class_docstr: Test class for regression tests.
docstr: Test that we can train a regressor on a non-sparse featureset.
docstr_tok: ["Test","that","we","can","train","a","regressor","on","a","non-sparse","featureset","."]
code: def test_train_non_sparse_featureset(self):
      """Test that we can train a regressor on a non-sparse featureset."""
      train_file = other_dir / "test_int_labels_cv.jsonlines"
      train_fs = NDJReader.for_path(train_file, sparse=False).read()
      learner = Learner("LinearRegression")
      learner.train(train_fs, grid_search=False)
      self.assertTrue(hasattr(learner.model, "coef_"))
  
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 33 for calling the TestRegression obj's underlying member method code with example usage: obj.test_train_non_sparse_featureset() without return types