Source code for pyod.models.deep_svdd

# -*- 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 ..utils.utility import check_parameter

from .base import BaseDetector
from .base_dl import _get_tensorflow_version

# if tensorflow 2, import from tf directly
if _get_tensorflow_version() == 2:
    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+')


[docs]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], activation=self.hidden_activation, activity_regularizer=l2(self.l2_regularizer))(inputs) for hidden_neurons in self.hidden_neurons_[1:-1]: x = Dense(hidden_neurons, 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], 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, activation=self.hidden_activation, activity_regularizer=l2(self.l2_regularizer))(x) x = Dropout(self.dropout_rate)(x) x = Dense(self.n_features_, 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
[docs] 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
[docs] 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