Loading deepchem/models/tests/test_reload.py +121 −0 Original line number Diff line number Diff line Loading @@ -278,6 +278,65 @@ def test_robust_multitask_classification_reload(): assert scores[classification_metric.name] > .9 # TODO: THIS DOESN'T WORK!! #def test_robust_multitask_regressor_reload(): # """Test that RobustMultitaskRegressor can be reloaded correctly.""" # n_tasks = 10 # n_samples = 10 # n_features = 3 # # # Generate dummy dataset # np.random.seed(123) # ids = np.arange(n_samples) # X = np.random.rand(n_samples, n_features) # y = np.random.rand(n_samples, n_tasks) # w = np.ones((n_samples, n_tasks)) # # dataset = dc.data.NumpyDataset(X, y, w, ids) # regression_metric = dc.metrics.Metric(dc.metrics.mean_squared_error) # # model_dir = tempfile.mkdtemp() # model = dc.models.RobustMultitaskRegressor( # n_tasks, # n_features, # layer_sizes=[50], # bypass_layer_sizes=[10], # dropouts=[0.], # learning_rate=0.003, # weight_init_stddevs=[.1], # batch_size=n_samples) # # # Fit trained model # model.fit(dataset, nb_epoch=100) # # # Eval model on train # scores = model.evaluate(dataset, [regression_metric]) # assert scores[regression_metric.name] < .1 # # # Reload trained model # reloaded_model = dc.models.RobustMultitaskRegressor( # n_tasks, # n_features, # layer_sizes=[50], # bypass_layer_sizes=[10], # dropouts=[0.], # learning_rate=0.003, # weight_init_stddevs=[.1], # batch_size=n_samples) # reloaded_model.restore() # # # Check predictions match on random sample # Xpred = np.random.rand(n_samples, n_features) # predset = dc.data.NumpyDataset(Xpred) # origpred = model.predict(predset) # reloadpred = reloaded_model.predict(predset) # assert np.all(origpred == reloadpred) # # # Eval model on train # scores = reloaded_model.evaluate(dataset, [regression_metric]) # assert scores[regression_metric.name] < 0.1 def test_IRV_multitask_classification_reload(): """Test IRV classifier can be reloaded.""" n_tasks = 5 Loading Loading @@ -398,6 +457,68 @@ def test_progressive_classification_reload(): assert scores[classification_metric.name] > .9 def test_progressivemultitaskregressor_reload(): """Test that ProgressiveMultitaskRegressor can be reloaded correctly.""" n_samples = 10 n_features = 3 n_tasks = 1 # Generate dummy dataset np.random.seed(123) ids = np.arange(n_samples) X = np.random.rand(n_samples, n_features) y = np.random.rand(n_samples, n_tasks) w = np.ones((n_samples, n_tasks)) dataset = dc.data.NumpyDataset(X, y, w, ids) regression_metric = dc.metrics.Metric(dc.metrics.mean_squared_error) model_dir = tempfile.mkdtemp() model = dc.models.ProgressiveMultitaskRegressor( n_tasks, n_features, layer_sizes=[50], bypass_layer_sizes=[10], dropouts=[0.], learning_rate=0.001, weight_init_stddevs=[.1], alpha_init_stddevs=[.02], batch_size=n_samples, model_dir=model_dir) # Fit trained model model.fit(dataset, nb_epoch=100) # Eval model on train scores = model.evaluate(dataset, [regression_metric]) assert scores[regression_metric.name] < .1 # Reload trained model reloaded_model = dc.models.ProgressiveMultitaskRegressor( n_tasks, n_features, layer_sizes=[50], bypass_layer_sizes=[10], dropouts=[0.], learning_rate=0.001, weight_init_stddevs=[.1], alpha_init_stddevs=[.02], batch_size=n_samples, model_dir=model_dir) reloaded_model.restore() # Check predictions match on random sample Xpred = np.random.rand(n_samples, n_features) predset = dc.data.NumpyDataset(Xpred) origpred = model.predict(predset) reloadpred = reloaded_model.predict(predset) assert np.all(origpred == reloadpred) # Eval model on train scores = reloaded_model.evaluate(dataset, [regression_metric]) assert scores[regression_metric.name] < 0.1 ## TODO: THIS IS FAILING! #def test_DAG_regression_reload(): # """Test DAG regressor reloads.""" Loading Loading
deepchem/models/tests/test_reload.py +121 −0 Original line number Diff line number Diff line Loading @@ -278,6 +278,65 @@ def test_robust_multitask_classification_reload(): assert scores[classification_metric.name] > .9 # TODO: THIS DOESN'T WORK!! #def test_robust_multitask_regressor_reload(): # """Test that RobustMultitaskRegressor can be reloaded correctly.""" # n_tasks = 10 # n_samples = 10 # n_features = 3 # # # Generate dummy dataset # np.random.seed(123) # ids = np.arange(n_samples) # X = np.random.rand(n_samples, n_features) # y = np.random.rand(n_samples, n_tasks) # w = np.ones((n_samples, n_tasks)) # # dataset = dc.data.NumpyDataset(X, y, w, ids) # regression_metric = dc.metrics.Metric(dc.metrics.mean_squared_error) # # model_dir = tempfile.mkdtemp() # model = dc.models.RobustMultitaskRegressor( # n_tasks, # n_features, # layer_sizes=[50], # bypass_layer_sizes=[10], # dropouts=[0.], # learning_rate=0.003, # weight_init_stddevs=[.1], # batch_size=n_samples) # # # Fit trained model # model.fit(dataset, nb_epoch=100) # # # Eval model on train # scores = model.evaluate(dataset, [regression_metric]) # assert scores[regression_metric.name] < .1 # # # Reload trained model # reloaded_model = dc.models.RobustMultitaskRegressor( # n_tasks, # n_features, # layer_sizes=[50], # bypass_layer_sizes=[10], # dropouts=[0.], # learning_rate=0.003, # weight_init_stddevs=[.1], # batch_size=n_samples) # reloaded_model.restore() # # # Check predictions match on random sample # Xpred = np.random.rand(n_samples, n_features) # predset = dc.data.NumpyDataset(Xpred) # origpred = model.predict(predset) # reloadpred = reloaded_model.predict(predset) # assert np.all(origpred == reloadpred) # # # Eval model on train # scores = reloaded_model.evaluate(dataset, [regression_metric]) # assert scores[regression_metric.name] < 0.1 def test_IRV_multitask_classification_reload(): """Test IRV classifier can be reloaded.""" n_tasks = 5 Loading Loading @@ -398,6 +457,68 @@ def test_progressive_classification_reload(): assert scores[classification_metric.name] > .9 def test_progressivemultitaskregressor_reload(): """Test that ProgressiveMultitaskRegressor can be reloaded correctly.""" n_samples = 10 n_features = 3 n_tasks = 1 # Generate dummy dataset np.random.seed(123) ids = np.arange(n_samples) X = np.random.rand(n_samples, n_features) y = np.random.rand(n_samples, n_tasks) w = np.ones((n_samples, n_tasks)) dataset = dc.data.NumpyDataset(X, y, w, ids) regression_metric = dc.metrics.Metric(dc.metrics.mean_squared_error) model_dir = tempfile.mkdtemp() model = dc.models.ProgressiveMultitaskRegressor( n_tasks, n_features, layer_sizes=[50], bypass_layer_sizes=[10], dropouts=[0.], learning_rate=0.001, weight_init_stddevs=[.1], alpha_init_stddevs=[.02], batch_size=n_samples, model_dir=model_dir) # Fit trained model model.fit(dataset, nb_epoch=100) # Eval model on train scores = model.evaluate(dataset, [regression_metric]) assert scores[regression_metric.name] < .1 # Reload trained model reloaded_model = dc.models.ProgressiveMultitaskRegressor( n_tasks, n_features, layer_sizes=[50], bypass_layer_sizes=[10], dropouts=[0.], learning_rate=0.001, weight_init_stddevs=[.1], alpha_init_stddevs=[.02], batch_size=n_samples, model_dir=model_dir) reloaded_model.restore() # Check predictions match on random sample Xpred = np.random.rand(n_samples, n_features) predset = dc.data.NumpyDataset(Xpred) origpred = model.predict(predset) reloadpred = reloaded_model.predict(predset) assert np.all(origpred == reloadpred) # Eval model on train scores = reloaded_model.evaluate(dataset, [regression_metric]) assert scores[regression_metric.name] < 0.1 ## TODO: THIS IS FAILING! #def test_DAG_regression_reload(): # """Test DAG regressor reloads.""" Loading
