def test_singletask_sklearn_rf_user_specified_regression_API(self):
"""Test of singletask RF USF regression API."""
splittype = "specified"
featurizer = UserDefinedFeaturizer(["user-specified1", "user-specified2"])
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, "user_specified_example.csv")
loader = DataLoader(tasks=tasks,
smiles_field=self.smiles_field,
featurizer=featurizer,
verbosity="low")
dataset = loader.featurize(input_file, self.data_dir, debug=True)
splitter = SpecifiedSplitter(input_file, "split")
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["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_user_specified_regression_API(self):
"""Test of singletask RF USF regression API."""
splittype = "specified"
featurizer = UserDefinedFeaturizer(["user-specified1", "user-specified2"])
tasks = ["log-solubility"]
task_type = "regression"
task_types = {task: task_type for task in tasks}
input_file = os.path.join(self.current_dir, "user_specified_example.csv")
loader = DataLoader(tasks=tasks,
smiles_field=self.smiles_field,
featurizer=featurizer,
verbosity="low")
dataset = loader.featurize(input_file, self.data_dir, debug=True)
splitter = SpecifiedSplitter(input_file, "split")
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)
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 load_bace(mode="regression", transform=True, split="20-80"):
"""Load BACE-1 dataset as regression/classification problem."""
reload = True
verbosity = "high"
regen = False
assert split in ["20-80", "80-20"]
current_dir = os.path.dirname(os.path.realpath(__file__))
if split == "20-80":
dataset_file = os.path.join(current_dir,
"../../datasets/desc_canvas_aug30.csv")
elif split == "80-20":
dataset_file = os.path.join(current_dir,
"../../datasets/rev8020split_desc.csv")
dataset = load_from_disk(dataset_file)
num_display = 10
pretty_columns = ("[" + ",".join(
["'%s'" % column
for column in dataset.columns.values[:num_display]]) + ",...]")
crystal_dataset_file = os.path.join(
current_dir, "../../datasets/crystal_desc_canvas_aug30.csv")
crystal_dataset = load_from_disk(crystal_dataset_file)
print("Columns of dataset: %s" % pretty_columns)
print("Number of examples in dataset: %s" % str(dataset.shape[0]))
print("Number of examples in crystal dataset: %s" %
str(crystal_dataset.shape[0]))
#Make directories to store the raw and featurized datasets.
base_dir = tempfile.mkdtemp()
data_dir = os.path.join(base_dir, "dataset")
train_dir = os.path.join(base_dir, "train_dataset")
valid_dir = os.path.join(base_dir, "valid_dataset")
test_dir = os.path.join(base_dir, "test_dataset")
model_dir = os.path.join(base_dir, "model")
crystal_dir = os.path.join(base_dir, "crystal")
if mode == "regression":
bace_tasks = ["pIC50"]
elif mode == "classification":
bace_tasks = ["Class"]
else:
raise ValueError("Unknown mode %s" % mode)
featurizer = UserDefinedFeaturizer(user_specified_features)
loader = DataLoader(tasks=bace_tasks,
smiles_field="mol",
id_field="CID",
featurizer=featurizer)
if not reload or not os.path.exists(data_dir):
dataset = loader.featurize(dataset_file, data_dir)
regen = True
else:
dataset = Dataset(data_dir, reload=True)
if not reload or not os.path.exists(crystal_dir):
crystal_dataset = loader.featurize(crystal_dataset_file, crystal_dir)
else:
crystal_dataset = Dataset(crystal_dir, reload=True)
if (not reload or not os.path.exists(train_dir)
or not os.path.exists(valid_dir) or not os.path.exists(test_dir)):
regen = True
splitter = SpecifiedSplitter(dataset_file,
"Model",
verbosity=verbosity)
train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split(
dataset, train_dir, valid_dir, test_dir)
else:
train_dataset = Dataset(train_dir, reload=True)
valid_dataset = Dataset(valid_dir, reload=True)
test_dataset = Dataset(test_dir, reload=True)
#NOTE THE RENAMING:
if split == "20-80":
valid_dataset, test_dataset = test_dataset, valid_dataset
print("Number of compounds in train set")
print(len(train_dataset))
print("Number of compounds in validation set")
print(len(valid_dataset))
print("Number of compounds in test set")
print(len(test_dataset))
print("Number of compounds in crystal set")
print(len(crystal_dataset))
if transform and regen:
input_transformers = [
NormalizationTransformer(transform_X=True, dataset=train_dataset),
ClippingTransformer(transform_X=True, dataset=train_dataset)
]
output_transformers = []
if mode == "regression":
output_transformers = [
NormalizationTransformer(transform_y=True,
dataset=train_dataset)
]
else:
output_transformers = []
else:
input_transformers, output_transformers = [], []
transformers = input_transformers + output_transformers
for dataset in [
train_dataset, valid_dataset, test_dataset, crystal_dataset
]:
for transformer in transformers:
dataset = transformer.transform(dataset)
return (bace_tasks, train_dataset, valid_dataset, test_dataset,
crystal_dataset, output_transformers)
def load_bace(mode="regression", transform=True, split="20-80"):
"""Load BACE-1 dataset as regression/classification problem."""
reload = True
verbosity = "high"
regen = False
assert split in ["20-80", "80-20"]
current_dir = os.path.dirname(os.path.realpath(__file__))
if split == "20-80":
dataset_file = os.path.join(
current_dir, "../../datasets/desc_canvas_aug30.csv")
elif split == "80-20":
dataset_file = os.path.join(
current_dir, "../../datasets/rev8020split_desc.csv")
dataset = load_from_disk(dataset_file)
num_display = 10
pretty_columns = (
"[" + ",".join(["'%s'" % column for column in
dataset.columns.values[:num_display]])
+ ",...]")
crystal_dataset_file = os.path.join(
current_dir, "../../datasets/crystal_desc_canvas_aug30.csv")
crystal_dataset = load_from_disk(crystal_dataset_file)
print("Columns of dataset: %s" % pretty_columns)
print("Number of examples in dataset: %s" % str(dataset.shape[0]))
print("Number of examples in crystal dataset: %s" %
str(crystal_dataset.shape[0]))
#Make directories to store the raw and featurized datasets.
base_dir = tempfile.mkdtemp()
data_dir = os.path.join(base_dir, "dataset")
train_dir = os.path.join(base_dir, "train_dataset")
valid_dir = os.path.join(base_dir, "valid_dataset")
test_dir = os.path.join(base_dir, "test_dataset")
model_dir = os.path.join(base_dir, "model")
crystal_dir = os.path.join(base_dir, "crystal")
if mode == "regression":
bace_tasks = ["pIC50"]
elif mode == "classification":
bace_tasks = ["Class"]
else:
raise ValueError("Unknown mode %s" % mode)
featurizer = UserDefinedFeaturizer(user_specified_features)
loader = DataLoader(tasks=bace_tasks,
smiles_field="mol",
id_field="CID",
featurizer=featurizer)
if not reload or not os.path.exists(data_dir):
dataset = loader.featurize(dataset_file, data_dir)
regen = True
else:
dataset = Dataset(data_dir, reload=True)
if not reload or not os.path.exists(crystal_dir):
crystal_dataset = loader.featurize(crystal_dataset_file, crystal_dir)
else:
crystal_dataset = Dataset(crystal_dir, reload=True)
if (not reload or not os.path.exists(train_dir) or not os.path.exists(valid_dir)
or not os.path.exists(test_dir)):
regen = True
splitter = SpecifiedSplitter(dataset_file, "Model", verbosity=verbosity)
train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split(
dataset, train_dir, valid_dir, test_dir)
else:
train_dataset = Dataset(train_dir, reload=True)
valid_dataset = Dataset(valid_dir, reload=True)
test_dataset = Dataset(test_dir, reload=True)
#NOTE THE RENAMING:
if split == "20-80":
valid_dataset, test_dataset = test_dataset, valid_dataset
print("Number of compounds in train set")
print(len(train_dataset))
print("Number of compounds in validation set")
print(len(valid_dataset))
print("Number of compounds in test set")
print(len(test_dataset))
print("Number of compounds in crystal set")
print(len(crystal_dataset))
if transform and regen:
input_transformers = [
NormalizationTransformer(transform_X=True, dataset=train_dataset),
ClippingTransformer(transform_X=True, dataset=train_dataset)]
output_transformers = []
if mode == "regression":
output_transformers = [
NormalizationTransformer(transform_y=True, dataset=train_dataset)]
else:
output_transformers = []
else:
input_transformers, output_transformers = [], []
transformers = input_transformers + output_transformers
for dataset in [train_dataset, valid_dataset, test_dataset, crystal_dataset]:
for transformer in transformers:
dataset = transformer.transform(dataset)
return (bace_tasks, train_dataset, valid_dataset, test_dataset,
crystal_dataset, output_transformers)
