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"""
This module contains the implementation of ``DAGRegressor``.
``DAGRegressor`` is a class which wraps the StructureModel in an sklearn interface for regression.
"""
from typing import Union
import numpy as np
import pandas as pd
from sklearn.base import RegressorMixin
from causalnex.structure.pytorch.sklearn._base import DAGBase
[docs]class DAGRegressor(RegressorMixin, DAGBase):
"""
Regressor wrapper of the StructureModel.
Implements the sklearn .fit and .predict interface.
Example:
::
>>> from causalnex.sklearn import DAGRegressor
>>>
>>> reg = DAGRegressor(threshold=0.1)
>>> reg.fit(X_train, y_train)
>>>
>>> y_preds = reg.predict(X_test)
>>> type(y_preds)
np.ndarray
>>>
>>> type(reg.feature_importances_)
np.ndarray
::
Attributes:
feature_importances_ (np.ndarray): An array of edge weights corresponding
positionally to the feature X.
coef_ (np.ndarray): An array of edge weights corresponding
positionally to the feature X.
intercept_ (float): The target node bias value.
"""
_supported_types = ("cont", "poiss")
[docs] def fit(
self, X: Union[pd.DataFrame, np.ndarray], y: Union[pd.Series, np.ndarray]
) -> "DAGRegressor":
"""
Fits the sm model using the concat of X and y.
Raises:
NotImplementedError: If unsupported _target_dist_type provided.
Returns:
Instance of DAGRegressor.
"""
# store the protected attr _target_dist_type
if self.target_dist_type is None:
self.target_dist_type = "cont"
# fit the NOTEARS model
super().fit(X, y)
return self