Source code for evalml.data_checks.uniqueness_data_check

"""Data check that checks if there are any columns in the input that are either too unique for classification problems or not unique enough for regression problems."""
from evalml.data_checks import (
    DataCheck,
    DataCheckActionCode,
    DataCheckActionOption,
    DataCheckMessageCode,
    DataCheckWarning,
)
from evalml.problem_types import (
    handle_problem_types,
    is_multiclass,
    is_regression,
)
from evalml.utils.woodwork_utils import infer_feature_types

warning_not_unique_enough = (
    "Input columns {} for {} problem type are not unique enough."
)
warning_too_unique = "Input columns {} for {} problem type are too unique."


[docs]class UniquenessDataCheck(DataCheck): """Check if there are any columns in the input that are either too unique for classification problems or not unique enough for regression problems. Args: problem_type (str or ProblemTypes): The specific problem type to data check for. e.g. 'binary', 'multiclass', 'regression, 'time series regression' threshold(float): The threshold to set as an upper bound on uniqueness for classification type problems or lower bound on for regression type problems. Defaults to 0.50. """ def __init__(self, problem_type, threshold=0.50): self.problem_type = handle_problem_types(problem_type) if threshold < 0 or threshold > 1: raise ValueError("threshold must be a float between 0 and 1, inclusive.") self.threshold = threshold
[docs] def validate(self, X, y=None): """Check if there are any columns in the input that are too unique in the case of classification problems or not unique enough in the case of regression problems. Args: X (pd.DataFrame, np.ndarray): Features. y (pd.Series, np.ndarray): Ignored. Defaults to None. Returns: dict: dict with a DataCheckWarning if there are any too unique or not unique enough columns. Examples: >>> import pandas as pd Because the problem type is regression, the column "regression_not_unique_enough" raises a warning for having just one value. >>> df = pd.DataFrame({ ... "regression_unique_enough": [float(x) for x in range(100)], ... "regression_not_unique_enough": [float(1) for x in range(100)] ... }) ... >>> uniqueness_check = UniquenessDataCheck(problem_type="regression", threshold=0.8) >>> assert uniqueness_check.validate(df) == [ ... { ... "message": "Input columns 'regression_not_unique_enough' for regression problem type are not unique enough.", ... "data_check_name": "UniquenessDataCheck", ... "level": "warning", ... "code": "NOT_UNIQUE_ENOUGH", ... "details": {"columns": ["regression_not_unique_enough"], "uniqueness_score": {"regression_not_unique_enough": 0.0}, "rows": None}, ... "action_options": [ ... { ... "code": "DROP_COL", ... "parameters": {}, ... "data_check_name": "UniquenessDataCheck", ... "metadata": {"columns": ["regression_not_unique_enough"], "rows": None} ... } ... ] ... } ... ] For multiclass, the column "regression_unique_enough" has too many unique values and will raise an appropriate warning. >>> y = pd.Series([1, 1, 1, 2, 2, 3, 3, 3]) >>> uniqueness_check = UniquenessDataCheck(problem_type="multiclass", threshold=0.8) >>> assert uniqueness_check.validate(df) == [ ... { ... "message": "Input columns 'regression_unique_enough' for multiclass problem type are too unique.", ... "data_check_name": "UniquenessDataCheck", ... "level": "warning", ... "details": { ... "columns": ["regression_unique_enough"], ... "rows": None, ... "uniqueness_score": {"regression_unique_enough": 0.99} ... }, ... "code": "TOO_UNIQUE", ... "action_options": [ ... { ... "code": "DROP_COL", ... "data_check_name": "UniquenessDataCheck", ... "parameters": {}, ... "metadata": {"columns": ["regression_unique_enough"], "rows": None} ... } ... ] ... } ... ] ... >>> assert UniquenessDataCheck.uniqueness_score(y) == 0.65625 """ messages = [] X = infer_feature_types(X) res = X.apply(UniquenessDataCheck.uniqueness_score) if is_regression(self.problem_type): not_unique_enough_cols = list(res.index[res < self.threshold]) messages.append( DataCheckWarning( message=warning_not_unique_enough.format( (", ").join( ["'{}'".format(str(col)) for col in not_unique_enough_cols] ), self.problem_type, ), data_check_name=self.name, message_code=DataCheckMessageCode.NOT_UNIQUE_ENOUGH, details={ "columns": not_unique_enough_cols, "uniqueness_score": { col: res.loc[col] for col in not_unique_enough_cols }, }, action_options=[ DataCheckActionOption( action_code=DataCheckActionCode.DROP_COL, data_check_name=self.name, metadata={"columns": not_unique_enough_cols}, ) ], ).to_dict() ) elif is_multiclass(self.problem_type): too_unique_cols = list(res.index[res > self.threshold]) messages.append( DataCheckWarning( message=warning_too_unique.format( (", ").join( ["'{}'".format(str(col)) for col in too_unique_cols] ), self.problem_type, ), data_check_name=self.name, message_code=DataCheckMessageCode.TOO_UNIQUE, details={ "columns": too_unique_cols, "uniqueness_score": { col: res.loc[col] for col in too_unique_cols }, }, action_options=[ DataCheckActionOption( action_code=DataCheckActionCode.DROP_COL, data_check_name=self.name, metadata={"columns": too_unique_cols}, ), ], ).to_dict() ) return messages
[docs] @staticmethod def uniqueness_score(col, drop_na=True): """Calculate a uniqueness score for the provided field. NaN values are not considered as unique values in the calculation. Based on the Herfindahl-Hirschman Index. Args: col (pd.Series): Feature values. drop_na (bool): Whether to drop null values when computing the uniqueness score. Defaults to True. Returns: (float): Uniqueness score. """ norm_counts = ( col.value_counts(dropna=drop_na) / col.value_counts(dropna=drop_na).sum() ) square_counts = norm_counts ** 2 score = 1 - square_counts.sum() return score