def test_sklearn_reload(self):
"""Test that trained model can be reloaded correctly."""
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 = Dataset.from_numpy(self.train_dir, X, y, w, ids, tasks)
model_params = {
"batch_size": None,
"data_shape": dataset.get_data_shape()
}
verbosity = "high"
classification_metric = Metric(metrics.roc_auc_score,
verbosity=verbosity)
model = SklearnModel(tasks,
task_types,
model_params,
self.model_dir,
mode="classification",
model_instance=RandomForestClassifier())
# Fit trained model
model.fit(dataset)
model.save()
# Load trained model
reloaded_model = SklearnModel(tasks,
task_types,
model_params,
self.model_dir,
mode="classification")
reloaded_model.reload()
# 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] > .9
def test_sklearn_reload(self):
"""Test that trained model can be reloaded correctly."""
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 = Dataset.from_numpy(self.train_dir, X, y, w, ids, tasks)
model_params = {"batch_size": None, "data_shape": dataset.get_data_shape()}
verbosity = "high"
classification_metric = Metric(metrics.roc_auc_score, verbosity=verbosity)
model = SklearnModel(
tasks,
task_types,
model_params,
self.model_dir,
mode="classification",
model_instance=RandomForestClassifier(),
)
# Fit trained model
model.fit(dataset)
model.save()
# Load trained model
reloaded_model = SklearnModel(tasks, task_types, model_params, self.model_dir, mode="classification")
reloaded_model.reload()
# 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] > 0.9
class RFConvexHullPocketFinder(BindingPocketFinder):
"""Uses pre-trained RF model + ConvexHulPocketFinder to select pockets."""
def __init__(self, pad=5):
self.pad = pad
self.convex_finder = ConvexHullPocketFinder(pad)
# Load binding pocket model
self.base_dir = tempfile.mkdtemp()
logger.info("About to download trained model.")
# TODO(rbharath): Shift refined to full once trained.
call((
"wget -nv -c http://deepchem.io.s3-website-us-west-1.amazonaws.com/trained_models/pocket_random_refined_RF.tar.gz"
).split())
call(("tar -zxvf pocket_random_refined_RF.tar.gz").split())
call(("mv pocket_random_refined_RF %s" % (self.base_dir)).split())
self.model_dir = os.path.join(self.base_dir, "pocket_random_refined_RF")
# Fit model on dataset
self.model = SklearnModel(model_dir=self.model_dir)
self.model.reload()
# Create featurizers
self.pocket_featurizer = BindingPocketFeaturizer()
self.ligand_featurizer = CircularFingerprint(size=1024)
def find_pockets(self, protein_file, ligand_file):
"""Compute features for a given complex
TODO(rbharath): This has a log of code overlap with
compute_binding_pocket_features in
examples/binding_pockets/binding_pocket_datasets.py. Find way to refactor
to avoid code duplication.
"""
# if not ligand_file.endswith(".sdf"):
# raise ValueError("Only .sdf ligand files can be featurized.")
# ligand_basename = os.path.basename(ligand_file).split(".")[0]
# ligand_mol2 = os.path.join(
# self.base_dir, ligand_basename + ".mol2")
#
# # Write mol2 file for ligand
# obConversion = ob.OBConversion()
# conv_out = obConversion.SetInAndOutFormats(str("sdf"), str("mol2"))
# ob_mol = ob.OBMol()
# obConversion.ReadFile(ob_mol, str(ligand_file))
# obConversion.WriteFile(ob_mol, str(ligand_mol2))
#
# # Featurize ligand
# mol = Chem.MolFromMol2File(str(ligand_mol2), removeHs=False)
# if mol is None:
# return None, None
# # Default for CircularFingerprint
# n_ligand_features = 1024
# ligand_features = self.ligand_featurizer.featurize([mol])
#
# # Featurize pocket
# pockets, pocket_atoms_map, pocket_coords = self.convex_finder.find_pockets(
# protein_file, ligand_file)
# n_pockets = len(pockets)
# n_pocket_features = BindingPocketFeaturizer.n_features
#
# features = np.zeros((n_pockets, n_pocket_features+n_ligand_features))
# pocket_features = self.pocket_featurizer.featurize(
# protein_file, pockets, pocket_atoms_map, pocket_coords)
# # Note broadcast operation
# features[:, :n_pocket_features] = pocket_features
# features[:, n_pocket_features:] = ligand_features
# dataset = NumpyDataset(X=features)
# pocket_preds = self.model.predict(dataset)
# pocket_pred_proba = np.squeeze(self.model.predict_proba(dataset))
#
# # Find pockets which are active
# active_pockets = []
# active_pocket_atoms_map = {}
# active_pocket_coords = []
# for pocket_ind in range(len(pockets)):
# #################################################### DEBUG
# # TODO(rbharath): For now, using a weak cutoff. Fix later.
# #if pocket_preds[pocket_ind] == 1:
# if pocket_pred_proba[pocket_ind][1] > .15:
# #################################################### DEBUG
# pocket = pockets[pocket_ind]
# active_pockets.append(pocket)
# active_pocket_atoms_map[pocket] = pocket_atoms_map[pocket]
# active_pocket_coords.append(pocket_coords[pocket_ind])
# return active_pockets, active_pocket_atoms_map, active_pocket_coords
# # TODO(LESWING)
raise ValueError("Karl Implement")
class RFConvexHullPocketFinder(BindingPocketFinder):
"""Uses pre-trained RF model + ConvexHulPocketFinder to select pockets."""
def __init__(self, pad=5):
self.pad = pad
self.convex_finder = ConvexHullPocketFinder(pad)
# Load binding pocket model
self.base_dir = tempfile.mkdtemp()
print("About to download trained model.")
# TODO(rbharath): Shift refined to full once trained.
call((
"wget -c http://deepchem.io.s3-website-us-west-1.amazonaws.com/trained_models/pocket_random_refined_RF.tar.gz"
).split())
call(("tar -zxvf pocket_random_refined_RF.tar.gz").split())
call(("mv pocket_random_refined_RF %s" % (self.base_dir)).split())
self.model_dir = os.path.join(self.base_dir, "pocket_random_refined_RF")
# Fit model on dataset
self.model = SklearnModel(model_dir=self.model_dir)
self.model.reload()
# Create featurizers
self.pocket_featurizer = BindingPocketFeaturizer()
self.ligand_featurizer = CircularFingerprint(size=1024)
def find_pockets(self, protein_file, ligand_file):
"""Compute features for a given complex
TODO(rbharath): This has a log of code overlap with
compute_binding_pocket_features in
examples/binding_pockets/binding_pocket_datasets.py. Find way to refactor
to avoid code duplication.
"""
# if not ligand_file.endswith(".sdf"):
# raise ValueError("Only .sdf ligand files can be featurized.")
# ligand_basename = os.path.basename(ligand_file).split(".")[0]
# ligand_mol2 = os.path.join(
# self.base_dir, ligand_basename + ".mol2")
#
# # Write mol2 file for ligand
# obConversion = ob.OBConversion()
# conv_out = obConversion.SetInAndOutFormats(str("sdf"), str("mol2"))
# ob_mol = ob.OBMol()
# obConversion.ReadFile(ob_mol, str(ligand_file))
# obConversion.WriteFile(ob_mol, str(ligand_mol2))
#
# # Featurize ligand
# mol = Chem.MolFromMol2File(str(ligand_mol2), removeHs=False)
# if mol is None:
# return None, None
# # Default for CircularFingerprint
# n_ligand_features = 1024
# ligand_features = self.ligand_featurizer.featurize([mol])
#
# # Featurize pocket
# pockets, pocket_atoms_map, pocket_coords = self.convex_finder.find_pockets(
# protein_file, ligand_file)
# n_pockets = len(pockets)
# n_pocket_features = BindingPocketFeaturizer.n_features
#
# features = np.zeros((n_pockets, n_pocket_features+n_ligand_features))
# pocket_features = self.pocket_featurizer.featurize(
# protein_file, pockets, pocket_atoms_map, pocket_coords)
# # Note broadcast operation
# features[:, :n_pocket_features] = pocket_features
# features[:, n_pocket_features:] = ligand_features
# dataset = NumpyDataset(X=features)
# pocket_preds = self.model.predict(dataset)
# pocket_pred_proba = np.squeeze(self.model.predict_proba(dataset))
#
# # Find pockets which are active
# active_pockets = []
# active_pocket_atoms_map = {}
# active_pocket_coords = []
# for pocket_ind in range(len(pockets)):
# #################################################### DEBUG
# # TODO(rbharath): For now, using a weak cutoff. Fix later.
# #if pocket_preds[pocket_ind] == 1:
# if pocket_pred_proba[pocket_ind][1] > .15:
# #################################################### DEBUG
# pocket = pockets[pocket_ind]
# active_pockets.append(pocket)
# active_pocket_atoms_map[pocket] = pocket_atoms_map[pocket]
# active_pocket_coords.append(pocket_coords[pocket_ind])
# return active_pockets, active_pocket_atoms_map, active_pocket_coords
# # TODO(LESWING)
raise ValueError("Karl Implement")