blocks|key|4686607|text|...
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)|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|4686608|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>...
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)
</code></pre>

blocks|key|4686647|text|我使用dill，它存储所有的数据，我想可能是模块信息？也许不是。我记得我曾尝试使用pickle来存储这些非常复杂的对象，但它对我不起作用。cPickle可能和dill做同样的事情，但是我从来没有尝试过cpickle。看起来它的工作原理与此完全相同。我使用了"obj“扩展名，但这并不意味着conventional...It对我来说是有意义的，因为我是在存储对象。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4686648|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+%2B+"filename.obj","wb"))|code-block|syntax|javascript|4686649|顺便说一句，不确定你是否使用iPython，但有时用这种方式写一个文件不会，所以你必须这样做：|4686650|with+open(wd+%2B+"filename.obj","wb")+as+f:
++++dill.dump(rf,f)|4686651|再次调用对象：|4686652|model+=+dill.load(open(wd+%2B+"filename.obj","rb"))|4686653|entityMap^0|15|6|1X|7|27|4|2S|7|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@$9|V|A|W|B|C]|$9|X|A|Y|B|C]|$9|Z|A|10|B|C]|$9|11|A|12|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|13|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|14|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|15|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|16|8|@]|D|@]|E|$]]|$1|Q|3|R|5|H|7|17|8|@]|D|@]|E|$I|J]]|$1|S|3|-4|5|6|7|18|8|@]|D|@]|E|$]]]|T|$]]

I use dill, it stores all the data and I think possibly module information? Maybe not. I remember trying to use <code>pickle</code> for storing these really complicated objects and it didn't work for me. <code>cPickle</code> probably does the same job as <code>dill</code> but i've never tried <code>cpickle</code>. 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.

<pre><code>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"))
</code></pre>

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

<pre><code>with open(wd + "filename.obj","wb") as f:
 dill.dump(rf,f)
</code></pre>

call the objects again:

<pre><code>model = dill.load(open(wd + "filename.obj","rb"))
</code></pre>

blocks|key|4686689|text|对于模型存储，您也可以使用.sav格式。它存储完整的模型和信息。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4686690|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

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

blocks|key|4686722|text|您可以使用joblib来保存和加载来自scikit+learn的随机森林(实际上，来自scikit+learn的任何模型)|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4686723|示例：|4686724|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")|code-block|syntax|javascript|4686725|更重要的是，joblib.dump+has+compress参数，因此模型可以压缩。我在虹膜数据集上做了非常简单的test，compress=3将文件的大小减少了大约5.6倍。|4686726|entityMap|0|LINK|mutability|MUTABLE|url|https://joblib.readthedocs.io/en/latest/index.html|1|https://mljar.com/blog/save-load-random-forest/^0|5|6|5|6|0|0|0|0|6|B|I|C|1Q|A|1L|4|1|0^^$0|@$1|2|3|4|5|6|7|Y|8|@$9|Z|A|10|B|C]]|D|@$9|11|A|12|1|13]]|E|$]]|$1|F|3|G|5|6|7|14|8|@]|D|@]|E|$]]|$1|H|3|I|5|J|7|15|8|@]|D|@]|E|$K|L]]|$1|M|3|N|5|6|7|16|8|@$9|17|A|18|B|C]|$9|19|A|1A|B|C]|$9|1B|A|1C|B|C]]|D|@$9|1D|A|1E|1|1F]]|E|$]]|$1|O|3|-4|5|6|7|1G|8|@]|D|@]|E|$]]]|P|$Q|$5|R|S|T|E|$U|V]]|W|$5|R|S|T|E|$U|X]]]]

You can use <a href="https://joblib.readthedocs.io/en/latest/index.html" rel="nofollow noreferrer"><code>joblib</code></a> to save and load the Random Forest from scikit-learn (in fact, any model from scikit-learn)
The example:
<pre class="lang-py prettyprint-override"><code>import joblib
from sklearn.ensemble import RandomForestClassifier
# create RF
rf = RandomForestClassifier()
# fit on some data
rf.fit(X, y)

# save
joblib.dump(rf, &quot;my_random_forest.joblib&quot;)

# load
loaded_rf = joblib.load(&quot;my_random_forest.joblib&quot;)

</code></pre>
What is more, the <code>joblib.dump</code> <code>has compress</code> argument, so the model can be compressed. I made very simple <a href="https://mljar.com/blog/save-load-random-forest/" rel="nofollow noreferrer">test</a> on iris dataset and <code>compress=3</code> reduces the size of the file about 5.6 times.

blocks|key|4686756|text|我要重申的是，joblib做得很好，it+provides+really+good+compression+options+(即lzma)。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4686757|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|code-block|syntax|javascript|4686758|entityMap|0|LINK|mutability|MUTABLE|url|https://joblib.readthedocs.io/en/latest/auto_examples/compressors_comparison.html#comparing-the-results^0|I|17|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@$A|R|B|S|1|T]]|C|$]]|$1|D|3|E|5|F|7|U|8|@]|9|@]|C|$G|H]]|$1|I|3|-4|5|6|7|V|8|@]|9|@]|C|$]]]|J|$K|$5|L|M|N|C|$O|P]]]]

I'd reiterate that joblib does the job well and <a href="https://joblib.readthedocs.io/en/latest/auto_examples/compressors_comparison.html#comparing-the-results" rel="nofollow noreferrer">it provides really good compression options</a> (ie lzma).
<pre><code>with open(&quot;clf.pkl&quot;, &quot;wb&quot;) as out: pickle.dump(clf, out)
with open(&quot;clf.dill&quot;, &quot;wb&quot;) as out: dill.dump(clf, out)
joblib.dump(clf, &quot;clf.jbl&quot;)
joblib.dump(clf, &quot;clf.jbl.lzma&quot;)
joblib.dump(clf, &quot;clf.jbl.gz&quot;)

!du clf.*
24576 clf.dill
24576 clf.jbl
5120 clf.jbl.gz
3072 clf.jbl.lzma
24576 clf.pkl
</code></pre>

In R, after running "random forest" model, I can use <code>save.image("***.RData")</code> 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:

<pre><code>rf= RandomForestRegressor(n_estimators=250, max_features=9,compute_importances=True)
fit= rf.fit(Predx, Predy)
</code></pre>

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

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

Save python random forest model to file

在R中，在运行“随机森林”模型之后，我可以使用save.image("***.RData")来存储模型。然后，我可以直接加载模型来进行预测。你能在python中做类似的事情吗？我将模型和预测分成两个文件。在模型文件中：rf= RandomForestRegressor(n_estimators=250, max_fea...

问将python随机森林模型保存到文件
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