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KI-KNN

+import librosa.feature
+import pandas as pd
+import numpy as np
+from pathlib import Path
+from os import chdir
+import os
+import csv
+from tensorflow import keras
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder, StandardScaler
+from keras import models
+from keras import layers
+from keras import optimizers
+import matplotlib.pyplot as plt
+from sklearn.metrics import classification_report
+import constants
+
+
+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
+
+
+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
+
+
+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
+    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"))
+    model.add(MaxPooling2D(pool_size=(2,2)))
+    model.add(Conv2D(64, kernel_size=(3, 3), activation="relu"))
+    model.add(MaxPooling2D(pool_size=(2,2)))
+    model.add(Dropout(0.5))
+    model.add(Flatten())
+    model.add(Dense(128, activation="relu"))
+    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])
+    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
+    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'])
+    plt.plot(history.history['val_accuracy'])
+    plt.title('Model Accuracy')
+    plt.ylabel('Accuracy')
+    plt.xlabel('Epoch')
+    plt.legend(['Train', 'Test'], loc='upper left')
+    plt.show()
+
+
+def plot_model_loss(history):
+    # Plot graph Model Loss
+    plt.plot(history.history['loss'])
+    plt.plot(history.history['val_loss'])
+    plt.title('Model loss')
+    plt.ylabel('Loss')
+    plt.xlabel('Epoch')
+    plt.legend(['Train', 'Test'], loc='upper right')
+    plt.show()
+
+
+# Changing Directory to Training Dataset Folder
+chdir(constants.TRAINING_DATA_DIRECTORY_NAME)
+trainingDataDir = Path.cwd()
+trainingDataSubDirs = os.listdir(trainingDataDir)
+chdir("..")
+if os.path.isfile(constants.FEATURES_CSV_NAME):
+    print("features.csv already exists, skip extraction")
+else:
+    extract_features(trainingDataDir, trainingDataSubDirs)
+#extract_features(trainingDataDir, trainingDataSubDirs)
+
+
+
+data = preprocessing_csv_data()
+target_labels, encoder = encode_labels(data)
+X = normalize_data(data)
+X_train, X_test, y_train, y_test = train_test_data_split(X, target_labels)
+model = create_and_compile_model()
+history = train_and_save_model(model, X_train, y_train, X_test, y_test)
+predict(X_test, y_test)
+plot_model_accuracy(history)
+plot_model_loss(history)
+