jsonvectorizer.vectorizers.StringVectorizer

class jsonvectorizer.vectorizers.StringVectorizer(min_df=1, **kwargs)

Vectorizer for strings

Tokenization using scikit-learn’s CountVectorizer.

Parameters:

min_df : int or float, optional (default=1)

When using tokenization, ignore terms that have a document frequency strictly lower than this threshold. An integer is taken as an absolute count, and a float indicates the proportion of n_total passed to the fit() method.

**kwargs

Passed to scikit-learn’s CountVectorizer class for initialization.

Raises:

ValueError

If min_df is not a positive number.

Attributes:

feature_names_ : list of str

Methods

fit(self, values[, n_total])	Fit vectorizer to the provided data
fit_transform(self, values, \*\*fit_params)	Fit vectorizer to the provided data, then transform it
get_params(self[, deep])	Get parameters for this estimator.
set_params(self, \*\*params)	Set the parameters of this estimator.
transform(self, values)	Transform values and return the resulting feature matrix

fit(self, values, n_total=None, **kwargs)

Fit vectorizer to the provided data

Parameters:

values : array-like, [n_samples]

n_total : int or None, optional (default=None)

Total Number of documents that values are extracted from. If None, defaults to len(values).

**kwargs:

Ignored keyword arguments.

Returns:

self or None

Returns None if no features were generated, otherwise returns self.

fit_transform(self, values, **fit_params)

Fit vectorizer to the provided data, then transform it

Parameters:

values : array-like, [n_samples]

**fit_params

Keyword arguments, passed to the fit() method.

Returns:

X : ndarray, [n_samples, n_features]

get_params(self, deep=True)

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 : mapping of string to any

Parameter names mapped to their values.

set_params(self, **params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params : dict

Estimator parameters.

Returns:

self : object

Estimator instance.

transform(self, values)

Transform values and return the resulting feature matrix

Parameters:

values : array-like, [n_samples]

Returns:

X : sparse matrix, shape [n_samples, n_features]

Raises:

NotFittedError

If the vectorizer has not yet been fitted.