class_docstr: Test class for output tests.
docstr: Test to check learning curve displays warning with less than 500 examples.
docstr_tok: ["Test","to","check","learning","curve","displays","warning","with","less","than","500","examples","."]
code: def test_learning_curve_min_examples_check_override(self):
      """Test to check learning curve displays warning with less than 500 examples."""
      # creates a logger which writes to a temporary log file
      log_file_path = (
          output_dir
          / "test_check_override_learning_curve_min_examples.log"
      )
      logger = get_skll_logger(
          "test_learning_curve_min_examples_check_override",
          filepath=log_file_path,
      )
  
      # generates a training split with less than 500 examples
      train_fs_less_than_500, _ = make_classification_data(
          num_examples=499, train_test_ratio=1.0, num_labels=3
      )
  
      # creating an example learner
      learner = Learner("LogisticRegression", logger=logger)
  
      # this must throw an error because `examples` has less than 500 items
      _ = learner.learning_curve(
          examples=train_fs_less_than_500,
          metric="accuracy",
          override_minimum=True,
      )
  
      # checks that the learning_curve warning message is contained in the log file
      with open(log_file_path) as tf:
          log_text = tf.read()
          learning_curve_warning_re = re.compile(
              r"Learning curves can be unreliable for examples fewer than "
              r"500. You provided \d+\."
          )
          assert learning_curve_warning_re.search(log_text)
  
      close_and_remove_logger_handlers(logger)
  
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 21 for calling the TestOutput obj's underlying member method code with example usage: obj.test_learning_curve_min_examples_check_override() without return types