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Merge pull request #520 from reneeotten/pytest
test-suite: use pytest features, improve coverage, fix mistakes
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,22 @@ | ||
| import numpy as np | ||
| import pytest | ||
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| import lmfit | ||
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| @pytest.fixture | ||
| def minimizer_Alpine02(): | ||
| """Return a lmfit Minimizer object for the Alpine02 function.""" | ||
| def residual_Alpine02(params): | ||
| x0 = params['x0'].value | ||
| x1 = params['x1'].value | ||
| return np.prod(np.sqrt(x0) * np.sin(x0)) * np.prod(np.sqrt(x1) * | ||
| np.sin(x1)) | ||
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| # create Parameters and set initial values and bounds | ||
| pars = lmfit.Parameters() | ||
| pars.add_many(('x0', 1., True, 0.0, 10.0), | ||
| ('x1', 1., True, 0.0, 10.0)) | ||
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| mini = lmfit.Minimizer(residual_Alpine02, pars) | ||
| return mini |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,49 +1,104 @@ | ||
| """Tests for the AMPGO global minimization algorithm.""" | ||
| import sys | ||
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| import numpy as np | ||
| from numpy.testing import assert_allclose | ||
| import pytest | ||
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| import lmfit | ||
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| # correct result for Alpine02 function | ||
| global_optimum = [7.91705268, 4.81584232] | ||
| fglob = -6.12950 | ||
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| def test_ampgo_Alpine02(): | ||
| @pytest.mark.parametrize("tabustrategy", ['farthest', 'oldest']) | ||
| def test_ampgo_Alpine02(minimizer_Alpine02, tabustrategy): | ||
| """Test AMPGO algorithm on Alpine02 function.""" | ||
| kws = {'tabustrategy': tabustrategy} | ||
| out = minimizer_Alpine02.minimize(method='ampgo', **kws) | ||
| out_x = np.array([out.params['x0'].value, out.params['x1'].value]) | ||
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| assert_allclose(out.residual, fglob, rtol=1e-5) | ||
| assert_allclose(min(out_x), min(global_optimum), rtol=1e-3) | ||
| assert_allclose(max(out_x), max(global_optimum), rtol=1e-3) | ||
| assert 'global' in out.ampgo_msg | ||
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| def test_ampgo_bounds(minimizer_Alpine02): | ||
| """Test AMPGO algorithm with bounds.""" | ||
| # change boundaries of parameters | ||
| pars_bounds = lmfit.Parameters() | ||
| pars_bounds.add_many(('x0', 1., True, 5.0, 15.0), | ||
| ('x1', 1., True, 2.5, 7.5)) | ||
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| global_optimum = [7.91705268, 4.81584232] | ||
| fglob = -6.12950 | ||
| out = minimizer_Alpine02.minimize(params=pars_bounds, method='ampgo') | ||
| assert 5.0 <= out.params['x0'].value <= 15.0 | ||
| assert 2.5 <= out.params['x1'].value <= 7.5 | ||
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| def residual_Alpine02(params): | ||
| x0 = params['x0'].value | ||
| x1 = params['x1'].value | ||
| return np.prod(np.sqrt(x0) * np.sin(x0)) * np.prod(np.sqrt(x1) * | ||
| np.sin(x1)) | ||
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| pars = lmfit.Parameters() | ||
| pars.add_many(('x0', 1., True, 0.0, 10.0), | ||
| ('x1', 1., True, 0.0, 10.0)) | ||
| def test_ampgo_disp_true(minimizer_Alpine02, capsys): | ||
| """Test AMPGO algorithm with disp is True.""" | ||
| # disp to False for L-BFGS-B to avoid too much output... | ||
| kws = {'disp': True, 'local_opts': {'disp': False}} | ||
| minimizer_Alpine02.minimize(method='ampgo', **kws) | ||
| captured = capsys.readouterr() | ||
| assert "Starting MINIMIZATION Phase" in captured.out | ||
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| mini = lmfit.Minimizer(residual_Alpine02, pars) | ||
| out = mini.minimize(method='ampgo') | ||
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| def test_ampgo_maxfunevals(minimizer_Alpine02): | ||
| """Test AMPGO algorithm with maxfunevals.""" | ||
| # disp to False for L-BFGS-B to avoid too much output... | ||
| kws = {'maxfunevals': 5, 'disp': True, 'local_opts': {'disp': False}} | ||
| out = minimizer_Alpine02.minimize(method='ampgo', **kws) | ||
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| assert out.ampgo_msg == 'Maximum number of function evaluations exceeded' | ||
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| def test_ampgo_local_solver(minimizer_Alpine02): | ||
| """Test AMPGO algorithm with local solver.""" | ||
| kws = {'local': 'Nelder-Mead'} | ||
| out = minimizer_Alpine02.minimize(method='ampgo', **kws) | ||
| out_x = np.array([out.params['x0'].value, out.params['x1'].value]) | ||
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| assert 'ampgo' and 'Nelder-Mead' in out.method | ||
| assert_allclose(out.residual, fglob, rtol=1e-5) | ||
| assert_allclose(min(out_x), min(global_optimum), rtol=1e-3) | ||
| assert_allclose(max(out_x), max(global_optimum), rtol=1e-3) | ||
| assert('global' in out.ampgo_msg) | ||
| assert 'global' in out.ampgo_msg | ||
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| def test_ampgo_Alpine02_maxfunevals(): | ||
| """Test AMPGO algorithm on Alpine02 function.""" | ||
| def test_ampgo_invalid_local_solver(minimizer_Alpine02): | ||
| """Test AMPGO algorithm with invalid local solvers.""" | ||
| kws = {'local': 'leastsq'} | ||
| with pytest.raises(Exception, match=r'Invalid local solver selected'): | ||
| minimizer_Alpine02.minimize(method='ampgo', **kws) | ||
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| def test_ampgo_invalid_tabulistsize(minimizer_Alpine02): | ||
| """Test AMPGO algorithm with invalid tabulistsize.""" | ||
| kws = {'tabulistsize': 0} | ||
| with pytest.raises(Exception, match=r'Invalid tabulistsize specified'): | ||
| minimizer_Alpine02.minimize(method='ampgo', **kws) | ||
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| def test_ampgo_invalid_tabustrategy(minimizer_Alpine02): | ||
| """Test AMPGO algorithm with invalid tabustrategy.""" | ||
| kws = {'tabustrategy': 'unknown'} | ||
| with pytest.raises(Exception, match=r'Invalid tabustrategy specified'): | ||
| minimizer_Alpine02.minimize(method='ampgo', **kws) | ||
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| def residual_Alpine02(params): | ||
| x0 = params['x0'].value | ||
| x1 = params['x1'].value | ||
| return np.prod(np.sqrt(x0) * np.sin(x0)) * np.prod(np.sqrt(x1) * | ||
| np.sin(x1)) | ||
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| pars = lmfit.Parameters() | ||
| pars.add_many(('x0', 1., True, 0.0, 10.0), | ||
| ('x1', 1., True, 0.0, 10.0)) | ||
| @pytest.mark.skipif(sys.version_info.major == 2, | ||
| reason="does not throw an exception in Python 2") | ||
| def test_ampgo_local_opts(minimizer_Alpine02): | ||
| """Test AMPGO algorithm, pass local_opts to solver.""" | ||
| # use local_opts to pass maxiter to the local optimizer: providing a string | ||
| # whereas an integer is required, this should throw an error. | ||
| kws = {'local_opts': {'maxiter': 'string'}} | ||
| with pytest.raises(TypeError): | ||
| minimizer_Alpine02.minimize(method='ampgo', **kws) | ||
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| mini = lmfit.Minimizer(residual_Alpine02, pars) | ||
| kws = {'maxfunevals': 50} | ||
| out = mini.minimize(method='ampgo', **kws) | ||
| assert('function' in out.ampgo_msg) | ||
| # for coverage: make sure that both occurences are reached | ||
| kws = {'local_opts': {'maxiter': 10}, 'maxfunevals': 50} | ||
| minimizer_Alpine02.minimize(method='ampgo', **kws) |
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