Merge branch 'release/0.5.3'

ChangeLog.rst

@@ -1,6 +1,12 @@
 ChangeLog
 ====================================================
 
+Release 0.5.3 (2017-12-18)
+---------------------------------------
+
+* New Features
+    * Add Clustering service (#93, #98)
+
 Release 0.5.2 (2017-10-30)
 ---------------------------------------
 

README.rst

@@ -16,7 +16,7 @@ jubakit is a Python module to access Jubatus features easily.
 jubakit can be used in conjunction with `scikit-learn <http://scikit-learn.org/>`_ so that you can use powerful features like cross validation and model evaluation.
 See the `Jubakit Documentation <http://jubat.us/en/jubakit>`_ for the detailed description.
 
-Currently jubakit supports `Classifier <http://jubat.us/en/api/api_classifier.html>`_, `Regression <http://jubat.us/en/api/api_regression.html>`_, `Anomaly <http://jubat.us/en/api/api_anomaly.html>`_, `Recommender <http://jubat.us/en/api/api_recommender.html>`_ and `Weight <http://jubat.us/en/api/api_weight.html>`_ engines.
+Currently jubakit supports `Classifier <http://jubat.us/en/api/api_classifier.html>`_, `Regression <http://jubat.us/en/api/api_regression.html>`_, `Anomaly <http://jubat.us/en/api/api_anomaly.html>`_, `Recommender <http://jubat.us/en/api/api_recommender.html>`_, `Clustering <http://jubat.us/en/api/api_clustering/html>`_ and `Weight <http://jubat.us/en/api/api_weight.html>`_ engines.
 
 Install
 -------
@@ -105,6 +105,8 @@ See the `example <https://github.com/jubatus/jubakit/tree/master/example>`_ dire
 +-----------------------------------+-----------------------------------------------+-----------------------+
 | recommender_npb.py                | Recommend similar items                       |                       |
 +-----------------------------------+-----------------------------------------------+-----------------------+
+| clustering_2d.py                  | Clustering 2-dimensional dataset              |                       |
++-----------------------------------+-----------------------------------------------+-----------------------+
 | weight_shogun.py                  | Tracing fv_converter behavior using Weight    |                       |
 +-----------------------------------+-----------------------------------------------+-----------------------+
 | weight_model_extract.py           | Extract contents of Weight model file         |                       |

example/blobs.csv

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example/clustering_2d.py

@@ -0,0 +1,53 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+"""
+Using Clustering
+========================================
+
+This is a simple example that illustrates Clustering service usage.
+
+"""
+
+from jubakit.clustering import Clustering, Schema, Dataset, Config
+from jubakit.loader.csv import CSVLoader
+
+# Load a CSV file.
+loader = CSVLoader('blobs.csv')
+
+# Define a Schema that defines types for each columns of the CSV file.
+schema = Schema({
+  'cluster': Schema.ID,
+}, Schema.NUMBER)
+
+# Create a Dataset.
+dataset = Dataset(loader, schema)
+
+# Create an Clustering Service.
+cfg = Config(method='kmeans')
+clustering = Clustering.run(cfg)
+
+# Update the Clustering model.
+for (idx, row_id, result) in clustering.push(dataset):
+  pass
+
+# Get clusters
+clusters = clustering.get_core_members(light=False)
+# Get centers of each cluster
+centers = clustering.get_k_center()
+
+# Calculate SSE: sum of squared errors
+sse = 0.0
+for cluster, center in zip(clusters, centers):
+  # Center of clusters
+  center = {"x1": center.num_values[0][1], "x2": center.num_values[1][1]}
+  for d in cluster:
+    vector = d.point.num_values
+    x1 = [x[1] for x in vector if x[0] == 'x1'][0]
+    x2 = [x[1] for x in vector if x[0] == 'x2'][0]
+    sse += (x1 - center["x1"])**2 + (x2- center["x2"])**2
+print('SSE:', sse)
+
+clustering.stop()