import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser()
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument('-w', '--weights', help='Input file to load h5 model trained weights.')
group.add_argument('-f', '--file', help='Input file to save trained model weights.')
parser.add_argument('-p', '--plot', action='store_true', help='Plot the latent space in a 2D scatter (if the latent space dimesion is greater than 2, PCA will be applied).')
args = parser.parse_args()
x, y_true = kdd.get_dataset()
x = MinMaxScaler().fit_transform(x)
x_train, x_test = train_test_split(x, test_size=0.2)
original_dim = x_train.shape[1]
batch_size = 192
epochs = 100
vae = Vae([original_dim, 96, 64, 32, 16])
if args.weights:
vae.model.load_weights(args.weights)
else:
vae_history = vae.model.fit(
import numpy as np
from utils import result_info
if __name__ == '__main__':
parser = argparse.ArgumentParser()
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument('-l', '--load', help='File to load SVC trained model.')
group.add_argument('-s', '--save', help='File to save SVC trained model.')
parser.add_argument('-e', '--encode', help='Encode the training data with a Variational Autoencoder.', action='store_true')
args = parser.parse_args()
x, y = kdd.get_dataset()
y = y.cat.add_categories(['anormal'])
y[y != 'normal'] = 'anormal'
y = y.cat.remove_unused_categories()
x = MinMaxScaler().fit_transform(x)
if args.encode:
vae = Vae([x.shape[1], 96, 64, 32, 16])
vae.model.load_weights('models/vae_full.h5')
x, _, _ = vae.encoder.predict(x)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.4)
'--weights',
help='Input file to load h5 model trained weights.')
group.add_argument('-f',
'--file',
help='Input file to save trained model weights.')
parser.add_argument(
'-p',
'--plot',
action='store_true',
help=
'Plot the latent space in a 2D scatter (if the latent space dimesion is greater than 2, PCA will be applied).'
)
args = parser.parse_args()
x, _ = kdd.get_dataset(mode='normal')
x = MinMaxScaler().fit_transform(x)
x_train, x_test = train_test_split(x, test_size=0.2)
original_dim = x_train.shape[1]
batch_size = 48
epochs = 100
vae = Vae([original_dim, 64, 32, 16, 8])
if args.file:
vae_history = vae.model.fit(x_train,
x_train,