summaryrefslogblamecommitdiff
path: root/site/docs/1.5.0/api/python/_modules/pyspark/ml/clustering.html
blob: 799549425f0ba166b5697c7982af4986d4ed96bd (plain) (tree) 
443d7fc27 ^








c7104724b ^

443d7fc27 ^







c7104724b ^

443d7fc27 ^








c7104724b ^

443d7fc27 ^






c7104724b ^

443d7fc27 ^



















































































































































































































c7104724b ^

443d7fc27 ^









1
2
3
4
5
6
7
8

9

10
11
12
13
14
15
16

17

18
19
20
21
22
23
24
25

26

27
28
29
30
31
32

33

34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244

245

246
247
248
249
250
251
252
253
254








                                                                         
                                                                            






                                                                                  
                             







                                                                                 
                                                                                     





                                                                           
                                                                                                                  


















































































































































































































                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             
                                                                                                                  








                                                                                                   
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
  "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">


<html xmlns="http://www.w3.org/1999/xhtml">
  <head>
    <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
    
    <title>pyspark.ml.clustering &mdash; PySpark 1.5.0 documentation</title>
    
    <link rel="stylesheet" href="../../../_static/nature.css" type="text/css" />
    <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
    
    <script type="text/javascript">
      var DOCUMENTATION_OPTIONS = {
        URL_ROOT:    '../../../',
        VERSION:     '1.5.0',
        COLLAPSE_INDEX: false,
        FILE_SUFFIX: '.html',
        HAS_SOURCE:  true
      };
    </script>
    <script type="text/javascript" src="../../../_static/jquery.js"></script>
    <script type="text/javascript" src="../../../_static/underscore.js"></script>
    <script type="text/javascript" src="../../../_static/doctools.js"></script>
    <link rel="top" title="PySpark 1.5.0 documentation" href="../../../index.html" />
    <link rel="up" title="Module code" href="../../index.html" /> 
  </head>
  <body role="document">
    <div class="related" role="navigation" aria-label="related navigation">
      <h3>Navigation</h3>
      <ul>
        <li class="nav-item nav-item-0"><a href="../../../index.html">PySpark 1.5.0 documentation</a> &raquo;</li>
          <li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &raquo;</li> 
      </ul>
    </div>  

    <div class="document">
      <div class="documentwrapper">
        <div class="bodywrapper">
          <div class="body" role="main">
            
  <h1>Source code for pyspark.ml.clustering</h1><div class="highlight"><pre>
<span class="c">#</span>
<span class="c"># Licensed to the Apache Software Foundation (ASF) under one or more</span>
<span class="c"># contributor license agreements.  See the NOTICE file distributed with</span>
<span class="c"># this work for additional information regarding copyright ownership.</span>
<span class="c"># The ASF licenses this file to You under the Apache License, Version 2.0</span>
<span class="c"># (the &quot;License&quot;); you may not use this file except in compliance with</span>
<span class="c"># the License.  You may obtain a copy of the License at</span>
<span class="c">#</span>
<span class="c">#    http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c">#</span>
<span class="c"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c"># See the License for the specific language governing permissions and</span>
<span class="c"># limitations under the License.</span>
<span class="c">#</span>

<span class="kn">from</span> <span class="nn">pyspark.ml.util</span> <span class="kn">import</span> <span class="n">keyword_only</span>
<span class="kn">from</span> <span class="nn">pyspark.ml.wrapper</span> <span class="kn">import</span> <span class="n">JavaEstimator</span><span class="p">,</span> <span class="n">JavaModel</span>
<span class="kn">from</span> <span class="nn">pyspark.ml.param.shared</span> <span class="kn">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">pyspark.mllib.common</span> <span class="kn">import</span> <span class="n">inherit_doc</span>

<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s">&#39;KMeans&#39;</span><span class="p">,</span> <span class="s">&#39;KMeansModel&#39;</span><span class="p">]</span>


<div class="viewcode-block" id="KMeansModel"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.clustering.KMeansModel">[docs]</a><span class="k">class</span> <span class="nc">KMeansModel</span><span class="p">(</span><span class="n">JavaModel</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Model fitted by KMeans.</span>
<span class="sd">    &quot;&quot;&quot;</span>

<div class="viewcode-block" id="KMeansModel.clusterCenters"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.clustering.KMeansModel.clusterCenters">[docs]</a>    <span class="k">def</span> <span class="nf">clusterCenters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Get the cluster centers, represented as a list of NumPy arrays.&quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="p">[</span><span class="n">c</span><span class="o">.</span><span class="n">toArray</span><span class="p">()</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_java</span><span class="p">(</span><span class="s">&quot;clusterCenters&quot;</span><span class="p">)]</span>

</div></div>
<span class="nd">@inherit_doc</span>
<div class="viewcode-block" id="KMeans"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.clustering.KMeans">[docs]</a><span class="k">class</span> <span class="nc">KMeans</span><span class="p">(</span><span class="n">JavaEstimator</span><span class="p">,</span> <span class="n">HasFeaturesCol</span><span class="p">,</span> <span class="n">HasPredictionCol</span><span class="p">,</span> <span class="n">HasMaxIter</span><span class="p">,</span> <span class="n">HasTol</span><span class="p">,</span> <span class="n">HasSeed</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    K-means clustering with support for multiple parallel runs and a k-means++ like initialization</span>
<span class="sd">    mode (the k-means|| algorithm by Bahmani et al). When multiple concurrent runs are requested,</span>
<span class="sd">    they are executed together with joint passes over the data for efficiency.</span>

<span class="sd">    &gt;&gt;&gt; from pyspark.mllib.linalg import Vectors</span>
<span class="sd">    &gt;&gt;&gt; data = [(Vectors.dense([0.0, 0.0]),), (Vectors.dense([1.0, 1.0]),),</span>
<span class="sd">    ...         (Vectors.dense([9.0, 8.0]),), (Vectors.dense([8.0, 9.0]),)]</span>
<span class="sd">    &gt;&gt;&gt; df = sqlContext.createDataFrame(data, [&quot;features&quot;])</span>
<span class="sd">    &gt;&gt;&gt; kmeans = KMeans(k=2, seed=1)</span>
<span class="sd">    &gt;&gt;&gt; model = kmeans.fit(df)</span>
<span class="sd">    &gt;&gt;&gt; centers = model.clusterCenters()</span>
<span class="sd">    &gt;&gt;&gt; len(centers)</span>
<span class="sd">    2</span>
<span class="sd">    &gt;&gt;&gt; transformed = model.transform(df).select(&quot;features&quot;, &quot;prediction&quot;)</span>
<span class="sd">    &gt;&gt;&gt; rows = transformed.collect()</span>
<span class="sd">    &gt;&gt;&gt; rows[0].prediction == rows[1].prediction</span>
<span class="sd">    True</span>
<span class="sd">    &gt;&gt;&gt; rows[2].prediction == rows[3].prediction</span>
<span class="sd">    True</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="c"># a placeholder to make it appear in the generated doc</span>
    <span class="n">k</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span> <span class="s">&quot;k&quot;</span><span class="p">,</span> <span class="s">&quot;number of clusters to create&quot;</span><span class="p">)</span>
    <span class="n">initMode</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span> <span class="s">&quot;initMode&quot;</span><span class="p">,</span>
                     <span class="s">&quot;the initialization algorithm. This can be either </span><span class="se">\&quot;</span><span class="s">random</span><span class="se">\&quot;</span><span class="s"> to &quot;</span> <span class="o">+</span>
                     <span class="s">&quot;choose random points as initial cluster centers, or </span><span class="se">\&quot;</span><span class="s">k-means||</span><span class="se">\&quot;</span><span class="s"> &quot;</span> <span class="o">+</span>
                     <span class="s">&quot;to use a parallel variant of k-means++&quot;</span><span class="p">)</span>
    <span class="n">initSteps</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span> <span class="s">&quot;initSteps&quot;</span><span class="p">,</span> <span class="s">&quot;steps for k-means initialization mode&quot;</span><span class="p">)</span>

    <span class="nd">@keyword_only</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">featuresCol</span><span class="o">=</span><span class="s">&quot;features&quot;</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s">&quot;prediction&quot;</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                 <span class="n">initMode</span><span class="o">=</span><span class="s">&quot;k-means||&quot;</span><span class="p">,</span> <span class="n">initSteps</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span> <span class="n">maxIter</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        __init__(self, featuresCol=&quot;features&quot;, predictionCol=&quot;prediction&quot;, k=2, \</span>
<span class="sd">                 initMode=&quot;k-means||&quot;, initSteps=5, tol=1e-4, maxIter=20, seed=None)</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">KMeans</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_java_obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_new_java_obj</span><span class="p">(</span><span class="s">&quot;org.apache.spark.ml.clustering.KMeans&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">uid</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">k</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">&quot;k&quot;</span><span class="p">,</span> <span class="s">&quot;number of clusters to create&quot;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">initMode</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">&quot;initMode&quot;</span><span class="p">,</span>
                              <span class="s">&quot;the initialization algorithm. This can be either </span><span class="se">\&quot;</span><span class="s">random</span><span class="se">\&quot;</span><span class="s"> to &quot;</span> <span class="o">+</span>
                              <span class="s">&quot;choose random points as initial cluster centers, or </span><span class="se">\&quot;</span><span class="s">k-means||</span><span class="se">\&quot;</span><span class="s"> &quot;</span> <span class="o">+</span>
                              <span class="s">&quot;to use a parallel variant of k-means++&quot;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">initSteps</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">&quot;initSteps&quot;</span><span class="p">,</span> <span class="s">&quot;steps for k-means initialization mode&quot;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_setDefault</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">initMode</span><span class="o">=</span><span class="s">&quot;k-means||&quot;</span><span class="p">,</span> <span class="n">initSteps</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span> <span class="n">maxIter</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
        <span class="n">kwargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__init__</span><span class="o">.</span><span class="n">_input_kwargs</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">setParams</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">_create_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">java_model</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">KMeansModel</span><span class="p">(</span><span class="n">java_model</span><span class="p">)</span>

    <span class="nd">@keyword_only</span>
<div class="viewcode-block" id="KMeans.setParams"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.clustering.KMeans.setParams">[docs]</a>    <span class="k">def</span> <span class="nf">setParams</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">featuresCol</span><span class="o">=</span><span class="s">&quot;features&quot;</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s">&quot;prediction&quot;</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                  <span class="n">initMode</span><span class="o">=</span><span class="s">&quot;k-means||&quot;</span><span class="p">,</span> <span class="n">initSteps</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span> <span class="n">maxIter</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        setParams(self, featuresCol=&quot;features&quot;, predictionCol=&quot;prediction&quot;, k=2, \</span>
<span class="sd">                  initMode=&quot;k-means||&quot;, initSteps=5, tol=1e-4, maxIter=20, seed=None)</span>

<span class="sd">        Sets params for KMeans.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">kwargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">setParams</span><span class="o">.</span><span class="n">_input_kwargs</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="KMeans.setK"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.clustering.KMeans.setK">[docs]</a>    <span class="k">def</span> <span class="nf">setK</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Sets the value of :py:attr:`k`.</span>

<span class="sd">        &gt;&gt;&gt; algo = KMeans().setK(10)</span>
<span class="sd">        &gt;&gt;&gt; algo.getK()</span>
<span class="sd">        10</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
        <span class="k">return</span> <span class="bp">self</span>
</div>
<div class="viewcode-block" id="KMeans.getK"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.clustering.KMeans.getK">[docs]</a>    <span class="k">def</span> <span class="nf">getK</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Gets the value of `k`</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="KMeans.setInitMode"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.clustering.KMeans.setInitMode">[docs]</a>    <span class="k">def</span> <span class="nf">setInitMode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Sets the value of :py:attr:`initMode`.</span>

<span class="sd">        &gt;&gt;&gt; algo = KMeans()</span>
<span class="sd">        &gt;&gt;&gt; algo.getInitMode()</span>
<span class="sd">        &#39;k-means||&#39;</span>
<span class="sd">        &gt;&gt;&gt; algo = algo.setInitMode(&quot;random&quot;)</span>
<span class="sd">        &gt;&gt;&gt; algo.getInitMode()</span>
<span class="sd">        &#39;random&#39;</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">initMode</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
        <span class="k">return</span> <span class="bp">self</span>
</div>
<div class="viewcode-block" id="KMeans.getInitMode"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.clustering.KMeans.getInitMode">[docs]</a>    <span class="k">def</span> <span class="nf">getInitMode</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Gets the value of `initMode`</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">initMode</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="KMeans.setInitSteps"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.clustering.KMeans.setInitSteps">[docs]</a>    <span class="k">def</span> <span class="nf">setInitSteps</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Sets the value of :py:attr:`initSteps`.</span>

<span class="sd">        &gt;&gt;&gt; algo = KMeans().setInitSteps(10)</span>
<span class="sd">        &gt;&gt;&gt; algo.getInitSteps()</span>
<span class="sd">        10</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">initSteps</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
        <span class="k">return</span> <span class="bp">self</span>
</div>
<div class="viewcode-block" id="KMeans.getInitSteps"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.clustering.KMeans.getInitSteps">[docs]</a>    <span class="k">def</span> <span class="nf">getInitSteps</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Gets the value of `initSteps`</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">initSteps</span><span class="p">)</span>

</div></div>
<span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s">&quot;__main__&quot;</span><span class="p">:</span>
    <span class="kn">import</span> <span class="nn">doctest</span>
    <span class="kn">from</span> <span class="nn">pyspark.context</span> <span class="kn">import</span> <span class="n">SparkContext</span>
    <span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SQLContext</span>
    <span class="n">globs</span> <span class="o">=</span> <span class="nb">globals</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
    <span class="c"># The small batch size here ensures that we see multiple batches,</span>
    <span class="c"># even in these small test examples:</span>
    <span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="p">(</span><span class="s">&quot;local[2]&quot;</span><span class="p">,</span> <span class="s">&quot;ml.clustering tests&quot;</span><span class="p">)</span>
    <span class="n">sqlContext</span> <span class="o">=</span> <span class="n">SQLContext</span><span class="p">(</span><span class="n">sc</span><span class="p">)</span>
    <span class="n">globs</span><span class="p">[</span><span class="s">&#39;sc&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sc</span>
    <span class="n">globs</span><span class="p">[</span><span class="s">&#39;sqlContext&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sqlContext</span>
    <span class="p">(</span><span class="n">failure_count</span><span class="p">,</span> <span class="n">test_count</span><span class="p">)</span> <span class="o">=</span> <span class="n">doctest</span><span class="o">.</span><span class="n">testmod</span><span class="p">(</span><span class="n">globs</span><span class="o">=</span><span class="n">globs</span><span class="p">,</span> <span class="n">optionflags</span><span class="o">=</span><span class="n">doctest</span><span class="o">.</span><span class="n">ELLIPSIS</span><span class="p">)</span>
    <span class="n">sc</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
    <span class="k">if</span> <span class="n">failure_count</span><span class="p">:</span>
        <span class="nb">exit</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
</pre></div>

          </div>
        </div>
      </div>
      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
        <div class="sphinxsidebarwrapper">
            <p class="logo"><a href="../../../index.html">
              <img class="logo" src="../../../_static/spark-logo-hd.png" alt="Logo"/>
            </a></p>
<div id="searchbox" style="display: none" role="search">
  <h3>Quick search</h3>
    <form class="search" action="../../../search.html" method="get">
      <input type="text" name="q" />
      <input type="submit" value="Go" />
      <input type="hidden" name="check_keywords" value="yes" />
      <input type="hidden" name="area" value="default" />
    </form>
    <p class="searchtip" style="font-size: 90%">
    Enter search terms or a module, class or function name.
    </p>
</div>
<script type="text/javascript">$('#searchbox').show(0);</script>
        </div>
      </div>
      <div class="clearer"></div>
    </div>
    <div class="related" role="navigation" aria-label="related navigation">
      <h3>Navigation</h3>
      <ul>
        <li class="nav-item nav-item-0"><a href="../../../index.html">PySpark 1.5.0 documentation</a> &raquo;</li>
          <li class="nav-item nav-item-1"><a href="../../index.html" >Module code</a> &raquo;</li> 
      </ul>
    </div>
    <div class="footer" role="contentinfo">
        &copy; Copyright .
      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 1.3.1.
    </div>
  </body>
</html>
generated by cgit v1.2.3 (git 2.39.1) at 2024-09-11 10:27:18 +0000