Loading deepchem/hyper/base_classes.py +3 −0 Original line number Diff line number Diff line Loading @@ -80,6 +80,7 @@ class HyperparamOpt(object): valid_dataset: Dataset, metric: Metric, output_transformers: List[Transformer] = [], nb_epoch: int = 5, use_max: bool = True, logdir: Optional[str] = None, **kwargs) -> Tuple[Model, Dict, Dict]: Loading Loading @@ -110,6 +111,8 @@ class HyperparamOpt(object): `train_dataset` and `valid_dataset` may have been transformed for learning and need the transform to be inverted before the metric can be evaluated on a model. nb_epoch: int, (default 5) Specifies the number of training epochs during each iteration of optimization. use_max: bool, optional If True, return the model with the highest score. Else return model with the minimum score. Loading deepchem/hyper/gaussian_process.py +1 −1 Original line number Diff line number Diff line Loading @@ -162,7 +162,7 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): for learning and need the transform to be inverted before the metric can be evaluated on a model. nb_epoch: int, (default 5) Specifies the number of epochs during each iteration of optimization Specifies the number of training epochs during each iteration of optimization. use_max: bool, (default True) Specifies whether to maximize or minimize `metric`. maximization(True) or minimization(False) Loading deepchem/hyper/grid_search.py +1 −0 Original line number Diff line number Diff line Loading @@ -93,6 +93,7 @@ class GridHyperparamOpt(HyperparamOpt): for learning and need the transform to be inverted before the metric can be evaluated on a model. nb_epoch: int, (default 5) Specifies the number of training epochs during each iteration of optimization. use_max: bool, optional If True, return the model with the highest score. Else return model with the minimum score. Loading Loading
deepchem/hyper/base_classes.py +3 −0 Original line number Diff line number Diff line Loading @@ -80,6 +80,7 @@ class HyperparamOpt(object): valid_dataset: Dataset, metric: Metric, output_transformers: List[Transformer] = [], nb_epoch: int = 5, use_max: bool = True, logdir: Optional[str] = None, **kwargs) -> Tuple[Model, Dict, Dict]: Loading Loading @@ -110,6 +111,8 @@ class HyperparamOpt(object): `train_dataset` and `valid_dataset` may have been transformed for learning and need the transform to be inverted before the metric can be evaluated on a model. nb_epoch: int, (default 5) Specifies the number of training epochs during each iteration of optimization. use_max: bool, optional If True, return the model with the highest score. Else return model with the minimum score. Loading
deepchem/hyper/gaussian_process.py +1 −1 Original line number Diff line number Diff line Loading @@ -162,7 +162,7 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): for learning and need the transform to be inverted before the metric can be evaluated on a model. nb_epoch: int, (default 5) Specifies the number of epochs during each iteration of optimization Specifies the number of training epochs during each iteration of optimization. use_max: bool, (default True) Specifies whether to maximize or minimize `metric`. maximization(True) or minimization(False) Loading
deepchem/hyper/grid_search.py +1 −0 Original line number Diff line number Diff line Loading @@ -93,6 +93,7 @@ class GridHyperparamOpt(HyperparamOpt): for learning and need the transform to be inverted before the metric can be evaluated on a model. nb_epoch: int, (default 5) Specifies the number of training epochs during each iteration of optimization. use_max: bool, optional If True, return the model with the highest score. Else return model with the minimum score. Loading
