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