Loading deepchem/models/tests/test_reload.py +77 −18 Original line number Diff line number Diff line """ Test reload for trained models. """ import pytest import unittest import tempfile import numpy as np Loading Loading @@ -465,8 +466,7 @@ def test_DAG_regression_reload(): assert scores[classification_metric.name] > .9 # TODO: THIS IS FAILING! def test_weave_classification_reload(): def test_weave_classification_reload_alt(): """Test weave model can be reloaded.""" np.random.seed(123) tf.random.set_seed(123) Loading @@ -483,41 +483,40 @@ def test_weave_classification_reload(): classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score) n_atom_feat = 75 n_pair_feat = 14 n_feat = 128 batch_size = 10 model_dir = tempfile.mkdtemp() model = dc.models.WeaveModel( n_tasks, n_atom_feat=n_atom_feat, n_pair_feat=n_pair_feat, n_graph_feat=n_feat, batch_size=batch_size, learning_rate=0.001, use_queue=False, learning_rate=0.0003, mode="classification", dropouts=0.0, model_dir=model_dir) # Fit trained model model.fit(dataset, nb_epoch=20) model.fit(dataset, nb_epoch=30) # Eval model on train scores = model.evaluate(dataset, [classification_metric]) assert scores[classification_metric.name] > .9 # Custom save save_dir = tempfile.mkdtemp() model.model.save(save_dir) from tensorflow import keras reloaded = keras.models.load_model(save_dir) reloaded_model = dc.models.WeaveModel( n_tasks, n_atom_feat=n_atom_feat, n_pair_feat=n_pair_feat, n_graph_feat=n_feat, batch_size=batch_size, learning_rate=0.001, use_queue=False, learning_rate=0.0003, mode="classification", dropouts=0.0, model_dir=model_dir) reloaded_model.restore() #reloaded_model.restore() reloaded_model.model = reloaded # Check predictions match on random sample predmols = ["CCCC", "CCCCCO", "CCCCC"] Loading @@ -525,8 +524,68 @@ def test_weave_classification_reload(): predset = dc.data.NumpyDataset(Xpred) origpred = model.predict(predset) reloadpred = reloaded_model.predict(predset) assert np.all(origpred == reloadpred) # Try re-restore # Eval model on train scores = reloaded_model.evaluate(dataset, [classification_metric]) assert scores[classification_metric.name] > .9 # TODO: THIS IS FAILING! @pytest.mark.slow def test_weave_classification_reload(): """Test weave model can be reloaded.""" np.random.seed(123) tf.random.set_seed(123) n_tasks = 1 # Load mini log-solubility dataset. featurizer = dc.feat.WeaveFeaturizer() tasks = ["outcome"] mols = ["C", "CO", "CC"] n_samples = len(mols) X = featurizer(mols) y = np.random.randint(2, size=(n_samples, n_tasks)) dataset = dc.data.NumpyDataset(X, y) classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score) batch_size = 10 #model_dir = tempfile.mkdtemp() model_dir = "/tmp/foobarbaz7" model = dc.models.WeaveModel( n_tasks, batch_size=batch_size, learning_rate=0.0003, mode="classification", dropouts=0.0, model_dir=model_dir) # Fit trained model model.fit(dataset, nb_epoch=30) # Eval model on train scores = model.evaluate(dataset, [classification_metric]) assert scores[classification_metric.name] > .9 # Check predictions match on random sample predmols = ["CCCC", "CCCCCO", "CCCCC"] Xpred = featurizer(predmols) predset = dc.data.NumpyDataset(Xpred) origpred = model.predict(predset) print("origpred") print(origpred) del model.model del model reloaded_model = dc.models.WeaveModel( n_tasks, batch_size=batch_size, learning_rate=0.0003, mode="classification", dropouts=0.0, model_dir=model_dir) reloaded_model.restore() reloadpred = reloaded_model.predict(predset) assert np.all(origpred == reloadpred) Loading deepchem/models/tests/test_weave_models.py +1 −1 Original line number Diff line number Diff line Loading @@ -117,7 +117,7 @@ def test_compute_features_on_distance_1(): # 10 pairs in total each with start/finish assert atom_to_pair.shape == (8, 2) assert np.all(atom_to_pair == np.array([[0, 0], [1, 1], [1, 3], [2, 2], [2, 3], [3, 1], [3, 2], [3, 3]])) [3, 3], [3, 1], [3, 2], [3, 3]])) @flaky Loading deepchem/feat/tests/test_coulomb_matrices.py +20 −20 File changed.Contains only whitespace changes. Show changes Loading
deepchem/models/tests/test_reload.py +77 −18 Original line number Diff line number Diff line """ Test reload for trained models. """ import pytest import unittest import tempfile import numpy as np Loading Loading @@ -465,8 +466,7 @@ def test_DAG_regression_reload(): assert scores[classification_metric.name] > .9 # TODO: THIS IS FAILING! def test_weave_classification_reload(): def test_weave_classification_reload_alt(): """Test weave model can be reloaded.""" np.random.seed(123) tf.random.set_seed(123) Loading @@ -483,41 +483,40 @@ def test_weave_classification_reload(): classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score) n_atom_feat = 75 n_pair_feat = 14 n_feat = 128 batch_size = 10 model_dir = tempfile.mkdtemp() model = dc.models.WeaveModel( n_tasks, n_atom_feat=n_atom_feat, n_pair_feat=n_pair_feat, n_graph_feat=n_feat, batch_size=batch_size, learning_rate=0.001, use_queue=False, learning_rate=0.0003, mode="classification", dropouts=0.0, model_dir=model_dir) # Fit trained model model.fit(dataset, nb_epoch=20) model.fit(dataset, nb_epoch=30) # Eval model on train scores = model.evaluate(dataset, [classification_metric]) assert scores[classification_metric.name] > .9 # Custom save save_dir = tempfile.mkdtemp() model.model.save(save_dir) from tensorflow import keras reloaded = keras.models.load_model(save_dir) reloaded_model = dc.models.WeaveModel( n_tasks, n_atom_feat=n_atom_feat, n_pair_feat=n_pair_feat, n_graph_feat=n_feat, batch_size=batch_size, learning_rate=0.001, use_queue=False, learning_rate=0.0003, mode="classification", dropouts=0.0, model_dir=model_dir) reloaded_model.restore() #reloaded_model.restore() reloaded_model.model = reloaded # Check predictions match on random sample predmols = ["CCCC", "CCCCCO", "CCCCC"] Loading @@ -525,8 +524,68 @@ def test_weave_classification_reload(): predset = dc.data.NumpyDataset(Xpred) origpred = model.predict(predset) reloadpred = reloaded_model.predict(predset) assert np.all(origpred == reloadpred) # Try re-restore # Eval model on train scores = reloaded_model.evaluate(dataset, [classification_metric]) assert scores[classification_metric.name] > .9 # TODO: THIS IS FAILING! @pytest.mark.slow def test_weave_classification_reload(): """Test weave model can be reloaded.""" np.random.seed(123) tf.random.set_seed(123) n_tasks = 1 # Load mini log-solubility dataset. featurizer = dc.feat.WeaveFeaturizer() tasks = ["outcome"] mols = ["C", "CO", "CC"] n_samples = len(mols) X = featurizer(mols) y = np.random.randint(2, size=(n_samples, n_tasks)) dataset = dc.data.NumpyDataset(X, y) classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score) batch_size = 10 #model_dir = tempfile.mkdtemp() model_dir = "/tmp/foobarbaz7" model = dc.models.WeaveModel( n_tasks, batch_size=batch_size, learning_rate=0.0003, mode="classification", dropouts=0.0, model_dir=model_dir) # Fit trained model model.fit(dataset, nb_epoch=30) # Eval model on train scores = model.evaluate(dataset, [classification_metric]) assert scores[classification_metric.name] > .9 # Check predictions match on random sample predmols = ["CCCC", "CCCCCO", "CCCCC"] Xpred = featurizer(predmols) predset = dc.data.NumpyDataset(Xpred) origpred = model.predict(predset) print("origpred") print(origpred) del model.model del model reloaded_model = dc.models.WeaveModel( n_tasks, batch_size=batch_size, learning_rate=0.0003, mode="classification", dropouts=0.0, model_dir=model_dir) reloaded_model.restore() reloadpred = reloaded_model.predict(predset) assert np.all(origpred == reloadpred) Loading
deepchem/models/tests/test_weave_models.py +1 −1 Original line number Diff line number Diff line Loading @@ -117,7 +117,7 @@ def test_compute_features_on_distance_1(): # 10 pairs in total each with start/finish assert atom_to_pair.shape == (8, 2) assert np.all(atom_to_pair == np.array([[0, 0], [1, 1], [1, 3], [2, 2], [2, 3], [3, 1], [3, 2], [3, 3]])) [3, 3], [3, 1], [3, 2], [3, 3]])) @flaky Loading
deepchem/feat/tests/test_coulomb_matrices.py +20 −20 File changed.Contains only whitespace changes. Show changes
