Update KI-KNN

KI-KNN

-import librosa.feature
 import pandas as pd
 import numpy as np
 from pathlib import Path
@@ -14,114 +13,26 @@ from keras import optimizers
 import matplotlib.pyplot as plt
 from sklearn.metrics import classification_report
 import constants
+from pandas import datetime
 
-
-max_len = 216
-
-
-def create_csv_header():
-    header = 'filename '
-    for i in range(constants.MFCC_FEATURE_START, ((constants.MFCC_FEATURE_END - 1) * max_len) + 1):
-        header += f' mfcc{i}'
-    header += ' label'
-    header = header.split()
-    file = open(constants.FEATURES_CSV_NAME, 'w', newline='')
-    with file:
-        writer = csv.writer(file)
-        writer.writerow(header)
-
-
-def extract_features(trainingDataDir, trainingDataSubDirs):
-    create_csv_header()
-    # Looping over every file inside the subdirectories for feature extraction
-    for trainingDataSubDir in trainingDataSubDirs:
-        for fileName in os.listdir(trainingDataDir/f'{trainingDataSubDir}'):
-            if fileName.endswith(".wav"):
-                audioFile = trainingDataDir/f'{trainingDataSubDir}/{fileName}'
-                print("Extracting Features from Directory "+trainingDataSubDir+" and file "+audioFile.name)
-                y, sr = librosa.load(audioFile, mono=True)
-                mfcc = librosa.feature.mfcc(y=y, sr=44100, n_mfcc=constants.N_MFCC)
-                to_append = f'{audioFile.name}'
-
-                print ("mfcc.shape before cutting/padding: ", mfcc.shape)
-                #the mfcc extraction results in slighty different time frames per mfcc segment
-                #in the case of 5seconds and 20 mfcc segments there are som time informations between 215 and 217
-                #in order to unify the size the ones that stores under 216 timeinformation there will be some padding
-                #for the ones that stores over 216 time information there will be a trim operation
-                #IMPORTANT:
-                #if the time of the audiofiles or the mfcc extraction number changes (current value is 20)
-                #there needs to be an adjustment to the MAX_LEN parameter
-                #1. check the new size of all the files
-                #2. find a suitable average for padding or cutting
-                #3. adjust the MAX_LEN variable in the constants.py file
-                if (constants.MFCC_MAX_LEN > mfcc.shape[1]):
-                    pad_width = max_len - mfcc.shape[1]
-                    mfcc = np.pad(mfcc, pad_width=((0, 0), (0, pad_width)), mode='constant')
-                elif constants.MFCC_MAX_LEN < mfcc.shape[1]:
-                    mfcc = mfcc[:, :constants.MFCC_MAX_LEN]
-                print ("mfcc.shape after cutting/padding: ", mfcc.shape)
-                #store every single value in the csv file
-                mfcc = np.reshape(mfcc, constants.MFCC_MAX_LEN * constants.N_MFCC)
-                #print ("new total size: ", mfcc.shape)
-                for g in mfcc:
-                    to_append += f' {g}'
-                if trainingDataSubDir == constants.CAR:
-                    to_append += f' {constants.LIGHT_WEIGHT}'
-                elif trainingDataSubDir == constants.BUS:
-                    to_append += f' {constants.MEDIUM_WEIGHT}'
-                elif trainingDataSubDir == constants.TRUCK:
-                    to_append += f' {constants.HEAVY_WEIGHT}'
-                elif trainingDataSubDir == constants.MOTORCYCLE:
-                    to_append += f' {constants.TWO_WHEELED}'
-                elif trainingDataSubDir == constants.TRAM:
-                    to_append += f' {constants.RAIL_BOUND}'
-
-                file = open(constants.FEATURES_CSV_NAME, 'a', newline='')
-                with file:
-                    writer = csv.writer(file)
-                    writer.writerow(to_append.split())
-
-def preprocessing_csv_data():
-    print("Reading Features... ")
-    data = pd.read_csv(constants.FEATURES_CSV_NAME)
-    data.head()
-    # Dropping unnecessary columns (Column Filename is dropped)
-    data = data.drop(['filename'], axis=1)
-    data.head()
-    return data
-
-
-def encode_labels(data):
-    # Extracting classes/label column as y from csv and converting string labels to numbers using LabelEncoder
-    audio_list = data.iloc[:, -1]
-    encoder = LabelEncoder()
-    target_labels = encoder.fit_transform(audio_list)
-    return target_labels, encoder
-
-
-def normalize_data(data):
-    # normalizing - Extracting Remaining Columns as X and normalizing them to a common scale
-    scaler = StandardScaler()
-    print (data.iloc[:, :-1])
-    X = scaler.fit_transform(np.array(data.iloc[:, :-1], dtype=float))
-    X = X.reshape(-1, constants.N_MFCC, constants.MFCC_MAX_LEN, constants.CHANNELS)
-    return X
-
+# splitting of dataset into train and test dataset
 
 def train_test_data_split(X, y):
-    # splitting of dataset into train and test dataset
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)
     print (X_train[0].shape)
     return X_train, X_test, y_train, y_test
 
+# creating a model
 
 def create_and_compile_model():
  print("Creating a Model")
-    # creating a model
  from keras.models import Sequential
-    from keras.layers import Conv2D, Dense, MaxPooling2D, Dropout, Flatten, BatchNormalization
+ from keras.layers import Conv2D, Dense, MaxPooling2D, Dropout, Flatten,
+BatchNormalization
  model = models.Sequential()
-    model.add(Conv2D(32, kernel_size=(3, 3), activation="relu", input_shape=(constants.N_MFCC, constants.MFCC_MAX_LEN, constants.CHANNELS)))
+ model.add(Conv2D(32, kernel_size=(3, 3), activation="relu",
+input_shape=(constants.N_FEATURE, constants.FEATURE_MAX_LEN,
+constants.CHANNELS)))
  model.add(Conv2D(32, kernel_size=(3, 3), activation="relu"))
  model.add(MaxPooling2D(pool_size=(2,2)))
  model.add(Conv2D(64, kernel_size=(3, 3), activation="relu"))
@@ -132,28 +43,26 @@ def create_and_compile_model():
  model.add(Dense(constants.OUTPUT_LAYER_DIMENSIONS, activation='softmax'))
  print("Compiling a Model")
  optimizer = keras.optimizers.RMSprop()
-    model.compile(optimizer=optimizer, loss=constants.LOSS_FUNCTION_SPARSE, metrics=[constants.ACCURACY_METRICS])
+ model.compile(optimizer=optimizer, loss=constants.LOSS_FUNCTION_SPARSE,
+metrics=[constants.ACCURACY_METRICS])
  print(model.summary())
  return model
 
-
 def train_and_save_model(model, X_train, y_train, X_test, y_test):
     logdir = constants.LOG_DIR_PATH
     tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)
     print("Start Training...")
     history = model.fit(X_train, y_train, batch_size=32, epochs=35, validation_data=(X_test, y_test), callbacks=[tensorboard_callback])
-    # Saving the trained model to avoid re-training
+# Saving the trained model to avoid re-training
     model.save(constants.TRAINED_MODEL)
     return history
 
-
 def predict(X_test, y_test):
     print("Predictions.....")
     predictions = np.argmax(model.predict(X_test), axis=-1)
     target_names = [constants.LIGHT_WEIGHT, constants.MEDIUM_WEIGHT, constants.HEAVY_WEIGHT,constants.TWO_WHEELED, constants.RAIL_BOUND]
     print(classification_report(y_test, predictions, target_names=target_names))
 
-
 def plot_model_accuracy(history):
     # Plot graph Model Accuracy
     plt.plot(history.history['accuracy'])
@@ -164,7 +73,6 @@ def plot_model_accuracy(history):
     plt.legend(['Train', 'Test'], loc='upper left')
     plt.show()
 
-
 def plot_model_loss(history):
     # Plot graph Model Loss
     plt.plot(history.history['loss'])
@@ -175,7 +83,6 @@ def plot_model_loss(history):
     plt.legend(['Train', 'Test'], loc='upper right')
     plt.show()
 
-
 # Changing Directory to Training Dataset Folder
 chdir(constants.TRAINING_DATA_DIRECTORY_NAME)
 trainingDataDir = Path.cwd()