def call_run(run_fn):
import numpy as np
params = NS.from_dict(json.loads(sys.stdin.read()))
def load_data(name, path, **ignored):
if isinstance(path, str) and data_keys.match(name):
return name, np.load(path, allow_pickle=True)
return name, path
print(params.dataset)
ds = NS.walk(params.dataset, load_data)
config = params.config
config.framework_params = NS.dict(config.framework_params)
try:
result = run_fn(ds, config)
res = dict(result)
for name in ['predictions', 'truth', 'probabilities']:
arr = result[name]
if arr is not None:
res[name] = os.path.join(config.result_dir, '.'.join([name, 'npy']))
np.save(res[name], arr, allow_pickle=True)
except Exception as e:
log.exception(e)
res = dict(
error_message=str(e),
models_count=0
)
print(config.result_token)
print(json.dumps(res, separators=(',', ':')))
def _add_default_params(framework):
if "params" not in framework:
framework.params = dict()
else:
framework.params = Namespace.dict(framework.params)
def call_run(run_fn):
import numpy as np
params = NS.from_dict({
"dataset": {
"train": {
"X_enc": "/tmp/dress/train.X_enc.npy",
"y_enc": "/tmp/dress/train.y_enc.npy"
},
"test": {
"X_enc": "/tmp/dress/test.X_enc.npy",
"y_enc": "/tmp/dress/test.y_enc.npy"
}
},
"config": {
"framewor k": "FEDOT",
"framework_params": {},
"type": "classification",
"name": "Australian",
"fold": 0,
"metrics": ["auc", "logloss", "acc"],
"metric": "auc",
"seed": 3029240368,
"max_runtime_seconds": 600,
"cores": 4,
"max_mem_size_mb": 91763,
"min_vol_size_mb": -1,
"input_dir": "/home/rosneft_user_2500/.openml/cache",
"output_dir":
"/home/rosneft_user_2500/bench/automlbenchmark/results/fedot.small.test.local.20201225T163641",
"output_predictions_file":
"/home/rosneft_user_2500/bench/automlbenchmark/results/fedot.small.test.local.20201225T163641/predictions/fedot.Australian.0.csv",
"result_token": "5e433616-46cf-11eb-a671-7957e32fc18d",
"result_dir": "/tmp/iris"
}
})
def load_data(name, path, **ignored):
if isinstance(path, str) and data_keys.match(name):
return name, np.load(path, allow_pickle=True)
return name, path
print(params.dataset)
ds = NS.walk(params.dataset, load_data)
config = params.config
config.framework_params = NS.dict(config.framework_params)
try:
result = run_fn(ds, config)
res = dict(result)
for name in ['predictions', 'truth', 'probabilities']:
arr = result[name]
if arr is not None:
res[name] = os.path.join(config.result_dir,
'.'.join([name, 'npy']))
np.save(res[name], arr, allow_pickle=True)
except Exception as e:
log.exception(e)
res = dict(error_message=str(e), models_count=0)
print(config.result_token)
print(json.dumps(res, separators=(',', ':')))
target_is_encoded=False)
if __name__ == '__main__':
params = json_loads(sys.stdin.read(), as_namespace=True)
def load_data(path):
return read_csv(path, as_data_frame=False, header=False)
ds = ns(train=ns(X_enc=load_data(params.dataset.train.X_enc),
y=load_data(params.dataset.train.y).squeeze()),
test=ns(
X_enc=load_data(params.dataset.test.X_enc),
y=load_data(params.dataset.test.y).squeeze(),
))
config = params.config
config.framework_params = ns.dict(config.framework_params)
result = run(ds, config)
res = copy.copy(result)
res.predictions = os.path.join(config.result_dir, 'predictions')
res.truth = os.path.join(config.result_dir, 'truth')
write_csv(result.predictions.reshape(-1, 1), res.predictions)
write_csv(result.truth.reshape(-1, 1), res.truth)
if result.probabilities is not None:
res.probabilities = os.path.join(config.result_dir, 'probabilities')
write_csv(result.probabilities, res.probabilities)
print(config.result_token)
print(json_dumps(res, style='compact'))
