Python 在 sklearn 中保存 MinMaxScaler 模型
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Save MinMaxScaler model in sklearn
提问by Luis Ramon Ramirez Rodriguez
I'm using the MinMaxScalermodel in sklearn to normalize the features of a model.
我MinMaxScaler在 sklearn 中使用模型来规范化模型的特征。
training_set = np.random.rand(4,4)*10
training_set
[[ 6.01144787, 0.59753007, 2.0014852 , 3.45433657],
[ 6.03041646, 5.15589559, 6.64992437, 2.63440202],
[ 2.27733136, 9.29927394, 0.03718093, 7.7679183 ],
[ 9.86934288, 7.59003904, 6.02363739, 2.78294206]]
scaler = MinMaxScaler()
scaler.fit(training_set)
scaler.transform(training_set)
[[ 0.49184811, 0. , 0.29704831, 0.15972182],
[ 0.4943466 , 0.52384506, 1. , 0. ],
[ 0. , 1. , 0. , 1. ],
[ 1. , 0.80357559, 0.9052909 , 0.02893534]]
Now I want to use the same scaler to normalize the test set:
现在我想使用相同的缩放器来规范化测试集：
[[ 8.31263467, 7.99782295, 0.02031658, 9.43249727],
[ 1.03761228, 9.53173021, 5.99539478, 4.81456067],
[ 0.19715961, 5.97702519, 0.53347403, 5.58747666],
[ 9.67505429, 2.76225253, 7.39944931, 8.46746594]]
But I don't want so use the scaler.fit()with the training data all the time. Is there a way to save the scaler and load it later from a different file?
但我不想一直使用scaler.fit()训练数据。有没有办法保存定标器并稍后从不同的文件加载它？
采纳答案by jlarks32
So I'm actually not an expert with this but from a bit of research and a few helpful links, I think pickleand sklearn.externals.joblibare going to be your friends here.
所以我实际上不是这方面的专家，但通过一些研究和一些有用的链接，我认为pickle并且sklearn.externals.joblib将成为您的朋友。
The package picklelets you save models or "dump" models to a file.
该软件包pickle可让您将模型或“转储”模型保存到文件中。
I think this linkis also helpful. It talks about creating a persistence model. Something that you're going to want to try is:
我认为这个链接也很有帮助。它讨论了创建持久性模型。您想要尝试的是：
# could use: import pickle... however let's do something else
from sklearn.externals import joblib
# this is more efficient than pickle for things like large numpy arrays
# ... which sklearn models often have.
# then just 'dump' your file
joblib.dump(clf, 'my_dope_model.pkl')
Hereis where you can learn more about the sklearn externals.
您可以在此处了解有关 sklearn 外部组件的更多信息。
Let me know if that doesn't help or I'm not understanding something about your model.
如果这没有帮助，或者我不了解您的模型，请告诉我。
Note: sklearn.externals.joblibis deprecated. Install and use the pure joblibinstead
注意：sklearn.externals.joblib已弃用。安装并使用 purejoblib代替
回答by Ivan Vegner
Even better than pickle(which creates much larger files than this method), you can use sklearn's built-in tool:
甚至比pickle（创建比此方法大得多的文件）更好，您可以使用sklearn的内置工具：
from sklearn.externals import joblib
scaler_filename = "scaler.save"
joblib.dump(scaler, scaler_filename)
# And now to load...
scaler = joblib.load(scaler_filename)
Note: sklearn.externals.joblibis deprecated. Install and use the pure joblibinstead
注意：sklearn.externals.joblib已弃用。安装并使用 purejoblib代替
回答by Engineero
Just a note that sklearn.externals.joblibhas been deprecated and is superseded by plain old joblib, which can be installed with pip install joblib:
只是一个sklearn.externals.joblib已被弃用并被普通 old 取代的注释，joblib可以安装pip install joblib：
import joblib
joblib.dump(my_scaler, 'scaler.gz')
my_scaler = joblib.load('scaler.gz')
Note that file extensions can be anything, but if it is one of ['.z', '.gz', '.bz2', '.xz', '.lzma']then the corresponding compression protocol will be used. Docs for joblib.dump()and joblib.load()methods.
请注意，文件扩展名可以是任何内容，但如果是其中之一，['.z', '.gz', '.bz2', '.xz', '.lzma']则将使用相应的压缩协议。文档joblib.dump()和joblib.load()方法。
回答by Psidom
You can use pickle, to save the scaler:
您可以使用pickle, 来保存缩放器：
import pickle
scalerfile = 'scaler.sav'
pickle.dump(scaler, open(scalerfile, 'wb'))
Load it back:
加载回来：
import pickle
scalerfile = 'scaler.sav'
scaler = pickle.load(open(scalerfile, 'rb'))
test_scaled_set = scaler.transform(test_set)
回答by PSN
The best way to do this is to create an ML pipeline like the following:
执行此操作的最佳方法是创建一个如下所示的 ML 管道：
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import MinMaxScaler
from sklearn.externals import joblib
pipeline = make_pipeline(MinMaxScaler(),YOUR_ML_MODEL() )
model = pipeline.fit(X_train, y_train)
Now you can save it to a file:
现在您可以将其保存到文件中：
joblib.dump(model, 'filename.mod')
Later you can load it like this:
稍后您可以像这样加载它：
model = joblib.load('filename.mod')
