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TextNormalizer


class atom.nlp.TextNormalizer(stopwords=True, custom_stopwords=None, stem=False, lemmatize=True, verbose=0)[source]

Normalize the corpus.

Convert words to a more uniform standard. The transformations are applied on the column named corpus, in the same order the parameters are presented. If there is no column with that name, an exception is raised. If the provided documents are strings, words are separated by spaces.

This class can be accessed from atom through the textnormalize method. Read more in the user guide.

Parameters stopwords: bool or str, default=True
Whether to remove a predefined dictionary of stopwords.

  • If False: Don't remove any predefined stopwords.
  • If True: Drop predefined english stopwords from the text.
  • If str: Language from nltk.corpus.stopwords.words.

custom_stopwords: sequence or None, default=None
Custom stopwords to remove from the text.

stem: bool or str, default=False
Whether to apply stemming using SnowballStemmer.

  • If False: Don't apply stemming.
  • If True: Apply stemmer based on the english language.
  • If str: Language from SnowballStemmer.languages.

lemmatize: bool, default=True
Whether to apply lemmatization using WordNetLemmatizer.

verbose: int, default=0
Verbosity level of the class. Choose from:

  • 0 to not print anything.
  • 1 to print basic information.
  • 2 to print detailed information.

Attributes feature_names_in_: np.ndarray
Names of features seen during fit.

n_features_in_: int
Number of features seen during fit.


See Also

TextCleaner

Applies standard text cleaning to the corpus.

Tokenizer

Tokenize the corpus.

Vectorizer

Vectorize text data.


Example

>>> from atom import ATOMClassifier

>>> X = [
...    ["I àm in ne'w york"],
...    ["New york is nice"],
...    ["new york"],
...    ["hi there this is a test!"],
...    ["another line..."],
...    ["new york is larger than washington"],
...    ["running the test"],
...    ["this is a test"],
... ]
>>> y = [1, 0, 0, 1, 1, 1, 0, 0]

>>> atom = ATOMClassifier(X, y, test_size=2, random_state=1)
>>> print(atom.dataset)

                               corpus  target
0                            new york       0
1                     another line...       1
2                    New york is nice       0
3  new york is larger than washington       1
4                    running the test       0
5                   I àm in ne'w york       1
6                      this is a test       0
7            hi there this is a test!       1

>>> atom.textnormalize(stopwords="english", lemmatize=True, verbose=2)

Fitting TextNormalizer...
Normalizing the corpus...
 --> Dropping stopwords.
 --> Applying lemmatization.

>>> print(atom.dataset)

                           corpus  target
0                     [new, york]       0
1              [another, line...]       1
2               [New, york, nice]       0
3  [new, york, large, washington]       1
4                     [run, test]       0
5             [I, àm, ne'w, york]       1
6                          [test]       0
7                     [hi, test!]       1
>>> from atom.nlp import TextNormalizer

>>> X = [
...    ["I àm in ne'w york"],
...    ["New york is nice"],
...    ["new york"],
...    ["hi there this is a test!"],
...    ["another line..."],
...    ["new york is larger than washington"],
...    ["running the test"],
...    ["this is a test"],
... ]

>>> textnormalizer = TextNormalizer(
...     stopwords="english",
...     lemmatize=True,
...     verbose=2,
... )
>>> X = textnormalizer.transform(X)

Normalizing the corpus...
 --> Dropping stopwords.
 --> Applying lemmatization.

>>> print(X)

                           corpus
0             [I, àm, ne'w, york]
1               [New, york, nice]
2                     [new, york]
3                     [hi, test!]
4              [another, line...]
5  [new, york, large, washington]
6                     [run, test]
7                          [test]


Methods

fitDo nothing.
fit_transformFit to data, then transform it.
get_feature_names_outGet output feature names for transformation.
get_paramsGet parameters for this estimator.
inverse_transformDo nothing.
set_outputSet output container.
set_paramsSet the parameters of this estimator.
transformNormalize the text.


method fit(X=None, y=None, **fit_params)[source]

Do nothing.

Implemented for continuity of the API.

Parameters X: dataframe-like or None, default=None
Feature set with shape=(n_samples, n_features). If None, X is ignored.

y: sequence, dataframe-like or None, default=None
Target column(s) corresponding to X. If None, y is ignored.

**fit_params
Additional keyword arguments for the fit method.

Returns self
Estimator instance.



method fit_transform(X=None, y=None, **fit_params)[source]

Fit to data, then transform it.

Parameters X: dataframe-like or None, default=None
Feature set with shape=(n_samples, n_features). If None, X is ignored.

y: sequence, dataframe-like or None, default=None
Target column(s) corresponding to X. If None, y is ignored.

**fit_params
Additional keyword arguments for the fit method.

Returns dataframe
Transformed feature set. Only returned if provided.

series or dataframe
Transformed target column. Only returned if provided.



method get_feature_names_out(input_features=None)[source]

Get output feature names for transformation.

Parameters input_features : array-like of str or None, default=None
Input features.

  • If input_features is None, then feature_names_in_ is used as feature names in. If feature_names_in_ is not defined, then the following input feature names are generated: ["x0", "x1", ..., "x(n_features_in_ - 1)"].
  • If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns feature_names_out : ndarray of str objects
Same as input features.



method get_params(deep=True)[source]

Get parameters for this estimator.

Parameters deep : bool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns params : dict
Parameter names mapped to their values.



method inverse_transform(X=None, y=None, **fit_params)[source]

Do nothing.

Returns the input unchanged. Implemented for continuity of the API.

Parameters X: dataframe-like or None, default=None
Feature set with shape=(n_samples, n_features). If None, X is ignored.

y: sequence, dataframe-like or None, default=None
Target column(s) corresponding to X. If None, y is ignored.

Returns dataframe
Feature set. Only returned if provided.

series or dataframe
Target column(s). Only returned if provided.



method set_output(transform=None)[source]

Set output container.

See sklearn's user guide on how to use the set_output API. See here a description of the choices.

Parameters transform: str or None, default=None
Configure the output of the transform, fit_transform, and inverse_transform method. If None, the configuration is not changed. Choose from:

  • "numpy"
  • "pandas" (default)
  • "pandas-pyarrow"
  • "polars"
  • "polars-lazy"
  • "pyarrow"
  • "modin"
  • "dask"
  • "pyspark"
  • "pyspark-pandas"

Returns Self
Estimator instance.



method set_params(**params)[source]

Set the parameters of this estimator.

Parameters **params : dict
Estimator parameters.

Returns self : estimator instance
Estimator instance.



method transform(X, y=None)[source]

Normalize the text.

Parameters X: dataframe-like
Feature set with shape=(n_samples, n_features). If X is not a dataframe, it should be composed of a single feature containing the text documents.

y: sequence, dataframe-like or None, default=None
Do nothing. Implemented for continuity of the API.

Returns dataframe
Transformed corpus.