def test_StratifiedShuffleSplit(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
sf1 = df.model_selection.StratifiedShuffleSplit(random_state=self.random_state)
sf2 = ms.StratifiedShuffleSplit(random_state=self.random_state)
# consume generator
ind1 = [x for x in sf1.split(df.data.values, df.target.values)]
ind2 = [x for x in sf2.split(iris.data, iris.target)]
for i1, i2 in zip(ind1, ind2):
self.assertIsInstance(i1, tuple)
self.assertEqual(len(i1), 2)
self.assertIsInstance(i2, tuple)
self.assertEqual(len(i2), 2)
tm.assert_numpy_array_equal(i1[0], i1[0])
tm.assert_numpy_array_equal(i1[1], i2[1])
sf1 = df.model_selection.StratifiedShuffleSplit(random_state=self.random_state)
with tm.assert_produces_warning(FutureWarning):
gen = df.model_selection.iterate(sf1)
# StratifiedShuffleSplit is not a subclass of BaseCrossValidator
for train_df, test_df in gen:
self.assertIsInstance(train_df, pdml.ModelFrame)
self.assertIsInstance(test_df, pdml.ModelFrame)
tm.assert_index_equal(df.columns, train_df.columns)
tm.assert_index_equal(df.columns, test_df.columns)
self.assertTrue(df.shape[0], train_df.shape[0] + test_df.shape[1])
def test_inverse_transform(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
models = ['PCA']
for model in models:
mod1 = getattr(df.decomposition, model)()
mod2 = getattr(decomposition, model)()
df.fit(mod1)
mod2.fit(iris.data, iris.target)
result = df.transform(mod1)
expected = mod2.transform(iris.data)
self.assertIsInstance(result, pdml.ModelFrame)
tm.assert_series_equal(df.target, result.target)
self.assert_numpy_array_almost_equal(result.data.values, expected)
result = df.inverse_transform(mod1)
expected = mod2.inverse_transform(iris.data)
self.assertIsInstance(result, pdml.ModelFrame)
tm.assert_series_equal(df.target, result.target)
self.assert_numpy_array_almost_equal(result.data.values, expected)
tm.assert_index_equal(result.columns, df.columns)
def test_preprocessing_assignment(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
original_columns = df.data.columns
df['sepal length (cm)'] = df[
'sepal length (cm)'].preprocessing.binarize(threshold=6)
self.assertIsInstance(df, pdml.ModelFrame)
binarized = pp.binarize(np.atleast_2d(iris.data[:, 0]), threshold=6)
expected = np.hstack([binarized.T, iris.data[:, 1:]])
self.assert_numpy_array_almost_equal(df.data.values, expected)
tm.assert_index_equal(df.data.columns, original_columns)
# recreate data
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
target_columns = ['sepal length (cm)', 'sepal width (cm)']
df[target_columns] = df[target_columns].preprocessing.binarize(
threshold=6)
self.assertIsInstance(df, pdml.ModelFrame)
binarized = pp.binarize(iris.data[:, 0:2], threshold=6)
expected = np.hstack([binarized, iris.data[:, 2:]])
self.assert_numpy_array_almost_equal(df.data.values, expected)
tm.assert_index_equal(df.data.columns, original_columns)
def test_binarize(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
result = df.preprocessing.binarize()
expected = pp.binarize(iris.data)
self.assertIsInstance(result, pdml.ModelFrame)
self.assert_numpy_array_almost_equal(result.data.values, expected)
tm.assert_index_equal(result.columns, df.data.columns)
result = df.preprocessing.binarize(threshold=5)
expected = pp.binarize(iris.data, threshold=5)
self.assertIsInstance(result, pdml.ModelFrame)
self.assert_numpy_array_almost_equal(result.data.values, expected)
tm.assert_index_equal(result.columns, df.data.columns)
s = df['sepal length (cm)']
self.assertIsInstance(s, pdml.ModelSeries)
result = s.preprocessing.binarize()
expected = pp.binarize(iris.data[:, 0].reshape(-1, 1))
self.assertIsInstance(result, pdml.ModelSeries)
self.assert_numpy_array_almost_equal(result.values, expected.flatten())
self.assertEqual(result.name, 'sepal length (cm)')
result = s.preprocessing.binarize(threshold=6)
expected = pp.binarize(iris.data[:, 0].reshape(-1, 1), threshold=6)
self.assertIsInstance(result, pdml.ModelSeries)
self.assert_numpy_array_almost_equal(result.values, expected.flatten())
self.assertEqual(result.name, 'sepal length (cm)')
def test_patsy_matrices(self):
df = pd.DataFrame({'A': [1, 2, 3],
'B': [4, 5, 6],
'C': [7, 8, 9]},
index=['a', 'b', 'c'],
columns=['A', 'B', 'C'])
s = pd.Series([10, 11, 12], index=['a', 'b', 'c'])
mdf = pdml.ModelFrame(df, target=s)
result = mdf.transform('A ~ B + C')
self.assertIsInstance(result, pdml.ModelFrame)
self.assertEqual(result.shape, (3, 4))
tm.assert_index_equal(result.index, pd.Index(['a', 'b', 'c']))
tm.assert_index_equal(result.columns, pd.Index(['A', 'Intercept', 'B', 'C']))
expected = pd.DataFrame({'A': [1, 2, 3],
'Intercept': [1, 1, 1],
'B': [4, 5, 6],
'C': [7, 8, 9]},
index=['a', 'b', 'c'],
columns=['A', 'Intercept', 'B', 'C'],
dtype=float)
tm.assert_frame_equal(result, expected)
expected = pd.Series([1, 2, 3], index=['a', 'b', 'c'], name='A', dtype=float)
tm.assert_series_equal(result.target, expected)
self.assertEqual(result.target.name, 'A')
self.assertEqual(result.target_name, 'A')
def test_GaussianProcess_ge_018(self):
X = np.atleast_2d([1., 3., 5., 6., 7., 8.]).T
y = np.sin(X).ravel()
df = pdml.ModelFrame(X, target=y)
k1 = (df.gp.kernels.ConstantKernel(1.0, (1e-3, 1e3)) *
df.gp.kernels.RBF(10, (1e-2, 1e2)))
g1 = df.gp.GaussianProcessRegressor(kernel=k1,
n_restarts_optimizer=9,
random_state=self.random_state)
k2 = (gp.kernels.ConstantKernel(1.0, (1e-3, 1e3)) *
gp.kernels.RBF(10, (1e-2, 1e2)))
g2 = gp.GaussianProcessRegressor(kernel=k2,
n_restarts_optimizer=9,
random_state=self.random_state)
g1.fit(X, y)
g2.fit(X, y)
x = np.atleast_2d(np.linspace(0, 10, 1000)).T
tdf = pdml.ModelFrame(x)
y_result = tdf.predict(g1)
y_expected = g2.predict(x)
self.assertIsInstance(y_result, pdml.ModelSeries)
tm.assert_index_equal(y_result.index, tdf.index)
self.assert_numpy_array_almost_equal(y_result, y_expected)
def test_binarize(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
result = df.preprocessing.binarize()
expected = pp.binarize(iris.data)
self.assertIsInstance(result, pdml.ModelFrame)
self.assert_numpy_array_almost_equal(result.data.values, expected)
tm.assert_index_equal(result.columns, df.data.columns)
result = df.preprocessing.binarize(threshold=5)
expected = pp.binarize(iris.data, threshold=5)
self.assertIsInstance(result, pdml.ModelFrame)
self.assert_numpy_array_almost_equal(result.data.values, expected)
tm.assert_index_equal(result.columns, df.data.columns)
s = df['sepal length (cm)']
self.assertIsInstance(s, pdml.ModelSeries)
result = s.preprocessing.binarize()
expected = pp.binarize(iris.data[:, 0].reshape(-1, 1))
self.assertIsInstance(result, pdml.ModelSeries)
self.assert_numpy_array_almost_equal(result.values, expected.flatten())
self.assertEqual(result.name, 'sepal length (cm)')
result = s.preprocessing.binarize(threshold=6)
expected = pp.binarize(iris.data[:, 0].reshape(-1, 1), threshold=6)
self.assertIsInstance(result, pdml.ModelSeries)
self.assert_numpy_array_almost_equal(result.values, expected.flatten())
self.assertEqual(result.name, 'sepal length (cm)')
def test_add_dummy_feature(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
result = df.preprocessing.add_dummy_feature()
expected = pp.add_dummy_feature(iris.data)
self.assertIsInstance(result, pdml.ModelFrame)
self.assert_numpy_array_almost_equal(result.data.values, expected)
result = df.preprocessing.add_dummy_feature(value=2)
expected = pp.add_dummy_feature(iris.data, value=2)
self.assertIsInstance(result, pdml.ModelFrame)
self.assert_numpy_array_almost_equal(result.data.values, expected)
tm.assert_index_equal(result.columns[1:], df.data.columns)
s = df['sepal length (cm)']
self.assertIsInstance(s, pdml.ModelSeries)
result = s.preprocessing.add_dummy_feature()
expected = pp.add_dummy_feature(iris.data[:, [0]])
self.assertIsInstance(result, pdml.ModelFrame)
self.assert_numpy_array_almost_equal(result.values, expected)
self.assertEqual(result.columns[1], 'sepal length (cm)')
def test_sample_ensemble(self):
from imblearn.ensemble import BalanceCascade, EasyEnsemble
models = [BalanceCascade, EasyEnsemble]
X = np.random.randn(100, 5)
y = np.array([0, 1]).repeat([80, 20])
df = pdml.ModelFrame(X, target=y, columns=list('ABCDE'))
for model in models:
mod1 = model(random_state=self.random_state)
mod2 = model(random_state=self.random_state)
df.fit(mod1)
mod2.fit(X, y)
results = df.fit_resample(mod1)
expected_X, expected_y = mod2.fit_resample(X, y)
self.assertIsInstance(results, list)
for r in results:
self.assertIsInstance(r, pdml.ModelFrame)
tm.assert_index_equal(r.columns, df.columns)
mod1 = model(random_state=self.random_state)
mod2 = model(random_state=self.random_state)
results = df.fit_sample(mod1)
expected_X, expected_y = mod2.fit_sample(X, y)
self.assertIsInstance(results, list)
for r in results:
self.assertIsInstance(r, pdml.ModelFrame)
tm.assert_index_equal(r.columns, df.columns)
def test_CCA_PLSCannonical(self, algo):
n = 500
with tm.RNGContext(1):
# 2 latents vars:
l1 = np.random.normal(size=n)
l2 = np.random.normal(size=n)
latents = np.array([l1, l1, l2, l2]).T
X = latents + np.random.normal(size=4 * n).reshape((n, 4))
Y = latents + np.random.normal(size=4 * n).reshape((n, 4))
X_train = X[:n // 2]
Y_train = Y[:n // 2]
X_test = X[n // 2:]
Y_test = Y[n // 2:]
train = pdml.ModelFrame(X_train, target=Y_train)
test = pdml.ModelFrame(X_test, target=Y_test)
# check multi target columns
self.assertTrue(train.has_target())
tm.assert_numpy_array_equal(train.data.values, X_train)
tm.assert_numpy_array_equal(train.target.values, Y_train)
tm.assert_numpy_array_equal(test.data.values, X_test)
tm.assert_numpy_array_equal(test.target.values, Y_test)
expected = pd.MultiIndex.from_tuples([('.target', 0), ('.target', 1),
('.target', 2), ('.target', 3)])
tm.assert_index_equal(train.target_name, expected)
self.assertEqual(train.data.shape, X_train.shape)
self.assertEqual(train.target.shape, Y_train.shape)
mod1 = getattr(train.cross_decomposition, algo)(n_components=2)
mod2 = getattr(cd, algo)(n_components=2)
train.fit(mod1)
mod2.fit(X_train, Y_train)
# 2nd cols are different on travis-CI
self.assert_numpy_array_almost_equal(mod1.x_weights_[:, 0],
mod2.x_weights_[:, 0])
self.assert_numpy_array_almost_equal(mod1.y_weights_[:, 0],
mod2.y_weights_[:, 0])
result_tr = train.transform(mod1)
result_test = test.transform(mod1)
expected_tr = mod2.transform(X_train, Y_train)
expected_test = mod2.transform(X_test, Y_test)
self.assertIsInstance(result_tr, pdml.ModelFrame)
self.assertIsInstance(result_test, pdml.ModelFrame)
self.assert_numpy_array_almost_equal(result_tr.data.values[:, 0],
expected_tr[0][:, 0])
self.assert_numpy_array_almost_equal(result_tr.target.values[:, 0],
expected_tr[1][:, 0])
self.assert_numpy_array_almost_equal(result_test.data.values[:, 0],
expected_test[0][:, 0])
self.assert_numpy_array_almost_equal(result_test.target.values[:, 0],
expected_test[1][:, 0])
def test_silhouette_samples(self):
result = self.df.metrics.silhouette_samples()
expected = metrics.silhouette_samples(self.data, self.pred)
self.assertTrue(isinstance(result, pdml.ModelSeries))
tm.assert_index_equal(result.index, self.df.index)
self.assert_numpy_array_almost_equal(result.values, expected)
def test_StratifiedShuffleSplit(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
sf1 = df.model_selection.StratifiedShuffleSplit(random_state=self.random_state)
sf2 = ms.StratifiedShuffleSplit(random_state=self.random_state)
# consume generator
ind1 = [x for x in sf1.split(df.data.values, df.target.values)]
ind2 = [x for x in sf2.split(iris.data, iris.target)]
for i1, i2 in zip(ind1, ind2):
self.assertIsInstance(i1, tuple)
self.assertEqual(len(i1), 2)
self.assertIsInstance(i2, tuple)
self.assertEqual(len(i2), 2)
tm.assert_numpy_array_equal(i1[0], i1[0])
tm.assert_numpy_array_equal(i1[1], i2[1])
sf1 = df.model_selection.StratifiedShuffleSplit(random_state=self.random_state)
with tm.assert_produces_warning(FutureWarning):
gen = df.model_selection.iterate(sf1)
# StratifiedShuffleSplit is not a subclass of BaseCrossValidator
for train_df, test_df in gen:
self.assertIsInstance(train_df, pdml.ModelFrame)
self.assertIsInstance(test_df, pdml.ModelFrame)
tm.assert_index_equal(df.columns, train_df.columns)
tm.assert_index_equal(df.columns, test_df.columns)
self.assertEqual(len(df), len(train_df) + len(test_df))
def test_sample_ensemble(self):
from imblearn.ensemble import BalanceCascade, EasyEnsemble
models = [BalanceCascade, EasyEnsemble]
X = np.random.randn(100, 5)
y = np.array([0, 1]).repeat([80, 20])
df = pdml.ModelFrame(X, target=y, columns=list('ABCDE'))
for model in models:
mod1 = model(random_state=self.random_state)
mod2 = model(random_state=self.random_state)
df.fit(mod1)
mod2.fit(X, y)
results = df.sample(mod1)
expected_X, expected_y = mod2.sample(X, y)
self.assertIsInstance(results, list)
for r in results:
self.assertIsInstance(r, pdml.ModelFrame)
tm.assert_index_equal(r.columns, df.columns)
mod1 = model(random_state=self.random_state)
mod2 = model(random_state=self.random_state)
results = df.fit_sample(mod1)
expected_X, expected_y = mod2.fit_sample(X, y)
self.assertIsInstance(results, list)
for r in results:
self.assertIsInstance(r, pdml.ModelFrame)
tm.assert_index_equal(r.columns, df.columns)
def test_add_dummy_feature(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
result = df.preprocessing.add_dummy_feature()
expected = pp.add_dummy_feature(iris.data)
self.assertIsInstance(result, pdml.ModelFrame)
self.assert_numpy_array_almost_equal(result.data.values, expected)
result = df.preprocessing.add_dummy_feature(value=2)
expected = pp.add_dummy_feature(iris.data, value=2)
self.assertIsInstance(result, pdml.ModelFrame)
self.assert_numpy_array_almost_equal(result.data.values, expected)
tm.assert_index_equal(result.columns[1:], df.data.columns)
s = df['sepal length (cm)']
self.assertIsInstance(s, pdml.ModelSeries)
result = s.preprocessing.add_dummy_feature()
expected = pp.add_dummy_feature(iris.data[:, [0]])
self.assertIsInstance(result, pdml.ModelFrame)
self.assert_numpy_array_almost_equal(result.values, expected)
self.assertEqual(result.columns[1], 'sepal length (cm)')
def test_silhouette_samples(self):
result = self.df.metrics.silhouette_samples()
expected = metrics.silhouette_samples(self.data, self.pred)
self.assertTrue(isinstance(result, pdml.ModelSeries))
tm.assert_index_equal(result.index, self.df.index)
self.assert_numpy_array_almost_equal(result.values, expected)
def test_anes96(self):
data = getattr(sm.datasets.anes96, self.load_method)()
df = pdml.ModelFrame(data)
self.assertEqual(df.shape, (944, 6))
self.assertEqual(df.target_name, 'PID')
tm.assert_index_equal(df.data.columns, pd.Index(data.exog_name))
def test_preprocessing_normalize(self):
s = pdml.ModelSeries([1, 2, 3, 4, 5], index=['A', 'B', 'C', 'D', 'E'])
self.assertIsInstance(s, pdml.ModelSeries)
result = s.preprocessing.normalize()
expected = pp.normalize(np.atleast_2d(s.values.astype(np.float)))[0, :]
self.assertIsInstance(result, pdml.ModelSeries)
self.assert_numpy_array_almost_equal(result.values, expected)
tm.assert_index_equal(result.index, s.index)
def test_empirical_covariance(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
result = df.covariance.empirical_covariance()
expected = covariance.empirical_covariance(iris.data)
self.assertIsInstance(result, pdml.ModelFrame)
tm.assert_index_equal(result.index, df.data.columns)
tm.assert_index_equal(result.columns, df.data.columns)
self.assert_numpy_array_almost_equal(result.values, expected)
def test_isotonic_regression(self):
data = np.abs(np.random.randn(100))
data = data.cumsum()
df = pdml.ModelFrame(np.arange(len(data)), target=data)
result = df.isotonic.isotonic_regression()
expected = isotonic.isotonic_regression(data)
self.assertIsInstance(result, pdml.ModelSeries)
tm.assert_index_equal(result.index, df.index)
tm.assert_numpy_array_equal(result.values, expected)
def test_iterate(self):
df = pdml.ModelFrame(datasets.load_iris())
kf = df.cross_validation.KFold(4, n_folds=2, random_state=self.random_state)
for train_df, test_df in df.cross_validation.iterate(kf):
self.assertIsInstance(train_df, pdml.ModelFrame)
self.assertIsInstance(test_df, pdml.ModelFrame)
tm.assert_index_equal(df.columns, train_df.columns)
tm.assert_index_equal(df.columns, test_df.columns)
self.assertTrue(df.shape[0], train_df.shape[0] + test_df.shape[1])
def test_precision_recall_fscore_support(self):
result = self.df.metrics.precision_recall_fscore_support()
expected = metrics.precision_recall_fscore_support(self.target, self.pred)
self.assert_numpy_array_almost_equal(result['precision'].values, expected[0])
self.assert_numpy_array_almost_equal(result['recall'].values, expected[1])
self.assert_numpy_array_almost_equal(result['f1-score'].values, expected[2])
self.assert_numpy_array_almost_equal(result['support'].values, expected[3])
expected = pd.Index(['precision', 'recall', 'f1-score', 'support'])
tm.assert_index_equal(result.columns, expected)
def test_precision_recall_fscore_support(self):
result = self.df.metrics.precision_recall_fscore_support()
expected = metrics.precision_recall_fscore_support(self.target, self.pred)
self.assert_numpy_array_almost_equal(result['precision'].values, expected[0])
self.assert_numpy_array_almost_equal(result['recall'].values, expected[1])
self.assert_numpy_array_almost_equal(result['f1-score'].values, expected[2])
self.assert_numpy_array_almost_equal(result['support'].values, expected[3])
expected = pd.Index(['precision', 'recall', 'f1-score', 'support'])
tm.assert_index_equal(result.columns, expected)
def test_isotonic_regression(self):
data = np.abs(np.random.randn(100))
data = data.cumsum()
df = pdml.ModelFrame(np.arange(len(data)), target=data)
result = df.isotonic.isotonic_regression()
expected = isotonic.isotonic_regression(data)
self.assertIsInstance(result, pdml.ModelSeries)
tm.assert_index_equal(result.index, df.index)
tm.assert_numpy_array_equal(result.values, expected)
def test_empirical_covariance(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
result = df.covariance.empirical_covariance()
expected = covariance.empirical_covariance(iris.data)
self.assertIsInstance(result, pdml.ModelFrame)
tm.assert_index_equal(result.index, df.data.columns)
tm.assert_index_equal(result.columns, df.data.columns)
self.assert_numpy_array_almost_equal(result.values, expected)
def test_iterate(self):
df = pdml.ModelFrame(datasets.load_iris())
kf = df.cross_validation.KFold(4,
n_folds=2,
random_state=self.random_state)
for train_df, test_df in df.cross_validation.iterate(kf):
self.assertIsInstance(train_df, pdml.ModelFrame)
self.assertIsInstance(test_df, pdml.ModelFrame)
tm.assert_index_equal(df.columns, train_df.columns)
tm.assert_index_equal(df.columns, test_df.columns)
self.assertTrue(df.shape[0], train_df.shape[0] + test_df.shape[1])
def test_mean_shift(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
result = df.cluster.mean_shift()
expected = cluster.mean_shift(iris.data)
self.assertEqual(len(result), 2)
self.assert_numpy_array_almost_equal(result[0], expected[0])
self.assertIsInstance(result[1], pdml.ModelSeries)
tm.assert_index_equal(result[1].index, df.index)
tm.assert_numpy_array_equal(result[1].values, expected[1])
def test_sparse_encode(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
_, dictionary, _ = decomposition.dict_learning(iris.data, 2, 1,
random_state=self.random_state)
result = df.decomposition.sparse_encode(dictionary)
expected = decomposition.sparse_encode(iris.data, dictionary)
self.assertIsInstance(result, pdml.ModelFrame)
tm.assert_index_equal(result.index, df.data.index)
self.assert_numpy_array_almost_equal(result.values, expected)
def test_mean_shift(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
result = df.cluster.mean_shift()
expected = cluster.mean_shift(iris.data)
self.assertEqual(len(result), 2)
self.assert_numpy_array_almost_equal(result[0], expected[0])
self.assertIsInstance(result[1], pdml.ModelSeries)
tm.assert_index_equal(result[1].index, df.index)
tm.assert_numpy_array_equal(result[1].values, expected[1])
def test_spectral_clustering(self):
N = 50
m = np.random.random_integers(1, 200, size=(N, N))
m = (m + m.T) / 2
df = pdml.ModelFrame(m)
result = df.cluster.spectral_clustering(random_state=self.random_state)
expected = cluster.spectral_clustering(m, random_state=self.random_state)
self.assertIsInstance(result, pdml.ModelSeries)
tm.assert_index_equal(result.index, df.index)
tm.assert_numpy_array_equal(result.values, expected)
def test_spectral_clustering(self):
N = 50
m = np.random.random_integers(1, 200, size=(N, N))
m = (m + m.T) / 2
df = pdml.ModelFrame(m)
result = df.cluster.spectral_clustering(random_state=self.random_state)
expected = cluster.spectral_clustering(m, random_state=self.random_state)
self.assertIsInstance(result, pdml.ModelSeries)
tm.assert_index_equal(result.index, df.index)
tm.assert_numpy_array_equal(result.values, expected)
def test_split(self):
df = pdml.ModelFrame(datasets.load_iris())
kf = df.model_selection.KFold(4, random_state=self.random_state)
gen = df.model_selection.split(kf)
for train_df, test_df in gen:
self.assertIsInstance(train_df, pdml.ModelFrame)
self.assertIsInstance(test_df, pdml.ModelFrame)
tm.assert_index_equal(df.columns, train_df.columns)
tm.assert_index_equal(df.columns, test_df.columns)
self.assertEqual(len(df), len(train_df) + len(test_df))
def test_MDS(self, algo):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
mod1 = getattr(df.manifold, algo)(random_state=self.random_state)
mod2 = getattr(manifold, algo)(random_state=self.random_state)
result = df.fit_transform(mod1)
expected = mod2.fit_transform(iris.data)
self.assertIsInstance(result, pdml.ModelFrame)
tm.assert_index_equal(result.index, df.index)
self.assert_numpy_array_almost_equal(result.data.values, expected)
def test_split(self):
df = pdml.ModelFrame(datasets.load_iris())
kf = df.model_selection.KFold(4, random_state=self.random_state)
gen = df.model_selection.split(kf)
for train_df, test_df in gen:
self.assertIsInstance(train_df, pdml.ModelFrame)
self.assertIsInstance(test_df, pdml.ModelFrame)
tm.assert_index_equal(df.columns, train_df.columns)
tm.assert_index_equal(df.columns, test_df.columns)
self.assertTrue(df.shape[0], train_df.shape[0] + test_df.shape[1])
def test_locally_linear_embedding(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
result = df.manifold.locally_linear_embedding(3, 3)
expected = manifold.locally_linear_embedding(iris.data, 3, 3)
self.assertEqual(len(result), 2)
self.assertIsInstance(result[0], pdml.ModelFrame)
tm.assert_index_equal(result[0].index, df.index)
tm.assert_numpy_array_equal(result[0].values, expected[0])
self.assertEqual(result[1], expected[1])
def test_locally_linear_embedding(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
result = df.manifold.locally_linear_embedding(3, 3)
expected = manifold.locally_linear_embedding(iris.data, 3, 3)
self.assertEqual(len(result), 2)
self.assertIsInstance(result[0], pdml.ModelFrame)
tm.assert_index_equal(result[0].index, df.index)
tm.assert_numpy_array_equal(result[0].values, expected[0])
self.assertEqual(result[1], expected[1])
def test_affinity_propagation(self):
iris = datasets.load_iris()
similality = np.cov(iris.data)
df = pdml.ModelFrame(similality)
result = df.cluster.affinity_propagation()
expected = cluster.affinity_propagation(similality)
self.assertEqual(len(result), 2)
self.assert_numpy_array_almost_equal(result[0], expected[0])
self.assertIsInstance(result[1], pdml.ModelSeries)
tm.assert_index_equal(result[1].index, df.index)
tm.assert_numpy_array_equal(result[1].values, expected[1])
def test_affinity_propagation(self):
iris = datasets.load_iris()
similality = np.cov(iris.data)
df = pdml.ModelFrame(similality)
result = df.cluster.affinity_propagation()
expected = cluster.affinity_propagation(similality)
self.assertEqual(len(result), 2)
self.assert_numpy_array_almost_equal(result[0], expected[0])
self.assertIsInstance(result[1], pdml.ModelSeries)
tm.assert_index_equal(result[1].index, df.index)
tm.assert_numpy_array_equal(result[1].values, expected[1])
def test_lasso_stability_path(self):
diabetes = datasets.load_diabetes()
df = pdml.ModelFrame(diabetes)
result = df.linear_model.lasso_stability_path(random_state=self.random_state)
expected = lm.lasso_stability_path(diabetes.data, diabetes.target,
random_state=self.random_state)
self.assertEqual(len(result), 2)
tm.assert_numpy_array_equal(result[0], expected[0])
self.assertIsInstance(result[1], pdml.ModelFrame)
tm.assert_index_equal(result[1].index, df.data.columns)
tm.assert_numpy_array_equal(result[1].values, expected[1])
def test_lars_path(self):
diabetes = datasets.load_diabetes()
df = pdml.ModelFrame(diabetes)
result = df.linear_model.lars_path()
expected = lm.lars_path(diabetes.data, diabetes.target)
self.assertEqual(len(result), 3)
tm.assert_numpy_array_equal(result[0], expected[0])
self.assertEqual(result[1], expected[1])
self.assertIsInstance(result[1], list)
self.assertIsInstance(result[2], pdml.ModelFrame)
tm.assert_index_equal(result[2].index, df.data.columns)
tm.assert_numpy_array_equal(result[2].values, expected[2])
def test_iterate(self):
df = pdml.ModelFrame(datasets.load_iris())
kf = df.model_selection.KFold(4, random_state=self.random_state)
with tm.assert_produces_warning(FutureWarning):
gen = df.model_selection.iterate(kf)
for train_df, test_df in gen:
self.assertIsInstance(train_df, pdml.ModelFrame)
self.assertIsInstance(test_df, pdml.ModelFrame)
tm.assert_index_equal(df.columns, train_df.columns)
tm.assert_index_equal(df.columns, test_df.columns)
self.assertEqual(len(df), len(train_df) + len(test_df))
def test_LabelEncoder_frame(self):
arr = np.array(['X', 'Y', 'Z', 'X'])
df = pdml.ModelFrame(arr, index=['a', 'b', 'c', 'd'], columns=['A'])
mod1 = df.pp.LabelEncoder()
df.fit(mod1)
result = df.transform(mod1)
expected = np.array([0, 1, 2, 0]).reshape(-1, 1)
self.assertIsInstance(result, pdml.ModelFrame)
self.assert_numpy_array_almost_equal(result.values, expected)
tm.assert_index_equal(result.columns, df.columns)
tm.assert_index_equal(result.index, df.index)
mod1 = df.pp.LabelEncoder()
result = df.fit_transform(mod1)
self.assertIsInstance(result, pdml.ModelFrame)
self.assert_numpy_array_almost_equal(result.values, expected)
tm.assert_index_equal(result.columns, df.columns)
tm.assert_index_equal(result.index, df.index)
inversed = result.inverse_transform(mod1)
self.assertIsInstance(inversed, pdml.ModelFrame)
tm.assert_frame_equal(inversed, df)
def test_iterate(self):
df = pdml.ModelFrame(datasets.load_iris())
kf = df.model_selection.KFold(4, random_state=self.random_state)
with tm.assert_produces_warning(FutureWarning):
gen = df.model_selection.iterate(kf)
for train_df, test_df in gen:
self.assertIsInstance(train_df, pdml.ModelFrame)
self.assertIsInstance(test_df, pdml.ModelFrame)
tm.assert_index_equal(df.columns, train_df.columns)
tm.assert_index_equal(df.columns, test_df.columns)
self.assertTrue(df.shape[0], train_df.shape[0] + test_df.shape[1])
def test_frame_target_object_set(self):
df = pd.DataFrame({datetime.datetime(2014, 1, 1): [1, 2, 3],
datetime.datetime(2015, 1, 1): [4, 5, 6],
datetime.datetime(2016, 1, 1): [7, 8, 9]},
index=['a', 'b', 'c'])
mdf = pdml.ModelFrame(df)
mdf.target = pd.Series(['A', 'B', 'C'], index=['a', 'b', 'c'], name=5)
self.assertIsInstance(mdf, pdml.ModelFrame)
self.assertEqual(mdf.shape, (3, 4))
tm.assert_index_equal(mdf.index, pd.Index(['a', 'b', 'c']))
expected = pd.Index([5, datetime.datetime(2014, 1, 1),
datetime.datetime(2015, 1, 1), datetime.datetime(2016, 1, 1)])
tm.assert_index_equal(mdf.columns, expected)
tm.assert_frame_equal(mdf.data, df)
expected = pd.Series(['A', 'B', 'C'], index=['a', 'b', 'c'], name=5)
tm.assert_series_equal(mdf.target, expected)
self.assertEqual(mdf.target.name, 5)
# name will be ignored if ModelFrame already has a target
mdf.target = pd.Series([10, 11, 12], index=['a', 'b', 'c'], name='X')
self.assertIsInstance(mdf, pdml.ModelFrame)
self.assertEqual(mdf.shape, (3, 4))
tm.assert_index_equal(mdf.index, pd.Index(['a', 'b', 'c']))
expected = pd.Index([5,
datetime.datetime(2014, 1, 1),
datetime.datetime(2015, 1, 1),
datetime.datetime(2016, 1, 1)])
tm.assert_index_equal(mdf.columns, expected)
tm.assert_frame_equal(mdf.data, df)
expected = pd.Series([10, 11, 12], index=['a', 'b', 'c'], name=5)
tm.assert_series_equal(mdf.target, expected)
self.assertEqual(mdf.target.name, 5)
def test_lasso_stability_path(self):
diabetes = datasets.load_diabetes()
df = pdml.ModelFrame(diabetes)
result = df.linear_model.lasso_stability_path(random_state=self.random_state)
expected = lm.lasso_stability_path(diabetes.data, diabetes.target,
random_state=self.random_state)
self.assertEqual(len(result), 2)
tm.assert_numpy_array_equal(result[0], expected[0])
self.assertIsInstance(result[1], pdml.ModelFrame)
tm.assert_index_equal(result[1].index, df.data.columns)
tm.assert_numpy_array_equal(result[1].values, expected[1])
def test_frame_init_df_series(self):
# initialization by dataframe and no-named series
df = pd.DataFrame({'A': [1, 2, 3],
'B': [4, 5, 6],
'C': [7, 8, 9]},
index=['a', 'b', 'c'],
columns=['A', 'B', 'C'])
s = pd.Series([1, 2, 3], index=['a', 'b', 'c'], name='.target')
mdf = pdml.ModelFrame(df, target=s)
self.assertIsInstance(mdf, pdml.ModelFrame)
self.assertEqual(mdf.shape, (3, 4))
tm.assert_index_equal(mdf.index, pd.Index(['a', 'b', 'c']))
tm.assert_index_equal(mdf.columns, pd.Index(['.target', 'A', 'B', 'C']))
tm.assert_frame_equal(mdf.data, df)
tm.assert_series_equal(mdf.target, s)
self.assertEqual(mdf.target.name, '.target')
self.assertEqual(mdf.target_name, '.target')
s = pd.Series([1, 2, 3])
with self.assertRaisesRegexp(ValueError, 'data and target must have equal index'):
mdf = pdml.ModelFrame(df, target=s)
s = pd.Series([1, 2, 3], index=['a', 'b', 'c'], name='XXX')
mdf = pdml.ModelFrame(df, target=s)
self.assertIsInstance(mdf, pdml.ModelFrame)
self.assertEqual(mdf.shape, (3, 4))
tm.assert_index_equal(mdf.index, pd.Index(['a', 'b', 'c']))
tm.assert_index_equal(mdf.columns, pd.Index(['XXX', 'A', 'B', 'C']))
tm.assert_frame_equal(mdf.data, df)
tm.assert_series_equal(mdf.target, s)
self.assertEqual(mdf.target.name, 'XXX')
self.assertEqual(mdf.target_name, 'XXX')
def test_lars_path(self):
diabetes = datasets.load_diabetes()
df = pdml.ModelFrame(diabetes)
result = df.linear_model.lars_path()
expected = lm.lars_path(diabetes.data, diabetes.target)
self.assertEqual(len(result), 3)
tm.assert_numpy_array_equal(result[0], expected[0])
self.assertEqual(result[1], expected[1])
self.assertIsInstance(result[1], list)
self.assertIsInstance(result[2], pdml.ModelFrame)
tm.assert_index_equal(result[2].index, df.data.columns)
tm.assert_numpy_array_equal(result[2].values, expected[2])
def test_LabelEncoder_frame(self):
arr = np.array(['X', 'Y', 'Z', 'X'])
df = pdml.ModelFrame(arr, index=['a', 'b', 'c', 'd'], columns=['A'])
mod1 = df.pp.LabelEncoder()
df.fit(mod1)
result = df.transform(mod1)
expected = np.array([0, 1, 2, 0]).reshape(-1, 1)
self.assertIsInstance(result, pdml.ModelFrame)
self.assert_numpy_array_almost_equal(result.values, expected)
tm.assert_index_equal(result.columns, df.columns)
tm.assert_index_equal(result.index, df.index)
mod1 = df.pp.LabelEncoder()
result = df.fit_transform(mod1)
self.assertIsInstance(result, pdml.ModelFrame)
self.assert_numpy_array_almost_equal(result.values, expected)
tm.assert_index_equal(result.columns, df.columns)
tm.assert_index_equal(result.index, df.index)
inversed = result.inverse_transform(mod1)
self.assertIsInstance(inversed, pdml.ModelFrame)
tm.assert_frame_equal(inversed, df)
def test_frame_init_dict_list_series_index(self):
# initialization by dataframe and list
df = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
target = pd.Series([9, 8, 7], name='X', index=['a', 'b', 'c'])
mdf = pdml.ModelFrame(df, target=target)
self.assertIsInstance(mdf, pdml.ModelFrame)
self.assertEqual(mdf.shape, (3, 4))
tm.assert_index_equal(mdf.index, pd.Index(['a', 'b', 'c']))
tm.assert_index_equal(mdf.columns, pd.Index(['X', 'A', 'B', 'C']))
expected = pd.DataFrame(df, index=['a', 'b', 'c'])
tm.assert_frame_equal(mdf.data, expected)
tm.assert_series_equal(mdf.target, target)
self.assertEqual(mdf.target.name, 'X')
self.assertEqual(mdf.target_name, 'X')
def test_frame_init_dict_list_series_index(self):
# initialization by dataframe and list
df = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
target = pd.Series([9, 8, 7], name='X', index=['a', 'b', 'c'])
mdf = pdml.ModelFrame(df, target=target)
self.assertIsInstance(mdf, pdml.ModelFrame)
self.assertEqual(mdf.shape, (3, 4))
tm.assert_index_equal(mdf.index, pd.Index(['a', 'b', 'c']))
tm.assert_index_equal(mdf.columns, pd.Index(['X', 'A', 'B', 'C']))
expected = pd.DataFrame(df, index=['a', 'b', 'c'])
tm.assert_frame_equal(mdf.data, expected)
tm.assert_series_equal(mdf.target, target)
self.assertEqual(mdf.target.name, 'X')
self.assertEqual(mdf.target_name, 'X')
def test_MDS(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
models = ['MDS']
for model in models:
mod1 = getattr(df.manifold, model)(random_state=self.random_state)
mod2 = getattr(manifold, model)(random_state=self.random_state)
result = df.fit_transform(mod1)
expected = mod2.fit_transform(iris.data)
self.assertIsInstance(result, pdml.ModelFrame)
tm.assert_index_equal(result.index, df.index)
self.assert_numpy_array_almost_equal(result.data.values, expected)
def test_k_means(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
result = df.cluster.k_means(3, random_state=self.random_state)
expected = cluster.k_means(iris.data, 3, random_state=self.random_state)
self.assertEqual(len(result), 3)
self.assert_numpy_array_almost_equal(result[0], expected[0])
self.assertIsInstance(result[1], pdml.ModelSeries)
tm.assert_index_equal(result[1].index, df.index)
tm.assert_numpy_array_equal(result[1].values, expected[1])
self.assertAlmostEqual(result[2], expected[2])
def test_oas(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
result = df.covariance.oas()
expected = covariance.oas(iris.data)
self.assertEqual(len(result), 2)
self.assertIsInstance(result[0], pdml.ModelFrame)
tm.assert_index_equal(result[0].index, df.data.columns)
tm.assert_index_equal(result[0].columns, df.data.columns)
self.assert_numpy_array_almost_equal(result[0].values, expected[0])
self.assert_numpy_array_almost_equal(result[1], expected[1])
def test_oas(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
result = df.covariance.oas()
expected = covariance.oas(iris.data)
self.assertEqual(len(result), 2)
self.assertIsInstance(result[0], pdml.ModelFrame)
tm.assert_index_equal(result[0].index, df.data.columns)
tm.assert_index_equal(result[0].columns, df.data.columns)
self.assert_numpy_array_almost_equal(result[0].values, expected[0])
self.assert_numpy_array_almost_equal(result[1], expected[1])
def test_k_means(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
result = df.cluster.k_means(3, random_state=self.random_state)
expected = cluster.k_means(iris.data, 3, random_state=self.random_state)
self.assertEqual(len(result), 3)
self.assert_numpy_array_almost_equal(result[0], expected[0])
self.assertIsInstance(result[1], pdml.ModelSeries)
tm.assert_index_equal(result[1].index, df.index)
tm.assert_numpy_array_equal(result[1].values, expected[1])
self.assertAlmostEqual(result[2], expected[2])
def _assert_fit_transform(self, df, exp_data, model1, model2):
result = df.fit_transform(model1)
expected = model2.fit_transform(exp_data)
self.assertIsInstance(result, pdml.ModelFrame)
# target is unchanged
if df.has_target():
# target is unchanged
tm.assert_series_equal(df.target, result.target)
else:
self.assertIsNone(result.target)
self.assert_numpy_array_almost_equal(result.data.values, expected)
# index and columns are kept
tm.assert_index_equal(result.index, df.index)
tm.assert_index_equal(result.columns, df.columns)
def test_spectral_embedding(self):
N = 10
m = np.random.random_integers(50, 200, size=(N, N))
m = (m + m.T) / 2
df = pdml.ModelFrame(m)
self.assert_numpy_array_almost_equal(df.data.values, m)
result = df.manifold.spectral_embedding(random_state=self.random_state)
expected = manifold.spectral_embedding(m, random_state=self.random_state)
self.assertIsInstance(result, pdml.ModelFrame)
tm.assert_index_equal(result.index, df.index)
# signs can be inversed
self.assert_numpy_array_almost_equal(np.abs(result.data.values),
np.abs(expected))
