G1-Diplodatos_Clase 06 - ML Pipelines.json

{"paragraphs":[{"text":"print(s\"\"\"%html\n<center>\n    <h1><a href=\"http://diplodatos.famaf.unc.edu.ar/\">Diplomatura en Ciencia de Datos, Aprendizaje Automático y sus Aplicaciones</a></h1>\n    <h2>Curso <a href=\"https://sites.google.com/view/eleccion-optativas-diplodatos/programaci%C3%B3n-distribu%C3%ADda-sobre-grandes-vol%C3%BAmenes-de-datos\">Programación Distribuida sobre Grandes Volúmenes de Datos</a></h2>\n</center>\n\n<br>\n\n<h3 style=\"text-align:center;\"> Damián Barsotti  </h3>\n\n<h3 style=\"text-align:center;\">\n    <a href=\"http://www.famaf.unc.edu.ar\">\n    Facultad de Matemática Astronomía Física y Computación\n    </a>\n<br/>\n    <a href=\"http://www.unc.edu.ar\">\n    Universidad Nacional de Córdoba\n    </a>\n<br/>\n    <center>\n    <a href=\"http://www.famaf.unc.edu.ar\">\n    <img src=\"$baseDir/comun/logo%20UNC%20FAMAF%202016.png\" alt=\"Drawing\" style=\"width:50%;\"/>\n    </a>\n    </center>\n</h3>\n\n<p style=\"font-size:15px;\">\n    <br />\n        This work is licensed under a\n        <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.\n    <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n        <img alt=\"Creative Commons License\" style=\"border-width:0;vertical-align:middle;float:right\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n    </a>\n</p>\n\"\"\")\n","user":"anonymous","dateUpdated":"2020-11-08T19:52:58-0300","config":{"editorSetting":{"language":"scala"},"colWidth":12,"editorMode":"ace/mode/scala","editorHide":true,"results":[{"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}}}],"enabled":false,"fontSize":9},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<center>\n    <h1><a href=\"http://diplodatos.famaf.unc.edu.ar/\">Diplomatura en Ciencia de Datos, Aprendizaje Automático y sus Aplicaciones</a></h1>\n    <h2>Curso <a href=\"https://sites.google.com/view/eleccion-optativas-diplodatos/programaci%C3%B3n-distribu%C3%ADda-sobre-grandes-vol%C3%BAmenes-de-datos\">Programación Distribuida sobre Grandes Volúmenes de Datos</a></h2>\n</center>\n\n<br>\n\n<h3 style=\"text-align:center;\"> Damián Barsotti  </h3>\n\n<h3 style=\"text-align:center;\">\n    <a href=\"http://www.famaf.unc.edu.ar\">\n    Facultad de Matemática Astronomía Física y Computación\n    </a>\n<br/>\n    <a href=\"http://www.unc.edu.ar\">\n    Universidad Nacional de Córdoba\n    </a>\n<br/>\n    <center>\n    <a href=\"http://www.famaf.unc.edu.ar\">\n    <img src=\"https://git.cs.famaf.unc.edu.ar/dbarsotti/diplodatos_bigdata/raw/master/clases/comun/logo%20UNC%20FAMAF%202016.png\" alt=\"Drawing\" style=\"width:50%;\"/>\n    </a>\n    </center>\n</h3>\n\n<p style=\"font-size:15px;\">\n    <br />\n        This work is licensed under a\n        <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.\n    <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">\n        <img alt=\"Creative Commons License\" style=\"border-width:0;vertical-align:middle;float:right\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" />\n    </a>\n</p>\n"}]},"apps":[],"jobName":"paragraph_1604845764797_-7721393","id":"20161027-185750_417030348","dateCreated":"2020-11-08T11:29:24-0300","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"focus":true,"$$hashKey":"object:54838"},{"text":"%md\n## Abstracciones Básicas\n\n### Transformer\n\n* Toma un `DataFrame` y devuelven un `DataFrame` transformado.\n    - método `transform(<DataFrame>)`\n* Ejemplos:\n\n","user":"anonymous","dateUpdated":"2020-11-08T19:52:58-0300","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","editorHide":true,"fontSize":9,"results":[{"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}}}],"enabled":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<h2>Abstracciones Básicas</h2>\n<h3>Transformer</h3>\n<ul>\n  <li>Toma un <code>DataFrame</code> y devuelven un <code>DataFrame</code> transformado.\n    <ul>\n      <li>método <code>transform(&lt;DataFrame&gt;)</code></li>\n    </ul>\n  </li>\n  <li>Ejemplos:</li>\n</ul>\n</div>"}]},"apps":[],"jobName":"paragraph_1604845764800_2015332065","id":"20161027-185927_1249905112","dateCreated":"2020-11-08T11:29:24-0300","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:54839"},{"title":"Transformador de features","text":"println(s\"\"\"%html\n<center>\n<img src=\"$baseDir/05_ml/06-transformador-features.svg\" alt=\"Drawing\" style=\"width: 80%;\"/>\n</center>\n\"\"\")\n/*\n%md\n\n    - Transformador de features\n\n        %%% yuml type=class style=plain format=svg\n          [note:Dataframe;Entrada{bg:cornsilk}]->[Transformer;(featurizer)|transform(){bg:orange}]\n          [Transformer;(featurizer)|transform(){bg:orange}]->[note:Dataframe; features{bg:cornsilk}]\n        %%%\n*/\n","user":"anonymous","dateUpdated":"2020-11-08T19:52:58-0300","config":{"tableHide":false,"editorSetting":{"language":"scala","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/scala","fontSize":9,"editorHide":true,"title":true,"results":{},"enabled":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<center>\n<img src=\"https://bitbucket.org/bigdata_famaf/diplodatos_bigdata/raw/HEAD/clases/05_ml/06-transformador-features.svg\" alt=\"Drawing\" style=\"width: 80%;\"/>\n</center>\n\n"}]},"apps":[],"jobName":"paragraph_1604845764801_1276630693","id":"20201029-111103_1838666173","dateCreated":"2020-11-08T11:29:24-0300","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:54840"},{"title":"Modelo","text":"println(s\"\"\"%html\n<center>\n<img src=\"$baseDir/05_ml/05-uso.svg\" alt=\"Drawing\" style=\"width: 75%;\"/>\n</center>\n\"\"\")\n/*\n%md\n\n    - Modelo\n\n        %%% yuml type=class style=plain format=svg\n          [note:Dataframe; features{bg:cornsilk}]->[Transformer;(modelo)|transform(){bg:orange}]\n          [Transformer;(modelo)|transform(){bg:orange}]->[note:Dataframe; predicción{bg:cornsilk}]\n        %%%\n*/","user":"anonymous","dateUpdated":"2020-11-08T19:52:58-0300","config":{"tableHide":false,"editorSetting":{"language":"scala","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/scala","fontSize":9,"editorHide":true,"title":true,"results":{},"enabled":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<center>\n<img src=\"https://bitbucket.org/bigdata_famaf/diplodatos_bigdata/raw/HEAD/clases/05_ml/05-uso.svg\" alt=\"Drawing\" style=\"width: 75%;\"/>\n</center>\n\n"}]},"apps":[],"jobName":"paragraph_1604845764801_-2076915008","id":"20201029-111212_842896971","dateCreated":"2020-11-08T11:29:24-0300","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:54841"},{"text":"%md\n### Estimator\n\n* Encapsula un algoritmo ML.\n* Toma un `DataFrame` de entrenamiento y devuelve un `Transformer`\n","user":"anonymous","dateUpdated":"2020-11-08T19:52:58-0300","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<h3>Estimator</h3>\n<ul>\n  <li>Encapsula un algoritmo ML.</li>\n  <li>Toma un <code>DataFrame</code> de entrenamiento y devuelve un <code>Transformer</code></li>\n</ul>\n</div>"}]},"apps":[],"jobName":"paragraph_1604845764802_723993519","id":"20201029-110945_2080913668","dateCreated":"2020-11-08T11:29:24-0300","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:54842"},{"text":"println(s\"\"\"%html\n<center>\n<img src=\"$baseDir/05_ml/04-entrenamiento.svg\" alt=\"Drawing\" style=\"width: 90%;\"/>\n</center>\n\"\"\")\n/*\n%md\n\n    - método `fit(<DataFrame>)`\n\n      %%% yuml type=class style=plain format=svg\n         [note:Dataframe; features;(entrenamiento){bg:cornsilk}]->[Estimator;(algoritmo ML)|fit()]\n         [Estimator;(algoritmo ML)|fit(){bg:red}]->[note:Transformer;(modelo){bg:orange}]\n      %%%\n*/\n","user":"anonymous","dateUpdated":"2020-11-08T19:52:58-0300","config":{"tableHide":false,"editorSetting":{"language":"scala","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/scala","fontSize":9,"editorHide":true,"results":{},"enabled":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<center>\n<img src=\"https://bitbucket.org/bigdata_famaf/diplodatos_bigdata/raw/HEAD/clases/05_ml/04-entrenamiento.svg\" alt=\"Drawing\" style=\"width: 90%;\"/>\n</center>\n\n"}]},"apps":[],"jobName":"paragraph_1604845764802_958376671","id":"20201029-111909_2094645311","dateCreated":"2020-11-08T11:29:24-0300","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:54843"},{"text":"%md\n\n## Ejemplo flujo de trabajo clásico ML.\n","user":"anonymous","dateUpdated":"2020-11-08T19:52:58-0300","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<h2>Ejemplo flujo de trabajo clásico ML.</h2>\n</div>"}]},"apps":[],"jobName":"paragraph_1604845764803_1842187506","id":"20201029-112000_2031171035","dateCreated":"2020-11-08T11:29:24-0300","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:54844"},{"title":"Datos de entrenamiento","text":"%pyspark\n\ntraining = spark.createDataFrame([\n        (0, \"a b c d e spark a\", 1.0),\n        (1, \"b d\", 0.0),\n        (2, \"spark f g h\", 1.0),\n        (3, \"hadoop a mapreduce\", 0.0)\n    ], [\"id\", \"text\", \"label\"])\n","user":"anonymous","dateUpdated":"2020-11-08T19:52:58-0300","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":14,"title":true,"results":{},"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1604845764803_-1342190865","id":"20161027-192753_1880015042","dateCreated":"2020-11-08T11:29:24-0300","dateStarted":"2020-11-08T19:52:58-0300","dateFinished":"2020-11-08T19:52:58-0300","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:54845"},{"text":"%md\n\n### Featurization\n","user":"anonymous","dateUpdated":"2020-11-08T19:52:58-0300","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<h3>Featurization</h3>\n</div>"}]},"apps":[],"jobName":"paragraph_1604845764803_63011981","id":"20191205-181810_1376282739","dateCreated":"2020-11-08T11:29:24-0300","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:54846"},{"title":"Tokenizer","text":"%pyspark\n\nfrom pyspark.ml.feature import HashingTF, Tokenizer\n\n# Creo Tokenizer Transformer\ntokenizer = Tokenizer(\n    inputCol=\"text\",\n    outputCol=\"words\")\n\n# Aplico la transformación devolviendo un nuevo dataframe\ntokenizerOut = tokenizer.transform(training)\n\nz.show(tokenizerOut)\n","user":"anonymous","dateUpdated":"2020-11-08T19:52:58-0300","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":14,"title":true,"results":{"0":{"graph":{"mode":"table","height":300,"optionOpen":false,"setting":{"table":{"tableGridState":{},"tableColumnTypeState":{"names":{"id":"string","text":"string","label":"string","words":"string"},"updated":false},"tableOptionSpecHash":"[{\"name\":\"useFilter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable filter for columns\"},{\"name\":\"showPagination\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable pagination for better navigation\"},{\"name\":\"showAggregationFooter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable a footer for displaying aggregated values\"}]","tableOptionValue":{"useFilter":false,"showPagination":false,"showAggregationFooter":false},"updated":false,"initialized":false}},"commonSetting":{}}}},"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TABLE","data":"id\ttext\tlabel\twords\n0\ta b c d e spark a\t1.0\tWrappedArray(a, b, c, d, e, spark, a)\n1\tb d\t0.0\tWrappedArray(b, d)\n2\tspark f g h\t1.0\tWrappedArray(spark, f, g, h)\n3\thadoop a mapreduce\t0.0\tWrappedArray(hadoop, a, mapreduce)\n"},{"type":"TEXT","data":"\n"}]},"apps":[],"jobName":"paragraph_1604845764804_18065765","id":"20161027-193120_515869320","dateCreated":"2020-11-08T11:29:24-0300","dateStarted":"2020-11-08T19:52:58-0300","dateFinished":"2020-11-08T19:52:58-0300","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:54847"},{"title":"Feature vectors","text":"%pyspark\n# Creo un Term Frecuency Transformer con hash\nhashingTF = HashingTF(\n    numFeatures=1000,\n    inputCol = tokenizer.getOutputCol(), \n    outputCol=\"features\")\n\n# Aplico la transformación al resultado de la transformación previa\nhashingTFOut = hashingTF.transform(tokenizerOut)\n\nhashingTFOut.printSchema() \n# Notar tipo de la columna \"features\"\n# El primer elemento de la tupla es el tamaño\n\nz.show(hashingTFOut)\n","user":"anonymous","dateUpdated":"2020-11-08T19:52:58-0300","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":14,"title":true,"results":{"1":{"graph":{"mode":"table","height":300,"optionOpen":false,"setting":{"table":{"tableGridState":{},"tableColumnTypeState":{"names":{"id":"string","text":"string","label":"string","words":"string","features":"string"},"updated":false},"tableOptionSpecHash":"[{\"name\":\"useFilter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable filter for columns\"},{\"name\":\"showPagination\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable pagination for better navigation\"},{\"name\":\"showAggregationFooter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable a footer for displaying aggregated values\"}]","tableOptionValue":{"useFilter":false,"showPagination":false,"showAggregationFooter":false},"updated":false,"initialized":false}},"commonSetting":{}}}},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"root\n |-- id: long (nullable = true)\n |-- text: string (nullable = true)\n |-- label: double (nullable = true)\n |-- words: array (nullable = true)\n |    |-- element: string (containsNull = true)\n |-- features: vector (nullable = true)\n\n"},{"type":"TABLE","data":"id\ttext\tlabel\twords\tfeatures\n0\ta b c d e spark a\t1.0\tWrappedArray(a, b, c, d, e, spark, a)\t(1000,[94,105,170,361,722,878],[1.0,1.0,2.0,1.0,1.0,1.0])\n1\tb d\t0.0\tWrappedArray(b, d)\t(1000,[94,361],[1.0,1.0])\n2\tspark f g h\t1.0\tWrappedArray(spark, f, g, h)\t(1000,[105,248,282,417],[1.0,1.0,1.0,1.0])\n3\thadoop a mapreduce\t0.0\tWrappedArray(hadoop, a, mapreduce)\t(1000,[170,181,953],[1.0,1.0,1.0])\n"},{"type":"TEXT","data":"\n"}]},"apps":[],"jobName":"paragraph_1604845764804_756861843","id":"20171115-095457_1156237442","dateCreated":"2020-11-08T11:29:24-0300","dateStarted":"2020-11-08T19:52:58-0300","dateFinished":"2020-11-08T19:52:58-0300","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:54848"},{"text":"%md\n\n### Modelo\n","user":"anonymous","dateUpdated":"2020-11-08T19:52:58-0300","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<h3>Modelo</h3>\n</div>"}]},"apps":[],"jobName":"paragraph_1604845764804_1758337167","id":"20191205-182631_90559665","dateCreated":"2020-11-08T11:29:24-0300","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:54849"},{"title":"Creación","text":"%pyspark\n\nfrom pyspark.ml.classification import LogisticRegression\n\n# Creo el Estimator\nlr = LogisticRegression(\n    maxIter=10,\n    regParam=0.01)\n  \n# Entreno el Estimador obteniendo un modelo\nmodel = lr.fit(hashingTFOut)\n","user":"anonymous","dateUpdated":"2020-11-08T19:52:58-0300","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":14,"title":true,"results":{},"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1604845764804_-260447479","id":"20161027-194846_129661804","dateCreated":"2020-11-08T11:29:24-0300","dateStarted":"2020-11-08T19:52:58-0300","dateFinished":"2020-11-08T19:52:59-0300","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:54850"},{"title":"Predicción","text":"%pyspark\n\n# Creo un DF para testearel modelo\ntest = spark.createDataFrame([\n  (4, \"spark i j k a\"),\n  (5, \"l m n\"),\n  (6, \"mapreduce spark\"),\n  (7, \"apache hadoop\"),\n  (8, \"spark f j k\")\n], [\"id\", \"text\"])\n\n\n# Featurizo el test\ntestTokens = tokenizer.transform(test)\n\ntestFeatures = hashingTF.transform(testTokens)\n\npredictions = model.transform(testFeatures)\n\nz.show(predictions.select(\"id\", \"text\", \"probability\", \"prediction\"))\n","user":"anonymous","dateUpdated":"2020-11-08T19:52:59-0300","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":14,"title":true,"results":{"0":{"graph":{"mode":"table","height":300,"optionOpen":false,"setting":{"table":{"tableGridState":{},"tableColumnTypeState":{"names":{"id":"string","text":"string","probability":"string","prediction":"string"},"updated":false},"tableOptionSpecHash":"[{\"name\":\"useFilter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable filter for columns\"},{\"name\":\"showPagination\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable pagination for better navigation\"},{\"name\":\"showAggregationFooter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable a footer for displaying aggregated values\"}]","tableOptionValue":{"useFilter":false,"showPagination":false,"showAggregationFooter":false},"updated":false,"initialized":false}},"commonSetting":{}}}},"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TABLE","data":"id\ttext\tprobability\tprediction\n4\tspark i j k a\t[0.4178708877031535,0.5821291122968465]\t1.0\n5\tl m n\t[0.954126238851537,0.045873761148463005]\t0.0\n6\tmapreduce spark\t[0.8365768944495406,0.16342310555045933]\t0.0\n7\tapache hadoop\t[0.9857864449916622,0.014213555008337634]\t0.0\n8\tspark f j k\t[0.25910961733038906,0.7408903826696108]\t1.0\n"},{"type":"TEXT","data":"\n"}]},"apps":[],"jobName":"paragraph_1604845764804_-1903070377","id":"20161027-195542_1318506384","dateCreated":"2020-11-08T11:29:24-0300","dateStarted":"2020-11-08T19:52:59-0300","dateFinished":"2020-11-08T19:52:59-0300","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:54851"},{"text":"%md\n\n## Pipelines\n\n","user":"anonymous","dateUpdated":"2020-11-08T19:52:59-0300","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","editorHide":true,"fontSize":9,"results":[{"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}}}],"enabled":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<h2>Pipelines</h2>\n</div>"}]},"apps":[],"jobName":"paragraph_1604845764805_-1997593537","id":"20161028-141905_1713895534","dateCreated":"2020-11-08T11:29:24-0300","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:54852"},{"text":"println(s\"\"\"%html\n<center>\n<img src=\"$baseDir/06_ml_pipelines/pipeline.svg\" alt=\"Drawing\" style=\"width: 90%;\"/>\n</center>\n\"\"\")\n\n/*\n%md\n* Abstracción del flujo de trabajo en ML:\n\n      %%% yuml style=plain scale=80 dir=LR format=svg\n        [Featurization]->[Entrenamiento]\n        [Entrenamiento]->[Modelo]\n        [Modelo]->[Predicción]\n        [Modelo]->[Evaluación]\n      %%%\n*/","user":"anonymous","dateUpdated":"2020-11-08T19:52:59-0300","config":{"tableHide":false,"editorSetting":{"language":"scala","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/scala","fontSize":9,"editorHide":true,"results":{},"enabled":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<center>\n<img src=\"https://bitbucket.org/bigdata_famaf/diplodatos_bigdata/raw/HEAD/clases/06_ml_pipelines/pipeline.svg\" alt=\"Drawing\" style=\"width: 90%;\"/>\n</center>\n\n"}]},"apps":[],"jobName":"paragraph_1604845764805_1158932760","id":"20201029-114011_107220819","dateCreated":"2020-11-08T11:29:24-0300","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:54853"},{"text":"%md\n\n* Para su construcción se usan **Transformers** y **Estimators** (llamados Stages)\n    - los concatena\n\n* Un pipeline es un **Estimator**\n    - método `fit(<DataFramee>)` devuelve un `PipelineModel` (Transformer)\n","user":"anonymous","dateUpdated":"2020-11-08T19:52:59-0300","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<ul>\n  <li>\n    <p>Para su construcción se usan <strong>Transformers</strong> y <strong>Estimators</strong> (llamados Stages)</p>\n    <ul>\n      <li>los concatena</li>\n    </ul>\n  </li>\n  <li>\n    <p>Un pipeline es un <strong>Estimator</strong></p>\n    <ul>\n      <li>método <code>fit(&lt;DataFramee&gt;)</code> devuelve un <code>PipelineModel</code> (Transformer)</li>\n    </ul>\n  </li>\n</ul>\n</div>"}]},"apps":[],"jobName":"paragraph_1604845764805_10535246","id":"20201029-114054_1255735097","dateCreated":"2020-11-08T11:29:24-0300","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:54854"},{"text":"%pyspark\n\nfrom pyspark.ml import Pipeline\n\n# Creo el pipeline con los Transformers y el Estimator\npipeline = Pipeline(stages=[tokenizer, hashingTF, lr])\n\n# Con fit el pipeline genera los fetures y entrena.\npipModel = pipeline.fit(training)\n","user":"anonymous","dateUpdated":"2020-11-08T19:52:59-0300","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":14,"results":{},"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1604845764805_526958664","id":"20161028-160715_1700211199","dateCreated":"2020-11-08T11:29:24-0300","dateStarted":"2020-11-08T19:52:59-0300","dateFinished":"2020-11-08T19:52:59-0300","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:54855"},{"title":"Pipeline de entrenamiento","text":"print(s\"\"\"%html\n<img src=\"$baseDir/06_ml_pipelines/ml-Pipeline.png\" alt=\"Drawing\" style=\"width: 70%;\"/>\n\"\"\")","user":"anonymous","dateUpdated":"2020-11-08T19:52:59-0300","config":{"editorSetting":{"language":"scala"},"colWidth":12,"editorMode":"ace/mode/scala","editorHide":true,"title":true,"results":[{"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}}}],"enabled":false,"fontSize":9},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<img src=\"https://git.cs.famaf.unc.edu.ar/dbarsotti/diplodatos_bigdata/raw/master/clases/06_ml_pipelines/ml-Pipeline.png\" alt=\"Drawing\" style=\"width: 70%;\"/>\n"}]},"apps":[],"jobName":"paragraph_1604845764806_-1758657346","id":"20161028-155205_837419355","dateCreated":"2020-11-08T11:29:24-0300","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:54856"},{"text":"%md\n### PipelineModel\n\n* El resultado anterior es un modelo (Transformer) que se puede usar para predecir\n    - método `transform(<DataFrame>)`\n* Usa los mismos Transformers de *featurizacion* que para el entrenamiento\n    - ahorra código\n","user":"anonymous","dateUpdated":"2020-11-08T19:52:59-0300","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","editorHide":true,"fontSize":9,"results":[{"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}}}],"enabled":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<h3>PipelineModel</h3>\n<ul>\n  <li>El resultado anterior es un modelo (Transformer) que se puede usar para predecir\n    <ul>\n      <li>método <code>transform(&lt;DataFrame&gt;)</code></li>\n    </ul>\n  </li>\n  <li>Usa los mismos Transformers de <em>featurizacion</em> que para el entrenamiento\n    <ul>\n      <li>ahorra código</li>\n    </ul>\n  </li>\n</ul>\n</div>"}]},"apps":[],"jobName":"paragraph_1604845764806_833147084","id":"20161028-160002_1792875570","dateCreated":"2020-11-08T11:29:24-0300","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:54857"},{"text":"%pyspark\n# El modelo en el pipeline solo necesita el Raw Test:\n# automaticamente aplica los transformadores de features\n# y hace predicciones.\n\npredictions = pipModel.transform(test)\n\nz.show(predictions.select(\"id\", \"text\", \"probability\", \"prediction\"))\n","user":"anonymous","dateUpdated":"2020-11-08T19:52:59-0300","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","editorHide":false,"fontSize":14,"results":{"0":{"graph":{"mode":"table","height":300,"optionOpen":false,"setting":{"table":{"tableGridState":{},"tableColumnTypeState":{"names":{"id":"string","text":"string","probability":"string","prediction":"string"},"updated":false},"tableOptionSpecHash":"[{\"name\":\"useFilter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable filter for columns\"},{\"name\":\"showPagination\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable pagination for better navigation\"},{\"name\":\"showAggregationFooter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable a footer for displaying aggregated values\"}]","tableOptionValue":{"useFilter":false,"showPagination":false,"showAggregationFooter":false},"updated":false,"initialized":false}},"commonSetting":{}}}},"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TABLE","data":"id\ttext\tprobability\tprediction\n4\tspark i j k a\t[0.4178708877031535,0.5821291122968465]\t1.0\n5\tl m n\t[0.954126238851537,0.045873761148463005]\t0.0\n6\tmapreduce spark\t[0.8365768944495406,0.16342310555045933]\t0.0\n7\tapache hadoop\t[0.9857864449916622,0.014213555008337634]\t0.0\n8\tspark f j k\t[0.25910961733038906,0.7408903826696108]\t1.0\n"},{"type":"TEXT","data":"\n"}]},"apps":[],"jobName":"paragraph_1604845764806_1597893766","id":"20171115-101137_579818646","dateCreated":"2020-11-08T11:29:24-0300","dateStarted":"2020-11-08T19:52:59-0300","dateFinished":"2020-11-08T19:52:59-0300","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:54858"},{"text":"print(s\"\"\"%html\n<img src=\"$baseDir/06_ml_pipelines/ml-PipelineModel.png\" alt=\"Drawing\" style=\"width: 70%;\"/>\n\"\"\")\n","user":"anonymous","dateUpdated":"2020-11-08T19:52:59-0300","config":{"editorSetting":{"language":"scala"},"colWidth":12,"editorMode":"ace/mode/scala","editorHide":true,"results":[{"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}}}],"enabled":false,"fontSize":9},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<img src=\"https://git.cs.famaf.unc.edu.ar/dbarsotti/diplodatos_bigdata/raw/master/clases/06_ml_pipelines/ml-PipelineModel.png\" alt=\"Drawing\" style=\"width: 70%;\"/>\n"}]},"apps":[],"jobName":"paragraph_1604845764807_-622934242","id":"20161028-160514_1612337572","dateCreated":"2020-11-08T11:29:24-0300","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:54859"},{"title":"Persistencia","text":"%pyspark\n\nfrom pyspark.ml import PipelineModel\n\n# El pipeline entrenado se puede guardar\npipModel.write().overwrite().save(\"/tmp/spark-logistic-regression-model\")\n\n# Y cargarlo para la etapa de uso\nsamePipModel = PipelineModel.load(\"/tmp/spark-logistic-regression-model\")\n\n# Tambien el pipeline sin entrenar\npipeline.write().overwrite().save(\"/tmp/unfit-lr-model\")\n","user":"anonymous","dateUpdated":"2020-11-08T19:52:59-0300","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","editorHide":false,"fontSize":14,"title":true,"results":{},"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1604845764807_493313591","id":"20171115-101044_1013070913","dateCreated":"2020-11-08T11:29:24-0300","dateStarted":"2020-11-08T19:52:59-0300","dateFinished":"2020-11-08T19:53:00-0300","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:54860"},{"text":"%md\n\n### Ejercicio\n\nHacer con pipelines uno de los ejemplos de clasificacionde clase anterior. Plotear el resultado.\n","user":"anonymous","dateUpdated":"2020-11-08T19:53:00-0300","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<h3>Ejercicio</h3>\n<p>Hacer con pipelines uno de los ejemplos de clasificacionde clase anterior. Plotear el resultado.</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1604845764807_708442401","id":"20191205-195354_363578557","dateCreated":"2020-11-08T11:29:24-0300","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:54861"},{"text":"%pyspark\n\ninputFile = \"../../diplodatos_bigdata/ds/people_sex_height_age_weight.json\"\n\npeople = spark.read.json(inputFile) \\\n    .select(\"kgs\", \"mts\", \"sex\") \\\n    .repartition(sc.defaultParallelism) \\\n    .cache()\n    \nprint(\"Cantidad de filas: {}. Cantidad de particiones: {}.\".format(people.count(), people.rdd.getNumPartitions()))\n\nz.show(people.sample(False,0.001))","user":"anonymous","dateUpdated":"2020-11-08T19:53:00-0300","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{"1":{"graph":{"mode":"table","height":300,"optionOpen":false,"setting":{"table":{"tableGridState":{},"tableColumnTypeState":{"names":{"kgs":"string","mts":"string","sex":"string"},"updated":false},"tableOptionSpecHash":"[{\"name\":\"useFilter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable filter for columns\"},{\"name\":\"showPagination\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable pagination for better navigation\"},{\"name\":\"showAggregationFooter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable a footer for displaying aggregated values\"}]","tableOptionValue":{"useFilter":false,"showPagination":false,"showAggregationFooter":false},"updated":false,"initialized":false}},"commonSetting":{}}}},"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"Cantidad de filas: 10000. Cantidad de particiones: 12.\n"},{"type":"TABLE","data":"kgs\tmts\tsex\n65.89937650007067\t1.6724093300652543\tM\n109.22102275870417\t1.738424249810114\tF\n67.54188695657949\t1.7137927756202476\tF\n100.16299341419716\t1.8941557153673048\tM\n94.82993621631624\t1.8731020690228546\tM\n47.748343776772764\t1.6473874506700923\tF\n90.92637477401604\t1.7968563471803358\tM\n54.62329512723526\t1.6574851870249974\tF\n"},{"type":"TEXT","data":"\n"}]},"apps":[],"jobName":"paragraph_1604860357665_-637065499","id":"20201108-153237_1534814412","dateCreated":"2020-11-08T15:32:37-0300","dateStarted":"2020-11-08T19:53:00-0300","dateFinished":"2020-11-08T19:53:00-0300","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:54862"},{"text":"%pyspark\n\n\ntrainDF, testDF = people.randomSplit([0.95, 0.05]) \n\nprint(testDF.count(), trainDF.count())\n","user":"anonymous","dateUpdated":"2020-11-08T19:53:00-0300","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"553 9447\n"}]},"apps":[],"jobName":"paragraph_1604860758723_986904390","id":"20201108-153918_410180257","dateCreated":"2020-11-08T15:39:18-0300","dateStarted":"2020-11-08T19:53:00-0300","dateFinished":"2020-11-08T19:53:00-0300","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:54863"},{"text":"%pyspark\nfrom pyspark.ml.feature import VectorAssembler\n\nvAssembler = VectorAssembler(\n    inputCols=[\"kgs\", \"mts\"],\n    outputCol=\"features\")\n    \nstrIndexer = StringIndexer(\n  inputCol=\"sex\",\n  outputCol=\"label\")\n \ndtEstimator = DecisionTreeClassifier(\n                featuresCol=\"features\",\n                labelCol=\"label\")","user":"anonymous","dateUpdated":"2020-11-08T19:53:00-0300","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1604860833837_-1550597570","id":"20201108-154033_1123777857","dateCreated":"2020-11-08T15:40:33-0300","dateStarted":"2020-11-08T19:53:00-0300","dateFinished":"2020-11-08T19:53:00-0300","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:54864"},{"text":"%pyspark\npipeline = Pipeline(stages=[vAssembler, strIndexer, dtEstimator])\npipModel = pipeline.fit(trainDF)","user":"anonymous","dateUpdated":"2020-11-08T19:53:00-0300","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1604860891143_-152777339","id":"20201108-154131_1567295523","dateCreated":"2020-11-08T15:41:31-0300","dateStarted":"2020-11-08T19:53:00-0300","dateFinished":"2020-11-08T19:53:01-0300","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:54865"},{"text":"%pyspark\nz.show(pipModel.transform(trainDF))","user":"anonymous","dateUpdated":"2020-11-08T19:53:01-0300","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{"0":{"graph":{"mode":"table","height":300,"optionOpen":false,"setting":{"table":{"tableGridState":{},"tableColumnTypeState":{"names":{"kgs":"string","mts":"string","sex":"string","features":"string","label":"string","rawPrediction":"string","probability":"string","prediction":"string"},"updated":false},"tableOptionSpecHash":"[{\"name\":\"useFilter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable 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class=\"result-alert alert-warning\" role=\"alert\"><button type=\"button\" class=\"close\" data-dismiss=\"alert\" aria-label=\"Close\"><span aria-hidden=\"true\">&times;</span></button><strong>Output is truncated</strong> to 102400 bytes. Learn more about <strong>ZEPPELIN_INTERPRETER_OUTPUT_LIMIT</strong></div>"}]},"apps":[],"jobName":"paragraph_1604860952600_-945509382","id":"20201108-154232_29704159","dateCreated":"2020-11-08T15:42:32-0300","dateStarted":"2020-11-08T19:53:01-0300","dateFinished":"2020-11-08T19:53:01-0300","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:54866"},{"text":"%pyspark\n\n\ndef createGrid():\n\n    from pyspark.sql.functions import min, max\n    \n    maxTics = 256\n\n    def fgrid (x, min, max, tics):\n        tics2 = tics - 2\n        return (float(x-1)*(max-min)+tics2*min)/tics2\n\n\n    minKgs, maxKgs, minMts, maxMts = people.agg(min(\"kgs\"), max(\"kgs\"), min(\"mts\"), max(\"mts\")).first()\n\n    # 1.- numeracion ticks en x \n    # 2.- numeracion ticks en (x, y)\n    # 3.- todos los (kgs, mts) \n    return sc.parallelize(range(maxTics)) \\\n            .flatMap(lambda x: [ (x, y) for y in range(maxTics)]) \\\n            .map(lambda x_y: (fgrid(x_y[0], minKgs, maxKgs, maxTics), fgrid(x_y[1], minMts, maxMts, maxTics))) \\\n            .toDF([\"kgs\", \"mts\"])\n\n\ndfGrid = createGrid()\n","user":"anonymous","dateUpdated":"2020-11-08T19:53:01-0300","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{"0":{"graph":{"mode":"table","height":300,"optionOpen":false,"setting":{"table":{"tableGridState":{},"tableColumnTypeState":{"names":{"kgs":"string","mts":"string","features":"string"},"updated":false},"tableOptionSpecHash":"[{\"name\":\"useFilter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable filter for columns\"},{\"name\":\"showPagination\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable pagination for better navigation\"},{\"name\":\"showAggregationFooter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable a footer for displaying aggregated values\"}]","tableOptionValue":{"useFilter":false,"showPagination":false,"showAggregationFooter":false},"updated":false,"initialized":false}},"commonSetting":{}}}},"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1604861825300_-1164711342","id":"20201108-155705_1196354086","dateCreated":"2020-11-08T15:57:05-0300","dateStarted":"2020-11-08T19:53:01-0300","dateFinished":"2020-11-08T19:53:01-0300","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:54867"},{"text":"%pyspark\n# Creo predicciones de la grilla\ngridPredictionDT = pipModel.transform(dfGrid)\n\nz.show(gridPredictionDT.sample(False, 0.001))","user":"anonymous","dateUpdated":"2020-11-08T19:53:01-0300","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{"0":{"graph":{"mode":"table","height":300,"optionOpen":false,"setting":{"table":{"tableGridState":{},"tableColumnTypeState":{"names":{"kgs":"string","mts":"string","features":"string","rawPrediction":"string","probability":"string","prediction":"string"},"updated":false},"tableOptionSpecHash":"[{\"name\":\"useFilter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable filter for columns\"},{\"name\":\"showPagination\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable pagination for better navigation\"},{\"name\":\"showAggregationFooter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable a footer for displaying aggregated 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plot_classification(df, surfaceDF=None, prob=False):\n    import matplotlib.pyplot as plt\n    import numpy as np\n    import matplotlib.cm as cm\n\n    from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n\n    cmap_light = LinearSegmentedColormap.from_list(\"light\", ['#FF5555', '#5555FF'])\n    cmap_pastel = LinearSegmentedColormap.from_list(\"pastel\", ['#FF8888', '#8888FF'])\n    cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n\n    # helper function to display in Zeppelin\n    def show(p):\n        from io import BytesIO\n        import base64\n        #img = StringIO()\n        img = BytesIO()\n        p.savefig(img, format='png')#,dpi=600)\n        img.seek(0)\n        print(\"%html <div style='width:600px'><img src=\\\"data:image/png;base64,\" + base64.encodebytes(img.getvalue()).decode().replace('\\n', '') + \"\\\"/></div>\")\n        #print(\"%html <div style='width:600px'><img src=\\\"data:image/png;base64,\" + img.buf.encode('base64').replace('\\n', '') + \"\\\"/></div>\")\n\n    plt.close('all')\n\n    plt.autoscale(enable=True, axis='x', tight=True)\n\n    if surfaceDF:\n\n        if prob:\n            pSTable = surfaceDF.select('kgs','mts','prob').toPandas()\n            Z = pSTable['prob']\n        else:\n            pSTable = surfaceDF.select('kgs','mts','prediction').toPandas()\n            Z = pSTable['prediction']\n\n        xs = pSTable['kgs']\n        ys = pSTable['mts']\n\n        xUs = xs.unique()\n        yUs = ys.unique()\n\n        xx, yy = np.meshgrid(xUs,yUs)\n\n        Z = Z.values.reshape(xx.shape).transpose()\n\n        if prob:\n            plt.pcolormesh(xx, yy, Z, shading='auto', cmap=cmap_light)\n        else:\n            plt.pcolormesh(xx, yy, Z, shading='auto', cmap=cmap_pastel)\n\n        plt.axis([xs.min(), xs.max(), ys.min(), ys.max()])\n\n    pTable = df.select('kgs','mts','label').toPandas().sample(frac=1)\n\n    xs = pTable['kgs']\n    ys = pTable['mts']\n    cs = pTable['label']\n    plt.scatter(xs, ys, c=cs, cmap=cmap_bold, alpha=0.5, s=16)\n    plt.xlabel('kgs')\n    plt.ylabel('mts')\n\n    show(plt)\n    plt.close()\n","user":"anonymous","dateUpdated":"2020-11-08T19:53:02-0300","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1604861968085_295998114","id":"20201108-155928_1193216155","dateCreated":"2020-11-08T15:59:28-0300","dateStarted":"2020-11-08T19:53:02-0300","dateFinished":"2020-11-08T19:53:02-0300","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:54869"},{"text":"%pyspark\nplot_classification(testFeaturized, surfaceDF=gridPredictionDT)","user":"anonymous","dateUpdated":"2020-11-08T19:53:02-0300","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div style='width:600px'><img 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