def test_duplication(self):
"""
Test de-duplication of array data
"""
arr = [0, 0, 5, 6, 6, 7, 8, 8]
sm = smoothing.SmoothingPy()
de_dupl, duplications = sm.de_duplicate_array(arr)
re_dupl = sm.re_duplicate_array(de_dupl, duplications)
# the input and output need to be identical!
self.assertEqual(re_dupl, arr)
def test_smooth_spline_scikit(self):
"""Test the smoothing spline using scikit"""
sm = smoothing.SmoothingPy()
r = sm._smooth_spline_scikit(self.data1, self.data2)
self.assertEqual(len(r), 8)
self.assertAlmostEqual(r[0], 4.41118020)
self.assertAlmostEqual(r[2], 8.7900826361)
self.assertAlmostEqual(r[5], 14.1276411901)
self.assertAlmostEqual(r[7], 5.90758396)
r = sm._smooth_spline_scikit(self.data1, self.data2, [5, 7, 10.0])
self.assertEqual(len(r), 3)
self.assertAlmostEqual(r[0], 4.6129201633)
self.assertAlmostEqual(r[1], 7.73837621136)
self.assertAlmostEqual(r[2], 10.3726686328)
r = sm._smooth_spline_scikit(self.data1, self.data2, [10.0, 5.0, 7])
self.assertEqual(len(r), 3)
self.assertAlmostEqual(r[0], 10.3726686328)
self.assertAlmostEqual(r[1], 4.6129201633)
self.assertAlmostEqual(r[2], 7.73837621136)
# Since the (xhat,r) is optimized, different lamda values are estimated
# and a different estimation is used here...
r = sm._smooth_spline_scikit(self.data1, self.data2,
[10.0, 5.0, 7, 10.00001])
self.assertEqual(len(r), 4)
self.assertAlmostEqual(r[0], 11.60276344181)
self.assertAlmostEqual(r[1], 4.3279294106253)
self.assertAlmostEqual(r[2], 7.3603529478143)
self.assertAlmostEqual(r[3], 11.60276823628391)
r = sm._smooth_spline_scikit(self.data1, self.data2,
[10.0, 5.0, 7, 10.0], True)
self.assertEqual(len(r), 4)
expected = [
10.372668632871067, 4.6129201633602159, 7.738376211369804,
10.372668632871067
]
for res, exp in zip(r, expected):
self.assertAlmostEqual(res, exp)
def test_smooth_spline_scikit_wrap(self):
"""Test the smoothing spline using scikit and the wrapper"""
sm = smoothing.SmoothingPy()
import numpy
sm.initialize(self.data1,
self.data2,
xmin=numpy.min(numpy.array(self.data1)),
xmax=numpy.max(numpy.array(self.data1)))
r_pred = sm.predict(self.data1)
r = sm._smooth_scikit_legacy(self.data1, self.data2, self.data1)
self.assertEqual(len(r), 8)
expected = [
4.340432925607892, 7.412884078435387, 8.865581929053054,
10.157652480992748, 11.196560644581082, 14.158931578788266,
7.561207213410677, 5.90694677222806
]
for res, exp in zip(r, expected):
self.assertAlmostEqual(res, exp)
for res, exp in zip(r_pred, expected):
self.assertAlmostEqual(res, exp)
r = sm._smooth_scikit_legacy(self.data1, self.data2, [5, 7, 10.0])
self.assertEqual(len(r), 3)
expected = [4.307319862409416, 7.423679061650855, 11.15630836580813]
for res, exp in zip(r, expected):
self.assertAlmostEqual(res, exp)
r = sm._smooth_scikit_legacy(self.data1, self.data2, [10.0, 5.0, 7])
self.assertEqual(len(r), 3)
expected = [11.15630836580813, 4.307319862409416, 7.423679061650855]
for res, exp in zip(r, expected):
self.assertAlmostEqual(res, exp)
# Here, we expect the exact same results
r = sm._smooth_scikit_legacy(self.data1, self.data2,
[10.0, 5.0, 7, 10.0])
self.assertEqual(len(r), 4)
expected = [
11.15630836580813, 4.307319862409416, 7.423679061650855,
11.15630836580813
]
for res, exp in zip(r, expected):
self.assertAlmostEqual(res, exp)
# However, if we also evaluate at a point outside the previous window, we expect the results to change slightly
r = sm._smooth_scikit_legacy(self.data1, self.data2,
[10.0, 5.0, 7, 10.0, 15.0])
self.assertEqual(len(r), 5)
expected = [
11.196560644581082, 4.340432925607892, 7.412884078435387,
11.196560644581082, 14.158931578788266
]
for res, exp in zip(r, expected):
self.assertAlmostEqual(res, exp)
# If we chose a point that is very far away, we still expect a "reasonable" result (e.g. -100 becores -96)
r = sm._smooth_scikit_legacy(self.data1, self.data2,
[10.0, 5.0, 7, -100.0, 15.0])
self.assertEqual(len(r), 5)
expected = [
10.265638884711272, 5.411029040286351, 7.352910860216361,
-96.53810165019972, 15.119857967104132
]
for res, exp in zip(r, expected):
self.assertAlmostEqual(res, exp, 1)