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Commit 900d9ac7 authored by DIANE's avatar DIANE
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parents 02cd06cc 8265fde5
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...@@ -15,12 +15,15 @@ LWPLSR.Jchemo_lwplsr_fit(Reg) ...@@ -15,12 +15,15 @@ LWPLSR.Jchemo_lwplsr_fit(Reg)
print('now predict') print('now predict')
LWPLSR.Jchemo_lwplsr_predict(Reg) LWPLSR.Jchemo_lwplsr_predict(Reg)
pred = ['pred_data_train', 'pred_data_cv1', 'pred_data_cv2', 'pred_data_cv3', 'pred_data_test']
json_export = {} json_export = {}
json_export['pred_data_train'] = Reg.pred_data_[0].to_dict() for i in pred:
json_export['pred_data_cv1'] = Reg.pred_data_[1].to_dict() json_export[i] = Reg.pred_data_[pred.index(i)].to_dict()
json_export['pred_data_cv2'] = Reg.pred_data_[2].to_dict() # json_export['pred_data_train'] = Reg.pred_data_[0].to_dict()
json_export['pred_data_cv3'] = Reg.pred_data_[3].to_dict() # json_export['pred_data_cv1'] = Reg.pred_data_[1].to_dict()
json_export['pred_data_test'] = Reg.pred_data_[4].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['model'] = str(Reg.model_)
# json_export['metrics'] = Reg.metrics_.to_dict() # json_export['metrics'] = Reg.metrics_.to_dict()
with open(temp_path / "lwplsr_outputs.json", "w+") as outfile: with open(temp_path / "lwplsr_outputs.json", "w+") as outfile:
......
...@@ -152,17 +152,25 @@ if not spectra.empty and not y.empty: ...@@ -152,17 +152,25 @@ if not spectra.empty and not y.empty:
Reg_json = json.load(outfile) Reg_json = json.load(outfile)
for i in data_to_work_with: os.unlink(temp_path / str(i + ".csv")) for i in data_to_work_with: os.unlink(temp_path / str(i + ".csv"))
os.unlink(temp_path / "lwplsr_outputs.json") os.unlink(temp_path / "lwplsr_outputs.json")
Reg = type('obj', (object,), {'model' : pd.json_normalize(Reg_json['model']), 'pred_data_' : [pd.json_normalize(Reg_json['pred_data_train']), pd.json_normalize(Reg_json['pred_data_cv1']), pd.json_normalize(Reg_json['pred_data_cv2']), pd.json_normalize(Reg_json['pred_data_cv3']), pd.json_normalize(Reg_json['pred_data_test'])]}) # Reg = type('obj', (object,), {'model' : pd.json_normalize(Reg_json['model']), 'pred_data_' : [pd.json_normalize(Reg_json['pred_data_train']), pd.json_normalize(Reg_json['pred_data_cv1']), pd.json_normalize(Reg_json['pred_data_cv2']), pd.json_normalize(Reg_json['pred_data_cv3']), pd.json_normalize(Reg_json['pred_data_test'])]})
Reg.pred_data_[0] = Reg.pred_data_[0].T.reset_index().drop(columns = ['index']) pred = ['pred_data_train', 'pred_data_cv1', 'pred_data_cv2', 'pred_data_cv3', 'pred_data_test']
Reg.pred_data_[0].index = list(y_train.index) Reg = type('obj', (object,), {'model' : pd.json_normalize(Reg_json['model']), 'pred_data_' : [pd.json_normalize(Reg_json[i]) for i in pred]})
for i in range(len(pred)):
Reg.pred_data_[i] = Reg.pred_data_[i].T.reset_index().drop(columns = ['index'])
if i is not 4:
Reg.pred_data_[i].index = list(y_train.index)
else:
Reg.pred_data_[i].index = list(y_test.index)
# Reg.pred_data_[0] = Reg.pred_data_[0].T.reset_index().drop(columns = ['index'])
# Reg.pred_data_[0].index = list(y_train.index)
# Reg.pred_data_[1] = Reg.pred_data_[1].T.reset_index().drop(columns = ['index']) # Reg.pred_data_[1] = Reg.pred_data_[1].T.reset_index().drop(columns = ['index'])
# Reg.pred_data_[1].index = list(y_train_cv1.index) # Reg.pred_data_[1].index = list(y_train_cv1.index)
# Reg.pred_data_[2] = Reg.pred_data_[2].T.reset_index().drop(columns = ['index']) # Reg.pred_data_[2] = Reg.pred_data_[2].T.reset_index().drop(columns = ['index'])
# Reg.pred_data_[2].index = list(y_train_cv2.index) # Reg.pred_data_[2].index = list(y_train_cv2.index)
# Reg.pred_data_[3] = Reg.pred_data_[3].T.reset_index().drop(columns = ['index']) # Reg.pred_data_[3] = Reg.pred_data_[3].T.reset_index().drop(columns = ['index'])
# Reg.pred_data_[3].index = list(y_train_cv3.index) # Reg.pred_data_[3].index = list(y_train_cv3.index)
Reg.pred_data_[4] = Reg.pred_data_[4].T.reset_index().drop(columns = ['index']) # Reg.pred_data_[4] = Reg.pred_data_[4].T.reset_index().drop(columns = ['index'])
Reg.pred_data_[4].index = list(y_test.index) # Reg.pred_data_[4].index = list(y_test.index)
elif regression_algo == reg_algo[3]: elif regression_algo == reg_algo[3]:
s = M1.number_input(label='Enter the maximum number of intervals', min_value=1, max_value=6, value=3) s = M1.number_input(label='Enter the maximum number of intervals', min_value=1, max_value=6, value=3)
......
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