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¶
Checks each column in the input for natural language features and will issue an error if NaN values are present. |
Attributes Summary¶
Contents¶
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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...¶
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class
evalml.data_checks.natural_language_nan_data_check.
NaturalLanguageNaNDataCheck
[source]¶ Checks each column in the input for natural language features and will issue an error if NaN values are present.
Methods
Return a name describing the data check.
Check if any natural language columns contain NaN values.
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name
(cls)¶ Return a name describing the data check.
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validate
(self, X, y=None)[source]¶ 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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