diff --git a/helloworld/notebooks/ListOfFiles.ipynb b/helloworld/notebooks/ListOfFiles.ipynb new file mode 100644 index 0000000000..2f6e608ced --- /dev/null +++ b/helloworld/notebooks/ListOfFiles.ipynb @@ -0,0 +1,128 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "import java.io.File\n" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import java.io.File" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "getListOfFiles: (dir: String)List[java.io.File]\n" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def getListOfFiles(dir: String):List[File] = {\n", + " val d = new File(dir)\n", + " if (d.exists && d.isDirectory) {\n", + " d.listFiles.filter(_.isFile).toList\n", + " } else {\n", + " List[File]()\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[/home/beakerx/helloworld/src/main/resources/TitanicDataset/TitanicPassengersTrainData.csv]]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val files = getListOfFiles(\"/home/beakerx/helloworld/src/main/resources/TitanicDataset\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[/home/beakerx/helloworld/src/main/resources/IrisDataset/iris.names, /home/beakerx/helloworld/src/main/resources/IrisDataset/Index.txt, /home/beakerx/helloworld/src/main/resources/IrisDataset/iris.data, /home/beakerx/helloworld/src/main/resources/IrisDataset/bezdekIris.data]]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val filesIris = getListOfFiles(\"/home/beakerx/helloworld/src/main/resources/IrisDataset\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Scala", + "language": "scala", + "name": "scala" + }, + "language_info": { + "codemirror_mode": "text/x-scala", + "file_extension": ".scala", + "mimetype": "", + "name": "Scala", + "nbconverter_exporter": "", + "version": "2.11.12" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": false, + "sideBar": false, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": false, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/helloworld/notebooks/OpIris.ipynb b/helloworld/notebooks/OpIris.ipynb new file mode 100644 index 0000000000..53cb4a0df1 --- /dev/null +++ b/helloworld/notebooks/OpIris.ipynb @@ -0,0 +1,1301 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Op Iris Sample" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "method": "display_data" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9eb764df-f864-4dde-a2e0-4e4044b78872", + "version_major": 2, + "version_minor": 0 + }, + "method": "display_data" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%classpath add mvn com.salesforce.transmogrifai transmogrifai-core_2.11 0.5.1" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "method": "display_data" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6a3b6e84-5880-4ef7-909f-9daef8f16ca4", + "version_major": 2, + "version_minor": 0 + }, + "method": "display_data" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%classpath add mvn org.apache.spark spark-mllib_2.11 2.3.0" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "defined class Iris\n" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "case class Iris\n", + "(\n", + " sepalLength: Double,\n", + " sepalWidth: Double,\n", + " petalLength: Double,\n", + " petalWidth: Double,\n", + " irisClass: String\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Feature(name = irisClass, uid = Text_000000000005, isResponse = true, originStage = FeatureGeneratorStage_000000000005, parents = [], distributions = [])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import com.salesforce.op.features.FeatureBuilder\n", + "import com.salesforce.op.features.types._\n", + "\n", + "val sepalLength = FeatureBuilder.Real[Iris].extract(_.sepalLength.toReal).asPredictor\n", + "val sepalWidth = FeatureBuilder.Real[Iris].extract(_.sepalWidth.toReal).asPredictor\n", + "val petalLength = FeatureBuilder.Real[Iris].extract(_.petalLength.toReal).asPredictor\n", + "val petalWidth = FeatureBuilder.Real[Iris].extract(_.petalWidth.toReal).asPredictor\n", + "val irisClass = FeatureBuilder.Text[Iris].extract(_.irisClass.toText).asResponse" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "import org.apache.spark.SparkConf\n", + "import org.apache.spark.sql.SparkSession\n", + "import org.apache.spark.SparkContext\n", + "import org.apache.spark.sql.functions.udf\n" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import org.apache.spark.SparkConf\n", + "import org.apache.spark.sql.SparkSession\n", + "import org.apache.spark.SparkContext\n", + "import org.apache.spark.sql.functions.udf" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "org.apache.spark.sql.SparkSession@43282772" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val conf = new SparkConf().setMaster(\"local[*]\").setAppName(\"TitanicPrediction\")\n", + "implicit val spark = SparkSession.builder.config(conf).getOrCreate()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "import com.salesforce.op._\n", + "import com.salesforce.op.evaluators.Evaluators\n", + "import com.salesforce.op.readers.DataReaders\n", + "import com.salesforce.op.stages.impl.classification.MultiClassificationModelSelector\n", + "import com.salesforce.op.stages.impl.tuning.DataCutter\n", + "import org.apache.spark.sql.Encoders\n" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import com.salesforce.op._\n", + "import com.salesforce.op.evaluators.Evaluators\n", + "import com.salesforce.op.readers.DataReaders\n", + "import com.salesforce.op.stages.impl.classification.MultiClassificationModelSelector\n", + "import com.salesforce.op.stages.impl.tuning.DataCutter\n", + "import org.apache.spark.sql.Encoders" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "class[sepalLength[0]: double, sepalWidth[0]: double, petalLength[0]: double, petalWidth[0]: double, irisClass[0]: string]" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "implicit val irisEncoder = Encoders.product[Iris]" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "com.salesforce.op.readers.CSVProductReader@7185e0f1" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val irisReader = DataReaders.Simple.csvCase[Iris]()" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Feature(name = petalLength-petalWidth-sepalLength-sepalWidth_2-stagesApplied_OPVector_000000000040, uid = OPVector_000000000040, isResponse = false, originStage = VectorsCombiner_000000000040, parents = [OPVector_00000000003f], distributions = [])" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val labels = irisClass.indexed()\n", + "val features = Seq(sepalLength, sepalWidth, petalLength, petalWidth).transmogrify()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DataCutter_000000000041" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val randomSeed = 42L\n", + "val cutter = DataCutter(reserveTestFraction = 0.2, seed = randomSeed)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Feature(name = irisClass-petalLength-petalWidth-sepalLength-sepalWidth_4-stagesApplied_Prediction_000000000049, uid = Prediction_000000000049, isResponse = true, originStage = ModelSelector_000000000049, parents = [RealNN_00000000003e,OPVector_000000000040], distributions = [])" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val prediction = MultiClassificationModelSelector\n", + " .withCrossValidation(splitter = Option(cutter), seed = randomSeed)\n", + " .setInput(labels, features).getOutput()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "OpMultiClassificationEvaluator_00000000004a" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val evaluator = Evaluators.MultiClassification.f1().setLabelCol(labels).setPredictionCol(prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "org.apache.spark.sql.SparkSession$implicits$@616837d5" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "implicit val spark = SparkSession.builder.config(conf).getOrCreate()\n", + "import spark.implicits._ // Needed for Encoders for the Passenger case class\n", + "import com.salesforce.op.readers.DataReaders\n", + "\n", + "val trainFilePath = \"/home/beakerx/helloworld/src/main/resources/IrisDataset/iris.data\"\n", + " // Define a way to read data into our Passenger class from our CSV file\n", + "val trainDataReader = DataReaders.Simple.csvCase[Iris](\n", + " path = Option(trainFilePath)\n", + " //key = _.id.toString\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "com.salesforce.op.OpWorkflow@52f8d3a4" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "/*val workflow =\n", + " new OpWorkflow()\n", + " .setResultFeatures(survived, prediction)\n", + " .setReader(trainDataReader)*/\n", + "\n", + "val workflow = new OpWorkflow().setResultFeatures(prediction, labels).setReader(trainDataReader)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary: {\n", + " \"ModelSelector_000000000049\" : {\n", + " \"ValidationResults\" : [ {\n", + " \"ModelName\" : \"OpRandomForestClassifier_000000000045_0\",\n", + " \"ModelParameters\" : {\n", + " \"outputFeatureName\" : \"irisClass-petalLength-petalWidth-sepalLength-sepalWidth_4-stagesApplied_Prediction_000000000049\",\n", + " \"seed\" : 329511018,\n", + " \"inputSchema\" : \"StructType()\",\n", + " \"sparkMlStage\" : \"org.apache.spark.ml.classification.RandomForestClassifier\",\n", + " \"subsamplingRate\" : 1.0,\n", + " \"impurity\" : \"gini\",\n", + " \"featuresCol\" : \"features\",\n", + " \"maxDepth\" : 3,\n", + " \"outputMetadata\" : { },\n", + " \"minInstancesPerNode\" : 10,\n", + " \"featureSubsetStrategy\" : \"auto\",\n", + " \"checkpointInterval\" : 10,\n", + " \"minInfoGain\" : 0.001,\n", + " \"cacheNodeIds\" : false,\n", + " \"labelCol\" : \"label\",\n", + " \"inputFeatures\" : [ \"{\\n \\\"name\\\" : \\\"irisClass_1-stagesApplied_RealNN_00000000003e\\\",\\n \\\"isResponse\\\" : true,\\n \\\"isRaw\\\" : true,\\n \\\"uid\\\" : \\\"RealNN_00000000005b\\\",\\n \\\"typeName\\\" : \\\"com.salesforce.op.features.types.RealNN\\\",\\n \\\"originFeatures\\\" : [ \\\"irisClass_1-stagesApplied_RealNN_00000000003e\\\" ],\\n \\\"stages\\\" : [ ]\\n}\", \"{\\n \\\"name\\\" : \\\"petalLength-petalWidth-sepalLength-sepalWidth_2-stagesApplied_OPVector_000000000040\\\",\\n \\\"isResponse\\\" : false,\\n \\\"isRaw\\\" : true,\\n \\\"uid\\\" : \\\"OPVector_00000000005c\\\",\\n \\\"typeName\\\" : \\\"com.salesforce.op.features.types.OPVector\\\",\\n \\\"originFeatures\\\" : [ \\\"petalLength-petalWidth-sepalLength-sepalWidth_2-stagesApplied_OPVector_000000000040\\\" ],\\n \\\"stages\\\" : [ ]\\n}\" ],\n", + " \"predictionCol\" : \"prediction\",\n", + " \"maxMemoryInMB\" : 256,\n", + " \"maxBins\" : 32,\n", + " \"rawPredictionCol\" : \"rawPrediction\",\n", + " \"probabilityCol\" : \"probability\",\n", + " \"numTrees\" : 50\n", + " },\n", + " \"ModelUID\" : \"OpRandomForestClassifier_000000000045\",\n", + " \"ModelType\" : \"OpRandomForestClassifier\",\n", + " \"MetricValues\" : [ \"com.salesforce.op.evaluators.SingleMetric\", \"{\\\"name\\\":\\\"error\\\",\\\"value\\\":0.034882037921551604}\" ]\n", + " }, {\n", + " \"ModelName\" : \"OpRandomForestClassifier_000000000045_1\",\n", + " \"ModelParameters\" : {\n", + " \"outputFeatureName\" : \"irisClass-petalLength-petalWidth-sepalLength-sepalWidth_4-stagesApplied_Prediction_000000000049\",\n", + " \"seed\" : 329511018,\n", + " \"inputSchema\" : \"StructType()\",\n", + " \"sparkMlStage\" : \"org.apache.spark.ml.classification.RandomForestClassifier\",\n", + " \"subsamplingRate\" : 1.0,\n", + " \"impurity\" : \"gini\",\n", + " \"featuresCol\" : \"features\",\n", + " \"maxDepth\" : 3,\n", + " \"outputMetadata\" : { },\n", + " \"minInstancesPerNode\" : 10,\n", + " \"featureSubsetStrategy\" : \"auto\",\n", + " \"checkpointInterval\" : 10,\n", + " \"minInfoGain\" : 0.01,\n", + " \"cacheNodeIds\" : false,\n", + " \"labelCol\" : \"label\",\n", + " \"inputFeatures\" : [ \"{\\n \\\"name\\\" : \\\"irisClass_1-stagesApplied_RealNN_00000000003e\\\",\\n \\\"isResponse\\\" : true,\\n \\\"isRaw\\\" : true,\\n \\\"uid\\\" : \\\"RealNN_00000000005b\\\",\\n \\\"typeName\\\" : \\\"com.salesforce.op.features.types.RealNN\\\",\\n \\\"originFeatures\\\" : [ \\\"irisClass_1-stagesApplied_RealNN_00000000003e\\\" ],\\n \\\"stages\\\" : [ ]\\n}\", \"{\\n \\\"name\\\" : \\\"petalLength-petalWidth-sepalLength-sepalWidth_2-stagesApplied_OPVector_000000000040\\\",\\n \\\"isResponse\\\" : false,\\n \\\"isRaw\\\" : true,\\n \\\"uid\\\" : \\\"OPVector_00000000005c\\\",\\n \\\"typeName\\\" : \\\"com.salesforce.op.features.types.OPVector\\\",\\n \\\"originFeatures\\\" : [ \\\"petalLength-petalWidth-sepalLength-sepalWidth_2-stagesApplied_OPVector_000000000040\\\" ],\\n \\\"stages\\\" : [ ]\\n}\" ],\n", + " \"predictionCol\" : \"prediction\",\n", + " \"maxMemoryInMB\" : 256,\n", + " \"maxBins\" : 32,\n", + " \"rawPredictionCol\" : \"rawPrediction\",\n", + " \"probabilityCol\" : \"probability\",\n", + " \"numTrees\" : 50\n", + " },\n", + " \"ModelUID\" : \"OpRandomForestClassifier_000000000045\",\n", + " \"ModelType\" : \"OpRandomForestClassifier\",\n", + " \"MetricValues\" : [ \"com.salesforce.op.evaluators.SingleMetric\", \"{\\\"name\\\":\\\"error\\\",\\\"value\\\":0.034882037921551604}\" ]\n", + " }, {\n", + " \"ModelName\" : \"OpRandomForestClassifier_000000000045_2\",\n", + " \"ModelParameters\" : {\n", + " \"outputFeatureName\" : \"irisClass-petalLength-petalWidth-sepalLength-sepalWidth_4-stagesApplied_Prediction_000000000049\",\n", + " \"seed\" : 329511018,\n", + " \"inputSchema\" : \"StructType()\",\n", + " \"sparkMlStage\" : \"org.apache.spark.ml.classification.RandomForestClassifier\",\n", + " \"subsamplingRate\" : 1.0,\n", + " \"impurity\" : \"gini\",\n", + " \"featuresCol\" : \"features\",\n", + " \"maxDepth\" : 3,\n", + " \"outputMetadata\" : { },\n", + " \"minInstancesPerNode\" : 10,\n", + " \"featureSubsetStrategy\" : \"auto\",\n", + " \"checkpointInterval\" : 10,\n", + " \"minInfoGain\" : 0.1,\n", + " \"cacheNodeIds\" : false,\n", + " \"labelCol\" : \"label\",\n", + " \"inputFeatures\" : [ \"{\\n \\\"name\\\" : \\\"irisClass_1-stagesApplied_RealNN_00000000003e\\\",\\n \\\"isResponse\\\" : true,\\n \\\"isRaw\\\" : true,\\n \\\"uid\\\" : \\\"RealNN_00000000005b\\\",\\n \\\"typeName\\\" : \\\"com.salesforce.op.features.types.RealNN\\\",\\n \\\"originFeatures\\\" : [ \\\"irisClass_1-stagesApplied_RealNN_00000000003e\\\" ],\\n \\\"stages\\\" : [ ]\\n}\", \"{\\n \\\"name\\\" : \\\"petalLength-petalWidth-sepalLength-sepalWidth_2-stagesApplied_OPVector_000000000040\\\",\\n \\\"isResponse\\\" : false,\\n \\\"isRaw\\\" : true,\\n \\\"uid\\\" : \\\"OPVector_00000000005c\\\",\\n \\\"typeName\\\" : \\\"com.salesforce.op.features.types.OPVector\\\",\\n \\\"originFeatures\\\" : [ \\\"petalLength-petalWidth-sepalLength-sepalWidth_2-stagesApplied_OPVector_000000000040\\\" ],\\n \\\"stages\\\" : [ ]\\n}\" ],\n", + " \"predictionCol\" : \"prediction\",\n", + " \"maxMemoryInMB\" : 256,\n", + " \"maxBins\" : 32,\n", + " \"rawPredictionCol\" : \"rawPrediction\",\n", + " \"probabilityCol\" : \"probability\",\n", + " \"numTrees\" : 50\n", + " },\n", + " \"ModelUID\" : \"OpRandomForestClassifier_000000000045\",\n", + " \"ModelType\" : \"OpRandomForestClassifier\",\n", + " \"MetricValues\" : [ \"com.salesforce.op.evaluators.SingleMetric\", \"{\\\"name\\\":\\\"error\\\",\\\"value\\\":0.034882037921551604}\" ]\n", + " }, {\n", + " \"ModelName\" : \"OpRandomForestClassifier_000000000045_3\",\n", + " \"ModelParameters\" : {\n", + " \"outputFeatureName\" : \"irisClass-petalLength-petalWidth-sepalLength-sepalWidth_4-stagesApplied_Prediction_000000000049\",\n", + " \"seed\" : 329511018,\n", + " \"inputSchema\" : \"StructType()\",\n", + " \"sparkMlStage\" : \"org.apache.spark.ml.classification.RandomForestClassifier\",\n", + " \"subsamplingRate\" : 1.0,\n", + " \"impurity\" : \"gini\",\n", + " \"featuresCol\" : \"features\",\n", + " \"maxDepth\" : 6,\n", + " \"outputMetadata\" : { },\n", + " \"minInstancesPerNode\" : 10,\n", + " \"featureSubsetStrategy\" : \"auto\",\n", + " \"checkpointInterval\" : 10,\n", + " \"minInfoGain\" : 0.001,\n", + " \"cacheNodeIds\" : false,\n", + " \"labelCol\" : \"label\",\n", + " \"inputFeatures\" : [ \"{\\n \\\"name\\\" : \\\"irisClass_1-stagesApplied_RealNN_00000000003e\\\",\\n \\\"isResponse\\\" : true,\\n \\\"isRaw\\\" : true,\\n \\\"uid\\\" : \\\"RealNN_00000000005b\\\",\\n \\\"typeName\\\" : \\\"com.salesforce.op.features.types.RealNN\\\",\\n \\\"originFeatures\\\" : [ \\\"irisClass_1-stagesApplied_RealNN_00000000003e\\\" ],\\n \\\"stages\\\" : [ ]\\n}\", \"{\\n \\\"name\\\" : \\\"petalLength-petalWidth-sepalLength-sepalWidth_2-stagesApplied_OPVector_000000000040\\\",\\n \\\"isResponse\\\" : false,\\n \\\"isRaw\\\" : true,\\n \\\"uid\\\" : \\\"OPVector_00000000005c\\\",\\n \\\"typeName\\\" : \\\"com.salesforce.op.features.types.OPVector\\\",\\n \\\"originFeatures\\\" : [ \\\"petalLength-petalWidth-sepalLength-sepalWidth_2-stagesApplied_OPVector_000000000040\\\" ],\\n \\\"stages\\\" : [ ]\\n}\" ],\n", + " \"predictionCol\" : \"prediction\",\n", + " \"maxMemoryInMB\" : 256,\n", + " \"maxBins\" : 32,\n", + " \"rawPredictionCol\" : \"rawPrediction\",\n", + " \"probabilityCol\" : \"probability\",\n", + " \"numTrees\" : 50\n", + " },\n", + " \"ModelUID\" : \"OpRandomForestClassifier_000000000045\",\n", + " \"ModelType\" : \"OpRandomForestClassifier\",\n", + " \"MetricValues\" : [ \"com.salesforce.op.evaluators.SingleMetric\", \"{\\\"name\\\":\\\"error\\\",\\\"value\\\":0.034882037921551604}\" ]\n", + " }, {\n", + " \"ModelName\" : \"OpRandomForestClassifier_000000000045_4\",\n", + " \"ModelParameters\" : {\n", + " \"outputFeatureName\" : \"irisClass-petalLength-petalWidth-sepalLength-sepalWidth_4-stagesApplied_Prediction_000000000049\",\n", + " \"seed\" : 329511018,\n", + " \"inputSchema\" : \"StructType()\",\n", + " \"sparkMlStage\" : \"org.apache.spark.ml.classification.RandomForestClassifier\",\n", + " \"subsamplingRate\" : 1.0,\n", + " \"impurity\" : \"gini\",\n", + " \"featuresCol\" : \"features\",\n", + " \"maxDepth\" : 6,\n", + " \"outputMetadata\" : { },\n", + " \"minInstancesPerNode\" : 10,\n", + " \"featureSubsetStrategy\" : \"auto\",\n", + " \"checkpointInterval\" : 10,\n", + " \"minInfoGain\" : 0.01,\n", + " \"cacheNodeIds\" : false,\n", + " \"labelCol\" : \"label\",\n", + " \"inputFeatures\" : [ \"{\\n \\\"name\\\" : \\\"irisClass_1-stagesApplied_RealNN_00000000003e\\\",\\n \\\"isResponse\\\" : 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\"MetricValues\" : [ \"com.salesforce.op.evaluators.SingleMetric\", \"{\\\"name\\\":\\\"error\\\",\\\"value\\\":0.051778158071739355}\" ]\n", + " }, {\n", + " \"ModelName\" : \"OpLogisticRegression_000000000044_5\",\n", + " \"ModelParameters\" : {\n", + " \"elasticNetParam\" : 0.5,\n", + " \"outputFeatureName\" : \"irisClass-petalLength-petalWidth-sepalLength-sepalWidth_4-stagesApplied_Prediction_000000000049\",\n", + " \"inputSchema\" : \"StructType()\",\n", + " \"sparkMlStage\" : \"org.apache.spark.ml.classification.LogisticRegression\",\n", + " \"featuresCol\" : \"features\",\n", + " \"outputMetadata\" : { },\n", + " \"fitIntercept\" : true,\n", + " \"labelCol\" : \"label\",\n", + " \"inputFeatures\" : [ \"{\\n \\\"name\\\" : \\\"irisClass_1-stagesApplied_RealNN_00000000003e\\\",\\n \\\"isResponse\\\" : true,\\n \\\"isRaw\\\" : true,\\n \\\"uid\\\" : \\\"RealNN_00000000004e\\\",\\n \\\"typeName\\\" : \\\"com.salesforce.op.features.types.RealNN\\\",\\n \\\"originFeatures\\\" : [ \\\"irisClass_1-stagesApplied_RealNN_00000000003e\\\" ],\\n \\\"stages\\\" : [ ]\\n}\", \"{\\n \\\"name\\\" : \\\"petalLength-petalWidth-sepalLength-sepalWidth_2-stagesApplied_OPVector_000000000040\\\",\\n \\\"isResponse\\\" : false,\\n \\\"isRaw\\\" : true,\\n \\\"uid\\\" : \\\"OPVector_00000000004f\\\",\\n \\\"typeName\\\" : \\\"com.salesforce.op.features.types.OPVector\\\",\\n \\\"originFeatures\\\" : [ \\\"petalLength-petalWidth-sepalLength-sepalWidth_2-stagesApplied_OPVector_000000000040\\\" ],\\n \\\"stages\\\" : [ ]\\n}\" ],\n", + " \"predictionCol\" : \"prediction\",\n", + " \"standardization\" : true,\n", + " \"rawPredictionCol\" : \"rawPrediction\",\n", + " \"probabilityCol\" : \"probability\",\n", + " \"regParam\" : 0.01,\n", + " \"tol\" : 1.0E-6,\n", + " \"family\" : \"auto\",\n", + " \"maxIter\" : 50,\n", + " \"aggregationDepth\" : 2\n", + " },\n", + " \"ModelUID\" : \"OpLogisticRegression_000000000044\",\n", + " \"ModelType\" : \"OpLogisticRegression\",\n", + " \"MetricValues\" : [ \"com.salesforce.op.evaluators.SingleMetric\", \"{\\\"name\\\":\\\"error\\\",\\\"value\\\":0.051778158071739355}\" ]\n", + " }, {\n", + " \"ModelName\" : \"OpLogisticRegression_000000000044_6\",\n", + " \"ModelParameters\" : {\n", + " \"elasticNetParam\" : 0.5,\n", + " \"outputFeatureName\" : \"irisClass-petalLength-petalWidth-sepalLength-sepalWidth_4-stagesApplied_Prediction_000000000049\",\n", + " \"inputSchema\" : \"StructType()\",\n", + " \"sparkMlStage\" : \"org.apache.spark.ml.classification.LogisticRegression\",\n", + " \"featuresCol\" : \"features\",\n", + " \"outputMetadata\" : { },\n", + " \"fitIntercept\" : true,\n", + " \"labelCol\" : \"label\",\n", + " \"inputFeatures\" : [ \"{\\n \\\"name\\\" : \\\"irisClass_1-stagesApplied_RealNN_00000000003e\\\",\\n \\\"isResponse\\\" : true,\\n \\\"isRaw\\\" : true,\\n \\\"uid\\\" : \\\"RealNN_00000000004e\\\",\\n \\\"typeName\\\" : \\\"com.salesforce.op.features.types.RealNN\\\",\\n \\\"originFeatures\\\" : [ \\\"irisClass_1-stagesApplied_RealNN_00000000003e\\\" ],\\n \\\"stages\\\" : [ ]\\n}\", \"{\\n \\\"name\\\" : \\\"petalLength-petalWidth-sepalLength-sepalWidth_2-stagesApplied_OPVector_000000000040\\\",\\n \\\"isResponse\\\" : false,\\n \\\"isRaw\\\" : true,\\n \\\"uid\\\" : \\\"OPVector_00000000004f\\\",\\n \\\"typeName\\\" : \\\"com.salesforce.op.features.types.OPVector\\\",\\n \\\"originFeatures\\\" : [ \\\"petalLength-petalWidth-sepalLength-sepalWidth_2-stagesApplied_OPVector_000000000040\\\" ],\\n \\\"stages\\\" : [ ]\\n}\" ],\n", + " \"predictionCol\" : \"prediction\",\n", + " \"standardization\" : true,\n", + " \"rawPredictionCol\" : \"rawPrediction\",\n", + " \"probabilityCol\" : \"probability\",\n", + " \"regParam\" : 0.1,\n", + " \"tol\" : 1.0E-6,\n", + " \"family\" : \"auto\",\n", + " \"maxIter\" : 50,\n", + " \"aggregationDepth\" : 2\n", + " },\n", + " \"ModelUID\" : \"OpLogisticRegression_000000000044\",\n", + " \"ModelType\" : \"OpLogisticRegression\",\n", + " \"MetricValues\" : [ \"com.salesforce.op.evaluators.SingleMetric\", \"{\\\"name\\\":\\\"error\\\",\\\"value\\\":0.04139528151686204}\" ]\n", + " }, {\n", + " \"ModelName\" : \"OpLogisticRegression_000000000044_7\",\n", + " \"ModelParameters\" : {\n", + " \"elasticNetParam\" : 0.5,\n", + " \"outputFeatureName\" : \"irisClass-petalLength-petalWidth-sepalLength-sepalWidth_4-stagesApplied_Prediction_000000000049\",\n", + " \"inputSchema\" : \"StructType()\",\n", + " \"sparkMlStage\" : \"org.apache.spark.ml.classification.LogisticRegression\",\n", + " \"featuresCol\" : \"features\",\n", + " \"outputMetadata\" : { },\n", + " \"fitIntercept\" : true,\n", + " \"labelCol\" : \"label\",\n", + " \"inputFeatures\" : [ \"{\\n \\\"name\\\" : \\\"irisClass_1-stagesApplied_RealNN_00000000003e\\\",\\n \\\"isResponse\\\" : true,\\n \\\"isRaw\\\" : true,\\n \\\"uid\\\" : \\\"RealNN_00000000004e\\\",\\n \\\"typeName\\\" : \\\"com.salesforce.op.features.types.RealNN\\\",\\n \\\"originFeatures\\\" : [ \\\"irisClass_1-stagesApplied_RealNN_00000000003e\\\" ],\\n \\\"stages\\\" : [ ]\\n}\", \"{\\n \\\"name\\\" : \\\"petalLength-petalWidth-sepalLength-sepalWidth_2-stagesApplied_OPVector_000000000040\\\",\\n \\\"isResponse\\\" : false,\\n \\\"isRaw\\\" : true,\\n \\\"uid\\\" : \\\"OPVector_00000000004f\\\",\\n \\\"typeName\\\" : \\\"com.salesforce.op.features.types.OPVector\\\",\\n \\\"originFeatures\\\" : [ \\\"petalLength-petalWidth-sepalLength-sepalWidth_2-stagesApplied_OPVector_000000000040\\\" ],\\n \\\"stages\\\" : [ ]\\n}\" ],\n", + " \"predictionCol\" : \"prediction\",\n", + " \"standardization\" : true,\n", + " \"rawPredictionCol\" : \"rawPrediction\",\n", + " \"probabilityCol\" : \"probability\",\n", + " \"regParam\" : 0.2,\n", + " \"tol\" : 1.0E-6,\n", + " \"family\" : \"auto\",\n", + " \"maxIter\" : 50,\n", + " \"aggregationDepth\" : 2\n", + " },\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \"ModelUID\" : \"OpLogisticRegression_000000000044\",\n", + " \"ModelType\" : \"OpLogisticRegression\",\n", + " \"MetricValues\" : [ \"com.salesforce.op.evaluators.SingleMetric\", \"{\\\"name\\\":\\\"error\\\",\\\"value\\\":0.06238239976841801}\" ]\n", + " } ],\n", + " \"ValidationType\" : \"CrossValidation\",\n", + " \"BestModelType\" : \"OpRandomForestClassifier\",\n", + " \"DataPrepParameters\" : {\n", + " \"seed\" : 42,\n", + " \"labelsToKeep\" : [ 0.0, 1.0, 2.0 ],\n", + " \"reserveTestFraction\" : 0.2,\n", + " \"minLabelFraction\" : 0.0,\n", + " \"labelsDropped\" : [ ],\n", + " \"maxLabelCategories\" : 100\n", + " },\n", + " \"BestModelName\" : \"OpRandomForestClassifier_000000000045_0\",\n", + " \"HoldoutEvaluation\" : [ \"com.salesforce.op.evaluators.MultiMetrics\", \"{\\\"metrics\\\":{\\\"multiclass evaluation metrics\\\":{\\\"Precision\\\":0.9249999999999999,\\\"Recall\\\":0.8999999999999999,\\\"F1\\\":0.9123287671232877,\\\"Error\\\":0.09999999999999998,\\\"ThresholdMetrics\\\":{\\\"topNs\\\":[1,3],\\\"thresholds\\\":[0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.09,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.91,0.92,0.93,0.94,0.95,0.96,0.97,0.98,0.99,1.0],\\\"correctCounts\\\":{\\\"1\\\":[18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,16,16,16,16,15,15,15,15,14,14,14,12,10,10,9,6,6,3,2,1,1,1,0,0,0,0,0,0,0],\\\"3\\\":[20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,18,18,18,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,16,16,16,16,15,15,15,15,14,14,14,12,10,10,9,6,6,3,2,1,1,1,0,0,0,0,0,0,0]},\\\"incorrectCounts\\\":{\\\"1\\\":[2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],\\\"3\\\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]},\\\"noPredictionCounts\\\":{\\\"1\\\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,4,4,4,4,5,5,5,5,6,6,6,8,10,10,11,14,14,17,18,19,19,19,20,20,20,20,20,20,20],\\\"3\\\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,4,4,4,4,5,5,5,5,6,6,6,8,10,10,11,14,14,17,18,19,19,19,20,20,20,20,20,20,20]}}}}}\" ],\n", + " \"ValidationParameters\" : {\n", + " \"parallelism\" : 8,\n", + " \"seed\" : 42,\n", + " \"evaluator\" : \"error\",\n", + " \"stratify\" : false,\n", + " \"numFolds\" : 3\n", + " },\n", + " \"ProblemType\" : \"MultiClassification\",\n", + " \"EvaluationMetric\" : \"Error\",\n", + " \"TrainEvaluation\" : [ \"com.salesforce.op.evaluators.MultiMetrics\", \"{\\\"metrics\\\":{\\\"multiclass evaluation metrics\\\":{\\\"Precision\\\":0.9699378141238606,\\\"Recall\\\":0.9692307692307692,\\\"F1\\\":0.9695841627786504,\\\"Error\\\":0.03076923076923077,\\\"ThresholdMetrics\\\":{\\\"topNs\\\":[1,3],\\\"thresholds\\\":[0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.09,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.91,0.92,0.93,0.94,0.95,0.96,0.97,0.98,0.99,1.0],\\\"correctCounts\\\":{\\\"1\\\":[126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,126,125,125,125,125,124,123,123,123,123,123,122,121,121,121,121,121,121,121,120,119,118,118,116,115,115,114,114,113,111,110,103,101,95,92,83,75,68,61,46,36,29,25,23,17,0,0,0,0,0,0,0],\\\"3\\\":[130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,130,129,129,129,129,128,128,127,127,126,126,126,126,125,125,125,125,124,123,123,123,123,123,122,121,121,121,121,121,121,121,120,119,118,118,116,115,115,114,114,113,111,110,103,101,95,92,83,75,68,61,46,36,29,25,23,17,0,0,0,0,0,0,0]},\\\"incorrectCounts\\\":{\\\"1\\\":[4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,3,3,2,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],\\\"3\\\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,2,2,3,3,4,4,4,4,4,4,3,3,2,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]},\\\"noPredictionCounts\\\":{\\\"1\\\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,2,4,6,6,6,6,7,8,9,9,9,9,9,9,9,10,11,12,12,14,15,15,16,16,17,19,20,27,29,35,38,47,55,62,69,84,94,101,105,107,113,130,130,130,130,130,130,130],\\\"3\\\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,2,4,6,6,6,6,7,8,9,9,9,9,9,9,9,10,11,12,12,14,15,15,16,16,17,19,20,27,29,35,38,47,55,62,69,84,94,101,105,107,113,130,130,130,130,130,130,130]}}}}}\" ],\n", + " \"DataPrepResults\" : {\n", + " \"labelsKept\" : [ 0.0, 1.0, 2.0 ],\n", + " \"className\" : \"com.salesforce.op.stages.impl.tuning.DataCutterSummary\",\n", + " \"labelsDropped\" : [ ]\n", + " },\n", + " \"BestModelUID\" : \"OpRandomForestClassifier_000000000045\"\n", + " }\n", + "}\n" + ] + }, + { + "data": { + "text/plain": [ + "null" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val fittedWorkflow = workflow.train()\n", + "println(s\"Summary: ${fittedWorkflow.summary()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scoring the model:\n", + "=================\n", + "Transformed dataframe columns:\n", + "--------------------------\n", + "key\n", + "irisClass_1-stagesApplied_RealNN_00000000003e\n", + "irisClass-petalLength-petalWidth-sepalLength-sepalWidth_4-stagesApplied_Prediction_000000000049\n", + "Metrics:\n", + "------------\n", + "{\n", + " \"Precision\" : 0.9600000000000001,\n", + " \"Recall\" : 0.9600000000000001,\n", + " \"F1\" : 0.9600000000000001,\n", + " \"Error\" : 0.040000000000000036,\n", + " \"ThresholdMetrics\" : {\n", + " \"topNs\" : [ 1, 3 ],\n", + " \"thresholds\" : [ 0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1.0 ],\n", + " \"correctCounts\" : {\n", + " \"1\" : [ 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 143, 142, 142, 142, 141, 140, 140, 140, 140, 140, 139, 138, 138, 138, 138, 138, 138, 138, 137, 136, 135, 135, 132, 131, 131, 130, 129, 128, 126, 125, 117, 115, 109, 104, 93, 85, 77, 67, 52, 39, 31, 26, 24, 18, 0, 0, 0, 0, 0, 0, 0 ],\n", + " \"3\" : [ 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 148, 148, 148, 148, 147, 147, 146, 146, 145, 145, 144, 144, 143, 142, 142, 142, 141, 140, 140, 140, 140, 140, 139, 138, 138, 138, 138, 138, 138, 138, 137, 136, 135, 135, 132, 131, 131, 130, 129, 128, 126, 125, 117, 115, 109, 104, 93, 85, 77, 67, 52, 39, 31, 26, 24, 18, 0, 0, 0, 0, 0, 0, 0 ]\n", + " },\n", + " \"incorrectCounts\" : {\n", + " \"1\" : [ 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 5, 5, 4, 4, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ],\n", + " \"3\" : [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 4, 4, 5, 5, 6, 5, 5, 5, 4, 4, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]\n", + " },\n", + " \"noPredictionCounts\" : {\n", + " \"1\" : [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 4, 6, 8, 8, 8, 8, 9, 10, 11, 11, 11, 11, 11, 11, 12, 13, 14, 15, 15, 18, 19, 19, 20, 21, 22, 24, 25, 33, 35, 41, 46, 57, 65, 73, 83, 98, 111, 119, 124, 126, 132, 150, 150, 150, 150, 150, 150, 150 ],\n", + " \"3\" : [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 4, 6, 8, 8, 8, 8, 9, 10, 11, 11, 11, 11, 11, 11, 12, 13, 14, 15, 15, 18, 19, 19, 20, 21, 22, 24, 25, 33, 35, 41, 46, 57, 65, 73, 83, 98, 111, 119, 124, 126, 132, 150, 150, 150, 150, 150, 150, 150 ]\n", + " }\n", + " }\n", + "}\n" + ] + }, + { + "data": { + "text/plain": [ + "null" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "println(\"Scoring the model:\\n=================\")\n", + "val (dataframe, metrics) = fittedWorkflow.scoreAndEvaluate(evaluator = evaluator)\n", + "\n", + "println(\"Transformed dataframe columns:\\n--------------------------\")\n", + "dataframe.columns.foreach(println)\n", + "\n", + "println(\"Metrics:\\n------------\")\n", + "println(metrics)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Scala", + "language": "scala", + "name": "scala" + }, + "language_info": { + "codemirror_mode": "text/x-scala", + "file_extension": ".scala", + "mimetype": "", + "name": "Scala", + "nbconverter_exporter": "", + "version": "2.11.12" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": false, + "sideBar": false, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": false, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/helloworld/notebooks/OpTitanicSimple.ipynb b/helloworld/notebooks/OpTitanicSimple.ipynb new file mode 100644 index 0000000000..6a9157948f --- /dev/null +++ b/helloworld/notebooks/OpTitanicSimple.ipynb @@ -0,0 +1,951 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Op Titanic Simple Sample" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "method": "display_data" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%classpath add mvn com.salesforce.transmogrifai transmogrifai-core_2.11 0.5.1" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "method": "display_data" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "79c80a8e-9522-4667-87b4-7700649b32f2", + "version_major": 2, + "version_minor": 0 + }, + "method": "display_data" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%classpath add mvn org.apache.spark spark-mllib_2.11 2.3.0" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "import org.apache.spark.SparkConf\n", + "import org.apache.spark.sql.SparkSession\n", + "import org.apache.spark.SparkContext\n", + "import org.apache.spark.sql.functions.udf\n", + "import com.salesforce.op._\n", + "import com.salesforce.op.features._\n", + "import com.salesforce.op.features.types._\n", + "import com.salesforce.op.stages.impl.classification._\n", + "import com.salesforce.op.evaluators.Evaluators\n" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import org.apache.spark.SparkConf\n", + "import org.apache.spark.sql.SparkSession\n", + "import org.apache.spark.SparkContext\n", + "import org.apache.spark.sql.functions.udf\n", + "\n", + "import com.salesforce.op._\n", + "import com.salesforce.op.features._\n", + "import com.salesforce.op.features.types._\n", + "import com.salesforce.op.stages.impl.classification._\n", + "import com.salesforce.op.evaluators.Evaluators" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "org.apache.spark.sql.SparkSession@68cc6276" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val conf = new SparkConf().setMaster(\"local[*]\").setAppName(\"TitanicPrediction\")\n", + "implicit val spark = SparkSession.builder.config(conf).getOrCreate()\n", + "//spark.conf.set(\"spark.executor.memory\", \"2g\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "defined class Passenger\n" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "case class Passenger(\n", + " id: Int,\n", + " survived: Int,\n", + " pClass: Option[Int],\n", + " name: Option[String],\n", + " sex: Option[String],\n", + " age: Option[Double],\n", + " sibSp: Option[Int],\n", + " parCh: Option[Int],\n", + " ticket: Option[String],\n", + " fare: Option[Double],\n", + " cabin: Option[String],\n", + " embarked: Option[String]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Feature(name = embarked, uid = PickList_00000000000b, isResponse = false, originStage = FeatureGeneratorStage_00000000000b, parents = [], distributions = [])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val survived = FeatureBuilder.RealNN[Passenger].extract(_.survived.toRealNN).asResponse\n", + "val pClass = FeatureBuilder.PickList[Passenger].extract(_.pClass.map(_.toString).toPickList).asPredictor\n", + "val name = FeatureBuilder.Text[Passenger].extract(_.name.toText).asPredictor\n", + "val sex = FeatureBuilder.PickList[Passenger].extract(_.sex.map(_.toString).toPickList).asPredictor\n", + "val age = FeatureBuilder.Real[Passenger].extract(_.age.toReal).asPredictor\n", + "val sibSp = FeatureBuilder.Integral[Passenger].extract(_.sibSp.toIntegral).asPredictor\n", + "val parCh = FeatureBuilder.Integral[Passenger].extract(_.parCh.toIntegral).asPredictor\n", + "val ticket = FeatureBuilder.PickList[Passenger].extract(_.ticket.map(_.toString).toPickList).asPredictor\n", + "val fare = FeatureBuilder.Real[Passenger].extract(_.fare.toReal).asPredictor\n", + "val cabin = FeatureBuilder.PickList[Passenger].extract(_.cabin.map(_.toString).toPickList).asPredictor\n", + "val embarked = FeatureBuilder.PickList[Passenger].extract(_.embarked.map(_.toString).toPickList).asPredictor" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Feature(name = age_1-stagesApplied_PickList_000000000012, uid = PickList_000000000012, isResponse = false, originStage = UnaryLambdaTransformer_000000000012, parents = [Real_000000000005], distributions = [])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val familySize = sibSp + parCh + 1\n", + "val estimatedCostOfTickets = familySize * fare\n", + "val pivotedSex = sex.pivot()\n", + "val normedAge = age.fillMissingWithMean().zNormalize()\n", + "val ageGroup = age.map[PickList](_.value.map(v => if (v > 18) \"adult\" else \"child\").toPickList)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Feature(name = age-cabin-embarked-fare-name-pClass-parCh-sex-sibSp-ticket_10-stagesApplied_OPVector_000000000017, uid = OPVector_000000000017, isResponse = false, originStage = VectorsCombiner_000000000017, parents = [OPVector_000000000013,OPVector_00000000000f,OPVector_000000000014,OPVector_000000000015,OPVector_000000000016], distributions = [])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val passengerFeatures = Seq(\n", + " pClass, name, age, sibSp, parCh, ticket,\n", + " cabin, embarked, familySize, estimatedCostOfTickets,\n", + " pivotedSex, ageGroup\n", + " ).transmogrify()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Feature(name = age-cabin-embarked-fare-name-pClass-parCh-sex-sibSp-survived-ticket_11-stagesApplied_OPVector_000000000018, uid = OPVector_000000000018, isResponse = false, originStage = SanityChecker_000000000018, parents = [RealNN_000000000001,OPVector_000000000017], distributions = [])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val sanityCheck = true\n", + "val finalFeatures = if (sanityCheck) survived.sanityCheck(passengerFeatures) else passengerFeatures" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Feature(name = age-cabin-embarked-fare-name-pClass-parCh-sex-sibSp-survived-ticket_12-stagesApplied_Prediction_000000000023, uid = Prediction_000000000023, isResponse = true, originStage = ModelSelector_000000000023, parents = [RealNN_000000000001,OPVector_000000000018], distributions = [])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import com.salesforce.op.stages.impl.classification.BinaryClassificationModelSelector\n", + "import com.salesforce.op.stages.impl.classification.BinaryClassificationModelsToTry._\n", + "\n", + "val prediction =\n", + " BinaryClassificationModelSelector.withTrainValidationSplit(\n", + " modelTypesToUse = Seq(OpLogisticRegression)\n", + " ).setInput(survived, finalFeatures).getOutput()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "OpBinaryClassificationEvaluator_000000000024" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val evaluator = Evaluators.BinaryClassification().setLabelCol(survived).setPredictionCol(prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "org.apache.spark.sql.SparkSession$implicits$@1ae3651" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import spark.implicits._ // Needed for Encoders for the Passenger case class\n", + "import com.salesforce.op.readers.DataReaders\n", + "\n", + "val trainFilePath = \"/home/beakerx/helloworld/src/main/resources/TitanicDataset/TitanicPassengersTrainData.csv\"\n", + " // Define a way to read data into our Passenger class from our CSV file\n", + "val trainDataReader = DataReaders.Simple.csvCase[Passenger](\n", + " path = Option(trainFilePath),\n", + " key = _.id.toString\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "com.salesforce.op.OpWorkflow@25f08c3" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val workflow =\n", + " new OpWorkflow()\n", + " .setResultFeatures(survived, prediction)\n", + " .setReader(trainDataReader)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary: {\n", + " \"SanityChecker_000000000018\" : {\n", + " \"statistics\" : {\n", + " \"sampleFraction\" : 1.0,\n", + " \"count\" : 803.0,\n", + " \"variance\" : [ 1.2644882190538604, 0.0, 0.6773259876460778, 0.0, 0.2299543793070251, 0.2299543793070251, 0.0, 0.0, 0.2480101117070338, 0.18345481253280246, 0.16637422632708393, 0.0, 0.0, 0.0, 0.0, 0.17685549513513849, 0.17685549513513849, 0.20083663816796737, 0.15365074238438772, 0.0796731707468564, 0.0, 0.0012453300124533001, 0.2299543793070251, 0.1324552876836551, 0.0, 0.16049850467231672, 168.68875319508854, 0.16049850467231672, 2.695434514585262, 0.0, 26530.387843169614, 0.0, 0.003726673353974963, 0.0, 0.003726673353974963, 0.009875684388033651, 0.0012453300124533001, 0.021940789371527596, 0.0012453300124533001, 0.004962686683043326, 0.002487554463778288, 0.0012453300124533001, 0.004962686683043326, 0.0012453300124533001, 0.0012453300124533001, 0.002487554463778288, 0.002487554463778288, 0.008652093303478539, 0.003726673353974963, 0.003726673353974963, 0.002487554463778288, 0.003726673353974963, 0.006195594450983376, 0.003726673353974963, 0.0012453300124533001, 0.003726673353974963, 0.006195594450983376, 0.002487554463778288, 0.002487554463778288, 0.01231354987375894, 0.007425396657795113, 0.002487554463778288, 0.007425396657795113, 0.003726673353974963, 0.004962686683043326, 0.0012453300124533001, 0.006195594450983376, 0.0012453300124533001, 0.006195594450983376, 0.009875684388033651, 0.003726673353974963, 0.01594705639388453, 0.004962686683043326, 0.002487554463778288, 0.0, 0.017232758701005893, 0.008652093303478539, 0.0012453300124533001, 0.003726673353974963, 0.006195594450983376, 0.019552612863855307, 0.0012453300124533001, 0.003726673353974963, 0.004962686683043326, 0.002487554463778288, 0.008652093303478539, 0.004962686683043326, 0.007425396657795113, 0.003726673353974963, 0.002487554463778288, 0.0, 0.0012453300124533001, 0.006195594450983376, 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],\n", + " \"DataPrepResults\" : {\n", + " \"className\" : \"com.salesforce.op.stages.impl.tuning.DataSplitterSummary\"\n", + " },\n", + " \"BestModelUID\" : \"OpLogisticRegression_00000000001c\"\n", + " }\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "}\n" + ] + }, + { + "data": { + "text/plain": [ + "null" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val fittedWorkflow = workflow.train()\n", + "println(s\"Summary: ${fittedWorkflow.summary()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scoring the model:\n", + "=================\n", + "Transformed dataframe columns:\n", + "--------------------------\n", + "key\n", + "survived\n", + "age-cabin-embarked-fare-name-pClass-parCh-sex-sibSp-survived-ticket_12-stagesApplied_Prediction_000000000023\n", + "Metrics:\n", + "------------\n", + "{\n", + " \"Precision\" : 0.9063545150501672,\n", + " \"Recall\" : 0.7923976608187134,\n", + " \"F1\" : 0.8455538221528861,\n", + " \"AuROC\" : 0.9438559209194816,\n", + " \"AuPR\" : 0.9285558825451835,\n", + " \"Error\" : 0.1111111111111111,\n", + " \"TP\" : 271.0,\n", + " \"TN\" : 521.0,\n", + " \"FP\" : 28.0,\n", + " \"FN\" : 71.0,\n", + " \"thresholds\" : [ 0.9611258128782662, 0.9423413118230444, 0.9256381144668346, 0.9054001487100896, 0.8887166190747181, 0.8843083167821855, 0.8754749577959478, 0.8630421473591078, 0.8569195054633844, 0.8503335513716453, 0.845900533639715, 0.8412523587229193, 0.8335609362703046, 0.8180550027616863, 0.8059673391921474, 0.7963016886663534, 0.7867468025791261, 0.7718668692622118, 0.7551827711721667, 0.7428100730208298, 0.7247361237239153, 0.719415116539814, 0.7111767011453167, 0.70016212724526, 0.6921190251813505, 0.6809086941999787, 0.6693044913472638, 0.658405606615863, 0.638393503031702, 0.6238345251163069, 0.6095517766976727, 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"execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Scala", + "language": "scala", + "name": "scala" + }, + "language_info": { + "codemirror_mode": "text/x-scala", + "file_extension": ".scala", + "mimetype": "", + "name": "Scala", + "nbconverter_exporter": "", + "version": "2.11.12" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": false, + "sideBar": false, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": false, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/helloworld/notebooks/README.md b/helloworld/notebooks/README.md new file mode 100644 index 0000000000..818279b286 --- /dev/null +++ b/helloworld/notebooks/README.md @@ -0,0 +1,94 @@ +# Transmogrify on Jupyter + +In this section we will look at how Transmogrify can be run within Scala notebooks on +Jupyter. + +We are going to leverage [BeakerX](http://beakerx.com/) scala kernel for Jupyter + +## Setup BeakerX on Linux / Ubuntu + +Prequisities : + +* Apache Maven +* Python 3 +* JDK 8 (JDK 10 or above can cause issues with Transmogrify) + +Installation using pip + +```$xslt +sudo pip install beakerx +sudo beakerx install +``` + +Installation using conda + +```$xslt +conda create -y -n beakerx 'python>=3' +source activate beakerx +conda config --env --add pinned_packages 'openjdk>8.0.121' +conda install -y -c conda-forge ipywidgets beakerx +``` + +Reference : [BeakerX Documentation](http://beakerx.com/documentation) + +## Setup BeakerX on Mac with Docker + +BeakerX provides a [docker container image](https://hub.docker.com/r/beakerx/beakerx/) on docker hub. + +Assuming your Transmogrify source code is downloaded at `/Users/rdua/work/github/rajdeepd/TransmogrifAI`. You can use +the following command to start the container. We need the directory above so that we can mount sample notebooks and dataset +into the container using docker volumes. + +### Increase the RAM available to Docker container + +Increase the Memory available to docker containers from the docker UI as shown below + + +![docker-settings][docker-settings] + +[docker-settings]: ./images/docker_memory_settings.png + +```bash +docker run -p 8888:8888 -v /Users/rdua/work/github/rajdeepd/TransmogrifAI/helloworld/jupyter:/home/beakerx/helloworld-jupyter \ +-v /Users/rdua/work/github/rajdeepd/TransmogrifAI/helloworld:/home/beakerx/helloworld beakerx/beakerx +``` + +This will download the image (which takes a few minutes first time) and start the container. It will also publish the url +and the token to access the container + +Sample url is shown below. + +```$xslt +http://localhost:8888/?token= +``` + +On opening the image in the browser you will notice that in the home page + +![jupyter_home][jupyter_home] + +helloworld-jupyter mounted folder (/home/beakerx/helloworld-jupyter)is where all our samples are located. + +![helloworld_jupyter][helloworld_jupyter] + +[jupyter_home]: ./images/jupyter_home.png +[helloworld_jupyter]: ./images/helloworld_jupyter.png + + +### Sample Notebooks + +Following notebooks are currently available + +#### OpTitanicSimple + +[OpTitanicSimple.ipynb](http://localhost:8888/notebooks/helloworld-jupyter/OpTitanicSimple.ipynb) + +![op_titanic][op_titanic] + +#### OpIris + +[OpIris.ipynb](http://localhost:8888/notebooks/helloworld-jupyter/OpIris.ipynb) + +![op_iris][op_iris] + +[op_titanic]: ./images/op_titanic.png +[op_iris]: ./images/op_iris.png \ No newline at end of file diff --git a/helloworld/notebooks/images/docker_memory_settings.png b/helloworld/notebooks/images/docker_memory_settings.png new file mode 100644 index 0000000000..e15ac8395e Binary files /dev/null and b/helloworld/notebooks/images/docker_memory_settings.png differ diff --git a/helloworld/notebooks/images/helloworld_jupyter.png b/helloworld/notebooks/images/helloworld_jupyter.png new file mode 100644 index 0000000000..61c2638aea Binary files /dev/null and b/helloworld/notebooks/images/helloworld_jupyter.png differ diff --git a/helloworld/notebooks/images/jupyter_home.png b/helloworld/notebooks/images/jupyter_home.png new file mode 100644 index 0000000000..a0caf6a5f9 Binary files /dev/null and b/helloworld/notebooks/images/jupyter_home.png differ diff --git a/helloworld/notebooks/images/op_iris.png b/helloworld/notebooks/images/op_iris.png new file mode 100644 index 0000000000..ccd63b422d Binary files /dev/null and b/helloworld/notebooks/images/op_iris.png differ diff --git a/helloworld/notebooks/images/op_titanic.png b/helloworld/notebooks/images/op_titanic.png new file mode 100644 index 0000000000..8c6279aa9a Binary files /dev/null and b/helloworld/notebooks/images/op_titanic.png differ