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| <div class="section" id="module-apache_beam.transforms.stats"> |
| <span id="apache-beam-transforms-stats-module"></span><h1>apache_beam.transforms.stats module<a class="headerlink" href="#module-apache_beam.transforms.stats" title="Permalink to this headline">¶</a></h1> |
| <p>This module has all statistic related transforms.</p> |
| <p>This ApproximateUnique class will be deprecated [1]. PLease look into using |
| HLLCount in the zetasketch extension module [2].</p> |
| <p>[1] <a class="reference external" href="https://lists.apache.org/thread.html/501605df5027567099b81f18c080469661fb426">https://lists.apache.org/thread.html/501605df5027567099b81f18c080469661fb426</a> |
| 4a002615fa1510502%40%3Cdev.beam.apache.org%3E |
| [2] <a class="reference external" href="https://beam.apache.org/releases/javadoc/2.16.0/org/apache/beam/sdk/extensio">https://beam.apache.org/releases/javadoc/2.16.0/org/apache/beam/sdk/extensio</a> |
| ns/zetasketch/HllCount.html</p> |
| <dl class="class"> |
| <dt id="apache_beam.transforms.stats.ApproximateQuantiles"> |
| <em class="property">class </em><code class="descclassname">apache_beam.transforms.stats.</code><code class="descname">ApproximateQuantiles</code><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateQuantiles" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p> |
| <p>PTransform for getting the idea of data distribution using approximate N-tile |
| (e.g. quartiles, percentiles etc.) either globally or per-key.</p> |
| <p class="rubric">Examples</p> |
| <p>in: list(range(101)), num_quantiles=5</p> |
| <p>out: [0, 25, 50, 75, 100]</p> |
| <dl class="docutils"> |
| <dt>in: [(i, 1 if i<10 else 1e-5) for i in range(101)], num_quantiles=5,</dt> |
| <dd>weighted=True</dd> |
| </dl> |
| <p>out: [0, 2, 5, 7, 100]</p> |
| <dl class="docutils"> |
| <dt>in: [list(range(10)), …, list(range(90, 101))], num_quantiles=5,</dt> |
| <dd>input_batched=True</dd> |
| </dl> |
| <p>out: [0, 25, 50, 75, 100]</p> |
| <dl class="docutils"> |
| <dt>in: [(list(range(10)), [1]*10), (list(range(10)), [0]*10), …,</dt> |
| <dd>(list(range(90, 101)), [0]*11)], num_quantiles=5, input_batched=True, |
| weighted=True</dd> |
| </dl> |
| <p>out: [0, 2, 5, 7, 100]</p> |
| <dl class="class"> |
| <dt id="apache_beam.transforms.stats.ApproximateQuantiles.Globally"> |
| <em class="property">class </em><code class="descname">Globally</code><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateQuantiles.Globally" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="apache_beam.transforms.ptransform.html#apache_beam.transforms.ptransform.PTransform" title="apache_beam.transforms.ptransform.PTransform"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.transforms.ptransform.PTransform</span></code></a></p> |
| <p>PTransform takes PCollection and returns a list whose single value is |
| approximate N-tiles of the input collection globally.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> |
| <li><strong>num_quantiles</strong> – number of elements in the resulting quantiles values list.</li> |
| <li><strong>key</strong> – (optional) Key is a mapping of elements to a comparable key, similar |
| to the key argument of Python’s sorting methods.</li> |
| <li><strong>reverse</strong> – (optional) whether to order things smallest to largest, rather |
| than largest to smallest.</li> |
| <li><strong>weighted</strong> – (optional) if set to True, the transform returns weighted |
| quantiles. The input PCollection is then expected to contain tuples of |
| input values with the corresponding weight.</li> |
| <li><strong>input_batched</strong> – (optional) if set to True, the transform expects each |
| element of input PCollection to be a batch, which is a list of elements |
| for non-weighted case and a tuple of lists of elements and weights for |
| weighted. Provides a way to accumulate multiple elements at a time more |
| efficiently.</li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="attribute"> |
| <dt id="apache_beam.transforms.stats.ApproximateQuantiles.Globally.display_data"> |
| <code class="descname">display_data</code><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateQuantiles.Globally.display_data" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.transforms.stats.ApproximateQuantiles.Globally.expand"> |
| <code class="descname">expand</code><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateQuantiles.Globally.expand" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="apache_beam.transforms.stats.ApproximateQuantiles.PerKey"> |
| <em class="property">class </em><code class="descname">PerKey</code><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateQuantiles.PerKey" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="apache_beam.transforms.ptransform.html#apache_beam.transforms.ptransform.PTransform" title="apache_beam.transforms.ptransform.PTransform"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.transforms.ptransform.PTransform</span></code></a></p> |
| <p>PTransform takes PCollection of KV and returns a list based on each key |
| whose single value is list of approximate N-tiles of the input element of |
| the key.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> |
| <li><strong>num_quantiles</strong> – number of elements in the resulting quantiles values list.</li> |
| <li><strong>key</strong> – (optional) Key is a mapping of elements to a comparable key, similar |
| to the key argument of Python’s sorting methods.</li> |
| <li><strong>reverse</strong> – (optional) whether to order things smallest to largest, rather |
| than largest to smallest.</li> |
| <li><strong>weighted</strong> – (optional) if set to True, the transform returns weighted |
| quantiles. The input PCollection is then expected to contain tuples of |
| input values with the corresponding weight.</li> |
| <li><strong>input_batched</strong> – (optional) if set to True, the transform expects each |
| element of input PCollection to be a batch, which is a list of elements |
| for non-weighted case and a tuple of lists of elements and weights for |
| weighted. Provides a way to accumulate multiple elements at a time more |
| efficiently.</li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="attribute"> |
| <dt id="apache_beam.transforms.stats.ApproximateQuantiles.PerKey.display_data"> |
| <code class="descname">display_data</code><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateQuantiles.PerKey.display_data" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.transforms.stats.ApproximateQuantiles.PerKey.expand"> |
| <code class="descname">expand</code><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateQuantiles.PerKey.expand" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="apache_beam.transforms.stats.ApproximateUnique"> |
| <em class="property">class </em><code class="descclassname">apache_beam.transforms.stats.</code><code class="descname">ApproximateUnique</code><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateUnique" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p> |
| <p>Hashes input elements and uses those to extrapolate the size of the entire |
| set of hash values by assuming the rest of the hash values are as densely |
| distributed as the sample space.</p> |
| <dl class="class"> |
| <dt id="apache_beam.transforms.stats.ApproximateUnique.Globally"> |
| <em class="property">class </em><code class="descname">Globally</code><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateUnique.Globally" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="apache_beam.transforms.ptransform.html#apache_beam.transforms.ptransform.PTransform" title="apache_beam.transforms.ptransform.PTransform"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.transforms.ptransform.PTransform</span></code></a></p> |
| <p>Approximate.Globally approximate number of unique values</p> |
| <dl class="attribute"> |
| <dt id="apache_beam.transforms.stats.ApproximateUnique.Globally.expand"> |
| <code class="descname">expand</code><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateUnique.Globally.expand" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="apache_beam.transforms.stats.ApproximateUnique.PerKey"> |
| <em class="property">class </em><code class="descname">PerKey</code><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateUnique.PerKey" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="apache_beam.transforms.ptransform.html#apache_beam.transforms.ptransform.PTransform" title="apache_beam.transforms.ptransform.PTransform"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.transforms.ptransform.PTransform</span></code></a></p> |
| <p>Approximate.PerKey approximate number of unique values per key</p> |
| <dl class="attribute"> |
| <dt id="apache_beam.transforms.stats.ApproximateUnique.PerKey.expand"> |
| <code class="descname">expand</code><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateUnique.PerKey.expand" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.transforms.stats.ApproximateUnique.parse_input_params"> |
| <code class="descname">parse_input_params</code><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateUnique.parse_input_params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Check if input params are valid and return sample size.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>size</strong> – an int not smaller than 16, which we would use to estimate |
| number of unique values.</li> |
| <li><strong>error</strong> – max estimation error, which is a float between 0.01 and 0.50. |
| If error is given, sample size will be calculated from error with |
| _get_sample_size_from_est_error function.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">sample size</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Raises:</th><td class="field-body"><p class="first last">ValueError: If both size and error are given, or neither is given, or |
| values are out of range.</p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
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| </html> |