paddlets.models.forecasting.ml.ml_model_wrapper

class MLModelBaseWrapper(model_class: Type, in_chunk_len: int, out_chunk_len: int, skip_chunk_len: int = 0, sampling_stride: int = 1, model_init_params: Optional[Dict[str, Any]] = None, fit_params: Optional[Dict[str, Any]] = None, predict_params: Optional[Dict[str, Any]] = None)

Bases: MLBaseModel

Time series model base wrapper for third party models.

Parameters

• model_class (Type) – Class type of the third party model.

• in_chunk_len (int) – The size of the loopback window, i.e., the number of time steps feed to the model.

• out_chunk_len (int) – The size of the forecasting horizon, i.e., the number of time steps output by the model.

• skip_chunk_len (int, optional) – The number of time steps between in_chunk and out_chunk for a single sample. The skip chunk is neither used as a feature (i.e. X) nor a label (i.e. Y) for a single sample. By default, it will NOT skip any time steps.

• sampling_stride (int, optional) – Time steps to stride over the i-th sample and (i+1)-th sample. More precisely, let t be the time index of target time series, t[i] be the start time of the i-th sample, t[i+1] be the start time of the (i+1)-th sample, then sampling_stride represents the result of t[i+1] - t[i].

• model_init_params (Dict[str, Any]) – All params for initializing the third party model.

• fit_params (Dict[str, Any], optional) – All params for fitting third party model except x_train / y_train.

• predict_params (Dict[str, Any], optional) – All params for forecasting third party model except x_test / y_test.

class SklearnModelWrapper(model_class: Type, in_chunk_len: int, out_chunk_len: int, skip_chunk_len: int = 0, sampling_stride: int = 1, model_init_params: Optional[Dict[str, Any]] = None, fit_params: Optional[Dict[str, Any]] = None, predict_params: Optional[Dict[str, Any]] = None, udf_ml_dataloader_to_fit_ndarray: Optional[Callable] = None, udf_ml_dataloader_to_predict_ndarray: Optional[Callable] = None)

Bases: MLModelBaseWrapper

Time series model wrapper for sklearn third party models.

Parameters

• model_class (Type) – Class type of the third party model.

• in_chunk_len (int) – The size of the loopback window, i.e., the number of time steps feed to the model.

• out_chunk_len (int) – The size of the forecasting horizon, i.e., the number of time steps output by the model.

• skip_chunk_len (int, optional) – The number of time steps between in_chunk and out_chunk for a single sample. The skip chunk is neither used as a feature (i.e. X) nor a label (i.e. Y) for a single sample. By default, it will NOT skip any time steps.

• sampling_stride (int, optional) – Time steps to stride over the i-th sample and (i+1)-th sample. More precisely, let t be the time index of target time series, t[i] be the start time of the i-th sample, t[i+1] be the start time of the (i+1)-th sample, then sampling_stride represents the result of t[i+1] - t[i].

• model_init_params (Dict[str, Any]) – All params for initializing the third party model.

• fit_params (Dict[str, Any], optional) – All params for fitting third party model except x_train / y_train.

• predict_params (Dict[str, Any], optional) – All params for forecasting third party model except x_test / y_test.

• udf_ml_dataloader_to_fit_ndarray (Callable, optional) – User defined function for converting MLDataLoader object to a numpy.ndarray object that can be processed by fit method of the third party model.

• udf_ml_dataloader_to_predict_ndarray (Callable, optional) – User defined function for converting MLDataLoader object to a numpy.ndarray object that can be processed by predict method of the third party model.

fit(train_data: TSDataset, valid_data: Optional[TSDataset] = None) → None

Fit a machine learning model.

Parameters

• train_data (TSDataset) – training dataset.

• valid_data (TSDataset, optional) – validation dataset.

predict(tsdataset: TSDataset) → TSDataset

Make prediction.

Parameters

tsdataset (TSDataset) – TSDataset to predict.

Returns

TSDataset with predictions.

Return type

TSDataset

default_ml_dataloader_to_fit_ndarray(ml_dataloader: MLDataLoader, model_init_params: Dict[str, Any], in_chunk_len: int, skip_chunk_len: int, out_chunk_len: int) → Tuple[ndarray, Optional[ndarray]]

Default function for converting MLDataLoader to a numpy array that can be used for fitting the sklearn model.

Parameters

• ml_dataloader (MLDataLoader) – MLDataLoader object to be converted.

• model_init_params (Dict) – parameters when initializing sklearn models, possibly be used while converting.

• in_chunk_len (int) – The size of the loopback window, i.e., the number of time steps feed to the model. Possibly be used while converting.

• skip_chunk_len (int, optional) – The number of time steps between in_chunk and out_chunk for a single sample. The skip chunk is neither used as a feature (i.e. X) nor a label (i.e. Y) for a single sample. By default, it will NOT skip any time steps. Possibly be used while converting.

• out_chunk_len (int) – The size of the forecasting horizon, i.e., the number of time steps output by the model. Possibly be used while converting.

Returns

Converted numpy array. The first and second element in the tuple represent x_train and y_train, respectively.

Return type

Tuple[np.ndarray, Optional[np.ndarray]]

default_ml_dataloader_to_predict_ndarray(ml_dataloader: MLDataLoader, model_init_params: Dict[str, Any], in_chunk_len: int, skip_chunk_len: int, out_chunk_len: int) → Tuple[ndarray, Optional[ndarray]]

Default function for converting MLDataLoader to a numpy array that can be predicted by the sklearn model.

Parameters

• ml_dataloader (MLDataLoader) – MLDataLoader object to be converted.

• model_init_params (Dict) – parameters when initializing sklearn models, possibly be used while converting.

• in_chunk_len (int) – The size of the loopback window, i.e., the number of time steps feed to the model. Possibly be used while converting.

• skip_chunk_len (int, optional) – The number of time steps between in_chunk and out_chunk for a single sample. The skip chunk is neither used as a feature (i.e. X) nor a label (i.e. Y) for a single sample. By default, it will NOT skip any time steps. Possibly be used while converting.

• out_chunk_len (int) – The size of the forecasting horizon, i.e., the number of time steps output by the model. Possibly be used while converting.

Returns

Converted numpy array. The first and second element in the tuple represent x and y, respectively, where y is optional.

Return type

Tuple[np.ndarray, Optional[np.ndarray]]

make_ml_model(model_class: Type, in_chunk_len: int, out_chunk_len: int, skip_chunk_len: int = 0, sampling_stride: int = 1, model_init_params: Optional[Dict[str, Any]] = None, fit_params: Optional[Dict[str, Any]] = None, predict_params: Optional[Dict[str, Any]] = None, udf_ml_dataloader_to_fit_ndarray: Optional[Callable] = None, udf_ml_dataloader_to_predict_ndarray: Optional[Callable] = None) → MLModelBaseWrapper

Make Wrapped time series model based on the third-party model.

Parameters

• model_class (Type) – Class type of the third party model.

• in_chunk_len (int) – The size of the loopback window, i.e., the number of time steps feed to the model.

• out_chunk_len (int) – The size of the forecasting horizon, i.e., the number of time steps output by the model.

• skip_chunk_len (int, optional) – The number of time steps between in_chunk and out_chunk for a single sample. The skip chunk is neither used as a feature (i.e. X) nor a label (i.e. Y) for a single sample. By default, it will NOT skip any time steps.

• sampling_stride (int, optional) – Time steps to stride over the i-th sample and (i+1)-th sample. More precisely, let t be the time index of target time series, t[i] be the start time of the i-th sample, t[i+1] be the start time of the (i+1)-th sample, then sampling_stride represents the result of t[i+1] - t[i].

• model_init_params (Dict[str, Any]) – All params for initializing the third party model.

• fit_params (Dict[str, Any], optional) – All params for fitting third party model except x_train / y_train.

• predict_params (Dict[str, Any], optional) – All params for forecasting third party model except x_test / y_test.

• udf_ml_dataloader_to_fit_ndarray (Callable, optional) – User defined function for converting MLDataLoader object to a numpy.ndarray object that can be processed by fit method of the third party model. Any third party models that accept numpy array as fit inputs can use this function to build the data for training.

• udf_ml_dataloader_to_predict_ndarray (Callable, optional) – User defined function for converting MLDataLoader object to a numpy.ndarray object that can be processed by predict method of the third party model. Any third-party models that accept numpy array as predict inputs can use this function to build the data for prediction.

Returns

Wrapped time series model wrapper object.

Return type

SklearnModelWrapper