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Merge pull request #129 from sanity/genSplitFract
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Gen split fract
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@athawk81
athawk81 committed Jun 16, 2015
2 parents 2f97f4c + c0eacea commit 268521e
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Showing 2 changed files with 32 additions and 16 deletions.
26 changes: 21 additions & 5 deletions src/main/java/quickml/supervised/classifier/decisionTree/TreeBuilder.java
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Original file line number Diff line number Diff line change
Expand Up @@ -43,12 +43,14 @@ public final class TreeBuilder<T extends ClassifierInstance> implements Predicti
private static final int HARD_MINIMUM_INSTANCES_PER_CATEGORICAL_VALUE = 10;
public static final String MIN_SPLIT_FRACTION = "minSplitFraction";
public static final String EXEMPT_ATTRIBUTES = "exemptAttributes";
public static final String IMBALANCE_PENALTY_POWER = "impbalancePenaltyPower";

private Scorer scorer;
private int maxDepth = 5;
private double minimumScore = 0.00000000000001;
private int minDiscreteAttributeValueOccurances = 0;
private double minSplitFraction = .005;
private double imbalancePenaltyPower = 0;
private Set<String> exemptAttributes = Sets.newHashSet();

private int minLeafInstances = 0;
Expand Down Expand Up @@ -77,6 +79,10 @@ public TreeBuilder attributeIgnoringStrategy(AttributeIgnoringStrategy attribute
this.attributeIgnoringStrategy = attributeIgnoringStrategy;
return this;
}
public TreeBuilder imbalancePenaltyPower(double imbalancePenaltyPower ) {
this.imbalancePenaltyPower = imbalancePenaltyPower;
return this;
}

public TreeBuilder exemptAttributes(Set<String> exemptAttributes) {
this.exemptAttributes = exemptAttributes;
Expand Down Expand Up @@ -108,6 +114,7 @@ public TreeBuilder copy() {
copy.fractionOfDataToUseInHoldOutSet = fractionOfDataToUseInHoldOutSet;
copy.minSplitFraction = minSplitFraction;
copy.exemptAttributes = exemptAttributes;
copy.imbalancePenaltyPower = imbalancePenaltyPower;
return copy;
}

Expand Down Expand Up @@ -137,8 +144,9 @@ public void updateBuilderConfig(final Map<String, Object> cfg) {
if (cfg.containsKey(ATTRIBUTE_IGNORING_STRATEGY))
attributeIgnoringStrategy((AttributeIgnoringStrategy) cfg.get(ATTRIBUTE_IGNORING_STRATEGY));
if (cfg.containsKey(IGNORE_ATTR_PROB))
ignoreAttributeAtNodeProbability((Double)cfg.get(IGNORE_ATTR_PROB));

ignoreAttributeAtNodeProbability((Double) cfg.get(IGNORE_ATTR_PROB));
if (cfg.containsKey(IMBALANCE_PENALTY_POWER))
imbalancePenaltyPower((Double)cfg.get(IMBALANCE_PENALTY_POWER));
penalizeCategoricalSplitsBySplitAttributeIntrinsicValue(cfg.containsKey(PENALIZE_CATEGORICAL_SPLITS) ? (Boolean) cfg.get(PENALIZE_CATEGORICAL_SPLITS) : true);
}

Expand Down Expand Up @@ -434,7 +442,7 @@ private Pair<? extends Branch, Double> createTwoClassCategoricalNode(Node parent

double bestScore = 0;
final Pair<ClassificationCounter, List<AttributeValueWithClassificationCounter>> valueOutcomeCountsPairs =
ClassificationCounter.getSortedListOfAttributeValuesWithClassificationCounters(instances, attribute, minorityClassification); //returs a list of ClassificationCounterList
ClassificationCounter.getSortedListOfAttributeValuesWithClassificationCounters(instances, attribute, minorityClassification); //returns a list of ClassificationCounterList

ClassificationCounter outCounts = new ClassificationCounter(valueOutcomeCountsPairs.getValue0()); //classification counter treating all values the same
ClassificationCounter inCounts = new ClassificationCounter(); //the histogram of counts by classification for the in-set
Expand Down Expand Up @@ -474,8 +482,11 @@ private Pair<? extends Branch, Double> createTwoClassCategoricalNode(Node parent
double thisScore = scorer.scoreSplit(inCounts, outCounts);
valuesInTheInset++;
if (penalizeCategoricalSplitsBySplitAttributeIntrinsicValue) {
thisScore = thisScore * (1 - degreeOfGainRatioPenalty) + degreeOfGainRatioPenalty * (thisScore / intrinsicValueOfAttribute); }

thisScore = thisScore * (1 - degreeOfGainRatioPenalty) + degreeOfGainRatioPenalty * (thisScore / intrinsicValueOfAttribute);
}
if (imbalancePenaltyPower!=0) {
thisScore/=Math.pow(Math.min(inCounts.getTotal(), outCounts.getTotal()), imbalancePenaltyPower);
}
if (thisScore > bestScore) {
bestScore = thisScore;
lastValOfInset = valueWithClassificationCounter.attributeValue;
Expand Down Expand Up @@ -712,6 +723,11 @@ private Pair<? extends Branch, Double> createNumericBranch(Node parent, final St


double thisScore = scorer.scoreSplit(inClassificationCounts, outClassificationCounts);

if (imbalancePenaltyPower!=0) {
thisScore/=Math.pow(Math.min(inClassificationCounts.getTotal(), outClassificationCounts.getTotal()), imbalancePenaltyPower);
}

if (thisScore > bestScore) {
bestScore = thisScore;
bestThreshold = threshold;
Expand Down
22 changes: 11 additions & 11 deletions .../quickml/supervised/predictiveModelOptimizer/PredictiveModelOptimizerIntegrationTest.java
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Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,7 @@ public class PredictiveModelOptimizerIntegrationTest {
@Before
public void setUp() throws Exception {
List<ClassifierInstance> advertisingInstances = getAdvertisingInstances();
advertisingInstances = advertisingInstances.subList(0, 3000);
// advertisingInstances = advertisingInstances.subList(0, 3000);
optimizer = new PredictiveModelOptimizerBuilder<Classifier, ClassifierInstance>()
.modelBuilder(new RandomForestBuilder<>())
.dataCycler(new OutOfTimeData<>(advertisingInstances, 0.2, 12, new OnespotDateTimeExtractor()))
Expand All @@ -62,17 +62,17 @@ private Map<String, FieldValueRecommender> createConfig() {
CompositeAttributeIgnoringStrategy compositeAttributeIgnoringStrategy = new CompositeAttributeIgnoringStrategy(Arrays.asList(
new IgnoreAttributesWithConstantProbability(0.7), new IgnoreAttributesInSet(attributesToIgnore, probabilityOfDiscardingFromAttributesToIgnore)
));
config.put(ATTRIBUTE_IGNORING_STRATEGY, new FixedOrderRecommender(new IgnoreAttributesWithConstantProbability(0.7), compositeAttributeIgnoringStrategy ));
config.put(NUM_TREES, new MonotonicConvergenceRecommender(asList(2, 4, 8), 0.02));
config.put(MAX_DEPTH, new FixedOrderRecommender( 4, 8, 16));//Integer.MAX_VALUE, 2, 3, 5, 6, 9));
config.put(ATTRIBUTE_IGNORING_STRATEGY, new FixedOrderRecommender(new IgnoreAttributesWithConstantProbability(0.7)));//, compositeAttributeIgnoringStrategy ));
config.put(NUM_TREES, new MonotonicConvergenceRecommender(asList(8), 0.02));
config.put(MAX_DEPTH, new FixedOrderRecommender(8));//, 16));//Integer.MAX_VALUE, 2, 3, 5, 6, 9));
config.put(MIN_SCORE, new FixedOrderRecommender(0.00000000000001));//, Double.MIN_VALUE, 0.0, 0.000001, 0.0001, 0.001, 0.01, 0.1));
config.put(MIN_OCCURRENCES_OF_ATTRIBUTE_VALUE, new FixedOrderRecommender(2, 11, 16, 30 ));
config.put(MIN_LEAF_INSTANCES, new FixedOrderRecommender(0, 20, 40));
config.put(SCORER, new FixedOrderRecommender(new InformationGainScorer(), new GiniImpurityScorer()));
config.put(DEGREE_OF_GAIN_RATIO_PENALTY, new FixedOrderRecommender(1.0, 0.75, .5 ));
config.put(MIN_SPLIT_FRACTION, new FixedOrderRecommender(0.01, 0.25, .5 ));
config.put(EXEMPT_ATTRIBUTES, new FixedOrderRecommender(exemptAttributes));

config.put(MIN_OCCURRENCES_OF_ATTRIBUTE_VALUE, new FixedOrderRecommender(11));//;, 16, 30 ));
config.put(MIN_LEAF_INSTANCES, new FixedOrderRecommender(20));//, 40));
config.put(SCORER, new FixedOrderRecommender(new GiniImpurityScorer()));//, new InformationGainScorer())), ;
config.put(DEGREE_OF_GAIN_RATIO_PENALTY, new FixedOrderRecommender(1.0));//, 0.75, .5 ));
config.put(MIN_SPLIT_FRACTION, new FixedOrderRecommender(0.001));// 0.25, .5 ));
// config.put(EXEMPT_ATTRIBUTES, new FixedOrderRecommender(exemptAttributes));
config.put(IMBALANCE_PENALTY_POWER, new FixedOrderRecommender(0.0, 1.0, 2.0));
return config;
}

Expand Down

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