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)