From fe03f0c83cd4b0e4319bad1ad7bcb4e51005e232 Mon Sep 17 00:00:00 2001 From: Alex Li <7Alex7Li@gmail.com> Date: Tue, 21 May 2019 17:29:39 -0400 Subject: [PATCH 01/31] Moved eigenpro files to sklearn-extra --- .idea/misc.xml | 4 + .idea/modules.xml | 8 + .idea/scikit-learn-extra.iml | 12 + .idea/vcs.xml | 6 + .idea/workspace.xml | 291 ++++++++++++ doc/images/fast_kernel_mnist.png | Bin 0 -> 72335 bytes doc/images/fast_kernel_noisy_mnist.png | Bin 0 -> 76762 bytes doc/images/fast_kernel_synthetic.png | Bin 0 -> 71927 bytes doc/modules/fast_kernel.rst | 62 +++ examples/fast_kernel/README.txt | 6 + examples/fast_kernel/plot_mnist.py | 114 +++++ examples/fast_kernel/plot_noisy_mnist.py | 122 +++++ examples/fast_kernel/plot_synthetic.py | 119 +++++ sklearn_extra/fast_kernel.py | 544 +++++++++++++++++++++++ sklearn_extra/tests/test_fast_kernel.py | 157 +++++++ 15 files changed, 1445 insertions(+) create mode 100644 .idea/misc.xml create mode 100644 .idea/modules.xml create mode 100644 .idea/scikit-learn-extra.iml create mode 100644 .idea/vcs.xml create mode 100644 .idea/workspace.xml create mode 100644 doc/images/fast_kernel_mnist.png create mode 100644 doc/images/fast_kernel_noisy_mnist.png create mode 100644 doc/images/fast_kernel_synthetic.png create mode 100644 doc/modules/fast_kernel.rst create mode 100644 examples/fast_kernel/README.txt create mode 100644 examples/fast_kernel/plot_mnist.py create mode 100644 examples/fast_kernel/plot_noisy_mnist.py create mode 100644 examples/fast_kernel/plot_synthetic.py create mode 100644 sklearn_extra/fast_kernel.py create mode 100644 sklearn_extra/tests/test_fast_kernel.py diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 00000000..65531ca9 --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 00000000..75659b6b --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/scikit-learn-extra.iml b/.idea/scikit-learn-extra.iml new file mode 100644 index 00000000..7c9d48f0 --- /dev/null +++ b/.idea/scikit-learn-extra.iml @@ -0,0 +1,12 @@ + + + + + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 00000000..94a25f7f --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/.idea/workspace.xml b/.idea/workspace.xml new file mode 100644 index 00000000..c77cdf2a --- /dev/null +++ b/.idea/workspace.xml @@ -0,0 +1,291 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 1558472799529 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file From e42e447e81b762a54d6f7bb0ebf337dfc43c7807 Mon Sep 17 00:00:00 2001 From: Alex Li <7Alex7Li@gmail.com> Date: Tue, 21 May 2019 18:58:06 -0400 Subject: [PATCH 03/31] Import from sklearn_extra not sklearn --- examples/fast_kernel/plot_mnist.py | 2 +- examples/fast_kernel/plot_noisy_mnist.py | 2 +- examples/fast_kernel/plot_synthetic.py | 2 +- sklearn_extra/tests/test_fast_kernel.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/fast_kernel/plot_mnist.py b/examples/fast_kernel/plot_mnist.py index 58ea92ea..68085b8d 100644 --- a/examples/fast_kernel/plot_mnist.py +++ b/examples/fast_kernel/plot_mnist.py @@ -16,7 +16,7 @@ import numpy as np from time import time -from sklearn.fast_kernel import FKC_EigenPro +from sklearn_extra.fast_kernel import FKC_EigenPro from sklearn.svm import SVC from sklearn.datasets import fetch_mldata diff --git a/examples/fast_kernel/plot_noisy_mnist.py b/examples/fast_kernel/plot_noisy_mnist.py index e95e46bb..66223376 100644 --- a/examples/fast_kernel/plot_noisy_mnist.py +++ b/examples/fast_kernel/plot_noisy_mnist.py @@ -19,7 +19,7 @@ from time import time from sklearn.datasets import fetch_mldata -from sklearn.fast_kernel import FKC_EigenPro +from sklearn_extra.fast_kernel import FKC_EigenPro from sklearn.svm import SVC rng = np.random.RandomState(1) diff --git a/examples/fast_kernel/plot_synthetic.py b/examples/fast_kernel/plot_synthetic.py index b7af293f..5f9646f0 100644 --- a/examples/fast_kernel/plot_synthetic.py +++ b/examples/fast_kernel/plot_synthetic.py @@ -20,7 +20,7 @@ from time import time from sklearn.datasets import make_classification -from sklearn.fast_kernel import FKC_EigenPro +from sklearn_extra.fast_kernel import FKC_EigenPro from sklearn.svm import SVC rng = np.random.RandomState(1) diff --git a/sklearn_extra/tests/test_fast_kernel.py b/sklearn_extra/tests/test_fast_kernel.py index e0c1b00b..fc9ce470 100644 --- a/sklearn_extra/tests/test_fast_kernel.py +++ b/sklearn_extra/tests/test_fast_kernel.py @@ -2,7 +2,7 @@ from sklearn.datasets import make_regression, make_classification from sklearn.utils.testing import assert_array_almost_equal -from sklearn.fast_kernel import FKR_EigenPro, FKC_EigenPro +from sklearn_extra.fast_kernel import FKR_EigenPro, FKC_EigenPro np.random.seed(1) # Tests for Fast Kernel Regression and Classification From 850d6ecabf9971f1f77db80f8b27216d73aa0a3c Mon Sep 17 00:00:00 2001 From: Alex Li <7Alex7Li@gmail.com> Date: Tue, 21 May 2019 20:26:01 -0400 Subject: [PATCH 04/31] Updated website to work with EigenPro --- doc/api.rst | 14 ++++++++++++++ doc/modules/fast_kernel.rst | 2 +- 2 files changed, 15 insertions(+), 1 deletion(-) diff --git a/doc/api.rst b/doc/api.rst index e8de935e..bdaaafe9 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -12,3 +12,17 @@ Kernel approximation :template: class.rst kernel_approximation.Fastfood + +EigenPro +======== + +.. currentmodule:: doc + +.. toctree:: + modules/fast_kernel + +.. currentmodule:: sklearn_extra + +.. autosummary:: + fast_kernel.FKR_EigenPro + fast_kernel.FKC_EigenPro diff --git a/doc/modules/fast_kernel.rst b/doc/modules/fast_kernel.rst index 72e55308..d8abbf23 100644 --- a/doc/modules/fast_kernel.rst +++ b/doc/modules/fast_kernel.rst @@ -4,7 +4,7 @@ Fast Kernel Machine (EigenPro) for Regression and Classification ================================================================ -.. currentmodule:: sklearn.fast_kernel +.. currentmodule:: sklearn_extra.fast_kernel Fast Kernel Machine is a very efficient implementation of kernel regression/classification using *EigenPro iteration* [MB17]_, From ac61cc1f66668998da3ec4c50d7bc93d1a4e7ee3 Mon Sep 17 00:00:00 2001 From: Alex Li <7Alex7Li@gmail.com> Date: Tue, 21 May 2019 20:31:14 -0400 Subject: [PATCH 05/31] Modify examples --- .gitignore | 1 + examples/fast_kernel/README.txt | 2 +- examples/fast_kernel/plot_mnist.py | 7 ++++--- examples/fast_kernel/plot_noisy_mnist.py | 8 ++++---- examples/fast_kernel/plot_synthetic.py | 10 +++++----- sklearn_extra/fast_kernel.py | 4 ++-- 6 files changed, 17 insertions(+), 15 deletions(-) diff --git a/.gitignore b/.gitignore index f241c34e..7f7c45b2 100644 --- a/.gitignore +++ b/.gitignore @@ -68,4 +68,5 @@ doc/generated/ # PyBuilder target/ +# Pycharm .idea \ No newline at end of file diff --git a/examples/fast_kernel/README.txt b/examples/fast_kernel/README.txt index 35d94786..04b8b128 100644 --- a/examples/fast_kernel/README.txt +++ b/examples/fast_kernel/README.txt @@ -1,6 +1,6 @@ .. _fast_kernel_examples: Fast Kernel -------------- +=========== Examples concerning the :mod:`sklearn.fast_kernel` module. diff --git a/examples/fast_kernel/plot_mnist.py b/examples/fast_kernel/plot_mnist.py index 68085b8d..5397c5a0 100644 --- a/examples/fast_kernel/plot_mnist.py +++ b/examples/fast_kernel/plot_mnist.py @@ -1,7 +1,8 @@ """ -================================================================ -Comparison of Fast Kernel Classifier (EigenPro) and SVC on MNIST -================================================================ +=========================================== +Comparison of FKC_EigenPro and SVC on MNIST +=========================================== + Here we train a Fast Kernel Classifier (EigenPro) and a Support Vector Classifier (SVC) on subsets of MNIST of various sizes. We halt the training of EigenPro in two epochs. diff --git a/examples/fast_kernel/plot_noisy_mnist.py b/examples/fast_kernel/plot_noisy_mnist.py index 66223376..9bc586cc 100644 --- a/examples/fast_kernel/plot_noisy_mnist.py +++ b/examples/fast_kernel/plot_noisy_mnist.py @@ -1,8 +1,8 @@ """ -================================================================ -Comparison of Fast Kernel Classifier (EigenPro) and SVC on MNIST -with added label noise -================================================================ +================================================================== +Comparison of FKC_EigenPro and SVC on MNIST with added label noise +================================================================== + Here we train a Fast Kernel Machine (EigenPro) and a Support Vector Classifier (SVC) on subsets of MNIST with added label noises. Specifically, we randomly reset the label (0-9) of 20% diff --git a/examples/fast_kernel/plot_synthetic.py b/examples/fast_kernel/plot_synthetic.py index 5f9646f0..a6039d2f 100644 --- a/examples/fast_kernel/plot_synthetic.py +++ b/examples/fast_kernel/plot_synthetic.py @@ -1,15 +1,15 @@ """ -======================================================= -Comparison of Fast Kernel Machine (Eigenpro) and SVC on -synthetic dataset -======================================================= +========================================================================= +Comparison of Fast Kernel Machine (Eigenpro) and SVC on synthetic dataset +========================================================================= + Here we train a Fast Kernel Machine (EigenPro) and a Support Vector Classifier (SVC) on subsets of a synthetic dataset. Features of this dataset are sampled from two independent Gaussian distributions. We halt the training for EigenPro in 3 epochs. Experimental results demonstrate that EigenPro achieves high test accuracy, -competitive to that of SVC, while completes training in +competitive to that of SVC, while completing training in significant less time (8 times speedup). """ print(__doc__) diff --git a/sklearn_extra/fast_kernel.py b/sklearn_extra/fast_kernel.py index c315f471..363b3a09 100644 --- a/sklearn_extra/fast_kernel.py +++ b/sklearn_extra/fast_kernel.py @@ -80,7 +80,7 @@ class FKR_EigenPro(BaseEstimator, RegressorMixin): Examples -------- - >>> from sklearn.fast_kernel import FKR_EigenPro + >>> from sklearn_extra.fast_kernel import FKR_EigenPro >>> import numpy as np >>> n_samples, n_features, n_targets = 4000, 20, 3 >>> rng = np.random.RandomState(1) @@ -454,7 +454,7 @@ class FKC_EigenPro(BaseEstimator, ClassifierMixin): Examples -------- - >>> from sklearn.fast_kernel import FKC_EigenPro + >>> from sklearn_extra.fast_kernel import FKC_EigenPro >>> import numpy as np >>> n_samples, n_features, n_targets = 4000, 20, 3 >>> rng = np.random.RandomState(1) From 9f7ef751b49ccc441fdec4952fbde278a1863c6d Mon Sep 17 00:00:00 2001 From: Alex Li <7Alex7Li@gmail.com> Date: Tue, 21 May 2019 20:53:30 -0400 Subject: [PATCH 06/31] Update examples --- examples/fast_kernel/plot_mnist.py | 4 ++-- examples/fast_kernel/plot_noisy_mnist.py | 4 ++-- .../kernel_approximation/_fastfood.py | 2 +- vim.exe.stackdump | 19 +++++++++++++++++++ 4 files changed, 24 insertions(+), 5 deletions(-) create mode 100644 vim.exe.stackdump diff --git a/examples/fast_kernel/plot_mnist.py b/examples/fast_kernel/plot_mnist.py index 5397c5a0..a398e446 100644 --- a/examples/fast_kernel/plot_mnist.py +++ b/examples/fast_kernel/plot_mnist.py @@ -19,12 +19,12 @@ from sklearn_extra.fast_kernel import FKC_EigenPro from sklearn.svm import SVC -from sklearn.datasets import fetch_mldata +from sklearn.datasets import fetch_openml rng = np.random.RandomState(1) # Generate sample data from mnist -mnist = fetch_mldata('MNIST original') +mnist = fetch_openml('mnist_784') mnist.data = mnist.data / 255. p = np.random.permutation(60000) diff --git a/examples/fast_kernel/plot_noisy_mnist.py b/examples/fast_kernel/plot_noisy_mnist.py index 9bc586cc..5684c173 100644 --- a/examples/fast_kernel/plot_noisy_mnist.py +++ b/examples/fast_kernel/plot_noisy_mnist.py @@ -18,13 +18,13 @@ import numpy as np from time import time -from sklearn.datasets import fetch_mldata +from sklearn.datasets import fetch_openml from sklearn_extra.fast_kernel import FKC_EigenPro from sklearn.svm import SVC rng = np.random.RandomState(1) # Generate sample data from mnist -mnist = fetch_mldata('MNIST original') +mnist = fetch_openml('mnist_784') mnist.data = mnist.data / 255. p = rng.permutation(60000) diff --git a/sklearn_extra/kernel_approximation/_fastfood.py b/sklearn_extra/kernel_approximation/_fastfood.py index bb2d32d2..2b5daa0e 100644 --- a/sklearn_extra/kernel_approximation/_fastfood.py +++ b/sklearn_extra/kernel_approximation/_fastfood.py @@ -7,7 +7,7 @@ from sklearn.base import TransformerMixin from sklearn.utils import check_array, check_random_state -from ..utils._cyfht import fht2 as cyfht +#from ..utils._cyfht import fht2 as cyfht class Fastfood(BaseEstimator, TransformerMixin): diff --git a/vim.exe.stackdump b/vim.exe.stackdump new file mode 100644 index 00000000..ac4e8065 --- /dev/null +++ b/vim.exe.stackdump @@ -0,0 +1,19 @@ +Stack trace: +Frame Function Args +00180000000 0018005E0DE (00180230639, 00180230C39, 001802412F0, 000FFFFB730) +00180000000 001800468F9 (C0C0C000008080, FF000000808080, FFFF000000FF00, 0000024F930) +00180000000 00180046932 (00180230616, 000000001E7, 001802412F0, 80808000C0C0C0) +00180000000 00180043543 (00000000000, 00180000000, 7FFB9BEA888E, 001800004EC) +00180000000 0018006BF01 (C0C0C000008080, FF000000808080, FFFF000000FF00, FF00FF000000FF) +00180000000 0018006CD8E (00000000000, 00000000000, 00000000000, 00000000000) +00180000000 0018006ED24 (00000000000, 00000000008, 00000000000, 00000000000) +00600049320 001801372B1 (00100666960, 00000000008, 00000000000, 00000000000) +00600049320 0018011DE4B (00100666960, 00000000008, 00000000000, 00000000000) +00600049320 001004F8574 (00100577CA4, 0010066D398, 00000000000, 0010066D39C) +00600049320 001005872D3 (00600000008, 0010066E9E0, 00000000000, 00000000000) +00600049320 00100578C76 (00022EF0B88, 0005CA5387C, 00024A85574, 00000010000) +00600049320 001005DB4DF (0017FF845B0, 0010000000E, 000FFFFCCD0, 00000000000) +00600049320 001005E9807 (00000000020, 001802F7920, 00180047EB8, 00180046E80) +000FFFFCCD0 00180047F24 (00000000000, 00000000000, 00000000000, 00000000000) +00000000000 00180045A03 (00000000000, 00000000000, 00000000000, 00000000000) +End of stack trace (more stack frames may be present) From 462c053e60594863076f79bae71cf436175bc0b5 Mon Sep 17 00:00:00 2001 From: Alex Li <7Alex7Li@gmail.com> Date: Tue, 21 May 2019 21:43:58 -0400 Subject: [PATCH 07/31] Trying to find out why examples are crashing --- doc/api.rst | 3 +-- examples/fast_kernel/plot_mnist.py | 4 ++-- examples/fast_kernel/plot_noisy_mnist.py | 2 +- examples/fast_kernel/plot_synthetic.py | 10 ++++++---- sklearn_extra/fast_kernel.py | 5 +++-- .../kernel_approximation/_fastfood.py | 2 +- vim.exe.stackdump | 19 ------------------- 7 files changed, 14 insertions(+), 31 deletions(-) delete mode 100644 vim.exe.stackdump diff --git a/doc/api.rst b/doc/api.rst index bdaaafe9..77ded21b 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -24,5 +24,4 @@ EigenPro .. currentmodule:: sklearn_extra .. autosummary:: - fast_kernel.FKR_EigenPro - fast_kernel.FKC_EigenPro + fast_kernel diff --git a/examples/fast_kernel/plot_mnist.py b/examples/fast_kernel/plot_mnist.py index a398e446..aaebff9a 100644 --- a/examples/fast_kernel/plot_mnist.py +++ b/examples/fast_kernel/plot_mnist.py @@ -25,7 +25,7 @@ # Generate sample data from mnist mnist = fetch_openml('mnist_784') -mnist.data = mnist.data / 255. +mnist.data = mnist.data / 255.0 p = np.random.permutation(60000) x_train = mnist.data[p][:60000] @@ -43,7 +43,7 @@ train_sizes = [500, 1000, 2000] -bandwidth = 5 +bandwidth = 5.0 # Fit models to data for train_size in train_sizes: diff --git a/examples/fast_kernel/plot_noisy_mnist.py b/examples/fast_kernel/plot_noisy_mnist.py index 5684c173..894aed1b 100644 --- a/examples/fast_kernel/plot_noisy_mnist.py +++ b/examples/fast_kernel/plot_noisy_mnist.py @@ -53,7 +53,7 @@ for train_size in train_sizes: for name, estimator in [ ("FastKernel", - FKC_EigenPro(n_epoch=2, bandwidth=5, random_state=rng)), + FKC_EigenPro(n_epoch=2, bandwidth=5., random_state=rng)), ("SupportVector", SVC(C=5, gamma=1. / (2 * 5 * 5)))]: stime = time() estimator.fit(x_train[:train_size], y_train[:train_size]) diff --git a/examples/fast_kernel/plot_synthetic.py b/examples/fast_kernel/plot_synthetic.py index a6039d2f..809e3de0 100644 --- a/examples/fast_kernel/plot_synthetic.py +++ b/examples/fast_kernel/plot_synthetic.py @@ -47,13 +47,15 @@ svc_pred_times = [] svc_err = [] -train_sizes = [2000, 5000, 10000] +train_sizes = [2000, 4000, 8000] +bandwidth = 10. for train_size in train_sizes: for name, estimator in [ - ("FastKernel", FKC_EigenPro(n_epoch=3, bandwidth=10, - random_state=rng)), - ("SupportVector", SVC(C=5, gamma=1./(2 * 10 * 10)))]: + ("FastKernel", + FKC_EigenPro(n_epoch=3, bandwidth=bandwidth, n_components=30, + subsample_size=1000, random_state=rng)), + ("SupportVector", SVC(C=5, gamma=1./(2 * bandwidth * bandwidth)))]: stime = time() estimator.fit(x_train[:train_size], y_train[:train_size]) fit_t = time() - stime diff --git a/sklearn_extra/fast_kernel.py b/sklearn_extra/fast_kernel.py index 363b3a09..42d113bc 100644 --- a/sklearn_extra/fast_kernel.py +++ b/sklearn_extra/fast_kernel.py @@ -262,7 +262,7 @@ def _initialize_params(self, X, Y): # Each batch will require about 1 gb memory mem_bytes = 1024 ** 3 mem_usages = (d + n_label + 2 * np.arange(sample_size)) * n * 4 - mG = np.sum(mem_usages < mem_bytes) + mG = np.int32(np.sum(mem_usages < mem_bytes)) # Calculate largest eigenvalue and max{k(x,x)} using subsamples. self.pinx_ = self.random_state_.choice(n, sample_size, @@ -507,7 +507,8 @@ def fit(self, X, Y): bandwidth=self.bandwidth, gamma=self.gamma, degree=self.degree, coef0=self.coef0, kernel_params=self.kernel_params, random_state=self.random_state) - X, Y = check_X_y(X, Y, multi_output=False, ensure_min_samples=3) + X, Y = check_X_y(X, Y, dtype=np.float32, + multi_output=False, ensure_min_samples=3) check_classification_targets(Y) self.classes_ = np.unique(Y) diff --git a/sklearn_extra/kernel_approximation/_fastfood.py b/sklearn_extra/kernel_approximation/_fastfood.py index 2b5daa0e..bb2d32d2 100644 --- a/sklearn_extra/kernel_approximation/_fastfood.py +++ b/sklearn_extra/kernel_approximation/_fastfood.py @@ -7,7 +7,7 @@ from sklearn.base import TransformerMixin from sklearn.utils import check_array, check_random_state -#from ..utils._cyfht import fht2 as cyfht +from ..utils._cyfht import fht2 as cyfht class Fastfood(BaseEstimator, TransformerMixin): diff --git a/vim.exe.stackdump b/vim.exe.stackdump deleted file mode 100644 index ac4e8065..00000000 --- a/vim.exe.stackdump +++ /dev/null @@ -1,19 +0,0 @@ -Stack trace: -Frame Function Args -00180000000 0018005E0DE (00180230639, 00180230C39, 001802412F0, 000FFFFB730) -00180000000 001800468F9 (C0C0C000008080, FF000000808080, FFFF000000FF00, 0000024F930) -00180000000 00180046932 (00180230616, 000000001E7, 001802412F0, 80808000C0C0C0) -00180000000 00180043543 (00000000000, 00180000000, 7FFB9BEA888E, 001800004EC) -00180000000 0018006BF01 (C0C0C000008080, FF000000808080, FFFF000000FF00, FF00FF000000FF) -00180000000 0018006CD8E (00000000000, 00000000000, 00000000000, 00000000000) -00180000000 0018006ED24 (00000000000, 00000000008, 00000000000, 00000000000) -00600049320 001801372B1 (00100666960, 00000000008, 00000000000, 00000000000) -00600049320 0018011DE4B (00100666960, 00000000008, 00000000000, 00000000000) -00600049320 001004F8574 (00100577CA4, 0010066D398, 00000000000, 0010066D39C) -00600049320 001005872D3 (00600000008, 0010066E9E0, 00000000000, 00000000000) -00600049320 00100578C76 (00022EF0B88, 0005CA5387C, 00024A85574, 00000010000) -00600049320 001005DB4DF (0017FF845B0, 0010000000E, 000FFFFCCD0, 00000000000) -00600049320 001005E9807 (00000000020, 001802F7920, 00180047EB8, 00180046E80) -000FFFFCCD0 00180047F24 (00000000000, 00000000000, 00000000000, 00000000000) -00000000000 00180045A03 (00000000000, 00000000000, 00000000000, 00000000000) -End of stack trace (more stack frames may be present) From 4d3da41be46b6a7f0b7df282f8185042f4f85e4a Mon Sep 17 00:00:00 2001 From: Alex Li <7Alex7Li@gmail.com> Date: Tue, 21 May 2019 21:53:59 -0400 Subject: [PATCH 08/31] removed Y squared parameter from the call to euclidean distances --- sklearn_extra/fast_kernel.py | 21 ++++++--------------- 1 file changed, 6 insertions(+), 15 deletions(-) diff --git a/sklearn_extra/fast_kernel.py b/sklearn_extra/fast_kernel.py index 42d113bc..73a3fcdc 100644 --- a/sklearn_extra/fast_kernel.py +++ b/sklearn_extra/fast_kernel.py @@ -111,7 +111,7 @@ def __init__(self, batch_size="auto", n_epoch=2, n_components=1000, self.kernel_params = kernel_params self.random_state = random_state - def _kernel(self, X, Y, Y_squared=None): + def _kernel(self, X, Y): """Calculate the kernel matrix Parameters @@ -122,9 +122,6 @@ def _kernel(self, X, Y, Y_squared=None): Y : {float, array}, shape = [n_centers, n_targets] Kernel centers. - Y_squared : {float, array}, shape = [1, n_centers] - Square of L2 norms of centers. - Returns ------- K : {float, array}, shape = [n_samples, n_centers] @@ -141,8 +138,7 @@ def _kernel(self, X, Y, Y_squared=None): "coef0": self.coef0} return pairwise_kernels(X, Y, metric=self.kernel, filter_params=True, **params) - distance = euclidean_distances(X, Y, squared=True, - Y_norm_squared=Y_squared) + distance = euclidean_distances(X, Y, squared=True) bandwidth = np.float32(self.bandwidth) if self.kernel == "gaussian": K = np.exp(-distance / (2 * np.square(bandwidth))) @@ -307,7 +303,7 @@ def fit(self, X, Y): """ X, Y = check_X_y(X, Y, dtype=np.float32, multi_output=True, ensure_min_samples=3, y_numeric=True) - Y = Y.astype(np.float32) # check_X_y does not seem to do this + Y = Y.astype(np.float32) """Parameter Initialization""" Y = self._initialize_params(X, Y) @@ -315,8 +311,6 @@ def fit(self, X, Y): n = self.centers_.shape[0] self.coef_ = np.zeros((n, Y.shape[1]), dtype=np.float32) - self.centers_squared_ = \ - np.square(self.centers_).sum(axis=1, keepdims=True).T step = np.float32(self.eta_ / self.bs_) for epoch in range(0, self.n_epoch): epoch_inds = \ @@ -325,9 +319,7 @@ def fit(self, X, Y): for batch_inds in np.array_split(epoch_inds, n // self.bs_): batch_x = self.centers_[batch_inds] - kfeat = self._kernel(batch_x, self.centers_, - Y_squared=self.centers_squared_) - + kfeat = self._kernel(batch_x, self.centers_) batch_y = Y[batch_inds] # Update 1: Sampled Coordinate Block. @@ -356,7 +348,7 @@ def predict(self, X): Y : {float, array}, shape = [n_samples, n_targets] Predicted targets. """ - check_is_fitted(self, ["bs_", "centers_", "centers_squared_", "coef_", + check_is_fitted(self, ["bs_", "centers_", "coef_", "eta_", "random_state_", "pinx_", "Q_", "V_", "was_1D_"]) X = np.asarray(X, dtype=np.float64) @@ -369,8 +361,7 @@ def predict(self, X): Ys = [] for batch_inds in np.array_split(range(n), max(1, n // self.bs_)): batch_x = X[batch_inds] - kfeat = self._kernel(batch_x, self.centers_, - Y_squared=self.centers_squared_) + kfeat = self._kernel(batch_x, self.centers_) pred = np.dot(kfeat, self.coef_) Ys.append(pred) From 4430b9944e64647c0ade9049e657eedba8758e63 Mon Sep 17 00:00:00 2001 From: Alex Li <7Alex7Li@gmail.com> Date: Tue, 21 May 2019 22:02:09 -0400 Subject: [PATCH 09/31] Fixed documentation spaces --- sklearn_extra/fast_kernel.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/sklearn_extra/fast_kernel.py b/sklearn_extra/fast_kernel.py index 73a3fcdc..764fe766 100644 --- a/sklearn_extra/fast_kernel.py +++ b/sklearn_extra/fast_kernel.py @@ -89,8 +89,8 @@ class FKR_EigenPro(BaseEstimator, RegressorMixin): >>> rgs = FKR_EigenPro(n_epoch=3, bandwidth=1, subsample_size=50) >>> rgs.fit(x_train, y_train) FKR_EigenPro(bandwidth=1, batch_size='auto', coef0=1, degree=3, gamma=None, - kernel='gaussian', kernel_params=None, n_components=1000, - n_epoch=3, random_state=None, subsample_size=50) + kernel='gaussian', kernel_params=None, n_components=1000, + n_epoch=3, random_state=None, subsample_size=50) >>> y_pred = rgs.predict(x_train) >>> loss = np.mean(np.square(y_train - y_pred)) """ @@ -141,12 +141,12 @@ def _kernel(self, X, Y): distance = euclidean_distances(X, Y, squared=True) bandwidth = np.float32(self.bandwidth) if self.kernel == "gaussian": - K = np.exp(-distance / (2 * np.square(bandwidth))) + K = np.exp(-distance / (2.0 * bandwidth * bandwidth)) elif self.kernel == "laplace": d = np.maximum(distance, 0) K = np.exp(-np.sqrt(d) / bandwidth) else: # self.kernel == "cauchy": - K = 1 / (1 + distance / np.square(bandwidth)) + K = 1 / (1 + distance / (bandwidth * bandwidth)) return K def _nystrom_svd(self, X, n_components): @@ -454,8 +454,8 @@ class FKC_EigenPro(BaseEstimator, ClassifierMixin): >>> rgs = FKC_EigenPro(n_epoch=3, bandwidth=1, subsample_size=50) >>> rgs.fit(x_train, y_train) FKC_EigenPro(bandwidth=1, batch_size='auto', coef0=1, degree=3, gamma=None, - kernel='gaussian', kernel_params=None, n_components=1000, - n_epoch=3, random_state=None, subsample_size=50) + kernel='gaussian', kernel_params=None, n_components=1000, + n_epoch=3, random_state=None, subsample_size=50) >>> y_pred = rgs.predict(x_train) >>> loss = np.mean(y_train != y_pred) """ From bfe5513fa1b15783c355d42560a43473f14c8025 Mon Sep 17 00:00:00 2001 From: Alex Li <7Alex7Li@gmail.com> Date: Tue, 21 May 2019 22:52:33 -0400 Subject: [PATCH 10/31] Changed documentation as required and modified broken test --- examples/fast_kernel/plot_synthetic.py | 17 ++++++++--------- sklearn_extra/fast_kernel.py | 10 +++++----- sklearn_extra/kernel_approximation/_fastfood.py | 2 +- 3 files changed, 14 insertions(+), 15 deletions(-) diff --git a/examples/fast_kernel/plot_synthetic.py b/examples/fast_kernel/plot_synthetic.py index 809e3de0..e492db0c 100644 --- a/examples/fast_kernel/plot_synthetic.py +++ b/examples/fast_kernel/plot_synthetic.py @@ -9,8 +9,8 @@ independent Gaussian distributions. We halt the training for EigenPro in 3 epochs. Experimental results demonstrate that EigenPro achieves high test accuracy, -competitive to that of SVC, while completing training in -significant less time (8 times speedup). +competitive to that of SVC, while completing training almost +10 times faster. """ print(__doc__) @@ -25,16 +25,15 @@ rng = np.random.RandomState(1) -centers = np.zeros((2, 50)) -centers[0][0] = 2 -max_size = 10000 -test_size = 10000 +max_size = 5000 +test_size = 5000 # Get data for testing x, y = make_classification(n_samples=max_size+test_size, - n_features=200, - n_informative=10, + n_features=500, + n_informative=6, random_state=rng) + x_train = x[:max_size] y_train = y[:max_size] x_test = x[max_size:] @@ -47,7 +46,7 @@ svc_pred_times = [] svc_err = [] -train_sizes = [2000, 4000, 8000] +train_sizes = [1000, 2500, 5000] bandwidth = 10. for train_size in train_sizes: diff --git a/sklearn_extra/fast_kernel.py b/sklearn_extra/fast_kernel.py index 764fe766..f1daf765 100644 --- a/sklearn_extra/fast_kernel.py +++ b/sklearn_extra/fast_kernel.py @@ -89,8 +89,8 @@ class FKR_EigenPro(BaseEstimator, RegressorMixin): >>> rgs = FKR_EigenPro(n_epoch=3, bandwidth=1, subsample_size=50) >>> rgs.fit(x_train, y_train) FKR_EigenPro(bandwidth=1, batch_size='auto', coef0=1, degree=3, gamma=None, - kernel='gaussian', kernel_params=None, n_components=1000, - n_epoch=3, random_state=None, subsample_size=50) + kernel='gaussian', kernel_params=None, n_components=1000, n_epoch=3, + random_state=None, subsample_size=50) >>> y_pred = rgs.predict(x_train) >>> loss = np.mean(np.square(y_train - y_pred)) """ @@ -138,7 +138,7 @@ def _kernel(self, X, Y): "coef0": self.coef0} return pairwise_kernels(X, Y, metric=self.kernel, filter_params=True, **params) - distance = euclidean_distances(X, Y, squared=True) + distance = np.float32(euclidean_distances(X, Y, squared=True)) bandwidth = np.float32(self.bandwidth) if self.kernel == "gaussian": K = np.exp(-distance / (2.0 * bandwidth * bandwidth)) @@ -454,8 +454,8 @@ class FKC_EigenPro(BaseEstimator, ClassifierMixin): >>> rgs = FKC_EigenPro(n_epoch=3, bandwidth=1, subsample_size=50) >>> rgs.fit(x_train, y_train) FKC_EigenPro(bandwidth=1, batch_size='auto', coef0=1, degree=3, gamma=None, - kernel='gaussian', kernel_params=None, n_components=1000, - n_epoch=3, random_state=None, subsample_size=50) + kernel='gaussian', kernel_params=None, n_components=1000, n_epoch=3, + random_state=None, subsample_size=50) >>> y_pred = rgs.predict(x_train) >>> loss = np.mean(y_train != y_pred) """ diff --git a/sklearn_extra/kernel_approximation/_fastfood.py b/sklearn_extra/kernel_approximation/_fastfood.py index bb2d32d2..2b5daa0e 100644 --- a/sklearn_extra/kernel_approximation/_fastfood.py +++ b/sklearn_extra/kernel_approximation/_fastfood.py @@ -7,7 +7,7 @@ from sklearn.base import TransformerMixin from sklearn.utils import check_array, check_random_state -from ..utils._cyfht import fht2 as cyfht +#from ..utils._cyfht import fht2 as cyfht class Fastfood(BaseEstimator, TransformerMixin): From 0db284f30ea7a7b989cecac62e260a4ea6a69877 Mon Sep 17 00:00:00 2001 From: Alex Li <7Alex7Li@gmail.com> Date: Tue, 21 May 2019 23:53:21 -0400 Subject: [PATCH 11/31] Removed synthetic example --- .gitignore | 3 +- examples/fast_kernel/plot_synthetic.py | 120 ------------------------- sklearn_extra/fast_kernel.py | 7 +- 3 files changed, 6 insertions(+), 124 deletions(-) delete mode 100644 examples/fast_kernel/plot_synthetic.py diff --git a/.gitignore b/.gitignore index 7f7c45b2..098d0fd7 100644 --- a/.gitignore +++ b/.gitignore @@ -69,4 +69,5 @@ doc/generated/ target/ # Pycharm -.idea \ No newline at end of file +.idea +venv/ \ No newline at end of file diff --git a/examples/fast_kernel/plot_synthetic.py b/examples/fast_kernel/plot_synthetic.py deleted file mode 100644 index e492db0c..00000000 --- a/examples/fast_kernel/plot_synthetic.py +++ /dev/null @@ -1,120 +0,0 @@ -""" -========================================================================= -Comparison of Fast Kernel Machine (Eigenpro) and SVC on synthetic dataset -========================================================================= - -Here we train a Fast Kernel Machine (EigenPro) and a -Support Vector Classifier (SVC) on subsets of a synthetic -dataset. Features of this dataset are sampled from two -independent Gaussian distributions. We halt the training -for EigenPro in 3 epochs. Experimental results -demonstrate that EigenPro achieves high test accuracy, -competitive to that of SVC, while completing training almost -10 times faster. -""" -print(__doc__) - -import matplotlib -import matplotlib.pyplot as plt -import numpy as np -from time import time - -from sklearn.datasets import make_classification -from sklearn_extra.fast_kernel import FKC_EigenPro -from sklearn.svm import SVC - -rng = np.random.RandomState(1) - -max_size = 5000 -test_size = 5000 - -# Get data for testing -x, y = make_classification(n_samples=max_size+test_size, - n_features=500, - n_informative=6, - random_state=rng) - -x_train = x[:max_size] -y_train = y[:max_size] -x_test = x[max_size:] -y_test = y[max_size:] - -fkc_fit_times = [] -fkc_pred_times = [] -fkc_err = [] -svc_fit_times = [] -svc_pred_times = [] -svc_err = [] - -train_sizes = [1000, 2500, 5000] - -bandwidth = 10. -for train_size in train_sizes: - for name, estimator in [ - ("FastKernel", - FKC_EigenPro(n_epoch=3, bandwidth=bandwidth, n_components=30, - subsample_size=1000, random_state=rng)), - ("SupportVector", SVC(C=5, gamma=1./(2 * bandwidth * bandwidth)))]: - stime = time() - estimator.fit(x_train[:train_size], y_train[:train_size]) - fit_t = time() - stime - - stime = time() - y_pred_test = estimator.predict(x_test) - pred_t = time() - stime - - err = 100. * np.sum(y_pred_test != y_test) / len(y_test) - if name == "FastKernel": - fkc_fit_times.append(fit_t) - fkc_pred_times.append(pred_t) - fkc_err.append(err) - else: - svc_fit_times.append(fit_t) - svc_pred_times.append(pred_t) - svc_err.append(err) - print("%s Classification with %i training samples in %0.2f seconds." % - (name, train_size, fit_t + pred_t)) - -# set up grid for figures -fig = plt.figure(num=None, figsize=(6, 4), dpi=160) -ax = plt.subplot2grid((2, 2), (0, 0), rowspan=2) -train_size_labels = [str(s) for s in train_sizes] - -# Graph fit(train) time -ax.plot(train_sizes, svc_fit_times, 'o--', color='g', label='SVC') -ax.plot(train_sizes, fkc_fit_times, 'o-', color='r', label='FKC (EigenPro)') -ax.set_xscale('log') -ax.set_yscale('log', nonposy='clip') -ax.set_xlabel('train size') -ax.set_ylabel('time (seconds)') - -ax.legend() -ax.set_title('Train set') -ax.set_xticks(train_sizes) -ax.set_xticklabels(train_size_labels) -ax.set_xticks([], minor=True) -ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) - -# Graph prediction(test) time -ax = plt.subplot2grid((2, 2), (0, 1), rowspan=1) -ax.plot(train_sizes, fkc_pred_times, 'o-', color='r') -ax.plot(train_sizes, svc_pred_times, 'o--', color='g') -ax.set_xscale('log') -ax.set_yscale('log', nonposy='clip') -ax.set_ylabel('time (seconds)') -ax.set_title('Test set') -ax.set_xticks([]) -ax.set_xticks([], minor=True) - -# Graph training error -ax = plt.subplot2grid((2, 2), (1, 1), rowspan=1) -ax.plot(train_sizes, fkc_err, 'o-', color='r') -ax.plot(train_sizes, svc_err, 'o-', color='g') -ax.set_xscale('log') -ax.set_xticks(train_sizes) -ax.set_xticklabels(train_size_labels) -ax.set_xticks([], minor=True) -ax.set_xlabel('train size') -ax.set_ylabel('classification error %') -plt.tight_layout() -plt.show() diff --git a/sklearn_extra/fast_kernel.py b/sklearn_extra/fast_kernel.py index f1daf765..a565712c 100644 --- a/sklearn_extra/fast_kernel.py +++ b/sklearn_extra/fast_kernel.py @@ -138,7 +138,7 @@ def _kernel(self, X, Y): "coef0": self.coef0} return pairwise_kernels(X, Y, metric=self.kernel, filter_params=True, **params) - distance = np.float32(euclidean_distances(X, Y, squared=True)) + distance = euclidean_distances(X, Y, squared=True) bandwidth = np.float32(self.bandwidth) if self.kernel == "gaussian": K = np.exp(-distance / (2.0 * bandwidth * bandwidth)) @@ -235,7 +235,8 @@ def _setup(self, feat, max_components, mG, alpha): return max_S / scale, np.max(kxx) def _initialize_params(self, X, Y): - """Validate parameters passed to the model, choose parameters + """ + Validate parameters passed to the model, choose parameters that have not been passed in, and run setup for EigenPro iteration. """ self.random_state_ = check_random_state(self.random_state) @@ -498,7 +499,7 @@ def fit(self, X, Y): bandwidth=self.bandwidth, gamma=self.gamma, degree=self.degree, coef0=self.coef0, kernel_params=self.kernel_params, random_state=self.random_state) - X, Y = check_X_y(X, Y, dtype=np.float32, + X, Y = check_X_y(X, Y, dtype=np.float32, force_all_finite=True, multi_output=False, ensure_min_samples=3) check_classification_targets(Y) self.classes_ = np.unique(Y) From c3590516e694b96446854452976f69c40941e531 Mon Sep 17 00:00:00 2001 From: Alex Li <7Alex7Li@gmail.com> Date: Wed, 22 May 2019 13:58:33 -0400 Subject: [PATCH 12/31] Undo accidental deletion of line from _fastfood --- examples/fast_kernel/plot_synthetic.py | 121 ++++++++++++++++++ sklearn_extra/__init__.py | 2 +- sklearn_extra/fast_kernel.py | 2 +- .../kernel_approximation/_fastfood.py | 2 +- 4 files changed, 124 insertions(+), 3 deletions(-) create mode 100644 examples/fast_kernel/plot_synthetic.py diff --git a/examples/fast_kernel/plot_synthetic.py b/examples/fast_kernel/plot_synthetic.py new file mode 100644 index 00000000..eb6a715f --- /dev/null +++ b/examples/fast_kernel/plot_synthetic.py @@ -0,0 +1,121 @@ +""" +========================================================================= +Comparison of Fast Kernel Machine (Eigenpro) and SVC on synthetic dataset +========================================================================= + +Here we train a Fast Kernel Machine (EigenPro) and a +Support Vector Classifier (SVC) on subsets of a synthetic +dataset. Features of this dataset are sampled from two +independent Gaussian distributions. We halt the training +for EigenPro in 3 epochs. Experimental results +demonstrate that EigenPro achieves high test accuracy, +competitive to that of SVC, while completing training almost +10 times faster. +""" +print(__doc__) + +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +from time import time + +from sklearn.datasets import make_classification +from sklearn_extra.fast_kernel import FKC_EigenPro +from sklearn.svm import SVC + +rng = np.random.RandomState(1) + +max_size = 5000 +test_size = 5000 + +# Get data for testing + +x, y = make_classification(n_samples=max_size+test_size, + n_features=400, + n_informative=6, + random_state=rng) + +x_train = x[:max_size] +y_train = y[:max_size] +x_test = x[max_size:] +y_test = y[max_size:] + +fkc_fit_times = [] +fkc_pred_times = [] +fkc_err = [] +svc_fit_times = [] +svc_pred_times = [] +svc_err = [] + +train_sizes = [1000, 2500, 5000] + +bandwidth = 10 +for train_size in train_sizes: + for name, estimator in [ + ("FastKernel", + FKC_EigenPro(n_epoch=3, bandwidth=bandwidth, n_components=30, + subsample_size=1000, random_state=rng)), + ("SupportVector", SVC(C=5, gamma=1./(2 * bandwidth * bandwidth)))]: + stime = time() + estimator.fit(x_train[:train_size], y_train[:train_size]) + fit_t = time() - stime + + stime = time() + y_pred_test = estimator.predict(x_test) + pred_t = time() - stime + + err = 100. * np.sum(y_pred_test != y_test) / len(y_test) + if name == "FastKernel": + fkc_fit_times.append(fit_t) + fkc_pred_times.append(pred_t) + fkc_err.append(err) + else: + svc_fit_times.append(fit_t) + svc_pred_times.append(pred_t) + svc_err.append(err) + print("%s Classification with %i training samples in %0.2f seconds." % + (name, train_size, fit_t + pred_t)) + +# set up grid for figures +fig = plt.figure(num=None, figsize=(6, 4), dpi=160) +ax = plt.subplot2grid((2, 2), (0, 0), rowspan=2) +train_size_labels = [str(s) for s in train_sizes] + +# Graph fit(train) time +ax.plot(train_sizes, svc_fit_times, 'o--', color='g', label='SVC') +ax.plot(train_sizes, fkc_fit_times, 'o-', color='r', label='FKC (EigenPro)') +ax.set_xscale('log') +ax.set_yscale('log', nonposy='clip') +ax.set_xlabel('train size') +ax.set_ylabel('time (seconds)') + +ax.legend() +ax.set_title('Train set') +ax.set_xticks(train_sizes) +ax.set_xticklabels(train_size_labels) +ax.set_xticks([], minor=True) +ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) + +# Graph prediction(test) time +ax = plt.subplot2grid((2, 2), (0, 1), rowspan=1) +ax.plot(train_sizes, fkc_pred_times, 'o-', color='r') +ax.plot(train_sizes, svc_pred_times, 'o--', color='g') +ax.set_xscale('log') +ax.set_yscale('log', nonposy='clip') +ax.set_ylabel('time (seconds)') +ax.set_title('Test set') +ax.set_xticks([]) +ax.set_xticks([], minor=True) + +# Graph training error +ax = plt.subplot2grid((2, 2), (1, 1), rowspan=1) +ax.plot(train_sizes, fkc_err, 'o-', color='r') +ax.plot(train_sizes, svc_err, 'o-', color='g') +ax.set_xscale('log') +ax.set_xticks(train_sizes) +ax.set_xticklabels(train_size_labels) +ax.set_xticks([], minor=True) +ax.set_xlabel('train size') +ax.set_ylabel('classification error %') +plt.tight_layout() +plt.show() diff --git a/sklearn_extra/__init__.py b/sklearn_extra/__init__.py index 35691dd2..b994d34a 100644 --- a/sklearn_extra/__init__.py +++ b/sklearn_extra/__init__.py @@ -2,4 +2,4 @@ from ._version import __version__ -__all__ = ['__version__'] +__all__ = ['__version__', 'fast_kernel'] diff --git a/sklearn_extra/fast_kernel.py b/sklearn_extra/fast_kernel.py index a565712c..b2db56e9 100644 --- a/sklearn_extra/fast_kernel.py +++ b/sklearn_extra/fast_kernel.py @@ -1,8 +1,8 @@ # Authors: Alex Li <7Alex7Li@gmail.com> # Siyuan Ma -import numpy as np import scipy as sp +import numpy as np from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin from sklearn.metrics.pairwise import pairwise_kernels, euclidean_distances from sklearn.utils import check_random_state diff --git a/sklearn_extra/kernel_approximation/_fastfood.py b/sklearn_extra/kernel_approximation/_fastfood.py index 2b5daa0e..bb2d32d2 100644 --- a/sklearn_extra/kernel_approximation/_fastfood.py +++ b/sklearn_extra/kernel_approximation/_fastfood.py @@ -7,7 +7,7 @@ from sklearn.base import TransformerMixin from sklearn.utils import check_array, check_random_state -#from ..utils._cyfht import fht2 as cyfht +from ..utils._cyfht import fht2 as cyfht class Fastfood(BaseEstimator, TransformerMixin): From 7ed17966bc0279b14591d1854ca0ee791b7aaa0e Mon Sep 17 00:00:00 2001 From: Alex <7alex7li@gmail.com> Date: Sat, 29 Jun 2019 18:11:57 -0400 Subject: [PATCH 13/31] Updated tests and refactored eigenpro to add a base class --- examples/fast_kernel/plot_mnist.py | 18 +- examples/fast_kernel/plot_noisy_mnist.py | 122 --------- examples/fast_kernel/plot_synthetic.py | 121 --------- sklearn_extra/fast_kernel.py | 311 ++++++++++++----------- sklearn_extra/tests/test_common.py | 3 +- sklearn_extra/tests/test_fast_kernel.py | 253 +++++++++--------- 6 files changed, 315 insertions(+), 513 deletions(-) delete mode 100644 examples/fast_kernel/plot_noisy_mnist.py delete mode 100644 examples/fast_kernel/plot_synthetic.py diff --git a/examples/fast_kernel/plot_mnist.py b/examples/fast_kernel/plot_mnist.py index aaebff9a..e34fab8a 100644 --- a/examples/fast_kernel/plot_mnist.py +++ b/examples/fast_kernel/plot_mnist.py @@ -1,7 +1,7 @@ """ -=========================================== -Comparison of FKC_EigenPro and SVC on MNIST -=========================================== +=================================================== +Comparison of FKC_EigenPro and SVC on Fashion-MNIST +=================================================== Here we train a Fast Kernel Classifier (EigenPro) and a Support Vector Classifier (SVC) on subsets of MNIST of various sizes. @@ -24,12 +24,12 @@ rng = np.random.RandomState(1) # Generate sample data from mnist -mnist = fetch_openml('mnist_784') +mnist = fetch_openml('mnist_784') # 'Fashion-MNIST') mnist.data = mnist.data / 255.0 p = np.random.permutation(60000) -x_train = mnist.data[p][:60000] -y_train = np.int32(mnist.target[p][:60000]) +x_train = mnist.data[p] +y_train = np.int32(mnist.target[p]) x_test = mnist.data[60000:] y_test = np.int32(mnist.target[60000:]) @@ -41,7 +41,7 @@ svc_pred_times = [] svc_err = [] -train_sizes = [500, 1000, 2000] +train_sizes = [5000, 60000] bandwidth = 5.0 @@ -84,7 +84,7 @@ ax.set_xlabel('train size') ax.set_ylabel('time (seconds)') ax.legend() -ax.set_title('Train set') +ax.set_title('Training Time') ax.set_xticks(train_sizes) ax.set_xticklabels(train_size_labels) ax.set_xticks([], minor=True) @@ -97,7 +97,7 @@ ax.set_xscale('log') ax.set_yscale('log', nonposy='clip') ax.set_ylabel('time (seconds)') -ax.set_title('Test set') +ax.set_title('Prediction Time') ax.set_xticks([]) ax.set_xticks([], minor=True) diff --git a/examples/fast_kernel/plot_noisy_mnist.py b/examples/fast_kernel/plot_noisy_mnist.py deleted file mode 100644 index 894aed1b..00000000 --- a/examples/fast_kernel/plot_noisy_mnist.py +++ /dev/null @@ -1,122 +0,0 @@ -""" -================================================================== -Comparison of FKC_EigenPro and SVC on MNIST with added label noise -================================================================== - -Here we train a Fast Kernel Machine (EigenPro) and a Support -Vector Classifier (SVC) on subsets of MNIST with added label -noises. Specifically, we randomly reset the label (0-9) of 20% -samples. We halt the training of EigenPro in two epochs. -Experimental results on this dataset demonstrate more than 19 times -speedup of EigenPro over SVC in training time. -EigenPro also shows consistently lower classification error on -test set. -""" - -import matplotlib -import matplotlib.pyplot as plt -import numpy as np -from time import time - -from sklearn.datasets import fetch_openml -from sklearn_extra.fast_kernel import FKC_EigenPro -from sklearn.svm import SVC - -rng = np.random.RandomState(1) -# Generate sample data from mnist -mnist = fetch_openml('mnist_784') -mnist.data = mnist.data / 255. - -p = rng.permutation(60000) -x_train = mnist.data[p][:60000] -y_train = np.int32(mnist.target[p][:60000]) -x_test = mnist.data[60000:] -y_test = np.int32(mnist.target[60000:]) - -# randomize 20% of labels -p = rng.choice(len(y_train), np.int32(len(y_train) * .2), False) -y_train[p] = rng.choice(10, np.int32(len(y_train) * .2)) -p = rng.choice(len(y_test), np.int32(len(y_test) * .2), False) -y_test[p] = rng.choice(10, np.int32(len(y_test) * .2)) - -# Run tests comparing fkc to svc -fkc_fit_times = [] -fkc_pred_times = [] -fkc_err = [] -svc_fit_times = [] -svc_pred_times = [] -svc_err = [] - -train_sizes = [500, 1000, 2000] - -# Fit models to data -for train_size in train_sizes: - for name, estimator in [ - ("FastKernel", - FKC_EigenPro(n_epoch=2, bandwidth=5., random_state=rng)), - ("SupportVector", SVC(C=5, gamma=1. / (2 * 5 * 5)))]: - stime = time() - estimator.fit(x_train[:train_size], y_train[:train_size]) - fit_t = time() - stime - - y_pred_train = estimator.predict(x_train[:train_size]) - - stime = time() - y_pred_test = estimator.predict(x_test) - pred_t = time() - stime - train_err = 100. * np.sum(y_pred_train != y_train) / train_size - err = 100. * np.sum(y_pred_test != y_test) / len(y_test) - if name == "FastKernel": - fkc_fit_times.append(fit_t) - fkc_pred_times.append(pred_t) - fkc_err.append(err) - else: - svc_fit_times.append(fit_t) - svc_pred_times.append(pred_t) - svc_err.append(err) - print("%s Classification with %i training samples in %0.2f seconds. " - "Train error %.4f" % - (name, train_size, fit_t + pred_t, train_err)) - -# set up grid for figures -fig = plt.figure(num=None, figsize=(6, 4), dpi=160) -ax = plt.subplot2grid((2, 2), (0, 0), rowspan=2) -train_size_labels = [str(s) for s in train_sizes] - -# Graph fit(train) time -ax.plot(train_sizes, svc_fit_times, 'o--', color='g', label='SVC') -ax.plot(train_sizes, fkc_fit_times, 'o-', color='r', label='FKC (EigenPro)') -ax.set_xscale('log') -ax.set_yscale('log', nonposy='clip') -ax.set_xlabel('train size') -ax.set_ylabel('time (seconds)') -ax.legend() -ax.set_title('Train set') -ax.set_xticks(train_sizes) -ax.set_xticklabels(train_size_labels) -ax.set_xticks([], minor=True) -ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) - -# Graph prediction(test) time -ax = plt.subplot2grid((2, 2), (0, 1), rowspan=1) -ax.plot(train_sizes, fkc_pred_times, 'o-', color='r') -ax.plot(train_sizes, svc_pred_times, 'o--', color='g') -ax.set_xscale('log') -ax.set_yscale('log', nonposy='clip') -ax.set_ylabel('time (seconds)') -ax.set_title('Test set') -ax.set_xticks([]) -ax.set_xticks([], minor=True) - -# Graph training error -ax = plt.subplot2grid((2, 2), (1, 1), rowspan=1) -ax.plot(train_sizes, fkc_err, 'o-', color='r') -ax.plot(train_sizes, svc_err, 'o-', color='g') -ax.set_xscale('log') -ax.set_xticks(train_sizes) -ax.set_xticklabels(train_size_labels) -ax.set_xticks([], minor=True) -ax.set_xlabel('train size') -ax.set_ylabel('classification error %') -plt.tight_layout() -plt.show() diff --git a/examples/fast_kernel/plot_synthetic.py b/examples/fast_kernel/plot_synthetic.py deleted file mode 100644 index eb6a715f..00000000 --- a/examples/fast_kernel/plot_synthetic.py +++ /dev/null @@ -1,121 +0,0 @@ -""" -========================================================================= -Comparison of Fast Kernel Machine (Eigenpro) and SVC on synthetic dataset -========================================================================= - -Here we train a Fast Kernel Machine (EigenPro) and a -Support Vector Classifier (SVC) on subsets of a synthetic -dataset. Features of this dataset are sampled from two -independent Gaussian distributions. We halt the training -for EigenPro in 3 epochs. Experimental results -demonstrate that EigenPro achieves high test accuracy, -competitive to that of SVC, while completing training almost -10 times faster. -""" -print(__doc__) - -import matplotlib -import matplotlib.pyplot as plt -import numpy as np -from time import time - -from sklearn.datasets import make_classification -from sklearn_extra.fast_kernel import FKC_EigenPro -from sklearn.svm import SVC - -rng = np.random.RandomState(1) - -max_size = 5000 -test_size = 5000 - -# Get data for testing - -x, y = make_classification(n_samples=max_size+test_size, - n_features=400, - n_informative=6, - random_state=rng) - -x_train = x[:max_size] -y_train = y[:max_size] -x_test = x[max_size:] -y_test = y[max_size:] - -fkc_fit_times = [] -fkc_pred_times = [] -fkc_err = [] -svc_fit_times = [] -svc_pred_times = [] -svc_err = [] - -train_sizes = [1000, 2500, 5000] - -bandwidth = 10 -for train_size in train_sizes: - for name, estimator in [ - ("FastKernel", - FKC_EigenPro(n_epoch=3, bandwidth=bandwidth, n_components=30, - subsample_size=1000, random_state=rng)), - ("SupportVector", SVC(C=5, gamma=1./(2 * bandwidth * bandwidth)))]: - stime = time() - estimator.fit(x_train[:train_size], y_train[:train_size]) - fit_t = time() - stime - - stime = time() - y_pred_test = estimator.predict(x_test) - pred_t = time() - stime - - err = 100. * np.sum(y_pred_test != y_test) / len(y_test) - if name == "FastKernel": - fkc_fit_times.append(fit_t) - fkc_pred_times.append(pred_t) - fkc_err.append(err) - else: - svc_fit_times.append(fit_t) - svc_pred_times.append(pred_t) - svc_err.append(err) - print("%s Classification with %i training samples in %0.2f seconds." % - (name, train_size, fit_t + pred_t)) - -# set up grid for figures -fig = plt.figure(num=None, figsize=(6, 4), dpi=160) -ax = plt.subplot2grid((2, 2), (0, 0), rowspan=2) -train_size_labels = [str(s) for s in train_sizes] - -# Graph fit(train) time -ax.plot(train_sizes, svc_fit_times, 'o--', color='g', label='SVC') -ax.plot(train_sizes, fkc_fit_times, 'o-', color='r', label='FKC (EigenPro)') -ax.set_xscale('log') -ax.set_yscale('log', nonposy='clip') -ax.set_xlabel('train size') -ax.set_ylabel('time (seconds)') - -ax.legend() -ax.set_title('Train set') -ax.set_xticks(train_sizes) -ax.set_xticklabels(train_size_labels) -ax.set_xticks([], minor=True) -ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) - -# Graph prediction(test) time -ax = plt.subplot2grid((2, 2), (0, 1), rowspan=1) -ax.plot(train_sizes, fkc_pred_times, 'o-', color='r') -ax.plot(train_sizes, svc_pred_times, 'o--', color='g') -ax.set_xscale('log') -ax.set_yscale('log', nonposy='clip') -ax.set_ylabel('time (seconds)') -ax.set_title('Test set') -ax.set_xticks([]) -ax.set_xticks([], minor=True) - -# Graph training error -ax = plt.subplot2grid((2, 2), (1, 1), rowspan=1) -ax.plot(train_sizes, fkc_err, 'o-', color='r') -ax.plot(train_sizes, svc_err, 'o-', color='g') -ax.set_xscale('log') -ax.set_xticks(train_sizes) -ax.set_xticklabels(train_size_labels) -ax.set_xticks([], minor=True) -ax.set_xlabel('train size') -ax.set_ylabel('classification error %') -plt.tight_layout() -plt.show() diff --git a/sklearn_extra/fast_kernel.py b/sklearn_extra/fast_kernel.py index b2db56e9..c20ea509 100644 --- a/sklearn_extra/fast_kernel.py +++ b/sklearn_extra/fast_kernel.py @@ -1,8 +1,9 @@ # Authors: Alex Li <7Alex7Li@gmail.com> # Siyuan Ma -import scipy as sp import numpy as np +from scipy.linalg import eigh +from abc import ABC, abstractmethod from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin from sklearn.metrics.pairwise import pairwise_kernels, euclidean_distances from sklearn.utils import check_random_state @@ -10,91 +11,11 @@ from sklearn.utils.validation import check_is_fitted, check_X_y -class FKR_EigenPro(BaseEstimator, RegressorMixin): - """Fast kernel regression using EigenPro iteration. - - Train least squared kernel regression model with mini-batch EigenPro - iteration. - - Parameters - ---------- - batch_size : int, default = 'auto' - Mini-batch size for gradient descent. - - n_epoch : int, default = 1 - The number of passes over the training data. - - n_components : int, default = 1000 - the maximum number of eigendirections used in modifying the kernel - operator. Convergence rate speedup over normal gradient descent is - approximately the largest eigenvalue over the n_componentth - eigenvalue, however, it may take time to compute eigenvalues for - large n_components - - subsample_size : int, default = 'auto' - The number of subsamples used for estimating the largest - n_component eigenvalues and eigenvectors. When it is set to 'auto', - it will be 4000 if there are less than 100,000 samples - (for training), and otherwise 10000. - - kernel : string or callable, default = "gaussian" - Kernel mapping used internally. Strings can be anything supported - by sklearn's library, however, it is recommended to use a radial - kernel. There is special support for gaussian, laplace, and cauchy - kernels. A callable should accept two arguments and return a - floating point number. - - bandwidth : float, default=5 - Bandwidth to use with the gaussian, laplacian, and cauchy kernels. - Ignored by other kernels. - - gamma : float, default=None - Gamma parameter for the RBF, polynomial, exponential chi2 and - sigmoid kernels. Interpretation of the default value is left to - the kernel; see the documentation for sklearn.metrics.pairwise. - Ignored by other kernels. - - degree : float, default=3 - Degree of the polynomial kernel. Ignored by other kernels. - - coef0 : float, default=1 - Zero coefficient for polynomial and sigmoid kernels. - Ignored by other kernels. - - kernel_params : mapping of string to any - Additional parameters (keyword arguments) for kernel function - passed as callable object. - - random_state : int, RandomState instance or None, (default=None) - The seed of the pseudo random number generator to use when - shuffling the data. 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`. - - References - ---------- - * Siyuan Ma, Mikhail Belkin - "Diving into the shallows: a computational perspective on - large-scale machine learning", NIPS 2017. - - Examples - -------- - >>> from sklearn_extra.fast_kernel import FKR_EigenPro - >>> import numpy as np - >>> n_samples, n_features, n_targets = 4000, 20, 3 - >>> rng = np.random.RandomState(1) - >>> x_train = rng.randn(n_samples, n_features) - >>> y_train = rng.randn(n_samples, n_targets) - >>> rgs = FKR_EigenPro(n_epoch=3, bandwidth=1, subsample_size=50) - >>> rgs.fit(x_train, y_train) - FKR_EigenPro(bandwidth=1, batch_size='auto', coef0=1, degree=3, gamma=None, - kernel='gaussian', kernel_params=None, n_components=1000, n_epoch=3, - random_state=None, subsample_size=50) - >>> y_pred = rgs.predict(x_train) - >>> loss = np.mean(np.square(y_train - y_pred)) +class BaseEigenPro(BaseEstimator, ABC): """ - + Base class for Fast Kernel/Eigenpro iteration. + """ + @abstractmethod def __init__(self, batch_size="auto", n_epoch=2, n_components=1000, subsample_size="auto", kernel="gaussian", bandwidth=5, gamma=None, degree=3, coef0=1, @@ -174,7 +95,7 @@ def _nystrom_svd(self, X, n_components): m, _ = X.shape K = self._kernel(X, X) W = K / m - S, V = sp.linalg.eigh(W, eigvals=(m - n_components, m - 1)) + S, V = eigh(W, eigvals=(m - n_components, m - 1)) # Flip so eigenvalues are in descending order. S = np.maximum(np.float32(1e-7), np.flipud(S)) @@ -190,7 +111,7 @@ def _setup(self, feat, max_components, mG, alpha): feat : {float, array}, shape = [n_samples, n_features] Feature matrix (normally from training data). - max_components : float + max_components : int Maximum number of components to be used in EigenPro iteration. mG : int @@ -222,24 +143,23 @@ def _setup(self, feat, max_components, mG, alpha): if n_components < 2: n_components = min(S.shape[0] - 1, 2) - self.V_ = V[:, :n_components] + V = V[:, :n_components] scale = np.power(S[0] / S[n_components], alpha) # Compute part of the preconditioner for step 2 of gradient descent in # the eigenpro model - self.Q_ = (1 - np.power(S[n_components] / S[:n_components], - alpha)) / S[:n_components] + Q = (1 - np.power(S[n_components] / S[:n_components], + alpha)) / S[:n_components] max_S = S[0].astype(np.float32) - kxx = 1 - np.sum(self.V_ ** 2, axis=1) * n_subsamples - return max_S / scale, np.max(kxx) + kxx = 1 - np.sum(V ** 2, axis=1) * n_subsamples + return max_S / scale, np.max(kxx), Q, V - def _initialize_params(self, X, Y): + def _initialize_params(self, X, Y, random_state): """ Validate parameters passed to the model, choose parameters that have not been passed in, and run setup for EigenPro iteration. """ - self.random_state_ = check_random_state(self.random_state) n, d = X.shape n_label = 1 if len(Y.shape) == 1 else Y.shape[1] self.centers_ = X @@ -247,11 +167,12 @@ def _initialize_params(self, X, Y): # Calculate the subsample size to be used. if self.subsample_size == "auto": if n < 100000: - sample_size = min(n, 4000) + sample_size = 4000 else: sample_size = 12000 else: - sample_size = min(n, self.subsample_size) + sample_size = self.subsample_size + sample_size = min(n, sample_size) n_components = min(sample_size - 1, self.n_components) n_components = max(1, n_components) @@ -262,12 +183,12 @@ def _initialize_params(self, X, Y): mG = np.int32(np.sum(mem_usages < mem_bytes)) # Calculate largest eigenvalue and max{k(x,x)} using subsamples. - self.pinx_ = self.random_state_.choice(n, sample_size, - replace=False).astype('int32') - max_S, beta = self._setup(X[self.pinx_], n_components, mG, alpha=.95) + pinx = random_state.choice(n, sample_size, + replace=False).astype('int32') + max_S, beta, Q, V = self._setup(X[pinx], n_components, mG, alpha=.95) # Calculate best batch size. if self.batch_size == "auto": - bs = min(np.int32(beta / max_S + 1), mG) + bs = min(np.int32(beta / max_S), mG)+1 else: bs = self.batch_size self.bs_ = min(bs, n) @@ -279,15 +200,31 @@ def _initialize_params(self, X, Y): eta = 2. * self.bs_ / (beta + (self.bs_ - 1) * max_S) else: eta = 0.95 * 2 / max_S - self.eta_ = np.float32(eta) # Remember the shape of Y for predict() and ensure it's shape is 2-D. self.was_1D_ = False if len(Y.shape) == 1: Y = np.reshape(Y, (Y.shape[0], 1)) self.was_1D_ = True - return Y - - def fit(self, X, Y): + return Y, Q, V, np.float32(eta), pinx + + def validate_parameters(self): + if self.n_epoch <= 0: + raise ValueError('n_epoch should be positive, was ' + + str(self.n_epoch)) + if self.n_components < 0: + raise ValueError('n_components should be non-negative, was ' + + str(self.n_components)) + if self.subsample_size != 'auto' and self.subsample_size < 0: + raise ValueError('subsample_size should be non-negative, was ' + + str(self.subsample_size)) + if self.batch_size != 'auto' and self.batch_size <= 0: + raise ValueError('batch_size should be positive, was ' + + str(self.batch_size)) + if self.bandwidth <= 0: + raise ValueError('bandwidth should be positive, was ' + + str(self.bandwidth)) + + def _raw_fit(self, X, Y): """Train fast kernel regression model Parameters @@ -305,18 +242,20 @@ def fit(self, X, Y): X, Y = check_X_y(X, Y, dtype=np.float32, multi_output=True, ensure_min_samples=3, y_numeric=True) Y = Y.astype(np.float32) + random_state = check_random_state(self.random_state) + + self.validate_parameters() """Parameter Initialization""" - Y = self._initialize_params(X, Y) + Y, Q, V, eta, pinx = self._initialize_params(X, Y, random_state) """Training loop""" n = self.centers_.shape[0] self.coef_ = np.zeros((n, Y.shape[1]), dtype=np.float32) - step = np.float32(self.eta_ / self.bs_) + step = np.float32(eta / self.bs_) for epoch in range(0, self.n_epoch): - epoch_inds = \ - self.random_state_.choice(n, n // self.bs_ * self.bs_, - replace=False).astype('int32') + epoch_inds = random_state.choice(n, n // self.bs_ * self.bs_, + replace=False).astype('int32') for batch_inds in np.array_split(epoch_inds, n // self.bs_): batch_x = self.centers_[batch_inds] @@ -330,13 +269,12 @@ def fit(self, X, Y): self.coef_[batch_inds] - step * gradient # Update 2: Fixed Coordinate Block - delta = np.dot(self.V_ * self.Q_, - np.dot(self.V_.T, np.dot( - kfeat[:, self.pinx_].T, gradient))) - self.coef_[self.pinx_] += step * delta + delta = np.dot(V * Q, np.dot(V.T, np.dot( + kfeat[:, pinx].T, gradient))) + self.coef_[pinx] += step * delta return self - def predict(self, X): + def _raw_predict(self, X): """Predict using the kernel regression model Parameters @@ -349,9 +287,7 @@ def predict(self, X): Y : {float, array}, shape = [n_samples, n_targets] Predicted targets. """ - check_is_fitted(self, ["bs_", "centers_", "coef_", - "eta_", "random_state_", "pinx_", - "Q_", "V_", "was_1D_"]) + check_is_fitted(self, ["bs_", "centers_", "coef_", "was_1D_"]) X = np.asarray(X, dtype=np.float64) if len(X.shape) == 1: @@ -375,7 +311,109 @@ def _get_tags(self): return {'multioutput': True} -class FKC_EigenPro(BaseEstimator, ClassifierMixin): +class FKR_EigenPro(BaseEigenPro, RegressorMixin): + """Fast kernel regression using EigenPro iteration. + + Train least squared kernel regression model with mini-batch EigenPro + iteration. + + Parameters + ---------- + batch_size : int, default = 'auto' + Mini-batch size for gradient descent. + + n_epoch : int, default = 1 + The number of passes over the training data. + + n_components : int, default = 1000 + the maximum number of eigendirections used in modifying the kernel + operator. Convergence rate speedup over normal gradient descent is + approximately the largest eigenvalue over the n_componentth + eigenvalue, however, it may take time to compute eigenvalues for + large n_components + + subsample_size : int, default = 'auto' + The number of subsamples used for estimating the largest + n_component eigenvalues and eigenvectors. When it is set to 'auto', + it will be 4000 if there are less than 100,000 samples + (for training), and otherwise 10000. + + kernel : string or callable, default = "gaussian" + Kernel mapping used internally. Strings can be anything supported + by sklearn's library, however, it is recommended to use a radial + kernel. There is special support for gaussian, laplace, and cauchy + kernels. A callable should accept two arguments and return a + floating point number. + + bandwidth : float, default=5 + Bandwidth to use with the gaussian, laplacian, and cauchy kernels. + Ignored by other kernels. + + gamma : float, default=None + Gamma parameter for the RBF, polynomial, exponential chi2 and + sigmoid kernels. Interpretation of the default value is left to + the kernel; see the documentation for sklearn.metrics.pairwise. + Ignored by other kernels. + + degree : float, default=3 + Degree of the polynomial kernel. Ignored by other kernels. + + coef0 : float, default=1 + Zero coefficient for polynomial and sigmoid kernels. + Ignored by other kernels. + + kernel_params : mapping of string to any + Additional parameters (keyword arguments) for kernel function + passed as callable object. + + random_state : int, RandomState instance or None, (default=None) + The seed of the pseudo random number generator to use when + shuffling the data. 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`. + + References + ---------- + * Siyuan Ma, Mikhail Belkin + "Diving into the shallows: a computational perspective on + large-scale machine learning", NIPS 2017. + + Examples + -------- + >>> from sklearn_extra.fast_kernel import FKR_EigenPro + >>> import numpy as np + >>> n_samples, n_features, n_targets = 4000, 20, 3 + >>> rng = np.random.RandomState(1) + >>> x_train = rng.randn(n_samples, n_features) + >>> y_train = rng.randn(n_samples, n_targets) + >>> rgs = FKR_EigenPro(n_epoch=3, bandwidth=1, subsample_size=50) + >>> rgs.fit(x_train, y_train) + FKR_EigenPro(bandwidth=1, batch_size='auto', coef0=1, degree=3, gamma=None, + kernel='gaussian', kernel_params=None, n_components=1000, n_epoch=3, + random_state=None, subsample_size=50) + >>> y_pred = rgs.predict(x_train) + >>> loss = np.mean(np.square(y_train - y_pred)) + """ + def __init__(self, batch_size="auto", n_epoch=2, n_components=1000, + subsample_size="auto", kernel="gaussian", + bandwidth=5, gamma=None, degree=3, coef0=1, + kernel_params=None, random_state=None): + super().__init__( + batch_size=batch_size, n_epoch=n_epoch, + n_components=n_components, subsample_size=subsample_size, + kernel=kernel, bandwidth=bandwidth, gamma=gamma, + degree=degree, coef0=coef0, + kernel_params=kernel_params, random_state=random_state) + + def fit(self, X, Y): + return self._raw_fit(X, Y) + + def predict(self, X): + return self._raw_predict(X) + + +class FKC_EigenPro(BaseEigenPro, ClassifierMixin): """Fast kernel classification using EigenPro iteration. Train least squared kernel classification model with mini-batch EigenPro @@ -455,8 +493,8 @@ class FKC_EigenPro(BaseEstimator, ClassifierMixin): >>> rgs = FKC_EigenPro(n_epoch=3, bandwidth=1, subsample_size=50) >>> rgs.fit(x_train, y_train) FKC_EigenPro(bandwidth=1, batch_size='auto', coef0=1, degree=3, gamma=None, - kernel='gaussian', kernel_params=None, n_components=1000, n_epoch=3, - random_state=None, subsample_size=50) + kernel='gaussian', kernel_params=None, n_components=1000, + n_epoch=3, random_state=None, subsample_size=50) >>> y_pred = rgs.predict(x_train) >>> loss = np.mean(y_train != y_pred) """ @@ -465,17 +503,12 @@ def __init__(self, batch_size="auto", n_epoch=2, n_components=1000, subsample_size="auto", kernel="gaussian", bandwidth=5, gamma=None, degree=3, coef0=1, kernel_params=None, random_state=None): - self.batch_size = batch_size - self.n_epoch = n_epoch - self.n_components = n_components - self.subsample_size = subsample_size - self.kernel = kernel - self.bandwidth = bandwidth - self.gamma = gamma - self.degree = degree - self.coef0 = coef0 - self.kernel_params = kernel_params - self.random_state = random_state + super().__init__( + batch_size=batch_size, n_epoch=n_epoch, + n_components=n_components, subsample_size=subsample_size, + kernel=kernel, bandwidth=bandwidth, gamma=gamma, + degree=degree, coef0=coef0, + kernel_params=kernel_params, random_state=random_state) def fit(self, X, Y): """ Train fast kernel classification model @@ -492,13 +525,6 @@ def fit(self, X, Y): ------- self : returns an instance of self. """ - self.regressor_ = FKR_EigenPro( - batch_size=self.batch_size, n_epoch=self.n_epoch, - n_components=self.n_components, - subsample_size=self.subsample_size, kernel=self.kernel, - bandwidth=self.bandwidth, gamma=self.gamma, - degree=self.degree, coef0=self.coef0, - kernel_params=self.kernel_params, random_state=self.random_state) X, Y = check_X_y(X, Y, dtype=np.float32, force_all_finite=True, multi_output=False, ensure_min_samples=3) check_classification_targets(Y) @@ -512,8 +538,7 @@ def fit(self, X, Y): for ind, label in enumerate(Y): class_matrix[ind][loc[label]] = 1 - self.regressor_.fit(X, class_matrix) - + self._raw_fit(X, class_matrix) return self def predict(self, X): @@ -529,9 +554,5 @@ def predict(self, X): y : {float, array}, shape = [n_samples] Predicted labels. """ - check_is_fitted(self, ["regressor_"]) - Y = self.regressor_.predict(X) + Y = self._raw_predict(X) return self.classes_[np.argmax(Y, axis=1)] - - def _get_tags(self): - return {'multioutput': True} diff --git a/sklearn_extra/tests/test_common.py b/sklearn_extra/tests/test_common.py index f22cdab0..eee108d4 100644 --- a/sklearn_extra/tests/test_common.py +++ b/sklearn_extra/tests/test_common.py @@ -3,11 +3,12 @@ from sklearn.utils.estimator_checks import check_estimator from sklearn_extra.kernel_approximation import Fastfood +from sklearn_extra import fast_kernel @pytest.mark.parametrize( "Estimator", - [Fastfood] + [Fastfood, fast_kernel.FKC_EigenPro, fast_kernel.FKR_EigenPro] ) def test_all_estimators(Estimator, request): return check_estimator(Estimator) diff --git a/sklearn_extra/tests/test_fast_kernel.py b/sklearn_extra/tests/test_fast_kernel.py index fc9ce470..a157d249 100644 --- a/sklearn_extra/tests/test_fast_kernel.py +++ b/sklearn_extra/tests/test_fast_kernel.py @@ -4,78 +4,104 @@ from sklearn.utils.testing import assert_array_almost_equal from sklearn_extra.fast_kernel import FKR_EigenPro, FKC_EigenPro -np.random.seed(1) -# Tests for Fast Kernel Regression and Classification - - -def test_fast_kernel_regression_gaussian(): - X, y = make_regression(n_features=100, random_state=1) - FKR_prediction = FKR_EigenPro( - kernel="gaussian", n_epoch=100, bandwidth=10, - random_state=1).fit(X, y).predict(X) - assert_array_almost_equal(abs(FKR_prediction / y)/2.0, .5, decimal=2) - - -def test_fast_kernel_regression_laplace(): - X, y = make_regression(random_state=1) - FKR_prediction = FKR_EigenPro( - kernel="laplace", n_epoch=100, bandwidth=8, - random_state=1).fit(X, y).predict(X) - assert_array_almost_equal(abs(FKR_prediction / y)/2.0, .5, decimal=2) - - -def test_fast_kernel_regression_cauchy(): - X, y = make_regression(random_state=1) - FKR_prediction = FKR_EigenPro( - kernel="cauchy", n_epoch=100, bandwidth=10, subsample_size=1000, - random_state=1).fit(X, y).predict(X) - assert_array_almost_equal(abs(FKR_prediction / y)/2.0, .5, decimal=2) - - -def test_fast_kernel_regression_2d(): - X, y = make_regression(n_features=200, n_targets=30, random_state=1) - FKR_prediction = FKR_EigenPro( - kernel="gaussian", n_epoch=100, bandwidth=14, - random_state=1).fit(X, y).predict(X) - assert_array_almost_equal(abs(FKR_prediction / y)/2.0, .5, decimal=2) - - -def test_fast_kernel_regression_many_features(): - X, y = make_regression(n_features=10000, random_state=1) - FKR_prediction = FKR_EigenPro( - kernel="gaussian", n_epoch=100, bandwidth=1, - random_state=1).fit(X, y).predict(X) - assert_array_almost_equal(abs(FKR_prediction / y)/2.0, .5, decimal=2) - - -def test_fast_kernel_regression_simple(): - X, y = make_regression(n_features=100, n_informative=1, - random_state=1) - FKR_prediction = FKR_EigenPro( - batch_size=500, kernel="gaussian", n_epoch=100, bandwidth=10, - random_state=1).fit(X, y).predict(X) - assert_array_almost_equal(abs(FKR_prediction / y)/2.0, .5, decimal=2) - +import pytest -def test_fast_kernel_regression_complex(): - X, y = make_regression(n_samples=500, n_informative=100, - random_state=1) - FKR_prediction = FKR_EigenPro( - kernel="gaussian", n_epoch=60, bandwidth=10, - random_state=1).fit(X, y).predict(X) - assert_array_almost_equal(abs(FKR_prediction / y)/2.0, .5, decimal=2) +np.random.seed(1) +# Tests for Fast Kernel Regression and Classification. + + +def gen_regression(params): + """Generate a regression problem with make_regression + where random_state=1""" + return make_regression(**params, random_state=1) + + +def gen_classification(params): + """Generate a classification problem with make_classification + where random_state=1""" + return make_classification(**params, random_state=1) + + +@pytest.mark.parametrize('estimator, data', [ + (FKR_EigenPro, gen_regression({})), + (FKC_EigenPro, gen_classification({})) +]) +@pytest.mark.parametrize('params, err_msg', [ + ({'kernel': "not_a_kernel"}, 'Unknown kernel \'not_a_kernel\''), + ({'n_epoch': 0}, 'n_epoch should be positive, was 0'), + ({'n_epoch': -1}, 'n_epoch should be positive, was -1'), + ({'n_components': -1}, 'n_components should be non-negative, was -1'), + ({'subsample_size': -1}, 'subsample_size should be non-negative, was -1'), + ({'batch_size': 0}, 'batch_size should be positive, was 0'), + ({'batch_size': -1}, 'batch_size should be positive, was -1'), + ({'bandwidth': 0}, 'bandwidth should be positive, was 0'), + ({'bandwidth': -1}, 'bandwidth should be positive, was -1') +]) +def test_parameter_validation(estimator, data, params, err_msg): + X, y = data + with pytest.raises(ValueError, match=err_msg): + estimator(**params).fit(X, y) + + +@pytest.mark.parametrize( + "data, estimator", + [ + # Test gaussian kernel + (gen_regression({}), FKR_EigenPro( + kernel="gaussian", n_epoch=100, bandwidth=10, random_state=1)), + # Test laplacian kernel + (gen_regression({}), FKR_EigenPro( + kernel="laplace", n_epoch=100, bandwidth=8, + random_state=1)), + # Test cauchy kernel + (gen_regression({}), FKR_EigenPro( + kernel="cauchy", n_epoch=100, bandwidth=10, subsample_size=1000, + random_state=1)), + # Test with multiple outputs + (gen_regression({'n_features': 200, 'n_targets': 30}), + FKR_EigenPro(kernel="gaussian", n_epoch=100, bandwidth=14, + random_state=1)), + # Test with a very large number of input features + (gen_regression({'n_features': 10000}), + FKR_EigenPro(kernel="gaussian", n_epoch=100, + bandwidth=1, random_state=1)), + # Test a very simple underlying distribution + (gen_regression({'n_informative': 1}), + FKR_EigenPro(batch_size=500, kernel="gaussian", n_epoch=100, + bandwidth=10, random_state=1)), + # Test a very complex underlying distribution + (gen_regression({'n_samples': 500, 'n_informative': 100}), + FKR_EigenPro(kernel="gaussian", n_epoch=60, bandwidth=10, + random_state=1)) + ] +) +def test_regressor_accuracy(data, estimator): + """ + Test the accuracy of the Fast Kernel Regressor on multiple + data sets with different parameter inputs. We expect that the + regressor should achieve near-zero training error after sufficient + training time. + :param data: A tuple containing the input and output training data + :param Estimator: The regressor to do predictions with. + """ + X, y = data + prediction = estimator.fit(X, y).predict(X) + assert_array_almost_equal(abs(prediction / y)/2.0, .5, decimal=2) def test_fast_kernel_regression_duplicate_data(): + """Test the performance when some data is repeated""" X, y = make_regression(random_state=1) X, y = np.concatenate([X, X]), np.concatenate([y, y]) - FKR_prediction = FKR_EigenPro( - kernel="gaussian", n_epoch=100, bandwidth=1, + fkr_prediction = FKR_EigenPro( + kernel="gaussian", n_epoch=100, bandwidth=5, random_state=1).fit(X, y).predict(X) - assert_array_almost_equal(abs(FKR_prediction / y)/2.0, .5, decimal=2) + assert_array_almost_equal(abs(fkr_prediction / y)/2.0, .5, decimal=2) def test_fast_kernel_regression_conflict_data(): + """Make sure the regressor doesn't crash when conflicting + data is given""" X, y = make_regression(random_state=1) y = np.reshape(y, (-1, 1)) X, y = X, np.hstack([y, y+2]) @@ -87,69 +113,66 @@ def test_fast_kernel_regression_conflict_data(): # Tests for FastKernelClassification -def test_fast_kernel_classification_gaussian(): - X, y = make_classification(n_samples=10, hypercube=False, - random_state=1) - FKC_prediction = FKC_EigenPro( - batch_size=9, kernel="gaussian", bandwidth=2.5, - n_epoch=100, random_state=1)\ - .fit(X, y).predict(X) - assert_array_almost_equal(FKC_prediction, y) - - -def test_fast_kernel_classification_laplace(): - X, y = make_classification(random_state=1) - FKC_prediction = FKC_EigenPro( - kernel="laplace", n_epoch=100, - bandwidth=13, random_state=1).fit(X, y).predict(X) - assert_array_almost_equal(FKC_prediction, y) - - -def test_fast_kernel_classification_cauchy(): - X, y = make_classification(random_state=1) - FKC_prediction = FKC_EigenPro( - kernel="cauchy", n_epoch=100, bandwidth=10, - random_state=1).fit(X, y).predict(X) - assert_array_almost_equal(FKC_prediction, y) - - -def test_fast_kernel_classification_complex(): - X, y = make_classification(n_samples=500, n_features=500, - n_informative=170, scale=30, shift=6, - random_state=1) - FKC_prediction = FKC_EigenPro( - kernel="gaussian", bandwidth=5, - n_epoch=100, random_state=1).fit(X, y).predict(X) - assert_array_almost_equal(FKC_prediction, y) - - -def test_fast_kernel_classification_redundant(): - X, y = make_classification(n_redundant=18, random_state=1) - FKC_prediction = FKC_EigenPro( - kernel="laplace", bandwidth=1, - n_epoch=100, random_state=1).fit(X, y).predict(X) - assert_array_almost_equal(FKC_prediction, y) - - -def test_fast_kernel_classification_shift(): - X, y = make_classification(shift=1, hypercube=False, - random_state=1) - FKC_prediction = FKC_EigenPro( - kernel="gaussian", bandwidth=5, - n_epoch=100, random_state=1).fit(X, y).predict(X) - assert_array_almost_equal(FKC_prediction, y) +@pytest.mark.parametrize( + "data, estimator", + [ + # Test gaussian kernel + (gen_classification({'n_samples': 10, 'hypercube': False}), + FKC_EigenPro(batch_size=9, kernel="gaussian", bandwidth=2.5, + n_epoch=100, random_state=1)), + # Test laplacian kernel + (gen_classification({}), FKC_EigenPro( + kernel="laplace", n_epoch=100, bandwidth=13, + random_state=1)), + # Test cauchy kernel + (gen_classification({}), FKC_EigenPro( + kernel="cauchy", n_epoch=100, bandwidth=10, random_state=1)), + # Test with a very large number of input features + # and samples, shifted around and scaled + (gen_classification({'n_samples': 500, 'n_features': 500, + 'n_informative': 160, 'scale': 30, 'shift': 6}), + FKC_EigenPro(kernel="gaussian", n_epoch=100, + bandwidth=20, random_state=1)), + # Test a distribution that has been shifted + (gen_classification({'shift': 1, 'hypercube': False}), + FKC_EigenPro(kernel="gaussian", n_epoch=200, bandwidth=8, + random_state=1)), + # Test with many redundant features. + (gen_classification({'n_redundant': 18}), + FKC_EigenPro(kernel="laplace", n_epoch=100, bandwidth=20, + random_state=1)) + ] +) +def test_classifier_accuracy(data, estimator): + """ + Test the accuracy of the Fast Kernel Classification on multiple + data sets with different parameter inputs. We expect that the + classification should achieve zero training error after sufficient + training time. + :param data: A tuple containing the input and output training data + :param Estimator: The classifier to do predictions with. + """ + X, y = data + prediction = estimator.fit(X, y).predict(X) + assert_array_almost_equal(prediction, y) def test_fast_kernel_classification_duplicate_data(): + """ + Make sure that the classifier correctly handles cases + where some data is repeated. + """ X, y = make_classification(n_features=200, n_repeated=50, random_state=1) - FKC_prediction = FKC_EigenPro( + fkc_prediction = FKC_EigenPro( kernel="gaussian", n_epoch=60, bandwidth=1, random_state=1).fit(X, y).predict(X) - assert_array_almost_equal(FKC_prediction, y) + assert_array_almost_equal(fkc_prediction, y) def test_fast_kernel_classification_conflict_data(): + """Make sure that the classifier doesn't crash + when given conflicting input data""" X, y = make_classification(random_state=1) X, y = np.concatenate([X, X]), np.concatenate([y, 1-y]) # Make sure we don't throw an error when fitting or predicting From 82de694600c5bb0fc8dc283e25b767187fe01071 Mon Sep 17 00:00:00 2001 From: Alex <7alex7li@gmail.com> Date: Tue, 2 Jul 2019 10:01:51 -0400 Subject: [PATCH 14/31] Added lines to help diagnose error, reduced size of plot_mnist.py --- doc/api.rst | 6 +++++- examples/fast_kernel/plot_mnist.py | 10 ++++++---- sklearn_extra/fast_kernel.py | 12 ++++++++++-- 3 files changed, 21 insertions(+), 7 deletions(-) diff --git a/doc/api.rst b/doc/api.rst index 77ded21b..6e404c00 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -24,4 +24,8 @@ EigenPro .. currentmodule:: sklearn_extra .. autosummary:: - fast_kernel + :toctree: generated/ + :template: class.rst + + fast_kernel.FKR_EigenPro + fast_kernel.FKC_EigenPro diff --git a/examples/fast_kernel/plot_mnist.py b/examples/fast_kernel/plot_mnist.py index e34fab8a..80265402 100644 --- a/examples/fast_kernel/plot_mnist.py +++ b/examples/fast_kernel/plot_mnist.py @@ -24,10 +24,11 @@ rng = np.random.RandomState(1) # Generate sample data from mnist -mnist = fetch_openml('mnist_784') # 'Fashion-MNIST') +mnist = fetch_openml('Fashion-MNIST') mnist.data = mnist.data / 255.0 +print("Data has loaded") -p = np.random.permutation(60000) +p = np.random.permutation(600) x_train = mnist.data[p] y_train = np.int32(mnist.target[p]) x_test = mnist.data[60000:] @@ -41,7 +42,8 @@ svc_pred_times = [] svc_err = [] -train_sizes = [5000, 60000] +train_sizes = [500, 1000] +print("Train Sizes: " + str(train_sizes)) bandwidth = 5.0 @@ -49,7 +51,7 @@ for train_size in train_sizes: for name, estimator in [ ("FastKernel", FKC_EigenPro( - n_epoch=2, bandwidth=bandwidth, random_state=rng)), + n_epoch=2, bandwidth=bandwidth, random_state=rng)), ("SupportVector", SVC(C=5, gamma=1./(2 * bandwidth * bandwidth)))]: stime = time() estimator.fit(x_train[:train_size], y_train[:train_size]) diff --git a/sklearn_extra/fast_kernel.py b/sklearn_extra/fast_kernel.py index c20ea509..b7451710 100644 --- a/sklearn_extra/fast_kernel.py +++ b/sklearn_extra/fast_kernel.py @@ -93,10 +93,12 @@ def _nystrom_svd(self, X, n_components): operator). """ m, _ = X.shape + K = self._kernel(X, X) W = K / m S, V = eigh(W, eigvals=(m - n_components, m - 1)) + # Flip so eigenvalues are in descending order. S = np.maximum(np.float32(1e-7), np.flipud(S)) V = np.fliplr(V)[:, :n_components] / np.sqrt(m, dtype='float32') @@ -187,11 +189,16 @@ def _initialize_params(self, X, Y, random_state): replace=False).astype('int32') max_S, beta, Q, V = self._setup(X[pinx], n_components, mG, alpha=.95) # Calculate best batch size. + print("mG:" + str(mG)) + print("BETA:" + str(beta)) + print("max_S:" + str(max_S)) + print("N:" + str(n)) if self.batch_size == "auto": bs = min(np.int32(beta / max_S), mG)+1 else: bs = self.batch_size self.bs_ = min(bs, n) + print("BS: " + str(bs)) # Calculate best step size. if self.bs_ < beta / max_S + 1: @@ -200,6 +207,7 @@ def _initialize_params(self, X, Y, random_state): eta = 2. * self.bs_ / (beta + (self.bs_ - 1) * max_S) else: eta = 0.95 * 2 / max_S + print("eta" + str(eta)) # Remember the shape of Y for predict() and ensure it's shape is 2-D. self.was_1D_ = False if len(Y.shape) == 1: @@ -390,8 +398,8 @@ class FKR_EigenPro(BaseEigenPro, RegressorMixin): >>> rgs = FKR_EigenPro(n_epoch=3, bandwidth=1, subsample_size=50) >>> rgs.fit(x_train, y_train) FKR_EigenPro(bandwidth=1, batch_size='auto', coef0=1, degree=3, gamma=None, - kernel='gaussian', kernel_params=None, n_components=1000, n_epoch=3, - random_state=None, subsample_size=50) + kernel='gaussian', kernel_params=None, n_components=1000, + n_epoch=3, random_state=None, subsample_size=50) >>> y_pred = rgs.predict(x_train) >>> loss = np.mean(np.square(y_train - y_pred)) """ From db77d84b2f2385a0b74a347aae94b5a0b2cf31a4 Mon Sep 17 00:00:00 2001 From: Alex <7alex7li@gmail.com> Date: Tue, 2 Jul 2019 11:50:21 -0400 Subject: [PATCH 15/31] Fix plot mnist using wrong permutation number and remove print statments --- examples/fast_kernel/plot_mnist.py | 4 ++-- sklearn_extra/fast_kernel.py | 5 ----- 2 files changed, 2 insertions(+), 7 deletions(-) diff --git a/examples/fast_kernel/plot_mnist.py b/examples/fast_kernel/plot_mnist.py index 80265402..b6445148 100644 --- a/examples/fast_kernel/plot_mnist.py +++ b/examples/fast_kernel/plot_mnist.py @@ -28,7 +28,7 @@ mnist.data = mnist.data / 255.0 print("Data has loaded") -p = np.random.permutation(600) +p = np.random.permutation(60000) x_train = mnist.data[p] y_train = np.int32(mnist.target[p]) x_test = mnist.data[60000:] @@ -42,7 +42,7 @@ svc_pred_times = [] svc_err = [] -train_sizes = [500, 1000] +train_sizes = [500, 1000, 5000] print("Train Sizes: " + str(train_sizes)) bandwidth = 5.0 diff --git a/sklearn_extra/fast_kernel.py b/sklearn_extra/fast_kernel.py index b7451710..8abfdba8 100644 --- a/sklearn_extra/fast_kernel.py +++ b/sklearn_extra/fast_kernel.py @@ -189,16 +189,11 @@ def _initialize_params(self, X, Y, random_state): replace=False).astype('int32') max_S, beta, Q, V = self._setup(X[pinx], n_components, mG, alpha=.95) # Calculate best batch size. - print("mG:" + str(mG)) - print("BETA:" + str(beta)) - print("max_S:" + str(max_S)) - print("N:" + str(n)) if self.batch_size == "auto": bs = min(np.int32(beta / max_S), mG)+1 else: bs = self.batch_size self.bs_ = min(bs, n) - print("BS: " + str(bs)) # Calculate best step size. if self.bs_ < beta / max_S + 1: From df4afa440ae57f45f9693258d8c8cfa95c0de657 Mon Sep 17 00:00:00 2001 From: Alex <7alex7li@gmail.com> Date: Tue, 2 Jul 2019 12:54:57 -0400 Subject: [PATCH 16/31] Convert to float64 before doing conversion --- sklearn_extra/fast_kernel.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/sklearn_extra/fast_kernel.py b/sklearn_extra/fast_kernel.py index 8abfdba8..4ed00bf0 100644 --- a/sklearn_extra/fast_kernel.py +++ b/sklearn_extra/fast_kernel.py @@ -2,7 +2,7 @@ # Siyuan Ma import numpy as np -from scipy.linalg import eigh +from scipy.linalg import eigh, eig, LinAlgError from abc import ABC, abstractmethod from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin from sklearn.metrics.pairwise import pairwise_kernels, euclidean_distances @@ -95,15 +95,13 @@ def _nystrom_svd(self, X, n_components): m, _ = X.shape K = self._kernel(X, X) - W = K / m + W = np.float64(K) / m S, V = eigh(W, eigvals=(m - n_components, m - 1)) - - # Flip so eigenvalues are in descending order. S = np.maximum(np.float32(1e-7), np.flipud(S)) V = np.fliplr(V)[:, :n_components] / np.sqrt(m, dtype='float32') - return S, V + return np.float32(S), np.float32(V) def _setup(self, feat, max_components, mG, alpha): """Compute preconditioner and scale factors for EigenPro iteration From e90c64667bce793391daa14e76d9e060a569a70c Mon Sep 17 00:00:00 2001 From: Alex <7alex7li@gmail.com> Date: Tue, 2 Jul 2019 13:01:44 -0400 Subject: [PATCH 17/31] Convert to float64 for computing eigenvalues --- sklearn_extra/fast_kernel.py | 1 - 1 file changed, 1 deletion(-) diff --git a/sklearn_extra/fast_kernel.py b/sklearn_extra/fast_kernel.py index 4ed00bf0..a19178cf 100644 --- a/sklearn_extra/fast_kernel.py +++ b/sklearn_extra/fast_kernel.py @@ -200,7 +200,6 @@ def _initialize_params(self, X, Y, random_state): eta = 2. * self.bs_ / (beta + (self.bs_ - 1) * max_S) else: eta = 0.95 * 2 / max_S - print("eta" + str(eta)) # Remember the shape of Y for predict() and ensure it's shape is 2-D. self.was_1D_ = False if len(Y.shape) == 1: From 9a921100343ff4de4ce1f66ff821e0ab73ca0093 Mon Sep 17 00:00:00 2001 From: Alex <7alex7li@gmail.com> Date: Tue, 2 Jul 2019 13:01:44 -0400 Subject: [PATCH 18/31] Convert to float64 for computing eigenvalues, try to conform to all code style --- examples/fast_kernel/plot_mnist.py | 11 ++++++----- sklearn_extra/fast_kernel.py | 1 - 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/fast_kernel/plot_mnist.py b/examples/fast_kernel/plot_mnist.py index b6445148..dbc8f860 100644 --- a/examples/fast_kernel/plot_mnist.py +++ b/examples/fast_kernel/plot_mnist.py @@ -23,7 +23,7 @@ rng = np.random.RandomState(1) -# Generate sample data from mnist +# Generate sample data from mnist mnist = fetch_openml('Fashion-MNIST') mnist.data = mnist.data / 255.0 print("Data has loaded") @@ -42,7 +42,7 @@ svc_pred_times = [] svc_err = [] -train_sizes = [500, 1000, 5000] +train_sizes = [500, 1000, 2000] print("Train Sizes: " + str(train_sizes)) bandwidth = 5.0 @@ -50,9 +50,10 @@ # Fit models to data for train_size in train_sizes: for name, estimator in [ - ("FastKernel", FKC_EigenPro( - n_epoch=2, bandwidth=bandwidth, random_state=rng)), - ("SupportVector", SVC(C=5, gamma=1./(2 * bandwidth * bandwidth)))]: + ("FastKernel", + FKC_EigenPro(n_epoch=2, bandwidth=bandwidth, random_state=rng)), + ("SupportVector", + SVC(C=5, gamma=1. / (2 * bandwidth * bandwidth)))]: stime = time() estimator.fit(x_train[:train_size], y_train[:train_size]) fit_t = time() - stime diff --git a/sklearn_extra/fast_kernel.py b/sklearn_extra/fast_kernel.py index 4ed00bf0..a19178cf 100644 --- a/sklearn_extra/fast_kernel.py +++ b/sklearn_extra/fast_kernel.py @@ -200,7 +200,6 @@ def _initialize_params(self, X, Y, random_state): eta = 2. * self.bs_ / (beta + (self.bs_ - 1) * max_S) else: eta = 0.95 * 2 / max_S - print("eta" + str(eta)) # Remember the shape of Y for predict() and ensure it's shape is 2-D. self.was_1D_ = False if len(Y.shape) == 1: From 289115f5a1d3cb2593c1fed2ce55de2a6a6f20c0 Mon Sep 17 00:00:00 2001 From: Alex <7alex7li@gmail.com> Date: Tue, 2 Jul 2019 15:36:24 -0400 Subject: [PATCH 19/31] Apparently the doc tests want different docs again horray now watch as a completely different set of tests appears and causes even more failures despite only comments changing --- sklearn_extra/fast_kernel.py | 8 ++++---- sklearn_extra/tests/test_common.py | 3 +-- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/sklearn_extra/fast_kernel.py b/sklearn_extra/fast_kernel.py index e1371753..ae4b4513 100644 --- a/sklearn_extra/fast_kernel.py +++ b/sklearn_extra/fast_kernel.py @@ -391,8 +391,8 @@ class FKR_EigenPro(BaseEigenPro, RegressorMixin): >>> rgs = FKR_EigenPro(n_epoch=3, bandwidth=1, subsample_size=50) >>> rgs.fit(x_train, y_train) FKR_EigenPro(bandwidth=1, batch_size='auto', coef0=1, degree=3, gamma=None, - kernel='gaussian', kernel_params=None, n_components=1000, - n_epoch=3, random_state=None, subsample_size=50) + kernel='gaussian', kernel_params=None, n_components=1000, + n_epoch=3, random_state=None, subsample_size=50) >>> y_pred = rgs.predict(x_train) >>> loss = np.mean(np.square(y_train - y_pred)) """ @@ -494,8 +494,8 @@ class FKC_EigenPro(BaseEigenPro, ClassifierMixin): >>> rgs = FKC_EigenPro(n_epoch=3, bandwidth=1, subsample_size=50) >>> rgs.fit(x_train, y_train) FKC_EigenPro(bandwidth=1, batch_size='auto', coef0=1, degree=3, gamma=None, - kernel='gaussian', kernel_params=None, n_components=1000, - n_epoch=3, random_state=None, subsample_size=50) + kernel='gaussian', kernel_params=None, n_components=1000, + n_epoch=3, random_state=None, subsample_size=50) >>> y_pred = rgs.predict(x_train) >>> loss = np.mean(y_train != y_pred) """ diff --git a/sklearn_extra/tests/test_common.py b/sklearn_extra/tests/test_common.py index eee108d4..5a8cc925 100644 --- a/sklearn_extra/tests/test_common.py +++ b/sklearn_extra/tests/test_common.py @@ -7,8 +7,7 @@ @pytest.mark.parametrize( - "Estimator", - [Fastfood, fast_kernel.FKC_EigenPro, fast_kernel.FKR_EigenPro] + "Estimator", [Fastfood, fast_kernel.FKC_EigenPro, fast_kernel.FKR_EigenPro] ) def test_all_estimators(Estimator, request): return check_estimator(Estimator) From cc842ac04bf54c2311ded7406982991c2c1c1729 Mon Sep 17 00:00:00 2001 From: Alex <7alex7li@gmail.com> Date: Wed, 3 Jul 2019 21:25:45 -0400 Subject: [PATCH 20/31] Reformated Files using black and implement try-except for error in comment 13 --- examples/fast_kernel/plot_mnist.py | 56 +++--- sklearn_extra/__init__.py | 2 +- sklearn_extra/fast_kernel.py | 220 ++++++++++++++++-------- sklearn_extra/tests/test_fast_kernel.py | 216 +++++++++++++++-------- 4 files changed, 329 insertions(+), 165 deletions(-) diff --git a/examples/fast_kernel/plot_mnist.py b/examples/fast_kernel/plot_mnist.py index dbc8f860..981407b2 100644 --- a/examples/fast_kernel/plot_mnist.py +++ b/examples/fast_kernel/plot_mnist.py @@ -24,7 +24,7 @@ rng = np.random.RandomState(1) # Generate sample data from mnist -mnist = fetch_openml('Fashion-MNIST') +mnist = fetch_openml("Fashion-MNIST") mnist.data = mnist.data / 255.0 print("Data has loaded") @@ -50,10 +50,12 @@ # Fit models to data for train_size in train_sizes: for name, estimator in [ - ("FastKernel", - FKC_EigenPro(n_epoch=2, bandwidth=bandwidth, random_state=rng)), - ("SupportVector", - SVC(C=5, gamma=1. / (2 * bandwidth * bandwidth)))]: + ( + "FastKernel", + FKC_EigenPro(n_epoch=2, bandwidth=bandwidth, random_state=rng), + ), + ("SupportVector", SVC(C=5, gamma=1.0 / (2 * bandwidth * bandwidth))), + ]: stime = time() estimator.fit(x_train[:train_size], y_train[:train_size]) fit_t = time() - stime @@ -62,7 +64,7 @@ y_pred_test = estimator.predict(x_test) pred_t = time() - stime - err = 100. * np.sum(y_pred_test != y_test) / len(y_test) + err = 100.0 * np.sum(y_pred_test != y_test) / len(y_test) if name == "FastKernel": fkc_fit_times.append(fit_t) fkc_pred_times.append(pred_t) @@ -71,8 +73,10 @@ svc_fit_times.append(fit_t) svc_pred_times.append(pred_t) svc_err.append(err) - print("%s Classification with %i training samples in %0.2f seconds." % - (name, train_size, fit_t + pred_t)) + print( + "%s Classification with %i training samples in %0.2f seconds." + % (name, train_size, fit_t + pred_t) + ) # set up grid for figures fig = plt.figure(num=None, figsize=(6, 4), dpi=160) @@ -80,14 +84,14 @@ # Graph fit(train) time train_size_labels = [str(s) for s in train_sizes] -ax.plot(train_sizes, svc_fit_times, 'o--', color='g', label='SVC') -ax.plot(train_sizes, fkc_fit_times, 'o-', color='r', label='FKC (EigenPro)') -ax.set_xscale('log') -ax.set_yscale('log', nonposy='clip') -ax.set_xlabel('train size') -ax.set_ylabel('time (seconds)') +ax.plot(train_sizes, svc_fit_times, "o--", color="g", label="SVC") +ax.plot(train_sizes, fkc_fit_times, "o-", color="r", label="FKC (EigenPro)") +ax.set_xscale("log") +ax.set_yscale("log", nonposy="clip") +ax.set_xlabel("train size") +ax.set_ylabel("time (seconds)") ax.legend() -ax.set_title('Training Time') +ax.set_title("Training Time") ax.set_xticks(train_sizes) ax.set_xticklabels(train_size_labels) ax.set_xticks([], minor=True) @@ -95,24 +99,24 @@ # Graph prediction(test) time ax = plt.subplot2grid((2, 2), (0, 1), rowspan=1) -ax.plot(train_sizes, fkc_pred_times, 'o-', color='r') -ax.plot(train_sizes, svc_pred_times, 'o--', color='g') -ax.set_xscale('log') -ax.set_yscale('log', nonposy='clip') -ax.set_ylabel('time (seconds)') -ax.set_title('Prediction Time') +ax.plot(train_sizes, fkc_pred_times, "o-", color="r") +ax.plot(train_sizes, svc_pred_times, "o--", color="g") +ax.set_xscale("log") +ax.set_yscale("log", nonposy="clip") +ax.set_ylabel("time (seconds)") +ax.set_title("Prediction Time") ax.set_xticks([]) ax.set_xticks([], minor=True) # Graph training error ax = plt.subplot2grid((2, 2), (1, 1), rowspan=1) -ax.plot(train_sizes, fkc_err, 'o-', color='r') -ax.plot(train_sizes, svc_err, 'o-', color='g') -ax.set_xscale('log') +ax.plot(train_sizes, fkc_err, "o-", color="r") +ax.plot(train_sizes, svc_err, "o-", color="g") +ax.set_xscale("log") ax.set_xticks(train_sizes) ax.set_xticklabels(train_size_labels) ax.set_xticks([], minor=True) -ax.set_xlabel('train size') -ax.set_ylabel('classification error %') +ax.set_xlabel("train size") +ax.set_ylabel("classification error %") plt.tight_layout() plt.show() diff --git a/sklearn_extra/__init__.py b/sklearn_extra/__init__.py index b994d34a..55ba93d3 100644 --- a/sklearn_extra/__init__.py +++ b/sklearn_extra/__init__.py @@ -2,4 +2,4 @@ from ._version import __version__ -__all__ = ['__version__', 'fast_kernel'] +__all__ = ["__version__", "fast_kernel"] diff --git a/sklearn_extra/fast_kernel.py b/sklearn_extra/fast_kernel.py index ae4b4513..32a34b01 100644 --- a/sklearn_extra/fast_kernel.py +++ b/sklearn_extra/fast_kernel.py @@ -2,7 +2,7 @@ # Siyuan Ma import numpy as np -from scipy.linalg import eigh +from scipy.linalg import eigh, LinAlgError from abc import ABC, abstractmethod from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin from sklearn.metrics.pairwise import pairwise_kernels, euclidean_distances @@ -15,11 +15,22 @@ class BaseEigenPro(BaseEstimator, ABC): """ Base class for Fast Kernel/Eigenpro iteration. """ + @abstractmethod - def __init__(self, batch_size="auto", n_epoch=2, n_components=1000, - subsample_size="auto", kernel="gaussian", - bandwidth=5, gamma=None, degree=3, coef0=1, - kernel_params=None, random_state=None): + def __init__( + self, + batch_size="auto", + n_epoch=2, + n_components=1000, + subsample_size="auto", + kernel="gaussian", + bandwidth=5, + gamma=None, + degree=3, + coef0=1, + kernel_params=None, + random_state=None, + ): self.batch_size = batch_size self.n_epoch = n_epoch self.n_components = n_components @@ -48,17 +59,22 @@ def _kernel(self, X, Y): K : {float, array}, shape = [n_samples, n_centers] Kernel matrix. """ - if (self.kernel != "gaussian" - and self.kernel != "laplace" - and self.kernel != "cauchy"): + if ( + self.kernel != "gaussian" + and self.kernel != "laplace" + and self.kernel != "cauchy" + ): if callable(self.kernel): params = self.kernel_params or {} else: - params = {"gamma": self.gamma, - "degree": self.degree, - "coef0": self.coef0} - return pairwise_kernels(X, Y, metric=self.kernel, - filter_params=True, **params) + params = { + "gamma": self.gamma, + "degree": self.degree, + "coef0": self.coef0, + } + return pairwise_kernels( + X, Y, metric=self.kernel, filter_params=True, **params + ) distance = euclidean_distances(X, Y, squared=True) bandwidth = np.float32(self.bandwidth) if self.kernel == "gaussian": @@ -96,13 +112,18 @@ def _nystrom_svd(self, X, n_components): K = self._kernel(X, X) # Use float64 so eigh doesn't occassionally crash and burn and fail - W = np.float64(K) / m - S, V = eigh(W, eigvals=(m - n_components, m - 1)) + W = K / m + try: + S, V = eigh(W, eigvals=(m - n_components, m - 1)) + except LinAlgError: + W = np.float64(W) + S, V = eigh(W, eigvals=(m - n_components, m - 1)) + S, V = np.float32(S), np.float32(V) # Flip so eigenvalues are in descending order. S = np.maximum(np.float32(1e-7), np.flipud(S)) - V = np.fliplr(V)[:, :n_components] / np.sqrt(m, dtype='float32') + V = np.fliplr(V)[:, :n_components] / np.sqrt(m, dtype="float32") - return np.float32(S), np.float32(V) + return S, V def _setup(self, feat, max_components, mG, alpha): """Compute preconditioner and scale factors for EigenPro iteration @@ -149,8 +170,9 @@ def _setup(self, feat, max_components, mG, alpha): # Compute part of the preconditioner for step 2 of gradient descent in # the eigenpro model - Q = (1 - np.power(S[n_components] / S[:n_components], - alpha)) / S[:n_components] + Q = (1 - np.power(S[n_components] / S[:n_components], alpha)) / S[ + :n_components + ] max_S = S[0].astype(np.float32) kxx = 1 - np.sum(V ** 2, axis=1) * n_subsamples @@ -184,9 +206,10 @@ def _initialize_params(self, X, Y, random_state): mG = np.int32(np.sum(mem_usages < mem_bytes)) # Calculate largest eigenvalue and max{k(x,x)} using subsamples. - pinx = random_state.choice(n, sample_size, - replace=False).astype('int32') - max_S, beta, Q, V = self._setup(X[pinx], n_components, mG, alpha=.95) + pinx = random_state.choice(n, sample_size, replace=False).astype( + "int32" + ) + max_S, beta, Q, V = self._setup(X[pinx], n_components, mG, alpha=0.95) # Calculate best batch size. if self.batch_size == "auto": bs = min(np.int32(beta / max_S), mG) + 1 @@ -198,7 +221,7 @@ def _initialize_params(self, X, Y, random_state): if self.bs_ < beta / max_S + 1: eta = self.bs_ / beta elif self.bs_ < n: - eta = 2. * self.bs_ / (beta + (self.bs_ - 1) * max_S) + eta = 2.0 * self.bs_ / (beta + (self.bs_ - 1) * max_S) else: eta = 0.95 * 2 / max_S # Remember the shape of Y for predict() and ensure it's shape is 2-D. @@ -210,20 +233,27 @@ def _initialize_params(self, X, Y, random_state): def validate_parameters(self): if self.n_epoch <= 0: - raise ValueError('n_epoch should be positive, was ' - + str(self.n_epoch)) + raise ValueError( + "n_epoch should be positive, was " + str(self.n_epoch) + ) if self.n_components < 0: - raise ValueError('n_components should be non-negative, was ' - + str(self.n_components)) - if self.subsample_size != 'auto' and self.subsample_size < 0: - raise ValueError('subsample_size should be non-negative, was ' - + str(self.subsample_size)) - if self.batch_size != 'auto' and self.batch_size <= 0: - raise ValueError('batch_size should be positive, was ' - + str(self.batch_size)) + raise ValueError( + "n_components should be non-negative, was " + + str(self.n_components) + ) + if self.subsample_size != "auto" and self.subsample_size < 0: + raise ValueError( + "subsample_size should be non-negative, was " + + str(self.subsample_size) + ) + if self.batch_size != "auto" and self.batch_size <= 0: + raise ValueError( + "batch_size should be positive, was " + str(self.batch_size) + ) if self.bandwidth <= 0: - raise ValueError('bandwidth should be positive, was ' - + str(self.bandwidth)) + raise ValueError( + "bandwidth should be positive, was " + str(self.bandwidth) + ) def _raw_fit(self, X, Y): """Train fast kernel regression model @@ -240,8 +270,14 @@ def _raw_fit(self, X, Y): ------- self : returns an instance of self. """ - X, Y = check_X_y(X, Y, dtype=np.float32, multi_output=True, - ensure_min_samples=3, y_numeric=True) + X, Y = check_X_y( + X, + Y, + dtype=np.float32, + multi_output=True, + ensure_min_samples=3, + y_numeric=True, + ) Y = Y.astype(np.float32) random_state = check_random_state(self.random_state) @@ -255,8 +291,9 @@ def _raw_fit(self, X, Y): self.coef_ = np.zeros((n, Y.shape[1]), dtype=np.float32) step = np.float32(eta / self.bs_) for epoch in range(0, self.n_epoch): - epoch_inds = random_state.choice(n, n // self.bs_ * self.bs_, - replace=False).astype('int32') + epoch_inds = random_state.choice( + n, n // self.bs_ * self.bs_, replace=False + ).astype("int32") for batch_inds in np.array_split(epoch_inds, n // self.bs_): batch_x = self.centers_[batch_inds] @@ -266,12 +303,14 @@ def _raw_fit(self, X, Y): # Update 1: Sampled Coordinate Block. gradient = np.dot(kfeat, self.coef_) - batch_y - self.coef_[batch_inds] = \ + self.coef_[batch_inds] = ( self.coef_[batch_inds] - step * gradient + ) # Update 2: Fixed Coordinate Block - delta = np.dot(V * Q, np.dot(V.T, np.dot( - kfeat[:, pinx].T, gradient))) + delta = np.dot( + V * Q, np.dot(V.T, np.dot(kfeat[:, pinx].T, gradient)) + ) self.coef_[pinx] += step * delta return self @@ -292,8 +331,10 @@ def _raw_predict(self, X): X = np.asarray(X, dtype=np.float64) if len(X.shape) == 1: - raise ValueError("Reshape your data. X should be a matrix of shape" - " (n_samples, n_features).") + raise ValueError( + "Reshape your data. X should be a matrix of shape" + " (n_samples, n_features)." + ) n = X.shape[0] Ys = [] @@ -309,7 +350,7 @@ def _raw_predict(self, X): return Y def _get_tags(self): - return {'multioutput': True} + return {"multioutput": True} class FKR_EigenPro(BaseEigenPro, RegressorMixin): @@ -391,21 +432,39 @@ class FKR_EigenPro(BaseEigenPro, RegressorMixin): >>> rgs = FKR_EigenPro(n_epoch=3, bandwidth=1, subsample_size=50) >>> rgs.fit(x_train, y_train) FKR_EigenPro(bandwidth=1, batch_size='auto', coef0=1, degree=3, gamma=None, - kernel='gaussian', kernel_params=None, n_components=1000, - n_epoch=3, random_state=None, subsample_size=50) + kernel='gaussian', kernel_params=None, n_components=1000, + n_epoch=3, random_state=None, subsample_size=50) >>> y_pred = rgs.predict(x_train) >>> loss = np.mean(np.square(y_train - y_pred)) """ - def __init__(self, batch_size="auto", n_epoch=2, n_components=1000, - subsample_size="auto", kernel="gaussian", - bandwidth=5, gamma=None, degree=3, coef0=1, - kernel_params=None, random_state=None): + + def __init__( + self, + batch_size="auto", + n_epoch=2, + n_components=1000, + subsample_size="auto", + kernel="gaussian", + bandwidth=5, + gamma=None, + degree=3, + coef0=1, + kernel_params=None, + random_state=None, + ): super().__init__( - batch_size=batch_size, n_epoch=n_epoch, - n_components=n_components, subsample_size=subsample_size, - kernel=kernel, bandwidth=bandwidth, gamma=gamma, - degree=degree, coef0=coef0, - kernel_params=kernel_params, random_state=random_state) + batch_size=batch_size, + n_epoch=n_epoch, + n_components=n_components, + subsample_size=subsample_size, + kernel=kernel, + bandwidth=bandwidth, + gamma=gamma, + degree=degree, + coef0=coef0, + kernel_params=kernel_params, + random_state=random_state, + ) def fit(self, X, Y): return self._raw_fit(X, Y) @@ -494,22 +553,39 @@ class FKC_EigenPro(BaseEigenPro, ClassifierMixin): >>> rgs = FKC_EigenPro(n_epoch=3, bandwidth=1, subsample_size=50) >>> rgs.fit(x_train, y_train) FKC_EigenPro(bandwidth=1, batch_size='auto', coef0=1, degree=3, gamma=None, - kernel='gaussian', kernel_params=None, n_components=1000, - n_epoch=3, random_state=None, subsample_size=50) + kernel='gaussian', kernel_params=None, n_components=1000, + n_epoch=3, random_state=None, subsample_size=50) >>> y_pred = rgs.predict(x_train) >>> loss = np.mean(y_train != y_pred) """ - def __init__(self, batch_size="auto", n_epoch=2, n_components=1000, - subsample_size="auto", kernel="gaussian", - bandwidth=5, gamma=None, degree=3, coef0=1, - kernel_params=None, random_state=None): + def __init__( + self, + batch_size="auto", + n_epoch=2, + n_components=1000, + subsample_size="auto", + kernel="gaussian", + bandwidth=5, + gamma=None, + degree=3, + coef0=1, + kernel_params=None, + random_state=None, + ): super().__init__( - batch_size=batch_size, n_epoch=n_epoch, - n_components=n_components, subsample_size=subsample_size, - kernel=kernel, bandwidth=bandwidth, gamma=gamma, - degree=degree, coef0=coef0, - kernel_params=kernel_params, random_state=random_state) + batch_size=batch_size, + n_epoch=n_epoch, + n_components=n_components, + subsample_size=subsample_size, + kernel=kernel, + bandwidth=bandwidth, + gamma=gamma, + degree=degree, + coef0=coef0, + kernel_params=kernel_params, + random_state=random_state, + ) def fit(self, X, Y): """ Train fast kernel classification model @@ -526,8 +602,14 @@ def fit(self, X, Y): ------- self : returns an instance of self. """ - X, Y = check_X_y(X, Y, dtype=np.float32, force_all_finite=True, - multi_output=False, ensure_min_samples=3) + X, Y = check_X_y( + X, + Y, + dtype=np.float32, + force_all_finite=True, + multi_output=False, + ensure_min_samples=3, + ) check_classification_targets(Y) self.classes_ = np.unique(Y) diff --git a/sklearn_extra/tests/test_fast_kernel.py b/sklearn_extra/tests/test_fast_kernel.py index b8f1e8bd..12f97bc6 100644 --- a/sklearn_extra/tests/test_fast_kernel.py +++ b/sklearn_extra/tests/test_fast_kernel.py @@ -22,21 +22,30 @@ def gen_classification(params): return make_classification(**params, random_state=1) -@pytest.mark.parametrize('estimator, data', [ - (FKR_EigenPro, gen_regression({})), - (FKC_EigenPro, gen_classification({})) -]) -@pytest.mark.parametrize('params, err_msg', [ - ({'kernel': "not_a_kernel"}, 'Unknown kernel \'not_a_kernel\''), - ({'n_epoch': 0}, 'n_epoch should be positive, was 0'), - ({'n_epoch': -1}, 'n_epoch should be positive, was -1'), - ({'n_components': -1}, 'n_components should be non-negative, was -1'), - ({'subsample_size': -1}, 'subsample_size should be non-negative, was -1'), - ({'batch_size': 0}, 'batch_size should be positive, was 0'), - ({'batch_size': -1}, 'batch_size should be positive, was -1'), - ({'bandwidth': 0}, 'bandwidth should be positive, was 0'), - ({'bandwidth': -1}, 'bandwidth should be positive, was -1') -]) +@pytest.mark.parametrize( + "estimator, data", + [ + (FKR_EigenPro, gen_regression({})), + (FKC_EigenPro, gen_classification({})), + ], +) +@pytest.mark.parametrize( + "params, err_msg", + [ + ({"kernel": "not_a_kernel"}, "Unknown kernel 'not_a_kernel'"), + ({"n_epoch": 0}, "n_epoch should be positive, was 0"), + ({"n_epoch": -1}, "n_epoch should be positive, was -1"), + ({"n_components": -1}, "n_components should be non-negative, was -1"), + ( + {"subsample_size": -1}, + "subsample_size should be non-negative, was -1", + ), + ({"batch_size": 0}, "batch_size should be positive, was 0"), + ({"batch_size": -1}, "batch_size should be positive, was -1"), + ({"bandwidth": 0}, "bandwidth should be positive, was 0"), + ({"bandwidth": -1}, "bandwidth should be positive, was -1"), + ], +) def test_parameter_validation(estimator, data, params, err_msg): X, y = data with pytest.raises(ValueError, match=err_msg): @@ -47,33 +56,63 @@ def test_parameter_validation(estimator, data, params, err_msg): "data, estimator", [ # Test gaussian kernel - (gen_regression({}), FKR_EigenPro( - kernel="gaussian", n_epoch=100, bandwidth=10, random_state=1)), + ( + gen_regression({}), + FKR_EigenPro( + kernel="gaussian", n_epoch=100, bandwidth=10, random_state=1 + ), + ), # Test laplacian kernel - (gen_regression({}), FKR_EigenPro( - kernel="laplace", n_epoch=100, bandwidth=8, - random_state=1)), + ( + gen_regression({}), + FKR_EigenPro( + kernel="laplace", n_epoch=100, bandwidth=8, random_state=1 + ), + ), # Test cauchy kernel - (gen_regression({}), FKR_EigenPro( - kernel="cauchy", n_epoch=100, bandwidth=10, subsample_size=1000, - random_state=1)), + ( + gen_regression({}), + FKR_EigenPro( + kernel="cauchy", + n_epoch=100, + bandwidth=10, + subsample_size=1000, + random_state=1, + ), + ), # Test with multiple outputs - (gen_regression({'n_features': 200, 'n_targets': 30}), - FKR_EigenPro(kernel="gaussian", n_epoch=100, bandwidth=14, - random_state=1)), + ( + gen_regression({"n_features": 200, "n_targets": 30}), + FKR_EigenPro( + kernel="gaussian", n_epoch=100, bandwidth=14, random_state=1 + ), + ), # Test with a very large number of input features - (gen_regression({'n_features': 10000}), - FKR_EigenPro(kernel="gaussian", n_epoch=100, - bandwidth=1, random_state=1)), + ( + gen_regression({"n_features": 10000}), + FKR_EigenPro( + kernel="gaussian", n_epoch=100, bandwidth=1, random_state=1 + ), + ), # Test a very simple underlying distribution - (gen_regression({'n_informative': 1}), - FKR_EigenPro(batch_size=500, kernel="gaussian", n_epoch=100, - bandwidth=10, random_state=1)), + ( + gen_regression({"n_informative": 1}), + FKR_EigenPro( + batch_size=500, + kernel="gaussian", + n_epoch=100, + bandwidth=10, + random_state=1, + ), + ), # Test a very complex underlying distribution - (gen_regression({'n_samples': 500, 'n_informative': 100}), - FKR_EigenPro(kernel="gaussian", n_epoch=60, bandwidth=10, - random_state=1)) - ] + ( + gen_regression({"n_samples": 500, "n_informative": 100}), + FKR_EigenPro( + kernel="gaussian", n_epoch=60, bandwidth=10, random_state=1 + ), + ), + ], ) def test_regressor_accuracy(data, estimator): """ @@ -86,17 +125,21 @@ def test_regressor_accuracy(data, estimator): """ X, y = data prediction = estimator.fit(X, y).predict(X) - assert_array_almost_equal(abs(prediction / y) / 2.0, .5, decimal=2) + assert_array_almost_equal(abs(prediction / y) / 2.0, 0.5, decimal=2) def test_fast_kernel_regression_duplicate_data(): """Test the performance when some data is repeated""" X, y = make_regression(random_state=1) X, y = np.concatenate([X, X]), np.concatenate([y, y]) - fkr_prediction = FKR_EigenPro( - kernel="gaussian", n_epoch=100, bandwidth=5, - random_state=1).fit(X, y).predict(X) - assert_array_almost_equal(abs(fkr_prediction / y) / 2.0, .5, decimal=2) + fkr_prediction = ( + FKR_EigenPro( + kernel="gaussian", n_epoch=100, bandwidth=5, random_state=1 + ) + .fit(X, y) + .predict(X) + ) + assert_array_almost_equal(abs(fkr_prediction / y) / 2.0, 0.5, decimal=2) def test_fast_kernel_regression_conflict_data(): @@ -106,8 +149,9 @@ def test_fast_kernel_regression_conflict_data(): y = np.reshape(y, (-1, 1)) X, y = X, np.hstack([y, y + 2]) # Make sure we don't throw an error when fitting or predicting - FKR_EigenPro(kernel="linear", n_epoch=5, bandwidth=1, - random_state=1).fit(X, y).predict(X) + FKR_EigenPro(kernel="linear", n_epoch=5, bandwidth=1, random_state=1).fit( + X, y + ).predict(X) # Tests for FastKernelClassification @@ -117,31 +161,61 @@ def test_fast_kernel_regression_conflict_data(): "data, estimator", [ # Test gaussian kernel - (gen_classification({'n_samples': 10, 'hypercube': False}), - FKC_EigenPro(batch_size=9, kernel="gaussian", bandwidth=2.5, - n_epoch=100, random_state=1)), + ( + gen_classification({"n_samples": 10, "hypercube": False}), + FKC_EigenPro( + batch_size=9, + kernel="gaussian", + bandwidth=2.5, + n_epoch=100, + random_state=1, + ), + ), # Test laplacian kernel - (gen_classification({}), FKC_EigenPro( - kernel="laplace", n_epoch=100, bandwidth=13, - random_state=1)), + ( + gen_classification({}), + FKC_EigenPro( + kernel="laplace", n_epoch=100, bandwidth=13, random_state=1 + ), + ), # Test cauchy kernel - (gen_classification({}), FKC_EigenPro( - kernel="cauchy", n_epoch=100, bandwidth=10, random_state=1)), + ( + gen_classification({}), + FKC_EigenPro( + kernel="cauchy", n_epoch=100, bandwidth=10, random_state=1 + ), + ), # Test with a very large number of input features # and samples, shifted around and scaled - (gen_classification({'n_samples': 500, 'n_features': 500, - 'n_informative': 160, 'scale': 30, 'shift': 6}), - FKC_EigenPro(kernel="gaussian", n_epoch=100, - bandwidth=20, random_state=1)), + ( + gen_classification( + { + "n_samples": 500, + "n_features": 500, + "n_informative": 160, + "scale": 30, + "shift": 6, + } + ), + FKC_EigenPro( + kernel="gaussian", n_epoch=100, bandwidth=20, random_state=1 + ), + ), # Test a distribution that has been shifted - (gen_classification({'shift': 1, 'hypercube': False}), - FKC_EigenPro(kernel="gaussian", n_epoch=200, bandwidth=8, - random_state=1)), + ( + gen_classification({"shift": 1, "hypercube": False}), + FKC_EigenPro( + kernel="gaussian", n_epoch=200, bandwidth=8, random_state=1 + ), + ), # Test with many redundant features. - (gen_classification({'n_redundant': 18}), - FKC_EigenPro(kernel="laplace", n_epoch=100, bandwidth=20, - random_state=1)) - ] + ( + gen_classification({"n_redundant": 18}), + FKC_EigenPro( + kernel="laplace", n_epoch=100, bandwidth=20, random_state=1 + ), + ), + ], ) def test_classifier_accuracy(data, estimator): """ @@ -162,11 +236,14 @@ def test_fast_kernel_classification_duplicate_data(): Make sure that the classifier correctly handles cases where some data is repeated. """ - X, y = make_classification(n_features=200, n_repeated=50, - random_state=1) - fkc_prediction = FKC_EigenPro( - kernel="gaussian", n_epoch=60, bandwidth=1, - random_state=1).fit(X, y).predict(X) + X, y = make_classification(n_features=200, n_repeated=50, random_state=1) + fkc_prediction = ( + FKC_EigenPro( + kernel="gaussian", n_epoch=60, bandwidth=1, random_state=1 + ) + .fit(X, y) + .predict(X) + ) assert_array_almost_equal(fkc_prediction, y) @@ -176,5 +253,6 @@ def test_fast_kernel_classification_conflict_data(): X, y = make_classification(random_state=1) X, y = np.concatenate([X, X]), np.concatenate([y, 1 - y]) # Make sure we don't throw an error when fitting or predicting - FKC_EigenPro(kernel="linear", n_epoch=5, bandwidth=5, - random_state=1).fit(X, y).predict(X) + FKC_EigenPro(kernel="linear", n_epoch=5, bandwidth=5, random_state=1).fit( + X, y + ).predict(X) From 587cb7f23ad9b8763b61c2ababbffddb60b65b99 Mon Sep 17 00:00:00 2001 From: Alex <7alex7li@gmail.com> Date: Wed, 10 Jul 2019 18:09:30 -0400 Subject: [PATCH 21/31] Change gaussian to rbf and update docs --- sklearn_extra/fast_kernel.py | 51 +++++++++++-------------- sklearn_extra/tests/test_fast_kernel.py | 24 ++++++------ 2 files changed, 35 insertions(+), 40 deletions(-) diff --git a/sklearn_extra/fast_kernel.py b/sklearn_extra/fast_kernel.py index 32a34b01..d2550138 100644 --- a/sklearn_extra/fast_kernel.py +++ b/sklearn_extra/fast_kernel.py @@ -23,7 +23,7 @@ def __init__( n_epoch=2, n_components=1000, subsample_size="auto", - kernel="gaussian", + kernel="rbf", bandwidth=5, gamma=None, degree=3, @@ -60,7 +60,7 @@ def _kernel(self, X, Y): Kernel matrix. """ if ( - self.kernel != "gaussian" + self.kernel != "rbf" and self.kernel != "laplace" and self.kernel != "cauchy" ): @@ -68,7 +68,7 @@ def _kernel(self, X, Y): params = self.kernel_params or {} else: params = { - "gamma": self.gamma, + "gamma": np.float32(.5/(self.bandwidth*self.bandwidth)), "degree": self.degree, "coef0": self.coef0, } @@ -77,7 +77,7 @@ def _kernel(self, X, Y): ) distance = euclidean_distances(X, Y, squared=True) bandwidth = np.float32(self.bandwidth) - if self.kernel == "gaussian": + if self.kernel == "rbf": K = np.exp(-distance / (2.0 * bandwidth * bandwidth)) elif self.kernel == "laplace": d = np.maximum(distance, 0) @@ -380,20 +380,15 @@ class FKR_EigenPro(BaseEigenPro, RegressorMixin): it will be 4000 if there are less than 100,000 samples (for training), and otherwise 10000. - kernel : string or callable, default = "gaussian" + kernel : string or callable, default = "rbf" Kernel mapping used internally. Strings can be anything supported - by sklearn's library, however, it is recommended to use a radial - kernel. There is special support for gaussian, laplace, and cauchy - kernels. A callable should accept two arguments and return a - floating point number. + by sklearn's library, however, there is special support for the + rbf, laplace, and cauchy kernels. If a callable is given, it should + accept two arguments and return a floating point number. bandwidth : float, default=5 - Bandwidth to use with the gaussian, laplacian, and cauchy kernels. - Ignored by other kernels. - - gamma : float, default=None - Gamma parameter for the RBF, polynomial, exponential chi2 and - sigmoid kernels. Interpretation of the default value is left to + Bandwidth to use with the given kernel. For kernels that use gamma, + gamma = .5/(bandwidth^2). Interpretation of the default value is left to the kernel; see the documentation for sklearn.metrics.pairwise. Ignored by other kernels. @@ -432,7 +427,7 @@ class FKR_EigenPro(BaseEigenPro, RegressorMixin): >>> rgs = FKR_EigenPro(n_epoch=3, bandwidth=1, subsample_size=50) >>> rgs.fit(x_train, y_train) FKR_EigenPro(bandwidth=1, batch_size='auto', coef0=1, degree=3, gamma=None, - kernel='gaussian', kernel_params=None, n_components=1000, + kernel='rbf', kernel_params=None, n_components=1000, n_epoch=3, random_state=None, subsample_size=50) >>> y_pred = rgs.predict(x_train) >>> loss = np.mean(np.square(y_train - y_pred)) @@ -444,7 +439,7 @@ def __init__( n_epoch=2, n_components=1000, subsample_size="auto", - kernel="gaussian", + kernel="rbf", bandwidth=5, gamma=None, degree=3, @@ -500,17 +495,17 @@ class FKC_EigenPro(BaseEigenPro, ClassifierMixin): 'auto', it will be 4000 if there are less than 100,000 samples (for training), and otherwise 10000. - kernel : string or callable, default = "gaussian" - Kernel mapping used internally. Strings can be anything - supported by sklearn's library, however, it is recommended to - use a radial kernel. There is special support for gaussian, - laplace, and cauchy kernels. A callable should accept two - arguments and return a floating point number. + kernel : string or callable, default = "rbf" + Kernel mapping used internally. Strings can be anything supported + by sklearn's library, however, there is special support for the + rbf, laplace, and cauchy kernels. If a callable is given, it should + accept two arguments and return a floating point number. bandwidth : float, default=5 - Bandwidth to use with the gaussian, laplacian, and cauchy - kernels. Ignored by other kernels. - + Bandwidth to use with the given kernel. For kernels that use gamma, + gamma = .5/(bandwidth^2). Interpretation of the default value is left to + the kernel; see the documentation for sklearn.metrics.pairwise. + Ignored by other kernels. gamma : float, default=None Gamma parameter for the RBF, polynomial, exponential chi2 and sigmoid kernels. Interpretation of the default value is left @@ -553,7 +548,7 @@ class FKC_EigenPro(BaseEigenPro, ClassifierMixin): >>> rgs = FKC_EigenPro(n_epoch=3, bandwidth=1, subsample_size=50) >>> rgs.fit(x_train, y_train) FKC_EigenPro(bandwidth=1, batch_size='auto', coef0=1, degree=3, gamma=None, - kernel='gaussian', kernel_params=None, n_components=1000, + kernel='rbf', kernel_params=None, n_components=1000, n_epoch=3, random_state=None, subsample_size=50) >>> y_pred = rgs.predict(x_train) >>> loss = np.mean(y_train != y_pred) @@ -565,7 +560,7 @@ def __init__( n_epoch=2, n_components=1000, subsample_size="auto", - kernel="gaussian", + kernel="rbf", bandwidth=5, gamma=None, degree=3, diff --git a/sklearn_extra/tests/test_fast_kernel.py b/sklearn_extra/tests/test_fast_kernel.py index 12f97bc6..9e9a0a69 100644 --- a/sklearn_extra/tests/test_fast_kernel.py +++ b/sklearn_extra/tests/test_fast_kernel.py @@ -55,11 +55,11 @@ def test_parameter_validation(estimator, data, params, err_msg): @pytest.mark.parametrize( "data, estimator", [ - # Test gaussian kernel + # Test rbf kernel ( gen_regression({}), FKR_EigenPro( - kernel="gaussian", n_epoch=100, bandwidth=10, random_state=1 + kernel="rbf", n_epoch=100, bandwidth=10, random_state=1 ), ), # Test laplacian kernel @@ -84,14 +84,14 @@ def test_parameter_validation(estimator, data, params, err_msg): ( gen_regression({"n_features": 200, "n_targets": 30}), FKR_EigenPro( - kernel="gaussian", n_epoch=100, bandwidth=14, random_state=1 + kernel="rbf", n_epoch=100, bandwidth=14, random_state=1 ), ), # Test with a very large number of input features ( gen_regression({"n_features": 10000}), FKR_EigenPro( - kernel="gaussian", n_epoch=100, bandwidth=1, random_state=1 + kernel="rbf", n_epoch=100, bandwidth=1, random_state=1 ), ), # Test a very simple underlying distribution @@ -99,7 +99,7 @@ def test_parameter_validation(estimator, data, params, err_msg): gen_regression({"n_informative": 1}), FKR_EigenPro( batch_size=500, - kernel="gaussian", + kernel="rbf", n_epoch=100, bandwidth=10, random_state=1, @@ -109,7 +109,7 @@ def test_parameter_validation(estimator, data, params, err_msg): ( gen_regression({"n_samples": 500, "n_informative": 100}), FKR_EigenPro( - kernel="gaussian", n_epoch=60, bandwidth=10, random_state=1 + kernel="rbf", n_epoch=60, bandwidth=10, random_state=1 ), ), ], @@ -134,7 +134,7 @@ def test_fast_kernel_regression_duplicate_data(): X, y = np.concatenate([X, X]), np.concatenate([y, y]) fkr_prediction = ( FKR_EigenPro( - kernel="gaussian", n_epoch=100, bandwidth=5, random_state=1 + kernel="rbf", n_epoch=100, bandwidth=5, random_state=1 ) .fit(X, y) .predict(X) @@ -160,12 +160,12 @@ def test_fast_kernel_regression_conflict_data(): @pytest.mark.parametrize( "data, estimator", [ - # Test gaussian kernel + # Test rbf kernel ( gen_classification({"n_samples": 10, "hypercube": False}), FKC_EigenPro( batch_size=9, - kernel="gaussian", + kernel="rbf", bandwidth=2.5, n_epoch=100, random_state=1, @@ -198,14 +198,14 @@ def test_fast_kernel_regression_conflict_data(): } ), FKC_EigenPro( - kernel="gaussian", n_epoch=100, bandwidth=20, random_state=1 + kernel="rbf", n_epoch=100, bandwidth=20, random_state=1 ), ), # Test a distribution that has been shifted ( gen_classification({"shift": 1, "hypercube": False}), FKC_EigenPro( - kernel="gaussian", n_epoch=200, bandwidth=8, random_state=1 + kernel="rbf", n_epoch=200, bandwidth=8, random_state=1 ), ), # Test with many redundant features. @@ -239,7 +239,7 @@ def test_fast_kernel_classification_duplicate_data(): X, y = make_classification(n_features=200, n_repeated=50, random_state=1) fkc_prediction = ( FKC_EigenPro( - kernel="gaussian", n_epoch=60, bandwidth=1, random_state=1 + kernel="rbf", n_epoch=60, bandwidth=1, random_state=1 ) .fit(X, y) .predict(X) From d9d3f936b3988b5ce8f1783932fe55a9525e57cf Mon Sep 17 00:00:00 2001 From: Alex <7alex7li@gmail.com> Date: Wed, 10 Jul 2019 18:12:36 -0400 Subject: [PATCH 22/31] Fixed lint issue --- sklearn_extra/fast_kernel.py | 4 +++- sklearn_extra/tests/test_fast_kernel.py | 8 ++------ 2 files changed, 5 insertions(+), 7 deletions(-) diff --git a/sklearn_extra/fast_kernel.py b/sklearn_extra/fast_kernel.py index d2550138..503dcf18 100644 --- a/sklearn_extra/fast_kernel.py +++ b/sklearn_extra/fast_kernel.py @@ -68,7 +68,9 @@ def _kernel(self, X, Y): params = self.kernel_params or {} else: params = { - "gamma": np.float32(.5/(self.bandwidth*self.bandwidth)), + "gamma": np.float32( + 0.5 / (self.bandwidth * self.bandwidth) + ), "degree": self.degree, "coef0": self.coef0, } diff --git a/sklearn_extra/tests/test_fast_kernel.py b/sklearn_extra/tests/test_fast_kernel.py index 9e9a0a69..9c651a81 100644 --- a/sklearn_extra/tests/test_fast_kernel.py +++ b/sklearn_extra/tests/test_fast_kernel.py @@ -133,9 +133,7 @@ def test_fast_kernel_regression_duplicate_data(): X, y = make_regression(random_state=1) X, y = np.concatenate([X, X]), np.concatenate([y, y]) fkr_prediction = ( - FKR_EigenPro( - kernel="rbf", n_epoch=100, bandwidth=5, random_state=1 - ) + FKR_EigenPro(kernel="rbf", n_epoch=100, bandwidth=5, random_state=1) .fit(X, y) .predict(X) ) @@ -238,9 +236,7 @@ def test_fast_kernel_classification_duplicate_data(): """ X, y = make_classification(n_features=200, n_repeated=50, random_state=1) fkc_prediction = ( - FKC_EigenPro( - kernel="rbf", n_epoch=60, bandwidth=1, random_state=1 - ) + FKC_EigenPro(kernel="rbf", n_epoch=60, bandwidth=1, random_state=1) .fit(X, y) .predict(X) ) From 7504399562e085fc709b298662d9fc76b251194e Mon Sep 17 00:00:00 2001 From: Alex <7alex7li@gmail.com> Date: Mon, 22 Jul 2019 23:30:55 -0400 Subject: [PATCH 23/31] Renamed classes and some variables, edited and added documentation for the changes --- doc/api.rst | 4 +- examples/fast_kernel/plot_mnist.py | 13 ++- sklearn_extra/fast_kernel.py | 108 ++++++++++++++++-------- sklearn_extra/tests/test_common.py | 2 +- sklearn_extra/tests/test_fast_kernel.py | 41 +++++---- 5 files changed, 106 insertions(+), 62 deletions(-) diff --git a/doc/api.rst b/doc/api.rst index 6e404c00..4d03f11a 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -27,5 +27,5 @@ EigenPro :toctree: generated/ :template: class.rst - fast_kernel.FKR_EigenPro - fast_kernel.FKC_EigenPro + fast_kernel.FKREigenPro + fast_kernel.FKCEigenPro diff --git a/examples/fast_kernel/plot_mnist.py b/examples/fast_kernel/plot_mnist.py index 981407b2..7c07b07e 100644 --- a/examples/fast_kernel/plot_mnist.py +++ b/examples/fast_kernel/plot_mnist.py @@ -17,7 +17,7 @@ import numpy as np from time import time -from sklearn_extra.fast_kernel import FKC_EigenPro +from sklearn_extra.fast_kernel import FKCEigenPro from sklearn.svm import SVC from sklearn.datasets import fetch_openml @@ -28,7 +28,7 @@ mnist.data = mnist.data / 255.0 print("Data has loaded") -p = np.random.permutation(60000) +p = rng.permutation(60000) x_train = mnist.data[p] y_train = np.int32(mnist.target[p]) x_test = mnist.data[60000:] @@ -52,9 +52,14 @@ for name, estimator in [ ( "FastKernel", - FKC_EigenPro(n_epoch=2, bandwidth=bandwidth, random_state=rng), + FKCEigenPro(n_epoch=2, bandwidth=bandwidth, random_state=rng), + ), + ( + "SupportVector", + SVC( + C=5, gamma=1.0 / (2 * bandwidth * bandwidth), random_state=rng + ), ), - ("SupportVector", SVC(C=5, gamma=1.0 / (2 * bandwidth * bandwidth))), ]: stime = time() estimator.fit(x_train[:train_size], y_train[:train_size]) diff --git a/sklearn_extra/fast_kernel.py b/sklearn_extra/fast_kernel.py index 503dcf18..6a60880c 100644 --- a/sklearn_extra/fast_kernel.py +++ b/sklearn_extra/fast_kernel.py @@ -102,10 +102,10 @@ def _nystrom_svd(self, X, n_components): Returns ------- - S : {float, array}, shape = [k] + E : {float, array}, shape = [k] Top eigenvalues. - V : {float, array}, shape = [n_subsamples, k] + Lambda : {float, array}, shape = [n_subsamples, k] Top eigenvectors of a subsample kernel matrix (which can be directly used to approximate the eigenfunctions of the kernel operator). @@ -116,16 +116,18 @@ def _nystrom_svd(self, X, n_components): # Use float64 so eigh doesn't occassionally crash and burn and fail W = K / m try: - S, V = eigh(W, eigvals=(m - n_components, m - 1)) + E, Lambda = eigh(W, eigvals=(m - n_components, m - 1)) except LinAlgError: W = np.float64(W) - S, V = eigh(W, eigvals=(m - n_components, m - 1)) - S, V = np.float32(S), np.float32(V) + E, Lambda = eigh(W, eigvals=(m - n_components, m - 1)) + E, Lambda = np.float32(E), np.float32(Lambda) # Flip so eigenvalues are in descending order. - S = np.maximum(np.float32(1e-7), np.flipud(S)) - V = np.fliplr(V)[:, :n_components] / np.sqrt(m, dtype="float32") + E = np.maximum(np.float32(1e-7), np.flipud(E)) + Lambda = np.fliplr(Lambda)[:, :n_components] / np.sqrt( + m, dtype="float32" + ) - return S, V + return E, Lambda def _setup(self, feat, max_components, mG, alpha): """Compute preconditioner and scale factors for EigenPro iteration @@ -151,39 +153,73 @@ def _setup(self, feat, max_components, mG, alpha): max_kxx : float Maximum of k(x,x) where k is the EigenPro kernel. + + E : {float, array}, shape = [k] + Preconditioner for EigenPro + + Lambda : {float, array}, shape = [n_subsamples, k] + Top eigenvectors of a subsample kernel matrix """ alpha = np.float32(alpha) # Estimate eigenvalues (S) and eigenvectors (V) of the kernel matrix # corresponding to the feature matrix. - S, V = self._nystrom_svd(feat, max_components) + E, Lambda = self._nystrom_svd(feat, max_components) n_subsamples = feat.shape[0] # Calculate the number of components to be used such that the # corresponding batch size is bounded by the subsample size and the # memory size. max_bs = min(max(n_subsamples / 5, mG), n_subsamples) - n_components = np.sum(np.power(1 / S, alpha) < max_bs) - 1 + n_components = np.sum(np.power(1 / E, alpha) < max_bs) - 1 if n_components < 2: - n_components = min(S.shape[0] - 1, 2) + n_components = min(E.shape[0] - 1, 2) - V = V[:, :n_components] - scale = np.power(S[0] / S[n_components], alpha) + Lambda = Lambda[:, :n_components] + scale = np.power(E[0] / E[n_components], alpha) # Compute part of the preconditioner for step 2 of gradient descent in # the eigenpro model - Q = (1 - np.power(S[n_components] / S[:n_components], alpha)) / S[ + D = (1 - np.power(E[n_components] / E[:n_components], alpha)) / E[ :n_components ] - max_S = S[0].astype(np.float32) - kxx = 1 - np.sum(V ** 2, axis=1) * n_subsamples - return max_S / scale, np.max(kxx), Q, V + max_S = E[0].astype(np.float32) + kxx = 1 - np.sum(Lambda ** 2, axis=1) * n_subsamples + return max_S / scale, np.max(kxx), D, Lambda def _initialize_params(self, X, Y, random_state): """ Validate parameters passed to the model, choose parameters that have not been passed in, and run setup for EigenPro iteration. + Parameters + ---------- + X : {float, array}, shape = [n_samples, n_features] + Training data. + + Y : {float, array}, shape = [n_samples, n_targets] + Training targets. + + random_state : RandomState instance + The random state to use for random number generation + + Returns + ------- + Y : {float, array}, shape = [n_samples, n_targets] + Training targets. If Y was originally of shape + [n_samples], it is now [n_samples, 1]. + + E : {float, array}, shape = [k] + Preconditioner for EigenPro + + Lambda : {float, array}, shape = [n_subsamples, k] + Top eigenvectors of a subsample kernel matrix + + eta : float + The learning rate + + pinx : {int, array}, shape = [sample_size] + The rows of X used to calculate E and Lambda """ n, d = X.shape n_label = 1 if len(Y.shape) == 1 else Y.shape[1] @@ -211,7 +247,9 @@ def _initialize_params(self, X, Y, random_state): pinx = random_state.choice(n, sample_size, replace=False).astype( "int32" ) - max_S, beta, Q, V = self._setup(X[pinx], n_components, mG, alpha=0.95) + max_S, beta, E, Lambda = self._setup( + X[pinx], n_components, mG, alpha=0.95 + ) # Calculate best batch size. if self.batch_size == "auto": bs = min(np.int32(beta / max_S), mG) + 1 @@ -231,9 +269,13 @@ def _initialize_params(self, X, Y, random_state): if len(Y.shape) == 1: Y = np.reshape(Y, (Y.shape[0], 1)) self.was_1D_ = True - return Y, Q, V, np.float32(eta), pinx + return Y, E, Lambda, np.float32(eta), pinx def validate_parameters(self): + """ + Validate the parameters of the model to ensure that no unreasonable + values were passed in. + """ if self.n_epoch <= 0: raise ValueError( "n_epoch should be positive, was " + str(self.n_epoch) @@ -285,7 +327,7 @@ def _raw_fit(self, X, Y): self.validate_parameters() """Parameter Initialization""" - Y, Q, V, eta, pinx = self._initialize_params(X, Y, random_state) + Y, D, V, eta, pinx = self._initialize_params(X, Y, random_state) """Training loop""" n = self.centers_.shape[0] @@ -311,7 +353,7 @@ def _raw_fit(self, X, Y): # Update 2: Fixed Coordinate Block delta = np.dot( - V * Q, np.dot(V.T, np.dot(kfeat[:, pinx].T, gradient)) + V * D, np.dot(V.T, np.dot(kfeat[:, pinx].T, gradient)) ) self.coef_[pinx] += step * delta return self @@ -355,7 +397,7 @@ def _get_tags(self): return {"multioutput": True} -class FKR_EigenPro(BaseEigenPro, RegressorMixin): +class FKREigenPro(BaseEigenPro, RegressorMixin): """Fast kernel regression using EigenPro iteration. Train least squared kernel regression model with mini-batch EigenPro @@ -420,17 +462,17 @@ class FKR_EigenPro(BaseEigenPro, RegressorMixin): Examples -------- - >>> from sklearn_extra.fast_kernel import FKR_EigenPro + >>> from sklearn_extra.fast_kernel import FKREigenPro >>> import numpy as np >>> n_samples, n_features, n_targets = 4000, 20, 3 >>> rng = np.random.RandomState(1) >>> x_train = rng.randn(n_samples, n_features) >>> y_train = rng.randn(n_samples, n_targets) - >>> rgs = FKR_EigenPro(n_epoch=3, bandwidth=1, subsample_size=50) + >>> rgs = FKREigenPro(n_epoch=3, bandwidth=1, subsample_size=50) >>> rgs.fit(x_train, y_train) - FKR_EigenPro(bandwidth=1, batch_size='auto', coef0=1, degree=3, gamma=None, - kernel='rbf', kernel_params=None, n_components=1000, - n_epoch=3, random_state=None, subsample_size=50) + FKREigenPro(bandwidth=1, batch_size='auto', coef0=1, degree=3, gamma=None, + kernel='rbf', kernel_params=None, n_components=1000, n_epoch=3, + random_state=None, subsample_size=50) >>> y_pred = rgs.predict(x_train) >>> loss = np.mean(np.square(y_train - y_pred)) """ @@ -470,7 +512,7 @@ def predict(self, X): return self._raw_predict(X) -class FKC_EigenPro(BaseEigenPro, ClassifierMixin): +class FKCEigenPro(BaseEigenPro, ClassifierMixin): """Fast kernel classification using EigenPro iteration. Train least squared kernel classification model with mini-batch EigenPro @@ -541,17 +583,17 @@ class FKC_EigenPro(BaseEigenPro, ClassifierMixin): Examples -------- - >>> from sklearn_extra.fast_kernel import FKC_EigenPro + >>> from sklearn_extra.fast_kernel import FKCEigenPro >>> import numpy as np >>> n_samples, n_features, n_targets = 4000, 20, 3 >>> rng = np.random.RandomState(1) >>> x_train = rng.randn(n_samples, n_features) >>> y_train = rng.randint(n_targets, size=n_samples) - >>> rgs = FKC_EigenPro(n_epoch=3, bandwidth=1, subsample_size=50) + >>> rgs = FKCEigenPro(n_epoch=3, bandwidth=1, subsample_size=50) >>> rgs.fit(x_train, y_train) - FKC_EigenPro(bandwidth=1, batch_size='auto', coef0=1, degree=3, gamma=None, - kernel='rbf', kernel_params=None, n_components=1000, - n_epoch=3, random_state=None, subsample_size=50) + FKCEigenPro(bandwidth=1, batch_size='auto', coef0=1, degree=3, gamma=None, + kernel='rbf', kernel_params=None, n_components=1000, n_epoch=3, + random_state=None, subsample_size=50) >>> y_pred = rgs.predict(x_train) >>> loss = np.mean(y_train != y_pred) """ diff --git a/sklearn_extra/tests/test_common.py b/sklearn_extra/tests/test_common.py index 5a8cc925..c4c4cdbc 100644 --- a/sklearn_extra/tests/test_common.py +++ b/sklearn_extra/tests/test_common.py @@ -7,7 +7,7 @@ @pytest.mark.parametrize( - "Estimator", [Fastfood, fast_kernel.FKC_EigenPro, fast_kernel.FKR_EigenPro] + "Estimator", [Fastfood, fast_kernel.FKCEigenPro, fast_kernel.FKREigenPro] ) def test_all_estimators(Estimator, request): return check_estimator(Estimator) diff --git a/sklearn_extra/tests/test_fast_kernel.py b/sklearn_extra/tests/test_fast_kernel.py index 9c651a81..13b430c0 100644 --- a/sklearn_extra/tests/test_fast_kernel.py +++ b/sklearn_extra/tests/test_fast_kernel.py @@ -2,7 +2,7 @@ from sklearn.datasets import make_regression, make_classification from sklearn.utils.testing import assert_array_almost_equal -from sklearn_extra.fast_kernel import FKR_EigenPro, FKC_EigenPro +from sklearn_extra.fast_kernel import FKREigenPro, FKCEigenPro import pytest @@ -24,10 +24,7 @@ def gen_classification(params): @pytest.mark.parametrize( "estimator, data", - [ - (FKR_EigenPro, gen_regression({})), - (FKC_EigenPro, gen_classification({})), - ], + [(FKREigenPro, gen_regression({})), (FKCEigenPro, gen_classification({}))], ) @pytest.mark.parametrize( "params, err_msg", @@ -58,21 +55,21 @@ def test_parameter_validation(estimator, data, params, err_msg): # Test rbf kernel ( gen_regression({}), - FKR_EigenPro( + FKREigenPro( kernel="rbf", n_epoch=100, bandwidth=10, random_state=1 ), ), # Test laplacian kernel ( gen_regression({}), - FKR_EigenPro( + FKREigenPro( kernel="laplace", n_epoch=100, bandwidth=8, random_state=1 ), ), # Test cauchy kernel ( gen_regression({}), - FKR_EigenPro( + FKREigenPro( kernel="cauchy", n_epoch=100, bandwidth=10, @@ -83,21 +80,21 @@ def test_parameter_validation(estimator, data, params, err_msg): # Test with multiple outputs ( gen_regression({"n_features": 200, "n_targets": 30}), - FKR_EigenPro( + FKREigenPro( kernel="rbf", n_epoch=100, bandwidth=14, random_state=1 ), ), # Test with a very large number of input features ( gen_regression({"n_features": 10000}), - FKR_EigenPro( + FKREigenPro( kernel="rbf", n_epoch=100, bandwidth=1, random_state=1 ), ), # Test a very simple underlying distribution ( gen_regression({"n_informative": 1}), - FKR_EigenPro( + FKREigenPro( batch_size=500, kernel="rbf", n_epoch=100, @@ -108,7 +105,7 @@ def test_parameter_validation(estimator, data, params, err_msg): # Test a very complex underlying distribution ( gen_regression({"n_samples": 500, "n_informative": 100}), - FKR_EigenPro( + FKREigenPro( kernel="rbf", n_epoch=60, bandwidth=10, random_state=1 ), ), @@ -133,7 +130,7 @@ def test_fast_kernel_regression_duplicate_data(): X, y = make_regression(random_state=1) X, y = np.concatenate([X, X]), np.concatenate([y, y]) fkr_prediction = ( - FKR_EigenPro(kernel="rbf", n_epoch=100, bandwidth=5, random_state=1) + FKREigenPro(kernel="rbf", n_epoch=100, bandwidth=5, random_state=1) .fit(X, y) .predict(X) ) @@ -147,7 +144,7 @@ def test_fast_kernel_regression_conflict_data(): y = np.reshape(y, (-1, 1)) X, y = X, np.hstack([y, y + 2]) # Make sure we don't throw an error when fitting or predicting - FKR_EigenPro(kernel="linear", n_epoch=5, bandwidth=1, random_state=1).fit( + FKREigenPro(kernel="linear", n_epoch=5, bandwidth=1, random_state=1).fit( X, y ).predict(X) @@ -161,7 +158,7 @@ def test_fast_kernel_regression_conflict_data(): # Test rbf kernel ( gen_classification({"n_samples": 10, "hypercube": False}), - FKC_EigenPro( + FKCEigenPro( batch_size=9, kernel="rbf", bandwidth=2.5, @@ -172,14 +169,14 @@ def test_fast_kernel_regression_conflict_data(): # Test laplacian kernel ( gen_classification({}), - FKC_EigenPro( + FKCEigenPro( kernel="laplace", n_epoch=100, bandwidth=13, random_state=1 ), ), # Test cauchy kernel ( gen_classification({}), - FKC_EigenPro( + FKCEigenPro( kernel="cauchy", n_epoch=100, bandwidth=10, random_state=1 ), ), @@ -195,21 +192,21 @@ def test_fast_kernel_regression_conflict_data(): "shift": 6, } ), - FKC_EigenPro( + FKCEigenPro( kernel="rbf", n_epoch=100, bandwidth=20, random_state=1 ), ), # Test a distribution that has been shifted ( gen_classification({"shift": 1, "hypercube": False}), - FKC_EigenPro( + FKCEigenPro( kernel="rbf", n_epoch=200, bandwidth=8, random_state=1 ), ), # Test with many redundant features. ( gen_classification({"n_redundant": 18}), - FKC_EigenPro( + FKCEigenPro( kernel="laplace", n_epoch=100, bandwidth=20, random_state=1 ), ), @@ -236,7 +233,7 @@ def test_fast_kernel_classification_duplicate_data(): """ X, y = make_classification(n_features=200, n_repeated=50, random_state=1) fkc_prediction = ( - FKC_EigenPro(kernel="rbf", n_epoch=60, bandwidth=1, random_state=1) + FKCEigenPro(kernel="rbf", n_epoch=60, bandwidth=1, random_state=1) .fit(X, y) .predict(X) ) @@ -249,6 +246,6 @@ def test_fast_kernel_classification_conflict_data(): X, y = make_classification(random_state=1) X, y = np.concatenate([X, X]), np.concatenate([y, 1 - y]) # Make sure we don't throw an error when fitting or predicting - FKC_EigenPro(kernel="linear", n_epoch=5, bandwidth=5, random_state=1).fit( + FKCEigenPro(kernel="linear", n_epoch=5, bandwidth=5, random_state=1).fit( X, y ).predict(X) From fed866f34e776ae6c0b0d973112879a3d37349ec Mon Sep 17 00:00:00 2001 From: Alex <7alex7li@gmail.com> Date: Fri, 26 Jul 2019 20:08:05 -0400 Subject: [PATCH 24/31] Renaming and addressing issues from Roman --- sklearn_extra/__init__.py | 2 +- sklearn_extra/{fast_kernel.py => eigenpro.py} | 55 ++++++------- sklearn_extra/tests/test_common.py | 5 +- .../{test_fast_kernel.py => test_eigenpro.py} | 82 ++++++++++--------- 4 files changed, 74 insertions(+), 70 deletions(-) rename sklearn_extra/{fast_kernel.py => eigenpro.py} (93%) rename sklearn_extra/tests/{test_fast_kernel.py => test_eigenpro.py} (79%) diff --git a/sklearn_extra/__init__.py b/sklearn_extra/__init__.py index 55ba93d3..e1162fdb 100644 --- a/sklearn_extra/__init__.py +++ b/sklearn_extra/__init__.py @@ -2,4 +2,4 @@ from ._version import __version__ -__all__ = ["__version__", "fast_kernel"] +__all__ = ["__version__", "eigenpro"] diff --git a/sklearn_extra/fast_kernel.py b/sklearn_extra/eigenpro.py similarity index 93% rename from sklearn_extra/fast_kernel.py rename to sklearn_extra/eigenpro.py index 6a60880c..94d24d91 100644 --- a/sklearn_extra/fast_kernel.py +++ b/sklearn_extra/eigenpro.py @@ -3,7 +3,6 @@ import numpy as np from scipy.linalg import eigh, LinAlgError -from abc import ABC, abstractmethod from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin from sklearn.metrics.pairwise import pairwise_kernels, euclidean_distances from sklearn.utils import check_random_state @@ -11,12 +10,11 @@ from sklearn.utils.validation import check_is_fitted, check_X_y -class BaseEigenPro(BaseEstimator, ABC): +class BaseEigenPro(BaseEstimator): """ - Base class for Fast Kernel/Eigenpro iteration. + Base class for EigenPro iteration. """ - @abstractmethod def __init__( self, batch_size="auto", @@ -300,7 +298,7 @@ def validate_parameters(self): ) def _raw_fit(self, X, Y): - """Train fast kernel regression model + """Train eigenpro regression model Parameters ---------- @@ -347,9 +345,7 @@ def _raw_fit(self, X, Y): # Update 1: Sampled Coordinate Block. gradient = np.dot(kfeat, self.coef_) - batch_y - self.coef_[batch_inds] = ( - self.coef_[batch_inds] - step * gradient - ) + self.coef_[batch_inds] -= step * gradient # Update 2: Fixed Coordinate Block delta = np.dot( @@ -397,8 +393,8 @@ def _get_tags(self): return {"multioutput": True} -class FKREigenPro(BaseEigenPro, RegressorMixin): - """Fast kernel regression using EigenPro iteration. +class EigenProRegressor(BaseEigenPro, RegressorMixin): + """Regression using EigenPro iteration. Train least squared kernel regression model with mini-batch EigenPro iteration. @@ -408,7 +404,7 @@ class FKREigenPro(BaseEigenPro, RegressorMixin): batch_size : int, default = 'auto' Mini-batch size for gradient descent. - n_epoch : int, default = 1 + n_epoch : int, default = 2 The number of passes over the training data. n_components : int, default = 1000 @@ -422,11 +418,11 @@ class FKREigenPro(BaseEigenPro, RegressorMixin): The number of subsamples used for estimating the largest n_component eigenvalues and eigenvectors. When it is set to 'auto', it will be 4000 if there are less than 100,000 samples - (for training), and otherwise 10000. + (for training), and otherwise 12000. kernel : string or callable, default = "rbf" Kernel mapping used internally. Strings can be anything supported - by sklearn's library, however, there is special support for the + by scikit-learn, however, there is special support for the rbf, laplace, and cauchy kernels. If a callable is given, it should accept two arguments and return a floating point number. @@ -462,17 +458,17 @@ class FKREigenPro(BaseEigenPro, RegressorMixin): Examples -------- - >>> from sklearn_extra.fast_kernel import FKREigenPro + >>> from sklearn_extra.eigenpro import EigenProRegressor >>> import numpy as np >>> n_samples, n_features, n_targets = 4000, 20, 3 >>> rng = np.random.RandomState(1) >>> x_train = rng.randn(n_samples, n_features) >>> y_train = rng.randn(n_samples, n_targets) - >>> rgs = FKREigenPro(n_epoch=3, bandwidth=1, subsample_size=50) + >>> rgs = EigenProRegressor(n_epoch=3, bandwidth=1, subsample_size=50) >>> rgs.fit(x_train, y_train) - FKREigenPro(bandwidth=1, batch_size='auto', coef0=1, degree=3, gamma=None, - kernel='rbf', kernel_params=None, n_components=1000, n_epoch=3, - random_state=None, subsample_size=50) + EigenProRegressor(bandwidth=1, batch_size='auto', coef0=1, degree=3, gamma=None, + kernel='rbf', kernel_params=None, n_components=1000, + n_epoch=3, random_state=None, subsample_size=50) >>> y_pred = rgs.predict(x_train) >>> loss = np.mean(np.square(y_train - y_pred)) """ @@ -512,7 +508,7 @@ def predict(self, X): return self._raw_predict(X) -class FKCEigenPro(BaseEigenPro, ClassifierMixin): +class EigenProClassifier(BaseEigenPro, ClassifierMixin): """Fast kernel classification using EigenPro iteration. Train least squared kernel classification model with mini-batch EigenPro @@ -523,7 +519,7 @@ class FKCEigenPro(BaseEigenPro, ClassifierMixin): batch_size : int, default = 'auto' Mini-batch size for gradient descent. - n_epoch : int, default = 1 + n_epoch : int, default = 2 The number of passes over the training data. n_components : int, default = 1000 @@ -537,11 +533,11 @@ class FKCEigenPro(BaseEigenPro, ClassifierMixin): The size of subsamples used for estimating the largest n_component eigenvalues and eigenvectors. When it is set to 'auto', it will be 4000 if there are less than 100,000 samples - (for training), and otherwise 10000. + (for training), and otherwise 12000. kernel : string or callable, default = "rbf" Kernel mapping used internally. Strings can be anything supported - by sklearn's library, however, there is special support for the + by scikit-learn, however, there is special support for the rbf, laplace, and cauchy kernels. If a callable is given, it should accept two arguments and return a floating point number. @@ -583,17 +579,18 @@ class FKCEigenPro(BaseEigenPro, ClassifierMixin): Examples -------- - >>> from sklearn_extra.fast_kernel import FKCEigenPro + >>> from sklearn_extra.eigenpro import EigenProClassifier >>> import numpy as np >>> n_samples, n_features, n_targets = 4000, 20, 3 >>> rng = np.random.RandomState(1) >>> x_train = rng.randn(n_samples, n_features) >>> y_train = rng.randint(n_targets, size=n_samples) - >>> rgs = FKCEigenPro(n_epoch=3, bandwidth=1, subsample_size=50) + >>> rgs = EigenProClassifier(n_epoch=3, bandwidth=1, subsample_size=50) >>> rgs.fit(x_train, y_train) - FKCEigenPro(bandwidth=1, batch_size='auto', coef0=1, degree=3, gamma=None, - kernel='rbf', kernel_params=None, n_components=1000, n_epoch=3, - random_state=None, subsample_size=50) + EigenProClassifier(bandwidth=1, batch_size='auto', coef0=1, degree=3, + gamma=None, kernel='rbf', kernel_params=None, + n_components=1000, n_epoch=3, random_state=None, + subsample_size=50) >>> y_pred = rgs.predict(x_train) >>> loss = np.mean(y_train != y_pred) """ @@ -627,7 +624,7 @@ def __init__( ) def fit(self, X, Y): - """ Train fast kernel classification model + """ Train eigenpro classification model Parameters ---------- @@ -659,7 +656,7 @@ def fit(self, X, Y): class_matrix = np.zeros((Y.shape[0], self.classes_.shape[0])) for ind, label in enumerate(Y): - class_matrix[ind][loc[label]] = 1 + class_matrix[ind, loc[label]] = 1 self._raw_fit(X, class_matrix) return self diff --git a/sklearn_extra/tests/test_common.py b/sklearn_extra/tests/test_common.py index c4c4cdbc..795e8150 100644 --- a/sklearn_extra/tests/test_common.py +++ b/sklearn_extra/tests/test_common.py @@ -3,11 +3,12 @@ from sklearn.utils.estimator_checks import check_estimator from sklearn_extra.kernel_approximation import Fastfood -from sklearn_extra import fast_kernel +from sklearn_extra import eigenpro @pytest.mark.parametrize( - "Estimator", [Fastfood, fast_kernel.FKCEigenPro, fast_kernel.FKREigenPro] + "Estimator", + [Fastfood, eigenpro.EigenProClassifier, eigenpro.EigenProRegressor], ) def test_all_estimators(Estimator, request): return check_estimator(Estimator) diff --git a/sklearn_extra/tests/test_fast_kernel.py b/sklearn_extra/tests/test_eigenpro.py similarity index 79% rename from sklearn_extra/tests/test_fast_kernel.py rename to sklearn_extra/tests/test_eigenpro.py index 13b430c0..a85dabb6 100644 --- a/sklearn_extra/tests/test_fast_kernel.py +++ b/sklearn_extra/tests/test_eigenpro.py @@ -1,13 +1,12 @@ import numpy as np from sklearn.datasets import make_regression, make_classification -from sklearn.utils.testing import assert_array_almost_equal -from sklearn_extra.fast_kernel import FKREigenPro, FKCEigenPro +from sklearn.utils.testing import assert_allclose +from sklearn_extra.eigenpro import EigenProRegressor, EigenProClassifier import pytest -np.random.seed(1) -# Tests for Fast Kernel Regression and Classification. +# Tests for EigenPro Regression and Classification. def gen_regression(params): @@ -24,7 +23,10 @@ def gen_classification(params): @pytest.mark.parametrize( "estimator, data", - [(FKREigenPro, gen_regression({})), (FKCEigenPro, gen_classification({}))], + [ + (EigenProRegressor, gen_regression({})), + (EigenProClassifier, gen_classification({})), + ], ) @pytest.mark.parametrize( "params, err_msg", @@ -55,21 +57,21 @@ def test_parameter_validation(estimator, data, params, err_msg): # Test rbf kernel ( gen_regression({}), - FKREigenPro( + EigenProRegressor( kernel="rbf", n_epoch=100, bandwidth=10, random_state=1 ), ), # Test laplacian kernel ( gen_regression({}), - FKREigenPro( + EigenProRegressor( kernel="laplace", n_epoch=100, bandwidth=8, random_state=1 ), ), # Test cauchy kernel ( gen_regression({}), - FKREigenPro( + EigenProRegressor( kernel="cauchy", n_epoch=100, bandwidth=10, @@ -80,21 +82,21 @@ def test_parameter_validation(estimator, data, params, err_msg): # Test with multiple outputs ( gen_regression({"n_features": 200, "n_targets": 30}), - FKREigenPro( + EigenProRegressor( kernel="rbf", n_epoch=100, bandwidth=14, random_state=1 ), ), # Test with a very large number of input features ( gen_regression({"n_features": 10000}), - FKREigenPro( + EigenProRegressor( kernel="rbf", n_epoch=100, bandwidth=1, random_state=1 ), ), # Test a very simple underlying distribution ( gen_regression({"n_informative": 1}), - FKREigenPro( + EigenProRegressor( batch_size=500, kernel="rbf", n_epoch=100, @@ -105,7 +107,7 @@ def test_parameter_validation(estimator, data, params, err_msg): # Test a very complex underlying distribution ( gen_regression({"n_samples": 500, "n_informative": 100}), - FKREigenPro( + EigenProRegressor( kernel="rbf", n_epoch=60, bandwidth=10, random_state=1 ), ), @@ -113,7 +115,7 @@ def test_parameter_validation(estimator, data, params, err_msg): ) def test_regressor_accuracy(data, estimator): """ - Test the accuracy of the Fast Kernel Regressor on multiple + Test the accuracy of the EigenPro Regressor on multiple data sets with different parameter inputs. We expect that the regressor should achieve near-zero training error after sufficient training time. @@ -122,31 +124,33 @@ def test_regressor_accuracy(data, estimator): """ X, y = data prediction = estimator.fit(X, y).predict(X) - assert_array_almost_equal(abs(prediction / y) / 2.0, 0.5, decimal=2) + assert_allclose(prediction, y, rtol=5e-3) -def test_fast_kernel_regression_duplicate_data(): +def test_eigenpro_regression_duplicate_data(): """Test the performance when some data is repeated""" X, y = make_regression(random_state=1) X, y = np.concatenate([X, X]), np.concatenate([y, y]) - fkr_prediction = ( - FKREigenPro(kernel="rbf", n_epoch=100, bandwidth=5, random_state=1) + prediction = ( + EigenProRegressor( + kernel="rbf", n_epoch=100, bandwidth=5, random_state=1 + ) .fit(X, y) .predict(X) ) - assert_array_almost_equal(abs(fkr_prediction / y) / 2.0, 0.5, decimal=2) + assert_allclose(prediction, y, rtol=5e-3) -def test_fast_kernel_regression_conflict_data(): +def test_eigenpro_regression_conflict_data(): """Make sure the regressor doesn't crash when conflicting data is given""" X, y = make_regression(random_state=1) y = np.reshape(y, (-1, 1)) X, y = X, np.hstack([y, y + 2]) # Make sure we don't throw an error when fitting or predicting - FKREigenPro(kernel="linear", n_epoch=5, bandwidth=1, random_state=1).fit( - X, y - ).predict(X) + EigenProRegressor( + kernel="linear", n_epoch=5, bandwidth=1, random_state=1 + ).fit(X, y).predict(X) # Tests for FastKernelClassification @@ -158,7 +162,7 @@ def test_fast_kernel_regression_conflict_data(): # Test rbf kernel ( gen_classification({"n_samples": 10, "hypercube": False}), - FKCEigenPro( + EigenProClassifier( batch_size=9, kernel="rbf", bandwidth=2.5, @@ -169,14 +173,14 @@ def test_fast_kernel_regression_conflict_data(): # Test laplacian kernel ( gen_classification({}), - FKCEigenPro( + EigenProClassifier( kernel="laplace", n_epoch=100, bandwidth=13, random_state=1 ), ), # Test cauchy kernel ( gen_classification({}), - FKCEigenPro( + EigenProClassifier( kernel="cauchy", n_epoch=100, bandwidth=10, random_state=1 ), ), @@ -192,21 +196,21 @@ def test_fast_kernel_regression_conflict_data(): "shift": 6, } ), - FKCEigenPro( + EigenProClassifier( kernel="rbf", n_epoch=100, bandwidth=20, random_state=1 ), ), # Test a distribution that has been shifted ( gen_classification({"shift": 1, "hypercube": False}), - FKCEigenPro( + EigenProClassifier( kernel="rbf", n_epoch=200, bandwidth=8, random_state=1 ), ), # Test with many redundant features. ( gen_classification({"n_redundant": 18}), - FKCEigenPro( + EigenProClassifier( kernel="laplace", n_epoch=100, bandwidth=20, random_state=1 ), ), @@ -214,7 +218,7 @@ def test_fast_kernel_regression_conflict_data(): ) def test_classifier_accuracy(data, estimator): """ - Test the accuracy of the Fast Kernel Classification on multiple + Test the accuracy of the EigenPro Classification on multiple data sets with different parameter inputs. We expect that the classification should achieve zero training error after sufficient training time. @@ -223,29 +227,31 @@ def test_classifier_accuracy(data, estimator): """ X, y = data prediction = estimator.fit(X, y).predict(X) - assert_array_almost_equal(prediction, y) + assert_allclose(prediction, y, rtol=5e-3) -def test_fast_kernel_classification_duplicate_data(): +def test_eigenpro_classification_duplicate_data(): """ Make sure that the classifier correctly handles cases where some data is repeated. """ X, y = make_classification(n_features=200, n_repeated=50, random_state=1) - fkc_prediction = ( - FKCEigenPro(kernel="rbf", n_epoch=60, bandwidth=1, random_state=1) + prediction = ( + EigenProClassifier( + kernel="rbf", n_epoch=60, bandwidth=1, random_state=1 + ) .fit(X, y) .predict(X) ) - assert_array_almost_equal(fkc_prediction, y) + assert_allclose(prediction, y, rtol=5e-3) -def test_fast_kernel_classification_conflict_data(): +def test_eigenpro_classification_conflict_data(): """Make sure that the classifier doesn't crash when given conflicting input data""" X, y = make_classification(random_state=1) X, y = np.concatenate([X, X]), np.concatenate([y, 1 - y]) # Make sure we don't throw an error when fitting or predicting - FKCEigenPro(kernel="linear", n_epoch=5, bandwidth=5, random_state=1).fit( - X, y - ).predict(X) + EigenProClassifier( + kernel="linear", n_epoch=5, bandwidth=5, random_state=1 + ).fit(X, y).predict(X) From 5a338af16c3979710bddbe79adb06172aa37ab7d Mon Sep 17 00:00:00 2001 From: Alex <7alex7li@gmail.com> Date: Fri, 26 Jul 2019 20:12:41 -0400 Subject: [PATCH 25/31] Renaming and addressing issues from Roman --- doc/api.rst | 6 +-- doc/modules/{fast_kernel.rst => eigenpro.rst} | 2 +- doc/user_guide.rst | 5 +++ examples/{fast_kernel => eigenpro}/README.txt | 6 +-- .../{fast_kernel => eigenpro}/plot_mnist.py | 42 ++++++++++--------- 5 files changed, 35 insertions(+), 26 deletions(-) rename doc/modules/{fast_kernel.rst => eigenpro.rst} (98%) rename examples/{fast_kernel => eigenpro}/README.txt (54%) rename examples/{fast_kernel => eigenpro}/plot_mnist.py (77%) diff --git a/doc/api.rst b/doc/api.rst index 4d03f11a..928038b3 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -19,7 +19,7 @@ EigenPro .. currentmodule:: doc .. toctree:: - modules/fast_kernel + modules/eigenpro .. currentmodule:: sklearn_extra @@ -27,5 +27,5 @@ EigenPro :toctree: generated/ :template: class.rst - fast_kernel.FKREigenPro - fast_kernel.FKCEigenPro + eigenpro.EigenProRegressor + eigenpro.EigenProClassifier diff --git a/doc/modules/fast_kernel.rst b/doc/modules/eigenpro.rst similarity index 98% rename from doc/modules/fast_kernel.rst rename to doc/modules/eigenpro.rst index d8abbf23..0e86a727 100644 --- a/doc/modules/fast_kernel.rst +++ b/doc/modules/eigenpro.rst @@ -4,7 +4,7 @@ Fast Kernel Machine (EigenPro) for Regression and Classification ================================================================ -.. currentmodule:: sklearn_extra.fast_kernel +.. currentmodule:: sklearn_extra.eigenpro Fast Kernel Machine is a very efficient implementation of kernel regression/classification using *EigenPro iteration* [MB17]_, diff --git a/doc/user_guide.rst b/doc/user_guide.rst index a190e568..72598c27 100644 --- a/doc/user_guide.rst +++ b/doc/user_guide.rst @@ -2,6 +2,11 @@ .. _user_guide: +.. toctree:: + :numbered: + + modules/eigenpro.rst + ========== User guide ========== diff --git a/examples/fast_kernel/README.txt b/examples/eigenpro/README.txt similarity index 54% rename from examples/fast_kernel/README.txt rename to examples/eigenpro/README.txt index 04b8b128..82d2996d 100644 --- a/examples/fast_kernel/README.txt +++ b/examples/eigenpro/README.txt @@ -1,6 +1,6 @@ -.. _fast_kernel_examples: +.. _eigenpro_examples: -Fast Kernel -=========== +Eigenpro +======== Examples concerning the :mod:`sklearn.fast_kernel` module. diff --git a/examples/fast_kernel/plot_mnist.py b/examples/eigenpro/plot_mnist.py similarity index 77% rename from examples/fast_kernel/plot_mnist.py rename to examples/eigenpro/plot_mnist.py index 7c07b07e..768e0a1f 100644 --- a/examples/fast_kernel/plot_mnist.py +++ b/examples/eigenpro/plot_mnist.py @@ -1,11 +1,11 @@ """ -=================================================== -Comparison of FKC_EigenPro and SVC on Fashion-MNIST -=================================================== +=============================================== +Comparison of EigenPro and SVC on Fashion-MNIST +=============================================== -Here we train a Fast Kernel Classifier (EigenPro) and a Support +Here we train a EigenPro Classifier and a Support Vector Classifier (SVC) on subsets of MNIST of various sizes. -We halt the training of EigenPro in two epochs. +We halt the training of EigenPro after two epochs. Experimental results on MNIST demonstrate more than 3 times speedup of EigenPro over SVC in training time. EigenPro also shows consistently lower classification error on test set. @@ -17,7 +17,7 @@ import numpy as np from time import time -from sklearn_extra.fast_kernel import FKCEigenPro +from sklearn_extra.eigenpro import EigenProClassifier from sklearn.svm import SVC from sklearn.datasets import fetch_openml @@ -34,10 +34,10 @@ x_test = mnist.data[60000:] y_test = np.int32(mnist.target[60000:]) -# Run tests comparing fkc to svc -fkc_fit_times = [] -fkc_pred_times = [] -fkc_err = [] +# Run tests comparing eig to svc +eig_fit_times = [] +eig_pred_times = [] +eig_err = [] svc_fit_times = [] svc_pred_times = [] svc_err = [] @@ -51,8 +51,10 @@ for train_size in train_sizes: for name, estimator in [ ( - "FastKernel", - FKCEigenPro(n_epoch=2, bandwidth=bandwidth, random_state=rng), + "EigenPro", + EigenProClassifier( + n_epoch=2, bandwidth=bandwidth, random_state=rng + ), ), ( "SupportVector", @@ -70,10 +72,10 @@ pred_t = time() - stime err = 100.0 * np.sum(y_pred_test != y_test) / len(y_test) - if name == "FastKernel": - fkc_fit_times.append(fit_t) - fkc_pred_times.append(pred_t) - fkc_err.append(err) + if name == "EigenPro": + eig_fit_times.append(fit_t) + eig_pred_times.append(pred_t) + eig_err.append(err) else: svc_fit_times.append(fit_t) svc_pred_times.append(pred_t) @@ -90,7 +92,9 @@ # Graph fit(train) time train_size_labels = [str(s) for s in train_sizes] ax.plot(train_sizes, svc_fit_times, "o--", color="g", label="SVC") -ax.plot(train_sizes, fkc_fit_times, "o-", color="r", label="FKC (EigenPro)") +ax.plot( + train_sizes, eig_fit_times, "o-", color="r", label="EigenPro Classifier" +) ax.set_xscale("log") ax.set_yscale("log", nonposy="clip") ax.set_xlabel("train size") @@ -104,7 +108,7 @@ # Graph prediction(test) time ax = plt.subplot2grid((2, 2), (0, 1), rowspan=1) -ax.plot(train_sizes, fkc_pred_times, "o-", color="r") +ax.plot(train_sizes, eig_pred_times, "o-", color="r") ax.plot(train_sizes, svc_pred_times, "o--", color="g") ax.set_xscale("log") ax.set_yscale("log", nonposy="clip") @@ -115,7 +119,7 @@ # Graph training error ax = plt.subplot2grid((2, 2), (1, 1), rowspan=1) -ax.plot(train_sizes, fkc_err, "o-", color="r") +ax.plot(train_sizes, eig_err, "o-", color="r") ax.plot(train_sizes, svc_err, "o-", color="g") ax.set_xscale("log") ax.set_xticks(train_sizes) From 8da6edcd3c0953f28d66ba28a39bfcdaf7469d95 Mon Sep 17 00:00:00 2001 From: Alex <7alex7li@gmail.com> Date: Sat, 27 Jul 2019 19:21:19 -0400 Subject: [PATCH 26/31] Added code for static tests and ran the code to produce new images. --- benchmarks/__init__.py | 0 benchmarks/_bench/__init__.py | 0 benchmarks/_bench/eigenpro_plot_mnist.py | 115 +++++++++++++++++ .../_bench/eigenpro_plot_noisy_mnist.py | 113 +++++++++++++++++ benchmarks/_bench/eigenpro_plot_synthetic.py | 117 ++++++++++++++++++ doc/images/eigenpro_mnist.png | Bin 0 -> 199137 bytes doc/images/eigenpro_mnist_noisy.png | Bin 0 -> 209770 bytes doc/images/eigenpro_synthetic.png | Bin 0 -> 183881 bytes doc/images/fast_kernel_mnist.png | Bin 72335 -> 0 bytes doc/images/fast_kernel_noisy_mnist.png | Bin 76762 -> 0 bytes doc/images/fast_kernel_synthetic.png | Bin 71927 -> 0 bytes doc/modules/eigenpro.rst | 30 ++--- .../{plot_mnist.py => plot_eigenpro_mnist.py} | 0 sklearn_extra/eigenpro.py | 5 +- 14 files changed, 363 insertions(+), 17 deletions(-) create mode 100644 benchmarks/__init__.py create mode 100644 benchmarks/_bench/__init__.py create mode 100644 benchmarks/_bench/eigenpro_plot_mnist.py create mode 100644 benchmarks/_bench/eigenpro_plot_noisy_mnist.py create mode 100644 benchmarks/_bench/eigenpro_plot_synthetic.py create mode 100644 doc/images/eigenpro_mnist.png create mode 100644 doc/images/eigenpro_mnist_noisy.png create mode 100644 doc/images/eigenpro_synthetic.png delete mode 100644 doc/images/fast_kernel_mnist.png delete mode 100644 doc/images/fast_kernel_noisy_mnist.png delete mode 100644 doc/images/fast_kernel_synthetic.png rename examples/eigenpro/{plot_mnist.py => plot_eigenpro_mnist.py} (100%) diff --git a/benchmarks/__init__.py b/benchmarks/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/benchmarks/_bench/__init__.py b/benchmarks/_bench/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/benchmarks/_bench/eigenpro_plot_mnist.py b/benchmarks/_bench/eigenpro_plot_mnist.py new file mode 100644 index 00000000..553c6931 --- /dev/null +++ b/benchmarks/_bench/eigenpro_plot_mnist.py @@ -0,0 +1,115 @@ +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +from time import time + +from sklearn_extra.eigenpro import EigenProClassifier +from sklearn.svm import SVC +from sklearn.datasets import fetch_openml + +rng = np.random.RandomState(1) + +# Generate sample data from mnist +mnist = fetch_openml("mnist_784") +mnist.data = mnist.data / 255.0 +print("Data has loaded") + +p = rng.permutation(60000) +x_train = mnist.data[p] +y_train = np.int32(mnist.target[p]) +x_test = mnist.data[60000:] +y_test = np.int32(mnist.target[60000:]) + +# Run tests comparing eig to svc +eig_fit_times = [] +eig_pred_times = [] +eig_err = [] +svc_fit_times = [] +svc_pred_times = [] +svc_err = [] + +train_sizes = [500, 1000, 2000, 5000, 10000, 20000, 40000, 60000] + +bandwidth = 5.0 + +# Fit models to data +for train_size in train_sizes: + for name, estimator in [ + ( + "EigenPro", + EigenProClassifier( + n_epoch=2, bandwidth=bandwidth, random_state=rng + ), + ), + ( + "SupportVector", + SVC( + C=5, gamma=1.0 / (2 * bandwidth * bandwidth), random_state=rng + ), + ), + ]: + stime = time() + estimator.fit(x_train[:train_size], y_train[:train_size]) + fit_t = time() - stime + + stime = time() + y_pred_test = estimator.predict(x_test) + pred_t = time() - stime + + err = 100.0 * np.sum(y_pred_test != y_test) / len(y_test) + if name == "EigenPro": + eig_fit_times.append(fit_t) + eig_pred_times.append(pred_t) + eig_err.append(err) + else: + svc_fit_times.append(fit_t) + svc_pred_times.append(pred_t) + svc_err.append(err) + print( + "%s Classification with %i training samples in %0.2f seconds." + "Test error %.4f" % (name, train_size, fit_t + pred_t, err) + ) + +# set up grid for figures +fig = plt.figure(num=None, figsize=(6, 4), dpi=160) +ax = plt.subplot2grid((2, 2), (0, 0), rowspan=2) +train_size_labels = ["500", "1k", "2k", "5k", "10k", "20k", "40k", "60k"] + +# Graph fit(train) time +ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) +ax.plot(train_sizes, svc_fit_times, "o--", color="g", label="SVC") +ax.plot(train_sizes, eig_fit_times, "o-", color="r", label="EigenPro") +ax.set_xscale("log") +ax.set_yscale("log", nonposy="clip") +ax.set_xlabel("train size") +ax.set_ylabel("time (seconds)") +ax.legend() +ax.set_title("Train set") +ax.set_xticks(train_sizes) +ax.set_xticks([], minor=True) +ax.set_xticklabels(train_size_labels) + +# Graph prediction(test) time +ax = plt.subplot2grid((2, 2), (0, 1), rowspan=1) +ax.plot(train_sizes, eig_pred_times, "o-", color="r") +ax.plot(train_sizes, svc_pred_times, "o--", color="g") +ax.set_xscale("log") +ax.set_yscale("log", nonposy="clip") +ax.set_ylabel("time (seconds)") +ax.set_title("Test set") +ax.set_xticks(train_sizes) +ax.set_xticks([], minor=True) +ax.set_xticklabels(train_size_labels) + +# Graph training error +ax = plt.subplot2grid((2, 2), (1, 1), rowspan=1) +ax.plot(train_sizes, eig_err, "o-", color="r") +ax.plot(train_sizes, svc_err, "o-", color="g") +ax.set_xscale("log") +ax.set_xticks(train_sizes) +ax.set_xticklabels(train_size_labels) +ax.set_xticks([], minor=True) +ax.set_xlabel("train size") +ax.set_ylabel("classification error %") +plt.tight_layout() +plt.show() diff --git a/benchmarks/_bench/eigenpro_plot_noisy_mnist.py b/benchmarks/_bench/eigenpro_plot_noisy_mnist.py new file mode 100644 index 00000000..23c1ce5d --- /dev/null +++ b/benchmarks/_bench/eigenpro_plot_noisy_mnist.py @@ -0,0 +1,113 @@ +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +from time import time + +from sklearn.datasets import fetch_openml +from sklearn_extra.eigenpro import EigenProClassifier +from sklearn.svm import SVC + +rng = np.random.RandomState(1) + +# Generate sample data from mnist +mnist = fetch_openml("mnist_784") +mnist.data = mnist.data / 255.0 + +p = rng.permutation(60000) +x_train = mnist.data[p][:60000] +y_train = np.int32(mnist.target[p][:60000]) +x_test = mnist.data[60000:] +y_test = np.int32(mnist.target[60000:]) + +# randomize 20% of labels +p = rng.choice(len(y_train), np.int32(len(y_train) * 0.2), False) +y_train[p] = rng.choice(10, np.int32(len(y_train) * 0.2)) +p = rng.choice(len(y_test), np.int32(len(y_test) * 0.2), False) +y_test[p] = rng.choice(10, np.int32(len(y_test) * 0.2)) + +# Run tests comparing fkc to svc +eig_fit_times = [] +eig_pred_times = [] +eig_err = [] +svc_fit_times = [] +svc_pred_times = [] +svc_err = [] + +train_sizes = [500, 1000, 2000, 5000, 10000, 20000, 40000, 60000] + +bandwidth = 5.0 +# Fit models to data +for train_size in train_sizes: + for name, estimator in [ + ( + "EigenPro", + EigenProClassifier( + n_epoch=2, bandwidth=bandwidth, random_state=rng + ), + ), + ("SupportVector", SVC(C=5, gamma=1.0 / (2 * bandwidth * bandwidth))), + ]: + stime = time() + estimator.fit(x_train[:train_size], y_train[:train_size]) + fit_t = time() - stime + + stime = time() + y_pred_test = estimator.predict(x_test) + pred_t = time() - stime + err = 100.0 * np.sum(y_pred_test != y_test) / len(y_test) + if name == "EigenPro": + eig_fit_times.append(fit_t) + eig_pred_times.append(pred_t) + eig_err.append(err) + else: + svc_fit_times.append(fit_t) + svc_pred_times.append(pred_t) + svc_err.append(err) + print( + "%s Classification with %i training samples in %0.2f seconds. " + "Test error %.4f" % (name, train_size, fit_t + pred_t, err) + ) + +# set up grid for figures +fig = plt.figure(num=None, figsize=(6, 4), dpi=160) +ax = plt.subplot2grid((2, 2), (0, 0), rowspan=2) +train_size_labels = ["500", "1k", "2k", "5k", "10k", "20k", "40k", "60k"] + +# Graph fit(train) time +ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) +ax.plot(train_sizes, svc_fit_times, "o--", color="g", label="SVC") +ax.plot(train_sizes, eig_fit_times, "o-", color="r", label="EigenPro") +ax.set_xscale("log") +ax.set_yscale("log", nonposy="clip") +ax.set_xlabel("train size") +ax.set_ylabel("time (seconds)") +ax.legend() +ax.set_title("Train set") +ax.set_xticks(train_sizes) +ax.set_xticks([], minor=True) +ax.set_xticklabels(train_size_labels) + +# Graph prediction(test) time +ax = plt.subplot2grid((2, 2), (0, 1), rowspan=1) +ax.plot(train_sizes, eig_pred_times, "o-", color="r") +ax.plot(train_sizes, svc_pred_times, "o--", color="g") +ax.set_xscale("log") +ax.set_yscale("log", nonposy="clip") +ax.set_ylabel("time (seconds)") +ax.set_title("Test set") +ax.set_xticks(train_sizes) +ax.set_xticks([], minor=True) +ax.set_xticklabels(train_size_labels) + +# Graph training error +ax = plt.subplot2grid((2, 2), (1, 1), rowspan=1) +ax.plot(train_sizes, eig_err, "o-", color="r") +ax.plot(train_sizes, svc_err, "o-", color="g") +ax.set_xscale("log") +ax.set_xticks(train_sizes) +ax.set_xticklabels(train_size_labels) +ax.set_xticks([], minor=True) +ax.set_xlabel("train size") +ax.set_ylabel("classification error %") +plt.tight_layout() +plt.show() diff --git a/benchmarks/_bench/eigenpro_plot_synthetic.py b/benchmarks/_bench/eigenpro_plot_synthetic.py new file mode 100644 index 00000000..7e137ed9 --- /dev/null +++ b/benchmarks/_bench/eigenpro_plot_synthetic.py @@ -0,0 +1,117 @@ +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +from time import time + +from sklearn.datasets import make_classification +from sklearn_extra.eigenpro import EigenProClassifier +from sklearn.svm import SVC + +rng = np.random.RandomState(1) + +max_size = 50000 +test_size = 10000 + +# Get data for testing + +x, y = make_classification( + n_samples=max_size + test_size, + n_features=400, + n_informative=6, + random_state=rng, +) + +x_train = x[:max_size] +y_train = y[:max_size] +x_test = x[max_size:] +y_test = y[max_size:] + +eig_fit_times = [] +eig_pred_times = [] +eig_err = [] +svc_fit_times = [] +svc_pred_times = [] +svc_err = [] + +train_sizes = [2000, 5000, 10000, 20000, 50000] + +bandwidth = 10.0 +for train_size in train_sizes: + for name, estimator in [ + ( + "EigenPro", + EigenProClassifier( + n_epoch=3, + bandwidth=bandwidth, + n_components=30, + subsample_size=1000, + random_state=rng, + ), + ), + ("SupportVector", SVC(C=5, gamma=1.0 / (2 * bandwidth * bandwidth))), + ]: + stime = time() + estimator.fit(x_train[:train_size], y_train[:train_size]) + fit_t = time() - stime + + stime = time() + y_pred_test = estimator.predict(x_test) + pred_t = time() - stime + + err = 100.0 * np.sum(y_pred_test != y_test) / len(y_test) + if name == "EigenPro": + eig_fit_times.append(fit_t) + eig_pred_times.append(pred_t) + eig_err.append(err) + else: + svc_fit_times.append(fit_t) + svc_pred_times.append(pred_t) + svc_err.append(err) + print( + "%s Classification with %i training samples in %0.2f seconds." + % (name, train_size, fit_t + pred_t) + ) + +# set up grid for figures +fig = plt.figure(num=None, figsize=(6, 4), dpi=160) +ax = plt.subplot2grid((2, 2), (0, 0), rowspan=2) +train_size_labels = [str(s) for s in train_sizes] + +# Graph fit(train) time +ax.plot(train_sizes, svc_fit_times, "o--", color="g", label="SVC") +ax.plot(train_sizes, eig_fit_times, "o-", color="r", label="FKC (EigenPro)") +ax.set_xscale("log") +ax.set_yscale("log", nonposy="clip") +ax.set_xlabel("train size") +ax.set_ylabel("time (seconds)") + +ax.legend() +ax.set_title("Train set") +ax.set_xticks(train_sizes) +ax.set_xticklabels(train_size_labels) +ax.set_xticks([], minor=True) +ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) + +# Graph prediction(test) time +ax = plt.subplot2grid((2, 2), (0, 1), rowspan=1) +ax.plot(train_sizes, eig_pred_times, "o-", color="r") +ax.plot(train_sizes, svc_pred_times, "o--", color="g") +ax.set_xscale("log") +ax.set_yscale("log", nonposy="clip") +ax.set_ylabel("time (seconds)") +ax.set_title("Test set") +ax.set_xticks([]) +ax.set_xticks([], minor=True) + +# Graph training error +ax = plt.subplot2grid((2, 2), (1, 1), rowspan=1) +ax.plot(train_sizes, eig_err, "o-", color="r") +ax.plot(train_sizes, svc_err, "o-", color="g") +ax.set_xscale("log") +ax.set_xticks(train_sizes) +ax.set_xticklabels(train_size_labels) +ax.set_xticks([], minor=True) +ax.set_xlabel("train size") +ax.set_ylabel("classification error %") +plt.tight_layout() +plt.show() diff --git a/doc/images/eigenpro_mnist.png b/doc/images/eigenpro_mnist.png new file mode 100644 index 0000000000000000000000000000000000000000..77601d7a65e67f50ca7f7f7132433e3c23694a3c GIT binary patch literal 199137 zcmeEu`9IWe`?eA)TL=lGM93c5mxNSAS}a+{PId-k8B58QWs+>!qN1|z#=h@nsAM0z zQO1lV`}SO;&wYRI`}qT&U!L##^=h%s`+Z&4d7j649LG5k541IC&t5!BMn*<^_m0{_ zGBTU{@SoQz!&bl$TeH+YR?Y!5e&0{ndGamAN>bENDZ(>C>lOvE1-=V#CpBNb;i`?WUn2 z)QIISvf(Zr&2I7RV(UFBa)jFv%=F_d^4nz35a}2RbpN_+d@Sed>OKoYgywNH&(RfpGb**kw7(U2G)_mEP4mEnX{i$H+w?W!=@+080?a{^NEtHj8p0o)bGj6kyUmoRT`R{Emj#U793LKkR@V#cBDIbQlS@MWXtihVy56eIKiO+eWu?OB^GP#`MsDun zyWnTS!_^Eu_^`&$`|c7VnD|_O{}3>m=wluc>S{9^7#bSV*7lY^I%e(`q~?x@vgbeJKhL7L^?u9slY*9?kHdr zKVpQ$?5)VI%EbEMJJ0TUBRG`Omu{^4tj^*&+lKmZ2h0>5`oMh|7dV_F zVA*@Gz@%p7aF2M!;b&fFinM6`(V^d-ZwLhZ?m*%O;_tq53tl}rRnu22(;ydkSlhS6 zEs;1qj;xE6-8v)RFyn?vm9?xao;b(#{RYmXFW-wW9syp-==3|~z<~Wf-*udEdbKVd zI6Z`x684j3VsoA8VHzd{W{8Ch7>h|_(L;D=?XIDoSywE9Zc?* zVLB=&10oYmeY?Ek7kZPwzi4X-qnB4^l45|?BV^^^geW=B-|y8T7^Uu_)8s4j6GTj^ zB`-?anJIpdVW?xpO6#O6@IT6?38m+9j@$3Q@Kwrb^t^f`i~5&`pYkft^DU<)dEti# zcJCJlig6u*GuiG(tP6|1qA|)Nm+hK`?pXTviQ4{WUIZ@P51lT-Rq8t3%x_r2gZ062 z?yq)mCCS4J?pF|{%yE8oR$YvKE=u-w)NrkXS(H*D|FFXtE*w$w+?<5dG*topfneG8R{m;xUm+(2s2n*Y4< ziZe_mwCnKjFiF9uq;C@Hw|pZ-Q)zec1OMaC=dS4&M6n1zH3LW2c~YLTRkUHhWZ8RR zX<_Jfp`vOiO+}*&-hoTf?zMjm2o~gV(T^xu)UF-Lh^W!nkxj*4-#!4z#_?OSpuWh* zd-2^@>(4OqG$uj=BFL%eWR#hoeHg)Z4aQR!Y-v1k8 z7R?8z8fiogeUqA8cGl)wzz)iESjlwxaN;D!zjQZ#=ft+7xQIPI{J2M{`lx1Oh_p$y zhZj35Cw#HL$&2$l2sx($HeWjCfvI(Ke$F%$Gkjo^I6293bp0(lN&G{f$qt9fx&iuw zY@+q+9$9NOA|I|tGf||CF9Y!cml23vh{=RX9L+7wkC)vFIm;pgGxu8LtQ|k->6rGP zsQX#;!4{LgDGLgf;&G9*h|v+?eV5aR1iz3DzE4$qcy_}Q!X02c4D2s^Lc$o5n3 z>*FpZq`VfJo%;%LA5mbl!1D(zPs0->!K^pBx@Uq+;=BmE^W8R~CA92PW$#gUo}=wqHC(}Zj!hee;6M>J+W{}WjtO`z^2F*#6%(`d;KSe+3jAm+Or zwZE$BY*x7^BfCth#wuMp20mywF-uY%p)p7680BT}@wA?ZKUHqCdCIZ!VDDrF<_q)+ z7;&u$(?VQvOx^`|Y*xCv6!leLpQOnjnwo{%NjqD)p6pGlrm>QJX1gO;-(jrY*I_M? zcd)l*lq{kYsQq!LzUX5zb&FBL$A#APju~|t)5lXG^z~30@1214r2WyAgLzB0X7m

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Updated eigenpro.rst to give accurate descriptions of the new graphs. --- doc/modules/eigenpro.rst | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/doc/modules/eigenpro.rst b/doc/modules/eigenpro.rst index bcefb5f3..19d7919a 100644 --- a/doc/modules/eigenpro.rst +++ b/doc/modules/eigenpro.rst @@ -22,7 +22,7 @@ The figure below compares the Fast Kernel Classifier (EigenPro) and the Support Classifier (:class:`SVC`) on MNIST digits classification task. We see that EigenPro and SVC give competitive and similar accuracy on test set. Notably, on the full MNIST training and testing using EigenPro are -approximately 3 times and 6 times faster than that using SVC, respectively. +approximately 2 times and 5 times faster than that using SVC, respectively. .. |mnist| image:: ../images/eigenpro_mnist.png :target: ../auto_examples/eigenpro/plot_mnist.html @@ -32,9 +32,9 @@ approximately 3 times and 6 times faster than that using SVC, respectively. We then repeat the same experiments on MNIST with added label noise. Specifically, we randomly reset the label (0-9) of 20% samples. -We see that EigenPro has a significant larger advantage over SVC -on this noisy MNIST. Especially, training and testing using EigenPro are -up to 20 times faster than that using SVC. +We see that EigenPro has a significant advantage over SVC +on this noisy MNIST. Training and testing using EigenPro are +both 10 to 20 times faster than they are when using SVC. .. |mnist_noisy| image:: ../images/eigenpro_noisy_mnist.png :target: ../auto_examples/eigenpro/plot_noisy_mnist.html @@ -44,7 +44,7 @@ up to 20 times faster than that using SVC. The next figure compares the two methods on a binary classification problem -with synthetic features. Here EigenPro demonstrates nearly 8 times +with 400 synthetic features. Again, EigenPro demonstrates 10~20 times acceleration on training and testing without loss of accuracy. .. |synthetic| image:: ../images/eigenpro_synthetic.png From 6fef3d29f8968b958f83f8bf4fb339a7923ea1eb Mon Sep 17 00:00:00 2001 From: Alex <7alex7li@gmail.com> Date: Thu, 1 Aug 2019 01:57:28 -0400 Subject: [PATCH 28/31] Removed extra file --- doc/modules/eigenpro.html | 440 ------------------ doc/modules/eigenpro.rst | 2 +- sklearn_extra/kernel_methods/__init__.py | 2 +- .../{eigenpro.py => _eigenpro.py} | 0 sklearn_extra/tests/test_common.py | 6 +- 5 files changed, 5 insertions(+), 445 deletions(-) delete mode 100644 doc/modules/eigenpro.html rename sklearn_extra/kernel_methods/{eigenpro.py => _eigenpro.py} (100%) diff --git a/doc/modules/eigenpro.html b/doc/modules/eigenpro.html deleted file mode 100644 index f628d8ff..00000000 --- a/doc/modules/eigenpro.html +++ /dev/null @@ -1,440 +0,0 @@ - - - - - - -EigenPro for Regression and Classification - - - -

- -

EigenPro for Regression and Classification

- -
-

System Message: ERROR/3 (C:/Users/Alex Li/Dropbox/sklearn/doc/modules/eigenpro.rst, line 7)

-

Unknown directive type "currentmodule".

-
-.. currentmodule:: sklearn_extra.kernel_methods.eigenpro
-
-
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-

EigenPro iteration [MB17] is a very efficient implementation of kernel -regression/classification that uses an optimization method based on -preconditioned stochastic gradient descent. It essentially implements a -"ridgeless" kernel regression. Regularization, when necessary, can be -achieved by early stopping.

-

Optimization parameters, such as step size, batch size, and the size of the preconditioning -block are chosen automatically and optimally. (They can also be set up manually.) -This results in a simple and user-friendly interface.

-

Next, we present several experimental results using a server equipped with one -Intel Xeon E5-1620 CPU. -The figure below compares the EigenPro Classifier and the Support Vector -Classifier (:class:`SVC`) on MNIST digits classification task. -We see that EigenPro and SVC give competitive and similar accuracy on test set. -Notably, on the full MNIST training and testing using EigenPro are -approximately 2 times and 5 times faster than that using SVC, respectively.

-
-

System Message: ERROR/3 (C:/Users/Alex Li/Dropbox/sklearn/doc/modules/eigenpro.rst, line 19); backlink

-Unknown interpreted text role "class".
-
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System Message: ERROR/3 (C:/Users/Alex Li/Dropbox/sklearn/doc/modules/eigenpro.rst, line 31)

-

Unknown directive type "centered".

-
-.. centered:: |mnist|
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-
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-

We then repeat the same experiments on MNIST with added label noise. -Specifically, we randomly reset the label (0-9) of 20% samples. -We see that EigenPro has a significant advantage over SVC -on this noisy MNIST. Training and testing using EigenPro are -both 10 to 20 times faster than they are when using SVC.

-
-

System Message: ERROR/3 (C:/Users/Alex Li/Dropbox/sklearn/doc/modules/eigenpro.rst, line 43)

-

Unknown directive type "centered".

-
-.. centered:: |mnist_noisy|
-
-
-
-
-

The next figure compares the two methods on a binary classification problem -with 400 synthetic features. Again, EigenPro demonstrates 10~20 times -acceleration on training and testing without loss of accuracy.

-
-

System Message: ERROR/3 (C:/Users/Alex Li/Dropbox/sklearn/doc/modules/eigenpro.rst, line 54)

-

Unknown directive type "centered".

-
-.. centered:: |synthetic|
-
-
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References:

- - - - - -
[MB17]Siyuan Ma and Mikhail Belkin, -"Diving into the shallows: a computational perspective on large-scale shallow learning", -Advances in Neural Information Processing Systems, 2017.
-
-
- - diff --git a/doc/modules/eigenpro.rst b/doc/modules/eigenpro.rst index 6d7903f7..b42de4ce 100644 --- a/doc/modules/eigenpro.rst +++ b/doc/modules/eigenpro.rst @@ -4,7 +4,7 @@ EigenPro for Regression and Classification ========================================== -.. currentmodule:: sklearn_extra.kernel_methods.eigenpro +.. currentmodule:: sklearn_extra.kernel_methods *EigenPro iteration* [MB17]_ is a very efficient implementation of kernel regression/classification that uses an optimization method based on diff --git a/sklearn_extra/kernel_methods/__init__.py b/sklearn_extra/kernel_methods/__init__.py index 9804c509..53be76dc 100644 --- a/sklearn_extra/kernel_methods/__init__.py +++ b/sklearn_extra/kernel_methods/__init__.py @@ -1,3 +1,3 @@ -from .eigenpro import BaseEigenPro, EigenProClassifier, EigenProRegressor +from ._eigenpro import BaseEigenPro, EigenProClassifier, EigenProRegressor __all__ = ["BaseEigenPro", "EigenProClassifier", "EigenProRegressor"] diff --git a/sklearn_extra/kernel_methods/eigenpro.py b/sklearn_extra/kernel_methods/_eigenpro.py similarity index 100% rename from sklearn_extra/kernel_methods/eigenpro.py rename to sklearn_extra/kernel_methods/_eigenpro.py diff --git a/sklearn_extra/tests/test_common.py b/sklearn_extra/tests/test_common.py index 9c90d563..6563d42b 100644 --- a/sklearn_extra/tests/test_common.py +++ b/sklearn_extra/tests/test_common.py @@ -3,7 +3,7 @@ from sklearn.utils.estimator_checks import check_estimator from sklearn_extra.kernel_approximation import Fastfood -from sklearn_extra.kernel_methods import eigenpro +from sklearn_extra.kernel_methods import _eigenpro from sklearn_extra.cluster import KMedoids @@ -12,8 +12,8 @@ [ Fastfood, KMedoids, - eigenpro.EigenProClassifier, - eigenpro.EigenProRegressor, + _eigenpro.EigenProClassifier, + _eigenpro.EigenProRegressor, ], ) def test_all_estimators(Estimator, request): From ccd34a198f669794ab5250fb7d5d061914860c15 Mon Sep 17 00:00:00 2001 From: Alex <7alex7li@gmail.com> Date: Thu, 1 Aug 2019 19:21:02 -0400 Subject: [PATCH 29/31] Trying to commit again to see if ci buidls are still there --- examples/eigenpro/plot_eigenpro_mnist.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/eigenpro/plot_eigenpro_mnist.py b/examples/eigenpro/plot_eigenpro_mnist.py index 18ef4adc..c3ab9e10 100644 --- a/examples/eigenpro/plot_eigenpro_mnist.py +++ b/examples/eigenpro/plot_eigenpro_mnist.py @@ -127,6 +127,6 @@ ax.set_xticklabels(train_size_labels) ax.set_xticks([], minor=True) ax.set_xlabel("train size") -ax.set_ylabel("classification error %") +ax.set_ylabel("Classification error %") plt.tight_layout() plt.show() From 9d6b0330d11fe881dd039b7eddc3215b766ffbef Mon Sep 17 00:00:00 2001 From: Alex <7alex7li@gmail.com> Date: Thu, 1 Aug 2019 19:35:30 -0400 Subject: [PATCH 30/31] Using old commit that worked previously to see if the problem is me --- .gitignore | 4 - .travis.yml | 6 +- README.rst | 2 +- benchmarks/_bench/eigenpro_plot_mnist.py | 2 +- .../_bench/eigenpro_plot_noisy_mnist.py | 2 +- benchmarks/_bench/eigenpro_plot_synthetic.py | 2 +- benchmarks/bench_rbfsampler_fastfood.py | 23 +- doc/api.rst | 14 +- doc/conf.py | 207 ++++----- doc/images/eigenpro_synthetic.png | Bin 195613 -> 183881 bytes doc/install.rst | 2 +- doc/modules/eigenpro.rst | 4 +- doc/user_guide.rst | 58 +-- examples/eigenpro/plot_eigenpro_mnist.py | 5 +- examples/plot_kmedoids_digits.py | 104 ----- setup.cfg | 1 - sklearn_extra/__init__.py | 4 +- sklearn_extra/cluster/__init__.py | 3 - sklearn_extra/cluster/_k_medoids.py | 431 ------------------ sklearn_extra/cluster/tests/__init__.py | 0 sklearn_extra/cluster/tests/test_k_medoids.py | 312 ------------- .../_eigenpro.py => eigenpro.py} | 14 +- sklearn_extra/kernel_methods/__init__.py | 3 - .../kernel_methods/tests/__init__.py | 0 sklearn_extra/tests/test_common.py | 10 +- .../tests/test_eigenpro.py | 2 +- 26 files changed, 130 insertions(+), 1085 deletions(-) delete mode 100644 examples/plot_kmedoids_digits.py delete mode 100644 sklearn_extra/cluster/__init__.py delete mode 100644 sklearn_extra/cluster/_k_medoids.py delete mode 100644 sklearn_extra/cluster/tests/__init__.py delete mode 100644 sklearn_extra/cluster/tests/test_k_medoids.py rename sklearn_extra/{kernel_methods/_eigenpro.py => eigenpro.py} (98%) delete mode 100644 sklearn_extra/kernel_methods/__init__.py delete mode 100644 sklearn_extra/kernel_methods/tests/__init__.py rename sklearn_extra/{kernel_methods => }/tests/test_eigenpro.py (99%) diff --git a/.gitignore b/.gitignore index 71d1c71a..098d0fd7 100644 --- a/.gitignore +++ b/.gitignore @@ -8,9 +8,6 @@ __pycache__/ # C extensions *.so -# Text Editors -.vscode/ - # scikit-learn specific doc/_build/ doc/auto_examples/ @@ -20,7 +17,6 @@ doc/datasets/generated/ # Distribution / packaging .Python -venv/ env/ build/ develop-eggs/ diff --git a/.travis.yml b/.travis.yml index 241445d6..9ddc4b90 100644 --- a/.travis.yml +++ b/.travis.yml @@ -10,9 +10,9 @@ cache: matrix: include: - env: PYTHON_VERSION="3.5" NUMPY_VERSION="1.13.1" SCIPY_VERSION="0.19.1" - SKLEARN_VERSION="0.21.2" + SKLEARN_VERSION="0.19.1" - env: PYTHON_VERSION="3.6" NUMPY_VERSION="1.13.1" SCIPY_VERSION="0.19.1" - SKLEARN_VERSION="0.21.2" + SKLEARN_VERSION="0.20.2" - env: PYTHON_VERSION="3.7" NUMPY_VERSION="*" SCIPY_VERSION="*" SKLEARN_VERSION="*" - env: PYTHON_VERSION="3.7" NUMPY_VERSION="*" SCIPY_VERSION="*" @@ -25,7 +25,7 @@ install: - MINICONDA_PATH=/home/travis/miniconda - chmod +x miniconda.sh && ./miniconda.sh -b -p $MINICONDA_PATH - export PATH=$MINICONDA_PATH/bin:$PATH - - conda install --yes conda==4.6.14 + - conda update --yes conda # create the testing environment - conda create -n testenv --yes python=$PYTHON_VERSION pip - source activate testenv diff --git a/README.rst b/README.rst index 25d1d12a..20ea7fb7 100644 --- a/README.rst +++ b/README.rst @@ -30,7 +30,7 @@ Dependencies scikit-learn-extra requires, - Python (>=3.5) -- scikit-learn (>=0.21), and its dependencies +- scikit-learn (>=0.20), and its dependencies - Cython (>0.28) diff --git a/benchmarks/_bench/eigenpro_plot_mnist.py b/benchmarks/_bench/eigenpro_plot_mnist.py index 1dbe6fdd..553c6931 100644 --- a/benchmarks/_bench/eigenpro_plot_mnist.py +++ b/benchmarks/_bench/eigenpro_plot_mnist.py @@ -3,7 +3,7 @@ import numpy as np from time import time -from sklearn_extra.kernel_methods import EigenProClassifier +from sklearn_extra.eigenpro import EigenProClassifier from sklearn.svm import SVC from sklearn.datasets import fetch_openml diff --git a/benchmarks/_bench/eigenpro_plot_noisy_mnist.py b/benchmarks/_bench/eigenpro_plot_noisy_mnist.py index dd0c5abd..23c1ce5d 100644 --- a/benchmarks/_bench/eigenpro_plot_noisy_mnist.py +++ b/benchmarks/_bench/eigenpro_plot_noisy_mnist.py @@ -4,7 +4,7 @@ from time import time from sklearn.datasets import fetch_openml -from sklearn_extra.kernel_methods import EigenProClassifier +from sklearn_extra.eigenpro import EigenProClassifier from sklearn.svm import SVC rng = np.random.RandomState(1) diff --git a/benchmarks/_bench/eigenpro_plot_synthetic.py b/benchmarks/_bench/eigenpro_plot_synthetic.py index 475d9a97..7e137ed9 100644 --- a/benchmarks/_bench/eigenpro_plot_synthetic.py +++ b/benchmarks/_bench/eigenpro_plot_synthetic.py @@ -4,7 +4,7 @@ from time import time from sklearn.datasets import make_classification -from sklearn_extra.kernel_methods import EigenProClassifier +from sklearn_extra.eigenpro import EigenProClassifier from sklearn.svm import SVC rng = np.random.RandomState(1) diff --git a/benchmarks/bench_rbfsampler_fastfood.py b/benchmarks/bench_rbfsampler_fastfood.py index 11f5df9b..42bea9b4 100644 --- a/benchmarks/bench_rbfsampler_fastfood.py +++ b/benchmarks/bench_rbfsampler_fastfood.py @@ -15,9 +15,9 @@ Y /= Y.sum(axis=1)[:, np.newaxis] # calculate feature maps -gamma = 10.0 +gamma = 10. sigma = np.sqrt(1 / (2 * gamma)) -number_of_features_to_generate = 4096 * 4 +number_of_features_to_generate = 4096*4 exact_start = datetime.datetime.utcnow() # original rbf kernel method: @@ -27,24 +27,23 @@ exact_spent_time = exact_end - exact_start print("Timimg exact rbf: \t\t", exact_spent_time) -rbf_transform = Fastfood( - sigma=sigma, - n_components=number_of_features_to_generate, - tradeoff_mem_accuracy="mem", - random_state=42, -) +rbf_transform = Fastfood(sigma=sigma, + n_components=number_of_features_to_generate, + tradeoff_mem_accuracy='mem', + random_state=42) _ = rbf_transform.fit(X) fastfood_fast_vec_start = datetime.datetime.utcnow() # Fastfood: approximate kernel mapping _ = rbf_transform.transform(X) _ = rbf_transform.transform(Y) fastfood_fast_vec_end = datetime.datetime.utcnow() -fastfood_fast_vec_spent_time = fastfood_fast_vec_end - fastfood_fast_vec_start +fastfood_fast_vec_spent_time = fastfood_fast_vec_end - \ + fastfood_fast_vec_start print("Timimg fastfood fast vectorized: \t\t", fastfood_fast_vec_spent_time) -rks_rbf_transform = RBFSampler( - gamma=gamma, n_components=number_of_features_to_generate, random_state=42 -) +rks_rbf_transform = RBFSampler(gamma=gamma, + n_components=number_of_features_to_generate, + random_state=42) _ = rks_rbf_transform.fit(X) rks_start = datetime.datetime.utcnow() # Random Kitchens Sinks: approximate kernel mapping diff --git a/doc/api.rst b/doc/api.rst index 61e1bd7f..928038b3 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -27,15 +27,5 @@ EigenPro :toctree: generated/ :template: class.rst - kernel_methods.EigenProRegressor - kernel_methods.EigenProClassifier - -Clustering -==================== - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - cluster.KMedoids - + eigenpro.EigenProRegressor + eigenpro.EigenProClassifier diff --git a/doc/conf.py b/doc/conf.py index c39936a0..eb7aadf6 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -21,65 +21,61 @@ # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. -# sys.path.insert(0, os.path.abspath('.')) +#sys.path.insert(0, os.path.abspath('.')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. -# needs_sphinx = '1.0' +#needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ - "sphinx.ext.autodoc", - "sphinx.ext.autosummary", - "sphinx.ext.doctest", - "sphinx.ext.intersphinx", - "sphinx.ext.viewcode", - "numpydoc", - "sphinx_gallery.gen_gallery", + 'sphinx.ext.autodoc', + 'sphinx.ext.autosummary', + 'sphinx.ext.doctest', + 'sphinx.ext.intersphinx', + 'sphinx.ext.viewcode', + 'numpydoc', + 'sphinx_gallery.gen_gallery', ] # this is needed for some reason... # see https://github.com/numpy/numpydoc/issues/69 numpydoc_show_class_members = False -autodoc_default_flags = ["members", "inherited-members"] - -# For maths, use mathjax by default and svg if NO_MATHJAX env variable is set -# (useful for viewing the doc offline) -if os.environ.get("NO_MATHJAX"): - extensions.append("sphinx.ext.imgmath") - imgmath_image_format = "svg" +# pngmath / imgmath compatibility layer for different sphinx versions +import sphinx +from distutils.version import LooseVersion +if LooseVersion(sphinx.__version__) < LooseVersion('1.4'): + extensions.append('sphinx.ext.pngmath') else: - extensions.append("sphinx.ext.mathjax") - mathjax_path = ( - "https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/" - "MathJax.js?config=TeX-AMS_SVG" - ) + extensions.append('sphinx.ext.imgmath') + +autodoc_default_flags = ['members', 'inherited-members'] # Add any paths that contain templates here, relative to this directory. -templates_path = ["_templates"] +templates_path = ['_templates'] # generate autosummary even if no references autosummary_generate = True # The suffix of source filenames. -source_suffix = ".rst" +source_suffix = '.rst' # The encoding of source files. -# source_encoding = 'utf-8-sig' +#source_encoding = 'utf-8-sig' # Generate the plots for the gallery plot_gallery = True # The master toctree document. -master_doc = "index" +master_doc = 'index' # General information about the project. -project = u"scikit-learn-extra" -copyright = u"2019, scikit-learn-extra developpers" +project = u'scikit-learn-extra' +copyright = u'2019, scikit-learn-extra developpers' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the @@ -87,181 +83,177 @@ # # The short X.Y version. from sklearn_extra import __version__ - version = __version__ # The full version, including alpha/beta/rc tags. release = __version__ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. -# language = None +#language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: -# today = '' +#today = '' # Else, today_fmt is used as the format for a strftime call. -# today_fmt = '%B %d, %Y' +#today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. -exclude_patterns = ["_build", "_templates"] +exclude_patterns = ['_build', '_templates'] # The reST default role (used for this markup: `text`) to use for all # documents. -# default_role = None +#default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. -# add_function_parentheses = True +#add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). -# add_module_names = True +#add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. -# show_authors = False +#show_authors = False # The name of the Pygments (syntax highlighting) style to use. -pygments_style = "sphinx" +pygments_style = 'sphinx' # Custom style -html_style = "css/project-template.css" +html_style = 'css/project-template.css' # A list of ignored prefixes for module index sorting. -# modindex_common_prefix = [] +#modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. -# keep_warnings = False +#keep_warnings = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. -html_theme = "sphinx_rtd_theme" +html_theme = 'sphinx_rtd_theme' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. -# html_theme_options = {} +#html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". -# html_title = None +#html_title = None # A shorter title for the navigation bar. Default is the same as html_title. -# html_short_title = None +#html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. -# html_logo = None +#html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. -# html_favicon = None +#html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ["_static"] +html_static_path = ['_static'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. -# html_extra_path = [] +#html_extra_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. -# html_last_updated_fmt = '%b %d, %Y' +#html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. -# html_use_smartypants = True +#html_use_smartypants = True # Custom sidebar templates, maps document names to template names. -# html_sidebars = {} +#html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. -# html_additional_pages = {} +#html_additional_pages = {} # If false, no module index is generated. -# html_domain_indices = True +#html_domain_indices = True # If false, no index is generated. -# html_use_index = True +#html_use_index = True # If true, the index is split into individual pages for each letter. -# html_split_index = False +#html_split_index = False # If true, links to the reST sources are added to the pages. -# html_show_sourcelink = True +#html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. -# html_show_sphinx = True +#html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. -# html_show_copyright = True +#html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. -# html_use_opensearch = '' +#html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). -# html_file_suffix = None +#html_file_suffix = None # Output file base name for HTML help builder. -htmlhelp_basename = "project-templatedoc" +htmlhelp_basename = 'project-templatedoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { - # The paper size ('letterpaper' or 'a4paper'). - #'papersize': 'letterpaper', - # The font size ('10pt', '11pt' or '12pt'). - #'pointsize': '10pt', - # Additional stuff for the LaTeX preamble. - #'preamble': '', +# The paper size ('letterpaper' or 'a4paper'). +#'papersize': 'letterpaper', + +# The font size ('10pt', '11pt' or '12pt'). +#'pointsize': '10pt', + +# Additional stuff for the LaTeX preamble. +#'preamble': '', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ - ( - "index", - "project-template.tex", - u"project-template Documentation", - u"Vighnesh Birodkar", - "manual", - ) + ('index', 'project-template.tex', u'project-template Documentation', + u'Vighnesh Birodkar', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. -# latex_logo = None +#latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. -# latex_use_parts = False +#latex_use_parts = False # If true, show page references after internal links. -# latex_show_pagerefs = False +#latex_show_pagerefs = False # If true, show URL addresses after external links. -# latex_show_urls = False +#latex_show_urls = False # Documents to append as an appendix to all manuals. -# latex_appendices = [] +#latex_appendices = [] # If false, no module index is generated. -# latex_domain_indices = True +#latex_domain_indices = True # -- Options for manual page output --------------------------------------- @@ -269,17 +261,12 @@ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ - ( - "index", - "project-template", - u"project-template Documentation", - [u"Vighnesh Birodkar"], - 1, - ) + ('index', 'project-template', u'project-template Documentation', + [u'Vighnesh Birodkar'], 1) ] # If true, show URL addresses after external links. -# man_show_urls = False +#man_show_urls = False # -- Options for Texinfo output ------------------------------------------- @@ -288,51 +275,43 @@ # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ - ( - "index", - "project-template", - u"project-template Documentation", - u"Vighnesh Birodkar", - "project-template", - "One line description of project.", - "Miscellaneous", - ) + ('index', 'project-template', u'project-template Documentation', + u'Vighnesh Birodkar', 'project-template', 'One line description of project.', + 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. -# texinfo_appendices = [] +#texinfo_appendices = [] # If false, no module index is generated. -# texinfo_domain_indices = True +#texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. -# texinfo_show_urls = 'footnote' +#texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. -# texinfo_no_detailmenu = False +#texinfo_no_detailmenu = False # Example configuration for intersphinx: refer to the Python standard library. # intersphinx configuration intersphinx_mapping = { - "python": ( - "https://docs.python.org/{.major}".format(sys.version_info), - None, - ), - "numpy": ("https://docs.scipy.org/doc/numpy/", None), - "scipy": ("https://docs.scipy.org/doc/scipy/reference", None), - "matplotlib": ("https://matplotlib.org/", None), - "sklearn": ("http://scikit-learn.org/stable", None), + 'python': ('https://docs.python.org/{.major}'.format( + sys.version_info), None), + 'numpy': ('https://docs.scipy.org/doc/numpy/', None), + 'scipy': ('https://docs.scipy.org/doc/scipy/reference', None), + 'matplotlib': ('https://matplotlib.org/', None), + 'sklearn': ('http://scikit-learn.org/stable', None) } # sphinx-gallery configuration sphinx_gallery_conf = { - "doc_module": "sklearn_extra", - "backreferences_dir": os.path.join("generated"), - "reference_url": {"sklearn_extra": None}, + 'doc_module': 'sklearn_extra', + 'backreferences_dir': os.path.join('generated'), + 'reference_url': { + 'sklearn_extra': None} } - def setup(app): # a copy button to copy snippet of code from the documentation - app.add_javascript("js/copybutton.js") + app.add_javascript('js/copybutton.js') diff --git a/doc/images/eigenpro_synthetic.png b/doc/images/eigenpro_synthetic.png index b0be911c2d6cf107846b72ca62231f4925699fb8..0059682db7187108581448c340d4e5542b74a632 100644 GIT binary patch literal 183881 zcmeFZWmr{R+civs(uizn1qmfKA>9a)qM!mIu<7oWl#Wd(t(1T$NSAa=BPpGm+H@lg 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Next, we present several experimental results using a server equipped with one Intel Xeon E5-1620 CPU. -The figure below compares the EigenPro Classifier and the Support Vector +The figure below compares the Fast Kernel Classifier (EigenPro) and the Support Vector Classifier (:class:`SVC`) on MNIST digits classification task. We see that EigenPro and SVC give competitive and similar accuracy on test set. Notably, on the full MNIST training and testing using EigenPro are diff --git a/doc/user_guide.rst b/doc/user_guide.rst index 0224d330..72598c27 100644 --- a/doc/user_guide.rst +++ b/doc/user_guide.rst @@ -11,60 +11,4 @@ User guide ========== -.. _k_medoids: - -K-Medoids -========= - -:class:`KMedoids` is related to the :class:`KMeans` algorithm. While -:class:`KMeans` tries to minimize the within cluster sum-of-squares, -:class:`KMedoids` tries to minimize the sum of distances between each point and -the medoid of its cluster. The medoid is a data point (unlike the centroid) -which has least total distance to the other members of its cluster. The use of -a data point to represent each cluster's center allows the use of any distance -metric for clustering. - -:class:`KMedoids` can be more robust to noise and outliers than :class:`KMeans` -as it will choose one of the cluster members as the medoid while -:class:`KMeans` will move the center of the cluster towards the outlier which -might in turn move other points away from the cluster centre. - -:class:`KMedoids` is also different from K-Medians, which is analogous to :class:`KMeans` -except that the Manhattan Median is used for each cluster center instead of -the centroid. K-Medians is robust to outliers, but it is limited to the -Manhattan Distance metric and, similar to :class:`KMeans`, it does not guarantee -that the center of each cluster will be a member of the original dataset. - -The complexity of K-Medoids is :math:`O(N^2 K T)` where :math:`N` is the number -of samples, :math:`T` is the number of iterations and :math:`K` is the number of -clusters. This makes it more suitable for smaller datasets in comparison to -:class:`KMeans` which is :math:`O(N K T)`. - -.. topic:: Examples: - - * :ref:`sphx_glr_auto_examples_plot_kmedoids_digits.py`: Applying K-Medoids on digits - with various distance metrics. - - -**Algorithm description:** -There are several algorithms to compute K-Medoids, though :class:`KMedoids` -currently only supports Partitioning Around Medoids (PAM). The PAM algorithm -uses a greedy search, which may fail to find the global optimum. It consists of -two alternating steps commonly called the -Assignment and Update steps (BUILD and SWAP in Kaufmann and Rousseeuw, 1987). - -PAM works as follows: - -* Initialize: Select ``n_clusters`` from the dataset as the medoids using - a heuristic, random, or k-medoids++ approach (configurable using the ``init`` parameter). -* Assignment step: assign each element from the dataset to the closest medoid. -* Update step: Identify the new medoid of each cluster. -* Repeat the assignment and update step while the medoids keep changing or - maximum number of iterations ``max_iter`` is reached. - -.. topic:: References: - - * "Clustering by Means of Medoids'" - Kaufman, L. and Rousseeuw, P.J., - Statistical Data Analysis Based on the L1Norm and Related Methods, edited - by Y. Dodge, North-Holland, 405416. 1987 \ No newline at end of file +To add. diff --git a/examples/eigenpro/plot_eigenpro_mnist.py b/examples/eigenpro/plot_eigenpro_mnist.py index c3ab9e10..768e0a1f 100644 --- a/examples/eigenpro/plot_eigenpro_mnist.py +++ b/examples/eigenpro/plot_eigenpro_mnist.py @@ -17,7 +17,7 @@ import numpy as np from time import time -from sklearn_extra.kernel_methods import EigenProClassifier +from sklearn_extra.eigenpro import EigenProClassifier from sklearn.svm import SVC from sklearn.datasets import fetch_openml @@ -43,7 +43,6 @@ svc_err = [] train_sizes = [500, 1000, 2000] - print("Train Sizes: " + str(train_sizes)) bandwidth = 5.0 @@ -127,6 +126,6 @@ ax.set_xticklabels(train_size_labels) ax.set_xticks([], minor=True) ax.set_xlabel("train size") -ax.set_ylabel("Classification error %") +ax.set_ylabel("classification error %") plt.tight_layout() plt.show() diff --git a/examples/plot_kmedoids_digits.py b/examples/plot_kmedoids_digits.py deleted file mode 100644 index 28c7659d..00000000 --- a/examples/plot_kmedoids_digits.py +++ /dev/null @@ -1,104 +0,0 @@ -# -*- coding: utf-8 -*- -""" -============================================================= -A demo of K-Medoids clustering on the handwritten digits data -============================================================= -In this example we compare different pairwise distance -metrics for K-Medoids. -""" -import numpy as np -import matplotlib.pyplot as plt - -from sklearn.cluster import KMeans -from sklearn_extra.cluster import KMedoids -from sklearn.datasets import load_digits -from sklearn.decomposition import PCA -from sklearn.preprocessing import scale - -print(__doc__) - -# Authors: Timo Erkkilä -# Antti Lehmussola -# Kornel Kiełczewski -# License: BSD 3 clause - -np.random.seed(42) - -digits = load_digits() -data = scale(digits.data) -n_digits = len(np.unique(digits.target)) - -reduced_data = PCA(n_components=2).fit_transform(data) - -# Step size of the mesh. Decrease to increase the quality of the VQ. -h = 0.02 # point in the mesh [x_min, m_max]x[y_min, y_max]. - -# Plot the decision boundary. For that, we will assign a color to each -x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1 -y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1 -xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) - -plt.figure() -plt.clf() - -plt.suptitle( - "Comparing multiple K-Medoids metrics to K-Means and each other", - fontsize=14, -) - - -selected_models = [ - ( - KMedoids(metric="manhattan", n_clusters=n_digits), - "KMedoids (manhattan)", - ), - ( - KMedoids(metric="euclidean", n_clusters=n_digits), - "KMedoids (euclidean)", - ), - (KMedoids(metric="cosine", n_clusters=n_digits), "KMedoids (cosine)"), - (KMeans(n_clusters=n_digits), "KMeans"), -] - -plot_rows = int(np.ceil(len(selected_models) / 2.0)) -plot_cols = 2 - -for i, (model, description) in enumerate(selected_models): - - # Obtain labels for each point in mesh. Use last trained model. - model.fit(reduced_data) - Z = model.predict(np.c_[xx.ravel(), yy.ravel()]) - - # Put the result into a color plot - Z = Z.reshape(xx.shape) - plt.subplot(plot_cols, plot_rows, i + 1) - plt.imshow( - Z, - interpolation="nearest", - extent=(xx.min(), xx.max(), yy.min(), yy.max()), - cmap=plt.cm.Paired, - aspect="auto", - origin="lower", - ) - - plt.plot( - reduced_data[:, 0], reduced_data[:, 1], "k.", markersize=2, alpha=0.3 - ) - # Plot the centroids as a white X - centroids = model.cluster_centers_ - plt.scatter( - centroids[:, 0], - centroids[:, 1], - marker="x", - s=169, - linewidths=3, - color="w", - zorder=10, - ) - plt.title(description) - plt.xlim(x_min, x_max) - plt.ylim(y_min, y_max) - plt.xticks(()) - plt.yticks(()) - -plt.show() diff --git a/setup.cfg b/setup.cfg index 8c7675f8..a199e77a 100644 --- a/setup.cfg +++ b/setup.cfg @@ -5,5 +5,4 @@ description-file = README.rst test = pytest [tool:pytest] -doctest_optionflags = NORMALIZE_WHITESPACE ELLIPSIS addopts = --doctest-modules diff --git a/sklearn_extra/__init__.py b/sklearn_extra/__init__.py index b855d4eb..e1162fdb 100644 --- a/sklearn_extra/__init__.py +++ b/sklearn_extra/__init__.py @@ -1,5 +1,5 @@ -from . import kernel_approximation, kernel_methods # noqa +from . import kernel_approximation # noqa from ._version import __version__ -__all__ = ["__version__"] +__all__ = ["__version__", "eigenpro"] diff --git a/sklearn_extra/cluster/__init__.py b/sklearn_extra/cluster/__init__.py deleted file mode 100644 index bbdaaf41..00000000 --- a/sklearn_extra/cluster/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from ._k_medoids import KMedoids - -__all__ = ["KMedoids"] diff --git a/sklearn_extra/cluster/_k_medoids.py b/sklearn_extra/cluster/_k_medoids.py deleted file mode 100644 index 298195d9..00000000 --- a/sklearn_extra/cluster/_k_medoids.py +++ /dev/null @@ -1,431 +0,0 @@ -# -*- coding: utf-8 -*- -"""K-medoids clustering""" - -# Authors: Timo Erkkilä -# Antti Lehmussola -# Kornel Kiełczewski -# Zane Dufour -# License: BSD 3 clause - -import warnings - -import numpy as np - -from sklearn.base import BaseEstimator, ClusterMixin, TransformerMixin -from sklearn.metrics.pairwise import ( - pairwise_distances, - pairwise_distances_argmin, -) -from sklearn.utils import check_array, check_random_state -from sklearn.utils.extmath import stable_cumsum -from sklearn.utils.validation import check_is_fitted -from sklearn.exceptions import ConvergenceWarning - - -class KMedoids(BaseEstimator, ClusterMixin, TransformerMixin): - """k-medoids clustering. - - Read more in the :ref:`User Guide `. - - Parameters - ---------- - n_clusters : int, optional, default: 8 - The number of clusters to form as well as the number of medoids to - generate. - - metric : string, or callable, optional, default: 'euclidean' - What distance metric to use. See :func:metrics.pairwise_distances - - init : {'random', 'heuristic', 'k-medoids++'}, optional, default: 'heuristic' - Specify medoid initialization method. 'random' selects n_clusters - elements from the dataset. 'heuristic' picks the n_clusters points - with the smallest sum distance to every other point. 'k-medoids++' - follows an approach based on k-means++_, and in general, gives initial - medoids which are more separated than those generated by the other methods. - - .. _k-means++: https://theory.stanford.edu/~sergei/papers/kMeansPP-soda.pdf - - max_iter : int, optional, default : 300 - Specify the maximum number of iterations when fitting. - - random_state : int, RandomState instance or None, optional - Specify random state for the random number generator. Used to - initialise medoids when init='random'. - - Attributes - ---------- - cluster_centers_ : array, shape = (n_clusters, n_features) - or None if metric == 'precomputed' - Cluster centers, i.e. medoids (elements from the original dataset) - - medoid_indices_ : array, shape = (n_clusters,) - The indices of the medoid rows in X - - labels_ : array, shape = (n_samples,) - Labels of each point - - inertia_ : float - Sum of distances of samples to their closest cluster center. - - Examples - -------- - >>> from sklearn_extra.cluster import KMedoids - >>> import numpy as np - - >>> X = np.asarray([[1, 2], [1, 4], [1, 0], - ... [4, 2], [4, 4], [4, 0]]) - >>> kmedoids = KMedoids(n_clusters=2, random_state=0).fit(X) - >>> kmedoids.labels_ - array([0, 0, 0, 1, 1, 1]) - >>> kmedoids.predict([[0,0], [4,4]]) - array([0, 1]) - >>> kmedoids.cluster_centers_ - array([[1, 2], - [4, 2]]) - >>> kmedoids.inertia_ - 8.0 - - See scikit-learn-extra/examples/plot_kmedoids_digits.py for examples - of KMedoids with various distance metrics. - - References - ---------- - Kaufman, L. and Rousseeuw, P.J., Statistical Data Analysis Based on - the L1–Norm and Related Methods, edited by Y. Dodge, North-Holland, - 405–416. 1987 - - See also - -------- - - KMeans - The KMeans algorithm minimizes the within-cluster sum-of-squares - criterion. It scales well to large number of samples. - - Notes - ----- - Since all pairwise distances are calculated and stored in memory for - the duration of fit, the space complexity is O(n_samples ** 2). - - """ - - def __init__( - self, - n_clusters=8, - metric="euclidean", - init="heuristic", - max_iter=300, - random_state=None, - ): - self.n_clusters = n_clusters - self.metric = metric - self.init = init - self.max_iter = max_iter - self.random_state = random_state - - def _check_nonnegative_int(self, value, desc): - """Validates if value is a valid integer > 0""" - - if ( - value is None - or value <= 0 - or not isinstance(value, (int, np.integer)) - ): - raise ValueError( - "%s should be a nonnegative integer. " - "%s was given" % (desc, value) - ) - - def _check_init_args(self): - """Validates the input arguments. """ - - # Check n_clusters and max_iter - self._check_nonnegative_int(self.n_clusters, "n_clusters") - self._check_nonnegative_int(self.max_iter, "max_iter") - - # Check init - init_methods = ["random", "heuristic", "k-medoids++"] - if self.init not in init_methods: - raise ValueError( - "init needs to be one of " - + "the following: " - + "%s" % init_methods - ) - - def fit(self, X, y=None): - """Fit K-Medoids to the provided data. - - Parameters - ---------- - X : {array-like, sparse matrix}, shape = (n_samples, n_features), \ - or (n_samples, n_samples) if metric == 'precomputed' - Dataset to cluster. - - y : Ignored - - Returns - ------- - self - """ - random_state_ = check_random_state(self.random_state) - - self._check_init_args() - X = check_array(X, accept_sparse=["csr", "csc"]) - if self.n_clusters > X.shape[0]: - raise ValueError( - "The number of medoids (%d) must be less " - "than the number of samples %d." - % (self.n_clusters, X.shape[0]) - ) - - D = pairwise_distances(X, metric=self.metric) - medoid_idxs = self._initialize_medoids( - D, self.n_clusters, random_state_ - ) - labels = None - - # Continue the algorithm as long as - # the medoids keep changing and the maximum number - # of iterations is not exceeded - for self.n_iter_ in range(0, self.max_iter): - old_medoid_idxs = np.copy(medoid_idxs) - labels = np.argmin(D[medoid_idxs, :], axis=0) - - # Update medoids with the new cluster indices - self._update_medoid_idxs_in_place(D, labels, medoid_idxs) - if np.all(old_medoid_idxs == medoid_idxs): - break - elif self.n_iter_ == self.max_iter - 1: - warnings.warn( - "Maximum number of iteration reached before " - "convergence. Consider increasing max_iter to " - "improve the fit.", - ConvergenceWarning, - ) - - # Set the resulting instance variables. - if self.metric == "precomputed": - self.cluster_centers_ = None - else: - self.cluster_centers_ = X[medoid_idxs] - - # Expose labels_ which are the assignments of - # the training data to clusters - self.labels_ = labels - self.medoid_indices_ = medoid_idxs - self.inertia_ = self._compute_inertia(self.transform(X)) - - # Return self to enable method chaining - return self - - def _update_medoid_idxs_in_place(self, D, labels, medoid_idxs): - """In-place update of the medoid indices""" - - # Update the medoids for each cluster - for k in range(self.n_clusters): - # Extract the distance matrix between the data points - # inside the cluster k - cluster_k_idxs = np.where(labels == k)[0] - - if len(cluster_k_idxs) == 0: - warnings.warn( - "Cluster {k} is empty! " - "self.labels_[self.medoid_indices_[{k}]] " - "may not be labeled with " - "its corresponding cluster ({k}).".format(k=k) - ) - continue - - in_cluster_distances = D[ - cluster_k_idxs, cluster_k_idxs[:, np.newaxis] - ] - - # Calculate all costs from each point to all others in the cluster - in_cluster_all_costs = np.sum(in_cluster_distances, axis=1) - - min_cost_idx = np.argmin(in_cluster_all_costs) - min_cost = in_cluster_all_costs[min_cost_idx] - curr_cost = in_cluster_all_costs[ - np.argmax(cluster_k_idxs == medoid_idxs[k]) - ] - - # Adopt a new medoid if its distance is smaller then the current - if min_cost < curr_cost: - medoid_idxs[k] = cluster_k_idxs[min_cost_idx] - - def transform(self, X): - """Transforms X to cluster-distance space. - - Parameters - ---------- - X : {array-like, sparse matrix}, shape (n_query, n_features), \ - or (n_query, n_indexed) if metric == 'precomputed' - Data to transform. - - Returns - ------- - X_new : {array-like, sparse matrix}, shape=(n_query, n_clusters) - X transformed in the new space of distances to cluster centers. - """ - X = check_array(X, accept_sparse=["csr", "csc"]) - - if self.metric == "precomputed": - check_is_fitted(self, "medoid_indices_") - return X[:, self.medoid_indices_] - else: - check_is_fitted(self, "cluster_centers_") - - Y = self.cluster_centers_ - return pairwise_distances(X, Y=Y, metric=self.metric) - - def predict(self, X): - """Predict the closest cluster for each sample in X. - - Parameters - ---------- - X : {array-like, sparse matrix}, shape (n_query, n_features), \ - or (n_query, n_indexed) if metric == 'precomputed' - New data to predict. - - Returns - ------- - labels : array, shape = (n_query,) - Index of the cluster each sample belongs to. - """ - X = check_array(X, accept_sparse=["csr", "csc"]) - - if self.metric == "precomputed": - check_is_fitted(self, "medoid_indices_") - return np.argmin(X[:, self.medoid_indices_], axis=1) - else: - check_is_fitted(self, "cluster_centers_") - - # Return data points to clusters based on which cluster assignment - # yields the smallest distance - return pairwise_distances_argmin( - X, Y=self.cluster_centers_, metric=self.metric - ) - - def _compute_inertia(self, distances): - """Compute inertia of new samples. Inertia is defined as the sum of the - sample distances to closest cluster centers. - - Parameters - ---------- - distances : {array-like, sparse matrix}, shape=(n_samples, n_clusters) - Distances to cluster centers. - - Returns - ------- - Sum of sample distances to closest cluster centers. - """ - - # Define inertia as the sum of the sample-distances - # to closest cluster centers - inertia = np.sum(np.min(distances, axis=1)) - - return inertia - - def _initialize_medoids(self, D, n_clusters, random_state_): - """Select initial mediods when beginning clustering.""" - - if self.init == "random": # Random initialization - # Pick random k medoids as the initial ones. - medoids = random_state_.choice(len(D), n_clusters) - elif self.init == "k-medoids++": - medoids = self._kpp_init(D, n_clusters, random_state_) - elif self.init == "heuristic": # Initialization by heuristic - # Pick K first data points that have the smallest sum distance - # to every other point. These are the initial medoids. - medoids = np.argpartition(np.sum(D, axis=1), n_clusters - 1)[ - :n_clusters - ] - else: - raise ValueError( - "init value '{init}' not recognized".format(init=self.init) - ) - - return medoids - - # Copied from sklearn.cluster.k_means_._k_init - def _kpp_init(self, D, n_clusters, random_state_, n_local_trials=None): - """Init n_clusters seeds with a method similar to k-means++ - - Parameters - ----------- - D : array, shape (n_samples, n_samples) - The distance matrix we will use to select medoid indices. - - n_clusters : integer - The number of seeds to choose - - random_state : RandomState - The generator used to initialize the centers. - - n_local_trials : integer, optional - The number of seeding trials for each center (except the first), - of which the one reducing inertia the most is greedily chosen. - Set to None to make the number of trials depend logarithmically - on the number of seeds (2+log(k)); this is the default. - - Notes - ----- - Selects initial cluster centers for k-medoid clustering in a smart way - to speed up convergence. see: Arthur, D. and Vassilvitskii, S. - "k-means++: the advantages of careful seeding". ACM-SIAM symposium - on Discrete algorithms. 2007 - - Version ported from http://www.stanford.edu/~darthur/kMeansppTest.zip, - which is the implementation used in the aforementioned paper. - """ - n_samples, _ = D.shape - - centers = np.empty(n_clusters, dtype=int) - - # Set the number of local seeding trials if none is given - if n_local_trials is None: - # This is what Arthur/Vassilvitskii tried, but did not report - # specific results for other than mentioning in the conclusion - # that it helped. - n_local_trials = 2 + int(np.log(n_clusters)) - - center_id = random_state_.randint(n_samples) - centers[0] = center_id - - # Initialize list of closest distances and calculate current potential - closest_dist_sq = D[centers[0], :] ** 2 - current_pot = closest_dist_sq.sum() - - # pick the remaining n_clusters-1 points - for cluster_index in range(1, n_clusters): - rand_vals = ( - random_state_.random_sample(n_local_trials) * current_pot - ) - candidate_ids = np.searchsorted( - stable_cumsum(closest_dist_sq), rand_vals - ) - - # Compute distances to center candidates - distance_to_candidates = D[candidate_ids, :] ** 2 - - # Decide which candidate is the best - best_candidate = None - best_pot = None - best_dist_sq = None - for trial in range(n_local_trials): - # Compute potential when including center candidate - new_dist_sq = np.minimum( - closest_dist_sq, distance_to_candidates[trial] - ) - new_pot = new_dist_sq.sum() - - # Store result if it is the best local trial so far - if (best_candidate is None) or (new_pot < best_pot): - best_candidate = candidate_ids[trial] - best_pot = new_pot - best_dist_sq = new_dist_sq - - centers[cluster_index] = best_candidate - current_pot = best_pot - closest_dist_sq = best_dist_sq - - return centers diff --git a/sklearn_extra/cluster/tests/__init__.py b/sklearn_extra/cluster/tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/sklearn_extra/cluster/tests/test_k_medoids.py b/sklearn_extra/cluster/tests/test_k_medoids.py deleted file mode 100644 index 0b125f36..00000000 --- a/sklearn_extra/cluster/tests/test_k_medoids.py +++ /dev/null @@ -1,312 +0,0 @@ -"""Testing for K-Medoids""" -import warnings -import numpy as np -from unittest import mock -from scipy.sparse import csc_matrix - -from sklearn.datasets import load_iris -from sklearn.metrics.pairwise import PAIRWISE_DISTANCE_FUNCTIONS -from sklearn.metrics.pairwise import euclidean_distances -from sklearn.utils.testing import assert_array_equal, assert_equal -from sklearn.utils.testing import assert_raise_message, assert_warns_message -from sklearn.utils.testing import assert_allclose - -from sklearn_extra.cluster import KMedoids -from sklearn.cluster import KMeans - -seed = 0 -X = np.random.RandomState(seed).rand(100, 5) - - -def test_kmedoids_input_validation_and_fit_check(): - rng = np.random.RandomState(seed) - # Invalid parameters - assert_raise_message( - ValueError, - "n_clusters should be a nonnegative " "integer. 0 was given", - KMedoids(n_clusters=0).fit, - X, - ) - - assert_raise_message( - ValueError, - "n_clusters should be a nonnegative " "integer. None was given", - KMedoids(n_clusters=None).fit, - X, - ) - - assert_raise_message( - ValueError, - "max_iter should be a nonnegative " "integer. 0 was given", - KMedoids(n_clusters=1, max_iter=0).fit, - X, - ) - - assert_raise_message( - ValueError, - "max_iter should be a nonnegative " "integer. None was given", - KMedoids(n_clusters=1, max_iter=None).fit, - X, - ) - - assert_raise_message( - ValueError, - "init needs to be one of the following: " - "['random', 'heuristic', 'k-medoids++']", - KMedoids(init=None).fit, - X, - ) - - # Trying to fit 3 samples to 8 clusters - Xsmall = rng.rand(5, 2) - assert_raise_message( - ValueError, - "The number of medoids (8) must be less " - "than the number of samples 5.", - KMedoids(n_clusters=8).fit, - Xsmall, - ) - - -def test_random_deterministic(): - """Random_state should determine 'random' init output.""" - rng = np.random.RandomState(seed) - - X = load_iris()["data"] - D = euclidean_distances(X) - - medoids = KMedoids(init="random")._initialize_medoids(D, 4, rng) - assert_array_equal(medoids, [47, 117, 67, 103]) - - -def test_heuristic_deterministic(): - """Result of heuristic init method should not depend on rnadom state.""" - rng1 = np.random.RandomState(1) - rng2 = np.random.RandomState(2) - X = load_iris()["data"] - D = euclidean_distances(X) - - medoids_1 = KMedoids(init="heuristic")._initialize_medoids(D, 10, rng1) - - medoids_2 = KMedoids(init="heuristic")._initialize_medoids(D, 10, rng2) - - assert_array_equal(medoids_1, medoids_2) - - -def test_update_medoid_idxs_empty_cluster(): - """Label is unchanged for an empty cluster.""" - D = np.zeros((3, 3)) - labels = np.array([0, 0, 0]) - medoid_idxs = np.array([0, 1]) - kmedoids = KMedoids(n_clusters=2) - - # Swallow empty cluster warning - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - kmedoids._update_medoid_idxs_in_place(D, labels, medoid_idxs) - - assert_array_equal(medoid_idxs, [0, 1]) - - -def test_kmedoids_empty_clusters(): - """When a cluster is empty, it should throw a warning.""" - rng = np.random.RandomState(seed) - X = [[1], [1], [1]] - kmedoids = KMedoids(n_clusters=2, random_state=rng) - assert_warns_message(UserWarning, "Cluster 1 is empty!", kmedoids.fit, X) - - -@mock.patch.object(KMedoids, "_kpp_init", return_value=object()) -def test_kpp_called(_kpp_init_mocked): - """KMedoids._kpp_init method should be called by _initialize_medoids""" - D = np.array([[0, 1], [1, 0]]) - n_clusters = 2 - rng = np.random.RandomState(seed) - kmedoids = KMedoids() - kmedoids.init = "k-medoids++" - # set _kpp_init_mocked.return_value to a singleton - initial_medoids = kmedoids._initialize_medoids(D, n_clusters, rng) - - # assert that _kpp_init was called and its result was returned. - _kpp_init_mocked.assert_called_once_with(D, n_clusters, rng) - assert initial_medoids == _kpp_init_mocked.return_value - - -def test_kmedoids_pp(): - """Initial clusters should be well-separated for k-medoids++""" - rng = np.random.RandomState(seed) - kmedoids = KMedoids() - X = [ - [10, 0], - [11, 0], - [0, 10], - [0, 11], - [10, 10], - [11, 10], - [12, 10], - [10, 11], - ] - D = euclidean_distances(X) - - centers = kmedoids._kpp_init(D, n_clusters=3, random_state_=rng) - - assert len(centers) == 3 - - inter_medoid_distances = D[centers][:, centers] - assert np.all((inter_medoid_distances > 5) | (inter_medoid_distances == 0)) - - -def test_precomputed(): - """Test the 'precomputed' distance metric.""" - rng = np.random.RandomState(seed) - X_1 = [[1.0, 0.0], [1.1, 0.0], [0.0, 1.0], [0.0, 1.1]] - D_1 = euclidean_distances(X_1) - X_2 = [[1.1, 0.0], [0.0, 0.9]] - D_2 = euclidean_distances(X_2, X_1) - - kmedoids = KMedoids(metric="precomputed", n_clusters=2, random_state=rng) - kmedoids.fit(D_1) - - assert_allclose(kmedoids.inertia_, 0.2) - assert_array_equal(kmedoids.medoid_indices_, [2, 0]) - assert_array_equal(kmedoids.labels_, [1, 1, 0, 0]) - assert kmedoids.cluster_centers_ is None - - med_1, med_2 = tuple(kmedoids.medoid_indices_) - predictions = kmedoids.predict(D_2) - assert_array_equal(predictions, [med_1 // 2, med_2 // 2]) - - transformed = kmedoids.transform(D_2) - assert_array_equal(transformed, D_2[:, kmedoids.medoid_indices_]) - - -def test_kmedoids_fit_naive(): - n_clusters = 3 - metric = "euclidean" - - model = KMedoids(n_clusters=n_clusters, metric=metric) - Xnaive = np.asarray([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) - - model.fit(Xnaive) - - assert_array_equal( - model.cluster_centers_, [[1, 0, 0], [0, 1, 0], [0, 0, 1]] - ) - assert_array_equal(model.labels_, [0, 1, 2]) - assert model.inertia_ == 0.0 - - # diagonal must be zero, off-diagonals must be positive - X_new = model.transform(Xnaive) - for c in range(n_clusters): - assert X_new[c, c] == 0 - for c2 in range(n_clusters): - if c != c2: - assert X_new[c, c2] > 0 - - -def test_max_iter(): - """Test that warning message is thrown when max_iter is reached.""" - rng = np.random.RandomState(seed) - X_iris = load_iris()["data"] - - model = KMedoids( - n_clusters=10, init="random", random_state=rng, max_iter=1 - ) - assert_warns_message( - UserWarning, - "Maximum number of iteration reached before", - model.fit, - X_iris, - ) - - -def test_kmedoids_iris(): - """Test kmedoids on the Iris dataset""" - rng = np.random.RandomState(seed) - X_iris = load_iris()["data"] - - ref_model = KMeans(n_clusters=3).fit(X_iris) - - avg_dist_to_closest_centroid = ( - ref_model.transform(X_iris).min(axis=1).mean() - ) - - for init in ["random", "heuristic", "k-medoids++"]: - distance_metric = "euclidean" - model = KMedoids( - n_clusters=3, metric=distance_metric, init=init, random_state=rng - ) - model.fit(X_iris) - - # test convergence in reasonable number of steps - assert model.n_iter_ < (len(X_iris) // 10) - - distances = PAIRWISE_DISTANCE_FUNCTIONS[distance_metric](X_iris) - avg_dist_to_random_medoid = np.mean(distances.ravel()) - avg_dist_to_closest_medoid = model.inertia_ / X_iris.shape[0] - # We want distance-to-closest-medoid to be reduced from average - # distance by more than 50% - assert avg_dist_to_random_medoid > 2 * avg_dist_to_closest_medoid - # When K-Medoids is using Euclidean distance, - # we can compare its performance to - # K-Means. We want the average distance to cluster centers - # to be similar between K-Means and K-Medoids - assert_allclose( - avg_dist_to_closest_medoid, avg_dist_to_closest_centroid, rtol=0.1 - ) - - -def test_kmedoids_fit_predict_transform(): - rng = np.random.RandomState(seed) - model = KMedoids(random_state=rng) - - labels1 = model.fit_predict(X) - assert_equal(len(labels1), 100) - assert_array_equal(labels1, model.labels_) - - labels2 = model.predict(X) - assert_array_equal(labels1, labels2) - - Xt1 = model.fit_transform(X) - assert_array_equal(Xt1.shape, (100, model.n_clusters)) - - Xt2 = model.transform(X) - assert_array_equal(Xt1, Xt2) - - -def test_callable_distance_metric(): - rng = np.random.RandomState(seed) - - def my_metric(a, b): - return np.sqrt(np.sum(np.power(a - b, 2))) - - model = KMedoids(random_state=rng, metric=my_metric) - labels1 = model.fit_predict(X) - assert_equal(len(labels1), 100) - assert_array_equal(labels1, model.labels_) - - -def test_outlier_robustness(): - rng = np.random.RandomState(seed) - kmeans = KMeans(n_clusters=2, random_state=rng) - kmedoids = KMedoids(n_clusters=2, random_state=rng) - - X = [[-11, 0], [-10, 0], [-9, 0], [0, 0], [1, 0], [2, 0], [1000, 0]] - - kmeans.fit(X) - kmedoids.fit(X) - - assert_array_equal(kmeans.labels_, [0, 0, 0, 0, 0, 0, 1]) - assert_array_equal(kmedoids.labels_, [0, 0, 0, 1, 1, 1, 1]) - - -def test_kmedoids_on_sparse_input(): - rng = np.random.RandomState(seed) - model = KMedoids(n_clusters=2, random_state=rng) - row = np.array([1, 0]) - col = np.array([0, 4]) - data = np.array([1, 1]) - X = csc_matrix((data, (row, col)), shape=(2, 5)) - labels = model.fit_predict(X) - assert_equal(len(labels), 2) - assert_array_equal(labels, model.labels_) diff --git a/sklearn_extra/kernel_methods/_eigenpro.py b/sklearn_extra/eigenpro.py similarity index 98% rename from sklearn_extra/kernel_methods/_eigenpro.py rename to sklearn_extra/eigenpro.py index e7df64ba..6a28cd01 100644 --- a/sklearn_extra/kernel_methods/_eigenpro.py +++ b/sklearn_extra/eigenpro.py @@ -78,8 +78,7 @@ def _kernel(self, X, Y): distance = euclidean_distances(X, Y, squared=True) bandwidth = np.float32(self.bandwidth) if self.kernel == "rbf": - distance = distance / (-2.0 * bandwidth * bandwidth) - K = np.exp(distance) + K = np.exp(-distance / (2.0 * bandwidth * bandwidth)) elif self.kernel == "laplace": d = np.maximum(distance, 0) K = np.exp(-np.sqrt(d) / bandwidth) @@ -237,9 +236,8 @@ def _initialize_params(self, X, Y, random_state): n_components = min(sample_size - 1, self.n_components) n_components = max(1, n_components) - # Approximate amount of memory that we want to use - mem_bytes = 0.1 * 1024 ** 3 - # Memory used with a certain sample size + # Each batch will require about 1 gb memory + mem_bytes = 1024 ** 3 mem_usages = (d + n_label + 2 * np.arange(sample_size)) * n * 4 mG = np.int32(np.sum(mem_usages < mem_bytes)) @@ -370,7 +368,7 @@ def _raw_predict(self, X): Predicted targets. """ check_is_fitted(self, ["bs_", "centers_", "coef_", "was_1D_"]) - X = np.asarray(X, dtype=np.float32) + X = np.asarray(X, dtype=np.float64) if len(X.shape) == 1: raise ValueError( @@ -460,7 +458,7 @@ class EigenProRegressor(BaseEigenPro, RegressorMixin): Examples -------- - >>> from sklearn_extra.kernel_methods import EigenProRegressor + >>> from sklearn_extra.eigenpro import EigenProRegressor >>> import numpy as np >>> n_samples, n_features, n_targets = 4000, 20, 3 >>> rng = np.random.RandomState(1) @@ -582,7 +580,7 @@ class EigenProClassifier(BaseEigenPro, ClassifierMixin): Examples -------- - >>> from sklearn_extra.kernel_methods import EigenProClassifier + >>> from sklearn_extra.eigenpro import EigenProClassifier >>> import numpy as np >>> n_samples, n_features, n_targets = 4000, 20, 3 >>> rng = np.random.RandomState(1) diff --git a/sklearn_extra/kernel_methods/__init__.py b/sklearn_extra/kernel_methods/__init__.py deleted file mode 100644 index 53be76dc..00000000 --- a/sklearn_extra/kernel_methods/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from ._eigenpro import BaseEigenPro, EigenProClassifier, EigenProRegressor - -__all__ = ["BaseEigenPro", "EigenProClassifier", "EigenProRegressor"] diff --git a/sklearn_extra/kernel_methods/tests/__init__.py b/sklearn_extra/kernel_methods/tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/sklearn_extra/tests/test_common.py b/sklearn_extra/tests/test_common.py index 6563d42b..795e8150 100644 --- a/sklearn_extra/tests/test_common.py +++ b/sklearn_extra/tests/test_common.py @@ -3,18 +3,12 @@ from sklearn.utils.estimator_checks import check_estimator from sklearn_extra.kernel_approximation import Fastfood -from sklearn_extra.kernel_methods import _eigenpro -from sklearn_extra.cluster import KMedoids +from sklearn_extra import eigenpro @pytest.mark.parametrize( "Estimator", - [ - Fastfood, - KMedoids, - _eigenpro.EigenProClassifier, - _eigenpro.EigenProRegressor, - ], + [Fastfood, eigenpro.EigenProClassifier, eigenpro.EigenProRegressor], ) def test_all_estimators(Estimator, request): return check_estimator(Estimator) diff --git a/sklearn_extra/kernel_methods/tests/test_eigenpro.py b/sklearn_extra/tests/test_eigenpro.py similarity index 99% rename from sklearn_extra/kernel_methods/tests/test_eigenpro.py rename to sklearn_extra/tests/test_eigenpro.py index fc60c076..a85dabb6 100644 --- a/sklearn_extra/kernel_methods/tests/test_eigenpro.py +++ b/sklearn_extra/tests/test_eigenpro.py @@ -2,7 +2,7 @@ from sklearn.datasets import make_regression, make_classification from sklearn.utils.testing import assert_allclose -from sklearn_extra.kernel_methods import EigenProRegressor, EigenProClassifier +from sklearn_extra.eigenpro import EigenProRegressor, EigenProClassifier import pytest From 24cbaca564fd4b1723a5a1ebad8928ac329a4b37 Mon Sep 17 00:00:00 2001 From: Alex <7alex7li@gmail.com> Date: Thu, 1 Aug 2019 19:47:36 -0400 Subject: [PATCH 31/31] Back to current version, seems like the problem is me or the merge, not really sure how to fix it --- .gitignore | 4 + .travis.yml | 6 +- README.rst | 2 +- benchmarks/_bench/eigenpro_plot_mnist.py | 2 +- .../_bench/eigenpro_plot_noisy_mnist.py | 2 +- benchmarks/_bench/eigenpro_plot_synthetic.py | 2 +- benchmarks/bench_rbfsampler_fastfood.py | 23 +- doc/api.rst | 14 +- doc/conf.py | 207 +++++---- doc/images/eigenpro_synthetic.png | Bin 183881 -> 195613 bytes doc/install.rst | 2 +- doc/modules/eigenpro.rst | 4 +- doc/user_guide.rst | 58 ++- examples/eigenpro/plot_eigenpro_mnist.py | 5 +- examples/plot_kmedoids_digits.py | 104 +++++ setup.cfg | 1 + sklearn_extra/__init__.py | 4 +- sklearn_extra/cluster/__init__.py | 3 + sklearn_extra/cluster/_k_medoids.py | 431 ++++++++++++++++++ sklearn_extra/cluster/tests/__init__.py | 0 sklearn_extra/cluster/tests/test_k_medoids.py | 312 +++++++++++++ sklearn_extra/kernel_methods/__init__.py | 3 + .../_eigenpro.py} | 14 +- .../kernel_methods/tests/__init__.py | 0 .../tests/test_eigenpro.py | 2 +- sklearn_extra/tests/test_common.py | 10 +- 26 files changed, 1085 insertions(+), 130 deletions(-) create mode 100644 examples/plot_kmedoids_digits.py create mode 100644 sklearn_extra/cluster/__init__.py create mode 100644 sklearn_extra/cluster/_k_medoids.py create mode 100644 sklearn_extra/cluster/tests/__init__.py create mode 100644 sklearn_extra/cluster/tests/test_k_medoids.py create mode 100644 sklearn_extra/kernel_methods/__init__.py rename sklearn_extra/{eigenpro.py => kernel_methods/_eigenpro.py} (98%) create mode 100644 sklearn_extra/kernel_methods/tests/__init__.py rename sklearn_extra/{ => kernel_methods}/tests/test_eigenpro.py (99%) diff --git a/.gitignore b/.gitignore index 098d0fd7..71d1c71a 100644 --- a/.gitignore +++ b/.gitignore @@ -8,6 +8,9 @@ __pycache__/ # C extensions *.so +# Text Editors +.vscode/ + # scikit-learn specific doc/_build/ doc/auto_examples/ @@ -17,6 +20,7 @@ doc/datasets/generated/ # Distribution / packaging .Python +venv/ env/ build/ develop-eggs/ diff --git a/.travis.yml b/.travis.yml index 9ddc4b90..241445d6 100644 --- a/.travis.yml +++ b/.travis.yml @@ -10,9 +10,9 @@ cache: matrix: include: - env: PYTHON_VERSION="3.5" NUMPY_VERSION="1.13.1" SCIPY_VERSION="0.19.1" - SKLEARN_VERSION="0.19.1" + SKLEARN_VERSION="0.21.2" - env: PYTHON_VERSION="3.6" NUMPY_VERSION="1.13.1" SCIPY_VERSION="0.19.1" - SKLEARN_VERSION="0.20.2" + SKLEARN_VERSION="0.21.2" - env: PYTHON_VERSION="3.7" NUMPY_VERSION="*" SCIPY_VERSION="*" SKLEARN_VERSION="*" - env: PYTHON_VERSION="3.7" NUMPY_VERSION="*" SCIPY_VERSION="*" @@ -25,7 +25,7 @@ install: - MINICONDA_PATH=/home/travis/miniconda - chmod +x miniconda.sh && ./miniconda.sh -b -p $MINICONDA_PATH - export PATH=$MINICONDA_PATH/bin:$PATH - - conda update --yes conda + - conda install --yes conda==4.6.14 # create the testing environment - conda create -n testenv --yes python=$PYTHON_VERSION pip - source activate testenv diff --git a/README.rst b/README.rst index 20ea7fb7..25d1d12a 100644 --- a/README.rst +++ b/README.rst @@ -30,7 +30,7 @@ Dependencies scikit-learn-extra requires, - Python (>=3.5) -- scikit-learn (>=0.20), and its dependencies +- scikit-learn (>=0.21), and its dependencies - Cython (>0.28) diff --git a/benchmarks/_bench/eigenpro_plot_mnist.py b/benchmarks/_bench/eigenpro_plot_mnist.py index 553c6931..1dbe6fdd 100644 --- a/benchmarks/_bench/eigenpro_plot_mnist.py +++ b/benchmarks/_bench/eigenpro_plot_mnist.py @@ -3,7 +3,7 @@ import numpy as np from time import time -from sklearn_extra.eigenpro import EigenProClassifier +from sklearn_extra.kernel_methods import EigenProClassifier from sklearn.svm import SVC from sklearn.datasets import fetch_openml diff --git a/benchmarks/_bench/eigenpro_plot_noisy_mnist.py b/benchmarks/_bench/eigenpro_plot_noisy_mnist.py index 23c1ce5d..dd0c5abd 100644 --- a/benchmarks/_bench/eigenpro_plot_noisy_mnist.py +++ b/benchmarks/_bench/eigenpro_plot_noisy_mnist.py @@ -4,7 +4,7 @@ from time import time from sklearn.datasets import fetch_openml -from sklearn_extra.eigenpro import EigenProClassifier +from sklearn_extra.kernel_methods import EigenProClassifier from sklearn.svm import SVC rng = np.random.RandomState(1) diff --git a/benchmarks/_bench/eigenpro_plot_synthetic.py b/benchmarks/_bench/eigenpro_plot_synthetic.py index 7e137ed9..475d9a97 100644 --- a/benchmarks/_bench/eigenpro_plot_synthetic.py +++ b/benchmarks/_bench/eigenpro_plot_synthetic.py @@ -4,7 +4,7 @@ from time import time from sklearn.datasets import make_classification -from sklearn_extra.eigenpro import EigenProClassifier +from sklearn_extra.kernel_methods import EigenProClassifier from sklearn.svm import SVC rng = np.random.RandomState(1) diff --git a/benchmarks/bench_rbfsampler_fastfood.py b/benchmarks/bench_rbfsampler_fastfood.py index 42bea9b4..11f5df9b 100644 --- a/benchmarks/bench_rbfsampler_fastfood.py +++ b/benchmarks/bench_rbfsampler_fastfood.py @@ -15,9 +15,9 @@ Y /= Y.sum(axis=1)[:, np.newaxis] # calculate feature maps -gamma = 10. +gamma = 10.0 sigma = np.sqrt(1 / (2 * gamma)) -number_of_features_to_generate = 4096*4 +number_of_features_to_generate = 4096 * 4 exact_start = datetime.datetime.utcnow() # original rbf kernel method: @@ -27,23 +27,24 @@ exact_spent_time = exact_end - exact_start print("Timimg exact rbf: \t\t", exact_spent_time) -rbf_transform = Fastfood(sigma=sigma, - n_components=number_of_features_to_generate, - tradeoff_mem_accuracy='mem', - random_state=42) +rbf_transform = Fastfood( + sigma=sigma, + n_components=number_of_features_to_generate, + tradeoff_mem_accuracy="mem", + random_state=42, +) _ = rbf_transform.fit(X) fastfood_fast_vec_start = datetime.datetime.utcnow() # Fastfood: approximate kernel mapping _ = rbf_transform.transform(X) _ = rbf_transform.transform(Y) fastfood_fast_vec_end = datetime.datetime.utcnow() -fastfood_fast_vec_spent_time = fastfood_fast_vec_end - \ - fastfood_fast_vec_start +fastfood_fast_vec_spent_time = fastfood_fast_vec_end - fastfood_fast_vec_start print("Timimg fastfood fast vectorized: \t\t", fastfood_fast_vec_spent_time) -rks_rbf_transform = RBFSampler(gamma=gamma, - n_components=number_of_features_to_generate, - random_state=42) +rks_rbf_transform = RBFSampler( + gamma=gamma, n_components=number_of_features_to_generate, random_state=42 +) _ = rks_rbf_transform.fit(X) rks_start = datetime.datetime.utcnow() # Random Kitchens Sinks: approximate kernel mapping diff --git a/doc/api.rst b/doc/api.rst index 928038b3..61e1bd7f 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -27,5 +27,15 @@ EigenPro :toctree: generated/ :template: class.rst - eigenpro.EigenProRegressor - eigenpro.EigenProClassifier + kernel_methods.EigenProRegressor + kernel_methods.EigenProClassifier + +Clustering +==================== + +.. autosummary:: + :toctree: generated/ + :template: class.rst + + cluster.KMedoids + diff --git a/doc/conf.py b/doc/conf.py index eb7aadf6..c39936a0 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -21,61 +21,65 @@ # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. -#sys.path.insert(0, os.path.abspath('.')) +# sys.path.insert(0, os.path.abspath('.')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. -#needs_sphinx = '1.0' +# needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ - 'sphinx.ext.autodoc', - 'sphinx.ext.autosummary', - 'sphinx.ext.doctest', - 'sphinx.ext.intersphinx', - 'sphinx.ext.viewcode', - 'numpydoc', - 'sphinx_gallery.gen_gallery', + "sphinx.ext.autodoc", + "sphinx.ext.autosummary", + "sphinx.ext.doctest", + "sphinx.ext.intersphinx", + "sphinx.ext.viewcode", + "numpydoc", + "sphinx_gallery.gen_gallery", ] # this is needed for some reason... # see https://github.com/numpy/numpydoc/issues/69 numpydoc_show_class_members = False -# pngmath / imgmath compatibility layer for different sphinx versions -import sphinx -from distutils.version import LooseVersion -if LooseVersion(sphinx.__version__) < LooseVersion('1.4'): - extensions.append('sphinx.ext.pngmath') -else: - extensions.append('sphinx.ext.imgmath') +autodoc_default_flags = ["members", "inherited-members"] -autodoc_default_flags = ['members', 'inherited-members'] +# For maths, use mathjax by default and svg if NO_MATHJAX env variable is set +# (useful for viewing the doc offline) +if os.environ.get("NO_MATHJAX"): + extensions.append("sphinx.ext.imgmath") + imgmath_image_format = "svg" +else: + extensions.append("sphinx.ext.mathjax") + mathjax_path = ( + "https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/" + "MathJax.js?config=TeX-AMS_SVG" + ) # Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] +templates_path = ["_templates"] # generate autosummary even if no references autosummary_generate = True # The suffix of source filenames. -source_suffix = '.rst' +source_suffix = ".rst" # The encoding of source files. -#source_encoding = 'utf-8-sig' +# source_encoding = 'utf-8-sig' # Generate the plots for the gallery plot_gallery = True # The master toctree document. -master_doc = 'index' +master_doc = "index" # General information about the project. -project = u'scikit-learn-extra' -copyright = u'2019, scikit-learn-extra developpers' +project = u"scikit-learn-extra" +copyright = u"2019, scikit-learn-extra developpers" # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the @@ -83,177 +87,181 @@ # # The short X.Y version. from sklearn_extra import __version__ + version = __version__ # The full version, including alpha/beta/rc tags. release = __version__ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. -#language = None +# language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: -#today = '' +# today = '' # Else, today_fmt is used as the format for a strftime call. -#today_fmt = '%B %d, %Y' +# today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. -exclude_patterns = ['_build', '_templates'] +exclude_patterns = ["_build", "_templates"] # The reST default role (used for this markup: `text`) to use for all # documents. -#default_role = None +# default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. -#add_function_parentheses = True +# add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). -#add_module_names = True +# add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. -#show_authors = False +# show_authors = False # The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' +pygments_style = "sphinx" # Custom style -html_style = 'css/project-template.css' +html_style = "css/project-template.css" # A list of ignored prefixes for module index sorting. -#modindex_common_prefix = [] +# modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. -#keep_warnings = False +# keep_warnings = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. -html_theme = 'sphinx_rtd_theme' +html_theme = "sphinx_rtd_theme" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. -#html_theme_options = {} +# html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". -#html_title = None +# html_title = None # A shorter title for the navigation bar. Default is the same as html_title. -#html_short_title = None +# html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. -#html_logo = None +# html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. -#html_favicon = None +# html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] +html_static_path = ["_static"] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. -#html_extra_path = [] +# html_extra_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. -#html_last_updated_fmt = '%b %d, %Y' +# html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. -#html_use_smartypants = True +# html_use_smartypants = True # Custom sidebar templates, maps document names to template names. -#html_sidebars = {} +# html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. -#html_additional_pages = {} +# html_additional_pages = {} # If false, no module index is generated. -#html_domain_indices = True +# html_domain_indices = True # If false, no index is generated. -#html_use_index = True +# html_use_index = True # If true, the index is split into individual pages for each letter. -#html_split_index = False +# html_split_index = False # If true, links to the reST sources are added to the pages. -#html_show_sourcelink = True +# html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. -#html_show_sphinx = True +# html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. -#html_show_copyright = True +# html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. -#html_use_opensearch = '' +# html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). -#html_file_suffix = None +# html_file_suffix = None # Output file base name for HTML help builder. -htmlhelp_basename = 'project-templatedoc' +htmlhelp_basename = "project-templatedoc" # -- Options for LaTeX output --------------------------------------------- latex_elements = { -# The paper size ('letterpaper' or 'a4paper'). -#'papersize': 'letterpaper', - -# The font size ('10pt', '11pt' or '12pt'). -#'pointsize': '10pt', - -# Additional stuff for the LaTeX preamble. -#'preamble': '', + # The paper size ('letterpaper' or 'a4paper'). + #'papersize': 'letterpaper', + # The font size ('10pt', '11pt' or '12pt'). + #'pointsize': '10pt', + # Additional stuff for the LaTeX preamble. + #'preamble': '', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ - ('index', 'project-template.tex', u'project-template Documentation', - u'Vighnesh Birodkar', 'manual'), + ( + "index", + "project-template.tex", + u"project-template Documentation", + u"Vighnesh Birodkar", + "manual", + ) ] # The name of an image file (relative to this directory) to place at the top of # the title page. -#latex_logo = None +# latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. -#latex_use_parts = False +# latex_use_parts = False # If true, show page references after internal links. -#latex_show_pagerefs = False +# latex_show_pagerefs = False # If true, show URL addresses after external links. -#latex_show_urls = False +# latex_show_urls = False # Documents to append as an appendix to all manuals. -#latex_appendices = [] +# latex_appendices = [] # If false, no module index is generated. -#latex_domain_indices = True +# latex_domain_indices = True # -- Options for manual page output --------------------------------------- @@ -261,12 +269,17 @@ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ - ('index', 'project-template', u'project-template Documentation', - [u'Vighnesh Birodkar'], 1) + ( + "index", + "project-template", + u"project-template Documentation", + [u"Vighnesh Birodkar"], + 1, + ) ] # If true, show URL addresses after external links. -#man_show_urls = False +# man_show_urls = False # -- Options for Texinfo output ------------------------------------------- @@ -275,43 +288,51 @@ # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ - ('index', 'project-template', u'project-template Documentation', - u'Vighnesh Birodkar', 'project-template', 'One line description of project.', - 'Miscellaneous'), + ( + "index", + "project-template", + u"project-template Documentation", + u"Vighnesh Birodkar", + "project-template", + "One line description of project.", + "Miscellaneous", + ) ] # Documents to append as an appendix to all manuals. -#texinfo_appendices = [] +# texinfo_appendices = [] # If false, no module index is generated. -#texinfo_domain_indices = True +# texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. -#texinfo_show_urls = 'footnote' +# texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. -#texinfo_no_detailmenu = False +# texinfo_no_detailmenu = False # Example configuration for intersphinx: refer to the Python standard library. # intersphinx configuration intersphinx_mapping = { - 'python': ('https://docs.python.org/{.major}'.format( - sys.version_info), None), - 'numpy': ('https://docs.scipy.org/doc/numpy/', None), - 'scipy': ('https://docs.scipy.org/doc/scipy/reference', None), - 'matplotlib': ('https://matplotlib.org/', None), - 'sklearn': ('http://scikit-learn.org/stable', None) + "python": ( + "https://docs.python.org/{.major}".format(sys.version_info), + None, + ), + "numpy": ("https://docs.scipy.org/doc/numpy/", None), + "scipy": ("https://docs.scipy.org/doc/scipy/reference", None), + "matplotlib": ("https://matplotlib.org/", None), + "sklearn": ("http://scikit-learn.org/stable", None), } # sphinx-gallery configuration sphinx_gallery_conf = { - 'doc_module': 'sklearn_extra', - 'backreferences_dir': os.path.join('generated'), - 'reference_url': { - 'sklearn_extra': None} + "doc_module": "sklearn_extra", + "backreferences_dir": os.path.join("generated"), + "reference_url": {"sklearn_extra": None}, } + def setup(app): # a copy button to copy snippet of code from the documentation - app.add_javascript('js/copybutton.js') + app.add_javascript("js/copybutton.js") diff --git a/doc/images/eigenpro_synthetic.png b/doc/images/eigenpro_synthetic.png index 0059682db7187108581448c340d4e5542b74a632..b0be911c2d6cf107846b72ca62231f4925699fb8 100644 GIT binary patch literal 195613 zcmeFZXH=7G*EJeNKqY{HQlxDK1*L`+3J1XN>dveD@e`hi*yktb1K+%{Av-iN39)#d!SmaR>y$ 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results in a simple and user-friendly interface. Next, we present several experimental results using a server equipped with one Intel Xeon E5-1620 CPU. -The figure below compares the Fast Kernel Classifier (EigenPro) and the Support Vector +The figure below compares the EigenPro Classifier and the Support Vector Classifier (:class:`SVC`) on MNIST digits classification task. We see that EigenPro and SVC give competitive and similar accuracy on test set. Notably, on the full MNIST training and testing using EigenPro are diff --git a/doc/user_guide.rst b/doc/user_guide.rst index 72598c27..0224d330 100644 --- a/doc/user_guide.rst +++ b/doc/user_guide.rst @@ -11,4 +11,60 @@ User guide ========== -To add. +.. _k_medoids: + +K-Medoids +========= + +:class:`KMedoids` is related to the :class:`KMeans` algorithm. While +:class:`KMeans` tries to minimize the within cluster sum-of-squares, +:class:`KMedoids` tries to minimize the sum of distances between each point and +the medoid of its cluster. The medoid is a data point (unlike the centroid) +which has least total distance to the other members of its cluster. The use of +a data point to represent each cluster's center allows the use of any distance +metric for clustering. + +:class:`KMedoids` can be more robust to noise and outliers than :class:`KMeans` +as it will choose one of the cluster members as the medoid while +:class:`KMeans` will move the center of the cluster towards the outlier which +might in turn move other points away from the cluster centre. + +:class:`KMedoids` is also different from K-Medians, which is analogous to :class:`KMeans` +except that the Manhattan Median is used for each cluster center instead of +the centroid. K-Medians is robust to outliers, but it is limited to the +Manhattan Distance metric and, similar to :class:`KMeans`, it does not guarantee +that the center of each cluster will be a member of the original dataset. + +The complexity of K-Medoids is :math:`O(N^2 K T)` where :math:`N` is the number +of samples, :math:`T` is the number of iterations and :math:`K` is the number of +clusters. This makes it more suitable for smaller datasets in comparison to +:class:`KMeans` which is :math:`O(N K T)`. + +.. topic:: Examples: + + * :ref:`sphx_glr_auto_examples_plot_kmedoids_digits.py`: Applying K-Medoids on digits + with various distance metrics. + + +**Algorithm description:** +There are several algorithms to compute K-Medoids, though :class:`KMedoids` +currently only supports Partitioning Around Medoids (PAM). The PAM algorithm +uses a greedy search, which may fail to find the global optimum. It consists of +two alternating steps commonly called the +Assignment and Update steps (BUILD and SWAP in Kaufmann and Rousseeuw, 1987). + +PAM works as follows: + +* Initialize: Select ``n_clusters`` from the dataset as the medoids using + a heuristic, random, or k-medoids++ approach (configurable using the ``init`` parameter). +* Assignment step: assign each element from the dataset to the closest medoid. +* Update step: Identify the new medoid of each cluster. +* Repeat the assignment and update step while the medoids keep changing or + maximum number of iterations ``max_iter`` is reached. + +.. topic:: References: + + * "Clustering by Means of Medoids'" + Kaufman, L. and Rousseeuw, P.J., + Statistical Data Analysis Based on the L1Norm and Related Methods, edited + by Y. Dodge, North-Holland, 405416. 1987 \ No newline at end of file diff --git a/examples/eigenpro/plot_eigenpro_mnist.py b/examples/eigenpro/plot_eigenpro_mnist.py index 768e0a1f..c3ab9e10 100644 --- a/examples/eigenpro/plot_eigenpro_mnist.py +++ b/examples/eigenpro/plot_eigenpro_mnist.py @@ -17,7 +17,7 @@ import numpy as np from time import time -from sklearn_extra.eigenpro import EigenProClassifier +from sklearn_extra.kernel_methods import EigenProClassifier from sklearn.svm import SVC from sklearn.datasets import fetch_openml @@ -43,6 +43,7 @@ svc_err = [] train_sizes = [500, 1000, 2000] + print("Train Sizes: " + str(train_sizes)) bandwidth = 5.0 @@ -126,6 +127,6 @@ ax.set_xticklabels(train_size_labels) ax.set_xticks([], minor=True) ax.set_xlabel("train size") -ax.set_ylabel("classification error %") +ax.set_ylabel("Classification error %") plt.tight_layout() plt.show() diff --git a/examples/plot_kmedoids_digits.py b/examples/plot_kmedoids_digits.py new file mode 100644 index 00000000..28c7659d --- /dev/null +++ b/examples/plot_kmedoids_digits.py @@ -0,0 +1,104 @@ +# -*- coding: utf-8 -*- +""" +============================================================= +A demo of K-Medoids clustering on the handwritten digits data +============================================================= +In this example we compare different pairwise distance +metrics for K-Medoids. +""" +import numpy as np +import matplotlib.pyplot as plt + +from sklearn.cluster import KMeans +from sklearn_extra.cluster import KMedoids +from sklearn.datasets import load_digits +from sklearn.decomposition import PCA +from sklearn.preprocessing import scale + +print(__doc__) + +# Authors: Timo Erkkilä +# Antti Lehmussola +# Kornel Kiełczewski +# License: BSD 3 clause + +np.random.seed(42) + +digits = load_digits() +data = scale(digits.data) +n_digits = len(np.unique(digits.target)) + +reduced_data = PCA(n_components=2).fit_transform(data) + +# Step size of the mesh. Decrease to increase the quality of the VQ. +h = 0.02 # point in the mesh [x_min, m_max]x[y_min, y_max]. + +# Plot the decision boundary. For that, we will assign a color to each +x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1 +y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1 +xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) + +plt.figure() +plt.clf() + +plt.suptitle( + "Comparing multiple K-Medoids metrics to K-Means and each other", + fontsize=14, +) + + +selected_models = [ + ( + KMedoids(metric="manhattan", n_clusters=n_digits), + "KMedoids (manhattan)", + ), + ( + KMedoids(metric="euclidean", n_clusters=n_digits), + "KMedoids (euclidean)", + ), + (KMedoids(metric="cosine", n_clusters=n_digits), "KMedoids (cosine)"), + (KMeans(n_clusters=n_digits), "KMeans"), +] + +plot_rows = int(np.ceil(len(selected_models) / 2.0)) +plot_cols = 2 + +for i, (model, description) in enumerate(selected_models): + + # Obtain labels for each point in mesh. Use last trained model. + model.fit(reduced_data) + Z = model.predict(np.c_[xx.ravel(), yy.ravel()]) + + # Put the result into a color plot + Z = Z.reshape(xx.shape) + plt.subplot(plot_cols, plot_rows, i + 1) + plt.imshow( + Z, + interpolation="nearest", + extent=(xx.min(), xx.max(), yy.min(), yy.max()), + cmap=plt.cm.Paired, + aspect="auto", + origin="lower", + ) + + plt.plot( + reduced_data[:, 0], reduced_data[:, 1], "k.", markersize=2, alpha=0.3 + ) + # Plot the centroids as a white X + centroids = model.cluster_centers_ + plt.scatter( + centroids[:, 0], + centroids[:, 1], + marker="x", + s=169, + linewidths=3, + color="w", + zorder=10, + ) + plt.title(description) + plt.xlim(x_min, x_max) + plt.ylim(y_min, y_max) + plt.xticks(()) + plt.yticks(()) + +plt.show() diff --git a/setup.cfg b/setup.cfg index a199e77a..8c7675f8 100644 --- a/setup.cfg +++ b/setup.cfg @@ -5,4 +5,5 @@ description-file = README.rst test = pytest [tool:pytest] +doctest_optionflags = NORMALIZE_WHITESPACE ELLIPSIS addopts = --doctest-modules diff --git a/sklearn_extra/__init__.py b/sklearn_extra/__init__.py index e1162fdb..b855d4eb 100644 --- a/sklearn_extra/__init__.py +++ b/sklearn_extra/__init__.py @@ -1,5 +1,5 @@ -from . import kernel_approximation # noqa +from . import kernel_approximation, kernel_methods # noqa from ._version import __version__ -__all__ = ["__version__", "eigenpro"] +__all__ = ["__version__"] diff --git a/sklearn_extra/cluster/__init__.py b/sklearn_extra/cluster/__init__.py new file mode 100644 index 00000000..bbdaaf41 --- /dev/null +++ b/sklearn_extra/cluster/__init__.py @@ -0,0 +1,3 @@ +from ._k_medoids import KMedoids + +__all__ = ["KMedoids"] diff --git a/sklearn_extra/cluster/_k_medoids.py b/sklearn_extra/cluster/_k_medoids.py new file mode 100644 index 00000000..298195d9 --- /dev/null +++ b/sklearn_extra/cluster/_k_medoids.py @@ -0,0 +1,431 @@ +# -*- coding: utf-8 -*- +"""K-medoids clustering""" + +# Authors: Timo Erkkilä +# Antti Lehmussola +# Kornel Kiełczewski +# Zane Dufour +# License: BSD 3 clause + +import warnings + +import numpy as np + +from sklearn.base import BaseEstimator, ClusterMixin, TransformerMixin +from sklearn.metrics.pairwise import ( + pairwise_distances, + pairwise_distances_argmin, +) +from sklearn.utils import check_array, check_random_state +from sklearn.utils.extmath import stable_cumsum +from sklearn.utils.validation import check_is_fitted +from sklearn.exceptions import ConvergenceWarning + + +class KMedoids(BaseEstimator, ClusterMixin, TransformerMixin): + """k-medoids clustering. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + n_clusters : int, optional, default: 8 + The number of clusters to form as well as the number of medoids to + generate. + + metric : string, or callable, optional, default: 'euclidean' + What distance metric to use. See :func:metrics.pairwise_distances + + init : {'random', 'heuristic', 'k-medoids++'}, optional, default: 'heuristic' + Specify medoid initialization method. 'random' selects n_clusters + elements from the dataset. 'heuristic' picks the n_clusters points + with the smallest sum distance to every other point. 'k-medoids++' + follows an approach based on k-means++_, and in general, gives initial + medoids which are more separated than those generated by the other methods. + + .. _k-means++: https://theory.stanford.edu/~sergei/papers/kMeansPP-soda.pdf + + max_iter : int, optional, default : 300 + Specify the maximum number of iterations when fitting. + + random_state : int, RandomState instance or None, optional + Specify random state for the random number generator. Used to + initialise medoids when init='random'. + + Attributes + ---------- + cluster_centers_ : array, shape = (n_clusters, n_features) + or None if metric == 'precomputed' + Cluster centers, i.e. medoids (elements from the original dataset) + + medoid_indices_ : array, shape = (n_clusters,) + The indices of the medoid rows in X + + labels_ : array, shape = (n_samples,) + Labels of each point + + inertia_ : float + Sum of distances of samples to their closest cluster center. + + Examples + -------- + >>> from sklearn_extra.cluster import KMedoids + >>> import numpy as np + + >>> X = np.asarray([[1, 2], [1, 4], [1, 0], + ... [4, 2], [4, 4], [4, 0]]) + >>> kmedoids = KMedoids(n_clusters=2, random_state=0).fit(X) + >>> kmedoids.labels_ + array([0, 0, 0, 1, 1, 1]) + >>> kmedoids.predict([[0,0], [4,4]]) + array([0, 1]) + >>> kmedoids.cluster_centers_ + array([[1, 2], + [4, 2]]) + >>> kmedoids.inertia_ + 8.0 + + See scikit-learn-extra/examples/plot_kmedoids_digits.py for examples + of KMedoids with various distance metrics. + + References + ---------- + Kaufman, L. and Rousseeuw, P.J., Statistical Data Analysis Based on + the L1–Norm and Related Methods, edited by Y. Dodge, North-Holland, + 405–416. 1987 + + See also + -------- + + KMeans + The KMeans algorithm minimizes the within-cluster sum-of-squares + criterion. It scales well to large number of samples. + + Notes + ----- + Since all pairwise distances are calculated and stored in memory for + the duration of fit, the space complexity is O(n_samples ** 2). + + """ + + def __init__( + self, + n_clusters=8, + metric="euclidean", + init="heuristic", + max_iter=300, + random_state=None, + ): + self.n_clusters = n_clusters + self.metric = metric + self.init = init + self.max_iter = max_iter + self.random_state = random_state + + def _check_nonnegative_int(self, value, desc): + """Validates if value is a valid integer > 0""" + + if ( + value is None + or value <= 0 + or not isinstance(value, (int, np.integer)) + ): + raise ValueError( + "%s should be a nonnegative integer. " + "%s was given" % (desc, value) + ) + + def _check_init_args(self): + """Validates the input arguments. """ + + # Check n_clusters and max_iter + self._check_nonnegative_int(self.n_clusters, "n_clusters") + self._check_nonnegative_int(self.max_iter, "max_iter") + + # Check init + init_methods = ["random", "heuristic", "k-medoids++"] + if self.init not in init_methods: + raise ValueError( + "init needs to be one of " + + "the following: " + + "%s" % init_methods + ) + + def fit(self, X, y=None): + """Fit K-Medoids to the provided data. + + Parameters + ---------- + X : {array-like, sparse matrix}, shape = (n_samples, n_features), \ + or (n_samples, n_samples) if metric == 'precomputed' + Dataset to cluster. + + y : Ignored + + Returns + ------- + self + """ + random_state_ = check_random_state(self.random_state) + + self._check_init_args() + X = check_array(X, accept_sparse=["csr", "csc"]) + if self.n_clusters > X.shape[0]: + raise ValueError( + "The number of medoids (%d) must be less " + "than the number of samples %d." + % (self.n_clusters, X.shape[0]) + ) + + D = pairwise_distances(X, metric=self.metric) + medoid_idxs = self._initialize_medoids( + D, self.n_clusters, random_state_ + ) + labels = None + + # Continue the algorithm as long as + # the medoids keep changing and the maximum number + # of iterations is not exceeded + for self.n_iter_ in range(0, self.max_iter): + old_medoid_idxs = np.copy(medoid_idxs) + labels = np.argmin(D[medoid_idxs, :], axis=0) + + # Update medoids with the new cluster indices + self._update_medoid_idxs_in_place(D, labels, medoid_idxs) + if np.all(old_medoid_idxs == medoid_idxs): + break + elif self.n_iter_ == self.max_iter - 1: + warnings.warn( + "Maximum number of iteration reached before " + "convergence. Consider increasing max_iter to " + "improve the fit.", + ConvergenceWarning, + ) + + # Set the resulting instance variables. + if self.metric == "precomputed": + self.cluster_centers_ = None + else: + self.cluster_centers_ = X[medoid_idxs] + + # Expose labels_ which are the assignments of + # the training data to clusters + self.labels_ = labels + self.medoid_indices_ = medoid_idxs + self.inertia_ = self._compute_inertia(self.transform(X)) + + # Return self to enable method chaining + return self + + def _update_medoid_idxs_in_place(self, D, labels, medoid_idxs): + """In-place update of the medoid indices""" + + # Update the medoids for each cluster + for k in range(self.n_clusters): + # Extract the distance matrix between the data points + # inside the cluster k + cluster_k_idxs = np.where(labels == k)[0] + + if len(cluster_k_idxs) == 0: + warnings.warn( + "Cluster {k} is empty! " + "self.labels_[self.medoid_indices_[{k}]] " + "may not be labeled with " + "its corresponding cluster ({k}).".format(k=k) + ) + continue + + in_cluster_distances = D[ + cluster_k_idxs, cluster_k_idxs[:, np.newaxis] + ] + + # Calculate all costs from each point to all others in the cluster + in_cluster_all_costs = np.sum(in_cluster_distances, axis=1) + + min_cost_idx = np.argmin(in_cluster_all_costs) + min_cost = in_cluster_all_costs[min_cost_idx] + curr_cost = in_cluster_all_costs[ + np.argmax(cluster_k_idxs == medoid_idxs[k]) + ] + + # Adopt a new medoid if its distance is smaller then the current + if min_cost < curr_cost: + medoid_idxs[k] = cluster_k_idxs[min_cost_idx] + + def transform(self, X): + """Transforms X to cluster-distance space. + + Parameters + ---------- + X : {array-like, sparse matrix}, shape (n_query, n_features), \ + or (n_query, n_indexed) if metric == 'precomputed' + Data to transform. + + Returns + ------- + X_new : {array-like, sparse matrix}, shape=(n_query, n_clusters) + X transformed in the new space of distances to cluster centers. + """ + X = check_array(X, accept_sparse=["csr", "csc"]) + + if self.metric == "precomputed": + check_is_fitted(self, "medoid_indices_") + return X[:, self.medoid_indices_] + else: + check_is_fitted(self, "cluster_centers_") + + Y = self.cluster_centers_ + return pairwise_distances(X, Y=Y, metric=self.metric) + + def predict(self, X): + """Predict the closest cluster for each sample in X. + + Parameters + ---------- + X : {array-like, sparse matrix}, shape (n_query, n_features), \ + or (n_query, n_indexed) if metric == 'precomputed' + New data to predict. + + Returns + ------- + labels : array, shape = (n_query,) + Index of the cluster each sample belongs to. + """ + X = check_array(X, accept_sparse=["csr", "csc"]) + + if self.metric == "precomputed": + check_is_fitted(self, "medoid_indices_") + return np.argmin(X[:, self.medoid_indices_], axis=1) + else: + check_is_fitted(self, "cluster_centers_") + + # Return data points to clusters based on which cluster assignment + # yields the smallest distance + return pairwise_distances_argmin( + X, Y=self.cluster_centers_, metric=self.metric + ) + + def _compute_inertia(self, distances): + """Compute inertia of new samples. Inertia is defined as the sum of the + sample distances to closest cluster centers. + + Parameters + ---------- + distances : {array-like, sparse matrix}, shape=(n_samples, n_clusters) + Distances to cluster centers. + + Returns + ------- + Sum of sample distances to closest cluster centers. + """ + + # Define inertia as the sum of the sample-distances + # to closest cluster centers + inertia = np.sum(np.min(distances, axis=1)) + + return inertia + + def _initialize_medoids(self, D, n_clusters, random_state_): + """Select initial mediods when beginning clustering.""" + + if self.init == "random": # Random initialization + # Pick random k medoids as the initial ones. + medoids = random_state_.choice(len(D), n_clusters) + elif self.init == "k-medoids++": + medoids = self._kpp_init(D, n_clusters, random_state_) + elif self.init == "heuristic": # Initialization by heuristic + # Pick K first data points that have the smallest sum distance + # to every other point. These are the initial medoids. + medoids = np.argpartition(np.sum(D, axis=1), n_clusters - 1)[ + :n_clusters + ] + else: + raise ValueError( + "init value '{init}' not recognized".format(init=self.init) + ) + + return medoids + + # Copied from sklearn.cluster.k_means_._k_init + def _kpp_init(self, D, n_clusters, random_state_, n_local_trials=None): + """Init n_clusters seeds with a method similar to k-means++ + + Parameters + ----------- + D : array, shape (n_samples, n_samples) + The distance matrix we will use to select medoid indices. + + n_clusters : integer + The number of seeds to choose + + random_state : RandomState + The generator used to initialize the centers. + + n_local_trials : integer, optional + The number of seeding trials for each center (except the first), + of which the one reducing inertia the most is greedily chosen. + Set to None to make the number of trials depend logarithmically + on the number of seeds (2+log(k)); this is the default. + + Notes + ----- + Selects initial cluster centers for k-medoid clustering in a smart way + to speed up convergence. see: Arthur, D. and Vassilvitskii, S. + "k-means++: the advantages of careful seeding". ACM-SIAM symposium + on Discrete algorithms. 2007 + + Version ported from http://www.stanford.edu/~darthur/kMeansppTest.zip, + which is the implementation used in the aforementioned paper. + """ + n_samples, _ = D.shape + + centers = np.empty(n_clusters, dtype=int) + + # Set the number of local seeding trials if none is given + if n_local_trials is None: + # This is what Arthur/Vassilvitskii tried, but did not report + # specific results for other than mentioning in the conclusion + # that it helped. + n_local_trials = 2 + int(np.log(n_clusters)) + + center_id = random_state_.randint(n_samples) + centers[0] = center_id + + # Initialize list of closest distances and calculate current potential + closest_dist_sq = D[centers[0], :] ** 2 + current_pot = closest_dist_sq.sum() + + # pick the remaining n_clusters-1 points + for cluster_index in range(1, n_clusters): + rand_vals = ( + random_state_.random_sample(n_local_trials) * current_pot + ) + candidate_ids = np.searchsorted( + stable_cumsum(closest_dist_sq), rand_vals + ) + + # Compute distances to center candidates + distance_to_candidates = D[candidate_ids, :] ** 2 + + # Decide which candidate is the best + best_candidate = None + best_pot = None + best_dist_sq = None + for trial in range(n_local_trials): + # Compute potential when including center candidate + new_dist_sq = np.minimum( + closest_dist_sq, distance_to_candidates[trial] + ) + new_pot = new_dist_sq.sum() + + # Store result if it is the best local trial so far + if (best_candidate is None) or (new_pot < best_pot): + best_candidate = candidate_ids[trial] + best_pot = new_pot + best_dist_sq = new_dist_sq + + centers[cluster_index] = best_candidate + current_pot = best_pot + closest_dist_sq = best_dist_sq + + return centers diff --git a/sklearn_extra/cluster/tests/__init__.py b/sklearn_extra/cluster/tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/sklearn_extra/cluster/tests/test_k_medoids.py b/sklearn_extra/cluster/tests/test_k_medoids.py new file mode 100644 index 00000000..0b125f36 --- /dev/null +++ b/sklearn_extra/cluster/tests/test_k_medoids.py @@ -0,0 +1,312 @@ +"""Testing for K-Medoids""" +import warnings +import numpy as np +from unittest import mock +from scipy.sparse import csc_matrix + +from sklearn.datasets import load_iris +from sklearn.metrics.pairwise import PAIRWISE_DISTANCE_FUNCTIONS +from sklearn.metrics.pairwise import euclidean_distances +from sklearn.utils.testing import assert_array_equal, assert_equal +from sklearn.utils.testing import assert_raise_message, assert_warns_message +from sklearn.utils.testing import assert_allclose + +from sklearn_extra.cluster import KMedoids +from sklearn.cluster import KMeans + +seed = 0 +X = np.random.RandomState(seed).rand(100, 5) + + +def test_kmedoids_input_validation_and_fit_check(): + rng = np.random.RandomState(seed) + # Invalid parameters + assert_raise_message( + ValueError, + "n_clusters should be a nonnegative " "integer. 0 was given", + KMedoids(n_clusters=0).fit, + X, + ) + + assert_raise_message( + ValueError, + "n_clusters should be a nonnegative " "integer. None was given", + KMedoids(n_clusters=None).fit, + X, + ) + + assert_raise_message( + ValueError, + "max_iter should be a nonnegative " "integer. 0 was given", + KMedoids(n_clusters=1, max_iter=0).fit, + X, + ) + + assert_raise_message( + ValueError, + "max_iter should be a nonnegative " "integer. None was given", + KMedoids(n_clusters=1, max_iter=None).fit, + X, + ) + + assert_raise_message( + ValueError, + "init needs to be one of the following: " + "['random', 'heuristic', 'k-medoids++']", + KMedoids(init=None).fit, + X, + ) + + # Trying to fit 3 samples to 8 clusters + Xsmall = rng.rand(5, 2) + assert_raise_message( + ValueError, + "The number of medoids (8) must be less " + "than the number of samples 5.", + KMedoids(n_clusters=8).fit, + Xsmall, + ) + + +def test_random_deterministic(): + """Random_state should determine 'random' init output.""" + rng = np.random.RandomState(seed) + + X = load_iris()["data"] + D = euclidean_distances(X) + + medoids = KMedoids(init="random")._initialize_medoids(D, 4, rng) + assert_array_equal(medoids, [47, 117, 67, 103]) + + +def test_heuristic_deterministic(): + """Result of heuristic init method should not depend on rnadom state.""" + rng1 = np.random.RandomState(1) + rng2 = np.random.RandomState(2) + X = load_iris()["data"] + D = euclidean_distances(X) + + medoids_1 = KMedoids(init="heuristic")._initialize_medoids(D, 10, rng1) + + medoids_2 = KMedoids(init="heuristic")._initialize_medoids(D, 10, rng2) + + assert_array_equal(medoids_1, medoids_2) + + +def test_update_medoid_idxs_empty_cluster(): + """Label is unchanged for an empty cluster.""" + D = np.zeros((3, 3)) + labels = np.array([0, 0, 0]) + medoid_idxs = np.array([0, 1]) + kmedoids = KMedoids(n_clusters=2) + + # Swallow empty cluster warning + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + kmedoids._update_medoid_idxs_in_place(D, labels, medoid_idxs) + + assert_array_equal(medoid_idxs, [0, 1]) + + +def test_kmedoids_empty_clusters(): + """When a cluster is empty, it should throw a warning.""" + rng = np.random.RandomState(seed) + X = [[1], [1], [1]] + kmedoids = KMedoids(n_clusters=2, random_state=rng) + assert_warns_message(UserWarning, "Cluster 1 is empty!", kmedoids.fit, X) + + +@mock.patch.object(KMedoids, "_kpp_init", return_value=object()) +def test_kpp_called(_kpp_init_mocked): + """KMedoids._kpp_init method should be called by _initialize_medoids""" + D = np.array([[0, 1], [1, 0]]) + n_clusters = 2 + rng = np.random.RandomState(seed) + kmedoids = KMedoids() + kmedoids.init = "k-medoids++" + # set _kpp_init_mocked.return_value to a singleton + initial_medoids = kmedoids._initialize_medoids(D, n_clusters, rng) + + # assert that _kpp_init was called and its result was returned. + _kpp_init_mocked.assert_called_once_with(D, n_clusters, rng) + assert initial_medoids == _kpp_init_mocked.return_value + + +def test_kmedoids_pp(): + """Initial clusters should be well-separated for k-medoids++""" + rng = np.random.RandomState(seed) + kmedoids = KMedoids() + X = [ + [10, 0], + [11, 0], + [0, 10], + [0, 11], + [10, 10], + [11, 10], + [12, 10], + [10, 11], + ] + D = euclidean_distances(X) + + centers = kmedoids._kpp_init(D, n_clusters=3, random_state_=rng) + + assert len(centers) == 3 + + inter_medoid_distances = D[centers][:, centers] + assert np.all((inter_medoid_distances > 5) | (inter_medoid_distances == 0)) + + +def test_precomputed(): + """Test the 'precomputed' distance metric.""" + rng = np.random.RandomState(seed) + X_1 = [[1.0, 0.0], [1.1, 0.0], [0.0, 1.0], [0.0, 1.1]] + D_1 = euclidean_distances(X_1) + X_2 = [[1.1, 0.0], [0.0, 0.9]] + D_2 = euclidean_distances(X_2, X_1) + + kmedoids = KMedoids(metric="precomputed", n_clusters=2, random_state=rng) + kmedoids.fit(D_1) + + assert_allclose(kmedoids.inertia_, 0.2) + assert_array_equal(kmedoids.medoid_indices_, [2, 0]) + assert_array_equal(kmedoids.labels_, [1, 1, 0, 0]) + assert kmedoids.cluster_centers_ is None + + med_1, med_2 = tuple(kmedoids.medoid_indices_) + predictions = kmedoids.predict(D_2) + assert_array_equal(predictions, [med_1 // 2, med_2 // 2]) + + transformed = kmedoids.transform(D_2) + assert_array_equal(transformed, D_2[:, kmedoids.medoid_indices_]) + + +def test_kmedoids_fit_naive(): + n_clusters = 3 + metric = "euclidean" + + model = KMedoids(n_clusters=n_clusters, metric=metric) + Xnaive = np.asarray([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) + + model.fit(Xnaive) + + assert_array_equal( + model.cluster_centers_, [[1, 0, 0], [0, 1, 0], [0, 0, 1]] + ) + assert_array_equal(model.labels_, [0, 1, 2]) + assert model.inertia_ == 0.0 + + # diagonal must be zero, off-diagonals must be positive + X_new = model.transform(Xnaive) + for c in range(n_clusters): + assert X_new[c, c] == 0 + for c2 in range(n_clusters): + if c != c2: + assert X_new[c, c2] > 0 + + +def test_max_iter(): + """Test that warning message is thrown when max_iter is reached.""" + rng = np.random.RandomState(seed) + X_iris = load_iris()["data"] + + model = KMedoids( + n_clusters=10, init="random", random_state=rng, max_iter=1 + ) + assert_warns_message( + UserWarning, + "Maximum number of iteration reached before", + model.fit, + X_iris, + ) + + +def test_kmedoids_iris(): + """Test kmedoids on the Iris dataset""" + rng = np.random.RandomState(seed) + X_iris = load_iris()["data"] + + ref_model = KMeans(n_clusters=3).fit(X_iris) + + avg_dist_to_closest_centroid = ( + ref_model.transform(X_iris).min(axis=1).mean() + ) + + for init in ["random", "heuristic", "k-medoids++"]: + distance_metric = "euclidean" + model = KMedoids( + n_clusters=3, metric=distance_metric, init=init, random_state=rng + ) + model.fit(X_iris) + + # test convergence in reasonable number of steps + assert model.n_iter_ < (len(X_iris) // 10) + + distances = PAIRWISE_DISTANCE_FUNCTIONS[distance_metric](X_iris) + avg_dist_to_random_medoid = np.mean(distances.ravel()) + avg_dist_to_closest_medoid = model.inertia_ / X_iris.shape[0] + # We want distance-to-closest-medoid to be reduced from average + # distance by more than 50% + assert avg_dist_to_random_medoid > 2 * avg_dist_to_closest_medoid + # When K-Medoids is using Euclidean distance, + # we can compare its performance to + # K-Means. We want the average distance to cluster centers + # to be similar between K-Means and K-Medoids + assert_allclose( + avg_dist_to_closest_medoid, avg_dist_to_closest_centroid, rtol=0.1 + ) + + +def test_kmedoids_fit_predict_transform(): + rng = np.random.RandomState(seed) + model = KMedoids(random_state=rng) + + labels1 = model.fit_predict(X) + assert_equal(len(labels1), 100) + assert_array_equal(labels1, model.labels_) + + labels2 = model.predict(X) + assert_array_equal(labels1, labels2) + + Xt1 = model.fit_transform(X) + assert_array_equal(Xt1.shape, (100, model.n_clusters)) + + Xt2 = model.transform(X) + assert_array_equal(Xt1, Xt2) + + +def test_callable_distance_metric(): + rng = np.random.RandomState(seed) + + def my_metric(a, b): + return np.sqrt(np.sum(np.power(a - b, 2))) + + model = KMedoids(random_state=rng, metric=my_metric) + labels1 = model.fit_predict(X) + assert_equal(len(labels1), 100) + assert_array_equal(labels1, model.labels_) + + +def test_outlier_robustness(): + rng = np.random.RandomState(seed) + kmeans = KMeans(n_clusters=2, random_state=rng) + kmedoids = KMedoids(n_clusters=2, random_state=rng) + + X = [[-11, 0], [-10, 0], [-9, 0], [0, 0], [1, 0], [2, 0], [1000, 0]] + + kmeans.fit(X) + kmedoids.fit(X) + + assert_array_equal(kmeans.labels_, [0, 0, 0, 0, 0, 0, 1]) + assert_array_equal(kmedoids.labels_, [0, 0, 0, 1, 1, 1, 1]) + + +def test_kmedoids_on_sparse_input(): + rng = np.random.RandomState(seed) + model = KMedoids(n_clusters=2, random_state=rng) + row = np.array([1, 0]) + col = np.array([0, 4]) + data = np.array([1, 1]) + X = csc_matrix((data, (row, col)), shape=(2, 5)) + labels = model.fit_predict(X) + assert_equal(len(labels), 2) + assert_array_equal(labels, model.labels_) diff --git a/sklearn_extra/kernel_methods/__init__.py b/sklearn_extra/kernel_methods/__init__.py new file mode 100644 index 00000000..53be76dc --- /dev/null +++ b/sklearn_extra/kernel_methods/__init__.py @@ -0,0 +1,3 @@ +from ._eigenpro import BaseEigenPro, EigenProClassifier, EigenProRegressor + +__all__ = ["BaseEigenPro", "EigenProClassifier", "EigenProRegressor"] diff --git a/sklearn_extra/eigenpro.py b/sklearn_extra/kernel_methods/_eigenpro.py similarity index 98% rename from sklearn_extra/eigenpro.py rename to sklearn_extra/kernel_methods/_eigenpro.py index 6a28cd01..e7df64ba 100644 --- a/sklearn_extra/eigenpro.py +++ b/sklearn_extra/kernel_methods/_eigenpro.py @@ -78,7 +78,8 @@ def _kernel(self, X, Y): distance = euclidean_distances(X, Y, squared=True) bandwidth = np.float32(self.bandwidth) if self.kernel == "rbf": - K = np.exp(-distance / (2.0 * bandwidth * bandwidth)) + distance = distance / (-2.0 * bandwidth * bandwidth) + K = np.exp(distance) elif self.kernel == "laplace": d = np.maximum(distance, 0) K = np.exp(-np.sqrt(d) / bandwidth) @@ -236,8 +237,9 @@ def _initialize_params(self, X, Y, random_state): n_components = min(sample_size - 1, self.n_components) n_components = max(1, n_components) - # Each batch will require about 1 gb memory - mem_bytes = 1024 ** 3 + # Approximate amount of memory that we want to use + mem_bytes = 0.1 * 1024 ** 3 + # Memory used with a certain sample size mem_usages = (d + n_label + 2 * np.arange(sample_size)) * n * 4 mG = np.int32(np.sum(mem_usages < mem_bytes)) @@ -368,7 +370,7 @@ def _raw_predict(self, X): Predicted targets. """ check_is_fitted(self, ["bs_", "centers_", "coef_", "was_1D_"]) - X = np.asarray(X, dtype=np.float64) + X = np.asarray(X, dtype=np.float32) if len(X.shape) == 1: raise ValueError( @@ -458,7 +460,7 @@ class EigenProRegressor(BaseEigenPro, RegressorMixin): Examples -------- - >>> from sklearn_extra.eigenpro import EigenProRegressor + >>> from sklearn_extra.kernel_methods import EigenProRegressor >>> import numpy as np >>> n_samples, n_features, n_targets = 4000, 20, 3 >>> rng = np.random.RandomState(1) @@ -580,7 +582,7 @@ class EigenProClassifier(BaseEigenPro, ClassifierMixin): Examples -------- - >>> from sklearn_extra.eigenpro import EigenProClassifier + >>> from sklearn_extra.kernel_methods import EigenProClassifier >>> import numpy as np >>> n_samples, n_features, n_targets = 4000, 20, 3 >>> rng = np.random.RandomState(1) diff --git a/sklearn_extra/kernel_methods/tests/__init__.py b/sklearn_extra/kernel_methods/tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/sklearn_extra/tests/test_eigenpro.py b/sklearn_extra/kernel_methods/tests/test_eigenpro.py similarity index 99% rename from sklearn_extra/tests/test_eigenpro.py rename to sklearn_extra/kernel_methods/tests/test_eigenpro.py index a85dabb6..fc60c076 100644 --- a/sklearn_extra/tests/test_eigenpro.py +++ b/sklearn_extra/kernel_methods/tests/test_eigenpro.py @@ -2,7 +2,7 @@ from sklearn.datasets import make_regression, make_classification from sklearn.utils.testing import assert_allclose -from sklearn_extra.eigenpro import EigenProRegressor, EigenProClassifier +from sklearn_extra.kernel_methods import EigenProRegressor, EigenProClassifier import pytest diff --git a/sklearn_extra/tests/test_common.py b/sklearn_extra/tests/test_common.py index 795e8150..6563d42b 100644 --- a/sklearn_extra/tests/test_common.py +++ b/sklearn_extra/tests/test_common.py @@ -3,12 +3,18 @@ from sklearn.utils.estimator_checks import check_estimator from sklearn_extra.kernel_approximation import Fastfood -from sklearn_extra import eigenpro +from sklearn_extra.kernel_methods import _eigenpro +from sklearn_extra.cluster import KMedoids @pytest.mark.parametrize( "Estimator", - [Fastfood, eigenpro.EigenProClassifier, eigenpro.EigenProRegressor], + [ + Fastfood, + KMedoids, + _eigenpro.EigenProClassifier, + _eigenpro.EigenProRegressor, + ], ) def test_all_estimators(Estimator, request): return check_estimator(Estimator)