XGBoost原理及在Python中使用XGBoost

原理见：http://www.myexception.cn/operating-system/2084839.html

译文转自：http://blog.csdn.net/zc02051126/article/details/46771793

在Python中使用XGBoost

下面将介绍XGBoost的Python模块，内容如下： 
* 编译及导入Python模块 
* 数据接口 
* 参数设置 
* 训练模型l 
* 提前终止程序 
* 预测

A walk through python example for UCI Mushroom dataset is provided.

安装

首先安装XGBoost的C++版本，然后进入源文件的根目录下的 wrappers文件夹执行如下脚本安装Python模块

<code class="language-shell hljs cmake 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;">python setup.py <span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">install</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>

安装完成后按照如下方式导入XGBoost的Python模块

<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-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">import</span> xgboost <span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">as</span> xgb</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>

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数据接口

XGBoost可以加载libsvm格式的文本数据，加载的数据格式可以为Numpy的二维数组和XGBoost的二进制的缓存文件。加载的数据存储在对象DMatrix中。

• 加载libsvm格式的数据和二进制的缓存文件时可以使用如下方式

<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;">dtrain = xgb.DMatrix(<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'train.svm.txt'</span>)dtest = xgb.DMatrix(<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'test.svm.buffer'</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></ul>

• 加载numpy的数组到DMatrix对象时，可以用如下方式

<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;">data = np.random.rand(<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">5</span>,<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">10</span>) <span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># 5 entities, each contains 10 features</span>label = np.random.randint(<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">2</span>, size=<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">5</span>) <span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># binary target</span>dtrain = xgb.DMatrix( data, label=label)</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></ul>

• 将scipy.sparse格式的数据转化为 DMatrix格式时，可以使用如下方式

<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;">csr = scipy.sparse.csr_matrix( (dat, (row,col)) )dtrain = xgb.DMatrix( csr )</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></ul>

• 将 DMatrix 格式的数据保存成XGBoost的二进制格式，在下次加载时可以提高加载速度，使用方式如下

<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;">dtrain = xgb.DMatrix(<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'train.svm.txt'</span>)dtrain.save_binary(<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">"train.buffer"</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></ul>

• 可以用如下方式处理 DMatrix中的缺失值：

<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;">dtrain = xgb.DMatrix( data, label=label, missing = -<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">999.0</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>

• 当需要给样本设置权重时，可以用如下方式

<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;">w = np.random.rand(<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">5</span>,<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">1</span>)dtrain = xgb.DMatrix( data, label=label, missing = -<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">999.0</span>, weight=w)</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></ul>

参数设置

XGBoost使用key-value格式保存参数. Eg 
* Booster（基本学习器）参数

<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;">param = {<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'bst:max_depth'</span>:<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">2</span>, <span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'bst:eta'</span>:<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">1</span>, <span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'silent'</span>:<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">1</span>, <span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'objective'</span>:<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'binary:logistic'</span> }param[<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'nthread'</span>] = <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">4</span>plst = param.items()plst += [(<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'eval_metric'</span>, <span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'auc'</span>)] <span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># Multiple evals can be handled in this way</span>plst += [(<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'eval_metric'</span>, <span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'ams@0'</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></ul>

• 还可以定义验证数据集，验证算法的性能

<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;">evallist  = [(dtest,<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'eval'</span>), (dtrain,<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'train'</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>

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训练模型

有了参数列表和数据就可以训练模型了 
* 训练

<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;">num_round = <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">10</span>bst = xgb.train( plst, dtrain, num_round, evallist )</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></ul>

• 保存模型 
  在训练完成之后可以将模型保存下来，也可以查看模型内部的结构

<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;">bst.save_model(<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'0001.model'</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>

• Dump Model and Feature Map 
  You can dump the model to txt and review the meaning of model

<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-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># dump model</span>bst.dump_model(<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'dump.raw.txt'</span>)<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># dump model with feature map</span>bst.dump_model(<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'dump.raw.txt'</span>,<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'featmap.txt'</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></ul>

• 加载模型 
  通过如下方式可以加载模型

<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;">bst = xgb.Booster({<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'nthread'</span>:<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">4</span>}) <span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;">#init model</span>bst.load_model(<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">"model.bin"</span>) <span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># load data</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></ul>

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提前终止程序

如果有评价数据，可以提前终止程序，这样可以找到最优的迭代次数。如果要提前终止程序必须至少有一个评价数据在参数evals中。 If there’s more than one, it will use the last.

train(..., evals=evals, early_stopping_rounds=10)

The model will train until the validation score stops improving. Validation error needs to decrease at least every early_stopping_rounds to continue training.

If early stopping occurs, the model will have two additional fields: bst.best_score and bst.best_iteration. Note that train() will return a model from the last iteration, not the best one.

This works with both metrics to minimize (RMSE, log loss, etc.) and to maximize (MAP, NDCG, AUC).

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Prediction

After you training/loading a model and preparing the data, you can start to do prediction.

<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;">data = np.random.rand(<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">7</span>,<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">10</span>) <span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># 7 entities, each contains 10 features</span>dtest = xgb.DMatrix( data, missing = -<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">999.0</span> )ypred = bst.predict( xgmat )</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></ul>

If early stopping is enabled during training, you can predict with the best iteration.

<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;">ypred = bst.predict(xgmat,ntree_limit=bst.best_iteration)</code>