Commit 0430d59d authored by Bharath Ramsundar's avatar Bharath Ramsundar
Browse files

Fix some broken tests

parent 46c403da

deepchem/models/tests/test_api.py

+1 −2
Original line number Diff line number Diff line
@@ -203,9 +203,8 @@ class TestAPI(unittest.TestCase): 
                              dc.metrics.Metric(dc.metrics.recall_score),

                              dc.metrics.Metric(dc.metrics.accuracy_score)]



    tensorflow_model = dc.models.TensorflowMultiTaskClassifier(

    model = dc.models.TensorflowMultiTaskClassifier(

        len(tasks), n_features)

    model = dc.models.TensorflowModel(tensorflow_model)



    # Fit trained model

    model.fit(train_dataset)

deepchem/models/tests/test_overfit.py

+9 −18
Original line number Diff line number Diff line
@@ -169,11 +169,10 @@ class TestOverfit(test_util.TensorFlowTestCase): 
    regression_metric = dc.metrics.Metric(

        dc.metrics.mean_squared_error, verbosity=verbosity)

    # TODO(rbharath): This breaks with optimizer="momentum". Why?

    tensorflow_model = dc.models.TensorflowMultiTaskRegressor(

    model = dc.models.TensorflowMultiTaskRegressor(

        n_tasks, n_features, dropouts=[0.],

        learning_rate=0.003, weight_init_stddevs=[np.sqrt(6)/np.sqrt(1000)],

        batch_size=n_samples, verbosity=verbosity)

    model = dc.models.TensorflowModel(tensorflow_model)



    # Fit trained model

    model.fit(dataset, nb_epoch=100)
@@ -272,11 +271,10 @@ class TestOverfit(test_util.TensorFlowTestCase): 
    verbosity = "high"

    classification_metric = dc.metrics.Metric(

        dc.metrics.accuracy_score, verbosity=verbosity)

    tensorflow_model = dc.models.TensorflowMultiTaskClassifier(

    model = dc.models.TensorflowMultiTaskClassifier(

        n_tasks, n_features, dropouts=[0.],

        learning_rate=0.0003, weight_init_stddevs=[.1],

        batch_size=n_samples, verbosity=verbosity)

    model = dc.models.TensorflowModel(tensorflow_model)



    # Fit trained model

    model.fit(dataset, nb_epoch=100)
@@ -307,11 +305,10 @@ class TestOverfit(test_util.TensorFlowTestCase): 
    verbosity = "high"

    classification_metric = dc.metrics.Metric(

        dc.metrics.roc_auc_score, verbosity=verbosity)

    tensorflow_model = dc.models.TensorflowMultiTaskClassifier(

    model = dc.models.TensorflowMultiTaskClassifier(

        n_tasks, n_features, dropouts=[0.],

        learning_rate=0.003, weight_init_stddevs=[.1],

        batch_size=n_samples, verbosity=verbosity)

    model = dc.models.TensorflowModel(tensorflow_model)



    # Fit trained model

    model.fit(dataset, nb_epoch=100)
@@ -352,11 +349,10 @@ class TestOverfit(test_util.TensorFlowTestCase): 
    verbosity = "high"

    classification_metric = dc.metrics.Metric(

        dc.metrics.roc_auc_score, verbosity=verbosity)

    tensorflow_model = dc.models.TensorflowMultiTaskClassifier(

    model = dc.models.TensorflowMultiTaskClassifier(

        n_tasks, n_features, dropouts=[0.],

        learning_rate=0.003, weight_init_stddevs=[1.],

        batch_size=n_samples, verbosity=verbosity)

    model = dc.models.TensorflowModel(tensorflow_model)



    # Fit trained model

    model.fit(dataset, nb_epoch=50)
@@ -449,11 +445,10 @@ class TestOverfit(test_util.TensorFlowTestCase): 
    verbosity = "high"

    classification_metric = dc.metrics.Metric(

      dc.metrics.accuracy_score, verbosity=verbosity, task_averager=np.mean)

    tensorflow_model = dc.models.TensorflowMultiTaskClassifier(

    model = dc.models.TensorflowMultiTaskClassifier(

        n_tasks, n_features, dropouts=[0.],

        learning_rate=0.0003, weight_init_stddevs=[.1],

        batch_size=n_samples, verbosity=verbosity)

    model = dc.models.TensorflowModel(tensorflow_model)



    # Fit trained model

    model.fit(dataset)
@@ -481,12 +476,11 @@ class TestOverfit(test_util.TensorFlowTestCase): 
    verbosity = "high"

    classification_metric = dc.metrics.Metric(

      dc.metrics.accuracy_score, verbosity=verbosity, task_averager=np.mean)

    tensorflow_model = dc.models.RobustMultitaskClassifier(

    model = dc.models.RobustMultitaskClassifier(

        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, verbosity=verbosity)

    model = dc.models.TensorflowModel(tensorflow_model)



    # Fit trained model

    model.fit(dataset, nb_epoch=25)
@@ -514,10 +508,9 @@ class TestOverfit(test_util.TensorFlowTestCase): 
    verbosity = "high"

    classification_metric = dc.metrics.Metric(

      dc.metrics.accuracy_score, verbosity=verbosity, task_averager=np.mean)

    tensorflow_model = dc.models.TensorflowLogisticRegression(

    model = dc.models.TensorflowLogisticRegression(

        n_tasks, n_features, learning_rate=0.5, weight_init_stddevs=[.01],

        batch_size=n_samples, verbosity=verbosity)

    model = dc.models.TensorflowModel(tensorflow_model)



    # Fit trained model

    model.fit(dataset)
@@ -613,11 +606,10 @@ class TestOverfit(test_util.TensorFlowTestCase): 
    regression_metric = dc.metrics.Metric(

        dc.metrics.mean_squared_error, verbosity=verbosity,

        task_averager=np.mean, mode="regression")

    tensorflow_model = dc.models.TensorflowMultiTaskRegressor(

    model = dc.models.TensorflowMultiTaskRegressor(

        n_tasks, n_features, dropouts=[0.],

        learning_rate=0.0003, weight_init_stddevs=[.1],

        batch_size=n_samples, verbosity=verbosity)

    model = dc.models.TensorflowModel(tensorflow_model)



    # Fit trained model

    model.fit(dataset, nb_epoch=50)
@@ -649,12 +641,11 @@ class TestOverfit(test_util.TensorFlowTestCase): 
    regression_metric = dc.metrics.Metric(

        dc.metrics.mean_squared_error, verbosity=verbosity,

        task_averager=np.mean, mode="regression")

    tensorflow_model = dc.models.RobustMultitaskRegressor(

    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, verbosity=verbosity)

    model = dc.models.TensorflowModel(tensorflow_model)



    # Fit trained model

    model.fit(dataset, nb_epoch=25)

deepchem/models/tests/test_reload.py

+2 −5
Original line number Diff line number Diff line
@@ -110,20 +110,17 @@ class TestReload(unittest.TestCase): 
    classification_metric = dc.metrics.Metric(dc.metrics.accuracy_score)



    model_dir = tempfile.mkdtemp()

    tensorflow_model = dc.models.TensorflowMultiTaskClassifier(

    model = dc.models.TensorflowMultiTaskClassifier(

          n_tasks, n_features, model_dir, dropouts=[0.], verbosity="high")

    model = dc.models.TensorflowModel(tensorflow_model)



    # Fit trained model

    model.fit(dataset)

    model.save()



    # Load trained model

    reloaded_tensorflow_model = dc.models.TensorflowMultiTaskClassifier(

    reloaded_model = dc.models.TensorflowMultiTaskClassifier(

        n_tasks, n_features, model_dir, dropouts=[0.],

        verbosity="high")

    reloaded_model = dc.models.TensorflowModel(

        reloaded_tensorflow_model)

    reloaded_model.reload()



    # Eval model on train

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