From 896f9d97cfc9945f83ce89b4287c53a3d22a94d4 Mon Sep 17 00:00:00 2001
From: Andreas Mueller <t3kcit@gmail.com>
Date: Fri, 30 Jun 2017 12:16:10 -0400
Subject: [PATCH] Fix handling of Nystroem params with a callable kernel
(#9166)
---
doc/whats_new.rst | 7 +++++
sklearn/kernel_approximation.py | 23 +++++++++-----
sklearn/metrics/pairwise.py | 3 +-
sklearn/tests/test_kernel_approximation.py | 36 ++++++++++++++++++++--
4 files changed, 58 insertions(+), 11 deletions(-)
diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index d367c627c2..ecfc65de35 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -431,6 +431,13 @@ Bug fixes
hence :func:`metrics.cohen_kappa_score`. :issue:`8354`, :issue:`7929`
by `Joel Nothman`_ and :user:`Jon Crall <Erotemic>`.
+ - Made default kernel parameters kernel-dependent in :class:`kernel_approximation.Nystroem`
+ :issue:`5229` by :user:`mth4saurabh` and `Andreas Müller`_.
+
+ - Fixed passing of ``gamma`` parameter to the ``chi2`` kernel in
+ :func:`metrics.pairwise_kernels` :issue:`5211` by :user:`nrhine1`,
+ :user:`mth4saurabh` and `Andreas Müller`_.
+
- Fixed a bug in :class:`gaussian_process.GaussianProcessRegressor`
when the standard deviation and covariance predicted without fit
would fail with a unmeaningful error by default.
diff --git a/sklearn/kernel_approximation.py b/sklearn/kernel_approximation.py
index 984b1a2a95..68b2e82772 100644
--- a/sklearn/kernel_approximation.py
+++ b/sklearn/kernel_approximation.py
@@ -18,7 +18,7 @@ from .base import TransformerMixin
from .utils import check_array, check_random_state, as_float_array
from .utils.extmath import safe_sparse_dot
from .utils.validation import check_is_fitted
-from .metrics.pairwise import pairwise_kernels
+from .metrics.pairwise import pairwise_kernels, KERNEL_PARAMS
class RBFSampler(BaseEstimator, TransformerMixin):
@@ -389,10 +389,10 @@ class Nystroem(BaseEstimator, TransformerMixin):
the kernel; see the documentation for sklearn.metrics.pairwise.
Ignored by other kernels.
- degree : float, default=3
+ degree : float, default=None
Degree of the polynomial kernel. Ignored by other kernels.
- coef0 : float, default=1
+ coef0 : float, default=None
Zero coefficient for polynomial and sigmoid kernels.
Ignored by other kernels.
@@ -438,7 +438,7 @@ class Nystroem(BaseEstimator, TransformerMixin):
sklearn.metrics.pairwise.kernel_metrics : List of built-in kernels.
"""
- def __init__(self, kernel="rbf", gamma=None, coef0=1, degree=3,
+ def __init__(self, kernel="rbf", gamma=None, coef0=None, degree=None,
kernel_params=None, n_components=100, random_state=None):
self.kernel = kernel
self.gamma = gamma
@@ -521,8 +521,17 @@ class Nystroem(BaseEstimator, TransformerMixin):
if params is None:
params = {}
if not callable(self.kernel):
- params['gamma'] = self.gamma
- params['degree'] = self.degree
- params['coef0'] = self.coef0
+ for param in (KERNEL_PARAMS[self.kernel]):
+ if getattr(self, param) is not None:
+ params[param] = getattr(self, param)
+ else:
+ if (self.gamma is not None or
+ self.coef0 is not None or
+ self.degree is not None):
+ warnings.warn(
+ "Passing gamma, coef0 or degree to Nystroem when using a"
+ " callable kernel is deprecated in version 0.19 and will"
+ " raise an error in 0.21, as they are ignored. Use "
+ "kernel_params instead.", DeprecationWarning)
return params
diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py
index 9af3afd0c9..0b63653672 100644
--- a/sklearn/metrics/pairwise.py
+++ b/sklearn/metrics/pairwise.py
@@ -1298,9 +1298,8 @@ def kernel_metrics():
KERNEL_PARAMS = {
"additive_chi2": (),
- "chi2": (),
+ "chi2": frozenset(["gamma"]),
"cosine": (),
- "exp_chi2": frozenset(["gamma"]),
"linear": (),
"poly": frozenset(["gamma", "degree", "coef0"]),
"polynomial": frozenset(["gamma", "degree", "coef0"]),
diff --git a/sklearn/tests/test_kernel_approximation.py b/sklearn/tests/test_kernel_approximation.py
index b2e826e262..8a2208b20a 100644
--- a/sklearn/tests/test_kernel_approximation.py
+++ b/sklearn/tests/test_kernel_approximation.py
@@ -5,13 +5,14 @@ from sklearn.utils.testing import assert_array_equal, assert_equal, assert_true
from sklearn.utils.testing import assert_not_equal
from sklearn.utils.testing import assert_array_almost_equal, assert_raises
from sklearn.utils.testing import assert_less_equal
+from sklearn.utils.testing import assert_warns_message
from sklearn.metrics.pairwise import kernel_metrics
from sklearn.kernel_approximation import RBFSampler
from sklearn.kernel_approximation import AdditiveChi2Sampler
from sklearn.kernel_approximation import SkewedChi2Sampler
from sklearn.kernel_approximation import Nystroem
-from sklearn.metrics.pairwise import polynomial_kernel, rbf_kernel
+from sklearn.metrics.pairwise import polynomial_kernel, rbf_kernel, chi2_kernel
# generate data
rng = np.random.RandomState(0)
@@ -165,7 +166,8 @@ def test_nystroem_approximation():
assert_equal(X_transformed.shape, (X.shape[0], 2))
# test callable kernel
- linear_kernel = lambda X, Y: np.dot(X, Y.T)
+ def linear_kernel(X, Y):
+ return np.dot(X, Y.T)
trans = Nystroem(n_components=2, kernel=linear_kernel, random_state=rnd)
X_transformed = trans.fit(X).transform(X)
assert_equal(X_transformed.shape, (X.shape[0], 2))
@@ -178,6 +180,26 @@ def test_nystroem_approximation():
assert_equal(X_transformed.shape, (X.shape[0], 2))
+def test_nystroem_default_parameters():
+ rnd = np.random.RandomState(42)
+ X = rnd.uniform(size=(10, 4))
+
+ # rbf kernel should behave as gamma=None by default
+ # aka gamma = 1 / n_features
+ nystroem = Nystroem(n_components=10)
+ X_transformed = nystroem.fit_transform(X)
+ K = rbf_kernel(X, gamma=None)
+ K2 = np.dot(X_transformed, X_transformed.T)
+ assert_array_almost_equal(K, K2)
+
+ # chi2 kernel should behave as gamma=1 by default
+ nystroem = Nystroem(kernel='chi2', n_components=10)
+ X_transformed = nystroem.fit_transform(X)
+ K = chi2_kernel(X, gamma=1)
+ K2 = np.dot(X_transformed, X_transformed.T)
+ assert_array_almost_equal(K, K2)
+
+
def test_nystroem_singular_kernel():
# test that nystroem works with singular kernel matrix
rng = np.random.RandomState(0)
@@ -223,3 +245,13 @@ def test_nystroem_callable():
n_components=(n_samples - 1),
kernel_params={'log': kernel_log}).fit(X)
assert_equal(len(kernel_log), n_samples * (n_samples - 1) / 2)
+
+ def linear_kernel(X, Y):
+ return np.dot(X, Y.T)
+
+ # if degree, gamma or coef0 is passed, we raise a warning
+ msg = "Passing gamma, coef0 or degree to Nystroem"
+ params = ({'gamma': 1}, {'coef0': 1}, {'degree': 2})
+ for param in params:
+ ny = Nystroem(kernel=linear_kernel, **param)
+ assert_warns_message(DeprecationWarning, msg, ny.fit, X)
--
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