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 ValidatePred(ndata, par, steps, nplasmids, npromoters, variants, random=100, timespan=3600):
""" Simulating all combinations will become too expensive with large sets! """
""" Alternative ask for a random sample """
if random is None:
points = np.arange(ndata.shape[0])
else:
points = np.hstack( [ [0,ndata.shape[0]-1],
np.random.choice(ndata.shape[0],
min(ndata.shape[0],random-2),
replace=False) ] )
library = []
results = []
for i in points:
select = [ int( re.sub('L', '',x)) for x in np.array( ndata.iloc[i,0:-1] ) ]
design = Assembly( select, steps, nplasmids, npromoters, variants )
pw = Construct(par,design)
library.append(pw)
target = SelectCurves(pw)
s = pw.simulate(0,timespan,1000)
ds = pd.DataFrame(s,columns=s.colnames)
results.append( s[target][-1] )
ndata.loc[points,'sim'] = results
ix = np.logical_not( np.isnan( ndata['sim'] ) )
sim = ndata.loc[ix,'sim']
pred = ndata.loc[ix,'pred']
rms = rmse(pred, sim)
iq = iqr(pred,sim)
ym = np.mean(sim)
ols1 = smf.ols(formula="sim ~ pred", data=ndata )
res1 = ols1.fit()
performance = { 'rms': rms, 'lib': library, 'ndata': ndata, 'res': res1,
'iqr': float(iq), 'ym': ym }
return performance
def test_iqr_axis(reset_randomstate):
x1 = np.random.standard_normal((100, 100))
x2 = np.random.standard_normal((100, 100))
ax_none = iqr(x1, x2, axis=None)
ax_none_direct = iqr(x1.ravel(), x2.ravel())
assert_equal(ax_none, ax_none_direct)
ax_0 = iqr(x1, x2, axis=0)
assert ax_0.shape == (100, )
ax_0_direct = [iqr(x1[:, i], x2[:, i]) for i in range(100)]
assert_almost_equal(ax_0, np.array(ax_0_direct))
ax_1 = iqr(x1, x2, axis=1)
assert ax_1.shape == (100, )
ax_1_direct = [iqr(x1[i, :], x2[i, :]) for i in range(100)]
assert_almost_equal(ax_1, np.array(ax_1_direct))
assert any(ax_0 != ax_1)