[MRG+2] switch to multinomial composition for mixture sampling (sciki…

…t-learn#7702)

* switch to multinomial composition for mixture sampling

* add shape assertions to test

* Use n_components=3 to test actual regression

n_components and n_features were equal and one was used for the other in
some places.

sklearn/mixture/base.py

@@ -385,7 +385,7 @@ def sample(self, n_samples=1):
 
  _, n_features = self.means_.shape
  rng = check_random_state(self.random_state)
- n_samples_comp = np.round(self.weights_ * n_samples).astype(int)
+ n_samples_comp = rng.multinomial(n_samples, self.weights_)
 
  if self.covariance_type == 'full':
  X = np.vstack([

sklearn/mixture/tests/test_gaussian_mixture.py

@@ -918,7 +918,7 @@ def test_property():
 
 def test_sample():
  rng = np.random.RandomState(0)
- rand_data = RandomData(rng, scale=7)
+ rand_data = RandomData(rng, scale=7, n_components=3)
  n_features, n_components = rand_data.n_features, rand_data.n_components
 
  for covar_type in COVARIANCE_TYPE:
@@ -935,8 +935,10 @@ def test_sample():
  gmm.sample, 0)
 
  # Just to make sure the class samples correctly
- X_s, y_s = gmm.sample(20000)
- for k in range(n_features):
+ n_samples = 20000
+ X_s, y_s = gmm.sample(n_samples)
+
+ for k in range(n_components):
  if covar_type == 'full':
  assert_array_almost_equal(gmm.covariances_[k],
  np.cov(X_s[y_s == k].T), decimal=1)
@@ -953,9 +955,17 @@ def test_sample():
  decimal=1)
 
  means_s = np.array([np.mean(X_s[y_s == k], 0)
- for k in range(n_features)])
+ for k in range(n_components)])
  assert_array_almost_equal(gmm.means_, means_s, decimal=1)
 
+ # Check shapes of sampled data, see
+ # https://github.com/scikit-learn/scikit-learn/issues/7701
+ assert_equal(X_s.shape, (n_samples, n_features))
+
+ for sample_size in range(1, 100):
+ X_s, _ = gmm.sample(sample_size)
+ assert_equal(X_s.shape, (sample_size, n_features))
+
 
 @ignore_warnings(category=ConvergenceWarning)
 def test_init():