Refactored 'k' parameter o 'n_neighbors'.

Signed-off-by: Daniel Scheffler <danschef@gfz-potsdam.de>

gms_preprocessing/algorithms/classification.py

@@ -323,12 +323,12 @@ class SAM_Classifier(_ImageClassifier):
 
 
 class kNN_SAM_Classifier(SAM_Classifier):
-    def __init__(self, train_spectra, k=3, CPUs=1):
+    def __init__(self, train_spectra, n_neighbors=3, CPUs=1):
         # type: (np.ndarray, int, Union[int, None]) -> None
         super(kNN_SAM_Classifier, self).__init__(train_spectra, CPUs=CPUs)
 
         self.clf_name = 'k-nearest neighbour spectral angle mapper (SAM)'
-        self.k = k
+        self.n_neighbors = n_neighbors
 
     def _predict(self, tilepos):
         assert global_shared_endmembers is not None and global_shared_im2classify is not None
@@ -337,8 +337,8 @@ class kNN_SAM_Classifier(SAM_Classifier):
         tileimdata = global_shared_im2classify[rS: rE + 1, cS: cE + 1, :]
 
         angles = self._calc_sam(tileimdata, global_shared_endmembers)
-        cmap = np.argpartition(angles, self.k, axis=2)[:, :, :self.k].astype(np.int16)
-        angles_min_k = np.partition(angles, self.k, axis=2)[:, :, :self.k].astype(np.float32)
+        cmap = np.argpartition(angles, self.n_neighbors, axis=2)[:, :, :self.n_neighbors].astype(np.int16)
+        angles_min_k = np.partition(angles, self.n_neighbors, axis=2)[:, :, :self.n_neighbors].astype(np.float32)
 
         if global_shared_im2classify.nodata is not None and self._cmap_nodataVal is not None:
             cmap = self.overwrite_cmap_at_nodata_positions(cmap, tileimdata,

tests/test_image_classifier.py

@@ -121,24 +121,24 @@ class Test_SAM_Classifier(unittest.TestCase):
         SC.label_unclassified_pixels(label_unclassified=-1, threshold='10%')
 
 
-class Test_KNN_SAM_Classifier(unittest.TestCase):
+class Test_kNN_SAM_Classifier(unittest.TestCase):
     def setUp(self) -> None:
-        self.k = 3
+        self.n_neighbors = 3
 
     def test_classify(self):
-        SC = kNN_SAM_Classifier(cluster_centers, k=self.k, CPUs=1)
+        SC = kNN_SAM_Classifier(cluster_centers, n_neighbors=self.n_neighbors, CPUs=1)
         cmap_sp = SC.classify(test_gA, in_nodataVal=-9999, cmap_nodataVal=-9999, tiledims=(400, 200))
         self.assertIsInstance(cmap_sp, GeoArray)
-        self.assertEqual(cmap_sp.shape, (1010, 1010, self.k))
+        self.assertEqual(cmap_sp.shape, (1010, 1010, self.n_neighbors))
 
-        SC = kNN_SAM_Classifier(cluster_centers, k=self.k, CPUs=None)
+        SC = kNN_SAM_Classifier(cluster_centers, n_neighbors=self.n_neighbors, CPUs=None)
         cmap_mp = SC.classify(test_gA, in_nodataVal=-9999, cmap_nodataVal=-9999, tiledims=(400, 200))
         self.assertIsInstance(cmap_mp, GeoArray)
-        self.assertEqual(cmap_mp.shape, (1010, 1010, self.k))
+        self.assertEqual(cmap_mp.shape, (1010, 1010, self.n_neighbors))
 
         self.assertTrue(np.array_equal(cmap_sp, cmap_mp))
 
-        SC = kNN_SAM_Classifier(cluster_centers, k=self.k, CPUs=None)
+        SC = kNN_SAM_Classifier(cluster_centers, n_neighbors=self.n_neighbors, CPUs=None)
         cmap_mp = SC.classify(test_gA_pure_endmembers, in_nodataVal=-9999, cmap_nodataVal=-9999)
 
         for i, cl in enumerate(cluster_labels):
@@ -146,12 +146,12 @@ class Test_KNN_SAM_Classifier(unittest.TestCase):
         # self.assertTrue(np.array_equal(cmap_mp.flatten(), cluster_labels))  # TODO sort cmap by SC.angles_deg
 
     def test_label_unclassified_pixels_absolute_th(self):
-        SC = kNN_SAM_Classifier(cluster_centers, k=self.k, CPUs=None)
+        SC = kNN_SAM_Classifier(cluster_centers, n_neighbors=self.n_neighbors, CPUs=None)
         SC.classify(test_gA, in_nodataVal=-9999, cmap_nodataVal=-9999, tiledims=(400, 200))
         SC.label_unclassified_pixels(label_unclassified=-1, threshold=10)
 
     def test_label_unclassified_pixels_relative_th(self):
-        SC = kNN_SAM_Classifier(cluster_centers, self.k, CPUs=None)
+        SC = kNN_SAM_Classifier(cluster_centers, self.n_neighbors, CPUs=None)
         SC.classify(test_gA, in_nodataVal=-9999, cmap_nodataVal=-9999, tiledims=(400, 200))
         SC.label_unclassified_pixels(label_unclassified=-1, threshold='10%')