I’m trying to use PyMC3 Minibatch ADVI for Bayesian Regression. The pm.fit function throws the following error and I’m not sure how to fix it.
It says that the ‘str’ object has no attribute ‘type’. What is any ‘str’ object from the error message here? I’ve mapped float tensors for more_replacements to the best of what I know.
advi = pm.ADVI()
tracker = pm.callbacks.Tracker(mean=advi.approx.mean.eval,std=advi.approx.std.eval)
map_tensor_batch = {'x_tensor': pm.Minibatch(X_train, dtype=float),'y_tensor':pm.Minibatch(y_train['target'],dtype=float)}
approx = advi.fit(20000, obj_optimizer=pm.sgd(learning_rate=0.01), callbacks=[tracker], more_replacements = map_tensor_batch)
Your answers will be appreciated.
Addendum: If I just say
pm.Minibatch(np.array([tuple(y.iloc[i,[0]]) for i in train_index])).type()
(Or)
map_tensor_batch['y_tensor'].type()
I get the following result:
<TensorType(float64, matrix)>
Then why does it throw the attribute error below? Again, what’s my ‘str’ object?
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-24-2824def803dc> in <module>
56 tracker = pm.callbacks.Tracker(mean=advi.approx.mean.eval,std=advi.approx.std.eval)
57 map_tensor_batch = {'x_tensor': pm.Minibatch(X_train, dtype=float),'y_tensor': pm.Minibatch(y_train['target'],dtype=float)}
---> 58 approx = advi.fit(20000, obj_optimizer=pm.sgd(learning_rate=0.01), callbacks=[tracker], more_replacements = map_tensor_batch)
59 fig = plt.figure(figsize=(16, 9))
60 mu_ax = fig.add_subplot(221)
/opt/conda/lib/python3.7/site-packages/pymc3/variational/inference.py in fit(self, n, score, callbacks, progressbar, **kwargs)
142 callbacks = []
143 score = self._maybe_score(score)
--> 144 step_func = self.objective.step_function(score=score, **kwargs)
145 if progressbar:
146 progress = progress_bar(range(n), display=progressbar)
/opt/conda/lib/python3.7/site-packages/theano/configparser.py in res(*args, **kwargs)
46 def res(*args, **kwargs):
47 with self:
---> 48 return f(*args, **kwargs)
49
50 return res
/opt/conda/lib/python3.7/site-packages/pymc3/variational/opvi.py in step_function(self, obj_n_mc, tf_n_mc, obj_optimizer, test_optimizer, more_obj_params, more_tf_params, more_updates, more_replacements, total_grad_norm_constraint, score, fn_kwargs)
358 more_updates=more_updates,
359 more_replacements=more_replacements,
--> 360 total_grad_norm_constraint=total_grad_norm_constraint,
361 )
362 if score:
/opt/conda/lib/python3.7/site-packages/pymc3/variational/opvi.py in updates(self, obj_n_mc, tf_n_mc, obj_optimizer, test_optimizer, more_obj_params, more_tf_params, more_updates, more_replacements, total_grad_norm_constraint)
244 more_obj_params=more_obj_params,
245 more_replacements=more_replacements,
--> 246 total_grad_norm_constraint=total_grad_norm_constraint,
247 )
248 resulting_updates.update(more_updates)
/opt/conda/lib/python3.7/site-packages/pymc3/variational/opvi.py in add_obj_updates(self, updates, obj_n_mc, obj_optimizer, more_obj_params, more_replacements, total_grad_norm_constraint)
284 more_replacements = dict()
285 obj_target = self(
--> 286 obj_n_mc, more_obj_params=more_obj_params, more_replacements=more_replacements
287 )
288 grads = pm.updates.get_or_compute_grads(obj_target, self.obj_params + more_obj_params)
/opt/conda/lib/python3.7/site-packages/theano/configparser.py in res(*args, **kwargs)
46 def res(*args, **kwargs):
47 with self:
---> 48 return f(*args, **kwargs)
49
50 return res
/opt/conda/lib/python3.7/site-packages/pymc3/variational/opvi.py in __call__(self, nmc, **kwargs)
401 m = 1.0
402 a = self.op.apply(self.tf)
--> 403 a = self.approx.set_size_and_deterministic(a, nmc, 0, kwargs.get("more_replacements"))
404 return m * self.op.T(a)
405
/opt/conda/lib/python3.7/site-packages/theano/configparser.py in res(*args, **kwargs)
46 def res(*args, **kwargs):
47 with self:
---> 48 return f(*args, **kwargs)
49
50 return res
/opt/conda/lib/python3.7/site-packages/pymc3/variational/opvi.py in set_size_and_deterministic(self, node, s, d, more_replacements)
1496 _node = node
1497 optimizations = self.get_optimization_replacements(s, d)
-> 1498 flat2rand = self.make_size_and_deterministic_replacements(s, d, more_replacements)
1499 node = theano.clone(node, optimizations)
1500 node = theano.clone(node, flat2rand)
/opt/conda/lib/python3.7/site-packages/pymc3/variational/opvi.py in make_size_and_deterministic_replacements(self, s, d, more_replacements)
1470 flat2rand = collections.OrderedDict()
1471 for g in self.groups:
-> 1472 flat2rand.update(g.make_size_and_deterministic_replacements(s, d, more_replacements))
1473 flat2rand.update(more_replacements)
1474 return flat2rand
/opt/conda/lib/python3.7/site-packages/pymc3/variational/opvi.py in make_size_and_deterministic_replacements(self, s, d, more_replacements)
1186 initial = tt.patternbroadcast(initial, self.symbolic_initial.broadcastable)
1187 if more_replacements:
-> 1188 initial = theano.clone(initial, more_replacements)
1189 return {self.symbolic_initial: initial}
1190
/opt/conda/lib/python3.7/site-packages/theano/scan/utils.py in clone(output, replace, strict, share_inputs)
212 )
213 )
--> 214 tmp_replace = [(x, x.type()) for x, y in items]
215 new_replace = [(x, y) for ((_, x), (_, y)) in zip(tmp_replace, items)]
216 _, _outs, _ = rebuild_collect_shared(
/opt/conda/lib/python3.7/site-packages/theano/scan/utils.py in <listcomp>(.0)
212 )
213 )
--> 214 tmp_replace = [(x, x.type()) for x, y in items]
215 new_replace = [(x, y) for ((_, x), (_, y)) in zip(tmp_replace, items)]
216 _, _outs, _ = rebuild_collect_shared(
AttributeError: 'str' object has no attribute 'type'
A workaround seems to be to use a tuple instead of a dictionary. The following doesn’t throw the error as before.
advi = pm.ADVI()
tracker = pm.callbacks.Tracker(mean=advi.approx.mean.eval,std=advi.approx.std.eval)
#map_tensor_batch = {'x_tensor': pm.Minibatch(x_tensor.eval()),'y_tensor': pm.Minibatch(y_tensor.eval())}
map_tensor_batch = (pm.Minibatch(x_tensor.eval()),pm.Minibatch(y_tensor.eval()))
approx = advi.fit(20000, obj_optimizer=pm.sgd(learning_rate=0.00001), callbacks=[tracker], more_replacements = map_tensor_batch)
But there’s another error as below:
TypeError: TensorType does not support iteration. Maybe you are using builtins.sum instead of theano.tensor.sum? (Maybe .max?)
Any fix to this?
This (https://alexioannides.com/2018/11/07/bayesian-regression-in-pymc3-using-mcmc-variational-inference/) is the example I’m trying to follow.
The blog post you are working from shows
import theano
y_tensor = theano.shared(train.y.values.astype('float64'))
x_tensor = theano.shared(train.x.values.astype('float64'))
map_tensor_batch = {y_tensor: pm.Minibatch(train.y.values, 100),
x_tensor: pm.Minibatch(train.x.values, 100)}
That is, map_tensor_batch should be a dict, but the keys are Theano tensors, not mere strings.