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   <div class="section" id="minhashlshmodel">
 <h1>MinHashLSHModel<a class="headerlink" href="#minhashlshmodel" title="Permalink to this headline">¶</a></h1>
 <dl class="py class">
 <dt id="pyspark.ml.feature.MinHashLSHModel">
 <em class="property">class </em><code class="sig-prename descclassname">pyspark.ml.feature.</code><code class="sig-name descname">MinHashLSHModel</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">java_model</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#MinHashLSHModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel" title="Permalink to this definition">¶</a></dt>
 <dd><p>Model produced by <a class="reference internal" href="pyspark.ml.feature.MinHashLSH.html#pyspark.ml.feature.MinHashLSH" title="pyspark.ml.feature.MinHashLSH"><code class="xref py py-class docutils literal notranslate"><span class="pre">MinHashLSH</span></code></a>, where where multiple hash functions are stored. Each
 hash function is picked from the following family of hash functions, where <span class="math notranslate nohighlight">\(a_i\)</span> and
 <span class="math notranslate nohighlight">\(b_i\)</span> are randomly chosen integers less than prime:
 <span class="math notranslate nohighlight">\(h_i(x) = ((x \cdot a_i + b_i) \mod prime)\)</span> This hash family is approximately min-wise
 independent according to the reference.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">New in version 2.2.0.</span></p>
 </div>
 <p class="rubric">Notes</p>
 <p>See Tom Bohman, Colin Cooper, and Alan Frieze. “Min-wise independent linear permutations.”
 Electronic Journal of Combinatorics 7 (2000): R26.</p>
 <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.MinHashLSHModel.approxNearestNeighbors" title="pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.approxSimilarityJoin" title="pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.clear" title="pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.copy" title="pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.explainParam" title="pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.explainParams" title="pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.extractParamMap" title="pyspark.ml.feature.MinHashLSHModel.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 &lt; user-supplied values &lt; extra.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.getInputCol" title="pyspark.ml.feature.MinHashLSHModel.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-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.getNumHashTables" title="pyspark.ml.feature.MinHashLSHModel.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-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.getOrDefault" title="pyspark.ml.feature.MinHashLSHModel.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-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.getOutputCol" title="pyspark.ml.feature.MinHashLSHModel.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-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.getParam" title="pyspark.ml.feature.MinHashLSHModel.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-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.hasDefault" title="pyspark.ml.feature.MinHashLSHModel.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-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.hasParam" title="pyspark.ml.feature.MinHashLSHModel.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-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.isDefined" title="pyspark.ml.feature.MinHashLSHModel.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-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.isSet" title="pyspark.ml.feature.MinHashLSHModel.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-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.load" title="pyspark.ml.feature.MinHashLSHModel.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-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.read" title="pyspark.ml.feature.MinHashLSHModel.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-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.save" title="pyspark.ml.feature.MinHashLSHModel.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-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.set" title="pyspark.ml.feature.MinHashLSHModel.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-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.setInputCol" title="pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.inputCol" title="pyspark.ml.feature.MinHashLSHModel.inputCol"><code class="xref py py-attr docutils literal notranslate"><span class="pre">inputCol</span></code></a>.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.setOutputCol" title="pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.outputCol" title="pyspark.ml.feature.MinHashLSHModel.outputCol"><code class="xref py py-attr docutils literal notranslate"><span class="pre">outputCol</span></code></a>.</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.transform" title="pyspark.ml.feature.MinHashLSHModel.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-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.write" title="pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.inputCol" title="pyspark.ml.feature.MinHashLSHModel.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-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.numHashTables" title="pyspark.ml.feature.MinHashLSHModel.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-odd"><td><p><a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.outputCol" title="pyspark.ml.feature.MinHashLSHModel.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-even"><td><p><a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.params" title="pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.approxNearestNeighbors">
 <code class="sig-name descname">approxNearestNeighbors</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="n">key</span></em>, <em class="sig-param"><span class="n">numNearestNeighbors</span></em>, <em class="sig-param"><span class="n">distCol</span><span class="o">=</span><span class="default_value">'distCol'</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.outputCol" title="pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.outputCol" title="pyspark.ml.feature.MinHashLSHModel.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.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.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.MinHashLSHModel.approxSimilarityJoin">
 <code class="sig-name descname">approxSimilarityJoin</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">datasetA</span></em>, <em class="sig-param"><span class="n">datasetB</span></em>, <em class="sig-param"><span class="n">threshold</span></em>, <em class="sig-param"><span class="n">distCol</span><span class="o">=</span><span class="default_value">'distCol'</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.outputCol" title="pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.outputCol" title="pyspark.ml.feature.MinHashLSHModel.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.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.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.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.MinHashLSHModel.clear">
 <code class="sig-name descname">clear</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.copy">
 <code class="sig-name descname">copy</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">extra</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.explainParam">
 <code class="sig-name descname">explainParam</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.explainParams">
 <code class="sig-name descname">explainParams</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.extractParamMap">
 <code class="sig-name descname">extractParamMap</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">extra</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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 &lt;
 user-supplied values &lt; 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.MinHashLSHModel.getInputCol">
 <code class="sig-name descname">getInputCol</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.getNumHashTables">
 <code class="sig-name descname">getNumHashTables</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.getOrDefault">
 <code class="sig-name descname">getOrDefault</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.getOutputCol">
 <code class="sig-name descname">getOutputCol</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.getParam">
 <code class="sig-name descname">getParam</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">paramName</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.hasDefault">
 <code class="sig-name descname">hasDefault</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.hasParam">
 <code class="sig-name descname">hasParam</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">paramName</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.isDefined">
 <code class="sig-name descname">isDefined</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.isSet">
 <code class="sig-name descname">isSet</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.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></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.read">
 <em class="property">classmethod </em><code class="sig-name descname">read</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.save">
 <code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">path</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.set">
 <code class="sig-name descname">set</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span></em>, <em class="sig-param"><span class="n">value</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.setInputCol">
 <code class="sig-name descname">setInputCol</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.setInputCol" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the value of <a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.inputCol" title="pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.setOutputCol">
 <code class="sig-name descname">setOutputCol</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.setOutputCol" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the value of <a class="reference internal" href="#pyspark.ml.feature.MinHashLSHModel.outputCol" title="pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.transform">
 <code class="sig-name descname">transform</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="n">params</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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.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.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.MinHashLSHModel.write">
 <code class="sig-name descname">write</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.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.MinHashLSHModel.inputCol" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="py attribute">
 <dt id="pyspark.ml.feature.MinHashLSHModel.numHashTables">
 <code class="sig-name descname">numHashTables</code><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.MinHashLSHModel.numHashTables" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="py attribute">
 <dt id="pyspark.ml.feature.MinHashLSHModel.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.MinHashLSHModel.outputCol" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="py attribute">
 <dt id="pyspark.ml.feature.MinHashLSHModel.params">
 <code class="sig-name descname">params</code><a class="headerlink" href="#pyspark.ml.feature.MinHashLSHModel.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>


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