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(self): from Base.Cython.cosine_similarity import Cosine_Similarity as Cosine_Similarity_Cython from Base.cosine_similarity import Cosine_Similarity as Cosine_Similarity_Python 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 = Cosine_Similarity_Python(data_matrix, topK=TopK, normalize=False) W_dense_Python = 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"
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 Cosine_Similarity as Cosine_Similarity_Python 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() 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"
def test_cosine_similarity_dense_external_cfr(self): from Base.Cython.cosine_similarity import Cosine_Similarity as Cosine_Similarity_Cython from Base.cosine_similarity import Cosine_Similarity as Cosine_Similarity_Python 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 = Cosine_Similarity_Python(data_matrix, topK=TopK, normalize=True, shrink=shrink) W_dense_Python = 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" 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() 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"