Loading deepchem/dock/pose_generation.py +1 −0 Original line number Diff line number Diff line Loading @@ -298,6 +298,7 @@ class VinaPoseGenerator(PoseGenerator): else: # I'm not sure why specifying the args as a list fails on other platforms, # but for some reason it only works if I pass it as a string. # FIXME: Incompatible types in assignment args = "%s --config %s --log %s --out %s" % ( # type: ignore self.vina_cmd, conf_file, log_file, out_pdbqt) # FIXME: We should use `subprocess.run` instead of `call` Loading deepchem/hyper/base_classes.py +23 −20 Original line number Diff line number Diff line import logging from typing import Any, Callable, Dict, Optional, Tuple from deepchem.data import Dataset from deepchem.models import Model from deepchem.metrics import Metric logger = logging.getLogger(__name__) def _convert_hyperparam_dict_to_filename(hyper_params): def _convert_hyperparam_dict_to_filename(hyper_params: Dict) -> str: """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. hyper_params: Dict Maps string of hyperparameter name to int/float/string/list etc. Returns ------- Loading Loading @@ -47,7 +52,7 @@ class HyperparamOpt(object): instantiated. """ def __init__(self, model_builder): def __init__(self, model_builder: Callable[..., Model]): """Initialize Hyperparameter Optimizer. Note this is an abstract constructor which should only be used by Loading @@ -64,18 +69,19 @@ class HyperparamOpt(object): """ 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." ) "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): def hyperparam_search( self, params_dict: Dict[str, Any], train_dataset: Dataset, valid_dataset: Dataset, metric: Metric, use_max: bool = True, logdir: Optional[str] = None, **kwargs) -> Tuple[Model, Dict[str, Any], Dict[str, float]]: """Conduct Hyperparameter search. This method defines the common API shared by all hyperparameter Loading @@ -84,7 +90,7 @@ class HyperparamOpt(object): Parameters ---------- params_dict: dict 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 Loading @@ -96,11 +102,8 @@ class HyperparamOpt(object): 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. metric: `dc.metrics.Metric` metric used for evaluation use_max: bool, optional If True, return the model with the highest score. Else return model with the minimum score. Loading deepchem/hyper/gaussian_process.py +43 −33 Original line number Diff line number Diff line Loading @@ -4,23 +4,30 @@ Contains class for gaussian process hyperparameter optimizations. import os import logging import tempfile from typing import Dict, List, Optional, Tuple, Union from deepchem.data import Dataset from deepchem.metrics import Metric from deepchem.hyper.base_classes import HyperparamOpt from deepchem.hyper.base_classes import _convert_hyperparam_dict_to_filename logger = logging.getLogger(__name__) def compute_parameter_range(params_dict, search_range): def compute_parameter_range( params_dict: Dict[str, Union[int, float]], search_range: Union[int, float, Dict[str, Union[int, float]]] ) -> Dict[str, Tuple[str, List[float]]]: """Convenience Function to compute parameter search space. Parameters ---------- params_dict: dict params_dict: Dict Dictionary mapping strings to Ints/Floats. An explicit list of parameters is computed with `search_range`. The optimization range computed is specified in the documentation for `search_range` below. search_range: int(float)/dict (default 4) 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 Loading @@ -41,7 +48,7 @@ def compute_parameter_range(params_dict, search_range): Returns ------- param_range: dict param_range: Dict Dictionary mapping hyperparameter names to 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 Loading Loading @@ -115,22 +122,24 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): >>> optimizer = dc.hyper.GaussianProcessHyperparamOpt(model_builder) """ def hyperparam_search(self, params_dict, train_dataset, valid_dataset, transformers, metric, use_max=True, logdir=None, max_iter=20, search_range=4, logfile=None): # NOTE: mypy prohibits changing the number of arguments # FIXME: Signature of "hyperparam_search" incompatible with supertype "HyperparamOpt" def hyperparam_search( # type: ignore[override] self, params_dict: Dict[str, Union[int, float]], train_dataset: Dataset, valid_dataset: Dataset, metric: Metric, use_max: bool = True, logdir: Optional[str] = None, max_iter: int = 20, search_range: Union[int, float, Dict[str, Union[int, float]]] = 4, logfile: Optional[str] = None): """Perform hyperparameter search using a gaussian process. Parameters ---------- params_dict: dict params_dict: Dict Maps hyperparameter names (strings) to possible parameter values. The semantics of this list are different than for `GridHyperparamOpt`. `params_dict[hp]` must map to an int/float, Loading @@ -141,19 +150,17 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): dataset used for training valid_dataset: `dc.data.Dataset` dataset used for validation(optimization on valid scores) transformers: list[dc.trans.Transformer] transformers for evaluation metric: `dc.metrics.Metric` metric used for evaluation use_max: bool, (default True) Specifies whether to maximize or minimize `metric`. maximization(True) or minimization(False) logdir: str, optional logdir: str, optional, (default None) The directory in which to store created models. If not set, will use a temporary directory. max_iter: int, (default 20) number of optimization trials search_range: int(float)/dict (default 4) 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 Loading @@ -171,7 +178,7 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): optimization on hp on [initial value[hp] / search_range[hp], initial value[hp] * search_range[hp]] logfile: str logfile: str, optional (default None) Name of logfile to write results to. If specified, this is must be a valid file. If not specified, results of hyperparameter search will be written to `logdir/.txt`. Loading @@ -180,12 +187,21 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): Returns ------- `(best_model, best_hyperparams, all_scores)` where `best_model` is an instance of `dc.model.Models`, `best_hyperparams` is a an instance of `dc.model.Model`, `best_hyperparams` is a dictionary of parameters, and `all_scores` is a dictionary mapping string representations of hyperparameter sets to validation scores. """ try: from pyGPGO.covfunc import matern32 from pyGPGO.acquisition import Acquisition from pyGPGO.surrogates.GaussianProcess import GaussianProcess from pyGPGO.GPGO import GPGO except ModuleNotFoundError: raise ValueError("This class requires pyGPGO to be installed.") # Specify logfile log_file = None if logfile: log_file = logfile elif logdir is not None: Loading @@ -193,8 +209,6 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): if not os.path.exists(logdir): os.makedirs(logdir, exist_ok=True) log_file = os.path.join(logdir, "results.txt") else: log_file = None # setup range param_range = compute_parameter_range(params_dict, search_range) Loading @@ -208,7 +222,6 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): model_locations = {} # Demarcating internal function for readability ######################## def optimizing_function(**placeholders): """Private Optimizing function Loading Loading @@ -275,18 +288,14 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): # Store reference to model all_models[hp_str] = model model_locations[hp_str] = model_dir # GPGO maximize performance by default, set performance to its negative value for minimization # GPGO maximize performance by default # set performance to its negative value for minimization if use_max: return score else: return -score ######################## from pyGPGO.covfunc import matern32 from pyGPGO.acquisition import Acquisition from pyGPGO.surrogates.GaussianProcess import GaussianProcess from pyGPGO.GPGO import GPGO # execute GPGO cov = matern32() gp = GaussianProcess(cov) acq = Acquisition(mode='ExpectedImprovement') Loading @@ -300,7 +309,8 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): if param_range[hp][0] == "int": hyper_parameters[hp] = int(hp_opt[hp]) else: hyper_parameters[hp] = float(hp_opt[hp]) # Incompatible types in assignment hyper_parameters[hp] = float(hp_opt[hp]) # type: ignore hp_str = _convert_hyperparam_dict_to_filename(hyper_parameters) # Let's fetch the model with the best parameters Loading deepchem/hyper/grid_search.py +24 −12 Original line number Diff line number Diff line Loading @@ -10,6 +10,11 @@ import collections import logging from functools import reduce from operator import mul from typing import Dict, List, Optional from deepchem.data import Dataset from deepchem.trans import Transformer from deepchem.metrics import Metric from deepchem.utils.evaluate import Evaluator from deepchem.hyper.base_classes import HyperparamOpt from deepchem.hyper.base_classes import _convert_hyperparam_dict_to_filename Loading Loading @@ -55,14 +60,18 @@ class GridHyperparamOpt(HyperparamOpt): """ def hyperparam_search(self, params_dict, train_dataset, valid_dataset, output_transformers, metric, use_max=True, logdir=None): # NOTE: mypy prohibits changing the number of arguments # FIXME: Signature of "hyperparam_search" incompatible with supertype "HyperparamOpt" def hyperparam_search( # type: ignore[override] self, params_dict: Dict[str, List], train_dataset: Dataset, valid_dataset: Dataset, output_transformers: List[Transformer], metric: Metric, use_max: bool = True, logdir: Optional[str] = None, ): """Perform hyperparams search according to params_dict. Each key to hyperparams_dict is a model_param. The values should Loading @@ -70,15 +79,13 @@ class GridHyperparamOpt(HyperparamOpt): Parameters ---------- params_dict: Dict[str, list] params_dict: Dict Maps hyperparameter names (strings) to lists of possible parameter values. 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 metric: dc.metrics.Metric metric used for evaluation use_max: bool, optional Loading @@ -87,11 +94,16 @@ class GridHyperparamOpt(HyperparamOpt): logdir: str, optional The directory in which to store created models. If not set, will use a temporary directory. 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. Returns ------- `(best_model, best_hyperparams, all_scores)` where `best_model` is an instance of `dc.model.Models`, `best_hyperparams` is a an instance of `dc.model.Model`, `best_hyperparams` is a dictionary of parameters, and `all_scores` is a dictionary mapping string representations of hyperparameter sets to validation scores. Loading deepchem/hyper/tests/test_gaussian_hyperparam_opt.py +6 −6 Original line number Diff line number Diff line Loading @@ -108,10 +108,10 @@ class TestGaussianHyperparamOpt(unittest.TestCase): np.random.rand(5, 3), np.zeros((5, 2)), np.ones((5, 2)), np.arange(5)) optimizer = dc.hyper.GaussianProcessHyperparamOpt( lambda **p: dc.models.MultitaskRegressor(n_tasks=2, lambda **params: dc.models.MultitaskRegressor(n_tasks=2, n_features=3, dropouts=[0.], weight_init_stddevs=[np.sqrt(6) / np.sqrt(1000)], learning_rate=0.003, **p)) learning_rate=0.003, **params)) params_dict = {"batch_size": 10} transformers = [] Loading Loading @@ -143,12 +143,12 @@ class TestGaussianHyperparamOpt(unittest.TestCase): np.random.rand(5, 3), np.zeros((5, 2)), np.ones((5, 2)), np.arange(5)) optimizer = dc.hyper.GaussianProcessHyperparamOpt( lambda **p: dc.models.MultitaskRegressor( lambda **params: dc.models.MultitaskRegressor( n_tasks=2, n_features=3, dropouts=[0.], weight_init_stddevs=[np.sqrt(6) / np.sqrt(1000)], **p)) **params)) params_dict = {"learning_rate": 0.003, "batch_size": 10} # These are per-example multiplier Loading Loading
deepchem/dock/pose_generation.py +1 −0 Original line number Diff line number Diff line Loading @@ -298,6 +298,7 @@ class VinaPoseGenerator(PoseGenerator): else: # I'm not sure why specifying the args as a list fails on other platforms, # but for some reason it only works if I pass it as a string. # FIXME: Incompatible types in assignment args = "%s --config %s --log %s --out %s" % ( # type: ignore self.vina_cmd, conf_file, log_file, out_pdbqt) # FIXME: We should use `subprocess.run` instead of `call` Loading
deepchem/hyper/base_classes.py +23 −20 Original line number Diff line number Diff line import logging from typing import Any, Callable, Dict, Optional, Tuple from deepchem.data import Dataset from deepchem.models import Model from deepchem.metrics import Metric logger = logging.getLogger(__name__) def _convert_hyperparam_dict_to_filename(hyper_params): def _convert_hyperparam_dict_to_filename(hyper_params: Dict) -> str: """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. hyper_params: Dict Maps string of hyperparameter name to int/float/string/list etc. Returns ------- Loading Loading @@ -47,7 +52,7 @@ class HyperparamOpt(object): instantiated. """ def __init__(self, model_builder): def __init__(self, model_builder: Callable[..., Model]): """Initialize Hyperparameter Optimizer. Note this is an abstract constructor which should only be used by Loading @@ -64,18 +69,19 @@ class HyperparamOpt(object): """ 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." ) "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): def hyperparam_search( self, params_dict: Dict[str, Any], train_dataset: Dataset, valid_dataset: Dataset, metric: Metric, use_max: bool = True, logdir: Optional[str] = None, **kwargs) -> Tuple[Model, Dict[str, Any], Dict[str, float]]: """Conduct Hyperparameter search. This method defines the common API shared by all hyperparameter Loading @@ -84,7 +90,7 @@ class HyperparamOpt(object): Parameters ---------- params_dict: dict 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 Loading @@ -96,11 +102,8 @@ class HyperparamOpt(object): 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. metric: `dc.metrics.Metric` metric used for evaluation use_max: bool, optional If True, return the model with the highest score. Else return model with the minimum score. Loading
deepchem/hyper/gaussian_process.py +43 −33 Original line number Diff line number Diff line Loading @@ -4,23 +4,30 @@ Contains class for gaussian process hyperparameter optimizations. import os import logging import tempfile from typing import Dict, List, Optional, Tuple, Union from deepchem.data import Dataset from deepchem.metrics import Metric from deepchem.hyper.base_classes import HyperparamOpt from deepchem.hyper.base_classes import _convert_hyperparam_dict_to_filename logger = logging.getLogger(__name__) def compute_parameter_range(params_dict, search_range): def compute_parameter_range( params_dict: Dict[str, Union[int, float]], search_range: Union[int, float, Dict[str, Union[int, float]]] ) -> Dict[str, Tuple[str, List[float]]]: """Convenience Function to compute parameter search space. Parameters ---------- params_dict: dict params_dict: Dict Dictionary mapping strings to Ints/Floats. An explicit list of parameters is computed with `search_range`. The optimization range computed is specified in the documentation for `search_range` below. search_range: int(float)/dict (default 4) 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 Loading @@ -41,7 +48,7 @@ def compute_parameter_range(params_dict, search_range): Returns ------- param_range: dict param_range: Dict Dictionary mapping hyperparameter names to 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 Loading Loading @@ -115,22 +122,24 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): >>> optimizer = dc.hyper.GaussianProcessHyperparamOpt(model_builder) """ def hyperparam_search(self, params_dict, train_dataset, valid_dataset, transformers, metric, use_max=True, logdir=None, max_iter=20, search_range=4, logfile=None): # NOTE: mypy prohibits changing the number of arguments # FIXME: Signature of "hyperparam_search" incompatible with supertype "HyperparamOpt" def hyperparam_search( # type: ignore[override] self, params_dict: Dict[str, Union[int, float]], train_dataset: Dataset, valid_dataset: Dataset, metric: Metric, use_max: bool = True, logdir: Optional[str] = None, max_iter: int = 20, search_range: Union[int, float, Dict[str, Union[int, float]]] = 4, logfile: Optional[str] = None): """Perform hyperparameter search using a gaussian process. Parameters ---------- params_dict: dict params_dict: Dict Maps hyperparameter names (strings) to possible parameter values. The semantics of this list are different than for `GridHyperparamOpt`. `params_dict[hp]` must map to an int/float, Loading @@ -141,19 +150,17 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): dataset used for training valid_dataset: `dc.data.Dataset` dataset used for validation(optimization on valid scores) transformers: list[dc.trans.Transformer] transformers for evaluation metric: `dc.metrics.Metric` metric used for evaluation use_max: bool, (default True) Specifies whether to maximize or minimize `metric`. maximization(True) or minimization(False) logdir: str, optional logdir: str, optional, (default None) The directory in which to store created models. If not set, will use a temporary directory. max_iter: int, (default 20) number of optimization trials search_range: int(float)/dict (default 4) 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 Loading @@ -171,7 +178,7 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): optimization on hp on [initial value[hp] / search_range[hp], initial value[hp] * search_range[hp]] logfile: str logfile: str, optional (default None) Name of logfile to write results to. If specified, this is must be a valid file. If not specified, results of hyperparameter search will be written to `logdir/.txt`. Loading @@ -180,12 +187,21 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): Returns ------- `(best_model, best_hyperparams, all_scores)` where `best_model` is an instance of `dc.model.Models`, `best_hyperparams` is a an instance of `dc.model.Model`, `best_hyperparams` is a dictionary of parameters, and `all_scores` is a dictionary mapping string representations of hyperparameter sets to validation scores. """ try: from pyGPGO.covfunc import matern32 from pyGPGO.acquisition import Acquisition from pyGPGO.surrogates.GaussianProcess import GaussianProcess from pyGPGO.GPGO import GPGO except ModuleNotFoundError: raise ValueError("This class requires pyGPGO to be installed.") # Specify logfile log_file = None if logfile: log_file = logfile elif logdir is not None: Loading @@ -193,8 +209,6 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): if not os.path.exists(logdir): os.makedirs(logdir, exist_ok=True) log_file = os.path.join(logdir, "results.txt") else: log_file = None # setup range param_range = compute_parameter_range(params_dict, search_range) Loading @@ -208,7 +222,6 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): model_locations = {} # Demarcating internal function for readability ######################## def optimizing_function(**placeholders): """Private Optimizing function Loading Loading @@ -275,18 +288,14 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): # Store reference to model all_models[hp_str] = model model_locations[hp_str] = model_dir # GPGO maximize performance by default, set performance to its negative value for minimization # GPGO maximize performance by default # set performance to its negative value for minimization if use_max: return score else: return -score ######################## from pyGPGO.covfunc import matern32 from pyGPGO.acquisition import Acquisition from pyGPGO.surrogates.GaussianProcess import GaussianProcess from pyGPGO.GPGO import GPGO # execute GPGO cov = matern32() gp = GaussianProcess(cov) acq = Acquisition(mode='ExpectedImprovement') Loading @@ -300,7 +309,8 @@ class GaussianProcessHyperparamOpt(HyperparamOpt): if param_range[hp][0] == "int": hyper_parameters[hp] = int(hp_opt[hp]) else: hyper_parameters[hp] = float(hp_opt[hp]) # Incompatible types in assignment hyper_parameters[hp] = float(hp_opt[hp]) # type: ignore hp_str = _convert_hyperparam_dict_to_filename(hyper_parameters) # Let's fetch the model with the best parameters Loading
deepchem/hyper/grid_search.py +24 −12 Original line number Diff line number Diff line Loading @@ -10,6 +10,11 @@ import collections import logging from functools import reduce from operator import mul from typing import Dict, List, Optional from deepchem.data import Dataset from deepchem.trans import Transformer from deepchem.metrics import Metric from deepchem.utils.evaluate import Evaluator from deepchem.hyper.base_classes import HyperparamOpt from deepchem.hyper.base_classes import _convert_hyperparam_dict_to_filename Loading Loading @@ -55,14 +60,18 @@ class GridHyperparamOpt(HyperparamOpt): """ def hyperparam_search(self, params_dict, train_dataset, valid_dataset, output_transformers, metric, use_max=True, logdir=None): # NOTE: mypy prohibits changing the number of arguments # FIXME: Signature of "hyperparam_search" incompatible with supertype "HyperparamOpt" def hyperparam_search( # type: ignore[override] self, params_dict: Dict[str, List], train_dataset: Dataset, valid_dataset: Dataset, output_transformers: List[Transformer], metric: Metric, use_max: bool = True, logdir: Optional[str] = None, ): """Perform hyperparams search according to params_dict. Each key to hyperparams_dict is a model_param. The values should Loading @@ -70,15 +79,13 @@ class GridHyperparamOpt(HyperparamOpt): Parameters ---------- params_dict: Dict[str, list] params_dict: Dict Maps hyperparameter names (strings) to lists of possible parameter values. 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 metric: dc.metrics.Metric metric used for evaluation use_max: bool, optional Loading @@ -87,11 +94,16 @@ class GridHyperparamOpt(HyperparamOpt): logdir: str, optional The directory in which to store created models. If not set, will use a temporary directory. 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. Returns ------- `(best_model, best_hyperparams, all_scores)` where `best_model` is an instance of `dc.model.Models`, `best_hyperparams` is a an instance of `dc.model.Model`, `best_hyperparams` is a dictionary of parameters, and `all_scores` is a dictionary mapping string representations of hyperparameter sets to validation scores. Loading
deepchem/hyper/tests/test_gaussian_hyperparam_opt.py +6 −6 Original line number Diff line number Diff line Loading @@ -108,10 +108,10 @@ class TestGaussianHyperparamOpt(unittest.TestCase): np.random.rand(5, 3), np.zeros((5, 2)), np.ones((5, 2)), np.arange(5)) optimizer = dc.hyper.GaussianProcessHyperparamOpt( lambda **p: dc.models.MultitaskRegressor(n_tasks=2, lambda **params: dc.models.MultitaskRegressor(n_tasks=2, n_features=3, dropouts=[0.], weight_init_stddevs=[np.sqrt(6) / np.sqrt(1000)], learning_rate=0.003, **p)) learning_rate=0.003, **params)) params_dict = {"batch_size": 10} transformers = [] Loading Loading @@ -143,12 +143,12 @@ class TestGaussianHyperparamOpt(unittest.TestCase): np.random.rand(5, 3), np.zeros((5, 2)), np.ones((5, 2)), np.arange(5)) optimizer = dc.hyper.GaussianProcessHyperparamOpt( lambda **p: dc.models.MultitaskRegressor( lambda **params: dc.models.MultitaskRegressor( n_tasks=2, n_features=3, dropouts=[0.], weight_init_stddevs=[np.sqrt(6) / np.sqrt(1000)], **p)) **params)) params_dict = {"learning_rate": 0.003, "batch_size": 10} # These are per-example multiplier Loading
