diff --git a/.gitignore b/.gitignore
index c8c9b25f2e86d55951711db6771ee526fdac145b..a025df3d4195260756c39427f4deb4e013be7dc4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,4 +6,5 @@ src/data/predictions/*
src/data/sample_selections/*
src/Report/*.pdf
src/Report/*.tex
-src/Report/figures/
\ No newline at end of file
+src/Report/figures/
+src/temp/*
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 042ceb548ca24a8dc4783d89bccb86fc5bf130d6..414efc3ad8b5918734821eba724e563efedb3118 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -15,4 +15,4 @@ umap-learn>=0.5.5
jcamp>=1.2.2
mkdocs>=1.5.3
mkdocs-material>=9.5.18
-mkdocstrings[python]>=0.24.3
\ No newline at end of file
+mkdocstrings[python]>=0.24.3
diff --git a/src/Class_Mod/LWPLSR_.py b/src/Class_Mod/LWPLSR_.py
index 5e86c902e4926d77b818531284492abffa9d584c..877ca504ebe13bf7b39ad942cba3e6ad80c3f097 100644
--- a/src/Class_Mod/LWPLSR_.py
+++ b/src/Class_Mod/LWPLSR_.py
@@ -1,5 +1,6 @@
-from Packages import *
-from Class_Mod.Miscellaneous import *
+from juliacall import Main as jl
+import numpy as np
+import pandas as pd
class LWPLSR:
"""
@@ -11,36 +12,40 @@ class LWPLSR:
# 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
# Pre-treatment of x_train and x_test
- jl.seval("""
- # using DataFrames
- # using Pandas
- using Jchemo
- mod1 = Jchemo.model(snv; centr = true, scal = true)
- mod2 = Jchemo.model(savgol; npoint = 15, deriv = 1, degree = 2)
- mod = Jchemo.pip(mod1, mod2)
- Jchemo.fit!(mod, x_train)
- x_train = Jchemo.transf(mod1, x_train)
- Jchemo.fit!(mod, x_test)
- x_test = Jchemo.transf(mod1, x_test)
- """)
- jl.seval("""
- ntrain = nro(x_train)
- segm = segmkf(ntrain, 4; rep = 5)
- nlvdis = [5; 10; 15] ; metric = [:mah]
- h = [1; 2; 6; Inf] ; k = [10; 30; 100]
- nlv = 0:15
- pars = mpar(nlvdis = nlvdis, metric = metric, h = h, k = k)
- println(pars)
- """)
+ # jl.seval("""
+ # # using DataFrames
+ # # using Pandas
+ # using Jchemo
+ # mod1 = Jchemo.model(snv; centr = true, scal = true)
+ # mod2 = Jchemo.model(savgol; npoint = 15, deriv = 1, degree = 2)
+ # mod = Jchemo.pip(mod1, mod2)
+ # Jchemo.fit!(mod, x_train)
+ # x_train = Jchemo.transf(mod1, x_train)
+ # Jchemo.fit!(mod, x_test)
+ # x_test = Jchemo.transf(mod1, x_test)
+ # """)
+ # jl.seval("""
+ # ntrain = nro(x_train)
+ # segm = segmkf(ntrain, 4; rep = 5)
+ # nlvdis = [5; 10; 15] ; metric = [:mah]
+ # h = [1; 2; 6; Inf] ; k = [10; 30; 100]
+ # nlv = 0:15
+ # pars = mpar(nlvdis = nlvdis, metric = metric, h = h, k = k)
+ # println(pars)
+ # """)
# initialize vars from the class
y_shape = y_test.shape
self.scores = pd.DataFrame
self.predicted_results_on_test = pd.DataFrame
- self.pred = np.zeros(shape=(y_shape[0], 1))
+ self.predicted_results_on_train = pd.DataFrame
+ self.predicted_results_on_cv = pd.DataFrame
+ self.pred_test = np.zeros(shape=(y_shape[0], 1))
+ self.pred_train = np.zeros(shape=(y_shape[0], 1))
+ self.pred_cv = np.zeros(shape=(y_shape[0], 1))
self.mod = ""
- def Jchemo_lwplsr(self):
+ def Jchemo_lwplsr_fit(self):
"""Send data to Julia to compute lwplsr.
Args:
@@ -64,35 +69,44 @@ class LWPLSR:
y_test |> Pandas.DataFrame |> DataFrames.DataFrame
""")
# Create LWPLSR model and fit
- # jl.seval("""
- # nlvdis = 5 ; metric = :mah
- # h = 1 ; k = 200 ; nlv = 15 #; scal = true
- # mod = Jchemo.model(Jchemo.lwplsr; nlvdis, metric, h, k, nlv)
- # # Fit model
- # Jchemo.fit!(mod, x_train, y_train)
- # """)
-
jl.seval("""
- mod = Jchemo.model(Jchemo.lwplsr)
- res = Jchemo.gridcv(mod, x_train, y_train; segm, score = Jchemo.rmsep, pars, nlv, verbose = true).res
- # u = findall(res.y1 .== minimum(res.y1))[1]
- # mod = Jchemo.model(lwplsr; nlvdis = res.nlvdis[u], metric = res.metric[u], h = res.h[u], k = res.k[u], nlv = res.nlv[u]) ;
- # Jchemo.fit!(mod, x_train, y_train)
+ nlvdis = 5 ; metric = :mah
+ h = 1 ; k = 200 ; nlv = 15 #; scal = true
+ mod = Jchemo.model(Jchemo.lwplsr; nlvdis, metric, h, k, nlv)
+ # Fit model
+ Jchemo.fit!(mod, x_train, y_train)
""")
+
+ # jl.seval("""
+ # mod = Jchemo.model(Jchemo.lwplsr)
+ # res = Jchemo.gridcv(mod, x_train, y_train; segm, score = Jchemo.rmsep, pars, nlv, verbose = true).res
+ # # u = findall(res.y1 .== minimum(res.y1))[1]
+ # # mod = Jchemo.model(lwplsr; nlvdis = res.nlvdis[u], metric = res.metric[u], h = res.h[u], k = res.k[u], nlv = res.nlv[u]) ;
+ # # Jchemo.fit!(mod, x_train, y_train)
+ # """)
self.mod = jl.mod
def Jchemo_lwplsr_predict(self):
# Predictions on x_test and store in self.pred
- self.pred = jl.seval("""
+ self.pred_test = jl.seval("""
res = Jchemo.predict(mod, x_test)
res.pred
""")
- # convert predicted data from x_test to Pandas DataFrame
- self.predicted_results_on_test = pd.DataFrame(self.pred)
+ self.pred_train = jl.seval("""
+ res = Jchemo.predict(mod, x_train)
+ res.pred
+ """)
+ self.pred_cv = self.pred_train
+
@property
def pred_data_(self):
- return self.predicted_results_on_test, self.predicted_results_on_test, self.predicted_results_on_test
+ # convert predicted data from x_test to Pandas DataFrame
+ self.predicted_results_on_test = pd.DataFrame(self.pred_test)
+ self.predicted_results_on_train = pd.DataFrame(self.pred_train)
+ # self.predicted_results_on_cv = pd.DataFrame(self.pred_cv)
+ self.predicted_results_on_cv = pd.DataFrame(self.pred_train)
+ return self.predicted_results_on_train, self.predicted_results_on_cv, self.predicted_results_on_test
@property
def model_(self):
@@ -100,24 +114,50 @@ class LWPLSR:
@property
def metrics_(self):
- jl.pred = self.pred
- st.dataframe(self.pred)
- st.dataframe(self.predicted_results_on_test)
- st.write('starting metrics')
+ jl.pred_test = self.pred_test
jl.seval("""
using Jchemo
""")
- scorermsep = jl.seval("""
- first(Jchemo.rmsep(pred, y_test))
+ scorermsep_test = jl.seval("""
+ first(Jchemo.rmsep(pred_test, y_test))
+ """)
+ scoremr2_test = jl.seval("""
+ first(Jchemo.r2(pred_test, y_test))
+ """)
+ scorerpd_test = jl.seval("""
+ first(Jchemo.rpd(pred_test, y_test))
+ """)
+ scoremsep_test = jl.seval("""
+ first(Jchemo.sep(pred_test, y_test))
""")
- scoremr2 = jl.seval("""
- first(Jchemo.r2(pred, y_test))
+ jl.pred_train = self.pred_train
+ scorermsep_train = jl.seval("""
+ first(Jchemo.rmsep(pred_train, y_train))
""")
- scorerpd = jl.seval("""
- first(Jchemo.rpd(pred, y_test))
+ scoremr2_train = jl.seval("""
+ first(Jchemo.r2(pred_train, y_train))
""")
- scoremsep = jl.seval("""
- first(Jchemo.sep(pred, y_test))
+ scorerpd_train = jl.seval("""
+ first(Jchemo.rpd(pred_train, y_train))
""")
- self.scores = pd.DataFrame([[scoremr2, scorermsep, scoremsep, scorerpd]], columns=['r2', 'rmsep', 'msep', 'rpd'], index=['scores'])
+ scoremsep_train = jl.seval("""
+ first(Jchemo.sep(pred_train, y_train))
+ """)
+ jl.pred_cv = self.pred_cv
+ scorermsep_cv = jl.seval("""
+ first(Jchemo.rmsep(pred_cv, y_train))
+ """)
+ scoremr2_cv = jl.seval("""
+ first(Jchemo.r2(pred_cv, y_train))
+ """)
+ scorerpd_cv = jl.seval("""
+ first(Jchemo.rpd(pred_cv, y_train))
+ """)
+ scoremsep_cv = jl.seval("""
+ first(Jchemo.sep(pred_cv, y_train))
+ """)
+
+
+ self.scores = pd.DataFrame([[scoremr2_test, scorermsep_test, scoremsep_test, scorerpd_test]], columns=['r2', 'rmsep', 'msep', 'rpd'], index=['test'])
+ self.scores = pd.concat([self.scores, pd.DataFrame([[scoremr2_train, scorermsep_train, scoremsep_train, scorerpd_train]], columns=['r2', 'rmsep', 'msep', 'rpd'], index = ["train"]), pd.DataFrame([[scoremr2_cv, scorermsep_cv, scoremsep_cv, scorerpd_cv]], columns=['r2', 'rmsep', 'msep', 'rpd'], index = ["cv"])])#
return self.scores
diff --git a/src/Class_Mod/LWPLSR_Call.py b/src/Class_Mod/LWPLSR_Call.py
new file mode 100644
index 0000000000000000000000000000000000000000..47bf8c82ef890af6db341d1f359504064d15e421
--- /dev/null
+++ b/src/Class_Mod/LWPLSR_Call.py
@@ -0,0 +1,27 @@
+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']:
+ 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)
+print('model created. \n now fit')
+LWPLSR.Jchemo_lwplsr_fit(Reg)
+print('now predict')
+LWPLSR.Jchemo_lwplsr_predict(Reg)
+
+json_export = {}
+data_to_export = ['model', 'pred_data', 'metrics']
+json_export['pred_data_train'] = Reg.pred_data_[0].to_dict()
+json_export['pred_data_cv'] = Reg.pred_data_[1].to_dict()
+json_export['pred_data_test'] = Reg.pred_data_[2].to_dict()
+json_export['metrics'] = Reg.metrics_.to_dict()
+json_export['model'] = str(Reg.model_)
+with open(temp_path / "lwplsr_outputs.json", "w+") as outfile:
+ json.dump(json_export, outfile)
+print(Reg.metrics_)
\ No newline at end of file
diff --git a/src/Class_Mod/__init__.py b/src/Class_Mod/__init__.py
index 540a09349398300160773edb419b0bf9f1dec2c9..155f62559033d087dcb7c42c65e1bdaac7fb5aba 100644
--- a/src/Class_Mod/__init__.py
+++ b/src/Class_Mod/__init__.py
@@ -14,4 +14,4 @@ from .HDBSCAN_Clustering import Hdbscan
from .SK_PLSR_ import PlsR
from .PLSR_Preprocess import PlsProcess
from .NMF_ import Nmf
-from .Ap import AP
\ No newline at end of file
+from .Ap import AP
diff --git a/src/pages/2-model_creation.py b/src/pages/2-model_creation.py
index 986d249bb2c254db09665c5e6137f7d70b88824b..efc2022f954b5b1d8c49326e08bcfd6431bd2c46 100644
--- a/src/pages/2-model_creation.py
+++ b/src/pages/2-model_creation.py
@@ -1,20 +1,11 @@
+import streamlit
from Packages import *
st.set_page_config(page_title="NIRS Utils", page_icon=":goat:", layout="wide")
from Modules import *
from Class_Mod.DATA_HANDLING import *
-
-# HTML pour le bandeau "CEFE - CNRS"
-# bandeau_html = """
-# <div style="width: 100%; background-color: #4682B4; padding: 10px; margin-bottom: 10px;">
-# <h1 style="text-align: center; color: white;">CEFE - CNRS / UM</h1>
-# </div>
-# """
-# # Injecter le code HTML du bandeau
-# st.markdown(bandeau_html, unsafe_allow_html=True)
add_header()
-
st.session_state["interface"] = st.session_state.get('interface')
if st.session_state["interface"] == 'simple':
hide_pages("Predictions")
@@ -134,13 +125,24 @@ if not spectra.empty and not y.empty:
reg_model = Reg.model_
#M2.dataframe(Pin.pred_data_)
elif regression_algo == reg_algo[2]:
- x_train, y_train, x_test, y_test = X_train.to_numpy(), y_train.to_numpy(), X_test.to_numpy(), y_test.to_numpy()
- Reg = LWPLSR(x_train, y_train, x_test, y_test)
- LWPLSR.Jchemo_lwplsr(Reg)
- reg_model = Reg.model_
- # LWPLSR.Jchemo_lwplsr_predict(Reg)
- # st.dataframe(Reg.pred_data_)
- # st.dataframe(Reg.metrics_)
+ 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()
+ temp_path = Path('temp/')
+ for i in data_to_work_with: np.savetxt(temp_path / str(i + ".csv"), vars()[i], delimiter=",")
+ import subprocess
+ subprocess_path = Path("Class_Mod/")
+ subprocess.run([f"{sys.executable}", subprocess_path / "LWPLSR_Call.py"])
+ 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")
+ Reg = type('obj', (object,), {'metrics_' : pd.json_normalize(Reg_json['metrics']), 'pred_data_' : [pd.json_normalize(Reg_json['pred_data_train']), pd.json_normalize(Reg_json['pred_data_cv']),pd.json_normalize(Reg_json['pred_data_test'])]})
+ 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].index = list(y_train.index)
+ Reg.pred_data_[2] = Reg.pred_data_[2].T.reset_index().drop(columns = ['index'])
+ Reg.pred_data_[2].index = list(y_test.index)
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)