[GitHub] incubator-hivemall pull request #107: [HIVEMALL-132] Generalize f1score UDAF...

[myui]

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 actual, final 
List 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 

[GitHub] incubator-hivemall issue #107: [HIVEMALL-132] Generalize f1score UDAF to sup...

[nzw0301]

Github user nzw0301 commented on the issue:

https://github.com/apache/incubator-hivemall/pull/107
  
But I found another issue for f1score and fmeasure.

Both function cannot work on `EMR v5.8.0`

```sql
hive> select f1score(array(1), array(1));
FAILED: IllegalArgumentException Size requested for unknown type: 
org.apache.hadoop.hive.ql.exec.UDAFEvaluator
```

```sql
hive> select fmeasure(array(1), array(1));
FAILED: IllegalArgumentException Size requested for unknown type: 
java.lang.String
```

However, they can work on `EMR v5.0.0`.
I don't know why the failures occur on newer EMR.


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[GitHub] incubator-hivemall issue #107: [HIVEMALL-132] Generalize f1score UDAF to sup...

[nzw0301]

Github user nzw0301 commented on the issue:

https://github.com/apache/incubator-hivemall/pull/107
  
I tested @takuti's query. The buggy code (previous code) returns `1.0`.
On the other hand, the fixed code return correct value`0.5`.



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[GitHub] incubator-hivemall issue #110: [HIVEMALL-142] Implement SingularizeUDF

[coveralls]

Github user coveralls commented on the issue:

https://github.com/apache/incubator-hivemall/pull/110
  

[![Coverage 
Status](https://coveralls.io/builds/13016205/badge)](https://coveralls.io/builds/13016205)

Coverage increased (+0.5%) to 41.301% when pulling 
**e230581d26c2a794ed0b6ccbb26c90122eb7dccd on takuti:singularize** into 
**7205de1e959f0d9b96ac756e415d8a8ada7e92af on apache:master**.



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[GitHub] incubator-hivemall pull request #107: [HIVEMALL-132] Generalize f1score UDAF...

[myui]

Github user myui commented on a diff in the pull request:

https://github.com/apache/incubator-hivemall/pull/107#discussion_r135514455
  
--- Diff: core/src/main/java/hivemall/evaluation/F1ScoreUDAF.java ---
@@ -0,0 +1,134 @@
+/*
+ * 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 hivemall.utils.hadoop.WritableUtils;
+
+import java.util.List;
+
+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.serde2.io.DoubleWritable;
+import org.apache.hadoop.io.IntWritable;
+
+@SuppressWarnings("deprecation")
+@Description(name = "f1score", value = "_FUNC_(array[int], array[int]) - 
Return a F1 score")
+public final class F1ScoreUDAF extends UDAF {
+
+public static class Evaluator implements UDAFEvaluator {
+
+public static class PartialResult {
+long tp;
+/** tp + fn */
+long totalActual;
+/** tp + fp */
+long totalPredicted;
+
+PartialResult() {
+this.tp = 0L;
+this.totalPredicted = 0L;
+this.totalActual = 0L;
+}
+
+void updateScore(final List actual, final 
List 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++;
+}
+}
+this.tp += countTp;
+this.totalActual += numActual;
+this.totalPredicted += numPredicted;
+}
+
+void merge(PartialResult other) {
--- End diff --

oops. This should be fixed.


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[GitHub] incubator-hivemall issue #107: [HIVEMALL-132] Generalize f1score UDAF to sup...

[takuti]

Github user takuti commented on the issue:

https://github.com/apache/incubator-hivemall/pull/107
  
Importantly, in case that the number of mappers is 1, fixing the bug in 
`merge()` does not change the output value; you might see the same result 
`0.42483920860540153` even if the bug exists.

Alternatively, let you test the following query (6 mappers will be shown 
for each `select` statement), and check if its output is same as 
`fmeasure(truth, predicted, '-average micro')`:

```sql
WITH data as (
  select 1 as truth, 0 as predicted
union all
  select 0 as truth, 1 as predicted
union all
  select 0 as truth, 0 as predicted
union all
  select 1 as truth, 1 as predicted
union all
  select 0 as truth, 1 as predicted
union all
  select 0 as truth, 0 as predicted
)
select
  f1score(array(truth), array(predicted))
from data
;
```

If the bug has been fixed correctly, output should be same as 
`fmeasure(truth, predicted, '-average micro) = 0.5`, while the buggy code 
returns `f1score(array(truth), array(predicted)) = 1.0`


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[GitHub] incubator-hivemall issue #107: [HIVEMALL-132] Generalize f1score UDAF to sup...

[nzw0301]

Github user nzw0301 commented on the issue:

https://github.com/apache/incubator-hivemall/pull/107
  
I will check whether the return value is the same tomorrow.


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[GitHub] incubator-hivemall issue #105: [WIP][HIVEMALL-24] Scalable field-aware facto...

[coveralls]

Github user coveralls commented on the issue:

https://github.com/apache/incubator-hivemall/pull/105
  

[![Coverage 
Status](https://coveralls.io/builds/13013516/badge)](https://coveralls.io/builds/13013516)

Coverage decreased (-0.6%) to 40.215% when pulling 
**dba8bf64e3a29ba4da373bfbec01a37439a2cdea on myui:HIVEMALL-24-2** into 
**7205de1e959f0d9b96ac756e415d8a8ada7e92af on apache:master**.



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[GitHub] incubator-hivemall pull request #107: [HIVEMALL-132] Generalize f1score UDAF...

[nzw0301]

Github user nzw0301 commented on a diff in the pull request:

https://github.com/apache/incubator-hivemall/pull/107#discussion_r135471771
  
--- Diff: docs/gitbook/eval/binary_classification_measures.md ---
@@ -0,0 +1,261 @@
+
+
+
+
+# Binary problems
+
+Binary classification problem is the task to predict the label of each 
data given two categorized dataset.
+
+Hivemall provides some tutorials to deal with binary classification 
problems as follows:
+
+- [Online advertisement click prediction](../binaryclass/general.html)
+- [News classification](../binaryclass/news20_dataset.html)
+
+This page focuses on the evaluation of the results from such binary 
classification problems.
+If your classifier outputs probability rather than 0/1 label, evaluation 
based on [Area Under the ROC Curve](./auc.md) would be more appropriate.
+
+
+# Example
+
+For the metrics explanation, this page introduces toy example data and two 
metrics.
+
+## Data
+
+The following table shows the sample of binary classification's prediction.
+In this case, `1` means positive label and `0` means negative label.
+Left column includes supervised label data,
+and center column includes predicted label by a binary classifier.
+
+| truth label| predicted label | |
+|:---:|:---:|:---:|
+| 1 | 0 |False Negative|
+| 0 | 1 |False Positive|
+| 0 | 0 |True Negative|
+| 1 | 1 |True Positive|
+| 0 | 1 |False Positive|
+| 0 | 0 |True Negative|
+
+## Preliminary metrics
+
+Some evaluation metrics are calculated based on 4 values:
+
+- True Positive (TP): truth label is positive and predicted label is also 
positive
+- True Negative (TN): truth label is negative and predicted label is also 
negative
+- False Positive (FP): truth label is negative but predicted label is 
positive
+- False Negative (FN): truth label is positive but predicted label is 
negative
+
+`TR` and `TN` represent correct classification, and `FP` and `FN` 
illustrate incorrect ones.
+
+In this example, we can obtain those values:
+
+- TP: 1
+- TN: 2
+- FP: 2
+- FN: 1
+
+if you want to know about those metrics, Wikipedia provides [more detail 
information](https://en.wikipedia.org/wiki/Sensitivity_and_specificity).
+
+### Recall
+
+Recall indicates the true positive rate in truth positive labels.
+The value is computed by the following equation:
+
+$$
+\mathrm{recall} = \frac{\mathrm{\#TP}}{\mathrm{\#TP} + \mathrm{\#FN}}
+$$
+
+In the previous example, $$\mathrm{precision} = \frac{1}{2}$$.
+
+### Precision
+
+Precision indicates the true positive rate in positive predictive labels.
+The value is computed by the following equation:
+
+$$
+\mathrm{precision} = \frac{\mathrm{\#TP}}{\mathrm{\#TP} + \mathrm{\#FP}}
+$$
+
+In the previous example, $$\mathrm{precision} = \frac{1}{3}$$.
+
+# Metrics
+
+## F1-score
+
+F1-score is the harmonic mean of recall and precision.
+F1-score is computed by the following equation:
+
+$$
+\mathrm{F}_1 = 2 \frac{\mathrm{precision} * 
\mathrm{recall}}{\mathrm{precision} + \mathrm{recall}}
+$$
+
+Hivemall's `fmeasure` function provides the option which can switch 
`micro`(default) or `binary` by passing `average` argument.
+
+
+>  Caution
+> Hivemall also provides `f1score` function, but it is old function to 
obtain F1-score. The value of `f1score` is based on set operation. So, we 
recommend to use `fmeasure` function to get F1-score based on this article.
+
+You can learn more about this from the following external resource:
+
+- [scikit-learn's 
F1-score](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html)
+
+
+### Micro average
+
+If `micro` is passed to `average`, 
+recall and precision are modified to consider True Negative.
+So, micro f1score are calculated by those modified recall and precision.
+
+$$
+\mathrm{recall} = \frac{\mathrm{\#TP} + \mathrm{\#TN}}{\mathrm{\#TP} + 
\mathrm{\#FN} + \mathrm{\#TN}}
+$$
+
+$$
+\mathrm{precision} = \frac{\mathrm{\#TP} + \mathrm{\#TN}}{\mathrm{\#TP} + 
\mathrm{\#FP} + \mathrm{\#TN}}
+$$
+
+If `average` argument is omitted, `fmeasure` use default value: `'-average 
micro'`.
+
+The following query shows the example to obtain F1-score.
+Each row value has the same type (`int` or `boolean`).
+If row value's type is `int`, `1` is considered as the positive label, and 
`-1` or `0` is considered as the negative label.
+
+
+```sql
+WITH data as (
+  select 1 as truth, 0 as predicted
+union all
+  select 0 as truth, 1 as predicted
+union all
+  select 0 as truth, 0 as 

[GitHub] incubator-hivemall pull request #107: [HIVEMALL-132] Generalize f1score UDAF...

[nzw0301]

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();
   

[GitHub] incubator-hivemall pull request #110: [HIVEMALL-142] Implement SingularizeUD...

[takuti]

Github user takuti commented on a diff in the pull request:

https://github.com/apache/incubator-hivemall/pull/110#discussion_r135462565
  
--- Diff: core/src/main/java/hivemall/utils/lang/StringUtils.java ---
@@ -172,12 +172,17 @@ public static void clear(@Nonnull final StringBuilder 
buf) {
 
 public static String concat(@Nonnull final List list, @Nonnull 
final String sep) {
--- End diff --

done


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[GitHub] incubator-hivemall pull request #110: [HIVEMALL-142] Implement SingularizeUD...

[takuti]

Github user takuti commented on a diff in the pull request:

https://github.com/apache/incubator-hivemall/pull/110#discussion_r135459734
  
--- Diff: core/src/main/java/hivemall/utils/lang/StringUtils.java ---
@@ -172,12 +172,17 @@ public static void clear(@Nonnull final StringBuilder 
buf) {
 
 public static String concat(@Nonnull final List list, @Nonnull 
final String sep) {
--- End diff --

okay, so I keep the update here


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[GitHub] incubator-hivemall pull request #110: [HIVEMALL-142] Implement SingularizeUD...

[myui]

Github user myui commented on a diff in the pull request:

https://github.com/apache/incubator-hivemall/pull/110#discussion_r135458769
  
--- Diff: core/src/main/java/hivemall/utils/lang/StringUtils.java ---
@@ -172,12 +172,17 @@ public static void clear(@Nonnull final StringBuilder 
buf) {
 
 public static String concat(@Nonnull final List list, @Nonnull 
final String sep) {
--- End diff --

former is expected. 


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[GitHub] incubator-hivemall pull request #111: [WIP][HIVEMALL-17] Support SLIM

[nzw0301]

GitHub user nzw0301 opened a pull request:

https://github.com/apache/incubator-hivemall/pull/111

[WIP][HIVEMALL-17] Support SLIM

## What changes were proposed in this pull request?

Add new UDTF: `train_slim`

## What type of PR is it?

Improvement

## What is the Jira issue?

https://issues.apache.org/jira/browse/HIVEMALL-17

## How was this patch tested?

- Add `recommend/SlimUDTFTest`
- manual tests on EMR

## How to use this feature?

- Please see markdown file: `movielens_slim.md` in this PR.

## Checklist

- [x] Did you apply source code formatter, i.e., `mvn formatter:format`, 
for your commit?


You can merge this pull request into a Git repository by running:

$ git pull https://github.com/nzw0301/incubator-hivemall HIVEMALL-17

Alternatively you can review and apply these changes as the patch at:

https://github.com/apache/incubator-hivemall/pull/111.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

This closes #111


commit a9b89656ef4b54cb938e59e3bb546a2aa33a9fd3
Author: Kento NOZAWA 
Date:   2017-08-18T03:30:14Z

Init Slim UDTF

commit 5728499405400d68faec2be58cd11e788ce8c607
Author: Kento NOZAWA 
Date:   2017-08-25T07:15:18Z

Move comment

commit 7ac96304f06b41c0a72c1be885cd66d75acfd966
Author: Kento NOZAWA 
Date:   2017-08-25T09:58:27Z

Init slim document

commit 23672cf5ddeceb06cad10e9a0496d860d786d601
Author: Kento NOZAWA 
Date:   2017-08-28T07:22:39Z

Update slim

- Update docs
- Remove degub print in test
- Rename function for unification: `slim_train` to `train_slim`




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[GitHub] incubator-hivemall pull request #107: [HIVEMALL-132] Generalize f1score UDAF...

[takuti]

Github user takuti commented on a diff in the pull request:

https://github.com/apache/incubator-hivemall/pull/107#discussion_r135453504
  
--- Diff: core/src/main/java/hivemall/evaluation/F1ScoreUDAF.java ---
@@ -0,0 +1,134 @@
+/*
+ * 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 hivemall.utils.hadoop.WritableUtils;
+
+import java.util.List;
+
+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.serde2.io.DoubleWritable;
+import org.apache.hadoop.io.IntWritable;
+
+@SuppressWarnings("deprecation")
+@Description(name = "f1score", value = "_FUNC_(array[int], array[int]) - 
Return a F1 score")
+public final class F1ScoreUDAF extends UDAF {
+
+public static class Evaluator implements UDAFEvaluator {
+
+public static class PartialResult {
+long tp;
+/** tp + fn */
+long totalActual;
+/** tp + fp */
+long totalPredicted;
+
+PartialResult() {
+this.tp = 0L;
+this.totalPredicted = 0L;
+this.totalActual = 0L;
+}
+
+void updateScore(final List actual, final 
List 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++;
+}
+}
+this.tp += countTp;
+this.totalActual += numActual;
+this.totalPredicted += numPredicted;
+}
+
+void merge(PartialResult other) {
--- End diff --

Oh, there is a bug here! 🐛 

Correct:

```java
void merge(PartialResult other) {
this.tp += other.tp;
this.totalActual += other.totalActual;
this.totalPredicted += other.totalPredicted;
}
```


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[GitHub] incubator-hivemall pull request #107: [HIVEMALL-132] Generalize f1score UDAF...

[takuti]

Github user takuti commented on a diff in the pull request:

https://github.com/apache/incubator-hivemall/pull/107#discussion_r135447663
  
--- 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 actual, final 
List 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 actual` is invalid 
form: " + typeInfo[1]);
+}
+
+if (typeInfo[0] != typeInfo[1]) {
+throw new UDFArgumentTypeException(1, "The first argument's 
`actual` type is "
++ typeInfo[0] + ", but the second argument 
`predicated`'s type is not match: "
--- End diff --

Typo: `predicated` => `predicted`


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[GitHub] incubator-hivemall pull request #107: [HIVEMALL-132] Generalize f1score UDAF...

[takuti]

Github user takuti commented on a diff in the pull request:

https://github.com/apache/incubator-hivemall/pull/107#discussion_r135450159
  
--- 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();

[GitHub] incubator-hivemall pull request #107: [HIVEMALL-132] Generalize f1score UDAF...

[takuti]

Github user takuti commented on a diff in the pull request:

https://github.com/apache/incubator-hivemall/pull/107#discussion_r135450892
  
--- Diff: docs/gitbook/eval/binary_classification_measures.md ---
@@ -0,0 +1,261 @@
+
+
+
+
+# Binary problems
+
+Binary classification problem is the task to predict the label of each 
data given two categorized dataset.
+
+Hivemall provides some tutorials to deal with binary classification 
problems as follows:
+
+- [Online advertisement click prediction](../binaryclass/general.html)
+- [News classification](../binaryclass/news20_dataset.html)
+
+This page focuses on the evaluation of the results from such binary 
classification problems.
+If your classifier outputs probability rather than 0/1 label, evaluation 
based on [Area Under the ROC Curve](./auc.md) would be more appropriate.
+
+
+# Example
+
+For the metrics explanation, this page introduces toy example data and two 
metrics.
+
+## Data
+
+The following table shows the sample of binary classification's prediction.
+In this case, `1` means positive label and `0` means negative label.
+Left column includes supervised label data,
+and center column includes predicted label by a binary classifier.
+
+| truth label| predicted label | |
+|:---:|:---:|:---:|
+| 1 | 0 |False Negative|
+| 0 | 1 |False Positive|
+| 0 | 0 |True Negative|
+| 1 | 1 |True Positive|
+| 0 | 1 |False Positive|
+| 0 | 0 |True Negative|
+
+## Preliminary metrics
+
+Some evaluation metrics are calculated based on 4 values:
+
+- True Positive (TP): truth label is positive and predicted label is also 
positive
+- True Negative (TN): truth label is negative and predicted label is also 
negative
+- False Positive (FP): truth label is negative but predicted label is 
positive
+- False Negative (FN): truth label is positive but predicted label is 
negative
+
+`TR` and `TN` represent correct classification, and `FP` and `FN` 
illustrate incorrect ones.
+
+In this example, we can obtain those values:
+
+- TP: 1
+- TN: 2
+- FP: 2
+- FN: 1
+
+if you want to know about those metrics, Wikipedia provides [more detail 
information](https://en.wikipedia.org/wiki/Sensitivity_and_specificity).
+
+### Recall
+
+Recall indicates the true positive rate in truth positive labels.
+The value is computed by the following equation:
+
+$$
+\mathrm{recall} = \frac{\mathrm{\#TP}}{\mathrm{\#TP} + \mathrm{\#FN}}
+$$
+
+In the previous example, $$\mathrm{precision} = \frac{1}{2}$$.
+
+### Precision
+
+Precision indicates the true positive rate in positive predictive labels.
+The value is computed by the following equation:
+
+$$
+\mathrm{precision} = \frac{\mathrm{\#TP}}{\mathrm{\#TP} + \mathrm{\#FP}}
+$$
+
+In the previous example, $$\mathrm{precision} = \frac{1}{3}$$.
+
+# Metrics
+
+## F1-score
+
+F1-score is the harmonic mean of recall and precision.
+F1-score is computed by the following equation:
+
+$$
+\mathrm{F}_1 = 2 \frac{\mathrm{precision} * 
\mathrm{recall}}{\mathrm{precision} + \mathrm{recall}}
+$$
+
+Hivemall's `fmeasure` function provides the option which can switch 
`micro`(default) or `binary` by passing `average` argument.
+
+
+>  Caution
+> Hivemall also provides `f1score` function, but it is old function to 
obtain F1-score. The value of `f1score` is based on set operation. So, we 
recommend to use `fmeasure` function to get F1-score based on this article.
+
+You can learn more about this from the following external resource:
+
+- [scikit-learn's 
F1-score](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html)
+
+
+### Micro average
+
+If `micro` is passed to `average`, 
--- End diff --

👍 


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[GitHub] incubator-hivemall pull request #107: [HIVEMALL-132] Generalize f1score UDAF...

[takuti]

Github user takuti commented on a diff in the pull request:

https://github.com/apache/incubator-hivemall/pull/107#discussion_r135454567
  
--- Diff: docs/gitbook/eval/binary_classification_measures.md ---
@@ -0,0 +1,261 @@
+
+
+
+
+# Binary problems
+
+Binary classification problem is the task to predict the label of each 
data given two categorized dataset.
+
+Hivemall provides some tutorials to deal with binary classification 
problems as follows:
+
+- [Online advertisement click prediction](../binaryclass/general.html)
+- [News classification](../binaryclass/news20_dataset.html)
+
+This page focuses on the evaluation of the results from such binary 
classification problems.
+If your classifier outputs probability rather than 0/1 label, evaluation 
based on [Area Under the ROC Curve](./auc.md) would be more appropriate.
+
+
+# Example
+
+For the metrics explanation, this page introduces toy example data and two 
metrics.
+
+## Data
+
+The following table shows the sample of binary classification's prediction.
+In this case, `1` means positive label and `0` means negative label.
+Left column includes supervised label data,
+and center column includes predicted label by a binary classifier.
+
+| truth label| predicted label | |
+|:---:|:---:|:---:|
+| 1 | 0 |False Negative|
+| 0 | 1 |False Positive|
+| 0 | 0 |True Negative|
+| 1 | 1 |True Positive|
+| 0 | 1 |False Positive|
+| 0 | 0 |True Negative|
+
+## Preliminary metrics
+
+Some evaluation metrics are calculated based on 4 values:
+
+- True Positive (TP): truth label is positive and predicted label is also 
positive
+- True Negative (TN): truth label is negative and predicted label is also 
negative
+- False Positive (FP): truth label is negative but predicted label is 
positive
+- False Negative (FN): truth label is positive but predicted label is 
negative
+
+`TR` and `TN` represent correct classification, and `FP` and `FN` 
illustrate incorrect ones.
+
+In this example, we can obtain those values:
+
+- TP: 1
+- TN: 2
+- FP: 2
+- FN: 1
+
+if you want to know about those metrics, Wikipedia provides [more detail 
information](https://en.wikipedia.org/wiki/Sensitivity_and_specificity).
+
+### Recall
+
+Recall indicates the true positive rate in truth positive labels.
+The value is computed by the following equation:
+
+$$
+\mathrm{recall} = \frac{\mathrm{\#TP}}{\mathrm{\#TP} + \mathrm{\#FN}}
+$$
+
+In the previous example, $$\mathrm{precision} = \frac{1}{2}$$.
+
+### Precision
+
+Precision indicates the true positive rate in positive predictive labels.
+The value is computed by the following equation:
+
+$$
+\mathrm{precision} = \frac{\mathrm{\#TP}}{\mathrm{\#TP} + \mathrm{\#FP}}
+$$
+
+In the previous example, $$\mathrm{precision} = \frac{1}{3}$$.
+
+# Metrics
+
+## F1-score
+
+F1-score is the harmonic mean of recall and precision.
+F1-score is computed by the following equation:
+
+$$
+\mathrm{F}_1 = 2 \frac{\mathrm{precision} * 
\mathrm{recall}}{\mathrm{precision} + \mathrm{recall}}
+$$
+
+Hivemall's `fmeasure` function provides the option which can switch 
`micro`(default) or `binary` by passing `average` argument.
+
+
+>  Caution
+> Hivemall also provides `f1score` function, but it is old function to 
obtain F1-score. The value of `f1score` is based on set operation. So, we 
recommend to use `fmeasure` function to get F1-score based on this article.
+
+You can learn more about this from the following external resource:
+
+- [scikit-learn's 
F1-score](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html)
+
+
+### Micro average
+
+If `micro` is passed to `average`, 
+recall and precision are modified to consider True Negative.
+So, micro f1score are calculated by those modified recall and precision.
+
+$$
+\mathrm{recall} = \frac{\mathrm{\#TP} + \mathrm{\#TN}}{\mathrm{\#TP} + 
\mathrm{\#FN} + \mathrm{\#TN}}
+$$
+
+$$
+\mathrm{precision} = \frac{\mathrm{\#TP} + \mathrm{\#TN}}{\mathrm{\#TP} + 
\mathrm{\#FP} + \mathrm{\#TN}}
+$$
+
+If `average` argument is omitted, `fmeasure` use default value: `'-average 
micro'`.
+
+The following query shows the example to obtain F1-score.
+Each row value has the same type (`int` or `boolean`).
+If row value's type is `int`, `1` is considered as the positive label, and 
`-1` or `0` is considered as the negative label.
+
+
+```sql
+WITH data as (
+  select 1 as truth, 0 as predicted
+union all
+  select 0 as truth, 1 as predicted
+union all
+  select 0 as truth, 0 as 

[GitHub] incubator-hivemall pull request #107: [HIVEMALL-132] Generalize f1score UDAF...

[takuti]

Github user takuti commented on a diff in the pull request:

https://github.com/apache/incubator-hivemall/pull/107#discussion_r135449941
  
--- 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 actual, final 
List 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 actual` is invalid 
form: " + typeInfo[1]);
+}
+
+if (typeInfo[0] != typeInfo[1]) {
+throw new UDFArgumentTypeException(1, "The first argument's 
`actual` type is "
++ typeInfo[0] + ", but the second argument 
`predicated`'s type is not match: "
++ typeInfo[1]);
+}
+
+return new Evaluator();
+}
+
+public static class Evaluator