CODEWITHSUNDEEP
pythonscikit-learndaskdask-distributed
slaw
slaw

Reputation: 6869

Parallel Sklearn Model Building with Dask or Joblib

I have a large set of sklearn pipelines that I'd like to build in parallel with Dask. Here's a simple but naive sequential approach:

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)

Note that this is not GridSearchCV or RandomSearchCV problem

In the case of RandomSearchCV, I know how to parallelize it with Dask:

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)

However, I'm not interested in hyperparameter tuning and it isn't clear how to modify this code in order to fit a set of multiple different models with their own specific parameters in parallel with Dask.

Upvotes: 6

Views: 2798

Answers (1)

TomAugspurger
TomAugspurger

Reputation: 28946

dask.delayed is probably the easiest solution here.

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_)

Upvotes: 7

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