support_vector_machine_regressor

Provides a support vector machine regressor, which predicts next timesteps with.

class Kernel(value)

Bases: Enum

Specifies the kernel type to be used in the algorithm.

class SVMRegressorConfig(name: str = 'SVM Regressor', normalize: bool = True, kernel: Kernel = Kernel.linear)

Bases: SkLearnModelConfig

Defines the configuration for the SVMRegressor.

name

name of the model.

Type:

str

kernel

kernel type to be used in the algorithm.

Type:

simba_ml.prediction.time_series.models.sk_learn.support_vector_machine_regressor.Kernel

class SVMRegressorModel(time_series_params: TimeSeriesConfig, model_params: SVMRegressorConfig)

Bases: SkLearnModel

Defines a model, which uses a SVM regressor.

Initializes the configuration for the SVMRegressor.

Parameters:

• time_series_params – Time-series parameters that affect the training and architecture of models

• model_params – configuration for the model.

get_model(model_params: SVMRegressorConfig) → BaseEstimator

Returns the model.

Parameters:

model_params – configuration for the model.

Returns:

The model.

property name: str

Returns the models name.

Returns:

The models name.

predict(data: ndarray[Any, dtype[float64]]) → ndarray[Any, dtype[float64]]

Predicts the next time steps.

Parameters:

data – 3 dimensional numpy array.

Returns:

The predicted next time steps.

train(train: list[numpy.ndarray[Any, numpy.dtype[numpy.float64]]]) → None

Trains the model with the train data flattened to two dimensions.

Parameters:

train – training data.

validate_prediction_input(data: ndarray[Any, dtype[float64]]) → None

Validates the input of the predict function.

Parameters:

data – a single dataframe containing the input data, where the output will be predicted.

Raises:

ValueError – if data has incorrect shape (row length does not equal )