ALinkLab

连接算法与应用！

Alink教程

Alink组件列表

同类别的组件
该文档涉及的组件
相似的组件

网格搜索TV (GridSearchTVSplit)

Java 类名：com.alibaba.alink.pipeline.tuning.GridSearchTVSplit

Python 类名：GridSearchTVSplit

功能介绍

gridsearch是通过参数数组组成的网格，对其中的每一组输入参数的组很分别进行训练，预测，评估。取得评估参数最优的模型，作为最终的返回模型

tv为训练验证，将数据按照比例切分为两份，对其中一份数据做训练，对剩余一份数据做预测和评估，得到一个评估结果。

此函数用tv方法得到每一个grid对应参数的评估结果，得到最优模型

参数说明

名称 中文名称 描述 类型 是否必须？ 默认值
trainRatio 训练集比例 训练集与验证集的划分比例，取值范围为(0, 1]。 Double 0.8
ParamGrid 参数网格 指定参数的网格 ParamGrid ✓
Estimator Estimator 用于调优的Estimator Estimator ✓
TuningEvaluator 评估指标 用于选择最优模型的评估指标 TuningEvaluator ✓

代码示例

Python 代码

from pyalink.alink import *

import pandas as pd

useLocalEnv(1)

def adult(url):
    data = (
        CsvSourceBatchOp()
            .setFilePath('https://alink-test-data.oss-cn-hangzhou.aliyuncs.com/adult_train.csv')
            .setSchemaStr(
            'age bigint, workclass string, fnlwgt bigint,'
            'education string, education_num bigint,'
            'marital_status string, occupation string,'
            'relationship string, race string, sex string,'
            'capital_gain bigint, capital_loss bigint,'
            'hours_per_week bigint, native_country string,'
            'label string'
        )
    )
    return data

def adult_train():
    return adult('https://alink-test-data.oss-cn-hangzhou.aliyuncs.com/adult_train.csv')

def adult_test():
    return adult('https://alink-test-data.oss-cn-hangzhou.aliyuncs.com/adult_test.csv')

def adult_numerical_feature_strs():
    return [
        "age", "fnlwgt", "education_num",
        "capital_gain", "capital_loss", "hours_per_week"
    ]

def adult_categorical_feature_strs():
    return [
        "workclass", "education", "marital_status",
        "occupation", "relationship", "race", "sex",
        "native_country"
    ]

def adult_features_strs():
    feature = adult_numerical_feature_strs()
    feature.extend(adult_categorical_feature_strs())

    return feature

def rf_grid_search_cv(featureCols, categoryFeatureCols, label, metric):
    rf = (
        RandomForestClassifier()
            .setFeatureCols(featureCols)
            .setCategoricalCols(categoryFeatureCols)
            .setLabelCol(label)
            .setPredictionCol('prediction')
            .setPredictionDetailCol('prediction_detail')
    )
    paramGrid = (
        ParamGrid()
            .addGrid(rf, 'SUBSAMPLING_RATIO', [1.0, 0.99, 0.98])
            .addGrid(rf, 'NUM_TREES', [3, 6, 9])
    )
    tuningEvaluator = (
        BinaryClassificationTuningEvaluator()
            .setLabelCol(label)
            .setPredictionDetailCol("prediction_detail")
            .setTuningBinaryClassMetric(metric)
    )
    cv = (
        GridSearchCV()
            .setEstimator(rf)
            .setParamGrid(paramGrid)
            .setTuningEvaluator(tuningEvaluator)
            .setNumFolds(2)
            .enableLazyPrintTrainInfo("TrainInfo")
    )

    return cv

def rf_grid_search_tv(featureCols, categoryFeatureCols, label, metric):
    rf = (
        RandomForestClassifier()
            .setFeatureCols(featureCols)
            .setCategoricalCols(categoryFeatureCols)
            .setLabelCol(label)
            .setPredictionCol('prediction')
            .setPredictionDetailCol('prediction_detail')
    )
    paramGrid = (
        ParamGrid()
            .addGrid(rf, 'SUBSAMPLING_RATIO', [1.0, 0.99, 0.98])
            .addGrid(rf, 'NUM_TREES', [3, 6, 9])
    )
    tuningEvaluator = (
        BinaryClassificationTuningEvaluator()
            .setLabelCol(label)
            .setPredictionDetailCol("prediction_detail")
            .setTuningBinaryClassMetric(metric)
    )
    cv = (
        GridSearchTVSplit()
            .setEstimator(rf)
            .setParamGrid(paramGrid)
            .setTuningEvaluator(tuningEvaluator)
            .enableLazyPrintTrainInfo("TrainInfo")
    )

    return cv

def tuningcv(cv_estimator, input):
    return cv_estimator.fit(input)

def tuningtv(tv_estimator, input):
    return tv_estimator.fit(input)

def main():
    print('rf cv tuning')
    model = tuningcv(
        rf_grid_search_cv(adult_features_strs(),
                          adult_categorical_feature_strs(), 'label', 'AUC'),
        adult_train()
    )

    print('rf tv tuning')
    model = tuningtv(
        rf_grid_search_tv(adult_features_strs(),
                          adult_categorical_feature_strs(), 'label', 'AUC'),
        adult_train()
    )

main()

Java 代码

import com.alibaba.alink.operator.batch.source.CsvSourceBatchOp;
import com.alibaba.alink.pipeline.classification.RandomForestClassifier;
import com.alibaba.alink.pipeline.tuning.BinaryClassificationTuningEvaluator;
import com.alibaba.alink.pipeline.tuning.GridSearchTVSplit;
import com.alibaba.alink.pipeline.tuning.GridSearchTVSplitModel;
import com.alibaba.alink.pipeline.tuning.ParamGrid;
import org.junit.Test;

public class GridSearchTVSplitTest {
	@Test
	public void testGridSearchTVSplit() throws Exception {
		String[] featureCols = new String[] {
			"age", "fnlwgt", "education_num",
			"capital_gain", "capital_loss", "hours_per_week",
			"workclass", "education", "marital_status",
			"occupation", "relationship", "race", "sex",
			"native_country"
		};
		String[] categoryFeatureCols = new String[] {
			"workclass", "education", "marital_status",
			"occupation", "relationship", "race", "sex",
			"native_country"
		};
		String label = "label";
		CsvSourceBatchOp data = new CsvSourceBatchOp()
			.setFilePath("https://alink-test-data.oss-cn-hangzhou.aliyuncs.com/adult_train.csv")
			.setSchemaStr(
				"age bigint, workclass string, fnlwgt bigint, education string, education_num bigint, marital_status "
					+ "string, occupation string, relationship string, race string, sex string, capital_gain bigint, "
					+ "capital_loss bigint, hours_per_week bigint, native_country string, label string");
		RandomForestClassifier rf = new RandomForestClassifier()
			.setFeatureCols(featureCols)
			.setCategoricalCols(categoryFeatureCols)
			.setLabelCol(label)
			.setPredictionCol("prediction")
			.setPredictionDetailCol("prediction_detail");
		ParamGrid paramGrid = new ParamGrid()
			.addGrid(rf, RandomForestClassifier.SUBSAMPLING_RATIO, new Double[] {1.0, 0.99, 0.98})
			.addGrid(rf, RandomForestClassifier.NUM_TREES, new Integer[] {3, 6, 9});
		BinaryClassificationTuningEvaluator tuningEvaluator = new BinaryClassificationTuningEvaluator()
			.setLabelCol(label)
			.setPredictionDetailCol("prediction_detail")
			.setTuningBinaryClassMetric("AUC");
		GridSearchTVSplit cv = new GridSearchTVSplit()
			.setEstimator(rf)
			.setParamGrid(paramGrid)
			.setTuningEvaluator(tuningEvaluator)
			.setTrainRatio(0.8)
			.enableLazyPrintTrainInfo("TrainInfo");
		GridSearchTVSplitModel model = cv.fit(data);
	}
}

运行结果

TrainInfo
Metric information:
Metric name: AUC
Larger is better: true
Tuning information:
| AUC| stage| param|value| stage 2| param 2|value 2|
|——————|———————-|——–|—–|———————-|—————-|——-|
|0.9146840488554084|RandomForestClassifier|numTrees| 9|RandomForestClassifier|subsamplingRatio| 0.99|
|0.9125303612307454|RandomForestClassifier|numTrees| 9|RandomForestClassifier|subsamplingRatio| 1.0|
|0.9098037018824784|RandomForestClassifier|numTrees| 6|RandomForestClassifier|subsamplingRatio| 0.98|
|0.9091959987727252|RandomForestClassifier|numTrees| 6|RandomForestClassifier|subsamplingRatio| 0.99|
|0.9078221190139827|RandomForestClassifier|numTrees| 9|RandomForestClassifier|subsamplingRatio| 0.98|
|0.9025009982071417|RandomForestClassifier|numTrees| 6|RandomForestClassifier|subsamplingRatio| 1.0|
|0.9015572276497046|RandomForestClassifier|numTrees| 3|RandomForestClassifier|subsamplingRatio| 1.0|
| 0.893774245901412|RandomForestClassifier|numTrees| 3|RandomForestClassifier|subsamplingRatio| 0.98|
| 0.892706752181599|RandomForestClassifier|numTrees| 3|RandomForestClassifier|subsamplingRatio| 0.99|

运行结果

rf cv tuning
com.alibaba.alink.pipeline.tuning.GridSearchCV
[ {
  "param" : [ {
    "stage" : "RandomForestClassifier",
    "paramName" : "numTrees",
    "paramValue" : 3
  }, {
    "stage" : "RandomForestClassifier",
    "paramName" : "subsamplingRatio",
    "paramValue" : 1.0
  } ],
  "metric" : 0.8922549257899725
}, {
  "param" : [ {
    "stage" : "RandomForestClassifier",
    "paramName" : "numTrees",
    "paramValue" : 3
  }, {
    "stage" : "RandomForestClassifier",
    "paramName" : "subsamplingRatio",
    "paramValue" : 0.99
  } ],
  "metric" : 0.8920255970548456
}, {
  "param" : [ {
    "stage" : "RandomForestClassifier",
    "paramName" : "numTrees",
    "paramValue" : 3
  }, {
    "stage" : "RandomForestClassifier",
    "paramName" : "subsamplingRatio",
    "paramValue" : 0.98
  } ],
  "metric" : 0.8944982480437225
}, {
  "param" : [ {
    "stage" : "RandomForestClassifier",
    "paramName" : "numTrees",
    "paramValue" : 6
  }, {
    "stage" : "RandomForestClassifier",
    "paramName" : "subsamplingRatio",
    "paramValue" : 1.0
  } ],
  "metric" : 0.8923867598288401
}, {
  "param" : [ {
    "stage" : "RandomForestClassifier",
    "paramName" : "numTrees",
    "paramValue" : 6
  }, {
    "stage" : "RandomForestClassifier",
    "paramName" : "subsamplingRatio",
    "paramValue" : 0.99
  } ],
  "metric" : 0.9012141767959505
}, {
  "param" : [ {
    "stage" : "RandomForestClassifier",
    "paramName" : "numTrees",
    "paramValue" : 6
  }, {
    "stage" : "RandomForestClassifier",
    "paramName" : "subsamplingRatio",
    "paramValue" : 0.98
  } ],
  "metric" : 0.8993774036693788
}, {
  "param" : [ {
    "stage" : "RandomForestClassifier",
    "paramName" : "numTrees",
    "paramValue" : 9
  }, {
    "stage" : "RandomForestClassifier",
    "paramName" : "subsamplingRatio",
    "paramValue" : 1.0
  } ],
  "metric" : 0.8981738808130779
}, {
  "param" : [ {
    "stage" : "RandomForestClassifier",
    "paramName" : "numTrees",
    "paramValue" : 9
  }, {
    "stage" : "RandomForestClassifier",
    "paramName" : "subsamplingRatio",
    "paramValue" : 0.99
  } ],
  "metric" : 0.9029671873892725
}, {
  "param" : [ {
    "stage" : "RandomForestClassifier",
    "paramName" : "numTrees",
    "paramValue" : 9
  }, {
    "stage" : "RandomForestClassifier",
    "paramName" : "subsamplingRatio",
    "paramValue" : 0.98
  } ],
  "metric" : 0.905228896323363
} ]
rf tv tuning
com.alibaba.alink.pipeline.tuning.GridSearchTVSplit
[ {
  "param" : [ {
    "stage" : "RandomForestClassifier",
    "paramName" : "numTrees",
    "paramValue" : 3
  }, {
    "stage" : "RandomForestClassifier",
    "paramName" : "subsamplingRatio",
    "paramValue" : 1.0
  } ],
  "metric" : 0.9022694229691741
}, {
  "param" : [ {
    "stage" : "RandomForestClassifier",
    "paramName" : "numTrees",
    "paramValue" : 3
  }, {
    "stage" : "RandomForestClassifier",
    "paramName" : "subsamplingRatio",
    "paramValue" : 0.99
  } ],
  "metric" : 0.8963559966080328
}, {
  "param" : [ {
    "stage" : "RandomForestClassifier",
    "paramName" : "numTrees",
    "paramValue" : 3
  }, {
    "stage" : "RandomForestClassifier",
    "paramName" : "subsamplingRatio",
    "paramValue" : 0.98
  } ],
  "metric" : 0.9041948454957178
}, {
  "param" : [ {
    "stage" : "RandomForestClassifier",
    "paramName" : "numTrees",
    "paramValue" : 6
  }, {
    "stage" : "RandomForestClassifier",
    "paramName" : "subsamplingRatio",
    "paramValue" : 1.0
  } ],
  "metric" : 0.8982021117392784
}, {
  "param" : [ {
    "stage" : "RandomForestClassifier",
    "paramName" : "numTrees",
    "paramValue" : 6
  }, {
    "stage" : "RandomForestClassifier",
    "paramName" : "subsamplingRatio",
    "paramValue" : 0.99
  } ],
  "metric" : 0.9031851535310546
}, {
  "param" : [ {
    "stage" : "RandomForestClassifier",
    "paramName" : "numTrees",
    "paramValue" : 6
  }, {
    "stage" : "RandomForestClassifier",
    "paramName" : "subsamplingRatio",
    "paramValue" : 0.98
  } ],
  "metric" : 0.9034443322241488
}, {
  "param" : [ {
    "stage" : "RandomForestClassifier",
    "paramName" : "numTrees",
    "paramValue" : 9
  }, {
    "stage" : "RandomForestClassifier",
    "paramName" : "subsamplingRatio",
    "paramValue" : 1.0
  } ],
  "metric" : 0.8993474753000145
}, {
  "param" : [ {
    "stage" : "RandomForestClassifier",
    "paramName" : "numTrees",
    "paramValue" : 9
  }, {
    "stage" : "RandomForestClassifier",
    "paramName" : "subsamplingRatio",
    "paramValue" : 0.99
  } ],
  "metric" : 0.9090250137144916
}, {
  "param" : [ {
    "stage" : "RandomForestClassifier",
    "paramName" : "numTrees",
    "paramValue" : 9
  }, {
    "stage" : "RandomForestClassifier",
    "paramName" : "subsamplingRatio",
    "paramValue" : 0.98
  } ],
  "metric" : 0.9129786771786127
} ]