From e83a720d3346224220cea724d284afdfc5c8f593 Mon Sep 17 00:00:00 2001 From: Villu Ruusmann Date: Wed, 12 Jun 2024 12:14:04 +0300 Subject: [PATCH] Improved support for the 'maxLik' model type --- .../java/org/jpmml/rexp/MaxLikConverter.java | 368 +++++- pmml-rexp/src/test/R/apollo.R | 96 +- .../rexp/testing/MaxLikConverterTest.java | 7 +- .../src/test/resources/csv/NLModeChoice.csv | 1001 +++++++++++++++++ .../src/test/resources/rds/NLModeChoice.rds | Bin 0 -> 188789 bytes 5 files changed, 1428 insertions(+), 44 deletions(-) create mode 100644 pmml-rexp/src/test/resources/csv/NLModeChoice.csv create mode 100644 pmml-rexp/src/test/resources/rds/NLModeChoice.rds diff --git a/pmml-rexp/src/main/java/org/jpmml/rexp/MaxLikConverter.java b/pmml-rexp/src/main/java/org/jpmml/rexp/MaxLikConverter.java index 3af9930..26852f5 100644 --- a/pmml-rexp/src/main/java/org/jpmml/rexp/MaxLikConverter.java +++ b/pmml-rexp/src/main/java/org/jpmml/rexp/MaxLikConverter.java @@ -26,13 +26,17 @@ import java.util.Map; import java.util.Objects; +import org.dmg.pmml.Apply; import org.dmg.pmml.DataField; import org.dmg.pmml.DataType; import org.dmg.pmml.DerivedField; import org.dmg.pmml.Expression; +import org.dmg.pmml.Field; import org.dmg.pmml.FieldRef; import org.dmg.pmml.MiningFunction; +import org.dmg.pmml.Model; import org.dmg.pmml.OpType; +import org.dmg.pmml.PMMLFunctions; import org.dmg.pmml.regression.RegressionModel; import org.dmg.pmml.regression.RegressionTable; import org.jpmml.converter.CategoricalLabel; @@ -41,22 +45,32 @@ import org.jpmml.converter.Feature; import org.jpmml.converter.FieldNameUtil; import org.jpmml.converter.ModelUtil; +import org.jpmml.converter.PMMLEncoder; import org.jpmml.converter.Schema; import org.jpmml.converter.TypeUtil; import org.jpmml.converter.regression.RegressionModelUtil; public class MaxLikConverter extends ModelConverter { + private Map utilityFunctions = null; + + private RFunctionCall nlNests = null; + + private Map nlStructures = null; + + public MaxLikConverter(RGenericVector maxLik){ super(maxLik); } @Override public void encodeSchema(RExpEncoder encoder){ + parseApolloProbabilities(); + RGenericVector maxLik = getObject(); RDoubleVector estimate = maxLik.getDoubleElement("estimate"); - RClosure apolloProbabilities = (RClosure)maxLik.getElement("apollo_probabilities"); + RStringVector modelTypeList = maxLik.getStringElement("modelTypeList"); RStringVector estimateNames = estimate.names(); @@ -66,37 +80,7 @@ public void encodeSchema(RExpEncoder encoder){ betas.put(estimateNames.getDequotedValue(i), estimate.getValue(i)); } - RFunctionCall body = (RFunctionCall)apolloProbabilities.getBody(); - - if(!body.hasValue("{")){ - throw new IllegalArgumentException(); - } - - Map utilityFunctions = new LinkedHashMap<>(); - - for(Iterator it = body.argumentValues(); it.hasNext(); ){ - RExp argValue = it.next(); - - if(argValue instanceof RFunctionCall){ - RFunctionCall functionCall = (RFunctionCall)argValue; - - if(functionCall.hasValue("=")){ - Iterator it2 = functionCall.argumentValues(); - - RExp firstArgValue = it2.next(); - RExp secondArgValue = it2.next(); - - Object choice = matchUtilityFunction(firstArgValue); - if(choice != null){ - utilityFunctions.put(choice, secondArgValue); - } - } - } else - - { - throw new IllegalArgumentException(); - } - } + Map utilityFunctions = this.utilityFunctions; if(utilityFunctions.isEmpty()){ throw new IllegalArgumentException(); @@ -108,8 +92,10 @@ public void encodeSchema(RExpEncoder encoder){ encoder.setLabel(choiceField); + Map choiceFeatures = new LinkedHashMap<>(); + for(Object choice : choices){ - RFunctionCall functionCall = (RFunctionCall)utilityFunctions.get(choice); + RFunctionCall functionCall = utilityFunctions.get(choice); Expression expression = toPMML(functionCall, betas, encoder); @@ -117,37 +103,280 @@ public void encodeSchema(RExpEncoder encoder){ Feature feature = new ContinuousFeature(encoder, derivedField); + choiceFeatures.put(choice, feature); + + // XXX encoder.addFeature(feature); } + + String modelType = modelTypeList.getValue(0); + switch(modelType){ + case "MNL": + break; + case "NL": + { + RFunctionCall nlNests = this.nlNests; + Map nlStructures = this.nlStructures; + + if(nlNests == null){ + throw new IllegalArgumentException(); + } + + Map lambdas = parseList(nlNests); + + if(nlStructures.isEmpty()){ + throw new IllegalArgumentException(); + } // End if + + if(!Objects.equals(lambdas.keySet(), nlStructures.keySet())){ + throw new IllegalArgumentException(); + } + + choices = new ArrayList<>(nlStructures.keySet()); + + // XXX + Collections.reverse(choices); + + for(Object choice : choices){ + Number lambda = (Number)lambdas.get(choice); + + RFunctionCall functionCall = nlStructures.get(choice); + + List childChoices = parseVector(functionCall); + if(childChoices.isEmpty()){ + throw new IllegalArgumentException(); + } + + Apply apply = ExpressionUtil.createApply(PMMLFunctions.SUM); + + for(Object childChoice : childChoices){ + Feature choiceFeature = choiceFeatures.get(childChoice); + + if(choiceFeature == null){ + throw new IllegalArgumentException(); + } + + Apply choiceApply; + + if(lambda.doubleValue() != 1d){ + choiceApply = ExpressionUtil.createApply(PMMLFunctions.EXP, + ExpressionUtil.createApply(PMMLFunctions.DIVIDE, choiceFeature.ref(), ExpressionUtil.createConstant(lambda)) + ); + } else + + { + choiceApply = ExpressionUtil.createApply(PMMLFunctions.EXP, + choiceFeature.ref() + ); + } + + apply.addExpressions(choiceApply); + } + + apply = ExpressionUtil.createApply(PMMLFunctions.LN, apply); + + if(lambda.doubleValue() != 1d){ + apply = ExpressionUtil.createApply(PMMLFunctions.MULTIPLY, apply, ExpressionUtil.createConstant(lambda)); + } + + DerivedField derivedField = encoder.createDerivedField(FieldNameUtil.create("utility", choice), OpType.CONTINUOUS, DataType.DOUBLE, apply); + + Feature feature = new ContinuousFeature(encoder, derivedField); + + choiceFeatures.put(choice, feature); + } + } + break; + default: + throw new IllegalArgumentException(); + } } @Override - public RegressionModel encodeModel(Schema schema){ + public Model encodeModel(Schema schema){ RGenericVector maxLik = getObject(); + RStringVector modelTypeList = maxLik.getStringElement("modelTypeList"); + + PMMLEncoder encoder = schema.getEncoder(); CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel(); List features = schema.getFeatures(); List regressionTables = new ArrayList<>(); - for(int i = 0; i < categoricalLabel.size(); i++){ - Feature feature = features.get(i); + String modelType = modelTypeList.getValue(0); + switch(modelType){ + case "MNL": + { + for(int i = 0; i < categoricalLabel.size(); i++){ + Object choice = categoricalLabel.getValue(i); + + Feature feature = toExpFeature(getUtilityFeature(choice, encoder), encoder); + + RegressionTable regressionTable = RegressionModelUtil.createRegressionTable(Collections.singletonList(feature), Collections.singletonList(1d), null) + .setTargetCategory(choice); + + regressionTables.add(regressionTable); + } + } + break; + case "NL": + { + RFunctionCall nlNests = this.nlNests; + Map nlStructures = this.nlStructures; + + Map lambdas = parseList(nlNests); + + Map nlTree = new LinkedHashMap<>(); + + List choices = new ArrayList<>(nlStructures.keySet()); + + for(Object choice : choices){ + RFunctionCall functionCall = nlStructures.get(choice); + + List childChoices = parseVector(functionCall); + for(Object childChoice : childChoices){ + nlTree.put(childChoice, choice); + } + } + + for(int i = 0; i < categoricalLabel.size(); i++){ + Object choice = categoricalLabel.getValue(i); + + Apply apply = ExpressionUtil.createApply(PMMLFunctions.PRODUCT); + + for(Object currentChoice = choice, nextChoice = nlTree.get(currentChoice); nextChoice != null; currentChoice = nextChoice, nextChoice = nlTree.get(currentChoice)){ + Number lambda = (Number)lambdas.get(nextChoice); - RegressionTable regressionTable = RegressionModelUtil.createRegressionTable(Collections.singletonList(feature), Collections.singletonList(1d), null) - .setTargetCategory(categoricalLabel.getValue(i)); + Feature currentUtilityFeature = toExpFeature(getUtilityFeature(currentChoice, encoder), encoder); + Feature nextUtilityFeature = toExpFeature(getUtilityFeature(nextChoice, encoder), encoder); - regressionTables.add(regressionTable); + DerivedField derivedField = encoder.ensureDerivedField(FieldNameUtil.create("term", currentChoice, nextChoice), OpType.CONTINUOUS, DataType.DOUBLE, () -> { + Apply choiceApply = ExpressionUtil.createApply(PMMLFunctions.DIVIDE, currentUtilityFeature.ref(), nextUtilityFeature.ref()); + + if(lambda.doubleValue() != 1d){ + choiceApply = ExpressionUtil.createApply(PMMLFunctions.POW, choiceApply, ExpressionUtil.createConstant(1d / lambda.doubleValue())); + } + + return choiceApply; + }); + + apply.addExpressions(new FieldRef(derivedField)); + } + + DerivedField derivedField = encoder.createDerivedField(FieldNameUtil.create("term", choice), OpType.CONTINUOUS, DataType.DOUBLE, apply); + + Feature feature = new ContinuousFeature(encoder, derivedField); + + RegressionTable regressionTable = RegressionModelUtil.createRegressionTable(Collections.singletonList(feature), Collections.singletonList(1d), null) + .setTargetCategory(choice); + + regressionTables.add(regressionTable); + } + } + break; + default: + throw new IllegalArgumentException(modelType); } RegressionModel regressionModel = new RegressionModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel), regressionTables) - .setNormalizationMethod(RegressionModel.NormalizationMethod.SOFTMAX) + .setNormalizationMethod(RegressionModel.NormalizationMethod.SIMPLEMAX) .setOutput(ModelUtil.createProbabilityOutput(DataType.DOUBLE, categoricalLabel)); return regressionModel; } + private void parseApolloProbabilities(){ + RGenericVector maxLik = getObject(); + + RClosure apolloProbabilities = (RClosure)maxLik.getElement("apollo_probabilities"); + + RFunctionCall body = (RFunctionCall)apolloProbabilities.getBody(); + + if(!body.hasValue("{")){ + throw new IllegalArgumentException(); + } + + Map utilityFunctions = new LinkedHashMap<>(); + + RFunctionCall nlNests = null; + Map nlStructures = new LinkedHashMap<>(); + + for(Iterator it = body.argumentValues(); it.hasNext(); ){ + RExp argValue = it.next(); + + if(argValue instanceof RFunctionCall){ + RFunctionCall functionCall = (RFunctionCall)argValue; + + if(functionCall.hasValue("=")){ + Iterator it2 = functionCall.argumentValues(); + + RExp firstArgValue = it2.next(); + RExp secondArgValue = it2.next(); + + if(matchVariable(firstArgValue, "nlNests")){ + nlNests = (RFunctionCall)secondArgValue; + + continue; + } + + Object choice; + + choice = matchUtilityFunction(firstArgValue); + if(choice != null){ + utilityFunctions.put(choice, (RFunctionCall)secondArgValue); + + continue; + } + + choice = matchNLStructure(firstArgValue); + if(choice != null){ + nlStructures.put(choice, (RFunctionCall)secondArgValue); + + continue; + } + } + } else + + { + throw new IllegalArgumentException(); + } + } + + if(!Collections.disjoint(utilityFunctions.keySet(), nlStructures.keySet())){ + throw new IllegalArgumentException(); + } + + this.utilityFunctions = utilityFunctions; + + this.nlNests = nlNests; + this.nlStructures = nlStructures; + } + + static + private boolean matchVariable(RExp argValue, String variableName){ + + if(argValue instanceof RString){ + RString string = (RString)argValue; + + return Objects.equals(variableName, string.getValue()); + } + + return false; + } + static private Object matchUtilityFunction(RExp argValue){ + return matchListAssignment(argValue, "V"); + } + + static + private Object matchNLStructure(RExp argValue){ + return matchListAssignment(argValue, "nlStructure"); + } + + static + private Object matchListAssignment(RExp argValue, String variableName){ if(argValue instanceof RFunctionCall){ RFunctionCall functionCall = (RFunctionCall)argValue; @@ -160,7 +389,7 @@ private Object matchUtilityFunction(RExp argValue){ if(firstArgValue instanceof RString){ RString string = (RString)firstArgValue; - if(Objects.equals("V", string.getValue())){ + if(Objects.equals(variableName, string.getValue())){ RExp secondArgValue = it.next(); if(secondArgValue instanceof RVector){ @@ -176,6 +405,22 @@ private Object matchUtilityFunction(RExp argValue){ return null; } + static + private ContinuousFeature getUtilityFeature(Object choice, PMMLEncoder encoder){ + Field field = encoder.getField(FieldNameUtil.create("utility", choice)); + + return new ContinuousFeature(encoder, field); + } + + static + private ContinuousFeature toExpFeature(Feature feature, PMMLEncoder encoder){ + DerivedField derivedField = encoder.ensureDerivedField(FieldNameUtil.create(PMMLFunctions.EXP, feature), OpType.CONTINUOUS, DataType.DOUBLE, () -> { + return ExpressionUtil.createApply(PMMLFunctions.EXP, feature.ref()); + }); + + return new ContinuousFeature(encoder, derivedField); + } + static private Expression toPMML(RExp argumentValue, Map betas, RExpEncoder encoder){ @@ -192,7 +437,7 @@ private Expression toPMML(RExp argumentValue, Map betas, RExpEnc dataField = encoder.createDataField(stringValue, OpType.CONTINUOUS, DataType.DOUBLE); } - return new FieldRef(stringValue); + return new FieldRef(dataField); } else if(argumentValue instanceof RNumberVector){ @@ -235,4 +480,43 @@ private Expression toPMML(RExp argumentValue, Map betas, RExpEnc throw new IllegalArgumentException(); } } + + static + private Map parseList(RFunctionCall functionCall){ + + if(!functionCall.hasValue("list")){ + throw new IllegalArgumentException(); + } + + Map result = new LinkedHashMap<>(); + + for(Iterator it = functionCall.arguments(); it.hasNext(); ){ + RPair arg = it.next(); + + RString tag = (RString)arg.getTag(); + RVector value = (RVector)arg.getValue(); + + result.put(tag.getValue(), value.asScalar()); + } + + return result; + } + + static + private List parseVector(RFunctionCall functionCall){ + + if(!functionCall.hasValue("c")){ + throw new IllegalArgumentException(); + } + + List result = new ArrayList<>(); + + for(Iterator it = functionCall.argumentValues(); it.hasNext(); ){ + RVector argValue = (RVector)it.next(); + + result.add(argValue.asScalar()); + } + + return result; + } } \ No newline at end of file diff --git a/pmml-rexp/src/test/R/apollo.R b/pmml-rexp/src/test/R/apollo.R index a2023a3..d058573 100644 --- a/pmml-rexp/src/test/R/apollo.R +++ b/pmml-rexp/src/test/R/apollo.R @@ -82,8 +82,102 @@ generateMNLModeChoice = function(){ storeCsv(choice, "MNLModeChoice") } +# See http://www.apollochoicemodelling.com/files/examples/1%20MNL/MNL_RP.r +generateNLModeChoice = function(){ + database <<- loadModeChoiceCsv("ModeChoice") + + apollo_control <<- list( + modelName = "NL_RP", + modelDescr = "Simple NL model on mode choice RP data", + indivID = "ID", + outputDirectory = "/dev/null" + ) + + apollo_beta <<- c( + asc_car = 0, + asc_bus = 0, + asc_air = 0, + asc_rail = 0, + b_tt_car = 0, + b_tt_bus = 0, + b_tt_air = 0, + b_tt_rail = 0, + b_access = 0, + b_cost = 0 + ) + + apollo_fixed <<- c("asc_car") + + apollo_inputs = apollo_validateInputs() + + apollo_probabilities = function(apollo_beta, apollo_inputs, functionality="estimate"){ + + ### Attach inputs and detach after function exit + apollo_attach(apollo_beta, apollo_inputs) + on.exit(apollo_detach(apollo_beta, apollo_inputs)) + + ### Create list of probabilities P + P = list() + + ### List of utilities: these must use the same names as in nl_settings, order is irrelevant + V = list() + V[["car"]] = asc_car + b_tt_car * time_car + b_cost * cost_car + V[["bus"]] = asc_bus + b_tt_bus * time_bus + b_access * access_bus + b_cost * cost_bus + V[["air"]] = asc_air + b_tt_air * time_air + b_access * access_air + b_cost * cost_air + V[["rail"]] = asc_rail + b_tt_rail * time_rail + b_access * access_rail + b_cost * cost_rail + + ### Specify nests for NL model + nlNests = list(root = 1, PT = 0.75) + + ### Specify tree structure for NL model + nlStructure = list() + nlStructure[["root"]] = c("car", "PT") + nlStructure[["PT"]] = c("bus", "air", "rail") + + ### Define settings for MNL model component + nl_settings = list( + alternatives = c(car=1, bus=2, air=3, rail=4), + #avail = list(car=av_car, bus=av_bus, air=av_air, rail=av_rail), + choiceVar = choice, + utilities = V, + nlNests = nlNests, + nlStructure = nlStructure + ) + + ### Compute probabilities using MNL model + P[["model"]] = apollo_nl(nl_settings, functionality) + + ### Take product across observation for same individual + P = apollo_panelProd(P, apollo_inputs, functionality) + + ### Prepare and return outputs of function + P = apollo_prepareProb(P, apollo_inputs, functionality) + + return(P) + } + + model = apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities, apollo_inputs) + print(model) + + choices = c("car", "bus", "air", "rail") + + choice = apollo_prediction(model, apollo_probabilities, apollo_inputs) + choice = choice[choices] + colnames(choice) = lapply(choices, function(x){ paste("probability(", x, ")", sep = "")}) + choice$choice = apply(choice, 1, function(x) { choices[which.max(x)] }) + + storeRds(model, "NLModeChoice") + storeCsv(choice, "NLModeChoice") +} + +rm(list = c("database", "apollo_control", "apollo_beta", "apollo_fixed")) + set.seed(42) +generateMNLModeChoice() + rm(list = c("database", "apollo_control", "apollo_beta", "apollo_fixed")) -generateMNLModeChoice() \ No newline at end of file +set.seed(42) + +generateNLModeChoice() diff --git a/pmml-rexp/src/test/java/org/jpmml/rexp/testing/MaxLikConverterTest.java b/pmml-rexp/src/test/java/org/jpmml/rexp/testing/MaxLikConverterTest.java index 58bb4bb..f3c3fbb 100644 --- a/pmml-rexp/src/test/java/org/jpmml/rexp/testing/MaxLikConverterTest.java +++ b/pmml-rexp/src/test/java/org/jpmml/rexp/testing/MaxLikConverterTest.java @@ -23,7 +23,12 @@ public class MaxLikConverterTest extends RExpEncoderBatchTest { @Test - public void evaluateIris() throws Exception { + public void evaluateMNLModeChoice() throws Exception { evaluate("MNL", "ModeChoice"); } + + @Test + public void evaluateNLModeChoice() throws Exception { + evaluate("NL", "ModeChoice"); + } } \ No newline at end of file diff --git a/pmml-rexp/src/test/resources/csv/NLModeChoice.csv b/pmml-rexp/src/test/resources/csv/NLModeChoice.csv new file mode 100644 index 0000000..d968c8f --- /dev/null +++ b/pmml-rexp/src/test/resources/csv/NLModeChoice.csv @@ -0,0 +1,1001 @@ +probability(car),probability(bus),probability(air),probability(rail),choice +0.0709145454763794,0.0573580845540812,0.377713990700472,0.494013379269067,rail +0.0483763553545917,0.0341708053798255,0.278244932311088,0.639207906954494,rail 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