Package hivemall.io

Examples of hivemall.io.PredictionModel

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        if(actual_label.equals(missed_label)) {
            throw new IllegalArgumentException("Actual label equals to missed label: "
                    + actual_label);
        }

        PredictionModel model2add = label2model.get(actual_label);
        if(model2add == null) {
            model2add = createModel();
            label2model.put(actual_label, model2add);
        }
        PredictionModel model2sub = null;
        if(missed_label != null) {
            model2sub = label2model.get(missed_label);
            if(model2sub == null) {
                model2sub = createModel();
                label2model.put(missed_label, model2sub);
            }
        }

        final ObjectInspector featureInspector = featureListOI.getListElementObjectInspector();
        for(Object f : features) {// w[f] += y * x[f]
            if(f == null) {
                continue;
            }
            final Object k;
            final float v;
            if(parseFeature) {
                FeatureValue fv = FeatureValue.parse(f);
                k = fv.getFeature();
                v = fv.getValue();
            } else {
                k = ObjectInspectorUtils.copyToStandardObject(f, featureInspector);
                v = 1.f;
            }
            IWeightValue old_correctclass_w = model2add.get(k);
            IWeightValue new_correctclass_w = getNewWeight(old_correctclass_w, v, alpha, beta, true);
            model2add.set(k, new_correctclass_w);

            if(model2sub != null) {
                IWeightValue old_wrongclass_w = model2sub.get(k);
                IWeightValue new_wrongclass_w = getNewWeight(old_wrongclass_w, v, alpha, beta, false);
                model2sub.set(k, new_wrongclass_w);
            }
        }
    }
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        if(actual_label.equals(missed_label)) {
            throw new IllegalArgumentException("Actual label equals to missed label: "
                    + actual_label);
        }

        PredictionModel model2add = label2model.get(actual_label);
        if(model2add == null) {
            model2add = createModel();
            label2model.put(actual_label, model2add);
        }
        PredictionModel model2sub = null;
        if(missed_label != null) {
            model2sub = label2model.get(missed_label);
            if(model2sub == null) {
                model2sub = createModel();
                label2model.put(missed_label, model2sub);
            }
        }

        final ObjectInspector featureInspector = featureListOI.getListElementObjectInspector();
        for(Object f : features) {// w[f] += y * x[f]
            if(f == null) {
                continue;
            }
            final Object k;
            final float v;
            if(parseFeature) {
                FeatureValue fv = FeatureValue.parse(f);
                k = fv.getFeature();
                v = fv.getValue();
            } else {
                k = ObjectInspectorUtils.copyToStandardObject(f, featureInspector);
                v = 1.f;
            }
            IWeightValue old_correctclass_w = model2add.get(k);
            IWeightValue new_correctclass_w = getNewWeight(old_correctclass_w, v, alpha, phi, true);
            model2add.set(k, new_correctclass_w);

            if(model2sub != null) {
                IWeightValue old_wrongclass_w = model2sub.get(k);
                IWeightValue new_wrongclass_w = getNewWeight(old_wrongclass_w, v, alpha, phi, false);
                model2sub.set(k, new_wrongclass_w);
            }
        }
    }
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        if(actual_label.equals(missed_label)) {
            throw new IllegalArgumentException("Actual label equals to missed label: "
                    + actual_label);
        }

        PredictionModel model2add = label2model.get(actual_label);
        if(model2add == null) {
            model2add = createModel();
            label2model.put(actual_label, model2add);
        }
        PredictionModel model2sub = null;
        if(missed_label != null) {
            model2sub = label2model.get(missed_label);
            if(model2sub == null) {
                model2sub = createModel();
                label2model.put(missed_label, model2sub);
            }
        }

        final ObjectInspector featureInspector = featureListOI.getListElementObjectInspector();
        for(Object f : features) {// w[f] += y * x[f]
            if(f == null) {
                continue;
            }
            final Object k;
            final float v;
            if(parseFeature) {
                FeatureValue fv = FeatureValue.parse(f);
                k = fv.getFeature();
                v = fv.getValue();
            } else {
                k = ObjectInspectorUtils.copyToStandardObject(f, featureInspector);
                v = 1.f;
            }
            IWeightValue old_correctclass_w = model2add.get(k);
            IWeightValue new_correctclass_w = getNewWeight(old_correctclass_w, v, alpha, beta, true);
            model2add.set(k, new_correctclass_w);

            if(model2sub != null) {
                IWeightValue old_wrongclass_w = model2sub.get(k);
                IWeightValue new_wrongclass_w = getNewWeight(old_wrongclass_w, v, alpha, beta, false);
                model2sub.set(k, new_wrongclass_w);
            }
        }
    }
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        float maxScore = Float.MIN_VALUE;
        Object maxScoredLabel = null;

        for(Map.Entry<Object, PredictionModel> label2map : label2model.entrySet()) {// for each class
            Object label = label2map.getKey();
            PredictionModel model = label2map.getValue();
            float score = calcScore(model, features);
            if(maxScoredLabel == null || score > maxScore) {
                maxScore = score;
                maxScoredLabel = label;
            }
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        Object maxAnotherLabel = null;
        float maxAnotherScore = 0.f;

        for(Map.Entry<Object, PredictionModel> label2map : label2model.entrySet()) {// for each class
            Object label = label2map.getKey();
            PredictionModel model = label2map.getValue();
            float score = calcScore(model, features);
            if(label.equals(actual_label)) {
                correctScore = score;
            } else {
                if(maxAnotherLabel == null || score > maxAnotherScore) {
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            return new Margin(correctScore, maxAnotherLabel, maxAnotherScore).variance(var);
        }

        for(Map.Entry<Object, PredictionModel> label2map : label2model.entrySet()) {// for each class
            Object label = label2map.getKey();
            PredictionModel model = label2map.getValue();
            PredictionResult predicted = calcScoreAndVariance(model, features);
            float score = predicted.getScore();

            if(label.equals(actual_label)) {
                correctScore = score;
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        if(actual_label.equals(missed_label)) {
            throw new IllegalArgumentException("Actual label equals to missed label: "
                    + actual_label);
        }

        PredictionModel model2add = label2model.get(actual_label);
        if(model2add == null) {
            model2add = createModel();
            label2model.put(actual_label, model2add);
        }
        PredictionModel model2sub = null;
        if(missed_label != null) {
            model2sub = label2model.get(missed_label);
            if(model2sub == null) {
                model2sub = createModel();
                label2model.put(missed_label, model2sub);
            }
        }

        final ObjectInspector featureInspector = featureListOI.getListElementObjectInspector();
        for(Object f : features) {// w[f] += y * x[f]
            if(f == null) {
                continue;
            }
            final Object k;
            final float v;
            if(parseFeature) {
                FeatureValue fv = FeatureValue.parse(f);
                k = fv.getFeature();
                v = fv.getValue();
            } else {
                k = ObjectInspectorUtils.copyToStandardObject(f, featureInspector);
                v = 1.f;
            }
            float old_trueclass_w = model2add.getWeight(k);
            float add_w = old_trueclass_w + (coeff * v);
            model2add.set(k, new WeightValue(add_w));

            if(model2sub != null) {
                float old_falseclass_w = model2sub.getWeight(k);
                float sub_w = old_falseclass_w - (coeff * v);
                model2sub.set(k, new WeightValue(sub_w));
            }
        }
    }
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                final FloatWritable fv = new FloatWritable();
                final FloatWritable cov = new FloatWritable();
                for(Map.Entry<Object, PredictionModel> entry : label2model.entrySet()) {
                    Object label = entry.getKey();
                    forwardMapObj[0] = label;
                    PredictionModel model = entry.getValue();
                    numMixed += model.getNumMixed();
                    IMapIterator<Object, IWeightValue> itor = model.entries();
                    while(itor.next() != -1) {
                        itor.getValue(probe);
                        if(!probe.isTouched()) {
                            continue; // skip outputting untouched weights
                        }
                        Object k = itor.getKey();
                        fv.set(probe.get());
                        cov.set(probe.getCovariance());
                        forwardMapObj[1] = k;
                        forwardMapObj[2] = fv;
                        forwardMapObj[3] = cov;
                        forward(forwardMapObj);
                        numForwarded++;
                    }
                }
            } else {
                final WeightValue probe = new WeightValue();
                final Object[] forwardMapObj = new Object[3];
                final FloatWritable fv = new FloatWritable();
                for(Map.Entry<Object, PredictionModel> entry : label2model.entrySet()) {
                    Object label = entry.getKey();
                    forwardMapObj[0] = label;
                    PredictionModel model = entry.getValue();
                    numMixed += model.getNumMixed();
                    IMapIterator<Object, IWeightValue> itor = model.entries();
                    while(itor.next() != -1) {
                        itor.getValue(probe);
                        if(!probe.isTouched()) {
                            continue; // skip outputting untouched weights
                        }
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                        Object f2 = fields.get(2);
                        if(f0 == null || f1 == null || f2 == null) {
                            continue; // avoid the case that key or value is null
                        }
                        Object label = c1refOI.getPrimitiveWritableObject(c1refOI.copyObject(f0));
                        PredictionModel model = label2model.get(label);
                        if(model == null) {
                            model = createModel();
                            label2model.put(label, model);
                        }
                        Object k = c2refOI.getPrimitiveWritableObject(c2refOI.copyObject(f1));
                        float v = c3refOI.get(f2);
                        model.set(k, new WeightValue(v, false));
                    }
                } finally {
                    IOUtils.closeQuietly(reader);
                }
            }
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                        Object f3 = fields.get(3);
                        if(f0 == null || f1 == null || f2 == null) {
                            continue; // avoid unexpected case
                        }
                        Object label = c1refOI.getPrimitiveWritableObject(c1refOI.copyObject(f0));
                        PredictionModel model = label2model.get(label);
                        if(model == null) {
                            model = createModel();
                            label2model.put(label, model);
                        }
                        Object k = c2refOI.getPrimitiveWritableObject(c2refOI.copyObject(f1));
                        float v = c3refOI.get(f2);
                        float cov = (f3 == null) ? WeightValueWithCovar.DEFAULT_COVAR
                                : c4refOI.get(f3);
                        model.set(k, new WeightValueWithCovar(v, cov, false));
                    }
                } finally {
                    IOUtils.closeQuietly(reader);
                }
            }
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Related Classes of hivemall.io.PredictionModel

  • hivemall.classifier.multiclass.MulticlassAROWClassifierUDTF
  • hivemall.classifier.multiclass.MulticlassConfidenceWeightedUDTF
  • hivemall.classifier.multiclass.MulticlassOnlineClassifierUDTF
  • hivemall.classifier.multiclass.MulticlassSoftConfidenceWeightedUDTF
  • hivemall.LearnerBaseUDTF
  • hivemall.mix.server.MixServerTest
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