Loading datasets/muv.csv.gz −3.95 KiB (1.67 MiB) File changed.No diff preview for this file type. View original file View changed file datasets/pcba.csv.gz 0 → 100644 +14.8 MiB File added.No diff preview for this file type. View file datasets/tox21.csv.gz (122 KiB) File changed.No diff preview for this file type. View original file View changed file deepchem/featurizers/featurize.py +0 −3 Original line number Diff line number Diff line Loading @@ -442,9 +442,6 @@ class FeaturizedSamples(object): raise ValueError("improper splittype.") train_samples, valid_samples, test_samples = None, None, None dataset_files = self.dataset_files print("FeaturizedSamples.train_valid_test_split") print("dataset_files") print(dataset_files) if train_dir is not None: train_samples = FeaturizedSamples(samples_dir=train_dir, dataset_files=dataset_files, Loading deepchem/hyperparameters/__init__.py +10 −2 Original line number Diff line number Diff line Loading @@ -50,11 +50,16 @@ class HyperparamOpt(object): for hyperparam, hyperparam_val in zip(hyperparams, hyperparameter_tuple): model_params[hyperparam] = hyperparam_val if logdir is not None: model_dir = logdir else: model_dir = tempfile.mkdtemp() if logdir is not None: model = self.model_class(self.task_types, model_params, logdir=logdir) model = self.model_class(self.task_types, model_params, model_dir, verbosity=self.verbosity) else: model = self.model_class(self.task_types, model_params) model = self.model_class(self.task_types, model_params, verbosity=self.verbosity) model.fit(train_dataset) model.save(model_dir) Loading Loading @@ -84,6 +89,9 @@ class HyperparamOpt(object): log("\tbest_validation_score so far: %f" % best_validation_score, self.verbosity) if best_model is None: log("No models trained correctly.", self.verbosity) return best_model, best_hyperparams, all_scores train_csv_out = tempfile.NamedTemporaryFile() train_stats_out = tempfile.NamedTemporaryFile() train_evaluator = Evaluator(best_model, train_dataset, output_transformers) Loading Loading
datasets/muv.csv.gz −3.95 KiB (1.67 MiB) File changed.No diff preview for this file type. View original file View changed file
datasets/tox21.csv.gz (122 KiB) File changed.No diff preview for this file type. View original file View changed file
deepchem/featurizers/featurize.py +0 −3 Original line number Diff line number Diff line Loading @@ -442,9 +442,6 @@ class FeaturizedSamples(object): raise ValueError("improper splittype.") train_samples, valid_samples, test_samples = None, None, None dataset_files = self.dataset_files print("FeaturizedSamples.train_valid_test_split") print("dataset_files") print(dataset_files) if train_dir is not None: train_samples = FeaturizedSamples(samples_dir=train_dir, dataset_files=dataset_files, Loading
deepchem/hyperparameters/__init__.py +10 −2 Original line number Diff line number Diff line Loading @@ -50,11 +50,16 @@ class HyperparamOpt(object): for hyperparam, hyperparam_val in zip(hyperparams, hyperparameter_tuple): model_params[hyperparam] = hyperparam_val if logdir is not None: model_dir = logdir else: model_dir = tempfile.mkdtemp() if logdir is not None: model = self.model_class(self.task_types, model_params, logdir=logdir) model = self.model_class(self.task_types, model_params, model_dir, verbosity=self.verbosity) else: model = self.model_class(self.task_types, model_params) model = self.model_class(self.task_types, model_params, verbosity=self.verbosity) model.fit(train_dataset) model.save(model_dir) Loading Loading @@ -84,6 +89,9 @@ class HyperparamOpt(object): log("\tbest_validation_score so far: %f" % best_validation_score, self.verbosity) if best_model is None: log("No models trained correctly.", self.verbosity) return best_model, best_hyperparams, all_scores train_csv_out = tempfile.NamedTemporaryFile() train_stats_out = tempfile.NamedTemporaryFile() train_evaluator = Evaluator(best_model, train_dataset, output_transformers) Loading
