Prophet预测 (ProphetPredictBatchOp)

Java 类名：com.alibaba.alink.operator.batch.timeseries.ProphetPredictBatchOp

Python 类名：ProphetPredictBatchOp

功能介绍

指定模型(通过ProphetTrainBatchOp训练得到),对每一行的MTable数据, 进行Prophet时间序列预测，给出下一时间段的预测结果。

算法原理

Prophet是facebook开源的一个时间序列预测算法, github地址：https://github.com/facebook/prophet.

Prophet适用于具有明显的内在规律的数据, 例如：

有一定的历史数据，有至少几个月的每小时、每天或每周观察的历史数据
有较强的季节性趋势：每周的一些天，每年的一些时间
有已知的以不定期的间隔发生的重要节假日（比如国庆节）
缺失的历史数据或较大的异常数据的数量在合理范围内
对于数据中蕴含的非线性增长的趋势都有一个自然极限或饱和状态

使用方式

参考文档 https://www.yuque.com/pinshu/alink_guide/xbp5ky

参数说明

名称	中文名称	描述	类型	是否必须？	取值范围	默认值
predictionCol	预测结果列名	预测结果列名	String	✓
valueCol	value列，类型为MTable	value列，类型为MTable	String	✓	所选列类型为 [M_TABLE, STRING]
cap	cap	cap	Double			null
changePointPriorScale	changepoint_prior_scale	changepoint_prior_scale	Double			0.05
changePointRange	change_point_range	change_point_range	Double			0.8
changePoints	changepoints	changepoints	String			null
dailySeasonality	daily_seasonality	daily_seasonality	String			“auto”
floor	floor	floor	Double			null
growth	growth	growth	String		“LINEAR”, “LOGISTIC”, “FLAT”	“LINEAR”
holidays	节假日	节假日，格式是 playoff:2008-01-13,2009-01-03 superbowl: 2010-02-07,2014-02-02	String			null
holidaysPriorScale	holidays_prior_scale	holidays_prior_scale	Double			10.0
includeHistory	include_history	include_history	Boolean			false
intervalWidth	interval_width	interval_width	Double			0.8
mcmcSamples	mcmc_samples	mcmc_samples	Integer			0
modelFilePath	模型的文件路径	模型的文件路径	String			null
nChangePoint	n_change_point	n_change_point	Integer			25
predictNum	预测条数	预测条数	Integer			1
predictionDetailCol	预测详细信息列名	预测详细信息列名	String
reservedCols	算法保留列名	算法保留列	String[]			null
seasonalityMode	seasonality_mode	seasonality_mode	String		“MULTIPLICATIVE”, “ADDITIVE”	“ADDITIVE”
seasonalityPriorScale	seasonality_prior_scale	seasonality_prior_scale	Double			10.0
stanInit	初始值	初始值	String			null
uncertaintySamples	用来计算指标的采样数目	用来计算指标的采样数目，设置成0，不计算指标。	Integer			1000
weeklySeasonality	weekly_seasonality	weekly_seasonality	String			“auto”
yearlySeasonality	yearly_seasonality	yearly_seasonality	String			“auto”
numThreads	组件多线程线程个数	组件多线程线程个数	Integer			1

代码示例

Python 代码

from pyalink.alink import *

import pandas as pd

useLocalEnv(1)

import time, datetime
import numpy as np
import pandas as pd

downloader = AlinkGlobalConfiguration.getPluginDownloader()
downloader.downloadPlugin('tf115_python_env_linux')

data = pd.DataFrame([
			[1,  datetime.datetime.fromtimestamp(1000), 10.0],
			[1,  datetime.datetime.fromtimestamp(2000), 11.0],
			[1,  datetime.datetime.fromtimestamp(3000), 12.0],
			[1,  datetime.datetime.fromtimestamp(4000), 13.0],
			[1,  datetime.datetime.fromtimestamp(5000), 14.0],
			[1,  datetime.datetime.fromtimestamp(6000), 15.0],
			[1,  datetime.datetime.fromtimestamp(7000), 16.0],
			[1,  datetime.datetime.fromtimestamp(8000), 17.0],
			[1,  datetime.datetime.fromtimestamp(9000), 18.0],
			[1,  datetime.datetime.fromtimestamp(10000), 19.0]
])

source = dataframeToOperator(data, schemaStr='id int, ts timestamp, val double', op_type='batch')

prophetModel = source.link(\
                    ProphetTrainBatchOp()\
                        .setTimeCol("ts")\
                        .setValueCol("val")
                )

ProphetPredictBatchOp()\
    .setValueCol("data")\
    .setPredictNum(4)\
    .setPredictionCol("pred")\
    .linkFrom(
         prophetModel,
         source.link(\
            GroupByBatchOp()\
			    .setGroupByPredicate("id")\
			    .setSelectClause("id, mtable_agg(ts, val) as data")
            )
    )\
    .print()

Java 代码

package com.alibaba.alink.operator.batch.timeseries;

import org.apache.flink.types.Row;

import com.alibaba.alink.common.AlinkGlobalConfiguration;
import com.alibaba.alink.operator.batch.sql.GroupByBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import org.junit.Test;

import java.sql.Timestamp;
import java.util.Arrays;

public class ProphetBatchOpTest {

	@Test
	public void testModel() throws Exception {
		Row[] rowsData =
			new Row[] {
				Row.of(1, new Timestamp(1000), 10.0),
				Row.of(1, new Timestamp(2000), 11.0),
				Row.of(1, new Timestamp(3000), 12.0),
				Row.of(1, new Timestamp(4000), 13.0),
				Row.of(1, new Timestamp(5000), 14.0),
				Row.of(1, new Timestamp(6000), 15.0),
				Row.of(1, new Timestamp(7000), 16.0),
				Row.of(1, new Timestamp(8000), 17.0),
				Row.of(1, new Timestamp(9000), 18.0),
				Row.of(1, new Timestamp(10000), 19.0)
			};
		String[] colNames = new String[] {"id", "ds1", "y1"};

		//train batch model.
		MemSourceBatchOp source = new MemSourceBatchOp(Arrays.asList(rowsData), colNames);

		ProphetTrainBatchOp trainOp = new ProphetTrainBatchOp()
			.setTimeCol("ds1")
			.setValueCol("y1");

		source.link(trainOp);

		trainOp.lazyPrint();

		//construct times series by id.
		GroupByBatchOp groupData = new GroupByBatchOp()
			.setGroupByPredicate("id")
			.setSelectClause("mtable_agg(ds1, y1) as data");

		ProphetPredictBatchOp predictOp = new ProphetPredictBatchOp()
			.setValueCol("data")
			.setPredictNum(4)
			.setPredictionCol("pred");

		predictOp.linkFrom(trainOp, source.link(groupData)).print();
	}
}

运行结果

id	data	predict
1	{“data”:{“ts”:[“1970-01-01 08:00:00.001”,“1970-01-01 08:00:00.002”,“1970-01-01 08:00:00.003”,“1970-01-01 08:00:00.004”,“1970-01-01 08:00:00.005”,“1970-01-01 08:00:00.006”,“1970-01-01 08:00:00.007”,“1970-01-01 08:00:00.008”,“1970-01-01 08:00:00.009”,“1970-01-01 08:00:00.01”],“val”:[10.0,11.0,12.0,13.0,14.0,15.0,16.0,17.0,18.0,19.0]},“schema”:“ts TIMESTAMP,val DOUBLE”}	{“data”:{“ts”:[“1970-01-01 08:00:00.011”,“1970-01-01 08:00:00.012”,“1970-01-01 08:00:00.013”,“1970-01-01 08:00:00.014”,“1970-01-01 08:00:00.015”,“1970-01-01 08:00:00.016”,“1970-01-01 08:00:00.017”,“1970-01-01 08:00:00.018”,“1970-01-01 08:00:00.019”,“1970-01-01 08:00:00.02”,“1970-01-01 08:00:00.021”,“1970-01-01 08:00:00.022”],“val”:[20.0,21.0,22.0,23.0,24.0,25.0,26.0,27.0,28.0,29.0,30.0,31.0]},“schema”:“ts TIMESTAMP,val DOUBLE”}

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