yunfengzhou-hub commented on code in PR #172: URL: https://github.com/apache/flink-ml/pull/172#discussion_r1021035116
########## flink-ml-lib/src/main/java/org/apache/flink/ml/feature/robustscaler/RobustScaler.java: ########## @@ -0,0 +1,183 @@ +/* + * 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.robustscaler; + +import org.apache.flink.api.common.functions.AggregateFunction; +import org.apache.flink.api.common.functions.MapFunction; +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.linalg.DenseVector; +import org.apache.flink.ml.linalg.Vector; +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; +import java.util.stream.Collectors; + +/** + * Scale features using statistics that are robust to outliers. + * + * <p>This Scaler removes the median and scales the data according to the quantile range (defaults + * to IQR: Interquartile Range). The IQR is the range between the 1st quartile (25th quantile) and + * the 3rd quartile (75th quantile) but can be configured. + * + * <p>Centering and scaling happen independently on each feature by computing the relevant + * statistics on the samples in the training set. Median and quantile range are then stored to be + * used on later data using the transform method. + * + * <p>Standardization of a dataset is a common requirement for many machine learning estimators. + * Typically this is done by removing the mean and scaling to unit variance. However, outliers can + * often influence the sample mean / variance in a negative way. In such cases, the median and the + * interquartile range often give better results. Review Comment: Sorry that I mistook the meaning as "the median range and the interquartile range". I agree that there is no grammar error now. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: issues-unsubscr...@flink.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
