natural_language_nan_data_check

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

Module Contents

Classes Summary

NaturalLanguageNaNDataCheck

Checks each column in the input for natural language features and will issue an error if NaN values are present.

Attributes Summary

error_contains_nan

Contents

evalml.data_checks.natural_language_nan_data_check.error_contains_nan = Input natural language column(s) ({}) contains NaN values. Please impute NaN values or drop...

class evalml.data_checks.natural_language_nan_data_check.NaturalLanguageNaNDataCheck

Checks each column in the input for natural language features and will issue an error if NaN values are present.

Methods

name	Return a name describing the data check.
validate	Check if any natural language columns contain NaN values.

name(cls)

Return a name describing the data check.

validate(self, X, y=None)

Check if any natural language columns contain NaN values.

Parameters

• X (pd.DataFrame, np.ndarray) – Features.

• y (pd.Series, np.ndarray) – Ignored. Defaults to None.

Returns

dict with a DataCheckError if NaN values are present in natural language columns.

Return type

dict

Example

>>> import pandas as pd
>>> import woodwork as ww
>>> import numpy as np
>>> data = pd.DataFrame()
>>> data['A'] = [None, "string_that_is_long_enough_for_natural_language"]
>>> data['B'] = ['string_that_is_long_enough_for_natural_language', 'string_that_is_long_enough_for_natural_language']
>>> data['C'] = np.random.randint(0, 3, size=len(data))
>>> data.ww.init(logical_types={'A': 'NaturalLanguage', 'B': 'NaturalLanguage'})
>>> nl_nan_check = NaturalLanguageNaNDataCheck()
>>> assert nl_nan_check.validate(data) == {
...        "warnings": [],
...        "actions": [],
...        "errors": [DataCheckError(message='Input natural language column(s) (A) contains NaN values. Please impute NaN values or drop these rows or columns.',
...                      data_check_name=NaturalLanguageNaNDataCheck.name,
...                      message_code=DataCheckMessageCode.NATURAL_LANGUAGE_HAS_NAN,
...                      details={"columns": 'A'}).to_dict()]
...    }

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