Stuck on an issue?
Lightrun Answers was designed to reduce the constant googling that comes with debugging 3rd party libraries. It collects links to all the places you might be looking at while hunting down a tough bug.
And, if you’re still stuck at the end, we’re happy to hop on a call to see how we can help out.
Unexpected Error when testing Bambi package
See original GitHub issueI’m working on a laptop with Windows 10 and using Anaconda 64-bit. I have created an environment for working with Pymc3 and bambi. I have been able to test Pymc3 and it is working for an Hierarchical Linear Regression model. For bambi I was testing the example described at this link (growth curves of Pigs example) [https://bambinos.github.io/bambi/master/notebooks/multi-level_regression.html]. I run into an error I had not seen with bambi before (actually from the output this looks like it is occurring at Theano, but I’m not a programmer or a developer).
Here is my environment and the complete traceback of the error
# packages in environment at C:\ProgramData\Anaconda3\envs\pm3env:
#
# Name Version Build Channel
argon2-cffi 20.1.0 py37hcc03f2d_2 conda-forge
arviz 0.11.1 pypi_0 pypi
async_generator 1.10 py_0 conda-forge
attrs 21.2.0 pyhd8ed1ab_0 conda-forge
backcall 0.2.0 pyh9f0ad1d_0 conda-forge
backports 1.0 py_2 conda-forge
backports.functools_lru_cache 1.6.4 pyhd8ed1ab_0 conda-forge
bambi 0.5.0 pypi_0 pypi
blas 1.0 mkl
bleach 4.0.0 pyhd8ed1ab_0 conda-forge
ca-certificates 2021.5.30 h5b45459_0 conda-forge
cached-property 1.5.2 pypi_0 pypi
cachetools 4.2.2 pypi_0 pypi
cairo 1.16.0 hb19e0ff_1008 conda-forge
certifi 2021.5.30 py37h03978a9_0 conda-forge
cffi 1.14.6 py37hd8e9650_0 conda-forge
cftime 1.5.0 pypi_0 pypi
colorama 0.4.4 pyh9f0ad1d_0 conda-forge
cycler 0.10.0 py_2 conda-forge
decorator 5.0.9 pyhd8ed1ab_0 conda-forge
defusedxml 0.7.1 pyhd8ed1ab_0 conda-forge
dill 0.3.4 pypi_0 pypi
entrypoints 0.3 pyhd8ed1ab_1003 conda-forge
et-xmlfile 1.1.0 pypi_0 pypi
expat 2.4.1 h39d44d4_0 conda-forge
fastprogress 1.0.0 pypi_0 pypi
filelock 3.0.12 pypi_0 pypi
font-ttf-dejavu-sans-mono 2.37 hab24e00_0 conda-forge
font-ttf-inconsolata 3.000 h77eed37_0 conda-forge
font-ttf-source-code-pro 2.038 h77eed37_0 conda-forge
font-ttf-ubuntu 0.83 hab24e00_0 conda-forge
fontconfig 2.13.1 h1989441_1005 conda-forge
fonts-conda-ecosystem 1 0 conda-forge
fonts-conda-forge 1 0 conda-forge
formulae 0.1.3 pypi_0 pypi
freetype 2.10.4 h546665d_1 conda-forge
fribidi 1.0.10 h8d14728_0 conda-forge
getopt-win32 0.1 h8ffe710_0 conda-forge
gettext 0.19.8.1 h1a89ca6_1005 conda-forge
graphite2 1.3.13 1000 conda-forge
graphviz 2.48.0 hefbd956_0 conda-forge
gts 0.7.6 h7c369d9_2 conda-forge
h5py 3.1.0 pypi_0 pypi
harfbuzz 2.8.2 hc601d6f_0 conda-forge
icc_rt 2019.0.0 h0cc432a_1
icu 68.1 h0e60522_0 conda-forge
importlib-metadata 2.1.1 pypi_0 pypi
intel-openmp 2021.3.0 h57928b3_3372 conda-forge
ipykernel 5.5.5 py37h7813e69_0 conda-forge
ipython 7.26.0 py37h4038f58_0 conda-forge
ipython_genutils 0.2.0 py_1 conda-forge
ipywidgets 7.6.3 pyhd3eb1b0_1
jbig 2.1 h8d14728_2003 conda-forge
jedi 0.18.0 py37h03978a9_2 conda-forge
jinja2 3.0.1 pyhd8ed1ab_0 conda-forge
jpeg 9d h8ffe710_0 conda-forge
jsonschema 3.2.0 pyhd8ed1ab_3 conda-forge
jupyter 1.0.0 py37_7
jupyter_client 6.1.12 pyhd8ed1ab_0 conda-forge
jupyter_console 6.4.0 pyhd8ed1ab_0 conda-forge
jupyter_core 4.7.1 py37h03978a9_0 conda-forge
jupyterlab_pygments 0.1.2 pyh9f0ad1d_0 conda-forge
jupyterlab_widgets 1.0.0 pyhd8ed1ab_1 conda-forge
kiwisolver 1.3.1 py37h8c56517_1 conda-forge
lcms2 2.12 h2a16943_0 conda-forge
lerc 2.2.1 h0e60522_0 conda-forge
libclang 11.1.0 default_h5c34c98_1 conda-forge
libdeflate 1.7 h8ffe710_5 conda-forge
libffi 3.3 h0e60522_2 conda-forge
libgd 2.3.2 h138e682_0 conda-forge
libglib 2.68.3 h1e62bf3_0 conda-forge
libiconv 1.16 he774522_0 conda-forge
libpng 1.6.37 h1d00b33_2 conda-forge
libpython 2.1 py37_0
libsodium 1.0.18 h8d14728_1 conda-forge
libtiff 4.3.0 h0c97f57_1 conda-forge
libwebp 1.2.0 h57928b3_0 conda-forge
libwebp-base 1.2.0 h8ffe710_2 conda-forge
libxcb 1.13 hcd874cb_1003 conda-forge
libxml2 2.9.12 hf5bbc77_0 conda-forge
llvmlite 0.36.0 py37habb0c8c_0 conda-forge
lz4-c 1.9.3 h8ffe710_1 conda-forge
m2w64-gcc-libgfortran 5.3.0 6 conda-forge
m2w64-gcc-libs 5.3.0 7 conda-forge
m2w64-gcc-libs-core 5.3.0 7 conda-forge
m2w64-gmp 6.1.0 2 conda-forge
m2w64-libwinpthread-git 5.0.0.4634.697f757 2 conda-forge
markupsafe 2.0.1 py37hcc03f2d_0 conda-forge
matplotlib 3.3.2 haa95532_0
matplotlib-base 3.3.2 py37h3379fd5_1 conda-forge
matplotlib-inline 0.1.2 pyhd8ed1ab_2 conda-forge
mistune 0.8.4 py37hcc03f2d_1004 conda-forge
mkl 2020.4 hb70f87d_311 conda-forge
mkl-service 2.3.0 py37h196d8e1_0
mkl_fft 1.3.0 py37hda49f71_1 conda-forge
mkl_random 1.2.0 py37h414f9d2_1 conda-forge
mpmath 1.2.1 pyhd8ed1ab_0 conda-forge
msys2-conda-epoch 20160418 1 conda-forge
nbclient 0.5.3 pyhd8ed1ab_0 conda-forge
nbconvert 6.1.0 py37h03978a9_0 conda-forge
nbformat 5.1.3 pyhd8ed1ab_0 conda-forge
nest-asyncio 1.5.1 pyhd8ed1ab_0 conda-forge
netcdf4 1.5.7 pypi_0 pypi
notebook 6.4.0 pyha770c72_0 conda-forge
numba 0.53.1 py37h4e635f9_0 conda-forge
numpy 1.19.2 py37hadc3359_0
numpy-base 1.19.2 py37ha3acd2a_0
olefile 0.46 pyh9f0ad1d_1 conda-forge
openjpeg 2.4.0 hb211442_1 conda-forge
openpyxl 3.0.7 pypi_0 pypi
openssl 1.1.1k h8ffe710_0 conda-forge
packaging 21.0 pyhd8ed1ab_0 conda-forge
pandas 1.2.1 py37hf11a4ad_0
pandoc 2.14.1 h8ffe710_0 conda-forge
pandocfilters 1.4.2 py_1 conda-forge
pango 1.48.7 hd84fcdd_0 conda-forge
parso 0.8.2 pyhd8ed1ab_0 conda-forge
patsy 0.5.1 pypi_0 pypi
pcre 8.45 h0e60522_0 conda-forge
pickleshare 0.7.5 py_1003 conda-forge
pillow 8.3.1 py37hd7d9ad0_0 conda-forge
pip 20.3.3 py37haa95532_0
pixman 0.40.0 h8ffe710_0 conda-forge
prometheus_client 0.11.0 pyhd8ed1ab_0 conda-forge
prompt-toolkit 3.0.19 pyha770c72_0 conda-forge
prompt_toolkit 3.0.19 hd8ed1ab_0 conda-forge
pthread-stubs 0.4 hcd874cb_1001 conda-forge
pycparser 2.20 pyh9f0ad1d_2 conda-forge
pygments 2.9.0 pyhd8ed1ab_0 conda-forge
pymc3 3.11.2 pypi_0 pypi
pyparsing 2.4.7 pyh9f0ad1d_0 conda-forge
pyqt 5.12.3 py37h03978a9_7 conda-forge
pyqt-impl 5.12.3 py37hf2a7229_7 conda-forge
pyqt5-sip 4.19.18 py37hf2a7229_7 conda-forge
pyqtchart 5.12 py37hf2a7229_7 conda-forge
pyqtwebengine 5.12.1 py37hf2a7229_7 conda-forge
pyrsistent 0.17.3 py37hcc03f2d_2 conda-forge
python 3.7.9 h60c2a47_0
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python-graphviz 0.16 pyh243d235_2 conda-forge
python_abi 3.7 2_cp37m conda-forge
pytz 2021.1 pyhd8ed1ab_0 conda-forge
pywin32 300 py37hcc03f2d_0 conda-forge
pywinpty 1.1.3 py37h7f67f24_0 conda-forge
pyzmq 22.2.0 py37hcce574b_0 conda-forge
qt 5.12.9 h5909a2a_4 conda-forge
qtconsole 5.1.1 pyhd8ed1ab_0 conda-forge
qtpy 1.9.0 py_0 conda-forge
scipy 1.7.1 pypi_0 pypi
semver 2.13.0 pypi_0 pypi
send2trash 1.7.1 pyhd8ed1ab_0 conda-forge
setuptools 49.6.0 py37h03978a9_3 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
sqlite 3.36.0 h8ffe710_0 conda-forge
statsmodels 0.12.2 pypi_0 pypi
sympy 1.7.1 py37h03978a9_1 conda-forge
terminado 0.10.1 py37h03978a9_0 conda-forge
testpath 0.5.0 pyhd8ed1ab_0 conda-forge
theano-pymc 1.1.2 pypi_0 pypi
tk 8.6.10 h8ffe710_1 conda-forge
tornado 6.1 py37hcc03f2d_1 conda-forge
traitlets 5.0.5 py_0 conda-forge
typing 3.7.4.3 pypi_0 pypi
typing_extensions 3.10.0.0 pyha770c72_0 conda-forge
ucrt 10.0.20348.0 h57928b3_0 conda-forge
vc 14.2 hb210afc_5 conda-forge
vs2015_runtime 14.29.30037 h902a5da_5 conda-forge
watermark 2.2.0 pypi_0 pypi
wcwidth 0.2.5 pyh9f0ad1d_2 conda-forge
webencodings 0.5.1 py_1 conda-forge
wheel 0.36.2 pyhd3deb0d_0 conda-forge
widgetsnbextension 3.5.1 py37h03978a9_4 conda-forge
wincertstore 0.2 py37h03978a9_1006 conda-forge
winpty 0.4.3 4 conda-forge
xarray 0.16.2 pypi_0 pypi
xorg-kbproto 1.0.7 hcd874cb_1002 conda-forge
xorg-libice 1.0.10 hcd874cb_0 conda-forge
xorg-libsm 1.2.3 hcd874cb_1000 conda-forge
xorg-libx11 1.7.2 hcd874cb_0 conda-forge
xorg-libxau 1.0.9 hcd874cb_0 conda-forge
xorg-libxdmcp 1.1.3 hcd874cb_0 conda-forge
xorg-libxext 1.3.4 hcd874cb_1 conda-forge
xorg-libxpm 3.5.13 hcd874cb_0 conda-forge
xorg-libxt 1.2.1 hcd874cb_2 conda-forge
xorg-xextproto 7.3.0 hcd874cb_1002 conda-forge
xorg-xproto 7.0.31 hcd874cb_1007 conda-forge
xz 5.2.5 h62dcd97_1 conda-forge
zeromq 4.3.4 h0e60522_0 conda-forge
zipp 3.5.0 pyhd8ed1ab_0 conda-forge
zlib 1.2.11 h62dcd97_1010 conda-forge
zstd 1.5.0 h6255e5f_0 conda-forge
Model I tried to run is:
model = bmb.Model("Weight ~ Time + (Time|Pig)", data)
results = model.fit()
And the error I get is:
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-11-99071b9bde96> in <module>
1 model = bmb.Model("Weight ~ Time + (Time|Pig)", data)
----> 2 results = model.fit()
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\bambi\models.py in fit(self, omit_offsets, backend, **kwargs)
213 )
214
--> 215 return self.backend.run(omit_offsets=omit_offsets, **kwargs)
216
217 def build(self, backend="pymc"):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\bambi\backends\pymc.py in run(self, start, method, init, n_init, omit_offsets, **kwargs)
139 n_init=n_init,
140 return_inferencedata=True,
--> 141 **kwargs,
142 )
143
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\pymc3\sampling.py in sample(draws, step, init, n_init, start, trace, chain_idx, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, callback, jitter_max_retries, return_inferencedata, idata_kwargs, mp_ctx, pickle_backend, **kwargs)
502 progressbar=progressbar,
503 jitter_max_retries=jitter_max_retries,
--> 504 **kwargs,
505 )
506 if start is None:
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\pymc3\sampling.py in init_nuts(init, chains, n_init, model, random_seed, progressbar, jitter_max_retries, **kwargs)
2185 raise ValueError(f"Unknown initializer: {init}.")
2186
-> 2187 step = pm.NUTS(potential=potential, model=model, **kwargs)
2188
2189 return start, step
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\pymc3\step_methods\hmc\nuts.py in __init__(self, vars, max_treedepth, early_max_treedepth, **kwargs)
166 `pm.sample` to the desired number of tuning steps.
167 """
--> 168 super().__init__(vars, **kwargs)
169
170 self.max_treedepth = max_treedepth
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\pymc3\step_methods\hmc\base_hmc.py in __init__(self, vars, scaling, step_scale, is_cov, model, blocked, potential, dtype, Emax, target_accept, gamma, k, t0, adapt_step_size, step_rand, **theano_kwargs)
86 vars = inputvars(vars)
87
---> 88 super().__init__(vars, blocked=blocked, model=model, dtype=dtype, **theano_kwargs)
89
90 self.adapt_step_size = adapt_step_size
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\pymc3\step_methods\arraystep.py in __init__(self, vars, model, blocked, dtype, logp_dlogp_func, **theano_kwargs)
252
253 if logp_dlogp_func is None:
--> 254 func = model.logp_dlogp_function(vars, dtype=dtype, **theano_kwargs)
255 else:
256 func = logp_dlogp_func
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\pymc3\model.py in logp_dlogp_function(self, grad_vars, tempered, **kwargs)
1002 varnames = [var.name for var in grad_vars]
1003 extra_vars = [var for var in self.free_RVs if var.name not in varnames]
-> 1004 return ValueGradFunction(costs, grad_vars, extra_vars, **kwargs)
1005
1006 @property
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\pymc3\model.py in __init__(self, costs, grad_vars, extra_vars, dtype, casting, compute_grads, **kwargs)
689
690 if compute_grads:
--> 691 grad = tt.grad(self._cost_joined, self._vars_joined)
692 grad.name = "__grad"
693 outputs = [self._cost_joined, grad]
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in grad(cost, wrt, consider_constant, disconnected_inputs, add_names, known_grads, return_disconnected, null_gradients)
637 assert g.type.dtype in theano.tensor.float_dtypes
638
--> 639 rval = _populate_grad_dict(var_to_app_to_idx, grad_dict, wrt, cost_name)
640
641 for i in range(len(rval)):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in _populate_grad_dict(var_to_app_to_idx, grad_dict, wrt, cost_name)
1438 return grad_dict[var]
1439
-> 1440 rval = [access_grad_cache(elem) for elem in wrt]
1441
1442 return rval
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1438 return grad_dict[var]
1439
-> 1440 rval = [access_grad_cache(elem) for elem in wrt]
1441
1442 return rval
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1218 )
1219
-> 1220 input_grads = node.op.L_op(inputs, node.outputs, new_output_grads)
1221
1222 if input_grads is None:
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\tensor\elemwise.py in L_op(self, inputs, outs, ograds)
562
563 # compute grad with respect to broadcasted input
--> 564 rval = self._bgrad(inputs, outs, ograds)
565
566 # TODO: make sure that zeros are clearly identifiable
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\tensor\elemwise.py in _bgrad(self, inputs, outputs, ograds)
666 ret.append(None)
667 continue
--> 668 ret.append(transform(scalar_igrad))
669
670 return ret
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\tensor\elemwise.py in transform(r)
657 return DimShuffle((), ["x"] * nd)(res)
658
--> 659 new_r = Elemwise(node.op, {})(*[transform(ipt) for ipt in node.inputs])
660 return new_r
661
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\tensor\elemwise.py in <listcomp>(.0)
657 return DimShuffle((), ["x"] * nd)(res)
658
--> 659 new_r = Elemwise(node.op, {})(*[transform(ipt) for ipt in node.inputs])
660 return new_r
661
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\tensor\elemwise.py in transform(r)
657 return DimShuffle((), ["x"] * nd)(res)
658
--> 659 new_r = Elemwise(node.op, {})(*[transform(ipt) for ipt in node.inputs])
660 return new_r
661
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\graph\op.py in __call__(self, *inputs, **kwargs)
251
252 if config.compute_test_value != "off":
--> 253 compute_test_value(node)
254
255 if self.default_output is not None:
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\graph\op.py in compute_test_value(node)
124
125 # Create a thunk that performs the computation
--> 126 thunk = node.op.make_thunk(node, storage_map, compute_map, no_recycling=[])
127 thunk.inputs = [storage_map[v] for v in node.inputs]
128 thunk.outputs = [storage_map[v] for v in node.outputs]
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\graph\op.py in make_thunk(self, node, storage_map, compute_map, no_recycling, impl)
632 )
633 try:
--> 634 return self.make_c_thunk(node, storage_map, compute_map, no_recycling)
635 except (NotImplementedError, MethodNotDefined):
636 # We requested the c code, so don't catch the error.
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\graph\op.py in make_c_thunk(self, node, storage_map, compute_map, no_recycling)
599 raise NotImplementedError("float16")
600 outputs = cl.make_thunk(
--> 601 input_storage=node_input_storage, output_storage=node_output_storage
602 )
603 thunk, node_input_filters, node_output_filters = outputs
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\link\c\basic.py in make_thunk(self, input_storage, output_storage, storage_map)
1202 init_tasks, tasks = self.get_init_tasks()
1203 cthunk, module, in_storage, out_storage, error_storage = self.__compile__(
-> 1204 input_storage, output_storage, storage_map
1205 )
1206
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\link\c\basic.py in __compile__(self, input_storage, output_storage, storage_map)
1140 input_storage,
1141 output_storage,
-> 1142 storage_map,
1143 )
1144 return (
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\link\c\basic.py in cthunk_factory(self, error_storage, in_storage, out_storage, storage_map)
1632 for node in self.node_order:
1633 node.op.prepare_node(node, storage_map, None, "c")
-> 1634 module = get_module_cache().module_from_key(key=key, lnk=self)
1635
1636 vars = self.inputs + self.outputs + self.orphans
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\link\c\cmodule.py in module_from_key(self, key, lnk)
1189 try:
1190 location = dlimport_workdir(self.dirname)
-> 1191 module = lnk.compile_cmodule(location)
1192 name = module.__file__
1193 assert name.startswith(location)
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\link\c\basic.py in compile_cmodule(self, location)
1548 lib_dirs=self.lib_dirs(),
1549 libs=libs,
-> 1550 preargs=preargs,
1551 )
1552 except Exception as e:
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\link\c\cmodule.py in compile_str(module_name, src_code, location, include_dirs, lib_dirs, libs, preargs, py_module, hide_symbols)
2545 compile_stderr = compile_stderr.replace("\n", ". ")
2546 raise Exception(
-> 2547 f"Compilation failed (return status={status}): {compile_stderr}"
2548 )
2549 elif config.cmodule__compilation_warning and compile_stderr:
Exception: ("Compilation failed (return status=1): C:\\Users\\sreedatta\\AppData\\Local\\Theano\\compiledir_Windows-10-10.0.19041-SP0-Intel64_Family_6_Model_140_Stepping_1_GenuineIntel-3.7.9-64\\tmp81eb_5sq\\mod.cpp: In member function 'int {anonymous}::__struct_compiled_op_m67599e776bb0a5edbe20464e4ef6902fada5652e9f038845aa3f408620203691::run()':. C:\\Users\\sreedatta\\AppData\\Local\\Theano\\compiledir_Windows-10-10.0.19041-SP0-Intel64_Family_6_Model_140_Stepping_1_GenuineIntel-3.7.9-64\\tmp81eb_5sq\\mod.cpp:506:39: warning: narrowing conversion of 'V5_n0' from 'npy_intp' {aka 'long long int'} to 'int' inside { } [-Wnarrowing]. int init_totals[2] = {V5_n0, V1_n1};. ^. C:\\Users\\sreedatta\\AppData\\Local\\Theano\\compiledir_Windows-10-10.0.19041-SP0-Intel64_Family_6_Model_140_Stepping_1_GenuineIntel-3.7.9-64\\tmp81eb_5sq\\mod.cpp:506:39: warning: narrowing conversion of 'V1_n1' from 'npy_intp' {aka 'long long int'} to 'int' inside { } [-Wnarrowing]. C:\\Users\\sreedatta\\AppData\\Local\\Theano\\compiledir_Windows-10-10.0.19041-SP0-Intel64_Family_6_Model_140_Stepping_1_GenuineIntel-3.7.9-64\\tmp81eb_5sq\\mod.cpp:521:5: warning: narrowing conversion of 'V5_stride0' from 'ssize_t' {aka 'long long int'} to 'int' inside { } [-Wnarrowing]. };. ^. C:\\Users\\sreedatta\\AppData\\Local\\Theano\\compiledir_Windows-10-10.0.19041-SP0-Intel64_Family_6_Model_140_Stepping_1_GenuineIntel-3.7.9-64\\tmp81eb_5sq\\mod.cpp:521:5: warning: narrowing conversion of 'V1_stride0' from 'ssize_t' {aka 'long long int'} to 'int' inside { } [-Wnarrowing]. C:\\Users\\sreedatta\\AppData\\Local\\Theano\\compiledir_Windows-10-10.0.19041-SP0-Intel64_Family_6_Model_140_Stepping_1_GenuineIntel-3.7.9-64\\tmp81eb_5sq\\mod.cpp:521:5: warning: narrowing conversion of 'V1_stride1' from 'ssize_t' {aka 'long long int'} to 'int' inside { } [-Wnarrowing]. At global scope:. cc1plus.exe: warning: unrecognized command line option '-Wno-c++11-narrowing'. C:\\Users\\SREEDATTA\\AppData\\Local\\Temp\\ccjNNew1.s: Assembler messages:\r. C:\\Users\\SREEDATTA\\AppData\\Local\\Temp\\ccjNNew1.s:4410: Error: invalid register for .seh_savexmm\r. ", 'FunctionGraph(Elemwise{mul}(<TensorType(float64, col)>, <TensorType(int8, (True, True))>))')
Can one of you help?
Sree
Issue Analytics
- State:
- Created 2 years ago
- Comments:20
Top Results From Across the Web
bambi.models — Bambi 0.8.0 documentation
All chains use the test value (usually the prior mean) as starting point. ... terms in the model # If that happens, raise...
Read more >BAMBI: Bivariate Angular Mixture Models
BAMBI : An R package for Bivariate Angular Mixture Models ... standard error of the estimator is used to construct the test statistic....
Read more >Unexpected errors while testing Python code with doctest
I'm using doctest to test my code, and I'm getting unexpected error while testing a simple function which produces two lines of text...
Read more >A Simple Interface for Fitting Bayesian Linear Models in Python
Here we introduce an open source Python package named Bambi. (BAyesian Model Building Interface) that is built on top of the PyMC probabilistic...
Read more >Airstream Bambi Travel Trailer Owner's Manual
packet included with this manual, to be more detailed. ... Test units used in RVs after the vehicle has been in ... Common...
Read more >
Top Related Medium Post
No results found
Top Related StackOverflow Question
No results found
Troubleshoot Live Code
Lightrun enables developers to add logs, metrics and snapshots to live code - no restarts or redeploys required.
Start Free
Top Related Reddit Thread
No results found
Top Related Hackernoon Post
No results found
Top Related Tweet
No results found
Top Related Dev.to Post
No results found
Top Related Hashnode Post
No results found
@tomicapretto thank you for a detailed step by step process description. I will get familiar with this process in a couple of days. Beginning this Friday, I will start sharing content. I will reach out if I get stuck or have a question.
Sree
@tomicapretto thanks for the additional information regarding the version with Aesara. As one last attempt to get it to work on Windows 10, I have created an
environment.ymlfile from the Windows 8.1 install where it is working and will test with Windows 10. I have found that when installingpymc3=3.11.2orbambi=0.6.0,matplotlib=3.4.3is unable to be compiled. It is breaking the entire installation process. I manually forced on Windows 8.1 to install the previousmatplotlib=3.4.2. This compiles well. The issue is cropping on Windows 10 as well where '``matplotlib=3.4.3``` fails compilation.I now have a good working environment for
pymc3=3.11.2on WIndows 10 and Windows 8.1; I have a good workingpymc3=3.11.2andbambi=0.5.0together on Windows 8.1. I will see if the approach with the environment file will do the trick for bambi on Windows 10. If I get them to work, I will post those environment files for others to try and use.Thanks again - Sree