def explain_predictions(pipeline,
input_features,
training_data=None,
top_k_features=3,
include_shap_values=False,
output_format="text"):
"""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.
Arguments:
pipeline (PipelineBase): Fitted pipeline whose predictions we want to explain with SHAP.
input_features (ww.DataTable, pd.DataFrame): Dataframe of input data to evaluate the pipeline on.
training_data (ww.DataTable, pd.DataFrame): Dataframe of data the pipeline was fit on. This can be omitted for pipelines
with tree-based estimators.
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.
output_format (str): Either "text" or "dict". Default is "text".
Returns:
str or dict - 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).
"""
input_features = _convert_to_woodwork_structure(input_features)
input_features = _convert_woodwork_types_wrapper(
input_features.to_dataframe())
if training_data is not None:
training_data = _convert_to_woodwork_structure(training_data)
training_data = _convert_woodwork_types_wrapper(
training_data.to_dataframe())
if input_features.empty:
raise ValueError(
"Parameter input_features must be a non-empty dataframe.")
if output_format not in {"text", "dict"}:
raise ValueError(
f"Parameter output_format must be either text or dict. Received {output_format}"
)
data = _ReportData(pipeline,
input_features,
y_true=None,
y_pred=None,
y_pred_values=None,
errors=None,
index_list=range(input_features.shape[0]),
metric=None)
report_creator = _report_creator_factory(
data,
report_type="explain_predictions",
output_format=output_format,
top_k_features=top_k_features,
include_shap_values=include_shap_values)
return report_creator(data)
def 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"):
"""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.
Arguments:
pipeline (PipelineBase): Fitted pipeline whose predictions we want to explain with SHAP.
input_features (ww.DataTable, pd.DataFrame): Input data to evaluate the pipeline on.
y_true (ww.DataColumn, 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".
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.
"""
input_features = infer_feature_types(input_features)
input_features = _convert_woodwork_types_wrapper(
input_features.to_dataframe())
y_true = infer_feature_types(y_true)
y_true = _convert_woodwork_types_wrapper(y_true.to_series())
if not (input_features.shape[0] >= num_to_explain * 2):
raise ValueError(
f"Input features must be a dataframe with more than {num_to_explain * 2} rows! "
"Convert to a dataframe and select a smaller value for num_to_explain if you do not have "
"enough data.")
if y_true.shape[0] != input_features.shape[0]:
raise ValueError(
"Parameters y_true and input_features must have the same number of data points. Received: "
f"true labels: {y_true.shape[0]} and {input_features.shape[0]}")
if output_format not in {"text", "dict", "dataframe"}:
raise ValueError(
f"Parameter output_format must be either text, dict, or dataframe. Received {output_format}"
)
if not metric:
metric = DEFAULT_METRICS[pipeline.problem_type]
try:
if is_regression(pipeline.problem_type):
if is_time_series(pipeline.problem_type):
y_pred = pipeline.predict(input_features, y=y_true).to_series()
else:
y_pred = pipeline.predict(input_features).to_series()
y_pred_values = None
y_true_no_nan, y_pred_no_nan = drop_rows_with_nans(y_true, y_pred)
errors = metric(y_true_no_nan, y_pred_no_nan)
else:
if is_time_series(pipeline.problem_type):
y_pred = pipeline.predict_proba(input_features,
y=y_true).to_dataframe()
y_pred_values = pipeline.predict(input_features,
y=y_true).to_series()
else:
y_pred = pipeline.predict_proba(input_features).to_dataframe()
y_pred_values = pipeline.predict(input_features).to_series()
y_true_no_nan, y_pred_no_nan, y_pred_values_no_nan = drop_rows_with_nans(
y_true, y_pred, y_pred_values)
errors = metric(pipeline._encode_targets(y_true_no_nan),
y_pred_no_nan)
except Exception as e:
tb = traceback.format_tb(sys.exc_info()[2])
raise PipelineScoreError(exceptions={metric.__name__: (e, tb)},
scored_successfully={})
errors = pd.Series(errors, index=y_pred_no_nan.index)
sorted_scores = errors.sort_values()
best_indices = sorted_scores.index[:num_to_explain]
worst_indices = sorted_scores.index[-num_to_explain:]
index_list = best_indices.tolist() + worst_indices.tolist()
pipeline_features = pipeline.compute_estimator_features(
input_features, y_true).to_dataframe()
data = _ReportData(pipeline, pipeline_features, input_features, y_true,
y_pred, y_pred_values, errors, index_list, metric)
report_creator = _report_creator_factory(
data,
report_type="explain_predictions_best_worst",
output_format=output_format,
top_k_features=top_k_features,
include_shap_values=include_shap_values,
num_to_explain=num_to_explain)
return report_creator(data)
def explain_predictions(pipeline,
input_features,
y,
indices_to_explain,
top_k_features=3,
include_shap_values=False,
output_format="text"):
"""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.
Arguments:
pipeline (PipelineBase): Fitted pipeline whose predictions we want to explain with SHAP.
input_features (ww.DataTable, pd.DataFrame): Dataframe of input data to evaluate the pipeline on.
y (ww.DataColumn, 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.
output_format (str): Either "text", "dict", or "dataframe". Default is "text".
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.
"""
input_features = infer_feature_types(input_features)
input_features = _convert_woodwork_types_wrapper(
input_features.to_dataframe())
if input_features.empty:
raise ValueError(
"Parameter input_features must be a non-empty dataframe.")
if output_format not in {"text", "dict", "dataframe"}:
raise ValueError(
f"Parameter output_format must be either text, dict, or dataframe. Received {output_format}"
)
if any([x < 0 or x >= len(input_features) for x in indices_to_explain]):
raise ValueError(
f"Explained indices should be between 0 and {len(input_features) - 1}"
)
pipeline_features = pipeline.compute_estimator_features(input_features,
y).to_dataframe()
data = _ReportData(pipeline,
pipeline_features,
input_features,
y_true=y,
y_pred=None,
y_pred_values=None,
errors=None,
index_list=indices_to_explain,
metric=None)
report_creator = _report_creator_factory(
data,
report_type="explain_predictions",
output_format=output_format,
top_k_features=top_k_features,
include_shap_values=include_shap_values)
return report_creator(data)
