def test_fit_float(self):
domain = ContinuousDomain(with_data = False, with_statistics = False)
self.assertTrue(domain._empty_fit())
domain = ContinuousDomain(missing_values = float("NaN"), missing_value_treatment = "as_value", missing_value_replacement = -1.0, invalid_value_treatment = "as_is", invalid_value_replacement = 0.0)
self.assertTrue(numpy.isnan(domain.missing_values))
self.assertEqual("as_value", domain.missing_value_treatment)
self.assertEqual(-1.0, domain.missing_value_replacement)
self.assertEqual("as_is", domain.invalid_value_treatment)
self.assertEqual(0.0, domain.invalid_value_replacement)
self.assertFalse(hasattr(domain, "data_min_"))
self.assertFalse(hasattr(domain, "data_max_"))
self.assertFalse(hasattr(domain, "counts_"))
self.assertFalse(hasattr(domain, "numeric_info_"))
self.assertFalse(domain._empty_fit())
X = DataFrame(numpy.array([1.0, float("NaN"), 3.0, 2.0, float("NaN"), 2.0]))
Xt = domain.fit_transform(X)
self.assertIsInstance(Xt, DataFrame)
self.assertEqual(1.0, domain.data_min_)
self.assertEqual(3.0, domain.data_max_)
self.assertEqual({"totalFreq" : 6, "missingFreq" : 2, "invalidFreq" : 0}, domain.counts_)
self.assertEqual({"minimum" : [1.0], "maximum" : [3.0], "mean" : [2.0], "standardDeviation" : [0.7071067811865476], "median" : [2.0], "interQuartileRange" : [0.5]}, _array_to_list(domain.numeric_info_))
self.assertEqual([1.0, -1.0, 3.0, 2.0, -1.0, 2.0], Xt[0].tolist())
X = numpy.array([float("NaN"), None])
Xt = domain.transform(X)
self.assertEqual([-1.0, -1.0], Xt.tolist())
def test_fit_float_missing(self):
domain = ContinuousDomain(missing_values=-1.0,
missing_value_replacement=4.0)
domain = clone(domain)
self.assertEqual(-1.0, domain.missing_values)
self.assertEqual(4.0, domain.missing_value_replacement)
self.assertFalse(domain._empty_fit())
X = DataFrame([1.0, -1.0, 3.0, 2.0, -1.0, 2.0])
Xt = domain.fit_transform(X)
self.assertIsInstance(Xt, DataFrame)
self.assertEqual(1.0, domain.data_min_)
self.assertEqual(3.0, domain.data_max_)
self.assertEqual({
"totalFreq": 6,
"missingFreq": 2,
"invalidFreq": 0
}, domain.counts_)
self.assertEqual(
{
"minimum": [1.0],
"maximum": [3.0],
"mean": [2.0],
"standardDeviation": [0.7071067811865476],
"median": [2.0],
"interQuartileRange": [0.5]
}, _array_to_list(domain.numeric_info_))
self.assertEqual([1.0, 4.0, 3.0, 2.0, 4.0, 2.0], Xt[0].tolist())
X = numpy.array([-1.0, -1.0])
Xt = domain.transform(X)
self.assertEqual([4.0, 4.0], Xt.tolist())
def test_fit_float(self):
domain = ContinuousDomain(missing_value_treatment="as_value",
missing_value_replacement=-1.0,
invalid_value_treatment="as_is")
self.assertEqual("as_value", domain.missing_value_treatment)
self.assertEqual(-1.0, domain.missing_value_replacement)
self.assertEqual("as_is", domain.invalid_value_treatment)
self.assertFalse(hasattr(domain, "data_min_"))
self.assertFalse(hasattr(domain, "data_max_"))
X = DataFrame(
numpy.array([1.0, float('NaN'), 3.0, 2.0,
float('NaN'), 2.0]))
Xt = domain.fit_transform(X)
self.assertEqual(1.0, domain.data_min_)
self.assertEqual(3.0, domain.data_max_)
self.assertEqual(
numpy.array([1.0, -1.0, 3.0, 2.0, -1.0, 2.0]).tolist(),
Xt[0].tolist())
X = numpy.array([float('NaN'), None])
Xt = domain.transform(X)
self.assertEqual(numpy.array([-1.0, -1.0]).tolist(), Xt.tolist())