CODEWITHSUNDEEP
pythonmachine-learningscikit-learnrandom-forest
user3013706
user3013706

Reputation: 451

Save python random forest model to file

In R, after running "random forest" model, I can use save.image("***.RData") to store the model. Afterwards, I can just load the model to do predictions directly.

Can you do a similar thing in python? I separate the Model and Prediction into two files. And in Model file:

rf= RandomForestRegressor(n_estimators=250, max_features=9,compute_importances=True)
fit= rf.fit(Predx, Predy)

I tried to return rf or fit, but still can't load the model in the prediction file.

Can you separate the model and prediction using the sklearn random forest package?

Upvotes: 36

Views: 54536

Answers (5)

Leszek
Leszek

Reputation: 1360

I'd reiterate that joblib does the job well and it provides really good compression options (ie lzma).

with open("clf.pkl", "wb") as out: pickle.dump(clf, out)
with open("clf.dill", "wb") as out: dill.dump(clf, out)
joblib.dump(clf, "clf.jbl")
joblib.dump(clf, "clf.jbl.lzma")
joblib.dump(clf, "clf.jbl.gz")

!du clf.*
24576   clf.dill
24576   clf.jbl
5120    clf.jbl.gz
3072    clf.jbl.lzma
24576   clf.pkl

Upvotes: 0

pplonski
pplonski

Reputation: 5859

You can use joblib to save and load the Random Forest from scikit-learn (in fact, any model from scikit-learn)

The example:

import joblib
from sklearn.ensemble import RandomForestClassifier
# create RF
rf = RandomForestClassifier()
# fit on some data
rf.fit(X, y)

# save
joblib.dump(rf, "my_random_forest.joblib")

# load
loaded_rf = joblib.load("my_random_forest.joblib")

What is more, the joblib.dump has compress argument, so the model can be compressed. I made very simple test on iris dataset and compress=3 reduces the size of the file about 5.6 times.

Upvotes: 22

Ch HaXam
Ch HaXam

Reputation: 499

for the model storing you can also use .sav formate. it stores complete model and information.

Upvotes: 0

O.rka
O.rka

Reputation: 30737

I use dill, it stores all the data and I think possibly module information? Maybe not. I remember trying to use pickle for storing these really complicated objects and it didn't work for me. cPickle probably does the same job as dill but i've never tried cpickle. it looks like it works in literally the exact same way. I use "obj" extension but that's by no means conventional...It just made sense for me since I was storing an object.

import dill
wd = "/whatever/you/want/your/working/directory/to/be/"
rf= RandomForestRegressor(n_estimators=250, max_features=9,compute_importances=True)
rf.fit(Predx, Predy)
dill.dump(rf, open(wd + "filename.obj","wb"))

btw, not sure if you use iPython, but sometimes writing a file that way doesn't so you have to do the:

with open(wd + "filename.obj","wb") as f:
    dill.dump(rf,f)

call the objects again:

model = dill.load(open(wd + "filename.obj","rb"))

Upvotes: 2

Jake Burkhead
Jake Burkhead

Reputation: 6545

...
import cPickle

rf = RandomForestRegresor()
rf.fit(X, y)

with open('path/to/file', 'wb') as f:
    cPickle.dump(rf, f)


# in your prediction file                                                                                                                                                                                                           

with open('path/to/file', 'rb') as f:
    rf = cPickle.load(f)


preds = rf.predict(new_X)

Upvotes: 45

Related Questions