Tokenizer

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

logger: str, Logger or None, default=None

If None: Doesn't save a logging file.
If str: Name of the log file. Use "auto" for automatic naming.
Else: Python logging.Logger instance.

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.

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)
>>> print(atom.dataset)

                               corpus  target
0                            new york       0
1  new york is larger than washington       1
2                    New york is nice       0
3                   I àm in ne'w york       1
4                      this is a test       0
5                     another line...       1
6                    running the 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  [new, york, is, larger, than, washington]       1
2                      [New, york, is, nice]       0
3                [I, àm, in, ne, ', w, york]       1
4                        [this, is, a, test]       0
5                       [another, line, ...]       1
6                       [running, the, 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"],
... ]
>>> y = [1, 0, 0, 1, 1, 1, 0, 0]

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

Fitting Tokenizer...
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	Does nothing.
fit_transform	Fit to data, then transform it.
get_params	Get parameters for this estimator.
inverse_transform	Does nothing.
log	Print message and save to log file.
save	Save the instance to a pickle file.
set_params	Set the parameters of this estimator.
transform	Tokenize the text.

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

Does 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: int, str, dict, sequence or None, default=None

Target column corresponding to X.

If None: y is ignored.
If int: Position of the target column in X.
If str: Name of the target column in X.
Else: Array with shape=(n_samples,) to use as target.

**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: int, str, dict, sequence or None, default=None

Target column corresponding to X.

If None: y is ignored.
If int: Position of the target column in X.
If str: Name of the target column in X.
Else: Array with shape=(n_samples,) to use as target.

**fit_params

Additional keyword arguments for the fit method.

Returns

pd.DataFrame

Transformed feature set. Only returned if provided.

pd.Series

Transformed target column. Only returned if provided.

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)[source]

Does nothing.

Parameters	X: dataframe-like or None, default=None Feature set with shape=(n_samples, n_features). If None, X is ignored. y: int, str, dict, sequence or None, default=None Target column corresponding to X. If None: y is ignored. If int: Position of the target column in X. If str: Name of the target column in X. Else: Array with shape=(n_samples,) to use as target.
Returns	pd.DataFrame Transformed feature set. Only returned if provided. pd.Series Transformed target column. Only returned if provided.

method log(msg, level=0, severity="info")[source]

Print message and save to log file.

Parameters

msg: int, float or str

Message to save to the logger and print to stdout.

level: int, default=0

Minimum verbosity level to print the message.

severity: str, default="info"

Severity level of the message. Choose from: debug, info, warning, error, critical.

method save(filename="auto", save_data=True)[source]

Save the instance to a pickle file.

Parameters

filename: str, default="auto"

Name of the file. Use "auto" for automatic naming.

save_data: bool, default=True

Whether to save the dataset with the instance. This parameter is ignored if the method is not called from atom. If False, remember to add the data to ATOMLoader when loading the file.

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 pd.DataFrame, it should be composed of a single feature containing the text documents. y: int, str, dict, sequence or None, default=None Does nothing. Implemented for continuity of the API.
Returns	pd.DataFrame Transformed corpus.