docstr: Test that the model improves on toy data with no. of batches
code: def test_training_and_prediction(self):
      """Test that the model improves on toy data with no. of batches"""
      input_batches = [
          self.sc.parallelize(
              self.generateLogisticInput(0, 1.5, 100, 42 + i)
          )
          for i in range(40)
      ]
      predict_batches = [
          b.map(lambda lp: (lp.label, lp.features))
          for b in input_batches
      ]
  
      slr = StreamingLogisticRegressionWithSGD(
          stepSize=0.01, numIterations=25
      )
      slr.setInitialWeights([-0.1])
      errors = []
  
      def collect_errors(rdd):
          true, predicted = zip(*rdd.collect())
          errors.append(self.calculate_accuracy_error(true, predicted))
  
      input_stream = self.ssc.queueStream(input_batches)
      predict_stream = self.ssc.queueStream(predict_batches)
      slr.trainOn(input_stream)
      ps = slr.predictOnValues(predict_stream)
      ps.foreachRDD(lambda x: collect_errors(x))
  
      self.ssc.start()
  
      def condition():
          # Test that the improvement in error is > 0.3
          if len(errors) == len(predict_batches):
              self.assertGreater(errors[1] - errors[-1], 0.3)
          if len(errors) >= 3 and errors[1] - errors[-1] > 0.3:
              return True
          return "Latest errors: " + ", ".join(
              map(lambda x: str(x), errors)
          )
  
      eventually(timeout=180.0)(condition)()
  
imports: import os
  import unittest
  from numpy import array, random, exp, dot, all, mean, abs
  from numpy import sum
  from pyspark import SparkContext
  from pyspark.mllib.clustering import StreamingKMeans, StreamingKMeansModel
  from pyspark.mllib.classification import StreamingLogisticRegressionWithSGD
  from pyspark.mllib.linalg import Vectors
  from pyspark.mllib.regression import LabeledPoint, StreamingLinearRegressionWithSGD
  from pyspark.mllib.util import LinearDataGenerator
  from pyspark.streaming import StreamingContext
  from pyspark.testing.utils import eventually
label_desc: Use image node_id 6 for calling the StreamingLogisticRegressionWithSGDTests obj's underlying member method code with example usage: obj.test_training_and_prediction() and returns: unknown, True