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Module classification
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<h1 class="epydoc">Source Code for <a href="pyspark.mllib.classification-module.html">Module pyspark.mllib.classification</a></h1>
<pre class="py-src">
<a name="L1"></a><tt class="py-lineno"> 1</tt> <tt class="py-line"><tt class="py-comment">#</tt> </tt>
<a name="L2"></a><tt class="py-lineno"> 2</tt> <tt class="py-line"><tt class="py-comment"># Licensed to the Apache Software Foundation (ASF) under one or more</tt> </tt>
<a name="L3"></a><tt class="py-lineno"> 3</tt> <tt class="py-line"><tt class="py-comment"># contributor license agreements. See the NOTICE file distributed with</tt> </tt>
<a name="L4"></a><tt class="py-lineno"> 4</tt> <tt class="py-line"><tt class="py-comment"># this work for additional information regarding copyright ownership.</tt> </tt>
<a name="L5"></a><tt class="py-lineno"> 5</tt> <tt class="py-line"><tt class="py-comment"># The ASF licenses this file to You under the Apache License, Version 2.0</tt> </tt>
<a name="L6"></a><tt class="py-lineno"> 6</tt> <tt class="py-line"><tt class="py-comment"># (the "License"); you may not use this file except in compliance with</tt> </tt>
<a name="L7"></a><tt class="py-lineno"> 7</tt> <tt class="py-line"><tt class="py-comment"># the License. You may obtain a copy of the License at</tt> </tt>
<a name="L8"></a><tt class="py-lineno"> 8</tt> <tt class="py-line"><tt class="py-comment">#</tt> </tt>
<a name="L9"></a><tt class="py-lineno"> 9</tt> <tt class="py-line"><tt class="py-comment"># http://www.apache.org/licenses/LICENSE-2.0</tt> </tt>
<a name="L10"></a><tt class="py-lineno"> 10</tt> <tt class="py-line"><tt class="py-comment">#</tt> </tt>
<a name="L11"></a><tt class="py-lineno"> 11</tt> <tt class="py-line"><tt class="py-comment"># Unless required by applicable law or agreed to in writing, software</tt> </tt>
<a name="L12"></a><tt class="py-lineno"> 12</tt> <tt class="py-line"><tt class="py-comment"># distributed under the License is distributed on an "AS IS" BASIS,</tt> </tt>
<a name="L13"></a><tt class="py-lineno"> 13</tt> <tt class="py-line"><tt class="py-comment"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</tt> </tt>
<a name="L14"></a><tt class="py-lineno"> 14</tt> <tt class="py-line"><tt class="py-comment"># See the License for the specific language governing permissions and</tt> </tt>
<a name="L15"></a><tt class="py-lineno"> 15</tt> <tt class="py-line"><tt class="py-comment"># limitations under the License.</tt> </tt>
<a name="L16"></a><tt class="py-lineno"> 16</tt> <tt class="py-line"><tt class="py-comment">#</tt> </tt>
<a name="L17"></a><tt class="py-lineno"> 17</tt> <tt class="py-line"> </tt>
<a name="L18"></a><tt class="py-lineno"> 18</tt> <tt class="py-line"><tt class="py-keyword">import</tt> <tt class="py-name">numpy</tt> </tt>
<a name="L19"></a><tt class="py-lineno"> 19</tt> <tt class="py-line"> </tt>
<a name="L20"></a><tt class="py-lineno"> 20</tt> <tt class="py-line"><tt class="py-keyword">from</tt> <tt class="py-name">numpy</tt> <tt class="py-keyword">import</tt> <tt class="py-name">array</tt><tt class="py-op">,</tt> <tt class="py-name">dot</tt><tt class="py-op">,</tt> <tt class="py-name">shape</tt> </tt>
<a name="L21"></a><tt class="py-lineno"> 21</tt> <tt class="py-line"><tt class="py-keyword">from</tt> <tt id="link-0" class="py-name" targets="Package pyspark=pyspark-module.html"><a title="pyspark" class="py-name" href="#" onclick="return doclink('link-0', 'pyspark', 'link-0');">pyspark</a></tt> <tt class="py-keyword">import</tt> <tt id="link-1" class="py-name" targets="Class pyspark.context.SparkContext=pyspark.context.SparkContext-class.html"><a title="pyspark.context.SparkContext" class="py-name" href="#" onclick="return doclink('link-1', 'SparkContext', 'link-1');">SparkContext</a></tt> </tt>
<a name="L22"></a><tt class="py-lineno"> 22</tt> <tt class="py-line"><tt class="py-keyword">from</tt> <tt id="link-2" class="py-name"><a title="pyspark" class="py-name" href="#" onclick="return doclink('link-2', 'pyspark', 'link-0');">pyspark</a></tt><tt class="py-op">.</tt><tt id="link-3" class="py-name" targets="Package pyspark.mllib=pyspark.mllib-module.html"><a title="pyspark.mllib" class="py-name" href="#" onclick="return doclink('link-3', 'mllib', 'link-3');">mllib</a></tt><tt class="py-op">.</tt><tt class="py-name">_common</tt> <tt class="py-keyword">import</tt> \ </tt>
<a name="L23"></a><tt class="py-lineno"> 23</tt> <tt class="py-line"> <tt class="py-name">_get_unmangled_rdd</tt><tt class="py-op">,</tt> <tt class="py-name">_get_unmangled_double_vector_rdd</tt><tt class="py-op">,</tt> \ </tt>
<a name="L24"></a><tt class="py-lineno"> 24</tt> <tt class="py-line"> <tt class="py-name">_serialize_double_matrix</tt><tt class="py-op">,</tt> <tt class="py-name">_deserialize_double_matrix</tt><tt class="py-op">,</tt> \ </tt>
<a name="L25"></a><tt class="py-lineno"> 25</tt> <tt class="py-line"> <tt class="py-name">_serialize_double_vector</tt><tt class="py-op">,</tt> <tt class="py-name">_deserialize_double_vector</tt><tt class="py-op">,</tt> \ </tt>
<a name="L26"></a><tt class="py-lineno"> 26</tt> <tt class="py-line"> <tt class="py-name">_get_initial_weights</tt><tt class="py-op">,</tt> <tt class="py-name">_serialize_rating</tt><tt class="py-op">,</tt> <tt class="py-name">_regression_train_wrapper</tt><tt class="py-op">,</tt> \ </tt>
<a name="L27"></a><tt class="py-lineno"> 27</tt> <tt class="py-line"> <tt id="link-4" class="py-name" targets="Class pyspark.mllib.regression.LinearModel=pyspark.mllib.regression.LinearModel-class.html"><a title="pyspark.mllib.regression.LinearModel" class="py-name" href="#" onclick="return doclink('link-4', 'LinearModel', 'link-4');">LinearModel</a></tt><tt class="py-op">,</tt> <tt class="py-name">_linear_predictor_typecheck</tt> </tt>
<a name="L28"></a><tt class="py-lineno"> 28</tt> <tt class="py-line"><tt class="py-keyword">from</tt> <tt class="py-name">math</tt> <tt class="py-keyword">import</tt> <tt class="py-name">exp</tt><tt class="py-op">,</tt> <tt class="py-name">log</tt> </tt>
<a name="LogisticRegressionModel"></a><div id="LogisticRegressionModel-def"><a name="L29"></a><tt class="py-lineno"> 29</tt> <tt class="py-line"> </tt>
<a name="L30"></a><tt class="py-lineno"> 30</tt> <a class="py-toggle" href="#" id="LogisticRegressionModel-toggle" onclick="return toggle('LogisticRegressionModel');">-</a><tt class="py-line"><tt class="py-keyword">class</tt> <a class="py-def-name" href="pyspark.mllib.classification.LogisticRegressionModel-class.html">LogisticRegressionModel</a><tt class="py-op">(</tt><tt class="py-base-class">LinearModel</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="LogisticRegressionModel-collapsed" style="display:none;" pad="+++" indent="++++"></div><div id="LogisticRegressionModel-expanded"><a name="L31"></a><tt class="py-lineno"> 31</tt> <tt class="py-line"> <tt class="py-docstring">"""A linear binary classification model derived from logistic regression.</tt> </tt>
<a name="L32"></a><tt class="py-lineno"> 32</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L33"></a><tt class="py-lineno"> 33</tt> <tt class="py-line"><tt class="py-docstring"> >>> data = array([0.0, 0.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0]).reshape(4,2)</tt> </tt>
<a name="L34"></a><tt class="py-lineno"> 34</tt> <tt class="py-line"><tt class="py-docstring"> >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(data))</tt> </tt>
<a name="L35"></a><tt class="py-lineno"> 35</tt> <tt class="py-line"><tt class="py-docstring"> >>> lrm.predict(array([1.0])) > 0</tt> </tt>
<a name="L36"></a><tt class="py-lineno"> 36</tt> <tt class="py-line"><tt class="py-docstring"> True</tt> </tt>
<a name="L37"></a><tt class="py-lineno"> 37</tt> <tt class="py-line"><tt class="py-docstring"> """</tt> </tt>
<a name="LogisticRegressionModel.predict"></a><div id="LogisticRegressionModel.predict-def"><a name="L38"></a><tt class="py-lineno"> 38</tt> <a class="py-toggle" href="#" id="LogisticRegressionModel.predict-toggle" onclick="return toggle('LogisticRegressionModel.predict');">-</a><tt class="py-line"> <tt class="py-keyword">def</tt> <a class="py-def-name" href="pyspark.mllib.classification.LogisticRegressionModel-class.html#predict">predict</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">x</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="LogisticRegressionModel.predict-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="LogisticRegressionModel.predict-expanded"><a name="L39"></a><tt class="py-lineno"> 39</tt> <tt class="py-line"> <tt class="py-name">_linear_predictor_typecheck</tt><tt class="py-op">(</tt><tt class="py-name">x</tt><tt class="py-op">,</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">_coeff</tt><tt class="py-op">)</tt> </tt>
<a name="L40"></a><tt class="py-lineno"> 40</tt> <tt class="py-line"> <tt class="py-name">margin</tt> <tt class="py-op">=</tt> <tt class="py-name">dot</tt><tt class="py-op">(</tt><tt class="py-name">x</tt><tt class="py-op">,</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">_coeff</tt><tt class="py-op">)</tt> <tt class="py-op">+</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">_intercept</tt> </tt>
<a name="L41"></a><tt class="py-lineno"> 41</tt> <tt class="py-line"> <tt class="py-name">prob</tt> <tt class="py-op">=</tt> <tt class="py-number">1</tt><tt class="py-op">/</tt><tt class="py-op">(</tt><tt class="py-number">1</tt> <tt class="py-op">+</tt> <tt class="py-name">exp</tt><tt class="py-op">(</tt><tt class="py-op">-</tt><tt class="py-name">margin</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
<a name="L42"></a><tt class="py-lineno"> 42</tt> <tt class="py-line"> <tt class="py-keyword">return</tt> <tt class="py-number">1</tt> <tt class="py-keyword">if</tt> <tt class="py-name">prob</tt> <tt class="py-op">></tt> <tt class="py-number">0.5</tt> <tt class="py-keyword">else</tt> <tt class="py-number">0</tt> </tt>
</div></div><a name="L43"></a><tt class="py-lineno"> 43</tt> <tt class="py-line"> </tt>
<a name="LogisticRegressionWithSGD"></a><div id="LogisticRegressionWithSGD-def"><a name="L44"></a><tt class="py-lineno"> 44</tt> <a class="py-toggle" href="#" id="LogisticRegressionWithSGD-toggle" onclick="return toggle('LogisticRegressionWithSGD');">-</a><tt class="py-line"><tt class="py-keyword">class</tt> <a class="py-def-name" href="pyspark.mllib.classification.LogisticRegressionWithSGD-class.html">LogisticRegressionWithSGD</a><tt class="py-op">(</tt><tt class="py-base-class">object</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="LogisticRegressionWithSGD-collapsed" style="display:none;" pad="+++" indent="++++"></div><div id="LogisticRegressionWithSGD-expanded"><a name="L45"></a><tt class="py-lineno"> 45</tt> <tt class="py-line"> <tt class="py-decorator">@</tt><tt class="py-decorator">classmethod</tt> </tt>
<a name="LogisticRegressionWithSGD.train"></a><div id="LogisticRegressionWithSGD.train-def"><a name="L46"></a><tt class="py-lineno"> 46</tt> <a class="py-toggle" href="#" id="LogisticRegressionWithSGD.train-toggle" onclick="return toggle('LogisticRegressionWithSGD.train');">-</a><tt class="py-line"> <tt class="py-keyword">def</tt> <a class="py-def-name" href="pyspark.mllib.classification.LogisticRegressionWithSGD-class.html#train">train</a><tt class="py-op">(</tt><tt class="py-param">cls</tt><tt class="py-op">,</tt> <tt class="py-param">data</tt><tt class="py-op">,</tt> <tt class="py-param">iterations</tt><tt class="py-op">=</tt><tt class="py-number">100</tt><tt class="py-op">,</tt> <tt class="py-param">step</tt><tt class="py-op">=</tt><tt class="py-number">1.0</tt><tt class="py-op">,</tt> </tt>
<a name="L47"></a><tt class="py-lineno"> 47</tt> <tt class="py-line"> <tt class="py-param">miniBatchFraction</tt><tt class="py-op">=</tt><tt class="py-number">1.0</tt><tt class="py-op">,</tt> <tt class="py-param">initialWeights</tt><tt class="py-op">=</tt><tt class="py-name">None</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="LogisticRegressionWithSGD.train-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="LogisticRegressionWithSGD.train-expanded"><a name="L48"></a><tt class="py-lineno"> 48</tt> <tt class="py-line"> <tt class="py-docstring">"""Train a logistic regression model on the given data."""</tt> </tt>
<a name="L49"></a><tt class="py-lineno"> 49</tt> <tt class="py-line"> <tt class="py-name">sc</tt> <tt class="py-op">=</tt> <tt class="py-name">data</tt><tt class="py-op">.</tt><tt id="link-5" class="py-name" targets="Module pyspark.context=pyspark.context-module.html,Method pyspark.rdd.RDD.context()=pyspark.rdd.RDD-class.html#context"><a title="pyspark.context
pyspark.rdd.RDD.context" class="py-name" href="#" onclick="return doclink('link-5', 'context', 'link-5');">context</a></tt> </tt>
<a name="L50"></a><tt class="py-lineno"> 50</tt> <tt class="py-line"> <tt class="py-keyword">return</tt> <tt class="py-name">_regression_train_wrapper</tt><tt class="py-op">(</tt><tt class="py-name">sc</tt><tt class="py-op">,</tt> <tt class="py-keyword">lambda</tt> <tt class="py-name">d</tt><tt class="py-op">,</tt> <tt class="py-name">i</tt><tt class="py-op">:</tt> </tt>
<a name="L51"></a><tt class="py-lineno"> 51</tt> <tt class="py-line"> <tt class="py-name">sc</tt><tt class="py-op">.</tt><tt id="link-6" class="py-name" targets="Variable pyspark.context.SparkContext._jvm=pyspark.context.SparkContext-class.html#_jvm"><a title="pyspark.context.SparkContext._jvm" class="py-name" href="#" onclick="return doclink('link-6', '_jvm', 'link-6');">_jvm</a></tt><tt class="py-op">.</tt><tt class="py-name">PythonMLLibAPI</tt><tt class="py-op">(</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt class="py-name">trainLogisticRegressionModelWithSGD</tt><tt class="py-op">(</tt><tt class="py-name">d</tt><tt class="py-op">.</tt><tt class="py-name">_jrdd</tt><tt class="py-op">,</tt> </tt>
<a name="L52"></a><tt class="py-lineno"> 52</tt> <tt class="py-line"> <tt class="py-name">iterations</tt><tt class="py-op">,</tt> <tt class="py-name">step</tt><tt class="py-op">,</tt> <tt class="py-name">miniBatchFraction</tt><tt class="py-op">,</tt> <tt class="py-name">i</tt><tt class="py-op">)</tt><tt class="py-op">,</tt> </tt>
<a name="L53"></a><tt class="py-lineno"> 53</tt> <tt class="py-line"> <tt id="link-7" class="py-name" targets="Class pyspark.mllib.classification.LogisticRegressionModel=pyspark.mllib.classification.LogisticRegressionModel-class.html"><a title="pyspark.mllib.classification.LogisticRegressionModel" class="py-name" href="#" onclick="return doclink('link-7', 'LogisticRegressionModel', 'link-7');">LogisticRegressionModel</a></tt><tt class="py-op">,</tt> <tt class="py-name">data</tt><tt class="py-op">,</tt> <tt class="py-name">initialWeights</tt><tt class="py-op">)</tt> </tt>
</div></div><a name="L54"></a><tt class="py-lineno"> 54</tt> <tt class="py-line"> </tt>
<a name="SVMModel"></a><div id="SVMModel-def"><a name="L55"></a><tt class="py-lineno"> 55</tt> <a class="py-toggle" href="#" id="SVMModel-toggle" onclick="return toggle('SVMModel');">-</a><tt class="py-line"><tt class="py-keyword">class</tt> <a class="py-def-name" href="pyspark.mllib.classification.SVMModel-class.html">SVMModel</a><tt class="py-op">(</tt><tt class="py-base-class">LinearModel</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="SVMModel-collapsed" style="display:none;" pad="+++" indent="++++"></div><div id="SVMModel-expanded"><a name="L56"></a><tt class="py-lineno"> 56</tt> <tt class="py-line"> <tt class="py-docstring">"""A support vector machine.</tt> </tt>
<a name="L57"></a><tt class="py-lineno"> 57</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L58"></a><tt class="py-lineno"> 58</tt> <tt class="py-line"><tt class="py-docstring"> >>> data = array([0.0, 0.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0]).reshape(4,2)</tt> </tt>
<a name="L59"></a><tt class="py-lineno"> 59</tt> <tt class="py-line"><tt class="py-docstring"> >>> svm = SVMWithSGD.train(sc.parallelize(data))</tt> </tt>
<a name="L60"></a><tt class="py-lineno"> 60</tt> <tt class="py-line"><tt class="py-docstring"> >>> svm.predict(array([1.0])) > 0</tt> </tt>
<a name="L61"></a><tt class="py-lineno"> 61</tt> <tt class="py-line"><tt class="py-docstring"> True</tt> </tt>
<a name="L62"></a><tt class="py-lineno"> 62</tt> <tt class="py-line"><tt class="py-docstring"> """</tt> </tt>
<a name="SVMModel.predict"></a><div id="SVMModel.predict-def"><a name="L63"></a><tt class="py-lineno"> 63</tt> <a class="py-toggle" href="#" id="SVMModel.predict-toggle" onclick="return toggle('SVMModel.predict');">-</a><tt class="py-line"> <tt class="py-keyword">def</tt> <a class="py-def-name" href="pyspark.mllib.classification.SVMModel-class.html#predict">predict</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">x</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="SVMModel.predict-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="SVMModel.predict-expanded"><a name="L64"></a><tt class="py-lineno"> 64</tt> <tt class="py-line"> <tt class="py-name">_linear_predictor_typecheck</tt><tt class="py-op">(</tt><tt class="py-name">x</tt><tt class="py-op">,</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">_coeff</tt><tt class="py-op">)</tt> </tt>
<a name="L65"></a><tt class="py-lineno"> 65</tt> <tt class="py-line"> <tt class="py-name">margin</tt> <tt class="py-op">=</tt> <tt class="py-name">dot</tt><tt class="py-op">(</tt><tt class="py-name">x</tt><tt class="py-op">,</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">_coeff</tt><tt class="py-op">)</tt> <tt class="py-op">+</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">_intercept</tt> </tt>
<a name="L66"></a><tt class="py-lineno"> 66</tt> <tt class="py-line"> <tt class="py-keyword">return</tt> <tt class="py-number">1</tt> <tt class="py-keyword">if</tt> <tt class="py-name">margin</tt> <tt class="py-op">>=</tt> <tt class="py-number">0</tt> <tt class="py-keyword">else</tt> <tt class="py-number">0</tt> </tt>
</div></div><a name="L67"></a><tt class="py-lineno"> 67</tt> <tt class="py-line"> </tt>
<a name="SVMWithSGD"></a><div id="SVMWithSGD-def"><a name="L68"></a><tt class="py-lineno"> 68</tt> <a class="py-toggle" href="#" id="SVMWithSGD-toggle" onclick="return toggle('SVMWithSGD');">-</a><tt class="py-line"><tt class="py-keyword">class</tt> <a class="py-def-name" href="pyspark.mllib.classification.SVMWithSGD-class.html">SVMWithSGD</a><tt class="py-op">(</tt><tt class="py-base-class">object</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="SVMWithSGD-collapsed" style="display:none;" pad="+++" indent="++++"></div><div id="SVMWithSGD-expanded"><a name="L69"></a><tt class="py-lineno"> 69</tt> <tt class="py-line"> <tt class="py-decorator">@</tt><tt class="py-decorator">classmethod</tt> </tt>
<a name="SVMWithSGD.train"></a><div id="SVMWithSGD.train-def"><a name="L70"></a><tt class="py-lineno"> 70</tt> <a class="py-toggle" href="#" id="SVMWithSGD.train-toggle" onclick="return toggle('SVMWithSGD.train');">-</a><tt class="py-line"> <tt class="py-keyword">def</tt> <a class="py-def-name" href="pyspark.mllib.classification.SVMWithSGD-class.html#train">train</a><tt class="py-op">(</tt><tt class="py-param">cls</tt><tt class="py-op">,</tt> <tt class="py-param">data</tt><tt class="py-op">,</tt> <tt class="py-param">iterations</tt><tt class="py-op">=</tt><tt class="py-number">100</tt><tt class="py-op">,</tt> <tt class="py-param">step</tt><tt class="py-op">=</tt><tt class="py-number">1.0</tt><tt class="py-op">,</tt> <tt class="py-param">regParam</tt><tt class="py-op">=</tt><tt class="py-number">1.0</tt><tt class="py-op">,</tt> </tt>
<a name="L71"></a><tt class="py-lineno"> 71</tt> <tt class="py-line"> <tt class="py-param">miniBatchFraction</tt><tt class="py-op">=</tt><tt class="py-number">1.0</tt><tt class="py-op">,</tt> <tt class="py-param">initialWeights</tt><tt class="py-op">=</tt><tt class="py-name">None</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="SVMWithSGD.train-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="SVMWithSGD.train-expanded"><a name="L72"></a><tt class="py-lineno"> 72</tt> <tt class="py-line"> <tt class="py-docstring">"""Train a support vector machine on the given data."""</tt> </tt>
<a name="L73"></a><tt class="py-lineno"> 73</tt> <tt class="py-line"> <tt class="py-name">sc</tt> <tt class="py-op">=</tt> <tt class="py-name">data</tt><tt class="py-op">.</tt><tt id="link-8" class="py-name"><a title="pyspark.context
pyspark.rdd.RDD.context" class="py-name" href="#" onclick="return doclink('link-8', 'context', 'link-5');">context</a></tt> </tt>
<a name="L74"></a><tt class="py-lineno"> 74</tt> <tt class="py-line"> <tt class="py-keyword">return</tt> <tt class="py-name">_regression_train_wrapper</tt><tt class="py-op">(</tt><tt class="py-name">sc</tt><tt class="py-op">,</tt> <tt class="py-keyword">lambda</tt> <tt class="py-name">d</tt><tt class="py-op">,</tt> <tt class="py-name">i</tt><tt class="py-op">:</tt> </tt>
<a name="L75"></a><tt class="py-lineno"> 75</tt> <tt class="py-line"> <tt class="py-name">sc</tt><tt class="py-op">.</tt><tt id="link-9" class="py-name"><a title="pyspark.context.SparkContext._jvm" class="py-name" href="#" onclick="return doclink('link-9', '_jvm', 'link-6');">_jvm</a></tt><tt class="py-op">.</tt><tt class="py-name">PythonMLLibAPI</tt><tt class="py-op">(</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt class="py-name">trainSVMModelWithSGD</tt><tt class="py-op">(</tt><tt class="py-name">d</tt><tt class="py-op">.</tt><tt class="py-name">_jrdd</tt><tt class="py-op">,</tt> </tt>
<a name="L76"></a><tt class="py-lineno"> 76</tt> <tt class="py-line"> <tt class="py-name">iterations</tt><tt class="py-op">,</tt> <tt class="py-name">step</tt><tt class="py-op">,</tt> <tt class="py-name">regParam</tt><tt class="py-op">,</tt> <tt class="py-name">miniBatchFraction</tt><tt class="py-op">,</tt> <tt class="py-name">i</tt><tt class="py-op">)</tt><tt class="py-op">,</tt> </tt>
<a name="L77"></a><tt class="py-lineno"> 77</tt> <tt class="py-line"> <tt id="link-10" class="py-name" targets="Class pyspark.mllib.classification.SVMModel=pyspark.mllib.classification.SVMModel-class.html"><a title="pyspark.mllib.classification.SVMModel" class="py-name" href="#" onclick="return doclink('link-10', 'SVMModel', 'link-10');">SVMModel</a></tt><tt class="py-op">,</tt> <tt class="py-name">data</tt><tt class="py-op">,</tt> <tt class="py-name">initialWeights</tt><tt class="py-op">)</tt> </tt>
</div></div><a name="L78"></a><tt class="py-lineno"> 78</tt> <tt class="py-line"> </tt>
<a name="NaiveBayesModel"></a><div id="NaiveBayesModel-def"><a name="L79"></a><tt class="py-lineno"> 79</tt> <a class="py-toggle" href="#" id="NaiveBayesModel-toggle" onclick="return toggle('NaiveBayesModel');">-</a><tt class="py-line"><tt class="py-keyword">class</tt> <a class="py-def-name" href="pyspark.mllib.classification.NaiveBayesModel-class.html">NaiveBayesModel</a><tt class="py-op">(</tt><tt class="py-base-class">object</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="NaiveBayesModel-collapsed" style="display:none;" pad="+++" indent="++++"></div><div id="NaiveBayesModel-expanded"><a name="L80"></a><tt class="py-lineno"> 80</tt> <tt class="py-line"> <tt class="py-docstring">"""</tt> </tt>
<a name="L81"></a><tt class="py-lineno"> 81</tt> <tt class="py-line"><tt class="py-docstring"> Model for Naive Bayes classifiers.</tt> </tt>
<a name="L82"></a><tt class="py-lineno"> 82</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L83"></a><tt class="py-lineno"> 83</tt> <tt class="py-line"><tt class="py-docstring"> Contains two parameters:</tt> </tt>
<a name="L84"></a><tt class="py-lineno"> 84</tt> <tt class="py-line"><tt class="py-docstring"> - pi: vector of logs of class priors (dimension C)</tt> </tt>
<a name="L85"></a><tt class="py-lineno"> 85</tt> <tt class="py-line"><tt class="py-docstring"> - theta: matrix of logs of class conditional probabilities (CxD)</tt> </tt>
<a name="L86"></a><tt class="py-lineno"> 86</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L87"></a><tt class="py-lineno"> 87</tt> <tt class="py-line"><tt class="py-docstring"> >>> data = array([0.0, 0.0, 1.0, 0.0, 0.0, 2.0, 1.0, 1.0, 0.0]).reshape(3,3)</tt> </tt>
<a name="L88"></a><tt class="py-lineno"> 88</tt> <tt class="py-line"><tt class="py-docstring"> >>> model = NaiveBayes.train(sc.parallelize(data))</tt> </tt>
<a name="L89"></a><tt class="py-lineno"> 89</tt> <tt class="py-line"><tt class="py-docstring"> >>> model.predict(array([0.0, 1.0]))</tt> </tt>
<a name="L90"></a><tt class="py-lineno"> 90</tt> <tt class="py-line"><tt class="py-docstring"> 0</tt> </tt>
<a name="L91"></a><tt class="py-lineno"> 91</tt> <tt class="py-line"><tt class="py-docstring"> >>> model.predict(array([1.0, 0.0]))</tt> </tt>
<a name="L92"></a><tt class="py-lineno"> 92</tt> <tt class="py-line"><tt class="py-docstring"> 1</tt> </tt>
<a name="L93"></a><tt class="py-lineno"> 93</tt> <tt class="py-line"><tt class="py-docstring"> """</tt> </tt>
<a name="L94"></a><tt class="py-lineno"> 94</tt> <tt class="py-line"> </tt>
<a name="NaiveBayesModel.__init__"></a><div id="NaiveBayesModel.__init__-def"><a name="L95"></a><tt class="py-lineno"> 95</tt> <a class="py-toggle" href="#" id="NaiveBayesModel.__init__-toggle" onclick="return toggle('NaiveBayesModel.__init__');">-</a><tt class="py-line"> <tt class="py-keyword">def</tt> <a class="py-def-name" href="pyspark.mllib.classification.NaiveBayesModel-class.html#__init__">__init__</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">pi</tt><tt class="py-op">,</tt> <tt class="py-param">theta</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="NaiveBayesModel.__init__-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="NaiveBayesModel.__init__-expanded"><a name="L96"></a><tt class="py-lineno"> 96</tt> <tt class="py-line"> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">pi</tt> <tt class="py-op">=</tt> <tt class="py-name">pi</tt> </tt>
<a name="L97"></a><tt class="py-lineno"> 97</tt> <tt class="py-line"> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">theta</tt> <tt class="py-op">=</tt> <tt class="py-name">theta</tt> </tt>
</div><a name="L98"></a><tt class="py-lineno"> 98</tt> <tt class="py-line"> </tt>
<a name="NaiveBayesModel.predict"></a><div id="NaiveBayesModel.predict-def"><a name="L99"></a><tt class="py-lineno"> 99</tt> <a class="py-toggle" href="#" id="NaiveBayesModel.predict-toggle" onclick="return toggle('NaiveBayesModel.predict');">-</a><tt class="py-line"> <tt class="py-keyword">def</tt> <a class="py-def-name" href="pyspark.mllib.classification.NaiveBayesModel-class.html#predict">predict</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">x</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="NaiveBayesModel.predict-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="NaiveBayesModel.predict-expanded"><a name="L100"></a><tt class="py-lineno">100</tt> <tt class="py-line"> <tt class="py-docstring">"""Return the most likely class for a data vector x"""</tt> </tt>
<a name="L101"></a><tt class="py-lineno">101</tt> <tt class="py-line"> <tt class="py-keyword">return</tt> <tt class="py-name">numpy</tt><tt class="py-op">.</tt><tt class="py-name">argmax</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">pi</tt> <tt class="py-op">+</tt> <tt class="py-name">dot</tt><tt class="py-op">(</tt><tt class="py-name">x</tt><tt class="py-op">,</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">theta</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
</div></div><a name="L102"></a><tt class="py-lineno">102</tt> <tt class="py-line"> </tt>
<a name="NaiveBayes"></a><div id="NaiveBayes-def"><a name="L103"></a><tt class="py-lineno">103</tt> <a class="py-toggle" href="#" id="NaiveBayes-toggle" onclick="return toggle('NaiveBayes');">-</a><tt class="py-line"><tt class="py-keyword">class</tt> <a class="py-def-name" href="pyspark.mllib.classification.NaiveBayes-class.html">NaiveBayes</a><tt class="py-op">(</tt><tt class="py-base-class">object</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="NaiveBayes-collapsed" style="display:none;" pad="+++" indent="++++"></div><div id="NaiveBayes-expanded"><a name="L104"></a><tt class="py-lineno">104</tt> <tt class="py-line"> <tt class="py-decorator">@</tt><tt class="py-decorator">classmethod</tt> </tt>
<a name="NaiveBayes.train"></a><div id="NaiveBayes.train-def"><a name="L105"></a><tt class="py-lineno">105</tt> <a class="py-toggle" href="#" id="NaiveBayes.train-toggle" onclick="return toggle('NaiveBayes.train');">-</a><tt class="py-line"> <tt class="py-keyword">def</tt> <a class="py-def-name" href="pyspark.mllib.classification.NaiveBayes-class.html#train">train</a><tt class="py-op">(</tt><tt class="py-param">cls</tt><tt class="py-op">,</tt> <tt class="py-param">data</tt><tt class="py-op">,</tt> <tt class="py-param">lambda_</tt><tt class="py-op">=</tt><tt class="py-number">1.0</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="NaiveBayes.train-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="NaiveBayes.train-expanded"><a name="L106"></a><tt class="py-lineno">106</tt> <tt class="py-line"> <tt class="py-docstring">"""</tt> </tt>
<a name="L107"></a><tt class="py-lineno">107</tt> <tt class="py-line"><tt class="py-docstring"> Train a Naive Bayes model given an RDD of (label, features) vectors.</tt> </tt>
<a name="L108"></a><tt class="py-lineno">108</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L109"></a><tt class="py-lineno">109</tt> <tt class="py-line"><tt class="py-docstring"> This is the Multinomial NB (U{http://tinyurl.com/lsdw6p}) which can</tt> </tt>
<a name="L110"></a><tt class="py-lineno">110</tt> <tt class="py-line"><tt class="py-docstring"> handle all kinds of discrete data. For example, by converting</tt> </tt>
<a name="L111"></a><tt class="py-lineno">111</tt> <tt class="py-line"><tt class="py-docstring"> documents into TF-IDF vectors, it can be used for document</tt> </tt>
<a name="L112"></a><tt class="py-lineno">112</tt> <tt class="py-line"><tt class="py-docstring"> classification. By making every vector a 0-1 vector, it can also be</tt> </tt>
<a name="L113"></a><tt class="py-lineno">113</tt> <tt class="py-line"><tt class="py-docstring"> used as Bernoulli NB (U{http://tinyurl.com/p7c96j6}).</tt> </tt>
<a name="L114"></a><tt class="py-lineno">114</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L115"></a><tt class="py-lineno">115</tt> <tt class="py-line"><tt class="py-docstring"> @param data: RDD of NumPy vectors, one per element, where the first</tt> </tt>
<a name="L116"></a><tt class="py-lineno">116</tt> <tt class="py-line"><tt class="py-docstring"> coordinate is the label and the rest is the feature vector</tt> </tt>
<a name="L117"></a><tt class="py-lineno">117</tt> <tt class="py-line"><tt class="py-docstring"> (e.g. a count vector).</tt> </tt>
<a name="L118"></a><tt class="py-lineno">118</tt> <tt class="py-line"><tt class="py-docstring"> @param lambda_: The smoothing parameter</tt> </tt>
<a name="L119"></a><tt class="py-lineno">119</tt> <tt class="py-line"><tt class="py-docstring"> """</tt> </tt>
<a name="L120"></a><tt class="py-lineno">120</tt> <tt class="py-line"> <tt class="py-name">sc</tt> <tt class="py-op">=</tt> <tt class="py-name">data</tt><tt class="py-op">.</tt><tt id="link-11" class="py-name"><a title="pyspark.context
pyspark.rdd.RDD.context" class="py-name" href="#" onclick="return doclink('link-11', 'context', 'link-5');">context</a></tt> </tt>
<a name="L121"></a><tt class="py-lineno">121</tt> <tt class="py-line"> <tt class="py-name">dataBytes</tt> <tt class="py-op">=</tt> <tt class="py-name">_get_unmangled_double_vector_rdd</tt><tt class="py-op">(</tt><tt class="py-name">data</tt><tt class="py-op">)</tt> </tt>
<a name="L122"></a><tt class="py-lineno">122</tt> <tt class="py-line"> <tt class="py-name">ans</tt> <tt class="py-op">=</tt> <tt class="py-name">sc</tt><tt class="py-op">.</tt><tt id="link-12" class="py-name"><a title="pyspark.context.SparkContext._jvm" class="py-name" href="#" onclick="return doclink('link-12', '_jvm', 'link-6');">_jvm</a></tt><tt class="py-op">.</tt><tt class="py-name">PythonMLLibAPI</tt><tt class="py-op">(</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt class="py-name">trainNaiveBayes</tt><tt class="py-op">(</tt><tt class="py-name">dataBytes</tt><tt class="py-op">.</tt><tt class="py-name">_jrdd</tt><tt class="py-op">,</tt> <tt class="py-name">lambda_</tt><tt class="py-op">)</tt> </tt>
<a name="L123"></a><tt class="py-lineno">123</tt> <tt class="py-line"> <tt class="py-keyword">return</tt> <tt id="link-13" class="py-name" targets="Class pyspark.mllib.classification.NaiveBayesModel=pyspark.mllib.classification.NaiveBayesModel-class.html"><a title="pyspark.mllib.classification.NaiveBayesModel" class="py-name" href="#" onclick="return doclink('link-13', 'NaiveBayesModel', 'link-13');">NaiveBayesModel</a></tt><tt class="py-op">(</tt> </tt>
<a name="L124"></a><tt class="py-lineno">124</tt> <tt class="py-line"> <tt class="py-name">_deserialize_double_vector</tt><tt class="py-op">(</tt><tt class="py-name">ans</tt><tt class="py-op">[</tt><tt class="py-number">0</tt><tt class="py-op">]</tt><tt class="py-op">)</tt><tt class="py-op">,</tt> </tt>
<a name="L125"></a><tt class="py-lineno">125</tt> <tt class="py-line"> <tt class="py-name">_deserialize_double_matrix</tt><tt class="py-op">(</tt><tt class="py-name">ans</tt><tt class="py-op">[</tt><tt class="py-number">1</tt><tt class="py-op">]</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
</div></div><a name="L126"></a><tt class="py-lineno">126</tt> <tt class="py-line"> </tt>
<a name="_test"></a><div id="_test-def"><a name="L127"></a><tt class="py-lineno">127</tt> <tt class="py-line"> </tt>
<a name="L128"></a><tt class="py-lineno">128</tt> <a class="py-toggle" href="#" id="_test-toggle" onclick="return toggle('_test');">-</a><tt class="py-line"><tt class="py-keyword">def</tt> <a class="py-def-name" href="pyspark.mllib.classification-module.html#_test">_test</a><tt class="py-op">(</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="_test-collapsed" style="display:none;" pad="+++" indent="++++"></div><div id="_test-expanded"><a name="L129"></a><tt class="py-lineno">129</tt> <tt class="py-line"> <tt class="py-keyword">import</tt> <tt class="py-name">doctest</tt> </tt>
<a name="L130"></a><tt class="py-lineno">130</tt> <tt class="py-line"> <tt class="py-name">globs</tt> <tt class="py-op">=</tt> <tt class="py-name">globals</tt><tt class="py-op">(</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt id="link-14" class="py-name" targets="Method pyspark.statcounter.StatCounter.copy()=pyspark.statcounter.StatCounter-class.html#copy"><a title="pyspark.statcounter.StatCounter.copy" class="py-name" href="#" onclick="return doclink('link-14', 'copy', 'link-14');">copy</a></tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L131"></a><tt class="py-lineno">131</tt> <tt class="py-line"> <tt class="py-name">globs</tt><tt class="py-op">[</tt><tt class="py-string">'sc'</tt><tt class="py-op">]</tt> <tt class="py-op">=</tt> <tt id="link-15" class="py-name"><a title="pyspark.context.SparkContext" class="py-name" href="#" onclick="return doclink('link-15', 'SparkContext', 'link-1');">SparkContext</a></tt><tt class="py-op">(</tt><tt class="py-string">'local[4]'</tt><tt class="py-op">,</tt> <tt class="py-string">'PythonTest'</tt><tt class="py-op">,</tt> <tt class="py-name">batchSize</tt><tt class="py-op">=</tt><tt class="py-number">2</tt><tt class="py-op">)</tt> </tt>
<a name="L132"></a><tt class="py-lineno">132</tt> <tt class="py-line"> <tt class="py-op">(</tt><tt class="py-name">failure_count</tt><tt class="py-op">,</tt> <tt class="py-name">test_count</tt><tt class="py-op">)</tt> <tt class="py-op">=</tt> <tt class="py-name">doctest</tt><tt class="py-op">.</tt><tt class="py-name">testmod</tt><tt class="py-op">(</tt><tt class="py-name">globs</tt><tt class="py-op">=</tt><tt class="py-name">globs</tt><tt class="py-op">,</tt> </tt>
<a name="L133"></a><tt class="py-lineno">133</tt> <tt class="py-line"> <tt class="py-name">optionflags</tt><tt class="py-op">=</tt><tt class="py-name">doctest</tt><tt class="py-op">.</tt><tt class="py-name">ELLIPSIS</tt><tt class="py-op">)</tt> </tt>
<a name="L134"></a><tt class="py-lineno">134</tt> <tt class="py-line"> <tt class="py-name">globs</tt><tt class="py-op">[</tt><tt class="py-string">'sc'</tt><tt class="py-op">]</tt><tt class="py-op">.</tt><tt id="link-16" class="py-name" targets="Method pyspark.context.SparkContext.stop()=pyspark.context.SparkContext-class.html#stop"><a title="pyspark.context.SparkContext.stop" class="py-name" href="#" onclick="return doclink('link-16', 'stop', 'link-16');">stop</a></tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L135"></a><tt class="py-lineno">135</tt> <tt class="py-line"> <tt class="py-keyword">if</tt> <tt class="py-name">failure_count</tt><tt class="py-op">:</tt> </tt>
<a name="L136"></a><tt class="py-lineno">136</tt> <tt class="py-line"> <tt class="py-name">exit</tt><tt class="py-op">(</tt><tt class="py-op">-</tt><tt class="py-number">1</tt><tt class="py-op">)</tt> </tt>
</div><a name="L137"></a><tt class="py-lineno">137</tt> <tt class="py-line"> </tt>
<a name="L138"></a><tt class="py-lineno">138</tt> <tt class="py-line"><tt class="py-keyword">if</tt> <tt class="py-name">__name__</tt> <tt class="py-op">==</tt> <tt class="py-string">"__main__"</tt><tt class="py-op">:</tt> </tt>
<a name="L139"></a><tt class="py-lineno">139</tt> <tt class="py-line"> <tt class="py-name">_test</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L140"></a><tt class="py-lineno">140</tt> <tt class="py-line"> </tt><script type="text/javascript">
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