Package quickml.supervised.predictiveModelOptimizer.fieldValueRecommenders

Examples of quickml.supervised.predictiveModelOptimizer.fieldValueRecommenders.FixedOrderRecommender

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    @Override
    public Map<String, FieldValueRecommender> createDefaultParametersToOptimize() {
        if (!parametersToOptimize.containsKey(REGULARIZATION_CONSTANT)) {
            parametersToOptimize.put(REGULARIZATION_CONSTANT, new FixedOrderRecommender(0.001, 0.003, .01, 0.03, 0.1, 0.3));
        }
        return parametersToOptimize;
    }
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        this.header = header;
        return this;
    }

    public RidgeLinearModelBuilderFactory includeBiasTerm(Boolean includeBiasTerm) {
        parametersToOptimize.put(INCLUDE_BIAS_TERM, new FixedOrderRecommender(includeBiasTerm));
        return this;
    }
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    private static final String SCORER= "scorer";

    @Override
    public Map<String, FieldValueRecommender> createDefaultParametersToOptimize() {
        Map<String, FieldValueRecommender> parametersToOptimize = Maps.newHashMap();
        parametersToOptimize.put(IGNORE_ATTR_PROB, new FixedOrderRecommender(0.5, 0.0, 0.1, 0.2, 0.4, 0.7, 0.8, 0.9, 0.95, 0.98, 0.99));
        parametersToOptimize.put(MAX_DEPTH, new FixedOrderRecommender(Integer.MAX_VALUE, 2, 3, 4, 5, 6, 7, 9));
        parametersToOptimize.put(MIN_SCORE, new FixedOrderRecommender(0.00000000000001, Double.MIN_VALUE, 0.0, 0.000001, 0.0001, 0.001, 0.01, 0.1));
        parametersToOptimize.put(MIN_CAT_ATTR_OCC, new FixedOrderRecommender(5, 0, 1, 64, 1024, 4098));
        parametersToOptimize.put(MIN_LEAF_INSTANCES, new FixedOrderRecommender(0, 10, 100, 1000, 10000, 100000));
        parametersToOptimize.put(SCORER, new FixedOrderRecommender(new MSEScorer(MSEScorer.CrossValidationCorrection.FALSE), new MSEScorer(MSEScorer.CrossValidationCorrection.TRUE), new SplitDiffScorer(), new InformationGainScorer(), new GiniImpurityScorer()));
        return parametersToOptimize;
    }
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    @Override
    public Map<String, FieldValueRecommender> createDefaultParametersToOptimize() {
        Map<String, FieldValueRecommender> parametersToOptimize = Maps.newHashMap();
               parametersToOptimize.putAll(wrappedBuilderBuilder.createDefaultParametersToOptimize());
               parametersToOptimize.put(MINORITY_INSTANCE_PROPORTION, new FixedOrderRecommender(0.1, 0.2, 0.3, 0.4, 0.5));
               return parametersToOptimize;
    }
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    }

    @Override
    public Map<String, FieldValueRecommender> createDefaultParametersToOptimize() {
        Map<String, FieldValueRecommender> map = predictiveModelBuilderBuilder.createDefaultParametersToOptimize();
        map.put(REBUILD_THRESHOLD, new FixedOrderRecommender(0, 25));
        map.put(SPLIT_THRESHOLD, new FixedOrderRecommender(0, 5));
        return map;
    }
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    @Override
    public Map<String, FieldValueRecommender> createDefaultParametersToOptimize() {
        Map<String, FieldValueRecommender> parametersToOptimize = Maps.newHashMap();
        parametersToOptimize.putAll(wrappedBuilderBuilder.createDefaultParametersToOptimize());
        parametersToOptimize.put(HALF_LIFE_OF_NEGATIVE, new FixedOrderRecommender(1.0, 7.0, 30.0));
        parametersToOptimize.put(HALF_LIFE_OF_POSITIVE, new FixedOrderRecommender(1.0, 7.0, 30.0));
        parametersToOptimize.put(DATE_EXTRACTOR, new FixedOrderRecommender(new SimpleDateFormatExtractor()));
        return parametersToOptimize;
    }
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    @Override
    public Map<String, FieldValueRecommender> createDefaultParametersToOptimize() {
        Map<String, FieldValueRecommender> parametersToOptimize = Maps.newHashMap();
        parametersToOptimize.putAll(wrappedBuilderBuilder.createDefaultParametersToOptimize());
        parametersToOptimize.put(MIN_AMOUNT_TOTAL_CROSS_DATA, new FixedOrderRecommender(0, 100, 1000));
        parametersToOptimize.put(PERCENT_CROSS_DATA, new FixedOrderRecommender(0.1, 0.2, 0.5));
        parametersToOptimize.put(MIN_AMOUNT_CROSS_DATA_CLASSIFICATION, new FixedOrderRecommender(0, 10, 100));
        return parametersToOptimize;
    }
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    @Override
    public Map<String, FieldValueRecommender> createDefaultParametersToOptimize() {
        Map<String, FieldValueRecommender> parametersToOptimize = Maps.newHashMap();
        parametersToOptimize.putAll(treeBuilderBuilder.createDefaultParametersToOptimize());
        parametersToOptimize.put(NUM_TREES, new FixedOrderRecommender(5, 10, 20, 40));
        parametersToOptimize.put(BAG_SIZE, new FixedOrderRecommender(0, 1000, 10000, Integer.MAX_VALUE));
        return parametersToOptimize;
    }
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    @Override
    public Map<String, FieldValueRecommender> createDefaultParametersToOptimize() {
        Map<String, FieldValueRecommender> parametersToOptimize = Maps.newHashMap();
        parametersToOptimize.putAll(wrappedBuilderFactory.createDefaultParametersToOptimize());
        parametersToOptimize.put(BINS_IN_CALIBRATOR, new FixedOrderRecommender(5, 10, 20, 40));
        return parametersToOptimize;
    }
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    @Test
    public void ridgePMOTest() {
        List<Instance<double[]>> trainingData = setUp();
        CrossValidator<double[], Double> crossValidator = new StationaryCrossValidator<>(4, new SingleVariableRealValuedFunctionMSECVLossFunction());
        RidgeLinearModelBuilderFactory ridgeLinearModelBuilderFactory = new RidgeLinearModelBuilderFactory().header(header).includeBiasTerm(true).regularizationConstants(new FixedOrderRecommender(0.001, 0.01, 0.1));
        PredictiveModelOptimizer<double[], Double, RidgeLinearModel, RidgeLinearModelBuilder> predictiveModelOptimizer = new PredictiveModelOptimizer<>(ridgeLinearModelBuilderFactory, trainingData, crossValidator);
        Map<String, Object> optimalParams = predictiveModelOptimizer.determineOptimalConfiguration();
        for (String key : optimalParams.keySet())
            logger.info(key+ " : " + optimalParams.get(key));
    }
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Related Classes of quickml.supervised.predictiveModelOptimizer.fieldValueRecommenders.FixedOrderRecommender

  • quickml.supervised.calibratedPredictiveModel.CalibratedPredictiveModelBuilderFactory
  • quickml.supervised.classifier.decisionTree.TreeBuilderFactory
  • quickml.supervised.classifier.downsampling.DownsamplingClassifierBuilderFactory
  • quickml.supervised.classifier.randomForest.RandomForestBuilderFactory
  • quickml.supervised.classifier.splitOnAttribute.SplitOnAttributeClassifierBuilderFactory
  • quickml.supervised.classifier.temporallyWeightClassifier.TemporallyReweightedClassifierBuilderFactory
  • quickml.supervised.PredictiveModelWithDataBuilderFactory
  • quickml.supervised.regressionModel.LinearRegression.RidgeLinearModelBuilderFactory
  • quickml.supervised.regressionModel.RidgeRegressionBuilderTest
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