def test_integration_dense(params_in):
n_clusters = 5
n_examples = 20
n_features = 100
X = np.random.randn(n_examples, n_features)
for ee in range(n_examples):
X[ee, :] /= np.linalg.norm(X[ee, :])
params_in.update({'n_clusters': n_clusters})
movmf = VonMisesFisherMixture(**params_in)
movmf.fit(X)
assert movmf.cluster_centers_.shape == (n_clusters, n_features)
assert len(movmf.concentrations_) == n_clusters
assert len(movmf.weights_) == n_clusters
assert len(movmf.labels_) == n_examples
for center in movmf.cluster_centers_:
assert_almost_equal(np.linalg.norm(center), 1.0)
for concentration in movmf.concentrations_:
assert concentration > 0
for weight in movmf.weights_:
assert not np.isnan(weight)
plabels = movmf.predict(X)
assert_array_equal(plabels, movmf.labels_)
ll = movmf.log_likelihood(X)
ll_labels = np.zeros(movmf.labels_.shape)
for ee in range(n_examples):
ll_labels[ee] = np.argmax(ll[:, ee])
assert_array_equal(ll_labels, movmf.labels_)
def VonMisesFisherMixture_Model(vocab_embeddings, vocab, topics, rerank, rand):
# vmf_soft = VonMisesFisherMixture(n_clusters=topics, posterior_type='hard', n_jobs=-1, random_state=rand).fit(vocab_embeddings)
print("fitting vmf...")
vmf_soft = VonMisesFisherMixture(n_clusters=topics,
posterior_type='soft',
n_jobs=-1,
random_state=rand).fit(vocab_embeddings)
llh = vmf_soft.log_likelihood(vocab_embeddings)
indices = []
for i in range(topics):
topk_vals = llh[i, :].argsort()[::-1].astype(int)
if rerank:
indices.append(find_top_k_words(100, topk_vals, vocab))
else:
indices.append(find_top_k_words(10, topk_vals, vocab))
return vmf_soft.predict(vocab_embeddings), indices
def test_integration_sparse(params_in):
n_clusters = 5
n_examples = 20
n_features = 100
n_nonzero = 10
X = sp.sparse.csr_matrix((n_examples, n_features))
for ee in range(n_examples):
ridx = np.random.randint(n_features, size=(n_nonzero))
random_values = np.random.randn(n_nonzero)
random_values = random_values / np.linalg.norm(random_values)
X[ee, ridx] = random_values
params_in.update({"n_clusters": n_clusters})
movmf = VonMisesFisherMixture(**params_in)
movmf.fit(X)
assert movmf.cluster_centers_.shape == (n_clusters, n_features)
assert len(movmf.concentrations_) == n_clusters
assert len(movmf.weights_) == n_clusters
assert len(movmf.labels_) == n_examples
assert len(movmf.posterior_) == n_clusters
for center in movmf.cluster_centers_:
assert_almost_equal(np.linalg.norm(center), 1.0)
for concentration in movmf.concentrations_:
assert concentration > 0
for weight in movmf.weights_:
assert not np.isnan(weight)
plabels = movmf.predict(X)
assert_array_equal(plabels, movmf.labels_)
ll = movmf.log_likelihood(X)
ll_labels = np.zeros(movmf.labels_.shape)
for ee in range(n_examples):
ll_labels[ee] = np.argmax(ll[:, ee])
assert_array_equal(ll_labels, movmf.labels_)