def main():
parser = ArgumentParser()
action_parser = parser.add_subparsers(title="actions",
dest="action",
required=True,
help="select action to execute")
# args for training
train_parser = action_parser.add_parser("train",
help="train the classifier")
train_parser.add_argument("-r",
"--root-dir",
dest="root_dir",
required=True,
help="root directory of the dataset")
train_parser.add_argument("-d",
"--data-key",
dest="data_key",
required=True,
help="name of the dataset")
train_parser.add_argument("-m",
"--model-name",
dest="model_key",
required=True,
help="model to be used for training")
train_parser.add_argument("-s",
"--save-path",
dest="save_path",
required=True,
help="save path for trained model")
# args for testing
test_parser = action_parser.add_parser("test", help="test the classifier")
test_parser.add_argument("-r",
"--root-dir",
dest="root_dir",
required=True,
help="root directory of the dataset")
test_parser.add_argument("-d",
"--data-key",
dest="data_key",
required=True,
help="name of the dataset")
test_parser.add_argument("-m",
"--model-name",
dest="model_key",
required=True,
help="model to be used for testing")
test_parser.add_argument("-s",
"--save-path",
dest="save_path",
required=True,
help="save path of the trained model")
args_dict = vars(parser.parse_args())
init_logger(args_dict)
logging.info(f'User Arguments: {args_dict}!!!')
run(**vars(parser.parse_args()))
def main(n: str, compute_qf: str):
params = {**RUN_PARAMS}
if n is not None:
params['GITCOIN_TIMESTEPS'] = n
if compute_qf is not None:
params['GITCOIN_COMPUTE_QF'] = compute_qf
for (key, value) in params.items():
os.environ[key] = value
print("Preparing simulation")
from model.run import run
print("Run simuation")
result = run()
print(f"Simulation executed! Pickling result to {PICKLE_PATH}")
with open(PICKLE_PATH, 'wb') as fid:
cloudpickle.dump(result, fid)
print("Results pickled sucessfuly")
from model import run # Run training and test run.run()
# In[1]:
# Dependences
import pandas as pd
import numpy as np
# Experiments
from model import run
pd.options.display.float_format = '{:.2f}'.format
# get_ipython().run_line_magic('matplotlib', 'inline')
# %matplotlib inline
# run the simulation
df = run.run()
# In[ ]:
# observe the dataset
# In[2]:
df.head()
# In[ ]:
# plot the data
# In[3]: