KAFKA-ML-PipeLine-V1.ipynb

{"cells":[{"cell_type":"code","source":["%fs rm /FileStore/tables/card-logs-data -r"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">res0: Boolean = true\n</div>"]}}],"execution_count":1},{"cell_type":"code","source":["%fs rm /FileStore/tables/card-logs-check/ -r"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">res1: Boolean = true\n</div>"]}}],"execution_count":2},{"cell_type":"code","source":["%fs rm /FileStore/tables/card-logs-data-archieve/ -r"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">res2: Boolean = true\n</div>"]}}],"execution_count":3},{"cell_type":"code","source":["%fs rm /FileStore/tables/card-logs-data-table/ -r"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">res3: Boolean = true\n</div>"]}}],"execution_count":4},{"cell_type":"code","source":["%fs rm /FileStore/tables/card-logs-data-withOutPuts/ -r"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">res4: Boolean = true\n</div>"]}}],"execution_count":5},{"cell_type":"code","source":["%fs ls /FileStore/tables/"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>path</th><th>name</th><th>size</th></tr></thead><tbody><tr><td>dbfs:/FileStore/tables/1_Spark_Core/</td><td>1_Spark_Core/</td><td>0</td></tr><tr><td>dbfs:/FileStore/tables/2_Spark_Streaming/</td><td>2_Spark_Streaming/</td><td>0</td></tr><tr><td>dbfs:/FileStore/tables/3_Spark_SQL/</td><td>3_Spark_SQL/</td><td>0</td></tr><tr><td>dbfs:/FileStore/tables/4_Spark_GraphsLib/</td><td>4_Spark_GraphsLib/</td><td>0</td></tr><tr><td>dbfs:/FileStore/tables/5_Spark_MachineLearning/</td><td>5_Spark_MachineLearning/</td><td>0</td></tr><tr><td>dbfs:/FileStore/tables/7_Assignments/</td><td>7_Assignments/</td><td>0</td></tr><tr><td>dbfs:/FileStore/tables/creditcard.csv</td><td>creditcard.csv</td><td>150828752</td></tr><tr><td>dbfs:/FileStore/tables/creditcard_databricks.csv</td><td>creditcard_databricks.csv</td><td>111104688</td></tr></tbody></table></div>"]}}],"execution_count":6},{"cell_type":"code","source":["\"\"\"importing the usual pythonic libraries\"\"\"\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport collections\nimport time\nfrom datetime import datetime \n\n\"\"\"spark MLlib libraries\"\"\"\nfrom pyspark.ml import Pipeline\nfrom pyspark.ml.classification import GBTClassifier, RandomForestClassifier\nfrom pyspark.ml.feature import VectorIndexer, VectorAssembler\nfrom pyspark.ml.evaluation import BinaryClassificationEvaluator\nfrom pyspark.ml.linalg import DenseVector\n\n\n\n\"\"\"Spark Streaming and sql libaries\"\"\"\nfrom pyspark.sql.functions import *\nfrom pyspark.sql.window import Window\n# from pyspark.sql.functions import col\n\n\"\"\"SciKit Libraries\"\"\"\nfrom sklearn.preprocessing import RobustScaler\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.model_selection import train_test_split\n\n\n"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":7},{"cell_type":"code","source":["%scala\n\nimport org.apache.spark.sql.functions._\nimport org.apache.spark.sql.Row;\nimport org.apache.spark.sql.types.{StructType, StructField, StringType, IntegerType,DoubleType};\nimport org.apache.spark.sql.streaming.Trigger"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">import org.apache.spark.sql.functions._\nimport org.apache.spark.sql.Row\nimport org.apache.spark.sql.types.{StructType, StructField, StringType, IntegerType, DoubleType}\nimport org.apache.spark.sql.streaming.Trigger\n</div>"]}}],"execution_count":8},{"cell_type":"code","source":["columns = [\"MessageID\",\"Time\",\"V1\",\"V2\",\"V3\",\"V4\",\"V5\",\"V6\",\"V7\",\"V8\",\"V9\",\"V10\",\"V11\",\"V12\",\"V13\",\"V14\",\"V15\",\"V16\",\"V17\",\"V18\",\"V19\",\"V20\",\"V21\",\"V22\",\"V23\",\"V24\",\"V25\",\"V26\",\"V27\",\"V28\",\"Amount\",\"Class\"]"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":9},{"cell_type":"code","source":["# Set isCheckFileSystem flag False only if you are sure that the file exists and there is no filesize as 0\ndef transform_for_ccfd(path, isCheckFileSystem = True):\n  list_offiles=dbutils.fs.ls(path)\n  list_offiles_paths = []\n\n  if isCheckFileSystem:\n    for fileDetails  in list_offiles[:]:\n      if (fileDetails[1].startswith(\"part-00000-\") and  (fileDetails[2] > 0 )):\n          print(\"This is the one Name {0} size {1}\".format(fileDetails[1],fileDetails[2]))\n          list_offiles_paths.append(fileDetails[0])\n      path= list_offiles_paths\n  data_O = spark.read.load(path, format='csv', header='true', inferSchema='true')\n                          \n  data= data_O.toPandas()\n  data.columns = [\"MessageID\",\"Time\",\"V1\",\"V2\",\"V3\",\"V4\",\"V5\",\"V6\",\"V7\",\"V8\",\"V9\",\"V10\",\"V11\",\"V12\",\"V13\",\n                  \"V14\",\"V15\",\"V16\",\"V17\",\"V18\",\"V19\",\"V20\",\"V21\",\"V22\",\"V23\",\"V24\",\"V25\",\"V26\",\"V27\",\"V28\",\"Amount\",\"Class\"]\n\n  selectedFeatures = ['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V9', 'V10', 'V11', 'V12', 'V14', 'V16', 'V17', 'V18', 'V19']\n  selectedFeatures.append('Class')\n\n  equal_data=data.loc[:,selectedFeatures]\n  equal_data.shape\n\n  dfff = spark.createDataFrame(equal_data)\n  training_df = dfff.rdd.map(lambda x: (DenseVector(x[:-1]),x['Class']))\n  training_df = spark.createDataFrame(training_df,[\"features\",\"label\"])\n \n  return [training_df,data]"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":10},{"cell_type":"code","source":["# Data on which the estimator will be trained on\n# This data is differnt than the data coming from KAFKA server\npath_train = '/FileStore/tables/creditcard_databricks.csv'\n\n# Transform the data file into a spark data frame with all 30 features compressed into\n# a single dense vector\ntrain_data = transform_for_ccfd(path=path_train, isCheckFileSystem = False)[0]\n\n# Create an instance of Random Forest Classifier with 200 Trees\nestimatr = RandomForestClassifier(labelCol=\"label\", featuresCol=\"features\", numTrees=200)\n\n# Create a pipeline which can then be used for predictions and knowledge upgrade (active learning)\npipeline = Pipeline(stages = [estimatr])\n\n\n# Fit the classifier with training data\nmodel = pipeline.fit(train_data)"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":11},{"cell_type":"code","source":["%sh telnet ec2-3-249-245-26.eu-west-1.compute.amazonaws.com 9092"],"metadata":{},"outputs":[],"execution_count":12},{"cell_type":"code","source":["\n%scala\nval schema = StructType(\n  Seq(\n    StructField(\"Unnamed: 0\", DoubleType, true),\n    StructField(\"Time\", DoubleType, true),\n    StructField(\"V1\", DoubleType, true),\n    StructField(\"V2\", DoubleType, true),\n    StructField(\"V3\", DoubleType, true),\n    StructField(\"V4\", DoubleType, true),\n    StructField(\"V5\", DoubleType, true),\n    StructField(\"V6\", DoubleType, true),\n    StructField(\"V7\", DoubleType, true),\n    StructField(\"V8\", DoubleType, true),\n    StructField(\"V9\", DoubleType, true),\n    StructField(\"V10\", DoubleType, true),\n    StructField(\"V11\", DoubleType, true),\n    StructField(\"V12\", DoubleType, true),\n    StructField(\"V13\", DoubleType, true),\n    StructField(\"V14\", DoubleType, true),\n    StructField(\"V15\", DoubleType, true),\n    StructField(\"V16\", DoubleType, true),\n    StructField(\"V17\", DoubleType, true),\n    StructField(\"V18\", DoubleType, true),\n    StructField(\"V19\", DoubleType, true),\n    StructField(\"V20\", DoubleType, true),\n    StructField(\"V21\", DoubleType, true),\n    StructField(\"V22\", DoubleType, true),\n    StructField(\"V23\", DoubleType, true),\n    StructField(\"V24\", DoubleType, true),\n    StructField(\"V25\", DoubleType, true),\n    StructField(\"V26\", DoubleType, true),\n    StructField(\"V27\", DoubleType, true),\n    StructField(\"V28\", DoubleType, true),\n    StructField(\"Amount\", DoubleType, true),\n    StructField(\"Class\", DoubleType, true)\n  )\n)"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">schema: org.apache.spark.sql.types.StructType = StructType(StructField(Unnamed: 0,DoubleType,true), StructField(Time,DoubleType,true), StructField(V1,DoubleType,true), StructField(V2,DoubleType,true), StructField(V3,DoubleType,true), StructField(V4,DoubleType,true), StructField(V5,DoubleType,true), StructField(V6,DoubleType,true), StructField(V7,DoubleType,true), StructField(V8,DoubleType,true), StructField(V9,DoubleType,true), StructField(V10,DoubleType,true), StructField(V11,DoubleType,true), StructField(V12,DoubleType,true), StructField(V13,DoubleType,true), StructField(V14,DoubleType,true), StructField(V15,DoubleType,true), StructField(V16,DoubleType,true), StructField(V17,DoubleType,true), StructField(V18,DoubleType,true), StructField(V19,DoubleType,true), StructField(V20,DoubleType,true), StructField(V21,DoubleType,true), StructField(V22,DoubleType,true), StructField(V23,DoubleType,true), StructField(V24,DoubleType,true), StructField(V25,DoubleType,true), StructField(V26,DoubleType,true), StructField(V27,DoubleType,true), StructField(V28,DoubleType,true), StructField(Amount,DoubleType,true), StructField(Class,DoubleType,true))\n</div>"]}}],"execution_count":13},{"cell_type":"code","source":["%scala\nval DNS_host = \"ec2-3-249-245-26.eu-west-1.compute.amazonaws.com:9092\""],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">DNS_host: String = ec2-3-249-245-26.eu-west-1.compute.amazonaws.com:9092\n</div>"]}}],"execution_count":14},{"cell_type":"code","source":["%scala\nvar streamingInputDF = \n  spark.readStream\n    .format(\"kafka\")\n    .option(\"kafka.bootstrap.servers\", DNS_host)\n    .option(\"subscribe\", \"card\")     \n    .option(\"startingOffsets\", \"latest\")  \n    .option(\"minPartitions\", \"1\")  \n    .option(\"failOnDataLoss\", \"true\")\n    .load()"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">streamingInputDF: org.apache.spark.sql.DataFrame = [key: binary, value: binary ... 5 more fields]\n</div>"]}}],"execution_count":15},{"cell_type":"code","source":["%scala\nvar streamingSelectDF = \n    streamingInputDF\n   .selectExpr(\"cast (value as string) as json\")\n   .select(from_json($\"json\", schema).as(\"data\"))\n   .select(\"data.*\")"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">streamingSelectDF: org.apache.spark.sql.DataFrame = [Unnamed: 0: double, Time: double ... 30 more fields]\n</div>"]}}],"execution_count":16},{"cell_type":"code","source":["%scala\n\n\nval query =\n    streamingSelectDF\n    .writeStream\n    .format(\"console\")\n    .outputMode(\"append\")\n    .trigger(Trigger.ProcessingTime(\"30 seconds\"))\n    .start()\n\nquery.awaitTermination"],"metadata":{},"outputs":[],"execution_count":17},{"cell_type":"code","source":["%scala\n\nval query =\n    streamingSelectDF\n    .writeStream\n    .format(\"csv\")\n    .outputMode(\"append\")\n    .option(\"path\", \"/FileStore/tables/card-logs-data\")\n    .option(\"checkpointLocation\", \"/FileStore/tables/card-logs-check\")\n    .trigger(Trigger.ProcessingTime(\"30 seconds\"))\n    .start()"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">query: org.apache.spark.sql.streaming.StreamingQuery = org.apache.spark.sql.execution.streaming.StreamingQueryWrapper@4df24267\n</div>"]}}],"execution_count":18},{"cell_type":"code","source":["data_O = spark.read.load(\"/FileStore/tables/card-logs-data\", \n                          format='csv', \n                          header='true', \n                          inferSchema='true')\ndisplay(data_O)"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>15515.0</th><th>26904.0</th><th>1.2049381848</th><th>-0.1223084958</th><th>0.5558263777</th><th>-0.0987149449</th><th>-0.6104550098</th><th>-0.5449173344</th><th>-0.253801833</th><th>-0.0382008416</th><th>0.37184417</th><th>-0.2687736619</th><th>0.0151005234</th><th>0.370770316</th><th>0.228823525</th><th>0.1392470514</th><th>1.4932959828</th><th>0.1105542153</th><th>-0.0914952695</th><th>-0.8783661051</th><th>-0.336630052</th><th>-0.0524476197</th><th>-0.093961322</th><th>-0.2588618581</th><th>0.1093105753</th><th>0.1408489313</th><th>0.0419692437</th><th>0.93172843</th><th>-0.0569546528</th><th>0.0094759678</th><th>20.47</th><th>0.0</th></tr></thead><tbody><tr><td>15516.0</td><td>26905.0</td><td>-1.059690124</td><td>1.4442398159</td><td>1.4021601297</td><td>-0.0039575924</td><td>0.4313087845</td><td>-0.2663826561</td><td>1.0118127759</td><td>-0.8165845873</td><td>-0.7181505012</td><td>-0.5022710758</td><td>-0.1476371245</td><td>0.5345015985</td><td>0.5620484673</td><td>0.2037363817</td><td>0.3994687835</td><td>0.0159510764</td><td>-0.3823352274</td><td>-1.1333129331</td><td>-1.10877904</td><td>-0.1788836032</td><td>0.3560744273</td><td>-1.3700931848</td><td>0.1424650394</td><td>-0.0051327916</td><td>-0.0826111597</td><td>-0.9387589102</td><td>0.4060907504</td><td>0.1668482393</td><td>52.0</td><td>0.0</td></tr><tr><td>15517.0</td><td>26906.0</td><td>1.1609567794</td><td>1.2656213441</td><td>-1.5764729803</td><td>1.4729879638</td><td>1.1621726912</td><td>-1.0135323569</td><td>0.6581328268</td><td>-0.1526238089</td><td>-0.8473259374</td><td>-1.4524985804</td><td>2.8939231012</td><td>0.7741492136</td><td>0.6635058078</td><td>-3.3337206434</td><td>0.5232978065</td><td>0.8978112902</td><td>2.6826126385</td><td>1.2693141465</td><td>-0.9122617532</td><td>0.0024655761</td><td>-0.1203645055</td><td>-0.2049968528</td><td>-0.203189095</td><td>-0.2821715296</td><td>0.7852781366</td><td>-0.2849579915</td><td>0.0583991252</td><td>0.0854358811</td><td>1.79</td><td>0.0</td></tr><tr><td>15518.0</td><td>26906.0</td><td>1.1945546066</td><td>-0.1467282003</td><td>0.4597760204</td><td>-0.2333314876</td><td>-0.5436549225</td><td>-0.4818937894</td><td>-0.2221221155</td><td>-0.0470851329</td><td>0.0461698221</td><td>-0.081033111</td><td>1.3007436261</td><td>1.1831935259</td><td>0.8197624487</td><td>0.1733737919</td><td>0.5018370619</td><td>0.6695586948</td><td>-0.7734134266</td><td>0.1036205026</td><td>0.4252988957</td><td>0.0751826789</td><td>-0.0739031165</td><td>-0.2818232173</td><td>0.0040417289</td><td>0.087200732</td><td>0.1421305978</td><td>0.9010407832</td><td>-0.077138038</td><td>0.0037395343</td><td>38.16</td><td>0.0</td></tr><tr><td>15519.0</td><td>26909.0</td><td>-0.4372562536</td><td>-3.4599029903</td><td>-0.8510696436</td><td>0.1737636945</td><td>-1.7108014185</td><td>-0.0771364774</td><td>0.748273876</td><td>-0.2165807824</td><td>-1.0740082459</td><td>0.3488182003</td><td>1.2536747619</td><td>0.4642410898</td><td>-0.5421979735</td><td>0.6032509306</td><td>-0.3149753313</td><td>-1.8002366066</td><td>0.4831637421</td><td>0.8473124677</td><td>-0.8918122825</td><td>1.2630127695</td><td>0.1815352891</td><td>-0.9567472968</td><td>-0.7486900442</td><td>0.3104933337</td><td>-0.0515741823</td><td>0.9973787173</td><td>-0.2309269844</td><td>0.1420098916</td><td>908.6</td><td>0.0</td></tr><tr><td>15520.0</td><td>26911.0</td><td>0.7750096633</td><td>-0.9586281014</td><td>1.0055755342</td><td>0.597924481</td><td>-1.1679624651</td><td>0.576904844</td><td>-0.8664704836</td><td>0.4349765924</td><td>0.7539800101</td><td>-0.0062420383</td><td>0.901491218</td><td>-0.1311530719</td><td>-1.5579372871</td><td>0.3897912198</td><td>1.3952161912</td><td>0.9543525749</td><td>-0.6768584986</td><td>0.8369574757</td><td>-0.6102196693</td><td>0.1744140539</td><td>0.3712255091</td><td>0.6228807701</td><td>-0.1972479979</td><td>-0.3013604219</td><td>0.0219622709</td><td>0.4905795651</td><td>-0.0125631129</td><td>0.0435216447</td><td>190.0</td><td>0.0</td></tr><tr><td>15521.0</td><td>26912.0</td><td>1.0935532844</td><td>0.0138924535</td><td>1.1491267785</td><td>1.3743511399</td><td>-0.7353302398</td><td>-0.0127907553</td><td>-0.4106614462</td><td>0.0873984866</td><td>0.6282664827</td><td>-0.242893922</td><td>-0.1672665655</td><td>1.0776346664</td><td>0.7697052099</td><td>-0.3979425201</td><td>0.1885448555</td><td>-0.5253359311</td><td>0.1622708763</td><td>-0.6623085006</td><td>-0.723796919</td><td>-0.115906188</td><td>0.1524005881</td><td>0.7413398691</td><td>-0.0859163073</td><td>0.4540305779</td><td>0.5615818187</td><td>-0.2093641896</td><td>0.0815190169</td><td>0.0327433607</td><td>15.13</td><td>0.0</td></tr><tr><td>15522.0</td><td>26913.0</td><td>-0.5429222088</td><td>0.6817533002</td><td>1.6166490124</td><td>-0.4498867407</td><td>0.2777079565</td><td>-1.1569001686</td><td>0.6713976286</td><td>-0.0988143118</td><td>-0.3953690724</td><td>-0.7327828012</td><td>-0.4811612102</td><td>0.0566578808</td><td>0.0652545596</td><td>0.098350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-0.3491512975</td><td>-0.4796301368</td><td>1.2473680411</td><td>-0.457567795</td><td>0.4846221656</td><td>-0.4371148483</td><td>0.4659410803</td><td>-0.1346184403</td><td>-0.248638708</td><td>-0.2877178552</td><td>-0.2079733038</td><td>-0.1603112088</td><td>0.0294385263</td><td>0.3954821646</td><td>0.4142556295</td><td>-0.2089275762</td><td>0.0354996968</td><td>0.0220715802</td><td>10.0</td><td>0.0</td></tr></tbody></table></div>"]}}],"execution_count":19},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":20},{"cell_type":"code","source":["path_test = \"/FileStore/tables/card-logs-data\"\n\n# Archival Directory\nto_locations = \"dbfs:/FileStore/tables/card-logs-data-archieve/\"\n\n# List of file to archive post prediction\nlist_offiles=dbutils.fs.ls(\"dbfs:/FileStore/tables/card-logs-data\")\n\n# Read the CSV and transform in a dense vector for ML Lib\ntransformed_result = transform_for_ccfd(path=path_test)\n\n# Dense Vector\ntest_data  = transformed_result[0]\n\n# Retain the original dataframe for further use \noriginal_data  = transformed_result[1]\n\n# Archive all files post prediction except meta-data and current file\nfor fileDetails  in list_offiles[:]:\n  if (fileDetails[1].startswith(\"part-00000-\") and  (fileDetails[2] > 0 )):\n      print(\"This is the one Name {0} size {1}\".format(fileDetails[1],fileDetails[2]))\n      dbutils.fs.mv(fileDetails[0], to_locations + fileDetails[1] , False)\n\n"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">This is the one Name part-00000-5bfae047-376f-47f0-b900-f86117559989-c000.csv size 10416\nThis is the one Name part-00000-99384ec9-1c1f-451d-a64d-cc4de6325910-c000.csv size 62407\nThis is the one Name part-00000-5bfae047-376f-47f0-b900-f86117559989-c000.csv size 10416\nThis is the one Name part-00000-99384ec9-1c1f-451d-a64d-cc4de6325910-c000.csv size 62407\n</div>"]}}],"execution_count":21},{"cell_type":"code","source":["predictions = model.transform(test_data)\ndisplay(predictions)"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>features</th><th>label</th><th>rawPrediction</th><th>probability</th><th>prediction</th></tr></thead><tbody><tr><td>List(1, 16, List(), List(-1.059690124, 1.4442398159, 1.4021601297, -0.0039575924, 0.4313087845, -0.2663826561, 1.0118127759, -0.7181505012, -0.5022710758, -0.1476371245, 0.5345015985, 0.2037363817, 0.0159510764, -0.3823352274, -1.1333129331, -1.10877904))</td><td>0.0</td><td>List(1, 2, List(), List(199.94624926699498, 0.053750733005020195))</td><td>List(1, 2, List(), List(0.9997312463349749, 2.6875366502510095E-4))</td><td>0.0</td></tr><tr><td>List(1, 16, List(), List(1.1609567794, 1.2656213441, -1.5764729803, 1.4729879638, 1.1621726912, -1.0135323569, 0.6581328268, -0.8473259374, -1.4524985804, 2.8939231012, 0.7741492136, -3.3337206434, 0.8978112902, 2.6826126385, 1.2693141465, -0.9122617532))</td><td>0.0</td><td>List(1, 2, List(), List(199.3489729026484, 0.6510270973515901))</td><td>List(1, 2, List(), List(0.996744864513242, 0.003255135486757951))</td><td>0.0</td></tr><tr><td>List(1, 16, List(), List(1.1945546066, -0.1467282003, 0.4597760204, -0.2333314876, -0.5436549225, -0.4818937894, -0.2221221155, 0.0461698221, -0.081033111, 1.3007436261, 1.1831935259, 0.1733737919, 0.6695586948, -0.7734134266, 0.1036205026, 0.4252988957))</td><td>0.0</td><td>List(1, 2, List(), List(199.9484006334307, 0.051599366569324574))</td><td>List(1, 2, List(), List(0.9997420031671534, 2.5799683284662286E-4))</td><td>0.0</td></tr><tr><td>List(1, 16, List(), List(-0.4372562536, -3.4599029903, -0.8510696436, 0.1737636945, -1.7108014185, -0.0771364774, 0.748273876, -1.0740082459, 0.3488182003, 1.2536747619, 0.4642410898, 0.6032509306, -1.8002366066, 0.4831637421, 0.8473124677, -0.8918122825))</td><td>0.0</td><td>List(1, 2, List(), List(199.9468790532071, 0.05312094679292595))</td><td>List(1, 2, List(), List(0.9997343952660355, 2.656047339646297E-4))</td><td>0.0</td></tr><tr><td>List(1, 16, List(), List(0.7750096633, -0.9586281014, 1.0055755342, 0.597924481, -1.1679624651, 0.576904844, -0.8664704836, 0.7539800101, -0.0062420383, 0.901491218, -0.1311530719, 0.3897912198, 0.9543525749, -0.6768584986, 0.8369574757, -0.6102196693))</td><td>0.0</td><td>List(1, 2, List(), List(199.94808702148154, 0.05191297851848245))</td><td>List(1, 2, List(), List(0.9997404351074075, 2.595648925924122E-4))</td><td>0.0</td></tr><tr><td>List(1, 16, List(), List(1.0935532844, 0.0138924535, 1.1491267785, 1.3743511399, -0.7353302398, -0.0127907553, -0.4106614462, 0.6282664827, -0.242893922, -0.1672665655, 1.0776346664, -0.3979425201, -0.5253359311, 0.1622708763, -0.6623085006, -0.723796919))</td><td>0.0</td><td>List(1, 2, List(), List(199.9484006334307, 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0.2772886322, 0.7043725766, -0.4865197817, -1.1020481975, -0.0044193795, 0.2335740266, -0.2433313762, -0.2968562662, -0.4714500172, -0.0243618451, 0.542120522, 0.068463232, -0.1350783813, -0.1871556097))</td><td>0.0</td><td>List(1, 2, List(), List(199.9484006334307, 0.051599366569324574))</td><td>List(1, 2, List(), List(0.9997420031671534, 2.5799683284662286E-4))</td><td>0.0</td></tr><tr><td>List(1, 16, List(), List(1.1886028076, 0.1351413097, 0.5979758128, 0.5652322876, -0.5871871732, -0.7979911403, -0.0711052658, -0.1313158562, 0.1185417896, 1.5551375306, 0.7808200265, 0.5788359625, 0.4709794397, -0.5621906614, -0.0326069532, 0.0956550098))</td><td>0.0</td><td>List(1, 2, List(), List(199.9484006334307, 0.051599366569324574))</td><td>List(1, 2, List(), List(0.9997420031671534, 2.5799683284662286E-4))</td><td>0.0</td></tr><tr><td>List(1, 16, List(), List(-1.5184218774, 1.5053440104, 1.0622675848, -1.7974506499, -0.1025451823, -0.6296360901, 0.7177008014, 1.3983360814, 1.7983358973, 1.1455349686, 0.0979306306, -0.6130403396, 0.6792363729, -1.286752243, 0.1609668881, -0.5167677241))</td><td>0.0</td><td>List(1, 2, List(), List(199.92577297230798, 0.07422702769199106))</td><td>List(1, 2, List(), List(0.99962886486154, 3.711351384599554E-4))</td><td>0.0</td></tr><tr><td>List(1, 16, List(), List(-1.1464108105, 1.9637108176, -3.6118062847, 1.7238959704, 0.0415065736, -1.2993319606, -0.0939004828, -0.1827458244, -1.2919122057, -0.1584600221, -1.0768221001, -2.3849132599, 1.162139321, 3.4902819374, 1.5103179627, -0.6566445687))</td><td>0.0</td><td>List(1, 2, List(), List(199.3570829120314, 0.6429170879686206))</td><td>List(1, 2, List(), List(0.9967854145601569, 0.0032145854398431028))</td><td>0.0</td></tr><tr><td>List(1, 16, List(), List(1.5271404117, -0.8801251503, 0.4624684886, -1.3326511615, -1.4788329621, -1.0081192881, -0.8638479513, -1.9589066272, 1.4204990359, -0.3491512975, -0.4796301368, -0.457567795, -0.4371148483, 0.4659410803, -0.1346184403, -0.248638708))</td><td>0.0</td><td>List(1, 2, List(), List(199.9484006334307, 0.051599366569324574))</td><td>List(1, 2, List(), List(0.9997420031671534, 2.5799683284662286E-4))</td><td>0.0</td></tr></tbody></table></div>"]}}],"execution_count":22},{"cell_type":"code","source":["predictions.groupBy(\"prediction\").count().orderBy('prediction').show()\npredictions.groupBy(\"label\").count().orderBy('label').show()"],"metadata":{},"outputs":[],"execution_count":23},{"cell_type":"code","source":["columns_with_prediction = [\"MessageID\",\"Time\",\"V1\",\"V2\",\"V3\",\"V4\",\"V5\",\"V6\",\"V7\",\"V8\",\"V9\",\"V10\",\"V11\",\"V12\",\"V13\",\"V14\",\"V15\",\"V16\",\"V17\",\"V18\",\"V19\",\"V20\",\"V21\",\"V22\",\"V23\",\"V24\",\"V25\",\"V26\",\"V27\",\"V28\",\"Amount\",\"Class\",\"Prediction\"]"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":24},{"cell_type":"code","source":["few_columns = [\"MessageID\",\"Time\",\"V1\",\"V2\",\"V27\",\"V28\",\"Amount\",\"Class\",\"Prediction\"]\noriginal_data[\"Prediction\"] = predictions.select(\"prediction\").toPandas()\ndisplay(original_data[few_columns])"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>MessageID</th><th>Time</th><th>V1</th><th>V2</th><th>V27</th><th>V28</th><th>Amount</th><th>Class</th><th>Prediction</th></tr></thead><tbody><tr><td>15516.0</td><td>26905.0</td><td>-1.059690124</td><td>1.4442398159</td><td>0.4060907504</td><td>0.1668482393</td><td>52.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15517.0</td><td>26906.0</td><td>1.1609567794</td><td>1.2656213441</td><td>0.0583991252</td><td>0.0854358811</td><td>1.79</td><td>0.0</td><td>0.0</td></tr><tr><td>15518.0</td><td>26906.0</td><td>1.1945546066</td><td>-0.1467282003</td><td>-0.077138038</td><td>0.0037395343</td><td>38.16</td><td>0.0</td><td>0.0</td></tr><tr><td>15519.0</td><td>26909.0</td><td>-0.4372562536</td><td>-3.4599029903</td><td>-0.2309269844</td><td>0.1420098916</td><td>908.6</td><td>0.0</td><td>0.0</td></tr><tr><td>15520.0</td><td>26911.0</td><td>0.7750096633</td><td>-0.9586281014</td><td>-0.0125631129</td><td>0.0435216447</td><td>190.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15521.0</td><td>26912.0</td><td>1.0935532844</td><td>0.0138924535</td><td>0.0815190169</td><td>0.0327433607</td><td>15.13</td><td>0.0</td><td>0.0</td></tr><tr><td>15522.0</td><td>26913.0</td><td>-0.5429222088</td><td>0.6817533002</td><td>-0.0419142832</td><td>0.0181621336</td><td>1.98</td><td>0.0</td><td>0.0</td></tr><tr><td>15523.0</td><td>26914.0</td><td>-1.1408428333</td><td>0.4961850912</td><td>0.3296998865</td><td>0.0328464517</td><td>14.2</td><td>0.0</td><td>0.0</td></tr><tr><td>15524.0</td><td>26915.0</td><td>1.2176597757</td><td>-0.4987122047</td><td>0.0300083193</td><td>0.0479370565</td><td>66.44</td><td>0.0</td><td>0.0</td></tr><tr><td>15525.0</td><td>26916.0</td><td>-0.5618932475</td><td>1.9514347829</td><td>0.1190286479</td><td>0.2021055974</td><td>4.8</td><td>0.0</td><td>0.0</td></tr><tr><td>15526.0</td><td>26916.0</td><td>1.3393225315</td><td>-0.4237128067</td><td>-0.005772103</td><td>0.0072728573</td><td>26.85</td><td>0.0</td><td>0.0</td></tr><tr><td>15527.0</td><td>26918.0</td><td>-0.837742382</td><td>0.4005232608</td><td>0.377935202</td><td>0.2069396006</td><td>126.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15528.0</td><td>26919.0</td><td>1.0942639032</td><td>0.3575283533</td><td>0.0025737506</td><td>0.0319862876</td><td>23.39</td><td>0.0</td><td>0.0</td></tr><tr><td>15529.0</td><td>26919.0</td><td>-0.7550625971</td><td>0.7961666486</td><td>-0.3688115675</td><td>-0.2087463308</td><td>12.31</td><td>0.0</td><td>0.0</td></tr><tr><td>15530.0</td><td>26919.0</td><td>-0.6918403403</td><td>1.1733242262</td><td>0.0567288048</td><td>0.0647707608</td><td>20.98</td><td>0.0</td><td>0.0</td></tr><tr><td>15531.0</td><td>26921.0</td><td>-0.6272103645</td><td>0.6223189833</td><td>-0.0178989651</td><td>-0.0956748349</td><td>4.99</td><td>0.0</td><td>0.0</td></tr><tr><td>15532.0</td><td>26922.0</td><td>1.2705537901</td><td>0.1865074447</td><td>-0.0328815756</td><td>0.0014489477</td><td>4.49</td><td>0.0</td><td>0.0</td></tr><tr><td>15533.0</td><td>26922.0</td><td>-0.4808832534</td><td>0.8630581691</td><td>0.2540443539</td><td>0.1072163495</td><td>1.79</td><td>0.0</td><td>0.0</td></tr><tr><td>15534.0</td><td>26924.0</td><td>1.2885247658</td><td>-1.3285435617</td><td>-0.0079612138</td><td>0.0154117157</td><td>109.95</td><td>0.0</td><td>0.0</td></tr><tr><td>15535.0</td><td>26928.0</td><td>-1.333996119</td><td>0.4836354087</td><td>-0.1599899953</td><td>-0.0085949799</td><td>29.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15536.0</td><td>26929.0</td><td>-0.9053716729</td><td>1.3286559791</td><td>-0.1657818036</td><td>0.2328888754</td><td>2.69</td><td>0.0</td><td>0.0</td></tr><tr><td>15537.0</td><td>26930.0</td><td>-0.7220361421</td><td>1.000738303</td><td>0.1535832116</td><td>0.2499247279</td><td>25.82</td><td>0.0</td><td>0.0</td></tr><tr><td>15538.0</td><td>26930.0</td><td>-2.0494435961</td><td>-2.1206514112</td><td>0.0757458829</td><td>-0.0626370983</td><td>9.1</td><td>0.0</td><td>0.0</td></tr><tr><td>15539.0</td><td>26931.0</td><td>-22.5616992591</td><td>13.2089042844</td><td>1.50442466</td><td>0.4459198261</td><td>99.99</td><td>1.0</td><td>1.0</td></tr><tr><td>15540.0</td><td>26931.0</td><td>-0.0703856056</td><td>0.0377482052</td><td>-0.1055046122</td><td>-0.161399888</td><td>95.65</td><td>0.0</td><td>0.0</td></tr><tr><td>15541.0</td><td>26932.0</td><td>-0.4545407846</td><td>1.0188442835</td><td>0.2830631233</td><td>0.1181399484</td><td>4.56</td><td>0.0</td><td>0.0</td></tr><tr><td>15542.0</td><td>26933.0</td><td>1.2690109035</td><td>-0.432589955</td><td>0.0061309821</td><td>0.0289541646</td><td>55.85</td><td>0.0</td><td>0.0</td></tr><tr><td>15543.0</td><td>26934.0</td><td>-0.3269380586</td><td>1.2787231082</td><td>-0.0184300013</td><td>-0.1276348082</td><td>0.71</td><td>0.0</td><td>0.0</td></tr><tr><td>15544.0</td><td>26935.0</td><td>1.0494973133</td><td>0.2062365253</td><td>0.060884274</td><td>0.0224801269</td><td>7.57</td><td>0.0</td><td>0.0</td></tr><tr><td>15545.0</td><td>26936.0</td><td>1.2158145508</td><td>-0.9040532199</td><td>0.0674684178</td><td>0.0397700468</td><td>79.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15546.0</td><td>26936.0</td><td>-1.111726216</td><td>-0.2812557838</td><td>-0.0853962833</td><td>0.112927278</td><td>79.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15547.0</td><td>26941.0</td><td>-0.5194108806</td><td>2.0564700982</td><td>0.035847352</td><td>0.2159574182</td><td>1.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15548.0</td><td>26942.0</td><td>-1.4653127747</td><td>-0.0972459715</td><td>-0.2682994182</td><td>-0.0805687104</td><td>76.17</td><td>0.0</td><td>0.0</td></tr><tr><td>15549.0</td><td>26946.0</td><td>0.9947228702</td><td>-0.1233161985</td><td>-0.0925169224</td><td>0.0213138722</td><td>133.9</td><td>0.0</td><td>0.0</td></tr><tr><td>15550.0</td><td>26947.0</td><td>-2.0813343493</td><td>-1.5345279258</td><td>0.1092870571</td><td>0.3527852402</td><td>30.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15551.0</td><td>26948.0</td><td>-0.9389784325</td><td>0.9742482593</td><td>-0.0606859768</td><td>-0.1637040549</td><td>44.7</td><td>0.0</td><td>0.0</td></tr><tr><td>15552.0</td><td>26948.0</td><td>-0.3593775554</td><td>1.1252236012</td><td>0.2444113959</td><td>0.100760984</td><td>2.67</td><td>0.0</td><td>0.0</td></tr><tr><td>15553.0</td><td>26951.0</td><td>0.3634520214</td><td>-2.0998421763</td><td>0.0984061549</td><td>0.0664885277</td><td>361.31</td><td>0.0</td><td>0.0</td></tr><tr><td>15554.0</td><td>26951.0</td><td>-0.1090304868</td><td>0.7981771045</td><td>0.1965751499</td><td>0.2058932345</td><td>3.58</td><td>0.0</td><td>0.0</td></tr><tr><td>15555.0</td><td>26952.0</td><td>-3.1926194487</td><td>-7.5584617273</td><td>-0.4229661475</td><td>0.381602962</td><td>2217.18</td><td>0.0</td><td>0.0</td></tr><tr><td>15556.0</td><td>26953.0</td><td>1.2202033411</td><td>0.6268109051</td><td>0.0292301067</td><td>0.0382687924</td><td>1.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15557.0</td><td>26953.0</td><td>0.3512938847</td><td>1.4484734478</td><td>-0.4372756154</td><td>-0.2176493954</td><td>18.96</td><td>0.0</td><td>0.0</td></tr><tr><td>15558.0</td><td>26953.0</td><td>-0.5604294266</td><td>1.0367771586</td><td>-0.3683461906</td><td>-0.253107085</td><td>12.13</td><td>0.0</td><td>0.0</td></tr><tr><td>15559.0</td><td>26954.0</td><td>1.1201340712</td><td>-0.2530475383</td><td>-0.1362004999</td><td>-0.0012343161</td><td>129.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15560.0</td><td>26954.0</td><td>-0.3931704873</td><td>1.0618707538</td><td>0.2879713404</td><td>0.1114168331</td><td>3.57</td><td>0.0</td><td>0.0</td></tr><tr><td>15561.0</td><td>26955.0</td><td>-1.7515084793</td><td>-0.7908957889</td><td>-0.1241415058</td><td>0.0735695079</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15562.0</td><td>26958.0</td><td>-1.1839456738</td><td>0.9659896745</td><td>0.0694110093</td><td>0.0612014179</td><td>0.89</td><td>0.0</td><td>0.0</td></tr><tr><td>15563.0</td><td>26959.0</td><td>1.093338892</td><td>0.2498802811</td><td>0.0381707116</td><td>0.0227449111</td><td>21.99</td><td>0.0</td><td>0.0</td></tr><tr><td>15564.0</td><td>26959.0</td><td>-0.8628809421</td><td>0.7416898023</td><td>-0.1290966868</td><td>-0.0952742512</td><td>2.04</td><td>0.0</td><td>0.0</td></tr><tr><td>15565.0</td><td>26960.0</td><td>-1.2402836223</td><td>0.1505589271</td><td>0.2108791125</td><td>0.036163554</td><td>10.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15566.0</td><td>26961.0</td><td>-23.2379202445</td><td>13.4873857909</td><td>1.4812326064</td><td>0.4381248048</td><td>99.99</td><td>1.0</td><td>1.0</td></tr><tr><td>15567.0</td><td>26961.0</td><td>1.48567578</td><td>-1.3421849664</td><td>0.0308239399</td><td>0.006589499</td><td>26.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15568.0</td><td>26961.0</td><td>1.2137902535</td><td>-0.2587017548</td><td>-0.0696785333</td><td>-0.0104738478</td><td>4.99</td><td>0.0</td><td>0.0</td></tr><tr><td>15569.0</td><td>26965.0</td><td>-1.1607940029</td><td>0.1767899723</td><td>-0.081655833</td><td>0.3375511313</td><td>1.29</td><td>0.0</td><td>0.0</td></tr><tr><td>15570.0</td><td>26966.0</td><td>-0.8594104706</td><td>-0.3759136568</td><td>0.0826319504</td><td>0.0757270457</td><td>39.98</td><td>0.0</td><td>0.0</td></tr><tr><td>15571.0</td><td>26966.0</td><td>1.2145223793</td><td>0.0070665914</td><td>0.0258803567</td><td>-0.0107823623</td><td>23.15</td><td>0.0</td><td>0.0</td></tr><tr><td>15572.0</td><td>26966.0</td><td>1.1886028076</td><td>0.1351413097</td><td>-0.0382061829</td><td>0.0107741125</td><td>8.99</td><td>0.0</td><td>0.0</td></tr><tr><td>15573.0</td><td>26967.0</td><td>-0.7157350869</td><td>0.5992244001</td><td>0.1138934392</td><td>0.1479745829</td><td>122.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15574.0</td><td>26968.0</td><td>-0.1332442243</td><td>1.0551265348</td><td>-0.4869468932</td><td>-0.1445425246</td><td>30.85</td><td>0.0</td><td>0.0</td></tr><tr><td>15575.0</td><td>26969.0</td><td>1.0822733147</td><td>0.1920016314</td><td>0.0391378373</td><td>0.0398711988</td><td>22.78</td><td>0.0</td><td>0.0</td></tr><tr><td>15576.0</td><td>26970.0</td><td>1.2406578017</td><td>0.120544397</td><td>-0.0397011278</td><td>0.0033026563</td><td>1.29</td><td>0.0</td><td>0.0</td></tr><tr><td>15577.0</td><td>26971.0</td><td>-0.5101418693</td><td>-3.6182757542</td><td>-0.2390701683</td><td>0.1622057273</td><td>976.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15578.0</td><td>26971.0</td><td>-0.6546736764</td><td>1.2324060167</td><td>0.0236248066</td><td>0.1022354203</td><td>66.43</td><td>0.0</td><td>0.0</td></tr><tr><td>15579.0</td><td>26972.0</td><td>-1.0892640989</td><td>0.079704557</td><td>-0.0805242011</td><td>-0.0652387456</td><td>1.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15580.0</td><td>26973.0</td><td>-1.54675291</td><td>1.351154754</td><td>0.6217357576</td><td>0.2454761863</td><td>2.23</td><td>0.0</td><td>0.0</td></tr><tr><td>15581.0</td><td>26977.0</td><td>-0.5517254712</td><td>-3.0771080651</td><td>-0.195205472</td><td>0.1409484937</td><td>871.62</td><td>0.0</td><td>0.0</td></tr><tr><td>15582.0</td><td>26977.0</td><td>1.2353831587</td><td>0.1817172092</td><td>-0.0375177327</td><td>0.0139576824</td><td>1.98</td><td>0.0</td><td>0.0</td></tr><tr><td>15583.0</td><td>26978.0</td><td>-0.4282274248</td><td>0.7137473719</td><td>0.0777903627</td><td>0.1396916265</td><td>47.55</td><td>0.0</td><td>0.0</td></tr><tr><td>15584.0</td><td>26979.0</td><td>-0.9671432421</td><td>0.3779661092</td><td>-0.0498754611</td><td>0.0379043311</td><td>2.02</td><td>0.0</td><td>0.0</td></tr><tr><td>15585.0</td><td>26979.0</td><td>0.990367426</td><td>-0.7399893239</td><td>-0.0212010507</td><td>0.0029122492</td><td>55.06</td><td>0.0</td><td>0.0</td></tr><tr><td>15586.0</td><td>26980.0</td><td>1.1053356249</td><td>0.1080701837</td><td>0.0303788257</td><td>0.0338362409</td><td>10.05</td><td>0.0</td><td>0.0</td></tr><tr><td>15587.0</td><td>26980.0</td><td>1.2583533937</td><td>0.3910686653</td><td>-0.0198588405</td><td>0.0316689537</td><td>0.89</td><td>0.0</td><td>0.0</td></tr><tr><td>15588.0</td><td>26980.0</td><td>1.088173158</td><td>-0.6025252547</td><td>-0.028444353</td><td>0.0020374134</td><td>60.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15589.0</td><td>26982.0</td><td>1.4601225975</td><td>-1.268057299</td><td>0.0575119304</td><td>0.0044964913</td><td>17.4</td><td>0.0</td><td>0.0</td></tr><tr><td>15590.0</td><td>26983.0</td><td>1.232612754</td><td>0.3132497621</td><td>-0.0074546568</td><td>0.0189194663</td><td>9.99</td><td>0.0</td><td>0.0</td></tr><tr><td>15591.0</td><td>26985.0</td><td>-1.1333968453</td><td>1.4502219576</td><td>0.750683756</td><td>0.3851780866</td><td>17.99</td><td>0.0</td><td>0.0</td></tr><tr><td>15592.0</td><td>26985.0</td><td>-0.4077139666</td><td>-0.7596031185</td><td>-0.0788720693</td><td>-0.1273224945</td><td>15.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15593.0</td><td>26986.0</td><td>-1.8826685562</td><td>-0.6709026178</td><td>-0.2149266154</td><td>-0.1453210849</td><td>2.42</td><td>0.0</td><td>0.0</td></tr><tr><td>15594.0</td><td>26987.0</td><td>-1.7718028172</td><td>0.2003549955</td><td>0.1117478687</td><td>0.0063798912</td><td>4.49</td><td>0.0</td><td>0.0</td></tr><tr><td>15595.0</td><td>26988.0</td><td>1.2161114192</td><td>-0.0795368006</td><td>-0.0732445208</td><td>-0.0111694216</td><td>0.77</td><td>0.0</td><td>0.0</td></tr><tr><td>15596.0</td><td>26988.0</td><td>-0.7153131747</td><td>-0.2157540732</td><td>0.3137870445</td><td>0.200983983</td><td>62.85</td><td>0.0</td><td>0.0</td></tr><tr><td>15597.0</td><td>26990.0</td><td>-2.3479490235</td><td>-0.0364162612</td><td>-0.1292127113</td><td>-0.2633194896</td><td>158.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15598.0</td><td>26991.0</td><td>-1.7297285446</td><td>-1.2121049222</td><td>0.074134989</td><td>-0.0781344765</td><td>142.25</td><td>0.0</td><td>0.0</td></tr><tr><td>15599.0</td><td>26992.0</td><td>0.8878167169</td><td>0.0515122851</td><td>0.0436475941</td><td>0.0327779858</td><td>71.07</td><td>0.0</td><td>0.0</td></tr><tr><td>15600.0</td><td>26993.0</td><td>-1.1431130377</td><td>-0.3781897468</td><td>-1.5839418791</td><td>-0.8279931508</td><td>116.6</td><td>0.0</td><td>0.0</td></tr><tr><td>15601.0</td><td>26999.0</td><td>-0.0111779128</td><td>-2.6418817386</td><td>-0.1178803641</td><td>0.1049088337</td><td>601.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15602.0</td><td>27000.0</td><td>0.9376159456</td><td>-0.7107136172</td><td>0.0190014025</td><td>0.013617255</td><td>35.96</td><td>0.0</td><td>0.0</td></tr><tr><td>15603.0</td><td>27001.0</td><td>-0.3342442111</td><td>1.1918717174</td><td>0.2508751445</td><td>0.0995559442</td><td>1.78</td><td>0.0</td><td>0.0</td></tr><tr><td>15604.0</td><td>27003.0</td><td>1.4106161695</td><td>-1.1618030772</td><td>0.0319021501</td><td>0.0374285272</td><td>60.73</td><td>0.0</td><td>0.0</td></tr><tr><td>15605.0</td><td>27006.0</td><td>1.2389711468</td><td>0.0834028869</td><td>-0.0429842632</td><td>0.0023870219</td><td>1.98</td><td>0.0</td><td>0.0</td></tr><tr><td>15606.0</td><td>27006.0</td><td>-0.8973967558</td><td>-0.4719901391</td><td>0.2938456125</td><td>0.2200877257</td><td>130.75</td><td>0.0</td><td>0.0</td></tr><tr><td>15607.0</td><td>27007.0</td><td>-0.2640025013</td><td>-0.3071185497</td><td>0.1137544793</td><td>0.1319904278</td><td>43.9</td><td>0.0</td><td>0.0</td></tr><tr><td>15608.0</td><td>27012.0</td><td>-0.9204990082</td><td>-0.134392891</td><td>-0.0921021962</td><td>-0.0882373314</td><td>11.5</td><td>0.0</td><td>0.0</td></tr><tr><td>15609.0</td><td>27013.0</td><td>0.3190051041</td><td>-1.6152731185</td><td>-0.0601836784</td><td>0.1069554127</td><td>519.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15610.0</td><td>27013.0</td><td>-0.4319940846</td><td>0.7169321348</td><td>0.1294793517</td><td>-0.1028272252</td><td>104.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15611.0</td><td>27015.0</td><td>-2.7634533924</td><td>2.1749049303</td><td>-0.0233240014</td><td>0.1349838292</td><td>15.99</td><td>0.0</td><td>0.0</td></tr><tr><td>15612.0</td><td>27016.0</td><td>-1.419030399</td><td>0.6377032423</td><td>-0.3118541213</td><td>-0.0603374846</td><td>3.28</td><td>0.0</td><td>0.0</td></tr><tr><td>15613.0</td><td>27020.0</td><td>1.3977631894</td><td>-1.4107743856</td><td>0.0948318468</td><td>0.0282336161</td><td>25.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15614.0</td><td>27021.0</td><td>1.2397664847</td><td>0.123236624</td><td>-0.0393836892</td><td>0.0035160508</td><td>1.98</td><td>0.0</td><td>0.0</td></tr><tr><td>15615.0</td><td>27023.0</td><td>-1.8938260162</td><td>0.0912614059</td><td>-0.5933052334</td><td>0.4663111645</td><td>1.29</td><td>0.0</td><td>0.0</td></tr><tr><td>15616.0</td><td>27026.0</td><td>1.125928273</td><td>0.102848157</td><td>-0.0031150079</td><td>0.0106449109</td><td>1.29</td><td>0.0</td><td>0.0</td></tr><tr><td>15617.0</td><td>27027.0</td><td>-1.2119283792</td><td>1.3434898067</td><td>0.2941955457</td><td>0.0885605324</td><td>17.99</td><td>0.0</td><td>0.0</td></tr><tr><td>15618.0</td><td>27027.0</td><td>-1.0555562276</td><td>-0.3769569247</td><td>0.2182637464</td><td>0.056284612</td><td>60.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15619.0</td><td>27027.0</td><td>1.4188803502</td><td>-0.3464861632</td><td>-0.0116600997</td><td>-0.0179354503</td><td>15.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15620.0</td><td>27030.0</td><td>-0.6306940062</td><td>0.8051987175</td><td>0.1561634417</td><td>0.1754920525</td><td>39.94</td><td>0.0</td><td>0.0</td></tr><tr><td>15621.0</td><td>27033.0</td><td>1.2836613914</td><td>0.2770244085</td><td>-0.008081525</td><td>0.0171893865</td><td>1.98</td><td>0.0</td><td>0.0</td></tr><tr><td>15622.0</td><td>27033.0</td><td>0.9174785729</td><td>-0.2318512671</td><td>0.0130497445</td><td>0.0227411877</td><td>126.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15623.0</td><td>27033.0</td><td>1.1076587699</td><td>-0.4994257841</td><td>0.0802123414</td><td>0.0359574782</td><td>53.2</td><td>0.0</td><td>0.0</td></tr><tr><td>15624.0</td><td>27034.0</td><td>0.0880366245</td><td>1.3245411603</td><td>0.4852547379</td><td>0.2905493308</td><td>1.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15625.0</td><td>27035.0</td><td>-1.3964513937</td><td>0.3168929263</td><td>-0.1001815567</td><td>-0.2586565749</td><td>31.84</td><td>0.0</td><td>0.0</td></tr><tr><td>15626.0</td><td>27035.0</td><td>1.2279262459</td><td>-0.127732844</td><td>-0.0796389651</td><td>-9.023213E-4</td><td>0.77</td><td>0.0</td><td>0.0</td></tr><tr><td>15627.0</td><td>27036.0</td><td>-0.277617777</td><td>1.0515192829</td><td>0.0812988525</td><td>-0.1208778604</td><td>9.03</td><td>0.0</td><td>0.0</td></tr><tr><td>15628.0</td><td>27037.0</td><td>-0.8842656657</td><td>0.3594648317</td><td>0.0879323352</td><td>0.1510582444</td><td>1.98</td><td>0.0</td><td>0.0</td></tr><tr><td>15629.0</td><td>27037.0</td><td>-0.8003760222</td><td>1.0669706892</td><td>-0.2199273342</td><td>0.0735786324</td><td>10.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15630.0</td><td>27039.0</td><td>1.1799956101</td><td>-0.3165820866</td><td>-0.005656575</td><td>0.0021478942</td><td>59.9</td><td>0.0</td><td>0.0</td></tr><tr><td>15631.0</td><td>27040.0</td><td>1.2165702809</td><td>0.2880139924</td><td>0.018967939</td><td>0.0294799833</td><td>9.99</td><td>0.0</td><td>0.0</td></tr><tr><td>15632.0</td><td>27041.0</td><td>-1.1573722823</td><td>1.5776457316</td><td>-0.1220014041</td><td>-0.1235149395</td><td>0.32</td><td>0.0</td><td>0.0</td></tr><tr><td>15633.0</td><td>27041.0</td><td>-0.391709737</td><td>-0.3278975881</td><td>0.0740845943</td><td>0.0564978888</td><td>43.74</td><td>0.0</td><td>0.0</td></tr><tr><td>15634.0</td><td>27042.0</td><td>-5.3661230704</td><td>4.6433699724</td><td>-0.2984902214</td><td>-0.1074669835</td><td>1.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15635.0</td><td>27043.0</td><td>1.304407088</td><td>-0.617224391</td><td>0.0017412475</td><td>0.0405897047</td><td>85.8</td><td>0.0</td><td>0.0</td></tr><tr><td>15636.0</td><td>27043.0</td><td>-0.7986368449</td><td>0.1944577124</td><td>0.0173828348</td><td>0.1345462252</td><td>38.9</td><td>0.0</td><td>0.0</td></tr><tr><td>15637.0</td><td>27048.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d>0.0</td><td>0.0</td></tr><tr><td>15668.0</td><td>27089.0</td><td>0.9390775677</td><td>-0.2902731413</td><td>-0.0147144983</td><td>0.0094442422</td><td>76.1</td><td>0.0</td><td>0.0</td></tr><tr><td>15669.0</td><td>27089.0</td><td>-0.4139494642</td><td>1.1126838491</td><td>0.2718481918</td><td>0.1229775299</td><td>0.89</td><td>0.0</td><td>0.0</td></tr><tr><td>15670.0</td><td>27090.0</td><td>1.4071979834</td><td>-0.6612575524</td><td>-0.0250585095</td><td>-0.0146691223</td><td>40.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15490.0</td><td>26875.0</td><td>-11.7010580289</td><td>-15.2953956761</td><td>-0.1882416574</td><td>-1.477740797</td><td>1746.8</td><td>0.0</td><td>0.0</td></tr><tr><td>15491.0</td><td>26877.0</td><td>1.1609567794</td><td>1.2656213441</td><td>0.0583991252</td><td>0.0854358811</td><td>1.79</td><td>0.0</td><td>0.0</td></tr><tr><td>15492.0</td><td>26877.0</td><td>-1.6593825439</td><td>0.9713257226</td><td>0.6489859026</td><td>0.3271103072</td><td>8.4</td><td>0.0</td><td>0.0</td></tr><tr><td>15493.0</td><td>26878.0</td><td>1.1001752674</td><td>0.3359219863</td><td>0.0239517873</td><td>0.0276638176</td><td>15.18</td><td>0.0</td><td>0.0</td></tr><tr><td>15494.0</td><td>26880.0</td><td>1.4351802594</td><td>-0.5481925288</td><td>-0.0156213507</td><td>0.0051961099</td><td>36.85</td><td>0.0</td><td>0.0</td></tr><tr><td>15495.0</td><td>26883.0</td><td>1.0714481289</td><td>0.0396587041</td><td>0.0143728724</td><td>0.0240952066</td><td>49.45</td><td>0.0</td><td>0.0</td></tr><tr><td>15496.0</td><td>26887.0</td><td>1.2048594677</td><td>1.239464516</td><td>0.0551762374</td><td>0.0930827842</td><td>0.75</td><td>0.0</td><td>0.0</td></tr><tr><td>15497.0</td><td>26887.0</td><td>-1.3946064653</td><td>-0.8116303649</td><td>-0.1701458596</td><td>-0.0683090471</td><td>116.6</td><td>0.0</td><td>0.0</td></tr><tr><td>15498.0</td><td>26888.0</td><td>1.2687533215</td><td>0.5552954301</td><td>-0.0184319786</td><td>0.0332276833</td><td>0.76</td><td>0.0</td><td>0.0</td></tr><tr><td>15499.0</td><td>26888.0</td><td>-1.9594420891</td><td>0.185794644</td><td>0.1048682877</td><td>-0.1776233469</td><td>63.35</td><td>0.0</td><td>0.0</td></tr><tr><td>15500.0</td><td>26889.0</td><td>1.2123119571</td><td>0.2595686688</td><td>-0.0107655559</td><td>0.0260842683</td><td>1.98</td><td>0.0</td><td>0.0</td></tr><tr><td>15501.0</td><td>26890.0</td><td>-1.7970623557</td><td>-5.2523893709</td><td>-0.2864746496</td><td>0.2558237921</td><td>1494.8</td><td>0.0</td><td>0.0</td></tr><tr><td>15502.0</td><td>26891.0</td><td>-0.9414339911</td><td>-3.6380574872</td><td>-0.3135742587</td><td>0.1948040723</td><td>1164.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15503.0</td><td>26895.0</td><td>-1.319792119</td><td>1.3850808968</td><td>0.2124651712</td><td>0.3788055453</td><td>14.32</td><td>0.0</td><td>0.0</td></tr><tr><td>15504.0</td><td>26898.0</td><td>1.1695544882</td><td>-0.6772156032</td><td>-0.0567072915</td><td>-0.0035964037</td><td>50.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15505.0</td><td>26898.0</td><td>-0.7570749921</td><td>0.5021860242</td><td>0.2549187566</td><td>0.1734429131</td><td>19.26</td><td>0.0</td><td>0.0</td></tr><tr><td>15506.0</td><td>26899.0</td><td>-21.8854339052</td><td>12.930505125</td><td>1.5276552781</td><td>0.453698639</td><td>99.99</td><td>1.0</td><td>1.0</td></tr><tr><td>15507.0</td><td>26901.0</td><td>-1.1351124382</td><td>0.5801510316</td><td>0.2223343473</td><td>0.1410120736</td><td>49.99</td><td>0.0</td><td>0.0</td></tr><tr><td>15508.0</td><td>26901.0</td><td>-0.3865887058</td><td>0.9527221869</td><td>-0.1238882717</td><td>-0.0418590732</td><td>42.81</td><td>0.0</td><td>0.0</td></tr><tr><td>15509.0</td><td>26901.0</td><td>1.068480555</td><td>0.1605146247</td><td>0.0371173416</td><td>0.0263854433</td><td>19.78</td><td>0.0</td><td>0.0</td></tr><tr><td>15510.0</td><td>26902.0</td><td>1.2574309918</td><td>0.261199634</td><td>-0.0315716819</td><td>0.027767072</td><td>0.89</td><td>0.0</td><td>0.0</td></tr><tr><td>15511.0</td><td>26902.0</td><td>1.1886028076</td><td>0.1351413097</td><td>-0.0382061829</td><td>0.0107741125</td><td>8.99</td><td>0.0</td><td>0.0</td></tr><tr><td>15512.0</td><td>26902.0</td><td>-1.5184218774</td><td>1.5053440104</td><td>0.4693812844</td><td>-0.0309497228</td><td>2.22</td><td>0.0</td><td>0.0</td></tr><tr><td>15513.0</td><td>26904.0</td><td>-1.1464108105</td><td>1.9637108176</td><td>0.2067964512</td><td>-0.2029332989</td><td>89.99</td><td>0.0</td><td>0.0</td></tr><tr><td>15514.0</td><td>26904.0</td><td>1.5271404117</td><td>-0.8801251503</td><td>0.0354996968</td><td>0.0220715802</td><td>10.0</td><td>0.0</td><td>0.0</td></tr></tbody></table></div>"]}}],"execution_count":25},{"cell_type":"code","source":["# Save the predictions along with the transacrtion on the DBFS\noutput_directory = \"/FileStore/tables/card-logs-data-withOutPuts/\"\n\n# Use current timestamp as a file name of CSV\ncurrent_time = datetime.now().strftime(\"%c\")\nfile_name=current_time.replace(\" \", \"_\")+\".csv\"\nfile_name=file_name.replace(\":\", \"_\")\n\nprint(\"File Name\",output_directory+file_name)\n\nfilepath_with_name = output_directory + file_name\noriginal_data_sparkDF = spark.createDataFrame(original_data,columns_with_prediction)\n\n\n\noriginal_data_sparkDF.coalesce(1).write.format(\"com.databricks.spark.csv\").save(output_directory+file_name)"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">File Name /FileStore/tables/card-logs-data-withOutPuts/Sun_May_24_16_43_42_2020.csv\n</div>"]}}],"execution_count":26},{"cell_type":"code","source":["%fs ls /FileStore/tables/card-logs-data-withOutPuts/Sun_May_24_16_43_42_2020.csv"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>path</th><th>name</th><th>size</th></tr></thead><tbody><tr><td>dbfs:/FileStore/tables/card-logs-data-withOutPuts/Sun_May_24_16_43_42_2020.csv/_SUCCESS</td><td>_SUCCESS</td><td>0</td></tr><tr><td>dbfs:/FileStore/tables/card-logs-data-withOutPuts/Sun_May_24_16_43_42_2020.csv/_committed_5136772917708194473</td><td>_committed_5136772917708194473</td><td>114</td></tr><tr><td>dbfs:/FileStore/tables/card-logs-data-withOutPuts/Sun_May_24_16_43_42_2020.csv/_started_5136772917708194473</td><td>_started_5136772917708194473</td><td>0</td></tr><tr><td>dbfs:/FileStore/tables/card-logs-data-withOutPuts/Sun_May_24_16_43_42_2020.csv/part-00000-tid-5136772917708194473-df142781-9de4-448d-bda2-a93f58e82d2a-4737-1-c000.csv</td><td>part-00000-tid-5136772917708194473-df142781-9de4-448d-bda2-a93f58e82d2a-4737-1-c000.csv</td><td>72750</td></tr></tbody></table></div>"]}}],"execution_count":27},{"cell_type":"code","source":["# time.sleep(10)\n\"\"\" Major issue is coalesce splits a dataframe into multiple csv while collecting from slave nodes\"\"\"\n\"\"\"Move the files to the final table space to reflect on the DATABASE\"\"\"\n\nto_locations =\"/FileStore/tables/card-logs-data-table/\"\nlist_offiles=dbutils.fs.ls(filepath_with_name)\nprint(list_offiles)\n\n\"\"\"Copy All the CSV files from the timestamped directory to the table space\"\"\"\nfor fileDetails  in list_offiles[:]:\n  if (fileDetails[1].startswith(\"part-00\") and  (fileDetails[2] > 10 )):\n      print(\"File Name {0} size {1} moved\".format(fileDetails[1],fileDetails[2]))\n\n      dbutils.fs.mv(fileDetails[0], to_locations + file_name , False)"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">[FileInfo(path=&#39;dbfs:/FileStore/tables/card-logs-data-withOutPuts/Sun_May_24_16_43_42_2020.csv/_SUCCESS&#39;, name=&#39;_SUCCESS&#39;, size=0), FileInfo(path=&#39;dbfs:/FileStore/tables/card-logs-data-withOutPuts/Sun_May_24_16_43_42_2020.csv/_committed_5136772917708194473&#39;, name=&#39;_committed_5136772917708194473&#39;, size=114), FileInfo(path=&#39;dbfs:/FileStore/tables/card-logs-data-withOutPuts/Sun_May_24_16_43_42_2020.csv/_started_5136772917708194473&#39;, name=&#39;_started_5136772917708194473&#39;, size=0), FileInfo(path=&#39;dbfs:/FileStore/tables/card-logs-data-withOutPuts/Sun_May_24_16_43_42_2020.csv/part-00000-tid-5136772917708194473-df142781-9de4-448d-bda2-a93f58e82d2a-4737-1-c000.csv&#39;, name=&#39;part-00000-tid-5136772917708194473-df142781-9de4-448d-bda2-a93f58e82d2a-4737-1-c000.csv&#39;, size=72750)]\nFile Name part-00000-tid-5136772917708194473-df142781-9de4-448d-bda2-a93f58e82d2a-4737-1-c000.csv size 72750 moved\n</div>"]}}],"execution_count":28},{"cell_type":"code","source":["%fs ls   /FileStore/tables/card-logs-data-table/ "],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>path</th><th>name</th><th>size</th></tr></thead><tbody><tr><td>dbfs:/FileStore/tables/card-logs-data-table/Sun_May_24_16_43_42_2020.csv</td><td>Sun_May_24_16_43_42_2020.csv</td><td>72750</td></tr></tbody></table></div>"]}}],"execution_count":29},{"cell_type":"code","source":["data_for_table = spark.read.load(\"dbfs:/FileStore/tables/card-logs-data-table/Sun_May_24_16_43_42_2020.csv\", format='csv', header='true', inferSchema='true')\n\ndisplay(data_for_table)"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: 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>-0.1025451823</td><td>-0.6296360901</td><td>0.7177008014</td><td>-0.2088284287</td><td>1.3983360814</td><td>1.7983358973</td><td>1.1455349686</td><td>0.0979306306</td><td>-0.7361438268</td><td>-0.6130403396</td><td>0.058439156</td><td>0.6792363729</td><td>-1.286752243</td><td>0.1609668881</td><td>-0.5167677241</td><td>0.9104823219</td><td>-0.3631210561</td><td>-0.340175206</td><td>-0.0798242582</td><td>-0.0039862592</td><td>-0.0071697878</td><td>0.6982630753</td><td>0.4693812844</td><td>-0.0309497228</td><td>2.22</td><td>0.0</td><td>0.0</td></tr><tr><td>15513.0</td><td>26904.0</td><td>-1.1464108105</td><td>1.9637108176</td><td>-3.6118062847</td><td>1.7238959704</td><td>0.0415065736</td><td>-1.2993319606</td><td>-0.0939004828</td><td>1.2073432297</td><td>-0.1827458244</td><td>-1.2919122057</td><td>-0.1584600221</td><td>-1.0768221001</td><td>-1.5004101718</td><td>-2.3849132599</td><td>1.5181662221</td><td>1.162139321</td><td>3.4902819374</td><td>1.5103179627</td><td>-0.6566445687</td><td>-0.105189827</td><td>-0.1250073189</td><td>-0.4115658356</td><td>0.0741355257</td><td>-0.7999730654</td><td>-0.3890728365</td><td>-0.324837731</td><td>0.2067964512</td><td>-0.2029332989</td><td>89.99</td><td>0.0</td><td>0.0</td></tr><tr><td>15514.0</td><td>26904.0</td><td>1.5271404117</td><td>-0.8801251503</td><td>0.4624684886</td><td>-1.3326511615</td><td>-1.4788329621</td><td>-1.0081192881</td><td>-0.8638479513</td><td>-0.2914468732</td><td>-1.9589066272</td><td>1.4204990359</td><td>-0.3491512975</td><td>-0.4796301368</td><td>1.2473680411</td><td>-0.457567795</td><td>0.4846221656</td><td>-0.4371148483</td><td>0.4659410803</td><td>-0.1346184403</td><td>-0.248638708</td><td>-0.2877178552</td><td>-0.2079733038</td><td>-0.1603112088</td><td>0.0294385263</td><td>0.3954821646</td><td>0.4142556295</td><td>-0.2089275762</td><td>0.0354996968</td><td>0.0220715802</td><td>10.0</td><td>0.0</td><td>0.0</td></tr></tbody></table></div>"]}}],"execution_count":30},{"cell_type":"code","source":["%fs ls /FileStore/tables/card-logs-data-table/"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>path</th><th>name</th><th>size</th></tr></thead><tbody><tr><td>dbfs:/FileStore/tables/card-logs-data-table/Sun_May_24_16_43_42_2020.csv</td><td>Sun_May_24_16_43_42_2020.csv</td><td>72750</td></tr></tbody></table></div>"]}}],"execution_count":31},{"cell_type":"code","source":["%sql\n\nDROP TABLE  IF  EXISTS  card_logs_data"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr></tr></thead><tbody></tbody></table></div>"]}}],"execution_count":32},{"cell_type":"code","source":["%sql\nCREATE EXTERNAL TABLE IF NOT EXISTS  card_logs_data(\n  MessageID DOUBLE,\n  Time DOUBLE,\n  \n  V1 DOUBLE,   V2 DOUBLE,   V3 DOUBLE,   V4 DOUBLE,   V5 DOUBLE,   V6 DOUBLE,   V7 DOUBLE,\n  V8 DOUBLE,   V9 DOUBLE,   V10 DOUBLE,  V11 DOUBLE,  V12 DOUBLE,  V13 DOUBLE,  V14 DOUBLE,  \n  V15 DOUBLE,  V16 DOUBLE,  V17 DOUBLE,  V18 DOUBLE,  V19 DOUBLE,  V20 DOUBLE,  V21 DOUBLE,\n  V22 DOUBLE,  V23 DOUBLE,  V24 DOUBLE,  V25 DOUBLE,  V26 DOUBLE,  V27 DOUBLE,  V28 DOUBLE, \n  Amount DOUBLE,  Class DOUBLE,  Prediction DOUBLE\n\n  \n) ROW FORMAT DELIMITED FIELDS TERMINATED BY ','\nLOCATION '/FileStore/tables/card-logs-data-table/';\n     \n"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr></tr></thead><tbody></tbody></table></div>"]}}],"execution_count":33},{"cell_type":"code","source":["%sql \nSELECT    * FROM card_logs_data \n-- MessageID,  Time, V1,  V28, Amount, Class, Prediction\n\n-- SELECT    * FROM card_logs_data GROUP BY Prediction  \n\n-- TRUE POSITIVES 24\n-- SELECT    COUNT(*) FROM card_logs_data WHERE Class ==1 AND  Prediction ==1\n\n-- TRUE NEGATIVES 3695\n-- SELECT    COUNT(*) FROM card_logs_data WHERE Class ==0 AND  Prediction ==0\n\n-- FALSE POSTIVES 0\n-- SELECT    COUNT(*) FROM card_logs_data WHERE Class ==0 AND  Prediction ==1\n\n-- FALSE NEGATIVES 2\n-- SELECT    COUNT(*) FROM card_logs_data WHERE Class ==1 AND  Prediction ==0\n\n"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>MessageID</th><th>Time</th><th>V1</th><th>V2</th><th>V3</th><th>V4</th><th>V5</th><th>V6</th><th>V7</th><th>V8</th><th>V9</th><th>V10</th><th>V11</th><th>V12</th><th>V13</th><th>V14</th><th>V15</th><th>V16</th><th>V17</th><th>V18</th><th>V19</th><th>V20</th><th>V21</th><th>V22</th><th>V23</th><th>V24</th><th>V25</th><th>V26</th><th>V27</th><th>V28</th><th>Amount</th><th>Class</th><th>Prediction</th></tr></thead><tbody><tr><td>15516.0</td><td>26905.0</td><td>-1.059690124</td><td>1.4442398159</td><td>1.4021601297</td><td>-0.0039575924</td><td>0.4313087845</td><td>-0.2663826561</td><td>1.0118127759</td><td>-0.8165845873</td><td>-0.7181505012</td><td>-0.5022710758</td><td>-0.1476371245</td><td>0.5345015985</td><td>0.5620484673</td><td>0.2037363817</td><td>0.3994687835</td><td>0.0159510764</td><td>-0.3823352274</td><td>-1.1333129331</td><td>-1.10877904</td><td>-0.1788836032</td><td>0.3560744273</td><td>-1.3700931848</td><td>0.1424650394</td><td>-0.0051327916</td><td>-0.0826111597</td><td>-0.9387589102</td><td>0.4060907504</td><td>0.1668482393</td><td>52.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15517.0</td><td>26906.0</td><td>1.1609567794</td><td>1.2656213441</td><td>-1.5764729803</td><td>1.4729879638</td><td>1.1621726912</td><td>-1.0135323569</td><td>0.6581328268</td><td>-0.1526238089</td><td>-0.8473259374</td><td>-1.4524985804</td><td>2.8939231012</td><td>0.7741492136</td><td>0.6635058078</td><td>-3.3337206434</td><td>0.5232978065</td><td>0.8978112902</td><td>2.6826126385</td><td>1.2693141465</td><td>-0.9122617532</td><td>0.0024655761</td><td>-0.1203645055</td><td>-0.2049968528</td><td>-0.203189095</td><td>-0.2821715296</td><td>0.7852781366</td><td>-0.2849579915</td><td>0.0583991252</td><td>0.0854358811</td><td>1.79</td><td>0.0</td><td>0.0</td></tr><tr><td>15518.0</td><td>26906.0</td><td>1.1945546066</td><td>-0.1467282003</td><td>0.4597760204</td><td>-0.2333314876</td><td>-0.5436549225</td><td>-0.4818937894</td><td>-0.2221221155</td><td>-0.0470851329</td><td>0.0461698221</td><td>-0.081033111</td><td>1.3007436261</td><td>1.1831935259</td><td>0.8197624487</td><td>0.1733737919</td><td>0.5018370619</td><td>0.6695586948</td><td>-0.7734134266</td><td>0.1036205026</td><td>0.4252988957</td><td>0.0751826789</td><td>-0.0739031165</td><td>-0.2818232173</td><td>0.0040417289</td><td>0.087200732</td><td>0.1421305978</td><td>0.9010407832</td><td>-0.077138038</td><td>0.0037395343</td><td>38.16</td><td>0.0</td><td>0.0</td></tr><tr><td>15519.0</td><td>26909.0</td><td>-0.4372562536</td><td>-3.4599029903</td><td>-0.8510696436</td><td>0.1737636945</td><td>-1.7108014185</td><td>-0.0771364774</td><td>0.748273876</td><td>-0.2165807824</td><td>-1.0740082459</td><td>0.3488182003</td><td>1.2536747619</td><td>0.4642410898</td><td>-0.5421979735</td><td>0.6032509306</td><td>-0.3149753313</td><td>-1.8002366066</td><td>0.4831637421</td><td>0.8473124677</td><td>-0.8918122825</td><td>1.2630127695</td><td>0.1815352891</td><td>-0.9567472968</td><td>-0.7486900442</td><td>0.3104933337</td><td>-0.0515741823</td><td>0.9973787173</td><td>-0.2309269844</td><td>0.1420098916</td><td>908.6</td><td>0.0</td><td>0.0</td></tr><tr><td>15520.0</td><td>26911.0</td><td>0.7750096633</td><td>-0.9586281014</td><td>1.0055755342</td><td>0.597924481</td><td>-1.1679624651</td><td>0.576904844</td><td>-0.8664704836</td><td>0.4349765924</td><td>0.7539800101</td><td>-0.0062420383</td><td>0.901491218</td><td>-0.1311530719</td><td>-1.5579372871</td><td>0.3897912198</td><td>1.3952161912</td><td>0.9543525749</td><td>-0.6768584986</td><td>0.8369574757</td><td>-0.6102196693</td><td>0.1744140539</td><td>0.3712255091</td><td>0.6228807701</td><td>-0.1972479979</td><td>-0.3013604219</td><td>0.0219622709</td><td>0.4905795651</td><td>-0.0125631129</td><td>0.0435216447</td><td>190.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15521.0</td><td>26912.0</td><td>1.0935532844</td><td>0.0138924535</td><td>1.1491267785</td><td>1.3743511399</td><td>-0.7353302398</td><td>-0.0127907553</td><td>-0.4106614462</td><td>0.0873984866</td><td>0.6282664827</td><td>-0.242893922</td><td>-0.1672665655</td><td>1.0776346664</td><td>0.7697052099</td><td>-0.3979425201</td><td>0.1885448555</td><td>-0.5253359311</td><td>0.1622708763</td><td>-0.6623085006</td><td>-0.723796919</td><td>-0.115906188</td><td>0.1524005881</td><td>0.7413398691</td><td>-0.0859163073</td><td>0.4540305779</td><td>0.5615818187</td><td>-0.2093641896</td><td>0.0815190169</td><td>0.0327433607</td><td>15.13</td><td>0.0</td><td>0.0</td></tr><tr><td>15522.0</td><td>26913.0</td><td>-0.5429222088</td><td>0.6817533002</td><td>1.6166490124</td><td>-0.4498867407</td><td>0.2777079565</td><td>-1.1569001686</td><td>0.6713976286</td><td>-0.0988143118</td><td>-0.3953690724</td><td>-0.7327828012</td><td>-0.4811612102</td><td>0.0566578808</td><td>0.0652545596</td><td>0.0983507493</td><td>0.3387078731</td><td>0.5460849569</td><td>-0.7225444862</td><td>-0.4616007772</td><td>-0.8385104799</td><td>-0.0965455292</td><td>-0.1770919703</td><td>-0.6754190764</td><td>-0.008519805</td><td>0.353517433</td><td>-0.0592324406</td><td>-0.0740869974</td><td>-0.0419142832</td><td>0.0181621336</td><td>1.98</td><td>0.0</td><td>0.0</td></tr><tr><td>15523.0</td><td>26914.0</td><td>-1.1408428333</td><td>0.4961850912</td><td>2.2659645064</td><td>-2.0187499872</td><td>-0.7714001059</td><td>-0.6017134866</td><td>0.0924590047</td><td>-0.044847159</td><td>-0.8247261246</td><td>1.0325617175</td><td>1.5050281634</td><td>-0.330327818</td><td>-0.1380918455</td><td>-0.6434771677</td><td>-0.2172504241</td><td>1.5186724785</td><td>-0.3921966294</td><td>-0.5664354918</td><td>-0.044446401</td><td>0.536691544</td><td>0.245405728</td><td>0.9576891285</td><td>-0.3874707091</td><td>0.5594096332</td><td>0.5432786584</td><td>-0.2199400899</td><td>0.3296998865</td><td>0.0328464517</td><td>14.2</td><td>0.0</td><td>0.0</td></tr><tr><td>15524.0</td><td>26915.0</td><td>1.2176597757</td><td>-0.4987122047</td><td>0.1473877244</td><td>-0.3186396103</td><td>-0.8838321191</td><td>-0.7747624371</td><td>-0.4448082007</td><td>-0.1057747992</td><td>-0.6890894278</td><td>0.0483141491</td><td>0.403465033</td><td>-0.3003488918</td><td>0.1751505771</td><td>-1.3383513964</td><td>0.273333967</td><td>0.7195206921</td><td>1.6954709623</td><td>-1.8220070373</td><td>0.1671676528</td><td>0.1700617561</td><td>0.0035965365</td><td>-0.0457202484</td><td>0.0084800445</td><td>0.3470659179</td><td>0.3475651167</td><td>-0.2984976151</td><td>0.0300083193</td><td>0.0479370565</td><td>66.44</td><td>0.0</td><td>0.0</td></tr><tr><td>15525.0</td><td>26916.0</td><td>-0.5618932475</td><td>1.9514347829</td><td>-0.3057757985</td><td>0.5634692309</td><td>1.2138858485</td><td>-2.057169198</td><td>1.5634520614</td><td>-0.2857910443</td><td>-0.8646585893</td><td>-2.2408546224</td><td>0.6811632711</td><td>-1.1444222427</td><td>-1.2529720665</td><td>-3.3198601044</td><td>0.397398774</td><td>1.1862390849</td><td>2.570602955</td><td>1.4216355674</td><td>-1.6372004818</td><td>-0.1425226813</td><td>-0.0975438915</td><td>-0.3160905749</td><td>-0.3309493851</td><td>0.3483059098</td><td>0.2586325638</td><td>-0.515197401</td><td>0.1190286479</td><td>0.2021055974</td><td>4.8</td><td>0.0</td><td>0.0</td></tr><tr><td>15526.0</td><td>26916.0</td><td>1.3393225315</td><td>-0.4237128067</td><td>0.3332655092</td><td>-0.7136928152</td><td>-0.9019485472</td><td>-0.9448449696</td><td>-0.3738208066</td><td>-0.2179119217</td><td>-1.3253645796</td><td>0.788885807</td><td>1.8011395836</td><td>0.6726171295</td><td>1.0540904092</td><td>-0.0580083289</td><td>-0.1697540276</td><td>1.0809452461</td><td>0.0583383015</td><td>-1.0815644049</td><td>0.7194506515</td><td>0.1689706925</td><td>0.2985129938</td><td>0.7759045743</td><td>-0.1272814277</td><td>0.5990662918</td><td>0.6021874767</td><td>-0.1514966899</td><td>-0.005772103</td><td>0.0072728573</td><td>26.85</td><td>0.0</td><td>0.0</td></tr><tr><td>15527.0</td><td>26918.0</td><td>-0.837742382</td><td>0.4005232608</td><td>1.1907848646</td><td>1.2440710536</td><td>0.4159323799</td><td>0.5816097399</td><td>0.7429837725</td><td>0.2274056543</td><td>-0.6532328477</td><td>-0.0308560761</td><td>0.8895682312</td><td>1.0768978611</td><td>0.5002352285</td><td>0.0888559238</td><td>-0.5113993212</td><td>-0.9418087325</td><td>0.2192694824</td><td>-0.1021301417</td><td>1.1061739894</td><td>0.5340082047</td><td>0.096809437</td><td>0.3490714139</td><td>0.1212458718</td><td>-0.2810071875</td><td>0.0070469933</td><td>-0.2265577801</td><td>0.377935202</td><td>0.2069396006</td><td>126.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15528.0</td><td>26919.0</td><td>1.0942639032</td><td>0.3575283533</td><td>1.06238595</td><td>2.7906670661</td><td>-0.5905326759</td><td>-0.3921660268</td><td>-0.0442058838</td><td>-2.844424E-4</td><td>-0.2256908008</td><td>0.5296192972</td><td>-0.6787862878</td><td>0.0151493666</td><td>-0.6721626732</td><td>0.0184883247</td><td>-0.5985573169</td><td>0.2070313949</td><td>-0.0896907991</td><td>-0.7197618961</td><td>-0.7083642039</td><td>-0.1778024036</td><td>-0.2631584537</td><td>-0.7172267381</td><td>0.1159337896</td><td>0.6664863503</td><td>0.3072891849</td><td>-0.2000820593</td><td>0.0025737506</td><td>0.0319862876</td><td>23.39</td><td>0.0</td><td>0.0</td></tr><tr><td>15529.0</td><td>26919.0</td><td>-0.7550625971</td><td>0.7961666486</td><td>1.6305425092</td><td>1.0526324363</td><td>0.3301996099</td><td>-0.2496408971</td><td>0.5566902117</td><td>-0.2482605527</td><td>-0.0689999019</td><td>-0.6357651272</td><td>0.4251934001</td><td>0.5517900405</td><td>0.9277737535</td><td>-2.1028844146</td><td>0.3434541133</td><td>-0.8869908477</td><td>1.7418035949</td><td>-0.434194666</td><td>0.6625889837</td><td>0.1054615577</td><td>-0.1336523061</td><td>0.1362282129</td><td>-0.1546835044</td><td>0.378072087</td><td>0.3252795696</td><td>0.5569908726</td><td>-0.3688115675</td><td>-0.2087463308</td><td>12.31</td><td>0.0</td><td>0.0</td></tr><tr><td>15530.0</td><td>26919.0</td><td>-0.6918403403</td><td>1.1733242262</td><td>1.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06614</td><td>-0.0326069532</td><td>0.0956550098</td><td>-0.1156977262</td><td>-0.1953990789</td><td>-0.6512188491</td><td>0.1507345308</td><td>0.512968488</td><td>0.135489969</td><td>0.0658707071</td><td>-0.0382061829</td><td>0.0107741125</td><td>8.99</td><td>0.0</td><td>0.0</td></tr><tr><td>15512.0</td><td>26902.0</td><td>-1.5184218774</td><td>1.5053440104</td><td>1.0622675848</td><td>-1.7974506499</td><td>-0.1025451823</td><td>-0.6296360901</td><td>0.7177008014</td><td>-0.2088284287</td><td>1.3983360814</td><td>1.7983358973</td><td>1.1455349686</td><td>0.0979306306</td><td>-0.7361438268</td><td>-0.6130403396</td><td>0.058439156</td><td>0.6792363729</td><td>-1.286752243</td><td>0.1609668881</td><td>-0.5167677241</td><td>0.9104823219</td><td>-0.3631210561</td><td>-0.340175206</td><td>-0.0798242582</td><td>-0.0039862592</td><td>-0.0071697878</td><td>0.6982630753</td><td>0.4693812844</td><td>-0.0309497228</td><td>2.22</td><td>0.0</td><td>0.0</td></tr><tr><td>15513.0</td><td>26904.0</td><td>-1.1464108105</td><td>1.9637108176</td><td>-3.6118062847</td><td>1.7238959704</td><td>0.0415065736</td><td>-1.2993319606</td><td>-0.0939004828</td><td>1.2073432297</td><td>-0.1827458244</td><td>-1.2919122057</td><td>-0.1584600221</td><td>-1.0768221001</td><td>-1.5004101718</td><td>-2.3849132599</td><td>1.5181662221</td><td>1.162139321</td><td>3.4902819374</td><td>1.5103179627</td><td>-0.6566445687</td><td>-0.105189827</td><td>-0.1250073189</td><td>-0.4115658356</td><td>0.0741355257</td><td>-0.7999730654</td><td>-0.3890728365</td><td>-0.324837731</td><td>0.2067964512</td><td>-0.2029332989</td><td>89.99</td><td>0.0</td><td>0.0</td></tr><tr><td>15514.0</td><td>26904.0</td><td>1.5271404117</td><td>-0.8801251503</td><td>0.4624684886</td><td>-1.3326511615</td><td>-1.4788329621</td><td>-1.0081192881</td><td>-0.8638479513</td><td>-0.2914468732</td><td>-1.9589066272</td><td>1.4204990359</td><td>-0.3491512975</td><td>-0.4796301368</td><td>1.2473680411</td><td>-0.457567795</td><td>0.4846221656</td><td>-0.4371148483</td><td>0.4659410803</td><td>-0.1346184403</td><td>-0.248638708</td><td>-0.2877178552</td><td>-0.2079733038</td><td>-0.1603112088</td><td>0.0294385263</td><td>0.3954821646</td><td>0.4142556295</td><td>-0.2089275762</td><td>0.0354996968</td><td>0.0220715802</td><td>10.0</td><td>0.0</td><td>0.0</td></tr></tbody></table></div>"]}}],"execution_count":34},{"cell_type":"code","source":["%sql\n-- TRUE POSITIVES \nSELECT    COUNT(*) FROM card_logs_data WHERE Class ==1 AND  Prediction ==1"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>count(1)</th></tr></thead><tbody><tr><td>3</td></tr></tbody></table></div>"]}}],"execution_count":35},{"cell_type":"code","source":["%sql\n-- TRUE NEGATIVES\nSELECT    COUNT(*) FROM card_logs_data WHERE Class ==0 AND  Prediction ==0"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>count(1)</th></tr></thead><tbody><tr><td>177</td></tr></tbody></table></div>"]}}],"execution_count":36},{"cell_type":"code","source":["%sql\n-- FALSE POSTIVES \nSELECT    COUNT(*) FROM card_logs_data WHERE Class ==0 AND  Prediction ==1"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>count(1)</th></tr></thead><tbody><tr><td>0</td></tr></tbody></table></div>"]}}],"execution_count":37},{"cell_type":"code","source":["%sql\n-- FALSE NEGATIVES 2\nSELECT    COUNT(*) FROM card_logs_data WHERE Class ==1 AND  Prediction ==0"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>count(1)</th></tr></thead><tbody><tr><td>0</td></tr></tbody></table></div>"]}}],"execution_count":38},{"cell_type":"code","source":["%sql\n-- TOTAL CLASS DISTRIBUTION\nSELECT    COUNT(*) FROM card_logs_data GROUP BY CLASS \n"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>count(1)</th></tr></thead><tbody><tr><td>177</td></tr><tr><td>3</td></tr></tbody></table></div>"]}}],"execution_count":39},{"cell_type":"code","source":["%sql\n\n-- Generate a list of transactions for the investigators ordered by importance\n\nSELECT     MESSAGEID, AMOUNT, PREDICTION FROM card_logs_data ORDER BY PREDICTION DESC, AMOUNT DESC"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>MESSAGEID</th><th>AMOUNT</th><th>PREDICTION</th></tr></thead><tbody><tr><td>15506.0</td><td>99.99</td><td>1.0</td></tr><tr><td>15566.0</td><td>99.99</td><td>1.0</td></tr><tr><td>15539.0</td><td>99.99</td><td>1.0</td></tr><tr><td>15555.0</td><td>2217.18</td><td>0.0</td></tr><tr><td>15490.0</td><td>1746.8</td><td>0.0</td></tr><tr><td>15501.0</td><td>1494.8</td><td>0.0</td></tr><tr><td>15502.0</td><td>1164.0</td><td>0.0</td></tr><tr><td>15577.0</td><td>976.0</td><td>0.0</td></tr><tr><td>15519.0</td><td>908.6</td><td>0.0</td></tr><tr><td>15581.0</td><td>871.62</td><td>0.0</td></tr><tr><td>15601.0</td><td>601.0</td><td>0.0</td></tr><tr><td>15658.0</td><td>597.05</td><td>0.0</td></tr><tr><td>15609.0</td><td>519.0</td><td>0.0</td></tr><tr><td>15553.0</td><td>361.31</td><td>0.0</td></tr><tr><td>15520.0</td><td>190.0</td><td>0.0</td></tr><tr><td>15597.0</td><td>158.0</td><td>0.0</td></tr><tr><td>15598.0</td><td>142.25</td><td>0.0</td></tr><tr><td>15549.0</td><td>133.9</td><td>0.0</td></tr><tr><td>15606.0</td><td>130.75</td><td>0.0</td></tr><tr><td>15559.0</td><td>129.0</td><td>0.0</td></tr><tr><td>15527.0</td><td>126.0</td><td>0.0</td></tr><tr><td>15622.0</td><td>126.0</td><td>0.0</td></tr><tr><td>15660.0</td><td>123.42</td><td>0.0</td></tr><tr><td>15573.0</td><td>122.0</td><td>0.0</td></tr><tr><td>15497.0</td><td>116.6</td><td>0.0</td></tr><tr><td>15600.0</td><td>116.6</td><td>0.0</td></tr><tr><td>15534.0</td><td>109.95</td><td>0.0</td></tr><tr><td>15641.0</td><td>107.46</td><td>0.0</td></tr><tr><td>15610.0</td><td>104.0</td><td>0.0</td></tr><tr><td>15540.0</td><td>95.65</td><td>0.0</td></tr><tr><td>15513.0</td><td>89.99</td><td>0.0</td></tr><tr><td>15648.0</td><td>88.0</td><td>0.0</td></tr><tr><td>15635.0</td><td>85.8</td><td>0.0</td></tr><tr><td>15545.0</td><td>79.0</td><td>0.0</td></tr><tr><td>15546.0</td><td>79.0</td><td>0.0</td></tr><tr><td>15649.0</td><td>79.0</td><td>0.0</td></tr><tr><td>15548.0</td><td>76.17</td><td>0.0</td></tr><tr><td>15668.0</td><td>76.1</td><td>0.0</td></tr><tr><td>15663.0</td><td>76.07</td><td>0.0</td></tr><tr><td>15599.0</td><td>71.07</td><td>0.0</td></tr><tr><td>15524.0</td><td>66.44</td><td>0.0</td></tr><tr><td>15578.0</td><td>66.43</td><td>0.0</td></tr><tr><td>15499.0</td><td>63.35</td><td>0.0</td></tr><tr><td>15596.0</td><td>62.85</td><td>0.0</td></tr><tr><td>15651.0</td><td>61.28</td><td>0.0</td></tr><tr><td>15604.0</td><td>60.73</td><td>0.0</td></tr><tr><td>15588.0</td><td>60.0</td><td>0.0</td></tr><tr><td>15618.0</td><td>60.0</td><td>0.0</td></tr><tr><td>15630.0</td><td>59.9</td><td>0.0</td></tr><tr><td>15659.0</td><td>59.46</td><td>0.0</td></tr><tr><td>15662.0</td><td>59.0</td><td>0.0</td></tr><tr><td>15667.0</td><td>57.8</td><td>0.0</td></tr><tr><td>15542.0</td><td>55.85</td><td>0.0</td></tr><tr><td>15585.0</td><td>55.06</td><td>0.0</td></tr><tr><td>15623.0</td><td>53.2</td><td>0.0</td></tr><tr><td>15516.0</td><td>52.0</td><td>0.0</td></tr><tr><td>15504.0</td><td>50.0</td><td>0.0</td></tr><tr><td>15507.0</td><td>49.99</td><td>0.0</td></tr><tr><td>15650.0</td><td>49.9</td><td>0.0</td></tr><tr><td>15495.0</td><td>49.45</td><td>0.0</td></tr><tr><td>15583.0</td><td>47.55</td><td>0.0</td></tr><tr><td>15551.0</td><td>44.7</td><td>0.0</td></tr><tr><td>15607.0</td><td>43.9</td><td>0.0</td></tr><tr><td>15633.0</td><td>43.74</td><td>0.0</td></tr><tr><td>15508.0</td><td>42.81</td><td>0.0</td></tr><tr><td>15670.0</td><td>40.0</td><td>0.0</td></tr><tr><td>15570.0</td><td>39.98</td><td>0.0</td></tr><tr><td>15620.0</td><td>39.94</td><td>0.0</td></tr><tr><td>15636.0</td><td>38.9</td><td>0.0</td></tr><tr><td>15518.0</td><td>38.16</td><td>0.0</td></tr><tr><td>15494.0</td><td>36.85</td><td>0.0</td></tr><tr><td>15646.0</td><td>36.26</td><td>0.0</td></tr><tr><td>15602.0</td><td>35.96</td><td>0.0</td></tr><tr><td>15625.0</td><td>31.84</td><td>0.0</td></tr><tr><td>15574.0</td><td>30.85</td><td>0.0</td></tr><tr><td>15639.0</td><td>30.0</td><td>0.0</td></tr><tr><td>15640.0</td><td>30.0</td><td>0.0</td></tr><tr><td>15550.0</td><td>30.0</td><td>0.0</td></tr><tr><td>15535.0</td><td>29.0</td><td>0.0</td></tr><tr><td>15526.0</td><td>26.85</td><td>0.0</td></tr><tr><td>15567.0</td><td>26.0</td><td>0.0</td></tr><tr><td>15537.0</td><td>25.82</td><td>0.0</td></tr><tr><td>15613.0</td><td>25.0</td><td>0.0</td></tr><tr><td>15528.0</td><td>23.39</td><td>0.0</td></tr><tr><td>15571.0</td><td>23.15</td><td>0.0</td></tr><tr><td>15575.0</td><td>22.78</td><td>0.0</td></tr><tr><td>15563.0</td><td>21.99</td><td>0.0</td></tr><tr><td>15661.0</td><td>21.99</td><td>0.0</td></tr><tr><td>15530.0</td><td>20.98</td><td>0.0</td></tr><tr><td>15509.0</td><td>19.78</td><td>0.0</td></tr><tr><td>15505.0</td><td>19.26</td><td>0.0</td></tr><tr><td>15557.0</td><td>18.96</td><td>0.0</td></tr><tr><td>15591.0</td><td>17.99</td><td>0.0</td></tr><tr><td>15617.0</td><td>17.99</td><td>0.0</td></tr><tr><td>15645.0</td><td>17.8</td><td>0.0</td></tr><tr><td>15589.0</td><td>17.4</td><td>0.0</td></tr><tr><td>15611.0</td><td>15.99</td><td>0.0</td></tr><tr><td>15493.0</td><td>15.18</td><td>0.0</td></tr><tr><td>15521.0</td><td>15.13</td><td>0.0</td></tr><tr><td>15619.0</td><td>15.0</td><td>0.0</td></tr><tr><td>15592.0</td><td>15.0</td><td>0.0</td></tr><tr><td>15503.0</td><td>14.32</td><td>0.0</td></tr><tr><td>15523.0</td><td>14.2</td><td>0.0</td></tr><tr><td>15638.0</td><td>13.98</td><td>0.0</td></tr><tr><td>15652.0</td><td>12.99</td><td>0.0</td></tr><tr><td>15529.0</td><td>12.31</td><td>0.0</td></tr><tr><td>15558.0</td><td>12.13</td><td>0.0</td></tr><tr><td>15608.0</td><td>11.5</td><td>0.0</td></tr><tr><td>15586.0</td><td>10.05</td><td>0.0</td></tr><tr><td>15642.0</td><td>10.0</td><td>0.0</td></tr><tr><td>15514.0</td><td>10.0</td><td>0.0</td></tr><tr><td>15629.0</td><td>10.0</td><td>0.0</td></tr><tr><td>15565.0</td><td>10.0</td><td>0.0</td></tr><tr><td>15590.0</td><td>9.99</td><td>0.0</td></tr><tr><td>15631.0</td><td>9.99</td><td>0.0</td></tr><tr><td>15538.0</td><td>9.1</td><td>0.0</td></tr><tr><td>15627.0</td><td>9.03</td><td>0.0</td></tr><tr><td>15572.0</td><td>8.99</td><td>0.0</td></tr><tr><td>15511.0</td><td>8.99</td><td>0.0</td></tr><tr><td>15637.0</td><td>8.95</td><td>0.0</td></tr><tr><td>15492.0</td><td>8.4</td><td>0.0</td></tr><tr><td>15664.0</td><td>7.99</td><td>0.0</td></tr><tr><td>15544.0</td><td>7.57</td><td>0.0</td></tr><tr><td>15531.0</td><td>4.99</td><td>0.0</td></tr><tr><td>15568.0</td><td>4.99</td><td>0.0</td></tr><tr><td>15525.0</td><td>4.8</td><td>0.0</td></tr><tr><td>15541.0</td><td>4.56</td><td>0.0</td></tr><tr><td>15532.0</td><td>4.49</td><td>0.0</td></tr><tr><td>15594.0</td><td>4.49</td><td>0.0</td></tr><tr><td>15644.0</td><td>4.49</td><td>0.0</td></tr><tr><td>15654.0</td><td>4.49</td><td>0.0</td></tr><tr><td>15554.0</td><td>3.58</td><td>0.0</td></tr><tr><td>15560.0</td><td>3.57</td><td>0.0</td></tr><tr><td>15612.0</td><td>3.28</td><td>0.0</td></tr><tr><td>15536.0</td><td>2.69</td><td>0.0</td></tr><tr><td>15552.0</td><td>2.67</td><td>0.0</td></tr><tr><td>15593.0</td><td>2.42</td><td>0.0</td></tr><tr><td>15666.0</td><td>2.28</td><td>0.0</td></tr><tr><td>15580.0</td><td>2.23</td><td>0.0</td></tr><tr><td>15512.0</td><td>2.22</td><td>0.0</td></tr><tr><td>15564.0</td><td>2.04</td><td>0.0</td></tr><tr><td>15584.0</td><td>2.02</td><td>0.0</td></tr><tr><td>15614.0</td><td>1.98</td><td>0.0</td></tr><tr><td>15621.0</td><td>1.98</td><td>0.0</td></tr><tr><td>15522.0</td><td>1.98</td><td>0.0</td></tr><tr><td>15653.0</td><td>1.98</td><td>0.0</td></tr><tr><td>15628.0</td><td>1.98</td><td>0.0</td></tr><tr><td>15582.0</td><td>1.98</td><td>0.0</td></tr><tr><td>15500.0</td><td>1.98</td><td>0.0</td></tr><tr><td>15605.0</td><td>1.98</td><td>0.0</td></tr><tr><td>15517.0</td><td>1.79</td><td>0.0</td></tr><tr><td>15533.0</td><td>1.79</td><td>0.0</td></tr><tr><td>15491.0</td><td>1.79</td><td>0.0</td></tr><tr><td>15643.0</td><td>1.79</td><td>0.0</td></tr><tr><td>15647.0</td><td>1.79</td><td>0.0</td></tr><tr><td>15603.0</td><td>1.78</td><td>0.0</td></tr><tr><td>15615.0</td><td>1.29</td><td>0.0</td></tr><tr><td>15616.0</td><td>1.29</td><td>0.0</td></tr><tr><td>15576.0</td><td>1.29</td><td>0.0</td></tr><tr><td>15569.0</td><td>1.29</td><td>0.0</td></tr><tr><td>15579.0</td><td>1.0</td><td>0.0</td></tr><tr><td>15624.0</td><td>1.0</td><td>0.0</td></tr><tr><td>15634.0</td><td>1.0</td><td>0.0</td></tr><tr><td>15547.0</td><td>1.0</td><td>0.0</td></tr><tr><td>15556.0</td><td>1.0</td><td>0.0</td></tr><tr><td>15657.0</td><td>0.99</td><td>0.0</td></tr><tr><td>15562.0</td><td>0.89</td><td>0.0</td></tr><tr><td>15587.0</td><td>0.89</td><td>0.0</td></tr><tr><td>15655.0</td><td>0.89</td><td>0.0</td></tr><tr><td>15510.0</td><td>0.89</td><td>0.0</td></tr><tr><td>15665.0</td><td>0.89</td><td>0.0</td></tr><tr><td>15669.0</td><td>0.89</td><td>0.0</td></tr><tr><td>15595.0</td><td>0.77</td><td>0.0</td></tr><tr><td>15626.0</td><td>0.77</td><td>0.0</td></tr><tr><td>15498.0</td><td>0.76</td><td>0.0</td></tr><tr><td>15496.0</td><td>0.75</td><td>0.0</td></tr><tr><td>15543.0</td><td>0.71</td><td>0.0</td></tr><tr><td>15632.0</td><td>0.32</td><td>0.0</td></tr><tr><td>15561.0</td><td>0.0</td><td>0.0</td></tr><tr><td>15656.0</td><td>0.0</td><td>0.0</td></tr></tbody></table></div>"]}}],"execution_count":40},{"cell_type":"code","source":[" # path_test = '/FileStore/tables/card-logs-data-archievepart-00000-e63cacf7-b5e7-4a37-8654-e76f4ec8fc29-c000.csv' \n# test_data = transform_for_ccfd(path=path_test)\n# predictions = model.transform(test_data)\n\n\n# predictions.groupBy(\"prediction\").count().orderBy('prediction').show()\n# predictions.groupBy(\"label\").count().orderBy('label').show()"],"metadata":{},"outputs":[],"execution_count":41},{"cell_type":"code","source":["# data.columns = [\"Time\",\"V1\",\"V2\",\"V3\",\"V4\",\"V5\",\"V6\",\"V7\",\"V8\",\"V9\",\"V10\",\"V11\",\"V12\",\"V13\",\"V14\",\"V15\",\"V16\",\"V17\",\"V18\",\"V19\",\"V20\",\"V21\",\"V22\",\"V23\",\"V24\",\"V25\",\"V26\",\"V27\",\"V28\",\"Amount\",\"Class\"]"],"metadata":{},"outputs":[],"execution_count":42},{"cell_type":"code","source":["# selectedFeatures = ['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V9', 'V10', 'V11', 'V12', 'V14', 'V16', 'V17', 'V18', 'V19']\n# selectedFeatures.append('Class')\n# equal_data=equal_data.loc[:,selectedFeatures]\n# equal_data.head()"],"metadata":{},"outputs":[],"execution_count":43},{"cell_type":"code","source":["# dfff = spark.createDataFrame(equal_data)"],"metadata":{},"outputs":[],"execution_count":44},{"cell_type":"code","source":["# training_df = dfff.rdd.map(lambda x: (DenseVector(x[:-1]),x['Class']))\n\n# training_df = spark.createDataFrame(training_df,[\"features\",\"label\"])"],"metadata":{},"outputs":[],"execution_count":45},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":46},{"cell_type":"code","source":["# training_df.groupBy(\"label\").count().show()"],"metadata":{},"outputs":[],"execution_count":47},{"cell_type":"code","source":["# path_test = '/FileStore/tables/card-logs-data-archievepart-00000-e63cacf7-b5e7-4a37-8654-e76f4ec8fc29-c000.csv' \n# test_data = transform_for_ccfd(path=path_test)\n# predictions = model.transform(test_data)\n\n\n# predictions.groupBy(\"prediction\").count().orderBy('prediction').show()\n# predictions.groupBy(\"label\").count().orderBy('label').show()"],"metadata":{},"outputs":[],"execution_count":48}],"metadata":{"name":"KAFKA-ML-PipeLine-V1","notebookId":239732575911112},"nbformat":4,"nbformat_minor":0}