Pystan,运行时错误 - 初始化失败
Pystan, Runtime error - Initialization failed
我正在尝试使用 Pystan 开发贝叶斯模型。我能够成功编译模型。但是当我对数据进行采样时,我遇到了 运行 时间错误。参考以下代码:
my_code = '''
data {
int N;
int K1;
int K2;
real max_intercept;
matrix[N, K1] X1;
matrix[N, K2] X2;
vector[N] y;
}
parameters {
vector<lower=0>[K1] beta1;
vector[K2] beta2;
real<lower=0, upper=max_intercept> alpha;
real<lower=0> noise_var;
}
model {
beta1 ~ normal(0, 1);
beta2 ~ normal(0, 1);
noise_var ~ inv_gamma(0.05, 0.05 * 0.01);
y ~ normal(X1*beta1 + X2*beta2 + alpha, sqrt(noise_var));
}
'''
fit1 = sm1.sampling(data=input_data, iter=2000, chains=4, init=0.5,n_jobs=-1) #Getting an error here
我已经检查了所有数据点(没有丢失数据或没有相同编号的列)及其数据类型(都是 float 64)。我还使用 MinMaxScaler
缩放了数据
input_data = {
'N': len(data_scaled), #836
'K1': len(pos_var), #17
'K2': len(pos_neg_var),#29
'X1': X1, #(836,17)
'X2': X2, #(836,17)
'y': data['orders'].values,
'max_intercept': min(data['orders']) #0
}
以下是我遇到的错误。
RemoteTraceback Traceback (most recent call last)
RemoteTraceback:
"""
Traceback (most recent call last):
File "C:\Users\abc\.conda\envs\stan_env\lib\multiprocessing\pool.py", line 121, in worker
result = (True, func(*args, **kwds))
File "C:\Users\abc\.conda\envs\stan_env\lib\multiprocessing\pool.py", line 44, in mapstar
return list(map(*args))
File "stanfit4anon_model_a396b59aabedfaa132f3a814776a219f_7619586994410633893.pyx", line 371, in stanfit4anon_model_a396b59aabedfaa132f3a814776a219f_7619586994410633893._call_sampler_star
File "stanfit4anon_model_a396b59aabedfaa132f3a814776a219f_7619586994410633893.pyx", line 404, in stanfit4anon_model_a396b59aabedfaa132f3a814776a219f_7619586994410633893._call_sampler
RuntimeError: Initialization failed.
"""
The above exception was the direct cause of the following exception:
RuntimeError Traceback (most recent call last)
<timed exec> in <module>
~\.conda\envs\stan_env\lib\site-packages\pystan\model.py in sampling(self, data, pars, chains, iter, warmup, thin, seed, init, sample_file, diagnostic_file, verbose, algorithm, control, n_jobs, **kwargs)
776 call_sampler_args = izip(itertools.repeat(data), args_list, itertools.repeat(pars))
777 call_sampler_star = self.module._call_sampler_star
--> 778 ret_and_samples = _map_parallel(call_sampler_star, call_sampler_args, n_jobs)
779 samples = [smpl for _, smpl in ret_and_samples]
780
~\.conda\envs\stan_env\lib\site-packages\pystan\model.py in _map_parallel(function, args, n_jobs)
83 try:
84 pool = multiprocessing.Pool(processes=n_jobs)
---> 85 map_result = pool.map(function, args)
86 finally:
87 pool.close()
~\.conda\envs\stan_env\lib\multiprocessing\pool.py in map(self, func, iterable, chunksize)
266 in a list that is returned.
267 '''
--> 268 return self._map_async(func, iterable, mapstar, chunksize).get()
269
270 def starmap(self, func, iterable, chunksize=None):
~\.conda\envs\stan_env\lib\multiprocessing\pool.py in get(self, timeout)
655 return self._value
656 else:
--> 657 raise self._value
658
659 def _set(self, i, obj):
RuntimeError: Initialization failed.
我对 Pystan 比较陌生。我很感激在这里得到的任何指导。
我解决了这个问题!当数据不满足模型中定义的约束时,通常会出现运行时错误。
- 例如,当约束为模型中定义的 X>0 时,X 值具有一些 -ve 个数字。
- 也是最常见的错误,需要确保 Y 值没有关闭。在我的数据中,很少有 Y 值为 0,这些值通过了缺失值和 pos 值检查。在用 Y 的平均值估算值后,问题就解决了。
学习愉快!
我正在尝试使用 Pystan 开发贝叶斯模型。我能够成功编译模型。但是当我对数据进行采样时,我遇到了 运行 时间错误。参考以下代码:
my_code = '''
data {
int N;
int K1;
int K2;
real max_intercept;
matrix[N, K1] X1;
matrix[N, K2] X2;
vector[N] y;
}
parameters {
vector<lower=0>[K1] beta1;
vector[K2] beta2;
real<lower=0, upper=max_intercept> alpha;
real<lower=0> noise_var;
}
model {
beta1 ~ normal(0, 1);
beta2 ~ normal(0, 1);
noise_var ~ inv_gamma(0.05, 0.05 * 0.01);
y ~ normal(X1*beta1 + X2*beta2 + alpha, sqrt(noise_var));
}
'''
fit1 = sm1.sampling(data=input_data, iter=2000, chains=4, init=0.5,n_jobs=-1) #Getting an error here
我已经检查了所有数据点(没有丢失数据或没有相同编号的列)及其数据类型(都是 float 64)。我还使用 MinMaxScaler
缩放了数据input_data = {
'N': len(data_scaled), #836
'K1': len(pos_var), #17
'K2': len(pos_neg_var),#29
'X1': X1, #(836,17)
'X2': X2, #(836,17)
'y': data['orders'].values,
'max_intercept': min(data['orders']) #0
}
以下是我遇到的错误。
RemoteTraceback Traceback (most recent call last)
RemoteTraceback:
"""
Traceback (most recent call last):
File "C:\Users\abc\.conda\envs\stan_env\lib\multiprocessing\pool.py", line 121, in worker
result = (True, func(*args, **kwds))
File "C:\Users\abc\.conda\envs\stan_env\lib\multiprocessing\pool.py", line 44, in mapstar
return list(map(*args))
File "stanfit4anon_model_a396b59aabedfaa132f3a814776a219f_7619586994410633893.pyx", line 371, in stanfit4anon_model_a396b59aabedfaa132f3a814776a219f_7619586994410633893._call_sampler_star
File "stanfit4anon_model_a396b59aabedfaa132f3a814776a219f_7619586994410633893.pyx", line 404, in stanfit4anon_model_a396b59aabedfaa132f3a814776a219f_7619586994410633893._call_sampler
RuntimeError: Initialization failed.
"""
The above exception was the direct cause of the following exception:
RuntimeError Traceback (most recent call last)
<timed exec> in <module>
~\.conda\envs\stan_env\lib\site-packages\pystan\model.py in sampling(self, data, pars, chains, iter, warmup, thin, seed, init, sample_file, diagnostic_file, verbose, algorithm, control, n_jobs, **kwargs)
776 call_sampler_args = izip(itertools.repeat(data), args_list, itertools.repeat(pars))
777 call_sampler_star = self.module._call_sampler_star
--> 778 ret_and_samples = _map_parallel(call_sampler_star, call_sampler_args, n_jobs)
779 samples = [smpl for _, smpl in ret_and_samples]
780
~\.conda\envs\stan_env\lib\site-packages\pystan\model.py in _map_parallel(function, args, n_jobs)
83 try:
84 pool = multiprocessing.Pool(processes=n_jobs)
---> 85 map_result = pool.map(function, args)
86 finally:
87 pool.close()
~\.conda\envs\stan_env\lib\multiprocessing\pool.py in map(self, func, iterable, chunksize)
266 in a list that is returned.
267 '''
--> 268 return self._map_async(func, iterable, mapstar, chunksize).get()
269
270 def starmap(self, func, iterable, chunksize=None):
~\.conda\envs\stan_env\lib\multiprocessing\pool.py in get(self, timeout)
655 return self._value
656 else:
--> 657 raise self._value
658
659 def _set(self, i, obj):
RuntimeError: Initialization failed.
我对 Pystan 比较陌生。我很感激在这里得到的任何指导。
我解决了这个问题!当数据不满足模型中定义的约束时,通常会出现运行时错误。
- 例如,当约束为模型中定义的 X>0 时,X 值具有一些 -ve 个数字。
- 也是最常见的错误,需要确保 Y 值没有关闭。在我的数据中,很少有 Y 值为 0,这些值通过了缺失值和 pos 值检查。在用 Y 的平均值估算值后,问题就解决了。
学习愉快!