samples selection

Class_Mod/UMAP_.py

@@ -10,7 +10,7 @@ class Umap:
         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)
+        self.scores = pd.DataFrame(self.scores)
 
     @property
     def scores_(self):

pages/1-samples_selection.py

@@ -101,17 +101,18 @@ if not spectra.empty:
 
 # clustering
 if not t.empty:
+    tcr = standardize(t)
         # Clustering
-        if clus_method == cluster_methods[1]:
-            ncluster = scores.number_input(min_value=2, max_value=30, value=3, label = 'Select the desired number of clusters')
-            cl_model = Sk_Kmeans(t, max_clusters = 30)
-            fig2 = px.scatter(cl_model.inertia_.T, y = 'inertia')
-            scores.plotly_chart(fig2)
-            data, labels = cl_model.fit_optimal(nclusters = ncluster)
-
-        elif clus_method == cluster_methods[2]:
-                from hdbscan import HDBSCAN_function
-                labels, hdbscan_score = HDBSCAN_function(t, min_cluster_size=10)
+    if clus_method == cluster_methods[1]:
+        ncluster = scores.number_input(min_value=2, max_value=30, value=3, label = 'Select the desired number of clusters')
+        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)
+
+    elif clus_method == cluster_methods[2]:
+            from hdbscan import HDBSCAN_function
+            labels, hdbscan_score = HDBSCAN_function(tcr, min_cluster_size=10)
                 
 
 ##### Plots 
@@ -122,16 +123,16 @@ if not t.empty:
         st.write('Scores plot')
         # scores plot with clustering
         if list(labels) and meta_data.empty:
-            fig = px.scatter_3d(t, x=axis1, y=axis2, z = axis3, color = labels)
+            fig = px.scatter_3d(tcr, x=axis1, y=axis2, z = axis3, color = labels)
 
         # scores plot with metadata
         elif len(list(labels)) == 0 and not meta_data.empty:
             filter = meta_data.columns[1:]
             col = st.selectbox('Group by:', options= filter)
             if col == 0:
-                fig = px.scatter_3d(t, x=axis1, y=axis2, z = axis3)
+                fig = px.scatter_3d(tcr, x=axis1, y=axis2, z = axis3)
             else:
-                fig = px.scatter_3d(t, x=axis1, y=axis2, z = axis3, color = list(map(str.lower,meta_data[col])) )
+                fig = px.scatter_3d(tcr, x=axis1, y=axis2, z = axis3, color = list(map(str.lower,meta_data[col])) )
 
         # color with scores and metadata
         elif len(list(labels)) > 0  and not meta_data.empty:
@@ -143,14 +144,14 @@ if not t.empty:
 
             col = st.selectbox('Group by:', options= filter)
             if col == "None":
-                fig = px.scatter_3d(t, x=axis1, y=axis2, z = axis3) 
+                fig = px.scatter_3d(tcr, x=axis1, y=axis2, z = axis3) 
             elif col == clus_method:
-                fig = px.scatter_3d(t, x=axis1, y=axis2, z = axis3, color = labels)
+                fig = px.scatter_3d(tcr, x=axis1, y=axis2, z = axis3, color = labels)
             else:
-                fig = px.scatter_3d(t, x=axis1, y=axis2, z = axis3, color = list(map(str.lower,meta_data[col])))
+                fig = px.scatter_3d(tcr, x=axis1, y=axis2, z = axis3, color = list(map(str.lower,meta_data[col])))
 
         else:
-            fig = px.scatter_3d(t, x=axis1, y=axis2, z = axis3)        
+            fig = px.scatter_3d(tcr, x=axis1, y=axis2, z = axis3)        
         fig.update_traces(marker=dict(size=4))
         st.plotly_chart(fig)
 
@@ -183,7 +184,4 @@ 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)
-
-
-
+                st.plotly_chart(fig)
\ No newline at end of file