class_docstr: Test class for output tests.
docstr: Test learning curve plots for experiment with objectives option.
docstr_tok: ["Test","learning","curve","plots","for","experiment","with","objectives","option","."]
code: def test_learning_curve_plots_with_objectives(self):
      """Test learning curve plots for experiment with objectives option."""
      # Test to validate learning curve output
      self.make_learning_curve_data()
  
      config_template_path = (
          config_dir / "test_learning_curve.template.cfg"
      )
      config_path = fill_in_config_paths(config_template_path)
  
      # run the learning curve experiment
      run_configuration(config_path, quiet=True, local=True)
      outprefix = "test_learning_curve"
  
      # make sure that the four PNG files (two per featureset) are created
      for featureset_name in [
          "test_learning_curve1",
          "test_learning_curve2",
      ]:
          path_score = output_dir / f"{outprefix}_{featureset_name}.png"
          path_time = (
              output_dir / f"{outprefix}_{featureset_name}_times.png"
          )
          self.assertTrue(path_score.exists())
          self.assertTrue(path_time.exists())
  
imports: import csv
  import json
  import re
  import unittest
  import warnings
  from ast import literal_eval
  from collections import defaultdict
  from itertools import chain, product
  from pathlib import Path
  from unittest.mock import Mock, patch
  import numpy
  import pandas
  from numpy.testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal
  from sklearn.model_selection import ShuffleSplit, learning_curve
  from sklearn.naive_bayes import MultinomialNB
  from skll.data import FeatureSet, NDJReader, NDJWriter, Reader
  from skll.experiments import run_configuration
  from skll.experiments.output import _compute_ylimits_for_featureset
  from skll.learner import Learner
  from skll.utils.constants import VALID_TASKS
  from skll.utils.logging import close_and_remove_logger_handlers, get_skll_logger
  from skll.utils.testing import backward_compatibility_dir, config_dir, create_jsonlines_feature_files, fill_in_config_options, fill_in_config_paths, fill_in_config_paths_for_single_file, make_classification_data, make_digits_data, make_regression_data, other_dir, output_dir, remove_jsonlines_feature_files, test_dir, tests_dir, train_dir, unlink
label_desc: Use image node_id 18 for calling the TestOutput obj's underlying member method code with example usage: obj.test_learning_curve_plots_with_objectives() without return types