def __init__(self,
data,
n_cluster,
method="soft-movMF",
init="random-class",
n_init=10,
n_jobs=1):
self.data = data
self.n_cluster = n_cluster
self.method = method
if method == "spk":
self.clus = SphericalKMeans(n_clusters=n_cluster)
elif method == "hard-movMF":
self.clus = VonMisesFisherMixture(n_clusters=n_cluster,
posterior_type='hard',
init=init,
n_init=n_init,
n_jobs=n_jobs)
elif method == "soft-movMF":
self.clus = VonMisesFisherMixture(n_clusters=n_cluster,
posterior_type='soft',
init=init,
n_init=n_init,
n_jobs=n_jobs)
self.clusters = {
} # cluster id -> dict(element_id: distance to center)
self.clusters_phrase = {} # cluster id -> representative words
self.membership = None # a list contain the membership of the data points
self.center_ids = None # a list contain the ids of the cluster centers
self.inertia_scores = None
def mandatory_questions():
# 1.2.1
# ang_data = unistroke_to_angular(read_data(""))
# plt.hist(ang_data[:, 0], 150, density=True)
# plt.hist(ang_data[:, 1], 150, density=True)
# 1.2.5
mix = VonMisesFisherMixture(2)
data = unistroke_to_angular(read_data(""))
mix = mix.fit(data)
def label_expansion(class_labels, write_path, vocabulary_inv, embedding_mat):
print("Retrieving top-t nearest words...")
n_classes = len(class_labels)
prob_sup_array = []
current_szes = []
all_class_labels = []
for class_label in class_labels:
current_sz = len(class_label)
current_szes.append(current_sz)
prob_sup_array.append([1 / current_sz] * current_sz)
all_class_labels += list(class_label)
current_sz = np.min(current_szes)
while len(all_class_labels) == len(set(all_class_labels)):
current_sz += 1
expanded_array = seed_expansion(class_labels, prob_sup_array,
current_sz, None, vocabulary_inv,
embedding_mat)
all_class_labels = [w for w_class in expanded_array for w in w_class]
expanded_array = seed_expansion(class_labels, prob_sup_array,
current_sz - 1, None, vocabulary_inv,
embedding_mat)
print("Final expansion size t = {}".format(len(expanded_array[0])))
centers = []
kappas = []
print("Top-t nearest words for each class:")
for i in range(n_classes):
expanded_class = expanded_array[i]
vocab_expanded = [vocabulary_inv[w] for w in expanded_class]
print("Class {}:".format(i))
print(vocab_expanded)
expanded_mat = embedding_mat[np.asarray(expanded_class)]
vmf_soft = VonMisesFisherMixture(n_clusters=1)
vmf_soft.fit(expanded_mat)
center = vmf_soft.cluster_centers_[0]
kappa = vmf_soft.concentrations_[0]
centers.append(center)
kappas.append(kappa)
for j, expanded_class in enumerate(expanded_array):
if write_path is not None:
if not os.path.exists(write_path):
os.makedirs(write_path)
f = open(write_path + 'class' + str(j) + '.txt', 'w')
for i, word in enumerate(expanded_class):
f.write(vocabulary_inv[word] + ' ')
f.close()
print("Finished vMF distribution fitting.")
return expanded_array, centers, kappas
def estimate_vMF_params(self):
"""
Returns:
"""
vmf_soft = VonMisesFisherMixture(n_clusters=1, posterior_type='soft')
try:
vmf_soft.fit(self.samples_xyz)
self.kappa = vmf_soft.concentrations_[0]
self.mean = vmf_soft.cluster_centers_[0]
print('concentration parameter ', self.kappa.astype(int),
'mean direction ',
self._cartesian2spherical(self.mean).astype(int))
except AttributeError:
print(
'object has no orientations. Use add_orientations to load orientation data manually or sample from a vMF distribution with the .sample method'
)
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 fit_movmf(theta, **kwargs):
""" Fit a mixture of 2 von-Mises-Fisher distributions
Args:
theta (np.array): angles
**kwargs: keyword arguments to spherecluster.VonMisesFisherMixture()
Returns:
pi, mu, kappa
"""
X = np.array([np.cos(theta * 2), np.sin(theta * 2)]).T
vmf_soft = VonMisesFisherMixture(n_clusters=2, **kwargs)
vmf_soft.fit(X)
mu = np.arctan2(vmf_soft.cluster_centers_[:, 1],
vmf_soft.cluster_centers_[:, 0])
pi = vmf_soft.weights_
kappa = vmf_soft.concentrations_
return pi, mu, kappa
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 soft_clustering(terms: List[str], wv: Dict[str, np.ndarray],
n_clusters: int) -> Tuple[List[int], List[str]]:
"""Use spherical vmf to cluster word vectors"""
X = []
X_terms = []
n_out_of_vocab = 0
for term in terms:
try:
phrase = term
emb = wv[phrase]
X.append(emb)
X_terms.append(phrase)
except KeyError as e:
n_out_of_vocab += 1
logger.debug(f"{n_out_of_vocab} / {len(terms)} words out of vocab")
logger.debug(f"Clustering {len(X)} words")
vmf_soft = VonMisesFisherMixture(n_clusters=n_clusters,
posterior_type='soft')
vmf_soft.fit(X)
return vmf_soft.predict(X), X_terms, vmf_soft
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_)
print()
table.append([
'spherical k-means',
metrics.homogeneity_score(labels, skm.labels_),
metrics.completeness_score(labels, skm.labels_),
metrics.v_measure_score(labels, skm.labels_),
metrics.adjusted_rand_score(labels, skm.labels_),
metrics.adjusted_mutual_info_score(labels, skm.labels_),
metrics.silhouette_score(X, skm.labels_, metric='cosine')])
###############################################################################
# Mixture of von Mises Fisher clustering (soft)
vmf_soft = VonMisesFisherMixture(n_clusters=true_k, posterior_type='soft',
init='random-class', n_init=20, force_weights=np.ones((true_k,))/true_k)
print("Clustering with %s" % vmf_soft)
vmf_soft.fit(X)
print()
print('weights: {}'.format(vmf_soft.weights_))
print('concentrations: {}'.format(vmf_soft.concentrations_))
print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels, vmf_soft.labels_))
print("Completeness: %0.3f" % metrics.completeness_score(labels, vmf_soft.labels_))
print("V-measure: %0.3f" % metrics.v_measure_score(labels, vmf_soft.labels_))
print("Adjusted Rand-Index: %.3f"
% metrics.adjusted_rand_score(labels, vmf_soft.labels_))
print("Adjusted Mututal Information: %.3f"
% metrics.adjusted_mutual_info_score(labels, vmf_soft.labels_))
print("Silhouette Coefficient (euclidean): %0.3f"
return args.x_s, args.y_s, args.mu_s, args.mode, args.verbose
input_X_s, input_Y_s, input_mu_s, processMode, verboseMode = parseArguments()
X_s = np.load(input_X_s)
Y_s = np.load(input_Y_s)
mu_s = np.load(input_mu_s)
num_clusters = np.unique(Y_s).size
print(num_clusters)
if processMode == 'soft':
vmf_model = VonMisesFisherMixture(n_clusters=num_clusters,
posterior_type='soft')
vmf_model.fit(X_s)
elif processMode == 'hard':
vmf_model = VonMisesFisherMixture(n_clusters=num_clusters,
posterior_type='hard')
vmf_model.fit(X_s)
estimated_kappa_s = vmf_model.concentrations_
estimated_mu_s = vmf_model.cluster_centers_
print(estimated_kappa_s)
cross_distance_s = cdist(estimated_mu_s, mu_s, metric='cosine')
print(cross_distance_s)
#print( vmf_model.labels_ )
# coding: utf-8
# In[6]:
import scipy.io as sio
from spherecluster import VonMisesFisherMixture # using spherecluster package : https://pypi.org/project/spherecluster/0.1.2/
# In[10]:
train = sio.loadmat('./vmf_data.mat') # load segmented frames or superframes
data = train['ps']
print(data.shape)
# In[11]:
vmf_soft = VonMisesFisherMixture(n_clusters=15, posterior_type='soft')
vmf_soft.fit(data)
# In[15]:
mean_directions = vmf_soft.cluster_centers_
print(mean_directions.shape)
# In[18]:
concen = vmf_soft.concentrations_
ind = concen.argsort()[-10:][::-1]
print(concen)
print(ind)
mean_directions = mean_directions[ind, :]
print(mean_directions.shape)
def sim_multimodal():
to_save = []
data_cells = []
# Data pre-processing
for i in range(40):
read_sim(i,
f_in='datasets/multimodal_sim2.npy',
f_out='datasets/transformed/multimodal_sim2_cell' + str(i))
# Angles to query
Thq = np.linspace(-np.pi, np.pi, 360)[:, None]
Xq = np.hstack((np.cos(Thq), np.sin(Thq)))
# Fit one cell at a time
for i in range(40):
print('\ncell no={}'.format(i))
try:
# Read data
read_data = np.load('datasets/transformed/multimodal_sim2_cell' +
str(i) + '.npz')
data, xx, yy = read_data['data'], read_data['xx'], read_data['yy']
if data.shape[0] <= 1:
continue
# Data
Th = data[:, 4][:, None]
X = np.hstack((np.cos(Th), np.sin(Th)))
db = DBSCAN().fit(X)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
labels = db.labels_
# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
unique_labels = set(labels)
print("n_clusters_={}, labels={}".format(n_clusters_,
unique_labels))
for k in unique_labels:
if k == -1: # noisy samples
continue
class_member_mask = (labels == k)
xy = X[class_member_mask & core_samples_mask]
if k == 0:
db_centers = np.mean(xy, axis=0)[None, :]
else:
db_centers = np.concatenate(
(db_centers, np.mean(xy, axis=0)[None, :]), axis=0)
print("db_centers=", db_centers)
# TBD: "NOTE:: play with max_iter if you get the denom=inf error"
# Mixture of von Mises Fisher clustering (soft)
vmf_soft = VonMisesFisherMixture(n_clusters=n_clusters_,
posterior_type='soft',
init=db_centers,
n_init=1,
verbose=True,
max_iter=20)
vmf_soft.fit(X)
y = 0
for cn in range(n_clusters_):
y += vmf_soft.weights_[cn] * np.exp(
von_mises_fisher_mixture._vmf_log(
Xq, vmf_soft.concentrations_[cn],
vmf_soft.cluster_centers_[cn]))
yq = np.array(y)[:, None]
to_save.append(yq)
data_cells.append(i)
# Plot
pl.figure(figsize=(15, 4))
pl.subplot(131)
mesh = np.vstack((xx.ravel(), yy.ravel())).T
pl.scatter(mesh[:, 0], mesh[:, 1], c='k', marker='.')
pl.scatter(data[:, 1],
data[:, 2],
c=data[:, 0],
marker='*',
cmap='jet')
pl.colorbar()
pl.xlim([0, 20])
pl.ylim([-5, 30])
pl.title('data')
pl.subplot(132)
pl.scatter(Xq[:, 0], Xq[:, 1], c=yq[:], cmap='jet')
pl.colorbar()
pl.scatter(X[:, 0] * 0.9, X[:, 1] * 0.9, c='k', marker='+')
pl.title('data and extimated distribution')
pl.subplot(133, projection='polar')
pl.polar(Thq, yq)
pl.title('polar plot')
pl.savefig('outputs/multimodal_sim2_cell{}'.format(i))
#pl.show()
except:
print(' skipped...')
continue
def label_expansion(relevant_nodes,
write_path,
vocabulary_inv,
embedding_mat,
manual_num=None,
fitting='mix'):
print("Retrieving top-t nearest words...")
vocab_dict = {v: k for k, v in vocabulary_inv.items()}
prob_sup_array = []
current_szes = []
all_class_keywords = []
children_nodes = []
for relevant_node in relevant_nodes:
if relevant_node.children:
children_nodes += relevant_node.children
else:
children_nodes += [relevant_node]
for children_node in children_nodes:
current_sz = len(children_node.keywords)
current_szes.append(current_sz)
prob_sup_array.append([1 / current_sz] * current_sz)
all_class_keywords += children_node.keywords
current_sz = np.min(current_szes)
if manual_num is None:
while len(all_class_keywords) == len(set(all_class_keywords)):
print(f'current_sz: {current_sz}')
current_sz += 1
# print(f'len_kw: {len(all_class_keywords)}')
seed_expansion(children_nodes, prob_sup_array, current_sz,
vocab_dict, embedding_mat)
all_class_keywords = [
w for relevant_node in children_nodes
for w in relevant_node.expanded
]
seed_expansion(children_nodes, prob_sup_array, current_sz - 1,
vocab_dict, embedding_mat)
# seed_expansion(children_nodes, prob_sup_array, current_sz, vocab_dict, embedding_mat)
else:
seed_expansion(children_nodes, prob_sup_array, manual_num, vocab_dict,
embedding_mat)
if manual_num is None:
print(f"Final expansion size t = {len(children_nodes[0].expanded)}")
else:
print(f"Manual expansion size t = {manual_num}")
centers = []
kappas = []
weights = []
if write_path is not None:
if not os.path.exists(write_path):
os.makedirs(write_path)
else:
f = open(os.path.join(write_path, 'expanded.txt'), 'w')
f.close()
for relevant_node in relevant_nodes:
children_nodes = relevant_node.children if relevant_node.children else [
relevant_node
]
num_children = len(children_nodes)
expanded_class = []
if fitting == 'mix':
for child in children_nodes:
# assert child.expanded != []
expanded_class = np.concatenate(
(expanded_class, child.expanded))
print([vocabulary_inv[w] for w in child.expanded])
vocab_expanded = [vocabulary_inv[w] for w in expanded_class]
expanded_mat = embedding_mat[np.asarray(list(set(expanded_class)),
dtype='int32')]
vmf_soft = VonMisesFisherMixture(n_clusters=num_children,
n_jobs=15,
random_state=0)
vmf_soft.fit(expanded_mat)
center = vmf_soft.cluster_centers_
kappa = vmf_soft.concentrations_
weight = vmf_soft.weights_
print(f'weight: {weight}')
print(f'kappa: {kappa}')
centers.append(center)
kappas.append(kappa)
weights.append(weight)
elif fitting == 'separate':
center = []
kappa = []
weight = []
for child in children_nodes:
assert child.expanded != []
expanded_class = np.concatenate(
(expanded_class, child.expanded))
expanded_mat = embedding_mat[np.asarray(child.expanded,
dtype='int32')]
vmf_soft = VonMisesFisherMixture(n_clusters=1,
n_jobs=15,
random_state=0)
vmf_soft.fit(expanded_mat)
center.append(vmf_soft.cluster_centers_[0])
kappa.append(vmf_soft.concentrations_[0])
weight.append(1 / num_children)
expanded = np.dot(embedding_mat, center[-1])
word_expanded = sorted(range(len(expanded)),
key=lambda k: expanded[k],
reverse=True)
vocab_expanded = [vocabulary_inv[w] for w in expanded_class]
print(f'Class {relevant_node.name}:')
print(vocab_expanded)
print(f'weight: {weight}')
print(f'kappa: {kappa}')
centers.append(center)
kappas.append(kappa)
weights.append(weight)
if write_path is not None:
f = open(os.path.join(write_path, 'expanded.txt'), 'a')
f.write(relevant_node.name + '\t')
f.write(' '.join(vocab_expanded) + '\n')
f.close()
print("Finished vMF distribution fitting.")
return centers, kappas, weights
def sim_unimodal():
to_save = []
data_cells = []
# Data pre-processing
for i in range(40):
read_sim(i,
f_in='datasets/unimodal_sim1.npy',
f_out='datasets/transformed/unimodal_sim1_cell' + str(i))
# Angles to query
Thq = np.linspace(-np.pi, np.pi, 360)[:, None]
Xq = np.hstack((np.cos(Thq), np.sin(Thq)))
# Fit one cell at a time
for i in range(40):
print('cell no={}'.format(i))
try:
# Read data
read_data = np.load('datasets/transformed/unimodal_sim1_cell' +
str(i) + '.npz')
data, xx, yy = read_data['data'], read_data['xx'], read_data['yy']
if data.shape[0] <= 1:
continue
# Data
Th = data[:, 4][:, None]
X = np.hstack((np.cos(Th), np.sin(Th)))
# Von Mises clustering (soft)
vmf_soft = VonMisesFisherMixture(n_clusters=1,
posterior_type='soft',
n_init=20)
vmf_soft.fit(X)
y0 = np.exp(
von_mises_fisher_mixture._vmf_log(
Xq, vmf_soft.concentrations_[0],
vmf_soft.cluster_centers_[0]))
y = y0 * vmf_soft.weights_[0]
# Query
yq = np.array(y)[:, None]
to_save.append(yq)
data_cells.append(i)
# Plot
pl.figure(figsize=(15, 4))
pl.subplot(131)
mesh = np.vstack((xx.ravel(), yy.ravel())).T
pl.scatter(mesh[:, 0], mesh[:, 1], c='k', marker='.')
pl.scatter(data[:, 1],
data[:, 2],
c=data[:, 0],
marker='*',
cmap='jet')
pl.colorbar()
pl.xlim([0, 20])
pl.ylim([-5, 30])
pl.title('data')
pl.subplot(132)
pl.scatter(Xq[:, 0], Xq[:, 1], c=y0[:], cmap='jet')
pl.colorbar()
pl.scatter(X[:, 0] * 0.9, X[:, 1] * 0.9, c='k', marker='+')
pl.title('data and extimated distribution')
pl.subplot(133, projection='polar')
pl.polar(Thq, yq)
pl.title('polar plot')
#pl.show()
pl.savefig('outputs/unimodal_sim1_cell{}'.format(i))
except:
print(' skipped...')
continue
skm.fit(X)
cdists = []
for center in skm.cluster_centers_:
cdists.append(np.linalg.norm(mus[0] - center))
skm_mu_0_idx = np.argmin(cdists)
skm_mu_1_idx = 1 - skm_mu_0_idx
skm_mu_0_error = np.linalg.norm(mus[0] - skm.cluster_centers_[skm_mu_0_idx])
skm_mu_1_error = np.linalg.norm(mus[1] - skm.cluster_centers_[skm_mu_1_idx])
###############################################################################
# Mixture of von Mises Fisher clustering (soft)
vmf_soft = VonMisesFisherMixture(n_clusters=2,
posterior_type='soft',
n_init=20)
vmf_soft.fit(X)
cdists = []
for center in vmf_soft.cluster_centers_:
cdists.append(np.linalg.norm(mus[0] - center))
vmf_soft_mu_0_idx = np.argmin(cdists)
vmf_soft_mu_1_idx = 1 - vmf_soft_mu_0_idx
vmf_soft_mu_0_error = np.linalg.norm(
mus[0] - vmf_soft.cluster_centers_[vmf_soft_mu_0_idx])
vmf_soft_mu_1_error = np.linalg.norm(
mus[1] - vmf_soft.cluster_centers_[vmf_soft_mu_1_idx])
skm.fit(X)
cdists = []
for center in skm.cluster_centers_:
cdists.append(np.linalg.norm(mus[0] - center))
skm_mu_0_idx = np.argmin(cdists)
skm_mu_1_idx = 1 - skm_mu_0_idx
skm_mu_0_error = np.linalg.norm(mus[0] - skm.cluster_centers_[skm_mu_0_idx])
skm_mu_1_error = np.linalg.norm(mus[1] - skm.cluster_centers_[skm_mu_1_idx])
###############################################################################
# Mixture of von Mises Fisher clustering (soft)
vmf_soft = VonMisesFisherMixture(n_clusters=2,
posterior_type='soft',
n_init=20)
vmf_soft.fit(X)
cdists = []
for center in vmf_soft.cluster_centers_:
cdists.append(np.linalg.norm(mus[0] - center))
vmf_soft_mu_0_idx = np.argmin(cdists)
vmf_soft_mu_1_idx = 1 - vmf_soft_mu_0_idx
vmf_soft_mu_0_error = np.linalg.norm(
mus[0] - vmf_soft.cluster_centers_[vmf_soft_mu_0_idx])
vmf_soft_mu_1_error = np.linalg.norm(
mus[1] - vmf_soft.cluster_centers_[vmf_soft_mu_1_idx])
class Clusterer:
def __init__(self,
data,
n_cluster,
method="soft-movMF",
init="random-class",
n_init=10,
n_jobs=1):
self.data = data
self.n_cluster = n_cluster
self.method = method
if method == "spk":
self.clus = SphericalKMeans(n_clusters=n_cluster)
elif method == "hard-movMF":
self.clus = VonMisesFisherMixture(n_clusters=n_cluster,
posterior_type='hard',
init=init,
n_init=n_init,
n_jobs=n_jobs)
elif method == "soft-movMF":
self.clus = VonMisesFisherMixture(n_clusters=n_cluster,
posterior_type='soft',
init=init,
n_init=n_init,
n_jobs=n_jobs)
self.clusters = {
} # cluster id -> dict(element_id: distance to center)
self.clusters_phrase = {} # cluster id -> representative words
self.membership = None # a list contain the membership of the data points
self.center_ids = None # a list contain the ids of the cluster centers
self.inertia_scores = None
def fit(self, debug=False):
start = time.time()
self.clus.fit(self.data)
end = time.time()
print("Finish fitting data of size %s using %s seconds" %
(self.data.shape, (end - start)))
self.inertia_scores = self.clus.inertia_
print('Clustering inertia score (smaller is better):',
self.inertia_scores)
labels = self.clus.labels_
self.membership = labels
if debug:
print("Labels:", labels)
# print("cluster_centers_:", self.clus.cluster_centers_)
if self.method != "spk":
print("concentrations_:", self.clus.concentrations_)
print("weights_:", self.clus.weights_)
print("posterior_:", self.clus.posterior_)
for idx, label in enumerate(labels):
cluster_center = self.clus.cluster_centers_[int(label)]
consine_sim = self.calc_cosine(self.data[idx], cluster_center)
if label not in self.clusters:
self.clusters[label] = {}
self.clusters[label][idx] = consine_sim
else:
self.clusters[label][idx] = consine_sim
for cluster_id in range(self.n_cluster):
self.clusters_phrase[cluster_id] = sorted(
self.clusters[cluster_id].items(), key=lambda x: -x[1])
# self.center_ids = self.gen_center_idx()
# find the idx of each cluster center
def gen_center_idx(self):
ret = []
for cluster_id in range(self.n_cluster):
center_idx = self.find_center_idx_for_one_cluster(cluster_id)
ret.append((cluster_id, center_idx))
return ret
def find_center_idx_for_one_cluster(self, cluster_id):
query_vec = self.clus.cluster_centers_[cluster_id]
members = self.clusters[cluster_id]
best_similarity, ret = -1, -1
for member_idx in members:
member_vec = self.data[member_idx]
cosine_sim = self.calc_cosine(query_vec, member_vec)
if cosine_sim > best_similarity:
best_similarity = cosine_sim
ret = member_idx
return ret
def calc_cosine(self, vec_a, vec_b):
return 1 - cosine(vec_a, vec_b)
def find_phrase_rank(self, phrase, cluster_id):
for idx, ele in enumerate(self.clusters_phrase[cluster_id]):
if ele[0] == phrase:
return (idx + 1)
return -1
def senity_check(self):
for phrase_id in range(self.data.shape[0]):
cluster_member = self.membership[phrase_id]
result = []
for cluster_id in range(self.n_cluster):
cluster_rank = self.find_phrase_rank(phrase_id, cluster_id)
sim = self.calc_cosine(self.data[phrase_id],
self.clus.cluster_centers_[cluster_id])
if sim < 0:
print(phrase_id, sim)
return
# result.append((cluster_id, cluster_rank, sim))
# print("Put in cluster: %s" % cluster_member)
# print("Rank information in all clusters: %s" % str(result))
def explore(self, keyword2id=None, id2keyword=None, iteractive=False):
for cluster_id in range(self.n_cluster):
print("Cluster %s top keywords" % cluster_id)
for rank, keyword_id in enumerate(
self.clusters_phrase[cluster_id][0:10]):
print("Rank:%s keywords:%s (score=%s)" %
(rank + 1, id2keyword[keyword_id[0]], keyword_id[1]))
print("=" * 80)
if iteractive:
while (True):
phrase = input(
'Input keyword (use "_" to concat tokens for phrase): ')
if len(phrase) == 0:
break
if phrase not in keyword2id:
print("Out of vocabulary keyword, please try again")
continue
else:
phrase_id = keyword2id[phrase]
cluster_member = self.membership[phrase_id]
result = []
for cluster_id in range(self.n_cluster):
cluster_rank = self.find_phrase_rank(
phrase_id, cluster_id)
sim = self.calc_cosine(
self.data[phrase_id],
self.clus.cluster_centers_[cluster_id])
result.append((cluster_id, cluster_rank, sim))
print("Put in cluster: %s" % cluster_member)
print("Rank information in all clusters: %s" % str(result))
sns.scatterplot(
data=scatter_df,
x="x",
y="y",
hue="Label",
ax=ax,
legend="full",
hue_order=true_color_dict.keys(),
palette=true_color_dict.values(),
)
plt.legend(bbox_to_anchor=(1.0, 1), loc=2, borderaxespad=0.0)
# %% [markdown]
# #
vmm = VonMisesFisherMixture(n_clusters=2, init="spherical-k-means")
pred_labels = vmm.fit_predict(latent)
pred_color_dict = get_color_dict(pred_labels, pal=cc.glasbey_warm)
# %% [markdown]
# #
fig, ax = plt.subplots(1, 1, figsize=(20, 20))
scatter_df["Clusters"] = pred_labels
sns.scatterplot(
data=scatter_df,
x="x",
y="y",
hue="Clusters",
ax=ax,
legend="full",
hue_order=pred_color_dict.keys(),
palette=pred_color_dict.values(),