class_docstr: Test class for preprocessing tests.
code: def make_scaling_data(self, use_feature_hashing=False):
      X, y = make_classification(
          n_samples=1000,
          n_classes=2,
          n_features=5,
          n_informative=5,
          n_redundant=0,
          random_state=1234567890,
      )
  
      # we want to arbitrary scale the various features to test the scaling
      scalers = np.array([1, 10, 100, 1000, 10000])
      X = X * scalers
  
      # since we want to use SKLL's FeatureSet class, we need to
      # create a list of IDs
      ids = [f"EXAMPLE_{n}" for n in range(1, 1001)]
  
      # create a list of dictionaries as the features
      feature_names = [f"f{n}" for n in range(1, 6)]
      features = []
      for row in X:
          features.append(dict(zip(feature_names, row)))
  
      # split everything into training and testing portions
      train_features, test_features = features[:800], features[800:]
      train_y, test_y = y[:800], y[800:]
      train_ids, test_ids = ids[:800], ids[800:]
  
      vectorizer = (
          FeatureHasher(n_features=4) if use_feature_hashing else None
      )
      train_fs = FeatureSet(
          "train_scaling",
          train_ids,
          features=train_features,
          labels=train_y,
          vectorizer=vectorizer,
      )
      test_fs = FeatureSet(
          "test_scaling",
          test_ids,
          features=test_features,
          labels=test_y,
          vectorizer=vectorizer,
      )
  
      return (train_fs, test_fs)
  
imports: import json
  import os
  import re
  import unittest
  import numpy
  import scipy.sparse
  from numpy.testing import assert_almost_equal, assert_array_equal
  from sklearn.datasets import make_classification
  from sklearn.feature_extraction import FeatureHasher
  from skll.data import FeatureSet, NDJWriter
  from skll.experiments import run_configuration
  from skll.learner import Learner
  from skll.learner.utils import SelectByMinCount
  from skll.utils.testing import config_dir, fill_in_config_paths, output_dir, test_dir, train_dir, unlink
label_desc: Use image node_id 7 for calling the TestPreprocessing obj's underlying member method code with example usage: obj.make_scaling_data(use_feature_hashing) and returns: train_fs,  test_fs