{"id":13701231,"url":"https://github.com/Cascading/pattern","last_synced_at":"2025-05-04T21:30:37.601Z","repository":{"id":66877596,"uuid":"10251840","full_name":"Cascading/pattern","owner":"Cascading","description":"Machine Learning for Cascading","archived":false,"fork":false,"pushed_at":"2015-06-12T23:33:43.000Z","size":2220,"stargazers_count":82,"open_issues_count":3,"forks_count":23,"subscribers_count":21,"default_branch":"wip-1.0","last_synced_at":"2024-11-13T07:35:37.606Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Java","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Cascading.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2013-05-23T19:38:36.000Z","updated_at":"2024-07-09T09:43:53.000Z","dependencies_parsed_at":"2023-02-20T16:45:32.587Z","dependency_job_id":null,"html_url":"https://github.com/Cascading/pattern","commit_stats":null,"previous_names":[],"tags_count":14,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Cascading%2Fpattern","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Cascading%2Fpattern/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Cascading%2Fpattern/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Cascading%2Fpattern/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Cascading","download_url":"https://codeload.github.com/Cascading/pattern/tar.gz/refs/heads/wip-1.0","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252403630,"owners_count":21742399,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-02T20:01:23.408Z","updated_at":"2025-05-04T21:30:36.612Z","avatar_url":"https://github.com/Cascading.png","language":"Java","funding_links":[],"categories":["Java","人工智能"],"sub_categories":["模型训练"],"readme":"# Overview\n\n[Pattern](http://www.cascading.org/pattern/) is a Cascading framework and library for machine learning model\nscoring at scale.\n\nPattern can read [PMML](http://en.wikipedia.org/wiki/Predictive_Model_Markup_Language) models as workflow\nspecifications for generating Cascading flows which can run on Apache Hadoop.\n\nPattern is still under active development under the wip-1.0 branch. Thus all wip releases are made available\nfrom the `files.concurrentinc.com` domain. When Pattern hits 1.0 and beyond, final releases will be under\n`files.cascading.org`.\n\nSee the `pattern-examples` subdirectory for sample apps.\n\nFor more information, visit: http://www.cascading.org/pattern/\n\n# PMML\n\nPattern currently supports the following PMML model types:\n\n  * General Regression\n  * Regression\n  * Clustering\n  * Tree\n  * Mining - ensembles of the above models like Random Forest\n\nIn progress are:\n\n  * Neural Network\n  * Support Vector Machine\n\nNot all aspects of each of the above models are supported. To request support for a particular model or model\nparameter, [report an issue](#reporting-issues).\n\nThese PMML model types translate or compose into:\n\n  * [Random Forest](http://en.wikipedia.org/wiki/Random_forest) in [PMML 4.0+](http://www.dmg.org/v4-0-1/MultipleModels.html) exported from [R/Rattle](http://cran.r-project.org/web/packages/rattle/index.html)\n  * [Linear Regression](http://en.wikipedia.org/wiki/Linear_regression) in [PMML 1.1+](http://www.dmg.org/v1-1/generalregression.html)\n  * [K-Means Clustering](http://en.wikipedia.org/wiki/K-means_clustering) in [PMML 2.0+](http://www.dmg.org/v2-0/ClusteringModel.html)\n  * [Logistic Regression](http://en.wikipedia.org/wiki/Logistic_regression) in [PMML 4.0.1+](http://www.dmg.org/v4-0-1/Regression.html)\n  * [Multinomial Model](http://en.wikipedia.org/wiki/Multinomial_distribution) in [PMML 2.0+](http://www.dmg.org/v2-0/Regression.html)\n\nNote: [Hierarchical Clustering](http://en.wikipedia.org/wiki/Hierarchical_clustering) is also implemented. The unit test\nfor that algorithm currently excludes two data points. In regression tests with the Iris data set, we've\nisolated edge cases where the classifiers in R and Pattern do not agree. Then again, Iris data gets used to illustrate model behaviors\nwith such properties. This will take some digging into numerical operations inside R.\n\n# Using\n\nTo use Pattern, there is no installation other than adding the necessary dependencies to Maven, Ivy, or Gradle.\n\nTo include the base core model libraries, use:\n\n    \u003cdependency\u003e\n      \u003cgroupId\u003ecascading\u003c/groupId\u003e\n      \u003cartifactId\u003epattern-core\u003c/artifactId\u003e\n      \u003cversion\u003ex.y.z\u003c/version\u003e\n    \u003c/dependency\u003e\n\nTo include the PMML parsing libraries and the `PMMLPlanner`, use:\n\n    \u003cdependency\u003e\n      \u003cgroupId\u003ecascading\u003c/groupId\u003e\n      \u003cartifactId\u003epattern-pmml\u003c/artifactId\u003e\n      \u003cversion\u003ex.y.z\u003c/version\u003e\n    \u003c/dependency\u003e\n\nOther sub-projects and artifacts are simply in place to faciliate testing on various platforms, the above dependencies\nhave no dependencies on Cascading Hadoop or local modes, they are completely independent of the underying platforms.\n\n# Reporting Issues\n\nThe best way to report an issue is to add a new test to `SimplePMMLPlatformTest` along with the expected result set\nand submit a pull request on GitHub.\n\nFailing that, feel free to open an [issue](https://github.com/Cascading/pattern/issues) on the [Cascading/Pattern](https://github.com/Cascading/pattern)\nproject site or mail the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pattern-user).\n\n# Developing\n\nRunning:\n\n    \u003e gradle idea\n\nfrom the root of the project will create all IntelliJ project and module files, and retrieve all dependencies.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FCascading%2Fpattern","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FCascading%2Fpattern","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FCascading%2Fpattern/lists"}