resetting changes out of scope of this PR

examples/classifiers/wrn_gru_mesa.py

@@ -15,9 +15,9 @@
     set_nsrr_token,
 )
 
-set_nsrr_token("YOUR TOKEN HERE")
+set_nsrr_token("your-token-here")
 
-TRAIN = False  # set to False to skip training and load classifier from disk
+TRAIN = True  # set to False to skip training and load classifier from disk
 
 # silence warnings (which might pop up during feature extraction)
 warnings.filterwarnings(
@@ -38,7 +38,6 @@
             "recording_start_time",
             "age",
             "gender",
-            "activity_counts",
         ],
         "min_rri": 0.3,
         "max_rri": 2,

examples/classifiers/ws_gru_mesa.py

@@ -10,13 +10,14 @@
     prepare_data_keras,
     print_class_balance,
     read_mesa,
+    read_shhs,
     save_classifier,
     set_nsrr_token,
 )
 
-set_nsrr_token("YOUR TOKEN HERE")
+set_nsrr_token("your-token-here")
 
-TRAIN = False  # set to False to skip training and load classifier from disk
+TRAIN = True  # set to False to skip training and load classifier from disk
 
 # silence warnings (which might pop up during feature extraction)
 warnings.filterwarnings(
@@ -37,7 +38,6 @@
             "recording_start_time",
             "age",
             "gender",
-            "activity_counts",
         ],
         "min_rri": 0.3,
         "max_rri": 2,
@@ -105,18 +105,10 @@
 clf = load_classifier("ws-gru-mesa", "./classifiers")
 
 print("‣‣ Extracting features...")
-# shhs = list(read_shhs(offline=False))
-test_data = list(
-    read_mesa(
-        offline=False,
-        activity_source="actigraphy",
-        keep_edfs=True,
-        data_dir="D:/SleepData/",
-        records_pattern="2***",
-    )
-)
+shhs = list(read_shhs(offline=False))
+
 features_test, stages_test, feature_ids = extract_features(
-    tqdm(test_data),
+    tqdm(shhs),
     **clf.feature_extraction_params,
     n_jobs=-1,
 )

src/sleepecg/feature_extraction.py

@@ -56,7 +56,6 @@
         "LF_HF_ratio",
     ),
     "metadata": ("recording_start_time", "age", "gender", "weight"),
-    "actigraphy": ("activity_counts", "dummy_feature"),
 }
 _FEATURE_ID_TO_GROUP = {id: group for group, ids in _FEATURE_GROUPS.items() for id in ids}
 
@@ -657,9 +656,6 @@ def _extract_features_single(
             )
         elif feature_group == "metadata":
             X.append(_metadata_features(record, num_stages))
-        elif feature_group == "actigraphy":
-            if record.activity_counts is not None:
-                X.append(record.activity_counts.reshape(-1, 1))
     features = np.hstack(X)[:, col_indices]
 
     if record.sleep_stages is None or sleep_stage_duration == record.sleep_stage_duration: