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| "text": "%md\n\n# Introduction\n\nThis note is to demonstrate how to do machine learning in flink. Here we use [Alink](https://github.com/alibaba/Alink/). \nWe use logics regression to do classification task. We use the same data as other tutorials [bank](https://archive.ics.uci.edu/ml/datasets/bank+marketing).\n", |
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| "dateUpdated": "2020-04-29 16:09:44.080", |
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| "data": "\u003cdiv class\u003d\"markdown-body\"\u003e\n\u003ch1\u003eIntroduction\u003c/h1\u003e\n\u003cp\u003eThis note is to demonstrate how to do machine learning in flink. Here we use \u003ca href\u003d\"https://github.com/alibaba/Alink/\"\u003eAlink\u003c/a\u003e.\u003cbr /\u003e\nWe use logics regression to do classification task. We use the same data as other tutorials \u003ca href\u003d\"https://archive.ics.uci.edu/ml/datasets/bank+marketing\"\u003ebank\u003c/a\u003e.\u003c/p\u003e\n\n\u003c/div\u003e" |
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| "dateFinished": "2020-04-29 16:09:44.102", |
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| "text": "%flink.pyflink\n\nimport pyflink\nfrom pyflink.dataset import ExecutionEnvironment\nfrom pyflink.datastream import StreamExecutionEnvironment\nfrom pyalink.alink.env import useCustomEnv\nmlenv \u003d useCustomEnv(gateway,\n b_env,bt_env_2, s_env, st_env_2)\nfrom pyalink.alink import *\n\nt \u003d bt_env_2.from_elements([(1, 2), (2, 5), (3, 1)], [\u0027a\u0027, \u0027b\u0027])\nsource \u003d TableSourceBatchOp(t)\nsource.print()", |
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| "dateUpdated": "2020-04-27 13:48:06.523", |
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| { |
| "type": "TEXT", |
| "data": "\nUse one of the following commands to start using PyAlink:\n - useLocalEnv(parallelism, flinkHome\u003dNone, config\u003dNone)\n - useRemoteEnv(host, port, parallelism, flinkHome\u003dNone, localIp\u003d\"localhost\", config\u003dNone)\nCall resetEnv() to reset environment and switch to another.\n\n a b\n0 1 2\n1 2 5\n2 3 1\n" |
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| "dateCreated": "2020-03-09 23:47:52.979", |
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| "dateFinished": "2020-04-27 13:48:29.623", |
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| "text": "%flink.pyflink\n\n\ntest_data_path \u003d \"/tmp/bank.csv\"\nfull_data_path \u003d \"/tmp/bank-full.csv\"\nschema_str \u003d \"age int, job string, marital string, education string, default string, balance string, housing string, loan string, contact string, day string, month string, duration int, campaign int, pdays int, previous int, poutcome string, y string\"\n\ntest_data \u003d CsvSourceBatchOp() \\\n .setFilePath(test_data_path) \\\n .setSchemaStr(schema_str) \\\n .setIgnoreFirstLine(True) \\\n .setFieldDelimiter(\";\")\n \nfull_data \u003d CsvSourceBatchOp() \\\n .setFilePath(full_data_path) \\\n .setSchemaStr(schema_str) \\\n .setIgnoreFirstLine(True) \\\n .setFieldDelimiter(\";\")\n \ntrain_set \u003d UnionAllBatchOp().linkFrom(\n full_data.filter(\"y\u003d\u0027yes\u0027\"),\n full_data.filter(\"y\u003d\u0027no\u0027\").sample(0.25)\n \n)", |
| "user": "anonymous", |
| "dateUpdated": "2020-04-29 16:10:03.433", |
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| "dateCreated": "2020-03-09 23:47:52.982", |
| "dateStarted": "2020-04-27 13:48:33.820", |
| "dateFinished": "2020-04-27 13:48:34.969", |
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| { |
| "text": "%flink.pyflink\n\n\ncategoricalColNames \u003d [\"job\", \"marital\", \"education\", \"default\",\n \"balance\", \"housing\", \"loan\", \"contact\", \"poutcome\" ]\nnumerialColNames \u003d [\"age\", \"duration\", \"campaign\", \"pdays\",\n \"previous\"]\nlabelColName \u003d \"y\"\n\nonehot \u003d OneHotEncoder().setSelectedCols(categoricalColNames) \\\n .setOutputCols([\"output\"])\nassembler \u003d VectorAssembler().setSelectedCols([\"output\"] + numerialColNames) \\\n .setOutputCol(\"vec\")\npipeline \u003d Pipeline().add(onehot).add(assembler)", |
| "user": "anonymous", |
| "dateUpdated": "2020-04-27 13:48:37.221", |
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| "id": "20200309-234752_1624274051", |
| "dateCreated": "2020-03-09 23:47:52.983", |
| "dateStarted": "2020-04-27 13:48:37.242", |
| "dateFinished": "2020-04-27 13:48:37.911", |
| "status": "FINISHED" |
| }, |
| { |
| "text": "%flink.pyflink\n\n\nlogistic \u003d LogisticRegression().setVectorCol(\"vec\").setLabelCol(labelColName) \\\n .setPredictionCol(\"pred\").setPredictionDetailCol(\"detail\")\nmodel \u003d pipeline.add(logistic).fit(train_set)\n\npredict \u003d model.transform(test_data)\n\nmetrics \u003d EvalBinaryClassBatchOp().setLabelCol(labelColName) \\\n .setPredictionDetailCol(\"detail\").linkFrom(predict).collectMetrics()\n \n \n ", |
| "user": "anonymous", |
| "dateUpdated": "2020-04-27 13:48:39.232", |
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| "text": "%flink.pyflink\n\nprint(\"AUC:\", metrics.getAuc())\nprint(\"KS:\", metrics.getKs())\nprint(\"PRC:\", metrics.getPrc())\nprint(\"Precision:\", metrics.getPrecision())\nprint(\"Recall:\", metrics.getRecall())\nprint(\"F1:\", metrics.getF1())\nprint(\"ConfusionMatrix:\", metrics.getConfusionMatrix())\nprint(\"LabelArray:\", metrics.getLabelArray())\nprint(\"LogLoss:\", metrics.getLogLoss())\nprint(\"TotalSamples:\", metrics.getTotalSamples())\nprint(\"ActualLabelProportion:\", metrics.getActualLabelProportion())\nprint(\"ActualLabelFrequency:\", metrics.getActualLabelFrequency())\nprint(\"Accuracy:\", metrics.getAccuracy())\nprint(\"Kappa:\", metrics.getKappa())", |
| "user": "anonymous", |
| "dateUpdated": "2020-04-27 13:48:51.441", |
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| "code": "SUCCESS", |
| "msg": [ |
| { |
| "type": "TEXT", |
| "data": "AUC: 0.9076425143954201\nKS: 0.6844817658349329\nPRC: 0.5349186786420718\nPrecision: 0.4748803827751196\nRecall: 0.761996161228407\nF1: 0.5851142225497421\nConfusionMatrix: [[397, 439], [124, 3561]]\nLabelArray: [\u0027yes\u0027, \u0027no\u0027]\nLogLoss: 0.3427333415817235\nTotalSamples: 4521\nActualLabelProportion: [0.11523999115239991, 0.8847600088476001]\nActualLabelFrequency: [521, 4000]\nAccuracy: 0.8754700287547003\nKappa: 0.5164554316821129\n" |
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| "dateCreated": "2020-03-09 23:47:52.984", |
| "dateStarted": "2020-04-27 13:48:51.451", |
| "dateFinished": "2020-04-27 13:48:52.073", |
| "status": "FINISHED" |
| }, |
| { |
| "text": "%flink.pyflink\n\ndf \u003d predict.filter(\"y\u003c\u003epred\").firstN(300).collectToDataframe()\n\nz.show(df)", |
| "user": "anonymous", |
| "dateUpdated": "2020-04-27 13:48:54.873", |
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| "job": "string", |
| "marital": "string", |
| "education": "string", |
| "default": "string", |
| "balance": "string", |
| "housing": "string", |
| "loan": "string", |
| "contact": "string", |
| "day": "string", |
| "month": "string", |
| "duration": "string", |
| "campaign": "string", |
| "pdays": "string", |
| "previous": "string", |
| "poutcome": "string", |
| "y": "string", |
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| "data": "age\tjob\tmarital\teducation\tdefault\tbalance\thousing\tloan\tcontact\tday\tmonth\tduration\tcampaign\tpdays\tprevious\tpoutcome\ty\toutput\tvec\tpred\tdetail\n36\tself-employed\tmarried\ttertiary\tno\t307\tyes\tno\tcellular\t14\tmay\t341\t1\t330\t2\tother\tno\t$4563$6:1.0 13:1.0 17:1.0 19:1.0 2518:1.0 4554:1.0 4556:1.0 4560:1.0\t$4568$6:1.0 13:1.0 17:1.0 19:1.0 2518:1.0 4554:1.0 4556:1.0 4560:1.0 4563:36.0 4564:341.0 4565:1.0 4566:330.0 4567:2.0\tyes\t{\"no\":\"0.24364838869421468\",\"yes\":\"0.7563516113057853\"}\n31\tservices\tmarried\tsecondary\tno\t132\tno\tno\tcellular\t7\tjul\t148\t1\t152\t1\tother\tno\t$4563$7:1.0 13:1.0 16:1.0 19:1.0 1043:1.0 4552:1.0 4554:1.0 4556:1.0 4560:1.0\t$4568$7:1.0 13:1.0 16:1.0 19:1.0 1043:1.0 4552:1.0 4554:1.0 4556:1.0 4560:1.0 4563:31.0 4564:148.0 4565:1.0 4566:152.0 4567:1.0\tyes\t{\"no\":\"0.3501427245267339\",\"yes\":\"0.6498572754732661\"}\n78\tretired\tdivorced\tprimary\tno\t229\tno\tno\ttelephone\t22\toct\t97\t1\t-1\t0\tunknown\tyes\t$4563$5:1.0 12:1.0 15:1.0 19:1.0 1962:1.0 4552:1.0 4554:1.0 4557:1.0\t$4568$5:1.0 12:1.0 15:1.0 19:1.0 1962:1.0 4552:1.0 4554:1.0 4557:1.0 4563:78.0 4564:97.0 4565:1.0 4566:-1.0 4567:0.0\tno\t{\"no\":\"0.6104429520406811\",\"yes\":\"0.3895570479593189\"}\n32\tblue-collar\tmarried\tsecondary\tno\t2089\tyes\tno\tcellular\t14\tnov\t132\t1\t-1\t0\tunknown\tyes\t$4563$1:1.0 13:1.0 16:1.0 19:1.0 1790:1.0 4554:1.0 4556:1.0\t$4568$1:1.0 13:1.0 16:1.0 19:1.0 1790:1.0 4554:1.0 4556:1.0 4563:32.0 4564:132.0 4565:1.0 4566:-1.0 4567:0.0\tno\t{\"no\":\"0.955472844471085\",\"yes\":\"0.04452715552891495\"}\n37\ttechnician\tsingle\tsecondary\tno\t228\tyes\tno\tcellular\t20\taug\t1740\t2\t-1\t0\tunknown\tno\t$4563$9:1.0 16:1.0 19:1.0 1952:1.0 4554:1.0 4556:1.0\t$4568$9:1.0 16:1.0 19:1.0 1952:1.0 4554:1.0 4556:1.0 4563:37.0 4564:1740.0 4565:2.0 4566:-1.0 4567:0.0\tyes\t{\"no\":\"0.0015312625158320214\",\"yes\":\"0.998468737484168\"}\n33\tself-employed\tsingle\ttertiary\tno\t2155\tno\tno\tcellular\t17\tnov\t295\t1\t-1\t0\tunknown\tno\t$4563$6:1.0 17:1.0 19:1.0 1846:1.0 4552:1.0 4554:1.0 4556:1.0\t$4568$6:1.0 17:1.0 19:1.0 1846:1.0 4552:1.0 4554:1.0 4556:1.0 4563:33.0 4564:295.0 4565:1.0 4566:-1.0 4567:0.0\tyes\t{\"no\":\"0.12667119287866468\",\"yes\":\"0.8733288071213353\"}\n41\tblue-collar\tmarried\tprimary\tno\t-516\tno\tyes\ttelephone\t8\tjul\t554\t3\t-1\t0\tunknown\tno\t$4563$1:1.0 13:1.0 15:1.0 19:1.0 4551:1.0 4552:1.0 4557:1.0\t$4568$1:1.0 13:1.0 15:1.0 19:1.0 4551:1.0 4552:1.0 4557:1.0 4563:41.0 4564:554.0 4565:3.0 4566:-1.0 4567:0.0\tyes\t{\"no\":\"0.47900049763976393\",\"yes\":\"0.5209995023602361\"}\n41\tmanagement\tmarried\tsecondary\tno\t0\tno\tyes\tcellular\t7\tjul\t630\t3\t-1\t0\tunknown\tno\t$4563$4:1.0 13:1.0 16:1.0 19:1.0 591:1.0 4552:1.0 4556:1.0\t$4568$4:1.0 13:1.0 16:1.0 19:1.0 591:1.0 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