def test_nan(self):
var = DiscreteVariable("d", tuple("abcde"))
col = np.array([1.0, 4, 2, np.nan, 2, 0])
transform = Indicator(var, 2).transform
expected = [0, 0, 1, np.nan, 1, 0]
np.testing.assert_equal(transform(col), expected)
sparse = transform(sp.csr_matrix(col))
self.assertTrue(sp.issparse(sparse))
np.testing.assert_equal(sparse.toarray().ravel(), expected)
self.assertEqual(transform(1), 0)
self.assertEqual(transform(2), 1)
self.assertTrue(np.isnan(transform(np.nan)))
transform = Indicator(var, 0).transform
expected = [0, 0, 0, np.nan, 0, 1]
np.testing.assert_equal(transform(col), expected)
sparse = transform(sp.csr_matrix(col))
# Currently, this always returns dense array
assert not sp.issparse(sparse)
np.testing.assert_equal(sparse, expected)
self.assertEqual(transform(1), 0)
self.assertEqual(transform(0), 1)
self.assertTrue(np.isnan(transform(np.nan)))
transform = Indicator1(var, 2).transform
expected = [-1, -1, 1, np.nan, 1, -1]
np.testing.assert_equal(transform(col), expected)
np.testing.assert_equal(
transform(sp.csr_matrix(col).toarray().ravel()), expected)
self.assertEqual(transform(1), -1)
self.assertEqual(transform(2), 1)
self.assertTrue(np.isnan(transform(np.nan)))
def make_indicator_var(source, value_ind, weight=None, zero_based=True):
if zero_based and weight is None:
indicator = Indicator(source, value=value_ind)
elif zero_based:
indicator = WeightedIndicator(source, value=value_ind, weight=weight)
elif weight is None:
indicator = Indicator1(source, value=value_ind)
else:
indicator = WeightedIndicator_1(source, value=value_ind, weight=weight)
return Orange.data.ContinuousVariable("{}={}".format(
source.name, source.values[value_ind]),
compute_value=indicator)