Thank you. Following your advice I have installed pymc v4 in a separate conda environment but am stuck with a strange theano error when I try to run my model - see below.
I have tried the solutions suggested in this dicussion but nothing has worked out so far.
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ImportError Traceback (most recent call last)
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\aesara\link\c\lazylinker_c.py:79, in <module>
78 if version != actual_version:
---> 79 raise ImportError(
80 "Version check of the existing lazylinker compiled file."
81 f" Looking for version {version}, but found {actual_version}. "
82 f"Extra debug information: force_compile={force_compile}, _need_reload={_need_reload}"
83 )
84 except ImportError:
ImportError: Version check of the existing lazylinker compiled file. Looking for version 0.212, but found 0.211. Extra debug information: force_compile=False, _need_reload=True
During handling of the above exception, another exception occurred:
ImportError Traceback (most recent call last)
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\aesara\link\c\lazylinker_c.py:100, in <module>
99 if version != actual_version:
--> 100 raise ImportError(
101 "Version check of the existing lazylinker compiled file."
102 f" Looking for version {version}, but found {actual_version}. "
103 f"Extra debug information: force_compile={force_compile}, _need_reload={_need_reload}"
104 )
105 except ImportError:
106 # It is useless to try to compile if there isn't any
107 # compiler! But we still want to try to load it, in case
108 # the cache was copied from another computer.
ImportError: Version check of the existing lazylinker compiled file. Looking for version 0.212, but found 0.211. Extra debug information: force_compile=False, _need_reload=True
During handling of the above exception, another exception occurred:
AssertionError Traceback (most recent call last)
Input In [40], in <cell line: 8>()
51 Y_obs =pm.Gamma("Y_obs", alpha=theta_home, beta =1 , observed= df["homegoals_minsremaining"])
53 #Y_obs =pm.Poisson("Y_obs", mu=f1(theta_home,df["timedelta"]) ,observed= df["homegoals_remaining"], dims="observations")
54
55
56 # Can sample and look at distribution of our priors and thetas
---> 57 trace = pm.sample(2, chains=1, cores=4)
58 pm.traceplot(trace)
59 plt.show()
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\pymc\sampling.py:524, in sample(draws, step, init, n_init, initvals, 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, **kwargs)
521 auto_nuts_init = False
523 initial_points = None
--> 524 step = assign_step_methods(model, step, methods=pm.STEP_METHODS, step_kwargs=kwargs)
526 if isinstance(step, list):
527 step = CompoundStep(step)
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\pymc\sampling.py:229, in assign_step_methods(model, step, methods, step_kwargs)
221 selected = max(
222 methods,
223 key=lambda method, var=rv_var, has_gradient=has_gradient: method._competence(
224 var, has_gradient
225 ),
226 )
227 selected_steps[selected].append(var)
--> 229 return instantiate_steppers(model, steps, selected_steps, step_kwargs)
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\pymc\sampling.py:147, in instantiate_steppers(model, steps, selected_steps, step_kwargs)
145 args = step_kwargs.get(step_class.name, {})
146 used_keys.add(step_class.name)
--> 147 step = step_class(vars=vars, model=model, **args)
148 steps.append(step)
150 unused_args = set(step_kwargs).difference(used_keys)
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\pymc\step_methods\hmc\nuts.py:178, in NUTS.__init__(self, vars, max_treedepth, early_max_treedepth, **kwargs)
120 def __init__(self, vars=None, max_treedepth=10, early_max_treedepth=8, **kwargs):
121 r"""Set up the No-U-Turn sampler.
122
123 Parameters
(...)
176 `pm.sample` to the desired number of tuning steps.
177 """
--> 178 super().__init__(vars, **kwargs)
180 self.max_treedepth = max_treedepth
181 self.early_max_treedepth = early_max_treedepth
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\pymc\step_methods\hmc\base_hmc.py:95, in BaseHMC.__init__(self, vars, scaling, step_scale, is_cov, model, blocked, potential, dtype, Emax, target_accept, gamma, k, t0, adapt_step_size, step_rand, **aesara_kwargs)
92 else:
93 vars = [self._model.rvs_to_values.get(var, var) for var in vars]
---> 95 super().__init__(vars, blocked=blocked, model=self._model, dtype=dtype, **aesara_kwargs)
97 self.adapt_step_size = adapt_step_size
98 self.Emax = Emax
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\pymc\step_methods\arraystep.py:276, in GradientSharedStep.__init__(self, vars, model, blocked, dtype, logp_dlogp_func, **aesara_kwargs)
273 model = modelcontext(model)
275 if logp_dlogp_func is None:
--> 276 func = model.logp_dlogp_function(vars, dtype=dtype, **aesara_kwargs)
277 else:
278 func = logp_dlogp_func
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\pymc\model.py:637, in Model.logp_dlogp_function(self, grad_vars, tempered, **kwargs)
635 input_vars = {i for i in graph_inputs(costs) if not isinstance(i, Constant)}
636 extra_vars = [self.rvs_to_values.get(var, var) for var in self.free_RVs]
--> 637 ip = self.initial_point(0)
638 extra_vars_and_values = {
639 var: ip[var.name] for var in extra_vars if var in input_vars and var not in grad_vars
640 }
641 return ValueGradFunction(costs, grad_vars, extra_vars_and_values, **kwargs)
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\pymc\model.py:1067, in Model.initial_point(self, seed)
1059 def initial_point(self, seed=None) -> Dict[str, np.ndarray]:
1060 """Computes the initial point of the model.
1061
1062 Returns
(...)
1065 Maps names of transformed variables to numeric initial values in the transformed space.
1066 """
-> 1067 fn = make_initial_point_fn(model=self, return_transformed=True)
1068 return Point(fn(seed), model=self)
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\pymc\initial_point.py:181, in make_initial_point_fn(model, overrides, jitter_rvs, default_strategy, return_transformed)
179 new_rng_nodes.append(aesara.shared(rng_cls(np.random.PCG64())))
180 graph.replace_all(zip(rng_nodes, new_rng_nodes), import_missing=True)
--> 181 func = compile_pymc(inputs=[], outputs=graph.outputs, mode=aesara.compile.mode.FAST_COMPILE)
183 varnames = []
184 for var in model.free_RVs:
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\pymc\aesaraf.py:1034, in compile_pymc(inputs, outputs, random_seed, mode, **kwargs)
1032 opt_qry = mode.provided_optimizer.including("random_make_inplace", check_parameter_opt)
1033 mode = Mode(linker=mode.linker, optimizer=opt_qry)
-> 1034 aesara_function = aesara.function(
1035 inputs,
1036 outputs,
1037 updates={**rng_updates, **kwargs.pop("updates", {})},
1038 mode=mode,
1039 **kwargs,
1040 )
1041 return aesara_function
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\aesara\compile\function\__init__.py:317, in function(inputs, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input)
311 fn = orig_function(
312 inputs, outputs, mode=mode, accept_inplace=accept_inplace, name=name
313 )
314 else:
315 # note: pfunc will also call orig_function -- orig_function is
316 # a choke point that all compilation must pass through
--> 317 fn = pfunc(
318 params=inputs,
319 outputs=outputs,
320 mode=mode,
321 updates=updates,
322 givens=givens,
323 no_default_updates=no_default_updates,
324 accept_inplace=accept_inplace,
325 name=name,
326 rebuild_strict=rebuild_strict,
327 allow_input_downcast=allow_input_downcast,
328 on_unused_input=on_unused_input,
329 profile=profile,
330 output_keys=output_keys,
331 )
332 return fn
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\aesara\compile\function\pfunc.py:374, in pfunc(params, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input, output_keys, fgraph)
360 profile = ProfileStats(message=profile)
362 inputs, cloned_outputs = construct_pfunc_ins_and_outs(
363 params,
364 outputs,
(...)
371 fgraph=fgraph,
372 )
--> 374 return orig_function(
375 inputs,
376 cloned_outputs,
377 mode,
378 accept_inplace=accept_inplace,
379 name=name,
380 profile=profile,
381 on_unused_input=on_unused_input,
382 output_keys=output_keys,
383 fgraph=fgraph,
384 )
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\aesara\compile\function\types.py:1763, in orig_function(inputs, outputs, mode, accept_inplace, name, profile, on_unused_input, output_keys, fgraph)
1751 m = Maker(
1752 inputs,
1753 outputs,
(...)
1760 fgraph=fgraph,
1761 )
1762 with config.change_flags(compute_test_value="off"):
-> 1763 fn = m.create(defaults)
1764 finally:
1765 t2 = time.time()
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\aesara\compile\function\types.py:1656, in FunctionMaker.create(self, input_storage, trustme, storage_map)
1653 start_import_time = aesara.link.c.cmodule.import_time
1655 with config.change_flags(traceback__limit=config.traceback__compile_limit):
-> 1656 _fn, _i, _o = self.linker.make_thunk(
1657 input_storage=input_storage_lists, storage_map=storage_map
1658 )
1660 end_linker = time.time()
1662 linker_time = end_linker - start_linker
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\aesara\link\basic.py:254, in LocalLinker.make_thunk(self, input_storage, output_storage, storage_map, **kwargs)
247 def make_thunk(
248 self,
249 input_storage: Optional["InputStorageType"] = None,
(...)
252 **kwargs,
253 ) -> Tuple["BasicThunkType", "InputStorageType", "OutputStorageType"]:
--> 254 return self.make_all(
255 input_storage=input_storage,
256 output_storage=output_storage,
257 storage_map=storage_map,
258 )[:3]
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\aesara\link\vm.py:1299, in VMLinker.make_all(self, profiler, input_storage, output_storage, storage_map)
1296 else:
1297 post_thunk_clear = None
-> 1299 vm = self.make_vm(
1300 order,
1301 thunks,
1302 input_storage,
1303 output_storage,
1304 storage_map,
1305 post_thunk_clear,
1306 computed,
1307 compute_map,
1308 self.updated_vars,
1309 )
1311 vm.storage_map = storage_map
1312 vm.compute_map = compute_map
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\aesara\link\vm.py:1020, in VMLinker.make_vm(self, nodes, thunks, input_storage, output_storage, storage_map, post_thunk_clear, computed, compute_map, updated_vars)
1017 pre_call_clear = [storage_map[v] for v in self.no_recycling]
1019 try:
-> 1020 from aesara.link.c.cvm import CVM
1021 except (MissingGXX, ImportError):
1022 CVM = None
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\aesara\link\c\cvm.py:13, in <module>
9 if not config.cxx:
10 raise MissingGXX(
11 "lazylinker will not be imported if aesara.config.cxx is not set."
12 )
---> 13 from aesara.link.c.lazylinker_c import CLazyLinker
15 class CVM(CLazyLinker, VM):
16 def __init__(self, fgraph, *args, **kwargs):
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\aesara\link\c\lazylinker_c.py:143, in <module>
140 assert os.path.exists(loc)
142 args = GCC_compiler.compile_args()
--> 143 GCC_compiler.compile_str(dirname, code, location=loc, preargs=args)
144 # Save version into the __init__.py file.
145 init_py = os.path.join(loc, "__init__.py")
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\aesara\link\c\cmodule.py:2648, in GCC_compiler.compile_str(module_name, src_code, location, include_dirs, lib_dirs, libs, preargs, py_module, hide_symbols)
2646 pass
2647 assert os.path.isfile(lib_filename)
-> 2648 return dlimport(lib_filename)
File C:\ProgramData\Anaconda3\envs\pymc_env\lib\site-packages\aesara\link\c\cmodule.py:331, in dlimport(fullpath, suffix)
328 finally:
329 del sys.path[0]
--> 331 assert fullpath.startswith(rval.__file__)
332 return rval
AssertionError: