350 lines
12 KiB
Python
350 lines
12 KiB
Python
# -*- coding: utf-8 -*-
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# seasonality tests
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from pmdarima.arima.seasonality import CHTest, decompose, OCSBTest
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from pmdarima.arima.utils import nsdiffs
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from pmdarima.compat.pytest import pytest_error_str
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from pmdarima.datasets import \
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load_airpassengers, load_ausbeer, load_austres, load_wineind
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import numpy as np
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from numpy.testing import assert_almost_equal, assert_array_equal
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from sklearn.utils.validation import check_random_state
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import pytest
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from unittest import mock
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airpassengers = load_airpassengers()
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austres = load_austres()
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ausbeer = load_ausbeer()
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wineind = load_wineind()
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# change the length to be longer so we can actually test the large case
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aus_list = austres.tolist() # type: list
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austres_long = np.asarray(aus_list * 10) # type: np.ndarray
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@pytest.mark.parametrize(
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'x,type_,m,filter_', [
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pytest.param(ausbeer, 'additive', 4, None),
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pytest.param(airpassengers, 'multiplicative', 12, None),
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pytest.param(wineind, 'additive', 12, None),
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pytest.param(np.array([1., 2., 3., 4., 5., 6.]), 'additive', 3, None)
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]
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)
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def test_decompose_happy_path(x, type_, m, filter_):
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decomposed_tuple = decompose(x, type_, m, filter_)
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first_ind = int(m / 2)
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last_ind = -int(m / 2)
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x = decomposed_tuple.x[first_ind:last_ind]
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trend = decomposed_tuple.trend[first_ind:last_ind]
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seasonal = decomposed_tuple.seasonal[first_ind:last_ind]
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random = decomposed_tuple.random[first_ind:last_ind]
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if type_ == 'multiplicative':
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reconstructed_x = trend * seasonal * random
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else:
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reconstructed_x = trend + seasonal + random
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assert_almost_equal(x, reconstructed_x)
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def test_decompose_corner_cases():
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with pytest.raises(ValueError):
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decompose(ausbeer, 'dummy_type', 4, None), # bad `type_`
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with pytest.raises(ValueError):
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decompose(airpassengers, 'multiplicative', -0.5, None), # bad `m`
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with pytest.raises(ValueError):
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decompose(ausbeer[:1], 'multiplicative', 4, None) # bad `x`
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@pytest.mark.parametrize(
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'm,expected', [
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pytest.param(3, 0),
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pytest.param(24, 0),
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pytest.param(52, 0),
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pytest.param(365, 0)
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]
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)
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def test_ch_test_m_values(m, expected):
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assert CHTest(m=m).estimate_seasonal_differencing_term(austres) == expected
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@pytest.mark.parametrize(
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'm,chstat,expected', [
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pytest.param(365, 66., 1),
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pytest.param(365, 63., 0),
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pytest.param(366, 65., 1),
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pytest.param(366, 60., 0),
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]
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)
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def test_ch_test_long(m, chstat, expected):
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chtest = CHTest(m=m)
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y = np.random.rand(m * 3) # very long, but mock makes it not matter
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mock_sdtest = (lambda *args, **kwargs: chstat)
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with mock.patch.object(chtest, '_sd_test', mock_sdtest):
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res = chtest.estimate_seasonal_differencing_term(y)
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assert expected == res
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def test_ch_base():
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test = CHTest(m=2)
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assert test.estimate_seasonal_differencing_term(None) == 0
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# test really long m for random array
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random_state = check_random_state(42)
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CHTest(m=365).estimate_seasonal_differencing_term(random_state.rand(400))
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@pytest.mark.parametrize(
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'tst', ('ocsb', 'ch')
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)
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def test_nsdiffs_corner_cases(tst):
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# max_D must be a positive int
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with pytest.raises(ValueError):
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nsdiffs(austres, m=2, max_D=0, test=tst)
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# assert 0 for constant
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assert nsdiffs([1, 1, 1, 1], m=2, test=tst) == 0
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# show fails for m <= 1
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for m in (0, 1):
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with pytest.raises(ValueError):
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nsdiffs(austres, m=m, test=tst)
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def test_ch_seas_dummy():
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x = austres
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# Results from R. Don't try this in the console; it tends to
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# freak out and fall apart...
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expected = np.array([
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[6.123234e-17, 1.000000e+00, -1],
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[-1.000000e+00, 1.224647e-16, 1],
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[-1.836970e-16, -1.000000e+00, -1],
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[1.000000e+00, -2.449294e-16, 1],
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[3.061617e-16, 1.000000e+00, -1],
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[-1.000000e+00, 3.673940e-16, 1],
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[-4.286264e-16, -1.000000e+00, -1],
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[1.000000e+00, -4.898587e-16, 1],
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[5.510911e-16, 1.000000e+00, -1],
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[-1.000000e+00, 6.123234e-16, 1],
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[-2.449913e-15, -1.000000e+00, -1],
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[1.000000e+00, -7.347881e-16, 1],
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[-9.803364e-16, 1.000000e+00, -1],
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[-1.000000e+00, 8.572528e-16, 1],
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[-2.694842e-15, -1.000000e+00, -1],
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[1.000000e+00, -9.797174e-16, 1],
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[-7.354071e-16, 1.000000e+00, -1],
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[-1.000000e+00, 1.102182e-15, 1],
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[-2.939771e-15, -1.000000e+00, -1],
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[1.000000e+00, -1.224647e-15, 1],
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[-4.904777e-16, 1.000000e+00, -1],
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[-1.000000e+00, 4.899825e-15, 1],
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[-3.184701e-15, -1.000000e+00, -1],
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[1.000000e+00, -1.469576e-15, 1],
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[-2.455483e-16, 1.000000e+00, -1],
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[-1.000000e+00, -1.960673e-15, 1],
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[-3.429630e-15, -1.000000e+00, -1],
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[1.000000e+00, -1.714506e-15, 1],
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[-6.189806e-19, 1.000000e+00, -1],
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[-1.000000e+00, 5.389684e-15, 1],
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[-3.674559e-15, -1.000000e+00, -1],
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[1.000000e+00, -1.959435e-15, 1],
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[2.443104e-16, 1.000000e+00, -1],
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[-1.000000e+00, -1.470814e-15, 1],
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[-3.919489e-15, -1.000000e+00, -1],
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[1.000000e+00, -2.204364e-15, 1],
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[4.892397e-16, 1.000000e+00, -1],
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[-1.000000e+00, 5.879543e-15, 1],
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[-4.164418e-15, -1.000000e+00, -1],
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[1.000000e+00, -2.449294e-15, 1],
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[7.839596e-15, 1.000000e+00, -1],
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[-1.000000e+00, -9.809554e-16, 1],
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[-4.409347e-15, -1.000000e+00, -1],
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[1.000000e+00, -9.799650e-15, 1],
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[9.790985e-16, 1.000000e+00, -1],
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[-1.000000e+00, 6.369401e-15, 1],
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[2.451151e-15, -1.000000e+00, -1],
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[1.000000e+00, -2.939152e-15, 1],
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[8.329455e-15, 1.000000e+00, -1],
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[-1.000000e+00, -4.910967e-16, 1],
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[-4.899206e-15, -1.000000e+00, -1],
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[1.000000e+00, 3.921346e-15, 1],
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[1.468957e-15, 1.000000e+00, -1],
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[-1.000000e+00, 6.859260e-15, 1],
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[1.961292e-15, -1.000000e+00, -1],
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[1.000000e+00, -3.429011e-15, 1],
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[8.819314e-15, 1.000000e+00, -1],
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[-1.000000e+00, -1.237961e-18, 1],
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[-5.389065e-15, -1.000000e+00, -1],
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[1.000000e+00, -1.077937e-14, 1],
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[1.958816e-15, 1.000000e+00, -1],
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[-1.000000e+00, 7.349119e-15, 1],
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[1.471433e-15, -1.000000e+00, -1],
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[1.000000e+00, -3.918870e-15, 1],
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[9.309173e-15, 1.000000e+00, -1],
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[-1.000000e+00, 4.886208e-16, 1],
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[-5.878924e-15, -1.000000e+00, -1],
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[1.000000e+00, 2.941628e-15, 1],
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[2.448675e-15, 1.000000e+00, -1],
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[-1.000000e+00, 7.838977e-15, 1],
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[9.815744e-16, -1.000000e+00, -1],
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[1.000000e+00, -4.408728e-15, 1],
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[9.799031e-15, 1.000000e+00, -1],
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[-1.000000e+00, 9.784795e-16, 1],
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[-6.368782e-15, -1.000000e+00, -1],
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[1.000000e+00, -1.175909e-14, 1],
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[2.938533e-15, 1.000000e+00, -1],
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[-1.000000e+00, 8.328836e-15, 1],
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[4.917157e-16, -1.000000e+00, -1],
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[1.000000e+00, -4.898587e-15, 1],
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[1.028889e-14, 1.000000e+00, -1],
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[-1.000000e+00, 1.567919e-14, 1],
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[7.352214e-15, -1.000000e+00, -1],
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[1.000000e+00, 1.961911e-15, 1],
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[3.428392e-15, 1.000000e+00, -1],
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[-1.000000e+00, 8.818695e-15, 1],
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[-1.420900e-14, -1.000000e+00, -1],
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[1.000000e+00, -1.959930e-14, 1],
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[-3.432106e-15, 1.000000e+00, -1]
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])
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actual = CHTest._seas_dummy(x, 4)
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assert_almost_equal(actual, expected)
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@pytest.mark.parametrize(
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'x,m,expected', [
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pytest.param(austres, 3, 0.07956102), # R code produces 0.07956102
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pytest.param(austres, 4, 0.1935046), # Expected from R: 0.1935046
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pytest.param(austres, 24, 4.134289) # R res: 4.134289
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]
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)
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def test_ch_sd_test(x, m, expected):
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res = CHTest._sd_test(x, m)
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assert np.allclose(res, expected)
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def test_ocsb_do_lag():
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q = np.arange(5)
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assert_array_equal(OCSBTest._do_lag(q, 1, False),
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[[0.],
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[1.],
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[2.],
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[3.],
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[4.]])
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assert_array_equal(OCSBTest._do_lag(q, 1, True),
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[[0.],
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[1.],
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[2.],
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[3.],
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[4.]])
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assert_array_equal(OCSBTest._do_lag(q, 2, False),
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[[0., np.nan],
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[1., 0.],
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[2., 1.],
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[3., 2.],
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[4., 3.],
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[np.nan, 4.]])
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assert_array_equal(OCSBTest._do_lag(q, 2, True),
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[[1., 0.],
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[2., 1.],
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[3., 2.],
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[4., 3.]])
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assert_array_equal(OCSBTest._do_lag(q, 3, False),
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[[0., np.nan, np.nan],
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[1., 0., np.nan],
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[2., 1., 0.],
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[3., 2., 1.],
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[4., 3., 2.],
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[np.nan, 4., 3.],
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[np.nan, np.nan, 4.]])
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assert_array_equal(OCSBTest._do_lag(q, 3, True),
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[[2., 1., 0.],
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[3., 2., 1.],
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[4., 3., 2.]])
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assert_array_equal(OCSBTest._do_lag(q, 4, False),
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[[0., np.nan, np.nan, np.nan],
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[1., 0., np.nan, np.nan],
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[2., 1., 0., np.nan],
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[3., 2., 1., 0.],
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[4., 3., 2., 1.],
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[np.nan, 4., 3., 2.],
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[np.nan, np.nan, 4., 3.],
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[np.nan, np.nan, np.nan, 4.]])
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assert_array_equal(OCSBTest._do_lag(q, 4, True),
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[[3., 2., 1., 0.],
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[4., 3., 2., 1.]])
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def test_ocsb_gen_lags():
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z_res = OCSBTest._gen_lags(austres, 0)
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assert z_res.shape == austres.shape
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assert (z_res == 0).all()
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@pytest.mark.parametrize(
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'lag_method,expected,max_lag', [
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# ocsb.test(austres, lag.method='fixed', maxlag=2)$stat -> -5.673749
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pytest.param('fixed', -5.6737, 2),
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# ocsb.test(austres, lag.method='fixed', maxlag=3)$stat -> -5.632227
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pytest.param('fixed', -5.6280, 3),
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# ocsb.test(austres, lag.method='AIC', maxlag=2)$stat -> -6.834392
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# We get a singular matrix error in Python that doesn't show up in R,
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# but we found a way to recover. Unforunately, it means our results are
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# different...
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pytest.param('aic', -5.66870, 2),
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pytest.param('aic', -6.03761, 3),
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pytest.param('bic', -5.66870, 2),
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pytest.param('bic', -6.03761, 3),
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pytest.param('aicc', -5.66870, 2),
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pytest.param('aicc', -6.03761, 3),
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]
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)
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def test_ocsb_test_statistic(lag_method, expected, max_lag):
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test = OCSBTest(m=4, max_lag=max_lag, lag_method=lag_method)
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test_stat = test._compute_test_statistic(austres)
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assert np.allclose(test_stat, expected, rtol=0.01)
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def test_ocsb_regression():
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# fitOCSB is a closure function inside of forecast::ocsb.test
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# > fitOCSB(austres, 1, 1)
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# Coefficients:
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# xregmf.x xregZ4 xregZ5
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# 0.2169 0.2111 -0.8625
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# We get different results here, but only marginally...
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reg = OCSBTest._fit_ocsb(austres, m=4, lag=1, max_lag=1)
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coef = reg.params
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assert np.allclose(coef, [0.2169, 0.2111, -0.8625], rtol=0.01)
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def test_failing_ocsb():
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# TODO: should this pass?
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# This passes in R, but statsmodels can't compute the regression...
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with pytest.raises(ValueError):
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OCSBTest(m=4, max_lag=0).estimate_seasonal_differencing_term(austres)
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# Fail for bad method
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with pytest.raises(ValueError) as v:
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OCSBTest(m=4, max_lag=3, lag_method="bad_method")\
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.estimate_seasonal_differencing_term(austres)
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assert "invalid method" in pytest_error_str(v)
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