def test_cosine_similarity_dense_row_weighted(self):
from Base.Cython.cosine_similarity import Cosine_Similarity as Cosine_Similarity_Cython
from Base.cosine_similarity_parallel import Cosine_Similarity_Parallel as Cosine_Similarity_Parallel
TopK = 0
data_matrix = np.array([[1, 2, 0, 1], [0, 1, 4, 1], [3, 0, 1, 0]])
data_matrix = sps.csr_matrix(data_matrix, dtype=np.float)
row_weights = [2, 3, 0, 4]
cosine_similarity = Cosine_Similarity_Cython(data_matrix.T, topK=TopK, normalize=False, row_weights=row_weights)
W_dense_Cython = cosine_similarity.compute_similarity()
cosine_similarity = Compute_Similarity_Python(data_matrix.T, topK=TopK, normalize=False,
row_weights=row_weights)
W_dense_Python = cosine_similarity.compute_similarity()
cosine_similarity = Cosine_Similarity_Parallel(data_matrix.T, topK=TopK, normalize=False,
row_weights=row_weights)
W_dense_Parallel = cosine_similarity.compute_similarity()
W_dense_mul = data_matrix.dot(sps.diags(row_weights)).dot(data_matrix.T).toarray()
W_dense_mul[np.arange(W_dense_mul.shape[0]), np.arange(W_dense_mul.shape[0])] = 0.0
assert np.allclose(W_dense_Cython, W_dense_mul, atol=1e-4), "W_dense_Cython not matching control"
assert np.allclose(W_dense_Python, W_dense_mul, atol=1e-4), "W_dense_Python not matching control"
assert np.allclose(W_dense_Parallel, W_dense_mul, atol=1e-4), "W_dense_Parallel not matching control"
def test_cosine_similarity_dense(self):
from Base.Cython.cosine_similarity import Cosine_Similarity as Cosine_Similarity_Cython
from Base.cosine_similarity_parallel import Cosine_Similarity_Parallel as Cosine_Similarity_Parallel
TopK = 0
data_matrix = np.array([[1, 1, 0, 1], [0, 1, 1, 1], [1, 0, 1, 0]])
data_matrix = sps.csr_matrix(data_matrix)
cosine_similarity = Cosine_Similarity_Cython(data_matrix,
topK=TopK,
normalize=False)
W_dense_Cython = cosine_similarity.compute_similarity()
cosine_similarity = Compute_Similarity_Python(data_matrix,
topK=TopK,
normalize=False)
W_dense_Python = cosine_similarity.compute_similarity()
cosine_similarity = Cosine_Similarity_Parallel(data_matrix,
topK=TopK,
normalize=False)
W_dense_Parallel = cosine_similarity.compute_similarity()
W_dense_mul = data_matrix.T.dot(data_matrix)
W_dense_mul[np.arange(W_dense_mul.shape[0]),
np.arange(W_dense_mul.shape[0])] = 0.0
assert np.all(W_dense_Cython ==
W_dense_mul), "W_dense_Cython not matching control"
assert np.all(W_dense_Python ==
W_dense_mul), "W_dense_Python not matching control"
assert np.all(W_dense_Parallel ==
W_dense_mul), "W_dense_Parallel not matching control"
def test_cosine_similarity_TopK_big(self):
from Base.Cython.cosine_similarity import Cosine_Similarity as Cosine_Similarity_Cython
from Base.cosine_similarity import Compute_Similarity as Cosine_Similarity_Python
from Base.cosine_similarity_parallel import Cosine_Similarity_Parallel as Cosine_Similarity_Parallel
n_items = 500
n_users = 1000
TopK = n_items
data_matrix = sps.random(n_users, n_items, density=0.1)
cosine_similarity = Cosine_Similarity_Cython(data_matrix, topK=TopK, normalize=False)
W_dense_Cython = cosine_similarity.compute_similarity().toarray()
cosine_similarity = Cosine_Similarity_Python(data_matrix, topK=TopK, normalize=False)
W_dense_Python = cosine_similarity.compute_similarity().toarray()
cosine_similarity = Cosine_Similarity_Parallel(data_matrix, topK=TopK, normalize=False)
W_dense_Parallel = cosine_similarity.compute_similarity().toarray()
W_dense_mul = data_matrix.T.dot(data_matrix)
W_dense_mul[np.arange(W_dense_mul.shape[0]), np.arange(W_dense_mul.shape[0])] = 0.0
W_dense_mul = similarityMatrixTopK(W_dense_mul, k=TopK).toarray()
assert np.allclose(W_dense_Cython, W_dense_mul, atol=1e-4), "W_sparse_Cython not matching control"
assert np.allclose(W_dense_Python, W_dense_mul, atol=1e-4), "W_dense_Python not matching control"
assert np.allclose(W_dense_Parallel, W_dense_mul, atol=1e-4), "W_dense_Parallel not matching control"
def test_cosine_similarity_TopK(self):
from Base.Cython.cosine_similarity import Cosine_Similarity as Cosine_Similarity_Cython
from Base.cosine_similarity import Compute_Similarity as Cosine_Similarity_Python
from Base.cosine_similarity_parallel import Cosine_Similarity_Parallel as Cosine_Similarity_Parallel
TopK = 4
data_matrix = np.array([[1, 1, 0, 1], [0, 1, 1, 1], [1, 0, 1, 0]])
data_matrix = sps.csr_matrix(data_matrix)
cosine_similarity = Cosine_Similarity_Cython(data_matrix, topK=TopK, normalize=False)
W_dense_Cython = cosine_similarity.compute_similarity().toarray()
cosine_similarity = Cosine_Similarity_Python(data_matrix, topK=TopK, normalize=False)
W_dense_Python = cosine_similarity.compute_similarity().toarray()
cosine_similarity = Cosine_Similarity_Parallel(data_matrix, topK=TopK, normalize=False)
W_dense_Parallel = cosine_similarity.compute_similarity().toarray()
W_dense_mul = data_matrix.T.dot(data_matrix)
W_dense_mul[np.arange(W_dense_mul.shape[0]), np.arange(W_dense_mul.shape[0])] = 0.0
W_dense_mul = similarityMatrixTopK(W_dense_mul, k=TopK).toarray()
assert np.allclose(W_dense_Cython, W_dense_mul, atol=1e-4), "W_sparse_Cython not matching control"
assert np.allclose(W_dense_Python, W_dense_mul, atol=1e-4), "W_dense_Python not matching control"
assert np.allclose(W_dense_Parallel, W_dense_mul, atol=1e-4), "W_dense_Parallel not matching control"
def test_cosine_similarity_dense_normalize(self):
from Base.Cython.cosine_similarity import Cosine_Similarity as Cosine_Similarity_Cython
from Base.cosine_similarity import Compute_Similarity as Cosine_Similarity_Python
from Base.cosine_similarity_parallel import Cosine_Similarity_Parallel as Cosine_Similarity_Parallel
import numpy.matlib
TopK = 0
shrink = 5
data_matrix = np.array([[1, 1, 0, 1], [0, 1, 1, 1], [1, 0, 1, 0]])
data_matrix = sps.csr_matrix(data_matrix)
cosine_similarity = Cosine_Similarity_Cython(data_matrix, topK=TopK, normalize=True, shrink=shrink)
W_dense_Cython = cosine_similarity.compute_similarity()
cosine_similarity = Cosine_Similarity_Python(data_matrix, topK=TopK, normalize=True, shrink=shrink)
W_dense_Python = cosine_similarity.compute_similarity()
cosine_similarity = Cosine_Similarity_Parallel(data_matrix, topK=TopK, normalize=True, shrink=shrink)
W_dense_Parallel = cosine_similarity.compute_similarity()
W_dense_denominator = np.matlib.repmat(data_matrix.power(2).sum(axis=0), data_matrix.shape[1], 1)
W_dense_denominator = np.sqrt(W_dense_denominator)
W_dense_denominator = np.multiply(W_dense_denominator, W_dense_denominator.T) + shrink
W_dense_mul = data_matrix.T.dot(data_matrix)
W_dense_mul /= W_dense_denominator
W_dense_mul[np.arange(W_dense_mul.shape[0]), np.arange(W_dense_mul.shape[0])] = 0.0
assert np.allclose(W_dense_Cython, W_dense_mul, atol=1e-4), "W_dense_Cython not matching control"
assert np.allclose(W_dense_Python, W_dense_mul, atol=1e-4), "W_dense_Python not matching control"
assert np.allclose(W_dense_Parallel, W_dense_mul, atol=1e-4), "W_dense_Parallel not matching control"
def test_cosine_similarity_dense_jaccard(self):
from Base.Cython.cosine_similarity import Cosine_Similarity as Cosine_Similarity_Cython
from Base.cosine_similarity import Compute_Similarity as Cosine_Similarity_Python
from Base.cosine_similarity_parallel import Cosine_Similarity_Parallel as Cosine_Similarity_Parallel
import numpy.matlib
TopK = 0
shrink = 0
data_matrix = np.array([[1, 2, 0, 1], [0, 1, 4, 1], [1, 3, 1, 0]])
data_matrix = sps.csr_matrix(data_matrix)
cosine_similarity = Cosine_Similarity_Cython(data_matrix,
topK=TopK,
normalize=True,
shrink=shrink,
mode='jaccard')
W_dense_Cython = cosine_similarity.compute_similarity()
cosine_similarity = Cosine_Similarity_Python(data_matrix,
topK=TopK,
normalize=True,
shrink=shrink,
mode='jaccard')
W_dense_Python = cosine_similarity.compute_similarity()
cosine_similarity = Cosine_Similarity_Parallel(data_matrix,
topK=TopK,
normalize=True,
shrink=shrink,
mode='jaccard')
W_dense_Parallel = cosine_similarity.compute_similarity()
data_matrix.data = np.ones_like(data_matrix.data)
data_matrix = data_matrix.toarray().astype(np.float64)
W_dense_mul = data_matrix.T.dot(data_matrix)
W_dense_denominator = np.matlib.repmat((data_matrix**2).sum(axis=0),
data_matrix.shape[1], 1)
W_dense_denominator = W_dense_denominator + W_dense_denominator.T - W_dense_mul + shrink
W_dense_mul[W_dense_denominator > 0] /= W_dense_denominator[
W_dense_denominator > 0]
W_dense_mul[np.arange(W_dense_mul.shape[0]),
np.arange(W_dense_mul.shape[0])] = 0.0
assert np.allclose(W_dense_Cython, W_dense_mul,
atol=1e-4), "W_dense_Cython not matching control"
assert np.allclose(W_dense_Python, W_dense_mul,
atol=1e-4), "W_dense_Python not matching control"
assert np.allclose(W_dense_Parallel, W_dense_mul,
atol=1e-4), "W_dense_Parallel not matching control"
def test_cosine_similarity_dense_pearson(self):
from Base.Cython.cosine_similarity import Cosine_Similarity as Cosine_Similarity_Cython
from Base.cosine_similarity import Compute_Similarity as Cosine_Similarity_Python
from Base.cosine_similarity_parallel import Cosine_Similarity_Parallel as Cosine_Similarity_Parallel
import numpy.matlib
TopK = 0
shrink = 0
data_matrix = np.array([[1,2,0,1],[0,1,4,1],[1,3,1,0]])
data_matrix = sps.csr_matrix(data_matrix)
cosine_similarity = Cosine_Similarity_Cython(data_matrix, topK=TopK, normalize = True,
shrink=shrink, mode='pearson')
W_dense_Cython = cosine_similarity.compute_similarity()
cosine_similarity = Cosine_Similarity_Python(data_matrix, topK=TopK, normalize = True,
shrink=shrink, mode='pearson')
W_dense_Python = cosine_similarity.compute_similarity()
cosine_similarity = Cosine_Similarity_Parallel(data_matrix, topK=TopK, normalize = True,
shrink=shrink, mode='pearson')
W_dense_Parallel = cosine_similarity.compute_similarity()
data_matrix = data_matrix.toarray().astype(np.float64)
for col in range(data_matrix.shape[1]):
nonzeroMask = data_matrix[:,col]>0
data_matrix[:,col][nonzeroMask] -= np.mean(data_matrix[:,col][nonzeroMask])
W_dense_denominator = np.matlib.repmat((data_matrix**2).sum(axis=0), data_matrix.shape[1], 1)
W_dense_denominator = np.sqrt(W_dense_denominator)
W_dense_denominator = np.multiply(W_dense_denominator, W_dense_denominator.T) + shrink
W_dense_mul = data_matrix.T.dot(data_matrix)
W_dense_mul[W_dense_denominator>0] /= W_dense_denominator[W_dense_denominator>0]
W_dense_mul[np.arange(W_dense_mul.shape[0]),np.arange(W_dense_mul.shape[0])] = 0.0
assert np.allclose(W_dense_Cython, W_dense_mul, atol=1e-4), "W_dense_Cython not matching control"
assert np.allclose(W_dense_Python, W_dense_mul, atol=1e-4), "W_dense_Python not matching control"
assert np.allclose(W_dense_Parallel, W_dense_mul, atol=1e-4), "W_dense_Parallel not matching control"
def test_cosine_similarity_dense_external_cfr(self):
from Base.Cython.cosine_similarity import Cosine_Similarity as Cosine_Similarity_Cython
from Base.cosine_similarity_parallel import Cosine_Similarity_Parallel as Cosine_Similarity_Parallel
from sklearn.metrics.pairwise import cosine_similarity as Cosine_Similarity_Sklearn
from scipy.spatial.distance import jaccard as Jaccard_Distance_Scipy
TopK = 0
shrink = 0
data_matrix = np.array([[1, 2, 0, 1], [0, 1, 4, 1], [1, 3, 1, 0]])
data_matrix = sps.csr_matrix(data_matrix)
cosine_similarity = Cosine_Similarity_Cython(data_matrix,
topK=TopK,
normalize=True,
shrink=shrink)
W_dense_Cython = cosine_similarity.compute_similarity()
cosine_similarity = Compute_Similarity_Python(data_matrix,
topK=TopK,
normalize=True,
shrink=shrink)
W_dense_Python = cosine_similarity.compute_similarity()
cosine_similarity = Cosine_Similarity_Parallel(data_matrix,
topK=TopK,
normalize=True,
shrink=shrink)
W_dense_Parallel = cosine_similarity.compute_similarity()
W_dense_sklearn = Cosine_Similarity_Sklearn(data_matrix.copy().T)
W_dense_sklearn[np.arange(W_dense_sklearn.shape[0]),
np.arange(W_dense_sklearn.shape[0])] = 0.0
assert np.allclose(
W_dense_Cython, W_dense_sklearn,
atol=1e-4), "W_dense_Cython Cosine not matching Sklearn control"
assert np.allclose(
W_dense_Python, W_dense_sklearn,
atol=1e-4), "W_dense_Python Cosine not matching Sklearn control"
assert np.allclose(
W_dense_Parallel, W_dense_sklearn,
atol=1e-4), "W_dense_Parallel Cosine not matching Sklearn control"
data_matrix = np.array([[1, 2, 0, 1], [0, 1, 4, 1], [1, 3, 1, 0]])
data_matrix = sps.csr_matrix(data_matrix)
cosine_similarity = Cosine_Similarity_Cython(data_matrix,
topK=TopK,
normalize=True,
shrink=shrink,
mode='jaccard')
W_dense_Cython = cosine_similarity.compute_similarity()
cosine_similarity = Compute_Similarity_Python(data_matrix,
topK=TopK,
normalize=True,
shrink=shrink,
mode='jaccard')
W_dense_Python = cosine_similarity.compute_similarity()
cosine_similarity = Cosine_Similarity_Parallel(data_matrix,
topK=TopK,
normalize=True,
shrink=shrink,
mode='jaccard')
W_dense_Parallel = cosine_similarity.compute_similarity()
W_dense_Scipy = np.zeros_like(W_dense_Python)
data_matrix.data = np.ones_like(data_matrix.data)
data_matrix = data_matrix.toarray()
for row in range(W_dense_Scipy.shape[0]):
for col in range(W_dense_Scipy.shape[1]):
if row != col:
W_dense_Scipy[row, col] = 1 - Jaccard_Distance_Scipy(
data_matrix[:, row], data_matrix[:, col])
assert np.allclose(
W_dense_Cython, W_dense_Scipy,
atol=1e-4), "W_dense_Cython Jaccard not matching Scipy control"
assert np.allclose(
W_dense_Python, W_dense_Scipy,
atol=1e-4), "W_dense_Python Jaccard not matching Scipy control"
assert np.allclose(
W_dense_Parallel, W_dense_Scipy,
atol=1e-4), "W_dense_Parallel Jaccard not matching Scipy control"
