def test_grunfeld(self):
data = getattr(sm.datasets.grunfeld, self.load_method)()
df = pdml.ModelFrame(data)
self.assertEqual(df.shape, (220, 5))
self.assertEqual(df.target_name, 'invest')
tm.assert_index_equal(df.data.columns, pd.Index(data.exog_name))
def test_objectmapper(self):
df = pdml.ModelFrame([])
self.assertIs(df.semi_supervised.LabelPropagation, ss.LabelPropagation)
self.assertIs(df.semi_supervised.LabelSpreading, ss.LabelSpreading)
def test_frame_data_proparty(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([1, 2, 3], index=['a', 'b', 'c'], name='.target')
mdf = pdml.ModelFrame(df, target=s)
self.assertIsInstance(mdf, pdml.ModelFrame)
new = pd.DataFrame({
'X': [1, 2, 3],
'Y': [4, 5, 6]
},
index=['a', 'b', 'c'],
columns=['X', 'Y'])
# set data property
mdf.data = new
self.assertIsInstance(mdf, pdml.ModelFrame)
self.assertEqual(mdf.shape, (3, 3))
tm.assert_index_equal(mdf.index, pd.Index(['a', 'b', 'c']))
tm.assert_index_equal(mdf.columns, pd.Index(['.target', 'X', 'Y']))
tm.assert_frame_equal(mdf.data, new)
tm.assert_series_equal(mdf.target, s)
self.assertEqual(mdf.target.name, '.target')
self.assertEqual(mdf.target_name, '.target')
new = pdml.ModelFrame({
'M': [1, 2, 3],
'N': [4, 5, 6]
},
index=['a', 'b', 'c'],
columns=['M', 'N'])
# set data property
mdf.data = new
self.assertIsInstance(mdf, pdml.ModelFrame)
self.assertEqual(mdf.shape, (3, 3))
tm.assert_index_equal(mdf.index, pd.Index(['a', 'b', 'c']))
tm.assert_index_equal(mdf.columns, pd.Index(['.target', 'M', 'N']))
tm.assert_frame_equal(mdf.data, new)
tm.assert_series_equal(mdf.target, s)
self.assertEqual(mdf.target.name, '.target')
self.assertEqual(mdf.target_name, '.target')
new = pd.DataFrame({
'.target': [1, 2, 3],
'K': [4, 5, 6]
},
index=['a', 'b', 'c'])
# unable to set data if passed value has the same column as the target
msg = "Passed data has the same column name as the target '.target'"
with pytest.raises(ValueError, match=msg):
mdf.data = new
# unable to set ModelFrame with target attribute
msg = "Cannot update with ModelFrame which has target attribute"
with pytest.raises(ValueError, match=msg):
mdf.data = mdf
# set delete property
del mdf.data
self.assertIsInstance(mdf, pdml.ModelFrame)
self.assertEqual(mdf.shape, (3, 1))
tm.assert_index_equal(mdf.index, pd.Index(['a', 'b', 'c']))
tm.assert_index_equal(mdf.columns, pd.Index(['.target']))
self.assertTrue(mdf.data is None)
tm.assert_series_equal(mdf.target, s)
self.assertEqual(mdf.target.name, '.target')
self.assertEqual(mdf.target_name, '.target')
def test_plot_partial_dependence(self):
df = pdml.ModelFrame(datasets.load_iris())
clf = df.ensemble.GradientBoostingRegressor(n_estimators=10)
df.fit(clf)
"""
def test_objectmapper(self):
df = pdml.ModelFrame([])
self.assertIs(df.multioutput.MultiOutputRegressor,
multioutput.MultiOutputRegressor)
self.assertIs(df.multioutput.MultiOutputClassifier,
multioutput.MultiOutputClassifier)
def test_objectmapper(self):
df = pdml.ModelFrame([])
self.assertIs(df.xgboost.XGBRegressor, xgb.XGBRegressor)
self.assertIs(df.xgboost.XGBClassifier, xgb.XGBClassifier)
def test_objectmapper(self):
df = pdml.ModelFrame([])
self.assertIs(df.grid_search.GridSearchCV, gs.GridSearchCV)
self.assertIs(df.grid_search.ParameterGrid, gs.ParameterGrid)
self.assertIs(df.grid_search.ParameterSampler, gs.ParameterSampler)
self.assertIs(df.grid_search.RandomizedSearchCV, gs.RandomizedSearchCV)
def test_objectmapper(self):
df = pdml.ModelFrame([])
self.assertIs(df.random_projection.GaussianRandomProjection, rp.GaussianRandomProjection)
self.assertIs(df.random_projection.SparseRandomProjection, rp.SparseRandomProjection)
self.assertIs(df.random_projection.johnson_lindenstrauss_min_dim, rp.johnson_lindenstrauss_min_dim)
def test_check_cv(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
result = df.cross_validation.check_cv(cv=5)
self.assertIsInstance(result, cv.KFold)
browser_dummies.drop(['Browser_other'], axis=1, inplace=True)
adex_dummies = get_dummies(ads['AdExchange'], prefix='AdExchange')
adex_dummies.drop(['AdExchange_1'], axis=1, inplace=True)
advi_dummies = get_dummies(ads['Adslotvisibility'], prefix='Adslotvisibility')
advi_dummies.drop(['Adslotvisibility_Na'], axis=1, inplace=True)
adfo_dummies = get_dummies(ads['Adslotformat'], prefix='Adslotformat')
adfo_dummies.drop(['Adslotformat_Na'], axis=1, inplace=True)
os_dummies = get_dummies(ads['OS'], prefix='OS')
os_dummies.drop(['OS_other'], axis=1, inplace=True)
ads = ads.join(
[browser_dummies, adex_dummies, advi_dummies, adfo_dummies, os_dummies])
ads.drop(['Browser', 'AdExchange', 'Adslotvisibility', 'Adslotformat', 'OS'],
axis=1,
inplace=True)
ads_ml = pdml.ModelFrame(ads, target='click')
sampler = ads_ml.imbalance.over_sampling.RandomOverSampler()
ads = ads_ml.fit_sample(sampler)
if mode == 'rfe':
from sklearn.feature_selection import RFECV
from sklearn.linear_model import LogisticRegression
logit_rfe = LogisticRegression()
rfe = RFECV(logit_rfe, cv=5)
rfe = rfe.fit(ads.drop(['click'], axis=1), ads['click'])
print(rfe.support_)
result = rfe
elif mode == 'stat':
import statsmodels.api as sm
ads['intercept'] = 1.0
ads = ads.astype(int)
def test_objectmapper(self):
df = pdml.ModelFrame([])
self.assertIs(df.neural_network.BernoulliRBM, nn.BernoulliRBM)
self.assertIs(df.neural_network.MLPClassifier, nn.MLPClassifier)
self.assertIs(df.neural_network.MLPRegressor, nn.MLPRegressor)
def test_nile(self):
data = getattr(sm.datasets.nile, self.load_method)()
msg = "Unable to read statsmodels Dataset without exog"
with self.assertRaisesRegexp(ValueError, msg):
pdml.ModelFrame(data)
def test_modechoice(self):
data = getattr(sm.datasets.modechoice, self.load_method)()
df = pdml.ModelFrame(data)
self.assertEqual(df.shape, (840, 7))
self.assertEqual(df.target_name, 'choice')
tm.assert_index_equal(df.data.columns, pd.Index(data.exog_name))
def test_longley(self):
data = getattr(sm.datasets.longley, self.load_method)()
df = pdml.ModelFrame(data)
self.assertEqual(df.shape, (16, 7))
self.assertEqual(df.target_name, 'TOTEMP')
tm.assert_index_equal(df.data.columns, pd.Index(data.exog_name))
def test_frame_init_dict_list(self):
# initialization by dataframe and list
df = pd.DataFrame({
'A': [1, 2, 3],
'B': [4, 5, 6],
'C': [7, 8, 9]
},
index=['a', 'b', 'c'],
columns=['A', 'B', 'C'])
s = [1, 2, 3]
mdf = pdml.ModelFrame(df, target=s)
self.assertTrue(isinstance(mdf, pdml.ModelFrame))
self.assertEqual(mdf.shape, (3, 4))
self.assert_index_equal(mdf.index, pd.Index(['a', 'b', 'c']))
self.assert_index_equal(mdf.columns,
pd.Index(['.target', 'A', 'B', 'C']))
self.assert_frame_equal(mdf.data, df)
expected = pd.Series([1, 2, 3], index=['a', 'b', 'c'], name='.target')
self.assert_series_equal(mdf.target, expected)
self.assertEqual(mdf.target.name, '.target')
self.assertEqual(mdf.target_name, '.target')
df = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
s = [1, 2, 3]
mdf = pdml.ModelFrame(df, target=s)
self.assertTrue(isinstance(mdf, pdml.ModelFrame))
self.assertEqual(mdf.shape, (3, 4))
self.assert_index_equal(mdf.index, pd.Index([0, 1, 2]))
self.assert_index_equal(mdf.columns,
pd.Index(['.target', 'A', 'B', 'C']))
expected = pd.DataFrame(df)
self.assert_frame_equal(mdf.data, expected)
expected = pd.Series([1, 2, 3], index=[0, 1, 2], name='.target')
self.assert_series_equal(mdf.target, expected)
self.assertEqual(mdf.target.name, '.target')
self.assertEqual(mdf.target_name, '.target')
mdf = pdml.ModelFrame(df, target='A')
self.assertTrue(isinstance(mdf, pdml.ModelFrame))
self.assertEqual(mdf.shape, (3, 3))
self.assert_index_equal(mdf.index, pd.Index([0, 1, 2]))
self.assert_index_equal(mdf.columns, pd.Index(['A', 'B', 'C']))
expected = pd.DataFrame(df)
self.assert_frame_equal(mdf.data, expected[['B', 'C']])
self.assert_series_equal(mdf.target, expected['A'])
self.assertEqual(mdf.target.name, 'A')
self.assertEqual(mdf.target_name, 'A')
mdf = pdml.ModelFrame({
'A': [1, 2, 3],
'B': [4, 5, 6],
'C': [7, 8, 9]
},
index=['a', 'b', 'c'],
columns=['A', 'B', 'C'])
self.assertTrue(isinstance(mdf, pdml.ModelFrame))
self.assertEqual(mdf.shape, (3, 3))
self.assert_index_equal(mdf.index, pd.Index(['a', 'b', 'c']))
self.assert_index_equal(mdf.columns, pd.Index(['A', 'B', 'C']))
self.assert_frame_equal(mdf.data, mdf)
self.assertEqual(mdf.target_name, '.target')
elif aresults.loc[i, "Payment Typology 1"] == "Medicare":
aresults.loc[i, "Payment Typology 1"] = 1
elif aresults.loc[i, "Payment Typology 1"] == "Blue Cross/Blue Shield":
aresults.loc[i, "Payment Typology 1"] = 2
else:
aresults.loc[i, "Payment Typology 1"] = 3
print "aresults"
#print df
test1 = test1.replace("120 +", 120)
test2 = test2.replace("120 +", 120)
test3 = test3.replace("120 +", 120)
test4 = test4.replace("120 +", 120)
test5 = test5.replace("120 +", 120)
scaler = MinMaxScaler()
test1 = pdml.ModelFrame(scaler.fit_transform(test1), columns=test1.columns)
test2 = pdml.ModelFrame(scaler.fit_transform(test2), columns=test2.columns)
test3 = pdml.ModelFrame(scaler.fit_transform(test3), columns=test3.columns)
test4 = pdml.ModelFrame(scaler.fit_transform(test4), columns=test4.columns)
test5 = pdml.ModelFrame(scaler.fit_transform(test5), columns=test5.columns)
bresults = aresults.replace("120 +", "120")
bresults = pdml.ModelFrame(scaler.fit_transform(bresults),
columns=bresults.columns)
import matplotlib.pyplot as plt
plt.rc("font", size=14)
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import train_test_split
import seaborn as sns
def test_objectmapper(self):
df = pdml.ModelFrame([])
self.assertIs(df.naive_bayes.GaussianNB, nb.GaussianNB)
self.assertIs(df.naive_bayes.MultinomialNB, nb.MultinomialNB)
self.assertIs(df.naive_bayes.BernoulliNB, nb.BernoulliNB)
def test_diabetes(self):
data = datasets.load_diabetes()
df = pdml.ModelFrame(data)
self.assertEqual(df.shape, (442, 11))
def test_objectmapper(self):
df = pdml.ModelFrame([])
self.assertIs(df.dummy.DummyClassifier, dummy.DummyClassifier)
self.assertIs(df.dummy.DummyRegressor, dummy.DummyRegressor)
def test_digits(self):
data = datasets.load_digits()
df = pdml.ModelFrame(data)
self.assertEqual(df.shape, (1797, 65))
def test_objectmapper(self):
df = pdml.ModelFrame([])
self.assertIs(df.metrics.make_scorer, metrics.make_scorer)
def test_iris(self):
data = datasets.load_iris()
df = pdml.ModelFrame(data)
self.assertEqual(df.shape, (150, 5))
y = df['Class']
df.head()
from pandas_ml import ConfusionMatrix
from sklearn.decomposition import PCA
from sklearn.preprocessing import scale
data = scale(X)
pca = PCA(n_components=10)
X = pca.fit_transform(data)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
df = pdml.ModelFrame(X_train, target=y_train)
sampler = df.imbalance.over_sampling.SMOTE()
oversampled = df.fit_sample(sampler)
X2, y2 = oversampled.iloc[:, 1:11], oversampled['Class']
print(X2)
print(y2)
X2 = X2.as_matrix()
y2 = y2.as_matrix()
model = Sequential()
model.add(Dense(27, input_dim=10, activation='relu'))
model.add(Dense(20, activation='relu'))
model.add(Dense(15, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adagrad',
def test_boston(self):
data = datasets.load_boston()
df = pdml.ModelFrame(data)
self.assertEqual(df.shape, (506, 14))
def test_check_cv(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
result = df.model_selection.check_cv(cv=5)
self.assertIsInstance(result, ms.KFold)
def test_frame_target_proparty(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([1, 2, 3], index=['a', 'b', 'c'])
mdf = pdml.ModelFrame(df, target=s)
new = pd.Series([4, 5, 6], index=['a', 'b', 'c'], name='.target')
# set target property
mdf.target = new
self.assertTrue(isinstance(mdf, pdml.ModelFrame))
self.assertEqual(mdf.shape, (3, 4))
self.assert_index_equal(mdf.index, pd.Index(['a', 'b', 'c']))
self.assert_index_equal(mdf.columns,
pd.Index(['.target', 'A', 'B', 'C']))
self.assert_frame_equal(mdf.data, df)
self.assert_series_equal(mdf.target, new)
self.assertEqual(mdf.target.name, '.target')
self.assertEqual(mdf.target_name, '.target')
with tm.assert_produces_warning(UserWarning):
new = pd.Series([4, 5, 6], index=['a', 'b', 'c'], name='xxx')
# set target property
mdf.target = new
self.assertTrue(isinstance(mdf, pdml.ModelFrame))
self.assertEqual(mdf.shape, (3, 4))
self.assert_index_equal(mdf.index, pd.Index(['a', 'b', 'c']))
self.assert_index_equal(mdf.columns,
pd.Index(['.target', 'A', 'B', 'C']))
self.assert_frame_equal(mdf.data, df)
exp_target = pd.Series(new, name='.target')
self.assert_series_equal(mdf.target, exp_target)
self.assertEqual(mdf.target.name, '.target')
self.assertEqual(mdf.target_name, '.target')
new = pd.Series([4, 5, 6], name='.target')
with self.assertRaisesRegexp(ValueError,
'data and target must have equal index'):
mdf.target = new
# set target property
mdf.target = [7, 8, 9]
self.assertTrue(isinstance(mdf, pdml.ModelFrame))
self.assertEqual(mdf.shape, (3, 4))
self.assert_index_equal(mdf.index, pd.Index(['a', 'b', 'c']))
self.assert_index_equal(mdf.columns,
pd.Index(['.target', 'A', 'B', 'C']))
self.assert_frame_equal(mdf.data, df)
expected = pd.Series([7, 8, 9], index=['a', 'b', 'c'], name='.target')
self.assert_series_equal(mdf.target, expected)
self.assertEqual(mdf.target.name, '.target')
self.assertEqual(mdf.target_name, '.target')
with self.assertRaisesRegexp(
ValueError,
'Wrong number of items passed 2, placement implies 3'):
mdf.target = [1, 2]
# set target property
mdf.target = None
self.assertTrue(isinstance(mdf, pdml.ModelFrame))
self.assertEqual(mdf.shape, (3, 3))
self.assert_index_equal(mdf.index, pd.Index(['a', 'b', 'c']))
self.assert_index_equal(mdf.columns, pd.Index(['A', 'B', 'C']))
self.assert_frame_equal(mdf.data, df)
self.assertEqual(mdf.target_name, '.target')
def test_objectmapper(self):
df = pdml.ModelFrame([])
self.assertIs(df.mixture.GMM, mixture.GMM)
self.assertIs(df.mixture.DPGMM, mixture.DPGMM)
self.assertIs(df.mixture.VBGMM, mixture.VBGMM)
def test_frame_init_df_target_setter(self):
# initialization by dataframe and dataframe
df = pd.DataFrame({
'A': [1, 2, 3],
'B': [4, 5, 6],
'C': [7, 8, 9]
},
index=['a', 'b', 'c'],
columns=['A', 'B', 'C'])
mdf = pdml.ModelFrame(df)
self.assertFalse(mdf.has_target())
target = pd.DataFrame({
't1': [10, 11, 12],
't2': [13, 14, 15]
},
index=['a', 'b', 'c'])
mdf.target = target
self.assertTrue(isinstance(mdf, pdml.ModelFrame))
self.assertEqual(mdf.shape, (3, 5))
expected = pd.DataFrame(
{
't1': [10, 11, 12],
't2': [13, 14, 15],
'A': [1, 2, 3],
'B': [4, 5, 6],
'C': [7, 8, 9]
},
index=['a', 'b', 'c'],
columns=['t1', 't2', 'A', 'B', 'C'])
self.assert_frame_equal(mdf, expected)
self.assert_index_equal(mdf.index, pd.Index(['a', 'b', 'c']))
self.assert_index_equal(mdf.columns,
pd.Index(['t1', 't2', 'A', 'B', 'C']))
self.assert_frame_equal(mdf.data, df)
self.assert_frame_equal(mdf.target, target)
self.assert_index_equal(mdf.target.columns, pd.Index(['t1', 't2']))
self.assert_index_equal(mdf.target_name, pd.Index(['t1', 't2']))
self.assertTrue(mdf.has_multi_targets())
target = pd.DataFrame({
'x1': [20, 21, 22],
'x2': [23, 24, 25]
},
index=['a', 'b', 'c'])
with tm.assert_produces_warning(UserWarning):
# when the target has the same length as the target_name,
# is renamed to existing target ['t1', 't2']
mdf.target = target
self.assertTrue(isinstance(mdf, pdml.ModelFrame))
self.assertEqual(mdf.shape, (3, 5))
expected = pd.DataFrame(
{
't1': [20, 21, 22],
't2': [23, 24, 25],
'A': [1, 2, 3],
'B': [4, 5, 6],
'C': [7, 8, 9]
},
index=['a', 'b', 'c'],
columns=['t1', 't2', 'A', 'B', 'C'])
self.assert_frame_equal(mdf, expected)
self.assert_index_equal(mdf.index, pd.Index(['a', 'b', 'c']))
self.assert_index_equal(mdf.columns,
pd.Index(['t1', 't2', 'A', 'B', 'C']))
self.assert_frame_equal(mdf.data, df)
expected = pd.DataFrame({
't1': [20, 21, 22],
't2': [23, 24, 25]
},
index=['a', 'b', 'c'])
self.assert_frame_equal(mdf.target, expected)
self.assert_index_equal(mdf.target.columns, pd.Index(['t1', 't2']))
self.assert_index_equal(mdf.target_name, pd.Index(['t1', 't2']))
self.assertTrue(mdf.has_multi_targets())
target = pd.DataFrame(
{
'x1': [20, 21, 22],
'x2': [23, 24, 25],
'x3': [25, 26, 27]
},
index=['a', 'b', 'c'])
# when the target has the different length as the target_name,
# target is being replaced
mdf.target = target
self.assertTrue(isinstance(mdf, pdml.ModelFrame))
self.assertEqual(mdf.shape, (3, 6))
expected = pd.DataFrame(
{
'x1': [20, 21, 22],
'x2': [23, 24, 25],
'x3': [25, 26, 27],
'A': [1, 2, 3],
'B': [4, 5, 6],
'C': [7, 8, 9]
},
index=['a', 'b', 'c'],
columns=['x1', 'x2', 'x3', 'A', 'B', 'C'])
self.assert_frame_equal(mdf, expected)
self.assert_index_equal(mdf.index, pd.Index(['a', 'b', 'c']))
self.assert_index_equal(mdf.columns,
pd.Index(['x1', 'x2', 'x3', 'A', 'B', 'C']))
self.assert_frame_equal(mdf.data, df)
self.assert_frame_equal(mdf.target, target)
self.assert_index_equal(mdf.target.columns,
pd.Index(['x1', 'x2', 'x3']))
self.assert_index_equal(mdf.target_name, pd.Index(['x1', 'x2', 'x3']))
self.assertTrue(mdf.has_multi_targets())
def test_frame_target_proparty(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([1, 2, 3], index=['a', 'b', 'c'])
mdf = pdml.ModelFrame(df, target=s)
new = pd.Series([4, 5, 6], index=['a', 'b', 'c'], name='.target')
# set target property
mdf.target = new
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, new)
self.assertEqual(mdf.target.name, '.target')
self.assertEqual(mdf.target_name, '.target')
with tm.assert_produces_warning(UserWarning):
new = pd.Series([4, 5, 6], index=['a', 'b', 'c'], name='xxx')
# set target property
mdf.target = new
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)
exp_target = pd.Series(new, name='.target')
tm.assert_series_equal(mdf.target, exp_target)
self.assertEqual(mdf.target.name, '.target')
self.assertEqual(mdf.target_name, '.target')
new = pd.Series([4, 5, 6], name='.target')
with pytest.raises(ValueError,
match='data and target must have equal index'):
mdf.target = new
# set target property
mdf.target = [7, 8, 9]
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)
expected = pd.Series([7, 8, 9], index=['a', 'b', 'c'], name='.target')
tm.assert_series_equal(mdf.target, expected)
self.assertEqual(mdf.target.name, '.target')
self.assertEqual(mdf.target_name, '.target')
if pdml.compat._PANDAS_ge_023:
msg = 'Length of passed values is 2, index implies 3'
else:
msg = 'Wrong number of items passed 2, placement implies 3'
with pytest.raises(ValueError, match=msg):
mdf.target = [1, 2]
# set target property
mdf.target = None
self.assertIsInstance(mdf, pdml.ModelFrame)
self.assertEqual(mdf.shape, (3, 3))
tm.assert_index_equal(mdf.index, pd.Index(['a', 'b', 'c']))
tm.assert_index_equal(mdf.columns, pd.Index(['A', 'B', 'C']))
tm.assert_frame_equal(mdf.data, df)
self.assertEqual(mdf.target_name, '.target')
def test_engel(self):
data = getattr(sm.datasets.engel, self.load_method)()
df = pdml.ModelFrame(data)
self.assertEqual(df.shape, (235, 2))
self.assertEqual(df.target_name, 'income')
tm.assert_index_equal(df.data.columns, pd.Index(data.exog_name))
