ValueError: design matrix must be real-valued floating point

Questions
9

Hi falk/junpenglao,
the issue with the data has been resolved. I am able to run model with few independent variables.

The problem arises when I am running with more number of variable. Model is getting run but with NO estimation,

input data ----------------------------

var1 var2 var3 var4 var5 var6 var7 var8 var9 var10 var11 var12 var13 var14 var15 Sales
65.968675 15.2701 2.1527 5.4806 0.489 1.460987 1.741694 0 3.910838 1.092921 0.285847 0.244336 0 0 0 529.9619
67.5334 17.9691 1.6467 13.1201 4.6723 1.44204 1.496044 21.818191 0 6.651632 0.285974 0.194165 0 0 0 620.3295
67.3809 17.9222 2.5687 22.6372 9.0627 1.403944 1.628209 23.465386 2.306421 4.922758 0.119166 0.201742 0 0 0 632.1304
65.38 31.9901 2.8427 20.4549 7.7223 1.348839 1.650558 24.083962 0 3.643249 0.209231 0.211424 0 0 0 638.6615
62.811887 35.289 2.4745 10.1979 14.6548 1.308521 1.447921 23.924864 0 2.696306 0.161398 0.130767 0 0 2.874286 574.2733
61.20905 32.243 1.5991 14.9932 3.5393 1.308449 1.396404 15.929017 0 1.99549 0.068879 0.208567 0 0 3.18655 556.783
60.266263 18.7617 1.4808 7.09 3.9164 1.307377 1.257778 6.291788 0 1.476827 0.189545 0.246374 0 0 2.003989 482.9076
59.085637 22.6611 2.4573 11.7184 12.8453 1.340698 1.298065 14.435666 2.683821 1.092975 0.157764 0.095394 0 0 2.025502 554.5634
57.670538 14.0938 1.9221 11.0503 5.2115 1.334293 1.704465 11.761712 2.512806 0.808892 0.062262 0.133791 0 0 0 480.2632
56.11065 10.0776 1.7768 11.1672 0.7745 1.400468 1.56978 13.348649 0 0.598647 0.182504 0.142478 0 0 0.404979 442.1669
56.774025 9.251 2.4151 3.7361 1.7296 1.383044 1.448474 16.523113 0.177587 6.65008 0.13767 0.152971 0 0 6.103887 461.18
57.19335 8.0343 2.7391 29.1426 20.1989 1.370721 1.491971 17.767906 2.581828 4.92161 0.057027 0.086603 0 0 0.07709 674.1602
58.681025 9.9183 3.4747 16.6473 8.2476 1.322674 1.452471 16.70324 3.72689 3.642399 0.207987 0.283549 0 0 0 547.6149
58.648762 18.516 3.1345 8.6557 3.2639 1.393997 1.474499 16.911993 3.169332 2.695677 0.149747 0.341321 0 0 7.341655 473.3629
58.530862 22.5153 2.2564 2.3721 1.0061 1.404794 1.480851 16.431272 3.427617 1.995024 0.056095 0.35609 113.780051 1.747999 76.160462 468.2492
58.646275 16.261 2.0446 5.1886 2.5959 1.372736 1.509665 15.15974 2.008483 1.476483 0.203494 0.31686 143.419071 1.624762 7.994356 451.0772
57.79665 12.6764 3.9441 22.6553 10.5435 1.330009 1.440223 14.612009 2.892945 1.09272 0.1532 0.264008 154.581761 1.708611 3.551518 617.1936
57.963487 10.7354 1.911 6.7582 1.963 1.252337 1.367364 15.60003 2.600795 0.808703 0.053883 0.633877 167.418532 0 4.07991 431.7602
57.326037 15.0732 2.4045 8.2784 2.3125 1.429363 1.343116 16.637302 2.534635 0.598507 0.053354 0.557943 140.072393 0 2.649223 464.4708
57.3714 12.4054 2.0728 4.4784 1.662 1.327184 1.379131 16.589011 3.355133 0.442945 0.210256 0.440227 117.192972 0 0.461549 427.4129
56.567125 11.7413 2.5183 27.7579 16.0378 1.269452 1.391455 16.579956 3.110303 6.650057 0.187285 0.4531 141.805624 0 2.436883 667.2775
56.91355 15.787 2.1519 7.2272 6.1375 1.213862 1.291586 16.102183 2.117948 4.921593 0.055441 0.257682 151.47998 0 0.088704 476.0278

model - prior

{‘Intercept’: <pymc3.distributions.continuous.Uniform at 0x2c9b448f780>,
‘var1’: <pymc3.distributions.continuous.Uniform at 0x2c9b1b87438>,
‘var10’: <pymc3.distributions.continuous.Uniform at 0x2c9ab968eb8>,
‘var11’: <pymc3.distributions.continuous.Uniform at 0x2c9abae67b8>,
‘var12’: <pymc3.distributions.continuous.Uniform at 0x2c9b4482e80>,
‘var13’: <pymc3.distributions.continuous.Uniform at 0x2c9ab88e6a0>,
‘var14’: <pymc3.distributions.continuous.Uniform at 0x2c9ab7914a8>,
‘var15’: <pymc3.distributions.continuous.Uniform at 0x2c9b4482a90>,
‘var2’: <pymc3.distributions.continuous.Uniform at 0x2c9aaa252b0>,
‘var3’: <pymc3.distributions.continuous.Uniform at 0x2c9a5df7780>,
‘var4’: <pymc3.distributions.continuous.Uniform at 0x2c9b1ed77f0>,
‘var5’: <pymc3.distributions.continuous.Uniform at 0x2c9aafec080>,
‘var6’: <pymc3.distributions.continuous.Uniform at 0x2c9b43d0898>,
‘var7’: <pymc3.distributions.continuous.Uniform at 0x2c9b267af60>,
‘var8’: <pymc3.distributions.continuous.Uniform at 0x2c9b2b45780>,
‘var9’: <pymc3.distributions.continuous.Uniform at 0x2c9aa76b860>}

I am seeing following while running the models,

logp = nan:   4%|▍         | 200/5000 [00:00<00:04, 1179.25it/s]Optimization terminated successfully.
         Current function value: -10000000000000000159028911097599180468360808563945281389781327557747838772170381060813469985856815104.000000
         Iterations: 1
         Function evaluations: 205
logp = nan:   4%|▍         | 205/5000 [00:00<00:11, 399.62it/s] 
100%|██████████| 10500/10500 [01:21<00:00, 129.22it/s]
WAIC WAIC_r(WAIC=nan, WAIC_se=nan, p_WAIC=nan)
DIC nan
BPIC nan

this is the output I am getting at the end.

mean sd mc_error hpd_2.5 hpd_97.5
Intercept NaN NaN NaN NaN NaN
var1 NaN NaN NaN NaN NaN
var2 NaN NaN NaN NaN NaN
var3 NaN NaN NaN NaN NaN
var4 NaN NaN NaN NaN NaN
var5 NaN NaN NaN NaN NaN
var6 NaN NaN NaN NaN NaN
var7 NaN NaN NaN NaN NaN
var8 NaN NaN NaN NaN NaN
var9 NaN NaN NaN NaN NaN
var10 NaN NaN NaN NaN NaN
var11 NaN NaN NaN NaN NaN
var12 NaN NaN NaN NaN NaN
var13 NaN NaN NaN NaN NaN
var14 NaN NaN NaN NaN NaN
var15 NaN NaN NaN NaN NaN
sd 13.302194 5.33E-15 0 13.302194 13.302194

Can you please help me what could be the possible source of error?