python-使用Dask或Joblib并行Sklearn模型构建

我想与Dask并行构建大量的sklearn管道.这是一个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

对于RandomSearchCV,我知道如何使用Dask将其并行化：

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并行地适合一组具有各自特定参数的多个不同模型.

解决方法:

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_)

标签：dask,scikit-learn,dask-distributed-2,python
来源： https://codeday.me/bug/20191024/1923441.html