hotfix to handle_nan reshape

added to do for pure BSOID clf
added conversion for BSOID clf

Author: JensBlack

convert_classifier.py

@@ -1,29 +1,52 @@
 import pickle
+import joblib
 import os
 from pure_sklearn.map import convert_estimator
 
 
-def load_classifier(path_to_sav):
+def load_classifier_SIMBA(path_to_sav):
     """Load saved classifier"""
     file = open(path_to_sav, "rb")
     classifier = pickle.load(file)
     file.close()
     return classifier
 
+def load_classifier_BSOID(path_to_sav):
+    """Load saved classifier"""
+    file = open(path_to_sav, "rb")
+    clf = joblib.load(file)
+    file.close()
+    return clf
 
-def convert_classifier(path):
+
+def convert_classifier(path, origin: str):
     # convert to pure python estimator
-    print("Loading classifier...")
-    clf = load_classifier(path)
     dir_path = os.path.dirname(path)
     filename = os.path.basename(path)
     filename, _ = filename.split(".")
-    clf_pure_predict = convert_estimator(clf)
-    with open(dir_path + "/" + filename + "_pure.sav", "wb") as f:
-        pickle.dump(clf_pure_predict, f)
+
+    print("Loading classifier...")
+    if origin.lower() == 'simba':
+        clf = load_classifier_SIMBA(path)
+        clf_pure_predict = convert_estimator(clf)
+        with open(dir_path + "/" + filename + "_pure.sav", "wb") as f:
+            pickle.dump(clf_pure_predict, f)
+
+    elif origin.lower() == 'bsoid':
+        clf_pack = load_classifier_BSOID(path)
+        # bsoid exported classfier has format [a, b, c, clf, d, e]
+        clf_pure_predict = convert_estimator(clf_pack[3])
+        clf_pack[3] =clf_pure_predict
+        with open(dir_path + "/" + filename + "_pure.sav", "wb") as f:
+            joblib.dump(clf_pack, f)
+    else:
+        raise ValueError(f'{origin} is not a valid classifier origin.')
+
     print(f"Converted Classifier {filename}")
 
 
 if __name__ == "__main__":
+
+    """Converted BSOID Classifiers are not integrated yet, although you can already convert them here"""
     path_to_classifier = "PATH_TO_CLASSIFIER"
-    convert_classifier(path_to_classifier)
+    convert_classifier(path_to_classifier, origin= 'SIMBA')

experiments/custom/classifier.py

@@ -117,6 +117,7 @@ def classify(self, features):
         Adapted from BSOID; https://github.com/YttriLab/B-SOID
         """
         labels_fslow = []
+        # TODO: adapt to pure version of BSOID Classifier
         for i in range(0, len(features)):
             labels = self._classifier.predict(features[i].T)
             labels_fslow.append(labels)
@@ -191,13 +192,18 @@ def bsoid_feat_classifier_pool_run(input_q: mp.Queue, output_q: mp.Queue):
         if input_q.full():
             skel_time_window, feature_id = input_q.get()
         if skel_time_window is not None:
-            start_time = time.time()
+            start_time_feat = time.time()
             features = feature_extractor.extract_features(skel_time_window)
+            end_time_feat = time.time()
+            start_time_clf = time.time()
             last_prob = classifier.classify(features)
             output_q.put((last_prob, feature_id))
             end_time = time.time()
-            # print("Classification time: {:.2f} msec".format((end_time-start_time)*1000))
-            # print("Feature ID: "+ feature_id)
+            print("Feature Extraction time: {:.2f} msec".format((end_time_feat - start_time_feat) * 1000))
+            print("Classification time: {:.2f} msec".format((end_time-start_time_clf)*1000))
+            print("Total time: {:.2f} msec".format((end_time-start_time_feat)*1000))
+            print("Current motif: ", *last_prob)
+            print("Feature ID: "+ str(feature_id))
         else:
             pass
 

experiments/custom/featureextraction.py

@@ -1281,7 +1281,7 @@ def handle_nan(self, pose_window):
                     # no NaN? then update the last valid pose for this body part
                     self._last_valid_pose[bp_num] = bp
         #reshape to match requirements of bsoid feature extraction: time_window , bodyparts*2
-        pro_window = np.reshape(pro_window, (pro_window.shape[1], pro_window.shape[2]*pro_window.shape[3]))# converted to np.array
+        pro_window = np.reshape(pro_window, (pro_window.shape[0], pro_window.shape[1]*pro_window.shape[2]))# converted to np.array
 
         return pro_window