# data_set, n_clusters = 'math_garden-multiplication', 3
# data_set, n_clusters = 'math_garden-addition', 3
# data_set, n_clusters = 'math_garden-subtraction', 3
# data_set, n_clusters = 'math_garden-all', 3
answers = pd.read_pickle('data/{}-answers.pd'.format(data_set))
items = pd.read_pickle('data/{}-items.pd'.format(data_set))
true_cluster_names = list(items['concept'].unique())
print(len(answers), len(items))
projection = tsne
similarity, euclid = similarity_pearson, True
X = similarity(answers)
xs, ys = projection(X, euclid=euclid, perplexity=10)
items_ids = X.index
ground_truth = np.array([true_cluster_names.index(items.get_value(item, 'concept')) for item in items_ids])
plot_clustering(
items_ids, xs, ys,
labels=ground_truth,
texts=[items.get_value(item, 'name') for item in items_ids],
shapes=ground_truth,
)
if False:
df = pd.read_csv('tsne-prid-jmena-data.csv', sep=';')
print(df)
similarities_names.append("{} -> {}".format(f.__name__.replace('similarity_', ''), g.__name__.replace('similarity_', '')))
else:
similarities.append(lambda x, f=f: f(x, cache=data_set + str(modificator)))
similarities_names.append(f.__name__.replace('similarity_', ''))
if True:
euclid = True
clusters = []
for i, similarity in enumerate(similarities):
print(similarities_names[i])
X = similarity(answers)
labels = kmeans(X, concepts=n_clusters, euclid=euclid)
clusters.append(labels)
items_ids = X.index
xs, ys = projection(X, euclid=euclid)
ground_truth = [true_cluster_names.index(items.get_value(item, 'concept')) for item in items_ids]
plt.subplot(2, len(similarities) / 2 + 1, i + 1)
plt.title(similarities_names[i])
plot_clustering(
items_ids, xs, ys,
labels=labels,
texts=[items.get_value(item, 'name') for item in items_ids],
shapes=ground_truth,
)
plt.legend(handles=[
mlines.Line2D([], [], color='black', linewidth=0, marker=markers[i], label=v)
for i, v in enumerate(true_cluster_names)
])