From 11ef0f4858a097f9cbc59676f64a8377feb403cc Mon Sep 17 00:00:00 2001 From: condecon Date: Wed, 17 Sep 2025 13:26:45 +0200 Subject: [PATCH 001/137] add ability to add content categories to test items --- adaptivetesting/models/__item_pool.py | 42 ++++++++++----- adaptivetesting/models/__test_item.py | 12 +++-- adaptivetesting/tests/test_load_test_items.py | 51 +++++++++++++++++++ 3 files changed, 90 insertions(+), 15 deletions(-) diff --git a/adaptivetesting/models/__item_pool.py b/adaptivetesting/models/__item_pool.py index 661ca7c..4081a63 100644 --- a/adaptivetesting/models/__item_pool.py +++ b/adaptivetesting/models/__item_pool.py @@ -95,22 +95,20 @@ def load_from_list( c: List[float] | None = None, d: List[float] | None = None, simulated_responses: List[int] | None = None, - ids: List[int] | None = None) -> "ItemPool": + ids: List[int] | None = None, + content_categories: list[list[str]] | None = None) -> "ItemPool": """ Creates test items from a list of floats. Args: a (List[float]): discrimination parameter - b (List[float]): difficulty parameter - c (List[float]): guessing parameter - d (List[float]): slipping parameter - simulated_responses (List[int]): simulated responses - ids (List[int]): item IDs + content_categories (list[list[str]], optional): List of categories for each item. + This list is used for content balancing. Returns: List[TestItem]: item pool @@ -148,6 +146,12 @@ def load_from_list( for i, id_ in enumerate(ids): items[i].id = id_ + if content_categories is not None: + if len(content_categories) != len(b): + raise ValueError("Length of content_categories and b has to be the same.") + for i, groups in enumerate(content_categories): + items[i].additional_properties["category"] = groups + item_pool = ItemPool(items) item_pool.simulated_responses = simulated_responses @@ -156,7 +160,8 @@ def load_from_list( @staticmethod def load_from_dict(source: dict[str, List[float]], simulated_responses: List[int] | None = None, - ids: List[int] | None = None) -> "ItemPool": + ids: List[int] | None = None, + content_categories: list[list[str]] | None = None) -> "ItemPool": """Creates test items from a dictionary. The dictionary has to have the following keys: @@ -169,7 +174,9 @@ def load_from_dict(source: dict[str, List[float]], Args: source (dict[str, List[float]]): item pool dictionary simulated_responses (List[int]): simulated responses - ids (List[int]): item IDs + ids (List[int], optional): item IDs. Default `None`. + content_categories (list[list[str]], optional): List of categories for each item. + This list is used for content balancing. Returns: List[TestItem]: item pool @@ -200,6 +207,10 @@ def load_from_dict(source: dict[str, List[float]], if len(ids) != len(b): raise ValueError("Length of ids and b has to be the same.") + if content_categories is not None: + if len(content_categories) != len(b): + raise ValueError("Length of content_categories and b has to be the same.") + n_items = len(b) items: List[TestItem] = [] for i in range(n_items): @@ -212,6 +223,9 @@ def load_from_dict(source: dict[str, List[float]], if ids is not None: item.id = ids[i] + if content_categories is not None: + item.additional_properties["category"] = content_categories[i] + items.append(item) item_pool = ItemPool(items, simulated_responses) @@ -226,12 +240,11 @@ def load_from_dataframe(source: DataFrame) -> "ItemPool": A `simulated_responses` (int values) column can be added to the DataFrame to provide simulated responses. - Args: - source (DataFrame): _description_ + source (DataFrame): source data frame Returns: - ItemPool: _description_ + ItemPool: parsed item pool """ # check if columns are present @@ -258,8 +271,13 @@ def load_from_dataframe(source: DataFrame) -> "ItemPool": else: ids = None + if "content_categories" in source.columns: + groups: list[list[str]] | None = source["content_categories"].values.tolist() + else: + groups = None + # create item pool - item_pool = ItemPool.load_from_list(a=a, b=b, c=c, d=d, ids=ids) + item_pool = ItemPool.load_from_list(a=a, b=b, c=c, d=d, ids=ids, content_categories=groups) # check if simulated responses are present if "simulated_responses" in source.columns: diff --git a/adaptivetesting/models/__test_item.py b/adaptivetesting/models/__test_item.py index 7203208..275ead5 100644 --- a/adaptivetesting/models/__test_item.py +++ b/adaptivetesting/models/__test_item.py @@ -9,6 +9,11 @@ def __init__(self): - b (float): difficulty parameter - c (float): guessing parameter - d (float): slipping parameter / upper asymptote + - additional_properties (dict): addtional properties can be set if required. + This functionality is used for content balancing. + To use content balancing, set set `category` key of the class instance + to a list of string which indicate the corresponding constraint classes. + Example: `item.additional_properties["category"] = ["Math"]` """ self.id: int | None = None @@ -16,15 +21,16 @@ def __init__(self): self.b: float = float("nan") self.c: float = 0 self.d: float = 1 + self.additional_properties: dict = {} def as_dict(self, with_id: bool = False) -> dict[str, float | int | None]: - """Returns the item as a dictionary""" - item_dict: dict[str, float | int | None] = { + item_dict: dict[str, float | int | dict | None] = { "a": self.a, "b": self.b, "c": self.c, - "d": self.d + "d": self.d, + "additional_properties": self.additional_properties } if with_id and self.id is not None: diff --git a/adaptivetesting/tests/test_load_test_items.py b/adaptivetesting/tests/test_load_test_items.py index 91c8fa2..65b9f17 100644 --- a/adaptivetesting/tests/test_load_test_items.py +++ b/adaptivetesting/tests/test_load_test_items.py @@ -174,3 +174,54 @@ def test_load_pandas_no_responses(self): generated = ItemPool.load_from_dataframe(df) self.assertIsNone(generated.simulated_responses) + + def test_load_dict_content_balancing(self): + source_dictionary: dict[str, list[float]] = { + "a": [0.9, 1.9], + "b": [5, 3], + "c": [0.9, 1.9], + "d": [1, 1] + } + + item_pool = ItemPool.load_from_dict(source=source_dictionary, + content_categories=[["math"], ["english"]]) + items = item_pool.test_items + assigned_groups = [item.additional_properties["category"] for item in items] + + self.assertListEqual(assigned_groups, [["math"], ["english"]]) + + def test_load_list_content_balancing(self): + source_dictionary: dict[str, list[float | str]] = { + "a": [0.9, 1.9], + "b": [5, 3], + "c": [0.9, 1.9], + "d": [1, 1], + "group": [["math"], ["english"]] + } + + item_pool = ItemPool.load_from_list( + a=source_dictionary["a"], + b=source_dictionary["b"], + c=source_dictionary["c"], + d=source_dictionary["d"], + content_categories=source_dictionary["group"] + ) + items = item_pool.test_items + assigned_groups = [item.additional_properties["category"] for item in items] + + self.assertListEqual(assigned_groups, [["math"], ["english"]]) + + def test_load_dataframe_content_balancing(self): + source_dictionary: dict[str, list[float | str]] = { + "a": [0.9, 1.9], + "b": [5, 3], + "c": [0.9, 1.9], + "d": [1, 1], + "content_categories": [["math"], ["english"]] + } + + item_pool = ItemPool.load_from_dataframe(pd.DataFrame(source_dictionary)) + items = item_pool.test_items + assigned_groups = [item.additional_properties["category"] for item in items] + + self.assertListEqual(assigned_groups, [["math"], ["english"]]) \ No newline at end of file From 28c1911af4ef527d169ba159786aa27ab319fa70 Mon Sep 17 00:00:00 2001 From: condecon Date: Wed, 17 Sep 2025 13:27:31 +0200 Subject: [PATCH 002/137] add basic content balacing functions --- adaptivetesting/__init__.py | 1 + .../item_selection/__content_balancing.py | 94 +++++++++++++++++++ .../math/item_selection/__init__.py | 1 + .../tests/test_content_balacing.py | 69 ++++++++++++++ 4 files changed, 165 insertions(+) create mode 100644 adaptivetesting/math/item_selection/__content_balancing.py create mode 100644 adaptivetesting/tests/test_content_balacing.py diff --git a/adaptivetesting/__init__.py b/adaptivetesting/__init__.py index ca0fad1..774345f 100644 --- a/adaptivetesting/__init__.py +++ b/adaptivetesting/__init__.py @@ -24,6 +24,7 @@ from .math.estimators.__test_information import test_information_function, item_information_function, prior_information_function from .math.item_selection.__maximum_information_criterion import maximum_information_criterion from .math.item_selection.__urrys_rule import urrys_rule +from .math.item_selection.__content_balancing import ContentBalancing, MaximumPriorityIndexMethod, calculate_priority_index, calculate_quota_left from .models.__adaptive_test import AdaptiveTest from .models.__algorithm_exception import AlgorithmException diff --git a/adaptivetesting/math/item_selection/__content_balancing.py b/adaptivetesting/math/item_selection/__content_balancing.py new file mode 100644 index 0000000..ac77c6d --- /dev/null +++ b/adaptivetesting/math/item_selection/__content_balancing.py @@ -0,0 +1,94 @@ +from ...models.__test_item import TestItem +from ...models.__item_selection_exception import ItemSelectionException +from ...math.estimators.__test_information import item_information_function +import numpy as np +import abc + + +class ContentBalancing(abc.ABC): + def __init__(self): + super().__init__() + + +# WORK IN PROGRESS +class MaximumPriorityIndexMethod(ContentBalancing): + def __init__(self, + available_items: list[TestItem], + shown_items: list[TestItem], + fisher_information: np.ndarray, + groups: list[str], + quota: list[float] + ): + self.available_items = available_items + self.fisher_information = fisher_information + self.shown_items = shown_items + self.groups = groups + self.quota = quota + + # sort items by id + self.available_items.sort(key=lambda item: item.id) + + super().__init__() + +def calculate_priority_index(item: TestItem, + group_weights: dict[str, float], + required_items: int, + shown_item: int, + current_ability: float) -> float: + """Calculates the priority index of a given item. + + Args: + item (TestItem): Item for which to calcualte the priority index + group_weights (dict[str, float]): Dictionary with the group names and their weights + Example: `{"math": 1, "english": 1}`. These weight show how important a specific + constraint is for the item selection process + required_items (int): number of items required to be shown per constraint + shown_item (int): number of items already shown per constraint + + Returns: + float: priority index of an item + + Raises: + ItemSelectionException: Raised if the additional properties of the items are not correctly formatted. + """ + try: + item_groups: list[str] = item.additional_properties["category"] + + if not isinstance(item_groups, list): + raise ItemSelectionException("Additional properties of the items are not correctly formatted.") + except KeyError: + raise ItemSelectionException("Additional properties of the items are not correctly formatted.") + priority_index: float = 1.0 + + for group in item_groups: + priority_index = priority_index * group_weights[group] * calculate_quota_left(required_items=required_items, shown_items=shown_item) + + # weight fisher information + priority_index = priority_index * float(item_information_function( + mu=np.array(current_ability), + a=np.array(item.a), + b=np.array(item.b), + c=np.array(item.c), + d=np.array(item.d) + )) + + return priority_index + + +def calculate_quota_left(required_items: int, + shown_items: int) -> float: + """ + Claculates the quota left (items left allowed to be shown) for a given constraint/group. + + Args: + required_items (int): number of required items per constraint + shown_items (int): number of already shown items per constraint + + Returns: + float: calculated quota for the given constraint. Results in a float between 0 and 1. + + Example: + `calculate_quota_left(10, 8)` + """ + quota = (required_items - shown_items) / required_items + return quota diff --git a/adaptivetesting/math/item_selection/__init__.py b/adaptivetesting/math/item_selection/__init__.py index 5e6afe4..27d8eaf 100644 --- a/adaptivetesting/math/item_selection/__init__.py +++ b/adaptivetesting/math/item_selection/__init__.py @@ -1,2 +1,3 @@ from .__urrys_rule import urrys_rule from .__maximum_information_criterion import maximum_information_criterion +from .__content_balancing import ContentBalancing, MaximumPriorityIndexMethod, calculate_priority_index, calculate_quota_left diff --git a/adaptivetesting/tests/test_content_balacing.py b/adaptivetesting/tests/test_content_balacing.py new file mode 100644 index 0000000..d6dee76 --- /dev/null +++ b/adaptivetesting/tests/test_content_balacing.py @@ -0,0 +1,69 @@ +import unittest +import adaptivetesting as adt +import pandas as pd +import numpy as np + +class TestMaximumPriorityIndex(unittest.TestCase): + def __init__(self, methodName = "runTest"): + + items = pd.DataFrame({ + "a": [1.32, 1.07, 0.84], + "b": [-0.63, 0.18, -0.84], + "c": [0.17, 0.10, 0.19], + "d": [0.87, 0.93, 1], + "id": [1, 2, 3] + }) + + self.available_items = adt.ItemPool.load_from_dataframe(items).test_items + self.content_categories = ["Math", "English", "Math"] + + for i, _ in enumerate(self.available_items): + self.available_items[i].additional_properties = { + "category": [self.content_categories[i]] + } + + super().__init__(methodName) + + def test_basic_calculation(self): + adt.calculate_priority_index( + item=self.available_items[0], + group_weights={ + "Math": 0.2, + "English": 0.8 + }, + required_items=10, + shown_item=0, + current_ability=0 + ) + + def test_quota_calculation(self): + result = adt.calculate_quota_left(10, 5) + self.assertAlmostEqual(result, 0.5) + + def test_exception_list(self): + with self.assertRaises(adt.ItemSelectionException): + self.available_items[0].additional_properties["category"] = 0 + result = adt.calculate_priority_index( + item=self.available_items[0], + group_weights={ + "Math": 0.2, + "English": 0.8 + }, + required_items=10, + shown_item=0, + current_ability=0 + ) + + def test_exception_key(self): + with self.assertRaises(adt.ItemSelectionException): + self.available_items[0].additional_properties.pop("category") + result = adt.calculate_priority_index( + item=self.available_items[0], + group_weights={ + "Math": 0.2, + "English": 0.8 + }, + required_items=10, + shown_item=0, + current_ability=0 + ) \ No newline at end of file From 5d547aac190df38249a48f3ea9f7a213c895ae06 Mon Sep 17 00:00:00 2001 From: condecon Date: Wed, 17 Sep 2025 13:37:40 +0200 Subject: [PATCH 003/137] fix mypy and linting --- adaptivetesting/models/__test_item.py | 2 +- adaptivetesting/tests/test_load_test_items.py | 18 +++++++++--------- adaptivetesting/tests/test_test_assembler.py | 2 +- 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/adaptivetesting/models/__test_item.py b/adaptivetesting/models/__test_item.py index 275ead5..fff1242 100644 --- a/adaptivetesting/models/__test_item.py +++ b/adaptivetesting/models/__test_item.py @@ -23,7 +23,7 @@ def __init__(self): self.d: float = 1 self.additional_properties: dict = {} - def as_dict(self, with_id: bool = False) -> dict[str, float | int | None]: + def as_dict(self, with_id: bool = False) -> dict[str, float | int | dict | None]: item_dict: dict[str, float | int | dict | None] = { "a": self.a, diff --git a/adaptivetesting/tests/test_load_test_items.py b/adaptivetesting/tests/test_load_test_items.py index 65b9f17..5ebf413 100644 --- a/adaptivetesting/tests/test_load_test_items.py +++ b/adaptivetesting/tests/test_load_test_items.py @@ -184,14 +184,14 @@ def test_load_dict_content_balancing(self): } item_pool = ItemPool.load_from_dict(source=source_dictionary, - content_categories=[["math"], ["english"]]) + content_categories=[["math"], ["english"]]) items = item_pool.test_items assigned_groups = [item.additional_properties["category"] for item in items] self.assertListEqual(assigned_groups, [["math"], ["english"]]) def test_load_list_content_balancing(self): - source_dictionary: dict[str, list[float | str]] = { + source_dictionary: dict[str, list[float] | list[int] | list[list[str]]] = { "a": [0.9, 1.9], "b": [5, 3], "c": [0.9, 1.9], @@ -200,11 +200,11 @@ def test_load_list_content_balancing(self): } item_pool = ItemPool.load_from_list( - a=source_dictionary["a"], - b=source_dictionary["b"], - c=source_dictionary["c"], - d=source_dictionary["d"], - content_categories=source_dictionary["group"] + a=source_dictionary["a"], # type: ignore + b=source_dictionary["b"], # type: ignore + c=source_dictionary["c"], # type: ignore + d=source_dictionary["d"], # type: ignore + content_categories=source_dictionary["group"] # type: ignore ) items = item_pool.test_items assigned_groups = [item.additional_properties["category"] for item in items] @@ -212,7 +212,7 @@ def test_load_list_content_balancing(self): self.assertListEqual(assigned_groups, [["math"], ["english"]]) def test_load_dataframe_content_balancing(self): - source_dictionary: dict[str, list[float | str]] = { + source_dictionary: dict[str, list[float] | list[int] | list[list[str]]] = { "a": [0.9, 1.9], "b": [5, 3], "c": [0.9, 1.9], @@ -224,4 +224,4 @@ def test_load_dataframe_content_balancing(self): items = item_pool.test_items assigned_groups = [item.additional_properties["category"] for item in items] - self.assertListEqual(assigned_groups, [["math"], ["english"]]) \ No newline at end of file + self.assertListEqual(assigned_groups, [["math"], ["english"]]) diff --git a/adaptivetesting/tests/test_test_assembler.py b/adaptivetesting/tests/test_test_assembler.py index 4a09d7e..58423f7 100644 --- a/adaptivetesting/tests/test_test_assembler.py +++ b/adaptivetesting/tests/test_test_assembler.py @@ -11,7 +11,7 @@ class DummyTestItem(TestItem): def __init__(self, id=0): self.id = id - def as_dict(self, with_id: bool = True) -> dict[str, float | int | None]: + def as_dict(self, with_id: bool = True): return {"id": self.id} From ad2b1aa4a8fa76eb28f7ae133376cff13e64ae97 Mon Sep 17 00:00:00 2001 From: codecon Date: Mon, 22 Sep 2025 17:40:52 +0200 Subject: [PATCH 004/137] move to setuptools and add cython support --- pyproject.toml | 10 +- setup.py | 22 + uv.lock | 4476 ++++++++++++++++++++++++------------------------ 3 files changed, 2301 insertions(+), 2207 deletions(-) create mode 100644 setup.py diff --git a/pyproject.toml b/pyproject.toml index 1d969bd..e8e49c0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,16 +17,18 @@ license-files=["LICENSE"] keywords=["statistics", "psychology", "item-response-theory", "computerized-adaptive-testing"] [build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" +requires = ["setuptools", "Cython"] +build-backend = "setuptools.build_meta" [dependency-groups] dev = [ "adaptivetesting", + "cython>=3.1.4", "flake8>=7.2.0", "mypy>=1.15.0", "pandas-stubs>=2.2.3.250308", "scipy-stubs>=1.15.2.2", + "setuptools>=70.3.0", "snakeviz>=2.2.2", "types-tqdm>=4.67.0.20250516", ] @@ -46,6 +48,10 @@ test = [ [tool.uv.sources] adaptivetesting = { workspace = true } +[tool.setuptools] +packages=["adaptivetesting"] + + [project.urls] Homepage = "https://github.com/condecon/adaptivetesting" Documentation = "https://github.com/condecon/adaptivetesting/tree/main/docs" diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..0884638 --- /dev/null +++ b/setup.py @@ -0,0 +1,22 @@ +from setuptools import setup, Extension, find_packages +from Cython.Build import cythonize +import glob +import os + +# Recursively find all .pyx files +pyx_files = glob.glob("adaptivetesting/**/*.pyx", recursive=True) + +# Convert each file into an Extension +extensions = [ + Extension( + name=os.path.splitext(path.replace("/", ".").replace("\\", "."))[0], + sources=[path], + ) + for path in pyx_files +] + +setup( + name="adaptivetesting", + packages=find_packages(where="."), + ext_modules=cythonize(extensions, language_level="3"), +) diff --git a/uv.lock b/uv.lock index 2fab80c..55ac896 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 1 +revision = 3 requires-python = ">=3.10" resolution-markers = [ "python_full_version >= '3.12' and sys_platform == 'darwin'", @@ -23,14 +23,14 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "pygments" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/bc/c1/bbac6a50d02774f91572938964c582fff4270eee73ab822a4aeea4d8b11b/accessible_pygments-0.0.5.tar.gz", hash = "sha256:40918d3e6a2b619ad424cb91e556bd3bd8865443d9f22f1dcdf79e33c8046872", size = 1377899 } +sdist = { url = "https://files.pythonhosted.org/packages/bc/c1/bbac6a50d02774f91572938964c582fff4270eee73ab822a4aeea4d8b11b/accessible_pygments-0.0.5.tar.gz", hash = "sha256:40918d3e6a2b619ad424cb91e556bd3bd8865443d9f22f1dcdf79e33c8046872", size = 1377899, upload-time = "2024-05-10T11:23:10.216Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl", hash = "sha256:88ae3211e68a1d0b011504b2ffc1691feafce124b845bd072ab6f9f66f34d4b7", size = 1395903 }, + { url = "https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl", hash = "sha256:88ae3211e68a1d0b011504b2ffc1691feafce124b845bd072ab6f9f66f34d4b7", size = 1395903, upload-time = "2024-05-10T11:23:08.421Z" }, ] [[package]] name = "adaptivetesting" -version = "1.1.1" +version = "1.1.2" source = { editable = "." } dependencies = [ { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, @@ -44,11 +44,13 @@ dependencies = 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cython support --- pyproject.toml | 11 +++++++---- setup.py | 1 - 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index e8e49c0..c1dbec4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,13 +13,14 @@ dependencies = [ "scipy>=1.15.0", "tqdm>=4.67.1", ] -license-files=["LICENSE"] +license-files = ["LICENSE"] keywords=["statistics", "psychology", "item-response-theory", "computerized-adaptive-testing"] [build-system] requires = ["setuptools", "Cython"] build-backend = "setuptools.build_meta" + [dependency-groups] dev = [ "adaptivetesting", @@ -48,9 +49,11 @@ test = [ [tool.uv.sources] adaptivetesting = { workspace = true } -[tool.setuptools] -packages=["adaptivetesting"] - +[tool.uv.workspace] +members = [ + ".", + ".", +] [project.urls] Homepage = "https://github.com/condecon/adaptivetesting" diff --git a/setup.py b/setup.py index 0884638..ba8b68f 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,6 @@ # Recursively find all .pyx files pyx_files = glob.glob("adaptivetesting/**/*.pyx", recursive=True) - # Convert each file into an Extension extensions = [ Extension( From ea2bca1c69ace05071d186230e288a1e0a0bbf2d Mon Sep 17 00:00:00 2001 From: codecon Date: Mon, 22 Sep 2025 18:12:31 +0200 Subject: [PATCH 007/137] move content balacing functions to cython files --- .../math/content_balancing/__functions.pyi | 42 ++++++++++++ .../math/content_balancing/__functions.pyx | 67 +++++++++++++++++++ .../math/content_balancing/__init__.py | 1 + .../item_selection/__content_balancing.py | 62 ----------------- 4 files changed, 110 insertions(+), 62 deletions(-) create mode 100644 adaptivetesting/math/content_balancing/__functions.pyi create mode 100644 adaptivetesting/math/content_balancing/__functions.pyx create mode 100644 adaptivetesting/math/content_balancing/__init__.py diff --git a/adaptivetesting/math/content_balancing/__functions.pyi b/adaptivetesting/math/content_balancing/__functions.pyi new file mode 100644 index 0000000..8da7382 --- /dev/null +++ b/adaptivetesting/math/content_balancing/__functions.pyi @@ -0,0 +1,42 @@ +from ...models.__test_item import TestItem + +def calculate_priority_index(item: TestItem, + group_weights: dict[str, float], + required_items: int, + shown_item: int, + current_ability: float) -> float: + """Calculates the priority index of a given item. + + Args: + item (TestItem): Item for which to calcualte the priority index + group_weights (dict[str, float]): Dictionary with the group names and their weights + Example: `{"math": 1, "english": 1}`. These weight show how important a specific + constraint is for the item selection process + required_items (int): number of items required to be shown per constraint + shown_item (int): number of items already shown per constraint + + Returns: + float: priority index of an item + + Raises: + ItemSelectionException: Raised if the additional properties of the items are not correctly formatted. + """ + ... + + +def calculate_quota_left(required_items: int, + shown_items: int) -> float: + """ + Claculates the quota left (items left allowed to be shown) for a given constraint/group. + + Args: + required_items (int): number of required items per constraint + shown_items (int): number of already shown items per constraint + + Returns: + float: calculated quota for the given constraint. Results in a float between 0 and 1. + + Example: + `calculate_quota_left(10, 8)` + """ + ... \ No newline at end of file diff --git a/adaptivetesting/math/content_balancing/__functions.pyx b/adaptivetesting/math/content_balancing/__functions.pyx new file mode 100644 index 0000000..46fb863 --- /dev/null +++ b/adaptivetesting/math/content_balancing/__functions.pyx @@ -0,0 +1,67 @@ +from ...models.__test_item import TestItem +from ...models.__item_selection_exception import ItemSelectionException +from ...math.estimators.__test_information import item_information_function +import numpy as np + +def calculate_priority_index(item: TestItem, + group_weights: dict[str, float], + required_items: int, + shown_item: int, + current_ability: float) -> float: + """Calculates the priority index of a given item. + + Args: + item (TestItem): Item for which to calcualte the priority index + group_weights (dict[str, float]): Dictionary with the group names and their weights + Example: `{"math": 1, "english": 1}`. These weight show how important a specific + constraint is for the item selection process + required_items (int): number of items required to be shown per constraint + shown_item (int): number of items already shown per constraint + + Returns: + float: priority index of an item + + Raises: + ItemSelectionException: Raised if the additional properties of the items are not correctly formatted. + """ + try: + item_groups: list[str] = item.additional_properties["category"] + + if not isinstance(item_groups, list): + raise ItemSelectionException("Additional properties of the items are not correctly formatted.") + except KeyError: + raise ItemSelectionException("Additional properties of the items are not correctly formatted.") + priority_index: float = 1.0 + + for group in item_groups: + priority_index = priority_index * group_weights[group] * calculate_quota_left(required_items=required_items, shown_items=shown_item) + + # weight fisher information + priority_index = priority_index * float(item_information_function( + mu=np.array(current_ability), + a=np.array(item.a), + b=np.array(item.b), + c=np.array(item.c), + d=np.array(item.d) + )) + + return priority_index + + +def calculate_quota_left(required_items: int, + shown_items: int) -> float: + """ + Claculates the quota left (items left allowed to be shown) for a given constraint/group. + + Args: + required_items (int): number of required items per constraint + shown_items (int): number of already shown items per constraint + + Returns: + float: calculated quota for the given constraint. Results in a float between 0 and 1. + + Example: + `calculate_quota_left(10, 8)` + """ + quota = (required_items - shown_items) / required_items + return quota diff --git a/adaptivetesting/math/content_balancing/__init__.py b/adaptivetesting/math/content_balancing/__init__.py new file mode 100644 index 0000000..ddfe710 --- /dev/null +++ b/adaptivetesting/math/content_balancing/__init__.py @@ -0,0 +1 @@ +from .__functions import calculate_priority_index, calculate_quota_left \ No newline at end of file diff --git a/adaptivetesting/math/item_selection/__content_balancing.py b/adaptivetesting/math/item_selection/__content_balancing.py index ac77c6d..a69fd6b 100644 --- a/adaptivetesting/math/item_selection/__content_balancing.py +++ b/adaptivetesting/math/item_selection/__content_balancing.py @@ -30,65 +30,3 @@ def __init__(self, super().__init__() -def calculate_priority_index(item: TestItem, - group_weights: dict[str, float], - required_items: int, - shown_item: int, - current_ability: float) -> float: - """Calculates the priority index of a given item. - - Args: - item (TestItem): Item for which to calcualte the priority index - group_weights (dict[str, float]): Dictionary with the group names and their weights - Example: `{"math": 1, "english": 1}`. These weight show how important a specific - constraint is for the item selection process - required_items (int): number of items required to be shown per constraint - shown_item (int): number of items already shown per constraint - - Returns: - float: priority index of an item - - Raises: - ItemSelectionException: Raised if the additional properties of the items are not correctly formatted. - """ - try: - item_groups: list[str] = item.additional_properties["category"] - - if not isinstance(item_groups, list): - raise ItemSelectionException("Additional properties of the items are not correctly formatted.") - except KeyError: - raise ItemSelectionException("Additional properties of the items are not correctly formatted.") - priority_index: float = 1.0 - - for group in item_groups: - priority_index = priority_index * group_weights[group] * calculate_quota_left(required_items=required_items, shown_items=shown_item) - - # weight fisher information - priority_index = priority_index * float(item_information_function( - mu=np.array(current_ability), - a=np.array(item.a), - b=np.array(item.b), - c=np.array(item.c), - d=np.array(item.d) - )) - - return priority_index - - -def calculate_quota_left(required_items: int, - shown_items: int) -> float: - """ - Claculates the quota left (items left allowed to be shown) for a given constraint/group. - - Args: - required_items (int): number of required items per constraint - shown_items (int): number of already shown items per constraint - - Returns: - float: calculated quota for the given constraint. Results in a float between 0 and 1. - - Example: - `calculate_quota_left(10, 8)` - """ - quota = (required_items - shown_items) / required_items - return quota From 5a600d61541dc884d9e561ecff0f101bbb7a4484 Mon Sep 17 00:00:00 2001 From: codecon Date: Mon, 22 Sep 2025 18:13:59 +0200 Subject: [PATCH 008/137] fix imports --- adaptivetesting/__init__.py | 2 +- adaptivetesting/math/item_selection/__init__.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/adaptivetesting/__init__.py b/adaptivetesting/__init__.py index 774345f..7f0f8cd 100644 --- a/adaptivetesting/__init__.py +++ b/adaptivetesting/__init__.py @@ -24,7 +24,7 @@ from .math.estimators.__test_information import test_information_function, item_information_function, prior_information_function from .math.item_selection.__maximum_information_criterion import maximum_information_criterion from .math.item_selection.__urrys_rule import urrys_rule -from .math.item_selection.__content_balancing import ContentBalancing, MaximumPriorityIndexMethod, calculate_priority_index, calculate_quota_left +from .math.item_selection.__content_balancing import ContentBalancing, MaximumPriorityIndexMethod from .models.__adaptive_test import AdaptiveTest from .models.__algorithm_exception import AlgorithmException diff --git a/adaptivetesting/math/item_selection/__init__.py b/adaptivetesting/math/item_selection/__init__.py index 27d8eaf..8c6d2f9 100644 --- a/adaptivetesting/math/item_selection/__init__.py +++ b/adaptivetesting/math/item_selection/__init__.py @@ -1,3 +1,3 @@ from .__urrys_rule import urrys_rule from .__maximum_information_criterion import maximum_information_criterion -from .__content_balancing import ContentBalancing, MaximumPriorityIndexMethod, calculate_priority_index, calculate_quota_left +from .__content_balancing import ContentBalancing, MaximumPriorityIndexMethod From f4a8548b33acbea419486983f0580df551522c86 Mon Sep 17 00:00:00 2001 From: codecon Date: Tue, 23 Sep 2025 10:49:10 +0200 Subject: [PATCH 009/137] add functions for WPM --- .../math/content_balancing/__constraint.py | 9 + .../math/content_balancing/__functions.py | 225 ++++++++++++++++++ .../math/content_balancing/__functions.pyi | 42 ---- .../math/content_balancing/__functions.pyx | 67 ------ .../item_selection/__content_balancing.py | 32 --- 5 files changed, 234 insertions(+), 141 deletions(-) create mode 100644 adaptivetesting/math/content_balancing/__constraint.py create mode 100644 adaptivetesting/math/content_balancing/__functions.py delete mode 100644 adaptivetesting/math/content_balancing/__functions.pyi delete mode 100644 adaptivetesting/math/content_balancing/__functions.pyx delete mode 100644 adaptivetesting/math/item_selection/__content_balancing.py diff --git a/adaptivetesting/math/content_balancing/__constraint.py b/adaptivetesting/math/content_balancing/__constraint.py new file mode 100644 index 0000000..d1796ed --- /dev/null +++ b/adaptivetesting/math/content_balancing/__constraint.py @@ -0,0 +1,9 @@ +from dataclasses import dataclass + +@dataclass +class Constraint: + name: str + weight: float + proportion: float + lower: float | None = None + upper: float | None = None \ No newline at end of file diff --git a/adaptivetesting/math/content_balancing/__functions.py b/adaptivetesting/math/content_balancing/__functions.py new file mode 100644 index 0000000..a77b47c --- /dev/null +++ b/adaptivetesting/math/content_balancing/__functions.py @@ -0,0 +1,225 @@ +from .__constraint import Constraint +from ...models.__test_item import TestItem +from ...models.__item_selection_exception import ItemSelectionException +from ...math.estimators.__test_information import item_information_function +import numpy as np + + +def compute_priority_index(item: TestItem, + group_weights: dict[str, float], + required_items: int, + shown_item: int, + current_ability: float) -> float: + """Calculates the priority index of a given item. + + Args: + item (TestItem): Item for which to calcualte the priority index + group_weights (dict[str, float]): Dictionary with the group names and their weights + Example: `{"math": 1, "english": 1}`. These weight show how important a specific + constraint is for the item selection process + required_items (int): number of items required to be shown per constraint + shown_item (int): number of items already shown per constraint + + Returns: + float: priority index of an item + + Raises: + ItemSelectionException: Raised if the additional properties of the items are not correctly formatted. + """ + try: + item_groups: list[str] = item.additional_properties["category"] + + if not isinstance(item_groups, list): + raise ItemSelectionException("Additional properties of the items are not correctly formatted.") + except KeyError: + raise ItemSelectionException("Additional properties of the items are not correctly formatted.") + priority_index: float = 1.0 + + for group in item_groups: + priority_index = priority_index * group_weights[group] * calculate_quota_left(required_items=required_items, shown_items=shown_item) + + # weight fisher information + priority_index = priority_index * float(item_information_function( + mu=np.array(current_ability, dtype=np.float64), + a=np.array(item.a, dtype=np.float64), + b=np.array(item.b, dtype=np.float64), + c=np.array(item.c, dtype=np.float64), + d=np.array(item.d, dtype=np.float64) + )) + + return priority_index + + +def compute_quota_left(required_items: int, + shown_items: int) -> float: + """ + Claculates the quota left (items left allowed to be shown) for a given constraint/group. + + Args: + required_items (int): number of required items per constraint + shown_items (int): number of already shown items per constraint + + Returns: + float: calculated quota for the given constraint. Results in a float between 0 and 1. + + Example: + `compute_quota_left(10, 8)` + """ + quota = (required_items - shown_items) / required_items + return quota + + +def compute_prop(n_administered: int, + prevalence: float, + n_remaining: int, + test_length: int) -> float: + """Calculates the expected proportion + of items with a specific constraint + + Args: + n_administered (int): number of items constraint items administered + prevalence (float): proportion of items in the pool with given constraint + n_remaining (int): remaining items in pool with constraint + test_length (int): lenght of the test + + Returns: + float: expected proportion + """ + expected_proportion = (n_administered + prevalence * n_remaining) / test_length + return expected_proportion + +def compute_expected_difference(proportion: float, + constraint_target: float) -> float: + """Calculates expected difference between + expected proportion and the constraint target + + Args: + proportion (float): expected proportion + constraint_target (float): constraint target + + Returns: + float: _description_ + """ + expected_difference = proportion - constraint_target + return expected_difference + +def compute_penalty_value(prop: float, + lower: float, + upper: float) -> float: + """Calculates the penalty value for an item + + Args: + prop (float): expected proportion + lower (float): lower bound for proportion of items + upper (float): upper bound for proportion of items + + Returns: + float: penalty value + """ + penalty_value: float = float("NaN") + mid: float = (upper + lower) / 2 + + if prop < lower: + d = lower - mid + k = 2 + penalty_value = ((1 / k * d) * + (compute_expected_difference(proportion=prop, constraint_target=mid) ** 2) + + (d / k)) + + if prop >= upper: + a = upper - mid + k = 2 + penalty_value = ((1 / k * a) * + (compute_expected_difference(proportion=prop, constraint_target=mid) ** 2) + + (d / a)) + + if upper > prop and prop >= lower: + penalty_value = compute_expected_difference(proportion=prop, + constraint_target=mid) + + return penalty_value + +def compute_total_content_penalty_value_for_item(item: TestItem, + constraints: list[Constraint]) -> float: + """Calculates the total content penalty value for a given item + + Args: + item (TestItem): given test item + group_weights (dict[str, float]): dictionary of group key and weight + + Returns: + float: total content penalty value + """ + assigned_item_constraints: list[str] = item.additional_properties["category"] + assigned_constraints = [constraint for constraint in constraints if constraint.name in assigned_item_constraints] + + total_penalty_value = 0.0 + for constraint in assigned_constraints: + total_penalty_value = total_penalty_value + compute_penalty_value( + prop=constraint.proportion, + lower=constraint.lower, + upper=constraint.upper + ) * constraint.weight + +def standardize_total_content_constraint_penalty_value(item_penality_value: float, + min: float, + max: float) -> float: + """Standardize total content contraint penalty values. + + Args: + item_penality_value (float): unstandardized item penalty value + min (float): minimum of the item penalty values over all eligible items + max (float): maximum of the item penalty values over all eligible items + + Returns: + float: standardized total content constraint penalty value + """ + standardized_value = (item_penality_value - min) / (max - min) + return standardized_value + +def standardize_item_information(item_information: float, + max: float) -> float: + """Standardize the item information + + Args: + item_information (float): information of an item + max (float): maximum information value across all eligible items + + Returns: + float: standardized item information + """ + standardized_item_information = item_information / max + return standardize_item_information + +def compute_information_penalty_value(standardized_item_information: float) -> float: + """Compute information penalty value with respect to the information + + Args: + standardized_item_information (float): standardized item information + + Returns: + float: information penalty + """ + information_penalty = -(standardized_item_information ** 2) + return information_penalty + +def compute_weighted_penalty_value(constraint_weight: float, + standardized_constraint_penalty_value: float, + information_weight: float, + information_penalty_value: float) -> float: + """Calculates the weighted penalty value. + The `constraint_weight` and `information_weight` parameters can be used + to balance the content constraint and item information. + + Args: + constraint_weight (float): constraint weight + standardized_constraint_penalty_value (float): standardized constraint penalty value + information_weight (float): information weight + information_penalty_value (float): information penalty value + + Returns: + float: weighted penalty value + """ + weighted_penalty = constraint_weight * standardized_constraint_penalty_value + information_weight * information_penalty_value + return weighted_penalty + diff --git a/adaptivetesting/math/content_balancing/__functions.pyi b/adaptivetesting/math/content_balancing/__functions.pyi deleted file mode 100644 index 8da7382..0000000 --- a/adaptivetesting/math/content_balancing/__functions.pyi +++ /dev/null @@ -1,42 +0,0 @@ -from ...models.__test_item import TestItem - -def calculate_priority_index(item: TestItem, - group_weights: dict[str, float], - required_items: int, - shown_item: int, - current_ability: float) -> float: - """Calculates the priority index of a given item. - - Args: - item (TestItem): Item for which to calcualte the priority index - group_weights (dict[str, float]): Dictionary with the group names and their weights - Example: `{"math": 1, "english": 1}`. These weight show how important a specific - constraint is for the item selection process - required_items (int): number of items required to be shown per constraint - shown_item (int): number of items already shown per constraint - - Returns: - float: priority index of an item - - Raises: - ItemSelectionException: Raised if the additional properties of the items are not correctly formatted. - """ - ... - - -def calculate_quota_left(required_items: int, - shown_items: int) -> float: - """ - Claculates the quota left (items left allowed to be shown) for a given constraint/group. - - Args: - required_items (int): number of required items per constraint - shown_items (int): number of already shown items per constraint - - Returns: - float: calculated quota for the given constraint. Results in a float between 0 and 1. - - Example: - `calculate_quota_left(10, 8)` - """ - ... \ No newline at end of file diff --git a/adaptivetesting/math/content_balancing/__functions.pyx b/adaptivetesting/math/content_balancing/__functions.pyx deleted file mode 100644 index 46fb863..0000000 --- a/adaptivetesting/math/content_balancing/__functions.pyx +++ /dev/null @@ -1,67 +0,0 @@ -from ...models.__test_item import TestItem -from ...models.__item_selection_exception import ItemSelectionException -from ...math.estimators.__test_information import item_information_function -import numpy as np - -def calculate_priority_index(item: TestItem, - group_weights: dict[str, float], - required_items: int, - shown_item: int, - current_ability: float) -> float: - """Calculates the priority index of a given item. - - Args: - item (TestItem): Item for which to calcualte the priority index - group_weights (dict[str, float]): Dictionary with the group names and their weights - Example: `{"math": 1, "english": 1}`. These weight show how important a specific - constraint is for the item selection process - required_items (int): number of items required to be shown per constraint - shown_item (int): number of items already shown per constraint - - Returns: - float: priority index of an item - - Raises: - ItemSelectionException: Raised if the additional properties of the items are not correctly formatted. - """ - try: - item_groups: list[str] = item.additional_properties["category"] - - if not isinstance(item_groups, list): - raise ItemSelectionException("Additional properties of the items are not correctly formatted.") - except KeyError: - raise ItemSelectionException("Additional properties of the items are not correctly formatted.") - priority_index: float = 1.0 - - for group in item_groups: - priority_index = priority_index * group_weights[group] * calculate_quota_left(required_items=required_items, shown_items=shown_item) - - # weight fisher information - priority_index = priority_index * float(item_information_function( - mu=np.array(current_ability), - a=np.array(item.a), - b=np.array(item.b), - c=np.array(item.c), - d=np.array(item.d) - )) - - return priority_index - - -def calculate_quota_left(required_items: int, - shown_items: int) -> float: - """ - Claculates the quota left (items left allowed to be shown) for a given constraint/group. - - Args: - required_items (int): number of required items per constraint - shown_items (int): number of already shown items per constraint - - Returns: - float: calculated quota for the given constraint. Results in a float between 0 and 1. - - Example: - `calculate_quota_left(10, 8)` - """ - quota = (required_items - shown_items) / required_items - return quota diff --git a/adaptivetesting/math/item_selection/__content_balancing.py b/adaptivetesting/math/item_selection/__content_balancing.py deleted file mode 100644 index a69fd6b..0000000 --- a/adaptivetesting/math/item_selection/__content_balancing.py +++ /dev/null @@ -1,32 +0,0 @@ -from ...models.__test_item import TestItem -from ...models.__item_selection_exception import ItemSelectionException -from ...math.estimators.__test_information import item_information_function -import numpy as np -import abc - - -class ContentBalancing(abc.ABC): - def __init__(self): - super().__init__() - - -# WORK IN PROGRESS -class MaximumPriorityIndexMethod(ContentBalancing): - def __init__(self, - available_items: list[TestItem], - shown_items: list[TestItem], - fisher_information: np.ndarray, - groups: list[str], - quota: list[float] - ): - self.available_items = available_items - self.fisher_information = fisher_information - self.shown_items = shown_items - self.groups = groups - self.quota = quota - - # sort items by id - self.available_items.sort(key=lambda item: item.id) - - super().__init__() - From 1a36cd075f6c24bc9c237431f52a29056dbb4b01 Mon Sep 17 00:00:00 2001 From: codecon Date: Tue, 23 Sep 2025 10:50:10 +0200 Subject: [PATCH 010/137] fix naming issue --- adaptivetesting/math/content_balancing/__functions.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/adaptivetesting/math/content_balancing/__functions.py b/adaptivetesting/math/content_balancing/__functions.py index a77b47c..5da7fe2 100644 --- a/adaptivetesting/math/content_balancing/__functions.py +++ b/adaptivetesting/math/content_balancing/__functions.py @@ -36,7 +36,7 @@ def compute_priority_index(item: TestItem, priority_index: float = 1.0 for group in item_groups: - priority_index = priority_index * group_weights[group] * calculate_quota_left(required_items=required_items, shown_items=shown_item) + priority_index = priority_index * group_weights[group] * compute_quota_left(required_items=required_items, shown_items=shown_item) # weight fisher information priority_index = priority_index * float(item_information_function( From 26ed59635594f02b92869c4e06e520fbb669b50e Mon Sep 17 00:00:00 2001 From: codecon Date: Tue, 23 Sep 2025 11:01:25 +0200 Subject: [PATCH 011/137] fix return statements --- adaptivetesting/math/content_balancing/__functions.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/adaptivetesting/math/content_balancing/__functions.py b/adaptivetesting/math/content_balancing/__functions.py index 5da7fe2..329ed40 100644 --- a/adaptivetesting/math/content_balancing/__functions.py +++ b/adaptivetesting/math/content_balancing/__functions.py @@ -161,6 +161,8 @@ def compute_total_content_penalty_value_for_item(item: TestItem, upper=constraint.upper ) * constraint.weight + return total_penalty_value + def standardize_total_content_constraint_penalty_value(item_penality_value: float, min: float, max: float) -> float: @@ -188,8 +190,8 @@ def standardize_item_information(item_information: float, Returns: float: standardized item information """ - standardized_item_information = item_information / max - return standardize_item_information + standardized_item_information_value = item_information / max + return standardized_item_information_value def compute_information_penalty_value(standardized_item_information: float) -> float: """Compute information penalty value with respect to the information From 6f79c6ae8d27a598aa9d9998a17b66e8e8704208 Mon Sep 17 00:00:00 2001 From: codecon Date: Tue, 23 Sep 2025 11:07:30 +0200 Subject: [PATCH 012/137] update imports --- adaptivetesting/__init__.py | 1 - adaptivetesting/math/content_balancing/__init__.py | 2 +- adaptivetesting/math/item_selection/__init__.py | 1 - 3 files changed, 1 insertion(+), 3 deletions(-) diff --git a/adaptivetesting/__init__.py b/adaptivetesting/__init__.py index 7f0f8cd..ca0fad1 100644 --- a/adaptivetesting/__init__.py +++ b/adaptivetesting/__init__.py @@ -24,7 +24,6 @@ from .math.estimators.__test_information import test_information_function, item_information_function, prior_information_function from .math.item_selection.__maximum_information_criterion import maximum_information_criterion from .math.item_selection.__urrys_rule import urrys_rule -from .math.item_selection.__content_balancing import ContentBalancing, MaximumPriorityIndexMethod from .models.__adaptive_test import AdaptiveTest from .models.__algorithm_exception import AlgorithmException diff --git a/adaptivetesting/math/content_balancing/__init__.py b/adaptivetesting/math/content_balancing/__init__.py index ddfe710..4f3e61e 100644 --- a/adaptivetesting/math/content_balancing/__init__.py +++ b/adaptivetesting/math/content_balancing/__init__.py @@ -1 +1 @@ -from .__functions import calculate_priority_index, calculate_quota_left \ No newline at end of file +from .__functions import compute_priority_index, compute_quota_left \ No newline at end of file diff --git a/adaptivetesting/math/item_selection/__init__.py b/adaptivetesting/math/item_selection/__init__.py index 8c6d2f9..5e6afe4 100644 --- a/adaptivetesting/math/item_selection/__init__.py +++ b/adaptivetesting/math/item_selection/__init__.py @@ -1,3 +1,2 @@ from .__urrys_rule import urrys_rule from .__maximum_information_criterion import maximum_information_criterion -from .__content_balancing import ContentBalancing, MaximumPriorityIndexMethod From 4fdef43cc70661dbb4de31295b11d4a614659069 Mon Sep 17 00:00:00 2001 From: codecon Date: Tue, 23 Sep 2025 11:20:33 +0200 Subject: [PATCH 013/137] update imports --- adaptivetesting/__init__.py | 2 ++ adaptivetesting/math/content_balancing/__init__.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/adaptivetesting/__init__.py b/adaptivetesting/__init__.py index ca0fad1..f178fe7 100644 --- a/adaptivetesting/__init__.py +++ b/adaptivetesting/__init__.py @@ -24,6 +24,8 @@ from .math.estimators.__test_information import test_information_function, item_information_function, prior_information_function from .math.item_selection.__maximum_information_criterion import maximum_information_criterion from .math.item_selection.__urrys_rule import urrys_rule +from .math.content_balancing.__constraint import * +from .math.content_balancing.__functions import * from .models.__adaptive_test import AdaptiveTest from .models.__algorithm_exception import AlgorithmException diff --git a/adaptivetesting/math/content_balancing/__init__.py b/adaptivetesting/math/content_balancing/__init__.py index 4f3e61e..d55c95f 100644 --- a/adaptivetesting/math/content_balancing/__init__.py +++ b/adaptivetesting/math/content_balancing/__init__.py @@ -1 +1 @@ -from .__functions import compute_priority_index, compute_quota_left \ No newline at end of file +from .__functions import * \ No newline at end of file From e55f9768bd745bf931bbfad2efe22867ea4ed382 Mon Sep 17 00:00:00 2001 From: codecon Date: Tue, 23 Sep 2025 11:20:42 +0200 Subject: [PATCH 014/137] fix typing issue --- adaptivetesting/math/content_balancing/__functions.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/adaptivetesting/math/content_balancing/__functions.py b/adaptivetesting/math/content_balancing/__functions.py index 329ed40..e87ca9b 100644 --- a/adaptivetesting/math/content_balancing/__functions.py +++ b/adaptivetesting/math/content_balancing/__functions.py @@ -155,6 +155,12 @@ def compute_total_content_penalty_value_for_item(item: TestItem, total_penalty_value = 0.0 for constraint in assigned_constraints: + # type checks + if constraint.lower is None: + raise ValueError("lower cannot be None here.") + if constraint.upper is None: + raise ValueError("upper cannot be None here.") + # calculation total_penalty_value = total_penalty_value + compute_penalty_value( prop=constraint.proportion, lower=constraint.lower, From 5dd3a5776657df6fca038b74efafb3b166074e92 Mon Sep 17 00:00:00 2001 From: codecon Date: Tue, 23 Sep 2025 11:20:49 +0200 Subject: [PATCH 015/137] update function names --- adaptivetesting/tests/test_content_balacing.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/adaptivetesting/tests/test_content_balacing.py b/adaptivetesting/tests/test_content_balacing.py index d6dee76..53223ee 100644 --- a/adaptivetesting/tests/test_content_balacing.py +++ b/adaptivetesting/tests/test_content_balacing.py @@ -25,7 +25,7 @@ def __init__(self, methodName = "runTest"): super().__init__(methodName) def test_basic_calculation(self): - adt.calculate_priority_index( + adt.compute_priority_index( item=self.available_items[0], group_weights={ "Math": 0.2, @@ -37,13 +37,13 @@ def test_basic_calculation(self): ) def test_quota_calculation(self): - result = adt.calculate_quota_left(10, 5) + result = adt.compute_quota_left(10, 5) self.assertAlmostEqual(result, 0.5) def test_exception_list(self): with self.assertRaises(adt.ItemSelectionException): self.available_items[0].additional_properties["category"] = 0 - result = adt.calculate_priority_index( + result = adt.compute_priority_index( item=self.available_items[0], group_weights={ "Math": 0.2, @@ -57,7 +57,7 @@ def test_exception_list(self): def test_exception_key(self): with self.assertRaises(adt.ItemSelectionException): self.available_items[0].additional_properties.pop("category") - result = adt.calculate_priority_index( + result = adt.compute_priority_index( item=self.available_items[0], group_weights={ "Math": 0.2, From ac4e1a0bb04b88d2b84ea83bdec6ba7085de85f2 Mon Sep 17 00:00:00 2001 From: Tim Schaefer Date: Wed, 24 Sep 2025 13:52:29 +0200 Subject: [PATCH 016/137] FIX: use more stable versions of functions item_information_function() and probability_y1() --- .../estimators/__functions/__estimators.py | 71 +++++++++++++------ .../math/estimators/__test_information.py | 27 +++---- 2 files changed, 57 insertions(+), 41 deletions(-) diff --git a/adaptivetesting/math/estimators/__functions/__estimators.py b/adaptivetesting/math/estimators/__functions/__estimators.py index 6d30e91..182f168 100644 --- a/adaptivetesting/math/estimators/__functions/__estimators.py +++ b/adaptivetesting/math/estimators/__functions/__estimators.py @@ -2,7 +2,6 @@ from scipy.optimize import minimize_scalar, OptimizeResult # type: ignore from ....models.__algorithm_exception import AlgorithmException - def probability_y1(mu: np.ndarray, a: np.ndarray, b: np.ndarray, @@ -12,22 +11,50 @@ def probability_y1(mu: np.ndarray, Args: mu (np.ndarray): latent ability level - a (np.ndarray): item discrimination parameter - b (np.ndarray): item difficulty parameter - c (np.ndarray): pseudo guessing parameter - d (np.ndarray): inattention parameter Returns: np.ndarray: probability of getting the item correct """ + # Compute the exponent safely + z = a * (mu - b) + + # Use log-sum-exp trick for numerical stability + # For large positive z: exp(z)/(1+exp(z)) ≈ 1 + # For large negative z: exp(z)/(1+exp(z)) ≈ exp(z) + + # Clip z to prevent overflow in exp() + z_clipped = np.clip(z, -500, 500) # exp(-500) and exp(500) are safe + + # Compute the logistic function safely + # Use different formulas based on the sign of z for stability + result = np.empty_like(z_clipped) + + # For large positive values, use alternative formula to avoid overflow + mask_positive = z_clipped > 20 # exp(20) is large but manageable + mask_negative = z_clipped < -20 # exp(-20) is very small + mask_medium = ~(mask_positive | mask_negative) + + # Large positive z: 1/(1+exp(-z)) is more stable + result[mask_positive] = 1.0 / (1.0 + np.exp(-z_clipped[mask_positive])) + + # Large negative z: exp(z)/(1+exp(z)) is stable + exp_z_neg = np.exp(z_clipped[mask_negative]) + result[mask_negative] = exp_z_neg / (1.0 + exp_z_neg) + + # Medium values: use standard formula + exp_z_med = np.exp(z_clipped[mask_medium]) + result[mask_medium] = exp_z_med / (1.0 + exp_z_med) + + # Apply the 4PL transformation + value = c + (d - c) * result + + # Ensure probabilities are within valid range [c, d] + value = np.clip(value, c, d) - value = c + (d - c) * (np.exp(a * (mu - b))) / \ - (1 + np.exp(a * (mu - b))) - return np.squeeze(value) @@ -40,13 +67,13 @@ def probability_y0(mu: np.ndarray, Args: mu (np.ndarray): latent ability level - + a (np.ndarray): item discrimination parameter - + b (np.ndarray): item difficulty parameter - + c (np.ndarray): pseudo guessing parameter - + d (np.ndarray): inattention parameter Returns: @@ -68,13 +95,13 @@ def likelihood(mu: np.ndarray, Args: mu (np.ndarray): ability level - + a (np.ndarray): item discrimination parameter - + b (np.ndarray): item difficulty parameter - + c (np.ndarray): pseudo guessing parameter - + d (np.ndarray): inattention parameter Returns: @@ -88,7 +115,7 @@ def likelihood(mu: np.ndarray, terms = (probability_y1(mu, a, b, c, d)**response_pattern) * \ (probability_y0(mu, a, b, c, d) ** (1 - response_pattern)) - + return -np.prod(terms) @@ -100,14 +127,14 @@ def maximize_likelihood_function(a: np.ndarray, border: tuple[float, float] = (-10, 10)) -> float: """Find the ability value that maximizes the likelihood function. This function uses the minimize_scalar function from scipy and the "bounded" method. - + Args: a (np.ndarray): item discrimination parameter - + b (np.ndarray): item difficulty parameter - + c (np.ndarray): pseudo guessing parameter - + d (np.ndarray): inattention parameter response_pattern (np.ndarray): response pattern of the item @@ -129,7 +156,7 @@ def maximize_likelihood_function(a: np.ndarray, "Response pattern is invalid. It consists of only one type of response.") raise AlgorithmException( "Response pattern is invalid. It consists of only one type of response.") - + result: OptimizeResult = minimize_scalar(likelihood, args=(a, b, c, d, response_pattern), bounds=border, method='bounded') # type: ignore diff --git a/adaptivetesting/math/estimators/__test_information.py b/adaptivetesting/math/estimators/__test_information.py index 01de668..e6ff67b 100644 --- a/adaptivetesting/math/estimators/__test_information.py +++ b/adaptivetesting/math/estimators/__test_information.py @@ -5,7 +5,6 @@ import numpy from scipy.differentiate import derivative - def item_information_function( mu: np.ndarray, a: np.ndarray, @@ -13,27 +12,17 @@ def item_information_function( c: np.ndarray, d: np.ndarray ) -> np.ndarray: - """Calculate the information of a test item given the currently - estimated ability `mu`. - - Args: - mu (np.ndarray): currently estimated ability - a (np.ndarray): _description_ - b (np.ndarray): _description_ - c (np.ndarray): _description_ - d (np.ndarray): _description_ - - Returns: - np.ndarray: _description_ - """ p_y1 = probability_y1(mu, a, b, c, d) + # Clip probabilities + p_y1_clipped = np.clip(p_y1, 1e-10, 1 - 1e-10) + def p_y1_grad(x: np.ndarray) -> np.ndarray: - # Central finite difference for gradient - h = 1e-5 + # Use a more robust finite difference scheme + h = np.maximum(1e-8, 1e-8 * np.abs(x)) # Adaptive step size return (probability_y1(x + h, a, b, c, d) - probability_y1(x - h, a, b, c, d)) / (2 * h) - - product = (p_y1_grad(mu) ** 2) / (p_y1 * (1 - p_y1)) + + product = (p_y1_grad(mu) ** 2) / (p_y1_clipped * (1 - p_y1_clipped)) information = np.sum(product) return information @@ -59,7 +48,7 @@ def log_prior(x): (score_values ** 2) * prior.pdf(x_vals), x_vals ) - + return information From 4e53b91fd116b62e60c9ddeb4206b24870c3c0ca Mon Sep 17 00:00:00 2001 From: Tim Schaefer Date: Wed, 24 Sep 2025 13:58:27 +0200 Subject: [PATCH 017/137] CHG: adapt number of empty lines --- adaptivetesting/math/estimators/__functions/__estimators.py | 1 + adaptivetesting/math/estimators/__test_information.py | 1 + 2 files changed, 2 insertions(+) diff --git a/adaptivetesting/math/estimators/__functions/__estimators.py b/adaptivetesting/math/estimators/__functions/__estimators.py index 182f168..8dd1b11 100644 --- a/adaptivetesting/math/estimators/__functions/__estimators.py +++ b/adaptivetesting/math/estimators/__functions/__estimators.py @@ -2,6 +2,7 @@ from scipy.optimize import minimize_scalar, OptimizeResult # type: ignore from ....models.__algorithm_exception import AlgorithmException + def probability_y1(mu: np.ndarray, a: np.ndarray, b: np.ndarray, diff --git a/adaptivetesting/math/estimators/__test_information.py b/adaptivetesting/math/estimators/__test_information.py index e6ff67b..1fd02ba 100644 --- a/adaptivetesting/math/estimators/__test_information.py +++ b/adaptivetesting/math/estimators/__test_information.py @@ -5,6 +5,7 @@ import numpy from scipy.differentiate import derivative + def item_information_function( mu: np.ndarray, a: np.ndarray, From 27a8e26d8379446d8768a9e50e98c4957576fdae Mon Sep 17 00:00:00 2001 From: codecon Date: Thu, 25 Sep 2025 10:23:18 +0200 Subject: [PATCH 018/137] progress --- .../math/content_balancing/__constraint.py | 2 +- .../math/content_balancing/__functions.py | 60 +- .../__weighted_penalty_model.py | 156 ++++ .../tests/test_weighted_penalty_model.py | 814 ++++++++++++++++++ 4 files changed, 1011 insertions(+), 21 deletions(-) create mode 100644 adaptivetesting/math/content_balancing/__weighted_penalty_model.py create mode 100644 adaptivetesting/tests/test_weighted_penalty_model.py diff --git a/adaptivetesting/math/content_balancing/__constraint.py b/adaptivetesting/math/content_balancing/__constraint.py index d1796ed..0f74825 100644 --- a/adaptivetesting/math/content_balancing/__constraint.py +++ b/adaptivetesting/math/content_balancing/__constraint.py @@ -4,6 +4,6 @@ class Constraint: name: str weight: float - proportion: float + prevalence: float lower: float | None = None upper: float | None = None \ No newline at end of file diff --git a/adaptivetesting/math/content_balancing/__functions.py b/adaptivetesting/math/content_balancing/__functions.py index e87ca9b..be2762d 100644 --- a/adaptivetesting/math/content_balancing/__functions.py +++ b/adaptivetesting/math/content_balancing/__functions.py @@ -13,12 +13,13 @@ def compute_priority_index(item: TestItem, """Calculates the priority index of a given item. Args: - item (TestItem): Item for which to calcualte the priority index + item (TestItem): Item for which to calculate the priority index group_weights (dict[str, float]): Dictionary with the group names and their weights Example: `{"math": 1, "english": 1}`. These weight show how important a specific constraint is for the item selection process required_items (int): number of items required to be shown per constraint shown_item (int): number of items already shown per constraint + current_ability (float): currently estimated ability level Returns: float: priority index of an item @@ -53,7 +54,7 @@ def compute_priority_index(item: TestItem, def compute_quota_left(required_items: int, shown_items: int) -> float: """ - Claculates the quota left (items left allowed to be shown) for a given constraint/group. + Calculates the quota left (items left allowed to be shown) for a given constraint/group. Args: required_items (int): number of required items per constraint @@ -77,10 +78,10 @@ def compute_prop(n_administered: int, of items with a specific constraint Args: - n_administered (int): number of items constraint items administered + n_administered (int): number of constraint items administered prevalence (float): proportion of items in the pool with given constraint n_remaining (int): remaining items in pool with constraint - test_length (int): lenght of the test + test_length (int): length of the test Returns: float: expected proportion @@ -122,30 +123,35 @@ def compute_penalty_value(prop: float, if prop < lower: d = lower - mid k = 2 - penalty_value = ((1 / k * d) * + penalty_value = ((1 / (k * d)) * (compute_expected_difference(proportion=prop, constraint_target=mid) ** 2) + (d / k)) if prop >= upper: a = upper - mid k = 2 - penalty_value = ((1 / k * a) * + penalty_value = ((1 / (k * a)) * (compute_expected_difference(proportion=prop, constraint_target=mid) ** 2) + - (d / a)) + (a / k)) - if upper > prop and prop >= lower: + if upper > prop >= lower: penalty_value = compute_expected_difference(proportion=prop, constraint_target=mid) return penalty_value + def compute_total_content_penalty_value_for_item(item: TestItem, - constraints: list[Constraint]) -> float: + shown_items: list[TestItem], + available_items: list[TestItem], + constraints: list[Constraint]) -> float: """Calculates the total content penalty value for a given item Args: item (TestItem): given test item - group_weights (dict[str, float]): dictionary of group key and weight + shown_items: + available_items: + constraints (list[Constraint]): YYYYYY Returns: float: total content penalty value @@ -160,9 +166,23 @@ def compute_total_content_penalty_value_for_item(item: TestItem, raise ValueError("lower cannot be None here.") if constraint.upper is None: raise ValueError("upper cannot be None here.") + # find number of administered items within constraint + n_administered = len([ + item for item in shown_items if constraint.name in item.additional_properties["category"] + ]) + n_remaining = len(available_items) + test_length = len(shown_items) + # calculation + proportion = compute_prop( + n_administered=n_administered, + prevalence=constraint.prevalence, + n_remaining=n_remaining, + test_length=test_length + ) + total_penalty_value = total_penalty_value + compute_penalty_value( - prop=constraint.proportion, + prop=proportion, lower=constraint.lower, upper=constraint.upper ) * constraint.weight @@ -170,33 +190,33 @@ def compute_total_content_penalty_value_for_item(item: TestItem, return total_penalty_value def standardize_total_content_constraint_penalty_value(item_penality_value: float, - min: float, - max: float) -> float: - """Standardize total content contraint penalty values. + minimum: float, + maximum: float) -> float: + """Standardize total content constraint penalty values. Args: item_penality_value (float): unstandardized item penalty value - min (float): minimum of the item penalty values over all eligible items - max (float): maximum of the item penalty values over all eligible items + minimum (float): minimum of the item penalty values over all eligible items + maximum (float): maximum of the item penalty values over all eligible items Returns: float: standardized total content constraint penalty value """ - standardized_value = (item_penality_value - min) / (max - min) + standardized_value = (item_penality_value - minimum) / (maximum - minimum) return standardized_value def standardize_item_information(item_information: float, - max: float) -> float: + maximum: float) -> float: """Standardize the item information Args: item_information (float): information of an item - max (float): maximum information value across all eligible items + maximum (float): maximum information value across all eligible items Returns: float: standardized item information """ - standardized_item_information_value = item_information / max + standardized_item_information_value = item_information / maximum return standardized_item_information_value def compute_information_penalty_value(standardized_item_information: float) -> float: diff --git a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py new file mode 100644 index 0000000..efcd4a2 --- /dev/null +++ b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py @@ -0,0 +1,156 @@ +from typing import Literal +import numpy as np +from ..estimators.__test_information import item_information_function +from ...models.__test_item import TestItem +from .__functions import ( + compute_prop, + compute_expected_difference, + compute_penalty_value, + compute_total_content_penalty_value_for_item, + standardize_total_content_constraint_penalty_value, + standardize_item_information, + compute_information_penalty_value, + compute_weighted_penalty_value +) +from .__constraint import Constraint + + +CONSTRAINT_GROUP = Literal["A", "B", "C"] +ITEM_GROUP = Literal["green", "orange", "yellow", "red", None] + + +class WeightedPenaltyModel: + def __int__(self, + items: list[TestItem], + shown_items: list[TestItem], + ability: float, + constraints: list[Constraint], + constraint_weight: float, + information_weight: float + ): + self.items = items + self.ability = ability + self.constraints = constraints + + + # calculate weighted penality value for each eligible item in the pool + eligible_items: list[tuple[TestItem, float, ITEM_GROUP]] = [] + # calculate item information for every item + item_information_list = [ + float(item_information_function( + mu=np.array(ability), + a=np.array(item.a), + b=np.array(item.b), + c=np.array(item.c), + d=np.array(item.d) + )) + for item in self.items + ] + + max_item = max(item_information_list) + + content_penalties = [ + compute_total_content_penalty_value_for_item( + item=item, + shown_items=shown_items, + available_items=items, + constraints=constraints + ) + for item in items + ] + + max_content_penalties = max(content_penalties) + min_content_penalties = min(content_penalties) + + for i, item in enumerate(self.items): + weighted_penalty_value = self.__calculate_weighted_penalty( + item_information=item_information_list[i], + max_information=max_item, + constraint_weight=constraint_weight, + information_weight=information_weight, + content_penalty=content_penalties[i], + maximum_total_content_penalty=max_content_penalties, + minimum_total_content_penalty=min_content_penalties, + ) + + eligible_items.append((item, weighted_penalty_value, None)) + + group_assignment: list[tuple[Constraint, CONSTRAINT_GROUP]] = [] + # assign each constraint to a color group + for constraint in constraints: + # calculate proportion of the constraint + n_administered: int = len( + [item for item in shown_items if constraint.name in item.additional_properties["category"]] + ) + n_remaining: int = len( + [item for item in self.items if constraint.name in item.additional_properties["category"]] + ) + test_length: int = len(shown_items) + prop = compute_prop( + n_administered=n_administered, + n_remaining=n_remaining, + prevalence=constraint.prevalence, + test_length=test_length, + ) + # assign color group per proportion + if prop <= constraint.lower: + group_assignment.append((constraint, "A")) + if constraint.lower <= prop <= constraint.upper: + group_assignment.append((constraint, "B")) + if constraint.upper <= prop: + group_assignment.append((constraint, "C")) + + # form a list of candidate items + for i, item in enumerate(eligible_items): + # find associated constraint + associated_constraints = [constraint_assignment + for constraint_assignment in group_assignment if constraint_assignment[0].name in item[0].additional_properties["category"] + ] + + # if all associated constraints A or B -> green group + # if all A, B, C, or A, C -> orange + # if all B -> yellow + # if all B, C -> red + + # exposure control? + # select first item in the list to be administered + + @staticmethod + def __calculate_weighted_penalty(content_penalty: float, + minimum_total_content_penalty: float, + maximum_total_content_penalty: float, + item_information: float, + max_information: float, + constraint_weight: float, + information_weight: float + ) -> float: + # reference content penalty + total_content_penalty_value = content_penalty + + # standardize total content constraint penalty value + standardized_total_content_penalty_value = standardize_total_content_constraint_penalty_value( + item_penality_value=total_content_penalty_value, + minimum=minimum_total_content_penalty, + maximum=maximum_total_content_penalty + ) + + # calculate standardized item information + standardized_item_information = standardize_item_information( + item_information=item_information, + maximum=max_information + ) + + # calculate information penalty value + information_penalty_value = compute_information_penalty_value( + standardized_item_information=standardized_item_information + ) + + # compute weighted penality value + weighted_penalty_value = compute_weighted_penalty_value( + constraint_weight=constraint_weight, + standardized_constraint_penalty_value=standardized_total_content_penalty_value, + information_weight=information_weight, + information_penalty_value=information_penalty_value, + ) + return weighted_penalty_value + diff --git a/adaptivetesting/tests/test_weighted_penalty_model.py b/adaptivetesting/tests/test_weighted_penalty_model.py new file mode 100644 index 0000000..7e5e4c0 --- /dev/null +++ b/adaptivetesting/tests/test_weighted_penalty_model.py @@ -0,0 +1,814 @@ +""" +Comprehensive test suite for the weighted penalty model functions used in content balancing. + +This module tests all functions related to content balancing penalty calculations, +including proportion calculations, penalty computations, standardization functions, +and weighted penalty value calculations. +""" + +import unittest +import numpy as np +from unittest.mock import patch, MagicMock + +from adaptivetesting.models import TestItem, ItemSelectionException +from adaptivetesting.math.content_balancing.__constraint import Constraint +from adaptivetesting.math.content_balancing.__functions import ( + compute_prop, + compute_expected_difference, + compute_penalty_value, + compute_total_content_penalty_value_for_item, + standardize_total_content_constraint_penalty_value, + standardize_item_information, + compute_information_penalty_value, + compute_weighted_penalty_value +) + + +class TestWeightedPenaltyModel(unittest.TestCase): + """ + Comprehensive test suite for weighted penalty model functions. + + This test class is organized into several test groups: + 1. Basic utility functions (proportion, expected difference) + 2. Penalty value calculations + 3. Standardization functions + 4. Integration and workflow tests + """ + + def setUp(self): + """Set up comprehensive test fixtures for all test scenarios.""" + # Create diverse test items with different parameters + self.item_math_algebra = self._create_test_item( + item_id=1, + a=1.5, b=-0.5, c=0.2, d=0.9, + categories=["Math", "Algebra"] + ) + + self.item_english = self._create_test_item( + item_id=2, + a=1.2, b=0.3, c=0.1, d=1.0, + categories=["English"] + ) + + self.item_science = self._create_test_item( + item_id=3, + a=2.0, b=0.0, c=0.15, d=1.0, + categories=["Science"] + ) + + self.item_multiple_categories = self._create_test_item( + item_id=4, + a=1.8, b=-1.0, c=0.25, d=0.8, + categories=["Math", "Science", "Advanced"] + ) + + # Create comprehensive constraint set + self.constraint_math = Constraint( + name="Math", + weight=0.8, + proportion=0.6, + lower=0.4, + upper=0.7 + ) + + self.constraint_algebra = Constraint( + name="Algebra", + weight=0.5, + proportion=0.3, + lower=0.2, + upper=0.4 + ) + + self.constraint_english = Constraint( + name="English", + weight=1.0, + proportion=0.8, + lower=0.3, + upper=0.9 + ) + + self.constraint_science = Constraint( + name="Science", + weight=0.7, + proportion=0.25, + lower=0.1, + upper=0.4 + ) + + self.constraint_advanced = Constraint( + name="Advanced", + weight=1.2, + proportion=0.15, + lower=0.05, + upper=0.25 + ) + + self.all_constraints = [ + self.constraint_math, + self.constraint_algebra, + self.constraint_english, + self.constraint_science, + self.constraint_advanced + ] + + def _create_test_item(self, item_id: int, a: float, b: float, c: float, d: float, categories: list[str]) -> TestItem: + """Helper method to create test items with consistent setup.""" + item = TestItem() + item.id = item_id + item.a = a + item.b = b + item.c = c + item.d = d + item.additional_properties = {"category": categories} + return item + + # =================================================================== + # GROUP 1: Basic Utility Function Tests + # =================================================================== + + def test_compute_prop_standard_cases(self): + """Test basic proportion calculation with standard scenarios.""" + # Standard case with some administered items + result = compute_prop(n_administered=5, prevalence=0.3, n_remaining=10, test_length=20) + expected = (5 + 0.3 * 10) / 20 # (5 + 3) / 20 = 0.4 + self.assertAlmostEqual(result, expected, places=6) + + # Different values to ensure calculation is correct + result = compute_prop(n_administered=3, prevalence=0.5, n_remaining=12, test_length=15) + expected = (3 + 0.5 * 12) / 15 # (3 + 6) / 15 = 0.6 + self.assertAlmostEqual(result, expected, places=6) + + def test_compute_prop_edge_cases(self): + """Test proportion calculation with edge cases and boundary conditions.""" + # No items administered yet + result = compute_prop(n_administered=0, prevalence=0.5, n_remaining=20, test_length=20) + expected = (0 + 0.5 * 20) / 20 # 0.5 + self.assertAlmostEqual(result, expected) + + # All items administered (no remaining items) + result = compute_prop(n_administered=20, prevalence=0.5, n_remaining=0, test_length=20) + expected = (20 + 0.5 * 0) / 20 # 1.0 + self.assertAlmostEqual(result, expected) + + # Zero prevalence + result = compute_prop(n_administered=5, prevalence=0.0, n_remaining=15, test_length=20) + expected = (5 + 0.0 * 15) / 20 # 0.25 + self.assertAlmostEqual(result, expected) + + # Full prevalence (all remaining items have the constraint) + result = compute_prop(n_administered=2, prevalence=1.0, n_remaining=8, test_length=10) + expected = (2 + 1.0 * 8) / 10 # 1.0 + self.assertAlmostEqual(result, expected) + + def test_compute_prop_extreme_cases(self): + """Test proportion calculation with extreme but valid inputs.""" + # Very small test + result = compute_prop(n_administered=1, prevalence=0.5, n_remaining=1, test_length=2) + expected = (1 + 0.5 * 1) / 2 # 0.75 + self.assertAlmostEqual(result, expected) + + # Very large test + result = compute_prop(n_administered=500, prevalence=0.3, n_remaining=1500, test_length=2000) + expected = (500 + 0.3 * 1500) / 2000 # (500 + 450) / 2000 = 0.475 + self.assertAlmostEqual(result, expected) + + def test_compute_expected_difference_standard_cases(self): + """Test expected difference calculation with various scenarios.""" + # Positive difference (proportion exceeds target) + result = compute_expected_difference(proportion=0.6, constraint_target=0.5) + self.assertAlmostEqual(result, 0.1) + + # Negative difference (proportion below target) + result = compute_expected_difference(proportion=0.3, constraint_target=0.7) + self.assertAlmostEqual(result, -0.4) + + # Zero difference (exactly at target) + result = compute_expected_difference(proportion=0.5, constraint_target=0.5) + self.assertAlmostEqual(result, 0.0) + + def test_compute_expected_difference_extreme_cases(self): + """Test expected difference calculation with extreme values.""" + # Maximum positive difference + result = compute_expected_difference(proportion=1.0, constraint_target=0.0) + self.assertAlmostEqual(result, 1.0) + + # Maximum negative difference + result = compute_expected_difference(proportion=0.0, constraint_target=1.0) + self.assertAlmostEqual(result, -1.0) + + # Very small differences (precision test) + result = compute_expected_difference(proportion=0.501, constraint_target=0.5) + self.assertAlmostEqual(result, 0.001, places=6) + + # =================================================================== + # GROUP 2: Penalty Value Calculation Tests + # =================================================================== + + def test_compute_penalty_value_within_bounds(self): + """Test penalty value calculation when proportion is within acceptable bounds.""" + lower, upper = 0.3, 0.7 + mid = (upper + lower) / 2 # 0.5 + + # Exactly at midpoint + result = compute_penalty_value(prop=0.5, lower=lower, upper=upper) + expected = 0.5 - mid # 0.0 + self.assertAlmostEqual(result, expected, places=6) + + # Slightly above midpoint + result = compute_penalty_value(prop=0.6, lower=lower, upper=upper) + expected = 0.6 - mid # 0.1 + self.assertAlmostEqual(result, expected, places=6) + + # Slightly below midpoint + result = compute_penalty_value(prop=0.4, lower=lower, upper=upper) + expected = 0.4 - mid # -0.1 + self.assertAlmostEqual(result, expected, places=6) + + def test_compute_penalty_value_at_boundaries(self): + """Test penalty value calculation exactly at boundary conditions.""" + lower, upper = 0.3, 0.7 + mid = (upper + lower) / 2 # 0.5 + + # At lower bound (should be treated as within bounds) + result = compute_penalty_value(prop=0.3, lower=lower, upper=upper) + expected = 0.3 - mid # -0.2 + self.assertAlmostEqual(result, expected, places=6) + + # Just above lower bound + result = compute_penalty_value(prop=0.300001, lower=lower, upper=upper) + expected = 0.300001 - mid + self.assertAlmostEqual(result, expected, places=6) + + def test_compute_penalty_value_below_lower_bound(self): + """Test penalty value calculation when proportion is below lower bound.""" + lower, upper = 0.3, 0.7 + prop = 0.2 # Below lower bound + + mid = (upper + lower) / 2 # 0.5 + d = lower - mid # 0.3 - 0.5 = -0.2 + k = 2 + expected_diff = prop - mid # 0.2 - 0.5 = -0.3 + + # Based on actual implementation: (1 / (k * d)) * expected_diff^2 + (d / k) + expected = (1 / (k * d)) * (expected_diff ** 2) + (d / k) + # expected = (1 / (2 * -0.2)) * (-0.3)^2 + (-0.2 / 2) + # expected = (1 / -0.4) * 0.09 + (-0.1) + # expected = -2.5 * 0.09 - 0.1 = -0.225 - 0.1 = -0.325 + + result = compute_penalty_value(prop=prop, lower=lower, upper=upper) + self.assertAlmostEqual(result, expected, places=6) + + def test_compute_penalty_value_above_upper_bound(self): + """Test penalty value calculation when proportion is above upper bound.""" + lower, upper = 0.3, 0.7 + prop = 0.8 # Above upper bound + + mid = (upper + lower) / 2 # 0.5 + a = upper - mid # 0.7 - 0.5 = 0.2 + k = 2 + expected_diff = prop - mid # 0.8 - 0.5 = 0.3 + + # Based on actual implementation: (1 / (k * a)) * expected_diff^2 + (a / k) + expected = (1 / (k * a)) * (expected_diff ** 2) + (a / k) + # expected = (1 / (2 * 0.2)) * (0.3)^2 + (0.2 / 2) + # expected = (1 / 0.4) * 0.09 + 0.1 + # expected = 2.5 * 0.09 + 0.1 = 0.225 + 0.1 = 0.325 + + result = compute_penalty_value(prop=prop, lower=lower, upper=upper) + self.assertAlmostEqual(result, expected, places=6) + + def test_compute_penalty_value_extreme_violations(self): + """Test penalty value calculation with extreme constraint violations.""" + lower, upper = 0.4, 0.6 + + # Very far below lower bound + result = compute_penalty_value(prop=0.1, lower=lower, upper=upper) + self.assertIsInstance(result, float) + self.assertFalse(np.isnan(result)) + self.assertLess(result, 0) # Should be negative penalty for being below + + # Very far above upper bound + result = compute_penalty_value(prop=0.9, lower=lower, upper=upper) + self.assertIsInstance(result, float) + self.assertFalse(np.isnan(result)) + self.assertGreater(result, 0) # Should be positive penalty for being above + + def test_compute_penalty_value_narrow_bounds(self): + """Test penalty value calculation with very narrow acceptable bounds.""" + lower, upper = 0.49, 0.51 + mid = (upper + lower) / 2 # 0.5 + + # Within narrow bounds + result = compute_penalty_value(prop=0.5, lower=lower, upper=upper) + expected = 0.5 - mid # 0.0 + self.assertAlmostEqual(result, expected, places=6) + + # Just outside narrow bounds (below) + result = compute_penalty_value(prop=0.48, lower=lower, upper=upper) + self.assertIsInstance(result, float) + self.assertFalse(np.isnan(result)) + + # =================================================================== + # GROUP 3: Total Content Penalty Tests + # =================================================================== + + def test_compute_total_content_penalty_value_single_constraint(self): + """Test total content penalty calculation for items with single constraint.""" + # Mock penalty calculation to focus on the aggregation logic + with patch('adaptivetesting.math.content_balancing.__functions.compute_penalty_value') as mock_penalty: + mock_penalty.return_value = 0.15 + + result = compute_total_content_penalty_value_for_item( + item=self.item_english, # Has only "English" category + constraints=self.all_constraints + ) + + # Should only match English constraint with weight 1.0 + expected = 0.15 * 1.0 + self.assertAlmostEqual(result, expected, places=6) + mock_penalty.assert_called_once() + + def test_compute_total_content_penalty_value_multiple_constraints(self): + """Test total content penalty calculation for items with multiple constraints.""" + with patch('adaptivetesting.math.content_balancing.__functions.compute_penalty_value') as mock_penalty: + # Return different penalty values for different constraints + mock_penalty.side_effect = [0.1, 0.2] # Math=0.1, Algebra=0.2 + + result = compute_total_content_penalty_value_for_item( + item=self.item_math_algebra, # Has ["Math", "Algebra"] categories + constraints=self.all_constraints + ) + + # Math: penalty=0.1, weight=0.8 -> 0.08 + # Algebra: penalty=0.2, weight=0.5 -> 0.10 + # Total: 0.08 + 0.10 = 0.18 + expected = 0.1 * 0.8 + 0.2 * 0.5 + self.assertAlmostEqual(result, expected, places=6) + self.assertEqual(mock_penalty.call_count, 2) + + def test_compute_total_content_penalty_value_many_constraints(self): + """Test total content penalty calculation for items with many constraints.""" + with patch('adaptivetesting.math.content_balancing.__functions.compute_penalty_value') as mock_penalty: + # Return different penalty values for Math, Science, and Advanced + mock_penalty.side_effect = [0.3, 0.1, 0.4] + + result = compute_total_content_penalty_value_for_item( + item=self.item_multiple_categories, # Has ["Math", "Science", "Advanced"] + constraints=self.all_constraints + ) + + # Math: penalty=0.3, weight=0.8 -> 0.24 + # Science: penalty=0.1, weight=0.7 -> 0.07 + # Advanced: penalty=0.4, weight=1.2 -> 0.48 + # Total: 0.24 + 0.07 + 0.48 = 0.79 + expected = 0.3 * 0.8 + 0.1 * 0.7 + 0.4 * 1.2 + self.assertAlmostEqual(result, expected, places=6) + self.assertEqual(mock_penalty.call_count, 3) + + def test_compute_total_content_penalty_value_no_matching_constraints(self): + """Test total content penalty calculation with no matching constraints.""" + # Create item with category not in any constraint + item_orphan = self._create_test_item( + item_id=99, a=1.0, b=0.0, c=0.0, d=1.0, + categories=["Unmatched", "Category"] + ) + + result = compute_total_content_penalty_value_for_item( + item=item_orphan, + constraints=self.all_constraints + ) + + # No matching constraints should result in zero penalty + self.assertEqual(result, 0.0) + + def test_compute_total_content_penalty_value_constraint_bounds_validation(self): + """Test that function validates constraint bounds are not None.""" + # Create constraint with None bounds + invalid_constraint = Constraint( + name="Math", weight=1.0, proportion=0.5, + lower=None, upper=0.7 # lower is None + ) + + with self.assertRaises(ValueError) as context: + compute_total_content_penalty_value_for_item( + item=self.item_math_algebra, + constraints=[invalid_constraint] + ) + self.assertIn("lower cannot be None", str(context.exception)) + + # Test upper bound None + invalid_constraint_upper = Constraint( + name="Math", weight=1.0, proportion=0.5, + lower=0.3, upper=None # upper is None + ) + + with self.assertRaises(ValueError) as context: + compute_total_content_penalty_value_for_item( + item=self.item_math_algebra, + constraints=[invalid_constraint_upper] + ) + self.assertIn("upper cannot be None", str(context.exception)) + + # =================================================================== + # GROUP 4: Standardization Function Tests + # =================================================================== + + def test_standardize_total_content_constraint_penalty_value_normal_cases(self): + """Test standardization of content constraint penalty values in normal scenarios.""" + # Standard case: value in middle of range + result = standardize_total_content_constraint_penalty_value( + item_penality_value=0.6, + min=0.2, + max=1.0 + ) + expected = (0.6 - 0.2) / (1.0 - 0.2) # 0.4 / 0.8 = 0.5 + self.assertAlmostEqual(result, expected, places=6) + + # Different range + result = standardize_total_content_constraint_penalty_value( + item_penality_value=0.75, + min=0.5, + max=1.5 + ) + expected = (0.75 - 0.5) / (1.5 - 0.5) # 0.25 / 1.0 = 0.25 + self.assertAlmostEqual(result, expected, places=6) + + def test_standardize_total_content_constraint_penalty_value_boundary_cases(self): + """Test standardization at boundary conditions.""" + # At minimum value + result = standardize_total_content_constraint_penalty_value( + item_penality_value=0.2, + min=0.2, + max=1.0 + ) + self.assertAlmostEqual(result, 0.0, places=6) + + # At maximum value + result = standardize_total_content_constraint_penalty_value( + item_penality_value=1.0, + min=0.2, + max=1.0 + ) + self.assertAlmostEqual(result, 1.0, places=6) + + def test_standardize_total_content_constraint_penalty_value_edge_cases(self): + """Test standardization with edge cases and error conditions.""" + # Zero range (min == max) should cause division by zero + with self.assertRaises(ZeroDivisionError): + standardize_total_content_constraint_penalty_value( + item_penality_value=0.5, + min=0.5, + max=0.5 + ) + + # Negative values (valid mathematically) + result = standardize_total_content_constraint_penalty_value( + item_penality_value=-0.5, + min=-1.0, + max=0.0 + ) + expected = (-0.5 - (-1.0)) / (0.0 - (-1.0)) # 0.5 / 1.0 = 0.5 + self.assertAlmostEqual(result, expected, places=6) + + # Value outside range (above max) + result = standardize_total_content_constraint_penalty_value( + item_penality_value=1.5, + min=0.2, + max=1.0 + ) + expected = (1.5 - 0.2) / (1.0 - 0.2) # 1.3 / 0.8 = 1.625 + self.assertAlmostEqual(result, expected, places=6) + + # Value outside range (below min) + result = standardize_total_content_constraint_penalty_value( + item_penality_value=0.1, + min=0.2, + max=1.0 + ) + expected = (0.1 - 0.2) / (1.0 - 0.2) # -0.1 / 0.8 = -0.125 + self.assertAlmostEqual(result, expected, places=6) + + def test_standardize_item_information_normal_cases(self): + """Test item information standardization in normal scenarios.""" + # Standard case + result = standardize_item_information(item_information=0.8, max=2.0) + expected = 0.8 / 2.0 # 0.4 + self.assertAlmostEqual(result, expected, places=6) + + # Different values + result = standardize_item_information(item_information=1.5, max=3.0) + expected = 1.5 / 3.0 # 0.5 + self.assertAlmostEqual(result, expected, places=6) + + def test_standardize_item_information_boundary_cases(self): + """Test item information standardization at boundaries.""" + # Zero information + result = standardize_item_information(item_information=0.0, max=2.0) + self.assertAlmostEqual(result, 0.0, places=6) + + # Maximum information + result = standardize_item_information(item_information=2.0, max=2.0) + self.assertAlmostEqual(result, 1.0, places=6) + + # Information exceeds maximum (mathematically valid) + result = standardize_item_information(item_information=3.0, max=2.0) + self.assertAlmostEqual(result, 1.5, places=6) + + def test_standardize_item_information_error_cases(self): + """Test item information standardization error conditions.""" + # Division by zero when max is zero + with self.assertRaises(ZeroDivisionError): + standardize_item_information(item_information=1.0, max=0.0) + + # Negative maximum (mathematically valid but unusual) + result = standardize_item_information(item_information=1.0, max=-2.0) + expected = 1.0 / -2.0 # -0.5 + self.assertAlmostEqual(result, expected, places=6) + + # =================================================================== + # GROUP 5: Information Penalty Function Tests + # =================================================================== + + def test_compute_information_penalty_value_standard_cases(self): + """Test information penalty value computation with standard inputs.""" + # Mid-range value + result = compute_information_penalty_value(standardized_item_information=0.8) + expected = -(0.8 ** 2) # -0.64 + self.assertAlmostEqual(result, expected, places=6) + + # Different value + result = compute_information_penalty_value(standardized_item_information=0.5) + expected = -(0.5 ** 2) # -0.25 + self.assertAlmostEqual(result, expected, places=6) + + def test_compute_information_penalty_value_boundary_cases(self): + """Test information penalty value at boundary conditions.""" + # Zero information (no penalty) + result = compute_information_penalty_value(standardized_item_information=0.0) + self.assertAlmostEqual(result, 0.0, places=6) + + # Perfect information (maximum penalty) + result = compute_information_penalty_value(standardized_item_information=1.0) + self.assertAlmostEqual(result, -1.0, places=6) + + def test_compute_information_penalty_value_extreme_cases(self): + """Test information penalty value with extreme inputs.""" + # Information above 1.0 (possible due to standardization issues) + result = compute_information_penalty_value(standardized_item_information=1.5) + expected = -(1.5 ** 2) # -2.25 + self.assertAlmostEqual(result, expected, places=6) + + # Very small positive information + result = compute_information_penalty_value(standardized_item_information=0.001) + expected = -(0.001 ** 2) # -0.000001 + self.assertAlmostEqual(result, expected, places=9) + + # Negative information (mathematically unusual but handled) + result = compute_information_penalty_value(standardized_item_information=-0.5) + expected = -((-0.5) ** 2) # -0.25 + self.assertAlmostEqual(result, expected, places=6) + + # =================================================================== + # GROUP 6: Weighted Penalty Function Tests + # =================================================================== + + def test_compute_weighted_penalty_value_balanced_weights(self): + """Test weighted penalty value computation with balanced weights.""" + result = compute_weighted_penalty_value( + constraint_weight=0.5, + standardized_constraint_penalty_value=0.4, + information_weight=0.5, + information_penalty_value=-0.6 + ) + expected = 0.5 * 0.4 + 0.5 * (-0.6) # 0.2 - 0.3 = -0.1 + self.assertAlmostEqual(result, expected, places=6) + + def test_compute_weighted_penalty_value_constraint_focused(self): + """Test weighted penalty value computation favoring constraint penalties.""" + result = compute_weighted_penalty_value( + constraint_weight=0.8, + standardized_constraint_penalty_value=0.6, + information_weight=0.2, + information_penalty_value=-0.8 + ) + expected = 0.8 * 0.6 + 0.2 * (-0.8) # 0.48 - 0.16 = 0.32 + self.assertAlmostEqual(result, expected, places=6) + + def test_compute_weighted_penalty_value_information_focused(self): + """Test weighted penalty value computation favoring information penalties.""" + result = compute_weighted_penalty_value( + constraint_weight=0.2, + standardized_constraint_penalty_value=0.7, + information_weight=0.8, + information_penalty_value=-0.9 + ) + expected = 0.2 * 0.7 + 0.8 * (-0.9) # 0.14 - 0.72 = -0.58 + self.assertAlmostEqual(result, expected, places=6) + + def test_compute_weighted_penalty_value_extreme_weights(self): + """Test weighted penalty value computation with extreme weight distributions.""" + # Only constraint weight (information weight = 0) + result = compute_weighted_penalty_value( + constraint_weight=1.0, + standardized_constraint_penalty_value=0.5, + information_weight=0.0, + information_penalty_value=-0.8 + ) + self.assertAlmostEqual(result, 0.5, places=6) + + # Only information weight (constraint weight = 0) + result = compute_weighted_penalty_value( + constraint_weight=0.0, + standardized_constraint_penalty_value=0.5, + information_weight=1.0, + information_penalty_value=-0.8 + ) + self.assertAlmostEqual(result, -0.8, places=6) + + # Both weights zero + result = compute_weighted_penalty_value( + constraint_weight=0.0, + standardized_constraint_penalty_value=0.5, + information_weight=0.0, + information_penalty_value=-0.8 + ) + self.assertAlmostEqual(result, 0.0, places=6) + + def test_compute_weighted_penalty_value_negative_penalties(self): + """Test weighted penalty value computation with negative penalty values.""" + result = compute_weighted_penalty_value( + constraint_weight=0.6, + standardized_constraint_penalty_value=-0.3, # Negative constraint penalty + information_weight=0.4, + information_penalty_value=-0.7 # Negative information penalty + ) + expected = 0.6 * (-0.3) + 0.4 * (-0.7) # -0.18 - 0.28 = -0.46 + self.assertAlmostEqual(result, expected, places=6) + + def test_compute_weighted_penalty_value_weights_exceeding_one(self): + """Test weighted penalty value computation with weights that sum to more than 1.""" + # Weights summing to more than 1.0 (mathematically valid) + result = compute_weighted_penalty_value( + constraint_weight=0.8, + standardized_constraint_penalty_value=0.5, + information_weight=0.7, # Total weight = 1.5 + information_penalty_value=-0.4 + ) + expected = 0.8 * 0.5 + 0.7 * (-0.4) # 0.4 - 0.28 = 0.12 + self.assertAlmostEqual(result, expected, places=6) + + # =================================================================== + # GROUP 7: Integration and Workflow Tests + # =================================================================== + + def test_complete_penalty_calculation_workflow(self): + """Integration test for the complete weighted penalty calculation workflow.""" + # This test simulates a realistic end-to-end penalty calculation + + # Step 1: Calculate total content penalty using actual function (not mocked) + # We'll use constraint with known bounds to get predictable results + test_constraint = Constraint( + name="Math", + weight=0.8, + proportion=0.4, # This will be used in penalty calculation + lower=0.3, + upper=0.7 + ) + + content_penalty = compute_total_content_penalty_value_for_item( + item=self.item_math_algebra, + constraints=[test_constraint] + ) + + # Step 2: Standardize content penalty (using realistic min/max) + standardized_content_penalty = standardize_total_content_constraint_penalty_value( + item_penality_value=content_penalty, + min=-0.5, # Realistic minimum penalty + max=0.8 # Realistic maximum penalty + ) + + # Step 3: Calculate and standardize item information + mock_item_information = 1.2 # Realistic information value + standardized_information = standardize_item_information( + item_information=mock_item_information, + max=2.5 # Realistic maximum information + ) + + # Step 4: Calculate information penalty + information_penalty = compute_information_penalty_value(standardized_information) + + # Step 5: Calculate weighted penalty + weighted_penalty = compute_weighted_penalty_value( + constraint_weight=0.6, + standardized_constraint_penalty_value=standardized_content_penalty, + information_weight=0.4, + information_penalty_value=information_penalty + ) + + # Verify the result is mathematically valid + self.assertIsInstance(weighted_penalty, float) + self.assertFalse(np.isnan(weighted_penalty)) + self.assertFalse(np.isinf(weighted_penalty)) + + def test_penalty_calculation_with_multiple_items_and_constraints(self): + """Integration test comparing penalty calculations across multiple items.""" + penalties = {} + + # Calculate penalties for different items using the same constraints + test_items = [ + self.item_math_algebra, + self.item_english, + self.item_science, + self.item_multiple_categories + ] + + # Use a subset of constraints for predictable results + test_constraints = [self.constraint_math, self.constraint_english, self.constraint_science] + + for item in test_items: + content_penalty = compute_total_content_penalty_value_for_item( + item=item, + constraints=test_constraints + ) + penalties[item.id] = content_penalty + + # Verify all penalties are valid numbers + for item_id, penalty in penalties.items(): + self.assertIsInstance(penalty, float) + self.assertFalse(np.isnan(penalty)) + self.assertFalse(np.isinf(penalty)) + + # Items with no matching constraints should have zero penalty + # (item_science doesn't match any of our test constraints initially) + # This assumes our test setup doesn't include Science in the first three constraints + + def test_standardization_workflow_robustness(self): + """Test that standardization functions handle various realistic scenarios.""" + # Test various penalty value ranges + penalty_values = [-0.8, -0.2, 0.0, 0.3, 0.9, 1.5] + information_values = [0.0, 0.2, 0.8, 1.0, 1.3, 2.0] + + # Test content penalty standardization + for penalty in penalty_values: + result = standardize_total_content_constraint_penalty_value( + item_penality_value=penalty, + min=-1.0, + max=2.0 + ) + self.assertIsInstance(result, float) + self.assertFalse(np.isnan(result)) + + # Test information standardization + for info in information_values: + result = standardize_item_information( + item_information=info, + max=2.5 + ) + self.assertIsInstance(result, float) + self.assertFalse(np.isnan(result)) + + # Test information penalty calculation + info_penalty = compute_information_penalty_value(result) + self.assertIsInstance(info_penalty, float) + self.assertFalse(np.isnan(info_penalty)) + self.assertLessEqual(info_penalty, 0.0) # Should always be negative or zero + + def test_edge_case_combinations(self): + """Test combinations of edge cases that might occur in practice.""" + # Scenario 1: Item with constraints at exact boundaries + boundary_constraint = Constraint( + name="Math", + weight=1.0, + proportion=0.5, # Exactly at midpoint + lower=0.3, + upper=0.7 + ) + + penalty = compute_total_content_penalty_value_for_item( + item=self.item_math_algebra, + constraints=[boundary_constraint] + ) + + # Should be zero since proportion is at midpoint + self.assertAlmostEqual(penalty, 0.0, places=6) + + # Scenario 2: Very narrow constraint bounds + narrow_constraint = Constraint( + name="Math", + weight=0.5, + proportion=0.501, # Just above midpoint + lower=0.499, + upper=0.502 + ) + + penalty = compute_total_content_penalty_value_for_item( + item=self.item_math_algebra, + constraints=[narrow_constraint] + ) + + self.assertIsInstance(penalty, float) + self.assertFalse(np.isnan(penalty)) + + +if __name__ == '__main__': + unittest.main() \ No newline at end of file From 60f59d015c1013e299ea413508a3b3adfc44dfcb Mon Sep 17 00:00:00 2001 From: codecon Date: Thu, 25 Sep 2025 10:23:38 +0200 Subject: [PATCH 019/137] Fix typo --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 85d3f51..4753037 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,7 @@ - Maximum Likelihood Estimation (MLE) - Bayesian Modal Estimation (BM) - Expected A Posteriori (EAP) -- **Item Selection Strategies**: Kaximum information criterion and Urry's rule +- **Item Selection Strategies**: Maximum information criterion and Urry's rule - **Simulation Framework**: Comprehensive tools for CAT simulation and evaluation - **Real-world Application**: Direct transition from simulation to production testing - **Stopping Criteria**: Support for standard error and test length criteria From a7fb0c22cfe327c26ef5a240fb09fb9438b2ec40 Mon Sep 17 00:00:00 2001 From: codecon Date: Fri, 26 Sep 2025 13:07:43 +0200 Subject: [PATCH 020/137] update version number 1.1.3 --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 1d969bd..120a818 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name="adaptivetesting" -version="1.1.2" +version="1.1.3" description="adaptivetesting is a Python package that can be used to simulate and evaluate custom CAT scenarios as well as implement them in real-world testing scenarios from a single codebase" readme = "README.md" authors = [ From 08141b28aec804f05b4f195addfab1d047b69c09 Mon Sep 17 00:00:00 2001 From: condecon Date: Fri, 3 Oct 2025 09:43:11 +0200 Subject: [PATCH 021/137] progress --- .../math/content_balancing/__constraint.py | 3 +- .../math/content_balancing/__functions.py | 43 +- .../__weighted_penalty_model.py | 78 +- .../math/exposure_control/__init__.py | 0 .../math/exposure_control/__randomesque.py | 40 + .../tests/test_content_balacing.py | 10 +- .../tests/test_weighted_penalty_model.py | 1218 ++++++----------- 7 files changed, 553 insertions(+), 839 deletions(-) create mode 100644 adaptivetesting/math/exposure_control/__init__.py create mode 100644 adaptivetesting/math/exposure_control/__randomesque.py diff --git a/adaptivetesting/math/content_balancing/__constraint.py b/adaptivetesting/math/content_balancing/__constraint.py index 0f74825..e8d60f4 100644 --- a/adaptivetesting/math/content_balancing/__constraint.py +++ b/adaptivetesting/math/content_balancing/__constraint.py @@ -1,9 +1,10 @@ from dataclasses import dataclass + @dataclass class Constraint: name: str weight: float prevalence: float lower: float | None = None - upper: float | None = None \ No newline at end of file + upper: float | None = None diff --git a/adaptivetesting/math/content_balancing/__functions.py b/adaptivetesting/math/content_balancing/__functions.py index be2762d..13599da 100644 --- a/adaptivetesting/math/content_balancing/__functions.py +++ b/adaptivetesting/math/content_balancing/__functions.py @@ -6,10 +6,10 @@ def compute_priority_index(item: TestItem, - group_weights: dict[str, float], - required_items: int, - shown_item: int, - current_ability: float) -> float: + group_weights: dict[str, float], + required_items: int, + shown_item: int, + current_ability: float) -> float: """Calculates the priority index of a given item. Args: @@ -37,7 +37,8 @@ def compute_priority_index(item: TestItem, priority_index: float = 1.0 for group in item_groups: - priority_index = priority_index * group_weights[group] * compute_quota_left(required_items=required_items, shown_items=shown_item) + priority_index = priority_index * group_weights[group] \ + * compute_quota_left(required_items=required_items, shown_items=shown_item) # weight fisher information priority_index = priority_index * float(item_information_function( @@ -52,7 +53,7 @@ def compute_priority_index(item: TestItem, def compute_quota_left(required_items: int, - shown_items: int) -> float: + shown_items: int) -> float: """ Calculates the quota left (items left allowed to be shown) for a given constraint/group. @@ -80,7 +81,7 @@ def compute_prop(n_administered: int, Args: n_administered (int): number of constraint items administered prevalence (float): proportion of items in the pool with given constraint - n_remaining (int): remaining items in pool with constraint + n_remaining (int): remaining items in pool with constraint test_length (int): length of the test Returns: @@ -89,6 +90,7 @@ def compute_prop(n_administered: int, expected_proportion = (n_administered + prevalence * n_remaining) / test_length return expected_proportion + def compute_expected_difference(proportion: float, constraint_target: float) -> float: """Calculates expected difference between @@ -104,6 +106,7 @@ def compute_expected_difference(proportion: float, expected_difference = proportion - constraint_target return expected_difference + def compute_penalty_value(prop: float, lower: float, upper: float) -> float: @@ -123,16 +126,16 @@ def compute_penalty_value(prop: float, if prop < lower: d = lower - mid k = 2 - penalty_value = ((1 / (k * d)) * - (compute_expected_difference(proportion=prop, constraint_target=mid) ** 2) + - (d / k)) + penalty_value = ((1 / (k * d)) * (compute_expected_difference( + proportion=prop, + constraint_target=mid) ** 2) + (d / k)) if prop >= upper: a = upper - mid k = 2 - penalty_value = ((1 / (k * a)) * - (compute_expected_difference(proportion=prop, constraint_target=mid) ** 2) + - (a / k)) + penalty_value = ((1 / (k * a)) * (compute_expected_difference( + proportion=prop, + constraint_target=mid) ** 2) + (a / k)) if upper > prop >= lower: penalty_value = compute_expected_difference(proportion=prop, @@ -188,10 +191,11 @@ def compute_total_content_penalty_value_for_item(item: TestItem, ) * constraint.weight return total_penalty_value - + + def standardize_total_content_constraint_penalty_value(item_penality_value: float, - minimum: float, - maximum: float) -> float: + minimum: float, + maximum: float) -> float: """Standardize total content constraint penalty values. Args: @@ -205,6 +209,7 @@ def standardize_total_content_constraint_penalty_value(item_penality_value: floa standardized_value = (item_penality_value - minimum) / (maximum - minimum) return standardized_value + def standardize_item_information(item_information: float, maximum: float) -> float: """Standardize the item information @@ -219,6 +224,7 @@ def standardize_item_information(item_information: float, standardized_item_information_value = item_information / maximum return standardized_item_information_value + def compute_information_penalty_value(standardized_item_information: float) -> float: """Compute information penalty value with respect to the information @@ -231,6 +237,7 @@ def compute_information_penalty_value(standardized_item_information: float) -> f information_penalty = -(standardized_item_information ** 2) return information_penalty + def compute_weighted_penalty_value(constraint_weight: float, standardized_constraint_penalty_value: float, information_weight: float, @@ -248,6 +255,6 @@ def compute_weighted_penalty_value(constraint_weight: float, Returns: float: weighted penalty value """ - weighted_penalty = constraint_weight * standardized_constraint_penalty_value + information_weight * information_penalty_value + weighted_penalty = constraint_weight * standardized_constraint_penalty_value \ + + information_weight * information_penalty_value return weighted_penalty - diff --git a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py index efcd4a2..80189e7 100644 --- a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py +++ b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py @@ -4,8 +4,6 @@ from ...models.__test_item import TestItem from .__functions import ( compute_prop, - compute_expected_difference, - compute_penalty_value, compute_total_content_penalty_value_for_item, standardize_total_content_constraint_penalty_value, standardize_item_information, @@ -20,7 +18,7 @@ class WeightedPenaltyModel: - def __int__(self, + def __init__(self, items: list[TestItem], shown_items: list[TestItem], ability: float, @@ -32,19 +30,18 @@ def __int__(self, self.ability = ability self.constraints = constraints - # calculate weighted penality value for each eligible item in the pool eligible_items: list[tuple[TestItem, float, ITEM_GROUP]] = [] # calculate item information for every item item_information_list = [ float(item_information_function( - mu=np.array(ability), - a=np.array(item.a), - b=np.array(item.b), - c=np.array(item.c), - d=np.array(item.d) - )) - for item in self.items + mu=np.array(ability), + a=np.array(item.a), + b=np.array(item.b), + c=np.array(item.c), + d=np.array(item.d) + )) + for item in self.items ] max_item = max(item_information_list) @@ -93,27 +90,61 @@ def __int__(self, test_length=test_length, ) # assign color group per proportion - if prop <= constraint.lower: - group_assignment.append((constraint, "A")) - if constraint.lower <= prop <= constraint.upper: - group_assignment.append((constraint, "B")) - if constraint.upper <= prop: - group_assignment.append((constraint, "C")) + if constraint.lower is not None and constraint.upper is not None: + if prop <= constraint.lower: + group_assignment.append((constraint, "A")) + if constraint.lower <= prop <= constraint.upper: + group_assignment.append((constraint, "B")) + if constraint.upper <= prop: + group_assignment.append((constraint, "C")) + else: + raise ValueError("constraint.lower and constraint upper may not be None.") # form a list of candidate items - for i, item in enumerate(eligible_items): + for i, item_entry in enumerate(eligible_items): + item, weighted_penalty_value, _ = item_entry # find associated constraint - associated_constraints = [constraint_assignment - for constraint_assignment in group_assignment if constraint_assignment[0].name in item[0].additional_properties["category"] - ] + associated_constraints = [constraint_assignment + for constraint_assignment in group_assignment + if constraint_assignment[0].name in item.additional_properties["category"] + ] # if all associated constraints A or B -> green group + if all(group in ["A", "B"] for _, group in associated_constraints): + eligible_items[i] = (item, weighted_penalty_value, "green") # if all A, B, C, or A, C -> orange + if all(group in ["A", "B", "C"] or group in ["A", "C"] for _, group in associated_constraints): + eligible_items[i] = (item, weighted_penalty_value, "orange") # if all B -> yellow + if all(group in ["B"] for _, group in associated_constraints): + eligible_items[i] = (item, weighted_penalty_value, "yellow") # if all B, C -> red + if all(group in ["B", "C"] for _, group in associated_constraints): + eligible_items[i] = (item, weighted_penalty_value, "red") + else: + eligible_items[i] = (item, weighted_penalty_value, None) + + # order items + # between group ordering: green, orange, yellow, red + # within group ordering: ascending order of weighted penalty value + self.eligible_items = sorted( + eligible_items, + key=lambda x: ( + {"green": 0, "orange": 1, "yellow": 2, "red": 3, None: 4}[x[2]], + x[1] + ) + ) - # exposure control? - # select first item in the list to be administered + def select_item(self) -> TestItem | None: + """Select the next item to administer based on the weighted penalty model. + + Returns: + TestItem | None: The selected TestItem or None if no eligible items are available. + """ + if len(self.eligible_items) > 0: + return self.eligible_items[0][0] + else: + return None @staticmethod def __calculate_weighted_penalty(content_penalty: float, @@ -153,4 +184,3 @@ def __calculate_weighted_penalty(content_penalty: float, information_penalty_value=information_penalty_value, ) return weighted_penalty_value - diff --git a/adaptivetesting/math/exposure_control/__init__.py b/adaptivetesting/math/exposure_control/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/adaptivetesting/math/exposure_control/__randomesque.py b/adaptivetesting/math/exposure_control/__randomesque.py new file mode 100644 index 0000000..3e96616 --- /dev/null +++ b/adaptivetesting/math/exposure_control/__randomesque.py @@ -0,0 +1,40 @@ +from typing import Callable, Any +import numpy as np +from ...models.__test_item import TestItem +from ..estimators.__test_information import item_information_function + + +def radomesque_item_selection( + items: list[TestItem], + ability_estimate: float, + n_items: int, + reverse: bool = True, + item_selection_function: Callable = item_information_function, # use item selection strategy + **kwargs: Any +) -> list[TestItem]: + item_information_list: list[tuple[float, int]] = [] + for i, item in enumerate(items): + information = float(item_selection_function( + a=np.array(item.a), + b=np.array(item.b), + c=np.array(item.c), + d=np.array(item.d), + mu=np.array(ability_estimate), + **kwargs + )) + + item_information_list.append((information, i)) + + def sort_by_information(item_entry: tuple[float, int]) -> float: + return item_entry[0] + + # sort items in ascending order to have the first item be the one with the hightest + # information value (default or expected settings) + item_information_list.sort(key=sort_by_information, reverse=reverse) + + # select first n items + selected_items = item_information_list[0:n_items] + # return specific items + return_items = [items[item[1]] for item in selected_items] + return return_items + diff --git a/adaptivetesting/tests/test_content_balacing.py b/adaptivetesting/tests/test_content_balacing.py index 53223ee..2e09833 100644 --- a/adaptivetesting/tests/test_content_balacing.py +++ b/adaptivetesting/tests/test_content_balacing.py @@ -1,10 +1,10 @@ import unittest import adaptivetesting as adt import pandas as pd -import numpy as np + class TestMaximumPriorityIndex(unittest.TestCase): - def __init__(self, methodName = "runTest"): + def __init__(self, methodName="runTest"): items = pd.DataFrame({ "a": [1.32, 1.07, 0.84], @@ -43,7 +43,7 @@ def test_quota_calculation(self): def test_exception_list(self): with self.assertRaises(adt.ItemSelectionException): self.available_items[0].additional_properties["category"] = 0 - result = adt.compute_priority_index( + adt.compute_priority_index( item=self.available_items[0], group_weights={ "Math": 0.2, @@ -57,7 +57,7 @@ def test_exception_list(self): def test_exception_key(self): with self.assertRaises(adt.ItemSelectionException): self.available_items[0].additional_properties.pop("category") - result = adt.compute_priority_index( + adt.compute_priority_index( item=self.available_items[0], group_weights={ "Math": 0.2, @@ -66,4 +66,4 @@ def test_exception_key(self): required_items=10, shown_item=0, current_ability=0 - ) \ No newline at end of file + ) diff --git a/adaptivetesting/tests/test_weighted_penalty_model.py b/adaptivetesting/tests/test_weighted_penalty_model.py index 7e5e4c0..dec6e91 100644 --- a/adaptivetesting/tests/test_weighted_penalty_model.py +++ b/adaptivetesting/tests/test_weighted_penalty_model.py @@ -1,813 +1,449 @@ -""" -Comprehensive test suite for the weighted penalty model functions used in content balancing. - -This module tests all functions related to content balancing penalty calculations, -including proportion calculations, penalty computations, standardization functions, -and weighted penalty value calculations. -""" - +# flake8: noqa +# type: ignore import unittest +from unittest.mock import patch import numpy as np -from unittest.mock import patch, MagicMock - -from adaptivetesting.models import TestItem, ItemSelectionException -from adaptivetesting.math.content_balancing.__constraint import Constraint -from adaptivetesting.math.content_balancing.__functions import ( - compute_prop, - compute_expected_difference, - compute_penalty_value, - compute_total_content_penalty_value_for_item, - standardize_total_content_constraint_penalty_value, - standardize_item_information, - compute_information_penalty_value, - compute_weighted_penalty_value -) +from adaptivetesting.math.content_balancing.__weighted_penalty_model import WeightedPenaltyModel -class TestWeightedPenaltyModel(unittest.TestCase): - """ - Comprehensive test suite for weighted penalty model functions. - - This test class is organized into several test groups: - 1. Basic utility functions (proportion, expected difference) - 2. Penalty value calculations - 3. Standardization functions - 4. Integration and workflow tests - """ +class MockTestItem: + def __init__(self, id: int, a: float = 1.0, b: float = 0.0, c: float = 0.0, d: float = 1.0, category: list = None): + self.id = id + self.a = a + self.b = b + self.c = c + self.d = d + self.additional_properties = {"category": category or []} - def setUp(self): - """Set up comprehensive test fixtures for all test scenarios.""" - # Create diverse test items with different parameters - self.item_math_algebra = self._create_test_item( - item_id=1, - a=1.5, b=-0.5, c=0.2, d=0.9, - categories=["Math", "Algebra"] - ) - self.item_english = self._create_test_item( - item_id=2, - a=1.2, b=0.3, c=0.1, d=1.0, - categories=["English"] - ) +class MockConstraint: + def __init__(self, name: str, prevalence: float = 0.5, lower: float = 0.4, upper: float = 0.6): + self.name = name + self.prevalence = prevalence + self.lower = lower + self.upper = upper - self.item_science = self._create_test_item( - item_id=3, - a=2.0, b=0.0, c=0.15, d=1.0, - categories=["Science"] - ) - - self.item_multiple_categories = self._create_test_item( - item_id=4, - a=1.8, b=-1.0, c=0.25, d=0.8, - categories=["Math", "Science", "Advanced"] - ) - - # Create comprehensive constraint set - self.constraint_math = Constraint( - name="Math", - weight=0.8, - proportion=0.6, - lower=0.4, - upper=0.7 - ) - - self.constraint_algebra = Constraint( - name="Algebra", - weight=0.5, - proportion=0.3, - lower=0.2, - upper=0.4 - ) - self.constraint_english = Constraint( - name="English", - weight=1.0, - proportion=0.8, - lower=0.3, - upper=0.9 - ) - - self.constraint_science = Constraint( - name="Science", - weight=0.7, - proportion=0.25, - lower=0.1, - upper=0.4 - ) - - self.constraint_advanced = Constraint( - name="Advanced", - weight=1.2, - proportion=0.15, - lower=0.05, - upper=0.25 - ) +class TestWeightedPenaltyModel(unittest.TestCase): - self.all_constraints = [ - self.constraint_math, - self.constraint_algebra, - self.constraint_english, - self.constraint_science, - self.constraint_advanced + def setUp(self): + self.items = [ + MockTestItem(1, a=1.5, b=0.5, c=0.2, d=1.0, category=["math"]), + MockTestItem(2, a=1.2, b=-0.3, c=0.1, d=1.0, category=["reading"]), + MockTestItem(3, a=1.8, b=1.0, c=0.0, d=1.0, category=["math", "geometry"]) + ] + self.shown_items = [MockTestItem(4, category=["math"])] + self.ability = 1.0 + self.constraints = [ + MockConstraint("math", prevalence=0.6, lower=0.5, upper=0.7), + MockConstraint("reading", prevalence=0.4, lower=0.3, upper=0.5), + MockConstraint("geometry", prevalence=0.2, lower=0.1, upper=0.3) + ] + self.constraint_weight = 0.7 + self.information_weight = 0.3 + + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.item_information_function') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_total_content_penalty_value_for_item') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_prop') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_total_content_constraint_penalty_value') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_item_information') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_information_penalty_value') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_weighted_penalty_value') + def test_init_basic_functionality(self, mock_weighted_penalty, mock_info_penalty, mock_std_info, + mock_std_content, mock_compute_prop, mock_compute_penalty, mock_item_info): + # Setup mocks + mock_item_info.side_effect = [2.0, 1.5, 2.5] + mock_compute_penalty.side_effect = [1.0, 2.0, 1.5] + mock_compute_prop.side_effect = [0.45, 0.35, 0.25] # Below lower bounds -> group A + mock_std_content.side_effect = [0.4, 0.8, 0.6] + mock_std_info.side_effect = [0.8, 0.6, 1.0] + mock_info_penalty.side_effect = [0.2, 0.4, 0.0] + mock_weighted_penalty.side_effect = [0.45, 0.68, 0.42] + + model = WeightedPenaltyModel( + items=self.items, + shown_items=self.shown_items, + ability=self.ability, + constraints=self.constraints, + constraint_weight=self.constraint_weight, + information_weight=self.information_weight + ) + + # Verify basic attributes + self.assertEqual(model.items, self.items) + self.assertEqual(model.ability, self.ability) + self.assertEqual(model.constraints, self.constraints) + self.assertTrue(hasattr(model, 'eligible_items')) + self.assertEqual(len(model.eligible_items), 3) + + # Verify function calls + self.assertEqual(mock_item_info.call_count, 3) + self.assertEqual(mock_compute_penalty.call_count, 3) + + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.item_information_function') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_total_content_penalty_value_for_item') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_prop') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_total_content_constraint_penalty_value') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_item_information') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_information_penalty_value') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_weighted_penalty_value') + def test_group_assignment_green(self, mock_weighted_penalty, mock_info_penalty, mock_std_info, + mock_std_content, mock_compute_prop, mock_compute_penalty, mock_item_info): + # Setup for green group (all constraints A or B) + mock_item_info.side_effect = [2.0, 1.5, 2.5] + mock_compute_penalty.side_effect = [1.0, 2.0, 1.5] + mock_compute_prop.side_effect = [0.3, 0.2, 0.15] # All below lower bounds -> group A + mock_std_content.side_effect = [0.4, 0.8, 0.6] + mock_std_info.side_effect = [0.8, 0.6, 1.0] + mock_info_penalty.side_effect = [0.2, 0.4, 0.0] + mock_weighted_penalty.side_effect = [0.45, 0.68, 0.42] + + model = WeightedPenaltyModel( + items=self.items, + shown_items=self.shown_items, + ability=self.ability, + constraints=self.constraints, + constraint_weight=self.constraint_weight, + information_weight=self.information_weight + ) + + # Check that items with constraints in group A get green assignment + green_items = [item for item in model.eligible_items if item[2] == "green"] + self.assertTrue(len(green_items) > 0) + + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.item_information_function') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_total_content_penalty_value_for_item') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_prop') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_total_content_constraint_penalty_value') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_item_information') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_information_penalty_value') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_weighted_penalty_value') + def test_group_assignment_yellow(self, mock_weighted_penalty, mock_info_penalty, mock_std_info, + mock_std_content, mock_compute_prop, mock_compute_penalty, mock_item_info): + # Setup for yellow group (all constraints B) + mock_item_info.side_effect = [2.0, 1.5, 2.5] + mock_compute_penalty.side_effect = [1.0, 2.0, 1.5] + mock_compute_prop.side_effect = [0.55, 0.45, 0.25] # Math & reading within bounds, geometry below + mock_std_content.side_effect = [0.4, 0.8, 0.6] + mock_std_info.side_effect = [0.8, 0.6, 1.0] + mock_info_penalty.side_effect = [0.2, 0.4, 0.0] + mock_weighted_penalty.side_effect = [0.45, 0.68, 0.42] + + model = WeightedPenaltyModel( + items=self.items, + shown_items=self.shown_items, + ability=self.ability, + constraints=self.constraints, + constraint_weight=self.constraint_weight, + information_weight=self.information_weight + ) + + # Check that items with only constraint B get yellow assignment + yellow_items = [item for item in model.eligible_items if item[2] == "yellow"] + self.assertTrue(len(yellow_items) >= 0) # May be 0 based on test setup + + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.item_information_function') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_total_content_penalty_value_for_item') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_prop') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_total_content_constraint_penalty_value') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_item_information') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_information_penalty_value') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_weighted_penalty_value') + def test_group_assignment_red(self, mock_weighted_penalty, mock_info_penalty, mock_std_info, + mock_std_content, mock_compute_prop, mock_compute_penalty, mock_item_info): + # Setup for red group (all constraints B or C) + mock_item_info.side_effect = [2.0, 1.5, 2.5] + mock_compute_penalty.side_effect = [1.0, 2.0, 1.5] + mock_compute_prop.side_effect = [0.8, 0.7, 0.55] # Above upper bounds or within + mock_std_content.side_effect = [0.4, 0.8, 0.6] + mock_std_info.side_effect = [0.8, 0.6, 1.0] + mock_info_penalty.side_effect = [0.2, 0.4, 0.0] + mock_weighted_penalty.side_effect = [0.45, 0.68, 0.42] + + model = WeightedPenaltyModel( + items=self.items, + shown_items=self.shown_items, + ability=self.ability, + constraints=self.constraints, + constraint_weight=self.constraint_weight, + information_weight=self.information_weight + ) + + # Check that items with constraints in group B,C get red assignment + red_items = [item for item in model.eligible_items if item[2] == "red"] + self.assertTrue(len(red_items) >= 0) + + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.item_information_function') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_total_content_penalty_value_for_item') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_total_content_constraint_penalty_value') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_item_information') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_information_penalty_value') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_weighted_penalty_value') + def test_item_sorting_by_penalty_value(self, mock_weighted_penalty, mock_info_penalty, mock_std_info, + mock_std_content, mock_compute_penalty, mock_item_info): + # Setup different penalty values for sorting test + mock_item_info.side_effect = [2.0, 1.5, 2.5] + mock_compute_penalty.side_effect = [3.0, 1.0, 2.0] + mock_std_content.side_effect = [1.0, 0.0, 0.5] + mock_std_info.side_effect = [0.8, 0.6, 1.0] + mock_info_penalty.side_effect = [0.2, 0.4, 0.0] + mock_weighted_penalty.side_effect = [3.0, 1.0, 2.0] # Different penalty values + + model = WeightedPenaltyModel( + items=self.items, + shown_items=self.shown_items, + ability=self.ability, + constraints=[], # No constraints to avoid group assignment + constraint_weight=self.constraint_weight, + information_weight=self.information_weight + ) + + # Items should be sorted by penalty value (ascending) when no groups + penalty_values = [item[1] for item in model.eligible_items] + self.assertEqual(penalty_values, [1.0, 2.0, 3.0]) + + def test_init_empty_items_list(self): + model = WeightedPenaltyModel( + items=[], + shown_items=self.shown_items, + ability=self.ability, + constraints=self.constraints, + constraint_weight=self.constraint_weight, + information_weight=self.information_weight + ) + + self.assertEqual(len(model.eligible_items), 0) + self.assertEqual(model.items, []) + + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.item_information_function') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_total_content_penalty_value_for_item') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_total_content_constraint_penalty_value') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_item_information') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_information_penalty_value') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_weighted_penalty_value') + def test_init_empty_constraints_list(self, mock_weighted_penalty, mock_info_penalty, mock_std_info, + mock_std_content, mock_compute_penalty, mock_item_info): + mock_item_info.side_effect = [2.0, 1.5, 2.5] + mock_compute_penalty.side_effect = [1.0, 2.0, 1.5] + mock_std_content.side_effect = [0.4, 0.8, 0.6] + mock_std_info.side_effect = [0.8, 0.6, 1.0] + mock_info_penalty.side_effect = [0.2, 0.4, 0.0] + mock_weighted_penalty.side_effect = [0.45, 0.68, 0.42] + + model = WeightedPenaltyModel( + items=self.items, + shown_items=self.shown_items, + ability=self.ability, + constraints=[], + constraint_weight=self.constraint_weight, + information_weight=self.information_weight + ) + + # All items should have None group assignment + self.assertTrue(all(item[2] is None for item in model.eligible_items)) + + def test_select_item_with_eligible_items(self): + model = WeightedPenaltyModel.__new__(WeightedPenaltyModel) + model.eligible_items = [ + (self.items[0], 1.0, "green"), + (self.items[1], 2.0, "orange"), + (self.items[2], 1.5, "green") ] - def _create_test_item(self, item_id: int, a: float, b: float, c: float, d: float, categories: list[str]) -> TestItem: - """Helper method to create test items with consistent setup.""" - item = TestItem() - item.id = item_id - item.a = a - item.b = b - item.c = c - item.d = d - item.additional_properties = {"category": categories} - return item - - # =================================================================== - # GROUP 1: Basic Utility Function Tests - # =================================================================== - - def test_compute_prop_standard_cases(self): - """Test basic proportion calculation with standard scenarios.""" - # Standard case with some administered items - result = compute_prop(n_administered=5, prevalence=0.3, n_remaining=10, test_length=20) - expected = (5 + 0.3 * 10) / 20 # (5 + 3) / 20 = 0.4 - self.assertAlmostEqual(result, expected, places=6) - - # Different values to ensure calculation is correct - result = compute_prop(n_administered=3, prevalence=0.5, n_remaining=12, test_length=15) - expected = (3 + 0.5 * 12) / 15 # (3 + 6) / 15 = 0.6 - self.assertAlmostEqual(result, expected, places=6) - - def test_compute_prop_edge_cases(self): - """Test proportion calculation with edge cases and boundary conditions.""" - # No items administered yet - result = compute_prop(n_administered=0, prevalence=0.5, n_remaining=20, test_length=20) - expected = (0 + 0.5 * 20) / 20 # 0.5 - self.assertAlmostEqual(result, expected) - - # All items administered (no remaining items) - result = compute_prop(n_administered=20, prevalence=0.5, n_remaining=0, test_length=20) - expected = (20 + 0.5 * 0) / 20 # 1.0 - self.assertAlmostEqual(result, expected) - - # Zero prevalence - result = compute_prop(n_administered=5, prevalence=0.0, n_remaining=15, test_length=20) - expected = (5 + 0.0 * 15) / 20 # 0.25 - self.assertAlmostEqual(result, expected) - - # Full prevalence (all remaining items have the constraint) - result = compute_prop(n_administered=2, prevalence=1.0, n_remaining=8, test_length=10) - expected = (2 + 1.0 * 8) / 10 # 1.0 - self.assertAlmostEqual(result, expected) - - def test_compute_prop_extreme_cases(self): - """Test proportion calculation with extreme but valid inputs.""" - # Very small test - result = compute_prop(n_administered=1, prevalence=0.5, n_remaining=1, test_length=2) - expected = (1 + 0.5 * 1) / 2 # 0.75 - self.assertAlmostEqual(result, expected) - - # Very large test - result = compute_prop(n_administered=500, prevalence=0.3, n_remaining=1500, test_length=2000) - expected = (500 + 0.3 * 1500) / 2000 # (500 + 450) / 2000 = 0.475 - self.assertAlmostEqual(result, expected) - - def test_compute_expected_difference_standard_cases(self): - """Test expected difference calculation with various scenarios.""" - # Positive difference (proportion exceeds target) - result = compute_expected_difference(proportion=0.6, constraint_target=0.5) - self.assertAlmostEqual(result, 0.1) - - # Negative difference (proportion below target) - result = compute_expected_difference(proportion=0.3, constraint_target=0.7) - self.assertAlmostEqual(result, -0.4) - - # Zero difference (exactly at target) - result = compute_expected_difference(proportion=0.5, constraint_target=0.5) - self.assertAlmostEqual(result, 0.0) - - def test_compute_expected_difference_extreme_cases(self): - """Test expected difference calculation with extreme values.""" - # Maximum positive difference - result = compute_expected_difference(proportion=1.0, constraint_target=0.0) - self.assertAlmostEqual(result, 1.0) - - # Maximum negative difference - result = compute_expected_difference(proportion=0.0, constraint_target=1.0) - self.assertAlmostEqual(result, -1.0) - - # Very small differences (precision test) - result = compute_expected_difference(proportion=0.501, constraint_target=0.5) - self.assertAlmostEqual(result, 0.001, places=6) - - # =================================================================== - # GROUP 2: Penalty Value Calculation Tests - # =================================================================== - - def test_compute_penalty_value_within_bounds(self): - """Test penalty value calculation when proportion is within acceptable bounds.""" - lower, upper = 0.3, 0.7 - mid = (upper + lower) / 2 # 0.5 - - # Exactly at midpoint - result = compute_penalty_value(prop=0.5, lower=lower, upper=upper) - expected = 0.5 - mid # 0.0 - self.assertAlmostEqual(result, expected, places=6) - - # Slightly above midpoint - result = compute_penalty_value(prop=0.6, lower=lower, upper=upper) - expected = 0.6 - mid # 0.1 - self.assertAlmostEqual(result, expected, places=6) - - # Slightly below midpoint - result = compute_penalty_value(prop=0.4, lower=lower, upper=upper) - expected = 0.4 - mid # -0.1 - self.assertAlmostEqual(result, expected, places=6) - - def test_compute_penalty_value_at_boundaries(self): - """Test penalty value calculation exactly at boundary conditions.""" - lower, upper = 0.3, 0.7 - mid = (upper + lower) / 2 # 0.5 - - # At lower bound (should be treated as within bounds) - result = compute_penalty_value(prop=0.3, lower=lower, upper=upper) - expected = 0.3 - mid # -0.2 - self.assertAlmostEqual(result, expected, places=6) - - # Just above lower bound - result = compute_penalty_value(prop=0.300001, lower=lower, upper=upper) - expected = 0.300001 - mid - self.assertAlmostEqual(result, expected, places=6) - - def test_compute_penalty_value_below_lower_bound(self): - """Test penalty value calculation when proportion is below lower bound.""" - lower, upper = 0.3, 0.7 - prop = 0.2 # Below lower bound - - mid = (upper + lower) / 2 # 0.5 - d = lower - mid # 0.3 - 0.5 = -0.2 - k = 2 - expected_diff = prop - mid # 0.2 - 0.5 = -0.3 - - # Based on actual implementation: (1 / (k * d)) * expected_diff^2 + (d / k) - expected = (1 / (k * d)) * (expected_diff ** 2) + (d / k) - # expected = (1 / (2 * -0.2)) * (-0.3)^2 + (-0.2 / 2) - # expected = (1 / -0.4) * 0.09 + (-0.1) - # expected = -2.5 * 0.09 - 0.1 = -0.225 - 0.1 = -0.325 - - result = compute_penalty_value(prop=prop, lower=lower, upper=upper) - self.assertAlmostEqual(result, expected, places=6) - - def test_compute_penalty_value_above_upper_bound(self): - """Test penalty value calculation when proportion is above upper bound.""" - lower, upper = 0.3, 0.7 - prop = 0.8 # Above upper bound - - mid = (upper + lower) / 2 # 0.5 - a = upper - mid # 0.7 - 0.5 = 0.2 - k = 2 - expected_diff = prop - mid # 0.8 - 0.5 = 0.3 - - # Based on actual implementation: (1 / (k * a)) * expected_diff^2 + (a / k) - expected = (1 / (k * a)) * (expected_diff ** 2) + (a / k) - # expected = (1 / (2 * 0.2)) * (0.3)^2 + (0.2 / 2) - # expected = (1 / 0.4) * 0.09 + 0.1 - # expected = 2.5 * 0.09 + 0.1 = 0.225 + 0.1 = 0.325 - - result = compute_penalty_value(prop=prop, lower=lower, upper=upper) - self.assertAlmostEqual(result, expected, places=6) - - def test_compute_penalty_value_extreme_violations(self): - """Test penalty value calculation with extreme constraint violations.""" - lower, upper = 0.4, 0.6 - - # Very far below lower bound - result = compute_penalty_value(prop=0.1, lower=lower, upper=upper) - self.assertIsInstance(result, float) - self.assertFalse(np.isnan(result)) - self.assertLess(result, 0) # Should be negative penalty for being below - - # Very far above upper bound - result = compute_penalty_value(prop=0.9, lower=lower, upper=upper) - self.assertIsInstance(result, float) - self.assertFalse(np.isnan(result)) - self.assertGreater(result, 0) # Should be positive penalty for being above - - def test_compute_penalty_value_narrow_bounds(self): - """Test penalty value calculation with very narrow acceptable bounds.""" - lower, upper = 0.49, 0.51 - mid = (upper + lower) / 2 # 0.5 - - # Within narrow bounds - result = compute_penalty_value(prop=0.5, lower=lower, upper=upper) - expected = 0.5 - mid # 0.0 - self.assertAlmostEqual(result, expected, places=6) - - # Just outside narrow bounds (below) - result = compute_penalty_value(prop=0.48, lower=lower, upper=upper) - self.assertIsInstance(result, float) - self.assertFalse(np.isnan(result)) - - # =================================================================== - # GROUP 3: Total Content Penalty Tests - # =================================================================== - - def test_compute_total_content_penalty_value_single_constraint(self): - """Test total content penalty calculation for items with single constraint.""" - # Mock penalty calculation to focus on the aggregation logic - with patch('adaptivetesting.math.content_balancing.__functions.compute_penalty_value') as mock_penalty: - mock_penalty.return_value = 0.15 - - result = compute_total_content_penalty_value_for_item( - item=self.item_english, # Has only "English" category - constraints=self.all_constraints - ) - - # Should only match English constraint with weight 1.0 - expected = 0.15 * 1.0 - self.assertAlmostEqual(result, expected, places=6) - mock_penalty.assert_called_once() - - def test_compute_total_content_penalty_value_multiple_constraints(self): - """Test total content penalty calculation for items with multiple constraints.""" - with patch('adaptivetesting.math.content_balancing.__functions.compute_penalty_value') as mock_penalty: - # Return different penalty values for different constraints - mock_penalty.side_effect = [0.1, 0.2] # Math=0.1, Algebra=0.2 - - result = compute_total_content_penalty_value_for_item( - item=self.item_math_algebra, # Has ["Math", "Algebra"] categories - constraints=self.all_constraints - ) - - # Math: penalty=0.1, weight=0.8 -> 0.08 - # Algebra: penalty=0.2, weight=0.5 -> 0.10 - # Total: 0.08 + 0.10 = 0.18 - expected = 0.1 * 0.8 + 0.2 * 0.5 - self.assertAlmostEqual(result, expected, places=6) - self.assertEqual(mock_penalty.call_count, 2) - - def test_compute_total_content_penalty_value_many_constraints(self): - """Test total content penalty calculation for items with many constraints.""" - with patch('adaptivetesting.math.content_balancing.__functions.compute_penalty_value') as mock_penalty: - # Return different penalty values for Math, Science, and Advanced - mock_penalty.side_effect = [0.3, 0.1, 0.4] - - result = compute_total_content_penalty_value_for_item( - item=self.item_multiple_categories, # Has ["Math", "Science", "Advanced"] - constraints=self.all_constraints - ) - - # Math: penalty=0.3, weight=0.8 -> 0.24 - # Science: penalty=0.1, weight=0.7 -> 0.07 - # Advanced: penalty=0.4, weight=1.2 -> 0.48 - # Total: 0.24 + 0.07 + 0.48 = 0.79 - expected = 0.3 * 0.8 + 0.1 * 0.7 + 0.4 * 1.2 - self.assertAlmostEqual(result, expected, places=6) - self.assertEqual(mock_penalty.call_count, 3) - - def test_compute_total_content_penalty_value_no_matching_constraints(self): - """Test total content penalty calculation with no matching constraints.""" - # Create item with category not in any constraint - item_orphan = self._create_test_item( - item_id=99, a=1.0, b=0.0, c=0.0, d=1.0, - categories=["Unmatched", "Category"] - ) - - result = compute_total_content_penalty_value_for_item( - item=item_orphan, - constraints=self.all_constraints - ) - - # No matching constraints should result in zero penalty - self.assertEqual(result, 0.0) - - def test_compute_total_content_penalty_value_constraint_bounds_validation(self): - """Test that function validates constraint bounds are not None.""" - # Create constraint with None bounds - invalid_constraint = Constraint( - name="Math", weight=1.0, proportion=0.5, - lower=None, upper=0.7 # lower is None - ) - - with self.assertRaises(ValueError) as context: - compute_total_content_penalty_value_for_item( - item=self.item_math_algebra, - constraints=[invalid_constraint] - ) - self.assertIn("lower cannot be None", str(context.exception)) - - # Test upper bound None - invalid_constraint_upper = Constraint( - name="Math", weight=1.0, proportion=0.5, - lower=0.3, upper=None # upper is None - ) - - with self.assertRaises(ValueError) as context: - compute_total_content_penalty_value_for_item( - item=self.item_math_algebra, - constraints=[invalid_constraint_upper] - ) - self.assertIn("upper cannot be None", str(context.exception)) - - # =================================================================== - # GROUP 4: Standardization Function Tests - # =================================================================== - - def test_standardize_total_content_constraint_penalty_value_normal_cases(self): - """Test standardization of content constraint penalty values in normal scenarios.""" - # Standard case: value in middle of range - result = standardize_total_content_constraint_penalty_value( - item_penality_value=0.6, - min=0.2, - max=1.0 - ) - expected = (0.6 - 0.2) / (1.0 - 0.2) # 0.4 / 0.8 = 0.5 - self.assertAlmostEqual(result, expected, places=6) - - # Different range - result = standardize_total_content_constraint_penalty_value( - item_penality_value=0.75, - min=0.5, - max=1.5 - ) - expected = (0.75 - 0.5) / (1.5 - 0.5) # 0.25 / 1.0 = 0.25 - self.assertAlmostEqual(result, expected, places=6) - - def test_standardize_total_content_constraint_penalty_value_boundary_cases(self): - """Test standardization at boundary conditions.""" - # At minimum value - result = standardize_total_content_constraint_penalty_value( - item_penality_value=0.2, - min=0.2, - max=1.0 - ) - self.assertAlmostEqual(result, 0.0, places=6) + selected_item = model.select_item() + # Should return first item (lowest penalty in green group) + self.assertEqual(selected_item, self.items[0]) - # At maximum value - result = standardize_total_content_constraint_penalty_value( - item_penality_value=1.0, - min=0.2, - max=1.0 - ) - self.assertAlmostEqual(result, 1.0, places=6) - - def test_standardize_total_content_constraint_penalty_value_edge_cases(self): - """Test standardization with edge cases and error conditions.""" - # Zero range (min == max) should cause division by zero - with self.assertRaises(ZeroDivisionError): - standardize_total_content_constraint_penalty_value( - item_penality_value=0.5, - min=0.5, - max=0.5 - ) + def test_select_item_with_no_eligible_items(self): + model = WeightedPenaltyModel.__new__(WeightedPenaltyModel) + model.eligible_items = [] - # Negative values (valid mathematically) - result = standardize_total_content_constraint_penalty_value( - item_penality_value=-0.5, - min=-1.0, - max=0.0 - ) - expected = (-0.5 - (-1.0)) / (0.0 - (-1.0)) # 0.5 / 1.0 = 0.5 - self.assertAlmostEqual(result, expected, places=6) - - # Value outside range (above max) - result = standardize_total_content_constraint_penalty_value( - item_penality_value=1.5, - min=0.2, - max=1.0 - ) - expected = (1.5 - 0.2) / (1.0 - 0.2) # 1.3 / 0.8 = 1.625 - self.assertAlmostEqual(result, expected, places=6) - - # Value outside range (below min) - result = standardize_total_content_constraint_penalty_value( - item_penality_value=0.1, - min=0.2, - max=1.0 - ) - expected = (0.1 - 0.2) / (1.0 - 0.2) # -0.1 / 0.8 = -0.125 - self.assertAlmostEqual(result, expected, places=6) - - def test_standardize_item_information_normal_cases(self): - """Test item information standardization in normal scenarios.""" - # Standard case - result = standardize_item_information(item_information=0.8, max=2.0) - expected = 0.8 / 2.0 # 0.4 - self.assertAlmostEqual(result, expected, places=6) - - # Different values - result = standardize_item_information(item_information=1.5, max=3.0) - expected = 1.5 / 3.0 # 0.5 - self.assertAlmostEqual(result, expected, places=6) - - def test_standardize_item_information_boundary_cases(self): - """Test item information standardization at boundaries.""" - # Zero information - result = standardize_item_information(item_information=0.0, max=2.0) - self.assertAlmostEqual(result, 0.0, places=6) - - # Maximum information - result = standardize_item_information(item_information=2.0, max=2.0) - self.assertAlmostEqual(result, 1.0, places=6) - - # Information exceeds maximum (mathematically valid) - result = standardize_item_information(item_information=3.0, max=2.0) - self.assertAlmostEqual(result, 1.5, places=6) - - def test_standardize_item_information_error_cases(self): - """Test item information standardization error conditions.""" - # Division by zero when max is zero - with self.assertRaises(ZeroDivisionError): - standardize_item_information(item_information=1.0, max=0.0) - - # Negative maximum (mathematically valid but unusual) - result = standardize_item_information(item_information=1.0, max=-2.0) - expected = 1.0 / -2.0 # -0.5 - self.assertAlmostEqual(result, expected, places=6) - - # =================================================================== - # GROUP 5: Information Penalty Function Tests - # =================================================================== - - def test_compute_information_penalty_value_standard_cases(self): - """Test information penalty value computation with standard inputs.""" - # Mid-range value - result = compute_information_penalty_value(standardized_item_information=0.8) - expected = -(0.8 ** 2) # -0.64 - self.assertAlmostEqual(result, expected, places=6) - - # Different value - result = compute_information_penalty_value(standardized_item_information=0.5) - expected = -(0.5 ** 2) # -0.25 - self.assertAlmostEqual(result, expected, places=6) - - def test_compute_information_penalty_value_boundary_cases(self): - """Test information penalty value at boundary conditions.""" - # Zero information (no penalty) - result = compute_information_penalty_value(standardized_item_information=0.0) - self.assertAlmostEqual(result, 0.0, places=6) - - # Perfect information (maximum penalty) - result = compute_information_penalty_value(standardized_item_information=1.0) - self.assertAlmostEqual(result, -1.0, places=6) - - def test_compute_information_penalty_value_extreme_cases(self): - """Test information penalty value with extreme inputs.""" - # Information above 1.0 (possible due to standardization issues) - result = compute_information_penalty_value(standardized_item_information=1.5) - expected = -(1.5 ** 2) # -2.25 - self.assertAlmostEqual(result, expected, places=6) - - # Very small positive information - result = compute_information_penalty_value(standardized_item_information=0.001) - expected = -(0.001 ** 2) # -0.000001 - self.assertAlmostEqual(result, expected, places=9) - - # Negative information (mathematically unusual but handled) - result = compute_information_penalty_value(standardized_item_information=-0.5) - expected = -((-0.5) ** 2) # -0.25 - self.assertAlmostEqual(result, expected, places=6) - - # =================================================================== - # GROUP 6: Weighted Penalty Function Tests - # =================================================================== - - def test_compute_weighted_penalty_value_balanced_weights(self): - """Test weighted penalty value computation with balanced weights.""" - result = compute_weighted_penalty_value( - constraint_weight=0.5, - standardized_constraint_penalty_value=0.4, - information_weight=0.5, - information_penalty_value=-0.6 - ) - expected = 0.5 * 0.4 + 0.5 * (-0.6) # 0.2 - 0.3 = -0.1 - self.assertAlmostEqual(result, expected, places=6) - - def test_compute_weighted_penalty_value_constraint_focused(self): - """Test weighted penalty value computation favoring constraint penalties.""" - result = compute_weighted_penalty_value( - constraint_weight=0.8, - standardized_constraint_penalty_value=0.6, - information_weight=0.2, - information_penalty_value=-0.8 - ) - expected = 0.8 * 0.6 + 0.2 * (-0.8) # 0.48 - 0.16 = 0.32 - self.assertAlmostEqual(result, expected, places=6) - - def test_compute_weighted_penalty_value_information_focused(self): - """Test weighted penalty value computation favoring information penalties.""" - result = compute_weighted_penalty_value( - constraint_weight=0.2, - standardized_constraint_penalty_value=0.7, - information_weight=0.8, - information_penalty_value=-0.9 - ) - expected = 0.2 * 0.7 + 0.8 * (-0.9) # 0.14 - 0.72 = -0.58 - self.assertAlmostEqual(result, expected, places=6) - - def test_compute_weighted_penalty_value_extreme_weights(self): - """Test weighted penalty value computation with extreme weight distributions.""" - # Only constraint weight (information weight = 0) - result = compute_weighted_penalty_value( - constraint_weight=1.0, - standardized_constraint_penalty_value=0.5, - information_weight=0.0, - information_penalty_value=-0.8 - ) - self.assertAlmostEqual(result, 0.5, places=6) + selected_item = model.select_item() + self.assertIsNone(selected_item) - # Only information weight (constraint weight = 0) - result = compute_weighted_penalty_value( - constraint_weight=0.0, - standardized_constraint_penalty_value=0.5, - information_weight=1.0, - information_penalty_value=-0.8 - ) - self.assertAlmostEqual(result, -0.8, places=6) + def test_select_item_returns_first_item(self): + model = WeightedPenaltyModel.__new__(WeightedPenaltyModel) + model.eligible_items = [ + (self.items[1], 2.0, "orange"), + (self.items[0], 1.0, "green"), + (self.items[2], 3.0, "red") + ] - # Both weights zero - result = compute_weighted_penalty_value( - constraint_weight=0.0, + selected_item = model.select_item() + # Should return the first item in the sorted list + self.assertEqual(selected_item, self.items[1]) + + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_total_content_constraint_penalty_value') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_item_information') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_information_penalty_value') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_weighted_penalty_value') + def test_calculate_weighted_penalty_static_method(self, mock_weighted_penalty, mock_info_penalty, + mock_std_info, mock_std_content): + mock_std_content.return_value = 0.5 + mock_std_info.return_value = 0.8 + mock_info_penalty.return_value = 0.2 + mock_weighted_penalty.return_value = 0.45 + + result = WeightedPenaltyModel._WeightedPenaltyModel__calculate_weighted_penalty( + content_penalty=2.0, + minimum_total_content_penalty=1.0, + maximum_total_content_penalty=3.0, + item_information=1.5, + max_information=2.0, + constraint_weight=0.7, + information_weight=0.3 + ) + + self.assertEqual(result, 0.45) + + # Verify function calls with correct parameters + mock_std_content.assert_called_once_with( + item_penality_value=2.0, + minimum=1.0, + maximum=3.0 + ) + mock_std_info.assert_called_once_with( + item_information=1.5, + maximum=2.0 + ) + mock_info_penalty.assert_called_once_with( + standardized_item_information=0.8 + ) + mock_weighted_penalty.assert_called_once_with( + constraint_weight=0.7, standardized_constraint_penalty_value=0.5, - information_weight=0.0, - information_penalty_value=-0.8 - ) - self.assertAlmostEqual(result, 0.0, places=6) - - def test_compute_weighted_penalty_value_negative_penalties(self): - """Test weighted penalty value computation with negative penalty values.""" - result = compute_weighted_penalty_value( - constraint_weight=0.6, - standardized_constraint_penalty_value=-0.3, # Negative constraint penalty - information_weight=0.4, - information_penalty_value=-0.7 # Negative information penalty - ) - expected = 0.6 * (-0.3) + 0.4 * (-0.7) # -0.18 - 0.28 = -0.46 - self.assertAlmostEqual(result, expected, places=6) - - def test_compute_weighted_penalty_value_weights_exceeding_one(self): - """Test weighted penalty value computation with weights that sum to more than 1.""" - # Weights summing to more than 1.0 (mathematically valid) - result = compute_weighted_penalty_value( - constraint_weight=0.8, - standardized_constraint_penalty_value=0.5, - information_weight=0.7, # Total weight = 1.5 - information_penalty_value=-0.4 - ) - expected = 0.8 * 0.5 + 0.7 * (-0.4) # 0.4 - 0.28 = 0.12 - self.assertAlmostEqual(result, expected, places=6) - - # =================================================================== - # GROUP 7: Integration and Workflow Tests - # =================================================================== - - def test_complete_penalty_calculation_workflow(self): - """Integration test for the complete weighted penalty calculation workflow.""" - # This test simulates a realistic end-to-end penalty calculation - - # Step 1: Calculate total content penalty using actual function (not mocked) - # We'll use constraint with known bounds to get predictable results - test_constraint = Constraint( - name="Math", - weight=0.8, - proportion=0.4, # This will be used in penalty calculation - lower=0.3, - upper=0.7 - ) - - content_penalty = compute_total_content_penalty_value_for_item( - item=self.item_math_algebra, - constraints=[test_constraint] - ) - - # Step 2: Standardize content penalty (using realistic min/max) - standardized_content_penalty = standardize_total_content_constraint_penalty_value( - item_penality_value=content_penalty, - min=-0.5, # Realistic minimum penalty - max=0.8 # Realistic maximum penalty - ) - - # Step 3: Calculate and standardize item information - mock_item_information = 1.2 # Realistic information value - standardized_information = standardize_item_information( - item_information=mock_item_information, - max=2.5 # Realistic maximum information - ) - - # Step 4: Calculate information penalty - information_penalty = compute_information_penalty_value(standardized_information) - - # Step 5: Calculate weighted penalty - weighted_penalty = compute_weighted_penalty_value( - constraint_weight=0.6, - standardized_constraint_penalty_value=standardized_content_penalty, - information_weight=0.4, - information_penalty_value=information_penalty - ) - - # Verify the result is mathematically valid - self.assertIsInstance(weighted_penalty, float) - self.assertFalse(np.isnan(weighted_penalty)) - self.assertFalse(np.isinf(weighted_penalty)) - - def test_penalty_calculation_with_multiple_items_and_constraints(self): - """Integration test comparing penalty calculations across multiple items.""" - penalties = {} - - # Calculate penalties for different items using the same constraints - test_items = [ - self.item_math_algebra, - self.item_english, - self.item_science, - self.item_multiple_categories + information_weight=0.3, + information_penalty_value=0.2 + ) + + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.item_information_function') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_total_content_penalty_value_for_item') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_prop') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_total_content_constraint_penalty_value') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_item_information') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_information_penalty_value') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_weighted_penalty_value') + def test_compute_prop_calls_with_correct_parameters(self, mock_weighted_penalty, mock_info_penalty, + mock_std_info, mock_std_content, mock_compute_prop, + mock_compute_penalty, mock_item_info): + mock_item_info.side_effect = [2.0, 1.5, 2.5] + mock_compute_penalty.side_effect = [1.0, 2.0, 1.5] + mock_compute_prop.side_effect = [0.45, 0.35, 0.25] + mock_std_content.side_effect = [0.4, 0.8, 0.6] + mock_std_info.side_effect = [0.8, 0.6, 1.0] + mock_info_penalty.side_effect = [0.2, 0.4, 0.0] + mock_weighted_penalty.side_effect = [0.45, 0.68, 0.42] + + model = WeightedPenaltyModel( + items=self.items, + shown_items=self.shown_items, + ability=self.ability, + constraints=self.constraints, + constraint_weight=self.constraint_weight, + information_weight=self.information_weight + ) + + # Verify compute_prop was called for each constraint + self.assertEqual(mock_compute_prop.call_count, 3) + + # Check first call parameters + first_call = mock_compute_prop.call_args_list[0] + self.assertEqual(first_call[1]['n_administered'], 1) # One shown item with "math" + self.assertEqual(first_call[1]['n_remaining'], 2) # Two items with "math" + self.assertEqual(first_call[1]['prevalence'], 0.6) + self.assertEqual(first_call[1]['test_length'], 1) + + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.item_information_function') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_total_content_penalty_value_for_item') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_prop') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_total_content_constraint_penalty_value') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_item_information') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_information_penalty_value') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_weighted_penalty_value') + def test_item_information_function_calls(self, mock_weighted_penalty, mock_info_penalty, mock_std_info, + mock_std_content, mock_compute_prop, mock_compute_penalty, mock_item_info): + mock_item_info.side_effect = [2.0, 1.5, 2.5] + mock_compute_penalty.side_effect = [1.0, 2.0, 1.5] + mock_compute_prop.side_effect = [0.45, 0.35, 0.25] + mock_std_content.side_effect = [0.4, 0.8, 0.6] + mock_std_info.side_effect = [0.8, 0.6, 1.0] + mock_info_penalty.side_effect = [0.2, 0.4, 0.0] + mock_weighted_penalty.side_effect = [0.45, 0.68, 0.42] + + model = WeightedPenaltyModel( + items=self.items, + shown_items=self.shown_items, + ability=self.ability, + constraints=self.constraints, + constraint_weight=self.constraint_weight, + information_weight=self.information_weight + ) + + # Verify item_information_function was called for each item + self.assertEqual(mock_item_info.call_count, 3) + + # Check that it was called with correct parameters for first item + first_call = mock_item_info.call_args_list[0] + np.testing.assert_array_equal(first_call[1]['mu'], np.array(self.ability)) + np.testing.assert_array_equal(first_call[1]['a'], np.array(self.items[0].a)) + + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.item_information_function') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_total_content_penalty_value_for_item') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_prop') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_total_content_constraint_penalty_value') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_item_information') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_information_penalty_value') + @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_weighted_penalty_value') + def test_single_item_processing(self, mock_weighted_penalty, mock_info_penalty, mock_std_info, + mock_std_content, mock_compute_prop, mock_compute_penalty, mock_item_info): + mock_item_info.return_value = 2.0 + mock_compute_penalty.return_value = 1.0 + mock_compute_prop.return_value = 0.45 + mock_std_content.return_value = 0.5 + mock_std_info.return_value = 0.8 + mock_info_penalty.return_value = 0.2 + mock_weighted_penalty.return_value = 0.45 + + single_item = [MockTestItem(1, category=["math"])] + single_constraint = [MockConstraint("math")] + + model = WeightedPenaltyModel( + items=single_item, + shown_items=self.shown_items, + ability=self.ability, + constraints=single_constraint, + constraint_weight=self.constraint_weight, + information_weight=self.information_weight + ) + + self.assertEqual(len(model.eligible_items), 1) + self.assertEqual(model.eligible_items[0][0], single_item[0]) + self.assertEqual(model.eligible_items[0][1], 0.45) + self.assertEqual(model.eligible_items[0][2], "green") # Single constraint A -> green + + def test_group_ordering_priority(self): + # Test that group ordering follows: green, orange, yellow, red, None + model = WeightedPenaltyModel.__new__(WeightedPenaltyModel) + model.eligible_items = [ + (self.items[0], 3.0, "red"), + (self.items[1], 1.0, "green"), + (self.items[2], 2.0, "yellow"), + (MockTestItem(5), 4.0, None), + (MockTestItem(6), 1.5, "orange") ] - - # Use a subset of constraints for predictable results - test_constraints = [self.constraint_math, self.constraint_english, self.constraint_science] - - for item in test_items: - content_penalty = compute_total_content_penalty_value_for_item( - item=item, - constraints=test_constraints - ) - penalties[item.id] = content_penalty - - # Verify all penalties are valid numbers - for item_id, penalty in penalties.items(): - self.assertIsInstance(penalty, float) - self.assertFalse(np.isnan(penalty)) - self.assertFalse(np.isinf(penalty)) - - # Items with no matching constraints should have zero penalty - # (item_science doesn't match any of our test constraints initially) - # This assumes our test setup doesn't include Science in the first three constraints - - def test_standardization_workflow_robustness(self): - """Test that standardization functions handle various realistic scenarios.""" - # Test various penalty value ranges - penalty_values = [-0.8, -0.2, 0.0, 0.3, 0.9, 1.5] - information_values = [0.0, 0.2, 0.8, 1.0, 1.3, 2.0] - - # Test content penalty standardization - for penalty in penalty_values: - result = standardize_total_content_constraint_penalty_value( - item_penality_value=penalty, - min=-1.0, - max=2.0 - ) - self.assertIsInstance(result, float) - self.assertFalse(np.isnan(result)) - - # Test information standardization - for info in information_values: - result = standardize_item_information( - item_information=info, - max=2.5 + + # Manually sort using the same key function as in the class + sorted_items = sorted( + model.eligible_items, + key=lambda x: ( + {"green": 0, "orange": 1, "yellow": 2, "red": 3, None: 4}[x[2]], + x[1] ) - self.assertIsInstance(result, float) - self.assertFalse(np.isnan(result)) - - # Test information penalty calculation - info_penalty = compute_information_penalty_value(result) - self.assertIsInstance(info_penalty, float) - self.assertFalse(np.isnan(info_penalty)) - self.assertLessEqual(info_penalty, 0.0) # Should always be negative or zero - - def test_edge_case_combinations(self): - """Test combinations of edge cases that might occur in practice.""" - # Scenario 1: Item with constraints at exact boundaries - boundary_constraint = Constraint( - name="Math", - weight=1.0, - proportion=0.5, # Exactly at midpoint - lower=0.3, - upper=0.7 - ) - - penalty = compute_total_content_penalty_value_for_item( - item=self.item_math_algebra, - constraints=[boundary_constraint] - ) - - # Should be zero since proportion is at midpoint - self.assertAlmostEqual(penalty, 0.0, places=6) - - # Scenario 2: Very narrow constraint bounds - narrow_constraint = Constraint( - name="Math", - weight=0.5, - proportion=0.501, # Just above midpoint - lower=0.499, - upper=0.502 - ) - - penalty = compute_total_content_penalty_value_for_item( - item=self.item_math_algebra, - constraints=[narrow_constraint] ) - - self.assertIsInstance(penalty, float) - self.assertFalse(np.isnan(penalty)) + + expected_order = ["green", "orange", "yellow", "red", None] + actual_order = [item[2] for item in sorted_items] + self.assertEqual(actual_order, expected_order) if __name__ == '__main__': From c1184f2c4dbbe0f6fe2db739ca7f71ed1de31485 Mon Sep 17 00:00:00 2001 From: codecon Date: Fri, 3 Oct 2025 10:38:52 +0200 Subject: [PATCH 022/137] progress --- .../math/content_balancing/__functions.py | 6 +- .../__maximum_priority_index.py | 0 .../math/exposure_control/__randomesque.py | 18 +- .../tests/test_weighted_penalty_model.py | 450 ------------------ 4 files changed, 15 insertions(+), 459 deletions(-) create mode 100644 adaptivetesting/math/content_balancing/__maximum_priority_index.py delete mode 100644 adaptivetesting/tests/test_weighted_penalty_model.py diff --git a/adaptivetesting/math/content_balancing/__functions.py b/adaptivetesting/math/content_balancing/__functions.py index 13599da..c03bbbb 100644 --- a/adaptivetesting/math/content_balancing/__functions.py +++ b/adaptivetesting/math/content_balancing/__functions.py @@ -8,7 +8,7 @@ def compute_priority_index(item: TestItem, group_weights: dict[str, float], required_items: int, - shown_item: int, + shown_items: int, current_ability: float) -> float: """Calculates the priority index of a given item. @@ -18,7 +18,7 @@ def compute_priority_index(item: TestItem, Example: `{"math": 1, "english": 1}`. These weight show how important a specific constraint is for the item selection process required_items (int): number of items required to be shown per constraint - shown_item (int): number of items already shown per constraint + shown_items (int): number of items already shown per constraint current_ability (float): currently estimated ability level Returns: @@ -38,7 +38,7 @@ def compute_priority_index(item: TestItem, for group in item_groups: priority_index = priority_index * group_weights[group] \ - * compute_quota_left(required_items=required_items, shown_items=shown_item) + * compute_quota_left(required_items=required_items, shown_items=shown_items) # weight fisher information priority_index = priority_index * float(item_information_function( diff --git a/adaptivetesting/math/content_balancing/__maximum_priority_index.py b/adaptivetesting/math/content_balancing/__maximum_priority_index.py new file mode 100644 index 0000000..e69de29 diff --git a/adaptivetesting/math/exposure_control/__randomesque.py b/adaptivetesting/math/exposure_control/__randomesque.py index 3e96616..bfee40f 100644 --- a/adaptivetesting/math/exposure_control/__randomesque.py +++ b/adaptivetesting/math/exposure_control/__randomesque.py @@ -1,4 +1,5 @@ from typing import Callable, Any +import random import numpy as np from ...models.__test_item import TestItem from ..estimators.__test_information import item_information_function @@ -9,12 +10,13 @@ def radomesque_item_selection( ability_estimate: float, n_items: int, reverse: bool = True, - item_selection_function: Callable = item_information_function, # use item selection strategy + item_rating_function: Callable = item_information_function, + seed: int | None = None, **kwargs: Any -) -> list[TestItem]: +) -> TestItem: item_information_list: list[tuple[float, int]] = [] for i, item in enumerate(items): - information = float(item_selection_function( + information = float(item_rating_function( a=np.array(item.a), b=np.array(item.b), c=np.array(item.c), @@ -34,7 +36,11 @@ def sort_by_information(item_entry: tuple[float, int]) -> float: # select first n items selected_items = item_information_list[0:n_items] - # return specific items - return_items = [items[item[1]] for item in selected_items] - return return_items + # select only items + sub_item_pool = [items[item[1]] for item in selected_items] + # randomly select items from sub item pool + if seed is not None: + random.seed(seed) + sampled_item = random.sample(sub_item_pool, k=1)[0] + return sampled_item diff --git a/adaptivetesting/tests/test_weighted_penalty_model.py b/adaptivetesting/tests/test_weighted_penalty_model.py deleted file mode 100644 index dec6e91..0000000 --- a/adaptivetesting/tests/test_weighted_penalty_model.py +++ /dev/null @@ -1,450 +0,0 @@ -# flake8: noqa -# type: ignore -import unittest -from unittest.mock import patch -import numpy as np -from adaptivetesting.math.content_balancing.__weighted_penalty_model import WeightedPenaltyModel - - -class MockTestItem: - def __init__(self, id: int, a: float = 1.0, b: float = 0.0, c: float = 0.0, d: float = 1.0, category: list = None): - self.id = id - self.a = a - self.b = b - self.c = c - self.d = d - self.additional_properties = {"category": category or []} - - -class MockConstraint: - def __init__(self, name: str, prevalence: float = 0.5, lower: float = 0.4, upper: float = 0.6): - self.name = name - self.prevalence = prevalence - self.lower = lower - self.upper = upper - - -class TestWeightedPenaltyModel(unittest.TestCase): - - def setUp(self): - self.items = [ - MockTestItem(1, a=1.5, b=0.5, c=0.2, d=1.0, category=["math"]), - MockTestItem(2, a=1.2, b=-0.3, c=0.1, d=1.0, category=["reading"]), - MockTestItem(3, a=1.8, b=1.0, c=0.0, d=1.0, category=["math", "geometry"]) - ] - self.shown_items = [MockTestItem(4, category=["math"])] - self.ability = 1.0 - self.constraints = [ - MockConstraint("math", prevalence=0.6, lower=0.5, upper=0.7), - MockConstraint("reading", prevalence=0.4, lower=0.3, upper=0.5), - MockConstraint("geometry", prevalence=0.2, lower=0.1, upper=0.3) - ] - self.constraint_weight = 0.7 - self.information_weight = 0.3 - - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.item_information_function') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_total_content_penalty_value_for_item') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_prop') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_total_content_constraint_penalty_value') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_item_information') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_information_penalty_value') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_weighted_penalty_value') - def test_init_basic_functionality(self, mock_weighted_penalty, mock_info_penalty, mock_std_info, - mock_std_content, mock_compute_prop, mock_compute_penalty, mock_item_info): - # Setup mocks - mock_item_info.side_effect = [2.0, 1.5, 2.5] - mock_compute_penalty.side_effect = [1.0, 2.0, 1.5] - mock_compute_prop.side_effect = [0.45, 0.35, 0.25] # Below lower bounds -> group A - mock_std_content.side_effect = [0.4, 0.8, 0.6] - mock_std_info.side_effect = [0.8, 0.6, 1.0] - mock_info_penalty.side_effect = [0.2, 0.4, 0.0] - mock_weighted_penalty.side_effect = [0.45, 0.68, 0.42] - - model = WeightedPenaltyModel( - items=self.items, - shown_items=self.shown_items, - ability=self.ability, - constraints=self.constraints, - constraint_weight=self.constraint_weight, - information_weight=self.information_weight - ) - - # Verify basic attributes - self.assertEqual(model.items, self.items) - self.assertEqual(model.ability, self.ability) - self.assertEqual(model.constraints, self.constraints) - self.assertTrue(hasattr(model, 'eligible_items')) - self.assertEqual(len(model.eligible_items), 3) - - # Verify function calls - self.assertEqual(mock_item_info.call_count, 3) - self.assertEqual(mock_compute_penalty.call_count, 3) - - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.item_information_function') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_total_content_penalty_value_for_item') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_prop') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_total_content_constraint_penalty_value') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_item_information') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_information_penalty_value') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_weighted_penalty_value') - def test_group_assignment_green(self, mock_weighted_penalty, mock_info_penalty, mock_std_info, - mock_std_content, mock_compute_prop, mock_compute_penalty, mock_item_info): - # Setup for green group (all constraints A or B) - mock_item_info.side_effect = [2.0, 1.5, 2.5] - mock_compute_penalty.side_effect = [1.0, 2.0, 1.5] - mock_compute_prop.side_effect = [0.3, 0.2, 0.15] # All below lower bounds -> group A - mock_std_content.side_effect = [0.4, 0.8, 0.6] - mock_std_info.side_effect = [0.8, 0.6, 1.0] - mock_info_penalty.side_effect = [0.2, 0.4, 0.0] - mock_weighted_penalty.side_effect = [0.45, 0.68, 0.42] - - model = WeightedPenaltyModel( - items=self.items, - shown_items=self.shown_items, - ability=self.ability, - constraints=self.constraints, - constraint_weight=self.constraint_weight, - information_weight=self.information_weight - ) - - # Check that items with constraints in group A get green assignment - green_items = [item for item in model.eligible_items if item[2] == "green"] - self.assertTrue(len(green_items) > 0) - - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.item_information_function') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_total_content_penalty_value_for_item') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_prop') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_total_content_constraint_penalty_value') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_item_information') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_information_penalty_value') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_weighted_penalty_value') - def test_group_assignment_yellow(self, mock_weighted_penalty, mock_info_penalty, mock_std_info, - mock_std_content, mock_compute_prop, mock_compute_penalty, mock_item_info): - # Setup for yellow group (all constraints B) - mock_item_info.side_effect = [2.0, 1.5, 2.5] - mock_compute_penalty.side_effect = [1.0, 2.0, 1.5] - mock_compute_prop.side_effect = [0.55, 0.45, 0.25] # Math & reading within bounds, geometry below - mock_std_content.side_effect = [0.4, 0.8, 0.6] - mock_std_info.side_effect = [0.8, 0.6, 1.0] - mock_info_penalty.side_effect = [0.2, 0.4, 0.0] - mock_weighted_penalty.side_effect = [0.45, 0.68, 0.42] - - model = WeightedPenaltyModel( - items=self.items, - shown_items=self.shown_items, - ability=self.ability, - constraints=self.constraints, - constraint_weight=self.constraint_weight, - information_weight=self.information_weight - ) - - # Check that items with only constraint B get yellow assignment - yellow_items = [item for item in model.eligible_items if item[2] == "yellow"] - self.assertTrue(len(yellow_items) >= 0) # May be 0 based on test setup - - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.item_information_function') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_total_content_penalty_value_for_item') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_prop') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_total_content_constraint_penalty_value') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_item_information') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_information_penalty_value') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_weighted_penalty_value') - def test_group_assignment_red(self, mock_weighted_penalty, mock_info_penalty, mock_std_info, - mock_std_content, mock_compute_prop, mock_compute_penalty, mock_item_info): - # Setup for red group (all constraints B or C) - mock_item_info.side_effect = [2.0, 1.5, 2.5] - mock_compute_penalty.side_effect = [1.0, 2.0, 1.5] - mock_compute_prop.side_effect = [0.8, 0.7, 0.55] # Above upper bounds or within - mock_std_content.side_effect = [0.4, 0.8, 0.6] - mock_std_info.side_effect = [0.8, 0.6, 1.0] - mock_info_penalty.side_effect = [0.2, 0.4, 0.0] - mock_weighted_penalty.side_effect = [0.45, 0.68, 0.42] - - model = WeightedPenaltyModel( - items=self.items, - shown_items=self.shown_items, - ability=self.ability, - constraints=self.constraints, - constraint_weight=self.constraint_weight, - information_weight=self.information_weight - ) - - # Check that items with constraints in group B,C get red assignment - red_items = [item for item in model.eligible_items if item[2] == "red"] - self.assertTrue(len(red_items) >= 0) - - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.item_information_function') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_total_content_penalty_value_for_item') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_total_content_constraint_penalty_value') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_item_information') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_information_penalty_value') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_weighted_penalty_value') - def test_item_sorting_by_penalty_value(self, mock_weighted_penalty, mock_info_penalty, mock_std_info, - mock_std_content, mock_compute_penalty, mock_item_info): - # Setup different penalty values for sorting test - mock_item_info.side_effect = [2.0, 1.5, 2.5] - mock_compute_penalty.side_effect = [3.0, 1.0, 2.0] - mock_std_content.side_effect = [1.0, 0.0, 0.5] - mock_std_info.side_effect = [0.8, 0.6, 1.0] - mock_info_penalty.side_effect = [0.2, 0.4, 0.0] - mock_weighted_penalty.side_effect = [3.0, 1.0, 2.0] # Different penalty values - - model = WeightedPenaltyModel( - items=self.items, - shown_items=self.shown_items, - ability=self.ability, - constraints=[], # No constraints to avoid group assignment - constraint_weight=self.constraint_weight, - information_weight=self.information_weight - ) - - # Items should be sorted by penalty value (ascending) when no groups - penalty_values = [item[1] for item in model.eligible_items] - self.assertEqual(penalty_values, [1.0, 2.0, 3.0]) - - def test_init_empty_items_list(self): - model = WeightedPenaltyModel( - items=[], - shown_items=self.shown_items, - ability=self.ability, - constraints=self.constraints, - constraint_weight=self.constraint_weight, - information_weight=self.information_weight - ) - - self.assertEqual(len(model.eligible_items), 0) - self.assertEqual(model.items, []) - - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.item_information_function') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_total_content_penalty_value_for_item') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_total_content_constraint_penalty_value') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_item_information') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_information_penalty_value') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_weighted_penalty_value') - def test_init_empty_constraints_list(self, mock_weighted_penalty, mock_info_penalty, mock_std_info, - mock_std_content, mock_compute_penalty, mock_item_info): - mock_item_info.side_effect = [2.0, 1.5, 2.5] - mock_compute_penalty.side_effect = [1.0, 2.0, 1.5] - mock_std_content.side_effect = [0.4, 0.8, 0.6] - mock_std_info.side_effect = [0.8, 0.6, 1.0] - mock_info_penalty.side_effect = [0.2, 0.4, 0.0] - mock_weighted_penalty.side_effect = [0.45, 0.68, 0.42] - - model = WeightedPenaltyModel( - items=self.items, - shown_items=self.shown_items, - ability=self.ability, - constraints=[], - constraint_weight=self.constraint_weight, - information_weight=self.information_weight - ) - - # All items should have None group assignment - self.assertTrue(all(item[2] is None for item in model.eligible_items)) - - def test_select_item_with_eligible_items(self): - model = WeightedPenaltyModel.__new__(WeightedPenaltyModel) - model.eligible_items = [ - (self.items[0], 1.0, "green"), - (self.items[1], 2.0, "orange"), - (self.items[2], 1.5, "green") - ] - - selected_item = model.select_item() - # Should return first item (lowest penalty in green group) - self.assertEqual(selected_item, self.items[0]) - - def test_select_item_with_no_eligible_items(self): - model = WeightedPenaltyModel.__new__(WeightedPenaltyModel) - model.eligible_items = [] - - selected_item = model.select_item() - self.assertIsNone(selected_item) - - def test_select_item_returns_first_item(self): - model = WeightedPenaltyModel.__new__(WeightedPenaltyModel) - model.eligible_items = [ - (self.items[1], 2.0, "orange"), - (self.items[0], 1.0, "green"), - (self.items[2], 3.0, "red") - ] - - selected_item = model.select_item() - # Should return the first item in the sorted list - self.assertEqual(selected_item, self.items[1]) - - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_total_content_constraint_penalty_value') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_item_information') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_information_penalty_value') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_weighted_penalty_value') - def test_calculate_weighted_penalty_static_method(self, mock_weighted_penalty, mock_info_penalty, - mock_std_info, mock_std_content): - mock_std_content.return_value = 0.5 - mock_std_info.return_value = 0.8 - mock_info_penalty.return_value = 0.2 - mock_weighted_penalty.return_value = 0.45 - - result = WeightedPenaltyModel._WeightedPenaltyModel__calculate_weighted_penalty( - content_penalty=2.0, - minimum_total_content_penalty=1.0, - maximum_total_content_penalty=3.0, - item_information=1.5, - max_information=2.0, - constraint_weight=0.7, - information_weight=0.3 - ) - - self.assertEqual(result, 0.45) - - # Verify function calls with correct parameters - mock_std_content.assert_called_once_with( - item_penality_value=2.0, - minimum=1.0, - maximum=3.0 - ) - mock_std_info.assert_called_once_with( - item_information=1.5, - maximum=2.0 - ) - mock_info_penalty.assert_called_once_with( - standardized_item_information=0.8 - ) - mock_weighted_penalty.assert_called_once_with( - constraint_weight=0.7, - standardized_constraint_penalty_value=0.5, - information_weight=0.3, - information_penalty_value=0.2 - ) - - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.item_information_function') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_total_content_penalty_value_for_item') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_prop') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_total_content_constraint_penalty_value') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_item_information') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_information_penalty_value') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_weighted_penalty_value') - def test_compute_prop_calls_with_correct_parameters(self, mock_weighted_penalty, mock_info_penalty, - mock_std_info, mock_std_content, mock_compute_prop, - mock_compute_penalty, mock_item_info): - mock_item_info.side_effect = [2.0, 1.5, 2.5] - mock_compute_penalty.side_effect = [1.0, 2.0, 1.5] - mock_compute_prop.side_effect = [0.45, 0.35, 0.25] - mock_std_content.side_effect = [0.4, 0.8, 0.6] - mock_std_info.side_effect = [0.8, 0.6, 1.0] - mock_info_penalty.side_effect = [0.2, 0.4, 0.0] - mock_weighted_penalty.side_effect = [0.45, 0.68, 0.42] - - model = WeightedPenaltyModel( - items=self.items, - shown_items=self.shown_items, - ability=self.ability, - constraints=self.constraints, - constraint_weight=self.constraint_weight, - information_weight=self.information_weight - ) - - # Verify compute_prop was called for each constraint - self.assertEqual(mock_compute_prop.call_count, 3) - - # Check first call parameters - first_call = mock_compute_prop.call_args_list[0] - self.assertEqual(first_call[1]['n_administered'], 1) # One shown item with "math" - self.assertEqual(first_call[1]['n_remaining'], 2) # Two items with "math" - self.assertEqual(first_call[1]['prevalence'], 0.6) - self.assertEqual(first_call[1]['test_length'], 1) - - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.item_information_function') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_total_content_penalty_value_for_item') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_prop') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_total_content_constraint_penalty_value') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_item_information') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_information_penalty_value') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_weighted_penalty_value') - def test_item_information_function_calls(self, mock_weighted_penalty, mock_info_penalty, mock_std_info, - mock_std_content, mock_compute_prop, mock_compute_penalty, mock_item_info): - mock_item_info.side_effect = [2.0, 1.5, 2.5] - mock_compute_penalty.side_effect = [1.0, 2.0, 1.5] - mock_compute_prop.side_effect = [0.45, 0.35, 0.25] - mock_std_content.side_effect = [0.4, 0.8, 0.6] - mock_std_info.side_effect = [0.8, 0.6, 1.0] - mock_info_penalty.side_effect = [0.2, 0.4, 0.0] - mock_weighted_penalty.side_effect = [0.45, 0.68, 0.42] - - model = WeightedPenaltyModel( - items=self.items, - shown_items=self.shown_items, - ability=self.ability, - constraints=self.constraints, - constraint_weight=self.constraint_weight, - information_weight=self.information_weight - ) - - # Verify item_information_function was called for each item - self.assertEqual(mock_item_info.call_count, 3) - - # Check that it was called with correct parameters for first item - first_call = mock_item_info.call_args_list[0] - np.testing.assert_array_equal(first_call[1]['mu'], np.array(self.ability)) - np.testing.assert_array_equal(first_call[1]['a'], np.array(self.items[0].a)) - - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.item_information_function') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_total_content_penalty_value_for_item') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_prop') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_total_content_constraint_penalty_value') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.standardize_item_information') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_information_penalty_value') - @patch('adaptivetesting.math.content_balancing.__weighted_penalty_model.compute_weighted_penalty_value') - def test_single_item_processing(self, mock_weighted_penalty, mock_info_penalty, mock_std_info, - mock_std_content, mock_compute_prop, mock_compute_penalty, mock_item_info): - mock_item_info.return_value = 2.0 - mock_compute_penalty.return_value = 1.0 - mock_compute_prop.return_value = 0.45 - mock_std_content.return_value = 0.5 - mock_std_info.return_value = 0.8 - mock_info_penalty.return_value = 0.2 - mock_weighted_penalty.return_value = 0.45 - - single_item = [MockTestItem(1, category=["math"])] - single_constraint = [MockConstraint("math")] - - model = WeightedPenaltyModel( - items=single_item, - shown_items=self.shown_items, - ability=self.ability, - constraints=single_constraint, - constraint_weight=self.constraint_weight, - information_weight=self.information_weight - ) - - self.assertEqual(len(model.eligible_items), 1) - self.assertEqual(model.eligible_items[0][0], single_item[0]) - self.assertEqual(model.eligible_items[0][1], 0.45) - self.assertEqual(model.eligible_items[0][2], "green") # Single constraint A -> green - - def test_group_ordering_priority(self): - # Test that group ordering follows: green, orange, yellow, red, None - model = WeightedPenaltyModel.__new__(WeightedPenaltyModel) - model.eligible_items = [ - (self.items[0], 3.0, "red"), - (self.items[1], 1.0, "green"), - (self.items[2], 2.0, "yellow"), - (MockTestItem(5), 4.0, None), - (MockTestItem(6), 1.5, "orange") - ] - - # Manually sort using the same key function as in the class - sorted_items = sorted( - model.eligible_items, - key=lambda x: ( - {"green": 0, "orange": 1, "yellow": 2, "red": 3, None: 4}[x[2]], - x[1] - ) - ) - - expected_order = ["green", "orange", "yellow", "red", None] - actual_order = [item[2] for item in sorted_items] - self.assertEqual(actual_order, expected_order) - - -if __name__ == '__main__': - unittest.main() \ No newline at end of file From 404adfd8c4701fe063f92623a53968905e5a9a0a Mon Sep 17 00:00:00 2001 From: condecon Date: Mon, 13 Oct 2025 09:55:01 +0200 Subject: [PATCH 023/137] Refactor WeightedPenaltyModel: Enhance item selection and penalty calculation logic --- .../__weighted_penalty_model.py | 143 +++++++++++------- 1 file changed, 91 insertions(+), 52 deletions(-) diff --git a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py index 80189e7..25c7797 100644 --- a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py +++ b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py @@ -29,13 +29,56 @@ def __init__(self, self.items = items self.ability = ability self.constraints = constraints + self.eligible_items: list[tuple[TestItem, float, ITEM_GROUP]] = [] + self.shown_items = shown_items + self.constraint_weight = constraint_weight + self.information_weight = information_weight - # calculate weighted penality value for each eligible item in the pool - eligible_items: list[tuple[TestItem, float, ITEM_GROUP]] = [] + def select_item(self) -> TestItem | None: + """Select the next item to administer based on the weighted penalty model. + + Returns: + TestItem | None: The selected TestItem or None if no eligible items are available. + """ + self.prepare_item_pool() + if len(self.eligible_items) > 0: + return self.eligible_items[0][0] + else: + return None + + def prepare_item_pool(self): + """Prepares item pool for the item selection + and calls are functions required to perform the necessary calculations""" # calculate item information for every item - item_information_list = [ + item_information_list = self.calculate_information() + + max_item = max(item_information_list) + + content_penalties = self.calculate_content_penalties() + + max_content_penalty = max(content_penalties) + min_content_penalty = min(content_penalties) + + self.calcualte_weighted_penalty_for_all_items( + item_information_list=item_information_list, + max_item=max_item, + content_penalties=content_penalties, + max_content_penalty=max_content_penalty, + min_content_penalty=min_content_penalty + ) + + group_assignment: list[tuple[Constraint, CONSTRAINT_GROUP]] = self.get_constraint_group_assignments() + + # form a list of candidate items + self.form_list_of_candidate_items(group_assignment) + + # order items + self.order_candidate_items() + + def calculate_information(self) -> list[float]: + information_list = [ float(item_information_function( - mu=np.array(ability), + mu=np.array(self.ability), a=np.array(item.a), b=np.array(item.b), c=np.array(item.c), @@ -43,46 +86,50 @@ def __init__(self, )) for item in self.items ] + return information_list - max_item = max(item_information_list) - + def calculate_content_penalties(self) -> list[float]: content_penalties = [ compute_total_content_penalty_value_for_item( item=item, - shown_items=shown_items, - available_items=items, - constraints=constraints + shown_items=self.shown_items, + available_items=self.items, + constraints=self.constraints ) - for item in items + for item in self.items ] - - max_content_penalties = max(content_penalties) - min_content_penalties = min(content_penalties) - + return content_penalties + + def calcualte_weighted_penalty_for_all_items(self, + item_information_list: list[float], + max_item: float, + content_penalties: list[float], + max_content_penalty: float, + min_content_penalty: float): for i, item in enumerate(self.items): - weighted_penalty_value = self.__calculate_weighted_penalty( + weighted_penalty_value = self.calculate_weighted_penalty_value( item_information=item_information_list[i], max_information=max_item, - constraint_weight=constraint_weight, - information_weight=information_weight, + constraint_weight=self.constraint_weight, + information_weight=self.information_weight, content_penalty=content_penalties[i], - maximum_total_content_penalty=max_content_penalties, - minimum_total_content_penalty=min_content_penalties, + maximum_total_content_penalty=max_content_penalty, + minimum_total_content_penalty=min_content_penalty, ) - eligible_items.append((item, weighted_penalty_value, None)) + self.eligible_items.append((item, weighted_penalty_value, None)) + def get_constraint_group_assignments(self) -> list[tuple[Constraint, CONSTRAINT_GROUP]]: group_assignment: list[tuple[Constraint, CONSTRAINT_GROUP]] = [] - # assign each constraint to a color group - for constraint in constraints: + for constraint in self.constraints: # calculate proportion of the constraint n_administered: int = len( - [item for item in shown_items if constraint.name in item.additional_properties["category"]] + [item for item in self.shown_items if constraint.name in item.additional_properties["category"]] ) n_remaining: int = len( [item for item in self.items if constraint.name in item.additional_properties["category"]] ) - test_length: int = len(shown_items) + test_length: int = len(self.shown_items) prop = compute_prop( n_administered=n_administered, n_remaining=n_remaining, @@ -100,8 +147,11 @@ def __init__(self, else: raise ValueError("constraint.lower and constraint upper may not be None.") - # form a list of candidate items - for i, item_entry in enumerate(eligible_items): + return group_assignment + + def form_list_of_candidate_items(self, + group_assignment: list[tuple[Constraint, CONSTRAINT_GROUP]]) -> None: + for i, item_entry in enumerate(self.eligible_items): item, weighted_penalty_value, _ = item_entry # find associated constraint associated_constraints = [constraint_assignment @@ -111,50 +161,39 @@ def __init__(self, # if all associated constraints A or B -> green group if all(group in ["A", "B"] for _, group in associated_constraints): - eligible_items[i] = (item, weighted_penalty_value, "green") + self.eligible_items[i] = (item, weighted_penalty_value, "green") # if all A, B, C, or A, C -> orange if all(group in ["A", "B", "C"] or group in ["A", "C"] for _, group in associated_constraints): - eligible_items[i] = (item, weighted_penalty_value, "orange") + self.eligible_items[i] = (item, weighted_penalty_value, "orange") # if all B -> yellow if all(group in ["B"] for _, group in associated_constraints): - eligible_items[i] = (item, weighted_penalty_value, "yellow") + self.eligible_items[i] = (item, weighted_penalty_value, "yellow") # if all B, C -> red if all(group in ["B", "C"] for _, group in associated_constraints): - eligible_items[i] = (item, weighted_penalty_value, "red") + self.eligible_items[i] = (item, weighted_penalty_value, "red") else: - eligible_items[i] = (item, weighted_penalty_value, None) + self.eligible_items[i] = (item, weighted_penalty_value, None) - # order items + def order_candidate_items(self): # between group ordering: green, orange, yellow, red # within group ordering: ascending order of weighted penalty value self.eligible_items = sorted( - eligible_items, + self.eligible_items, key=lambda x: ( {"green": 0, "orange": 1, "yellow": 2, "red": 3, None: 4}[x[2]], x[1] ) ) - def select_item(self) -> TestItem | None: - """Select the next item to administer based on the weighted penalty model. - - Returns: - TestItem | None: The selected TestItem or None if no eligible items are available. - """ - if len(self.eligible_items) > 0: - return self.eligible_items[0][0] - else: - return None - @staticmethod - def __calculate_weighted_penalty(content_penalty: float, - minimum_total_content_penalty: float, - maximum_total_content_penalty: float, - item_information: float, - max_information: float, - constraint_weight: float, - information_weight: float - ) -> float: + def calculate_weighted_penalty_value(content_penalty: float, + minimum_total_content_penalty: float, + maximum_total_content_penalty: float, + item_information: float, + max_information: float, + constraint_weight: float, + information_weight: float + ) -> float: # reference content penalty total_content_penalty_value = content_penalty From fa0cccd4aa77825c5e43daa4c50d5342228d3312 Mon Sep 17 00:00:00 2001 From: condecon Date: Mon, 13 Oct 2025 09:55:46 +0200 Subject: [PATCH 024/137] improve value error message --- .../math/content_balancing/__weighted_penalty_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py index 25c7797..3a3db11 100644 --- a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py +++ b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py @@ -145,7 +145,7 @@ def get_constraint_group_assignments(self) -> list[tuple[Constraint, CONSTRAINT_ if constraint.upper <= prop: group_assignment.append((constraint, "C")) else: - raise ValueError("constraint.lower and constraint upper may not be None.") + raise ValueError("constraint.lower and constraint.upper may not be None.") return group_assignment From b628eddab3c571438b2d30c810f75d232c6c9e0c Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Tue, 21 Oct 2025 13:35:07 +0200 Subject: [PATCH 025/137] Add plot functions and descriptive statistics (#39) * update global module imports * progress * add read test results capabilities * remove uncalled urrys rule * add bias, rmse, aad functions * add function to read final test results * add basic plots (wip) * add iif plot * update item ID assignment logic in ItemPool class to handle None case and improve documentation * add missing functions * linting and type checking * refactor: enhance plotting functions and add unit tests for descriptives --- adaptivetesting/__init__.py | 35 +++ adaptivetesting/data/__csv_context.py | 27 +- adaptivetesting/data/__pickle_context.py | 22 +- adaptivetesting/models/__adaptive_test.py | 3 - adaptivetesting/models/__item_pool.py | 20 +- adaptivetesting/tests/test_descriptives.py | 50 ++++ adaptivetesting/tests/test_plots.py | 97 +++++++ adaptivetesting/utils/__descriptives.py | 63 +++++ adaptivetesting/utils/__funcs.py | 78 ++++++ adaptivetesting/utils/__init__.py | 13 + adaptivetesting/utils/__plots.py | 300 +++++++++++++++++++++ 11 files changed, 687 insertions(+), 21 deletions(-) create mode 100644 adaptivetesting/tests/test_descriptives.py create mode 100644 adaptivetesting/tests/test_plots.py create mode 100644 adaptivetesting/utils/__descriptives.py create mode 100644 adaptivetesting/utils/__funcs.py create mode 100644 adaptivetesting/utils/__init__.py create mode 100644 adaptivetesting/utils/__plots.py diff --git a/adaptivetesting/__init__.py b/adaptivetesting/__init__.py index 8939eaf..fc88426 100644 --- a/adaptivetesting/__init__.py +++ b/adaptivetesting/__init__.py @@ -5,3 +5,38 @@ from . import services from . import simulation from . import tests + +from .data.__csv_context import CSVContext +from .data.__pickle_context import PickleContext + +from .implementations.__default_implementation import DefaultImplementation +from .implementations.__pre_test import PreTest +from .implementations.__semi_implementation import SemiAdaptiveImplementation +from .implementations.__test_assembler import TestAssembler + +from .math.__gen_response_pattern import generate_response_pattern +from .math.estimators.__ml_estimation import MLEstimator +from .math.estimators.__bayes_modal_estimation import BayesModal +from .math.estimators.__expect_a_posteriori import ExpectedAPosteriori +from .math.estimators.__prior import Prior, NormalPrior, CustomPrior, CustomPriorException +from .math.estimators.__functions.__estimators import probability_y0, probability_y1, maximize_likelihood_function, likelihood +from .math.estimators.__functions.__bayes import maximize_posterior +from .math.estimators.__test_information import test_information_function, item_information_function, prior_information_function +from .math.item_selection.__maximum_information_criterion import maximum_information_criterion +from .math.item_selection.__urrys_rule import urrys_rule + +from .models.__adaptive_test import AdaptiveTest +from .models.__algorithm_exception import AlgorithmException +from .models.__item_pool import ItemPool +from .models.__item_selection_exception import ItemSelectionException +from .models.__test_item import TestItem +from .models.__test_result import TestResult +from .models.__misc import ResultOutputFormat, StoppingCriterion + +from .services.__estimator_interface import IEstimator +from .services.__test_results_interface import ITestResults +from .services.__item_selection_protocol import ItemSelectionStrategy + +from .simulation.__simulation import Simulation, SimulationPool, setup_simulation_and_start + +from .utils.__descriptives import bias, average_absolute_deviation, rmse \ No newline at end of file diff --git a/adaptivetesting/data/__csv_context.py b/adaptivetesting/data/__csv_context.py index 9f71cbb..ed8decd 100644 --- a/adaptivetesting/data/__csv_context.py +++ b/adaptivetesting/data/__csv_context.py @@ -3,6 +3,7 @@ import pathlib from ..models.__test_result import TestResult from ..services.__test_results_interface import ITestResults +from dataclasses import fields class CSVContext(ITestResults): @@ -46,12 +47,22 @@ def save(self, test_results: List[TestResult]) -> None: file.close() def load(self) -> List[TestResult]: - """Loads results from the database. - The implementation of this method is required - by the interface. However, it does not have - any implemented functionality and will throw an error - if used. - - Returns: List[TestResult] + """Loads test results from a CSV file for a specific participant and simulation. + Reads the CSV file located at `data/{simulation_id}/{participant_id}.csv`, + parses each row into a `TestResult` object, + and returns a list of these objects. + + Returns: + List[TestResult]: A list of `TestResult` objects loaded from the CSV file. """ - raise NotImplementedError("This function is not implemented.") + foldername = f"data/{self.simulation_id}" + + fieldnames = list(fields(TestResult)) + test_results: list[TestResult] = [] + with open(f"{foldername}/{self.participant_id}.csv", "r", encoding="utf-8") as file: + reader = csv.DictReader(file, fieldnames=fieldnames) + for row in reader: + test_result = TestResult.from_dict(row) + test_results.append(test_result) + file.close() + return test_results diff --git a/adaptivetesting/data/__pickle_context.py b/adaptivetesting/data/__pickle_context.py index 91b9c7b..b250717 100644 --- a/adaptivetesting/data/__pickle_context.py +++ b/adaptivetesting/data/__pickle_context.py @@ -40,12 +40,18 @@ def save(self, test_results: List[TestResult]) -> None: file.close() def load(self) -> List[TestResult]: - """Loads results from the database. - The implementation of this method is required - by the interface. However, is does not have - any implemented functionality and will throw an error - if used. - - Returns: List[TestResult] + """Loads and returns a list of TestResult objects for a specific participant and simulation. + The method reads a pickle file located at 'data/{simulation_id}/{participant_id}.pickle' + and deserializes its contents into a list of TestResult instances. + Returns: + List[TestResult]: The list of test results loaded from the pickle file. + Raises: + FileNotFoundError: If the specified pickle file does not exist. + pickle.UnpicklingError: If the file cannot be unpickled. """ - raise NotImplementedError("This function is not implemented.") + foldername = f"data/{self.simulation_id}" + + with open(f"{foldername}/{self.participant_id}.pickle", "rb") as file: + test_results: list[TestResult] = pickle.load(file) + file.close() + return test_results diff --git a/adaptivetesting/models/__adaptive_test.py b/adaptivetesting/models/__adaptive_test.py index 43ee370..19d9816 100644 --- a/adaptivetesting/models/__adaptive_test.py +++ b/adaptivetesting/models/__adaptive_test.py @@ -2,7 +2,6 @@ import abc import copy from .__test_item import TestItem -from ..math.item_selection.__urrys_rule import urrys_rule from ..math.__gen_response_pattern import generate_response_pattern from .__test_result import TestResult from .__item_pool import ItemPool @@ -114,8 +113,6 @@ def get_next_item(self) -> TestItem: TestItem: selected item """ raise NotImplementedError("This functionality is not implemented by default.") - item = urrys_rule(self.item_pool.test_items, self.ability_level) - return item @abc.abstractmethod def estimate_ability_level(self) -> tuple[float, float]: diff --git a/adaptivetesting/models/__item_pool.py b/adaptivetesting/models/__item_pool.py index 661ca7c..ced5deb 100644 --- a/adaptivetesting/models/__item_pool.py +++ b/adaptivetesting/models/__item_pool.py @@ -110,7 +110,9 @@ def load_from_list( simulated_responses (List[int]): simulated responses - ids (List[int]): item IDs + ids (List[int]): item IDs. If the argument is set to `None` + all items are numbered in the order in which they are + passed to the function. Returns: List[TestItem]: item pool @@ -147,6 +149,9 @@ def load_from_list( raise ValueError("Length of ids and b has to be the same.") for i, id_ in enumerate(ids): items[i].id = id_ + else: + for i in range(len(b)): + items[i].id = i item_pool = ItemPool(items) item_pool.simulated_responses = simulated_responses @@ -169,7 +174,9 @@ def load_from_dict(source: dict[str, List[float]], Args: source (dict[str, List[float]]): item pool dictionary simulated_responses (List[int]): simulated responses - ids (List[int]): item IDs + ids (List[int]): item IDs. If the argument is set to `None` + all items are numbered in the order in which they are + passed to the function. Returns: List[TestItem]: item pool @@ -211,6 +218,8 @@ def load_from_dict(source: dict[str, List[float]], if ids is not None: item.id = ids[i] + else: + item.id = i items.append(item) @@ -223,6 +232,13 @@ def load_from_dataframe(source: DataFrame) -> "ItemPool": """Creates item pool from a pandas DataFrame. Required columns are: `a`, `b`, `c`, `d`. Each column has to contain float values. + + A `id` column can be added to assign + each test item a unique identifier. + If there is no `id` column + all items are numbered in the order in which they are + passed to the function. + A `simulated_responses` (int values) column can be added to the DataFrame to provide simulated responses. diff --git a/adaptivetesting/tests/test_descriptives.py b/adaptivetesting/tests/test_descriptives.py new file mode 100644 index 0000000..02c7de4 --- /dev/null +++ b/adaptivetesting/tests/test_descriptives.py @@ -0,0 +1,50 @@ +import unittest +from unittest.mock import patch +import numpy as np +from adaptivetesting import rmse, bias, average_absolute_deviation, TestResult +from adaptivetesting.models.__misc import ResultOutputFormat + + +class DummyResult: + def __init__(self, ability_estimation, true_ability_level): + self.ability_estimation = ability_estimation + self.true_ability_level = true_ability_level + + +class TestDescriptives(unittest.TestCase): + @patch('adaptivetesting.utils.__descriptives.load_final_test_results') + def test_bias(self, mock_load): + mock_load.return_value = [ + TestResult(None, 1.0, None, None, None, 0.5), # type: ignore + TestResult(None, 2.0, None, None, None, 1.5), # type: ignore + TestResult(None, 3.0, None, None, None, 2.5), # type: ignore + ] + result = bias('sim1', ['p1', 'p2', 'p3'], ResultOutputFormat.CSV) + expected = np.mean([1.0 - 0.5, 2.0 - 1.5, 3.0 - 2.5]) + self.assertAlmostEqual(result, expected) # type: ignore + + @patch('adaptivetesting.utils.__descriptives.load_final_test_results') + def test_average_absolute_deviation(self, mock_load): + mock_load.return_value = [ + TestResult(None, 1.0, None, None, None, 0.5), # type: ignore + TestResult(None, 2.0, None, None, None, 1.5), # type: ignore + TestResult(None, 3.0, None, None, None, 2.5), # type: ignore + ] + result = average_absolute_deviation('sim1', ['p1', 'p2', 'p3'], ResultOutputFormat.CSV) + expected = np.mean([abs(1.0 - 0.5), abs(2.0 - 1.5), abs(3.0 - 2.5)]) + self.assertAlmostEqual(result, expected) # type: ignore + + @patch('adaptivetesting.utils.__descriptives.load_final_test_results') + def test_rmse(self, mock_load): + mock_load.return_value = [ + TestResult(None, 1.0, None, None, None, 0.5), # type: ignore + TestResult(None, 2.0, None, None, None, 1.5), # type: ignore + TestResult(None, 3.0, None, None, None, 2.5), # type: ignore + ] + result = rmse('sim1', ['p1', 'p2', 'p3'], ResultOutputFormat.CSV) + expected = np.sqrt(np.mean([(1.0 - 0.5) ** 2, (2.0 - 1.5) ** 2, (3.0 - 2.5) ** 2])) + self.assertAlmostEqual(result, expected) + + +if __name__ == '__main__': + unittest.main() diff --git a/adaptivetesting/tests/test_plots.py b/adaptivetesting/tests/test_plots.py new file mode 100644 index 0000000..12f4694 --- /dev/null +++ b/adaptivetesting/tests/test_plots.py @@ -0,0 +1,97 @@ +# type: ignore +import unittest +from unittest.mock import patch +from adaptivetesting.utils import ( + plot_final_ability_estimates, + plot_icc, + plot_iif, + plot_exposure_rate, + plot_test_information, + plot_theta_estimation_trace +) +from adaptivetesting.models.__test_item import TestItem +from adaptivetesting.models.__misc import ResultOutputFormat +import matplotlib + + +class TestPlots(unittest.TestCase): + @classmethod + def setUpClass(cls): + cls.dummy_item = TestItem() + cls.dummy_item.id = 1 + cls.dummy_item.a = 1.0 + cls.dummy_item.b = 0.0 + cls.dummy_item.c = 0.2 + cls.dummy_item.d = 1.0 + cls.dummy_items = [cls.dummy_item for _ in range(3)] + cls.simulation_id = 'sim1' + cls.participant_ids = ['p1', 'p2'] + cls.output_format = ResultOutputFormat.CSV + + @patch('adaptivetesting.utils.__plots.load_final_test_results') + def test_plot_final_ability_estimates(self, mock_load): + # Mock return value: list of objects with .ability_estimation and .true_ability_level + class DummyResult: + def __init__(self, est, true): + self.ability_estimation = est + self.true_ability_level = true + mock_load.return_value = [DummyResult(1.0, 1.2), DummyResult(0.5, 0.7)] + fig, ax = plot_final_ability_estimates( + self.simulation_id, + self.participant_ids, + self.output_format + ) + self.assertIsInstance(fig, matplotlib.figure.Figure) + self.assertIsNotNone(ax) + + def test_plot_icc(self): + fig, ax = plot_icc(self.dummy_item) + self.assertIsInstance(fig, matplotlib.figure.Figure) + self.assertIsNotNone(ax) + + def test_plot_iif(self): + fig, ax = plot_iif(self.dummy_item) + self.assertIsInstance(fig, matplotlib.figure.Figure) + self.assertIsNotNone(ax) + + @patch('adaptivetesting.utils.__plots.load_test_results_single_participant') + def test_plot_exposure_rate(self, mock_load): + # Mock return value: list of objects with .showed_item["id"] + class DummyResult: + def __init__(self, item_id): + self.showed_item = {"id": item_id} + mock_load.return_value = [ + DummyResult("item1"), DummyResult("item2") + ] + fig, ax = plot_exposure_rate( + self.simulation_id, + self.participant_ids, + self.output_format + ) + self.assertIsInstance(fig, matplotlib.figure.Figure) + self.assertIsNotNone(ax) + + def test_plot_test_information(self): + fig, ax = plot_test_information(self.dummy_items) + self.assertIsInstance(fig, matplotlib.figure.Figure) + self.assertIsNotNone(ax) + + @patch('adaptivetesting.utils.__plots.load_test_results_single_participant') + def test_plot_theta_estimation_trace(self, mock_load): + # Mock return value: list of objects with .true_ability_level and .ability_estimation + class DummyResult: + def __init__(self, true, est): + self.true_ability_level = true + self.ability_estimation = est + mock_load.return_value = [DummyResult(1.0, 0.9), DummyResult(1.2, 1.1)] + fig, ax = plot_theta_estimation_trace( + self.simulation_id, + self.participant_ids[0], + self.output_format + ) + self.assertIsInstance(fig, matplotlib.figure.Figure) + self.assertIsNotNone(ax) + + +if __name__ == "__main__": + unittest.main() diff --git a/adaptivetesting/utils/__descriptives.py b/adaptivetesting/utils/__descriptives.py new file mode 100644 index 0000000..e1c5585 --- /dev/null +++ b/adaptivetesting/utils/__descriptives.py @@ -0,0 +1,63 @@ +from ..models.__misc import ResultOutputFormat +import numpy as np +from .__funcs import load_final_test_results + + +def bias(simulation_id: str, participant_ids: list[str], output_format: ResultOutputFormat) -> np.floating: + """ + Calculates the bias of ability estimations for a set of participants in a simulation. + Bias is defined as the mean difference between estimated abilities and true ability levels. + Args: + simulation_id (str): Identifier for the simulation. + participant_ids (list[str]): List of participant identifiers. + output_format (ResultOutputFormat): Format in which the results are output. + Returns: + np.floating: The mean bias of ability estimations. + """ + final_test_results = load_final_test_results(simulation_id, participant_ids, output_format) + estimations = np.array([result.ability_estimation for result in final_test_results], dtype=np.float64) + true_abilities = np.array([result.true_ability_level for result in final_test_results], dtype=np.float64) + + return np.mean(estimations - true_abilities) + + +def average_absolute_deviation(simulation_id: str, + participant_ids: list[str], + output_format: ResultOutputFormat) -> np.floating: + """ + Calculates the average absolute deviation between estimated abilities + and true ability levels for a set of participants. + + Args: + simulation_id (str): Identifier for the simulation run. + participant_ids (list[str]): List of participant identifiers. + output_format (ResultOutputFormat): Format in which the results are returned. + Returns: + np.floating: The mean absolute deviation between estimated and true abilities. + """ + final_test_results = load_final_test_results(simulation_id, participant_ids, output_format) + estimations = np.array([result.ability_estimation for result in final_test_results], dtype=np.float64) + true_abilities = np.array([result.true_ability_level for result in final_test_results], dtype=np.float64) + + return np.mean(np.abs(estimations - true_abilities)) + + +def rmse(simulation_id: str, + participant_ids: list[str], + output_format: ResultOutputFormat) -> np.floating: + """ + Calculates the root mean squared error (RMSE) between + estimated abilities and true ability levels for a set of participants. + + Args: + simulation_id (str): Identifier for the simulation run. + participant_ids (list[str]): List of participant IDs to include in the calculation. + output_format (ResultOutputFormat): Format in which the test results are returned. + Returns: + np.floating: The RMSE value between estimated and true abilities. + """ + final_test_results = load_final_test_results(simulation_id, participant_ids, output_format) + estimations = np.array([result.ability_estimation for result in final_test_results], dtype=np.float64) + true_abilities = np.array([result.true_ability_level for result in final_test_results], dtype=np.float64) + + return np.sqrt(np.mean((estimations - true_abilities) ** 2)) diff --git a/adaptivetesting/utils/__funcs.py b/adaptivetesting/utils/__funcs.py new file mode 100644 index 0000000..f5dd034 --- /dev/null +++ b/adaptivetesting/utils/__funcs.py @@ -0,0 +1,78 @@ +from ..models.__test_result import TestResult +from ..data.__csv_context import CSVContext +from ..data.__pickle_context import PickleContext +from ..services.__test_results_interface import ITestResults +from ..models.__misc import ResultOutputFormat + + +def load_final_test_results(simulation_id: str, + participant_ids: list[str], + output_format: ResultOutputFormat) -> list[TestResult]: + """ + Loads the final test results for a list of participants from the specified simulation or adaptive test. + Depending on the output format (CSV or PICKLE), this function initializes the appropriate context, + loads all test results for each participant, and selects the final result (assumed to be the last entry). + The final results are collected and returned as a list. + Args: + simulation_id (str): The identifier for the simulation from which to load results. + participant_ids (list[str]): A list of participant IDs whose results are to be loaded. + output_format (ResultOutputFormat): The format in which results are stored (CSV or PICKLE). + Returns: + list[TestResult]: A list containing the final test result for each participant. + Raises: + ValueError: If the output_format is not set to either ResultOutputFormat.CSV or ResultOutputFormat.PICKLE. + """ + # load data + final_test_results: list[TestResult] = [] + context: ITestResults + + if output_format is ResultOutputFormat.CSV: + for id in participant_ids: + context = CSVContext(simulation_id, participant_id=id) + test_results = context.load() + # select final result + final_result = test_results[-1] + final_test_results.append(final_result) + + if output_format is ResultOutputFormat.PICKLE: + for id in participant_ids: + context = PickleContext(simulation_id, participant_id=id) + test_results = context.load() + # select final result + final_result = test_results[-1] + final_test_results.append(final_result) + else: + raise ValueError("output_format is not correctly set to either PICKLE of CSV.") + + return final_test_results + + +def load_test_results_single_participant(simulation_id: str, + participant_id: str, + output_format: ResultOutputFormat) -> list[TestResult]: + """ + Loads the test results for a participant from the specified simulation or adaptive test. + Depending on the output format (CSV or PICKLE), this function reads the available CSV or PICKLE files. + + Args: + simulation_id (str): The identifier for the simulation from which to load results. + participant_id (str): Participant ID whose results are to be loaded. + output_format (ResultOutputFormat): The format in which results are stored (CSV or PICKLE). + Returns: + list[TestResult]: A list containing the test results for the participant. + Raises: + ValueError: If the output_format is not set to either ResultOutputFormat.CSV or ResultOutputFormat.PICKLE. + """ + context: ITestResults + + if output_format is ResultOutputFormat.CSV: + context = CSVContext(simulation_id=simulation_id, + participant_id=participant_id) + return context.load() + + if output_format is ResultOutputFormat.PICKLE: + context = PickleContext(simulation_id=simulation_id, + participant_id=participant_id) + return context.load() + else: + raise ValueError("output_format is not correctly set to either PICKLE of CSV.") diff --git a/adaptivetesting/utils/__init__.py b/adaptivetesting/utils/__init__.py new file mode 100644 index 0000000..acae14c --- /dev/null +++ b/adaptivetesting/utils/__init__.py @@ -0,0 +1,13 @@ +from .__descriptives import bias, average_absolute_deviation, rmse + +from .__funcs import load_final_test_results, load_test_results_single_participant + +from .__plots import ( + plot_exposure_rate, + plot_final_ability_estimates, + plot_icc, + plot_iif, + plot_exposure_rate, + plot_test_information, + plot_theta_estimation_trace +) \ No newline at end of file diff --git a/adaptivetesting/utils/__plots.py b/adaptivetesting/utils/__plots.py new file mode 100644 index 0000000..385acb4 --- /dev/null +++ b/adaptivetesting/utils/__plots.py @@ -0,0 +1,300 @@ +import matplotlib.pyplot as plt +from matplotlib.axes import Axes +from matplotlib.figure import Figure, SubFigure +from ..models.__misc import ResultOutputFormat +from ..models.__test_item import TestItem +from ..math.estimators.__functions.__estimators import probability_y1 +from ..math.estimators.__test_information import item_information_function +from .__funcs import load_final_test_results, load_test_results_single_participant +import numpy as np + + +def plot_final_ability_estimates(simulation_id: str, + participant_ids: list[str], + output_format: ResultOutputFormat, + ax: Axes | None = None, **kwargs): + """ + Plots the final ability estimates against the true ability levels for a set of participants in a simulation. + Args: + simulation_id (str): Identifier for the simulation whose results are to be plotted. + participant_ids (list[str]): List of participant IDs to include in the plot. + output_format (ResultOutputFormat): Format in which the results are stored and should be read. + ax (Axes | None, optional): Matplotlib Axes object to plot on. If None, a new figure and axes are created. + **kwargs: Additional keyword arguments passed to `ax.scatter`. + Returns: + tuple: (fig, ax) where `fig` is the Matplotlib Figure object and `ax` is the Axes object containing the plot. + Notes: + - The function reads the final test results for the specified participants and simulation. + - It plots the estimated ability levels against the true ability levels using a scatter plot. + """ + # get old attributes + fig: Figure | SubFigure + if ax is None: + fig, ax = plt.subplots() + else: + fig = ax.figure + + # read final test results data + final_test_results = load_final_test_results(simulation_id, participant_ids, output_format) + # extract true and finally estimated ability levels + true_and_final_abilities = [ + (result.ability_estimation, result.true_ability_level) + for result in final_test_results + ] + + final_estimates, true_abilities = zip(*true_and_final_abilities) + + if "color" not in kwargs: + ax.scatter(true_abilities, final_estimates, color="blue", **kwargs) + else: + ax.scatter(true_abilities, final_estimates, **kwargs) + ax.plot(true_abilities, true_abilities, color="black") + ax.set_xlabel("True ability level") + ax.set_ylabel("Estimated ability level") + + return fig, ax + + +def plot_icc(item: TestItem, + range: tuple[float, float] = (-10, 10), + ax: Axes | None = None, + **kwargs): + """ + Plots the Item Characteristic Curve (ICC) for a given test item. + Parameters: + item (TestItem): The test item containing parameters (a, b, c, d) for the ICC. + range (tuple[float, float], optional): The range of ability levels (theta) to plot. Defaults to (-10, 10). + ax (Axes, optional): Matplotlib Axes object to plot on. If None, a new figure and axes are created. + **kwargs: Additional keyword arguments passed to matplotlib's plot function. + Returns: + tuple: A tuple containing the matplotlib Figure and Axes objects. + """ + thetas = np.linspace(range[0], range[1], 1000) + probabilities = probability_y1( + mu=np.array(thetas).T, + a=np.array(item.a), + b=np.array(item.b), + c=np.array(item.c), + d=np.array(item.d), + ) + + fig: Figure | SubFigure + if ax is None: + fig, ax = plt.subplots() + else: + fig = ax.figure + + # create plot + ax.plot(thetas, probabilities, **kwargs) + ax.set_xlabel("Ability level") + ax.set_ylabel("Probability of correct response") + + return fig, ax + + +def plot_iif(item: TestItem, + range: tuple[float, float] = (-10, 10), + ax: Axes | None = None, + **kwargs): + """ + Plots the Item Information Function (IIF) for a given test item over a specified ability range. + Parameters: + item (TestItem): The test item for which to plot the information function. + range (tuple[float, float], optional): The range of ability levels (theta) to plot over. Defaults to (-10, 10). + ax (Axes, optional): Matplotlib Axes object to plot on. If None, a new figure and axes are created. + **kwargs: Additional keyword arguments passed to matplotlib's plot function. + Returns: + tuple[Figure, Axes]: The matplotlib Figure and Axes objects containing the plot. + """ + # calculate item information + thetas: list[np.ndarray] = list(np.linspace(range[0], range[1], 100)) + + information_array = [] + + for theta in thetas: + info = item_information_function( + mu=theta, + a=np.array(item.a), + b=np.array(item.b), + c=np.array(item.c), + d=np.array(item.d), + ) + information_array.append(info) + + # setup figure + fig: Figure | SubFigure + if ax is None: + fig, ax = plt.subplots() + else: + fig = ax.figure + + ax.plot(thetas, information_array, **kwargs) + ax.set_xlabel("Ability level") + ax.set_ylabel("Item information") + return fig, ax + + +def plot_exposure_rate(simulation_id: str, + participant_ids: list[str], + output_format: ResultOutputFormat): + """This function returns the exposure rates for all shown items + in a series of adaptive tests or CAT simulations. + + Args: + simulation_id (str): Simulation identifyer + participant_ids (list[str]): List of unique participant IDs + output_format (ResultOutputFormat): Format in which the test results have been previously saved + + Returns: + tuple[Figure, Axes]: matplotlib figure and axes + """ + # read test results for each participant and collect shown item ids + all_item_ids: list[str] = [] + for participant in participant_ids: + test_results = load_test_results_single_participant( + simulation_id=simulation_id, + participant_id=participant, + output_format=output_format, + ) + + # each TestResult has a 'showed_item' dict with an 'id' key + participant_item_ids = [res.showed_item["id"] for res in test_results] + all_item_ids.extend(participant_item_ids) + + if len(all_item_ids) == 0: + # nothing to plot, return empty figure + fig, ax = plt.subplots() + ax.set_title("No items shown - no exposure data") + return fig, ax + + # unique item ids and counts + unique_ids, counts = np.unique(np.array(all_item_ids, dtype=object), return_counts=True) + + # sort by counts descending for better readability + order = np.argsort(counts)[::-1] + unique_ids_sorted = unique_ids[order] + counts_sorted = counts[order] + + # compute exposure percentage relative to number of participants (or total exposures) + total_exposures = len(all_item_ids) + exposure_pct = counts_sorted / total_exposures * 100.0 + + # create bar plot + fig, ax = plt.subplots(figsize=(max(6, len(unique_ids_sorted) * 0.2), 4)) + bars = ax.bar(range(len(unique_ids_sorted)), counts_sorted, tick_label=unique_ids_sorted) + ax.set_xlabel("Item id") + ax.set_ylabel("Times shown") + ax.set_title("Item exposure counts (sorted)") + plt.xticks(rotation=90) + + # annotate bars with percentage labels + for rect, pct in zip(bars, exposure_pct): + height = rect.get_height() + ax.annotate(f"{pct:.1f}%", + xy=(rect.get_x() + rect.get_width() / 2, height), + xytext=(0, 3), # 3 points vertical offset + textcoords="offset points", + ha='center', va='bottom', fontsize=8) + + fig.tight_layout() + return fig, ax + + +def plot_test_information( + items: list[TestItem], + range: tuple[float, float] = (-10, 10), + ax: Axes | None = None, + **kwargs): + """ + Plots the Item Information Function (IIF) for a given test item over a specified ability range. + Args: + items (list[TestItem]): Test items in an item pool for which to calculate the test information + range (tuple[float, float], optional): The range of ability levels (theta) to plot over. Defaults to (-10, 10). + ax (Axes, optional): Matplotlib Axes object to plot on. If None, a new figure and axes are created. + **kwargs: Additional keyword arguments passed to matplotlib's plot function. + Returns: + tuple[Figure, Axes]: The matplotlib Figure and Axes objects containing the plot. + """ + # calculate test information by summing item information across items + thetas = np.linspace(range[0], range[1], 100) + information_array = np.zeros_like(thetas, dtype=float) + for item in items: + information_array += item_information_function( + mu=np.array(thetas).T, + a=np.array(item.a), + b=np.array(item.b), + c=np.array(item.c), + d=np.array(item.d), + ) + + # setup figure + fig: Figure | SubFigure + if ax is None: + fig, ax = plt.subplots() + else: + fig = ax.figure + + ax.plot(thetas, information_array, **kwargs) + ax.set_xlabel("Ability level") + ax.set_ylabel("Test information") + return fig, ax + + +def plot_theta_estimation_trace(simulation_id: str, + participant_id: str, + output_format: ResultOutputFormat, + ax: Axes | None = None,): + """ + Plot the time (step) trace of true ability levels and estimated abilities for a single participant. + This function loads per-item test results for the given simulation and participant using + load_test_results_single_participant, extracts the true ability levels and the model's + ability estimations at each step, and renders a two-line plot showing how the estimated + ability evolves relative to the true ability across test steps. + + Args: + simulation_id (str): Identifier of the simulation run to load results from. + participant_id (str): Identifier of the participant whose results will be plotted. + output_format (ResultOutputFormat) + Format or storage hint forwarded to load_test_results_single_participant to control + how results are retrieved/returned. + ax (Axes (optional)): matplotlib axis object. If None is passed to the function, + an Axes object is created internally. + + + Returns: + tuple[Figure, Axes]:A tuple containing the Matplotlib Figure and Axes objects with the rendered plot. + The plot includes: + - a black line for "True ability" (true_ability_level per step) + - a blue line for "Ability Estimations" (ability_estimation per step) + The x-axis corresponds to the sequential test step index. + """ + test_results = load_test_results_single_participant( + simulation_id=simulation_id, + participant_id=participant_id, + output_format=output_format + ) + + true_abilities = np.array([ + result.true_ability_level + for result in test_results + ]) + + estimations = np.array([ + result.ability_estimation + for result in test_results + ]) + + steps = np.array(range(len(test_results))) + + # setup figure + fig: Figure | SubFigure + if ax is None: + fig, ax = plt.subplots() + else: + fig = ax.figure + + ax.plot(steps, true_abilities, label="True ability", color="black") + ax.plot(steps, estimations, label="Ability Estimations", color="blue") + ax.legend() + + return fig, ax From 74ad28852e4aae6001355ff1ca4d9c5001257f48 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Tue, 21 Oct 2025 13:36:25 +0200 Subject: [PATCH 026/137] Revert "Add plot functions and descriptive statistics (#39)" (#40) This reverts commit b628eddab3c571438b2d30c810f75d232c6c9e0c. --- adaptivetesting/__init__.py | 35 --- adaptivetesting/data/__csv_context.py | 27 +- adaptivetesting/data/__pickle_context.py | 22 +- adaptivetesting/models/__adaptive_test.py | 3 + adaptivetesting/models/__item_pool.py | 20 +- adaptivetesting/tests/test_descriptives.py | 50 ---- adaptivetesting/tests/test_plots.py | 97 ------- adaptivetesting/utils/__descriptives.py | 63 ----- adaptivetesting/utils/__funcs.py | 78 ------ adaptivetesting/utils/__init__.py | 13 - adaptivetesting/utils/__plots.py | 300 --------------------- 11 files changed, 21 insertions(+), 687 deletions(-) delete mode 100644 adaptivetesting/tests/test_descriptives.py delete mode 100644 adaptivetesting/tests/test_plots.py delete mode 100644 adaptivetesting/utils/__descriptives.py delete mode 100644 adaptivetesting/utils/__funcs.py delete mode 100644 adaptivetesting/utils/__init__.py delete mode 100644 adaptivetesting/utils/__plots.py diff --git a/adaptivetesting/__init__.py b/adaptivetesting/__init__.py index fc88426..8939eaf 100644 --- a/adaptivetesting/__init__.py +++ b/adaptivetesting/__init__.py @@ -5,38 +5,3 @@ from . import services from . import simulation from . import tests - -from .data.__csv_context import CSVContext -from .data.__pickle_context import PickleContext - -from .implementations.__default_implementation import DefaultImplementation -from .implementations.__pre_test import PreTest -from .implementations.__semi_implementation import SemiAdaptiveImplementation -from .implementations.__test_assembler import TestAssembler - -from .math.__gen_response_pattern import generate_response_pattern -from .math.estimators.__ml_estimation import MLEstimator -from .math.estimators.__bayes_modal_estimation import BayesModal -from .math.estimators.__expect_a_posteriori import ExpectedAPosteriori -from .math.estimators.__prior import Prior, NormalPrior, CustomPrior, CustomPriorException -from .math.estimators.__functions.__estimators import probability_y0, probability_y1, maximize_likelihood_function, likelihood -from .math.estimators.__functions.__bayes import maximize_posterior -from .math.estimators.__test_information import test_information_function, item_information_function, prior_information_function -from .math.item_selection.__maximum_information_criterion import maximum_information_criterion -from .math.item_selection.__urrys_rule import urrys_rule - -from .models.__adaptive_test import AdaptiveTest -from .models.__algorithm_exception import AlgorithmException -from .models.__item_pool import ItemPool -from .models.__item_selection_exception import ItemSelectionException -from .models.__test_item import TestItem -from .models.__test_result import TestResult -from .models.__misc import ResultOutputFormat, StoppingCriterion - -from .services.__estimator_interface import IEstimator -from .services.__test_results_interface import ITestResults -from .services.__item_selection_protocol import ItemSelectionStrategy - -from .simulation.__simulation import Simulation, SimulationPool, setup_simulation_and_start - -from .utils.__descriptives import bias, average_absolute_deviation, rmse \ No newline at end of file diff --git a/adaptivetesting/data/__csv_context.py b/adaptivetesting/data/__csv_context.py index ed8decd..9f71cbb 100644 --- a/adaptivetesting/data/__csv_context.py +++ b/adaptivetesting/data/__csv_context.py @@ -3,7 +3,6 @@ import pathlib from ..models.__test_result import TestResult from ..services.__test_results_interface import ITestResults -from dataclasses import fields class CSVContext(ITestResults): @@ -47,22 +46,12 @@ def save(self, test_results: List[TestResult]) -> None: file.close() def load(self) -> List[TestResult]: - """Loads test results from a CSV file for a specific participant and simulation. - Reads the CSV file located at `data/{simulation_id}/{participant_id}.csv`, - parses each row into a `TestResult` object, - and returns a list of these objects. - - Returns: - List[TestResult]: A list of `TestResult` objects loaded from the CSV file. + """Loads results from the database. + The implementation of this method is required + by the interface. However, it does not have + any implemented functionality and will throw an error + if used. + + Returns: List[TestResult] """ - foldername = f"data/{self.simulation_id}" - - fieldnames = list(fields(TestResult)) - test_results: list[TestResult] = [] - with open(f"{foldername}/{self.participant_id}.csv", "r", encoding="utf-8") as file: - reader = csv.DictReader(file, fieldnames=fieldnames) - for row in reader: - test_result = TestResult.from_dict(row) - test_results.append(test_result) - file.close() - return test_results + raise NotImplementedError("This function is not implemented.") diff --git a/adaptivetesting/data/__pickle_context.py b/adaptivetesting/data/__pickle_context.py index b250717..91b9c7b 100644 --- a/adaptivetesting/data/__pickle_context.py +++ b/adaptivetesting/data/__pickle_context.py @@ -40,18 +40,12 @@ def save(self, test_results: List[TestResult]) -> None: file.close() def load(self) -> List[TestResult]: - """Loads and returns a list of TestResult objects for a specific participant and simulation. - The method reads a pickle file located at 'data/{simulation_id}/{participant_id}.pickle' - and deserializes its contents into a list of TestResult instances. - Returns: - List[TestResult]: The list of test results loaded from the pickle file. - Raises: - FileNotFoundError: If the specified pickle file does not exist. - pickle.UnpicklingError: If the file cannot be unpickled. - """ - foldername = f"data/{self.simulation_id}" + """Loads results from the database. + The implementation of this method is required + by the interface. However, is does not have + any implemented functionality and will throw an error + if used. - with open(f"{foldername}/{self.participant_id}.pickle", "rb") as file: - test_results: list[TestResult] = pickle.load(file) - file.close() - return test_results + Returns: List[TestResult] + """ + raise NotImplementedError("This function is not implemented.") diff --git a/adaptivetesting/models/__adaptive_test.py b/adaptivetesting/models/__adaptive_test.py index 19d9816..43ee370 100644 --- a/adaptivetesting/models/__adaptive_test.py +++ b/adaptivetesting/models/__adaptive_test.py @@ -2,6 +2,7 @@ import abc import copy from .__test_item import TestItem +from ..math.item_selection.__urrys_rule import urrys_rule from ..math.__gen_response_pattern import generate_response_pattern from .__test_result import TestResult from .__item_pool import ItemPool @@ -113,6 +114,8 @@ def get_next_item(self) -> TestItem: TestItem: selected item """ raise NotImplementedError("This functionality is not implemented by default.") + item = urrys_rule(self.item_pool.test_items, self.ability_level) + return item @abc.abstractmethod def estimate_ability_level(self) -> tuple[float, float]: diff --git a/adaptivetesting/models/__item_pool.py b/adaptivetesting/models/__item_pool.py index ced5deb..661ca7c 100644 --- a/adaptivetesting/models/__item_pool.py +++ b/adaptivetesting/models/__item_pool.py @@ -110,9 +110,7 @@ def load_from_list( simulated_responses (List[int]): simulated responses - ids (List[int]): item IDs. If the argument is set to `None` - all items are numbered in the order in which they are - passed to the function. + ids (List[int]): item IDs Returns: List[TestItem]: item pool @@ -149,9 +147,6 @@ def load_from_list( raise ValueError("Length of ids and b has to be the same.") for i, id_ in enumerate(ids): items[i].id = id_ - else: - for i in range(len(b)): - items[i].id = i item_pool = ItemPool(items) item_pool.simulated_responses = simulated_responses @@ -174,9 +169,7 @@ def load_from_dict(source: dict[str, List[float]], Args: source (dict[str, List[float]]): item pool dictionary simulated_responses (List[int]): simulated responses - ids (List[int]): item IDs. If the argument is set to `None` - all items are numbered in the order in which they are - passed to the function. + ids (List[int]): item IDs Returns: List[TestItem]: item pool @@ -218,8 +211,6 @@ def load_from_dict(source: dict[str, List[float]], if ids is not None: item.id = ids[i] - else: - item.id = i items.append(item) @@ -232,13 +223,6 @@ def load_from_dataframe(source: DataFrame) -> "ItemPool": """Creates item pool from a pandas DataFrame. Required columns are: `a`, `b`, `c`, `d`. Each column has to contain float values. - - A `id` column can be added to assign - each test item a unique identifier. - If there is no `id` column - all items are numbered in the order in which they are - passed to the function. - A `simulated_responses` (int values) column can be added to the DataFrame to provide simulated responses. diff --git a/adaptivetesting/tests/test_descriptives.py b/adaptivetesting/tests/test_descriptives.py deleted file mode 100644 index 02c7de4..0000000 --- a/adaptivetesting/tests/test_descriptives.py +++ /dev/null @@ -1,50 +0,0 @@ -import unittest -from unittest.mock import patch -import numpy as np -from adaptivetesting import rmse, bias, average_absolute_deviation, TestResult -from adaptivetesting.models.__misc import ResultOutputFormat - - -class DummyResult: - def __init__(self, ability_estimation, true_ability_level): - self.ability_estimation = ability_estimation - self.true_ability_level = true_ability_level - - -class TestDescriptives(unittest.TestCase): - @patch('adaptivetesting.utils.__descriptives.load_final_test_results') - def test_bias(self, mock_load): - mock_load.return_value = [ - TestResult(None, 1.0, None, None, None, 0.5), # type: ignore - TestResult(None, 2.0, None, None, None, 1.5), # type: ignore - TestResult(None, 3.0, None, None, None, 2.5), # type: ignore - ] - result = bias('sim1', ['p1', 'p2', 'p3'], ResultOutputFormat.CSV) - expected = np.mean([1.0 - 0.5, 2.0 - 1.5, 3.0 - 2.5]) - self.assertAlmostEqual(result, expected) # type: ignore - - @patch('adaptivetesting.utils.__descriptives.load_final_test_results') - def test_average_absolute_deviation(self, mock_load): - mock_load.return_value = [ - TestResult(None, 1.0, None, None, None, 0.5), # type: ignore - TestResult(None, 2.0, None, None, None, 1.5), # type: ignore - TestResult(None, 3.0, None, None, None, 2.5), # type: ignore - ] - result = average_absolute_deviation('sim1', ['p1', 'p2', 'p3'], ResultOutputFormat.CSV) - expected = np.mean([abs(1.0 - 0.5), abs(2.0 - 1.5), abs(3.0 - 2.5)]) - self.assertAlmostEqual(result, expected) # type: ignore - - @patch('adaptivetesting.utils.__descriptives.load_final_test_results') - def test_rmse(self, mock_load): - mock_load.return_value = [ - TestResult(None, 1.0, None, None, None, 0.5), # type: ignore - TestResult(None, 2.0, None, None, None, 1.5), # type: ignore - TestResult(None, 3.0, None, None, None, 2.5), # type: ignore - ] - result = rmse('sim1', ['p1', 'p2', 'p3'], ResultOutputFormat.CSV) - expected = np.sqrt(np.mean([(1.0 - 0.5) ** 2, (2.0 - 1.5) ** 2, (3.0 - 2.5) ** 2])) - self.assertAlmostEqual(result, expected) - - -if __name__ == '__main__': - unittest.main() diff --git a/adaptivetesting/tests/test_plots.py b/adaptivetesting/tests/test_plots.py deleted file mode 100644 index 12f4694..0000000 --- a/adaptivetesting/tests/test_plots.py +++ /dev/null @@ -1,97 +0,0 @@ -# type: ignore -import unittest -from unittest.mock import patch -from adaptivetesting.utils import ( - plot_final_ability_estimates, - plot_icc, - plot_iif, - plot_exposure_rate, - plot_test_information, - plot_theta_estimation_trace -) -from adaptivetesting.models.__test_item import TestItem -from adaptivetesting.models.__misc import ResultOutputFormat -import matplotlib - - -class TestPlots(unittest.TestCase): - @classmethod - def setUpClass(cls): - cls.dummy_item = TestItem() - cls.dummy_item.id = 1 - cls.dummy_item.a = 1.0 - cls.dummy_item.b = 0.0 - cls.dummy_item.c = 0.2 - cls.dummy_item.d = 1.0 - cls.dummy_items = [cls.dummy_item for _ in range(3)] - cls.simulation_id = 'sim1' - cls.participant_ids = ['p1', 'p2'] - cls.output_format = ResultOutputFormat.CSV - - @patch('adaptivetesting.utils.__plots.load_final_test_results') - def test_plot_final_ability_estimates(self, mock_load): - # Mock return value: list of objects with .ability_estimation and .true_ability_level - class DummyResult: - def __init__(self, est, true): - self.ability_estimation = est - self.true_ability_level = true - mock_load.return_value = [DummyResult(1.0, 1.2), DummyResult(0.5, 0.7)] - fig, ax = plot_final_ability_estimates( - self.simulation_id, - self.participant_ids, - self.output_format - ) - self.assertIsInstance(fig, matplotlib.figure.Figure) - self.assertIsNotNone(ax) - - def test_plot_icc(self): - fig, ax = plot_icc(self.dummy_item) - self.assertIsInstance(fig, matplotlib.figure.Figure) - self.assertIsNotNone(ax) - - def test_plot_iif(self): - fig, ax = plot_iif(self.dummy_item) - self.assertIsInstance(fig, matplotlib.figure.Figure) - self.assertIsNotNone(ax) - - @patch('adaptivetesting.utils.__plots.load_test_results_single_participant') - def test_plot_exposure_rate(self, mock_load): - # Mock return value: list of objects with .showed_item["id"] - class DummyResult: - def __init__(self, item_id): - self.showed_item = {"id": item_id} - mock_load.return_value = [ - DummyResult("item1"), DummyResult("item2") - ] - fig, ax = plot_exposure_rate( - self.simulation_id, - self.participant_ids, - self.output_format - ) - self.assertIsInstance(fig, matplotlib.figure.Figure) - self.assertIsNotNone(ax) - - def test_plot_test_information(self): - fig, ax = plot_test_information(self.dummy_items) - self.assertIsInstance(fig, matplotlib.figure.Figure) - self.assertIsNotNone(ax) - - @patch('adaptivetesting.utils.__plots.load_test_results_single_participant') - def test_plot_theta_estimation_trace(self, mock_load): - # Mock return value: list of objects with .true_ability_level and .ability_estimation - class DummyResult: - def __init__(self, true, est): - self.true_ability_level = true - self.ability_estimation = est - mock_load.return_value = [DummyResult(1.0, 0.9), DummyResult(1.2, 1.1)] - fig, ax = plot_theta_estimation_trace( - self.simulation_id, - self.participant_ids[0], - self.output_format - ) - self.assertIsInstance(fig, matplotlib.figure.Figure) - self.assertIsNotNone(ax) - - -if __name__ == "__main__": - unittest.main() diff --git a/adaptivetesting/utils/__descriptives.py b/adaptivetesting/utils/__descriptives.py deleted file mode 100644 index e1c5585..0000000 --- a/adaptivetesting/utils/__descriptives.py +++ /dev/null @@ -1,63 +0,0 @@ -from ..models.__misc import ResultOutputFormat -import numpy as np -from .__funcs import load_final_test_results - - -def bias(simulation_id: str, participant_ids: list[str], output_format: ResultOutputFormat) -> np.floating: - """ - Calculates the bias of ability estimations for a set of participants in a simulation. - Bias is defined as the mean difference between estimated abilities and true ability levels. - Args: - simulation_id (str): Identifier for the simulation. - participant_ids (list[str]): List of participant identifiers. - output_format (ResultOutputFormat): Format in which the results are output. - Returns: - np.floating: The mean bias of ability estimations. - """ - final_test_results = load_final_test_results(simulation_id, participant_ids, output_format) - estimations = np.array([result.ability_estimation for result in final_test_results], dtype=np.float64) - true_abilities = np.array([result.true_ability_level for result in final_test_results], dtype=np.float64) - - return np.mean(estimations - true_abilities) - - -def average_absolute_deviation(simulation_id: str, - participant_ids: list[str], - output_format: ResultOutputFormat) -> np.floating: - """ - Calculates the average absolute deviation between estimated abilities - and true ability levels for a set of participants. - - Args: - simulation_id (str): Identifier for the simulation run. - participant_ids (list[str]): List of participant identifiers. - output_format (ResultOutputFormat): Format in which the results are returned. - Returns: - np.floating: The mean absolute deviation between estimated and true abilities. - """ - final_test_results = load_final_test_results(simulation_id, participant_ids, output_format) - estimations = np.array([result.ability_estimation for result in final_test_results], dtype=np.float64) - true_abilities = np.array([result.true_ability_level for result in final_test_results], dtype=np.float64) - - return np.mean(np.abs(estimations - true_abilities)) - - -def rmse(simulation_id: str, - participant_ids: list[str], - output_format: ResultOutputFormat) -> np.floating: - """ - Calculates the root mean squared error (RMSE) between - estimated abilities and true ability levels for a set of participants. - - Args: - simulation_id (str): Identifier for the simulation run. - participant_ids (list[str]): List of participant IDs to include in the calculation. - output_format (ResultOutputFormat): Format in which the test results are returned. - Returns: - np.floating: The RMSE value between estimated and true abilities. - """ - final_test_results = load_final_test_results(simulation_id, participant_ids, output_format) - estimations = np.array([result.ability_estimation for result in final_test_results], dtype=np.float64) - true_abilities = np.array([result.true_ability_level for result in final_test_results], dtype=np.float64) - - return np.sqrt(np.mean((estimations - true_abilities) ** 2)) diff --git a/adaptivetesting/utils/__funcs.py b/adaptivetesting/utils/__funcs.py deleted file mode 100644 index f5dd034..0000000 --- a/adaptivetesting/utils/__funcs.py +++ /dev/null @@ -1,78 +0,0 @@ -from ..models.__test_result import TestResult -from ..data.__csv_context import CSVContext -from ..data.__pickle_context import PickleContext -from ..services.__test_results_interface import ITestResults -from ..models.__misc import ResultOutputFormat - - -def load_final_test_results(simulation_id: str, - participant_ids: list[str], - output_format: ResultOutputFormat) -> list[TestResult]: - """ - Loads the final test results for a list of participants from the specified simulation or adaptive test. - Depending on the output format (CSV or PICKLE), this function initializes the appropriate context, - loads all test results for each participant, and selects the final result (assumed to be the last entry). - The final results are collected and returned as a list. - Args: - simulation_id (str): The identifier for the simulation from which to load results. - participant_ids (list[str]): A list of participant IDs whose results are to be loaded. - output_format (ResultOutputFormat): The format in which results are stored (CSV or PICKLE). - Returns: - list[TestResult]: A list containing the final test result for each participant. - Raises: - ValueError: If the output_format is not set to either ResultOutputFormat.CSV or ResultOutputFormat.PICKLE. - """ - # load data - final_test_results: list[TestResult] = [] - context: ITestResults - - if output_format is ResultOutputFormat.CSV: - for id in participant_ids: - context = CSVContext(simulation_id, participant_id=id) - test_results = context.load() - # select final result - final_result = test_results[-1] - final_test_results.append(final_result) - - if output_format is ResultOutputFormat.PICKLE: - for id in participant_ids: - context = PickleContext(simulation_id, participant_id=id) - test_results = context.load() - # select final result - final_result = test_results[-1] - final_test_results.append(final_result) - else: - raise ValueError("output_format is not correctly set to either PICKLE of CSV.") - - return final_test_results - - -def load_test_results_single_participant(simulation_id: str, - participant_id: str, - output_format: ResultOutputFormat) -> list[TestResult]: - """ - Loads the test results for a participant from the specified simulation or adaptive test. - Depending on the output format (CSV or PICKLE), this function reads the available CSV or PICKLE files. - - Args: - simulation_id (str): The identifier for the simulation from which to load results. - participant_id (str): Participant ID whose results are to be loaded. - output_format (ResultOutputFormat): The format in which results are stored (CSV or PICKLE). - Returns: - list[TestResult]: A list containing the test results for the participant. - Raises: - ValueError: If the output_format is not set to either ResultOutputFormat.CSV or ResultOutputFormat.PICKLE. - """ - context: ITestResults - - if output_format is ResultOutputFormat.CSV: - context = CSVContext(simulation_id=simulation_id, - participant_id=participant_id) - return context.load() - - if output_format is ResultOutputFormat.PICKLE: - context = PickleContext(simulation_id=simulation_id, - participant_id=participant_id) - return context.load() - else: - raise ValueError("output_format is not correctly set to either PICKLE of CSV.") diff --git a/adaptivetesting/utils/__init__.py b/adaptivetesting/utils/__init__.py deleted file mode 100644 index acae14c..0000000 --- a/adaptivetesting/utils/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -from .__descriptives import bias, average_absolute_deviation, rmse - -from .__funcs import load_final_test_results, load_test_results_single_participant - -from .__plots import ( - plot_exposure_rate, - plot_final_ability_estimates, - plot_icc, - plot_iif, - plot_exposure_rate, - plot_test_information, - plot_theta_estimation_trace -) \ No newline at end of file diff --git a/adaptivetesting/utils/__plots.py b/adaptivetesting/utils/__plots.py deleted file mode 100644 index 385acb4..0000000 --- a/adaptivetesting/utils/__plots.py +++ /dev/null @@ -1,300 +0,0 @@ -import matplotlib.pyplot as plt -from matplotlib.axes import Axes -from matplotlib.figure import Figure, SubFigure -from ..models.__misc import ResultOutputFormat -from ..models.__test_item import TestItem -from ..math.estimators.__functions.__estimators import probability_y1 -from ..math.estimators.__test_information import item_information_function -from .__funcs import load_final_test_results, load_test_results_single_participant -import numpy as np - - -def plot_final_ability_estimates(simulation_id: str, - participant_ids: list[str], - output_format: ResultOutputFormat, - ax: Axes | None = None, **kwargs): - """ - Plots the final ability estimates against the true ability levels for a set of participants in a simulation. - Args: - simulation_id (str): Identifier for the simulation whose results are to be plotted. - participant_ids (list[str]): List of participant IDs to include in the plot. - output_format (ResultOutputFormat): Format in which the results are stored and should be read. - ax (Axes | None, optional): Matplotlib Axes object to plot on. If None, a new figure and axes are created. - **kwargs: Additional keyword arguments passed to `ax.scatter`. - Returns: - tuple: (fig, ax) where `fig` is the Matplotlib Figure object and `ax` is the Axes object containing the plot. - Notes: - - The function reads the final test results for the specified participants and simulation. - - It plots the estimated ability levels against the true ability levels using a scatter plot. - """ - # get old attributes - fig: Figure | SubFigure - if ax is None: - fig, ax = plt.subplots() - else: - fig = ax.figure - - # read final test results data - final_test_results = load_final_test_results(simulation_id, participant_ids, output_format) - # extract true and finally estimated ability levels - true_and_final_abilities = [ - (result.ability_estimation, result.true_ability_level) - for result in final_test_results - ] - - final_estimates, true_abilities = zip(*true_and_final_abilities) - - if "color" not in kwargs: - ax.scatter(true_abilities, final_estimates, color="blue", **kwargs) - else: - ax.scatter(true_abilities, final_estimates, **kwargs) - ax.plot(true_abilities, true_abilities, color="black") - ax.set_xlabel("True ability level") - ax.set_ylabel("Estimated ability level") - - return fig, ax - - -def plot_icc(item: TestItem, - range: tuple[float, float] = (-10, 10), - ax: Axes | None = None, - **kwargs): - """ - Plots the Item Characteristic Curve (ICC) for a given test item. - Parameters: - item (TestItem): The test item containing parameters (a, b, c, d) for the ICC. - range (tuple[float, float], optional): The range of ability levels (theta) to plot. Defaults to (-10, 10). - ax (Axes, optional): Matplotlib Axes object to plot on. If None, a new figure and axes are created. - **kwargs: Additional keyword arguments passed to matplotlib's plot function. - Returns: - tuple: A tuple containing the matplotlib Figure and Axes objects. - """ - thetas = np.linspace(range[0], range[1], 1000) - probabilities = probability_y1( - mu=np.array(thetas).T, - a=np.array(item.a), - b=np.array(item.b), - c=np.array(item.c), - d=np.array(item.d), - ) - - fig: Figure | SubFigure - if ax is None: - fig, ax = plt.subplots() - else: - fig = ax.figure - - # create plot - ax.plot(thetas, probabilities, **kwargs) - ax.set_xlabel("Ability level") - ax.set_ylabel("Probability of correct response") - - return fig, ax - - -def plot_iif(item: TestItem, - range: tuple[float, float] = (-10, 10), - ax: Axes | None = None, - **kwargs): - """ - Plots the Item Information Function (IIF) for a given test item over a specified ability range. - Parameters: - item (TestItem): The test item for which to plot the information function. - range (tuple[float, float], optional): The range of ability levels (theta) to plot over. Defaults to (-10, 10). - ax (Axes, optional): Matplotlib Axes object to plot on. If None, a new figure and axes are created. - **kwargs: Additional keyword arguments passed to matplotlib's plot function. - Returns: - tuple[Figure, Axes]: The matplotlib Figure and Axes objects containing the plot. - """ - # calculate item information - thetas: list[np.ndarray] = list(np.linspace(range[0], range[1], 100)) - - information_array = [] - - for theta in thetas: - info = item_information_function( - mu=theta, - a=np.array(item.a), - b=np.array(item.b), - c=np.array(item.c), - d=np.array(item.d), - ) - information_array.append(info) - - # setup figure - fig: Figure | SubFigure - if ax is None: - fig, ax = plt.subplots() - else: - fig = ax.figure - - ax.plot(thetas, information_array, **kwargs) - ax.set_xlabel("Ability level") - ax.set_ylabel("Item information") - return fig, ax - - -def plot_exposure_rate(simulation_id: str, - participant_ids: list[str], - output_format: ResultOutputFormat): - """This function returns the exposure rates for all shown items - in a series of adaptive tests or CAT simulations. - - Args: - simulation_id (str): Simulation identifyer - participant_ids (list[str]): List of unique participant IDs - output_format (ResultOutputFormat): Format in which the test results have been previously saved - - Returns: - tuple[Figure, Axes]: matplotlib figure and axes - """ - # read test results for each participant and collect shown item ids - all_item_ids: list[str] = [] - for participant in participant_ids: - test_results = load_test_results_single_participant( - simulation_id=simulation_id, - participant_id=participant, - output_format=output_format, - ) - - # each TestResult has a 'showed_item' dict with an 'id' key - participant_item_ids = [res.showed_item["id"] for res in test_results] - all_item_ids.extend(participant_item_ids) - - if len(all_item_ids) == 0: - # nothing to plot, return empty figure - fig, ax = plt.subplots() - ax.set_title("No items shown - no exposure data") - return fig, ax - - # unique item ids and counts - unique_ids, counts = np.unique(np.array(all_item_ids, dtype=object), return_counts=True) - - # sort by counts descending for better readability - order = np.argsort(counts)[::-1] - unique_ids_sorted = unique_ids[order] - counts_sorted = counts[order] - - # compute exposure percentage relative to number of participants (or total exposures) - total_exposures = len(all_item_ids) - exposure_pct = counts_sorted / total_exposures * 100.0 - - # create bar plot - fig, ax = plt.subplots(figsize=(max(6, len(unique_ids_sorted) * 0.2), 4)) - bars = ax.bar(range(len(unique_ids_sorted)), counts_sorted, tick_label=unique_ids_sorted) - ax.set_xlabel("Item id") - ax.set_ylabel("Times shown") - ax.set_title("Item exposure counts (sorted)") - plt.xticks(rotation=90) - - # annotate bars with percentage labels - for rect, pct in zip(bars, exposure_pct): - height = rect.get_height() - ax.annotate(f"{pct:.1f}%", - xy=(rect.get_x() + rect.get_width() / 2, height), - xytext=(0, 3), # 3 points vertical offset - textcoords="offset points", - ha='center', va='bottom', fontsize=8) - - fig.tight_layout() - return fig, ax - - -def plot_test_information( - items: list[TestItem], - range: tuple[float, float] = (-10, 10), - ax: Axes | None = None, - **kwargs): - """ - Plots the Item Information Function (IIF) for a given test item over a specified ability range. - Args: - items (list[TestItem]): Test items in an item pool for which to calculate the test information - range (tuple[float, float], optional): The range of ability levels (theta) to plot over. Defaults to (-10, 10). - ax (Axes, optional): Matplotlib Axes object to plot on. If None, a new figure and axes are created. - **kwargs: Additional keyword arguments passed to matplotlib's plot function. - Returns: - tuple[Figure, Axes]: The matplotlib Figure and Axes objects containing the plot. - """ - # calculate test information by summing item information across items - thetas = np.linspace(range[0], range[1], 100) - information_array = np.zeros_like(thetas, dtype=float) - for item in items: - information_array += item_information_function( - mu=np.array(thetas).T, - a=np.array(item.a), - b=np.array(item.b), - c=np.array(item.c), - d=np.array(item.d), - ) - - # setup figure - fig: Figure | SubFigure - if ax is None: - fig, ax = plt.subplots() - else: - fig = ax.figure - - ax.plot(thetas, information_array, **kwargs) - ax.set_xlabel("Ability level") - ax.set_ylabel("Test information") - return fig, ax - - -def plot_theta_estimation_trace(simulation_id: str, - participant_id: str, - output_format: ResultOutputFormat, - ax: Axes | None = None,): - """ - Plot the time (step) trace of true ability levels and estimated abilities for a single participant. - This function loads per-item test results for the given simulation and participant using - load_test_results_single_participant, extracts the true ability levels and the model's - ability estimations at each step, and renders a two-line plot showing how the estimated - ability evolves relative to the true ability across test steps. - - Args: - simulation_id (str): Identifier of the simulation run to load results from. - participant_id (str): Identifier of the participant whose results will be plotted. - output_format (ResultOutputFormat) - Format or storage hint forwarded to load_test_results_single_participant to control - how results are retrieved/returned. - ax (Axes (optional)): matplotlib axis object. If None is passed to the function, - an Axes object is created internally. - - - Returns: - tuple[Figure, Axes]:A tuple containing the Matplotlib Figure and Axes objects with the rendered plot. - The plot includes: - - a black line for "True ability" (true_ability_level per step) - - a blue line for "Ability Estimations" (ability_estimation per step) - The x-axis corresponds to the sequential test step index. - """ - test_results = load_test_results_single_participant( - simulation_id=simulation_id, - participant_id=participant_id, - output_format=output_format - ) - - true_abilities = np.array([ - result.true_ability_level - for result in test_results - ]) - - estimations = np.array([ - result.ability_estimation - for result in test_results - ]) - - steps = np.array(range(len(test_results))) - - # setup figure - fig: Figure | SubFigure - if ax is None: - fig, ax = plt.subplots() - else: - fig = ax.figure - - ax.plot(steps, true_abilities, label="True ability", color="black") - ax.plot(steps, estimations, label="Ability Estimations", color="blue") - ax.legend() - - return fig, ax From f4597cf9a005bce641ef076dee700c1c3516f822 Mon Sep 17 00:00:00 2001 From: condecon Date: Mon, 3 Nov 2025 10:23:24 +0100 Subject: [PATCH 027/137] fix function typo --- adaptivetesting/math/content_balancing/__functions.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/adaptivetesting/math/content_balancing/__functions.py b/adaptivetesting/math/content_balancing/__functions.py index c03bbbb..ce5b92b 100644 --- a/adaptivetesting/math/content_balancing/__functions.py +++ b/adaptivetesting/math/content_balancing/__functions.py @@ -193,20 +193,20 @@ def compute_total_content_penalty_value_for_item(item: TestItem, return total_penalty_value -def standardize_total_content_constraint_penalty_value(item_penality_value: float, +def standardize_total_content_constraint_penalty_value(item_penalty_value: float, minimum: float, maximum: float) -> float: """Standardize total content constraint penalty values. Args: - item_penality_value (float): unstandardized item penalty value + item_penalty_value (float): unstandardized item penalty value minimum (float): minimum of the item penalty values over all eligible items maximum (float): maximum of the item penalty values over all eligible items Returns: float: standardized total content constraint penalty value """ - standardized_value = (item_penality_value - minimum) / (maximum - minimum) + standardized_value = (item_penalty_value - minimum) / (maximum - minimum) return standardized_value From a44c4bec942a0ece79dcdc40661d111f07ce50d8 Mon Sep 17 00:00:00 2001 From: condecon Date: Mon, 3 Nov 2025 10:23:36 +0100 Subject: [PATCH 028/137] add example item pool --- adaptivetesting/tests/example_item_pool.json | 182 +++++++++++++++++++ 1 file changed, 182 insertions(+) create mode 100644 adaptivetesting/tests/example_item_pool.json diff --git a/adaptivetesting/tests/example_item_pool.json b/adaptivetesting/tests/example_item_pool.json new file mode 100644 index 0000000..473aabf --- /dev/null +++ b/adaptivetesting/tests/example_item_pool.json @@ -0,0 +1,182 @@ +[ + { + "a": 1.3024, + "b": -0.6265, + "c": 0.2052, + "d": 0.8694, + "content_categories": [ + "math" + ] + }, + { + "a": 1.078, + "b": 0.1836, + "c": 0.1618, + "d": 0.9653, + "content_categories": [ + "math" + ] + }, + { + "a": 0.8758, + "b": -0.8356, + "c": 0.1957, + "d": 0.8595, + "content_categories": [ + "math" + ] + }, + { + "a": 0.5571, + "b": 1.5953, + "c": 0.1383, + "d": 0.8112, + "content_categories": [ + "math" + ] + }, + { + "a": 1.225, + "b": 0.3295, + "c": 0.1324, + "d": 0.7677, + "content_categories": [ + "math" + ] + }, + { + "a": 0.991, + "b": -0.8205, + "c": 0.1973, + "d": 0.7749, + "content_categories": [ + "math" + ] + }, + { + "a": 0.9968, + "b": 0.4874, + "c": 0.0058, + "d": 0.8291, + "content_categories": [ + "math" + ] + }, + { + "a": 1.1888, + "b": 0.7383, + "c": 0.1193, + "d": 0.8797, + "content_categories": [ + "math" + ] + }, + { + "a": 1.1642, + "b": 0.5758, + "c": 0.1831, + "d": 0.9155, + "content_categories": [ + "math" + ] + }, + { + "a": 1.1188, + "b": -0.3054, + "c": 0.1732, + "d": 0.8517, + "content_categories": [ + "math" + ] + }, + { + "a": 1.3024, + "b": -0.6265, + "c": 0.2052, + "d": 0.8694, + "content_categories": [ + "english" + ] + }, + { + "a": 1.078, + "b": 0.1836, + "c": 0.1618, + "d": 0.9653, + "content_categories": [ + "english" + ] + }, + { + "a": 0.8758, + "b": -0.8356, + "c": 0.1957, + "d": 0.8595, + "content_categories": [ + "english" + ] + }, + { + "a": 0.5571, + "b": 1.5953, + "c": 0.1383, + "d": 0.8112, + "content_categories": [ + "english" + ] + }, + { + "a": 1.225, + "b": 0.3295, + "c": 0.1324, + "d": 0.7677, + "content_categories": [ + "english" + ] + }, + { + "a": 0.991, + "b": -0.8205, + "c": 0.1973, + "d": 0.7749, + "content_categories": [ + "english" + ] + }, + { + "a": 0.9968, + "b": 0.4874, + "c": 0.0058, + "d": 0.8291, + "content_categories": [ + "english" + ] + }, + { + "a": 1.1888, + "b": 0.7383, + "c": 0.1193, + "d": 0.8797, + "content_categories": [ + "english" + ] + }, + { + "a": 1.1642, + "b": 0.5758, + "c": 0.1831, + "d": 0.9155, + "content_categories": [ + "english" + ] + }, + { + "a": 1.1188, + "b": -0.3054, + "c": 0.1732, + "d": 0.8517, + "content_categories": [ + "english" + ] + } +] \ No newline at end of file From 02d4d75a0ad0a14d13ac6434cbcc2f82eaf3ed5a Mon Sep 17 00:00:00 2001 From: condecon Date: Mon, 3 Nov 2025 10:24:10 +0100 Subject: [PATCH 029/137] fix item function tests --- adaptivetesting/tests/test_item_selection.py | 4 ++-- adaptivetesting/tests/test_load_test_items.py | 12 ++++++++---- 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/adaptivetesting/tests/test_item_selection.py b/adaptivetesting/tests/test_item_selection.py index cf9f035..8a72f76 100644 --- a/adaptivetesting/tests/test_item_selection.py +++ b/adaptivetesting/tests/test_item_selection.py @@ -48,7 +48,7 @@ def test_selection_when_0(self): 0 ) self.assertDictEqual(selected_item.as_dict(), - {"a": 1.0975, "b": 0.1836, "c": 0.053, "d": 0.7533}) + {"a": 1.0975, "b": 0.1836, "c": 0.053, "d": 0.7533, "additional_properties": {}}) def test_selection_when_minus_0_5(self): items = self.load_items() @@ -57,4 +57,4 @@ def test_selection_when_minus_0_5(self): -0.5 ) self.assertDictEqual(selected_item.as_dict(), - {"a": 1.1477, "b": -0.8356, "c": 0.1629, "d": 0.8456}) + {"a": 1.1477, "b": -0.8356, "c": 0.1629, "d": 0.8456, "additional_properties": {}}) diff --git a/adaptivetesting/tests/test_load_test_items.py b/adaptivetesting/tests/test_load_test_items.py index 5ebf413..abb0390 100644 --- a/adaptivetesting/tests/test_load_test_items.py +++ b/adaptivetesting/tests/test_load_test_items.py @@ -96,7 +96,8 @@ def test_load_items_from_pandas_success(self): "a": 0.9, "b": 5, "c": 0.9, - "d": 1 + "d": 1, + "additional_properties": {} }, generated.test_items[0].as_dict() ) @@ -106,7 +107,8 @@ def test_load_items_from_pandas_success(self): "a": 1.9, "b": 3, "c": 1.9, - "d": 1 + "d": 1, + "additional_properties": {} }, generated.test_items[1].as_dict() ) @@ -133,7 +135,8 @@ def test_load_items_from_pandas_with_ids_success(self): "b": 5, "c": 0.9, "d": 1, - "id": 101 + "id": 101, + "additional_properties": {} }, generated.test_items[0].as_dict(with_id=True) ) @@ -144,7 +147,8 @@ def test_load_items_from_pandas_with_ids_success(self): "b": 3, "c": 1.9, "d": 1, - "id": 202 + "id": 202, + "additional_properties": {} }, generated.test_items[1].as_dict(with_id=True) ) From 49607e209141a2669cead52e64b088797dc1f585 Mon Sep 17 00:00:00 2001 From: condecon Date: Mon, 3 Nov 2025 10:27:08 +0100 Subject: [PATCH 030/137] fix typos --- adaptivetesting/tests/test_content_balacing.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/adaptivetesting/tests/test_content_balacing.py b/adaptivetesting/tests/test_content_balacing.py index 2e09833..ee15fd8 100644 --- a/adaptivetesting/tests/test_content_balacing.py +++ b/adaptivetesting/tests/test_content_balacing.py @@ -32,7 +32,7 @@ def test_basic_calculation(self): "English": 0.8 }, required_items=10, - shown_item=0, + shown_items=0, current_ability=0 ) @@ -50,7 +50,7 @@ def test_exception_list(self): "English": 0.8 }, required_items=10, - shown_item=0, + shown_items=0, current_ability=0 ) @@ -64,6 +64,6 @@ def test_exception_key(self): "English": 0.8 }, required_items=10, - shown_item=0, + shown_items=0, current_ability=0 ) From d9fe96ddfa3aae7e562973dda8174ba22f7e0571 Mon Sep 17 00:00:00 2001 From: condecon Date: Mon, 3 Nov 2025 10:27:31 +0100 Subject: [PATCH 031/137] functioning weighted penalty model tests --- adaptivetesting/__init__.py | 1 + .../__weighted_penalty_model.py | 66 ++++-- .../tests/test_weighted_penalty_model.py | 214 ++++++++++++++++++ 3 files changed, 258 insertions(+), 23 deletions(-) create mode 100644 adaptivetesting/tests/test_weighted_penalty_model.py diff --git a/adaptivetesting/__init__.py b/adaptivetesting/__init__.py index f178fe7..b62cd89 100644 --- a/adaptivetesting/__init__.py +++ b/adaptivetesting/__init__.py @@ -24,6 +24,7 @@ from .math.estimators.__test_information import test_information_function, item_information_function, prior_information_function from .math.item_selection.__maximum_information_criterion import maximum_information_criterion from .math.item_selection.__urrys_rule import urrys_rule +from .math.content_balancing.__weighted_penalty_model import WeightedPenaltyModel from .math.content_balancing.__constraint import * from .math.content_balancing.__functions import * diff --git a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py index 3a3db11..745a8d2 100644 --- a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py +++ b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py @@ -137,17 +137,24 @@ def get_constraint_group_assignments(self) -> list[tuple[Constraint, CONSTRAINT_ test_length=test_length, ) # assign color group per proportion - if constraint.lower is not None and constraint.upper is not None: - if prop <= constraint.lower: - group_assignment.append((constraint, "A")) - if constraint.lower <= prop <= constraint.upper: - group_assignment.append((constraint, "B")) - if constraint.upper <= prop: - group_assignment.append((constraint, "C")) - else: - raise ValueError("constraint.lower and constraint.upper may not be None.") + group_assignment.append(self.assign_color_group_per_proportion( + constraint, prop + )) return group_assignment + + @staticmethod + def assign_color_group_per_proportion(constraint: Constraint, + prop: float): + if constraint.lower is not None and constraint.upper is not None: + if prop <= constraint.lower: + return (constraint, "A") + if constraint.lower <= prop <= constraint.upper: + return (constraint, "B") + if constraint.upper <= prop: + return (constraint, "C") + else: + raise ValueError("constraint.lower and constraint.upper may not be None.") def form_list_of_candidate_items(self, group_assignment: list[tuple[Constraint, CONSTRAINT_GROUP]]) -> None: @@ -158,21 +165,34 @@ def form_list_of_candidate_items(self, for constraint_assignment in group_assignment if constraint_assignment[0].name in item.additional_properties["category"] ] + self.eligible_items[i] = self.assign_items_to_item_group( + item, + associated_constraints, + weighted_penalty_value + ) - # if all associated constraints A or B -> green group - if all(group in ["A", "B"] for _, group in associated_constraints): - self.eligible_items[i] = (item, weighted_penalty_value, "green") - # if all A, B, C, or A, C -> orange - if all(group in ["A", "B", "C"] or group in ["A", "C"] for _, group in associated_constraints): - self.eligible_items[i] = (item, weighted_penalty_value, "orange") - # if all B -> yellow - if all(group in ["B"] for _, group in associated_constraints): - self.eligible_items[i] = (item, weighted_penalty_value, "yellow") - # if all B, C -> red - if all(group in ["B", "C"] for _, group in associated_constraints): - self.eligible_items[i] = (item, weighted_penalty_value, "red") - else: - self.eligible_items[i] = (item, weighted_penalty_value, None) + @staticmethod + def assign_items_to_item_group( + item: TestItem, + associated_constraints: list[tuple[Constraint, Literal['A', 'B', 'C']]], + weighted_penalty_value: float + ): + group_set = set([group for _, group in associated_constraints]) + + # if all associated constraints A or B -> green group + if group_set.issubset({"A", "B"}) and "A" in group_set: + return (item, weighted_penalty_value, "green") + # if all A, B, C, or A, C -> orange + elif group_set == {"A", "C"} or group_set == {"A", "B", "C"}: + return (item, weighted_penalty_value, "orange") + # if all B -> yellow + elif group_set == {"B"}: + return (item, weighted_penalty_value, "yellow") + # if all B, C -> red + elif group_set == {"B", "C"} or group_set == {"C"}: + return (item, weighted_penalty_value, "red") + else: + return (item, weighted_penalty_value, None) def order_candidate_items(self): # between group ordering: green, orange, yellow, red diff --git a/adaptivetesting/tests/test_weighted_penalty_model.py b/adaptivetesting/tests/test_weighted_penalty_model.py new file mode 100644 index 0000000..dbd3c89 --- /dev/null +++ b/adaptivetesting/tests/test_weighted_penalty_model.py @@ -0,0 +1,214 @@ +import unittest +import pandas as pd +import adaptivetesting as adt +import copy +from typing import Literal + +class MockItem(adt.TestItem): + def __init__(self, category: list[str]): + super().__init__() + self.additional_properties["category"] = category + + +class TestWeightedPenaltyModel(unittest.TestCase): + def __init__(self, methodName="runTest"): + super().__init__(methodName) + + # load test item pool + data_frame = pd.read_json("adaptivetesting/tests/example_item_pool.json") + # convert list[dict] into item pool + self.item_pool = adt.ItemPool.load_from_dataframe(data_frame) + + # create two example items fitting a rasch model + self.example_item1 = adt.TestItem() + self.example_item1.id = 1 + self.example_item1.a = 1; self.example_item1.b = 0.5; self.example_item1.c = 0; self.example_item1.d = 1 + self.example_item1.additional_properties["category"] = ["math"] + + self.example_item2 = adt.TestItem() + self.example_item2.id = 2 + self.example_item2.a = 1; self.example_item2.b = 0.5; self.example_item2.c = 0; self.example_item2.d = 1 + self.example_item2.additional_properties["category"] = ["english"] + + def test_content_penalty_calculation(self): + """compute_total_content_penalty_value_for_item""" + # create target item + item = copy.deepcopy(self.example_item1) + item.additional_properties["category"] = ["math", "science"] + + # create shown items for math and science + shown_items = [ + MockItem(["math"]), + MockItem(["math"]), + MockItem(["science"]) + ] + + # setup constraints + constraints = [ + adt.Constraint( + name="math", + prevalence=0.75, + lower=0, + upper=1, + weight=1 + ), + adt.Constraint( + name="science", + prevalence=0.25, + lower=0, + upper=1, + weight=1 + ) + ] + + total_content_penalty_value = adt.compute_total_content_penalty_value_for_item( + item, + shown_items=shown_items, + available_items=[item], + constraints=constraints + ) + + manual_result = 0.4166 - 0.083 + + self.assertAlmostEqual(total_content_penalty_value, + manual_result, places=2) + + def test_calcualte_weighted_content_penalty(self): + """calculate_weighted_penalty_value""" + item_information = 0.5 + max_information = 1 + constraint_weight = 1 + information_weight = 1 + total_content_penalty = 0.5 + maximum_total_content_penalty = 0.8 + minimum_total_content_penalty = 0.2 + + # standardize total content constraint penalty value + standardized_total_content_penalty_value = adt.standardize_total_content_constraint_penalty_value( + item_penalty_value=total_content_penalty, + minimum=minimum_total_content_penalty, + maximum=maximum_total_content_penalty + ) + + self.assertAlmostEqual(standardized_total_content_penalty_value, 0.5, places=2) + + # standardize item information + standardized_item_information = adt.standardize_item_information( + item_information=item_information, + maximum=max_information + ) + self.assertAlmostEqual(standardized_item_information, 0.5, places=2) + + # calculate information penalty value + information_penalty_value = adt.compute_information_penalty_value( + standardized_item_information + ) + + self.assertAlmostEqual(information_penalty_value, -0.25, places=2) + + # compute weighted penalty vlaue + weighted_penalty_value = adt.compute_weighted_penalty_value( + constraint_weight=constraint_weight, + standardized_constraint_penalty_value=standardized_total_content_penalty_value, + information_weight=information_weight, + information_penalty_value=information_penalty_value + ) + + self.assertAlmostEqual(weighted_penalty_value, 0.25, places=2) + + def test_constraint_group_assignment(self): + """get_constraint_group_assignments""" + + # create shown items for math and science + shown_items = [ + MockItem(["math"]), + MockItem(["math"]), + MockItem(["science"]) + ] + + # setup constraints + constraints = [ + adt.Constraint( + name="math", + prevalence=0.75, + lower=0, + upper=1, + weight=1 + ), + adt.Constraint( + name="science", + prevalence=0.25, + lower=0.9, + upper=1, + weight=1 + ) + ] + + assignments = [] + for constraint in constraints: + # calculate proportion + prop = adt.compute_prop( + n_administered=1, + n_remaining=1, + prevalence=constraint.prevalence, + test_length=2 + ) + + # assign color group per proportion + _, assignment = adt.WeightedPenaltyModel.assign_color_group_per_proportion( + constraint, + prop + ) + assignments.append(assignment) + + self.assertListEqual(assignments, ["B", "A"]) + + + def test_candidate_group_assignment(self): + """form_list_of_candidate_items""" + # create item + item = MockItem("None") + weighted_penalty_value = float("NaN") + # test green + associated_constraints: list[tuple[adt.Constraint, Literal['A', 'B', 'C']]] = [ + (adt.Constraint(None, None, None), "A"), + (adt.Constraint(None, None, None), "B") + ] + _, _, assigned_group = adt.WeightedPenaltyModel.assign_items_to_item_group( + item, associated_constraints, + weighted_penalty_value + ) + self.assertEqual(assigned_group, "green") + + # test organge + associated_constraints: list[tuple[adt.Constraint, Literal['A', 'B', 'C']]] = [ + (adt.Constraint(None, None, None), "A"), + (adt.Constraint(None, None, None), "C") + ] + _, _, assigned_group = adt.WeightedPenaltyModel.assign_items_to_item_group( + item, associated_constraints, + weighted_penalty_value + ) + self.assertEqual(assigned_group, "orange") + + # test yellow + associated_constraints: list[tuple[adt.Constraint, Literal['A', 'B', 'C']]] = [ + (adt.Constraint(None, None, None), "B"), + (adt.Constraint(None, None, None), "B") + ] + _, _, assigned_group = adt.WeightedPenaltyModel.assign_items_to_item_group( + item, associated_constraints, + weighted_penalty_value + ) + self.assertEqual(assigned_group, "yellow") + + # test red + associated_constraints: list[tuple[adt.Constraint, Literal['A', 'B', 'C']]] = [ + (adt.Constraint(None, None, None), "B"), + (adt.Constraint(None, None, None), "C") + ] + _, _, assigned_group = adt.WeightedPenaltyModel.assign_items_to_item_group( + item, associated_constraints, + weighted_penalty_value + ) + self.assertEqual(assigned_group, "red") From 2f807491e41993f6952e20a3d8a97411b0183351 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Tue, 4 Nov 2025 09:19:16 +0100 Subject: [PATCH 032/137] Bring bug fixes to revision branch (#45) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Update version number to 1.1.2 (#33) * add contributing guidelines * move * update docs build * add .readthedocs.yml * update deps * fix path * fix path * update install * update deps * update deps * update theme * 29 honor id field when loading itempool (#31) * Make ItemPool loading functions respects IDs if given * Update instructions on running unit tests * Add new unit tests for loading TestItems with IDs * fix typing issue --------- Co-authored-by: Tim Schäfer * honor id field for item selection * update example in readme * update changelog * update version to 1.1.2 --------- Co-authored-by: Tim Schäfer * Bug Fix #34 (#37) * add contributing guidelines * move * update docs build * add .readthedocs.yml * update deps * fix path * fix path * update install * update deps * update deps * update theme * 29 honor id field when loading itempool (#31) * Make ItemPool loading functions respects IDs if given * Update instructions on running unit tests * Add new unit tests for loading TestItems with IDs * fix typing issue --------- Co-authored-by: Tim Schäfer * honor id field for item selection * update example in readme * update changelog * update version to 1.1.2 * FIX: use more stable versions of functions item_information_function() and probability_y1() * CHG: adapt number of empty lines * update version number 1.1.3 --------- Co-authored-by: Tim Schäfer Co-authored-by: Tim Schaefer * Fix #43 (#44) * use custom optimization interval for minimize_scalar * update version and changelog --------- Co-authored-by: Tim Schäfer Co-authored-by: Tim Schaefer --- CHANGELOG.md | 6 ++ .../math/estimators/__functions/__bayes.py | 21 +++--- .../estimators/__functions/__estimators.py | 70 +++++++++++++------ .../math/estimators/__test_information.py | 26 +++---- pyproject.toml | 2 +- 5 files changed, 76 insertions(+), 49 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 5c977e3..fc93870 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,9 @@ +# Version 1.1.4 +- Bug Fix #43 (optimization interval in `maximize_posterior` function) + +# Version 1.1.3 +- Bug Fix #34 (numerical stability issue concerning the ability estimation and standard error calculation) + # Version 1.1.2 - The `ItemPool` class now correctly honors the `id` field of items. (#30, #31) diff --git a/adaptivetesting/math/estimators/__functions/__bayes.py b/adaptivetesting/math/estimators/__functions/__bayes.py index e2e8cad..788e47c 100644 --- a/adaptivetesting/math/estimators/__functions/__bayes.py +++ b/adaptivetesting/math/estimators/__functions/__bayes.py @@ -11,22 +11,25 @@ def maximize_posterior( c: np.ndarray, d: np.ndarray, response_pattern: np.ndarray, - prior: Prior + prior: Prior, + optimization_interval: tuple[float, float] = (-10, 10) ) -> float: """_summary_ Args: - a (np.ndarray): _description_ - - b (np.ndarray): _description_ + a (np.ndarray): item parameter a + + b (np.ndarray): item parameter b - c (np.ndarray): _description_ + c (np.ndarray): item parameter c - d (np.ndarray): _description_ + d (np.ndarray): item parameter d - response_pattern (np.ndarray): _description_ + response_pattern (np.ndarray): response pattern (simulated or user generated) - prior (Prior): _description_ + prior (Prior): prior distribution + + optimization_interval (Tuple[float, float]): interval used for the optimization function Returns: float: Bayes Modal estimator for the given parameters @@ -35,7 +38,7 @@ def posterior(mu) -> np.ndarray: return likelihood(mu, a, b, c, d, response_pattern) * prior.pdf(mu) result: OptimizeResult = minimize_scalar(lambda mu: posterior(mu), - bounds=(-10, 10), + bounds=optimization_interval, method="bounded") # type: ignore if not result.success: diff --git a/adaptivetesting/math/estimators/__functions/__estimators.py b/adaptivetesting/math/estimators/__functions/__estimators.py index 6d30e91..8dd1b11 100644 --- a/adaptivetesting/math/estimators/__functions/__estimators.py +++ b/adaptivetesting/math/estimators/__functions/__estimators.py @@ -12,22 +12,50 @@ def probability_y1(mu: np.ndarray, Args: mu (np.ndarray): latent ability level - a (np.ndarray): item discrimination parameter - b (np.ndarray): item difficulty parameter - c (np.ndarray): pseudo guessing parameter - d (np.ndarray): inattention parameter Returns: np.ndarray: probability of getting the item correct """ + # Compute the exponent safely + z = a * (mu - b) + + # Use log-sum-exp trick for numerical stability + # For large positive z: exp(z)/(1+exp(z)) ≈ 1 + # For large negative z: exp(z)/(1+exp(z)) ≈ exp(z) + + # Clip z to prevent overflow in exp() + z_clipped = np.clip(z, -500, 500) # exp(-500) and exp(500) are safe + + # Compute the logistic function safely + # Use different formulas based on the sign of z for stability + result = np.empty_like(z_clipped) + + # For large positive values, use alternative formula to avoid overflow + mask_positive = z_clipped > 20 # exp(20) is large but manageable + mask_negative = z_clipped < -20 # exp(-20) is very small + mask_medium = ~(mask_positive | mask_negative) + + # Large positive z: 1/(1+exp(-z)) is more stable + result[mask_positive] = 1.0 / (1.0 + np.exp(-z_clipped[mask_positive])) + + # Large negative z: exp(z)/(1+exp(z)) is stable + exp_z_neg = np.exp(z_clipped[mask_negative]) + result[mask_negative] = exp_z_neg / (1.0 + exp_z_neg) + + # Medium values: use standard formula + exp_z_med = np.exp(z_clipped[mask_medium]) + result[mask_medium] = exp_z_med / (1.0 + exp_z_med) + + # Apply the 4PL transformation + value = c + (d - c) * result + + # Ensure probabilities are within valid range [c, d] + value = np.clip(value, c, d) - value = c + (d - c) * (np.exp(a * (mu - b))) / \ - (1 + np.exp(a * (mu - b))) - return np.squeeze(value) @@ -40,13 +68,13 @@ def probability_y0(mu: np.ndarray, Args: mu (np.ndarray): latent ability level - + a (np.ndarray): item discrimination parameter - + b (np.ndarray): item difficulty parameter - + c (np.ndarray): pseudo guessing parameter - + d (np.ndarray): inattention parameter Returns: @@ -68,13 +96,13 @@ def likelihood(mu: np.ndarray, Args: mu (np.ndarray): ability level - + a (np.ndarray): item discrimination parameter - + b (np.ndarray): item difficulty parameter - + c (np.ndarray): pseudo guessing parameter - + d (np.ndarray): inattention parameter Returns: @@ -88,7 +116,7 @@ def likelihood(mu: np.ndarray, terms = (probability_y1(mu, a, b, c, d)**response_pattern) * \ (probability_y0(mu, a, b, c, d) ** (1 - response_pattern)) - + return -np.prod(terms) @@ -100,14 +128,14 @@ def maximize_likelihood_function(a: np.ndarray, border: tuple[float, float] = (-10, 10)) -> float: """Find the ability value that maximizes the likelihood function. This function uses the minimize_scalar function from scipy and the "bounded" method. - + Args: a (np.ndarray): item discrimination parameter - + b (np.ndarray): item difficulty parameter - + c (np.ndarray): pseudo guessing parameter - + d (np.ndarray): inattention parameter response_pattern (np.ndarray): response pattern of the item @@ -129,7 +157,7 @@ def maximize_likelihood_function(a: np.ndarray, "Response pattern is invalid. It consists of only one type of response.") raise AlgorithmException( "Response pattern is invalid. It consists of only one type of response.") - + result: OptimizeResult = minimize_scalar(likelihood, args=(a, b, c, d, response_pattern), bounds=border, method='bounded') # type: ignore diff --git a/adaptivetesting/math/estimators/__test_information.py b/adaptivetesting/math/estimators/__test_information.py index 01de668..1fd02ba 100644 --- a/adaptivetesting/math/estimators/__test_information.py +++ b/adaptivetesting/math/estimators/__test_information.py @@ -13,27 +13,17 @@ def item_information_function( c: np.ndarray, d: np.ndarray ) -> np.ndarray: - """Calculate the information of a test item given the currently - estimated ability `mu`. - - Args: - mu (np.ndarray): currently estimated ability - a (np.ndarray): _description_ - b (np.ndarray): _description_ - c (np.ndarray): _description_ - d (np.ndarray): _description_ - - Returns: - np.ndarray: _description_ - """ p_y1 = probability_y1(mu, a, b, c, d) + # Clip probabilities + p_y1_clipped = np.clip(p_y1, 1e-10, 1 - 1e-10) + def p_y1_grad(x: np.ndarray) -> np.ndarray: - # Central finite difference for gradient - h = 1e-5 + # Use a more robust finite difference scheme + h = np.maximum(1e-8, 1e-8 * np.abs(x)) # Adaptive step size return (probability_y1(x + h, a, b, c, d) - probability_y1(x - h, a, b, c, d)) / (2 * h) - - product = (p_y1_grad(mu) ** 2) / (p_y1 * (1 - p_y1)) + + product = (p_y1_grad(mu) ** 2) / (p_y1_clipped * (1 - p_y1_clipped)) information = np.sum(product) return information @@ -59,7 +49,7 @@ def log_prior(x): (score_values ** 2) * prior.pdf(x_vals), x_vals ) - + return information diff --git a/pyproject.toml b/pyproject.toml index ef8441e..8ec5427 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name="adaptivetesting" -version="1.1.2" +version="1.1.4" description="adaptivetesting is a Python package that can be used to simulate and evaluate custom CAT scenarios as well as implement them in real-world testing scenarios from a single codebase" readme = "README.md" authors = [ From d0c40088edd634f403ab4b05aab87f8fe91da1a8 Mon Sep 17 00:00:00 2001 From: condecon Date: Tue, 4 Nov 2025 12:43:15 +0100 Subject: [PATCH 033/137] Squashed commit of the following: MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit commit 471ff7f2a0a901d0503398b74dc3f5991b4fa331 Author: condecon Date: Tue Nov 4 12:35:52 2025 +0100 Bug Fix #46 commit 35b5276f87efa2df9b960be4e2a12449f42617b1 Merge: 9a9f1f1 acff4be Author: Jonas Engicht Date: Tue Nov 4 09:18:31 2025 +0100 Merge branch 'revision' into main commit 9a9f1f1769ebfccf895aca5b5a732c0e2916f292 Author: Jonas Engicht Date: Tue Nov 4 09:10:01 2025 +0100 Fix #43 (#44) * use custom optimization interval for minimize_scalar * update version and changelog commit 292b6e02546930f3abcfe24b753d00977b0fac07 Author: Jonas Engicht Date: Fri Sep 26 13:09:42 2025 +0200 Bug Fix #34 (#37) * add contributing guidelines * move * update docs build * add .readthedocs.yml * update deps * fix path * fix path * update install * update deps * update deps * update theme * 29 honor id field when loading itempool (#31) * Make ItemPool loading functions respects IDs if given * Update instructions on running unit tests * Add new unit tests for loading TestItems with IDs * fix typing issue --------- Co-authored-by: Tim Schäfer * honor id field for item selection * update example in readme * update changelog * update version to 1.1.2 * FIX: use more stable versions of functions item_information_function() and probability_y1() * CHG: adapt number of empty lines * update version number 1.1.3 --------- Co-authored-by: Tim Schäfer Co-authored-by: Tim Schaefer commit 28a6c976de0b940c0239dba00f0f62e295d69c10 Author: Jonas Engicht Date: Wed Sep 10 09:41:27 2025 +0200 Update version number to 1.1.2 (#33) * add contributing guidelines * move * update docs build * add .readthedocs.yml * update deps * fix path * fix path * update install * update deps * update deps * update theme * 29 honor id field when loading itempool (#31) * Make ItemPool loading functions respects IDs if given * Update instructions on running unit tests * Add new unit tests for loading TestItems with IDs * fix typing issue --------- Co-authored-by: Tim Schäfer * honor id field for item selection * update example in readme * update changelog * update version to 1.1.2 --------- Co-authored-by: Tim Schäfer --- CHANGELOG.md | 3 +++ .../math/estimators/__functions/__bayes.py | 14 +++++++++++--- pyproject.toml | 2 +- 3 files changed, 15 insertions(+), 4 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index fc93870..0e21e3a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,6 @@ +# Version 1.1.5 +- Bug Fix #46 (numerical stability issue concerning Bayesian ability estimation) + # Version 1.1.4 - Bug Fix #43 (optimization interval in `maximize_posterior` function) diff --git a/adaptivetesting/math/estimators/__functions/__bayes.py b/adaptivetesting/math/estimators/__functions/__bayes.py index 788e47c..bb963cc 100644 --- a/adaptivetesting/math/estimators/__functions/__bayes.py +++ b/adaptivetesting/math/estimators/__functions/__bayes.py @@ -3,6 +3,7 @@ from .__estimators import likelihood from ..__prior import Prior from ....models.__algorithm_exception import AlgorithmException +from .__estimators import probability_y0, probability_y1 def maximize_posterior( @@ -34,10 +35,17 @@ def maximize_posterior( Returns: float: Bayes Modal estimator for the given parameters """ - def posterior(mu) -> np.ndarray: - return likelihood(mu, a, b, c, d, response_pattern) * prior.pdf(mu) + def log_posterior(mu) -> np.ndarray: + p1 = probability_y1(mu, a, b, c, d) + p0 = probability_y0(mu, a, b, c, d) + + log_likelihood = np.sum((response_pattern * np.log(p1 + 1e-300)) + \ + ((1 - response_pattern) * np.log(p0 + 1e-300))) + log_prior = np.log(prior.pdf(mu) + 1e-300) + + return log_likelihood + log_prior - result: OptimizeResult = minimize_scalar(lambda mu: posterior(mu), + result: OptimizeResult = minimize_scalar(lambda mu: -log_posterior(mu), bounds=optimization_interval, method="bounded") # type: ignore diff --git a/pyproject.toml b/pyproject.toml index 8ec5427..6c608d8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name="adaptivetesting" -version="1.1.4" +version="1.1.5" description="adaptivetesting is a Python package that can be used to simulate and evaluate custom CAT scenarios as well as implement them in real-world testing scenarios from a single codebase" readme = "README.md" authors = [ From c5a62b39fbb4f36d06d86f7d6a4efcd0a5d82f54 Mon Sep 17 00:00:00 2001 From: condecon Date: Tue, 4 Nov 2025 12:49:00 +0100 Subject: [PATCH 034/137] fix linting --- adaptivetesting/math/estimators/__functions/__bayes.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/adaptivetesting/math/estimators/__functions/__bayes.py b/adaptivetesting/math/estimators/__functions/__bayes.py index bb963cc..0918056 100644 --- a/adaptivetesting/math/estimators/__functions/__bayes.py +++ b/adaptivetesting/math/estimators/__functions/__bayes.py @@ -1,6 +1,5 @@ import numpy as np from scipy.optimize import minimize_scalar, OptimizeResult # type: ignore -from .__estimators import likelihood from ..__prior import Prior from ....models.__algorithm_exception import AlgorithmException from .__estimators import probability_y0, probability_y1 @@ -39,8 +38,8 @@ def log_posterior(mu) -> np.ndarray: p1 = probability_y1(mu, a, b, c, d) p0 = probability_y0(mu, a, b, c, d) - log_likelihood = np.sum((response_pattern * np.log(p1 + 1e-300)) + \ - ((1 - response_pattern) * np.log(p0 + 1e-300))) + log_likelihood = np.sum((response_pattern * np.log(p1 + 1e-300)) + + ((1 - response_pattern) * np.log(p0 + 1e-300))) # noqa: W503 log_prior = np.log(prior.pdf(mu) + 1e-300) return log_likelihood + log_prior From 130ba46e6d36e7c0d68e31e1b43348c969412cb9 Mon Sep 17 00:00:00 2001 From: codecon Date: Tue, 4 Nov 2025 15:43:50 +0100 Subject: [PATCH 035/137] progress --- .../content_balancing/__content_balancing.py | 14 ++++++++++++++ .../math/content_balancing/__types.py | 5 +++++ .../__weighted_penalty_model.py | 16 ++++++++++++---- 3 files changed, 31 insertions(+), 4 deletions(-) create mode 100644 adaptivetesting/math/content_balancing/__content_balancing.py create mode 100644 adaptivetesting/math/content_balancing/__types.py diff --git a/adaptivetesting/math/content_balancing/__content_balancing.py b/adaptivetesting/math/content_balancing/__content_balancing.py new file mode 100644 index 0000000..43f314f --- /dev/null +++ b/adaptivetesting/math/content_balancing/__content_balancing.py @@ -0,0 +1,14 @@ +from abc import ABC, abstractmethod +from ...models.__test_item import TestItem +from ...models.__adaptive_test import AdaptiveTest +from .__constraint import Constraint + + +class ContentBalancing(ABC): + def __init__(self, adaptive_test: AdaptiveTest, + constraints: list[Constraint]): + pass + + @abstractmethod + def select_item(self) -> TestItem | None: + pass diff --git a/adaptivetesting/math/content_balancing/__types.py b/adaptivetesting/math/content_balancing/__types.py new file mode 100644 index 0000000..e45b092 --- /dev/null +++ b/adaptivetesting/math/content_balancing/__types.py @@ -0,0 +1,5 @@ +from .__content_balancing import ContentBalancing +from .__weighted_penalty_model import WeightedPenaltyModel + + +type ContentBalancingOptions = ContentBalancing | WeightedPenaltyModel diff --git a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py index 745a8d2..2153d75 100644 --- a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py +++ b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py @@ -11,14 +11,17 @@ compute_weighted_penalty_value ) from .__constraint import Constraint +from .__content_balancing import ContentBalancing +from ...models.__adaptive_test import AdaptiveTest CONSTRAINT_GROUP = Literal["A", "B", "C"] ITEM_GROUP = Literal["green", "orange", "yellow", "red", None] -class WeightedPenaltyModel: +class WeightedPenaltyModel(ContentBalancing): def __init__(self, + adaptive_test: AdaptiveTest, items: list[TestItem], shown_items: list[TestItem], ability: float, @@ -26,11 +29,16 @@ def __init__(self, constraint_weight: float, information_weight: float ): - self.items = items - self.ability = ability + super().__init__() + self.items = adaptive_test.item_pool.test_items + self.ability = adaptive_test.ability_level + self.shown_items = adaptive_test.answered_items self.constraints = constraints + + # setup self.eligible_items: list[tuple[TestItem, float, ITEM_GROUP]] = [] - self.shown_items = shown_items + + # only used internally self.constraint_weight = constraint_weight self.information_weight = information_weight From 17b4dff234d959dbbe31fa784bf747c9b0c8f803 Mon Sep 17 00:00:00 2001 From: condecon Date: Wed, 5 Nov 2025 10:55:53 +0100 Subject: [PATCH 036/137] implement content balancing classes and unittests --- .../content_balancing/__content_balancing.py | 16 ++++ .../math/content_balancing/__functions.py | 14 ++-- .../__maximum_priority_index.py | 80 +++++++++++++++++++ .../math/content_balancing/__types.py | 5 -- .../__weighted_penalty_model.py | 29 +++++-- .../tests/test_content_balacing.py | 34 ++++++-- .../tests/test_weighted_penalty_model.py | 20 ++--- 7 files changed, 163 insertions(+), 35 deletions(-) delete mode 100644 adaptivetesting/math/content_balancing/__types.py diff --git a/adaptivetesting/math/content_balancing/__content_balancing.py b/adaptivetesting/math/content_balancing/__content_balancing.py index 43f314f..931762f 100644 --- a/adaptivetesting/math/content_balancing/__content_balancing.py +++ b/adaptivetesting/math/content_balancing/__content_balancing.py @@ -5,10 +5,26 @@ class ContentBalancing(ABC): + """Abstract base class for content balancing methods. + + Abstract Methods + ------------------ + - `select_item` + """ def __init__(self, adaptive_test: AdaptiveTest, constraints: list[Constraint]): + """ + Args: + adaptive_test (AdaptiveTest): instance of the adaptive test + constraints (list[Constraint]): constraints that are applied to the item selection + """ pass @abstractmethod def select_item(self) -> TestItem | None: + """Select an item based on the implemented selection rules + + Returns: + TestItem | None: selected test item + """ pass diff --git a/adaptivetesting/math/content_balancing/__functions.py b/adaptivetesting/math/content_balancing/__functions.py index ce5b92b..aad421f 100644 --- a/adaptivetesting/math/content_balancing/__functions.py +++ b/adaptivetesting/math/content_balancing/__functions.py @@ -7,8 +7,8 @@ def compute_priority_index(item: TestItem, group_weights: dict[str, float], - required_items: int, - shown_items: int, + required_items: dict[str, float], + shown_items: dict[str, float], current_ability: float) -> float: """Calculates the priority index of a given item. @@ -38,7 +38,7 @@ def compute_priority_index(item: TestItem, for group in item_groups: priority_index = priority_index * group_weights[group] \ - * compute_quota_left(required_items=required_items, shown_items=shown_items) + * compute_quota_left(required_items=required_items[group], shown_items=shown_items[group]) # weight fisher information priority_index = priority_index * float(item_information_function( @@ -52,14 +52,14 @@ def compute_priority_index(item: TestItem, return priority_index -def compute_quota_left(required_items: int, - shown_items: int) -> float: +def compute_quota_left(required_items: int | float, + shown_items: int | float) -> float: """ Calculates the quota left (items left allowed to be shown) for a given constraint/group. Args: - required_items (int): number of required items per constraint - shown_items (int): number of already shown items per constraint + required_items (int | float): number of required items per constraint + shown_items (int | float): number of already shown items per constraint Returns: float: calculated quota for the given constraint. Results in a float between 0 and 1. diff --git a/adaptivetesting/math/content_balancing/__maximum_priority_index.py b/adaptivetesting/math/content_balancing/__maximum_priority_index.py index e69de29..5370ace 100644 --- a/adaptivetesting/math/content_balancing/__maximum_priority_index.py +++ b/adaptivetesting/math/content_balancing/__maximum_priority_index.py @@ -0,0 +1,80 @@ +from .__content_balancing import ContentBalancing +from ...models.__adaptive_test import AdaptiveTest +from .__constraint import Constraint +from .__functions import compute_priority_index +import numpy as np + + +class MaximumPriorityIndex(ContentBalancing): + """This content balancing method follows Cheng & Chang (2009). + A weight it applied to the item information considering the + current state of constraint fulfillment. + + References + ----------- + Cheng, Y., & Chang, H. (2009). The maximum priority index method for severely constrained item selection + in computerized adaptive testing. + British Journal of Mathematical and Statistical Psychology, 62(2), 369–383. + https://doi.org/10.1348/000711008X304376 + + """ + def __init__(self, adaptive_test: AdaptiveTest, constraints: list[Constraint]): + """This content balancing method follows Cheng & Chang (2009). + A weight it applied to the item information considering the + current state of constraint fulfillment. + + Args: + adaptive_test (AdaptiveTest): instance of the adaptive test + constraints (list[Constraint]): constraints that are applied to the item selection + """ + self.adaptive_test = adaptive_test + self.constraints = constraints + + def select_item(self): + """Select the next item to administer based on the maximum priority index method. + Returns: + TestItem: The selected test item. + """ + # compute priority index for every item + available_items = self.adaptive_test.item_pool.test_items + shown_items = self.adaptive_test.answered_items + priority_indices: list[float] = [] + + for item in available_items: + # get associated constraints + associated_constraints = [ + constraint + for constraint in self.constraints + if constraint.name in item.additional_properties["category"] + ] + + group_weights: dict[str, float] = {} + required_items: dict[str, float] = {} + shown_items_per_constraint: dict[str, float] = {} + + for constraint in associated_constraints: + group_weights[constraint.name] = constraint.weight + required_items[constraint.name] = constraint.prevalence + n_shown_items = len([ + shown_item + for shown_item in shown_items + if constraint.name in shown_item.additional_properties["category"] + ]) + shown_items_per_constraint[constraint.name] = float(n_shown_items) + + # calculate priority index + pix = compute_priority_index( + item=item, + group_weights=group_weights, + required_items=required_items, + shown_items=shown_items_per_constraint, + current_ability=self.adaptive_test.ability_level + ) + + priority_indices.append(pix) + + # select the item with the highest priority index + max_p_i = np.argmax(priority_indices) + selected_item = available_items[max_p_i] + + return selected_item diff --git a/adaptivetesting/math/content_balancing/__types.py b/adaptivetesting/math/content_balancing/__types.py deleted file mode 100644 index e45b092..0000000 --- a/adaptivetesting/math/content_balancing/__types.py +++ /dev/null @@ -1,5 +0,0 @@ -from .__content_balancing import ContentBalancing -from .__weighted_penalty_model import WeightedPenaltyModel - - -type ContentBalancingOptions = ContentBalancing | WeightedPenaltyModel diff --git a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py index 2153d75..3e5b4d6 100644 --- a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py +++ b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py @@ -20,16 +20,35 @@ class WeightedPenaltyModel(ContentBalancing): + """This content balancing method follows Shin et al. (2009) + and allows to apply constraints to the item selection. + The users can define a custom weight for the item information and the constriant. + + References + ------------ + Shin, C. D., Chien, Y., Way, W. D., & Swanson, L. (2009). Weighted Penalty Model for Content Balancing in CATS. + Pearson. + https://www.pearsonassessments.com/content/dam/school/global/clinical/us/assets/testnav/weighted-penalty-model.pdf + + """ def __init__(self, adaptive_test: AdaptiveTest, - items: list[TestItem], - shown_items: list[TestItem], - ability: float, constraints: list[Constraint], constraint_weight: float, information_weight: float ): - super().__init__() + """ + This content balancing method follows Shin et al. (2009) + and allows to apply constraints to the item selection. + The users can define a custom weight for the item information and the constriant. + + Args: + adaptive_test (AdaptiveTest): instance of the adaptive test + constraints (list[Constraint]): constraints that are applied to the item selection + constraint_weight (float): weight of the constraints + information_weight (float): weight of the item information + """ + super().__init__(adaptive_test, constraints) self.items = adaptive_test.item_pool.test_items self.ability = adaptive_test.ability_level self.shown_items = adaptive_test.answered_items @@ -227,7 +246,7 @@ def calculate_weighted_penalty_value(content_penalty: float, # standardize total content constraint penalty value standardized_total_content_penalty_value = standardize_total_content_constraint_penalty_value( - item_penality_value=total_content_penalty_value, + item_penalty_value=total_content_penalty_value, minimum=minimum_total_content_penalty, maximum=maximum_total_content_penalty ) diff --git a/adaptivetesting/tests/test_content_balacing.py b/adaptivetesting/tests/test_content_balacing.py index ee15fd8..9899ad9 100644 --- a/adaptivetesting/tests/test_content_balacing.py +++ b/adaptivetesting/tests/test_content_balacing.py @@ -16,7 +16,7 @@ def __init__(self, methodName="runTest"): self.available_items = adt.ItemPool.load_from_dataframe(items).test_items self.content_categories = ["Math", "English", "Math"] - + for i, _ in enumerate(self.available_items): self.available_items[i].additional_properties = { "category": [self.content_categories[i]] @@ -31,15 +31,21 @@ def test_basic_calculation(self): "Math": 0.2, "English": 0.8 }, - required_items=10, - shown_items=0, + required_items={ + "Math": 5, + "English": 10 + }, + shown_items={ + "Math": 0, + "English": 10 + }, current_ability=0 ) def test_quota_calculation(self): result = adt.compute_quota_left(10, 5) self.assertAlmostEqual(result, 0.5) - + def test_exception_list(self): with self.assertRaises(adt.ItemSelectionException): self.available_items[0].additional_properties["category"] = 0 @@ -49,8 +55,14 @@ def test_exception_list(self): "Math": 0.2, "English": 0.8 }, - required_items=10, - shown_items=0, + required_items={ + "Math": 5, + "English": 10 + }, + shown_items={ + "Math": 0, + "English": 10 + }, current_ability=0 ) @@ -63,7 +75,13 @@ def test_exception_key(self): "Math": 0.2, "English": 0.8 }, - required_items=10, - shown_items=0, + required_items={ + "Math": 5, + "English": 10 + }, + shown_items={ + "Math": 0, + "English": 10 + }, current_ability=0 ) diff --git a/adaptivetesting/tests/test_weighted_penalty_model.py b/adaptivetesting/tests/test_weighted_penalty_model.py index dbd3c89..e4a871a 100644 --- a/adaptivetesting/tests/test_weighted_penalty_model.py +++ b/adaptivetesting/tests/test_weighted_penalty_model.py @@ -1,9 +1,11 @@ +# type: ignore import unittest import pandas as pd import adaptivetesting as adt import copy from typing import Literal + class MockItem(adt.TestItem): def __init__(self, category: list[str]): super().__init__() @@ -22,12 +24,18 @@ def __init__(self, methodName="runTest"): # create two example items fitting a rasch model self.example_item1 = adt.TestItem() self.example_item1.id = 1 - self.example_item1.a = 1; self.example_item1.b = 0.5; self.example_item1.c = 0; self.example_item1.d = 1 + self.example_item1.a = 1 + self.example_item1.b = 0.5 + self.example_item1.c = 0 + self.example_item1.d = 1 self.example_item1.additional_properties["category"] = ["math"] self.example_item2 = adt.TestItem() self.example_item2.id = 2 - self.example_item2.a = 1; self.example_item2.b = 0.5; self.example_item2.c = 0; self.example_item2.d = 1 + self.example_item2.a = 1 + self.example_item2.b = 0.5 + self.example_item2.c = 0 + self.example_item2.d = 1 self.example_item2.additional_properties["category"] = ["english"] def test_content_penalty_calculation(self): @@ -119,13 +127,6 @@ def test_calcualte_weighted_content_penalty(self): def test_constraint_group_assignment(self): """get_constraint_group_assignments""" - # create shown items for math and science - shown_items = [ - MockItem(["math"]), - MockItem(["math"]), - MockItem(["science"]) - ] - # setup constraints constraints = [ adt.Constraint( @@ -163,7 +164,6 @@ def test_constraint_group_assignment(self): self.assertListEqual(assignments, ["B", "A"]) - def test_candidate_group_assignment(self): """form_list_of_candidate_items""" # create item From f0462f3c99085a72c97f8a537bdd945950eb9d49 Mon Sep 17 00:00:00 2001 From: codecon Date: Tue, 11 Nov 2025 10:47:57 +0100 Subject: [PATCH 037/137] add content balancing to test assembler --- .../implementations/__test_assembler.py | 150 +++++++++++++----- .../math/content_balancing/__constraint.py | 13 ++ .../content_balancing/__content_balancing.py | 4 + .../__maximum_priority_index.py | 4 +- 4 files changed, 130 insertions(+), 41 deletions(-) diff --git a/adaptivetesting/implementations/__test_assembler.py b/adaptivetesting/implementations/__test_assembler.py index d9bc5a1..97e4221 100644 --- a/adaptivetesting/implementations/__test_assembler.py +++ b/adaptivetesting/implementations/__test_assembler.py @@ -1,49 +1,38 @@ +from .. import ItemSelectionException, Constraint from ..models.__adaptive_test import AdaptiveTest from ..models.__test_item import TestItem from ..services.__estimator_interface import IEstimator -from typing import Any, Type +from typing import Any, Type, TypedDict, Protocol from ..math.item_selection.__maximum_information_criterion import maximum_information_criterion from ..models.__algorithm_exception import AlgorithmException from ..implementations.__pre_test import PreTest from ..models.__test_result import TestResult from ..services.__item_selection_protocol import ItemSelectionStrategy +from ..math.content_balancing.__content_balancing import CONTENT_BALANCING +from ..math.content_balancing.__weighted_penalty_model import WeightedPenaltyModel +from ..math.content_balancing.__maximum_priority_index import MaximumPriorityIndex +from ..math.estimators.__prior import Prior +from copy import deepcopy +import inspect + + +class EstimatorArgs(TypedDict): + prior: Prior | None + optimization_interval: tuple[int, int] + + +class ContentBalancingArgs(TypedDict): + constraints: list[Constraint] | None + constraint_weight: float | None + information_weight: float | None + + class TestAssembler(AdaptiveTest): """ TestAssembler is a subclass of AdaptiveTest designed to assemble and administer adaptive tests, optionally including a pretest phase. It supports customizable ability estimation and item selection strategies. - Args: - - item_pool: The pool of test items available for selection. - - simulation_id: Identifier for the simulation run. - - participant_id: Identifier for the participant. - - ability_estimator (Type[IEstimator]): The estimator class used for ability estimation. - - estimator_args (dict[str, Any], optional): - Arguments for the ability estimator. Defaults to {"prior": None, "optimization_interval": (-10, 10)}. - - item_selector (ItemSelectionStrategy, optional): - Function or strategy for selecting the next item. Defaults to maximum_information_criterion. - - item_selector_args (dict[str, Any], optional): Arguments for the item selector. Defaults to {}. - - pretest (bool, optional): Whether to run a pretest phase before the main test. Defaults to False. - - pretest_seed (int | None, optional): Random seed for pretest item selection. Defaults to None. - - true_ability_level (optional): The true ability level of the participant (for simulation). - - initial_ability_level (optional): The initial ability estimate. Defaults to 0. - - simulation (bool, optional): Whether the test is run in simulation mode. Defaults to True. - - debug (bool, optional): Whether to enable debug output. Defaults to False. - - **kwargs: Additional keyword arguments passed to the AdaptiveTest superclass. Methods: estimate_ability_level(): @@ -75,12 +64,14 @@ def __init__(self, simulation_id, participant_id, ability_estimator: Type[IEstimator], - estimator_args: dict[str, Any] = { + estimator_args: EstimatorArgs = { "prior": None, "optimization_interval": (-10, 10) }, item_selector: ItemSelectionStrategy = maximum_information_criterion, # type: ignore item_selector_args: dict[str, Any] = {}, + content_balancing: None | CONTENT_BALANCING = None, + content_balancing_args: ContentBalancingArgs = None, pretest: bool = False, pretest_seed: int | None = None, true_ability_level=None, @@ -88,10 +79,48 @@ def __init__(self, simulation=True, debug=False, **kwargs): + """ + Args: + + item_pool: The pool of test items available for selection. + + simulation_id: Identifier for the simulation run. + + participant_id: Identifier for the participant. + + ability_estimator (Type[IEstimator]): The estimator class used for ability estimation. + + estimator_args (EstimatorArgs, optional): + Arguments for the ability estimator. Defaults to {"prior": None, "optimization_interval": (-10, 10)}. + + item_selector (ItemSelectionStrategy, optional): + Function or strategy for selecting the next item. Defaults to maximum_information_criterion. + + item_selector_args (dict[str, Any], optional): Arguments for the item selector. Defaults to {}. + + content_balancing (CONTENT_BALANCING, optional): Selected content balancing strategy. Defaults to None. + If a content balancing strategy is specified, the item selection strategy will be ignored. + + pretest (bool, optional): Whether to run a pretest phase before the main test. Defaults to False. + + pretest_seed (int | None, optional): Random seed for pretest item selection. Defaults to None. + + true_ability_level (optional): The true ability level of the participant (for simulation). + + initial_ability_level (optional): The initial ability estimate. Defaults to 0. + + simulation (bool, optional): Whether the test is run in simulation mode. Defaults to True. + + debug (bool, optional): Whether to enable debug output. Defaults to False. + + **kwargs: Additional keyword arguments passed to the AdaptiveTest superclass. + """ self.__ability_estimator = ability_estimator self.__estimator_args = estimator_args self.__item_selector = item_selector self.__item_selector_args = item_selector_args + self.content_balancing = content_balancing + self.content_balancing_args = content_balancing_args self.__pretest = pretest self.__pretest_seed = pretest_seed @@ -120,10 +149,16 @@ def estimate_ability_level(self): Raises: AlgorithmException: If estimation fails for reasons other than all responses being identical. """ + # filter estimator args + sig = inspect.signature(self.__ability_estimator) + allowed = set(sig.parameters.keys()) + filtered_estimator_args = {k: v for k, v in self.__estimator_args.items() if k in allowed} + + # setup estimator estimator = self.__ability_estimator( self.response_pattern, self.answered_items, - **self.__estimator_args + **filtered_estimator_args ) try: @@ -147,6 +182,8 @@ def estimate_ability_level(self): def get_next_item(self) -> TestItem: """ Selects and returns the next test item based on the current ability level and item selector strategy. + If a content balancing strategy is specified, the item selection strategy will be ignored. + Instead, the item selected by the content balancing strategy will be returned. Returns: TestItem: The next item to be administered in the test, as determined by the item selector. @@ -154,12 +191,45 @@ def get_next_item(self) -> TestItem: Raises: Any exceptions raised by the item selector function. """ - item = self.__item_selector( - self.item_pool.test_items, - self.ability_level, - **self.__item_selector_args - ) - return item + if self.content_balancing is None: + # filter item selection args + sig = inspect.signature(self.__item_selector) + allowed = set(sig.parameters.keys()) + filtered_item_selector_args = {k: v for k, v in self.__item_selector_args.items() if k in allowed} + + item = self.__item_selector( + self.item_pool.test_items, + self.ability_level, + **filtered_item_selector_args + ) + return item + else: + # which strategy has been selected + if self.content_balancing == "WeightedPenaltyModel": + # parse content balancing args + + # setup wep + adaptive_test = deepcopy(self) + wep = WeightedPenaltyModel(adaptive_test, + constraints=self.content_balancing_args["constraints"], + constraint_weight=self.content_balancing_args["constraint_weight"], + information_weight=self.content_balancing_args["information_weight"],) + item = wep.select_item() + if item is None: + raise ItemSelectionException(f"""Something went wrong when selecting an item using {self.content_balancing}""") + else: + return item + elif self.content_balancing == "MaximumPriorityIndex": + adaptive_test = deepcopy(self) + mpi = MaximumPriorityIndex(adaptive_test, constraints=self.content_balancing_args["constraints"]) + item = mpi.select_item() + + if item is None: + raise ItemSelectionException( + f"""Something went wrong when selecting an item using {self.content_balancing}""") + else: + return item + raise ValueError(f"Something went wrong when selecting an item using {self.content_balancing}.") def run_test_once(self): """ diff --git a/adaptivetesting/math/content_balancing/__constraint.py b/adaptivetesting/math/content_balancing/__constraint.py index e8d60f4..25d91e3 100644 --- a/adaptivetesting/math/content_balancing/__constraint.py +++ b/adaptivetesting/math/content_balancing/__constraint.py @@ -3,8 +3,21 @@ @dataclass class Constraint: + """Constraint for Exposure Constrol and Content Balancing + """ name: str + """Constraint name. This string has to be the same + in the associated items. + """ weight: float + """Weight of this constraint. + """ prevalence: float + """Frequency / Relative Frequency of a constraint and its items. + For MPI, this value has to be an integer > 0. + For WEP, this may be a float between 0 and 1. + """ lower: float | None = None + """Lower bound of the constraint. Only required for WEP.""" upper: float | None = None + """Upper bound of the constraint. Only required for WEP.""" diff --git a/adaptivetesting/math/content_balancing/__content_balancing.py b/adaptivetesting/math/content_balancing/__content_balancing.py index 931762f..52168b3 100644 --- a/adaptivetesting/math/content_balancing/__content_balancing.py +++ b/adaptivetesting/math/content_balancing/__content_balancing.py @@ -2,8 +2,12 @@ from ...models.__test_item import TestItem from ...models.__adaptive_test import AdaptiveTest from .__constraint import Constraint +from typing import Literal +type CONTENT_BALANCING = Literal["WeightedPenaltyModel", "MaximumPriorityIndex"] +"""Default available content balancing methods.""" + class ContentBalancing(ABC): """Abstract base class for content balancing methods. diff --git a/adaptivetesting/math/content_balancing/__maximum_priority_index.py b/adaptivetesting/math/content_balancing/__maximum_priority_index.py index 5370ace..d3b0e3f 100644 --- a/adaptivetesting/math/content_balancing/__maximum_priority_index.py +++ b/adaptivetesting/math/content_balancing/__maximum_priority_index.py @@ -1,4 +1,5 @@ from .__content_balancing import ContentBalancing +from ... import TestItem from ...models.__adaptive_test import AdaptiveTest from .__constraint import Constraint from .__functions import compute_priority_index @@ -27,10 +28,11 @@ def __init__(self, adaptive_test: AdaptiveTest, constraints: list[Constraint]): adaptive_test (AdaptiveTest): instance of the adaptive test constraints (list[Constraint]): constraints that are applied to the item selection """ + super().__init__(adaptive_test, constraints) self.adaptive_test = adaptive_test self.constraints = constraints - def select_item(self): + def select_item(self) -> TestItem |None: """Select the next item to administer based on the maximum priority index method. Returns: TestItem: The selected test item. From 5998fe680ef581cec7e6549ce5be7c50aabe93b8 Mon Sep 17 00:00:00 2001 From: codecon Date: Fri, 14 Nov 2025 15:59:38 +0100 Subject: [PATCH 038/137] progress exposure control --- .../exposure_control/__exposure_control.py | 31 ++++ .../math/exposure_control/__init__.py | 2 + .../math/exposure_control/__randomesque.py | 140 +++++++++++++----- 3 files changed, 137 insertions(+), 36 deletions(-) create mode 100644 adaptivetesting/math/exposure_control/__exposure_control.py diff --git a/adaptivetesting/math/exposure_control/__exposure_control.py b/adaptivetesting/math/exposure_control/__exposure_control.py new file mode 100644 index 0000000..3e914ac --- /dev/null +++ b/adaptivetesting/math/exposure_control/__exposure_control.py @@ -0,0 +1,31 @@ +from abc import ABC, abstractmethod +from typing import Literal +from ... import TestItem +from ...models.__adaptive_test import AdaptiveTest + + +type EXPOSURECONTROL = Literal["Randomesque"] + +class ExposureControl(ABC): + """ + Abstract base class for exposure control + + Abstract Methods + ------------------ + - `select_item` + """ + def __init__(self, adaptive_test: AdaptiveTest): + """ + Args: + adaptive_test (AdaptiveTest): instance of the adaptive test + """ + self.adaptive_test = adaptive_test + + @abstractmethod + def select_item(self, **kwargs) -> TestItem: + """Select an item based on the implemented selection rules + + Returns: + TestItem | None: selected test item + """ + pass \ No newline at end of file diff --git a/adaptivetesting/math/exposure_control/__init__.py b/adaptivetesting/math/exposure_control/__init__.py index e69de29..75fe831 100644 --- a/adaptivetesting/math/exposure_control/__init__.py +++ b/adaptivetesting/math/exposure_control/__init__.py @@ -0,0 +1,2 @@ +from .__exposure_control import ExposureControl +from .__randomesque import Randomesque \ No newline at end of file diff --git a/adaptivetesting/math/exposure_control/__randomesque.py b/adaptivetesting/math/exposure_control/__randomesque.py index bfee40f..d359ce4 100644 --- a/adaptivetesting/math/exposure_control/__randomesque.py +++ b/adaptivetesting/math/exposure_control/__randomesque.py @@ -1,46 +1,114 @@ from typing import Callable, Any import random import numpy as np +from .__exposure_control import ExposureControl +from ...models.__adaptive_test import AdaptiveTest from ...models.__test_item import TestItem from ..estimators.__test_information import item_information_function -def radomesque_item_selection( - items: list[TestItem], - ability_estimate: float, - n_items: int, - reverse: bool = True, - item_rating_function: Callable = item_information_function, - seed: int | None = None, - **kwargs: Any -) -> TestItem: - item_information_list: list[tuple[float, int]] = [] - for i, item in enumerate(items): - information = float(item_rating_function( - a=np.array(item.a), - b=np.array(item.b), - c=np.array(item.c), - d=np.array(item.d), - mu=np.array(ability_estimate), - **kwargs - )) - - item_information_list.append((information, i)) +class Randomesque(ExposureControl): + def __init__(self, + adaptive_test: AdaptiveTest, + n_items: int, + seed: int | None = None, + reverse: bool = True): + """ + Exposure Control using randomesque item selection. + Instead of the most informative item, the `n` most informative items are selected. + From this selection, the final item is drawn randomly. For this process, a seed + can be specified. + + Args: + adaptive_test (AdaptiveTest): instance of the adaptive test + n_items (int): number of items to select + seed (int | None): random seed for the final item selection + reverse (bool): If `True` the most informative items are selected. Default `True`. + + + References + ------------ + Kingsbury, C. G., & Zara, A. R. (1991). A Comparison of Procedures for Content-Sensitive + Item Selection in Computerized Adaptive Tests. Applied Measurement in Education, 4(3), 241–261. + Psychology and Behavioral Sciences Collection. https://doi.org/10.1207/s15324818ame0403_4 + + Kingsbury, G. G., & Zara, A. R. (1989). Procedures for selecting items for + computerized adaptive tests. Applied Measurement in Education, 2(4), 359–375. + + """ + super().__init__(adaptive_test) + + # extract items + self.items = adaptive_test.item_pool.test_items + self.ability_estimate = adaptive_test.ability_level + self.n_items = n_items + self.seed = seed + self.reverse = reverse + + def select_item(self) -> TestItem: + """Select an item based on the implemented selection rules + + Returns: + TestItem | None: selected test item + """ + selected_items = self.radomesque_item_selection( + self.items, + self.ability_estimate, + self.n_items, + self.reverse, + self.sort_by_information, + self.seed + ) + return selected_items + + @staticmethod def sort_by_information(item_entry: tuple[float, int]) -> float: + """ + Internal function for sorting items by their information value + + Args: + item_entry: tuple of information value and item index + + Returns: + information value of given item + """ return item_entry[0] - - # sort items in ascending order to have the first item be the one with the hightest - # information value (default or expected settings) - item_information_list.sort(key=sort_by_information, reverse=reverse) - - # select first n items - selected_items = item_information_list[0:n_items] - # select only items - sub_item_pool = [items[item[1]] for item in selected_items] - - # randomly select items from sub item pool - if seed is not None: - random.seed(seed) - sampled_item = random.sample(sub_item_pool, k=1)[0] - return sampled_item + + + @staticmethod + def radomesque_item_selection(items: list[TestItem], + ability_estimate: float, + n_items: int, + reverse: bool = True, + item_rating_function: Callable = item_information_function, + seed: int | None = None, + **kwargs: Any + ) -> TestItem: + item_information_list: list[tuple[float, int]] = [] + for i, item in enumerate(items): + information = float(item_rating_function( + a=np.array(item.a), + b=np.array(item.b), + c=np.array(item.c), + d=np.array(item.d), + mu=np.array(ability_estimate), + **kwargs + )) + + item_information_list.append((information, i)) + + # sort items in ascending order to have the first item be the one with the highest + # information value (default or expected settings) + item_information_list.sort(key=Randomesque.sort_by_information, reverse=reverse) + + # select first n items + selected_items = item_information_list[0:n_items] + # select only items + sub_item_pool = [items[item[1]] for item in selected_items] + + # randomly select items from sub item pool + if seed is not None: + random.seed(seed) + sampled_item = random.sample(sub_item_pool, k=1)[0] + return sampled_item From 3f7c8d4a9e9e0e46b9808e1168cf7ce0c5c8bcd1 Mon Sep 17 00:00:00 2001 From: condecon Date: Fri, 14 Nov 2025 16:20:43 +0100 Subject: [PATCH 039/137] call functions for wep weights --- .../implementations/__test_assembler.py | 17 ++++++++--- .../__weighted_penalty_model.py | 28 ++++++++++++++----- 2 files changed, 34 insertions(+), 11 deletions(-) diff --git a/adaptivetesting/implementations/__test_assembler.py b/adaptivetesting/implementations/__test_assembler.py index 97e4221..47a1dd8 100644 --- a/adaptivetesting/implementations/__test_assembler.py +++ b/adaptivetesting/implementations/__test_assembler.py @@ -2,7 +2,7 @@ from ..models.__adaptive_test import AdaptiveTest from ..models.__test_item import TestItem from ..services.__estimator_interface import IEstimator -from typing import Any, Type, TypedDict, Protocol +from typing import Any, Type, TypedDict, Callable from ..math.item_selection.__maximum_information_criterion import maximum_information_criterion from ..models.__algorithm_exception import AlgorithmException from ..implementations.__pre_test import PreTest @@ -23,8 +23,17 @@ class EstimatorArgs(TypedDict): class ContentBalancingArgs(TypedDict): constraints: list[Constraint] | None - constraint_weight: float | None - information_weight: float | None + """constraints that are applied to the item selection""" + constraint_weight: float | Callable[[AdaptiveTest], float] | None + """weight of the constraints + This can also be a function taking adaptive test as an input argument. + This allows the user to specify custom weight values depending + on the specific states and progress of the test.""" + information_weight: float | Callable[[AdaptiveTest], float] | None + """weight of the item information + This can also be a function taking adaptive test as an input argument. + This allows the user to specify custom weight values depending + on the specific states and progress of the test.""" @@ -71,7 +80,7 @@ def __init__(self, item_selector: ItemSelectionStrategy = maximum_information_criterion, # type: ignore item_selector_args: dict[str, Any] = {}, content_balancing: None | CONTENT_BALANCING = None, - content_balancing_args: ContentBalancingArgs = None, + content_balancing_args: ContentBalancingArgs | None = None, pretest: bool = False, pretest_seed: int | None = None, true_ability_level=None, diff --git a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py index 3e5b4d6..9cde3db 100644 --- a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py +++ b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py @@ -1,4 +1,4 @@ -from typing import Literal +from typing import Literal, Callable import numpy as np from ..estimators.__test_information import item_information_function from ...models.__test_item import TestItem @@ -34,8 +34,8 @@ class WeightedPenaltyModel(ContentBalancing): def __init__(self, adaptive_test: AdaptiveTest, constraints: list[Constraint], - constraint_weight: float, - information_weight: float + constraint_weight: float | Callable[[AdaptiveTest], float], + information_weight: float | Callable[[AdaptiveTest], float] ): """ This content balancing method follows Shin et al. (2009) @@ -45,8 +45,14 @@ def __init__(self, Args: adaptive_test (AdaptiveTest): instance of the adaptive test constraints (list[Constraint]): constraints that are applied to the item selection - constraint_weight (float): weight of the constraints - information_weight (float): weight of the item information + constraint_weight (float | Callable[[AdaptiveTest], float]): weight of the constraints + This can also be a function taking adaptive test as an input argument. + This allows the user to specify custom weight values depending + on the specific states and progress of the test. + information_weight (float | Callable[[AdaptiveTest], float]): weight of the item information + This can also be a function taking adaptive test as an input argument. + This allows the user to specify custom weight values depending + on the specific states and progress of the test. """ super().__init__(adaptive_test, constraints) self.items = adaptive_test.item_pool.test_items @@ -58,8 +64,16 @@ def __init__(self, self.eligible_items: list[tuple[TestItem, float, ITEM_GROUP]] = [] # only used internally - self.constraint_weight = constraint_weight - self.information_weight = information_weight + if callable(constraint_weight): + self.constraint_weight = constraint_weight(adaptive_test) + else: + self.constraint_weight = constraint_weight + + if callable(information_weight): + self.information_weight = information_weight(adaptive_test) + else: + self.information_weight = information_weight + def select_item(self) -> TestItem | None: """Select the next item to administer based on the weighted penalty model. From 2dde99a090896ca322f2aa54a7376d8802027da4 Mon Sep 17 00:00:00 2001 From: condecon Date: Mon, 17 Nov 2025 14:48:44 +0100 Subject: [PATCH 040/137] refactor: update imports and enhance item selection compatibility --- .../implementations/__test_assembler.py | 89 ++++++++++++------- .../__maximum_priority_index.py | 2 +- .../math/exposure_control/__randomesque.py | 6 +- adaptivetesting/tests/test_test_assembler.py | 12 +-- 4 files changed, 71 insertions(+), 38 deletions(-) diff --git a/adaptivetesting/implementations/__test_assembler.py b/adaptivetesting/implementations/__test_assembler.py index 47a1dd8..d9ff6f4 100644 --- a/adaptivetesting/implementations/__test_assembler.py +++ b/adaptivetesting/implementations/__test_assembler.py @@ -1,4 +1,5 @@ -from .. import ItemSelectionException, Constraint +from ..models.__item_selection_exception import ItemSelectionException +from ..math.content_balancing.__constraint import Constraint from ..models.__adaptive_test import AdaptiveTest from ..models.__test_item import TestItem from ..services.__estimator_interface import IEstimator @@ -18,7 +19,7 @@ class EstimatorArgs(TypedDict): prior: Prior | None - optimization_interval: tuple[int, int] + optimization_interval: tuple[float, float] class ContentBalancingArgs(TypedDict): @@ -27,12 +28,12 @@ class ContentBalancingArgs(TypedDict): constraint_weight: float | Callable[[AdaptiveTest], float] | None """weight of the constraints This can also be a function taking adaptive test as an input argument. - This allows the user to specify custom weight values depending + This allows the user to specify custom weight values depending on the specific states and progress of the test.""" information_weight: float | Callable[[AdaptiveTest], float] | None """weight of the item information This can also be a function taking adaptive test as an input argument. - This allows the user to specify custom weight values depending + This allows the user to specify custom weight values depending on the specific states and progress of the test.""" @@ -167,7 +168,7 @@ def estimate_ability_level(self): estimator = self.__ability_estimator( self.response_pattern, self.answered_items, - **filtered_estimator_args + **filtered_estimator_args # type: ignore ) try: @@ -200,6 +201,7 @@ def get_next_item(self) -> TestItem: Raises: Any exceptions raised by the item selector function. """ + item: TestItem | None if self.content_balancing is None: # filter item selection args sig = inspect.signature(self.__item_selector) @@ -213,31 +215,39 @@ def get_next_item(self) -> TestItem: ) return item else: - # which strategy has been selected - if self.content_balancing == "WeightedPenaltyModel": - # parse content balancing args - - # setup wep - adaptive_test = deepcopy(self) - wep = WeightedPenaltyModel(adaptive_test, - constraints=self.content_balancing_args["constraints"], - constraint_weight=self.content_balancing_args["constraint_weight"], - information_weight=self.content_balancing_args["information_weight"],) - item = wep.select_item() - if item is None: - raise ItemSelectionException(f"""Something went wrong when selecting an item using {self.content_balancing}""") - else: - return item - elif self.content_balancing == "MaximumPriorityIndex": - adaptive_test = deepcopy(self) - mpi = MaximumPriorityIndex(adaptive_test, constraints=self.content_balancing_args["constraints"]) - item = mpi.select_item() - - if item is None: - raise ItemSelectionException( - f"""Something went wrong when selecting an item using {self.content_balancing}""") - else: - return item + if self.content_balancing_args is not None: + # which strategy has been selected + if self.content_balancing == "WeightedPenaltyModel": + # parse content balancing args + + # setup wep + adaptive_test = deepcopy(self) + wep = WeightedPenaltyModel(adaptive_test, + constraints=self.check_args_are_not_none("constraints", + self.content_balancing_args["constraints"]), + constraint_weight=self.check_args_are_not_none("constraint_weights", + self.content_balancing_args["constraint_weight"]), + information_weight=self.check_args_are_not_none("information_weight", + self.content_balancing_args["information_weight"]) + ) + item = wep.select_item() + if item is None: + raise ItemSelectionException(f"""Something went wrong when selecting an item using {self.content_balancing}""") + else: + return item + elif self.content_balancing == "MaximumPriorityIndex": + adaptive_test = deepcopy(self) + mpi = MaximumPriorityIndex(adaptive_test, constraints=self.check_args_are_not_none("constraints", + self.content_balancing_args["constraints"])) + item = mpi.select_item() + + if item is None: + raise ItemSelectionException( + f"""Something went wrong when selecting an item using {self.content_balancing}""") + else: + return item + else: + raise ValueError("content_balancing_args cannot be None when using content balancing.") raise ValueError(f"Something went wrong when selecting an item using {self.content_balancing}.") def run_test_once(self): @@ -308,3 +318,22 @@ def run_test_once(self): self.test_results.append(intermediate_result) return super().run_test_once() + + def check_args_are_not_none(self, key: str, x: Any | None) -> Any: + """This functions checks if an object is none. + If not a ValueError is raised. + + Args: + key (str): parameter name where the object is typically assigned + x (Any | None): object + + Raises: + ValueError: raised if the object is None. + + Returns: + Any: object + """ + if x is not None: + return x + else: + raise ValueError(f"{key} cannot be None.") diff --git a/adaptivetesting/math/content_balancing/__maximum_priority_index.py b/adaptivetesting/math/content_balancing/__maximum_priority_index.py index d3b0e3f..2c1b7fd 100644 --- a/adaptivetesting/math/content_balancing/__maximum_priority_index.py +++ b/adaptivetesting/math/content_balancing/__maximum_priority_index.py @@ -1,5 +1,5 @@ from .__content_balancing import ContentBalancing -from ... import TestItem +from ...models.__test_item import TestItem from ...models.__adaptive_test import AdaptiveTest from .__constraint import Constraint from .__functions import compute_priority_index diff --git a/adaptivetesting/math/exposure_control/__randomesque.py b/adaptivetesting/math/exposure_control/__randomesque.py index d359ce4..7ef86e9 100644 --- a/adaptivetesting/math/exposure_control/__randomesque.py +++ b/adaptivetesting/math/exposure_control/__randomesque.py @@ -46,9 +46,13 @@ def __init__(self, self.seed = seed self.reverse = reverse - def select_item(self) -> TestItem: + def select_item(self, **kwargs) -> TestItem: """Select an item based on the implemented selection rules + Args: + **kwargs: additional arguments for item selection function. + Not used here but implemented for compatibility. + Returns: TestItem | None: selected test item """ diff --git a/adaptivetesting/tests/test_test_assembler.py b/adaptivetesting/tests/test_test_assembler.py index 58423f7..14d72cf 100644 --- a/adaptivetesting/tests/test_test_assembler.py +++ b/adaptivetesting/tests/test_test_assembler.py @@ -57,7 +57,7 @@ def setUp(self): simulation_id="sim1", participant_id="p1", ability_estimator=DummyEstimator, - estimator_args={}, + estimator_args={}, # type: ignore item_selector=dummy_item_selector, item_selector_args={}, pretest=False, @@ -71,7 +71,7 @@ def test_init_sets_attributes(self): simulation_id="sim1", participant_id="p1", ability_estimator=DummyEstimator, - estimator_args={"foo": "bar"}, + estimator_args={"foo": "bar"}, # type: ignore item_selector=dummy_item_selector, item_selector_args={"baz": 1}, pretest=True, @@ -99,7 +99,7 @@ def test_estimate_ability_level_all_correct(self): simulation_id="sim1", participant_id="p1", ability_estimator=DummyEstimator, - estimator_args={}, + estimator_args={}, # type: ignore item_selector=dummy_item_selector, item_selector_args={}, pretest=False, @@ -120,7 +120,7 @@ def test_estimate_ability_level_all_incorrect(self): simulation_id="sim1", participant_id="p1", ability_estimator=DummyEstimator, - estimator_args={}, + estimator_args={}, # type: ignore item_selector=dummy_item_selector, item_selector_args={}, pretest=False, @@ -141,7 +141,7 @@ def test_estimate_ability_level_raises_on_other_exception(self): simulation_id="sim1", participant_id="p1", ability_estimator=DummyEstimator, - estimator_args={}, + estimator_args={}, # type: ignore item_selector=dummy_item_selector, item_selector_args={}, pretest=False, @@ -168,7 +168,7 @@ def test_run_test_once_calls_super(self): simulation_id="sim1", participant_id="p1", ability_estimator=DummyEstimator, - estimator_args={}, + estimator_args={}, # type: ignore item_selector=dummy_item_selector, item_selector_args={}, pretest=True, From 3b6d30a8b48f1d4e5be04b9a9d7fdffe195300bd Mon Sep 17 00:00:00 2001 From: condecon Date: Mon, 17 Nov 2025 15:05:20 +0100 Subject: [PATCH 041/137] linting --- .../implementations/__test_assembler.py | 68 +++++++++++-------- .../content_balancing/__content_balancing.py | 1 + .../__maximum_priority_index.py | 2 +- .../__weighted_penalty_model.py | 5 +- .../exposure_control/__exposure_control.py | 3 +- .../math/exposure_control/__randomesque.py | 16 ++--- 6 files changed, 51 insertions(+), 44 deletions(-) diff --git a/adaptivetesting/implementations/__test_assembler.py b/adaptivetesting/implementations/__test_assembler.py index d9ff6f4..436a8cf 100644 --- a/adaptivetesting/implementations/__test_assembler.py +++ b/adaptivetesting/implementations/__test_assembler.py @@ -37,38 +37,37 @@ class ContentBalancingArgs(TypedDict): on the specific states and progress of the test.""" - - class TestAssembler(AdaptiveTest): """ TestAssembler is a subclass of AdaptiveTest designed to assemble and administer adaptive tests, optionally including a pretest phase. It supports customizable ability estimation and item selection strategies. - + Methods: estimate_ability_level(): Estimates the current ability level using the specified estimator and handles exceptions for specific response patterns (all correct or all incorrect). - + get_next_item() -> TestItem: Selects the next item to administer using the specified item selection strategy. - + run_test_once(): Runs a single iteration of the test, including an optional pretest phase. Handles item administration, response collection, ability estimation, and result recording. - + Attributes: __ability_estimator: The estimator class for ability estimation. - + __estimator_args: Arguments for the ability estimator. - + __item_selector: The item selection strategy. - + __item_selector_args: Arguments for the item selector. - + __pretest: Whether to run a pretest phase. - + __pretest_seed: Random seed for pretest item selection. """ + def __init__(self, item_pool, simulation_id, @@ -133,7 +132,7 @@ def __init__(self, self.content_balancing_args = content_balancing_args self.__pretest = pretest self.__pretest_seed = pretest_seed - + super().__init__(item_pool, simulation_id, participant_id, @@ -142,7 +141,7 @@ def __init__(self, simulation, debug, **kwargs) - + def estimate_ability_level(self): """ Estimates the ability level of a test-taker based on their response pattern and answered items. @@ -152,10 +151,10 @@ def estimate_ability_level(self): it assigns a default estimation value (-10 for all incorrect, 10 for all correct) and recalculates the standard error. Otherwise, it raises an AlgorithmException with additional context. - + Returns: tuple[float, float]: A tuple containing the estimated ability level (float) and its standard error (float). - + Raises: AlgorithmException: If estimation fails for reasons other than all responses being identical. """ @@ -188,7 +187,7 @@ def estimate_ability_level(self): when wrong when running {type(estimator)}""") from exception return estimation, standard_error - + def get_next_item(self) -> TestItem: """ Selects and returns the next test item based on the current ability level and item selector strategy. @@ -222,23 +221,32 @@ def get_next_item(self) -> TestItem: # setup wep adaptive_test = deepcopy(self) - wep = WeightedPenaltyModel(adaptive_test, - constraints=self.check_args_are_not_none("constraints", - self.content_balancing_args["constraints"]), - constraint_weight=self.check_args_are_not_none("constraint_weights", - self.content_balancing_args["constraint_weight"]), - information_weight=self.check_args_are_not_none("information_weight", - self.content_balancing_args["information_weight"]) - ) + wep = WeightedPenaltyModel( + adaptive_test, + constraints=self.check_args_are_not_none( + "constraints", + self.content_balancing_args["constraints"]), + constraint_weight=self.check_args_are_not_none( + "constraint_weights", + self.content_balancing_args["constraint_weight"]), + information_weight=self.check_args_are_not_none( + "information_weight", + self.content_balancing_args["information_weight"])) + item = wep.select_item() if item is None: - raise ItemSelectionException(f"""Something went wrong when selecting an item using {self.content_balancing}""") + raise ItemSelectionException( + f"""Something went wrong when selecting an item using {self.content_balancing}""") else: return item elif self.content_balancing == "MaximumPriorityIndex": adaptive_test = deepcopy(self) - mpi = MaximumPriorityIndex(adaptive_test, constraints=self.check_args_are_not_none("constraints", - self.content_balancing_args["constraints"])) + mpi = MaximumPriorityIndex( + adaptive_test, + constraints=self.check_args_are_not_none( + "constraints", + self.content_balancing_args["constraints"])) + item = mpi.select_item() if item is None: @@ -261,7 +269,7 @@ def run_test_once(self): - Removes the item from the item pool. - Estimates the ability level and standard error after pretest responses. - Records test results for each pretest item, with the final item including the first ability estimation. - + Returns: The result of the superclass's run_test_once() method. """ @@ -318,7 +326,7 @@ def run_test_once(self): self.test_results.append(intermediate_result) return super().run_test_once() - + def check_args_are_not_none(self, key: str, x: Any | None) -> Any: """This functions checks if an object is none. If not a ValueError is raised. @@ -332,7 +340,7 @@ def check_args_are_not_none(self, key: str, x: Any | None) -> Any: Returns: Any: object - """ + """ if x is not None: return x else: diff --git a/adaptivetesting/math/content_balancing/__content_balancing.py b/adaptivetesting/math/content_balancing/__content_balancing.py index 52168b3..59e1aaa 100644 --- a/adaptivetesting/math/content_balancing/__content_balancing.py +++ b/adaptivetesting/math/content_balancing/__content_balancing.py @@ -8,6 +8,7 @@ type CONTENT_BALANCING = Literal["WeightedPenaltyModel", "MaximumPriorityIndex"] """Default available content balancing methods.""" + class ContentBalancing(ABC): """Abstract base class for content balancing methods. diff --git a/adaptivetesting/math/content_balancing/__maximum_priority_index.py b/adaptivetesting/math/content_balancing/__maximum_priority_index.py index 2c1b7fd..099eeca 100644 --- a/adaptivetesting/math/content_balancing/__maximum_priority_index.py +++ b/adaptivetesting/math/content_balancing/__maximum_priority_index.py @@ -32,7 +32,7 @@ def __init__(self, adaptive_test: AdaptiveTest, constraints: list[Constraint]): self.adaptive_test = adaptive_test self.constraints = constraints - def select_item(self) -> TestItem |None: + def select_item(self) -> TestItem | None: """Select the next item to administer based on the maximum priority index method. Returns: TestItem: The selected test item. diff --git a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py index 9cde3db..fd830ba 100644 --- a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py +++ b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py @@ -47,11 +47,11 @@ def __init__(self, constraints (list[Constraint]): constraints that are applied to the item selection constraint_weight (float | Callable[[AdaptiveTest], float]): weight of the constraints This can also be a function taking adaptive test as an input argument. - This allows the user to specify custom weight values depending + This allows the user to specify custom weight values depending on the specific states and progress of the test. information_weight (float | Callable[[AdaptiveTest], float]): weight of the item information This can also be a function taking adaptive test as an input argument. - This allows the user to specify custom weight values depending + This allows the user to specify custom weight values depending on the specific states and progress of the test. """ super().__init__(adaptive_test, constraints) @@ -74,7 +74,6 @@ def __init__(self, else: self.information_weight = information_weight - def select_item(self) -> TestItem | None: """Select the next item to administer based on the weighted penalty model. diff --git a/adaptivetesting/math/exposure_control/__exposure_control.py b/adaptivetesting/math/exposure_control/__exposure_control.py index 3e914ac..ccaeaa3 100644 --- a/adaptivetesting/math/exposure_control/__exposure_control.py +++ b/adaptivetesting/math/exposure_control/__exposure_control.py @@ -6,6 +6,7 @@ type EXPOSURECONTROL = Literal["Randomesque"] + class ExposureControl(ABC): """ Abstract base class for exposure control @@ -28,4 +29,4 @@ def select_item(self, **kwargs) -> TestItem: Returns: TestItem | None: selected test item """ - pass \ No newline at end of file + pass diff --git a/adaptivetesting/math/exposure_control/__randomesque.py b/adaptivetesting/math/exposure_control/__randomesque.py index 7ef86e9..a972146 100644 --- a/adaptivetesting/math/exposure_control/__randomesque.py +++ b/adaptivetesting/math/exposure_control/__randomesque.py @@ -7,7 +7,6 @@ from ..estimators.__test_information import item_information_function - class Randomesque(ExposureControl): def __init__(self, adaptive_test: AdaptiveTest, @@ -79,16 +78,15 @@ def sort_by_information(item_entry: tuple[float, int]) -> float: """ return item_entry[0] - @staticmethod def radomesque_item_selection(items: list[TestItem], - ability_estimate: float, - n_items: int, - reverse: bool = True, - item_rating_function: Callable = item_information_function, - seed: int | None = None, - **kwargs: Any - ) -> TestItem: + ability_estimate: float, + n_items: int, + reverse: bool = True, + item_rating_function: Callable = item_information_function, + seed: int | None = None, + **kwargs: Any + ) -> TestItem: item_information_list: list[tuple[float, int]] = [] for i, item in enumerate(items): information = float(item_rating_function( From 6bd39924b4bea8f3fa75ac7698ae44a412ef2a85 Mon Sep 17 00:00:00 2001 From: codecon Date: Mon, 17 Nov 2025 17:06:53 +0100 Subject: [PATCH 042/137] change python version to 3.13 for development --- .python-version | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.python-version b/.python-version index e4fba21..24ee5b1 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.12 +3.13 From ea23cc9569b1294d924502a696dcb7cc696702c3 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Mon, 24 Nov 2025 09:48:22 +0100 Subject: [PATCH 043/137] Add SkewNormalPrior and EmpiricalPrior (#47) * fix typos * fix import error * add new priors * add unittests for new priors * add priors to global import * test priors and numerical stability for bayes modal * improve numerical stability for bayes ability estimates * type checking and linting * refactor: update log_posterior return types for consistency * refactor: enhance numerical stability in get_standard_error method * update documentation and simplify expressions --- adaptivetesting/__init__.py | 2 +- .../estimators/__bayes_modal_estimation.py | 70 ++------ .../math/estimators/__expect_a_posteriori.py | 68 +++++--- .../math/estimators/__functions/__bayes.py | 29 ++-- .../estimators/__functions/__estimators.py | 53 +++--- adaptivetesting/math/estimators/__init__.py | 2 +- adaptivetesting/math/estimators/__prior.py | 97 ++++++++++- .../math/estimators/__test_information.py | 23 ++- adaptivetesting/models/__adaptive_test.py | 21 +-- adaptivetesting/models/__item_pool.py | 9 +- .../models/__item_selection_exception.py | 1 + .../services/__estimator_interface.py | 1 - .../services/__item_selection_protocol.py | 2 - .../services/__test_results_interface.py | 2 - adaptivetesting/simulation/__simulation.py | 18 +-- adaptivetesting/tests/test_bayes_modal.py | 153 +++++++++++++++--- adaptivetesting/tests/test_eap.py | 138 +++++++++++++++- adaptivetesting/tests/test_priors.py | 56 +++++++ 18 files changed, 549 insertions(+), 196 deletions(-) create mode 100644 adaptivetesting/tests/test_priors.py diff --git a/adaptivetesting/__init__.py b/adaptivetesting/__init__.py index fc88426..74440b9 100644 --- a/adaptivetesting/__init__.py +++ b/adaptivetesting/__init__.py @@ -18,7 +18,7 @@ from .math.estimators.__ml_estimation import MLEstimator from .math.estimators.__bayes_modal_estimation import BayesModal from .math.estimators.__expect_a_posteriori import ExpectedAPosteriori -from .math.estimators.__prior import Prior, NormalPrior, CustomPrior, CustomPriorException +from .math.estimators.__prior import Prior, NormalPrior, CustomPrior, CustomPriorException, SkewNormalPrior, EmpiricalPrior from .math.estimators.__functions.__estimators import probability_y0, probability_y1, maximize_likelihood_function, likelihood from .math.estimators.__functions.__bayes import maximize_posterior from .math.estimators.__test_information import test_information_function, item_information_function, prior_information_function diff --git a/adaptivetesting/math/estimators/__bayes_modal_estimation.py b/adaptivetesting/math/estimators/__bayes_modal_estimation.py index 045c6fa..98046e1 100644 --- a/adaptivetesting/math/estimators/__bayes_modal_estimation.py +++ b/adaptivetesting/math/estimators/__bayes_modal_estimation.py @@ -2,9 +2,8 @@ import numpy as np from ...services.__estimator_interface import IEstimator from ...models.__test_item import TestItem -from ...models.__algorithm_exception import AlgorithmException -from .__functions.__bayes import maximize_posterior, likelihood -from .__prior import Prior, NormalPrior, CustomPrior, CustomPriorException +from .__functions.__bayes import maximize_posterior +from .__prior import Prior from .__test_information import test_information_function @@ -36,67 +35,24 @@ def __init__(self, def get_estimation(self) -> float: """Estimate the current ability level using Bayes Modal. - If a `NormalPrior` is used, the `bounded` optimizer is used + The `bounded` optimizer is used to get the ability estimate. - For any other prior, it cannot be guaranteed that the optimizer will converge correctly. - Therefore, the full posterior distribution is calculated - and the maximum posterior value is selected. - - Because this function uses a switch internally to determine - whether a optimizer is used for the estimate or not, - you have to create your custom priors from the correct base class (`CustomPrior`). - Otherwise, the estimate may not necessarily be correct! - + Raises: AlgorithmException: Raised when maximum could not be found. - CustomPriorException: Raised when custom prior is not based on the `CustomPrior` class. Returns: float: ability estimation """ - if type(self.prior) is NormalPrior: - # get estimate using a classical optimizers approach - return maximize_posterior( - self.a, - self.b, - self.c, - self.d, - self.response_pattern, - self.prior - ) - # else, we have to calculate the full posterior distribution - # because the optimizers do not correctly identify the maximum of the function - else: - # check that the used prior is really inherited from - # the CustomPrior base class - if not isinstance(self.prior, CustomPrior): - raise CustomPriorException("It seems like you are using a non-normal prior but", - "did not use the CustomPrior base class!") - - mu = np.linspace(self.optimization_interval[0], - self.optimization_interval[1], - num=1000) - # calculate likelihood values for every mu - try: - lik_values = np.array([ - likelihood( - i, - self.a, - self.b, - self.c, - self.d, - self.response_pattern - ) - for i in mu - ]) - - # add prior - unmarginalized_posterior = lik_values * self.prior.pdf(mu) - # find argmin and return mu - estimate_index = np.argmin(unmarginalized_posterior) - return float(mu[estimate_index].astype(float)) - except Exception as e: - raise AlgorithmException(e) + + return maximize_posterior( + self.a, + self.b, + self.c, + self.d, + self.response_pattern, + self.prior + ) def get_standard_error(self, estimation: float) -> float: """Calculates the standard error for the given estimated ability level. diff --git a/adaptivetesting/math/estimators/__expect_a_posteriori.py b/adaptivetesting/math/estimators/__expect_a_posteriori.py index b31f51a..ec2426f 100644 --- a/adaptivetesting/math/estimators/__expect_a_posteriori.py +++ b/adaptivetesting/math/estimators/__expect_a_posteriori.py @@ -2,7 +2,7 @@ from scipy.integrate import trapezoid from .__bayes_modal_estimation import BayesModal from ...models.__test_item import TestItem -from .__functions.__bayes import likelihood +from .__functions.__estimators import log_likelihood from .__prior import Prior from math import pow @@ -38,21 +38,34 @@ def get_estimation(self) -> float: """ x = np.linspace(self.optimization_interval[0], self.optimization_interval[1], 1000) - prior_pdf = self.prior.pdf(x) + if hasattr(self.prior, "logpdf"): + log_prior = self.prior.logpdf(x) + else: + log_prior = np.log(self.prior.pdf(x) + 1e-300) - likelihood_vals = np.vectorize(lambda mu: likelihood(mu, - self.a, - self.b, - self.c, - self.d, - self.response_pattern))(x) + log_likelihood_vals = np.vectorize( + lambda mu: log_likelihood(mu, + self.a, + self.b, + self.c, + self.d, + self.response_pattern) + )(x) - numerator = trapezoid(x * likelihood_vals * prior_pdf, x) - - denominator = trapezoid(likelihood_vals * prior_pdf, x) + log_posterior = log_likelihood_vals + log_prior + # use log-sum-exp stabilization + max_log = np.nanmax(log_posterior) + weights = np.exp(log_posterior - max_log) + + numerator = trapezoid(x * weights, x) + denominator = trapezoid(weights, x) + np.finfo(float).eps + + if denominator == 0 or not np.isfinite(denominator): + raise ValueError("Denominator (integral of posterior) is zero or " + "non-finite — check interval/prior/likelihood.") + estimation = numerator / denominator - return estimation def get_standard_error(self, estimated_ability: float) -> float: @@ -63,27 +76,30 @@ def get_standard_error(self, estimated_ability: float) -> float: Args: estimated_ability (float): _description_ - Raises: - NotImplementedError: Either an instance of NormalPrior or CustomPrior has to be used. - If you want to use another calculation method for the standard, - you have to specifically override this method. - Returns: float: standard error of the ability estimation """ x = np.linspace(self.optimization_interval[0], self.optimization_interval[1], 1000) - prior_pdf = self.prior.pdf(x) - likelihood_vals = np.vectorize(lambda mu: likelihood(mu, - self.a, - self.b, - self.c, - self.d, - self.response_pattern))(x) + # get log-prior in a numerically stable way + if hasattr(self.prior, "logpdf"): + log_prior = self.prior.logpdf(x) + else: + log_prior = np.log(self.prior.pdf(x) + 1e-300) - numerator = trapezoid((x - estimated_ability) ** 2 * likelihood_vals * prior_pdf, x) + log_likelihood_vals = np.vectorize(lambda mu: log_likelihood(mu, + self.a, + self.b, + self.c, + self.d, + self.response_pattern))(x) + log_posterior = log_likelihood_vals + log_prior + max_log = np.nanmax(log_posterior) + weights = np.exp(log_posterior - max_log) + + numerator = trapezoid((x - estimated_ability) ** 2 * weights, x) - denominator = trapezoid(likelihood_vals * prior_pdf, x) + denominator = trapezoid(weights, x) standard_error_result = pow(numerator / denominator, 0.5) diff --git a/adaptivetesting/math/estimators/__functions/__bayes.py b/adaptivetesting/math/estimators/__functions/__bayes.py index 0918056..d756222 100644 --- a/adaptivetesting/math/estimators/__functions/__bayes.py +++ b/adaptivetesting/math/estimators/__functions/__bayes.py @@ -2,7 +2,7 @@ from scipy.optimize import minimize_scalar, OptimizeResult # type: ignore from ..__prior import Prior from ....models.__algorithm_exception import AlgorithmException -from .__estimators import probability_y0, probability_y1 +from .__estimators import log_likelihood def maximize_posterior( @@ -14,35 +14,34 @@ def maximize_posterior( prior: Prior, optimization_interval: tuple[float, float] = (-10, 10) ) -> float: - """_summary_ + """Get the maximum of the posterior distribution Args: a (np.ndarray): item parameter a - b (np.ndarray): item parameter b - c (np.ndarray): item parameter c - d (np.ndarray): item parameter d - response_pattern (np.ndarray): response pattern (simulated or user generated) - prior (Prior): prior distribution - optimization_interval (Tuple[float, float]): interval used for the optimization function Returns: float: Bayes Modal estimator for the given parameters """ - def log_posterior(mu) -> np.ndarray: - p1 = probability_y1(mu, a, b, c, d) - p0 = probability_y0(mu, a, b, c, d) + def log_posterior(mu): + log_likelihood_res = log_likelihood(mu, a, b, c, d, response_pattern) - log_likelihood = np.sum((response_pattern * np.log(p1 + 1e-300)) - + ((1 - response_pattern) * np.log(p0 + 1e-300))) # noqa: W503 - log_prior = np.log(prior.pdf(mu) + 1e-300) + if hasattr(prior, "logpdf"): + log_prior = prior.logpdf(mu) + else: + log_prior = np.log(np.clip(prior.pdf(mu), 1e-300, None)) - return log_likelihood + log_prior + log_post = log_likelihood_res + log_prior + + if not np.isfinite(log_post): + return -1e300 + else: + return float(log_post.ravel()[0]) result: OptimizeResult = minimize_scalar(lambda mu: -log_posterior(mu), bounds=optimization_interval, diff --git a/adaptivetesting/math/estimators/__functions/__estimators.py b/adaptivetesting/math/estimators/__functions/__estimators.py index 8dd1b11..21e4644 100644 --- a/adaptivetesting/math/estimators/__functions/__estimators.py +++ b/adaptivetesting/math/estimators/__functions/__estimators.py @@ -68,13 +68,9 @@ def probability_y0(mu: np.ndarray, Args: mu (np.ndarray): latent ability level - a (np.ndarray): item discrimination parameter - b (np.ndarray): item difficulty parameter - c (np.ndarray): pseudo guessing parameter - d (np.ndarray): inattention parameter Returns: @@ -96,14 +92,11 @@ def likelihood(mu: np.ndarray, Args: mu (np.ndarray): ability level - a (np.ndarray): item discrimination parameter - b (np.ndarray): item difficulty parameter - c (np.ndarray): pseudo guessing parameter - d (np.ndarray): inattention parameter + response_pattern (np.ndarray): response pattern of the answered items Returns: float: negative likelihood value of given ability value @@ -120,6 +113,33 @@ def likelihood(mu: np.ndarray, return -np.prod(terms) +def log_likelihood(mu: np.ndarray, + a: np.ndarray, + b: np.ndarray, + c: np.ndarray, + d: np.ndarray, + response_pattern: np.ndarray): + """Log-likelihood function of the 4-PL model. + For optimization purposes, the function returns the negative value of the likelihood function. + + Args: + mu (np.ndarray): ability level + a (np.ndarray): item discrimination parameter + b (np.ndarray): item difficulty parameter + c (np.ndarray): pseudo guessing parameter + d (np.ndarray): inattention parameter + response_pattern (np.ndarray): response pattern of the answered items + + Returns: + float: log-likelihood value of given ability value + """ + p1 = probability_y1(mu, a, b, c, d) + p0 = probability_y0(mu, a, b, c, d) + result = np.sum((response_pattern * np.log(p1 + 1e-300)) + ((1 - response_pattern) * np.log(p0 + 1e-300))) + + return result + + def maximize_likelihood_function(a: np.ndarray, b: np.ndarray, c: np.ndarray, @@ -131,22 +151,18 @@ def maximize_likelihood_function(a: np.ndarray, Args: a (np.ndarray): item discrimination parameter - b (np.ndarray): item difficulty parameter - c (np.ndarray): pseudo guessing parameter - d (np.ndarray): inattention parameter - response_pattern (np.ndarray): response pattern of the item border (tuple[float, float], optional): border of the optimization interval. - Defaults to (-10, 10). + Defaults to (-10, 10). Raises: AlgorithmException: if the optimization fails or the response - pattern consists of only one type of response. + pattern consists of only one type of response. AlgorithmException: if the optimization fails or the response - pattern consists of only one type of response. + pattern consists of only one type of response. Returns: float: optimized ability value @@ -155,11 +171,10 @@ def maximize_likelihood_function(a: np.ndarray, if len(set(response_pattern.tolist())) == 1: raise AlgorithmException( "Response pattern is invalid. It consists of only one type of response.") - raise AlgorithmException( - "Response pattern is invalid. It consists of only one type of response.") - result: OptimizeResult = minimize_scalar(likelihood, args=(a, b, c, d, response_pattern), - bounds=border, method='bounded') # type: ignore + result: OptimizeResult = minimize_scalar(lambda mu: -log_likelihood(mu, a, b, c, d, response_pattern), + bounds=border, + method='bounded') # type: ignore if not result.success: raise AlgorithmException(f"Optimization failed: {result.message}") diff --git a/adaptivetesting/math/estimators/__init__.py b/adaptivetesting/math/estimators/__init__.py index 1193efe..4555085 100644 --- a/adaptivetesting/math/estimators/__init__.py +++ b/adaptivetesting/math/estimators/__init__.py @@ -1,7 +1,7 @@ from .__ml_estimation import MLEstimator from .__bayes_modal_estimation import BayesModal from .__expect_a_posteriori import ExpectedAPosteriori -from .__prior import Prior, NormalPrior, CustomPrior, CustomPriorException +from .__prior import Prior, NormalPrior, CustomPrior, CustomPriorException, EmpiricalPrior, SkewNormalPrior from .__functions.__estimators import probability_y0, probability_y1, maximize_likelihood_function, likelihood from .__functions.__bayes import maximize_posterior from .__test_information import test_information_function, item_information_function, prior_information_function diff --git a/adaptivetesting/math/estimators/__prior.py b/adaptivetesting/math/estimators/__prior.py index fb1b630..4e311b9 100644 --- a/adaptivetesting/math/estimators/__prior.py +++ b/adaptivetesting/math/estimators/__prior.py @@ -1,6 +1,6 @@ import numpy as np from abc import ABC, abstractmethod -from scipy.stats import norm, rv_continuous +from scipy.stats import norm, skewnorm, rv_continuous, gaussian_kde class Prior(ABC): @@ -24,7 +24,7 @@ def pdf(self, x: float | np.ndarray) -> np.ndarray: class NormalPrior(Prior): def __init__(self, mean: float, sd: float): - """Normal distribution as prior for Bayes Modal estimation + """Normal distribution as prior for Bayes Modal or EAP estimation Args: mean (float): mean of the distribution @@ -45,6 +45,43 @@ def pdf(self, x: float | np.ndarray) -> np.ndarray: ndarray: function value """ return norm.pdf(x, self.mean, self.sd) # type: ignore + + def logpdf(self, x: float | np.ndarray): + return norm.logpdf(x, self.mean, self.sd) + + +class SkewNormalPrior(Prior): + def __init__(self, skewness: float, loc: float, scale: float): + """Skew normal distribution as prior for Bayes Modal or EAP estimation + + Args: + loc (float): location parameter + + scale (float): scale parameter + """ + super().__init__() + self.skewness = skewness + self.loc = loc + self.scale = scale + + def pdf(self, x): + """Probability density function for a prior distribution + + Args: + x (float | np.ndarray): point at which to calculate the function value + + Returns: + ndarray: function value + """ + return skewnorm.pdf(x, + self.skewness, + loc=self.loc, + scale=self.scale) + + def logpdf(self, x: float | np.ndarray): + return skewnorm.logpdf(x, self.skewness, + loc=self.loc, + scale=self.scale) class CustomPrior(Prior): @@ -55,12 +92,12 @@ def __init__(self, scale: float = 1): """This class is for using a custom prior in the ability estimation in Bayes Modal or Expected a Posteriori. - Any continous, univariate random variable from the scipy.stats module can be used. + Any continuous, univariate random variable from the scipy.stats module can be used. However, you have to consult to the scipy documentation for the required parameters for the probability density function (pdf) of that particular random variable. Args: - random_variable (rv_continuous): Any continous, univariate random variable from the scipy.stats module. + random_variable (rv_continuous): Any continuous, univariate random variable from the scipy.stats module. *args (float): Custom parameters required to calculate the pdf of that specific random variable. @@ -84,6 +121,58 @@ def pdf(self, x: float | np.ndarray) -> np.ndarray: return np.array(result) +class EmpiricalPrior(Prior): + """ + A prior distribution constructed from empirical samples using a kernel density estimate (KDE). + This class wraps scipy.stats.gaussian_kde to provide a nonparametric prior estimated + from observed data. The KDE is built from the provided dataset at initialization and + used to evaluate the probability density (pdf) at query points. + + + Parameters + ---------- + dataset : np.ndarray + Samples used to fit the prior. For univariate data this can be a 1-D array of + shape (n_samples,). For multivariate data, provide an array of shape (d, n_samples) + (as expected by scipy.stats.gaussian_kde) or an array that can be transposed to + that shape. The dataset must contain at least one sample. + + Attributes + ---------- + kde : scipy.stats.kde.gaussian_kde + The fitted kernel density estimator built from the provided dataset. + + """ + def __init__(self, dataset: np.ndarray): + """ + Args: + dataset (np.ndarray): Samples used to fit the prior. For univariate data this can be a 1-D array of + shape (n_samples,). For multivariate data, provide an array of shape (d, n_samples) + (as expected by scipy.stats.gaussian_kde) or an array that can be transposed to + that shape. The dataset must contain at least one sample. + """ + super().__init__() + + self.kde = gaussian_kde(dataset) + + def pdf(self, x): + """Evaluate the estimated probability density at x. Accepts inputs compatible with + scipy.stats.gaussian_kde.__call__: for univariate data x can be a float, 1-D array + of points, or similarly shaped array for multivariate queries. + + Args: + x (float | np.ndarray): point at which to evaluate the pdf + + Raises: + ValueError: + If `dataset` is empty. + numpy.linalg.LinAlgError: + If the covariance estimate used by gaussian_kde is singular (this is raised by + scipy's implementation when the data are degenerate). + """ + return self.kde(x) + + class CustomPriorException(Exception): """This exception can be used is the custom prior is not correctly specified. diff --git a/adaptivetesting/math/estimators/__test_information.py b/adaptivetesting/math/estimators/__test_information.py index 1fd02ba..90bb0e2 100644 --- a/adaptivetesting/math/estimators/__test_information.py +++ b/adaptivetesting/math/estimators/__test_information.py @@ -13,6 +13,19 @@ def item_information_function( c: np.ndarray, d: np.ndarray ) -> np.ndarray: + """ + Calculates the item information for given parameters. + + Args: + mu (np.ndarray): ability level + a (np.ndarray): discrimination parameter + b (np.ndarray): difficulty parameter + c (np.ndarray): guessing parameter + d (np.ndarray): slipping parameter + + Returns: + np.ndarray: item information + """ p_y1 = probability_y1(mu, a, b, c, d) # Clip probabilities @@ -34,10 +47,12 @@ def prior_information_function(prior: Prior, of the specified prior Args: - prior (Prior): _description_ + prior (Prior): prior distribution + optimization_interval (tuple[float, float], optional): interval used for numerical integration. + Defaults to (-10, 10). Returns: - np.ndarray: _description_ + np.ndarray: calculated fisher information of the prior """ def log_prior(x): epsilon = 1e-12 # Small value to avoid log(0) @@ -75,11 +90,13 @@ def test_information_function( b (np.ndarray): difficulty parameter c (np.ndarray): guessing parameter d (np.ndarray): slipping parameter + prior (Prior | None, optional): prior distribution. Defaults to None. + optimization_interval (tuple[float, float], optional): interval used for numerical integration. Returns: float: test information """ - # calcualte information for every item + # calculate information for every item item_information = np.vectorize(item_information_function)( mu, a, b, c, d ) diff --git a/adaptivetesting/models/__adaptive_test.py b/adaptivetesting/models/__adaptive_test.py index 19d9816..039a845 100644 --- a/adaptivetesting/models/__adaptive_test.py +++ b/adaptivetesting/models/__adaptive_test.py @@ -25,15 +25,10 @@ def __init__(self, item_pool: ItemPool, Args: item_pool (ItemPool): item pool used for the test - simulation_id (str): simulation id - participant_id (str): participant id - true_ability_level (float): true ability level (must always be set) - initial_ability_level (float): initially assumed ability level - simulation (bool): will the test be simulated. If it is simulated and a response pattern is not yet set in the item pool, it will be generated for the given true ability level. @@ -63,7 +58,7 @@ def __init__(self, item_pool: ItemPool, # if simulation is True # generate a response pattern if # it is not yet set in the item pool - if simulation is True: + if simulation: if self.item_pool.simulated_responses is None: if self.true_ability_level is not None: self.item_pool.simulated_responses = generate_response_pattern( @@ -93,18 +88,6 @@ def get_answered_items(self) -> List[TestItem]: """ return self.answered_items - # def get_ability_se(self) -> float: - # """ - # Calculate the current standard error - # of the ability estimation. - - # Returns: - # float: standard error of the ability estimation - - # """ - # answered_items = self.get_answered_items() - # return standard_error(answered_items, self.ability_level) - @abc.abstractmethod def get_next_item(self) -> TestItem: """Select next item. @@ -151,7 +134,7 @@ def run_test_once(self): # check if simulation is running response = None - if self.simulation is True: + if self.simulation: response = self.item_pool.get_item_response(item) else: # not simulation diff --git a/adaptivetesting/models/__item_pool.py b/adaptivetesting/models/__item_pool.py index ced5deb..365c670 100644 --- a/adaptivetesting/models/__item_pool.py +++ b/adaptivetesting/models/__item_pool.py @@ -101,15 +101,10 @@ def load_from_list( Args: a (List[float]): discrimination parameter - b (List[float]): difficulty parameter - c (List[float]): guessing parameter - d (List[float]): slipping parameter - simulated_responses (List[int]): simulated responses - ids (List[int]): item IDs. If the argument is set to `None` all items are numbered in the order in which they are passed to the function. @@ -244,10 +239,10 @@ def load_from_dataframe(source: DataFrame) -> "ItemPool": Args: - source (DataFrame): _description_ + source (DataFrame): DataFrame containing item parameters. Returns: - ItemPool: _description_ + ItemPool: item pool """ # check if columns are present diff --git a/adaptivetesting/models/__item_selection_exception.py b/adaptivetesting/models/__item_selection_exception.py index 9ca309f..b989eb9 100644 --- a/adaptivetesting/models/__item_selection_exception.py +++ b/adaptivetesting/models/__item_selection_exception.py @@ -1,3 +1,4 @@ class ItemSelectionException(Exception): + """Custom exception for item selection errors in adaptive testing.""" def __init__(self, *args): super().__init__(*args) diff --git a/adaptivetesting/services/__estimator_interface.py b/adaptivetesting/services/__estimator_interface.py index d97bee6..51b899f 100644 --- a/adaptivetesting/services/__estimator_interface.py +++ b/adaptivetesting/services/__estimator_interface.py @@ -16,7 +16,6 @@ def __init__(self, Args: response_pattern (List[int]): list of responses (0: wrong, 1:right) - items (List[TestItem]): list of answered items """ if type(response_pattern) is not np.ndarray: diff --git a/adaptivetesting/services/__item_selection_protocol.py b/adaptivetesting/services/__item_selection_protocol.py index c9cd042..bb9944b 100644 --- a/adaptivetesting/services/__item_selection_protocol.py +++ b/adaptivetesting/services/__item_selection_protocol.py @@ -13,9 +13,7 @@ class ItemSelectionStrategy(Protocol): Args: items (list[TestItem]): The list of available test items to select from. - ability (float): The current ability estimate of the test taker. - **kwargs: Additional keyword arguments that may be required by specific selection strategies. Returns: diff --git a/adaptivetesting/services/__test_results_interface.py b/adaptivetesting/services/__test_results_interface.py index 21745b3..af01038 100644 --- a/adaptivetesting/services/__test_results_interface.py +++ b/adaptivetesting/services/__test_results_interface.py @@ -4,14 +4,12 @@ class ITestResults(ABC): - def __init__(self, simulation_id: str, participant_id: str): """Interface for saving and reading test results. This interface may mainly be used for saving simulation results. Args: simulation_id (str): The simulation ID. Name of the results file. - participant_id (str): The participant ID. """ self.simulation_id = simulation_id diff --git a/adaptivetesting/simulation/__simulation.py b/adaptivetesting/simulation/__simulation.py index 22a1102..e0dc989 100644 --- a/adaptivetesting/simulation/__simulation.py +++ b/adaptivetesting/simulation/__simulation.py @@ -18,7 +18,6 @@ def __init__(self, Args: test (AdaptiveTest): instance of an adaptive test implementation (see implementations module) - test_result_output (ResultOutputFormat): test results output format """ self.test = test @@ -41,7 +40,7 @@ def simulate(self, """ stop_test = False - while stop_test is False: + while not stop_test: # run test self.test.run_test_once() # check available items @@ -82,12 +81,9 @@ def setup_simulation_and_start(test: AdaptiveTest, Args: test (AdaptiveTest): The adaptive test instance to be simulated. - test_result_output (ResultOutputFormat): The format or handler for outputting test results. - criterion (StoppingCriterion | list[StoppingCriterion]): The criterion used to determine when the simulation should stop. - value (float): The value associated with the stopping criterion (e.g., maximum number of items, target standard error). """ @@ -99,7 +95,7 @@ def setup_simulation_and_start(test: AdaptiveTest, simulation.save_test_results() -class SimulationPool(): +class SimulationPool: def __init__(self, adaptive_tests: list[AdaptiveTest], test_result_output: ResultOutputFormat, @@ -110,9 +106,7 @@ def __init__(self, Args: adaptive_tests (list[AdaptiveTest]): List of adaptive test instances to be simulated. - - test_results_output (ResultOutputFormat): Format for outputting test results. - + test_result_output (ResultOutputFormat): Format for outputting test results. criterion (StoppingCriterion | list[StoppingCriterion]): Stopping criterion or list of criteria for the simulations. @@ -138,15 +132,15 @@ def start(self): value=self.value ) # check for platform - # this is because multiprocessing is not as well supported on windows + # this is because multiprocessing is not as well-supported on windows # therefore, multithreading is used instead if platform.system() == "Windows": with ThreadPoolExecutor(max_workers=60) as executor: - futures = [executor.submit(func, test) for test in self.adaptive_tests] + futures = [executor.submit(func, (test,)) for test in self.adaptive_tests] for _ in tqdm(as_completed(futures), total=len(futures)): pass else: with ProcessPoolExecutor() as executor: - futures = [executor.submit(func, test) for test in self.adaptive_tests] + futures = [executor.submit(func, (test,)) for test in self.adaptive_tests] for _ in tqdm(as_completed(futures), total=len(futures)): pass diff --git a/adaptivetesting/tests/test_bayes_modal.py b/adaptivetesting/tests/test_bayes_modal.py index 70d57fc..3fe14f1 100644 --- a/adaptivetesting/tests/test_bayes_modal.py +++ b/adaptivetesting/tests/test_bayes_modal.py @@ -1,9 +1,14 @@ import unittest from adaptivetesting.models import ItemPool -from adaptivetesting.math.estimators import BayesModal, NormalPrior, CustomPrior +from adaptivetesting.math.estimators import ( + BayesModal, + NormalPrior, + CustomPrior, + SkewNormalPrior, + EmpiricalPrior) import pandas as pd from scipy.stats import beta -from adaptivetesting.math.estimators import CustomPriorException +import numpy as np class TestBayesModal(unittest.TestCase): @@ -18,9 +23,7 @@ def test_estimation_4pl(self): "d": [0.87, 0.93, 1] }) item_pool = ItemPool.load_from_dataframe(items) - item_pool = ItemPool.load_from_dataframe(items) - response_pattern = [0, 1, 0] response_pattern = [0, 1, 0] estimator = BayesModal( response_pattern=response_pattern, @@ -30,7 +33,6 @@ def test_estimation_4pl(self): ) result = estimator.get_estimation() - self.assertAlmostEqual(result, -0.4741753, 4) @@ -61,29 +63,132 @@ def test_estimation_4pl(self): ) result = estimator.get_estimation() - self.assertAlmostEqual(result, 0.01, places=3) + self.assertAlmostEqual(result, 0, places=2) - def test_wrong_prior_implementation(self): - items = pd.DataFrame({"a": [1.3024, 1.078, 0.8758, 0.5571, 1.225, 0.991, 0.9968, 1.1888, 1.1642, 1.1188], - "b": [-0.6265, 0.1836, -0.8356, 1.5953, 0.3295, -0.8205, 0.4874, 0.7383, 0.5758, -0.3054], - "c": [0.2052, 0.1618, 0.1957, 0.1383, 0.1324, 0.1973, 0.0058, 0.1193, 0.1831, 0.1732], - "d": [0.8694, 0.9653, 0.8595, 0.8112, 0.7677, 0.7749, 0.8291, 0.8797, 0.9155, 0.8517]}) - item_pool = ItemPool.load_from_dataframe(items) - response_pattern = [0, 0, 1, 1, 1, 0, 0, 0, 0, 1] +class TestSkewNormalPriorIntegration(unittest.TestCase): + def setUp(self): + # simple 3-item pool used elsewhere in tests + items = pd.DataFrame({ + "a": [1.32, 1.07, 0.84], + "b": [-0.63, 0.18, -0.84], + "c": [0.17, 0.10, 0.19], + "d": [0.87, 0.93, 1.0] + }) + self.item_pool = ItemPool.load_from_dataframe(items).test_items + self.response_pattern = [0, 1, 0] + + def test_skewnormal_pdf_basic(self): + prior = SkewNormalPrior(skewness=4.0, loc=0.0, scale=1.0) + # scalar + p0 = prior.pdf(0.0) + self.assertTrue(np.isfinite(np.asarray(p0)).all()) + self.assertGreaterEqual(float(np.asarray(p0).ravel()[0]), 0.0) + # array + xs = np.array([-2.0, 0.0, 2.0]) + pa = prior.pdf(xs) + pa = np.asarray(pa) + self.assertEqual(pa.shape[-1], xs.shape[-1]) + self.assertTrue(np.all(pa >= 0.0)) + + def test_skewnormal_changes_estimate_vs_normal(self): + prior_norm = NormalPrior(0.0, 1.0) + prior_skew = SkewNormalPrior(skewness=6.0, loc=0.0, scale=1.0) + + est_norm = BayesModal( + response_pattern=self.response_pattern, + items=self.item_pool, + prior=prior_norm, + optimization_interval=(-4, 4), + ).get_estimation() + + est_skew = BayesModal( + response_pattern=self.response_pattern, + items=self.item_pool, + prior=prior_skew, + optimization_interval=(-4, 4), + ).get_estimation() + + self.assertTrue(np.isfinite(est_norm)) + self.assertTrue(np.isfinite(est_skew)) + # skew prior should typically shift the estimate compared to symmetric prior + self.assertNotAlmostEqual(est_norm, est_skew, places=6) + + +class TestEmpiricalPriorIntegration(unittest.TestCase): + def setUp(self): + rng = np.random.default_rng(0) + self.dataset = rng.normal(loc=0.0, scale=1.0, size=300) + self.prior = EmpiricalPrior(self.dataset) + items = pd.DataFrame({ + "a": [1.0, 1.2, 0.9], + "b": [0.0, 0.5, -0.5], + "c": [0.2, 0.2, 0.2], + "d": [1.0, 0.9, 0.95] + }) + self.item_pool = ItemPool.load_from_dataframe(items).test_items + self.response_pattern = [1, 0, 1] - # create custom prior - class WrongPrior(NormalPrior): - pass + def test_empirical_pdf_scalar_and_array(self): + scalar = self.prior.pdf(0.0) + self.assertTrue(np.isfinite(np.asarray(scalar)).all()) + self.assertGreaterEqual(float(np.asarray(scalar).ravel()[0]), 0.0) - prior = WrongPrior(0, 1) - + xs = np.array([-1.0, 0.0, 1.0]) + vals = np.asarray(self.prior.pdf(xs)) + # gaussian_kde returns shape (n_points,) for 1-D data + self.assertEqual(vals.shape[-1], xs.shape[-1]) + self.assertTrue(np.all(vals >= 0.0)) + + def test_empirical_prior_works_with_bayesmodal(self): estimator = BayesModal( - response_pattern=response_pattern, - items=item_pool.test_items, - prior=prior, - optimization_interval=(-10, 10) + response_pattern=self.response_pattern, + items=self.item_pool, + prior=self.prior, + optimization_interval=(-4, 4), ) + result = estimator.get_estimation() + self.assertTrue(np.isfinite(result)) - with self.assertRaises(CustomPriorException): - estimator.get_estimation() + +class TestNumericalStability(unittest.TestCase): + def setUp(self): + # moderate 4PL pool for stability checks + self.items = pd.DataFrame({ + "a": [1.3024, 1.078, 0.8758, 0.5571], + "b": [-0.6265, 0.1836, -0.8356, 1.5953], + "c": [0.2052, 0.1618, 0.1957, 0.1383], + "d": [0.8694, 0.9653, 0.8595, 0.8112] + }) + self.item_pool = ItemPool.load_from_dataframe(self.items).test_items + + def test_tight_normal_prior_keeps_estimate_near_mean(self): + # extremely tight prior around mean 2.0 should pull estimate close to 2.0 + prior = NormalPrior(mean=2.0, sd=1e-6) + responses = [1, 1, 1, 1] # all-correct + estimator = BayesModal( + response_pattern=responses, + items=self.item_pool, + prior=prior, + optimization_interval=(-10, 10), + ) + result = estimator.get_estimation() + self.assertTrue(np.isfinite(result)) + # result should be extremely close to the prior mean + self.assertAlmostEqual(result, 2.0, places=2) + + def test_extreme_response_patterns_do_not_crash(self): + # all incorrect + prior = NormalPrior(0.0, 1.0) + for responses in ([0, 0, 0, 0], [1, 1, 1, 1]): + estimator = BayesModal( + response_pattern=responses, + items=self.item_pool, + prior=prior, + optimization_interval=(-10, 10), + ) + result = estimator.get_estimation() + self.assertTrue(np.isfinite(result)) + # result lies within the provided optimization interval + self.assertGreaterEqual(result, -10) + self.assertLessEqual(result, 10) diff --git a/adaptivetesting/tests/test_eap.py b/adaptivetesting/tests/test_eap.py index 3f53e9d..9ee33fe 100644 --- a/adaptivetesting/tests/test_eap.py +++ b/adaptivetesting/tests/test_eap.py @@ -1,10 +1,14 @@ -from unittest import TestCase +import unittest import pandas as pd +import numpy as np from adaptivetesting.models import ItemPool -from adaptivetesting.math.estimators import ExpectedAPosteriori, NormalPrior +from adaptivetesting.math.estimators import (ExpectedAPosteriori, + NormalPrior, + SkewNormalPrior, + EmpiricalPrior) -class TestEAP(TestCase): +class TestEAP(unittest.TestCase): def test_estimation_4pl(self): items = pd.DataFrame({ "a": [1.32, 1.07, 0.84], @@ -25,3 +29,131 @@ def test_estimation_4pl(self): result = estimator.get_estimation() self.assertAlmostEqual(result, -0.4565068, 4) + + +class TestSkewNormalPriorIntegration(unittest.TestCase): + def setUp(self): + # simple 3-item pool used elsewhere in tests + items = pd.DataFrame({ + "a": [1.32, 1.07, 0.84], + "b": [-0.63, 0.18, -0.84], + "c": [0.17, 0.10, 0.19], + "d": [0.87, 0.93, 1.0] + }) + self.item_pool = ItemPool.load_from_dataframe(items).test_items + self.response_pattern = [0, 1, 0] + + def test_skewnormal_pdf_basic(self): + prior = SkewNormalPrior(skewness=4.0, loc=0.0, scale=1.0) + # scalar + p0 = prior.pdf(0.0) + self.assertTrue(np.isfinite(np.asarray(p0)).all()) + self.assertGreaterEqual(float(np.asarray(p0).ravel()[0]), 0.0) + # array + xs = np.array([-2.0, 0.0, 2.0]) + pa = prior.pdf(xs) + pa = np.asarray(pa) + self.assertEqual(pa.shape[-1], xs.shape[-1]) + self.assertTrue(np.all(pa >= 0.0)) + + def test_skewnormal_changes_estimate_vs_normal(self): + prior_norm = NormalPrior(0.0, 1.0) + prior_skew = SkewNormalPrior(skewness=6.0, loc=0.0, scale=1.0) + + est_norm = ExpectedAPosteriori( + response_pattern=self.response_pattern, + items=self.item_pool, + prior=prior_norm, + optimization_interval=(-4, 4), + ).get_estimation() + + est_skew = ExpectedAPosteriori( + response_pattern=self.response_pattern, + items=self.item_pool, + prior=prior_skew, + optimization_interval=(-4, 4), + ).get_estimation() + + self.assertTrue(np.isfinite(est_norm)) + self.assertTrue(np.isfinite(est_skew)) + # skew prior should typically shift the estimate compared to symmetric prior + self.assertNotAlmostEqual(est_norm, est_skew, places=6) + + +class TestEmpiricalPriorIntegration(unittest.TestCase): + def setUp(self): + rng = np.random.default_rng(0) + self.dataset = rng.normal(loc=0.0, scale=1.0, size=300) + self.prior = EmpiricalPrior(self.dataset) + items = pd.DataFrame({ + "a": [1.0, 1.2, 0.9], + "b": [0.0, 0.5, -0.5], + "c": [0.2, 0.2, 0.2], + "d": [1.0, 0.9, 0.95] + }) + self.item_pool = ItemPool.load_from_dataframe(items).test_items + self.response_pattern = [1, 0, 1] + + def test_empirical_pdf_scalar_and_array(self): + scalar = self.prior.pdf(0.0) + self.assertTrue(np.isfinite(np.asarray(scalar)).all()) + self.assertGreaterEqual(float(np.asarray(scalar).ravel()[0]), 0.0) + + xs = np.array([-1.0, 0.0, 1.0]) + vals = np.asarray(self.prior.pdf(xs)) + # gaussian_kde returns shape (n_points,) for 1-D data + self.assertEqual(vals.shape[-1], xs.shape[-1]) + self.assertTrue(np.all(vals >= 0.0)) + + def test_empirical_prior_works_with_ExpectedAPosteriori(self): + estimator = ExpectedAPosteriori( + response_pattern=self.response_pattern, + items=self.item_pool, + prior=self.prior, + optimization_interval=(-4, 4), + ) + result = estimator.get_estimation() + self.assertTrue(np.isfinite(result)) + + +class TestNumericalStability(unittest.TestCase): + def setUp(self): + # moderate 4PL pool for stability checks + self.items = pd.DataFrame({ + "a": [1.3024, 1.078, 0.8758, 0.5571], + "b": [-0.6265, 0.1836, -0.8356, 1.5953], + "c": [0.2052, 0.1618, 0.1957, 0.1383], + "d": [0.8694, 0.9653, 0.8595, 0.8112] + }) + self.item_pool = ItemPool.load_from_dataframe(self.items).test_items + + def test_tight_normal_prior_keeps_estimate_near_mean(self): + # extremely tight prior around mean 2.0 should pull estimate close to 2.0 + prior = NormalPrior(mean=2.0, sd=1e-6) + responses = [1, 1, 1, 1] # all-correct + estimator = ExpectedAPosteriori( + response_pattern=responses, + items=self.item_pool, + prior=prior, + optimization_interval=(-10, 10), + ) + result = estimator.get_estimation() + self.assertTrue(np.isfinite(result)) + # result should be extremely close to the prior mean + self.assertAlmostEqual(result, 2.0, delta=0.1) + + def test_extreme_response_patterns_do_not_crash(self): + # all incorrect + prior = NormalPrior(0.0, 1.0) + for responses in ([0, 0, 0, 0], [1, 1, 1, 1]): + estimator = ExpectedAPosteriori( + response_pattern=responses, + items=self.item_pool, + prior=prior, + optimization_interval=(-10, 10), + ) + result = estimator.get_estimation() + self.assertTrue(np.isfinite(result)) + # result lies within the provided optimization interval + self.assertGreaterEqual(result, -10) + self.assertLessEqual(result, 10) diff --git a/adaptivetesting/tests/test_priors.py b/adaptivetesting/tests/test_priors.py new file mode 100644 index 0000000..bb056b3 --- /dev/null +++ b/adaptivetesting/tests/test_priors.py @@ -0,0 +1,56 @@ +import unittest +import numpy as np +from adaptivetesting.math.estimators import EmpiricalPrior, SkewNormalPrior + + +class TestEmpiricalPrior(unittest.TestCase): + def setUp(self): + self.rng = np.random.default_rng(0) + # univariate dataset of moderate size to avoid singular covariance + self.dataset = self.rng.normal(loc=0.0, scale=1.0, size=200) + + def test_pdf_array_and_scalar(self): + prior = EmpiricalPrior(self.dataset) + points = np.array([-1.0, 0.0, 1.0]) + dens = prior.pdf(points) + # gaussian_kde returns an array of densities for array input + self.assertEqual(np.asarray(dens).shape[-1], points.shape[-1]) + self.assertTrue(np.all(np.asarray(dens) >= 0.0)) + + # scalar input - ensure convertible to float and non-negative + scalar_d = prior.pdf(0.0) + scalar_val = float(np.asarray(scalar_d).ravel()[0]) + self.assertTrue(np.isfinite(scalar_val)) + self.assertGreaterEqual(scalar_val, 0.0) + + def test_pdf_peak_near_mean(self): + prior = EmpiricalPrior(self.dataset) + mean = float(np.mean(self.dataset)) + dens_mean = float(np.asarray(prior.pdf(np.array([mean]))).ravel()[0]) + dens_far = float(np.asarray(prior.pdf(np.array([mean + 5.0]))).ravel()[0]) + self.assertGreater(dens_mean, dens_far) + + +class TestSkewNormalPrior(unittest.TestCase): + def test_pdf_scalar_and_array(self): + prior = SkewNormalPrior(skewness=2.0, loc=0.0, scale=1.0) + scalar = prior.pdf(0.0) + scalar_val = float(np.asarray(scalar).ravel()[0]) if hasattr(scalar, "__array__") else float(scalar) + self.assertTrue(np.isfinite(scalar_val)) + self.assertGreaterEqual(scalar_val, 0.0) + + arr = np.array([-3.0, 0.0, 3.0]) + dens_arr = prior.pdf(arr) + dens_arr = np.asarray(dens_arr) + self.assertEqual(dens_arr.shape[-1], arr.shape[-1]) + self.assertTrue(np.all(dens_arr >= 0.0)) + + def test_pdf_decreases_far_from_loc(self): + prior = SkewNormalPrior(skewness=0.0, loc=1.0, scale=0.5) + dens_loc = float(np.asarray(prior.pdf(1.0)).ravel()[0]) + dens_far = float(np.asarray(prior.pdf(1.0 + 5.0)).ravel()[0]) + self.assertGreater(dens_loc, dens_far) + + +if __name__ == "__main__": + unittest.main() From bcf4bc13d398110352a1752e3bd769c460ec370a Mon Sep 17 00:00:00 2001 From: condecon Date: Mon, 1 Dec 2025 10:47:09 +0100 Subject: [PATCH 044/137] progress exposure control --- .../implementations/__test_assembler.py | 27 +++++++++++++++++-- .../exposure_control/__exposure_control.py | 4 +-- .../__mpi_exposure_control.py | 17 ++++++++++++ .../math/exposure_control/__randomesque.py | 3 +-- 4 files changed, 45 insertions(+), 6 deletions(-) create mode 100644 adaptivetesting/math/exposure_control/__mpi_exposure_control.py diff --git a/adaptivetesting/implementations/__test_assembler.py b/adaptivetesting/implementations/__test_assembler.py index 436a8cf..4d2452f 100644 --- a/adaptivetesting/implementations/__test_assembler.py +++ b/adaptivetesting/implementations/__test_assembler.py @@ -15,6 +15,7 @@ from ..math.estimators.__prior import Prior from copy import deepcopy import inspect +from ..math.exposure_control.__exposure_control import EXPOSURE_CONTROL class EstimatorArgs(TypedDict): @@ -37,6 +38,15 @@ class ContentBalancingArgs(TypedDict): on the specific states and progress of the test.""" +class ExposureControlArgs(TypedDict): + constraints: list[Constraint] | None + """Constraints applied for Maximum Priority Index exposure control.""" + n_items: int | None + """number of items to select for Randomesque items selection""" + seed: int | None + """random seed for the final item selection during Randomesque item selection""" + + class TestAssembler(AdaptiveTest): """ TestAssembler is a subclass of AdaptiveTest designed to assemble and administer adaptive tests, @@ -81,6 +91,8 @@ def __init__(self, item_selector_args: dict[str, Any] = {}, content_balancing: None | CONTENT_BALANCING = None, content_balancing_args: ContentBalancingArgs | None = None, + exposure_control: None | EXPOSURE_CONTROL = None, + exposure_control_args: None | ExposureControlArgs = None, pretest: bool = False, pretest_seed: int | None = None, true_ability_level=None, @@ -130,6 +142,8 @@ def __init__(self, self.__item_selector_args = item_selector_args self.content_balancing = content_balancing self.content_balancing_args = content_balancing_args + self.exposure_control = exposure_control + self.exposure_control_args = exposure_control_args self.__pretest = pretest self.__pretest_seed = pretest_seed @@ -193,6 +207,8 @@ def get_next_item(self) -> TestItem: Selects and returns the next test item based on the current ability level and item selector strategy. If a content balancing strategy is specified, the item selection strategy will be ignored. Instead, the item selected by the content balancing strategy will be returned. + This also applies to exposure control. + However, content balancing and exposure control cannot be specified at the same time Returns: TestItem: The next item to be administered in the test, as determined by the item selector. @@ -201,7 +217,8 @@ def get_next_item(self) -> TestItem: Any exceptions raised by the item selector function. """ item: TestItem | None - if self.content_balancing is None: + if self.content_balancing is None and self.exposure_control_args is None: + # content balancing an exposure control are not specified # filter item selection args sig = inspect.signature(self.__item_selector) allowed = set(sig.parameters.keys()) @@ -213,7 +230,10 @@ def get_next_item(self) -> TestItem: **filtered_item_selector_args ) return item - else: + elif self.content_balancing and self.exposure_control: + raise ValueError("Content balancing and exposure cannot be specified at the same time!") + # content balancing only + elif self.content_balancing and self.exposure_control is None: if self.content_balancing_args is not None: # which strategy has been selected if self.content_balancing == "WeightedPenaltyModel": @@ -256,6 +276,9 @@ def get_next_item(self) -> TestItem: return item else: raise ValueError("content_balancing_args cannot be None when using content balancing.") + # exposure control only + elif self.content_balancing is None and self.exposure_control: + raise ValueError(f"Something went wrong when selecting an item using {self.content_balancing}.") def run_test_once(self): diff --git a/adaptivetesting/math/exposure_control/__exposure_control.py b/adaptivetesting/math/exposure_control/__exposure_control.py index ccaeaa3..e57995e 100644 --- a/adaptivetesting/math/exposure_control/__exposure_control.py +++ b/adaptivetesting/math/exposure_control/__exposure_control.py @@ -1,10 +1,10 @@ from abc import ABC, abstractmethod -from typing import Literal +from typing import Literal, TypedDict from ... import TestItem from ...models.__adaptive_test import AdaptiveTest -type EXPOSURECONTROL = Literal["Randomesque"] +type EXPOSURE_CONTROL = Literal["Randomesque", "MaximumPriorityIndex"] class ExposureControl(ABC): diff --git a/adaptivetesting/math/exposure_control/__mpi_exposure_control.py b/adaptivetesting/math/exposure_control/__mpi_exposure_control.py new file mode 100644 index 0000000..3b43613 --- /dev/null +++ b/adaptivetesting/math/exposure_control/__mpi_exposure_control.py @@ -0,0 +1,17 @@ +from ..content_balancing.__maximum_priority_index import MaximumPriorityIndex +from .__exposure_control import ExposureControl + + +class MaximumPriorityIndexExposureControl(MaximumPriorityIndex, ExposureControl): + """This is a reimplementation of the Maximum Priority Index for Exposure Control. + For further information see `MaximumPriorityIndex`. + """ + def __init__(self, adaptive_test, constraints): + super().__init__(adaptive_test, constraints) + + def select_item(self): + """Select the next item to administer based on the maximum priority index method. + Returns: + TestItem: The selected test item. + """ + return super().select_item() \ No newline at end of file diff --git a/adaptivetesting/math/exposure_control/__randomesque.py b/adaptivetesting/math/exposure_control/__randomesque.py index a972146..02dd4ec 100644 --- a/adaptivetesting/math/exposure_control/__randomesque.py +++ b/adaptivetesting/math/exposure_control/__randomesque.py @@ -1,4 +1,4 @@ -from typing import Callable, Any +from typing import Callable, Any, TypedDict import random import numpy as np from .__exposure_control import ExposureControl @@ -6,7 +6,6 @@ from ...models.__test_item import TestItem from ..estimators.__test_information import item_information_function - class Randomesque(ExposureControl): def __init__(self, adaptive_test: AdaptiveTest, From c40363f3011eddd674a4ba3fedc0269cb70e58ce Mon Sep 17 00:00:00 2001 From: condecon Date: Mon, 1 Dec 2025 10:58:32 +0100 Subject: [PATCH 045/137] todo!! --- .../implementations/__test_assembler.py | 15 +++++++++++++++ .../exposure_control/__mpi_exposure_control.py | 5 ++++- 2 files changed, 19 insertions(+), 1 deletion(-) diff --git a/adaptivetesting/implementations/__test_assembler.py b/adaptivetesting/implementations/__test_assembler.py index 4d2452f..55e1c93 100644 --- a/adaptivetesting/implementations/__test_assembler.py +++ b/adaptivetesting/implementations/__test_assembler.py @@ -16,6 +16,8 @@ from copy import deepcopy import inspect from ..math.exposure_control.__exposure_control import EXPOSURE_CONTROL +from ..math.exposure_control.__randomesque import Randomesque +from ..math.exposure_control.__mpi_exposure_control import MaximumPriorityIndexExposureControl class EstimatorArgs(TypedDict): @@ -278,6 +280,19 @@ def get_next_item(self) -> TestItem: raise ValueError("content_balancing_args cannot be None when using content balancing.") # exposure control only elif self.content_balancing is None and self.exposure_control: + if self.exposure_control_args is None: + raise ValueError("exposure_control_args cannot be None when using exposure control.") + # which strategy has been selected + if self.exposure_control == "Randomesque": + # Randomesque + adaptive_test = deepcopy(self) + randomesque = Randomesque( + adaptive_test=adaptive_test, + n_items=self.exposure_control_args["n_items"], + seed=self.exposure_control_args["seed"] + ) + if self.exposure_control == "MaximumPriorityIndex": + raise ValueError(f"Something went wrong when selecting an item using {self.content_balancing}.") diff --git a/adaptivetesting/math/exposure_control/__mpi_exposure_control.py b/adaptivetesting/math/exposure_control/__mpi_exposure_control.py index 3b43613..e5d1734 100644 --- a/adaptivetesting/math/exposure_control/__mpi_exposure_control.py +++ b/adaptivetesting/math/exposure_control/__mpi_exposure_control.py @@ -14,4 +14,7 @@ def select_item(self): Returns: TestItem: The selected test item. """ - return super().select_item() \ No newline at end of file + return super().select_item() + + + ##### TODO: Das geht so noch nicht!!!! \ No newline at end of file From 3f1ee3d541557f9352b9a361c5073403c4c93b31 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Thu, 4 Dec 2025 15:50:51 +0100 Subject: [PATCH 046/137] use previously administered items for exposure control --- .../data/__read_prev_items_exp_cont.py | 35 ++++++++ .../implementations/__test_assembler.py | 28 +++++- .../exposure_control/__exposure_control.py | 6 +- .../__mpi_exposure_control.py | 71 +++++++++++++-- .../math/exposure_control/__randomesque.py | 3 +- adaptivetesting/models/__test_item.py | 19 ++++ .../tests/test_exposure_control_mpi.py | 87 +++++++++++++++++++ adaptivetesting/tests/test_load_test_items.py | 33 +++++++ 8 files changed, 272 insertions(+), 10 deletions(-) create mode 100644 adaptivetesting/data/__read_prev_items_exp_cont.py create mode 100644 adaptivetesting/tests/test_exposure_control_mpi.py diff --git a/adaptivetesting/data/__read_prev_items_exp_cont.py b/adaptivetesting/data/__read_prev_items_exp_cont.py new file mode 100644 index 0000000..dc8e026 --- /dev/null +++ b/adaptivetesting/data/__read_prev_items_exp_cont.py @@ -0,0 +1,35 @@ +from typing import Literal +from warnings import deprecated +from ..models.__test_item import TestItem +from ..models.__test_result import TestResult +from .__csv_context import CSVContext +from .__pickle_context import PickleContext + + +def read_prev_items(format: Literal["CSV", "PICKLE"], + test_id: str, + participant_ids: list[str]) -> list[TestItem]: + """Read previously administered items + to perform exposure control + """ + all_shown_test_items = [] + + for part_id in participant_ids: + test_results = read_single_participant(test_id, part_id, format) + items = [TestItem.from_dict(res.showed_item) for res in test_results] + + all_shown_test_items += items + + return all_shown_test_items + + +@deprecated("This function should be replaced after merging" + " with revision branch") +def read_single_participant(test_id: str, participant_id: str, format: Literal["CSV", "PICKLE"]) -> list[TestResult]: + context: CSVContext | PickleContext + if format == "CSV": + context = CSVContext(test_id, participant_id) + return context.load() + if format == "PICKLE": + context = PickleContext(test_id, participant_id) + return context.load() diff --git a/adaptivetesting/implementations/__test_assembler.py b/adaptivetesting/implementations/__test_assembler.py index 55e1c93..772a71d 100644 --- a/adaptivetesting/implementations/__test_assembler.py +++ b/adaptivetesting/implementations/__test_assembler.py @@ -8,6 +8,7 @@ from ..models.__algorithm_exception import AlgorithmException from ..implementations.__pre_test import PreTest from ..models.__test_result import TestResult +from ..models.__misc import ResultOutputFormat from ..services.__item_selection_protocol import ItemSelectionStrategy from ..math.content_balancing.__content_balancing import CONTENT_BALANCING from ..math.content_balancing.__weighted_penalty_model import WeightedPenaltyModel @@ -43,6 +44,12 @@ class ContentBalancingArgs(TypedDict): class ExposureControlArgs(TypedDict): constraints: list[Constraint] | None """Constraints applied for Maximum Priority Index exposure control.""" + participant_ids: list[str] | None + """Participant ids to read the previously administered test in order to + perform exposure control (MPI only). + """ + output_format: ResultOutputFormat | None + """Format in which the previous tests have been saved (MPI only).""" n_items: int | None """number of items to select for Randomesque items selection""" seed: int | None @@ -286,14 +293,33 @@ def get_next_item(self) -> TestItem: if self.exposure_control == "Randomesque": # Randomesque adaptive_test = deepcopy(self) + if self.exposure_control_args["seed"] is None or self.exposure_control_args["n_items"] is None: + raise ValueError("exposure_control_args are not correctly specified") randomesque = Randomesque( adaptive_test=adaptive_test, n_items=self.exposure_control_args["n_items"], seed=self.exposure_control_args["seed"] ) + + return randomesque.select_item() + if self.exposure_control == "MaximumPriorityIndex": - + if (self.exposure_control_args["participant_ids"] + is None or self.exposure_control_args["output_format"] is None): + raise ValueError("exposure_control_args are not correctly specified") + mpi = MaximumPriorityIndexExposureControl( + adaptive_test, + constraints=self.exposure_control_args["constraints"], + participant_ids=self.exposure_control_args["participant_ids"], + format=self.exposure_control_args["output_format"] + ) + selected_item = mpi.select_item() + if selected_item is None: + raise ItemSelectionException("Fatal! Not appropriated item was " + "selected using MPI for exposure control") + else: + return selected_item raise ValueError(f"Something went wrong when selecting an item using {self.content_balancing}.") def run_test_once(self): diff --git a/adaptivetesting/math/exposure_control/__exposure_control.py b/adaptivetesting/math/exposure_control/__exposure_control.py index e57995e..b8c1281 100644 --- a/adaptivetesting/math/exposure_control/__exposure_control.py +++ b/adaptivetesting/math/exposure_control/__exposure_control.py @@ -1,6 +1,6 @@ from abc import ABC, abstractmethod -from typing import Literal, TypedDict -from ... import TestItem +from typing import Literal +from ...models.__test_item import TestItem from ...models.__adaptive_test import AdaptiveTest @@ -23,7 +23,7 @@ def __init__(self, adaptive_test: AdaptiveTest): self.adaptive_test = adaptive_test @abstractmethod - def select_item(self, **kwargs) -> TestItem: + def select_item(self, **kwargs) -> TestItem | None: """Select an item based on the implemented selection rules Returns: diff --git a/adaptivetesting/math/exposure_control/__mpi_exposure_control.py b/adaptivetesting/math/exposure_control/__mpi_exposure_control.py index e5d1734..5c6bca2 100644 --- a/adaptivetesting/math/exposure_control/__mpi_exposure_control.py +++ b/adaptivetesting/math/exposure_control/__mpi_exposure_control.py @@ -1,20 +1,81 @@ from ..content_balancing.__maximum_priority_index import MaximumPriorityIndex from .__exposure_control import ExposureControl +from ...models.__test_item import TestItem +from ...models.__misc import ResultOutputFormat +from ..content_balancing.__functions import compute_priority_index +import numpy as np +from ...data.__read_prev_items_exp_cont import read_prev_items +from typing import Literal class MaximumPriorityIndexExposureControl(MaximumPriorityIndex, ExposureControl): """This is a reimplementation of the Maximum Priority Index for Exposure Control. For further information see `MaximumPriorityIndex`. """ - def __init__(self, adaptive_test, constraints): + def __init__(self, adaptive_test, constraints, participant_ids: list[str], format: ResultOutputFormat): super().__init__(adaptive_test, constraints) + self.participant_ids = participant_ids + self.format = format - def select_item(self): + def select_item(self, **kwargs) -> TestItem | None: """Select the next item to administer based on the maximum priority index method. Returns: TestItem: The selected test item. """ - return super().select_item() - + # compute priority index for every item + available_items = self.adaptive_test.item_pool.test_items + # skip seletion if item pool is empty + if len(available_items) == 0: + return None - ##### TODO: Das geht so noch nicht!!!! \ No newline at end of file + # load items that have been shown to other users + format: Literal["CSV", "PICKLE"] + if self.format is ResultOutputFormat.CSV: + format = "CSV" + if self.format is ResultOutputFormat.PICKLE: + format = "PICKLE" + shown_items = read_prev_items( + test_id=self.adaptive_test.simulation_id, + participant_ids=self.participant_ids, + format=format + ) + priority_indices: list[float] = [] + + for item in available_items: + # get associated constraints + associated_constraints = [ + constraint + for constraint in self.constraints + if constraint.name in item.additional_properties["category"] + ] + + group_weights: dict[str, float] = {} + required_items: dict[str, float] = {} + shown_items_per_constraint: dict[str, float] = {} + + for constraint in associated_constraints: + group_weights[constraint.name] = constraint.weight + required_items[constraint.name] = constraint.prevalence + n_shown_items = len([ + shown_item + for shown_item in shown_items + if constraint.name in shown_item.additional_properties["category"] + ]) + shown_items_per_constraint[constraint.name] = float(n_shown_items) + + # calculate priority index + pix = compute_priority_index( + item=item, + group_weights=group_weights, + required_items=required_items, + shown_items=shown_items_per_constraint, + current_ability=self.adaptive_test.ability_level + ) + + priority_indices.append(pix) + + # select the item with the highest priority index + max_p_i = np.argmax(priority_indices) + selected_item = available_items[max_p_i] + + return selected_item diff --git a/adaptivetesting/math/exposure_control/__randomesque.py b/adaptivetesting/math/exposure_control/__randomesque.py index 02dd4ec..a972146 100644 --- a/adaptivetesting/math/exposure_control/__randomesque.py +++ b/adaptivetesting/math/exposure_control/__randomesque.py @@ -1,4 +1,4 @@ -from typing import Callable, Any, TypedDict +from typing import Callable, Any import random import numpy as np from .__exposure_control import ExposureControl @@ -6,6 +6,7 @@ from ...models.__test_item import TestItem from ..estimators.__test_information import item_information_function + class Randomesque(ExposureControl): def __init__(self, adaptive_test: AdaptiveTest, diff --git a/adaptivetesting/models/__test_item.py b/adaptivetesting/models/__test_item.py index fff1242..32cf3f5 100644 --- a/adaptivetesting/models/__test_item.py +++ b/adaptivetesting/models/__test_item.py @@ -37,3 +37,22 @@ def as_dict(self, with_id: bool = False) -> dict[str, float | int | dict | None] item_dict["id"] = self.id return item_dict + + @staticmethod + def from_dict(source: dict) -> "TestItem": + item = TestItem() + # copy known fields, preserving defaults if keys are missing + if "a" in source and source["a"] is not None: + item.a = source["a"] + if "b" in source and source["b"] is not None: + item.b = source["b"] + if "c" in source and source["c"] is not None: + item.c = source["c"] + if "d" in source and source["d"] is not None: + item.d = source["d"] + if "additional_properties" in source and source["additional_properties"] is not None: + item.additional_properties = source["additional_properties"] + if "id" in source and source["id"] is not None: + item.id = source["id"] + return item + diff --git a/adaptivetesting/tests/test_exposure_control_mpi.py b/adaptivetesting/tests/test_exposure_control_mpi.py new file mode 100644 index 0000000..1b01290 --- /dev/null +++ b/adaptivetesting/tests/test_exposure_control_mpi.py @@ -0,0 +1,87 @@ +import unittest +from unittest.mock import patch + +from adaptivetesting.math.exposure_control.__mpi_exposure_control import ( + MaximumPriorityIndexExposureControl, +) +from adaptivetesting import ResultOutputFormat, Constraint + + +class DummyConstraint: + def __init__(self, name, weight=1.0, prevalence=0.0): + self.name = name + self.weight = weight + self.prevalence = prevalence + + +class DummyItem: + def __init__(self, identifier, categories): + # production code expects item.additional_properties["category"] + self.identifier = identifier + self.additional_properties = {"category": categories} + + +class DummyItemPool: + def __init__(self, items): + self.test_items = items + + +class DummyAdaptiveTest: + def __init__(self, items, simulation_id="sim1", ability_level=0.0): + self.item_pool = DummyItemPool(items) + self.simulation_id = simulation_id + self.ability_level = ability_level + + +class MPIExposureControlTests(unittest.TestCase): + @patch( + "adaptivetesting.math.exposure_control.__mpi_exposure_control.read_prev_items", + return_value=[], + ) + @patch( + "adaptivetesting.math.exposure_control.__mpi_exposure_control.compute_priority_index" + ) + def test_selects_item_with_highest_priority_index(self, mock_compute, _mock_read): + # arrange: three items with different categories + i1 = DummyItem("i1", ["A"]) + i2 = DummyItem("i2", ["B"]) + i3 = DummyItem("i3", ["A"]) + adaptive = DummyAdaptiveTest([i1, i2, i3]) + + constraints = [ + DummyConstraint("A", weight=1.0, prevalence=2.0), + DummyConstraint("B", weight=1.0, prevalence=1.0), + ] + + scores = {"i1": 0.2, "i2": 0.8, "i3": 0.5} + + def fake_compute(item, **kwargs): + res = scores.get(str(getattr(item, "identifier", None)), 0.0) + return res + + mock_compute.side_effect = fake_compute + + controller = MaximumPriorityIndexExposureControl( + adaptive, constraints, participant_ids=["p1"], + format=ResultOutputFormat.CSV + ) + selected = controller.select_item() + + self.assertIs(selected, i2) # item with score 0.8 should be chosen + + @patch( + "adaptivetesting.math.exposure_control.__mpi_exposure_control.read_prev_items", + return_value=[], + ) + def test_returns_none_on_empty_pool(self, _mock_read): + adaptive = DummyAdaptiveTest([]) + constraints: list[Constraint] = [] + controller = MaximumPriorityIndexExposureControl(adaptive, + constraints, + participant_ids=[], + format=ResultOutputFormat.CSV) + self.assertIsNone(controller.select_item()) + + +if __name__ == "__main__": + unittest.main() diff --git a/adaptivetesting/tests/test_load_test_items.py b/adaptivetesting/tests/test_load_test_items.py index abb0390..a9ab38f 100644 --- a/adaptivetesting/tests/test_load_test_items.py +++ b/adaptivetesting/tests/test_load_test_items.py @@ -229,3 +229,36 @@ def test_load_dataframe_content_balancing(self): assigned_groups = [item.additional_properties["category"] for item in items] self.assertListEqual(assigned_groups, [["math"], ["english"]]) + + +class TestTestItemRoundTrip(TestCase): + def test_roundtrip_preserves_fields(self): + # create and populate original item + original = TestItem() + original.id = 42 + original.a = 1.2 + original.b = -0.5 + original.c = 0.25 + original.d = 0.95 + original.additional_properties = { + "category": ["Math", "Science"], + "meta": {"difficulty": "hard", "tags": ["algebra", "geometry"]}, + } + + # serialize including id + data_with_id = original.as_dict(with_id=True) + + # deserialize + restored = TestItem.from_dict(data_with_id) + + # verify fields preserved + self.assertEqual(restored.id, original.id) + self.assertEqual(restored.a, original.a) + self.assertEqual(restored.b, original.b) + self.assertEqual(restored.c, original.c) + self.assertEqual(restored.d, original.d) + self.assertEqual(restored.additional_properties, original.additional_properties) + + # verify as_dict omits id when with_id is False + data_no_id = original.as_dict(with_id=False) + self.assertNotIn("id", data_no_id) From 90304e74d7eb073c13727885cfe2d82cdd6e6374 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Thu, 4 Dec 2025 16:31:46 +0100 Subject: [PATCH 047/137] working implementation progress --- .../implementations/__test_assembler.py | 11 +-- .../math/content_balancing/__functions.py | 7 +- adaptivetesting/tests/test_full_tests.py | 91 +++++++++++++++++++ .../tests/test_weighted_penalty_model.py | 59 ++++++++++++ 4 files changed, 160 insertions(+), 8 deletions(-) create mode 100644 adaptivetesting/tests/test_full_tests.py diff --git a/adaptivetesting/implementations/__test_assembler.py b/adaptivetesting/implementations/__test_assembler.py index 772a71d..4e8c07f 100644 --- a/adaptivetesting/implementations/__test_assembler.py +++ b/adaptivetesting/implementations/__test_assembler.py @@ -14,11 +14,10 @@ from ..math.content_balancing.__weighted_penalty_model import WeightedPenaltyModel from ..math.content_balancing.__maximum_priority_index import MaximumPriorityIndex from ..math.estimators.__prior import Prior -from copy import deepcopy -import inspect from ..math.exposure_control.__exposure_control import EXPOSURE_CONTROL from ..math.exposure_control.__randomesque import Randomesque from ..math.exposure_control.__mpi_exposure_control import MaximumPriorityIndexExposureControl +import inspect class EstimatorArgs(TypedDict): @@ -249,7 +248,7 @@ def get_next_item(self) -> TestItem: # parse content balancing args # setup wep - adaptive_test = deepcopy(self) + adaptive_test = self wep = WeightedPenaltyModel( adaptive_test, constraints=self.check_args_are_not_none( @@ -269,7 +268,7 @@ def get_next_item(self) -> TestItem: else: return item elif self.content_balancing == "MaximumPriorityIndex": - adaptive_test = deepcopy(self) + adaptive_test = self mpi = MaximumPriorityIndex( adaptive_test, constraints=self.check_args_are_not_none( @@ -292,7 +291,7 @@ def get_next_item(self) -> TestItem: # which strategy has been selected if self.exposure_control == "Randomesque": # Randomesque - adaptive_test = deepcopy(self) + adaptive_test = self if self.exposure_control_args["seed"] is None or self.exposure_control_args["n_items"] is None: raise ValueError("exposure_control_args are not correctly specified") randomesque = Randomesque( @@ -308,7 +307,7 @@ def get_next_item(self) -> TestItem: is None or self.exposure_control_args["output_format"] is None): raise ValueError("exposure_control_args are not correctly specified") mpi = MaximumPriorityIndexExposureControl( - adaptive_test, + self, constraints=self.exposure_control_args["constraints"], participant_ids=self.exposure_control_args["participant_ids"], format=self.exposure_control_args["output_format"] diff --git a/adaptivetesting/math/content_balancing/__functions.py b/adaptivetesting/math/content_balancing/__functions.py index aad421f..48f2fec 100644 --- a/adaptivetesting/math/content_balancing/__functions.py +++ b/adaptivetesting/math/content_balancing/__functions.py @@ -87,8 +87,11 @@ def compute_prop(n_administered: int, Returns: float: expected proportion """ - expected_proportion = (n_administered + prevalence * n_remaining) / test_length - return expected_proportion + if test_length != 0: + expected_proportion = (n_administered + prevalence * n_remaining) / test_length + return expected_proportion + else: + return 0.0 def compute_expected_difference(proportion: float, diff --git a/adaptivetesting/tests/test_full_tests.py b/adaptivetesting/tests/test_full_tests.py new file mode 100644 index 0000000..4ca0f23 --- /dev/null +++ b/adaptivetesting/tests/test_full_tests.py @@ -0,0 +1,91 @@ +# This file is used to perform a full run of +# specific adaptiv test specifications. +import unittest +import adaptivetesting as adt +import pandas as pd + +class TestContentBalancing(unittest.TestCase): + def __init__(self, methodName = "runTest"): + super().__init__(methodName) + + items = pd.DataFrame({ + "a": [1.32, 1.07, 0.84], + "b": [-0.63, 0.18, -0.84], + "c": [0.17, 0.10, 0.19], + "d": [0.87, 0.93, 1], + "id": [1, 2, 3] + }) + + self.available_items = adt.ItemPool.load_from_dataframe(items).test_items + self.content_categories = ["Math", "English", "Math"] + + for i, _ in enumerate(self.available_items): + self.available_items[i].additional_properties = { + "category": [self.content_categories[i]] + } + + def test_maximum_priority_index(self): + item_pool = adt.ItemPool(self.available_items, + [0, 1, 0]) + + + adaptive_test = adt.TestAssembler( + item_pool=item_pool, + simulation_id="1", + participant_id="12", + ability_estimator=adt.MLEstimator, + content_balancing="MaximumPriorityIndex", + content_balancing_args={ + "constraints": [ + adt.Constraint( + "Math", + 0.5, + 0.2 + ), + adt.Constraint( + "English", + 0.5, + 0.1 + ) + ] + } + ) + + sim = adt.Simulation(adaptive_test, adt.ResultOutputFormat.CSV) + sim.simulate() + + def test_weighted_penalty_model(self): + item_pool = adt.ItemPool(self.available_items, + [0, 1, 0]) + + + adaptive_test = adt.TestAssembler( + item_pool=item_pool, + simulation_id="1", + participant_id="12", + ability_estimator=adt.MLEstimator, + content_balancing="WeightedPenaltyModel", + content_balancing_args={ + "constraints": [ + adt.Constraint( + "Math", + 0.5, + 0.2, + lower=0, + upper=1 + ), + adt.Constraint( + "English", + 0.5, + 0.1, + lower=0, + upper=1 + ) + ], + "constraint_weight": 0.5, + "information_weight": 0.5 + } + ) + + sim = adt.Simulation(adaptive_test, adt.ResultOutputFormat.CSV) + sim.simulate() diff --git a/adaptivetesting/tests/test_weighted_penalty_model.py b/adaptivetesting/tests/test_weighted_penalty_model.py index e4a871a..6c3e018 100644 --- a/adaptivetesting/tests/test_weighted_penalty_model.py +++ b/adaptivetesting/tests/test_weighted_penalty_model.py @@ -212,3 +212,62 @@ def test_candidate_group_assignment(self): weighted_penalty_value ) self.assertEqual(assigned_group, "red") + + def test_select_item_with_no_shown_items(self): + """Test that select_item returns the highest information item when no previous items have been shown""" + # Create a mock AdaptiveTest with no shown items + class MockAdaptiveTest: + def __init__(self, item_pool, ability_level): + self.item_pool = item_pool + self.ability_level = ability_level + self.answered_items = [] # No items shown + + # Create item pool with example items + item_pool_obj = adt.ItemPool([self.example_item1, self.example_item2]) + + # Create mock adaptive test with empty answered_items + mock_test = MockAdaptiveTest(item_pool_obj, 0) + + # Create constraints + constraints = [ + adt.Constraint( + name="math", + prevalence=0.5, + lower=0, + upper=1, + weight=1 + ), + adt.Constraint( + name="english", + prevalence=0.5, + lower=0, + upper=1, + weight=1 + ) + ] + + # Create WeightedPenaltyModel + model = adt.WeightedPenaltyModel( + adaptive_test=mock_test, + constraints=constraints, + constraint_weight=1.0, + information_weight=1.0 + ) + + # Select item + selected_item = model.select_item() + + # select item using maximum fisher information + information_selected_item: adt.TestItem = adt.maximum_information_criterion( + items=item_pool_obj.test_items, + 0 + ) + + # Verify an item is returned + self.assertIsNotNone(selected_item) + self.assertIsInstance(selected_item, adt.TestItem) + + self.assertDictEqual( + selected_item.as_dict(), + information_selected_item.as_dict() + ) \ No newline at end of file From aeb7b497e81d4302c81053cffef7640115e0aabf Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 5 Dec 2025 16:47:00 +0100 Subject: [PATCH 048/137] fix weighted penalty model tests --- adaptivetesting/math/content_balancing/__functions.py | 8 ++++++-- adaptivetesting/tests/test_full_tests.py | 3 ++- adaptivetesting/tests/test_weighted_penalty_model.py | 2 +- 3 files changed, 9 insertions(+), 4 deletions(-) diff --git a/adaptivetesting/math/content_balancing/__functions.py b/adaptivetesting/math/content_balancing/__functions.py index 48f2fec..df062ad 100644 --- a/adaptivetesting/math/content_balancing/__functions.py +++ b/adaptivetesting/math/content_balancing/__functions.py @@ -209,8 +209,12 @@ def standardize_total_content_constraint_penalty_value(item_penalty_value: float Returns: float: standardized total content constraint penalty value """ - standardized_value = (item_penalty_value - minimum) / (maximum - minimum) - return standardized_value + # TODO: ok? + try: + standardized_value = (item_penalty_value - minimum) / (maximum - minimum) + return standardized_value + except ZeroDivisionError: + return 0 def standardize_item_information(item_information: float, diff --git a/adaptivetesting/tests/test_full_tests.py b/adaptivetesting/tests/test_full_tests.py index 4ca0f23..ebe34e1 100644 --- a/adaptivetesting/tests/test_full_tests.py +++ b/adaptivetesting/tests/test_full_tests.py @@ -84,7 +84,8 @@ def test_weighted_penalty_model(self): ], "constraint_weight": 0.5, "information_weight": 0.5 - } + }, + debug=True ) sim = adt.Simulation(adaptive_test, adt.ResultOutputFormat.CSV) diff --git a/adaptivetesting/tests/test_weighted_penalty_model.py b/adaptivetesting/tests/test_weighted_penalty_model.py index 6c3e018..a15e9b4 100644 --- a/adaptivetesting/tests/test_weighted_penalty_model.py +++ b/adaptivetesting/tests/test_weighted_penalty_model.py @@ -260,7 +260,7 @@ def __init__(self, item_pool, ability_level): # select item using maximum fisher information information_selected_item: adt.TestItem = adt.maximum_information_criterion( items=item_pool_obj.test_items, - 0 + ability=0 ) # Verify an item is returned From b49f2642bc4ee674fab5569883732361b2dfa12d Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Mon, 8 Dec 2025 09:43:01 +0100 Subject: [PATCH 049/137] test randomesque exposure control --- .../implementations/__test_assembler.py | 3 +- .../math/exposure_control/__randomesque.py | 3 +- adaptivetesting/tests/test_full_tests.py | 41 +++++++++++++++++++ 3 files changed, 44 insertions(+), 3 deletions(-) diff --git a/adaptivetesting/implementations/__test_assembler.py b/adaptivetesting/implementations/__test_assembler.py index 4e8c07f..2c696e2 100644 --- a/adaptivetesting/implementations/__test_assembler.py +++ b/adaptivetesting/implementations/__test_assembler.py @@ -292,7 +292,8 @@ def get_next_item(self) -> TestItem: if self.exposure_control == "Randomesque": # Randomesque adaptive_test = self - if self.exposure_control_args["seed"] is None or self.exposure_control_args["n_items"] is None: + dict_keys = list(self.exposure_control_args.keys()) + if "seed" not in dict_keys or "n_items" not in dict_keys: raise ValueError("exposure_control_args are not correctly specified") randomesque = Randomesque( adaptive_test=adaptive_test, diff --git a/adaptivetesting/math/exposure_control/__randomesque.py b/adaptivetesting/math/exposure_control/__randomesque.py index a972146..5edd062 100644 --- a/adaptivetesting/math/exposure_control/__randomesque.py +++ b/adaptivetesting/math/exposure_control/__randomesque.py @@ -60,8 +60,7 @@ def select_item(self, **kwargs) -> TestItem: self.ability_estimate, self.n_items, self.reverse, - self.sort_by_information, - self.seed + seed=self.seed ) return selected_items diff --git a/adaptivetesting/tests/test_full_tests.py b/adaptivetesting/tests/test_full_tests.py index ebe34e1..370fbf7 100644 --- a/adaptivetesting/tests/test_full_tests.py +++ b/adaptivetesting/tests/test_full_tests.py @@ -90,3 +90,44 @@ def test_weighted_penalty_model(self): sim = adt.Simulation(adaptive_test, adt.ResultOutputFormat.CSV) sim.simulate() + +class TestExposureControl(unittest.TestCase): + def __init__(self, methodName = "runTest"): + super().__init__(methodName) + + items = pd.DataFrame({ + "a": [1.32, 1.07, 0.84], + "b": [-0.63, 0.18, -0.84], + "c": [0.17, 0.10, 0.19], + "d": [0.87, 0.93, 1], + "id": [1, 2, 3] + }) + + self.available_items = adt.ItemPool.load_from_dataframe(items).test_items + self.content_categories = ["Math", "English", "Math"] + + for i, _ in enumerate(self.available_items): + self.available_items[i].additional_properties = { + "category": [self.content_categories[i]] + } + + def test_randomesque(self): + item_pool = adt.ItemPool(self.available_items, + [0, 1, 0]) + + + adaptive_test = adt.TestAssembler( + item_pool=item_pool, + simulation_id="1", + participant_id="12", + ability_estimator=adt.MLEstimator, + exposure_control="Randomesque", + exposure_control_args={ + "n_items": 2, + "seed": None + }, + debug=True + ) + + sim = adt.Simulation(adaptive_test, adt.ResultOutputFormat.CSV) + sim.simulate() \ No newline at end of file From ba23c32d8a3c222344826429eb930ad72e1e1201 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Mon, 8 Dec 2025 14:04:46 +0100 Subject: [PATCH 050/137] progress --- adaptivetesting/__init__.py | 2 +- adaptivetesting/data/__csv_context.py | 13 ++++- adaptivetesting/tests/test_full_tests.py | 61 +++++++++++++++++++++++- 3 files changed, 73 insertions(+), 3 deletions(-) diff --git a/adaptivetesting/__init__.py b/adaptivetesting/__init__.py index b62cd89..34e9bde 100644 --- a/adaptivetesting/__init__.py +++ b/adaptivetesting/__init__.py @@ -12,7 +12,7 @@ from .implementations.__default_implementation import DefaultImplementation from .implementations.__pre_test import PreTest from .implementations.__semi_implementation import SemiAdaptiveImplementation -from .implementations.__test_assembler import TestAssembler +from .implementations.__test_assembler import TestAssembler, ContentBalancingArgs, ExposureControlArgs from .math.__gen_response_pattern import generate_response_pattern from .math.estimators.__ml_estimation import MLEstimator diff --git a/adaptivetesting/data/__csv_context.py b/adaptivetesting/data/__csv_context.py index 9f71cbb..c5bf33c 100644 --- a/adaptivetesting/data/__csv_context.py +++ b/adaptivetesting/data/__csv_context.py @@ -3,6 +3,7 @@ import pathlib from ..models.__test_result import TestResult from ..services.__test_results_interface import ITestResults +from dataclasses import fields class CSVContext(ITestResults): @@ -54,4 +55,14 @@ def load(self) -> List[TestResult]: Returns: List[TestResult] """ - raise NotImplementedError("This function is not implemented.") + foldername = f"data/{self.simulation_id}" + + fieldnames = list(fields(TestResult)) + test_results: list[TestResult] = [] + with open(f"{foldername}/{self.participant_id}.csv", "r", encoding="utf-8") as file: + reader = csv.DictReader(file, fieldnames=fieldnames) + for row in reader: + test_result = TestResult.from_dict(row) + test_results.append(test_result) + file.close() + return test_results diff --git a/adaptivetesting/tests/test_full_tests.py b/adaptivetesting/tests/test_full_tests.py index 370fbf7..e1b344e 100644 --- a/adaptivetesting/tests/test_full_tests.py +++ b/adaptivetesting/tests/test_full_tests.py @@ -3,6 +3,10 @@ import unittest import adaptivetesting as adt import pandas as pd +import shutil +import os +import pathlib + class TestContentBalancing(unittest.TestCase): def __init__(self, methodName = "runTest"): @@ -111,6 +115,15 @@ def __init__(self, methodName = "runTest"): "category": [self.content_categories[i]] } + def setUp(self): + def clean_up_sim(): + path = pathlib.Path("./data/1") + if path.exists(): + shutil.rmtree(path) + + self.addCleanup(clean_up_sim) + + def test_randomesque(self): item_pool = adt.ItemPool(self.available_items, [0, 1, 0]) @@ -130,4 +143,50 @@ def test_randomesque(self): ) sim = adt.Simulation(adaptive_test, adt.ResultOutputFormat.CSV) - sim.simulate() \ No newline at end of file + sim.simulate() + + + def test_MPI_exposure_control(self): + item_pool = adt.ItemPool(self.available_items, + [0, 1, 0]) + + ex_args: adt.ExposureControlArgs = { + "constraints": [ + adt.Constraint("Math", 0.5, 0.5), + adt.Constraint("English", 0.5, 0.5) + ], + "participant_ids": ["1"], + "output_format": adt.ResultOutputFormat.CSV + } + def run_previous_tests(sim_id: str, par_id: str): + adaptive_test = adt.TestAssembler( + item_pool=item_pool, + simulation_id=sim_id, + participant_id=par_id, + ability_estimator=adt.MLEstimator, + #exposure_control="MaximumPriorityIndex", + #exposure_control_args={ + # "n_items": 2, + # "seed": None + #}, + debug=False + ) + + sim = adt.Simulation(adaptive_test, adt.ResultOutputFormat.CSV) + sim.simulate() + sim.save_test_results() + run_previous_tests("1", "1") + + # acutal test + adaptive_test = adt.TestAssembler( + item_pool=item_pool, + simulation_id="1", + participant_id="2", + ability_estimator=adt.MLEstimator, + exposure_control="MaximumPriorityIndex", + exposure_control_args=ex_args, + debug=False + ) + sim = adt.Simulation(adaptive_test, adt.ResultOutputFormat.CSV) + sim.simulate() + sim.save_test_results() \ No newline at end of file From 034455c75aab95a59af46dfb180901c2b1995c3b Mon Sep 17 00:00:00 2001 From: condecon Date: Mon, 8 Dec 2025 14:31:13 +0100 Subject: [PATCH 051/137] working true sim tests --- adaptivetesting/data/__csv_context.py | 8 ++++- adaptivetesting/data/__pickle_context.py | 7 +++- adaptivetesting/tests/test_full_tests.py | 45 ++++++++++++++++++++++++ 3 files changed, 58 insertions(+), 2 deletions(-) diff --git a/adaptivetesting/data/__csv_context.py b/adaptivetesting/data/__csv_context.py index c5bf33c..40631b7 100644 --- a/adaptivetesting/data/__csv_context.py +++ b/adaptivetesting/data/__csv_context.py @@ -4,6 +4,7 @@ from ..models.__test_result import TestResult from ..services.__test_results_interface import ITestResults from dataclasses import fields +import json class CSVContext(ITestResults): @@ -60,9 +61,14 @@ def load(self) -> List[TestResult]: fieldnames = list(fields(TestResult)) test_results: list[TestResult] = [] with open(f"{foldername}/{self.participant_id}.csv", "r", encoding="utf-8") as file: - reader = csv.DictReader(file, fieldnames=fieldnames) + reader = csv.DictReader(file) + next(reader, None) # skip header row for row in reader: test_result = TestResult.from_dict(row) + # showed_item is read as a json string + # this has to be manually converted into a dictionary + json_string = str(test_result.showed_item).replace("'", '"') + test_result.showed_item = json.loads(json_string) test_results.append(test_result) file.close() return test_results diff --git a/adaptivetesting/data/__pickle_context.py b/adaptivetesting/data/__pickle_context.py index 91b9c7b..c859cc3 100644 --- a/adaptivetesting/data/__pickle_context.py +++ b/adaptivetesting/data/__pickle_context.py @@ -48,4 +48,9 @@ def load(self) -> List[TestResult]: Returns: List[TestResult] """ - raise NotImplementedError("This function is not implemented.") + foldername = f"data/{self.simulation_id}" + + with open(f"{foldername}/{self.participant_id}.pickle", "rb") as file: + test_results: list[TestResult] = pickle.load(file) + file.close() + return test_results diff --git a/adaptivetesting/tests/test_full_tests.py b/adaptivetesting/tests/test_full_tests.py index e1b344e..3f880ec 100644 --- a/adaptivetesting/tests/test_full_tests.py +++ b/adaptivetesting/tests/test_full_tests.py @@ -189,4 +189,49 @@ def run_previous_tests(sim_id: str, par_id: str): ) sim = adt.Simulation(adaptive_test, adt.ResultOutputFormat.CSV) sim.simulate() + sim.save_test_results() + + def test_MPI_exposure_control_pickle(self): + item_pool = adt.ItemPool(self.available_items, + [0, 1, 0]) + + ex_args: adt.ExposureControlArgs = { + "constraints": [ + adt.Constraint("Math", 0.5, 0.5), + adt.Constraint("English", 0.5, 0.5) + ], + "participant_ids": ["1"], + "output_format": adt.ResultOutputFormat.PICKLE + } + def run_previous_tests(sim_id: str, par_id: str): + adaptive_test = adt.TestAssembler( + item_pool=item_pool, + simulation_id=sim_id, + participant_id=par_id, + ability_estimator=adt.MLEstimator, + #exposure_control="MaximumPriorityIndex", + #exposure_control_args={ + # "n_items": 2, + # "seed": None + #}, + debug=False + ) + + sim = adt.Simulation(adaptive_test, adt.ResultOutputFormat.PICKLE) + sim.simulate() + sim.save_test_results() + run_previous_tests("1", "1") + + # acutal test + adaptive_test = adt.TestAssembler( + item_pool=item_pool, + simulation_id="1", + participant_id="2", + ability_estimator=adt.MLEstimator, + exposure_control="MaximumPriorityIndex", + exposure_control_args=ex_args, + debug=False + ) + sim = adt.Simulation(adaptive_test, adt.ResultOutputFormat.PICKLE) + sim.simulate() sim.save_test_results() \ No newline at end of file From b3e71be12271721ab8b79936f4c0b315ae79c259 Mon Sep 17 00:00:00 2001 From: condecon Date: Mon, 8 Dec 2025 14:38:30 +0100 Subject: [PATCH 052/137] remove comments --- adaptivetesting/tests/test_full_tests.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/adaptivetesting/tests/test_full_tests.py b/adaptivetesting/tests/test_full_tests.py index 3f880ec..26e2b08 100644 --- a/adaptivetesting/tests/test_full_tests.py +++ b/adaptivetesting/tests/test_full_tests.py @@ -209,11 +209,6 @@ def run_previous_tests(sim_id: str, par_id: str): simulation_id=sim_id, participant_id=par_id, ability_estimator=adt.MLEstimator, - #exposure_control="MaximumPriorityIndex", - #exposure_control_args={ - # "n_items": 2, - # "seed": None - #}, debug=False ) From ffaa685dd4bee873d651b701bd0e9ba77ffebb8e Mon Sep 17 00:00:00 2001 From: condecon Date: Mon, 8 Dec 2025 15:29:02 +0100 Subject: [PATCH 053/137] linting and type checking --- adaptivetesting/data/__csv_context.py | 2 - .../implementations/__test_assembler.py | 23 ++++---- adaptivetesting/tests/test_full_tests.py | 58 ++++++++----------- .../tests/test_weighted_penalty_model.py | 2 +- 4 files changed, 39 insertions(+), 46 deletions(-) diff --git a/adaptivetesting/data/__csv_context.py b/adaptivetesting/data/__csv_context.py index 40631b7..39141d3 100644 --- a/adaptivetesting/data/__csv_context.py +++ b/adaptivetesting/data/__csv_context.py @@ -3,7 +3,6 @@ import pathlib from ..models.__test_result import TestResult from ..services.__test_results_interface import ITestResults -from dataclasses import fields import json @@ -58,7 +57,6 @@ def load(self) -> List[TestResult]: """ foldername = f"data/{self.simulation_id}" - fieldnames = list(fields(TestResult)) test_results: list[TestResult] = [] with open(f"{foldername}/{self.participant_id}.csv", "r", encoding="utf-8") as file: reader = csv.DictReader(file) diff --git a/adaptivetesting/implementations/__test_assembler.py b/adaptivetesting/implementations/__test_assembler.py index 2c696e2..f803c14 100644 --- a/adaptivetesting/implementations/__test_assembler.py +++ b/adaptivetesting/implementations/__test_assembler.py @@ -260,7 +260,7 @@ def get_next_item(self) -> TestItem: information_weight=self.check_args_are_not_none( "information_weight", self.content_balancing_args["information_weight"])) - + item = wep.select_item() if item is None: raise ItemSelectionException( @@ -293,16 +293,19 @@ def get_next_item(self) -> TestItem: # Randomesque adaptive_test = self dict_keys = list(self.exposure_control_args.keys()) - if "seed" not in dict_keys or "n_items" not in dict_keys: + if "seed" not in dict_keys or "n_items" not in dict_keys: raise ValueError("exposure_control_args are not correctly specified") - randomesque = Randomesque( - adaptive_test=adaptive_test, - n_items=self.exposure_control_args["n_items"], - seed=self.exposure_control_args["seed"] - ) - - return randomesque.select_item() - + + if self.exposure_control_args["n_items"] is not None: + randomesque = Randomesque( + adaptive_test=adaptive_test, + n_items=self.exposure_control_args["n_items"], + seed=self.exposure_control_args["seed"] + ) + return randomesque.select_item() + else: + raise ValueError("n_items cannot be None.") + if self.exposure_control == "MaximumPriorityIndex": if (self.exposure_control_args["participant_ids"] is None or self.exposure_control_args["output_format"] is None): diff --git a/adaptivetesting/tests/test_full_tests.py b/adaptivetesting/tests/test_full_tests.py index 26e2b08..336c723 100644 --- a/adaptivetesting/tests/test_full_tests.py +++ b/adaptivetesting/tests/test_full_tests.py @@ -4,12 +4,11 @@ import adaptivetesting as adt import pandas as pd import shutil -import os import pathlib class TestContentBalancing(unittest.TestCase): - def __init__(self, methodName = "runTest"): + def __init__(self, methodName="runTest"): super().__init__(methodName) items = pd.DataFrame({ @@ -29,9 +28,8 @@ def __init__(self, methodName = "runTest"): } def test_maximum_priority_index(self): - item_pool = adt.ItemPool(self.available_items, + item_pool = adt.ItemPool(self.available_items, [0, 1, 0]) - adaptive_test = adt.TestAssembler( item_pool=item_pool, @@ -39,7 +37,7 @@ def test_maximum_priority_index(self): participant_id="12", ability_estimator=adt.MLEstimator, content_balancing="MaximumPriorityIndex", - content_balancing_args={ + content_balancing_args={ # type: ignore "constraints": [ adt.Constraint( "Math", @@ -59,10 +57,9 @@ def test_maximum_priority_index(self): sim.simulate() def test_weighted_penalty_model(self): - item_pool = adt.ItemPool(self.available_items, + item_pool = adt.ItemPool(self.available_items, [0, 1, 0]) - adaptive_test = adt.TestAssembler( item_pool=item_pool, simulation_id="1", @@ -95,8 +92,9 @@ def test_weighted_penalty_model(self): sim = adt.Simulation(adaptive_test, adt.ResultOutputFormat.CSV) sim.simulate() + class TestExposureControl(unittest.TestCase): - def __init__(self, methodName = "runTest"): + def __init__(self, methodName="runTest"): super().__init__(methodName) items = pd.DataFrame({ @@ -123,19 +121,17 @@ def clean_up_sim(): self.addCleanup(clean_up_sim) - def test_randomesque(self): - item_pool = adt.ItemPool(self.available_items, + item_pool = adt.ItemPool(self.available_items, [0, 1, 0]) - adaptive_test = adt.TestAssembler( item_pool=item_pool, simulation_id="1", participant_id="12", ability_estimator=adt.MLEstimator, exposure_control="Randomesque", - exposure_control_args={ + exposure_control_args={ # type: ignore "n_items": 2, "seed": None }, @@ -145,12 +141,11 @@ def test_randomesque(self): sim = adt.Simulation(adaptive_test, adt.ResultOutputFormat.CSV) sim.simulate() - def test_MPI_exposure_control(self): - item_pool = adt.ItemPool(self.available_items, + item_pool = adt.ItemPool(self.available_items, [0, 1, 0]) - ex_args: adt.ExposureControlArgs = { + ex_args: adt.ExposureControlArgs = { # type: ignore "constraints": [ adt.Constraint("Math", 0.5, 0.5), adt.Constraint("English", 0.5, 0.5) @@ -158,18 +153,14 @@ def test_MPI_exposure_control(self): "participant_ids": ["1"], "output_format": adt.ResultOutputFormat.CSV } + def run_previous_tests(sim_id: str, par_id: str): adaptive_test = adt.TestAssembler( - item_pool=item_pool, - simulation_id=sim_id, - participant_id=par_id, - ability_estimator=adt.MLEstimator, - #exposure_control="MaximumPriorityIndex", - #exposure_control_args={ - # "n_items": 2, - # "seed": None - #}, - debug=False + item_pool=item_pool, + simulation_id=sim_id, + participant_id=par_id, + ability_estimator=adt.MLEstimator, + debug=False ) sim = adt.Simulation(adaptive_test, adt.ResultOutputFormat.CSV) @@ -192,10 +183,10 @@ def run_previous_tests(sim_id: str, par_id: str): sim.save_test_results() def test_MPI_exposure_control_pickle(self): - item_pool = adt.ItemPool(self.available_items, + item_pool = adt.ItemPool(self.available_items, [0, 1, 0]) - ex_args: adt.ExposureControlArgs = { + ex_args: adt.ExposureControlArgs = { # type: ignore "constraints": [ adt.Constraint("Math", 0.5, 0.5), adt.Constraint("English", 0.5, 0.5) @@ -203,13 +194,14 @@ def test_MPI_exposure_control_pickle(self): "participant_ids": ["1"], "output_format": adt.ResultOutputFormat.PICKLE } + def run_previous_tests(sim_id: str, par_id: str): adaptive_test = adt.TestAssembler( - item_pool=item_pool, - simulation_id=sim_id, - participant_id=par_id, - ability_estimator=adt.MLEstimator, - debug=False + item_pool=item_pool, + simulation_id=sim_id, + participant_id=par_id, + ability_estimator=adt.MLEstimator, + debug=False ) sim = adt.Simulation(adaptive_test, adt.ResultOutputFormat.PICKLE) @@ -229,4 +221,4 @@ def run_previous_tests(sim_id: str, par_id: str): ) sim = adt.Simulation(adaptive_test, adt.ResultOutputFormat.PICKLE) sim.simulate() - sim.save_test_results() \ No newline at end of file + sim.save_test_results() diff --git a/adaptivetesting/tests/test_weighted_penalty_model.py b/adaptivetesting/tests/test_weighted_penalty_model.py index a15e9b4..fa913d4 100644 --- a/adaptivetesting/tests/test_weighted_penalty_model.py +++ b/adaptivetesting/tests/test_weighted_penalty_model.py @@ -270,4 +270,4 @@ def __init__(self, item_pool, ability_level): self.assertDictEqual( selected_item.as_dict(), information_selected_item.as_dict() - ) \ No newline at end of file + ) From de1f0f8b48d34a2802acfdea8dabef5d2b6a6e50 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Tue, 9 Dec 2025 14:54:40 +0100 Subject: [PATCH 054/137] update python environment to latest supported packages --- requirements.txt | Bin 92676 -> 92612 bytes uv.lock | 4102 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zzpuS{Ci1!SlDDfjZnl1Et}p)=o%*W0T&}M^mv_})Cw1WPI*HO#jF^|?Jg6sauGh#n zjq*CyLW|CWA+GC`g|^9jcdHqF;7MpFR8vEB(xGNM0Z4`bh&6k=Xfg9#qEFS^8R2Vv z+=-vwL}z)wR0(dZsAxK=!I}DruHPLxc6;KR72B@pdM9D zDqFCjvSRIQ->fi#=VEal%hlkezJ|-9H#cn2ojN4#Z)a>wRdNkH>NKwGbi|7Kw`|v$ XSZ!`r%oR8N%%`r|9OaBYb^ZSWwZMih diff --git a/uv.lock b/uv.lock index 55ac896..39cb69a 100644 --- a/uv.lock +++ b/uv.lock @@ -2,10 +2,10 @@ version = 1 revision = 3 requires-python = ">=3.10" resolution-markers = [ + "python_full_version >= '3.12' and sys_platform == 'win32'", "python_full_version >= '3.12' and sys_platform == 'darwin'", "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'", "(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')", - "python_full_version >= '3.12' and sys_platform == 'win32'", "python_full_version == '3.11.*' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')", @@ -34,10 +34,10 @@ version = "1.1.2" source = { editable = "." } dependencies = [ { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, - { name = "numpy", version = "2.3.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, + { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "pandas" }, { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, - { name = "scipy", 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zd*7|>8v`SZPk}faY{7}x4%rkeW^Y$R-{PrUiL_qJX;GE>v}(g2?8Zw#>^qeSs!l5zRVN({U@;#)&9rViM?s{}U#CKKlHs`ideqP1>y#IguMnV0i`%U%a zXLjz3!5n}JSrHWL1+>b(Mvkn#?w~VpGVMgYMtdk_-O^80nYyWA-J1V^`nnJF(`6lc zf;7nB&iIm?7{sLWRtesoBzKyx|f%$!NzU_wCi_Go)EVry!KW zhc#I(Ce_ES4!*?AAHx+aVxZdmI+s3wmOr_DzH!^Hc>M18*CT0V_5It5;bK+w;?wVJ z*W;$YDG#f5Bs693PSZzvZ{K3YIL6U*GGc}DSXQaC>*$8uQdDg6tUXe`7jj)ig;Zbn zG47<2TsOyz<7O}4vOvv64?hr?rsIMts1{b^jEcGf+EmFs`4MYWaeZDzIj&Tg+FH@) zHQVP>xBHuGKCY9gH^*<&>-Tu3e>tC;bper!Mn3H$)Cx1yi`VeD-rl-950oP)w02HJ zRdkQmp0>bcr$KmOR9U1&>f#xAnAAqL^cQ)Se;tt~ Date: Fri, 12 Dec 2025 10:55:34 +0100 Subject: [PATCH 057/137] add math functions for poly models --- .../estimators/__functions/__poly/__gpcm.py | 63 ++ .../estimators/__functions/__poly/__grm.py | 67 ++ .../estimators/__functions/__poly/__init__.py | 4 + .../__functions/__poly/__poly_math.py | 29 + mypy.ini | 3 +- pyproject.toml | 3 +- uv.lock | 867 +++++++++--------- 7 files changed, 608 insertions(+), 428 deletions(-) create mode 100644 adaptivetesting/math/estimators/__functions/__poly/__gpcm.py create mode 100644 adaptivetesting/math/estimators/__functions/__poly/__grm.py create mode 100644 adaptivetesting/math/estimators/__functions/__poly/__init__.py create mode 100644 adaptivetesting/math/estimators/__functions/__poly/__poly_math.py diff --git a/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py b/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py new file mode 100644 index 0000000..980d7de --- /dev/null +++ b/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py @@ -0,0 +1,63 @@ +import numpy as np +import numdifftools as nd +from .__poly_math import PolyModelFunctions +from math import log + + +class GPCM(PolyModelFunctions): + @staticmethod + def category_prob(theta: float, + a: float, + thresholds_list: list[float], + response_pattern: int): + # numerator + numerator = 0.0 + for i in range(response_pattern): + # print(f"i: {i}") + numerator += a * (theta - thresholds_list[i]) + + # denominator + denominator = 1 + possible_categories = len(thresholds_list) + for c in range(possible_categories): + local_sum = 0.0 + for i in range(c): + local_sum += a * (theta - thresholds_list[i]) + + denominator += np.exp(local_sum) + + return numerator / denominator + + @staticmethod + def log_likelihood(theta: float, + a_params: list[float], + thresholds_list: list[list[float]], + response_pattern: list[int]): + log_lik = 0.0 + # Iterate over item indices + for item_idx in range(len(a_params)): + prob = GPCM.category_prob( + theta=theta, + a=a_params[item_idx], + thresholds_list=thresholds_list[item_idx], + response_pattern=response_pattern[item_idx] + ) + if prob <= 0: # Handle cases where probability is zero or negative + log_lik += -np.inf # Log of zero is negative infinity + else: + log_lik += np.log(prob) + return -log_lik # Return negative log-likelihood for minimization + + @staticmethod + def fisher_information(theta, + a, + thresholds, + response): + """Embretson, S. E., & Reise, S. P. (2000). Item Response Theory for Psychologists.""" + def log_prob(x): + p = GPCM.category_prob(x, a, thresholds, response) + p = max(p, 1e-12) + return log(p) + + log_prob_d2 = nd.Derivative(log_prob, order=2) + return -log_prob_d2(theta) \ No newline at end of file diff --git a/adaptivetesting/math/estimators/__functions/__poly/__grm.py b/adaptivetesting/math/estimators/__functions/__poly/__grm.py new file mode 100644 index 0000000..d77165c --- /dev/null +++ b/adaptivetesting/math/estimators/__functions/__poly/__grm.py @@ -0,0 +1,67 @@ +import numpy as np +from math import log +from scipy.integrate import quad +import numdifftools as nd +from .__poly_math import PolyModelFunctions + + +class GRM(PolyModelFunctions): + @staticmethod + def category_prob(theta, a: float, thresholds: list[float], k: int): + # k is the category index (0, 1, ..., num_thresholds) + num_thresholds = len(thresholds) + + # Calculate P(Y >= k) + if k == 0: + p_ge_k = 1.0 + elif k > 0 and k <= num_thresholds: + p_ge_k = 1 / (1 + np.exp(-a * (theta - thresholds[k-1]))) + else: + # Invalid category index k or k > num_categories (num_thresholds + 1) + # For likelihood calculation, return a very small positive number to avoid log(0) + # Ensure the returned type matches the expected type if theta was an array + return np.full_like(theta, 1e-10, dtype=float) if isinstance(theta, np.ndarray) else 1e-10 + + # Calculate P(Y >= k+1) + if k == num_thresholds: # k+1 would correspond to P(Y >= num_thresholds + 1) which is 0.0 + p_ge_k_plus_1 = 0.0 + elif k < num_thresholds: # k+1 is <= num_thresholds, so it uses thresholds[k] + p_ge_k_plus_1 = 1 / (1 + np.exp(-a * (theta - thresholds[k]))) + else: + # Invalid k+1 (should not be less than 0 or something) + # Ensure the returned type matches the expected type if theta was an array + return np.full_like(theta, 1e-10, dtype=float) if isinstance(theta, np.ndarray) else 1e-10 + + prob_k = p_ge_k - p_ge_k_plus_1 + return np.maximum(prob_k, 1e-10) # Use np.maximum for element-wise comparison with arrays + + @staticmethod + def log_likelihood(theta: float, a_params: list[float], thresholds_list: list[list[float]], response_pattern: list[int]): + log_lik = 0.0 + # Iterate over item indices + for item_idx in range(len(a_params)): + prob = GRM.category_prob( + theta=theta, + a=a_params[item_idx], + thresholds=thresholds_list[item_idx], + k=response_pattern[item_idx] + ) + if prob <= 0: # Handle cases where probability is zero or negative + log_lik += -np.inf # Log of zero is negative infinity + else: + log_lik += np.log(prob) + return -log_lik # Return negative log-likelihood for minimization + + @staticmethod + def fisher_information(theta: float, + a: float, + thresholds: list[float], + response: int): + """Embretson, S. E., & Reise, S. P. (2000). Item Response Theory for Psychologists.""" + def log_prob(x): + p = GRM.category_prob(x, a, thresholds, response) + p = max(p, 1e-12) + return log(p) + + log_prob_d2 = nd.Derivative(log_prob, order=2) + return -log_prob_d2(theta) \ No newline at end of file diff --git a/adaptivetesting/math/estimators/__functions/__poly/__init__.py b/adaptivetesting/math/estimators/__functions/__poly/__init__.py new file mode 100644 index 0000000..f0852e4 --- /dev/null +++ b/adaptivetesting/math/estimators/__functions/__poly/__init__.py @@ -0,0 +1,4 @@ +""" +This submodule holds functions used for the ability +estimation in polytomous IRT models. +""" \ No newline at end of file diff --git a/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py b/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py new file mode 100644 index 0000000..0c951ac --- /dev/null +++ b/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py @@ -0,0 +1,29 @@ +from abc import ABC, abstractmethod + + +class PolyModelFunctions(ABC): + + @abstractmethod + @staticmethod + def category_prob(theta: float, + a: float, + thresholds_list: list[float], + response_pattern: int): + pass + + @abstractmethod + @staticmethod + def log_likelihood(theta: float, + a_params: list[float], + thresholds_list: list[list[float]], + response_pattern: list[int]): + pass + + @abstractmethod + @staticmethod + def fisher_information(theta: float, + a: float, + thresholds: list[float], + response: int): + pass + \ No newline at end of file diff --git a/mypy.ini b/mypy.ini index f1a742f..061d4ff 100644 --- a/mypy.ini +++ b/mypy.ini @@ -5,4 +5,5 @@ files = adaptivetesting # Exclude the 'env' directory from type checking exclude = ^(setup\.py|env/.*) -check_untyped_defs = True \ No newline at end of file +check_untyped_defs = True +follow_untyped_imports = True \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 0320f8b..1173723 100644 --- a/pyproject.toml +++ 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7c7da4e9cd71d942b1338248ed8cd7a6e2efcae7 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 12 Dec 2025 10:58:36 +0100 Subject: [PATCH 058/137] add todo list --- todo.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 todo.md diff --git a/todo.md b/todo.md new file mode 100644 index 0000000..d2fec43 --- /dev/null +++ b/todo.md @@ -0,0 +1,8 @@ +# Todo + +- [ ] TestAssembler +- [ ] Ability Estimation +- [ ] Item Selection +- [ ] Item Pool +- [ ] Content Balancing +- [ ] Exposure Control \ No newline at end of file From 6fd717ed18a8c614ae02ae98cf0bb0c6e9f07531 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Tue, 16 Dec 2025 13:24:21 +0100 Subject: [PATCH 059/137] progress --- .../implementations/__poly_test_assembler.py | 34 +++++++++++ .../math/estimators/__ml_estimation.py | 7 ++- adaptivetesting/models/__test_item.py | 59 ++++++++++++++++++- .../services/__estimator_interface.py | 25 +++++--- 4 files changed, 112 insertions(+), 13 deletions(-) create mode 100644 adaptivetesting/implementations/__poly_test_assembler.py diff --git a/adaptivetesting/implementations/__poly_test_assembler.py b/adaptivetesting/implementations/__poly_test_assembler.py new file mode 100644 index 0000000..7dc0cb5 --- /dev/null +++ b/adaptivetesting/implementations/__poly_test_assembler.py @@ -0,0 +1,34 @@ +from ..models.__adaptive_test import AdaptiveTest +from typing import Type, Any +from ..services.__estimator_interface import IEstimator +from .__test_assembler import EstimatorArgs + + +class PolyTestAssembler(AdaptiveTest): + def __init__(self, + item_pool, + simulation_id, + participant_id, + ability_estimator: Type[IEstimator], + estimator_args: EstimatorArgs = { + "prior": None, + "optimization_interval": (-10, 10) + }, + item_selector: ItemSelectionStrategy = maximum_information_criterion, # type: ignore + item_selector_args: dict[str, Any] = {}, + pretest: bool = False, + pretest_seed: int | None = None, + true_ability_level=None, + initial_ability_level=0, + simulation=True, + DEBUG=False, + **kwargs): + super().__init__(item_pool, simulation_id, participant_id, + true_ability_level, initial_ability_level, simulation, DEBUG, **kwargs) + + self.__ability_estimator = ability_estimator + self.__estimator_args = estimator_args + self.__item_selector = item_selector + self.__item_selector_args = item_selector_args + self.__pretest = pretest + self.__pretest_seed = pretest_seed \ No newline at end of file diff --git a/adaptivetesting/math/estimators/__ml_estimation.py b/adaptivetesting/math/estimators/__ml_estimation.py index fd60343..df7708f 100644 --- a/adaptivetesting/math/estimators/__ml_estimation.py +++ b/adaptivetesting/math/estimators/__ml_estimation.py @@ -1,15 +1,16 @@ from typing import List, Tuple import numpy as np -from ...models.__test_item import TestItem +from ...models.__test_item import TestItem, BaseItem, PolyItem from ...services.__estimator_interface import IEstimator from .__functions.__estimators import maximize_likelihood_function from .__test_information import test_information_function +# TODO: introduce switch for estimation depending on the item type class MLEstimator(IEstimator): def __init__(self, response_pattern: List[int] | np.ndarray, - items: List[TestItem], + items: List[BaseItem], optimization_interval: Tuple[float, float] = (-10, 10), **kwargs): """This class can be used to estimate the current ability level of a respondent given the response pattern and the corresponding @@ -19,7 +20,7 @@ def __init__(self, Args: response_pattern (List[int]): list of response patterns (0: wrong, 1:right) - items (List[TestItem]): list of answered items + items (List[BaseItem]): list of answered items """ IEstimator.__init__(self, response_pattern, items, optimization_interval) diff --git a/adaptivetesting/models/__test_item.py b/adaptivetesting/models/__test_item.py index 32cf3f5..dcee5ff 100644 --- a/adaptivetesting/models/__test_item.py +++ b/adaptivetesting/models/__test_item.py @@ -1,4 +1,21 @@ -class TestItem: +from abc import abstractmethod + +class BaseItem: + def __init__(self): + self.id: int | None + self.a: float + self.b: float | list[float] + + @abstractmethod + def as_dict(self, with_id: bool = False): + pass + + @staticmethod + @abstractmethod + def from_dict(source: dict) -> "BaseItem": + pass + +class TestItem(BaseItem): def __init__(self): """Representation of a test item in the item pool. The format is equal to the implementation in catR. @@ -56,3 +73,43 @@ def from_dict(source: dict) -> "TestItem": item.id = source["id"] return item + +class PolyItem(BaseItem): + def __init__(self): + self.id: int | None = None + self.a: float = 1 + self.b: list[float] = [] # thresholds + self.additional_properties: dict = {} + self.num_categories: int = 2 + + def as_dict(self, with_id = False): + item_dict: dict[str, float | int | list | dict | None] = { + "a": self.a, + "b": self.b, + "num_categories": self.num_categories, + "additional_properties": self.additional_properties + } + if with_id and self.id is not None: + item_dict["id"] = self.id + return item_dict + + @staticmethod + def from_dict(source) -> "PolyItem": + item = PolyItem() + if "a" in source and source["a"] is not None: + item.a = source["a"] + if "b" in source and source["b"] is not None: + if not isinstance(source["b"], list): + raise TypeError("b is not a list") + item.b = source["b"] + if "num_categories" in source and source["num_categories"] is not None: + item.num_categories = source["num_categories"] + if "additional_properties" in source and source["additional_properties"] is not None: + if not isinstance(source["additional_properties"], dict): + raise TypeError("additional_properties is not dict") + item.additional_properties = source["additional_properties"] + if "id" in source and source["id"] is not None: + item.id = source["id"] + + return item + diff --git a/adaptivetesting/services/__estimator_interface.py b/adaptivetesting/services/__estimator_interface.py index 51b899f..aa887c1 100644 --- a/adaptivetesting/services/__estimator_interface.py +++ b/adaptivetesting/services/__estimator_interface.py @@ -1,13 +1,13 @@ from abc import ABC, abstractmethod -from typing import List, Tuple +from typing import List, Tuple, cast, Sequence import numpy as np -from ..models.__test_item import TestItem +from ..models.__test_item import TestItem, BaseItem, PolyItem class IEstimator(ABC): def __init__(self, response_pattern: List[int] | np.ndarray, - items: List[TestItem], + items: List[BaseItem], optimization_interval: Tuple[float, float] = (-10, 10)): """This is the interface required for every possible estimator. @@ -16,7 +16,7 @@ def __init__(self, Args: response_pattern (List[int]): list of responses (0: wrong, 1:right) - items (List[TestItem]): list of answered items + items (List[BaseItem]): list of answered items """ if type(response_pattern) is not np.ndarray: self.response_pattern = np.array(response_pattern) @@ -24,11 +24,18 @@ def __init__(self, self.response_pattern = response_pattern self.optimization_interval = optimization_interval - # convert items to parameter arrays - self.a = np.array([i.a for i in items]) - self.b = np.array([i.b for i in items]) - self.c = np.array([i.c for i in items]) - self.d = np.array([i.d for i in items]) + if isinstance(items[0], TestItem): + # convert items to parameter arrays + items_t = cast(list[TestItem], items) + self.a = np.array([i.a for i in items_t]) + self.b = np.array([i.b for i in items_t]) + self.c = np.array([i.c for i in items_t]) + self.d = np.array([i.d for i in items_t]) + + if isinstance(items[0], PolyItem): + items_tp = cast(list[PolyItem], items) + self.a_params = [i.a for i in items_tp] + self.thresholds_list: list[list[float]] = [i.b for i in items_tp] @abstractmethod def get_estimation(self) -> float: From 144fcd2b2c798b2d799e3067240bfa80073d4062 Mon Sep 17 00:00:00 2001 From: condecon Date: Tue, 16 Dec 2025 13:36:12 +0100 Subject: [PATCH 060/137] fix sequence issue --- adaptivetesting/math/estimators/__bayes_modal_estimation.py | 6 +++--- adaptivetesting/math/estimators/__expect_a_posteriori.py | 5 +++-- adaptivetesting/math/estimators/__ml_estimation.py | 5 +++-- adaptivetesting/services/__estimator_interface.py | 4 ++-- 4 files changed, 11 insertions(+), 9 deletions(-) diff --git a/adaptivetesting/math/estimators/__bayes_modal_estimation.py b/adaptivetesting/math/estimators/__bayes_modal_estimation.py index 98046e1..761ddb1 100644 --- a/adaptivetesting/math/estimators/__bayes_modal_estimation.py +++ b/adaptivetesting/math/estimators/__bayes_modal_estimation.py @@ -1,4 +1,4 @@ -from typing import List, Tuple +from typing import List, Tuple, Sequence import numpy as np from ...services.__estimator_interface import IEstimator from ...models.__test_item import TestItem @@ -10,7 +10,7 @@ class BayesModal(IEstimator): def __init__(self, response_pattern: List[int] | np.ndarray, - items: List[TestItem], + items: Sequence[TestItem], prior: Prior, optimization_interval: Tuple[float, float] = (-10, 10)): """This class can be used to estimate the current ability level @@ -23,7 +23,7 @@ def __init__(self, Args: response_pattern (List[int] | np.ndarray ): list of response patterns (0: wrong, 1:right) - items (List[TestItem]): list of answered items + items (Sequence[TestItem]): list of answered items prior (Prior): prior distribution diff --git a/adaptivetesting/math/estimators/__expect_a_posteriori.py b/adaptivetesting/math/estimators/__expect_a_posteriori.py index ec2426f..205042d 100644 --- a/adaptivetesting/math/estimators/__expect_a_posteriori.py +++ b/adaptivetesting/math/estimators/__expect_a_posteriori.py @@ -5,12 +5,13 @@ from .__functions.__estimators import log_likelihood from .__prior import Prior from math import pow +from typing import Sequence class ExpectedAPosteriori(BayesModal): def __init__(self, response_pattern: list[int] | np.ndarray, - items: list[TestItem], + items: Sequence[TestItem], prior: Prior, optimization_interval: tuple[float, float] = (-10, 10)): """This class can be used to estimate the current ability level @@ -22,7 +23,7 @@ def __init__(self, Args: response_pattern (List[int] | np.ndarray): list of response patterns (0: wrong, 1:right) - items (List[TestItem]): list of answered items + items (Sequence[TestItem]): list of answered items prior (Prior): prior distribution diff --git a/adaptivetesting/math/estimators/__ml_estimation.py b/adaptivetesting/math/estimators/__ml_estimation.py index df7708f..d426654 100644 --- a/adaptivetesting/math/estimators/__ml_estimation.py +++ b/adaptivetesting/math/estimators/__ml_estimation.py @@ -4,13 +4,14 @@ from ...services.__estimator_interface import IEstimator from .__functions.__estimators import maximize_likelihood_function from .__test_information import test_information_function +from typing import Sequence # TODO: introduce switch for estimation depending on the item type class MLEstimator(IEstimator): def __init__(self, response_pattern: List[int] | np.ndarray, - items: List[BaseItem], + items: Sequence[BaseItem], optimization_interval: Tuple[float, float] = (-10, 10), **kwargs): """This class can be used to estimate the current ability level of a respondent given the response pattern and the corresponding @@ -20,7 +21,7 @@ def __init__(self, Args: response_pattern (List[int]): list of response patterns (0: wrong, 1:right) - items (List[BaseItem]): list of answered items + items (Sequence[BaseItem]): list of answered items """ IEstimator.__init__(self, response_pattern, items, optimization_interval) diff --git a/adaptivetesting/services/__estimator_interface.py b/adaptivetesting/services/__estimator_interface.py index aa887c1..bceebb3 100644 --- a/adaptivetesting/services/__estimator_interface.py +++ b/adaptivetesting/services/__estimator_interface.py @@ -7,7 +7,7 @@ class IEstimator(ABC): def __init__(self, response_pattern: List[int] | np.ndarray, - items: List[BaseItem], + items: Sequence[BaseItem], optimization_interval: Tuple[float, float] = (-10, 10)): """This is the interface required for every possible estimator. @@ -16,7 +16,7 @@ def __init__(self, Args: response_pattern (List[int]): list of responses (0: wrong, 1:right) - items (List[BaseItem]): list of answered items + items (Sequence[BaseItem]): list of answered items """ if type(response_pattern) is not np.ndarray: self.response_pattern = np.array(response_pattern) From 339ff68441544d5148ea45d5a65649b234fe0073 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 19 Dec 2025 10:03:36 +0100 Subject: [PATCH 061/137] progress ml estimation and estimators --- .../estimators/__functions/__estimators.py | 2 +- .../math/estimators/__ml_estimation.py | 20 +++++++++++++++---- 2 files changed, 17 insertions(+), 5 deletions(-) diff --git a/adaptivetesting/math/estimators/__functions/__estimators.py b/adaptivetesting/math/estimators/__functions/__estimators.py index 21e4644..3b4cb61 100644 --- a/adaptivetesting/math/estimators/__functions/__estimators.py +++ b/adaptivetesting/math/estimators/__functions/__estimators.py @@ -139,7 +139,7 @@ def log_likelihood(mu: np.ndarray, return result - +# TODO: pass function to this for poly model compatability def maximize_likelihood_function(a: np.ndarray, b: np.ndarray, c: np.ndarray, diff --git a/adaptivetesting/math/estimators/__ml_estimation.py b/adaptivetesting/math/estimators/__ml_estimation.py index df7708f..4823cba 100644 --- a/adaptivetesting/math/estimators/__ml_estimation.py +++ b/adaptivetesting/math/estimators/__ml_estimation.py @@ -1,4 +1,4 @@ -from typing import List, Tuple +from typing import List, Tuple, Literal import numpy as np from ...models.__test_item import TestItem, BaseItem, PolyItem from ...services.__estimator_interface import IEstimator @@ -11,7 +11,9 @@ class MLEstimator(IEstimator): def __init__(self, response_pattern: List[int] | np.ndarray, items: List[BaseItem], - optimization_interval: Tuple[float, float] = (-10, 10), **kwargs): + optimization_interval: Tuple[float, float] = (-10, 10), + model: Literal["GRM", "GPCM"] | None = None, + **kwargs): """This class can be used to estimate the current ability level of a respondent given the response pattern and the corresponding item parameters. @@ -24,6 +26,13 @@ def __init__(self, """ IEstimator.__init__(self, response_pattern, items, optimization_interval) + # decide type of model used + if isinstance(items, PolyItem): + self.type: Literal["poly", "dich"] = "poly" + self.model = model + else: + self.type = "dich" + # ignore additional kwargs del kwargs @@ -35,13 +44,16 @@ def get_estimation(self) -> float: Returns: float: ability estimation """ - - return maximize_likelihood_function(a=self.a, + if self.type == "dich": + return maximize_likelihood_function(a=self.a, b=self.b, c=self.c, d=self.d, response_pattern=self.response_pattern, border=self.optimization_interval) + if self.type == "poly": + if self.model == "GRM": + def get_standard_error(self, estimation) -> float: """Calculates the standard error for the given estimated ability level. From 88d7e734c099f1d23f46d378ed721285753e974a Mon Sep 17 00:00:00 2001 From: condecon Date: Fri, 19 Dec 2025 10:55:18 +0100 Subject: [PATCH 062/137] add ml estimation for poly models --- .../estimators/__functions/__estimators.py | 1 - .../__functions/__poly/__poly_math.py | 20 ++++++++++++++++++- .../math/estimators/__ml_estimation.py | 20 +++++++++++++++++-- 3 files changed, 37 insertions(+), 4 deletions(-) diff --git a/adaptivetesting/math/estimators/__functions/__estimators.py b/adaptivetesting/math/estimators/__functions/__estimators.py index 3b4cb61..c9c75d3 100644 --- a/adaptivetesting/math/estimators/__functions/__estimators.py +++ b/adaptivetesting/math/estimators/__functions/__estimators.py @@ -139,7 +139,6 @@ def log_likelihood(mu: np.ndarray, return result -# TODO: pass function to this for poly model compatability def maximize_likelihood_function(a: np.ndarray, b: np.ndarray, c: np.ndarray, diff --git a/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py b/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py index 0c951ac..0ff4be4 100644 --- a/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py +++ b/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py @@ -1,4 +1,8 @@ from abc import ABC, abstractmethod +from scipy.optimize import minimize_scalar, OptimizeResult +from .....models.__algorithm_exception import AlgorithmException +from .....models.__test_item import PolyItem +import numpy as np class PolyModelFunctions(ABC): @@ -26,4 +30,18 @@ def fisher_information(theta: float, thresholds: list[float], response: int): pass - \ No newline at end of file + + def maximize_likelihood_function(self, + a_params: list[float], + thresholds_list: list[list[float]], + response_pattern: list[int], + border: tuple[float, float] = (-10, 10)): + + result: OptimizeResult = minimize_scalar(lambda mu: -self.log_likelihood(mu, a_params, thresholds_list, response_pattern), + bounds=border, + method='bounded') # type: ignore + + if not result.success: + raise AlgorithmException(f"Optimization failed: {result.message}") + else: + return result.x \ No newline at end of file diff --git a/adaptivetesting/math/estimators/__ml_estimation.py b/adaptivetesting/math/estimators/__ml_estimation.py index d042d01..a062868 100644 --- a/adaptivetesting/math/estimators/__ml_estimation.py +++ b/adaptivetesting/math/estimators/__ml_estimation.py @@ -1,10 +1,12 @@ -from typing import List, Tuple, Literal +from typing import List, Tuple, Literal, cast import numpy as np from ...models.__test_item import TestItem, BaseItem, PolyItem from ...services.__estimator_interface import IEstimator from .__functions.__estimators import maximize_likelihood_function from .__test_information import test_information_function from typing import Sequence +from .__functions.__poly.__gpcm import GPCM +from .__functions.__poly.__grm import GRM # TODO: introduce switch for estimation depending on the item type @@ -54,7 +56,21 @@ def get_estimation(self) -> float: border=self.optimization_interval) if self.type == "poly": if self.model == "GRM": - + grm = GRM() + return grm.maximize_likelihood_function( + self.a_params, + self.thresholds_list, + cast(list[int], self.response_pattern.tolist()) + ) + + if self.model == "GPCM": + gpcm = GPCM() + return gpcm.maximize_likelihood_function( + self.a_params, + self.thresholds_list, + cast(list[int], self.response_pattern.tolist()) + ) + raise ValueError("model and/or type have not been correctly specified") def get_standard_error(self, estimation) -> float: """Calculates the standard error for the given estimated ability level. From 6845d0acca951e839a04751337eb9b04785c70cb Mon Sep 17 00:00:00 2001 From: condecon Date: Fri, 19 Dec 2025 12:07:52 +0100 Subject: [PATCH 063/137] fix linting --- .../implementations/__poly_test_assembler.py | 4 +++- .../math/estimators/__functions/__estimators.py | 1 + .../math/estimators/__functions/__poly/__gpcm.py | 2 +- .../math/estimators/__functions/__poly/__grm.py | 10 ++++++---- .../estimators/__functions/__poly/__poly_math.py | 13 +++++++------ .../math/estimators/__ml_estimation.py | 15 +++++++-------- adaptivetesting/models/__test_item.py | 6 ++++-- 7 files changed, 29 insertions(+), 22 deletions(-) diff --git a/adaptivetesting/implementations/__poly_test_assembler.py b/adaptivetesting/implementations/__poly_test_assembler.py index 7dc0cb5..afce0d5 100644 --- a/adaptivetesting/implementations/__poly_test_assembler.py +++ b/adaptivetesting/implementations/__poly_test_assembler.py @@ -2,6 +2,8 @@ from typing import Type, Any from ..services.__estimator_interface import IEstimator from .__test_assembler import EstimatorArgs +from ..services.__item_selection_protocol import ItemSelectionStrategy +from ..math.item_selection.__maximum_information_criterion import maximum_information_criterion class PolyTestAssembler(AdaptiveTest): @@ -31,4 +33,4 @@ def __init__(self, self.__item_selector = item_selector self.__item_selector_args = item_selector_args self.__pretest = pretest - self.__pretest_seed = pretest_seed \ No newline at end of file + self.__pretest_seed = pretest_seed diff --git a/adaptivetesting/math/estimators/__functions/__estimators.py b/adaptivetesting/math/estimators/__functions/__estimators.py index c9c75d3..21e4644 100644 --- a/adaptivetesting/math/estimators/__functions/__estimators.py +++ b/adaptivetesting/math/estimators/__functions/__estimators.py @@ -139,6 +139,7 @@ def log_likelihood(mu: np.ndarray, return result + def maximize_likelihood_function(a: np.ndarray, b: np.ndarray, c: np.ndarray, diff --git a/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py b/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py index 980d7de..e656cbc 100644 --- a/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py +++ b/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py @@ -60,4 +60,4 @@ def log_prob(x): return log(p) log_prob_d2 = nd.Derivative(log_prob, order=2) - return -log_prob_d2(theta) \ No newline at end of file + return -log_prob_d2(theta) diff --git a/adaptivetesting/math/estimators/__functions/__poly/__grm.py b/adaptivetesting/math/estimators/__functions/__poly/__grm.py index d77165c..1ab31b7 100644 --- a/adaptivetesting/math/estimators/__functions/__poly/__grm.py +++ b/adaptivetesting/math/estimators/__functions/__poly/__grm.py @@ -1,6 +1,5 @@ import numpy as np from math import log -from scipy.integrate import quad import numdifftools as nd from .__poly_math import PolyModelFunctions @@ -15,7 +14,7 @@ def category_prob(theta, a: float, thresholds: list[float], k: int): if k == 0: p_ge_k = 1.0 elif k > 0 and k <= num_thresholds: - p_ge_k = 1 / (1 + np.exp(-a * (theta - thresholds[k-1]))) + p_ge_k = 1 / (1 + np.exp(-a * (theta - thresholds[k - 1]))) else: # Invalid category index k or k > num_categories (num_thresholds + 1) # For likelihood calculation, return a very small positive number to avoid log(0) @@ -36,7 +35,10 @@ def category_prob(theta, a: float, thresholds: list[float], k: int): return np.maximum(prob_k, 1e-10) # Use np.maximum for element-wise comparison with arrays @staticmethod - def log_likelihood(theta: float, a_params: list[float], thresholds_list: list[list[float]], response_pattern: list[int]): + def log_likelihood(theta: float, + a_params: list[float], + thresholds_list: list[list[float]], + response_pattern: list[int]): log_lik = 0.0 # Iterate over item indices for item_idx in range(len(a_params)): @@ -64,4 +66,4 @@ def log_prob(x): return log(p) log_prob_d2 = nd.Derivative(log_prob, order=2) - return -log_prob_d2(theta) \ No newline at end of file + return -log_prob_d2(theta) diff --git a/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py b/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py index 0ff4be4..8a4fbb6 100644 --- a/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py +++ b/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py @@ -1,8 +1,6 @@ from abc import ABC, abstractmethod from scipy.optimize import minimize_scalar, OptimizeResult from .....models.__algorithm_exception import AlgorithmException -from .....models.__test_item import PolyItem -import numpy as np class PolyModelFunctions(ABC): @@ -37,11 +35,14 @@ def maximize_likelihood_function(self, response_pattern: list[int], border: tuple[float, float] = (-10, 10)): - result: OptimizeResult = minimize_scalar(lambda mu: -self.log_likelihood(mu, a_params, thresholds_list, response_pattern), - bounds=border, - method='bounded') # type: ignore + result: OptimizeResult = minimize_scalar(lambda mu: -self.log_likelihood(mu, + a_params, + thresholds_list, + response_pattern), + bounds=border, + method='bounded') if not result.success: raise AlgorithmException(f"Optimization failed: {result.message}") else: - return result.x \ No newline at end of file + return result.x diff --git a/adaptivetesting/math/estimators/__ml_estimation.py b/adaptivetesting/math/estimators/__ml_estimation.py index a062868..0c78fc4 100644 --- a/adaptivetesting/math/estimators/__ml_estimation.py +++ b/adaptivetesting/math/estimators/__ml_estimation.py @@ -1,6 +1,6 @@ from typing import List, Tuple, Literal, cast import numpy as np -from ...models.__test_item import TestItem, BaseItem, PolyItem +from ...models.__test_item import BaseItem, PolyItem from ...services.__estimator_interface import IEstimator from .__functions.__estimators import maximize_likelihood_function from .__test_information import test_information_function @@ -9,7 +9,6 @@ from .__functions.__poly.__grm import GRM -# TODO: introduce switch for estimation depending on the item type class MLEstimator(IEstimator): def __init__(self, response_pattern: List[int] | np.ndarray, @@ -47,13 +46,13 @@ def get_estimation(self) -> float: Returns: float: ability estimation """ - if self.type == "dich": + if self.type == "dich": return maximize_likelihood_function(a=self.a, - b=self.b, - c=self.c, - d=self.d, - response_pattern=self.response_pattern, - border=self.optimization_interval) + b=self.b, + c=self.c, + d=self.d, + response_pattern=self.response_pattern, + border=self.optimization_interval) if self.type == "poly": if self.model == "GRM": grm = GRM() diff --git a/adaptivetesting/models/__test_item.py b/adaptivetesting/models/__test_item.py index dcee5ff..4dd4f05 100644 --- a/adaptivetesting/models/__test_item.py +++ b/adaptivetesting/models/__test_item.py @@ -1,5 +1,6 @@ from abc import abstractmethod + class BaseItem: def __init__(self): self.id: int | None @@ -15,6 +16,7 @@ def as_dict(self, with_id: bool = False): def from_dict(source: dict) -> "BaseItem": pass + class TestItem(BaseItem): def __init__(self): """Representation of a test item in the item pool. @@ -82,7 +84,7 @@ def __init__(self): self.additional_properties: dict = {} self.num_categories: int = 2 - def as_dict(self, with_id = False): + def as_dict(self, with_id=False): item_dict: dict[str, float | int | list | dict | None] = { "a": self.a, "b": self.b, @@ -106,7 +108,7 @@ def from_dict(source) -> "PolyItem": item.num_categories = source["num_categories"] if "additional_properties" in source and source["additional_properties"] is not None: if not isinstance(source["additional_properties"], dict): - raise TypeError("additional_properties is not dict") + raise TypeError("additional_properties is not dict") item.additional_properties = source["additional_properties"] if "id" in source and source["id"] is not None: item.id = source["id"] From 3145967f20e6078568210ce3408f9bd4885a9073 Mon Sep 17 00:00:00 2001 From: condecon Date: Fri, 19 Dec 2025 12:48:17 +0100 Subject: [PATCH 064/137] add estimation ability for poly models --- .../estimators/__bayes_modal_estimation.py | 59 +++++++++++---- .../math/estimators/__expect_a_posteriori.py | 46 ++++++++++-- .../__functions/__poly/__poly_math.py | 74 +++++++++++++++++++ .../math/estimators/__ml_estimation.py | 9 ++- 4 files changed, 166 insertions(+), 22 deletions(-) diff --git a/adaptivetesting/math/estimators/__bayes_modal_estimation.py b/adaptivetesting/math/estimators/__bayes_modal_estimation.py index 761ddb1..bb413cd 100644 --- a/adaptivetesting/math/estimators/__bayes_modal_estimation.py +++ b/adaptivetesting/math/estimators/__bayes_modal_estimation.py @@ -1,18 +1,21 @@ -from typing import List, Tuple, Sequence +from typing import List, Tuple, Sequence, Literal, cast import numpy as np from ...services.__estimator_interface import IEstimator -from ...models.__test_item import TestItem +from ...models.__test_item import BaseItem, PolyItem from .__functions.__bayes import maximize_posterior from .__prior import Prior from .__test_information import test_information_function +from .__functions.__poly.__gpcm import GPCM +from .__functions.__poly.__grm import GRM class BayesModal(IEstimator): def __init__(self, response_pattern: List[int] | np.ndarray, - items: Sequence[TestItem], + items: Sequence[BaseItem], prior: Prior, - optimization_interval: Tuple[float, float] = (-10, 10)): + optimization_interval: Tuple[float, float] = (-10, 10), + model: Literal["GRM", "GPCM"] | None = None,): """This class can be used to estimate the current ability level of a respondent given the response pattern and the corresponding item difficulties. @@ -23,7 +26,7 @@ def __init__(self, Args: response_pattern (List[int] | np.ndarray ): list of response patterns (0: wrong, 1:right) - items (Sequence[TestItem]): list of answered items + items (Sequence[BaseItem]): list of answered items prior (Prior): prior distribution @@ -33,6 +36,13 @@ def __init__(self, self.prior = prior + # decide type of model used + if isinstance(items, PolyItem): + self.type: Literal["poly", "dich"] = "poly" + self.model = model + else: + self.type = "dich" + def get_estimation(self) -> float: """Estimate the current ability level using Bayes Modal. The `bounded` optimizer is used @@ -44,16 +54,37 @@ def get_estimation(self) -> float: Returns: float: ability estimation """ - - return maximize_posterior( - self.a, - self.b, - self.c, - self.d, - self.response_pattern, - self.prior - ) + if self.type == "dich": + return maximize_posterior(self.a, + self.b, + self.c, + self.d, + self.response_pattern, + self.prior) + + if self.type == "poly": + if self.model == "GRM": + grm = GRM() + return grm.maximize_posterior( + self.a_params, + self.thresholds_list, + cast(list[int], self.response_pattern.tolist()), + self.prior, + self.optimization_interval + ) + + if self.model == "GPCM": + gpcm = GPCM() + return gpcm.maximize_posterior( + self.a_params, + self.thresholds_list, + cast(list[int], self.response_pattern.tolist()), + self.prior, + self.optimization_interval + ) + raise ValueError("model and/or type have not been correctly specified") + # TODO: Standard error POLY def get_standard_error(self, estimation: float) -> float: """Calculates the standard error for the given estimated ability level. diff --git a/adaptivetesting/math/estimators/__expect_a_posteriori.py b/adaptivetesting/math/estimators/__expect_a_posteriori.py index 205042d..e394977 100644 --- a/adaptivetesting/math/estimators/__expect_a_posteriori.py +++ b/adaptivetesting/math/estimators/__expect_a_posteriori.py @@ -1,19 +1,22 @@ import numpy as np from scipy.integrate import trapezoid from .__bayes_modal_estimation import BayesModal -from ...models.__test_item import TestItem +from ...models.__test_item import BaseItem, PolyItem from .__functions.__estimators import log_likelihood from .__prior import Prior from math import pow -from typing import Sequence +from typing import Sequence, Literal, cast +from .__functions.__poly.__gpcm import GPCM +from .__functions.__poly.__grm import GRM class ExpectedAPosteriori(BayesModal): def __init__(self, response_pattern: list[int] | np.ndarray, - items: Sequence[TestItem], + items: Sequence[BaseItem], prior: Prior, - optimization_interval: tuple[float, float] = (-10, 10)): + optimization_interval: tuple[float, float] = (-10, 10), + model: Literal["GRM", "GPCM"] | None = None,): """This class can be used to estimate the current ability level of a respondent given the response pattern and the corresponding item difficulties. @@ -23,7 +26,7 @@ def __init__(self, Args: response_pattern (List[int] | np.ndarray): list of response patterns (0: wrong, 1:right) - items (Sequence[TestItem]): list of answered items + items (Sequence[BaseItem]): list of answered items prior (Prior): prior distribution @@ -31,12 +34,45 @@ def __init__(self, """ super().__init__(response_pattern, items, prior, optimization_interval) + # decide type of model used + if isinstance(items, PolyItem): + self.type: Literal["poly", "dich"] = "poly" + self.model = model + else: + self.type = "dich" + def get_estimation(self) -> float: """Estimate the current ability level using EAP. Returns: float: ability estimation """ + if self.type == "dich": + return self.get_estimation_4pl() + + if self.type == "poly": + if self.model == "GRM": + grm = GRM() + return grm.posterior_mean( + self.a_params, + self.thresholds_list, + cast(list[int], self.response_pattern.tolist()), + self.prior, + self.optimization_interval + ) + + if self.model == "GPCM": + gpcm = GPCM() + return gpcm.posterior_mean( + self.a_params, + self.thresholds_list, + cast(list[int], self.response_pattern.tolist()), + self.prior, + self.optimization_interval + ) + raise ValueError("model and/or type have not been correctly specified") + + def get_estimation_4pl(self) -> float: x = np.linspace(self.optimization_interval[0], self.optimization_interval[1], 1000) if hasattr(self.prior, "logpdf"): diff --git a/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py b/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py index 8a4fbb6..01ef473 100644 --- a/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py +++ b/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py @@ -1,6 +1,9 @@ from abc import ABC, abstractmethod from scipy.optimize import minimize_scalar, OptimizeResult from .....models.__algorithm_exception import AlgorithmException +from ...__prior import Prior +import numpy as np +from scipy.integrate import trapezoid class PolyModelFunctions(ABC): @@ -46,3 +49,74 @@ def maximize_likelihood_function(self, raise AlgorithmException(f"Optimization failed: {result.message}") else: return result.x + + def maximize_posterior(self, + a_params: list[float], + thresholds_list: list[list[float]], + response_pattern: list[int], + prior: Prior, + optimization_interval: tuple[float, float] = (-10, 10) + ) -> float: + + def log_posterior(mu): + log_likelihood_res = np.array(self.log_likelihood(mu, + a_params, + thresholds_list, + response_pattern)) + + if hasattr(prior, "logpdf"): + log_prior = prior.logpdf(mu) + else: + log_prior = np.log(np.clip(prior.pdf(mu), 1e-300, None)) + + log_post = log_likelihood_res + log_prior + + if not np.isfinite(log_post): + return -1e300 + else: + return float(log_post.ravel()[0]) + + result: OptimizeResult = minimize_scalar(lambda mu: -log_posterior(mu), + bounds=optimization_interval, + method="bounded") # type: ignore + + if not result.success: + raise AlgorithmException(f"Optimization failed: {result.message}") + + else: + return float(result.x) + + def posterior_mean(self, + a_params: list[float], + thresholds_list: list[list[float]], + response_pattern: list[int], + prior: Prior, + optimization_interval: tuple[float, float] = (-10, 10)) -> float: + x = np.linspace(optimization_interval[0], optimization_interval[1], 1000) + + if hasattr(prior, "logpdf"): + log_prior = prior.logpdf(x) + else: + log_prior = np.log(prior.pdf(x) + 1e-300) + + log_likelihood_vec = np.vectorize( + lambda mu: self.log_likelihood(mu, a_params, thresholds_list, response_pattern) + ) + + log_likelihood_vals = log_likelihood_vec(x) + + log_posterior = log_likelihood_vals + log_prior + + # use log-sum-exp stabilization + max_log = np.nanmax(log_posterior) + weights = np.exp(log_posterior - max_log) + + numerator = trapezoid(x * weights, x) + denominator = trapezoid(weights, x) + np.finfo(float).eps + + if denominator == 0 or not np.isfinite(denominator): + raise ValueError("Denominator (integral of posterior) is zero or " + "non-finite — check interval/prior/likelihood.") + + estimation = numerator / denominator + return estimation diff --git a/adaptivetesting/math/estimators/__ml_estimation.py b/adaptivetesting/math/estimators/__ml_estimation.py index 0c78fc4..2949237 100644 --- a/adaptivetesting/math/estimators/__ml_estimation.py +++ b/adaptivetesting/math/estimators/__ml_estimation.py @@ -59,7 +59,8 @@ def get_estimation(self) -> float: return grm.maximize_likelihood_function( self.a_params, self.thresholds_list, - cast(list[int], self.response_pattern.tolist()) + cast(list[int], self.response_pattern.tolist()), + self.optimization_interval ) if self.model == "GPCM": @@ -67,10 +68,12 @@ def get_estimation(self) -> float: return gpcm.maximize_likelihood_function( self.a_params, self.thresholds_list, - cast(list[int], self.response_pattern.tolist()) + cast(list[int], self.response_pattern.tolist()), + self.optimization_interval ) raise ValueError("model and/or type have not been correctly specified") - + +# TODO: Standard error POLY def get_standard_error(self, estimation) -> float: """Calculates the standard error for the given estimated ability level. From f866ecc3484bba9316c65e3d298d205dd651419e Mon Sep 17 00:00:00 2001 From: condecon Date: Fri, 19 Dec 2025 13:08:38 +0100 Subject: [PATCH 065/137] add marker --- .../math/estimators/__test_information.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/adaptivetesting/math/estimators/__test_information.py b/adaptivetesting/math/estimators/__test_information.py index 90bb0e2..863e94b 100644 --- a/adaptivetesting/math/estimators/__test_information.py +++ b/adaptivetesting/math/estimators/__test_information.py @@ -4,6 +4,9 @@ from scipy.integrate import trapezoid import numpy from scipy.differentiate import derivative +from typing import Literal +from .__functions.__poly.__gpcm import GPCM +from .__functions.__poly.__grm import GRM def item_information_function( @@ -106,3 +109,19 @@ def test_information_function( return float(item_information.sum() + prior_information) else: return float(item_information.sum()) + +def poly_test_information_function( + mu: float, + a_params: list[float], + thresholds_list: list[list[float]], + prior: Prior, + model_type: Literal["GRM", "GPCM"], + optimization_interval: tuple[float, float] = (-10, 10), + ) -> float: + # calculate information for every test item + if model_type == "GRM": + GRM.fisher_information() + + item_information = + + ############## CONTINUE HERE! \ No newline at end of file From f81996a24f07ddef1393a989282c42081d240fb9 Mon Sep 17 00:00:00 2001 From: condecon Date: Tue, 13 Jan 2026 10:12:28 +0100 Subject: [PATCH 066/137] add standard error calculation for poly models --- .../estimators/__bayes_modal_estimation.py | 36 +++++++++---- .../math/estimators/__expect_a_posteriori.py | 35 ++++++++++--- .../estimators/__functions/__poly/__gpcm.py | 2 +- .../estimators/__functions/__poly/__grm.py | 2 +- .../math/estimators/__ml_estimation.py | 38 +++++++++----- .../math/estimators/__test_information.py | 50 +++++++++++++++---- 6 files changed, 120 insertions(+), 43 deletions(-) diff --git a/adaptivetesting/math/estimators/__bayes_modal_estimation.py b/adaptivetesting/math/estimators/__bayes_modal_estimation.py index bb413cd..c30205d 100644 --- a/adaptivetesting/math/estimators/__bayes_modal_estimation.py +++ b/adaptivetesting/math/estimators/__bayes_modal_estimation.py @@ -4,7 +4,7 @@ from ...models.__test_item import BaseItem, PolyItem from .__functions.__bayes import maximize_posterior from .__prior import Prior -from .__test_information import test_information_function +from .__test_information import test_information_function, poly_test_information_function from .__functions.__poly.__gpcm import GPCM from .__functions.__poly.__grm import GRM @@ -84,7 +84,6 @@ def get_estimation(self) -> float: ) raise ValueError("model and/or type have not been correctly specified") - # TODO: Standard error POLY def get_standard_error(self, estimation: float) -> float: """Calculates the standard error for the given estimated ability level. @@ -94,15 +93,30 @@ def get_standard_error(self, estimation: float) -> float: Returns: float: standard error of the ability estimation """ - test_information = test_information_function( - np.array(estimation, dtype=float), - a=self.a, - b=self.b, - c=self.c, - d=self.d, - prior=self.prior, - optimization_interval=self.optimization_interval - ) + if self.type == "dich": + test_information = test_information_function( + np.array(estimation, dtype=float), + a=self.a, + b=self.b, + c=self.c, + d=self.d, + prior=self.prior, + optimization_interval=self.optimization_interval + ) + + else: + if self.model is None: + raise ValueError("model cannot be None") + else: + test_information = poly_test_information_function( + mu=estimation, + a_params=self.a_params, + thresholds_list=self.thresholds_list, + response_pattern=self.response_pattern.tolist(), + model_type=self.model, + optimization_interval=self.optimization_interval, + prior=self.prior + ) sd_error = 1 / np.sqrt(test_information) return float(sd_error) diff --git a/adaptivetesting/math/estimators/__expect_a_posteriori.py b/adaptivetesting/math/estimators/__expect_a_posteriori.py index e394977..dc3d5e5 100644 --- a/adaptivetesting/math/estimators/__expect_a_posteriori.py +++ b/adaptivetesting/math/estimators/__expect_a_posteriori.py @@ -111,7 +111,7 @@ def get_standard_error(self, estimated_ability: float) -> float: The currently estimated ability level is required as parameter. Args: - estimated_ability (float): _description_ + estimated_ability (float): estimated ability level Returns: float: standard error of the ability estimation @@ -124,12 +124,33 @@ def get_standard_error(self, estimated_ability: float) -> float: else: log_prior = np.log(self.prior.pdf(x) + 1e-300) - log_likelihood_vals = np.vectorize(lambda mu: log_likelihood(mu, - self.a, - self.b, - self.c, - self.d, - self.response_pattern))(x) + log_likelihood_vals: np.ndarray + + if self.type == "dich": + log_likelihood_vals = np.vectorize(lambda mu: log_likelihood(mu, + self.a, + self.b, + self.c, + self.d, + self.response_pattern))(x) + if self.type == "poly": + if self.model == "GPCM": + log_likelihood_vec = np.vectorize( + lambda mu: GPCM.log_likelihood(mu, + self.a_params, + self.thresholds_list, + cast(list[int], self.response_pattern.tolist())) + ) + log_likelihood_vals = log_likelihood_vec(x) + if self.model == "GRM": + log_likelihood_vec = np.vectorize( + lambda mu: GRM.log_likelihood(mu, + self.a_params, + self.thresholds_list, + cast(list[int], self.response_pattern.tolist())) + ) + log_likelihood_vals = log_likelihood_vec(x) + log_posterior = log_likelihood_vals + log_prior max_log = np.nanmax(log_posterior) weights = np.exp(log_posterior - max_log) diff --git a/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py b/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py index e656cbc..611abca 100644 --- a/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py +++ b/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py @@ -52,7 +52,7 @@ def log_likelihood(theta: float, def fisher_information(theta, a, thresholds, - response): + response) -> float: """Embretson, S. E., & Reise, S. P. (2000). Item Response Theory for Psychologists.""" def log_prob(x): p = GPCM.category_prob(x, a, thresholds, response) diff --git a/adaptivetesting/math/estimators/__functions/__poly/__grm.py b/adaptivetesting/math/estimators/__functions/__poly/__grm.py index 1ab31b7..8894a51 100644 --- a/adaptivetesting/math/estimators/__functions/__poly/__grm.py +++ b/adaptivetesting/math/estimators/__functions/__poly/__grm.py @@ -58,7 +58,7 @@ def log_likelihood(theta: float, def fisher_information(theta: float, a: float, thresholds: list[float], - response: int): + response: int) -> float: """Embretson, S. E., & Reise, S. P. (2000). Item Response Theory for Psychologists.""" def log_prob(x): p = GRM.category_prob(x, a, thresholds, response) diff --git a/adaptivetesting/math/estimators/__ml_estimation.py b/adaptivetesting/math/estimators/__ml_estimation.py index 2949237..a71d7d7 100644 --- a/adaptivetesting/math/estimators/__ml_estimation.py +++ b/adaptivetesting/math/estimators/__ml_estimation.py @@ -3,7 +3,7 @@ from ...models.__test_item import BaseItem, PolyItem from ...services.__estimator_interface import IEstimator from .__functions.__estimators import maximize_likelihood_function -from .__test_information import test_information_function +from .__test_information import test_information_function, poly_test_information_function from typing import Sequence from .__functions.__poly.__gpcm import GPCM from .__functions.__poly.__grm import GRM @@ -62,7 +62,7 @@ def get_estimation(self) -> float: cast(list[int], self.response_pattern.tolist()), self.optimization_interval ) - + if self.model == "GPCM": gpcm = GPCM() return gpcm.maximize_likelihood_function( @@ -73,7 +73,6 @@ def get_estimation(self) -> float: ) raise ValueError("model and/or type have not been correctly specified") -# TODO: Standard error POLY def get_standard_error(self, estimation) -> float: """Calculates the standard error for the given estimated ability level. @@ -83,15 +82,30 @@ def get_standard_error(self, estimation) -> float: Returns: float: standard error of the ability estimation """ - test_information = test_information_function( - np.array(estimation, dtype=float), - a=self.a, - b=self.b, - c=self.c, - d=self.d, - prior=None, - optimization_interval=self.optimization_interval - ) + if self.type == "dich": + test_information = test_information_function( + np.array(estimation, dtype=float), + a=self.a, + b=self.b, + c=self.c, + d=self.d, + prior=None, + optimization_interval=self.optimization_interval + ) + + else: + if self.model is None: + raise ValueError("model cannot be None") + else: + test_information = poly_test_information_function( + mu=estimation, + a_params=self.a_params, + thresholds_list=self.thresholds_list, + response_pattern=self.response_pattern.tolist(), + model_type=self.model, + optimization_interval=self.optimization_interval, + prior=None + ) sd_error = 1 / np.sqrt(test_information) return float(sd_error) diff --git a/adaptivetesting/math/estimators/__test_information.py b/adaptivetesting/math/estimators/__test_information.py index 863e94b..89d3eca 100644 --- a/adaptivetesting/math/estimators/__test_information.py +++ b/adaptivetesting/math/estimators/__test_information.py @@ -110,18 +110,46 @@ def test_information_function( else: return float(item_information.sum()) + def poly_test_information_function( - mu: float, - a_params: list[float], - thresholds_list: list[list[float]], - prior: Prior, - model_type: Literal["GRM", "GPCM"], - optimization_interval: tuple[float, float] = (-10, 10), - ) -> float: + mu: float, + a_params: list[float], + thresholds_list: list[list[float]], + response_pattern: list[int], + prior: Prior | None, + model_type: Literal["GRM", "GPCM"], + optimization_interval: tuple[float, float] = (-10, 10), +) -> float: # calculate information for every test item + item_information = 0.0 if model_type == "GRM": - GRM.fisher_information() - - item_information = + for i, _ in enumerate(a_params): + inf_item_i = GRM.fisher_information( + mu, + a_params[i], + thresholds_list[i], + response_pattern[i] + ) + item_information = item_information + inf_item_i + elif model_type == "GPCM": + for i, _ in enumerate(a_params): + inf_item_i = GPCM.fisher_information( + mu, + a_params[i], + thresholds_list[i], + response_pattern[i] + ) + item_information = item_information + inf_item_i + else: + raise ValueError("model_type must be GRM or GPCM") + + test_information = item_information + # add prior information + if prior: + prior_information = prior_information_function( + prior=prior, + optimization_interval=optimization_interval + ) + test_information = test_information + float(prior_information) - ############## CONTINUE HERE! \ No newline at end of file + return test_information From 7751b92068aaecf1fc0b53c0e969d8e5a2a27d2c Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 16 Jan 2026 10:12:19 +0100 Subject: [PATCH 067/137] update item information function for GRM --- .../estimators/__functions/__poly/__grm.py | 26 +++++++++++++------ 1 file changed, 18 insertions(+), 8 deletions(-) diff --git a/adaptivetesting/math/estimators/__functions/__poly/__grm.py b/adaptivetesting/math/estimators/__functions/__poly/__grm.py index 8894a51..19d8fdd 100644 --- a/adaptivetesting/math/estimators/__functions/__poly/__grm.py +++ b/adaptivetesting/math/estimators/__functions/__poly/__grm.py @@ -57,13 +57,23 @@ def log_likelihood(theta: float, @staticmethod def fisher_information(theta: float, a: float, - thresholds: list[float], - response: int) -> float: - """Embretson, S. E., & Reise, S. P. (2000). Item Response Theory for Psychologists.""" - def log_prob(x): - p = GRM.category_prob(x, a, thresholds, response) + thresholds: list[float]): + """Embretson, S. E., & Reise, S. P. (2000). Item Response Theory for Psychologists. + p. 185 + primary citation Dodd, DeAyala & Koch 1995 + """ + def prob(x: float, category: int): + p = GRM.category_prob(x, a, thresholds, category) p = max(p, 1e-12) - return log(p) + return p + prob_d1 = nd.Derivative(prob, order=1) + + def category_information(x: float, category: int): + cat_inf = (prob_d1(x, category) ** 2) / prob(x, category) + return cat_inf + + item_inf = 0 + for cat in range(len(thresholds) + 1): + item_inf = item_inf + category_information(theta, cat) - log_prob_d2 = nd.Derivative(log_prob, order=2) - return -log_prob_d2(theta) + return item_inf \ No newline at end of file From 2b322a9095d5682f21f39503dc40bac533d2c57f Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 16 Jan 2026 10:15:37 +0100 Subject: [PATCH 068/137] update GPCM item information function and superclass signature --- .../estimators/__functions/__poly/__gpcm.py | 30 ++++++++++++------- .../__functions/__poly/__poly_math.py | 3 +- 2 files changed, 21 insertions(+), 12 deletions(-) diff --git a/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py b/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py index 611abca..b4a84c4 100644 --- a/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py +++ b/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py @@ -49,15 +49,25 @@ def log_likelihood(theta: float, return -log_lik # Return negative log-likelihood for minimization @staticmethod - def fisher_information(theta, - a, - thresholds, - response) -> float: - """Embretson, S. E., & Reise, S. P. (2000). Item Response Theory for Psychologists.""" - def log_prob(x): - p = GPCM.category_prob(x, a, thresholds, response) + def fisher_information(theta: float, + a: float, + thresholds: list[float]): + """Embretson, S. E., & Reise, S. P. (2000). Item Response Theory for Psychologists. + p. 185 + primary citation Dodd, DeAyala & Koch 1995 + """ + def prob(x: float, category: int): + p = GPCM.category_prob(x, a, thresholds, category) p = max(p, 1e-12) - return log(p) + return p + prob_d1 = nd.Derivative(prob, order=1) - log_prob_d2 = nd.Derivative(log_prob, order=2) - return -log_prob_d2(theta) + def category_information(x: float, category: int): + cat_inf = (prob_d1(x, category) ** 2) / prob(x, category) + return cat_inf + + item_inf = 0 + for cat in range(len(thresholds) + 1): + item_inf = item_inf + category_information(theta, cat) + + return item_inf diff --git a/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py b/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py index 01ef473..dd12a25 100644 --- a/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py +++ b/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py @@ -28,8 +28,7 @@ def log_likelihood(theta: float, @staticmethod def fisher_information(theta: float, a: float, - thresholds: list[float], - response: int): + thresholds: list[float]): pass def maximize_likelihood_function(self, From 264ec16aa3eb997e39a37a8fe1a15a5c3f6c5ed4 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 16 Jan 2026 10:20:54 +0100 Subject: [PATCH 069/137] remove old information calculation residuals --- .../math/estimators/__bayes_modal_estimation.py | 1 - .../math/estimators/__functions/__poly/__gpcm.py | 1 - .../math/estimators/__functions/__poly/__grm.py | 3 +-- adaptivetesting/math/estimators/__ml_estimation.py | 1 - adaptivetesting/math/estimators/__test_information.py | 7 ++----- 5 files changed, 3 insertions(+), 10 deletions(-) diff --git a/adaptivetesting/math/estimators/__bayes_modal_estimation.py b/adaptivetesting/math/estimators/__bayes_modal_estimation.py index c30205d..2558b15 100644 --- a/adaptivetesting/math/estimators/__bayes_modal_estimation.py +++ b/adaptivetesting/math/estimators/__bayes_modal_estimation.py @@ -112,7 +112,6 @@ def get_standard_error(self, estimation: float) -> float: mu=estimation, a_params=self.a_params, thresholds_list=self.thresholds_list, - response_pattern=self.response_pattern.tolist(), model_type=self.model, optimization_interval=self.optimization_interval, prior=self.prior diff --git a/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py b/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py index b4a84c4..31068dd 100644 --- a/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py +++ b/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py @@ -1,7 +1,6 @@ import numpy as np import numdifftools as nd from .__poly_math import PolyModelFunctions -from math import log class GPCM(PolyModelFunctions): diff --git a/adaptivetesting/math/estimators/__functions/__poly/__grm.py b/adaptivetesting/math/estimators/__functions/__poly/__grm.py index 19d8fdd..836a35c 100644 --- a/adaptivetesting/math/estimators/__functions/__poly/__grm.py +++ b/adaptivetesting/math/estimators/__functions/__poly/__grm.py @@ -1,5 +1,4 @@ import numpy as np -from math import log import numdifftools as nd from .__poly_math import PolyModelFunctions @@ -76,4 +75,4 @@ def category_information(x: float, category: int): for cat in range(len(thresholds) + 1): item_inf = item_inf + category_information(theta, cat) - return item_inf \ No newline at end of file + return item_inf diff --git a/adaptivetesting/math/estimators/__ml_estimation.py b/adaptivetesting/math/estimators/__ml_estimation.py index a71d7d7..3ac23da 100644 --- a/adaptivetesting/math/estimators/__ml_estimation.py +++ b/adaptivetesting/math/estimators/__ml_estimation.py @@ -101,7 +101,6 @@ def get_standard_error(self, estimation) -> float: mu=estimation, a_params=self.a_params, thresholds_list=self.thresholds_list, - response_pattern=self.response_pattern.tolist(), model_type=self.model, optimization_interval=self.optimization_interval, prior=None diff --git a/adaptivetesting/math/estimators/__test_information.py b/adaptivetesting/math/estimators/__test_information.py index 89d3eca..703ad05 100644 --- a/adaptivetesting/math/estimators/__test_information.py +++ b/adaptivetesting/math/estimators/__test_information.py @@ -115,7 +115,6 @@ def poly_test_information_function( mu: float, a_params: list[float], thresholds_list: list[list[float]], - response_pattern: list[int], prior: Prior | None, model_type: Literal["GRM", "GPCM"], optimization_interval: tuple[float, float] = (-10, 10), @@ -127,8 +126,7 @@ def poly_test_information_function( inf_item_i = GRM.fisher_information( mu, a_params[i], - thresholds_list[i], - response_pattern[i] + thresholds_list[i] ) item_information = item_information + inf_item_i elif model_type == "GPCM": @@ -136,8 +134,7 @@ def poly_test_information_function( inf_item_i = GPCM.fisher_information( mu, a_params[i], - thresholds_list[i], - response_pattern[i] + thresholds_list[i] ) item_information = item_information + inf_item_i else: From 8b8d77b26b11c108cfc79cde5f7749028dedb150 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Mon, 19 Jan 2026 15:10:04 +0100 Subject: [PATCH 070/137] remove poly test assembler --- .../implementations/__poly_test_assembler.py | 36 ------------------- 1 file changed, 36 deletions(-) delete mode 100644 adaptivetesting/implementations/__poly_test_assembler.py diff --git a/adaptivetesting/implementations/__poly_test_assembler.py b/adaptivetesting/implementations/__poly_test_assembler.py deleted file mode 100644 index afce0d5..0000000 --- a/adaptivetesting/implementations/__poly_test_assembler.py +++ /dev/null @@ -1,36 +0,0 @@ -from ..models.__adaptive_test import AdaptiveTest -from typing import Type, Any -from ..services.__estimator_interface import IEstimator -from .__test_assembler import EstimatorArgs -from ..services.__item_selection_protocol import ItemSelectionStrategy -from ..math.item_selection.__maximum_information_criterion import maximum_information_criterion - - -class PolyTestAssembler(AdaptiveTest): - def __init__(self, - item_pool, - simulation_id, - participant_id, - ability_estimator: Type[IEstimator], - estimator_args: EstimatorArgs = { - "prior": None, - "optimization_interval": (-10, 10) - }, - item_selector: ItemSelectionStrategy = maximum_information_criterion, # type: ignore - item_selector_args: dict[str, Any] = {}, - pretest: bool = False, - pretest_seed: int | None = None, - true_ability_level=None, - initial_ability_level=0, - simulation=True, - DEBUG=False, - **kwargs): - super().__init__(item_pool, simulation_id, participant_id, - true_ability_level, initial_ability_level, simulation, DEBUG, **kwargs) - - self.__ability_estimator = ability_estimator - self.__estimator_args = estimator_args - self.__item_selector = item_selector - self.__item_selector_args = item_selector_args - self.__pretest = pretest - self.__pretest_seed = pretest_seed From 9f9df2b5e0353eac19cd8b75a375d7e1c4b41e7d Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Mon, 19 Jan 2026 15:11:09 +0100 Subject: [PATCH 071/137] add BaseItemPool and PolyItemPool classes with loading methods --- adaptivetesting/models/__base_item_pool.py | 61 +++++++++ adaptivetesting/models/__item_pool.py | 101 ++------------ adaptivetesting/models/__poly_item_pool.py | 145 +++++++++++++++++++++ 3 files changed, 220 insertions(+), 87 deletions(-) create mode 100644 adaptivetesting/models/__base_item_pool.py create mode 100644 adaptivetesting/models/__poly_item_pool.py diff --git a/adaptivetesting/models/__base_item_pool.py b/adaptivetesting/models/__base_item_pool.py new file mode 100644 index 0000000..6116863 --- /dev/null +++ b/adaptivetesting/models/__base_item_pool.py @@ -0,0 +1,61 @@ +from .__test_item import BaseItem +from typing import List, Tuple + + +class BaseItemPool: + def __init__(self, + test_items: List[BaseItem], + simulated_responses: List[int] | None = None): + self.test_items: List[BaseItem] = test_items + self.simulated_responses: List[int] | None = simulated_responses + + def get_item_by_index(self, index: int) -> Tuple[BaseItem, int] | BaseItem: + selected_item = self.test_items[index] + if self.simulated_responses is not None: + simulated_response = self.simulated_responses[index] + return selected_item, simulated_response + else: + return selected_item + + def get_item_by_item(self, item: BaseItem) -> Tuple[BaseItem, int] | BaseItem: + index = self.test_items.index(item) + selected_item = self.test_items[index] + if self.simulated_responses is not None: + simulated_response = self.simulated_responses[index] + return selected_item, simulated_response + else: + return selected_item + + def get_item_response(self, item: BaseItem) -> int: + if self.simulated_responses is None: + raise ValueError("Simulated responses not provided") + else: + i, res = self.get_item_by_item(item) # type: ignore + return res + + def delete_item(self, item: BaseItem) -> None: + index = self.test_items.index(item) + self.test_items.pop(index) + if self.simulated_responses is not None: + self.simulated_responses.pop(index) + + @staticmethod + def load_from_list(*args, **kwargs): + """ + Abstract static method for loading from list. Should be overridden in subclasses. + """ + raise NotImplementedError("load_from_list must be implemented in subclass") + + @staticmethod + def load_from_dict(*args, **kwargs): + """ + Abstract static method for loading from dict. Should be overridden in subclasses. + """ + raise NotImplementedError("load_from_dict must be implemented in subclass") + + @staticmethod + def load_from_dataframe(*args, **kwargs): + """ + Abstract static method for loading from dataframe. Should be overridden in subclasses. + """ + raise NotImplementedError("load_from_dataframe must be implemented in subclass") diff --git a/adaptivetesting/models/__item_pool.py b/adaptivetesting/models/__item_pool.py index 4081a63..5b4c2a9 100644 --- a/adaptivetesting/models/__item_pool.py +++ b/adaptivetesting/models/__item_pool.py @@ -1,9 +1,12 @@ -from .__test_item import TestItem + +from .__test_item import TestItem, BaseItem +from .__base_item_pool import BaseItemPool from typing import List, Tuple from pandas import DataFrame -class ItemPool: + +class ItemPool(BaseItemPool): def __init__(self, test_items: List[TestItem], simulated_responses: List[int] | None = None): @@ -15,88 +18,20 @@ def __init__(self, Args: test_items (List[TestItem]): A list of test items. Necessary for any adaptive test. - simulated_responses (List[int]): A list of simulated responses. - Required for CAT simulations. - """ - self.test_items: List[TestItem] = test_items - self.simulated_responses: List[int] | None = simulated_responses - - def get_item_by_index(self, index: int) -> Tuple[TestItem, int] | TestItem: - """Returns item and if defined the simulated response. - - Args: - index (int): Index of the test item in the item pool to return. - - Returns: - TestItem or (TestItem, Simulated Response) - """ - selected_item = self.test_items[index] - if self.simulated_responses is not None: - simulated_response = self.simulated_responses[index] - return selected_item, simulated_response - else: - return selected_item - - def get_item_by_item(self, item: TestItem) -> Tuple[TestItem, int] | TestItem: - """Returns item and if defined the simulated response. - - Args: - item (TestItem): item to return. - - Returns: - TestItem or (TestItem, Simulated Response) - """ - index = self.test_items.index(item) - selected_item = self.test_items[index] - if self.simulated_responses is not None: - simulated_response = self.simulated_responses[index] - return selected_item, simulated_response - else: - return selected_item - - def get_item_response(self, item: TestItem) -> int: + Required for CAT simulations. """ - Gets the simulated response to an item if available. - A `ValueError` will be raised if a simulated response is not available. - - Args: - item (TestItem): item to get the corresponding response + super().__init__(test_items, simulated_responses) - Returns: - (int): response (either `0` or `1`) - """ - if self.simulated_responses is None: - raise ValueError("Simulated responses not provided") - else: - i, res = self.get_item_by_item(item) # type: ignore - return res - - def delete_item(self, item: TestItem) -> None: - """Deletes item from item pool. - If simulated responses are defined, they will be deleted as well. - - Args: - item (TestItem): The test item to delete. - """ - # get index - index = self.test_items.index(item) - # remove item at index - self.test_items.pop(index) - # remove response at index - if self.simulated_responses is not None: - self.simulated_responses.pop(index) - -# STATIC LOAD METHODS @staticmethod def load_from_list( - b: List[float], - a: List[float] | None = None, - c: List[float] | None = None, - d: List[float] | None = None, - simulated_responses: List[int] | None = None, - ids: List[int] | None = None, - content_categories: list[list[str]] | None = None) -> "ItemPool": + b: List[float], + a: List[float] | None = None, + c: List[float] | None = None, + d: List[float] | None = None, + simulated_responses: List[int] | None = None, + ids: List[int] | None = None, + content_categories: list[list[str]] | None = None) -> "ItemPool": """ Creates test items from a list of floats. @@ -115,7 +50,6 @@ def load_from_list( """ items: List[TestItem] = [] - for difficulty in b: item = TestItem() item.b = difficulty @@ -154,7 +88,6 @@ def load_from_list( item_pool = ItemPool(items) item_pool.simulated_responses = simulated_responses - return item_pool @staticmethod @@ -219,17 +152,12 @@ def load_from_dict(source: dict[str, List[float]], item.b = b[i] item.c = c[i] item.d = d[i] - if ids is not None: item.id = ids[i] - if content_categories is not None: item.additional_properties["category"] = content_categories[i] - items.append(item) - item_pool = ItemPool(items, simulated_responses) - return item_pool @staticmethod @@ -283,5 +211,4 @@ def load_from_dataframe(source: DataFrame) -> "ItemPool": if "simulated_responses" in source.columns: simulated_responses: List[int] = source["simulated_responses"].values.tolist() # type: ignore item_pool.simulated_responses = simulated_responses - return item_pool diff --git a/adaptivetesting/models/__poly_item_pool.py b/adaptivetesting/models/__poly_item_pool.py new file mode 100644 index 0000000..fac0846 --- /dev/null +++ b/adaptivetesting/models/__poly_item_pool.py @@ -0,0 +1,145 @@ +from .__item_pool import ItemPool +from ..models.__test_item import PolyItem +from .__base_item_pool import BaseItemPool +from pandas import DataFrame + + +# TODO: Static load functions +# TODO: replace in assembler +class PolyItemPool(BaseItemPool): + def __init__(self, + test_items: list[PolyItem], + simulated_responses: list[int] | None = None): + + self.test_items: list[PolyItem] = test_items + self.simulated_responses: list[int] | None = simulated_responses + + @staticmethod + def load_from_list(a: list[float], + b: list[list[float]], + simulated_responses: list[int] | None = None, + ids: list[int] | None = None, + content_categories: list[list[str]] | None = None) -> "PolyItemPool": + items: list[PolyItem] = [] + + if len(a) != len(b): + raise ValueError("Length of a and b has to be the same.") + + for i in range(len(a)): + item = PolyItem() + item.a = a[i] + item.b = b[i] + items.append(item) + + if content_categories is not None: + if len(content_categories) != len(b): + raise ValueError("Length of content_categories and b has to be the same.") + for i, groups in enumerate(content_categories): + items[i].additional_properties["category"] = groups + + item_pool = PolyItemPool(item, simulated_responses) + return item_pool + + @staticmethod + def load_from_dict(source, simulated_responses = None, ids = None, content_categories = None): + """Creates test items from a dictionary. + The dictionary has to have the following keys: + + - a + - b + + each containing a list of float. + """ + a = source.get("a") + b = source.get("b") + + # check none + if a is None: + raise ValueError("a cannot be None") + + if b is None: + raise ValueError("b cannot be None") + + # check if a, b, c, and d have the same length + if not len(a) == len(b): + raise ValueError("All lists in the source dictionary must have the same length") + + if ids is not None: + if len(ids) != len(b): + raise ValueError("Length of ids and b has to be the same.") + + if content_categories is not None: + if len(content_categories) != len(b): + raise ValueError("Length of content_categories and b has to be the same.") + + n_items = len(b) + items: list[PolyItem] = [] + for i in range(n_items): + item = PolyItem() + item.a = a[i] + item.b = b[i] + + if ids is not None: + item.id = ids[i] + if content_categories is not None: + item.additional_properties["category"] = content_categories[i] + + items.append(item) + + item_pool = PolyItemPool(items, simulated_responses) + return item_pool + + + @staticmethod + def load_from_dataframe(source: DataFrame) -> "ItemPool": + """Creates item pool from a pandas DataFrame. + Required columns are: `a`, `b`. + Each column has to contain float values. + A `simulated_responses` (int values) column can be added to + the DataFrame to provide simulated responses. + + Args: + source (DataFrame): source data frame + + Returns: + ItemPool: parsed item pool + """ + + # check if columns are present + if "a" not in source.columns: + raise ValueError("Column 'a' not found.") + + if "b" not in source.columns: + raise ValueError("Column 'b' not found.") + + if "c" not in source.columns: + raise ValueError("Column 'c' not found.") + + if "d" not in source.columns: + raise ValueError("Column 'd' not found.") + + # get values + a: list[float] = source["a"].values.tolist() # type: ignore + b: list[float] = source["b"].values.tolist() # type: ignore + + if "ids" in source.columns: + ids: List[int] | None = source["ids"].values.tolist() # type: ignore + else: + ids = None + + if "content_categories" in source.columns: + groups: list[list[str]] | None = source["content_categories"].values.tolist() + else: + groups = None + + # create item pool + item_pool = ItemPool.load_from_list(a=a, b=b, ids=ids, content_categories=groups) + + # check if simulated responses are present + if "simulated_responses" in source.columns: + simulated_responses: list[int] = source["simulated_responses"].values.tolist() # type: ignore + item_pool.simulated_responses = simulated_responses + return item_pool + + + \ No newline at end of file From a212cd4ff48c7387a28747696f84afee48b7bfe0 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Tue, 20 Jan 2026 13:40:38 +0100 Subject: [PATCH 072/137] Revert "add BaseItemPool and PolyItemPool classes with loading methods" This reverts commit 9f9df2b5e0353eac19cd8b75a375d7e1c4b41e7d. --- adaptivetesting/models/__base_item_pool.py | 61 --------- adaptivetesting/models/__item_pool.py | 101 ++++++++++++-- adaptivetesting/models/__poly_item_pool.py | 145 --------------------- 3 files changed, 87 insertions(+), 220 deletions(-) delete mode 100644 adaptivetesting/models/__base_item_pool.py delete mode 100644 adaptivetesting/models/__poly_item_pool.py diff --git a/adaptivetesting/models/__base_item_pool.py b/adaptivetesting/models/__base_item_pool.py deleted file mode 100644 index 6116863..0000000 --- a/adaptivetesting/models/__base_item_pool.py +++ /dev/null @@ -1,61 +0,0 @@ -from .__test_item import BaseItem -from typing import List, Tuple - - -class BaseItemPool: - def __init__(self, - test_items: List[BaseItem], - simulated_responses: List[int] | None = None): - self.test_items: List[BaseItem] = test_items - self.simulated_responses: List[int] | None = simulated_responses - - def get_item_by_index(self, index: int) -> Tuple[BaseItem, int] | BaseItem: - selected_item = self.test_items[index] - if self.simulated_responses is not None: - simulated_response = self.simulated_responses[index] - return selected_item, simulated_response - else: - return selected_item - - def get_item_by_item(self, item: BaseItem) -> Tuple[BaseItem, int] | BaseItem: - index = self.test_items.index(item) - selected_item = self.test_items[index] - if self.simulated_responses is not None: - simulated_response = self.simulated_responses[index] - return selected_item, simulated_response - else: - return selected_item - - def get_item_response(self, item: BaseItem) -> int: - if self.simulated_responses is None: - raise ValueError("Simulated responses not provided") - else: - i, res = self.get_item_by_item(item) # type: ignore - return res - - def delete_item(self, item: BaseItem) -> None: - index = self.test_items.index(item) - self.test_items.pop(index) - if self.simulated_responses is not None: - self.simulated_responses.pop(index) - - @staticmethod - def load_from_list(*args, **kwargs): - """ - Abstract static method for loading from list. Should be overridden in subclasses. - """ - raise NotImplementedError("load_from_list must be implemented in subclass") - - @staticmethod - def load_from_dict(*args, **kwargs): - """ - Abstract static method for loading from dict. Should be overridden in subclasses. - """ - raise NotImplementedError("load_from_dict must be implemented in subclass") - - @staticmethod - def load_from_dataframe(*args, **kwargs): - """ - Abstract static method for loading from dataframe. Should be overridden in subclasses. - """ - raise NotImplementedError("load_from_dataframe must be implemented in subclass") diff --git a/adaptivetesting/models/__item_pool.py b/adaptivetesting/models/__item_pool.py index 5b4c2a9..4081a63 100644 --- a/adaptivetesting/models/__item_pool.py +++ b/adaptivetesting/models/__item_pool.py @@ -1,12 +1,9 @@ - -from .__test_item import TestItem, BaseItem -from .__base_item_pool import BaseItemPool +from .__test_item import TestItem from typing import List, Tuple from pandas import DataFrame - -class ItemPool(BaseItemPool): +class ItemPool: def __init__(self, test_items: List[TestItem], simulated_responses: List[int] | None = None): @@ -18,20 +15,88 @@ def __init__(self, Args: test_items (List[TestItem]): A list of test items. Necessary for any adaptive test. + simulated_responses (List[int]): A list of simulated responses. - Required for CAT simulations. + Required for CAT simulations. + """ + self.test_items: List[TestItem] = test_items + self.simulated_responses: List[int] | None = simulated_responses + + def get_item_by_index(self, index: int) -> Tuple[TestItem, int] | TestItem: + """Returns item and if defined the simulated response. + + Args: + index (int): Index of the test item in the item pool to return. + + Returns: + TestItem or (TestItem, Simulated Response) + """ + selected_item = self.test_items[index] + if self.simulated_responses is not None: + simulated_response = self.simulated_responses[index] + return selected_item, simulated_response + else: + return selected_item + + def get_item_by_item(self, item: TestItem) -> Tuple[TestItem, int] | TestItem: + """Returns item and if defined the simulated response. + + Args: + item (TestItem): item to return. + + Returns: + TestItem or (TestItem, Simulated Response) + """ + index = self.test_items.index(item) + selected_item = self.test_items[index] + if self.simulated_responses is not None: + simulated_response = self.simulated_responses[index] + return selected_item, simulated_response + else: + return selected_item + + def get_item_response(self, item: TestItem) -> int: """ - super().__init__(test_items, simulated_responses) + Gets the simulated response to an item if available. + A `ValueError` will be raised if a simulated response is not available. + + Args: + item (TestItem): item to get the corresponding response + Returns: + (int): response (either `0` or `1`) + """ + if self.simulated_responses is None: + raise ValueError("Simulated responses not provided") + else: + i, res = self.get_item_by_item(item) # type: ignore + return res + + def delete_item(self, item: TestItem) -> None: + """Deletes item from item pool. + If simulated responses are defined, they will be deleted as well. + + Args: + item (TestItem): The test item to delete. + """ + # get index + index = self.test_items.index(item) + # remove item at index + self.test_items.pop(index) + # remove response at index + if self.simulated_responses is not None: + self.simulated_responses.pop(index) + +# STATIC LOAD METHODS @staticmethod def load_from_list( - b: List[float], - a: List[float] | None = None, - c: List[float] | None = None, - d: List[float] | None = None, - simulated_responses: List[int] | None = None, - ids: List[int] | None = None, - content_categories: list[list[str]] | None = None) -> "ItemPool": + b: List[float], + a: List[float] | None = None, + c: List[float] | None = None, + d: List[float] | None = None, + simulated_responses: List[int] | None = None, + ids: List[int] | None = None, + content_categories: list[list[str]] | None = None) -> "ItemPool": """ Creates test items from a list of floats. @@ -50,6 +115,7 @@ def load_from_list( """ items: List[TestItem] = [] + for difficulty in b: item = TestItem() item.b = difficulty @@ -88,6 +154,7 @@ def load_from_list( item_pool = ItemPool(items) item_pool.simulated_responses = simulated_responses + return item_pool @staticmethod @@ -152,12 +219,17 @@ def load_from_dict(source: dict[str, List[float]], item.b = b[i] item.c = c[i] item.d = d[i] + if ids is not None: item.id = ids[i] + if content_categories is not None: item.additional_properties["category"] = content_categories[i] + items.append(item) + item_pool = ItemPool(items, simulated_responses) + return item_pool @staticmethod @@ -211,4 +283,5 @@ def load_from_dataframe(source: DataFrame) -> "ItemPool": if "simulated_responses" in source.columns: simulated_responses: List[int] = source["simulated_responses"].values.tolist() # type: ignore item_pool.simulated_responses = simulated_responses + return item_pool diff --git a/adaptivetesting/models/__poly_item_pool.py b/adaptivetesting/models/__poly_item_pool.py deleted file mode 100644 index fac0846..0000000 --- a/adaptivetesting/models/__poly_item_pool.py +++ /dev/null @@ -1,145 +0,0 @@ -from .__item_pool import ItemPool -from ..models.__test_item import PolyItem -from .__base_item_pool import BaseItemPool -from pandas import DataFrame - - -# TODO: Static load functions -# TODO: replace in assembler -class PolyItemPool(BaseItemPool): - def __init__(self, - test_items: list[PolyItem], - simulated_responses: list[int] | None = None): - - self.test_items: list[PolyItem] = test_items - self.simulated_responses: list[int] | None = simulated_responses - - @staticmethod - def load_from_list(a: list[float], - b: list[list[float]], - simulated_responses: list[int] | None = None, - ids: list[int] | None = None, - content_categories: list[list[str]] | None = None) -> "PolyItemPool": - items: list[PolyItem] = [] - - if len(a) != len(b): - raise ValueError("Length of a and b has to be the same.") - - for i in range(len(a)): - item = PolyItem() - item.a = a[i] - item.b = b[i] - items.append(item) - - if content_categories is not None: - if len(content_categories) != len(b): - raise ValueError("Length of content_categories and b has to be the same.") - for i, groups in enumerate(content_categories): - items[i].additional_properties["category"] = groups - - item_pool = PolyItemPool(item, simulated_responses) - return item_pool - - @staticmethod - def load_from_dict(source, simulated_responses = None, ids = None, content_categories = None): - """Creates test items from a dictionary. - The dictionary has to have the following keys: - - - a - - b - - each containing a list of float. - """ - a = source.get("a") - b = source.get("b") - - # check none - if a is None: - raise ValueError("a cannot be None") - - if b is None: - raise ValueError("b cannot be None") - - # check if a, b, c, and d have the same length - if not len(a) == len(b): - raise ValueError("All lists in the source dictionary must have the same length") - - if ids is not None: - if len(ids) != len(b): - raise ValueError("Length of ids and b has to be the same.") - - if content_categories is not None: - if len(content_categories) != len(b): - raise ValueError("Length of content_categories and b has to be the same.") - - n_items = len(b) - items: list[PolyItem] = [] - for i in range(n_items): - item = PolyItem() - item.a = a[i] - item.b = b[i] - - if ids is not None: - item.id = ids[i] - if content_categories is not None: - item.additional_properties["category"] = content_categories[i] - - items.append(item) - - item_pool = PolyItemPool(items, simulated_responses) - return item_pool - - - @staticmethod - def load_from_dataframe(source: DataFrame) -> "ItemPool": - """Creates item pool from a pandas DataFrame. - Required columns are: `a`, `b`. - Each column has to contain float values. - A `simulated_responses` (int values) column can be added to - the DataFrame to provide simulated responses. - - Args: - source (DataFrame): source data frame - - Returns: - ItemPool: parsed item pool - """ - - # check if columns are present - if "a" not in source.columns: - raise ValueError("Column 'a' not found.") - - if "b" not in source.columns: - raise ValueError("Column 'b' not found.") - - if "c" not in source.columns: - raise ValueError("Column 'c' not found.") - - if "d" not in source.columns: - raise ValueError("Column 'd' not found.") - - # get values - a: list[float] = source["a"].values.tolist() # type: ignore - b: list[float] = source["b"].values.tolist() # type: ignore - - if "ids" in source.columns: - ids: List[int] | None = source["ids"].values.tolist() # type: ignore - else: - ids = None - - if "content_categories" in source.columns: - groups: list[list[str]] | None = source["content_categories"].values.tolist() - else: - groups = None - - # create item pool - item_pool = ItemPool.load_from_list(a=a, b=b, ids=ids, content_categories=groups) - - # check if simulated responses are present - if "simulated_responses" in source.columns: - simulated_responses: list[int] = source["simulated_responses"].values.tolist() # type: ignore - item_pool.simulated_responses = simulated_responses - return item_pool - - - \ No newline at end of file From 5b9d0584bc8061126c787eb27f1374d26607ffb4 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Tue, 20 Jan 2026 13:45:48 +0100 Subject: [PATCH 073/137] refactor TestItem class by removing BaseItem inheritance and cleaning up code --- adaptivetesting/models/__test_item.py | 67 ++------------------------- 1 file changed, 5 insertions(+), 62 deletions(-) diff --git a/adaptivetesting/models/__test_item.py b/adaptivetesting/models/__test_item.py index 4dd4f05..b104438 100644 --- a/adaptivetesting/models/__test_item.py +++ b/adaptivetesting/models/__test_item.py @@ -1,23 +1,7 @@ from abc import abstractmethod -class BaseItem: - def __init__(self): - self.id: int | None - self.a: float - self.b: float | list[float] - - @abstractmethod - def as_dict(self, with_id: bool = False): - pass - - @staticmethod - @abstractmethod - def from_dict(source: dict) -> "BaseItem": - pass - - -class TestItem(BaseItem): +class TestItem: def __init__(self): """Representation of a test item in the item pool. The format is equal to the implementation in catR. @@ -25,9 +9,9 @@ def __init__(self): Properties: - id (int | None): item ID - a (float): discrimination parameter - - b (float): difficulty parameter - - c (float): guessing parameter - - d (float): slipping parameter / upper asymptote + - b (float | list[float]): difficulty parameter. For polytomous models, list of threshold parameters + - c (float): guessing parameter. Ignored for polytomours models. + - d (float): slipping parameter / upper asymptote. Ignored for polytomours models. - additional_properties (dict): addtional properties can be set if required. This functionality is used for content balancing. To use content balancing, set set `category` key of the class instance @@ -37,7 +21,7 @@ def __init__(self): """ self.id: int | None = None self.a: float = 1 - self.b: float = float("nan") + self.b: float | list[float]= float("nan") self.c: float = 0 self.d: float = 1 self.additional_properties: dict = {} @@ -74,44 +58,3 @@ def from_dict(source: dict) -> "TestItem": if "id" in source and source["id"] is not None: item.id = source["id"] return item - - -class PolyItem(BaseItem): - def __init__(self): - self.id: int | None = None - self.a: float = 1 - self.b: list[float] = [] # thresholds - self.additional_properties: dict = {} - self.num_categories: int = 2 - - def as_dict(self, with_id=False): - item_dict: dict[str, float | int | list | dict | None] = { - "a": self.a, - "b": self.b, - "num_categories": self.num_categories, - "additional_properties": self.additional_properties - } - if with_id and self.id is not None: - item_dict["id"] = self.id - return item_dict - - @staticmethod - def from_dict(source) -> "PolyItem": - item = PolyItem() - if "a" in source and source["a"] is not None: - item.a = source["a"] - if "b" in source and source["b"] is not None: - if not isinstance(source["b"], list): - raise TypeError("b is not a list") - item.b = source["b"] - if "num_categories" in source and source["num_categories"] is not None: - item.num_categories = source["num_categories"] - if "additional_properties" in source and source["additional_properties"] is not None: - if not isinstance(source["additional_properties"], dict): - raise TypeError("additional_properties is not dict") - item.additional_properties = source["additional_properties"] - if "id" in source and source["id"] is not None: - item.id = source["id"] - - return item - From 51c2b1714a204348720152842b785ab7aff30210 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Tue, 20 Jan 2026 13:58:27 +0100 Subject: [PATCH 074/137] refactor TestItem.as_dict method to use TypedDict and update deprecated argument handling --- adaptivetesting/models/__test_item.py | 26 +++++++++++++++++++------- 1 file changed, 19 insertions(+), 7 deletions(-) diff --git a/adaptivetesting/models/__test_item.py b/adaptivetesting/models/__test_item.py index b104438..547d8d2 100644 --- a/adaptivetesting/models/__test_item.py +++ b/adaptivetesting/models/__test_item.py @@ -1,5 +1,4 @@ -from abc import abstractmethod - +from typing import TypedDict class TestItem: def __init__(self): @@ -26,9 +25,25 @@ def __init__(self): self.d: float = 1 self.additional_properties: dict = {} - def as_dict(self, with_id: bool = False) -> dict[str, float | int | dict | None]: + def as_dict(self, with_id = True): + """Convert test item to a dictionary. + + Args: + with_id (bool, optional): Deprecated. This argument will be ignored. + Defaults to True. - item_dict: dict[str, float | int | dict | None] = { + """ + ItemDict = TypedDict("ItemDict", { + "id": int | None, + "a": float, + "b": float | list[float], + "c": float, + "d": float, + "additional_properties": dict + }) + + item_dict: ItemDict = { + "id": self.id, "a": self.a, "b": self.b, "c": self.c, @@ -36,9 +51,6 @@ def as_dict(self, with_id: bool = False) -> dict[str, float | int | dict | None] "additional_properties": self.additional_properties } - if with_id and self.id is not None: - item_dict["id"] = self.id - return item_dict @staticmethod From 76965751241e8d2be7e49c974cca6f9c60826f57 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 23 Jan 2026 09:28:47 +0100 Subject: [PATCH 075/137] add basic functionality support for polytomous items --- adaptivetesting/data/__csv_context.py | 17 +++++++-- adaptivetesting/implementations/__pre_test.py | 13 +++++-- .../estimators/__bayes_modal_estimation.py | 10 ++--- .../math/estimators/__expect_a_posteriori.py | 10 ++--- .../__functions/__poly/__poly_math.py | 7 ++-- .../math/estimators/__ml_estimation.py | 9 ++--- .../math/item_selection/__urrys_rule.py | 37 +++++++++++-------- adaptivetesting/models/__adaptive_test.py | 14 ------- adaptivetesting/models/__item_pool.py | 3 ++ adaptivetesting/models/__test_item.py | 7 ++-- .../services/__estimator_interface.py | 19 +++++----- adaptivetesting/tests/test_adaptive_test.py | 4 -- adaptivetesting/tests/test_item_selection.py | 14 ++++++- adaptivetesting/tests/test_load_test_items.py | 11 +++--- 14 files changed, 95 insertions(+), 80 deletions(-) diff --git a/adaptivetesting/data/__csv_context.py b/adaptivetesting/data/__csv_context.py index 39141d3..89e37fe 100644 --- a/adaptivetesting/data/__csv_context.py +++ b/adaptivetesting/data/__csv_context.py @@ -4,6 +4,7 @@ from ..models.__test_result import TestResult from ..services.__test_results_interface import ITestResults import json +import ast class CSVContext(ITestResults): @@ -63,10 +64,18 @@ def load(self) -> List[TestResult]: next(reader, None) # skip header row for row in reader: test_result = TestResult.from_dict(row) - # showed_item is read as a json string - # this has to be manually converted into a dictionary - json_string = str(test_result.showed_item).replace("'", '"') - test_result.showed_item = json.loads(json_string) + # showed_item is read as a string representation of a Python + # literal (e.g. a dict). Use ast.literal_eval to safely parse + # it back into a dict. Fallback to a JSON-style conversion + # if literal_eval fails. + try: + test_result.showed_item = ast.literal_eval(str(test_result.showed_item)) + except Exception: + json_string = str(test_result.showed_item).replace("'", '"').replace("None", "null") + try: + test_result.showed_item = json.loads(json_string) + except Exception: + test_result.showed_item = {} test_results.append(test_result) file.close() return test_results diff --git a/adaptivetesting/implementations/__pre_test.py b/adaptivetesting/implementations/__pre_test.py index b7ae15c..bccd12d 100644 --- a/adaptivetesting/implementations/__pre_test.py +++ b/adaptivetesting/implementations/__pre_test.py @@ -1,4 +1,4 @@ -from typing import List +from typing import List, cast import numpy as np from ..models.__test_item import TestItem import random @@ -10,6 +10,7 @@ def __init__(self, items: List[TestItem], seed: int | None = None): The pretest class can be used to draw items randomly from difficulty quantiles of the item pool. + This pretest can only be used with dichotomous IRT models and items. Args: items: Item pool @@ -18,15 +19,21 @@ def __init__(self, items: List[TestItem], seed: int | None = None): If not, the item selection will be drawn randomly, and you will not be able to reproduce the results. + Raises: + ValueError: Raised if the items are specified for polytomous IRT models. + """ self.items = items self.seed = seed + if any([isinstance(item.b, list) for item in items]): + raise ValueError("The pretest can only be used with dichotomous IRT models and items.") + def calculate_quantiles(self) -> np.ndarray: """Calculates quantiles 0.25, 0.5, 0.75 """ # get difficulties - difficulties: List[float] = [item.b for item in self.items] + difficulties: List[float] = [cast(float, item.b) for item in self.items] quantiles = np.array([]) # calculate quantiles @@ -50,7 +57,7 @@ def select_item_in_interval(self, lower: float, upper: float) -> TestItem: """ # select only items with difficulty in interval items_in_interval: List[TestItem] = [item for item in list(self.items) - if lower < item.b <= upper] + if lower < cast(float, item.b) <= upper] # draw one item randomly if self.seed is not None: random.seed(self.seed) diff --git a/adaptivetesting/math/estimators/__bayes_modal_estimation.py b/adaptivetesting/math/estimators/__bayes_modal_estimation.py index 2558b15..a8f6ce4 100644 --- a/adaptivetesting/math/estimators/__bayes_modal_estimation.py +++ b/adaptivetesting/math/estimators/__bayes_modal_estimation.py @@ -1,7 +1,7 @@ -from typing import List, Tuple, Sequence, Literal, cast +from typing import List, Tuple, Literal, cast import numpy as np from ...services.__estimator_interface import IEstimator -from ...models.__test_item import BaseItem, PolyItem +from ...models.__test_item import TestItem from .__functions.__bayes import maximize_posterior from .__prior import Prior from .__test_information import test_information_function, poly_test_information_function @@ -12,7 +12,7 @@ class BayesModal(IEstimator): def __init__(self, response_pattern: List[int] | np.ndarray, - items: Sequence[BaseItem], + items: list[TestItem], prior: Prior, optimization_interval: Tuple[float, float] = (-10, 10), model: Literal["GRM", "GPCM"] | None = None,): @@ -26,7 +26,7 @@ def __init__(self, Args: response_pattern (List[int] | np.ndarray ): list of response patterns (0: wrong, 1:right) - items (Sequence[BaseItem]): list of answered items + items (list[TestItem]): list of answered items prior (Prior): prior distribution @@ -37,7 +37,7 @@ def __init__(self, self.prior = prior # decide type of model used - if isinstance(items, PolyItem): + if all([isinstance(item.b, list) for item in items]): self.type: Literal["poly", "dich"] = "poly" self.model = model else: diff --git a/adaptivetesting/math/estimators/__expect_a_posteriori.py b/adaptivetesting/math/estimators/__expect_a_posteriori.py index dc3d5e5..7de17ad 100644 --- a/adaptivetesting/math/estimators/__expect_a_posteriori.py +++ b/adaptivetesting/math/estimators/__expect_a_posteriori.py @@ -1,11 +1,11 @@ import numpy as np from scipy.integrate import trapezoid from .__bayes_modal_estimation import BayesModal -from ...models.__test_item import BaseItem, PolyItem +from ...models.__test_item import TestItem from .__functions.__estimators import log_likelihood from .__prior import Prior from math import pow -from typing import Sequence, Literal, cast +from typing import Literal, cast from .__functions.__poly.__gpcm import GPCM from .__functions.__poly.__grm import GRM @@ -13,7 +13,7 @@ class ExpectedAPosteriori(BayesModal): def __init__(self, response_pattern: list[int] | np.ndarray, - items: Sequence[BaseItem], + items: list[TestItem], prior: Prior, optimization_interval: tuple[float, float] = (-10, 10), model: Literal["GRM", "GPCM"] | None = None,): @@ -26,7 +26,7 @@ def __init__(self, Args: response_pattern (List[int] | np.ndarray): list of response patterns (0: wrong, 1:right) - items (Sequence[BaseItem]): list of answered items + items (list[TestItem]): list of answered items prior (Prior): prior distribution @@ -35,7 +35,7 @@ def __init__(self, super().__init__(response_pattern, items, prior, optimization_interval) # decide type of model used - if isinstance(items, PolyItem): + if all([isinstance(item.b, list) for item in items]): self.type: Literal["poly", "dich"] = "poly" self.model = model else: diff --git a/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py b/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py index dd12a25..e82efd5 100644 --- a/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py +++ b/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py @@ -8,24 +8,25 @@ class PolyModelFunctions(ABC): - @abstractmethod + @staticmethod + @abstractmethod def category_prob(theta: float, a: float, thresholds_list: list[float], response_pattern: int): pass - @abstractmethod @staticmethod + @abstractmethod def log_likelihood(theta: float, a_params: list[float], thresholds_list: list[list[float]], response_pattern: list[int]): pass - @abstractmethod @staticmethod + @abstractmethod def fisher_information(theta: float, a: float, thresholds: list[float]): diff --git a/adaptivetesting/math/estimators/__ml_estimation.py b/adaptivetesting/math/estimators/__ml_estimation.py index 3ac23da..bdcc0ad 100644 --- a/adaptivetesting/math/estimators/__ml_estimation.py +++ b/adaptivetesting/math/estimators/__ml_estimation.py @@ -1,10 +1,9 @@ from typing import List, Tuple, Literal, cast import numpy as np -from ...models.__test_item import BaseItem, PolyItem +from ...models.__test_item import TestItem from ...services.__estimator_interface import IEstimator from .__functions.__estimators import maximize_likelihood_function from .__test_information import test_information_function, poly_test_information_function -from typing import Sequence from .__functions.__poly.__gpcm import GPCM from .__functions.__poly.__grm import GRM @@ -12,7 +11,7 @@ class MLEstimator(IEstimator): def __init__(self, response_pattern: List[int] | np.ndarray, - items: Sequence[BaseItem], + items: list[TestItem], optimization_interval: Tuple[float, float] = (-10, 10), model: Literal["GRM", "GPCM"] | None = None, **kwargs): @@ -29,12 +28,12 @@ def __init__(self, IEstimator.__init__(self, response_pattern, items, optimization_interval) # decide type of model used - if isinstance(items, PolyItem): + if all([isinstance(item.b, list) for item in items]): self.type: Literal["poly", "dich"] = "poly" self.model = model else: self.type = "dich" - + # ignore additional kwargs del kwargs diff --git a/adaptivetesting/math/item_selection/__urrys_rule.py b/adaptivetesting/math/item_selection/__urrys_rule.py index 1eab960..4defb12 100644 --- a/adaptivetesting/math/item_selection/__urrys_rule.py +++ b/adaptivetesting/math/item_selection/__urrys_rule.py @@ -1,8 +1,10 @@ from ...models.__test_item import TestItem -from typing import List +from typing import List, cast from ...models.__item_selection_exception import ItemSelectionException +from warnings import deprecated +@deprecated("Use maximum information criterion instead.") def urrys_rule(items: List[TestItem], ability: float) -> TestItem: """Urry's rule selects the test item which has the minimal difference between @@ -16,18 +18,21 @@ def urrys_rule(items: List[TestItem], ability: float) -> TestItem: Returns: TestItem: selected test item """ - # create difference array from absolute value - difference: List[float] = [] - for item in items: - difference.append(abs(ability - item.b)) - - # get minimal difference - minimal_difference = min(difference) - - # find the item where minimal difference is equal to absolut - # value of difference - for item in items: - if abs(ability - item.b) == minimal_difference: - return item - - raise ItemSelectionException("No appropriate item could be selected.") + if all([isinstance(item.b, float) for item in items]): + # create difference array from absolute value + difference: List[float] = [] + for item in items: + difference.append(abs(ability - cast(float, item.b))) + + # get minimal difference + minimal_difference = min(difference) + + # find the item where minimal difference is equal to absolut + # value of difference + for item in items: + if abs(ability - cast(float, item.b)) == minimal_difference: + return item + + raise ItemSelectionException("No appropriate item could be selected.") + else: + raise ValueError("Urry's rule cannot be used with polytomous IRT model items!") diff --git a/adaptivetesting/models/__adaptive_test.py b/adaptivetesting/models/__adaptive_test.py index 039a845..b749dc7 100644 --- a/adaptivetesting/models/__adaptive_test.py +++ b/adaptivetesting/models/__adaptive_test.py @@ -67,20 +67,6 @@ def __init__(self, item_pool: ItemPool, seed=kwargs["seed"] if "seed" in kwargs.keys() else None ) - def get_item_difficulties(self) -> List[float]: - """ - Returns: - List[float]: difficulties of items in the item pool - """ - return [item.b for item in self.item_pool.test_items] - - def get_answered_items_difficulties(self) -> List[float]: - """ - Returns: - List[float]: difficulties of answered items - """ - return [item.b for item in self.answered_items] - def get_answered_items(self) -> List[TestItem]: """ Returns: diff --git a/adaptivetesting/models/__item_pool.py b/adaptivetesting/models/__item_pool.py index 4081a63..b26e4d5 100644 --- a/adaptivetesting/models/__item_pool.py +++ b/adaptivetesting/models/__item_pool.py @@ -222,6 +222,9 @@ def load_from_dict(source: dict[str, List[float]], if ids is not None: item.id = ids[i] + + if "id" in source.keys(): + item.id = source["id"][i] if content_categories is not None: item.additional_properties["category"] = content_categories[i] diff --git a/adaptivetesting/models/__test_item.py b/adaptivetesting/models/__test_item.py index 547d8d2..cbde588 100644 --- a/adaptivetesting/models/__test_item.py +++ b/adaptivetesting/models/__test_item.py @@ -1,5 +1,6 @@ from typing import TypedDict + class TestItem: def __init__(self): """Representation of a test item in the item pool. @@ -20,16 +21,16 @@ def __init__(self): """ self.id: int | None = None self.a: float = 1 - self.b: float | list[float]= float("nan") + self.b: float | list[float] = float("nan") self.c: float = 0 self.d: float = 1 self.additional_properties: dict = {} - def as_dict(self, with_id = True): + def as_dict(self, with_id=True): """Convert test item to a dictionary. Args: - with_id (bool, optional): Deprecated. This argument will be ignored. + with_id (bool, optional): Deprecated. This argument will be ignored. Defaults to True. """ diff --git a/adaptivetesting/services/__estimator_interface.py b/adaptivetesting/services/__estimator_interface.py index bceebb3..bdb5bc9 100644 --- a/adaptivetesting/services/__estimator_interface.py +++ b/adaptivetesting/services/__estimator_interface.py @@ -1,13 +1,13 @@ from abc import ABC, abstractmethod -from typing import List, Tuple, cast, Sequence +from typing import List, Tuple, cast import numpy as np -from ..models.__test_item import TestItem, BaseItem, PolyItem +from ..models.__test_item import TestItem class IEstimator(ABC): def __init__(self, response_pattern: List[int] | np.ndarray, - items: Sequence[BaseItem], + items: list[TestItem], optimization_interval: Tuple[float, float] = (-10, 10)): """This is the interface required for every possible estimator. @@ -16,7 +16,7 @@ def __init__(self, Args: response_pattern (List[int]): list of responses (0: wrong, 1:right) - items (Sequence[BaseItem]): list of answered items + items (list[TestItem]): list of answered items """ if type(response_pattern) is not np.ndarray: self.response_pattern = np.array(response_pattern) @@ -24,18 +24,17 @@ def __init__(self, self.response_pattern = response_pattern self.optimization_interval = optimization_interval - if isinstance(items[0], TestItem): + # decide type of model used + if all([isinstance(item.b, list) for item in items]): + self.a_params = [i.a for i in items] + self.thresholds_list: list[list[float]] = [cast(list, i.b) for i in items] + else: # convert items to parameter arrays items_t = cast(list[TestItem], items) self.a = np.array([i.a for i in items_t]) self.b = np.array([i.b for i in items_t]) self.c = np.array([i.c for i in items_t]) self.d = np.array([i.d for i in items_t]) - - if isinstance(items[0], PolyItem): - items_tp = cast(list[PolyItem], items) - self.a_params = [i.a for i in items_tp] - self.thresholds_list: list[list[float]] = [i.b for i in items_tp] @abstractmethod def get_estimation(self) -> float: diff --git a/adaptivetesting/tests/test_adaptive_test.py b/adaptivetesting/tests/test_adaptive_test.py index c24beac..27c30bf 100644 --- a/adaptivetesting/tests/test_adaptive_test.py +++ b/adaptivetesting/tests/test_adaptive_test.py @@ -39,10 +39,6 @@ def get_next_item(self): def estimate_ability_level(self) -> tuple[float, float]: return 0, float("NaN") - def test_get_difficulties(self): - difficulties = self.get_item_difficulties() - self.assertEqual(difficulties, [0.24, 0.89, -0.6]) - def test_standard_error(self): """This should calculate a standard error without failing""" self.answered_items = [item1, item2] diff --git a/adaptivetesting/tests/test_item_selection.py b/adaptivetesting/tests/test_item_selection.py index 8a72f76..5cd40e2 100644 --- a/adaptivetesting/tests/test_item_selection.py +++ b/adaptivetesting/tests/test_item_selection.py @@ -48,7 +48,12 @@ def test_selection_when_0(self): 0 ) self.assertDictEqual(selected_item.as_dict(), - {"a": 1.0975, "b": 0.1836, "c": 0.053, "d": 0.7533, "additional_properties": {}}) + {"a": 1.0975, + "b": 0.1836, + "c": 0.053, + "d": 0.7533, + "additional_properties": {}, + "id": None}) def test_selection_when_minus_0_5(self): items = self.load_items() @@ -57,4 +62,9 @@ def test_selection_when_minus_0_5(self): -0.5 ) self.assertDictEqual(selected_item.as_dict(), - {"a": 1.1477, "b": -0.8356, "c": 0.1629, "d": 0.8456, "additional_properties": {}}) + {"a": 1.1477, + "b": -0.8356, + "c": 0.1629, + "d": 0.8456, + "additional_properties": {}, + "id": None}) diff --git a/adaptivetesting/tests/test_load_test_items.py b/adaptivetesting/tests/test_load_test_items.py index a9ab38f..52a5ca3 100644 --- a/adaptivetesting/tests/test_load_test_items.py +++ b/adaptivetesting/tests/test_load_test_items.py @@ -97,7 +97,8 @@ def test_load_items_from_pandas_success(self): "b": 5, "c": 0.9, "d": 1, - "additional_properties": {} + "additional_properties": {}, + "id": None }, generated.test_items[0].as_dict() ) @@ -108,7 +109,8 @@ def test_load_items_from_pandas_success(self): "b": 3, "c": 1.9, "d": 1, - "additional_properties": {} + "additional_properties": {}, + "id": None }, generated.test_items[1].as_dict() ) @@ -246,7 +248,7 @@ def test_roundtrip_preserves_fields(self): } # serialize including id - data_with_id = original.as_dict(with_id=True) + data_with_id = original.as_dict() # deserialize restored = TestItem.from_dict(data_with_id) @@ -259,6 +261,3 @@ def test_roundtrip_preserves_fields(self): self.assertEqual(restored.d, original.d) self.assertEqual(restored.additional_properties, original.additional_properties) - # verify as_dict omits id when with_id is False - data_no_id = original.as_dict(with_id=False) - self.assertNotIn("id", data_no_id) From 8647e59a1c759edf7799d50e15806b45cf253dd0 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 23 Jan 2026 10:24:35 +0100 Subject: [PATCH 076/137] refactor ItemPool to support polytomous item parameters and improve type handling --- adaptivetesting/models/__item_pool.py | 92 +++++++++++++++------------ 1 file changed, 52 insertions(+), 40 deletions(-) diff --git a/adaptivetesting/models/__item_pool.py b/adaptivetesting/models/__item_pool.py index b26e4d5..f101a37 100644 --- a/adaptivetesting/models/__item_pool.py +++ b/adaptivetesting/models/__item_pool.py @@ -1,5 +1,5 @@ from .__test_item import TestItem -from typing import List, Tuple +from typing import List, Tuple, cast from pandas import DataFrame @@ -90,7 +90,7 @@ def delete_item(self, item: TestItem) -> None: # STATIC LOAD METHODS @staticmethod def load_from_list( - b: List[float], + b: List[float] | list[list[float]], a: List[float] | None = None, c: List[float] | None = None, d: List[float] | None = None, @@ -102,9 +102,10 @@ def load_from_list( Args: a (List[float]): discrimination parameter - b (List[float]): difficulty parameter - c (List[float]): guessing parameter - d (List[float]): slipping parameter + b (List[float] | list[list[float]]): For dichotomous models, this is the difficulty parameter. + For polytomous items, this is the list of threshold parameters. + c (List[float]): guessing parameter. Ignored for polytomous items. + d (List[float]): slipping parameter. Ignored for polytomous items. simulated_responses (List[int]): simulated responses ids (List[int]): item IDs content_categories (list[list[str]], optional): List of categories for each item. @@ -116,9 +117,9 @@ def load_from_list( """ items: List[TestItem] = [] - for difficulty in b: + for b_i in b: item = TestItem() - item.b = difficulty + item.b = b_i items.append(item) # check if a, b, c, d are the same length @@ -158,7 +159,7 @@ def load_from_list( return item_pool @staticmethod - def load_from_dict(source: dict[str, List[float]], + def load_from_dict(source: dict[str, List[float] | list[float]], simulated_responses: List[int] | None = None, ids: List[int] | None = None, content_categories: list[list[str]] | None = None) -> "ItemPool": @@ -166,13 +167,13 @@ def load_from_dict(source: dict[str, List[float]], The dictionary has to have the following keys: - a - - b - - c - - d + - b (list of float, for polytomous models list[list[float]] as threshold list) + - c (ignored for polytomous items) + - d (ignored for polytomous items) each containing a list of float. Args: - source (dict[str, List[float]]): item pool dictionary + source (dict[str, List[float] | list[float]]): item pool dictionary simulated_responses (List[int]): simulated responses ids (List[int], optional): item IDs. Default `None`. content_categories (list[list[str]], optional): List of categories for each item. @@ -181,10 +182,10 @@ def load_from_dict(source: dict[str, List[float]], Returns: List[TestItem]: item pool """ - a = source.get("a") - b = source.get("b") - c = source.get("c") - d = source.get("d") + a = cast(list[float], source.get("a")) + b = cast(list[list[float]] | list[float], source.get("b")) + c = cast(list[float] | None, source.get("c")) + d = cast(list[float] | None, source.get("d")) # check none if a is None: @@ -192,17 +193,20 @@ def load_from_dict(source: dict[str, List[float]], if b is None: raise ValueError("b cannot be None") - - if c is None: - raise ValueError("c cannot be None") - - if d is None: - raise ValueError("d cannot be None") - - # check if a, b, c, and d have the same length - if not (len(a) == len(b) == len(c) == len(d)): - raise ValueError("All lists in the source dictionary must have the same length") - + + if all([isinstance(b_i, float) for b_i in b]): + if c is None: + raise ValueError("c cannot be None") + + if d is None: + raise ValueError("d cannot be None") + # check if a, b, c, and d have the same length + if not (len(a) == len(b) == len(c) == len(d)): + raise ValueError("a, b, c, and d must have the same length.") + else: + if not (len(a) == len(b)): + raise ValueError("a and b must have the same length.") + if ids is not None: if len(ids) != len(b): raise ValueError("Length of ids and b has to be the same.") @@ -217,14 +221,17 @@ def load_from_dict(source: dict[str, List[float]], item = TestItem() item.a = a[i] item.b = b[i] - item.c = c[i] - item.d = d[i] + + if c is not None: + item.c = c[i] + if d is not None: + item.d = d[i] if ids is not None: item.id = ids[i] if "id" in source.keys(): - item.id = source["id"][i] + item.id = cast(int, source["id"][i]) if content_categories is not None: item.additional_properties["category"] = content_categories[i] @@ -238,7 +245,8 @@ def load_from_dict(source: dict[str, List[float]], @staticmethod def load_from_dataframe(source: DataFrame) -> "ItemPool": """Creates item pool from a pandas DataFrame. - Required columns are: `a`, `b`, `c`, `d`. + Required columns are: `a`, `b`. + `c`, `d` are optional (ignored for polytomous items). Each column has to contain float values. A `simulated_responses` (int values) column can be added to the DataFrame to provide simulated responses. @@ -257,17 +265,21 @@ def load_from_dataframe(source: DataFrame) -> "ItemPool": if "b" not in source.columns: raise ValueError("Column 'b' not found.") - if "c" not in source.columns: - raise ValueError("Column 'c' not found.") - - if "d" not in source.columns: - raise ValueError("Column 'd' not found.") - # get values a: List[float] = source["a"].values.tolist() # type: ignore - b: List[float] = source["b"].values.tolist() # type: ignore - c: List[float] = source["c"].values.tolist() # type: ignore - d: List[float] = source["d"].values.tolist() # type: ignore + b: List[float] | list[list[float]] = source["b"].values.tolist() # type: ignore + + c: list[float] | None + if "c" in source.columns: + c: List[float] = source["c"].values.tolist() # type: ignore + else: + c = None + + d: list[float] | None + if "d" in source.columns: + d: List[float] = source["d"].values.tolist() # type: ignore + else: + d = None if "ids" in source.columns: ids: List[int] | None = source["ids"].values.tolist() # type: ignore From 28e27deab6d84c5966632b1b48955042a54cb3a3 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 23 Jan 2026 10:25:27 +0100 Subject: [PATCH 077/137] linting --- .../math/estimators/__functions/__poly/__poly_math.py | 2 -- adaptivetesting/models/__item_pool.py | 4 ++-- adaptivetesting/tests/test_load_test_items.py | 1 - 3 files changed, 2 insertions(+), 5 deletions(-) diff --git a/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py b/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py index e82efd5..17a96b5 100644 --- a/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py +++ b/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py @@ -7,8 +7,6 @@ class PolyModelFunctions(ABC): - - @staticmethod @abstractmethod def category_prob(theta: float, diff --git a/adaptivetesting/models/__item_pool.py b/adaptivetesting/models/__item_pool.py index f101a37..5583768 100644 --- a/adaptivetesting/models/__item_pool.py +++ b/adaptivetesting/models/__item_pool.py @@ -204,7 +204,7 @@ def load_from_dict(source: dict[str, List[float] | list[float]], if not (len(a) == len(b) == len(c) == len(d)): raise ValueError("a, b, c, and d must have the same length.") else: - if not (len(a) == len(b)): + if not (len(a) == len(b)): raise ValueError("a and b must have the same length.") if ids is not None: @@ -245,7 +245,7 @@ def load_from_dict(source: dict[str, List[float] | list[float]], @staticmethod def load_from_dataframe(source: DataFrame) -> "ItemPool": """Creates item pool from a pandas DataFrame. - Required columns are: `a`, `b`. + Required columns are: `a`, `b`. `c`, `d` are optional (ignored for polytomous items). Each column has to contain float values. A `simulated_responses` (int values) column can be added to diff --git a/adaptivetesting/tests/test_load_test_items.py b/adaptivetesting/tests/test_load_test_items.py index 52a5ca3..3ca4864 100644 --- a/adaptivetesting/tests/test_load_test_items.py +++ b/adaptivetesting/tests/test_load_test_items.py @@ -260,4 +260,3 @@ def test_roundtrip_preserves_fields(self): self.assertEqual(restored.c, original.c) self.assertEqual(restored.d, original.d) self.assertEqual(restored.additional_properties, original.additional_properties) - From 2518ffc8c19e38a5d1dd4009f40bfeb06ccc4bb2 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Mon, 23 Feb 2026 09:52:27 +0100 Subject: [PATCH 078/137] add generation for poly response patterns --- .../math/__gen_response_pattern.py | 71 ++++++++++++++++++- .../estimators/__functions/__poly/__grm.py | 5 +- adaptivetesting/models/__test_item.py | 8 +++ .../tests/test_gen_response_pattern.py | 61 ++++++++++++++++ .../tests/test_generate_response_pattern.py | 35 ++++++++- todo.md | 16 +++-- 6 files changed, 185 insertions(+), 11 deletions(-) create mode 100644 adaptivetesting/tests/test_gen_response_pattern.py diff --git a/adaptivetesting/math/__gen_response_pattern.py b/adaptivetesting/math/__gen_response_pattern.py index 7731194..2da2c11 100644 --- a/adaptivetesting/math/__gen_response_pattern.py +++ b/adaptivetesting/math/__gen_response_pattern.py @@ -1,10 +1,15 @@ from .estimators.__functions.__estimators import probability_y1 +from .estimators.__functions.__poly.__gpcm import GPCM +from .estimators.__functions.__poly.__grm import GRM from ..models.__test_item import TestItem import numpy as np +from typing import Literal, cast +from scipy.stats import multinomial def generate_response_pattern(ability: float, items: list[TestItem], + model: Literal["GRM", "GPCM"] | None = None, seed: int | None = None) -> list[int]: """Generates a response pattern for a given ability level and item difficulties. Also, a seed can be set. @@ -12,6 +17,7 @@ def generate_response_pattern(ability: float, Args: ability (float): participants ability items (list[TestItem]): test items + model (Literal["GRM", "GPCM"], optional): model type (required for polytomous models) seed (int, optional): Seed for the random process. Returns: @@ -23,6 +29,25 @@ def generate_response_pattern(ability: float, else: rng = np.random.RandomState() + if all([item.is_polytomous() for item in items]): + if model is None: + raise ValueError("model has to be specified for polytomous items") + else: + return gen_pattern_poly( + ability, + items, + model, + rng + ) + else: + return gen_pattern_dichotomous(ability, + items, + rng) + + +def gen_pattern_dichotomous(ability: float, + items: list[TestItem], + rng: np.random.RandomState): responses: list[int] = [] for item in items: @@ -31,20 +56,60 @@ def generate_response_pattern(ability: float, b=np.array(item.b), c=np.array(item.c), d=np.array(item.d)) - + # Handle numpy scalar/array return properly if hasattr(probability_of_success, 'item'): prob_scalar = probability_of_success.item() else: prob_scalar = float(probability_of_success) - + # Validate probability bounds if not (0 <= prob_scalar <= 1): raise ValueError(f"Invalid probability: {prob_scalar}. Must be between 0 and 1.") - + # simulate response based on probability of success random_val = rng.random_sample() response = 1 if random_val < prob_scalar else 0 responses.append(response) return responses + + +def gen_pattern_poly( + ability: float, + items: list[TestItem], + model: Literal["GRM", "GPCM"], + rng: np.random.RandomState +) -> list[int]: + responses: list[int] = [] + # loop through all items + for item in items: + # calculate probability for every class + probabilities: list[float] = [] + if model == "GRM": + for t_i in range(len(cast(list, item.b)) + 1): + prob = GRM.category_prob(ability, + item.a, + cast(list, item.b), + response_pattern=t_i) + probabilities.append(prob) + elif model == "GPCM": + for t_i in range(len(cast(list, item.b)) + 1): + prob = GPCM.category_prob(ability, + item.a, + cast(list, item.b), + response_pattern=t_i) + probabilities.append(prob) + + # draw from multinomial distribution for final response + # the probability for a response in k categories is 1 + mn_draw = cast(np.ndarray, multinomial.rvs( + n=1, + p=probabilities, + size=(), + random_state=rng + )).astype(int) + response = np.argmax(mn_draw).item() + responses.append(response) + + return responses diff --git a/adaptivetesting/math/estimators/__functions/__poly/__grm.py b/adaptivetesting/math/estimators/__functions/__poly/__grm.py index 836a35c..1cf892a 100644 --- a/adaptivetesting/math/estimators/__functions/__poly/__grm.py +++ b/adaptivetesting/math/estimators/__functions/__poly/__grm.py @@ -5,7 +5,8 @@ class GRM(PolyModelFunctions): @staticmethod - def category_prob(theta, a: float, thresholds: list[float], k: int): + def category_prob(theta, a: float, thresholds: list[float], response_pattern: int): + k = response_pattern # k is the category index (0, 1, ..., num_thresholds) num_thresholds = len(thresholds) @@ -45,7 +46,7 @@ def log_likelihood(theta: float, theta=theta, a=a_params[item_idx], thresholds=thresholds_list[item_idx], - k=response_pattern[item_idx] + response_pattern=response_pattern[item_idx] ) if prob <= 0: # Handle cases where probability is zero or negative log_lik += -np.inf # Log of zero is negative infinity diff --git a/adaptivetesting/models/__test_item.py b/adaptivetesting/models/__test_item.py index cbde588..b09f2ca 100644 --- a/adaptivetesting/models/__test_item.py +++ b/adaptivetesting/models/__test_item.py @@ -71,3 +71,11 @@ def from_dict(source: dict) -> "TestItem": if "id" in source and source["id"] is not None: item.id = source["id"] return item + + def is_polytomous(self) -> bool: + """Checks whether a item is polytomous or dichotomous. + + Returns: + bool: True if item is polytomous. + """ + return isinstance(self.b, list) diff --git a/adaptivetesting/tests/test_gen_response_pattern.py b/adaptivetesting/tests/test_gen_response_pattern.py new file mode 100644 index 0000000..a6e98b0 --- /dev/null +++ b/adaptivetesting/tests/test_gen_response_pattern.py @@ -0,0 +1,61 @@ +import unittest +import numpy as np + +from adaptivetesting.math.__gen_response_pattern import generate_response_pattern +from adaptivetesting.models.__test_item import TestItem + + +def make_dichotomous_item(a=1.0, b=0.0, c=0.0, d=1.0): + it = TestItem() + it.a = a + it.b = b + it.c = c + it.d = d + return it + + +def make_polyt_item(a=1.0, b_list=None): + it = TestItem() + it.a = a + it.b = b_list if b_list is not None else [-1.0, 0.0] + it.c = 0.0 + it.d = 1.0 + return it + + +class TestGenerateResponsePattern(unittest.TestCase): + def test_dichotomous_reproducible_seed(self): + items = [make_dichotomous_item() for _ in range(5)] + r1 = generate_response_pattern(ability=0.0, items=items, seed=123) + r2 = generate_response_pattern(ability=0.0, items=items, seed=123) + self.assertEqual(r1, r2) + self.assertEqual(len(r1), len(items)) + for x in r1: + self.assertIsInstance(x, int) + self.assertIn(x, (0, 1)) + + def test_polytomous_requires_model(self): + items = [make_polyt_item() for _ in range(3)] + with self.assertRaises(ValueError): + generate_response_pattern(ability=0.0, items=items, model=None, seed=1) + + def test_polytomous_one_hot_and_reproducible(self): + items = [make_polyt_item(b_list=[-1.0, 0.0, 1.0]) for _ in range(4)] + r1 = generate_response_pattern(ability=0.5, items=items, model="GRM", seed=42) + r2 = generate_response_pattern(ability=0.5, items=items, model="GRM", seed=42) + + self.assertEqual(len(r1), len(items)) + + for a, b in zip(r1, r2): + self.assertTrue(np.array_equal(np.asarray(a), np.asarray(b))) + + for resp in r1: + arr = np.asarray(resp) + self.assertTrue(np.issubdtype(arr.dtype, np.integer)) + self.assertEqual(arr.ndim, 1) + self.assertEqual(int(arr.sum()), 1) + self.assertTrue(set(arr.flatten()).issubset({0, 1})) + + +if __name__ == "__main__": + unittest.main() diff --git a/adaptivetesting/tests/test_generate_response_pattern.py b/adaptivetesting/tests/test_generate_response_pattern.py index 57d41ce..2c381ad 100644 --- a/adaptivetesting/tests/test_generate_response_pattern.py +++ b/adaptivetesting/tests/test_generate_response_pattern.py @@ -1,7 +1,7 @@ # flake8: noqa import unittest -import math -from adaptivetesting.models import ItemPool +import numpy as np +from adaptivetesting.models import ItemPool, TestItem from adaptivetesting.math.estimators import MLEstimator from adaptivetesting.math import generate_response_pattern @@ -36,6 +36,8 @@ 0.4029, -0.4667, 0.78, -0.0834], "c": [0.0597, 0.2406, 0.1503, 0.1288, 0.1006, 0.2201, 0.091, 0.0721, 0.0427, 0.043, 0.1205, 0.0632, 0.0541, 0.1686, 0.0119, 0.1752, 0.088, 0.1022, 0.2052, 0.2297, 0.0706, 0.2403, 0.1821, 0.1716, 0.0132, 0.0988, 0.1195, 0.1401, 0.1746, 0.2289, 0.1546, 0.1071, 0.1355, 0.0146, 0.0652, 0.0993, 0.0494, 0.208, 0.0382, 0.2009, 0.1367, 0.1656, 0.0429, 0.1583, 0.078, 0.1811, 0.0997, 0.2423, 0.2418, 0.1817], "d": [0.8143, 0.8054, 0.8983, 0.8169, 0.8828, 0.9463, 0.792, 0.8511, 0.8679, 0.967, 0.9814, 0.9705, 0.9185, 0.9875, 0.8791, 0.8941, 0.8341, 0.8368, 0.755, 0.8757, 0.9678, 0.7516, 0.768, 0.7911, 0.9426, 0.9338, 0.993, 0.8666, 0.7686, 0.9122, 0.9396, 0.7843, 0.8491, 0.8062, 0.7645, 0.849, 0.7662, 0.8065, 0.7637, 0.9176, 0.8244, 0.7752, 0.768, 0.9701, 0.9386, 0.9542, 0.9955, 0.7759, 0.7748, 0.9497]} + + class TestGenerateResponsePattern(unittest.TestCase): def test_compare_generation_to_estimation(self): item_pool = ItemPool.load_from_dict(source_dictionary) @@ -104,3 +106,32 @@ def test_calculate_expected_vs_actual(self): # The difference should be within reasonable bounds for random sampling # With 50 items, we expect some variation self.assertAlmostEqual(actual_percentage, expected_percentage, delta=3) + + +def make_polyt_item(a=1.0, b_list=None): + it = TestItem() + it.a = a + it.b = b_list if b_list is not None else [0.2, 0.7] + return it + +class TestGenerateResponsePattern(unittest.TestCase): + def test_no_break(self): + items = [make_polyt_item() for _ in range(3)] + pattern = generate_response_pattern(ability=0.5, items=items, model="GRM") + print(pattern) + + + def test_polytomous_requires_model(self): + items = [make_polyt_item() for _ in range(3)] + with self.assertRaises(ValueError): + generate_response_pattern(ability=0.0, items=items, model=None, seed=1) + + def test_polytomous_one_hot_and_reproducible(self): + items = [make_polyt_item(b_list=[-1.0, 0.0, 1.0]) for _ in range(4)] + r1 = generate_response_pattern(ability=0.5, items=items, model="GRM", seed=42) + r2 = generate_response_pattern(ability=0.5, items=items, model="GRM", seed=42) + + self.assertEqual(len(r1), len(items)) + + for a, b in zip(r1, r2): + self.assertTrue(np.array_equal(np.asarray(a), np.asarray(b))) diff --git a/todo.md b/todo.md index d2fec43..44d656e 100644 --- a/todo.md +++ b/todo.md @@ -1,8 +1,16 @@ # Todo - [ ] TestAssembler -- [ ] Ability Estimation -- [ ] Item Selection -- [ ] Item Pool +- [X] Response Pattern generation +- [X] Ability Estimation + - [ ] unittest +- [X] Item Selection + - [ ] unittest +- [X] Item Pool + - [X] Load item pool with polytomous items + - [ ] unittest - [ ] Content Balancing -- [ ] Exposure Control \ No newline at end of file + - [ ] item information function calculation +- [ ] Exposure Control + +- [ ] showed item -> shown item \ No newline at end of file From e5ccc98150b62ec7abd010ef234a54a07325650b Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Mon, 23 Feb 2026 10:54:28 +0100 Subject: [PATCH 079/137] change item information function --- .../math/content_balancing/__functions.py | 14 ++++---- .../__weighted_penalty_model.py | 11 +++--- .../math/estimators/__test_information.py | 34 +++++++++++++++++-- .../__maximum_information_criterion.py | 20 ++++------- .../tests/test_generate_response_pattern.py | 2 +- adaptivetesting/utils/__plots.py | 24 +++++++------ 6 files changed, 64 insertions(+), 41 deletions(-) diff --git a/adaptivetesting/math/content_balancing/__functions.py b/adaptivetesting/math/content_balancing/__functions.py index df062ad..595a0f1 100644 --- a/adaptivetesting/math/content_balancing/__functions.py +++ b/adaptivetesting/math/content_balancing/__functions.py @@ -3,13 +3,15 @@ from ...models.__item_selection_exception import ItemSelectionException from ...math.estimators.__test_information import item_information_function import numpy as np +from typing import Literal - +# TODO: implement model in function call def compute_priority_index(item: TestItem, group_weights: dict[str, float], required_items: dict[str, float], shown_items: dict[str, float], - current_ability: float) -> float: + current_ability: float, + model: Literal['GRM', 'GPCM'] | None = None) -> float: """Calculates the priority index of a given item. Args: @@ -42,11 +44,9 @@ def compute_priority_index(item: TestItem, # weight fisher information priority_index = priority_index * float(item_information_function( - mu=np.array(current_ability, dtype=np.float64), - a=np.array(item.a, dtype=np.float64), - b=np.array(item.b, dtype=np.float64), - c=np.array(item.c, dtype=np.float64), - d=np.array(item.d, dtype=np.float64) + ability=current_ability, + item=item, + model=model )) return priority_index diff --git a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py index fd830ba..8960036 100644 --- a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py +++ b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py @@ -115,14 +115,13 @@ def prepare_item_pool(self): # order items self.order_candidate_items() - def calculate_information(self) -> list[float]: + # TODO: implement function call + def calculate_information(self, model: Literal['GRM', 'GPCM'] | None = None) -> list[float]: information_list = [ float(item_information_function( - mu=np.array(self.ability), - a=np.array(item.a), - b=np.array(item.b), - c=np.array(item.c), - d=np.array(item.d) + ability=self.ability, + item=item, + model=model )) for item in self.items ] diff --git a/adaptivetesting/math/estimators/__test_information.py b/adaptivetesting/math/estimators/__test_information.py index 703ad05..bfd0425 100644 --- a/adaptivetesting/math/estimators/__test_information.py +++ b/adaptivetesting/math/estimators/__test_information.py @@ -4,12 +4,40 @@ from scipy.integrate import trapezoid import numpy from scipy.differentiate import derivative -from typing import Literal +from typing import Literal, cast from .__functions.__poly.__gpcm import GPCM from .__functions.__poly.__grm import GRM +from ...models.__test_item import TestItem def item_information_function( + ability: float, + item: TestItem, + model: Literal["GRM", "GPCM"] | None = None +) -> float: + if model == "GRM": + return GRM.fisher_information( + ability, + item.a, + cast(list, item.b), + ) + elif model == "GPCM": + return GPCM.fisher_information( + ability, + item.a, + cast(list, item.b) + ) + + else: # dichotmous + return dicho_item_information_function( + mu=np.array(ability), + a=np.array(item.a), + b=np.array(item.b), + c=np.array(item.c), + d=np.array(item.d) + ).astype(float).item() + +def dicho_item_information_function( mu: np.ndarray, a: np.ndarray, b: np.ndarray, @@ -81,7 +109,7 @@ def test_information_function( optimization_interval: tuple[float, float] = (-10, 10) ) -> float: """ - Calculates test information. + Calculates test information for dichotmous items. Therefore, the information is calculated for every item and then summed up. If a prior is specified, the fisher information of the prior @@ -100,7 +128,7 @@ def test_information_function( float: test information """ # calculate information for every item - item_information = np.vectorize(item_information_function)( + item_information = np.vectorize(dicho_item_information_function)( mu, a, b, c, d ) diff --git a/adaptivetesting/math/item_selection/__maximum_information_criterion.py b/adaptivetesting/math/item_selection/__maximum_information_criterion.py index dcb2d0c..4a57d24 100644 --- a/adaptivetesting/math/item_selection/__maximum_information_criterion.py +++ b/adaptivetesting/math/item_selection/__maximum_information_criterion.py @@ -3,10 +3,12 @@ from ..estimators.__test_information import item_information_function from ...models.__algorithm_exception import AlgorithmException import numpy as np +from typing import Literal - +# Todo: implement model parameter in function calls def maximum_information_criterion(items: list[TestItem], - ability: float) -> TestItem: + ability: float, + model: Literal["GRM", "GPCM"] | None = None) -> TestItem: """The maximum information criterion selected the next item for the respondent by finding the item that has the highest information value. @@ -25,20 +27,12 @@ def maximum_information_criterion(items: list[TestItem], best_item = None for item in items: - # extract parameters from the current item - a = np.array([item.a]) - b = np.array([item.b]) - c = np.array([item.c]) - d = np.array([item.d]) - # calculate information for the current item try: information = float(item_information_function( - mu=np.array(ability, dtype=float), - a=a, - b=b, - c=c, - d=d + ability=ability, + item=item, + model=model, )) # if information is higher than before diff --git a/adaptivetesting/tests/test_generate_response_pattern.py b/adaptivetesting/tests/test_generate_response_pattern.py index 2c381ad..3bae53a 100644 --- a/adaptivetesting/tests/test_generate_response_pattern.py +++ b/adaptivetesting/tests/test_generate_response_pattern.py @@ -114,7 +114,7 @@ def make_polyt_item(a=1.0, b_list=None): it.b = b_list if b_list is not None else [0.2, 0.7] return it -class TestGenerateResponsePattern(unittest.TestCase): +class TestGeneratePolyPattern(unittest.TestCase): def test_no_break(self): items = [make_polyt_item() for _ in range(3)] pattern = generate_response_pattern(ability=0.5, items=items, model="GRM") diff --git a/adaptivetesting/utils/__plots.py b/adaptivetesting/utils/__plots.py index 385acb4..8db2a27 100644 --- a/adaptivetesting/utils/__plots.py +++ b/adaptivetesting/utils/__plots.py @@ -7,6 +7,7 @@ from ..math.estimators.__test_information import item_information_function from .__funcs import load_final_test_results, load_test_results_single_participant import numpy as np +from typing import Literal def plot_final_ability_estimates(simulation_id: str, @@ -94,6 +95,7 @@ def plot_icc(item: TestItem, def plot_iif(item: TestItem, range: tuple[float, float] = (-10, 10), + model: Literal["GRM", "GPCM"] | None = None, ax: Axes | None = None, **kwargs): """ @@ -101,6 +103,7 @@ def plot_iif(item: TestItem, Parameters: item (TestItem): The test item for which to plot the information function. range (tuple[float, float], optional): The range of ability levels (theta) to plot over. Defaults to (-10, 10). + model (Literal["GRM", "GPCM"], optional): Type of IRT model. Defaults to dichotmous IRT models. ax (Axes, optional): Matplotlib Axes object to plot on. If None, a new figure and axes are created. **kwargs: Additional keyword arguments passed to matplotlib's plot function. Returns: @@ -113,11 +116,9 @@ def plot_iif(item: TestItem, for theta in thetas: info = item_information_function( - mu=theta, - a=np.array(item.a), - b=np.array(item.b), - c=np.array(item.c), - d=np.array(item.d), + ability=theta.astype(float).item(), + item=item, + model=model ) information_array.append(info) @@ -203,6 +204,7 @@ def plot_exposure_rate(simulation_id: str, def plot_test_information( items: list[TestItem], range: tuple[float, float] = (-10, 10), + model: Literal["GRM", "GPCM"] | None = None, ax: Axes | None = None, **kwargs): """ @@ -210,6 +212,7 @@ def plot_test_information( Args: items (list[TestItem]): Test items in an item pool for which to calculate the test information range (tuple[float, float], optional): The range of ability levels (theta) to plot over. Defaults to (-10, 10). + model (Literal["GRM", "GPCM"], optional): Type of IRT model. Defaults to dichotmous IRT models. ax (Axes, optional): Matplotlib Axes object to plot on. If None, a new figure and axes are created. **kwargs: Additional keyword arguments passed to matplotlib's plot function. Returns: @@ -218,13 +221,12 @@ def plot_test_information( # calculate test information by summing item information across items thetas = np.linspace(range[0], range[1], 100) information_array = np.zeros_like(thetas, dtype=float) + item_information_function_vec = np.vectorize(item_information_function) for item in items: - information_array += item_information_function( - mu=np.array(thetas).T, - a=np.array(item.a), - b=np.array(item.b), - c=np.array(item.c), - d=np.array(item.d), + information_array += item_information_function_vec( + np.array(thetas).T, + item=item, + model=model ) # setup figure From 3791ea0e00d0e74d12af50b202e0364e9c16dfda Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Mon, 23 Feb 2026 10:54:39 +0100 Subject: [PATCH 080/137] remove duplicate test cases for response pattern generation --- .../tests/test_gen_response_pattern.py | 61 ------------------- 1 file changed, 61 deletions(-) delete mode 100644 adaptivetesting/tests/test_gen_response_pattern.py diff --git a/adaptivetesting/tests/test_gen_response_pattern.py b/adaptivetesting/tests/test_gen_response_pattern.py deleted file mode 100644 index a6e98b0..0000000 --- a/adaptivetesting/tests/test_gen_response_pattern.py +++ /dev/null @@ -1,61 +0,0 @@ -import unittest -import numpy as np - -from adaptivetesting.math.__gen_response_pattern import generate_response_pattern -from adaptivetesting.models.__test_item import TestItem - - -def make_dichotomous_item(a=1.0, b=0.0, c=0.0, d=1.0): - it = TestItem() - it.a = a - it.b = b - it.c = c - it.d = d - return it - - -def make_polyt_item(a=1.0, b_list=None): - it = TestItem() - it.a = a - it.b = b_list if b_list is not None else [-1.0, 0.0] - it.c = 0.0 - it.d = 1.0 - return it - - -class TestGenerateResponsePattern(unittest.TestCase): - def test_dichotomous_reproducible_seed(self): - items = [make_dichotomous_item() for _ in range(5)] - r1 = generate_response_pattern(ability=0.0, items=items, seed=123) - r2 = generate_response_pattern(ability=0.0, items=items, seed=123) - self.assertEqual(r1, r2) - self.assertEqual(len(r1), len(items)) - for x in r1: - self.assertIsInstance(x, int) - self.assertIn(x, (0, 1)) - - def test_polytomous_requires_model(self): - items = [make_polyt_item() for _ in range(3)] - with self.assertRaises(ValueError): - generate_response_pattern(ability=0.0, items=items, model=None, seed=1) - - def test_polytomous_one_hot_and_reproducible(self): - items = [make_polyt_item(b_list=[-1.0, 0.0, 1.0]) for _ in range(4)] - r1 = generate_response_pattern(ability=0.5, items=items, model="GRM", seed=42) - r2 = generate_response_pattern(ability=0.5, items=items, model="GRM", seed=42) - - self.assertEqual(len(r1), len(items)) - - for a, b in zip(r1, r2): - self.assertTrue(np.array_equal(np.asarray(a), np.asarray(b))) - - for resp in r1: - arr = np.asarray(resp) - self.assertTrue(np.issubdtype(arr.dtype, np.integer)) - self.assertEqual(arr.ndim, 1) - self.assertEqual(int(arr.sum()), 1) - self.assertTrue(set(arr.flatten()).issubset({0, 1})) - - -if __name__ == "__main__": - unittest.main() From f37b22c3413a0536241ad8ed62f36f2bad25d7ed Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Mon, 23 Feb 2026 11:09:16 +0100 Subject: [PATCH 081/137] fix unittest bugs --- .../math/exposure_control/__randomesque.py | 11 +++---- adaptivetesting/models/__item_pool.py | 30 +++++++++++-------- 2 files changed, 24 insertions(+), 17 deletions(-) diff --git a/adaptivetesting/math/exposure_control/__randomesque.py b/adaptivetesting/math/exposure_control/__randomesque.py index 5edd062..1d17134 100644 --- a/adaptivetesting/math/exposure_control/__randomesque.py +++ b/adaptivetesting/math/exposure_control/__randomesque.py @@ -5,6 +5,7 @@ from ...models.__adaptive_test import AdaptiveTest from ...models.__test_item import TestItem from ..estimators.__test_information import item_information_function +from typing import Literal class Randomesque(ExposureControl): @@ -78,22 +79,22 @@ def sort_by_information(item_entry: tuple[float, int]) -> float: return item_entry[0] @staticmethod + # TODO: implement model call def radomesque_item_selection(items: list[TestItem], ability_estimate: float, n_items: int, reverse: bool = True, item_rating_function: Callable = item_information_function, seed: int | None = None, + model: Literal["GRM", "GPCM"] | None = None, **kwargs: Any ) -> TestItem: item_information_list: list[tuple[float, int]] = [] for i, item in enumerate(items): information = float(item_rating_function( - a=np.array(item.a), - b=np.array(item.b), - c=np.array(item.c), - d=np.array(item.d), - mu=np.array(ability_estimate), + ability=ability_estimate, + item=item, + model=model, **kwargs )) diff --git a/adaptivetesting/models/__item_pool.py b/adaptivetesting/models/__item_pool.py index 5583768..b0d01c7 100644 --- a/adaptivetesting/models/__item_pool.py +++ b/adaptivetesting/models/__item_pool.py @@ -129,17 +129,22 @@ def load_from_list( for i, discrimination in enumerate(a): items[i].a = discrimination - if c is not None: - if len(c) != len(b): - raise ValueError("Length of c and b has to be the same.") - for i, guessing in enumerate(c): - items[i].c = guessing - - if d is not None: - if len(d) != len(b): - raise ValueError("Length of d and b has to be the same.") - for i, slipping in enumerate(d): - items[i].d = slipping + if not all([isinstance(b_i, list) for b_i in b]): + if c is not None: + if len(c) != len(b): + raise ValueError("Length of c and b has to be the same.") + for i, guessing in enumerate(c): + items[i].c = guessing + if c is None: + raise ValueError("c cannot be None.") + + if d is not None: + if len(d) != len(b): + raise ValueError("Length of d and b has to be the same.") + for i, slipping in enumerate(d): + items[i].d = slipping + if d is None: + raise ValueError("d cannot be None.") if ids is not None: if len(ids) != len(b): @@ -194,7 +199,8 @@ def load_from_dict(source: dict[str, List[float] | list[float]], if b is None: raise ValueError("b cannot be None") - if all([isinstance(b_i, float) for b_i in b]): + if not all([isinstance(b_i, list) for b_i in b]): + # if b is not a list of thresholds check of c and d if c is None: raise ValueError("c cannot be None") From 00a8b2589a0795122e0ac092ff09d703b4d41531 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Mon, 23 Feb 2026 11:15:20 +0100 Subject: [PATCH 082/137] revert loading item change, fix unittest instead --- adaptivetesting/models/__item_pool.py | 28 ++++++++----------- adaptivetesting/tests/test_load_test_items.py | 20 +++++++++++-- 2 files changed, 29 insertions(+), 19 deletions(-) diff --git a/adaptivetesting/models/__item_pool.py b/adaptivetesting/models/__item_pool.py index b0d01c7..914840b 100644 --- a/adaptivetesting/models/__item_pool.py +++ b/adaptivetesting/models/__item_pool.py @@ -129,22 +129,18 @@ def load_from_list( for i, discrimination in enumerate(a): items[i].a = discrimination - if not all([isinstance(b_i, list) for b_i in b]): - if c is not None: - if len(c) != len(b): - raise ValueError("Length of c and b has to be the same.") - for i, guessing in enumerate(c): - items[i].c = guessing - if c is None: - raise ValueError("c cannot be None.") - - if d is not None: - if len(d) != len(b): - raise ValueError("Length of d and b has to be the same.") - for i, slipping in enumerate(d): - items[i].d = slipping - if d is None: - raise ValueError("d cannot be None.") + + if c is not None: + if len(c) != len(b): + raise ValueError("Length of c and b has to be the same.") + for i, guessing in enumerate(c): + items[i].c = guessing + + if d is not None: + if len(d) != len(b): + raise ValueError("Length of d and b has to be the same.") + for i, slipping in enumerate(d): + items[i].d = slipping if ids is not None: if len(ids) != len(b): diff --git a/adaptivetesting/tests/test_load_test_items.py b/adaptivetesting/tests/test_load_test_items.py index 3ca4864..7e271a6 100644 --- a/adaptivetesting/tests/test_load_test_items.py +++ b/adaptivetesting/tests/test_load_test_items.py @@ -164,9 +164,23 @@ def test_load_items_pandas_error_missing_column(self): "c": [0.9, 1.9] } df = pd.DataFrame(dictionary) - - with self.assertRaises(ValueError): - ItemPool.load_from_dataframe(df) + # this should work because d can be ignored when creating items + pool = ItemPool.load_from_dataframe(df) + # check that the pool contains the correct items + item1 = TestItem() + item1.a = 0.9 + item1.b = 5 + item1.c = 0.9 + item1.d = 1 + + item2 = TestItem() + item2.a = 1.9 + item2.b = 3 + item2.c = 1.9 + item2.d = 1 + + self.assertDictEqual(item1.as_dict(), pool.test_items[0].as_dict()) + self.assertDictEqual(item2.as_dict(), pool.test_items[1].as_dict()) def test_load_pandas_no_responses(self): dictionary = { From 5558c25409ee32a5af0e9e233cd87a03680ecae8 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Tue, 24 Feb 2026 10:15:29 +0100 Subject: [PATCH 083/137] add tests for content balancing and exposure control --- .../tests/test_content_balacing.py | 44 +++ .../tests/test_weighted_penalty_model.py | 256 ++++++++++++++++++ 2 files changed, 300 insertions(+) diff --git a/adaptivetesting/tests/test_content_balacing.py b/adaptivetesting/tests/test_content_balacing.py index 9899ad9..9cab706 100644 --- a/adaptivetesting/tests/test_content_balacing.py +++ b/adaptivetesting/tests/test_content_balacing.py @@ -85,3 +85,47 @@ def test_exception_key(self): }, current_ability=0 ) + + +class TestMPIPolyModels(unittest.TestCase): + def __init__(self, methodName="runTest"): + + items = pd.DataFrame({ + "a": [1.32, 1.07, 0.84], + "b": [[0.2, 0.9], + [0.2, 0.9], + [0.2, 0.9]], + "id": [1, 2, 3] + }) + + self.available_items = adt.ItemPool.load_from_dataframe(items).test_items + self.content_categories = ["Math", "English", "Math"] + + for i, _ in enumerate(self.available_items): + self.available_items[i].additional_properties = { + "category": [self.content_categories[i]] + } + + super().__init__(methodName) + + def test_basic_calculation(self): + adt.compute_priority_index( + item=self.available_items[0], + group_weights={ + "Math": 0.2, + "English": 0.8 + }, + required_items={ + "Math": 5, + "English": 10 + }, + shown_items={ + "Math": 0, + "English": 10 + }, + current_ability=0 + ) + + def test_quota_calculation(self): + result = adt.compute_quota_left(10, 5) + self.assertAlmostEqual(result, 0.5) \ No newline at end of file diff --git a/adaptivetesting/tests/test_weighted_penalty_model.py b/adaptivetesting/tests/test_weighted_penalty_model.py index fa913d4..704e3a2 100644 --- a/adaptivetesting/tests/test_weighted_penalty_model.py +++ b/adaptivetesting/tests/test_weighted_penalty_model.py @@ -271,3 +271,259 @@ def __init__(self, item_pool, ability_level): selected_item.as_dict(), information_selected_item.as_dict() ) + +class TestWeightedPenaltyModelPoly(unittest.TestCase): + def __init__(self, methodName="runTest"): + super().__init__(methodName) + + # load test item pool + data_frame = pd.read_json("adaptivetesting/tests/example_item_pool.json") + # convert list[dict] into item pool + self.item_pool = adt.ItemPool.load_from_dataframe(data_frame) + + # create two example items fitting a rasch model + self.example_item1 = adt.TestItem() + self.example_item1.id = 1 + self.example_item1.a = 1 + self.example_item1.b = [0.5, 0.9] + self.example_item1.additional_properties["category"] = ["math"] + + self.example_item2 = adt.TestItem() + self.example_item2.id = 2 + self.example_item2.a = 1 + self.example_item2.b = [0.5, 0.9] + self.example_item2.additional_properties["category"] = ["english"] + + def test_content_penalty_calculation(self): + """compute_total_content_penalty_value_for_item""" + # create target item + item = copy.deepcopy(self.example_item1) + item.additional_properties["category"] = ["math", "science"] + + # create shown items for math and science + shown_items = [ + MockItem(["math"]), + MockItem(["math"]), + MockItem(["science"]) + ] + + # setup constraints + constraints = [ + adt.Constraint( + name="math", + prevalence=0.75, + lower=0, + upper=1, + weight=1 + ), + adt.Constraint( + name="science", + prevalence=0.25, + lower=0, + upper=1, + weight=1 + ) + ] + + total_content_penalty_value = adt.compute_total_content_penalty_value_for_item( + item, + shown_items=shown_items, + available_items=[item], + constraints=constraints + ) + + manual_result = 0.4166 - 0.083 + + self.assertAlmostEqual(total_content_penalty_value, + manual_result, places=2) + + def test_calcualte_weighted_content_penalty(self): + """calculate_weighted_penalty_value""" + item_information = 0.5 + max_information = 1 + constraint_weight = 1 + information_weight = 1 + total_content_penalty = 0.5 + maximum_total_content_penalty = 0.8 + minimum_total_content_penalty = 0.2 + + # standardize total content constraint penalty value + standardized_total_content_penalty_value = adt.standardize_total_content_constraint_penalty_value( + item_penalty_value=total_content_penalty, + minimum=minimum_total_content_penalty, + maximum=maximum_total_content_penalty + ) + + self.assertAlmostEqual(standardized_total_content_penalty_value, 0.5, places=2) + + # standardize item information + standardized_item_information = adt.standardize_item_information( + item_information=item_information, + maximum=max_information + ) + self.assertAlmostEqual(standardized_item_information, 0.5, places=2) + + # calculate information penalty value + information_penalty_value = adt.compute_information_penalty_value( + standardized_item_information + ) + + self.assertAlmostEqual(information_penalty_value, -0.25, places=2) + + # compute weighted penalty vlaue + weighted_penalty_value = adt.compute_weighted_penalty_value( + constraint_weight=constraint_weight, + standardized_constraint_penalty_value=standardized_total_content_penalty_value, + information_weight=information_weight, + information_penalty_value=information_penalty_value + ) + + self.assertAlmostEqual(weighted_penalty_value, 0.25, places=2) + + def test_constraint_group_assignment(self): + """get_constraint_group_assignments""" + + # setup constraints + constraints = [ + adt.Constraint( + name="math", + prevalence=0.75, + lower=0, + upper=1, + weight=1 + ), + adt.Constraint( + name="science", + prevalence=0.25, + lower=0.9, + upper=1, + weight=1 + ) + ] + + assignments = [] + for constraint in constraints: + # calculate proportion + prop = adt.compute_prop( + n_administered=1, + n_remaining=1, + prevalence=constraint.prevalence, + test_length=2 + ) + + # assign color group per proportion + _, assignment = adt.WeightedPenaltyModel.assign_color_group_per_proportion( + constraint, + prop + ) + assignments.append(assignment) + + self.assertListEqual(assignments, ["B", "A"]) + + def test_candidate_group_assignment(self): + """form_list_of_candidate_items""" + # create item + item = MockItem("None") + weighted_penalty_value = float("NaN") + # test green + associated_constraints: list[tuple[adt.Constraint, Literal['A', 'B', 'C']]] = [ + (adt.Constraint(None, None, None), "A"), + (adt.Constraint(None, None, None), "B") + ] + _, _, assigned_group = adt.WeightedPenaltyModel.assign_items_to_item_group( + item, associated_constraints, + weighted_penalty_value + ) + self.assertEqual(assigned_group, "green") + + # test organge + associated_constraints: list[tuple[adt.Constraint, Literal['A', 'B', 'C']]] = [ + (adt.Constraint(None, None, None), "A"), + (adt.Constraint(None, None, None), "C") + ] + _, _, assigned_group = adt.WeightedPenaltyModel.assign_items_to_item_group( + item, associated_constraints, + weighted_penalty_value + ) + self.assertEqual(assigned_group, "orange") + + # test yellow + associated_constraints: list[tuple[adt.Constraint, Literal['A', 'B', 'C']]] = [ + (adt.Constraint(None, None, None), "B"), + (adt.Constraint(None, None, None), "B") + ] + _, _, assigned_group = adt.WeightedPenaltyModel.assign_items_to_item_group( + item, associated_constraints, + weighted_penalty_value + ) + self.assertEqual(assigned_group, "yellow") + + # test red + associated_constraints: list[tuple[adt.Constraint, Literal['A', 'B', 'C']]] = [ + (adt.Constraint(None, None, None), "B"), + (adt.Constraint(None, None, None), "C") + ] + _, _, assigned_group = adt.WeightedPenaltyModel.assign_items_to_item_group( + item, associated_constraints, + weighted_penalty_value + ) + self.assertEqual(assigned_group, "red") + + def test_select_item_with_no_shown_items(self): + """Test that select_item returns the highest information item when no previous items have been shown""" + # Create a mock AdaptiveTest with no shown items + class MockAdaptiveTest: + def __init__(self, item_pool, ability_level): + self.item_pool = item_pool + self.ability_level = ability_level + self.answered_items = [] # No items shown + + # Create item pool with example items + item_pool_obj = adt.ItemPool([self.example_item1, self.example_item2]) + + # Create mock adaptive test with empty answered_items + mock_test = MockAdaptiveTest(item_pool_obj, 0) + + # Create constraints + constraints = [ + adt.Constraint( + name="math", + prevalence=0.5, + lower=0, + upper=1, + weight=1 + ), + adt.Constraint( + name="english", + prevalence=0.5, + lower=0, + upper=1, + weight=1 + ) + ] + + # Create WeightedPenaltyModel + model = adt.WeightedPenaltyModel( + adaptive_test=mock_test, + constraints=constraints, + constraint_weight=1.0, + information_weight=1.0 + ) + + # Select item + selected_item = model.select_item() + + # select item using maximum fisher information + information_selected_item: adt.TestItem = adt.maximum_information_criterion( + items=item_pool_obj.test_items, + ability=0 + ) + + # Verify an item is returned + self.assertIsNotNone(selected_item) + self.assertIsInstance(selected_item, adt.TestItem) + + self.assertDictEqual( + selected_item.as_dict(), + information_selected_item.as_dict() + ) From 56012f59430ce20d189f284ed2ccdde4f1806689 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Tue, 24 Feb 2026 11:01:34 +0100 Subject: [PATCH 084/137] make changes to allow simulation of GRM and GPCM models --- .../implementations/__test_assembler.py | 4 +- .../math/__gen_response_pattern.py | 14 +++-- .../estimators/__functions/__poly/__gpcm.py | 35 +++++------ .../math/estimators/__ml_estimation.py | 4 +- adaptivetesting/models/__item_pool.py | 2 +- .../services/__estimator_interface.py | 5 +- adaptivetesting/tests/test_simulation.py | 62 +++++++++++++++++++ 7 files changed, 96 insertions(+), 30 deletions(-) diff --git a/adaptivetesting/implementations/__test_assembler.py b/adaptivetesting/implementations/__test_assembler.py index f803c14..4428e96 100644 --- a/adaptivetesting/implementations/__test_assembler.py +++ b/adaptivetesting/implementations/__test_assembler.py @@ -3,7 +3,7 @@ from ..models.__adaptive_test import AdaptiveTest from ..models.__test_item import TestItem from ..services.__estimator_interface import IEstimator -from typing import Any, Type, TypedDict, Callable +from typing import Any, Type, TypedDict, Callable, Literal from ..math.item_selection.__maximum_information_criterion import maximum_information_criterion from ..models.__algorithm_exception import AlgorithmException from ..implementations.__pre_test import PreTest @@ -97,6 +97,7 @@ def __init__(self, }, item_selector: ItemSelectionStrategy = maximum_information_criterion, # type: ignore item_selector_args: dict[str, Any] = {}, + model_type: Literal["GRM", "GPCM"] | None = None, content_balancing: None | CONTENT_BALANCING = None, content_balancing_args: ContentBalancingArgs | None = None, exposure_control: None | EXPOSURE_CONTROL = None, @@ -154,6 +155,7 @@ def __init__(self, self.exposure_control_args = exposure_control_args self.__pretest = pretest self.__pretest_seed = pretest_seed + self.__estimator_args["model"] = model_type super().__init__(item_pool, simulation_id, diff --git a/adaptivetesting/math/__gen_response_pattern.py b/adaptivetesting/math/__gen_response_pattern.py index 2da2c11..bacf129 100644 --- a/adaptivetesting/math/__gen_response_pattern.py +++ b/adaptivetesting/math/__gen_response_pattern.py @@ -5,6 +5,7 @@ import numpy as np from typing import Literal, cast from scipy.stats import multinomial +from scipy.special import softmax def generate_response_pattern(ability: float, @@ -85,24 +86,27 @@ def gen_pattern_poly( # loop through all items for item in items: # calculate probability for every class - probabilities: list[float] = [] + log_probabilities: list[float] = [] if model == "GRM": for t_i in range(len(cast(list, item.b)) + 1): - prob = GRM.category_prob(ability, + log_prob = GRM.category_prob(ability, item.a, cast(list, item.b), response_pattern=t_i) - probabilities.append(prob) + log_probabilities.append(log_prob) elif model == "GPCM": for t_i in range(len(cast(list, item.b)) + 1): - prob = GPCM.category_prob(ability, + log_prob = GPCM.category_prob(ability, item.a, cast(list, item.b), response_pattern=t_i) - probabilities.append(prob) + log_probabilities.append(log_prob) + + # draw from multinomial distribution for final response # the probability for a response in k categories is 1 + probabilities = softmax(np.array(log_probabilities)) mn_draw = cast(np.ndarray, multinomial.rvs( n=1, p=probabilities, diff --git a/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py b/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py index 31068dd..79e4304 100644 --- a/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py +++ b/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py @@ -1,4 +1,5 @@ import numpy as np +from scipy.special import logsumexp import numdifftools as nd from .__poly_math import PolyModelFunctions @@ -9,23 +10,21 @@ def category_prob(theta: float, a: float, thresholds_list: list[float], response_pattern: int): - # numerator - numerator = 0.0 - for i in range(response_pattern): - # print(f"i: {i}") - numerator += a * (theta - thresholds_list[i]) + m = len(thresholds_list) - # denominator - denominator = 1 - possible_categories = len(thresholds_list) - for c in range(possible_categories): - local_sum = 0.0 - for i in range(c): - local_sum += a * (theta - thresholds_list[i]) + # Compute eta_k values (log numerators) + etas = np.zeros(m + 1) - denominator += np.exp(local_sum) + for k in range(1, m + 1): + etas[k] = etas[k - 1] + a * (theta - thresholds_list[k - 1]) - return numerator / denominator + # Compute log denominator safely + log_denom = logsumexp(etas) + + # log probability + log_prob = etas[response_pattern] - log_denom + + return log_prob @staticmethod def log_likelihood(theta: float, @@ -35,16 +34,14 @@ def log_likelihood(theta: float, log_lik = 0.0 # Iterate over item indices for item_idx in range(len(a_params)): - prob = GPCM.category_prob( + log_prob = GPCM.category_prob( theta=theta, a=a_params[item_idx], thresholds_list=thresholds_list[item_idx], response_pattern=response_pattern[item_idx] ) - if prob <= 0: # Handle cases where probability is zero or negative - log_lik += -np.inf # Log of zero is negative infinity - else: - log_lik += np.log(prob) + + log_lik += log_prob return -log_lik # Return negative log-likelihood for minimization @staticmethod diff --git a/adaptivetesting/math/estimators/__ml_estimation.py b/adaptivetesting/math/estimators/__ml_estimation.py index bdcc0ad..ebc7738 100644 --- a/adaptivetesting/math/estimators/__ml_estimation.py +++ b/adaptivetesting/math/estimators/__ml_estimation.py @@ -12,8 +12,8 @@ class MLEstimator(IEstimator): def __init__(self, response_pattern: List[int] | np.ndarray, items: list[TestItem], - optimization_interval: Tuple[float, float] = (-10, 10), model: Literal["GRM", "GPCM"] | None = None, + optimization_interval: Tuple[float, float] = (-10, 10), **kwargs): """This class can be used to estimate the current ability level of a respondent given the response pattern and the corresponding @@ -28,7 +28,7 @@ def __init__(self, IEstimator.__init__(self, response_pattern, items, optimization_interval) # decide type of model used - if all([isinstance(item.b, list) for item in items]): + if all([item.is_polytomous() for item in items]): self.type: Literal["poly", "dich"] = "poly" self.model = model else: diff --git a/adaptivetesting/models/__item_pool.py b/adaptivetesting/models/__item_pool.py index 914840b..918e20e 100644 --- a/adaptivetesting/models/__item_pool.py +++ b/adaptivetesting/models/__item_pool.py @@ -64,7 +64,7 @@ def get_item_response(self, item: TestItem) -> int: item (TestItem): item to get the corresponding response Returns: - (int): response (either `0` or `1`) + (int): response (either `0` or `1` for dichotmous items or `k` for polytomous items) """ if self.simulated_responses is None: raise ValueError("Simulated responses not provided") diff --git a/adaptivetesting/services/__estimator_interface.py b/adaptivetesting/services/__estimator_interface.py index bdb5bc9..8019b29 100644 --- a/adaptivetesting/services/__estimator_interface.py +++ b/adaptivetesting/services/__estimator_interface.py @@ -1,5 +1,5 @@ from abc import ABC, abstractmethod -from typing import List, Tuple, cast +from typing import List, Tuple, cast, Literal import numpy as np from ..models.__test_item import TestItem @@ -8,7 +8,8 @@ class IEstimator(ABC): def __init__(self, response_pattern: List[int] | np.ndarray, items: list[TestItem], - optimization_interval: Tuple[float, float] = (-10, 10)): + optimization_interval: Tuple[float, float] = (-10, 10), + model: Literal["GRM", "GPCM"] | None = None): """This is the interface required for every possible estimator. Any estimator inherits from this class and implements diff --git a/adaptivetesting/tests/test_simulation.py b/adaptivetesting/tests/test_simulation.py index bd056a0..b03f61a 100644 --- a/adaptivetesting/tests/test_simulation.py +++ b/adaptivetesting/tests/test_simulation.py @@ -3,6 +3,8 @@ from adaptivetesting.simulation.__simulation import Simulation from adaptivetesting.models.__adaptive_test import AdaptiveTest from adaptivetesting.models.__misc import ResultOutputFormat, StoppingCriterion +import pandas as pd +import adaptivetesting as adt def get_mock_adaptive_test(): @@ -97,3 +99,63 @@ def test_save_test_results_unsupported_format(self): sim = Simulation(test=mock_adaptive_test, test_result_output="UNSUPPORTED") # type: ignore with self.assertRaises(KeyError): sim.save_test_results() + + +class TestSimulation(unittest.TestCase): + def test_run_basic_simulation_GRM(self): + items = pd.DataFrame({ + "a": [1.32, 1.07, 0.84], + "b": [[0.2, 0.9], + [0.2, 0.9], + [0.2, 0.9]], + "id": [1, 2, 3] + }) + + item_pool = adt.ItemPool.load_from_dataframe(items) + res_pattern = adt.generate_response_pattern( + 0, + item_pool.test_items, + model="GRM", + seed=123 + ) + item_pool.simulated_responses = res_pattern + + adaptive_test = adt.TestAssembler( + item_pool=item_pool, + simulation_id="2", + participant_id="2", + ability_estimator=adt.MLEstimator, + model_type="GRM" + ) + + sim = adt.Simulation(adaptive_test, adt.ResultOutputFormat.CSV) + sim.simulate() + + def test_run_basic_simulation_GPCM(self): + items = pd.DataFrame({ + "a": [0.926, 1.595, 0.330], + "b": [[1.595, 0.330], + [-0.820, 0.487], + [0.738, 0.578]], + "id": [1, 2, 3] + }) + + item_pool = adt.ItemPool.load_from_dataframe(items) + res_pattern = adt.generate_response_pattern( + 0, + item_pool.test_items, + model="GPCM", + seed=123 + ) + item_pool.simulated_responses = res_pattern + + adaptive_test = adt.TestAssembler( + item_pool=item_pool, + simulation_id="2", + participant_id="2", + ability_estimator=adt.MLEstimator, + model_type="GPCM" + ) + + sim = adt.Simulation(adaptive_test, adt.ResultOutputFormat.CSV) + sim.simulate() From a01e192de55de2ac7ed62c331134ed7867c76c9f Mon Sep 17 00:00:00 2001 From: Tim Schaefer Date: Sat, 21 Feb 2026 10:04:36 +0100 Subject: [PATCH 085/137] FIX: pass optimization_interval argument on in NormalPrior branch of BayesModal.get_estimation --- adaptivetesting/math/estimators/__bayes_modal_estimation.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/adaptivetesting/math/estimators/__bayes_modal_estimation.py b/adaptivetesting/math/estimators/__bayes_modal_estimation.py index 045c6fa..6f35f65 100644 --- a/adaptivetesting/math/estimators/__bayes_modal_estimation.py +++ b/adaptivetesting/math/estimators/__bayes_modal_estimation.py @@ -62,7 +62,8 @@ def get_estimation(self) -> float: self.c, self.d, self.response_pattern, - self.prior + self.prior, + self.optimization_interval ) # else, we have to calculate the full posterior distribution # because the optimizers do not correctly identify the maximum of the function From aca0f9d03323de657498dd9b8b7d0b83963cb77a Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Tue, 10 Mar 2026 15:31:54 +0100 Subject: [PATCH 086/137] fix: bayes modal does not honor the optimization interval --- adaptivetesting/math/estimators/__bayes_modal_estimation.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/adaptivetesting/math/estimators/__bayes_modal_estimation.py b/adaptivetesting/math/estimators/__bayes_modal_estimation.py index a8f6ce4..3a62c66 100644 --- a/adaptivetesting/math/estimators/__bayes_modal_estimation.py +++ b/adaptivetesting/math/estimators/__bayes_modal_estimation.py @@ -60,7 +60,8 @@ def get_estimation(self) -> float: self.c, self.d, self.response_pattern, - self.prior) + self.prior, + optimization_interval=self.optimization_interval) if self.type == "poly": if self.model == "GRM": From a658f699fb0342c1a5f89b180c1450b02940eb67 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Wed, 11 Mar 2026 14:29:14 +0100 Subject: [PATCH 087/137] update version and dependencies --- pyproject.toml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 0320f8b..23344a3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name="adaptivetesting" -version="1.1.5" +version="1.2b1" description="adaptivetesting is a Python package that can be used to simulate and evaluate custom CAT scenarios as well as implement them in real-world testing scenarios from a single codebase" readme = "README.md" authors = [ @@ -9,8 +9,8 @@ authors = [ requires-python = ">=3.12" dependencies = [ "numpy>=2.0.0", - "pandas>=2.2.0", - "scipy>=1.12.0", + "pandas>=2.3.0", + "scipy>=1.13.0", "tqdm>=4.66.0", "matplotlib>=3.9.0" ] From 08a1df1cacd24cffa1b3c8504270d9aa8851637d Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Wed, 11 Mar 2026 17:56:37 +0100 Subject: [PATCH 088/137] add wiki build workflow --- .github/workflows/wiki.yml | 30 + .gitlab-ci.yml | 13 - .readthedocs.yml | 22 - README.md | 4 +- docs/_Sidebar.md | 7 + docs/{source => }/_static/SPEC-0.svg | 0 docs/{source => }/_static/logo.svg | 0 docs/{source => }/_static/main.css | 0 docs/data.md | 69 -- docs/{ => docs_source}/Makefile | 0 docs/{ => docs_source}/make.bat | 0 docs/{ => docs_source}/requirements.txt | Bin .../source/API-data.rst} | 0 .../source/API-implementations.rst} | 0 docs/docs_source/source/API-index.rst | 15 + .../source/API-math.rst} | 0 .../source/API-models.rst} | 0 .../source/API-services.rst} | 0 .../source/API-simulation.rst} | 0 .../source/API-tests.rst} | 0 docs/docs_source/source/API-utils.rst | 9 + docs/{ => docs_source}/source/conf.py | 2 +- docs/implementations.md | 117 --- docs/index.md | 32 +- docs/math.md | 254 ------ docs/models.md | 248 ----- docs/readme.md | 32 - docs/services.md | 42 - docs/simulation.md | 53 -- docs/source/index.rst | 37 - docs/tests.md | 4 - examples/.gitignore | 3 - examples/simulation.py | 134 --- examples/testing.py | 108 --- images/default.svg | 208 ----- images/semi-adaptive.svg | 254 ------ uv.lock | 858 +++++++++--------- 37 files changed, 499 insertions(+), 2056 deletions(-) create mode 100644 .github/workflows/wiki.yml delete mode 100644 .gitlab-ci.yml delete mode 100644 .readthedocs.yml create mode 100644 docs/_Sidebar.md rename docs/{source => }/_static/SPEC-0.svg (100%) rename docs/{source => }/_static/logo.svg (100%) rename docs/{source => }/_static/main.css (100%) delete mode 100644 docs/data.md rename docs/{ => docs_source}/Makefile (100%) rename docs/{ => docs_source}/make.bat (100%) rename docs/{ => docs_source}/requirements.txt (100%) rename docs/{source/data.rst => docs_source/source/API-data.rst} (100%) rename docs/{source/implementations.rst => docs_source/source/API-implementations.rst} (100%) create mode 100644 docs/docs_source/source/API-index.rst rename docs/{source/math.rst => docs_source/source/API-math.rst} (100%) rename docs/{source/models.rst => docs_source/source/API-models.rst} (100%) rename docs/{source/services.rst => docs_source/source/API-services.rst} (100%) rename docs/{source/simulation.rst => docs_source/source/API-simulation.rst} (100%) rename docs/{source/tests.rst => docs_source/source/API-tests.rst} (100%) create mode 100644 docs/docs_source/source/API-utils.rst rename docs/{ => docs_source}/source/conf.py (96%) delete mode 100644 docs/implementations.md delete mode 100644 docs/math.md delete mode 100644 docs/models.md delete mode 100644 docs/readme.md delete mode 100644 docs/services.md delete mode 100644 docs/simulation.md delete mode 100644 docs/source/index.rst delete mode 100644 docs/tests.md delete mode 100644 examples/.gitignore delete mode 100644 examples/simulation.py delete mode 100644 examples/testing.py delete mode 100644 images/default.svg delete mode 100644 images/semi-adaptive.svg diff --git a/.github/workflows/wiki.yml b/.github/workflows/wiki.yml new file mode 100644 index 0000000..6b0e72f --- /dev/null +++ b/.github/workflows/wiki.yml @@ -0,0 +1,30 @@ +name: Publish wiki +on: + push: + branches: [revision] + paths: + - wiki/** + - .github/workflows/publish-wiki.yml +permissions: + contents: write +jobs: + publish-wiki: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v6 + with: + python-version: '3.13' + - name: build wiki + run: | + cd docs/docs_source + pip install -r requirements.txt + .\Make markdown + - name: move markdown pages + run: | + cd .. + mkdir API + mv docs_source/build/markdown ./API + rm -r docs_source + - uses: Andrew-Chen-Wang/github-wiki-action@v4 + path: docs/ \ No newline at end of file diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml deleted file mode 100644 index c1f1ad4..0000000 --- a/.gitlab-ci.yml +++ /dev/null @@ -1,13 +0,0 @@ -image: python:latest - -deploy: - variables: - TWINE_USERNAME: gitlab-ci-token - TWINE_PASSWORD: $CI_JOB_TOKEN - script: - - pip install -r requirements.txt - - python -m build - - python -m twine upload --repository-url ${CI_API_V4_URL}/projects/${CI_PROJECT_ID}/packages/pypi dist/* - only: - refs: - - tags \ No newline at end of file diff --git a/.readthedocs.yml b/.readthedocs.yml deleted file mode 100644 index d786e48..0000000 --- a/.readthedocs.yml +++ /dev/null @@ -1,22 +0,0 @@ -# Read the Docs configuration file -# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details - -# Required -version: 2 - -# Set the OS, Python version, and other tools you might need -build: - os: ubuntu-24.04 - tools: - python: "3.13" - -# Build documentation in the "docs/" directory with Sphinx -sphinx: - configuration: docs/source/conf.py - -python: - install: - - method: pip - path: . - - requirements: docs/requirements.txt - \ No newline at end of file diff --git a/README.md b/README.md index 63a5c3a..27e091a 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,9 @@ # adaptivetesting -[![SPEC 0 — Minimum Supported Dependencies](docs/source/_static/SPEC-0.svg)](https://scientific-python.org/specs/spec-0000/) +[![SPEC 0 — Minimum Supported Dependencies](docs/docs_source/source/_static/SPEC-0.svg)](https://scientific-python.org/specs/spec-0000/) - + **An open-source Python package for simplified, customizable Computerized Adaptive Testing (CAT) using Bayesian methods.** diff --git a/docs/_Sidebar.md b/docs/_Sidebar.md new file mode 100644 index 0000000..c9e8bee --- /dev/null +++ b/docs/_Sidebar.md @@ -0,0 +1,7 @@ +### [Home](Home) + +**Getting Started** + +**Tutorials** + +**API Reference** \ No newline at end of file diff --git a/docs/source/_static/SPEC-0.svg b/docs/_static/SPEC-0.svg similarity index 100% rename from docs/source/_static/SPEC-0.svg rename to docs/_static/SPEC-0.svg diff --git a/docs/source/_static/logo.svg b/docs/_static/logo.svg similarity index 100% rename from docs/source/_static/logo.svg rename to docs/_static/logo.svg diff --git a/docs/source/_static/main.css b/docs/_static/main.css similarity index 100% rename from docs/source/_static/main.css rename to docs/_static/main.css diff --git a/docs/data.md b/docs/data.md deleted file mode 100644 index 918d11e..0000000 --- a/docs/data.md +++ /dev/null @@ -1,69 +0,0 @@ -# data Module - -## PickleContext - -### *class* adaptivetesting.data.PickleContext(simulation_id: str, participant_id: int) - -Bases: [`ITestResults`](services.md#adaptivetesting.services.ITestResults) - -Implementation of the ITestResults interface for -saving test results to the pickle format. -The resulting pickle file .pickle -will be of the standard pickle format which depends -on the used python version. - -Args: -: simulation_id (str): Not used but required by the interface -
- participant_id (int): participant id and table name - -#### load() → List[[TestResult](models.md#adaptivetesting.models.TestResult)] - -Loads results from the database. -The implementation of this method is required -by the interface. However, is does not have -any implemented functionality and will throw an error -if used. - -Returns: List[TestResult] - -#### save(test_results: List[[TestResult](models.md#adaptivetesting.models.TestResult)]) → None - -Saves a list of test results to a pickle binary file -(.pickle). - -Args: -: test_results (List[TestResult]): list of test results - -## SQLiteContext - -### *class* adaptivetesting.data.SQLiteContext(simulation_id: str, participant_id: int) - -Bases: [`ITestResults`](services.md#adaptivetesting.services.ITestResults) - -Implementation of the ITestResults interface for -saving test results to a SQLITE database. -The resulting sqlite file .db -will be of the SQLITE3 format. - -Args: -: simulation_id (str): db filename -
- participant_id (int): participant id and table name - -#### load() → List[[TestResult](models.md#adaptivetesting.models.TestResult)] - -Loads results from the database. -The implementation of this method is required -by the interface. However, is does not have -any implemented functionality and will throw an error. - -Returns: List[TestResult] - -#### save(test_results: List[[TestResult](models.md#adaptivetesting.models.TestResult)]) → None - -Saves a list of test results to the database -in the table . - -Args: -: test_results (List[TestResult]): list of test results diff --git a/docs/Makefile b/docs/docs_source/Makefile similarity index 100% rename from docs/Makefile rename to docs/docs_source/Makefile diff --git a/docs/make.bat b/docs/docs_source/make.bat similarity index 100% rename from docs/make.bat rename to docs/docs_source/make.bat diff --git a/docs/requirements.txt b/docs/docs_source/requirements.txt similarity index 100% rename from docs/requirements.txt rename to docs/docs_source/requirements.txt diff --git a/docs/source/data.rst b/docs/docs_source/source/API-data.rst similarity index 100% rename from docs/source/data.rst rename to docs/docs_source/source/API-data.rst diff --git a/docs/source/implementations.rst b/docs/docs_source/source/API-implementations.rst similarity index 100% rename from docs/source/implementations.rst rename to docs/docs_source/source/API-implementations.rst diff --git a/docs/docs_source/source/API-index.rst b/docs/docs_source/source/API-index.rst new file mode 100644 index 0000000..3d5732c --- /dev/null +++ b/docs/docs_source/source/API-index.rst @@ -0,0 +1,15 @@ +API +======== + +.. toctree:: + :maxdepth: 1 + :caption: Contents: + + API-data + API-implementations + API-math + API-models + API-simulation + API-services + API-utils + API-tests \ No newline at end of file diff --git a/docs/source/math.rst b/docs/docs_source/source/API-math.rst similarity index 100% rename from docs/source/math.rst rename to docs/docs_source/source/API-math.rst diff --git a/docs/source/models.rst b/docs/docs_source/source/API-models.rst similarity index 100% rename from docs/source/models.rst rename to docs/docs_source/source/API-models.rst diff --git a/docs/source/services.rst b/docs/docs_source/source/API-services.rst similarity index 100% rename from docs/source/services.rst rename to docs/docs_source/source/API-services.rst diff --git a/docs/source/simulation.rst b/docs/docs_source/source/API-simulation.rst similarity index 100% rename from docs/source/simulation.rst rename to docs/docs_source/source/API-simulation.rst diff --git a/docs/source/tests.rst b/docs/docs_source/source/API-tests.rst similarity index 100% rename from docs/source/tests.rst rename to docs/docs_source/source/API-tests.rst diff --git a/docs/docs_source/source/API-utils.rst b/docs/docs_source/source/API-utils.rst new file mode 100644 index 0000000..f3a1f90 --- /dev/null +++ b/docs/docs_source/source/API-utils.rst @@ -0,0 +1,9 @@ +utils Module +================= + + +.. automodule:: adaptivetesting.data + :members: + :undoc-members: + :show-inheritance: + :imported-members: diff --git a/docs/source/conf.py b/docs/docs_source/source/conf.py similarity index 96% rename from docs/source/conf.py rename to docs/docs_source/source/conf.py index e054868..0ea63ed 100644 --- a/docs/source/conf.py +++ b/docs/docs_source/source/conf.py @@ -1,7 +1,7 @@ import os import sys from sphinx.application import Sphinx -sys.path.insert(0, os.path.abspath('../')) +sys.path.insert(0, os.path.abspath('../../')) # Configuration file for the Sphinx documentation builder. # diff --git a/docs/implementations.md b/docs/implementations.md deleted file mode 100644 index c625d6e..0000000 --- a/docs/implementations.md +++ /dev/null @@ -1,117 +0,0 @@ -# implementations Module - -## DefaultImplementation - -### *class* adaptivetesting.implementations.DefaultImplementation(item_pool: [ItemPool](models.md#adaptivetesting.models.ItemPool), simulation_id: str, participant_id: int, true_ability_level: float, initial_ability_level: float = 0, simulation=True, debug=False) - -Bases: [`AdaptiveTest`](models.md#adaptivetesting.models.AdaptiveTest) - -This class represents the Default implementation using -Maximum Likelihood Estimation and Urry’s rule during the test. - -Args: -: item_pool (ItemPool): item pool used for the test -
- simulation_id (str): simulation id -
- participant_id (int): participant id -
- true_ability_level (float): true ability level (must always be set) -
- initial_ability_level (float): initially assumed ability level -
- simulation (bool): will the test be simulated -
- debug (bool): enables debug mode - -#### estimate_ability_level() → float - -Estimates latent ability level using ML. -If responses are only 1 or 0, -the ability will be set to one -of the boundaries of the estimation interval ([-10,10]). - -Returns: -: float: ability estimation - -## PreTest - -### *class* adaptivetesting.implementations.PreTest(items: List[[TestItem](models.md#adaptivetesting.models.TestItem)], seed: int | None = None) - -Bases: `object` - -The pretest class can be used to draw items randomly from -difficulty quantiles -of the item pool. - -Args: -: items: Item pool -
- seed (int): A seed for the item selection can be provided. - : If not, the item selection will be drawn randomly, and you will not be able - to reproduce the results. - -#### calculate_quantiles() → Array - -Calculates quantiles 0.25, 0.5, 0.75 - -#### select_item_in_interval(lower: float, upper: float) → [TestItem](models.md#adaptivetesting.models.TestItem) - -Draws item from a pool in the specified interval. -The item difficulty is > than the lower limit and <= the higher limit. - -Args: -: lower (float): Lower bound of the item difficulty interval. -
- upper (float): Upper bound of the item difficulty interval. - -Returns: -: TestItem: Selected item. - -#### select_random_item_quantile() → List[[TestItem](models.md#adaptivetesting.models.TestItem)] - -Selects a random item from the 0.25, 0.5 and 0.75 quantiles. - -Returns: -: List[TestItem]: Selected item. - -## SemiAdaptiveImplementation - -### *class* adaptivetesting.implementations.SemiAdaptiveImplementation(item_pool: [ItemPool](models.md#adaptivetesting.models.ItemPool), simulation_id: str, participant_id: int, true_ability_level: float, initial_ability_level: float = 0, simulation=True, debug=False, pretest_seed=12345) - -Bases: [`AdaptiveTest`](models.md#adaptivetesting.models.AdaptiveTest) - -This class represents the Semi-Adaptive implementation using -Maximum Likelihood Estimation and Urry’s rule during the test. -The pretest is 4 items long. - -Args: -: item_pool (ItemPool): item pool used for the test -
- simulation_id (str): simulation id -
- participant_id (int): participant id -
- true_ability_level (float): true ability level (must always be set) -
- initial_ability_level (float): initially assumed ability level -
- simulation (bool): will the test be simulated -
- debug (bool): enables debug mode -
- pretest_seed (int): seed used for the random number generator to draw pretest items. - -#### estimate_ability_level() → float - -Estimates latent ability level using ML. -If responses are only 1 or 0, -the ability will be set to one -of the boundaries of the estimation interval ([-10,10]). - -Returns: -: float: ability estimation - -#### pre_test() - -Runs pretest diff --git a/docs/index.md b/docs/index.md index 61fa3e8..47c11af 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,32 +1,12 @@ # adaptivetesting -*adaptivetesting* is a Python package for computer-aided adaptive -testing that can be used to simulate and implement custom adaptive tests -in real-world testing scenarios. +[![SPEC 0 — Minimum Supported Dependencies](docs/_static/SPEC-0.svg)](https://scientific-python.org/specs/spec-0000/) +![](https://anaconda.org/conda-forge/adaptivetesting/badges/version.svg) +![](https://anaconda.org/conda-forge/adaptivetesting/badges/license.svg) +![](https://anaconda.org/conda-forge/adaptivetesting/badges/downloads.svg) -# Getting Started ---- +![Logo of the adaptivetesting package](/docs/_static/logo.svg) -Required Python version: >= 3.11 (other versions may work, but they are not officially supported) -```bash -pip install adaptivetesting -``` - -If you want to install the current development version, -you can do so by running the following command: - -```bash -pip install git+https://github.com/condecon/adaptivetesting -``` - -# Contents: - -* [data Module](data.md) -* [implementations Module](implementations.md) -* [math Module](math.md) -* [models Module](models.md) -* [simulation Module](simulation.md) -* [services Module](services.md) -* [tests Module](tests.md) +## Features \ No newline at end of file diff --git a/docs/math.md b/docs/math.md deleted file mode 100644 index f224155..0000000 --- a/docs/math.md +++ /dev/null @@ -1,254 +0,0 @@ -# math Module - -## Estimators - -### MLEstimator - -### *class* adaptivetesting.math.estimators.MLEstimator(response_pattern: List[int] | Array, items: List[[TestItem](models.md#adaptivetesting.models.TestItem)], optimization_interval: Tuple[float, float] = (-10, 10)) - -Bases: [`IEstimator`](services.md#adaptivetesting.services.IEstimator) - -This class can be used to estimate the current ability level -of a respondent given the response pattern and the corresponding -item parameters. -The estimation uses Maximum Likelihood Estimation. - -Args: -: response_pattern (List[int]): list of response patterns (0: wrong, 1:right) -
- items (List[TestItem]): list of answered items - -#### get_estimation() → float - -Estimate the current ability level by searching -for the maximum of the likelihood function. -A line-search algorithm is used. - -Returns: -: float: ability estimation - -### Prior - -### *class* adaptivetesting.math.estimators.Prior - -Bases: `ABC` - -Base class for prior distributions - -#### *abstractmethod* pdf(x: float | Array) → Array - -Probability density function for a prior distribution - -Args: -: x (float | np.ndarray): point at which to calculate the function value - -Returns: -: ndarray: function value - -### NormalPrior - -### *class* adaptivetesting.math.estimators.NormalPrior(mean: float, sd: float) - -Bases: [`Prior`](#adaptivetesting.math.estimators.Prior) - -Normal distribution as prior for Bayes Modal estimation - -Args: -: mean (float): mean of the distribution -
- sd (float): standard deviation of the distribution - -#### pdf(x: float | Array) → Array - -Probability density function for a prior distribution - -Args: -: x (float | np.ndarray): point at which to calculate the function value - -Returns: -: ndarray: function value - -### probability_y1 - -### adaptivetesting.math.estimators.probability_y1(mu: Array, a: Array, b: Array, c: Array, d: Array) → Array - -Probability of getting the item correct given the ability level. - -Args: -: mu (jnp.ndarray): latent ability level -
- a (jnp.ndarray): item discrimination parameter -
- b (jnp.ndarray): item difficulty parameter -
- c (jnp.ndarray): pseudo guessing parameter -
- d (jnp.ndarray): inattention parameter - -Returns: -: jnp.ndarray: probability of getting the item correct - -### probability_y0 - -### adaptivetesting.math.estimators.probability_y0(mu: Array, a: Array, b: Array, c: Array, d: Array) → Array - -Probability of getting the item wrong given the ability level. - -Args: -: mu (jnp.ndarray): latent ability level -
- a (jnp.ndarray): item discrimination parameter -
- b (jnp.ndarray): item difficulty parameter -
- c (jnp.ndarray): pseudo guessing parameter -
- d (jnp.ndarray): inattention parameter - -Returns: -: jnp.ndarray: probability of getting the item wrong - -### maximize_likelihood_function - -### adaptivetesting.math.estimators.maximize_likelihood_function(a: Array, b: Array, c: Array, d: Array, response_pattern: Array, border: tuple[float, float] = (-10, 10)) → float - -Find the ability value that maximizes the likelihood function. -This function uses the minimize_scalar function from scipy and the “bounded” method. - -Args: -: a (jnp.ndarray): item discrimination parameter -
- b (jnp.ndarray): item difficulty parameter -
- c (jnp.ndarray): pseudo guessing parameter -
- d (jnp.ndarray): inattention parameter -
- response_pattern (jnp.ndarray): response pattern of the item - border (tuple[float, float], optional): border of the optimization interval. - Defaults to (-10, 10). - -Raises: -: AlgorithmException: if the optimization fails or the response - pattern consists of only one type of response. - -Returns: -: float: optimized ability value - -### likelihood - -### adaptivetesting.math.estimators.likelihood(mu: Array, a: Array, b: Array, c: Array, d: Array, response_pattern: Array) → Array - -Likelihood function of the 4-PL model. -For optimization purposes, the function returns the negative value of the likelihood function. -To get the *real* value, multiply the result by -1. - -Args: -: mu (jnp.ndarray): ability level -
- a (jnp.ndarray): item discrimination parameter -
- b (jnp.ndarray): item difficulty parameter -
- c (jnp.ndarray): pseudo guessing parameter -
- d (jnp.ndarray): inattention parameter - -Returns: -: float: likelihood value of given ability value - -### maximize_posterior - -### adaptivetesting.math.estimators.maximize_posterior(a: Array, b: Array, c: Array, d: Array, response_pattern: Array, prior: [Prior](#adaptivetesting.math.estimators.Prior)) → float - -\_summary_ - -Args: -: a (np.ndarray): \_description_ -
- b (np.ndarray): \_description_ -
- c (np.ndarray): \_description_ -
- d (np.ndarray): \_description_ -
- response_pattern (np.ndarray): \_description_ -
- prior (Prior): \_description_ - -Returns: -: float: Bayes Modal estimator for the given parameters - -## Item Selection - -### urrys_rule - -### adaptivetesting.math.item_selection.urrys_rule(items: List[[TestItem](models.md#adaptivetesting.models.TestItem)], ability: float) → [TestItem](models.md#adaptivetesting.models.TestItem) - -Urry’s rule selects the test item -which has the minimal difference between -the item’s difficulty and the ability level. - -Args: -: items (List[TestItem]): Test items (item pool) -
- ability (float): Ability level (current ability estimation) - -Returns: -: TestItem: selected test item - -## standard_error - -### adaptivetesting.math.standard_error(answered_items: List[[TestItem](models.md#adaptivetesting.models.TestItem)], estimated_ability_level: float, estimator: Literal['ML', 'BM'] = 'ML', sd: float | None = None) → float - -Calculates the standard error using the test information function. -If Bayes Modal is used for the ability estimation, a standard deviation value -of the prior distribution has to be provided. - -Args: -: answered_items (List[float]): List of answered items -
- estimated_ability_level (float): Currently estimated ability level -
- estimator (Literal[“ML”, “BM”]): Ability estimator (Default: ML) -
- sd (float | None): Standard deviation of the prior distribution. Only required for BM. - -Raises: -: ValueError - -Returns: -: float: Standard error - -## test_information_function - -### adaptivetesting.math.test_information_function(mu: Array, a: Array, b: Array, c: Array, d: Array) → float - -Calculates test information. - -Args: -: mu (np.ndarray): ability level - a (np.ndarray): discrimination parameter - b (np.ndarray): difficulty parameter - c (np.ndarray): guessing parameter - d (np.ndarray): slipping parameter - -Returns: -: float: test information - -## Utilities - -### generate_response_pattern - -### adaptivetesting.math.generate_response_pattern(ability: float, items: list[[TestItem](models.md#adaptivetesting.models.TestItem)], seed: int | None = None) → list[int] - -Generates a response pattern for a given ability level -and item difficulties. Also, a seed can be set. - -Args: -: ability (float): participants ability - items (list[TestItem]): test items - seed (int, optional): Seed for the random process. - -Returns: -: list[int]: response pattern diff --git a/docs/models.md b/docs/models.md deleted file mode 100644 index 7150fbc..0000000 --- a/docs/models.md +++ /dev/null @@ -1,248 +0,0 @@ -# models Module - -## AdaptiveTest - -### *class* adaptivetesting.models.AdaptiveTest(item_pool: [ItemPool](#adaptivetesting.models.ItemPool), simulation_id: str, participant_id: int, true_ability_level: float, initial_ability_level: float = 0, simulation: bool = True, DEBUG=False) - -Bases: `ABC` - -Abstract implementation of an adaptive test. -All abstract methods have to be overridden -to create an instance of this class. - -Abstract methods: -: - estimate_ability_level - -Args: -: item_pool (ItemPool): item pool used for the test -
- simulation_id (str): simulation id -
- participant_id (int): participant id -
- true_ability_level (float): true ability level (must always be set) -
- initial_ability_level (float): initially assumed ability level -
- simulation (bool): will the test be simulated -
- DEBUG (bool): enables debug mode - -#### check_length_criterion(value: float) → bool - -#### check_se_criterion(value: float) → bool - -#### *abstractmethod* estimate_ability_level() → float - -Estimates ability level. -The method has to be implemented by subclasses. - -Returns: -: float: estimated ability level - -#### get_ability_se() → float - -Calculate the current standard error -of the ability estimation. - -Returns: -: float: standard error of the ability estimation - -#### get_answered_items() → List[[TestItem](#adaptivetesting.models.TestItem)] - -Returns: -: List[TestItem]: answered items - -#### get_answered_items_difficulties() → List[float] - -Returns: -: List[float]: difficulties of answered items - -#### get_item_difficulties() → List[float] - -Returns: -: List[float]: difficulties of items in the item pool - -#### get_next_item() → [TestItem](#adaptivetesting.models.TestItem) - -Select next item using Urry’s rule. - -Returns: -: TestItem: selected item - -#### get_response(item: [TestItem](#adaptivetesting.models.TestItem)) → int - -If the adaptive test is not used for simulation. -This method is used to get user feedback. - -Args: -: item (TestItem): test item shown to the participant - -Return: -: int: participant’s response - -#### run_test_once() - -Runs the test procedure once. -Saves the result to test_results of -the current instance. - -## AlgorithmException - -### *class* adaptivetesting.models.AlgorithmException - -Bases: `Exception` - -Exception that is thrown when the estimation process did not find a maximum. - -## ItemPool - -### *class* adaptivetesting.models.ItemPool(test_items: List[[TestItem](#adaptivetesting.models.TestItem)], simulated_responses: List[int] | None = None) - -Bases: `object` - -An item pool has to be created for an adaptive test. -For that, a list of test items has to be provided. If the package is used -to simulate adaptive tests, simulated responses have to be supplied as well. -The responses are matched to the items internally. -Therefore, both have to be in the same order. - -Args: -: test_items (List[TestItem]): A list of test items. Necessary for any adaptive test. -
- simulated_responses (List[int]): A list of simulated responses. - Required for CAT simulations. - -#### delete_item(item: [TestItem](#adaptivetesting.models.TestItem)) → None - -Deletes item from item pool. -If simulated responses are defined, they will be deleted as well. - -Args: -: item (TestItem): The test item to delete. - -#### get_item_by_index(index: int) → Tuple[[TestItem](#adaptivetesting.models.TestItem), int] | [TestItem](#adaptivetesting.models.TestItem) - -Returns item and if defined the simulated response. - -Args: -: index (int): Index of the test item in the item pool to return. - -Returns: -: TestItem or (TestItem, Simulated Response) - -#### get_item_by_item(item: [TestItem](#adaptivetesting.models.TestItem)) → Tuple[[TestItem](#adaptivetesting.models.TestItem), int] | [TestItem](#adaptivetesting.models.TestItem) - -Returns item and if defined the simulated response. - -Args: -: item (TestItem): item to return. - -Returns: -: TestItem or (TestItem, Simulated Response) - -#### get_item_response(item: [TestItem](#adaptivetesting.models.TestItem)) → int - -Gets the simulated response to an item if available. -A ValueError will be raised if a simulated response is not available. - -Args: -: item (TestItem): item to get the corresponding response - -Returns: -: (int): response (either 0 or 1) - -#### *static* load_from_dataframe(source: DataFrame) → [ItemPool](#adaptivetesting.models.ItemPool) - -Creates item pool from a pandas DataFrame. -Required columns are: a, b, c, d. -Each column has to contain float values. -A simulated_responses (int values) column can be added to -the DataFrame to provide simulated responses. - -Args: -: source (DataFrame): \_description_ - -Returns: -: ItemPool: \_description_ - -#### *static* load_from_dict(source: dict[str, List[float]], simulated_responses: List[int] | None = None) → [ItemPool](#adaptivetesting.models.ItemPool) - -Creates test items from a dictionary. -The dictionary has to have the following keys: - -> - a -> - b -> - c -> - d - -each containing a list of float. - -Args: -: source (dict[str, List[float]]): item pool dictionary - -Returns: -: List[TestItem]: item pool - -#### *static* load_from_list(b: List[float], a: List[float] | None = None, c: List[float] | None = None, d: List[float] | None = None, simulated_responses: List[int] | None = None) → [ItemPool](#adaptivetesting.models.ItemPool) - -Creates test items from a list of floats. - -Args: -: a (List[float]): discrimination parameter -
- b (List[float]): difficulty parameter -
- c (List[float]): guessing parameter -
- d (List[float]): slipping parameter -
- simulated_responses (List[int]): simulated responses - -Returns: -: List[TestItem]: item pool - -## TestItem - -### *class* adaptivetesting.models.TestItem - -Bases: `object` - -Representation of a test item in the item pool. -The format is equal to the implementation in catR. - -Properties: -: - a (float): - - b (float): difficulty - - c (float): - - d (float): - -#### as_dict() → dict[str, float] - -## TestResult - -### *class* adaptivetesting.models.TestResult(test_id: str, ability_estimation: float, standard_error: float, showed_item: float, response: int, true_ability_level: float) - -Bases: `object` - -Representation of simulation test results - -#### ability_estimation *: float* - -#### *static* from_dict(dictionary: Dict) → [TestResult](#adaptivetesting.models.TestResult) - -Create a TestResult from a dictionary - -Args: -: dictionary: with the fields test_id, ability_estimation, standard_error, showed_item, response, - true_ability_level - -#### response *: int* - -#### showed_item *: float* - -#### standard_error *: float* - -#### test_id *: str* - -#### true_ability_level *: float* diff --git a/docs/readme.md b/docs/readme.md deleted file mode 100644 index 8562210..0000000 --- a/docs/readme.md +++ /dev/null @@ -1,32 +0,0 @@ -# adaptivetesting - -*adaptivetesting* is a Python package for computer-aided adaptive -testing that can be used to simulate and implement custom adaptive tests -in real-world testing scenarios. - -# Getting Started - ---- - -Required Python version: >= 3.11 (other versions may work, but they are not officially supported) - -```bash -pip install adaptivetesting -``` - -If you want to install the current development version, -you can do so by running the following command: - -```bash -pip install git+https://github.com/condecon/adaptivetesting -``` - -# Contents: - -* [data Module](adaptivetesting.data.md) -* [implementations Module](adaptivetesting.implementations.md) -* [math Module](adaptivetesting.math.md) -* [models Module](adaptivetesting.models.md) -* [simulation Module](adaptivetesting.simulation.md) -* [services Module](adaptivetesting.services.md) -* [tests Module](adaptivetesting.tests.md) diff --git a/docs/services.md b/docs/services.md deleted file mode 100644 index 082614a..0000000 --- a/docs/services.md +++ /dev/null @@ -1,42 +0,0 @@ -# services Module - -## IEstimator - -### *class* adaptivetesting.services.IEstimator(response_pattern: List[int] | Array, items: List[[TestItem](models.md#adaptivetesting.models.TestItem)], optimization_interval: Tuple[float, float] = (-10, 10)) - -Bases: `ABC` - -This is the interface required for every possible -estimator. -Any estimator inherits from this class and implements -the get_estimation method. - -Args: -: response_pattern (List[int]): list of responses (0: wrong, 1:right) -
- items (List[TestItem]): list of answered items - -#### *abstractmethod* get_estimation() → float - -Get the currently estimated ability. - -Returns: -: float: ability - -## ITestResults - -### *class* adaptivetesting.services.ITestResults(simulation_id: str, participant_id: int) - -Bases: `ABC` - -Interface for saving and reading test results. -This interface may mainly be used for saving simulation results. - -Args: -: simulation_id (str): The simulation ID. Name of the results file. -
- participant_id (int): The participant ID. - -#### *abstractmethod* load() → List[[TestResult](models.md#adaptivetesting.models.TestResult)] - -#### *abstractmethod* save(test_results: List[[TestResult](models.md#adaptivetesting.models.TestResult)]) → None diff --git a/docs/simulation.md b/docs/simulation.md deleted file mode 100644 index 6b6f4f5..0000000 --- a/docs/simulation.md +++ /dev/null @@ -1,53 +0,0 @@ -# simulation Module - -## ResultOutputFormat - -### *class* adaptivetesting.simulation.ResultOutputFormat(\*values) - -Bases: `Enum` - -Enum for selecting the output format for -the test results - -#### PICKLE *= 2* - -#### SQLITE *= 1* - -## StoppingCriterion - -### *class* adaptivetesting.simulation.StoppingCriterion(\*values) - -Bases: `Enum` - -Enum for selecting the stopping criterion -for the adaptive test - -#### LENGTH *= 2* - -#### SE *= 1* - -## Simulation - -### *class* adaptivetesting.simulation.Simulation(test: [AdaptiveTest](models.md#adaptivetesting.models.AdaptiveTest), test_result_output: [ResultOutputFormat](#adaptivetesting.simulation.ResultOutputFormat)) - -Bases: `object` - -This class can be used for simulating CAT. - -Args: -: test (AdaptiveTest): instance of an adaptive test implementation (see implementations module) -
- test_result_output (ResultOutputFormat): test results output format - -#### save_test_results() - -Saves the test results to the specified output format. - -#### simulate(criterion: [StoppingCriterion](#adaptivetesting.simulation.StoppingCriterion) = StoppingCriterion.SE, value: float = 0.4) - -Runs test until the stopping criterion is met. - -Args: -: criterion (StoppingCriterion): selected stopping criterion -
- value (float): either standard error value or test length value that has to be met by the test diff --git a/docs/source/index.rst b/docs/source/index.rst deleted file mode 100644 index d7dd04f..0000000 --- a/docs/source/index.rst +++ /dev/null @@ -1,37 +0,0 @@ -adaptivetesting -=========================================== - -*adaptivetesting* is a Python package for computer-aided adaptive -testing that can be used to simulate and implement custom adaptive tests -in real-world testing scenarios. - -Getting Started -================ -================ - -Required Python version: >= 3.11 (other versions may work, but they are not officially supported) - -.. code-block:: bash - - pip install adaptivetesting - - -If you want to install the current development version, -you can do so by running the following command: - -.. code-block:: bash - - pip install git+https://github.com/condecon/adaptivetesting - - -.. toctree:: - :maxdepth: 1 - :caption: Contents: - - data - implementations - math - models - simulation - services - tests diff --git a/docs/tests.md b/docs/tests.md deleted file mode 100644 index 8d3068d..0000000 --- a/docs/tests.md +++ /dev/null @@ -1,4 +0,0 @@ -# tests Module - -This module includes unit tests for the functionality of the package. -There is no explicit documentation available. diff --git a/examples/.gitignore b/examples/.gitignore deleted file mode 100644 index ff6bdd5..0000000 --- a/examples/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -item_pool.csv -data/ -*.pdf \ No newline at end of file diff --git a/examples/simulation.py b/examples/simulation.py deleted file mode 100644 index dcf5a6a..0000000 --- a/examples/simulation.py +++ /dev/null @@ -1,134 +0,0 @@ -# flake8: noqa -import pandas as pd -previc_item_pool = pd.read_csv("item_pool.csv") -previc_item_pool.head() - -from adaptivetesting.models import ItemPool -item_pool = ItemPool.load_from_list( - b=previc_item_pool["Difficulty"] -) - -from scipy.stats import norm -from numpy.random import seed -seed(1234) -theta_samples = norm.rvs(loc=0, scale=2, size=100) - - -from adaptivetesting.implementations import TestAssembler -from adaptivetesting.simulation import SimulationPool -from adaptivetesting.models import ResultOutputFormat, StoppingCriterion -from adaptivetesting.math.item_selection import maximum_information_criterion -from adaptivetesting.math.estimators import BayesModal, NormalPrior - -tests = [ - TestAssembler( - item_pool=item_pool, - simulation_id="example_bm", - participant_id=str(index), - ability_estimator=BayesModal, - estimator_args={ - "prior": NormalPrior(0,1), - "optimization_interval":(-10, 10) - }, - item_selector=maximum_information_criterion, - true_ability_level=theta, - simulation=True, - seed=index - ) - for index, theta in enumerate(theta_samples) -] - -sim_pool = SimulationPool( - adaptive_tests=tests, - test_result_output=ResultOutputFormat.CSV, - criterion=StoppingCriterion.SE, - value=0.4 -) -sim_pool.start() - -import os - -dfs = [] -for i in range(100): - filename = f"data/example_bm/{i}.csv" - if os.path.exists(filename): - try: - df = pd.read_csv(filename) - # Get the last row of every file - last_row = df.tail(1) - dfs.append(last_row) - # If there is an error, skip the file - except Exception: - pass -results_df = pd.concat(dfs, ignore_index=True) -mse = ((results_df["ability_estimation"] - results_df["true_ability_level"]) ** 2).mean() -print(f"MSE of ability estimates: {mse:.4f}") - - -import matplotlib.pyplot as plt - -fig, ax = plt.subplots() -ax.scatter(results_df["true_ability_level"], - results_df["ability_estimation"], - label="BM") -ax.plot(results_df["ability_estimation"], - results_df["ability_estimation"], - color="black") -ax.set_xlabel("True ability level") -ax.set_ylabel("Estimated ability level") -fig.savefig("sim_results_bm.pdf") - -# other example -from adaptivetesting.math.estimators import ExpectedAPosteriori, CustomPrior -from scipy.stats.distributions import t - -tests = [ - TestAssembler( - item_pool=item_pool, - simulation_id="example_eap", - participant_id=str(index), - ability_estimator=ExpectedAPosteriori, - estimator_args={ - "prior": CustomPrior(t, 3), - "optimization_interval":(-10, 10) - }, - item_selector=maximum_information_criterion, - true_ability_level=theta, - simulation=True, - seed=index - ) - for index, theta in enumerate(theta_samples) -] - -sim_pool = SimulationPool( - adaptive_tests=tests, - test_result_output=ResultOutputFormat.CSV, - criterion=StoppingCriterion.SE, - value=0.4 -) -sim_pool.start() - -dfs = [] -for i in range(100): - filename = f"data/example_eap/{i}.csv" - if os.path.exists(filename): - try: - df = pd.read_csv(filename) - # Get the last row of every file - last_row = df.tail(1) - dfs.append(last_row) - # If there is an error, skip the file - except Exception: - pass -results_df = pd.concat(dfs, ignore_index=True) -mse = ((results_df["ability_estimation"] - results_df["true_ability_level"]) ** 2).mean() -print(f"MSE of ability estimates: {mse:.4f}") - - -ax.scatter(results_df["true_ability_level"], - results_df["ability_estimation"], - label="EAP") -ax.set_xlabel("True ability level") -ax.set_ylabel("Estimated ability level") -ax.legend() -fig.savefig("sim_results_eap.pdf") diff --git a/examples/testing.py b/examples/testing.py deleted file mode 100644 index 0bca296..0000000 --- a/examples/testing.py +++ /dev/null @@ -1,108 +0,0 @@ -# flake8: noqa -# setup item pool -# the item pool is retrieved from the PREVIC -# https://github.com/manuelbohn/previc/tree/main/saves -import pandas as pd -previc_item_pool = pd.read_csv("item_pool.csv") -# add item column -previc_item_pool["id"] = list(range(1, 90)) -previc_item_pool.head() - -from adaptivetesting.models import ItemPool -item_pool = ItemPool.load_from_list( - b=previc_item_pool["Difficulty"], - ids=previc_item_pool["id"] -) - -# import psychopy -from psychopy import visual, event -from psychopy.hardware import keyboard -from adaptivetesting.implementations import TestAssembler -from adaptivetesting.models import AdaptiveTest, ItemPool, TestItem -from adaptivetesting.data import CSVContext -from adaptivetesting.math.estimators import ExpectedAPosteriori, CustomPrior -from adaptivetesting.math.item_selection import maximum_information_criterion -from scipy.stats import t -import pandas as pd - -# Create adaptive test -adaptive_test: AdaptiveTest = TestAssembler( - item_pool=item_pool, - simulation_id="example", - participant_id="dummy", - ability_estimator=ExpectedAPosteriori, - estimator_args={ - "prior": CustomPrior(t, 3), - "optimization_interval":(-10, 10) - }, - item_selector=maximum_information_criterion, - simulation=False, - debug=False -) - -# ==================== -# Setup PsychoPy -# ==================== - -# general setup -win = visual.Window([800, 600], - monitor="testMonitor", - units="deg", - fullscr=False) - -# init keyboard -keyboard.Keyboard() - - -# define function to get user input -def get_response(item: TestItem) -> int: - # select corresponding word from item pool data frame - stimuli: str = previc_item_pool[previc_item_pool["id"] == item.id]["word"].values[0] - - # create text box and display stimulus - text_box = visual.TextBox2(win=win, - text=stimuli, - alignment="center", - size=24) - # draw text - text_box.draw() - # update window - win.flip() - - # wait for pressed keys - while True: - keys = event.getKeys() - # if keys are not None - if keys: - # if the right arrow keys is pressed - # return 1 - if keys[0] == "right": - return 1 - # if the left arrow keys is pressed - # return 0 - if keys[0] == "left": - return 0 - - -# override adaptive test default function -adaptive_test.get_response = get_response - -# start adaptive test -while True: - adaptive_test.run_test_once() - - # check stopping criterion - if adaptive_test.standard_error <= 0.4: - break - - # end test if all items have been shown - if len(adaptive_test.item_pool.test_items) == 0: - break - -# save test results -data_context = CSVContext( - adaptive_test.simulation_id, - adaptive_test.participant_id -) - -data_context.save(adaptive_test.test_results) \ No newline at end of file diff --git a/images/default.svg b/images/default.svg deleted file mode 100644 index 844cbeb..0000000 --- a/images/default.svg +++ /dev/null @@ -1,208 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Item selection(Urry’s rule) - - - - - - - Presenting item to respondent - - - - - - - Ability estimation(MLE) - - - - - - - Checking stopping criterion - - - - - - - Initial ability level - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - End test - - - - - - - - - - - - - 0 - - - - - - true - - - - - - - - \ No newline at end of file diff --git a/images/semi-adaptive.svg b/images/semi-adaptive.svg deleted file mode 100644 index 96f20d1..0000000 --- a/images/semi-adaptive.svg +++ /dev/null @@ -1,254 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Ability estimation (MLE) - - - - - - - Checking stopping criterion - - - - - - - Item selection(Urry’s rule) - - - - - - - Presenting item to respondent - - - - - - - Random item selection - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - End test - - - - - - - - - - - - - - - - - Presenting items to respondent - - - - - - - - - - - - Pretest - - - - - - Adaptive test - - - - - - - - - - - - - true - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/uv.lock b/uv.lock index 276f0bf..33a3abe 100644 --- a/uv.lock +++ b/uv.lock @@ -22,7 +22,7 @@ wheels = [ [[package]] name = "adaptivetesting" -version = "1.1.5" +version = 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671c69d4b8e057d54992b191cf7e688270254f49 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Wed, 11 Mar 2026 17:58:11 +0100 Subject: [PATCH 089/137] fix schema --- .github/workflows/wiki.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/wiki.yml b/.github/workflows/wiki.yml index 6b0e72f..5f33a2d 100644 --- a/.github/workflows/wiki.yml +++ b/.github/workflows/wiki.yml @@ -27,4 +27,5 @@ jobs: mv docs_source/build/markdown ./API rm -r docs_source - uses: Andrew-Chen-Wang/github-wiki-action@v4 - path: docs/ \ No newline at end of file + with: + path: docs/ \ No newline at end of file From 95e6c6a9d9d6ae48e7c21112d5a4d34171f68993 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Wed, 11 Mar 2026 17:59:33 +0100 Subject: [PATCH 090/137] change path --- .github/workflows/wiki.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/wiki.yml b/.github/workflows/wiki.yml index 5f33a2d..dbe7f08 100644 --- a/.github/workflows/wiki.yml +++ b/.github/workflows/wiki.yml @@ -3,7 +3,7 @@ on: push: branches: [revision] paths: - - wiki/** + - docs/** - .github/workflows/publish-wiki.yml permissions: contents: write From 4216612adcdb4690a6cf5c33f0b9beb69078c33c Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Wed, 11 Mar 2026 18:03:14 +0100 Subject: [PATCH 091/137] add workflow dispatch --- .github/workflows/wiki.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/wiki.yml b/.github/workflows/wiki.yml index dbe7f08..6182604 100644 --- a/.github/workflows/wiki.yml +++ b/.github/workflows/wiki.yml @@ -5,6 +5,7 @@ on: paths: - docs/** - .github/workflows/publish-wiki.yml + workflow_dispatch: permissions: contents: write jobs: From 16d6a00cb5b4463750d3a0724f5d77010fa4949c Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Wed, 11 Mar 2026 18:11:14 +0100 Subject: [PATCH 092/137] fix make --- .github/workflows/wiki.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/wiki.yml b/.github/workflows/wiki.yml index 6182604..219ee84 100644 --- a/.github/workflows/wiki.yml +++ b/.github/workflows/wiki.yml @@ -20,7 +20,7 @@ jobs: run: | cd docs/docs_source pip install -r requirements.txt - .\Make markdown + make markdown - name: move markdown pages run: | cd .. From 9a9afe72781f90cbef6f6d5ab304ff37d6211547 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Wed, 11 Mar 2026 18:13:38 +0100 Subject: [PATCH 093/137] install package before building docs --- .github/workflows/wiki.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/wiki.yml b/.github/workflows/wiki.yml index 219ee84..d9f3280 100644 --- a/.github/workflows/wiki.yml +++ b/.github/workflows/wiki.yml @@ -18,6 +18,7 @@ jobs: python-version: '3.13' - name: build wiki run: | + pip install -e . cd docs/docs_source pip install -r requirements.txt make markdown From 0781d8b63e05154748e4dbb304f25f2520e4223f Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Thu, 12 Mar 2026 11:20:45 +0100 Subject: [PATCH 094/137] rename file --- docs/docs_source/source/{API-index.rst => index.rst} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename docs/docs_source/source/{API-index.rst => index.rst} (100%) diff --git a/docs/docs_source/source/API-index.rst b/docs/docs_source/source/index.rst similarity index 100% rename from docs/docs_source/source/API-index.rst rename to docs/docs_source/source/index.rst From e14a9d1f40387bb68a17a78199e109cd37a05149 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Thu, 12 Mar 2026 11:23:32 +0100 Subject: [PATCH 095/137] add ls output --- .github/workflows/wiki.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/wiki.yml b/.github/workflows/wiki.yml index d9f3280..8a3c9b8 100644 --- a/.github/workflows/wiki.yml +++ b/.github/workflows/wiki.yml @@ -26,6 +26,7 @@ jobs: run: | cd .. mkdir API + ls mv docs_source/build/markdown ./API rm -r docs_source - uses: Andrew-Chen-Wang/github-wiki-action@v4 From c9c8407ca37771066624b0689ed1f2354966dfb7 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Thu, 12 Mar 2026 11:28:44 +0100 Subject: [PATCH 096/137] update paths --- .github/workflows/wiki.yml | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/.github/workflows/wiki.yml b/.github/workflows/wiki.yml index 8a3c9b8..4ee09ba 100644 --- a/.github/workflows/wiki.yml +++ b/.github/workflows/wiki.yml @@ -19,16 +19,15 @@ jobs: - name: build wiki run: | pip install -e . - cd docs/docs_source - pip install -r requirements.txt - make markdown + pip install -r docs/docs_source/requirements.txt + make -C docs/docs_source markdown - name: move markdown pages run: | - cd .. - mkdir API - ls - mv docs_source/build/markdown ./API - rm -r docs_source + mkdir -p docs/API + # Move the files using the full path from the repo root + # Note: check if 'make' creates 'build' inside 'docs_source' + mv docs/docs_source/build/markdown/* docs/API/ + rm -r docs/docs_source - uses: Andrew-Chen-Wang/github-wiki-action@v4 with: path: docs/ \ No newline at end of file From 3f5e4feae0e095837465ccf775aad5788a9cc84a Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Thu, 12 Mar 2026 11:31:26 +0100 Subject: [PATCH 097/137] delete sphinx index md --- .github/workflows/wiki.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/wiki.yml b/.github/workflows/wiki.yml index 4ee09ba..922f876 100644 --- a/.github/workflows/wiki.yml +++ b/.github/workflows/wiki.yml @@ -26,7 +26,9 @@ jobs: mkdir -p docs/API # Move the files using the full path from the repo root # Note: check if 'make' creates 'build' inside 'docs_source' + rm docs/docs_source/build/markdown/index.md mv docs/docs_source/build/markdown/* docs/API/ + # remove source_dir rm -r docs/docs_source - uses: Andrew-Chen-Wang/github-wiki-action@v4 with: From f9c2cce37cd4f54644e46900873c0866fc8da7b2 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Thu, 12 Mar 2026 11:36:55 +0100 Subject: [PATCH 098/137] update sidebar and index.md --- docs/_Sidebar.md | 9 ++++++++- docs/index.md | 19 ++++++++++++++++++- 2 files changed, 26 insertions(+), 2 deletions(-) diff --git a/docs/_Sidebar.md b/docs/_Sidebar.md index c9e8bee..0178fb9 100644 --- a/docs/_Sidebar.md +++ b/docs/_Sidebar.md @@ -4,4 +4,11 @@ **Tutorials** -**API Reference** \ No newline at end of file +**API Reference** +- [data module](API-data) +- [implementations module](API-implementations) +- [math module](API-math) +- [models module](API-models) +- [services module](API-services) +- [simulation module](API-simulation) +- [utils module](API-utils) \ No newline at end of file diff --git a/docs/index.md b/docs/index.md index 47c11af..deaedf7 100644 --- a/docs/index.md +++ b/docs/index.md @@ -9,4 +9,21 @@ ![Logo of the adaptivetesting package](/docs/_static/logo.svg) -## Features \ No newline at end of file +## Features + +| **Ability Estimators** | | +|-------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------| +| Maximum Likelihood | $\arg \max\, L(Y=1\| \theta)$ | +| Bayes Modal | $\arg \max \frac{L(\theta)P_\text{prior}(\theta)}{\int_\infty^\infty L(\theta)P_\text{prior}(\theta)}$ | +| Expected a Posteriori | $\frac{\int_{-\infty}^{\infty} \theta L(\theta) P_\text{prior}(\theta)\, d\theta}{\int_{-\infty}^{\infty} L(\theta) P_\text{prior}(\theta)\, d\theta}$ | +| **Item Selection** | | +| MFI | $\arg \max I_i(\theta)$ | +| **Exposure Control** | | +| Randomesque | | +| Maximum Priority Index | $PI_i = I_i \prod_{k=1}^K (w_k f_k)^{c_{ik}}$ | +| **Content Balancing** | | +| Maximum Priority Index | $PI_i = I_i \prod_{k=1}^K (w_k f_k)^{c_{ik}}$ | +| Weighted Penalty Model | $F_i = w'F_i' + w'' F_i''$ | +| **Stopping Criterion** | | +| Standard Error | $\leq \frac{1}{\sqrt{I(\theta)}}$ | +| Test Length | $\#\, \text{of items} $ | From ab459c90d779d523314724a5bde0642a8c4c2aeb Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Thu, 12 Mar 2026 11:40:19 +0100 Subject: [PATCH 099/137] update image paths and rename index file --- docs/{index.md => Home.md} | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) rename docs/{index.md => Home.md} (91%) diff --git a/docs/index.md b/docs/Home.md similarity index 91% rename from docs/index.md rename to docs/Home.md index deaedf7..9438a70 100644 --- a/docs/index.md +++ b/docs/Home.md @@ -1,12 +1,12 @@ # adaptivetesting -[![SPEC 0 — Minimum Supported Dependencies](docs/_static/SPEC-0.svg)](https://scientific-python.org/specs/spec-0000/) +[![SPEC 0 — Minimum Supported Dependencies](_static/SPEC-0.svg)](https://scientific-python.org/specs/spec-0000/) ![](https://anaconda.org/conda-forge/adaptivetesting/badges/version.svg) ![](https://anaconda.org/conda-forge/adaptivetesting/badges/license.svg) ![](https://anaconda.org/conda-forge/adaptivetesting/badges/downloads.svg) -![Logo of the adaptivetesting package](/docs/_static/logo.svg) +![Logo of the adaptivetesting package](_static/logo.svg) ## Features @@ -19,11 +19,11 @@ | **Item Selection** | | | MFI | $\arg \max I_i(\theta)$ | | **Exposure Control** | | -| Randomesque | | +| Randomesque | | | Maximum Priority Index | $PI_i = I_i \prod_{k=1}^K (w_k f_k)^{c_{ik}}$ | | **Content Balancing** | | | Maximum Priority Index | $PI_i = I_i \prod_{k=1}^K (w_k f_k)^{c_{ik}}$ | | Weighted Penalty Model | $F_i = w'F_i' + w'' F_i''$ | | **Stopping Criterion** | | | Standard Error | $\leq \frac{1}{\sqrt{I(\theta)}}$ | -| Test Length | $\#\, \text{of items} $ | +| Test Length | # of items | From a5e56b9663623f8222b0d6d109227f494e7ba11f Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Thu, 12 Mar 2026 12:07:32 +0100 Subject: [PATCH 100/137] update doc config --- adaptivetesting/simulation/__init__.py | 6 +++++- docs/docs_source/source/conf.py | 20 +------------------- 2 files changed, 6 insertions(+), 20 deletions(-) diff --git a/adaptivetesting/simulation/__init__.py b/adaptivetesting/simulation/__init__.py index 0eeb141..fccb158 100644 --- a/adaptivetesting/simulation/__init__.py +++ b/adaptivetesting/simulation/__init__.py @@ -1 +1,5 @@ -from .__simulation import * +from .__simulation import ( + Simulation, + setup_simulation_and_start, + SimulationPool +) \ No newline at end of file diff --git a/docs/docs_source/source/conf.py b/docs/docs_source/source/conf.py index 0ea63ed..fd87166 100644 --- a/docs/docs_source/source/conf.py +++ b/docs/docs_source/source/conf.py @@ -12,9 +12,6 @@ # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information project = 'adaptivetesting' -copyright = '2025, Jonas Engicht' -author = 'Jonas Engicht' -release = '2025' # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration @@ -31,19 +28,4 @@ html_theme = 'sphinx_book_theme' html_static_path = ['_static'] -html_css_files = [ - 'main.css', -] -html_logo = "_static/logo.svg" -html_theme_options = { - "navigation_depth": -1, - "repository_url": "https://github.com/condecon/adaptivetesting", - "use_issues_button": True - -} - - - - -def setup(app: Sphinx): - app.add_css_file("main.css") +markdown_uri_doc_suffix = "" \ No newline at end of file From 25fbabc31ea72b72e1703e37d8d40957ece681a3 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Thu, 12 Mar 2026 12:20:04 +0100 Subject: [PATCH 101/137] remove images --- README.md | 3 --- docs/Home.md | 7 ------- docs/_static/SPEC-0.svg | 1 - docs/_static/main.css | 24 ------------------------ 4 files changed, 35 deletions(-) delete mode 100644 docs/_static/SPEC-0.svg delete mode 100644 docs/_static/main.css diff --git a/README.md b/README.md index 27e091a..a7bdb8e 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,4 @@ # adaptivetesting -[![SPEC 0 — Minimum Supported Dependencies](docs/docs_source/source/_static/SPEC-0.svg)](https://scientific-python.org/specs/spec-0000/) - - diff --git a/docs/Home.md b/docs/Home.md index 9438a70..e223fa3 100644 --- a/docs/Home.md +++ b/docs/Home.md @@ -1,11 +1,4 @@ # adaptivetesting - -[![SPEC 0 — Minimum Supported Dependencies](_static/SPEC-0.svg)](https://scientific-python.org/specs/spec-0000/) -![](https://anaconda.org/conda-forge/adaptivetesting/badges/version.svg) -![](https://anaconda.org/conda-forge/adaptivetesting/badges/license.svg) -![](https://anaconda.org/conda-forge/adaptivetesting/badges/downloads.svg) - - ![Logo of the adaptivetesting package](_static/logo.svg) diff --git a/docs/_static/SPEC-0.svg b/docs/_static/SPEC-0.svg deleted file mode 100644 index d3e13e9..0000000 --- a/docs/_static/SPEC-0.svg +++ /dev/null @@ -1 +0,0 @@ -SPEC: 0SPEC0 \ No newline at end of file diff --git a/docs/_static/main.css b/docs/_static/main.css deleted file mode 100644 index 9298215..0000000 --- a/docs/_static/main.css +++ /dev/null @@ -1,24 +0,0 @@ -.logo{ - display: block; - margin: 0; - height: auto; - width: 100% !important; - border-radius: 0; - max-width: 100%; - background: transparent; -} - -.wy-side-nav-search { - display: block; - width: 300px; - padding: .809em; - margin-bottom: .809em; - z-index: 200; - background-color: white !important; - text-align: center; - color: #fcfcfc; -} - -.caption-text{ - color: white !important; -} \ No newline at end of file From c9b84e62165fd92bd80701a11a2064bbb4f22d6d Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Mon, 16 Mar 2026 15:24:08 +0100 Subject: [PATCH 102/137] create status badges --- .gitignore | 2 +- docs/Home.md | 3 ++ docs/_static/create_badges.js | 51 +++++++++++++++++++ docs/_static/package-lock.json | 92 ++++++++++++++++++++++++++++++++++ docs/_static/package.json | 22 ++++++++ docs/_static/package.svg | 1 + docs/_static/python.svg | 1 + docs/_static/spec0.svg | 1 + 8 files changed, 172 insertions(+), 1 deletion(-) create mode 100644 docs/_static/create_badges.js create mode 100644 docs/_static/package-lock.json create mode 100644 docs/_static/package.json create mode 100644 docs/_static/package.svg create mode 100644 docs/_static/python.svg create mode 100644 docs/_static/spec0.svg diff --git a/.gitignore b/.gitignore index 99ce1eb..7c71191 100644 --- a/.gitignore +++ b/.gitignore @@ -167,5 +167,5 @@ cython_debug/ #.idea/ .DS_Store - +node_modules/ diff --git a/docs/Home.md b/docs/Home.md index e223fa3..1686d14 100644 --- a/docs/Home.md +++ b/docs/Home.md @@ -1,6 +1,9 @@ # adaptivetesting ![Logo of the adaptivetesting package](_static/logo.svg) +![Following SPEC 0](_static/spec0.svg) +![Supported Python Versions](_static/python.svg) +![Package Repositories: PyPi, conda-forge](_static/package.svg) ## Features diff --git a/docs/_static/create_badges.js b/docs/_static/create_badges.js new file mode 100644 index 0000000..b8adfd4 --- /dev/null +++ b/docs/_static/create_badges.js @@ -0,0 +1,51 @@ +const { makeBadge } = require("badge-maker"); +const fs = require("node:fs"); + +function SPEC0() { + const format = { + label: 'SPEC', // (Optional) Badge label + message: '0', // (Required) Badge message + labelColor: '#555', // (Optional) Label color + color: '#4c1', // (Optional) Message color + } + const badgeString = makeBadge(format); + fs.writeFile("spec0.svg", badgeString, e => { + if (e != null) { + console.log(e) + } + }); +} + +function PythonVersion() { + const format = { + label: 'Python', // (Optional) Badge label + message: '3.12 | 3.13 | 3.14', // (Required) Badge message + labelColor: '#555', // (Optional) Label color + color: 'rgb(17, 86, 204)', // (Optional) Message color + } + const badgeString = makeBadge(format); + fs.writeFile("python.svg", badgeString, e => { + if (e != null) { + console.log(e) + } + }); +} + +function PackageManager(){ + const format = { + label: "Package Repositories", + message: "PyPI | conda-forge", + labelColor: "#555", + color: "rgb(17, 86, 204)" + } + const badgeString = makeBadge(format); + fs.writeFile("package.svg", badgeString, e => { + if(e != null){ + console.log(e) + } + }) +} + +SPEC0() +PythonVersion() +PackageManager(); \ No newline at end of file diff --git a/docs/_static/package-lock.json b/docs/_static/package-lock.json new file mode 100644 index 0000000..55d7d7f --- /dev/null +++ b/docs/_static/package-lock.json @@ -0,0 +1,92 @@ +{ + "name": "icons", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "icons", + "version": "1.0.0", + "license": "ISC", + "dependencies": { + "anafanafo": "^2.0.0", + "badge-maker": "^5.0.2", + "binary-search": "^1.3.6", + "char-width-table-consumer": "^1.0.0", + "color-convert": "^0.5.3", + "color-name": "^1.1.4", + "css-color-converter": "^2.0.0", + "css-unit-converter": "^1.1.2" + }, + "devDependencies": {} + }, + "node_modules/anafanafo": { + "version": "2.0.0", + "resolved": "https://registry.npmjs.org/anafanafo/-/anafanafo-2.0.0.tgz", + "integrity": "sha512-Nlfq7NC4AOkTJerWRIZcOAiMNtIDVIGWGvQ98O7Jl6Kr2Dk0dX5u4MqN778kSRTy5KRqchpLdF2RtLFEz9FVkQ==", + "license": "MIT", + "dependencies": { + "char-width-table-consumer": "^1.0.0" + } + }, + "node_modules/badge-maker": { + "version": "5.0.2", + "resolved": "https://registry.npmjs.org/badge-maker/-/badge-maker-5.0.2.tgz", + "integrity": "sha512-Xd3YUmKPEShQcn6PFB03Wxq0RNJRFwVVroyRz0qIjSXwniYUGsGWNHqtNsQYi/Sbs8Ni7qAEf7LKgDOtcAoCDg==", + "license": "CC0-1.0", + "dependencies": { + "anafanafo": "2.0.0", + "css-color-converter": "^2.0.0" + }, + "bin": { + "badge": "lib/badge-cli.js" + }, + "engines": { + "node": ">=20" + } + }, + "node_modules/binary-search": { + "version": "1.3.6", + "resolved": "https://registry.npmjs.org/binary-search/-/binary-search-1.3.6.tgz", + "integrity": "sha512-nbE1WxOTTrUWIfsfZ4aHGYu5DOuNkbxGokjV6Z2kxfJK3uaAb8zNK1muzOeipoLHZjInT4Br88BHpzevc681xA==", + "license": "CC0-1.0" + }, + "node_modules/char-width-table-consumer": { + "version": "1.0.0", + "resolved": "https://registry.npmjs.org/char-width-table-consumer/-/char-width-table-consumer-1.0.0.tgz", + "integrity": "sha512-Fz4UD0LBpxPgL9i29CJ5O4KANwaMnX/OhhbxzvNa332h+9+nRKyeuLw4wA51lt/ex67+/AdsoBQJF3kgX2feYQ==", + "license": "MIT", + "dependencies": { + "binary-search": "^1.3.5" + } + }, + "node_modules/color-convert": { + "version": "0.5.3", + "resolved": "https://registry.npmjs.org/color-convert/-/color-convert-0.5.3.tgz", + "integrity": "sha512-RwBeO/B/vZR3dfKL1ye/vx8MHZ40ugzpyfeVG5GsiuGnrlMWe2o8wxBbLCpw9CsxV+wHuzYlCiWnybrIA0ling==" + }, + "node_modules/color-name": { + "version": "1.1.4", + "resolved": "https://registry.npmjs.org/color-name/-/color-name-1.1.4.tgz", + "integrity": "sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==", + "license": "MIT" + }, + "node_modules/css-color-converter": { + "version": "2.0.0", + "resolved": "https://registry.npmjs.org/css-color-converter/-/css-color-converter-2.0.0.tgz", + "integrity": "sha512-oLIG2soZz3wcC3aAl/7Us5RS8Hvvc6I8G8LniF/qfMmrm7fIKQ8RIDDRZeKyGL2SrWfNqYspuLShbnjBMVWm8g==", + "license": "MIT", + "dependencies": { + "color-convert": "^0.5.2", + "color-name": "^1.1.4", + "css-unit-converter": "^1.1.2" + } + }, + "node_modules/css-unit-converter": { + "version": "1.1.2", + "resolved": "https://registry.npmjs.org/css-unit-converter/-/css-unit-converter-1.1.2.tgz", + "integrity": "sha512-IiJwMC8rdZE0+xiEZHeru6YoONC4rfPMqGm2W85jMIbkFvv5nFTwJVFHam2eFrN6txmoUYFAFXiv8ICVeTO0MA==", + "license": "MIT" + } + } +} diff --git a/docs/_static/package.json b/docs/_static/package.json new file mode 100644 index 0000000..2bb20dd --- /dev/null +++ b/docs/_static/package.json @@ -0,0 +1,22 @@ +{ + "name": "icons", + "version": "1.0.0", + "description": "", + "license": "ISC", + "author": "", + "type": "commonjs", + "main": "create_badges.js", + "scripts": { + "test": "echo \"Error: no test specified\" && exit 1" + }, + "dependencies": { + "anafanafo": "^2.0.0", + "badge-maker": "^5.0.2", + "binary-search": "^1.3.6", + "char-width-table-consumer": "^1.0.0", + "color-convert": "^0.5.3", + "color-name": "^1.1.4", + "css-color-converter": "^2.0.0", + "css-unit-converter": "^1.1.2" + } +} diff --git a/docs/_static/package.svg b/docs/_static/package.svg new file mode 100644 index 0000000..0c6540c --- /dev/null +++ b/docs/_static/package.svg @@ -0,0 +1 @@ +Package Repositories: PyPI | conda-forgePackage RepositoriesPyPI | conda-forge \ No newline at end of file diff --git a/docs/_static/python.svg b/docs/_static/python.svg new file mode 100644 index 0000000..19c79aa --- /dev/null +++ b/docs/_static/python.svg @@ -0,0 +1 @@ +Python: 3.12 | 3.13 | 3.14Python3.12 | 3.13 | 3.14 \ No newline at end of file diff --git a/docs/_static/spec0.svg b/docs/_static/spec0.svg new file mode 100644 index 0000000..e6cb613 --- /dev/null +++ b/docs/_static/spec0.svg @@ -0,0 +1 @@ +SPEC: 0SPEC0 \ No newline at end of file From 9ff05d4e5086ecce09a1b484c44a4bd770ec5183 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Thu, 19 Mar 2026 09:57:13 +0100 Subject: [PATCH 103/137] fix ability estimates and add unit tests --- .../estimators/__functions/__poly/__gpcm.py | 5 +- .../estimators/__functions/__poly/__grm.py | 9 +- adaptivetesting/tests/test_poly_models.py | 102 ++++++++++++++++++ adaptivetesting/tests/test_test_assembler.py | 2 +- 4 files changed, 110 insertions(+), 8 deletions(-) create mode 100644 adaptivetesting/tests/test_poly_models.py diff --git a/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py b/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py index 79e4304..0085ad6 100644 --- a/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py +++ b/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py @@ -1,6 +1,7 @@ import numpy as np from scipy.special import logsumexp import numdifftools as nd +import math from .__poly_math import PolyModelFunctions @@ -42,7 +43,7 @@ def log_likelihood(theta: float, ) log_lik += log_prob - return -log_lik # Return negative log-likelihood for minimization + return log_lik # Return negative log-likelihood for minimization @staticmethod def fisher_information(theta: float, @@ -53,7 +54,7 @@ def fisher_information(theta: float, primary citation Dodd, DeAyala & Koch 1995 """ def prob(x: float, category: int): - p = GPCM.category_prob(x, a, thresholds, category) + p = math.exp(GPCM.category_prob(x, a, thresholds, category)) p = max(p, 1e-12) return p prob_d1 = nd.Derivative(prob, order=1) diff --git a/adaptivetesting/math/estimators/__functions/__poly/__grm.py b/adaptivetesting/math/estimators/__functions/__poly/__grm.py index 1cf892a..c551adc 100644 --- a/adaptivetesting/math/estimators/__functions/__poly/__grm.py +++ b/adaptivetesting/math/estimators/__functions/__poly/__grm.py @@ -1,6 +1,7 @@ import numpy as np import numdifftools as nd from .__poly_math import PolyModelFunctions +import math class GRM(PolyModelFunctions): @@ -48,11 +49,9 @@ def log_likelihood(theta: float, thresholds=thresholds_list[item_idx], response_pattern=response_pattern[item_idx] ) - if prob <= 0: # Handle cases where probability is zero or negative - log_lik += -np.inf # Log of zero is negative infinity - else: - log_lik += np.log(prob) - return -log_lik # Return negative log-likelihood for minimization + + log_lik += np.log(np.maximum(prob, 1e-12)) + return log_lik # Return negative log-likelihood for minimization @staticmethod def fisher_information(theta: float, diff --git a/adaptivetesting/tests/test_poly_models.py b/adaptivetesting/tests/test_poly_models.py new file mode 100644 index 0000000..6ed60c8 --- /dev/null +++ b/adaptivetesting/tests/test_poly_models.py @@ -0,0 +1,102 @@ +import adaptivetesting as adt +import unittest + +class TestPolyMLEstimation(unittest.TestCase): + def test_grm(self): + items = adt.ItemPool.load_from_list( + a = [0.943, 0.972, 1.210], + b = [ + [0.071, 0.129], + [0.461, 1.715], + [-1.265, -0.687] + ] + ).test_items + + pattern = [2, 1, 2] + estimator = adt.MLEstimator( + response_pattern=pattern, + items=items, + model="GRM" + ) + estimate = estimator.get_estimation() + print(estimate) + self.assertAlmostEqual(1.6684, round(estimate, 3), delta=0.001) + + pattern = [0, 1, 2] + estimator = adt.MLEstimator( + response_pattern=pattern, + items=items, + model="GRM" + ) + estimate = estimator.get_estimation() + self.assertAlmostEqual(0.4076371, estimate, delta=0.01) + + def test_gpcm(self): + items = adt.ItemPool.load_from_list( + a = [0.934, 0.972, 1.210], + b = [ + [0.071, 0.129], + [1.715, 0.461], + [-1.265, -0.687] + ] + ).test_items + + pattern = [2,1,2] + estimator = adt.MLEstimator( + pattern, + items, + "GPCM" + ) + estimate = estimator.get_estimation() + self.assertAlmostEqual( + 1.581, + estimate, + delta=0.001 + ) + + pattern = [0, 1, 2] + estimator = adt.MLEstimator( + pattern, + items, + "GPCM" + ) + estimate = estimator.get_estimation() + self.assertAlmostEqual( + 0.181, + estimate, + delta=0.001 + ) + + def test_gpcm_information(self): + items = adt.ItemPool.load_from_list( + a = [0.934, 0.972, 1.210], + b = [ + [0.071, 0.129], + [1.715, 0.461], + [-1.265, -0.687] + ] + ).test_items + + pattern = [2, 1, 2] # required for spec, has no influence on the result + estimator = adt.MLEstimator(pattern, + items, + "GPCM") + sde = estimator.get_standard_error(0) + self.assertAlmostEqual(sde, 0.819, delta=0.001) + + def test_grm_information(self): + items = adt.ItemPool.load_from_list( + a = [0.943, 0.972, 1.210], + b = [ + [0.071, 0.129], + [0.461, 1.715], + [-1.265, -0.687] + ] + ).test_items + + pattern = [2, 1, 2] # required for spec, has no influence on the result + estimator = adt.MLEstimator(pattern, + items, + "GRM") + sde = estimator.get_standard_error(0) + self.assertAlmostEqual(sde, 1.133, delta=0.003) diff --git a/adaptivetesting/tests/test_test_assembler.py b/adaptivetesting/tests/test_test_assembler.py index 14d72cf..39b764d 100644 --- a/adaptivetesting/tests/test_test_assembler.py +++ b/adaptivetesting/tests/test_test_assembler.py @@ -80,7 +80,7 @@ def test_init_sets_attributes(self): debug=True ) self.assertEqual(assembler._TestAssembler__ability_estimator, DummyEstimator) # type: ignore - self.assertEqual(assembler._TestAssembler__estimator_args, {"foo": "bar"}) # type: ignore + self.assertEqual(assembler._TestAssembler__estimator_args, {"foo": "bar", "model": None}) # type: ignore self.assertEqual(assembler._TestAssembler__item_selector, dummy_item_selector) # type: ignore self.assertEqual(assembler._TestAssembler__item_selector_args, {"baz": 1}) # type: ignore self.assertTrue(assembler._TestAssembler__pretest) # type: ignore From 315e56f4efa412073c567c63644b75fe900b9e74 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Thu, 19 Mar 2026 10:11:30 +0100 Subject: [PATCH 104/137] add unittests for item loading --- adaptivetesting/tests/test_load_test_items.py | 48 +++++++++++++++++++ 1 file changed, 48 insertions(+) diff --git a/adaptivetesting/tests/test_load_test_items.py b/adaptivetesting/tests/test_load_test_items.py index 7e271a6..1f9ecbb 100644 --- a/adaptivetesting/tests/test_load_test_items.py +++ b/adaptivetesting/tests/test_load_test_items.py @@ -274,3 +274,51 @@ def test_roundtrip_preserves_fields(self): self.assertEqual(restored.c, original.c) self.assertEqual(restored.d, original.d) self.assertEqual(restored.additional_properties, original.additional_properties) + + +class TestPolyItems(TestCase): + def test_loading_items_list(self): + items = ItemPool.load_from_list( + a = [0.934, 0.972, 1.210], + b = [ + [0.071, 0.129], + [1.715, 0.461], + [-1.265, -0.687] + ] + ).test_items + + self.assertTrue(all([isinstance(item.a, float) for item in items])) + self.assertTrue(all([isinstance(item.b, list) for item in items])) + + def test_loading_items_dict(self): + items_dict = { + "a" : [0.934, 0.972, 1.210], + "b": [ + [0.071, 0.129], + [1.715, 0.461], + [-1.265, -0.687] + ] + } + + items = ItemPool.load_from_dict( + items_dict + ).test_items + + self.assertTrue(all([isinstance(item.a, float) for item in items])) + self.assertTrue(all([isinstance(item.b, list) for item in items])) + + def test_loading_items_df(self): + items_dict = { + "a" : [0.934, 0.972, 1.210], + "b": [ + [0.071, 0.129], + [1.715, 0.461], + [-1.265, -0.687] + ] + } + + df = pd.DataFrame(items_dict) + items = ItemPool.load_from_dataframe(df).test_items + + self.assertTrue(all([isinstance(item.a, float) for item in items])) + self.assertTrue(all([isinstance(item.b, list) for item in items])) From 24ec1243ab977ad84663618eb6abf0c046530723 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Thu, 19 Mar 2026 10:12:57 +0100 Subject: [PATCH 105/137] update todo and remove readthedocs.yml --- .readthedocs.yml | 22 ---------------------- todo.md | 13 ++++--------- 2 files changed, 4 insertions(+), 31 deletions(-) delete mode 100644 .readthedocs.yml diff --git a/.readthedocs.yml b/.readthedocs.yml deleted file mode 100644 index d786e48..0000000 --- a/.readthedocs.yml +++ /dev/null @@ -1,22 +0,0 @@ -# Read the Docs configuration file -# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details - -# Required -version: 2 - -# Set the OS, Python version, and other tools you might need -build: - os: ubuntu-24.04 - tools: - python: "3.13" - -# Build documentation in the "docs/" directory with Sphinx -sphinx: - configuration: docs/source/conf.py - -python: - install: - - method: pip - path: . - - requirements: docs/requirements.txt - \ No newline at end of file diff --git a/todo.md b/todo.md index 44d656e..c0ef9ea 100644 --- a/todo.md +++ b/todo.md @@ -1,16 +1,11 @@ # Todo -- [ ] TestAssembler +- [X] TestAssembler - [X] Response Pattern generation - [X] Ability Estimation - - [ ] unittest + - [X] unittest - [X] Item Selection - - [ ] unittest + - [X] unittest - [X] Item Pool - [X] Load item pool with polytomous items - - [ ] unittest -- [ ] Content Balancing - - [ ] item information function calculation -- [ ] Exposure Control - -- [ ] showed item -> shown item \ No newline at end of file + - [X] unittest \ No newline at end of file From 5e23be00211788690626049c874de77fc75093f7 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Thu, 19 Mar 2026 10:37:02 +0100 Subject: [PATCH 106/137] fix lintinig issues --- .../implementations/__test_assembler.py | 4 +++- adaptivetesting/math/__gen_response_pattern.py | 14 ++++++-------- .../math/content_balancing/__functions.py | 2 +- .../content_balancing/__weighted_penalty_model.py | 1 - .../math/estimators/__functions/__poly/__grm.py | 1 - .../math/estimators/__test_information.py | 3 ++- .../math/exposure_control/__randomesque.py | 1 - .../__maximum_information_criterion.py | 2 +- adaptivetesting/models/__item_pool.py | 3 +-- adaptivetesting/tests/test_content_balacing.py | 2 +- adaptivetesting/tests/test_load_test_items.py | 3 ++- adaptivetesting/tests/test_poly_models.py | 1 + adaptivetesting/tests/test_simulation.py | 4 ++-- .../tests/test_weighted_penalty_model.py | 1 + 14 files changed, 21 insertions(+), 21 deletions(-) diff --git a/adaptivetesting/implementations/__test_assembler.py b/adaptivetesting/implementations/__test_assembler.py index 4428e96..5c1f7be 100644 --- a/adaptivetesting/implementations/__test_assembler.py +++ b/adaptivetesting/implementations/__test_assembler.py @@ -23,6 +23,7 @@ class EstimatorArgs(TypedDict): prior: Prior | None optimization_interval: tuple[float, float] + model: Literal["GRM", "GPCM"] | None class ContentBalancingArgs(TypedDict): @@ -93,7 +94,8 @@ def __init__(self, ability_estimator: Type[IEstimator], estimator_args: EstimatorArgs = { "prior": None, - "optimization_interval": (-10, 10) + "optimization_interval": (-10, 10), + "model": None }, item_selector: ItemSelectionStrategy = maximum_information_criterion, # type: ignore item_selector_args: dict[str, Any] = {}, diff --git a/adaptivetesting/math/__gen_response_pattern.py b/adaptivetesting/math/__gen_response_pattern.py index bacf129..3c8c09a 100644 --- a/adaptivetesting/math/__gen_response_pattern.py +++ b/adaptivetesting/math/__gen_response_pattern.py @@ -90,20 +90,18 @@ def gen_pattern_poly( if model == "GRM": for t_i in range(len(cast(list, item.b)) + 1): log_prob = GRM.category_prob(ability, - item.a, - cast(list, item.b), - response_pattern=t_i) + item.a, + cast(list, item.b), + response_pattern=t_i) log_probabilities.append(log_prob) elif model == "GPCM": for t_i in range(len(cast(list, item.b)) + 1): log_prob = GPCM.category_prob(ability, - item.a, - cast(list, item.b), - response_pattern=t_i) + item.a, + cast(list, item.b), + response_pattern=t_i) log_probabilities.append(log_prob) - - # draw from multinomial distribution for final response # the probability for a response in k categories is 1 probabilities = softmax(np.array(log_probabilities)) diff --git a/adaptivetesting/math/content_balancing/__functions.py b/adaptivetesting/math/content_balancing/__functions.py index 595a0f1..669b04d 100644 --- a/adaptivetesting/math/content_balancing/__functions.py +++ b/adaptivetesting/math/content_balancing/__functions.py @@ -2,9 +2,9 @@ from ...models.__test_item import TestItem from ...models.__item_selection_exception import ItemSelectionException from ...math.estimators.__test_information import item_information_function -import numpy as np from typing import Literal + # TODO: implement model in function call def compute_priority_index(item: TestItem, group_weights: dict[str, float], diff --git a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py index 8960036..162acc0 100644 --- a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py +++ b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py @@ -1,5 +1,4 @@ from typing import Literal, Callable -import numpy as np from ..estimators.__test_information import item_information_function from ...models.__test_item import TestItem from .__functions import ( diff --git a/adaptivetesting/math/estimators/__functions/__poly/__grm.py b/adaptivetesting/math/estimators/__functions/__poly/__grm.py index c551adc..c225a0b 100644 --- a/adaptivetesting/math/estimators/__functions/__poly/__grm.py +++ b/adaptivetesting/math/estimators/__functions/__poly/__grm.py @@ -1,7 +1,6 @@ import numpy as np import numdifftools as nd from .__poly_math import PolyModelFunctions -import math class GRM(PolyModelFunctions): diff --git a/adaptivetesting/math/estimators/__test_information.py b/adaptivetesting/math/estimators/__test_information.py index bfd0425..12ffd1a 100644 --- a/adaptivetesting/math/estimators/__test_information.py +++ b/adaptivetesting/math/estimators/__test_information.py @@ -28,7 +28,7 @@ def item_information_function( cast(list, item.b) ) - else: # dichotmous + else: # dichotmous return dicho_item_information_function( mu=np.array(ability), a=np.array(item.a), @@ -37,6 +37,7 @@ def item_information_function( d=np.array(item.d) ).astype(float).item() + def dicho_item_information_function( mu: np.ndarray, a: np.ndarray, diff --git a/adaptivetesting/math/exposure_control/__randomesque.py b/adaptivetesting/math/exposure_control/__randomesque.py index 1d17134..68d7569 100644 --- a/adaptivetesting/math/exposure_control/__randomesque.py +++ b/adaptivetesting/math/exposure_control/__randomesque.py @@ -1,6 +1,5 @@ from typing import Callable, Any import random -import numpy as np from .__exposure_control import ExposureControl from ...models.__adaptive_test import AdaptiveTest from ...models.__test_item import TestItem diff --git a/adaptivetesting/math/item_selection/__maximum_information_criterion.py b/adaptivetesting/math/item_selection/__maximum_information_criterion.py index 4a57d24..943b16d 100644 --- a/adaptivetesting/math/item_selection/__maximum_information_criterion.py +++ b/adaptivetesting/math/item_selection/__maximum_information_criterion.py @@ -2,9 +2,9 @@ from ...models.__item_selection_exception import ItemSelectionException from ..estimators.__test_information import item_information_function from ...models.__algorithm_exception import AlgorithmException -import numpy as np from typing import Literal + # Todo: implement model parameter in function calls def maximum_information_criterion(items: list[TestItem], ability: float, diff --git a/adaptivetesting/models/__item_pool.py b/adaptivetesting/models/__item_pool.py index 918e20e..79e6f47 100644 --- a/adaptivetesting/models/__item_pool.py +++ b/adaptivetesting/models/__item_pool.py @@ -129,7 +129,6 @@ def load_from_list( for i, discrimination in enumerate(a): items[i].a = discrimination - if c is not None: if len(c) != len(b): raise ValueError("Length of c and b has to be the same.") @@ -160,7 +159,7 @@ def load_from_list( return item_pool @staticmethod - def load_from_dict(source: dict[str, List[float] | list[float]], + def load_from_dict(source: dict[str, list[float]] | dict[str, list], simulated_responses: List[int] | None = None, ids: List[int] | None = None, content_categories: list[list[str]] | None = None) -> "ItemPool": diff --git a/adaptivetesting/tests/test_content_balacing.py b/adaptivetesting/tests/test_content_balacing.py index 9cab706..8b41d02 100644 --- a/adaptivetesting/tests/test_content_balacing.py +++ b/adaptivetesting/tests/test_content_balacing.py @@ -128,4 +128,4 @@ def test_basic_calculation(self): def test_quota_calculation(self): result = adt.compute_quota_left(10, 5) - self.assertAlmostEqual(result, 0.5) \ No newline at end of file + self.assertAlmostEqual(result, 0.5) diff --git a/adaptivetesting/tests/test_load_test_items.py b/adaptivetesting/tests/test_load_test_items.py index 1f9ecbb..12cf26e 100644 --- a/adaptivetesting/tests/test_load_test_items.py +++ b/adaptivetesting/tests/test_load_test_items.py @@ -1,3 +1,4 @@ +# flake8: noqa from unittest import TestCase from adaptivetesting.models import TestItem, ItemPool import pandas as pd @@ -291,7 +292,7 @@ def test_loading_items_list(self): self.assertTrue(all([isinstance(item.b, list) for item in items])) def test_loading_items_dict(self): - items_dict = { + items_dict: dict[str, list] = { "a" : [0.934, 0.972, 1.210], "b": [ [0.071, 0.129], diff --git a/adaptivetesting/tests/test_poly_models.py b/adaptivetesting/tests/test_poly_models.py index 6ed60c8..7043fc6 100644 --- a/adaptivetesting/tests/test_poly_models.py +++ b/adaptivetesting/tests/test_poly_models.py @@ -1,3 +1,4 @@ +# flake8: noqa import adaptivetesting as adt import unittest diff --git a/adaptivetesting/tests/test_simulation.py b/adaptivetesting/tests/test_simulation.py index b03f61a..7634a61 100644 --- a/adaptivetesting/tests/test_simulation.py +++ b/adaptivetesting/tests/test_simulation.py @@ -34,7 +34,7 @@ def get_mock_adaptive_test_empty_pool(): return mock_test -class TestSimulation(unittest.TestCase): +class TestSimulationMock(unittest.TestCase): @patch("adaptivetesting.simulation.__simulation.PickleContext") @patch("adaptivetesting.simulation.__simulation.CSVContext") @@ -101,7 +101,7 @@ def test_save_test_results_unsupported_format(self): sim.save_test_results() -class TestSimulation(unittest.TestCase): +class TestFullSimulation(unittest.TestCase): def test_run_basic_simulation_GRM(self): items = pd.DataFrame({ "a": [1.32, 1.07, 0.84], diff --git a/adaptivetesting/tests/test_weighted_penalty_model.py b/adaptivetesting/tests/test_weighted_penalty_model.py index 704e3a2..e18841a 100644 --- a/adaptivetesting/tests/test_weighted_penalty_model.py +++ b/adaptivetesting/tests/test_weighted_penalty_model.py @@ -272,6 +272,7 @@ def __init__(self, item_pool, ability_level): information_selected_item.as_dict() ) + class TestWeightedPenaltyModelPoly(unittest.TestCase): def __init__(self, methodName="runTest"): super().__init__(methodName) From 48a80185d441c9d594cef47c6a5498fd87fc1920 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Thu, 19 Mar 2026 10:41:20 +0100 Subject: [PATCH 107/137] update workflow parameters --- .github/workflows/publish.yml | 2 +- .github/workflows/python-test.yml | 4 ++-- .github/workflows/wiki.yml | 4 +--- 3 files changed, 4 insertions(+), 6 deletions(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index b939cba..3bab44c 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -25,7 +25,7 @@ jobs: - name: Set up Python uses: actions/setup-python@v3 with: - python-version: '3.x' + python-version: '3.13' - name: Install dependencies run: | python -m pip install --upgrade pip diff --git a/.github/workflows/python-test.yml b/.github/workflows/python-test.yml index fb9ea27..1ea85c1 100644 --- a/.github/workflows/python-test.yml +++ b/.github/workflows/python-test.yml @@ -16,7 +16,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: ["3.10", "3.11", "3.12"] + python-version: ["3.12", "3.13", "3.14"] steps: - uses: actions/checkout@v3 @@ -42,7 +42,7 @@ jobs: strategy: fail-fast: false matrix: 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--git a/pyproject.toml b/pyproject.toml index 27d0689..b8736c8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -56,6 +56,6 @@ members = [ [project.urls] Homepage = "https://github.com/condecon/adaptivetesting" -Documentation = "https://github.com/condecon/adaptivetesting/tree/main/docs" +Documentation = "https://github.com/condecon/adaptivetesting/wiki" Repository = "https://github.com/condecon/adaptivetesting.git" Issues = "https://github.com/condecon/adaptivetesting/issues" From 8db28deced6179ed95cc23d1005b91a169d6cf3c Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Thu, 19 Mar 2026 10:56:10 +0100 Subject: [PATCH 110/137] update workflow permissions --- .github/workflows/python-test.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.github/workflows/python-test.yml b/.github/workflows/python-test.yml index 1ea85c1..ab8bac7 100644 --- a/.github/workflows/python-test.yml +++ b/.github/workflows/python-test.yml @@ -3,6 +3,9 @@ name: Package test +permissions: + contents: read + on: push: branches: [ "main", "dev"] From 478afa0f0d76ae92a574172b1701b02460b4b5f1 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Thu, 19 Mar 2026 10:56:57 +0100 Subject: [PATCH 111/137] update workflow permissions --- .github/workflows/publish.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 3bab44c..338433c 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -7,7 +7,6 @@ # documentation. name: Upload Python Package - on: release: types: [published] From 3f0c31b6e6ee6522aae6674f3fe510b721a7f9f9 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Thu, 19 Mar 2026 11:05:58 +0100 Subject: [PATCH 112/137] fix setup tools security issue --- pyproject.toml | 4 ++-- uv.lock | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index b8736c8..6f15c3d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -31,7 +31,7 @@ dev = [ "mypy>=1.15.0", "pandas-stubs>=2.2.3.250308", "scipy-stubs>=1.15.2.2", - "setuptools>=70.3.0", + "setuptools>=78.1.1", "snakeviz>=2.2.2", "types-tqdm>=4.67.0.20250516", ] @@ -42,7 +42,7 @@ docs = [ "sphinx-markdown-builder>=0.6.8", ] test = [ - "psychopy>=2025.1.1", + "psychopy>=2026.1.1", ] [tool.uv.sources] diff --git a/uv.lock b/uv.lock index 1132f60..f69ba6a 100644 --- a/uv.lock +++ b/uv.lock @@ -74,7 +74,7 @@ dev = [ { name = "mypy", specifier = ">=1.15.0" }, { name = "pandas-stubs", specifier = ">=2.2.3.250308" }, { name = "scipy-stubs", specifier = ">=1.15.2.2" }, - { name = "setuptools", specifier = ">=70.3.0" }, + { name = "setuptools", specifier = ">=78.1.1" }, { name = "snakeviz", specifier = ">=2.2.2" }, { name = "types-tqdm", specifier = ">=4.67.0.20250516" }, ] @@ -83,7 +83,7 @@ docs = [ { name = "sphinx-book-theme", specifier = ">=1.1.3" }, { name = "sphinx-markdown-builder", specifier = ">=0.6.8" }, ] -test = [{ name = "psychopy", specifier = ">=2025.1.1" }] +test = [{ name = "psychopy", specifier = ">=2026.1.1" }] [[package]] name = "alabaster" From ed69a91ecac5e3520d7c101f0351c15fa3955672 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Thu, 19 Mar 2026 11:09:36 +0100 Subject: [PATCH 113/137] remove todo tags --- adaptivetesting/math/content_balancing/__functions.py | 2 -- .../math/content_balancing/__weighted_penalty_model.py | 1 - adaptivetesting/math/exposure_control/__randomesque.py | 1 - 3 files changed, 4 deletions(-) diff --git a/adaptivetesting/math/content_balancing/__functions.py b/adaptivetesting/math/content_balancing/__functions.py index 669b04d..858a1dc 100644 --- a/adaptivetesting/math/content_balancing/__functions.py +++ b/adaptivetesting/math/content_balancing/__functions.py @@ -5,7 +5,6 @@ from typing import Literal -# TODO: implement model in function call def compute_priority_index(item: TestItem, group_weights: dict[str, float], required_items: dict[str, float], @@ -209,7 +208,6 @@ def standardize_total_content_constraint_penalty_value(item_penalty_value: float Returns: float: standardized total content constraint penalty value """ - # TODO: ok? try: standardized_value = (item_penalty_value - minimum) / (maximum - minimum) return standardized_value diff --git a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py index 162acc0..b699d20 100644 --- a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py +++ b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py @@ -114,7 +114,6 @@ def prepare_item_pool(self): # order items self.order_candidate_items() - # TODO: implement function call def calculate_information(self, model: Literal['GRM', 'GPCM'] | None = None) -> list[float]: information_list = [ float(item_information_function( diff --git a/adaptivetesting/math/exposure_control/__randomesque.py b/adaptivetesting/math/exposure_control/__randomesque.py index 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.../math/content_balancing/__functions.py | 9 ++- .../__weighted_penalty_model.py | 63 ++++++++++++++- .../estimators/__bayes_modal_estimation.py | 2 + .../math/estimators/__expect_a_posteriori.py | 2 + .../__functions/__poly/__poly_math.py | 77 ++++++++++++++++++- .../math/estimators/__ml_estimation.py | 4 + .../math/estimators/__test_information.py | 42 +++++++++- .../__mpi_exposure_control.py | 2 +- .../__maximum_information_criterion.py | 2 +- adaptivetesting/models/__test_item.py | 10 +-- .../services/__estimator_interface.py | 1 + adaptivetesting/utils/__plots.py | 6 +- 16 files changed, 251 insertions(+), 32 deletions(-) diff --git a/adaptivetesting/data/__read_prev_items_exp_cont.py b/adaptivetesting/data/__read_prev_items_exp_cont.py index dc8e026..7f7a8a7 100644 --- a/adaptivetesting/data/__read_prev_items_exp_cont.py +++ b/adaptivetesting/data/__read_prev_items_exp_cont.py @@ -9,8 +9,16 @@ def read_prev_items(format: Literal["CSV", "PICKLE"], test_id: str, participant_ids: list[str]) -> list[TestItem]: - """Read previously administered items - to perform exposure control + """Read previously administered items. + This function is internally used to perform exposure control. + + Args: + format (Literal["CSV", "PICKLE"]): The format of the results: CSV or PICKLE. + test_id (str): The test ID / simulation ID. + participant_ids (list[str]): The participant IDs. + + Returns: + list[TestItem]: A list of TestItem objects. """ all_shown_test_items = [] @@ -23,9 +31,21 @@ def read_prev_items(format: Literal["CSV", "PICKLE"], return all_shown_test_items -@deprecated("This function should be replaced after merging" - " with revision branch") +@deprecated("This function will be removed in future releases.") def read_single_participant(test_id: str, participant_id: str, format: Literal["CSV", "PICKLE"]) -> list[TestResult]: + """ + Reads the test results of a particular participant. + Args: + test_id (str): The test ID / simulation ID. + participant_id (str): The participant ID. + format (Literal["CSV", "PICKLE"]): The format of the results: CSV or PICKLE. + + Returns: + list[TestResult]: A list of TestResult objects. + + Raises: + ValueError: If the format is not supported. + """ context: CSVContext | PickleContext if format == "CSV": context = CSVContext(test_id, participant_id) @@ -33,3 +53,4 @@ def read_single_participant(test_id: str, participant_id: str, format: Literal[" if format == "PICKLE": context = PickleContext(test_id, participant_id) return context.load() + raise ValueError(f"Unknown format: {format}") diff --git a/adaptivetesting/implementations/__test_assembler.py b/adaptivetesting/implementations/__test_assembler.py index 5c1f7be..31f3f94 100644 --- a/adaptivetesting/implementations/__test_assembler.py +++ b/adaptivetesting/implementations/__test_assembler.py @@ -345,7 +345,7 @@ def run_test_once(self): The result of the superclass's run_test_once() method. """ # check if to run pretest - if self.__pretest is True: + if self.__pretest: pretest = PreTest( self.item_pool.test_items, self.__pretest_seed @@ -353,7 +353,7 @@ def run_test_once(self): # get selected items random_items = pretest.select_random_item_quantile() for item in random_items: - if self.simulation is True: + if self.simulation: response = self.item_pool.get_item_response(item) else: # not simulation diff --git a/adaptivetesting/math/__gen_response_pattern.py b/adaptivetesting/math/__gen_response_pattern.py index 3c8c09a..37355a6 100644 --- a/adaptivetesting/math/__gen_response_pattern.py +++ b/adaptivetesting/math/__gen_response_pattern.py @@ -13,7 +13,7 @@ def generate_response_pattern(ability: float, model: Literal["GRM", "GPCM"] | None = None, seed: int | None = None) -> list[int]: """Generates a response pattern for a given ability level - and item difficulties. Also, a seed can be set. + and item difficulties. A seed may be set for reproducibility. Args: ability (float): participants ability @@ -48,7 +48,19 @@ def generate_response_pattern(ability: float, def gen_pattern_dichotomous(ability: float, items: list[TestItem], - rng: np.random.RandomState): + rng: np.random.RandomState) -> list[int]: + """ + Generates a response pattern for a given ability level for an IRT + model with dichotomous test items (e.g., 4PL, 3PL). + + Args: + ability (float): participants ability + items (list[TestItem]): test items + rng (np.random.RandomState): random state (numpy object) + + Returns: + list[int]: generated response pattern + """ responses: list[int] = [] for item in items: @@ -82,6 +94,18 @@ def gen_pattern_poly( model: Literal["GRM", "GPCM"], rng: np.random.RandomState ) -> list[int]: + """ + Generates a response pattern for a given ability level for polytomous items. + + Args: + ability (float): participants ability + items (list[TestItem]): test items + model (literal, optional): model type (GRM, or GPCM) + rng (np.random.RandomState, optional): random state (numpy object) + + Returns: + list[int]: response pattern + """ responses: list[int] = [] # loop through all items for item in items: diff --git a/adaptivetesting/math/content_balancing/__constraint.py b/adaptivetesting/math/content_balancing/__constraint.py index 25d91e3..98a86b1 100644 --- a/adaptivetesting/math/content_balancing/__constraint.py +++ b/adaptivetesting/math/content_balancing/__constraint.py @@ -3,7 +3,7 @@ @dataclass class Constraint: - """Constraint for Exposure Constrol and Content Balancing + """Constraint for Exposure Control and Content Balancing """ name: str """Constraint name. This string has to be the same diff --git a/adaptivetesting/math/content_balancing/__functions.py b/adaptivetesting/math/content_balancing/__functions.py index 858a1dc..cd83fdf 100644 --- a/adaptivetesting/math/content_balancing/__functions.py +++ b/adaptivetesting/math/content_balancing/__functions.py @@ -21,6 +21,7 @@ def compute_priority_index(item: TestItem, required_items (int): number of items required to be shown per constraint shown_items (int): number of items already shown per constraint current_ability (float): currently estimated ability level + model (Literal['GRM', 'GPCM'] | None): model type. Required for polytomous items. Returns: float: priority index of an item @@ -103,7 +104,7 @@ def compute_expected_difference(proportion: float, constraint_target (float): constraint target Returns: - float: _description_ + float: expected difference """ expected_difference = proportion - constraint_target return expected_difference @@ -154,9 +155,9 @@ def compute_total_content_penalty_value_for_item(item: TestItem, Args: item (TestItem): given test item - shown_items: - available_items: - constraints (list[Constraint]): YYYYYY + shown_items: shown item + available_items: available items in the test + constraints (list[Constraint]): constraints assigned to the test and its items Returns: float: total content penalty value diff --git a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py index b699d20..2b0502a 100644 --- a/adaptivetesting/math/content_balancing/__weighted_penalty_model.py +++ b/adaptivetesting/math/content_balancing/__weighted_penalty_model.py @@ -21,7 +21,7 @@ class WeightedPenaltyModel(ContentBalancing): """This content balancing method follows Shin et al. (2009) and allows to apply constraints to the item selection. - The users can define a custom weight for the item information and the constriant. + The users can define a custom weight for the item information and the constraint. References ------------ @@ -98,7 +98,7 @@ def prepare_item_pool(self): max_content_penalty = max(content_penalties) min_content_penalty = min(content_penalties) - self.calcualte_weighted_penalty_for_all_items( + self.calculate_weighted_penalty_for_all_items( item_information_list=item_information_list, max_item=max_item, content_penalties=content_penalties, @@ -114,7 +114,16 @@ def prepare_item_pool(self): # order items self.order_candidate_items() - def calculate_information(self, model: Literal['GRM', 'GPCM'] | None = None) -> list[float]: + def calculate_information(self, + model: Literal['GRM', 'GPCM'] | None = None) -> list[float]: + """ + Calculates the item information for every item + Args: + model: model type. Required for polytomous models. + + Returns: + list of item information + """ information_list = [ float(item_information_function( ability=self.ability, @@ -126,6 +135,7 @@ def calculate_information(self, model: Literal['GRM', 'GPCM'] | None = None) -> return information_list def calculate_content_penalties(self) -> list[float]: + """Calculate content penalty values for every item""" content_penalties = [ compute_total_content_penalty_value_for_item( item=item, @@ -137,12 +147,13 @@ def calculate_content_penalties(self) -> list[float]: ] return content_penalties - def calcualte_weighted_penalty_for_all_items(self, + def calculate_weighted_penalty_for_all_items(self, item_information_list: list[float], max_item: float, content_penalties: list[float], max_content_penalty: float, min_content_penalty: float): + """Calculate the weighted penalty value for every item""" for i, item in enumerate(self.items): weighted_penalty_value = self.calculate_weighted_penalty_value( item_information=item_information_list[i], @@ -157,6 +168,8 @@ def calcualte_weighted_penalty_for_all_items(self, self.eligible_items.append((item, weighted_penalty_value, None)) def get_constraint_group_assignments(self) -> list[tuple[Constraint, CONSTRAINT_GROUP]]: + """Assign every constraint to a constraint group depending on the number + of items shown from each specific constraint.""" group_assignment: list[tuple[Constraint, CONSTRAINT_GROUP]] = [] for constraint in self.constraints: # calculate proportion of the constraint @@ -183,6 +196,15 @@ def get_constraint_group_assignments(self) -> list[tuple[Constraint, CONSTRAINT_ @staticmethod def assign_color_group_per_proportion(constraint: Constraint, prop: float): + """ + Implements constraint assignment logic. + Args: + constraint (Constraint): constraint + prop (float): proportion + + Returns: + tuple[Constraint, str]: constraint and assigned group + """ if constraint.lower is not None and constraint.upper is not None: if prop <= constraint.lower: return (constraint, "A") @@ -195,6 +217,12 @@ def assign_color_group_per_proportion(constraint: Constraint, def form_list_of_candidate_items(self, group_assignment: list[tuple[Constraint, CONSTRAINT_GROUP]]) -> None: + """ + Form a list of candidate items according to the constraint group assignment. + Args: + group_assignment (list[tuple[Constraint, CONSTRAINT_GROUP]): list of constraints and assigned groups + + """ for i, item_entry in enumerate(self.eligible_items): item, weighted_penalty_value, _ = item_entry # find associated constraint @@ -214,6 +242,17 @@ def assign_items_to_item_group( associated_constraints: list[tuple[Constraint, Literal['A', 'B', 'C']]], weighted_penalty_value: float ): + """ + Assign items according to the constraint group assignment to an item group (color group) + Args: + item (TestItem): item + associated_constraints (list[tuple[Constraint, Literal['A', 'B', 'C']]]): + constraints relevant for this item + weighted_penalty_value (float): calculated weighted penalty value of this item + + Returns: + tuple[TestItem, float, str]: item, weighted penalty value and assigned color + """ group_set = set([group for _, group in associated_constraints]) # if all associated constraints A or B -> green group @@ -232,6 +271,8 @@ def assign_items_to_item_group( return (item, weighted_penalty_value, None) def order_candidate_items(self): + """Order candidate items according to the color group assignment. + """ # between group ordering: green, orange, yellow, red # within group ordering: ascending order of weighted penalty value self.eligible_items = sorted( @@ -251,6 +292,20 @@ def calculate_weighted_penalty_value(content_penalty: float, constraint_weight: float, information_weight: float ) -> float: + """ + Calculate the weighted penalty value of an item. + Args: + content_penalty: content penalty value + minimum_total_content_penalty: minimum total content penalty value + maximum_total_content_penalty: maximum total content penalty value + item_information: item information + max_information: maximum item information + constraint_weight: constraint weight + information_weight: information weight + + Returns: + weighted penalty value + """ # reference content penalty total_content_penalty_value = content_penalty diff --git a/adaptivetesting/math/estimators/__bayes_modal_estimation.py b/adaptivetesting/math/estimators/__bayes_modal_estimation.py index 3a62c66..2fd7313 100644 --- a/adaptivetesting/math/estimators/__bayes_modal_estimation.py +++ b/adaptivetesting/math/estimators/__bayes_modal_estimation.py @@ -31,6 +31,8 @@ def __init__(self, prior (Prior): prior distribution optimization_interval (Tuple[float, float]): interval used for the optimization function + + model (Literal["GRM", "GPCM"], optional): model type (required for polytomous models) """ super().__init__(response_pattern, items, optimization_interval) diff --git a/adaptivetesting/math/estimators/__expect_a_posteriori.py b/adaptivetesting/math/estimators/__expect_a_posteriori.py index 7de17ad..f07612f 100644 --- a/adaptivetesting/math/estimators/__expect_a_posteriori.py +++ b/adaptivetesting/math/estimators/__expect_a_posteriori.py @@ -31,6 +31,8 @@ def __init__(self, prior (Prior): prior distribution optimization_interval (Tuple[float, float]): interval used for the optimization function + + model (Literal["GRM", "GPCM"], optional): model type (required for polytomous models) """ super().__init__(response_pattern, items, prior, optimization_interval) diff --git a/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py b/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py index 17a96b5..6fba718 100644 --- a/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py +++ b/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py @@ -7,12 +7,27 @@ class PolyModelFunctions(ABC): + """ + This is an abstract base class for polytomous IRT models and + their functions used for Computerized Adaptive Testing. + + This class may be reimplemented by subclasses. + """ @staticmethod @abstractmethod def category_prob(theta: float, a: float, thresholds_list: list[float], - response_pattern: int): + response_pattern: int) -> float: + """ + Calculates the probability of a specific category. + Args: + theta (float): ability + a (float): item parameter a + thresholds_list (list[float]): list of thresholds + response_pattern (int): item response / category + + """ pass @staticmethod @@ -21,6 +36,16 @@ def log_likelihood(theta: float, a_params: list[float], thresholds_list: list[list[float]], response_pattern: list[int]): + """ + Calculates the log likelihood function of the model. + + Args: + theta (float): ability + a_params (list[float]): item parameters a + thresholds_list (list[list[float]]): list of thresholds for each item + response_pattern (list[int]): response pattern + + """ pass @staticmethod @@ -28,6 +53,14 @@ def log_likelihood(theta: float, def fisher_information(theta: float, a: float, thresholds: list[float]): + """ + Calculates the fisher information of a specific item. + + Args: + theta (float): ability + a (float): item parameter a + thresholds (list[float]): list of thresholds + """ pass def maximize_likelihood_function(self, @@ -35,6 +68,18 @@ def maximize_likelihood_function(self, thresholds_list: list[list[float]], response_pattern: list[int], border: tuple[float, float] = (-10, 10)): + """ + Maximize the likelihood function of the model. + + Args: + a_params (list[float]): item parameters a + thresholds_list (list[list[float]]): list of thresholds for each item + response_pattern (list[int]): response pattern + border (tuple[float, float]): interval used for numerical optimization. Defaults to (-10, 10). + + Returns: + float: point (theta) where the likelihood function is maximized + """ result: OptimizeResult = minimize_scalar(lambda mu: -self.log_likelihood(mu, a_params, @@ -55,7 +100,20 @@ def maximize_posterior(self, prior: Prior, optimization_interval: tuple[float, float] = (-10, 10) ) -> float: - + """ + Maximize the posterior function of the model. + + Args: + a_params (list[float]): item parameters a + thresholds_list (list[list[float]]): list of thresholds for each item + response_pattern (list[int]): response pattern + optimization_interval (tuple[float, float]): interval used for numerical optimization. + Defaults to (-10, 10). + prior (Prior): prior distribution used + + Returns: + float: point (theta) where the posterior function is maximized + """ def log_posterior(mu): log_likelihood_res = np.array(self.log_likelihood(mu, a_params, @@ -90,6 +148,21 @@ def posterior_mean(self, response_pattern: list[int], prior: Prior, optimization_interval: tuple[float, float] = (-10, 10)) -> float: + """ + Calculate the posterior mean of the model. + + Args: + a_params (list[float]): item parameters a + thresholds_list (list[list[float]]): list of thresholds for each item + response_pattern (list[int]): response pattern + optimization_interval (tuple[float, float]): interval used for numerical optimization. + Defaults to (-10, 10). + prior (Prior): prior distribution used + + Returns: + float: posterior mean + """ + x = np.linspace(optimization_interval[0], optimization_interval[1], 1000) if hasattr(prior, "logpdf"): diff --git a/adaptivetesting/math/estimators/__ml_estimation.py b/adaptivetesting/math/estimators/__ml_estimation.py index ebc7738..48aea73 100644 --- a/adaptivetesting/math/estimators/__ml_estimation.py +++ b/adaptivetesting/math/estimators/__ml_estimation.py @@ -24,6 +24,10 @@ def __init__(self, response_pattern (List[int]): list of response patterns (0: wrong, 1:right) items (Sequence[BaseItem]): list of answered items + + model (Literal["GRM", "GPCM"], optional): model type (required for polytomous models) + + optimization_interval (Tuple[float, float]): tuple of (min, max) intervals used for numerical optimization. """ IEstimator.__init__(self, response_pattern, items, optimization_interval) diff --git a/adaptivetesting/math/estimators/__test_information.py b/adaptivetesting/math/estimators/__test_information.py index 12ffd1a..8e1686d 100644 --- a/adaptivetesting/math/estimators/__test_information.py +++ b/adaptivetesting/math/estimators/__test_information.py @@ -15,6 +15,19 @@ def item_information_function( item: TestItem, model: Literal["GRM", "GPCM"] | None = None ) -> float: + """ + Calculates the item information given an item and ability level. + If the item is polytomous, the model parameter has to be specified to + correctly calculated the item information. + + Args: + ability (float): ability level + item (TestItem): test item + model (literal["GRM", "GPCM"] | None): model parameter. Required for polytomous response variables. + + Returns: + float: item information + """ if model == "GRM": return GRM.fisher_information( ability, @@ -28,7 +41,7 @@ def item_information_function( cast(list, item.b) ) - else: # dichotmous + else: # dichotomous return dicho_item_information_function( mu=np.array(ability), a=np.array(item.a), @@ -46,7 +59,7 @@ def dicho_item_information_function( d: np.ndarray ) -> np.ndarray: """ - Calculates the item information for given parameters. + Internal function to calculate the item information for dichotomous items. Args: mu (np.ndarray): ability level @@ -110,7 +123,7 @@ def test_information_function( optimization_interval: tuple[float, float] = (-10, 10) ) -> float: """ - Calculates test information for dichotmous items. + Calculates test information for dichotomous items. Therefore, the information is calculated for every item and then summed up. If a prior is specified, the fisher information of the prior @@ -124,6 +137,7 @@ def test_information_function( d (np.ndarray): slipping parameter prior (Prior | None, optional): prior distribution. Defaults to None. optimization_interval (tuple[float, float], optional): interval used for numerical integration. + Defaults to (-10, 10). Returns: float: test information @@ -148,6 +162,28 @@ def poly_test_information_function( model_type: Literal["GRM", "GPCM"], optimization_interval: tuple[float, float] = (-10, 10), ) -> float: + """ + Calculates test information for polytomous items. + Therefore, the information is calculated for every item + and then summed up. + If a prior is specified, the fisher information of the prior + is calculated as well and added to the information sum. + + Args: + mu (float): ability level + a_params (list[float]): discrimination parameters + thresholds_list (list[list[float]]): list of thresholds + model_type (Literal["GRM", "GPCM"]): model type. Supported models: GRM, GPCM. + prior (Prior | None, optional): prior distribution. Defaults to None. + optimization_interval (tuple[float, float], optional): interval used for numerical integration. + Defaults to (-10, 10). + + Returns: + float: test information + + Raises: + ValueError: model type must be either GRM or GPCM. + """ # calculate information for every test item item_information = 0.0 if model_type == "GRM": diff --git a/adaptivetesting/math/exposure_control/__mpi_exposure_control.py b/adaptivetesting/math/exposure_control/__mpi_exposure_control.py index 5c6bca2..7e74389 100644 --- a/adaptivetesting/math/exposure_control/__mpi_exposure_control.py +++ b/adaptivetesting/math/exposure_control/__mpi_exposure_control.py @@ -24,7 +24,7 @@ def select_item(self, **kwargs) -> TestItem | None: """ # compute priority index for every item available_items = self.adaptive_test.item_pool.test_items - # skip seletion if item pool is empty + # skip selection if item pool is empty if len(available_items) == 0: return None diff --git a/adaptivetesting/math/item_selection/__maximum_information_criterion.py b/adaptivetesting/math/item_selection/__maximum_information_criterion.py index 943b16d..1f1e894 100644 --- a/adaptivetesting/math/item_selection/__maximum_information_criterion.py +++ b/adaptivetesting/math/item_selection/__maximum_information_criterion.py @@ -5,7 +5,6 @@ from typing import Literal -# Todo: implement model parameter in function calls def maximum_information_criterion(items: list[TestItem], ability: float, model: Literal["GRM", "GPCM"] | None = None) -> TestItem: @@ -15,6 +14,7 @@ def maximum_information_criterion(items: list[TestItem], Args: items (list[TestItem]): list of available items ability (float): currently estimated ability + model (Literal["GRM", "GPCM"] | None): model type. Required for polytomous models. Defaults to dichotomous variables. Returns: TestItem: item that has the highest information value diff --git a/adaptivetesting/models/__test_item.py b/adaptivetesting/models/__test_item.py index b09f2ca..f0a27be 100644 --- a/adaptivetesting/models/__test_item.py +++ b/adaptivetesting/models/__test_item.py @@ -10,11 +10,11 @@ def __init__(self): - id (int | None): item ID - a (float): discrimination parameter - b (float | list[float]): difficulty parameter. For polytomous models, list of threshold parameters - - c (float): guessing parameter. Ignored for polytomours models. - - d (float): slipping parameter / upper asymptote. Ignored for polytomours models. - - additional_properties (dict): addtional properties can be set if required. + - c (float): guessing parameter. Ignored for polytomous models. + - d (float): slipping parameter / upper asymptote. Ignored for polytomous models. + - additional_properties (dict): additional properties can be set if required. This functionality is used for content balancing. - To use content balancing, set set `category` key of the class instance + To use content balancing, set `category` key of the class instance to a list of string which indicate the corresponding constraint classes. Example: `item.additional_properties["category"] = ["Math"]` @@ -73,7 +73,7 @@ def from_dict(source: dict) -> "TestItem": return item def is_polytomous(self) -> bool: - """Checks whether a item is polytomous or dichotomous. + """Checks whether an item is polytomous or dichotomous. Returns: bool: True if item is polytomous. diff --git a/adaptivetesting/services/__estimator_interface.py b/adaptivetesting/services/__estimator_interface.py index 8019b29..45d4725 100644 --- a/adaptivetesting/services/__estimator_interface.py +++ b/adaptivetesting/services/__estimator_interface.py @@ -18,6 +18,7 @@ def __init__(self, Args: response_pattern (List[int]): list of responses (0: wrong, 1:right) items (list[TestItem]): list of answered items + model: model type. Required for polytomous IRT models. """ if type(response_pattern) is not np.ndarray: self.response_pattern = np.array(response_pattern) diff --git a/adaptivetesting/utils/__plots.py b/adaptivetesting/utils/__plots.py index 8db2a27..7df14c1 100644 --- a/adaptivetesting/utils/__plots.py +++ b/adaptivetesting/utils/__plots.py @@ -103,7 +103,7 @@ def plot_iif(item: TestItem, Parameters: item (TestItem): The test item for which to plot the information function. range (tuple[float, float], optional): The range of ability levels (theta) to plot over. Defaults to (-10, 10). - model (Literal["GRM", "GPCM"], optional): Type of IRT model. Defaults to dichotmous IRT models. + model (Literal["GRM", "GPCM"], optional): Type of IRT model. Defaults to dichotomous IRT models. ax (Axes, optional): Matplotlib Axes object to plot on. If None, a new figure and axes are created. **kwargs: Additional keyword arguments passed to matplotlib's plot function. Returns: @@ -142,7 +142,7 @@ def plot_exposure_rate(simulation_id: str, in a series of adaptive tests or CAT simulations. Args: - simulation_id (str): Simulation identifyer + simulation_id (str): Simulation identifier participant_ids (list[str]): List of unique participant IDs output_format (ResultOutputFormat): Format in which the test results have been previously saved @@ -212,7 +212,7 @@ def plot_test_information( Args: items (list[TestItem]): Test items in an item pool for which to calculate the test information range (tuple[float, float], optional): The range of ability levels (theta) to plot over. Defaults to (-10, 10). - model (Literal["GRM", "GPCM"], optional): Type of IRT model. Defaults to dichotmous IRT models. + model (Literal["GRM", "GPCM"], optional): Type of IRT model. Defaults to dichotomous IRT models. ax (Axes, optional): Matplotlib Axes object to plot on. If None, a new figure and axes are created. **kwargs: Additional keyword arguments passed to matplotlib's plot function. Returns: From b9cab41d818c8b09c19b9272199cbedfafcd9024 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 20 Mar 2026 16:38:52 +0100 Subject: [PATCH 116/137] add global import for plot functions --- adaptivetesting/__init__.py | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/adaptivetesting/__init__.py b/adaptivetesting/__init__.py index 17a7e3d..88b1811 100644 --- a/adaptivetesting/__init__.py +++ b/adaptivetesting/__init__.py @@ -42,4 +42,14 @@ from .simulation.__simulation import Simulation, SimulationPool, setup_simulation_and_start -from .utils.__descriptives import bias, average_absolute_deviation, rmse \ No newline at end of file +from .utils.__descriptives import bias, average_absolute_deviation, rmse +from .utils.__funcs import load_final_test_results, load_test_results_single_participant +from .utils.__plots import ( + plot_exposure_rate, + plot_final_ability_estimates, + plot_icc, + plot_iif, + plot_exposure_rate, + plot_test_information, + plot_theta_estimation_trace +) \ No newline at end of file From 57fa079eaffcfedf3d3c132aea8426512176d417 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 20 Mar 2026 17:01:02 +0100 Subject: [PATCH 117/137] fix importing issue of submodules --- adaptivetesting/__init__.py | 4 ++-- adaptivetesting/math/__gen_response_pattern.py | 6 +++--- adaptivetesting/math/estimators/__expect_a_posteriori.py | 6 +++--- adaptivetesting/math/estimators/__init__.py | 4 ++-- adaptivetesting/math/estimators/__test_information.py | 6 +++--- .../math/estimators/{__functions => functions}/__bayes.py | 0 .../estimators/{__functions => functions}/__estimators.py | 0 .../{__functions/__poly => functions/poly}/__gpcm.py | 0 .../{__functions/__poly => functions/poly}/__grm.py | 0 .../{__functions/__poly => functions/poly}/__init__.py | 0 .../{__functions/__poly => functions/poly}/__poly_math.py | 0 .../math/item_selection/__maximum_information_criterion.py | 3 ++- 12 files changed, 15 insertions(+), 14 deletions(-) rename adaptivetesting/math/estimators/{__functions => functions}/__bayes.py (100%) rename adaptivetesting/math/estimators/{__functions => functions}/__estimators.py (100%) rename adaptivetesting/math/estimators/{__functions/__poly => functions/poly}/__gpcm.py (100%) rename adaptivetesting/math/estimators/{__functions/__poly => functions/poly}/__grm.py (100%) rename adaptivetesting/math/estimators/{__functions/__poly => functions/poly}/__init__.py (100%) rename adaptivetesting/math/estimators/{__functions/__poly => functions/poly}/__poly_math.py (100%) diff --git a/adaptivetesting/__init__.py b/adaptivetesting/__init__.py index 88b1811..bef16c1 100644 --- a/adaptivetesting/__init__.py +++ b/adaptivetesting/__init__.py @@ -19,8 +19,8 @@ from .math.estimators.__bayes_modal_estimation import BayesModal from .math.estimators.__expect_a_posteriori import ExpectedAPosteriori from .math.estimators.__prior import Prior, NormalPrior, CustomPrior, CustomPriorException, SkewNormalPrior, EmpiricalPrior -from .math.estimators.__functions.__estimators import probability_y0, probability_y1, maximize_likelihood_function, likelihood -from .math.estimators.__functions.__bayes import maximize_posterior +from .math.estimators.functions.__estimators import probability_y0, probability_y1, maximize_likelihood_function, likelihood +from .math.estimators.functions.__bayes import maximize_posterior from .math.estimators.__test_information import test_information_function, item_information_function, prior_information_function from .math.item_selection.__maximum_information_criterion import maximum_information_criterion from .math.item_selection.__urrys_rule import urrys_rule diff --git a/adaptivetesting/math/__gen_response_pattern.py b/adaptivetesting/math/__gen_response_pattern.py index 37355a6..78c8871 100644 --- a/adaptivetesting/math/__gen_response_pattern.py +++ b/adaptivetesting/math/__gen_response_pattern.py @@ -1,6 +1,6 @@ -from .estimators.__functions.__estimators import probability_y1 -from .estimators.__functions.__poly.__gpcm import GPCM -from .estimators.__functions.__poly.__grm import GRM +from .estimators.functions.__estimators import probability_y1 +from .estimators.functions.poly.__gpcm import GPCM +from .estimators.functions.poly.__grm import GRM from ..models.__test_item import TestItem import numpy as np from typing import Literal, cast diff --git a/adaptivetesting/math/estimators/__expect_a_posteriori.py b/adaptivetesting/math/estimators/__expect_a_posteriori.py index f07612f..36fa7c9 100644 --- a/adaptivetesting/math/estimators/__expect_a_posteriori.py +++ b/adaptivetesting/math/estimators/__expect_a_posteriori.py @@ -2,12 +2,12 @@ from scipy.integrate import trapezoid from .__bayes_modal_estimation import BayesModal from ...models.__test_item import TestItem -from .__functions.__estimators import log_likelihood +from .functions.__estimators import log_likelihood from .__prior import Prior from math import pow from typing import Literal, cast -from .__functions.__poly.__gpcm import GPCM -from .__functions.__poly.__grm import GRM +from .functions.poly.__gpcm import GPCM +from .functions.poly.__grm import GRM class ExpectedAPosteriori(BayesModal): diff --git a/adaptivetesting/math/estimators/__init__.py b/adaptivetesting/math/estimators/__init__.py index 4555085..60211c0 100644 --- a/adaptivetesting/math/estimators/__init__.py +++ b/adaptivetesting/math/estimators/__init__.py @@ -2,6 +2,6 @@ from .__bayes_modal_estimation import BayesModal from .__expect_a_posteriori import ExpectedAPosteriori from .__prior import Prior, NormalPrior, CustomPrior, CustomPriorException, EmpiricalPrior, SkewNormalPrior -from .__functions.__estimators import probability_y0, probability_y1, maximize_likelihood_function, likelihood -from .__functions.__bayes import maximize_posterior +from .functions.__estimators import probability_y0, probability_y1, maximize_likelihood_function, likelihood +from .functions.__bayes import maximize_posterior from .__test_information import test_information_function, item_information_function, prior_information_function diff --git a/adaptivetesting/math/estimators/__test_information.py b/adaptivetesting/math/estimators/__test_information.py index 8e1686d..61372cd 100644 --- a/adaptivetesting/math/estimators/__test_information.py +++ b/adaptivetesting/math/estimators/__test_information.py @@ -1,12 +1,12 @@ import numpy as np -from .__functions.__estimators import probability_y1 +from .functions.__estimators import probability_y1 from .__prior import Prior from scipy.integrate import trapezoid import numpy from scipy.differentiate import derivative from typing import Literal, cast -from .__functions.__poly.__gpcm import GPCM -from .__functions.__poly.__grm import GRM +from .functions.poly.__gpcm import GPCM +from .functions.poly.__grm import GRM from ...models.__test_item import TestItem diff --git a/adaptivetesting/math/estimators/__functions/__bayes.py b/adaptivetesting/math/estimators/functions/__bayes.py similarity index 100% rename from adaptivetesting/math/estimators/__functions/__bayes.py rename to adaptivetesting/math/estimators/functions/__bayes.py diff --git a/adaptivetesting/math/estimators/__functions/__estimators.py b/adaptivetesting/math/estimators/functions/__estimators.py similarity index 100% rename from adaptivetesting/math/estimators/__functions/__estimators.py rename to adaptivetesting/math/estimators/functions/__estimators.py diff --git a/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py b/adaptivetesting/math/estimators/functions/poly/__gpcm.py similarity index 100% rename from adaptivetesting/math/estimators/__functions/__poly/__gpcm.py rename to adaptivetesting/math/estimators/functions/poly/__gpcm.py diff --git a/adaptivetesting/math/estimators/__functions/__poly/__grm.py b/adaptivetesting/math/estimators/functions/poly/__grm.py similarity index 100% rename from adaptivetesting/math/estimators/__functions/__poly/__grm.py rename to adaptivetesting/math/estimators/functions/poly/__grm.py diff --git a/adaptivetesting/math/estimators/__functions/__poly/__init__.py b/adaptivetesting/math/estimators/functions/poly/__init__.py similarity index 100% rename from adaptivetesting/math/estimators/__functions/__poly/__init__.py rename to adaptivetesting/math/estimators/functions/poly/__init__.py diff --git a/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py b/adaptivetesting/math/estimators/functions/poly/__poly_math.py similarity index 100% rename from adaptivetesting/math/estimators/__functions/__poly/__poly_math.py rename to adaptivetesting/math/estimators/functions/poly/__poly_math.py diff --git a/adaptivetesting/math/item_selection/__maximum_information_criterion.py b/adaptivetesting/math/item_selection/__maximum_information_criterion.py index 1f1e894..19e1ca1 100644 --- a/adaptivetesting/math/item_selection/__maximum_information_criterion.py +++ b/adaptivetesting/math/item_selection/__maximum_information_criterion.py @@ -14,7 +14,8 @@ def maximum_information_criterion(items: list[TestItem], Args: items (list[TestItem]): list of available items ability (float): currently estimated ability - model (Literal["GRM", "GPCM"] | None): model type. Required for polytomous models. Defaults to dichotomous variables. + model (Literal["GRM", "GPCM"] | None): model type. Required for polytomous models. + Defaults to dichotomous variables. Returns: TestItem: item that has the highest information value From a513bf96ab93b04c7f87004a568c73ffd929dab1 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 20 Mar 2026 17:06:19 +0100 Subject: [PATCH 118/137] fix import issue again --- adaptivetesting/math/estimators/__bayes_modal_estimation.py | 6 +++--- adaptivetesting/math/estimators/__ml_estimation.py | 6 +++--- adaptivetesting/tests/test_generate_response_pattern.py | 4 ++-- adaptivetesting/utils/__plots.py | 2 +- 4 files changed, 9 insertions(+), 9 deletions(-) diff --git a/adaptivetesting/math/estimators/__bayes_modal_estimation.py b/adaptivetesting/math/estimators/__bayes_modal_estimation.py index 2fd7313..241490f 100644 --- a/adaptivetesting/math/estimators/__bayes_modal_estimation.py +++ b/adaptivetesting/math/estimators/__bayes_modal_estimation.py @@ -2,11 +2,11 @@ import numpy as np from ...services.__estimator_interface import IEstimator from ...models.__test_item import TestItem -from .__functions.__bayes import maximize_posterior +from .functions.__bayes import maximize_posterior from .__prior import Prior from .__test_information import test_information_function, poly_test_information_function -from .__functions.__poly.__gpcm import GPCM -from .__functions.__poly.__grm import GRM +from .functions.poly.__gpcm import GPCM +from .functions.poly.__grm import GRM class BayesModal(IEstimator): diff --git a/adaptivetesting/math/estimators/__ml_estimation.py b/adaptivetesting/math/estimators/__ml_estimation.py index 48aea73..1ce4eb6 100644 --- a/adaptivetesting/math/estimators/__ml_estimation.py +++ b/adaptivetesting/math/estimators/__ml_estimation.py @@ -2,10 +2,10 @@ import numpy as np from ...models.__test_item import TestItem from ...services.__estimator_interface import IEstimator -from .__functions.__estimators import maximize_likelihood_function +from .functions.__estimators import maximize_likelihood_function from .__test_information import test_information_function, poly_test_information_function -from .__functions.__poly.__gpcm import GPCM -from .__functions.__poly.__grm import GRM +from .functions.poly.__gpcm import GPCM +from .functions.poly.__grm import GRM class MLEstimator(IEstimator): diff --git a/adaptivetesting/tests/test_generate_response_pattern.py b/adaptivetesting/tests/test_generate_response_pattern.py index 3bae53a..cfee1db 100644 --- a/adaptivetesting/tests/test_generate_response_pattern.py +++ b/adaptivetesting/tests/test_generate_response_pattern.py @@ -59,7 +59,7 @@ def test_compare_generation_to_estimation(self): self.assertAlmostEqual(mean, 0, delta=0.3) def test_debug_probabilities(self): - from adaptivetesting.math.estimators.__functions.__estimators import probability_y1 + from adaptivetesting.math.estimators.functions.__estimators import probability_y1 import numpy as np item_pool = ItemPool.load_from_dict(source_dictionary) @@ -74,7 +74,7 @@ def test_debug_probabilities(self): print(f"Item {i}: a={item.a:.3f}, b={item.b:.3f}, c={item.c:.3f}, d={item.d:.3f} -> P={float(prob):.3f}") def test_calculate_expected_vs_actual(self): - from adaptivetesting.math.estimators.__functions.__estimators import probability_y1 + from adaptivetesting.math.estimators.functions.__estimators import probability_y1 import numpy as np item_pool = ItemPool.load_from_dict(source_dictionary) diff --git a/adaptivetesting/utils/__plots.py b/adaptivetesting/utils/__plots.py index 7df14c1..1b60007 100644 --- a/adaptivetesting/utils/__plots.py +++ b/adaptivetesting/utils/__plots.py @@ -3,7 +3,7 @@ from matplotlib.figure import Figure, SubFigure from ..models.__misc import ResultOutputFormat from ..models.__test_item import TestItem -from ..math.estimators.__functions.__estimators import probability_y1 +from ..math.estimators.functions.__estimators import probability_y1 from ..math.estimators.__test_information import item_information_function from .__funcs import load_final_test_results, load_test_results_single_participant import numpy as np From 951b6ac3c45e5677c560983a9ddf32d52d8dad92 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 20 Mar 2026 17:12:46 +0100 Subject: [PATCH 119/137] add missing init file --- adaptivetesting/math/estimators/functions/__init__.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 adaptivetesting/math/estimators/functions/__init__.py diff --git a/adaptivetesting/math/estimators/functions/__init__.py b/adaptivetesting/math/estimators/functions/__init__.py new file mode 100644 index 0000000..e69de29 From 2db978e4e07cd38d96eb595abbf905e65b60be62 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 20 Mar 2026 17:19:55 +0100 Subject: [PATCH 120/137] Revert "fix import issue again" This reverts commit a513bf96ab93b04c7f87004a568c73ffd929dab1. --- adaptivetesting/math/estimators/__bayes_modal_estimation.py | 6 +++--- adaptivetesting/math/estimators/__ml_estimation.py | 6 +++--- adaptivetesting/tests/test_generate_response_pattern.py | 4 ++-- adaptivetesting/utils/__plots.py | 2 +- 4 files changed, 9 insertions(+), 9 deletions(-) diff --git a/adaptivetesting/math/estimators/__bayes_modal_estimation.py b/adaptivetesting/math/estimators/__bayes_modal_estimation.py index 241490f..2fd7313 100644 --- a/adaptivetesting/math/estimators/__bayes_modal_estimation.py +++ b/adaptivetesting/math/estimators/__bayes_modal_estimation.py @@ -2,11 +2,11 @@ import numpy as np from ...services.__estimator_interface import IEstimator from ...models.__test_item import TestItem -from .functions.__bayes import maximize_posterior +from .__functions.__bayes import maximize_posterior from .__prior import Prior from .__test_information import test_information_function, poly_test_information_function -from .functions.poly.__gpcm import GPCM -from .functions.poly.__grm import GRM +from .__functions.__poly.__gpcm import GPCM +from .__functions.__poly.__grm import GRM class BayesModal(IEstimator): diff --git a/adaptivetesting/math/estimators/__ml_estimation.py b/adaptivetesting/math/estimators/__ml_estimation.py index 1ce4eb6..48aea73 100644 --- a/adaptivetesting/math/estimators/__ml_estimation.py +++ b/adaptivetesting/math/estimators/__ml_estimation.py @@ -2,10 +2,10 @@ import numpy as np from ...models.__test_item import TestItem from ...services.__estimator_interface import IEstimator -from .functions.__estimators import maximize_likelihood_function +from .__functions.__estimators import maximize_likelihood_function from .__test_information import test_information_function, poly_test_information_function -from .functions.poly.__gpcm import GPCM -from .functions.poly.__grm import GRM +from .__functions.__poly.__gpcm import GPCM +from .__functions.__poly.__grm import GRM class MLEstimator(IEstimator): diff --git a/adaptivetesting/tests/test_generate_response_pattern.py b/adaptivetesting/tests/test_generate_response_pattern.py index cfee1db..3bae53a 100644 --- a/adaptivetesting/tests/test_generate_response_pattern.py +++ b/adaptivetesting/tests/test_generate_response_pattern.py @@ -59,7 +59,7 @@ def test_compare_generation_to_estimation(self): self.assertAlmostEqual(mean, 0, delta=0.3) def test_debug_probabilities(self): - from adaptivetesting.math.estimators.functions.__estimators import probability_y1 + from adaptivetesting.math.estimators.__functions.__estimators import probability_y1 import numpy as np item_pool = ItemPool.load_from_dict(source_dictionary) @@ -74,7 +74,7 @@ def test_debug_probabilities(self): print(f"Item {i}: a={item.a:.3f}, b={item.b:.3f}, c={item.c:.3f}, d={item.d:.3f} -> P={float(prob):.3f}") def test_calculate_expected_vs_actual(self): - from adaptivetesting.math.estimators.functions.__estimators import probability_y1 + from adaptivetesting.math.estimators.__functions.__estimators import probability_y1 import numpy as np item_pool = ItemPool.load_from_dict(source_dictionary) diff --git a/adaptivetesting/utils/__plots.py b/adaptivetesting/utils/__plots.py index 1b60007..7df14c1 100644 --- a/adaptivetesting/utils/__plots.py +++ b/adaptivetesting/utils/__plots.py @@ -3,7 +3,7 @@ from matplotlib.figure import Figure, SubFigure from ..models.__misc import ResultOutputFormat from ..models.__test_item import TestItem -from ..math.estimators.functions.__estimators import probability_y1 +from ..math.estimators.__functions.__estimators import probability_y1 from ..math.estimators.__test_information import item_information_function from .__funcs import load_final_test_results, load_test_results_single_participant import numpy as np From eff6dcb828d99241a2402614263f33a6c21904d6 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 20 Mar 2026 17:20:54 +0100 Subject: [PATCH 121/137] Revert "fix importing issue of submodules" This reverts commit 57fa079eaffcfedf3d3c132aea8426512176d417. --- adaptivetesting/__init__.py | 4 ++-- adaptivetesting/math/__gen_response_pattern.py | 6 +++--- adaptivetesting/math/estimators/__expect_a_posteriori.py | 6 +++--- .../math/estimators/{functions => __functions}/__bayes.py | 0 .../estimators/{functions => __functions}/__estimators.py | 0 .../{functions/poly => __functions/__poly}/__gpcm.py | 0 .../{functions/poly => __functions/__poly}/__grm.py | 0 .../{functions/poly => __functions/__poly}/__init__.py | 0 .../{functions/poly => __functions/__poly}/__poly_math.py | 0 adaptivetesting/math/estimators/__init__.py | 4 ++-- adaptivetesting/math/estimators/__test_information.py | 6 +++--- adaptivetesting/math/estimators/functions/__init__.py | 0 .../math/item_selection/__maximum_information_criterion.py | 3 +-- 13 files changed, 14 insertions(+), 15 deletions(-) rename adaptivetesting/math/estimators/{functions => __functions}/__bayes.py (100%) rename adaptivetesting/math/estimators/{functions => __functions}/__estimators.py (100%) rename adaptivetesting/math/estimators/{functions/poly => __functions/__poly}/__gpcm.py (100%) rename adaptivetesting/math/estimators/{functions/poly => __functions/__poly}/__grm.py (100%) rename adaptivetesting/math/estimators/{functions/poly => __functions/__poly}/__init__.py (100%) rename adaptivetesting/math/estimators/{functions/poly => __functions/__poly}/__poly_math.py (100%) delete mode 100644 adaptivetesting/math/estimators/functions/__init__.py diff --git a/adaptivetesting/__init__.py b/adaptivetesting/__init__.py index bef16c1..88b1811 100644 --- a/adaptivetesting/__init__.py +++ b/adaptivetesting/__init__.py @@ -19,8 +19,8 @@ from .math.estimators.__bayes_modal_estimation import BayesModal from .math.estimators.__expect_a_posteriori import ExpectedAPosteriori from .math.estimators.__prior import Prior, NormalPrior, CustomPrior, CustomPriorException, SkewNormalPrior, EmpiricalPrior -from .math.estimators.functions.__estimators import probability_y0, probability_y1, maximize_likelihood_function, likelihood -from .math.estimators.functions.__bayes import maximize_posterior +from .math.estimators.__functions.__estimators import probability_y0, probability_y1, maximize_likelihood_function, likelihood +from .math.estimators.__functions.__bayes import maximize_posterior from .math.estimators.__test_information import test_information_function, item_information_function, prior_information_function from .math.item_selection.__maximum_information_criterion import maximum_information_criterion from .math.item_selection.__urrys_rule import urrys_rule diff --git a/adaptivetesting/math/__gen_response_pattern.py b/adaptivetesting/math/__gen_response_pattern.py index 78c8871..37355a6 100644 --- a/adaptivetesting/math/__gen_response_pattern.py +++ b/adaptivetesting/math/__gen_response_pattern.py @@ -1,6 +1,6 @@ -from .estimators.functions.__estimators import probability_y1 -from .estimators.functions.poly.__gpcm import GPCM -from .estimators.functions.poly.__grm import GRM +from .estimators.__functions.__estimators import probability_y1 +from .estimators.__functions.__poly.__gpcm import GPCM +from .estimators.__functions.__poly.__grm import GRM from ..models.__test_item import TestItem import numpy as np from typing import Literal, cast diff --git a/adaptivetesting/math/estimators/__expect_a_posteriori.py b/adaptivetesting/math/estimators/__expect_a_posteriori.py index 36fa7c9..f07612f 100644 --- a/adaptivetesting/math/estimators/__expect_a_posteriori.py +++ b/adaptivetesting/math/estimators/__expect_a_posteriori.py @@ -2,12 +2,12 @@ from scipy.integrate import trapezoid from .__bayes_modal_estimation import BayesModal from ...models.__test_item import TestItem -from .functions.__estimators import log_likelihood +from .__functions.__estimators import log_likelihood from .__prior import Prior from math import pow from typing import Literal, cast -from .functions.poly.__gpcm import GPCM -from .functions.poly.__grm import GRM +from .__functions.__poly.__gpcm import GPCM +from .__functions.__poly.__grm import GRM class ExpectedAPosteriori(BayesModal): diff --git a/adaptivetesting/math/estimators/functions/__bayes.py b/adaptivetesting/math/estimators/__functions/__bayes.py similarity index 100% rename from adaptivetesting/math/estimators/functions/__bayes.py rename to adaptivetesting/math/estimators/__functions/__bayes.py diff --git a/adaptivetesting/math/estimators/functions/__estimators.py b/adaptivetesting/math/estimators/__functions/__estimators.py similarity index 100% rename from adaptivetesting/math/estimators/functions/__estimators.py rename to adaptivetesting/math/estimators/__functions/__estimators.py diff --git a/adaptivetesting/math/estimators/functions/poly/__gpcm.py b/adaptivetesting/math/estimators/__functions/__poly/__gpcm.py similarity index 100% rename from adaptivetesting/math/estimators/functions/poly/__gpcm.py rename to adaptivetesting/math/estimators/__functions/__poly/__gpcm.py diff --git a/adaptivetesting/math/estimators/functions/poly/__grm.py b/adaptivetesting/math/estimators/__functions/__poly/__grm.py similarity index 100% rename from adaptivetesting/math/estimators/functions/poly/__grm.py rename to adaptivetesting/math/estimators/__functions/__poly/__grm.py diff --git a/adaptivetesting/math/estimators/functions/poly/__init__.py b/adaptivetesting/math/estimators/__functions/__poly/__init__.py similarity index 100% rename from adaptivetesting/math/estimators/functions/poly/__init__.py rename to adaptivetesting/math/estimators/__functions/__poly/__init__.py diff --git a/adaptivetesting/math/estimators/functions/poly/__poly_math.py b/adaptivetesting/math/estimators/__functions/__poly/__poly_math.py similarity index 100% rename from adaptivetesting/math/estimators/functions/poly/__poly_math.py rename to adaptivetesting/math/estimators/__functions/__poly/__poly_math.py diff --git a/adaptivetesting/math/estimators/__init__.py b/adaptivetesting/math/estimators/__init__.py index 60211c0..4555085 100644 --- a/adaptivetesting/math/estimators/__init__.py +++ b/adaptivetesting/math/estimators/__init__.py @@ -2,6 +2,6 @@ from .__bayes_modal_estimation import BayesModal from .__expect_a_posteriori import ExpectedAPosteriori from .__prior import Prior, NormalPrior, CustomPrior, CustomPriorException, EmpiricalPrior, SkewNormalPrior -from .functions.__estimators import probability_y0, probability_y1, maximize_likelihood_function, likelihood -from .functions.__bayes import maximize_posterior +from .__functions.__estimators import probability_y0, probability_y1, maximize_likelihood_function, likelihood +from .__functions.__bayes import maximize_posterior from .__test_information import test_information_function, item_information_function, prior_information_function diff --git a/adaptivetesting/math/estimators/__test_information.py b/adaptivetesting/math/estimators/__test_information.py index 61372cd..8e1686d 100644 --- a/adaptivetesting/math/estimators/__test_information.py +++ b/adaptivetesting/math/estimators/__test_information.py @@ -1,12 +1,12 @@ import numpy as np -from .functions.__estimators import probability_y1 +from .__functions.__estimators import probability_y1 from .__prior import Prior from scipy.integrate import trapezoid import numpy from scipy.differentiate import derivative from typing import Literal, cast -from .functions.poly.__gpcm import GPCM -from .functions.poly.__grm import GRM +from .__functions.__poly.__gpcm import GPCM +from .__functions.__poly.__grm import GRM from ...models.__test_item import TestItem diff --git a/adaptivetesting/math/estimators/functions/__init__.py b/adaptivetesting/math/estimators/functions/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/adaptivetesting/math/item_selection/__maximum_information_criterion.py b/adaptivetesting/math/item_selection/__maximum_information_criterion.py index 19e1ca1..1f1e894 100644 --- a/adaptivetesting/math/item_selection/__maximum_information_criterion.py +++ b/adaptivetesting/math/item_selection/__maximum_information_criterion.py @@ -14,8 +14,7 @@ def maximum_information_criterion(items: list[TestItem], Args: items (list[TestItem]): list of available items ability (float): currently estimated ability - model (Literal["GRM", "GPCM"] | None): model type. Required for polytomous models. - Defaults to dichotomous variables. + model (Literal["GRM", "GPCM"] | None): model type. Required for polytomous models. Defaults to dichotomous variables. Returns: TestItem: item that has the highest information value From d656dcabd35ee1c146318bbeb03b8540fa71d064 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 20 Mar 2026 17:21:16 +0100 Subject: [PATCH 122/137] add missing init --- adaptivetesting/math/estimators/__functions/__init__.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 adaptivetesting/math/estimators/__functions/__init__.py diff --git a/adaptivetesting/math/estimators/__functions/__init__.py b/adaptivetesting/math/estimators/__functions/__init__.py new file mode 100644 index 0000000..e69de29 From a3e5270107379374975c147d0b2a35e00f24ef2c Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 20 Mar 2026 17:23:43 +0100 Subject: [PATCH 123/137] add testing script --- test.ps1 | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 test.ps1 diff --git a/test.ps1 b/test.ps1 new file mode 100644 index 0000000..b7523a3 --- /dev/null +++ b/test.ps1 @@ -0,0 +1,3 @@ +uv run mypy +uv run flake8 +uv run python -m unittest \ No newline at end of file From 4687f7134d6b6175894587b3794354685fdf9031 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 20 Mar 2026 17:28:54 +0100 Subject: [PATCH 124/137] Enhance simulation start method to support optional parallel execution --- .../__maximum_information_criterion.py | 3 +- adaptivetesting/simulation/__simulation.py | 55 ++++++++++++------- 2 files changed, 38 insertions(+), 20 deletions(-) diff --git a/adaptivetesting/math/item_selection/__maximum_information_criterion.py b/adaptivetesting/math/item_selection/__maximum_information_criterion.py index 1f1e894..19e1ca1 100644 --- a/adaptivetesting/math/item_selection/__maximum_information_criterion.py +++ b/adaptivetesting/math/item_selection/__maximum_information_criterion.py @@ -14,7 +14,8 @@ def maximum_information_criterion(items: list[TestItem], Args: items (list[TestItem]): list of available items ability (float): currently estimated ability - model (Literal["GRM", "GPCM"] | None): model type. Required for polytomous models. Defaults to dichotomous variables. + model (Literal["GRM", "GPCM"] | None): model type. Required for polytomous models. + Defaults to dichotomous variables. Returns: TestItem: item that has the highest information value diff --git a/adaptivetesting/simulation/__simulation.py b/adaptivetesting/simulation/__simulation.py index e0dc989..11e08c1 100644 --- a/adaptivetesting/simulation/__simulation.py +++ b/adaptivetesting/simulation/__simulation.py @@ -117,30 +117,47 @@ def __init__(self, self.criterion = criterion self.value = value - def start(self): + def start(self, parallel: bool = True): """ Starts the simulation by executing adaptive tests in parallel. Depending on the operating system, uses either multithreading (on Windows) or multiprocessing (on other platforms) to run the simulation for each adaptive test. Progress is displayed using a progress bar. + + Note that parallel processing is not supported for the use in jupyter notebooks. + For that, `parallel` has to be set to `False`. + + Args: + parallel (bool): process all simulations in parallel. Not supported in jupyter notebooks. + Default `True`. + """ - func = partial( - setup_simulation_and_start, - test_result_output=self.test_results_output, - criterion=self.criterion, - value=self.value - ) - # check for platform - # this is because multiprocessing is not as well-supported on windows - # therefore, multithreading is used instead - if platform.system() == "Windows": - with ThreadPoolExecutor(max_workers=60) as executor: - futures = [executor.submit(func, (test,)) for test in self.adaptive_tests] - for _ in tqdm(as_completed(futures), total=len(futures)): - pass + if parallel: + func = partial( + setup_simulation_and_start, + test_result_output=self.test_results_output, + criterion=self.criterion, + value=self.value + ) + # check for platform + # this is because multiprocessing is not as well-supported on windows + # therefore, multithreading is used instead + if platform.system() == "Windows": + with ThreadPoolExecutor(max_workers=60) as executor: + futures = [executor.submit(func, (test,)) for test in self.adaptive_tests] + for _ in tqdm(as_completed(futures), total=len(futures)): + pass + else: + with ProcessPoolExecutor() as executor: + futures = [executor.submit(func, (test,)) for test in self.adaptive_tests] + for _ in tqdm(as_completed(futures), total=len(futures)): + pass else: - with ProcessPoolExecutor() as executor: - futures = [executor.submit(func, (test,)) for test in self.adaptive_tests] - for _ in tqdm(as_completed(futures), total=len(futures)): - pass + for test in tqdm(self.adaptive_tests): + setup_simulation_and_start( + test=test, + test_result_output=self.test_results_output, + criterion=self.criterion, + value=self.value + ) From e0027bb5caf4f6720167285a8b15472d49cbfe45 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 20 Mar 2026 18:06:47 +0100 Subject: [PATCH 125/137] change dtypes of plot functions --- adaptivetesting/utils/__plots.py | 35 +++++++++++++++++++------------- 1 file changed, 21 insertions(+), 14 deletions(-) diff --git a/adaptivetesting/utils/__plots.py b/adaptivetesting/utils/__plots.py index 7df14c1..e8dcf08 100644 --- a/adaptivetesting/utils/__plots.py +++ b/adaptivetesting/utils/__plots.py @@ -38,18 +38,25 @@ def plot_final_ability_estimates(simulation_id: str, # read final test results data final_test_results = load_final_test_results(simulation_id, participant_ids, output_format) # extract true and finally estimated ability levels - true_and_final_abilities = [ - (result.ability_estimation, result.true_ability_level) - for result in final_test_results - ] - - final_estimates, true_abilities = zip(*true_and_final_abilities) + estimates = [] + true_abilities = [] + + for result in final_test_results: + estimates.append(result.ability_estimation) + true_abilities.append(result.true_ability_level) + + + if "color" not in kwargs: - ax.scatter(true_abilities, final_estimates, color="blue", **kwargs) + ax.scatter(np.array(true_abilities, dtype=float), + np.array(estimates, dtype=float), + color="blue", **kwargs) else: - ax.scatter(true_abilities, final_estimates, **kwargs) - ax.plot(true_abilities, true_abilities, color="black") + ax.scatter(np.array(true_abilities, dtype=float), + np.array(estimates, dtype=float), **kwargs) + ax.plot(np.array(true_abilities, dtype=float), + np.array(true_abilities, dtype=float), color="black") ax.set_xlabel("True ability level") ax.set_ylabel("Estimated ability level") @@ -70,7 +77,7 @@ def plot_icc(item: TestItem, Returns: tuple: A tuple containing the matplotlib Figure and Axes objects. """ - thetas = np.linspace(range[0], range[1], 1000) + thetas = np.linspace(range[0], range[1], 1000, dtype=float) probabilities = probability_y1( mu=np.array(thetas).T, a=np.array(item.a), @@ -219,7 +226,7 @@ def plot_test_information( tuple[Figure, Axes]: The matplotlib Figure and Axes objects containing the plot. """ # calculate test information by summing item information across items - thetas = np.linspace(range[0], range[1], 100) + thetas = np.linspace(range[0], range[1], 100, dtype=float) information_array = np.zeros_like(thetas, dtype=float) item_information_function_vec = np.vectorize(item_information_function) for item in items: @@ -279,14 +286,14 @@ def plot_theta_estimation_trace(simulation_id: str, true_abilities = np.array([ result.true_ability_level for result in test_results - ]) + ], dtype=float) estimations = np.array([ result.ability_estimation for result in test_results - ]) + ], dtype=float) - steps = np.array(range(len(test_results))) + steps = np.array(range(len(test_results)), dtype=float) # setup figure fig: Figure | SubFigure From 6b214db39c0df31647347ae316f814ea9ac45ab9 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 20 Mar 2026 18:09:03 +0100 Subject: [PATCH 126/137] Fix conditional structure in load_final_test_results for PICKLE output format --- adaptivetesting/utils/__funcs.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/adaptivetesting/utils/__funcs.py b/adaptivetesting/utils/__funcs.py index f5dd034..8c678dc 100644 --- a/adaptivetesting/utils/__funcs.py +++ b/adaptivetesting/utils/__funcs.py @@ -34,7 +34,7 @@ def load_final_test_results(simulation_id: str, final_result = test_results[-1] final_test_results.append(final_result) - if output_format is ResultOutputFormat.PICKLE: + elif output_format is ResultOutputFormat.PICKLE: for id in participant_ids: context = PickleContext(simulation_id, participant_id=id) test_results = context.load() From 8aa8a078108449149248bc0578a37bffc25a5cf0 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 20 Mar 2026 18:27:59 +0100 Subject: [PATCH 127/137] add x/y lab to plot --- adaptivetesting/utils/__plots.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/adaptivetesting/utils/__plots.py b/adaptivetesting/utils/__plots.py index e8dcf08..1088cd0 100644 --- a/adaptivetesting/utils/__plots.py +++ b/adaptivetesting/utils/__plots.py @@ -304,6 +304,8 @@ def plot_theta_estimation_trace(simulation_id: str, ax.plot(steps, true_abilities, label="True ability", color="black") ax.plot(steps, estimations, label="Ability Estimations", color="blue") + ax.set_xlabel("Step") + ax.set_ylabel("Ability level") ax.legend() return fig, ax From bae3bda244cc3957f87741723114fe75d873687b Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Fri, 10 Apr 2026 12:40:40 +0200 Subject: [PATCH 128/137] Add Getting Started Tutorial --- docs/Getting_Started.md | 69 +++++++++++++++++++++++++++++++++++++++++ docs/Home.md | 48 ++++++++++++++++++---------- docs/_Sidebar.md | 2 +- 3 files changed, 102 insertions(+), 17 deletions(-) create mode 100644 docs/Getting_Started.md diff --git a/docs/Getting_Started.md b/docs/Getting_Started.md new file mode 100644 index 0000000..302d861 --- /dev/null +++ b/docs/Getting_Started.md @@ -0,0 +1,69 @@ +# Getting Started + +This small tutorial will give a general introduction to the package, +show the installation and how a small simulation is performed. + +### 1. Package Installation + +The package can be installed through `pip` or `conda` - depending on the user's +preference. +```bash +pip install adaptivetesting +``` +or +```bash +conda install conda-forge::adaptivetesting +``` + +### 2. Load the items and set up the item pool +At first, the package should be imported. We recommend using a short alias, such as `adt`. +Then, the item pool can be loaded from a compatible format, such as a dictionary or simple lists. +In this example, we will load the item parameters from python lists. +```python +import adaptivetesting as adt +item_pool = adt.ItemPool.load_from_list( + a = [0.42, 0.3, 1.5], + b = [0.5, 0.9, 1.1] +) +``` +Here, we use a 2PL model which is a simplified version of the 4PL model so that +the `c` and `d` parameters are not manually set but inferred by the package. + +### 3. Define the adaptive test +The general adaptive testing procedure can be easily defined with the +`TestAssembler` class. This class supports numerous arguments to +also allow rather complex procedures. +For this example, we will only focus on a very basic configuration. + +We configure a test for a single participant using ML for the ability estimation +and MFI for the item selection. Because we want to simulate the test later, +we have to specify a true ability level (here `0`) so that the package +may draw corresponding response patterns. For reproducibility, we also +set a seed (`123`). + +```python +test = adt.TestAssembler( + item_pool = item_pool, + simulation_id="test", + participant_id="1", + ability_estimator=adt.MLEstimator, + true_ability_level=0, + seed=123 + ) + +``` + +### 4. Set up the simulation +The simulation can now simply be set up with the `Simulation` class. +Additionally, we have to specify a format in which the test results will be saved. +```python +simulation = adt.Simulation(test, adt.ResultOutputFormat.CSV) +``` + +### 5. Run the simulation +To start the actual simulation, we have to specify a stopping criterion. +We will use a standard error value of $0.4$ in this example. +```python +simulation.simulate(criterion=adt.StoppingCriterion.SE, value=0.4) +simulation.save_test_results() +``` \ No newline at end of file diff --git a/docs/Home.md b/docs/Home.md index 1686d14..3ba968f 100644 --- a/docs/Home.md +++ b/docs/Home.md @@ -7,19 +7,35 @@ ## Features -| **Ability Estimators** | | -|-------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------| -| Maximum Likelihood | $\arg \max\, L(Y=1\| \theta)$ | -| Bayes Modal | $\arg \max \frac{L(\theta)P_\text{prior}(\theta)}{\int_\infty^\infty L(\theta)P_\text{prior}(\theta)}$ | -| Expected a Posteriori | $\frac{\int_{-\infty}^{\infty} \theta L(\theta) P_\text{prior}(\theta)\, d\theta}{\int_{-\infty}^{\infty} L(\theta) P_\text{prior}(\theta)\, d\theta}$ | -| **Item Selection** | | -| MFI | $\arg \max I_i(\theta)$ | -| **Exposure Control** | | -| Randomesque | | -| Maximum Priority Index | $PI_i = I_i \prod_{k=1}^K (w_k f_k)^{c_{ik}}$ | -| **Content Balancing** | | -| Maximum Priority Index | $PI_i = I_i \prod_{k=1}^K (w_k f_k)^{c_{ik}}$ | -| Weighted Penalty Model | $F_i = w'F_i' + w'' F_i''$ | -| **Stopping Criterion** | | -| Standard Error | $\leq \frac{1}{\sqrt{I(\theta)}}$ | -| Test Length | # of items | +* **IRT Models** + * 4PL, 3PL, 2PL, Rasch + * GRM + * GPCM +* **Ability Estimators** + * Maximum Likelihood + * Bayes Modal + * Expected a Posteriori +* **Item Selection** + * MFI +* **Exposure Control** + * Randomesque + * Maximum Priority Index +* **Content Balancing** + * Maximum Priority Index + * Weighted Penalty Model + * **Stopping Criterion** + * Standard Error + * Test Length + + +## Package Installation +### PyPi + +```bash +pip install adaptivetesting +``` + +### Conda +```bash +conda install conda-forge::adaptivetesting +``` \ No newline at end of file diff --git a/docs/_Sidebar.md b/docs/_Sidebar.md index 0178fb9..eea9698 100644 --- a/docs/_Sidebar.md +++ b/docs/_Sidebar.md @@ -1,8 +1,8 @@ ### [Home](Home) -**Getting Started** **Tutorials** +[**Getting Started**](Getting_Started) **API Reference** - [data module](API-data) From 132cd477747a10d68c39636252529064240b03a8 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Mon, 13 Apr 2026 11:16:20 +0200 Subject: [PATCH 129/137] Add documentation --- .../math/exposure_control/__randomesque.py | 2 +- docs/Content_Balancing.md | 126 ++++++++++++++++++ docs/Exposure_Control.md | 103 ++++++++++++++ docs/Simulation.md | 72 ++++++++++ docs/Testing_Software.md | 61 +++++++++ docs/_Sidebar.md | 6 +- 6 files changed, 368 insertions(+), 2 deletions(-) create mode 100644 docs/Content_Balancing.md create mode 100644 docs/Exposure_Control.md create mode 100644 docs/Simulation.md create mode 100644 docs/Testing_Software.md diff --git a/adaptivetesting/math/exposure_control/__randomesque.py b/adaptivetesting/math/exposure_control/__randomesque.py index 2af9ef7..fe21cce 100644 --- a/adaptivetesting/math/exposure_control/__randomesque.py +++ b/adaptivetesting/math/exposure_control/__randomesque.py @@ -28,7 +28,7 @@ def __init__(self, References ------------ - Kingsbury, C. G., & Zara, A. R. (1991). A Comparison of Procedures for Content-Sensitive + Kingsbury, G. G., & Zara, A. R. (1991). A Comparison of Procedures for Content-Sensitive Item Selection in Computerized Adaptive Tests. Applied Measurement in Education, 4(3), 241–261. Psychology and Behavioral Sciences Collection. https://doi.org/10.1207/s15324818ame0403_4 diff --git a/docs/Content_Balancing.md b/docs/Content_Balancing.md new file mode 100644 index 0000000..58e5eca --- /dev/null +++ b/docs/Content_Balancing.md @@ -0,0 +1,126 @@ +# Content Balancing +This package currently supports two methods for Content Balancing: +- Maximum Priority Index (Cheng & Chang, 2009) +- Weighted Penalty Model (Shin et al., 2009) + +## Maximum Priority Index +n MPI, items are assigned to groups. These groups, in turn, belong to constraints. +These constraints are implemented in the package as follows: +- Constraint name (important for the correct assignment of items) +- Weight +- Prevalence (Frequency / Relative Frequency of a constraint and its items) +For more background information, please refer to Cheng & Chang (2009). + +### Setup Item Pool +It is important to note that each item in the item pool +must be assigned to one or more content categories. +These specify which constraints the item is relevant to. +Therefore of course, an item can belong to multiple constraints. + +````python +import adaptivetesting as adt +import pandas as pd + +items = pd.DataFrame({ + "a": [1.32, 1.07, 0.84], + "b": [-0.63, 0.18, -0.84], + "c": [0.17, 0.10, 0.19], + "d": [0.87, 0.93, 1], + "content_categories": ["Math", "English", "Math"], + "id": [1, 2, 3] + }) +```` +In this example, we have two items belonging to the content category `Math` and one +to `English`. + +### Setup Adaptive Test +The adaptive test can, in general, be set up as usual. +But some additional properties, i.e. the constraints, have to be defined. +For simplicity, we will define all the arguments required for Content Balancing +prior to the test object. +```python +cb_args: adt.ContentBalancingArgs = { + "constraints": [ + adt.Constraint("Math", weight=0.5, prevalence=0.5), + adt.Constraint("English", weight=0.5, prevalence=0.5) + ] + } +``` +In this example, we set the prevalence and the weight of both constraints to `0.5`. +The test can then be assembled. +```python +adaptive_test = adt.TestAssembler( + item_pool=item_pool, + simulation_id="1", + participant_id="2", + ability_estimator=adt.MLEstimator, + content_balancing="MaximumPriorityIndex", + content_balancing_args=cb_args + ) +``` + +## Weighted Penalty Model +The WPM itself follows a very complex ruleset. +For more background on the operation of the WPM, see Shin et al. (2009). + +To implement the WPM in an adaptive test, constraints have to be defined. +These consist of the following properties: +- name +- weight +- prevalence (0 < x < 1) +- lower bound +- upper bound + +Again, we will define all the arguments required for Content Balancing +before the actual test object. +```python +cb_args: adt.ContentBalancingArgs = { + "constraints": [ + adt.Constraint( + "Math", + weight=0.5, + prevalence=0.2, + lower=0, + upper=1 + ), + adt.Constraint( + "English", + weight=0.5, + prevalence=0.2, + lower=0, + upper=1 + ) + ], + "constraint_weight": 0.5, + "information_weight": 0.5 +} +``` +WEP also requires to weight the constraints and item information. +In this example, the weights are fixed but can also be set to functions so that +the weights may be adapted during the testing procedure. +For that, `"constraint_weight"` and/or `"information_weight"` must be set to +functions that take an instance of the adaptive test as argument +and return a float (`Callable[[AdaptiveTest], float]`). + +Finally, the adaptive test can be specified as usual. +```python +adaptive_test = adt.TestAssembler( + item_pool=item_pool, + simulation_id="1", + participant_id="12", + ability_estimator=adt.MLEstimator, + content_balancing="WeightedPenaltyModel", + content_balancing_args=cb_args + ) +``` + +## References +Cheng, Y., & Chang, H. (2009). The maximum priority index method for severely constrained item selection +in computerized adaptive testing. +British Journal of Mathematical and Statistical Psychology, 62(2), 369–383. +https://doi.org/10.1348/000711008X304376 + +Shin, C. D., Chien, Y., Way, W. D., & Swanson, L. (2009, April). Weighted Penalty +Model for Content Balancing in CATS. +https://www.pearsonassessments.com/content/dam/school/global/clinical/us/ +assets/testnav/weighted-penalty-model.pdf \ No newline at end of file diff --git a/docs/Exposure_Control.md b/docs/Exposure_Control.md new file mode 100644 index 0000000..2e12b60 --- /dev/null +++ b/docs/Exposure_Control.md @@ -0,0 +1,103 @@ +# Exposure Control + +This package currently supports two methods for Exposure Control: +- Randomesque Item Selection (Kingsbury & Zara, 1989, 1991) +- Maximum Priority Index (Cheng & Chang, 2009) + +## Randomesque +Randomesque item selection does not select the *most* informative item, +instead a random draw is made from the $n$ most informative items. +This can be easily set up in the `TestAssembler` by specifying +the `exposure_control` argument. +`exposure_control_args` takes a dictionary in which we can define the number +items and, if required, a seed for the random draw. + +```python +import adaptivetesting as adt + +test = adt.TestAssembler( + ..., + exposure_control="Randomesque", + exposure_control_args={ + "n_items": 4, + "seed": None + } +) +``` +The final test can then be used and run as usual. + +## Maximum Priority Index +In MPI, items are assigned to groups. These groups, in turn, belong to constraints. +These constraints are implemented in the package as follows: +- Constraint name (important for the correct assignment of items) +- Weight +- Prevalence (Frequency / Relative Frequency of a constraint and its items) +For more background information, please refer to Cheng & Chang (2009). + +### Setup Item Pool +It is important to note that each item in the item pool +must be assigned to one or more content categories. +These specify which constraints the item is relevant to. +Therefore of course, an item can belong to multiple constraints. + +````python +import adaptivetesting as adt +import pandas as pd + +items = pd.DataFrame({ + "a": [1.32, 1.07, 0.84], + "b": [-0.63, 0.18, -0.84], + "c": [0.17, 0.10, 0.19], + "d": [0.87, 0.93, 1], + "content_categories": ["Math", "English", "Math"], + "id": [1, 2, 3] + }) +```` +In this example, we have two items belonging to the content category `Math` and one +to `English`. + +### Setup Adaptive Test +The adaptive test can, in general, be set up as usual. +But some additional properties, i.e. the constraints, have to be defined. +For simplicity, we will define all the arguments required for Exposure Control +prior to the test object. +```python +ex_args: adt.ExposureControlArgs = { + "constraints": [ + adt.Constraint("Math", weight=0.5, prevalence=0.5), + adt.Constraint("English", weight=0.5, prevalence=0.5) + ], + "participant_ids": ["1"], + "output_format": adt.ResultOutputFormat.CSV + } +``` +In this example, we set the prevalence and the weight of both constraints to `0.5`. +Additionally, we expect to have a single previous test results (from participant `"1"`) +saved in the CSV file format. + +The test can then be assembled. +```python +adaptive_test = adt.TestAssembler( + item_pool=item_pool, + simulation_id="1", + participant_id="2", + ability_estimator=adt.MLEstimator, + exposure_control="MaximumPriorityIndex", + exposure_control_args=ex_args, + debug=False + ) +``` + +## References + +Cheng, Y., & Chang, H. (2009). The maximum priority index method for severely constrained item selection +in computerized adaptive testing. +British Journal of Mathematical and Statistical Psychology, 62(2), 369–383. +https://doi.org/10.1348/000711008X304376 + +Kingsbury, G. G., & Zara, A. R. (1991). A Comparison of Procedures for Content-Sensitive +Item Selection in Computerized Adaptive Tests. Applied Measurement in Education, 4(3), 241–261. +Psychology and Behavioral Sciences Collection. https://doi.org/10.1207/s15324818ame0403_4 + +Kingsbury, G. G., & Zara, A. R. (1989). Procedures for selecting items for +computerized adaptive tests. Applied Measurement in Education, 2(4), 359–375. \ No newline at end of file diff --git a/docs/Simulation.md b/docs/Simulation.md new file mode 100644 index 0000000..a59b287 --- /dev/null +++ b/docs/Simulation.md @@ -0,0 +1,72 @@ +# Simulation + +## Single Simulation + +For simulating an adaptive test, first the item pool has to be initialized. +```python +import adaptivetesting as adt +item_pool = adt.ItemPool.load_from_list( + a = [0.42, 0.3, 1.5], + b = [0.5, 0.9, 1.1] +) +``` +Then, the test object can be created using the `TestAssembler` class. For reproducibility, a seed can be set. +```python +test = adt.TestAssembler( + item_pool = item_pool, + simulation_id="test", + participant_id="1", + ability_estimator=adt.MLEstimator, + true_ability_level=0, + seed=123 +) +``` + +The simulation itself is set up with the `Simulation` class which specifies the output format for +the test results and the stopping criteria. +```python +simulation = adt.Simulation(test, adt.ResultOutputFormat.CSV) + +simulation.simulate(criterion=adt.StoppingCriterion.SE, value=0.4) +simulation.save_test_results() +``` + +## Simulation Pool +For large scale simulations, the `SimulationPool` class can be used. +For every single simulation, a test object has to be created which needs to be saved in a list. +```python +tests = [ + adt.TestAssembler( + item_pool=item_pool, + simulation_id="example", + participant_id="1", + ability_estimator=adt.MLEstimator, + item_selector=adt.maximum_information_criterion, + true_ability_level=0, + seed=123 + ), + adt.TestAssembler( + item_pool=item_pool, + simulation_id="example", + participant_id="2", + ability_estimator=adt.MLEstimator, + item_selector=adt.maximum_information_criterion, + true_ability_level=1, + seed=123 + ), +] +``` +The simulation pool allows the tests to run sequentially without additional setup but also in parallel. +```python +sim_pool = adt.SimulationPool( + adaptive_tests=tests, + test_result_output=adt.ResultOutputFormat.CSV, + criterion=adt.StoppingCriterion.SE, + value=0.4 +) +sim_pool.start() +``` +Depending on the operating system, the simulation pool uses either multithreading (on Windows) +or multiprocessing (on other platforms) to run the simulation for each adaptive test. +Note that parallel processing is not supported for the use in jupyter notebooks. +For that, `parallel` has to be set to `False`. \ No newline at end of file diff --git a/docs/Testing_Software.md b/docs/Testing_Software.md new file mode 100644 index 0000000..f1d47bb --- /dev/null +++ b/docs/Testing_Software.md @@ -0,0 +1,61 @@ +# Testing Software +With `adaptivetesting`, users can simulate adaptive tests +but also collect real data. +For data collection, a function has to be defined +which allows interaction with the examinees. +This can be via testing software such as PsychoPy or any +other appropriate interface. + +In this example, we will just collect responses from the commandline. +In real-world data collection, items selected from the test can be used to match the appropriate stimuli +which are then displayed to the participants. +```python +import adaptivetesting as adt + +def get_response (item : adt.TestItem) -> int: + print(f"Selected item: {item.id}") + response = input("Response >") + return int(response) +``` + +Then, we can set up the adaptive test object. +```python +adaptive_test = adt.TestAssembler ( + item_pool=item_pool, + simulation_id="example_data_collection", + participant_id="dummy", + ability_estimator=adt.MLEstimator, + item_selector=adt.maximum_information_criterion, + simulation=False + ) +``` +It is important that the `simulation` parameter is set to `False` so that the package +does not simulate responses but expects real user input. +To enable data collection, the `get_response` method of the +test object has to be overridden. +```python +adaptive_test.get_response = get_response +``` + +Simple additional code is required to let the test run until a stopping criterion is met +and the test results may be saved. +```python +# start adaptive test +while True: + adaptive_test.run_test_once() + + # check stopping criterion + if adaptive_test.standard_error <= 0.4: + break + + # end test if all items have been shown + if len(adaptive_test.item_pool.test_items) == 0: + break + +data_context = adt.CSVContext( + adaptive_test.simulation_id, + adaptive_test.participant_id +) + +data_context.save(adaptive_test.test_results) +``` \ No newline at end of file diff --git a/docs/_Sidebar.md b/docs/_Sidebar.md index eea9698..607706d 100644 --- a/docs/_Sidebar.md +++ b/docs/_Sidebar.md @@ -2,7 +2,11 @@ **Tutorials** -[**Getting Started**](Getting_Started) +- [Getting Started](Getting_Started) +- [Exposure Control](Exposure_Control) +- [Content Balancing](Content_Balancing) +- [Testing Software](Testing_Software) +- [Simulation](Simulation) **API Reference** - [data module](API-data) From 109610f208c3aba049b1340c932760b1436dcdee Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Mon, 13 Apr 2026 12:28:33 +0200 Subject: [PATCH 130/137] add missing docstring arguments --- adaptivetesting/implementations/__test_assembler.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/adaptivetesting/implementations/__test_assembler.py b/adaptivetesting/implementations/__test_assembler.py index 31f3f94..a335192 100644 --- a/adaptivetesting/implementations/__test_assembler.py +++ b/adaptivetesting/implementations/__test_assembler.py @@ -133,6 +133,15 @@ def __init__(self, content_balancing (CONTENT_BALANCING, optional): Selected content balancing strategy. Defaults to None. If a content balancing strategy is specified, the item selection strategy will be ignored. + content_balancing_args (ContentBalancingArgs, optional): Arguments for the content balancing strategy. + Defaults to None. + + exposure_control (EXPOSURE_CONTROL, optional): Exposure control strategy. Defaults to None. + If an exposure control strategy is specified, the item selection strategy will be ignored. + + exposure_control_args (ExposureControlArgs, optional): Arguments for the exposure control strategy. + Defaults to None. + pretest (bool, optional): Whether to run a pretest phase before the main test. Defaults to False. pretest_seed (int | None, optional): Random seed for pretest item selection. Defaults to None. From cee035da8456758029d47a9c22cf1cfbdf9e31b5 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Mon, 13 Apr 2026 12:42:50 +0200 Subject: [PATCH 131/137] fix linting --- .../math/item_selection/__maximum_information_criterion.py | 2 +- adaptivetesting/simulation/__simulation.py | 2 +- adaptivetesting/utils/__plots.py | 5 +---- 3 files changed, 3 insertions(+), 6 deletions(-) diff --git a/adaptivetesting/math/item_selection/__maximum_information_criterion.py b/adaptivetesting/math/item_selection/__maximum_information_criterion.py index 19e1ca1..330d32d 100644 --- a/adaptivetesting/math/item_selection/__maximum_information_criterion.py +++ b/adaptivetesting/math/item_selection/__maximum_information_criterion.py @@ -14,7 +14,7 @@ def maximum_information_criterion(items: list[TestItem], Args: items (list[TestItem]): list of available items ability (float): currently estimated ability - model (Literal["GRM", "GPCM"] | None): model type. Required for polytomous models. + model (Literal["GRM", "GPCM"] | None): model type. Required for polytomous models. Defaults to dichotomous variables. Returns: diff --git a/adaptivetesting/simulation/__simulation.py b/adaptivetesting/simulation/__simulation.py index 11e08c1..037bd55 100644 --- a/adaptivetesting/simulation/__simulation.py +++ b/adaptivetesting/simulation/__simulation.py @@ -125,7 +125,7 @@ def start(self, parallel: bool = True): or multiprocessing (on other platforms) to run the simulation for each adaptive test. Progress is displayed using a progress bar. - Note that parallel processing is not supported for the use in jupyter notebooks. + Note that parallel processing is not supported for the use in jupyter notebooks. For that, `parallel` has to be set to `False`. Args: diff --git a/adaptivetesting/utils/__plots.py b/adaptivetesting/utils/__plots.py index 1088cd0..3ae0ac4 100644 --- a/adaptivetesting/utils/__plots.py +++ b/adaptivetesting/utils/__plots.py @@ -40,14 +40,11 @@ def plot_final_ability_estimates(simulation_id: str, # extract true and finally estimated ability levels estimates = [] true_abilities = [] - - + for result in final_test_results: estimates.append(result.ability_estimation) true_abilities.append(result.true_ability_level) - - if "color" not in kwargs: ax.scatter(np.array(true_abilities, dtype=float), np.array(estimates, dtype=float), From 7c9a879ab3af7005e5eb07e071b32b7143d0f0a1 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Mon, 13 Apr 2026 14:08:18 +0200 Subject: [PATCH 132/137] ad PR template --- .github/pull_request_template.md | 9 +++++++++ 1 file changed, 9 insertions(+) create mode 100644 .github/pull_request_template.md diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md new file mode 100644 index 0000000..9bb8f0d --- /dev/null +++ b/.github/pull_request_template.md @@ -0,0 +1,9 @@ +Fixes: #--- + +## Quality Assurance Checklist +- [ ] This PR is opened against the dev branch. +- [ ] Code follows the project style guidelines and passes flake8 linting. +- [ ] Type annotations are correct and pass mypy static analysis. +- [ ] All existing and new unittests pass locally. + +## Proposed Changes \ No newline at end of file From 785ecc32ecac24ecf2ff60673796abd01c554c56 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Tue, 14 Apr 2026 13:54:38 +0200 Subject: [PATCH 133/137] remove todo --- todo.md | 11 ----------- 1 file changed, 11 deletions(-) delete mode 100644 todo.md diff --git a/todo.md b/todo.md deleted file mode 100644 index c0ef9ea..0000000 --- a/todo.md +++ /dev/null @@ -1,11 +0,0 @@ -# Todo - -- [X] TestAssembler -- [X] Response Pattern generation -- [X] Ability Estimation - - [X] unittest -- [X] Item Selection - - [X] unittest -- [X] Item Pool - - [X] Load item pool with polytomous items - - [X] unittest \ No newline at end of file From 0d42b0bb5fac558fbe8754da091812757b5342de Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Tue, 14 Apr 2026 13:58:27 +0200 Subject: [PATCH 134/137] add node script to build badges --- .github/workflows/wiki.yml | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/.github/workflows/wiki.yml b/.github/workflows/wiki.yml index 79bf9d5..416dc0f 100644 --- a/.github/workflows/wiki.yml +++ b/.github/workflows/wiki.yml @@ -13,6 +13,15 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 + - uses: actions/setup-node@v6 + with: + node-version: "latest" + - name: render badge images + run: | + cd docs/_static + npm ci + node create_badges.js + - uses: actions/setup-python@v6 with: python-version: '3.13' From 2e03796f256188917d587919d58e1f7dbc2a4a37 Mon Sep 17 00:00:00 2001 From: Jonas Engicht Date: Tue, 14 Apr 2026 14:02:36 +0200 Subject: [PATCH 135/137] update Readme.md --- README.md | 282 +----------------------------------------------------- 1 file changed, 4 insertions(+), 278 deletions(-) diff --git a/README.md b/README.md index a7bdb8e..a022d00 100644 --- a/README.md +++ b/README.md @@ -10,12 +10,14 @@ - **Bayesian Methods**: Built-in support for Bayesian ability estimation with customizable priors - **Flexible Architecture**: Object-oriented design with abstract classes for easy extension -- **Item Response Theory**: Full support for 1PL, 2PL, 3PL, and 4PL models +- **Item Response Theory**: Full support for 1PL, 2PL, 3PL, and 4PL models, GRM, GPCM - **Multiple Estimators**: - Maximum Likelihood Estimation (MLE) - Bayesian Modal Estimation (BM) - Expected A Posteriori (EAP) -- **Item Selection Strategies**: Maximum information criterion and Urry's rule +- **Item Selection Strategies**: Maximum information criterion +- **Content Balancing**: Maximum Priority Index, Weighted Penalty Model +- **Exposure Control**: Randomesque Item Selection, Maximum Priority Index - **Simulation Framework**: Comprehensive tools for CAT simulation and evaluation - **Real-world Application**: Direct transition from simulation to production testing - **Stopping Criteria**: Support for standard error and test length criteria @@ -35,282 +37,6 @@ For the latest development version: pip install git+https://github.com/condecon/adaptivetesting ``` -## Requirements - -- Python >= 3.10 -- NumPy >= 2.0.0 -- Pandas >= 2.2.0 -- SciPy >= 1.15.0 -- tqdm >= 4.67.1 - -## Quick Start - -### Basic Example: Setting Up an Adaptive Test - -```python -from adaptivetesting.models import ItemPool, TestItem -from adaptivetesting.implementations import TestAssembler -from adaptivetesting.math.estimators import BayesModal, NormalPrior -from adaptivetesting.simulation import Simulation, StoppingCriterion, ResultOutputFormat -import pandas as pd - -# Create item pool from DataFrame -items_data = pd.DataFrame({ - "a": [1.32, 1.07, 0.84, 1.19, 0.95], # discrimination - "b": [-0.63, 0.18, -0.84, 0.41, -0.25], # difficulty - "c": [0.17, 0.10, 0.19, 0.15, 0.12], # guessing - "d": [0.87, 0.93, 1.0, 0.89, 0.94] # upper asymptote -}) -item_pool = ItemPool.load_from_dataframe(items_data) - -# Set up adaptive test -adaptive_test = TestAssembler( - item_pool=item_pool, - simulation_id="sim_001", - participant_id="participant_001", - ability_estimator=BayesModal, - estimator_args={"prior": NormalPrior(mean=0, sd=1)}, - true_ability_level=0.5, # For simulation - simulation=True -) - -# Run simulation -simulation = Simulation( - test=adaptive_test, - test_result_output=ResultOutputFormat.CSV -) - -simulation.simulate( - criterion=StoppingCriterion.SE, - value=0.3 # Stop when standard error <= 0.3 -) - -# Save results -simulation.save_test_results() -``` - -### Custom Prior Example - -```python -from adaptivetesting.math.estimators import CustomPrior -from scipy.stats import t - -# Create custom t prior -custom_prior = CustomPrior(t, 100) - -# Use in estimator -adaptive_test = TestAssembler( - item_pool=item_pool, - simulation_id="custom_prior_sim", - participant_id="participant_002", - ability_estimator=BayesModal, - estimator_args={"prior": custom_prior}, - true_ability_level=0.0, - simulation=True -) -``` - -### Real-world Testing (Non-simulation) with PsychoPy - -```python -# setup item pool -# the item pool is retrieved from the PREVIC -# https://github.com/manuelbohn/previc/tree/main/saves -import pandas as pd -from adaptivetesting.models import ItemPool -from psychopy import visual, event -from psychopy.hardware import keyboard -from adaptivetesting.implementations import TestAssembler -from adaptivetesting.models import AdaptiveTest, ItemPool, TestItem -from adaptivetesting.data import CSVContext -from adaptivetesting.math.estimators import ExpectedAPosteriori, CustomPrior -from adaptivetesting.math.item_selection import maximum_information_criterion -from scipy.stats import t -import pandas as pd - -previc_item_pool = pd.read_csv("item_pool.csv") -# add item column -previc_item_pool["id"] = list(range(1, 90)) -previc_item_pool.head() - - -item_pool = ItemPool.load_from_list( - b=previc_item_pool["Difficulty"], - ids=previc_item_pool["id"] -) - -# Create adaptive test -adaptive_test: AdaptiveTest = TestAssembler( - item_pool=item_pool, - simulation_id="example", - participant_id="dummy", - ability_estimator=BayesModal, - estimator_args={ - "prior": CustomPrior(t, 100), - "optimization_interval":(-10, 10) - }, - item_selector=maximum_information_criterion, - simulation=False, - debug=False -) - -# ==================== -# Setup PsychoPy -# ==================== - -# general setup -win = visual.Window([800, 600], - monitor="testMonitor", - units="deg", - fullscr=False) - -# init keyboard -keyboard.Keyboard() - -## FIX THIS - -# define function to get user input -def get_response(item: TestItem) -> int: - # select corresponding word from item pool data frame - stimuli: str = previc_item_pool[previc_item_pool["id"] == item.id]["word"].values[0] - - # create text box and display stimulus - text_box = visual.TextBox2(win=win, - text=stimuli, - alignment="center", - size=24) - # draw text - text_box.draw() - # update window - win.flip() - - # wait for pressed keys - while True: - keys = event.getKeys() - # if keys are not None - if keys: - # if the right arrow keys is pressed - # return 1 - if keys[0] == "right": - return 1 - # if the left arrow keys is pressed - # return 0 - if keys[0] == "left": - return 0 - - -# override adaptive test default function -adaptive_test.get_response = get_response - -# start adaptive test -while True: - adaptive_test.run_test_once() - - # check stopping criterion - if adaptive_test.standard_error <= 0.4: - break - - # end test if all items have been shown - if len(adaptive_test.item_pool.test_items) == 0: - break - -# save test results -data_context = CSVContext( - adaptive_test.simulation_id, - adaptive_test.participant_id -) - -data_context.save(adaptive_test.test_results) -``` - -## Package Structure - -The package is organized into several key modules: - -- **`adaptivetesting.models`**: Core classes including `AdaptiveTest`, `ItemPool`, and `TestItem` -- **`adaptivetesting.implementations`**: Ready-to-use test implementations like `TestAssembler` -- **`adaptivetesting.math`**: Mathematical functions for IRT, ability estimation, and item selection -- **`adaptivetesting.simulation`**: Simulation framework and result management -- **`adaptivetesting.data`**: Data management utilities for CSV and pickle formats -- **`adaptivetesting.services`**: Abstract interfaces and protocols - -## Advanced Features - -### Multiple Stopping Criteria - -```python -simulation.simulate( - criterion=[StoppingCriterion.SE, StoppingCriterion.LENGTH], - value=[0.3, 20] # Stop at SE ≤ 0.3 OR length ≥ 20 items -) -``` - -### Pretest Phase - -```python -adaptive_test = TestAssembler( - item_pool=item_pool, - simulation_id="pretest_sim", - participant_id="participant_003", - ability_estimator=BayesModal, - estimator_args={"prior": NormalPrior(0, 1)}, - pretest=True, - pretest_seed=42, - simulation=True -) -``` - -### Custom Item Selection - -```python -from adaptivetesting.math.item_selection import maximum_information_criterion - -adaptive_test = TestAssembler( - item_pool=item_pool, - simulation_id="custom_selection", - participant_id="participant_004", - ability_estimator=BayesModal, - estimator_args={"prior": NormalPrior(0, 1)}, - item_selector=maximum_information_criterion, - item_selector_args={"additional_param": "value"}, - simulation=True -) -``` - -### Custom Optimization Interval -```python -adaptive_test = TestAssembler( - item_pool=item_pool, - simulation_id="pretest_sim", - participant_id="participant_003", - ability_estimator=BayesModal, - estimator_args={ - "prior": NormalPrior(0, 1), - "optimization_interval": (-5, 5)}, - pretest_seed=42, - simulation=True -) -``` - -## Documentation - -Full documentation is available in the `docs/` directory: - -- [API Reference](docs/readme.md) -- [Models Module](docs/models.md) -- [Math Module](docs/math.md) -- [Implementation Examples](docs/implementations.md) -- [Simulation Guide](docs/simulation.md) - -## Testing - -The package includes comprehensive tests. Run them using: - -```bash -uv sync -uv run python -m unittest -``` - ## Contributing We welcome contributions! 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pyproject.toml | 2 +- 2 files changed, 14 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 0e21e3a..401e022 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,16 @@ +# Version 1.2 +- Required Python version: >= 3.12 +- The package now follows SPEC0 +- All functions can be now be imported from the top-level namespace +- Adding descriptive statistics and plotting functions +- Added support for the GRM and GPCM models (polytomous response variables) +- Added `SkewNormalPrior` and `EmpiricalPrior` for Bayesian ability estimation +- Added support for Content Balancing and Exposure Control +- The package is now also published on conda-forge + +*Note*: We have tried our best to eliminate breaking changes. +But still, please be careful when updating to this version. + # Version 1.1.5 - Bug Fix #46 (numerical stability issue concerning Bayesian ability estimation) diff --git a/pyproject.toml b/pyproject.toml index 69db95a..2de1878 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name="adaptivetesting" -version="1.2b1" +version="1.2" description="adaptivetesting is a Python package that can be used to simulate and evaluate custom CAT scenarios as well as implement them in real-world testing scenarios from a single codebase" readme = "README.md" authors = [