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)