changes cluster centers to json
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a188b2e104
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@ -15,14 +15,14 @@ CREATE OR REPLACE FUNCTION CDB_KMeansNonspatial(
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query TEXT,
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colnames TEXT[],
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num_clusters INTEGER,
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id_col TEXT DEFAULT 'cartodb_id',
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id_colname TEXT DEFAULT 'cartodb_id',
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standarize BOOLEAN DEFAULT true
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)
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RETURNS TABLE(cluster_label text, cluster_center text, rowid bigint) AS $$
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RETURNS TABLE(cluster_label text, cluster_center json, rowid bigint) AS $$
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from crankshaft.clustering import kmeans_nonspatial
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return kmeans_nonspatial(query, colnames, num_clusters,
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id_col, standarize)
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id_colname, standarize)
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$$ LANGUAGE plpythonu;
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@ -40,6 +40,7 @@ def kmeans_nonspatial(query, colnames, num_clusters=5,
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num_clusters (int): number of clusters (greater than zero)
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id_col (string): name of the input id_column
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"""
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import json
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out_id_colname = 'rowids'
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# TODO: need a random seed?
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@ -60,7 +61,7 @@ def kmeans_nonspatial(query, colnames, num_clusters=5,
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# fill array with values for k-means clustering
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if standarize:
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cluster_columns = _scale_data(
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_extract_columns(db_resp, id_col=out_id_colname))
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_extract_columns(db_resp, out_id_colname))
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else:
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cluster_columns = _extract_columns(db_resp, id_col=out_id_colname)
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@ -69,8 +70,9 @@ def kmeans_nonspatial(query, colnames, num_clusters=5,
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kmeans = KMeans(n_clusters=num_clusters,
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random_state=0).fit(cluster_columns)
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centers = [json.dumps(dict(zip(colnames, c))) for c in kmeans.cluster_centers_[kmeans.labels_]]
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return zip(kmeans.labels_,
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map(str, kmeans.cluster_centers_[kmeans.labels_]),
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centers,
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db_resp[0][out_id_colname])
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