PyMC3 小批量 ADVI
PyMC3 Minibatch ADVI
我正在尝试使用 PyMC3 Minibatch ADVI 进行贝叶斯回归。 pm.fit 函数抛出以下错误，我不确定如何修复它。
它说‘str’对象没有属性‘type’。此处错误消息中的任何“str”对象是什么？我已经将 more_replacements 的浮点张量映射到我所知道的最好的。
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)
您的回答将不胜感激。
附录：如果我只是说
pm.Minibatch(np.array([tuple(y.iloc[i,[0]]) for i in train_index])).type()
（或）
map_tensor_batch['y_tensor'].type()
我得到以下结果：
那为什么会抛出下面的属性错误呢？同样，我的“str”对象是什么？
---------------------------------------------------------------------------
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'
解决方法似乎是使用元组而不是字典。下面就不会像之前那样报错了
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)
但是还有一个错误如下：
TypeError: TensorType does not support iteration. Maybe you are using builtins.sum instead of theano.tensor.sum? (Maybe .max?)
有什么办法解决这个问题吗？
这 (https://alexioannides.com/2018/11/07/bayesian-regression-in-pymc3-using-mcmc-variational-inference/) 是我试图效仿的例子。
您正在工作的博客post显示
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)}
也就是说，map_tensor_batch应该是一个dict，但是键是Theano张量，而不仅仅是字符串。
我正在尝试使用 PyMC3 Minibatch ADVI 进行贝叶斯回归。 pm.fit 函数抛出以下错误，我不确定如何修复它。
它说‘str’对象没有属性‘type’。此处错误消息中的任何“str”对象是什么？我已经将 more_replacements 的浮点张量映射到我所知道的最好的。
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)
您的回答将不胜感激。
附录：如果我只是说
pm.Minibatch(np.array([tuple(y.iloc[i,[0]]) for i in train_index])).type()
（或）
map_tensor_batch['y_tensor'].type()
我得到以下结果：
那为什么会抛出下面的属性错误呢？同样，我的“str”对象是什么？
---------------------------------------------------------------------------
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'
解决方法似乎是使用元组而不是字典。下面就不会像之前那样报错了
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)
但是还有一个错误如下：
TypeError: TensorType does not support iteration. Maybe you are using builtins.sum instead of theano.tensor.sum? (Maybe .max?)
有什么办法解决这个问题吗？
这 (https://alexioannides.com/2018/11/07/bayesian-regression-in-pymc3-using-mcmc-variational-inference/) 是我试图效仿的例子。
您正在工作的博客post显示
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)}
也就是说，map_tensor_batch应该是一个dict，但是键是Theano张量，而不仅仅是字符串。