Python，在多个CPU上运行循环

Question

我创建了一个类似于sklearn gridsearch的小代码，它在一组超参数上训练模型（在下面的代码中为X和y），使用一组超参数有关验证数据的几个指标（Xt，yt_class），并将结果存储在pandas DataFrame中。

from sklearn.grid_search import ParameterGrid 
    from sklearn.metrics import precision_score,f1_score 

    grid = {'C':[1,10.0,50,100.0],'gamma':[0.00001,0.0001,0.001,0.01,0.1]} 
    param_grid = ParameterGrid(grid) 
    results = pd.DataFrame(list(param_grid)) 
    precision = [] 
    f1 = [] 
    for params in param_grid: 
     model = SVC(kernel='rbf',cache_size=1000,class_weight='balanced',**params) 
     model.fit(X,y) 
     precision.append(precision_score(yt_class, model.predict(Xt), average='weighted')) 
     f1.append(f1_score(yt_class, model.predict(Xt), average='weighted')) 
     print(params) 
     print(precision_score(yt_class, model.predict(Xt), average='weighted')) 
     print(f1_score(yt_class, model.predict(Xt), average='weighted')) 

    results['precision'] = precision 
    results['f1'] = f1 

现在我努力让自己在多个CPU我的循环运行，我尝试以下基本的例子多处理模块，但作为新的Python和总体规划的无法弄清楚它的作品在我的情况。什么不起作用

例子：

import multiprocessing as mp 
pool = mp.Pool(processes=8) 

def get_scores(param_grid): 
    precision = [] 
    f1 = [] 
    for params in param_grid: 
     model = SVC(kernel='rbf',cache_size=1000,class_weight='balanced',**params) 
     model.fit(X,y) 
     model.predict(Xt) 
     precision.append(precision_score(yt_class, model.predict(Xt), average='weighted')) 
     f1.append(f1_score(yt_class, model.predict(Xt), average='weighted')) 
    return precision,f1  

scores = pool.apply(get_scores,param_grid) 

Answer 1

你get_scores方法应该只包含循环

的内部的试试这个：

import multiprocessing as mp 
from sklearn.grid_search import ParameterGrid 
from sklearn.metrics import precision_score,f1_score 

def get_scores(params): 
    model = SVC(kernel='rbf',cache_size=1000,class_weight='balanced',**params) 
    model.fit(X,y) 
    model.predict(Xt) 
    precision = precision_score(yt_class, model.predict(Xt), average='weighted') 
    f1 = f1_score(yt_class, model.predict(Xt), average='weighted') 
    return precision, f1  


grid = {'C':[1,10.0,50,100.0],'gamma':[0.00001,0.0001,0.001,0.01,0.1]} 
param_grid = ParameterGrid(grid) 
pool = mp.Pool(processes=8) 

scores = pool.map_async(get_scores, param_grid).get() 
# scores is a list of tuples [(precision_1, f1_1), (precision_2, f1_2)...] 
# you can "unzip" it like this 

precision, f1 = zip(*scores)