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Posted to issues@flink.apache.org by GitBox <gi...@apache.org> on 2022/11/03 13:14:18 UTC
[GitHub] [flink-ml] lindong28 commented on a diff in pull request #166: [FLINK-29598] Add Estimator and Transformer for Imputer
lindong28 commented on code in PR #166:
URL: https://github.com/apache/flink-ml/pull/166#discussion_r1012805730
##########
flink-ml-lib/src/main/java/org/apache/flink/ml/feature/imputer/ImputerModel.java:
##########
@@ -0,0 +1,170 @@
+/*
+ * 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.imputer;
+
+import org.apache.flink.api.common.functions.RichMapFunction;
+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.Model;
+import org.apache.flink.ml.common.broadcast.BroadcastUtils;
+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.Preconditions;
+
+import org.apache.commons.lang3.ArrayUtils;
+
+import java.io.IOException;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.Map;
+
+/** A Model which replace the missing values using the model data computed by {@link Imputer}. */
Review Comment:
replace ->replaces
Same for similar docs in this PR.
##########
flink-ml-core/src/main/java/org/apache/flink/ml/param/DoubleParam.java:
##########
@@ -32,4 +34,12 @@ public DoubleParam(
public DoubleParam(String name, String description, Double defaultValue) {
this(name, description, defaultValue, ParamValidators.alwaysTrue());
}
+
+ @Override
+ public Double jsonDecode(Object json) throws IOException {
+ if (json instanceof String && json.equals(String.valueOf(Double.NaN))) {
+ return Double.NaN;
+ }
+ return (Double) json;
Review Comment:
What other special value do we expect to handle?
If it is just `NaN`, it seems simpler to just do the following
```
if (json.equals("NaN")) {
return Double.NaN;
}
```
Depending on what other special value we plan to handle, we might need to update e.g. `FloatParam` for consistency.
##########
docs/content/docs/operators/feature/imputer.md:
##########
@@ -0,0 +1,196 @@
+---
+title: "Imputer"
+weight: 1
+type: docs
+aliases:
+- /operators/feature/imputer.html
+---
+
+<!--
+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 dand limitations
+under the License.
+-->
+
+## Imputer
+The imputer for completing missing values of the input columns.
+
+Missing values can be imputed using the statistics(mean, median or
+most frequent) of each column in which the missing values are located.
+The input columns should be of numeric type.
+
+__Note__ The `mean`/`median`/`most frequent` value is computed after
+filtering out missing values and null values, null values are always
+treated as missing, and so are also imputed.
+
+__Note__ The parameter `relativeError` is only effective when the strategy
+ is `median`.
+
+### Input Columns
+
+| Param name | Type | Default | Description |
+|:-----------|:-------|:--------|:------------------------|
+| inputCols | Number | `null` | Features to be imputed. |
+
+### Output Columns
+
+| Param name | Type | Default | Description |
+|:-----------|:-------|:--------|:------------------|
+| outputCols | Double | `null` | Imputed features. |
+
+### Parameters
+
+Below are the parameters required by `ImputerModel`.
+
+| Key | Default | Type | Required | Description |
+|---------------|-----------|-------------|----------|--------------------------------------------------------------------------------|
+| inputCols | `null` | String[] | yes | Input column names. |
+| outputCols | `null` | String[] | yes | Output column names. |
+| strategy | `"mean"` | String | no | The imputation strategy. Supported values: 'mean', 'median', 'most_frequent'. |
+| relativeError | `0.001` | Double | no | The relative target precision for the approximate quantile algorithm. |
+
+`Imputer` needs parameters above and also below.
+
+| Key | Default | Type | Required | Description |
+|---------------|--------------|-------------|----------|--------------------------------------------------------------------------------------------|
+| missingValue | `Double.NaN` | Double | no | The placeholder for the missing values. All occurrences of missing values will be imputed. |
+
+### Examples
+
+{{< tabs examples >}}
+
+{{< tab "Java">}}
+
+```java
+import org.apache.flink.ml.feature.imputer.Imputer;
+import org.apache.flink.ml.feature.imputer.ImputerModel;
+import org.apache.flink.streaming.api.datastream.DataStream;
+import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
+import org.apache.flink.table.api.Table;
+import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
+import org.apache.flink.types.Row;
+import org.apache.flink.util.CloseableIterator;
+
+import java.util.Arrays;
+
+/** Simple program that trains a {@link Imputer} model and uses it for feature engineering. */
+public class ImputerExample {
+
+ public static void main(String[] args) {
+ StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
+ StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);
+
+ // Generates input training and prediction data.
+ DataStream<Row> trainStream =
+ env.fromElements(
+ Row.of(Double.NaN, 9.0),
+ Row.of(1.0, 9.0),
+ Row.of(1.5, 9.0),
+ Row.of(2.5, Double.NaN),
+ Row.of(5.0, 5.0),
+ Row.of(5.0, 4.0));
+ Table trainTable = tEnv.fromDataStream(trainStream).as("input1", "input2");
+
+ // Create a Imputer object and initialize its parameters
Review Comment:
a -> an
Same for other comments in this PR.
##########
flink-ml-lib/src/main/java/org/apache/flink/ml/feature/imputer/Imputer.java:
##########
@@ -0,0 +1,333 @@
+/*
+ * 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.imputer;
+
+import org.apache.flink.api.common.functions.AggregateFunction;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.ml.api.Estimator;
+import org.apache.flink.ml.common.datastream.DataStreamUtils;
+import org.apache.flink.ml.common.util.QuantileSummary;
+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.Preconditions;
+
+import java.io.IOException;
+import java.util.Arrays;
+import java.util.HashMap;
+import java.util.Map;
+
+/**
+ * The imputer for completing missing values of the input columns.
+ *
+ * <p>Missing values can be imputed using the statistics(mean, median or most frequent) of each
+ * column in which the missing values are located. The input columns should be of numeric type.
Review Comment:
Is this issue addressed?
##########
docs/content/docs/operators/feature/imputer.md:
##########
@@ -0,0 +1,196 @@
+---
+title: "Imputer"
+weight: 1
+type: docs
+aliases:
+- /operators/feature/imputer.html
+---
+
+<!--
+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 dand limitations
+under the License.
+-->
+
+## Imputer
+The imputer for completing missing values of the input columns.
+
+Missing values can be imputed using the statistics(mean, median or
+most frequent) of each column in which the missing values are located.
+The input columns should be of numeric type.
+
+__Note__ The `mean`/`median`/`most frequent` value is computed after
+filtering out missing values and null values, null values are always
+treated as missing, and so are also imputed.
+
+__Note__ The parameter `relativeError` is only effective when the strategy
+ is `median`.
+
+### Input Columns
+
+| Param name | Type | Default | Description |
+|:-----------|:-------|:--------|:------------------------|
+| inputCols | Number | `null` | Features to be imputed. |
+
+### Output Columns
+
+| Param name | Type | Default | Description |
+|:-----------|:-------|:--------|:------------------|
+| outputCols | Double | `null` | Imputed features. |
+
+### Parameters
+
+Below are the parameters required by `ImputerModel`.
+
+| Key | Default | Type | Required | Description |
+|---------------|-----------|-------------|----------|--------------------------------------------------------------------------------|
+| inputCols | `null` | String[] | yes | Input column names. |
+| outputCols | `null` | String[] | yes | Output column names. |
+| strategy | `"mean"` | String | no | The imputation strategy. Supported values: 'mean', 'median', 'most_frequent'. |
+| relativeError | `0.001` | Double | no | The relative target precision for the approximate quantile algorithm. |
+
+`Imputer` needs parameters above and also below.
+
+| Key | Default | Type | Required | Description |
+|---------------|--------------|-------------|----------|--------------------------------------------------------------------------------------------|
+| missingValue | `Double.NaN` | Double | no | The placeholder for the missing values. All occurrences of missing values will be imputed. |
Review Comment:
Is the parameter documented here consistent with the actual code?
##########
docs/content/docs/operators/feature/imputer.md:
##########
@@ -0,0 +1,196 @@
+---
+title: "Imputer"
+weight: 1
+type: docs
+aliases:
+- /operators/feature/imputer.html
+---
+
+<!--
+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 dand limitations
+under the License.
+-->
+
+## Imputer
+The imputer for completing missing values of the input columns.
+
+Missing values can be imputed using the statistics(mean, median or
+most frequent) of each column in which the missing values are located.
+The input columns should be of numeric type.
+
+__Note__ The `mean`/`median`/`most frequent` value is computed after
+filtering out missing values and null values, null values are always
+treated as missing, and so are also imputed.
+
+__Note__ The parameter `relativeError` is only effective when the strategy
+ is `median`.
+
+### Input Columns
+
+| Param name | Type | Default | Description |
+|:-----------|:-------|:--------|:------------------------|
+| inputCols | Number | `null` | Features to be imputed. |
+
+### Output Columns
+
+| Param name | Type | Default | Description |
+|:-----------|:-------|:--------|:------------------|
+| outputCols | Double | `null` | Imputed features. |
+
+### Parameters
+
+Below are the parameters required by `ImputerModel`.
+
+| Key | Default | Type | Required | Description |
+|---------------|-----------|-------------|----------|--------------------------------------------------------------------------------|
+| inputCols | `null` | String[] | yes | Input column names. |
+| outputCols | `null` | String[] | yes | Output column names. |
+| strategy | `"mean"` | String | no | The imputation strategy. Supported values: 'mean', 'median', 'most_frequent'. |
Review Comment:
Should we describe the behavior for each option, similar to what is described in the Java doc?
##########
docs/content/docs/operators/feature/imputer.md:
##########
@@ -0,0 +1,196 @@
+---
+title: "Imputer"
+weight: 1
+type: docs
+aliases:
+- /operators/feature/imputer.html
+---
+
+<!--
+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 dand limitations
+under the License.
+-->
+
+## Imputer
+The imputer for completing missing values of the input columns.
+
+Missing values can be imputed using the statistics(mean, median or
+most frequent) of each column in which the missing values are located.
+The input columns should be of numeric type.
+
+__Note__ The `mean`/`median`/`most frequent` value is computed after
+filtering out missing values and null values, null values are always
+treated as missing, and so are also imputed.
+
+__Note__ The parameter `relativeError` is only effective when the strategy
+ is `median`.
+
+### Input Columns
+
+| Param name | Type | Default | Description |
+|:-----------|:-------|:--------|:------------------------|
+| inputCols | Number | `null` | Features to be imputed. |
+
+### Output Columns
+
+| Param name | Type | Default | Description |
+|:-----------|:-------|:--------|:------------------|
+| outputCols | Double | `null` | Imputed features. |
+
+### Parameters
+
+Below are the parameters required by `ImputerModel`.
+
+| Key | Default | Type | Required | Description |
+|---------------|-----------|-------------|----------|--------------------------------------------------------------------------------|
+| inputCols | `null` | String[] | yes | Input column names. |
+| outputCols | `null` | String[] | yes | Output column names. |
+| strategy | `"mean"` | String | no | The imputation strategy. Supported values: 'mean', 'median', 'most_frequent'. |
+| relativeError | `0.001` | Double | no | The relative target precision for the approximate quantile algorithm. |
+
+`Imputer` needs parameters above and also below.
+
+| Key | Default | Type | Required | Description |
+|---------------|--------------|-------------|----------|--------------------------------------------------------------------------------------------|
+| missingValue | `Double.NaN` | Double | no | The placeholder for the missing values. All occurrences of missing values will be imputed. |
+
+### Examples
+
+{{< tabs examples >}}
+
+{{< tab "Java">}}
+
+```java
+import org.apache.flink.ml.feature.imputer.Imputer;
+import org.apache.flink.ml.feature.imputer.ImputerModel;
+import org.apache.flink.streaming.api.datastream.DataStream;
+import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
+import org.apache.flink.table.api.Table;
+import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
+import org.apache.flink.types.Row;
+import org.apache.flink.util.CloseableIterator;
+
+import java.util.Arrays;
+
+/** Simple program that trains a {@link Imputer} model and uses it for feature engineering. */
+public class ImputerExample {
+
+ public static void main(String[] args) {
+ StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
+ StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);
+
+ // Generates input training and prediction data.
+ DataStream<Row> trainStream =
+ env.fromElements(
+ Row.of(Double.NaN, 9.0),
+ Row.of(1.0, 9.0),
+ Row.of(1.5, 9.0),
+ Row.of(2.5, Double.NaN),
+ Row.of(5.0, 5.0),
+ Row.of(5.0, 4.0));
+ Table trainTable = tEnv.fromDataStream(trainStream).as("input1", "input2");
+
+ // Create a Imputer object and initialize its parameters
+ Imputer imputer =
+ new Imputer()
+ .setInputCols("input1", "input2")
+ .setOutputCols("output1", "output2")
+ .setStrategy("mean")
+ .setMissingValue(Double.NaN);
+
+ // Train the Imputer model.
+ ImputerModel model = imputer.fit(trainTable);
+
+ // Uses the Imputer model for predictions.
Review Comment:
It seems inconsistent to start the coment with `Uses` here but use `Train` above. Can you make the comment style consistent with the exiting doc?
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