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,
    DataCheckAction,
    DataCheckActionCode,
    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 ... >>> 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) == { ... "errors": [], ... "warnings": [{"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}}], ... "actions": [{"code": "DROP_COL", ... "metadata": {"columns": ["regression_not_unique_enough"], "rows": None}}]} ... ... >>> uniqueness_check = UniquenessDataCheck(problem_type="multiclass", threshold=0.8) >>> assert uniqueness_check.validate(df) == { ... 'warnings': [{'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'}], ... 'errors': [], ... 'actions': [{'code': 'DROP_COL', ... 'metadata': {'columns': ['regression_unique_enough'], 'rows': None}}]} ... >>> y = pd.Series([1, 1, 1, 2, 2, 3, 3, 3]) >>> assert UniquenessDataCheck.uniqueness_score(y) == 0.65625 """ results = {"warnings": [], "errors": [], "actions": []} 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]) results["warnings"].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 }, }, ).to_dict() ) results["actions"].append( DataCheckAction( action_code=DataCheckActionCode.DROP_COL, metadata={"columns": not_unique_enough_cols}, ).to_dict() ) elif is_multiclass(self.problem_type): too_unique_cols = list(res.index[res > self.threshold]) results["warnings"].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 }, }, ).to_dict() ) results["actions"].append( DataCheckAction( action_code=DataCheckActionCode.DROP_COL, metadata={"columns": too_unique_cols}, ).to_dict() ) return results
[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