pyod/models/anogan.py
# -*- coding: utf-8 -*-
"""Anomaly Detection with Generative Adversarial Networks (AnoGAN)
Paper: https://arxiv.org/pdf/1703.05921.pdf
Note, that this is another implementation of AnoGAN as the one from https://github.com/fuchami/ANOGAN
"""
# Author: Michiel Bongaerts (but not author of the AnoGAN method)
# License: BSD 2 clause
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
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 AnoGAN(BaseDetector):
"""Anomaly Detection with Generative Adversarial Networks (AnoGAN).
See the original paper "Unsupervised anomaly detection with generative
adversarial networks to guide marker discovery".
See :cite:`schlegl2017unsupervised` for details.
Parameters
----------
output_activation : str, optional (default=None)
Activation function to use for output layer.
See https://keras.io/activations/
activation_hidden : str, optional (default='tanh')
Activation function to use for output layer.
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.
G_layers : list, optional (default=[20,10,3,10,20])
List that indicates the number of nodes per hidden layer for the
generator. Thus, [10,10] indicates 2 hidden layers having each 10 nodes.
D_layers : list, optional (default=[20,10,5])
List that indicates the number of nodes per hidden layer for the
discriminator. Thus, [10,10] indicates 2 hidden layers having each 10
nodes.
learning_rate: float in (0., 1), optional (default=0.001)
learning rate of training the network
index_D_layer_for_recon_error: int, optional (default = 1)
This is the index of the hidden layer in the discriminator for which
the reconstruction error will be determined between query sample and
the sample created from the latent space.
learning_rate_query: float in (0., 1), optional (default=0.001)
learning rate for the backpropagation steps needed to find a point in
the latent space of the generator that approximate the query sample
epochs_query: int, optional (default=20)
Number of epochs to approximate the query sample in the latent space
of the generator
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='tanh',
dropout_rate=0.2,
latent_dim_G=2,
G_layers=[20, 10, 3, 10, 20],
verbose=0,
D_layers=[20, 10, 5],
index_D_layer_for_recon_error=1,
epochs=500,
preprocessing=False,
learning_rate=0.001,
learning_rate_query=0.01,
epochs_query=20,
batch_size=32,
output_activation=None,
contamination=0.1):
super(AnoGAN, self).__init__(contamination=contamination)
self.activation_hidden = activation_hidden
self.dropout_rate = dropout_rate
self.latent_dim_G = latent_dim_G
self.G_layers = G_layers
self.D_layers = D_layers
self.index_D_layer_for_recon_error = index_D_layer_for_recon_error
self.output_activation = output_activation
self.contamination = contamination
self.epochs = epochs
self.learning_rate = learning_rate
self.learning_rate_query = learning_rate_query
self.epochs_query = epochs_query
self.preprocessing = preprocessing
self.batch_size = batch_size
self.verbose = verbose
check_parameter(dropout_rate, 0, 1, param_name='dropout_rate',
include_left=True)
def _build_model(self):
#### Generator #####
G_in = Input(shape=(self.latent_dim_G,), name='I1')
G_1 = Dropout(self.dropout_rate, input_shape=(self.n_features_,))(G_in)
last_layer = G_1
G_hl_dict = {}
for i, l_dim in enumerate(self.G_layers):
layer_name = 'hl_{}'.format(i)
G_hl_dict[layer_name] = Dropout(self.dropout_rate)(
Dense(l_dim, activation=self.activation_hidden)(last_layer))
last_layer = G_hl_dict[layer_name]
G_out = Dense(self.n_features_, activation=self.output_activation)(
last_layer)
self.generator = Model(inputs=(G_in), outputs=[G_out])
self.hist_loss_generator = []
#### Discriminator #####
D_in = Input(shape=(self.n_features_,), name='I1')
D_1 = Dropout(self.dropout_rate, input_shape=(self.n_features_,))(D_in)
last_layer = D_1
D_hl_dict = {}
for i, l_dim in enumerate(self.D_layers):
layer_name = 'hl_{}'.format(i)
D_hl_dict[layer_name] = Dropout(self.dropout_rate)(
Dense(l_dim, activation=self.activation_hidden)(last_layer))
last_layer = D_hl_dict[layer_name]
classifier_node = Dense(1, activation='sigmoid')(last_layer)
self.discriminator = Model(inputs=(D_in),
outputs=[classifier_node,
D_hl_dict['hl_{}'.format(
self.index_D_layer_for_recon_error)]])
self.hist_loss_discriminator = []
# Set optimizer
opt = Adam(learning_rate=self.learning_rate)
self.generator.compile(optimizer=opt)
self.discriminator.compile(optimizer=opt)
def plot_learning_curves(self, start_ind=0,
window_smoothening=10): # pragma: no cover
fig = plt.figure(figsize=(12, 5))
l_gen = pd.Series(self.hist_loss_generator[start_ind:]).rolling(
window_smoothening).mean()
l_disc = pd.Series(self.hist_loss_discriminator[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')
ax.set_ylabel('Loss')
ax.set_xlabel('Iter')
plt.show()
def train_step(self, data):
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=False)
X_original, latent_noise = data
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
X_gen = self.generator({'I1': latent_noise}, training=True)
real_output, _ = self.discriminator({'I1': X_original},
training=True)
fake_output, _ = self.discriminator({'I1': X_gen}, training=True)
# Correctly predicted
loss_discriminator = cross_entropy(tf.ones_like(fake_output),
fake_output)
total_loss_generator = loss_discriminator
## Losses discriminator
real_loss = cross_entropy(
tf.ones_like(real_output, dtype='float32') * 0.9,
real_output) # one-sided label smoothening
fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
total_loss_discriminator = real_loss + fake_loss
# Compute gradients
gradients_gen = gen_tape.gradient(total_loss_generator,
self.generator.trainable_variables)
# Update weights
self.generator.optimizer.apply_gradients(
zip(gradients_gen, self.generator.trainable_variables))
# Compute gradients
gradients_disc = disc_tape.gradient(total_loss_discriminator,
self.discriminator.trainable_variables)
# Update weights
self.discriminator.optimizer.apply_gradients(
zip(gradients_disc, self.discriminator.trainable_variables))
self.hist_loss_generator.append(
np.float64(total_loss_generator.numpy()))
self.hist_loss_discriminator.append(
np.float64(total_loss_discriminator.numpy()))
def fit_query(self, query_sample):
assert (query_sample.shape[0] == 1)
assert (query_sample.shape[1] == self.n_features_)
# Make pseudo input (just zeros)
zeros = np.zeros((1, self.latent_dim_G))
# build model for back-propagating a approximate latent space where
# reconstruction with query sample is optimal
pseudo_in = Input(shape=(self.latent_dim_G,), name='I1')
z_gamma = Dense(self.latent_dim_G, activation=None, use_bias=True)(
pseudo_in)
sample_gen = self.generator({'I1': z_gamma}, training=False)
_, sample_disc_latent = self.discriminator({'I1': sample_gen},
training=False)
self.query_model = Model(inputs=(pseudo_in),
outputs=[z_gamma, sample_gen,
sample_disc_latent])
opt = Adam(learning_rate=self.learning_rate_query)
self.query_model.compile(optimizer=opt)
###############
for i in range(self.epochs_query):
if ((i % 25 == 0) and (self.verbose == 1)):
print('iter:', i)
with tf.GradientTape() as tape:
z, sample_gen, sample_disc_latent = self.query_model(
{'I1': zeros}, training=True)
_, sample_disc_latent_original = self.discriminator(
{'I1': query_sample}, training=False)
# Reconstruction loss generator
abs_err = tf.keras.backend.abs(query_sample - sample_gen)
loss_recon_gen = tf.keras.backend.mean(
tf.keras.backend.mean(abs_err, axis=-1))
# Reconstruction loss latent space of discrimator
abs_err = tf.keras.backend.abs(
sample_disc_latent_original - sample_disc_latent)
loss_recon_disc = tf.keras.backend.mean(
tf.keras.backend.mean(abs_err, axis=-1))
total_loss = loss_recon_gen + loss_recon_disc # equal weighting both terms
# Compute gradients
gradients = tape.gradient(total_loss,
self.query_model.trainable_variables[
0:2])
# Update weights
self.query_model.optimizer.apply_gradients(
zip(gradients, self.query_model.trainable_variables[0:2]))
return total_loss.numpy()
def fit(self, X, y=None):
"""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)
# Verify and construct the hidden units
self.n_samples_, self.n_features_ = X.shape[0], X.shape[1]
self._build_model()
# Standardize data for better performance
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 % 100 == 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_G))
self.train_step((np.float32(X_train_sel),
np.float32(latent_noise)))
# Predict on X itself and calculate the reconstruction error as
# the outlier scores. Noted X_norm was shuffled has to recreate
if self.preprocessing:
X_norm = self.scaler_.transform(X)
else:
X_norm = np.copy(X)
scores = []
# For each sample we use a few backpropagation steps, to obtain a point in the latent
# space, that best resembles the query sample
for i in range(X_norm.shape[0]):
if (self.verbose == 1):
print('query sample {} / {}'.format(i + 1, X_norm.shape[0]))
sample = X_norm[[i],]
score = self.fit_query(sample)
scores.append(score)
self.decision_scores_ = np.array(scores)
self._process_decision_scores()
return self
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 = []
for i in range(X_norm.shape[0]):
if (self.verbose == 1):
print('query sample {} / {}'.format(i + 1, X_norm.shape[0]))
sample = X_norm[[i],]
score = self.fit_query(sample)
pred_scores.append(score)
pred_scores = np.array(pred_scores)
return pred_scores