"""Utility methods for the ProblemTypes enum in EvalML."""
import pandas as pd
from pandas.api.types import is_numeric_dtype
from .problem_types import ProblemTypes
[docs]def handle_problem_types(problem_type):
"""Handles problem_type by either returning the ProblemTypes or converting from a str.
Args:
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.
"""
if isinstance(problem_type, str):
try:
tpe = ProblemTypes._all_values[problem_type.upper()]
except KeyError:
raise KeyError("Problem type '{}' does not exist".format(problem_type))
return tpe
if isinstance(problem_type, ProblemTypes):
return problem_type
raise ValueError(
"`handle_problem_types` was not passed a str or ProblemTypes object"
)
[docs]def 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.
Args:
y (pd.Series): The target labels to predict.
Returns:
ProblemType: ProblemType Enum
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.
"""
y = pd.Series(y).dropna()
num_classes = y.nunique()
if num_classes < 2:
raise ValueError("Less than 2 classes detected! Target unusable for modeling")
if num_classes == 2:
return ProblemTypes.BINARY
if is_numeric_dtype(y.dtype):
if num_classes > 10:
return ProblemTypes.REGRESSION
return ProblemTypes.MULTICLASS
[docs]def is_regression(problem_type):
"""Determines if the provided problem_type is a regression problem type.
Args:
problem_type (str or ProblemTypes): type of supervised learning problem. See evalml.problem_types.ProblemType.all_problem_types for a full list.
Returns:
bool: Whether or not the provided problem_type is a regression problem type.
"""
return handle_problem_types(problem_type) in [
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
]
[docs]def is_binary(problem_type):
"""Determines if the provided problem_type is a binary classification problem type.
Args:
problem_type (str or ProblemTypes): type of supervised learning problem. See evalml.problem_types.ProblemType.all_problem_types for a full list.
Returns:
bool: Whether or not the provided problem_type is a binary classification problem type.
"""
return handle_problem_types(problem_type) in [
ProblemTypes.BINARY,
ProblemTypes.TIME_SERIES_BINARY,
]
[docs]def is_multiclass(problem_type):
"""Determines if the provided problem_type is a multiclass classification problem type.
Args:
problem_type (str or ProblemTypes): type of supervised learning problem. See evalml.problem_types.ProblemType.all_problem_types for a full list.
Returns:
bool: Whether or not the provided problem_type is a multiclass classification problem type.
"""
return handle_problem_types(problem_type) in [
ProblemTypes.MULTICLASS,
ProblemTypes.TIME_SERIES_MULTICLASS,
]
[docs]def is_classification(problem_type):
"""Determines if the provided problem_type is a classification problem type.
Args:
problem_type (str or ProblemTypes): type of supervised learning problem. See evalml.problem_types.ProblemType.all_problem_types for a full list.
Returns:
bool: Whether or not the provided problem_type is a classification problem type.
"""
return is_binary(problem_type) or is_multiclass(problem_type)
[docs]def is_time_series(problem_type):
"""Determines if the provided problem_type is a time series problem type.
Args:
problem_type (str or ProblemTypes): type of supervised learning problem. See evalml.problem_types.ProblemType.all_problem_types for a full list.
Returns:
bool: Whether or not the provided problem_type is a time series problem type.
"""
return handle_problem_types(problem_type) in [
ProblemTypes.TIME_SERIES_BINARY,
ProblemTypes.TIME_SERIES_MULTICLASS,
ProblemTypes.TIME_SERIES_REGRESSION,
]