Loading deepchem/models/multitask.py +0 −5 Original line number Diff line number Diff line Loading @@ -18,16 +18,11 @@ class SingletaskToMultitask(Model): """ def __init__(self, task_types, model_params, model_dir, model_builder, verbosity=None): print("ENTERING SingletaskToMultitask") print("verbosity") print(verbosity) self.task_types = task_types self.model_params = model_params self.models = {} self.model_dir = model_dir self.verbosity = verbosity print("verbosity") print(verbosity) log("About to initialize singletask to multitask model", self.verbosity, "high") if not os.path.exists(self.model_dir): Loading deepchem/models/test/test_reload.py +20 −70 Original line number Diff line number Diff line Loading @@ -33,48 +33,23 @@ class TestReload(TestAPI): """ Test reload for datasets. """ #def setUp(self): # self.current_dir = os.path.dirname(os.path.abspath(__file__)) # self.smiles_field = "smiles" # sys_temp = tempfile.gettempdir() # self.base_dir = os.path.join(sys_temp, "base_dir") # # Make sure to remove an alternate instance of this dir if it exists. # if os.path.exists(self.base_dir): # shutil.rmtree(self.base_dir) # os.makedirs(self.base_dir) # self.feature_dir = os.path.join(self.base_dir, "features") # #if not os.path.exists(self.feature_dir): # # os.makedirs(self.feature_dir) # self.samples_dir = os.path.join(self.base_dir, "samples") # #if not os.path.exists(self.samples_dir): # # os.makedirs(self.samples_dir) # self.train_dir = os.path.join(self.base_dir, "train_dataset") # #if not os.path.exists(self.train_dir): # # os.makedirs(self.train_dir) # self.valid_dir = os.path.join(self.base_dir, "valid_dataset") # #if not os.path.exists(self.valid_dir): # # os.makedirs(self.valid_dir) # self.test_dir = os.path.join(self.base_dir, "test_dataset") # #if not os.path.exists(self.test_dir): # # os.makedirs(self.test_dir) # print("self.base_dir") # print(self.base_dir) # print("self.feature_dir") # print(self.feature_dir) # print("self.samples_dir") # print(self.samples_dir) # print("self.train_dir") # print(self.train_dir) # print("self.valid_dir") # print(self.valid_dir) # print("self.test_dir") # print(self.test_dir) #def tearDown(self): # shutil.rmtree(self.base_dir) # # TODO(rbharath): Removing this causes crashes for some reason. Need to # # debug. # #shutil.rmtree(self.model_dir) def setUp(self): self.current_dir = os.path.dirname(os.path.abspath(__file__)) self.smiles_field = "smiles" sys_temp = tempfile.gettempdir() self.base_dir = os.path.join(sys_temp, "base_dir") # Make sure to remove an alternate instance of this dir if it exists. if os.path.exists(self.base_dir): shutil.rmtree(self.base_dir) os.makedirs(self.base_dir) self.feature_dir = os.path.join(self.base_dir, "features") self.samples_dir = os.path.join(self.base_dir, "samples") self.train_dir = os.path.join(self.base_dir, "train_dataset") self.valid_dir = os.path.join(self.base_dir, "valid_dataset") self.test_dir = os.path.join(self.base_dir, "test_dataset") def tearDown(self): shutil.rmtree(self.base_dir) def _run_muv_experiment(self, dataset_file, reload=False, verbosity=None): """Loads or reloads a small version of MUV dataset.""" Loading Loading @@ -106,9 +81,8 @@ class TestReload(TestAPI): featurized_samples, self.train_dir, self.valid_dir, self.test_dir, log_every_n=1000, reload=reload, frac_train=frac_train, frac_test=frac_test, frac_valid=frac_valid) #assert len(train_samples) == frac_train * len(featurized_samples) #assert len(valid_samples) == frac_valid * len(featurized_samples) #assert len(test_samples) == frac_test * len(featurized_samples) # Do an approximate comparison since splits are sometimes slightly off from # the exact fraction. assert relative_difference( len(train_samples), frac_train * len(featurized_samples)) < 1e-3 assert relative_difference( Loading Loading @@ -137,7 +111,7 @@ class TestReload(TestAPI): assert len(valid_samples) == len(valid_dataset) assert len(test_samples) == len(test_dataset) # TODO(rbharath): TRANSFORMERS DON'T PLAY NICE WITH RELOAD! Namely, # TODO(rbharath): Transformers don't play nice with reload! Namely, # reloading will cause the transform to be reapplied. This is undesirable in # almost all cases. Need to understand a method to fix this. input_transformers = [] Loading Loading @@ -206,27 +180,3 @@ class TestReload(TestAPI): assert len_train_dataset == len_reload_train_dataset assert len_valid_dataset == len_reload_valid_dataset assert len_test_dataset == len_reload_valid_dataset # def test_reload_twice_large(self): # """Check ability to repeatedly run experiments with reload set True.""" # reload = True # verbosity = "high" # dataset_file = os.path.join( # self.current_dir, "../../../datasets/medium_muv.csv.gz") # print("Running experiment for first time with reload.") # (len_train_samples, len_valid_samples, len_test_samples, # len_train_dataset, len_valid_dataset, len_test_dataset) = \ # self._run_muv_experiment(dataset_file, reload, verbosity) # # print("Running experiment for second time with reload.") # (len_reload_train_samples, len_reload_valid_samples, len_reload_test_samples, # len_reload_train_dataset, len_reload_valid_dataset, len_reload_test_dataset) = \ # self._run_muv_experiment(dataset_file, reload, verbosity) # print("len_train_samples, len_reload_train_samples") # print(len_train_samples, len_reload_train_samples) # assert len_train_samples == len_reload_train_samples # assert len_valid_samples == len_reload_valid_samples # assert len_test_samples == len_reload_valid_samples # assert len_train_dataset == len_reload_train_dataset # assert len_valid_dataset == len_reload_valid_dataset # assert len_test_dataset == len_reload_valid_dataset deepchem/models/test/test_singletask_to_multitask.py +2 −1 Original line number Diff line number Diff line Loading @@ -45,6 +45,7 @@ class TestSingletasktoMultitaskAPI(TestAPI): def model_builder(task_types, model_params, verbosity=None): return SklearnModel(task_types, model_params, model_instance=LogisticRegression()) multitask_model = SingletaskToMultitask(task_types, params_dict, model_builder) multitask_model = SingletaskToMultitask(task_types, params_dict, self.model_dir, model_builder) self._create_model(train_dataset, test_dataset, multitask_model, output_transformers, classification_metrics) Loading
deepchem/models/multitask.py +0 −5 Original line number Diff line number Diff line Loading @@ -18,16 +18,11 @@ class SingletaskToMultitask(Model): """ def __init__(self, task_types, model_params, model_dir, model_builder, verbosity=None): print("ENTERING SingletaskToMultitask") print("verbosity") print(verbosity) self.task_types = task_types self.model_params = model_params self.models = {} self.model_dir = model_dir self.verbosity = verbosity print("verbosity") print(verbosity) log("About to initialize singletask to multitask model", self.verbosity, "high") if not os.path.exists(self.model_dir): Loading
deepchem/models/test/test_reload.py +20 −70 Original line number Diff line number Diff line Loading @@ -33,48 +33,23 @@ class TestReload(TestAPI): """ Test reload for datasets. """ #def setUp(self): # self.current_dir = os.path.dirname(os.path.abspath(__file__)) # self.smiles_field = "smiles" # sys_temp = tempfile.gettempdir() # self.base_dir = os.path.join(sys_temp, "base_dir") # # Make sure to remove an alternate instance of this dir if it exists. # if os.path.exists(self.base_dir): # shutil.rmtree(self.base_dir) # os.makedirs(self.base_dir) # self.feature_dir = os.path.join(self.base_dir, "features") # #if not os.path.exists(self.feature_dir): # # os.makedirs(self.feature_dir) # self.samples_dir = os.path.join(self.base_dir, "samples") # #if not os.path.exists(self.samples_dir): # # os.makedirs(self.samples_dir) # self.train_dir = os.path.join(self.base_dir, "train_dataset") # #if not os.path.exists(self.train_dir): # # os.makedirs(self.train_dir) # self.valid_dir = os.path.join(self.base_dir, "valid_dataset") # #if not os.path.exists(self.valid_dir): # # os.makedirs(self.valid_dir) # self.test_dir = os.path.join(self.base_dir, "test_dataset") # #if not os.path.exists(self.test_dir): # # os.makedirs(self.test_dir) # print("self.base_dir") # print(self.base_dir) # print("self.feature_dir") # print(self.feature_dir) # print("self.samples_dir") # print(self.samples_dir) # print("self.train_dir") # print(self.train_dir) # print("self.valid_dir") # print(self.valid_dir) # print("self.test_dir") # print(self.test_dir) #def tearDown(self): # shutil.rmtree(self.base_dir) # # TODO(rbharath): Removing this causes crashes for some reason. Need to # # debug. # #shutil.rmtree(self.model_dir) def setUp(self): self.current_dir = os.path.dirname(os.path.abspath(__file__)) self.smiles_field = "smiles" sys_temp = tempfile.gettempdir() self.base_dir = os.path.join(sys_temp, "base_dir") # Make sure to remove an alternate instance of this dir if it exists. if os.path.exists(self.base_dir): shutil.rmtree(self.base_dir) os.makedirs(self.base_dir) self.feature_dir = os.path.join(self.base_dir, "features") self.samples_dir = os.path.join(self.base_dir, "samples") self.train_dir = os.path.join(self.base_dir, "train_dataset") self.valid_dir = os.path.join(self.base_dir, "valid_dataset") self.test_dir = os.path.join(self.base_dir, "test_dataset") def tearDown(self): shutil.rmtree(self.base_dir) def _run_muv_experiment(self, dataset_file, reload=False, verbosity=None): """Loads or reloads a small version of MUV dataset.""" Loading Loading @@ -106,9 +81,8 @@ class TestReload(TestAPI): featurized_samples, self.train_dir, self.valid_dir, self.test_dir, log_every_n=1000, reload=reload, frac_train=frac_train, frac_test=frac_test, frac_valid=frac_valid) #assert len(train_samples) == frac_train * len(featurized_samples) #assert len(valid_samples) == frac_valid * len(featurized_samples) #assert len(test_samples) == frac_test * len(featurized_samples) # Do an approximate comparison since splits are sometimes slightly off from # the exact fraction. assert relative_difference( len(train_samples), frac_train * len(featurized_samples)) < 1e-3 assert relative_difference( Loading Loading @@ -137,7 +111,7 @@ class TestReload(TestAPI): assert len(valid_samples) == len(valid_dataset) assert len(test_samples) == len(test_dataset) # TODO(rbharath): TRANSFORMERS DON'T PLAY NICE WITH RELOAD! Namely, # TODO(rbharath): Transformers don't play nice with reload! Namely, # reloading will cause the transform to be reapplied. This is undesirable in # almost all cases. Need to understand a method to fix this. input_transformers = [] Loading Loading @@ -206,27 +180,3 @@ class TestReload(TestAPI): assert len_train_dataset == len_reload_train_dataset assert len_valid_dataset == len_reload_valid_dataset assert len_test_dataset == len_reload_valid_dataset # def test_reload_twice_large(self): # """Check ability to repeatedly run experiments with reload set True.""" # reload = True # verbosity = "high" # dataset_file = os.path.join( # self.current_dir, "../../../datasets/medium_muv.csv.gz") # print("Running experiment for first time with reload.") # (len_train_samples, len_valid_samples, len_test_samples, # len_train_dataset, len_valid_dataset, len_test_dataset) = \ # self._run_muv_experiment(dataset_file, reload, verbosity) # # print("Running experiment for second time with reload.") # (len_reload_train_samples, len_reload_valid_samples, len_reload_test_samples, # len_reload_train_dataset, len_reload_valid_dataset, len_reload_test_dataset) = \ # self._run_muv_experiment(dataset_file, reload, verbosity) # print("len_train_samples, len_reload_train_samples") # print(len_train_samples, len_reload_train_samples) # assert len_train_samples == len_reload_train_samples # assert len_valid_samples == len_reload_valid_samples # assert len_test_samples == len_reload_valid_samples # assert len_train_dataset == len_reload_train_dataset # assert len_valid_dataset == len_reload_valid_dataset # assert len_test_dataset == len_reload_valid_dataset
deepchem/models/test/test_singletask_to_multitask.py +2 −1 Original line number Diff line number Diff line Loading @@ -45,6 +45,7 @@ class TestSingletasktoMultitaskAPI(TestAPI): def model_builder(task_types, model_params, verbosity=None): return SklearnModel(task_types, model_params, model_instance=LogisticRegression()) multitask_model = SingletaskToMultitask(task_types, params_dict, model_builder) multitask_model = SingletaskToMultitask(task_types, params_dict, self.model_dir, model_builder) self._create_model(train_dataset, test_dataset, multitask_model, output_transformers, classification_metrics)
