def train():
from source import run_train
run_train.run_train(config_name=config_name,
path_data_train=path_data_train,
path_output=path_model,
config_path=path_config_model,
n_sample=n_sample)
def train(config=None, nsample=None):
config_name = config if config is not None else config_default
mdict = globals()[config_name]()
print(mdict)
from source import run_train
run_train.run_train(config_name=config_name,
path_data_train=path_data_train,
path_output=path_model,
config_path=path_config_model,
n_sample=nsample if nsample is not None else n_sample)
def train(config=None, nsample=None):
config_name = config if config is not None else config_default
mdict = globals()[config_name]()
m = mdict['global_pars']
print(mdict)
from source import run_train
run_train.run_train(config_name = config_name,
config_path = m['config_path'],
n_sample = nsample if nsample is not None else m['n_sample'],
)
def train(config='', nsample=None):
config_uri, config_name = get_config_path(config)
mdict = get_global_pars(config_uri)
m = mdict['global_pars']
log(mdict)
from source import run_train
run_train.run_train(
config_name=config_name,
config_path=m['config_path'],
n_sample=nsample if nsample is not None else m['n_sample'],
)
def train(config=None, nsample=None):
model_class = config if config is not None else config_default
mdict = globals()[model_class]()
m = mdict['global_pars']
print(mdict)
from source import run_train
run_train.run_train(config_name= model_class,
path_data_train= m['path_data_train'],
path_output = m['path_model'],
config_path= m['config_path'],
n_sample = nsample if nsample is not None else m['n_sample']
)
def train(config='', nsample=None):
""" train a model with confi_name and nsample
:param config:
:param nsample:
:return:
"""
config_uri, config_name = get_config_path(config)
mdict = get_global_pars( config_uri)
m = mdict['global_pars']
log(mdict)
from source import run_train
run_train.run_train(config_name = config_name,
config_path = m['config_path'],
n_sample = nsample if nsample is not None else m['n_sample'],
# use_mlmflow = False
)
def objective_fun(mdict):
if debug : log(mdict)#
ddict = run_train(config_name="", config_path="", n_sample= n_sample,
mode="run_preprocess", model_dict=mdict,
return_mode='dict')
# print(ddict['stats']['metrics_test'].to_dict('records')[0])
#res = ddict['stats']['metrics_test'].to_dict('records')[0]['metric_val']
df = ddict['stats']['metrics_test']
#### Beware of the sign
res = -np.mean(df[ df['metric_name'] == metric_name ]['metric_val'].values)
return res
def objective1(ddict):
res = run_train.run_train(model_pars = model_pars,
n_sample = nsample,
)
return res
