如何在 sklearn 中并行化多个模型构建过程

Question

有没有办法在 scikit-learn 中并行化多个模型构建过程？我知道我可以在GridSearchCV 和cross_validate 中使用n_jobs 参数来实现某种并行化在一个模型构建过程中。但是，我在具有不同输入参数的 for 循环中运行多个模型构建过程，并将结果保存在列表中。举个例子，假设我有 15 个空闲 CPU，我在 cross_validate 中使用 n_jobs=5。如果我没记错的话，这意味着一个模型构建过程使用 5 个 CPU。现在有没有办法在我的 for 循环中开始接下来的 2 个模型构建过程，所以我正在使用所有 15 个 CPU？这是一个虚拟示例：

import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import KFold, GridSearchCV, cross_validate

# load breast cancer data set
X,y = load_breast_cancer(return_X_y=True)

# define different types of penalty strategies
# let's make a toy example and pretend we would be interested in
# running different penalty strategies (I use three times 'l2' here,
# but imagine these would be different)
penalty_types = ['l2','l2','l2']

# define output list where we add the results using different penalty strategies
nested_cv_scores_list = []

for penalty_type in penalty_types:
    
    # create a random number generator
    rng = np.random.RandomState(42)

    # z-standardize features
    scaler = StandardScaler()
    
    # use linear L2-regularized Logistic Regression as classifier
    lr = LogisticRegression(random_state=rng,penalty=penalty_type)
    
    # define parameter grid to optimize over (optimize C)
    lr_c = np.linspace(start=1,stop=16,num=11,endpoint=True)
    p_grid = {'lr__C':lr_c}
    
    # create pipeline
    lr_pipe = Pipeline([
        ('scaler',scaler),
        ('lr',lr)
        ])
    
    # define cross validation strategy
    cv = KFold(shuffle=True,random_state=rng)
    
    # implement GridSearch (inner cross validation)
    grid = GridSearchCV(lr_pipe,param_grid=p_grid,cv=cv)
    
    # implement cross_validate (outer cross validation)
    nested_cv_scores = cross_validate(grid,X,y,cv=cv,n_jobs=5)

    # append result to list
    nested_cv_scores_list.append(nested_cv_scores)

有没有办法并行化这个 for 循环？

Answer 1

joblib.parallel 是为这项工作而生的！只需将循环内容放入一个函数中，然后使用Parallel 和delayed 调用它

from joblib.parallel import Parallel, delayed
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import KFold, GridSearchCV, cross_validate

# load breast cancer data set
X,y = load_breast_cancer(return_X_y=True)

# define different types of penalty strategies
# let's make a toy example and pretend we would be interested in
# running different penalty strategies (I use three times 'l2' here,
# but imagine these would be different)
penalty_types = ['l2','l2','l2']

# define output list where we add the results using different penalty strategies
nested_cv_scores_list = []

# put rng-seed outside of loop so that not all results are the same
rng = np.random.RandomState(42)

def run_as_job(penalty_type, X, y):

    # create a random number generator
    

    # z-standardize features
    scaler = StandardScaler()
    
    # use linear L2-regularized Logistic Regression as classifier
    lr = LogisticRegression(random_state=rng,penalty=penalty_type)
    
    # define parameter grid to optimize over (optimize C)
    lr_c = np.linspace(start=1,stop=16,num=11,endpoint=True)
    p_grid = {'lr__C':lr_c}

    .... # additional calculation that is missing in the example
    .... # e.g. res = cross_val_score(clf, X, y, n_jobs=2)
    return res

if __name__ == '__main__':
    results = Parallel(n_jobs=2)(delayed(run_as_job)(penalty_type) for penalty_type in penalty_types)

更多使用选项请查看joblib: Embarrassingly parallel for loops