在诸如策略评估的任务中，比如， [Athey2020]_ ，除了因果效应，我们可能也对其他的问题感兴趣。例如:是否一个样例应该被分配一个治疗方案，如果答案是肯定的，哪一个选项 是所有可能的治疗值中最好的。YLearn为这样的目的实现了 :class:`PolicyTree` 。给定一个训练好的估计器模型或者估计的因果效应，它通过构建一个 目标是为每一个样例最大化因果效应的决策树模型，为每一个样例找到最优的策略。
训练树的准则是
S = \sum_i\sum_k g_{ik}e_{ki}
其中 g_{ik} = \phi(v_i)_k ，并且有 \phi: \mathbb{R}^D \to \mathbb{R}^K 是一个映射，从 v_i\in \mathbb{R}^D 到 一个仅有一个非零元素的在 \mathbb{R}^K 中的基向量，并且 e_{ki} 表明 对样本 i 采取治疗 k-th 值的因果效应。
.. seealso::
sklearn中 :py:class:`BaseDecisionTree` 。
注意：可以使用 :class:`PolicyInterpreter` 来解释策略模型的结果。
例子
import numpy as np
from ylearn.policy.policy_model import PolicyTree
from ylearn.utils._common import to_df
# build dataset
v = np.random.normal(size=(1000, 10))
y = np.hstack([v[:, [0]] < 0, v[:, [0]] > 0])
data = to_df(v=v)
covariate = data.columns
est = PolicyTree(criterion='policy_reg')
est.fit(data=data, covariate=covariate, effect_array=y.astype(float))>>> 06-23 14:53:14 I ylearn.p.policy_model.py 452 - Start building the policy tree with criterion PRegCriteria
>>> 06-23 14:53:14 I ylearn.p.policy_model.py 468 - Building the policy tree with splitter BestSplitter
>>> 06-23 14:53:14 I ylearn.p.policy_model.py 511 - Building the policy tree with builder DepthFirstTreeBuilder
>>> <ylearn.policy.policy_model.PolicyTree at 0x7ff1ee5f2eb0>.. py:class:: ylearn.policy.policy_model.PolicyTree(*, criterion='policy_reg', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, random_state=2022, max_leaf_nodes=None, max_features=None, min_impurity_decrease=0.0, ccp_alpha=0.0, min_weight_fraction_leaf=0.0)
:param {'policy_reg'}, default="'policy_reg'" criterion: The function to measure the quality of a split. The criterion for
training the tree is (in the Einstein notation)
.. math::
S = \sum_i g_{ik} e^k_{i},
where :math:`g_{ik} = \phi(v_i)_k` is a map from the covariates, :math:`v_i`, to a
basis vector which has only one nonzero element in the :math:`R^k` space. By
using this criterion, the aim of the model is to find the index of the
treatment which will render the max causal effect, i.e., finding the
optimal policy.
:param {"best", "random"}, default="best" splitter: 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.
:param int, default=None max_depth: 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.
:param int or float, default=2 min_samples_split: 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.
:param int or float, default=1 min_samples_leaf: 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.
:param float, default=0.0 min_weight_fraction_leaf: 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.
:param int, float or {"sqrt", "log2"}, default=None max_features: 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 "sqrt", then `max_features=sqrt(n_features)`.
- If "log2", then `max_features=log2(n_features)`.
- If None, then `max_features=n_features`.
:param int random_state: Controls the randomness of the estimator.
:param int, default to None max_leaf_nodes: 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.
:param float, default=0.0 min_impurity_decrease: 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.
.. py:method:: fit(data, covariate, *, effect=None, effect_array=None, est_modle=None, sample_weight=None)
Fit the PolicyInterpreter model to interpret the policy for the causal
effect estimated by the est_model on data. One has several options for passing the causal effects, which usually is a vector of (n, j, i)
where `n` is the number of the examples, `j` is the dimension of the outcome, and `i` is the number of possible treatment values or the dimension of the treatment:
1. Only pass `est_model`. Then `est_model` will be used to generate the causal effects.
2. Only pass `effect_array` which will be set as the causal effects and `effect` and `est_model` will be ignored.
3. Only pass `effect`. This usually is a list of names of the causal effect in `data` which will then be used as the causal effects for training the model.
:param pandas.DataFrame data: The input samples for the est_model to estimate the causal effects
and for the CEInterpreter to fit.
:param estimator_model est_model: est_model should be any valid estimator model of ylearn which was
already fitted and can estimate the CATE. If `effect=None` and `effect_array=None`, then `est_model` can not be None and the causal
effect will be estimated by the `est_model`.
:param list of str, optional, default=None covariate: Names of the covariate.
:param list of str, optional, default=None effect: Names of the causal effect in `data`. If `effect_array` is not None, then `effect` will be ignored.
:param numpy.ndarray, default=None effect_array: The causal effect that waited to be fitted by :class:`PolicyTree`. If this is not provided and `est_model` is None, then `effect` can not be None.
:returns: Fitted PolicyModel
:rtype: instance of PolicyModel
.. py:method:: predict_ind(data=None)
Estimate the optimal policy for the causal effects of the treatment
on the outcome in the data, i.e., return the index of the optimal treatment.
:param pandas.DataFrame, optional, default=None data: The test data in the form of the DataFrame. The model will only use this if v is set as None. In this case, if data is also None, then the data used for trainig will be used.
:returns: The index of the optimal treatment dimension.
:rtype: ndarray or int, optional
.. py:method:: predict_opt_effect(data=None)
Estimate the value of the optimal policy for the causal effects of the treatment
on the outcome in the data, i.e., return the value of the causal effects
when taking the optimal treatment.
:param pandas.DataFrame, optional, default=None data: The test data in the form of the DataFrame. The model will only use this if v is set as None. In this case, if data is also None, then the data used for trainig will be used.
:returns: The estimated causal effect with the optimal treatment value.
:rtype: ndarray or float, optional
.. py:method:: apply(*, v=None, data=None)
Return the index of the leaf that each sample is predicted as.
:param numpy.ndarray, default=None v: The input samples as an ndarray. If None, then the DataFrame data
will be used as the input samples.
:param pandas.DataFrame, default=None data: The input samples. The data must contains columns of the covariates
used for training the model. If None, the training data will be
passed as input samples.
:returns: For each datapoint v_i in v, return the index of the leaf v_i
ends up in. Leaves are numbered within ``[0; self.tree_.node_count)``, possibly with gaps in the
numbering.
:rtype: v_leaves : array-like of shape (n_samples, )
.. py:method:: decision_path(*, v=None, data=None)
Return the decision path.
:param numpy.ndarray, default=None v: The input samples as an ndarray. If None, then the DataFrame data
will be used as the input samples.
:param pandas.DataFrame, default=None data: The input samples. The data must contains columns of the covariates
used for training the model. If None, the training data will be
passed as input samples.
:returns: Return a node indicator CSR matrix where non zero elements
indicates that the samples goes through the nodes.
:rtype: indicator : sparse matrix of shape (n_samples, n_nodes)
.. py:method:: get_depth()
Return the depth of the policy tree.
The depth of a tree is the maximum distance between the root
and any leaf.
:returns: The maximum depth of the tree.
:rtype: int
.. py:method:: get_n_leaves()
Return the number of leaves of the policy tree.
:returns: Number of leaves
:rtype: int
.. py:property:: feature_importance
Return the feature importances.
The importance of a feature is computed as the (normalized) total
reduction of the criterion brought by that feature.
It is also known as the Gini importance.
Warning: impurity-based feature importances can be misleading for
high cardinality features (many unique values). See
:func:`sklearn.inspection.permutation_importance` as an alternative.
:returns: Normalized total reduction of criteria by feature
(Gini importance).
:rtype: ndarray of shape (n_features,)
.. py:property:: n_features_
:returns: number of features
:rtype: int
.. py:method:: plot(*, feature_names=None, max_depth=None, class_names=None, label='all', filled=False, node_ids=False, proportion=False, rounded=False, precision=3, ax=None, fontsize=None)
Plot the PolicyTree.
The sample counts that are shown are weighted with any sample_weights that
might be present.
The visualization is fit automatically to the size of the axis.
Use the ``figsize`` or ``dpi`` arguments of ``plt.figure`` to control
the size of the rendering.
:returns: List containing the artists for the annotation boxes making up the
tree.
:rtype: annotations : list of artists