from evalml.pipelines import PipelineBase
from evalml.problem_types import ProblemTypes
from evalml.utils import infer_feature_types
[docs]class RegressionPipeline(PipelineBase):
"""Pipeline subclass for all regression pipelines."""
problem_type = ProblemTypes.REGRESSION
[docs] def fit(self, X, y):
"""Build a regression model.
Arguments:
X (pd.DataFrame or np.ndarray): The input training data of shape [n_samples, n_features]
y (pd.Series, np.ndarray): The target training data of length [n_samples]
Returns:
self
"""
X = infer_feature_types(X)
y = infer_feature_types(y)
if "numeric" not in y.ww.semantic_tags:
raise ValueError(f"Regression pipeline can only handle numeric target data")
self._fit(X, y)
return self
[docs] def score(self, X, y, objectives):
"""Evaluate model performance on current and additional objectives
Arguments:
X (pd.DataFrame, or np.ndarray): Data of shape [n_samples, n_features]
y (pd.Series, or np.ndarray): True values of length [n_samples]
objectives (list): Non-empty list of objectives to score on
Returns:
dict: Ordered dictionary of objective scores
"""
objectives = self.create_objectives(objectives)
y_predicted = self.predict(X)
return self._score_all_objectives(X, y, y_predicted, y_pred_proba=None, objectives=objectives)