remove umap

Kapoorlabs-CAPED · Nov 4, 2023 · acec5c5 · acec5c5
1 parent 61784d7
commit acec5c5
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Showing 2 changed files with 4 additions and 39 deletions.
diff --git a/src/napatrackmater/Trackvector.py b/src/napatrackmater/Trackvector.py
@@ -19,7 +19,6 @@
 from joblib import dump
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.metrics import accuracy_score
-import umap
 
 
 class TrackVector(TrackMate):
@@ -839,9 +838,6 @@ def unsupervised_clustering(
     shape_dynamic_covariance_matrix = []
     shape_covariance_matrix = []
     dynamic_covariance_matrix = []
-    shape_dynamic_umap_matrix = []
-    shape_umap_matrix = []
-    dynamic_umap_matrix = []
     for track_id, (
         shape_dynamic_dataframe_list,
         shape_dataframe_list,
@@ -876,12 +872,7 @@ def unsupervised_clustering(
             dynamic_covaraince, dynamic_eigenvectors = compute_covariance_matrix(
                 dynamic_track_array
             )
-            shape_dynamic_umap = compute_umap_matrix(shape_dynamic_track_array)
-            shape_umap = compute_umap_matrix(shape_track_array)
-            dynamic_umap = compute_umap_matrix(dynamic_track_array)
-            shape_dynamic_umap_matrix.append(shape_dynamic_umap)
-            shape_umap_matrix.append(shape_umap)
-            dynamic_umap_matrix.append(dynamic_umap)
+
             shape_dynamic_covariance_matrix.append(shape_dynamic_covariance)
             shape_covariance_matrix.append(shape_covariance)
             dynamic_covariance_matrix.append(dynamic_covaraince)
@@ -894,10 +885,6 @@ def unsupervised_clustering(
     shape_covariance_matrix = np.mean(shape_covariance_matrix, axis=0)
     dynamic_covariance_matrix = np.mean(dynamic_covariance_matrix, axis=0)
 
-    shape_dynamic_umap_matrix = np.mean(shape_dynamic_umap_matrix, axis=0)
-    shape_umap_matrix = np.mean(shape_umap_matrix, axis=0)
-    dynamic_umap_matrix = np.mean(dynamic_umap_matrix, axis=0)
-
     shape_dynamic_covariance_2d = shape_dynamic_covariance_3d.reshape(
         len(analysis_track_ids), -1
     )
@@ -909,11 +896,7 @@ def unsupervised_clustering(
         shape_covariance_matrix,
         dynamic_covariance_matrix,
     ]
-    track_umap_array = [
-        shape_dynamic_umap_matrix,
-        shape_umap_matrix,
-        dynamic_umap_matrix,
-    ]
+
     track_arrays_array_names = ["shape_dynamic", "shape", "dynamic"]
     clusterable_track_arrays = [
         shape_dynamic_covariance_2d,
@@ -966,16 +949,6 @@ def unsupervised_clustering(
         )
         np.save(mean_matrix_file_name, track_arrays)
 
-        umap_matrix_file_name = (
-            csv_file_name_original
-            + track_arrays_array_names[track_arrays_array.index(track_arrays)]
-            + "_umap.npy"
-        )
-        np.save(
-            umap_matrix_file_name,
-            track_umap_array[track_arrays_array.index(track_arrays)],
-        )
-
         linkage_npy_file_name = (
             csv_file_name_original
             + track_arrays_array_names[track_arrays_array.index(track_arrays)]
@@ -1050,14 +1023,6 @@ def compute_covariance_matrix(track_arrays):
     return covariance_matrix, eigenvectors
 
 
-def compute_umap_matrix(track_arrays):
-
-    reducer = umap.UMAP()
-    umap_result = reducer.fit_transform(track_arrays)
-
-    return umap_result
-
-
 def _save_feature_importance(
     sorted_feature_names,
     normalized_importances,

diff --git a/src/napatrackmater/_version.py b/src/napatrackmater/_version.py
@@ -1,2 +1,2 @@
-__version__ = version = "4.4.5"
-__version_tuple__ = version_tuple = (4, 4, 5)
+__version__ = version = "4.4.6"
+__version_tuple__ = version_tuple = (4, 4, 6)