# Sampling issue: The following error happened while compiling the node

**URL:** <https://discourse.pymc.io/t/sampling-issue-the-following-error-happened-while-compiling-the-node/2554>\
**Category:** Questions\
**Tags:** theano\
**Created:** [January 19, 2019, 9:38pm UTC](https://discourse.pymc.io/t/sampling-issue-the-following-error-happened-while-compiling-the-node/2554 "2019-01-19T21:38:25Z")\
**Posts on this page:** 5\
**Page:** 1

<div class="post-metadata">

**Author:** ![Edderic](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/edderic/32/1422_2.png) [@Edderic](https://discourse.pymc.io/u/Edderic)\
**Post date:** [January 19, 2019, 9:38pm UTC](https://discourse.pymc.io/t/sampling-issue-the-following-error-happened-while-compiling-the-node/2554/1 "2019-01-19T21:38:25Z")

</div>

Greetings!

I’m having issues with sampling. I have a lot of predictors (~150+). Here’s my code with fake data:

```auto
fake_data = pd.DataFrame(np.random.poisson(size=(1000,150)))
fake_data.columns = ['col_' + str(i) for i in range(150)]

with pm.Model() as model:
    summation = 0
    for predictor in fake_data.columns[:-1]:
        summation += pm.Normal(name=predictor, mu=0, sd=2) * fake_data[predictor].astype(float)
    
    pm.Poisson(
        name='course_topic_interest_culture_count',
        mu=np.exp(summation),
        observed=fake_data['col_149']
    )
    
    traces['col_149'] = pm.sample()

```

Here’s the error that I got:

```auto
INFO (theano.gof.compilelock): Refreshing lock /Users/eddericugaddan/.theano/compiledir_Darwin-18.2.0-x86_64-i386-64bit-i386-3.6.0-64/lock_dir/lock
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
INFO (theano.gof.compilelock): Refreshing lock /Users/eddericugaddan/.theano/compiledir_Darwin-18.2.0-x86_64-i386-64bit-i386-3.6.0-64/lock_dir/lock

You can find the C code in this temporary file: /var/folders/_t/k13fpkjx41z2qd6ws3ry4tn00000gn/T/theano_compilation_error_hcy9krlp

---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-106-6190dd043682> in <module>()
     10 )
     11 
---> 12 traces['col_149'] = pm.sample()

~/anaconda3/lib/python3.6/site-packages/pymc3/sampling.py in sample(draws, step, init, n_init, start, trace, chain_idx, chains, cores, tune, nuts_kwargs, step_kwargs, progressbar, model, random_seed, live_plot, discard_tuned_samples, live_plot_kwargs, compute_convergence_checks, use_mmap, **kwargs)
    403 start_, step = init_nuts(init=init, chains=chains, n_init=n_init,
    404 model=model, random_seed=random_seed,
--> 405 progressbar=progressbar, **args)
    406 if start is None:
    407 start = start_

~/anaconda3/lib/python3.6/site-packages/pymc3/sampling.py in init_nuts(init, chains, n_init, model, random_seed, progressbar, **kwargs)
   1504 'Unknown initializer: {}.'.format(init))
   1505 
-> 1506 step = pm.NUTS(potential=potential, model=model, **kwargs)
   1507 
   1508 return start, step

~/anaconda3/lib/python3.6/site-packages/pymc3/step_methods/hmc/nuts.py in __init__ (self, vars, max_treedepth, early_max_treedepth, **kwargs)
    150 `pm.sample` to the desired number of tuning steps.
    151 """
--> 152 super(NUTS, self). __init__ (vars, **kwargs)
    153 
    154 self.max_treedepth = max_treedepth

~/anaconda3/lib/python3.6/site-packages/pymc3/step_methods/hmc/base_hmc.py in __init__ (self, vars, scaling, step_scale, is_cov, model, blocked, potential, integrator, dtype, Emax, target_accept, gamma, k, t0, adapt_step_size, step_rand, **theano_kwargs)
     61 
     62 super(BaseHMC, self). __init__ (vars, blocked=blocked, model=model,
---> 63 dtype=dtype, **theano_kwargs)
     64 
     65 self.adapt_step_size = adapt_step_size

~/anaconda3/lib/python3.6/site-packages/pymc3/step_methods/arraystep.py in __init__ (self, vars, model, blocked, dtype, **theano_kwargs)
    226 
    227 func = model.logp_dlogp_function(
--> 228 vars, dtype=dtype, **theano_kwargs)
    229 
    230 # handle edge case discovered in #2948

~/anaconda3/lib/python3.6/site-packages/pymc3/model.py in logp_dlogp_function(self, grad_vars, **kwargs)
    707 varnames = [var.name for var in grad_vars]
    708 extra_vars = [var for var in self.free_RVs if var.name not in varnames]
--> 709 return ValueGradFunction(self.logpt, grad_vars, extra_vars, **kwargs)
    710 
    711 @property

~/anaconda3/lib/python3.6/site-packages/pymc3/model.py in __init__ (self, cost, grad_vars, extra_vars, dtype, casting, **kwargs)
    446 
    447 self._theano_function = theano.function(
--> 448 inputs, [self._cost_joined, grad], givens=givens, **kwargs)
    449 
    450 def set_extra_values(self, extra_vars):

~/anaconda3/lib/python3.6/site-packages/theano/compile/function.py in function(inputs, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input)
    315 on_unused_input=on_unused_input,
    316 profile=profile,
--> 317 output_keys=output_keys)
    318 return fn

~/anaconda3/lib/python3.6/site-packages/theano/compile/pfunc.py in pfunc(params, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input, output_keys)
    484 accept_inplace=accept_inplace, name=name,
    485 profile=profile, on_unused_input=on_unused_input,
--> 486 output_keys=output_keys)
    487 
    488 

~/anaconda3/lib/python3.6/site-packages/theano/compile/function_module.py in orig_function(inputs, outputs, mode, accept_inplace, name, profile, on_unused_input, output_keys)
   1839 name=name)
   1840 with theano.change_flags(compute_test_value="off"):
-> 1841 fn = m.create(defaults)
   1842 finally:
   1843 t2 = time.time()

~/anaconda3/lib/python3.6/site-packages/theano/compile/function_module.py in create(self, input_storage, trustme, storage_map)
   1713 theano.config.traceback.limit = theano.config.traceback.compile_limit
   1714 _fn, _i, _o = self.linker.make_thunk(
-> 1715 input_storage=input_storage_lists, storage_map=storage_map)
   1716 finally:
   1717 theano.config.traceback.limit = limit_orig

~/anaconda3/lib/python3.6/site-packages/theano/gof/link.py in make_thunk(self, input_storage, output_storage, storage_map)
    697 return self.make_all(input_storage=input_storage,
    698 output_storage=output_storage,
--> 699 storage_map=storage_map)[:3]
    700 
    701 def make_all(self, input_storage, output_storage):

~/anaconda3/lib/python3.6/site-packages/theano/gof/vm.py in make_all(self, profiler, input_storage, output_storage, storage_map)
   1089 compute_map,
   1090 [],
-> 1091 impl=impl))
   1092 linker_make_thunk_time[node] = time.time() - thunk_start
   1093 if not hasattr(thunks[-1], 'lazy'):

~/anaconda3/lib/python3.6/site-packages/theano/gof/op.py in make_thunk(self, node, storage_map, compute_map, no_recycling, impl)
    953 try:
    954 return self.make_c_thunk(node, storage_map, compute_map,
--> 955 no_recycling)
    956 except (NotImplementedError, utils.MethodNotDefined):
    957 # We requested the c code, so don't catch the error.

~/anaconda3/lib/python3.6/site-packages/theano/gof/op.py in make_c_thunk(self, node, storage_map, compute_map, no_recycling)
    856 _logger.debug('Trying CLinker.make_thunk')
    857 outputs = cl.make_thunk(input_storage=node_input_storage,
--> 858 output_storage=node_output_storage)
    859 thunk, node_input_filters, node_output_filters = outputs
    860 

~/anaconda3/lib/python3.6/site-packages/theano/gof/cc.py in make_thunk(self, input_storage, output_storage, storage_map, keep_lock)
   1215 cthunk, module, in_storage, out_storage, error_storage = self. __compile__ (
   1216 input_storage, output_storage, storage_map,
-> 1217 keep_lock=keep_lock)
   1218 
   1219 res = _CThunk(cthunk, init_tasks, tasks, error_storage, module)

~/anaconda3/lib/python3.6/site-packages/theano/gof/cc.py in __compile__ (self, input_storage, output_storage, storage_map, keep_lock)
   1155 output_storage,
   1156 storage_map,
-> 1157 keep_lock=keep_lock)
   1158 return (thunk,
   1159 module,

~/anaconda3/lib/python3.6/site-packages/theano/gof/cc.py in cthunk_factory(self, error_storage, in_storage, out_storage, storage_map, keep_lock)
   1618 node.op.prepare_node(node, storage_map, None, 'c')
   1619 module = get_module_cache().module_from_key(
-> 1620 key=key, lnk=self, keep_lock=keep_lock)
   1621 
   1622 vars = self.inputs + self.outputs + self.orphans

~/anaconda3/lib/python3.6/site-packages/theano/gof/cmodule.py in module_from_key(self, key, lnk, keep_lock)
   1179 try:
   1180 location = dlimport_workdir(self.dirname)
-> 1181 module = lnk.compile_cmodule(location)
   1182 name = module. __file__
   1183 assert name.startswith(location)

~/anaconda3/lib/python3.6/site-packages/theano/gof/cc.py in compile_cmodule(self, location)
   1521 lib_dirs=self.lib_dirs(),
   1522 libs=libs,
-> 1523 preargs=preargs)
   1524 except Exception as e:
   1525 e.args += (str(self.fgraph),)

~/anaconda3/lib/python3.6/site-packages/theano/gof/cmodule.py in compile_str(module_name, src_code, location, include_dirs, lib_dirs, libs, preargs, py_module, hide_symbols)
   2386 # difficult to read.
   2387 raise Exception('Compilation failed (return status=%s): %s' %
-> 2388 (status, compile_stderr.replace('\n', '. ')))
   2389 elif config.cmodule.compilation_warning and compile_stderr:
   2390 # Print errors just below the command line.

Exception: ('The following error happened while compiling the node', Elemwise{Composite{exp(((i0 * i1) + (i2 * i3) + (i4 * i5) + (i6 * i7) + (i8 * i9) + (i10 * i11) + (i12 * i13) + (i14 * i15) + (i16 * i17) + (i18 * i19) + (i20 * i21) + (i22 * i23) + (i24 * i25) + (i26 * i27) + (i28 * i29) + (i30 * i31) + (i32 * i33) + (i34 * i35) + (i36 * i37) + (i38 * i39) + (i40 * i41) + (i42 * i43) + (i44 * i45) + (i46 * i47) + (i48 * i49) + (i50 * i51) + (i52 * i53) + (i54 * i55) + (i56 * i57) + (i58 * i59) + (i60 * i61) + (i62 * i63) + (i64 * i65) + (i66 * i67) + (i68 * i69) + (i70 * i71) + (i72 * i73) + (i74 * i75) + (i76 * i77) + (i78 * i79) + (i80 * i81) + (i82 * i83) + (i84 * i85) + (i86 * i87) + (i88 * i89) + (i90 * i91) + (i92 * i93) + (i94 * i95) + (i96 * i97) + (i98 * i99) + (i100 * i101) + (i102 * i103) + (i104 * i105) + (i106 * i107) + (i108 * i109) + (i110 * i111) + (i112 * i113) + (i114 * i115) + (i116 * i117) + (i118 * i119) + (i120 * i121) + (i122 * i123) + (i124 * i125) + (i126 * i127) + (i128 * i129) + (i130 * i131) + (i132 * i133) + (i134 * i135) + (i136 * i137) + (i138 * i139) + (i140 * i141) + (i142 * i143) + (i144 * i145) + (i146 * i147) + (i148 * i149) + (i150 * i151) + (i152 * i153) + (i154 * i155) + (i156 * i157) + (i158 * i159) + (i160 * i161) + (i162 * i163) + (i164 * i165) + (i166 * i167) + (i168 * i169) + (i170 * i171) + (i172 * i173) + (i174 * i175) + (i176 * i177) + (i178 * i179) + (i180 * i181) + (i182 * i183) + (i184 * i185) + (i186 * i187) + (i188 * i189) + (i190 * i191) + (i192 * i193) + (i194 * i195) + (i196 * i197) + (i198 * i199) + (i200 * i201) + (i202 * i203) + (i204 * i205) + (i206 * i207) + (i208 * i209) + (i210 * i211) + (i212 * i213) + (i214 * i215) + (i216 * i217) + (i218 * i219) + (i220 * i221) + (i222 * i223) + (i224 * i225) + (i226 * i227) + (i228 * i229) + (i230 * i231) + (i232 * i233) + (i234 * i235) + (i236 * i237) + (i238 * i239) + (i240 * i241) + (i242 * i243) + (i244 * i245) + (i246 * i247) + (i248 * i249) + (i250 * i251) + (i252 * i253) + (i254 * i255) + (i256 * i257) + (i258 * i259) + (i260 * i261) + (i262 * i263) + (i264 * i265) + (i266 * i267) + (i268 * i269) + (i270 * i271) + (i272 * i273) + (i274 * i275) + (i276 * i277) + (i278 * i279) + (i280 * i281) + (i282 * i283) + (i284 * i285) + (i286 * i287) + (i288 * i289) + (i290 * i291) + (i292 * i293) + (i294 * i295) + (i296 * i297)))}}(InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 2. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 0. ... 1. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 0. ... 3. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 2. ... 0. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 2. ... 3. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 2. ... 1. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[3. 1. ... 1. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 0. ... 5. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 1. ... 2. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 3. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 2. ... 0. 2.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 2. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 1. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 1. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 0. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 0. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 1. ... 0. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 1. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 0. ... 2. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 2. ... 2. 2.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 3. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 2. ... 1. 2.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 1. ... 2. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 1. ... 1. 2.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 3. ... 1. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[3. 1. ... 1. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 0. ... 2. 2.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 1. ... 2. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 3. ... 0. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 2. ... 1. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 1. ... 3. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 0. ... 0. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 2. ... 1. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 1. ... 1. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 0. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 0. ... 1. 2.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 2. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 2. ... 0. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 0. ... 1. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 1. ... 2. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[3. 0. ... 0. 3.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 4. ... 2. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 0. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 2. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 0. ... 2. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 1. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 0. ... 3. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 2. ... 0. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 2. ... 1. 2.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 0. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 0. ... 0. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 1. ... 3. 2.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 0. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 0. ... 0. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 3. ... 1. 2.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 2. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 0. ... 2. 2.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 2. ... 1. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 0. ... 2. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 2. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 1. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[3. 0. ... 1. 2.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 2. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 1. ... 1. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 1. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 1. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 2. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[3. 0. ... 2. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 1. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 1. ... 0. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 1. ... 0. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 0. 2.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 1. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 0. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 2. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[3. 1. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 1. 2.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 2. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 3. ... 0. 4.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 1. ... 0. 2.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 2. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 1. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 3. ... 2. 3.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 3. ... 1. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 2. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[3. 3. ... 2. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 0. ... 1. 3.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 1. ... 4. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 2. ... 2. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 2. ... 4. 3.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 3. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[4. 2. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 2. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 1. ... 2. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 2. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 1. ... 3. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[3. 0. ... 0. 4.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 1. ... 0. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 0. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 2. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 2. ... 4. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 0. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 1. ... 4. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 2. ... 0. 4.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 2. ... 1. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 2. ... 1. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 4. ... 2. 2.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 1. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 2. ... 0. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 0. ... 1. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 1. ... 1. 2.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 1. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[4. 0. ... 2. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 0. ... 1. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 3. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 0. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 5. ... 1. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 0. ... 0. 3.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 0. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 1. ... 2. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 3. ... 2. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 0. 2.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 1. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 2. ... 2. 2.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 1. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 1. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[3. 1. ... 0. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 0. ... 4. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 0. 3.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 0. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 2. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 3. ... 0. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 2. ... 0. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 1. ... 0. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 0. ... 2. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 1. ... 1. 2.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 0. ... 2. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 0. ... 2. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 2. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 2. ... 1. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 3. ... 2. 1.]}, InplaceDimShuffle{x}.0, TensorConstant{[1. 2. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[2. 1. ... 0. 0.]}, InplaceDimShuffle{x}.0, TensorConstant{[0. 0. ... 0. 1.]}), '\n', 'Compilation failed (return status=1): /Users/eddericugaddan/.theano/compiledir_Darwin-18.2.0-x86_64-i386-64bit-i386-3.6.0-64/tmpuxmwtlie/mod.cpp:25346:32: fatal error: bracket nesting level exceeded maximum of 256. if (!PyErr_Occurred()) {. ^. /Users/eddericugaddan/.theano/compiledir_Darwin-18.2.0-x86_64-i386-64bit-i386-3.6.0-64/tmpuxmwtlie/mod.cpp:25346:32: note: use -fbracket-depth=N to increase maximum nesting level. 1 error generated.. ', '[Elemwise{Composite{exp(((i0 * i1) + (i2 * i3) + (i4 * i5) + (i6 * i7) + (i8 * i9) + (i10 * i11) + (i12 * i13) + (i14 * i15) + (i16 * i17) + (i18 * i19) + (i20 * i21) + (i22 * i23) + (i24 * i25) + (i26 * i27) + (i28 * i29) + (i30 * i31) + (i32 * i33) + (i34 * i35) + (i36 * i37) + (i38 * i39) + (i40 * i41) + (i42 * i43) + (i44 * i45) + (i46 * i47) + (i48 * i49) + (i50 * i51) + (i52 * i53) + (i54 * i55) + (i56 * i57) + (i58 * i59) + (i60 * i61) + (i62 * i63) + (i64 * i65) + (i66 * i67) + (i68 * i69) + (i70 * i71) + (i72 * i73) + (i74 * i75) + (i76 * i77) + (i78 * i79) + (i80 * i81) + (i82 * i83) + (i84 * i85) + (i86 * i87) + (i88 * i89) + (i90 * i91) + (i92 * i93) + (i94 * i95) + (i96 * i97) + (i98 * i99) + (i100 * i101) + (i102 * i103) + (i104 * i105) + (i106 * i107) + (i108 * i109) + (i110 * i111) + (i112 * i113) + (i114 * i115) + (i116 * i117) + (i118 * i119) + (i120 * i121) + (i122 * i123) + (i124 * i125) + (i126 * i127) + (i128 * i129) + (i130 * i131) + (i132 * i133) + (i134 * i135) + (i136 * i137) + (i138 * i139) + (i140 * i141) + (i142 * i143) + (i144 * i145) + (i146 * i147) + (i148 * i149) + (i150 * i151) + (i152 * i153) + (i154 * i155) + (i156 * i157) + (i158 * i159) + (i160 * i161) + (i162 * i163) + (i164 * i165) + (i166 * i167) + (i168 * i169) + (i170 * i171) + (i172 * i173) + (i174 * i175) + (i176 * i177) + (i178 * i179) + (i180 * i181) + (i182 * i183) + (i184 * i185) + (i186 * i187) + (i188 * i189) + (i190 * i191) + (i192 * i193) + (i194 * i195) + (i196 * i197) + (i198 * i199) + (i200 * i201) + (i202 * i203) + (i204 * i205) + (i206 * i207) + (i208 * i209) + (i210 * i211) + (i212 * i213) + (i214 * i215) + (i216 * i217) + (i218 * i219) + (i220 * i221) + (i222 * i223) + (i224 * i225) + (i226 * i227) + (i228 * i229) + (i230 * i231) + (i232 * i233) + (i234 * i235) + (i236 * i237) + (i238 * i239) + (i240 * i241) + (i242 * i243) + (i244 * i245) + (i246 * i247) + (i248 * i249) + (i250 * i251) + (i252 * i253) + (i254 * i255) + (i256 * i257) + (i258 * i259) + (i260 * i261) + (i262 * i263) + (i264 * i265) + (i266 * i267) + (i268 * i269) + (i270 * i271) + (i272 * i273) + (i274 * i275) + (i276 * i277) + (i278 * i279) + (i280 * i281) + (i282 * i283) + (i284 * i285) + (i286 * i287) + (i288 * i289) + (i290 * i291) + (i292 * i293) + (i294 * i295) + (i296 * i297)))}}(<TensorType(float64, (True,))>, TensorConstant{[1. 1. ... 2. 1.]}, <TensorType(float64, (True,))>, TensorConstant{[0. 0. ... 1. 1.]}, <TensorType(float64, (True,))>, TensorConstant{[2. 0. ... 3. 1.]}, <TensorType(float64, (True,))>, TensorConstant{[1. 1. ... 0. 0.]}, <TensorType(float64, (True,))>, TensorConstant{[1. 2. ... 0. 1.]}, <TensorType(float64, (True,))>, TensorConstant{[1. 2. ... 3. 1.]}, <TensorType(float64, (True,))>, TensorConstant{[2. 2. ... 1. 1.]}, <TensorType(float64, (True,))>, TensorConstant{[3. 1. ... 1. 1.]}, 

...

```

I am able to make it work if I drop the number of predictors, but I wouldn’t want to for the sake of causal inference (i.e. satisfying the backdoor criterion, etc.). Do you think there’s a way around this? Thanks a lot!

Regards,  
Edderic

---

<div class="post-metadata">

**Author:** ![junpenglao](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/junpenglao/32/8_2.png) [@junpenglao](https://discourse.pymc.io/u/junpenglao)\
**Post date:** [January 20, 2019, 5:30pm UTC](https://discourse.pymc.io/t/sampling-issue-the-following-error-happened-while-compiling-the-node/2554/2 "2019-01-20T17:30:09Z")

</div>

The for loop could be quite inefficient, try something like:

```python
X = fake_data.iloc[:, :-1].values
with pm.Model() as model:
    beta = pm.Normal('beta', mu=0., sd=2., shape=(X.shape[1], 1))
    summation = pm.math.dot(X, beta)
    pm.Poisson(
        name='course_topic_interest_culture_count',
        mu=np.exp(summation),
        observed=fake_data['col_149']
    )
    
    traces = pm.sample()

```

---

<div class="post-metadata">

**Author:** ![Edderic](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/edderic/32/1422_2.png) [@Edderic](https://discourse.pymc.io/u/Edderic)\
**Post date:** [January 20, 2019, 5:39pm UTC](https://discourse.pymc.io/t/sampling-issue-the-following-error-happened-while-compiling-the-node/2554/3 "2019-01-20T17:39:01Z")

</div>

> [@junpenglao](#):
>
> X = fake\_data.iloc[:, :-1].values with pm.Model() as model: beta = pm.Normal(‘beta’, mu=0., sd=2., shape=(X.shape[1], 1)) summation = pm.math.dot(X, beta) pm.Poisson( name=‘course\_topic\_interest\_culture\_count’, mu=np.exp(summation), observed=fake\_data[‘col\_149’] ) traces = pm.sample()

Thanks for the response! I tried running the above, and it seemed to make more progress than last time, but now I’m getting a different failure:

```auto
INFO (theano.gof.compilelock): Refreshing lock /Users/eddericugaddan/.theano/compiledir_Darwin-18.2.0-x86_64-i386-64bit-i386-3.6.0-64/lock_dir/lock
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
INFO (theano.gof.compilelock): Refreshing lock /Users/eddericugaddan/.theano/compiledir_Darwin-18.2.0-x86_64-i386-64bit-i386-3.6.0-64/lock_dir/lock
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [beta]
Sampling 4 chains: 18%|█▊ | 723/4000 [01:03<04:45, 11.48draws/s]  

---------------------------------------------------------------------------
RemoteTraceback Traceback (most recent call last)
RemoteTraceback: 
"""
Traceback (most recent call last):
  File "/Users/eddericugaddan/anaconda3/lib/python3.6/site-packages/pymc3/parallel_sampling.py", line 73, in run
    self._start_loop()
  File "/Users/eddericugaddan/anaconda3/lib/python3.6/site-packages/pymc3/parallel_sampling.py", line 113, in _start_loop
    point, stats = self._compute_point()
  File "/Users/eddericugaddan/anaconda3/lib/python3.6/site-packages/pymc3/parallel_sampling.py", line 139, in _compute_point
    point, stats = self._step_method.step(self._point)
  File "/Users/eddericugaddan/anaconda3/lib/python3.6/site-packages/pymc3/step_methods/arraystep.py", line 247, in step
    apoint, stats = self.astep(array)
  File "/Users/eddericugaddan/anaconda3/lib/python3.6/site-packages/pymc3/step_methods/hmc/base_hmc.py", line 115, in astep
    self.potential.raise_ok(self._logp_dlogp_func._ordering.vmap)
  File "/Users/eddericugaddan/anaconda3/lib/python3.6/site-packages/pymc3/step_methods/hmc/quadpotential.py", line 201, in raise_ok
    raise ValueError('\n'.join(errmsg))
ValueError: Mass matrix contains zeros on the diagonal. 
The derivative of RV `beta`.ravel()[4] is zero.
The derivative of RV `beta`.ravel()[32] is zero.
The derivative of RV `beta`.ravel()[38] is zero.
The derivative of RV `beta`.ravel()[47] is zero.
The derivative of RV `beta`.ravel()[62] is zero.
The derivative of RV `beta`.ravel()[67] is zero.
The derivative of RV `beta`.ravel()[80] is zero.
The derivative of RV `beta`.ravel()[96] is zero.
The derivative of RV `beta`.ravel()[105] is zero.
The derivative of RV `beta`.ravel()[107] is zero.
The derivative of RV `beta`.ravel()[112] is zero.
The derivative of RV `beta`.ravel()[132] is zero.
The derivative of RV `beta`.ravel()[139] is zero.
"""

The above exception was the direct cause of the following exception:

ValueError Traceback (most recent call last)
ValueError: Mass matrix contains zeros on the diagonal. 
The derivative of RV `beta`.ravel()[4] is zero.
The derivative of RV `beta`.ravel()[32] is zero.
The derivative of RV `beta`.ravel()[38] is zero.
The derivative of RV `beta`.ravel()[47] is zero.
The derivative of RV `beta`.ravel()[62] is zero.
The derivative of RV `beta`.ravel()[67] is zero.
The derivative of RV `beta`.ravel()[80] is zero.
The derivative of RV `beta`.ravel()[96] is zero.
The derivative of RV `beta`.ravel()[105] is zero.
The derivative of RV `beta`.ravel()[107] is zero.
The derivative of RV `beta`.ravel()[112] is zero.
The derivative of RV `beta`.ravel()[132] is zero.
The derivative of RV `beta`.ravel()[139] is zero.

The above exception was the direct cause of the following exception:

RuntimeError Traceback (most recent call last)
<ipython-input-297-992e789ace0a> in <module>()
      9 )
     10 
---> 11 traces = pm.sample()

~/anaconda3/lib/python3.6/site-packages/pymc3/sampling.py in sample(draws, step, init, n_init, start, trace, chain_idx, chains, cores, tune, nuts_kwargs, step_kwargs, progressbar, model, random_seed, live_plot, discard_tuned_samples, live_plot_kwargs, compute_convergence_checks, use_mmap, **kwargs)
    447 _print_step_hierarchy(step)
    448 try:
--> 449 trace = _mp_sample(**sample_args)
    450 except pickle.PickleError:
    451 _log.warning("Could not pickle model, sampling singlethreaded.")

~/anaconda3/lib/python3.6/site-packages/pymc3/sampling.py in _mp_sample(draws, tune, step, chains, cores, chain, random_seed, start, progressbar, trace, model, use_mmap, **kwargs)
    997 try:
    998 with sampler:
--> 999 for draw in sampler:
   1000 trace = traces[draw.chain - chain]
   1001 if trace.supports_sampler_stats and draw.stats is not None:

~/anaconda3/lib/python3.6/site-packages/pymc3/parallel_sampling.py in __iter__ (self)
    303 
    304 while self._active:
--> 305 draw = ProcessAdapter.recv_draw(self._active)
    306 proc, is_last, draw, tuning, stats, warns = draw
    307 if self._progress is not None:

~/anaconda3/lib/python3.6/site-packages/pymc3/parallel_sampling.py in recv_draw(processes, timeout)
    221 if msg[0] == 'error':
    222 old = msg[1]
--> 223 six.raise_from(RuntimeError('Chain %s failed.' % proc.chain), old)
    224 elif msg[0] == 'writing_done':
    225 proc._readable = True

~/anaconda3/lib/python3.6/site-packages/six.py in raise_from(value, from_value)

RuntimeError: Chain 0 failed.

```

---

<div class="post-metadata">

**Author:** ![junpenglao](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/junpenglao/32/8_2.png) [@junpenglao](https://discourse.pymc.io/u/junpenglao)\
**Post date:** [January 20, 2019, 5:49pm UTC](https://discourse.pymc.io/t/sampling-issue-the-following-error-happened-while-compiling-the-node/2554/4 "2019-01-20T17:49:36Z")

</div>

Likely numerical issue from exp (for example, `np.exp(30) # ==> 10686474581524.463`), try scaling the input matrix `X`

---

<div class="post-metadata">

**Author:** ![Edderic](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/edderic/32/1422_2.png) [@Edderic](https://discourse.pymc.io/u/Edderic)\
**Post date:** [January 21, 2019, 1:15pm UTC](https://discourse.pymc.io/t/sampling-issue-the-following-error-happened-while-compiling-the-node/2554/5 "2019-01-21T13:15:37Z")

</div>

> [@junpenglao](#):
>
> beta = pm.Normal(‘beta’, mu=0., sd=2., shape=(X.shape[1], 1)) summation = pm.math.dot(X, beta)

Thanks, @junpenglao! That worked. Really appreciate the help!

Cheers,  
Edderic