Github user MechCoder commented on a diff in the pull request:
https://github.com/apache/spark/pull/6499#discussion_r32773505
--- Diff: python/pyspark/mllib/tests.py ---
@@ -863,6 +876,107 @@ def test_model_transform(self):
eprod.transform(sparsevec), SparseVector(3, [0], [3]))
+class StreamingKMeansTest(MLLibStreamingTestCase):
+ def test_model_params(self):
+ stkm = StreamingKMeans()
+ stkm.setK(5).setDecayFactor(0.0)
+ self.assertEquals(stkm._k, 5)
+ self.assertEquals(stkm._decayFactor, 0.0)
+
+ # Model not set yet.
+ self.assertIsNone(stkm.latestModel)
+ self.assertRaises(ValueError, stkm.trainOn, [0.0, 1.0])
+
+ stkm.setInitialCenters([[0.0, 0.0], [1.0, 1.0]], [1.0, 1.0])
+ self.assertEquals(stkm.latestModel.centers, [[0.0, 0.0], [1.0,
1.0]])
+ self.assertEquals(stkm.latestModel.getClusterWeights, [1.0, 1.0])
+
+ @staticmethod
+ def _ssc_wait(start_time, end_time, sleep_time):
+ while time() - start_time < end_time:
+ sleep(0.01)
+
+ def test_accuracy_for_single_center(self):
+ numBatches, numPoints, k, d, r, seed = 5, 5, 1, 5, 0.1, 0
+ centers, batches = self.streamingKMeansDataGenerator(
+ numBatches, numPoints, k, d, r, seed)
+ stkm = StreamingKMeans(1)
+ stkm.setInitialCenters([[0., 0., 0., 0., 0.]], [0.])
+ input_stream = self.ssc.queueStream(
+ [self.sc.parallelize(batch, 1) for batch in batches])
+ stkm.trainOn(input_stream)
+ t = time()
+ self.ssc.start()
+ self._ssc_wait(t, 10.0, 0.01)
+ self.assertEquals(stkm.latestModel.getClusterWeights, [25.0])
+ realCenters = array_sum(array(centers), axis=0)
+ for i in range(d):
+ modelCenters = stkm.latestModel.centers[0][i]
+ self.assertAlmostEqual(centers[0][i], modelCenters, 1)
+ self.assertAlmostEqual(realCenters[i], modelCenters, 1)
+
+ def streamingKMeansDataGenerator(self, batches, numPoints,
+ k, d, r, seed, centers=None):
+ rng = random.RandomState(seed)
+
+ # Generate centers.
+ centers = [rng.randn(d) for i in range(k)]
+
+ return centers, [[Vectors.dense(centers[j % k] + r * rng.randn(d))
+ for j in range(numPoints)]
+ for i in range(batches)]
+
+ def test_trainOn_model(self):
+ # Test the model on toy data with four clusters.
+ stkm = StreamingKMeans()
+ initCenters = [[1.0, 1.0], [-1.0, 1.0], [-1.0, -1.0], [1.0, -1.0]]
+ weights = [1.0, 1.0, 1.0, 1.0]
+ stkm.setInitialCenters(initCenters, weights)
+
+ # Create a toy dataset by setting a tiny offest for each point.
+ offsets = [[0, 0.1], [0, -0.1], [0.1, 0], [-0.1, 0]]
+ batches = []
+ for offset in offsets:
+ batches.append([[offset[0] + center[0], offset[1] + center[1]]
+ for center in initCenters])
+
+ batches = [self.sc.parallelize(batch, 1) for batch in batches]
+ input_stream = self.ssc.queueStream(batches)
+ stkm.trainOn(input_stream)
+ t = time()
+ self.ssc.start()
+
+ # Give enough time to train the model.
+ self._ssc_wait(t, 6.0, 0.01)
+ finalModel = stkm.latestModel
+ self.assertTrue(all(finalModel.centers == array(initCenters)))
+ self.assertEquals(finalModel.getClusterWeights, [5.0, 5.0, 5.0,
5.0])
+
+ def test_predictOn_model(self):
+ initCenters = [[1.0, 1.0], [-1.0, 1.0], [-1.0, -1.0], [1.0, -1.0]]
+ weights = [1.0, 1.0, 1.0, 1.0]
+ stkm = StreamingKMeans()
+ stkm.latestModel = StreamingKMeansModel(initCenters, weights)
+
+ predict_data = [[[1.5, 1.5]], [[-1.5, 1.5]], [[-1.5, -1.5]],
[[1.5, -1.5]]]
+ predict_data = [sc.parallelize(batch, 1) for batch in predict_data]
+ predict_stream = self.ssc.queueStream(predict_data)
+ predict_val = stkm.predictOn(predict_stream)
+
+ result = []
+
+ def update(rdd):
+ rdd_collect = rdd.collect()
+ if rdd_collect:
+ result.append(rdd_collect)
+
+ predict_val.foreachRDD(update)
+ t = time()
+ self.ssc.start()
+ self._ssc_wait(t, 6.0, 0.01)
+ self.assertEquals(result, [[0], [1], [2], [3]])
+
+
--- End diff --
I'm not sure what correct predictions are in clustering models where the
ground truth is not known. However I've added a test that does something
similar.
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