pyod/models/alad.py
# -*- coding: utf-8 -*-
"""Using Adversarially Learned Anomaly Detection
"""
# Author: Michiel Bongaerts (but not author of the ALAD method)
from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.utils import check_array
from sklearn.utils.validation import check_is_fitted
from .base import BaseDetector
from .base_dl import _get_tensorflow_version
from ..utils.utility import check_parameter
# if tensorflow 2, import from tf directly
if _get_tensorflow_version() < 200:
raise NotImplementedError('Model not implemented for Tensorflow version 1')
elif 200 <= _get_tensorflow_version() <= 209:
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, Dropout
from tensorflow.keras.optimizers import Adam
else:
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, Dropout
from tensorflow.keras.optimizers.legacy import Adam
class ALAD(BaseDetector):
"""Adversarially Learned Anomaly Detection (ALAD).
Paper: https://arxiv.org/pdf/1812.02288.pdf
See :cite:`zenati2018adversarially` for details.
Parameters
----------
output_activation : str, optional (default=None)
Activation function to use for output layers for encoder and dector.
See https://keras.io/activations/
activation_hidden_disc : str, optional (default='tanh')
Activation function to use for hidden layers in discrimators.
See https://keras.io/activations/
activation_hidden_gen : str, optional (default='tanh')
Activation function to use for hidden layers in encoder and decoder
(i.e. generator).
See https://keras.io/activations/
epochs : int, optional (default=500)
Number of epochs to train the model.
batch_size : int, optional (default=32)
Number of samples per gradient update.
dropout_rate : float in (0., 1), optional (default=0.2)
The dropout to be used across all layers.
dec_layers : list, optional (default=[5,10,25])
List that indicates the number of nodes per hidden layer for the d
ecoder network.
Thus, [10,10] indicates 2 hidden layers having each 10 nodes.
enc_layers : list, optional (default=[25,10,5])
List that indicates the number of nodes per hidden layer for the
encoder network.
Thus, [10,10] indicates 2 hidden layers having each 10 nodes.
disc_xx_layers : list, optional (default=[25,10,5])
List that indicates the number of nodes per hidden layer for
discriminator_xx.
Thus, [10,10] indicates 2 hidden layers having each 10 nodes.
disc_zz_layers : list, optional (default=[25,10,5])
List that indicates the number of nodes per hidden layer for
discriminator_zz.
Thus, [10,10] indicates 2 hidden layers having each 10 nodes.
disc_xz_layers : list, optional (default=[25,10,5])
List that indicates the number of nodes per hidden layer for
discriminator_xz.
Thus, [10,10] indicates 2 hidden layers having each 10 nodes.
learning_rate_gen: float in (0., 1), optional (default=0.001)
learning rate of training the encoder and decoder
learning_rate_disc: float in (0., 1), optional (default=0.001)
learning rate of training the discriminators
add_recon_loss: bool optional (default=False)
add an extra loss for encoder and decoder based on the reconstruction
error
lambda_recon_loss: float in (0., 1), optional (default=0.1)
if ``add_recon_loss= True``, the reconstruction loss gets multiplied
by ``lambda_recon_loss`` and added to the total loss for the generator
(i.e. encoder and decoder).
preprocessing : bool, optional (default=True)
If True, apply standardization on the data.
verbose : int, optional (default=1)
Verbosity mode.
- 0 = silent
- 1 = progress bar
contamination : float in (0., 0.5), optional (default=0.1)
The amount of contamination of the data set, i.e.
the proportion of outliers in the data set. When fitting this is used
to define the threshold on the decision function.
Attributes
----------
decision_scores_ : numpy array of shape (n_samples,)
The outlier scores of the training data [0,1].
The higher, the more abnormal. Outliers tend to have higher
scores. This value is available once the detector is
fitted.
threshold_ : float
The threshold is based on ``contamination``. It is the
``n_samples * contamination`` most abnormal samples in
``decision_scores_``. The threshold is calculated for generating
binary outlier labels.
labels_ : int, either 0 or 1
The binary labels of the training data. 0 stands for inliers
and 1 for outliers/anomalies. It is generated by applying
``threshold_`` on ``decision_scores_``.
"""
def __init__(self, activation_hidden_gen='tanh',
activation_hidden_disc='tanh',
output_activation=None,
dropout_rate=0.2,
latent_dim=2,
dec_layers=[5, 10, 25],
enc_layers=[25, 10, 5],
disc_xx_layers=[25, 10, 5],
disc_zz_layers=[25, 10, 5],
disc_xz_layers=[25, 10, 5],
learning_rate_gen=0.0001, learning_rate_disc=0.0001,
add_recon_loss=False, lambda_recon_loss=0.1,
epochs=200,
verbose=0,
preprocessing=False,
add_disc_zz_loss=True, spectral_normalization=False,
batch_size=32, contamination=0.1):
super(ALAD, self).__init__(contamination=contamination)
self.activation_hidden_disc = activation_hidden_disc
self.activation_hidden_gen = activation_hidden_gen
self.dropout_rate = dropout_rate
self.latent_dim = latent_dim
self.dec_layers = dec_layers
self.enc_layers = enc_layers
self.disc_xx_layers = disc_xx_layers
self.disc_zz_layers = disc_zz_layers
self.disc_xz_layers = disc_xz_layers
self.add_recon_loss = add_recon_loss
self.lambda_recon_loss = lambda_recon_loss
self.add_disc_zz_loss = add_disc_zz_loss
self.output_activation = output_activation
self.contamination = contamination
self.epochs = epochs
self.learning_rate_gen = learning_rate_gen
self.learning_rate_disc = learning_rate_disc
self.preprocessing = preprocessing
self.batch_size = batch_size
self.verbose = verbose
self.spectral_normalization = spectral_normalization
if (self.spectral_normalization == True):
try:
global tfa
import tensorflow_addons as tfa
except ModuleNotFoundError:
# Error handling
print(
'tensorflow_addons not found, cannot use spectral normalization. Install tensorflow_addons first.')
self.spectral_normalization = False
check_parameter(dropout_rate, 0, 1, param_name='dropout_rate',
include_left=True)
def _build_model(self):
#### Decoder #####
dec_in = Input(shape=(self.latent_dim,), name='I1')
dec_1 = Dropout(self.dropout_rate)(dec_in)
last_layer = dec_1
# Store all hidden layers in dict
dec_hl_dict = {}
for i, l_dim in enumerate(self.dec_layers):
layer_name = 'hl_{}'.format(i)
dec_hl_dict[layer_name] = Dropout(self.dropout_rate)(
Dense(l_dim, activation=self.activation_hidden_gen)(
last_layer))
last_layer = dec_hl_dict[layer_name]
dec_out = Dense(self.n_features_, activation=self.output_activation)(
last_layer)
self.dec = Model(inputs=(dec_in), outputs=[dec_out])
self.hist_loss_dec = []
#### Encoder #####
enc_in = Input(shape=(self.n_features_,), name='I1')
enc_1 = Dropout(self.dropout_rate)(enc_in)
last_layer = enc_1
# Store all hidden layers in dict
enc_hl_dict = {}
for i, l_dim in enumerate(self.enc_layers):
layer_name = 'hl_{}'.format(i)
enc_hl_dict[layer_name] = Dropout(self.dropout_rate)(
Dense(l_dim, activation=self.activation_hidden_gen)(
last_layer))
last_layer = enc_hl_dict[layer_name]
enc_out = Dense(self.latent_dim, activation=self.output_activation)(
last_layer)
self.enc = Model(inputs=(enc_in), outputs=[enc_out])
self.hist_loss_enc = []
#### Discriminator_xz #####
disc_xz_in_x = Input(shape=(self.n_features_,), name='I1')
disc_xz_in_z = Input(shape=(self.latent_dim,), name='I2')
disc_xz_in = tf.concat([disc_xz_in_x, disc_xz_in_z], axis=1)
disc_xz_1 = Dropout(self.dropout_rate)(disc_xz_in)
last_layer = disc_xz_1
# Store all hidden layers in dict
disc_xz_hl_dict = {}
for i, l_dim in enumerate(self.disc_xz_layers):
layer_name = 'hl_{}'.format(i)
if (self.spectral_normalization == True):
disc_xz_hl_dict[layer_name] = Dropout(self.dropout_rate)(
tfa.layers.SpectralNormalization(
Dense(l_dim, activation=self.activation_hidden_disc))(
last_layer))
else:
disc_xz_hl_dict[layer_name] = Dropout(self.dropout_rate)(
Dense(l_dim, activation=self.activation_hidden_disc)(
last_layer))
last_layer = disc_xz_hl_dict[layer_name]
disc_xz_out = Dense(1, activation='sigmoid')(last_layer)
self.disc_xz = Model(inputs=(disc_xz_in_x, disc_xz_in_z),
outputs=[disc_xz_out])
# self.hist_loss_disc_xz = []
#### Discriminator_xx #####
disc_xx_in_x = Input(shape=(self.n_features_,), name='I1')
disc_xx_in_x_hat = Input(shape=(self.n_features_,), name='I2')
disc_xx_in = tf.concat([disc_xx_in_x, disc_xx_in_x_hat], axis=1)
disc_xx_1 = Dropout(self.dropout_rate,
input_shape=(self.n_features_,))(disc_xx_in)
last_layer = disc_xx_1
# Store all hidden layers in dict
disc_xx_hl_dict = {}
for i, l_dim in enumerate(self.disc_xx_layers):
layer_name = 'hl_{}'.format(i)
if (self.spectral_normalization == True):
disc_xx_hl_dict[layer_name] = Dropout(self.dropout_rate)(
tfa.layers.SpectralNormalization(
Dense(l_dim, activation=self.activation_hidden_disc))(
last_layer))
else:
disc_xx_hl_dict[layer_name] = Dropout(self.dropout_rate)(
Dense(l_dim, activation=self.activation_hidden_disc)(
last_layer))
last_layer = disc_xx_hl_dict[layer_name]
disc_xx_out = Dense(1, activation='sigmoid')(last_layer)
self.disc_xx = Model(inputs=(disc_xx_in_x, disc_xx_in_x_hat),
outputs=[disc_xx_out, last_layer])
# self.hist_loss_disc_xx = []
#### Discriminator_zz #####
disc_zz_in_z = Input(shape=(self.latent_dim,), name='I1')
disc_zz_in_z_prime = Input(shape=(self.latent_dim,), name='I2')
disc_zz_in = tf.concat([disc_zz_in_z, disc_zz_in_z_prime], axis=1)
disc_zz_1 = Dropout(self.dropout_rate,
input_shape=(self.n_features_,))(disc_zz_in)
last_layer = disc_zz_1
# Store all hidden layers in dict
disc_zz_hl_dict = {}
for i, l_dim in enumerate(self.disc_zz_layers):
layer_name = 'hl_{}'.format(i)
if (self.spectral_normalization == True):
disc_zz_hl_dict[layer_name] = Dropout(self.dropout_rate)(
tfa.layers.SpectralNormalization(
Dense(l_dim, activation=self.activation_hidden_disc))(
last_layer))
else:
disc_zz_hl_dict[layer_name] = Dropout(self.dropout_rate)(
Dense(l_dim, activation=self.activation_hidden_disc)(
last_layer))
last_layer = disc_zz_hl_dict[layer_name]
disc_zz_out = Dense(1, activation='sigmoid')(last_layer)
self.disc_zz = Model(inputs=(disc_zz_in_z, disc_zz_in_z_prime),
outputs=[disc_zz_out])
# self.hist_loss_disc_zz = []
# Set optimizer
opt_gen = Adam(learning_rate=self.learning_rate_gen)
opt_disc = Adam(learning_rate=self.learning_rate_disc)
self.dec.compile(optimizer=opt_gen)
self.enc.compile(optimizer=opt_gen)
self.disc_xz.compile(optimizer=opt_disc)
self.disc_xx.compile(optimizer=opt_disc)
self.disc_zz.compile(optimizer=opt_disc)
self.hist_loss_disc = []
self.hist_loss_gen = []
def train_step(self, data):
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=False)
x_real, z_real = data
def get_losses():
y_true = tf.ones_like(x_real[:, [0]])
y_fake = tf.zeros_like(x_real[:, [0]])
# Generator
x_gen = self.dec({'I1': z_real}, training=True)
# Encoder
z_gen = self.enc({'I1': x_real}, training=True)
# Discriminatorxz
out_truexz = self.disc_xz({'I1': x_real, 'I2': z_gen},
training=True)
out_fakexz = self.disc_xz({'I1': x_gen, 'I2': z_real},
training=True)
# Discriminatorzz
if (self.add_disc_zz_loss == True):
out_truezz = self.disc_zz({'I1': z_real, 'I2': z_real},
training=True)
out_fakezz = self.disc_zz({'I1': z_real, 'I2': self.enc(
{'I1': self.dec({'I1': z_real}, training=True)})},
training=True)
# Discriminatorxx
out_truexx, _ = self.disc_xx({'I1': x_real, 'I2': x_real},
training=True) # self.Dxx(x_real, x_real)
out_fakexx, _ = self.disc_xx({'I1': x_real, 'I2': self.dec(
{'I1': self.enc({'I1': x_real}, training=True)})},
training=True)
# Losses for discriminators
loss_dxz = cross_entropy(y_true, out_truexz) + cross_entropy(
y_fake, out_fakexz)
loss_dxx = cross_entropy(y_true, out_truexx) + cross_entropy(
y_fake, out_fakexx)
if (self.add_disc_zz_loss == True):
loss_dzz = cross_entropy(y_true, out_truezz) + cross_entropy(
y_fake, out_fakezz)
loss_disc = loss_dxz + loss_dzz + loss_dxx
else:
loss_disc = loss_dxz + loss_dxx
# Losses for generator
loss_gexz = cross_entropy(y_true, out_fakexz) + cross_entropy(
y_fake, out_truexz)
loss_gexx = cross_entropy(y_true, out_fakexx) + cross_entropy(
y_fake, out_truexx)
if (self.add_disc_zz_loss == True):
loss_gezz = cross_entropy(y_true, out_fakezz) + cross_entropy(
y_fake, out_truezz)
cycle_consistency = loss_gezz + loss_gexx
loss_gen = loss_gexz + cycle_consistency
else:
cycle_consistency = loss_gexx
loss_gen = loss_gexz + cycle_consistency
if (self.add_recon_loss == True):
# Extra recon loss
x_recon = self.dec(
{'I1': self.enc({'I1': x_real}, training=True)})
loss_recon = tf.reduce_mean((x_real - x_recon) ** 2)
loss_gen += loss_recon * self.lambda_recon_loss
return loss_disc, loss_gen
with tf.GradientTape() as enc_tape, tf.GradientTape() as dec_tape, tf.GradientTape() as disc_xx_tape, tf.GradientTape() as disc_xz_tape, tf.GradientTape() as disc_zz_tape:
loss_disc, loss_gen = get_losses()
self.hist_loss_disc.append(np.float64(loss_disc.numpy()))
self.hist_loss_gen.append(np.float64(loss_gen.numpy()))
gradients_dec = dec_tape.gradient(loss_gen,
self.dec.trainable_variables)
self.dec.optimizer.apply_gradients(
zip(gradients_dec, self.dec.trainable_variables))
gradients_enc = enc_tape.gradient(loss_gen,
self.enc.trainable_variables)
self.enc.optimizer.apply_gradients(
zip(gradients_enc, self.enc.trainable_variables))
gradients_disc_xx = disc_xx_tape.gradient(loss_disc,
self.disc_xx.trainable_variables)
self.disc_xx.optimizer.apply_gradients(
zip(gradients_disc_xx, self.disc_xx.trainable_variables))
if (self.add_disc_zz_loss == True):
gradients_disc_zz = disc_zz_tape.gradient(loss_disc,
self.disc_zz.trainable_variables)
self.disc_zz.optimizer.apply_gradients(
zip(gradients_disc_zz, self.disc_zz.trainable_variables))
gradients_disc_xz = disc_xz_tape.gradient(loss_disc,
self.disc_xz.trainable_variables)
self.disc_xz.optimizer.apply_gradients(
zip(gradients_disc_xz, self.disc_xz.trainable_variables))
def plot_learning_curves(self, start_ind=0, window_smoothening=10):
fig = plt.figure(figsize=(12, 5))
l_gen = pd.Series(self.hist_loss_gen[start_ind:]).rolling(
window_smoothening).mean()
l_disc = pd.Series(self.hist_loss_disc[start_ind:]).rolling(
window_smoothening).mean()
ax = fig.add_subplot(1, 2, 1)
ax.plot(range(len(l_gen)), l_gen, )
ax.set_title('Generator')
ax.set_ylabel('Loss')
ax.set_xlabel('Iter')
ax = fig.add_subplot(1, 2, 2)
ax.plot(range(len(l_disc)), l_disc)
ax.set_title('Discriminator(s)')
ax.set_ylabel('Loss')
ax.set_xlabel('Iter')
plt.show()
def fit(self, X, y=None, noise_std=0.1):
"""Fit detector. y is ignored in unsupervised methods.
Parameters
----------
X : numpy array of shape (n_samples, n_features)
The input samples.
y : Ignored
Not used, present for API consistency by convention.
Returns
-------
self : object
Fitted estimator.
"""
# validate inputs X and y (optional)
X = check_array(X)
self._set_n_classes(y)
# Get number of sampels and features from train set
self.n_samples_, self.n_features_ = X.shape[0], X.shape[1]
self._build_model()
# Apply data scaling or not
if self.preprocessing:
self.scaler_ = StandardScaler()
X_norm = self.scaler_.fit_transform(X)
else:
X_norm = np.copy(X)
for n in range(self.epochs):
if ((n % 50 == 0) and (n != 0) and (self.verbose == 1)):
print('Train iter:{}'.format(n))
# Shuffle train
np.random.shuffle(X_norm)
X_train_sel = X_norm[0: min(self.batch_size, self.n_samples_), :]
latent_noise = np.random.normal(0, 1, (
X_train_sel.shape[0], self.latent_dim))
X_train_sel += np.random.normal(0, noise_std,
size=X_train_sel.shape)
self.train_step(
(np.float32(X_train_sel), np.float32(latent_noise)))
# Predict on X itself and calculate the the outlier scores.
# Note, X_norm was shuffled and needs to be recreated
if self.preprocessing:
X_norm = self.scaler_.transform(X)
else:
X_norm = np.copy(X)
pred_scores = self.get_outlier_scores(X_norm)
self.decision_scores_ = pred_scores
self._process_decision_scores()
return self
def train_more(self, X, epochs=100, noise_std=0.1):
"""This function allows the researcher to perform extra training instead of the fixed number determined
by the fit() function.
"""
# fit() should have been called first
check_is_fitted(self, ['decision_scores_'])
# Apply data scaling or not
if self.preprocessing:
X_norm = self.scaler_.transform(X)
else:
X_norm = np.copy(X)
for n in range(epochs):
if ((n % 50 == 0) and (n != 0) and (self.verbose == 1)):
print('Train iter:{}'.format(n))
# Shuffle train
np.random.shuffle(X_norm)
X_train_sel = X_norm[0: min(self.batch_size, self.n_samples_), :]
latent_noise = np.random.normal(0, 1, (
X_train_sel.shape[0], self.latent_dim))
X_train_sel += np.random.normal(0, noise_std,
size=X_train_sel.shape)
self.train_step(
(np.float32(X_train_sel), np.float32(latent_noise)))
# Predict on X itself and calculate the the outlier scores.
# Note, X_norm was shuffled and needs to be recreated
if self.preprocessing:
X_norm = self.scaler_.transform(X)
else:
X_norm = np.copy(X)
pred_scores = self.get_outlier_scores(X_norm)
self.decision_scores_ = pred_scores
self._process_decision_scores()
return self
def get_outlier_scores(self, X_norm):
X_enc = self.enc({'I1': X_norm}).numpy()
X_enc_gen = self.dec({'I1': X_enc}).numpy()
_, act_layer_xx = self.disc_xx({'I1': X_norm, 'I2': X_norm},
training=False)
act_layer_xx = act_layer_xx.numpy()
_, act_layer_xx_enc_gen = self.disc_xx({'I1': X_norm, 'I2': X_enc_gen},
training=False)
act_layer_xx_enc_gen = act_layer_xx_enc_gen.numpy()
outlier_scores = np.mean(
np.abs((act_layer_xx - act_layer_xx_enc_gen) ** 2), axis=1)
return outlier_scores
def decision_function(self, X):
"""Predict raw anomaly score of X using the fitted detector.
The anomaly score of an input sample is computed based on different
detector algorithms. For consistency, outliers are assigned with
larger anomaly scores.
Parameters
----------
X : numpy array of shape (n_samples, n_features)
The training input samples. Sparse matrices are accepted only
if they are supported by the base estimator.
Returns
-------
anomaly_scores : numpy array of shape (n_samples,)
The anomaly score of the input samples.
"""
check_is_fitted(self, ['decision_scores_'])
X = check_array(X)
if self.preprocessing:
X_norm = self.scaler_.transform(X)
else:
X_norm = np.copy(X)
# Predict on X
pred_scores = self.get_outlier_scores(X_norm)
return pred_scores