Trouble running examples

[rwaples]

I'm trying to get PAPI running on the examples:

I was able create a conda environment with:
conda create --name papi --file ./papi/papi_spec-file.txt

But, I am running into a number of different errors. Sorry to combine then into one issue, but it seems like these cmds may have worked in the past, but not anymore.

If I run:
python src/inference.py --inputfile ./examples/tracts.txt --ind 1 --tracefile ./example.trace --outfile OUTFILE --typ bin

I get the output:
Running inference in pymc mode
Warning: gradient not available.(E.g. vars contains discrete variables). MAP estimates may not be accurate for the default parameters. Defaulting to non-gradient minimization 'Powell'.
logp = -249.34: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 46/46 [00:00<00:00, 3164.68it/s]
Only 100 samples in chain.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Initializing NUTS failed. Falling back to elementwise auto-assignment.
Multiprocess sampling (4 chains in 4 jobs)
CompoundStep

Slice: [t2]
Slice: [t1]
Slice: [p2]
Slice: [p1]
Sampling 4 chains, 0 divergences: 100%|███████████████████████████████████████████████████████████████████| 2400/2400 [00:05<00:00, 475.56draws/s]
The number of effective samples is smaller than 25% for some parameters.
Traceback (most recent call last):
File "src/inference.py", line 310, in
trace['t1']=trace['t1']-1
TypeError: 'MultiTrace' object does not support item assignment

If I run python src/inference.py --inputfile ./examples/tracts.txt --ind 1 --tracefile ./example.trace --outfile OUTFILE --typ full

I get the output:
Running inference in pymc mode
Traceback (most recent call last):
File "src/inference.py", line 301, in
pm.DensityDist('likelihood', lambda v: logl(v), observed={'v': theta})
File "/home/kele/mambaforge/envs/papi/lib/python3.8/site-packages/pymc3/distributions/distribution.py", line 47, in new
return model.Var(name, dist, data, total_size)
File "/home/kele/mambaforge/envs/papi/lib/python3.8/site-packages/pymc3/model.py", line 940, in Var
var = MultiObservedRV(name=name, data=data, distribution=dist,
File "/home/kele/mambaforge/envs/papi/lib/python3.8/site-packages/pymc3/model.py", line 1543, in init
self.logp_elemwiset = distribution.logp(**self.data)
File "src/inference.py", line 301, in
pm.DensityDist('likelihood', lambda v: logl(v), observed={'v': theta})
File "/home/kele/mambaforge/envs/papi/lib/python3.8/site-packages/theano/gof/op.py", line 674, in call
required = thunk()
File "/home/kele/mambaforge/envs/papi/lib/python3.8/site-packages/theano/gof/op.py", line 892, in rval
r = p(n, [x[0] for x in i], o)
File "src/inference.py", line 49, in perform
logl = self.likelihood(theta,self.data)
TypeError: lik_func() missing 1 required positional argument: 'tau'

and with:
python src/inference.py --inputfile ./examples/tracts.txt --ind 1 --tracefile ./example.trace --outfile OUTFILE --typ mrkv

Running inference in pymc mode
Warning: gradient not available.(E.g. vars contains discrete variables). MAP estimates may not be accurate for the default parameters. Defaulting to non-gradient minimization 'Powell'.
logp = 141.33: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 136/136 [00:03<00:00, 36.25it/s]
Only 100 samples in chain.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Initializing NUTS failed. Falling back to elementwise auto-assignment.
Multiprocess sampling (4 chains in 4 jobs)
CompoundStep

Slice: [t2]
Slice: [t1]
Slice: [p2]
Slice: [p1]
Sampling 4 chains, 0 divergences: 100%|████████████████████████████████████████████████████████████████████| 2400/2400 [09:25<00:00, 4.25draws/s]
The rhat statistic is larger than 1.4 for some parameters. The sampler did not converge.
The number of effective samples is smaller than 10% for some parameters.
Traceback (most recent call last):
File "src/inference.py", line 310, in
trace['t1']=trace['t1']-1
TypeError: 'MultiTrace' object does not support item assignment