def scaffold_test_train_test_split(self):
"""Test of singletask RF ECFP regression API."""
splittype = "scaffold"
input_transforms = []
output_transforms = ["normalize"]
model_params = {}
tasks = ["log-solubility"]
task_type = "regression"
task_types = {task: task_type for task in tasks}
input_file = os.path.join(self.current_dir, "example.csv")
featurizer = CircularFingerprint(size=1024)
input_file = os.path.join(self.current_dir, input_file)
loader = DataLoader(tasks=tasks,
smiles_field=self.smiles_field,
featurizer=featurizer,
verbosity="low")
dataset = loader.featurize(input_file, self.data_dir)
# Splits featurized samples into train/test
splitter = ScaffoldSplitter()
train_dataset, test_dataset = splitter.train_test_split(
dataset, self.train_dir, self.test_dir)
assert len(train_dataset) == 8
assert len(test_dataset) == 2
def test_singletask_sklearn_rf_ECFP_regression_API(self):
"""Test of singletask RF ECFP regression API."""
splittype = "scaffold"
featurizer = CircularFingerprint(size=1024)
model_params = {}
tasks = ["log-solubility"]
task_type = "regression"
task_types = {task: task_type for task in tasks}
input_file = os.path.join(self.current_dir, "example.csv")
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)
input_transformers = []
output_transformers = [
NormalizationTransformer(transform_y=True, dataset=train_dataset)
]
transformers = input_transformers + output_transformers
model_params["data_shape"] = train_dataset.get_data_shape()
regression_metrics = [
Metric(metrics.r2_score),
Metric(metrics.mean_squared_error),
Metric(metrics.mean_absolute_error)
]
model = SklearnModel(tasks,
task_types,
model_params,
self.model_dir,
mode="regression",
model_instance=RandomForestRegressor())
# 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(regression_metrics)
# Eval model on test
evaluator = Evaluator(model,
test_dataset,
transformers,
verbosity=True)
_ = evaluator.compute_model_performance(regression_metrics)
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"
# TODO(rbharath): There should be some automatic check to ensure that all
# required model_params are specified.
# TODO(rbharath): Turning off dropout to make tests behave.
model_params = {"nb_hidden": 10, "activation": "relu",
"dropout": .0, "learning_rate": .01,
"momentum": .9, "nesterov": False,
"decay": 1e-4, "batch_size": 5,
"nb_epoch": 2, "init": "glorot_uniform",
"nb_layers": 1, "batchnorm": False}
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"]
task_types = {task: task_type for task in tasks}
featurizer = CircularFingerprint(size=1024)
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 = []
model_params["data_shape"] = train_dataset.get_data_shape()
classification_metrics = [Metric(metrics.roc_auc_score),
Metric(metrics.matthews_corrcoef),
Metric(metrics.recall_score),
Metric(metrics.accuracy_score)]
model = MultiTaskDNN(tasks, task_types, model_params, 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_singletask_sklearn_rf_RDKIT_descriptor_regression_API(self):
"""Test of singletask RF RDKIT-descriptor regression API."""
splittype = "scaffold"
featurizer = RDKitDescriptors()
tasks = ["log-solubility"]
task_type = "regression"
task_types = {task: task_type for task in tasks}
model_params = {}
input_file = os.path.join(self.current_dir, "example.csv")
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)
input_transformers = [
NormalizationTransformer(transform_X=True, dataset=train_dataset),
ClippingTransformer(transform_X=True, dataset=train_dataset)]
output_transformers = [
NormalizationTransformer(transform_y=True, dataset=train_dataset)]
transformers = input_transformers + output_transformers
for dataset in [train_dataset, test_dataset]:
for transformer in transformers:
transformer.transform(dataset)
model_params["data_shape"] = train_dataset.get_data_shape()
regression_metrics = [Metric(metrics.r2_score),
Metric(metrics.mean_squared_error),
Metric(metrics.mean_absolute_error)]
model = SklearnModel(tasks, task_types, model_params, self.model_dir,
mode="regression",
model_instance=RandomForestRegressor())
# 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(regression_metrics)
# Eval model on test
evaluator = Evaluator(model, test_dataset, transformers, verbosity=True)
_ = evaluator.compute_model_performance(regression_metrics)
def test_singletask_sklearn_rf_ECFP_regression_sharded_API(self):
"""Test of singletask RF ECFP regression API: sharded edition."""
splittype = "scaffold"
featurizer = CircularFingerprint(size=1024)
model_params = {}
tasks = ["label"]
task_type = "regression"
task_types = {task: task_type for task in tasks}
input_file = os.path.join(
self.current_dir, "../../../datasets/pdbbind_core_df.pkl.gz")
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)
input_transformers = []
output_transformers = [
NormalizationTransformer(transform_y=True, dataset=train_dataset)]
transformers = input_transformers + output_transformers
for dataset in [train_dataset, test_dataset]:
for transformer in transformers:
transformer.transform(dataset)
# We set shard size above to force the creation of multiple shards of the data.
# pdbbind_core has ~200 examples.
model_params["data_shape"] = train_dataset.get_data_shape()
regression_metrics = [Metric(metrics.r2_score),
Metric(metrics.mean_squared_error),
Metric(metrics.mean_absolute_error)]
model = SklearnModel(tasks, task_types, model_params, self.model_dir,
mode="regression",
model_instance=RandomForestRegressor())
# 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(regression_metrics)
# Eval model on test
evaluator = Evaluator(model, test_dataset, transformers, verbosity=True)
_ = evaluator.compute_model_performance(regression_metrics)
def test_singletask_sklearn_rf_RDKIT_descriptor_regression_API(self):
"""Test of singletask RF RDKIT-descriptor regression API."""
splittype = "scaffold"
featurizer = RDKitDescriptors()
tasks = ["log-solubility"]
task_type = "regression"
task_types = {task: task_type for task in tasks}
input_file = os.path.join(self.current_dir, "example.csv")
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)
input_transformers = [
NormalizationTransformer(transform_X=True, dataset=train_dataset),
ClippingTransformer(transform_X=True, dataset=train_dataset)]
output_transformers = [
NormalizationTransformer(transform_y=True, dataset=train_dataset)]
transformers = input_transformers + output_transformers
for dataset in [train_dataset, test_dataset]:
for transformer in transformers:
transformer.transform(dataset)
regression_metrics = [Metric(metrics.r2_score),
Metric(metrics.mean_squared_error),
Metric(metrics.mean_absolute_error)]
sklearn_model = RandomForestRegressor()
model = SklearnModel(sklearn_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(regression_metrics)
# Eval model on test
evaluator = Evaluator(model, test_dataset, transformers, verbosity=True)
_ = evaluator.compute_model_performance(regression_metrics)
def test_multitask_order(self):
"""Test that order of tasks in multitask datasets is preserved."""
from deepchem.models.keras_models.fcnet import MultiTaskDNN
splittype = "scaffold"
output_transformers = []
input_transformers = []
task_type = "classification"
# TODO(rbharath): There should be some automatic check to ensure that all
# required model_params are specified.
model_params = {
"nb_hidden": 10,
"activation": "relu",
"dropout": .5,
"learning_rate": .01,
"momentum": .9,
"nesterov": False,
"decay": 1e-4,
"batch_size": 5,
"nb_epoch": 2,
"init": "glorot_uniform",
"nb_layers": 1,
"batchnorm": False
}
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"
]
task_types = {task: task_type for task in tasks}
featurizer = CircularFingerprint(size=1024)
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)
assert train_dataset.get_task_names() == tasks
assert test_dataset.get_task_names() == tasks
def test_singletask_tf_mlp_ECFP_classification_API(self):
"""Straightforward test of Tensorflow singletask deepchem classification API."""
n_features = 1024
featurizer = CircularFingerprint(size=n_features)
tasks = ["outcome"]
input_file = os.path.join(self.current_dir, "example_classification.csv")
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 = [
NormalizationTransformer(transform_y=True, dataset=train_dataset)]
for dataset in [train_dataset, test_dataset]:
for transformer in transformers:
transformer.transform(dataset)
classification_metrics = [Metric(metrics.roc_auc_score),
Metric(metrics.matthews_corrcoef),
Metric(metrics.recall_score),
Metric(metrics.accuracy_score)]
tensorflow_model = TensorflowMultiTaskClassifier(
len(tasks), n_features, self.model_dir)
model = TensorflowModel(tensorflow_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_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_multitask_order(self):
"""Test that order of tasks in multitask datasets is preserved."""
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"
]
featurizer = CircularFingerprint(size=1024)
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)
assert train_dataset.get_task_names() == tasks
assert test_dataset.get_task_names() == tasks
def test_multitask_order(self):
"""Test that order of tasks in multitask datasets is preserved."""
from deepchem.models.keras_models.fcnet import MultiTaskDNN
splittype = "scaffold"
output_transformers = []
input_transformers = []
task_type = "classification"
# TODO(rbharath): There should be some automatic check to ensure that all
# required model_params are specified.
model_params = {"nb_hidden": 10, "activation": "relu",
"dropout": .5, "learning_rate": .01,
"momentum": .9, "nesterov": False,
"decay": 1e-4, "batch_size": 5,
"nb_epoch": 2, "init": "glorot_uniform",
"nb_layers": 1, "batchnorm": False}
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"]
task_types = {task: task_type for task in tasks}
featurizer = CircularFingerprint(size=1024)
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)
assert train_dataset.get_task_names() == tasks
assert test_dataset.get_task_names() == tasks
def test_singletask_tf_mlp_ECFP_classification_API(self):
"""Straightforward test of Tensorflow singletask deepchem classification API."""
splittype = "scaffold"
output_transformers = []
input_transformers = []
task_type = "classification"
featurizer = CircularFingerprint(size=1024)
tasks = ["outcome"]
task_type = "classification"
task_types = {task: task_type for task in tasks}
input_file = os.path.join(self.current_dir, "example_classification.csv")
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)
input_transformers = []
output_transformers = [
NormalizationTransformer(transform_y=True, dataset=train_dataset)]
transformers = input_transformers + output_transformers
for dataset in [train_dataset, test_dataset]:
for transformer in transformers:
transformer.transform(dataset)
model_params = {
"batch_size": 2,
"num_classification_tasks": 1,
"num_features": 1024,
"layer_sizes": [1024],
"weight_init_stddevs": [1.],
"bias_init_consts": [0.],
"dropouts": [.5],
"num_classes": 2,
"nb_epoch": 1,
"penalty": 0.0,
"optimizer": "adam",
"learning_rate": .001,
"data_shape": train_dataset.get_data_shape()
}
classification_metrics = [Metric(metrics.roc_auc_score),
Metric(metrics.matthews_corrcoef),
Metric(metrics.recall_score),
Metric(metrics.accuracy_score)]
model = TensorflowModel(
tasks, task_types, model_params, self.model_dir,
tf_class=TensorflowMultiTaskClassifier)
# 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_singletask_tf_mlp_ECFP_classification_API(self):
"""Straightforward test of Tensorflow singletask deepchem classification API."""
splittype = "scaffold"
output_transformers = []
input_transformers = []
task_type = "classification"
featurizer = CircularFingerprint(size=1024)
tasks = ["outcome"]
task_type = "classification"
task_types = {task: task_type for task in tasks}
input_file = os.path.join(self.current_dir,
"example_classification.csv")
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)
input_transformers = []
output_transformers = [
NormalizationTransformer(transform_y=True, dataset=train_dataset)
]
transformers = input_transformers + output_transformers
for dataset in [train_dataset, test_dataset]:
for transformer in transformers:
transformer.transform(dataset)
model_params = {
"batch_size": 2,
"num_classification_tasks": 1,
"num_features": 1024,
"layer_sizes": [1024],
"weight_init_stddevs": [1.],
"bias_init_consts": [0.],
"dropouts": [.5],
"num_classes": 2,
"nb_epoch": 1,
"penalty": 0.0,
"optimizer": "adam",
"learning_rate": .001,
"data_shape": train_dataset.get_data_shape()
}
classification_metrics = [
Metric(metrics.roc_auc_score),
Metric(metrics.matthews_corrcoef),
Metric(metrics.recall_score),
Metric(metrics.accuracy_score)
]
model = TensorflowModel(tasks,
task_types,
model_params,
self.model_dir,
tf_class=TensorflowMultiTaskClassifier)
# 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_multitask_keras_mlp_ECFP_classification_API(self):
"""Straightforward test of Keras multitask deepchem classification API."""
from deepchem.models.keras_models.fcnet import MultiTaskDNN
task_type = "classification"
# TODO(rbharath): There should be some automatic check to ensure that all
# required model_params are specified.
model_params = {
"nb_hidden": 10,
"activation": "relu",
"dropout": .5,
"learning_rate": .01,
"momentum": .9,
"nesterov": False,
"decay": 1e-4,
"batch_size": 5,
"nb_epoch": 2,
"init": "glorot_uniform",
"nb_layers": 1,
"batchnorm": False
}
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"
]
task_types = {task: task_type for task in tasks}
featurizer = CircularFingerprint(size=1024)
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 = []
model_params["data_shape"] = train_dataset.get_data_shape()
classification_metrics = [
Metric(metrics.roc_auc_score),
Metric(metrics.matthews_corrcoef),
Metric(metrics.recall_score),
Metric(metrics.accuracy_score)
]
model = MultiTaskDNN(tasks, task_types, model_params, 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)
