gluonts.mx.trainer.model_averaging module¶
-
class
gluonts.mx.trainer.model_averaging.AveragingStrategy(num_models: int = 5, metric: str = 'score', maximize: bool = False)[source]¶ Bases:
object-
apply(model_path: str) → str[source]¶ Averages model parameters of serialized models based on the selected model strategy and metric. IMPORTANT: Depending on the metric the user might want to minimize or maximize. The maximize flag has to be chosen appropriately to reflect this.
- Parameters
model_path – Path to the models directory.
- Returns
- Return type
Path to file with the averaged model.
-
average(param_paths: List[str], weights: List[float]) → Dict[source]¶ Averages parameters from a list of .params file paths.
- Parameters
param_paths – List of paths to parameter files.
weights – List of weights for the parameter average.
- Returns
- Return type
Averaged parameter dictionary.
-
static
average_arrays(arrays: List[mxnet.ndarray.ndarray.NDArray], weights: List[float]) → mxnet.ndarray.ndarray.NDArray[source]¶ Takes a list of arrays of the same shape and computes the element wise weighted average.
- Parameters
arrays – List of NDArrays with the same shape that will be averaged.
weights – List of weights for the parameter average.
- Returns
- Return type
The average of the NDArrays in the same context as arrays[0]
-
static
get_checkpoint_information(model_path: str) → List[Dict][source]¶ - Parameters
model_path – Path to the models directory.
- Returns
- Return type
List of checkpoint information dictionaries (metric, epoch_no, checkpoint path)
-
select_checkpoints(checkpoints: List[Dict]) → Tuple[List[str], List[float]][source]¶ Selects checkpoints and computes weights for the selected checkpoints.
- Parameters
checkpoints – List of checkpoint information dictionaries.
- Returns
- Return type
List of selected checkpoint paths and list of corresponding weights.
-
-
class
gluonts.mx.trainer.model_averaging.SelectNBestMean(num_models: int = 5, metric: str = 'score', maximize: bool = False)[source]¶ Bases:
gluonts.mx.trainer.model_averaging.AveragingStrategy-
select_checkpoints(checkpoints: List[Dict]) → Tuple[List[str], List[float]][source]¶ Selects the checkpoints with the best metric values. The weights are equal for all checkpoints, i.e., w_i = 1/N.
- Parameters
checkpoints – List of checkpoint information dictionaries.
- Returns
- Return type
List of selected checkpoint paths and list of corresponding weights.
-
-
class
gluonts.mx.trainer.model_averaging.SelectNBestSoftmax(num_models: int = 5, metric: str = 'score', maximize: bool = False)[source]¶ Bases:
gluonts.mx.trainer.model_averaging.AveragingStrategy-
select_checkpoints(checkpoints: List[Dict]) → Tuple[List[str], List[float]][source]¶ Selects the checkpoints with the best metric values. The weights are the softmax of the metric values, i.e., w_i = exp(v_i) / sum(exp(v_j)) if maximize=True w_i = exp(-v_i) / sum(exp(-v_j)) if maximize=False
- Parameters
checkpoints – List of checkpoint information dictionaries.
- Returns
- Return type
List of selected checkpoint paths and list of corresponding weights.
-
-
gluonts.mx.trainer.model_averaging.save_epoch_info(tmp_path: str, epoch_info: dict) → None[source]¶ Writes the current epoch information into a json file in the model path.
- Parameters
tmp_path – Temporary base path to save the epoch info.
epoch_info – Epoch information dictionary containing the parameters path, the epoch number and the tracking metric value.
- Returns
- Return type
None