forked from leibinghe/GAAL-based-outlier-detection
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MO-GAAL.py
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MO-GAAL.py
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from keras.layers import Input, Dense
from keras.models import Sequential, Model
from keras.optimizers import SGD
import numpy as np
import pandas as pd
from collections import defaultdict
import matplotlib.pyplot as plt
import keras
import math
import argparse
def parse_args():
parser = argparse.ArgumentParser(description="Run MO-GAAL.")
parser.add_argument('--path', nargs='?', default='Data/Annthyroid',
help='Input data path.')
parser.add_argument('--k', type=int, default=10,
help='Number of sub_generator.')
parser.add_argument('--stop_epochs', type=int, default=25,
help='Stop training generator after stop_epochs.')
parser.add_argument('--lr_d', type=float, default=0.01,
help='Learning rate of discriminator.')
parser.add_argument('--lr_g', type=float, default=0.0001,
help='Learning rate of generator.')
parser.add_argument('--decay', type=float, default=1e-6,
help='Decay.')
parser.add_argument('--momentum', type=float, default=0.9,
help='Momentum.')
return parser.parse_args()
# Generator
def create_generator(latent_size):
gen = Sequential()
gen.add(Dense(latent_size, input_dim=latent_size, activation='relu', kernel_initializer=keras.initializers.Identity(gain=1.0)))
gen.add(Dense(latent_size, activation='relu', kernel_initializer=keras.initializers.Identity(gain=1.0)))
latent = Input(shape=(latent_size,))
fake_data = gen(latent)
return Model(latent, fake_data)
# Discriminator
def create_discriminator():
dis = Sequential()
dis.add(Dense(math.ceil(math.sqrt(data_size)), input_dim=latent_size, activation='relu', kernel_initializer= keras.initializers.VarianceScaling(scale=1.0, mode='fan_in', distribution='normal', seed=None)))
dis.add(Dense(1, activation='sigmoid', kernel_initializer=keras.initializers.VarianceScaling(scale=1.0, mode='fan_in', distribution='normal', seed=None)))
data = Input(shape=(latent_size,))
fake = dis(data)
return Model(data, fake)
# Load data
def load_data():
data = pd.read_table('{path}'.format(path = args.path), sep=',', header=None)
data = data.sample(frac=1).reset_index(drop=True)
id = data.pop(0)
y = data.pop(1)
data_x = data.as_matrix()
data_id = id.values
data_y = y.values
return data_x, data_y, data_id
# Plot loss history
def plot(train_history, name):
dy = train_history['discriminator_loss']
gy = train_history['generator_loss']
auc_y = train_history['auc']
for i in range(k):
names['gy_' + str(i)] = train_history['sub_generator{}_loss'.format(i)]
x = np.linspace(1, len(dy), len(dy))
fig, ax = plt.subplots()
ax.plot(x, dy, color='blue')
ax.plot(x, gy,color='red')
ax.plot(x, auc_y, color='yellow', linewidth = '3')
for i in range(k):
ax.plot(x, names['gy_' + str(i)], color='green', linewidth='0.5')
plt.show()
if __name__ == '__main__':
train = True
# initilize arguments
args = parse_args()
# initialize dataset
data_x, data_y, data_id = load_data()
data_size = data_x.shape[0]
latent_size = data_x.shape[1]
print("The dimension of the training data :{}*{}".format(data_size, latent_size))
if train:
train_history = defaultdict(list)
names = locals()
epochs = args.stop_epochs * 3
stop = 0
k = args.k
# Create discriminator
discriminator = create_discriminator()
discriminator.compile(optimizer=SGD(lr=args.lr_d, decay=args.decay, momentum=args.momentum), loss='binary_crossentropy')
# Create k combine models
for i in range(k):
names['sub_generator' + str(i)] = create_generator(latent_size)
latent = Input(shape=(latent_size,))
names['fake' + str(i)] = names['sub_generator' + str(i)](latent)
discriminator.trainable = False
names['fake' + str(i)] = discriminator(names['fake' + str(i)])
names['combine_model' + str(i)] = Model(latent, names['fake' + str(i)])
names['combine_model' + str(i)].compile(optimizer=SGD(lr=args.lr_g, decay=args.decay, momentum=args.momentum), loss='binary_crossentropy')
# Start iteration
for epoch in range(epochs):
print('Epoch {} of {}'.format(epoch + 1, epochs))
batch_size = min(500, data_size)
num_batches = int(data_size / batch_size)
for index in range(num_batches):
print('\nTesting for epoch {} index {}:'.format(epoch + 1, index + 1))
# Generate noise
noise_size = batch_size
noise = np.random.uniform(0, 1, (int(noise_size), latent_size))
# Get training data
data_batch = data_x[index * batch_size: (index + 1) * batch_size]
# Generate potential outliers
block = ((1 + k) * k) // 2
for i in range(k):
if i != (k-1):
noise_start = int((((k + (k - i + 1)) * i) / 2) * (noise_size // block))
noise_end = int((((k + (k - i)) * (i + 1)) / 2) * (noise_size // block))
names['noise' + str(i)] = noise[noise_start : noise_end ]
names['generated_data' + str(i)] = names['sub_generator' + str(i)].predict(names['noise' + str(i)], verbose=0)
else:
noise_start = int((((k + (k - i + 1)) * i) / 2) * (noise_size // block))
names['noise' + str(i)] = noise[noise_start : noise_size]
names['generated_data' + str(i)] = names['sub_generator' + str(i)].predict(names['noise' + str(i)], verbose=0)
# Concatenate real data to generated data
for i in range(k):
if i == 0:
X = np.concatenate((data_batch, names['generated_data' + str(i)]))
else:
X = np.concatenate((X, names['generated_data' + str(i)]))
Y = np.array([1] * batch_size + [0] * int(noise_size))
# Train discriminator
discriminator_loss = discriminator.train_on_batch(X, Y)
train_history['discriminator_loss'].append(discriminator_loss)
# Get the target value of sub-generator
p_value = discriminator.predict(data_x)
p_value = pd.DataFrame(p_value)
for i in range(k):
names['T' + str(i)] = p_value.quantile(i/k)
names['trick' + str(i)] = np.array([float(names['T' + str(i)])] * noise_size)
# Train generator
noise = np.random.uniform(0, 1, (int(noise_size), latent_size))
if stop == 0:
for i in range(k):
names['sub_generator' + str(i) + '_loss'] = names['combine_model' + str(i)].train_on_batch(noise, names['trick' + str(i)])
train_history['sub_generator{}_loss'.format(i)].append(names['sub_generator' + str(i) + '_loss'])
else:
for i in range(k):
names['sub_generator' + str(i) + '_loss'] = names['combine_model' + str(i)].evaluate(noise, names['trick' + str(i)])
train_history['sub_generator{}_loss'.format(i)].append(names['sub_generator' + str(i) + '_loss'])
generator_loss = 0
for i in range(k):
generator_loss = generator_loss + names['sub_generator' + str(i) + '_loss']
generator_loss = generator_loss / k
train_history['generator_loss'].append(generator_loss)
# Stop training generator
if epoch +1 > args.stop_epochs:
stop = 1
# Detection result
data_y = pd.DataFrame(data_y)
result = np.concatenate((p_value,data_y), axis=1)
result = pd.DataFrame(result, columns=['p', 'y'])
result = result.sort_values('p', ascending=True)
# Calculate the AUC
inlier_parray = result.loc[lambda df: df.y == "nor", 'p'].values
outlier_parray = result.loc[lambda df: df.y == "out", 'p'].values
sum = 0.0
for o in outlier_parray:
for i in inlier_parray:
if o < i:
sum += 1.0
elif o == i:
sum += 0.5
else:
sum += 0
AUC = '{:.4f}'.format(sum / (len(inlier_parray) * len(outlier_parray)))
print('AUC:{}'.format(AUC))
for i in range(num_batches):
train_history['auc'].append(AUC)
plot(train_history, 'loss')