causalml/metrics/visualize.py [347:387]:
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):
    """Get TMLE based average uplifts of model estimates of segments.

    Args:
        df (pandas.DataFrame): a data frame with model estimates and actual data as columns
        inferenece_col (list of str): a list of columns that used in learner for inference
        learner (optional): a model used by TMLE to estimate the outcome
        outcome_col (str, optional): the column name for the actual outcome
        treatment_col (str, optional): the column name for the treatment indicator (0 or 1)
        p_col (str, optional): the column name for propensity score
        n_segment (int, optional): number of segment that TMLE will estimated for each
        cv (sklearn.model_selection._BaseKFold, optional): sklearn CV object
        calibrate_propensity (bool, optional): whether calibrate propensity score or not
        ci (bool, optional): whether return confidence intervals for ATE or not
    Returns:
        (pandas.DataFrame): cumulative gains of model estimates based of TMLE
    """
    assert (
        (outcome_col in df.columns and df[outcome_col].notnull().all())
        and (treatment_col in df.columns and df[treatment_col].notnull().all())
        or (p_col in df.columns and df[p_col].notnull().all())
    ), "{outcome_col} and {treatment_col}, or {p_col} should be present without null.".format(
        outcome_col=outcome_col,
        treatment_col=treatment_col,
        p_col=p_col,
    )

    inference_col = [x for x in inference_col if x in df.columns]

    # Initialize TMLE
    tmle = TMLELearner(learner, cv=cv, calibrate_propensity=calibrate_propensity)
    ate_all, ate_all_lb, ate_all_ub = tmle.estimate_ate(
        X=df[inference_col], p=df[p_col], treatment=df[treatment_col], y=df[outcome_col]
    )

    df = df.copy()
    model_names = [
        x
        for x in df.columns
        if x not in [outcome_col, treatment_col, p_col] + inference_col
    ]
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causalml/metrics/visualize.py [460:500]:
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):
    """Get TMLE based Qini of model estimates by segments.

    Args:
        df (pandas.DataFrame): a data frame with model estimates and actual data as columns
        inferenece_col (list of str): a list of columns that used in learner for inference
        learner(optional): a model used by TMLE to estimate the outcome
        outcome_col (str, optional): the column name for the actual outcome
        treatment_col (str, optional): the column name for the treatment indicator (0 or 1)
        p_col (str, optional): the column name for propensity score
        n_segment (int, optional): number of segment that TMLE will estimated for each
        cv (sklearn.model_selection._BaseKFold, optional): sklearn CV object
        calibrate_propensity (bool, optional): whether calibrate propensity score or not
        ci (bool, optional): whether return confidence intervals for ATE or not
    Returns:
        (pandas.DataFrame): cumulative gains of model estimates based of TMLE
    """
    assert (
        (outcome_col in df.columns and df[outcome_col].notnull().all())
        and (treatment_col in df.columns and df[treatment_col].notnull().all())
        or (p_col in df.columns and df[p_col].notnull().all())
    ), "{outcome_col} and {treatment_col}, or {p_col} should be present without null.".format(
        outcome_col=outcome_col,
        treatment_col=treatment_col,
        p_col=p_col,
    )

    inference_col = [x for x in inference_col if x in df.columns]

    # Initialize TMLE
    tmle = TMLELearner(learner, cv=cv, calibrate_propensity=calibrate_propensity)
    ate_all, ate_all_lb, ate_all_ub = tmle.estimate_ate(
        X=df[inference_col], p=df[p_col], treatment=df[treatment_col], y=df[outcome_col]
    )

    df = df.copy()
    model_names = [
        x
        for x in df.columns
        if x not in [outcome_col, treatment_col, p_col] + inference_col
    ]
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