text_quality.classifier.pipeline
Classification pipeline.
Module Contents
Classes
Reasons for the classification result. |
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A wrapper around an sklearn pipeline that adds a featurizer. |
Functions
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Generate a ClassifierScores dict with default values. |
Attributes
Container class for the scores returned by the classifier. |
- text_quality.classifier.pipeline.ClassifierScores[source]
Container class for the scores returned by the classifier.
- class text_quality.classifier.pipeline.Reason[source]
Bases:
enum.EnumReasons for the classification result.
- text_quality.classifier.pipeline.default_scores_dict(default_value, **fields) ClassifierScores[source]
Generate a ClassifierScores dict with default values.
- Parameters:
default_value – The default value for the scores.
fields – arguments to add to the dict, hence not taking the default value.
- class text_quality.classifier.pipeline.Pipeline(pipeline: sklearn.pipeline.Pipeline, featurizer: text_quality.feature.featurize.Featurizer, default_language: str = DEFAULT_LANGUAGE)[source]
A wrapper around an sklearn pipeline that adds a featurizer.
- classify(page: text_quality.page.page.Page | str) int[source]
Single instance classification.
- _classify_pagexml(pagexml: text_quality.page.page.Page) int[source]
Classify a Page object.
- classify_with_scores(page: text_quality.page.page.Page | str) tuple[int, ClassifierScores, Reason][source]
Single instance classification with scores.
- _classify_pagexml_with_scores(pagexml: text_quality.page.page.Page) tuple[int, ClassifierScores, Reason][source]
Classify a Page object with scores.
- classmethod from_file(pipeline_file: pathlib.Path, featurizer: text_quality.feature.featurize.Featurizer)[source]
Load a pipeline from a file.