zensols.deepnlp.classify package

Submodules

zensols.deepnlp.classify.domain

Domain objects for the natural language text classification atsk.

class zensols.deepnlp.classify.domain.LabeledBatch(batch_stash, id, split_name, data_points)

Bases: Batch

A batch used for labeled text, usually used for text classification. This batch class serves as a way for very basic funcationly, but also provides an example and template from which to desigh your own batch implementation for your custom application.

COUNTS_ATTRIBUTE = 'counts'

The feature counts attribute name.

DEPENDENCIES_ATTRIBUTE = 'dependencies'

The dependency feature attribute name.

DEPENDENCY_EXPANDER_ATTRIBTE = 'transformer_dep_expander'

Expands dependency tree spaCy features to transformer wordpiece alignment.

EMBEDDING_ATTRIBUTES = {'fasttext_crawl_300_embedding', 'fasttext_news_300_embedding', 'glove_300_embedding', 'glove_50_embedding', 'transformer_fixed_embedding', 'transformer_trainable_embedding', 'word2vec_300_embedding'}

All embedding feature section names.

ENUMS_ATTRIBUTE = 'enums'

The enumeration feature attribute name.

ENUM_EXPANDER_ATTRIBUTE = 'transformer_enum_expander'

Expands enumerated spaCy features to transformer wordpiece alignment.

FASTTEXT_CRAWL_300_EMBEDDING = 'fasttext_crawl_300_embedding'

The configuration section name of the fasttext crawl embedding FastTextEmbedModel class.

FASTTEXT_NEWS_300_EMBEDDING = 'fasttext_news_300_embedding'

The configuration section name of the fasttext news embedding FastTextEmbedModel class.

GLOVE_300_EMBEDDING = 'glove_300_embedding'

The configuration section name of the glove embedding GloveWordEmbedModel class.

GLOVE_50_EMBEDDING = 'glove_50_embedding'

The configuration section name of the glove embedding GloveWordEmbedModel class.

LANGUAGE_ATTRIBUTES = {'counts', 'dependencies', 'enums', 'stats', 'transformer_dep_expander', 'transformer_enum_expander'}

All linguistic feature attribute names.

LANGUAGE_FEATURE_MANAGER_NAME = 'language_vectorizer_manager'

The configuration section of the definition of the FeatureDocumentVectorizerManager.

MAPPINGS = BatchFeatureMapping(label_attribute_name='label', manager_mappings=[ManagerFeatureMapping(vectorizer_manager_name='classify_label_vectorizer_manager', fields=(FieldFeatureMapping(attr='label', feature_id='lblabel', is_agg=True, attr_access=None, is_label=False),)), ManagerFeatureMapping(vectorizer_manager_name='language_vectorizer_manager', fields=(FieldFeatureMapping(attr='glove_50_embedding', feature_id='wvglove50', is_agg=True, attr_access='doc', is_label=False), FieldFeatureMapping(attr='glove_300_embedding', feature_id='wvglove300', is_agg=True, attr_access='doc', is_label=False), FieldFeatureMapping(attr='word2vec_300_embedding', feature_id='w2v300', is_agg=True, attr_access='doc', is_label=False), FieldFeatureMapping(attr='fasttext_news_300_embedding', feature_id='wvftnews300', is_agg=True, attr_access='doc', is_label=False), FieldFeatureMapping(attr='fasttext_crawl_300_embedding', feature_id='wvftcrawl300', is_agg=True, attr_access='doc', is_label=False), FieldFeatureMapping(attr='transformer_trainable_embedding', feature_id='transformer_trainable', is_agg=True, attr_access='doc', is_label=False), FieldFeatureMapping(attr='transformer_fixed_embedding', feature_id='transformer_fixed', is_agg=True, attr_access='doc', is_label=False), FieldFeatureMapping(attr='stats', feature_id='stats', is_agg=False, attr_access='doc', is_label=False), FieldFeatureMapping(attr='enums', feature_id='enum', is_agg=True, attr_access='doc', is_label=False), FieldFeatureMapping(attr='counts', feature_id='count', is_agg=True, attr_access='doc', is_label=False), FieldFeatureMapping(attr='dependencies', feature_id='dep', is_agg=True, attr_access='doc', is_label=False), FieldFeatureMapping(attr='transformer_enum_expander', feature_id='tran_enum_expander', is_agg=True, attr_access='doc', is_label=False), FieldFeatureMapping(attr='transformer_dep_expander', feature_id='tran_dep_expander', is_agg=True, attr_access='doc', is_label=False)))])

The mapping from the labeled data’s feature attribute to feature ID and accessor information.

STATS_ATTRIBUTE = 'stats'

The statistics feature attribute name.

TRANSFORMER_FIXED_EMBEDDING = 'transformer_fixed_embedding'

Like TRANSFORMER_TRAINBLE_EMBEDDING, but all layers of the tranformer are frozen and only the static embeddings are used.

TRANSFORMER_TRAINBLE_EMBEDDING = 'transformer_trainable_embedding'

The configuration section name of the BERT transformer contextual embedding TransformerEmbedding class.

WORD2VEC_300_EMBEDDING = 'word2vec_300_embedding'

The configuration section name of the the Google word2vec embedding Word2VecModel class.

__init__(batch_stash, id, split_name, data_points)

class zensols.deepnlp.classify.domain.LabeledFeatureDocument(sents, text=None, spacy_doc=None, label=None, pred=None, softmax_logit=None)

Bases: FeatureDocument

A feature document with a label, used for text classification.

__init__(sents, text=None, spacy_doc=None, label=None, pred=None, softmax_logit=None)

label: str = None

The document level classification gold label.

pred: str = None

The document level prediction label.

See:

ClassificationPredictionMapper.pred_attribute

softmax_logit: float = None

The document level softmax of the logits.

See:

ClassificationPredictionMapper.softmax_logit_attribute

write(depth=0, writer=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>)

Write the document and optionally sentence features.

Parameters:

• n_sents – the number of sentences to write

• n_tokens – the number of tokens to print across all sentences

• include_original – whether to include the original text

• include_normalized – whether to include the normalized text

class zensols.deepnlp.classify.domain.LabeledFeatureDocumentDataPoint(id, batch_stash, container)

Bases: TokenContainerDataPoint

A representation of a data for a reivew document containing the sentiment polarity as the label.

__init__(id, batch_stash, container)

property label: str

The label for the textual data point.

class zensols.deepnlp.classify.domain.TokenContainerDataPoint(id, batch_stash, container)

Bases: DataPoint

A convenience class that uses data, such as tokens, a sentence or a document (TokenContainer) as a data point.

__init__(id, batch_stash, container)

container: TokenContainer

The token cotainer used for this data point.

property doc: FeatureDocument

The container as a document. If it is a sentence, it will create a document with the single sentence. This is usually used by the embeddings vectorizer.

property token_labels: Tuple[Any, ...]

The label that corresponds to each normalized token.

write(depth=0, writer=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>)

Write the contents of this instance to writer using indention depth.

Parameters:

• depth (int) – the starting indentation depth

• writer (TextIOBase) – the writer to dump the content of this writable

zensols.deepnlp.classify.facade

A facade for simple text classification tasks.

class zensols.deepnlp.classify.facade.ClassifyModelFacade(config, config_factory=<property object>, progress_bar=True, progress_bar_cols='term', executor_name='executor', writer=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>, predictions_dataframe_factory_class=<class 'zensols.deeplearn.result.pred.PredictionsDataFrameFactory'>, suppress_transformer_warnings=True)

Bases: LanguageModelFacade

A facade for the text classification. See super classes for more information on the purprose of this class.

All the set_* methods set parameters in the model.

LANGUAGE_MODEL_CONFIG = LanguageModelFacadeConfig(manager_name='language_vectorizer_manager', attribs={'transformer_enum_expander', 'transformer_dep_expander', 'dependencies', 'stats', 'counts', 'enums'}, embedding_attribs={'transformer_trainable_embedding', 'glove_50_embedding', 'fasttext_news_300_embedding', 'word2vec_300_embedding', 'glove_300_embedding', 'transformer_fixed_embedding', 'fasttext_crawl_300_embedding'})

The label model configuration constructed from the batch metadata.

See:

LabeledBatch

__init__(config, config_factory=<property object>, progress_bar=True, progress_bar_cols='term', executor_name='executor', writer=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>, predictions_dataframe_factory_class=<class 'zensols.deeplearn.result.pred.PredictionsDataFrameFactory'>, suppress_transformer_warnings=True)

property feature_stash: Stash

The stash containing feature instances.

get_predictions(*args, **kwargs)

Return a Pandas dataframe of the predictions with columns that include the correct label, the prediction, the text and the length of the text of the text.

Return type:

DataFrame

predict(datas)

Make ad-hoc predictions on batches without labels, and return the results.

Parameters:

datas (Iterable[Any]) – the data predict on, each as a separate element as a data point in a batch

Return type:

Any

class zensols.deepnlp.classify.facade.TokenClassifyModelFacade(config, config_factory=<property object>, progress_bar=True, progress_bar_cols='term', executor_name='executor', writer=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>, predictions_dataframe_factory_class=<class 'zensols.deeplearn.result.pred.SequencePredictionsDataFrameFactory'>, suppress_transformer_warnings=True)

Bases: ClassifyModelFacade

A token level classification model facade.

__init__(config, config_factory=<property object>, progress_bar=True, progress_bar_cols='term', executor_name='executor', writer=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>, predictions_dataframe_factory_class=<class 'zensols.deeplearn.result.pred.SequencePredictionsDataFrameFactory'>, suppress_transformer_warnings=True)

get_predictions(*args, **kwargs)

Return a Pandas dataframe of the predictions with columns that include the correct label, the prediction, the text and the length of the text of the text. This uses the token norms of the document.

See:

get_predictions_factory()

Parameters:

• args – arguments passed to get_predictions_factory()

• kwargs – arguments passed to get_predictions_factory()

Return type:

DataFrame

predictions_dataframe_factory_class

alias of SequencePredictionsDataFrameFactory

zensols.deepnlp.classify.model

Contains classes that make up a text classification model.

class zensols.deepnlp.classify.model.ClassifyNetwork(net_settings)

Bases: EmbeddingNetworkModule

A model that either allows for an RNN or a BERT transforemr to classify text.

MODULE_NAME: ClassVar[str] = 'classify'

The module name used in the logging message. This is set in each inherited class.

__init__(net_settings)

Initialize the embedding layer.

Parameters:

• net_settings (ClassifyNetworkSettings) – the embedding layer configuration

• logger – the logger to use for the forward process in this layer

• filter_attrib_fn – if provided, called with a BatchFieldMetadata for each field returning True if the batch field should be retained and used in the embedding layer (see class docs); if None all fields are considered

class zensols.deepnlp.classify.model.ClassifyNetworkSettings(name, config_factory, batch_stash, embedding_layer, dropout, recurrent_settings, linear_settings)

Bases: DropoutNetworkSettings, EmbeddingNetworkSettings

A utility container settings class for convulsion network models. This class also updates the recurrent network’s drop out settings when changed.

__init__(name, config_factory, batch_stash, embedding_layer, dropout, recurrent_settings, linear_settings)

get_module_class_name()

Returns the fully qualified class name of the module to create by ModelManager. This module takes as the first parameter an instance of this class.

Important: This method is not used for nested modules. You must declare specific class names in the configuration for those nested class naems.

Return type:

str

linear_settings: DeepLinearNetworkSettings

Contains the configuration for the model’s FF decoder.

recurrent_settings: RecurrentAggregationNetworkSettings

Contains the confgiuration for the models RNN.

zensols.deepnlp.classify.pred

Prediction mapper support for NLP applications.

class zensols.deepnlp.classify.pred.ClassificationPredictionMapper(datas, batch_stash, vec_manager, label_feature_id, pred_attribute='pred', softmax_logit_attribute='softmax_logit')

Bases: PredictionMapper

A prediction mapper for text classification. This mapper works at any level (document, sentence, token).

__init__(datas, batch_stash, vec_manager, label_feature_id, pred_attribute='pred', softmax_logit_attribute='softmax_logit')

label_feature_id: str

The feature ID for the label vectorizer.

property label_vectorizer: CategoryEncodableFeatureVectorizer

The label vectorizer used to map classes in get_classes().

map_results(result)

Map class predictions, logits, and documents generated during use of this instance. Each data point is aggregated across batches.

Return type:

Tuple[LabeledFeatureDocument]

Returns:

a Settings instance with classess, logits and docs attributes

pred_attribute: str = 'pred'

The prediction attribute to set on the FeatureDocument returned from map_results().

softmax_logit_attribute: str = 'softmax_logit'

The softmax of the logits attribute to set on the FeatureDocument returned from map_results().

See:

On Calibration of Modern Neural Networks

vec_manager: FeatureDocumentVectorizerManager

The vectorizer manager used to parse and get the label vectorizer.

class zensols.deepnlp.classify.pred.SequencePredictionMapper(datas, batch_stash, vec_manager, label_feature_id, pred_attribute='pred', softmax_logit_attribute='softmax_logit')

Bases: ClassificationPredictionMapper

Predicts sequences as a Settings with keys classes as the token level predictions and docs containing the parsed documents from the sentence text.

__init__(datas, batch_stash, vec_manager, label_feature_id, pred_attribute='pred', softmax_logit_attribute='softmax_logit')

map_results(result)

Map class predictions, logits, and documents generated during use of this instance. Each data point is aggregated across batches.

Return type:

Settings

Returns:

a Settings instance with classess, logits and docs attributes

Module contents