LWPLSR_Call.py

import numpy as np
from pathlib import Path
import json
from LWPLSR_ import LWPLSR

# loading the lwplsr_inputs.json
temp_path = Path("temp/")
for i in ['x_train_np', 'y_train_np', 'x_test_np', 'y_test_np']:#,'x_train_np_cv1', 'y_train_np_cv1', 'x_test_np_cv1', 'y_test_np_cv1', 'x_train_np_cv2', 'y_train_np_cv2', 'x_test_np_cv2', 'y_test_np_cv2', 'x_train_np_cv3', 'y_train_np_cv3', 'x_test_np_cv3', 'y_test_np_cv3',]:
    globals()[i] = np.genfromtxt(temp_path / str(i + ".csv"), delimiter=',')
print('CSV imported')
print('start model creation')
Reg = LWPLSR(x_train_np, y_train_np, x_test_np, y_test_np, x_train_np_cv1, y_train_np_cv1, x_test_np_cv1, y_test_np_cv1, x_train_np_cv2, y_train_np_cv2, x_test_np_cv2, y_test_np_cv2, x_train_np_cv3, y_train_np_cv3, x_test_np_cv3, y_test_np_cv3)
print('model created. \n now fit')
LWPLSR.Jchemo_lwplsr_fit(Reg)
print('now predict')
LWPLSR.Jchemo_lwplsr_predict(Reg)

pred = ['pred_data_train', 'pred_data_cv1', 'pred_data_cv2', 'pred_data_cv3', 'pred_data_test']
json_export = {}
for i in pred:
    json_export[i] = Reg.pred_data_[pred.index(i)].to_dict()
# json_export['pred_data_train'] = Reg.pred_data_[0].to_dict()
# json_export['pred_data_cv1'] = Reg.pred_data_[1].to_dict()
# json_export['pred_data_cv2'] = Reg.pred_data_[2].to_dict()
# json_export['pred_data_cv3'] = Reg.pred_data_[3].to_dict()
# json_export['pred_data_test'] = Reg.pred_data_[4].to_dict()
json_export['model'] = str(Reg.model_)
# json_export['metrics'] = Reg.metrics_.to_dict()
with open(temp_path / "lwplsr_outputs.json", "w+") as outfile:
    json.dump(json_export, outfile)
print(Reg.metrics_)