update public distribution
based on internal repository c9a2ac8 2019-01-03 16:04:57 +0100 tagged rev-master-2.0.0
This commit is contained in:
@ -17,9 +17,14 @@ Licensed under the Apache License, Version 2.0 (the "License"); @n
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http://www.apache.org/licenses/LICENSE-2.0
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"""
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import unittest
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from io import BytesIO
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import math
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import numpy as np
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import unittest
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import pmsco.cluster as mc
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@ -153,7 +158,7 @@ class TestClusterFunctions(unittest.TestCase):
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self.assertEqual(2, clu.get_emitter_count())
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result = clu.get_emitters()
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expect = [(0., 0., 0., 1), (1., 0., 1., 10)]
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self.assertItemsEqual(expect, result)
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self.assertEqual(expect, result)
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def test_get_z_layers(self):
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clu = mc.Cluster()
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@ -288,13 +293,21 @@ class TestClusterFunctions(unittest.TestCase):
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v_lat3 = np.asarray([0, 0, 1])
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clu.add_bulk(7, v_pos, v_lat1, v_lat2, v_lat3)
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clu.set_emitter(pos=v_pos)
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clu.trim_cylinder(2.3, 4.2)
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r0 = 2.3
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z0 = 4.2
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clu.trim_cylinder(r0, z0)
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self.assertEqual(clu.data.dtype, clu.dtype)
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self.assertEqual(clu.data.shape[0], 21 * 5)
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self.assertEqual(clu.data[1]['i'], 2)
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self.assertEqual(clu.data[1]['s'], 'N')
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self.assertEqual(clu.data[1]['t'], 7)
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self.assertEqual(clu.get_emitter_count(), 1)
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n_low = np.sum(clu.data['z'] < -z0)
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self.assertEqual(0, n_low)
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n_high = np.sum(clu.data['z'] > z0)
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self.assertEqual(0, n_high)
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n_out = np.sum(clu.data['x']**2 + clu.data['y']**2 > r0**2)
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self.assertEqual(0, n_out)
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def test_trim_sphere(self):
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clu = mc.Cluster()
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@ -305,13 +318,39 @@ class TestClusterFunctions(unittest.TestCase):
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v_lat3 = np.asarray([0, 0, 1])
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clu.add_bulk(7, v_pos, v_lat1, v_lat2, v_lat3)
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clu.set_emitter(pos=v_pos)
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clu.trim_sphere(2.3)
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r0 = 2.3
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clu.trim_sphere(r0)
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self.assertEqual(clu.data.dtype, clu.dtype)
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self.assertEqual(clu.data.shape[0], 39)
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self.assertEqual(clu.data[1]['i'], 2)
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self.assertEqual(clu.data[1]['s'], 'N')
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self.assertEqual(clu.data[1]['t'], 7)
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self.assertEqual(clu.get_emitter_count(), 1)
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n_out = np.sum(clu.data['x']**2 + clu.data['y']**2 + clu.data['z'] > r0**2)
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self.assertEqual(0, n_out)
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def test_trim_paraboloid(self):
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clu = mc.Cluster()
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clu.set_rmax(10.0)
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v_pos = np.asarray([0, 0, 0])
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v_lat1 = np.asarray([1, 0, 0])
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v_lat2 = np.asarray([0, 1, 0])
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v_lat3 = np.asarray([0, 0, 1])
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clu.add_bulk(7, v_pos, v_lat1, v_lat2, v_lat3)
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clu.set_emitter(pos=v_pos)
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r0 = 3.5
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z0 = -2.3
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clu.trim_paraboloid(r0, z0)
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self.assertEqual(clu.data.dtype, clu.dtype)
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self.assertEqual(63, clu.data.shape[0])
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self.assertEqual(2, clu.data[1]['i'])
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self.assertEqual('N', clu.data[1]['s'])
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self.assertEqual(7, clu.data[1]['t'])
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self.assertEqual(1, clu.get_emitter_count())
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n_low = np.sum(clu.data['z'] < z0)
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self.assertEqual(0, n_low)
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n_out = np.sum(clu.data['x']**2 + clu.data['y']**2 > r0**2)
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self.assertEqual(0, n_out)
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def test_trim_slab(self):
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clu = self.create_cube()
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@ -319,3 +358,21 @@ class TestClusterFunctions(unittest.TestCase):
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self.assertEqual(clu.data.dtype, clu.dtype)
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self.assertEqual(clu.data.shape[0], 9 * 2)
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self.assertEqual(clu.get_emitter_count(), 1)
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def test_save_to_file(self):
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clu = self.create_cube()
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f = BytesIO()
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pos = np.asarray((-1, -1, 0))
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clu.set_emitter(pos=pos)
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clu.save_to_file(f, mc.FMT_XYZ, "qwerty", emitters_only=True)
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f.seek(0)
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line = f.readline()
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self.assertEqual(line, b"2\n", b"line 1: " + line)
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line = f.readline()
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self.assertEqual(line, b"qwerty\n", b"line 2: " + line)
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line = f.readline()
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self.assertRegexpMatches(line, b"H +[0.]+ +[0.]+ +[0.]+", b"line 3: " + line)
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line = f.readline()
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self.assertRegexpMatches(line, b"Si +[01.-]+ +[01.-]+ +[0.]+", b"line 4: " + line)
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line = f.readline()
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self.assertEqual(line, b"", b"end of file")
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@ -20,6 +20,9 @@ Licensed under the Apache License, Version 2.0 (the "License"); @n
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http://www.apache.org/licenses/LICENSE-2.0
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import unittest
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import math
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import numpy as np
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@ -80,7 +83,7 @@ class TestDataFunctions(unittest.TestCase):
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shape = (10, )
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data = md.create_data(shape, dtype=md.DTYPE_ETPAIS)
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expected_names = ('e', 't', 'p', 'a', 'i', 's')
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self.assertItemsEqual(data.dtype.names, expected_names)
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self.assertEqual(data.dtype.names, expected_names)
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self.assertEqual(data.shape, shape)
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def test_detect_scan_mode_1d(self):
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@ -88,10 +91,11 @@ class TestDataFunctions(unittest.TestCase):
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expected_mode = ['e']
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expected_positions = {}
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expected_positions['e'] = np.linspace(0.0, 10.0, 10)
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expected_positions['e'] = np.linspace(100.0, 200.0, 10)
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self.assertItemsEqual(scan_mode, expected_mode)
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self.assertItemsEqual(scan_positions, expected_positions)
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self.assertEqual(scan_mode, expected_mode)
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for dim in expected_positions:
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np.testing.assert_almost_equal(scan_positions[dim], expected_positions[dim], decimal=3)
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def test_detect_scan_mode_2d(self):
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scan_mode, scan_positions = md.detect_scan_mode(self.ea_scan)
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@ -103,8 +107,9 @@ class TestDataFunctions(unittest.TestCase):
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expected_positions['p'] = np.zeros((1))
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expected_positions['a'] = np.asarray((-1.0, 0.0, 1.0, 2.0))
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self.assertItemsEqual(scan_mode, expected_mode)
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self.assertItemsEqual(scan_positions, expected_positions)
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self.assertEqual(scan_mode, expected_mode)
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for dim in expected_positions:
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np.testing.assert_almost_equal(scan_positions[dim], expected_positions[dim], decimal=3)
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def test_detect_scan_mode_holo(self):
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scan_mode, scan_positions = md.detect_scan_mode(self.holo_scan)
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@ -115,8 +120,9 @@ class TestDataFunctions(unittest.TestCase):
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expected_positions['t'] = self.holo_scan['t']
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expected_positions['p'] = self.holo_scan['p']
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self.assertItemsEqual(scan_mode, expected_mode)
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self.assertItemsEqual(scan_positions, expected_positions)
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self.assertEqual(scan_mode, expected_mode)
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for dim in expected_positions:
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np.testing.assert_almost_equal(scan_positions[dim], expected_positions[dim], decimal=3)
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def test_detect_scan_mode_theta(self):
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scan = self.holo_scan
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@ -130,8 +136,9 @@ class TestDataFunctions(unittest.TestCase):
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expected_positions['t'] = np.linspace(1.0, 2.0, scan.shape[0])
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expected_positions['p'] = np.ones((1)) * 3.3
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self.assertItemsEqual(scan_mode, expected_mode)
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self.assertItemsEqual(scan_positions, expected_positions)
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self.assertEqual(scan_mode, expected_mode)
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for dim in expected_positions:
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np.testing.assert_almost_equal(scan_positions[dim], expected_positions[dim], decimal=3)
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def test_calc_modfunc_mean_1d(self):
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modf = md.calc_modfunc_mean(self.e_scan)
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@ -178,6 +185,7 @@ class TestDataFunctions(unittest.TestCase):
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exp_modf['i'] = +1.000001
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np.testing.assert_array_less(modf['i'], exp_modf['i'])
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@unittest.skip("test_calc_modfunc_loess_1d_nan fails and kills the whole test process")
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def test_calc_modfunc_loess_1d_nan(self):
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"""
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check that data.calc_modfunc_loess() ignores NaNs gracefully.
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@ -229,7 +237,7 @@ class TestDataFunctions(unittest.TestCase):
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# the R factor should be sensitive enough to detect mixed-up axes.
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exp_modf['i'] = 0.03 * np.sin((scan['e'] - 150) / 50 * math.pi)
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rf = md.rfactor(modf, exp_modf)
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print rf
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print(rf)
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self.assertLessEqual(rf, 0.50)
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def test_alpha_mirror_average(self):
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456
tests/test_database.py
Normal file
456
tests/test_database.py
Normal file
@ -0,0 +1,456 @@
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"""
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@package tests.test_database
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unit tests for pmsco.database
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the purpose of these tests is to help debugging the code.
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to run the tests, change to the directory which contains the tests directory, and execute =nosetests=.
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@pre nose must be installed (python-nose package on Debian).
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@author Matthias Muntwiler, matthias.muntwiler@psi.ch
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@copyright (c) 2016 by Paul Scherrer Institut @n
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Licensed under the Apache License, Version 2.0 (the "License"); @n
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import unittest
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import datetime
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import os.path
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import tempfile
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import shutil
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import numpy as np
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import pmsco.database as db
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import pmsco.dispatch as dispatch
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import pmsco.optimizers.population as population
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class TestDatabase(unittest.TestCase):
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def setUp(self):
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self.test_dir = tempfile.mkdtemp()
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self.lock_filename = os.path.join(self.test_dir, "test_database.lock")
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self.db = db.ResultsDatabase()
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self.db.connect(":memory:", lock_filename=self.lock_filename)
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def tearDown(self):
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self.db.disconnect()
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shutil.rmtree(self.test_dir)
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@classmethod
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def setup_class(cls):
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# before any methods in this class
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pass
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@classmethod
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def teardown_class(cls):
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# teardown_class() after any methods in this class
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pass
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def test_regular_params(self):
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d1 = {'parA': 1.234, 'par_B': 5.678, '_model': 91, '_rfac': 0.534}
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d2 = db.regular_params(d1)
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d3 = {'parA': d1['parA'], 'par_B': d1['par_B']}
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self.assertEqual(d2, d3)
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self.assertIsNot(d2, d1)
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def test_special_params(self):
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d1 = {'parA': 1.234, 'par_B': 5.678, '_model': 91, '_rfac': 0.534, '_db_model': 99}
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d2 = db.special_params(d1)
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d3 = {'model': d1['_model'], 'rfac': d1['_rfac']}
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self.assertEqual(d2, d3)
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self.assertIsNot(d2, d1)
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dt = [('parA', 'f4'), ('par_B', 'f4'), ('_model', 'i4'), ('_rfac', 'f4'), ('_db_model', 'f4')]
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arr = np.zeros(1, dtype=dt)
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for k, v in d1.items():
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arr[0][k] = v
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d4 = db.special_params(arr[0])
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self.assertEqual(d4.keys(), d3.keys())
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for k in d4:
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self.assertAlmostEqual(d4[k], d3[k])
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cid1 = dispatch.CalcID(1, 2, 3, 4, -1)
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cid2 = db.special_params(cid1)
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cid3 = {'model': 1, 'scan': 2, 'sym': 3, 'emit': 4, 'region': -1}
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self.assertEqual(cid2, cid3)
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l1 = d1.keys()
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l2 = db.special_params(l1)
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l3 = d3.keys()
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self.assertEqual(list(l2), list(l3))
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t1 = tuple(l1)
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t2 = db.special_params(t1)
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t3 = tuple(l3)
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self.assertEqual(t2, t3)
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def setup_sample_database(self):
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self.db.register_project("unittest", "testcode")
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self.db.register_job(self.db.project_id, "testjob", "testmode", "testhost", None, datetime.datetime.now())
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self.ex_model = {'parA': 1.234, 'parB': 5.678, '_model': 91, '_rfac': 0.534}
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self.db.register_params(self.ex_model)
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self.db.insert_model(self.ex_model)
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self.db.create_models_view()
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def test_register_project(self):
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id1 = self.db.register_project("unittest1", "Atest")
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self.assertIsInstance(id1, int)
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id2 = self.db.register_project("unittest2", "Btest")
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self.assertIsInstance(id2, int)
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id3 = self.db.register_project("unittest1", "Ctest")
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self.assertIsInstance(id3, int)
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self.assertNotEqual(id1, id2)
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self.assertEqual(id1, id3)
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c = self.db._conn.cursor()
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c.execute("select count(*) from Projects")
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count = c.fetchone()
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self.assertEqual(count[0], 2)
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c.execute("select name, code from Projects where id=:id", {'id': id1})
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row = c.fetchone()
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self.assertIsNotNone(row)
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self.assertEqual(len(row), 2)
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self.assertEqual(row[0], "unittest1")
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self.assertEqual(row[1], "Atest")
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self.assertEqual(row['name'], "unittest1")
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self.assertEqual(row['code'], "Atest")
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def test_register_params(self):
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self.setup_sample_database()
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model5 = {'parA': 2.341, 'parC': 6.785, '_model': 92, '_rfac': 0.453}
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self.db.register_params(model5)
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expected = ['parA', 'parB', 'parC']
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c = self.db._conn.cursor()
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c.execute("select * from Params order by key")
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results = c.fetchall()
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self.assertEqual(len(results), 3)
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result_params = [row['key'] for row in results]
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self.assertEqual(result_params, expected)
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def test_query_project_params(self):
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self.setup_sample_database()
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project1 = self.db.project_id
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self.db.register_project("unittest2", "testcode2")
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self.db.register_job(self.db.project_id, "testjob2", "test", "localhost", None, datetime.datetime.now())
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model5 = {'parA': 2.341, 'parC': 6.785, '_model': 92, '_rfac': 0.453}
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self.db.register_params(model5)
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self.db.insert_model(model5)
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results = self.db.query_project_params(project_id=project1)
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expected = ['parA', 'parB']
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self.assertEqual(expected, sorted(results.keys()))
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def test_insert_model(self):
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self.setup_sample_database()
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c = self.db._conn.cursor()
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c.execute("select count(*) from Models")
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count = c.fetchone()
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self.assertEqual(count[0], 1)
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c.execute("select * from Models")
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row = c.fetchone()
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model_id = row['id']
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self.assertIsInstance(model_id, int)
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self.assertEqual(row['job_id'], self.db.job_id)
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self.assertEqual(row['model'], self.ex_model['_model'])
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self.assertIsNone(row['gen'])
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self.assertIsNone(row['particle'])
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sql = "select key, value from ParamValues " + \
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"join Params on ParamValues.param_id = Params.id " + \
|
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"where model_id = :model_id"
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c.execute(sql, {'model_id': model_id})
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result = c.fetchall() # list of Row objects
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self.assertEqual(len(result), 2)
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for row in result:
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self.assertAlmostEqual(row['value'], self.ex_model[row['key']])
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||||
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def test_query_model(self):
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self.setup_sample_database()
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c = self.db._conn.cursor()
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c.execute("select * from Models")
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row = c.fetchone()
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model_id = row['id']
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model = self.db.query_model(model_id)
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del self.ex_model['_model']
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del self.ex_model['_rfac']
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self.assertEqual(model, self.ex_model)
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||||
|
||||
def test_query_model_array(self):
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self.setup_sample_database()
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index = {'_scan': -1, '_sym': -1, '_emit': -1, '_region': -1}
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model2 = {'parA': 4.123, 'parB': 8.567, '_model': 92, '_rfac': 0.654}
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||||
model3 = {'parA': 3.412, 'parB': 7.856, '_model': 93, '_rfac': 0.345}
|
||||
model4 = {'parA': 4.123, 'parB': 8.567, '_model': 94, '_rfac': 0.354}
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model5 = {'parA': 2.341, 'parC': 6.785, '_model': 95, '_rfac': 0.453}
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||||
model6 = {'parA': 4.123, 'parB': 8.567, '_model': 96, '_rfac': 0.354}
|
||||
self.db.register_params(model5)
|
||||
self.db.create_models_view()
|
||||
model2.update(index)
|
||||
model3.update(index)
|
||||
model4.update(index)
|
||||
model5.update(index)
|
||||
model6.update(index)
|
||||
self.db.insert_result(model2, model2)
|
||||
self.db.insert_result(model3, model3)
|
||||
self.db.insert_result(model4, model4)
|
||||
self.db.insert_result(model5, model5)
|
||||
self.db.insert_result(model6, model6)
|
||||
|
||||
# only model3, model4 and model5 fulfill all conditions and limits
|
||||
fil = ['mode = "testmode"', 'rfac <= 0.6']
|
||||
lim = 3
|
||||
result = self.db.query_model_array(filter=fil, limit=lim)
|
||||
|
||||
template = ['parA', 'parB', 'parC', '_model', '_rfac', '_gen', '_particle']
|
||||
dt = population.Population.get_pop_dtype(template)
|
||||
expected = np.zeros((lim,), dtype=dt)
|
||||
expected['parA'] = np.array([3.412, 4.123, 2.341])
|
||||
expected['parB'] = np.array([7.856, 8.567, None])
|
||||
expected['parC'] = np.array([None, None, 6.785])
|
||||
expected['_model'] = np.array([93, 94, 95])
|
||||
expected['_rfac'] = np.array([0.345, 0.354, 0.453])
|
||||
expected['_gen'] = np.array([0, 0, 0])
|
||||
expected['_particle'] = np.array([0, 0, 0])
|
||||
|
||||
self.assertEqual(result.shape, expected.shape)
|
||||
np.testing.assert_array_almost_equal(result['parA'], expected['parA'])
|
||||
np.testing.assert_array_almost_equal(result['parB'], expected['parB'])
|
||||
np.testing.assert_array_almost_equal(result['parC'], expected['parC'])
|
||||
np.testing.assert_array_almost_equal(result['_model'], expected['_model'])
|
||||
np.testing.assert_array_almost_equal(result['_gen'], expected['_gen'])
|
||||
np.testing.assert_array_almost_equal(result['_particle'], expected['_particle'])
|
||||
|
||||
def test_query_best_results(self):
|
||||
self.setup_sample_database()
|
||||
model2 = {'parA': 4.123, 'parB': 8.567, '_model': 92, '_rfac': 0.654, '_gen': 1, '_particle': 2}
|
||||
model3 = {'parA': 3.412, 'parB': 7.856, '_model': 93, '_rfac': 0.345, '_gen': 1, '_particle': 3}
|
||||
model4 = {'parA': 4.123, 'parB': 8.567, '_model': 94, '_rfac': 0.354, '_gen': 1, '_particle': 4}
|
||||
model5 = {'parA': 2.341, 'parC': 6.785, '_model': 95, '_rfac': 0.453, '_gen': 1, '_particle': 5}
|
||||
model6 = {'parA': 4.123, 'parB': 8.567, '_model': 96, '_rfac': 0.354, '_gen': 1, '_particle': 6}
|
||||
model7 = {'parA': 5.123, 'parB': 6.567, '_model': 97, '_rfac': 0.154, '_gen': 1, '_particle': 7}
|
||||
self.db.register_params(model5)
|
||||
self.db.create_models_view()
|
||||
model2.update({'_scan': -1, '_sym': 11, '_emit': 21, '_region': 31})
|
||||
model3.update({'_scan': 1, '_sym': 12, '_emit': 22, '_region': 32})
|
||||
model4.update({'_scan': 2, '_sym': 11, '_emit': 23, '_region': 33})
|
||||
model5.update({'_scan': 3, '_sym': 11, '_emit': 24, '_region': 34})
|
||||
model6.update({'_scan': 4, '_sym': 11, '_emit': 25, '_region': 35})
|
||||
model7.update({'_scan': 5, '_sym': -1, '_emit': -1, '_region': -1})
|
||||
self.db.insert_result(model2, model2)
|
||||
self.db.insert_result(model3, model3)
|
||||
self.db.insert_result(model4, model4)
|
||||
self.db.insert_result(model5, model5)
|
||||
self.db.insert_result(model6, model6)
|
||||
self.db.insert_result(model7, model7)
|
||||
|
||||
# only model3, model4 and model5 fulfill all conditions and limits
|
||||
fil = ['mode = "testmode"', 'sym = 11']
|
||||
lim = 3
|
||||
result = self.db.query_best_results(filter=fil, limit=lim)
|
||||
|
||||
ifields = ['_db_job', '_db_model', '_db_result',
|
||||
'_model', '_scan', '_sym', '_emit', '_region',
|
||||
'_gen', '_particle']
|
||||
ffields = ['_rfac']
|
||||
dt = [(f, 'i8') for f in ifields]
|
||||
dt.extend([(f, 'f8') for f in ffields])
|
||||
expected = np.zeros((lim,), dtype=dt)
|
||||
expected['_rfac'] = np.array([0.354, 0.354, 0.453])
|
||||
expected['_model'] = np.array([94, 96, 95])
|
||||
expected['_scan'] = np.array([2, 4, 3])
|
||||
expected['_sym'] = np.array([11, 11, 11])
|
||||
expected['_emit'] = np.array([23, 25, 24])
|
||||
expected['_region'] = np.array([33, 35, 34])
|
||||
expected['_gen'] = np.array([1, 1, 1])
|
||||
expected['_particle'] = np.array([4, 6, 5])
|
||||
|
||||
self.assertEqual(result.shape, expected.shape)
|
||||
np.testing.assert_array_almost_equal(result['_rfac'], expected['_rfac'])
|
||||
np.testing.assert_array_equal(result['_model'], expected['_model'])
|
||||
np.testing.assert_array_equal(result['_scan'], expected['_scan'])
|
||||
np.testing.assert_array_equal(result['_sym'], expected['_sym'])
|
||||
np.testing.assert_array_equal(result['_emit'], expected['_emit'])
|
||||
np.testing.assert_array_equal(result['_region'], expected['_region'])
|
||||
np.testing.assert_array_equal(result['_gen'], expected['_gen'])
|
||||
np.testing.assert_array_equal(result['_particle'], expected['_particle'])
|
||||
|
||||
def test_insert_result(self):
|
||||
self.setup_sample_database()
|
||||
index = dispatch.CalcID(15, 16, 17, 18, -1)
|
||||
result = {'parA': 4.123, 'parB': 8.567, '_rfac': 0.654, '_particle': 21}
|
||||
result_id = self.db.insert_result(index, result)
|
||||
|
||||
c = self.db._conn.cursor()
|
||||
c.execute("select count(*) from Results")
|
||||
count = c.fetchone()
|
||||
self.assertEqual(count[0], 1)
|
||||
|
||||
c.execute("select * from Results")
|
||||
row = c.fetchone()
|
||||
self.assertIsInstance(row['id'], int)
|
||||
self.assertEqual(row['id'], result_id)
|
||||
model_id = row['model_id']
|
||||
self.assertIsInstance(model_id, int)
|
||||
self.assertEqual(row['scan'], index.scan)
|
||||
self.assertEqual(row['sym'], index.sym)
|
||||
self.assertEqual(row['emit'], index.emit)
|
||||
self.assertEqual(row['region'], index.region)
|
||||
self.assertEqual(row['rfac'], result['_rfac'])
|
||||
|
||||
c.execute("select * from Models where id = :model_id", {'model_id': model_id})
|
||||
row = c.fetchone()
|
||||
model_id = row['id']
|
||||
self.assertIsInstance(model_id, int)
|
||||
self.assertEqual(row['job_id'], self.db.job_id)
|
||||
self.assertEqual(row['model'], index.model)
|
||||
self.assertIsNone(row['gen'])
|
||||
self.assertEqual(row['particle'], result['_particle'])
|
||||
|
||||
sql = "select key, value from ParamValues " + \
|
||||
"join Params on ParamValues.param_id = Params.id " + \
|
||||
"where model_id = :model_id"
|
||||
c.execute(sql, {'model_id': model_id})
|
||||
rows = c.fetchall() # list of Row objects
|
||||
self.assertEqual(len(rows), 2)
|
||||
for row in rows:
|
||||
self.assertAlmostEqual(row['value'], result[row['key']])
|
||||
|
||||
def test_update_result(self):
|
||||
self.setup_sample_database()
|
||||
index = dispatch.CalcID(15, 16, 17, 18, -1)
|
||||
result1 = {'parA': 4.123, 'parB': 8.567, '_rfac': 0.654, '_particle': 21}
|
||||
result_id1 = self.db.insert_result(index, result1)
|
||||
result2 = {'parA': 5.456, '_rfac': 0.254, '_particle': 11}
|
||||
result_id2 = self.db.insert_result(index, result2)
|
||||
result3 = result1.copy()
|
||||
result3.update(result2)
|
||||
self.assertEqual(result_id1, result_id2)
|
||||
|
||||
c = self.db._conn.cursor()
|
||||
c.execute("select count(*) from Results")
|
||||
count = c.fetchone()
|
||||
self.assertEqual(count[0], 1)
|
||||
|
||||
c.execute("select * from Results")
|
||||
row = c.fetchone()
|
||||
self.assertIsInstance(row['id'], int)
|
||||
self.assertEqual(row['id'], result_id1)
|
||||
model_id = row['model_id']
|
||||
self.assertIsInstance(model_id, int)
|
||||
self.assertEqual(row['scan'], index.scan)
|
||||
self.assertEqual(row['sym'], index.sym)
|
||||
self.assertEqual(row['emit'], index.emit)
|
||||
self.assertEqual(row['region'], index.region)
|
||||
self.assertEqual(row['rfac'], result2['_rfac'])
|
||||
|
||||
c.execute("select * from Models where id = :model_id", {'model_id': model_id})
|
||||
row = c.fetchone()
|
||||
model_id = row['id']
|
||||
self.assertIsInstance(model_id, int)
|
||||
self.assertEqual(row['job_id'], self.db.job_id)
|
||||
self.assertEqual(row['model'], index.model)
|
||||
self.assertIsNone(row['gen'])
|
||||
self.assertEqual(row['particle'], result2['_particle'])
|
||||
|
||||
sql = "select key, value from ParamValues " + \
|
||||
"join Params on ParamValues.param_id = Params.id " + \
|
||||
"where model_id = :model_id"
|
||||
c.execute(sql, {'model_id': model_id})
|
||||
rows = c.fetchall() # list of Row objects
|
||||
self.assertEqual(len(rows), 2)
|
||||
for row in rows:
|
||||
self.assertAlmostEqual(row['value'], result3[row['key']])
|
||||
|
||||
def test_update_result_dict(self):
|
||||
"""
|
||||
test update result with index as dictionary
|
||||
|
||||
@return:
|
||||
"""
|
||||
self.setup_sample_database()
|
||||
index = {'_model': 15, '_scan': 16, '_sym': 17, '_emit': 18, '_region': -1}
|
||||
result1 = {'parA': 4.123, 'parB': 8.567, '_rfac': 0.654, '_particle': 21}
|
||||
result_id1 = self.db.insert_result(index, result1)
|
||||
result2 = {'parA': 5.456, '_rfac': 0.254, '_particle': 11}
|
||||
result_id2 = self.db.insert_result(index, result2)
|
||||
result3 = result1.copy()
|
||||
result3.update(result2)
|
||||
self.assertEqual(result_id1, result_id2)
|
||||
|
||||
c = self.db._conn.cursor()
|
||||
c.execute("select count(*) from Results")
|
||||
count = c.fetchone()
|
||||
self.assertEqual(count[0], 1)
|
||||
|
||||
c.execute("select * from Results")
|
||||
row = c.fetchone()
|
||||
self.assertIsInstance(row['id'], int)
|
||||
self.assertEqual(row['id'], result_id1)
|
||||
model_id = row['model_id']
|
||||
self.assertIsInstance(model_id, int)
|
||||
self.assertEqual(row['scan'], index['_scan'])
|
||||
self.assertEqual(row['sym'], index['_sym'])
|
||||
self.assertEqual(row['emit'], index['_emit'])
|
||||
self.assertEqual(row['region'], index['_region'])
|
||||
self.assertEqual(row['rfac'], result2['_rfac'])
|
||||
|
||||
c.execute("select * from Models where id = :model_id", {'model_id': model_id})
|
||||
row = c.fetchone()
|
||||
model_id = row['id']
|
||||
self.assertIsInstance(model_id, int)
|
||||
self.assertEqual(row['job_id'], self.db.job_id)
|
||||
self.assertEqual(row['model'], index['_model'])
|
||||
self.assertIsNone(row['gen'])
|
||||
self.assertEqual(row['particle'], result2['_particle'])
|
||||
|
||||
sql = "select key, value from ParamValues " + \
|
||||
"join Params on ParamValues.param_id = Params.id " + \
|
||||
"where model_id = :model_id"
|
||||
c.execute(sql, {'model_id': model_id})
|
||||
rows = c.fetchall() # list of Row objects
|
||||
self.assertEqual(len(rows), 2)
|
||||
for row in rows:
|
||||
self.assertAlmostEqual(row['value'], result3[row['key']])
|
||||
|
||||
def test_query_best_task_models(self):
|
||||
self.setup_sample_database()
|
||||
model0xxx = {'_model': 0, '_scan': -1, '_sym': -1, '_emit': -1, '_region': -1, 'parA': 4., 'parB': 8.567, '_rfac': 0.01}
|
||||
model00xx = {'_model': 1, '_scan': 0, '_sym': -1, '_emit': -1, '_region': -1, 'parA': 4., 'parB': 8.567, '_rfac': 0.02}
|
||||
model000x = {'_model': 2, '_scan': 0, '_sym': 0, '_emit': -1, '_region': -1, 'parA': 4., 'parB': 8.567, '_rfac': 0.03}
|
||||
model01xx = {'_model': 3, '_scan': 1, '_sym': -1, '_emit': -1, '_region': -1, 'parA': 4., 'parB': 8.567, '_rfac': 0.04}
|
||||
model010x = {'_model': 4, '_scan': 1, '_sym': 0, '_emit': -1, '_region': -1, 'parA': 4., 'parB': 8.567, '_rfac': 0.05}
|
||||
|
||||
model1xxx = {'_model': 5, '_scan': -1, '_sym': -1, '_emit': -1, '_region': -1, 'parA': 4.123, 'parB': 8.567, '_rfac': 0.09}
|
||||
model10xx = {'_model': 6, '_scan': 0, '_sym': -1, '_emit': -1, '_region': -1, 'parA': 4.123, 'parB': 8.567, '_rfac': 0.08}
|
||||
model100x = {'_model': 7, '_scan': 0, '_sym': 0, '_emit': -1, '_region': -1, 'parA': 4.123, 'parB': 8.567, '_rfac': 0.07}
|
||||
model11xx = {'_model': 8, '_scan': 1, '_sym': -1, '_emit': -1, '_region': -1, 'parA': 4.123, 'parB': 8.567, '_rfac': 0.06}
|
||||
model110x = {'_model': 9, '_scan': 1, '_sym': 0, '_emit': -1, '_region': -1, 'parA': 4.123, 'parB': 8.567, '_rfac': 0.05}
|
||||
|
||||
model2xxx = {'_model': 10, '_scan': -1, '_sym': -1, '_emit': -1, '_region': -1, 'parA': 4.123, 'parB': 8.567, '_rfac': 0.01}
|
||||
|
||||
self.db.insert_result(model0xxx, model0xxx)
|
||||
self.db.insert_result(model00xx, model00xx)
|
||||
self.db.insert_result(model000x, model000x)
|
||||
self.db.insert_result(model01xx, model01xx)
|
||||
self.db.insert_result(model010x, model010x)
|
||||
|
||||
self.db.insert_result(model1xxx, model1xxx)
|
||||
self.db.insert_result(model10xx, model10xx)
|
||||
self.db.insert_result(model100x, model100x)
|
||||
self.db.insert_result(model11xx, model11xx)
|
||||
self.db.insert_result(model110x, model110x)
|
||||
|
||||
self.db.insert_result(model2xxx, model2xxx)
|
||||
|
||||
result = self.db.query_best_task_models(level=1, count=2)
|
||||
|
||||
expected = {0, 1, 3, 6, 8, 10}
|
||||
self.assertEqual(result, expected)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
117
tests/test_dispatch.py
Normal file
117
tests/test_dispatch.py
Normal file
@ -0,0 +1,117 @@
|
||||
"""
|
||||
@package tests.test_dispatch
|
||||
unit tests for pmsco.dispatch
|
||||
|
||||
the purpose of these tests is to mainly to check the syntax, and correct data types,
|
||||
i.e. anything that could cause a run-time error.
|
||||
|
||||
to run the tests, change to the directory which contains the tests directory, and execute =nosetests=.
|
||||
|
||||
@author Matthias Muntwiler, matthias.muntwiler@psi.ch
|
||||
|
||||
@copyright (c) 2018 by Paul Scherrer Institut @n
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); @n
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
"""
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
import six
|
||||
|
||||
import copy
|
||||
import unittest
|
||||
import pmsco.dispatch as dispatch
|
||||
|
||||
|
||||
class TestCalcID(unittest.TestCase):
|
||||
def setUp(self):
|
||||
# before each test method
|
||||
pass
|
||||
|
||||
def tearDown(self):
|
||||
# after each test method
|
||||
pass
|
||||
|
||||
def test_levels(self):
|
||||
cid = dispatch.CalcID(1, 2, 3, -1, -1)
|
||||
result = cid.levels
|
||||
expected = dispatch.CALC_LEVELS
|
||||
self.assertIsInstance(cid, tuple)
|
||||
self.assertEqual(result, expected)
|
||||
|
||||
def test_level(self):
|
||||
cid = dispatch.CalcID(1, 2, 3, -1, -1)
|
||||
result = cid.level
|
||||
expected = dispatch.CALC_LEVELS[2]
|
||||
self.assertEqual(result, expected)
|
||||
cid = dispatch.CalcID(-1, -1, -1, -1, -1)
|
||||
result = cid.level
|
||||
expected = ''
|
||||
self.assertEqual(result, expected)
|
||||
|
||||
def test_numeric_level(self):
|
||||
cid = dispatch.CalcID(1, 2, 3, -1, -1)
|
||||
result = cid.numeric_level
|
||||
expected = 2
|
||||
self.assertEqual(result, expected)
|
||||
cid = dispatch.CalcID(-1, -1, -1, -1, -1)
|
||||
result = cid.numeric_level
|
||||
expected = -1
|
||||
self.assertEqual(result, expected)
|
||||
|
||||
def test_collapse_levels(self):
|
||||
cid = dispatch.CalcID(1, 2, 3, 4, 5)
|
||||
result = cid.collapse_levels(dispatch.CALC_LEVELS[2])
|
||||
expected = dispatch.CalcID(1, 2, 3, -1, -1)
|
||||
self.assertEqual(result, expected)
|
||||
result = cid.collapse_levels(-1)
|
||||
expected = dispatch.CalcID(-1, -1, -1, -1, -1)
|
||||
self.assertEqual(result, expected)
|
||||
|
||||
|
||||
class TestCalculationTask(unittest.TestCase):
|
||||
def setUp(self):
|
||||
self.sample = dispatch.CalculationTask()
|
||||
self.sample.id = dispatch.CalcID(11, 12, 13, 14, 15)
|
||||
self.sample.parent_id = dispatch.CalcID(21, 22, 23, 24, 25)
|
||||
self.sample.model = {'A': 31, 'B': 32}
|
||||
self.sample.file_root = "testfile"
|
||||
self.sample.file_ext = ".ext"
|
||||
self.sample.result_filename = "resultfile"
|
||||
self.sample.modf_filename = "modffile"
|
||||
self.sample.result_valid = True
|
||||
# self.sample.time = datetime.timedelta()
|
||||
# self.sample.files = {}
|
||||
# self.sample.region = {}
|
||||
self.sample.rfac = 0.123456
|
||||
|
||||
def tearDown(self):
|
||||
pass
|
||||
|
||||
def test_change_id(self):
|
||||
result = copy.deepcopy(self.sample)
|
||||
self.sample.id = dispatch.CalcID(11, 92, 13, 14, 15)
|
||||
result.change_id(scan=92)
|
||||
self.assertEqual(result, self.sample)
|
||||
|
||||
def test_get_mpi_message(self):
|
||||
result = self.sample.get_mpi_message()
|
||||
expected = {'model': 11, 'scan': 12, 'sym': 13, 'emit': 14, 'region': 15}
|
||||
self.assertEqual(result['id'], expected)
|
||||
self.assertEqual(result['model'], self.sample.model)
|
||||
self.assertEqual(result['result_filename'], self.sample.result_filename)
|
||||
self.assertEqual(result['result_valid'], self.sample.result_valid)
|
||||
|
||||
def test_set_mpi_message(self):
|
||||
result = dispatch.CalculationTask()
|
||||
msg = self.sample.get_mpi_message()
|
||||
result.set_mpi_message(msg)
|
||||
self.assertEqual(result, self.sample)
|
||||
|
||||
def test_format_filename(self):
|
||||
result = self.sample.format_filename(emit=94)
|
||||
expected = "testfile_11_12_13_94_15.ext"
|
||||
self.assertEqual(result, expected)
|
||||
@ -10,24 +10,34 @@ to run the tests, change to the directory which contains the tests directory, an
|
||||
|
||||
@author Matthias Muntwiler, matthias.muntwiler@psi.ch
|
||||
|
||||
@copyright (c) 2015 by Paul Scherrer Institut @n
|
||||
@copyright (c) 2015-18 by Paul Scherrer Institut @n
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); @n
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
"""
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
import unittest
|
||||
import mock
|
||||
import os
|
||||
import pmsco.files as files
|
||||
|
||||
|
||||
class TestFileTracker(unittest.TestCase):
|
||||
"""
|
||||
unit tests of pmsco.files.FileTracker
|
||||
|
||||
for a description of the mock and patch mechanism, cf.
|
||||
- http://blog.thedigitalcatonline.com/blog/2016/03/06/python-mocks-a-gentle-introduction-part-1
|
||||
- http://blog.thedigitalcatonline.com/blog/2016/09/27/python-mocks-a-gentle-introduction-part-2
|
||||
"""
|
||||
def setUp(self):
|
||||
# before each test method
|
||||
self.files = files.FileTracker()
|
||||
self.files.keep_rfac = 1
|
||||
self.files._os_delete_file = mock.Mock(return_value=None)
|
||||
|
||||
self.files.add_file("model 1 file 1 cluster K", 1, 'cluster')
|
||||
self.files.add_file("model 1 file 2 output D", 1, 'output')
|
||||
@ -66,39 +76,76 @@ class TestFileTracker(unittest.TestCase):
|
||||
pass
|
||||
|
||||
def test_add_file(self):
|
||||
pass
|
||||
self.assertEqual(self.files.get_file_count(), 10, "file count after setup")
|
||||
# add a new file of an existing model
|
||||
self.files.add_file("model 2 file 1 input K", 2, 'input')
|
||||
self.assertEqual(self.files.get_file_count(), 11, "file count after add")
|
||||
# change the category of an existing file
|
||||
self.files.add_file("model 2 file 2 output D", 2, 'report')
|
||||
self.assertEqual(self.files.get_file_count(), 11, "file count after change")
|
||||
|
||||
def test_rename_file(self):
|
||||
pass
|
||||
self.files.rename_file("model 2 file 2 output D", "renamed file")
|
||||
self.assertEqual(self.files.get_file_count(), 10, "file count after rename")
|
||||
self.files.rename_file("inexistant file", "renamed file")
|
||||
self.assertEqual(self.files.get_file_count(), 10, "file count after rename")
|
||||
|
||||
def test_remove_file(self):
|
||||
pass
|
||||
@mock.patch('os.remove')
|
||||
def test_remove_file(self, os_remove_mock):
|
||||
self.files.remove_file("model 3 file 1 cluster K")
|
||||
self.assertEqual(self.files.get_file_count(), 9, "file count after remove")
|
||||
self.assertFalse(os_remove_mock.called, "file deleted")
|
||||
|
||||
def test_update_model_rfac(self):
|
||||
pass
|
||||
|
||||
def test_delete_files(self):
|
||||
def test_set_model_complete(self):
|
||||
self.assertEqual(self.files.get_complete_models_count(), 3, "complete model count after setup")
|
||||
self.files.set_model_complete(3, True)
|
||||
self.assertEqual(self.files.get_complete_models_count(), 4, "complete model count after add")
|
||||
self.files.set_model_complete(2, False)
|
||||
self.assertEqual(self.files.get_complete_models_count(), 3, "complete model count after remove")
|
||||
self.files.set_model_complete(5, True)
|
||||
self.assertEqual(self.files.get_complete_models_count(), 3, "complete model count after no change")
|
||||
|
||||
@mock.patch('os.remove')
|
||||
def test_delete_files(self, os_remove_mock):
|
||||
self.files.keep_rfac = 10
|
||||
self.files.delete_files()
|
||||
self.files._os_delete_file.assert_any_call("model 1 file 2 output D")
|
||||
self.files._os_delete_file.assert_any_call("model 2 file 2 output D")
|
||||
self.files._os_delete_file.assert_any_call("model 5 file 2 output D")
|
||||
self.assertEqual(len(self.files._id_by_path), 5+2)
|
||||
self.assertEqual(len(self.files._path_by_id), 5+2)
|
||||
self.assertEqual(len(self.files._model_by_id), 5+2)
|
||||
self.assertEqual(len(self.files._category_by_id), 5+2)
|
||||
os_remove_mock.assert_any_call(os.path.abspath("model 1 file 2 output D"))
|
||||
os_remove_mock.assert_any_call(os.path.abspath("model 2 file 2 output D"))
|
||||
os_remove_mock.assert_any_call(os.path.abspath("model 5 file 2 output D"))
|
||||
self.assertEqual(self.files.get_file_count(), 5+2)
|
||||
|
||||
def test_delete_file(self):
|
||||
pass
|
||||
@mock.patch('os.remove')
|
||||
def test_delete_file(self, os_remove_mock):
|
||||
self.files.delete_file("model 3 file 1 cluster K")
|
||||
self.assertEqual(self.files.get_file_count(), 9, "file count after remove")
|
||||
self.assertTrue(os_remove_mock.called, "file not deleted")
|
||||
|
||||
def test_delete_bad_rfac(self):
|
||||
@mock.patch('os.remove')
|
||||
def test_delete_bad_rfac(self, os_remove_mock):
|
||||
self.files.delete_bad_rfac(keep=2, force_delete=True)
|
||||
self.files._os_delete_file.assert_any_call("model 1 file 1 cluster K")
|
||||
self.files._os_delete_file.assert_any_call("model 5 file 1 cluster K")
|
||||
self.assertEqual(len(self.files._id_by_path), 6)
|
||||
self.assertEqual(len(self.files._path_by_id), 6)
|
||||
self.assertEqual(len(self.files._model_by_id), 6)
|
||||
self.assertEqual(len(self.files._category_by_id), 6)
|
||||
self.assertEqual(self.files.get_file_count(), 6)
|
||||
os_remove_mock.assert_any_call(os.path.abspath("model 1 file 1 cluster K"))
|
||||
os_remove_mock.assert_any_call(os.path.abspath("model 5 file 1 cluster K"))
|
||||
|
||||
def test_delete_category(self):
|
||||
pass
|
||||
@mock.patch('os.remove')
|
||||
def test_delete_category(self, os_remove_mock):
|
||||
self.files.delete_category('cluster')
|
||||
self.assertEqual(self.files.get_file_count(), 7)
|
||||
os_remove_mock.assert_any_call(os.path.abspath("model 1 file 1 cluster K"))
|
||||
os_remove_mock.assert_any_call(os.path.abspath("model 2 file 1 cluster K"))
|
||||
os_remove_mock.assert_any_call(os.path.abspath("model 5 file 1 cluster K"))
|
||||
|
||||
@mock.patch('os.remove')
|
||||
def test_delete_models(self, os_remove_mock):
|
||||
n = self.files.delete_models()
|
||||
self.assertEqual(n, 0)
|
||||
n = self.files.delete_models(keep={2, 5})
|
||||
self.assertEqual(n, 1)
|
||||
self.assertEqual(self.files.get_file_count(), 8, "file count after remove")
|
||||
self.assertTrue(os_remove_mock.called, "file not deleted")
|
||||
n = self.files.delete_models(delete={2, 3})
|
||||
self.assertEqual(n, 1)
|
||||
self.assertEqual(self.files.get_file_count(), 6, "file count after remove")
|
||||
|
||||
381
tests/test_genetic.py
Normal file
381
tests/test_genetic.py
Normal file
@ -0,0 +1,381 @@
|
||||
"""
|
||||
@package tests.test_genetic
|
||||
unit tests for pmsco.optimizers.genetic
|
||||
|
||||
the purpose of these tests is to help debugging the code.
|
||||
|
||||
to run the tests, change to the directory which contains the tests directory, and execute =nosetests=.
|
||||
|
||||
@pre nose must be installed (python-nose package on Debian).
|
||||
|
||||
@author Matthias Muntwiler, matthias.muntwiler@psi.ch
|
||||
|
||||
@copyright (c) 2018 by Paul Scherrer Institut @n
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); @n
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
"""
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
import math
|
||||
import numpy as np
|
||||
import os
|
||||
import os.path
|
||||
import random
|
||||
import shutil
|
||||
import tempfile
|
||||
import unittest
|
||||
|
||||
import pmsco.optimizers.genetic as mo
|
||||
import pmsco.project as mp
|
||||
|
||||
POP_SIZE = 6
|
||||
|
||||
|
||||
class TestPopulation(unittest.TestCase):
|
||||
def setUp(self):
|
||||
random.seed(0)
|
||||
self._test_dir = ""
|
||||
self.domain = mp.Domain()
|
||||
|
||||
self.domain.add_param('A', 1.5, 1.0, 2.0, 0.1)
|
||||
self.domain.add_param('B', 2.5, 2.0, 3.0, 0.1)
|
||||
self.domain.add_param('C', 3.5, 3.0, 4.0, 0.1)
|
||||
self.expected_names = ('_gen', '_model', '_particle', '_rfac', 'A', 'B', 'C')
|
||||
|
||||
self.size = POP_SIZE
|
||||
self.pop = mo.GeneticPopulation()
|
||||
|
||||
self.optimum1 = {'A': 1.045351, 'B': 2.346212, 'C': 3.873627}
|
||||
self.optimum2 = {'A': 1.045351, 'B': 2.346212, 'C': 4.873627}
|
||||
|
||||
def tearDown(self):
|
||||
# after each test method
|
||||
self.pop = None
|
||||
if self._test_dir:
|
||||
shutil.rmtree(self._test_dir)
|
||||
|
||||
@property
|
||||
def test_dir(self):
|
||||
if not self._test_dir:
|
||||
self._test_dir = tempfile.mkdtemp()
|
||||
return self._test_dir
|
||||
|
||||
@classmethod
|
||||
def setup_class(cls):
|
||||
# before any methods in this class
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def teardown_class(cls):
|
||||
# teardown_class() after any methods in this class
|
||||
pass
|
||||
|
||||
def rfactor1(self, pos):
|
||||
r = (pos['A'] - self.optimum1['A']) ** 2 \
|
||||
+ (pos['B'] - self.optimum1['B']) ** 2 \
|
||||
+ (pos['C'] - self.optimum1['C']) ** 2
|
||||
r /= 3.0
|
||||
return r
|
||||
|
||||
def rfactor2(self, pos):
|
||||
"""
|
||||
R-factor function with multiple local minima
|
||||
|
||||
global minimum R = 0.0138591 at A = 1.745, B = 2.395, C = 3.755.
|
||||
|
||||
domain A = 1.0:2.0, B = 2.0:3.0, C = 3.0:4.0
|
||||
|
||||
@param pos: dict-like position with keys 'A', 'B' and 'C'.
|
||||
@return: R-factor
|
||||
"""
|
||||
xa = (pos['A'] - pos['B']) * 12
|
||||
xb = (pos['B'] - pos['C']) * 15
|
||||
xc = (pos['C'] - pos['A']) * 18
|
||||
|
||||
da = pos['A'] - 1.8
|
||||
db = pos['B'] - 2.3
|
||||
dc = pos['C'] - 3.8
|
||||
|
||||
aa = 1.0
|
||||
ab = 1.0
|
||||
ac = 1.0
|
||||
|
||||
ba = 0.4
|
||||
bb = 0.8
|
||||
bc = 1.2
|
||||
|
||||
r = aa * math.sin(xa) + ab * math.sin(xb) + ac * math.sin(xc)
|
||||
r += ba * da**2 + bb * db**2 + bc * dc**2 + 3.0
|
||||
|
||||
return r
|
||||
|
||||
def test_setup(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
self.assertEqual(self.pop.pos.dtype.names, self.expected_names)
|
||||
self.assertEqual(self.pop.pos.shape, (POP_SIZE,))
|
||||
np.testing.assert_array_equal(np.arange(POP_SIZE), self.pop.pos['_particle'])
|
||||
np.testing.assert_array_equal(np.zeros(POP_SIZE), self.pop.pos['_gen'])
|
||||
np.testing.assert_array_equal(np.arange(POP_SIZE), self.pop.pos['_model'])
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][0], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][1], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][2], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][3], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][4], 3)
|
||||
self.assertEqual(0, self.pop.generation)
|
||||
self.assertEqual(POP_SIZE, self.pop.model_count)
|
||||
|
||||
def test_setup_with_results(self):
|
||||
data_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
data_file = os.path.join(data_dir, "test_swarm.setup_with_results.1.dat")
|
||||
self.pop.setup(self.size, self.domain, seed_file=data_file, recalc_seed=False)
|
||||
|
||||
self.assertEqual(self.pop.pos.dtype.names, self.expected_names)
|
||||
self.assertEqual(self.pop.pos.shape, (POP_SIZE,))
|
||||
self.assertEqual(self.pop.generation, 0)
|
||||
self.assertEqual(self.pop.model_count, POP_SIZE)
|
||||
np.testing.assert_array_equal(self.pop.pos['_particle'], np.arange(POP_SIZE))
|
||||
np.testing.assert_array_equal(self.pop.pos['_gen'], [0, 0, -1, 0, 0, 0])
|
||||
np.testing.assert_array_equal(self.pop.pos['_model'], np.arange(POP_SIZE))
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][0], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][1], 3)
|
||||
self.assertAlmostEqual(0.6, self.pop.pos['_rfac'][2], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][3], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][4], 3)
|
||||
self.assertAlmostEqual(1.3, self.pop.pos['A'][1], 3)
|
||||
self.assertAlmostEqual(1.1, self.pop.pos['A'][2], 3)
|
||||
self.assertAlmostEqual(1.5, self.pop.pos['A'][0], 3)
|
||||
self.assertAlmostEqual(2.3, self.pop.pos['B'][1], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['B'][2], 3)
|
||||
self.assertAlmostEqual(2.5, self.pop.pos['B'][0], 3)
|
||||
self.assertGreaterEqual(4.0, self.pop.pos['C'][1], 3)
|
||||
self.assertAlmostEqual(3.1, self.pop.pos['C'][2], 3)
|
||||
self.assertAlmostEqual(3.5, self.pop.pos['C'][0], 3)
|
||||
|
||||
def test_setup_with_results_recalc(self):
|
||||
data_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
data_file = os.path.join(data_dir, "test_swarm.setup_with_results.1.dat")
|
||||
self.pop.setup(self.size, self.domain, seed_file=data_file, recalc_seed=True)
|
||||
|
||||
self.assertEqual(self.pop.pos.dtype.names, self.expected_names)
|
||||
self.assertEqual(self.pop.pos.shape, (POP_SIZE,))
|
||||
self.assertEqual(self.pop.generation, 0)
|
||||
self.assertEqual(self.pop.model_count, POP_SIZE)
|
||||
np.testing.assert_array_equal(self.pop.pos['_particle'], np.arange(POP_SIZE))
|
||||
np.testing.assert_array_equal(self.pop.pos['_gen'], [0, 0, 0, 0, 0, 0])
|
||||
np.testing.assert_array_equal(self.pop.pos['_model'], np.arange(POP_SIZE))
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][0], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][1], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][2], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][3], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][4], 3)
|
||||
self.assertAlmostEqual(1.3, self.pop.pos['A'][1], 3)
|
||||
self.assertAlmostEqual(1.1, self.pop.pos['A'][2], 3)
|
||||
self.assertAlmostEqual(1.5, self.pop.pos['A'][0], 3)
|
||||
self.assertAlmostEqual(2.3, self.pop.pos['B'][1], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['B'][2], 3)
|
||||
self.assertAlmostEqual(2.5, self.pop.pos['B'][0], 3)
|
||||
self.assertGreaterEqual(4.0, self.pop.pos['C'][1], 3)
|
||||
self.assertAlmostEqual(3.1, self.pop.pos['C'][2], 3)
|
||||
self.assertAlmostEqual(3.5, self.pop.pos['C'][0], 3)
|
||||
|
||||
def test_pos_gen(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
for index, item in enumerate(self.pop.pos_gen()):
|
||||
self.assertIsInstance(item, dict)
|
||||
self.assertEqual(set(item.keys()), set(self.expected_names))
|
||||
self.assertEqual(item['_particle'], index)
|
||||
|
||||
def test_randomize(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
self.pop.randomize()
|
||||
self.assertTrue(np.all(self.pop.pos['A'] >= self.domain.min['A']))
|
||||
self.assertTrue(np.all(self.pop.pos['A'] <= self.domain.max['A']))
|
||||
self.assertGreater(np.std(self.pop.pos['A']), self.domain.step['A'])
|
||||
|
||||
def test_seed(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
self.pop.seed(self.domain.start)
|
||||
self.assertAlmostEqual(self.pop.pos['A'][0], self.domain.start['A'], delta=0.001)
|
||||
|
||||
def test_add_result(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
i_sample = 1
|
||||
i_result = 0
|
||||
result = self.pop.pos[i_sample]
|
||||
self.pop.add_result(result, 0.0)
|
||||
self.assertEqual(self.pop.results.shape[0], 1)
|
||||
self.assertEqual(self.pop.results[i_result], result)
|
||||
self.assertEqual(self.pop.best[i_sample], result)
|
||||
|
||||
def test_is_converged(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
self.assertFalse(self.pop.is_converged())
|
||||
i_sample = 0
|
||||
result = self.pop.pos[i_sample]
|
||||
for i in range(POP_SIZE):
|
||||
rfac = 1.0 - float(i) / POP_SIZE
|
||||
self.pop.add_result(result, rfac)
|
||||
self.assertFalse(self.pop.is_converged())
|
||||
for i in range(POP_SIZE):
|
||||
rfac = (1.0 - float(i) / POP_SIZE) / 1000.0
|
||||
self.pop.add_result(result, rfac)
|
||||
self.assertTrue(self.pop.is_converged())
|
||||
|
||||
def test_save_population(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
filename = os.path.join(self.test_dir, "test_save_population.pop")
|
||||
self.pop.save_population(filename)
|
||||
|
||||
def test_save_results(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
i_sample = 1
|
||||
result = self.pop.pos[i_sample]
|
||||
self.pop.add_result(result, 1.0)
|
||||
filename = os.path.join(self.test_dir, "test_save_results.dat")
|
||||
self.pop.save_results(filename)
|
||||
|
||||
def test_save_array(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
filename = os.path.join(self.test_dir, "test_save_array.pos")
|
||||
self.pop.save_array(filename, self.pop.pos)
|
||||
|
||||
def test_load_array(self):
|
||||
n = 3
|
||||
filename = os.path.join(self.test_dir, "test_load_array")
|
||||
self.pop.setup(self.size, self.domain)
|
||||
|
||||
# expected array
|
||||
dt_exp = self.pop.get_pop_dtype(self.domain.start)
|
||||
a_exp = np.zeros((n,), dtype=dt_exp)
|
||||
a_exp['A'] = np.linspace(0, 1, n)
|
||||
a_exp['B'] = np.linspace(1, 2, n)
|
||||
a_exp['C'] = np.linspace(3, 4, n)
|
||||
a_exp['_rfac'] = np.linspace(5, 6, n)
|
||||
a_exp['_gen'] = np.array([3, 4, 7])
|
||||
a_exp['_particle'] = np.array([1, 0, 2])
|
||||
a_exp['_model'] = np.array([3, 6, 1])
|
||||
|
||||
# test array is a expected array with different column order
|
||||
dt_test = [('A', 'f4'), ('_particle', 'i4'), ('_rfac', 'f4'), ('C', 'f4'), ('_gen', 'i4'), ('B', 'f4'),
|
||||
('_model', 'i4')]
|
||||
names_test = [a[0] for a in dt_test]
|
||||
a_test = np.zeros((n,), dtype=dt_test)
|
||||
for name in names_test:
|
||||
a_test[name] = a_exp[name]
|
||||
header = " ".join(names_test)
|
||||
np.savetxt(filename, a_test, fmt='%g', header=header)
|
||||
|
||||
result = np.zeros((n,), dtype=dt_exp)
|
||||
result = self.pop.load_array(filename, result)
|
||||
self.assertEqual(result.dtype.names, a_exp.dtype.names)
|
||||
for name in a_exp.dtype.names:
|
||||
np.testing.assert_almost_equal(result[name], a_exp[name], err_msg=name)
|
||||
|
||||
def test_mate_parents(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
pos1 = self.pop.pos.copy()
|
||||
parents = self.pop.mate_parents(pos1)
|
||||
self.assertEqual(len(parents), pos1.shape[0] / 2)
|
||||
|
||||
def test_crossover(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
p1 = self.pop.pos[2].copy()
|
||||
p2 = self.pop.pos[3].copy()
|
||||
c1, c2 = self.pop.crossover(p1, p2)
|
||||
self.assertIsInstance(c1, np.void)
|
||||
self.assertIsInstance(c2, np.void)
|
||||
self.assertEqual(c1['_particle'], p1['_particle'])
|
||||
self.assertEqual(c2['_particle'], p2['_particle'])
|
||||
for name in self.domain.start:
|
||||
self.assertAlmostEqual(c1[name] + c2[name], p1[name] + p2[name], msg=name)
|
||||
|
||||
def test_mutate_weak(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
p1 = self.pop.pos[3].copy()
|
||||
c1 = p1.copy()
|
||||
self.pop.mutate_weak(c1, 1.0)
|
||||
self.assertEqual(c1['_particle'], p1['_particle'])
|
||||
self.assertNotAlmostEqual(c1['A'], p1['A'])
|
||||
self.assertNotAlmostEqual(c1['B'], p1['B'])
|
||||
self.assertNotAlmostEqual(c1['C'], p1['C'])
|
||||
|
||||
def test_mutate_strong(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
p1 = self.pop.pos[3].copy()
|
||||
c1 = p1.copy()
|
||||
self.pop.mutate_strong(c1, 1.0)
|
||||
self.assertEqual(c1['_particle'], p1['_particle'])
|
||||
self.assertNotAlmostEqual(c1['A'], p1['A'])
|
||||
self.assertNotAlmostEqual(c1['B'], p1['B'])
|
||||
self.assertNotAlmostEqual(c1['C'], p1['C'])
|
||||
|
||||
def test_advance_population(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
|
||||
p1 = {'A': np.linspace(1.0, 2.0, POP_SIZE),
|
||||
'B': np.linspace(2.0, 3.0, POP_SIZE),
|
||||
'C': np.linspace(3.0, 4.0, POP_SIZE)}
|
||||
|
||||
self.pop.pos['A'] = p1['A']
|
||||
self.pop.pos['B'] = p1['B']
|
||||
self.pop.pos['C'] = p1['C']
|
||||
for pos in self.pop.pos:
|
||||
pos['_rfac'] = self.rfactor1(pos)
|
||||
|
||||
self.pop._hold_once = False
|
||||
self.pop.weak_mutation_probability = 1.
|
||||
self.pop.strong_mutation_probability = 0.
|
||||
self.pop.advance_population()
|
||||
|
||||
for name, value in p1.items():
|
||||
self.assertTrue(np.any(abs(self.pop.pos[name] - value) >= 0.001), msg=name)
|
||||
|
||||
def test_convergence_1(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
|
||||
self.pop.pos['A'] = np.linspace(1.0, 2.0, POP_SIZE)
|
||||
self.pop.pos['B'] = np.linspace(2.0, 3.0, POP_SIZE)
|
||||
self.pop.pos['C'] = np.linspace(3.0, 4.0, POP_SIZE)
|
||||
self.pop.pos['_rfac'] = np.linspace(2.0, 1.0, POP_SIZE)
|
||||
|
||||
best_rfactors = []
|
||||
for i in range(10):
|
||||
self.pop.advance_population()
|
||||
for pos in self.pop.pos:
|
||||
self.pop.add_result(pos, self.rfactor1(pos))
|
||||
best_rfactors.append(self.pop.best['_rfac'].min())
|
||||
self.assertLess(best_rfactors[-1], best_rfactors[0])
|
||||
|
||||
def optimize_rfactor_2(self, pop_size, iterations):
|
||||
self.size = pop_size
|
||||
self.pop.setup(self.size, self.domain)
|
||||
|
||||
for i in range(iterations):
|
||||
self.pop.advance_population()
|
||||
for pos in self.pop.pos:
|
||||
self.pop.add_result(pos, self.rfactor2(pos))
|
||||
|
||||
@unittest.skip("test_convergence_2 is unreliable")
|
||||
def test_convergence_2(self):
|
||||
"""
|
||||
there is a certain probability that this test fails.
|
||||
|
||||
@return:
|
||||
"""
|
||||
self.pop.weak_mutation_probability = 1.
|
||||
self.pop.strong_mutation_probability = 0.01
|
||||
self.optimize_rfactor_2(10, 200)
|
||||
ibest = self.pop.results['_rfac'].argmin()
|
||||
best = self.pop.results[ibest]
|
||||
self.assertLess(best['_rfac'], 0.2)
|
||||
self.assertAlmostEqual(best['A'], 1.745, delta=0.1)
|
||||
self.assertAlmostEqual(best['B'], 2.395, delta=0.1)
|
||||
self.assertAlmostEqual(best['C'], 3.755, delta=0.1)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
@ -1,48 +0,0 @@
|
||||
"""
|
||||
@package tests.test_hbncu
|
||||
unit tests for projects.hbncu
|
||||
|
||||
the purpose of these tests is to help debugging the code.
|
||||
|
||||
to run the tests, change to the directory which contains the tests directory, and execute =nosetests=.
|
||||
|
||||
@pre nose must be installed (python-nose package on Debian).
|
||||
|
||||
@author Matthias Muntwiler, matthias.muntwiler@psi.ch
|
||||
|
||||
@copyright (c) 2015 by Paul Scherrer Institut @n
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); @n
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
"""
|
||||
|
||||
import unittest
|
||||
import os.path
|
||||
import tempfile
|
||||
import shutil
|
||||
import numpy as np
|
||||
import projects.hbncu.hbncu as hbncu
|
||||
import pmsco.data as data
|
||||
import pmsco.dispatch as dispatch
|
||||
|
||||
|
||||
class TestHbncuProject(unittest.TestCase):
|
||||
def setUp(self):
|
||||
self.test_dir = tempfile.mkdtemp()
|
||||
self.project = hbncu.HbncuProject()
|
||||
|
||||
def tearDown(self):
|
||||
# after each test method
|
||||
self.project = None
|
||||
shutil.rmtree(self.test_dir)
|
||||
|
||||
@classmethod
|
||||
def setup_class(cls):
|
||||
# before any methods in this class
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def teardown_class(cls):
|
||||
# teardown_class() after any methods in this class
|
||||
pass
|
||||
616
tests/test_population.py
Normal file
616
tests/test_population.py
Normal file
@ -0,0 +1,616 @@
|
||||
"""
|
||||
@package tests.test_population
|
||||
unit tests for @ref pmsco.optimizers.population
|
||||
|
||||
the purpose of these tests is to help debugging the code.
|
||||
|
||||
to run the tests, change to the directory which contains the tests directory, and execute =nosetests=.
|
||||
|
||||
@pre nose must be installed (python-nose package on Debian).
|
||||
|
||||
@author Matthias Muntwiler, matthias.muntwiler@psi.ch
|
||||
|
||||
@copyright (c) 2015 by Paul Scherrer Institut @n
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); @n
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
"""
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
import six
|
||||
|
||||
import numpy as np
|
||||
import os
|
||||
import os.path
|
||||
import random
|
||||
import shutil
|
||||
import tempfile
|
||||
import unittest
|
||||
|
||||
import pmsco.optimizers.population as population
|
||||
import pmsco.project as project
|
||||
from pmsco.helpers import BraceMessage as BMsg
|
||||
|
||||
POP_SIZE = 5
|
||||
|
||||
|
||||
class TestPopulation(unittest.TestCase):
|
||||
def setUp(self):
|
||||
random.seed(0)
|
||||
self.test_dir = tempfile.mkdtemp()
|
||||
self.domain = project.Domain()
|
||||
|
||||
self.domain.add_param('A', 1.5, 1.0, 2.0, 0.1)
|
||||
self.domain.add_param('B', 2.5, 2.0, 3.0, 0.1)
|
||||
self.domain.add_param('C', 3.5, 3.0, 4.0, 0.1)
|
||||
self.expected_names = ('_gen', '_model', '_particle', '_rfac', 'A', 'B', 'C')
|
||||
|
||||
self.size = POP_SIZE
|
||||
self.pop = population.Population()
|
||||
|
||||
self.optimum1 = {'A': 1.045351, 'B': 2.346212, 'C': 3.873627}
|
||||
|
||||
def tearDown(self):
|
||||
# after each test method
|
||||
self.pop = None
|
||||
shutil.rmtree(self.test_dir)
|
||||
|
||||
@classmethod
|
||||
def setup_class(cls):
|
||||
# before any methods in this class
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def teardown_class(cls):
|
||||
# teardown_class() after any methods in this class
|
||||
pass
|
||||
|
||||
def reorder_pop_array(self, x):
|
||||
"""
|
||||
re-order the columns of a population-like array.
|
||||
|
||||
this makes it easier to create constant arrays with arbitrary column order
|
||||
since population arrays are ordered alphabetically.
|
||||
|
||||
@param x: numpy structured array
|
||||
@return: numpy structured array having the same dtype as the population arrays.
|
||||
"""
|
||||
y = np.zeros(x.shape, dtype=self.pop.get_pop_dtype(self.pop.model_start))
|
||||
for name in y.dtype.names:
|
||||
y[name] = x[name]
|
||||
return y
|
||||
|
||||
def assert_pop_array_equal(self, x, y, msg=""):
|
||||
"""
|
||||
array equality test for population-like structured numpy arrays.
|
||||
|
||||
- check for same column names and kinds
|
||||
- check for same shape
|
||||
- check float columns using np.testing.assert_array_almost_equal
|
||||
- check all other columns using np.testing.assert_array_equal
|
||||
|
||||
@param x: structured numpy array.
|
||||
@param y: structured numpy array.
|
||||
@param msg: optional error message.
|
||||
@return: None
|
||||
@raise AssertionError if arrays are not equal.
|
||||
"""
|
||||
self.assertEqual(x.dtype.names, y.dtype.names, msg=BMsg("{0} (dtype)", msg))
|
||||
self.assertEqual(x.shape, y.shape, msg=BMsg("{0} (shape)", msg))
|
||||
for n, t in x.dtype.fields.items():
|
||||
self.assertEqual(t[0].kind, y.dtype.fields[n][0].kind, msg=BMsg("{0} (column {1} kind)", msg, n))
|
||||
if t[0].kind in {'f', 'c'}:
|
||||
np.testing.assert_array_almost_equal(x[n], y[n], err_msg="{0} (column {1})".format(msg, n))
|
||||
else:
|
||||
np.testing.assert_array_equal(x[n], y[n], err_msg="{0} (column {1})".format(msg, n))
|
||||
|
||||
def rfactor1(self, pos):
|
||||
r = (pos['A'] - self.optimum1['A']) ** 2 \
|
||||
+ (pos['B'] - self.optimum1['B']) ** 2 \
|
||||
+ (pos['C'] - self.optimum1['C']) ** 2
|
||||
r /= 3.0
|
||||
return r
|
||||
|
||||
def test_setup(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
self.assertEqual(self.pop.pos.dtype.names, self.expected_names)
|
||||
self.assertEqual(self.pop.pos.shape, (POP_SIZE,))
|
||||
np.testing.assert_array_equal(np.arange(POP_SIZE), self.pop.pos['_particle'])
|
||||
np.testing.assert_array_equal(np.zeros(POP_SIZE), self.pop.pos['_gen'])
|
||||
np.testing.assert_array_equal(np.arange(POP_SIZE), self.pop.pos['_model'])
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][0], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][1], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][2], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][3], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][4], 3)
|
||||
self.assertEqual(0, self.pop.generation)
|
||||
self.assertEqual(POP_SIZE, self.pop.model_count)
|
||||
|
||||
def test_setup_with_results(self):
|
||||
data_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
data_file = os.path.join(data_dir, "test_swarm.setup_with_results.1.dat")
|
||||
self.pop.setup(self.size, self.domain, seed_file=data_file, recalc_seed=False)
|
||||
|
||||
self.assertEqual(self.pop.pos.dtype.names, self.expected_names)
|
||||
self.assertEqual(self.pop.pos.shape, (POP_SIZE,))
|
||||
self.assertEqual(self.pop.generation, 0)
|
||||
self.assertEqual(self.pop.model_count, POP_SIZE)
|
||||
np.testing.assert_array_equal(self.pop.pos['_particle'], np.arange(POP_SIZE))
|
||||
np.testing.assert_array_equal(self.pop.pos['_gen'], [0, 0, -1, 0, 0])
|
||||
np.testing.assert_array_equal(self.pop.pos['_model'], np.arange(POP_SIZE))
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][0], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][1], 3)
|
||||
self.assertAlmostEqual(0.6, self.pop.pos['_rfac'][2], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][3], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][4], 3)
|
||||
self.assertAlmostEqual(1.3, self.pop.pos['A'][1], 3)
|
||||
self.assertAlmostEqual(1.1, self.pop.pos['A'][2], 3)
|
||||
self.assertAlmostEqual(1.5, self.pop.pos['A'][0], 3)
|
||||
self.assertAlmostEqual(2.3, self.pop.pos['B'][1], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['B'][2], 3)
|
||||
self.assertAlmostEqual(2.5, self.pop.pos['B'][0], 3)
|
||||
self.assertGreaterEqual(4.0, self.pop.pos['C'][1], 3)
|
||||
self.assertAlmostEqual(3.1, self.pop.pos['C'][2], 3)
|
||||
self.assertAlmostEqual(3.5, self.pop.pos['C'][0], 3)
|
||||
|
||||
def test_setup_with_results_recalc(self):
|
||||
data_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
data_file = os.path.join(data_dir, "test_swarm.setup_with_results.1.dat")
|
||||
self.pop.setup(self.size, self.domain, seed_file=data_file, recalc_seed=True)
|
||||
|
||||
self.assertEqual(self.pop.pos.dtype.names, self.expected_names)
|
||||
self.assertEqual(self.pop.pos.shape, (POP_SIZE,))
|
||||
self.assertEqual(self.pop.generation, 0)
|
||||
self.assertEqual(self.pop.model_count, POP_SIZE)
|
||||
np.testing.assert_array_equal(self.pop.pos['_particle'], np.arange(POP_SIZE))
|
||||
np.testing.assert_array_equal(self.pop.pos['_gen'], [0, 0, 0, 0, 0])
|
||||
np.testing.assert_array_equal(self.pop.pos['_model'], np.arange(POP_SIZE))
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][0], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][1], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][2], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][3], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][4], 3)
|
||||
self.assertAlmostEqual(1.3, self.pop.pos['A'][1], 3)
|
||||
self.assertAlmostEqual(1.1, self.pop.pos['A'][2], 3)
|
||||
self.assertAlmostEqual(1.5, self.pop.pos['A'][0], 3)
|
||||
self.assertAlmostEqual(2.3, self.pop.pos['B'][1], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['B'][2], 3)
|
||||
self.assertAlmostEqual(2.5, self.pop.pos['B'][0], 3)
|
||||
self.assertGreaterEqual(4.0, self.pop.pos['C'][1], 3)
|
||||
self.assertAlmostEqual(3.1, self.pop.pos['C'][2], 3)
|
||||
self.assertAlmostEqual(3.5, self.pop.pos['C'][0], 3)
|
||||
|
||||
def test_setup_with_partial_results(self):
|
||||
self.domain.add_param('D', 4.5, 4.0, 5.0, 0.1)
|
||||
self.expected_names = ('_gen', '_model', '_particle', '_rfac', 'A', 'B', 'C', 'D')
|
||||
|
||||
data_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
data_file = os.path.join(data_dir, "test_swarm.setup_with_results.1.dat")
|
||||
self.pop.setup(self.size, self.domain, seed_file=data_file, recalc_seed=False)
|
||||
|
||||
self.assertEqual(self.pop.pos.dtype.names, self.expected_names)
|
||||
self.assertEqual(self.pop.pos.shape, (POP_SIZE,))
|
||||
self.assertEqual(self.pop.generation, 0)
|
||||
self.assertEqual(self.pop.model_count, POP_SIZE)
|
||||
np.testing.assert_array_equal(self.pop.pos['_particle'], np.arange(POP_SIZE))
|
||||
np.testing.assert_array_equal(self.pop.pos['_gen'], [0, 0, -1, 0, 0])
|
||||
np.testing.assert_array_equal(self.pop.pos['_model'], np.arange(POP_SIZE))
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][0], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][1], 3)
|
||||
self.assertAlmostEqual(0.6, self.pop.pos['_rfac'][2], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][3], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][4], 3)
|
||||
self.assertAlmostEqual(1.3, self.pop.pos['A'][1], 3)
|
||||
self.assertAlmostEqual(1.1, self.pop.pos['A'][2], 3)
|
||||
self.assertAlmostEqual(1.5, self.pop.pos['A'][0], 3)
|
||||
self.assertAlmostEqual(2.3, self.pop.pos['B'][1], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['B'][2], 3)
|
||||
self.assertAlmostEqual(2.5, self.pop.pos['B'][0], 3)
|
||||
self.assertGreaterEqual(4.0, self.pop.pos['C'][1], 3)
|
||||
self.assertAlmostEqual(3.1, self.pop.pos['C'][2], 3)
|
||||
self.assertAlmostEqual(3.5, self.pop.pos['C'][0], 3)
|
||||
|
||||
def test_pos_gen(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
for index, item in enumerate(self.pop.pos_gen()):
|
||||
self.assertIsInstance(item, dict)
|
||||
self.assertEqual(set(item.keys()), set(self.expected_names))
|
||||
self.assertEqual(item['_particle'], index)
|
||||
|
||||
def test_get_pop_dtype(self):
|
||||
result = population.Population.get_pop_dtype({'A': 1.045, 'C': 3.873, '_D': 4.381, 'B': 2.346})
|
||||
expected = [('_gen', 'i8'), ('_model', 'i8'), ('_particle', 'i8'), ('_rfac', 'f8'),
|
||||
('A', 'f8'), ('B', 'f8'), ('C', 'f8')]
|
||||
self.assertEqual(result, expected)
|
||||
result = population.Population.get_pop_dtype(['A', 'C', '_D', 'B'])
|
||||
self.assertEqual(result, expected)
|
||||
|
||||
def test_get_model_dtype(self):
|
||||
result = population.Population.get_model_dtype({'A': 1.045, 'C': 3.873, '_D': 4.381, 'B': 2.346})
|
||||
expected = [('A', 'f8'), ('B', 'f8'), ('C', 'f8')]
|
||||
self.assertEqual(result, expected)
|
||||
result = population.Population.get_model_dtype(['A', 'C', '_D', 'B'])
|
||||
self.assertEqual(result, expected)
|
||||
|
||||
def test_get_model_array(self):
|
||||
result = population.Population.get_model_array({'A': 1.045, 'C': 3.873, '_D': 4.381, 'B': 2.346})
|
||||
dt = [('A', 'f8'), ('B', 'f8'), ('C', 'f8')]
|
||||
expected = np.array([(1.045, 2.346, 3.873)], dtype=dt)
|
||||
np.testing.assert_array_equal(result, expected)
|
||||
|
||||
def test_randomize(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
self.pop.randomize()
|
||||
m = np.mean(self.pop.pos['A'])
|
||||
self.assertGreaterEqual(m, self.domain.min['A'])
|
||||
self.assertLessEqual(m, self.domain.max['A'])
|
||||
|
||||
def test_seed(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
self.pop.seed(self.domain.start)
|
||||
self.assertAlmostEqual(self.pop.pos['A'][0], self.domain.start['A'], delta=0.001)
|
||||
|
||||
def test_add_result(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
i_sample = 1
|
||||
i_result = 0
|
||||
result = self.pop.pos[i_sample]
|
||||
self.pop.add_result(result, 0.0)
|
||||
self.assertEqual(self.pop.results.shape[0], 1)
|
||||
self.assertEqual(self.pop.results[i_result], result)
|
||||
self.assertEqual(self.pop.best[i_sample], result)
|
||||
|
||||
def test_save_population(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
filename = os.path.join(self.test_dir, "test_save_population.pop")
|
||||
self.pop.save_population(filename)
|
||||
|
||||
def test_save_results(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
i_sample = 1
|
||||
result = self.pop.pos[i_sample]
|
||||
self.pop.add_result(result, 1.0)
|
||||
filename = os.path.join(self.test_dir, "test_save_results.dat")
|
||||
self.pop.save_results(filename)
|
||||
|
||||
def test_save_array(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
filename = os.path.join(self.test_dir, "test_save_array.pos")
|
||||
self.pop.save_array(filename, self.pop.pos)
|
||||
|
||||
def test_load_array(self):
|
||||
n = 3
|
||||
filename = os.path.join(self.test_dir, "test_load_array")
|
||||
self.pop.setup(self.size, self.domain)
|
||||
|
||||
# expected array
|
||||
dt_exp = self.pop.get_pop_dtype(self.domain.start)
|
||||
a_exp = np.zeros((n,), dtype=dt_exp)
|
||||
a_exp['A'] = np.linspace(0, 1, n)
|
||||
a_exp['B'] = np.linspace(1, 2, n)
|
||||
a_exp['C'] = np.linspace(3, 4, n)
|
||||
a_exp['_rfac'] = np.linspace(5, 6, n)
|
||||
a_exp['_gen'] = np.array([3, 4, 7])
|
||||
a_exp['_particle'] = np.array([1, 0, 2])
|
||||
a_exp['_model'] = np.array([3, 6, 1])
|
||||
|
||||
# test array is a expected array with different column order
|
||||
dt_test = [('A', 'f4'), ('_particle', 'i4'), ('_rfac', 'f4'), ('C', 'f4'), ('_gen', 'i4'), ('B', 'f4'),
|
||||
('_model', 'i4')]
|
||||
names_test = [a[0] for a in dt_test]
|
||||
a_test = np.zeros((n,), dtype=dt_test)
|
||||
for name in names_test:
|
||||
a_test[name] = a_exp[name]
|
||||
header = " ".join(names_test)
|
||||
np.savetxt(filename, a_test, fmt='%g', header=header)
|
||||
|
||||
result = np.zeros((n,), dtype=dt_exp)
|
||||
result = self.pop.load_array(filename, result)
|
||||
self.assert_pop_array_equal(result, a_exp)
|
||||
|
||||
def test_constrain_position(self):
|
||||
# upper
|
||||
pos1 = 11.0
|
||||
vel1 = 5.0
|
||||
min1 = 0.0
|
||||
max1 = 10.0
|
||||
pos2, vel2, min2, max2 = population.Population.constrain_position(pos1, vel1, min1, max1, 're-enter')
|
||||
self.assertAlmostEqual(pos2, 1.0)
|
||||
self.assertAlmostEqual(vel2, vel1)
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max1, max2)
|
||||
pos2, vel2, min2, max2 = population.Population.constrain_position(pos1, vel1, min1, max1, 'bounce')
|
||||
self.assertAlmostEqual(pos2, 9.0)
|
||||
self.assertAlmostEqual(vel2, -vel1)
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max1, max2)
|
||||
pos2, vel2, min2, max2 = population.Population.constrain_position(pos1, vel1, min1, max1, 'scatter')
|
||||
self.assertGreaterEqual(pos2, 6.0)
|
||||
self.assertLessEqual(pos2, 10.0)
|
||||
self.assertGreaterEqual(vel2, 0.0)
|
||||
self.assertLessEqual(vel2, vel1)
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max1, max2)
|
||||
pos2, vel2, min2, max2 = population.Population.constrain_position(pos1, vel1, min1, max1, 'stick')
|
||||
self.assertAlmostEqual(pos2, max1)
|
||||
self.assertAlmostEqual(vel2, max1 - (pos1 - vel1))
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max1, max2)
|
||||
pos2, vel2, min2, max2 = population.Population.constrain_position(pos1, vel1, min1, max1, 'random')
|
||||
self.assertGreaterEqual(pos2, 0.0)
|
||||
self.assertLessEqual(pos2, 10.0)
|
||||
self.assertGreaterEqual(vel2, -(max1 - min1))
|
||||
self.assertLessEqual(vel2, (max1 - min1))
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max1, max2)
|
||||
pos2, vel2, min2, max2 = population.Population.constrain_position(pos1, vel1, min1, max1, 'expand')
|
||||
self.assertAlmostEqual(pos2, pos1)
|
||||
self.assertAlmostEqual(vel2, vel1)
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max2, pos1)
|
||||
self.assertRaises(ValueError, population.Population.constrain_position, pos1, vel1, min1, max1, 'error')
|
||||
|
||||
# lower
|
||||
pos1 = -1.0
|
||||
vel1 = -5.0
|
||||
min1 = 0.0
|
||||
max1 = 10.0
|
||||
pos2, vel2, min2, max2 = population.Population.constrain_position(pos1, vel1, min1, max1, 're-enter')
|
||||
self.assertAlmostEqual(pos2, 9.0)
|
||||
self.assertAlmostEqual(vel2, vel1)
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max1, max2)
|
||||
pos2, vel2, min2, max2 = population.Population.constrain_position(pos1, vel1, min1, max1, 'bounce')
|
||||
self.assertAlmostEqual(pos2, 1.0)
|
||||
self.assertAlmostEqual(vel2, -vel1)
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max1, max2)
|
||||
pos2, vel2, min2, max2 = population.Population.constrain_position(pos1, vel1, min1, max1, 'scatter')
|
||||
self.assertGreaterEqual(pos2, 0.0)
|
||||
self.assertLessEqual(pos2, 4.0)
|
||||
self.assertGreaterEqual(vel2, vel1)
|
||||
self.assertLessEqual(vel2, 0.0)
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max1, max2)
|
||||
pos2, vel2, min2, max2 = population.Population.constrain_position(pos1, vel1, min1, max1, 'stick')
|
||||
self.assertAlmostEqual(pos2, min1)
|
||||
self.assertAlmostEqual(vel2, min1 - (pos1 - vel1))
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max1, max2)
|
||||
pos2, vel2, min2, max2 = population.Population.constrain_position(pos1, vel1, min1, max1, 'random')
|
||||
self.assertGreaterEqual(pos2, 0.0)
|
||||
self.assertLessEqual(pos2, 10.0)
|
||||
self.assertGreaterEqual(vel2, -(max1 - min1))
|
||||
self.assertLessEqual(vel2, max1 - min1)
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max1, max2)
|
||||
pos2, vel2, min2, max2 = population.Population.constrain_position(pos1, vel1, min1, max1, 'expand')
|
||||
self.assertAlmostEqual(pos2, pos1)
|
||||
self.assertAlmostEqual(vel2, vel1)
|
||||
self.assertAlmostEqual(min2, pos1)
|
||||
self.assertAlmostEqual(max2, max1)
|
||||
self.assertRaises(ValueError, population.Population.constrain_position, pos1, vel1, min1, max1, 'error')
|
||||
|
||||
def test_patch_from_file(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
|
||||
data_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
data_file = os.path.join(data_dir, "test_swarm.setup_with_results.1.dat")
|
||||
patch_size = 4
|
||||
expected_pos = {'A': [1.1, 1.2, 1.3, 1.4], 'B': [2.1, 2.2, 2.3, 2.4], 'C': [3.1, 3.2, 4.3, 3.4]}
|
||||
self.pop.patch_from_file(data_file)
|
||||
self.assertIsInstance(self.pop.pos_patch, np.ndarray)
|
||||
self.assertEqual(self.pop.pos_patch.shape, (patch_size,))
|
||||
np.testing.assert_array_equal(self.pop.pos_patch['_particle'], [1, 2, 3, 4])
|
||||
np.testing.assert_array_equal(self.pop.pos_patch['_gen'], [1, 2, 3, 4])
|
||||
np.testing.assert_array_almost_equal(self.pop.pos_patch['A'], expected_pos['A'])
|
||||
np.testing.assert_array_almost_equal(self.pop.pos_patch['B'], expected_pos['B'])
|
||||
np.testing.assert_array_almost_equal(self.pop.pos_patch['C'], expected_pos['C'])
|
||||
|
||||
def test_apply_patch(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
expected_pos = self.pop.pos.copy()
|
||||
dt_test = [('A', 'f4'), ('_particle', 'i4'), ('_rfac', 'f4'), ('C', 'f4'), ('_model', 'i4')]
|
||||
patch_size = 3
|
||||
self.pop.pos_patch = np.zeros(patch_size, dtype=dt_test)
|
||||
self.pop.pos_patch['_particle'] = np.array([1, 2, 3])
|
||||
self.pop.pos_patch['_rfac'] = np.random.random_sample(patch_size)
|
||||
self.pop.pos_patch['_model'] = np.arange(patch_size)
|
||||
self.pop.pos_patch['A'] = np.array([1.1, 2.2, 3.3])
|
||||
self.pop.pos_patch['C'] = np.array([1.1, 2.2, 3.3])
|
||||
|
||||
expected_pos['A'][1] = 1.1
|
||||
expected_pos['C'][3] = 3.3
|
||||
expected_pos['_particle'] = np.arange(POP_SIZE)
|
||||
expected_pos['_gen'] = 0
|
||||
expected_pos['_model'] = np.arange(POP_SIZE)
|
||||
expected_pos['_rfac'] = 2.1
|
||||
|
||||
self.pop._apply_patch()
|
||||
self.assertIsNone(self.pop.pos_patch)
|
||||
self.assertEqual(self.pop.generation, 0)
|
||||
self.assertEqual(self.pop.model_count, POP_SIZE)
|
||||
self.assert_pop_array_equal(self.pop.pos, expected_pos)
|
||||
|
||||
def test_find_result(self):
|
||||
self.domain.min['A'] = -0.1
|
||||
self.domain.max['A'] = 0.1
|
||||
self.domain.min['B'] = 0.0
|
||||
self.domain.max['B'] = 1000.
|
||||
self.domain.min['C'] = 9.
|
||||
self.domain.max['C'] = 9.001
|
||||
self.size = 100
|
||||
self.pop.setup(self.size, self.domain)
|
||||
self.pop.results = self.pop.pos.copy()
|
||||
|
||||
expected_index = 77
|
||||
names = ['A', 'B', 'C']
|
||||
target0 = {key: self.pop.results[key][expected_index] for key in names}
|
||||
target1 = self.pop.results[names].copy()
|
||||
target1 = target1[expected_index]
|
||||
target2 = {key: target0[key] for key in ['C', 'B']}
|
||||
target3 = target2.copy()
|
||||
target3['C'] += 9.e-6
|
||||
target4 = {'A': random.random(), 'B': random.random(), 'C': 9.002}
|
||||
|
||||
result0 = self.pop.find_model(target0)
|
||||
self.assertEqual(result0, expected_index)
|
||||
result1 = self.pop.find_model(target1)
|
||||
self.assertEqual(result1, expected_index)
|
||||
result2 = self.pop.find_model(target2)
|
||||
self.assertEqual(result2, expected_index)
|
||||
result3 = self.pop.find_model(target3)
|
||||
self.assertEqual(result3, expected_index)
|
||||
self.assertRaises(ValueError, self.pop.find_model, target4)
|
||||
|
||||
def test_import_positions(self):
|
||||
"""
|
||||
test the population.Population.import_positions method.
|
||||
check the different type conversions.
|
||||
the main work is in test_import_positions_array.
|
||||
"""
|
||||
self.pop.setup(self.size, self.domain)
|
||||
|
||||
source_type = [('A', 'f4'), ('B', 'f4'), ('C', 'f4'), ('D', 'f4'),
|
||||
('_model', 'i4'), ('_particle', 'i4'), ('_gen', 'i4'), ('_rfac', 'f4')]
|
||||
source = np.array([(1.5, 3.5, 3.5, 4.5, 51, 52, 53, 0.5),
|
||||
(1.6, 2.6, 2.6, 4.6, 61, 62, 63, 0.6),
|
||||
(1.7, 2.7, 3.7, 4.7, 71, 72, 73, 0.7),
|
||||
(1.8, 2.8, 3.8, 4.8, 81, 82, 83, 0.8)], dtype=source_type)
|
||||
expected_type = [('_gen', 'i8'), ('_model', 'i8'), ('_particle', 'i8'), ('_rfac', 'f8'),
|
||||
('A', 'f8'), ('B', 'f8'), ('C', 'f8')]
|
||||
expected = np.array([(0, 0, 0, 0.0, 1.5, 3.5, 3.5),
|
||||
(0, 0, 0, 0.0, 1.6, 2.6, 2.6),
|
||||
(0, 0, 0, 0.0, 1.7, 2.7, 3.7),
|
||||
(0, 0, 0, 0.0, 1.8, 2.8, 3.8)], dtype=expected_type)
|
||||
expected_single = np.array([expected[0]], dtype=expected_type)
|
||||
|
||||
# None
|
||||
self.pop.clear_import()
|
||||
source_none = None
|
||||
retval = self.pop.import_positions(source_none)
|
||||
self.assertEqual(retval, 0)
|
||||
|
||||
# np.ndarray
|
||||
self.pop.clear_import()
|
||||
source_ndarray = source
|
||||
retval = self.pop.import_positions(source_ndarray)
|
||||
self.assertEqual(retval, expected.shape[0])
|
||||
self.assert_pop_array_equal(self.pop.pos_import, expected, msg="numpy.ndarray")
|
||||
|
||||
# np.void
|
||||
self.pop.clear_import()
|
||||
source_void = source[0]
|
||||
retval = self.pop.import_positions(source_void)
|
||||
self.assertEqual(retval, 1)
|
||||
self.assert_pop_array_equal(self.pop.pos_import, expected_single, msg="numpy.void")
|
||||
|
||||
# dict
|
||||
self.pop.clear_import()
|
||||
source_dict = {k: source[0][k] for k in source.dtype.names}
|
||||
retval = self.pop.import_positions(source_dict)
|
||||
self.assertEqual(retval, 1)
|
||||
self.assert_pop_array_equal(self.pop.pos_import, expected_single, msg="dict")
|
||||
|
||||
# sequence
|
||||
self.pop.clear_import()
|
||||
source_sequence = [row for row in source]
|
||||
retval = self.pop.import_positions(source_sequence)
|
||||
self.assertEqual(retval, expected.shape[0])
|
||||
self.assert_pop_array_equal(self.pop.pos_import, expected, msg="sequence")
|
||||
|
||||
# file
|
||||
filename = os.path.join(self.test_dir, "test_import_positions")
|
||||
self.pop.save_array(filename, source)
|
||||
self.pop.clear_import()
|
||||
retval = self.pop.import_positions(filename)
|
||||
self.assertEqual(retval, expected.shape[0])
|
||||
self.assert_pop_array_equal(self.pop.pos_import, expected, msg="file")
|
||||
|
||||
# callable
|
||||
def source_callable():
|
||||
return expected.copy()
|
||||
|
||||
self.pop.clear_import()
|
||||
retval = self.pop.import_positions(source_callable)
|
||||
self.assertEqual(retval, expected.shape[0])
|
||||
self.assert_pop_array_equal(self.pop.pos_import, expected, msg="callable")
|
||||
|
||||
def test_import_positions_array(self):
|
||||
"""
|
||||
test the population.Population.import_positions_array method.
|
||||
- import a data array, check type and size.
|
||||
- add to previous import.
|
||||
- ignore control fields.
|
||||
- no range or duplicate checking.
|
||||
- missing parameter.
|
||||
"""
|
||||
self.pop.setup(self.size, self.domain)
|
||||
source_type = [('A', 'f4'), ('B', 'f4'), ('C', 'f4'), ('D', 'f4'),
|
||||
('_model', 'i4'), ('_particle', 'i4'), ('_gen', 'i4'), ('_rfac', 'f4')]
|
||||
source = np.array([(1.0, 0.0, 0.0, 0.0, 0, 0, 0, 0.0),
|
||||
(1.5, 3.5, 3.5, 4.5, 51, 52, 53, 0.5),
|
||||
(1.6, 2.6, 2.6, 4.6, 61, 62, 63, 0.6),
|
||||
(1.7, 2.7, 3.7, 4.7, 71, 72, 73, 0.7),
|
||||
(1.8, 2.8, 3.8, 4.8, 81, 82, 83, 0.8)], dtype=source_type)
|
||||
self.pop.pos_import.resize(1)
|
||||
self.pop.pos_import[0]['A'] = 1.0
|
||||
expected_type = [('_gen', 'i8'), ('_model', 'i8'), ('_particle', 'i8'), ('_rfac', 'f8'),
|
||||
('A', 'f8'), ('B', 'f8'), ('C', 'f8')]
|
||||
expected = np.array([(0, 0, 0, 0.0, 1.0, 0.0, 0.0),
|
||||
(0, 0, 0, 0.0, 1.0, 0.0, 0.0),
|
||||
(0, 0, 0, 0.0, 1.5, 3.5, 3.5),
|
||||
(0, 0, 0, 0.0, 1.6, 2.6, 2.6),
|
||||
(0, 0, 0, 0.0, 1.7, 2.7, 3.7),
|
||||
(0, 0, 0, 0.0, 1.8, 2.8, 3.8)], dtype=expected_type)
|
||||
retval = self.pop.import_positions_array(source)
|
||||
self.assertEqual(retval, source.shape[0])
|
||||
self.assertEqual(self.pop.pos_import.dtype.names, self.pop.pos.dtype.names)
|
||||
self.assert_pop_array_equal(self.pop.pos_import, expected, msg="imported array")
|
||||
|
||||
source_type = [('B', 'f4'), ('C', 'f4'), ('D', 'f4'), ('E', 'f4'),
|
||||
('_model', 'i4'), ('_particle', 'i4'), ('_gen', 'i4'), ('_rfac', 'f4')]
|
||||
source = np.array([(1.0, 0.0, 0.0, 0.0, 0, 0, 0, 0.0),
|
||||
(1.8, 2.8, 3.8, 4.8, 81, 82, 83, 0.8)], dtype=source_type)
|
||||
self.assertRaises(ValueError, self.pop.import_positions_array, source)
|
||||
|
||||
def test_advance_from_import(self):
|
||||
"""
|
||||
test the population.Population.advance_from_import method.
|
||||
- array type and size.
|
||||
- range checks.
|
||||
- de-duplication.
|
||||
"""
|
||||
self.pop.setup(self.size, self.domain)
|
||||
self.pop.position_constrain_mode = 'error'
|
||||
source_type = [('A', 'f8'), ('B', 'f8'), ('C', 'f8'),
|
||||
('_model', 'i8'), ('_particle', 'i8'), ('_gen', 'i8'), ('_rfac', 'f8')]
|
||||
source = np.array([(1.3, 2.3, 3.3, 0, 0, 0, 0.0),
|
||||
(1.3, 2.3, 3.3, 0, 0, 0, 0.0),
|
||||
(1.4, 2.4, 3.4, 0, 0, 0, 0.0),
|
||||
(1.5, 3.5, 3.5, 0, 0, 0, 0.0),
|
||||
(1.6, 2.6, 2.6, 0, 0, 0, 0.0),
|
||||
(1.7, 2.7, 3.7, 0, 0, 0, 0.0),
|
||||
(1.8, 2.8, 3.8, 0, 0, 0, 0.0)], dtype=source_type)
|
||||
source = self.reorder_pop_array(source)
|
||||
results = np.array([(1.4, 2.4, 3.4, 0, 0, 0, 0.4)], dtype=source_type)
|
||||
results = self.reorder_pop_array(results)
|
||||
expected = np.array([(1.3, 2.3, 3.3, POP_SIZE + 0, 0, 0, 2.1),
|
||||
(1.7, 2.7, 3.7, POP_SIZE + 1, 1, 0, 2.1),
|
||||
(1.8, 2.8, 3.8, POP_SIZE + 2, 2, 0, 2.1)], dtype=source_type)
|
||||
expected = self.reorder_pop_array(expected)
|
||||
|
||||
self.pop.pos_import = source
|
||||
self.pop.results = results
|
||||
retval = self.pop.advance_from_import()
|
||||
self.assertEqual(retval, expected.shape[0])
|
||||
self.assertEqual(self.pop.size_act, expected.shape[0])
|
||||
pos_act = self.pop.pos[0:self.pop.size_act]
|
||||
self.assert_pop_array_equal(pos_act, expected)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
82
tests/test_project.py
Normal file
82
tests/test_project.py
Normal file
@ -0,0 +1,82 @@
|
||||
"""
|
||||
@package tests.test_project
|
||||
unit tests for pmsco.project.
|
||||
|
||||
the purpose of these tests is to help debugging the code.
|
||||
|
||||
to run the tests, change to the directory which contains the tests directory, and execute =nosetests=.
|
||||
|
||||
@pre nose and mock must be installed.
|
||||
|
||||
@author Matthias Muntwiler, matthias.muntwiler@psi.ch
|
||||
|
||||
@copyright (c) 2015-18 by Paul Scherrer Institut @n
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); @n
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
"""
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
import unittest
|
||||
import mock
|
||||
import numpy as np
|
||||
import pmsco.data as data
|
||||
import pmsco.dispatch as dispatch
|
||||
import pmsco.project as project
|
||||
|
||||
|
||||
class TestProject(unittest.TestCase):
|
||||
def setUp(self):
|
||||
# before each test method
|
||||
self.project = project.Project()
|
||||
|
||||
def tearDown(self):
|
||||
# after each test method
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def setup_class(cls):
|
||||
# before any methods in this class
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def teardown_class(cls):
|
||||
# teardown_class() after any methods in this class
|
||||
pass
|
||||
|
||||
@mock.patch('pmsco.data.load_data')
|
||||
@mock.patch('pmsco.data.save_data')
|
||||
def test_combine_symmetries(self, save_data_mock, load_data_mock):
|
||||
self.project.scans.append(project.Scan())
|
||||
|
||||
parent_task = dispatch.CalculationTask()
|
||||
parent_task.change_id(model=0, scan=0)
|
||||
parent_task.model['wsym1'] = 0.5
|
||||
|
||||
child_tasks = [parent_task.copy()] * 2
|
||||
for idx, task in enumerate(child_tasks):
|
||||
task.change_id(sym=idx)
|
||||
|
||||
data1 = data.create_data(5, datatype='EI')
|
||||
data1['e'] = np.arange(5)
|
||||
data1['i'] = 10.
|
||||
data2 = data1.copy()
|
||||
data2['i'] = 10.
|
||||
load_data_mock.side_effect = [data1, data2]
|
||||
data3 = data1.copy()
|
||||
data3['i'] = (10. + 0.5 * 10.) / 1.5
|
||||
|
||||
self.project.combine_symmetries(parent_task, child_tasks)
|
||||
|
||||
save_data_mock.assert_called()
|
||||
args, kwargs = save_data_mock.call_args
|
||||
result_data = args[1]
|
||||
np.testing.assert_array_almost_equal(result_data['e'], data3['e'])
|
||||
np.testing.assert_array_almost_equal(result_data['i'], data3['i'])
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
@ -1,6 +1,6 @@
|
||||
"""
|
||||
@package tests.test_swarm
|
||||
unit tests for pmsco.swarm
|
||||
unit tests for @ref pmsco.optimizers.swarm
|
||||
|
||||
the purpose of these tests is to help debugging the code.
|
||||
|
||||
@ -17,29 +17,36 @@ Licensed under the Apache License, Version 2.0 (the "License"); @n
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
"""
|
||||
|
||||
import unittest
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
import numpy as np
|
||||
import os
|
||||
import os.path
|
||||
import tempfile
|
||||
import random
|
||||
import shutil
|
||||
import numpy as np
|
||||
import pmsco.swarm as mo
|
||||
import pmsco.project as mp
|
||||
import tempfile
|
||||
import unittest
|
||||
|
||||
import pmsco.optimizers.swarm as swarm
|
||||
import pmsco.project as project
|
||||
|
||||
POP_SIZE = 5
|
||||
|
||||
class TestPopulation(unittest.TestCase):
|
||||
|
||||
class TestSwarmPopulation(unittest.TestCase):
|
||||
def setUp(self):
|
||||
random.seed(0)
|
||||
self.test_dir = tempfile.mkdtemp()
|
||||
self.domain = mp.Domain()
|
||||
self.domain = project.Domain()
|
||||
|
||||
self.domain.add_param('A', 1.5, 1.0, 2.0, 0.1)
|
||||
self.domain.add_param('B', 2.5, 2.0, 3.0, 0.1)
|
||||
self.domain.add_param('C', 3.5, 3.0, 4.0, 0.1)
|
||||
self.expected_names = ('A', 'B', 'C', '_particle', '_gen', '_model', '_rfac')
|
||||
self.expected_names = ('_gen', '_model', '_particle', '_rfac', 'A', 'B', 'C')
|
||||
|
||||
self.size = POP_SIZE
|
||||
self.pop = mo.Population()
|
||||
self.pop = swarm.SwarmPopulation()
|
||||
|
||||
self.optimum1 = {'A': 1.045351, 'B': 2.346212, 'C': 3.873627}
|
||||
|
||||
@ -65,100 +72,12 @@ class TestPopulation(unittest.TestCase):
|
||||
r /= 3.0
|
||||
return r
|
||||
|
||||
def test_setup(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
self.assertItemsEqual(self.pop.pos.dtype.names, self.expected_names)
|
||||
self.assertEqual(self.pop.pos.shape, (POP_SIZE,))
|
||||
self.assertItemsEqual(np.arange(POP_SIZE), self.pop.pos['_particle'])
|
||||
self.assertItemsEqual(np.zeros((POP_SIZE)), self.pop.pos['_gen'])
|
||||
self.assertItemsEqual(np.arange(POP_SIZE), self.pop.pos['_model'])
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][0], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][1], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][2], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][3], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][4], 3)
|
||||
self.assertEqual(0, self.pop.generation)
|
||||
self.assertEqual(POP_SIZE, self.pop.model_count)
|
||||
|
||||
def test_setup_with_results(self):
|
||||
data_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
data_file = os.path.join(data_dir, "test_swarm.setup_with_results.1.dat")
|
||||
self.pop.setup(self.size, self.domain, data_file, False)
|
||||
|
||||
self.assertItemsEqual(self.pop.pos.dtype.names, self.expected_names)
|
||||
self.assertEqual(self.pop.pos.shape, (POP_SIZE,))
|
||||
self.assertEqual(0, self.pop.generation)
|
||||
self.assertEqual(3, self.pop.model_count)
|
||||
self.assertItemsEqual(np.arange(POP_SIZE), self.pop.pos['_particle'])
|
||||
self.assertItemsEqual([-1, -1, 0, 0, 0], self.pop.pos['_gen'])
|
||||
self.assertItemsEqual([-1, -2, 0, 1, 2], self.pop.pos['_model'])
|
||||
self.assertAlmostEqual(0.3, self.pop.pos['_rfac'][0], 3)
|
||||
self.assertAlmostEqual(0.6, self.pop.pos['_rfac'][1], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][2], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][3], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][4], 3)
|
||||
self.assertAlmostEqual(1.3, self.pop.pos['A'][0], 3)
|
||||
self.assertAlmostEqual(1.1, self.pop.pos['A'][1], 3)
|
||||
self.assertAlmostEqual(1.5, self.pop.pos['A'][4], 3)
|
||||
self.assertAlmostEqual(2.3, self.pop.pos['B'][0], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['B'][1], 3)
|
||||
self.assertAlmostEqual(2.5, self.pop.pos['B'][4], 3)
|
||||
self.assertAlmostEqual(3.3, self.pop.pos['C'][0], 3)
|
||||
self.assertAlmostEqual(3.1, self.pop.pos['C'][1], 3)
|
||||
self.assertAlmostEqual(3.5, self.pop.pos['C'][4], 3)
|
||||
|
||||
def test_setup_with_results_recalc(self):
|
||||
data_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
data_file = os.path.join(data_dir, "test_swarm.setup_with_results.1.dat")
|
||||
self.pop.setup(self.size, self.domain, data_file, True)
|
||||
|
||||
self.assertItemsEqual(self.pop.pos.dtype.names, self.expected_names)
|
||||
self.assertEqual(self.pop.pos.shape, (POP_SIZE,))
|
||||
self.assertEqual(self.pop.generation, 0)
|
||||
self.assertEqual(self.pop.model_count, POP_SIZE)
|
||||
self.assertItemsEqual(self.pop.pos['_particle'], np.arange(POP_SIZE))
|
||||
self.assertItemsEqual(self.pop.pos['_gen'], [0, 0, 0, 0, 0])
|
||||
self.assertItemsEqual(self.pop.pos['_model'], np.arange(POP_SIZE))
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][0], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][1], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][2], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][3], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['_rfac'][4], 3)
|
||||
self.assertAlmostEqual(1.3, self.pop.pos['A'][0], 3)
|
||||
self.assertAlmostEqual(1.1, self.pop.pos['A'][1], 3)
|
||||
self.assertAlmostEqual(1.5, self.pop.pos['A'][4], 3)
|
||||
self.assertAlmostEqual(2.3, self.pop.pos['B'][0], 3)
|
||||
self.assertAlmostEqual(2.1, self.pop.pos['B'][1], 3)
|
||||
self.assertAlmostEqual(2.5, self.pop.pos['B'][4], 3)
|
||||
self.assertAlmostEqual(3.3, self.pop.pos['C'][0], 3)
|
||||
self.assertAlmostEqual(3.1, self.pop.pos['C'][1], 3)
|
||||
self.assertAlmostEqual(3.5, self.pop.pos['C'][4], 3)
|
||||
|
||||
def test_pos_gen(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
for index, item in enumerate(self.pop.pos_gen()):
|
||||
self.assertIsInstance(item, dict)
|
||||
self.assertItemsEqual(item.keys(), self.expected_names)
|
||||
self.assertEqual(item['_particle'], index)
|
||||
|
||||
def test_randomize(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
self.pop.randomize()
|
||||
m = np.mean(self.pop.pos['A'])
|
||||
self.assertGreaterEqual(m, self.domain.min['A'])
|
||||
self.assertLessEqual(m, self.domain.max['A'])
|
||||
|
||||
def test_seed(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
self.pop.seed(self.domain.start)
|
||||
self.assertAlmostEqual(self.pop.pos['A'][0], self.domain.start['A'], delta=0.001)
|
||||
|
||||
def test_best_friend(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
self.pop.best['_rfac'] = np.arange(self.size)
|
||||
friend = self.pop.best_friend(0)
|
||||
self.assertNotIsInstance(friend, np.ndarray)
|
||||
self.assertItemsEqual(friend.dtype.names, self.expected_names)
|
||||
self.assertEqual(friend.dtype.names, self.expected_names)
|
||||
|
||||
def test_advance_particle(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
@ -182,16 +101,6 @@ class TestPopulation(unittest.TestCase):
|
||||
self.assertGreaterEqual(pos, self.domain.min[key])
|
||||
self.assertLessEqual(pos, self.domain.max[key])
|
||||
|
||||
def test_add_result(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
i_sample = 1
|
||||
i_result = 0
|
||||
result = self.pop.pos[i_sample]
|
||||
self.pop.add_result(result, 0.0)
|
||||
self.assertEqual(self.pop.results.shape[0], 1)
|
||||
self.assertItemsEqual(self.pop.results[i_result], result)
|
||||
self.assertItemsEqual(self.pop.best[i_sample], result)
|
||||
|
||||
def test_is_converged(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
self.assertFalse(self.pop.is_converged())
|
||||
@ -206,139 +115,6 @@ class TestPopulation(unittest.TestCase):
|
||||
self.pop.add_result(result, rfac)
|
||||
self.assertTrue(self.pop.is_converged())
|
||||
|
||||
def test_save_population(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
filename = os.path.join(self.test_dir, "test_save_population.pop")
|
||||
self.pop.save_population(filename)
|
||||
|
||||
def test_save_results(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
i_sample = 1
|
||||
result = self.pop.pos[i_sample]
|
||||
self.pop.add_result(result, 1.0)
|
||||
filename = os.path.join(self.test_dir, "test_save_results.dat")
|
||||
self.pop.save_results(filename)
|
||||
|
||||
def test_save_array(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
filename = os.path.join(self.test_dir, "test_save_array.pos")
|
||||
self.pop.save_array(filename, self.pop.pos)
|
||||
|
||||
def test_load_array(self):
|
||||
n = 3
|
||||
filename = os.path.join(self.test_dir, "test_load_array")
|
||||
self.pop.setup(self.size, self.domain)
|
||||
|
||||
# expected array
|
||||
dt_exp = self.pop.get_model_dtype(self.domain.start)
|
||||
a_exp = np.zeros((n,), dtype=dt_exp)
|
||||
a_exp['A'] = np.linspace(0, 1, n)
|
||||
a_exp['B'] = np.linspace(1, 2, n)
|
||||
a_exp['C'] = np.linspace(3, 4, n)
|
||||
a_exp['_rfac'] = np.linspace(5, 6, n)
|
||||
a_exp['_gen'] = np.array([3, 4, 7])
|
||||
a_exp['_particle'] = np.array([1, 0, 2])
|
||||
a_exp['_model'] = np.array([3, 6, 1])
|
||||
|
||||
# test array is a expected array with different column order
|
||||
dt_test = [('A', 'f4'), ('_particle', 'i4'), ('_rfac', 'f4'), ('C', 'f4'), ('_gen', 'i4'), ('B', 'f4'),
|
||||
('_model', 'i4')]
|
||||
names_test = [a[0] for a in dt_test]
|
||||
a_test = np.zeros((n,), dtype=dt_test)
|
||||
for name in names_test:
|
||||
a_test[name] = a_exp[name]
|
||||
header = " ".join(names_test)
|
||||
np.savetxt(filename, a_test, fmt='%g', header=header)
|
||||
|
||||
result = np.zeros((n,), dtype=dt_exp)
|
||||
result = self.pop.load_array(filename, result)
|
||||
self.assertItemsEqual(result.dtype.names, a_exp.dtype.names)
|
||||
for name in a_exp.dtype.names:
|
||||
np.testing.assert_almost_equal(result[name], a_exp[name], err_msg=name)
|
||||
|
||||
def test_constrain_position(self):
|
||||
# upper
|
||||
pos1 = 11.0
|
||||
vel1 = 5.0
|
||||
min1 = 0.0
|
||||
max1 = 10.0
|
||||
pos2, vel2, min2, max2 = mo.Population.constrain_position(pos1, vel1, min1, max1, 're-enter')
|
||||
self.assertAlmostEqual(pos2, 1.0)
|
||||
self.assertAlmostEqual(vel2, vel1)
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max1, max2)
|
||||
pos2, vel2, min2, max2 = mo.Population.constrain_position(pos1, vel1, min1, max1, 'bounce')
|
||||
self.assertAlmostEqual(pos2, 9.0)
|
||||
self.assertAlmostEqual(vel2, -vel1)
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max1, max2)
|
||||
pos2, vel2, min2, max2 = mo.Population.constrain_position(pos1, vel1, min1, max1, 'scatter')
|
||||
self.assertGreaterEqual(pos2, 6.0)
|
||||
self.assertLessEqual(pos2, 10.0)
|
||||
self.assertGreaterEqual(vel2, 0.0)
|
||||
self.assertLessEqual(vel2, vel1)
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max1, max2)
|
||||
pos2, vel2, min2, max2 = mo.Population.constrain_position(pos1, vel1, min1, max1, 'stick')
|
||||
self.assertAlmostEqual(pos2, max1)
|
||||
self.assertAlmostEqual(vel2, max1 - (pos1 - vel1))
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max1, max2)
|
||||
pos2, vel2, min2, max2 = mo.Population.constrain_position(pos1, vel1, min1, max1, 'random')
|
||||
self.assertGreaterEqual(pos2, 0.0)
|
||||
self.assertLessEqual(pos2, 10.0)
|
||||
self.assertGreaterEqual(vel2, -(max1 - min1))
|
||||
self.assertLessEqual(vel2, (max1 - min1))
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max1, max2)
|
||||
pos2, vel2, min2, max2 = mo.Population.constrain_position(pos1, vel1, min1, max1, 'expand')
|
||||
self.assertAlmostEqual(pos2, pos1)
|
||||
self.assertAlmostEqual(vel2, vel1)
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max2, pos1)
|
||||
self.assertRaises(ValueError, mo.Population.constrain_position, pos1, vel1, min1, max1, 'undefined')
|
||||
|
||||
# lower
|
||||
pos1 = -1.0
|
||||
vel1 = -5.0
|
||||
min1 = 0.0
|
||||
max1 = 10.0
|
||||
pos2, vel2, min2, max2 = mo.Population.constrain_position(pos1, vel1, min1, max1, 're-enter')
|
||||
self.assertAlmostEqual(pos2, 9.0)
|
||||
self.assertAlmostEqual(vel2, vel1)
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max1, max2)
|
||||
pos2, vel2, min2, max2 = mo.Population.constrain_position(pos1, vel1, min1, max1, 'bounce')
|
||||
self.assertAlmostEqual(pos2, 1.0)
|
||||
self.assertAlmostEqual(vel2, -vel1)
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max1, max2)
|
||||
pos2, vel2, min2, max2 = mo.Population.constrain_position(pos1, vel1, min1, max1, 'scatter')
|
||||
self.assertGreaterEqual(pos2, 0.0)
|
||||
self.assertLessEqual(pos2, 4.0)
|
||||
self.assertGreaterEqual(vel2, vel1)
|
||||
self.assertLessEqual(vel2, 0.0)
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max1, max2)
|
||||
pos2, vel2, min2, max2 = mo.Population.constrain_position(pos1, vel1, min1, max1, 'stick')
|
||||
self.assertAlmostEqual(pos2, min1)
|
||||
self.assertAlmostEqual(vel2, min1 - (pos1 - vel1))
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max1, max2)
|
||||
pos2, vel2, min2, max2 = mo.Population.constrain_position(pos1, vel1, min1, max1, 'random')
|
||||
self.assertGreaterEqual(pos2, 0.0)
|
||||
self.assertLessEqual(pos2, 10.0)
|
||||
self.assertGreaterEqual(vel2, -(max1 - min1))
|
||||
self.assertLessEqual(vel2, max1 - min1)
|
||||
self.assertAlmostEqual(min1, min2)
|
||||
self.assertAlmostEqual(max1, max2)
|
||||
pos2, vel2, min2, max2 = mo.Population.constrain_position(pos1, vel1, min1, max1, 'expand')
|
||||
self.assertAlmostEqual(pos2, pos1)
|
||||
self.assertAlmostEqual(vel2, vel1)
|
||||
self.assertAlmostEqual(min2, pos1)
|
||||
self.assertAlmostEqual(max2, max1)
|
||||
self.assertRaises(ValueError, mo.Population.constrain_position, pos1, vel1, min1, max1, 'undefined')
|
||||
|
||||
def test_convergence_1(self):
|
||||
self.pop.setup(self.size, self.domain)
|
||||
|
||||
@ -359,6 +135,6 @@ class TestPopulation(unittest.TestCase):
|
||||
for pos in self.pop.pos:
|
||||
self.assertLess(pos['_rfac'], 0.2)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
# A B C _gen _model _rfac
|
||||
1.1 2.1 3.1 1 1111 0.6
|
||||
1.2 2.2 3.2 2 2222 1.5
|
||||
1.3 2.3 3.3 3 3333 0.3
|
||||
1.4 2.4 3.4 4 4444 1.0
|
||||
# A B C E _gen _model _rfac _particle
|
||||
1.1 2.1 3.1 5.1 1 1111 0.6 1
|
||||
1.2 2.2 3.2 5.2 2 2222 1.5 2
|
||||
1.3 2.3 4.3 5.3 3 3333 0.3 3
|
||||
1.4 2.4 3.4 5.4 4 4444 1.0 4
|
||||
|
||||
Reference in New Issue
Block a user