Loading deepchem/hyper/base_classes.py +3 −66 Original line number Diff line number Diff line def compute_parameter_range(params_dict, search_range): """Convenience Function to compute parameter search space. Parameters ---------- params_dict: dict Dictionary mapping strings to Ints/Floats/Lists. For those parameters in which int/float is specified, an explicit list of parameters is computed with `search_range`. 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]` Returns ------- expanded_params: dict Expanded dictionary of parameters where all int/float values in `params_dict` are expanded out into explicit search ranges. """ hp_list = list(params_dict.keys()) hp_list_class = [params_dict[hp].__class__ for hp in hp_list] # Check the type is correct if not (set(hp_list_class) <= set([list, int, float])): raise ValueError("params_dict must contain values that are lists/ints/floats.") # Float or int hyper parameters(ex. batch_size, learning_rate) hp_list_single = [ hp_list[i] for i in range(len(hp_list)) if not hp_list_class[i] is list ] # List of float or int hyper parameters(ex. layer_sizes) hp_list_multiple = [(hp_list[i], len(params_dict[hp_list[i]])) for i in range(len(hp_list)) if hp_list_class[i] is list] # Range of optimization param_range = [] for hp in hp_list_single: if params_dict[hp].__class__ is int: param_range.append((('int'), [ params_dict[hp] // search_range, params_dict[hp] * search_range ])) else: param_range.append((('cont'), [ params_dict[hp] / search_range, params_dict[hp] * search_range ])) for hp in hp_list_multiple: if params_dict[hp[0]][0].__class__ is int: param_range.extend([(('int'), [ params_dict[hp[0]][i] // search_range, params_dict[hp[0]][i] * search_range ]) for i in range(hp[1])]) else: param_range.extend([(('cont'), [ params_dict[hp[0]][i] / search_range, params_dict[hp[0]][i] * search_range ]) for i in range(hp[1])]) return hp_list_single, hp_list_multiple, param_range class HyperparamOpt(object): """Abstract superclass for hyperparameter search classes. Loading Loading @@ -127,9 +64,9 @@ class HyperparamOpt(object): Parameters ---------- params_dict: dict Dictionary mapping strings to Ints/Floats/Lists. For those parameters in which int/float is specified, an explicit list of parameters is computed with `search_range`. Dictionary mapping strings to Ints/Floats/Lists. Note that the precise semantics of `params_dict` will change depending on the optimizer that you're using. train_dataset: `dc.data.Dataset` dataset used for training valid_dataset: `dc.data.Dataset` Loading deepchem/hyper/gaussian_process.py +110 −13 Original line number Diff line number Diff line Loading @@ -12,17 +12,108 @@ from deepchem.utils.evaluate import Evaluator logger = logging.getLogger(__name__) def compute_parameter_range(params_dict, search_range): """Convenience Function to compute parameter search space. Parameters ---------- params_dict: dict Dictionary mapping strings to Ints/Floats/Lists. For those parameters in which int/float is specified, an explicit list of parameters is computed with `search_range`. 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]` Returns ------- param_range: list List of tuples. Each tuple is of form `(value_type, value_range)` where `value_type` is a string that is either "int" or "cont" and `value_range` is a list of two elements of the form `[low, hi]` """ #hp_list = list(params_dict.keys()) #hp_list_class = [params_dict[hp].__class__ for hp in hp_list] ## Check the type is correct #if not (set(hp_list_class) <= set([list, int, float])): # raise ValueError("params_dict must contain values that are lists/ints/floats.") ## Float or int hyper parameters(ex. batch_size, learning_rate) #hp_list_single = [ # hp_list[i] for i in range(len(hp_list)) if not hp_list_class[i] is list #] ## List of float or int hyper parameters(ex. layer_sizes) #hp_list_multiple = [(hp_list[i], len(params_dict[hp_list[i]])) # for i in range(len(hp_list)) # if hp_list_class[i] is list] # Range of optimization param_range = [] for hp, value in params_dict.items(): if isinstance(value, int): value_range = [value // search_range, value * search_range] param_range.append(("int", value_range)) pass elif isinstance(value, float): value_range = [value / search_range, value * search_range] param_range.append(("cont", value_range)) pass elif isinstance(value, list): if len(value) == 0: raise ValueError("Cannot specify empty lists for hyperparameter search.") if isinstance(value[0], int): # Expand out each of the possible values into a range for val in value: value_range = [value // search_range, value * search_range] param_range.append(("int", value_range)) elif isinstance(value[0], float): for val in value: value_range = [value / search_range, value * search_range] param_range.append(("cont", value_range)) return param_range #for hp in hp_list_single: # if params_dict[hp].__class__ is int: # param_range.append((('int'), [ # params_dict[hp] // search_range, # params_dict[hp] * search_range # ])) # else: # param_range.append((('cont'), [ # params_dict[hp] / search_range, # params_dict[hp] * search_range # ])) #for hp in hp_list_multiple: # if params_dict[hp[0]][0].__class__ is int: # param_range.extend([(('int'), [ # params_dict[hp[0]][i] // search_range, # params_dict[hp[0]][i] * search_range # ]) for i in range(hp[1])]) # else: # param_range.extend([(('cont'), [ # params_dict[hp[0]][i] / search_range, # params_dict[hp[0]][i] * search_range # ]) for i in range(hp[1])]) ##return hp_list_single, hp_list_multiple, param_range #return param_range class GaussianProcessHyperparamOpt(HyperparamOpt): """ Gaussian Process Global Optimization(GPGO) This class uses Gaussian Process optimization to select hyperparameters. Note that this class can only optimize 20 parameters at a time. hyperparameters. Underneath the hood it uses pyGPGO to optimize models. If you don't have pyGPGO installed, you won't be able to use this class. TODO: This class is too tied up with the MoleculeNet benchmarking. This needs to be refactored out cleanly. Note ---- This class can only optimize 20 parameters at a time. """ def hyperparam_search( Loading Loading @@ -83,23 +174,29 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): if logfile: log_file = logfile elif logdir is not None: log_file = os.path.join(model_dir, log_file) log_file = os.path.join(logdir, log_file) else: log_file = None hyper_parameters = params_dict hp_list_single, hp_list_multiple, param_range = compute_parameter_range(params_dict, search_range) #hyper_parameters = params_dict param_range = compute_parameter_range(params_dict, search_range) # Number of parameters n_param = len(hp_list_single) if len(hp_list_multiple) > 0: n_param = n_param + sum([hp[1] for hp in hp_list_multiple]) ## Number of parameters #n_param = len(hp_list_single) #if len(hp_list_multiple) > 0: # n_param = n_param + sum([hp[1] for hp in hp_list_multiple]) # Compute number of different params n_param = 0 for val in params_dict.items(): if isinstance(val, list): n_param += len(val) else: n_param += 1 # Dummy names param_name = ['l' + format(i, '02d') for i in range(20)] param = dict(zip(param_name[:n_param], param_range)) def f(l00=0, l01=0, l02=0, Loading @@ -120,7 +217,7 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): l17=0, l18=0, l19=0): """ Optimizing function """Private Optimizing function Take in hyper parameter values and return valid set performances Loading Loading
deepchem/hyper/base_classes.py +3 −66 Original line number Diff line number Diff line def compute_parameter_range(params_dict, search_range): """Convenience Function to compute parameter search space. Parameters ---------- params_dict: dict Dictionary mapping strings to Ints/Floats/Lists. For those parameters in which int/float is specified, an explicit list of parameters is computed with `search_range`. 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]` Returns ------- expanded_params: dict Expanded dictionary of parameters where all int/float values in `params_dict` are expanded out into explicit search ranges. """ hp_list = list(params_dict.keys()) hp_list_class = [params_dict[hp].__class__ for hp in hp_list] # Check the type is correct if not (set(hp_list_class) <= set([list, int, float])): raise ValueError("params_dict must contain values that are lists/ints/floats.") # Float or int hyper parameters(ex. batch_size, learning_rate) hp_list_single = [ hp_list[i] for i in range(len(hp_list)) if not hp_list_class[i] is list ] # List of float or int hyper parameters(ex. layer_sizes) hp_list_multiple = [(hp_list[i], len(params_dict[hp_list[i]])) for i in range(len(hp_list)) if hp_list_class[i] is list] # Range of optimization param_range = [] for hp in hp_list_single: if params_dict[hp].__class__ is int: param_range.append((('int'), [ params_dict[hp] // search_range, params_dict[hp] * search_range ])) else: param_range.append((('cont'), [ params_dict[hp] / search_range, params_dict[hp] * search_range ])) for hp in hp_list_multiple: if params_dict[hp[0]][0].__class__ is int: param_range.extend([(('int'), [ params_dict[hp[0]][i] // search_range, params_dict[hp[0]][i] * search_range ]) for i in range(hp[1])]) else: param_range.extend([(('cont'), [ params_dict[hp[0]][i] / search_range, params_dict[hp[0]][i] * search_range ]) for i in range(hp[1])]) return hp_list_single, hp_list_multiple, param_range class HyperparamOpt(object): """Abstract superclass for hyperparameter search classes. Loading Loading @@ -127,9 +64,9 @@ class HyperparamOpt(object): Parameters ---------- params_dict: dict Dictionary mapping strings to Ints/Floats/Lists. For those parameters in which int/float is specified, an explicit list of parameters is computed with `search_range`. Dictionary mapping strings to Ints/Floats/Lists. Note that the precise semantics of `params_dict` will change depending on the optimizer that you're using. train_dataset: `dc.data.Dataset` dataset used for training valid_dataset: `dc.data.Dataset` Loading
deepchem/hyper/gaussian_process.py +110 −13 Original line number Diff line number Diff line Loading @@ -12,17 +12,108 @@ from deepchem.utils.evaluate import Evaluator logger = logging.getLogger(__name__) def compute_parameter_range(params_dict, search_range): """Convenience Function to compute parameter search space. Parameters ---------- params_dict: dict Dictionary mapping strings to Ints/Floats/Lists. For those parameters in which int/float is specified, an explicit list of parameters is computed with `search_range`. 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]` Returns ------- param_range: list List of tuples. Each tuple is of form `(value_type, value_range)` where `value_type` is a string that is either "int" or "cont" and `value_range` is a list of two elements of the form `[low, hi]` """ #hp_list = list(params_dict.keys()) #hp_list_class = [params_dict[hp].__class__ for hp in hp_list] ## Check the type is correct #if not (set(hp_list_class) <= set([list, int, float])): # raise ValueError("params_dict must contain values that are lists/ints/floats.") ## Float or int hyper parameters(ex. batch_size, learning_rate) #hp_list_single = [ # hp_list[i] for i in range(len(hp_list)) if not hp_list_class[i] is list #] ## List of float or int hyper parameters(ex. layer_sizes) #hp_list_multiple = [(hp_list[i], len(params_dict[hp_list[i]])) # for i in range(len(hp_list)) # if hp_list_class[i] is list] # Range of optimization param_range = [] for hp, value in params_dict.items(): if isinstance(value, int): value_range = [value // search_range, value * search_range] param_range.append(("int", value_range)) pass elif isinstance(value, float): value_range = [value / search_range, value * search_range] param_range.append(("cont", value_range)) pass elif isinstance(value, list): if len(value) == 0: raise ValueError("Cannot specify empty lists for hyperparameter search.") if isinstance(value[0], int): # Expand out each of the possible values into a range for val in value: value_range = [value // search_range, value * search_range] param_range.append(("int", value_range)) elif isinstance(value[0], float): for val in value: value_range = [value / search_range, value * search_range] param_range.append(("cont", value_range)) return param_range #for hp in hp_list_single: # if params_dict[hp].__class__ is int: # param_range.append((('int'), [ # params_dict[hp] // search_range, # params_dict[hp] * search_range # ])) # else: # param_range.append((('cont'), [ # params_dict[hp] / search_range, # params_dict[hp] * search_range # ])) #for hp in hp_list_multiple: # if params_dict[hp[0]][0].__class__ is int: # param_range.extend([(('int'), [ # params_dict[hp[0]][i] // search_range, # params_dict[hp[0]][i] * search_range # ]) for i in range(hp[1])]) # else: # param_range.extend([(('cont'), [ # params_dict[hp[0]][i] / search_range, # params_dict[hp[0]][i] * search_range # ]) for i in range(hp[1])]) ##return hp_list_single, hp_list_multiple, param_range #return param_range class GaussianProcessHyperparamOpt(HyperparamOpt): """ Gaussian Process Global Optimization(GPGO) This class uses Gaussian Process optimization to select hyperparameters. Note that this class can only optimize 20 parameters at a time. hyperparameters. Underneath the hood it uses pyGPGO to optimize models. If you don't have pyGPGO installed, you won't be able to use this class. TODO: This class is too tied up with the MoleculeNet benchmarking. This needs to be refactored out cleanly. Note ---- This class can only optimize 20 parameters at a time. """ def hyperparam_search( Loading Loading @@ -83,23 +174,29 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): if logfile: log_file = logfile elif logdir is not None: log_file = os.path.join(model_dir, log_file) log_file = os.path.join(logdir, log_file) else: log_file = None hyper_parameters = params_dict hp_list_single, hp_list_multiple, param_range = compute_parameter_range(params_dict, search_range) #hyper_parameters = params_dict param_range = compute_parameter_range(params_dict, search_range) # Number of parameters n_param = len(hp_list_single) if len(hp_list_multiple) > 0: n_param = n_param + sum([hp[1] for hp in hp_list_multiple]) ## Number of parameters #n_param = len(hp_list_single) #if len(hp_list_multiple) > 0: # n_param = n_param + sum([hp[1] for hp in hp_list_multiple]) # Compute number of different params n_param = 0 for val in params_dict.items(): if isinstance(val, list): n_param += len(val) else: n_param += 1 # Dummy names param_name = ['l' + format(i, '02d') for i in range(20)] param = dict(zip(param_name[:n_param], param_range)) def f(l00=0, l01=0, l02=0, Loading @@ -120,7 +217,7 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): l17=0, l18=0, l19=0): """ Optimizing function """Private Optimizing function Take in hyper parameter values and return valid set performances Loading
