102 lines
2.4 KiB
Python
102 lines
2.4 KiB
Python
|
"""
|
||
|
|
|
||
|
|
Tests for bandwidth selection and calculation.
|
||
|
|
|
||
|
|
Author: Padarn Wilson
|
||
|
|
"""
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
from scipy import stats
|
||
|
|
|
||
|
|
from statsmodels.sandbox.nonparametric import kernels
|
||
|
|
from statsmodels.distributions.mixture_rvs import mixture_rvs
|
||
|
|
from statsmodels.nonparametric.bandwidths import select_bandwidth
|
||
|
|
from statsmodels.nonparametric.bandwidths import bw_normal_reference
|
||
|
|
|
||
|
|
|
||
|
|
from numpy.testing import assert_allclose
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
# setup test data
|
||
|
|
|
||
|
|
np.random.seed(12345)
|
||
|
|
Xi = mixture_rvs([.25,.75], size=200, dist=[stats.norm, stats.norm],
|
||
|
|
kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5)))
|
||
|
|
|
||
|
|
|
||
|
|
class TestBandwidthCalculation:
|
||
|
|
|
||
|
|
def test_calculate_bandwidth_gaussian(self):
|
||
|
|
|
||
|
|
bw_expected = [0.29774853596742024,
|
||
|
|
0.25304408155871411,
|
||
|
|
0.29781147113698891]
|
||
|
|
|
||
|
|
kern = kernels.Gaussian()
|
||
|
|
|
||
|
|
bw_calc = [0, 0, 0]
|
||
|
|
for ii, bw in enumerate(['scott','silverman','normal_reference']):
|
||
|
|
bw_calc[ii] = select_bandwidth(Xi, bw, kern)
|
||
|
|
|
||
|
|
assert_allclose(bw_expected, bw_calc)
|
||
|
|
|
||
|
|
def test_calculate_normal_reference_bandwidth(self):
|
||
|
|
# Should be the same as the Gaussian Kernel
|
||
|
|
bw_expected = 0.29781147113698891
|
||
|
|
bw = bw_normal_reference(Xi)
|
||
|
|
assert_allclose(bw, bw_expected)
|
||
|
|
|
||
|
|
|
||
|
|
class CheckNormalReferenceConstant:
|
||
|
|
|
||
|
|
def test_calculate_normal_reference_constant(self):
|
||
|
|
const = self.constant
|
||
|
|
kern = self.kern
|
||
|
|
assert_allclose(const, kern.normal_reference_constant, 1e-2)
|
||
|
|
|
||
|
|
|
||
|
|
class TestEpanechnikov(CheckNormalReferenceConstant):
|
||
|
|
|
||
|
|
kern = kernels.Epanechnikov()
|
||
|
|
constant = 2.34
|
||
|
|
|
||
|
|
|
||
|
|
class TestGaussian(CheckNormalReferenceConstant):
|
||
|
|
|
||
|
|
kern = kernels.Gaussian()
|
||
|
|
constant = 1.06
|
||
|
|
|
||
|
|
|
||
|
|
class TestBiweight(CheckNormalReferenceConstant):
|
||
|
|
|
||
|
|
kern = kernels.Biweight()
|
||
|
|
constant = 2.78
|
||
|
|
|
||
|
|
|
||
|
|
class TestTriweight(CheckNormalReferenceConstant):
|
||
|
|
|
||
|
|
kern = kernels.Triweight()
|
||
|
|
constant = 3.15
|
||
|
|
|
||
|
|
|
||
|
|
class BandwidthZero:
|
||
|
|
|
||
|
|
def test_bandwidth_zero(self):
|
||
|
|
|
||
|
|
kern = kernels.Gaussian()
|
||
|
|
for bw in ['scott', 'silverman', 'normal_reference']:
|
||
|
|
with pytest.raises(RuntimeError,
|
||
|
|
match="Selected KDE bandwidth is 0"):
|
||
|
|
select_bandwidth(self.xx, bw, kern)
|
||
|
|
|
||
|
|
|
||
|
|
class TestAllBandwidthZero(BandwidthZero):
|
||
|
|
|
||
|
|
xx = np.ones((100, 3))
|
||
|
|
|
||
|
|
|
||
|
|
class TestAnyBandwidthZero(BandwidthZero):
|
||
|
|
|
||
|
|
xx = np.random.normal(size=(100, 3))
|
||
|
|
xx[:, 0] = 1.0
|