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))