0.93.10

CHANGELOG.md

@@ -1,6 +1,11 @@
 Changelog
 =========
 
+Version 0.93.10
+--------------
+* added nan check for imported features
+* added LOGO result output
+
 Version 0.93.9
 --------------
 * added manual seed to torch models

nkululeko/constants.py

@@ -1,2 +1,2 @@
-VERSION="0.93.9"
+VERSION="0.93.10"
 SAMPLING_RATE = 16000

nkululeko/feat_extract/feats_import.py

@@ -30,7 +30,7 @@ def extract(self):
                 "feature type == import needs import_file = ['file1', 'filex']"
             )
         except SyntaxError:
-            if type(feat_import_files) == str:
+            if type(feat_import_files) is str:
                 feat_import_files = [feat_import_files]
             else:
                 self.util.error(f"import_file is wrong: {feat_import_files}")
@@ -40,6 +40,11 @@ def extract(self):
             if not os.path.isfile(feat_import_file):
                 self.util.error(f"no import file: {feat_import_file}")
             df = audformat.utils.read_csv(feat_import_file)
+            if df.isnull().values.any():
+                self.util.warn(
+                    f"imported features contain {df.isna().sum()} NAN, filling with zero."
+                )
+                df = df.fillna(0)
             df = self.util.make_segmented_index(df)
             df = df[df.index.isin(self.data_df.index)]
             if import_files_append:

nkululeko/models/model.py

@@ -171,6 +171,7 @@ def _do_logo(self):
             f"LOGO: {self.logo} folds: mean {results.mean():.3f}, std:"
             f" {results.std():.3f}"
         )
+        report.print_logo(results)
 
     def train(self):
         """Train the model."""

nkululeko/reporting/reporter.py

@@ -380,6 +380,18 @@ def _plot_confmat(self, truths, preds, plot_name, epoch=None, test_result=None):
     def set_filename_add(self, my_string):
         self.filenameadd = f"_{my_string}"
 
+    def print_logo(self, results):
+        res_dir = self.util.get_path("res_dir")
+        result_str = f"LOGO results: [{','.join(results.astype(str))}]"
+        file_name = f"{res_dir}/logo_results.txt"
+        with open(file_name, "w") as text_file:
+            text_file.write(
+                f"LOGO: mean {results.mean():.3f},  std: " + f"{results.std():.3f}"
+            )
+            text_file.write("\n")
+            text_file.write(result_str)
+        self.util.debug(result_str)
+
     def print_results(self, epoch=None):
         if epoch is None:
             epoch = self.epoch