fixed discrepancy between old and new data versions

EliHei2 · Oct 8, 2024 · b2adc96 · b2adc96
1 parent e1a7b05
commit b2adc96
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Showing 3 changed files with 6 additions and 5 deletions.
diff --git a/scripts/predict_model_sample.py b/scripts/predict_model_sample.py
@@ -40,13 +40,13 @@
     model,
     dm,
     save_dir=benchmarks_dir,
-    seg_tag='segger_embedding_1001_0.5',
+    seg_tag='segger_embedding_1001_0.5_cc',
     transcript_file=transcripts_file,
     file_format='anndata',
     receptive_field = receptive_field,
     min_transcripts=5,
     # max_transcripts=1500,
     cell_id_col='segger_cell_id',
-    use_cc=False,
+    use_cc=True,
     knn_method='cuda'
 )
diff --git a/src/segger/data/parquet/pyg_dataset.py b/src/segger/data/parquet/pyg_dataset.py
@@ -37,7 +37,8 @@ def processed_file_names(self) -> List[str]:
         Returns:
             List[str]: List of processed file names.
         """
-        paths = glob.glob(f'{self.processed_dir}/tiles_x=*_y=*_w=*_h=*.pt')
+        paths = glob.glob(f'{self.processed_dir}/tiles_x*_y*_*_*.pt')
+        # paths = paths.append(paths = glob.glob(f'{self.processed_dir}/tiles_x*_y*_*_*.pt'))
         file_names = list(map(os.path.basename, paths))
         return file_names
 

diff --git a/src/segger/prediction/predict.py b/src/segger/prediction/predict.py
@@ -157,7 +157,7 @@ def sparse_multiply(embeddings, edge_index, shape) -> coo_matrix:
         indices[1] = edge_index[edge_index != -1]
         rows = cp.fromDlpack(to_dlpack(indices[0,:].to('cuda')))
         columns = cp.fromDlpack(to_dlpack(indices[1,:].to('cuda')))
-        print(rows)
+        # print(rows)
         del indices
         values = similarity[edge_index != -1].flatten()
         sparse_result = coo_matrix((cp.fromDlpack(to_dlpack(values)), (rows, columns)), shape=shape)
@@ -419,7 +419,7 @@ def segment(
     seg_combined = pd.concat([segmentation_train, segmentation_val, segmentation_test], ignore_index=True)
 
     # seg_combined = segmentation_test
-    print(seg_combined.columns)
+    # print(seg_combined.columns)
     # print(transcripts_df.id)
     # Drop any unassigned rows
     seg_final = seg_combined.dropna(subset=['segger_cell_id']).reset_index(drop=True)