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<h1>Source code for pyspark.ml.recommendation</h1><div class="highlight"><pre>
<span></span><span class="c1">#</span>
<span class="c1"># Licensed to the Apache Software Foundation (ASF) under one or more</span>
<span class="c1"># contributor license agreements. See the NOTICE file distributed with</span>
<span class="c1"># this work for additional information regarding copyright ownership.</span>
<span class="c1"># The ASF licenses this file to You under the Apache License, Version 2.0</span>
<span class="c1"># (the &quot;License&quot;); you may not use this file except in compliance with</span>
<span class="c1"># the License. You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c1"># See the License for the specific language governing permissions and</span>
<span class="c1"># limitations under the License.</span>
<span class="c1">#</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">sys</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">TYPE_CHECKING</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark</span><span class="w"> </span><span class="kn">import</span> <span class="n">since</span><span class="p">,</span> <span class="n">keyword_only</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.ml.param.shared</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
<span class="n">HasPredictionCol</span><span class="p">,</span>
<span class="n">HasBlockSize</span><span class="p">,</span>
<span class="n">HasMaxIter</span><span class="p">,</span>
<span class="n">HasRegParam</span><span class="p">,</span>
<span class="n">HasCheckpointInterval</span><span class="p">,</span>
<span class="n">HasSeed</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.ml.wrapper</span><span class="w"> </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="w"> </span><span class="nn">pyspark.ml.common</span><span class="w"> </span><span class="kn">import</span> <span class="n">inherit_doc</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.ml.param</span><span class="w"> </span><span class="kn">import</span> <span class="n">Params</span><span class="p">,</span> <span class="n">TypeConverters</span><span class="p">,</span> <span class="n">Param</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.ml.util</span><span class="w"> </span><span class="kn">import</span> <span class="n">JavaMLWritable</span><span class="p">,</span> <span class="n">JavaMLReadable</span><span class="p">,</span> <span class="n">try_remote_attribute_relation</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.sql</span><span class="w"> </span><span class="kn">import</span> <span class="n">DataFrame</span>
<span class="k">if</span> <span class="n">TYPE_CHECKING</span><span class="p">:</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">py4j.java_gateway</span><span class="w"> </span><span class="kn">import</span> <span class="n">JavaObject</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;ALS&quot;</span><span class="p">,</span> <span class="s2">&quot;ALSModel&quot;</span><span class="p">]</span>
<span class="nd">@inherit_doc</span>
<span class="k">class</span><span class="w"> </span><span class="nc">_ALSModelParams</span><span class="p">(</span><span class="n">HasPredictionCol</span><span class="p">,</span> <span class="n">HasBlockSize</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Params for :py:class:`ALS` and :py:class:`ALSModel`.</span>
<span class="sd"> .. versionadded:: 3.0.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">userCol</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">str</span><span class="p">]</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="s2">&quot;userCol&quot;</span><span class="p">,</span>
<span class="s2">&quot;column name for user ids. Ids must be within &quot;</span> <span class="o">+</span> <span class="s2">&quot;the integer value range.&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toString</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">itemCol</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">str</span><span class="p">]</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="s2">&quot;itemCol&quot;</span><span class="p">,</span>
<span class="s2">&quot;column name for item ids. Ids must be within &quot;</span> <span class="o">+</span> <span class="s2">&quot;the integer value range.&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toString</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">coldStartStrategy</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">str</span><span class="p">]</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="s2">&quot;coldStartStrategy&quot;</span><span class="p">,</span>
<span class="s2">&quot;strategy for dealing with &quot;</span>
<span class="o">+</span> <span class="s2">&quot;unknown or new users/items at prediction time. This may be useful &quot;</span>
<span class="o">+</span> <span class="s2">&quot;in cross-validation or production scenarios, for handling &quot;</span>
<span class="o">+</span> <span class="s2">&quot;user/item ids the model has not seen in the training data. &quot;</span>
<span class="o">+</span> <span class="s2">&quot;Supported values: &#39;nan&#39;, &#39;drop&#39;.&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toString</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Any</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">_ALSModelParams</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">args</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">blockSize</span><span class="o">=</span><span class="mi">4096</span><span class="p">)</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">getUserCol</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the value of userCol or its default value.</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">userCol</span><span class="p">)</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">getItemCol</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the value of itemCol or its default value.</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">itemCol</span><span class="p">)</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;2.2.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">getColdStartStrategy</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the value of coldStartStrategy or its default value.</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">coldStartStrategy</span><span class="p">)</span>
<span class="nd">@inherit_doc</span>
<span class="k">class</span><span class="w"> </span><span class="nc">_ALSParams</span><span class="p">(</span><span class="n">_ALSModelParams</span><span class="p">,</span> <span class="n">HasMaxIter</span><span class="p">,</span> <span class="n">HasRegParam</span><span class="p">,</span> <span class="n">HasCheckpointInterval</span><span class="p">,</span> <span class="n">HasSeed</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Params for :py:class:`ALS`.</span>
<span class="sd"> .. versionadded:: 3.0.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">rank</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">int</span><span class="p">]</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="s2">&quot;rank&quot;</span><span class="p">,</span> <span class="s2">&quot;rank of the factorization&quot;</span><span class="p">,</span> <span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toInt</span>
<span class="p">)</span>
<span class="n">numUserBlocks</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">int</span><span class="p">]</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="s2">&quot;numUserBlocks&quot;</span><span class="p">,</span>
<span class="s2">&quot;number of user blocks&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toInt</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">numItemBlocks</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">int</span><span class="p">]</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="s2">&quot;numItemBlocks&quot;</span><span class="p">,</span>
<span class="s2">&quot;number of item blocks&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toInt</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">implicitPrefs</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</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="s2">&quot;implicitPrefs&quot;</span><span class="p">,</span>
<span class="s2">&quot;whether to use implicit preference&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toBoolean</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">alpha</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">float</span><span class="p">]</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="s2">&quot;alpha&quot;</span><span class="p">,</span>
<span class="s2">&quot;alpha for implicit preference&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toFloat</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">ratingCol</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">str</span><span class="p">]</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="s2">&quot;ratingCol&quot;</span><span class="p">,</span>
<span class="s2">&quot;column name for ratings&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toString</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">nonnegative</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</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="s2">&quot;nonnegative&quot;</span><span class="p">,</span>
<span class="s2">&quot;whether to use nonnegative constraint for least squares&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toBoolean</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">intermediateStorageLevel</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">str</span><span class="p">]</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="s2">&quot;intermediateStorageLevel&quot;</span><span class="p">,</span>
<span class="s2">&quot;StorageLevel for intermediate datasets. Cannot be &#39;NONE&#39;.&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toString</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">finalStorageLevel</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">str</span><span class="p">]</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="s2">&quot;finalStorageLevel&quot;</span><span class="p">,</span>
<span class="s2">&quot;StorageLevel for ALS model factors.&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toString</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Any</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">_ALSParams</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">args</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">rank</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="n">maxIter</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="n">regParam</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
<span class="n">numUserBlocks</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="n">numItemBlocks</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="n">implicitPrefs</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
<span class="n">userCol</span><span class="o">=</span><span class="s2">&quot;user&quot;</span><span class="p">,</span>
<span class="n">itemCol</span><span class="o">=</span><span class="s2">&quot;item&quot;</span><span class="p">,</span>
<span class="n">ratingCol</span><span class="o">=</span><span class="s2">&quot;rating&quot;</span><span class="p">,</span>
<span class="n">nonnegative</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">checkpointInterval</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="n">intermediateStorageLevel</span><span class="o">=</span><span class="s2">&quot;MEMORY_AND_DISK&quot;</span><span class="p">,</span>
<span class="n">finalStorageLevel</span><span class="o">=</span><span class="s2">&quot;MEMORY_AND_DISK&quot;</span><span class="p">,</span>
<span class="n">coldStartStrategy</span><span class="o">=</span><span class="s2">&quot;nan&quot;</span><span class="p">,</span>
<span class="p">)</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">getRank</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the value of rank or its default value.</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">rank</span><span class="p">)</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">getNumUserBlocks</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the value of numUserBlocks or its default value.</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">numUserBlocks</span><span class="p">)</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">getNumItemBlocks</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the value of numItemBlocks or its default value.</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">numItemBlocks</span><span class="p">)</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">getImplicitPrefs</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the value of implicitPrefs or its default value.</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">implicitPrefs</span><span class="p">)</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">getAlpha</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the value of alpha or its default value.</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">alpha</span><span class="p">)</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">getRatingCol</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the value of ratingCol or its default value.</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">ratingCol</span><span class="p">)</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">getNonnegative</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the value of nonnegative or its default value.</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">nonnegative</span><span class="p">)</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;2.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">getIntermediateStorageLevel</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the value of intermediateStorageLevel or its default value.</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">intermediateStorageLevel</span><span class="p">)</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;2.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">getFinalStorageLevel</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the value of finalStorageLevel or its default value.</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">finalStorageLevel</span><span class="p">)</span>
<div class="viewcode-block" id="ALS"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS">[docs]</a><span class="nd">@inherit_doc</span>
<span class="k">class</span><span class="w"> </span><span class="nc">ALS</span><span class="p">(</span><span class="n">JavaEstimator</span><span class="p">[</span><span class="s2">&quot;ALSModel&quot;</span><span class="p">],</span> <span class="n">_ALSParams</span><span class="p">,</span> <span class="n">JavaMLWritable</span><span class="p">,</span> <span class="n">JavaMLReadable</span><span class="p">[</span><span class="s2">&quot;ALS&quot;</span><span class="p">]):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Alternating Least Squares (ALS) matrix factorization.</span>
<span class="sd"> ALS attempts to estimate the ratings matrix `R` as the product of</span>
<span class="sd"> two lower-rank matrices, `X` and `Y`, i.e. `X * Yt = R`. Typically</span>
<span class="sd"> these approximations are called &#39;factor&#39; matrices. The general</span>
<span class="sd"> approach is iterative. During each iteration, one of the factor</span>
<span class="sd"> matrices is held constant, while the other is solved for using least</span>
<span class="sd"> squares. The newly-solved factor matrix is then held constant while</span>
<span class="sd"> solving for the other factor matrix.</span>
<span class="sd"> This is a blocked implementation of the ALS factorization algorithm</span>
<span class="sd"> that groups the two sets of factors (referred to as &quot;users&quot; and</span>
<span class="sd"> &quot;products&quot;) into blocks and reduces communication by only sending</span>
<span class="sd"> one copy of each user vector to each product block on each</span>
<span class="sd"> iteration, and only for the product blocks that need that user&#39;s</span>
<span class="sd"> feature vector. This is achieved by pre-computing some information</span>
<span class="sd"> about the ratings matrix to determine the &quot;out-links&quot; of each user</span>
<span class="sd"> (which blocks of products it will contribute to) and &quot;in-link&quot;</span>
<span class="sd"> information for each product (which of the feature vectors it</span>
<span class="sd"> receives from each user block it will depend on). This allows us to</span>
<span class="sd"> send only an array of feature vectors between each user block and</span>
<span class="sd"> product block, and have the product block find the users&#39; ratings</span>
<span class="sd"> and update the products based on these messages.</span>
<span class="sd"> For implicit preference data, the algorithm used is based on</span>
<span class="sd"> `&quot;Collaborative Filtering for Implicit Feedback Datasets&quot;,</span>
<span class="sd"> &lt;https://doi.org/10.1109/ICDM.2008.22&gt;`_, adapted for the blocked</span>
<span class="sd"> approach used here.</span>
<span class="sd"> Essentially instead of finding the low-rank approximations to the</span>
<span class="sd"> rating matrix `R`, this finds the approximations for a preference</span>
<span class="sd"> matrix `P` where the elements of `P` are 1 if r &gt; 0 and 0 if r &lt;= 0.</span>
<span class="sd"> The ratings then act as &#39;confidence&#39; values related to strength of</span>
<span class="sd"> indicated user preferences rather than explicit ratings given to</span>
<span class="sd"> items.</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> The input rating dataframe to the ALS implementation should be deterministic.</span>
<span class="sd"> Nondeterministic data can cause failure during fitting ALS model.</span>
<span class="sd"> For example, an order-sensitive operation like sampling after a repartition makes</span>
<span class="sd"> dataframe output nondeterministic, like `df.repartition(2).sample(False, 0.5, 1618)`.</span>
<span class="sd"> Checkpointing sampled dataframe or adding a sort before sampling can help make the</span>
<span class="sd"> dataframe deterministic.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; df = spark.createDataFrame(</span>
<span class="sd"> ... [(0, 0, 4.0), (0, 1, 2.0), (1, 1, 3.0), (1, 2, 4.0), (2, 1, 1.0), (2, 2, 5.0)],</span>
<span class="sd"> ... [&quot;user&quot;, &quot;item&quot;, &quot;rating&quot;])</span>
<span class="sd"> &gt;&gt;&gt; als = ALS(rank=10, seed=0)</span>
<span class="sd"> &gt;&gt;&gt; als.setMaxIter(5)</span>
<span class="sd"> ALS...</span>
<span class="sd"> &gt;&gt;&gt; als.getMaxIter()</span>
<span class="sd"> 5</span>
<span class="sd"> &gt;&gt;&gt; als.setRegParam(0.1)</span>
<span class="sd"> ALS...</span>
<span class="sd"> &gt;&gt;&gt; als.getRegParam()</span>
<span class="sd"> 0.1</span>
<span class="sd"> &gt;&gt;&gt; als.clear(als.regParam)</span>
<span class="sd"> &gt;&gt;&gt; model = als.fit(df)</span>
<span class="sd"> &gt;&gt;&gt; model.getBlockSize()</span>
<span class="sd"> 4096</span>
<span class="sd"> &gt;&gt;&gt; model.getUserCol()</span>
<span class="sd"> &#39;user&#39;</span>
<span class="sd"> &gt;&gt;&gt; model.setUserCol(&quot;user&quot;)</span>
<span class="sd"> ALSModel...</span>
<span class="sd"> &gt;&gt;&gt; model.getItemCol()</span>
<span class="sd"> &#39;item&#39;</span>
<span class="sd"> &gt;&gt;&gt; model.setPredictionCol(&quot;newPrediction&quot;)</span>
<span class="sd"> ALS...</span>
<span class="sd"> &gt;&gt;&gt; model.rank</span>
<span class="sd"> 10</span>
<span class="sd"> &gt;&gt;&gt; model.userFactors.orderBy(&quot;id&quot;).collect()</span>
<span class="sd"> [Row(id=0, features=[...]), Row(id=1, ...), Row(id=2, ...)]</span>
<span class="sd"> &gt;&gt;&gt; test = spark.createDataFrame([(0, 2), (1, 0), (2, 0)], [&quot;user&quot;, &quot;item&quot;])</span>
<span class="sd"> &gt;&gt;&gt; predictions = sorted(model.transform(test).collect(), key=lambda r: r[0])</span>
<span class="sd"> &gt;&gt;&gt; predictions[0]</span>
<span class="sd"> Row(user=0, item=2, newPrediction=0.6929...)</span>
<span class="sd"> &gt;&gt;&gt; predictions[1]</span>
<span class="sd"> Row(user=1, item=0, newPrediction=3.47356...)</span>
<span class="sd"> &gt;&gt;&gt; predictions[2]</span>
<span class="sd"> Row(user=2, item=0, newPrediction=-0.899198...)</span>
<span class="sd"> &gt;&gt;&gt; user_recs = model.recommendForAllUsers(3)</span>
<span class="sd"> &gt;&gt;&gt; user_recs.where(user_recs.user == 0)\</span>
<span class="sd"> .select(&quot;recommendations.item&quot;, &quot;recommendations.rating&quot;).collect()</span>
<span class="sd"> [Row(item=[0, 1, 2], rating=[3.910..., 1.997..., 0.692...])]</span>
<span class="sd"> &gt;&gt;&gt; item_recs = model.recommendForAllItems(3)</span>
<span class="sd"> &gt;&gt;&gt; item_recs.where(item_recs.item == 2)\</span>
<span class="sd"> .select(&quot;recommendations.user&quot;, &quot;recommendations.rating&quot;).collect()</span>
<span class="sd"> [Row(user=[2, 1, 0], rating=[4.892..., 3.991..., 0.692...])]</span>
<span class="sd"> &gt;&gt;&gt; user_subset = df.where(df.user == 2)</span>
<span class="sd"> &gt;&gt;&gt; user_subset_recs = model.recommendForUserSubset(user_subset, 3)</span>
<span class="sd"> &gt;&gt;&gt; user_subset_recs.select(&quot;recommendations.item&quot;, &quot;recommendations.rating&quot;).first()</span>
<span class="sd"> Row(item=[2, 1, 0], rating=[4.892..., 1.076..., -0.899...])</span>
<span class="sd"> &gt;&gt;&gt; item_subset = df.where(df.item == 0)</span>
<span class="sd"> &gt;&gt;&gt; item_subset_recs = model.recommendForItemSubset(item_subset, 3)</span>
<span class="sd"> &gt;&gt;&gt; item_subset_recs.select(&quot;recommendations.user&quot;, &quot;recommendations.rating&quot;).first()</span>
<span class="sd"> Row(user=[0, 1, 2], rating=[3.910..., 3.473..., -0.899...])</span>
<span class="sd"> &gt;&gt;&gt; als_path = temp_path + &quot;/als&quot;</span>
<span class="sd"> &gt;&gt;&gt; als.save(als_path)</span>
<span class="sd"> &gt;&gt;&gt; als2 = ALS.load(als_path)</span>
<span class="sd"> &gt;&gt;&gt; als.getMaxIter()</span>
<span class="sd"> 5</span>
<span class="sd"> &gt;&gt;&gt; model_path = temp_path + &quot;/als_model&quot;</span>
<span class="sd"> &gt;&gt;&gt; model.save(model_path)</span>
<span class="sd"> &gt;&gt;&gt; model2 = ALSModel.load(model_path)</span>
<span class="sd"> &gt;&gt;&gt; model.rank == model2.rank</span>
<span class="sd"> True</span>
<span class="sd"> &gt;&gt;&gt; sorted(model.userFactors.collect()) == sorted(model2.userFactors.collect())</span>
<span class="sd"> True</span>
<span class="sd"> &gt;&gt;&gt; sorted(model.itemFactors.collect()) == sorted(model2.itemFactors.collect())</span>
<span class="sd"> True</span>
<span class="sd"> &gt;&gt;&gt; model.transform(test).take(1) == model2.transform(test).take(1)</span>
<span class="sd"> True</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">_input_kwargs</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]</span>
<span class="nd">@keyword_only</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="o">*</span><span class="p">,</span>
<span class="n">rank</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span>
<span class="n">maxIter</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span>
<span class="n">regParam</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span>
<span class="n">numUserBlocks</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span>
<span class="n">numItemBlocks</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span>
<span class="n">implicitPrefs</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">alpha</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
<span class="n">userCol</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;user&quot;</span><span class="p">,</span>
<span class="n">itemCol</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;item&quot;</span><span class="p">,</span>
<span class="n">seed</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">ratingCol</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;rating&quot;</span><span class="p">,</span>
<span class="n">nonnegative</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">checkpointInterval</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span>
<span class="n">intermediateStorageLevel</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;MEMORY_AND_DISK&quot;</span><span class="p">,</span>
<span class="n">finalStorageLevel</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;MEMORY_AND_DISK&quot;</span><span class="p">,</span>
<span class="n">coldStartStrategy</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;nan&quot;</span><span class="p">,</span>
<span class="n">blockSize</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">4096</span><span class="p">,</span>
<span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> __init__(self, \\*, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10,</span>
<span class="sd"> numItemBlocks=10, implicitPrefs=False, alpha=1.0, userCol=&quot;user&quot;, itemCol=&quot;item&quot;, \</span>
<span class="sd"> seed=None, ratingCol=&quot;rating&quot;, nonnegative=False, checkpointInterval=10, \</span>
<span class="sd"> intermediateStorageLevel=&quot;MEMORY_AND_DISK&quot;, \</span>
<span class="sd"> finalStorageLevel=&quot;MEMORY_AND_DISK&quot;, coldStartStrategy=&quot;nan&quot;, blockSize=4096)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="nb">super</span><span class="p">(</span><span class="n">ALS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__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="s2">&quot;org.apache.spark.ml.recommendation.ALS&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="n">kwargs</span> <span class="o">=</span> <span class="bp">self</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>
<div class="viewcode-block" id="ALS.setParams"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS.setParams">[docs]</a> <span class="nd">@keyword_only</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">setParams</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="o">*</span><span class="p">,</span>
<span class="n">rank</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span>
<span class="n">maxIter</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span>
<span class="n">regParam</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span>
<span class="n">numUserBlocks</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span>
<span class="n">numItemBlocks</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span>
<span class="n">implicitPrefs</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">alpha</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
<span class="n">userCol</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;user&quot;</span><span class="p">,</span>
<span class="n">itemCol</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;item&quot;</span><span class="p">,</span>
<span class="n">seed</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">ratingCol</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;rating&quot;</span><span class="p">,</span>
<span class="n">nonnegative</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">checkpointInterval</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span>
<span class="n">intermediateStorageLevel</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;MEMORY_AND_DISK&quot;</span><span class="p">,</span>
<span class="n">finalStorageLevel</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;MEMORY_AND_DISK&quot;</span><span class="p">,</span>
<span class="n">coldStartStrategy</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;nan&quot;</span><span class="p">,</span>
<span class="n">blockSize</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">4096</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALS&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> setParams(self, \\*, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, \</span>
<span class="sd"> numItemBlocks=10, implicitPrefs=False, alpha=1.0, userCol=&quot;user&quot;, itemCol=&quot;item&quot;, \</span>
<span class="sd"> seed=None, ratingCol=&quot;rating&quot;, nonnegative=False, checkpointInterval=10, \</span>
<span class="sd"> intermediateStorageLevel=&quot;MEMORY_AND_DISK&quot;, \</span>
<span class="sd"> finalStorageLevel=&quot;MEMORY_AND_DISK&quot;, coldStartStrategy=&quot;nan&quot;, blockSize=4096)</span>
<span class="sd"> Sets params for ALS.</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">_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>
<span class="k">def</span><span class="w"> </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="s2">&quot;JavaObject&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALSModel&quot;</span><span class="p">:</span>
<span class="k">return</span> <span class="n">ALSModel</span><span class="p">(</span><span class="n">java_model</span><span class="p">)</span>
<div class="viewcode-block" id="ALS.setRank"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS.setRank">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">setRank</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="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALS&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`rank`.</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">_set</span><span class="p">(</span><span class="n">rank</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALS.setNumUserBlocks"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS.setNumUserBlocks">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">setNumUserBlocks</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="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALS&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`numUserBlocks`.</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">_set</span><span class="p">(</span><span class="n">numUserBlocks</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALS.setNumItemBlocks"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS.setNumItemBlocks">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">setNumItemBlocks</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="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALS&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`numItemBlocks`.</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">_set</span><span class="p">(</span><span class="n">numItemBlocks</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALS.setNumBlocks"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS.setNumBlocks">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">setNumBlocks</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="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALS&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets both :py:attr:`numUserBlocks` and :py:attr:`numItemBlocks` to the specific value.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_set</span><span class="p">(</span><span class="n">numUserBlocks</span><span class="o">=</span><span class="n">value</span><span class="p">)</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="n">numItemBlocks</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALS.setImplicitPrefs"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS.setImplicitPrefs">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">setImplicitPrefs</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="nb">bool</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALS&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`implicitPrefs`.</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">_set</span><span class="p">(</span><span class="n">implicitPrefs</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALS.setAlpha"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS.setAlpha">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">setAlpha</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="nb">float</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALS&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`alpha`.</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">_set</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALS.setUserCol"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS.setUserCol">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">setUserCol</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="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALS&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`userCol`.</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">_set</span><span class="p">(</span><span class="n">userCol</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALS.setItemCol"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS.setItemCol">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">setItemCol</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="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALS&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`itemCol`.</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">_set</span><span class="p">(</span><span class="n">itemCol</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALS.setRatingCol"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS.setRatingCol">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">setRatingCol</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="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALS&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`ratingCol`.</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">_set</span><span class="p">(</span><span class="n">ratingCol</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALS.setNonnegative"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS.setNonnegative">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">setNonnegative</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="nb">bool</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALS&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`nonnegative`.</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">_set</span><span class="p">(</span><span class="n">nonnegative</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALS.setIntermediateStorageLevel"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS.setIntermediateStorageLevel">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;2.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">setIntermediateStorageLevel</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="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALS&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`intermediateStorageLevel`.</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">_set</span><span class="p">(</span><span class="n">intermediateStorageLevel</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALS.setFinalStorageLevel"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS.setFinalStorageLevel">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;2.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">setFinalStorageLevel</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="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALS&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`finalStorageLevel`.</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">_set</span><span class="p">(</span><span class="n">finalStorageLevel</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALS.setColdStartStrategy"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS.setColdStartStrategy">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;2.2.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">setColdStartStrategy</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="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALS&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`coldStartStrategy`.</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">_set</span><span class="p">(</span><span class="n">coldStartStrategy</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALS.setMaxIter"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS.setMaxIter">[docs]</a> <span class="k">def</span><span class="w"> </span><span class="nf">setMaxIter</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="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALS&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`maxIter`.</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">_set</span><span class="p">(</span><span class="n">maxIter</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALS.setRegParam"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS.setRegParam">[docs]</a> <span class="k">def</span><span class="w"> </span><span class="nf">setRegParam</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="nb">float</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALS&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`regParam`.</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">_set</span><span class="p">(</span><span class="n">regParam</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALS.setPredictionCol"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS.setPredictionCol">[docs]</a> <span class="k">def</span><span class="w"> </span><span class="nf">setPredictionCol</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="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALS&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`predictionCol`.</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">_set</span><span class="p">(</span><span class="n">predictionCol</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALS.setCheckpointInterval"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS.setCheckpointInterval">[docs]</a> <span class="k">def</span><span class="w"> </span><span class="nf">setCheckpointInterval</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="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALS&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`checkpointInterval`.</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">_set</span><span class="p">(</span><span class="n">checkpointInterval</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALS.setSeed"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS.setSeed">[docs]</a> <span class="k">def</span><span class="w"> </span><span class="nf">setSeed</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="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALS&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`seed`.</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">_set</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALS.setBlockSize"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS.setBlockSize">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;3.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">setBlockSize</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="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALS&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`blockSize`.</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">_set</span><span class="p">(</span><span class="n">blockSize</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="ALSModel"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALSModel.html#pyspark.ml.recommendation.ALSModel">[docs]</a><span class="k">class</span><span class="w"> </span><span class="nc">ALSModel</span><span class="p">(</span><span class="n">JavaModel</span><span class="p">,</span> <span class="n">_ALSModelParams</span><span class="p">,</span> <span class="n">JavaMLWritable</span><span class="p">,</span> <span class="n">JavaMLReadable</span><span class="p">[</span><span class="s2">&quot;ALSModel&quot;</span><span class="p">]):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Model fitted by ALS.</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<div class="viewcode-block" id="ALSModel.setUserCol"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALSModel.html#pyspark.ml.recommendation.ALSModel.setUserCol">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;3.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">setUserCol</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="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALSModel&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`userCol`.</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">_set</span><span class="p">(</span><span class="n">userCol</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALSModel.setItemCol"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALSModel.html#pyspark.ml.recommendation.ALSModel.setItemCol">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;3.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">setItemCol</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="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALSModel&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`itemCol`.</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">_set</span><span class="p">(</span><span class="n">itemCol</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALSModel.setColdStartStrategy"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALSModel.html#pyspark.ml.recommendation.ALSModel.setColdStartStrategy">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;3.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">setColdStartStrategy</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="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALSModel&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`coldStartStrategy`.</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">_set</span><span class="p">(</span><span class="n">coldStartStrategy</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALSModel.setPredictionCol"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALSModel.html#pyspark.ml.recommendation.ALSModel.setPredictionCol">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;3.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">setPredictionCol</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="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALSModel&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`predictionCol`.</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">_set</span><span class="p">(</span><span class="n">predictionCol</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALSModel.setBlockSize"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALSModel.html#pyspark.ml.recommendation.ALSModel.setBlockSize">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;3.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">setBlockSize</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="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;ALSModel&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`blockSize`.</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">_set</span><span class="p">(</span><span class="n">blockSize</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<span class="nd">@property</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">rank</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;rank of the matrix factorization model&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_java</span><span class="p">(</span><span class="s2">&quot;rank&quot;</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="nd">@try_remote_attribute_relation</span>
<span class="k">def</span><span class="w"> </span><span class="nf">userFactors</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> a DataFrame that stores user factors in two columns: `id` and</span>
<span class="sd"> `features`</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">_call_java</span><span class="p">(</span><span class="s2">&quot;userFactors&quot;</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="nd">@try_remote_attribute_relation</span>
<span class="k">def</span><span class="w"> </span><span class="nf">itemFactors</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> a DataFrame that stores item factors in two columns: `id` and</span>
<span class="sd"> `features`</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">_call_java</span><span class="p">(</span><span class="s2">&quot;itemFactors&quot;</span><span class="p">)</span>
<div class="viewcode-block" id="ALSModel.recommendForAllUsers"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALSModel.html#pyspark.ml.recommendation.ALSModel.recommendForAllUsers">[docs]</a> <span class="nd">@try_remote_attribute_relation</span>
<span class="k">def</span><span class="w"> </span><span class="nf">recommendForAllUsers</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">numItems</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns top `numItems` items recommended for each user, for all users.</span>
<span class="sd"> .. versionadded:: 2.2.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> numItems : int</span>
<span class="sd"> max number of recommendations for each user</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :py:class:`pyspark.sql.DataFrame`</span>
<span class="sd"> a DataFrame of (userCol, recommendations), where recommendations are</span>
<span class="sd"> stored as an array of (itemCol, rating) Rows.</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">_call_java</span><span class="p">(</span><span class="s2">&quot;recommendForAllUsers&quot;</span><span class="p">,</span> <span class="n">numItems</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALSModel.recommendForAllItems"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALSModel.html#pyspark.ml.recommendation.ALSModel.recommendForAllItems">[docs]</a> <span class="nd">@try_remote_attribute_relation</span>
<span class="k">def</span><span class="w"> </span><span class="nf">recommendForAllItems</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">numUsers</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns top `numUsers` users recommended for each item, for all items.</span>
<span class="sd"> .. versionadded:: 2.2.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> numUsers : int</span>
<span class="sd"> max number of recommendations for each item</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :py:class:`pyspark.sql.DataFrame`</span>
<span class="sd"> a DataFrame of (itemCol, recommendations), where recommendations are</span>
<span class="sd"> stored as an array of (userCol, rating) Rows.</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">_call_java</span><span class="p">(</span><span class="s2">&quot;recommendForAllItems&quot;</span><span class="p">,</span> <span class="n">numUsers</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALSModel.recommendForUserSubset"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALSModel.html#pyspark.ml.recommendation.ALSModel.recommendForUserSubset">[docs]</a> <span class="nd">@try_remote_attribute_relation</span>
<span class="k">def</span><span class="w"> </span><span class="nf">recommendForUserSubset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span> <span class="n">numItems</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns top `numItems` items recommended for each user id in the input data set. Note that</span>
<span class="sd"> if there are duplicate ids in the input dataset, only one set of recommendations per unique</span>
<span class="sd"> id will be returned.</span>
<span class="sd"> .. versionadded:: 2.3.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> dataset : :py:class:`pyspark.sql.DataFrame`</span>
<span class="sd"> a DataFrame containing a column of user ids. The column name must match `userCol`.</span>
<span class="sd"> numItems : int</span>
<span class="sd"> max number of recommendations for each user</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :py:class:`pyspark.sql.DataFrame`</span>
<span class="sd"> a DataFrame of (userCol, recommendations), where recommendations are</span>
<span class="sd"> stored as an array of (itemCol, rating) Rows.</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">_call_java</span><span class="p">(</span><span class="s2">&quot;recommendForUserSubset&quot;</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">numItems</span><span class="p">)</span></div>
<div class="viewcode-block" id="ALSModel.recommendForItemSubset"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.recommendation.ALSModel.html#pyspark.ml.recommendation.ALSModel.recommendForItemSubset">[docs]</a> <span class="nd">@try_remote_attribute_relation</span>
<span class="k">def</span><span class="w"> </span><span class="nf">recommendForItemSubset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span> <span class="n">numUsers</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns top `numUsers` users recommended for each item id in the input data set. Note that</span>
<span class="sd"> if there are duplicate ids in the input dataset, only one set of recommendations per unique</span>
<span class="sd"> id will be returned.</span>
<span class="sd"> .. versionadded:: 2.3.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> dataset : :py:class:`pyspark.sql.DataFrame`</span>
<span class="sd"> a DataFrame containing a column of item ids. The column name must match `itemCol`.</span>
<span class="sd"> numUsers : int</span>
<span class="sd"> max number of recommendations for each item</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :py:class:`pyspark.sql.DataFrame`</span>
<span class="sd"> a DataFrame of (itemCol, recommendations), where recommendations are</span>
<span class="sd"> stored as an array of (userCol, rating) Rows.</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">_call_java</span><span class="p">(</span><span class="s2">&quot;recommendForItemSubset&quot;</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">numUsers</span><span class="p">)</span></div></div>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">doctest</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">pyspark.ml.recommendation</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.sql</span><span class="w"> </span><span class="kn">import</span> <span class="n">SparkSession</span>
<span class="n">globs</span> <span class="o">=</span> <span class="n">pyspark</span><span class="o">.</span><span class="n">ml</span><span class="o">.</span><span class="n">recommendation</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="c1"># The small batch size here ensures that we see multiple batches,</span>
<span class="c1"># even in these small test examples:</span>
<span class="n">spark</span> <span class="o">=</span> <span class="n">SparkSession</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">master</span><span class="p">(</span><span class="s2">&quot;local[2]&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">appName</span><span class="p">(</span><span class="s2">&quot;ml.recommendation tests&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">getOrCreate</span><span class="p">()</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">sparkContext</span>
<span class="n">globs</span><span class="p">[</span><span class="s2">&quot;sc&quot;</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="s2">&quot;spark&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">spark</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">tempfile</span>
<span class="n">temp_path</span> <span class="o">=</span> <span class="n">tempfile</span><span class="o">.</span><span class="n">mkdtemp</span><span class="p">()</span>
<span class="n">globs</span><span class="p">[</span><span class="s2">&quot;temp_path&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">temp_path</span>
<span class="k">try</span><span class="p">:</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">spark</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
<span class="k">finally</span><span class="p">:</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">shutil</span><span class="w"> </span><span class="kn">import</span> <span class="n">rmtree</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">rmtree</span><span class="p">(</span><span class="n">temp_path</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">OSError</span><span class="p">:</span>
<span class="k">pass</span>
<span class="k">if</span> <span class="n">failure_count</span><span class="p">:</span>
<span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
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