def ClusterHouses(matches, plot_groups=False):
groups = {}
try:
N = len(matches)
X = np.zeros((N, 2))
for m in range(N):
loc = RFAPI.house_location(matches[m])
#logging.debug("ClusterHouses({})".format(loc))
X[m] = (loc[0], loc[1])
params = {
'quantile': .3,
'eps': .15,
'damping': .9,
'preference': -5,
'n_neighbors': 2,
'n_clusters': 5
}
# a bit buggy..
spectral = cluster.SpectralClustering(
n_clusters=params['n_clusters'],
eigen_solver='arpack',
affinity="nearest_neighbors")
# best so far!
gmm = mixture.GaussianMixture(n_components=params['n_clusters'],
covariance_type='full')
# yielded one cluster..
affinity_propagation = cluster.AffinityPropagation(
damping=params['damping'], preference=params['preference'])
bandwidth = cluster.estimate_bandwidth(X,
quantile=params['quantile'])
ms = cluster.MeanShift(bandwidth=bandwidth, bin_seeding=True)
algorithm = ms
algorithm.fit(X)
if hasattr(algorithm, 'labels_'):
y_pred = algorithm.labels_.astype(np.int)
else:
y_pred = algorithm.predict(X)
for m in range(len(matches)):
key = str(y_pred[m])
if groups.get(key, None) == None:
groups[key] = []
groups[key].append({
"adress": RFAPI.house_address(matches[m]),
"location": [X[m][0], X[m][1]]
})
logging.debug("groups = {}".format(groups))
if plot_groups:
HouseScore._plot_groups(X, y_pred)
except Exception as e:
groups["error"] = str(e)
logging.error(groups["error"])
return groups
