local.py

from __future__ import annotations

import numpy as np
from typing import Tuple
from dte_adj.stratified import (
    SimpleStratifiedDistributionEstimator,
    AdjustedStratifiedDistributionEstimator,
)
from dte_adj.util import ArrayLike, compute_ldte, compute_lpte, _convert_to_ndarray


class SimpleLocalDistributionEstimator(SimpleStratifiedDistributionEstimator):
    """
    A class for computing Local Distribution Treatment Effects (LDTE) and Local Probability
    Treatment Effects (LPTE) using simple empirical estimation.

    This estimator computes treatment effects that are weighted by treatment propensity
    within each stratum, providing estimates that are locally robust to treatment assignment
    heterogeneity across strata. It uses empirical methods without ML adjustment.
    """

    def __init__(self):
        """
        Initializes the SimpleLocalDistributionEstimator.

        Returns:
            SimpleLocalDistributionEstimator: An instance of the estimator.
        """
        super().__init__()

    def fit(
        self,
        covariates: ArrayLike,
        treatment_arms: ArrayLike,
        treatment_indicator: ArrayLike,
        outcomes: ArrayLike,
        strata: ArrayLike,
    ) -> SimpleLocalDistributionEstimator:
        """
        Train the SimpleLocalDistributionEstimator.

        Args:
            covariates: Pre-treatment covariates.
            treatment_arms: Treatment assignment variable (Z).
            treatment_indicator: Treatment indicator variable (D).
            outcomes: Scalar-valued observed outcome.
            strata: Stratum indicators.

        Returns:
            SimpleLocalDistributionEstimator: The fitted estimator.
        """
        treatment_indicator = _convert_to_ndarray(treatment_indicator)
        super().fit(covariates, treatment_arms, outcomes, strata)
        self.treatment_indicator = treatment_indicator

        return self

    def predict_ldte(
        self,
        target_treatment_arm: int,
        control_treatment_arm: int,
        locations: np.ndarray,
        alpha: float = 0.05,
        verbose: bool = True,
    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        """
        Compute Local Distribution Treatment Effects (LDTE).

        LDTE measures the difference in cumulative distribution functions between treatment groups
        weighted by treatment propensity within each stratum. This provides estimates that are
        locally robust to treatment assignment heterogeneity across strata.

        Args:
            target_treatment_arm (int): The index of the treatment arm of the treatment group.
            control_treatment_arm (int): The index of the treatment arm of the control group.
            locations (np.ndarray): Scalar values to be used for computing the cumulative distribution.
            alpha (float, optional): Significance level of the confidence bound. Defaults to 0.05.
            verbose (bool, optional): Whether to display a progress bar. Defaults to True.

        Returns:
            Tuple[np.ndarray, np.ndarray, np.ndarray]: A tuple containing:
                - Expected LDTEs (np.ndarray): Local treatment effect estimates at each location
                - Lower bounds (np.ndarray): Lower confidence interval bounds
                - Upper bounds (np.ndarray): Upper confidence interval bounds

        Example:
            .. code-block:: python

                import numpy as np
                from sklearn.linear_model import LogisticRegression
                from dte_adj import AdjustedLocalDistributionEstimator

                # Generate sample data with strata
                np.random.seed(42)
                X = np.random.randn(1000, 5)
                strata = np.random.choice([0, 1], size=1000)  # Binary strata
                D = np.random.binomial(1, 0.3 + 0.4 * strata, 1000)  # Treatment depends on strata
                Y = X[:, 0] + 2 * D + strata + np.random.randn(1000)

                # Fit local estimator
                base_model = LogisticRegression()
                estimator = AdjustedLocalDistributionEstimator(base_model)
                estimator.fit(X, D, D, Y, strata)  # treatment_arms = treatment_indicator for binary case

                # Compute LDTE
                locations = np.linspace(Y.min(), Y.max(), 20)
                ldte, lower, upper = estimator.predict_ldte(
                    target_treatment_arm=1,
                    control_treatment_arm=0,
                    locations=locations
                )

                print(f"LDTE shape: {ldte.shape}")  # Should match locations.shape
                print(f"Average LDTE: {ldte.mean():.3f}")
        """
        return compute_ldte(
            self,
            target_treatment_arm,
            control_treatment_arm,
            locations,
            alpha,
            verbose,
        )

    def predict_lpte(
        self,
        target_treatment_arm: int,
        control_treatment_arm: int,
        locations: np.ndarray,
        alpha: float = 0.05,
        verbose: bool = True,
    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        """
        Compute Local Probability Treatment Effects (LPTE).

        LPTE measures the difference in probability mass between treatment groups for intervals
        defined by consecutive location pairs, weighted by treatment propensity within each stratum.
        This provides locally robust estimates of treatment effects on interval probabilities.

        Args:
            target_treatment_arm (int): The index of the treatment arm of the treatment group.
            control_treatment_arm (int): The index of the treatment arm of the control group.
            locations (np.ndarray): Scalar values defining interval boundaries for probability computation.
                For each interval (locations[i], locations[i+1]], the LPTE is computed.
            alpha (float, optional): Significance level of the confidence bound. Defaults to 0.05.
            verbose (bool, optional): Whether to display a progress bar. Defaults to True.

        Returns:
            Tuple[np.ndarray, np.ndarray, np.ndarray]: A tuple containing:
                - Expected LPTEs (np.ndarray): Local treatment effect estimates for each interval,
                  shape (len(locations)-1,)
                - Lower bounds (np.ndarray): Lower confidence interval bounds
                - Upper bounds (np.ndarray): Upper confidence interval bounds

        Example:
            .. code-block:: python

                import numpy as np
                from dte_adj import SimpleLocalDistributionEstimator

                # Generate sample data with strata
                np.random.seed(42)
                X = np.random.randn(1000, 5)
                strata = np.random.choice([0, 1], size=1000)  # Binary strata
                Z = np.random.binomial(1, 0.5, 1000)  # Treatment assignment
                D = np.random.binomial(1, 0.3 + 0.4 * Z, 1000)  # Treatment receipt
                Y = X[:, 0] + 2 * D + strata + np.random.randn(1000)

                # Fit local estimator
                estimator = SimpleLocalDistributionEstimator()
                estimator.fit(X, Z, D, Y, strata)

                # Define interval boundaries
                locations = np.array([-2, -1, 0, 1, 2])  # Creates intervals: (-2,-1], (-1,0], (0,1], (1,2]

                # Compute LPTE
                lpte, lower, upper = estimator.predict_lpte(
                    target_treatment_arm=1,
                    control_treatment_arm=0,
                    locations=locations
                )

                print(f"LPTE shape: {lpte.shape}")  # Should be (4,) for 4 intervals
                print(f"Interval effects: {lpte}")
        """
        return compute_lpte(
            self,
            target_treatment_arm,
            control_treatment_arm,
            locations,
            alpha,
            verbose,
        )


class AdjustedLocalDistributionEstimator(AdjustedStratifiedDistributionEstimator):
    """
    A class for computing Local Distribution Treatment Effects (LDTE) and Local Probability
    Treatment Effects (LPTE) using machine learning adjustment.

    This estimator combines the benefits of ML adjustment with local treatment effect estimation,
    providing precise estimates of treatment effects that are weighted by treatment propensity
    within each stratum. It uses cross-fitting to avoid overfitting issues.
    """

    def fit(
        self,
        covariates: ArrayLike,
        treatment_arms: ArrayLike,
        treatment_indicator: ArrayLike,
        outcomes: ArrayLike,
        strata: ArrayLike,
    ) -> AdjustedLocalDistributionEstimator:
        """
        Train the AdjustedLocalDistributionEstimator.

        Args:
            covariates: Pre-treatment covariates.
            treatment_arms: Treatment assignment variable (Z).
            treatment_indicator: Treatment indicator variable (D).
            outcomes: Scalar-valued observed outcome.
            strata: Stratum indicators.

        Returns:
            AdjustedLocalDistributionEstimator: The fitted estimator.
        """
        treatment_indicator = _convert_to_ndarray(treatment_indicator)
        super().fit(covariates, treatment_arms, outcomes, strata)
        self.treatment_indicator = treatment_indicator

        return self

    def predict_ldte(
        self,
        target_treatment_arm: int,
        control_treatment_arm: int,
        locations: np.ndarray,
        alpha: float = 0.05,
        verbose: bool = True,
    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        """
        Compute Local Distribution Treatment Effects (LDTE) using ML adjustment.

        This method combines machine learning adjustment with local treatment effect estimation
        to provide precise, locally robust estimates of distributional treatment effects.

        Args:
            target_treatment_arm (int): The index of the treatment arm of the treatment group.
            control_treatment_arm (int): The index of the treatment arm of the control group.
            locations (np.ndarray): Scalar values to be used for computing the cumulative distribution.
            alpha (float, optional): Significance level of the confidence bound. Defaults to 0.05.
            verbose (bool, optional): Whether to display a progress bar. Defaults to True.

        Returns:
            Tuple[np.ndarray, np.ndarray, np.ndarray]: A tuple containing:
                - Expected LDTEs (np.ndarray): Local treatment effect estimates at each location
                - Lower bounds (np.ndarray): Lower confidence interval bounds
                - Upper bounds (np.ndarray): Upper confidence interval bounds

        Example:
            .. code-block:: python

                import numpy as np
                from sklearn.ensemble import RandomForestClassifier
                from dte_adj import AdjustedLocalDistributionEstimator

                # Generate confounded data with strata
                np.random.seed(42)
                X = np.random.randn(1000, 5)
                strata = np.random.choice([0, 1], size=1000)
                # Treatment assignment depends on covariates
                Z_prob = 1 / (1 + np.exp(-(X[:, 0] + X[:, 1] + strata)))
                Z = np.random.binomial(1, Z_prob, 1000)
                D = np.random.binomial(1, 0.3 + 0.4 * Z, 1000)
                Y = X.sum(axis=1) + 2 * D + strata + np.random.randn(1000)

                # Fit adjusted local estimator
                base_model = RandomForestClassifier(n_estimators=100)
                estimator = AdjustedLocalDistributionEstimator(base_model, folds=3)
                estimator.fit(X, Z, D, Y, strata)

                # Compute LDTE with ML adjustment
                locations = np.linspace(Y.min(), Y.max(), 20)
                ldte, lower, upper = estimator.predict_ldte(
                    target_treatment_arm=1,
                    control_treatment_arm=0,
                    locations=locations
                )

                print(f"Adjusted LDTE: {ldte.mean():.3f}")
        """
        return compute_ldte(
            self,
            target_treatment_arm,
            control_treatment_arm,
            locations,
            alpha,
            verbose,
        )

    def predict_lpte(
        self,
        target_treatment_arm: int,
        control_treatment_arm: int,
        locations: np.ndarray,
        alpha: float = 0.05,
        verbose: bool = True,
    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        """
        Compute Local Probability Treatment Effects (LPTE) using ML adjustment.

        This method combines machine learning adjustment with local treatment effect estimation
        to provide precise estimates of treatment effects on interval probabilities.

        Args:
            target_treatment_arm (int): The index of the treatment arm of the treatment group.
            control_treatment_arm (int): The index of the treatment arm of the control group.
            locations (np.ndarray): Scalar values defining interval boundaries for probability computation.
                For each interval (locations[i], locations[i+1]], the LPTE is computed.
            alpha (float, optional): Significance level of the confidence bound. Defaults to 0.05.
            verbose (bool, optional): Whether to display a progress bar. Defaults to True.

        Returns:
            Tuple[np.ndarray, np.ndarray, np.ndarray]: A tuple containing:
                - Expected LPTEs (np.ndarray): Local treatment effect estimates for each interval,
                  shape (len(locations)-1,)
                - Lower bounds (np.ndarray): Lower confidence interval bounds
                - Upper bounds (np.ndarray): Upper confidence interval bounds

        Example:
            .. code-block:: python

                import numpy as np
                from sklearn.linear_model import LogisticRegression
                from dte_adj import AdjustedLocalDistributionEstimator

                # Generate confounded data with strata
                np.random.seed(42)
                X = np.random.randn(1000, 5)
                strata = np.random.choice([0, 1], size=1000)
                # Treatment assignment depends on covariates
                Z_prob = 1 / (1 + np.exp(-(X[:, 0] + strata)))
                Z = np.random.binomial(1, Z_prob, 1000)
                D = np.random.binomial(1, 0.3 + 0.4 * Z, 1000)
                Y = X.sum(axis=1) + 2 * D + strata + np.random.randn(1000)

                # Fit adjusted local estimator
                base_model = LogisticRegression()
                estimator = AdjustedLocalDistributionEstimator(base_model, folds=3)
                estimator.fit(X, Z, D, Y, strata)

                # Define interval boundaries
                locations = np.array([-2, -1, 0, 1, 2])

                # Compute LPTE with ML adjustment
                lpte, lower, upper = estimator.predict_lpte(
                    target_treatment_arm=1,
                    control_treatment_arm=0,
                    locations=locations
                )

                print(f"Adjusted LPTE: {lpte}")
        """
        return compute_lpte(
            self,
            target_treatment_arm,
            control_treatment_arm,
            locations,
            alpha,
            verbose,
        )