Exceptions

Exceptions used in EvalML.

Submodules

Package Contents

Classes Summary

PartialDependenceErrorCode

Enum identifying the type of error encountered in partial dependence.

Exceptions Summary

Contents

exception evalml.exceptions.AutoMLSearchException[source]

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

exception evalml.exceptions.ComponentNotYetFittedError[source]

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

exception evalml.exceptions.DataCheckInitError[source]

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

exception evalml.exceptions.EnsembleMissingPipelinesError[source]

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

exception evalml.exceptions.MethodPropertyNotFoundError[source]

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

exception evalml.exceptions.MissingComponentError[source]

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

exception evalml.exceptions.NoPositiveLabelException[source]

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

exception evalml.exceptions.NullsInColumnWarning[source]

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

exception evalml.exceptions.ObjectiveCreationError[source]

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

exception evalml.exceptions.ObjectiveNotFoundError[source]

Exception to raise when specified objective does not exist.

exception evalml.exceptions.ParameterNotUsedWarning(components)[source]

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

exception evalml.exceptions.PartialDependenceError(message, code)[source]

Exception raised for all errors that partial dependence can raise.

class evalml.exceptions.PartialDependenceErrorCode[source]

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[source]

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

exception evalml.exceptions.PipelineNotYetFittedError[source]

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)[source]

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.