AFFINITY PROPAGATION

src/Class_Mod/Ap.py

+from Packages import *
+
+class AP:
+    def __init__(self, X):
+        ## input matrix
+        self.__x = np.array(X)
+
+        # Fit PCA model
+        self.M = AffinityPropagation(damping=0.5, max_iter=200, convergence_iter=15, copy=True, preference=None,
+                                 affinity='euclidean', verbose=False, random_state=None)
+        self.M.fit(self.__x)
+        self.yp = self.M.predict(self.__x)+1
+    @property
+    def fit_optimal_(self):
+        clu = [f'cluster#{i}' for i in self.yp]
+        return self.__x, clu, self.M.cluster_centers_
\ No newline at end of file

src/Class_Mod/__init__.py

@@ -13,4 +13,5 @@ from .DxReader import DxRead, read_dx
 from .HDBSCAN_Clustering import Hdbscan
 from .SK_PLSR_ import PlsR
 from .PLSR_Preprocess import PlsProcess
-from .NMF_ import Nmf
\ No newline at end of file
+from .NMF_ import Nmf
+from .Ap import AP
\ No newline at end of file

src/Modules.py

 from Packages import *
-from Class_Mod import PlsR, LinearPCA, Umap, find_col_index, PinardPlsr, Nmf
+from Class_Mod import PlsR, LinearPCA, Umap, find_col_index, PinardPlsr, Nmf, AP
 from Class_Mod import LWPLSR, list_files, metrics, TpeIpls, reg_plot, resid_plot, Sk_Kmeans, DxRead, Hdbscan, read_dx, PlsProcess
 from Class_Mod.DATA_HANDLING import *
 from Class_Mod.Miscellaneous import prediction, download_results, plot_spectra, local_css

src/Packages.py

@@ -19,7 +19,7 @@ from umap.umap_ import UMAP
 from sklearn.decomposition import PCA, NMF
 
 # Clustering
-from sklearn.cluster import KMeans, HDBSCAN
+from sklearn.cluster import KMeans, HDBSCAN,AffinityPropagation
 from scipy.spatial.distance import euclidean, cdist
 from scipy.sparse.csgraph import minimum_spanning_tree
 from scipy.sparse import csgraph
@@ -36,7 +36,6 @@ 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
 from sklearn.cross_decomposition import PLSRegression
-
 ## Images and plots
 from PIL import Image
 import plotly.express as px

src/pages/1-samples_selection.py

@@ -157,6 +157,11 @@ if not t.empty:
         non_clustered = np.where(labels == -1)
         labels[non_clustered] = 1000
         labels = labels.tolist()
+    
+    elif clus_method == cluster_methods[3]:
+        cl_model = AP(X=tcr)
+        data, labels, clu_centers = cl_model.fit_optimal_
+
 
 ###### 3- Samples selection using the reduced data preentation ######
 selec_strategy = ['center','random']