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amd.compare.PDD_pdist() B

↳ Parent: amd.compare

Complexity

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Total Lines	75
Code Lines	29

Duplication

Lines	0
Ratio	0 %

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Metric	Value
eloc	29
dl	0
loc	75
rs	7.784
c	0
b	0
f	0
cc	7
nop	6

How to fix Long Method

"""Functions for comparing AMDs and PDDs of crystals."""

from typing import List, Optional, Union, Tuple
from functools import partial
from itertools import combinations

import numpy as np
import numba
from scipy.spatial.distance import cdist, pdist
from joblib import Parallel, delayed
import tqdm

from ._emd import network_simplex
from ._types import FloatArray

__all__ = ["EMD", "AMD_cdist", "AMD_pdist", "PDD_cdist", "PDD_pdist"]


def EMD(
    pdd: FloatArray,
    pdd_: FloatArray,
    metric: Optional[str] = "chebyshev",
    return_transport: Optional[bool] = False,
    **kwargs,
) -> Union[float, Tuple[float, FloatArray]]:
    r"""Calculate the Earth mover's distance (EMD) between two PDDs, aka
    the Wasserstein metric.

    Parameters
    ----------
    pdd : :class:`numpy.ndarray`
        PDD of a crystal.
    pdd\_ : :class:`numpy.ndarray`
        PDD of a crystal.
    metric : str or callable, default 'chebyshev'
        EMD between PDDs requires defining a distance between PDD rows.
        By default, Chebyshev (L-infinity) distance is chosen as with
        AMDs. Accepts any metric accepted by
        :func:`scipy.spatial.distance.cdist`.
    return_transport: bool, default False
        Instead return a tuple ``(emd, transport_plan)`` where
        transport_plan describes the optimal flow.

    Returns
    -------
    emd : float
        Earth mover's distance between two PDDs. If ``return_transport``
        is True, return a tuple (emd, transport_plan).

    Raises
    ------
    ValueError
        Thrown if ``pdd`` and ``pdd_`` do not have the same number of
        columns.
    """

    emd_dist, transport_plan = _EMD(
        pdd[:, 0], pdd_[:, 0], pdd[:, 1:], pdd_[:, 1:], metric=metric, **kwargs
    )
    if return_transport:
        return emd_dist, transport_plan
    return emd_dist


def _EMD(
    weights: FloatArray,
    weights_: FloatArray,
    dist: FloatArray,
    dist_: FloatArray,
    metric: Optional[str] = None,
    **kwargs,
) -> Tuple[float, FloatArray]:
    r"""Calculate the earth mover's distance (EMD) between two weighted
    distributions (collections of vectors).

    Parameters
    ----------
    dist : :class:`numpy.ndarray`
        ``(n, d)`` array of items in the first distribution.
    dist_ : :class:`numpy.ndarray`
        ``(m, d)`` array of items in the second distribution.
    weights : :class:`numpy.ndarray`
        Weights of items in ``dist``.
    weights\_ : :class:`numpy.ndarray`
        Weights of items in ``dist\_``.
    metric : str or callable, default 'chebyshev'
        Metric used as the base distance between items in ``dist`` and
        ``dist\_``. For a list of accepted metrics see
        :func:`scipy.spatial.distance.cdist`.

    Returns
    -------
    emd : float
        Earth mover's distance between two PDDs. If ``return_transport``
        is True, returns a tuple (emd, transport_plan).
    transport_plan : :class:`numpy.ndarray`
        Matrix of optimal flows between the two distributions.
    """

    dm = cdist(dist, dist_, metric=metric, **kwargs)
    return network_simplex(weights, weights_, dm)


def AMD_cdist(
    amds, amds_, metric: str = "chebyshev", low_memory: bool = False, **kwargs
) -> FloatArray:
    r"""Compare two sets of AMDs with each other, returning a distance
    matrix. This function is essentially
    :func:`scipy.spatial.distance.cdist` with the default metric
    ``chebyshev`` and a low memory option.

    Parameters
    ----------
    amds : ArrayLike
        A list/array of AMDs.
    amds\_ : ArrayLike
        A list/array of AMDs.
    metric : str or callable, default 'chebyshev'
        Usually AMDs are compared with the Chebyshev (L-infinitys)
        distance. Accepts any metric accepted by
        :func:`scipy.spatial.distance.cdist`.
    low_memory : bool, default False
        Use a slower but more memory efficient method for large
        collections of AMDs (metric 'chebyshev' only).
    **kwargs :
        Extra arguments for ``metric``, passed to
        :func:`scipy.spatial.distance.cdist`.

    Returns
    -------
    dm : :class:`numpy.ndarray`
        A distance matrix shape ``(len(amds), len(amds_))``. ``dm[ij]``
        is the distance (given by ``metric``) between ``amds[i]`` and
        ``amds[j]``.
    """

    amds = np.asarray(amds)

    if low_memory:
        if metric != "chebyshev":
            raise ValueError(
                "'low_memory' parameter of amd.AMD_cdist() only implemented "
                "with metric='chebyshev'"
            )
        dm = np.empty((len(amds), len(amds_)))
        for i, amd_vec in enumerate(amds):
            dm[i] = np.amax(np.abs(amds_ - amd_vec), axis=-1)
    else:
        dm = cdist(amds, amds_, metric=metric, **kwargs)
    return dm


def AMD_pdist(
    amds, metric: str = "chebyshev", low_memory: bool = False, **kwargs
) -> FloatArray:
    """Compare a set of AMDs pairwise, returning a condensed distance
    matrix. This function is essentially
    :func:`scipy.spatial.distance.pdist` with the default metric
    ``chebyshev`` and a low memory parameter.

    Parameters
    ----------
    amds : ArrayLike
        An list/array of AMDs.
    metric : str or callable, default 'chebyshev'
        Usually AMDs are compared with the Chebyshev (L-infinity)
        distance. Accepts any metric accepted by
        :func:`scipy.spatial.distance.pdist`.
    low_memory : bool, default False
        Use a slower but more memory efficient method for large
        collections of AMDs (metric 'chebyshev' only).
    **kwargs :
        Extra arguments for ``metric``, passed to
        :func:`scipy.spatial.distance.pdist`.

    Returns
    -------
    cdm : :class:`numpy.ndarray`
        Returns a condensed distance matrix. Collapses a square distance
        matrix into a vector, just keeping the upper half. See the
        function :func:`squareform <scipy.spatial.distance.squareform>`
        from SciPy to convert to a symmetric square distance matrix.
    """

    amds = np.asarray(amds)

    @numba.njit(cache=True, fastmath=True)
    def _pdist_lowmem(amds):
        m = amds.shape[0]
        cdm = np.empty((m * (m - 1)) // 2, dtype=np.float64)
        ind = 0
        for i in range(m):
            for j in range(i + 1, m):
                cdm[ind] = np.amax(np.abs(amds[i] - amds[j]))
        return cdm

    if low_memory:
        if metric != "chebyshev":
            raise ValueError(
                "'low_memory' parameter of amd.AMD_pdist() only implemented "
                "with metric='chebyshev'"
            )
        cdm = _pdist_lowmem(amds)
    else:
        cdm = pdist(amds, metric=metric, **kwargs)

    return cdm


def PDD_cdist(
    pdds: List[FloatArray],
    pdds_: List[FloatArray],
    metric: str = "chebyshev",
    backend: str = "multiprocessing",
    n_jobs: Optional[int] = None,
    verbose: bool = False,
    **kwargs,
) -> FloatArray:
    r"""Compare two sets of PDDs with each other, returning a distance
    matrix. Supports parallel processing via joblib. If using
    parallelisation, make sure to include an if __name__ == '__main__'
    guard around this function.

    Parameters
    ----------
    pdds : List[:class:`numpy.ndarray`]
        A list of PDDs.
    pdds\_ : List[:class:`numpy.ndarray`]
        A list of PDDs.
    metric : str or callable, default 'chebyshev'
        Usually PDD rows are compared with the Chebyshev/l-infinity
        distance. Accepts any metric accepted by
        :func:`scipy.spatial.distance.cdist`.
    backend : str, default 'multiprocessing'
        The parallelization backend implementation. For a list of
        supported backends, see the backend argument of
        :class:`joblib.Parallel`.
    n_jobs : int, default None
        Maximum number of concurrent jobs for parallel processing with
        ``joblib``. Set to -1 to use the maximum. Using parallel
        processing may be slower for small inputs.
    verbose : bool, default False
        Prints a progress bar. If using parallel processing
        (n_jobs > 1), the verbose argument of :class:`joblib.Parallel`
        is used, otherwise uses tqdm.
    **kwargs :
        Extra arguments for ``metric``, passed to
        :func:`scipy.spatial.distance.cdist`.

    Returns
    -------
    dm : :class:`numpy.ndarray`
        Returns a distance matrix shape ``(len(pdds), len(pdds_))``. The
        :math:`ij` th entry is the distance between ``pdds[i]`` and
        ``pdds_[j]`` given by Earth mover's distance.
    """

    kwargs.pop("return_transport", None)
    k = pdds[0].shape[-1] - 1
    _verbose = 3 if verbose else 0

    if n_jobs is not None and n_jobs not in (0, 1):
        # TODO: put results into preallocated empty array in place
        dm = Parallel(backend=backend, n_jobs=n_jobs, verbose=_verbose)(
            delayed(partial(EMD, metric=metric, **kwargs))(pdds[i], pdds_[j])
            for i in range(len(pdds))
            for j in range(len(pdds_))
        )
        dm = np.array(dm).reshape((len(pdds), len(pdds_)))

    else:
        n, m = len(pdds), len(pdds_)
        dm = np.empty((n, m))
        if verbose:
            desc = f"Comparing {len(pdds)}x{len(pdds_)} PDDs (k={k})"
            progress_bar = tqdm.tqdm(desc=desc, total=n * m)
            for i in range(n):
                for j in range(m):
                    dm[i, j] = EMD(pdds[i], pdds_[j], metric=metric, **kwargs)
                    progress_bar.update(1)
            progress_bar.close()
        else:
            for i in range(n):
                for j in range(m):
                    dm[i, j] = EMD(pdds[i], pdds_[j], metric=metric, **kwargs)

    return dm


def PDD_pdist(
    pdds: List[FloatArray],
    metric: str = "chebyshev",
    backend: str = "multiprocessing",
    n_jobs: Optional[int] = None,
    verbose: bool = False,
    **kwargs,
) -> FloatArray:
    """Compare a set of PDDs pairwise, returning a condensed distance
    matrix. Supports parallelisation via joblib. If using
    parallelisation, make sure to include a if __name__ == '__main__'
    guard around this function.

    Parameters
    ----------
    pdds : List[:class:`numpy.ndarray`]
        A list of PDDs.
    metric : str or callable, default 'chebyshev'
        Usually PDD rows are compared with the Chebyshev/l-infinity
        distance. Accepts any metric accepted by
        :func:`scipy.spatial.distance.cdist`.
    backend : str, default 'multiprocessing'
        The parallelization backend implementation. For a list of
        supported backends, see the backend argument of
        :class:`joblib.Parallel`.
    n_jobs : int, default None
        Maximum number of concurrent jobs for parallel processing with
        ``joblib``. Set to -1 to use the maximum. Using parallel
        processing may be slower for small inputs.
    verbose : bool, default False
        Prints a progress bar. If using parallel processing
        (n_jobs > 1), the verbose argument of :class:`joblib.Parallel`
        is used, otherwise uses tqdm.
    **kwargs :
        Extra arguments for ``metric``, passed to
        :func:`scipy.spatial.distance.cdist`.

    Returns
    -------
    cdm : :class:`numpy.ndarray`
        Returns a condensed distance matrix. Collapses a square distance
        matrix into a vector, just keeping the upper half. See the
        function :func:`squareform <scipy.spatial.distance.squareform>`
        from SciPy to convert to a symmetric square distance matrix.
    """

    kwargs.pop("return_transport", None)
    k = pdds[0].shape[-1] - 1
    _verbose = 3 if verbose else 0

    if n_jobs is not None and n_jobs > 1:
        # TODO: put results into preallocated empty array in place
        cdm = Parallel(backend=backend, n_jobs=n_jobs, verbose=_verbose)(
            delayed(partial(EMD, metric=metric, **kwargs))(pdds[i], pdds[j])
            for i, j in combinations(range(len(pdds)), 2)
        )
        cdm = np.array(cdm)

    else:
        m = len(pdds)
        cdm_len = (m * (m - 1)) // 2
        cdm = np.empty(cdm_len, dtype=np.float64)
        inds = ((i, j) for i in range(0, m - 1) for j in range(i + 1, m))
        if verbose:
            desc = f"Comparing {len(pdds)} PDDs pairwise (k={k})"
            progress_bar = tqdm.tqdm(desc=desc, total=cdm_len)
            for r, (i, j) in enumerate(inds):
                cdm[r] = EMD(pdds[i], pdds[j], metric=metric, **kwargs)
                progress_bar.update(1)
            progress_bar.close()
        else:
            for r, (i, j) in enumerate(inds):
                cdm[r] = EMD(pdds[i], pdds[j], metric=metric, **kwargs)

    return cdm


1			"""Functions for comparing AMDs and PDDs of crystals."""
2
3			from typing import List, Optional, Union, Tuple
4			from functools import partial
5			from itertools import combinations
6
7			import numpy as np
8			import numba
9			from scipy.spatial.distance import cdist, pdist
10			from joblib import Parallel, delayed
11			import tqdm
12
13			from ._emd import network_simplex
14			from ._types import FloatArray
15
16			__all__ = ["EMD", "AMD_cdist", "AMD_pdist", "PDD_cdist", "PDD_pdist"]
17
18
19			def EMD(
20			pdd: FloatArray,
21			pdd_: FloatArray,
22			metric: Optional[str] = "chebyshev",
23			return_transport: Optional[bool] = False,
24			**kwargs,
25			) -> Union[float, Tuple[float, FloatArray]]:
26			r"""Calculate the Earth mover's distance (EMD) between two PDDs, aka
27			the Wasserstein metric.
28
29			Parameters
30			----------
31			pdd : :class:`numpy.ndarray`
32			PDD of a crystal.
33			pdd\_ : :class:`numpy.ndarray`
34			PDD of a crystal.
35			metric : str or callable, default 'chebyshev'
36			EMD between PDDs requires defining a distance between PDD rows.
37			By default, Chebyshev (L-infinity) distance is chosen as with
38			AMDs. Accepts any metric accepted by
39			:func:`scipy.spatial.distance.cdist`.
40			return_transport: bool, default False
41			Instead return a tuple ``(emd, transport_plan)`` where
42			transport_plan describes the optimal flow.
43
44			Returns
45			-------
46			emd : float
47			Earth mover's distance between two PDDs. If ``return_transport``
48			is True, return a tuple (emd, transport_plan).
49
50			Raises
51			------
52			ValueError
53			Thrown if ``pdd`` and ``pdd_`` do not have the same number of
54			columns.
55			"""
56
57			emd_dist, transport_plan = _EMD(
58			pdd[:, 0], pdd_[:, 0], pdd[:, 1:], pdd_[:, 1:], metric=metric, **kwargs
59			)
60			if return_transport:
61			return emd_dist, transport_plan
62			return emd_dist
63
64
65			def _EMD(
66			weights: FloatArray,
67			weights_: FloatArray,
68			dist: FloatArray,
69			dist_: FloatArray,
70			metric: Optional[str] = None,
71			**kwargs,
72			) -> Tuple[float, FloatArray]:
73			r"""Calculate the earth mover's distance (EMD) between two weighted
74			distributions (collections of vectors).
75
76			Parameters
77			----------
78			dist : :class:`numpy.ndarray`
79			``(n, d)`` array of items in the first distribution.
80			dist_ : :class:`numpy.ndarray`
81			``(m, d)`` array of items in the second distribution.
82			weights : :class:`numpy.ndarray`
83			Weights of items in ``dist``.
84			weights\_ : :class:`numpy.ndarray`
85			Weights of items in ``dist\_``.
86			metric : str or callable, default 'chebyshev'
87			Metric used as the base distance between items in ``dist`` and
88			``dist\_``. For a list of accepted metrics see
89			:func:`scipy.spatial.distance.cdist`.
90
91			Returns
92			-------
93			emd : float
94			Earth mover's distance between two PDDs. If ``return_transport``
95			is True, returns a tuple (emd, transport_plan).
96			transport_plan : :class:`numpy.ndarray`
97			Matrix of optimal flows between the two distributions.
98			"""
99
100			dm = cdist(dist, dist_, metric=metric, **kwargs)
101			return network_simplex(weights, weights_, dm)
102
103
104			def AMD_cdist(
105			amds, amds_, metric: str = "chebyshev", low_memory: bool = False, **kwargs
106			) -> FloatArray:
107			r"""Compare two sets of AMDs with each other, returning a distance
108			matrix. This function is essentially
109			:func:`scipy.spatial.distance.cdist` with the default metric
110			``chebyshev`` and a low memory option.
111
112			Parameters
113			----------
114			amds : ArrayLike
115			A list/array of AMDs.
116			amds\_ : ArrayLike
117			A list/array of AMDs.
118			metric : str or callable, default 'chebyshev'
119			Usually AMDs are compared with the Chebyshev (L-infinitys)
120			distance. Accepts any metric accepted by
121			:func:`scipy.spatial.distance.cdist`.
122			low_memory : bool, default False
123			Use a slower but more memory efficient method for large
124			collections of AMDs (metric 'chebyshev' only).
125			**kwargs :
126			Extra arguments for ``metric``, passed to
127			:func:`scipy.spatial.distance.cdist`.
128
129			Returns
130			-------
131			dm : :class:`numpy.ndarray`
132			A distance matrix shape ``(len(amds), len(amds_))``. ``dm[ij]``
133			is the distance (given by ``metric``) between ``amds[i]`` and
134			``amds[j]``.
135			"""
136
137			amds = np.asarray(amds)
138
139			if low_memory:
140			if metric != "chebyshev":
141			raise ValueError(
142			"'low_memory' parameter of amd.AMD_cdist() only implemented "
143			"with metric='chebyshev'"
144			)
145			dm = np.empty((len(amds), len(amds_)))
146			for i, amd_vec in enumerate(amds):
147			dm[i] = np.amax(np.abs(amds_ - amd_vec), axis=-1)
148			else:
149			dm = cdist(amds, amds_, metric=metric, **kwargs)
150			return dm
151
152
153			def AMD_pdist(
154			amds, metric: str = "chebyshev", low_memory: bool = False, **kwargs
155			) -> FloatArray:
156			"""Compare a set of AMDs pairwise, returning a condensed distance
157			matrix. This function is essentially
158			:func:`scipy.spatial.distance.pdist` with the default metric
159			``chebyshev`` and a low memory parameter.
160
161			Parameters
162			----------
163			amds : ArrayLike
164			An list/array of AMDs.
165			metric : str or callable, default 'chebyshev'
166			Usually AMDs are compared with the Chebyshev (L-infinity)
167			distance. Accepts any metric accepted by
168			:func:`scipy.spatial.distance.pdist`.
169			low_memory : bool, default False
170			Use a slower but more memory efficient method for large
171			collections of AMDs (metric 'chebyshev' only).
172			**kwargs :
173			Extra arguments for ``metric``, passed to
174			:func:`scipy.spatial.distance.pdist`.
175
176			Returns
177			-------
178			cdm : :class:`numpy.ndarray`
179			Returns a condensed distance matrix. Collapses a square distance
180			matrix into a vector, just keeping the upper half. See the
181			function :func:`squareform <scipy.spatial.distance.squareform>`
182			from SciPy to convert to a symmetric square distance matrix.
183			"""
184
185			amds = np.asarray(amds)
186
187			@numba.njit(cache=True, fastmath=True)
188			def _pdist_lowmem(amds):
189			m = amds.shape[0]
190			cdm = np.empty((m * (m - 1)) // 2, dtype=np.float64)
191			ind = 0
192			for i in range(m):
193			for j in range(i + 1, m):
194			cdm[ind] = np.amax(np.abs(amds[i] - amds[j]))
195			return cdm
196
197			if low_memory:
198			if metric != "chebyshev":
199			raise ValueError(
200			"'low_memory' parameter of amd.AMD_pdist() only implemented "
201			"with metric='chebyshev'"
202			)
203			cdm = _pdist_lowmem(amds)
204			else:
205			cdm = pdist(amds, metric=metric, **kwargs)
206
207			return cdm
208
209
210			def PDD_cdist(
211			pdds: List[FloatArray],
212			pdds_: List[FloatArray],
213			metric: str = "chebyshev",
214			backend: str = "multiprocessing",
215			n_jobs: Optional[int] = None,
216			verbose: bool = False,
217			**kwargs,
218			) -> FloatArray:
219			r"""Compare two sets of PDDs with each other, returning a distance
220			matrix. Supports parallel processing via joblib. If using
221			parallelisation, make sure to include an if __name__ == '__main__'
222			guard around this function.
223
224			Parameters
225			----------
226			pdds : List[:class:`numpy.ndarray`]
227			A list of PDDs.
228			pdds\_ : List[:class:`numpy.ndarray`]
229			A list of PDDs.
230			metric : str or callable, default 'chebyshev'
231			Usually PDD rows are compared with the Chebyshev/l-infinity
232			distance. Accepts any metric accepted by
233			:func:`scipy.spatial.distance.cdist`.
234			backend : str, default 'multiprocessing'
235			The parallelization backend implementation. For a list of
236			supported backends, see the backend argument of
237			:class:`joblib.Parallel`.
238			n_jobs : int, default None
239			Maximum number of concurrent jobs for parallel processing with
240			``joblib``. Set to -1 to use the maximum. Using parallel
241			processing may be slower for small inputs.
242			verbose : bool, default False
243			Prints a progress bar. If using parallel processing
244			(n_jobs > 1), the verbose argument of :class:`joblib.Parallel`
245			is used, otherwise uses tqdm.
246			**kwargs :
247			Extra arguments for ``metric``, passed to
248			:func:`scipy.spatial.distance.cdist`.
249
250			Returns
251			-------
252			dm : :class:`numpy.ndarray`
253			Returns a distance matrix shape ``(len(pdds), len(pdds_))``. The
254			:math:`ij` th entry is the distance between ``pdds[i]`` and
255			``pdds_[j]`` given by Earth mover's distance.
256			"""
257
258			kwargs.pop("return_transport", None)
259			k = pdds[0].shape[-1] - 1
260			_verbose = 3 if verbose else 0
261
262			if n_jobs is not None and n_jobs not in (0, 1):
263			# TODO: put results into preallocated empty array in place
264			dm = Parallel(backend=backend, n_jobs=n_jobs, verbose=_verbose)(
265			delayed(partial(EMD, metric=metric, **kwargs))(pdds[i], pdds_[j])
266			for i in range(len(pdds))
267			for j in range(len(pdds_))
268			)
269			dm = np.array(dm).reshape((len(pdds), len(pdds_)))
270
271			else:
272			n, m = len(pdds), len(pdds_)
273			dm = np.empty((n, m))
274			if verbose:
275			desc = f"Comparing {len(pdds)}x{len(pdds_)} PDDs (k={k})"
276			progress_bar = tqdm.tqdm(desc=desc, total=n * m)
277			for i in range(n):
278			for j in range(m):
279			dm[i, j] = EMD(pdds[i], pdds_[j], metric=metric, **kwargs)
280			progress_bar.update(1)
281			progress_bar.close()
282			else:
283			for i in range(n):
284			for j in range(m):
285			dm[i, j] = EMD(pdds[i], pdds_[j], metric=metric, **kwargs)
286
287			return dm
288
289
290			def PDD_pdist(
291			pdds: List[FloatArray],
292			metric: str = "chebyshev",
293			backend: str = "multiprocessing",
294			n_jobs: Optional[int] = None,
295			verbose: bool = False,
296			**kwargs,
297			) -> FloatArray:
298			"""Compare a set of PDDs pairwise, returning a condensed distance
299			matrix. Supports parallelisation via joblib. If using
300			parallelisation, make sure to include a if __name__ == '__main__'
301			guard around this function.
302
303			Parameters
304			----------
305			pdds : List[:class:`numpy.ndarray`]
306			A list of PDDs.
307			metric : str or callable, default 'chebyshev'
308			Usually PDD rows are compared with the Chebyshev/l-infinity
309			distance. Accepts any metric accepted by
310			:func:`scipy.spatial.distance.cdist`.
311			backend : str, default 'multiprocessing'
312			The parallelization backend implementation. For a list of
313			supported backends, see the backend argument of
314			:class:`joblib.Parallel`.
315			n_jobs : int, default None
316			Maximum number of concurrent jobs for parallel processing with
317			``joblib``. Set to -1 to use the maximum. Using parallel
318			processing may be slower for small inputs.
319			verbose : bool, default False
320			Prints a progress bar. If using parallel processing
321			(n_jobs > 1), the verbose argument of :class:`joblib.Parallel`
322			is used, otherwise uses tqdm.
323			**kwargs :
324			Extra arguments for ``metric``, passed to
325			:func:`scipy.spatial.distance.cdist`.
326
327			Returns
328			-------
329			cdm : :class:`numpy.ndarray`
330			Returns a condensed distance matrix. Collapses a square distance
331			matrix into a vector, just keeping the upper half. See the
332			function :func:`squareform <scipy.spatial.distance.squareform>`
333			from SciPy to convert to a symmetric square distance matrix.
334			"""
335
336			kwargs.pop("return_transport", None)
337			k = pdds[0].shape[-1] - 1
338			_verbose = 3 if verbose else 0
339
340			if n_jobs is not None and n_jobs > 1:
341			# TODO: put results into preallocated empty array in place
342			cdm = Parallel(backend=backend, n_jobs=n_jobs, verbose=_verbose)(
343			delayed(partial(EMD, metric=metric, **kwargs))(pdds[i], pdds[j])
344			for i, j in combinations(range(len(pdds)), 2)
345			)
346			cdm = np.array(cdm)
347
348			else:
349			m = len(pdds)
350			cdm_len = (m * (m - 1)) // 2
351			cdm = np.empty(cdm_len, dtype=np.float64)
352			inds = ((i, j) for i in range(0, m - 1) for j in range(i + 1, m))
353			if verbose:
354			desc = f"Comparing {len(pdds)} PDDs pairwise (k={k})"
355			progress_bar = tqdm.tqdm(desc=desc, total=cdm_len)
356			for r, (i, j) in enumerate(inds):
357			cdm[r] = EMD(pdds[i], pdds[j], metric=metric, **kwargs)
358			progress_bar.update(1)
359			progress_bar.close()
360			else:
361			for r, (i, j) in enumerate(inds):
362			cdm[r] = EMD(pdds[i], pdds[j], metric=metric, **kwargs)
363
364			return cdm
365

dwiddo / average-minimum-distance

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