| |
| |
| <!DOCTYPE html> |
| |
| |
| <html > |
| |
| <head> |
| <meta charset="utf-8" /> |
| <meta name="viewport" content="width=device-width, initial-scale=1.0" /> |
| <title>pyspark.mllib.util — PySpark 4.0.0-preview1 documentation</title> |
| |
| |
| |
| <script data-cfasync="false"> |
| document.documentElement.dataset.mode = localStorage.getItem("mode") || ""; |
| document.documentElement.dataset.theme = localStorage.getItem("theme") || "light"; |
| </script> |
| |
| <!-- Loaded before other Sphinx assets --> |
| <link href="../../../_static/styles/theme.css?digest=e353d410970836974a52" rel="stylesheet" /> |
| <link href="../../../_static/styles/bootstrap.css?digest=e353d410970836974a52" rel="stylesheet" /> |
| <link href="../../../_static/styles/pydata-sphinx-theme.css?digest=e353d410970836974a52" rel="stylesheet" /> |
| |
| |
| <link href="../../../_static/vendor/fontawesome/6.1.2/css/all.min.css?digest=e353d410970836974a52" rel="stylesheet" /> |
| <link rel="preload" as="font" type="font/woff2" crossorigin href="../../../_static/vendor/fontawesome/6.1.2/webfonts/fa-solid-900.woff2" /> |
| <link rel="preload" as="font" type="font/woff2" crossorigin href="../../../_static/vendor/fontawesome/6.1.2/webfonts/fa-brands-400.woff2" /> |
| <link rel="preload" as="font" type="font/woff2" crossorigin href="../../../_static/vendor/fontawesome/6.1.2/webfonts/fa-regular-400.woff2" /> |
| |
| <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" /> |
| <link rel="stylesheet" type="text/css" href="../../../_static/copybutton.css" /> |
| <link rel="stylesheet" type="text/css" href="../../../_static/css/pyspark.css" /> |
| |
| <!-- Pre-loaded scripts that we'll load fully later --> |
| <link rel="preload" as="script" href="../../../_static/scripts/bootstrap.js?digest=e353d410970836974a52" /> |
| <link rel="preload" as="script" href="../../../_static/scripts/pydata-sphinx-theme.js?digest=e353d410970836974a52" /> |
| |
| <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script> |
| <script src="../../../_static/jquery.js"></script> |
| <script src="../../../_static/underscore.js"></script> |
| <script src="../../../_static/doctools.js"></script> |
| <script src="../../../_static/clipboard.min.js"></script> |
| <script src="../../../_static/copybutton.js"></script> |
| <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script> |
| <script>DOCUMENTATION_OPTIONS.pagename = '_modules/pyspark/mllib/util';</script> |
| <link rel="canonical" href="https://spark.apache.org/docs/latest/api/python/_modules/pyspark/mllib/util.html" /> |
| <link rel="search" title="Search" href="../../../search.html" /> |
| <meta name="viewport" content="width=device-width, initial-scale=1" /> |
| <meta name="docsearch:language" content="None"> |
| |
| |
| <!-- Matomo --> |
| <script type="text/javascript"> |
| var _paq = window._paq = window._paq || []; |
| /* tracker methods like "setCustomDimension" should be called before "trackPageView" */ |
| _paq.push(["disableCookies"]); |
| _paq.push(['trackPageView']); |
| _paq.push(['enableLinkTracking']); |
| (function() { |
| var u="https://analytics.apache.org/"; |
| _paq.push(['setTrackerUrl', u+'matomo.php']); |
| _paq.push(['setSiteId', '40']); |
| var d=document, g=d.createElement('script'), s=d.getElementsByTagName('script')[0]; |
| g.async=true; g.src=u+'matomo.js'; s.parentNode.insertBefore(g,s); |
| })(); |
| </script> |
| <!-- End Matomo Code --> |
| |
| </head> |
| |
| |
| <body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode=""> |
| |
| |
| |
| <a class="skip-link" href="#main-content">Skip to main content</a> |
| |
| <input type="checkbox" |
| class="sidebar-toggle" |
| name="__primary" |
| id="__primary"/> |
| <label class="overlay overlay-primary" for="__primary"></label> |
| |
| <input type="checkbox" |
| class="sidebar-toggle" |
| name="__secondary" |
| id="__secondary"/> |
| <label class="overlay overlay-secondary" for="__secondary"></label> |
| |
| <div class="search-button__wrapper"> |
| <div class="search-button__overlay"></div> |
| <div class="search-button__search-container"> |
| <form class="bd-search d-flex align-items-center" |
| action="../../../search.html" |
| method="get"> |
| <i class="fa-solid fa-magnifying-glass"></i> |
| <input type="search" |
| class="form-control" |
| name="q" |
| id="search-input" |
| placeholder="Search the docs ..." |
| aria-label="Search the docs ..." |
| autocomplete="off" |
| autocorrect="off" |
| autocapitalize="off" |
| spellcheck="false"/> |
| <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span> |
| </form></div> |
| </div> |
| |
| <nav class="bd-header navbar navbar-expand-lg bd-navbar"> |
| <div class="bd-header__inner bd-page-width"> |
| <label class="sidebar-toggle primary-toggle" for="__primary"> |
| <span class="fa-solid fa-bars"></span> |
| </label> |
| |
| <div class="navbar-header-items__start"> |
| |
| <div class="navbar-item"> |
| |
| |
| <a class="navbar-brand logo" href="../../../index.html"> |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| <img src="../../../_static/spark-logo-light.png" class="logo__image only-light" alt="Logo image"/> |
| <script>document.write(`<img src="../../../_static/spark-logo-dark.png" class="logo__image only-dark" alt="Logo image"/>`);</script> |
| |
| |
| </a></div> |
| |
| </div> |
| |
| |
| <div class="col-lg-9 navbar-header-items"> |
| |
| <div class="me-auto navbar-header-items__center"> |
| |
| <div class="navbar-item"><nav class="navbar-nav"> |
| <p class="sidebar-header-items__title" |
| role="heading" |
| aria-level="1" |
| aria-label="Site Navigation"> |
| Site Navigation |
| </p> |
| <ul class="bd-navbar-elements navbar-nav"> |
| |
| <li class="nav-item"> |
| <a class="nav-link nav-internal" href="../../../index.html"> |
| Overview |
| </a> |
| </li> |
| |
| |
| <li class="nav-item"> |
| <a class="nav-link nav-internal" href="../../../getting_started/index.html"> |
| Getting Started |
| </a> |
| </li> |
| |
| |
| <li class="nav-item"> |
| <a class="nav-link nav-internal" href="../../../user_guide/index.html"> |
| User Guides |
| </a> |
| </li> |
| |
| |
| <li class="nav-item"> |
| <a class="nav-link nav-internal" href="../../../reference/index.html"> |
| API Reference |
| </a> |
| </li> |
| |
| |
| <li class="nav-item"> |
| <a class="nav-link nav-internal" href="../../../development/index.html"> |
| Development |
| </a> |
| </li> |
| |
| |
| <li class="nav-item"> |
| <a class="nav-link nav-internal" href="../../../migration_guide/index.html"> |
| Migration Guides |
| </a> |
| </li> |
| |
| </ul> |
| </nav></div> |
| |
| </div> |
| |
| |
| <div class="navbar-header-items__end"> |
| |
| <div class="navbar-item navbar-persistent--container"> |
| |
| <script> |
| document.write(` |
| <button class="btn btn-sm navbar-btn search-button search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip"> |
| <i class="fa-solid fa-magnifying-glass"></i> |
| </button> |
| `); |
| </script> |
| </div> |
| |
| |
| <div class="navbar-item"><!-- |
| Licensed to the Apache Software Foundation (ASF) under one or more |
| contributor license agreements. See the NOTICE file distributed with |
| this work for additional information regarding copyright ownership. |
| The ASF licenses this file to You under the Apache License, Version 2.0 |
| (the "License"); you may not use this file except in compliance with |
| the License. You may obtain a copy of the License at |
| |
| http://www.apache.org/licenses/LICENSE-2.0 |
| |
| Unless required by applicable law or agreed to in writing, software |
| distributed under the License is distributed on an "AS IS" BASIS, |
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| See the License for the specific language governing permissions and |
| limitations under the License. |
| --> |
| |
| <div id="version-button" class="dropdown"> |
| <button type="button" class="btn btn-secondary btn-sm navbar-btn dropdown-toggle" id="version_switcher_button" data-toggle="dropdown"> |
| 4.0.0-preview1 |
| <span class="caret"></span> |
| </button> |
| <div id="version_switcher" class="dropdown-menu list-group-flush py-0" aria-labelledby="version_switcher_button"> |
| <!-- dropdown will be populated by javascript on page load --> |
| </div> |
| </div> |
| |
| <script type="text/javascript"> |
| // Function to construct the target URL from the JSON components |
| function buildURL(entry) { |
| var template = "https://spark.apache.org/docs/{version}/api/python/index.html"; // supplied by jinja |
| template = template.replace("{version}", entry.version); |
| return template; |
| } |
| |
| // Function to check if corresponding page path exists in other version of docs |
| // and, if so, go there instead of the homepage of the other docs version |
| function checkPageExistsAndRedirect(event) { |
| const currentFilePath = "_modules/pyspark/mllib/util.html", |
| otherDocsHomepage = event.target.getAttribute("href"); |
| let tryUrl = `${otherDocsHomepage}${currentFilePath}`; |
| $.ajax({ |
| type: 'HEAD', |
| url: tryUrl, |
| // if the page exists, go there |
| success: function() { |
| location.href = tryUrl; |
| } |
| }).fail(function() { |
| location.href = otherDocsHomepage; |
| }); |
| return false; |
| } |
| |
| // Function to populate the version switcher |
| (function () { |
| // get JSON config |
| $.getJSON("https://spark.apache.org/static/versions.json", function(data, textStatus, jqXHR) { |
| // create the nodes first (before AJAX calls) to ensure the order is |
| // correct (for now, links will go to doc version homepage) |
| $.each(data, function(index, entry) { |
| // if no custom name specified (e.g., "latest"), use version string |
| if (!("name" in entry)) { |
| entry.name = entry.version; |
| } |
| // construct the appropriate URL, and add it to the dropdown |
| entry.url = buildURL(entry); |
| const node = document.createElement("a"); |
| node.setAttribute("class", "list-group-item list-group-item-action py-1"); |
| node.setAttribute("href", `${entry.url}`); |
| node.textContent = `${entry.name}`; |
| node.onclick = checkPageExistsAndRedirect; |
| $("#version_switcher").append(node); |
| }); |
| }); |
| })(); |
| </script></div> |
| |
| <div class="navbar-item"> |
| <script> |
| document.write(` |
| <button class="theme-switch-button btn btn-sm btn-outline-primary navbar-btn rounded-circle" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip"> |
| <span class="theme-switch" data-mode="light"><i class="fa-solid fa-sun"></i></span> |
| <span class="theme-switch" data-mode="dark"><i class="fa-solid fa-moon"></i></span> |
| <span class="theme-switch" data-mode="auto"><i class="fa-solid fa-circle-half-stroke"></i></span> |
| </button> |
| `); |
| </script></div> |
| |
| <div class="navbar-item"><ul class="navbar-icon-links navbar-nav" |
| aria-label="Icon Links"> |
| <li class="nav-item"> |
| |
| |
| |
| |
| |
| |
| |
| |
| <a href="https://github.com/apache/spark" title="GitHub" class="nav-link" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><span><i class="fa-brands fa-github"></i></span> |
| <label class="sr-only">GitHub</label></a> |
| </li> |
| <li class="nav-item"> |
| |
| |
| |
| |
| |
| |
| |
| |
| <a href="https://pypi.org/project/pyspark" title="PyPI" class="nav-link" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><span><i class="fa-solid fa-box"></i></span> |
| <label class="sr-only">PyPI</label></a> |
| </li> |
| </ul></div> |
| |
| </div> |
| |
| </div> |
| |
| |
| <div class="navbar-persistent--mobile"> |
| <script> |
| document.write(` |
| <button class="btn btn-sm navbar-btn search-button search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip"> |
| <i class="fa-solid fa-magnifying-glass"></i> |
| </button> |
| `); |
| </script> |
| </div> |
| |
| |
| |
| </div> |
| |
| </nav> |
| |
| <div class="bd-container"> |
| <div class="bd-container__inner bd-page-width"> |
| |
| <div class="bd-sidebar-primary bd-sidebar hide-on-wide"> |
| |
| |
| |
| <div class="sidebar-header-items sidebar-primary__section"> |
| |
| |
| <div class="sidebar-header-items__center"> |
| |
| <div class="navbar-item"><nav class="navbar-nav"> |
| <p class="sidebar-header-items__title" |
| role="heading" |
| aria-level="1" |
| aria-label="Site Navigation"> |
| Site Navigation |
| </p> |
| <ul class="bd-navbar-elements navbar-nav"> |
| |
| <li class="nav-item"> |
| <a class="nav-link nav-internal" href="../../../index.html"> |
| Overview |
| </a> |
| </li> |
| |
| |
| <li class="nav-item"> |
| <a class="nav-link nav-internal" href="../../../getting_started/index.html"> |
| Getting Started |
| </a> |
| </li> |
| |
| |
| <li class="nav-item"> |
| <a class="nav-link nav-internal" href="../../../user_guide/index.html"> |
| User Guides |
| </a> |
| </li> |
| |
| |
| <li class="nav-item"> |
| <a class="nav-link nav-internal" href="../../../reference/index.html"> |
| API Reference |
| </a> |
| </li> |
| |
| |
| <li class="nav-item"> |
| <a class="nav-link nav-internal" href="../../../development/index.html"> |
| Development |
| </a> |
| </li> |
| |
| |
| <li class="nav-item"> |
| <a class="nav-link nav-internal" href="../../../migration_guide/index.html"> |
| Migration Guides |
| </a> |
| </li> |
| |
| </ul> |
| </nav></div> |
| |
| </div> |
| |
| |
| |
| <div class="sidebar-header-items__end"> |
| |
| <div class="navbar-item"><!-- |
| Licensed to the Apache Software Foundation (ASF) under one or more |
| contributor license agreements. See the NOTICE file distributed with |
| this work for additional information regarding copyright ownership. |
| The ASF licenses this file to You under the Apache License, Version 2.0 |
| (the "License"); you may not use this file except in compliance with |
| the License. You may obtain a copy of the License at |
| |
| http://www.apache.org/licenses/LICENSE-2.0 |
| |
| Unless required by applicable law or agreed to in writing, software |
| distributed under the License is distributed on an "AS IS" BASIS, |
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| See the License for the specific language governing permissions and |
| limitations under the License. |
| --> |
| |
| <div id="version-button" class="dropdown"> |
| <button type="button" class="btn btn-secondary btn-sm navbar-btn dropdown-toggle" id="version_switcher_button" data-toggle="dropdown"> |
| 4.0.0-preview1 |
| <span class="caret"></span> |
| </button> |
| <div id="version_switcher" class="dropdown-menu list-group-flush py-0" aria-labelledby="version_switcher_button"> |
| <!-- dropdown will be populated by javascript on page load --> |
| </div> |
| </div> |
| |
| <script type="text/javascript"> |
| // Function to construct the target URL from the JSON components |
| function buildURL(entry) { |
| var template = "https://spark.apache.org/docs/{version}/api/python/index.html"; // supplied by jinja |
| template = template.replace("{version}", entry.version); |
| return template; |
| } |
| |
| // Function to check if corresponding page path exists in other version of docs |
| // and, if so, go there instead of the homepage of the other docs version |
| function checkPageExistsAndRedirect(event) { |
| const currentFilePath = "_modules/pyspark/mllib/util.html", |
| otherDocsHomepage = event.target.getAttribute("href"); |
| let tryUrl = `${otherDocsHomepage}${currentFilePath}`; |
| $.ajax({ |
| type: 'HEAD', |
| url: tryUrl, |
| // if the page exists, go there |
| success: function() { |
| location.href = tryUrl; |
| } |
| }).fail(function() { |
| location.href = otherDocsHomepage; |
| }); |
| return false; |
| } |
| |
| // Function to populate the version switcher |
| (function () { |
| // get JSON config |
| $.getJSON("https://spark.apache.org/static/versions.json", function(data, textStatus, jqXHR) { |
| // create the nodes first (before AJAX calls) to ensure the order is |
| // correct (for now, links will go to doc version homepage) |
| $.each(data, function(index, entry) { |
| // if no custom name specified (e.g., "latest"), use version string |
| if (!("name" in entry)) { |
| entry.name = entry.version; |
| } |
| // construct the appropriate URL, and add it to the dropdown |
| entry.url = buildURL(entry); |
| const node = document.createElement("a"); |
| node.setAttribute("class", "list-group-item list-group-item-action py-1"); |
| node.setAttribute("href", `${entry.url}`); |
| node.textContent = `${entry.name}`; |
| node.onclick = checkPageExistsAndRedirect; |
| $("#version_switcher").append(node); |
| }); |
| }); |
| })(); |
| </script></div> |
| |
| <div class="navbar-item"> |
| <script> |
| document.write(` |
| <button class="theme-switch-button btn btn-sm btn-outline-primary navbar-btn rounded-circle" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip"> |
| <span class="theme-switch" data-mode="light"><i class="fa-solid fa-sun"></i></span> |
| <span class="theme-switch" data-mode="dark"><i class="fa-solid fa-moon"></i></span> |
| <span class="theme-switch" data-mode="auto"><i class="fa-solid fa-circle-half-stroke"></i></span> |
| </button> |
| `); |
| </script></div> |
| |
| <div class="navbar-item"><ul class="navbar-icon-links navbar-nav" |
| aria-label="Icon Links"> |
| <li class="nav-item"> |
| |
| |
| |
| |
| |
| |
| |
| |
| <a href="https://github.com/apache/spark" title="GitHub" class="nav-link" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><span><i class="fa-brands fa-github"></i></span> |
| <label class="sr-only">GitHub</label></a> |
| </li> |
| <li class="nav-item"> |
| |
| |
| |
| |
| |
| |
| |
| |
| <a href="https://pypi.org/project/pyspark" title="PyPI" class="nav-link" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><span><i class="fa-solid fa-box"></i></span> |
| <label class="sr-only">PyPI</label></a> |
| </li> |
| </ul></div> |
| |
| </div> |
| |
| </div> |
| |
| |
| <div class="sidebar-primary-items__end sidebar-primary__section"> |
| </div> |
| |
| <div id="rtd-footer-container"></div> |
| |
| |
| </div> |
| |
| <main id="main-content" class="bd-main"> |
| |
| |
| <div class="bd-content"> |
| <div class="bd-article-container"> |
| |
| <div class="bd-header-article"> |
| <div class="header-article-items header-article__inner"> |
| |
| <div class="header-article-items__start"> |
| |
| <div class="header-article-item"> |
| |
| |
| |
| <nav aria-label="Breadcrumbs"> |
| <ul class="bd-breadcrumbs" role="navigation" aria-label="Breadcrumb"> |
| |
| <li class="breadcrumb-item breadcrumb-home"> |
| <a href="../../../index.html" class="nav-link" aria-label="Home"> |
| <i class="fa-solid fa-home"></i> |
| </a> |
| </li> |
| |
| <li class="breadcrumb-item"><a href="../../index.html" class="nav-link">Module code</a></li> |
| |
| <li class="breadcrumb-item active" aria-current="page">pyspark.mllib.util</li> |
| </ul> |
| </nav> |
| </div> |
| |
| </div> |
| |
| |
| </div> |
| </div> |
| |
| |
| |
| |
| <div id="searchbox"></div> |
| <article class="bd-article" role="main"> |
| |
| <h1>Source code for pyspark.mllib.util</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="kn">import</span> <span class="nn">sys</span> |
| <span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">reduce</span> |
| |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| |
| <span class="kn">from</span> <span class="nn">pyspark</span> <span class="kn">import</span> <span class="n">SparkContext</span><span class="p">,</span> <span class="n">since</span> |
| <span class="kn">from</span> <span class="nn">pyspark.mllib.common</span> <span class="kn">import</span> <span class="n">callMLlibFunc</span><span class="p">,</span> <span class="n">inherit_doc</span> |
| <span class="kn">from</span> <span class="nn">pyspark.mllib.linalg</span> <span class="kn">import</span> <span class="n">Vectors</span><span class="p">,</span> <span class="n">SparseVector</span><span class="p">,</span> <span class="n">_convert_to_vector</span> |
| <span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">DataFrame</span> |
| <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Generic</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Type</span><span class="p">,</span> <span class="n">TypeVar</span><span class="p">,</span> <span class="n">cast</span><span class="p">,</span> <span class="n">TYPE_CHECKING</span> |
| <span class="kn">from</span> <span class="nn">pyspark.core.context</span> <span class="kn">import</span> <span class="n">SparkContext</span> |
| <span class="kn">from</span> <span class="nn">pyspark.mllib.linalg</span> <span class="kn">import</span> <span class="n">Vector</span> |
| <span class="kn">from</span> <span class="nn">pyspark.core.rdd</span> <span class="kn">import</span> <span class="n">RDD</span> |
| <span class="kn">from</span> <span class="nn">pyspark.sql.dataframe</span> <span class="kn">import</span> <span class="n">DataFrame</span> |
| |
| <span class="n">T</span> <span class="o">=</span> <span class="n">TypeVar</span><span class="p">(</span><span class="s2">"T"</span><span class="p">)</span> |
| <span class="n">L</span> <span class="o">=</span> <span class="n">TypeVar</span><span class="p">(</span><span class="s2">"L"</span><span class="p">,</span> <span class="n">bound</span><span class="o">=</span><span class="s2">"Loader"</span><span class="p">)</span> |
| <span class="n">JL</span> <span class="o">=</span> <span class="n">TypeVar</span><span class="p">(</span><span class="s2">"JL"</span><span class="p">,</span> <span class="n">bound</span><span class="o">=</span><span class="s2">"JavaLoader"</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">TYPE_CHECKING</span><span class="p">:</span> |
| <span class="kn">from</span> <span class="nn">pyspark.mllib._typing</span> <span class="kn">import</span> <span class="n">VectorLike</span> |
| <span class="kn">from</span> <span class="nn">py4j.java_gateway</span> <span class="kn">import</span> <span class="n">JavaObject</span> |
| <span class="kn">from</span> <span class="nn">pyspark.mllib.regression</span> <span class="kn">import</span> <span class="n">LabeledPoint</span> |
| |
| |
| <div class="viewcode-block" id="MLUtils"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.util.MLUtils.html#pyspark.mllib.tree.MLUtils">[docs]</a><span class="k">class</span> <span class="nc">MLUtils</span><span class="p">:</span> |
| |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Helper methods to load, save and pre-process data used in MLlib.</span> |
| |
| <span class="sd"> .. versionadded:: 1.0.0</span> |
| <span class="sd"> """</span> |
| |
| <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">_parse_libsvm_line</span><span class="p">(</span><span class="n">line</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Parses a line in LIBSVM format into (label, indices, values).</span> |
| <span class="sd"> """</span> |
| <span class="n">items</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">items</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| <span class="n">nnz</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">items</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span> |
| <span class="n">indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">nnz</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span> |
| <span class="n">values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">nnz</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nnz</span><span class="p">):</span> |
| <span class="n">index</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">items</span><span class="p">[</span><span class="mi">1</span> <span class="o">+</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">":"</span><span class="p">)</span> |
| <span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span> |
| <span class="n">values</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">label</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">values</span> |
| |
| <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">_convert_labeled_point_to_libsvm</span><span class="p">(</span><span class="n">p</span><span class="p">:</span> <span class="s2">"LabeledPoint"</span><span class="p">)</span> <span class="o">-></span> <span class="nb">str</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""Converts a LabeledPoint to a string in LIBSVM format."""</span> |
| <span class="kn">from</span> <span class="nn">pyspark.mllib.regression</span> <span class="kn">import</span> <span class="n">LabeledPoint</span> |
| |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">LabeledPoint</span><span class="p">)</span> |
| <span class="n">items</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">label</span><span class="p">)]</span> |
| <span class="n">v</span> <span class="o">=</span> <span class="n">_convert_to_vector</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">features</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">SparseVector</span><span class="p">):</span> |
| <span class="n">nnz</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">indices</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nnz</span><span class="p">):</span> |
| <span class="n">items</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="s2">":"</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">v</span><span class="p">)):</span> |
| <span class="n">items</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="s2">":"</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> <span class="c1"># type: ignore[index]</span> |
| <span class="k">return</span> <span class="s2">" "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">items</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="MLUtils.loadLibSVMFile"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.util.MLUtils.html#pyspark.mllib.tree.MLUtils.loadLibSVMFile">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">loadLibSVMFile</span><span class="p">(</span> |
| <span class="n">sc</span><span class="p">:</span> <span class="n">SparkContext</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">numFeatures</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">minPartitions</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="n">RDD</span><span class="p">[</span><span class="s2">"LabeledPoint"</span><span class="p">]:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Loads labeled data in the LIBSVM format into an RDD of</span> |
| <span class="sd"> LabeledPoint. The LIBSVM format is a text-based format used by</span> |
| <span class="sd"> LIBSVM and LIBLINEAR. Each line represents a labeled sparse</span> |
| <span class="sd"> feature vector using the following format:</span> |
| |
| <span class="sd"> label index1:value1 index2:value2 ...</span> |
| |
| <span class="sd"> where the indices are one-based and in ascending order. This</span> |
| <span class="sd"> method parses each line into a LabeledPoint, where the feature</span> |
| <span class="sd"> indices are converted to zero-based.</span> |
| |
| <span class="sd"> .. versionadded:: 1.0.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> sc : :py:class:`pyspark.SparkContext`</span> |
| <span class="sd"> Spark context</span> |
| <span class="sd"> path : str</span> |
| <span class="sd"> file or directory path in any Hadoop-supported file system URI</span> |
| <span class="sd"> numFeatures : int, optional</span> |
| <span class="sd"> number of features, which will be determined</span> |
| <span class="sd"> from the input data if a nonpositive value</span> |
| <span class="sd"> is given. This is useful when the dataset is</span> |
| <span class="sd"> already split into multiple files and you</span> |
| <span class="sd"> want to load them separately, because some</span> |
| <span class="sd"> features may not present in certain files,</span> |
| <span class="sd"> which leads to inconsistent feature</span> |
| <span class="sd"> dimensions.</span> |
| <span class="sd"> minPartitions : int, optional</span> |
| <span class="sd"> min number of partitions</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> :py:class:`pyspark.RDD`</span> |
| <span class="sd"> labeled data stored as an RDD of LabeledPoint</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> from tempfile import NamedTemporaryFile</span> |
| <span class="sd"> >>> from pyspark.mllib.util import MLUtils</span> |
| <span class="sd"> >>> from pyspark.mllib.regression import LabeledPoint</span> |
| <span class="sd"> >>> tempFile = NamedTemporaryFile(delete=True)</span> |
| <span class="sd"> >>> _ = tempFile.write(b"+1 1:1.0 3:2.0 5:3.0\\n-1\\n-1 2:4.0 4:5.0 6:6.0")</span> |
| <span class="sd"> >>> tempFile.flush()</span> |
| <span class="sd"> >>> examples = MLUtils.loadLibSVMFile(sc, tempFile.name).collect()</span> |
| <span class="sd"> >>> tempFile.close()</span> |
| <span class="sd"> >>> examples[0]</span> |
| <span class="sd"> LabeledPoint(1.0, (6,[0,2,4],[1.0,2.0,3.0]))</span> |
| <span class="sd"> >>> examples[1]</span> |
| <span class="sd"> LabeledPoint(-1.0, (6,[],[]))</span> |
| <span class="sd"> >>> examples[2]</span> |
| <span class="sd"> LabeledPoint(-1.0, (6,[1,3,5],[4.0,5.0,6.0]))</span> |
| <span class="sd"> """</span> |
| <span class="kn">from</span> <span class="nn">pyspark.mllib.regression</span> <span class="kn">import</span> <span class="n">LabeledPoint</span> |
| |
| <span class="n">lines</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">minPartitions</span><span class="p">)</span> |
| <span class="n">parsed</span> <span class="o">=</span> <span class="n">lines</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">l</span><span class="p">:</span> <span class="n">MLUtils</span><span class="o">.</span><span class="n">_parse_libsvm_line</span><span class="p">(</span><span class="n">l</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">numFeatures</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">parsed</span><span class="o">.</span><span class="n">cache</span><span class="p">()</span> |
| <span class="n">numFeatures</span> <span class="o">=</span> <span class="n">parsed</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span> <span class="k">if</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="nb">max</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span> |
| <span class="k">return</span> <span class="n">parsed</span><span class="o">.</span><span class="n">map</span><span class="p">(</span> |
| <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">LabeledPoint</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="n">Vectors</span><span class="o">.</span><span class="n">sparse</span><span class="p">(</span><span class="n">numFeatures</span><span class="p">,</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span> <span class="c1"># type: ignore[arg-type]</span> |
| <span class="p">)</span> |
| <span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="MLUtils.saveAsLibSVMFile"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.util.MLUtils.html#pyspark.mllib.tree.MLUtils.saveAsLibSVMFile">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">saveAsLibSVMFile</span><span class="p">(</span><span class="n">data</span><span class="p">:</span> <span class="n">RDD</span><span class="p">[</span><span class="s2">"LabeledPoint"</span><span class="p">],</span> <span class="nb">dir</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</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"> Save labeled data in LIBSVM format.</span> |
| |
| <span class="sd"> .. versionadded:: 1.0.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> data : :py:class:`pyspark.RDD`</span> |
| <span class="sd"> an RDD of LabeledPoint to be saved</span> |
| <span class="sd"> dir : str</span> |
| <span class="sd"> directory to save the data</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> from tempfile import NamedTemporaryFile</span> |
| <span class="sd"> >>> from fileinput import input</span> |
| <span class="sd"> >>> from pyspark.mllib.regression import LabeledPoint</span> |
| <span class="sd"> >>> from glob import glob</span> |
| <span class="sd"> >>> from pyspark.mllib.util import MLUtils</span> |
| <span class="sd"> >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, 1.23), (2, 4.56)])),</span> |
| <span class="sd"> ... LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]</span> |
| <span class="sd"> >>> tempFile = NamedTemporaryFile(delete=True)</span> |
| <span class="sd"> >>> tempFile.close()</span> |
| <span class="sd"> >>> MLUtils.saveAsLibSVMFile(sc.parallelize(examples), tempFile.name)</span> |
| <span class="sd"> >>> ''.join(sorted(input(glob(tempFile.name + "/part-0000*"))))</span> |
| <span class="sd"> '0.0 1:1.01 2:2.02 3:3.03\\n1.1 1:1.23 3:4.56\\n'</span> |
| <span class="sd"> """</span> |
| <span class="n">lines</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="n">MLUtils</span><span class="o">.</span><span class="n">_convert_labeled_point_to_libsvm</span><span class="p">(</span><span class="n">p</span><span class="p">))</span> |
| <span class="n">lines</span><span class="o">.</span><span class="n">saveAsTextFile</span><span class="p">(</span><span class="nb">dir</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="MLUtils.loadLabeledPoints"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.util.MLUtils.html#pyspark.mllib.tree.MLUtils.loadLabeledPoints">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">loadLabeledPoints</span><span class="p">(</span> |
| <span class="n">sc</span><span class="p">:</span> <span class="n">SparkContext</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">minPartitions</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="n">RDD</span><span class="p">[</span><span class="s2">"LabeledPoint"</span><span class="p">]:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Load labeled points saved using RDD.saveAsTextFile.</span> |
| |
| <span class="sd"> .. versionadded:: 1.0.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> sc : :py:class:`pyspark.SparkContext`</span> |
| <span class="sd"> Spark context</span> |
| <span class="sd"> path : str</span> |
| <span class="sd"> file or directory path in any Hadoop-supported file system URI</span> |
| <span class="sd"> minPartitions : int, optional</span> |
| <span class="sd"> min number of partitions</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> :py:class:`pyspark.RDD`</span> |
| <span class="sd"> labeled data stored as an RDD of LabeledPoint</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> from tempfile import NamedTemporaryFile</span> |
| <span class="sd"> >>> from pyspark.mllib.util import MLUtils</span> |
| <span class="sd"> >>> from pyspark.mllib.regression import LabeledPoint</span> |
| <span class="sd"> >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, -1.23), (2, 4.56e-7)])),</span> |
| <span class="sd"> ... LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]</span> |
| <span class="sd"> >>> tempFile = NamedTemporaryFile(delete=True)</span> |
| <span class="sd"> >>> tempFile.close()</span> |
| <span class="sd"> >>> sc.parallelize(examples, 1).saveAsTextFile(tempFile.name)</span> |
| <span class="sd"> >>> MLUtils.loadLabeledPoints(sc, tempFile.name).collect()</span> |
| <span class="sd"> [LabeledPoint(1.1, (3,[0,2],[-1.23,4.56e-07])), LabeledPoint(0.0, [1.01,2.02,3.03])]</span> |
| <span class="sd"> """</span> |
| <span class="n">minPartitions</span> <span class="o">=</span> <span class="n">minPartitions</span> <span class="ow">or</span> <span class="nb">min</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">defaultParallelism</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">callMLlibFunc</span><span class="p">(</span><span class="s2">"loadLabeledPoints"</span><span class="p">,</span> <span class="n">sc</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">minPartitions</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="MLUtils.appendBias"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.util.MLUtils.html#pyspark.mllib.tree.MLUtils.appendBias">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="nd">@since</span><span class="p">(</span><span class="s2">"1.5.0"</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">appendBias</span><span class="p">(</span><span class="n">data</span><span class="p">:</span> <span class="n">Vector</span><span class="p">)</span> <span class="o">-></span> <span class="n">Vector</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Returns a new vector with `1.0` (bias) appended to</span> |
| <span class="sd"> the end of the input vector.</span> |
| <span class="sd"> """</span> |
| <span class="n">vec</span> <span class="o">=</span> <span class="n">_convert_to_vector</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">vec</span><span class="p">,</span> <span class="n">SparseVector</span><span class="p">):</span> |
| <span class="n">newIndices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">vec</span><span class="o">.</span><span class="n">indices</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">vec</span><span class="p">))</span> |
| <span class="n">newValues</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">vec</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">SparseVector</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">vec</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">newIndices</span><span class="p">,</span> <span class="n">newValues</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">_convert_to_vector</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">vec</span><span class="o">.</span><span class="n">toArray</span><span class="p">(),</span> <span class="mf">1.0</span><span class="p">))</span></div> |
| |
| <div class="viewcode-block" id="MLUtils.loadVectors"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.util.MLUtils.html#pyspark.mllib.tree.MLUtils.loadVectors">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="nd">@since</span><span class="p">(</span><span class="s2">"1.5.0"</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">loadVectors</span><span class="p">(</span><span class="n">sc</span><span class="p">:</span> <span class="n">SparkContext</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="n">RDD</span><span class="p">[</span><span class="n">Vector</span><span class="p">]:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Loads vectors saved using `RDD[Vector].saveAsTextFile`</span> |
| <span class="sd"> with the default number of partitions.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">callMLlibFunc</span><span class="p">(</span><span class="s2">"loadVectors"</span><span class="p">,</span> <span class="n">sc</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="MLUtils.convertVectorColumnsToML"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.util.MLUtils.html#pyspark.mllib.tree.MLUtils.convertVectorColumnsToML">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">convertVectorColumnsToML</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="n">DataFrame</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Converts vector columns in an input DataFrame from the</span> |
| <span class="sd"> :py:class:`pyspark.mllib.linalg.Vector` type to the new</span> |
| <span class="sd"> :py:class:`pyspark.ml.linalg.Vector` type under the `spark.ml`</span> |
| <span class="sd"> package.</span> |
| |
| <span class="sd"> .. versionadded:: 2.0.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> dataset : :py:class:`pyspark.sql.DataFrame`</span> |
| <span class="sd"> input dataset</span> |
| <span class="sd"> \\*cols : str</span> |
| <span class="sd"> Vector columns to be converted.</span> |
| |
| <span class="sd"> New vector columns will be ignored. If unspecified, all old</span> |
| <span class="sd"> vector columns will be converted excepted nested ones.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> :py:class:`pyspark.sql.DataFrame`</span> |
| <span class="sd"> the input dataset with old vector columns converted to the</span> |
| <span class="sd"> new vector type</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> import pyspark</span> |
| <span class="sd"> >>> from pyspark.mllib.linalg import Vectors</span> |
| <span class="sd"> >>> from pyspark.mllib.util import MLUtils</span> |
| <span class="sd"> >>> df = spark.createDataFrame(</span> |
| <span class="sd"> ... [(0, Vectors.sparse(2, [1], [1.0]), Vectors.dense(2.0, 3.0))],</span> |
| <span class="sd"> ... ["id", "x", "y"])</span> |
| <span class="sd"> >>> r1 = MLUtils.convertVectorColumnsToML(df).first()</span> |
| <span class="sd"> >>> isinstance(r1.x, pyspark.ml.linalg.SparseVector)</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> isinstance(r1.y, pyspark.ml.linalg.DenseVector)</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> r2 = MLUtils.convertVectorColumnsToML(df, "x").first()</span> |
| <span class="sd"> >>> isinstance(r2.x, pyspark.ml.linalg.SparseVector)</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> isinstance(r2.y, pyspark.mllib.linalg.DenseVector)</span> |
| <span class="sd"> True</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">DataFrame</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"Input dataset must be a DataFrame but got </span><span class="si">{}</span><span class="s2">."</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">dataset</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">callMLlibFunc</span><span class="p">(</span><span class="s2">"convertVectorColumnsToML"</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="n">cols</span><span class="p">))</span></div> |
| |
| <div class="viewcode-block" id="MLUtils.convertVectorColumnsFromML"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.util.MLUtils.html#pyspark.mllib.tree.MLUtils.convertVectorColumnsFromML">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">convertVectorColumnsFromML</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="n">DataFrame</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Converts vector columns in an input DataFrame to the</span> |
| <span class="sd"> :py:class:`pyspark.mllib.linalg.Vector` type from the new</span> |
| <span class="sd"> :py:class:`pyspark.ml.linalg.Vector` type under the `spark.ml`</span> |
| <span class="sd"> package.</span> |
| |
| <span class="sd"> .. versionadded:: 2.0.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> dataset : :py:class:`pyspark.sql.DataFrame`</span> |
| <span class="sd"> input dataset</span> |
| <span class="sd"> \\*cols : str</span> |
| <span class="sd"> Vector columns to be converted.</span> |
| |
| <span class="sd"> Old vector columns will be ignored. If unspecified, all new</span> |
| <span class="sd"> vector columns will be converted except nested ones.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> :py:class:`pyspark.sql.DataFrame`</span> |
| <span class="sd"> the input dataset with new vector columns converted to the</span> |
| <span class="sd"> old vector type</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> import pyspark</span> |
| <span class="sd"> >>> from pyspark.ml.linalg import Vectors</span> |
| <span class="sd"> >>> from pyspark.mllib.util import MLUtils</span> |
| <span class="sd"> >>> df = spark.createDataFrame(</span> |
| <span class="sd"> ... [(0, Vectors.sparse(2, [1], [1.0]), Vectors.dense(2.0, 3.0))],</span> |
| <span class="sd"> ... ["id", "x", "y"])</span> |
| <span class="sd"> >>> r1 = MLUtils.convertVectorColumnsFromML(df).first()</span> |
| <span class="sd"> >>> isinstance(r1.x, pyspark.mllib.linalg.SparseVector)</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> isinstance(r1.y, pyspark.mllib.linalg.DenseVector)</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> r2 = MLUtils.convertVectorColumnsFromML(df, "x").first()</span> |
| <span class="sd"> >>> isinstance(r2.x, pyspark.mllib.linalg.SparseVector)</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> isinstance(r2.y, pyspark.ml.linalg.DenseVector)</span> |
| <span class="sd"> True</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">DataFrame</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"Input dataset must be a DataFrame but got </span><span class="si">{}</span><span class="s2">."</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">dataset</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">callMLlibFunc</span><span class="p">(</span><span class="s2">"convertVectorColumnsFromML"</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="n">cols</span><span class="p">))</span></div> |
| |
| <div class="viewcode-block" id="MLUtils.convertMatrixColumnsToML"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.util.MLUtils.html#pyspark.mllib.tree.MLUtils.convertMatrixColumnsToML">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">convertMatrixColumnsToML</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="n">DataFrame</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Converts matrix columns in an input DataFrame from the</span> |
| <span class="sd"> :py:class:`pyspark.mllib.linalg.Matrix` type to the new</span> |
| <span class="sd"> :py:class:`pyspark.ml.linalg.Matrix` type under the `spark.ml`</span> |
| <span class="sd"> package.</span> |
| |
| <span class="sd"> .. versionadded:: 2.0.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> dataset : :py:class:`pyspark.sql.DataFrame`</span> |
| <span class="sd"> input dataset</span> |
| <span class="sd"> \\*cols : str</span> |
| <span class="sd"> Matrix columns to be converted.</span> |
| |
| <span class="sd"> New matrix columns will be ignored. If unspecified, all old</span> |
| <span class="sd"> matrix columns will be converted excepted nested ones.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> :py:class:`pyspark.sql.DataFrame`</span> |
| <span class="sd"> the input dataset with old matrix columns converted to the</span> |
| <span class="sd"> new matrix type</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> import pyspark</span> |
| <span class="sd"> >>> from pyspark.mllib.linalg import Matrices</span> |
| <span class="sd"> >>> from pyspark.mllib.util import MLUtils</span> |
| <span class="sd"> >>> df = spark.createDataFrame(</span> |
| <span class="sd"> ... [(0, Matrices.sparse(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4]),</span> |
| <span class="sd"> ... Matrices.dense(2, 2, range(4)))], ["id", "x", "y"])</span> |
| <span class="sd"> >>> r1 = MLUtils.convertMatrixColumnsToML(df).first()</span> |
| <span class="sd"> >>> isinstance(r1.x, pyspark.ml.linalg.SparseMatrix)</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> isinstance(r1.y, pyspark.ml.linalg.DenseMatrix)</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> r2 = MLUtils.convertMatrixColumnsToML(df, "x").first()</span> |
| <span class="sd"> >>> isinstance(r2.x, pyspark.ml.linalg.SparseMatrix)</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> isinstance(r2.y, pyspark.mllib.linalg.DenseMatrix)</span> |
| <span class="sd"> True</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">DataFrame</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"Input dataset must be a DataFrame but got </span><span class="si">{}</span><span class="s2">."</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">dataset</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">callMLlibFunc</span><span class="p">(</span><span class="s2">"convertMatrixColumnsToML"</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="n">cols</span><span class="p">))</span></div> |
| |
| <div class="viewcode-block" id="MLUtils.convertMatrixColumnsFromML"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.util.MLUtils.html#pyspark.mllib.tree.MLUtils.convertMatrixColumnsFromML">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">convertMatrixColumnsFromML</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="n">DataFrame</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Converts matrix columns in an input DataFrame to the</span> |
| <span class="sd"> :py:class:`pyspark.mllib.linalg.Matrix` type from the new</span> |
| <span class="sd"> :py:class:`pyspark.ml.linalg.Matrix` type under the `spark.ml`</span> |
| <span class="sd"> package.</span> |
| |
| <span class="sd"> .. versionadded:: 2.0.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> dataset : :py:class:`pyspark.sql.DataFrame`</span> |
| <span class="sd"> input dataset</span> |
| <span class="sd"> \\*cols : str</span> |
| <span class="sd"> Matrix columns to be converted.</span> |
| |
| <span class="sd"> Old matrix columns will be ignored. If unspecified, all new</span> |
| <span class="sd"> matrix columns will be converted except nested ones.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> :py:class:`pyspark.sql.DataFrame`</span> |
| <span class="sd"> the input dataset with new matrix columns converted to the</span> |
| <span class="sd"> old matrix type</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> import pyspark</span> |
| <span class="sd"> >>> from pyspark.ml.linalg import Matrices</span> |
| <span class="sd"> >>> from pyspark.mllib.util import MLUtils</span> |
| <span class="sd"> >>> df = spark.createDataFrame(</span> |
| <span class="sd"> ... [(0, Matrices.sparse(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4]),</span> |
| <span class="sd"> ... Matrices.dense(2, 2, range(4)))], ["id", "x", "y"])</span> |
| <span class="sd"> >>> r1 = MLUtils.convertMatrixColumnsFromML(df).first()</span> |
| <span class="sd"> >>> isinstance(r1.x, pyspark.mllib.linalg.SparseMatrix)</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> isinstance(r1.y, pyspark.mllib.linalg.DenseMatrix)</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> r2 = MLUtils.convertMatrixColumnsFromML(df, "x").first()</span> |
| <span class="sd"> >>> isinstance(r2.x, pyspark.mllib.linalg.SparseMatrix)</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> isinstance(r2.y, pyspark.ml.linalg.DenseMatrix)</span> |
| <span class="sd"> True</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">DataFrame</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"Input dataset must be a DataFrame but got </span><span class="si">{}</span><span class="s2">."</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">dataset</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">callMLlibFunc</span><span class="p">(</span><span class="s2">"convertMatrixColumnsFromML"</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="n">cols</span><span class="p">))</span></div></div> |
| |
| |
| <div class="viewcode-block" id="Saveable"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.util.Saveable.html#pyspark.mllib.tree.Saveable">[docs]</a><span class="k">class</span> <span class="nc">Saveable</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Mixin for models and transformers which may be saved as files.</span> |
| |
| <span class="sd"> .. versionadded:: 1.3.0</span> |
| <span class="sd"> """</span> |
| |
| <div class="viewcode-block" id="Saveable.save"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.util.Saveable.html#pyspark.mllib.tree.Saveable.save">[docs]</a> <span class="k">def</span> <span class="nf">save</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sc</span><span class="p">:</span> <span class="n">SparkContext</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</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"> Save this model to the given path.</span> |
| |
| <span class="sd"> This saves:</span> |
| <span class="sd"> * human-readable (JSON) model metadata to path/metadata/</span> |
| <span class="sd"> * Parquet formatted data to path/data/</span> |
| |
| <span class="sd"> The model may be loaded using :py:meth:`Loader.load`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> sc : :py:class:`pyspark.SparkContext`</span> |
| <span class="sd"> Spark context used to save model data.</span> |
| <span class="sd"> path : str</span> |
| <span class="sd"> Path specifying the directory in which to save</span> |
| <span class="sd"> this model. If the directory already exists,</span> |
| <span class="sd"> this method throws an exception.</span> |
| <span class="sd"> """</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span></div></div> |
| |
| |
| <div class="viewcode-block" id="JavaSaveable"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.util.JavaSaveable.html#pyspark.mllib.tree.JavaSaveable">[docs]</a><span class="nd">@inherit_doc</span> |
| <span class="k">class</span> <span class="nc">JavaSaveable</span><span class="p">(</span><span class="n">Saveable</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Mixin for models that provide save() through their Scala</span> |
| <span class="sd"> implementation.</span> |
| |
| <span class="sd"> .. versionadded:: 1.3.0</span> |
| <span class="sd"> """</span> |
| |
| <span class="n">_java_model</span><span class="p">:</span> <span class="s2">"JavaObject"</span> |
| |
| <div class="viewcode-block" id="JavaSaveable.save"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.util.JavaSaveable.html#pyspark.mllib.tree.JavaSaveable.save">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">"1.3.0"</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">save</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sc</span><span class="p">:</span> <span class="n">SparkContext</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""Save this model to the given path."""</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">SparkContext</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"sc should be a SparkContext, got type </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="nb">type</span><span class="p">(</span><span class="n">sc</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">path</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"path should be a string, got type </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="nb">type</span><span class="p">(</span><span class="n">path</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_java_model</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">(),</span> <span class="n">path</span><span class="p">)</span></div></div> |
| |
| |
| <div class="viewcode-block" id="Loader"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.util.Loader.html#pyspark.mllib.tree.Loader">[docs]</a><span class="k">class</span> <span class="nc">Loader</span><span class="p">(</span><span class="n">Generic</span><span class="p">[</span><span class="n">T</span><span class="p">]):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Mixin for classes which can load saved models from files.</span> |
| |
| <span class="sd"> .. versionadded:: 1.3.0</span> |
| <span class="sd"> """</span> |
| |
| <div class="viewcode-block" id="Loader.load"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.util.Loader.html#pyspark.mllib.tree.Loader.load">[docs]</a> <span class="nd">@classmethod</span> |
| <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">:</span> <span class="n">Type</span><span class="p">[</span><span class="n">L</span><span class="p">],</span> <span class="n">sc</span><span class="p">:</span> <span class="n">SparkContext</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="n">L</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Load a model from the given path. The model should have been</span> |
| <span class="sd"> saved using :py:meth:`Saveable.save`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> sc : :py:class:`pyspark.SparkContext`</span> |
| <span class="sd"> Spark context used for loading model files.</span> |
| <span class="sd"> path : str</span> |
| <span class="sd"> Path specifying the directory to which the model was saved.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> object</span> |
| <span class="sd"> model instance</span> |
| <span class="sd"> """</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span></div></div> |
| |
| |
| <div class="viewcode-block" id="JavaLoader"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.util.JavaLoader.html#pyspark.mllib.tree.JavaLoader">[docs]</a><span class="nd">@inherit_doc</span> |
| <span class="k">class</span> <span class="nc">JavaLoader</span><span class="p">(</span><span class="n">Loader</span><span class="p">[</span><span class="n">T</span><span class="p">]):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Mixin for classes which can load saved models using its Scala</span> |
| <span class="sd"> implementation.</span> |
| |
| <span class="sd"> .. versionadded:: 1.3.0</span> |
| <span class="sd"> """</span> |
| |
| <span class="nd">@classmethod</span> |
| <span class="k">def</span> <span class="nf">_java_loader_class</span><span class="p">(</span><span class="bp">cls</span><span class="p">)</span> <span class="o">-></span> <span class="nb">str</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Returns the full class name of the Java loader. The default</span> |
| <span class="sd"> implementation replaces "pyspark" by "org.apache.spark" in</span> |
| <span class="sd"> the Python full class name.</span> |
| <span class="sd"> """</span> |
| <span class="n">java_package</span> <span class="o">=</span> <span class="bp">cls</span><span class="o">.</span><span class="vm">__module__</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"pyspark"</span><span class="p">,</span> <span class="s2">"org.apache.spark"</span><span class="p">)</span> |
| <span class="k">return</span> <span class="s2">"."</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">java_package</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="vm">__name__</span><span class="p">])</span> |
| |
| <span class="nd">@classmethod</span> |
| <span class="k">def</span> <span class="nf">_load_java</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">sc</span><span class="p">:</span> <span class="n">SparkContext</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="s2">"JavaObject"</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Load a Java model from the given path.</span> |
| <span class="sd"> """</span> |
| <span class="n">java_class</span> <span class="o">=</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_java_loader_class</span><span class="p">()</span> |
| <span class="n">java_obj</span><span class="p">:</span> <span class="s2">"JavaObject"</span> <span class="o">=</span> <span class="n">reduce</span><span class="p">(</span><span class="nb">getattr</span><span class="p">,</span> <span class="n">java_class</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"."</span><span class="p">),</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">java_obj</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">(),</span> <span class="n">path</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="JavaLoader.load"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.util.JavaLoader.html#pyspark.mllib.tree.JavaLoader.load">[docs]</a> <span class="nd">@classmethod</span> |
| <span class="nd">@since</span><span class="p">(</span><span class="s2">"1.3.0"</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">:</span> <span class="n">Type</span><span class="p">[</span><span class="n">JL</span><span class="p">],</span> <span class="n">sc</span><span class="p">:</span> <span class="n">SparkContext</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="n">JL</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""Load a model from the given path."""</span> |
| <span class="n">java_model</span> <span class="o">=</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_load_java</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">java_model</span><span class="p">)</span> <span class="c1"># type: ignore[call-arg]</span></div></div> |
| |
| |
| <div class="viewcode-block" id="LinearDataGenerator"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.util.LinearDataGenerator.html#pyspark.mllib.tree.LinearDataGenerator">[docs]</a><span class="k">class</span> <span class="nc">LinearDataGenerator</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""Utils for generating linear data.</span> |
| |
| <span class="sd"> .. versionadded:: 1.5.0</span> |
| <span class="sd"> """</span> |
| |
| <div class="viewcode-block" id="LinearDataGenerator.generateLinearInput"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.util.LinearDataGenerator.html#pyspark.mllib.tree.LinearDataGenerator.generateLinearInput">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">generateLinearInput</span><span class="p">(</span> |
| <span class="n">intercept</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> |
| <span class="n">weights</span><span class="p">:</span> <span class="s2">"VectorLike"</span><span class="p">,</span> |
| <span class="n">xMean</span><span class="p">:</span> <span class="s2">"VectorLike"</span><span class="p">,</span> |
| <span class="n">xVariance</span><span class="p">:</span> <span class="s2">"VectorLike"</span><span class="p">,</span> |
| <span class="n">nPoints</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> |
| <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> |
| <span class="n">eps</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="n">List</span><span class="p">[</span><span class="s2">"LabeledPoint"</span><span class="p">]:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> .. versionadded:: 1.5.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> intercept : float</span> |
| <span class="sd"> bias factor, the term c in X'w + c</span> |
| <span class="sd"> weights : :py:class:`pyspark.mllib.linalg.Vector` or convertible</span> |
| <span class="sd"> feature vector, the term w in X'w + c</span> |
| <span class="sd"> xMean : :py:class:`pyspark.mllib.linalg.Vector` or convertible</span> |
| <span class="sd"> Point around which the data X is centered.</span> |
| <span class="sd"> xVariance : :py:class:`pyspark.mllib.linalg.Vector` or convertible</span> |
| <span class="sd"> Variance of the given data</span> |
| <span class="sd"> nPoints : int</span> |
| <span class="sd"> Number of points to be generated</span> |
| <span class="sd"> seed : int</span> |
| <span class="sd"> Random Seed</span> |
| <span class="sd"> eps : float</span> |
| <span class="sd"> Used to scale the noise. If eps is set high,</span> |
| <span class="sd"> the amount of gaussian noise added is more.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> list</span> |
| <span class="sd"> of :py:class:`pyspark.mllib.regression.LabeledPoints` of length nPoints</span> |
| <span class="sd"> """</span> |
| <span class="n">weights</span> <span class="o">=</span> <span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="n">weight</span><span class="p">)</span> <span class="k">for</span> <span class="n">weight</span> <span class="ow">in</span> <span class="n">cast</span><span class="p">(</span><span class="n">Iterable</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span> <span class="n">weights</span><span class="p">)]</span> |
| <span class="n">xMean</span> <span class="o">=</span> <span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="n">mean</span><span class="p">)</span> <span class="k">for</span> <span class="n">mean</span> <span class="ow">in</span> <span class="n">cast</span><span class="p">(</span><span class="n">Iterable</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span> <span class="n">xMean</span><span class="p">)]</span> |
| <span class="n">xVariance</span> <span class="o">=</span> <span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="n">var</span><span class="p">)</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">cast</span><span class="p">(</span><span class="n">Iterable</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span> <span class="n">xVariance</span><span class="p">)]</span> |
| <span class="k">return</span> <span class="nb">list</span><span class="p">(</span> |
| <span class="n">callMLlibFunc</span><span class="p">(</span> |
| <span class="s2">"generateLinearInputWrapper"</span><span class="p">,</span> |
| <span class="nb">float</span><span class="p">(</span><span class="n">intercept</span><span class="p">),</span> |
| <span class="n">weights</span><span class="p">,</span> |
| <span class="n">xMean</span><span class="p">,</span> |
| <span class="n">xVariance</span><span class="p">,</span> |
| <span class="nb">int</span><span class="p">(</span><span class="n">nPoints</span><span class="p">),</span> |
| <span class="nb">int</span><span class="p">(</span><span class="n">seed</span><span class="p">),</span> |
| <span class="nb">float</span><span class="p">(</span><span class="n">eps</span><span class="p">),</span> |
| <span class="p">)</span> |
| <span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="LinearDataGenerator.generateLinearRDD"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.util.LinearDataGenerator.html#pyspark.mllib.tree.LinearDataGenerator.generateLinearRDD">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="nd">@since</span><span class="p">(</span><span class="s2">"1.5.0"</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">generateLinearRDD</span><span class="p">(</span> |
| <span class="n">sc</span><span class="p">:</span> <span class="n">SparkContext</span><span class="p">,</span> |
| <span class="n">nexamples</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> |
| <span class="n">nfeatures</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> |
| <span class="n">eps</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> |
| <span class="n">nParts</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span> |
| <span class="n">intercept</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="n">RDD</span><span class="p">[</span><span class="s2">"LabeledPoint"</span><span class="p">]:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Generate an RDD of LabeledPoints.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">callMLlibFunc</span><span class="p">(</span> |
| <span class="s2">"generateLinearRDDWrapper"</span><span class="p">,</span> |
| <span class="n">sc</span><span class="p">,</span> |
| <span class="nb">int</span><span class="p">(</span><span class="n">nexamples</span><span class="p">),</span> |
| <span class="nb">int</span><span class="p">(</span><span class="n">nfeatures</span><span class="p">),</span> |
| <span class="nb">float</span><span class="p">(</span><span class="n">eps</span><span class="p">),</span> |
| <span class="nb">int</span><span class="p">(</span><span class="n">nParts</span><span class="p">),</span> |
| <span class="nb">float</span><span class="p">(</span><span class="n">intercept</span><span class="p">),</span> |
| <span class="p">)</span></div></div> |
| |
| |
| <span class="k">def</span> <span class="nf">_test</span><span class="p">()</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span> |
| <span class="kn">import</span> <span class="nn">doctest</span> |
| <span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SparkSession</span> |
| |
| <span class="n">globs</span> <span class="o">=</span> <span class="nb">globals</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="c1"># The small batch size here ensures that we see multiple batches,</span> |
| <span class="c1"># even in these small test examples:</span> |
| <span class="n">spark</span> <span class="o">=</span> <span class="n">SparkSession</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">master</span><span class="p">(</span><span class="s2">"local[2]"</span><span class="p">)</span><span class="o">.</span><span class="n">appName</span><span class="p">(</span><span class="s2">"mllib.util tests"</span><span class="p">)</span><span class="o">.</span><span class="n">getOrCreate</span><span class="p">()</span> |
| <span class="n">globs</span><span class="p">[</span><span class="s2">"spark"</span><span class="p">]</span> <span class="o">=</span> <span class="n">spark</span> |
| <span class="n">globs</span><span class="p">[</span><span class="s2">"sc"</span><span class="p">]</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">sparkContext</span> |
| <span class="p">(</span><span class="n">failure_count</span><span class="p">,</span> <span class="n">test_count</span><span class="p">)</span> <span class="o">=</span> <span class="n">doctest</span><span class="o">.</span><span class="n">testmod</span><span class="p">(</span><span class="n">globs</span><span class="o">=</span><span class="n">globs</span><span class="p">,</span> <span class="n">optionflags</span><span class="o">=</span><span class="n">doctest</span><span class="o">.</span><span class="n">ELLIPSIS</span><span class="p">)</span> |
| <span class="n">spark</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">failure_count</span><span class="p">:</span> |
| <span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> |
| |
| |
| <span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">"__main__"</span><span class="p">:</span> |
| <span class="n">_test</span><span class="p">()</span> |
| </pre></div> |
| |
| </article> |
| |
| |
| |
| <footer class="bd-footer-article"> |
| |
| <div class="footer-article-items footer-article__inner"> |
| |
| <div class="footer-article-item"><!-- Previous / next buttons --> |
| <div class="prev-next-area"> |
| </div></div> |
| |
| </div> |
| |
| </footer> |
| |
| </div> |
| |
| |
| |
| |
| </div> |
| <footer class="bd-footer-content"> |
| |
| </footer> |
| |
| </main> |
| </div> |
| </div> |
| |
| <!-- Scripts loaded after <body> so the DOM is not blocked --> |
| <script src="../../../_static/scripts/bootstrap.js?digest=e353d410970836974a52"></script> |
| <script src="../../../_static/scripts/pydata-sphinx-theme.js?digest=e353d410970836974a52"></script> |
| |
| <footer class="bd-footer"> |
| <div class="bd-footer__inner bd-page-width"> |
| |
| <div class="footer-items__start"> |
| |
| <div class="footer-item"><p class="copyright"> |
| Copyright @ 2024 The Apache Software Foundation, Licensed under the <a href="https://www.apache.org/licenses/LICENSE-2.0">Apache License, Version 2.0</a>. |
| </p></div> |
| |
| <div class="footer-item"> |
| <p class="sphinx-version"> |
| Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0. |
| <br/> |
| </p> |
| </div> |
| |
| </div> |
| |
| |
| <div class="footer-items__end"> |
| |
| <div class="footer-item"><p class="theme-version"> |
| Built with the <a href="https://pydata-sphinx-theme.readthedocs.io/en/stable/index.html">PyData Sphinx Theme</a> 0.13.3. |
| </p></div> |
| |
| </div> |
| |
| </div> |
| |
| </footer> |
| </body> |
| </html> |
