Source code for econml.cate_interpreter._interpreters

# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.

import abc
import numbers
import numpy as np
from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier
from sklearn.utils import check_array
from ..policy import PolicyTree
from .._tree_exporter import (_SingleTreeExporterMixin,
                              _CateTreeDOTExporter, _CateTreeMPLExporter,
                              _PolicyTreeDOTExporter, _PolicyTreeMPLExporter)


class _SingleTreeInterpreter(_SingleTreeExporterMixin, metaclass=abc.ABCMeta):

    @abc.abstractmethod
    def interpret(self, cate_estimator, X):
        """
        Interpret a linear CATE estimator when applied to a set of features

        Parameters
        ----------
        cate_estimator : :class:`.LinearCateEstimator`
            The fitted estimator to interpret

        X : array-like
            The features against which to interpret the estimator;
            must be compatible shape-wise with the features used to fit
            the estimator
        """
        pass


[docs]class SingleTreeCateInterpreter(_SingleTreeInterpreter): """ An interpreter for the effect estimated by a CATE estimator Parameters ---------- include_model_uncertainty : bool, optional, default False Whether to include confidence interval information when building a simplified model of the cate model. If set to True, then cate estimator needs to support the `const_marginal_ate_inference` method. uncertainty_level : double, optional, default .1 The uncertainty level for the confidence intervals to be constructed and used in the simplified model creation. If value=alpha then a multitask decision tree will be built such that all samples in a leaf have similar target prediction but also similar alpha confidence intervals. uncertainty_only_on_leaves : bool, optional, default True Whether uncertainty information should be displayed only on leaf nodes. If False, then interpretation can be slightly slower, especially for cate models that have a computationally expensive inference method. splitter : string, optional, default "best" The strategy used to choose the split at each node. Supported strategies are "best" to choose the best split and "random" to choose the best random split. max_depth : int or None, optional, default None The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. min_samples_split : int, float, optional, default 2 The minimum number of samples required to split an internal node: - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a fraction and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. min_samples_leaf : int, float, optional, default 1 The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a fraction and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. min_weight_fraction_leaf : float, optional, default 0. The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. max_features : int, float or {"auto", "sqrt", "log2"}, default=None The number of features to consider when looking for the best split: - If int, then consider `max_features` features at each split. - If float, then `max_features` is a fraction and `int(max_features * n_features)` features are considered at each split. - If "auto", then `max_features=n_features`. - If "sqrt", then `max_features=sqrt(n_features)`. - If "log2", then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. random_state : int, RandomState instance or None, optional, default None If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. max_leaf_nodes : int or None, optional, default None Grow a tree with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. min_impurity_decrease : float, optional, default 0. A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following:: N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child. ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. """
[docs] def __init__(self, *, include_model_uncertainty=False, uncertainty_level=.1, uncertainty_only_on_leaves=True, splitter="best", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.): self.include_uncertainty = include_model_uncertainty self.uncertainty_level = uncertainty_level self.uncertainty_only_on_leaves = uncertainty_only_on_leaves self.criterion = "mse" self.splitter = splitter self.max_depth = max_depth self.min_samples_split = min_samples_split self.min_samples_leaf = min_samples_leaf self.min_weight_fraction_leaf = min_weight_fraction_leaf self.max_features = max_features self.random_state = random_state self.max_leaf_nodes = max_leaf_nodes self.min_impurity_decrease = min_impurity_decrease
[docs] def interpret(self, cate_estimator, X): """ Interpret the heterogeneity of a CATE estimator when applied to a set of features Parameters ---------- cate_estimator : :class:`.LinearCateEstimator` The fitted estimator to interpret X : array-like The features against which to interpret the estimator; must be compatible shape-wise with the features used to fit the estimator Returns ------- self: object instance """ self.tree_model_ = DecisionTreeRegressor(criterion=self.criterion, splitter=self.splitter, max_depth=self.max_depth, min_samples_split=self.min_samples_split, min_samples_leaf=self.min_samples_leaf, min_weight_fraction_leaf=self.min_weight_fraction_leaf, max_features=self.max_features, random_state=self.random_state, max_leaf_nodes=self.max_leaf_nodes, min_impurity_decrease=self.min_impurity_decrease) y_pred = cate_estimator.const_marginal_effect(X) self.tree_model_.fit(X, y_pred.reshape((y_pred.shape[0], -1))) paths = self.tree_model_.decision_path(X) node_dict = {} for node_id in range(paths.shape[1]): mask = paths.getcol(node_id).toarray().flatten().astype(bool) Xsub = X[mask] if (self.include_uncertainty and ((not self.uncertainty_only_on_leaves) or (self.tree_model_.tree_.children_left[node_id] < 0))): res = cate_estimator.const_marginal_ate_inference(Xsub) node_dict[node_id] = {'mean': res.mean_point, 'std': res.std_point, 'ci': res.conf_int_mean(alpha=self.uncertainty_level)} else: cate_node = y_pred[mask] node_dict[node_id] = {'mean': np.mean(cate_node, axis=0), 'std': np.std(cate_node, axis=0)} self.node_dict_ = node_dict return self
def _make_dot_exporter(self, *, out_file, feature_names, treatment_names, max_depth, filled, leaves_parallel, rotate, rounded, special_characters, precision): return _CateTreeDOTExporter(self.include_uncertainty, self.uncertainty_level, out_file=out_file, feature_names=feature_names, treatment_names=treatment_names, max_depth=max_depth, filled=filled, leaves_parallel=leaves_parallel, rotate=rotate, rounded=rounded, special_characters=special_characters, precision=precision) def _make_mpl_exporter(self, *, title, feature_names, treatment_names, max_depth, filled, rounded, precision, fontsize): return _CateTreeMPLExporter(self.include_uncertainty, self.uncertainty_level, title=title, feature_names=feature_names, treatment_names=treatment_names, max_depth=max_depth, filled=filled, rounded=rounded, precision=precision, fontsize=fontsize)
[docs]class SingleTreePolicyInterpreter(_SingleTreeInterpreter): """ An interpreter for a policy estimated based on a CATE estimation Parameters ---------- include_model_uncertainty : bool, optional, default False Whether to include confidence interval information when building a simplified model of the cate model. If set to True, then cate estimator needs to support the `const_marginal_ate_inference` method. uncertainty_level : double, optional, default .1 The uncertainty level for the confidence intervals to be constructed and used in the simplified model creation. If value=alpha then a multitask decision tree will be built such that all samples in a leaf have similar target prediction but also similar alpha confidence intervals. uncertainty_only_on_leaves : bool, optional, default True Whether uncertainty information should be displayed only on leaf nodes. If False, then interpretation can be slightly slower, especially for cate models that have a computationally expensive inference method. risk_level : float or None, optional (default=None) If None then the point estimate of the CATE of every point will be used as the effect of treatment. If any float alpha and risk_seeking=False (default), then the lower end point of an alpha confidence interval of the CATE will be used. Otherwise if risk_seeking=True, then the upper end of an alpha confidence interval will be used. risk_seeking : bool, optional, default False, Whether to use an optimistic or pessimistic value for the effect estimate at a sample point. Used only when risk_level is not None. max_depth : int or None, optional, default None The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. min_samples_split : int, float, optional, default 2 The minimum number of samples required to split an internal node: - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a fraction and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. min_samples_leaf : int, float, optional, default 1 The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a fraction and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. min_weight_fraction_leaf : float, optional, default 0. The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. max_features : int, float or {"auto", "sqrt", "log2"}, default=None The number of features to consider when looking for the best split: - If int, then consider `max_features` features at each split. - If float, then `max_features` is a fraction and `int(max_features * n_features)` features are considered at each split. - If "auto", then `max_features=n_features`. - If "sqrt", then `max_features=sqrt(n_features)`. - If "log2", then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. min_balancedness_tol: float in [0, .5], default=.45 How imbalanced a split we can tolerate. This enforces that each split leaves at least (.5 - min_balancedness_tol) fraction of samples on each side of the split; or fraction of the total weight of samples, when sample_weight is not None. Default value, ensures that at least 5% of the parent node weight falls in each side of the split. Set it to 0.0 for no balancedness and to .5 for perfectly balanced splits. For the formal inference theory to be valid, this has to be any positive constant bounded away from zero. min_impurity_decrease : float, optional, default 0. A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following:: N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child. ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. random_state : int, RandomState instance or None, optional, default None If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Attributes ---------- tree_model_ : :class:`~econml.policy.PolicyTree` The policy tree model that represents the learned policy; available only after :meth:`interpret` has been called. policy_value_ : float The value of applying the learned policy, applied to the sample used with :meth:`interpret` always_treat_value_ : float The value of the policy that always treats all units, applied to the sample used with :meth:`interpret` """
[docs] def __init__(self, *, include_model_uncertainty=False, uncertainty_level=.1, uncertainty_only_on_leaves=True, risk_level=None, risk_seeking=False, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_features=None, min_balancedness_tol=.45, min_impurity_decrease=0., random_state=None): self.include_uncertainty = include_model_uncertainty self.uncertainty_level = uncertainty_level self.uncertainty_only_on_leaves = uncertainty_only_on_leaves self.risk_level = risk_level self.risk_seeking = risk_seeking self.max_depth = max_depth self.min_samples_split = min_samples_split self.min_samples_leaf = min_samples_leaf self.min_weight_fraction_leaf = min_weight_fraction_leaf self.max_features = max_features self.random_state = random_state self.min_impurity_decrease = min_impurity_decrease self.min_balancedness_tol = min_balancedness_tol
[docs] def interpret(self, cate_estimator, X, sample_treatment_costs=None): """ Interpret a policy based on a linear CATE estimator when applied to a set of features Parameters ---------- cate_estimator : :class:`.LinearCateEstimator` The fitted estimator to interpret X : array-like The features against which to interpret the estimator; must be compatible shape-wise with the features used to fit the estimator sample_treatment_costs : array-like, optional The cost of treatment. Can be a scalar or have dimension (n_samples, n_treatments) or (n_samples,) if T is a vector Returns ------- self: object instance """ if X is not None: X = check_array(X) X_in = X else: X = np.empty(shape=(1, 0)) X_in = None self.tree_model_ = PolicyTree(criterion='neg_welfare', splitter='best', max_depth=self.max_depth, min_samples_split=self.min_samples_split, min_samples_leaf=self.min_samples_leaf, min_weight_fraction_leaf=self.min_weight_fraction_leaf, max_features=self.max_features, min_impurity_decrease=self.min_impurity_decrease, min_balancedness_tol=self.min_balancedness_tol, honest=False, random_state=self.random_state) if self.risk_level is None: y_pred = cate_estimator.const_marginal_effect(X_in) elif not self.risk_seeking: y_pred, _ = cate_estimator.const_marginal_effect_interval(X_in, alpha=self.risk_level) else: _, y_pred = cate_estimator.const_marginal_effect_interval(X_in, alpha=self.risk_level) # average the outcome dimension if it exists and ensure 2d y_pred if y_pred.ndim == 3: y_pred = np.mean(y_pred, axis=1) elif y_pred.ndim == 2: if (len(cate_estimator._d_y) > 0) and cate_estimator._d_y[0] > 1: y_pred = np.mean(y_pred, axis=1, keepdims=True) elif y_pred.ndim == 1: y_pred = y_pred.reshape((-1, 1)) if sample_treatment_costs is not None: if isinstance(sample_treatment_costs, numbers.Real): y_pred -= sample_treatment_costs else: sample_treatment_costs = check_array(sample_treatment_costs, ensure_2d=False) if sample_treatment_costs.ndim == 1: sample_treatment_costs = sample_treatment_costs.reshape((-1, 1)) if sample_treatment_costs.shape == y_pred.shape: y_pred -= sample_treatment_costs else: raise ValueError("`sample_treatment_costs` should be a double scalar " "or have dimension (n_samples, n_treatments) or (n_samples,) if T is a vector") # get index of best treatment all_y = np.hstack([np.zeros((y_pred.shape[0], 1)), np.atleast_1d(y_pred)]) self.tree_model_.fit(X, all_y) self.policy_value_ = np.mean(np.max(self.tree_model_.predict_value(X), axis=1)) self.always_treat_value_ = np.mean(y_pred, axis=0) paths = self.tree_model_.decision_path(X) node_dict = {} for node_id in range(paths.shape[1]): mask = paths.getcol(node_id).toarray().flatten().astype(bool) Xsub = X_in[mask] if X_in is not None else None if (self.include_uncertainty and ((not self.uncertainty_only_on_leaves) or (self.tree_model_.tree_.children_left[node_id] < 0))): res = cate_estimator.const_marginal_ate_inference(Xsub) node_dict[node_id] = {'mean': res.mean_point, 'std': res.std_point, 'ci': res.conf_int_mean(alpha=self.uncertainty_level)} else: cate_node = y_pred[mask] node_dict[node_id] = {'mean': np.mean(cate_node, axis=0), 'std': np.std(cate_node, axis=0)} self.node_dict_ = node_dict return self
[docs] def treat(self, X): """ Using the policy model learned by a call to :meth:`interpret`, assign treatment to a set of units Parameters ---------- X : array-like The features for the units to treat; must be compatible shape-wise with the features used during interpretation Returns ------- T : array-like The treatments implied by the policy learned by the interpreter, with treatment 0, meaning no treatment, and treatment 1 meains the first treatment, etc. """ assert self.tree_model_ is not None, "Interpret must be called prior to trying to assign treatment." return self.tree_model_.predict(X)
def _make_dot_exporter(self, *, out_file, feature_names, treatment_names, max_depth, filled, leaves_parallel, rotate, rounded, special_characters, precision): title = "Average policy gains over no treatment: {} \n".format(np.around(self.policy_value_, precision)) title += "Average policy gains over constant treatment policies for each treatment: {}".format( np.around(self.policy_value_ - self.always_treat_value_, precision)) return _PolicyTreeDOTExporter(out_file=out_file, title=title, treatment_names=treatment_names, feature_names=feature_names, max_depth=max_depth, filled=filled, leaves_parallel=leaves_parallel, rotate=rotate, rounded=rounded, special_characters=special_characters, precision=precision) def _make_mpl_exporter(self, *, title, feature_names, treatment_names, max_depth, filled, rounded, precision, fontsize): title = "" if title is None else title title += "Average policy gains over no treatment: {} \n".format(np.around(self.policy_value_, precision)) title += "Average policy gains over constant treatment policies for each treatment: {}".format( np.around(self.policy_value_ - self.always_treat_value_, precision)) return _PolicyTreeMPLExporter(treatment_names=treatment_names, title=title, feature_names=feature_names, max_depth=max_depth, filled=filled, rounded=rounded, precision=precision, fontsize=fontsize)