from .binary_classification_pipeline_mixin import (
BinaryClassificationPipelineMixin
)
from evalml.objectives import get_objective
from evalml.pipelines.classification_pipeline import ClassificationPipeline
from evalml.problem_types import ProblemTypes
from evalml.utils import infer_feature_types
[docs]class BinaryClassificationPipeline(BinaryClassificationPipelineMixin, ClassificationPipeline):
"""Pipeline subclass for all binary classification pipelines."""
problem_type = ProblemTypes.BINARY
def _predict(self, X, objective=None):
"""Make predictions using selected features.
Arguments:
X (pd.DataFrame): Data of shape [n_samples, n_features]
objective (Object or string): The objective to use to make predictions
Returns:
pd.Series: Estimated labels
"""
if objective is not None:
objective = get_objective(objective, return_instance=True)
if not objective.is_defined_for_problem_type(self.problem_type):
raise ValueError("You can only use a binary classification objective to make predictions for a binary classification pipeline.")
if self.threshold is None:
return self._component_graph.predict(X)
ypred_proba = self.predict_proba(X)
predictions = self._predict_with_objective(X, ypred_proba, objective)
return infer_feature_types(predictions)
[docs] def predict_proba(self, X):
"""Make probability estimates for labels. Assumes that the column at index 1 represents the positive label case.
Arguments:
X (pd.DataFrame or np.ndarray): Data of shape [n_samples, n_features]
Returns:
pd.Series: Probability estimates
"""
return super().predict_proba(X)
@staticmethod
def _score(X, y, predictions, objective):
"""Given data, model predictions or predicted probabilities computed on the data, and an objective, evaluate and return the objective score.
"""
if predictions.ndim > 1:
predictions = predictions.iloc[:, 1]
return ClassificationPipeline._score(X, y, predictions, objective)