Update normalization parameters and add estimator params validation #210
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@@ -8,7 +8,7 @@ | |
| } | ||
| }, | ||
| "data": { | ||
| "preprocessing_kwargs": { "normalize": "standard" } | ||
| } | ||
| }, | ||
| "sklearn knn parameters": { | ||
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@@ -10,7 +10,7 @@ | |
| "random_state": 42 | ||
| }, | ||
| "preprocessing_kwargs": { | ||
| "normalize": "standard" | ||
| } | ||
| }, | ||
| "bench": { "n_runs": 5 }, | ||
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@@ -4,14 +4,19 @@ | |||||||||
| "high-load dbscan datasets": { | ||||||||||
| "data": [ | ||||||||||
| { | ||||||||||
| "dataset" : "cifar", | ||||||||||
| "split_kwargs": { "ignore" : true }, | ||||||||||
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| "preprocessing_kwargs": { "normalize": "mean" } | ||||||||||
| }, | ||||||||||
| { | ||||||||||
| "dataset": ["road_network", "covtype"], | ||||||||||
| "split_kwargs": { "ignore": true }, | ||||||||||
| "preprocessing_kwargs": { "normalize": "standard" } | ||||||||||
| }, | ||||||||||
| { | ||||||||||
| "dataset": "susy", | ||||||||||
| "split_kwargs": { "train_size": 800000 }, | ||||||||||
| "preprocessing_kwargs": { "normalize": "standard" } | ||||||||||
| }, | ||||||||||
| { | ||||||||||
| "source": "make_blobs", | ||||||||||
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|---|---|---|
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@@ -15,7 +15,7 @@ | |
| "data": { | ||
| "dataset": ["susy", "hepmass"], | ||
| "split_kwargs": { "ignore": true }, | ||
| "preprocessing_kwargs": { "normalize": "standard" } | ||
| } | ||
| }, | ||
| { | ||
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@@ -37,7 +37,8 @@ | |
| { | ||
| "data": { | ||
| "dataset": "cifar", | ||
| "split_kwargs": { "ignore": true }, | ||
| "preprocessing_kwargs": { "normalize": "mean" } | ||
| } | ||
| } | ||
| ] | ||
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@@ -31,7 +31,7 @@ | |
| "susy" | ||
| ], | ||
| "preprocessing_kwargs": { | ||
| "normalize": "standard" | ||
| }, | ||
| "split_kwargs": { "ignore": true } | ||
| } | ||
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@@ -28,7 +28,7 @@ | |
| "data": { | ||
| "dataset": "mnist", | ||
| "split_kwargs": { "ignore": true }, | ||
| "preprocessing_kwargs": { "normalize": null } | ||
| }, | ||
| "algorithm": { "estimator_params": { "C": 1.0, "kernel": ["poly", "rbf"] } } | ||
| } | ||
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@@ -16,9 +16,16 @@ | |
| }, | ||
| { | ||
| "data": { | ||
| "dataset": ["sensit", "mnist"], | ||
| "split_kwargs": { "ignore": true } | ||
| } | ||
| }, | ||
| { | ||
| "data": { | ||
| "dataset" : "cifar", | ||
| "split_kwargs": { "ignore" : true }, | ||
| "preprocessing_kwargs": { "normalize": "mean" } | ||
| } | ||
| } | ||
| ] | ||
| }, | ||
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@@ -6,6 +6,7 @@ pandas | |
| tabulate | ||
| fastparquet | ||
| h5py | ||
| openml | ||
| openpyxl | ||
| tqdm | ||
| psutil | ||
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@@ -334,6 +334,48 @@ def verify_patching(stream: io.StringIO, function_name) -> bool: | |
| return acceleration_lines > 0 and fallback_lines == 0 | ||
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| def validate_estimator_params(estimator_class, estimator_params: Dict) -> Dict: | ||
| """Validates parameters and returns only those supported by the estimator. | ||
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| Args: | ||
| estimator_class: The estimator class to validate against | ||
| estimator_params: Dictionary of parameters to validate | ||
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| Returns: | ||
| Dictionary with only valid parameters | ||
| """ | ||
| try: | ||
| init_signature = inspect.signature(estimator_class.__init__) | ||
| valid_params = set(init_signature.parameters.keys()) - {"self"} | ||
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| # Check if estimator accepts **kwargs | ||
| has_var_keyword = any( | ||
| param.kind == inspect.Parameter.VAR_KEYWORD | ||
| for param in init_signature.parameters.values() | ||
| ) | ||
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| # If accepts **kwargs, return all params | ||
| if has_var_keyword: | ||
| return estimator_params | ||
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| # Filter out invalid params and warn | ||
| filtered_params = {} | ||
| for param_name, param_value in estimator_params.items(): | ||
| if param_name in valid_params: | ||
| filtered_params[param_name] = param_value | ||
| else: | ||
| logger.warning( | ||
| f"Parameter '{param_name}' is not supported by " | ||
| f"{estimator_class.__name__} and will be ignored" | ||
| ) | ||
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| return filtered_params | ||
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| except Exception as e: | ||
| logger.debug(f"Could not validate parameters for {estimator_class.__name__}: {e}") | ||
| return estimator_params | ||
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| def create_online_function(method_instance, data_args, batch_size): | ||
| n_batches = data_args[0].shape[0] // batch_size | ||
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@@ -491,6 +533,9 @@ def main(bench_case: BenchCase, filters: List[BenchCase]): | |
| bench_case, "algorithm:estimator_params", dict() | ||
| ) | ||
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| # validate and filter estimator parameters | ||
| estimator_params = validate_estimator_params(estimator_class, estimator_params) | ||
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| # get estimator methods for measurement | ||
| estimator_methods = get_estimator_methods(bench_case) | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -23,7 +23,12 @@ | |
| import pandas as pd | ||
| from scipy.sparse import csr_matrix | ||
| from sklearn.model_selection import train_test_split | ||
| from sklearn.preprocessing import ( | ||
| MinMaxScaler, | ||
| OneHotEncoder, | ||
| OrdinalEncoder, | ||
| StandardScaler, | ||
| ) | ||
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| from ..utils.custom_types import Array | ||
| from ..utils.logger import logger | ||
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@@ -167,7 +172,7 @@ def preprocess_x( | |
| x: Array, | ||
| replace_nan="auto", | ||
| category_encoding="ordinal", | ||
| normalize=None, | ||
| force_for_sparse=True, | ||
| **kwargs, | ||
| ) -> Array: | ||
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@@ -219,9 +224,18 @@ def preprocess_x( | |
| pass | ||
| else: | ||
| logger.warning(f'Unknown "{category_encoding}" category encoding type.') | ||
| # Normalization | ||
| if normalize: | ||
| if normalize == "standard": | ||
| scaler = StandardScaler(with_mean=True, with_std=True) | ||
| elif normalize == "mean": | ||
| scaler = StandardScaler(with_mean=True, with_std=False) | ||
| elif normalize == "minmax": | ||
| scaler = MinMaxScaler(feature_range=(0, 1)) | ||
| else: | ||
| logger.warning(f'Unknown "{normalize}" normalization type.') | ||
| if scaler is not None: | ||
| return pd.DataFrame(scaler.fit_transform(x), columns=x.columns, index=x.index) | ||
| if return_type == np.ndarray: | ||
| return x.values | ||
| else: | ||
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Seems like this is the only case where benchmark behavior changes - is it intended?
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I think it was done for a reason but let me check the convergence for both options