explainers¶

Module Contents¶

Classes Summary¶

ExplainPredictionsStage

Generic enumeration.

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)[source]¶

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)[source]¶

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_shap_values=False, include_expected_value=False, output_format='text', training_data=None, training_target=None)[source]¶

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

XGBoost and Stacked Ensemble models, as well as CatBoost multiclass classifiers, are not currently supported.

Parameters

pipeline (PipelineBase) – Fitted pipeline whose predictions we want to explain with SHAP.
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_shap_values (bool) – Whether SHAP 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.

Returns

str, dict, or pd.DataFrame - 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 SHAP Value (optional).

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_shap_values=False, metric=None, output_format='text', callback=None, training_data=None, training_target=None)[source]¶

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 and Stacked Ensemble models, as well as CatBoost multiclass classifiers, are not currently supported.

Parameters

pipeline (PipelineBase) – Fitted pipeline whose predictions we want to explain with SHAP.
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_shap_values (bool) – Whether SHAP 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.

Returns

str, dict, or pd.DataFrame - 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 SHAP Value (optional) will be listed.

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.

class evalml.model_understanding.prediction_explanations.explainers.ExplainPredictionsStage[source]¶

Generic enumeration.

Derive from this class to define new enumerations.

Attributes

COMPUTE_FEATURE_STAGE	compute_feature_stage
COMPUTE_SHAP_VALUES_STAGE	compute_shap_value_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.

prediction_explanations force_plots