def test_correlation_helper_for_group_with_one_row_and_length(self):
# this should return a data frames with nans for group with 1 row
retval = Analyzer.correlation_helper(self.df_features_with_groups_and_length[:6],
'sc1', 'group', include_length=True)
for df in retval:
assert_equal(len(df), 2)
assert_equal(len(df.columns), 3)
def test_that_correlation_helper_works_for_data_with_two_rows(self):
# this should return 1/-1 for marginal correlations and nans for
# partial correlations
retval = Analyzer.correlation_helper(self.df_features[:2], 'sc1',
'group')
assert_equal(abs(retval[0].values).sum(), 3)
assert_equal(retval[1].isnull().values.sum(), 3)
def test_that_correlation_helper_works_for_data_with_four_rows(self):
# this should compute marginal correlations and return a unity
# matrix for partial correlations
retval = Analyzer.correlation_helper(self.df_features[:4], 'sc1',
'group')
assert_equal(retval[0].isnull().values.sum(), 0)
assert_almost_equal(abs(retval[1].values).sum(), 3)
def test_that_correlation_helper_works_for_data_with_three_rows(self):
# this should compute marginal correlations but return Nans for
# partial correlations
retval = Analyzer.correlation_helper(self.df_features[:3], 'sc1',
'group')
assert_equal(retval[0].isnull().values.sum(), 0)
assert_equal(retval[1].isnull().values.sum(), 3)
def test_that_correlation_helper_works_for_data_with_the_same_label(self):
# this should return two data frames with nans
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
retval = Analyzer.correlation_helper(self.df_features_same_score, 'sc1', 'group')
assert_equal(retval[0].isnull().values.sum(), 3)
assert_equal(retval[1].isnull().values.sum(), 3)
def test_that_correlation_helper_works_for_data_with_one_row(self):
# this should return two data frames with nans
# we expect a runtime warning here so let's suppress it
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
retval = Analyzer.correlation_helper(self.df_features[:1], 'sc1', 'group')
assert_equal(retval[0].isnull().values.sum(), 3)
assert_equal(retval[1].isnull().values.sum(), 3)
def test_that_correlation_helper_works_for_data_with_the_same_label(self):
# this should return two data frames with nans
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
retval = Analyzer.correlation_helper(self.df_features_same_score,
'sc1', 'group')
assert_equal(retval[0].isnull().values.sum(), 3)
assert_equal(retval[1].isnull().values.sum(), 3)
def test_correlation_helper_for_data_with_four_rows(self):
# this should compute marginal correlations and return a unity
# matrix for partial correlations
# it should also raise a UserWarning
with warnings.catch_warnings(record=True) as warning_list:
retval = Analyzer.correlation_helper(self.df_features[:4], 'sc1', 'group')
assert_equal(retval[0].isnull().values.sum(), 0)
assert_almost_equal(np.abs(retval[1].values).sum(), 0.9244288637889855)
assert issubclass(warning_list[-1].category, UserWarning)
def test_that_correlation_helper_works_for_data_with_one_row(self):
# this should return two data frames with nans
# we expect a runtime warning here so let's suppress it
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
retval = Analyzer.correlation_helper(self.df_features[:1], 'sc1',
'group')
assert_equal(retval[0].isnull().values.sum(), 3)
assert_equal(retval[1].isnull().values.sum(), 3)
def test_that_correlation_helper_works_for_data_with_the_same_human_score(self):
# this test should raise UserWarning because the determinant is very close to
# zero. It also raises Runtime warning because
# variance of human scores is 0.
with warnings.catch_warnings(record=True) as warning_list:
warnings.filterwarnings('ignore', category=RuntimeWarning)
retval = Analyzer.correlation_helper(self.df_features_same_score, 'sc1', 'group')
assert_equal(retval[0].isnull().values.sum(), 3)
assert_equal(retval[1].isnull().values.sum(), 3)
assert issubclass(warning_list[-1].category, UserWarning)
def test_correlation_helper_for_data_with_one_row(self):
# this should return two data frames with nans
retval = Analyzer.correlation_helper(self.df_features[:1], 'sc1', 'group')
assert_equal(retval[0].isnull().values.sum(), 3)
assert_equal(retval[1].isnull().values.sum(), 3)
def test_correlation_helper(self):
# test that there are no nans for data frame with 10 values
retval = Analyzer.correlation_helper(self.df_features, 'sc1', 'group')
assert_equal(retval[0].isnull().values.sum(), 0)
assert_equal(retval[1].isnull().values.sum(), 0)
def test_correlation_helper_for_one_group_with_one_row(self):
# this should return a data frames with nans for group with 1 row
retval = Analyzer.correlation_helper(self.df_features_with_groups[:6], 'sc1', 'group')
assert_equal(len(retval[0]), 2)
assert_equal(len(retval[1]), 2)
assert_equal(retval[0].isnull().values.sum(), 3)
def test_that_correlation_helper_works_for_data_with_two_rows(self):
# this should return 1/-1 for marginal correlations and nans for
# partial correlations
retval = Analyzer.correlation_helper(self.df_features[:2], 'sc1', 'group')
assert_equal(abs(retval[0].values).sum(), 3)
assert_equal(retval[1].isnull().values.sum(), 3)
def test_correlation_helper(self):
# test that there are no nans for data frame with 10 values
retval = Analyzer.correlation_helper(self.df_features, 'sc1', 'group')
assert_equal(retval[0].isnull().values.sum(), 0)
assert_equal(retval[1].isnull().values.sum(), 0)
def test_correlation_helper_for_data_with_groups(self):
retval = Analyzer.correlation_helper(self.df_features_with_groups, 'sc1', 'group')
assert_equal(len(retval[0]), 2)
assert_equal(len(retval[1]), 2)
def test_that_correlation_helper_works_for_data_with_three_rows(self):
# this should compute marginal correlations but return Nans for
# partial correlations
retval = Analyzer.correlation_helper(self.df_features[:3], 'sc1', 'group')
assert_equal(retval[0].isnull().values.sum(), 0)
assert_equal(retval[1].isnull().values.sum(), 3)
def test_correlation_helper_for_groups_and_length(self):
retval = Analyzer.correlation_helper(self.df_features_with_groups_and_length,
'sc1', 'group', include_length=True)
for df in retval:
assert_equal(len(df), 2)
assert_equal(len(df.columns), 3)
def test_that_correlation_helper_works_for_data_with_four_rows(self):
# this should compute marginal correlations and return a unity
# matrix for partial correlations
retval = Analyzer.correlation_helper(self.df_features[:4], 'sc1', 'group')
assert_equal(retval[0].isnull().values.sum(), 0)
assert_almost_equal(abs(retval[1].values).sum(), 3)