python - 使用 Dask 或 Joblib 进行并行 Sklearn 模型构建

Question

我有一大套 sklearn 管道，我想与 Dask 并行构建。这是一个简单但天真的顺序方法:

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

对于 RandomSearchCV，我知道如何将它与 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，也不清楚如何修改这段代码，以便与 Dask 并行地适应一组具有自己特定参数的多个不同模型。

Answer 1

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_)