TypeError: Unknown parameter type: <class 'theano.tensor.var.TensorVariable'>

[Erikmeier18]

If you have questions about a specific use case, or you are not sure whether this is a bug or not, please post it to our discourse channel: https://discourse.pymc.io

Description of your problem

I'm new using pymc3 and wanted to learn how to use it with a simple example [https://docs.pymc.io/notebooks/GLM-linear.html]. When building the model and running it I get a problem related to theano.

Please provide a minimal, self-contained, and reproducible example.

import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pymc3 as pm

%config InlineBackend.figure_format = 'retina'
az.style.use("arviz-darkgrid")

size = 200
true_intercept = 1
true_slope = 2

x = np.linspace(0,1, size)
true_regression_line = true_intercept + true_slope * x
y = true_regression_line + np.random.normal(scale=0.5, size=size)
data = dict(x=x, y=y)

fig = plt.figure(figsize=((7,7)))
ax = fig.add_subplot(111, xlabel="x", ylabel="y", title="Generated data and underlying model")
ax.plot(x,y, "x", label="sampled data")
ax.plot(x, true_regression_line, label="true regression line", lw=2.0)
plt.legend(loc=0);

with pm.Model() as model:
    sigma = pm.HalfCauchy("sigma", beta=10, testval=1.0)
    intercept = pm.Normal("Intercept", 0, sigma=20)
    x_coeff = pm.Normal("x", 0, sigma=20)
    likelihood = pm.Normal("y", mu=intercept + x_coeff * x, sigma=sigma, observed=y)
    trace = pm.sample(3000, cores=2)
    
az.plot_trace(trace)

Please provide the full traceback.

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-1-807f8acca5be> in <module>
     27     x_coeff = pm.Normal("x", 0, sigma=20)
     28     likelihood = pm.Normal("y", mu=intercept + x_coeff * x, sigma=sigma, observed=y)
---> 29     trace = pm.sample(3000, cores=2)
     30 
     31 az.plot_trace(trace)

/opt/anaconda3/lib/python3.8/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, return_inferencedata, idata_kwargs, mp_ctx, pickle_backend, **kwargs)
    479             # By default, try to use NUTS
    480             _log.info("Auto-assigning NUTS sampler...")
--> 481             start_, step = init_nuts(
    482                 init=init,
    483                 chains=chains,

/opt/anaconda3/lib/python3.8/site-packages/pymc3/sampling.py in init_nuts(init, chains, n_init, model, random_seed, progressbar, **kwargs)
   2168         raise ValueError("Unknown initializer: {}.".format(init))
   2169 
-> 2170     step = pm.NUTS(potential=potential, model=model, **kwargs)
   2171 
   2172     return start, step

/opt/anaconda3/lib/python3.8/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

/opt/anaconda3/lib/python3.8/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)
     91         vars = inputvars(vars)
     92 
---> 93         super().__init__(vars, blocked=blocked, model=model, dtype=dtype, **theano_kwargs)
     94 
     95         self.adapt_step_size = adapt_step_size

/opt/anaconda3/lib/python3.8/site-packages/pymc3/step_methods/arraystep.py in __init__(self, vars, model, blocked, dtype, **theano_kwargs)
    241         self.blocked = blocked
    242 
--> 243         func = model.logp_dlogp_function(
    244             vars, dtype=dtype, **theano_kwargs)
    245 

/opt/anaconda3/lib/python3.8/site-packages/pymc3/model.py in logp_dlogp_function(self, grad_vars, **kwargs)
    933         varnames = [var.name for var in grad_vars]
    934         extra_vars = [var for var in self.free_RVs if var.name not in varnames]
--> 935         return ValueGradFunction(self.logpt, grad_vars, extra_vars, **kwargs)
    936 
    937     @property

/opt/anaconda3/lib/python3.8/site-packages/pymc3/model.py in __init__(self, cost, grad_vars, extra_vars, dtype, casting, **kwargs)
    652         inputs = [self._vars_joined]
    653 
--> 654         self._theano_function = theano.function(
    655             inputs, [self._cost_joined, grad], givens=givens, **kwargs
    656         )

/opt/anaconda3/lib/python3.8/site-packages/theano/compile/function/__init__.py in function(inputs, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input)
    335         # note: pfunc will also call orig_function -- orig_function is
    336         #      a choke point that all compilation must pass through
--> 337         fn = pfunc(
    338             params=inputs,
    339             outputs=outputs,

/opt/anaconda3/lib/python3.8/site-packages/theano/compile/function/pfunc.py in pfunc(params, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input, output_keys)
    424 
    425     # transform params into theano.compile.In objects.
--> 426     inputs = [
    427         _pfunc_param_to_in(p, allow_downcast=allow_input_downcast) for p in params
    428     ]

/opt/anaconda3/lib/python3.8/site-packages/theano/compile/function/pfunc.py in <listcomp>(.0)
    425     # transform params into theano.compile.In objects.
    426     inputs = [
--> 427         _pfunc_param_to_in(p, allow_downcast=allow_input_downcast) for p in params
    428     ]
    429 

/opt/anaconda3/lib/python3.8/site-packages/theano/compile/function/pfunc.py in _pfunc_param_to_in(param, strict, allow_downcast)
    541     elif isinstance(param, In):
    542         return param
--> 543     raise TypeError(f"Unknown parameter type: {type(param)}")
    544 
    545 

TypeError: Unknown parameter type: <class 'theano.tensor.var.TensorVariable'>

Please provide any additional information below.
Before I was able to get the trace and run into problems trying to az.plot_trace(), which I tried to solve. I updated the CLT, uninstalled pymc3 and theano, tried git clone and version 3.11 gave another type of theano error related to gcc, so I uninstalled again and installed it back with conda (giving version 3.9.3). I'm running Big Sur and the problem could be related to my machine, since a colleague of mine with older OS was able to run correctly the complete code. I would appreciate any help on getting it solved.

Versions and main components

• PyMC3 Version: 3.9.3
• Theano Version: 1.0.5
• Python Version: 3.8.3 (Clang 10.0.0)
• Operating system: macOS Big Sur 11.2
• How did you install PyMC3: (conda/pip) conda install pymc3 / conda install -c conda-forge mkl pymc3

[michaelosthege]

Closing this issue, after the problem was resolved via discourse: https://discourse.pymc.io/t/typeerror-unknown-parameter-type-class-theano-tensor-var-tensorvariable/6745/3

[philongvn99]

[Google Colab] Maybe this solution is too late but for me, it is because of theano package is too old. Then it lead to mismatch of theano.Variable and theano.tensor.var.TensorVariable . Let's try to remove theano folder (or at least all of files) and try again. Thanks for solution of mqin