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| <div class="section" id="bucketedrandomprojectionlshmodel"> |
| <h1>BucketedRandomProjectionLSHModel<a class="headerlink" href="#bucketedrandomprojectionlshmodel" title="Permalink to this headline">¶</a></h1> |
| <dl class="py class"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel"> |
| <em class="property">class </em><code class="sig-prename descclassname">pyspark.ml.feature.</code><code class="sig-name descname">BucketedRandomProjectionLSHModel</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">java_model</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>JavaObject<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/pyspark/ml/feature.html#BucketedRandomProjectionLSHModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Model fitted by <a class="reference internal" href="pyspark.ml.feature.BucketedRandomProjectionLSH.html#pyspark.ml.feature.BucketedRandomProjectionLSH" title="pyspark.ml.feature.BucketedRandomProjectionLSH"><code class="xref py py-class docutils literal notranslate"><span class="pre">BucketedRandomProjectionLSH</span></code></a>, where multiple random vectors are |
| stored. The vectors are normalized to be unit vectors and each vector is used in a hash |
| function: <span class="math notranslate nohighlight">\(h_i(x) = floor(r_i \cdot x / bucketLength)\)</span> where <span class="math notranslate nohighlight">\(r_i\)</span> is the |
| i-th random unit vector. The number of buckets will be <cite>(max L2 norm of input vectors) / |
| bucketLength</cite>.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 2.2.0.</span></p> |
| </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.ml.feature.BucketedRandomProjectionLSHModel.approxNearestNeighbors" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.approxNearestNeighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">approxNearestNeighbors</span></code></a>(dataset, key, …[, …])</p></td> |
| <td><p>Given a large dataset and an item, approximately find at most k items which have the closest distance to the item.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.approxSimilarityJoin" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.approxSimilarityJoin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">approxSimilarityJoin</span></code></a>(datasetA, datasetB, …)</p></td> |
| <td><p>Join two datasets to approximately find all pairs of rows whose distance are smaller than the threshold.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.clear" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.clear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">clear</span></code></a>(param)</p></td> |
| <td><p>Clears a param from the param map if it has been explicitly set.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.copy" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.copy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">copy</span></code></a>([extra])</p></td> |
| <td><p>Creates a copy of this instance with the same uid and some extra params.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.explainParam" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.explainParam"><code class="xref py py-obj docutils literal notranslate"><span class="pre">explainParam</span></code></a>(param)</p></td> |
| <td><p>Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.explainParams" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.explainParams"><code class="xref py py-obj docutils literal notranslate"><span class="pre">explainParams</span></code></a>()</p></td> |
| <td><p>Returns the documentation of all params with their optionally default values and user-supplied values.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.extractParamMap" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.extractParamMap"><code class="xref py py-obj docutils literal notranslate"><span class="pre">extractParamMap</span></code></a>([extra])</p></td> |
| <td><p>Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.getBucketLength" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.getBucketLength"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getBucketLength</span></code></a>()</p></td> |
| <td><p>Gets the value of bucketLength or its default value.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.getInputCol" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.getInputCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getInputCol</span></code></a>()</p></td> |
| <td><p>Gets the value of inputCol or its default value.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.getNumHashTables" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.getNumHashTables"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getNumHashTables</span></code></a>()</p></td> |
| <td><p>Gets the value of numHashTables or its default value.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.getOrDefault" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.getOrDefault"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getOrDefault</span></code></a>(param)</p></td> |
| <td><p>Gets the value of a param in the user-supplied param map or its default value.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.getOutputCol" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.getOutputCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getOutputCol</span></code></a>()</p></td> |
| <td><p>Gets the value of outputCol or its default value.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.getParam" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.getParam"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getParam</span></code></a>(paramName)</p></td> |
| <td><p>Gets a param by its name.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.hasDefault" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.hasDefault"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hasDefault</span></code></a>(param)</p></td> |
| <td><p>Checks whether a param has a default value.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.hasParam" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.hasParam"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hasParam</span></code></a>(paramName)</p></td> |
| <td><p>Tests whether this instance contains a param with a given (string) name.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.isDefined" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.isDefined"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isDefined</span></code></a>(param)</p></td> |
| <td><p>Checks whether a param is explicitly set by user or has a default value.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.isSet" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.isSet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isSet</span></code></a>(param)</p></td> |
| <td><p>Checks whether a param is explicitly set by user.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.load" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path)</p></td> |
| <td><p>Reads an ML instance from the input path, a shortcut of <cite>read().load(path)</cite>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.read" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.read"><code class="xref py py-obj docutils literal notranslate"><span class="pre">read</span></code></a>()</p></td> |
| <td><p>Returns an MLReader instance for this class.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.save" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(path)</p></td> |
| <td><p>Save this ML instance to the given path, a shortcut of ‘write().save(path)’.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.set" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.set"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set</span></code></a>(param, value)</p></td> |
| <td><p>Sets a parameter in the embedded param map.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.setInputCol" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.setInputCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setInputCol</span></code></a>(value)</p></td> |
| <td><p>Sets the value of <a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.inputCol" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.inputCol"><code class="xref py py-attr docutils literal notranslate"><span class="pre">inputCol</span></code></a>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.setOutputCol" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.setOutputCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setOutputCol</span></code></a>(value)</p></td> |
| <td><p>Sets the value of <a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.outputCol" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.outputCol"><code class="xref py py-attr docutils literal notranslate"><span class="pre">outputCol</span></code></a>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.transform" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(dataset[, params])</p></td> |
| <td><p>Transforms the input dataset with optional parameters.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.write" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.write"><code class="xref py py-obj docutils literal notranslate"><span class="pre">write</span></code></a>()</p></td> |
| <td><p>Returns an MLWriter instance for this ML instance.</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.ml.feature.BucketedRandomProjectionLSHModel.bucketLength" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.bucketLength"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bucketLength</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.inputCol" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.inputCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">inputCol</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.numHashTables" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.numHashTables"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numHashTables</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.outputCol" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.outputCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">outputCol</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.params" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">params</span></code></a></p></td> |
| <td><p>Returns all params ordered by name.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Methods Documentation</p> |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.approxNearestNeighbors"> |
| <code class="sig-name descname">approxNearestNeighbors</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span><span class="p">:</span> <span class="n">pyspark.sql.dataframe.DataFrame</span></em>, <em class="sig-param"><span class="n">key</span><span class="p">:</span> <span class="n"><a class="reference internal" href="pyspark.ml.linalg.Vector.html#pyspark.ml.linalg.Vector" title="pyspark.ml.linalg.Vector">pyspark.ml.linalg.Vector</a></span></em>, <em class="sig-param"><span class="n">numNearestNeighbors</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">distCol</span><span class="p">:</span> <span class="n">str</span> <span class="o">=</span> <span class="default_value">'distCol'</span></em><span class="sig-paren">)</span> → pyspark.sql.dataframe.DataFrame<a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.approxNearestNeighbors" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Given a large dataset and an item, approximately find at most k items which have the |
| closest distance to the item. If the <a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.outputCol" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.outputCol"><code class="xref py py-attr docutils literal notranslate"><span class="pre">outputCol</span></code></a> is missing, the method will |
| transform the data; if the <a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.outputCol" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.outputCol"><code class="xref py py-attr docutils literal notranslate"><span class="pre">outputCol</span></code></a> exists, it will use that. This allows |
| caching of the transformed data when necessary.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>dataset</strong><span class="classifier"><a class="reference internal" href="../pyspark.sql/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame" title="pyspark.sql.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.sql.DataFrame</span></code></a></span></dt><dd><p>The dataset to search for nearest neighbors of the key.</p> |
| </dd> |
| <dt><strong>key</strong><span class="classifier"><a class="reference internal" href="pyspark.ml.linalg.Vector.html#pyspark.ml.linalg.Vector" title="pyspark.ml.linalg.Vector"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.ml.linalg.Vector</span></code></a></span></dt><dd><p>Feature vector representing the item to search for.</p> |
| </dd> |
| <dt><strong>numNearestNeighbors</strong><span class="classifier">int</span></dt><dd><p>The maximum number of nearest neighbors.</p> |
| </dd> |
| <dt><strong>distCol</strong><span class="classifier">str</span></dt><dd><p>Output column for storing the distance between each result row and the key. |
| Use “distCol” as default value if it’s not specified.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="../pyspark.sql/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame" title="pyspark.sql.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.sql.DataFrame</span></code></a></dt><dd><p>A dataset containing at most k items closest to the key. A column “distCol” is |
| added to show the distance between each row and the key.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Notes</p> |
| <p>This method is experimental and will likely change behavior in the next release.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.approxSimilarityJoin"> |
| <code class="sig-name descname">approxSimilarityJoin</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">datasetA</span><span class="p">:</span> <span class="n">pyspark.sql.dataframe.DataFrame</span></em>, <em class="sig-param"><span class="n">datasetB</span><span class="p">:</span> <span class="n">pyspark.sql.dataframe.DataFrame</span></em>, <em class="sig-param"><span class="n">threshold</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">distCol</span><span class="p">:</span> <span class="n">str</span> <span class="o">=</span> <span class="default_value">'distCol'</span></em><span class="sig-paren">)</span> → pyspark.sql.dataframe.DataFrame<a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.approxSimilarityJoin" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Join two datasets to approximately find all pairs of rows whose distance are smaller than |
| the threshold. If the <a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.outputCol" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.outputCol"><code class="xref py py-attr docutils literal notranslate"><span class="pre">outputCol</span></code></a> is missing, the method will transform the data; |
| if the <a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.outputCol" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.outputCol"><code class="xref py py-attr docutils literal notranslate"><span class="pre">outputCol</span></code></a> exists, it will use that. This allows caching of the |
| transformed data when necessary.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>datasetA</strong><span class="classifier"><a class="reference internal" href="../pyspark.sql/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame" title="pyspark.sql.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.sql.DataFrame</span></code></a></span></dt><dd><p>One of the datasets to join.</p> |
| </dd> |
| <dt><strong>datasetB</strong><span class="classifier"><a class="reference internal" href="../pyspark.sql/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame" title="pyspark.sql.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.sql.DataFrame</span></code></a></span></dt><dd><p>Another dataset to join.</p> |
| </dd> |
| <dt><strong>threshold</strong><span class="classifier">float</span></dt><dd><p>The threshold for the distance of row pairs.</p> |
| </dd> |
| <dt><strong>distCol</strong><span class="classifier">str, optional</span></dt><dd><p>Output column for storing the distance between each pair of rows. Use |
| “distCol” as default value if it’s not specified.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="../pyspark.sql/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame" title="pyspark.sql.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.sql.DataFrame</span></code></a></dt><dd><p>A joined dataset containing pairs of rows. The original rows are in columns |
| “datasetA” and “datasetB”, and a column “distCol” is added to show the distance |
| between each pair.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.clear"> |
| <code class="sig-name descname">clear</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span><span class="p">:</span> <span class="n"><a class="reference internal" href="pyspark.ml.param.Param.html#pyspark.ml.param.Param" title="pyspark.ml.param.Param">pyspark.ml.param.Param</a></span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.clear" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Clears a param from the param map if it has been explicitly set.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.copy"> |
| <code class="sig-name descname">copy</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">extra</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>ParamMap<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span> → JP<a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.copy" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Creates a copy of this instance with the same uid and some |
| extra params. This implementation first calls Params.copy and |
| then make a copy of the companion Java pipeline component with |
| extra params. So both the Python wrapper and the Java pipeline |
| component get copied.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><strong>extra</strong><span class="classifier">dict, optional</span></dt><dd><p>Extra parameters to copy to the new instance</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><code class="xref py py-class docutils literal notranslate"><span class="pre">JavaParams</span></code></dt><dd><p>Copy of this instance</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.explainParam"> |
| <code class="sig-name descname">explainParam</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>str<span class="p">, </span><a class="reference internal" href="pyspark.ml.param.Param.html#pyspark.ml.param.Param" title="pyspark.ml.param.Param">pyspark.ml.param.Param</a><span class="p">]</span></span></em><span class="sig-paren">)</span> → str<a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.explainParam" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Explains a single param and returns its name, doc, and optional |
| default value and user-supplied value in a string.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.explainParams"> |
| <code class="sig-name descname">explainParams</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → str<a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.explainParams" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns the documentation of all params with their optionally |
| default values and user-supplied values.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.extractParamMap"> |
| <code class="sig-name descname">extractParamMap</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">extra</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>ParamMap<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span> → ParamMap<a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.extractParamMap" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Extracts the embedded default param values and user-supplied |
| values, and then merges them with extra values from input into |
| a flat param map, where the latter value is used if there exist |
| conflicts, i.e., with ordering: default param values < |
| user-supplied values < extra.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><strong>extra</strong><span class="classifier">dict, optional</span></dt><dd><p>extra param values</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt>dict</dt><dd><p>merged param map</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.getBucketLength"> |
| <code class="sig-name descname">getBucketLength</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → float<a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.getBucketLength" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of bucketLength or its default value.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 2.2.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.getInputCol"> |
| <code class="sig-name descname">getInputCol</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → str<a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.getInputCol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of inputCol or its default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.getNumHashTables"> |
| <code class="sig-name descname">getNumHashTables</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → int<a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.getNumHashTables" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of numHashTables or its default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.getOrDefault"> |
| <code class="sig-name descname">getOrDefault</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>str<span class="p">, </span><a class="reference internal" href="pyspark.ml.param.Param.html#pyspark.ml.param.Param" title="pyspark.ml.param.Param">pyspark.ml.param.Param</a><span class="p">[</span>T<span class="p">]</span><span class="p">]</span></span></em><span class="sig-paren">)</span> → Union<span class="p">[</span>Any<span class="p">, </span>T<span class="p">]</span><a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.getOrDefault" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of a param in the user-supplied param map or its |
| default value. Raises an error if neither is set.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.getOutputCol"> |
| <code class="sig-name descname">getOutputCol</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → str<a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.getOutputCol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of outputCol or its default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.getParam"> |
| <code class="sig-name descname">getParam</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">paramName</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → <a class="reference internal" href="pyspark.ml.param.Param.html#pyspark.ml.param.Param" title="pyspark.ml.param.Param">pyspark.ml.param.Param</a><a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.getParam" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets a param by its name.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.hasDefault"> |
| <code class="sig-name descname">hasDefault</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>str<span class="p">, </span><a class="reference internal" href="pyspark.ml.param.Param.html#pyspark.ml.param.Param" title="pyspark.ml.param.Param">pyspark.ml.param.Param</a><span class="p">[</span>Any<span class="p">]</span><span class="p">]</span></span></em><span class="sig-paren">)</span> → bool<a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.hasDefault" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Checks whether a param has a default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.hasParam"> |
| <code class="sig-name descname">hasParam</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">paramName</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → bool<a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.hasParam" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Tests whether this instance contains a param with a given |
| (string) name.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.isDefined"> |
| <code class="sig-name descname">isDefined</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>str<span class="p">, </span><a class="reference internal" href="pyspark.ml.param.Param.html#pyspark.ml.param.Param" title="pyspark.ml.param.Param">pyspark.ml.param.Param</a><span class="p">[</span>Any<span class="p">]</span><span class="p">]</span></span></em><span class="sig-paren">)</span> → bool<a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.isDefined" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Checks whether a param is explicitly set by user or has |
| a default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.isSet"> |
| <code class="sig-name descname">isSet</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>str<span class="p">, </span><a class="reference internal" href="pyspark.ml.param.Param.html#pyspark.ml.param.Param" title="pyspark.ml.param.Param">pyspark.ml.param.Param</a><span class="p">[</span>Any<span class="p">]</span><span class="p">]</span></span></em><span class="sig-paren">)</span> → bool<a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.isSet" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Checks whether a param is explicitly set by user.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.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">path</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → RL<a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.load" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Reads an ML instance from the input path, a shortcut of <cite>read().load(path)</cite>.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.read"> |
| <em class="property">classmethod </em><code class="sig-name descname">read</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → pyspark.ml.util.JavaMLReader<span class="p">[</span>RL<span class="p">]</span><a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.read" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns an MLReader instance for this class.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.save"> |
| <code class="sig-name descname">save</code><span class="sig-paren">(</span><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.ml.feature.BucketedRandomProjectionLSHModel.save" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save this ML instance to the given path, a shortcut of ‘write().save(path)’.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.set"> |
| <code class="sig-name descname">set</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span><span class="p">:</span> <span class="n"><a class="reference internal" href="pyspark.ml.param.Param.html#pyspark.ml.param.Param" title="pyspark.ml.param.Param">pyspark.ml.param.Param</a></span></em>, <em class="sig-param"><span class="n">value</span><span class="p">:</span> <span class="n">Any</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.set" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets a parameter in the embedded param map.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.setInputCol"> |
| <code class="sig-name descname">setInputCol</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → P<a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.setInputCol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets the value of <a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.inputCol" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.inputCol"><code class="xref py py-attr docutils literal notranslate"><span class="pre">inputCol</span></code></a>.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.setOutputCol"> |
| <code class="sig-name descname">setOutputCol</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → P<a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.setOutputCol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets the value of <a class="reference internal" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.outputCol" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel.outputCol"><code class="xref py py-attr docutils literal notranslate"><span class="pre">outputCol</span></code></a>.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.transform"> |
| <code class="sig-name descname">transform</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span><span class="p">:</span> <span class="n">pyspark.sql.dataframe.DataFrame</span></em>, <em class="sig-param"><span class="n">params</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>ParamMap<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span> → pyspark.sql.dataframe.DataFrame<a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.transform" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Transforms the input dataset with optional parameters.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.3.0.</span></p> |
| </div> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>dataset</strong><span class="classifier"><a class="reference internal" href="../pyspark.sql/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame" title="pyspark.sql.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.sql.DataFrame</span></code></a></span></dt><dd><p>input dataset</p> |
| </dd> |
| <dt><strong>params</strong><span class="classifier">dict, optional</span></dt><dd><p>an optional param map that overrides embedded params.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="../pyspark.sql/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame" title="pyspark.sql.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.sql.DataFrame</span></code></a></dt><dd><p>transformed dataset</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.write"> |
| <code class="sig-name descname">write</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → pyspark.ml.util.JavaMLWriter<a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.write" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns an MLWriter instance for this ML instance.</p> |
| </dd></dl> |
| |
| <p class="rubric">Attributes Documentation</p> |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.bucketLength"> |
| <code class="sig-name descname">bucketLength</code><em class="property"> = Param(parent='undefined', name='bucketLength', doc='the length of each hash bucket, a larger bucket lowers the false negative rate.')</em><a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.bucketLength" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.inputCol"> |
| <code class="sig-name descname">inputCol</code><em class="property"> = Param(parent='undefined', name='inputCol', doc='input column name.')</em><a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.inputCol" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.numHashTables"> |
| <code class="sig-name descname">numHashTables</code><em class="property">: pyspark.ml.param.Param[int]</em><em class="property"> = Param(parent='undefined', name='numHashTables', doc='number of hash tables, where increasing number of hash tables lowers the false negative rate, and decreasing it improves the running performance.')</em><a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.numHashTables" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.outputCol"> |
| <code class="sig-name descname">outputCol</code><em class="property"> = Param(parent='undefined', name='outputCol', doc='output column name.')</em><a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.outputCol" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.feature.BucketedRandomProjectionLSHModel.params"> |
| <code class="sig-name descname">params</code><a class="headerlink" href="#pyspark.ml.feature.BucketedRandomProjectionLSHModel.params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns all params ordered by name. The default implementation |
| uses <code class="xref py py-func docutils literal notranslate"><span class="pre">dir()</span></code> to get all attributes of type |
| <code class="xref py py-class docutils literal notranslate"><span class="pre">Param</span></code>.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| </div> |
| |
| |
| </div> |
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