Loading deepchem/hyper/gaussian_process.py +23 −2 Original line number Diff line number Diff line Loading @@ -163,6 +163,7 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): the metric can be evaluated on a model. nb_epoch: int, (default 5) Specifies the number of training epochs during each iteration of optimization. Not used by all model types. use_max: bool, (default True) Specifies whether to maximize or minimize `metric`. maximization(True) or minimization(False) Loading Loading @@ -235,6 +236,24 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): # Private opt_func to pass nb_epoch for optimizing_function def _optimize(nb_epoch, **placeholders): """Private Optimizing function Take in hyper parameter values and number of training epochs. Return valid set performances. Parameters ---------- nb_epoch: int Number of epochs to train model being optimized during each iteration. Not used by all model types. placeholders: keyword arguments Should be various hyperparameters as specified in `param_keys` above. Returns: -------- valid_scores: float valid set performances """ hyper_parameters = {} for hp in param_keys: if param_range[hp][0] == "int": Loading Loading @@ -300,9 +319,11 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): # Demarcating internal function for readability def optimizing_function(**placeholders): """Private Optimizing function """Wrapper function Take in hyper parameter values and return valid set performances Take in hyper parameter values. Calls a private optimize function (_optimize) with number of epochs. Returns valid set performances. Parameters ---------- Loading deepchem/hyper/grid_search.py +1 −0 Original line number Diff line number Diff line Loading @@ -94,6 +94,7 @@ class GridHyperparamOpt(HyperparamOpt): the metric can be evaluated on a model. nb_epoch: int, (default 5) Specifies the number of training epochs during each iteration of optimization. Not used by all model types. use_max: bool, optional If True, return the model with the highest score. Else return model with the minimum score. Loading Loading
deepchem/hyper/gaussian_process.py +23 −2 Original line number Diff line number Diff line Loading @@ -163,6 +163,7 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): the metric can be evaluated on a model. nb_epoch: int, (default 5) Specifies the number of training epochs during each iteration of optimization. Not used by all model types. use_max: bool, (default True) Specifies whether to maximize or minimize `metric`. maximization(True) or minimization(False) Loading Loading @@ -235,6 +236,24 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): # Private opt_func to pass nb_epoch for optimizing_function def _optimize(nb_epoch, **placeholders): """Private Optimizing function Take in hyper parameter values and number of training epochs. Return valid set performances. Parameters ---------- nb_epoch: int Number of epochs to train model being optimized during each iteration. Not used by all model types. placeholders: keyword arguments Should be various hyperparameters as specified in `param_keys` above. Returns: -------- valid_scores: float valid set performances """ hyper_parameters = {} for hp in param_keys: if param_range[hp][0] == "int": Loading Loading @@ -300,9 +319,11 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): # Demarcating internal function for readability def optimizing_function(**placeholders): """Private Optimizing function """Wrapper function Take in hyper parameter values and return valid set performances Take in hyper parameter values. Calls a private optimize function (_optimize) with number of epochs. Returns valid set performances. Parameters ---------- Loading
deepchem/hyper/grid_search.py +1 −0 Original line number Diff line number Diff line Loading @@ -94,6 +94,7 @@ class GridHyperparamOpt(HyperparamOpt): the metric can be evaluated on a model. nb_epoch: int, (default 5) Specifies the number of training epochs during each iteration of optimization. Not used by all model types. use_max: bool, optional If True, return the model with the highest score. Else return model with the minimum score. Loading
