diff --git a/README.md b/README.md
index cc9c29fe64dfb8b86d65e2565f29b7e6421d64d3..ae7fb90a88b005af80744358b0fc0ead39c35e41 100644
--- a/README.md
+++ b/README.md
@@ -17,8 +17,8 @@ If one wants to use data stored in a SQL database, the config file is in the con
## Installation
This package is written in python. You can clone the repository: git clone https://src.koda.cnrs.fr/CEFE/PACE/nirs_workflow.git
-Then install the requirements: pip install -r requirements.txt
-(OPTIONNAL) To use Locally weighted PLS Regression for creation model, you will need to install Jchemo.jl (https://github.com/mlesnoff/Jchemo.jl), a Julia package.
+Then install the requirements: pip install -r requirements.txt
+(OPTIONNAL) To use Locally weighted PLS Regression for creation model, you will need to install Jchemo.jl (https://github.com/mlesnoff/Jchemo.jl), a Julia package.
From the CLI: python
> python
'>>> import julia
@@ -29,7 +29,7 @@ From the CLI: python
To check if Jchemo is installed without errors:
> '>>> Pkg.status()
-You can then run (CLI): streamlit run ./app.py from within your folder.
+You can then run (CLI): streamlit run ./app.py from within your src/ folder.
The app will open in your default browser.
diff --git a/Sample_test.txt b/Sample_test.txt
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/src/Class_Mod/LWPLSR_.py b/src/Class_Mod/LWPLSR_.py
index e5682ac39c37e85dd6871faf0e1646b5c5626e30..5e86c902e4926d77b818531284492abffa9d584c 100644
--- a/src/Class_Mod/LWPLSR_.py
+++ b/src/Class_Mod/LWPLSR_.py
@@ -1,19 +1,44 @@
-import pandas as pd
-import streamlit
from Packages import *
from Class_Mod.Miscellaneous import *
class LWPLSR:
"""
- The UMAP dimension reduction algorithm from scikit learn
+ The lwpls regression model from Jchemo (M. Lesnoff)
"""
- def __init__(self, x_train, x_test, y_train, y_test):
+ def __init__(self, x_train, y_train, x_test, y_test):
"""Initiate the LWPLSR and prepare data for Julia computing."""
- self.x_train, self.y_train, self.x_test, self.y_test = x_train, x_test, y_train, y_test
+ self.x_train, self.y_train, self.x_test, self.y_test = x_train, y_train, x_test, y_test
# 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
+ 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)
+ """)
+
+ # 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.mod = ""
def Jchemo_lwplsr(self):
"""Send data to Julia to compute lwplsr.
@@ -29,38 +54,70 @@ class LWPLSR:
self.predicted_results_on_test (DataFrame):
"""
# launch Julia Jchemo lwplsr
- jl.eval("""
- using Pandas
+ jl.seval("""
using DataFrames
+ using Pandas
using Jchemo
- nlvdis = 5 ; metric = :mah
- h = 1 ; k = 200 ; nlv = 15 #; scal = true
- mod = Jchemo.model(Jchemo.lwplsr; nlvdis, metric, h, k, nlv)
- Jchemo.fit!(mod, X_train, y_train)
- # Jchemo.pnames(mod)
- # Jchemo.pnames(mod.fm)
- # predictions on test data calculation
- res = Jchemo.predict(mod, X_test)
- # Jchemo.pnames(res)
+ x_train |> Pandas.DataFrame |> DataFrames.DataFrame
+ y_train |> Pandas.DataFrame |> DataFrames.DataFrame
+ x_test |> Pandas.DataFrame |> DataFrames.DataFrame
+ y_test |> Pandas.DataFrame |> DataFrames.DataFrame
""")
- resjp = jl.seval("""
- Pandas.DataFrame(res.pred)
+ # 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)
""")
- scoresjp = jl.seval("""
- Jchemo.mse(res.pred, y_test)
+ self.mod = jl.mod
+
+ def Jchemo_lwplsr_predict(self):
+ # Predictions on x_test and store in self.pred
+ self.pred = jl.seval("""
+ res = Jchemo.predict(mod, x_test)
+ res.pred
""")
- self.scores = pd.Dataframe(scoresjp)
- self.predicted_results_on_test = pd.Dataframe(resjp)
+ # convert predicted data from x_test to Pandas DataFrame
+ self.predicted_results_on_test = pd.DataFrame(self.pred)
@property
def pred_data_(self):
return self.predicted_results_on_test, self.predicted_results_on_test, self.predicted_results_on_test
- # @property
- # def model_(self):
- # return self.trained
+ @property
+ def model_(self):
+ return self.mod
@property
def metrics_(self):
- # self.scores = pd.DataFrame(self.scores, index=['test'])
- return self.scores
\ No newline at end of file
+ jl.pred = self.pred
+ st.dataframe(self.pred)
+ st.dataframe(self.predicted_results_on_test)
+ st.write('starting metrics')
+ jl.seval("""
+ using Jchemo
+ """)
+ scorermsep = jl.seval("""
+ first(Jchemo.rmsep(pred, y_test))
+ """)
+ scoremr2 = jl.seval("""
+ first(Jchemo.r2(pred, y_test))
+ """)
+ scorerpd = jl.seval("""
+ first(Jchemo.rpd(pred, y_test))
+ """)
+ scoremsep = jl.seval("""
+ first(Jchemo.sep(pred, y_test))
+ """)
+ self.scores = pd.DataFrame([[scoremr2, scorermsep, scoremsep, scorerpd]], columns=['r2', 'rmsep', 'msep', 'rpd'], index=['scores'])
+ return self.scores
diff --git a/src/Class_Mod/Miscellaneous.py b/src/Class_Mod/Miscellaneous.py
index 0d726113ce995ce0dde5e53054cccb98765a568e..706a61de7a3d7a14f8867e1cdda7366796ae6464 100644
--- a/src/Class_Mod/Miscellaneous.py
+++ b/src/Class_Mod/Miscellaneous.py
@@ -6,7 +6,6 @@ from Packages import *
def local_css(file_name):
with open(file_name) as f:
st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
-local_css("style/style.css")
# predict module
def prediction(NIRS_csv, qsep, qhdr, model):
diff --git a/src/Modules.py b/src/Modules.py
index d5a0e2a3d3f0b7e2416c4cd7e980426ad3284ee7..cb51a5c8e3fc5e3b62af93dedb35b8b3eb0dc639 100644
--- a/src/Modules.py
+++ b/src/Modules.py
@@ -1,5 +1,6 @@
from Class_Mod import PlsR, LinearPCA, Umap, find_col_index, PinardPlsr
from Class_Mod import LWPLSR, list_files, metrics, TpeIpls, reg_plot, resid_plot, Sk_Kmeans, DxRead, Hdbscan, read_dx, PlsProcess
-from Class_Mod.Miscellaneous import prediction, download_results, plot_spectra
-from style.header import add_header
\ No newline at end of file
+from Class_Mod.Miscellaneous import prediction, download_results, plot_spectra, local_css
+from style.header import add_header
+local_css("style/style.css")
\ No newline at end of file
diff --git a/src/Packages.py b/src/Packages.py
index 5c1d19856500b2d04cd3b8e439af9323a5311888..bc4f15307d8cc98a24ffb6a07e0eb8df6e4ee115 100644
--- a/src/Packages.py
+++ b/src/Packages.py
@@ -8,8 +8,6 @@ import random
import datetime
import numpy as np
import pandas as pd
-from os import listdir
-from os.path import isfile, join
from sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder
import time
from scipy.stats import skew, kurtosis
@@ -38,7 +36,7 @@ from sklearn.compose import TransformedTargetRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error, r2_score
from sklearn.cross_decomposition import PLSRegression
-## Images and plots
+## Images and plots
from PIL import Image
import plotly.express as px
import plotly.graph_objects as go
diff --git a/src/app.py b/src/app.py
index 0ec3af010fb29c358eb9c9c12601849bf8a37c59..bfe8cb84b57d1d877904927ffb96f3f041af6e2b 100644
--- a/src/app.py
+++ b/src/app.py
@@ -6,15 +6,6 @@ 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</h1>
-# </div>
-# """
-
-# Injecter le code HTML du bandeau
-# st.markdown(bandeau_html, unsafe_allow_html=True)
add_header()
# # TOC menu on the left
@@ -27,10 +18,6 @@ show_pages(
]
)
-# hide_pages("Samples Selection")
-# hide_pages("Models Creation")
-# hide_pages("Predictions")
-
with st.sidebar:
interface = st.selectbox(label="Interface", options=['simple', 'advanced'], key='interface')
@@ -54,22 +41,6 @@ with st.sidebar:
# Page header
with st.container():
- # Centrer les boutons - CODE DEPLACE dans le style/style.css
- # st.markdown(
- # """
- # <style>
- # .stButton>button {
- # display: block;
- # margin: 0 auto;
- # width: 200px;
- # height: 50px;
- # font-size: 16px;
- # }
- # </style>
- # """,
- # unsafe_allow_html=True
- # )
-
header1, header2, header3,header4 = st.columns(4)
if header1.button("Inputs"):
st.switch_page('pages\\4-inputs.py')
@@ -83,4 +54,4 @@ with st.container():
st.title("NIRS Utils")
st.write("Samples selection (PCA, [UMAP](https://umap-learn.readthedocs.io/en/latest/how_umap_works.html), ...), Predictive Modelling ([Pinard](https://github.com/GBeurier/pinard), [LWPLSR](https://doi.org/10.1002/cem.3209), ...), and Predictions using your data (CSV or DX files) and/or PACE NIRS Database.")
#st.image(img_general)
- st.markdown("### We could add documentation here ###")
\ No newline at end of file
+ st.markdown("### We could add documentation here ###")
diff --git a/src/pages/2-model_creation.py b/src/pages/2-model_creation.py
index cfed211cd23bdeb57c997ec2eb27fa521bfdcd57..986d249bb2c254db09665c5e6137f7d70b88824b 100644
--- a/src/pages/2-model_creation.py
+++ b/src/pages/2-model_creation.py
@@ -134,9 +134,13 @@ if not spectra.empty and not y.empty:
reg_model = Reg.model_
#M2.dataframe(Pin.pred_data_)
elif regression_algo == reg_algo[2]:
- # Reg = LWPLSR(x_train = X_train, x_test = X_test, y_train = y_train, y_test = y_test)
- # LWPLSR.Jchemo_lwplsr(Reg)
- pass
+ 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_)
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)
diff --git a/src/pages/4-inputs.py b/src/pages/4-inputs.py
index 59fbb88694103f31806b91c16ed2510a18215e03..a32790659064caf4eb47158303ddd3b4e362d672 100644
--- a/src/pages/4-inputs.py
+++ b/src/pages/4-inputs.py
@@ -1,21 +1,10 @@
-import streamlit as st
-
from Packages import *
-st.set_page_config(page_title="NIRS Utils", page_icon=":goat:", layout="wide",)
-if 'interface' not in st.session_state:
- st.session_state['interface'] = 'simple'
+st.set_page_config(page_title="NIRS Utils", page_icon=":goat:", layout="wide")
from Modules import *
from Class_Mod.DATA_HANDLING import *
-# HTML for the banner "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</h1>
-# </div>
-# """
-#
-#
-# # Inject the HTML code for the banner
-# st.markdown(bandeau_html, unsafe_allow_html=True)
+if 'interface' not in st.session_state:
+ st.session_state['interface'] = 'simple'
+
add_header()
@@ -26,22 +15,6 @@ if 'form_submitted' not in st.session_state:
# Page header
with st.container():
- # Center the buttons
- st.markdown(
- """
- <style>
- .stButton>button {
- display: block;
- margin: 0 auto;
- width: 200px;
- height: 50px;
- font-size: 16px;
- }
- </style>
- """,
- unsafe_allow_html=True
- )
-
# Text input fields
with st.form(key='my_form'):
@@ -108,10 +81,3 @@ with st.container():
if header5.button("Prediction"):
st.session_state['current_page'] = 'pages\\3-prediction.py'
st.switch_page('pages\\3-prediction.py')
-
-
-
-# Bouton de retour à la page d'accueil
-st.markdown('<div style="text-align: left;"><a href="/"> <img src="house.jpg" alt="Home" style="width:50px;height:50px;"> </a></div>', unsafe_allow_html=True)
-
-
diff --git a/src/plot_axe1_axe2.png b/src/plot_axe1_axe2.png
deleted file mode 100644
index a52dfe7b11df2b892ef0bf0b54e86c47fd0714b9..0000000000000000000000000000000000000000
Binary files a/src/plot_axe1_axe2.png and /dev/null differ