From 92ad7361ebeb51c18bfcd3053e76b8466f10c328 Mon Sep 17 00:00:00 2001 From: Lucas Date: Thu, 1 Sep 2022 22:52:06 +0200 Subject: [PATCH 01/17] made sure auto_encoder_pytorch does not start with batch norm and clearned initialization --- pyod/models/auto_encoder_torch.py | 55 ++++++++++++++++++++----------- 1 file changed, 36 insertions(+), 19 deletions(-) diff --git a/pyod/models/auto_encoder_torch.py b/pyod/models/auto_encoder_torch.py index 58d5dc4ab..69fd9c615 100644 --- a/pyod/models/auto_encoder_torch.py +++ b/pyod/models/auto_encoder_torch.py @@ -44,50 +44,67 @@ def __getitem__(self, idx): return torch.from_numpy(sample), idx -class inner_autoencoder(nn.Module): +class InnerAutoencoder(nn.Module): def __init__(self, n_features, - hidden_neurons=[128, 64], + hidden_neurons=(128, 64), dropout_rate=0.2, batch_norm=True, hidden_activation='relu'): - super(inner_autoencoder, self).__init__() + + # initialize the super class + super(InnerAutoencoder, self).__init__() + + # save the default values self.n_features = n_features self.dropout_rate = dropout_rate self.batch_norm = batch_norm self.hidden_activation = hidden_activation + # create the dimensions for the input and hidden layers + self.layers_neurons_encoder_ = [self.n_features, *hidden_neurons] + self.layers_neurons_decoder_ = self.layers_neurons_encoder_[::-1] + + # get the object for the activations functions self.activation = get_activation_by_name(hidden_activation) - self.layers_neurons_ = [self.n_features, *hidden_neurons] - self.layers_neurons_decoder_ = self.layers_neurons_[::-1] + # initialize encoder and decoder as a sequential self.encoder = nn.Sequential() self.decoder = nn.Sequential() - for idx, layer in enumerate(self.layers_neurons_[:-1]): - if batch_norm: + # fill the encoder sequential with hidden layers + for idx, layer in enumerate(self.layers_neurons_encoder_[:-1]): + + # add a batch norm per layer if wanted (leave out first layer) + if batch_norm and idx > 0: self.encoder.add_module("batch_norm" + str(idx), - nn.BatchNorm1d( - self.layers_neurons_[idx])) + nn.BatchNorm1d(layer)) + + # create a linear layer of neurons self.encoder.add_module("linear" + str(idx), - torch.nn.Linear(self.layers_neurons_[idx], - self.layers_neurons_[ - idx + 1])) + torch.nn.Linear(layer, self.layers_neurons_encoder_[idx + 1])) + # create the activation self.encoder.add_module(self.hidden_activation + str(idx), self.activation) + # create a dropout layer self.encoder.add_module("dropout" + str(idx), torch.nn.Dropout(dropout_rate)) - for idx, layer in enumerate(self.layers_neurons_[:-1]): + # fill the decoder layer + for idx, layer in enumerate(self.layers_neurons_decoder_[:-1]): + + # create a batch norm per layer if wanted if batch_norm: self.decoder.add_module("batch_norm" + str(idx), - nn.BatchNorm1d( - self.layers_neurons_decoder_[idx])) - self.decoder.add_module("linear" + str(idx), torch.nn.Linear( - self.layers_neurons_decoder_[idx], - self.layers_neurons_decoder_[idx + 1])) + nn.BatchNorm1d(layer)) + + # create a linear layer of neurons + self.decoder.add_module("linear" + str(idx), + torch.nn.Linear(layer, self.layers_neurons_decoder_[idx + 1])) + # create the activation self.encoder.add_module(self.hidden_activation + str(idx), self.activation) + # create a dropout layer self.decoder.add_module("dropout" + str(idx), torch.nn.Dropout(dropout_rate)) @@ -294,7 +311,7 @@ def fit(self, X, y=None): shuffle=True) # initialize the model - self.model = inner_autoencoder( + self.model = InnerAutoencoder( n_features=n_features, hidden_neurons=self.hidden_neurons, dropout_rate=self.dropout_rate, From 8deaf883e873e711af8cb66ac662153eebe41e02 Mon Sep 17 00:00:00 2001 From: Lucas Date: Sat, 3 Sep 2022 13:22:37 +0200 Subject: [PATCH 02/17] refactored pytorch autoencoder. cleaned code and changed order of layers --- pyod/models/auto_encoder_torch.py | 34 ++++++++++++++++++------------- 1 file changed, 20 insertions(+), 14 deletions(-) diff --git a/pyod/models/auto_encoder_torch.py b/pyod/models/auto_encoder_torch.py index 69fd9c615..cb7bf1be1 100644 --- a/pyod/models/auto_encoder_torch.py +++ b/pyod/models/auto_encoder_torch.py @@ -63,6 +63,7 @@ def __init__(self, # create the dimensions for the input and hidden layers self.layers_neurons_encoder_ = [self.n_features, *hidden_neurons] + print(self.layers_neurons_encoder_) self.layers_neurons_decoder_ = self.layers_neurons_encoder_[::-1] # get the object for the activations functions @@ -75,17 +76,19 @@ def __init__(self, # fill the encoder sequential with hidden layers for idx, layer in enumerate(self.layers_neurons_encoder_[:-1]): - # add a batch norm per layer if wanted (leave out first layer) - if batch_norm and idx > 0: - self.encoder.add_module("batch_norm" + str(idx), - nn.BatchNorm1d(layer)) - # create a linear layer of neurons self.encoder.add_module("linear" + str(idx), torch.nn.Linear(layer, self.layers_neurons_encoder_[idx + 1])) + + # add a batch norm per layer if wanted (leave out first layer) + if batch_norm: + self.encoder.add_module("batch_norm" + str(idx), + nn.BatchNorm1d(self.layers_neurons_encoder_[idx + 1])) + # create the activation self.encoder.add_module(self.hidden_activation + str(idx), self.activation) + # create a dropout layer self.encoder.add_module("dropout" + str(idx), torch.nn.Dropout(dropout_rate)) @@ -93,20 +96,23 @@ def __init__(self, # fill the decoder layer for idx, layer in enumerate(self.layers_neurons_decoder_[:-1]): - # create a batch norm per layer if wanted - if batch_norm: - self.decoder.add_module("batch_norm" + str(idx), - nn.BatchNorm1d(layer)) - # create a linear layer of neurons self.decoder.add_module("linear" + str(idx), torch.nn.Linear(layer, self.layers_neurons_decoder_[idx + 1])) + + # create a batch norm per layer if wanted (only if it is not the last layer) + if batch_norm and idx < len(self.layers_neurons_decoder_[:-1]) - 1: + self.decoder.add_module("batch_norm" + str(idx), + nn.BatchNorm1d(self.layers_neurons_decoder_[idx + 1])) + # create the activation - self.encoder.add_module(self.hidden_activation + str(idx), + self.decoder.add_module(self.hidden_activation + str(idx), self.activation) - # create a dropout layer - self.decoder.add_module("dropout" + str(idx), - torch.nn.Dropout(dropout_rate)) + + # create a dropout layer (only if it is not the last layer) + if idx < len(self.layers_neurons_decoder_[:-1]) - 1: + self.decoder.add_module("dropout" + str(idx), + torch.nn.Dropout(dropout_rate)) def forward(self, x): # we could return the latent representation here after the encoder as the latent representation From 22ac2147d0929760325375008a770eff75217b8e Mon Sep 17 00:00:00 2001 From: Lucas Date: Sat, 3 Sep 2022 13:28:58 +0200 Subject: [PATCH 03/17] deleted leftover print statement --- pyod/models/auto_encoder_torch.py | 1 - 1 file changed, 1 deletion(-) diff --git a/pyod/models/auto_encoder_torch.py b/pyod/models/auto_encoder_torch.py index cb7bf1be1..f9729ae83 100644 --- a/pyod/models/auto_encoder_torch.py +++ b/pyod/models/auto_encoder_torch.py @@ -63,7 +63,6 @@ def __init__(self, # create the dimensions for the input and hidden layers self.layers_neurons_encoder_ = [self.n_features, *hidden_neurons] - print(self.layers_neurons_encoder_) self.layers_neurons_decoder_ = self.layers_neurons_encoder_[::-1] # get the object for the activations functions From a06dec3819680a1a5a92ed283d812d0ddaca9863 Mon Sep 17 00:00:00 2001 From: Akira Tamamori Date: Tue, 13 Sep 2022 15:11:03 +0900 Subject: [PATCH 04/17] Create kpca.py --- pyod/models/kpca.py | 400 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 400 insertions(+) create mode 100644 pyod/models/kpca.py diff --git a/pyod/models/kpca.py b/pyod/models/kpca.py new file mode 100644 index 000000000..c5f812718 --- /dev/null +++ b/pyod/models/kpca.py @@ -0,0 +1,400 @@ +# -*- coding: utf-8 -*- +"""Kernel Principal Component Analysis (KPCA) Outlier Detector +""" +# Author: Akira Tamamori +# License: BSD 2 clause + +import numpy as np +from pyod.models.base import BaseDetector +from pyod.utils.utility import check_parameter +from sklearn.decomposition import KernelPCA +from sklearn.utils import check_array, check_random_state +from sklearn.utils.validation import check_is_fitted + + +class PyODKernelPCA(KernelPCA): + """A wrapper class for KernelPCA class of scikit-learn.""" + + def __init__( + self, + n_components=None, + kernel="rbf", + gamma=None, + degree=3, + coef0=1, + kernel_params=None, + alpha=1.0, + fit_inverse_transform=False, + eigen_solver="auto", + tol=0, + max_iter=None, + iterated_power="auto", + remove_zero_eig=False, + copy_X=True, + n_jobs=None, + random_state=None, + ): + super().__init__( + kernel=kernel, + gamma=gamma, + degree=degree, + coef0=coef0, + kernel_params=kernel_params, + alpha=alpha, + fit_inverse_transform=fit_inverse_transform, + eigen_solver=eigen_solver, + tol=tol, + max_iter=max_iter, + iterated_power=iterated_power, + remove_zero_eig=remove_zero_eig, + n_jobs=n_jobs, + copy_X=copy_X, + random_state=check_random_state(random_state), + ) + + @property + def get_centerer(self): + """Return a protected member _centerer.""" + return self._centerer + + @property + def get_kernel(self): + """Return a protected member _get_kernel.""" + return self._get_kernel + + +class KPCA(BaseDetector): + """KPCA class for outlier detection. + + PCA is performed on the feature space uniquely determined by the kernel, + and the reconstruction error on the feature space is used as the anomaly score. + + Reference: See + Heiko Hoffmann, "Kernel PCA for novelty detection," + Pattern Recognition, vol.40, no.3, pp. 863-874, 2007. + https://www.sciencedirect.com/science/article/pii/S0031320306003414 + for details. + + Parameters + ---------- + n_components : int, optional (default=None) + Number of components. If None, all non-zero components are kept. + + n_selected_components : int, optional (default=None) + Number of selected principal components + for calculating the outlier scores. It is not necessarily equal to + the total number of the principal components. If not set, use + all principal components. + + kernel : string {'linear', 'poly', 'rbf', 'sigmoid', + 'cosine', 'precomputed'}, optional (default='rbf') + Kernel used for PCA. + + gamma : float, optional (default=None) + Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other + kernels. If ``gamma`` is ``None``, then it is set to ``1/n_features``. + + degree : int, optional (default=3) + Degree for poly kernels. Ignored by other kernels. + + coef0 : float, optional (default=1) + Independent term in poly and sigmoid kernels. + Ignored by other kernels. + + kernel_params : dict, optional (default=None) + Parameters (keyword arguments) and + values for kernel passed as callable object. + Ignored by other kernels. + + alpha : float, optional (default=1.0) + Hyperparameter of the ridge regression that learns the + inverse transform (when inverse_transform=True). + + eigen_solver : string, {'auto', 'dense', 'arpack', 'randomized'}, \ + default='auto' + Select eigensolver to use. If `n_components` is much + less than the number of training samples, randomized (or arpack to a + smaller extend) may be more efficient than the dense eigensolver. + Randomized SVD is performed according to the method of Halko et al. + + auto : + the solver is selected by a default policy based on n_samples + (the number of training samples) and `n_components`: + if the number of components to extract is less than 10 (strict) and + the number of samples is more than 200 (strict), the 'arpack' + method is enabled. Otherwise the exact full eigenvalue + decomposition is computed and optionally truncated afterwards + ('dense' method). + dense : + run exact full eigenvalue decomposition calling the standard + LAPACK solver via `scipy.linalg.eigh`, and select the components + by postprocessing. + arpack : + run SVD truncated to n_components calling ARPACK solver using + `scipy.sparse.linalg.eigsh`. It requires strictly + 0 < n_components < n_samples + randomized : + run randomized SVD. + implementation selects eigenvalues based on their module; therefore + using this method can lead to unexpected results if the kernel is + not positive semi-definite. + + tol : float, optional (default=0) + Convergence tolerance for arpack. + If 0, optimal value will be chosen by arpack. + + max_iter : int, optional (default=None) + Maximum number of iterations for arpack. + If None, optimal value will be chosen by arpack. + + iterated_power : int >= 0, or 'auto', optional (default='auto') + Number of iterations for the power method computed by + svd_solver == 'randomized'. When 'auto', it is set to 7 when + `n_components < 0.1 * min(X.shape)`, other it is set to 4. + + remove_zero_eig : bool, optional (default=False) + If True, then all components with zero eigenvalues are removed, so + that the number of components in the output may be < n_components + (and sometimes even zero due to numerical instability). + When n_components is None, this parameter is ignored and components + with zero eigenvalues are removed regardless. + + copy_X : bool, optional (default=True) + If True, input X is copied and stored by the model in the `X_fit_` + attribute. If no further changes will be done to X, setting + `copy_X=False` saves memory by storing a reference. + + n_jobs : int, optional (default=None) + The number of parallel jobs to run. + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. + ``-1`` means using all processors. + + sampling : bool, optional (default=False) + If True, sampling subset from the dataset is performed only once, + in order to reduce time complexity while keeping detection performance. + + subset_size : float in (0., 1.0) or int (0, n_samples), optional (default=20) + If sampling is True, the size of subset is specified. + + 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. + + Attributes + ---------- + 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, + contamination=0.1, + n_components=None, + n_selected_components=None, + kernel="rbf", + gamma=None, + degree=3, + coef0=1, + kernel_params=None, + alpha=1.0, + eigen_solver="auto", + tol=0, + max_iter=None, + iterated_power="auto", + remove_zero_eig=False, + copy_X=True, + n_jobs=None, + sampling=False, + subset_size=20, + random_state=None, + ): + super().__init__(contamination=contamination) + self.n_components = n_components + self.n_selected_components = n_selected_components + self.copy_x = copy_X + self.sampling = sampling + self.subset_size = subset_size + self.random_state = check_random_state(random_state) + self.decision_scores_ = None + self.n_selected_components_ = None + + self.kpca = PyODKernelPCA( + n_components=n_components, + kernel=kernel, + gamma=gamma, + degree=degree, + coef0=coef0, + kernel_params=kernel_params, + alpha=alpha, + fit_inverse_transform=False, + eigen_solver=eigen_solver, + tol=tol, + max_iter=max_iter, + iterated_power=iterated_power, + remove_zero_eig=remove_zero_eig, + copy_X=copy_X, + n_jobs=n_jobs, + ) + + def _check_subset_size(self, array): + """Check subset size.""" + n_samples, _ = array.shape + if isinstance(self.subset_size, int) is True: + if 0 < self.subset_size <= n_samples: + subset_size = self.subset_size + else: + raise ValueError( + f"subset_size={self.subset_size} " + f"must be between 0 and n_samples={n_samples}." + ) + + if isinstance(self.subset_size, float) is True: + if 0.0 < self.subset_size <= 1.0: + subset_size = int(self.subset_size * n_samples) + else: + raise ValueError("subset_size=%r must be between 0.0 and 1.0") + + return subset_size + + 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, copy=self.copy_x) + self._set_n_classes(y) + + # perform subsampling to reduce time complexity + if self.sampling is True: + subset_size = self._check_subset_size(X) + random_indices = self.random_state.choice( + X.shape[0], + size=subset_size, + replace=False, + ) + X = X[random_indices, :] + + # copy the attributes from the sklearn Kernel PCA object + if self.n_components is None: + n_components = X.shape[1] # use all dimensions + else: + if self.n_components < 1: + raise ValueError( + f"`n_components` should be >= 1, got: {self.n_components}" + ) + n_components = min(X.shape[0], self.n_components) + + # validate the number of components to be used for outlier detection + if self.n_selected_components is None: + self.n_selected_components_ = n_components + else: + self.n_selected_components_ = self.n_selected_components + check_parameter( + self.n_selected_components_, + 1, + n_components, + include_left=True, + include_right=True, + param_name="n_selected_components", + ) + + self.kpca.fit(X) + centerer = self.kpca.get_centerer + kernel = self.kpca.get_kernel + + x_transformed = self.kpca.eigenvectors_ * np.sqrt(self.kpca.eigenvalues_) + x_transformed = x_transformed[:, : self.n_selected_components_] + + potential = [] + for i in range(X.shape[0]): + sample = X[i, :].reshape(1, -1) + potential.append(kernel(sample)) + potential = np.array(potential).squeeze() + potential = potential - 2 * centerer.K_fit_rows_ + centerer.K_fit_all_ + + # reconstruction error + self.decision_scores_ = potential - np.sum(np.square(x_transformed), axis=1) + 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_", "threshold_", "labels_"]) + + X = check_array(X) + + # Compute centered gram matrix between X and training data X_fit_ + centerer = self.kpca.get_centerer + kernel = self.kpca.get_kernel + gram_matrix = kernel(X, self.kpca.X_fit_) + centered_g = centerer.transform(gram_matrix) + + # scale eigenvectors (properly account for null-space for dot product) + non_zeros = np.flatnonzero(self.kpca.eigenvalues_) + scaled_alphas = np.zeros_like(self.kpca.eigenvectors_) + scaled_alphas[:, non_zeros] = self.kpca.eigenvectors_[:, non_zeros] / np.sqrt( + self.kpca.eigenvalues_[non_zeros] + ) + + # Project with a scalar product between K and the scaled eigenvectors + x_transformed = np.dot(centered_g, scaled_alphas) + x_transformed = x_transformed[:, : self.n_selected_components_] + + potential = [] + for i in range(X.shape[0]): + sample = X[i, :].reshape(1, -1) + potential.append(kernel(sample)) + potential = np.array(potential).squeeze() + gram_fit_rows = np.sum(gram_matrix, axis=1) / gram_matrix.shape[1] + potential = potential - 2 * gram_fit_rows + centerer.K_fit_all_ + + # reconstruction error + anomaly_scores = potential - np.sum(np.square(x_transformed), axis=1) + + return anomaly_scores From dedff9b9fc796fa32f7d756910196f0773cd1697 Mon Sep 17 00:00:00 2001 From: Akira Tamamori Date: Tue, 13 Sep 2022 15:11:54 +0900 Subject: [PATCH 05/17] Create test_kpca.py --- pyod/test/test_kpca.py | 166 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 166 insertions(+) create mode 100644 pyod/test/test_kpca.py diff --git a/pyod/test/test_kpca.py b/pyod/test/test_kpca.py new file mode 100644 index 000000000..8c112946d --- /dev/null +++ b/pyod/test/test_kpca.py @@ -0,0 +1,166 @@ +# -*- coding: utf-8 -*- +from __future__ import division, print_function + +import os +import sys +import unittest + +# noinspection PyProtectedMember +from numpy.testing import (assert_allclose, assert_array_less, assert_equal, + assert_raises) +from pyod.models.kpca import KPCA +from pyod.utils.data import generate_data +from scipy.stats import rankdata +from sklearn.base import clone +from sklearn.metrics import roc_auc_score + +# temporary solution for relative imports in case pyod is not installed +# if pyod is installed, no need to use the following line +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) + + +class TestKPCA(unittest.TestCase): + def setUp(self): + self.n_train = 200 + self.n_test = 100 + self.contamination = 0.1 + self.roc_floor = 0.8 + self.X_train, self.X_test, self.y_train, self.y_test = generate_data( + n_train=self.n_train, + n_test=self.n_test, + contamination=self.contamination, + random_state=42, + ) + + self.clf = KPCA(contamination=self.contamination, random_state=42) + self.clf.fit(self.X_train) + + def test_parameters(self): + assert ( + hasattr(self.clf, "decision_scores_") + and self.clf.decision_scores_ is not None + ) + assert hasattr(self.clf, "labels_") and self.clf.labels_ is not None + assert hasattr(self.clf, "threshold_") and self.clf.threshold_ is not None + + def test_train_scores(self): + assert_equal(len(self.clf.decision_scores_), self.X_train.shape[0]) + + def test_prediction_scores(self): + pred_scores = self.clf.decision_function(self.X_test) + + # check score shapes + assert_equal(pred_scores.shape[0], self.X_test.shape[0]) + + # check performance + assert roc_auc_score(self.y_test, pred_scores) >= self.roc_floor + + def test_prediction_labels(self): + pred_labels = self.clf.predict(self.X_test) + assert_equal(pred_labels.shape, self.y_test.shape) + + def test_prediction_labels_confidence(self): + pred_labels, confidence = self.clf.predict(self.X_test, return_confidence=True) + assert_equal(pred_labels.shape, self.y_test.shape) + assert_equal(confidence.shape, self.y_test.shape) + assert confidence.min() >= 0 + assert confidence.max() <= 1 + + def test_predict_rank(self): + pred_socres = self.clf.decision_function(self.X_test) + pred_ranks = self.clf._predict_rank(self.X_test) + + # assert the order is reserved + assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=4) + assert_array_less(pred_ranks, self.X_train.shape[0] + 1) + assert_array_less(-0.1, pred_ranks) + + def test_model_clone(self): + clone_clf = clone(self.clf) + + def tearDown(self): + pass + + +class TestKPCASubsetBound(unittest.TestCase): + def setUp(self): + self.n_train = 200 + self.n_test = 100 + self.contamination = 0.1 + self.roc_floor = 0.8 + self.X_train, self.X_test, self.y_train, self.y_test = generate_data( + n_train=self.n_train, + n_test=self.n_test, + contamination=self.contamination, + random_state=42, + ) + + self.clf_float = KPCA( + sampling=True, + subset_size=0.1, + contamination=self.contamination, + random_state=42, + ) + self.clf_float_upper = KPCA(sampling=True, subset_size=1.5, random_state=42) + self.clf_float_lower = KPCA(sampling=True, subset_size=0, random_state=42) + self.clf_int_upper = KPCA( + sampling=True, subset_size=self.n_train + 100, random_state=42 + ) + self.clf_int_lower = KPCA(sampling=True, subset_size=-1, random_state=42) + + def test_bound(self): + self.clf_float.fit(self.X_train) + with assert_raises(ValueError): + self.clf_float_upper.fit(self.X_train) + with assert_raises(ValueError): + self.clf_float_lower.fit(self.X_train) + with assert_raises(ValueError): + self.clf_int_upper.fit(self.X_train) + with assert_raises(ValueError): + self.clf_int_lower.fit(self.X_train) + + def tearDown(self): + pass + + +class TestKPCAComponents(unittest.TestCase): + def setUp(self): + self.n_train = 200 + self.n_test = 100 + self.contamination = 0.1 + self.roc_floor = 0.8 + self.X_train, self.X_test, self.y_train, self.y_test = generate_data( + n_train=self.n_train, + n_test=self.n_test, + contamination=self.contamination, + random_state=42, + ) + + self.clf = KPCA(contamination=self.contamination, random_state=42) + self.clf_component_neg = KPCA(n_components=-1, random_state=42) + self.clf_selected_components = KPCA( + n_components=10, n_selected_components=5, random_state=42 + ) + self.clf_selected_components_upper = KPCA( + n_components=10, n_selected_components=50, random_state=42 + ) + self.clf_selected_components_lower = KPCA( + n_components=10, n_selected_components=0, random_state=42 + ) + + def test_bound(self): + self.clf.fit(self.X_train) + with assert_raises(ValueError): + self.clf_component_neg.fit(self.X_train) + self.clf_selected_components.fit(self.X_train) + with assert_raises(ValueError): + self.clf_selected_components_upper.fit(self.X_train) + with assert_raises(ValueError): + self.clf_selected_components_lower.fit(self.X_train) + + def tearDown(self): + pass + + +if __name__ == "__main__": + unittest.main() From 6cf5f636a9c019db7d90adbd123284b70d8db4e5 Mon Sep 17 00:00:00 2001 From: Akira Tamamori Date: Tue, 13 Sep 2022 15:13:08 +0900 Subject: [PATCH 06/17] Create kpca_example.py --- examples/kpca_example.py | 66 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 examples/kpca_example.py diff --git a/examples/kpca_example.py b/examples/kpca_example.py new file mode 100644 index 000000000..4ab3b34be --- /dev/null +++ b/examples/kpca_example.py @@ -0,0 +1,66 @@ +# -*- coding: utf-8 -*- +"""Example of outlier detection based on Kernel PCA. +""" +# Author: Akira Tamamori +# License: BSD 2 clause + +from __future__ import division, print_function + +import os +import sys + +from pyod.models.kpca import KPCA +from pyod.utils.data import evaluate_print, generate_data +from pyod.utils.example import visualize + +# temporary solution for relative imports in case pyod is not installed +# if pyod is installed, no need to use the following line +sys.path.append(os.path.abspath(os.path.join(os.path.dirname("__file__"), ".."))) + + +if __name__ == "__main__": + contamination = 0.1 # percentage of outliers + n_train = 200 # number of training points + n_test = 100 # number of testing points + n_features = 2 + + # Generate sample data + X_train, X_test, y_train, y_test = generate_data( + n_train=n_train, + n_test=n_test, + n_features=2, + contamination=contamination, + random_state=42, + behaviour="new", + ) + + # train kNN detector + clf_name = "KPCA" + clf = KPCA() + clf.fit(X_train) + + # get the prediction labels and outlier scores of the training data + y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers) + y_train_scores = clf.decision_scores_ # raw outlier scores + + # get the prediction on the test data + y_test_pred = clf.predict(X_test) # outlier labels (0 or 1) + y_test_scores = clf.decision_function(X_test) # outlier scores + + # evaluate and print the results + print("\nOn Training Data:") + evaluate_print(clf_name, y_train, y_train_scores) + print("\nOn Test Data:") + evaluate_print(clf_name, y_test, y_test_scores) + + # visualize the results + visualize( + clf_name, + X_train, + y_train, + X_test, + y_test, + y_train_pred, + y_test_pred, + show_figure=True, + ) From 80f382734a99e0dc25549b4bd89f968dc9a5bb7a Mon Sep 17 00:00:00 2001 From: Akira Tamamori Date: Tue, 13 Sep 2022 15:21:43 +0900 Subject: [PATCH 07/17] fix module import. --- pyod/models/kpca.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pyod/models/kpca.py b/pyod/models/kpca.py index c5f812718..6da8a07cc 100644 --- a/pyod/models/kpca.py +++ b/pyod/models/kpca.py @@ -5,12 +5,13 @@ # License: BSD 2 clause import numpy as np -from pyod.models.base import BaseDetector -from pyod.utils.utility import check_parameter from sklearn.decomposition import KernelPCA from sklearn.utils import check_array, check_random_state from sklearn.utils.validation import check_is_fitted +from ..utils.utility import check_parameter +from .base import BaseDetector + class PyODKernelPCA(KernelPCA): """A wrapper class for KernelPCA class of scikit-learn.""" From 8291ae29e9c3df20329c26bd7a65035327d50a06 Mon Sep 17 00:00:00 2001 From: Akira Tamamori Date: Tue, 13 Sep 2022 16:41:06 +0900 Subject: [PATCH 08/17] remove attribute of iterated_power. The iterated_power is valid since version 1.0 of scikit-learn. In Python 3.6 environment, version of scikit-learn is at most 0.24. --- pyod/models/kpca.py | 9 --------- 1 file changed, 9 deletions(-) diff --git a/pyod/models/kpca.py b/pyod/models/kpca.py index 6da8a07cc..c0fda2a46 100644 --- a/pyod/models/kpca.py +++ b/pyod/models/kpca.py @@ -29,7 +29,6 @@ def __init__( eigen_solver="auto", tol=0, max_iter=None, - iterated_power="auto", remove_zero_eig=False, copy_X=True, n_jobs=None, @@ -46,7 +45,6 @@ def __init__( eigen_solver=eigen_solver, tol=tol, max_iter=max_iter, - iterated_power=iterated_power, remove_zero_eig=remove_zero_eig, n_jobs=n_jobs, copy_X=copy_X, @@ -148,11 +146,6 @@ class KPCA(BaseDetector): Maximum number of iterations for arpack. If None, optimal value will be chosen by arpack. - iterated_power : int >= 0, or 'auto', optional (default='auto') - Number of iterations for the power method computed by - svd_solver == 'randomized'. When 'auto', it is set to 7 when - `n_components < 0.1 * min(X.shape)`, other it is set to 4. - remove_zero_eig : bool, optional (default=False) If True, then all components with zero eigenvalues are removed, so that the number of components in the output may be < n_components @@ -217,7 +210,6 @@ def __init__( eigen_solver="auto", tol=0, max_iter=None, - iterated_power="auto", remove_zero_eig=False, copy_X=True, n_jobs=None, @@ -247,7 +239,6 @@ def __init__( eigen_solver=eigen_solver, tol=tol, max_iter=max_iter, - iterated_power=iterated_power, remove_zero_eig=remove_zero_eig, copy_X=copy_X, n_jobs=n_jobs, From c7308454ba3aeba047d9e252a8ccf9d6aca6d5b0 Mon Sep 17 00:00:00 2001 From: Akira Tamamori Date: Tue, 13 Sep 2022 16:41:41 +0900 Subject: [PATCH 09/17] fix typo --- examples/kpca_example.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/kpca_example.py b/examples/kpca_example.py index 4ab3b34be..b92f6d0f0 100644 --- a/examples/kpca_example.py +++ b/examples/kpca_example.py @@ -34,7 +34,7 @@ behaviour="new", ) - # train kNN detector + # train KPCA detector clf_name = "KPCA" clf = KPCA() clf.fit(X_train) From 7d74379788723ea569858a6377ed001ebf1430d3 Mon Sep 17 00:00:00 2001 From: Akira Tamamori Date: Tue, 13 Sep 2022 16:42:31 +0900 Subject: [PATCH 10/17] fix tests --- pyod/test/test_kpca.py | 52 +++++++++++++++++++++++++++++++++++++++++- 1 file changed, 51 insertions(+), 1 deletion(-) diff --git a/pyod/test/test_kpca.py b/pyod/test/test_kpca.py index 8c112946d..94eefc66f 100644 --- a/pyod/test/test_kpca.py +++ b/pyod/test/test_kpca.py @@ -59,6 +59,25 @@ def test_prediction_labels(self): pred_labels = self.clf.predict(self.X_test) assert_equal(pred_labels.shape, self.y_test.shape) + def test_prediction_proba(self): + pred_proba = self.clf.predict_proba(self.X_test) + assert pred_proba.min() >= 0 + assert pred_proba.max() <= 1 + + def test_prediction_proba_linear(self): + pred_proba = self.clf.predict_proba(self.X_test, method="linear") + assert pred_proba.min() >= 0 + assert pred_proba.max() <= 1 + + def test_prediction_proba_unify(self): + pred_proba = self.clf.predict_proba(self.X_test, method="unify") + assert pred_proba.min() >= 0 + assert pred_proba.max() <= 1 + + def test_prediction_proba_parameter(self): + with assert_raises(ValueError): + self.clf.predict_proba(self.X_test, method="something") + def test_prediction_labels_confidence(self): pred_labels, confidence = self.clf.predict(self.X_test, return_confidence=True) assert_equal(pred_labels.shape, self.y_test.shape) @@ -66,6 +85,28 @@ def test_prediction_labels_confidence(self): assert confidence.min() >= 0 assert confidence.max() <= 1 + def test_prediction_proba_linear_confidence(self): + pred_proba, confidence = self.clf.predict_proba( + self.X_test, method="linear", return_confidence=True + ) + assert pred_proba.min() >= 0 + assert pred_proba.max() <= 1 + + assert_equal(confidence.shape, self.y_test.shape) + assert confidence.min() >= 0 + assert confidence.max() <= 1 + + def test_fit_predict(self): + pred_labels = self.clf.fit_predict(self.X_train) + assert_equal(pred_labels.shape, self.y_train.shape) + + def test_fit_predict_score(self): + self.clf.fit_predict_score(self.X_test, self.y_test) + self.clf.fit_predict_score(self.X_test, self.y_test, scoring="roc_auc_score") + self.clf.fit_predict_score(self.X_test, self.y_test, scoring="prc_n_score") + with assert_raises(NotImplementedError): + self.clf.fit_predict_score(self.X_test, self.y_test, scoring="something") + def test_predict_rank(self): pred_socres = self.clf.decision_function(self.X_test) pred_ranks = self.clf._predict_rank(self.X_test) @@ -75,6 +116,15 @@ def test_predict_rank(self): assert_array_less(pred_ranks, self.X_train.shape[0] + 1) assert_array_less(-0.1, pred_ranks) + def test_predict_rank_normalized(self): + pred_socres = self.clf.decision_function(self.X_test) + pred_ranks = self.clf._predict_rank(self.X_test, normalized=True) + + # assert the order is reserved + assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=4) + assert_array_less(pred_ranks, 1.01) + assert_array_less(-0.1, pred_ranks) + def test_model_clone(self): clone_clf = clone(self.clf) @@ -123,7 +173,7 @@ def tearDown(self): pass -class TestKPCAComponents(unittest.TestCase): +class TestKPCAComponentsBound(unittest.TestCase): def setUp(self): self.n_train = 200 self.n_test = 100 From 71bc7e145f3aeb8bf1e2cecda369f73f45f67093 Mon Sep 17 00:00:00 2001 From: Akira Tamamori Date: Tue, 13 Sep 2022 17:28:17 +0900 Subject: [PATCH 11/17] fix eigenvalues and eigenvectors The attributes of eigenvalues_ and eigenvectors_ are invalid in scikit-learn 0.24. If major version of scikit-learn is 0, use lambdas_ and alphas_. --- pyod/models/kpca.py | 26 ++++++++++++++++++++------ 1 file changed, 20 insertions(+), 6 deletions(-) diff --git a/pyod/models/kpca.py b/pyod/models/kpca.py index c0fda2a46..51b4e5885 100644 --- a/pyod/models/kpca.py +++ b/pyod/models/kpca.py @@ -5,6 +5,7 @@ # License: BSD 2 clause import numpy as np +import sklearn from sklearn.decomposition import KernelPCA from sklearn.utils import check_array, check_random_state from sklearn.utils.validation import check_is_fitted @@ -323,7 +324,14 @@ def fit(self, X, y=None): centerer = self.kpca.get_centerer kernel = self.kpca.get_kernel - x_transformed = self.kpca.eigenvectors_ * np.sqrt(self.kpca.eigenvalues_) + if int(sklearn.__version__[0]) < 1: + eigenvalues_ = self.kpca.lambdas_ + eigenvectors_ = self.kpca.alphas_ + else: + eigenvalues_ = self.kpca.eigenvalues_ + eigenvectors_ = self.kpca.eigenvectors_ + + x_transformed = eigenvectors_ * np.sqrt(eigenvalues_) x_transformed = x_transformed[:, : self.n_selected_components_] potential = [] @@ -358,7 +366,6 @@ def decision_function(self, X): The anomaly score of the input samples. """ check_is_fitted(self, ["decision_scores_", "threshold_", "labels_"]) - X = check_array(X) # Compute centered gram matrix between X and training data X_fit_ @@ -367,11 +374,18 @@ def decision_function(self, X): gram_matrix = kernel(X, self.kpca.X_fit_) centered_g = centerer.transform(gram_matrix) + if int(sklearn.__version__[0]) < 1: + eigenvalues_ = self.kpca.lambdas_ + eigenvectors_ = self.kpca.alphas_ + else: + eigenvalues_ = self.kpca.eigenvalues_ + eigenvectors_ = self.kpca.eigenvectors_ + # scale eigenvectors (properly account for null-space for dot product) - non_zeros = np.flatnonzero(self.kpca.eigenvalues_) - scaled_alphas = np.zeros_like(self.kpca.eigenvectors_) - scaled_alphas[:, non_zeros] = self.kpca.eigenvectors_[:, non_zeros] / np.sqrt( - self.kpca.eigenvalues_[non_zeros] + non_zeros = np.flatnonzero(eigenvalues_) + scaled_alphas = np.zeros_like(eigenvectors_) + scaled_alphas[:, non_zeros] = eigenvectors_[:, non_zeros] / np.sqrt( + eigenvalues_[non_zeros] ) # Project with a scalar product between K and the scaled eigenvectors From 9122d3c91053a61fdb9c5ce9dd4311299b035e8f Mon Sep 17 00:00:00 2001 From: Akira Tamamori Date: Tue, 13 Sep 2022 18:18:40 +0900 Subject: [PATCH 12/17] fix test to improve the coverage --- pyod/test/test_kpca.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/pyod/test/test_kpca.py b/pyod/test/test_kpca.py index 94eefc66f..6bbb26fb6 100644 --- a/pyod/test/test_kpca.py +++ b/pyod/test/test_kpca.py @@ -151,6 +151,12 @@ def setUp(self): contamination=self.contamination, random_state=42, ) + self.clf_int = KPCA( + sampling=True, + subset_size=50, + contamination=self.contamination, + random_state=42, + ) self.clf_float_upper = KPCA(sampling=True, subset_size=1.5, random_state=42) self.clf_float_lower = KPCA(sampling=True, subset_size=0, random_state=42) self.clf_int_upper = KPCA( @@ -160,6 +166,7 @@ def setUp(self): def test_bound(self): self.clf_float.fit(self.X_train) + self.clf_int.fit(self.X_train) with 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Date: Fri, 23 Sep 2022 09:14:37 -0400 Subject: [PATCH 14/17] Add example for COPOD interpretability. --- CHANGES.txt | 1 + README.rst | 29 +++++++++++++++++++---------- docs/index.rst | 29 +++++++++++++++++++---------- 3 files changed, 39 insertions(+), 20 deletions(-) diff --git a/CHANGES.txt b/CHANGES.txt index 391808116..19ac2e340 100644 --- a/CHANGES.txt +++ b/CHANGES.txt @@ -170,3 +170,4 @@ v<1.0.4>, <07/29/2022> -- Add LUNAR (#415). v<1.0.5>, <07/29/2022> -- Import optimization. v<1.0.5>, <08/27/2022> -- Code optimization. v<1.0.5>, <09/14/2022> -- Add ALAD. +v<1.0.6>, <09/23/2022> -- Update ADBench benchmark for NeruIPS 2022. diff --git a/README.rst b/README.rst index 1223ab8e1..ad53928a8 100644 --- a/README.rst +++ b/README.rst @@ -114,20 +114,29 @@ If you use PyOD in a scientific publication, we would appreciate citations to the following paper:: @article{zhao2019pyod, - author = {Zhao, Yue and Nasrullah, Zain and Li, Zheng}, - title = {PyOD: A Python Toolbox for Scalable Outlier Detection}, - journal = {Journal of Machine Learning Research}, - year = {2019}, - volume = {20}, - number = {96}, - pages = {1-7}, - url = {http://jmlr.org/papers/v20/19-011.html} + author = {Zhao, Yue and Nasrullah, Zain and Li, Zheng}, + title = {PyOD: A Python Toolbox for Scalable Outlier Detection}, + journal = {Journal of Machine Learning Research}, + year = {2019}, + volume = {20}, + number = {96}, + pages = {1-7}, + url = {http://jmlr.org/papers/v20/19-011.html} } or:: Zhao, Y., Nasrullah, Z. and Li, Z., 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of machine learning research (JMLR), 20(96), pp.1-7. +If you want more general insights of anomaly detection and/or algorithm performance comparison, please see our +NeurIPS 2022 paper `ADBench: Anomaly Detection Benchmark `_:: + + @inproceedings{han2022adbench, + title={ADBench: Anomaly Detection Benchmark}, + author={Songqiao Han and Xiyang Hu and Hailiang Huang and Mingqi Jiang and Yue Zhao}, + booktitle={Neural Information Processing Systems (NeurIPS)} + year={2022}, + } **Key Links and Resources**\ : @@ -238,8 +247,8 @@ Key Attributes of a fitted model: ADBench Benchmark ^^^^^^^^^^^^^^^^^ -We just released a 36-page, the most comprehensive `anomaly detection benchmark paper `_ [#Han2022ADBench]_. -The fully `open-sourced ADBench `_ compares 30 anomaly detection algorithms on 55 benchmark datasets. +We just released a 45-page, the most comprehensive `ADBench: Anomaly Detection Benchmark `_ [#Han2022ADBench]_. +The fully `open-sourced ADBench `_ compares 30 anomaly detection algorithms on 57 benchmark datasets. The organization of **ADBench** is provided below: diff --git a/docs/index.rst b/docs/index.rst index 1e0d41a17..7daa8a44b 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -121,20 +121,29 @@ If you use PyOD in a scientific publication, we would appreciate citations to the following paper:: @article{zhao2019pyod, - author = {Zhao, Yue and Nasrullah, Zain and Li, Zheng}, - title = {PyOD: A Python Toolbox for Scalable Outlier Detection}, - journal = {Journal of Machine Learning Research}, - year = {2019}, - volume = {20}, - number = {96}, - pages = {1-7}, - url = {http://jmlr.org/papers/v20/19-011.html} + author = {Zhao, Yue and Nasrullah, Zain and Li, Zheng}, + title = {PyOD: A Python Toolbox for Scalable Outlier Detection}, + journal = {Journal of Machine Learning Research}, + year = {2019}, + volume = {20}, + number = {96}, + pages = {1-7}, + url = {http://jmlr.org/papers/v20/19-011.html} } or:: Zhao, Y., Nasrullah, Z. and Li, Z., 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of machine learning research (JMLR), 20(96), pp.1-7. +If you want more general insights of anomaly detection and/or algorithm performance comparison, please see our +NeurIPS 2022 paper `ADBench: Anomaly Detection Benchmark `_:: + + @inproceedings{han2022adbench, + title={ADBench: Anomaly Detection Benchmark}, + author={Songqiao Han and Xiyang Hu and Hailiang Huang and Mingqi Jiang and Yue Zhao}, + booktitle={Neural Information Processing Systems (NeurIPS)} + year={2022}, + } **Key Links and Resources**\ : @@ -148,8 +157,8 @@ or:: Benchmark ========= -We just released a 36-page, the most comprehensive `anomaly detection benchmark paper `_. -The fully `open-sourced ADBench `_ compares 30 anomaly detection algorithms on 55 benchmark datasets. +We just released a 45-page, the most comprehensive `ADBench: Anomaly Detection Benchmark `_. +The fully `open-sourced ADBench `_ compares 30 anomaly detection algorithms on 57 benchmark datasets. The organization of **ADBench** is provided below: From 2bdc57fdf89f049bb9c299bc83fb15e6f331a5d5 Mon Sep 17 00:00:00 2001 From: "yzhao062@gmail.com" <9io9mZ9K#nNL> Date: Fri, 23 Sep 2022 09:33:19 -0400 Subject: [PATCH 15/17] minor documentation updates --- pyod/models/auto_encoder_torch.py | 24 ++++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/pyod/models/auto_encoder_torch.py b/pyod/models/auto_encoder_torch.py index ab418517a..1279b3986 100644 --- a/pyod/models/auto_encoder_torch.py +++ b/pyod/models/auto_encoder_torch.py @@ -75,13 +75,16 @@ def __init__(self, for idx, layer in enumerate(self.layers_neurons_encoder_[:-1]): # create a linear layer of neurons - self.encoder.add_module("linear" + str(idx), - torch.nn.Linear(layer, self.layers_neurons_encoder_[idx + 1])) + self.encoder.add_module( + "linear" + str(idx), + torch.nn.Linear(layer,self.layers_neurons_encoder_[idx + 1])) # add a batch norm per layer if wanted (leave out first layer) if batch_norm: self.encoder.add_module("batch_norm" + str(idx), - nn.BatchNorm1d(self.layers_neurons_encoder_[idx + 1])) + nn.BatchNorm1d( + self.layers_neurons_encoder_[ + idx + 1])) # create the activation self.encoder.add_module(self.hidden_activation + str(idx), @@ -95,13 +98,17 @@ def __init__(self, for idx, layer in enumerate(self.layers_neurons_decoder_[:-1]): # create a linear layer of neurons - self.decoder.add_module("linear" + str(idx), - torch.nn.Linear(layer, self.layers_neurons_decoder_[idx + 1])) + self.decoder.add_module( + "linear" + str(idx), + torch.nn.Linear(layer,self.layers_neurons_decoder_[idx + 1])) - # create a batch norm per layer if wanted (only if it is not the last layer) + # create a batch norm per layer if wanted (only if it is not the + # last layer) if batch_norm and idx < len(self.layers_neurons_decoder_[:-1]) - 1: self.decoder.add_module("batch_norm" + str(idx), - nn.BatchNorm1d(self.layers_neurons_decoder_[idx + 1])) + nn.BatchNorm1d( + self.layers_neurons_decoder_[ + idx + 1])) # create the activation self.decoder.add_module(self.hidden_activation + str(idx), @@ -113,7 +120,8 @@ def __init__(self, torch.nn.Dropout(dropout_rate)) def forward(self, x): - # we could return the latent representation here after the encoder as the latent representation + # we could return the latent representation here after the encoder + # as the latent representation x = self.encoder(x) x = self.decoder(x) return x From 4029c59573973fc2af1ecf02bcf1dd943cdeeeed Mon Sep 17 00:00:00 2001 From: "yzhao062@gmail.com" <9io9mZ9K#nNL> Date: Mon, 10 Oct 2022 11:21:39 -0400 Subject: [PATCH 16/17] fix #445 to remove cosine as a distance measure under balltree --- pyod/models/knn.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyod/models/knn.py b/pyod/models/knn.py index c94cdde3a..c9eae1e41 100644 --- a/pyod/models/knn.py +++ b/pyod/models/knn.py @@ -92,7 +92,7 @@ class KNN(BaseDetector): Valid values for metric are: - - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', + - from scikit-learn: ['cityblock', 'euclidean', 'l1', 'l2', 'manhattan'] - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', From 4bcb5643afbeabd59487c7b492f10652ccba5f9d Mon Sep 17 00:00:00 2001 From: beingreviewed <116504164+beingreviewed@users.noreply.github.com> Date: Sun, 23 Oct 2022 18:58:15 -0400 Subject: [PATCH 17/17] v1.0.6 --- CHANGES.txt | 1 + README.rst | 14 +++++--- docs/index.rst | 14 +++++--- docs/pyod.models.rst | 9 +++++ docs/zreferences.bib | 11 +++++++ pyod/models/kpca.py | 74 +++++++++++++++++++++--------------------- pyod/test/test_kpca.py | 9 ++--- pyod/version.py | 2 +- 8 files changed, 83 insertions(+), 51 deletions(-) diff --git a/CHANGES.txt b/CHANGES.txt index 19ac2e340..3b7b20df3 100644 --- a/CHANGES.txt +++ b/CHANGES.txt @@ -171,3 +171,4 @@ v<1.0.5>, <07/29/2022> -- Import optimization. v<1.0.5>, <08/27/2022> -- Code optimization. v<1.0.5>, <09/14/2022> -- Add ALAD. v<1.0.6>, <09/23/2022> -- Update ADBench benchmark for NeruIPS 2022. +v<1.0.6>, <10/23/2022> -- ADD KPCA. diff --git a/README.rst b/README.rst index ad53928a8..1b5c496b5 100644 --- a/README.rst +++ b/README.rst @@ -58,8 +58,11 @@ Python Outlier Detection (PyOD) ----- -**News**: We just released a 36-page, the most comprehensive `anomaly detection benchmark paper `_. -The fully `open-sourced ADBench `_ compares 30 anomaly detection algorithms on 55 benchmark datasets. +**News**: We just released a 45-page, the most comprehensive `anomaly detection benchmark paper `_. +The fully `open-sourced ADBench `_ compares 30 anomaly detection algorithms on 57 benchmark datasets. + +**For time-series outlier detection**, please use `TODS `_. +**For graph outlier detection**, please use `PyGOD `_. PyOD is the most comprehensive and scalable **Python library** for **detecting outlying objects** in multivariate data. This exciting yet challenging field is commonly referred as @@ -68,7 +71,7 @@ or `Anomaly Detection `_. PyOD includes more than 40 detection algorithms, from classical LOF (SIGMOD 2000) to the latest ECOD (TKDE 2022). Since 2017, PyOD has been successfully used in numerous academic researches and -commercial products with more than `8 million downloads `_. +commercial products with more than `10 million downloads `_. It is also well acknowledged by the machine learning community with various dedicated posts/tutorials, including `Analytics Vidhya `_, `KDnuggets `_, and @@ -129,7 +132,7 @@ or:: Zhao, Y., Nasrullah, Z. and Li, Z., 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of machine learning research (JMLR), 20(96), pp.1-7. If you want more general insights of anomaly detection and/or algorithm performance comparison, please see our -NeurIPS 2022 paper `ADBench: Anomaly Detection Benchmark `_:: +NeurIPS 2022 paper `ADBench: Anomaly Detection Benchmark `_:: @inproceedings{han2022adbench, title={ADBench: Anomaly Detection Benchmark}, @@ -351,6 +354,7 @@ Probabilistic KDE Outlier Detection with Kernel Density F Probabilistic Sampling Rapid distance-based outlier detection via sampling 2013 [#Sugiyama2013Rapid]_ Probabilistic GMM Probabilistic Mixture Modeling for Outlier Analysis [#Aggarwal2015Outlier]_ [Ch.2] Linear Model PCA Principal Component Analysis (the sum of weighted projected distances to the eigenvector hyperplanes) 2003 [#Shyu2003A]_ +Linear Model KPCA Kernel Principal Component Analysis 2007 [#Hoffmann2007Kernel]_ Linear Model MCD Minimum Covariance Determinant (use the mahalanobis distances as the outlier scores) 1999 [#Hardin2004Outlier]_ [#Rousseeuw1999A]_ Linear Model CD Use Cook's distance for outlier detection 1977 [#Cook1977Detection]_ Linear Model OCSVM One-Class Support Vector Machines 2001 [#Scholkopf2001Estimating]_ @@ -573,6 +577,8 @@ Reference .. [#He2003Discovering] He, Z., Xu, X. and Deng, S., 2003. Discovering cluster-based local outliers. *Pattern Recognition Letters*\ , 24(9-10), pp.1641-1650. +.. [#Hoffmann2007Kernel] Hoffmann, H., 2007. Kernel PCA for novelty detection. Pattern recognition, 40(3), pp.863-874. + .. [#Iglewicz1993How] Iglewicz, B. and Hoaglin, D.C., 1993. How to detect and handle outliers (Vol. 16). Asq Press. .. [#Janssens2012Stochastic] Janssens, J.H.M., Huszár, F., Postma, E.O. and van den Herik, H.J., 2012. Stochastic outlier selection. Technical report TiCC TR 2012-001, Tilburg University, Tilburg Center for Cognition and Communication, Tilburg, The Netherlands. diff --git a/docs/index.rst b/docs/index.rst index 7daa8a44b..1a00da268 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -64,8 +64,11 @@ Welcome to PyOD documentation! ---- -**News**: We just released a 36-page, the most comprehensive `anomaly detection benchmark paper `_. -The fully `open-sourced ADBench `_ compares 30 anomaly detection algorithms on 55 benchmark datasets. +**News**: We just released a 45-page, the most comprehensive `anomaly detection benchmark paper `_. +The fully `open-sourced ADBench `_ compares 30 anomaly detection algorithms on 57 benchmark datasets. + +**For time-series outlier detection**, please use `TODS `_. +**For graph outlier detection**, please use `PyGOD `_. PyOD is the most comprehensive and scalable **Python library** for **detecting outlying objects** in multivariate data. This exciting yet challenging field is commonly referred as @@ -73,8 +76,8 @@ multivariate data. This exciting yet challenging field is commonly referred as or `Anomaly Detection `_. PyOD includes more than 40 detection algorithms, from classical LOF (SIGMOD 2000) to -the latest ECOD (TKDE 2020). Since 2017, PyOD :cite:`a-zhao2019pyod` has been successfully used in numerous -academic researches and commercial products with more than `8 million downloads `_. +the latest ECOD (TKDE 2022). Since 2017, PyOD :cite:`a-zhao2019pyod` has been successfully used in numerous +academic researches and commercial products with more than `10 million downloads `_. It is also well acknowledged by the machine learning community with various dedicated posts/tutorials, including `Analytics Vidhya `_, `KDnuggets `_, and @@ -136,7 +139,7 @@ or:: Zhao, Y., Nasrullah, Z. and Li, Z., 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of machine learning research (JMLR), 20(96), pp.1-7. If you want more general insights of anomaly detection and/or algorithm performance comparison, please see our -NeurIPS 2022 paper `ADBench: Anomaly Detection Benchmark `_:: +NeurIPS 2022 paper `ADBench: Anomaly Detection Benchmark `_:: @inproceedings{han2022adbench, title={ADBench: Anomaly Detection Benchmark}, @@ -187,6 +190,7 @@ Probabilistic KDE Outlier Detection with Kernel Density Fun Probabilistic Sampling Rapid distance-based outlier detection via sampling 2013 :class:`pyod.models.sampling.Sampling` :cite:`a-sugiyama2013rapid` Probabilistic GMM Probabilistic Mixture Modeling for Outlier Analysis :class:`pyod.models.gmm.GMM` :cite:`a-aggarwal2015outlier` [Ch.2] Linear Model PCA Principal Component Analysis (the sum of weighted projected distances to the eigenvector hyperplanes) 2003 :class:`pyod.models.pca.PCA` :cite:`a-shyu2003novel` +Linear Model KPCA Kernel Principal Component Analysis 2007 :class:`pyod.models.kpca.KPCA` :cite:`a-hoffmann2007kernel` Linear Model MCD Minimum Covariance Determinant (use the mahalanobis distances as the outlier scores) 1999 :class:`pyod.models.mcd.MCD` :cite:`a-rousseeuw1999fast,a-hardin2004outlier` Linear Model CD Use Cook's distance for outlier detection 1977 :class:`pyod.models.cd.CD` :cite:`a-cook1977detection` Linear Model OCSVM One-Class Support Vector Machines 2001 :class:`pyod.models.ocsvm.OCSVM` :cite:`a-scholkopf2001estimating` diff --git a/docs/pyod.models.rst b/docs/pyod.models.rst index 2ce72bd3c..d67236c36 100644 --- a/docs/pyod.models.rst +++ b/docs/pyod.models.rst @@ -181,6 +181,15 @@ pyod.models.knn module :show-inheritance: :inherited-members: +pyod.models.kpca module +----------------------- + +.. automodule:: pyod.models.kpca + :members: + :undoc-members: + :show-inheritance: + :inherited-members: + pyod.models.lmdd module ----------------------- diff --git a/docs/zreferences.bib b/docs/zreferences.bib index 60375b761..3bdc1a8ec 100644 --- a/docs/zreferences.bib +++ b/docs/zreferences.bib @@ -467,4 +467,15 @@ @inproceedings{zenati2018adversarially pages={727--736}, year={2018}, organization={IEEE} +} + +@article{hoffmann2007kernel, + title={Kernel PCA for novelty detection}, + author={Hoffmann, Heiko}, + journal={Pattern recognition}, + volume={40}, + number={3}, + pages={863--874}, + year={2007}, + publisher={Elsevier} } \ No newline at end of file diff --git a/pyod/models/kpca.py b/pyod/models/kpca.py index 51b4e5885..02d081ff7 100644 --- a/pyod/models/kpca.py +++ b/pyod/models/kpca.py @@ -10,30 +10,30 @@ from sklearn.utils import check_array, check_random_state from sklearn.utils.validation import check_is_fitted -from ..utils.utility import check_parameter from .base import BaseDetector +from ..utils.utility import check_parameter class PyODKernelPCA(KernelPCA): """A wrapper class for KernelPCA class of scikit-learn.""" def __init__( - self, - n_components=None, - kernel="rbf", - gamma=None, - degree=3, - coef0=1, - kernel_params=None, - alpha=1.0, - fit_inverse_transform=False, - eigen_solver="auto", - tol=0, - max_iter=None, - remove_zero_eig=False, - copy_X=True, - n_jobs=None, - random_state=None, + self, + n_components=None, + kernel="rbf", + gamma=None, + degree=3, + coef0=1, + kernel_params=None, + alpha=1.0, + fit_inverse_transform=False, + eigen_solver="auto", + tol=0, + max_iter=None, + remove_zero_eig=False, + copy_X=True, + n_jobs=None, + random_state=None, ): super().__init__( kernel=kernel, @@ -69,7 +69,7 @@ class KPCA(BaseDetector): PCA is performed on the feature space uniquely determined by the kernel, and the reconstruction error on the feature space is used as the anomaly score. - Reference: See + See :cite:`hoffmann2007kernel` Heiko Hoffmann, "Kernel PCA for novelty detection," Pattern Recognition, vol.40, no.3, pp. 863-874, 2007. https://www.sciencedirect.com/science/article/pii/S0031320306003414 @@ -198,25 +198,25 @@ class KPCA(BaseDetector): """ def __init__( - self, - contamination=0.1, - n_components=None, - n_selected_components=None, - kernel="rbf", - gamma=None, - degree=3, - coef0=1, - kernel_params=None, - alpha=1.0, - eigen_solver="auto", - tol=0, - max_iter=None, - remove_zero_eig=False, - copy_X=True, - n_jobs=None, - sampling=False, - subset_size=20, - random_state=None, + self, + contamination=0.1, + n_components=None, + n_selected_components=None, + kernel="rbf", + gamma=None, + degree=3, + coef0=1, + kernel_params=None, + alpha=1.0, + eigen_solver="auto", + tol=0, + max_iter=None, + remove_zero_eig=False, + copy_X=True, + n_jobs=None, + sampling=False, + subset_size=20, + random_state=None, ): super().__init__(contamination=contamination) self.n_components = n_components diff --git a/pyod/test/test_kpca.py b/pyod/test/test_kpca.py index 6bbb26fb6..621efe5ec 100644 --- a/pyod/test/test_kpca.py +++ b/pyod/test/test_kpca.py @@ -8,12 +8,13 @@ # noinspection PyProtectedMember from numpy.testing import (assert_allclose, assert_array_less, assert_equal, assert_raises) -from pyod.models.kpca import KPCA -from pyod.utils.data import generate_data from scipy.stats import rankdata from sklearn.base import clone from sklearn.metrics import roc_auc_score +from pyod.models.kpca import KPCA +from pyod.utils.data import generate_data + # temporary solution for relative imports in case pyod is not installed # if pyod is installed, no need to use the following line sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) @@ -37,8 +38,8 @@ def setUp(self): def test_parameters(self): assert ( - hasattr(self.clf, "decision_scores_") - and self.clf.decision_scores_ is not None + hasattr(self.clf, "decision_scores_") + and self.clf.decision_scores_ is not None ) assert hasattr(self.clf, "labels_") and self.clf.labels_ is not None assert hasattr(self.clf, "threshold_") and self.clf.threshold_ is not None diff --git a/pyod/version.py b/pyod/version.py index 6c7894379..fde31e04f 100644 --- a/pyod/version.py +++ b/pyod/version.py @@ -20,4 +20,4 @@ # Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer. # 'X.Y.dev0' is the canonical version of 'X.Y.dev' # -__version__ = '1.0.5' # pragma: no cover +__version__ = '1.0.6' # pragma: no cover

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