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test_np_distances.py

Source:test_np_distances.py

1from gehm.utils.np_distances import *2from tests.test_data import create_test_data34import pytest5import numpy as np6import torch7from numpy import cos, sin89@pytest.mark.distances10def test_nx_second_order_proximity(create_test_data):1112    G,G_undir=create_test_data13    nodes=np.array(G.nodes)141516    # Test 1: Ordering when subset proximity17    sel1=nodes[[0,1,2,3]]18    sel2=nodes[[0,3,2,1]]19    prox1=nx_second_order_proximity(G,sel1,False)20    prox2=nx_second_order_proximity(G,sel2,False)21    prox1=np.round(prox1,5)22    prox2=np.round(prox2,5)23    assert (prox1[0,1]==prox2[0,3]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])24    assert (prox1[1,2]==prox2[3,2]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])252627    # Test 2: Ordering when whole network proximity28    prox1=nx_second_order_proximity(G,sel1,True)29    prox2=nx_second_order_proximity(G,sel2,True)30    prox1=np.round(prox1,5)31    prox2=np.round(prox2,5)32    assert (prox1[1,3]==prox2[3,3]), "Ordering problem, {} != {}".format(prox1[1,3],prox2[3,3])33    assert (prox1[3,1]==prox2[1,1]), "Ordering problem, {} != {}".format(prox1[3,1],prox2[1,1])3435    # Test 3+4: Without row normalization36    prox1=nx_second_order_proximity(G,sel1,False, norm_rows=False)37    prox2=nx_second_order_proximity(G,sel2,False, norm_rows=False)38    prox1=np.round(prox1,5)39    prox2=np.round(prox2,5)40    assert (prox1[0,1]==prox2[0,3]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])41    assert (prox1[1,2]==prox2[3,2]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])4243    prox1=nx_second_order_proximity(G,sel1,True, norm_rows=False)44    prox2=nx_second_order_proximity(G,sel2,True, norm_rows=False)45    prox1=np.round(prox1,5)46    prox2=np.round(prox2,5)47    assert (prox1[1,3]==prox2[3,3]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])48    assert (prox1[3,1]==prox2[1,1]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])4950    # Test 5+6: Without row normalization, but with batch normalization51    prox1=nx_second_order_proximity(G,sel1,False, norm_rows=False, norm_rows_in_sample=True)52    prox2=nx_second_order_proximity(G,sel2,False, norm_rows=False, norm_rows_in_sample=True)53    prox1=np.round(prox1,5)54    prox2=np.round(prox2,5)55    assert (prox1[0,1]==prox2[0,3]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])56    assert (prox1[1,2]==prox2[3,2]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])5758    prox1=nx_second_order_proximity(G,sel1,True, norm_rows=False, norm_rows_in_sample=True)59    prox2=nx_second_order_proximity(G,sel2,True, norm_rows=False, norm_rows_in_sample=True)60    prox1=np.round(prox1,5)61    prox2=np.round(prox2,5)62    assert (prox1[1,3]==prox2[3,3]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])63    assert (prox1[3,1]==prox2[1,1]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])646566    # Test 7: Whole network, but return batch order67    prox1=nx_second_order_proximity(G,sel1,True, norm_rows=True, to_batch=True)68    prox2=nx_second_order_proximity(G,sel2,True, norm_rows=True, to_batch=True)69    prox1=np.round(prox1,5)70    prox2=np.round(prox2,5)71    assert (prox1[1,0]==prox2[3,0]), "Ordering problem, {} != {}".format(prox1[1,0],prox2[3,0])72    assert (prox1[3,1]==prox2[1,3]), "Ordering problem, {} != {}".format(prox1[3,1],prox2[1,3])7374    # Test 8: Whole network, but return batch order, no norm75    prox1=nx_second_order_proximity(G,sel1,True, norm_rows=False, to_batch=True)76    prox2=nx_second_order_proximity(G,sel2,True, norm_rows=False, to_batch=True)77    prox1=np.round(prox1,5)78    prox2=np.round(prox2,5)79    assert (prox1[1,0]==prox2[3,0]), "Ordering problem, {} != {}".format(prox1[1,0],prox2[3,0])80    assert (prox1[3,1]==prox2[1,3]), "Ordering problem, {} != {}".format(prox1[3,1],prox2[1,3])81828384    # Now repeat everything with an undirected graph:8586    G=G_undir87    nodes=np.array(G.nodes)888990    # Test 1: Ordering when subset proximity91    sel1=nodes[[0,1,2,3]]92    sel2=nodes[[0,3,2,1]]93    prox1=nx_second_order_proximity(G,sel1,False)94    prox2=nx_second_order_proximity(G,sel2,False)95    prox1=np.round(prox1,5)96    prox2=np.round(prox2,5)97    assert (prox1[0,1]==prox2[0,3]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])98    assert (prox1[1,2]==prox2[3,2]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])99100101    # Test 2: Ordering when whole network proximity102    prox1=nx_second_order_proximity(G,sel1,True)103    prox2=nx_second_order_proximity(G,sel2,True)104    prox1=np.round(prox1,5)105    prox2=np.round(prox2,5)106    assert (prox1[1,3]==prox2[3,3]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])107    assert (prox1[3,1]==prox2[1,1]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])108109    # Test 3+4: Without row normalization110    prox1=nx_second_order_proximity(G,sel1,False, norm_rows=False)111    prox2=nx_second_order_proximity(G,sel2,False, norm_rows=False)112    prox1=np.round(prox1,5)113    prox2=np.round(prox2,5)114    assert (prox1[0,1]==prox2[0,3]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])115    assert (prox1[1,2]==prox2[3,2]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])116117    prox1=nx_second_order_proximity(G,sel1,True, norm_rows=False)118    prox2=nx_second_order_proximity(G,sel2,True, norm_rows=False)119    prox1=np.round(prox1,5)120    prox2=np.round(prox2,5)121    assert (prox1[1,3]==prox2[3,3]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])122    assert (prox1[3,1]==prox2[1,1]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])123124    # Test 5+6: Without row normalization, but with batch normalization125    prox1=nx_second_order_proximity(G,sel1,False, norm_rows=False, norm_rows_in_sample=True)126    prox2=nx_second_order_proximity(G,sel2,False, norm_rows=False, norm_rows_in_sample=True)127    prox1=np.round(prox1,5)128    prox2=np.round(prox2,5)129    assert (prox1[0,1]==prox2[0,3]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])130    assert (prox1[1,2]==prox2[3,2]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])131132    prox1=nx_second_order_proximity(G,sel1,True, norm_rows=False, norm_rows_in_sample=True)133    prox2=nx_second_order_proximity(G,sel2,True, norm_rows=False, norm_rows_in_sample=True)134    prox1=np.round(prox1,5)135    prox2=np.round(prox2,5)136    assert (prox1[1,3]==prox2[3,3]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])137    assert (prox1[3,1]==prox2[1,1]), "Ordering problem, {} != {}".format(prox1[0,1],prox2[0,3])138139140    # Test 7: Whole network, but return batch order141    prox1=nx_second_order_proximity(G,sel1,True, norm_rows=True, to_batch=True)142    prox2=nx_second_order_proximity(G,sel2,True, norm_rows=True, to_batch=True)143    prox1=np.round(prox1,5)144    prox2=np.round(prox2,5)145    assert (prox1[1,0]==prox2[3,0]), "Ordering problem, {} != {}".format(prox1[1,0],prox2[3,0])146    assert (prox1[3,1]==prox2[1,3]), "Ordering problem, {} != {}".format(prox1[3,1],prox2[1,3])147148    # Test 8: Whole network, but return batch order, no norm149    prox1=nx_second_order_proximity(G,sel1,True, norm_rows=False, to_batch=True)150    prox2=nx_second_order_proximity(G,sel2,True, norm_rows=False, to_batch=True)151    prox1=np.round(prox1,5)152    prox2=np.round(prox2,5)153    assert (prox1[1,0]==prox2[3,0]), "Ordering problem, {} != {}".format(prox1[1,0],prox2[3,0])154    assert (prox1[3,1]==prox2[1,3]), "Ordering problem, {} != {}".format(prox1[3,1],prox2[1,3])
...

cosine.py

Source:cosine.py

1from ..utils import _XXT, rowSum, reflect, setDiagonal2from numpy import zeros, newaxis3from numba import njit, prange4from ..sparse.csr import div_1 ,mult_1, _XXT as _XXT_sparse, rowSum as rowSum_sparse5from ..sparse.tcsr import _XXT as _XXT_triangular6def cosine_sim_triangular(N, D):7	"""8	[Added 21/10/2018]9	Quickly performs X / norm_rows / norm_rows.T on the TCSR matrix.10	"""11	n = len(N)12	move = 013	14	# loop *-2 and adds S[:, newaxis]15	for i in prange(n-1):16		i1 = i+117		18		left = i*i1 // 219		s = N[i1]20		for j in range(left, left+i1):			21			# div N[:, newaxis]22			D[j] /= s23	24	# loop div N[newaxis, :]25	for a in prange(n-1):26		s = N[a]27		for b in range(a, n-1):28			# div N[newaxis, :] or N29			c = b*(b+1) // 2 + a30			D[c] /= s31	return D32cosine_sim_triangular_single = njit(cosine_sim_triangular, fastmath = True, nogil = True, cache = True)33cosine_sim_triangular_parallel = njit(cosine_sim_triangular, fastmath = True, nogil = True, parallel = True)34@njit(fastmath = True, nogil = True, cache = True)35def cosine_dis(XXT):36	"""37	[Added 22/10/2018]38	Performs XXT*-1 + 1 quickly on the lower triangular part.39	"""40	n = len(XXT)41	for i in range(n):42		for j in range(i):43			XXT[i, j] *= -144			XXT[i, j] += 145	return XXT46@njit(fastmath = True, nogil = True, cache = True)47def cosine_dis_triangular(D):48	"""49	[Added 22/10/2018]50	Performs XXT*-1 + 1 quickly on the TCSR.51	"""52	D *= -153	D += 154	return D55def cosine_similarity(X, Y = None, triangular = False, n_jobs = 1, copy = False):56	"""57	[Added 20/10/2018] [Edited 22/201/2018]58	[Edited 22/10/2018 Added Y option]59	Note: when using Y, speed improvement is approx 5% only from Sklearn.60	Cosine similarity is approx the same speed as Sklearn, but uses approx 10%61	less memory. One clear advantage is if you set triangular to TRUE, then it's faster.62	"""63	norm_rows = rowSum(X, norm = True)64	if Y is X:65		# Force algo to be triangular cosine rather than normal CS.66		Y = None67	if Y is None:68		if copy:69			XXT = _XXT(X.T)70			XXT /= norm_rows[:, newaxis]71			XXT /= norm_rows #[newaxis, :]72		else:73			XXT = _XXT(  (X/norm_rows[:, newaxis]).T  )74		if not triangular:75			XXT = reflect(XXT, n_jobs)76		# diagonal is set to 177		setDiagonal(XXT, 1)78		return XXT79	else:80		D = X @ Y.T81		D /= norm_rows[:, newaxis]82		D /= rowSum(Y, norm = True)83		return D84def cosine_similarity_sparse(val, colPointer, rowIndices, n, p, triangular = False, dense_output = True,85	n_jobs = 1, copy = True):86	"""87	[Added 20/10/2018] [Edited 21/10/2018]88	Slightly faster than Sklearn's Cosine Similarity implementation.89	If dense_output is set to FALSE, then a TCSR Matrix (Triangular CSR Matrix) is90	provided and not a CSR matrix. This has the advantage of using only 1/2n^2 - n91	memory and not n^2 memory.92	"""93	norm_rows = rowSum_sparse(val, colPointer, rowIndices, norm = True)94	if dense_output:95		if copy:96			XXT = _XXT_sparse(val, colPointer, rowIndices, n, p, n_jobs)97			XXT /= norm_rows[:, newaxis]98			XXT /= norm_rows #[newaxis, :]99		else:100			val = div_1(val, colPointer, rowIndices, norm_rows, n, p, copy = False)101			XXT = _XXT_sparse(val, colPointer, rowIndices, n, p, n_jobs)102			val = mult_1(val, colPointer, rowIndices, norm_rows, n, p, copy = False)103		if not triangular: 104			XXT = reflect(XXT, n_jobs)105		# diagonal is set to 1106		setDiagonal(XXT, 1)107	else:108		XXT = _XXT_triangular(val, colPointer, rowIndices, n, p, n_jobs)109		XXT = cosine_triangular_parallel(norm_rows, XXT) if n_jobs != 1 else \110			cosine_triangular_single(norm_rows, XXT)111	return XXT112def cosine_distances(X, Y = None, triangular = False, n_jobs = 1, copy = False):113	"""114	[Added 15/10/2018] [Edited 18/10/2018]115	[Edited 22/10/2018 Added Y option]116	Note: when using Y, speed improvement is approx 5-10% only from Sklearn.117	Slightly faster than Sklearn's Cosine Distances implementation.118	If you set triangular to TRUE, the result is much much faster.119	(Approx 50% faster than Sklearn)120	"""121	norm_rows = rowSum(X, norm = True)122	if Y is X:123		# Force algo to be triangular cosine rather than normal CS.124		Y = None125	if Y is None:126		if copy:127			XXT = _XXT(X.T)128			XXT /= norm_rows[:, newaxis]129			XXT /= norm_rows #[newaxis, :]130		else:131			XXT = _XXT(  (X/norm_rows[:, newaxis]).T  )132		# XXT*-1 + 1133		XXT = cosine_dis(XXT)134		if not triangular:135			XXT = reflect(XXT, n_jobs)136		# diagonal is set to 0 as zero distance between row i and i137		setDiagonal(XXT, 0)138		return XXT139	else:140		D = X @ Y.T141		D /= norm_rows[:, newaxis]142		D /= rowSum(Y, norm = True)143		D *= -1144		D += 1145		return D146def cosine_distances_sparse(val, colPointer, rowIndices, n, p, triangular = False, dense_output = True,147	n_jobs = 1, copy = True):148	"""149	[Added 22/10/2018]150	Slightly faster than Sklearn's Cosine Distances implementation.151	If dense_output is set to FALSE, then a TCSR Matrix (Triangular CSR Matrix) is152	provided and not a CSR matrix. This has the advantage of using only 1/2n^2 - n153	memory and not n^2 memory.154	"""155	norm_rows = rowSum_sparse(val, colPointer, rowIndices, norm = True)156	if dense_output:157		if copy:158			XXT = _XXT_sparse(val, colPointer, rowIndices, n, p, n_jobs)159			XXT /= norm_rows[:, newaxis]160			XXT /= norm_rows #[newaxis, :]161		else:162			val = div_1(val, colPointer, rowIndices, norm_rows, n, p, copy = False)163			XXT = _XXT_sparse(val, colPointer, rowIndices, n, p, n_jobs)164			val = mult_1(val, colPointer, rowIndices, norm_rows, n, p, copy = False)165		# XXT*-1 + 1166		XXT = cosine_dis(XXT)167		if not triangular: 168			XXT = reflect(XXT, n_jobs)169		# diagonal is set to 0 as zero distance between row i and i170		setDiagonal(XXT, 0)171	else:172		XXT = _XXT_triangular(val, colPointer, rowIndices, n, p, n_jobs)173		# XXT*-1 + 1174		XXT = cosine_dis_triangular(XXT)175		XXT = cosine_triangular_parallel(norm_rows, XXT) if n_jobs != 1 else \176			cosine_triangular_single(norm_rows, XXT)...

distances.py

Source:distances.py

1import networkx as nx2from typing import Union3from torch import Tensor4import networkx as nx5import torch6from torch.nn.functional import cosine_similarity7from torch import cdist8from gehm.utils.funcs import row_norm91011def embedding_first_order_proximity(12    positions: Tensor, norm_rows: bool = True,13) -> Tensor:14    """15    A simple application of euclidian distance to the positions vector in the embedding space.16    Includes row normalization to get relative distances.1718    Parameters19    ----------20    positions : Tensor21        Input positions, usually nx222    norm_rows : bool, optional23        If True, rows will be normed to 1, by default True2425    Returns26    -------27    Tensor28        Similarity Matrix29    """3031    assert isinstance(positions, Tensor)3233    similarity_matrix = cdist(positions, positions, p=2).to(positions.device)34    if norm_rows:35        similarity_matrix = row_norm(similarity_matrix)36    similarity_matrix = 1-similarity_matrix373839    return similarity_matrix-torch.eye(similarity_matrix.shape[0]).to(positions.device)404142def embedding_second_order_proximity(43    positions: Tensor, norm_rows: bool = True,44) -> Tensor:45    """46    Derives first the pairwise distances, then compares these distance vectors47    between positions to derive proximities of second order.4849    Parameters50    ----------51    positions : Union[Tensor,ndarray]52        Input positions, usually nx253    norm_rows : bool, optional54        If True, rows will be normed to 1, by default True5556    Returns57    -------58    Tensor59        Similarity Matrix60    """61    similarity_matrix = embedding_first_order_proximity(positions, norm_rows=norm_rows)62    similarity_matrix = matrix_cosine(similarity_matrix)6364    if norm_rows:65        similarity_matrix = row_norm(similarity_matrix)66    return similarity_matrix.to(positions.device)676869def matrix_cosine(mat: Tensor) -> Tensor:70    """71    Applies cosine similarity to a matrix, since pdist or cdist in PyTorch72    only computes Minkowski metrices.7374    Parameters75    ----------76    mat : Tensor77        NxN Input7879    Returns80    -------81    Tensor82        Cosine Similarity Matrix83    """84    return cosine_similarity(85        mat[..., None, :, :], mat[..., :, None, :], dim=-1
...

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