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#!/usr/bin/env python
# CREATED: 2013-10-06 22:31:29 by Dawen Liang <dl2771@columbia.edu>
# unit tests for librosa.decompose
# Disable cache
import os
try:
os.environ.pop("LIBROSA_CACHE_DIR")
except KeyError:
pass
import numpy as np
import scipy.sparse
import librosa
import sklearn.decomposition
import pytest
from test_core import srand
def test_default_decompose():
X = np.array([[1, 2, 3, 4, 5, 6], [1, 1, 1.2, 1, 0.8, 1]])
(W, H) = librosa.decompose.decompose(X, random_state=0)
assert np.allclose(X, W.dot(H), rtol=1e-2, atol=1e-2)
def test_given_decompose():
D = sklearn.decomposition.NMF(random_state=0)
X = np.array([[1, 2, 3, 4, 5, 6], [1, 1, 1.2, 1, 0.8, 1]])
(W, H) = librosa.decompose.decompose(X, transformer=D)
assert np.allclose(X, W.dot(H), rtol=1e-2, atol=1e-2)
def test_decompose_fit():
srand()
D = sklearn.decomposition.NMF(random_state=0)
X = np.array([[1, 2, 3, 4, 5, 6], [1, 1, 1.2, 1, 0.8, 1]])
# Do a first fit
(W, H) = librosa.decompose.decompose(X, transformer=D, fit=True)
# Make random data and decompose with the same basis
X = np.asarray(np.random.randn(*X.shape) ** 2)
(W2, H2) = librosa.decompose.decompose(X, transformer=D, fit=False)
# Make sure the basis hasn't changed
assert np.allclose(W, W2)
@pytest.mark.xfail(raises=librosa.ParameterError)
def test_decompose_multi_sort():
librosa.decompose.decompose(np.zeros((3, 3, 3)), sort=True)
@pytest.mark.filterwarnings("ignore:Maximum number of iterations")
def test_decompose_multi():
srand()
X = np.random.random_sample(size=(2, 20, 100))
# Fit with multichannel data
components, activations = librosa.decompose.decompose(
X, n_components=20, random_state=0
)
# Reshape the data
Xflat = np.vstack([X[0], X[1]])
c_flat, a_flat = librosa.decompose.decompose(Xflat, n_components=20, random_state=0)
assert np.allclose(c_flat[: X.shape[1]], components[0])
assert np.allclose(c_flat[X.shape[1] :], components[1])
assert np.allclose(activations, a_flat)
@pytest.mark.xfail(raises=librosa.ParameterError)
def test_decompose_fit_false():
X = np.array([[1, 2, 3, 4, 5, 6], [1, 1, 1.2, 1, 0.8, 1]])
(W, H) = librosa.decompose.decompose(X, fit=False)
def test_sorted_decompose():
X = np.array([[1, 2, 3, 4, 5, 6], [1, 1, 1.2, 1, 0.8, 1]])
(W, H) = librosa.decompose.decompose(X, sort=True, random_state=0)
assert np.allclose(X, W.dot(H), rtol=1e-2, atol=1e-2)
@pytest.fixture
def y22050():
y, _ = librosa.load(os.path.join("tests", "data", "test1_22050.wav"))
return y
@pytest.fixture
def D22050(y22050):
return librosa.stft(y22050)
@pytest.fixture
def S22050(D22050):
return np.abs(D22050)
@pytest.mark.parametrize("window", [31, (5, 5)])
@pytest.mark.parametrize("power", [1, 2, 10])
@pytest.mark.parametrize("mask", [False, True])
@pytest.mark.parametrize("margin", [1.0, 3.0, (1.0, 1.0), (9.0, 10.0)])
def test_real_hpss(S22050, window, power, mask, margin):
H, P = librosa.decompose.hpss(
S22050, kernel_size=window, power=power, mask=mask, margin=margin
)
if margin == 1.0 or margin == (1.0, 1.0):
if mask:
assert np.allclose(H + P, np.ones_like(S22050))
else:
assert np.allclose(H + P, S22050)
else:
if mask:
assert np.all(H + P <= np.ones_like(S22050))
else:
assert np.all(H + P <= S22050)
@pytest.mark.xfail(raises=librosa.ParameterError)
def test_hpss_margin_error(S22050):
H, P = librosa.decompose.hpss(S22050, margin=0.9)
def test_complex_hpss(D22050):
H, P = librosa.decompose.hpss(D22050)
assert np.allclose(H + P, D22050)
def test_nn_filter_mean():
srand()
X = np.random.randn(10, 100)
# Build a recurrence matrix, just for testing purposes
rec = librosa.segment.recurrence_matrix(X)
X_filtered = librosa.decompose.nn_filter(X)
# Normalize the recurrence matrix so dotting computes an average
rec = librosa.util.normalize(rec.astype(float), axis=0, norm=1)
assert np.allclose(X_filtered, X.dot(rec))
def test_nn_filter_mean_rec():
srand()
X = np.random.randn(10, 100)
# Build a recurrence matrix, just for testing purposes
rec = librosa.segment.recurrence_matrix(X)
# Knock out the first three rows of links
rec[:, :3] = False
X_filtered = librosa.decompose.nn_filter(X, rec=rec)
for i in range(3):
assert np.allclose(X_filtered[:, i], X[:, i])
# Normalize the recurrence matrix
rec = librosa.util.normalize(rec.astype(float), axis=0, norm=1)
assert np.allclose(X_filtered[:, 3:], (X.dot(rec))[:, 3:])
def test_nn_filter_mean_rec_sparse():
srand()
X = np.random.randn(10, 100)
# Build a recurrence matrix, just for testing purposes
rec = librosa.segment.recurrence_matrix(X, sparse=True)
X_filtered = librosa.decompose.nn_filter(X, rec=rec)
# Normalize the recurrence matrix
rec = librosa.util.normalize(rec.toarray().astype(float), axis=0, norm=1)
assert np.allclose(X_filtered, (X.dot(rec)))
@pytest.fixture(scope="module")
def s_multi():
y, sr = librosa.load(
os.path.join("tests", "data", "test1_44100.wav"), sr=None, mono=False
)
return np.abs(librosa.stft(y))
@pytest.mark.parametrize("useR,sparse", [(False, False), (True, False), (True, True)])
def test_nn_filter_multi(s_multi, useR, sparse):
R = librosa.segment.recurrence_matrix(s_multi, mode="affinity", sparse=sparse)
if useR:
R_multi = R
else:
R_multi = None
s_filt = librosa.decompose.nn_filter(
s_multi, rec=R_multi, mode="affinity", sparse=sparse
)
# Always use the same recurrence matrix for comparison
s_filt0 = librosa.decompose.nn_filter(s_multi[0], rec=R)
s_filt1 = librosa.decompose.nn_filter(s_multi[1], rec=R)
assert np.allclose(s_filt[0], s_filt0)
assert np.allclose(s_filt[1], s_filt1)
assert not np.allclose(s_filt0, s_filt1)
def test_nn_filter_avg():
srand()
X = np.random.randn(10, 100)
# Build a recurrence matrix, just for testing purposes
rec = librosa.segment.recurrence_matrix(X, mode="affinity")
X_filtered = librosa.decompose.nn_filter(X, rec=rec, aggregate=np.average)
# Normalize the recurrence matrix so dotting computes an average
rec = librosa.util.normalize(rec, axis=0, norm=1)
assert np.allclose(X_filtered, X.dot(rec))
@pytest.mark.xfail(raises=librosa.ParameterError)
@pytest.mark.parametrize(
"x,y", [(10, 10), (100, 20), (20, 100), (100, 101), (101, 101)]
)
@pytest.mark.parametrize("sparse", [False, True])
@pytest.mark.parametrize("data", [np.zeros((10, 100))])
def test_nn_filter_badselfsim(data, x, y, sparse):
srand()
# Build a recurrence matrix, just for testing purposes
rec = np.random.randn(x, y)
if sparse:
rec = scipy.sparse.csr_matrix(rec)
librosa.decompose.nn_filter(data, rec=rec)
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