Github user nzw0301 commented on a diff in the pull request:
https://github.com/apache/incubator-hivemall/pull/107#discussion_r135464601
--- Diff: core/src/test/java/hivemall/evaluation/FMeasureUDAFTest.java ---
@@ -0,0 +1,393 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+package hivemall.evaluation;
+
+import org.apache.hadoop.hive.ql.metadata.HiveException;
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFEvaluator;
+import
org.apache.hadoop.hive.ql.udf.generic.SimpleGenericUDAFParameterInfo;
+import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
+import
org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory;
+import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils;
+import
org.apache.hadoop.hive.serde2.objectinspector.PrimitiveObjectInspector;
+import
org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import java.util.Arrays;
+import java.util.List;
+
+
+public class FMeasureUDAFTest {
+ FMeasureUDAF fmeasure;
+ GenericUDAFEvaluator evaluator;
+ ObjectInspector[] inputOIs;
+ FMeasureUDAF.FMeasureAggregationBuffer agg;
+
+ @Before
+ public void setUp() throws Exception {
+ fmeasure = new FMeasureUDAF();
+ inputOIs = new ObjectInspector[] {
+
ObjectInspectorFactory.getStandardListObjectInspector(PrimitiveObjectInspectorFactory.writableLongObjectInspector),
+
ObjectInspectorFactory.getStandardListObjectInspector(PrimitiveObjectInspectorFactory.writableLongObjectInspector),
+ ObjectInspectorUtils.getConstantObjectInspector(
+
PrimitiveObjectInspectorFactory.javaStringObjectInspector, "-beta 1.")};
+
+ evaluator = fmeasure.getEvaluator(new
SimpleGenericUDAFParameterInfo(inputOIs, false, false));
+
+ agg = (FMeasureUDAF.FMeasureAggregationBuffer)
evaluator.getNewAggregationBuffer();
+ }
+
+ private void setUpWithArguments(double beta, String average) throws
Exception {
+ fmeasure = new FMeasureUDAF();
+ inputOIs = new ObjectInspector[] {
+
ObjectInspectorFactory.getStandardListObjectInspector(PrimitiveObjectInspectorFactory.writableLongObjectInspector),
+
ObjectInspectorFactory.getStandardListObjectInspector(PrimitiveObjectInspectorFactory.writableLongObjectInspector),
+ ObjectInspectorUtils.getConstantObjectInspector(
+
PrimitiveObjectInspectorFactory.javaStringObjectInspector, "-beta " + beta
+ + " -average " + average)};
+
+ evaluator = fmeasure.getEvaluator(new
SimpleGenericUDAFParameterInfo(inputOIs, false, false));
+ agg = (FMeasureUDAF.FMeasureAggregationBuffer)
evaluator.getNewAggregationBuffer();
+ }
+
+ private void binarySetUp(Object actual, Object predicted, double beta,
String average)
+ throws Exception {
+ fmeasure = new FMeasureUDAF();
+ inputOIs = new ObjectInspector[3];
+
+ String actualClassName = actual.getClass().getName();
+ if (actualClassName.equals("java.lang.Integer")) {
+ inputOIs[0] =
PrimitiveObjectInspectorFactory.getPrimitiveJavaObjectInspector(PrimitiveObjectInspector.PrimitiveCategory.INT);
+ } else if (actualClassName.equals("java.lang.Boolean")) {
+ inputOIs[0] =
PrimitiveObjectInspectorFactory.getPrimitiveJavaObjectInspector(PrimitiveObjectInspector.PrimitiveCategory.BOOLEAN);
+ } else if ((actualClassName.equals("java.lang.String"))) {
+ inputOIs[0] =
PrimitiveObjectInspectorFactory.getPrimitiveJavaObjectInspector(PrimitiveObjectInspector.PrimitiveCategory.STRING);
+ }
+
+ String predicatedClassName = predicted.getClass().getName();
+ if (predicatedClassName.equals("java.lang.Integer")) {
+ inputOIs[1] =
PrimitiveObjectInspectorFactory.getPrimitiveJavaObjectInspector(PrimitiveObjectInspector.PrimitiveCategory.INT);
+ } else if (predicatedClassName.equals("java.lang.Boolean")) {
+ inputOIs[1] =
PrimitiveObjectInspectorFactory.getPrimitiveJavaObjectInspector(PrimitiveObjectInspector.PrimitiveCategory.BOOLEAN);
+ } else if ((predicatedClassName.equals("java.lang.String"))) {
+ inputOIs[1] =
PrimitiveObjectInspectorFactory.getPrimitiveJavaObjectInspector(PrimitiveObjectInspector.PrimitiveCategory.STRING);
+ }
+
+ inputOIs[2] = ObjectInspectorUtils.getConstantObjectInspector(
+ PrimitiveObjectInspectorFactory.javaStringObjectInspector,
"-beta " + beta
+ + " -average " + average);
+
+ evaluator = fmeasure.getEvaluator(new
SimpleGenericUDAFParameterInfo(inputOIs, false, false));
+ agg = (FMeasureUDAF.FMeasureAggregationBuffer)
evaluator.getNewAggregationBuffer();
+ }
+
+ @Test
+ public void testBinaryMultiSamplesAverageBinary() throws Exception {
+ final int[] actual = {0, 1, 0, 0, 0, 1, 0, 0};
+ final int[] predicted = {1, 0, 0, 1, 0, 1, 0, 1};
+ double beta = 1.;
+ String average = "binary";
+ binarySetUp(actual[0], predicted[0], beta, average);
+
+ evaluator.init(GenericUDAFEvaluator.Mode.PARTIAL1, inputOIs);
+ evaluator.reset(agg);
+
+ for (int i = 0; i < actual.length; i++) {
+ evaluator.iterate(agg, new Object[] {actual[i], predicted[i]});
+ }
+
+ // should equal to turi's result
+ //
https://turi.com/learn/userguide/evaluation/classification.html#fscores-f1-fbeta-
+ Assert.assertEquals(0.3333d, agg.get(), 1e-4);
+ }
+
+ @Test(expected = HiveException.class)
+ public void testBinaryMultiSamplesAverageMacro() throws Exception {
+ final int[] actual = {0, 1, 0, 0, 0, 1, 0, 0};
+ final int[] predicted = {1, 0, 0, 1, 0, 1, 0, 1};
+ double beta = 1.;
+ String average = "macro";
+ binarySetUp(actual[0], predicted[0], beta, average);
+
+ evaluator.init(GenericUDAFEvaluator.Mode.PARTIAL1, inputOIs);
+ evaluator.reset(agg);
+
+ for (int i = 0; i < actual.length; i++) {
+ evaluator.iterate(agg, new Object[] {actual[i], predicted[i]});
+ }
+
+ agg.get();
+ }
+
+ @Test
+ public void testBinaryMultiSamples() throws Exception {
+ final int[] actual = {0, 1, 0, 0, 0, 1, 0, 0};
+ final int[] predicted = {1, 0, 0, 1, 0, 1, 0, 1};
+ double beta = 1.;
+ String average = "micro";
+ binarySetUp(actual[0], predicted[0], beta, average);
+
+ evaluator.init(GenericUDAFEvaluator.Mode.PARTIAL1, inputOIs);
+ evaluator.reset(agg);
+
+ for (int i = 0; i < actual.length; i++) {
+ evaluator.iterate(agg, new Object[] {actual[i], predicted[i]});
+ }
+
+ Assert.assertEquals(0.5d, agg.get(), 1e-4);
+ }
+
+ @Test
+ public void testBinaryMultiSamplesBeta2() throws Exception {
+ final int[] actual = {0, 1, 0, 0, 0, 1, 0, 0};
+ final int[] predicted = {1, 0, 0, 1, 0, 1, 0, 1};
+ double beta = 2.0;
+ String average = "binary";
+ binarySetUp(actual[0], predicted[0], beta, average);
+
+ evaluator.init(GenericUDAFEvaluator.Mode.PARTIAL1, inputOIs);
+ evaluator.reset(agg);
+
+ for (int i = 0; i < actual.length; i++) {
+ evaluator.iterate(agg, new Object[] {actual[i], predicted[i]});
+ }
+
+ Assert.assertEquals(0.4166d, agg.get(), 1e-4);
+ }
+
+ @Test
+ public void testBinary() throws Exception {
+ int actual = 1;
+ int predicted = 1;
+ double beta = 1.0;
+ String average = "micro";
+ binarySetUp(actual, predicted, beta, average);
+
+ evaluator.init(GenericUDAFEvaluator.Mode.PARTIAL1, inputOIs);
+ evaluator.reset(agg);
+
+ evaluator.iterate(agg, new Object[] {actual, predicted});
+
+ Assert.assertEquals(1.d, agg.get(), 1e-4);
+ }
+
+ @Test
+ public void testBinaryNegativeInput() throws Exception {
+ int actual = 1;
+ int predicted = -1;
+ binarySetUp(actual, predicted, 1.0, "binary");
+
+ evaluator.init(GenericUDAFEvaluator.Mode.PARTIAL1, inputOIs);
+ evaluator.reset(agg);
+
+ evaluator.iterate(agg, new Object[] {actual, predicted});
+
+ Assert.assertEquals(0.d, agg.get(), 1e-4);
+ }
+
+ @Test
+ public void testBinaryBooleanInput() throws Exception {
+ boolean actual = true;
+ boolean predicted = false;
+ double beta = 1.0d;
+ binarySetUp(actual, predicted, beta, "binary");
+
+ evaluator.init(GenericUDAFEvaluator.Mode.PARTIAL1, inputOIs);
+ evaluator.reset(agg);
+
+ evaluator.iterate(agg, new Object[] {actual, predicted});
+
+ Assert.assertEquals(0.d, agg.get(), 1e-4);
+ }
+
+ @Test(expected = HiveException.class)
+ public void testBinaryInvalidStringInput() throws Exception {
+ String actual = "cat";
+ int predicted = 1;
+ binarySetUp(actual, predicted, 1.0, "micro");
+
+ evaluator.init(GenericUDAFEvaluator.Mode.PARTIAL1, inputOIs);
+ evaluator.reset(agg);
+
+ evaluator.iterate(agg, new Object[] {actual, predicted});
+
+ agg.get();
+ }
+
+ @Test(expected = HiveException.class)
+ public void testBinaryInvalidLargeIntInput() throws Exception {
+ int actual = 1;
+ int predicted = 3;
+ binarySetUp(actual, predicted, 1.0, "micro");
+
+ evaluator.init(GenericUDAFEvaluator.Mode.PARTIAL1, inputOIs);
+ evaluator.reset(agg);
+
+ evaluator.iterate(agg, new Object[] {actual, predicted});
+
+ agg.get();
+ }
+
+ @Test(expected = HiveException.class)
+ public void testMultiLabelZeroBeta() throws Exception {
+ List<Integer> actual = Arrays.asList(1, 3, 2, 6);
+ List<Integer> predicted = Arrays.asList(1, 2, 4);
+ double beta = 0.;
+ setUpWithArguments(beta, "micro");
+
+ evaluator.init(GenericUDAFEvaluator.Mode.PARTIAL1, inputOIs);
+ evaluator.reset(agg);
+
+ evaluator.iterate(agg, new Object[] {actual, predicted});
+
+ // FMeasure for beta has zero value is not defined
+ agg.get();
+ }
+
+ @Test(expected = HiveException.class)
+ public void testMultiLabelNegativeBeta() throws Exception {
+ List<Integer> actual = Arrays.asList(1, 3, 2, 6);
+ List<Integer> predicted = Arrays.asList(1, 2, 4);
+ double beta = -1.0d;
+ String average = "micro";
+ setUpWithArguments(beta, average);
+
+ evaluator.init(GenericUDAFEvaluator.Mode.PARTIAL1, inputOIs);
+ evaluator.reset(agg);
+
+ evaluator.iterate(agg, new Object[] {actual, predicted});
+
+ // FMeasure for beta has negative value is not defined
+ agg.get();
+ }
+
+ @Test
+ public void testMultiLabelF1score() throws Exception {
+ List<Integer> actual = Arrays.asList(1, 3, 2, 6);
+ List<Integer> predicted = Arrays.asList(1, 2, 4);
+ double beta = 1.0;
+ String average = " micro";
+ setUpWithArguments(beta, average);
+
+ evaluator.init(GenericUDAFEvaluator.Mode.PARTIAL1, inputOIs);
+ evaluator.reset(agg);
+
+ evaluator.iterate(agg, new Object[] {actual, predicted});
+
+ // TODO: describe the way to get this expected value by spark
--- End diff --
Oh, I forget to update this line. Thanks.
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