code stringlengths 3 6.57k |
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track_start_request(request) |
track_end_request(self, request, response) |
time.time() |
super(SlowQueryTweenView, self) |
track_end_request(request, response) |
includeme(config) |
stack (after authentication, after router, ...) |
settings.get("zipkin.tween_factory", "all") |
tween_factory.configure(settings) |
format(tween_factory.__module__, tween_factory.__name__) |
V1Bayes(object) |
openapi_types (dict) |
attribute_map (dict) |
dict(str, object) |
__init__(self, kind='bayes', params=None, num_initial_runs=None, max_iterations=None, utility_function=None, metric=None, seed=None, concurrency=None, tuner=None, early_stopping=None, local_vars_configuration=None) |
Configuration() |
kind(self) |
kind(self, kind) |
params(self) |
dict(str, object) |
params(self, params) |
dict(str, object) |
num_initial_runs(self) |
num_initial_runs(self, num_initial_runs) |
max_iterations(self) |
max_iterations(self, max_iterations) |
utility_function(self) |
utility_function(self, utility_function) |
metric(self) |
metric(self, metric) |
seed(self) |
seed(self, seed) |
concurrency(self) |
concurrency(self, concurrency) |
tuner(self) |
tuner(self, tuner) |
early_stopping(self) |
early_stopping(self, early_stopping) |
to_dict(self) |
six.iteritems(self.openapi_types) |
getattr(self, attr) |
isinstance(value, list) |
x.to_dict() |
hasattr(x, "to_dict") |
hasattr(value, "to_dict") |
value.to_dict() |
isinstance(value, dict) |
to_dict() |
hasattr(item[1], "to_dict") |
value.items() |
to_str(self) |
pprint.pformat(self.to_dict() |
__repr__(self) |
self.to_str() |
__eq__(self, other) |
isinstance(other, V1Bayes) |
self.to_dict() |
other.to_dict() |
__ne__(self, other) |
isinstance(other, V1Bayes) |
self.to_dict() |
other.to_dict() |
_parse_args() |
argparse.ArgumentParser(description="Run SolTranNet aqueous solubility predictor") |
parser.add_argument('input',nargs='?',type=argparse.FileType('r') |
parser.add_argument('output',nargs='?',type=argparse.FileType('w') |
parser.add_argument('--batchsize',default=32,type=int,help='Batch size for the data loader. Defaults to 32.') |
parser.add_argument('--cpus',default=multiprocessing.cpu_count() |
parser.add_argument('--cpu_predict',action='store_true',help='Flag to force the predictions to be made on only the CPU. Default behavior is to use GPU if available.') |
parser.parse_args() |
_run(args) |
x.rstrip() |
predict(smiles,batch_size=args.batchsize,num_workers=args.cpus,device=torch.device('cpu') |
predict(smiles,batch_size=args.batchsize,num_workers=args.cpus) |
args.output.write(f'{smi},{pred:.3f},{warn}\n') |
val (object) |
__init__(self, val) |
make_optimizer(optimizer_type, module, **kwargs) |
optimizer_type (Union[type, tuple[type, dict]]) |
e.g. (torch.optim.Adam, {'lr' = 1e-3}) |
module (torch.nn.Module) |
kwargs (dict) |
isinstance(optimizer_type, tuple) |
kwargs.items() |
isinstance(arg, _Default) |
format(name) |
opt_type(module.parameters() |
kwargs.items() |
isinstance(arg, _Default) |
optimizer_type(module.parameters() |
baseline (value function) |
Estimation (GAE) |
discount (float) |
factor (i.e. gamma) |
gae_lambda (float) |
Estimation (GAE) |
max_path_length (int) |
baselines (torch.Tensor) |
shape (N, T) |
dimension (number of episodes) |
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