linear_regressor

Provides a model, which predicts next timesteps from with a linear regressor.

class LinearRegressorConfig(name: str = 'Linear Regressor', normalize: bool = True, fit_intercept: bool = True, n_jobs: Optional[int] = None, positive: bool = False)

Bases: SkLearnModelConfig

Defines the configuration for the LinearRegressor.

name

name of the model.

Type:

str

criterion

the function to measure the quality of a split.

splitter

the strategy used to choose the split at each node.

class LinearRegressorModel(time_series_params: TimeSeriesConfig, model_params: LinearRegressorConfig)

Bases: SkLearnModel

Defines a model, which uses a Linear Regressor to predict the next timestamps.

Initializes the configuration for the LinearRegressor.

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: LinearRegressorConfig) → 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 )