Tokenizer

class atom.nlp.Tokenizer(bigram_freq=None, trigram_freq=None, quadgram_freq=None, verbose=0)[source]

Tokenize the corpus.

Convert documents into sequences of words. Additionally, create n-grams (represented by words united with underscores, e.g., "New_York") based on their frequency in the corpus. The transformations are applied on the column named corpus. If there is no column with that name, an exception is raised.

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

Parameters

bigram_freq: int, float or None, default=None

Frequency threshold for bigram creation.

If None: Don't create any bigrams.
If int: Minimum number of occurrences to make a bigram.
If float: Minimum frequency fraction to make a bigram.

trigram_freq: int, float or None, default=None

Frequency threshold for trigram creation.

If None: Don't create any trigrams.
If int: Minimum number of occurrences to make a trigram.
If float: Minimum frequency fraction to make a trigram.

quadgram_freq: int, float or None, default=None

Frequency threshold for quadgram creation.

If None: Don't create any quadgrams.
If int: Minimum number of occurrences to make a quadgram.
If float: Minimum frequency fraction to make a quadgram.

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

bigrams_: pd.DataFrame

Created bigrams and their frequencies.

trigrams_: pd.DataFrame

Created trigrams and their frequencies.

quadgrams_: pd.DataFrame

Created quadgrams and their frequencies.

feature_names_in_: np.ndarray

Names of features seen during fit.

n_features_in_: int

Number of features seen during fit.

Example

atomstand-alone

>>> 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.tokenize(verbose=2)

Fitting Tokenizer...
Tokenizing the corpus...


>>> 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

>>> from atom.nlp import Tokenizer

>>> 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"],
... ]

>>> tokenizer = Tokenizer(bigram_freq=2, verbose=2)
>>> X = tokenizer.transform(X)

Tokenizing the corpus...
 --> Creating 5 bigrams on 10 locations.


>>> print(X)

                                     corpus
0               [I, àm, in, ne, ', w, york]
1                      [New, york_is, nice]
2                                [new_york]
3           [hi, there, this_is, a_test, !]
4                      [another, line, ...]
5  [new, york_is, larger, than, washington]
6                      [running, the, test]
7                         [this_is, a_test]

Methods

fit	Do nothing.
fit_transform	Fit to data, then transform it.
get_feature_names_out	Get output feature names for transformation.
get_params	Get parameters for this estimator.
inverse_transform	Do nothing.
set_output	Set output container.
set_params	Set the parameters of this estimator.
transform	Tokenize 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]

Tokenize 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.