Exceptions

Exceptions used in EvalML.

Submodules

• exceptions

Package Contents

Classes Summary

PartialDependenceErrorCode

Enum identifying the type of error encountered in partial dependence.

Exceptions Summary

Contents

exception evalml.exceptions.AutoMLSearchException

Exception raised when all pipelines in an automl batch return a score of NaN for the primary objective.

exception evalml.exceptions.ComponentNotYetFittedError

An exception to be raised when predict/predict_proba/transform is called on a component without fitting first.

exception evalml.exceptions.DataCheckInitError

Exception raised when a data check can’t initialize with the parameters given.

exception evalml.exceptions.EnsembleMissingPipelinesError

An exception raised when an ensemble is missing estimators (list) as a parameter.

exception evalml.exceptions.MethodPropertyNotFoundError

Exception to raise when a class is does not have an expected method or property.

exception evalml.exceptions.MissingComponentError

An exception raised when a component is not found in all_components().

exception evalml.exceptions.NoPositiveLabelException

Exception when a particular classification label for the ‘positive’ class cannot be found in the column index or unique values.

exception evalml.exceptions.NullsInColumnWarning

Warning thrown when there are null values in the column of interest.

exception evalml.exceptions.ObjectiveCreationError

Exception when get_objective tries to instantiate an objective and required args are not provided.

exception evalml.exceptions.ObjectiveNotFoundError

Exception to raise when specified objective does not exist.

exception evalml.exceptions.ParameterNotUsedWarning(components)

Warning thrown when a pipeline parameter isn’t used in a defined pipeline’s component graph during initialization.

exception evalml.exceptions.PartialDependenceError(message, code)

Exception raised for all errors that partial dependence can raise.

class evalml.exceptions.PartialDependenceErrorCode

Enum identifying the type of error encountered in partial dependence.

Attributes

ALL_OTHER_ERRORS	all_other_errors
COMPUTED_PERCENTILES_TOO_CLOSE	computed_percentiles_too_close
FEATURE_IS_ALL_NANS	feature_is_all_nans
FEATURE_IS_MOSTLY_ONE_VALUE	feature_is_mostly_one_value
FEATURES_ARGUMENT_INCORRECT_TYPES	features_argument_incorrect_types
ICE_PLOT_REQUESTED_FOR_TWO_WAY_PLOT	ice_plot_requested_for_two_way_partial_dependence_plot
INVALID_CLASS_LABEL	invalid_class_label_requested_for_plot
INVALID_FEATURE_TYPE	invalid_feature_type
PIPELINE_IS_BASELINE	pipeline_is_baseline
TOO_MANY_FEATURES	too_many_features
TWO_WAY_REQUESTED_FOR_DATES	two_way_requested_for_dates
UNFITTED_PIPELINE	unfitted_pipeline

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.

exception evalml.exceptions.PipelineNotFoundError

An exception raised when a particular pipeline is not found in automl search results.

exception evalml.exceptions.PipelineNotYetFittedError

An exception to be raised when predict/predict_proba/transform is called on a pipeline without fitting first.

exception evalml.exceptions.PipelineScoreError(exceptions, scored_successfully)

An exception raised when a pipeline errors while scoring any objective in a list of objectives.

Parameters

• exceptions (dict) – A dictionary mapping an objective name (str) to a tuple of the form (exception, traceback). All of the objectives that errored will be stored here.

• scored_successfully (dict) – A dictionary mapping an objective name (str) to a score value. All of the objectives that did not error will be stored here.

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