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Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍 https://github.com/madlabunimib/PyCTBN
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PyCTBN/venv/lib/python3.9/site-packages/scipy/cluster/tests/test_hierarchy.py

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Refactor on docs; Add performance comparison
4 years ago
#
# Author: Damian Eads
# Date: April 17, 2008
#
# Copyright (C) 2008 Damian Eads
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
#
# 3. The name of the author may not be used to endorse or promote
# products derived from this software without specific prior
# written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
from numpy.testing import assert_allclose, assert_equal, assert_, assert_warns
import pytest
from pytest import raises as assert_raises
import scipy.cluster.hierarchy
from scipy.cluster.hierarchy import (
ClusterWarning, linkage, from_mlab_linkage, to_mlab_linkage,
num_obs_linkage, inconsistent, cophenet, fclusterdata, fcluster,
is_isomorphic, single, leaders,
correspond, is_monotonic, maxdists, maxinconsts, maxRstat,
is_valid_linkage, is_valid_im, to_tree, leaves_list, dendrogram,
set_link_color_palette, cut_tree, optimal_leaf_ordering,
_order_cluster_tree, _hierarchy, _LINKAGE_METHODS)
from scipy.spatial.distance import pdist
from scipy.cluster._hierarchy import Heap
from . import hierarchy_test_data
# Matplotlib is not a scipy dependency but is optionally used in dendrogram, so
# check if it's available
try:
import matplotlib # type: ignore[import]
# and set the backend to be Agg (no gui)
matplotlib.use('Agg')
# before importing pyplot
import matplotlib.pyplot as plt # type: ignore[import]
have_matplotlib = True
except Exception:
have_matplotlib = False
class TestLinkage(object):
def test_linkage_non_finite_elements_in_distance_matrix(self):
# Tests linkage(Y) where Y contains a non-finite element (e.g. NaN or Inf).
# Exception expected.
y = np.zeros((6,))
y[0] = np.nan
assert_raises(ValueError, linkage, y)
def test_linkage_empty_distance_matrix(self):
# Tests linkage(Y) where Y is a 0x4 linkage matrix. Exception expected.
y = np.zeros((0,))
assert_raises(ValueError, linkage, y)
def test_linkage_tdist(self):
for method in ['single', 'complete', 'average', 'weighted']:
self.check_linkage_tdist(method)
def check_linkage_tdist(self, method):
# Tests linkage(Y, method) on the tdist data set.
Z = linkage(hierarchy_test_data.ytdist, method)
expectedZ = getattr(hierarchy_test_data, 'linkage_ytdist_' + method)
assert_allclose(Z, expectedZ, atol=1e-10)
def test_linkage_X(self):
for method in ['centroid', 'median', 'ward']:
self.check_linkage_q(method)
def check_linkage_q(self, method):
# Tests linkage(Y, method) on the Q data set.
Z = linkage(hierarchy_test_data.X, method)
expectedZ = getattr(hierarchy_test_data, 'linkage_X_' + method)
assert_allclose(Z, expectedZ, atol=1e-06)
y = scipy.spatial.distance.pdist(hierarchy_test_data.X,
metric="euclidean")
Z = linkage(y, method)
assert_allclose(Z, expectedZ, atol=1e-06)
def test_compare_with_trivial(self):
rng = np.random.RandomState(0)
n = 20
X = rng.rand(n, 2)
d = pdist(X)
for method, code in _LINKAGE_METHODS.items():
Z_trivial = _hierarchy.linkage(d, n, code)
Z = linkage(d, method)
assert_allclose(Z_trivial, Z, rtol=1e-14, atol=1e-15)
def test_optimal_leaf_ordering(self):
Z = linkage(hierarchy_test_data.ytdist, optimal_ordering=True)
expectedZ = getattr(hierarchy_test_data, 'linkage_ytdist_single_olo')
assert_allclose(Z, expectedZ, atol=1e-10)
class TestLinkageTies(object):
_expectations = {
'single': np.array([[0, 1, 1.41421356, 2],
[2, 3, 1.41421356, 3]]),
'complete': np.array([[0, 1, 1.41421356, 2],
[2, 3, 2.82842712, 3]]),
'average': np.array([[0, 1, 1.41421356, 2],
[2, 3, 2.12132034, 3]]),
'weighted': np.array([[0, 1, 1.41421356, 2],
[2, 3, 2.12132034, 3]]),
'centroid': np.array([[0, 1, 1.41421356, 2],
[2, 3, 2.12132034, 3]]),
'median': np.array([[0, 1, 1.41421356, 2],
[2, 3, 2.12132034, 3]]),
'ward': np.array([[0, 1, 1.41421356, 2],
[2, 3, 2.44948974, 3]]),
}
def test_linkage_ties(self):
for method in ['single', 'complete', 'average', 'weighted', 'centroid', 'median', 'ward']:
self.check_linkage_ties(method)
def check_linkage_ties(self, method):
X = np.array([[-1, -1], [0, 0], [1, 1]])
Z = linkage(X, method=method)
expectedZ = self._expectations[method]
assert_allclose(Z, expectedZ, atol=1e-06)
class TestInconsistent(object):
def test_inconsistent_tdist(self):
for depth in hierarchy_test_data.inconsistent_ytdist:
self.check_inconsistent_tdist(depth)
def check_inconsistent_tdist(self, depth):
Z = hierarchy_test_data.linkage_ytdist_single
assert_allclose(inconsistent(Z, depth),
hierarchy_test_data.inconsistent_ytdist[depth])
class TestCopheneticDistance(object):
def test_linkage_cophenet_tdist_Z(self):
# Tests cophenet(Z) on tdist data set.
expectedM = np.array([268, 295, 255, 255, 295, 295, 268, 268, 295, 295,
295, 138, 219, 295, 295])
Z = hierarchy_test_data.linkage_ytdist_single
M = cophenet(Z)
assert_allclose(M, expectedM, atol=1e-10)
def test_linkage_cophenet_tdist_Z_Y(self):
# Tests cophenet(Z, Y) on tdist data set.
Z = hierarchy_test_data.linkage_ytdist_single
(c, M) = cophenet(Z, hierarchy_test_data.ytdist)
expectedM = np.array([268, 295, 255, 255, 295, 295, 268, 268, 295, 295,
295, 138, 219, 295, 295])
expectedc = 0.639931296433393415057366837573
assert_allclose(c, expectedc, atol=1e-10)
assert_allclose(M, expectedM, atol=1e-10)
class TestMLabLinkageConversion(object):
def test_mlab_linkage_conversion_empty(self):
# Tests from/to_mlab_linkage on empty linkage array.
X = np.asarray([])
assert_equal(from_mlab_linkage([]), X)
assert_equal(to_mlab_linkage([]), X)
def test_mlab_linkage_conversion_single_row(self):
# Tests from/to_mlab_linkage on linkage array with single row.
Z = np.asarray([[0., 1., 3., 2.]])
Zm = [[1, 2, 3]]
assert_equal(from_mlab_linkage(Zm), Z)
assert_equal(to_mlab_linkage(Z), Zm)
def test_mlab_linkage_conversion_multiple_rows(self):
# Tests from/to_mlab_linkage on linkage array with multiple rows.
Zm = np.asarray([[3, 6, 138], [4, 5, 219],
[1, 8, 255], [2, 9, 268], [7, 10, 295]])
Z = np.array([[2., 5., 138., 2.],
[3., 4., 219., 2.],
[0., 7., 255., 3.],
[1., 8., 268., 4.],
[6., 9., 295., 6.]],
dtype=np.double)
assert_equal(from_mlab_linkage(Zm), Z)
assert_equal(to_mlab_linkage(Z), Zm)
class TestFcluster(object):
def test_fclusterdata(self):
for t in hierarchy_test_data.fcluster_inconsistent:
self.check_fclusterdata(t, 'inconsistent')
for t in hierarchy_test_data.fcluster_distance:
self.check_fclusterdata(t, 'distance')
for t in hierarchy_test_data.fcluster_maxclust:
self.check_fclusterdata(t, 'maxclust')
def check_fclusterdata(self, t, criterion):
# Tests fclusterdata(X, criterion=criterion, t=t) on a random 3-cluster data set.
expectedT = getattr(hierarchy_test_data, 'fcluster_' + criterion)[t]
X = hierarchy_test_data.Q_X
T = fclusterdata(X, criterion=criterion, t=t)
assert_(is_isomorphic(T, expectedT))
def test_fcluster(self):
for t in hierarchy_test_data.fcluster_inconsistent:
self.check_fcluster(t, 'inconsistent')
for t in hierarchy_test_data.fcluster_distance:
self.check_fcluster(t, 'distance')
for t in hierarchy_test_data.fcluster_maxclust:
self.check_fcluster(t, 'maxclust')
def check_fcluster(self, t, criterion):
# Tests fcluster(Z, criterion=criterion, t=t) on a random 3-cluster data set.
expectedT = getattr(hierarchy_test_data, 'fcluster_' + criterion)[t]
Z = single(hierarchy_test_data.Q_X)
T = fcluster(Z, criterion=criterion, t=t)
assert_(is_isomorphic(T, expectedT))
def test_fcluster_monocrit(self):
for t in hierarchy_test_data.fcluster_distance:
self.check_fcluster_monocrit(t)
for t in hierarchy_test_data.fcluster_maxclust:
self.check_fcluster_maxclust_monocrit(t)
def check_fcluster_monocrit(self, t):
expectedT = hierarchy_test_data.fcluster_distance[t]
Z = single(hierarchy_test_data.Q_X)
T = fcluster(Z, t, criterion='monocrit', monocrit=maxdists(Z))
assert_(is_isomorphic(T, expectedT))
def check_fcluster_maxclust_monocrit(self, t):
expectedT = hierarchy_test_data.fcluster_maxclust[t]
Z = single(hierarchy_test_data.Q_X)
T = fcluster(Z, t, criterion='maxclust_monocrit', monocrit=maxdists(Z))
assert_(is_isomorphic(T, expectedT))
class TestLeaders(object):
def test_leaders_single(self):
# Tests leaders using a flat clustering generated by single linkage.
X = hierarchy_test_data.Q_X
Y = pdist(X)
Z = linkage(Y)
T = fcluster(Z, criterion='maxclust', t=3)
Lright = (np.array([53, 55, 56]), np.array([2, 3, 1]))
L = leaders(Z, T)
assert_equal(L, Lright)
class TestIsIsomorphic(object):
def test_is_isomorphic_1(self):
# Tests is_isomorphic on test case #1 (one flat cluster, different labellings)
a = [1, 1, 1]
b = [2, 2, 2]
assert_(is_isomorphic(a, b))
assert_(is_isomorphic(b, a))
def test_is_isomorphic_2(self):
# Tests is_isomorphic on test case #2 (two flat clusters, different labelings)
a = [1, 7, 1]
b = [2, 3, 2]
assert_(is_isomorphic(a, b))
assert_(is_isomorphic(b, a))
def test_is_isomorphic_3(self):
# Tests is_isomorphic on test case #3 (no flat clusters)
a = []
b = []
assert_(is_isomorphic(a, b))
def test_is_isomorphic_4A(self):
# Tests is_isomorphic on test case #4A (3 flat clusters, different labelings, isomorphic)
a = [1, 2, 3]
b = [1, 3, 2]
assert_(is_isomorphic(a, b))
assert_(is_isomorphic(b, a))
def test_is_isomorphic_4B(self):
# Tests is_isomorphic on test case #4B (3 flat clusters, different labelings, nonisomorphic)
a = [1, 2, 3, 3]
b = [1, 3, 2, 3]
assert_(is_isomorphic(a, b) == False)
assert_(is_isomorphic(b, a) == False)
def test_is_isomorphic_4C(self):
# Tests is_isomorphic on test case #4C (3 flat clusters, different labelings, isomorphic)
a = [7, 2, 3]
b = [6, 3, 2]
assert_(is_isomorphic(a, b))
assert_(is_isomorphic(b, a))
def test_is_isomorphic_5(self):
# Tests is_isomorphic on test case #5 (1000 observations, 2/3/5 random
# clusters, random permutation of the labeling).
for nc in [2, 3, 5]:
self.help_is_isomorphic_randperm(1000, nc)
def test_is_isomorphic_6(self):
# Tests is_isomorphic on test case #5A (1000 observations, 2/3/5 random
# clusters, random permutation of the labeling, slightly
# nonisomorphic.)
for nc in [2, 3, 5]:
self.help_is_isomorphic_randperm(1000, nc, True, 5)
def test_is_isomorphic_7(self):
# Regression test for gh-6271
assert_(not is_isomorphic([1, 2, 3], [1, 1, 1]))
def help_is_isomorphic_randperm(self, nobs, nclusters, noniso=False, nerrors=0):
for k in range(3):
a = np.int_(np.random.rand(nobs) * nclusters)
b = np.zeros(a.size, dtype=np.int_)
P = np.random.permutation(nclusters)
for i in range(0, a.shape[0]):
b[i] = P[a[i]]
if noniso:
Q = np.random.permutation(nobs)
b[Q[0:nerrors]] += 1
b[Q[0:nerrors]] %= nclusters
assert_(is_isomorphic(a, b) == (not noniso))
assert_(is_isomorphic(b, a) == (not noniso))
class TestIsValidLinkage(object):
def test_is_valid_linkage_various_size(self):
for nrow, ncol, valid in [(2, 5, False), (2, 3, False),
(1, 4, True), (2, 4, True)]:
self.check_is_valid_linkage_various_size(nrow, ncol, valid)
def check_is_valid_linkage_various_size(self, nrow, ncol, valid):
# Tests is_valid_linkage(Z) with linkage matrics of various sizes
Z = np.asarray([[0, 1, 3.0, 2, 5],
[3, 2, 4.0, 3, 3]], dtype=np.double)
Z = Z[:nrow, :ncol]
assert_(is_valid_linkage(Z) == valid)
if not valid:
assert_raises(ValueError, is_valid_linkage, Z, throw=True)
def test_is_valid_linkage_int_type(self):
# Tests is_valid_linkage(Z) with integer type.
Z = np.asarray([[0, 1, 3.0, 2],
[3, 2, 4.0, 3]], dtype=int)
assert_(is_valid_linkage(Z) == False)
assert_raises(TypeError, is_valid_linkage, Z, throw=True)
def test_is_valid_linkage_empty(self):
# Tests is_valid_linkage(Z) with empty linkage.
Z = np.zeros((0, 4), dtype=np.double)
assert_(is_valid_linkage(Z) == False)
assert_raises(ValueError, is_valid_linkage, Z, throw=True)
def test_is_valid_linkage_4_and_up(self):
# Tests is_valid_linkage(Z) on linkage on observation sets between
# sizes 4 and 15 (step size 3).
for i in range(4, 15, 3):
y = np.random.rand(i*(i-1)//2)
Z = linkage(y)
assert_(is_valid_linkage(Z) == True)
def test_is_valid_linkage_4_and_up_neg_index_left(self):
# Tests is_valid_linkage(Z) on linkage on observation sets between
# sizes 4 and 15 (step size 3) with negative indices (left).
for i in range(4, 15, 3):
y = np.random.rand(i*(i-1)//2)
Z = linkage(y)
Z[i//2,0] = -2
assert_(is_valid_linkage(Z) == False)
assert_raises(ValueError, is_valid_linkage, Z, throw=True)
def test_is_valid_linkage_4_and_up_neg_index_right(self):
# Tests is_valid_linkage(Z) on linkage on observation sets between
# sizes 4 and 15 (step size 3) with negative indices (right).
for i in range(4, 15, 3):
y = np.random.rand(i*(i-1)//2)
Z = linkage(y)
Z[i//2,1] = -2
assert_(is_valid_linkage(Z) == False)
assert_raises(ValueError, is_valid_linkage, Z, throw=True)
def test_is_valid_linkage_4_and_up_neg_dist(self):
# Tests is_valid_linkage(Z) on linkage on observation sets between
# sizes 4 and 15 (step size 3) with negative distances.
for i in range(4, 15, 3):
y = np.random.rand(i*(i-1)//2)
Z = linkage(y)
Z[i//2,2] = -0.5
assert_(is_valid_linkage(Z) == False)
assert_raises(ValueError, is_valid_linkage, Z, throw=True)
def test_is_valid_linkage_4_and_up_neg_counts(self):
# Tests is_valid_linkage(Z) on linkage on observation sets between
# sizes 4 and 15 (step size 3) with negative counts.
for i in range(4, 15, 3):
y = np.random.rand(i*(i-1)//2)
Z = linkage(y)
Z[i//2,3] = -2
assert_(is_valid_linkage(Z) == False)
assert_raises(ValueError, is_valid_linkage, Z, throw=True)
class TestIsValidInconsistent(object):
def test_is_valid_im_int_type(self):
# Tests is_valid_im(R) with integer type.
R = np.asarray([[0, 1, 3.0, 2],
[3, 2, 4.0, 3]], dtype=int)
assert_(is_valid_im(R) == False)
assert_raises(TypeError, is_valid_im, R, throw=True)
def test_is_valid_im_various_size(self):
for nrow, ncol, valid in [(2, 5, False), (2, 3, False),
(1, 4, True), (2, 4, True)]:
self.check_is_valid_im_various_size(nrow, ncol, valid)
def check_is_valid_im_various_size(self, nrow, ncol, valid):
# Tests is_valid_im(R) with linkage matrics of various sizes
R = np.asarray([[0, 1, 3.0, 2, 5],
[3, 2, 4.0, 3, 3]], dtype=np.double)
R = R[:nrow, :ncol]
assert_(is_valid_im(R) == valid)
if not valid:
assert_raises(ValueError, is_valid_im, R, throw=True)
def test_is_valid_im_empty(self):
# Tests is_valid_im(R) with empty inconsistency matrix.
R = np.zeros((0, 4), dtype=np.double)
assert_(is_valid_im(R) == False)
assert_raises(ValueError, is_valid_im, R, throw=True)
def test_is_valid_im_4_and_up(self):
# Tests is_valid_im(R) on im on observation sets between sizes 4 and 15
# (step size 3).
for i in range(4, 15, 3):
y = np.random.rand(i*(i-1)//2)
Z = linkage(y)
R = inconsistent(Z)
assert_(is_valid_im(R) == True)
def test_is_valid_im_4_and_up_neg_index_left(self):
# Tests is_valid_im(R) on im on observation sets between sizes 4 and 15
# (step size 3) with negative link height means.
for i in range(4, 15, 3):
y = np.random.rand(i*(i-1)//2)
Z = linkage(y)
R = inconsistent(Z)
R[i//2,0] = -2.0
assert_(is_valid_im(R) == False)
assert_raises(ValueError, is_valid_im, R, throw=True)
def test_is_valid_im_4_and_up_neg_index_right(self):
# Tests is_valid_im(R) on im on observation sets between sizes 4 and 15
# (step size 3) with negative link height standard deviations.
for i in range(4, 15, 3):
y = np.random.rand(i*(i-1)//2)
Z = linkage(y)
R = inconsistent(Z)
R[i//2,1] = -2.0
assert_(is_valid_im(R) == False)
assert_raises(ValueError, is_valid_im, R, throw=True)
def test_is_valid_im_4_and_up_neg_dist(self):
# Tests is_valid_im(R) on im on observation sets between sizes 4 and 15
# (step size 3) with negative link counts.
for i in range(4, 15, 3):
y = np.random.rand(i*(i-1)//2)
Z = linkage(y)
R = inconsistent(Z)
R[i//2,2] = -0.5
assert_(is_valid_im(R) == False)
assert_raises(ValueError, is_valid_im, R, throw=True)
class TestNumObsLinkage(object):
def test_num_obs_linkage_empty(self):
# Tests num_obs_linkage(Z) with empty linkage.
Z = np.zeros((0, 4), dtype=np.double)
assert_raises(ValueError, num_obs_linkage, Z)
def test_num_obs_linkage_1x4(self):
# Tests num_obs_linkage(Z) on linkage over 2 observations.
Z = np.asarray([[0, 1, 3.0, 2]], dtype=np.double)
assert_equal(num_obs_linkage(Z), 2)
def test_num_obs_linkage_2x4(self):
# Tests num_obs_linkage(Z) on linkage over 3 observations.
Z = np.asarray([[0, 1, 3.0, 2],
[3, 2, 4.0, 3]], dtype=np.double)
assert_equal(num_obs_linkage(Z), 3)
def test_num_obs_linkage_4_and_up(self):
# Tests num_obs_linkage(Z) on linkage on observation sets between sizes
# 4 and 15 (step size 3).
for i in range(4, 15, 3):
y = np.random.rand(i*(i-1)//2)
Z = linkage(y)
assert_equal(num_obs_linkage(Z), i)
class TestLeavesList(object):
def test_leaves_list_1x4(self):
# Tests leaves_list(Z) on a 1x4 linkage.
Z = np.asarray([[0, 1, 3.0, 2]], dtype=np.double)
to_tree(Z)
assert_equal(leaves_list(Z), [0, 1])
def test_leaves_list_2x4(self):
# Tests leaves_list(Z) on a 2x4 linkage.
Z = np.asarray([[0, 1, 3.0, 2],
[3, 2, 4.0, 3]], dtype=np.double)
to_tree(Z)
assert_equal(leaves_list(Z), [0, 1, 2])
def test_leaves_list_Q(self):
for method in ['single', 'complete', 'average', 'weighted', 'centroid',
'median', 'ward']:
self.check_leaves_list_Q(method)
def check_leaves_list_Q(self, method):
# Tests leaves_list(Z) on the Q data set
X = hierarchy_test_data.Q_X
Z = linkage(X, method)
node = to_tree(Z)
assert_equal(node.pre_order(), leaves_list(Z))
def test_Q_subtree_pre_order(self):
# Tests that pre_order() works when called on sub-trees.
X = hierarchy_test_data.Q_X
Z = linkage(X, 'single')
node = to_tree(Z)
assert_equal(node.pre_order(), (node.get_left().pre_order()
+ node.get_right().pre_order()))
class TestCorrespond(object):
def test_correspond_empty(self):
# Tests correspond(Z, y) with empty linkage and condensed distance matrix.
y = np.zeros((0,))
Z = np.zeros((0,4))
assert_raises(ValueError, correspond, Z, y)
def test_correspond_2_and_up(self):
# Tests correspond(Z, y) on linkage and CDMs over observation sets of
# different sizes.
for i in range(2, 4):
y = np.random.rand(i*(i-1)//2)
Z = linkage(y)
assert_(correspond(Z, y))
for i in range(4, 15, 3):
y = np.random.rand(i*(i-1)//2)
Z = linkage(y)
assert_(correspond(Z, y))
def test_correspond_4_and_up(self):
# Tests correspond(Z, y) on linkage and CDMs over observation sets of
# different sizes. Correspondence should be false.
for (i, j) in (list(zip(list(range(2, 4)), list(range(3, 5)))) +
list(zip(list(range(3, 5)), list(range(2, 4))))):
y = np.random.rand(i*(i-1)//2)
y2 = np.random.rand(j*(j-1)//2)
Z = linkage(y)
Z2 = linkage(y2)
assert_equal(correspond(Z, y2), False)
assert_equal(correspond(Z2, y), False)
def test_correspond_4_and_up_2(self):
# Tests correspond(Z, y) on linkage and CDMs over observation sets of
# different sizes. Correspondence should be false.
for (i, j) in (list(zip(list(range(2, 7)), list(range(16, 21)))) +
list(zip(list(range(2, 7)), list(range(16, 21))))):
y = np.random.rand(i*(i-1)//2)
y2 = np.random.rand(j*(j-1)//2)
Z = linkage(y)
Z2 = linkage(y2)
assert_equal(correspond(Z, y2), False)
assert_equal(correspond(Z2, y), False)
def test_num_obs_linkage_multi_matrix(self):
# Tests num_obs_linkage with observation matrices of multiple sizes.
for n in range(2, 10):
X = np.random.rand(n, 4)
Y = pdist(X)
Z = linkage(Y)
assert_equal(num_obs_linkage(Z), n)
class TestIsMonotonic(object):
def test_is_monotonic_empty(self):
# Tests is_monotonic(Z) on an empty linkage.
Z = np.zeros((0, 4))
assert_raises(ValueError, is_monotonic, Z)
def test_is_monotonic_1x4(self):
# Tests is_monotonic(Z) on 1x4 linkage. Expecting True.
Z = np.asarray([[0, 1, 0.3, 2]], dtype=np.double)
assert_equal(is_monotonic(Z), True)
def test_is_monotonic_2x4_T(self):
# Tests is_monotonic(Z) on 2x4 linkage. Expecting True.
Z = np.asarray([[0, 1, 0.3, 2],
[2, 3, 0.4, 3]], dtype=np.double)
assert_equal(is_monotonic(Z), True)
def test_is_monotonic_2x4_F(self):
# Tests is_monotonic(Z) on 2x4 linkage. Expecting False.
Z = np.asarray([[0, 1, 0.4, 2],
[2, 3, 0.3, 3]], dtype=np.double)
assert_equal(is_monotonic(Z), False)
def test_is_monotonic_3x4_T(self):
# Tests is_monotonic(Z) on 3x4 linkage. Expecting True.
Z = np.asarray([[0, 1, 0.3, 2],
[2, 3, 0.4, 2],
[4, 5, 0.6, 4]], dtype=np.double)
assert_equal(is_monotonic(Z), True)
def test_is_monotonic_3x4_F1(self):
# Tests is_monotonic(Z) on 3x4 linkage (case 1). Expecting False.
Z = np.asarray([[0, 1, 0.3, 2],
[2, 3, 0.2, 2],
[4, 5, 0.6, 4]], dtype=np.double)
assert_equal(is_monotonic(Z), False)
def test_is_monotonic_3x4_F2(self):
# Tests is_monotonic(Z) on 3x4 linkage (case 2). Expecting False.
Z = np.asarray([[0, 1, 0.8, 2],
[2, 3, 0.4, 2],
[4, 5, 0.6, 4]], dtype=np.double)
assert_equal(is_monotonic(Z), False)
def test_is_monotonic_3x4_F3(self):
# Tests is_monotonic(Z) on 3x4 linkage (case 3). Expecting False
Z = np.asarray([[0, 1, 0.3, 2],
[2, 3, 0.4, 2],
[4, 5, 0.2, 4]], dtype=np.double)
assert_equal(is_monotonic(Z), False)
def test_is_monotonic_tdist_linkage1(self):
# Tests is_monotonic(Z) on clustering generated by single linkage on
# tdist data set. Expecting True.
Z = linkage(hierarchy_test_data.ytdist, 'single')
assert_equal(is_monotonic(Z), True)
def test_is_monotonic_tdist_linkage2(self):
# Tests is_monotonic(Z) on clustering generated by single linkage on
# tdist data set. Perturbing. Expecting False.
Z = linkage(hierarchy_test_data.ytdist, 'single')
Z[2,2] = 0.0
assert_equal(is_monotonic(Z), False)
def test_is_monotonic_Q_linkage(self):
# Tests is_monotonic(Z) on clustering generated by single linkage on
# Q data set. Expecting True.
X = hierarchy_test_data.Q_X
Z = linkage(X, 'single')
assert_equal(is_monotonic(Z), True)
class TestMaxDists(object):
def test_maxdists_empty_linkage(self):
# Tests maxdists(Z) on empty linkage. Expecting exception.
Z = np.zeros((0, 4), dtype=np.double)
assert_raises(ValueError, maxdists, Z)
def test_maxdists_one_cluster_linkage(self):
# Tests maxdists(Z) on linkage with one cluster.
Z = np.asarray([[0, 1, 0.3, 4]], dtype=np.double)
MD = maxdists(Z)
expectedMD = calculate_maximum_distances(Z)
assert_allclose(MD, expectedMD, atol=1e-15)
def test_maxdists_Q_linkage(self):
for method in ['single', 'complete', 'ward', 'centroid', 'median']:
self.check_maxdists_Q_linkage(method)
def check_maxdists_Q_linkage(self, method):
# Tests maxdists(Z) on the Q data set
X = hierarchy_test_data.Q_X
Z = linkage(X, method)
MD = maxdists(Z)
expectedMD = calculate_maximum_distances(Z)
assert_allclose(MD, expectedMD, atol=1e-15)
class TestMaxInconsts(object):
def test_maxinconsts_empty_linkage(self):
# Tests maxinconsts(Z, R) on empty linkage. Expecting exception.
Z = np.zeros((0, 4), dtype=np.double)
R = np.zeros((0, 4), dtype=np.double)
assert_raises(ValueError, maxinconsts, Z, R)
def test_maxinconsts_difrow_linkage(self):
# Tests maxinconsts(Z, R) on linkage and inconsistency matrices with
# different numbers of clusters. Expecting exception.
Z = np.asarray([[0, 1, 0.3, 4]], dtype=np.double)
R = np.random.rand(2, 4)
assert_raises(ValueError, maxinconsts, Z, R)
def test_maxinconsts_one_cluster_linkage(self):
# Tests maxinconsts(Z, R) on linkage with one cluster.
Z = np.asarray([[0, 1, 0.3, 4]], dtype=np.double)
R = np.asarray([[0, 0, 0, 0.3]], dtype=np.double)
MD = maxinconsts(Z, R)
expectedMD = calculate_maximum_inconsistencies(Z, R)
assert_allclose(MD, expectedMD, atol=1e-15)
def test_maxinconsts_Q_linkage(self):
for method in ['single', 'complete', 'ward', 'centroid', 'median']:
self.check_maxinconsts_Q_linkage(method)
def check_maxinconsts_Q_linkage(self, method):
# Tests maxinconsts(Z, R) on the Q data set
X = hierarchy_test_data.Q_X
Z = linkage(X, method)
R = inconsistent(Z)
MD = maxinconsts(Z, R)
expectedMD = calculate_maximum_inconsistencies(Z, R)
assert_allclose(MD, expectedMD, atol=1e-15)
class TestMaxRStat(object):
def test_maxRstat_invalid_index(self):
for i in [3.3, -1, 4]:
self.check_maxRstat_invalid_index(i)
def check_maxRstat_invalid_index(self, i):
# Tests maxRstat(Z, R, i). Expecting exception.
Z = np.asarray([[0, 1, 0.3, 4]], dtype=np.double)
R = np.asarray([[0, 0, 0, 0.3]], dtype=np.double)
if isinstance(i, int):
assert_raises(ValueError, maxRstat, Z, R, i)
else:
assert_raises(TypeError, maxRstat, Z, R, i)
def test_maxRstat_empty_linkage(self):
for i in range(4):
self.check_maxRstat_empty_linkage(i)
def check_maxRstat_empty_linkage(self, i):
# Tests maxRstat(Z, R, i) on empty linkage. Expecting exception.
Z = np.zeros((0, 4), dtype=np.double)
R = np.zeros((0, 4), dtype=np.double)
assert_raises(ValueError, maxRstat, Z, R, i)
def test_maxRstat_difrow_linkage(self):
for i in range(4):
self.check_maxRstat_difrow_linkage(i)
def check_maxRstat_difrow_linkage(self, i):
# Tests maxRstat(Z, R, i) on linkage and inconsistency matrices with
# different numbers of clusters. Expecting exception.
Z = np.asarray([[0, 1, 0.3, 4]], dtype=np.double)
R = np.random.rand(2, 4)
assert_raises(ValueError, maxRstat, Z, R, i)
def test_maxRstat_one_cluster_linkage(self):
for i in range(4):
self.check_maxRstat_one_cluster_linkage(i)
def check_maxRstat_one_cluster_linkage(self, i):
# Tests maxRstat(Z, R, i) on linkage with one cluster.
Z = np.asarray([[0, 1, 0.3, 4]], dtype=np.double)
R = np.asarray([[0, 0, 0, 0.3]], dtype=np.double)
MD = maxRstat(Z, R, 1)
expectedMD = calculate_maximum_inconsistencies(Z, R, 1)
assert_allclose(MD, expectedMD, atol=1e-15)
def test_maxRstat_Q_linkage(self):
for method in ['single', 'complete', 'ward', 'centroid', 'median']:
for i in range(4):
self.check_maxRstat_Q_linkage(method, i)
def check_maxRstat_Q_linkage(self, method, i):
# Tests maxRstat(Z, R, i) on the Q data set
X = hierarchy_test_data.Q_X
Z = linkage(X, method)
R = inconsistent(Z)
MD = maxRstat(Z, R, 1)
expectedMD = calculate_maximum_inconsistencies(Z, R, 1)
assert_allclose(MD, expectedMD, atol=1e-15)
class TestDendrogram(object):
def test_dendrogram_single_linkage_tdist(self):
# Tests dendrogram calculation on single linkage of the tdist data set.
Z = linkage(hierarchy_test_data.ytdist, 'single')
R = dendrogram(Z, no_plot=True)
leaves = R["leaves"]
assert_equal(leaves, [2, 5, 1, 0, 3, 4])
def test_valid_orientation(self):
Z = linkage(hierarchy_test_data.ytdist, 'single')
assert_raises(ValueError, dendrogram, Z, orientation="foo")
def test_labels_as_array_or_list(self):
# test for gh-12418
Z = linkage(hierarchy_test_data.ytdist, 'single')
labels = np.array([1, 3, 2, 6, 4, 5])
result1 = dendrogram(Z, labels=labels, no_plot=True)
result2 = dendrogram(Z, labels=labels.tolist(), no_plot=True)
assert result1 == result2
@pytest.mark.skipif(not have_matplotlib, reason="no matplotlib")
def test_valid_label_size(self):
link = np.array([
[0, 1, 1.0, 4],
[2, 3, 1.0, 5],
[4, 5, 2.0, 6],
])
plt.figure()
with pytest.raises(ValueError) as exc_info:
dendrogram(link, labels=list(range(100)))
assert "Dimensions of Z and labels must be consistent."\
in str(exc_info.value)
with pytest.raises(
ValueError,
match="Dimensions of Z and labels must be consistent."):
dendrogram(link, labels=[])
plt.close()
@pytest.mark.skipif(not have_matplotlib, reason="no matplotlib")
def test_dendrogram_plot(self):
for orientation in ['top', 'bottom', 'left', 'right']:
self.check_dendrogram_plot(orientation)
def check_dendrogram_plot(self, orientation):
# Tests dendrogram plotting.
Z = linkage(hierarchy_test_data.ytdist, 'single')
expected = {'color_list': ['C1', 'C0', 'C0', 'C0', 'C0'],
'dcoord': [[0.0, 138.0, 138.0, 0.0],
[0.0, 219.0, 219.0, 0.0],
[0.0, 255.0, 255.0, 219.0],
[0.0, 268.0, 268.0, 255.0],
[138.0, 295.0, 295.0, 268.0]],
'icoord': [[5.0, 5.0, 15.0, 15.0],
[45.0, 45.0, 55.0, 55.0],
[35.0, 35.0, 50.0, 50.0],
[25.0, 25.0, 42.5, 42.5],
[10.0, 10.0, 33.75, 33.75]],
'ivl': ['2', '5', '1', '0', '3', '4'],
'leaves': [2, 5, 1, 0, 3, 4]}
fig = plt.figure()
ax = fig.add_subplot(221)
# test that dendrogram accepts ax keyword
R1 = dendrogram(Z, ax=ax, orientation=orientation)
assert_equal(R1, expected)
# test that dendrogram accepts and handle the leaf_font_size and
# leaf_rotation keywords
dendrogram(Z, ax=ax, orientation=orientation,
leaf_font_size=20, leaf_rotation=90)
testlabel = (
ax.get_xticklabels()[0]
if orientation in ['top', 'bottom']
else ax.get_yticklabels()[0]
)
assert_equal(testlabel.get_rotation(), 90)
assert_equal(testlabel.get_size(), 20)
dendrogram(Z, ax=ax, orientation=orientation,
leaf_rotation=90)
testlabel = (
ax.get_xticklabels()[0]
if orientation in ['top', 'bottom']
else ax.get_yticklabels()[0]
)
assert_equal(testlabel.get_rotation(), 90)
dendrogram(Z, ax=ax, orientation=orientation,
leaf_font_size=20)
testlabel = (
ax.get_xticklabels()[0]
if orientation in ['top', 'bottom']
else ax.get_yticklabels()[0]
)
assert_equal(testlabel.get_size(), 20)
plt.close()
# test plotting to gca (will import pylab)
R2 = dendrogram(Z, orientation=orientation)
plt.close()
assert_equal(R2, expected)
@pytest.mark.skipif(not have_matplotlib, reason="no matplotlib")
def test_dendrogram_truncate_mode(self):
Z = linkage(hierarchy_test_data.ytdist, 'single')
R = dendrogram(Z, 2, 'lastp', show_contracted=True)
plt.close()
assert_equal(R, {'color_list': ['C0'],
'dcoord': [[0.0, 295.0, 295.0, 0.0]],
'icoord': [[5.0, 5.0, 15.0, 15.0]],
'ivl': ['(2)', '(4)'],
'leaves': [6, 9]})
R = dendrogram(Z, 2, 'mtica', show_contracted=True)
plt.close()
assert_equal(R, {'color_list': ['C1', 'C0', 'C0', 'C0'],
'dcoord': [[0.0, 138.0, 138.0, 0.0],
[0.0, 255.0, 255.0, 0.0],
[0.0, 268.0, 268.0, 255.0],
[138.0, 295.0, 295.0, 268.0]],
'icoord': [[5.0, 5.0, 15.0, 15.0],
[35.0, 35.0, 45.0, 45.0],
[25.0, 25.0, 40.0, 40.0],
[10.0, 10.0, 32.5, 32.5]],
'ivl': ['2', '5', '1', '0', '(2)'],
'leaves': [2, 5, 1, 0, 7]})
def test_dendrogram_colors(self):
# Tests dendrogram plots with alternate colors
Z = linkage(hierarchy_test_data.ytdist, 'single')
set_link_color_palette(['c', 'm', 'y', 'k'])
R = dendrogram(Z, no_plot=True,
above_threshold_color='g', color_threshold=250)
set_link_color_palette(['g', 'r', 'c', 'm', 'y', 'k'])
color_list = R['color_list']
assert_equal(color_list, ['c', 'm', 'g', 'g', 'g'])
# reset color palette (global list)
set_link_color_palette(None)
def calculate_maximum_distances(Z):
# Used for testing correctness of maxdists.
n = Z.shape[0] + 1
B = np.zeros((n-1,))
q = np.zeros((3,))
for i in range(0, n - 1):
q[:] = 0.0
left = Z[i, 0]
right = Z[i, 1]
if left >= n:
q[0] = B[int(left) - n]
if right >= n:
q[1] = B[int(right) - n]
q[2] = Z[i, 2]
B[i] = q.max()
return B
def calculate_maximum_inconsistencies(Z, R, k=3):
# Used for testing correctness of maxinconsts.
n = Z.shape[0] + 1
B = np.zeros((n-1,))
q = np.zeros((3,))
for i in range(0, n - 1):
q[:] = 0.0
left = Z[i, 0]
right = Z[i, 1]
if left >= n:
q[0] = B[int(left) - n]
if right >= n:
q[1] = B[int(right) - n]
q[2] = R[i, k]
B[i] = q.max()
return B
def within_tol(a, b, tol):
return np.abs(a - b).max() < tol
def test_unsupported_uncondensed_distance_matrix_linkage_warning():
assert_warns(ClusterWarning, linkage, [[0, 1], [1, 0]])
def test_euclidean_linkage_value_error():
for method in scipy.cluster.hierarchy._EUCLIDEAN_METHODS:
assert_raises(ValueError, linkage, [[1, 1], [1, 1]],
method=method, metric='cityblock')
def test_2x2_linkage():
Z1 = linkage([1], method='single', metric='euclidean')
Z2 = linkage([[0, 1], [0, 0]], method='single', metric='euclidean')
assert_allclose(Z1, Z2)
def test_node_compare():
np.random.seed(23)
nobs = 50
X = np.random.randn(nobs, 4)
Z = scipy.cluster.hierarchy.ward(X)
tree = to_tree(Z)
assert_(tree > tree.get_left())
assert_(tree.get_right() > tree.get_left())
assert_(tree.get_right() == tree.get_right())
assert_(tree.get_right() != tree.get_left())
def test_cut_tree():
np.random.seed(23)
nobs = 50
X = np.random.randn(nobs, 4)
Z = scipy.cluster.hierarchy.ward(X)
cutree = cut_tree(Z)
assert_equal(cutree[:, 0], np.arange(nobs))
assert_equal(cutree[:, -1], np.zeros(nobs))
assert_equal(cutree.max(0), np.arange(nobs - 1, -1, -1))
assert_equal(cutree[:, [-5]], cut_tree(Z, n_clusters=5))
assert_equal(cutree[:, [-5, -10]], cut_tree(Z, n_clusters=[5, 10]))
assert_equal(cutree[:, [-10, -5]], cut_tree(Z, n_clusters=[10, 5]))
nodes = _order_cluster_tree(Z)
heights = np.array([node.dist for node in nodes])
assert_equal(cutree[:, np.searchsorted(heights, [5])],
cut_tree(Z, height=5))
assert_equal(cutree[:, np.searchsorted(heights, [5, 10])],
cut_tree(Z, height=[5, 10]))
assert_equal(cutree[:, np.searchsorted(heights, [10, 5])],
cut_tree(Z, height=[10, 5]))
def test_optimal_leaf_ordering():
# test with the distance vector y
Z = optimal_leaf_ordering(linkage(hierarchy_test_data.ytdist),
hierarchy_test_data.ytdist)
expectedZ = hierarchy_test_data.linkage_ytdist_single_olo
assert_allclose(Z, expectedZ, atol=1e-10)
# test with the observation matrix X
Z = optimal_leaf_ordering(linkage(hierarchy_test_data.X, 'ward'),
hierarchy_test_data.X)
expectedZ = hierarchy_test_data.linkage_X_ward_olo
assert_allclose(Z, expectedZ, atol=1e-06)
def test_Heap():
values = np.array([2, -1, 0, -1.5, 3])
heap = Heap(values)
pair = heap.get_min()
assert_equal(pair['key'], 3)
assert_equal(pair['value'], -1.5)
heap.remove_min()
pair = heap.get_min()
assert_equal(pair['key'], 1)
assert_equal(pair['value'], -1)
heap.change_value(1, 2.5)
pair = heap.get_min()
assert_equal(pair['key'], 2)
assert_equal(pair['value'], 0)
heap.remove_min()
heap.remove_min()
heap.change_value(1, 10)
pair = heap.get_min()
assert_equal(pair['key'], 4)
assert_equal(pair['value'], 3)
heap.remove_min()
pair = heap.get_min()
assert_equal(pair['key'], 1)
assert_equal(pair['value'], 10)