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tests: invalid fit functions, num_grad vs auto_grad

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ppetrak 2022-12-20 18:26:15 +01:00
parent 80c8a0f979
commit 4b3fac8ee4
1 changed files with 100 additions and 18 deletions
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118
tests/fits_test.py
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@ -108,24 +108,6 @@ def test_prior_fit_num_grad():
auto = pe.fits.least_squares(x, y, lambda a, x: anp.exp(a[0] * x) + a[1], num_grad=False, piors=y[:2])
def test_least_squares_num_grad():
x = []
y = []
for i in range(2, 5):
x.append(i * 0.01)
y.append(pe.pseudo_Obs(i * 0.01, 0.0001, "ens"))
num = pe.fits.least_squares(x, y, lambda a, x: np.exp(a[0] * x) + a[1], num_grad=True)
auto = pe.fits.least_squares(x, y, lambda a, x: anp.exp(a[0] * x) + a[1], num_grad=False)
assert(num[0] == auto[0])
assert(num[1] == auto[1])
assert(num[0] == auto[0])
assert(num[1] == auto[1])
def test_total_least_squares_num_grad():
x = []
y = []
@ -651,6 +633,106 @@ def test_combined_fit_vs_standard_fit():
assert np.isclose(0.0, (res[0].p_value - res[1].p_value), 1e-14, 1e-8)
assert (res[0][0] - res[1][0]).is_zero(atol=1e-8)
def test_combined_fit_invalid_fit_functions():
def func1(a, x):
return a[0] + a[1] * x + a[2] * anp.sinh(x) + a[199]
def func2(a, x, y):
return a[0] + a[1] * x
def func3(x):
return x
def func_valid(a,x):
return a[0] + a[1] * x
xvals =[]
yvals =[]
err = 0.1
for x in range(1, 8, 2):
xvals.append(x)
yvals.append(pe.pseudo_Obs(x + np.random.normal(0.0, err), err, 'test1') + pe.pseudo_Obs(0, err / 100, 'test2', samples=87))
[o.gamma_method() for o in yvals]
for func in [func1, func2, func3]:
with pytest.raises(Exception):
pe.least_squares({'a':xvals}, {'a':yvals}, {'a':func})
with pytest.raises(Exception):
pe.least_squares({'a':xvals, 'b':xvals}, {'a':yvals, 'b':yvals}, {'a':func, 'b':func_valid})
with pytest.raises(Exception):
pe.least_squares({'a':xvals, 'b':xvals}, {'a':yvals, 'b':yvals}, {'a':func_valid, 'b':func})
def test_combined_fit_no_autograd():
def func_exp1(x):
return 0.3*np.exp(0.5*x)
def func_exp2(x):
return 0.3*np.exp(0.8*x)
xvals_b = np.arange(0,6)
xvals_a = np.arange(0,8)
def func_a(a,x):
return a[0]*np.exp(a[1]*x)
def func_b(a,x):
return a[0]*np.exp(a[2]*x)
funcs = {'a':func_a, 'b':func_b}
xs = {'a':xvals_a, 'b':xvals_b}
ys = {'a':[pe.Obs([np.random.normal(item, item*1.5, 1000)],['ensemble1']) for item in func_exp1(xvals_a)],
'b':[pe.Obs([np.random.normal(item, item*1.4, 1000)],['ensemble1']) for item in func_exp2(xvals_b)]}
for key in funcs.keys():
[item.gamma_method() for item in ys[key]]
with pytest.raises(Exception):
pe.least_squares(xs, ys, funcs)
pe.least_squares(xs, ys, funcs, num_grad=True)
def test_combined_fit_num_grad():
def func_exp1(x):
return 0.3*np.exp(0.5*x)
def func_exp2(x):
return 0.3*np.exp(0.8*x)
xvals_b = np.arange(0,6)
xvals_a = np.arange(0,8)
def func_num_a(a,x):
return a[0]*np.exp(a[1]*x)
def func_num_b(a,x):
return a[0]*np.exp(a[2]*x)
def func_auto_a(a,x):
return a[0]*anp.exp(a[1]*x)
def func_auto_b(a,x):
return a[0]*anp.exp(a[2]*x)
funcs_num = {'a':func_num_a, 'b':func_num_b}
funcs_auto = {'a':func_auto_a, 'b':func_auto_b}
xs = {'a':xvals_a, 'b':xvals_b}
ys = {'a':[pe.Obs([np.random.normal(item, item*1.5, 1000)],['ensemble1']) for item in func_exp1(xvals_a)],
'b':[pe.Obs([np.random.normal(item, item*1.4, 1000)],['ensemble1']) for item in func_exp2(xvals_b)]}
for key in funcs_num.keys():
[item.gamma_method() for item in ys[key]]
num = pe.fits.least_squares(xs, ys, funcs_num, num_grad=True)
auto = pe.fits.least_squares(xs, ys, funcs_auto, num_grad=False)
assert(num[0] == auto[0])
assert(num[1] == auto[1])
def fit_general(x, y, func, silent=False, **kwargs):
"""Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.