From edd165e7d4d9abfc878c4967ff060c3efc762bca Mon Sep 17 00:00:00 2001
From: barthes <nicolas.barthes@cefe.cnrs.fr>
Date: Thu, 11 Apr 2024 17:51:09 +0200
Subject: [PATCH] added HDBSCAN clustering with optimization
---
Class_Mod/UMAP_.py | 2 +-
Packages.py | 5 ++---
app.py | 8 +++++++-
pages/1-samples_selection.py | 20 ++++++++++++++++----
4 files changed, 26 insertions(+), 9 deletions(-)
diff --git a/Class_Mod/UMAP_.py b/Class_Mod/UMAP_.py
index b563d4e..e9ae0dc 100644
--- a/Class_Mod/UMAP_.py
+++ b/Class_Mod/UMAP_.py
@@ -10,7 +10,7 @@ class Umap:
self.x = scaled_values
- self.model = UMAP(random_state=42, n_neighbors=20, n_components=4, min_dist=0.0)
+ self.model = UMAP(n_neighbors=20, n_components=4, min_dist=0.0,) # random_state=42,)
self.model.fit(self.x)
self.scores = self.model.transform(self.x)
self.scores = pd.DataFrame(self.scores, index = self.numerical_data.index)
diff --git a/Packages.py b/Packages.py
index 68c0bf0..2f04530 100644
--- a/Packages.py
+++ b/Packages.py
@@ -17,8 +17,7 @@ from umap.umap_ import UMAP
from sklearn.decomposition import PCA, NMF
# Clustering
-from sklearn.cluster import KMeans
-#import hdbscan
+from sklearn.cluster import KMeans, HDBSCAN
# Modelling
# import julia
@@ -40,7 +39,7 @@ import plotly.express as px
import matplotlib.pyplot as plt
import seaborn as sns
### Important Metrics
-from sklearn.metrics import pairwise_distances_argmin_min
+from sklearn.metrics import pairwise_distances_argmin_min, adjusted_rand_score, adjusted_mutual_info_score
## Web app construction
import streamlit as st
diff --git a/app.py b/app.py
index a68c036..8baa0d0 100644
--- a/app.py
+++ b/app.py
@@ -27,4 +27,10 @@ with st.container():
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 ###")
-
+ header1, header2, header3 = st.columns(3)
+ if header1.button("Samples Selection"):
+ st.switch_page('pages\\1-samples_selection.py')
+ if header2.button("Model Creation"):
+ st.switch_page('pages\\2-model_creation.py')
+ if header3.button("Predictions"):
+ st.switch_page('pages\\3-prediction.py')
\ No newline at end of file
diff --git a/pages/1-samples_selection.py b/pages/1-samples_selection.py
index 42ce389..f494d1c 100644
--- a/pages/1-samples_selection.py
+++ b/pages/1-samples_selection.py
@@ -88,7 +88,7 @@ with container1:
with container2:
if sselectx_csv is not None:
plot_type=['', 'PCA','UMAP', 'NMF']
- cluster_methods = ['', 'Kmeans','UMAP', 'AP']
+ cluster_methods = ['', 'Kmeans','HDBSCAN', 'AP']
with pc:
type_plot = st.selectbox("Dimensionality reduction techniques: ", options=plot_type, key=37)
@@ -110,9 +110,13 @@ with container2:
if type_cluster == 'Kmeans':
cl = Sk_Kmeans(pd.concat([model.scores_.loc[:,axis1], model.scores_.loc[:,axis2], model.scores_.loc[:,axis3]], axis = 1), max_clusters = 30)
+ elif type_cluster == 'HDBSCAN':
+ from hdbscan import HDBSCAN_function
+ labels, hdbscan_score = HDBSCAN_function(data_import, min_cluster_size=10)
+
with scores:
t = model.scores_
- if type_cluster in ['Kmeans','UMAP', 'AP']:
+ if type_cluster in ['AP', 'Kmeans']:
st.write('Scree plot')
fig2 = px.scatter(cl.inertia_.T, y = 'inertia')
st.plotly_chart(fig2)
@@ -123,14 +127,22 @@ with container2:
st.write('Scores plot')
fig = px.scatter_3d(data, x=axis1, y=axis2, z = axis3, color=colors)
fig.update_traces(marker=dict(size=4))
+ st.plotly_chart(fig)
+
+ elif type_cluster in ['HDBSCAN']:
+ st.write('plot HDBSCAN clustering')
+ fig_hdbscan = px.scatter_3d(t, x=axis1, y=axis2, z = axis3, color=labels)
+ fig_hdbscan.update_traces(marker=dict(size=4))
+ st.plotly_chart(fig_hdbscan)
+ st.write('DBCV score = ' + str(hdbscan_score))
+ # st.dataframe(min_score.stack().agg(['min']))
else:
fig = px.scatter_3d(t, x=axis1, y=axis2, z = axis3)
fig.update_traces(marker=dict(size=4))
-
- st.plotly_chart(fig)
+ st.plotly_chart(fig)
if type_plot =='PCA':
--
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