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| <div class="section" id="matrixfactorizationmodel"> |
| <h1>MatrixFactorizationModel<a class="headerlink" href="#matrixfactorizationmodel" title="Permalink to this headline">¶</a></h1> |
| <dl class="py class"> |
| <dt id="pyspark.mllib.recommendation.MatrixFactorizationModel"> |
| <em class="property">class </em><code class="sig-prename descclassname">pyspark.mllib.recommendation.</code><code class="sig-name descname">MatrixFactorizationModel</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">java_model</span><span class="p">:</span> <span class="n">py4j.java_gateway.JavaObject</span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/pyspark/mllib/recommendation.html#MatrixFactorizationModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.recommendation.MatrixFactorizationModel" title="Permalink to this definition">¶</a></dt> |
| <dd><p>A matrix factorisation model trained by regularized alternating |
| least-squares.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 0.9.0.</span></p> |
| </div> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">r1</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">r2</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">r3</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">ratings</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">([</span><span class="n">r1</span><span class="p">,</span> <span class="n">r2</span><span class="p">,</span> <span class="n">r3</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">model</span> <span class="o">=</span> <span class="n">ALS</span><span class="o">.</span><span class="n">trainImplicit</span><span class="p">(</span><span class="n">ratings</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="go">0.4...</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">testset</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">([(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)])</span> |
| <span class="gp">>>> </span><span class="n">model</span> <span class="o">=</span> <span class="n">ALS</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">ratings</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">model</span><span class="o">.</span><span class="n">predictAll</span><span class="p">(</span><span class="n">testset</span><span class="p">)</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span> |
| <span class="go">[Rating(user=1, product=1, rating=1.0...), Rating(user=1, product=2, rating=1.9...)]</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">model</span> <span class="o">=</span> <span class="n">ALS</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">ratings</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">model</span><span class="o">.</span><span class="n">userFeatures</span><span class="p">()</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span> |
| <span class="go">[(1, array('d', [...])), (2, array('d', [...]))]</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">model</span><span class="o">.</span><span class="n">recommendUsers</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="go">[Rating(user=2, product=1, rating=1.9...), Rating(user=1, product=1, rating=1.0...)]</span> |
| <span class="gp">>>> </span><span class="n">model</span><span class="o">.</span><span class="n">recommendProducts</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="go">[Rating(user=1, product=2, rating=1.9...), Rating(user=1, product=1, rating=1.0...)]</span> |
| <span class="gp">>>> </span><span class="n">model</span><span class="o">.</span><span class="n">rank</span> |
| <span class="go">4</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">first_user</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">userFeatures</span><span class="p">()</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">latents</span> <span class="o">=</span> <span class="n">first_user</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="nb">len</span><span class="p">(</span><span class="n">latents</span><span class="p">)</span> |
| <span class="go">4</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">model</span><span class="o">.</span><span class="n">productFeatures</span><span class="p">()</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span> |
| <span class="go">[(1, array('d', [...])), (2, array('d', [...]))]</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">first_product</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">productFeatures</span><span class="p">()</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">latents</span> <span class="o">=</span> <span class="n">first_product</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="nb">len</span><span class="p">(</span><span class="n">latents</span><span class="p">)</span> |
| <span class="go">4</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">products_for_users</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">recommendProductsForUsers</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="nb">len</span><span class="p">(</span><span class="n">products_for_users</span><span class="p">)</span> |
| <span class="go">2</span> |
| <span class="gp">>>> </span><span class="n">products_for_users</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="go">(1, (Rating(user=1, product=2, rating=...),))</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">users_for_products</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">recommendUsersForProducts</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="nb">len</span><span class="p">(</span><span class="n">users_for_products</span><span class="p">)</span> |
| <span class="go">2</span> |
| <span class="gp">>>> </span><span class="n">users_for_products</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="go">(1, (Rating(user=2, product=1, rating=...),))</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">model</span> <span class="o">=</span> <span class="n">ALS</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">ratings</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">nonnegative</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">123456789</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="go">3.73...</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">([</span><span class="n">Rating</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">),</span> <span class="n">Rating</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">),</span> <span class="n">Rating</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">)])</span> |
| <span class="gp">>>> </span><span class="n">model</span> <span class="o">=</span> <span class="n">ALS</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">nonnegative</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">123456789</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="go">3.73...</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">model</span> <span class="o">=</span> <span class="n">ALS</span><span class="o">.</span><span class="n">trainImplicit</span><span class="p">(</span><span class="n">ratings</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">nonnegative</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">123456789</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="go">0.4...</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">os</span><span class="o">,</span> <span class="nn">tempfile</span> |
| <span class="gp">>>> </span><span class="n">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="gp">>>> </span><span class="n">model</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">sameModel</span> <span class="o">=</span> <span class="n">MatrixFactorizationModel</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">sameModel</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="go">0.4...</span> |
| <span class="gp">>>> </span><span class="n">sameModel</span><span class="o">.</span><span class="n">predictAll</span><span class="p">(</span><span class="n">testset</span><span class="p">)</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span> |
| <span class="go">[Rating(...</span> |
| <span class="gp">>>> </span><span class="kn">from</span> <span class="nn">shutil</span> <span class="kn">import</span> <span class="n">rmtree</span> |
| <span class="gp">>>> </span><span class="k">try</span><span class="p">:</span> |
| <span class="gp">... </span> <span class="n">rmtree</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> |
| <span class="gp">... </span><span class="k">except</span> <span class="ne">OSError</span><span class="p">:</span> |
| <span class="gp">... </span> <span class="k">pass</span> |
| </pre></div> |
| </div> |
| <p class="rubric">Methods</p> |
| <table class="longtable table autosummary"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.call" title="pyspark.mllib.recommendation.MatrixFactorizationModel.call"><code class="xref py py-obj docutils literal notranslate"><span class="pre">call</span></code></a>(name, *a)</p></td> |
| <td><p>Call method of java_model</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.load" title="pyspark.mllib.recommendation.MatrixFactorizationModel.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(sc, path)</p></td> |
| <td><p>Load a model from the given path</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.predict" title="pyspark.mllib.recommendation.MatrixFactorizationModel.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(user, product)</p></td> |
| <td><p>Predicts rating for the given user and product.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.predictAll" title="pyspark.mllib.recommendation.MatrixFactorizationModel.predictAll"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predictAll</span></code></a>(user_product)</p></td> |
| <td><p>Returns a list of predicted ratings for input user and product pairs.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.productFeatures" title="pyspark.mllib.recommendation.MatrixFactorizationModel.productFeatures"><code class="xref py py-obj docutils literal notranslate"><span class="pre">productFeatures</span></code></a>()</p></td> |
| <td><p>Returns a paired RDD, where the first element is the product and the second is an array of features corresponding to that product.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.recommendProducts" title="pyspark.mllib.recommendation.MatrixFactorizationModel.recommendProducts"><code class="xref py py-obj docutils literal notranslate"><span class="pre">recommendProducts</span></code></a>(user, num)</p></td> |
| <td><p>Recommends the top “num” number of products for a given user and returns a list of Rating objects sorted by the predicted rating in descending order.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.recommendProductsForUsers" title="pyspark.mllib.recommendation.MatrixFactorizationModel.recommendProductsForUsers"><code class="xref py py-obj docutils literal notranslate"><span class="pre">recommendProductsForUsers</span></code></a>(num)</p></td> |
| <td><p>Recommends the top “num” number of products for all users.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.recommendUsers" title="pyspark.mllib.recommendation.MatrixFactorizationModel.recommendUsers"><code class="xref py py-obj docutils literal notranslate"><span class="pre">recommendUsers</span></code></a>(product, num)</p></td> |
| <td><p>Recommends the top “num” number of users for a given product and returns a list of Rating objects sorted by the predicted rating in descending order.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.recommendUsersForProducts" title="pyspark.mllib.recommendation.MatrixFactorizationModel.recommendUsersForProducts"><code class="xref py py-obj docutils literal notranslate"><span class="pre">recommendUsersForProducts</span></code></a>(num)</p></td> |
| <td><p>Recommends the top “num” number of users for all products.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.save" title="pyspark.mllib.recommendation.MatrixFactorizationModel.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(sc, path)</p></td> |
| <td><p>Save this model to the given path.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.userFeatures" title="pyspark.mllib.recommendation.MatrixFactorizationModel.userFeatures"><code class="xref py py-obj docutils literal notranslate"><span class="pre">userFeatures</span></code></a>()</p></td> |
| <td><p>Returns a paired RDD, where the first element is the user and the second is an array of features corresponding to that user.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Attributes</p> |
| <table class="longtable table autosummary"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.rank" title="pyspark.mllib.recommendation.MatrixFactorizationModel.rank"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rank</span></code></a></p></td> |
| <td><p>Rank for the features in this model</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Methods Documentation</p> |
| <dl class="py method"> |
| <dt id="pyspark.mllib.recommendation.MatrixFactorizationModel.call"> |
| <code class="sig-name descname">call</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">name</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="o">*</span><span class="n">a</span><span class="p">:</span> <span class="n">Any</span></em><span class="sig-paren">)</span> → Any<a class="headerlink" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.call" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Call method of java_model</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.recommendation.MatrixFactorizationModel.load"> |
| <em class="property">classmethod </em><code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sc</span><span class="p">:</span> <span class="n">pyspark.context.SparkContext</span></em>, <em class="sig-param"><span class="n">path</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → <a class="reference internal" href="#pyspark.mllib.recommendation.MatrixFactorizationModel" title="pyspark.mllib.recommendation.MatrixFactorizationModel">pyspark.mllib.recommendation.MatrixFactorizationModel</a><a class="reference internal" href="../../_modules/pyspark/mllib/recommendation.html#MatrixFactorizationModel.load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.load" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load a model from the given path</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.3.1.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.recommendation.MatrixFactorizationModel.predict"> |
| <code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">user</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">product</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span> → float<a class="reference internal" href="../../_modules/pyspark/mllib/recommendation.html#MatrixFactorizationModel.predict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.predict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Predicts rating for the given user and product.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 0.9.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.recommendation.MatrixFactorizationModel.predictAll"> |
| <code class="sig-name descname">predictAll</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">user_product</span><span class="p">:</span> <span class="n">pyspark.rdd.RDD<span class="p">[</span>Tuple<span class="p">[</span>int<span class="p">, </span>int<span class="p">]</span><span class="p">]</span></span></em><span class="sig-paren">)</span> → pyspark.rdd.RDD<span class="p">[</span><a class="reference internal" href="pyspark.mllib.recommendation.Rating.html#pyspark.mllib.recommendation.Rating" title="pyspark.mllib.recommendation.Rating">pyspark.mllib.recommendation.Rating</a><span class="p">]</span><a class="reference internal" href="../../_modules/pyspark/mllib/recommendation.html#MatrixFactorizationModel.predictAll"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.predictAll" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a list of predicted ratings for input user and product |
| pairs.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 0.9.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.recommendation.MatrixFactorizationModel.productFeatures"> |
| <code class="sig-name descname">productFeatures</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → pyspark.rdd.RDD<span class="p">[</span>Tuple<span class="p">[</span>int<span class="p">, </span>array.array<span class="p">]</span><span class="p">]</span><a class="reference internal" href="../../_modules/pyspark/mllib/recommendation.html#MatrixFactorizationModel.productFeatures"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.productFeatures" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a paired RDD, where the first element is the product and the |
| second is an array of features corresponding to that product.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.2.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.recommendation.MatrixFactorizationModel.recommendProducts"> |
| <code class="sig-name descname">recommendProducts</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">user</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">num</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span> → List<span class="p">[</span><a class="reference internal" href="pyspark.mllib.recommendation.Rating.html#pyspark.mllib.recommendation.Rating" title="pyspark.mllib.recommendation.Rating">pyspark.mllib.recommendation.Rating</a><span class="p">]</span><a class="reference internal" href="../../_modules/pyspark/mllib/recommendation.html#MatrixFactorizationModel.recommendProducts"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.recommendProducts" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Recommends the top “num” number of products for a given user and |
| returns a list of Rating objects sorted by the predicted rating in |
| descending order.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.4.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.recommendation.MatrixFactorizationModel.recommendProductsForUsers"> |
| <code class="sig-name descname">recommendProductsForUsers</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">num</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span> → pyspark.rdd.RDD[Tuple[int, Tuple[pyspark.mllib.recommendation.Rating, …]]]<a class="reference internal" href="../../_modules/pyspark/mllib/recommendation.html#MatrixFactorizationModel.recommendProductsForUsers"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.recommendProductsForUsers" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Recommends the top “num” number of products for all users. The |
| number of recommendations returned per user may be less than “num”.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.recommendation.MatrixFactorizationModel.recommendUsers"> |
| <code class="sig-name descname">recommendUsers</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">product</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">num</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span> → List<span class="p">[</span><a class="reference internal" href="pyspark.mllib.recommendation.Rating.html#pyspark.mllib.recommendation.Rating" title="pyspark.mllib.recommendation.Rating">pyspark.mllib.recommendation.Rating</a><span class="p">]</span><a class="reference internal" href="../../_modules/pyspark/mllib/recommendation.html#MatrixFactorizationModel.recommendUsers"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.recommendUsers" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Recommends the top “num” number of users for a given product and |
| returns a list of Rating objects sorted by the predicted rating in |
| descending order.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.4.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.recommendation.MatrixFactorizationModel.recommendUsersForProducts"> |
| <code class="sig-name descname">recommendUsersForProducts</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">num</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span> → pyspark.rdd.RDD[Tuple[int, Tuple[pyspark.mllib.recommendation.Rating, …]]]<a class="reference internal" href="../../_modules/pyspark/mllib/recommendation.html#MatrixFactorizationModel.recommendUsersForProducts"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.recommendUsersForProducts" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Recommends the top “num” number of users for all products. The |
| number of recommendations returned per product may be less than |
| “num”.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.recommendation.MatrixFactorizationModel.save"> |
| <code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sc</span><span class="p">:</span> <span class="n">pyspark.context.SparkContext</span></em>, <em class="sig-param"><span class="n">path</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.save" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save this model to the given path.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.3.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.recommendation.MatrixFactorizationModel.userFeatures"> |
| <code class="sig-name descname">userFeatures</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → pyspark.rdd.RDD<span class="p">[</span>Tuple<span class="p">[</span>int<span class="p">, </span>array.array<span class="p">]</span><span class="p">]</span><a class="reference internal" href="../../_modules/pyspark/mllib/recommendation.html#MatrixFactorizationModel.userFeatures"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.userFeatures" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a paired RDD, where the first element is the user and the |
| second is an array of features corresponding to that user.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.2.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <p class="rubric">Attributes Documentation</p> |
| <dl class="py attribute"> |
| <dt id="pyspark.mllib.recommendation.MatrixFactorizationModel.rank"> |
| <code class="sig-name descname">rank</code><a class="headerlink" href="#pyspark.mllib.recommendation.MatrixFactorizationModel.rank" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Rank for the features in this model</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.4.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| </div> |
| |
| |
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