def predict_sklearn(args: SklearnPredictArgs):
print('Loading data')
data = get_data(path=args.test_path,
smiles_column=args.smiles_column,
target_columns=[])
print('Loading training arguments')
with open(args.checkpoint_paths[0], 'rb') as f:
model = pickle.load(f)
train_args: SklearnTrainArgs = SklearnTrainArgs().from_dict(
model.train_args, skip_unsettable=True)
print('Computing morgan fingerprints')
morgan_fingerprint = get_features_generator('morgan')
for datapoint in tqdm(data, total=len(data)):
datapoint.set_features(
morgan_fingerprint(mol=datapoint.smiles,
radius=train_args.radius,
num_bits=train_args.num_bits))
print(
f'Predicting with an ensemble of {len(args.checkpoint_paths)} models')
sum_preds = np.zeros((len(data), train_args.num_tasks))
for checkpoint_path in tqdm(args.checkpoint_paths,
total=len(args.checkpoint_paths)):
with open(checkpoint_path, 'rb') as f:
model = pickle.load(f)
model_preds = predict(model=model,
model_type=train_args.model_type,
dataset_type=train_args.dataset_type,
features=data.features())
sum_preds += np.array(model_preds)
# Ensemble predictions
avg_preds = sum_preds / len(args.checkpoint_paths)
avg_preds = avg_preds.tolist()
print(f'Saving predictions to {args.preds_path}')
assert len(data) == len(avg_preds)
makedirs(args.preds_path, isfile=True)
# Copy predictions over to data
for datapoint, preds in zip(data, avg_preds):
for pred_name, pred in zip(train_args.task_names, preds):
datapoint.row[pred_name] = pred
# Save
with open(args.preds_path, 'w') as f:
writer = csv.DictWriter(f, fieldnames=data[0].row.keys())
writer.writeheader()
for datapoint in data:
writer.writerow(datapoint.row)
def predict_sklearn(args: Namespace):
print('Loading data')
data = get_data(path=args.test_path)
print('Computing morgan fingerprints')
morgan_fingerprint = get_features_generator('morgan')
for datapoint in tqdm(data, total=len(data)):
datapoint.set_features(morgan_fingerprint(mol=datapoint.smiles, radius=args.radius, num_bits=args.num_bits))
print(f'Predicting with an ensemble of {len(args.checkpoint_paths)} models')
sum_preds = np.zeros((len(data), args.num_tasks))
for checkpoint_path in tqdm(args.checkpoint_paths, total=len(args.checkpoint_paths)):
with open(checkpoint_path, 'rb') as f:
model = pickle.load(f)
model_preds = predict(
model=model,
model_type=args.model_type,
dataset_type=args.dataset_type,
features=data.features()
)
sum_preds += np.array(model_preds)
# Ensemble predictions
avg_preds = sum_preds / len(args.checkpoint_paths)
avg_preds = avg_preds.tolist()
print('Saving predictions')
assert len(data) == len(avg_preds)
makedirs(args.preds_path, isfile=True)
with open(args.preds_path, 'w') as f:
writer = csv.writer(f)
writer.writerow(['smiles'] + get_task_names(args.test_path))
for smiles, pred in zip(data.smiles(), avg_preds):
writer.writerow([smiles] + pred)
def predict_sklearn(args: SklearnPredictArgs) -> None:
"""
Loads data and a trained scikit-learn model and uses the model to make predictions on the data.
:param args: A :class:`~chemprop.args.SklearnPredictArgs` object containing arguments for
loading data, loading a trained scikit-learn model, and making predictions with the model.
"""
print('Loading data')
data = get_data(path=args.test_path,
smiles_columns=args.smiles_columns,
target_columns=[],
ignore_columns=[],
store_row=True)
print('Loading training arguments')
with open(args.checkpoint_paths[0], 'rb') as f:
model = pickle.load(f)
train_args: SklearnTrainArgs = SklearnTrainArgs().from_dict(
model.train_args, skip_unsettable=True)
print('Computing morgan fingerprints')
morgan_fingerprint = get_features_generator('morgan')
for datapoint in tqdm(data, total=len(data)):
for s in datapoint.smiles:
datapoint.extend_features(
morgan_fingerprint(mol=s,
radius=train_args.radius,
num_bits=train_args.num_bits))
print(
f'Predicting with an ensemble of {len(args.checkpoint_paths)} models')
sum_preds = np.zeros((len(data), train_args.num_tasks))
for checkpoint_path in tqdm(args.checkpoint_paths,
total=len(args.checkpoint_paths)):
with open(checkpoint_path, 'rb') as f:
model = pickle.load(f)
model_preds = predict(model=model,
model_type=train_args.model_type,
dataset_type=train_args.dataset_type,
features=data.features())
sum_preds += np.array(model_preds)
# Ensemble predictions
avg_preds = sum_preds / len(args.checkpoint_paths)
avg_preds = avg_preds.tolist()
print(f'Saving predictions to {args.preds_path}')
# assert len(data) == len(avg_preds) #TODO: address with unit test later
makedirs(args.preds_path, isfile=True)
# Copy predictions over to data
for datapoint, preds in zip(data, avg_preds):
for pred_name, pred in zip(train_args.task_names, preds):
datapoint.row[pred_name] = pred
# Save
with open(args.preds_path, 'w') as f:
writer = csv.DictWriter(f, fieldnames=data[0].row.keys())
writer.writeheader()
for datapoint in data:
writer.writerow(datapoint.row)
def predict_sklearn(args: SklearnPredictArgs):
if args.parcel_size and args.max_data_size:
num_iterations = math.ceil(args.max_data_size / args.parcel_size)
max_data_size = args.parcel_size
else:
num_iterations = 1
max_data_size = args.max_data_size
offset = 0
for iteration in range(num_iterations):
if iteration > 0:
offset = offset + args.parcel_size
max_data_size = max_data_size + args.parcel_size
print('Loading data')
data = get_data(path=args.test_path,
smiles_column=args.smiles_column,
target_columns=[],
max_data_size=max_data_size,
data_offset=offset)
print('Computing morgan fingerprints')
morgan_fingerprint = get_features_generator('morgan')
for datapoint in tqdm(data, total=len(data)):
datapoint.set_features(
morgan_fingerprint(mol=datapoint.smiles,
radius=args.radius,
num_bits=args.num_bits))
print(
f'Predicting with an ensemble of {len(args.checkpoint_paths)} models'
)
sum_preds = np.zeros((len(data), args.num_tasks))
for checkpoint_path in tqdm(args.checkpoint_paths,
total=len(args.checkpoint_paths)):
with open(checkpoint_path, 'rb') as f:
model = pickle.load(f)
model_preds = predict(model=model,
model_type=args.model_type,
dataset_type=args.dataset_type,
features=data.features())
sum_preds += np.array(model_preds)
# Ensemble predictions
avg_preds = sum_preds / len(args.checkpoint_paths)
avg_preds = avg_preds.tolist()
print(f'Saving predictions to {args.preds_path}')
assert len(data) == len(avg_preds)
makedirs(args.preds_path, isfile=True)
# Copy predictions over to data
task_names = get_task_names(path=args.test_path)
for datapoint, preds in zip(data, avg_preds):
for pred_name, pred in zip(task_names, preds):
datapoint.row[pred_name] = pred
# Save
if iteration != 0:
name, ext = os.path.splitext(args.preds_path)
preds_path = "{name}.{it}{ext}".format(name=name,
it=iteration,
ext=ext)
else:
preds_path = args.preds_path
with open(args.preds_path, 'w') as f:
writer = csv.DictWriter(f, fieldnames=data[0].row.keys())
writer.writeheader()
for datapoint in data:
writer.writerow(datapoint.row)