def test_eval_measures():
#mainly regression tests
x = np.arange(20).reshape(4,5)
y = np.ones((4,5))
assert_equal(iqr(x, y), 5*np.ones(5))
assert_equal(iqr(x, y, axis=1), 2*np.ones(4))
assert_equal(iqr(x, y, axis=None), 9)
assert_equal(mse(x, y),
np.array([ 73.5, 87.5, 103.5, 121.5, 141.5]))
assert_equal(mse(x, y, axis=1),
np.array([ 3., 38., 123., 258.]))
assert_almost_equal(rmse(x, y),
np.array([ 8.5732141 , 9.35414347, 10.17349497,
11.02270384, 11.89537725]))
assert_almost_equal(rmse(x, y, axis=1),
np.array([ 1.73205081, 6.164414,
11.09053651, 16.0623784 ]))
assert_equal(maxabs(x, y),
np.array([ 14., 15., 16., 17., 18.]))
assert_equal(maxabs(x, y, axis=1),
np.array([ 3., 8., 13., 18.]))
assert_equal(meanabs(x, y),
np.array([ 7. , 7.5, 8.5, 9.5, 10.5]))
assert_equal(meanabs(x, y, axis=1),
np.array([ 1.4, 6. , 11. , 16. ]))
assert_equal(meanabs(x, y, axis=0),
np.array([ 7. , 7.5, 8.5, 9.5, 10.5]))
assert_equal(medianabs(x, y),
np.array([ 6.5, 7.5, 8.5, 9.5, 10.5]))
assert_equal(medianabs(x, y, axis=1),
np.array([ 1., 6., 11., 16.]))
assert_equal(bias(x, y),
np.array([ 6.5, 7.5, 8.5, 9.5, 10.5]))
assert_equal(bias(x, y, axis=1),
np.array([ 1., 6., 11., 16.]))
assert_equal(medianbias(x, y),
np.array([ 6.5, 7.5, 8.5, 9.5, 10.5]))
assert_equal(medianbias(x, y, axis=1),
np.array([ 1., 6., 11., 16.]))
assert_equal(vare(x, y),
np.array([ 31.25, 31.25, 31.25, 31.25, 31.25]))
assert_equal(vare(x, y, axis=1),
np.array([ 2., 2., 2., 2.]))
def test_eval_measures():
#mainly regression tests
x = np.arange(20).reshape(4,5)
y = np.ones((4,5))
assert_equal(iqr(x, y), 5*np.ones(5))
assert_equal(iqr(x, y, axis=1), 2*np.ones(4))
assert_equal(iqr(x, y, axis=None), 9)
assert_equal(mse(x, y),
np.array([ 73.5, 87.5, 103.5, 121.5, 141.5]))
assert_equal(mse(x, y, axis=1),
np.array([ 3., 38., 123., 258.]))
assert_almost_equal(rmse(x, y),
np.array([ 8.5732141 , 9.35414347, 10.17349497,
11.02270384, 11.89537725]))
assert_almost_equal(rmse(x, y, axis=1),
np.array([ 1.73205081, 6.164414,
11.09053651, 16.0623784 ]))
assert_equal(maxabs(x, y),
np.array([ 14., 15., 16., 17., 18.]))
assert_equal(maxabs(x, y, axis=1),
np.array([ 3., 8., 13., 18.]))
assert_equal(meanabs(x, y),
np.array([ 7. , 7.5, 8.5, 9.5, 10.5]))
assert_equal(meanabs(x, y, axis=1),
np.array([ 1.4, 6. , 11. , 16. ]))
assert_equal(meanabs(x, y, axis=0),
np.array([ 7. , 7.5, 8.5, 9.5, 10.5]))
assert_equal(medianabs(x, y),
np.array([ 6.5, 7.5, 8.5, 9.5, 10.5]))
assert_equal(medianabs(x, y, axis=1),
np.array([ 1., 6., 11., 16.]))
assert_equal(bias(x, y),
np.array([ 6.5, 7.5, 8.5, 9.5, 10.5]))
assert_equal(bias(x, y, axis=1),
np.array([ 1., 6., 11., 16.]))
assert_equal(medianbias(x, y),
np.array([ 6.5, 7.5, 8.5, 9.5, 10.5]))
assert_equal(medianbias(x, y, axis=1),
np.array([ 1., 6., 11., 16.]))
assert_equal(vare(x, y),
np.array([ 31.25, 31.25, 31.25, 31.25, 31.25]))
assert_equal(vare(x, y, axis=1),
np.array([ 2., 2., 2., 2.]))
def test_eval_measures():
# mainly regression tests
x = np.arange(20).reshape(4, 5)
y = np.ones((4, 5))
assert_equal(iqr(x, y), 5 * np.ones(5))
assert_equal(iqr(x, y, axis=1), 2 * np.ones(4))
assert_equal(iqr(x, y, axis=None), 9)
assert_equal(mse(x, y), np.array([73.5, 87.5, 103.5, 121.5, 141.5]))
assert_equal(mse(x, y, axis=1), np.array([3.0, 38.0, 123.0, 258.0]))
assert_almost_equal(
rmse(x, y),
np.array(
[8.5732141, 9.35414347, 10.17349497, 11.02270384, 11.89537725]
),
)
assert_almost_equal(
rmse(x, y, axis=1),
np.array([1.73205081, 6.164414, 11.09053651, 16.0623784]),
)
err = x - y
loc = np.where(x != 0)
err[loc] /= x[loc]
err[np.where(x == 0)] = np.nan
expected = np.sqrt(np.nanmean(err ** 2, 0) * 100)
assert_almost_equal(rmspe(x, y), expected)
err[np.where(np.isnan(err))] = 0.0
expected = np.sqrt(np.nanmean(err ** 2, 0) * 100)
assert_almost_equal(rmspe(x, y, zeros=0), expected)
assert_equal(maxabs(x, y), np.array([14.0, 15.0, 16.0, 17.0, 18.0]))
assert_equal(maxabs(x, y, axis=1), np.array([3.0, 8.0, 13.0, 18.0]))
assert_equal(meanabs(x, y), np.array([7.0, 7.5, 8.5, 9.5, 10.5]))
assert_equal(meanabs(x, y, axis=1), np.array([1.4, 6.0, 11.0, 16.0]))
assert_equal(meanabs(x, y, axis=0), np.array([7.0, 7.5, 8.5, 9.5, 10.5]))
assert_equal(medianabs(x, y), np.array([6.5, 7.5, 8.5, 9.5, 10.5]))
assert_equal(medianabs(x, y, axis=1), np.array([1.0, 6.0, 11.0, 16.0]))
assert_equal(bias(x, y), np.array([6.5, 7.5, 8.5, 9.5, 10.5]))
assert_equal(bias(x, y, axis=1), np.array([1.0, 6.0, 11.0, 16.0]))
assert_equal(medianbias(x, y), np.array([6.5, 7.5, 8.5, 9.5, 10.5]))
assert_equal(medianbias(x, y, axis=1), np.array([1.0, 6.0, 11.0, 16.0]))
assert_equal(vare(x, y), np.array([31.25, 31.25, 31.25, 31.25, 31.25]))
assert_equal(vare(x, y, axis=1), np.array([2.0, 2.0, 2.0, 2.0]))
def gen_results(d):
#exp = d.query('Trial > 10 and Time > 5')
exp = d.query('Time > 5')
error = exp.groupby(('Day', 'Subject', 'Trial')).ae.mean().reset_index()
error.columns = ['Day', 'Subject', 'Trial', 'AbsoluteError']
rms = exp.groupby(
('Day', 'Subject',
'Trial')).apply(lambda x: rmse(x.y, x.yg)).reset_index()
rms.columns = ['Day', 'Subject', 'Trial', 'RMSE']
var = exp.groupby(
('Day', 'Subject',
'Trial')).apply(lambda x: vare(x.y, x.yg)).reset_index()
var.columns = ['Day', 'Subject', 'Trial', 'VARE']
crossings = exp.groupby(
('Day', 'Subject',
'Trial')).apply(lambda x: len(cross(x.e))).reset_index()
crossings.columns = ['Day', 'Subject', 'Trial', 'Crossings']
rt = find_response_times(exp, trials)
response_time = rt.groupby(
('Day', 'Subject', 'Trial')).mean().ResponseTime.reset_index()
td = exp.groupby(('Day', 'Subject', 'Trial')).apply(
lambda x: recover_shift(x['Time'], x['y'], x['yg'])).reset_index()
time_delay = td.groupby(
('Day', 'Subject', 'Trial')).mean()[0].reset_index().abs()
time_delay.columns = ['Day', 'Subject', 'Trial', 'LagTime']
#entropy = generate_entropy_results(exp)
#res = error.merge(rms).merge(var).merge(crossings).merge(response_time, how='outer').merge(time_delay).merge(entropy)
res = error.merge(rms).merge(var).merge(crossings).merge(
response_time, how='outer').merge(time_delay)
res['Feedback'] = res.Subject % 2 == 1
res = res.merge(trials[['Trial']])
res = res.sort(['Day', 'Subject', 'Trial'])
#res['SecondaryTask'] = res['Secondary_Task']
res = res[[
'Day', 'Subject', 'Trial', 'AbsoluteError', 'RMSE', 'VARE',
'ResponseTime', 'LagTime', 'Crossings', 'Feedback'
]]
res = res.reset_index(drop=True)
res['ID'] = (res.Day - 1) * res.Trial.max() + res.Trial
return res