def test_entropy(self):
probs = np.array([0.1, 0.5, 0.01, 0.07, 0.02, 0.3, 0, 0, 0], dtype="d")
self.assertEquals(entropy.entropy(probs), it_entropy(probs))
try:
entropy.entropy(np.array([-1], dtype="d"))
self.fail()
except AssertionError:
pass
try:
entropy.entropy(np.array([0.1, 0.8], dtype="d"))
self.fail()
except AssertionError:
pass
try:
entropy.entropy(np.array([2, -1], dtype="d"))
self.fail()
except AssertionError:
pass
try:
entropy.entropy(np.array([], dtype="d"))
self.fail()
except AssertionError:
pass
def _summarize(data, vocabulary, labels_column, num_cluster):
# Basic stats
print("Number of songs per cluster")
counter = Counter(labels_column)
print(counter)
print()
prob_Ct, prob_Tc, prob_T = compute_probs(data, num_cluster, labels_column, counter)
all_tags = range(len(prob_T))
print("Top tags per cluster")
for clust in xrange(num_cluster):
print(clust, "tags with max_freq_in_cluster")
songs_in_cluster = np.where(labels_column == clust)[0]
for tag in top_10_frequency(data[songs_in_cluster]):
print("\t", vocabulary[tag])
print()
print(clust, "tags with max_prob_p(c|t)")
sort_func = lambda to_sort: prob_Ct[to_sort][clust]
for tag in sorted(all_tags, key=sort_func, reverse=True)[:10]:
print("\t", vocabulary[tag])
print()
print()
print("Term entropies for each cluster")
term_entropies = []
for clust in xrange(num_cluster):
h = entropy.entropy(prob_Tc[clust])
term_entropies.append(h)
print(clust, h)
print()
# Number of shared tags between clusters
X = np.zeros((num_cluster, len(all_tags)))
for clust in xrange(num_cluster):
for tag in all_tags:
X[clust][tag] = prob_Tc[clust][tag]
distances = pairwise_kernels(X)
for i in xrange(num_cluster):
distances[i, i] = 0
plt.imshow(distances, cmap="bone_r", interpolation="nearest")
ax = plt.gca()
plt.xticks(np.arange(0, num_cluster))
plt.yticks(np.arange(0, num_cluster))
plt.colorbar()
plt.title("Confusion Matrix for Cluster Similarities")
plt.ylabel("ClusterID")
plt.xlabel("ClusterID")
for i in xrange(num_cluster):
ax.annotate("%.3f" % term_entropies[i], xy=(i, i), horizontalalignment="center", verticalalignment="center")
plt.show()
print("Mean difference")
to_corr_1 = []
to_corr_2 = []
for clust in xrange(num_cluster):
to_corr_1.append(term_entropies[clust])
to_corr_2.append(np.mean(distances[clust]))
print(clust, term_entropies[clust], np.mean(distances[clust]))
from scipy.stats import pearsonr
print("R2 ", pearsonr(to_corr_1, to_corr_2))
