# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/05i_meta.sensitivity.ipynb.

# %% auto 0
__all__ = ['logger', 'one_sided', 'alignment', 'one_sided_att', 'alignment_att', 'Sensitivity', 'SensitivityPlaceboTreatment',
           'SensitivityRandomCause', 'SensitivityRandomReplace', 'SensitivitySubsetData', 'SensitivitySelectionBias']

# %% ../../nbs/05i_meta.sensitivity.ipynb 4
# REFERENCE: https://github.com/uber/causalml

# Copyright 2019 Uber Technology, Inc.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#     http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import logging
import numpy as np
import pandas as pd
from collections import defaultdict
import matplotlib.pyplot as plt
from importlib import import_module

logger = logging.getLogger('sensitivity')


def one_sided(alpha, p, treatment):
    """One sided confounding function.
    Reference:  Blackwell, Matthew. "A selection bias approach to sensitivity analysis
    for causal effects." Political Analysis 22.2 (2014): 169-182.
    https://www.mattblackwell.org/files/papers/causalsens.pdf

    Args:
        alpha (np.array): a confounding values vector
        p (np.array): a propensity score vector between 0 and 1
        treatment (np.array): a treatment vector (1 if treated, otherwise 0)
    """
    assert p.shape[0] == treatment.shape[0]
    adj = alpha * (1 - p) * treatment - alpha * p * (1 - treatment)
    return adj


def alignment(alpha, p, treatment):
    """Alignment confounding function.
    Reference:  Blackwell, Matthew. "A selection bias approach to sensitivity analysis
    for causal effects." Political Analysis 22.2 (2014): 169-182.
    https://www.mattblackwell.org/files/papers/causalsens.pdf

    Args:
        alpha (np.array): a confounding values vector
        p (np.array): a propensity score vector between 0 and 1
        treatment (np.array): a treatment vector (1 if treated, otherwise 0)
    """

    assert p.shape[0] == treatment.shape[0]
    adj = alpha * (1 - p) * treatment + alpha * p * (1 - treatment)
    return adj


def one_sided_att(alpha, p, treatment):
    """One sided confounding function for the average effect of the treatment among the treated units (ATT)

    Reference:  Blackwell, Matthew. "A selection bias approach to sensitivity analysis
    for causal effects." Political Analysis 22.2 (2014): 169-182.
    https://www.mattblackwell.org/files/papers/causalsens.pdf

    Args:
        alpha (np.array): a confounding values vector
        p (np.array): a propensity score vector between 0 and 1
        treatment (np.array): a treatment vector (1 if treated, otherwise 0)
    """
    assert p.shape[0] == treatment.shape[0]
    adj = alpha * (1 - treatment)
    return adj


def alignment_att(alpha, p, treatment):
    """Alignment confounding function for the average effect of the treatment among the treated units (ATT)

    Reference:  Blackwell, Matthew. "A selection bias approach to sensitivity analysis
    for causal effects." Political Analysis 22.2 (2014): 169-182.
    https://www.mattblackwell.org/files/papers/causalsens.pdf

    Args:
        alpha (np.array): a confounding values vector
        p (np.array): a propensity score vector between 0 and 1
        treatment (np.array): a treatment vector (1 if treated, otherwise 0)
    """
    assert p.shape[0] == treatment.shape[0]
    adj = alpha * (1 - treatment)
    return adj


class Sensitivity(object):
    """ A Sensitivity Check class to support Placebo Treatment, Irrelevant Additional Confounder
    and Subset validation refutation methods to verify causal inference.

    Reference: https://github.com/microsoft/dowhy/blob/master/dowhy/causal_refuters/
    """

    def __init__(self, df, inference_features, p_col, treatment_col, outcome_col,
                learner, *args, **kwargs):
        """Initialize.

        Args:
            df (pd.DataFrame): input data frame
            inferenece_features (list of str): a list of columns that used in learner for inference
            p_col (str): column name of propensity score
            treatment_col (str): column name of whether in treatment of control
            outcome_col (str): column name of outcome
            learner (model): a model to estimate outcomes and treatment effects
        """

        self.df = df
        self.inference_features = inference_features
        self.p_col = p_col
        self.treatment_col = treatment_col
        self.outcome_col = outcome_col
        self.learner = learner

    def get_prediction(self, X, p, treatment, y):
        """Return the treatment effects prediction.

        Args:
            X (np.matrix): a feature matrix
            p (np.array): a propensity score vector between 0 and 1
            treatment (np.array): a treatment vector (1 if treated, otherwise 0)
            y (np.array): an outcome vector
        Returns:
            (numpy.ndarray): Predictions of treatment effects
        """

        learner = self.learner
        try:
            preds = learner.fit_predict(X=X, p=p, treatment=treatment, y=y).flatten()
        except TypeError:
            preds = learner.fit_predict(X=X, treatment=treatment, y=y).flatten()
        return preds

    def get_ate_ci(self, X, p, treatment, y):
        """Return the confidence intervals for treatment effects prediction.

        Args:
            X (np.matrix): a feature matrix
            p (np.array): a propensity score vector between 0 and 1
            treatment (np.array): a treatment vector (1 if treated, otherwise 0)
            y (np.array): an outcome vector
        Returns:
            (numpy.ndarray): Mean and confidence interval (LB, UB) of the ATE estimate.
        """

        learner = self.learner
        from causalnlp.meta.tlearner import BaseTLearner
        if isinstance(learner, BaseTLearner):
            ate, ate_lower, ate_upper = learner.estimate_ate(X=X, treatment=treatment, y=y)
        else:
            try:
                ate, ate_lower, ate_upper = learner.estimate_ate(X=X, p=p, treatment=treatment, y=y)
            except TypeError:
                ate, ate_lower, ate_upper = learner.estimate_ate(X=X, treatment=treatment, y=y, return_ci=True)
        return ate[0], ate_lower[0], ate_upper[0]

    @staticmethod
    def get_class_object(method_name, *args, **kwargs):
        """Return class object based on input method
        Args:
            method_name (list of str): a list of sensitivity analysis method
        Returns:
            (class): Sensitivy Class
        """

        method_list = ['Placebo Treatment', 'Random Cause', 'Subset Data', 'Random Replace', 'Selection Bias']
        class_name = 'Sensitivity' + method_name.replace(' ', '')

        try:
            getattr(import_module('causalnlp.meta.sensitivity'), class_name)
            return getattr(import_module('causalnlp.meta.sensitivity'), class_name)
        except AttributeError:
            raise AttributeError('{} is not an existing method for sensitiviy analysis.'.format(method_name) +
                              ' Select one of {}'.format(method_list))

    def sensitivity_analysis(self, methods, sample_size=None,
                             confound='one_sided', alpha_range=None):
        """Return the sensitivity data by different method

        Args:
            method (list of str): a list of sensitivity analysis method
            sample_size (float, optional): ratio for subset the original data
            confound (string, optional): the name of confouding function
            alpha_range (np.array, optional): a parameter to pass the confounding function

        Returns:
            X (np.matrix): a feature matrix
            p (np.array): a propensity score vector between 0 and 1
            treatment (np.array): a treatment vector (1 if treated, otherwise 0)
            y (np.array): an outcome vector
        """
        if alpha_range is None:
            y = self.df[self.outcome_col]
            iqr = y.quantile(.75) - y.quantile(.25)
            alpha_range = np.linspace(-iqr/2, iqr/2, 11)
            if 0 not in alpha_range:
                alpha_range = np.append(alpha_range, 0)
        else:
            alpha_range = alpha_range

        alpha_range.sort()

        summary_df = pd.DataFrame(columns=['Method', 'ATE', 'New ATE', 'New ATE LB', 'New ATE UB'])
        for method in methods:
            sens = self.get_class_object(method)
            sens = sens(self.df, self.inference_features, self.p_col, self.treatment_col, self.outcome_col,
                        self.learner, sample_size=sample_size, confound=confound, alpha_range=alpha_range)

            if method == 'Subset Data':
                method = method + '(sample size @{})'.format(sample_size)

            sens_df = sens.summary(method=method)
            summary_df = summary_df.append(sens_df)

        return summary_df

    def summary(self, method):
        """Summary report
        Args:
            method_name (str): sensitivity analysis method

        Returns:
            (pd.DataFrame): a summary dataframe
        """
        method_name = method

        X = self.df[self.inference_features].values
        p = self.df[self.p_col].values if self.p_col is not None else None
        treatment = self.df[self.treatment_col].values
        y = self.df[self.outcome_col].values

        preds = self.get_prediction(X, p, treatment, y)
        ate = preds.mean()
        ate_new, ate_new_lower, ate_new_upper = self.sensitivity_estimate()

        sensitivity_summary = pd.DataFrame([method_name, ate,
                                            ate_new, ate_new_lower, ate_new_upper]).T
        sensitivity_summary.columns = ['Method', 'ATE', 'New ATE', 'New ATE LB', 'New ATE UB']
        return sensitivity_summary

    def sensitivity_estimate(self):
        raise NotImplementedError


class SensitivityPlaceboTreatment(Sensitivity):
    """Replaces the treatment variable with a new variable randomly generated.
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def sensitivity_estimate(self):
        """Summary report
        Args:
           return_ci (str): sensitivity analysis method

        Returns:
            (pd.DataFrame): a summary dataframe
        """
        num_rows = self.df.shape[0]

        X = self.df[self.inference_features].values
        p = self.df[self.p_col].values if self.p_col is not None else None
        treatment_new = np.random.randint(2, size=num_rows)
        y = self.df[self.outcome_col].values

        ate_new, ate_new_lower, ate_new_upper = self.get_ate_ci(X, p, treatment_new, y)
        return ate_new, ate_new_lower, ate_new_upper


class SensitivityRandomCause(Sensitivity):
    """Adds an irrelevant random covariate to the dataframe.
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def sensitivity_estimate(self):
        num_rows = self.df.shape[0]
        new_data = np.random.randn(num_rows)

        X = self.df[self.inference_features].values
        p = self.df[self.p_col].values if self.p_col is not None else None
        treatment = self.df[self.treatment_col].values
        y = self.df[self.outcome_col].values
        X_new = np.hstack((X, new_data.reshape((-1, 1))))

        ate_new, ate_new_lower, ate_new_upper = self.get_ate_ci(X_new, p, treatment, y)
        return ate_new, ate_new_lower, ate_new_upper


class SensitivityRandomReplace(Sensitivity):
    """Replaces a random covariate with an irrelevant variable.
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if 'replaced_feature' not in kwargs:
            replaced_feature_index = np.random.randint(len(self.inference_features))
            self.replaced_feature = self.inference_features[replaced_feature_index]
        else:
            self.replaced_feature = kwargs["replaced_feature"]

    def sensitivity_estimate(self):
        """Replaces a random covariate with an irrelevant variable.
        """

        logger.info('Replace feature {} with an random irrelevant variable'.format(self.replaced_feature))
        df_new = self.df.copy()
        num_rows = self.df.shape[0]
        df_new[self.replaced_feature] = np.random.randn(num_rows)

        X_new = df_new[self.inference_features].values
        p_new = df_new[self.p_col].values if self.p_col is not None else None
        treatment_new = df_new[self.treatment_col].values
        y_new = df_new[self.outcome_col].values

        ate_new, ate_new_lower, ate_new_upper = self.get_ate_ci(X_new, p_new, treatment_new, y_new)
        return ate_new, ate_new_lower, ate_new_upper


class SensitivitySubsetData(Sensitivity):
    """Takes a random subset of size sample_size of the data.
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.sample_size = kwargs["sample_size"]
        assert (self.sample_size is not None)

    def sensitivity_estimate(self):
        df_new = self.df.sample(frac=self.sample_size).copy()

        X_new = df_new[self.inference_features].values
        p_new = df_new[self.p_col].values if self.p_col is not None else None
        treatment_new = df_new[self.treatment_col].values
        y_new = df_new[self.outcome_col].values

        ate_new, ate_new_lower, ate_new_upper = self.get_ate_ci(X_new, p_new, treatment_new, y_new)
        return ate_new, ate_new_lower, ate_new_upper


class SensitivitySelectionBias(Sensitivity):
    """Reference:

    [1] Blackwell, Matthew. "A selection bias approach to sensitivity analysis
    for causal effects." Political Analysis 22.2 (2014): 169-182.
    https://www.mattblackwell.org/files/papers/causalsens.pdf

    [2] Confouding parameter alpha_range using the same range as in:
    https://github.com/mattblackwell/causalsens/blob/master/R/causalsens.R

    """

    def __init__(self, *args, confound='one_sided', alpha_range=None,
                 sensitivity_features=None, **kwargs):
        super().__init__(*args, **kwargs)
        """Initialize.

        Args:
            confound (string): the name of confouding function
            alpha_range (np.array): a parameter to pass the confounding function
            sensitivity_features (list of str): ): a list of columns that to check each individual partial r-square
        """

        logger.info('Only works for linear outcome models right now. Check back soon.')
        confounding_functions = {'one_sided': one_sided,
                                 'alignment': alignment,
                                 'one_sided_att': one_sided_att,
                                 'alignment_att': alignment_att}

        try:
            confound_func = confounding_functions[confound]
        except KeyError:
            raise NotImplementedError(f'Confounding function, {confound} is not implemented. \
                                        Use one of {confounding_functions.keys()}')

        self.confound = confound_func

        if sensitivity_features is None:
            self.sensitivity_features = self.inference_features
        else:
            self.sensitivity_features = sensitivity_features

        if alpha_range is None:
            y = self.df[self.outcome_col]
            iqr = y.quantile(.75) - y.quantile(.25)
            self.alpha_range = np.linspace(-iqr/2, iqr/2, 11)
            if 0 not in self.alpha_range:
                self.alpha_range = np.append(self.alpha_range, 0)
        else:
            self.alpha_range = alpha_range

        self.alpha_range.sort()

    def causalsens(self):
        alpha_range = self.alpha_range
        confound = self.confound
        df = self.df
        X = df[self.inference_features].values
        p = df[self.p_col].values if self.p_col is not None else None
        treatment = df[self.treatment_col].values
        y = df[self.outcome_col].values

        preds = self.get_prediction(X, p, treatment, y)

        sens_df = pd.DataFrame()
        for a in alpha_range:
            sens = defaultdict(list)
            sens['alpha'] = a
            adj = confound(a, p, treatment)
            preds_adj = y - adj
            s_preds = self.get_prediction(X, p, treatment, preds_adj)
            ate, ate_lb, ate_ub = self.get_ate_ci(X, p, treatment, preds_adj)

            s_preds_residul = preds_adj - s_preds
            sens['rsqs'] = a**2*np.var(treatment)/np.var(s_preds_residul)
            sens['New ATE'] = ate
            sens['New ATE LB'] = ate_lb
            sens['New ATE UB'] = ate_ub
            sens_df = sens_df.append(pd.DataFrame(sens, index=[0]))

        rss = np.sum(np.square(y - preds))
        partial_rsqs = []
        for feature in self.sensitivity_features:
            df_new = df.copy()
            X_new = df_new[self.inference_features].drop(feature, axis=1).copy()
            y_new_preds = self.get_prediction(X_new, p, treatment, y)
            rss_new = np.sum(np.square(y - y_new_preds))
            partial_rsqs.append(((rss_new - rss)/rss))

        partial_rsqs_df = pd.DataFrame([self.sensitivity_features, partial_rsqs]).T
        partial_rsqs_df.columns = ['feature', 'partial_rsqs']

        return sens_df, partial_rsqs_df

    def summary(self, method='Selection Bias'):
        """Summary report for Selection Bias Method
        Args:
            method_name (str): sensitivity analysis method
        Returns:
            (pd.DataFrame): a summary dataframe
        """

        method_name = method
        sensitivity_summary = self.causalsens()[0]
        sensitivity_summary['Method'] = [method_name + ' (alpha@' + str(round(i, 5)) + ', with r-sqaure:'
                                         for i in sensitivity_summary.alpha]
        sensitivity_summary['Method'] = sensitivity_summary['Method'] + sensitivity_summary['rsqs'].round(5).astype(str)
        sensitivity_summary['ATE'] = sensitivity_summary[sensitivity_summary.alpha == 0]['New ATE']
        return sensitivity_summary[['Method', 'ATE', 'New ATE', 'New ATE LB', 'New ATE UB']]

    @staticmethod
    def plot(sens_df, partial_rsqs_df=None, type='raw', ci=False, partial_rsqs=False):
        """Plot the results of a sensitivity analysis against unmeasured
        Args:
            sens_df (pandas.DataFrame): a data frame output from causalsens
            partial_rsqs_d (pandas.DataFrame) : a data frame output from causalsens including partial rsqure
            type (str, optional): the type of plot to draw, 'raw' or 'r.squared' are supported
            ci (bool, optional): whether plot confidence intervals
            partial_rsqs (bool, optional): whether plot partial rsquare results
         """

        if type == 'raw' and not ci:
            fig, ax = plt.subplots()
            y_max = round(sens_df['New ATE UB'].max()*1.1, 4)
            y_min = round(sens_df['New ATE LB'].min()*0.9, 4)
            x_max = round(sens_df.alpha.max()*1.1, 4)
            x_min = round(sens_df.alpha.min()*0.9, 4)
            plt.ylim(y_min, y_max)
            plt.xlim(x_min, x_max)
            ax.plot(sens_df.alpha, sens_df['New ATE'])
        elif type == 'raw' and ci:
            fig, ax = plt.subplots()
            y_max = round(sens_df['New ATE UB'].max()*1.1, 4)
            y_min = round(sens_df['New ATE LB'].min()*0.9, 4)
            x_max = round(sens_df.alpha.max()*1.1, 4)
            x_min = round(sens_df.alpha.min()*0.9, 4)
            plt.ylim(y_min, y_max)
            plt.xlim(x_min, x_max)
            ax.fill_between(sens_df.alpha, sens_df['New ATE LB'], sens_df['New ATE UB'], color='gray', alpha=0.5)
            ax.plot(sens_df.alpha, sens_df['New ATE'])
        elif type == 'r.squared' and ci:
            fig, ax = plt.subplots()
            y_max = round(sens_df['New ATE UB'].max()*1.1, 4)
            y_min = round(sens_df['New ATE LB'].min()*0.9, 4)
            plt.ylim(y_min, y_max)
            ax.fill_between(sens_df.rsqs, sens_df['New ATE LB'], sens_df['New ATE UB'], color='gray', alpha=0.5)
            ax.plot(sens_df.rsqs, sens_df['New ATE'])
            if partial_rsqs:
                plt.scatter(partial_rsqs_df.partial_rsqs,
                        list(sens_df[sens_df.alpha == 0]['New ATE']) * partial_rsqs_df.shape[0],
                        marker='x', color="red", linewidth=10)
        elif type == 'r.squared' and not ci:
            fig, ax = plt.subplots()
            y_max = round(sens_df['New ATE UB'].max()*1.1, 4)
            y_min = round(sens_df['New ATE LB'].min()*0.9, 4)
            plt.ylim(y_min, y_max)
            plt.plot(sens_df.rsqs, sens_df['New ATE'])
            if partial_rsqs:
                plt.scatter(partial_rsqs_df.partial_rsqs,
                        list(sens_df[sens_df.alpha == 0]['New ATE']) * partial_rsqs_df.shape[0],
                        marker='x', color="red", linewidth=10)

    @staticmethod
    def partial_rsqs_confounding(sens_df, feature_name, partial_rsqs_value, range=0.01):
        """Check partial rsqs values of feature corresponding confounding amonunt of ATE
        Args:
            sens_df (pandas.DataFrame): a data frame output from causalsens
            feature_name (str): feature name to check
            partial_rsqs_value (float) : partial rsquare value of feature
            range (float) : range to search from sens_df

        Return: min and max value of confounding amount
        """

        rsqs_dict = []
        for i in sens_df.rsqs:
            if partial_rsqs_value - partial_rsqs_value*range < i < partial_rsqs_value + partial_rsqs_value*range:
                rsqs_dict.append(i)

        if rsqs_dict:
            confounding_min = sens_df[sens_df.rsqs.isin(rsqs_dict)].alpha.min()
            confounding_max = sens_df[sens_df.rsqs.isin(rsqs_dict)].alpha.max()
            logger.info('Only works for linear outcome models right now. Check back soon.')
            logger.info('For feature {} with partial rsquare {} confounding amount with possible values: {}, {}'.format(
                        feature_name, partial_rsqs_value, confounding_min, confounding_max))
            return [confounding_min, confounding_max]
        else:
            logger.info('Cannot find correponding rsquare value within the range for input, please edit confounding', 'values vector or use a larger range and try again')
