Loading deepchem/hyper/gaussian_process.py +51 −19 Original line number Diff line number Diff line Loading @@ -51,10 +51,24 @@ def compute_parameter_range(params_dict, search_range): parameters is computed with `search_range`. The optimization range computed is specified in the documentation for `search_range` below. search_range: int(float) (default 4) For int/float values in `params_dict`, computes optimization range on `[initial values / search_range, initial values * search_range]` search_range: int(float)/dict (default 4) The `search_range` specifies the range of parameter values to search for. If `search_range` is an int/float, it is used as the global search range for parameters. This creates a search problem on the following space: optimization on [initial value / search_range, initial value * search_range] If `search_range` is a dict, it must contain the same keys as for `params_dict`. In this case, `search_range` specifies a per-parameter search range. This is useful in case some parameters have a larger natural range than others. For a given hyperparameter `hp` this would create the following search range: optimization on hp on [initial value[hp] / search_range[hp], initial value[hp] * search_range[hp]] Returns ------- Loading Loading @@ -102,7 +116,11 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): >>> optimizer = dc.hyper.GaussianProcessHyperparamOpt(lambda **p: dc.models.GraphConvModel(**p)) Here's a more sophisticated example that shows how to optimize only some parameters of a model some parameters of a model. In this case, we have some parameters we want to optimize, and others which we don't. To handle this type of search, we create a `model_builder` which hard codes some arguments (in this case, `n_tasks` and `n_features` which are properties of a dataset and not hyperparameters to search over.) >>> def model_builder(**model_params): ... n_layers = model_params['layers'] Loading @@ -114,7 +132,7 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): ... layer_sizes=[layer_width]*n_layers, ... dropouts=dropout ... ) >> optimizer = dc.hyper.GaussianProcessHyperparamOpt(model_builder) >>> optimizer = dc.hyper.GaussianProcessHyperparamOpt(model_builder) """ def hyperparam_search(self, Loading Loading @@ -155,10 +173,24 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): use a temporary directory. max_iter: int, (default 20) number of optimization trials search_range: int(float) (default 4) optimization on [initial values / search_range, initial values * search_range] names of parameters that should not be optimized search_range: int(float)/dict (default 4) The `search_range` specifies the range of parameter values to search for. If `search_range` is an int/float, it is used as the global search range for parameters. This creates a search problem on the following space: optimization on [initial value / search_range, initial value * search_range] If `search_range` is a dict, it must contain the same keys as for `params_dict`. In this case, `search_range` specifies a per-parameter search range. This is useful in case some parameters have a larger natural range than others. For a given hyperparameter `hp` this would create the following search range: optimization on hp on [initial value[hp] / search_range[hp], initial value[hp] * search_range[hp]] logfile: str Name of logfile to write results to. If specified, this is must be a valid file. If not specified, results of hyperparameter Loading Loading
deepchem/hyper/gaussian_process.py +51 −19 Original line number Diff line number Diff line Loading @@ -51,10 +51,24 @@ def compute_parameter_range(params_dict, search_range): parameters is computed with `search_range`. The optimization range computed is specified in the documentation for `search_range` below. search_range: int(float) (default 4) For int/float values in `params_dict`, computes optimization range on `[initial values / search_range, initial values * search_range]` search_range: int(float)/dict (default 4) The `search_range` specifies the range of parameter values to search for. If `search_range` is an int/float, it is used as the global search range for parameters. This creates a search problem on the following space: optimization on [initial value / search_range, initial value * search_range] If `search_range` is a dict, it must contain the same keys as for `params_dict`. In this case, `search_range` specifies a per-parameter search range. This is useful in case some parameters have a larger natural range than others. For a given hyperparameter `hp` this would create the following search range: optimization on hp on [initial value[hp] / search_range[hp], initial value[hp] * search_range[hp]] Returns ------- Loading Loading @@ -102,7 +116,11 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): >>> optimizer = dc.hyper.GaussianProcessHyperparamOpt(lambda **p: dc.models.GraphConvModel(**p)) Here's a more sophisticated example that shows how to optimize only some parameters of a model some parameters of a model. In this case, we have some parameters we want to optimize, and others which we don't. To handle this type of search, we create a `model_builder` which hard codes some arguments (in this case, `n_tasks` and `n_features` which are properties of a dataset and not hyperparameters to search over.) >>> def model_builder(**model_params): ... n_layers = model_params['layers'] Loading @@ -114,7 +132,7 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): ... layer_sizes=[layer_width]*n_layers, ... dropouts=dropout ... ) >> optimizer = dc.hyper.GaussianProcessHyperparamOpt(model_builder) >>> optimizer = dc.hyper.GaussianProcessHyperparamOpt(model_builder) """ def hyperparam_search(self, Loading Loading @@ -155,10 +173,24 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): use a temporary directory. max_iter: int, (default 20) number of optimization trials search_range: int(float) (default 4) optimization on [initial values / search_range, initial values * search_range] names of parameters that should not be optimized search_range: int(float)/dict (default 4) The `search_range` specifies the range of parameter values to search for. If `search_range` is an int/float, it is used as the global search range for parameters. This creates a search problem on the following space: optimization on [initial value / search_range, initial value * search_range] If `search_range` is a dict, it must contain the same keys as for `params_dict`. In this case, `search_range` specifies a per-parameter search range. This is useful in case some parameters have a larger natural range than others. For a given hyperparameter `hp` this would create the following search range: optimization on hp on [initial value[hp] / search_range[hp], initial value[hp] * search_range[hp]] logfile: str Name of logfile to write results to. If specified, this is must be a valid file. If not specified, results of hyperparameter Loading
