def predict():
from source import run_inference
run_inference.run_predict(model_name,
path_model = path_model,
path_data = path_data_test,
path_output = path_output_pred,
n_sample = n_sample)
def predict(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_inference, run_inference
run_inference.run_predict(
model_name,
path_model=path_model,
path_data=path_data_test,
path_output=path_output_pred,
cols_group=mdict['data_pars']['cols_input_type'],
n_sample=nsample if nsample is not None else n_sample)
def predict(config=None, nsample=None):
model_class = config if config is not None else config_default
mdict = globals()[model_class]()
m = mdict['global_pars']
from source import run_inference,run_inference
run_inference.run_predict(model_class,
path_model = m['path_model'],
path_data = m['path_data_test'],
path_output = m['path_output_pred'],
pars={'cols_group': mdict['data_pars']['cols_input_type'],
'pipe_list': mdict['model_pars']['pre_process_pars']['pipe_list']},
n_sample = nsample if nsample is not None else m['n_sample']
)
def predict(config=None, nsample=None):
config_name = config if config is not None else config_default
mdict = globals()[config_name]()
m = mdict['global_pars']
from source import run_inference
run_inference.run_predict(
config_name=config_name,
config_path=m['config_path'],
n_sample=nsample if nsample is not None else m['n_sample'],
#### Optional
path_data=m['path_pred_data'],
path_output=m['path_pred_output'],
model_dict=None)
def predict(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['data_pars']['cols_input_type'])
print(m)
from source import run_inference, run_inference
run_inference.run_predict(
config_name,
path_model=m['path_model'],
path_data=m['path_data_test'],
path_output=m['path_output_pred'],
cols_group=mdict['data_pars']['cols_input_type'],
n_sample=nsample if nsample is not None else m['n_sample'])
def predict(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_inference
run_inference.run_predict(
config_name=config_name,
config_path=m['config_path'],
n_sample=nsample if nsample is not None else m['n_sample'],
#### Optional
path_data=m['path_pred_data'],
path_output=m['path_pred_output'],
model_dict=None)
