以前のPymc3をfrom_formulaで変更する

Question

私はKaggle（ヒューマンリソースのもの）からデータセットを作成しています。ベイジアン統計（ロジスティック回帰）を実装しようとしていますが、このモデル

import pymc3 as pm 

priors = {"Intercept": pm.Normal('alpha', mu=0, sd=100), 
     "Regressor": pm.Laplace('beta', mu=0, b=np.sqrt(2)) 
      } 

with pm.Model() as logistic_model: 
    pm.glm.GLM.from_formula('left ~ satisfaction_level + last_evaluation \ 
         + number_project + average_montly_hours \ 
         + time_spend_company + Work_accident + promotion_last_5years\ 
         + sales + salary', df, family=pm.glm.families.Binomial(), priors = priors) 
    trace_logistic_model = pm.sample(4000) 

、エラーメッセージ（私はインターセプトが情報価値ガウス、他のすべての予測因子についてラプラスになりたい）：

TypeError: No model on context stack, which is needed to instantiate distributions. Add variable inside a 'with model:' block, or use the '.dist' syntax for a standalone distribution. 

私は「DIST」を使用しようとしましたが、それを動作しません

012あなたの助けのためのの

おかげ

更新：

私は次の私のコードを変更しています

with pm.Model() as logistic_model: 

    priors = {"Intercept": pm.Normal('alpha', mu=0, sd=100), 
     "Regressor": pm.Laplace('beta', mu=0, b=np.sqrt(2)) 
      } 

    pm.glm.GLM.from_formula('left ~ satisfaction_level + last_evaluation \ 
         + number_project + average_montly_hours \ 
         + time_spend_company + Work_accident + promotion_last_5years\ 
         + sales + salary', df, family=pm.glm.families.Binomial(), priors = priors) 
    trace_logistic_model = pm.sample(4000) 

今、私はこのエラーを持っている：

--------------------------------------------------------------------------- 
TypeError         Traceback (most recent call last) 
<ipython-input-16-e3566f815877> in <module>() 
     5    } 
     6 
----> 7  pm.glm.GLM.from_formula('left ~ satisfaction_level + last_evaluation        + number_project + average_montly_hours        + time_spend_company + Work_accident + promotion_last_5years       + sales + salary', df, family=pm.glm.families.Binomial(), priors = priors) 
     8  trace_logistic_model = pm.sample(4000) 

/home/glouis/anaconda/lib/python3.5/site-packages/pymc3/glm/linear.py in from_formula(cls, formula, data, priors, vars, family, name, model) 
    134   labels = x.design_info.column_names 
    135   return cls(np.asarray(x), np.asarray(y)[:, 0], intercept=False, labels=labels, 
--> 136     priors=priors, vars=vars, family=family, name=name, model=model) 
    137 
    138 glm = GLM 

/home/glouis/anaconda/lib/python3.5/site-packages/pymc3/model.py in __call__(cls, *args, **kwargs) 
    238   instance = cls.__new__(cls, *args, **kwargs) 
    239   with instance: # appends context 
--> 240    instance.__init__(*args, **kwargs) 
    241   return instance 
    242 

/home/glouis/anaconda/lib/python3.5/site-packages/pymc3/glm/linear.py in __init__(self, x, y, intercept, labels, priors, vars, family, name, model) 
    111   super(GLM, self).__init__(
    112    x, y, intercept=intercept, labels=labels, 
--> 113    priors=priors, vars=vars, name=name, model=model 
    114  ) 
    115 

/home/glouis/anaconda/lib/python3.5/site-packages/pymc3/glm/linear.py in __init__(self, x, y, intercept, labels, priors, vars, name, model) 
    57       dist=priors.get(
    58        name, 
---> 59        self.default_intercept_prior 
    60      ) 
    61     ) 

/home/glouis/anaconda/lib/python3.5/site-packages/pymc3/model.py in Var(self, name, dist, data, total_size) 
    750     with self: 
    751      var = FreeRV(name=name, distribution=dist, 
--> 752         total_size=total_size, model=self) 
    753     self.free_RVs.append(var) 
    754    else: 

/home/glouis/anaconda/lib/python3.5/site-packages/pymc3/model.py in __init__(self, type, owner, index, name, distribution, total_size, model) 
    1117   if distribution is not None: 
    1118    self.dshape = tuple(distribution.shape) 
-> 1119    self.dsize = int(np.prod(distribution.shape)) 
    1120    self.distribution = distribution 
    1121    self.tag.test_value = np.ones(

/home/glouis/anaconda/lib/python3.5/site-packages/numpy/core/fromnumeric.py in prod(a, axis, dtype, out, keepdims) 
    2513    pass 
    2514   else: 
-> 2515    return prod(axis=axis, dtype=dtype, out=out, **kwargs) 
    2516 
    2517  return _methods._prod(a, axis=axis, dtype=dtype, 

TypeError: prod() got an unexpected keyword argument 'out' 

Answer 1

あなたはこのコードを試してみてください

import pymc3 as pm 
import numpy as np 

df = ... 
# pm.Normal('alpha', mu=0, sd=100) is random variable, named `alpha`, 
# pm.Normal.dist(...) is distribution object used to create random variables 
priors = {"Intercept": pm.Normal.dist(mu=0, sd=100), 
      "Regressor": pm.Laplace.dist(mu=0, b=np.sqrt(2)) 
      } 

with pm.Model() as logistic_model: 
    pm.glm.GLM.from_formula('left ~ satisfaction_level + last_evaluation \ 
         + number_project + average_montly_hours \ 
         + time_spend_company + Work_accident +  promotion_last_5years\ 
         + sales + salary', df, family=pm.glm.families.Binomial(), priors = priors) 
    trace_logistic_model = pm.sample(4000)