def test_stateful_transform():
data_train = patsy.demo_data("x1", "x2", "y")
data_train['x1'][:] = 1
# mean of x1 is 1
data_test = patsy.demo_data("x1", "x2", "y")
data_test['x1'][:] = 0
# center x1
est = PatsyTransformer("center(x1) + x2")
est.fit(data_train)
data_trans = est.transform(data_test)
# make sure that mean of training, not test data was removed
assert_array_equal(data_trans[:, 0], -1)
def test_stateful_transform():
data_train = patsy.demo_data("x1", "x2", "y")
data_train['x1'][:] = 1
# mean of x1 is 1
data_test = patsy.demo_data("x1", "x2", "y")
data_test['x1'][:] = 0
# center x1
est = PatsyTransformer("center(x1) + x2")
est.fit(data_train)
data_trans = est.transform(data_test)
# make sure that mean of training, not test data was removed
assert_array_equal(data_trans[:, 0], -1)
def test_intercept_transformer():
data = patsy.demo_data("x1", "x2", "x3", "y")
# check wether X contains only the two features, no intercept
est = PatsyTransformer("x1 + x2")
est.fit(data)
assert_equal(est.transform(data).shape[1], 2)
# check wether X does contain intercept
est = PatsyTransformer("x1 + x2", add_intercept=True)
est.fit(data)
data_transformed = est.transform(data)
assert_array_equal(data_transformed[:, 0], 1)
assert_equal(est.transform(data).shape[1], 3)
def test_intercept_transformer():
data = patsy.demo_data("x1", "x2", "x3", "y")
# check wether X contains only the two features, no intercept
est = PatsyTransformer("x1 + x2")
est.fit(data)
assert_equal(est.transform(data).shape[1], 2)
# check wether X does contain intercept
est = PatsyTransformer("x1 + x2", add_intercept=True)
est.fit(data)
data_transformed = est.transform(data)
assert_array_equal(data_transformed[:, 0], 1)
assert_equal(est.transform(data).shape[1], 3)
def test_scope_transformer():
data = patsy.demo_data("x1", "x2", "x3", "y")
def myfunc(x):
tmp = np.ones_like(x)
tmp.fill(42)
return tmp
est = PatsyTransformer("x1 + myfunc(x2)")
est.fit(data)
data_trans = est.transform(data)
assert_array_equal(data_trans[:, 1], 42)
est = PatsyTransformer("x1 + myfunc(x2)")
data_trans = est.fit_transform(data)
assert_array_equal(data_trans[:, 1], 42)
def test_stateful_transform_dataframe():
data_train = pd.DataFrame(patsy.demo_data("x1", "x2", "y"))
data_train['x1'][:] = 1
# mean of x1 is 1
data_test = pd.DataFrame(patsy.demo_data("x1", "x2", "y"))
data_test['x1'][:] = 0
# center x1
est = PatsyTransformer("center(x1) + x2", return_type='dataframe')
est.fit(data_train)
data_trans = est.transform(data_test)
# make sure result is pandas dataframe
assert type(data_trans) is pd.DataFrame
# make sure that mean of training, not test data was removed
assert_array_equal(data_trans['center(x1)'][:],-1)
def test_stateful_transform_dataframe():
data_train = pd.DataFrame(patsy.demo_data("x1", "x2", "y"))
data_train['x1'][:] = 1
# mean of x1 is 1
data_test = pd.DataFrame(patsy.demo_data("x1", "x2", "y"))
data_test['x1'][:] = 0
# center x1
est = PatsyTransformer("center(x1) + x2", return_type='dataframe')
est.fit(data_train)
data_trans = est.transform(data_test)
# make sure result is pandas dataframe
assert type(data_trans) is pd.DataFrame
# make sure that mean of training, not test data was removed
assert_array_equal(data_trans['center(x1)'][:], -1)
def test_scope_transformer():
data = patsy.demo_data("x1", "x2", "x3", "y")
def myfunc(x):
tmp = np.ones_like(x)
tmp.fill(42)
return tmp
est = PatsyTransformer("x1 + myfunc(x2)")
est.fit(data)
data_trans = est.transform(data)
assert_array_equal(data_trans[:, 1], 42)
est = PatsyTransformer("x1 + myfunc(x2)")
data_trans = est.fit_transform(data)
assert_array_equal(data_trans[:, 1], 42)
# test feature names
assert_equal(est.feature_names_, ["x1", "myfunc(x2)"])