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Posted to dev@madlib.apache.org by GitBox <gi...@apache.org> on 2019/09/04 18:26:30 UTC
[GitHub] [madlib] kaknikhil commented on a change in pull request #433:
Kmeans: Add automatic optimal cluster estimation
kaknikhil commented on a change in pull request #433: Kmeans: Add automatic optimal cluster estimation
URL: https://github.com/apache/madlib/pull/433#discussion_r320881235
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File path: src/ports/postgres/modules/kmeans/kmeans_auto.py_in
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@@ -0,0 +1,201 @@
+# coding=utf-8
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""
+@file kmeans_auto.py_in
+
+@brief
+
+"""
+
+import numpy as np
+import math
+import plpy
+from utilities.utilities import _assert
+from utilities.utilities import unique_string
+from utilities.validate_args import output_tbl_valid
+from utilities.validate_args import get_algorithm_name
+
+ELBOW = 'elbow'
+SILHOUETTE = 'silhouette'
+BOTH = 'both'
+
+def _validate(output_table, k):
+
+ output_tbl_valid(output_table, "kmeans_auto")
+ output_tbl_valid(output_table+'_summary', "kmeans_auto")
+
+ _assert(k, "kmeans_auto: k cannot be NULL.")
+ _assert(len(k)>1, "kmeans_auto: there has to be more than 1 k values to consider.")
+ _assert(min(k)>1, "kmeans_auto: the minimum k value has to be > 1.")
+
+def set_defaults(schema_madlib, fn_dist, agg_centroid, max_num_iterations, min_frac_reassigned, k_selection_algorithm, seeding, seeding_sample_ratio):
+
+ fn_dist = (fn_dist if fn_dist is not None
+ else '{0}.squared_dist_norm2'.format(schema_madlib))
+ agg_centroid = (agg_centroid if agg_centroid is not None
+ else '{0}.avg'.format(schema_madlib))
+ max_num_iterations = (max_num_iterations if max_num_iterations is not None
+ else 20)
+ min_frac_reassigned = (min_frac_reassigned if min_frac_reassigned is not None
+ else 0.001)
+
+ k_selection_algorithm = get_algorithm_name(k_selection_algorithm, ELBOW,
+ [ELBOW, SILHOUETTE, BOTH], 'kmeans_auto')
+
+ if seeding is 'pp':
+ seeding_sample_ratio = (seeding_sample_ratio
+ if seeding_sample_ratio is not None else 1.0)
+ return (fn_dist, agg_centroid, max_num_iterations, min_frac_reassigned,
+ k_selection_algorithm, seeding_sample_ratio)
+
+def kmeans_auto(schema_madlib, rel_source, output_table, expr_point, k,
+ fn_dist=None, agg_centroid=None, max_num_iterations=None,
+ min_frac_reassigned=None, k_selection_algorithm=None, seeding=None,
+ seeding_sample_ratio=None, **kwargs):
+
+ _validate(output_table, k)
+
+ (fn_dist, agg_centroid, max_num_iterations, min_frac_reassigned,
+ k_selection_algorithm, seeding_sample_ratio) = set_defaults(
+ schema_madlib, fn_dist, agg_centroid, max_num_iterations,
+ min_frac_reassigned, k_selection_algorithm, seeding,
+ seeding_sample_ratio)
+
+ silhouette = ""
+ elbow = ""
+
+ plpy.execute("""
+ CREATE TABLE {output_table} (
+ k INTEGER,
+ centroids DOUBLE PRECISION[][],
+ cluster_variance DOUBLE PRECISION[],
+ objective_fn DOUBLE PRECISION,
+ frac_reassigned DOUBLE PRECISION,
+ num_iterations INTEGER)
+ """.format(**locals()))
+
+ silhouette_vals = []
+
+ for current_k in k:
+ if seeding is 'random':
+ plpy.execute("""
+ INSERT INTO {output_table}
+ SELECT {current_k} as k, *
+ FROM {schema_madlib}.kmeans_random('{rel_source}',
+ '{expr_point}',
+ {current_k},
+ '{fn_dist}',
+ '{agg_centroid}',
+ {max_num_iterations},
+ {min_frac_reassigned});
+ """.format(**locals()))
+ else:
+ plpy.execute("""
+ INSERT INTO {output_table}
+ SELECT {current_k} as k, *
+ FROM {schema_madlib}.kmeanspp('{rel_source}',
+ '{expr_point}',
+ {current_k},
+ '{fn_dist}',
+ '{agg_centroid}',
+ {max_num_iterations},
+ {min_frac_reassigned},
+ {seeding_sample_ratio});
+ """.format(**locals()))
+
+ if k_selection_algorithm != 'elbow':
+ silhouette_query= """
+ SELECT * FROM {schema_madlib}.simple_silhouette(
+ '{rel_source}',
+ '{expr_point}',
+ (SELECT centroids
+ FROM {output_table}
+ WHERE k = {current_k}),
+ '{fn_dist}')
+ """.format(**locals())
+ silhouette_vals.append(
+ plpy.execute(silhouette_query)[0]['simple_silhouette'])
+
+ # If the selection is silhouette or both, calculate silhouette
+ if k_selection_algorithm != 'elbow':
+
+ silhouette_vals_np = np.array(silhouette_vals)
+ optimal_sil = k[np.argmax(silhouette_vals_np)]
+ silhouette = ", {0} AS silhouette".format(max(silhouette_vals))
+
+ # If the selection is elbow or both, calculate elbow
+ if k_selection_algorithm != 'silhouette':
+
+ index_with_elbow, second_order = _calculate_elbow(output_table)
+ optimal_elbow = index_with_elbow
+ elbow = ", {0} AS elbow".format(max(second_order))
+
+ optimal_k = (optimal_sil if k_selection_algorithm == 'silhouette'
+ else optimal_elbow)
+
+ plpy.execute("""
+ CREATE TABLE {output_table}_summary AS
+ SELECT {output_table}.*,
+ '{k_selection_algorithm}'::VARCHAR AS selection_algorithm
+ {elbow}
+ {silhouette}
+ FROM {output_table}
+ WHERE k = {optimal_k}
+ """.format(**locals()))
+
+ return
+
+def _calculate_elbow(output_table):
+
+ # We have to get the values in ordered fashion because the elbow is only defined for ordered values.
+ inertia_result = plpy.execute("""
+ SELECT k, objective_fn FROM {output_table} ORDER BY k ASC
+ """.format(**locals()))
+ k = [ i['k'] for i in inertia_result ]
+ inertia_list = [ i['objective_fn'] for i in inertia_result ]
+ inertia_list = np.array(inertia_list)
+
+ #TODO: what to do if optimal is the very first (or last) value?
+ first_order=np.gradient(inertia_list, k)
+ second_order=np.gradient(first_order, k)
+ index_with_elbow=k[np.argmax(second_order)]
+
+ return index_with_elbow, second_order
+
+def kmeans_random_auto(schema_madlib, rel_source, output_table, expr_point, k,
+ fn_dist=None, agg_centroid=None, max_num_iterations=None,
+ min_frac_reassigned=None, k_selection_algorithm=None, **kwargs):
+
+ kmeans_auto(schema_madlib, rel_source, output_table, expr_point, k,
+ fn_dist, agg_centroid, max_num_iterations, min_frac_reassigned,
+ k_selection_algorithm, 'random')
Review comment:
Consider making variables for the strings 'random' and 'pp' which can then also be used in the `kmeans_auto` function.
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