Source code for evalml.pipelines.classification_pipeline


import pandas as pd
from sklearn.preprocessing import LabelEncoder

from evalml.pipelines import PipelineBase
from evalml.utils import _convert_woodwork_types_wrapper, infer_feature_types


[docs]class ClassificationPipeline(PipelineBase): """Pipeline subclass for all classification pipelines."""
[docs] def __init__(self, parameters, random_seed=0): """Machine learning classification pipeline made out of transformers and a classifier. Required Class Variables: component_graph (list): List of components in order. Accepts strings or ComponentBase subclasses in the list Arguments: parameters (dict): Dictionary with component names as keys and dictionary of that component's parameters as values. An empty dictionary {} implies using all default values for component parameters. random_state (int): Deprecated - use random_seed instead. random_seed (int): Seed for the random number generator. Defaults to 0. """ self._encoder = LabelEncoder() super().__init__(parameters, random_seed=random_seed)
[docs] def fit(self, X, y): """Build a classification model. For string and categorical targets, classes are sorted by sorted(set(y)) and then are mapped to values between 0 and n_classes-1. Arguments: X (ww.DataTable, pd.DataFrame or np.ndarray): The input training data of shape [n_samples, n_features] y (ww.DataColumn, pd.Series, np.ndarray): The target training labels of length [n_samples] Returns: self """ X = infer_feature_types(X) y = infer_feature_types(y) y = _convert_woodwork_types_wrapper(y.to_series()) self._encoder.fit(y) y = self._encode_targets(y) self._fit(X, y) return self
def _encode_targets(self, y): """Converts target values from their original values to integer values that can be processed.""" try: return pd.Series(self._encoder.transform(y), index=y.index, name=y.name) except ValueError as e: raise ValueError(str(e)) def _decode_targets(self, y): """Converts encoded numerical values to their original target values. Note: we cast y as ints first to address boolean values that may be returned from calculating predictions which we would not be able to otherwise transform if we originally had integer targets.""" return self._encoder.inverse_transform(y.astype(int)) @property def classes_(self): """Gets the class names for the problem.""" if not hasattr(self._encoder, "classes_"): raise AttributeError("Cannot access class names before fitting the pipeline.") return self._encoder.classes_ def _predict(self, X, objective=None): """Make predictions using selected features. Arguments: X (ww.DataTable, pd.DataFrame): Data of shape [n_samples, n_features] objective (Object or string): The objective to use to make predictions Returns: ww.DataColumn: Estimated labels """ return self._component_graph.predict(X)
[docs] def predict(self, X, objective=None): """Make predictions using selected features. Arguments: X (ww.DataTable, pd.DataFrame, or np.ndarray): Data of shape [n_samples, n_features] objective (Object or string): The objective to use to make predictions Returns: ww.DataColumn: Estimated labels """ predictions = self._predict(X, objective=objective).to_series() predictions = pd.Series(self._decode_targets(predictions), name=self.input_target_name) return infer_feature_types(predictions)
[docs] def predict_proba(self, X): """Make probability estimates for labels. Arguments: X (ww.DataTable, pd.DataFrame or np.ndarray): Data of shape [n_samples, n_features] Returns: ww.DataTable: Probability estimates """ X = self.compute_estimator_features(X, y=None) proba = self.estimator.predict_proba(X).to_dataframe() proba.columns = self._encoder.classes_ return infer_feature_types(proba)
[docs] def score(self, X, y, objectives): """Evaluate model performance on objectives Arguments: X (ww.DataTable, pd.DataFrame or np.ndarray): Data of shape [n_samples, n_features] y (ww.DataColumn, pd.Series, or np.ndarray): True labels of length [n_samples] objectives (list): List of objectives to score Returns: dict: Ordered dictionary of objective scores """ y = infer_feature_types(y) y = _convert_woodwork_types_wrapper(y.to_series()) objectives = self.create_objectives(objectives) y = self._encode_targets(y) y_predicted, y_predicted_proba = self._compute_predictions(X, y, objectives) if y_predicted is not None: y_predicted = _convert_woodwork_types_wrapper(y_predicted.to_series()) if y_predicted_proba is not None: y_predicted_proba = _convert_woodwork_types_wrapper(y_predicted_proba.to_dataframe()) return self._score_all_objectives(X, y, y_predicted, y_predicted_proba, objectives)
def _compute_predictions(self, X, y, objectives, time_series=False): """Compute predictions/probabilities based on objectives.""" y_predicted = None y_predicted_proba = None if any(o.score_needs_proba for o in objectives): y_predicted_proba = self.predict_proba(X, y) if time_series else self.predict_proba(X) if any(not o.score_needs_proba for o in objectives): y_predicted = self._predict(X, y, pad=True) if time_series else self._predict(X) return y_predicted, y_predicted_proba