def train(config_name, nparallel):
with open(config_name, "r") as f:
config = json.load(f)
train_csv_root = config["learning_param"]["train_csv_root"]
train_target_table = config["learning_param"].get(
"train_target_table_file", "seishitu_codeblue_wo_future.csv")
train_loader = DataLoader(config, train_csv_root)
train_handler = DataHandler(train_loader, config, train_target_table)
test_loader = DataLoader(config, config["learning_param"]["test_csv_root"])
test_target_table = config["learning_param"].get(
"test_target_table_file", "seishitu_codeblue_wo_future.csv")
test_handler = DataHandler(test_loader, config, test_target_table)
evaluator = Evaluator(config, test_loader, test_handler)
print("building_test_data")
evaluator.build_dataset(100.0, nparallel)
n_repeat = config["learning_param"]["n_repeat"]
with Pool(processes=nparallel) as p:
models = [
p.apply_async(train_one, (config, train_loader, train_handler, i))
for i in range(n_repeat)
]
res = np.array([evaluator.predict(model.get()) for model in models])
res_file = config_name[:-5] + ".predict.npy"
np.save(res_file, res)
def train(config_name, nparallel):
with open(config_name, "r") as f:
config = json.load(f)
train_csv_root = config["learning_param"]["train_csv_root"]
train_target_table = config["learning_param"].get("train_target_table_file", "seishitu_codeblue_wo_future.csv")
train_loader = DataLoader(config, train_csv_root)
train_handler = DataHandler(train_loader,config, train_target_table)
test_loader = DataLoader(config, config["learning_param"]["test_csv_root"])
test_target_table = config["learning_param"].get("test_target_table_file", "seishitu_codeblue_wo_future.csv")
test_handler = DataHandler(test_loader,config,test_target_table)
evaluator = Evaluator(config, test_loader, test_handler)
print("building_test_data")
evaluator.build_dataset(100.0, nparallel)
n_repeat = config["learning_param"]["n_repeat"]
with Pool(processes=nparallel) as p:
models = [p.apply_async(train_one, (config, train_loader, train_handler, i)) for i in range(n_repeat)]
res = np.array([evaluator.evaluate(model.get()) for model in models])
averages = np.mean(res, axis = 0, keepdims=False)
variances = np.var(res, axis = 0, keepdims=False)
config["results"] = dict()
config["results"]["nData_mean"] = list(averages[:,0])
config["results"]["nData_var"] = list(variances[:,0])
config["results"]["nCPA_mean"] = list(averages[:,1])
config["results"]["nCPA_var"] = list(variances[:,1])
config["results"]["n_nonCPA_mean"] = list(averages[:,2])
config["results"]["n_nonCPA_var"] = list(variances[:,2])
config["results"]["accuracy_mean"] = list(averages[:,3])
config["results"]["accuracy_var"] = list(variances[:,3])
config["results"]["PPV_mean"] = list(averages[:,4])
config["results"]["PPV_var"] = list(variances[:,4])
config["results"]["NPV_mean"] = list(averages[:,5])
config["results"]["NPV_var"] = list(variances[:,5])
config["results"]["sensitivity_mean"] = list(averages[:,6])
config["results"]["sensitivity_var"] = list(variances[:,6])
config["results"]["specificity_mean"] = list(averages[:,7])
config["results"]["specificity_var"] = list(variances[:,7])
config["results"]["f_val_mean"] = list(averages[:,8])
config["results"]["f_val_var"] = list(variances[:,8])
config["results"]["auc_score_mean"] = list(averages[:,9])
config["results"]["auc_score_var"] = list(variances[:,9])
config["results"]["youden_index_mean"] = list(averages[:,10])
config["results"]["youden_index_var"] = list(variances[:,10])
config["results"]["tp_mean"] = list(averages[:,11])
config["results"]["tp_var"] = list(variances[:,11])
config["results"]["fp_mean"] = list(averages[:,12])
config["results"]["fp_var"] = list(variances[:,12])
config["results"]["fn_mean"] = list(averages[:,13])
config["results"]["fn_var"] = list(variances[:,13])
config["results"]["tn_mean"] = list(averages[:,14])
config["results"]["tn_var"] = list(variances[:,14])
with open(config_name[:-5]+".res.json", "w") as f:
json.dump(config, f)
