autointent.modules.scoring.SklearnScorer#

class autointent.modules.scoring.SklearnScorer(clf_name, embedder_config=None, **clf_args)#

Bases: autointent.modules.base.BaseScorer

Scoring module for classification using sklearn classifiers.

This module uses embeddings generated from a transformer model to train chosen sklearn classifier for intent classification.

Parameters:

  • clf_name (str) – Name of the sklearn classifier to use

  • embedder_config (autointent.configs.EmbedderConfig | str | dict[str, Any] | None) – Config of the embedder model

  • **clf_args (Any) – Arguments for the chosen sklearn classifier

Examples

>>> from autointent.modules.scoring import SklearnScorer
>>> utterances = ["hello", "how are you?"]
>>> labels = [0, 1]
>>> scorer = SklearnScorer(
...     clf_name="LogisticRegression",
...     embedder_config="sergeyzh/rubert-tiny-turbo",
... )
>>> scorer.fit(utterances, labels)
>>> test_utterances = ["hi", "what's up?"]
>>> probabilities = scorer.predict(test_utterances)
name = 'sklearn'#

Name of the module.

supports_multilabel = True#

Whether the module supports multilabel classification

supports_multiclass = True#

Whether the module supports multiclass classification

embedder_config#
clf_name#
classmethod from_context(context, clf_name, embedder_config=None, **clf_args)#

Create a SklearnScorer instance using a Context object.

Parameters:

  • context (autointent.Context) – Context containing configurations and utilities

  • clf_name (str) – Name of the sklearn classifier to use

  • embedder_config (autointent.configs.EmbedderConfig | str | None) – Config of the embedder, or None to use the best embedder

  • **clf_args (float | str | bool) – Arguments for the chosen sklearn classifier

Return type:

typing_extensions.Self

fit(utterances, labels)#

Train the chosen sklearn classifier.

Parameters:

  • utterances (list[str]) – List of training utterances

  • labels (autointent.custom_types.ListOfLabels) – List of labels corresponding to the utterances

Raises:

ValueError – If the vector index mismatches the provided utterances

Return type:

None

predict(utterances)#

Predict probabilities for the given utterances.

Parameters:

utterances (list[str]) – List of query utterances

Returns:

Array of predicted probabilities for each class

Return type:

numpy.typing.NDArray[Any]

clear_cache()#

Clear cached data in memory used by the embedder.

Return type:

None