Source code for evalml.problem_types.utils

import pandas as pd

from .problem_types import ProblemTypes

from evalml.utils.gen_utils import numeric_dtypes


[docs]def handle_problem_types(problem_type): """Handles problem_type by either returning the ProblemTypes or converting from a str. Arguments: problem_type (str or ProblemTypes): Problem type that needs to be handled Returns: ProblemTypes """ 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 Arguments: 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 """ 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 y.dtype in numeric_dtypes: if (num_classes > 10): return ProblemTypes.REGRESSION return ProblemTypes.MULTICLASS