rocelib.models package

Subpackages

• rocelib.models.keras_models package
  • Submodules
  • rocelib.models.keras_models.TrainableKerasModel module
    • TrainableKerasModel
      • TrainableKerasModel.model
      • TrainableKerasModel.train()
      • TrainableKerasModel.predict()
      • TrainableKerasModel.predict_single()
      • TrainableKerasModel.evaluate()
      • TrainableKerasModel.predict_proba()
      • TrainableKerasModel.evaluate()
      • TrainableKerasModel.get_keras_model()
      • TrainableKerasModel.predict()
      • TrainableKerasModel.predict_proba()
      • TrainableKerasModel.predict_single()
      • TrainableKerasModel.train()
  • Module contents
• rocelib.models.pytorch_models package
  • Submodules
  • rocelib.models.pytorch_models.TrainableCustomPyTorchModel module
    • TrainableCustomPyTorchModel
      • TrainableCustomPyTorchModel.model
      • TrainableCustomPyTorchModel.criterion
      • TrainableCustomPyTorchModel.optimizer
      • TrainableCustomPyTorchModel.train()
      • TrainableCustomPyTorchModel.predict()
      • TrainableCustomPyTorchModel.predict_single()
      • TrainableCustomPyTorchModel.predict_proba()
      • TrainableCustomPyTorchModel.evaluate()
      • TrainableCustomPyTorchModel.train()
  • rocelib.models.pytorch_models.TrainablePyTorchModel module
    • TrainablePyTorchModel
      • TrainablePyTorchModel.input_dim
      • TrainablePyTorchModel.hidden_dim
      • TrainablePyTorchModel.output_dim
      • TrainablePyTorchModel.criterion
      • TrainablePyTorchModel.optimizer
      • TrainablePyTorchModel.__create_model()
      • TrainablePyTorchModel.train()
      • TrainablePyTorchModel.set_weights()
      • TrainablePyTorchModel.predict()
      • TrainablePyTorchModel.predict_single()
      • TrainablePyTorchModel.evaluate()
      • TrainablePyTorchModel.predict_proba()
      • TrainablePyTorchModel.predict_proba_tensor()
      • TrainablePyTorchModel.get_torch_model()
      • TrainablePyTorchModel.get_torch_model()
      • TrainablePyTorchModel.set_weights()
      • TrainablePyTorchModel.train()
  • Module contents
• rocelib.models.sklearn_models package
  • Submodules
  • rocelib.models.sklearn_models.TrainableDecisionTreeModel module
    • TrainableDecisionTreeModel
  • rocelib.models.sklearn_models.TrainableLogisticRegressionModel module
    • TrainableLogisticRegressionModel
  • rocelib.models.sklearn_models.TrainableSVMModel module
    • TrainableSVMModel
  • rocelib.models.sklearn_models.TrainableSKLearnModel module
    • TrainableSKLearnModel
      • TrainableSKLearnModel.train()
  • Module contents

Submodules

rocelib.models.TrainableModel module

class rocelib.models.TrainableModel.TrainableModel(model)

Bases: ABC

Abstract base class to define the essential methods that all model types must implement, providing template for training, predicting, and evaluating models in a standardized way.

_model

The underlying model object (e.g., a scikit-learn model or a PyTorch model) that this class wraps.

Type:

object

train(X: pd.DataFrame, y: pd.DataFrame) → None:

Trains the model using the provided feature and target data.

predict(X: pd.DataFrame) → pd.DataFrame:

Predicts the outcomes for the given instances.

predict_single(X: pd.DataFrame) → int:

Predicts the outcome for a single instance and returns an integer.

predict_proba(X: pd.DataFrame) → pd.DataFrame:

Predicts the probabilities of outcomes for the given instances.

predict_proba_tensor(X: torch.Tensor) → torch.Tensor:

Predicts the probabilities of outcomes for tensor inputs.

evaluate(X: pd.DataFrame, y: pd.DataFrame):

Evaluates the model’s performance on the provided feature and target data.

Properties()

----------

model:

Returns the underlying model object.

property model

Returns the underlying model object.

@return: The model object.

abstract train(X, y, **kwargs)

Trains the model using X feature variables and y target variable. Each implementing class can decide how to train their model and can add additional parameters, but X and y must be of type DataFrame.

@return: None

Return type:

TrainedModel

rocelib.models.Models module

rocelib.models.Models.get_sklearn_model(name)

Retrieves an instance of a scikit-learn model based on the provided name.

@param name: The name of the desired model. Options are:

• “log_reg” for Logistic Regression

• “decision_tree” for Decision Tree

• “svm” for Support Vector Machine

@return: An instance of the requested scikit-learn model. The model class should be a subclass of TrainableModel.

@raises ValueError: If the provided model name does not match any of the predefined options.

Module contents