def test_transform_series_int(self):
arr = np.array([1, 2, 3, 1, 2, 3, 1, 2, 3])
s = pdml.ModelSeries(arr, index='a b c d e f g h i'.split(' '))
# reshape arr to 2d
arr = arr.reshape(-1, 1)
if pd.compat.PY3:
models = ['Binarizer', 'Imputer', 'StandardScaler']
# MinMaxScalar raises TypeError in ufunc
else:
models = ['Binarizer', 'Imputer', 'StandardScaler', 'MinMaxScaler']
for model in models:
mod1 = getattr(s.preprocessing, model)()
mod2 = getattr(pp, model)()
s.fit(mod1)
mod2.fit(arr)
result = s.transform(mod1)
expected = mod2.transform(arr).flatten()
self.assertIsInstance(result, pdml.ModelSeries)
self.assert_numpy_array_almost_equal(result.values, expected)
mod1 = getattr(s.preprocessing, model)()
mod2 = getattr(pp, model)()
result = s.fit_transform(mod1)
expected = mod2.fit_transform(arr).flatten()
self.assertIsInstance(result, pdml.ModelSeries)
self.assert_numpy_array_almost_equal(result.values, expected)
def test_LabelBinarizer2(self):
arr = np.array(['X', 'Y', 'Z', 'X'])
s = pdml.ModelSeries(arr)
lb = s.preprocessing.LabelBinarizer()
s.fit(lb)
binarized = s.transform(lb)
self.assertTrue(isinstance(binarized, pdml.ModelFrame))
expected = pd.DataFrame({0: [1, 0, 0, 1], 1: [0, 1, 0, 0], 2: [0, 0, 1, 0]})
self.assert_frame_equal(binarized, expected)
df = pdml.ModelFrame(datasets.load_iris())
df.target.fit(lb)
binarized = df.target.transform(lb)
expected = pd.DataFrame({0: [1] * 50 + [0] * 100,
1: [0] * 50 + [1] * 50 + [0] * 50,
2: [0] * 100 + [1] * 50})
self.assert_frame_equal(binarized, expected)
df = pdml.ModelFrame(datasets.load_iris())
df.target.fit(lb)
df.target = df.target.transform(lb)
self.assertEqual(df.shape, (150, 7))
self.assert_frame_equal(df.target, expected)
def test_transform_series_float(self):
arr = np.array([1, 2, 3, 1, 2, 3, 1, 2, 3], dtype=np.float)
s = pdml.ModelSeries(arr, index='a b c d e f g h i'.split(' '))
models = ['Binarizer', 'Imputer', 'StandardScaler', 'MinMaxScaler']
for model in models:
mod1 = getattr(s.preprocessing, model)()
mod2 = getattr(pp, model)()
s.fit(mod1)
mod2.fit(arr)
result = s.transform(mod1)
expected = mod2.transform(arr).flatten()
self.assertTrue(isinstance(result, pdml.ModelSeries))
self.assert_numpy_array_almost_equal(result.values, expected)
mod1 = getattr(s.preprocessing, model)()
mod2 = getattr(pp, model)()
result = s.fit_transform(mod1)
expected = mod2.fit_transform(arr).flatten()
self.assertTrue(isinstance(result, pdml.ModelSeries))
self.assert_numpy_array_almost_equal(result.values, expected)
def test_transform_series_float(self, algo):
arr = np.array([1, 2, 3, 1, 2, 3, 1, 2, 3], dtype=np.float)
s = pdml.ModelSeries(arr, index='a b c d e f g h i'.split(' '))
# reshape arr to 2d
arr = arr.reshape(-1, 1)
mod1 = getattr(s.preprocessing, algo)()
mod2 = getattr(pp, algo)()
s.fit(mod1)
mod2.fit(arr)
result = s.transform(mod1)
expected = mod2.transform(arr).flatten()
self.assertIsInstance(result, pdml.ModelSeries)
self.assert_numpy_array_almost_equal(result.values, expected)
mod1 = getattr(s.preprocessing, algo)()
mod2 = getattr(pp, algo)()
result = s.fit_transform(mod1)
expected = mod2.fit_transform(arr).flatten()
self.assertIsInstance(result, pdml.ModelSeries)
self.assert_numpy_array_almost_equal(result.values, expected)
def test_series_instance(self):
s = pdml.ModelSeries([1, 2, 3, 4, 5], index=['A', 'B', 'C', 'D', 'E'])
self.assertTrue(isinstance(s, pdml.ModelSeries))
s = s[['A', 'B']]
self.assertEqual(len(s), 2)
self.assertTrue(isinstance(s, pdml.ModelSeries))
def test_preprocessing_normalize(self):
s = pdml.ModelSeries([1, 2, 3, 4, 5], index=['A', 'B', 'C', 'D', 'E'])
self.assertTrue(isinstance(s, pdml.ModelSeries))
result = s.preprocessing.normalize()
expected = pp.normalize(np.atleast_2d(s.values.astype(np.float)))[0, :]
self.assertTrue(isinstance(result, pdml.ModelSeries))
self.assert_numpy_array_almost_equal(result.values, expected)
self.assert_index_equal(result.index, s.index)
def test_series_groupby(self):
s = pdml.ModelSeries([1, 2, 1, 2], name='X')
self.assertIsInstance(s, pdml.ModelSeries)
grouped = s.groupby([1, 1, 1, 2])
self.assertIsInstance(grouped, pdml.core.groupby.ModelSeriesGroupBy)
gs = grouped.get_group(1)
self.assertIsInstance(gs, pdml.ModelSeries)
expected = pd.Series([1, 2, 1], index=[0, 1, 2], name='X')
tm.assert_series_equal(gs, expected)
self.assertEqual(gs.name, 'X')
def test_series_to_frame(self):
s = pdml.ModelSeries([1, 2, 3, 4, 5])
self.assertTrue(isinstance(s, pdml.ModelSeries))
df = s.to_frame()
self.assertTrue(isinstance(df, pdml.ModelFrame))
self.assert_index_equal(df.columns, pd.Index([0]))
df = s.to_frame(name='x')
self.assertTrue(isinstance(df, pdml.ModelFrame))
self.assert_index_equal(df.columns, pd.Index(['x']))
s = pdml.ModelSeries([1, 2, 3, 4, 5], name='name')
self.assertTrue(isinstance(s, pdml.ModelSeries))
df = s.to_frame()
self.assertTrue(isinstance(df, pdml.ModelFrame))
self.assert_index_equal(df.columns, pd.Index(['name']))
df = s.to_frame(name='x')
self.assertTrue(isinstance(df, pdml.ModelFrame))
self.assert_index_equal(df.columns, pd.Index(['x']))
def test_Imputer(self):
arr = np.array([1, np.nan, 3, 2])
s = pdml.ModelSeries(arr)
mod1 = s.pp.Imputer(axis=1)
s.fit(mod1)
result = s.transform(mod1)
expected = np.array([1, 2, 3, 2])
self.assertTrue(isinstance(result, pdml.ModelSeries))
self.assert_numpy_array_almost_equal(result.values, expected)
mod1 = s.pp.Imputer(axis=1)
result = s.fit_transform(mod1)
self.assertTrue(isinstance(result, pdml.ModelSeries))
self.assert_numpy_array_almost_equal(result.values, expected)
def test_LabelEncoder_series(self):
arr = np.array(['X', 'Y', 'Z', 'X'])
s = pdml.ModelSeries(arr, index=['a', 'b', 'c', 'd'])
mod1 = s.pp.LabelEncoder()
s.fit(mod1)
result = s.transform(mod1)
expected = np.array([0, 1, 2, 0])
self.assertIsInstance(result, pdml.ModelSeries)
self.assert_numpy_array_almost_equal(result.values, expected)
tm.assert_index_equal(result.index, s.index)
mod1 = s.pp.LabelEncoder()
result = s.fit_transform(mod1)
self.assertIsInstance(result, pdml.ModelSeries)
self.assert_numpy_array_almost_equal(result.values, expected)
inversed = result.inverse_transform(mod1)
self.assertIsInstance(inversed, pdml.ModelSeries)
tm.assert_series_equal(inversed, s)
def test_LabelEncoder(self):
arr = np.array(['X', 'Y', 'Z', 'X'])
s = pdml.ModelSeries(arr, index=['a', 'b', 'c', 'd'])
mod1 = s.pp.LabelEncoder()
s.fit(mod1)
result = s.transform(mod1)
expected = np.array([0, 1, 2, 0])
self.assertTrue(isinstance(result, pdml.ModelSeries))
self.assert_numpy_array_almost_equal(result.values, expected)
self.assert_index_equal(result.index, pd.Index(['a', 'b', 'c', 'd']))
mod1 = s.pp.LabelEncoder()
result = s.fit_transform(mod1)
self.assertTrue(isinstance(result, pdml.ModelSeries))
self.assert_numpy_array_almost_equal(result.values, expected)
inversed = result.inverse_transform(mod1)
self.assertTrue(isinstance(inversed, pdml.ModelSeries))
self.assert_numpy_array_equal(inversed.values.flatten(), arr)
self.assert_index_equal(result.index, pd.Index(['a', 'b', 'c', 'd']))
def test_LabelBinarizer(self):
arr = np.array([1, 2, 3, 2])
s = pdml.ModelSeries(arr, index=['a', 'b', 'c', 'd'])
mod1 = s.pp.LabelBinarizer()
s.fit(mod1)
result = s.transform(mod1)
expected = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 1, 0]])
self.assertIsInstance(result, pdml.ModelFrame)
self.assert_numpy_array_almost_equal(result.values, expected)
tm.assert_index_equal(result.index, s.index)
mod1 = s.pp.LabelBinarizer()
result = s.fit_transform(mod1)
self.assertIsInstance(result, pdml.ModelFrame)
self.assert_numpy_array_almost_equal(result.values, expected)
inversed = result.inverse_transform(mod1)
self.assertIsInstance(inversed, pdml.ModelFrame)
self.assert_numpy_array_almost_equal(inversed.values.flatten(), arr)
tm.assert_index_equal(result.index, s.index)
