Exceptions#

Exceptions used in EvalML.

Submodules#

`PartialDependenceErrorCode`	Enum identifying the type of error encountered in partial dependence.
`ValidationErrorCode`	Enum identifying the type of error encountered in holdout validation.

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.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.

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.

class evalml.exceptions.ValidationErrorCode[source]#

Enum identifying the type of error encountered in holdout validation.

Attributes

INVALID_HOLDOUT_GAP_SEPARATION	invalid_holdout_gap_separation
INVALID_HOLDOUT_LENGTH	invalid_holdout_length

Methods

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

wine

exceptions