Error: Chain 0 failed with: 'Scratchpad' object has no attribute 'ufunc'

Questions version agnostic
1

I am running a very simple model but I am newbie to PyMC. I have no idea what I am doing wrong here, seems pretty straightforward, can anybody help?
My data is target variable and rest all are independents, no hierarchy or nulls in the data. All data is float.

This is the model code.

with pm.Model() as model:
    
    sigma = pm.HalfCauchy(f"sigma", beta=3)
    new=X_train.drop(columns={'target'})
    for col in new.columns:
        # Define priors
        coef = pm.Normal(f"Coef_{col}", sigma=3)
        data=X_train[col].values
        mu=coef*data
    

    # Define likelihood
    likelihood = pm.Normal("y", mu=mu, sigma=sigma, observed=X_train['target'])

    # Inference!
    # Draw 3000 posterior samples using NUTS sampling
    idata = pm.sample(300)

I am getting this error. I have no idea what this means, can anybody help?

ParallelSamplingError: Chain 0 failed with: 'Scratchpad' object has no attribute 'ufunc' 
---------------------------------------------------------------------------
RemoteTraceback                           Traceback (most recent call last)
RemoteTraceback: 
"""
Traceback (most recent call last):
  File "C:\Users\u2231753\AppData\Roaming\Python\Python39\site-packages\pytensor\link\vm.py", line 414, in __call__
    thunk()
  File "C:\Users\u2231753\AppData\Roaming\Python\Python39\site-packages\pytensor\graph\op.py", line 552, in rval
    r = p(n, [x[0] for x in i], o)
  File "C:\Users\u2231753\AppData\Roaming\Python\Python39\site-packages\pytensor\tensor\elemwise.py", line 748, in perform
    ufunc = node.tag.ufunc
  File "C:\Users\u2231753\AppData\Roaming\Python\Python39\site-packages\pytensor\graph\utils.py", line 285, in __getattribute__
    return super().__getattribute__(name)
AttributeError: 'Scratchpad' object has no attribute 'ufunc'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Users\u2231753\Anaconda3\lib\site-packages\pymc\sampling\parallel.py", line 122, in run
    self._start_loop()
  File "C:\Users\u2231753\Anaconda3\lib\site-packages\pymc\sampling\parallel.py", line 174, in _start_loop
    point, stats = self._step_method.step(self._point)
  File "C:\Users\u2231753\Anaconda3\lib\site-packages\pymc\step_methods\arraystep.py", line 174, in step
    return super().step(point)
  File "C:\Users\u2231753\Anaconda3\lib\site-packages\pymc\step_methods\arraystep.py", line 100, in step
    apoint, stats = self.astep(q)
  File "C:\Users\u2231753\Anaconda3\lib\site-packages\pymc\step_methods\hmc\base_hmc.py", line 168, in astep
    start = self.integrator.compute_state(q0, p0)
  File "C:\Users\u2231753\Anaconda3\lib\site-packages\pymc\step_methods\hmc\integration.py", line 56, in compute_state
    logp, dlogp = self._logp_dlogp_func(q)
  File "C:\Users\u2231753\Anaconda3\lib\site-packages\pymc\model\core.py", line 378, in __call__
    cost, *grads = self._pytensor_function(*grad_vars)
  File "C:\Users\u2231753\AppData\Roaming\Python\Python39\site-packages\pytensor\compile\function\types.py", line 970, in __call__
    self.vm()
  File "C:\Users\u2231753\AppData\Roaming\Python\Python39\site-packages\pytensor\link\vm.py", line 418, in __call__
    raise_with_op(self.fgraph, node, thunk)
  File "C:\Users\u2231753\AppData\Roaming\Python\Python39\site-packages\pytensor\link\utils.py", line 535, in raise_with_op
    raise exc_value.with_traceback(exc_trace)
  File "C:\Users\u2231753\AppData\Roaming\Python\Python39\site-packages\pytensor\link\vm.py", line 414, in __call__
    thunk()
  File "C:\Users\u2231753\AppData\Roaming\Python\Python39\site-packages\pytensor\graph\op.py", line 552, in rval
    r = p(n, [x[0] for x in i], o)
  File "C:\Users\u2231753\AppData\Roaming\Python\Python39\site-packages\pytensor\tensor\elemwise.py", line 748, in perform
    ufunc = node.tag.ufunc
  File "C:\Users\u2231753\AppData\Roaming\Python\Python39\site-packages\pytensor\graph\utils.py", line 285, in __getattribute__
    return super().__getattribute__(name)
AttributeError: 'Scratchpad' object has no attribute 'ufunc'
Apply node that caused the error: Add(Composite{...}.1, sigma_log__, Switch.0, Coef_TBTS_TT1_log__, Switch.0, Coef_LWA_SBC_log__, Switch.0, Coef_LWA_SPB_log__, Switch.0, Coef_LWA_SPU_log__, Switch.0, Coef_OAZDISP_log__, Switch.0, Coef_OPSALL_log__, Switch.0, Coef_TCD_TT_log__, Switch.0, Coef_LGSAL_log__, Switch.0, Coef_OAUDAL_log__, Switch.0, Coef_TCF_TT1_log__, Switch.0, Coef_LWA_SBB_log__, Switch.0, Coef_LWA_SBU_log__, Switch.0, Coef_LWA_SPC_log__, Switch.0, Coef_ODISPAL_log__, Switch.0, Coef_OSTVAL_log__, Switch.0, Coef_OVIAL_log__, Switch.0, Coef_BTSCLCW1_log__, Switch.0, Coef_BTSCLCW3_log__, Switch.0, Coef_BEST_DL_log__, Switch.0, Coef_CFLG0_log__, Switch.0, Coef_CFLG3_log__, Switch.0, Coef_CFLG11_log__, Switch.0, Coef_CLNBPE_log__, Switch.0, Coef_INVDS_log__, Switch.0, Coef_LWA_OS_log__, Switch.0, Coef_CLNBPE_Lysol_log__, Switch.0, Coef_CLNBPE_TubOTowels_log__, Switch.0, Coef_CLNBPE_Solimo_log__, Switch.0, Coef_CLNBPE_Greenworks_log__, Switch.0, Coef_CLNBPE_Pledge_log__, Switch.0, Coef_CLNBPE_CleanCut_log__, Switch.0, Coef_CLNBPE_Method_log__, Switch.0, Coef_CLNBPE_BetterLife_log__, Switch.0, Coef_CLNBPE_Wysiwash_log__, Switch.0, Coef_CLNBPE_RMM_log__, Switch.0, Coef_COUPON_log__, Switch.0, Coef_COVFLG1_log__, Switch.0, Coef_COVFLG2_log__, Switch.0, Coef_COVFLG3_log__, Switch.0, Coef_GTRNES03_log__, Composite{...}.2, Coef_MOBINDX2_log__, Sum{axes=None}.0)
Toposort index: 332
Inputs types: [TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=())]
Inputs shapes: [(), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), ()]
Inputs strides: [(), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), ()]
Inputs values: [array(-2.41414043), array(1.25693026), array(-0.34653621), array(-0.7304002), array(-0.90980041), array(-0.40126756), array(0.40058422), array(-1.45810826), array(-1.28113175), array(-0.23161933), array(0.7157729), array(-2.05875208), array(0.26854652), array(-1.28486263), array(-1.42873339), array(-0.17144266), array(0.54573187), array(-1.69122585), array(-1.24362643), array(-0.24750508), array(0.56904623), array(-1.73430967), array(-1.26865108), array(-0.23687768), array(0.38607233), array(-1.43753159), array(0.51573383), array(-1.6383889), array(-0.67794423), array(-0.52393533), array(-0.68558156), array(-0.51964559), array(-0.13081207), array(-0.89206622), array(-0.20007912), array(-0.83725513), array(0.02159843), array(-1.02442002), array(-0.24341786), array(-0.80442878), array(0.00171408), array(-1.0061259), array(0.72535842), array(-2.08410882), array(0.24160446), array(-1.25292052), array(-0.52092526), array(-0.61647163), array(0.23771552), array(-1.24839297), array(0.17145302), array(-1.17424221), array(0.10319292), array(-1.10320345), array(-0.15425114), array(-0.8731806), array(-1.31880719), array(-0.21591163), array(0.13575997), array(-1.13646753), array(0.72915731), array(-2.09433873), array(-0.95066778), array(-0.38112373), array(0.18082884), array(-1.18440611), array(-0.01522656), array(-0.99079982), array(0.14829983), array(-1.14957673), array(0.34399038), array(-1.38015073), array(0.66283434), array(-1.92923479), array(0.46517317), array(-1.55520369), array(-1.45295262), array(-0.16190541), array(0.33809355), array(-1.37236683), array(0.6513911), array(-1.90331431), array(-0.12759038), array(-0.89469015), array(-340.5077784)]
Outputs clients: [['output']]

HINT: Re-running with most PyTensor optimizations disabled could provide a back-trace showing when this node was created. This can be done by setting the PyTensor flag 'optimizer=fast_compile'. If that does not work, PyTensor optimizations can be disabled with 'optimizer=None'.
HINT: Use the PyTensor flag `exception_verbosity=high` for a debug print-out and storage map footprint of this Apply node.
"""

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

AttributeError                            Traceback (most recent call last)
~\Anaconda3\lib\site-packages\pymc\sampling\parallel.py in run()
    121             self._point = self._make_numpy_refs()
--> 122             self._start_loop()
    123         except KeyboardInterrupt:

~\Anaconda3\lib\site-packages\pymc\sampling\parallel.py in _start_loop()
    173                 try:
--> 174                     point, stats = self._step_method.step(self._point)
    175                 except SamplingError as e:

~\Anaconda3\lib\site-packages\pymc\step_methods\arraystep.py in step()
    173         self._logp_dlogp_func._extra_are_set = True
--> 174         return super().step(point)
    175 

~\Anaconda3\lib\site-packages\pymc\step_methods\arraystep.py in step()
     99 
--> 100         apoint, stats = self.astep(q)
    101 

~\Anaconda3\lib\site-packages\pymc\step_methods\hmc\base_hmc.py in astep()
    167 
--> 168         start = self.integrator.compute_state(q0, p0)
    169 

~\Anaconda3\lib\site-packages\pymc\step_methods\hmc\integration.py in compute_state()
     55 
---> 56         logp, dlogp = self._logp_dlogp_func(q)
     57 

~\Anaconda3\lib\site-packages\pymc\model\core.py in __call__()
    377 
--> 378         cost, *grads = self._pytensor_function(*grad_vars)
    379 

~\AppData\Roaming\Python\Python39\site-packages\pytensor\compile\function\types.py in __call__()
    969             outputs = (
--> 970                 self.vm()
    971                 if output_subset is None

~\AppData\Roaming\Python\Python39\site-packages\pytensor\link\vm.py in __call__()
    417             except Exception:
--> 418                 raise_with_op(self.fgraph, node, thunk)
    419 

~\AppData\Roaming\Python\Python39\site-packages\pytensor\link\utils.py in raise_with_op()
    534         # extra long error message in that case.
--> 535     raise exc_value.with_traceback(exc_trace)
    536 

~\AppData\Roaming\Python\Python39\site-packages\pytensor\link\vm.py in __call__()
    413                 ):
--> 414                     thunk()
    415                     for old_s in old_storage:

~\AppData\Roaming\Python\Python39\site-packages\pytensor\graph\op.py in rval()
    551             ):
--> 552                 r = p(n, [x[0] for x in i], o)
    553                 for o in node.outputs:

~\AppData\Roaming\Python\Python39\site-packages\pytensor\tensor\elemwise.py in perform()
    747                 else:
--> 748                     ufunc = node.tag.ufunc
    749             else:

~\AppData\Roaming\Python\Python39\site-packages\pytensor\graph\utils.py in __getattribute__()
    284     def __getattribute__(self, name):
--> 285         return super().__getattribute__(name)
    286 

AttributeError: 'Scratchpad' object has no attribute 'ufunc'
Apply node that caused the error: Add(Composite{...}.1, sigma_log__, Switch.0, Coef_TBTS_TT1_log__, Switch.0, Coef_LWA_SBC_log__, Switch.0, Coef_LWA_SPB_log__, Switch.0, Coef_LWA_SPU_log__, Switch.0, Coef_OAZDISP_log__, Switch.0, Coef_OPSALL_log__, Switch.0, Coef_TCD_TT_log__, Switch.0, Coef_LGSAL_log__, Switch.0, Coef_OAUDAL_log__, Switch.0, Coef_TCF_TT1_log__, Switch.0, Coef_LWA_SBB_log__, Switch.0, Coef_LWA_SBU_log__, Switch.0, Coef_LWA_SPC_log__, Switch.0, Coef_ODISPAL_log__, Switch.0, Coef_OSTVAL_log__, Switch.0, Coef_OVIAL_log__, Switch.0, Coef_BTSCLCW1_log__, Switch.0, Coef_BTSCLCW3_log__, Switch.0, Coef_BEST_DL_log__, Switch.0, Coef_CFLG0_log__, Switch.0, Coef_CFLG3_log__, Switch.0, Coef_CFLG11_log__, Switch.0, Coef_CLNBPE_log__, Switch.0, Coef_INVDS_log__, Switch.0, Coef_LWA_OS_log__, Switch.0, Coef_CLNBPE_Lysol_log__, Switch.0, Coef_CLNBPE_TubOTowels_log__, Switch.0, Coef_CLNBPE_Solimo_log__, Switch.0, Coef_CLNBPE_Greenworks_log__, Switch.0, Coef_CLNBPE_Pledge_log__, Switch.0, Coef_CLNBPE_CleanCut_log__, Switch.0, Coef_CLNBPE_Method_log__, Switch.0, Coef_CLNBPE_BetterLife_log__, Switch.0, Coef_CLNBPE_Wysiwash_log__, Switch.0, Coef_CLNBPE_RMM_log__, Switch.0, Coef_COUPON_log__, Switch.0, Coef_COVFLG1_log__, Switch.0, Coef_COVFLG2_log__, Switch.0, Coef_COVFLG3_log__, Switch.0, Coef_GTRNES03_log__, Composite{...}.2, Coef_MOBINDX2_log__, Sum{axes=None}.0)
Toposort index: 332
Inputs types: [TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=())]
Inputs shapes: [(), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), ()]
Inputs strides: [(), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), ()]
Inputs values: [array(-2.41414043), array(1.25693026), array(-0.34653621), array(-0.7304002), array(-0.90980041), array(-0.40126756), array(0.40058422), array(-1.45810826), array(-1.28113175), array(-0.23161933), array(0.7157729), array(-2.05875208), array(0.26854652), array(-1.28486263), array(-1.42873339), array(-0.17144266), array(0.54573187), array(-1.69122585), array(-1.24362643), array(-0.24750508), array(0.56904623), array(-1.73430967), array(-1.26865108), array(-0.23687768), array(0.38607233), array(-1.43753159), array(0.51573383), array(-1.6383889), array(-0.67794423), array(-0.52393533), array(-0.68558156), array(-0.51964559), array(-0.13081207), array(-0.89206622), array(-0.20007912), array(-0.83725513), array(0.02159843), array(-1.02442002), array(-0.24341786), array(-0.80442878), array(0.00171408), array(-1.0061259), array(0.72535842), array(-2.08410882), array(0.24160446), array(-1.25292052), array(-0.52092526), array(-0.61647163), array(0.23771552), array(-1.24839297), array(0.17145302), array(-1.17424221), array(0.10319292), array(-1.10320345), array(-0.15425114), array(-0.8731806), array(-1.31880719), array(-0.21591163), array(0.13575997), array(-1.13646753), array(0.72915731), array(-2.09433873), array(-0.95066778), array(-0.38112373), array(0.18082884), array(-1.18440611), array(-0.01522656), array(-0.99079982), array(0.14829983), array(-1.14957673), array(0.34399038), array(-1.38015073), array(0.66283434), array(-1.92923479), array(0.46517317), array(-1.55520369), array(-1.45295262), array(-0.16190541), array(0.33809355), array(-1.37236683), array(0.6513911), array(-1.90331431), array(-0.12759038), array(-0.89469015), array(-340.5077784)]
Outputs clients: [['output']]

HINT: Re-running with most PyTensor optimizations disabled could provide a back-trace showing when this node was created. This can be done by setting the PyTensor flag 'optimizer=fast_compile'. If that does not work, PyTensor optimizations can be disabled with 'optimizer=None'.
HINT: Use the PyTensor flag `exception_verbosity=high` for a debug print-out and storage map footprint of this Apply node.

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

ParallelSamplingError                     Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_8792\2342495399.py in <module>
     23     # Inference!
     24     # Draw 3000 posterior samples using NUTS sampling
---> 25     idata = pm.sample(300, init="auto", random_seed=42)

~\Anaconda3\lib\site-packages\pymc\sampling\mcmc.py in sample(draws, tune, chains, cores, random_seed, progressbar, step, nuts_sampler, initvals, init, jitter_max_retries, n_init, trace, discard_tuned_samples, compute_convergence_checks, keep_warning_stat, return_inferencedata, idata_kwargs, nuts_sampler_kwargs, callback, mp_ctx, model, **kwargs)
    762         _print_step_hierarchy(step)
    763         try:
--> 764             _mp_sample(**sample_args, **parallel_args)
    765         except pickle.PickleError:
    766             _log.warning("Could not pickle model, sampling singlethreaded.")

~\Anaconda3\lib\site-packages\pymc\sampling\mcmc.py in _mp_sample(draws, tune, step, chains, cores, random_seed, start, progressbar, traces, model, callback, mp_ctx, **kwargs)
   1151         try:
   1152             with sampler:
-> 1153                 for draw in sampler:
   1154                     strace = traces[draw.chain]
   1155                     strace.record(draw.point, draw.stats)

~\Anaconda3\lib\site-packages\pymc\sampling\parallel.py in __iter__(self)
    446 
    447         while self._active:
--> 448             draw = ProcessAdapter.recv_draw(self._active)
    449             proc, is_last, draw, tuning, stats = draw
    450             self._total_draws += 1

~\Anaconda3\lib\site-packages\pymc\sampling\parallel.py in recv_draw(processes, timeout)
    328             else:
    329                 error = RuntimeError(f"Chain {proc.chain} failed.")
--> 330             raise error from old_error
    331         elif msg[0] == "writing_done":
    332             proc._readable = True

ParallelSamplingError: Chain 0 failed with: 'Scratchpad' object has no attribute 'ufunc'
Apply node that caused the error: Add(Composite{...}.1, sigma_log__, Switch.0, Coef_TBTS_TT1_log__, Switch.0, Coef_LWA_SBC_log__, Switch.0, Coef_LWA_SPB_log__, Switch.0, Coef_LWA_SPU_log__, Switch.0, Coef_OAZDISP_log__, Switch.0, Coef_OPSALL_log__, Switch.0, Coef_TCD_TT_log__, Switch.0, Coef_LGSAL_log__, Switch.0, Coef_OAUDAL_log__, Switch.0, Coef_TCF_TT1_log__, Switch.0, Coef_LWA_SBB_log__, Switch.0, Coef_LWA_SBU_log__, Switch.0, Coef_LWA_SPC_log__, Switch.0, Coef_ODISPAL_log__, Switch.0, Coef_OSTVAL_log__, Switch.0, Coef_OVIAL_log__, Switch.0, Coef_BTSCLCW1_log__, Switch.0, Coef_BTSCLCW3_log__, Switch.0, Coef_BEST_DL_log__, Switch.0, Coef_CFLG0_log__, Switch.0, Coef_CFLG3_log__, Switch.0, Coef_CFLG11_log__, Switch.0, Coef_CLNBPE_log__, Switch.0, Coef_INVDS_log__, Switch.0, Coef_LWA_OS_log__, Switch.0, Coef_CLNBPE_Lysol_log__, Switch.0, Coef_CLNBPE_TubOTowels_log__, Switch.0, Coef_CLNBPE_Solimo_log__, Switch.0, Coef_CLNBPE_Greenworks_log__, Switch.0, Coef_CLNBPE_Pledge_log__, Switch.0, Coef_CLNBPE_CleanCut_log__, Switch.0, Coef_CLNBPE_Method_log__, Switch.0, Coef_CLNBPE_BetterLife_log__, Switch.0, Coef_CLNBPE_Wysiwash_log__, Switch.0, Coef_CLNBPE_RMM_log__, Switch.0, Coef_COUPON_log__, Switch.0, Coef_COVFLG1_log__, Switch.0, Coef_COVFLG2_log__, Switch.0, Coef_COVFLG3_log__, Switch.0, Coef_GTRNES03_log__, Composite{...}.2, Coef_MOBINDX2_log__, Sum{axes=None}.0)
Toposort index: 332
Inputs types: [TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=())]
Inputs shapes: [(), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), ()]
Inputs strides: [(), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), ()]
Inputs values: [array(-2.41414043), array(1.25693026), array(-0.34653621), array(-0.7304002), array(-0.90980041), array(-0.40126756), array(0.40058422), array(-1.45810826), array(-1.28113175), array(-0.23161933), array(0.7157729), array(-2.05875208), array(0.26854652), array(-1.28486263), array(-1.42873339), array(-0.17144266), array(0.54573187), array(-1.69122585), array(-1.24362643), array(-0.24750508), array(0.56904623), array(-1.73430967), array(-1.26865108), array(-0.23687768), array(0.38607233), array(-1.43753159), array(0.51573383), array(-1.6383889), array(-0.67794423), array(-0.52393533), array(-0.68558156), array(-0.51964559), array(-0.13081207), array(-0.89206622), array(-0.20007912), array(-0.83725513), array(0.02159843), array(-1.02442002), array(-0.24341786), array(-0.80442878), array(0.00171408), array(-1.0061259), array(0.72535842), array(-2.08410882), array(0.24160446), array(-1.25292052), array(-0.52092526), array(-0.61647163), array(0.23771552), array(-1.24839297), array(0.17145302), array(-1.17424221), array(0.10319292), array(-1.10320345), array(-0.15425114), array(-0.8731806), array(-1.31880719), array(-0.21591163), array(0.13575997), array(-1.13646753), array(0.72915731), array(-2.09433873), array(-0.95066778), array(-0.38112373), array(0.18082884), array(-1.18440611), array(-0.01522656), array(-0.99079982), array(0.14829983), array(-1.14957673), array(0.34399038), array(-1.38015073), array(0.66283434), array(-1.92923479), array(0.46517317), array(-1.55520369), array(-1.45295262), array(-0.16190541), array(0.33809355), array(-1.37236683), array(0.6513911), array(-1.90331431), array(-0.12759038), array(-0.89469015), array(-340.5077784)]
Outputs clients: [['output']]

HINT: Re-running with most PyTensor optimizations disabled could provide a back-trace showing when this node was created. This can be done by setting the PyTensor flag 'optimizer=fast_compile'. If that does not work, PyTensor optimizations can be disabled with 'optimizer=None'.
HINT: Use the PyTensor flag `exception_verbosity=high` for a debug print-out and storage map footprint of this Apply node.
2

What version of PyMC and PyTensor do you have installed?

3

I am using latest pymc. Simply installed using pip install pymc.

4

Can you share a fully reproducible example including the data or fake data?

Also I would suggest you try the official instructions using conda, not pip installing: Installation — PyMC 5.9.2 documentation

5

train_data.csv (50.4 KB)
Here is the uploaded data, code I am using is simply pasted above. But here is it again.

import numpy as np
import pandas as pd
import pymc as pm

Assuming X_train and y_train are pandas DataFrames or Series

#X_train_np = X_train.values.astype(float) # Convert to NumPy array
#y_train_np = y_train.values.astype(float) # Convert to NumPy array

with pm.Model() as model:
new=X_train.drop(columns={‘target’})
for col in new.columns:
# Define priors
coef = pm.Normal(f"Coef_{col}", sigma=1)
data=X_train[col]
mu=coef*data

# Define likelihood
likelihood = pm.Normal("y", mu=mu, sigma=3, observed=X_train['target'])

# Inference!
# Draw 3000 posterior samples using NUTS sampling
idata = pm.sample(3000)

I will try reinstalling pymc though. Thanks