@Override
public void doTransform(FeatureVector featureVector) {
Map<String, Map<String, Double>> floatFeatures = featureVector.getFloatFeatures();
if (floatFeatures == null) {
return;
}
Map<String, Double> feature1 = floatFeatures.get(fieldName1);
if (feature1 == null || feature1.isEmpty()) {
return;
}
Util.optionallyCreateStringFeatures(featureVector);
Map<String, Set<String>> stringFeatures = featureVector.getStringFeatures();
Set<String> output = Util.getOrCreateStringFeature(outputName, stringFeatures);
for (Entry<String, Double> feature : feature1.entrySet()) {
output.add(logQuantize(feature.getKey(), feature.getValue()));
}
}
@Override
public void doTransform(FeatureVector featureVector) {
Map<String, Map<String, Double>> floatFeatures = featureVector.getFloatFeatures();
if (floatFeatures == null) {
return;
}
Map<String, Double> feature1 = floatFeatures.get(fieldName1);
if (feature1 == null || feature1.isEmpty()) {
return;
}
Map<String, Double> output = Util.getOrCreateFloatFeature(outputName, floatFeatures);
for (Entry<String, Double> feature : feature1.entrySet()) {
Double dbl = LinearLogQuantizeTransform.quantize(feature.getValue(), bucket);
Double newVal = feature.getValue() - dbl;
String name = feature.getKey() + '[' + bucket + "]=" + dbl;
output.put(name, newVal);
}
}
@Override
public void doTransform(FeatureVector featureVector) {
Map<String, Map<String, Double>> floatFeatures = featureVector.getFloatFeatures();
if (floatFeatures == null) {
return;
}
Map<String, Double> feature1 = floatFeatures.get(fieldName1);
if (feature1 == null) {
return;
}
Map<String, Double> feature2 = floatFeatures.get(fieldName2);
if (feature2 == null) {
return;
}
Double div = feature2.get(key2);
if (div == null) {
return;
}
Double scale = 1.0 / (constant + div);
Map<String, Double> output = Util.getOrCreateFloatFeature(outputName, floatFeatures);
Set<String> allKeys = feature1.keySet();
for (String key : allKeys) {
if (keys == null || keys.contains(key)) {
Double val = feature1.get(key);
if (val != null) {
output.put(key + "-d-" + key2, val * scale);
}
}
}
}
@Test
public void testLoad() {
CharArrayWriter charWriter = new CharArrayWriter();
BufferedWriter writer = new BufferedWriter(charWriter);
ModelHeader header = new ModelHeader();
header.setModelType("additive");
header.setNumRecords(4);
ArrayList<Double> ws = new ArrayList<Double>();
ws.add(5.0);
ws.add(10.0);
ws.add(-20.0);
ArrayList<Double> wl = new ArrayList<Double>();
wl.add(1.0);
wl.add(2.0);
ModelRecord record1 = new ModelRecord();
record1.setModelHeader(header);
ModelRecord record2 = new ModelRecord();
record2.setFunctionForm(FunctionForm.Spline);
record2.setFeatureFamily("spline_float");
record2.setFeatureName("aaa");
record2.setWeightVector(ws);
record2.setMinVal(1.0);
record2.setMaxVal(3.0);
ModelRecord record3 = new ModelRecord();
record3.setFunctionForm(FunctionForm.Spline);
record3.setFeatureFamily("spline_string");
record3.setFeatureName("bbb");
record3.setWeightVector(ws);
record3.setMinVal(1.0);
record3.setMaxVal(2.0);
ModelRecord record4 = new ModelRecord();
record4.setFunctionForm(FunctionForm.Linear);
record4.setFeatureFamily("linear_float");
record4.setFeatureName("ccc");
record4.setWeightVector(wl);
ModelRecord record5 = new ModelRecord();
record5.setFunctionForm(FunctionForm.Linear);
record5.setFeatureFamily("linear_string");
record5.setFeatureName("ddd");
record5.setWeightVector(wl);
try {
writer.write(Util.encode(record1) + "\n");
writer.write(Util.encode(record2) + "\n");
writer.write(Util.encode(record3) + "\n");
writer.write(Util.encode(record4) + "\n");
writer.write(Util.encode(record5) + "\n");
writer.close();
} catch (IOException e) {
assertTrue("Could not write", false);
}
String serialized = charWriter.toString();
assertTrue(serialized.length() > 0);
StringReader strReader = new StringReader(serialized);
BufferedReader reader = new BufferedReader(strReader);
FeatureVector featureVector = makeFeatureVector(2.0f, 7.0f);
try {
Optional<AbstractModel> model = ModelFactory.createFromReader(reader);
assertTrue(model.isPresent());
float score = model.get().scoreItem(featureVector);
assertEquals(8.0f + 10.0f + 15.0f, score, 0.001f);
} catch (IOException e) {
assertTrue("Could not read", false);
}
}
