Loading deepchem/hyper/__init__.py +2 −1 Original line number Diff line number Diff line from deepchem.hyper.grid_search import HyperparamOpt from deepchem.hyper.base_classes import HyperparamOpt from deepchem.hyper.grid_search import GridHyperparamOpt from deepchem.hyper.gaussian_process import GaussianProcessHyperparamOpt deepchem/hyper/base_classes.py 0 → 100644 +119 −0 Original line number Diff line number Diff line import logging logger = logging.getLogger(__name__) def _convert_hyperparam_dict_to_filename(hyper_params): """Helper function that converts a dictionary of hyperparameters to a string that can be a filename. Parameters ---------- hyper_params: dict Maps string of hyperparameter name to int/float. Returns ------- filename: str A filename of form "_key1_value1_value2_..._key2..." """ filename = "" keys = sorted(hyper_params.keys()) for key in keys: filename += "_%s" % str(key) value = hyper_params[key] if isinstance(value, int): filename += "_%s" % str(value) elif isinstance(value, float): filename += "_%.2f" % value else: filename += "%s" % str(value) return filename class HyperparamOpt(object): """Abstract superclass for hyperparameter search classes. This class is an abstract base class for hyperparameter search classes in DeepChem. Hyperparameter search is performed on `dc.models.Model` classes. Each hyperparameter object accepts a `dc.models.Model` class upon construct. When the `hyperparam_search` class is invoked, this class is used to construct many different concrete models which are trained on the specified training set and evaluated on a given validation set. Different subclasses of `HyperparamOpt` differ in the choice of strategy for searching the hyperparameter evaluation space. This class itself is an abstract superclass and should never be directly instantiated. """ def __init__(self, model_builder): """Initialize Hyperparameter Optimizer. Note this is an abstract constructor which should only be used by subclasses. Parameters ---------- model_builder: constructor function. This parameter must be constructor function which returns an object which is an instance of `dc.models.Model`. This function must accept two arguments, `model_params` of type `dict` and `model_dir`, a string specifying a path to a model directory. See the example. """ if self.__class__.__name__ == "HyperparamOpt": raise ValueError( "HyperparamOpt is an abstract superclass and cannot be directly instantiated. You probably want to instantiate a concrete subclass instead." ) self.model_builder = model_builder def hyperparam_search(self, params_dict, train_dataset, valid_dataset, transformers, metric, use_max=True, logdir=None): """Conduct Hyperparameter search. This method defines the common API shared by all hyperparameter optimization subclasses. Different classes will implement different search methods but they must all follow this common API. Parameters ---------- params_dict: dict Dictionary mapping strings to values. Note that the precise semantics of `params_dict` will change depending on the optimizer that you're using. Depending on the type of hyperparameter optimization, these values can be ints/floats/strings/lists/etc. Read the documentation for the concrete hyperparameter optimization subclass you're using to learn more about what's expected. train_dataset: `dc.data.Dataset` dataset used for training valid_dataset: `dc.data.Dataset` dataset used for validation(optimization on valid scores) output_transformers: list[dc.trans.Transformer] Transformers for evaluation. This argument is needed since `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. use_max: bool, optional If True, return the model with the highest score. Else return model with the minimum score. logdir: str, optional The directory in which to store created models. If not set, will use a temporary directory. Returns ------- `(best_model, best_hyperparams, all_scores)` where `best_model` is an instance of `dc.models.Models`, `best_hyperparams` is a dictionary of parameters, and `all_scores` is a dictionary mapping string representations of hyperparameter sets to validation scores. """ raise NotImplementedError Loading
deepchem/hyper/__init__.py +2 −1 Original line number Diff line number Diff line from deepchem.hyper.grid_search import HyperparamOpt from deepchem.hyper.base_classes import HyperparamOpt from deepchem.hyper.grid_search import GridHyperparamOpt from deepchem.hyper.gaussian_process import GaussianProcessHyperparamOpt
deepchem/hyper/base_classes.py 0 → 100644 +119 −0 Original line number Diff line number Diff line import logging logger = logging.getLogger(__name__) def _convert_hyperparam_dict_to_filename(hyper_params): """Helper function that converts a dictionary of hyperparameters to a string that can be a filename. Parameters ---------- hyper_params: dict Maps string of hyperparameter name to int/float. Returns ------- filename: str A filename of form "_key1_value1_value2_..._key2..." """ filename = "" keys = sorted(hyper_params.keys()) for key in keys: filename += "_%s" % str(key) value = hyper_params[key] if isinstance(value, int): filename += "_%s" % str(value) elif isinstance(value, float): filename += "_%.2f" % value else: filename += "%s" % str(value) return filename class HyperparamOpt(object): """Abstract superclass for hyperparameter search classes. This class is an abstract base class for hyperparameter search classes in DeepChem. Hyperparameter search is performed on `dc.models.Model` classes. Each hyperparameter object accepts a `dc.models.Model` class upon construct. When the `hyperparam_search` class is invoked, this class is used to construct many different concrete models which are trained on the specified training set and evaluated on a given validation set. Different subclasses of `HyperparamOpt` differ in the choice of strategy for searching the hyperparameter evaluation space. This class itself is an abstract superclass and should never be directly instantiated. """ def __init__(self, model_builder): """Initialize Hyperparameter Optimizer. Note this is an abstract constructor which should only be used by subclasses. Parameters ---------- model_builder: constructor function. This parameter must be constructor function which returns an object which is an instance of `dc.models.Model`. This function must accept two arguments, `model_params` of type `dict` and `model_dir`, a string specifying a path to a model directory. See the example. """ if self.__class__.__name__ == "HyperparamOpt": raise ValueError( "HyperparamOpt is an abstract superclass and cannot be directly instantiated. You probably want to instantiate a concrete subclass instead." ) self.model_builder = model_builder def hyperparam_search(self, params_dict, train_dataset, valid_dataset, transformers, metric, use_max=True, logdir=None): """Conduct Hyperparameter search. This method defines the common API shared by all hyperparameter optimization subclasses. Different classes will implement different search methods but they must all follow this common API. Parameters ---------- params_dict: dict Dictionary mapping strings to values. Note that the precise semantics of `params_dict` will change depending on the optimizer that you're using. Depending on the type of hyperparameter optimization, these values can be ints/floats/strings/lists/etc. Read the documentation for the concrete hyperparameter optimization subclass you're using to learn more about what's expected. train_dataset: `dc.data.Dataset` dataset used for training valid_dataset: `dc.data.Dataset` dataset used for validation(optimization on valid scores) output_transformers: list[dc.trans.Transformer] Transformers for evaluation. This argument is needed since `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. use_max: bool, optional If True, return the model with the highest score. Else return model with the minimum score. logdir: str, optional The directory in which to store created models. If not set, will use a temporary directory. Returns ------- `(best_model, best_hyperparams, all_scores)` where `best_model` is an instance of `dc.models.Models`, `best_hyperparams` is a dictionary of parameters, and `all_scores` is a dictionary mapping string representations of hyperparameter sets to validation scores. """ raise NotImplementedError
