def test_regression_threshold(): steps = [(0, 1, 1.0, 1.0, 0.0), (1, 2, 1.1, 1.1, 0.0), (2, 3, 2.0, 2.0, 0.0)] latest, best, pos = detect_regressions(steps, threshold=0.05) assert latest == 2 assert best == 1 assert pos == [(0, 1, 1.0, 1.1), (1, 2, 1.1, 2.0)] latest, best, pos = detect_regressions(steps, threshold=0.2) assert latest == 2 assert best == 1 assert pos == [(1, 2, 1.1, 2.0)] latest, best, pos = detect_regressions(steps, threshold=0.8) assert latest == 2 assert best == 1 assert pos == [(1, 2, 1.1, 2.0)] latest, best, pos = detect_regressions(steps, threshold=1.1) assert latest == None assert best == None assert pos == None steps = [(0, 1, 1.0, 1.0, 0.0), (1, 2, 1.3, 1.3, 0.0), (2, 3, 1.1, 1.1, 0.0)] latest, best, pos = detect_regressions(steps, threshold=0.2) assert latest == None assert best == None assert pos == None
def test_regression_threshold(): steps = [(0, 1, 1.0, 1.0, 0.0), (1, 2, 1.1, 1.1, 0.0), (2, 3, 2.0, 2.0, 0.0)] latest, best, pos = detect_regressions(steps, threshold=0.05) assert latest == 2 assert best == 1 assert pos == [(0, 1, 1.0, 1.1), (1, 2, 1.1, 2.0)] latest, best, pos = detect_regressions(steps, threshold=0.2) assert latest == 2 assert best == 1 assert pos == [(1, 2, 1.1, 2.0)] latest, best, pos = detect_regressions(steps, threshold=0.9) assert latest == 2 assert best == 1 assert pos == [(1, 2, 1.1, 2.0)] latest, best, pos = detect_regressions(steps, threshold=1.1) assert latest == None assert best == None assert pos == None steps = [(0, 1, 1.0, 1.0, 0.0), (1, 2, 1.3, 1.3, 0.0), (2, 3, 1.1, 1.1, 0.0)] latest, best, pos = detect_regressions(steps, threshold=0.2) assert latest == None assert best == None assert pos == None
def test_regression_min_size(): steps = [(0, 2, 1.0, 1.0, 0.0), (2, 3, 0.0, 0.0, 0.0), (3, 5, 1.0, 1.0, 0.0)] latest, best, pos = detect_regressions(steps) assert latest is None and best is None and pos is None latest, best, pos = detect_regressions(steps, min_size=1) assert latest == 1.0 assert best == 0.0 assert pos == [(2, 3, 0.0, 1.0)] steps = [(0, 2, 1.0, 1.0, 0.0), (2, 3, 0.0, 0.0, 0.0), (3, 5, 2.0, 1.0, 0.0)] latest, best, pos = detect_regressions(steps) assert latest == 2.0 assert best == 0.0 assert pos == [(2, 3, 0.0, 2.0)]
def test_detect_regressions(use_rangemedian): try: np.random.seed(1234) except NameError: # work around a bug in old pypy/pytest pytest.skip("test needs numpy") return for seed in [1234, 5678, 8901, 2345]: np.random.seed(seed) t = np.arange(4000) y = 0.7 * np.random.rand(t.size) y -= 0.3 * (t >= 1000) y += 2.0 * (t >= 3234 + (seed % 123)) y += 2.0 * ((t >= 3264 + (seed % 53)) & (t < 3700)) y -= 2.7 * ((t >= 3350) & (t < 3500 + (seed % 71))) y = y.tolist() y[123] = None y[1234] = np.nan steps = detect_steps(y) steps_lr = [(l, r) for l, r, _, _, _ in steps] k = steps[0][1] assert 990 <= k <= 1010 assert steps_lr == [(0, k), (k, 3234 + (seed % 123)), (3234 + (seed % 123), 3264 + (seed % 53)), (3264 + (seed % 53), 3350), (3350, 3500 + (seed % 71)), (3500 + (seed % 71), 3700), (3700, 4000)] steps_v = [x[2] for x in steps] assert np.allclose(steps_v, [0.35, 0.05, 2.05, 4.05, 1.15, 4.05, 2.05], rtol=0.3) # The expected mean error is 0.7 <|U(0,1) - 1/2|> = 0.7/4 steps_err = [x[4] for x in steps] assert np.allclose(steps_err, [0.7 / 4] * 7, rtol=0.3) # Check detect_regressions new_value, best_value, regression_pos = detect_regressions(steps) assert regression_pos == [ (3233 + (seed % 123), (3233 + (seed % 123) + 1), steps_v[1], steps_v[2]), (3499 + (seed % 71), 3499 + (seed % 71) + 1, steps_v[4], steps_v[5]) ] assert np.allclose(best_value, 0.7 / 2 - 0.3, rtol=0.3, atol=0) assert np.allclose(new_value, 0.7 / 2 - 0.3 + 2, rtol=0.3, atol=0)
def test_detect_regressions(use_rangemedian): try: np.random.seed(1234) except NameError: # work around a bug in old pypy/pytest pytest.skip("test needs numpy") return for seed in [1234, 5678, 8901, 2345]: np.random.seed(seed) t = np.arange(4000) y = 0.7 * np.random.rand(t.size) y -= 0.3 * (t >= 1000) y += 2.0 * (t >= 3234 + (seed % 123)) y += 2.0 * ((t >= 3264 + (seed % 53)) & (t < 3700)) y -= 2.7 * ((t >= 3350) & (t < 3500 + (seed % 71))) y = y.tolist() y[123] = None y[1234] = np.nan steps = detect_steps(y) steps_lr = [(l, r) for l, r, _, _, _ in steps] k = steps[0][1] assert 990 <= k <= 1010 assert steps_lr == [(0, k), (k, 3234 + (seed % 123)), (3234 + (seed % 123), 3264 + (seed % 53)), (3264 + (seed % 53), 3350), (3350, 3500 + (seed % 71)), (3500 + (seed % 71), 3700), (3700, 4000)] steps_v = [x[2] for x in steps] assert np.allclose(steps_v, [0.35, 0.05, 2.05, 4.05, 1.15, 4.05, 2.05], rtol=0.3) # The expected mean error is 0.7 <|U(0,1) - 1/2|> = 0.7/4 steps_err = [x[4] for x in steps] assert np.allclose(steps_err, [0.7/4]*7, rtol=0.3) # Check detect_regressions new_value, best_value, regression_pos = detect_regressions(steps) assert regression_pos == [(3233 + (seed % 123), (3233 + (seed % 123) + 1), steps_v[1], steps_v[2]), (3499 + (seed % 71), 3499 + (seed % 71) + 1, steps_v[4], steps_v[5])] assert np.allclose(best_value, 0.7/2 - 0.3, rtol=0.3, atol=0) assert np.allclose(new_value, 0.7/2 - 0.3 + 2, rtol=0.3, atol=0)
def test_detect_regressions(): for seed in [1234, 5678, 8901, 2345]: np.random.seed(seed) t = np.arange(4000) y = 0.7 * np.random.rand(t.size) y -= 0.3 * (t >= 1000) y += 2.0 * (t >= 3234 + (seed % 123)) y += 2.0 * ((t >= 3264 + (seed % 53)) & (t < 3700)) y -= 2.7 * ((t >= 3350) & (t < 3500 + (seed % 71))) y = y.tolist() y[123] = None y[1234] = np.nan new_value, jump_pos, best_value = detect_regressions(y) assert jump_pos == [3233 + (seed % 123), 3499 + (seed % 71)] assert np.allclose(best_value, 0.7/2 - 0.3, rtol=0.3, atol=0) assert np.allclose(new_value, 0.7/2 - 0.3 + 2, rtol=0.3, atol=0)
def test_detect_regressions(): for seed in [1234, 5678, 8901, 2345]: np.random.seed(seed) t = np.arange(4000) y = 0.7 * np.random.rand(t.size) y -= 0.3 * (t >= 1000) y += 2.0 * (t >= 3234 + (seed % 123)) y += 2.0 * ((t >= 3264 + (seed % 53)) & (t < 3700)) y -= 2.7 * ((t >= 3350) & (t < 3500 + (seed % 71))) y = y.tolist() y[123] = None y[1234] = np.nan new_value, jump_pos, best_value = detect_regressions(y) assert jump_pos == [3233 + (seed % 123), 3499 + (seed % 71)] assert np.allclose(best_value, 0.7 / 2 - 0.3, rtol=0.3, atol=0) assert np.allclose(new_value, 0.7 / 2 - 0.3 + 2, rtol=0.3, atol=0)
def test_regression_threshold(): steps = [(0, 1, 1.0, 1.0, 0.0), (1, 2, 1.1, 1.1, 0.0), (2, 3, 2.0, 2.0, 0.0)] latest, best, pos = detect_regressions(steps, threshold=0.05, min_size=1) assert latest == 2 assert best == 1 assert pos == [(0, 1, 1.0, 1.1), (1, 2, 1.1, 2.0)] latest, best, pos = detect_regressions(steps, threshold=0.2, min_size=1) assert latest == 2 assert best == 1 assert pos == [(1, 2, 1.1, 2.0)] latest, best, pos = detect_regressions(steps, threshold=0.8, min_size=1) assert latest == 2 assert best == 1 assert pos == [(1, 2, 1.1, 2.0)] latest, best, pos = detect_regressions(steps, threshold=1.1, min_size=1) assert latest == None assert best == None assert pos == None steps = [(0, 1, 1.0, 1.0, 0.0), (1, 2, 1.3, 1.3, 0.0), (2, 3, 1.1, 1.1, 0.0)] latest, best, pos = detect_regressions(steps, threshold=0.2, min_size=1) assert latest == None assert best == None assert pos == None # Gradual change should result to a regression detected somewhere, # even if the individual steps are smaller than the threshold steps = [(0, 1, 1.0, 1.0, 0.0), (1, 2, 1.04, 1.04, 0.0), (2, 3, 1.08, 1.08, 0.0),] latest, best, pos = detect_regressions(steps, threshold=0.05) assert pos == [(0, 1, 1.0, 1.04)]
def time_detect_regressions(self): step_detect.detect_regressions(self.y)
def time_detect_regressions(self): steps = step_detect.detect_steps(self.y) step_detect.detect_regressions(steps)