训练模型的保存与恢复(sklearn模型持久化)

方法一(sklearn):  

from sklearn.externals import joblib

joblib.dump(lr,'文件')   ///lr可以换成其他训练好的模型


方法二(Python通用):

import pickle

filename=open('文件','wb')

pickle.dump(bins,filename)

filename.close()

在做模型训练的时候,尤其是在训练集上做交叉验证,通常想要将模型保存下来,然后放到独立的测试集上测试,下面介绍的是Python中训练模型的保存和再使用。

scikit-learn已经有了模型持久化的操作,导入joblib即可

<code class="hljs python has-numbering" style="display: block; padding: 0px; color: inherit; box-sizing: border-box; font-family: 'Source Code Pro', monospace;font-size:undefined; white-space: pre; border-radius: 0px; word-wrap: normal; background: transparent;"><span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">from</span> sklearn.externals <span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">import</span> joblib</code><ul class="pre-numbering" style="box-sizing: border-box; position: absolute; width: 50px; top: 0px; left: 0px; margin: 0px; padding: 6px 0px 40px; border-right-width: 1px; border-right-style: solid; border-right-color: rgb(221, 221, 221); list-style: none; text-align: right; background-color: rgb(238, 238, 238);"><li style="box-sizing: border-box; padding: 0px 5px;">1</li></ul><ul class="pre-numbering" style="box-sizing: border-box; position: absolute; width: 50px; top: 0px; left: 0px; margin: 0px; padding: 6px 0px 40px; border-right-width: 1px; border-right-style: solid; border-right-color: rgb(221, 221, 221); list-style: none; text-align: right; background-color: rgb(238, 238, 238);"><li style="box-sizing: border-box; padding: 0px 5px;">1</li></ul>

模型保存

<code class="language-python hljs  has-numbering" style="display: block; padding: 0px; color: inherit; box-sizing: border-box; font-family: 'Source Code Pro', monospace;font-size:undefined; white-space: pre; border-radius: 0px; word-wrap: normal; background: transparent;"><span class="hljs-prompt" style="color: rgb(0, 102, 102); box-sizing: border-box;">>>> </span>os.chdir(<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">"workspace/model_save"</span>)
<span class="hljs-prompt" style="color: rgb(0, 102, 102); box-sizing: border-box;">>>> </span><span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">from</span> sklearn <span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">import</span> svm
<span class="hljs-prompt" style="color: rgb(0, 102, 102); box-sizing: border-box;">>>> </span>X = [[<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">0</span>, <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">0</span>], [<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">1</span>, <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">1</span>]]
<span class="hljs-prompt" style="color: rgb(0, 102, 102); box-sizing: border-box;">>>> </span>y = [<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">0</span>, <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">1</span>]
<span class="hljs-prompt" style="color: rgb(0, 102, 102); box-sizing: border-box;">>>> </span>clf = svm.SVC()
<span class="hljs-prompt" style="color: rgb(0, 102, 102); box-sizing: border-box;">>>> </span>clf.fit(X, y)  
<span class="hljs-prompt" style="color: rgb(0, 102, 102); box-sizing: border-box;">>>> </span>clf.fit(train_X,train_y)
<span class="hljs-prompt" style="color: rgb(0, 102, 102); box-sizing: border-box;">>>> </span>joblib.dump(clf, <span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">"train_model.m"</span>)</code><ul class="pre-numbering" style="box-sizing: border-box; position: absolute; width: 50px; top: 0px; left: 0px; margin: 0px; padding: 6px 0px 40px; border-right-width: 1px; border-right-style: solid; border-right-color: rgb(221, 221, 221); list-style: none; text-align: right; background-color: rgb(238, 238, 238);"><li style="box-sizing: border-box; padding: 0px 5px;">1</li><li style="box-sizing: border-box; padding: 0px 5px;">2</li><li style="box-sizing: border-box; padding: 0px 5px;">3</li><li style="box-sizing: border-box; padding: 0px 5px;">4</li><li style="box-sizing: border-box; padding: 0px 5px;">5</li><li style="box-sizing: border-box; padding: 0px 5px;">6</li><li style="box-sizing: border-box; padding: 0px 5px;">7</li><li style="box-sizing: border-box; padding: 0px 5px;">8</li></ul><ul class="pre-numbering" style="box-sizing: border-box; position: absolute; width: 50px; top: 0px; left: 0px; margin: 0px; padding: 6px 0px 40px; border-right-width: 1px; border-right-style: solid; border-right-color: rgb(221, 221, 221); list-style: none; text-align: right; background-color: rgb(238, 238, 238);"><li style="box-sizing: border-box; padding: 0px 5px;">1</li><li style="box-sizing: border-box; padding: 0px 5px;">2</li><li style="box-sizing: border-box; padding: 0px 5px;">3</li><li style="box-sizing: border-box; padding: 0px 5px;">4</li><li style="box-sizing: border-box; padding: 0px 5px;">5</li><li style="box-sizing: border-box; padding: 0px 5px;">6</li><li style="box-sizing: border-box; padding: 0px 5px;">7</li><li style="box-sizing: border-box; padding: 0px 5px;">8</li></ul>

通过joblib的dump可以将模型保存到本地,clf是训练的分类器

模型从本地调回

<code class="language-python hljs  has-numbering" style="display: block; padding: 0px; color: inherit; box-sizing: border-box; font-family: 'Source Code Pro', monospace;font-size:undefined; white-space: pre; border-radius: 0px; word-wrap: normal; background: transparent;"><span class="hljs-prompt" style="color: rgb(0, 102, 102); box-sizing: border-box;">>>> </span>clf = joblib.load(<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">"train_model.m"</span>)</code><ul class="pre-numbering" style="box-sizing: border-box; position: absolute; width: 50px; top: 0px; left: 0px; margin: 0px; padding: 6px 0px 40px; border-right-width: 1px; border-right-style: solid; border-right-color: rgb(221, 221, 221); list-style: none; text-align: right; background-color: rgb(238, 238, 238);"><li style="box-sizing: border-box; padding: 0px 5px;">1</li></ul><ul class="pre-numbering" style="box-sizing: border-box; position: absolute; width: 50px; top: 0px; left: 0px; margin: 0px; padding: 6px 0px 40px; border-right-width: 1px; border-right-style: solid; border-right-color: rgb(221, 221, 221); list-style: none; text-align: right; background-color: rgb(238, 238, 238);"><li style="box-sizing: border-box; padding: 0px 5px;">1</li></ul>

通过joblib的load方法,加载保存的模型。


然后就可以在测试集上测试了

<code class="hljs avrasm has-numbering" style="display: block; padding: 0px; color: inherit; box-sizing: border-box; font-family: 'Source Code Pro', monospace;font-size:undefined; white-space: pre; border-radius: 0px; word-wrap: normal; background: transparent;">clf<span class="hljs-preprocessor" style="color: rgb(68, 68, 68); box-sizing: border-box;">.predit</span>(test_X,test_y)</code>
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