diff --git a/src/Class_Mod/LWPLSR_.py b/src/Class_Mod/LWPLSR_.py
index 2e3d40b734429ef97fa7eb5cff1950279716c564..b11eb6ed01e50e72ad918d8dde59caf8d04f274f 100644
--- a/src/Class_Mod/LWPLSR_.py
+++ b/src/Class_Mod/LWPLSR_.py
@@ -14,8 +14,17 @@ class LWPLSR:
def __init__(self, dataset):
"""Initiate the LWPLSR and prepare data for Julia computing."""
- self.x_train, self.y_train, self.x_test, self.y_test = [dataset[i] for i in range(len(dataset))]
+ # self.x_train, self.y_train, self.x_test, self.y_test = [dataset[i] for i in range(len(dataset))]
+ self.x_train, self.y_train, self.x_test, self.y_test = [dataset[i] for i in range(4)]
+ nb_fold = int((len(dataset)-4)/4)
+ for i in range(nb_fold):
+ vars()["self.xtr_fold"+str(i+1)] = dataset[i+7]
+ vars()["self.ytr_fold"+str(i+1)] = dataset[i+13]
+ vars()["self.xte_fold"+str(i+1)] = dataset[i+4]
+ vars()["self.yte_fold"+str(i+1)] = dataset[i+10]
+
+ print(self.xtr_fold1)
# prepare to send dataframes to julia and Jchemo
jl.x_train, jl.y_train, jl.x_test, jl.y_test = self.x_train, self.y_train, self.x_test, self.y_test
diff --git a/src/Class_Mod/LWPLSR_Call.py b/src/Class_Mod/LWPLSR_Call.py
index f8445d4b2930400cf0820fffab5804572beba60b..46a65e44ddb13a4a4e8097d35d58178065a3925b 100644
--- a/src/Class_Mod/LWPLSR_Call.py
+++ b/src/Class_Mod/LWPLSR_Call.py
@@ -2,10 +2,15 @@ import numpy as np
from pathlib import Path
import json
from LWPLSR_ import LWPLSR
+import os
# loading the lwplsr_inputs.json
temp_path = Path("temp/")
data_to_work_with = ['x_train_np', 'y_train_np', 'x_test_np', 'y_test_np']
+temp_files_list = os.listdir(temp_path)
+for i in temp_files_list:
+ if 'fold' in i:
+ data_to_work_with.append(str(i)[:-4])
dataset = []
for i in data_to_work_with:
dataset.append(np.genfromtxt(temp_path / str(i + ".csv"), delimiter=','))
@@ -17,6 +22,10 @@ LWPLSR.Jchemo_lwplsr_fit(Reg)
print('now predict')
LWPLSR.Jchemo_lwplsr_predict(Reg)
+print('now CV')
+
+
+
print('export to json')
pred = ['pred_data_train', 'pred_data_test']
json_export = {}
diff --git a/src/Modules.py b/src/Modules.py
index c399a6ec5019f5daba315b26bf8330ede04c53c3..8699e5616f258a6b0fad449942a497c1edf9aa87 100644
--- a/src/Modules.py
+++ b/src/Modules.py
@@ -10,10 +10,3 @@ from style.header import add_header, add_sidebar
from config.config import pdflatex_path
local_css(css_file / "style.css")
-# path = os.path.dirname(os.path.abspath(__file__)).replace('\\','/')
-# d1 = path.find('/')
-# css_file = path[:d1]+'/style'
-# st.session_state["interface"] = st.session_state.get('interface')
-# if st.session_state["interface"] == 'simple':
-# hide_pages("Predictions")
-# local_css(css_file +"/style.css")
diff --git a/src/pages/2-model_creation.py b/src/pages/2-model_creation.py
index bf4dd10f84148e940b7ed3c1bd26cc1941c2591b..f4b43c2938e041b53d182bd871f51085b1ee2a8a 100644
--- a/src/pages/2-model_creation.py
+++ b/src/pages/2-model_creation.py
@@ -1,4 +1,5 @@
# import streamlit
+import pandas as pd
from Packages import *
st.set_page_config(page_title="NIRS Utils", page_icon=":goat:", layout="wide")
from Modules import *
@@ -123,7 +124,7 @@ if not spectra.empty and not y.empty:
colnames = spectra.columns
else:
colnames = np.arange(spectra.shape[1])
-
+
#rd_seed = M1.slider("Customize Train-test split", min_value=1, max_value=100, value=42, format="%i")
# Split data into training and test sets using the kennard_stone method and correlation metric, 25% of data is used for testing
@@ -132,9 +133,9 @@ if not spectra.empty and not y.empty:
# Assign data to training and test sets
X_train, y_train = pd.DataFrame(spectra.iloc[train_index,:]), y.iloc[train_index]
X_test, y_test = pd.DataFrame(spectra.iloc[test_index,:]), y.iloc[test_index]
-
- #### insight on loaded data
+
+ #### insight on loaded data
fig, ax1 = plt.subplots( figsize = (12,3))
spectra.T.plot(legend=False, ax = ax1, linestyle = '--')
ax1.set_ylabel('Signal intensity')
@@ -167,29 +168,54 @@ if not spectra.empty and not y.empty:
reg_model = Reg.model_
#M2.dataframe(Pin.pred_data_)
elif regression_algo == reg_algo[2]:
- # export data to csv for Julia
+ info = M1.info('Starting LWPLSR model creation... Please wait a few minutes.')
+ # export data to csv for Julia train/test
data_to_work_with = ['x_train_np', 'y_train_np', 'x_test_np', 'y_test_np']
x_train_np, y_train_np, x_test_np, y_test_np = X_train.to_numpy(), y_train.to_numpy(), X_test.to_numpy(), y_test.to_numpy()
+ # Cross-Validation calculation
+ nb_folds = 3
+ st.write('KFold = ' + str(nb_folds))
+ folds = KF_CV.CV(x_train_np, y_train_np, nb_folds)
+ d = {}
+ for i in range(nb_folds):
+ d["xtr_fold{0}".format(i+1)], d["ytr_fold{0}".format(i+1)], d["xte_fold{0}".format(i+1)], d["yte_fold{0}".format(i+1)] = np.delete(x_train_np, folds[list(folds)[i]], axis=0), np.delete(y_train_np, folds[list(folds)[i]], axis=0), x_train_np[folds[list(folds)[i]]], y_train_np[folds[list(folds)[i]]]
+ data_to_work_with.append("xtr_fold{0}".format(i+1))
+ data_to_work_with.append("ytr_fold{0}".format(i+1))
+ data_to_work_with.append("xte_fold{0}".format(i+1))
+ data_to_work_with.append("yte_fold{0}".format(i+1))
temp_path = Path('temp/')
- for i in data_to_work_with: np.savetxt(temp_path / str(i + ".csv"), vars()[i], delimiter=",")
+ for i in data_to_work_with:
+ if 'fold' in i:
+ j = d[i]
+ else:
+ j = globals()[i]
+ np.savetxt(temp_path / str(i + ".csv"), j, delimiter=",")
# run Julia Jchemo
import subprocess
subprocess_path = Path("Class_Mod/")
subprocess.run([f"{sys.executable}", subprocess_path / "LWPLSR_Call.py"])
# retrieve json results from Julia JChemo
- with open(temp_path / "lwplsr_outputs.json", "r") as outfile:
- Reg_json = json.load(outfile)
- for i in data_to_work_with: os.unlink(temp_path / str(i + ".csv"))
- os.unlink(temp_path / "lwplsr_outputs.json")
- pred = ['pred_data_train', 'pred_data_test']
- Reg = type('obj', (object,), {'model' : Reg_json['model'], 'best_lwplsr_params' : Reg_json['best_lwplsr_params'], '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 != 1: # if not pred_data_test
- Reg.pred_data_[i].index = list(y_train.index)
- else:
- Reg.pred_data_[i].index = list(y_test.index)
-
+ try:
+ with open(temp_path / "lwplsr_outputs.json", "r") as outfile:
+ Reg_json = json.load(outfile)
+ for i in data_to_work_with: os.unlink(temp_path / str(i + ".csv"))
+ os.unlink(temp_path / "lwplsr_outputs.json")
+ pred = ['pred_data_train', 'pred_data_test']
+ Reg = type('obj', (object,), {'model' : Reg_json['model'], 'best_lwplsr_params' : Reg_json['best_lwplsr_params'], '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 != 1: # if not pred_data_test
+ Reg.pred_data_[i].index = list(y_train.index)
+ else:
+ Reg.pred_data_[i].index = list(y_test.index)
+ Reg.CV_results_ = pd.DataFrame()
+ Reg.cv_data_ = pd.DataFrame()
+ info.empty()
+ M1.success('Model created!')
+ except FileNotFoundError as e:
+ info.empty()
+ M1.warning('- ERROR during model creation -')
+ Reg = None
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)
it = M1.number_input(label='Enter the number of iterations', min_value=2, max_value=10, value=3)
@@ -218,7 +244,7 @@ if not spectra.empty and not y.empty:
################# Model analysis ############
- if regression_algo in reg_algo[1:]:
+ if regression_algo in reg_algo[1:] and Reg is not None:
#M2.write('-- Pretreated data (train) visualization and important spectral regions in the model -- ')
fig, (ax1, ax2) = plt.subplots(2,1, figsize = (12, 6))
@@ -368,7 +394,7 @@ with st.container():
if not spectra.empty and not y.empty:
- if regression_algo in reg_algo[1:]:
+ if regression_algo in reg_algo[1:] and Reg is not None:
fig, (ax1, ax2) = plt.subplots(2,1, figsize = (12, 4), sharex=True)
ax1.plot(colnames, np.mean(X_train, axis = 0), color = 'black', label = 'Average spectrum (Raw)')
ax2.plot(colnames, np.mean(Reg.pretreated_spectra_ , axis = 0), color = 'black', label = 'Average spectrum (pretreated)')