119 lines
3.9 KiB
Python
119 lines
3.9 KiB
Python
"""Tests for _sketches.py."""
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import numpy as np
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from numpy.testing import assert_, assert_equal
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from scipy.linalg import clarkson_woodruff_transform
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from scipy.linalg._sketches import cwt_matrix
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from scipy.sparse import issparse, rand
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from scipy.sparse.linalg import norm
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class TestClarksonWoodruffTransform:
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"""
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Testing the Clarkson Woodruff Transform
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"""
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# set seed for generating test matrices
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rng = np.random.RandomState(seed=1179103485)
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# Test matrix parameters
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n_rows = 2000
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n_cols = 100
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density = 0.1
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# Sketch matrix dimensions
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n_sketch_rows = 200
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# Seeds to test with
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seeds = [1755490010, 934377150, 1391612830, 1752708722, 2008891431,
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1302443994, 1521083269, 1501189312, 1126232505, 1533465685]
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A_dense = rng.randn(n_rows, n_cols)
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A_csc = rand(
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n_rows, n_cols, density=density, format='csc', random_state=rng,
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)
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A_csr = rand(
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n_rows, n_cols, density=density, format='csr', random_state=rng,
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)
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A_coo = rand(
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n_rows, n_cols, density=density, format='coo', random_state=rng,
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)
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# Collect the test matrices
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test_matrices = [
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A_dense, A_csc, A_csr, A_coo,
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]
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# Test vector with norm ~1
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x = rng.randn(n_rows, 1) / np.sqrt(n_rows)
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def test_sketch_dimensions(self):
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for A in self.test_matrices:
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for seed in self.seeds:
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sketch = clarkson_woodruff_transform(
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A, self.n_sketch_rows, seed=seed
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)
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assert_(sketch.shape == (self.n_sketch_rows, self.n_cols))
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def test_seed_returns_identical_transform_matrix(self):
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for A in self.test_matrices:
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for seed in self.seeds:
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S1 = cwt_matrix(
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self.n_sketch_rows, self.n_rows, seed=seed
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).toarray()
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S2 = cwt_matrix(
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self.n_sketch_rows, self.n_rows, seed=seed
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).toarray()
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assert_equal(S1, S2)
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def test_seed_returns_identically(self):
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for A in self.test_matrices:
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for seed in self.seeds:
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sketch1 = clarkson_woodruff_transform(
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A, self.n_sketch_rows, seed=seed
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)
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sketch2 = clarkson_woodruff_transform(
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A, self.n_sketch_rows, seed=seed
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)
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if issparse(sketch1):
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sketch1 = sketch1.toarray()
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if issparse(sketch2):
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sketch2 = sketch2.toarray()
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assert_equal(sketch1, sketch2)
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def test_sketch_preserves_frobenius_norm(self):
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# Given the probabilistic nature of the sketches
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# we run the test multiple times and check that
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# we pass all/almost all the tries.
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n_errors = 0
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for A in self.test_matrices:
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if issparse(A):
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true_norm = norm(A)
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else:
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true_norm = np.linalg.norm(A)
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for seed in self.seeds:
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sketch = clarkson_woodruff_transform(
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A, self.n_sketch_rows, seed=seed,
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)
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if issparse(sketch):
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sketch_norm = norm(sketch)
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else:
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sketch_norm = np.linalg.norm(sketch)
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if np.abs(true_norm - sketch_norm) > 0.1 * true_norm:
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n_errors += 1
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assert_(n_errors == 0)
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def test_sketch_preserves_vector_norm(self):
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n_errors = 0
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n_sketch_rows = int(np.ceil(2. / (0.01 * 0.5**2)))
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true_norm = np.linalg.norm(self.x)
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for seed in self.seeds:
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sketch = clarkson_woodruff_transform(
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self.x, n_sketch_rows, seed=seed,
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)
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sketch_norm = np.linalg.norm(sketch)
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if np.abs(true_norm - sketch_norm) > 0.5 * true_norm:
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n_errors += 1
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assert_(n_errors == 0)
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