econml.policy.PolicyTree

class econml.policy.PolicyTree(*, criterion='neg_welfare', splitter='best', max_depth=None, min_samples_split=10, min_samples_leaf=5, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, min_impurity_decrease=0.0, min_balancedness_tol=0.45, honest=True)[source]

Bases: _SingleTreeExporterMixin, BaseTree

Welfare maximization policy tree.

Trains a tree to maximize the objective: $1/n \sum_i \sum_j a_j(X_i) * y_{ij}$, where, where $a(X)$ is constrained to take value of 1 only on one coordinate and zero otherwise. This corresponds to a policy optimization problem.

Parameters:

criterion ({'neg_welfare'}, default ‘neg_welfare’) – The criterion type
splitter ({“best”}, default “best”) – The strategy used to choose the split at each node. Supported strategies are “best” to choose the best split.
max_depth (int, 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 or float, default 10) – 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 or float, default 5) – 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, default 0.0) – 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, {“auto”, “sqrt”, “log2”}, or None, 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, default None) – Controls the randomness of the estimator. The features are always randomly permuted at each split, even if splitter is set to "best". When max_features < n_features, the algorithm will select max_features at random at each split before finding the best split among them. But the best found split may vary across different runs, even if max_features=n_features. That is the case, if the improvement of the criterion is identical for several splits and one split has to be selected at random. To obtain a deterministic behaviour during fitting, random_state has to be fixed to an integer.
min_impurity_decrease (float, default 0.0) – 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.
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.
honest (bool, default True) – Whether the data should be split in two equally sized samples, such that the one half-sample is used to determine the optimal split at each node and the other sample is used to determine the value of every node.

feature_importances_

The feature importances based on the amount of parameter heterogeneity they create. The higher, the more important the feature.

Type:: ndarray of shape (n_features,)

max_features_

The inferred value of max_features.

Type:: int

n_features_in_

The number of features when fit is performed.

Type:: int

n_samples_

The number of training samples when fit is performed.

Type:: int

honest_

Whether honesty was enabled when fit was performed

Type:: int

tree_

The underlying Tree object. Please refer to help(econml.tree._tree.Tree) for attributes of Tree object.

Type:: Tree instance

policy_value_

The value achieved by the recommended policy

Type:: float

always_treat_value_

The value of the policy that treats all samples

Type:: float

__init__(*, criterion='neg_welfare', splitter='best', max_depth=None, min_samples_split=10, min_samples_leaf=5, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, min_impurity_decrease=0.0, min_balancedness_tol=0.45, honest=True)[source]

Methods

`__init__`(*[, criterion, splitter, ...])
`apply`(X[, check_input])	Return the index of the leaf that each sample is predicted as.
`decision_path`(X[, check_input])	Return the decision path in the tree.
`export_graphviz`([out_file, feature_names, ...])	Export a graphviz dot file representing the learned tree model.
`feature_importances`([max_depth, ...])	Get feature importances.
`fit`(X, y, *[, sample_weight, check_input])	Fit the tree from the data.
`get_depth`()	Return the depth of the decision tree.
`get_metadata_routing`()	Get metadata routing of this object.
`get_n_leaves`()	Return the number of leaves of the decision tree.
`get_params`([deep])	Get parameters for this estimator.
`get_train_test_split_inds`()	Regenerate the train_test_split of input sample indices.
`init`()
`plot`([ax, title, feature_names, ...])	Export policy trees to matplotlib.
`predict`(X[, check_input])	Predict the best treatment for each sample.
`predict_proba`(X[, check_input])	Predict the probability of recommending each treatment.
`predict_value`(X[, check_input])	Predict the expected value of each treatment for each sample.
`render`(out_file[, format, view, ...])	Render the tree to a flie.
`set_fit_request`(*[, check_input, sample_weight])	Request metadata passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_predict_proba_request`(*[, check_input])	Request metadata passed to the `predict_proba` method.
`set_predict_request`(*[, check_input])	Request metadata passed to the `predict` method.

Attributes

`feature_importances_`
`n_features_`
`node_dict_`
`tree_model_`

apply(X, check_input=True)

Return the index of the leaf that each sample is predicted as.

Parameters:

X ({array_like} of shape (n_samples, n_features)) – The input samples. Internally, it will be converted to dtype=np.float64
check_input (bool, default True) – Allow to bypass several input checking. Don’t use this parameter unless you know what you do.

Returns:

X_leaves – For each datapoint x in X, return the index of the leaf x ends up in. Leaves are numbered within [0; self.tree_.node_count), possibly with gaps in the numbering.

Return type:

array_like of shape (n_samples,)

decision_path(X, check_input=True)

Return the decision path in the tree.

Parameters:

X ({array_like} of shape (n_samples, n_features)) – The input samples. Internally, it will be converted to dtype=np.float64
check_input (bool, default True) – Allow to bypass several input checking. Don’t use this parameter unless you know what you do.

Returns:

indicator – Return a node indicator CSR matrix where non zero elements indicates that the samples goes through the nodes.

Return type:

sparse matrix of shape (n_samples, n_nodes)

export_graphviz(out_file=None, feature_names=None, treatment_names=None, max_depth=None, filled=True, leaves_parallel=True, rotate=False, rounded=True, special_characters=False, precision=3)

Export a graphviz dot file representing the learned tree model.

Parameters:

out_file (file object or str, optional) – Handle or name of the output file. If None, the result is returned as a string.
feature_names (list of str, optional) – Names of each of the features.
treatment_names (list of str, optional) – Names of each of the treatments
max_depth (int, optional) – The maximum tree depth to plot
filled (bool, default False) – When set to True, paint nodes to indicate majority class for classification, extremity of values for regression, or purity of node for multi-output.
leaves_parallel (bool, default True) – When set to True, draw all leaf nodes at the bottom of the tree.
rotate (bool, default False) – When set to True, orient tree left to right rather than top-down.
rounded (bool, default True) – When set to True, draw node boxes with rounded corners and use Helvetica fonts instead of Times-Roman.
special_characters (bool, default False) – When set to False, ignore special characters for PostScript compatibility.
precision (int, default 3) – Number of digits of precision for floating point in the values of impurity, threshold and value attributes of each node.

feature_importances(max_depth=4, depth_decay_exponent=2.0)[source]

Get feature importances.

Parameters:

max_depth (int, default 4) – Splits of depth larger than max_depth are not used in this calculation
depth_decay_exponent (double, default 2.0) – The contribution of each split to the total score is re-weighted by 1 / (1 + `depth`)**2.0.

Returns:

feature_importances_ – Normalized total parameter heterogeneity inducing importance of each feature

Return type:

ndarray of shape (n_features,)

fit(X, y, *, sample_weight=None, check_input=True)[source]

Fit the tree from the data.

Parameters:

X ((n, n_features) array) – The features to split on
y ((n, n_treatments) array) – The reward for each of the m treatments (including baseline treatment)
sample_weight ((n,) array, default None) – The sample weights
check_input (bool, defaul=True) – Whether to check the input parameters for validity. Should be set to False to improve running time in parallel execution, if the variables have already been checked by the forest class that spawned this tree.

Returns:

self

Return type:

object instance

get_depth()

Return the depth of the decision tree.

The depth of a tree is the maximum distance between the root and any leaf.

Returns:: self.tree_.max_depth – The maximum depth of the tree.
Return type:: int

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:: routing – A MetadataRequest encapsulating routing information.
Return type:: MetadataRequest

get_n_leaves()

Return the number of leaves of the decision tree.

Returns:: self.tree_.n_leaves – Number of leaves.
Return type:: int

get_params(deep=True)

Get parameters for this estimator.

Parameters:: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:: params – Parameter names mapped to their values.
Return type:: dict

get_train_test_split_inds()

Regenerate the train_test_split of input sample indices.

Produces the split that was used for the training and the evaluation split of the honest tree construction structure.

Uses the same random seed that was used at fit time and re-generates the indices.

plot(ax=None, title=None, feature_names=None, treatment_names=None, max_depth=None, filled=True, rounded=True, precision=3, fontsize=None)

Export policy trees to matplotlib.

Parameters:

ax (matplotlib.axes.Axes, optional) – The axes on which to plot
title (str, optional) – A title for the final figure to be printed at the top of the page.
feature_names (list of str, optional) – Names of each of the features.
treatment_names (list of str, optional) – Names of each of the treatments
max_depth (int, optional) – The maximum tree depth to plot
filled (bool, default False) – When set to True, paint nodes to indicate majority class for classification, extremity of values for regression, or purity of node for multi-output.
rounded (bool, default True) – When set to True, draw node boxes with rounded corners and use Helvetica fonts instead of Times-Roman.
precision (int, default 3) – Number of digits of precision for floating point in the values of impurity, threshold and value attributes of each node.
fontsize (int, optional) – Font size for text

predict(X, check_input=True)[source]

Predict the best treatment for each sample.

Parameters:

X ({array_like} of shape (n_samples, n_features)) – The input samples. Internally, it will be converted to dtype=np.float64.
check_input (bool, default True) – Allow to bypass several input checking. Don’t use this parameter unless you know what you do.

Returns:

treatment – The recommded treatment, i.e. the treatment index with the largest reward for each sample

Return type:

array_like of shape (n_samples)

predict_proba(X, check_input=True)[source]

Predict the probability of recommending each treatment.

Parameters:

X ({array_like} of shape (n_samples, n_features)) – The input samples. Internally, it will be converted to dtype=np.float64.
check_input (bool, default True) – Allow to bypass several input checking. Don’t use this parameter unless you know what you do.

Returns:

treatment_proba – The probability of each treatment recommendation

Return type:

array_like of shape (n_samples, n_treatments)

predict_value(X, check_input=True)[source]

Predict the expected value of each treatment for each sample.

Parameters:

X ({array_like} of shape (n_samples, n_features)) – The input samples. Internally, it will be converted to dtype=np.float64.
check_input (bool, default True) – Allow to bypass several input checking. Don’t use this parameter unless you know what you do.

Returns:

welfare – The conditional average welfare for each treatment for the group of each sample defined by the tree

Return type:

array_like of shape (n_samples, n_treatments)

render(out_file, format='pdf', view=True, feature_names=None, treatment_names=None, max_depth=None, filled=True, leaves_parallel=True, rotate=False, rounded=True, special_characters=False, precision=3)

Render the tree to a flie.

Parameters:

out_file (file name to save to)
format (str, default ‘pdf’) – The file format to render to; must be supported by graphviz
view (bool, default True) – Whether to open the rendered result with the default application.
feature_names (list of str, optional) – Names of each of the features.
treatment_names (list of str, optional) – Names of each of the treatments
max_depth (int, optional) – The maximum tree depth to plot
filled (bool, default False) – When set to True, paint nodes to indicate majority class for classification, extremity of values for regression, or purity of node for multi-output.
leaves_parallel (bool, default True) – When set to True, draw all leaf nodes at the bottom of the tree.
rotate (bool, default False) – When set to True, orient tree left to right rather than top-down.
rounded (bool, default True) – When set to True, draw node boxes with rounded corners and use Helvetica fonts instead of Times-Roman.
special_characters (bool, default False) – When set to False, ignore special characters for PostScript compatibility.
precision (int, default 3) – Number of digits of precision for floating point in the values of impurity, threshold and value attributes of each node.

set_fit_request(*, check_input: bool | None | str = '$UNCHANGED$', sample_weight: bool | None | str = '$UNCHANGED$') → PolicyTree

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

check_input (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for check_input parameter in fit.
sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: estimator instance

set_predict_proba_request(*, check_input: bool | None | str = '$UNCHANGED$') → PolicyTree

Request metadata passed to the predict_proba method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to predict_proba if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to predict_proba.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:: check_input (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for check_input parameter in predict_proba.
Returns:: self – The updated object.
Return type:: object

set_predict_request(*, check_input: bool | None | str = '$UNCHANGED$') → PolicyTree

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to predict.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:: check_input (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for check_input parameter in predict.
Returns:: self – The updated object.
Return type:: object