Source code for evalml.pipelines.time_series_classification_pipelines

"""Pipeline base class for time-series classification problems."""
import pandas as pd

from evalml.objectives import get_objective
from evalml.pipelines.binary_classification_pipeline_mixin import (
    BinaryClassificationPipelineMixin,
)
from evalml.pipelines.classification_pipeline import ClassificationPipeline
from evalml.pipelines.time_series_pipeline_base import TimeSeriesPipelineBase
from evalml.problem_types import ProblemTypes
from evalml.utils import infer_feature_types


[docs]class TimeSeriesClassificationPipeline(TimeSeriesPipelineBase, ClassificationPipeline): """Pipeline base class for time series classification problems. Args: component_graph (ComponentGraph, list, dict): ComponentGraph instance, list of components in order, or dictionary of components. Accepts strings or ComponentBase subclasses in the list. Note that when duplicate components are specified in a list, the duplicate component names will be modified with the component's index in the list. For example, the component graph [Imputer, One Hot Encoder, Imputer, Logistic Regression Classifier] will have names ["Imputer", "One Hot Encoder", "Imputer_2", "Logistic Regression Classifier"] 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. Pipeline-level parameters such as time_index, gap, and max_delay must be specified with the "pipeline" key. For example: Pipeline(parameters={"pipeline": {"time_index": "Date", "max_delay": 4, "gap": 2}}). random_seed (int): Seed for the random number generator. Defaults to 0. """ def _estimator_predict_proba(self, features): """Get estimator predicted probabilities. This helper passes y as an argument if needed by the estimator. """ return self.estimator.predict_proba(features)
[docs] def predict_proba_in_sample(self, X_holdout, y_holdout, X_train, y_train): """Predict on future data where the target is known, e.g. cross validation. Args: X_holdout (pd.DataFrame or np.ndarray): Future data of shape [n_samples, n_features]. y_holdout (pd.Series, np.ndarray): Future target of shape [n_samples]. X_train (pd.DataFrame, np.ndarray): Data the pipeline was trained on of shape [n_samples_train, n_features]. y_train (pd.Series, np.ndarray): Targets used to train the pipeline of shape [n_samples_train]. Returns: pd.Series: Estimated probabilities. Raises: ValueError: If the final component is not an Estimator. """ if self.estimator is None: raise ValueError( "Cannot call predict_proba_in_sample() on a component graph because the final component is not an Estimator.", ) features = self.transform_all_but_final(X_holdout, y_holdout, X_train, y_train) proba = self._estimator_predict_proba(features) proba.index = y_holdout.index proba = proba.ww.rename( columns={ col: new_col for col, new_col in zip(proba.columns, self.classes_) }, ) return infer_feature_types(proba)
[docs] def predict_in_sample(self, X, y, X_train, y_train, objective=None): """Predict on future data where the target is known, e.g. cross validation. 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. Args: X (pd.DataFrame or np.ndarray): Future data of shape [n_samples, n_features]. y (pd.Series, np.ndarray): Future target of shape [n_samples]. X_train (pd.DataFrame, np.ndarray): Data the pipeline was trained on of shape [n_samples_train, n_features]. y_train (pd.Series, np.ndarray): Targets used to train the pipeline of shape [n_samples_train]. objective (ObjectiveBase, str, None): Objective used to threshold predicted probabilities, optional. Returns: pd.Series: Estimated labels. Raises: ValueError: If final component is not an Estimator. """ if self.estimator is None: raise ValueError( "Cannot call predict_in_sample() on a component graph because the final component is not an Estimator.", ) features = self.transform_all_but_final(X, y, X_train, y_train) predictions = self._estimator_predict(features) predictions.index = y.index predictions = self.inverse_transform(predictions.astype(int)) predictions = pd.Series(predictions, name=self.input_target_name) predictions = predictions.rename(index=dict(zip(predictions.index, y.index))) return infer_feature_types(predictions)
[docs] def predict_proba(self, X, X_train=None, y_train=None): """Predict on future data where the target is unknown. Args: X (pd.DataFrame or np.ndarray): Future data of shape [n_samples, n_features]. X_train (pd.DataFrame, np.ndarray): Data the pipeline was trained on of shape [n_samples_train, n_features]. y_train (pd.Series, np.ndarray): Targets used to train the pipeline of shape [n_samples_train]. Returns: pd.Series: Estimated probabilities. Raises: ValueError: If final component is not an Estimator. """ if self.estimator is None: raise ValueError( "Cannot call predict_proba() on a component graph because the final component is not an Estimator.", ) X_train, y_train = self._convert_to_woodwork(X_train, y_train) X = infer_feature_types(X) X.index = self._move_index_forward( X_train.index[-X.shape[0] :], self.gap + X.shape[0], ) y_holdout = self._create_empty_series(y_train, X.shape[0]) y_holdout = infer_feature_types(y_holdout) y_holdout.index = X.index return self.predict_proba_in_sample(X, y_holdout, X_train, y_train)
def _compute_predictions(self, X, y, X_train, y_train, objectives): y_predicted = None y_predicted_proba = None if any(o.score_needs_proba for o in objectives): y_predicted_proba = self.predict_proba_in_sample(X, y, X_train, y_train) if any(not o.score_needs_proba for o in objectives): y_predicted = self.predict_in_sample(X, y, X_train, y_train) y_predicted = self._encode_targets(y_predicted) return y_predicted, y_predicted_proba
[docs] def score(self, X, y, objectives, X_train=None, y_train=None): """Evaluate model performance on current and additional objectives. Args: X (pd.DataFrame or np.ndarray): Data of shape [n_samples, n_features]. y (pd.Series): True labels of length [n_samples]. objectives (list): Non-empty list of objectives to score on. X_train (pd.DataFrame, np.ndarray): Data the pipeline was trained on of shape [n_samples_train, n_features]. y_train (pd.Series, np.ndarray): Targets used to train the pipeline of shape [n_samples_train]. Returns: dict: Ordered dictionary of objective scores. """ X, y = self._convert_to_woodwork(X, y) X_train, y_train = self._convert_to_woodwork(X_train, y_train) objectives = self.create_objectives(objectives) y_predicted, y_predicted_proba = self._compute_predictions( X, y, X_train, y_train, objectives, ) if self._encoder is not None: y = self._encode_targets(y) return self._score_all_objectives( X, y, y_predicted, y_pred_proba=y_predicted_proba, objectives=objectives, )
[docs]class TimeSeriesBinaryClassificationPipeline( TimeSeriesClassificationPipeline, BinaryClassificationPipelineMixin, ): """Pipeline base class for time series binary classification problems. Args: component_graph (list or dict): List of components in order. Accepts strings or ComponentBase subclasses in the list. Note that when duplicate components are specified in a list, the duplicate component names will be modified with the component's index in the list. For example, the component graph [Imputer, One Hot Encoder, Imputer, Logistic Regression Classifier] will have names ["Imputer", "One Hot Encoder", "Imputer_2", "Logistic Regression Classifier"] 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. Pipeline-level parameters such as time_index, gap, and max_delay must be specified with the "pipeline" key. For example: Pipeline(parameters={"pipeline": {"time_index": "Date", "max_delay": 4, "gap": 2}}). random_seed (int): Seed for the random number generator. Defaults to 0. Example: >>> pipeline = TimeSeriesBinaryClassificationPipeline(component_graph=["Simple Imputer", "Logistic Regression Classifier"], ... parameters={"Logistic Regression Classifier": {"penalty": "elasticnet", ... "solver": "liblinear"}, ... "pipeline": {"gap": 1, "max_delay": 1, "forecast_horizon": 1, "time_index": "date"}}, ... custom_name="My TimeSeriesBinary Pipeline") ... >>> assert pipeline.custom_name == "My TimeSeriesBinary Pipeline" >>> assert pipeline.component_graph.component_dict.keys() == {'Simple Imputer', 'Logistic Regression Classifier'} ... >>> assert pipeline.parameters == { ... 'Simple Imputer': {'impute_strategy': 'most_frequent', 'fill_value': None}, ... 'Logistic Regression Classifier': {'penalty': 'elasticnet', ... 'C': 1.0, ... 'n_jobs': -1, ... 'multi_class': 'auto', ... 'solver': 'liblinear'}, ... 'pipeline': {'gap': 1, 'max_delay': 1, 'forecast_horizon': 1, 'time_index': "date"}} """ problem_type = ProblemTypes.TIME_SERIES_BINARY def _select_y_pred_for_score(self, X, y, y_pred, y_pred_proba, objective): y_pred_to_use = y_pred if self.threshold is not None and not objective.score_needs_proba: y_pred_to_use = self._predict_with_objective(X, y_pred_proba, objective) return y_pred_to_use
[docs] def predict_in_sample(self, X, y, X_train, y_train, objective=None): """Predict on future data where the target is known, e.g. cross validation. Args: X (pd.DataFrame): Future data of shape [n_samples, n_features]. y (pd.Series): Future target of shape [n_samples]. X_train (pd.DataFrame): Data the pipeline was trained on of shape [n_samples_train, n_feautures]. y_train (pd.Series): Targets used to train the pipeline of shape [n_samples_train]. objective (ObjectiveBase, str): Objective used to threshold predicted probabilities, optional. Defaults to None. Returns: pd.Series: Estimated labels. Raises: ValueError: If objective is not defined for time-series binary classification problems. """ 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( f"Objective {objective.name} is not defined for time series binary classification.", ) if self.threshold is not None: proba = self.predict_proba_in_sample(X, y, X_train, y_train) proba = proba.iloc[:, 1] if objective is None: predictions = proba > self.threshold predictions = predictions.astype(int) else: predictions = objective.decision_function( proba, threshold=self.threshold, X=X, ) predictions = pd.Series( predictions, name=self.input_target_name, index=y.index, ) else: predictions = super().predict_in_sample(X, y, X_train, y_train) return infer_feature_types(predictions)
@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 TimeSeriesClassificationPipeline._score(X, y, predictions, objective)
[docs]class TimeSeriesMulticlassClassificationPipeline(TimeSeriesClassificationPipeline): """Pipeline base class for time series multiclass classification problems. Args: component_graph (list or dict): List of components in order. Accepts strings or ComponentBase subclasses in the list. Note that when duplicate components are specified in a list, the duplicate component names will be modified with the component's index in the list. For example, the component graph [Imputer, One Hot Encoder, Imputer, Logistic Regression Classifier] will have names ["Imputer", "One Hot Encoder", "Imputer_2", "Logistic Regression Classifier"] 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. Pipeline-level parameters such as time_index, gap, and max_delay must be specified with the "pipeline" key. For example: Pipeline(parameters={"pipeline": {"time_index": "Date", "max_delay": 4, "gap": 2}}). random_seed (int): Seed for the random number generator. Defaults to 0. Example: >>> pipeline = TimeSeriesMulticlassClassificationPipeline(component_graph=["Simple Imputer", "Logistic Regression Classifier"], ... parameters={"Logistic Regression Classifier": {"penalty": "elasticnet", ... "solver": "liblinear"}, ... "pipeline": {"gap": 1, "max_delay": 1, "forecast_horizon": 1, "time_index": "date"}}, ... custom_name="My TimeSeriesMulticlass Pipeline") >>> assert pipeline.custom_name == "My TimeSeriesMulticlass Pipeline" >>> assert pipeline.component_graph.component_dict.keys() == {'Simple Imputer', 'Logistic Regression Classifier'} >>> assert pipeline.parameters == { ... 'Simple Imputer': {'impute_strategy': 'most_frequent', 'fill_value': None}, ... 'Logistic Regression Classifier': {'penalty': 'elasticnet', ... 'C': 1.0, ... 'n_jobs': -1, ... 'multi_class': 'auto', ... 'solver': 'liblinear'}, ... 'pipeline': {'gap': 1, 'max_delay': 1, 'forecast_horizon': 1, 'time_index': "date"}} """ problem_type = ProblemTypes.TIME_SERIES_MULTICLASS """ProblemTypes.TIME_SERIES_MULTICLASS"""