explainers

Prediction explanation tools.

Module Contents

Classes Summary

ExplainPredictionsStage

Enum for prediction stage.

Functions

abs_error	Computes the absolute error per data point for regression problems.
cross_entropy	Computes Cross Entropy Loss per data point for classification problems.
explain_predictions	Creates a report summarizing the top contributing features for each data point in the input features.
explain_predictions_best_worst	Creates a report summarizing the top contributing features for the best and worst points in the dataset as measured by error to true labels.

Attributes Summary

DEFAULT_METRICS

Contents

evalml.model_understanding.prediction_explanations.explainers.abs_error(y_true, y_pred)

Computes the absolute error per data point for regression problems.

Parameters

• y_true (pd.Series) – True labels.

• y_pred (pd.Series) – Predicted values.

Returns

np.ndarray

evalml.model_understanding.prediction_explanations.explainers.cross_entropy(y_true, y_pred_proba)

Computes Cross Entropy Loss per data point for classification problems.

Parameters

• y_true (pd.Series) – True labels encoded as ints.

• y_pred_proba (pd.DataFrame) – Predicted probabilities. One column per class.

Returns

np.ndarray

evalml.model_understanding.prediction_explanations.explainers.DEFAULT_METRICS

evalml.model_understanding.prediction_explanations.explainers.explain_predictions(pipeline, input_features, y, indices_to_explain, top_k_features=3, include_explainer_values=False, include_expected_value=False, output_format='text', training_data=None, training_target=None, algorithm='shap')

Creates a report summarizing the top contributing features for each data point in the input features.

XGBoost models and CatBoost multiclass classifiers are not currently supported with the SHAP algorithm. To explain XGBoost model predictions, use the LIME algorithm. The LIME algorithm does not currently support any CatBoost models. For Stacked Ensemble models, the SHAP value for each input pipeline’s predict function into the metalearner is used.

Parameters

• pipeline (PipelineBase) – Fitted pipeline whose predictions we want to explain with SHAP or LIME.

• input_features (pd.DataFrame) – Dataframe of input data to evaluate the pipeline on.

• y (pd.Series) – Labels for the input data.

• indices_to_explain (list[int]) – List of integer indices to explain.

• top_k_features (int) – How many of the highest/lowest contributing feature to include in the table for each data point. Default is 3.

• include_explainer_values (bool) – Whether explainer (SHAP or LIME) values should be included in the table. Default is False.

• include_expected_value (bool) – Whether the expected value should be included in the table. Default is False.

• output_format (str) – Either “text”, “dict”, or “dataframe”. Default is “text”.

• training_data (pd.DataFrame, np.ndarray) – Data the pipeline was trained on. Required and only used for time series pipelines.

• training_target (pd.Series, np.ndarray) – Targets used to train the pipeline. Required and only used for time series pipelines.

• algorithm (str) – Algorithm to use while generating top contributing features, one of “shap” or “lime”. Defaults to “shap”.

Returns

A report explaining the top contributing features to each prediction for each row of input_features.

The report will include the feature names, prediction contribution, and explainer value (optional).

Return type

str, dict, or pd.DataFrame

Raises

• ValueError – if input_features is empty.

• ValueError – if an output_format outside of “text”, “dict” or “dataframe is provided.

• ValueError – if the requested index falls outside the input_feature’s boundaries.

evalml.model_understanding.prediction_explanations.explainers.explain_predictions_best_worst(pipeline, input_features, y_true, num_to_explain=5, top_k_features=3, include_explainer_values=False, metric=None, output_format='text', callback=None, training_data=None, training_target=None, algorithm='shap')

Creates a report summarizing the top contributing features for the best and worst points in the dataset as measured by error to true labels.

XGBoost models and CatBoost multiclass classifiers are not currently supported with the SHAP algorithm. To explain XGBoost model predictions, use the LIME algorithm. The LIME algorithm does not currently support any CatBoost models. For Stacked Ensemble models, the SHAP value for each input pipeline’s predict function into the metalearner is used.

Parameters

• pipeline (PipelineBase) – Fitted pipeline whose predictions we want to explain with SHAP or LIME.

• input_features (pd.DataFrame) – Input data to evaluate the pipeline on.

• y_true (pd.Series) – True labels for the input data.

• num_to_explain (int) – How many of the best, worst, random data points to explain.

• top_k_features (int) – How many of the highest/lowest contributing feature to include in the table for each data point.

• include_explainer_values (bool) – Whether explainer (SHAP or LIME) values should be included in the table. Default is False.

• metric (callable) – The metric used to identify the best and worst points in the dataset. Function must accept the true labels and predicted value or probabilities as the only arguments and lower values must be better. By default, this will be the absolute error for regression problems and cross entropy loss for classification problems.

• output_format (str) – Either “text” or “dict”. Default is “text”.

• callback (callable) – Function to be called with incremental updates. Has the following parameters: - progress_stage: stage of computation - time_elapsed: total time in seconds that has elapsed since start of call

• training_data (pd.DataFrame, np.ndarray) – Data the pipeline was trained on. Required and only used for time series pipelines.

• training_target (pd.Series, np.ndarray) – Targets used to train the pipeline. Required and only used for time series pipelines.

• algorithm (str) – Algorithm to use while generating top contributing features, one of “shap” or “lime”. Defaults to “shap”.

Returns

A report explaining the top contributing features for the best/worst predictions in the input_features.

For each of the best/worst rows of input_features, the predicted values, true labels, metric value, feature names, prediction contribution, and explainer value (optional) will be listed.

Return type

str, dict, or pd.DataFrame

Raises

• ValueError – If input_features does not have more than twice the requested features to explain.

• ValueError – If y_true and input_features have mismatched lengths.

• ValueError – If an output_format outside of “text”, “dict” or “dataframe is provided.

• PipelineScoreError – If the pipeline errors out while scoring.

class evalml.model_understanding.prediction_explanations.explainers.ExplainPredictionsStage

Enum for prediction stage.

Attributes

COMPUTE_EXPLAINER_VALUES_STAGE	compute_explainer_value_stage
COMPUTE_FEATURE_STAGE	compute_feature_stage
DONE	done
PREDICT_STAGE	predict_stage
PREPROCESSING_STAGE	preprocessing_stage

Methods

name	The name of the Enum member.
value	The value of the Enum member.

name(self)

The name of the Enum member.

value(self)

The value of the Enum member.