Github user myui commented on a diff in the pull request:
https://github.com/apache/incubator-hivemall/pull/107#discussion_r135693139
--- Diff: core/src/main/java/hivemall/evaluation/FMeasureUDAF.java ---
@@ -18,118 +18,387 @@
*/
package hivemall.evaluation;
-import hivemall.utils.hadoop.WritableUtils;
+import hivemall.UDAFEvaluatorWithOptions;
+import hivemall.utils.hadoop.HiveUtils;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
import java.util.List;
+import hivemall.utils.lang.Primitives;
+import org.apache.commons.cli.CommandLine;
+import org.apache.commons.cli.Options;
+
import org.apache.hadoop.hive.ql.exec.Description;
-import org.apache.hadoop.hive.ql.exec.UDAF;
-import org.apache.hadoop.hive.ql.exec.UDAFEvaluator;
+import org.apache.hadoop.hive.ql.exec.UDFArgumentTypeException;
+import org.apache.hadoop.hive.ql.exec.UDFArgumentException;
+import org.apache.hadoop.hive.ql.metadata.HiveException;
+import org.apache.hadoop.hive.ql.parse.SemanticException;
+import org.apache.hadoop.hive.ql.udf.generic.AbstractGenericUDAFResolver;
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFEvaluator;
import org.apache.hadoop.hive.serde2.io.DoubleWritable;
-import org.apache.hadoop.io.IntWritable;
-
-@SuppressWarnings("deprecation")
-@Description(name = "f1score",
- value = "_FUNC_(array[int], array[int]) - Return a F-measure/F1
score")
-public final class FMeasureUDAF extends UDAF {
-
- public static class Evaluator implements UDAFEvaluator {
-
- public static class PartialResult {
- long tp;
- /** tp + fn */
- long totalAcutal;
- /** tp + fp */
- long totalPredicted;
-
- PartialResult() {
- this.tp = 0L;
- this.totalPredicted = 0L;
- this.totalAcutal = 0L;
- }
+import org.apache.hadoop.hive.serde2.objectinspector.*;
+import
org.apache.hadoop.hive.serde2.objectinspector.primitive.BooleanObjectInspector;
+import
org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory;
+import
org.apache.hadoop.hive.serde2.objectinspector.primitive.IntObjectInspector;
+import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo;
+import org.apache.hadoop.io.LongWritable;
- void updateScore(final List<IntWritable> actual, final
List<IntWritable> predicted) {
- final int numActual = actual.size();
- final int numPredicted = predicted.size();
- int countTp = 0;
- for (int i = 0; i < numPredicted; i++) {
- IntWritable p = predicted.get(i);
- if (actual.contains(p)) {
- countTp++;
- }
+import javax.annotation.Nonnull;
+
+@Description(
+ name = "fmeasure",
+ value = "_FUNC_(array | int | boolean actual , array | int |
boolean predicted, String) - Return a F-measure (f1score is the special with
beta=1.)")
+public final class FMeasureUDAF extends AbstractGenericUDAFResolver {
+ @Override
+ public GenericUDAFEvaluator getEvaluator(@Nonnull TypeInfo[] typeInfo)
throws SemanticException {
+ if (typeInfo.length != 2 && typeInfo.length != 3) {
+ throw new UDFArgumentTypeException(typeInfo.length - 1,
+ "_FUNC_ takes two or three arguments");
+ }
+
+ boolean isArg1ListOrIntOrBoolean =
HiveUtils.isListTypeInfo(typeInfo[0])
+ || HiveUtils.isIntegerTypeInfo(typeInfo[0])
+ || HiveUtils.isBooleanTypeInfo(typeInfo[0]);
+ if (!isArg1ListOrIntOrBoolean) {
+ throw new UDFArgumentTypeException(0,
+ "The first argument `array/int/boolean actual` is invalid
form: " + typeInfo[0]);
+ }
+
+ boolean isArg2ListOrIntOrBoolean =
HiveUtils.isListTypeInfo(typeInfo[1])
+ || HiveUtils.isIntegerTypeInfo(typeInfo[1])
+ || HiveUtils.isBooleanTypeInfo(typeInfo[1]);
+ if (!isArg2ListOrIntOrBoolean) {
+ throw new UDFArgumentTypeException(1,
+ "The second argument `array/int/boolean predicted` is
invalid form: " + typeInfo[1]);
+ }
+
+ if (typeInfo[0] != typeInfo[1]) {
+ throw new UDFArgumentTypeException(1, "The first argument
`actual`'s type is "
+ + typeInfo[0] + ", but the second argument
`predicted`'s type is not match: "
+ + typeInfo[1]);
+ }
+
+ return new Evaluator();
+ }
+
+ public static class Evaluator extends UDAFEvaluatorWithOptions {
+
+ private ObjectInspector actualOI;
+ private ObjectInspector predictedOI;
+ private StructObjectInspector internalMergeOI;
+
+ private StructField tpField;
+ private StructField totalActualField;
+ private StructField totalPredictedField;
+ private StructField betaOptionField;
+ private StructField averageOptionFiled;
+
+ private double beta;
+ private String average;
+
+ public Evaluator() {}
+
+ @Override
+ protected Options getOptions() {
+ Options opts = new Options();
+ opts.addOption("beta", true, "The weight of precision
[default: 1.]");
+ opts.addOption("average", true, "The way of average
calculation [default: micro]");
+ return opts;
+ }
+
+ @Override
+ protected CommandLine processOptions(ObjectInspector[] argOIs)
throws UDFArgumentException {
+ CommandLine cl = null;
+
+ double beta = 1.0d;
+ String average = "micro";
+
+ if (argOIs.length >= 3) {
+ String rawArgs = HiveUtils.getConstString(argOIs[2]);
+ cl = parseOptions(rawArgs);
+
+ beta = Primitives.parseDouble(cl.getOptionValue("beta"),
beta);
+ if (beta <= 0.d) {
+ throw new UDFArgumentException(
+ "The third argument `double beta` must be greater
than 0.0: " + beta);
}
- this.tp += countTp;
- this.totalAcutal += numActual;
- this.totalPredicted += numPredicted;
+
+ average = cl.getOptionValue("average", average);
+
+ if (average.equals("macro")) {
+ throw new UDFArgumentException("\"-average macro\" is
not supported");
+ }
+
+ if (!(average.equals("binary") ||
average.equals("micro"))) {
+ throw new UDFArgumentException(
+ "The third argument `String average` must be one
of the {binary, micro, macro}: "
+ + average);
+ }
+ }
+
+ this.beta = beta;
+ this.average = average;
+ return cl;
+ }
+
+ @Override
+ public ObjectInspector init(Mode mode, ObjectInspector[]
parameters) throws HiveException {
+ assert (parameters.length == 2 || parameters.length == 3) :
parameters.length;
+ super.init(mode, parameters);
+
+ // initialize input
+ if (mode == Mode.PARTIAL1 || mode == Mode.COMPLETE) {// from
original data
+ this.processOptions(parameters);
+ this.actualOI = parameters[0];
+ this.predictedOI = parameters[1];
+ } else {// from partial aggregation
+ StructObjectInspector soi = (StructObjectInspector)
parameters[0];
+ this.internalMergeOI = soi;
+ this.tpField = soi.getStructFieldRef("tp");
+ this.totalActualField =
soi.getStructFieldRef("totalActual");
+ this.totalPredictedField =
soi.getStructFieldRef("totalPredicted");
+ this.betaOptionField = soi.getStructFieldRef("beta");
+ this.averageOptionFiled = soi.getStructFieldRef("average");
}
- void merge(PartialResult other) {
- this.tp = other.tp;
- this.totalAcutal = other.totalAcutal;
- this.totalPredicted = other.totalPredicted;
+ // initialize output
+ final ObjectInspector outputOI;
+ if (mode == Mode.PARTIAL1 || mode == Mode.PARTIAL2) {//
terminatePartial
+ outputOI = internalMergeOI();
+ } else {// terminate
+ outputOI =
PrimitiveObjectInspectorFactory.writableDoubleObjectInspector;
}
+ return outputOI;
}
- private PartialResult partial;
+ private static StructObjectInspector internalMergeOI() {
+ ArrayList<String> fieldNames = new ArrayList<>();
+ ArrayList<ObjectInspector> fieldOIs = new ArrayList<>();
+
+ fieldNames.add("tp");
+
fieldOIs.add(PrimitiveObjectInspectorFactory.writableLongObjectInspector);
+ fieldNames.add("totalActual");
+
fieldOIs.add(PrimitiveObjectInspectorFactory.writableLongObjectInspector);
+ fieldNames.add("totalPredicted");
+
fieldOIs.add(PrimitiveObjectInspectorFactory.writableLongObjectInspector);
+ fieldNames.add("beta");
+
fieldOIs.add(PrimitiveObjectInspectorFactory.writableDoubleObjectInspector);
+ fieldNames.add("average");
+
fieldOIs.add(PrimitiveObjectInspectorFactory.javaStringObjectInspector);
+
+ return
ObjectInspectorFactory.getStandardStructObjectInspector(fieldNames, fieldOIs);
+ }
@Override
- public void init() {
- this.partial = null;
+ public FMeasureAggregationBuffer getNewAggregationBuffer() throws
HiveException {
+ FMeasureAggregationBuffer myAggr = new
FMeasureAggregationBuffer();
+ reset(myAggr);
+ return myAggr;
}
- public boolean iterate(List<IntWritable> actual, List<IntWritable>
predicted) {
- if (partial == null) {
- this.partial = new PartialResult();
+ @Override
+ public void reset(@SuppressWarnings("deprecation")
AggregationBuffer agg)
+ throws HiveException {
+ FMeasureAggregationBuffer myAggr = (FMeasureAggregationBuffer)
agg;
+ myAggr.reset();
+ myAggr.setOptions(this.beta, this.average);
+ }
+
+ @Override
+ public void iterate(@SuppressWarnings("deprecation")
AggregationBuffer agg,
+ Object[] parameters) throws HiveException {
+ FMeasureAggregationBuffer myAggr = (FMeasureAggregationBuffer)
agg;
+ boolean isList = HiveUtils.isListOI(actualOI) &&
HiveUtils.isListOI(predictedOI);
+
+ final List<?> actual;
+ final List<?> predicted;
+
+ if (isList) {// array case
+ if (this.average.equals("binary")) {
+ throw new UDFArgumentException(
+ "\"-average binary\" is not supported when
`predict` is array");
+ }
+ actual = ((ListObjectInspector)
actualOI).getList(parameters[0]);
+ predicted = ((ListObjectInspector)
predictedOI).getList(parameters[1]);
+ } else {//binary case
+ if (HiveUtils.isBooleanOI(actualOI)) { // boolean case
+ actual = Arrays.asList(asIntLabel(parameters[0],
+ (BooleanObjectInspector) actualOI));
+ predicted = Arrays.asList(asIntLabel(parameters[1],
+ (BooleanObjectInspector) predictedOI));
+ } else { // int case
+ int actualLabel = asIntLabel(parameters[0],
(IntObjectInspector) actualOI);
+
+ if (actualLabel == 0 && this.average.equals("binary"))
{
+ actual = Collections.emptyList();
+ } else {
+ actual = Arrays.asList(actualLabel);
+ }
+
+ int predictedLabel = asIntLabel(parameters[1],
(IntObjectInspector) predictedOI);
+ if (predictedLabel == 0 &&
this.average.equals("binary")) {
+ predicted = Collections.emptyList();
+ } else {
+ predicted = Arrays.asList(predictedLabel);
+ }
+ }
}
- partial.updateScore(actual, predicted);
- return true;
+ myAggr.iterate(actual, predicted);
}
- public PartialResult terminatePartial() {
- return partial;
+ private int asIntLabel(@Nonnull Object o, @Nonnull
BooleanObjectInspector booleanOI) {
+ if (booleanOI.get(o)) {
+ return 1;
+ } else {
+ return 0;
+ }
}
- public boolean merge(PartialResult other) {
- if (other == null) {
- return true;
+ private int asIntLabel(@Nonnull Object o, @Nonnull
IntObjectInspector intOI)
+ throws HiveException {
+ int value = intOI.get(o);
+ if (!(value == 1 || value == 0 || value == -1)) {
+ throw new UDFArgumentException("Int label must be 1, 0 or
-1: " + value);
}
- if (partial == null) {
- this.partial = new PartialResult();
+ if (value == -1) {
+ value = 0;
}
- partial.merge(other);
- return true;
+ return value;
+ }
+
+
+ @Override
+ public Object terminatePartial(@SuppressWarnings("deprecation")
AggregationBuffer agg)
+ throws HiveException {
+ FMeasureAggregationBuffer myAggr = (FMeasureAggregationBuffer)
agg;
+
+ Object[] partialResult = new Object[5];
+ partialResult[0] = new LongWritable(myAggr.tp);
+ partialResult[1] = new LongWritable(myAggr.totalActual);
+ partialResult[2] = new LongWritable(myAggr.totalPredicted);
+ partialResult[3] = new DoubleWritable(myAggr.beta);
+ partialResult[4] = myAggr.average;
+ return partialResult;
}
- public DoubleWritable terminate() {
+ @Override
+ public void merge(@SuppressWarnings("deprecation")
AggregationBuffer agg, Object partial)
+ throws HiveException {
if (partial == null) {
- return null;
+ return;
}
- double score = f1Score(partial);
- return WritableUtils.val(score);
+
+ Object tpObj = internalMergeOI.getStructFieldData(partial,
tpField);
+ Object totalActualObj =
internalMergeOI.getStructFieldData(partial, totalActualField);
+ Object totalPredictedObj =
internalMergeOI.getStructFieldData(partial,
+ totalPredictedField);
+ Object betaObj = internalMergeOI.getStructFieldData(partial,
betaOptionField);
+ Object averageObj =
internalMergeOI.getStructFieldData(partial, averageOptionFiled);
+ long tp =
PrimitiveObjectInspectorFactory.writableLongObjectInspector.get(tpObj);
+ long totalActual =
PrimitiveObjectInspectorFactory.writableLongObjectInspector.get(totalActualObj);
+ long totalPredicted =
PrimitiveObjectInspectorFactory.writableLongObjectInspector.get(totalPredictedObj);
+ double beta =
PrimitiveObjectInspectorFactory.writableDoubleObjectInspector.get(betaObj);
+ String average =
PrimitiveObjectInspectorFactory.writableStringObjectInspector.getPrimitiveJavaObject(averageObj);
+
+ FMeasureAggregationBuffer myAggr = (FMeasureAggregationBuffer)
agg;
+ myAggr.merge(tp, totalActual, totalPredicted, beta, average);
}
- /**
- * @return 2 * precision * recall / (precision + recall)
- */
- private static double f1Score(final PartialResult partial) {
- double precision = precision(partial);
- double recall = recall(partial);
- double divisor = precision + recall;
+ @Override
+ public DoubleWritable terminate(@SuppressWarnings("deprecation")
AggregationBuffer agg)
+ throws HiveException {
+ FMeasureAggregationBuffer myAggr = (FMeasureAggregationBuffer)
agg;
+ double result = myAggr.get();
+ return new DoubleWritable(result);
+ }
+ }
+
+ public static class FMeasureAggregationBuffer extends
--- End diff --
`estimate` is required for estimating resulting size for
`AbstractAggregationBuffer`.
Refer
https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/udf/generic/GenericUDAFComputeStats.java#L195
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
