seldonian.models.sklearn_model.SupervisedSkLearnBaseModel

class SupervisedSkLearnBaseModel(**kwargs)

Bases: SupervisedModel

__init__(**kwargs)

Base class for Supervised learning Seldonian models implemented in scikit-learn

__repr__()

Return repr(self).

Methods

backward_pass(predictions, external_grad)

Do a backward pass through the model and return the (vector) gradient of the model with respect to theta as a numpy ndarray

create_model(**kwargs)

Create the sklearn model and return it

forward_pass(X, **kwargs)

Do a forward pass through the Sklearn model and return the model outputs (predicted labels or probabilities, depending on the model). Here, a forward pass is just a call to self.sklearn_model.predict()

Parameters:

X (numpy ndarray) – model features

Returns:

predictions

Return type:

numpy ndarray

get_model_params(*args)

Return weights of the model as a flattened 1D array

predict(theta, X, **kwargs)

Do a forward pass through the sklearn model. Must convert back to numpy array before returning

Parameters:

• theta (numpy ndarray) – model weights

• X (numpy ndarray) – model features

Returns:

model predictions

Return type:

numpy ndarray same shape as labels

update_model_params(theta, **kwargs)

Update all model parameters using theta, which must be reshaped

Parameters:

theta (numpy ndarray) – model weights