7205de1 replay starts

core/src/main/java/hivemall/regression/AdaDeltaUDTF.java

@@ -37,7 +37,7 @@
 /**
  * ADADELTA: AN ADAPTIVE LEARNING RATE METHOD.
  *
- * @deprecated Use {@link hivemall.regression.GeneralRegressionUDTF} instead
+ * @deprecated Use {@link hivemall.regression.GeneralRegressorUDTF} instead
  */
 @Deprecated
 @Description(

core/src/main/java/hivemall/regression/AdaGradUDTF.java

@@ -37,7 +37,7 @@
 /**
  * ADAGRAD algorithm with element-wise adaptive learning rates.
  *
- * @deprecated Use {@link hivemall.regression.GeneralRegressionUDTF} instead
+ * @deprecated Use {@link hivemall.regression.GeneralRegressorUDTF} instead
  */
 @Deprecated
 @Description(

core/src/main/java/hivemall/regression/GeneralRegressionUDTF.java → core/src/main/java/hivemall/regression/GeneralRegressorUDTF.java

@@ -32,12 +32,12 @@
 /**
  * A general regression class with replaceable optimization functions.
  */
-@Description(name = "train_regression",
+@Description(name = "train_regressor",
         value = "_FUNC_(list<string|int|bigint> features, double label [, const string options])"
                 + " - Returns a relation consists of <string|int|bigint feature, float weight>",
         extended = "Build a prediction model by a generic regressor")
 @Since(version = "0.5-rc.1")
-public final class GeneralRegressionUDTF extends GeneralLearnerBaseUDTF {
+public final class GeneralRegressorUDTF extends GeneralLearnerBaseUDTF {
 
     @Override
     protected String getLossOptionDescription() {

core/src/main/java/hivemall/regression/LogressUDTF.java

@@ -31,7 +31,7 @@
 /**
  * Logistic regression using SGD.
  * 
- * @deprecated Use {@link hivemall.regression.GeneralRegressionUDTF} instead
+ * @deprecated Use {@link hivemall.regression.GeneralRegressorUDTF} instead
  */
 @Deprecated
 @Description(

core/src/test/java/hivemall/regression/GeneralRegressionUDTFTest.java → core/src/test/java/hivemall/regression/GeneralRegressorUDTFTest.java

@@ -47,12 +47,12 @@
 import org.junit.Assert;
 import org.junit.Test;
 
-public class GeneralRegressionUDTFTest {
+public class GeneralRegressorUDTFTest {
     private static final boolean DEBUG = false;
 
     @Test(expected = UDFArgumentException.class)
     public void testUnsupportedOptimizer() throws Exception {
-        GeneralRegressionUDTF udtf = new GeneralRegressionUDTF();
+        GeneralRegressorUDTF udtf = new GeneralRegressorUDTF();
         ObjectInspector floatOI = PrimitiveObjectInspectorFactory.javaFloatObjectInspector;
         ObjectInspector stringOI = PrimitiveObjectInspectorFactory.javaStringObjectInspector;
         ListObjectInspector stringListOI = ObjectInspectorFactory.getStandardListObjectInspector(stringOI);
@@ -64,7 +64,7 @@ public void testUnsupportedOptimizer() throws Exception {
 
     @Test(expected = UDFArgumentException.class)
     public void testUnsupportedLossFunction() throws Exception {
-        GeneralRegressionUDTF udtf = new GeneralRegressionUDTF();
+        GeneralRegressorUDTF udtf = new GeneralRegressorUDTF();
         ObjectInspector floatOI = PrimitiveObjectInspectorFactory.javaFloatObjectInspector;
         ObjectInspector stringOI = PrimitiveObjectInspectorFactory.javaStringObjectInspector;
         ListObjectInspector stringListOI = ObjectInspectorFactory.getStandardListObjectInspector(stringOI);
@@ -76,7 +76,7 @@ public void testUnsupportedLossFunction() throws Exception {
 
     @Test(expected = UDFArgumentException.class)
     public void testInvalidLossFunction() throws Exception {
-        GeneralRegressionUDTF udtf = new GeneralRegressionUDTF();
+        GeneralRegressorUDTF udtf = new GeneralRegressorUDTF();
         ObjectInspector floatOI = PrimitiveObjectInspectorFactory.javaFloatObjectInspector;
         ObjectInspector stringOI = PrimitiveObjectInspectorFactory.javaStringObjectInspector;
         ListObjectInspector stringListOI = ObjectInspectorFactory.getStandardListObjectInspector(stringOI);
@@ -88,7 +88,7 @@ public void testInvalidLossFunction() throws Exception {
 
     @Test(expected = UDFArgumentException.class)
     public void testUnsupportedRegularization() throws Exception {
-        GeneralRegressionUDTF udtf = new GeneralRegressionUDTF();
+        GeneralRegressorUDTF udtf = new GeneralRegressorUDTF();
         ObjectInspector floatOI = PrimitiveObjectInspectorFactory.javaFloatObjectInspector;
         ObjectInspector stringOI = PrimitiveObjectInspectorFactory.javaStringObjectInspector;
         ListObjectInspector stringListOI = ObjectInspectorFactory.getStandardListObjectInspector(stringOI);
@@ -103,7 +103,7 @@ public void testNoOptions() throws Exception {
         List<String> x = Arrays.asList("1:-2", "2:-1");
         float y = 0.f;
 
-        GeneralRegressionUDTF udtf = new GeneralRegressionUDTF();
+        GeneralRegressorUDTF udtf = new GeneralRegressorUDTF();
         ObjectInspector intOI = PrimitiveObjectInspectorFactory.javaFloatObjectInspector;
         ObjectInspector stringOI = PrimitiveObjectInspectorFactory.javaStringObjectInspector;
         ListObjectInspector stringListOI = ObjectInspectorFactory.getStandardListObjectInspector(stringOI);
@@ -122,7 +122,7 @@ private <T> void testFeature(@Nonnull List<T> x, @Nonnull ObjectInspector featur
             @Nonnull Class<T> featureClass, @Nonnull Class<?> modelFeatureClass) throws Exception {
         float y = 0.f;
 
-        GeneralRegressionUDTF udtf = new GeneralRegressionUDTF();
+        GeneralRegressorUDTF udtf = new GeneralRegressorUDTF();
         ObjectInspector valueOI = PrimitiveObjectInspectorFactory.javaFloatObjectInspector;
         ListObjectInspector featureListOI = ObjectInspectorFactory.getStandardListObjectInspector(featureOI);
 
@@ -244,7 +244,7 @@ private void run(@Nonnull String options) throws Exception {
             x2 += x2Step;
         }
 
-        GeneralRegressionUDTF udtf = new GeneralRegressionUDTF();
+        GeneralRegressorUDTF udtf = new GeneralRegressorUDTF();
         ObjectInspector floatOI = PrimitiveObjectInspectorFactory.javaFloatObjectInspector;
         ObjectInspector stringOI = PrimitiveObjectInspectorFactory.javaStringObjectInspector;
         ListObjectInspector stringListOI = ObjectInspectorFactory.getStandardListObjectInspector(stringOI);

docs/gitbook/FOOTER.md

@@ -19,4 +19,4 @@
 
 <sub><font color="gray">
 Apache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.
-</font></sub>
+</font></sub>

docs/gitbook/SUMMARY.md

@@ -27,7 +27,7 @@
     * [Input Format](getting_started/input-format.md)
 
 * [Tips for Effective Hivemall](tips/README.md)
-    * [Explicit addBias() for better prediction](tips/addbias.md)
+    * [Explicit add_bias() for better prediction](tips/addbias.md)
     * [Use rand_amplify() to better prediction results](tips/rand_amplify.md)
     * [Real-time Prediction on RDBMS](tips/rt_prediction.md)
     * [Ensemble learning for stable prediction](tips/ensemble_learning.md)

docs/gitbook/anomaly/sst.md

@@ -151,4 +151,4 @@ For instance, partial outputs obtained as a result of this query are:
 |7560  |  {"changepoint_score":0.0010629833145070489,"is_changepoint":false}|
 |...|...|
 
-Obviously, the 7555-th sample is detected as a change-point in this example.
+Obviously, the 7555-th sample is detected as a change-point in this example.

docs/gitbook/binaryclass/a9a_dataset.md

@@ -59,4 +59,4 @@ create external table a9atest (
   label float,
   features ARRAY<STRING>
 ) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' COLLECTION ITEMS TERMINATED BY "," STORED AS TEXTFILE LOCATION '/dataset/a9a/test';
-```
+```

docs/gitbook/binaryclass/a9a_lr.md

@@ -39,7 +39,7 @@ select
  avg(weight) as weight
 from 
  (select 
-     logress(addBias(features),label,"-total_steps ${total_steps}") as (feature,weight)
+     logress(add_bias(features),label,"-total_steps ${total_steps}") as (feature,weight)
   from 
      a9atrain
  ) t 
@@ -59,7 +59,7 @@ select
   extract_feature(feature) as feature,
   extract_weight(feature) as value
 from 
-  a9atest LATERAL VIEW explode(addBias(features)) t AS feature
+  a9atest LATERAL VIEW explode(add_bias(features)) t AS feature
 )
 select
   t.rowid, 

docs/gitbook/binaryclass/a9a_minibatch.md

@@ -35,7 +35,7 @@ select
  avg(weight) as weight
 from 
  (select 
-     logress(addBias(features),label,"-total_steps ${total_steps} -mini_batch ${mini_batch_size}") as (feature,weight)
+     logress(add_bias(features),label,"-total_steps ${total_steps} -mini_batch ${mini_batch_size}") as (feature,weight)
   from 
      a9atrain
  ) t 
@@ -52,4 +52,4 @@ where actual == predicted;
 
 | Stochastic Gradient Descent | Minibatch Gradient Descent |
 | ------------- | ------------- |
-| 0.8430071862907684 | 0.8463239358761747 |
+| 0.8430071862907684 | 0.8463239358761747 |

docs/gitbook/binaryclass/general.md

@@ -133,4 +133,4 @@ from
 	a9a_train
 ```
 
-Likewise, you can generate many different classifiers based on its options.
+Likewise, you can generate many different classifiers based on its options.

docs/gitbook/binaryclass/kdd2010a_dataset.md

@@ -77,7 +77,7 @@ select
   split(feature,":")[0] as feature,
   cast(split(feature,":")[1] as float) as value
 from 
-  kdd10a_test LATERAL VIEW explode(addBias(features)) t AS feature;
+  kdd10a_test LATERAL VIEW explode(add_bias(features)) t AS feature;
 
 set hivevar:xtimes=3;
 set hivevar:shufflebuffersize=1000;
@@ -88,4 +88,4 @@ select
    rand_amplify(${xtimes}, ${shufflebuffersize}, *) as (rowid, label, features)
 from  
    kdd10a_train_orcfile;
-```
+```

docs/gitbook/binaryclass/kdd2010a_scw.md

@@ -28,7 +28,7 @@ select
  voted_avg(weight) as weight
 from 
  (select 
-     train_pa1(addBias(features),label) as (feature,weight)
+     train_pa1(add_bias(features),label) as (feature,weight)
   from 
      kdd10a_train_x3
  ) t 
@@ -76,7 +76,7 @@ select
  argmin_kld(weight, covar) as weight
 from 
  (select 
-     train_cw(addBias(features),label) as (feature,weight,covar)
+     train_cw(add_bias(features),label) as (feature,weight,covar)
   from 
      kdd10a_train_x3
  ) t 
@@ -119,8 +119,8 @@ select
  argmin_kld(weight, covar) as weight -- [hivemall v0.2alpha3 or later]
 from 
  (select 
-     -- train_arow(addBias(features),label) as (feature,weight) -- [hivemall v0.1]
-     train_arow(addBias(features),label) as (feature,weight,covar) -- [hivemall v0.2 or later]
+     -- train_arow(add_bias(features),label) as (feature,weight) -- [hivemall v0.1]
+     train_arow(add_bias(features),label) as (feature,weight,covar) -- [hivemall v0.2 or later]
   from 
      kdd10a_train_x3
  ) t 
@@ -162,7 +162,7 @@ select
  argmin_kld(weight, covar) as weight
 from 
  (select 
-     train_scw(addBias(features),label) as (feature,weight,covar)
+     train_scw(add_bias(features),label) as (feature,weight,covar)
   from 
      kdd10a_train_x3
  ) t 
@@ -201,4 +201,4 @@ where actual = predicted;
 | AROW | 0.8676038894615345 |
 | PA1 | 0.8677782959894337 |
 | CW | 0.8678037711002504 |
-| SCW1 | 0.8678096499719774 |
+| SCW1 | 0.8678096499719774 |

docs/gitbook/binaryclass/kdd2010b_arow.md

@@ -28,8 +28,8 @@ select
  argmin_kld(weight, covar) as weight -- [hivemall v0.2alpha3 or later]
 from 
  (select 
-     -- train_arow(addBias(features),label) as (feature,weight) -- [hivemall v0.1]
-     train_arow(addBias(features),label) as (feature,weight,covar) -- [hivemall v0.2 or later]
+     -- train_arow(add_bias(features),label) as (feature,weight) -- [hivemall v0.1]
+     train_arow(add_bias(features),label) as (feature,weight,covar) -- [hivemall v0.2 or later]
   from 
      kdd10b_train_x3
  ) t 
@@ -67,4 +67,4 @@ from
 select count(1)/748401 from kdd10b_arow_submit1 
 where actual = predicted;
 ```
-> 0.8565808971393678
+> 0.8565808971393678

docs/gitbook/binaryclass/kdd2010b_dataset.md

@@ -65,7 +65,7 @@ select
   split(feature,":")[0] as feature,
   cast(split(feature,":")[1] as float) as value
 from 
-  kdd10b_test LATERAL VIEW explode(addBias(features)) t AS feature;
+  kdd10b_test LATERAL VIEW explode(add_bias(features)) t AS feature;
 
 set hivevar:xtimes=3;
 set hivevar:shufflebuffersize=1000;