• gridsearchcv
    • 功能介绍
    • 参数说明
    • 脚本示例
      • 脚本代码
      • 脚本结果

    gridsearchcv

    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

    cv为交叉验证，将数据切分为k-folds，对每k-1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cv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
    NumFolds 折数 交叉验证的参数，数据的折数（大于等于2） Integer 10
    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 ✓ —-

    脚本示例

    脚本代码

    1. def adult(url):
    2.     data = (
    3.         CsvSourceBatchOp()
    4.         .setFilePath('http://alink-dataset.cn-hangzhou.oss.aliyun-inc.com/csv/adult_train.csv')
    5.         .setSchemaStr(
    6.             'age bigint, workclass string, fnlwgt bigint,'
    7.             'education string, education_num bigint,'
    8.             'marital_status string, occupation string,'
    9.             'relationship string, race string, sex string,'
    10.             'capital_gain bigint, capital_loss bigint,'
    11.             'hours_per_week bigint, native_country string,'
    12.             'label string'
    13.         )
    14.     )
    15.     return data
    18. def adult_train():
    19.     return adult('http://alink-dataset.cn-hangzhou.oss.aliyun-inc.com/csv/adult_train.csv')
    22. def adult_test():
    23.     return adult('http://alink-dataset.cn-hangzhou.oss.aliyun-inc.com/csv/adult_test.csv')
    26. def adult_numerical_feature_strs():
    27.     return [
    28.         "age", "fnlwgt", "education_num",
    29.         "capital_gain", "capital_loss", "hours_per_week"
    30.     ]
    33. def adult_categorical_feature_strs():
    34.     return [
    35.         "workclass", "education", "marital_status",
    36.         "occupation", "relationship", "race", "sex",
    37.         "native_country"
    38.     ]
    41. def adult_features_strs():
    42.     feature = adult_numerical_feature_strs()
    43.     feature.extend(adult_categorical_feature_strs())
    45.     return feature
    48. def rf_grid_search_cv(featureCols, categoryFeatureCols, label, metric):
    49.     rf = (
    50.         RandomForestClassifier()
    51.         .setFeatureCols(featureCols)
    52.         .setCategoricalCols(categoryFeatureCols)
    53.         .setLabelCol(label)
    54.         .setPredictionCol('prediction')
    55.         .setPredictionDetailCol('prediction_detail')
    56.     )
    57.     paramGrid = (
    58.         ParamGrid()
    59.         .addGrid(rf, 'SUBSAMPLING_RATIO', [1.0, 0.99, 0.98])
    60.         .addGrid(rf, 'NUM_TREES', [3, 6, 9])
    61.     )
    62.     tuningEvaluator = (
    63.         BinaryClassificationTuningEvaluator()
    64.         .setLabelCol(label)
    65.         .setPredictionDetailCol("prediction_detail")
    66.         .setMetricName(metric)
    67.     )
    68.     cv = (
    69.         GridSearchCV()
    70.         .setEstimator(rf)
    71.         .setParamGrid(paramGrid)
    72.         .setTuningEvaluator(tuningEvaluator)
    73.         .setNumFolds(2)
    74.     )
    76.     return cv
    79. def rf_grid_search_tv(featureCols, categoryFeatureCols, label, metric):
    80.     rf = (
    81.         RandomForestClassifier()
    82.         .setFeatureCols(featureCols)
    83.         .setCategoricalCols(categoryFeatureCols)
    84.         .setLabelCol(label)
    85.         .setPredictionCol('prediction')
    86.         .setPredictionDetailCol('prediction_detail')
    87.     )
    88.     paramGrid = (
    89.         ParamGrid()
    90.         .addGrid(rf, 'SUBSAMPLING_RATIO', [1.0, 0.99, 0.98])
    91.         .addGrid(rf, 'NUM_TREES', [3, 6, 9])
    92.     )
    93.     tuningEvaluator = (
    94.         BinaryClassificationTuningEvaluator()
    95.         .setLabelCol(label)
    96.         .setPredictionDetailCol("prediction_detail")
    97.         .setMetricName(metric)
    98.     )
    99.     cv = (
    100.         GridSearchTVSplit()
    101.         .setEstimator(rf)
    102.         .setParamGrid(paramGrid)
    103.         .setTuningEvaluator(tuningEvaluator)
    104.     )
    106.     return cv
    109. def tuningcv(cv_estimator, input):
    110.     return cv_estimator.fit(input)
    113. def tuningtv(tv_estimator, input):
    114.     return tv_estimator.fit(input)
    117. def main():
    118.     print('rf cv tuning')
    119.     model = tuningcv(
    120.         rf_grid_search_cv(adult_features_strs(),
    121.                           adult_categorical_feature_strs(), 'label', 'AUC'),
    122.         adult_train()
    123.     )
    125.     print(model.getReport())
    127.     print('rf tv tuning')
    128.     model = tuningtv(
    129.         rf_grid_search_tv(adult_features_strs(),
    130.                           adult_categorical_feature_strs(), 'label', 'AUC'),
    131.         adult_train()
    132.     )
    134.     print(model.getReport())
    135. main()

    脚本结果

    1. rf cv tuning
    2. com.alibaba.alink.pipeline.tuning.GridSearchCV
    3. [ {
    4.   "param" : [ {
    5.     "stage" : "RandomForestClassifier",
    6.     "paramName" : "numTrees",
    7.     "paramValue" : 3
    8.   }, {
    9.     "stage" : "RandomForestClassifier",
    10.     "paramName" : "subsamplingRatio",
    11.     "paramValue" : 1.0
    12.   } ],
    13.   "metric" : 0.8922549257899725
    14. }, {
    15.   "param" : [ {
    16.     "stage" : "RandomForestClassifier",
    17.     "paramName" : "numTrees",
    18.     "paramValue" : 3
    19.   }, {
    20.     "stage" : "RandomForestClassifier",
    21.     "paramName" : "subsamplingRatio",
    22.     "paramValue" : 0.99
    23.   } ],
    24.   "metric" : 0.8920255970548456
    25. }, {
    26.   "param" : [ {
    27.     "stage" : "RandomForestClassifier",
    28.     "paramName" : "numTrees",
    29.     "paramValue" : 3
    30.   }, {
    31.     "stage" : "RandomForestClassifier",
    32.     "paramName" : "subsamplingRatio",
    33.     "paramValue" : 0.98
    34.   } ],
    35.   "metric" : 0.8944982480437225
    36. }, {
    37.   "param" : [ {
    38.     "stage" : "RandomForestClassifier",
    39.     "paramName" : "numTrees",
    40.     "paramValue" : 6
    41.   }, {
    42.     "stage" : "RandomForestClassifier",
    43.     "paramName" : "subsamplingRatio",
    44.     "paramValue" : 1.0
    45.   } ],
    46.   "metric" : 0.8923867598288401
    47. }, {
    48.   "param" : [ {
    49.     "stage" : "RandomForestClassifier",
    50.     "paramName" : "numTrees",
    51.     "paramValue" : 6
    52.   }, {
    53.     "stage" : "RandomForestClassifier",
    54.     "paramName" : "subsamplingRatio",
    55.     "paramValue" : 0.99
    56.   } ],
    57.   "metric" : 0.9012141767959505
    58. }, {
    59.   "param" : [ {
    60.     "stage" : "RandomForestClassifier",
    61.     "paramName" : "numTrees",
    62.     "paramValue" : 6
    63.   }, {
    64.     "stage" : "RandomForestClassifier",
    65.     "paramName" : "subsamplingRatio",
    66.     "paramValue" : 0.98
    67.   } ],
    68.   "metric" : 0.8993774036693788
    69. }, {
    70.   "param" : [ {
    71.     "stage" : "RandomForestClassifier",
    72.     "paramName" : "numTrees",
    73.     "paramValue" : 9
    74.   }, {
    75.     "stage" : "RandomForestClassifier",
    76.     "paramName" : "subsamplingRatio",
    77.     "paramValue" : 1.0
    78.   } ],
    79.   "metric" : 0.8981738808130779
    80. }, {
    81.   "param" : [ {
    82.     "stage" : "RandomForestClassifier",
    83.     "paramName" : "numTrees",
    84.     "paramValue" : 9
    85.   }, {
    86.     "stage" : "RandomForestClassifier",
    87.     "paramName" : "subsamplingRatio",
    88.     "paramValue" : 0.99
    89.   } ],
    90.   "metric" : 0.9029671873892725
    91. }, {
    92.   "param" : [ {
    93.     "stage" : "RandomForestClassifier",
    94.     "paramName" : "numTrees",
    95.     "paramValue" : 9
    96.   }, {
    97.     "stage" : "RandomForestClassifier",
    98.     "paramName" : "subsamplingRatio",
    99.     "paramValue" : 0.98
    100.   } ],
    101.   "metric" : 0.905228896323363
    102. } ]
    103. rf tv tuning
    104. com.alibaba.alink.pipeline.tuning.GridSearchTVSplit
    105. [ {
    106.   "param" : [ {
    107.     "stage" : "RandomForestClassifier",
    108.     "paramName" : "numTrees",
    109.     "paramValue" : 3
    110.   }, {
    111.     "stage" : "RandomForestClassifier",
    112.     "paramName" : "subsamplingRatio",
    113.     "paramValue" : 1.0
    114.   } ],
    115.   "metric" : 0.9022694229691741
    116. }, {
    117.   "param" : [ {
    118.     "stage" : "RandomForestClassifier",
    119.     "paramName" : "numTrees",
    120.     "paramValue" : 3
    121.   }, {
    122.     "stage" : "RandomForestClassifier",
    123.     "paramName" : "subsamplingRatio",
    124.     "paramValue" : 0.99
    125.   } ],
    126.   "metric" : 0.8963559966080328
    127. }, {
    128.   "param" : [ {
    129.     "stage" : "RandomForestClassifier",
    130.     "paramName" : "numTrees",
    131.     "paramValue" : 3
    132.   }, {
    133.     "stage" : "RandomForestClassifier",
    134.     "paramName" : "subsamplingRatio",
    135.     "paramValue" : 0.98
    136.   } ],
    137.   "metric" : 0.9041948454957178
    138. }, {
    139.   "param" : [ {
    140.     "stage" : "RandomForestClassifier",
    141.     "paramName" : "numTrees",
    142.     "paramValue" : 6
    143.   }, {
    144.     "stage" : "RandomForestClassifier",
    145.     "paramName" : "subsamplingRatio",
    146.     "paramValue" : 1.0
    147.   } ],
    148.   "metric" : 0.8982021117392784
    149. }, {
    150.   "param" : [ {
    151.     "stage" : "RandomForestClassifier",
    152.     "paramName" : "numTrees",
    153.     "paramValue" : 6
    154.   }, {
    155.     "stage" : "RandomForestClassifier",
    156.     "paramName" : "subsamplingRatio",
    157.     "paramValue" : 0.99
    158.   } ],
    159.   "metric" : 0.9031851535310546
    160. }, {
    161.   "param" : [ {
    162.     "stage" : "RandomForestClassifier",
    163.     "paramName" : "numTrees",
    164.     "paramValue" : 6
    165.   }, {
    166.     "stage" : "RandomForestClassifier",
    167.     "paramName" : "subsamplingRatio",
    168.     "paramValue" : 0.98
    169.   } ],
    170.   "metric" : 0.9034443322241488
    171. }, {
    172.   "param" : [ {
    173.     "stage" : "RandomForestClassifier",
    174.     "paramName" : "numTrees",
    175.     "paramValue" : 9
    176.   }, {
    177.     "stage" : "RandomForestClassifier",
    178.     "paramName" : "subsamplingRatio",
    179.     "paramValue" : 1.0
    180.   } ],
    181.   "metric" : 0.8993474753000145
    182. }, {
    183.   "param" : [ {
    184.     "stage" : "RandomForestClassifier",
    185.     "paramName" : "numTrees",
    186.     "paramValue" : 9
    187.   }, {
    188.     "stage" : "RandomForestClassifier",
    189.     "paramName" : "subsamplingRatio",
    190.     "paramValue" : 0.99
    191.   } ],
    192.   "metric" : 0.9090250137144916
    193. }, {
    194.   "param" : [ {
    195.     "stage" : "RandomForestClassifier",
    196.     "paramName" : "numTrees",
    197.     "paramValue" : 9
    198.   }, {
    199.     "stage" : "RandomForestClassifier",
    200.     "paramName" : "subsamplingRatio",
    201.     "paramValue" : 0.98
    202.   } ],
    203.   "metric" : 0.9129786771786127
    204. } ]