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How to use norm_rows method in Testify
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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])
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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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