Loading deepchem/hyper/gaussian_process.py +24 −19 Original line number Diff line number Diff line Loading @@ -132,6 +132,7 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): valid_dataset: Dataset, metric: Metric, output_transformers: List[Transformer] = [], nb_epoch: int = 5, use_max: bool = True, logdir: Optional[str] = None, max_iter: int = 20, Loading Loading @@ -230,22 +231,8 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): # Stores all model locations model_locations = {} # Demarcating internal function for readability def optimizing_function(**placeholders): """Private Optimizing function Take in hyper parameter values and return valid set performances Parameters ---------- placeholders: keyword arguments Should be various hyperparameters as specified in `param_keys` above. Returns: -------- valid_scores: float valid set performances """ # Private opt_func to pass nb_epoch for optimizing_function def _optimize(nb_epoch, **placeholders): hyper_parameters = {} for hp in param_keys: if param_range[hp][0] == "int": Loading Loading @@ -277,7 +264,7 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): # Add it on to the information needed for the constructor hyper_parameters["model_dir"] = model_dir model = self.model_builder(**hyper_parameters) model.fit(train_dataset) model.fit(train_dataset, nb_epoch=nb_epoch) try: model.save() # Some models autosave Loading Loading @@ -305,6 +292,24 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): else: return -score # Demarcating internal function for readability def optimizing_function(**placeholders): """Private Optimizing function Take in hyper parameter values and return valid set performances Parameters ---------- placeholders: keyword arguments Should be various hyperparameters as specified in `param_keys` above. Returns: -------- valid_scores: float valid set performances """ return _optimize(nb_epoch=nb_epoch, **placeholders) # execute GPGO cov = matern32() gp = GaussianProcess(cov) Loading Loading
deepchem/hyper/gaussian_process.py +24 −19 Original line number Diff line number Diff line Loading @@ -132,6 +132,7 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): valid_dataset: Dataset, metric: Metric, output_transformers: List[Transformer] = [], nb_epoch: int = 5, use_max: bool = True, logdir: Optional[str] = None, max_iter: int = 20, Loading Loading @@ -230,22 +231,8 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): # Stores all model locations model_locations = {} # Demarcating internal function for readability def optimizing_function(**placeholders): """Private Optimizing function Take in hyper parameter values and return valid set performances Parameters ---------- placeholders: keyword arguments Should be various hyperparameters as specified in `param_keys` above. Returns: -------- valid_scores: float valid set performances """ # Private opt_func to pass nb_epoch for optimizing_function def _optimize(nb_epoch, **placeholders): hyper_parameters = {} for hp in param_keys: if param_range[hp][0] == "int": Loading Loading @@ -277,7 +264,7 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): # Add it on to the information needed for the constructor hyper_parameters["model_dir"] = model_dir model = self.model_builder(**hyper_parameters) model.fit(train_dataset) model.fit(train_dataset, nb_epoch=nb_epoch) try: model.save() # Some models autosave Loading Loading @@ -305,6 +292,24 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): else: return -score # Demarcating internal function for readability def optimizing_function(**placeholders): """Private Optimizing function Take in hyper parameter values and return valid set performances Parameters ---------- placeholders: keyword arguments Should be various hyperparameters as specified in `param_keys` above. Returns: -------- valid_scores: float valid set performances """ return _optimize(nb_epoch=nb_epoch, **placeholders) # execute GPGO cov = matern32() gp = GaussianProcess(cov) Loading
