{
	"dag_id": "N1wP690lywNj1xrkzP8qD",
	"user_name": "XXX",
	"user_id": 1,
	"nodes_task_name": "dfs",
	"nodes_task_id": 123,
	"itermidate_data": ["hdfs://host:port/uri"],
	"nodes": [{
		"id": "8CSbH3t1CTdxSKZ0xqHf3",
		"name": "datasourceNode1",
		"op": "datasource",
		"data": {
			"input_source": [{
				"dataSelect": true,
				"dataField": "uuid",
				"dataType": "string"
			}, {
				"dataSelect": true,
				"dataField": "label",
				"dataType": "int"
			}, {
				"dataSelect": true,
				"dataField": "feature1",
				"dataType": "double"
			}, {
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				"dataField": "feature2",
				"dataType": "double"
			}, {
				"dataSelect": true,
				"dataField": "feature3",
				"dataType": "double"
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				"dataField": "feature7",
				"dataType": "double"
			}, {
				"dataSelect": true,
				"dataField": "feature8",
				"dataType": "double"
			}, {
				"dataSelect": true,
				"dataField": "feature9",
				"dataType": "double"
			}],
			"input_table": 0
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	}, {
		"id": "GqZitaihZUjpafezqtLOf",
		"name": "test",
		"op": "datasource",
		"data": {
			"input_source": [{
				"dataSelect": true,
				"dataField": "uuid",
				"dataType": "string"
			}, {
				"dataSelect": true,
				"dataField": "label",
				"dataType": "int"
			}, {
				"dataSelect": true,
				"dataField": "feature1",
				"dataType": "double"
			}, {
				"dataSelect": true,
				"dataField": "feature2",
				"dataType": "double"
			}, {
				"dataSelect": true,
				"dataField": "feature3",
				"dataType": "double"
			}, {
				"dataSelect": true,
				"dataField": "feature4",
				"dataType": "double"
			}, {
				"dataSelect": true,
				"dataField": "feature5",
				"dataType": "double"
			}, {
				"dataSelect": true,
				"dataField": "feature6",
				"dataType": "double"
			}, {
				"dataSelect": true,
				"dataField": "feature7",
				"dataType": "double"
			}, {
				"dataSelect": true,
				"dataField": "feature8",
				"dataType": "double"
			}, {
				"dataSelect": true,
				"dataField": "feature9",
				"dataType": "double"
			}],
			"input_table": 1
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	}, {
		"id": "SHb3z_rKGtOvBFvcaKTKj",
		"name": "pysparkNode1",
		"op": "pyspark",
		"data": {
			"input_number": 2,
			"output": [{
				"outputVar": "r0"
			}],
			"script": "# import sys\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql.types import *\nfrom time import *\n\nfrom pyspark.ml.classification import LogisticRegression\nfrom pyspark.ml.feature import  VectorAssembler\nfrom pyspark.ml import Pipeline\nfrom pyspark.sql.functions import udf, col\n\ndef getFeatureName():\n    featureLst = ['feature1', 'feature2', 'feature3', 'feature4', 'feature5', 'feature6', 'feature7', 'feature8', 'feature9']\n    colLst = ['uuid', 'label'] + featureLst\n    return featureLst, colLst\n\ndef parseFloat(x):\n    try:\n        rx = float(x)\n    except:\n        rx = 0.0\n    return rx\n\n\ndef getDict(dictDataLst, colLst):\n    dictData = {}\n    for i in range(len(colLst)):\n        dictData[colLst[i]] = parseFloat(dictDataLst[i])\n    return dictData\n\ndef to_array(col):\n    def to_array_(v):\n        return v.toArray().tolist()\n    # Important: asNondeterministic requires Spark 2.3 or later\n    # It can be safely removed i.e.\n    # return udf(to_array_, ArrayType(DoubleType()))(col)\n    # but at the cost of decreased performance\n    return udf(to_array_, ArrayType(DoubleType())).asNondeterministic()(col)\n\n# def read_input(ss: SparkSession, t1, t2):\n#     trainDF = ss.sql(f'select * from {t1}')\n#     testDF = ss.sql(f'select * from {t2}')\n#     return trainDF, testDF\n\n# def write_output(df1, m1):\n#     df1.write.mode(\"overwrite\").saveAsTable('prediction')\n#     m1.write().overwrite().save(\"hdfs://192.168.199.27:9000/tmp/dag/target/model/lrModel\")\n\n\n\n\ndef main_func(input0, input1, spark, sc):\n    trainDF = input0\n    testDF = input1\n    \n\n    featureLst, colLst = getFeatureName()\n\n\n    #用于训练的特征featureLst\n    vectorAssembler = VectorAssembler().setInputCols(featureLst).setOutputCol(\"features\")\n\n    #### 训练 ####\n    print(\"step 1\")\n    lr = LogisticRegression(regParam=0.01, maxIter=100)  # regParam 正则项参数\n\n    pipeline = Pipeline(stages=[vectorAssembler, lr])\n    model = pipeline.fit(trainDF)\n    #打印参数\n    print(\"\\n-------------------------------------------------------------------------\")\n    print(\"LogisticRegression parameters:\\n\" + lr.explainParams() + \"\\n\")\n    print(\"-------------------------------------------------------------------------\\n\")\n\n    #### 预测, 保存结果 ####\n    print(\"step 2\")\n    labelsAndPreds = model.transform(testDF).withColumn(\"probability_xj\", to_array(col(\"probability\"))[1])\\\n                                            .select(\"uuid\", \"label\", \"prediction\", \"probability_xj\")\n    labelsAndPreds.show()\n    # labelsAndPreds.write.mode(\"overwrite\").options(header=\"true\").csv(outputHDFS + \"/target/output\")\n\n\n    #### 评估不同阈值下的准确率、召回率\n    print(\"step 3\")\n    labelsAndPreds_label_1 = labelsAndPreds.where(labelsAndPreds.label == 1)\n    labelsAndPreds_label_0 = labelsAndPreds.where(labelsAndPreds.label == 0)\n    labelsAndPreds_label_1.show(3)\n    labelsAndPreds_label_0.show(3)\n    t_cnt = labelsAndPreds_label_1.count()\n    f_cnt = labelsAndPreds_label_0.count()\n    print(\"thre\\ttp\\ttn\\tfp\\tfn\\taccuracy\\trecall\")\n    for thre in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]:\n        tp = labelsAndPreds_label_1.where(labelsAndPreds_label_1.probability_xj > thre).count()\n        tn = t_cnt - tp\n        fp = labelsAndPreds_label_0.where(labelsAndPreds_label_0.probability_xj > thre).count()\n        fn = f_cnt - fp\n        print(\"%.1f\\t%d\\t%d\\t%d\\t%d\\t%.4f\\t%.4f\"%(thre, tp, tn, fp, fn, float(tp)/(tp+fp), float(tp)/(t_cnt)))\n    \n    r0 = labelsAndPreds\n    return [r0]"
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	}],
	"dag_script": {
		"sub_nodes": [{
			"id": "8CSbH3t1CTdxSKZ0xqHf3",
			"name": "datasourceNode1",
			"type": "datasource",
			"op": "sql",
			"script": "select uuid, label, feature1, feature2, feature3, feature4, feature5, feature6, feature7, feature8, feature9 from train"
		}, {
			"id": "GqZitaihZUjpafezqtLOf",
			"name": "test",
			"type": "datasource",
			"op": "sql",
			"script": "select uuid, label, feature1, feature2, feature3, feature4, feature5, feature6, feature7, feature8, feature9 from test"
		}, {
			"id": "SHb3z_rKGtOvBFvcaKTKj",
			"type": "script",
			"name": "pysparkNode1",
			"op": "pyspark",
			"inputs": {
				"input0": ["8CSbH3t1CTdxSKZ0xqHf3", 0],
				"input1": ["GqZitaihZUjpafezqtLOf", 0]
			},
			"script": "# import sys\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql.types import *\nfrom time import *\n\nfrom pyspark.ml.classification import LogisticRegression\nfrom pyspark.ml.feature import  VectorAssembler\nfrom pyspark.ml import Pipeline\nfrom pyspark.sql.functions import udf, col\n\ndef getFeatureName():\n    featureLst = ['feature1', 'feature2', 'feature3', 'feature4', 'feature5', 'feature6', 'feature7', 'feature8', 'feature9']\n    colLst = ['uuid', 'label'] + featureLst\n    return featureLst, colLst\n\ndef parseFloat(x):\n    try:\n        rx = float(x)\n    except:\n        rx = 0.0\n    return rx\n\n\ndef getDict(dictDataLst, colLst):\n    dictData = {}\n    for i in range(len(colLst)):\n        dictData[colLst[i]] = parseFloat(dictDataLst[i])\n    return dictData\n\ndef to_array(col):\n    def to_array_(v):\n        return v.toArray().tolist()\n    # Important: asNondeterministic requires Spark 2.3 or later\n    # It can be safely removed i.e.\n    # return udf(to_array_, ArrayType(DoubleType()))(col)\n    # but at the cost of decreased performance\n    return udf(to_array_, ArrayType(DoubleType())).asNondeterministic()(col)\n\n# def read_input(ss: SparkSession, t1, t2):\n#     trainDF = ss.sql(f'select * from {t1}')\n#     testDF = ss.sql(f'select * from {t2}')\n#     return trainDF, testDF\n\n# def write_output(df1, m1):\n#     df1.write.mode(\"overwrite\").saveAsTable('prediction')\n#     m1.write().overwrite().save(\"hdfs://192.168.199.27:9000/tmp/dag/target/model/lrModel\")\n\n\n\n\ndef main_func(input0, input1, spark, sc):\n    trainDF = input0\n    testDF = input1\n    \n\n    featureLst, colLst = getFeatureName()\n\n\n    #用于训练的特征featureLst\n    vectorAssembler = VectorAssembler().setInputCols(featureLst).setOutputCol(\"features\")\n\n    #### 训练 ####\n    print(\"step 1\")\n    lr = LogisticRegression(regParam=0.01, maxIter=100)  # regParam 正则项参数\n\n    pipeline = Pipeline(stages=[vectorAssembler, lr])\n    model = pipeline.fit(trainDF)\n    #打印参数\n    print(\"\\n-------------------------------------------------------------------------\")\n    print(\"LogisticRegression parameters:\\n\" + lr.explainParams() + \"\\n\")\n    print(\"-------------------------------------------------------------------------\\n\")\n\n    #### 预测, 保存结果 ####\n    print(\"step 2\")\n    labelsAndPreds = model.transform(testDF).withColumn(\"probability_xj\", to_array(col(\"probability\"))[1])\\\n                                            .select(\"uuid\", \"label\", \"prediction\", \"probability_xj\")\n    labelsAndPreds.show()\n    # labelsAndPreds.write.mode(\"overwrite\").options(header=\"true\").csv(outputHDFS + \"/target/output\")\n\n\n    #### 评估不同阈值下的准确率、召回率\n    print(\"step 3\")\n    labelsAndPreds_label_1 = labelsAndPreds.where(labelsAndPreds.label == 1)\n    labelsAndPreds_label_0 = labelsAndPreds.where(labelsAndPreds.label == 0)\n    labelsAndPreds_label_1.show(3)\n    labelsAndPreds_label_0.show(3)\n    t_cnt = labelsAndPreds_label_1.count()\n    f_cnt = labelsAndPreds_label_0.count()\n    print(\"thre\\ttp\\ttn\\tfp\\tfn\\taccuracy\\trecall\")\n    for thre in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]:\n        tp = labelsAndPreds_label_1.where(labelsAndPreds_label_1.probability_xj > thre).count()\n        tn = t_cnt - tp\n        fp = labelsAndPreds_label_0.where(labelsAndPreds_label_0.probability_xj > thre).count()\n        fn = f_cnt - fp\n        print(\"%.1f\\t%d\\t%d\\t%d\\t%d\\t%.4f\\t%.4f\"%(thre, tp, tn, fp, fn, float(tp)/(tp+fp), float(tp)/(t_cnt)))\n    \n    r0 = labelsAndPreds\n    return [r0]"
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				"items": [{
					"group": "top",
					"id": "input0"
				}, {
					"group": "top",
					"id": "input1"
				}, {
					"group": "bottom",
					"id": "r0"
				}]
			},
			"id": "1aa2f6c2-fdb3-4241-ae06-8f8f71d84e68",
			"data": {
				"label": "pyspark",
				"status": "undone",
				"type": "script",
				"id": "1aa2f6c2-fdb3-4241-ae06-8f8f71d84e68",
				"nodeId": "SHb3z_rKGtOvBFvcaKTKj",
				"nodeName": "pysparkNode1",
				"scriptText": "# import sys\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql.types import *\nfrom time import *\n\nfrom pyspark.ml.classification import LogisticRegression\nfrom pyspark.ml.feature import  VectorAssembler\nfrom pyspark.ml import Pipeline\nfrom pyspark.sql.functions import udf, col\n\ndef getFeatureName():\n    featureLst = ['feature1', 'feature2', 'feature3', 'feature4', 'feature5', 'feature6', 'feature7', 'feature8', 'feature9']\n    colLst = ['uuid', 'label'] + featureLst\n    return featureLst, colLst\n\ndef parseFloat(x):\n    try:\n        rx = float(x)\n    except:\n        rx = 0.0\n    return rx\n\n\ndef getDict(dictDataLst, colLst):\n    dictData = {}\n    for i in range(len(colLst)):\n        dictData[colLst[i]] = parseFloat(dictDataLst[i])\n    return dictData\n\ndef to_array(col):\n    def to_array_(v):\n        return v.toArray().tolist()\n    # Important: asNondeterministic requires Spark 2.3 or later\n    # It can be safely removed i.e.\n    # return udf(to_array_, ArrayType(DoubleType()))(col)\n    # but at the cost of decreased performance\n    return udf(to_array_, ArrayType(DoubleType())).asNondeterministic()(col)\n\n# def read_input(ss: SparkSession, t1, t2):\n#     trainDF = ss.sql(f'select * from {t1}')\n#     testDF = ss.sql(f'select * from {t2}')\n#     return trainDF, testDF\n\n# def write_output(df1, m1):\n#     df1.write.mode(\"overwrite\").saveAsTable('prediction')\n#     m1.write().overwrite().save(\"hdfs://192.168.199.27:9000/tmp/dag/target/model/lrModel\")\n\n\n\n\ndef main_func(input0, input1, spark, sc):\n    trainDF = input0\n    testDF = input1\n    \n\n    featureLst, colLst = getFeatureName()\n\n\n    #用于训练的特征featureLst\n    vectorAssembler = VectorAssembler().setInputCols(featureLst).setOutputCol(\"features\")\n\n    #### 训练 ####\n    print(\"step 1\")\n    lr = LogisticRegression(regParam=0.01, maxIter=100)  # regParam 正则项参数\n\n    pipeline = Pipeline(stages=[vectorAssembler, lr])\n    model = pipeline.fit(trainDF)\n    #打印参数\n    print(\"\\n-------------------------------------------------------------------------\")\n    print(\"LogisticRegression parameters:\\n\" + lr.explainParams() + \"\\n\")\n    print(\"-------------------------------------------------------------------------\\n\")\n\n    #### 预测, 保存结果 ####\n    print(\"step 2\")\n    labelsAndPreds = model.transform(testDF).withColumn(\"probability_xj\", to_array(col(\"probability\"))[1])\\\n                                            .select(\"uuid\", \"label\", \"prediction\", \"probability_xj\")\n    labelsAndPreds.show()\n    # labelsAndPreds.write.mode(\"overwrite\").options(header=\"true\").csv(outputHDFS + \"/target/output\")\n\n\n    #### 评估不同阈值下的准确率、召回率\n    print(\"step 3\")\n    labelsAndPreds_label_1 = labelsAndPreds.where(labelsAndPreds.label == 1)\n    labelsAndPreds_label_0 = labelsAndPreds.where(labelsAndPreds.label == 0)\n    labelsAndPreds_label_1.show(3)\n    labelsAndPreds_label_0.show(3)\n    t_cnt = labelsAndPreds_label_1.count()\n    f_cnt = labelsAndPreds_label_0.count()\n    print(\"thre\\ttp\\ttn\\tfp\\tfn\\taccuracy\\trecall\")\n    for thre in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]:\n        tp = labelsAndPreds_label_1.where(labelsAndPreds_label_1.probability_xj > thre).count()\n        tn = t_cnt - tp\n        fp = labelsAndPreds_label_0.where(labelsAndPreds_label_0.probability_xj > thre).count()\n        fn = f_cnt - fp\n        print(\"%.1f\\t%d\\t%d\\t%d\\t%d\\t%.4f\\t%.4f\"%(thre, tp, tn, fp, fn, float(tp)/(tp+fp), float(tp)/(t_cnt)))\n    \n    r0 = labelsAndPreds\n    return [r0]",
				"outputData": [{
					"outputVar": "r0"
				}],
				"inputNumber": 2
			},
			"zIndex": 5
		}]
	}
}