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| <h1>Source code for apache_beam.transforms.stats</h1><div class="highlight"><pre> |
| <span></span><span class="c1">#</span> |
| <span class="c1"># Licensed to the Apache Software Foundation (ASF) under one or more</span> |
| <span class="c1"># contributor license agreements. See the NOTICE file distributed with</span> |
| <span class="c1"># this work for additional information regarding copyright ownership.</span> |
| <span class="c1"># The ASF licenses this file to You under the Apache License, Version 2.0</span> |
| <span class="c1"># (the "License"); you may not use this file except in compliance with</span> |
| <span class="c1"># the License. You may obtain a copy of the License at</span> |
| <span class="c1">#</span> |
| <span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span> |
| <span class="c1">#</span> |
| <span class="c1"># Unless required by applicable law or agreed to in writing, software</span> |
| <span class="c1"># distributed under the License is distributed on an "AS IS" BASIS,</span> |
| <span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span> |
| <span class="c1"># See the License for the specific language governing permissions and</span> |
| <span class="c1"># limitations under the License.</span> |
| <span class="c1">#</span> |
| |
| <span class="c1"># cython: language_level=3</span> |
| |
| <span class="sd">"""This module has all statistic related transforms.</span> |
| |
| <span class="sd">This ApproximateUnique class will be deprecated [1]. PLease look into using</span> |
| <span class="sd">HLLCount in the zetasketch extension module [2].</span> |
| |
| <span class="sd">[1] https://lists.apache.org/thread.html/501605df5027567099b81f18c080469661fb426</span> |
| <span class="sd">4a002615fa1510502%40%3Cdev.beam.apache.org%3E</span> |
| <span class="sd">[2] https://beam.apache.org/releases/javadoc/2.16.0/org/apache/beam/sdk/extensio</span> |
| <span class="sd">ns/zetasketch/HllCount.html</span> |
| <span class="sd">"""</span> |
| |
| <span class="c1"># pytype: skip-file</span> |
| |
| <span class="kn">import</span> <span class="nn">hashlib</span> |
| <span class="kn">import</span> <span class="nn">heapq</span> |
| <span class="kn">import</span> <span class="nn">itertools</span> |
| <span class="kn">import</span> <span class="nn">logging</span> |
| <span class="kn">import</span> <span class="nn">math</span> |
| <span class="kn">import</span> <span class="nn">typing</span> |
| <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span> |
| <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Callable</span> |
| <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span> |
| <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span> |
| |
| <span class="kn">from</span> <span class="nn">apache_beam</span> <span class="kn">import</span> <span class="n">coders</span> |
| <span class="kn">from</span> <span class="nn">apache_beam</span> <span class="kn">import</span> <span class="n">typehints</span> |
| <span class="kn">from</span> <span class="nn">apache_beam.transforms.core</span> <span class="kn">import</span> <span class="o">*</span> |
| <span class="kn">from</span> <span class="nn">apache_beam.transforms.display</span> <span class="kn">import</span> <span class="n">DisplayDataItem</span> |
| <span class="kn">from</span> <span class="nn">apache_beam.transforms.ptransform</span> <span class="kn">import</span> <span class="n">PTransform</span> |
| |
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span> |
| <span class="s1">'ApproximateQuantiles'</span><span class="p">,</span> |
| <span class="s1">'ApproximateUnique'</span><span class="p">,</span> |
| <span class="p">]</span> |
| |
| <span class="c1"># Type variables</span> |
| <span class="n">T</span> <span class="o">=</span> <span class="n">typing</span><span class="o">.</span><span class="n">TypeVar</span><span class="p">(</span><span class="s1">'T'</span><span class="p">)</span> |
| <span class="n">K</span> <span class="o">=</span> <span class="n">typing</span><span class="o">.</span><span class="n">TypeVar</span><span class="p">(</span><span class="s1">'K'</span><span class="p">)</span> |
| <span class="n">V</span> <span class="o">=</span> <span class="n">typing</span><span class="o">.</span><span class="n">TypeVar</span><span class="p">(</span><span class="s1">'V'</span><span class="p">)</span> |
| |
| <span class="k">try</span><span class="p">:</span> |
| <span class="kn">import</span> <span class="nn">mmh3</span> <span class="c1"># pylint: disable=import-error</span> |
| |
| <span class="k">def</span> <span class="nf">_mmh3_hash</span><span class="p">(</span><span class="n">value</span><span class="p">):</span> |
| <span class="c1"># mmh3.hash64 returns two 64-bit unsigned integers</span> |
| <span class="k">return</span> <span class="n">mmh3</span><span class="o">.</span><span class="n">hash64</span><span class="p">(</span><span class="n">value</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="n">signed</span><span class="o">=</span><span class="kc">False</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> |
| |
| <span class="n">_default_hash_fn</span> <span class="o">=</span> <span class="n">_mmh3_hash</span> |
| <span class="n">_default_hash_fn_type</span> <span class="o">=</span> <span class="s1">'mmh3'</span> |
| <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span> |
| |
| <span class="k">def</span> <span class="nf">_md5_hash</span><span class="p">(</span><span class="n">value</span><span class="p">):</span> |
| <span class="c1"># md5 is a 128-bit hash, so we truncate the hexdigest (string of 32</span> |
| <span class="c1"># hexadecimal digits) to 16 digits and convert to int to get the 64-bit</span> |
| <span class="c1"># integer fingerprint.</span> |
| <span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">hashlib</span><span class="o">.</span><span class="n">md5</span><span class="p">(</span><span class="n">value</span><span class="p">)</span><span class="o">.</span><span class="n">hexdigest</span><span class="p">()[:</span><span class="mi">16</span><span class="p">],</span> <span class="mi">16</span><span class="p">)</span> |
| |
| <span class="n">_default_hash_fn</span> <span class="o">=</span> <span class="n">_md5_hash</span> |
| <span class="n">_default_hash_fn_type</span> <span class="o">=</span> <span class="s1">'md5'</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_get_default_hash_fn</span><span class="p">():</span> |
| <span class="w"> </span><span class="sd">"""Returns either murmurhash or md5 based on installation."""</span> |
| <span class="k">if</span> <span class="n">_default_hash_fn_type</span> <span class="o">==</span> <span class="s1">'md5'</span><span class="p">:</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> |
| <span class="s1">'Couldn</span><span class="se">\'</span><span class="s1">t find murmurhash. Install mmh3 for a faster implementation of'</span> |
| <span class="s1">'ApproximateUnique.'</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">_default_hash_fn</span> |
| |
| |
| <div class="viewcode-block" id="ApproximateUnique"><a class="viewcode-back" href="../../../apache_beam.transforms.stats.html#apache_beam.transforms.stats.ApproximateUnique">[docs]</a><span class="k">class</span> <span class="nc">ApproximateUnique</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Hashes input elements and uses those to extrapolate the size of the entire</span> |
| <span class="sd"> set of hash values by assuming the rest of the hash values are as densely</span> |
| <span class="sd"> distributed as the sample space.</span> |
| <span class="sd"> """</span> |
| |
| <span class="n">_NO_VALUE_ERR_MSG</span> <span class="o">=</span> <span class="s1">'Either size or error should be set. Received </span><span class="si">{}</span><span class="s1">.'</span> |
| <span class="n">_MULTI_VALUE_ERR_MSG</span> <span class="o">=</span> <span class="s1">'Either size or error should be set. '</span> \ |
| <span class="s1">'Received {size = </span><span class="si">%s</span><span class="s1">, error = </span><span class="si">%s</span><span class="s1">}.'</span> |
| <span class="n">_INPUT_SIZE_ERR_MSG</span> <span class="o">=</span> <span class="s1">'ApproximateUnique needs a size >= 16 for an error '</span> \ |
| <span class="s1">'<= 0.50. In general, the estimation error is about '</span> \ |
| <span class="s1">'2 / sqrt(sample_size). Received {size = </span><span class="si">%s</span><span class="s1">}.'</span> |
| <span class="n">_INPUT_ERROR_ERR_MSG</span> <span class="o">=</span> <span class="s1">'ApproximateUnique needs an estimation error '</span> \ |
| <span class="s1">'between 0.01 and 0.50. Received {error = </span><span class="si">%s</span><span class="s1">}.'</span> |
| |
| <div class="viewcode-block" id="ApproximateUnique.parse_input_params"><a class="viewcode-back" href="../../../apache_beam.transforms.stats.html#apache_beam.transforms.stats.ApproximateUnique.parse_input_params">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">parse_input_params</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">error</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Check if input params are valid and return sample size.</span> |
| |
| <span class="sd"> :param size: an int not smaller than 16, which we would use to estimate</span> |
| <span class="sd"> number of unique values.</span> |
| <span class="sd"> :param error: max estimation error, which is a float between 0.01 and 0.50.</span> |
| <span class="sd"> If error is given, sample size will be calculated from error with</span> |
| <span class="sd"> _get_sample_size_from_est_error function.</span> |
| <span class="sd"> :return: sample size</span> |
| <span class="sd"> :raises:</span> |
| <span class="sd"> ValueError: If both size and error are given, or neither is given, or</span> |
| <span class="sd"> values are out of range.</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">if</span> <span class="kc">None</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">error</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">ApproximateUnique</span><span class="o">.</span><span class="n">_MULTI_VALUE_ERR_MSG</span> <span class="o">%</span> <span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">error</span><span class="p">))</span> |
| <span class="k">elif</span> <span class="n">size</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">error</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">ApproximateUnique</span><span class="o">.</span><span class="n">_NO_VALUE_ERR_MSG</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="ow">or</span> <span class="n">size</span> <span class="o"><</span> <span class="mi">16</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">ApproximateUnique</span><span class="o">.</span><span class="n">_INPUT_SIZE_ERR_MSG</span> <span class="o">%</span> <span class="p">(</span><span class="n">size</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">size</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">error</span> <span class="o"><</span> <span class="mf">0.01</span> <span class="ow">or</span> <span class="n">error</span> <span class="o">></span> <span class="mf">0.5</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">ApproximateUnique</span><span class="o">.</span><span class="n">_INPUT_ERROR_ERR_MSG</span> <span class="o">%</span> <span class="p">(</span><span class="n">error</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">ApproximateUnique</span><span class="o">.</span><span class="n">_get_sample_size_from_est_error</span><span class="p">(</span><span class="n">error</span><span class="p">)</span></div> |
| |
| <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">_get_sample_size_from_est_error</span><span class="p">(</span><span class="n">est_err</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> :return: sample size</span> |
| |
| <span class="sd"> Calculate sample size from estimation error</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="mf">4.0</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">est_err</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">))</span> |
| |
| <div class="viewcode-block" id="ApproximateUnique.Globally"><a class="viewcode-back" href="../../../apache_beam.transforms.stats.html#apache_beam.transforms.stats.ApproximateUnique.Globally">[docs]</a> <span class="nd">@typehints</span><span class="o">.</span><span class="n">with_input_types</span><span class="p">(</span><span class="n">T</span><span class="p">)</span> |
| <span class="nd">@typehints</span><span class="o">.</span><span class="n">with_output_types</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span> |
| <span class="k">class</span> <span class="nc">Globally</span><span class="p">(</span><span class="n">PTransform</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">""" Approximate.Globally approximate number of unique values"""</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">error</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_sample_size</span> <span class="o">=</span> <span class="n">ApproximateUnique</span><span class="o">.</span><span class="n">parse_input_params</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">error</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="ApproximateUnique.Globally.expand"><a class="viewcode-back" href="../../../apache_beam.transforms.stats.html#apache_beam.transforms.stats.ApproximateUnique.Globally.expand">[docs]</a> <span class="k">def</span> <span class="nf">expand</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pcoll</span><span class="p">):</span> |
| <span class="n">coder</span> <span class="o">=</span> <span class="n">coders</span><span class="o">.</span><span class="n">registry</span><span class="o">.</span><span class="n">get_coder</span><span class="p">(</span><span class="n">pcoll</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">pcoll</span> \ |
| <span class="o">|</span> <span class="s1">'CountGlobalUniqueValues'</span> \ |
| <span class="o">>></span> <span class="p">(</span><span class="n">CombineGlobally</span><span class="p">(</span><span class="n">ApproximateUniqueCombineFn</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_sample_size</span><span class="p">,</span> |
| <span class="n">coder</span><span class="p">)))</span></div></div> |
| |
| <div class="viewcode-block" id="ApproximateUnique.PerKey"><a class="viewcode-back" href="../../../apache_beam.transforms.stats.html#apache_beam.transforms.stats.ApproximateUnique.PerKey">[docs]</a> <span class="nd">@typehints</span><span class="o">.</span><span class="n">with_input_types</span><span class="p">(</span><span class="n">typing</span><span class="o">.</span><span class="n">Tuple</span><span class="p">[</span><span class="n">K</span><span class="p">,</span> <span class="n">V</span><span class="p">])</span> |
| <span class="nd">@typehints</span><span class="o">.</span><span class="n">with_output_types</span><span class="p">(</span><span class="n">typing</span><span class="o">.</span><span class="n">Tuple</span><span class="p">[</span><span class="n">K</span><span class="p">,</span> <span class="nb">int</span><span class="p">])</span> |
| <span class="k">class</span> <span class="nc">PerKey</span><span class="p">(</span><span class="n">PTransform</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">""" Approximate.PerKey approximate number of unique values per key"""</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">error</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_sample_size</span> <span class="o">=</span> <span class="n">ApproximateUnique</span><span class="o">.</span><span class="n">parse_input_params</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">error</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="ApproximateUnique.PerKey.expand"><a class="viewcode-back" href="../../../apache_beam.transforms.stats.html#apache_beam.transforms.stats.ApproximateUnique.PerKey.expand">[docs]</a> <span class="k">def</span> <span class="nf">expand</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pcoll</span><span class="p">):</span> |
| <span class="n">coder</span> <span class="o">=</span> <span class="n">coders</span><span class="o">.</span><span class="n">registry</span><span class="o">.</span><span class="n">get_coder</span><span class="p">(</span><span class="n">pcoll</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">pcoll</span> \ |
| <span class="o">|</span> <span class="s1">'CountPerKeyUniqueValues'</span> \ |
| <span class="o">>></span> <span class="p">(</span><span class="n">CombinePerKey</span><span class="p">(</span><span class="n">ApproximateUniqueCombineFn</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_sample_size</span><span class="p">,</span> |
| <span class="n">coder</span><span class="p">)))</span></div></div></div> |
| |
| |
| <span class="k">class</span> <span class="nc">_LargestUnique</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> An object to keep samples and calculate sample hash space. It is an</span> |
| <span class="sd"> accumulator of a combine function.</span> |
| <span class="sd"> """</span> |
| <span class="c1"># We use unsigned 64-bit integer hashes.</span> |
| <span class="n">_HASH_SPACE_SIZE</span> <span class="o">=</span> <span class="mf">2.0</span><span class="o">**</span><span class="mi">64</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_size</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_sample_size</span> <span class="o">=</span> <span class="n">sample_size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_min_hash</span> <span class="o">=</span> <span class="mf">2.0</span><span class="o">**</span><span class="mi">64</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_sample_heap</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_sample_set</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">element</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> :param an element from pcoll.</span> |
| <span class="sd"> :return: boolean type whether the value is in the heap</span> |
| |
| <span class="sd"> Adds a value to the heap, returning whether the value is (large enough to</span> |
| <span class="sd"> be) in the heap.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_sample_heap</span><span class="p">)</span> <span class="o">>=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sample_size</span> <span class="ow">and</span> <span class="n">element</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">_min_hash</span><span class="p">:</span> |
| <span class="k">return</span> <span class="kc">False</span> |
| |
| <span class="k">if</span> <span class="n">element</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sample_set</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_sample_set</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">element</span><span class="p">)</span> |
| <span class="n">heapq</span><span class="o">.</span><span class="n">heappush</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_sample_heap</span><span class="p">,</span> <span class="n">element</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_sample_heap</span><span class="p">)</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">_sample_size</span><span class="p">:</span> |
| <span class="n">temp</span> <span class="o">=</span> <span class="n">heapq</span><span class="o">.</span><span class="n">heappop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_sample_heap</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_sample_set</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">temp</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_min_hash</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sample_heap</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">elif</span> <span class="n">element</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">_min_hash</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_min_hash</span> <span class="o">=</span> <span class="n">element</span> |
| <span class="k">return</span> <span class="kc">True</span> |
| |
| <span class="k">def</span> <span class="nf">get_estimate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> :return: estimation count of unique values</span> |
| |
| <span class="sd"> If heap size is smaller than sample size, just return heap size.</span> |
| <span class="sd"> Otherwise, takes into account the possibility of hash collisions,</span> |
| <span class="sd"> which become more likely than not for 2^32 distinct elements.</span> |
| <span class="sd"> Note that log(1+x) ~ x for small x, so for sampleSize << maxHash</span> |
| <span class="sd"> log(1 - sample_size/sample_space) / log(1 - 1/sample_space) ~ sample_size</span> |
| <span class="sd"> and hence estimate ~ sample_size * hash_space / sample_space</span> |
| <span class="sd"> as one would expect.</span> |
| |
| <span class="sd"> Given sample_size / sample_space = est / hash_space</span> |
| <span class="sd"> est = sample_size * hash_space / sample_space</span> |
| |
| <span class="sd"> Given above sample_size approximate,</span> |
| <span class="sd"> est = log1p(-sample_size/sample_space) / log1p(-1/sample_space)</span> |
| <span class="sd"> * hash_space / sample_space</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_sample_heap</span><span class="p">)</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sample_size</span><span class="p">:</span> |
| <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_sample_heap</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">sample_space_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_HASH_SPACE_SIZE</span> <span class="o">-</span> <span class="mf">1.0</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_min_hash</span> |
| <span class="n">est</span> <span class="o">=</span> <span class="p">(</span> |
| <span class="n">math</span><span class="o">.</span><span class="n">log1p</span><span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">_sample_size</span> <span class="o">/</span> <span class="n">sample_space_size</span><span class="p">)</span> <span class="o">/</span> |
| <span class="n">math</span><span class="o">.</span><span class="n">log1p</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span> <span class="o">/</span> <span class="n">sample_space_size</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_HASH_SPACE_SIZE</span> <span class="o">/</span> |
| <span class="n">sample_space_size</span><span class="p">)</span> |
| <span class="k">return</span> <span class="nb">round</span><span class="p">(</span><span class="n">est</span><span class="p">)</span> |
| |
| |
| <span class="k">class</span> <span class="nc">ApproximateUniqueCombineFn</span><span class="p">(</span><span class="n">CombineFn</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> ApproximateUniqueCombineFn computes an estimate of the number of</span> |
| <span class="sd"> unique values that were combined.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_size</span><span class="p">,</span> <span class="n">coder</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_sample_size</span> <span class="o">=</span> <span class="n">sample_size</span> |
| <span class="n">coder</span> <span class="o">=</span> <span class="n">coders</span><span class="o">.</span><span class="n">typecoders</span><span class="o">.</span><span class="n">registry</span><span class="o">.</span><span class="n">verify_deterministic</span><span class="p">(</span> |
| <span class="n">coder</span><span class="p">,</span> <span class="s1">'ApproximateUniqueCombineFn'</span><span class="p">)</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_coder</span> <span class="o">=</span> <span class="n">coder</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_hash_fn</span> <span class="o">=</span> <span class="n">_get_default_hash_fn</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">create_accumulator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">_LargestUnique</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_sample_size</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">add_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">accumulator</span><span class="p">,</span> <span class="n">element</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">hashed_value</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_hash_fn</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_coder</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">element</span><span class="p">))</span> |
| <span class="n">accumulator</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">hashed_value</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">accumulator</span> |
| <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"Runtime exception: </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="n">e</span><span class="p">)</span> |
| |
| <span class="c1"># created an issue https://github.com/apache/beam/issues/19459 to speed up</span> |
| <span class="c1"># merge process.</span> |
| <span class="k">def</span> <span class="nf">merge_accumulators</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">accumulators</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="n">merged_accumulator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_accumulator</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">accumulator</span> <span class="ow">in</span> <span class="n">accumulators</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">accumulator</span><span class="o">.</span><span class="n">_sample_heap</span><span class="p">:</span> |
| <span class="n">merged_accumulator</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">merged_accumulator</span> |
| |
| <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">extract_output</span><span class="p">(</span><span class="n">accumulator</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">accumulator</span><span class="o">.</span><span class="n">get_estimate</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">display_data</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">{</span><span class="s1">'sample_size'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sample_size</span><span class="p">}</span> |
| |
| |
| <div class="viewcode-block" id="ApproximateQuantiles"><a class="viewcode-back" href="../../../apache_beam.transforms.stats.html#apache_beam.transforms.stats.ApproximateQuantiles">[docs]</a><span class="k">class</span> <span class="nc">ApproximateQuantiles</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> PTransform for getting the idea of data distribution using approximate N-tile</span> |
| <span class="sd"> (e.g. quartiles, percentiles etc.) either globally or per-key.</span> |
| |
| <span class="sd"> Examples:</span> |
| |
| <span class="sd"> in: list(range(101)), num_quantiles=5</span> |
| |
| <span class="sd"> out: [0, 25, 50, 75, 100]</span> |
| |
| <span class="sd"> in: [(i, 1 if i<10 else 1e-5) for i in range(101)], num_quantiles=5,</span> |
| <span class="sd"> weighted=True</span> |
| |
| <span class="sd"> out: [0, 2, 5, 7, 100]</span> |
| |
| <span class="sd"> in: [list(range(10)), ..., list(range(90, 101))], num_quantiles=5,</span> |
| <span class="sd"> input_batched=True</span> |
| |
| <span class="sd"> out: [0, 25, 50, 75, 100]</span> |
| |
| <span class="sd"> in: [(list(range(10)), [1]*10), (list(range(10)), [0]*10), ...,</span> |
| <span class="sd"> (list(range(90, 101)), [0]*11)], num_quantiles=5, input_batched=True,</span> |
| <span class="sd"> weighted=True</span> |
| |
| <span class="sd"> out: [0, 2, 5, 7, 100]</span> |
| <span class="sd"> """</span> |
| <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">_display_data</span><span class="p">(</span><span class="n">num_quantiles</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">reverse</span><span class="p">,</span> <span class="n">weighted</span><span class="p">,</span> <span class="n">input_batched</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">{</span> |
| <span class="s1">'num_quantiles'</span><span class="p">:</span> <span class="n">DisplayDataItem</span><span class="p">(</span><span class="n">num_quantiles</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Quantile Count'</span><span class="p">),</span> |
| <span class="s1">'key'</span><span class="p">:</span> <span class="n">DisplayDataItem</span><span class="p">(</span> |
| <span class="n">key</span><span class="o">.</span><span class="vm">__name__</span> |
| <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="s1">'__name__'</span><span class="p">)</span> <span class="k">else</span> <span class="n">key</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="n">label</span><span class="o">=</span><span class="s1">'Record Comparer Key'</span><span class="p">),</span> |
| <span class="s1">'reverse'</span><span class="p">:</span> <span class="n">DisplayDataItem</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">reverse</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Is Reversed'</span><span class="p">),</span> |
| <span class="s1">'weighted'</span><span class="p">:</span> <span class="n">DisplayDataItem</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">weighted</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Is Weighted'</span><span class="p">),</span> |
| <span class="s1">'input_batched'</span><span class="p">:</span> <span class="n">DisplayDataItem</span><span class="p">(</span> |
| <span class="nb">str</span><span class="p">(</span><span class="n">input_batched</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Is Input Batched'</span><span class="p">),</span> |
| <span class="p">}</span> |
| |
| <div class="viewcode-block" id="ApproximateQuantiles.Globally"><a class="viewcode-back" href="../../../apache_beam.transforms.stats.html#apache_beam.transforms.stats.ApproximateQuantiles.Globally">[docs]</a> <span class="nd">@typehints</span><span class="o">.</span><span class="n">with_input_types</span><span class="p">(</span> |
| <span class="n">typehints</span><span class="o">.</span><span class="n">Union</span><span class="p">[</span><span class="n">typing</span><span class="o">.</span><span class="n">Sequence</span><span class="p">[</span><span class="n">T</span><span class="p">],</span> <span class="n">typing</span><span class="o">.</span><span class="n">Tuple</span><span class="p">[</span><span class="n">T</span><span class="p">,</span> <span class="nb">float</span><span class="p">]])</span> |
| <span class="nd">@typehints</span><span class="o">.</span><span class="n">with_output_types</span><span class="p">(</span><span class="n">typing</span><span class="o">.</span><span class="n">List</span><span class="p">[</span><span class="n">T</span><span class="p">])</span> |
| <span class="k">class</span> <span class="nc">Globally</span><span class="p">(</span><span class="n">PTransform</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> PTransform takes PCollection and returns a list whose single value is</span> |
| <span class="sd"> approximate N-tiles of the input collection globally.</span> |
| |
| <span class="sd"> Args:</span> |
| <span class="sd"> num_quantiles: number of elements in the resulting quantiles values list.</span> |
| <span class="sd"> key: (optional) Key is a mapping of elements to a comparable key, similar</span> |
| <span class="sd"> to the key argument of Python's sorting methods.</span> |
| <span class="sd"> reverse: (optional) whether to order things smallest to largest, rather</span> |
| <span class="sd"> than largest to smallest.</span> |
| <span class="sd"> weighted: (optional) if set to True, the transform returns weighted</span> |
| <span class="sd"> quantiles. The input PCollection is then expected to contain tuples of</span> |
| <span class="sd"> input values with the corresponding weight.</span> |
| <span class="sd"> input_batched: (optional) if set to True, the transform expects each</span> |
| <span class="sd"> element of input PCollection to be a batch, which is a list of elements</span> |
| <span class="sd"> for non-weighted case and a tuple of lists of elements and weights for</span> |
| <span class="sd"> weighted. Provides a way to accumulate multiple elements at a time more</span> |
| <span class="sd"> efficiently.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> |
| <span class="n">num_quantiles</span><span class="p">,</span> |
| <span class="n">key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">reverse</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">weighted</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">input_batched</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_num_quantiles</span> <span class="o">=</span> <span class="n">num_quantiles</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_key</span> <span class="o">=</span> <span class="n">key</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_reverse</span> <span class="o">=</span> <span class="n">reverse</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_weighted</span> <span class="o">=</span> <span class="n">weighted</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_input_batched</span> <span class="o">=</span> <span class="n">input_batched</span> |
| |
| <div class="viewcode-block" id="ApproximateQuantiles.Globally.expand"><a class="viewcode-back" href="../../../apache_beam.transforms.stats.html#apache_beam.transforms.stats.ApproximateQuantiles.Globally.expand">[docs]</a> <span class="k">def</span> <span class="nf">expand</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pcoll</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">pcoll</span> <span class="o">|</span> <span class="n">CombineGlobally</span><span class="p">(</span> |
| <span class="n">ApproximateQuantilesCombineFn</span><span class="o">.</span><span class="n">create</span><span class="p">(</span> |
| <span class="n">num_quantiles</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_quantiles</span><span class="p">,</span> |
| <span class="n">key</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_key</span><span class="p">,</span> |
| <span class="n">reverse</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_reverse</span><span class="p">,</span> |
| <span class="n">weighted</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_weighted</span><span class="p">,</span> |
| <span class="n">input_batched</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_input_batched</span><span class="p">))</span></div> |
| |
| <div class="viewcode-block" id="ApproximateQuantiles.Globally.display_data"><a class="viewcode-back" href="../../../apache_beam.transforms.stats.html#apache_beam.transforms.stats.ApproximateQuantiles.Globally.display_data">[docs]</a> <span class="k">def</span> <span class="nf">display_data</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">ApproximateQuantiles</span><span class="o">.</span><span class="n">_display_data</span><span class="p">(</span> |
| <span class="n">num_quantiles</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_quantiles</span><span class="p">,</span> |
| <span class="n">key</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_key</span><span class="p">,</span> |
| <span class="n">reverse</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_reverse</span><span class="p">,</span> |
| <span class="n">weighted</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_weighted</span><span class="p">,</span> |
| <span class="n">input_batched</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_input_batched</span><span class="p">)</span></div></div> |
| |
| <div class="viewcode-block" id="ApproximateQuantiles.PerKey"><a class="viewcode-back" href="../../../apache_beam.transforms.stats.html#apache_beam.transforms.stats.ApproximateQuantiles.PerKey">[docs]</a> <span class="nd">@typehints</span><span class="o">.</span><span class="n">with_input_types</span><span class="p">(</span> |
| <span class="n">typehints</span><span class="o">.</span><span class="n">Union</span><span class="p">[</span><span class="n">typing</span><span class="o">.</span><span class="n">Tuple</span><span class="p">[</span><span class="n">K</span><span class="p">,</span> <span class="n">V</span><span class="p">],</span> |
| <span class="n">typing</span><span class="o">.</span><span class="n">Tuple</span><span class="p">[</span><span class="n">K</span><span class="p">,</span> <span class="n">typing</span><span class="o">.</span><span class="n">Tuple</span><span class="p">[</span><span class="n">V</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]])</span> |
| <span class="nd">@typehints</span><span class="o">.</span><span class="n">with_output_types</span><span class="p">(</span><span class="n">typing</span><span class="o">.</span><span class="n">Tuple</span><span class="p">[</span><span class="n">K</span><span class="p">,</span> <span class="n">typing</span><span class="o">.</span><span class="n">List</span><span class="p">[</span><span class="n">V</span><span class="p">]])</span> |
| <span class="k">class</span> <span class="nc">PerKey</span><span class="p">(</span><span class="n">PTransform</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> PTransform takes PCollection of KV and returns a list based on each key</span> |
| <span class="sd"> whose single value is list of approximate N-tiles of the input element of</span> |
| <span class="sd"> the key.</span> |
| |
| <span class="sd"> Args:</span> |
| <span class="sd"> num_quantiles: number of elements in the resulting quantiles values list.</span> |
| <span class="sd"> key: (optional) Key is a mapping of elements to a comparable key, similar</span> |
| <span class="sd"> to the key argument of Python's sorting methods.</span> |
| <span class="sd"> reverse: (optional) whether to order things smallest to largest, rather</span> |
| <span class="sd"> than largest to smallest.</span> |
| <span class="sd"> weighted: (optional) if set to True, the transform returns weighted</span> |
| <span class="sd"> quantiles. The input PCollection is then expected to contain tuples of</span> |
| <span class="sd"> input values with the corresponding weight.</span> |
| <span class="sd"> input_batched: (optional) if set to True, the transform expects each</span> |
| <span class="sd"> element of input PCollection to be a batch, which is a list of elements</span> |
| <span class="sd"> for non-weighted case and a tuple of lists of elements and weights for</span> |
| <span class="sd"> weighted. Provides a way to accumulate multiple elements at a time more</span> |
| <span class="sd"> efficiently.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> |
| <span class="n">num_quantiles</span><span class="p">,</span> |
| <span class="n">key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">reverse</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">weighted</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">input_batched</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_num_quantiles</span> <span class="o">=</span> <span class="n">num_quantiles</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_key</span> <span class="o">=</span> <span class="n">key</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_reverse</span> <span class="o">=</span> <span class="n">reverse</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_weighted</span> <span class="o">=</span> <span class="n">weighted</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_input_batched</span> <span class="o">=</span> <span class="n">input_batched</span> |
| |
| <div class="viewcode-block" id="ApproximateQuantiles.PerKey.expand"><a class="viewcode-back" href="../../../apache_beam.transforms.stats.html#apache_beam.transforms.stats.ApproximateQuantiles.PerKey.expand">[docs]</a> <span class="k">def</span> <span class="nf">expand</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pcoll</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">pcoll</span> <span class="o">|</span> <span class="n">CombinePerKey</span><span class="p">(</span> |
| <span class="n">ApproximateQuantilesCombineFn</span><span class="o">.</span><span class="n">create</span><span class="p">(</span> |
| <span class="n">num_quantiles</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_quantiles</span><span class="p">,</span> |
| <span class="n">key</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_key</span><span class="p">,</span> |
| <span class="n">reverse</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_reverse</span><span class="p">,</span> |
| <span class="n">weighted</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_weighted</span><span class="p">,</span> |
| <span class="n">input_batched</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_input_batched</span><span class="p">))</span></div> |
| |
| <div class="viewcode-block" id="ApproximateQuantiles.PerKey.display_data"><a class="viewcode-back" href="../../../apache_beam.transforms.stats.html#apache_beam.transforms.stats.ApproximateQuantiles.PerKey.display_data">[docs]</a> <span class="k">def</span> <span class="nf">display_data</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">ApproximateQuantiles</span><span class="o">.</span><span class="n">_display_data</span><span class="p">(</span> |
| <span class="n">num_quantiles</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_quantiles</span><span class="p">,</span> |
| <span class="n">key</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_key</span><span class="p">,</span> |
| <span class="n">reverse</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_reverse</span><span class="p">,</span> |
| <span class="n">weighted</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_weighted</span><span class="p">,</span> |
| <span class="n">input_batched</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_input_batched</span><span class="p">)</span></div></div></div> |
| |
| |
| <span class="k">class</span> <span class="nc">_QuantileSpec</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Quantiles computation specifications."""</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">buffer_size</span><span class="p">,</span> <span class="n">num_buffers</span><span class="p">,</span> <span class="n">weighted</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">reverse</span><span class="p">):</span> |
| <span class="c1"># type: (int, int, bool, Any, bool) -> None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">buffer_size</span> <span class="o">=</span> <span class="n">buffer_size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_buffers</span> <span class="o">=</span> <span class="n">num_buffers</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weighted</span> <span class="o">=</span> <span class="n">weighted</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">key</span> <span class="o">=</span> <span class="n">key</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">reverse</span> <span class="o">=</span> <span class="n">reverse</span> |
| |
| <span class="c1"># Used to sort tuples of values and weights.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weighted_key</span> <span class="o">=</span> <span class="kc">None</span> <span class="k">if</span> <span class="n">key</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">key</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span> |
| |
| <span class="c1"># Used to compare values.</span> |
| <span class="k">if</span> <span class="n">reverse</span> <span class="ow">and</span> <span class="n">key</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">less_than</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="n">a</span> <span class="o">></span> <span class="n">b</span> |
| <span class="k">elif</span> <span class="n">reverse</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">less_than</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="n">key</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o">></span> <span class="n">key</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">key</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">less_than</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="n">a</span> <span class="o"><</span> <span class="n">b</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">less_than</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="n">key</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o"><</span> <span class="n">key</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">get_argsort_key</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">elements</span><span class="p">):</span> |
| <span class="c1"># type: (List) -> Callable[[int], Any]</span> |
| |
| <span class="w"> </span><span class="sd">"""Returns a key for sorting indices of elements by element's value."""</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">key</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">elements</span><span class="o">.</span><span class="fm">__getitem__</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="k">lambda</span> <span class="n">idx</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">key</span><span class="p">(</span><span class="n">elements</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span> |
| |
| <span class="k">def</span> <span class="nf">__reduce__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">,</span> |
| <span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">buffer_size</span><span class="p">,</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_buffers</span><span class="p">,</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weighted</span><span class="p">,</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">key</span><span class="p">,</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">reverse</span><span class="p">))</span> |
| |
| |
| <span class="k">class</span> <span class="nc">_QuantileBuffer</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""A single buffer in the sense of the referenced algorithm.</span> |
| <span class="sd"> (see http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.6.6513&rep=rep1</span> |
| <span class="sd"> &type=pdf and ApproximateQuantilesCombineFn for further information)"""</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> <span class="n">elements</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">weighted</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">min_val</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_val</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="c1"># type: (List, List, bool, int, Any, Any) -> None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">elements</span> <span class="o">=</span> <span class="n">elements</span> |
| <span class="c1"># In non-weighted case weights contains a single element representing weight</span> |
| <span class="c1"># of the buffer in the sense of the original algorithm. In weighted case,</span> |
| <span class="c1"># it stores weights of individual elements.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weighted</span> <span class="o">=</span> <span class="n">weighted</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">level</span> <span class="o">=</span> <span class="n">level</span> |
| <span class="k">if</span> <span class="n">min_val</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">max_val</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="c1"># Buffer is always initialized with sorted elements.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">min_val</span> <span class="o">=</span> <span class="n">elements</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">max_val</span> <span class="o">=</span> <span class="n">elements</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="c1"># Note that collapsed buffer may not contain min and max in the list of</span> |
| <span class="c1"># elements.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">min_val</span> <span class="o">=</span> <span class="n">min_val</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">max_val</span> <span class="o">=</span> <span class="n">max_val</span> |
| |
| <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="nb">zip</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">elements</span><span class="p">,</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">weighted</span> <span class="k">else</span> <span class="n">itertools</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span> |
| |
| <span class="k">def</span> <span class="fm">__lt__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">level</span> <span class="o"><</span> <span class="n">other</span><span class="o">.</span><span class="n">level</span> |
| |
| |
| <span class="k">class</span> <span class="nc">_QuantileState</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Compact summarization of a collection on which quantiles can be estimated.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">unbuffered_elements</span><span class="p">,</span> <span class="n">unbuffered_weights</span><span class="p">,</span> <span class="n">buffers</span><span class="p">,</span> <span class="n">spec</span><span class="p">):</span> |
| <span class="c1"># type: (List, List, List[_QuantileBuffer], _QuantileSpec) -> None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">buffers</span> <span class="o">=</span> <span class="n">buffers</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">spec</span> <span class="o">=</span> <span class="n">spec</span> |
| <span class="k">if</span> <span class="n">spec</span><span class="o">.</span><span class="n">weighted</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">add_unbuffered</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_add_unbuffered_weighted</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">add_unbuffered</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_add_unbuffered</span> |
| |
| <span class="c1"># The algorithm requires that the manipulated buffers always be filled to</span> |
| <span class="c1"># capacity to perform the collapse operation. This operation can be extended</span> |
| <span class="c1"># to buffers of varying sizes by introducing the notion of fractional</span> |
| <span class="c1"># weights, but it's easier to simply combine the remainders from all shards</span> |
| <span class="c1"># into new, full buffers and then take them into account when computing the</span> |
| <span class="c1"># final output.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span> <span class="o">=</span> <span class="n">unbuffered_elements</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_weights</span> <span class="o">=</span> <span class="n">unbuffered_weights</span> |
| |
| <span class="c1"># This is needed for pickling to work when Cythonization is enabled.</span> |
| <span class="k">def</span> <span class="nf">__reduce__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">,</span> |
| <span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span><span class="p">,</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_weights</span><span class="p">,</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">buffers</span><span class="p">,</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">spec</span><span class="p">))</span> |
| |
| <span class="k">def</span> <span class="nf">is_empty</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="c1"># type: () -> bool</span> |
| |
| <span class="w"> </span><span class="sd">"""Check if the buffered & unbuffered elements are empty or not."""</span> |
| <span class="k">return</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">buffers</span> |
| |
| <span class="k">def</span> <span class="nf">_add_unbuffered</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">elements</span><span class="p">,</span> <span class="n">offset_fn</span><span class="p">):</span> |
| <span class="c1"># type: (List, Any) -> None</span> |
| |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Add elements to the unbuffered list, creating new buffers and</span> |
| <span class="sd"> collapsing if needed.</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">elements</span><span class="p">)</span> |
| <span class="n">num_new_buffers</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span><span class="p">)</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">spec</span><span class="o">.</span><span class="n">buffer_size</span> |
| <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_new_buffers</span><span class="p">):</span> |
| <span class="n">to_buffer</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span><span class="p">[</span><span class="n">idx</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">spec</span><span class="o">.</span><span class="n">buffer_size</span><span class="p">:(</span><span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">spec</span><span class="o">.</span><span class="n">buffer_size</span><span class="p">],</span> |
| <span class="n">key</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">spec</span><span class="o">.</span><span class="n">key</span><span class="p">,</span> |
| <span class="n">reverse</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">spec</span><span class="o">.</span><span class="n">reverse</span><span class="p">)</span> |
| <span class="n">heapq</span><span class="o">.</span><span class="n">heappush</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">buffers</span><span class="p">,</span> |
| <span class="n">_QuantileBuffer</span><span class="p">(</span><span class="n">elements</span><span class="o">=</span><span class="n">to_buffer</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">weighted</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span> |
| |
| <span class="k">if</span> <span class="n">num_new_buffers</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span><span class="p">[</span><span class="n">num_new_buffers</span> <span class="o">*</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">spec</span><span class="o">.</span> |
| <span class="n">buffer_size</span><span class="p">:]</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">collapse_if_needed</span><span class="p">(</span><span class="n">offset_fn</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_add_unbuffered_weighted</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">elements</span><span class="p">,</span> <span class="n">offset_fn</span><span class="p">):</span> |
| <span class="c1"># type: (List, Any) -> None</span> |
| |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Add elements with weights to the unbuffered list, creating new buffers and</span> |
| <span class="sd"> collapsing if needed.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">elements</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">elements</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_weights</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">elements</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">elements</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_weights</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">elements</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> |
| <span class="n">num_new_buffers</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span><span class="p">)</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">spec</span><span class="o">.</span><span class="n">buffer_size</span> |
| <span class="n">argsort_key</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">spec</span><span class="o">.</span><span class="n">get_argsort_key</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_new_buffers</span><span class="p">):</span> |
| <span class="n">argsort</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span> |
| <span class="nb">range</span><span class="p">(</span><span class="n">idx</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">spec</span><span class="o">.</span><span class="n">buffer_size</span><span class="p">,</span> <span class="p">(</span><span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">spec</span><span class="o">.</span><span class="n">buffer_size</span><span class="p">),</span> |
| <span class="n">key</span><span class="o">=</span><span class="n">argsort_key</span><span class="p">,</span> |
| <span class="n">reverse</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">spec</span><span class="o">.</span><span class="n">reverse</span><span class="p">)</span> |
| <span class="n">elements_to_buffer</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">argsort</span><span class="p">]</span> |
| <span class="n">weights_to_buffer</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_weights</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">argsort</span><span class="p">]</span> |
| <span class="n">heapq</span><span class="o">.</span><span class="n">heappush</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">buffers</span><span class="p">,</span> |
| <span class="n">_QuantileBuffer</span><span class="p">(</span> |
| <span class="n">elements</span><span class="o">=</span><span class="n">elements_to_buffer</span><span class="p">,</span> |
| <span class="n">weights</span><span class="o">=</span><span class="n">weights_to_buffer</span><span class="p">,</span> |
| <span class="n">weighted</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span> |
| |
| <span class="k">if</span> <span class="n">num_new_buffers</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span><span class="p">[</span><span class="n">num_new_buffers</span> <span class="o">*</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">spec</span><span class="o">.</span> |
| <span class="n">buffer_size</span><span class="p">:]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_weights</span><span class="p">[</span><span class="n">num_new_buffers</span> <span class="o">*</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">spec</span><span class="o">.</span><span class="n">buffer_size</span><span class="p">:]</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">collapse_if_needed</span><span class="p">(</span><span class="n">offset_fn</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">finalize</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="c1"># type: () -> None</span> |
| |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Creates a new buffer using all unbuffered elements. Called before</span> |
| <span class="sd"> extracting an output. Note that the buffer doesn't have to be put in a</span> |
| <span class="sd"> proper position since _collapse is not going to be called after.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">spec</span><span class="o">.</span><span class="n">weighted</span><span class="p">:</span> |
| <span class="n">argsort_key</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">spec</span><span class="o">.</span><span class="n">get_argsort_key</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span><span class="p">)</span> |
| <span class="n">argsort</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span> |
| <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span><span class="p">)),</span> |
| <span class="n">key</span><span class="o">=</span><span class="n">argsort_key</span><span class="p">,</span> |
| <span class="n">reverse</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">spec</span><span class="o">.</span><span class="n">reverse</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span> <span class="o">=</span> <span class="p">[</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">argsort</span> |
| <span class="p">]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_weights</span> <span class="o">=</span> <span class="p">[</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_weights</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">argsort</span> |
| <span class="p">]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">buffers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> |
| <span class="n">_QuantileBuffer</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_weights</span><span class="p">,</span> <span class="n">weighted</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_weights</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span> |
| <span class="n">key</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">spec</span><span class="o">.</span><span class="n">key</span><span class="p">,</span> <span class="n">reverse</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">spec</span><span class="o">.</span><span class="n">reverse</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">buffers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> |
| <span class="n">_QuantileBuffer</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">weighted</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">unbuffered_elements</span> <span class="o">=</span> <span class="p">[]</span> |
| |
| <span class="k">def</span> <span class="nf">collapse_if_needed</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">offset_fn</span><span class="p">):</span> |
| <span class="c1"># type: (Any) -> None</span> |
| |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Checks if summary has too many buffers and collapses some of them until the</span> |
| <span class="sd"> limit is restored.</span> |
| <span class="sd"> """</span> |
| <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">buffers</span><span class="p">)</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">spec</span><span class="o">.</span><span class="n">num_buffers</span><span class="p">:</span> |
| <span class="n">to_collapse</span> <span class="o">=</span> <span class="p">[</span><span class="n">heapq</span><span class="o">.</span><span class="n">heappop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">buffers</span><span class="p">),</span> <span class="n">heapq</span><span class="o">.</span><span class="n">heappop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">buffers</span><span class="p">)]</span> |
| <span class="n">min_level</span> <span class="o">=</span> <span class="n">to_collapse</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">level</span> |
| |
| <span class="k">while</span> <span class="bp">self</span><span class="o">.</span><span class="n">buffers</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">buffers</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">level</span> <span class="o"><=</span> <span class="n">min_level</span><span class="p">:</span> |
| <span class="n">to_collapse</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">heapq</span><span class="o">.</span><span class="n">heappop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">buffers</span><span class="p">))</span> |
| |
| <span class="n">heapq</span><span class="o">.</span><span class="n">heappush</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">buffers</span><span class="p">,</span> <span class="n">_collapse</span><span class="p">(</span><span class="n">to_collapse</span><span class="p">,</span> <span class="n">offset_fn</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">spec</span><span class="p">))</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_collapse</span><span class="p">(</span><span class="n">buffers</span><span class="p">,</span> <span class="n">offset_fn</span><span class="p">,</span> <span class="n">spec</span><span class="p">):</span> |
| <span class="c1"># type: (List[_QuantileBuffer], Any, _QuantileSpec) -> _QuantileBuffer</span> |
| |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Approximates elements from multiple buffers and produces a single buffer.</span> |
| <span class="sd"> """</span> |
| <span class="n">new_level</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">new_weight</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="k">for</span> <span class="n">buffer</span> <span class="ow">in</span> <span class="n">buffers</span><span class="p">:</span> |
| <span class="c1"># As presented in the paper, there should always be at least two</span> |
| <span class="c1"># buffers of the same (minimal) level to collapse, but it is possible</span> |
| <span class="c1"># to violate this condition when combining buffers from independently</span> |
| <span class="c1"># computed shards. If they differ we take the max.</span> |
| <span class="n">new_level</span> <span class="o">=</span> <span class="nb">max</span><span class="p">([</span><span class="n">new_level</span><span class="p">,</span> <span class="n">buffer</span><span class="o">.</span><span class="n">level</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span> |
| <span class="n">new_weight</span> <span class="o">=</span> <span class="n">new_weight</span> <span class="o">+</span> <span class="nb">sum</span><span class="p">(</span><span class="n">buffer</span><span class="o">.</span><span class="n">weights</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">spec</span><span class="o">.</span><span class="n">weighted</span><span class="p">:</span> |
| <span class="n">step</span> <span class="o">=</span> <span class="n">new_weight</span> <span class="o">/</span> <span class="p">(</span><span class="n">spec</span><span class="o">.</span><span class="n">buffer_size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="n">offset</span> <span class="o">=</span> <span class="n">new_weight</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">spec</span><span class="o">.</span><span class="n">buffer_size</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">step</span> <span class="o">=</span> <span class="n">new_weight</span> |
| <span class="n">offset</span> <span class="o">=</span> <span class="n">offset_fn</span><span class="p">(</span><span class="n">new_weight</span><span class="p">)</span> |
| <span class="n">new_elements</span><span class="p">,</span> <span class="n">new_weights</span><span class="p">,</span> <span class="n">min_val</span><span class="p">,</span> <span class="n">max_val</span> <span class="o">=</span> \ |
| <span class="n">_interpolate</span><span class="p">(</span><span class="n">buffers</span><span class="p">,</span> <span class="n">spec</span><span class="o">.</span><span class="n">buffer_size</span><span class="p">,</span> <span class="n">step</span><span class="p">,</span> <span class="n">offset</span><span class="p">,</span> <span class="n">spec</span><span class="p">)</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">spec</span><span class="o">.</span><span class="n">weighted</span><span class="p">:</span> |
| <span class="n">new_weights</span> <span class="o">=</span> <span class="p">[</span><span class="n">new_weight</span><span class="p">]</span> |
| <span class="k">return</span> <span class="n">_QuantileBuffer</span><span class="p">(</span> |
| <span class="n">new_elements</span><span class="p">,</span> <span class="n">new_weights</span><span class="p">,</span> <span class="n">spec</span><span class="o">.</span><span class="n">weighted</span><span class="p">,</span> <span class="n">new_level</span><span class="p">,</span> <span class="n">min_val</span><span class="p">,</span> <span class="n">max_val</span><span class="p">)</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_interpolate</span><span class="p">(</span><span class="n">buffers</span><span class="p">,</span> <span class="n">count</span><span class="p">,</span> <span class="n">step</span><span class="p">,</span> <span class="n">offset</span><span class="p">,</span> <span class="n">spec</span><span class="p">):</span> |
| <span class="c1"># type: (List[_QuantileBuffer], int, float, float, _QuantileSpec) -> Tuple[List, List, Any, Any]</span> |
| |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Emulates taking the ordered union of all elements in buffers, repeated</span> |
| <span class="sd"> according to their weight, and picking out the (k * step + offset)-th elements</span> |
| <span class="sd"> of this list for `0 <= k < count`.</span> |
| <span class="sd"> """</span> |
| <span class="n">buffer_iterators</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">min_val</span> <span class="o">=</span> <span class="n">buffers</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min_val</span> |
| <span class="n">max_val</span> <span class="o">=</span> <span class="n">buffers</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max_val</span> |
| <span class="k">for</span> <span class="n">buffer</span> <span class="ow">in</span> <span class="n">buffers</span><span class="p">:</span> |
| <span class="c1"># Calculate extreme values for the union of buffers.</span> |
| <span class="n">min_val</span> <span class="o">=</span> <span class="n">buffer</span><span class="o">.</span><span class="n">min_val</span> <span class="k">if</span> <span class="n">spec</span><span class="o">.</span><span class="n">less_than</span><span class="p">(</span> |
| <span class="n">buffer</span><span class="o">.</span><span class="n">min_val</span><span class="p">,</span> <span class="n">min_val</span><span class="p">)</span> <span class="k">else</span> <span class="n">min_val</span> |
| <span class="n">max_val</span> <span class="o">=</span> <span class="n">buffer</span><span class="o">.</span><span class="n">max_val</span> <span class="k">if</span> <span class="n">spec</span><span class="o">.</span><span class="n">less_than</span><span class="p">(</span> |
| <span class="n">max_val</span><span class="p">,</span> <span class="n">buffer</span><span class="o">.</span><span class="n">max_val</span><span class="p">)</span> <span class="k">else</span> <span class="n">max_val</span> |
| <span class="n">buffer_iterators</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">buffer</span><span class="p">))</span> |
| |
| <span class="c1"># Note that `heapq.merge` can also be used here since the buffers are sorted.</span> |
| <span class="c1"># In practice, however, `sorted` uses natural order in the union and</span> |
| <span class="c1"># significantly outperforms `heapq.merge`.</span> |
| <span class="n">sorted_elements</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span> |
| <span class="n">itertools</span><span class="o">.</span><span class="n">chain</span><span class="o">.</span><span class="n">from_iterable</span><span class="p">(</span><span class="n">buffer_iterators</span><span class="p">),</span> |
| <span class="n">key</span><span class="o">=</span><span class="n">spec</span><span class="o">.</span><span class="n">weighted_key</span><span class="p">,</span> |
| <span class="n">reverse</span><span class="o">=</span><span class="n">spec</span><span class="o">.</span><span class="n">reverse</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">spec</span><span class="o">.</span><span class="n">weighted</span><span class="p">:</span> |
| <span class="c1"># If all buffers have the same weight, then quantiles' indices are evenly</span> |
| <span class="c1"># distributed over a range [0, len(sorted_elements)].</span> |
| <span class="n">buffers_have_same_weight</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="n">weight</span> <span class="o">=</span> <span class="n">buffers</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">buffer</span> <span class="ow">in</span> <span class="n">buffers</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">buffer</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">weight</span><span class="p">:</span> |
| <span class="n">buffers_have_same_weight</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="k">break</span> |
| <span class="k">if</span> <span class="n">buffers_have_same_weight</span><span class="p">:</span> |
| <span class="n">offset</span> <span class="o">=</span> <span class="n">offset</span> <span class="o">/</span> <span class="n">weight</span> |
| <span class="n">step</span> <span class="o">=</span> <span class="n">step</span> <span class="o">/</span> <span class="n">weight</span> |
| <span class="n">max_idx</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">sorted_elements</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span> |
| <span class="n">result</span> <span class="o">=</span> <span class="p">[</span> |
| <span class="n">sorted_elements</span><span class="p">[</span><span class="nb">min</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">j</span> <span class="o">*</span> <span class="n">step</span> <span class="o">+</span> <span class="n">offset</span><span class="p">),</span> <span class="n">max_idx</span><span class="p">)][</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">count</span><span class="p">)</span> |
| <span class="p">]</span> |
| <span class="k">return</span> <span class="n">result</span><span class="p">,</span> <span class="p">[],</span> <span class="n">min_val</span><span class="p">,</span> <span class="n">max_val</span> |
| |
| <span class="n">sorted_elements_iter</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="n">sorted_elements</span><span class="p">)</span> |
| <span class="n">weighted_element</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">sorted_elements_iter</span><span class="p">)</span> |
| <span class="n">new_elements</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">new_weights</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">j</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">current_weight</span> <span class="o">=</span> <span class="n">weighted_element</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="n">previous_weight</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="k">while</span> <span class="n">j</span> <span class="o"><</span> <span class="n">count</span><span class="p">:</span> |
| <span class="n">target_weight</span> <span class="o">=</span> <span class="n">j</span> <span class="o">*</span> <span class="n">step</span> <span class="o">+</span> <span class="n">offset</span> |
| <span class="n">j</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="k">while</span> <span class="n">current_weight</span> <span class="o"><=</span> <span class="n">target_weight</span><span class="p">:</span> |
| <span class="n">weighted_element</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">sorted_elements_iter</span><span class="p">)</span> |
| <span class="n">current_weight</span> <span class="o">+=</span> <span class="n">weighted_element</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="k">except</span> <span class="ne">StopIteration</span><span class="p">:</span> |
| <span class="k">pass</span> |
| <span class="n">new_elements</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">weighted_element</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| <span class="k">if</span> <span class="n">spec</span><span class="o">.</span><span class="n">weighted</span><span class="p">:</span> |
| <span class="n">new_weights</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">current_weight</span> <span class="o">-</span> <span class="n">previous_weight</span><span class="p">)</span> |
| <span class="n">previous_weight</span> <span class="o">=</span> <span class="n">current_weight</span> |
| |
| <span class="k">return</span> <span class="n">new_elements</span><span class="p">,</span> <span class="n">new_weights</span><span class="p">,</span> <span class="n">min_val</span><span class="p">,</span> <span class="n">max_val</span> |
| |
| |
| <span class="k">class</span> <span class="nc">ApproximateQuantilesCombineFn</span><span class="p">(</span><span class="n">CombineFn</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> This combiner gives an idea of the distribution of a collection of values</span> |
| <span class="sd"> using approximate N-tiles. The output of this combiner is the list of size of</span> |
| <span class="sd"> the number of quantiles (num_quantiles), containing the input values of the</span> |
| <span class="sd"> minimum value item of the list, the intermediate values (n-tiles) and the</span> |
| <span class="sd"> maximum value item of the list, in the sort order provided via key (similar</span> |
| <span class="sd"> to the key argument of Python's sorting methods).</span> |
| |
| <span class="sd"> If there are fewer values to combine than the number of quantile</span> |
| <span class="sd"> (num_quantiles), then the resulting list will contain all the values being</span> |
| <span class="sd"> combined, in sorted order.</span> |
| |
| <span class="sd"> If no `key` is provided, then the results are sorted in the natural order.</span> |
| |
| <span class="sd"> To evaluate the quantiles, we use the "New Algorithm" described here:</span> |
| |
| <span class="sd"> [MRL98] Manku, Rajagopalan & Lindsay, "Approximate Medians and other</span> |
| <span class="sd"> Quantiles in One Pass and with Limited Memory", Proc. 1998 ACM SIGMOD,</span> |
| <span class="sd"> Vol 27, No 2, p 426-435, June 1998.</span> |
| <span class="sd"> http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.6.6513&rep=rep1</span> |
| <span class="sd"> &type=pdf</span> |
| |
| <span class="sd"> Note that the weighted quantiles are evaluated using a generalized version of</span> |
| <span class="sd"> the algorithm referenced in the paper.</span> |
| |
| <span class="sd"> The default error bound is (1 / num_quantiles) for uniformly distributed data</span> |
| <span class="sd"> and min(1e-2, 1 / num_quantiles) for weighted case, though in practice the</span> |
| <span class="sd"> accuracy tends to be much better.</span> |
| |
| <span class="sd"> Args:</span> |
| <span class="sd"> num_quantiles: Number of quantiles to produce. It is the size of the final</span> |
| <span class="sd"> output list, including the mininum and maximum value items.</span> |
| <span class="sd"> buffer_size: The size of the buffers, corresponding to k in the referenced</span> |
| <span class="sd"> paper.</span> |
| <span class="sd"> num_buffers: The number of buffers, corresponding to b in the referenced</span> |
| <span class="sd"> paper.</span> |
| <span class="sd"> key: (optional) Key is a mapping of elements to a comparable key, similar</span> |
| <span class="sd"> to the key argument of Python's sorting methods.</span> |
| <span class="sd"> reverse: (optional) whether to order things smallest to largest, rather</span> |
| <span class="sd"> than largest to smallest.</span> |
| <span class="sd"> weighted: (optional) if set to True, the combiner produces weighted</span> |
| <span class="sd"> quantiles. The input elements are then expected to be tuples of input</span> |
| <span class="sd"> values with the corresponding weight.</span> |
| <span class="sd"> input_batched: (optional) if set to True, inputs are expected to be batches</span> |
| <span class="sd"> of elements.</span> |
| <span class="sd"> """</span> |
| |
| <span class="c1"># For alternating between biasing up and down in the above even weight</span> |
| <span class="c1"># collapse operation.</span> |
| <span class="n">_offset_jitter</span> <span class="o">=</span> <span class="mi">0</span> |
| |
| <span class="c1"># The cost (in time and space) to compute quantiles to a given accuracy is a</span> |
| <span class="c1"># function of the total number of elements in the data set. If an estimate is</span> |
| <span class="c1"># not known or specified, we use this as an upper bound. If this is too low,</span> |
| <span class="c1"># errors may exceed the requested tolerance; if too high, efficiency may be</span> |
| <span class="c1"># non-optimal. The impact is logarithmic with respect to this value, so this</span> |
| <span class="c1"># default should be fine for most uses.</span> |
| <span class="n">_MAX_NUM_ELEMENTS</span> <span class="o">=</span> <span class="mf">1e9</span> |
| <span class="n">_qs</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># type: _QuantileState</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> |
| <span class="n">num_quantiles</span><span class="p">,</span> <span class="c1"># type: int</span> |
| <span class="n">buffer_size</span><span class="p">,</span> <span class="c1"># type: int</span> |
| <span class="n">num_buffers</span><span class="p">,</span> <span class="c1"># type: int</span> |
| <span class="n">key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">reverse</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">weighted</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">input_batched</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_num_quantiles</span> <span class="o">=</span> <span class="n">num_quantiles</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_spec</span> <span class="o">=</span> <span class="n">_QuantileSpec</span><span class="p">(</span><span class="n">buffer_size</span><span class="p">,</span> <span class="n">num_buffers</span><span class="p">,</span> <span class="n">weighted</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">reverse</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_input_batched</span> <span class="o">=</span> <span class="n">input_batched</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_input_batched</span><span class="p">:</span> |
| <span class="nb">setattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'add_input'</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_add_inputs</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__reduce__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">,</span> |
| <span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_num_quantiles</span><span class="p">,</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_spec</span><span class="o">.</span><span class="n">buffer_size</span><span class="p">,</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_spec</span><span class="o">.</span><span class="n">num_buffers</span><span class="p">,</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_spec</span><span class="o">.</span><span class="n">key</span><span class="p">,</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_spec</span><span class="o">.</span><span class="n">reverse</span><span class="p">,</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_spec</span><span class="o">.</span><span class="n">weighted</span><span class="p">,</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_input_batched</span><span class="p">))</span> |
| |
| <span class="nd">@classmethod</span> |
| <span class="k">def</span> <span class="nf">create</span><span class="p">(</span> |
| <span class="bp">cls</span><span class="p">,</span> |
| <span class="n">num_quantiles</span><span class="p">,</span> <span class="c1"># type: int</span> |
| <span class="n">epsilon</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">max_num_elements</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">reverse</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">weighted</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">input_batched</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="c1"># type: (...) -> ApproximateQuantilesCombineFn</span> |
| |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Creates an approximate quantiles combiner with the given key and desired</span> |
| <span class="sd"> number of quantiles.</span> |
| |
| <span class="sd"> Args:</span> |
| <span class="sd"> num_quantiles: Number of quantiles to produce. It is the size of the</span> |
| <span class="sd"> final output list, including the mininum and maximum value items.</span> |
| <span class="sd"> epsilon: (optional) The default error bound is `epsilon`, which holds as</span> |
| <span class="sd"> long as the number of elements is less than `_MAX_NUM_ELEMENTS`.</span> |
| <span class="sd"> Specifically, if one considers the input as a sorted list x_1, ...,</span> |
| <span class="sd"> x_N, then the distance between each exact quantile x_c and its</span> |
| <span class="sd"> approximation x_c' is bounded by `|c - c'| < epsilon * N`. Note that</span> |
| <span class="sd"> these errors are worst-case scenarios. In practice the accuracy tends</span> |
| <span class="sd"> to be much better.</span> |
| <span class="sd"> max_num_elements: (optional) The cost (in time and space) to compute</span> |
| <span class="sd"> quantiles to a given accuracy is a function of the total number of</span> |
| <span class="sd"> elements in the data set.</span> |
| <span class="sd"> key: (optional) Key is a mapping of elements to a comparable key, similar</span> |
| <span class="sd"> to the key argument of Python's sorting methods.</span> |
| <span class="sd"> reverse: (optional) whether to order things smallest to largest, rather</span> |
| <span class="sd"> than largest to smallest.</span> |
| <span class="sd"> weighted: (optional) if set to True, the combiner produces weighted</span> |
| <span class="sd"> quantiles. The input elements are then expected to be tuples of values</span> |
| <span class="sd"> with the corresponding weight.</span> |
| <span class="sd"> input_batched: (optional) if set to True, inputs are expected to be</span> |
| <span class="sd"> batches of elements.</span> |
| <span class="sd"> """</span> |
| <span class="n">max_num_elements</span> <span class="o">=</span> <span class="n">max_num_elements</span> <span class="ow">or</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_MAX_NUM_ELEMENTS</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">epsilon</span><span class="p">:</span> |
| <span class="n">epsilon</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mf">1e-2</span><span class="p">,</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">num_quantiles</span><span class="p">)</span> \ |
| <span class="k">if</span> <span class="n">weighted</span> <span class="k">else</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">num_quantiles</span><span class="p">)</span> |
| <span class="c1"># Note that calculation of the buffer size and the number of buffers here</span> |
| <span class="c1"># is based on technique used in the Munro-Paterson algorithm. Switching to</span> |
| <span class="c1"># the logic used in the "New Algorithm" may result in memory savings since</span> |
| <span class="c1"># it results in lower values for b and k in practice.</span> |
| <span class="n">b</span> <span class="o">=</span> <span class="mi">2</span> |
| <span class="k">while</span> <span class="p">(</span><span class="n">b</span> <span class="o">-</span> <span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o"><<</span> <span class="p">(</span><span class="n">b</span> <span class="o">-</span> <span class="mi">2</span><span class="p">))</span> <span class="o"><</span> <span class="n">epsilon</span> <span class="o">*</span> <span class="n">max_num_elements</span><span class="p">:</span> |
| <span class="n">b</span> <span class="o">=</span> <span class="n">b</span> <span class="o">+</span> <span class="mi">1</span> |
| <span class="n">b</span> <span class="o">=</span> <span class="n">b</span> <span class="o">-</span> <span class="mi">1</span> |
| <span class="n">k</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">max_num_elements</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="mi">1</span> <span class="o"><<</span> <span class="p">(</span><span class="n">b</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)))))</span> |
| <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span> |
| <span class="n">num_quantiles</span><span class="o">=</span><span class="n">num_quantiles</span><span class="p">,</span> |
| <span class="n">buffer_size</span><span class="o">=</span><span class="n">k</span><span class="p">,</span> |
| <span class="n">num_buffers</span><span class="o">=</span><span class="n">b</span><span class="p">,</span> |
| <span class="n">key</span><span class="o">=</span><span class="n">key</span><span class="p">,</span> |
| <span class="n">reverse</span><span class="o">=</span><span class="n">reverse</span><span class="p">,</span> |
| <span class="n">weighted</span><span class="o">=</span><span class="n">weighted</span><span class="p">,</span> |
| <span class="n">input_batched</span><span class="o">=</span><span class="n">input_batched</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_offset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_weight</span><span class="p">):</span> |
| <span class="c1"># type: (int) -> float</span> |
| |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> If the weight is even, we must round up or down. Alternate between these</span> |
| <span class="sd"> two options to avoid a bias.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">new_weight</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">new_weight</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_offset_jitter</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_offset_jitter</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">new_weight</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_offset_jitter</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span> |
| |
| <span class="c1"># TODO(https://github.com/apache/beam/issues/19737): Signature incompatible</span> |
| <span class="c1"># with supertype</span> |
| <span class="k">def</span> <span class="nf">create_accumulator</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="c1"># type: ignore[override]</span> |
| <span class="c1"># type: () -> _QuantileState</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_qs</span> <span class="o">=</span> <span class="n">_QuantileState</span><span class="p">(</span> |
| <span class="n">unbuffered_elements</span><span class="o">=</span><span class="p">[],</span> |
| <span class="n">unbuffered_weights</span><span class="o">=</span><span class="p">[],</span> |
| <span class="n">buffers</span><span class="o">=</span><span class="p">[],</span> |
| <span class="n">spec</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_spec</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_qs</span> |
| |
| <span class="k">def</span> <span class="nf">add_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">quantile_state</span><span class="p">,</span> <span class="n">element</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Add a new element to the collection being summarized by quantile state.</span> |
| <span class="sd"> """</span> |
| <span class="n">quantile_state</span><span class="o">.</span><span class="n">add_unbuffered</span><span class="p">([</span><span class="n">element</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_offset</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">quantile_state</span> |
| |
| <span class="k">def</span> <span class="nf">_add_inputs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">quantile_state</span><span class="p">,</span> <span class="n">elements</span><span class="p">):</span> |
| <span class="c1"># type: (_QuantileState, List) -> _QuantileState</span> |
| |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Add a batch of elements to the collection being summarized by quantile</span> |
| <span class="sd"> state.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">elements</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">quantile_state</span> |
| <span class="n">quantile_state</span><span class="o">.</span><span class="n">add_unbuffered</span><span class="p">(</span><span class="n">elements</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_offset</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">quantile_state</span> |
| |
| <span class="k">def</span> <span class="nf">merge_accumulators</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">accumulators</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Merges all the accumulators (quantile state) as one."""</span> |
| <span class="n">qs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_accumulator</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">accumulator</span> <span class="ow">in</span> <span class="n">accumulators</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">accumulator</span><span class="o">.</span><span class="n">is_empty</span><span class="p">():</span> |
| <span class="k">continue</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_spec</span><span class="o">.</span><span class="n">weighted</span><span class="p">:</span> |
| <span class="n">qs</span><span class="o">.</span><span class="n">add_unbuffered</span><span class="p">(</span> |
| <span class="p">[</span><span class="n">accumulator</span><span class="o">.</span><span class="n">unbuffered_elements</span><span class="p">,</span> <span class="n">accumulator</span><span class="o">.</span><span class="n">unbuffered_weights</span><span class="p">],</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_offset</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">qs</span><span class="o">.</span><span class="n">add_unbuffered</span><span class="p">(</span><span class="n">accumulator</span><span class="o">.</span><span class="n">unbuffered_elements</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_offset</span><span class="p">)</span> |
| |
| <span class="n">qs</span><span class="o">.</span><span class="n">buffers</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">accumulator</span><span class="o">.</span><span class="n">buffers</span><span class="p">)</span> |
| <span class="n">heapq</span><span class="o">.</span><span class="n">heapify</span><span class="p">(</span><span class="n">qs</span><span class="o">.</span><span class="n">buffers</span><span class="p">)</span> |
| <span class="n">qs</span><span class="o">.</span><span class="n">collapse_if_needed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_offset</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">qs</span> |
| |
| <span class="k">def</span> <span class="nf">extract_output</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">accumulator</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Outputs num_quantiles elements consisting of the minimum, maximum and</span> |
| <span class="sd"> num_quantiles - 2 evenly spaced intermediate elements. Returns the empty</span> |
| <span class="sd"> list if no elements have been added.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">accumulator</span><span class="o">.</span><span class="n">is_empty</span><span class="p">():</span> |
| <span class="k">return</span> <span class="p">[]</span> |
| <span class="n">accumulator</span><span class="o">.</span><span class="n">finalize</span><span class="p">()</span> |
| <span class="n">all_elems</span> <span class="o">=</span> <span class="n">accumulator</span><span class="o">.</span><span class="n">buffers</span> |
| <span class="n">total_weight</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_spec</span><span class="o">.</span><span class="n">weighted</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">buffer_elem</span> <span class="ow">in</span> <span class="n">all_elems</span><span class="p">:</span> |
| <span class="n">total_weight</span> <span class="o">+=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">buffer_elem</span><span class="o">.</span><span class="n">weights</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">buffer_elem</span> <span class="ow">in</span> <span class="n">all_elems</span><span class="p">:</span> |
| <span class="n">total_weight</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">buffer_elem</span><span class="o">.</span><span class="n">elements</span><span class="p">)</span> <span class="o">*</span> <span class="n">buffer_elem</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| |
| <span class="n">step</span> <span class="o">=</span> <span class="n">total_weight</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_quantiles</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="n">offset</span> <span class="o">=</span> <span class="p">(</span><span class="n">total_weight</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_quantiles</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> |
| |
| <span class="n">quantiles</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">min_val</span><span class="p">,</span> <span class="n">max_val</span> <span class="o">=</span> \ |
| <span class="n">_interpolate</span><span class="p">(</span><span class="n">all_elems</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_quantiles</span> <span class="o">-</span> <span class="mi">2</span><span class="p">,</span> <span class="n">step</span><span class="p">,</span> <span class="n">offset</span><span class="p">,</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_spec</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="p">[</span><span class="n">min_val</span><span class="p">]</span> <span class="o">+</span> <span class="n">quantiles</span> <span class="o">+</span> <span class="p">[</span><span class="n">max_val</span><span class="p">]</span> |
| </pre></div> |
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