new class to extract plan indices

Makefile

@@ -257,6 +257,15 @@ eval-opt: $p/berlin-initial-$V-25pct.experienced_plans.xml.gz
 	$(sc) run --mode "routeChoice" --iterations 20 --all-car --output "output/eval-$(ERROR_METRIC)" --25pct --population "berlin-$V-25pct.plans_$(ERROR_METRIC).xml.gz"\
 	 --config $p/berlin-$V.config.xml
 
+$p/berlin-$V-25pct.plans_cadyts.xml.gz:
+	$(sc) prepare extract-plans-idx\
+	 --input output/cadyts/cadyts.output_plans.xml.gz\
+	 --output $p/berlin-$V-25pct.plans_selection_cadyts.csv
+
+	$(sc) prepare select-plans-idx\
+	 --input $p/berlin-cadyts-input-$V-25pct.plans.xml.gz\
+	 --csv $p/berlin-$V-25pct.plans_selection_cadyts.csv\
+	 --output $@
 
 # These depend on the output of optimization runs
 $p/berlin-$V-25pct.plans-initial.xml.gz: $p/berlin-$V-facilities.xml.gz $p/berlin-$V-network.xml.gz $p/berlin-longHaulFreight-$V-25pct.plans.xml.gz

src/main/java/org/matsim/prepare/RunOpenBerlinCalibration.java

@@ -44,6 +44,7 @@
 import org.matsim.core.scoring.SumScoringFunction;
 import org.matsim.core.scoring.functions.*;
 import org.matsim.core.utils.geometry.CoordUtils;
+import org.matsim.prepare.opt.ExtractPlanIndexFromType;
 import org.matsim.prepare.population.AssignReferencePopulation;
 import org.matsim.prepare.choices.ComputePlanChoices;
 import org.matsim.prepare.choices.ComputeTripChoices;
@@ -86,7 +87,7 @@
 	CreateCountsFromGeoPortalBerlin.class, CreateCountsFromVMZOld.class, CreateCountsFromVMZ.class, ReprojectNetwork.class, RunActivitySampling.class,
 	MergePlans.class, SplitActivityTypesDuration.class, CleanPopulation.class, CleanAttributes.class,
 	GenerateSmallScaleCommercialTrafficDemand.class, CreateDataDistributionOfStructureData.class,
-	RunCountOptimization.class, SelectPlansFromIndex.class, AssignReferencePopulation.class,
+	RunCountOptimization.class, SelectPlansFromIndex.class, ExtractPlanIndexFromType.class, AssignReferencePopulation.class,
 	ExtractRelevantFreightTrips.class, CheckCarAvailability.class, FixSubtourModes.class, ComputeTripChoices.class, ComputePlanChoices.class,
 	ApplyNetworkParams.class, SetCarAvailabilityByAge.class
 })
@@ -390,7 +391,7 @@ public ScoringFunction createNewScoringFunction(Person person) {
 //					sumScoringFunction.addScoringFunction(new CharyparNagelAgentStuckScoring(params));
 
 					final CadytsScoring<Link> scoringFunction = new CadytsScoring<>(person.getSelectedPlan(), config, cadytsContext);
-					scoringFunction.setWeightOfCadytsCorrection(config.scoring().getBrainExpBeta());
+					scoringFunction.setWeightOfCadytsCorrection(30 * config.scoring().getBrainExpBeta());
 					sumScoringFunction.addScoringFunction(scoringFunction);
 
 					return sumScoringFunction;

src/main/java/org/matsim/prepare/opt/ExtractPlanIndexFromType.java

@@ -0,0 +1,53 @@
+package org.matsim.prepare.opt;
+
+import it.unimi.dsi.fastutil.objects.Object2IntMap;
+import it.unimi.dsi.fastutil.objects.Object2IntOpenHashMap;
+import org.apache.commons.csv.CSVFormat;
+import org.apache.commons.csv.CSVPrinter;
+import org.matsim.api.core.v01.population.Person;
+import org.matsim.application.MATSimAppCommand;
+import org.matsim.core.config.ConfigUtils;
+import org.matsim.core.population.algorithms.PersonAlgorithm;
+import org.matsim.core.population.io.StreamingPopulationReader;
+import org.matsim.core.scenario.ScenarioUtils;
+import picocli.CommandLine;
+
+import java.nio.file.Files;
+import java.nio.file.Path;
+
+@CommandLine.Command(name = "extract-plans-idx", description = "Extract plan index from population.")
+public class ExtractPlanIndexFromType implements MATSimAppCommand, PersonAlgorithm {
+
+	@CommandLine.Option(names = "--input", description = "Path to input plans.", required = true)
+	private Path input;
+
+	@CommandLine.Option(names = "--output", description = "Desired output plans.", required = true)
+	private Path output;
+
+	private final Object2IntMap<String> mapping = new Object2IntOpenHashMap<>();
+
+	@Override
+	public Integer call() throws Exception {
+
+		StreamingPopulationReader reader = new StreamingPopulationReader(ScenarioUtils.createScenario(ConfigUtils.createConfig()));
+
+		reader.addAlgorithm(this);
+
+		reader.readFile(input.toString());
+
+		try (CSVPrinter csv = new CSVPrinter(Files.newBufferedWriter(output), CSVFormat.DEFAULT)) {
+			csv.printRecord("id", "idx");
+			for (Object2IntMap.Entry<String> e : mapping.object2IntEntrySet()) {
+				csv.printRecord(e.getKey(), e.getIntValue());
+			}
+		}
+
+		return 0;
+	}
+
+	@Override
+	public void run(Person person) {
+		String type = person.getSelectedPlan().getType();
+		mapping.put(person.getId().toString(), Integer.parseInt(type));
+	}
+}

src/main/java/org/matsim/prepare/opt/SelectPlansFromIndex.java

@@ -77,7 +77,7 @@ public static void selectPlanWithIndex(Person person, int planIndex) {
 		List<? extends Plan> plans = person.getPlans();
 		Set<Plan> removePlans = new HashSet<>();
 
-		// make sure that one plan is always selected, even if there are less plans than index
+		// make sure that one plan is always selected, even if there are fewer plans than index
 		int idx = planIndex % plans.size();
 
 		for (int i = 0; i < plans.size(); i++) {

src/main/java/org/matsim/run/scoring/AdvancedScoringConfigGroup.java

@@ -16,9 +16,12 @@ public final class AdvancedScoringConfigGroup extends ReflectiveConfigGroup {
 
 	private final List<ScoringParameters> scoringParameters = new ArrayList<>();
 
+	/**
+	 * Different options for income dependent scoring.
+	 */
 	public enum IncomeDependentScoring {
 		none,
-		avg_by_personal_income,
+		avg_by_personal_income
 	}
 
 	@Parameter
@@ -32,7 +35,6 @@ public enum IncomeDependentScoring {
 
 	// TODO: maybe option to re-assign variations or use them from attributes
 	// TODO: could load the random variations from a file, helper function to only generate the variations
-	// TODO: reference population?, for which the loglikelihood could be calculated
 
 	public AdvancedScoringConfigGroup() {
 		super(GROUP_NAME);

src/main/python/calibrate.py

@@ -8,10 +8,10 @@
 modes = ["walk", "car", "pt", "bike", "ride"]
 fixed_mode = "walk"
 initial = {
-    "bike": -2.23,
-    "pt": -0.25,
-    "car": -0.62,
-    "ride": -1.37
+    "bike": -1.4,
+    "pt": 0.6,
+    "car": -1,
+    "ride": -1.4
 }
 
 # Original modal split

src/main/python/calibrate_dist.py

@@ -10,10 +10,10 @@
 modes = ["walk", "car", "pt", "bike", "ride"]
 fixed_mode = "walk"
 initial = {
-    "bike": -1.6,
-    "pt": 0.2,
-    "car": -0.4,
-    "ride": -1.2
+    "bike": -1.4,
+    "pt": 0.6,
+    "car": -1,
+    "ride": -1.4
 }
 
 target = pd.read_csv("mode_share_ref.csv")