2012 lines
76 KiB
Python
2012 lines
76 KiB
Python
#
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# Author: Damian Eads
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# Date: April 17, 2008
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#
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# Copyright (C) 2008 Damian Eads
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions
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# are met:
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#
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# 1. Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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#
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# 2. Redistributions in binary form must reproduce the above
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# copyright notice, this list of conditions and the following
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# disclaimer in the documentation and/or other materials provided
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# with the distribution.
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#
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# 3. The name of the author may not be used to endorse or promote
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# products derived from this software without specific prior
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# written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
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# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
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# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
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# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
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# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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from __future__ import division, print_function, absolute_import
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import os.path
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from functools import wraps, partial
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from scipy._lib.six import xrange, u
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import numpy as np
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import warnings
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from numpy.linalg import norm
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from numpy.testing import (verbose, assert_,
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assert_array_equal, assert_equal,
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assert_almost_equal, assert_allclose)
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from pytest import raises as assert_raises
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from scipy._lib._numpy_compat import suppress_warnings
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from scipy.spatial.distance import (squareform, pdist, cdist, num_obs_y,
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num_obs_dm, is_valid_dm, is_valid_y,
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_validate_vector, _METRICS_NAMES)
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# these were missing: chebyshev cityblock kulsinski
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from scipy.spatial.distance import (braycurtis, canberra, chebyshev, cityblock,
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correlation, cosine, dice, euclidean,
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hamming, jaccard, kulsinski, mahalanobis,
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matching, minkowski, rogerstanimoto,
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russellrao, seuclidean, sokalmichener,
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sokalsneath, sqeuclidean, yule)
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from scipy.spatial.distance import wminkowski as old_wminkowski
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_filenames = [
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"cdist-X1.txt",
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"cdist-X2.txt",
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"iris.txt",
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"pdist-boolean-inp.txt",
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"pdist-chebyshev-ml-iris.txt",
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"pdist-chebyshev-ml.txt",
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"pdist-cityblock-ml-iris.txt",
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"pdist-cityblock-ml.txt",
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"pdist-correlation-ml-iris.txt",
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"pdist-correlation-ml.txt",
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"pdist-cosine-ml-iris.txt",
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"pdist-cosine-ml.txt",
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"pdist-double-inp.txt",
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"pdist-euclidean-ml-iris.txt",
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"pdist-euclidean-ml.txt",
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"pdist-hamming-ml.txt",
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"pdist-jaccard-ml.txt",
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"pdist-minkowski-3.2-ml-iris.txt",
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"pdist-minkowski-3.2-ml.txt",
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"pdist-minkowski-5.8-ml-iris.txt",
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"pdist-seuclidean-ml-iris.txt",
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"pdist-seuclidean-ml.txt",
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"pdist-spearman-ml.txt",
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"random-bool-data.txt",
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"random-double-data.txt",
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"random-int-data.txt",
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"random-uint-data.txt",
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]
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_tdist = np.array([[0, 662, 877, 255, 412, 996],
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[662, 0, 295, 468, 268, 400],
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[877, 295, 0, 754, 564, 138],
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[255, 468, 754, 0, 219, 869],
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[412, 268, 564, 219, 0, 669],
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[996, 400, 138, 869, 669, 0]], dtype='double')
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_ytdist = squareform(_tdist)
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# A hashmap of expected output arrays for the tests. These arrays
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# come from a list of text files, which are read prior to testing.
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# Each test loads inputs and outputs from this dictionary.
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eo = {}
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def load_testing_files():
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for fn in _filenames:
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name = fn.replace(".txt", "").replace("-ml", "")
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fqfn = os.path.join(os.path.dirname(__file__), 'data', fn)
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fp = open(fqfn)
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eo[name] = np.loadtxt(fp)
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fp.close()
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eo['pdist-boolean-inp'] = np.bool_(eo['pdist-boolean-inp'])
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eo['random-bool-data'] = np.bool_(eo['random-bool-data'])
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eo['random-float32-data'] = np.float32(eo['random-double-data'])
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eo['random-int-data'] = np.int_(eo['random-int-data'])
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eo['random-uint-data'] = np.uint(eo['random-uint-data'])
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load_testing_files()
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def _chk_asarrays(arrays, axis=None):
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arrays = [np.asanyarray(a) for a in arrays]
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if axis is None:
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# np < 1.10 ravel removes subclass from arrays
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arrays = [np.ravel(a) if a.ndim != 1 else a
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for a in arrays]
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axis = 0
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arrays = tuple(np.atleast_1d(a) for a in arrays)
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if axis < 0:
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if not all(a.ndim == arrays[0].ndim for a in arrays):
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raise ValueError("array ndim must be the same for neg axis")
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axis = range(arrays[0].ndim)[axis]
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return arrays + (axis,)
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def _chk_weights(arrays, weights=None, axis=None,
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force_weights=False, simplify_weights=True,
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pos_only=False, neg_check=False,
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nan_screen=False, mask_screen=False,
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ddof=None):
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chked = _chk_asarrays(arrays, axis=axis)
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arrays, axis = chked[:-1], chked[-1]
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simplify_weights = simplify_weights and not force_weights
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if not force_weights and mask_screen:
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force_weights = any(np.ma.getmask(a) is not np.ma.nomask for a in arrays)
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if nan_screen:
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has_nans = [np.isnan(np.sum(a)) for a in arrays]
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if any(has_nans):
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mask_screen = True
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force_weights = True
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arrays = tuple(np.ma.masked_invalid(a) if has_nan else a
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for a, has_nan in zip(arrays, has_nans))
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if weights is not None:
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weights = np.asanyarray(weights)
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elif force_weights:
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weights = np.ones(arrays[0].shape[axis])
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else:
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return arrays + (weights, axis)
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if ddof:
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weights = _freq_weights(weights)
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if mask_screen:
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weights = _weight_masked(arrays, weights, axis)
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if not all(weights.shape == (a.shape[axis],) for a in arrays):
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raise ValueError("weights shape must match arrays along axis")
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if neg_check and (weights < 0).any():
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raise ValueError("weights cannot be negative")
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if pos_only:
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pos_weights = np.where(weights > 0)[0]
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if pos_weights.size < weights.size:
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arrays = tuple(np.take(a, pos_weights, axis=axis) for a in arrays)
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weights = weights[pos_weights]
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if simplify_weights and (weights == 1).all():
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weights = None
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return arrays + (weights, axis)
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def _freq_weights(weights):
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if weights is None:
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return weights
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int_weights = weights.astype(int)
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if (weights != int_weights).any():
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raise ValueError("frequency (integer count-type) weights required %s" % weights)
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return int_weights
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def _weight_masked(arrays, weights, axis):
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if axis is None:
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axis = 0
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weights = np.asanyarray(weights)
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for a in arrays:
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axis_mask = np.ma.getmask(a)
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if axis_mask is np.ma.nomask:
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continue
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if a.ndim > 1:
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not_axes = tuple(i for i in range(a.ndim) if i != axis)
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axis_mask = axis_mask.any(axis=not_axes)
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weights *= 1 - axis_mask.astype(int)
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return weights
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def within_tol(a, b, tol):
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return np.abs(a - b).max() < tol
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def _assert_within_tol(a, b, atol=0, rtol=0, verbose_=False):
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if verbose_:
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print(np.abs(a - b).max())
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assert_allclose(a, b, rtol=rtol, atol=atol)
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def _rand_split(arrays, weights, axis, split_per, seed=None):
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# inverse operation for stats.collapse_weights
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weights = np.array(weights, dtype=np.float64) # modified inplace; need a copy
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seeded_rand = np.random.RandomState(seed)
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def mytake(a, ix, axis):
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record = np.asanyarray(np.take(a, ix, axis=axis))
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return record.reshape([a.shape[i] if i != axis else 1
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for i in range(a.ndim)])
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n_obs = arrays[0].shape[axis]
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assert all(a.shape[axis] == n_obs for a in arrays), "data must be aligned on sample axis"
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for i in range(int(split_per) * n_obs):
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split_ix = seeded_rand.randint(n_obs + i)
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prev_w = weights[split_ix]
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q = seeded_rand.rand()
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weights[split_ix] = q * prev_w
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weights = np.append(weights, (1. - q) * prev_w)
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arrays = [np.append(a, mytake(a, split_ix, axis=axis),
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axis=axis) for a in arrays]
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return arrays, weights
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def _rough_check(a, b, compare_assert=partial(assert_allclose, atol=1e-5),
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key=lambda x: x, w=None):
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check_a = key(a)
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check_b = key(b)
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try:
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if np.array(check_a != check_b).any(): # try strict equality for string types
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compare_assert(check_a, check_b)
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except AttributeError: # masked array
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compare_assert(check_a, check_b)
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except (TypeError, ValueError): # nested data structure
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for a_i, b_i in zip(check_a, check_b):
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_rough_check(a_i, b_i, compare_assert=compare_assert)
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# diff from test_stats:
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# n_args=2, weight_arg='w', default_axis=None
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# ma_safe = False, nan_safe = False
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def _weight_checked(fn, n_args=2, default_axis=None, key=lambda x: x, weight_arg='w',
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squeeze=True, silent=False,
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ones_test=True, const_test=True, dup_test=True,
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split_test=True, dud_test=True, ma_safe=False, ma_very_safe=False, nan_safe=False,
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split_per=1.0, seed=0, compare_assert=partial(assert_allclose, atol=1e-5)):
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"""runs fn on its arguments 2 or 3 ways, checks that the results are the same,
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then returns the same thing it would have returned before"""
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@wraps(fn)
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def wrapped(*args, **kwargs):
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result = fn(*args, **kwargs)
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arrays = args[:n_args]
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rest = args[n_args:]
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weights = kwargs.get(weight_arg, None)
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axis = kwargs.get('axis', default_axis)
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chked = _chk_weights(arrays, weights=weights, axis=axis, force_weights=True, mask_screen=True)
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arrays, weights, axis = chked[:-2], chked[-2], chked[-1]
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if squeeze:
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arrays = [np.atleast_1d(a.squeeze()) for a in arrays]
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try:
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# WEIGHTS CHECK 1: EQUAL WEIGHTED OBESERVATIONS
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args = tuple(arrays) + rest
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if ones_test:
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kwargs[weight_arg] = weights
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_rough_check(result, fn(*args, **kwargs), key=key)
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if const_test:
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kwargs[weight_arg] = weights * 101.0
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_rough_check(result, fn(*args, **kwargs), key=key)
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kwargs[weight_arg] = weights * 0.101
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try:
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_rough_check(result, fn(*args, **kwargs), key=key)
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except Exception as e:
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raise type(e)((e, arrays, weights))
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# WEIGHTS CHECK 2: ADDL 0-WEIGHTED OBS
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if dud_test:
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# add randomly resampled rows, weighted at 0
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dud_arrays, dud_weights = _rand_split(arrays, weights, axis, split_per=split_per, seed=seed)
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dud_weights[:weights.size] = weights # not exactly 1 because of masked arrays
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dud_weights[weights.size:] = 0
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dud_args = tuple(dud_arrays) + rest
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kwargs[weight_arg] = dud_weights
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_rough_check(result, fn(*dud_args, **kwargs), key=key)
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# increase the value of those 0-weighted rows
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for a in dud_arrays:
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indexer = [slice(None)] * a.ndim
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indexer[axis] = slice(weights.size, None)
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a[indexer] = a[indexer] * 101
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dud_args = tuple(dud_arrays) + rest
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_rough_check(result, fn(*dud_args, **kwargs), key=key)
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# set those 0-weighted rows to NaNs
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for a in dud_arrays:
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indexer = [slice(None)] * a.ndim
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indexer[axis] = slice(weights.size, None)
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a[indexer] = a[indexer] * np.nan
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if kwargs.get("nan_policy", None) == "omit" and nan_safe:
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dud_args = tuple(dud_arrays) + rest
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_rough_check(result, fn(*dud_args, **kwargs), key=key)
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# mask out those nan values
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if ma_safe:
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dud_arrays = [np.ma.masked_invalid(a) for a in dud_arrays]
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dud_args = tuple(dud_arrays) + rest
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_rough_check(result, fn(*dud_args, **kwargs), key=key)
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if ma_very_safe:
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kwargs[weight_arg] = None
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_rough_check(result, fn(*dud_args, **kwargs), key=key)
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del dud_arrays, dud_args, dud_weights
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# WEIGHTS CHECK 3: DUPLICATE DATA (DUMB SPLITTING)
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if dup_test:
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dup_arrays = [np.append(a, a, axis=axis) for a in arrays]
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dup_weights = np.append(weights, weights) / 2.0
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dup_args = tuple(dup_arrays) + rest
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kwargs[weight_arg] = dup_weights
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_rough_check(result, fn(*dup_args, **kwargs), key=key)
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del dup_args, dup_arrays, dup_weights
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# WEIGHT CHECK 3: RANDOM SPLITTING
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if split_test and split_per > 0:
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split_arrays, split_weights = _rand_split(arrays, weights, axis, split_per=split_per, seed=seed)
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split_args = tuple(split_arrays) + rest
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kwargs[weight_arg] = split_weights
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_rough_check(result, fn(*split_args, **kwargs), key=key)
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except NotImplementedError as e:
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# when some combination of arguments makes weighting impossible,
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# this is the desired response
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if not silent:
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warnings.warn("%s NotImplemented weights: %s" % (fn.__name__, e))
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return result
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return wrapped
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wcdist = _weight_checked(cdist, default_axis=1, squeeze=False)
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wcdist_no_const = _weight_checked(cdist, default_axis=1, squeeze=False, const_test=False)
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wpdist = _weight_checked(pdist, default_axis=1, squeeze=False, n_args=1)
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wpdist_no_const = _weight_checked(pdist, default_axis=1, squeeze=False, const_test=False, n_args=1)
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wrogerstanimoto = _weight_checked(rogerstanimoto)
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wmatching = whamming = _weight_checked(hamming, dud_test=False)
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wyule = _weight_checked(yule)
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wdice = _weight_checked(dice)
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wcityblock = _weight_checked(cityblock)
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wchebyshev = _weight_checked(chebyshev)
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wcosine = _weight_checked(cosine)
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wcorrelation = _weight_checked(correlation)
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wkulsinski = _weight_checked(kulsinski)
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wminkowski = _weight_checked(minkowski, const_test=False)
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wjaccard = _weight_checked(jaccard)
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weuclidean = _weight_checked(euclidean, const_test=False)
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wsqeuclidean = _weight_checked(sqeuclidean, const_test=False)
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wbraycurtis = _weight_checked(braycurtis)
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wcanberra = _weight_checked(canberra, const_test=False)
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wsokalsneath = _weight_checked(sokalsneath)
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wsokalmichener = _weight_checked(sokalmichener)
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wrussellrao = _weight_checked(russellrao)
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class TestCdist(object):
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def setup_method(self):
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self.rnd_eo_names = ['random-float32-data', 'random-int-data',
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'random-uint-data', 'random-double-data',
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'random-bool-data']
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self.valid_upcasts = {'bool': [np.uint, np.int_, np.float32, np.double],
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'uint': [np.int_, np.float32, np.double],
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'int': [np.float32, np.double],
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'float32': [np.double]}
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def test_cdist_extra_args(self):
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# Tests that args and kwargs are correctly handled
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def _my_metric(x, y, arg, kwarg=1, kwarg2=2):
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return arg + kwarg + kwarg2
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X1 = [[1., 2., 3.], [1.2, 2.3, 3.4], [2.2, 2.3, 4.4]]
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X2 = [[7., 5., 8.], [7.5, 5.8, 8.4], [5.5, 5.8, 4.4]]
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kwargs = {'N0tV4l1D_p4raM': 3.14, "w":np.arange(3)}
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args = [3.14] * 200
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with suppress_warnings() as w:
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w.filter(DeprecationWarning)
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for metric in _METRICS_NAMES:
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assert_raises(TypeError, cdist, X1, X2,
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metric=metric, **kwargs)
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assert_raises(TypeError, cdist, X1, X2,
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metric=eval(metric), **kwargs)
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assert_raises(TypeError, cdist, X1, X2,
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metric="test_" + metric, **kwargs)
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assert_raises(TypeError, cdist, X1, X2,
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metric=metric, *args)
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assert_raises(TypeError, cdist, X1, X2,
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metric=eval(metric), *args)
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assert_raises(TypeError, cdist, X1, X2,
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metric="test_" + metric, *args)
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assert_raises(TypeError, cdist, X1, X2, _my_metric)
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assert_raises(TypeError, cdist, X1, X2, _my_metric, *args)
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assert_raises(TypeError, cdist, X1, X2, _my_metric, **kwargs)
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assert_raises(TypeError, cdist, X1, X2, _my_metric,
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kwarg=2.2, kwarg2=3.3)
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assert_raises(TypeError, cdist, X1, X2, _my_metric, 1, 2, kwarg=2.2)
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assert_raises(TypeError, cdist, X1, X2, _my_metric, 1.1, 2.2, 3.3)
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assert_raises(TypeError, cdist, X1, X2, _my_metric, 1.1, 2.2)
|
|
assert_raises(TypeError, cdist, X1, X2, _my_metric, 1.1)
|
|
assert_raises(TypeError, cdist, X1, X2, _my_metric, 1.1,
|
|
kwarg=2.2, kwarg2=3.3)
|
|
|
|
# this should work
|
|
assert_allclose(cdist(X1, X2, metric=_my_metric,
|
|
arg=1.1, kwarg2=3.3), 5.4)
|
|
|
|
def test_cdist_euclidean_random_unicode(self):
|
|
eps = 1e-07
|
|
X1 = eo['cdist-X1']
|
|
X2 = eo['cdist-X2']
|
|
Y1 = wcdist_no_const(X1, X2, u('euclidean'))
|
|
Y2 = wcdist_no_const(X1, X2, u('test_euclidean'))
|
|
_assert_within_tol(Y1, Y2, eps, verbose > 2)
|
|
|
|
def test_cdist_minkowski_random_p3d8(self):
|
|
eps = 1e-07
|
|
X1 = eo['cdist-X1']
|
|
X2 = eo['cdist-X2']
|
|
Y1 = wcdist_no_const(X1, X2, 'minkowski', p=3.8)
|
|
Y2 = wcdist_no_const(X1, X2, 'test_minkowski', p=3.8)
|
|
_assert_within_tol(Y1, Y2, eps, verbose > 2)
|
|
|
|
def test_cdist_minkowski_random_p4d6(self):
|
|
eps = 1e-07
|
|
X1 = eo['cdist-X1']
|
|
X2 = eo['cdist-X2']
|
|
Y1 = wcdist_no_const(X1, X2, 'minkowski', p=4.6)
|
|
Y2 = wcdist_no_const(X1, X2, 'test_minkowski', p=4.6)
|
|
_assert_within_tol(Y1, Y2, eps, verbose > 2)
|
|
|
|
def test_cdist_minkowski_random_p1d23(self):
|
|
eps = 1e-07
|
|
X1 = eo['cdist-X1']
|
|
X2 = eo['cdist-X2']
|
|
Y1 = wcdist_no_const(X1, X2, 'minkowski', p=1.23)
|
|
Y2 = wcdist_no_const(X1, X2, 'test_minkowski', p=1.23)
|
|
_assert_within_tol(Y1, Y2, eps, verbose > 2)
|
|
|
|
def test_cdist_cosine_random(self):
|
|
eps = 1e-07
|
|
X1 = eo['cdist-X1']
|
|
X2 = eo['cdist-X2']
|
|
Y1 = wcdist(X1, X2, 'cosine')
|
|
|
|
# Naive implementation
|
|
def norms(X):
|
|
return np.linalg.norm(X, axis=1).reshape(-1, 1)
|
|
|
|
Y2 = 1 - np.dot((X1 / norms(X1)), (X2 / norms(X2)).T)
|
|
|
|
_assert_within_tol(Y1, Y2, eps, verbose > 2)
|
|
|
|
def test_cdist_mahalanobis(self):
|
|
# 1-dimensional observations
|
|
x1 = np.array([[2], [3]])
|
|
x2 = np.array([[2], [5]])
|
|
dist = cdist(x1, x2, metric='mahalanobis')
|
|
assert_allclose(dist, [[0.0, np.sqrt(4.5)], [np.sqrt(0.5), np.sqrt(2)]])
|
|
|
|
# 2-dimensional observations
|
|
x1 = np.array([[0, 0], [-1, 0]])
|
|
x2 = np.array([[0, 2], [1, 0], [0, -2]])
|
|
dist = cdist(x1, x2, metric='mahalanobis')
|
|
rt2 = np.sqrt(2)
|
|
assert_allclose(dist, [[rt2, rt2, rt2], [2, 2 * rt2, 2]])
|
|
|
|
# Too few observations
|
|
assert_raises(ValueError,
|
|
cdist, [[0, 1]], [[2, 3]], metric='mahalanobis')
|
|
|
|
def test_cdist_custom_notdouble(self):
|
|
class myclass(object):
|
|
pass
|
|
|
|
def _my_metric(x, y):
|
|
if not isinstance(x[0], myclass) or not isinstance(y[0], myclass):
|
|
raise ValueError("Type has been changed")
|
|
return 1.123
|
|
data = np.array([[myclass()]], dtype=object)
|
|
cdist_y = cdist(data, data, metric=_my_metric)
|
|
right_y = 1.123
|
|
assert_equal(cdist_y, right_y, verbose=verbose > 2)
|
|
|
|
def _check_calling_conventions(self, X1, X2, metric, eps=1e-07, **kwargs):
|
|
# helper function for test_cdist_calling_conventions
|
|
try:
|
|
y1 = cdist(X1, X2, metric=metric, **kwargs)
|
|
y2 = cdist(X1, X2, metric=eval(metric), **kwargs)
|
|
y3 = cdist(X1, X2, metric="test_" + metric, **kwargs)
|
|
except Exception as e:
|
|
e_cls = e.__class__
|
|
if verbose > 2:
|
|
print(e_cls.__name__)
|
|
print(e)
|
|
assert_raises(e_cls, cdist, X1, X2, metric=metric, **kwargs)
|
|
assert_raises(e_cls, cdist, X1, X2, metric=eval(metric), **kwargs)
|
|
assert_raises(e_cls, cdist, X1, X2, metric="test_" + metric, **kwargs)
|
|
else:
|
|
_assert_within_tol(y1, y2, rtol=eps, verbose_=verbose > 2)
|
|
_assert_within_tol(y1, y3, rtol=eps, verbose_=verbose > 2)
|
|
|
|
def test_cdist_calling_conventions(self):
|
|
# Ensures that specifying the metric with a str or scipy function
|
|
# gives the same behaviour (i.e. same result or same exception).
|
|
# NOTE: The correctness should be checked within each metric tests.
|
|
for eo_name in self.rnd_eo_names:
|
|
# subsampling input data to speed-up tests
|
|
# NOTE: num samples needs to be > than dimensions for mahalanobis
|
|
X1 = eo[eo_name][::5, ::-2]
|
|
X2 = eo[eo_name][1::5, ::2]
|
|
for metric in _METRICS_NAMES:
|
|
if verbose > 2:
|
|
print("testing: ", metric, " with: ", eo_name)
|
|
if metric == 'wminkowski':
|
|
continue
|
|
if metric in {'dice', 'yule', 'kulsinski', 'matching',
|
|
'rogerstanimoto', 'russellrao', 'sokalmichener',
|
|
'sokalsneath'} and 'bool' not in eo_name:
|
|
# python version permits non-bools e.g. for fuzzy logic
|
|
continue
|
|
self._check_calling_conventions(X1, X2, metric)
|
|
|
|
# Testing built-in metrics with extra args
|
|
if metric == "seuclidean":
|
|
X12 = np.vstack([X1, X2]).astype(np.double)
|
|
V = np.var(X12, axis=0, ddof=1)
|
|
self._check_calling_conventions(X1, X2, metric, V=V)
|
|
elif metric == "mahalanobis":
|
|
X12 = np.vstack([X1, X2]).astype(np.double)
|
|
V = np.atleast_2d(np.cov(X12.T))
|
|
VI = np.array(np.linalg.inv(V).T)
|
|
self._check_calling_conventions(X1, X2, metric, VI=VI)
|
|
|
|
def test_cdist_dtype_equivalence(self):
|
|
# Tests that the result is not affected by type up-casting
|
|
eps = 1e-07
|
|
tests = [(eo['random-bool-data'], self.valid_upcasts['bool']),
|
|
(eo['random-uint-data'], self.valid_upcasts['uint']),
|
|
(eo['random-int-data'], self.valid_upcasts['int']),
|
|
(eo['random-float32-data'], self.valid_upcasts['float32'])]
|
|
for metric in _METRICS_NAMES:
|
|
for test in tests:
|
|
X1 = test[0][::5, ::-2]
|
|
X2 = test[0][1::5, ::2]
|
|
try:
|
|
y1 = cdist(X1, X2, metric=metric)
|
|
except Exception as e:
|
|
e_cls = e.__class__
|
|
if verbose > 2:
|
|
print(e_cls.__name__)
|
|
print(e)
|
|
for new_type in test[1]:
|
|
X1new = new_type(X1)
|
|
X2new = new_type(X2)
|
|
assert_raises(e_cls, cdist, X1new, X2new, metric=metric)
|
|
else:
|
|
for new_type in test[1]:
|
|
y2 = cdist(new_type(X1), new_type(X2), metric=metric)
|
|
_assert_within_tol(y1, y2, eps, verbose > 2)
|
|
|
|
def test_cdist_out(self):
|
|
# Test that out parameter works properly
|
|
eps = 1e-07
|
|
X1 = eo['cdist-X1']
|
|
X2 = eo['cdist-X2']
|
|
out_r, out_c = X1.shape[0], X2.shape[0]
|
|
for metric in _METRICS_NAMES:
|
|
kwargs = dict()
|
|
if metric in ['minkowski', 'wminkowski']:
|
|
kwargs['p'] = 1.23
|
|
if metric == 'wminkowski':
|
|
kwargs['w'] = 1.0 / X1.std(axis=0)
|
|
out1 = np.empty((out_r, out_c), dtype=np.double)
|
|
Y1 = cdist(X1, X2, metric, **kwargs)
|
|
Y2 = cdist(X1, X2, metric, out=out1, **kwargs)
|
|
# test that output is numerically equivalent
|
|
_assert_within_tol(Y1, Y2, eps, verbose > 2)
|
|
# test that Y_test1 and out1 are the same object
|
|
assert_(Y2 is out1)
|
|
# test for incorrect shape
|
|
out2 = np.empty((out_r-1, out_c+1), dtype=np.double)
|
|
assert_raises(ValueError, cdist, X1, X2, metric, out=out2, **kwargs)
|
|
# test for C-contiguous order
|
|
out3 = np.empty((2 * out_r, 2 * out_c), dtype=np.double)[::2, ::2]
|
|
out4 = np.empty((out_r, out_c), dtype=np.double, order='F')
|
|
assert_raises(ValueError, cdist, X1, X2, metric, out=out3, **kwargs)
|
|
assert_raises(ValueError, cdist, X1, X2, metric, out=out4, **kwargs)
|
|
# test for incorrect dtype
|
|
out5 = np.empty((out_r, out_c), dtype=np.int64)
|
|
assert_raises(ValueError, cdist, X1, X2, metric, out=out5, **kwargs)
|
|
|
|
def test_striding(self):
|
|
# test that striding is handled correct with calls to
|
|
# _copy_array_if_base_present
|
|
eps = 1e-07
|
|
X1 = eo['cdist-X1'][::2, ::2]
|
|
X2 = eo['cdist-X2'][::2, ::2]
|
|
X1_copy = X1.copy()
|
|
X2_copy = X2.copy()
|
|
|
|
# confirm equivalence
|
|
assert_equal(X1, X1_copy)
|
|
assert_equal(X2, X2_copy)
|
|
# confirm contiguity
|
|
assert_(not X1.flags.c_contiguous)
|
|
assert_(not X2.flags.c_contiguous)
|
|
assert_(X1_copy.flags.c_contiguous)
|
|
assert_(X2_copy.flags.c_contiguous)
|
|
|
|
for metric in _METRICS_NAMES:
|
|
kwargs = dict()
|
|
if metric in ['minkowski', 'wminkowski']:
|
|
kwargs['p'] = 1.23
|
|
if metric == 'wminkowski':
|
|
kwargs['w'] = 1.0 / X1.std(axis=0)
|
|
Y1 = cdist(X1, X2, metric, **kwargs)
|
|
Y2 = cdist(X1_copy, X2_copy, metric, **kwargs)
|
|
# test that output is numerically equivalent
|
|
_assert_within_tol(Y1, Y2, eps, verbose > 2)
|
|
|
|
class TestPdist(object):
|
|
|
|
def setup_method(self):
|
|
self.rnd_eo_names = ['random-float32-data', 'random-int-data',
|
|
'random-uint-data', 'random-double-data',
|
|
'random-bool-data']
|
|
self.valid_upcasts = {'bool': [np.uint, np.int_, np.float32, np.double],
|
|
'uint': [np.int_, np.float32, np.double],
|
|
'int': [np.float32, np.double],
|
|
'float32': [np.double]}
|
|
|
|
def test_pdist_extra_args(self):
|
|
# Tests that args and kwargs are correctly handled
|
|
def _my_metric(x, y, arg, kwarg=1, kwarg2=2):
|
|
return arg + kwarg + kwarg2
|
|
|
|
X1 = [[1., 2.], [1.2, 2.3], [2.2, 2.3]]
|
|
kwargs = {'N0tV4l1D_p4raM': 3.14, "w":np.arange(2)}
|
|
args = [3.14] * 200
|
|
with suppress_warnings() as w:
|
|
w.filter(DeprecationWarning)
|
|
for metric in _METRICS_NAMES:
|
|
assert_raises(TypeError, pdist, X1, metric=metric, **kwargs)
|
|
assert_raises(TypeError, pdist, X1,
|
|
metric=eval(metric), **kwargs)
|
|
assert_raises(TypeError, pdist, X1,
|
|
metric="test_" + metric, **kwargs)
|
|
assert_raises(TypeError, pdist, X1, metric=metric, *args)
|
|
assert_raises(TypeError, pdist, X1, metric=eval(metric), *args)
|
|
assert_raises(TypeError, pdist, X1,
|
|
metric="test_" + metric, *args)
|
|
|
|
assert_raises(TypeError, pdist, X1, _my_metric)
|
|
assert_raises(TypeError, pdist, X1, _my_metric, *args)
|
|
assert_raises(TypeError, pdist, X1, _my_metric, **kwargs)
|
|
assert_raises(TypeError, pdist, X1, _my_metric,
|
|
kwarg=2.2, kwarg2=3.3)
|
|
assert_raises(TypeError, pdist, X1, _my_metric, 1, 2, kwarg=2.2)
|
|
|
|
assert_raises(TypeError, pdist, X1, _my_metric, 1.1, 2.2, 3.3)
|
|
assert_raises(TypeError, pdist, X1, _my_metric, 1.1, 2.2)
|
|
assert_raises(TypeError, pdist, X1, _my_metric, 1.1)
|
|
assert_raises(TypeError, pdist, X1, _my_metric, 1.1,
|
|
kwarg=2.2, kwarg2=3.3)
|
|
|
|
# these should work
|
|
assert_allclose(pdist(X1, metric=_my_metric,
|
|
arg=1.1, kwarg2=3.3), 5.4)
|
|
|
|
def test_pdist_euclidean_random(self):
|
|
eps = 1e-07
|
|
X = eo['pdist-double-inp']
|
|
Y_right = eo['pdist-euclidean']
|
|
Y_test1 = wpdist_no_const(X, 'euclidean')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_euclidean_random_u(self):
|
|
eps = 1e-07
|
|
X = eo['pdist-double-inp']
|
|
Y_right = eo['pdist-euclidean']
|
|
Y_test1 = wpdist_no_const(X, u('euclidean'))
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_euclidean_random_float32(self):
|
|
eps = 1e-07
|
|
X = np.float32(eo['pdist-double-inp'])
|
|
Y_right = eo['pdist-euclidean']
|
|
Y_test1 = wpdist_no_const(X, 'euclidean')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_euclidean_random_nonC(self):
|
|
eps = 1e-07
|
|
X = eo['pdist-double-inp']
|
|
Y_right = eo['pdist-euclidean']
|
|
Y_test2 = wpdist_no_const(X, 'test_euclidean')
|
|
_assert_within_tol(Y_test2, Y_right, eps)
|
|
|
|
def test_pdist_euclidean_iris_double(self):
|
|
eps = 1e-07
|
|
X = eo['iris']
|
|
Y_right = eo['pdist-euclidean-iris']
|
|
Y_test1 = wpdist_no_const(X, 'euclidean')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_euclidean_iris_float32(self):
|
|
eps = 1e-06
|
|
X = np.float32(eo['iris'])
|
|
Y_right = eo['pdist-euclidean-iris']
|
|
Y_test1 = wpdist_no_const(X, 'euclidean')
|
|
_assert_within_tol(Y_test1, Y_right, eps, verbose > 2)
|
|
|
|
def test_pdist_euclidean_iris_nonC(self):
|
|
# Test pdist(X, 'test_euclidean') [the non-C implementation] on the
|
|
# Iris data set.
|
|
eps = 1e-07
|
|
X = eo['iris']
|
|
Y_right = eo['pdist-euclidean-iris']
|
|
Y_test2 = wpdist_no_const(X, 'test_euclidean')
|
|
_assert_within_tol(Y_test2, Y_right, eps)
|
|
|
|
def test_pdist_seuclidean_random(self):
|
|
eps = 1e-05
|
|
X = eo['pdist-double-inp']
|
|
Y_right = eo['pdist-seuclidean']
|
|
Y_test1 = pdist(X, 'seuclidean')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_seuclidean_random_float32(self):
|
|
eps = 1e-05
|
|
X = np.float32(eo['pdist-double-inp'])
|
|
Y_right = eo['pdist-seuclidean']
|
|
Y_test1 = pdist(X, 'seuclidean')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_seuclidean_random_nonC(self):
|
|
# Test pdist(X, 'test_sqeuclidean') [the non-C implementation]
|
|
eps = 1e-05
|
|
X = eo['pdist-double-inp']
|
|
Y_right = eo['pdist-seuclidean']
|
|
Y_test2 = pdist(X, 'test_seuclidean')
|
|
_assert_within_tol(Y_test2, Y_right, eps)
|
|
|
|
def test_pdist_seuclidean_iris(self):
|
|
eps = 1e-05
|
|
X = eo['iris']
|
|
Y_right = eo['pdist-seuclidean-iris']
|
|
Y_test1 = pdist(X, 'seuclidean')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_seuclidean_iris_float32(self):
|
|
# Tests pdist(X, 'seuclidean') on the Iris data set (float32).
|
|
eps = 1e-05
|
|
X = np.float32(eo['iris'])
|
|
Y_right = eo['pdist-seuclidean-iris']
|
|
Y_test1 = pdist(X, 'seuclidean')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_seuclidean_iris_nonC(self):
|
|
# Test pdist(X, 'test_seuclidean') [the non-C implementation] on the
|
|
# Iris data set.
|
|
eps = 1e-05
|
|
X = eo['iris']
|
|
Y_right = eo['pdist-seuclidean-iris']
|
|
Y_test2 = pdist(X, 'test_seuclidean')
|
|
_assert_within_tol(Y_test2, Y_right, eps)
|
|
|
|
def test_pdist_cosine_random(self):
|
|
eps = 1e-08
|
|
X = eo['pdist-double-inp']
|
|
Y_right = eo['pdist-cosine']
|
|
Y_test1 = wpdist(X, 'cosine')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_cosine_random_float32(self):
|
|
eps = 1e-08
|
|
X = np.float32(eo['pdist-double-inp'])
|
|
Y_right = eo['pdist-cosine']
|
|
Y_test1 = wpdist(X, 'cosine')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_cosine_random_nonC(self):
|
|
# Test pdist(X, 'test_cosine') [the non-C implementation]
|
|
eps = 1e-08
|
|
X = eo['pdist-double-inp']
|
|
Y_right = eo['pdist-cosine']
|
|
Y_test2 = wpdist(X, 'test_cosine')
|
|
_assert_within_tol(Y_test2, Y_right, eps)
|
|
|
|
def test_pdist_cosine_iris(self):
|
|
eps = 1e-08
|
|
X = eo['iris']
|
|
Y_right = eo['pdist-cosine-iris']
|
|
Y_test1 = wpdist(X, 'cosine')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_cosine_iris_float32(self):
|
|
eps = 1e-07
|
|
X = np.float32(eo['iris'])
|
|
Y_right = eo['pdist-cosine-iris']
|
|
Y_test1 = wpdist(X, 'cosine')
|
|
_assert_within_tol(Y_test1, Y_right, eps, verbose > 2)
|
|
|
|
def test_pdist_cosine_iris_nonC(self):
|
|
eps = 1e-08
|
|
X = eo['iris']
|
|
Y_right = eo['pdist-cosine-iris']
|
|
Y_test2 = wpdist(X, 'test_cosine')
|
|
_assert_within_tol(Y_test2, Y_right, eps)
|
|
|
|
def test_pdist_cosine_bounds(self):
|
|
# Test adapted from @joernhees's example at gh-5208: case where
|
|
# cosine distance used to be negative. XXX: very sensitive to the
|
|
# specific norm computation.
|
|
x = np.abs(np.random.RandomState(1337).rand(91))
|
|
X = np.vstack([x, x])
|
|
assert_(wpdist(X, 'cosine')[0] >= 0,
|
|
msg='cosine distance should be non-negative')
|
|
|
|
def test_pdist_cityblock_random(self):
|
|
eps = 1e-06
|
|
X = eo['pdist-double-inp']
|
|
Y_right = eo['pdist-cityblock']
|
|
Y_test1 = wpdist_no_const(X, 'cityblock')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_cityblock_random_float32(self):
|
|
eps = 1e-06
|
|
X = np.float32(eo['pdist-double-inp'])
|
|
Y_right = eo['pdist-cityblock']
|
|
Y_test1 = wpdist_no_const(X, 'cityblock')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_cityblock_random_nonC(self):
|
|
eps = 1e-06
|
|
X = eo['pdist-double-inp']
|
|
Y_right = eo['pdist-cityblock']
|
|
Y_test2 = wpdist_no_const(X, 'test_cityblock')
|
|
_assert_within_tol(Y_test2, Y_right, eps)
|
|
|
|
def test_pdist_cityblock_iris(self):
|
|
eps = 1e-14
|
|
X = eo['iris']
|
|
Y_right = eo['pdist-cityblock-iris']
|
|
Y_test1 = wpdist_no_const(X, 'cityblock')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_cityblock_iris_float32(self):
|
|
eps = 1e-06
|
|
X = np.float32(eo['iris'])
|
|
Y_right = eo['pdist-cityblock-iris']
|
|
Y_test1 = wpdist_no_const(X, 'cityblock')
|
|
_assert_within_tol(Y_test1, Y_right, eps, verbose > 2)
|
|
|
|
def test_pdist_cityblock_iris_nonC(self):
|
|
# Test pdist(X, 'test_cityblock') [the non-C implementation] on the
|
|
# Iris data set.
|
|
eps = 1e-14
|
|
X = eo['iris']
|
|
Y_right = eo['pdist-cityblock-iris']
|
|
Y_test2 = wpdist_no_const(X, 'test_cityblock')
|
|
_assert_within_tol(Y_test2, Y_right, eps)
|
|
|
|
def test_pdist_correlation_random(self):
|
|
eps = 1e-07
|
|
X = eo['pdist-double-inp']
|
|
Y_right = eo['pdist-correlation']
|
|
Y_test1 = wpdist(X, 'correlation')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_correlation_random_float32(self):
|
|
eps = 1e-07
|
|
X = np.float32(eo['pdist-double-inp'])
|
|
Y_right = eo['pdist-correlation']
|
|
Y_test1 = wpdist(X, 'correlation')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_correlation_random_nonC(self):
|
|
eps = 1e-07
|
|
X = eo['pdist-double-inp']
|
|
Y_right = eo['pdist-correlation']
|
|
Y_test2 = wpdist(X, 'test_correlation')
|
|
_assert_within_tol(Y_test2, Y_right, eps)
|
|
|
|
def test_pdist_correlation_iris(self):
|
|
eps = 1e-08
|
|
X = eo['iris']
|
|
Y_right = eo['pdist-correlation-iris']
|
|
Y_test1 = wpdist(X, 'correlation')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_correlation_iris_float32(self):
|
|
eps = 1e-07
|
|
X = eo['iris']
|
|
Y_right = np.float32(eo['pdist-correlation-iris'])
|
|
Y_test1 = wpdist(X, 'correlation')
|
|
_assert_within_tol(Y_test1, Y_right, eps, verbose > 2)
|
|
|
|
def test_pdist_correlation_iris_nonC(self):
|
|
eps = 1e-08
|
|
X = eo['iris']
|
|
Y_right = eo['pdist-correlation-iris']
|
|
Y_test2 = wpdist(X, 'test_correlation')
|
|
_assert_within_tol(Y_test2, Y_right, eps)
|
|
|
|
def test_pdist_minkowski_random(self):
|
|
eps = 1e-05
|
|
X = eo['pdist-double-inp']
|
|
Y_right = eo['pdist-minkowski-3.2']
|
|
Y_test1 = wpdist_no_const(X, 'minkowski', p=3.2)
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_minkowski_random_float32(self):
|
|
eps = 1e-05
|
|
X = np.float32(eo['pdist-double-inp'])
|
|
Y_right = eo['pdist-minkowski-3.2']
|
|
Y_test1 = wpdist_no_const(X, 'minkowski', p=3.2)
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_minkowski_random_nonC(self):
|
|
eps = 1e-05
|
|
X = eo['pdist-double-inp']
|
|
Y_right = eo['pdist-minkowski-3.2']
|
|
Y_test2 = wpdist_no_const(X, 'test_minkowski', p=3.2)
|
|
_assert_within_tol(Y_test2, Y_right, eps)
|
|
|
|
def test_pdist_minkowski_3_2_iris(self):
|
|
eps = 1e-07
|
|
X = eo['iris']
|
|
Y_right = eo['pdist-minkowski-3.2-iris']
|
|
Y_test1 = wpdist_no_const(X, 'minkowski', p=3.2)
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_minkowski_3_2_iris_float32(self):
|
|
eps = 1e-06
|
|
X = np.float32(eo['iris'])
|
|
Y_right = eo['pdist-minkowski-3.2-iris']
|
|
Y_test1 = wpdist_no_const(X, 'minkowski', p=3.2)
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_minkowski_3_2_iris_nonC(self):
|
|
eps = 1e-07
|
|
X = eo['iris']
|
|
Y_right = eo['pdist-minkowski-3.2-iris']
|
|
Y_test2 = wpdist_no_const(X, 'test_minkowski', p=3.2)
|
|
_assert_within_tol(Y_test2, Y_right, eps)
|
|
|
|
def test_pdist_minkowski_5_8_iris(self):
|
|
eps = 1e-07
|
|
X = eo['iris']
|
|
Y_right = eo['pdist-minkowski-5.8-iris']
|
|
Y_test1 = wpdist_no_const(X, 'minkowski', p=5.8)
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_minkowski_5_8_iris_float32(self):
|
|
eps = 1e-06
|
|
X = np.float32(eo['iris'])
|
|
Y_right = eo['pdist-minkowski-5.8-iris']
|
|
Y_test1 = wpdist_no_const(X, 'minkowski', p=5.8)
|
|
_assert_within_tol(Y_test1, Y_right, eps, verbose > 2)
|
|
|
|
def test_pdist_minkowski_5_8_iris_nonC(self):
|
|
eps = 1e-07
|
|
X = eo['iris']
|
|
Y_right = eo['pdist-minkowski-5.8-iris']
|
|
Y_test2 = wpdist_no_const(X, 'test_minkowski', p=5.8)
|
|
_assert_within_tol(Y_test2, Y_right, eps)
|
|
|
|
def test_pdist_mahalanobis(self):
|
|
# 1-dimensional observations
|
|
x = np.array([2.0, 2.0, 3.0, 5.0]).reshape(-1, 1)
|
|
dist = pdist(x, metric='mahalanobis')
|
|
assert_allclose(dist, [0.0, np.sqrt(0.5), np.sqrt(4.5),
|
|
np.sqrt(0.5), np.sqrt(4.5), np.sqrt(2.0)])
|
|
|
|
# 2-dimensional observations
|
|
x = np.array([[0, 0], [-1, 0], [0, 2], [1, 0], [0, -2]])
|
|
dist = pdist(x, metric='mahalanobis')
|
|
rt2 = np.sqrt(2)
|
|
assert_allclose(dist, [rt2, rt2, rt2, rt2, 2, 2 * rt2, 2, 2, 2 * rt2, 2])
|
|
|
|
# Too few observations
|
|
assert_raises(ValueError,
|
|
wpdist, [[0, 1], [2, 3]], metric='mahalanobis')
|
|
|
|
def test_pdist_hamming_random(self):
|
|
eps = 1e-07
|
|
X = eo['pdist-boolean-inp']
|
|
Y_right = eo['pdist-hamming']
|
|
Y_test1 = wpdist(X, 'hamming')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_hamming_random_float32(self):
|
|
eps = 1e-07
|
|
X = np.float32(eo['pdist-boolean-inp'])
|
|
Y_right = eo['pdist-hamming']
|
|
Y_test1 = wpdist(X, 'hamming')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_hamming_random_nonC(self):
|
|
eps = 1e-07
|
|
X = eo['pdist-boolean-inp']
|
|
Y_right = eo['pdist-hamming']
|
|
Y_test2 = wpdist(X, 'test_hamming')
|
|
_assert_within_tol(Y_test2, Y_right, eps)
|
|
|
|
def test_pdist_dhamming_random(self):
|
|
eps = 1e-07
|
|
X = np.float64(eo['pdist-boolean-inp'])
|
|
Y_right = eo['pdist-hamming']
|
|
Y_test1 = wpdist(X, 'hamming')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_dhamming_random_float32(self):
|
|
eps = 1e-07
|
|
X = np.float32(eo['pdist-boolean-inp'])
|
|
Y_right = eo['pdist-hamming']
|
|
Y_test1 = wpdist(X, 'hamming')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_dhamming_random_nonC(self):
|
|
eps = 1e-07
|
|
X = np.float64(eo['pdist-boolean-inp'])
|
|
Y_right = eo['pdist-hamming']
|
|
Y_test2 = wpdist(X, 'test_hamming')
|
|
_assert_within_tol(Y_test2, Y_right, eps)
|
|
|
|
def test_pdist_jaccard_random(self):
|
|
eps = 1e-08
|
|
X = eo['pdist-boolean-inp']
|
|
Y_right = eo['pdist-jaccard']
|
|
Y_test1 = wpdist(X, 'jaccard')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_jaccard_random_float32(self):
|
|
eps = 1e-08
|
|
X = np.float32(eo['pdist-boolean-inp'])
|
|
Y_right = eo['pdist-jaccard']
|
|
Y_test1 = wpdist(X, 'jaccard')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_jaccard_random_nonC(self):
|
|
eps = 1e-08
|
|
X = eo['pdist-boolean-inp']
|
|
Y_right = eo['pdist-jaccard']
|
|
Y_test2 = wpdist(X, 'test_jaccard')
|
|
_assert_within_tol(Y_test2, Y_right, eps)
|
|
|
|
def test_pdist_djaccard_random(self):
|
|
eps = 1e-08
|
|
X = np.float64(eo['pdist-boolean-inp'])
|
|
Y_right = eo['pdist-jaccard']
|
|
Y_test1 = wpdist(X, 'jaccard')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_djaccard_random_float32(self):
|
|
eps = 1e-08
|
|
X = np.float32(eo['pdist-boolean-inp'])
|
|
Y_right = eo['pdist-jaccard']
|
|
Y_test1 = wpdist(X, 'jaccard')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_djaccard_random_nonC(self):
|
|
eps = 1e-08
|
|
X = np.float64(eo['pdist-boolean-inp'])
|
|
Y_right = eo['pdist-jaccard']
|
|
Y_test2 = wpdist(X, 'test_jaccard')
|
|
_assert_within_tol(Y_test2, Y_right, eps)
|
|
|
|
def test_pdist_chebyshev_random(self):
|
|
eps = 1e-08
|
|
X = eo['pdist-double-inp']
|
|
Y_right = eo['pdist-chebyshev']
|
|
Y_test1 = pdist(X, 'chebyshev')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_chebyshev_random_float32(self):
|
|
eps = 1e-07
|
|
X = np.float32(eo['pdist-double-inp'])
|
|
Y_right = eo['pdist-chebyshev']
|
|
Y_test1 = pdist(X, 'chebyshev')
|
|
_assert_within_tol(Y_test1, Y_right, eps, verbose > 2)
|
|
|
|
def test_pdist_chebyshev_random_nonC(self):
|
|
eps = 1e-08
|
|
X = eo['pdist-double-inp']
|
|
Y_right = eo['pdist-chebyshev']
|
|
Y_test2 = pdist(X, 'test_chebyshev')
|
|
_assert_within_tol(Y_test2, Y_right, eps)
|
|
|
|
def test_pdist_chebyshev_iris(self):
|
|
eps = 1e-15
|
|
X = eo['iris']
|
|
Y_right = eo['pdist-chebyshev-iris']
|
|
Y_test1 = pdist(X, 'chebyshev')
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
|
|
def test_pdist_chebyshev_iris_float32(self):
|
|
eps = 1e-06
|
|
X = np.float32(eo['iris'])
|
|
Y_right = eo['pdist-chebyshev-iris']
|
|
Y_test1 = pdist(X, 'chebyshev')
|
|
_assert_within_tol(Y_test1, Y_right, eps, verbose > 2)
|
|
|
|
def test_pdist_chebyshev_iris_nonC(self):
|
|
eps = 1e-15
|
|
X = eo['iris']
|
|
Y_right = eo['pdist-chebyshev-iris']
|
|
Y_test2 = pdist(X, 'test_chebyshev')
|
|
_assert_within_tol(Y_test2, Y_right, eps)
|
|
|
|
def test_pdist_matching_mtica1(self):
|
|
# Test matching(*,*) with mtica example #1 (nums).
|
|
m = wmatching(np.array([1, 0, 1, 1, 0]),
|
|
np.array([1, 1, 0, 1, 1]))
|
|
m2 = wmatching(np.array([1, 0, 1, 1, 0], dtype=bool),
|
|
np.array([1, 1, 0, 1, 1], dtype=bool))
|
|
assert_allclose(m, 0.6, rtol=0, atol=1e-10)
|
|
assert_allclose(m2, 0.6, rtol=0, atol=1e-10)
|
|
|
|
def test_pdist_matching_mtica2(self):
|
|
# Test matching(*,*) with mtica example #2.
|
|
m = wmatching(np.array([1, 0, 1]),
|
|
np.array([1, 1, 0]))
|
|
m2 = wmatching(np.array([1, 0, 1], dtype=bool),
|
|
np.array([1, 1, 0], dtype=bool))
|
|
assert_allclose(m, 2 / 3, rtol=0, atol=1e-10)
|
|
assert_allclose(m2, 2 / 3, rtol=0, atol=1e-10)
|
|
|
|
def test_pdist_jaccard_mtica1(self):
|
|
m = wjaccard(np.array([1, 0, 1, 1, 0]),
|
|
np.array([1, 1, 0, 1, 1]))
|
|
m2 = wjaccard(np.array([1, 0, 1, 1, 0], dtype=bool),
|
|
np.array([1, 1, 0, 1, 1], dtype=bool))
|
|
assert_allclose(m, 0.6, rtol=0, atol=1e-10)
|
|
assert_allclose(m2, 0.6, rtol=0, atol=1e-10)
|
|
|
|
def test_pdist_jaccard_mtica2(self):
|
|
m = wjaccard(np.array([1, 0, 1]),
|
|
np.array([1, 1, 0]))
|
|
m2 = wjaccard(np.array([1, 0, 1], dtype=bool),
|
|
np.array([1, 1, 0], dtype=bool))
|
|
assert_allclose(m, 2 / 3, rtol=0, atol=1e-10)
|
|
assert_allclose(m2, 2 / 3, rtol=0, atol=1e-10)
|
|
|
|
def test_pdist_yule_mtica1(self):
|
|
m = wyule(np.array([1, 0, 1, 1, 0]),
|
|
np.array([1, 1, 0, 1, 1]))
|
|
m2 = wyule(np.array([1, 0, 1, 1, 0], dtype=bool),
|
|
np.array([1, 1, 0, 1, 1], dtype=bool))
|
|
if verbose > 2:
|
|
print(m)
|
|
assert_allclose(m, 2, rtol=0, atol=1e-10)
|
|
assert_allclose(m2, 2, rtol=0, atol=1e-10)
|
|
|
|
def test_pdist_yule_mtica2(self):
|
|
m = wyule(np.array([1, 0, 1]),
|
|
np.array([1, 1, 0]))
|
|
m2 = wyule(np.array([1, 0, 1], dtype=bool),
|
|
np.array([1, 1, 0], dtype=bool))
|
|
if verbose > 2:
|
|
print(m)
|
|
assert_allclose(m, 2, rtol=0, atol=1e-10)
|
|
assert_allclose(m2, 2, rtol=0, atol=1e-10)
|
|
|
|
def test_pdist_dice_mtica1(self):
|
|
m = wdice(np.array([1, 0, 1, 1, 0]),
|
|
np.array([1, 1, 0, 1, 1]))
|
|
m2 = wdice(np.array([1, 0, 1, 1, 0], dtype=bool),
|
|
np.array([1, 1, 0, 1, 1], dtype=bool))
|
|
if verbose > 2:
|
|
print(m)
|
|
assert_allclose(m, 3 / 7, rtol=0, atol=1e-10)
|
|
assert_allclose(m2, 3 / 7, rtol=0, atol=1e-10)
|
|
|
|
def test_pdist_dice_mtica2(self):
|
|
m = wdice(np.array([1, 0, 1]),
|
|
np.array([1, 1, 0]))
|
|
m2 = wdice(np.array([1, 0, 1], dtype=bool),
|
|
np.array([1, 1, 0], dtype=bool))
|
|
if verbose > 2:
|
|
print(m)
|
|
assert_allclose(m, 0.5, rtol=0, atol=1e-10)
|
|
assert_allclose(m2, 0.5, rtol=0, atol=1e-10)
|
|
|
|
def test_pdist_sokalsneath_mtica1(self):
|
|
m = sokalsneath(np.array([1, 0, 1, 1, 0]),
|
|
np.array([1, 1, 0, 1, 1]))
|
|
m2 = sokalsneath(np.array([1, 0, 1, 1, 0], dtype=bool),
|
|
np.array([1, 1, 0, 1, 1], dtype=bool))
|
|
if verbose > 2:
|
|
print(m)
|
|
assert_allclose(m, 3 / 4, rtol=0, atol=1e-10)
|
|
assert_allclose(m2, 3 / 4, rtol=0, atol=1e-10)
|
|
|
|
def test_pdist_sokalsneath_mtica2(self):
|
|
m = wsokalsneath(np.array([1, 0, 1]),
|
|
np.array([1, 1, 0]))
|
|
m2 = wsokalsneath(np.array([1, 0, 1], dtype=bool),
|
|
np.array([1, 1, 0], dtype=bool))
|
|
if verbose > 2:
|
|
print(m)
|
|
assert_allclose(m, 4 / 5, rtol=0, atol=1e-10)
|
|
assert_allclose(m2, 4 / 5, rtol=0, atol=1e-10)
|
|
|
|
def test_pdist_rogerstanimoto_mtica1(self):
|
|
m = wrogerstanimoto(np.array([1, 0, 1, 1, 0]),
|
|
np.array([1, 1, 0, 1, 1]))
|
|
m2 = wrogerstanimoto(np.array([1, 0, 1, 1, 0], dtype=bool),
|
|
np.array([1, 1, 0, 1, 1], dtype=bool))
|
|
if verbose > 2:
|
|
print(m)
|
|
assert_allclose(m, 3 / 4, rtol=0, atol=1e-10)
|
|
assert_allclose(m2, 3 / 4, rtol=0, atol=1e-10)
|
|
|
|
def test_pdist_rogerstanimoto_mtica2(self):
|
|
m = wrogerstanimoto(np.array([1, 0, 1]),
|
|
np.array([1, 1, 0]))
|
|
m2 = wrogerstanimoto(np.array([1, 0, 1], dtype=bool),
|
|
np.array([1, 1, 0], dtype=bool))
|
|
if verbose > 2:
|
|
print(m)
|
|
assert_allclose(m, 4 / 5, rtol=0, atol=1e-10)
|
|
assert_allclose(m2, 4 / 5, rtol=0, atol=1e-10)
|
|
|
|
def test_pdist_russellrao_mtica1(self):
|
|
m = wrussellrao(np.array([1, 0, 1, 1, 0]),
|
|
np.array([1, 1, 0, 1, 1]))
|
|
m2 = wrussellrao(np.array([1, 0, 1, 1, 0], dtype=bool),
|
|
np.array([1, 1, 0, 1, 1], dtype=bool))
|
|
if verbose > 2:
|
|
print(m)
|
|
assert_allclose(m, 3 / 5, rtol=0, atol=1e-10)
|
|
assert_allclose(m2, 3 / 5, rtol=0, atol=1e-10)
|
|
|
|
def test_pdist_russellrao_mtica2(self):
|
|
m = wrussellrao(np.array([1, 0, 1]),
|
|
np.array([1, 1, 0]))
|
|
m2 = wrussellrao(np.array([1, 0, 1], dtype=bool),
|
|
np.array([1, 1, 0], dtype=bool))
|
|
if verbose > 2:
|
|
print(m)
|
|
assert_allclose(m, 2 / 3, rtol=0, atol=1e-10)
|
|
assert_allclose(m2, 2 / 3, rtol=0, atol=1e-10)
|
|
|
|
def test_pdist_canberra_match(self):
|
|
D = eo['iris']
|
|
if verbose > 2:
|
|
print(D.shape, D.dtype)
|
|
eps = 1e-10
|
|
y1 = wpdist_no_const(D, "canberra")
|
|
y2 = wpdist_no_const(D, "test_canberra")
|
|
_assert_within_tol(y1, y2, eps, verbose > 2)
|
|
|
|
def test_pdist_canberra_ticket_711(self):
|
|
# Test pdist(X, 'canberra') to see if Canberra gives the right result
|
|
# as reported on gh-1238.
|
|
eps = 1e-8
|
|
pdist_y = wpdist_no_const(([3.3], [3.4]), "canberra")
|
|
right_y = 0.01492537
|
|
_assert_within_tol(pdist_y, right_y, eps, verbose > 2)
|
|
|
|
def test_pdist_custom_notdouble(self):
|
|
# tests that when using a custom metric the data type is not altered
|
|
class myclass(object):
|
|
pass
|
|
|
|
def _my_metric(x, y):
|
|
if not isinstance(x[0], myclass) or not isinstance(y[0], myclass):
|
|
raise ValueError("Type has been changed")
|
|
return 1.123
|
|
data = np.array([[myclass()], [myclass()]], dtype=object)
|
|
pdist_y = pdist(data, metric=_my_metric)
|
|
right_y = 1.123
|
|
assert_equal(pdist_y, right_y, verbose=verbose > 2)
|
|
|
|
def _check_calling_conventions(self, X, metric, eps=1e-07, **kwargs):
|
|
# helper function for test_pdist_calling_conventions
|
|
try:
|
|
y1 = pdist(X, metric=metric, **kwargs)
|
|
y2 = pdist(X, metric=eval(metric), **kwargs)
|
|
y3 = pdist(X, metric="test_" + metric, **kwargs)
|
|
except Exception as e:
|
|
e_cls = e.__class__
|
|
if verbose > 2:
|
|
print(e_cls.__name__)
|
|
print(e)
|
|
assert_raises(e_cls, pdist, X, metric=metric, **kwargs)
|
|
assert_raises(e_cls, pdist, X, metric=eval(metric), **kwargs)
|
|
assert_raises(e_cls, pdist, X, metric="test_" + metric, **kwargs)
|
|
else:
|
|
_assert_within_tol(y1, y2, rtol=eps, verbose_=verbose > 2)
|
|
_assert_within_tol(y1, y3, rtol=eps, verbose_=verbose > 2)
|
|
|
|
def test_pdist_calling_conventions(self):
|
|
# Ensures that specifying the metric with a str or scipy function
|
|
# gives the same behaviour (i.e. same result or same exception).
|
|
# NOTE: The correctness should be checked within each metric tests.
|
|
# NOTE: Extra args should be checked with a dedicated test
|
|
eps = 1e-07
|
|
for eo_name in self.rnd_eo_names:
|
|
# subsampling input data to speed-up tests
|
|
# NOTE: num samples needs to be > than dimensions for mahalanobis
|
|
X = eo[eo_name][::5, ::2]
|
|
for metric in _METRICS_NAMES:
|
|
if metric == 'wminkowski':
|
|
continue
|
|
if verbose > 2:
|
|
print("testing: ", metric, " with: ", eo_name)
|
|
if metric in {'dice', 'yule', 'kulsinski', 'matching',
|
|
'rogerstanimoto', 'russellrao', 'sokalmichener',
|
|
'sokalsneath'} and 'bool' not in eo_name:
|
|
# python version permits non-bools e.g. for fuzzy logic
|
|
continue
|
|
self._check_calling_conventions(X, metric)
|
|
|
|
# Testing built-in metrics with extra args
|
|
if metric == "seuclidean":
|
|
V = np.var(X.astype(np.double), axis=0, ddof=1)
|
|
self._check_calling_conventions(X, metric, V=V)
|
|
elif metric == "mahalanobis":
|
|
V = np.atleast_2d(np.cov(X.astype(np.double).T))
|
|
VI = np.array(np.linalg.inv(V).T)
|
|
self._check_calling_conventions(X, metric, VI=VI)
|
|
|
|
def test_pdist_dtype_equivalence(self):
|
|
# Tests that the result is not affected by type up-casting
|
|
eps = 1e-07
|
|
tests = [(eo['random-bool-data'], self.valid_upcasts['bool']),
|
|
(eo['random-uint-data'], self.valid_upcasts['uint']),
|
|
(eo['random-int-data'], self.valid_upcasts['int']),
|
|
(eo['random-float32-data'], self.valid_upcasts['float32'])]
|
|
for metric in _METRICS_NAMES:
|
|
for test in tests:
|
|
X1 = test[0][::5, ::2]
|
|
try:
|
|
y1 = pdist(X1, metric=metric)
|
|
except Exception as e:
|
|
e_cls = e.__class__
|
|
if verbose > 2:
|
|
print(e_cls.__name__)
|
|
print(e)
|
|
for new_type in test[1]:
|
|
X2 = new_type(X1)
|
|
assert_raises(e_cls, pdist, X2, metric=metric)
|
|
else:
|
|
for new_type in test[1]:
|
|
y2 = pdist(new_type(X1), metric=metric)
|
|
_assert_within_tol(y1, y2, eps, verbose > 2)
|
|
|
|
def test_pdist_out(self):
|
|
# Test that out parameter works properly
|
|
eps = 1e-07
|
|
X = eo['random-float32-data'][::5, ::2]
|
|
out_size = int((X.shape[0] * (X.shape[0] - 1)) / 2)
|
|
for metric in _METRICS_NAMES:
|
|
kwargs = dict()
|
|
if metric in ['minkowski', 'wminkowski']:
|
|
kwargs['p'] = 1.23
|
|
if metric == 'wminkowski':
|
|
kwargs['w'] = 1.0 / X.std(axis=0)
|
|
out1 = np.empty(out_size, dtype=np.double)
|
|
Y_right = pdist(X, metric, **kwargs)
|
|
Y_test1 = pdist(X, metric, out=out1, **kwargs)
|
|
# test that output is numerically equivalent
|
|
_assert_within_tol(Y_test1, Y_right, eps)
|
|
# test that Y_test1 and out1 are the same object
|
|
assert_(Y_test1 is out1)
|
|
# test for incorrect shape
|
|
out2 = np.empty(out_size + 3, dtype=np.double)
|
|
assert_raises(ValueError, pdist, X, metric, out=out2, **kwargs)
|
|
# test for (C-)contiguous output
|
|
out3 = np.empty(2 * out_size, dtype=np.double)[::2]
|
|
assert_raises(ValueError, pdist, X, metric, out=out3, **kwargs)
|
|
# test for incorrect dtype
|
|
out5 = np.empty(out_size, dtype=np.int64)
|
|
assert_raises(ValueError, pdist, X, metric, out=out5, **kwargs)
|
|
|
|
def test_striding(self):
|
|
# test that striding is handled correct with calls to
|
|
# _copy_array_if_base_present
|
|
eps = 1e-07
|
|
X = eo['random-float32-data'][::5, ::2]
|
|
X_copy = X.copy()
|
|
|
|
# confirm contiguity
|
|
assert_(not X.flags.c_contiguous)
|
|
assert_(X_copy.flags.c_contiguous)
|
|
|
|
for metric in _METRICS_NAMES:
|
|
kwargs = dict()
|
|
if metric in ['minkowski', 'wminkowski']:
|
|
kwargs['p'] = 1.23
|
|
if metric == 'wminkowski':
|
|
kwargs['w'] = 1.0 / X.std(axis=0)
|
|
Y1 = pdist(X, metric, **kwargs)
|
|
Y2 = pdist(X_copy, metric, **kwargs)
|
|
# test that output is numerically equivalent
|
|
_assert_within_tol(Y1, Y2, eps, verbose > 2)
|
|
|
|
class TestSomeDistanceFunctions(object):
|
|
|
|
def setup_method(self):
|
|
# 1D arrays
|
|
x = np.array([1.0, 2.0, 3.0])
|
|
y = np.array([1.0, 1.0, 5.0])
|
|
# 3x1 arrays
|
|
x31 = x[:, np.newaxis]
|
|
y31 = y[:, np.newaxis]
|
|
# 1x3 arrays
|
|
x13 = x31.T
|
|
y13 = y31.T
|
|
|
|
self.cases = [(x, y), (x31, y31), (x13, y13)]
|
|
|
|
def test_minkowski(self):
|
|
with suppress_warnings() as w:
|
|
w.filter(message="`wminkowski` is deprecated")
|
|
for x, y in self.cases:
|
|
dist1 = wminkowski(x, y, p=1)
|
|
assert_almost_equal(dist1, 3.0)
|
|
dist1p5 = wminkowski(x, y, p=1.5)
|
|
assert_almost_equal(dist1p5, (1.0 + 2.0**1.5)**(2. / 3))
|
|
dist2 = wminkowski(x, y, p=2)
|
|
|
|
def test_old_wminkowski(self):
|
|
with suppress_warnings() as wrn:
|
|
wrn.filter(message="`wminkowski` is deprecated")
|
|
w = np.array([1.0, 2.0, 0.5])
|
|
for x, y in self.cases:
|
|
dist1 = old_wminkowski(x, y, p=1, w=w)
|
|
assert_almost_equal(dist1, 3.0)
|
|
dist1p5 = old_wminkowski(x, y, p=1.5, w=w)
|
|
assert_almost_equal(dist1p5, (2.0**1.5+1.0)**(2./3))
|
|
dist2 = old_wminkowski(x, y, p=2, w=w)
|
|
assert_almost_equal(dist2, np.sqrt(5))
|
|
|
|
# test weights Issue #7893
|
|
arr = np.arange(4)
|
|
w = np.full_like(arr, 4)
|
|
assert_almost_equal(old_wminkowski(arr, arr + 1, p=2, w=w), 8.0)
|
|
assert_almost_equal(wminkowski(arr, arr + 1, p=2, w=w), 4.0)
|
|
|
|
def test_euclidean(self):
|
|
for x, y in self.cases:
|
|
dist = weuclidean(x, y)
|
|
assert_almost_equal(dist, np.sqrt(5))
|
|
|
|
def test_sqeuclidean(self):
|
|
for x, y in self.cases:
|
|
dist = wsqeuclidean(x, y)
|
|
assert_almost_equal(dist, 5.0)
|
|
|
|
def test_cosine(self):
|
|
for x, y in self.cases:
|
|
dist = wcosine(x, y)
|
|
assert_almost_equal(dist, 1.0 - 18.0 / (np.sqrt(14) * np.sqrt(27)))
|
|
|
|
def test_correlation(self):
|
|
xm = np.array([-1.0, 0, 1.0])
|
|
ym = np.array([-4.0 / 3, -4.0 / 3, 5.0 - 7.0 / 3])
|
|
for x, y in self.cases:
|
|
dist = wcorrelation(x, y)
|
|
assert_almost_equal(dist, 1.0 - np.dot(xm, ym) / (norm(xm) * norm(ym)))
|
|
|
|
def test_mahalanobis(self):
|
|
x = np.array([1.0, 2.0, 3.0])
|
|
y = np.array([1.0, 1.0, 5.0])
|
|
vi = np.array([[2.0, 1.0, 0.0], [1.0, 2.0, 1.0], [0.0, 1.0, 2.0]])
|
|
for x, y in self.cases:
|
|
dist = mahalanobis(x, y, vi)
|
|
assert_almost_equal(dist, np.sqrt(6.0))
|
|
|
|
|
|
class TestSquareForm(object):
|
|
checked_dtypes = [np.float64, np.float32, np.int32, np.int8, bool]
|
|
|
|
def test_squareform_matrix(self):
|
|
for dtype in self.checked_dtypes:
|
|
self.check_squareform_matrix(dtype)
|
|
|
|
def test_squareform_vector(self):
|
|
for dtype in self.checked_dtypes:
|
|
self.check_squareform_vector(dtype)
|
|
|
|
def check_squareform_matrix(self, dtype):
|
|
A = np.zeros((0, 0), dtype=dtype)
|
|
rA = squareform(A)
|
|
assert_equal(rA.shape, (0,))
|
|
assert_equal(rA.dtype, dtype)
|
|
|
|
A = np.zeros((1, 1), dtype=dtype)
|
|
rA = squareform(A)
|
|
assert_equal(rA.shape, (0,))
|
|
assert_equal(rA.dtype, dtype)
|
|
|
|
A = np.array([[0, 4.2], [4.2, 0]], dtype=dtype)
|
|
rA = squareform(A)
|
|
assert_equal(rA.shape, (1,))
|
|
assert_equal(rA.dtype, dtype)
|
|
assert_array_equal(rA, np.array([4.2], dtype=dtype))
|
|
|
|
def check_squareform_vector(self, dtype):
|
|
v = np.zeros((0,), dtype=dtype)
|
|
rv = squareform(v)
|
|
assert_equal(rv.shape, (1, 1))
|
|
assert_equal(rv.dtype, dtype)
|
|
assert_array_equal(rv, [[0]])
|
|
|
|
v = np.array([8.3], dtype=dtype)
|
|
rv = squareform(v)
|
|
assert_equal(rv.shape, (2, 2))
|
|
assert_equal(rv.dtype, dtype)
|
|
assert_array_equal(rv, np.array([[0, 8.3], [8.3, 0]], dtype=dtype))
|
|
|
|
def test_squareform_multi_matrix(self):
|
|
for n in xrange(2, 5):
|
|
self.check_squareform_multi_matrix(n)
|
|
|
|
def check_squareform_multi_matrix(self, n):
|
|
X = np.random.rand(n, 4)
|
|
Y = wpdist_no_const(X)
|
|
assert_equal(len(Y.shape), 1)
|
|
A = squareform(Y)
|
|
Yr = squareform(A)
|
|
s = A.shape
|
|
k = 0
|
|
if verbose >= 3:
|
|
print(A.shape, Y.shape, Yr.shape)
|
|
assert_equal(len(s), 2)
|
|
assert_equal(len(Yr.shape), 1)
|
|
assert_equal(s[0], s[1])
|
|
for i in xrange(0, s[0]):
|
|
for j in xrange(i + 1, s[1]):
|
|
if i != j:
|
|
assert_equal(A[i, j], Y[k])
|
|
k += 1
|
|
else:
|
|
assert_equal(A[i, j], 0)
|
|
|
|
|
|
class TestNumObsY(object):
|
|
|
|
def test_num_obs_y_multi_matrix(self):
|
|
for n in xrange(2, 10):
|
|
X = np.random.rand(n, 4)
|
|
Y = wpdist_no_const(X)
|
|
assert_equal(num_obs_y(Y), n)
|
|
|
|
def test_num_obs_y_1(self):
|
|
# Tests num_obs_y(y) on a condensed distance matrix over 1
|
|
# observations. Expecting exception.
|
|
assert_raises(ValueError, self.check_y, 1)
|
|
|
|
def test_num_obs_y_2(self):
|
|
# Tests num_obs_y(y) on a condensed distance matrix over 2
|
|
# observations.
|
|
assert_(self.check_y(2))
|
|
|
|
def test_num_obs_y_3(self):
|
|
assert_(self.check_y(3))
|
|
|
|
def test_num_obs_y_4(self):
|
|
assert_(self.check_y(4))
|
|
|
|
def test_num_obs_y_5_10(self):
|
|
for i in xrange(5, 16):
|
|
self.minit(i)
|
|
|
|
def test_num_obs_y_2_100(self):
|
|
# Tests num_obs_y(y) on 100 improper condensed distance matrices.
|
|
# Expecting exception.
|
|
a = set([])
|
|
for n in xrange(2, 16):
|
|
a.add(n * (n - 1) / 2)
|
|
for i in xrange(5, 105):
|
|
if i not in a:
|
|
assert_raises(ValueError, self.bad_y, i)
|
|
|
|
def minit(self, n):
|
|
assert_(self.check_y(n))
|
|
|
|
def bad_y(self, n):
|
|
y = np.random.rand(n)
|
|
return num_obs_y(y)
|
|
|
|
def check_y(self, n):
|
|
return num_obs_y(self.make_y(n)) == n
|
|
|
|
def make_y(self, n):
|
|
return np.random.rand((n * (n - 1)) // 2)
|
|
|
|
|
|
class TestNumObsDM(object):
|
|
|
|
def test_num_obs_dm_multi_matrix(self):
|
|
for n in xrange(1, 10):
|
|
X = np.random.rand(n, 4)
|
|
Y = wpdist_no_const(X)
|
|
A = squareform(Y)
|
|
if verbose >= 3:
|
|
print(A.shape, Y.shape)
|
|
assert_equal(num_obs_dm(A), n)
|
|
|
|
def test_num_obs_dm_0(self):
|
|
# Tests num_obs_dm(D) on a 0x0 distance matrix. Expecting exception.
|
|
assert_(self.check_D(0))
|
|
|
|
def test_num_obs_dm_1(self):
|
|
# Tests num_obs_dm(D) on a 1x1 distance matrix.
|
|
assert_(self.check_D(1))
|
|
|
|
def test_num_obs_dm_2(self):
|
|
assert_(self.check_D(2))
|
|
|
|
def test_num_obs_dm_3(self):
|
|
assert_(self.check_D(2))
|
|
|
|
def test_num_obs_dm_4(self):
|
|
assert_(self.check_D(4))
|
|
|
|
def check_D(self, n):
|
|
return num_obs_dm(self.make_D(n)) == n
|
|
|
|
def make_D(self, n):
|
|
return np.random.rand(n, n)
|
|
|
|
|
|
def is_valid_dm_throw(D):
|
|
return is_valid_dm(D, throw=True)
|
|
|
|
|
|
class TestIsValidDM(object):
|
|
|
|
def test_is_valid_dm_improper_shape_1D_E(self):
|
|
D = np.zeros((5,), dtype=np.double)
|
|
assert_raises(ValueError, is_valid_dm_throw, (D))
|
|
|
|
def test_is_valid_dm_improper_shape_1D_F(self):
|
|
D = np.zeros((5,), dtype=np.double)
|
|
assert_equal(is_valid_dm(D), False)
|
|
|
|
def test_is_valid_dm_improper_shape_3D_E(self):
|
|
D = np.zeros((3, 3, 3), dtype=np.double)
|
|
assert_raises(ValueError, is_valid_dm_throw, (D))
|
|
|
|
def test_is_valid_dm_improper_shape_3D_F(self):
|
|
D = np.zeros((3, 3, 3), dtype=np.double)
|
|
assert_equal(is_valid_dm(D), False)
|
|
|
|
def test_is_valid_dm_nonzero_diagonal_E(self):
|
|
y = np.random.rand(10)
|
|
D = squareform(y)
|
|
for i in xrange(0, 5):
|
|
D[i, i] = 2.0
|
|
assert_raises(ValueError, is_valid_dm_throw, (D))
|
|
|
|
def test_is_valid_dm_nonzero_diagonal_F(self):
|
|
y = np.random.rand(10)
|
|
D = squareform(y)
|
|
for i in xrange(0, 5):
|
|
D[i, i] = 2.0
|
|
assert_equal(is_valid_dm(D), False)
|
|
|
|
def test_is_valid_dm_asymmetric_E(self):
|
|
y = np.random.rand(10)
|
|
D = squareform(y)
|
|
D[1, 3] = D[3, 1] + 1
|
|
assert_raises(ValueError, is_valid_dm_throw, (D))
|
|
|
|
def test_is_valid_dm_asymmetric_F(self):
|
|
y = np.random.rand(10)
|
|
D = squareform(y)
|
|
D[1, 3] = D[3, 1] + 1
|
|
assert_equal(is_valid_dm(D), False)
|
|
|
|
def test_is_valid_dm_correct_1_by_1(self):
|
|
D = np.zeros((1, 1), dtype=np.double)
|
|
assert_equal(is_valid_dm(D), True)
|
|
|
|
def test_is_valid_dm_correct_2_by_2(self):
|
|
y = np.random.rand(1)
|
|
D = squareform(y)
|
|
assert_equal(is_valid_dm(D), True)
|
|
|
|
def test_is_valid_dm_correct_3_by_3(self):
|
|
y = np.random.rand(3)
|
|
D = squareform(y)
|
|
assert_equal(is_valid_dm(D), True)
|
|
|
|
def test_is_valid_dm_correct_4_by_4(self):
|
|
y = np.random.rand(6)
|
|
D = squareform(y)
|
|
assert_equal(is_valid_dm(D), True)
|
|
|
|
def test_is_valid_dm_correct_5_by_5(self):
|
|
y = np.random.rand(10)
|
|
D = squareform(y)
|
|
assert_equal(is_valid_dm(D), True)
|
|
|
|
|
|
def is_valid_y_throw(y):
|
|
return is_valid_y(y, throw=True)
|
|
|
|
|
|
class TestIsValidY(object):
|
|
# If test case name ends on "_E" then an exception is expected for the
|
|
# given input, if it ends in "_F" then False is expected for the is_valid_y
|
|
# check. Otherwise the input is expected to be valid.
|
|
|
|
def test_is_valid_y_improper_shape_2D_E(self):
|
|
y = np.zeros((3, 3,), dtype=np.double)
|
|
assert_raises(ValueError, is_valid_y_throw, (y))
|
|
|
|
def test_is_valid_y_improper_shape_2D_F(self):
|
|
y = np.zeros((3, 3,), dtype=np.double)
|
|
assert_equal(is_valid_y(y), False)
|
|
|
|
def test_is_valid_y_improper_shape_3D_E(self):
|
|
y = np.zeros((3, 3, 3), dtype=np.double)
|
|
assert_raises(ValueError, is_valid_y_throw, (y))
|
|
|
|
def test_is_valid_y_improper_shape_3D_F(self):
|
|
y = np.zeros((3, 3, 3), dtype=np.double)
|
|
assert_equal(is_valid_y(y), False)
|
|
|
|
def test_is_valid_y_correct_2_by_2(self):
|
|
y = self.correct_n_by_n(2)
|
|
assert_equal(is_valid_y(y), True)
|
|
|
|
def test_is_valid_y_correct_3_by_3(self):
|
|
y = self.correct_n_by_n(3)
|
|
assert_equal(is_valid_y(y), True)
|
|
|
|
def test_is_valid_y_correct_4_by_4(self):
|
|
y = self.correct_n_by_n(4)
|
|
assert_equal(is_valid_y(y), True)
|
|
|
|
def test_is_valid_y_correct_5_by_5(self):
|
|
y = self.correct_n_by_n(5)
|
|
assert_equal(is_valid_y(y), True)
|
|
|
|
def test_is_valid_y_2_100(self):
|
|
a = set([])
|
|
for n in xrange(2, 16):
|
|
a.add(n * (n - 1) / 2)
|
|
for i in xrange(5, 105):
|
|
if i not in a:
|
|
assert_raises(ValueError, self.bad_y, i)
|
|
|
|
def bad_y(self, n):
|
|
y = np.random.rand(n)
|
|
return is_valid_y(y, throw=True)
|
|
|
|
def correct_n_by_n(self, n):
|
|
y = np.random.rand((n * (n - 1)) // 2)
|
|
return y
|
|
|
|
|
|
def test_bad_p():
|
|
# Raise ValueError if p < 1.
|
|
p = 0.5
|
|
with suppress_warnings() as w:
|
|
w.filter(message="`wminkowski` is deprecated")
|
|
assert_raises(ValueError, wminkowski, [1, 2], [3, 4], p)
|
|
assert_raises(ValueError, wminkowski, [1, 2], [3, 4], p, [1, 1])
|
|
|
|
|
|
def test_sokalsneath_all_false():
|
|
# Regression test for ticket #876
|
|
assert_raises(ValueError, sokalsneath, [False, False, False], [False, False, False])
|
|
|
|
|
|
def test_canberra():
|
|
# Regression test for ticket #1430.
|
|
assert_equal(wcanberra([1, 2, 3], [2, 4, 6]), 1)
|
|
assert_equal(wcanberra([1, 1, 0, 0], [1, 0, 1, 0]), 2)
|
|
|
|
|
|
def test_braycurtis():
|
|
# Regression test for ticket #1430.
|
|
assert_almost_equal(wbraycurtis([1, 2, 3], [2, 4, 6]), 1. / 3, decimal=15)
|
|
assert_almost_equal(wbraycurtis([1, 1, 0, 0], [1, 0, 1, 0]), 0.5, decimal=15)
|
|
|
|
|
|
def test_euclideans():
|
|
# Regression test for ticket #1328.
|
|
x1 = np.array([1, 1, 1])
|
|
x2 = np.array([0, 0, 0])
|
|
|
|
# Basic test of the calculation.
|
|
assert_almost_equal(wsqeuclidean(x1, x2), 3.0, decimal=14)
|
|
assert_almost_equal(weuclidean(x1, x2), np.sqrt(3), decimal=14)
|
|
|
|
# Check flattening for (1, N) or (N, 1) inputs
|
|
assert_almost_equal(weuclidean(x1[np.newaxis, :], x2[np.newaxis, :]),
|
|
np.sqrt(3), decimal=14)
|
|
assert_almost_equal(wsqeuclidean(x1[np.newaxis, :], x2[np.newaxis, :]),
|
|
3.0, decimal=14)
|
|
assert_almost_equal(wsqeuclidean(x1[:, np.newaxis], x2[:, np.newaxis]),
|
|
3.0, decimal=14)
|
|
|
|
# Distance metrics only defined for vectors (= 1-D)
|
|
x = np.arange(4).reshape(2, 2)
|
|
assert_raises(ValueError, weuclidean, x, x)
|
|
assert_raises(ValueError, wsqeuclidean, x, x)
|
|
|
|
# Another check, with random data.
|
|
rs = np.random.RandomState(1234567890)
|
|
x = rs.rand(10)
|
|
y = rs.rand(10)
|
|
d1 = weuclidean(x, y)
|
|
d2 = wsqeuclidean(x, y)
|
|
assert_almost_equal(d1**2, d2, decimal=14)
|
|
|
|
|
|
def test_hamming_unequal_length():
|
|
# Regression test for gh-4290.
|
|
x = [0, 0, 1]
|
|
y = [1, 0, 1, 0]
|
|
# Used to give an AttributeError from ndarray.mean called on bool
|
|
assert_raises(ValueError, whamming, x, y)
|
|
|
|
|
|
def test_hamming_string_array():
|
|
# https://github.com/scikit-learn/scikit-learn/issues/4014
|
|
a = np.array(['eggs', 'spam', 'spam', 'eggs', 'spam', 'spam', 'spam',
|
|
'spam', 'spam', 'spam', 'spam', 'eggs', 'eggs', 'spam',
|
|
'eggs', 'eggs', 'eggs', 'eggs', 'eggs', 'spam'],
|
|
dtype='|S4')
|
|
b = np.array(['eggs', 'spam', 'spam', 'eggs', 'eggs', 'spam', 'spam',
|
|
'spam', 'spam', 'eggs', 'spam', 'eggs', 'spam', 'eggs',
|
|
'spam', 'spam', 'eggs', 'spam', 'spam', 'eggs'],
|
|
dtype='|S4')
|
|
desired = 0.45
|
|
assert_allclose(whamming(a, b), desired)
|
|
|
|
|
|
def test_minkowski_w():
|
|
# Regression test for gh-8142.
|
|
arr_in = np.array([[83.33333333, 100., 83.33333333, 100., 36.,
|
|
60., 90., 150., 24., 48.],
|
|
[83.33333333, 100., 83.33333333, 100., 36.,
|
|
60., 90., 150., 24., 48.]])
|
|
pdist(arr_in, metric='minkowski', p=1, w=None)
|
|
cdist(arr_in, arr_in, metric='minkowski', p=1, w=None)
|
|
pdist(arr_in, metric='minkowski', p=1)
|
|
cdist(arr_in, arr_in, metric='minkowski', p=1)
|
|
|
|
|
|
def test_sqeuclidean_dtypes():
|
|
# Assert that sqeuclidean returns the right types of values.
|
|
# Integer types should be converted to floating for stability.
|
|
# Floating point types should be the same as the input.
|
|
x = [1, 2, 3]
|
|
y = [4, 5, 6]
|
|
|
|
for dtype in [np.int8, np.int16, np.int32, np.int64]:
|
|
d = wsqeuclidean(np.asarray(x, dtype=dtype), np.asarray(y, dtype=dtype))
|
|
assert_(np.issubdtype(d.dtype, np.floating))
|
|
|
|
for dtype in [np.uint8, np.uint16, np.uint32, np.uint64]:
|
|
d1 = wsqeuclidean([0], np.asarray([-1], dtype=dtype))
|
|
d2 = wsqeuclidean(np.asarray([-1], dtype=dtype), [0])
|
|
|
|
assert_equal(d1, d2)
|
|
assert_equal(d1, np.float64(np.iinfo(dtype).max)**2)
|
|
|
|
dtypes = [np.float32, np.float64, np.complex64, np.complex128]
|
|
for dtype in ['float16', 'float128']:
|
|
# These aren't present in older numpy versions; float128 may also not
|
|
# be present on all platforms.
|
|
if hasattr(np, dtype):
|
|
dtypes.append(getattr(np, dtype))
|
|
|
|
for dtype in dtypes:
|
|
d = wsqeuclidean(np.asarray(x, dtype=dtype), np.asarray(y, dtype=dtype))
|
|
assert_equal(d.dtype, dtype)
|
|
|
|
|
|
def test_sokalmichener():
|
|
# Test that sokalmichener has the same result for bool and int inputs.
|
|
p = [True, True, False]
|
|
q = [True, False, True]
|
|
x = [int(b) for b in p]
|
|
y = [int(b) for b in q]
|
|
dist1 = sokalmichener(p, q)
|
|
dist2 = sokalmichener(x, y)
|
|
# These should be exactly the same.
|
|
assert_equal(dist1, dist2)
|
|
|
|
|
|
def test_modifies_input():
|
|
# test whether cdist or pdist modifies input arrays
|
|
X1 = np.asarray([[1., 2., 3.],
|
|
[1.2, 2.3, 3.4],
|
|
[2.2, 2.3, 4.4],
|
|
[22.2, 23.3, 44.4]])
|
|
X1_copy = X1.copy()
|
|
with suppress_warnings() as w:
|
|
w.filter(message="`wminkowski` is deprecated")
|
|
for metric in _METRICS_NAMES:
|
|
kwargs = {"w": 1.0 / X1.std(axis=0)} if metric == "wminkowski" else {}
|
|
cdist(X1, X1, metric, **kwargs)
|
|
pdist(X1, metric, **kwargs)
|
|
assert_array_equal(X1, X1_copy)
|
|
|
|
|
|
def test_Xdist_deprecated_args():
|
|
# testing both cdist and pdist deprecated warnings
|
|
X1 = np.asarray([[1., 2., 3.],
|
|
[1.2, 2.3, 3.4],
|
|
[2.2, 2.3, 4.4],
|
|
[22.2, 23.3, 44.4]])
|
|
weights = np.arange(3)
|
|
warn_msg_kwargs = "Got unexpected kwarg"
|
|
warn_msg_args = "[0-9]* metric parameters have been passed as positional"
|
|
for metric in _METRICS_NAMES:
|
|
kwargs = {"w": weights} if metric == "wminkowski" else dict()
|
|
with suppress_warnings() as w:
|
|
log = w.record(message=warn_msg_args)
|
|
w.filter(message=warn_msg_kwargs)
|
|
w.filter(message="`wminkowski` is deprecated")
|
|
cdist(X1, X1, metric, 2., **kwargs)
|
|
pdist(X1, metric, 2., **kwargs)
|
|
assert_(len(log) == 2)
|
|
|
|
for arg in ["p", "V", "VI"]:
|
|
kwargs = {arg:"foo"}
|
|
|
|
if metric == "wminkowski":
|
|
if "p" in kwargs or "w" in kwargs:
|
|
continue
|
|
kwargs["w"] = weights
|
|
|
|
if((arg == "V" and metric == "seuclidean") or
|
|
(arg == "VI" and metric == "mahalanobis") or
|
|
(arg == "p" and metric == "minkowski")):
|
|
continue
|
|
|
|
with suppress_warnings() as w:
|
|
log = w.record(message=warn_msg_kwargs)
|
|
w.filter(message="`wminkowski` is deprecated")
|
|
cdist(X1, X1, metric, **kwargs)
|
|
pdist(X1, metric, **kwargs)
|
|
assert_(len(log) == 2)
|
|
|
|
|
|
def test_Xdist_non_negative_weights():
|
|
X = eo['random-float32-data'][::5, ::2]
|
|
w = np.ones(X.shape[1])
|
|
w[::5] = -w[::5]
|
|
for metric in _METRICS_NAMES:
|
|
if metric in ['seuclidean', 'mahalanobis']:
|
|
continue
|
|
|
|
for m in [metric, eval(metric), "test_" + metric]:
|
|
assert_raises(ValueError, pdist, X, m, w=w)
|
|
assert_raises(ValueError, cdist, X, X, m, w=w)
|
|
|
|
|
|
def test__validate_vector():
|
|
x = [1, 2, 3]
|
|
y = _validate_vector(x)
|
|
assert_array_equal(y, x)
|
|
|
|
y = _validate_vector(x, dtype=np.float64)
|
|
assert_array_equal(y, x)
|
|
assert_equal(y.dtype, np.float64)
|
|
|
|
x = [1]
|
|
y = _validate_vector(x)
|
|
assert_equal(y.ndim, 1)
|
|
assert_equal(y, x)
|
|
|
|
x = 1
|
|
y = _validate_vector(x)
|
|
assert_equal(y.ndim, 1)
|
|
assert_equal(y, [x])
|
|
|
|
x = np.arange(5).reshape(1, -1, 1)
|
|
y = _validate_vector(x)
|
|
assert_equal(y.ndim, 1)
|
|
assert_array_equal(y, x[0, :, 0])
|
|
|
|
x = [[1, 2], [3, 4]]
|
|
assert_raises(ValueError, _validate_vector, x)
|