def test_score():
covar_type = 'full'
rng = np.random.RandomState(0)
rand_data = RandomData(rng, scale=7)
n_components = rand_data.n_components
X = rand_data.X[covar_type]
# Check the error message if we don't call fit
gmm1 = GaussianMixture(n_components=n_components, n_init=1,
max_iter=1, reg_covar=0, random_state=rng,
covariance_type=covar_type)
assert_raise_message(NotFittedError,
"This GaussianMixture instance is not fitted "
"yet. Call 'fit' with appropriate arguments "
"before using this method.", gmm1.score, X)
# Check score value
with warnings.catch_warnings():
warnings.simplefilter("ignore", ConvergenceWarning)
gmm1.fit(X)
gmm_score = gmm1.score(X)
gmm_score_proba = gmm1.score_samples(X).mean()
assert_almost_equal(gmm_score, gmm_score_proba)
# Check if the score increase
gmm2 = GaussianMixture(n_components=n_components, n_init=1, reg_covar=0,
random_state=rng,
covariance_type=covar_type).fit(X)
assert_greater(gmm2.score(X), gmm1.score(X))
def test_score():
covar_type = 'full'
rng = np.random.RandomState(0)
rand_data = RandomData(rng, scale=7)
n_components = rand_data.n_components
X = rand_data.X[covar_type]
# Check the error message if we don't call fit
gmm1 = GaussianMixture(n_components=n_components,
n_init=1,
max_iter=1,
reg_covar=0,
random_state=rng,
covariance_type=covar_type)
assert_raise_message(
NotFittedError, "This GaussianMixture instance is not fitted "
"yet. Call 'fit' with appropriate arguments "
"before using this method.", gmm1.score, X)
# Check score value
with warnings.catch_warnings():
warnings.simplefilter("ignore", ConvergenceWarning)
gmm1.fit(X)
gmm_score = gmm1.score(X)
gmm_score_proba = gmm1.score_samples(X).mean()
assert_almost_equal(gmm_score, gmm_score_proba)
# Check if the score increase
gmm2 = GaussianMixture(n_components=n_components,
n_init=1,
reg_covar=0,
random_state=rng,
covariance_type=covar_type).fit(X)
assert_greater(gmm2.score(X), gmm1.score(X))
np.linspace(np.min(x[:, 1]), np.max(x[:, 1]), 50))
x_grid = np.c_[xx.ravel(), yy.ravel()]
cov_type = 'full' # 'spherical', 'diag', 'tied', 'full'
n_classes = 3
max_iters = 20 if sample_type == "" else 100
gmm = GMM(n_components=n_classes,
covariance_type=cov_type,
max_iter=max_iters,
random_state=0)
gmm.fit(x)
logger.debug("Means:\n%s" % str(gmm.means_))
logger.debug("Covariances:\n%s" % str(gmm.covariances_))
scores = gmm.score_samples(x)
top_anoms = np.argsort(scores)[np.arange(10)]
if args.plot:
# colors = ["red", "blue"]
colors = ['navy', 'turquoise', 'darkorange']
pdfpath = "temp/gmm_%scontours.pdf" % sample_type
dp = DataPlotter(pdfpath=pdfpath, rows=1, cols=2)
pl = dp.get_next_plot()
make_ellipses(gmm, pl, colors)
dp.plot_points(x,
pl,
labels=y,
lbl_color_map={
