From f25a975c37536bb473e74b3d52c9b94c40a20109 Mon Sep 17 00:00:00 2001
From: DIANE <abderrahim.diane@cefe.cnrs.fr>
Date: Thu, 8 Aug 2024 16:15:39 +0200
Subject: [PATCH] download
---
src/pages/1-samples_selection.py | 76 +++++++++++++++++---------------
1 file changed, 40 insertions(+), 36 deletions(-)
diff --git a/src/pages/1-samples_selection.py b/src/pages/1-samples_selection.py
index f46e65d..bc12af6 100644
--- a/src/pages/1-samples_selection.py
+++ b/src/pages/1-samples_selection.py
@@ -263,18 +263,11 @@ if not t.empty:
case 'KS':
rset = scores.number_input(min_value=0, max_value=100, value=20, label = 'The ratio of data to be sampled (%)')
cl_model = KS(x = tcr, rset = rset)
- calset = cl_model.calset
- labels = ["ind"]*n_samples
- ncluster = "1"
- selection_number = 'None'
+
case 'RDM':
rset = scores.number_input(min_value=0, max_value=100, value=20, label = 'The ratio of data to be sampled (%)')
- cl_model = RDM(x = tcr, rset = rset)
- calset = cl_model.calset
- labels = ["ind"]*n_samples
- ncluster = "1"
- selection_number = 'None'
+ cl_model = RDM(x = tcr, rset = rset)
new_tcr = tcr.iloc[clustered,:]
@@ -291,35 +284,39 @@ elif labels:
custom_color_palette = px.colors.qualitative.Plotly[:num_clusters]
if clus_method:
if clus_method in ['KS', 'RDM']:
+ calset = cl_model.calset
+ labels = ["ind"]*n_samples
+ ncluster = "1"
+ selection_number = 'None'
selected_samples_idx = calset[1]
selection = 'None'
else:
selection = scores.radio('Select samples selection strategy:',
options = selec_strategy, index = default_sample_selection_option, key=102)
- match selection:
- # Strategy 0
- case 'center':
- # 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, new_tcr)
- selected_samples_idx = np.array(new_tcr.index)[list(closest)]
- selected_samples_idx = selected_samples_idx.tolist()
-
- #### Strategy 1
- case 'random':
- selection_number = scores.number_input('How many samples per cluster?',
- min_value = 1, step=1, value = 3)
- s = np.array(labels)[np.where(np.array(labels) !='Non clustered')[0]]
- for i in np.unique(s):
- 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_idx.extend(tcr.iloc[C,:].iloc[list(clos)].index)
- else:
- selected_samples_idx.extend(new_tcr.iloc[C,:].index.to_list())
+ match selection:
+ # Strategy 0
+ case 'center':
+ # 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, new_tcr)
+ selected_samples_idx = np.array(new_tcr.index)[list(closest)]
+ selected_samples_idx = selected_samples_idx.tolist()
+
+ #### Strategy 1
+ case 'random':
+ selection_number = scores.number_input('How many samples per cluster?',
+ min_value = 1, step=1, value = 3)
+ s = np.array(labels)[np.where(np.array(labels) !='Non clustered')[0]]
+ for i in np.unique(s):
+ 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_idx.extend(tcr.iloc[C,:].iloc[list(clos)].index)
+ else:
+ selected_samples_idx.extend(new_tcr.iloc[C,:].index.to_list())
# list indexes of selected samples for colored plot
################################ Plots visualization ############################################
@@ -591,14 +588,21 @@ if labels:
else:
sam = sam1
sel.write(sam)
-
+
+Nb_ech = str(n_samples)
+nb_clu = str(sam1.shape[0])
+for i in ['Representative subset selection', data_file.name, dim_red_method,
+ clus_method, Nb_ech, ncluster, selection, selection_number, nb_clu]:
+ st.write(i)
# st.write(hash_data(change = './Report/report.tex'))
-with open('./Report/report.tex') as myfile:
- filehash = hash_data(myfile.read())
+# file_pathtex = Path('./Report/report.tex')
+# if file_pathtex.is_file():
+# with open('./Report/report.tex') as myfile:
+# filehash = hash_data(myfile.read())
# figs_list = os.listdir("./Report/figures")
-if data_file:
+if not (sam.empty and sam1.empty):
Nb_ech = str(n_samples)
nb_clu = str(sam1.shape[0])
###################################################
--
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