{"paragraphs":[{"text":"%spark.pyspark\n","user":"walker","dateUpdated":"2019-10-12T05:15:04+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"python","editOnDblClick":false,"completionSupport":true},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"apps":[],"jobName":"paragraph_1570857304400_640746049","id":"20191012-051504_1791494737","dateCreated":"2019-10-12T05:15:04+0000","status":"READY","progressUpdateIntervalMs":500,"focus":true,"$$hashKey":"object:23686"},{"text":"%spark.pyspark\ndef isSick(x):\n    if x in (3,7):\n        return 0\n    else:\n        return 1","user":"walker","dateUpdated":"2019-10-12T05:13:57+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"python","editOnDblClick":false,"completionSupport":true,"completionKey":"TAB"},"editorMode":"ace/mode/python","editorHide":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1570785919759_-1457622604","id":"20191011-092519_94493470","dateCreated":"2019-10-11T09:25:19+0000","dateStarted":"2019-10-12T05:13:57+0000","dateFinished":"2019-10-12T05:13:57+0000","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:23687"},{"text":"%spark.pyspark\n","user":"walker","dateUpdated":"2019-10-12T05:13:57+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"python","editOnDblClick":false,"completionSupport":true,"completionKey":"TAB"},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"apps":[],"jobName":"paragraph_1570857227530_1796867328","id":"20191012-051347_1396539575","dateCreated":"2019-10-12T05:13:47+0000","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:23688"},{"text":"%spark.pyspark\n\nimport pandas as pd\nfrom pyspark.sql.types import StructType, StructField, NumericType\n\n \ncols = ('age',       \n      'sex',         \n       'chest pain',           \n       'resting blood pressure',    \n       'serum cholestoral',       \n       'fasting blood sugar',         \n       'resting electrocardiographic results', \n       'maximum heart rate achieved',  \n       'exercise induced angina',     \n       'ST depression induced by exercise relative to rest',  \n      'the slope of the peak exercise ST segment',     \n      'number of major vessels ',       \n       'thal',  \n       'last')\n      \n\n      \ndata = pd.read_csv('/home/ubuntu/Downloads/heart.csv', delimiter=' ', names=cols)\n\ndata = data.iloc[:,0:13]\n\ndata['isSick'] = data['thal'].apply(isSick)\n\ndf = spark.createDataFrame(data)\n\n \n                    \n \n\n ","user":"walker","dateUpdated":"2019-10-12T05:13:57+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"python","editOnDblClick":false,"completionSupport":true,"completionKey":"TAB"},"editorMode":"ace/mode/python","tableHide":false,"editorHide":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1570626673174_-397859394","id":"20191009-131113_1020772482","dateCreated":"2019-10-09T13:11:13+0000","dateStarted":"2019-10-12T05:13:57+0000","dateFinished":"2019-10-12T05:13:57+0000","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:23689"},{"text":"%spark.pyspark\nfrom pyspark.ml.feature import StandardScaler\nfrom pyspark.ml.feature import VectorAssembler\n\nfeatures =   ('age',       \n      'sex',         \n       'chest pain',           \n       'resting blood pressure',    \n       'serum cholestoral',       \n       'fasting blood sugar',         \n       'resting electrocardiographic results', \n       'maximum heart rate achieved',  \n       'exercise induced angina',     \n       'ST depression induced by exercise relative to rest',  \n      'the slope of the peak exercise ST segment',     \n      'number of major vessels ') \n\nassembler = VectorAssembler(inputCols=features,outputCol=\"features\")\n \nraw_data=assembler.transform(df)\nraw_data.select(\"features\").show(truncate=False)","user":"walker","dateUpdated":"2019-10-12T05:13:57+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"python","editOnDblClick":false,"completionSupport":true,"completionKey":"TAB"},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"+--------------------------------------------------------+\n|features                                                |\n+--------------------------------------------------------+\n|[70.0,1.0,4.0,130.0,322.0,0.0,2.0,109.0,0.0,2.4,2.0,3.0]|\n|[67.0,0.0,3.0,115.0,564.0,0.0,2.0,160.0,0.0,1.6,2.0,0.0]|\n|[57.0,1.0,2.0,124.0,261.0,0.0,0.0,141.0,0.0,0.3,1.0,0.0]|\n|[64.0,1.0,4.0,128.0,263.0,0.0,0.0,105.0,1.0,0.2,2.0,1.0]|\n|[74.0,0.0,2.0,120.0,269.0,0.0,2.0,121.0,1.0,0.2,1.0,1.0]|\n|[65.0,1.0,4.0,120.0,177.0,0.0,0.0,140.0,0.0,0.4,1.0,0.0]|\n|[56.0,1.0,3.0,130.0,256.0,1.0,2.0,142.0,1.0,0.6,2.0,1.0]|\n|[59.0,1.0,4.0,110.0,239.0,0.0,2.0,142.0,1.0,1.2,2.0,1.0]|\n|[60.0,1.0,4.0,140.0,293.0,0.0,2.0,170.0,0.0,1.2,2.0,2.0]|\n|[63.0,0.0,4.0,150.0,407.0,0.0,2.0,154.0,0.0,4.0,2.0,3.0]|\n|[59.0,1.0,4.0,135.0,234.0,0.0,0.0,161.0,0.0,0.5,2.0,0.0]|\n|[53.0,1.0,4.0,142.0,226.0,0.0,2.0,111.0,1.0,0.0,1.0,0.0]|\n|[44.0,1.0,3.0,140.0,235.0,0.0,2.0,180.0,0.0,0.0,1.0,0.0]|\n|[61.0,1.0,1.0,134.0,234.0,0.0,0.0,145.0,0.0,2.6,2.0,2.0]|\n|[57.0,0.0,4.0,128.0,303.0,0.0,2.0,159.0,0.0,0.0,1.0,1.0]|\n|[71.0,0.0,4.0,112.0,149.0,0.0,0.0,125.0,0.0,1.6,2.0,0.0]|\n|[46.0,1.0,4.0,140.0,311.0,0.0,0.0,120.0,1.0,1.8,2.0,2.0]|\n|[53.0,1.0,4.0,140.0,203.0,1.0,2.0,155.0,1.0,3.1,3.0,0.0]|\n|[64.0,1.0,1.0,110.0,211.0,0.0,2.0,144.0,1.0,1.8,2.0,0.0]|\n|[40.0,1.0,1.0,140.0,199.0,0.0,0.0,178.0,1.0,1.4,1.0,0.0]|\n+--------------------------------------------------------+\nonly showing top 20 rows\n\n"}]},"runtimeInfos":{"jobUrl":{"propertyName":"jobUrl","label":"SPARK JOB","tooltip":"View in Spark web UI","group":"spark","values":["http://parisx:4040/jobs/job?id=238"],"interpreterSettingId":"spark"}},"apps":[],"jobName":"paragraph_1570787472789_-2031757993","id":"20191011-095112_1692182483","dateCreated":"2019-10-11T09:51:12+0000","dateStarted":"2019-10-12T05:13:57+0000","dateFinished":"2019-10-12T05:13:57+0000","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:23690"},{"text":"%spark.pyspark\nfrom pyspark.ml.feature import StandardScaler\nstandardscaler=StandardScaler().setInputCol(\"features\").setOutputCol(\"Scaled_features\")\nraw_data=standardscaler.fit(raw_data).transform(raw_data)\nraw_data.select(\"features\",\"Scaled_features\").show(5)","user":"walker","dateUpdated":"2019-10-12T05:13:57+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"python","editOnDblClick":false,"completionSupport":true,"completionKey":"TAB"},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"+--------------------+--------------------+\n|            features|     Scaled_features|\n+--------------------+--------------------+\n|[70.0,1.0,4.0,130...|[7.68465131046640...|\n|[67.0,0.0,3.0,115...|[7.35530911144641...|\n|[57.0,1.0,2.0,124...|[6.25750178137978...|\n|[64.0,1.0,4.0,128...|[7.02596691242642...|\n|[74.0,0.0,2.0,120...|[8.12377424249305...|\n+--------------------+--------------------+\nonly showing top 5 rows\n\n"}]},"runtimeInfos":{"jobUrl":{"propertyName":"jobUrl","label":"SPARK JOB","tooltip":"View in Spark web UI","group":"spark","values":["http://parisx:4040/jobs/job?id=239","http://parisx:4040/jobs/job?id=240"],"interpreterSettingId":"spark"}},"apps":[],"jobName":"paragraph_1570787532506_804248666","id":"20191011-095212_884979848","dateCreated":"2019-10-11T09:52:12+0000","dateStarted":"2019-10-12T05:13:57+0000","dateFinished":"2019-10-12T05:13:58+0000","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:23691"},{"text":"%spark.pyspark\nfrom pyspark.ml.tuning import ParamGridBuilder, TrainValidationSplit\ntraining, test = raw_data.randomSplit([0.5, 0.5], seed=12345) ","user":"walker","dateUpdated":"2019-10-12T05:13:58+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"python","editOnDblClick":false,"completionSupport":true,"completionKey":"TAB"},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1570786856257_1989926248","id":"20191011-094056_1017703181","dateCreated":"2019-10-11T09:40:56+0000","dateStarted":"2019-10-12T05:13:58+0000","dateFinished":"2019-10-12T05:13:58+0000","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:23692"},{"text":"%spark.pyspark\nfrom pyspark.ml.classification import LogisticRegression\n\nlr = LogisticRegression(labelCol=\"isSick\", featuresCol=\"Scaled_features\",maxIter=10)\nmodel=lr.fit(training)\npredict_train=model.transform(training)\npredict_test=model.transform(test)\npredict_test.select(\"isSick\",\"prediction\").show(10)\n\n","user":"walker","dateUpdated":"2019-10-12T05:13:58+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{"0":{"graph":{"mode":"table","height":300,"optionOpen":false,"setting":{"table":{"tableGridState":{},"tableColumnTypeState":{"names":{"age":"string","sex":"string","chest pain":"string","resting blood pressure":"string","serum cholestoral":"string","fasting blood sugar":"string","resting electrocardiographic results":"string","maximum heart rate achieved":"string","exercise induced angina":"string","ST depression induced by exercise relative to rest":"string","the slope of the peak exercise ST segment":"string","number of major vessels ":"string","thal":"string","last":"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,"completionSupport":true,"completionKey":"TAB"},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"+------+----------+\n|isSick|prediction|\n+------+----------+\n|     0|       0.0|\n|     0|       0.0|\n|     0|       0.0|\n|     0|       0.0|\n|     0|       0.0|\n|     0|       0.0|\n|     0|       0.0|\n|     0|       0.0|\n|     0|       0.0|\n|     0|       0.0|\n+------+----------+\nonly showing top 10 rows\n\n"}]},"runtimeInfos":{"jobUrl":{"propertyName":"jobUrl","label":"SPARK JOB","tooltip":"View in Spark web UI","group":"spark","values":["http://parisx:4040/jobs/job?id=241","http://parisx:4040/jobs/job?id=242","http://parisx:4040/jobs/job?id=243","http://parisx:4040/jobs/job?id=244","http://parisx:4040/jobs/job?id=245","http://parisx:4040/jobs/job?id=246","http://parisx:4040/jobs/job?id=247","http://parisx:4040/jobs/job?id=248","http://parisx:4040/jobs/job?id=249","http://parisx:4040/jobs/job?id=250","http://parisx:4040/jobs/job?id=251","http://parisx:4040/jobs/job?id=252","http://parisx:4040/jobs/job?id=253","http://parisx:4040/jobs/job?id=254"],"interpreterSettingId":"spark"}},"apps":[],"jobName":"paragraph_1570780194447_-881313364","id":"20191011-074954_221759573","dateCreated":"2019-10-11T07:49:54+0000","dateStarted":"2019-10-12T05:13:58+0000","dateFinished":"2019-10-12T05:13:59+0000","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:23693"},{"text":"","user":"walker","dateUpdated":"2019-10-12T05:13:59+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"python","editOnDblClick":false,"completionSupport":true,"completionKey":"TAB"},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"apps":[],"jobName":"paragraph_1570791927022_-1684254656","id":"20191011-110527_322705203","dateCreated":"2019-10-11T11:05:27+0000","dateStarted":"2019-10-11T17:18:09+0000","dateFinished":"2019-10-11T17:18:10+0000","status":"FINISHED","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:23694"},{"text":"","user":"walker","dateUpdated":"2019-10-12T05:13:59+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"python","editOnDblClick":false,"completionSupport":true,"completionKey":"TAB"},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"runtimeInfos":{"jobUrl":{"propertyName":"jobUrl","label":"SPARK JOB","tooltip":"View in Spark web UI","group":"spark","values":["http://parisx:4040/jobs/job?id=90"],"interpreterSettingId":"spark"}},"apps":[],"jobName":"paragraph_1570792350392_-458139160","id":"20191011-111230_1736345948","dateCreated":"2019-10-11T11:12:30+0000","dateStarted":"2019-10-11T11:39:51+0000","dateFinished":"2019-10-11T11:39:51+0000","status":"FINISHED","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:23695"},{"text":"%spark.pyspark\n \n\n\nprint(\"Multinomial coefficients: \" + str(model.coefficientMatrix))\nprint(\"Multinomial intercepts: \" + str(model.interceptVector))","user":"walker","dateUpdated":"2019-10-12T05:13:59+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"python","editOnDblClick":false,"completionSupport":true,"completionKey":"TAB"},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"Multinomial coefficients: DenseMatrix([[ 0.6668908 ,  1.69441174, -0.12228916, -0.4890833 , -1.12192264,\n              -0.09436271, -0.94372679, -0.02751622, -0.19240091, -0.83972691,\n               1.17107336,  0.26495489]])\nMultinomial intercepts: [-2.7970145465614697]\n"}]},"apps":[],"jobName":"paragraph_1570791083763_-478407110","id":"20191011-105123_295762744","dateCreated":"2019-10-11T10:51:23+0000","dateStarted":"2019-10-12T05:13:59+0000","dateFinished":"2019-10-12T05:13:59+0000","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:23696"},{"text":"%spark.pyspark\n# Extract the summary from the returned LogisticRegressionModel instance trained\n# in the earlier example\ntrainingSummary = model.summary\n\n# Obtain the objective per iteration\nobjectiveHistory = trainingSummary.objectiveHistory\nprint(\"objectiveHistory:\")\nfor objective in objectiveHistory:\n    print(objective)\n\n# Obtain the receiver-operating characteristic as a dataframe and areaUnderROC.\n#trainingSummary.roc.show()\nprint(\"areaUnderROC: \" + str(trainingSummary.areaUnderROC))\n\n# Set the model threshold to maximize F-Measure\nfMeasure = trainingSummary.fMeasureByThreshold\nmaxFMeasure = fMeasure.groupBy().max('F-Measure').select('max(F-Measure)').head()\nbestThreshold = fMeasure.where(fMeasure['F-Measure'] == maxFMeasure['max(F-Measure)']) \\\n    .select('threshold').head()['threshold']\nlr.setThreshold(bestThreshold)","user":"walker","dateUpdated":"2019-10-12T05:13:59+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{"0":{"graph":{"mode":"table","height":300,"optionOpen":false,"setting":{"table":{"tableGridState":{},"tableColumnTypeState":{"names":{"age":"string","sex":"string","chest pain":"string","resting blood pressure":"string","serum cholestoral":"string","fasting blood sugar":"string","resting electrocardiographic results":"string","maximum heart rate achieved":"string","exercise induced angina":"string","ST depression induced by exercise relative to rest":"string","the slope of the peak exercise ST segment":"string","number of major vessels ":"string","thal":"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,"completionSupport":true,"completionKey":"TAB"},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"objectiveHistory:\n0.2183808333258687\n0.20816927260803536\n0.1881067217274369\n0.18205075266753093\n0.17703243938548047\n0.17640412785509704\n0.16477178254282865\n0.1630298030353237\n0.1624715046926652\n0.16229471593907993\n0.16197892605655764\nareaUnderROC: 0.8460591133004924\nLogisticRegression_4f78985a234065d79b08"}]},"runtimeInfos":{"jobUrl":{"propertyName":"jobUrl","label":"SPARK JOB","tooltip":"View in Spark web UI","group":"spark","values":["http://parisx:4040/jobs/job?id=255","http://parisx:4040/jobs/job?id=256","http://parisx:4040/jobs/job?id=257","http://parisx:4040/jobs/job?id=258","http://parisx:4040/jobs/job?id=259","http://parisx:4040/jobs/job?id=260","http://parisx:4040/jobs/job?id=261"],"interpreterSettingId":"spark"}},"apps":[],"jobName":"paragraph_1570785416983_-405639045","id":"20191011-091656_1871714335","dateCreated":"2019-10-11T09:16:56+0000","dateStarted":"2019-10-12T05:13:59+0000","dateFinished":"2019-10-12T05:14:00+0000","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:23697"},{"text":" ","user":"walker","dateUpdated":"2019-10-12T05:14:00+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"python","editOnDblClick":false,"completionSupport":true,"completionKey":"TAB"},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"apps":[],"jobName":"paragraph_1570810637687_831880407","id":"20191011-161717_1625998568","dateCreated":"2019-10-11T16:17:17+0000","dateStarted":"2019-10-11T17:00:00+0000","dateFinished":"2019-10-11T17:00:00+0000","status":"FINISHED","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:23698"},{"text":"%spark.pyspark\ntrainingSummary","user":"walker","dateUpdated":"2019-10-12T05:14:00+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{"0":{"graph":{"mode":"table","height":300,"optionOpen":false,"setting":{"table":{"tableGridState":{},"tableColumnTypeState":{"names":{"age":"string","sex":"string","chest pain":"string","resting blood pressure":"string","serum cholestoral":"string","fasting blood sugar":"string","resting electrocardiographic results":"string","maximum heart rate achieved":"string","exercise induced angina":"string","ST depression induced by exercise relative to rest":"string","the slope of the peak exercise ST segment":"string","number of major vessels ":"string","thal":"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,"completionSupport":true,"completionKey":"TAB"},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"<pyspark.ml.classification.BinaryLogisticRegressionTrainingSummary at 0x7f6cd606b3c8>"}]},"apps":[],"jobName":"paragraph_1570627395453_-1913081912","id":"20191009-132315_1652278208","dateCreated":"2019-10-09T13:23:15+0000","dateStarted":"2019-10-12T05:14:00+0000","dateFinished":"2019-10-12T05:14:00+0000","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:23699"},{"text":"%spark.pyspark\nimport pyspark.sql.functions as F\ncheck = predict_test.withColumn('correct', F.when(F.col('isSick') == F.col('prediction'), 1).otherwise(0))\ncheck.groupby(\"correct\").count().show()\n ","user":"walker","dateUpdated":"2019-10-12T05:14:00+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{"0":{"graph":{"mode":"table","height":300,"optionOpen":false,"setting":{"table":{"tableGridState":{},"tableColumnTypeState":{"names":{"Price":"string","Rooms":"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,"completionSupport":true,"completionKey":"TAB"},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"+-------+-----+\n|correct|count|\n+-------+-----+\n|      1|  137|\n|      0|   10|\n+-------+-----+\n\n"}]},"runtimeInfos":{"jobUrl":{"propertyName":"jobUrl","label":"SPARK JOB","tooltip":"View in Spark web UI","group":"spark","values":["http://parisx:4040/jobs/job?id=262","http://parisx:4040/jobs/job?id=263","http://parisx:4040/jobs/job?id=264","http://parisx:4040/jobs/job?id=265","http://parisx:4040/jobs/job?id=266"],"interpreterSettingId":"spark"}},"apps":[],"jobName":"paragraph_1570627594552_1531265333","id":"20191009-132634_932134512","dateCreated":"2019-10-09T13:26:34+0000","dateStarted":"2019-10-12T05:14:00+0000","dateFinished":"2019-10-12T05:14:02+0000","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:23700"},{"text":"%spark.pyspark\n \nimport matplotlib.pyplot as plt\n \nimport numpy as np\n \n \n \narr = [int(row.correct ) for row in check.select(['correct']).collect()]\n \n \n\n#plt.pie(arr,labels=labels,autopct='%1.1f%%')\nplt.pie(arr)\n\nplt.show()\n \n \n\n \n\n\n","user":"walker","dateUpdated":"2019-10-12T05:14:02+0000","config":{"colWidth":11,"fontSize":9,"enabled":true,"results":{"0":{"graph":{"mode":"table","height":227,"optionOpen":false}}},"editorSetting":{"language":"python","editOnDblClick":false,"completionSupport":true,"completionKey":"TAB"},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"<Figure size 432x288 with 1 Axes>\n"},{"type":"IMG","data":"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