pyod/models/deep_svdd.py
# -*- coding: utf-8 -*-
"""Deep One-Class Classification for outlier detection
"""
# Author: Rafal Bodziony <bodziony.rafal@gmail.com>
# License: BSD 2 clause
from __future__ import division
from __future__ import print_function
import numpy as np
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:
import tensorflow as tf
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.regularizers import l2
from tensorflow.keras import Model, Input
else:
raise ModuleNotFoundError('DeepSVDD runs only with TensorFlow 2.0+')
class DeepSVDD(BaseDetector):
"""Deep One-Class Classifier with AutoEncoder (AE) is a type of neural
networks for learning useful data representations in an unsupervised way.
DeepSVDD trains a neural network while minimizing the volume of a
hypersphere that encloses the network representations of the data,
forcing the network to extract the common factors of variation.
Similar to PCA, DeepSVDD could be used to detect outlying objects in the
data by calculating the distance from center
See :cite:`ruff2018deepsvdd` for details.
Parameters
----------
c: float, optional (default='forwad_nn_pass')
Deep SVDD center, the default will be calculated based on network
initialization first forward pass. To get repeated results set
random_state if c is set to None.
use_ae: bool, optional (default=False)
The AutoEncoder type of DeepSVDD it reverse neurons from hidden_neurons
if set to True.
hidden_neurons : list, optional (default=[64, 32])
The number of neurons per hidden layers. if use_ae is True, neurons
will be reversed eg. [64, 32] -> [64, 32, 32, 64, n_features]
hidden_activation : str, optional (default='relu')
Activation function to use for hidden layers.
All hidden layers are forced to use the same type of activation.
See https://keras.io/activations/
output_activation : str, optional (default='sigmoid')
Activation function to use for output layer.
See https://keras.io/activations/
optimizer : str, optional (default='adam')
String (name of optimizer) or optimizer instance.
See https://keras.io/optimizers/
epochs : int, optional (default=100)
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.
l2_regularizer : float in (0., 1), optional (default=0.1)
The regularization strength of activity_regularizer
applied on each layer. By default, l2 regularizer is used. See
https://keras.io/regularizers/
validation_size : float in (0., 1), optional (default=0.1)
The percentage of data to be used for validation.
preprocessing : bool, optional (default=True)
If True, apply standardization on the data.
verbose : int, optional (default=1)
Verbosity mode.
- 0 = silent
- 1 = progress bar
- 2 = one line per epoch.
For verbose >= 1, model summary may be printed.
random_state : random_state: int, RandomState instance or None, optional
(default=None)
If int, random_state is the seed used by the random
number generator; If RandomState instance, random_state is the random
number generator; If None, the random number generator is the
RandomState instance used by `np.random`.
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
----------
model_ : Keras Object
The underlying DeppSVDD in Keras.
history_: Keras Object
The AutoEncoder training history.
decision_scores_ : numpy array of shape (n_samples,)
The outlier scores of the training data.
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, c=None,
use_ae=False,
hidden_neurons=None,
hidden_activation='relu',
output_activation='sigmoid',
optimizer='adam',
epochs=100, batch_size=32, dropout_rate=0.2,
l2_regularizer=0.1, validation_size=0.1, preprocessing=True,
verbose=1, random_state=None, contamination=0.1):
super(DeepSVDD, self).__init__(contamination=contamination)
self.c = c
self.use_ae = use_ae
self.hidden_neurons = hidden_neurons
self.hidden_activation = hidden_activation
self.output_activation = output_activation
self.optimizer = optimizer
self.epochs = epochs
self.batch_size = batch_size
self.dropout_rate = dropout_rate
self.l2_regularizer = l2_regularizer
self.validation_size = validation_size
self.preprocessing = preprocessing
self.verbose = verbose
self.random_state = random_state
if self.random_state is not None:
tf.random.set_seed(self.random_state)
# default values
if self.hidden_neurons is None:
self.hidden_neurons = [64, 32]
self.hidden_neurons_ = self.hidden_neurons
check_parameter(dropout_rate, 0, 1, param_name='dropout_rate',
include_left=True)
def _init_c(self, X_norm, eps=0.1):
# create true Center value from model predict of intermediate layers
model_center = Model(self.model_.inputs,
self.model_.get_layer('net_output').output)
out_ = model_center.predict(X_norm)
nf_predict = out_.shape[0]
out_ = np.sum(out_, axis=0)
out_ /= nf_predict
self.c = out_
self.c[(abs(self.c) < eps) & (self.c < 0)] = -eps
self.c[(abs(self.c) < eps) & (self.c > 0)] = eps
return self
def _build_model(self, training=True):
inputs = Input(shape=(self.n_features_,))
x = Dense(self.hidden_neurons_[0], use_bias=False, activation=self.hidden_activation,
activity_regularizer=l2(self.l2_regularizer))(inputs)
for hidden_neurons in self.hidden_neurons_[1:-1]:
x = Dense(hidden_neurons, use_bias=False, activation=self.hidden_activation,
activity_regularizer=l2(self.l2_regularizer))(x)
x = Dropout(self.dropout_rate)(x)
# add name to last hidden layer
x = Dense(self.hidden_neurons_[-1], use_bias=False, activation=self.hidden_activation,
activity_regularizer=l2(self.l2_regularizer),
name='net_output')(x)
# build distance loss
dist = tf.math.reduce_sum((x - self.c) ** 2, axis=-1)
outputs = dist
loss = tf.math.reduce_mean(dist)
# Instantiate Deep SVDD
dsvd = Model(inputs, outputs)
# Weight decay
w_d = 1e-6 * sum([np.linalg.norm(w) for w in dsvd.get_weights()])
# Use AutoEncoder version of DeepSVDD
if self.use_ae:
for reversed_neurons in self.hidden_neurons_[::-1]:
x = Dense(reversed_neurons, use_bias=False, activation=self.hidden_activation,
activity_regularizer=l2(self.l2_regularizer))(x)
x = Dropout(self.dropout_rate)(x)
x = Dense(self.n_features_, use_bias=False, activation=self.output_activation,
activity_regularizer=l2(self.l2_regularizer))(x)
dsvd.add_loss(
loss + tf.math.reduce_mean(tf.math.square(x - inputs)) + w_d)
else:
dsvd.add_loss(loss + w_d)
dsvd.compile(optimizer=self.optimizer)
if self.verbose >= 1 and training:
print(dsvd.summary())
return dsvd
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]
# Standardize data for better performance
if self.preprocessing:
self.scaler_ = StandardScaler()
X_norm = self.scaler_.fit_transform(X)
else:
X_norm = np.copy(X)
# Shuffle the data for validation as Keras do not shuffling for
# Validation Split
np.random.shuffle(X_norm)
# Validate and complete the number of hidden neurons
if np.min(self.hidden_neurons) > self.n_features_ and self.use_ae:
raise ValueError("The number of neurons should not exceed "
"the number of features")
if self.c is None:
self.c = 0.0
self.model_ = self._build_model(training=False)
self._init_c(X_norm)
# Build DeepSVDD model & fit with X
self.model_ = self._build_model()
self.history_ = self.model_.fit(X_norm, X_norm,
epochs=self.epochs,
batch_size=self.batch_size,
shuffle=True,
validation_split=self.validation_size,
verbose=self.verbose).history
# 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)
self.decision_scores_ = self.model_.predict(X_norm)
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, ['model_', 'history_'])
X = check_array(X)
if self.preprocessing:
X_norm = self.scaler_.transform(X)
else:
X_norm = np.copy(X)
# Predict on X and return the reconstruction errors
pred_scores = self.model_.predict(X_norm)
return pred_scores