python - 使用Dask或Joblib进行并行Sklearn模型构建

我想与Dask并行构建大量的sklearn管道。这是一种简单但幼稚的顺序方法:

from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

iris = load_iris()
X_train, X_test, Y_train, Y_test = train_test_split(iris.data, iris.target, test_size=0.2)

pipe_nb = Pipeline([('clf', MultinomialNB())])
pipe_lr = Pipeline([('clf', LogisticRegression())])
pipe_rf = Pipeline([('clf', RandomForestClassifier())])

pipelines = [pipe_nb, pipe_lr, pipe_rf]  # In reality, this would include many more different types of models with varying but specific parameters

for pl in pipelines:
    pl.fit(X_train, Y_train)

请注意，这不是GridSearchCV或RandomSearchCV问题

在RandomSearchCV的情况下，我知道如何将其与Dask并行化:

dask_client = Client('tcp://some.host.com:8786')

clf_rf = RandomForestClassifier()
param_dist = {'n_estimators': scipy.stats.randint(100, 500}
search_rf = RandomizedSearchCV(
                clf_rf,
                param_distributions=param_dist,
                n_iter = 100,
                scoring = 'f1',
                cv=10,
                error_score = 0,
                verbose = 3,
               )

with joblib.parallel_backend('dask'):
    search_rf.fit(X_train, Y_train)

但是，我对超参数调整不感兴趣，也不清楚如何修改此代码，以使其与Dask并行地适应具有其自身特定参数的多个不同模型的集合。

最佳答案

dask.delayed可能是这里最简单的解决方案。

from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

iris = load_iris()
X_train, X_test, Y_train, Y_test = train_test_split(iris.data, iris.target, test_size=0.2)

pipe_nb = Pipeline([('clf', MultinomialNB())])
pipe_lr = Pipeline([('clf', LogisticRegression())])
pipe_rf = Pipeline([('clf', RandomForestClassifier())])

pipelines = [pipe_nb, pipe_lr, pipe_rf]  # In reality, this would include many more different types of models with varying but specific parameters

# Use dask.delayed instead of a for loop.
import dask.delayed

pipelines_ = [dask.delayed(pl).fit(X_train, Y_train) for pl in pipelines]
fit_pipelines = dask.compute(*pipelines_)

关于python - 使用Dask或Joblib进行并行Sklearn模型构建，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/54355236/