diff --git a/.streamlit/config.toml b/.streamlit/config.toml
index 54bad9eb6d873cc0f57103c4112a03be75af0a58..9b3d160d8bd8c8b1eb5980e18e5e3170f3ccfd85 100644
--- a/.streamlit/config.toml
+++ b/.streamlit/config.toml
@@ -1,15 +1,6 @@
[theme]
-#primaryColor="#E0A505"
-#backgroundColor="#b7e4f3"
-#secondaryBackgroundColor="#b5f5cb"
-#textColor="#0a23bf"
+#base="dark"
font="sans serif"
-
-#primaryColor="#E91E63"
-#backgroundColor="#FFE082"
-#secondaryBackgroundColor="#E6EE9C"
-#textColor="#0a23bf"
-
primaryColor="#EF8354"
backgroundColor="#FFFFFF"
secondaryBackgroundColor="#7E8987"
diff --git a/hdbscan.py b/Class_Mod/HDBSCAN_Clustering.py
similarity index 90%
rename from hdbscan.py
rename to Class_Mod/HDBSCAN_Clustering.py
index 0d9268bfed8e6c97db22f7165424052a13110f60..f01928254a72c2516d8f2093011b06130aaaea27 100644
--- a/hdbscan.py
+++ b/Class_Mod/HDBSCAN_Clustering.py
@@ -256,14 +256,18 @@ def _get_label_members(X, labels, cluster):
members = X[indices]
return members
-def HDBSCAN_function(data, min_cluster_size):
+def HDBSCAN_function(data):
# param_dist = {'min_samples': [1,5,10,30],
# 'min_cluster_size':[5,10,20,30,50,75,100],
# # 'cluster_selection_method' : ['eom','leaf'],
# # 'metric' : ['euclidean','manhattan']
# }
- param_dist = {'min_samples': [1,5],
- 'min_cluster_size':[5,10],
+ # param_dist = {'min_samples': [1,5,10,50],
+ # 'min_cluster_size':[5,10,30,50,100,300,500],
+ # }
+ param_dist = {'min_samples': [1,5, 10,],
+ 'min_cluster_size':[5,10,30,50,100],
+ 'metric' : ['euclidean','manhattan'],
}
clusterable_embedding = UMAP(
@@ -272,6 +276,15 @@ def HDBSCAN_function(data, min_cluster_size):
n_components=5,
random_state=42,
).fit_transform(data)
+
+ # RandomizedSearchCV not working...
+ # def scoring(model, clusterable_embedding):
+ # label = HDBSCAN().fit_predict(clusterable_embedding)
+ # hdbscan_score = DBCV(clusterable_embedding, label, dist_function=euclidean)
+ # return hdbscan_score
+ # tunning = RandomizedSearchCV(estimator=HDBSCAN(), param_distributions=param_dist, scoring=scoring)
+ # tunning.fit(clusterable_embedding)
+ # return tunning
min_score = pd.DataFrame()
for i in param_dist.get('min_samples'):
for j in param_dist.get('min_cluster_size'):
@@ -279,7 +292,8 @@ def HDBSCAN_function(data, min_cluster_size):
ij_hdbscan_score = DBCV(clusterable_embedding, ij_label, dist_function=euclidean)
min_score.at[i,j] = ij_hdbscan_score
hdbscan_score = max(min_score.max())
- # get the coordinates of the best clustering paramters and run HDBSCAN below
-
- labels = HDBSCAN(min_samples=1, min_cluster_size=min_cluster_size).fit_predict(clusterable_embedding)
+ # get the coordinates of the best clustering parameters and run HDBSCAN below
+ bparams = np.where(min_score == hdbscan_score)
+ # run HDBSCAN with best params
+ labels = HDBSCAN(min_samples=param_dist['min_samples'][bparams[0][0]], min_cluster_size=param_dist['min_cluster_size'][bparams[1][0]], metric=param_dist['metric'][bparams[1][0]]).fit_predict(clusterable_embedding)
return labels, hdbscan_score
diff --git a/Packages.py b/Packages.py
index 924b7888f5c5829ec4a62bc3308b8e9d97ef3620..12032738ef858e544f1c6483e9ed09a1edd140d7 100644
--- a/Packages.py
+++ b/Packages.py
@@ -27,7 +27,7 @@ from pinard import utils
from pinard import preprocessing as pp
from pinard.model_selection import train_test_split_idx
-from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict, cross_validate
+from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict, cross_validate, RandomizedSearchCV
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.compose import TransformedTargetRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error, r2_score
@@ -45,6 +45,7 @@ from sklearn.metrics import pairwise_distances_argmin_min, adjusted_rand_score,
## Web app construction
import streamlit as st
+from st_pages import Page, Section, show_pages, add_page_title, hide_pages
from tempfile import NamedTemporaryFile
# help on streamlit input https://docs.streamlit.io/library/api-reference/widgets
diff --git a/README.md b/README.md
index 1edf0f54ef393356556e0a87f77cc94840f214a9..d4047956224c137fc011b54219fea0b4d1ed7c19 100644
--- a/README.md
+++ b/README.md
@@ -15,10 +15,10 @@ The process includes:
If one wants to use data stored in a SQL database, the config file is in the config/ folder.
## Installation
-This package is written in python. You can clone the repository: git clone https://src.koda.cnrs.fr/nicolas.barthes.5/nirs_workflow.git
+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
-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.
+(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
> '>>> import julia
'>>> julia.install()
@@ -28,9 +28,9 @@ From the CLI: python
To check if Jchemo is installed without errors:
> '>>> Pkg.status()
-You can then run: streamlit run ./app.py from the CLI.
+You can then run (CLI): streamlit run ./app.py from within your folder.
-The app will then open in your default browser.
+The app will open in your default browser.
## Usage
The web app allows you to process sample selection, model creation and predictions.
diff --git a/app.py b/app.py
index 8baa0d0724465c6e6101c90bafa42dba54c44a00..027393b8079257a75cae2893f29cd52e15db1c8a 100644
--- a/app.py
+++ b/app.py
@@ -1,24 +1,39 @@
-#from Modules_manager.PCA_ import pca_maker
-
from Packages import *
-st.set_page_config(page_title="NIRS Utils", page_icon=":goat:", layout="wide")
+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'
from Modules import *
from Class_Mod.DATA_HANDLING import *
-# graphical delimiter
-# st.write("---")
-
-# load images for web interface
-# img_sselect = Image.open("images\sselect.JPG")
-# img_general = Image.open("images\general.JPG")
-# img_predict = Image.open("images\predict.JPG")
-# TOC menu on the left
-# with st.sidebar:
-# st.markdown("[Sample Selection](#sample-selection)")
-# st.markdown("[Model Development](#create-a-model)")
-# st.markdown("[Predictions Making](#predict)")
+# # TOC menu on the left
+show_pages(
+ [Page("app.py", "Home"),
+ Page("pages\\1-samples_selection.py", "Samples Selection"),
+ Page("pages\\2-model_creation.py", "Models Creation"),
+ Page("pages\\3-prediction.py", "Predictions"),
+ ]
+)
+hide_pages("Samples Selection")
+hide_pages("Models Creation")
+hide_pages("Predictions")
+with st.sidebar:
+ interface = st.selectbox(label="Interface", options=['simple', 'advanced'], key='interface')
+ st.page_link('pages\\1-samples_selection.py')
+ if st.session_state['interface'] == 'simple':
+ st.page_link('pages\\2-model_creation.py')
+ # if advanced interface, split Models Creation and Predictions
+ elif st.session_state['interface'] == 'advanced':
+ show_pages(
+ [Page("app.py", "Home"),
+ Page("pages\\1-samples_selection.py", "Samples Selection"),
+ Page("pages\\2-model_creation.py", "Models Creation"),
+ Page("pages\\3-prediction.py", "Predictions"),
+ ]
+ )
+ st.page_link('pages\\2-model_creation.py')
+ st.page_link('pages\\3-prediction.py')
# Page header
with st.container():
diff --git a/pages/1-samples_selection.py b/pages/1-samples_selection.py
index 07139bf0e0d09f615c10a9bfd4d4f3c31fd665ef..76c2454f85edeb074e1f9006febae570d1def43b 100644
--- a/pages/1-samples_selection.py
+++ b/pages/1-samples_selection.py
@@ -7,6 +7,10 @@ from Class_Mod.DATA_HANDLING import *
+st.session_state["interface"] = st.session_state.get('interface')
+if st.session_state["interface"] == 'simple':
+ hide_pages("Predictions")
+
################################### Data Loading and Visualization ########################################
# container1 = st.header("Data loading",border=True)
col2, col1 = st.columns([3, 1])
diff --git a/pages/2-model_creation.py b/pages/2-model_creation.py
index b66ac470edbcd7355c71d0d3fdf9e03f8be4a24f..3fadcb45393c71242b4876ccf32912c616a59ec4 100644
--- a/pages/2-model_creation.py
+++ b/pages/2-model_creation.py
@@ -3,7 +3,9 @@ st.set_page_config(page_title="NIRS Utils", page_icon=":goat:", layout="wide")
from Modules import *
from Class_Mod.DATA_HANDLING import *
-
+st.session_state["interface"] = st.session_state.get('interface')
+if st.session_state["interface"] == 'simple':
+ hide_pages("Predictions")
def nn(x):
return x is not None
########################################################################################
@@ -107,4 +109,8 @@ if xcal_csv is not None and ycal_csv is not None:
# create a report with information on the model
## see https://stackoverflow.com/a/59578663
- #M4.pyplot(reg_plot(meas==(ycal_csv,ycal_csv,ycal_csv], pred=[ycal_csv,ycal_csv,ycal_csv]))
\ No newline at end of file
+ #M4.pyplot(reg_plot(meas==(ycal_csv,ycal_csv,ycal_csv], pred=[ycal_csv,ycal_csv,ycal_csv]))
+
+
+ if st.session_state['interface'] == 'simple':
+ st.page_link('pages\\3-prediction.py', label = 'Keep on keepin\' on to predict your values !')
diff --git a/pages/3-prediction.py b/pages/3-prediction.py
index d215aa7e9c1bca0269efacccd63bb258afdda062..65130fd1dfcdde9f491dc7f8eaee4e19817ddc55 100644
--- a/pages/3-prediction.py
+++ b/pages/3-prediction.py
@@ -3,6 +3,7 @@ st.set_page_config(page_title="NIRS Utils", page_icon=":goat:", layout="wide")
from Modules import *
from Class_Mod.DATA_HANDLING import *
+st.session_state["interface"] = st.session_state.get('interface')
st.header("Predictions making", divider='blue')
diff --git a/requirements.txt b/requirements.txt
index de7027c587917f3f6fcd947ce5ab4b58af12de24..60c8fc3ba7f50429005b8d19db952fc27902ca47 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,5 @@
streamlit>=1.3.0
+st_pages-0.4.5>=0.4.5
requests>=2.24.0
Pillow>=8.4.0
protobuf>=3.19.0