class_docstr: Test class for regression tests.
docstr: Test that SKLL can take integer input.
  
  This is just to test that SKLL can take int labels in the input
  (rather than floats or strings).  For v1.0.0, it could not because the
  json package doesn't know how to serialize numpy.int64 objects.
docstr_tok: ["Test","that","SKLL","can","take","integer","input",".","This","is","just","to","test","that","SKLL","can","take","int","labels","in","the","input","(","rather","than","floats","or","strings",")",".","For","v1.0.0",",","it","could","not","because","the","json","package","does","n't","know","how","to","serialize","numpy.int64","objects","."]
code: def test_int_labels(self):
      """
      Test that SKLL can take integer input.
  
      This is just to test that SKLL can take int labels in the input
      (rather than floats or strings).  For v1.0.0, it could not because the
      json package doesn't know how to serialize numpy.int64 objects.
      """
      config_template_path = (
          config_dir / "test_int_labels_cv.template.cfg"
      )
      config_path = config_dir / "test_int_labels_cv.cfg"
  
      config = _setup_config_parser(
          config_template_path, validate=False
      )
      config.set(
          "Input",
          "train_file",
          str(other_dir / "test_int_labels_cv.jsonlines"),
      )
      config.set("Output", "results", str(output_dir))
      config.set("Output", "logs", str(output_dir))
      config.set("Output", "predictions", str(output_dir))
  
      with open(config_path, "w") as new_config_file:
          config.write(new_config_file)
  
      run_configuration(config_path, quiet=True, local=True)
  
imports: import math
  import re
  import unittest
  import warnings
  from itertools import chain, product
  import numpy
  from numpy.testing import assert_allclose, assert_array_equal
  from scipy.stats import pearsonr
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.linear_model import LinearRegression, LogisticRegression
  from skll.config import _setup_config_parser
  from skll.data import FeatureSet, NDJReader, NDJWriter
  from skll.experiments import run_configuration
  from skll.learner import Learner
  from skll.learner.utils import rescaled
  from skll.utils.constants import CLASSIFICATION_ONLY_METRICS
  from skll.utils.testing import config_dir, fill_in_config_paths_for_fancy_output, make_regression_data, other_dir, output_dir, test_dir, train_dir, unlink
label_desc: Use image node_id 14 for calling the TestRegression obj's underlying member method code with example usage: obj.test_int_labels() without return types