plexus.scores.ExplainableClassifier module
- class plexus.scores.ExplainableClassifier.ExplainableClassifier(**parameters)
Bases:
ScoreA classifier based on XGBoost that uses n-gram vectorization and produces a ranked list of features for a target class, by importance.
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.
- 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, top_n_features: int = 10000, leaderboard_n_features: int = 10, target_score_name: str, target_score_value: str, ngram_range: str = '2,3', decision_threshold: float = 0.5, scale_pos_weight_index: float = 0, include_explanations: bool = False, keywords: list = None)
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.
- decision_threshold: float
- include_explanations: bool
- keywords: list
- leaderboard_n_features: int
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- ngram_range: str
- scale_pos_weight_index: float
- target_score_name: str
- target_score_value: str
- top_n_features: int
- class Result(*, parameters: Parameters, value: str | bool, metadata: dict = {}, error: str | None = None, confidence: float, explanation: str)
Bases:
ResultThis Score has an additional output attribute, explanation, which is a string
Create 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.
- confidence: float
- explanation: str
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- __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.
- evaluate_model()
Evaluate the model on the validation data.
Returns
- dict
Dictionary containing evaluation metrics.
- explain_model()
- predict(context, model_input)
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.
- preprocess_text(text)
- register_model()
Register the model with the model registry.
- save_model()
Save the model to the model registry.
- train_model()
Train the XGBoost model with the specified positive class weight.
Parameters
- X_trainnumpy.ndarray
Training data features.
- y_trainnumpy.ndarray
Training data labels.
- X_valnumpy.ndarray
Validation data features.
- y_valnumpy.ndarray
Validation data labels.
- vectorize_transcript(transcript: str)