I try to run ‘trace = pm.sample(100,init=‘adapt_diag’,njobs=1)’, it takes about 65 minutes and returns as follows:
100%|██████████| 600/600 [38:52<00:00, 3.89s/it]
100%|██████████| 600/600 [27:59<00:00, 2.80s/it]
The chain contains only diverging samples. The model is probably misspecified.
Only 100 samples in chain.
The acceptance probability does not match the target. It is 0.8972248469793171, but should be close to 0.8. Try to increase the number of tuning steps.
The chain contains only diverging samples. The model is probably misspecified.
Only 200 samples in chain.
The gelman-rubin 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.
I also try to run the code as you told and returns the following message. I am really confused about the error message.
Auto-assigning NUTS sampler…
Initializing NUTS using adapt_diag…
/anaconda3/lib/python3.6/site-packages/pymc3/model.py:384: FutureWarning: Conversion of the second argument of issubdtype from float to np.floating is deprecated. In future, it will be treated as np.float64 == np.dtype(float).type.
if not np.issubdtype(var.dtype, float):
Multiprocess sampling (2 chains in 2 jobs)
NUTS: [beta_24, beta_23, beta_22, beta_21, beta_20, beta_19, beta_18, beta_17, beta_16, beta_15, beta_14, beta_13, beta_12, beta_11, beta_10, beta_9, beta_8, beta_7, beta_6, beta_5, beta_4, beta_3, beta_2, beta_1, alpha, C_triu_24_interval__, C_triu_23_interval__, C_triu_22_interval__, C_triu_21_interval__, C_triu_20_interval__, C_triu_19_interval__, C_triu_18_interval__, C_triu_17_interval__, C_triu_16_interval__, C_triu_15_interval__, C_triu_14_interval__, C_triu_13_interval__, C_triu_12_interval__, C_triu_11_interval__, C_triu_10_interval__, C_triu_9_interval__, C_triu_8_interval__, C_triu_7_interval__, C_triu_6_interval__, C_triu_5_interval__, C_triu_4_interval__, C_triu_3_interval__, C_triu_2_interval__, C_triu_1_interval__, sigma_24_log__, sigma_23_log__, sigma_22_log__, sigma_21_log__, sigma_20_log__, sigma_19_log__, sigma_18_log__, sigma_17_log__, sigma_16_log__, sigma_15_log__, sigma_14_log__, sigma_13_log__, sigma_12_log__, sigma_11_log__, sigma_10_log__, sigma_9_log__, sigma_8_log__, sigma_7_log__, sigma_6_log__, sigma_5_log__, sigma_4_log__, sigma_3_log__, sigma_2_log__, sigma_1_log__, tau_log__, psi_log__]
0%| | 0/2000 [00:00<?, ?it/s]
RemoteTraceback Traceback (most recent call last)
RemoteTraceback:
“”"
Traceback (most recent call last):
File “/anaconda3/lib/python3.6/site-packages/theano/compile/function_module.py”, line 903, in call
self.fn() if output_subset is None else
NotImplementedError: c_sync: expected an aligned array, got non-aligned array of type 12 with 1 dimensions, with 3 last dims -1, -1, 64612 and 3 last strides -1 -1, 8.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File “/anaconda3/lib/python3.6/site-packages/joblib/_parallel_backends.py”, line 350, in call
return self.func(*args, **kwargs)
File “/anaconda3/lib/python3.6/site-packages/joblib/parallel.py”, line 131, in call
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File “/anaconda3/lib/python3.6/site-packages/joblib/parallel.py”, line 131, in
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File “/anaconda3/lib/python3.6/site-packages/pymc3/sampling.py”, line 526, in _sample
for it, strace in enumerate(sampling):
File “/anaconda3/lib/python3.6/site-packages/tqdm/_tqdm.py”, line 941, in iter
for obj in iterable:
File “/anaconda3/lib/python3.6/site-packages/pymc3/sampling.py”, line 624, in _iter_sample
point, states = step.step(point)
File “/anaconda3/lib/python3.6/site-packages/pymc3/step_methods/arraystep.py”, line 222, in step
apoint, stats = self.astep(array)
File “/anaconda3/lib/python3.6/site-packages/pymc3/step_methods/hmc/base_hmc.py”, line 112, in astep
start = self.integrator.compute_state(q0, p0)
File “/anaconda3/lib/python3.6/site-packages/pymc3/step_methods/hmc/integration.py”, line 29, in compute_state
logp, dlogp = self._logp_dlogp_func(q)
File “/anaconda3/lib/python3.6/site-packages/pymc3/model.py”, line 435, in call
logp, dlogp = self._theano_function(array)
File “/anaconda3/lib/python3.6/site-packages/theano/compile/function_module.py”, line 917, in call
storage_map=getattr(self.fn, ‘storage_map’, None))
File “/anaconda3/lib/python3.6/site-packages/theano/gof/link.py”, line 325, in raise_with_op
reraise(exc_type, exc_value, exc_trace)
File “/anaconda3/lib/python3.6/site-packages/six.py”, line 692, in reraise
raise value.with_traceback(tb)
File “/anaconda3/lib/python3.6/site-packages/theano/compile/function_module.py”, line 903, in call
self.fn() if output_subset is None else
NotImplementedError: c_sync: expected an aligned array, got non-aligned array of type 12 with 1 dimensions, with 3 last dims -1, -1, 64612 and 3 last strides -1 -1, 8.
Apply node that caused the error: Subtensor{::, int64}(<TensorType(float64, matrix)>, Constant{0})
Toposort index: 2
Inputs types: [TensorType(float64, matrix), Scalar(int64)]
Inputs shapes: [(64612, 24), ()]
Inputs strides: [(8, 516896), ()]
Inputs values: [‘not shown’, 0]
Outputs clients: [[Elemwise{Composite{(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))}}(AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0, Subtensor{::, int64}.0, AdvancedSubtensor.0), Elemwise{Composite{(scalar_sigmoid(i0) * scalar_sigmoid(i1) * i2 * i3 * i4)}}(Elemwise{neg,no_inplace}.0, Elemwise{Composite{(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))}}.0, TensorConstant{(1,) of 0…9999999996}, Elemwise{Composite{Switch(i0, Switch(i1, inv(i2), i3), Switch(i1, (-inv((i4 - i5))), i3))}}[(0, 2)].0, Subtensor{::, int64}.0)]]