Python，在多個CPU上運行循環

Question

我創建了一個類似於sklearn gridsearch的小代碼，它在一組超參數上訓練模型（在下面的代碼中的X和y上） ，使用驗證數據（ Xt，yt_class）上的多個指標檢查模型的性能並存儲會產生pandas DataFrame。

    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

現在，我試圖使我的循環在多個CPU上運行，我嘗試了以下有關多處理模塊的基本示例，但是對於Python和編程而言，它是新手，因此無法確定它是否適用於我的情況。

不起作用的示例：

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

您的get_scores方法應僅包含循環的內部部分

嘗試這個：

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)