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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-1901) Create sample operator for Dataset
Date Fri, 31 Jul 2015 15:28:04 GMT

    [ https://issues.apache.org/jira/browse/FLINK-1901?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14649332#comment-14649332
] 

ASF GitHub Bot commented on FLINK-1901:
---------------------------------------

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

    https://github.com/apache/flink/pull/949#discussion_r35984232
  
    --- Diff: flink-core/src/main/java/org/apache/flink/api/common/operators/util/BernoulliSampler.java
---
    @@ -0,0 +1,105 @@
    +/*
    + * 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.api.common.operators.util;
    +
    +import com.google.common.base.Preconditions;
    +
    +import java.util.Iterator;
    +import java.util.Random;
    +
    +/**
    + * A sampler implementation built upon Bernoulli Trail. For sample without replacement,
each element sample choice is just a bernoulli trail.
    + *
    + * @param <T> The type of sample.
    + */
    +public class BernoulliSampler<T> extends RandomSampler<T> {
    +	
    +	private final double fraction;
    +	private final Random random;
    +	
    +	/**
    +	 * Create a bernoulli sampler sample fraction and default random number generator.
    +	 *
    +	 * @param fraction sample fraction, aka the bernoulli sampler possibility.
    +	 */
    +	public BernoulliSampler(double fraction) {
    +		this(fraction, new Random());
    +	}
    +	
    +	/**
    +	 * Create a bernoulli sampler sample fraction and random number generator seed.
    +	 *
    +	 * @param fraction sample fraction, aka the bernoulli sampler possibility.
    +	 * @param seed     random number generator seed.
    +	 */
    +	public BernoulliSampler(double fraction, long seed) {
    +		this(fraction, new Random(seed));
    +	}
    +	
    +	/**
    +	 * Create a bernoulli sampler sample fraction and random number generator.
    +	 *
    +	 * @param fraction sample fraction, aka the bernoulli sampler possibility.
    +	 * @param random   the random number generator.
    +	 */
    +	public BernoulliSampler(double fraction, Random random) {
    +		Preconditions.checkArgument(fraction >= 0 && fraction <= 1.0d, "fraction
fraction must between [0, 1].");
    +		this.fraction = fraction;
    +		this.random = random;
    +	}
    +	
    +	/**
    +	 * Sample the input elements, for each input element, take a Bernoulli Trail for sample.
    +	 *
    +	 * @param input elements to be sampled.
    +	 * @return the sampled result which is lazy computed upon input elements.
    +	 */
    +	@Override
    +	public Iterator<T> sample(final Iterator<T> input) {
    +		if (fraction == 0) {
    +			return EMPTY_ITERABLE;
    +		}
    +		
    +		return new SampledIterator<T>() {
    +			T current;
    +			
    +			@Override
    +			public boolean hasNext() {
    +				if (current == null) {
    +					while (input.hasNext()) {
    +						T element = input.next();
    +						if (random.nextDouble() <= fraction) {
    +							current = element;
    +							return true;
    +						}
    +					}
    +					current = null;
    +					return false;
    +				}
    +				return false;
    --- End diff --
    
    I think, if I'm not mistaken, that `hasNext` has to be idempotent. Thus it should return
`true` if `current != null`.


> Create sample operator for Dataset
> ----------------------------------
>
>                 Key: FLINK-1901
>                 URL: https://issues.apache.org/jira/browse/FLINK-1901
>             Project: Flink
>          Issue Type: Improvement
>          Components: Core
>            Reporter: Theodore Vasiloudis
>            Assignee: Chengxiang Li
>
> In order to be able to implement Stochastic Gradient Descent and a number of other machine
learning algorithms we need to have a way to take a random sample from a Dataset.
> We need to be able to sample with or without replacement from the Dataset, choose the
relative size of the sample, and set a seed for reproducibility.



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