jsonvectorizer.vectorizers.BaseVectorizer

class jsonvectorizer.vectorizers.BaseVectorizer

Base class for vectorizers

Base class for extracting features from individual fields in JSON documents. Any class that inherits from this one must implement a scikit-learn-like interface, i.e., fit() and transform() methods. The fit() method must accept arbitrary keyword arguments, i.e., **kwargs at the end of the method’s signature, and must return None when no features are generated.

Methods

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.

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.