yunfengzhou-hub commented on code in PR #82:
URL: https://github.com/apache/flink-ml/pull/82#discussion_r846885979
##########
flink-ml-lib/src/test/java/org/apache/flink/ml/feature/VectorAssemblerTest.java:
##########
@@ -141,10 +140,11 @@ public void testErrorInvalid() {
Table outputTable = vectorAssembler.transform(inputDataTable)[0];
outputTable.execute().collect().next();
Assert.fail("Expected IllegalArgumentException");
- } catch (Exception e) {
- assertEquals(
- "Input column value should not be null.",
-
e.getCause().getCause().getCause().getCause().getCause().getMessage());
+ } catch (Throwable e) {
+ while (e.getCause() != null) {
+ e = e.getCause();
+ }
+ assertEquals("Input column value should not be null.",
e.getMessage());
Review Comment:
Just found that we can directly use
`org.apache.commons.lang3.exception.ExceptionUtils.getRootCauseMessage()` for
such cases.
##########
flink-ml-lib/src/main/java/org/apache/flink/ml/feature/bucketizer/BucketizerParams.java:
##########
@@ -0,0 +1,78 @@
+/*
+ * 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 org.apache.flink.ml.feature.bucketizer;
+
+import org.apache.flink.ml.common.param.HasHandleInvalid;
+import org.apache.flink.ml.common.param.HasInputCols;
+import org.apache.flink.ml.common.param.HasOutputCols;
+import org.apache.flink.ml.param.DoubleArrayArrayParam;
+import org.apache.flink.ml.param.ParamValidator;
+import org.apache.flink.util.Preconditions;
+
+/**
+ * Params for {@link Bucketizer}.
+ *
+ * <p>The `keep` option of {@link HasHandleInvalid} means that we put the
invalid data in the last
+ * bucket of the splits, whose index is the number of the buckets.
+ *
+ * @param <T> The class type of this instance.
+ */
+public interface BucketizerParams<T>
+ extends HasInputCols<T>, HasOutputCols<T>, HasHandleInvalid<T> {
+ /**
+ * The array of split points for mapping continuous features into buckets
for multiple columns.
+ *
+ * <p>Each input column is supposed to be mapped into {numberOfSplitPoints
- 1} buckets. A
+ * bucket is defined by two split points. For example, bucket(x,y)
contains values in the range
+ * [x,y). An exception is that the last bucket also contains y. The array
should contain at
+ * least three split points and be strictly increasing.
+ */
+ DoubleArrayArrayParam SPLITS_ARRAY =
+ new DoubleArrayArrayParam(
+ "splitsArray",
+ "Array of split points for mapping continuous features
into buckets.",
+ null,
+ new SplitsArrayValidator());
+
+ default Double[][] getSplitsArray() {
+ return get(SPLITS_ARRAY);
+ }
+
+ default T setSplitsArray(Double[][] value) {
+ set(SPLITS_ARRAY, value);
+ return (T) this;
+ }
+
+ /** Param validator for splitsArray. */
+ class SplitsArrayValidator implements ParamValidator<Double[][]> {
+
+ @Override
+ public boolean validate(Double[][] splitsArray) {
+ Preconditions.checkNotNull(splitsArray);
+ Preconditions.checkArgument(splitsArray.length != 0);
+ for (Double[] splits : splitsArray) {
+ Preconditions.checkArgument(splits.length >= 3);
+ for (int j = 1; j < splits.length; j++) {
+ Preconditions.checkArgument(splits[j] > splits[j - 1]);
Review Comment:
Shall we return false so that the external infra can handle it, instead of
directly throw exception?
```suggestion
if (splits[j] <= splits[j-1]) {
return false;
}
```
##########
flink-ml-lib/src/main/java/org/apache/flink/ml/feature/bucketizer/Bucketizer.java:
##########
@@ -0,0 +1,160 @@
+/*
+ * 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 org.apache.flink.ml.feature.bucketizer;
+
+import org.apache.flink.api.common.functions.FlatMapFunction;
+import org.apache.flink.api.common.typeinfo.BasicTypeInfo;
+import org.apache.flink.api.common.typeinfo.TypeInformation;
+import org.apache.flink.api.java.typeutils.RowTypeInfo;
+import org.apache.flink.ml.api.Transformer;
+import org.apache.flink.ml.common.datastream.TableUtils;
+import org.apache.flink.ml.param.Param;
+import org.apache.flink.ml.util.ParamUtils;
+import org.apache.flink.ml.util.ReadWriteUtils;
+import org.apache.flink.streaming.api.datastream.DataStream;
+import org.apache.flink.table.api.Table;
+import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
+import org.apache.flink.table.api.internal.TableImpl;
+import org.apache.flink.types.Row;
+import org.apache.flink.util.Collector;
+import org.apache.flink.util.Preconditions;
+
+import org.apache.commons.lang3.ArrayUtils;
+
+import java.io.IOException;
+import java.util.Arrays;
+import java.util.HashMap;
+import java.util.Map;
+
+/**
+ * Bucketizer is a transformer that maps multiple columns of continuous
features to multiple columns
+ * of discrete features, i.e., buckets IDs.
+ */
+public class Bucketizer implements Transformer<Bucketizer>,
BucketizerParams<Bucketizer> {
+ private final Map<Param<?>, Object> paramMap = new HashMap<>();
+
+ public Bucketizer() {
+ ParamUtils.initializeMapWithDefaultValues(paramMap, this);
+ }
+
+ @Override
+ public Table[] transform(Table... inputs) {
+ Preconditions.checkArgument(inputs.length == 1);
+ String[] inputCols = getInputCols();
+ String[] outputCols = getOutputCols();
+ Double[][] splitsArray = getSplitsArray();
+ Preconditions.checkArgument(inputCols.length == outputCols.length);
+ Preconditions.checkArgument(inputCols.length == splitsArray.length);
+
+ StreamTableEnvironment tEnv =
+ (StreamTableEnvironment) ((TableImpl)
inputs[0]).getTableEnvironment();
+
+ RowTypeInfo inputTypeInfo =
TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema());
+ TypeInformation<?>[] outputTypes = new
TypeInformation[outputCols.length];
+ Arrays.fill(outputTypes, BasicTypeInfo.INT_TYPE_INFO);
+ RowTypeInfo outputTypeInfo =
+ new RowTypeInfo(
+ ArrayUtils.addAll(inputTypeInfo.getFieldTypes(),
outputTypes),
+ ArrayUtils.addAll(inputTypeInfo.getFieldNames(),
getOutputCols()));
+
+ DataStream<Row> result =
+ tEnv.toDataStream(inputs[0])
+ .flatMap(
+ new FindBucketFunction(inputCols, splitsArray,
getHandleInvalid()),
+ outputTypeInfo);
+ return new Table[] {tEnv.fromDataStream(result)};
+ }
+
+ /** Finds the bucket index for each continuous feature of an input data
point. */
+ private static class FindBucketFunction implements FlatMapFunction<Row,
Row> {
+ private final String[] inputCols;
+ private final String handleInvalid;
+ private final Double[][] splitsArray;
+
+ public FindBucketFunction(
+ String[] inputCols, Double[][] splitsArray, String
handleInvalid) {
+ this.inputCols = inputCols;
+ this.splitsArray = splitsArray;
+ this.handleInvalid = handleInvalid;
+ }
+
+ @Override
+ public void flatMap(Row value, Collector<Row> out) {
+ Row outputRow = new Row(inputCols.length);
+
+ for (int i = 0; i < inputCols.length; i++) {
+ double feature = ((Number)
value.getField(inputCols[i])).doubleValue();
+ Double[] splits = splitsArray[i];
+ boolean isInvalid = false;
+
+ if (!Double.isNaN(feature)) {
+ int index = Arrays.binarySearch(splits, feature);
+ if (index >= 0) {
+ if (index == inputCols.length - 1) {
+ index--;
+ }
+ outputRow.setField(i, index);
+ } else {
+ index = -index - 1;
+ if (index == 0 || index == inputCols.length) {
+ isInvalid = true;
+ } else {
+ outputRow.setField(i, index - 1);
+ }
+ }
+ } else {
+ isInvalid = true;
+ }
+
+ if (isInvalid) {
+ switch (handleInvalid) {
+ case ERROR_INVALID:
+ throw new RuntimeException(
+ "The input contains invalid value. See "
+ + HANDLE_INVALID
+ + " parameter for more options.");
+ case SKIP_INVALID:
+ return;
+ case KEEP_INVALID:
+ outputRow.setField(i, splits.length - 1);
+ break;
+ default:
+ throw new IllegalStateException(
Review Comment:
nit: `UnsupportedOperationException`
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