def test_keras_reload(self):
"""Test that trained keras models can be reloaded correctly."""
g = tf.Graph()
sess = tf.Session(graph=g)
K.set_session(sess)
with g.as_default():
tasks = ["task0"]
task_types = {task: "classification" for task in tasks}
n_samples = 10
n_features = 3
n_tasks = len(tasks)
# Generate dummy dataset
np.random.seed(123)
ids = np.arange(n_samples)
X = np.random.rand(n_samples, n_features)
y = np.random.randint(2, size=(n_samples, n_tasks))
w = np.ones((n_samples, n_tasks))
dataset = NumpyDataset(X, y, w, ids)
verbosity = "high"
classification_metric = Metric(metrics.roc_auc_score,
verbosity=verbosity)
keras_model = MultiTaskDNN(n_tasks,
n_features,
"classification",
dropout=0.)
model = KerasModel(keras_model, self.model_dir)
# Fit trained model
model.fit(dataset)
model.save()
# Load trained model
reloaded_keras_model = MultiTaskDNN(n_tasks,
n_features,
"classification",
dropout=0.)
reloaded_model = KerasModel(reloaded_keras_model, self.model_dir)
reloaded_model.reload(
custom_objects={"MultiTaskDNN": MultiTaskDNN})
# Eval model on train
transformers = []
evaluator = Evaluator(reloaded_model,
dataset,
transformers,
verbosity=verbosity)
scores = evaluator.compute_model_performance(
[classification_metric])
assert scores[classification_metric.name] > .6
def model_builder(model_params, model_dir):
keras_model = MultiTaskDNN(len(tasks),
n_features,
task_type,
dropout=0.,
**model_params)
return KerasModel(keras_model, model_dir)
def model_builder(model_params, model_dir):
keras_model = MultiTaskDNN(len(bace_tasks),
n_features,
"classification",
dropout=.5,
**model_params)
return KerasModel(keras_model, model_dir)
def test_keras_multitask_regression_overfit(self):
"""Test keras multitask overfits tiny data."""
g = tf.Graph()
sess = tf.Session(graph=g)
K.set_session(sess)
with g.as_default():
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.randint(2, size=(n_samples, n_tasks))
w = np.ones((n_samples, n_tasks))
dataset = NumpyDataset(X, y, w, ids)
verbosity = "high"
regression_metric = Metric(metrics.r2_score, verbosity=verbosity,
task_averager=np.mean, mode="regression")
keras_model = MultiTaskDNN(n_tasks, n_features, "regression",
dropout=0., learning_rate=.1, decay=1e-4)
model = KerasModel(keras_model, self.model_dir, verbosity=verbosity)
# Fit trained model
model.fit(dataset, nb_epoch=100)
model.save()
# Eval model on train
transformers = []
evaluator = Evaluator(model, dataset, transformers, verbosity=verbosity)
scores = evaluator.compute_model_performance([regression_metric])
assert scores[regression_metric.name] > .75
def test_multitask_keras_mlp_ECFP_classification_API(self):
"""Straightforward test of Keras multitask deepchem classification API."""
g = tf.Graph()
sess = tf.Session(graph=g)
K.set_session(sess)
with g.as_default():
task_type = "classification"
input_file = os.path.join(self.current_dir, "multitask_example.csv")
tasks = ["task0", "task1", "task2", "task3", "task4", "task5", "task6",
"task7", "task8", "task9", "task10", "task11", "task12",
"task13", "task14", "task15", "task16"]
n_features = 1024
featurizer = CircularFingerprint(size=n_features)
loader = DataLoader(tasks=tasks,
smiles_field=self.smiles_field,
featurizer=featurizer,
verbosity="low")
dataset = loader.featurize(input_file, self.data_dir)
splitter = ScaffoldSplitter()
train_dataset, test_dataset = splitter.train_test_split(
dataset, self.train_dir, self.test_dir)
transformers = []
classification_metrics = [Metric(metrics.roc_auc_score),
Metric(metrics.matthews_corrcoef),
Metric(metrics.recall_score),
Metric(metrics.accuracy_score)]
keras_model = MultiTaskDNN(len(tasks), n_features, "classification",
dropout=0.)
model = KerasModel(keras_model, self.model_dir)
# Fit trained model
model.fit(train_dataset)
model.save()
# Eval model on train
evaluator = Evaluator(model, train_dataset, transformers, verbosity=True)
_ = evaluator.compute_model_performance(classification_metrics)
# Eval model on test
evaluator = Evaluator(model, test_dataset, transformers, verbosity=True)
_ = evaluator.compute_model_performance(classification_metrics)
def test_keras_skewed_classification_overfit(self):
"""Test keras models can overfit 0/1 datasets with few actives."""
g = tf.Graph()
sess = tf.Session(graph=g)
K.set_session(sess)
with g.as_default():
n_samples = 100
n_features = 3
n_tasks = 1
# Generate dummy dataset
np.random.seed(123)
p = .05
ids = np.arange(n_samples)
X = np.random.rand(n_samples, n_features)
y = np.random.binomial(1, p, size=(n_samples, n_tasks))
w = np.ones((n_samples, n_tasks))
dataset = NumpyDataset(X, y, w, ids)
verbosity = "high"
classification_metric = Metric(metrics.roc_auc_score, verbosity=verbosity)
keras_model = MultiTaskDNN(n_tasks, n_features, "classification",
dropout=0., learning_rate=.15, decay=1e-4)
model = KerasModel(keras_model, self.model_dir)
# Fit trained model
model.fit(dataset, batch_size=n_samples, nb_epoch=200)
model.save()
# Eval model on train
transformers = []
evaluator = Evaluator(model, dataset, transformers, verbosity=verbosity)
scores = evaluator.compute_model_performance([classification_metric])
assert scores[classification_metric.name] > .9
train_dataset, valid_dataset = muv_datasets
n_features = 1024
# Build model
classification_metric = Metric(metrics.roc_auc_score,
np.mean,
verbosity=verbosity,
mode="classification")
keras_model = MultiTaskDNN(len(muv_tasks),
n_features,
"classification",
dropout=.25,
learning_rate=.001,
decay=1e-4)
model = KerasModel(keras_model, self.model_dir, verbosity=verbosity)
# Fit trained model
model.fit(train_dataset)
model.save()
train_evaluator = Evaluator(model,
train_dataset,
transformers,
verbosity=verbosity)
train_scores = train_evaluator.compute_model_performance(
[classification_metric])
print("Train scores")
print(train_scores)
classification_metric = Metric(metrics.roc_auc_score, np.mean,
verbosity=verbosity,
mode="classification")
learning_rates = [0.0003, 0.001, 0.003]
hidden_units = [1000, 500]
dropouts = [.5, .25]
num_hidden_layers = [1, 2]
# hyperparameter sweep here
for learning_rate in learning_rates:
for hidden_unit in hidden_units:
for dropout in dropouts:
keras_model = MultiTaskDNN(len(sider_tasks), n_features, "classification",
dropout=.25, learning_rate=.001, decay=1e-4)
model = KerasModel(keras_model, self.model_dir, verbosity=verbosity)
# Fit trained model
model.fit(train_dataset)
model.save()
train_evaluator = Evaluator(model, train_dataset, transformers, verbosity=verbosity)
train_scores = train_evaluator.compute_model_performance([classification_metric])
print("Train scores")
print(train_scores)
valid_evaluator = Evaluator(model, valid_dataset, transformers, verbosity=verbosity)
valid_scores = valid_evaluator.compute_model_performance([classification_metric])
print("Validation scores")