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
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
|
<!DOCTYPE html>
<!--[if lt IE 7]> <html class="no-js lt-ie9 lt-ie8 lt-ie7"> <![endif]-->
<!--[if IE 7]> <html class="no-js lt-ie9 lt-ie8"> <![endif]-->
<!--[if IE 8]> <html class="no-js lt-ie9"> <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js"> <!--<![endif]-->
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1">
<title>Old Migration Guides - MLlib - Spark 1.5.1 Documentation</title>
<meta name="description" content="MLlib migration guides from before Spark 1.5.1">
<link rel="stylesheet" href="css/bootstrap.min.css">
<style>
body {
padding-top: 60px;
padding-bottom: 40px;
}
</style>
<meta name="viewport" content="width=device-width">
<link rel="stylesheet" href="css/bootstrap-responsive.min.css">
<link rel="stylesheet" href="css/main.css">
<script src="js/vendor/modernizr-2.6.1-respond-1.1.0.min.js"></script>
<link rel="stylesheet" href="css/pygments-default.css">
<!-- Google analytics script -->
<script type="text/javascript">
var _gaq = _gaq || [];
_gaq.push(['_setAccount', 'UA-32518208-2']);
_gaq.push(['_trackPageview']);
(function() {
var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
})();
</script>
</head>
<body>
<!--[if lt IE 7]>
<p class="chromeframe">You are using an outdated browser. <a href="http://browsehappy.com/">Upgrade your browser today</a> or <a href="http://www.google.com/chromeframe/?redirect=true">install Google Chrome Frame</a> to better experience this site.</p>
<![endif]-->
<!-- This code is taken from http://twitter.github.com/bootstrap/examples/hero.html -->
<div class="navbar navbar-fixed-top" id="topbar">
<div class="navbar-inner">
<div class="container">
<div class="brand"><a href="index.html">
<img src="img/spark-logo-hd.png" style="height:50px;"/></a><span class="version">1.5.1</span>
</div>
<ul class="nav">
<!--TODO(andyk): Add class="active" attribute to li some how.-->
<li><a href="index.html">Overview</a></li>
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown">Programming Guides<b class="caret"></b></a>
<ul class="dropdown-menu">
<li><a href="quick-start.html">Quick Start</a></li>
<li><a href="programming-guide.html">Spark Programming Guide</a></li>
<li class="divider"></li>
<li><a href="streaming-programming-guide.html">Spark Streaming</a></li>
<li><a href="sql-programming-guide.html">DataFrames and SQL</a></li>
<li><a href="mllib-guide.html">MLlib (Machine Learning)</a></li>
<li><a href="graphx-programming-guide.html">GraphX (Graph Processing)</a></li>
<li><a href="bagel-programming-guide.html">Bagel (Pregel on Spark)</a></li>
<li><a href="sparkr.html">SparkR (R on Spark)</a></li>
</ul>
</li>
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown">API Docs<b class="caret"></b></a>
<ul class="dropdown-menu">
<li><a href="api/scala/index.html#org.apache.spark.package">Scala</a></li>
<li><a href="api/java/index.html">Java</a></li>
<li><a href="api/python/index.html">Python</a></li>
<li><a href="api/R/index.html">R</a></li>
</ul>
</li>
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown">Deploying<b class="caret"></b></a>
<ul class="dropdown-menu">
<li><a href="cluster-overview.html">Overview</a></li>
<li><a href="submitting-applications.html">Submitting Applications</a></li>
<li class="divider"></li>
<li><a href="spark-standalone.html">Spark Standalone</a></li>
<li><a href="running-on-mesos.html">Mesos</a></li>
<li><a href="running-on-yarn.html">YARN</a></li>
<li class="divider"></li>
<li><a href="ec2-scripts.html">Amazon EC2</a></li>
</ul>
</li>
<li class="dropdown">
<a href="api.html" class="dropdown-toggle" data-toggle="dropdown">More<b class="caret"></b></a>
<ul class="dropdown-menu">
<li><a href="configuration.html">Configuration</a></li>
<li><a href="monitoring.html">Monitoring</a></li>
<li><a href="tuning.html">Tuning Guide</a></li>
<li><a href="job-scheduling.html">Job Scheduling</a></li>
<li><a href="security.html">Security</a></li>
<li><a href="hardware-provisioning.html">Hardware Provisioning</a></li>
<li><a href="hadoop-third-party-distributions.html">3<sup>rd</sup>-Party Hadoop Distros</a></li>
<li class="divider"></li>
<li><a href="building-spark.html">Building Spark</a></li>
<li><a href="https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark">Contributing to Spark</a></li>
<li><a href="https://cwiki.apache.org/confluence/display/SPARK/Supplemental+Spark+Projects">Supplemental Projects</a></li>
</ul>
</li>
</ul>
<!--<p class="navbar-text pull-right"><span class="version-text">v1.5.1</span></p>-->
</div>
</div>
</div>
<div class="container" id="content">
<h1 class="title"><a href="mllib-guide.html">MLlib</a> - Old Migration Guides</h1>
<p>The migration guide for the current Spark version is kept on the <a href="mllib-guide.html#migration-guide">MLlib Programming Guide main page</a>.</p>
<h2 id="from-13-to-14">From 1.3 to 1.4</h2>
<p>In the <code>spark.mllib</code> package, there were several breaking changes, but all in <code>DeveloperApi</code> or <code>Experimental</code> APIs:</p>
<ul>
<li>Gradient-Boosted Trees
<ul>
<li><em>(Breaking change)</em> The signature of the <a href="api/scala/index.html#org.apache.spark.mllib.tree.loss.Loss"><code>Loss.gradient</code></a> method was changed. This is only an issues for users who wrote their own losses for GBTs.</li>
<li><em>(Breaking change)</em> The <code>apply</code> and <code>copy</code> methods for the case class <a href="api/scala/index.html#org.apache.spark.mllib.tree.configuration.BoostingStrategy"><code>BoostingStrategy</code></a> have been changed because of a modification to the case class fields. This could be an issue for users who use <code>BoostingStrategy</code> to set GBT parameters.</li>
</ul>
</li>
<li><em>(Breaking change)</em> The return value of <a href="api/scala/index.html#org.apache.spark.mllib.clustering.LDA"><code>LDA.run</code></a> has changed. It now returns an abstract class <code>LDAModel</code> instead of the concrete class <code>DistributedLDAModel</code>. The object of type <code>LDAModel</code> can still be cast to the appropriate concrete type, which depends on the optimization algorithm.</li>
</ul>
<p>In the <code>spark.ml</code> package, several major API changes occurred, including:</p>
<ul>
<li><code>Param</code> and other APIs for specifying parameters</li>
<li><code>uid</code> unique IDs for Pipeline components</li>
<li>Reorganization of certain classes</li>
</ul>
<p>Since the <code>spark.ml</code> API was an alpha component in Spark 1.3, we do not list all changes here.
However, since 1.4 <code>spark.ml</code> is no longer an alpha component, we will provide details on any API
changes for future releases.</p>
<h2 id="from-12-to-13">From 1.2 to 1.3</h2>
<p>In the <code>spark.mllib</code> package, there were several breaking changes. The first change (in <code>ALS</code>) is the only one in a component not marked as Alpha or Experimental.</p>
<ul>
<li><em>(Breaking change)</em> In <a href="api/scala/index.html#org.apache.spark.mllib.recommendation.ALS"><code>ALS</code></a>, the extraneous method <code>solveLeastSquares</code> has been removed. The <code>DeveloperApi</code> method <code>analyzeBlocks</code> was also removed.</li>
<li><em>(Breaking change)</em> <a href="api/scala/index.html#org.apache.spark.mllib.feature.StandardScalerModel"><code>StandardScalerModel</code></a> remains an Alpha component. In it, the <code>variance</code> method has been replaced with the <code>std</code> method. To compute the column variance values returned by the original <code>variance</code> method, simply square the standard deviation values returned by <code>std</code>.</li>
<li><em>(Breaking change)</em> <a href="api/scala/index.html#org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD"><code>StreamingLinearRegressionWithSGD</code></a> remains an Experimental component. In it, there were two changes:
<ul>
<li>The constructor taking arguments was removed in favor of a builder pattern using the default constructor plus parameter setter methods.</li>
<li>Variable <code>model</code> is no longer public.</li>
</ul>
</li>
<li><em>(Breaking change)</em> <a href="api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree"><code>DecisionTree</code></a> remains an Experimental component. In it and its associated classes, there were several changes:
<ul>
<li>In <code>DecisionTree</code>, the deprecated class method <code>train</code> has been removed. (The object/static <code>train</code> methods remain.)</li>
<li>In <code>Strategy</code>, the <code>checkpointDir</code> parameter has been removed. Checkpointing is still supported, but the checkpoint directory must be set before calling tree and tree ensemble training.</li>
</ul>
</li>
<li><code>PythonMLlibAPI</code> (the interface between Scala/Java and Python for MLlib) was a public API but is now private, declared <code>private[python]</code>. This was never meant for external use.</li>
<li>In linear regression (including Lasso and ridge regression), the squared loss is now divided by 2.
So in order to produce the same result as in 1.2, the regularization parameter needs to be divided by 2 and the step size needs to be multiplied by 2.</li>
</ul>
<p>In the <code>spark.ml</code> package, the main API changes are from Spark SQL. We list the most important changes here:</p>
<ul>
<li>The old <a href="http://spark.apache.org/docs/1.2.1/api/scala/index.html#org.apache.spark.sql.SchemaRDD">SchemaRDD</a> has been replaced with <a href="api/scala/index.html#org.apache.spark.sql.DataFrame">DataFrame</a> with a somewhat modified API. All algorithms in Spark ML which used to use SchemaRDD now use DataFrame.</li>
<li>In Spark 1.2, we used implicit conversions from <code>RDD</code>s of <code>LabeledPoint</code> into <code>SchemaRDD</code>s by calling <code>import sqlContext._</code> where <code>sqlContext</code> was an instance of <code>SQLContext</code>. These implicits have been moved, so we now call <code>import sqlContext.implicits._</code>.</li>
<li>Java APIs for SQL have also changed accordingly. Please see the examples above and the <a href="sql-programming-guide.html">Spark SQL Programming Guide</a> for details.</li>
</ul>
<p>Other changes were in <code>LogisticRegression</code>:</p>
<ul>
<li>The <code>scoreCol</code> output column (with default value “score”) was renamed to be <code>probabilityCol</code> (with default value “probability”). The type was originally <code>Double</code> (for the probability of class 1.0), but it is now <code>Vector</code> (for the probability of each class, to support multiclass classification in the future).</li>
<li>In Spark 1.2, <code>LogisticRegressionModel</code> did not include an intercept. In Spark 1.3, it includes an intercept; however, it will always be 0.0 since it uses the default settings for <a href="api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS">spark.mllib.LogisticRegressionWithLBFGS</a>. The option to use an intercept will be added in the future.</li>
</ul>
<h2 id="from-11-to-12">From 1.1 to 1.2</h2>
<p>The only API changes in MLlib v1.2 are in
<a href="api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree"><code>DecisionTree</code></a>,
which continues to be an experimental API in MLlib 1.2:</p>
<ol>
<li>
<p><em>(Breaking change)</em> The Scala API for classification takes a named argument specifying the number
of classes. In MLlib v1.1, this argument was called <code>numClasses</code> in Python and
<code>numClassesForClassification</code> in Scala. In MLlib v1.2, the names are both set to <code>numClasses</code>.
This <code>numClasses</code> parameter is specified either via
<a href="api/scala/index.html#org.apache.spark.mllib.tree.configuration.Strategy"><code>Strategy</code></a>
or via <a href="api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree"><code>DecisionTree</code></a>
static <code>trainClassifier</code> and <code>trainRegressor</code> methods.</p>
</li>
<li>
<p><em>(Breaking change)</em> The API for
<a href="api/scala/index.html#org.apache.spark.mllib.tree.model.Node"><code>Node</code></a> has changed.
This should generally not affect user code, unless the user manually constructs decision trees
(instead of using the <code>trainClassifier</code> or <code>trainRegressor</code> methods).
The tree <code>Node</code> now includes more information, including the probability of the predicted label
(for classification).</p>
</li>
<li>
<p>Printing methods’ output has changed. The <code>toString</code> (Scala/Java) and <code>__repr__</code> (Python) methods used to print the full model; they now print a summary. For the full model, use <code>toDebugString</code>.</p>
</li>
</ol>
<p>Examples in the Spark distribution and examples in the
<a href="mllib-decision-tree.html#examples">Decision Trees Guide</a> have been updated accordingly.</p>
<h2 id="from-10-to-11">From 1.0 to 1.1</h2>
<p>The only API changes in MLlib v1.1 are in
<a href="api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree"><code>DecisionTree</code></a>,
which continues to be an experimental API in MLlib 1.1:</p>
<ol>
<li>
<p><em>(Breaking change)</em> The meaning of tree depth has been changed by 1 in order to match
the implementations of trees in
<a href="http://scikit-learn.org/stable/modules/classes.html#module-sklearn.tree">scikit-learn</a>
and in <a href="http://cran.r-project.org/web/packages/rpart/index.html">rpart</a>.
In MLlib v1.0, a depth-1 tree had 1 leaf node, and a depth-2 tree had 1 root node and 2 leaf nodes.
In MLlib v1.1, a depth-0 tree has 1 leaf node, and a depth-1 tree has 1 root node and 2 leaf nodes.
This depth is specified by the <code>maxDepth</code> parameter in
<a href="api/scala/index.html#org.apache.spark.mllib.tree.configuration.Strategy"><code>Strategy</code></a>
or via <a href="api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree"><code>DecisionTree</code></a>
static <code>trainClassifier</code> and <code>trainRegressor</code> methods.</p>
</li>
<li>
<p><em>(Non-breaking change)</em> We recommend using the newly added <code>trainClassifier</code> and <code>trainRegressor</code>
methods to build a <a href="api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree"><code>DecisionTree</code></a>,
rather than using the old parameter class <code>Strategy</code>. These new training methods explicitly
separate classification and regression, and they replace specialized parameter types with
simple <code>String</code> types.</p>
</li>
</ol>
<p>Examples of the new, recommended <code>trainClassifier</code> and <code>trainRegressor</code> are given in the
<a href="mllib-decision-tree.html#examples">Decision Trees Guide</a>.</p>
<h2 id="from-09-to-10">From 0.9 to 1.0</h2>
<p>In MLlib v1.0, we support both dense and sparse input in a unified way, which introduces a few
breaking changes. If your data is sparse, please store it in a sparse format instead of dense to
take advantage of sparsity in both storage and computation. Details are described below.</p>
</div> <!-- /container -->
<script src="js/vendor/jquery-1.8.0.min.js"></script>
<script src="js/vendor/bootstrap.min.js"></script>
<script src="js/vendor/anchor.min.js"></script>
<script src="js/main.js"></script>
<!-- MathJax Section -->
<script type="text/x-mathjax-config">
MathJax.Hub.Config({
TeX: { equationNumbers: { autoNumber: "AMS" } }
});
</script>
<script>
// Note that we load MathJax this way to work with local file (file://), HTTP and HTTPS.
// We could use "//cdn.mathjax...", but that won't support "file://".
(function(d, script) {
script = d.createElement('script');
script.type = 'text/javascript';
script.async = true;
script.onload = function(){
MathJax.Hub.Config({
tex2jax: {
inlineMath: [ ["$", "$"], ["\\\\(","\\\\)"] ],
displayMath: [ ["$$","$$"], ["\\[", "\\]"] ],
processEscapes: true,
skipTags: ['script', 'noscript', 'style', 'textarea', 'pre']
}
});
};
script.src = ('https:' == document.location.protocol ? 'https://' : 'http://') +
'cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML';
d.getElementsByTagName('head')[0].appendChild(script);
}(document));
</script>
</body>
</html>
|
