Initial commit

analysis.py

@@ -0,0 +1,95 @@
+from pathlib import Path
+import pandas as pd
+import numpy as np
+from copy import copy
+from sklearn.model_selection import cross_val_score
+from sklearn import svm
+from sklearn.neural_network import MLPClassifier
+from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+
+def load_measurements(csv_file: Path):
+  def cleanup_column_name(col_name: str):
+    clean_name = col_name.replace('#', '').strip()
+    if clean_name == 'room':
+      return 'tracker'
+    return clean_name
+
+  df = pd.read_csv(str(csv_file))
+
+  # String cleanup in column names and room names
+  df = df.rename(columns=cleanup_column_name)
+  df.applymap(lambda x: x.strip() if isinstance(x, str) else x)
+
+  df['tracker'] = df['tracker'].astype("category")
+  df['real_room'] = df['real_room'].astype("category")
+
+  return df
+
+
+FAR_AWAY_FEATURE_VALUE = 1
+def get_feature_value(rssi, tx_power):
+  MIN_RSSI = -90
+  MAX_TRANSFORMED_RSSI = 40
+  v = tx_power - rssi - MAX_TRANSFORMED_RSSI
+  if v < 0:
+    v = 0
+  return v / (-MIN_RSSI)
+
+
+def make_training_data(df: pd.DataFrame, device_to_map):
+  idx_to_tracker = dict(enumerate(df['tracker'].cat.categories ))
+  tracker_to_idx = {v: k for k, v in idx_to_tracker.items()}
+  idx_to_room = dict(enumerate(df['real_room'].cat.categories ))
+  room_to_idx = {v: k for k, v in idx_to_room.items()}
+
+  last_real_room = None
+  start_time = None
+  current_feature = [FAR_AWAY_FEATURE_VALUE] * len(idx_to_tracker)
+  
+  features = []
+  labels = []
+
+  # Feature vectors - rssi column for each room
+  for i, row in df.iterrows():
+    time, device, tracker, rssi, tx_power, real_room = row
+    if device != device_to_map:
+      continue
+    if last_real_room != real_room:
+      start_time = time
+      last_real_room = real_room
+
+    tracker_idx = tracker_to_idx[tracker]
+    current_feature[tracker_idx] = get_feature_value(rssi, tx_power)
+    if time - start_time > 20:
+      features.append(copy(current_feature))
+      labels.append(room_to_idx[real_room])
+
+  return np.array(features), np.array(labels)
+
+def train(features, labels, classes):
+  clf = svm.SVC(kernel='rbf')
+  print("Training")
+  scores = cross_val_score(clf, features, labels, cv=5)
+  print(scores)
+  print("%0.2f accuracy with a standard deviation of %0.2f" % (scores.mean(), scores.std()))
+
+  X_train, X_test, y_train, y_test = train_test_split(features, labels, random_state=0)  
+  clf.fit(X_train, y_train)
+  cm = confusion_matrix(clf.predict(X_test), y_test)
+  print(cm)
+  print(classes)
+  disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=classes)
+  disp.plot()
+  plt.show()
+
+
+
+if __name__ == "__main__":
+  csv_path = Path("/home/martin/code/ansible/roles/bluetooth-monitor/other/collected.csv")
+  df = load_measurements(csv_path)
+  features, labels = make_training_data(df, "martins_apple_watch")
+  print(np.unique(labels))
+  print(features.shape, labels.shape)
+  train(features, labels, list(df['real_room'].dtype.categories))