具有单个样本的Sklearn火车模型引发了DeprecationWarning [英] Sklearn train model with single sample raises a DeprecationWarning

问题描述

下面是我的代码:

我有一个features数组和一个用于训练model.pkl

I have a features array, and a labels array which I use to train the model.pkl

但是当我想在模型中添加single sample时，会出现warning波纹管.

But when I want to add a single sample to the model, I get the warning bellow.

from sklearn import tree
from sklearn.externals import joblib

features = [[140, 1], [130, 1], [150, 0], [170, 0]]
labels = [0, 0, 1, 1] 

# Here I train the model with the above arrays
clf = tree.DecisionTreeClassifier()
clf = clf.fit(features, labels)
joblib.dump(clf, 'model.pkl') 

# Now I want to train the model with a new single sample
clf = joblib.load('model.pkl')
clf = clf.fit([130, 1], 0) # WARNING MESSAGE HERE!!

这是warning:

        /usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.py:386:
 DeprecationWarning: 
    Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. 
    Reshape your data either using X.reshape(-1, 1) 
if your data has a single feature or X.reshape(1, -1) 
if it contains a single sample.  DeprecationWarning)

我已经阅读了此. 但是看来我的例子不一样.

I've already read this. But it seems my example is different.

如何每次使用一个样本训练模型?

谢谢

推荐答案

如果阅读错误消息，您会看到很快将不支持传递一维数组.相反，您必须确保单个样本看起来像一个样本列表，其中只有一个.在处理NumPy数组(推荐)时，可以使用reshape(-1, 1)，但是在使用列表时，将执行以下操作:

If you read the error message you can see that passing single dimensional arrays will soon not be supported. Instead you have to ensure that your single sample looks like a list of samples, in which there is just one. When dealing with NumPy arrays (which is recommended), you can use reshape(-1, 1) however as you're using lists then the following will do:

clf = clf.fit([[130, 1]], [0])

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