def test_robust_scaler_zero_variance_features():
"""Check RobustScaler on toy data with zero variance features"""
X = [[0., 1., +0.5],
[0., 1., -0.1],
[0., 1., +1.1]]
scaler = RobustScaler()
X_trans = scaler.fit_transform(X)
# NOTE: for such a small sample size, what we expect in the third column
# depends HEAVILY on the method used to calculate quantiles. The values
# here were calculated to fit the quantiles produces by np.percentile
# using numpy 1.9 Calculating quantiles with
# scipy.stats.mstats.scoreatquantile or scipy.stats.mstats.mquantiles
# would yield very different results!
X_expected = [[0., 0., +0.0],
[0., 0., -1.0],
[0., 0., +1.0]]
assert_array_almost_equal(X_trans, X_expected)
X_trans_inv = scaler.inverse_transform(X_trans)
assert_array_almost_equal(X, X_trans_inv)
# make sure new data gets transformed correctly
X_new = [[+0., 2., 0.5],
[-1., 1., 0.0],
[+0., 1., 1.5]]
X_trans_new = scaler.transform(X_new)
X_expected_new = [[+0., 1., +0.],
[-1., 0., -0.83333],
[+0., 0., +1.66667]]
assert_array_almost_equal(X_trans_new, X_expected_new, decimal=3)
class RobustScalerImpl():
def __init__(self, with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True):
self._hyperparams = {
'with_centering': with_centering,
'with_scaling': with_scaling,
'quantile_range': quantile_range,
'copy': copy}
self._wrapped_model = Op(**self._hyperparams)
def fit(self, X, y=None):
if (y is not None):
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def transform(self, X):
return self._wrapped_model.transform(X)
class CreateRobustScaler(CreateModel):
def fit(self, data, args):
self.model = RobustScaler()
with Timer() as t:
self.model.fit(data.X_train, data.y_train)
return t.interval
def test(self, data):
assert self.model is not None
return self.model.transform(data.X_test)
def predict(self, data):
with Timer() as t:
self.predictions = self.test(data)
data.learning_task = LearningTask.REGRESSION
return t.interval
def test_robust_scaler_zero_variance_features():
"""Check RobustScaler on toy data with zero variance features"""
X = [[0., 1., +0.5], [0., 1., -0.1], [0., 1., +1.1]]
scaler = RobustScaler()
X_trans = scaler.fit_transform(X)
# NOTE: for such a small sample size, what we expect in the third column
# depends HEAVILY on the method used to calculate quantiles. The values
# here were calculated to fit the quantiles produces by np.percentile
# using numpy 1.9 Calculating quantiles with
# scipy.stats.mstats.scoreatquantile or scipy.stats.mstats.mquantiles
# would yield very different results!
X_expected = [[0., 0., +0.0], [0., 0., -1.0], [0., 0., +1.0]]
assert_array_almost_equal(X_trans, X_expected)
X_trans_inv = scaler.inverse_transform(X_trans)
assert_array_almost_equal(X, X_trans_inv)
# make sure new data gets transformed correctly
X_new = [[+0., 2., 0.5], [-1., 1., 0.0], [+0., 1., 1.5]]
X_trans_new = scaler.transform(X_new)
X_expected_new = [[+0., 1., +0.], [-1., 0., -0.83333], [+0., 0., +1.66667]]
assert_array_almost_equal(X_trans_new, X_expected_new, decimal=3)
