sk_learn

Provides the sklearn models.

class DecisionTreeRegressorModel(time_series_params: TimeSeriesConfig, model_params: DecisionTreeRegressorConfig)

Bases: SkLearnModel

Defines a decisision tree regressor model for predictions.

Initializes the configuration for the DecisionTreeRegressor.

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: DecisionTreeRegressorConfig) → 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 )

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 )

class NearestNeighborsRegressorModel(time_series_params: TimeSeriesConfig, model_params: NearestNeighborsConfig)

Bases: SkLearnModel

Defines a model, which uses a nearest neighbors regressor.

Initializes the configuration for the DecisionTreeRegressor.

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: NearestNeighborsConfig) → 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 )

class RandomForestRegressorModel(time_series_params: TimeSeriesConfig, model_params: RandomForestRegressorConfig)

Bases: SkLearnModel

Defines a model, which uses a Random Forest regressor.

Initializes the configuration for the RandomForestRegressor.

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: RandomForestRegressorConfig) → 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 )

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 )

simba_ml.prediction.time_series.models.sk_learn.decision_tree_regressor	Provides a decision tree regressor for prediction.
simba_ml.prediction.time_series.models.sk_learn.linear_regressor	Provides a model, which predicts next timesteps from with a linear regressor.
simba_ml.prediction.time_series.models.sk_learn.nearest_neighbors_regressor	Provides a nearest neighbors regressor, which predicts next timesteps.
simba_ml.prediction.time_series.models.sk_learn.random_forest_regressor	Provides a model, which predicts next timesteps with a random forest regressor.
simba_ml.prediction.time_series.models.sk_learn.sk_learn_model	Provides an arbitrary sk learn model architecture for prediction.
simba_ml.prediction.time_series.models.sk_learn.support_vector_machine_regressor	Provides a support vector machine regressor, which predicts next timesteps with.