SklearnClassifier#

class SklearnClassifier(config)#

Bases: Classifier

The sklearn Classifier is a generic scikit-learn classifier.

Note - currently we support following scikit-learn classifier:

Input - the input field need to be of type list [ float or int ] or numpy.ndarray. In addition also the types str, float, int, list [ str ] and numpy.sparse matrices are supported.

Output - the output field is filled with data of type str or dict { str: float } if the model has the property “predict_proba”. The key of the dict is the predicted class name and the value is the probability/confidence returned by the model.

Parameters

  • type (str) – sklearn

  • model_type (str) – Type of scikit learn model

  • model_kwargs (dict, {}) – Keyword arguments for scikit learn model

  • use_sparse (bool, False) – Whether or not to use a sparse input matrix

Example

{
    "step": "classifier",
    "type": "sklearn",
    "model_type": "GaussianNB",
    "model_kwargs": {},
    "label_field": "label",
    "input_fields": ["embedded_extract"],
    "output_field": "prediction",
}

Methods Summary

load()

Load a step

process_batch(batch)

Process a batch of documents.

save()

Save a step

train(docs)

Train on a step of a set of documents

Methods Documentation

load()#

Load a step

process_batch(batch)#

Process a batch of documents. If not defined will default to using self.process_doc for each document in the batch.

Parameters

batch (list(Document)) – List of documents

Returns

List of processed documents

Return type

list(Document)

save()#

Save a step

train(docs)#

Train on a step of a set of documents

Parameters

docs (generator(Document)) – Generator of documents

Returns

Generator of processed documents

Return type

generator(Document)