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Merge branch 'adds-nonspatial-kmeans' of https://github.com/CartoDB/crankshaft into adds-nonspatial-kmeans

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This commit is contained in:
Andy Eschbacher 2016-10-13 12:52:07 -04:00
commit 5404589058
1 changed files with 3 additions and 4 deletions
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7
src/py/crankshaft/crankshaft/clustering/kmeans.py
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@ -1,5 +1,6 @@
from sklearn.cluster import KMeans
import plpy
import numpy as np
def kmeans(query, no_clusters, no_init=20):
@ -39,7 +40,6 @@ def kmeans_nonspatial(query, colnames, num_clusters=5,
num_clusters (int): number of clusters (greater than zero)
id_col (string): name of the input id_column
"""
import numpy as np
out_id_colname = 'rowids'
# TODO: need a random seed?
@ -54,14 +54,13 @@ def kmeans_nonspatial(query, colnames, num_clusters=5,
try:
db_resp = plpy.execute(full_query)
plpy.notice('query: %s' % full_query)
except plpy.SPIError, err:
plpy.error('k-means cluster analysis failed: %s' % err)
# fill array with values for kmeans clustering
if standarize:
cluster_columns = _scale_data(
_extract_columns(db_resp, id_col='cartodb_id'))
_extract_columns(db_resp, id_col=out_id_colname))
else:
cluster_columns = _extract_columns(db_resp)
@ -70,7 +69,7 @@ def kmeans_nonspatial(query, colnames, num_clusters=5,
kmeans = KMeans(n_clusters=num_clusters,
random_state=0).fit(cluster_columns)
return zip(kmeans.predict(X),
return zip(kmeans.labels_,
map(str, kmeans.cluster_centers_[kmeans.labels_]),
db_resp[0][out_id_colname])