我在sklearn中使用MinMaxScaler模型来规范化模型的功能。
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]]
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]]
但我不想这样,所以一直使用scaler.fit()和训练数据。有没有办法保存缩放器并稍后从其他文件加载?
答案 0 :(得分:48)
甚至比pickle
(创建比此方法更大的文件)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)
答案 1 :(得分:12)
所以我实际上不是这方面的专家,但是通过一些研究和一些有用的links,我认为pickle
和sklearn.externals.joblib
将成为你的朋友。
包pickle
允许您将模型或“转储”模型保存到文件中。
我认为这个link也很有帮助。它讨论了创建持久性模型。你想要尝试的东西是:
# 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')
Here是您可以了解更多关于sklearn外部的信息。
如果这没有帮助,或者我不了解您的模型,请告诉我。
答案 2 :(得分:7)
您可以使用pickle
来保存缩放器:
import pickle
scalerfile = 'scaler.sav'
pickle.dump(scaler, open(scalerfile, 'wb'))
加载回来:
import pickle
scalerfile = 'scaler.sav'
scaler = pickle.load(open(scalerfile, 'rb'))
test_scaled_set = scaler.transform(test_set)
答案 3 :(得分:2)
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)
joblib.dump(model, 'filename.mod')
model = joblib.load('filename.mod')
答案 4 :(得分:0)
仅需注意,sklearn.externals.joblib
已被弃用并由普通的旧joblib
(可以与pip install joblib
一起安装)取代:
import joblib
joblib.dump(my_scaler, 'scaler.pkl')
my_scaler = joblib.load('scaler.pkl')
有关joblib.dump()
和joblib.load()
方法的文档。