From a3c30c9fbc06ea78aef9417a9f1cc52cd7520fe1 Mon Sep 17 00:00:00 2001
From: DIANE <abderrahim.diane@cefe.cnrs.fr>
Date: Wed, 17 Apr 2024 16:16:34 +0200
Subject: [PATCH] samples_selection: - export samples for wet-chem analysis -
export metadata if it exists - color selected samples on scores plot
---
Class_Mod/KMEANS_.py | 3 +-
pages/1-samples_selection.py | 67 ++++++++++++++++++++++++++++++++++--
2 files changed, 66 insertions(+), 4 deletions(-)
diff --git a/Class_Mod/KMEANS_.py b/Class_Mod/KMEANS_.py
index 526a435..60d77ae 100644
--- a/Class_Mod/KMEANS_.py
+++ b/Class_Mod/KMEANS_.py
@@ -21,4 +21,5 @@ class Sk_Kmeans:
model.fit(self.x)
yp = model.predict(self.x)+1
clu = [f'cluster#{i}' for i in yp]
- return self.x, clu
\ No newline at end of file
+
+ return self.x, clu, model.cluster_centers_
\ No newline at end of file
diff --git a/pages/1-samples_selection.py b/pages/1-samples_selection.py
index 2edb875..a75dba6 100644
--- a/pages/1-samples_selection.py
+++ b/pages/1-samples_selection.py
@@ -68,6 +68,9 @@ container2 = st.container(border=True)
container2.header("Exploratory Data Analysis-Multivariable Data Analysis", divider='blue')
scores, loadings, pc = st.columns([2, 3, 0.5])
influence, hotelling, qexp = st.columns([2, 2, 1])
+st.header('Selected samples for chemical analysis')
+selected_s, selected_samples_metd = st.columns([3, 3])
+selected_s.write('Samples scores')
dim_red_methods=['', 'PCA','UMAP', 'NMF'] # List of dimensionality reduction algos
cluster_methods = ['', 'Kmeans','HDBSCAN', 'AP'] # List of clustering algos
@@ -106,7 +109,7 @@ if not t.empty:
cl_model = Sk_Kmeans(tcr, max_clusters = 30)
fig2 = px.scatter(cl_model.inertia_.T, y = 'inertia')
scores.plotly_chart(fig2)
- data, labels = cl_model.fit_optimal(nclusters = ncluster)
+ data, labels, clu_centers = cl_model.fit_optimal(nclusters = ncluster)
elif clus_method == cluster_methods[2]:
optimized_hdbscan = Hdbscan(model.scores_raw_)
@@ -114,7 +117,51 @@ if not t.empty:
##### Plots
-## Scores
+
+#####################################################################################################
+selec_strategy = ['center','random']
+samples_df_chem = pd.DataFrame
+selected_samples = []
+selected_samples_idx = []
+
+if labels:
+ selection = scores.radio('Select samples selection strategy:', options = selec_strategy)
+#################### selection strategy to be corrected
+ if selection == selec_strategy[0]:
+ # list samples at clusters centers - Use sklearn.metrics.pairwise_distances_argmin if you want more than 1 sample per cluster
+ closest, _ = pairwise_distances_argmin_min(clu_centers, tcr)
+ selected_samples_idx = list(closest)
+ elif selection == selec_strategy[1]:
+ selection_number = scores.number_input('How many samples per cluster?', min_value = 1, step=1, value = 3)
+ for i in np.unique(labels):
+ C = np.where(np.array(labels) ==i)[0]
+ if C.shape[0] >= selection_number:
+ #scores.write(list(tcr.index)[labels== i])
+ km2 = KMeans(n_clusters = selection_number)
+ km2.fit(tcr.iloc[C,:])
+ clos, _ = pairwise_distances_argmin_min(km2.cluster_centers_, tcr.iloc[C,:])
+ selected_samples_idx2 = list(clos)
+ selected_samples_idx.extend(tcr.iloc[C,:].index[selected_samples_idx2])
+ # selected_samples_idx.extend(tcr.iloc[C,:].sample(n=selection_number).index.to_list())
+ else:
+ selected_samples_idx.extend(tcr.iloc[C,:].index.to_list())
+ # list indexes of selected samples for colored plot
+
+if labels:
+ if selected_samples_idx:
+ sam = pd.DataFrame({'cluster':np.array(labels)[selected_samples_idx],
+ 'index': spectra.index[selected_samples_idx]})
+ selected_s.write(sam)
+
+ if not meta_data.empty:
+ selected_samples_metd.write('Corresponding meta-data')
+ meta = meta_data.iloc[selected_samples_idx,:]
+ meta['cluster'] = np.array(labels)[selected_samples_idx]
+ meta['index'] = spectra.index[selected_samples_idx]
+ selected_samples_metd.write(meta)
+
+
+ ## Scores
if not t.empty:
with scores:
st.write('Scores plot')
@@ -150,6 +197,12 @@ if not t.empty:
else:
fig = px.scatter_3d(tcr, x=axis1, y=axis2, z = axis3)
fig.update_traces(marker=dict(size=4))
+
+ if selected_samples_idx:
+ tt = tcr.iloc[selected_samples_idx,:]
+ fig.add_scatter3d(x = tt.loc[:,axis1], y = tt.loc[:,axis2],
+ z = tt.loc[:,axis3], mode ='markers', marker = dict(size = 7, color = 'black'),
+ name = 'selected samples')
st.plotly_chart(fig)
@@ -181,4 +234,12 @@ if not spectra.empty:
hotelling = dr_model.hotelling_
fig = px.scatter(t, x=hotelling[ax2], y=residuals[ax2]).update_layout(xaxis_title="T²",yaxis_title="Residuals")
- st.plotly_chart(fig)
\ No newline at end of file
+ st.plotly_chart(fig)
+
+
+
+
+
+
+
+
--
GitLab