Source code for evalml.data_checks.datetime_nan_data_check

"""Data check that checks each column in the input for datetime features and will issue an error if NaN values are present."""

from evalml.data_checks import DataCheck, DataCheckError, DataCheckMessageCode
from evalml.utils.woodwork_utils import infer_feature_types

error_contains_nan = "Input datetime column(s) ({}) contains NaN values. Please impute NaN values or drop these rows or columns."


[docs]class DateTimeNaNDataCheck(DataCheck): """Check each column in the input for datetime features and will issue an error if NaN values are present."""
[docs] def validate(self, X, y=None): """Check if any datetime columns contain NaN values. Args: X (pd.DataFrame, np.ndarray): Features. y (pd.Series, np.ndarray): Ignored. Defaults to None. Returns: dict: dict with a DataCheckError if NaN values are present in datetime columns. Examples: >>> import pandas as pd >>> import numpy as np ... >>> dates = [['2-1-21', '3-1-21'], ... ['2-2-21', '3-2-21'], ... ['2-3-21', '3-3-21'], ... ['2-4-21', '3-4-21']] >>> df = pd.DataFrame(dates, columns=['index', "days"]) >>> dt_nan_dc = DateTimeNaNDataCheck() >>> assert dt_nan_dc.validate(df) == {'warnings': [], 'errors': [], 'actions': []} ... ... >>> dates[0][0] = np.datetime64('NaT') >>> df = pd.DataFrame(dates, columns=['index', "days"]) >>> assert dt_nan_dc.validate(df) == { ... 'warnings': [], ... 'errors': [{'message': 'Input datetime column(s) (index) contains NaN values. Please impute NaN values or drop these rows or columns.', ... 'data_check_name': 'DateTimeNaNDataCheck', ... 'level': 'error', ... 'details': {'columns': ['index'], 'rows': None}, ... 'code': 'DATETIME_HAS_NAN'}], ... 'actions': []} ... ... >>> dates[0][1] = None >>> df = pd.DataFrame(dates, columns=['index', "days"]) >>> assert dt_nan_dc.validate(df) == { ... 'warnings': [], ... 'errors': [{'message': 'Input datetime column(s) (index, days) contains NaN values. Please impute NaN values or drop these rows or columns.', ... 'data_check_name': 'DateTimeNaNDataCheck', ... 'level': 'error', ... 'details': {'columns': ['index', 'days'], 'rows': None}, ... 'code': 'DATETIME_HAS_NAN'}], ... 'actions': []} ... ... >>> dates[0][1] = pd.NA >>> df = pd.DataFrame(dates, columns=['index', "days"]) >>> assert dt_nan_dc.validate(df) == { ... 'warnings': [], ... 'errors': [{'message': 'Input datetime column(s) (index, days) contains NaN values. Please impute NaN values or drop these rows or columns.', ... 'data_check_name': 'DateTimeNaNDataCheck', ... 'level': 'error', ... 'details': {'columns': ['index', 'days'], 'rows': None}, ... 'code': 'DATETIME_HAS_NAN'}], ... 'actions': []} """ results = {"warnings": [], "errors": [], "actions": []} X = infer_feature_types(X) datetime_cols = X.ww.select("datetime") nan_columns = datetime_cols.columns[datetime_cols.isna().any()].tolist() if len(nan_columns) > 0: nan_columns = [str(col) for col in nan_columns] cols_str = ( ", ".join(nan_columns) if len(nan_columns) > 1 else nan_columns[0] ) results["errors"].append( DataCheckError( message=error_contains_nan.format(cols_str), data_check_name=self.name, message_code=DataCheckMessageCode.DATETIME_HAS_NAN, details={"columns": nan_columns}, ).to_dict() ) return results