"""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) == []
The first value in the column "index" is replaced with NaT, which will raise an error in this data check.
>>> dates[0][0] = np.datetime64("NaT")
>>> df = pd.DataFrame(dates, columns=["index", "days"])
>>> assert dt_nan_dc.validate(df) == [
... {
... "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",
... "action_options": []
... }
... ]
...
The value None will be treated the same way.
>>> dates[0][1] = None
>>> df = pd.DataFrame(dates, columns=["index", "days"])
>>> assert dt_nan_dc.validate(df) == [
... {
... "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",
... "action_options": []
... }
... ]
...
As will pd.NA.
>>> dates[0][1] = pd.NA
>>> df = pd.DataFrame(dates, columns=["index", "days"])
>>> assert dt_nan_dc.validate(df) == [
... {
... "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",
... "action_options": []
... }
... ]
"""
messages = []
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]
)
messages.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 messages