Delete KI-KNN

KI-KNN

-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
-from pandas import datetime
-
-# splitting of dataset into train and test dataset
-
-def train_test_data_split(X, y):
-    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")
-    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_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"))
-    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 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
-
-# Extracting classes/label column as y from csv and converting string labels to numbers using LabelEncoder 
-    def encode_labels(data):
-    list = data.iloc[:, -1]
-    encoder = LabelEncoder()
-    target_labels = encoder.fit_transform(list)
-    return target_labels, encoder
-
-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)
-
-
-
-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)
-
-def predict(X_test, y_test):
-    print("Predictions.....")
-    predictions = np.argmax(model.predict(X_test), axis=-1)
-    target_names = [constants.0, constants.1]
-    print(classification_report(y_test, predictions, target_names=target_names))
-
-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_, constants.MAX_LEN, constants.CHANNELS)
-    return X
-
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