def test_binning_nominal(self):
X = [[1], [2], [3], [4], [5], [6], [7], [8], [9], [10]]
y = [1, 2, 3]
data_info = ['numeric']
dataset = Dataset(X, y, data_info)
expected_X = [[1, 0], [2, 1], [3, 2], [4, 3], [5, 4], [6, 5], [7, 6],
[8, 7], [9, 8], [10, 9]]
transformation = t.TransformationBinningNominal()
d, tc = transformation.transform(dataset)
np.testing.assert_equal(d.data_info, ['numeric'] + ['nominal'])
np.testing.assert_equal(d.X, expected_X)
expected_X = [[10, 9], [10, 9], [3, 2], [3, 2], [4, 3], [4, 3], [5, 4],
[5, 4], [6, 5], [6, 5]]
X = [[10], [10], [3], [3], [4], [4], [5], [5], [6], [6]]
y = [1, 2, 3]
data_info = ['numeric']
dataset = Dataset(X, y, data_info)
d, _ = tc.transform(dataset)
np.testing.assert_equal(d.data_info, ['numeric'] + ['nominal'])
np.testing.assert_equal(d.X, expected_X)
self.assertNotEqual(transformation, tc)
self.assertTrue(transformation.binner is None)
self.assertTrue(tc.binner is not None)
def test_ohe(self):
X = [[1, 0.5, 'red'], [1, 0.5, 'blue'], [1, 1, 'red'],
['?', 0.5, 'red'], [2, 0.5, 'red']]
y = [1, 2, 1, 1, 2]
data_info = ['numeric', 'numeric', 'nominal']
dataset = Dataset(X, y, data_info, '?', '%Y-%m-%d', True)
transformation = t.TransformationOneHotEncoder()
d, tc = transformation.transform(dataset)
expected_X = [[1, 0.5, 1, 0], [1, 0.5, 0, 1], [1, 1, 1, 0],
[0, 0.5, 1, 0], [2, 0.5, 1, 0]]
self.assertEqual(d.data_info,
['numeric', 'numeric', 'nominal', 'boolean'])
np.testing.assert_equal(d.X, expected_X)
X = [[1, 0.5, 'red'], [1, 0.5, 'red'], [1, 1, 'blue'],
['?', 0.5, 'red'], [2, 0.5, 'red']]
y = [1, 2, 1, 1, 2]
data_info = ['numeric', 'numeric', 'nominal']
dataset = Dataset(X, y, data_info, '?', '%Y-%m-%d', True)
d, _ = tc.transform(dataset)
expected_X = [[1, 0.5, 1, 0], [1, 0.5, 1, 0], [1, 1, 0, 1],
[0, 0.5, 1, 0], [2, 0.5, 1, 0]]
self.assertEqual(d.data_info,
['numeric', 'numeric', 'nominal', 'boolean'])
np.testing.assert_equal(d.X, expected_X)
self.assertNotEqual(transformation, tc)
self.assertTrue(transformation.encoder is None)
self.assertTrue(tc.encoder is not None)
def test_binning_mean(self):
X = [[1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12],
[13], [14], [15], [16], [17], [18], [19], [20]]
y = [1, 2, 3]
data_info = ['numeric']
dataset = Dataset(X, y, data_info)
expected_X = [[1, 1.95], [2, 1.95], [3, 3.85], [4, 3.85], [5, 5.75],
[6, 5.75], [7, 7.65], [8, 7.65], [9, 9.55], [10, 9.55],
[11, 11.45], [12, 11.45], [13, 13.35], [14, 13.35],
[15, 15.25], [16, 15.25], [17, 17.15], [18, 17.15],
[19, 19.05], [20, 19.05]]
transformation = t.TransformationBinningMean()
d, tc = transformation.transform(dataset)
np.testing.assert_equal(d.data_info, ['numeric'] + ['numeric'])
np.testing.assert_allclose(d.X, expected_X)
X = [[5], [4], [3], [2], [1]]
y = [1, 2, 3]
data_info = ['numeric']
dataset = Dataset(X, y, data_info)
expected_X = [[5, 5.75], [4, 3.85], [3, 3.85], [2, 1.95], [1, 1.95]]
d, _ = tc.transform(dataset)
np.testing.assert_equal(d.data_info, ['numeric'] + ['numeric'])
np.testing.assert_allclose(d.X, expected_X)
self.assertNotEqual(transformation, tc)
self.assertTrue(transformation.binner is None)
self.assertTrue(tc.binner is not None)
def test_impsim(self):
X = [[1, 1, 'red'], [2, 2, 'blue'], [3, '?', 'red'], [5, 5, '?'],
[5, 5, 'blue'], ['?', 2, 'red']]
y = [1, 2, 1, 1, 2]
data_info = ['numeric', 'numeric', 'nominal']
dataset = Dataset(X, y, data_info, '?', '%Y-%m-%d', True)
transformation = t.TransformationImputeSimple()
d, tc = transformation.transform(dataset)
expected_X = [[1, 1, 1], [2, 2, 0], [3, 2, 1], [5, 5, 1], [5, 5, 0],
[3, 2, 1]]
self.assertEqual(d.data_info, ['numeric', 'numeric', 'nominal'])
self.assertFalse(d.contains_missing_values())
np.testing.assert_equal(d.X, expected_X)
d, _ = tc.transform(dataset)
self.assertEqual(d.data_info, ['numeric', 'numeric', 'nominal'])
self.assertFalse(d.contains_missing_values())
self.assertNotEqual(transformation, tc)
self.assertTrue(transformation.imputer is None)
self.assertTrue(tc.imputer is not None)
def test_select_percentile(self):
X = [[-1, 1, 1, 1, -1], [-3, 1, 2, 1, -3], [1, 2, 3, 1, 1]]
y = [1, 2, 3]
data_info = ['numeric', 'numeric', 'numeric', 'numeric', 'numeric']
dataset = Dataset(X, y, data_info)
transformation = t.TransformationSelectPercentile(25)
d, tc = transformation.transform(dataset)
np.testing.assert_equal(d.data_info, ['numeric'])
self.assertNotEqual(transformation, tc)
d, _ = tc.transform(dataset)
np.testing.assert_equal(d.data_info, ['numeric'])
transformation = t.TransformationSelectPercentile(50)
d, tc = transformation.transform(dataset)
np.testing.assert_equal(d.data_info, ['numeric', 'numeric'])
self.assertNotEqual(transformation, tc)
d, _ = tc.transform(dataset)
np.testing.assert_equal(d.data_info, ['numeric', 'numeric'])
transformation = t.TransformationSelectPercentile(75)
d, tc = transformation.transform(dataset)
np.testing.assert_equal(d.data_info, ['numeric', 'numeric', 'numeric'])
self.assertNotEqual(transformation, tc)
d, _ = tc.transform(dataset)
np.testing.assert_equal(d.data_info, ['numeric', 'numeric', 'numeric'])
def test_chain(self):
X = [[1, 1, 1], [2, 2, 0], [3, 2, 1], [5, 5, 1], [5, 5, 0], [3, 2, 1]]
y = [1, 2, 1, 1, 2, 3]
data_info = ['numeric', 'numeric', 'numeric']
d = Dataset(X, y, data_info)
t1 = t.TransformationMul()
t2 = t.TransformationSelectPercentile(50)
t3 = t.TransformationMinMax()
t4 = t.TransformationSelectPercentile(20)
t5 = t.TransformationBinningNominal()
d1, tc1 = t1.transform(d)
d2, tc2 = t2.transform(d1)
d3, tc3 = t3.transform(d2)
d4, tc4 = t4.transform(d3)
d5, tc5 = t5.transform(d4)
d11, _ = tc1.transform(d)
d22, _ = tc2.transform(d11)
d33, _ = tc3.transform(d22)
d44, _ = tc4.transform(d33)
d55, _ = tc5.transform(d44)
np.testing.assert_equal(d5.X, d55.X)
np.testing.assert_equal(d5.data_info, d55.data_info)
def test_square(self):
X = [[1, 0.5, 123], [1, 0.5, 123], [1, 2, 123], [1, 0.5, 123],
[3, 0.5, 123]]
y = [1, 2, 1, 1, 2]
data_info = ['numeric', 'numeric', 'nominal']
dataset = Dataset(X, y, data_info)
transformation = t.TransformationSquare()
d, tc = transformation.transform(dataset)
expected_X = np.array([[1, 0.5, 123, 1.0, 0.25],
[1.0, 0.5, 123.0, 1.0, 0.25],
[1.0, 2.0, 123.0, 1.0, 4.],
[1.0, 0.5, 123.0, 1.0, 0.25],
[3.0, 0.5, 123.0, 9.0, 0.25]])
np.testing.assert_equal(d.X, expected_X)
np.testing.assert_equal(d.data_info, ['numeric'] * 2 + ['nominal'] +
['numeric'] * 2)
d, _ = tc.transform(dataset)
expected_X = np.array([[1, 0.5, 123, 1.0, 0.25],
[1.0, 0.5, 123.0, 1.0, 0.25],
[1.0, 2.0, 123.0, 1.0, 4.],
[1.0, 0.5, 123.0, 1.0, 0.25],
[3.0, 0.5, 123.0, 9.0, 0.25]])
np.testing.assert_equal(d.X, expected_X)
np.testing.assert_equal(d.data_info, ['numeric'] * 2 + ['nominal'] +
['numeric'] * 2)
self.assertNotEqual(transformation, tc)
def test_get_type_indexes(self):
d = Dataset(self.X, self.y, self.data_info, '?', '%Y-%m-%d', True)
type_indexes = d.get_type_indexes('numeric')
expected_indexes = [0, 1]
np.testing.assert_equal(type_indexes, expected_indexes)
def test_contains_type(self):
d = Dataset(self.X, self.y, self.data_info, '?', '%Y-%m-%d', True)
self.assertEqual(d.contains_type('numeric'), True)
self.assertEqual(d.contains_type('string'), False)
d.remove_type('numeric')
self.assertEqual(d.contains_type('numeric'), False)
def test_copy(self):
d = Dataset(self.X, self.y, self.data_info, '?', '%Y-%m-%d', True)
d_copy = d.copy()
np.testing.assert_equal(d.X, d_copy.X)
np.testing.assert_equal(d.data_info, d_copy.data_info)
np.testing.assert_equal(d.missing_values, d_copy.missing_values)
self.assertEqual(d == d_copy, False)
def transform(self, X, y, missing_value_mark=None):
transformations = self._get_best_transformations()
dataset = Dataset(X,
y,
self.graph.root.dataset.data_info,
missing_value_mark=missing_value_mark,
is_initial=True)
for transformation in transformations:
dataset, _ = transformation.transform(dataset)
return dataset.X
def test_remove_type(self):
d = Dataset(self.X, self.y, self.data_info, '?', '%Y-%m-%d', True)
d.remove_type('nominal')
expected_X = np.array([[1.0, 0.5, 1355266800.0],
[1.0, 0.5,
1355266800.0], [1.0, 0, 1385161200.0],
[1.0, 0.5, 1326236400.0], [0, 0.5,
955749600.0]])
expected_data_info = ['numeric', 'numeric', 'timestamp']
np.testing.assert_equal(d.X, expected_X)
np.testing.assert_equal(d.data_info, expected_data_info)
def test_init(self):
d = Dataset(self.X, self.y, self.data_info, '?', '%Y-%m-%d', True)
expected_X = np.array([[1.0, 0.5, 1.0, 1355266800.0],
[1.0, 0.5, 0.0, 1355266800.0],
[1.0, 0, 1.0, 1385161200.0],
[1.0, 0.5, 0, 1326236400.0],
[0, 0.5, 1.0, 955749600.0]])
expected_data_info = ['numeric', 'numeric', 'nominal', 'timestamp']
np.testing.assert_equal(d.X, expected_X)
self.assertEqual(d.data_info, expected_data_info)
self.assertEqual(d.X.dtype, 'float64')
def test_minmax(self):
X = [[-1, 0, 1, 5, 0, -3], [-3, 1, 2, 5, 0, 0], [1, 3, 2, 5, 0, 3]]
y = [1, 2, 3]
data_info = [
'numeric', 'nominal', 'nominal', 'numeric', 'numeric', 'numeric'
]
dataset = Dataset(X, y, data_info)
transformation = t.TransformationMinMax()
d, tc = transformation.transform(dataset)
expected_X = np.array([[-1, 0, 1, 5, 0, -3, 0.5, 0],
[-3, 1, 2, 5, 0, 0, 0, 0.5],
[1, 3, 2, 5, 0, 3, 1, 1]])
np.testing.assert_equal(d.X, expected_X)
np.testing.assert_equal(
d.data_info,
['numeric', 'nominal', 'nominal', 'numeric', 'numeric', 'numeric'
] + ['numeric'] * 2)
X = [[-1, 0, 1, 5, 0, -1.5], [-3, 1, 2, 5, 0, 0], [1, 3, 2, 5, 0, 1.5]]
y = [1, 2, 3]
data_info = [
'numeric', 'nominal', 'nominal', 'numeric', 'numeric', 'numeric'
]
dataset = Dataset(X, y, data_info)
d, _ = tc.transform(dataset)
expected_X = np.array([[-1, 0, 1, 5, 0, -1.5, 0.5, 0.25],
[-3, 1, 2, 5, 0, 0, 0, 0.5],
[1, 3, 2, 5, 0, 1.5, 1, 0.75]])
np.testing.assert_equal(d.X, expected_X)
np.testing.assert_equal(
d.data_info,
['numeric', 'nominal', 'nominal', 'numeric', 'numeric', 'numeric'
] + ['numeric'] * 2)
self.assertNotEqual(transformation, tc)
self.assertTrue(transformation.scaler is None)
self.assertTrue(tc.scaler is not None)
def test_remove_column(self):
X = [[1, 0.5, 'red', '2012-12-12'], [1, 0.5, 'blue', '2012-12-12'],
[1, '?', 'red', '?'], [1, 0.5, '?', '2012-01-11'],
['?', 0.5, 'red', '2000-04-15']]
d = Dataset(X, self.y, self.data_info, '?', '%Y-%m-%d', True)
d.remove_columns([0, 2])
expected_X = [[0.5, 1355266800.0], [0.5, 1355266800.0], [0, 0],
[0.5, 1326236400.0], [0.5, 955749600.0]]
expected_data_info = ['numeric', 'timestamp']
expected_missing_values = (np.array([2, 2]), np.array([0, 1]))
np.testing.assert_equal(d.X, expected_X)
np.testing.assert_equal(d.data_info, expected_data_info)
np.testing.assert_equal(d.missing_values, expected_missing_values)
def test_mul(self):
X = [[-1, 0], [-3, 1], [1, 3]]
y = [1, 2, 3]
data_info = ['numeric', 'numeric']
dataset = Dataset(X, y, data_info)
transformation = t.TransformationMul()
d, tc = transformation.transform(dataset)
expected_X = np.array([[-1, 0, 0], [-3, 1, -3], [1, 3, 3]])
np.testing.assert_equal(d.X, expected_X)
np.testing.assert_equal(d.data_info, ['numeric'] * 3)
d, _ = transformation.transform(dataset)
expected_X = np.array([[-1, 0, 0], [-3, 1, -3], [1, 3, 3]])
np.testing.assert_equal(d.X, expected_X)
np.testing.assert_equal(d.data_info, ['numeric'] * 3)
self.assertNotEqual(transformation, tc)
def test_filter_data(self):
d = Dataset(self.X, self.y, self.data_info, '?', '%Y-%m-%d', True)
actual = d.filter_data(['numeric', 'timestamp'])
expected = np.array([[1.0, 0.5, 1355266800.0],
[1.0, 0.5, 1355266800.0], [1.0, 0, 1385161200.0],
[1.0, 0.5, 1326236400.0], [0, 0.5, 955749600.0]])
expected_X = np.array([[1.0, 0.5, 1.0, 1355266800.0],
[1.0, 0.5, 0.0, 1355266800.0],
[1.0, 0, 1.0, 1385161200.0],
[1.0, 0.5, 0, 1326236400.0],
[0, 0.5, 1.0, 955749600.0]])
expected_data_info = ['numeric', 'numeric', 'nominal', 'timestamp']
np.testing.assert_equal(actual, expected)
np.testing.assert_equal(d.X, expected_X)
np.testing.assert_equal(d.data_info, expected_data_info)
def _transform(self, dataset):
if dataset.contains_missing_values() == False:
return dataset
dataset.readd_missing_values()
if self.is_copied_instance == False or self.imputer is None:
self.imputer = self._get_imputer()
if isinstance(self.imputer, TransformationImputeSimple.Imputer):
self.imputer.fit(dataset)
else:
self.imputer.fit(dataset.X)
X = self.imputer.transform(dataset.X)
new_dataset = Dataset(X, dataset.y, dataset.data_info,
dataset.missing_value_mark)
new_dataset.mark_as_imputed()
return new_dataset
def test_extend(self):
d = Dataset(self.X, self.y, self.data_info, '?', '%Y-%m-%d', True)
d.extend(np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]),
['numeric', 'numeric', 'aaa'])
expected_X = np.array([[1.0, 0.5, 1.0, 1355266800.0, 1, 2],
[1.0, 0.5, 0.0, 1355266800.0, 3, 4],
[1.0, 0, 1.0, 1385161200.0, 5, 6],
[1.0, 0.5, 0, 1326236400.0, 7, 8],
[0, 0.5, 1.0, 955749600.0, 9, 10]])
expected_data_info = [
'numeric', 'numeric', 'nominal', 'timestamp', 'numeric', 'numeric',
'aaa'
]
np.testing.assert_equal(d.X, expected_X)
self.assertEqual(d.data_info, expected_data_info)
self.assertEqual(d.X.dtype, 'float64')
self.assertEqual(d.contains_type('aaa'), True)
def test_sqrt(self):
X = [[-1, 0, 1], [-3, 1, 2]]
y = [1, 2]
data_info = ['numeric', 'numeric', 'numeric']
dataset = Dataset(X, y, data_info)
transformation = t.TransformationSqrt()
d, tc = transformation.transform(dataset)
expected_X = np.array([[-1, 0, 1, sqrt(2), sqrt(1)],
[-3, 1, 2, sqrt(0), sqrt(2)]])
np.testing.assert_equal(d.X, expected_X)
np.testing.assert_equal(d.data_info, ['numeric'] * 5)
d, _ = tc.transform(dataset)
expected_X = np.array([[-1, 0, 1, sqrt(2), sqrt(1)],
[-3, 1, 2, sqrt(0), sqrt(2)]])
np.testing.assert_equal(d.X, expected_X)
np.testing.assert_equal(d.data_info, ['numeric'] * 5)
self.assertNotEqual(transformation, tc)
def test_aggregation(self):
X = [[0, -1], [-3, 1], [1, 3]]
y = [1, 2, 3]
data_info = ['numeric', 'numeric']
dataset = Dataset(X, y, data_info)
transformation = t.TransformationAggregation()
d, tc = transformation.transform(dataset)
expected_X = np.array([[0, -1, -1, 0, -0.5, 0.5],
[-3, 1, -3, 1, -1, 2], [1, 3, 1, 3, 2, 1]])
np.testing.assert_equal(d.X, expected_X)
np.testing.assert_equal(d.data_info, ['numeric'] * 6)
d, _ = transformation.transform(dataset)
expected_X = np.array([[0, -1, -1, 0, -0.5, 0.5],
[-3, 1, -3, 1, -1, 2], [1, 3, 1, 3, 2, 1]])
np.testing.assert_equal(d.X, expected_X)
np.testing.assert_equal(d.data_info, ['numeric'] * 6)
self.assertNotEqual(transformation, tc)
def test_zscore(self):
X = [[-1, 0, 1, 5, 0, -3], [-3, 1, 2, 5, 0, 0], [1, 3, 2, 5, 0, 3]]
y = [1, 2, 3]
data_info = [
'numeric', 'nominal', 'nominal', 'numeric', 'numeric', 'numeric'
]
dataset = Dataset(X, y, data_info)
transformation = t.TransformationMinMax()
d, tc = transformation.transform(dataset)
self.assertFalse(d.contains_missing_values())
np.testing.assert_equal(
d.data_info, ['numeric', 'nominal', 'nominal'] + ['numeric'] * 5)
d, _ = tc.transform(dataset)
self.assertFalse(d.contains_missing_values())
np.testing.assert_equal(
d.data_info, ['numeric', 'nominal', 'nominal'] + ['numeric'] * 5)
self.assertNotEqual(transformation, tc)
self.assertTrue(transformation.scaler is None)
self.assertTrue(tc.scaler is not None)
def test_init_2(self):
X = [[1, 0.5, 'red', '2012-12-12', -1],
[1, 0.5, 'blue', '2012-12-12', 1],
[1, '?', 'red', '2013-11-23', -1], [1, 0.5, '?', '2012-01-11', 1],
['?', 0.5, 'red', '2000-04-15', '?']]
data_info = ['numeric', 'numeric', 'nominal', 'date', 'nominal']
d = Dataset(X, self.y, data_info, '?', '%Y-%m-%d', True)
expected_X = np.array([[1.0, 0.5, 1.0, 1355266800.0, 0],
[1.0, 0.5, 0.0, 1355266800.0, 1],
[1.0, 0, 1.0, 1385161200.0, 0],
[1.0, 0.5, 0, 1326236400.0, 1],
[0, 0.5, 1.0, 955749600.0, 0]])
expected_data_info = [
'numeric', 'numeric', 'nominal', 'timestamp', 'nominal'
]
expected_missing_values = (np.array([2, 3, 4,
4]), np.array([1, 2, 0, 4]))
np.testing.assert_equal(d.X, expected_X)
np.testing.assert_equal(d.missing_values, expected_missing_values)
self.assertEqual(d.data_info, expected_data_info)
self.assertEqual(d.X.dtype, 'float64')
def fit(self, X, y, data_info=None, missing_value_mark=None):
if type(self.max_iter) is not list:
self.max_iter = [self.max_iter]
for i in range(0, len(self.max_iter)):
print("Max iter: " + str(self.max_iter[i]))
if data_info == None:
data_info = X.shape[1] * ['numeric']
dataset = Dataset(X.copy(),
y.copy(),
data_info.copy(),
missing_value_mark=missing_value_mark,
is_initial=True)
self.graph = TransformationGraph(dataset,
self.transformation_factory,
self.estimator, self.cv,
self.max_iter[i], self.h_max,
self.w, self.random_state,
self.scoring)
self._fit(self.max_iter[i])
return self.w
def test_iteimp(self):
X = [[1, 0.5, 'red', '2012-12-12'], [1, 0.5, 'blue', '2012-12-12'],
[1, '?', 'red', '2013-11-23'], [1, 0.5, '?', '2012-01-11'],
['?', 0.5, 'red', '2000-04-15']]
y = [1, 2, 1, 1, 2]
data_info = ['numeric', 'numeric', 'nominal', 'date']
dataset = Dataset(X, y, data_info, '?', '%Y-%m-%d', True)
transformation = t.TransformationImputeIterative()
d, tc = transformation.transform(dataset)
self.assertEqual(d.data_info,
['numeric', 'numeric', 'nominal', 'timestamp'])
self.assertFalse(d.contains_missing_values())
d, _ = tc.transform(dataset)
self.assertEqual(d.data_info,
['numeric', 'numeric', 'nominal', 'timestamp'])
self.assertFalse(d.contains_missing_values())
self.assertNotEqual(transformation, tc)
self.assertTrue(transformation.imputer is None)
self.assertTrue(tc.imputer is not None)
def test_contains_missing_value(self):
d = Dataset(self.X, self.y, self.data_info, '?', '%Y-%m-%d', True)
self.assertEqual(d.contains_missing_values(), True)
d.mark_as_imputed()
self.assertEqual(d.contains_missing_values(), False)