utils

Utility methods for the ProblemTypes enum in EvalML.

Module Contents

Functions

detect_problem_type	Determine the type of problem is being solved based on the targets (binary vs multiclass classification, regression). Ignores missing and null data.
handle_problem_types	Handles problem_type by either returning the ProblemTypes or converting from a str.
is_binary	Determines if the provided problem_type is a binary classification problem type.
is_classification	Determines if the provided problem_type is a classification problem type.
is_multiclass	Determines if the provided problem_type is a multiclass classification problem type.
is_regression	Determines if the provided problem_type is a regression problem type.
is_time_series	Determines if the provided problem_type is a time series problem type.

Contents

evalml.problem_types.utils.detect_problem_type(y)

Determine the type of problem is being solved based on the targets (binary vs multiclass classification, regression). Ignores missing and null data.

Parameters

y (pd.Series) – The target labels to predict.

Returns

ProblemType Enum

Return type

ProblemType

Example

>>> y = pd.Series([0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1])
>>> problem_type = detect_problem_type(y)
>>> assert problem_type == ProblemTypes.BINARY

Raises

ValueError – If the input has less than two classes.

evalml.problem_types.utils.handle_problem_types(problem_type)

Handles problem_type by either returning the ProblemTypes or converting from a str.

Parameters

problem_type (str or ProblemTypes) – Problem type that needs to be handled.

Returns

ProblemTypes enum

Raises

• KeyError – If input is not a valid ProblemTypes enum value.

• ValueError – If input is not a string or ProblemTypes object.

evalml.problem_types.utils.is_binary(problem_type)

Determines if the provided problem_type is a binary classification problem type.

Parameters

problem_type (str or ProblemTypes) – type of supervised learning problem. See evalml.problem_types.ProblemType.all_problem_types for a full list.

Returns

Whether or not the provided problem_type is a binary classification problem type.

Return type

bool

evalml.problem_types.utils.is_classification(problem_type)

Determines if the provided problem_type is a classification problem type.

Parameters

problem_type (str or ProblemTypes) – type of supervised learning problem. See evalml.problem_types.ProblemType.all_problem_types for a full list.

Returns

Whether or not the provided problem_type is a classification problem type.

Return type

bool

evalml.problem_types.utils.is_multiclass(problem_type)

Determines if the provided problem_type is a multiclass classification problem type.

Parameters

problem_type (str or ProblemTypes) – type of supervised learning problem. See evalml.problem_types.ProblemType.all_problem_types for a full list.

Returns

Whether or not the provided problem_type is a multiclass classification problem type.

Return type

bool

evalml.problem_types.utils.is_regression(problem_type)

Determines if the provided problem_type is a regression problem type.

Parameters

problem_type (str or ProblemTypes) – type of supervised learning problem. See evalml.problem_types.ProblemType.all_problem_types for a full list.

Returns

Whether or not the provided problem_type is a regression problem type.

Return type

bool

evalml.problem_types.utils.is_time_series(problem_type)

Determines if the provided problem_type is a time series problem type.

Parameters

problem_type (str or ProblemTypes) – type of supervised learning problem. See evalml.problem_types.ProblemType.all_problem_types for a full list.

Returns

Whether or not the provided problem_type is a time series problem type.

Return type

bool

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