clusterer = cl.DBSCAN(eps=5, min_samples=8, metric='cityblock')
clusterer = cl.AffinityPropagation(damping=.55, affinity='euclidean')
clusterer = cl.SpectralClustering(3, affinity='cosine', n_init=3)
hel = cn.load_matrix(city)
features = hel['v']
scale = pp.MinMaxScaler(copy=False)
scale.fit_transform(features[:, 0:3])
scores = []
for k in range(3, 16):
clusterer = cl.KMeans(k, n_init=10, tol=1e-5, max_iter=500)
labels = clusterer.fit_predict(features)
scores.append(mt.silhouette_score(features, labels))
print(Counter(labels))
np.argsort(scores)[::-1]+3
ppl.plot(range(3, 16), scores[0:], '+')
clusterer = cl.MeanShift(min_bin_freq=3, cluster_all=False)
clusterer = cl.KMeans(6, n_init=20, tol=1e-5, max_iter=500)
visits = xp.get_visits(CLIENT, xp.Entity.venue, city)
visitors = xp.get_visitors(CLIENT, city)
density = vf.estimate_density(city)
c0, _ = vf.venues_info([_ for _ in hel['i'][labels == 0].tolist()
if _ in visits],
visits, visitors, density, depth=2, tags_freq=False)
c5, v = vf.venues_info([v for v in hel['i'][labels == 5].tolist()
if v in visits],
visits, visitors, density, depth=2, tags_freq=False)
c0.describe()
c5.describe()
scale = pp.MinMaxScaler(copy=False)
scale.fit_transform(features[:, 0:3])
scores = []
for k in range(3, 16):
clusterer = cl.KMeans(k, n_init=10, tol=1e-5, max_iter=500)
labels = clusterer.fit_predict(features)
scores.append(mt.silhouette_score(features, labels))
print(Counter(labels))
np.argsort(scores)[::-1] + 3
ppl.plot(range(3, 16), scores[0:], '+')
clusterer = cl.MeanShift(min_bin_freq=3, cluster_all=False)
clusterer = cl.KMeans(6, n_init=20, tol=1e-5, max_iter=500)
visits = xp.get_visits(CLIENT, xp.Entity.venue, city)
visitors = xp.get_visitors(CLIENT, city)
density = vf.estimate_density(city)
c0, _ = vf.venues_info(
[_ for _ in hel['i'][labels == 0].tolist() if _ in visits],
visits,
visitors,
density,
depth=2,
tags_freq=False)
c5, v = vf.venues_info(
[v for v in hel['i'][labels == 5].tolist() if v in visits],
visits,
visitors,
density,
depth=2,
tags_freq=False)
c0.describe()
