plexus.scores.FastTextClassifier module
- class plexus.scores.FastTextClassifier.FastTextClassifier(**parameters)
Bases:
ScoreInitialize the Score instance with the given parameters.
Parameters
- **parametersdict
Arbitrary keyword arguments that are used to initialize the Parameters instance.
Raises
- ValidationError
If the provided parameters do not pass validation.
- class Parameters(*, scorecard_name: str | None = None, name: str | None = None, id: str | int | None = None, key: str | None = None, dependencies: List[dict] | None = None, data: dict | None = None, number_of_classes: int | None = None, label_score_name: str | None = None, label_field: str | None = None, learning_rate: float = 0.1, dimension: int = 100, window_size: int = 5, number_of_epochs: int = 5, minimum_word_count: int = 1, minimum_label_count: int = 1, minimum_character_ngram_length: int = 0, maximum_character_ngram_length: int = 0, number_of_negative_samples: int = 5, word_ngram_count: int = 1, loss_function: str = 'softmax', bucket_size: int = 2000000, number_of_threads: int = 4, learning_rate_update_rate: int = 100, sampling_threshold: float = 0.0001)
Bases:
ParametersCreate a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- bucket_size: int
- dimension: int
- learning_rate: float
- learning_rate_update_rate: int
- loss_function: str
- maximum_character_ngram_length: int
- minimum_character_ngram_length: int
- minimum_label_count: int
- minimum_word_count: int
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- number_of_epochs: int
- number_of_negative_samples: int
- number_of_threads: int
- sampling_threshold: float
- window_size: int
- word_ngram_count: int
- __init__(**parameters)
Initialize the Score instance with the given parameters.
Parameters
- **parametersdict
Arbitrary keyword arguments that are used to initialize the Parameters instance.
Raises
- ValidationError
If the provided parameters do not pass validation.
- data_filename()
- get_model_artifact_path()
- load_context(context)
Load the trained model and any necessary artifacts based on the MLflow context.
Parameters
- contextmlflow.pyfunc.PythonModelContext
The context object containing artifacts and other information.
- load_model(model_path)
- predict(model_input, text_column='text')
Make predictions on the input data.
Parameters
- contextAny
Context for the prediction (e.g., MLflow context)
- model_inputScore.Input
The input data for making predictions.
Returns
- Union[Score.Result, List[Score.Result]]
Either a single Score.Result or a list of Score.Results
- predict_validation()
Predict on the validation set.
This method should be implemented by subclasses to provide the prediction logic on the validation set.
- process_data()
Handle any pre-processing of the training data, including the training/validation splits.
- register_model()
Register the model with the model registry.
- save_model()
Save the model to the model registry.
- save_model_binary()