PySpark机器学习-分类与回归实例

男娘i 2022-01-21 03:29 363阅读 0赞

### 1. 二元分类 ###

预测网页是 暂时性的， 还是 长青的 （ephemeral, evergreen）

》读取文件，创建DataFrame 格式数据

from pyspark.context import SparkContext 
    from pyspark.sql.session import SparkSession
    # sc = SparkContext("local")
    # spark = SparkSession(sc)
    
    # 读取文本文件， 创建为DataFrame 结构
    row_df = spark.read.format("csv").option("header", "true").option("delimiter", "\t").load(r"D:\data\spark\train.tsv")
    row_df.count()
    # 7395

row_df.printSchema()

row_df.select('url', 'alchemy_category', 'alchemy_category_score', 'is_news', 'label').show(10)

+--------------------+------------------+----------------------+-------+-----+
    |                 url|  alchemy_category|alchemy_category_score|is_news|label|
    +--------------------+------------------+----------------------+-------+-----+
    |http://www.bloomb...|          business|              0.789131|      1|    0|
    |http://www.popsci...|        recreation|              0.574147|      1|    1|
    |http://www.menshe...|            health|              0.996526|      1|    1|
    |http://www.dumbli...|            health|              0.801248|      1|    1|
    |http://bleacherre...|            sports|              0.719157|      1|    0|
    |http://www.conven...|                 ?|                     ?|      ?|    0|
    |http://gofashionl...|arts_entertainment|               0.22111|      1|    1|
    |http://www.inside...|                 ?|                     ?|      ?|    0|
    |http://www.valetm...|                 ?|                     ?|      1|    1|
    |http://www.howswe...|                 ?|                     ?|      ?|    1|
    +--------------------+------------------+----------------------+-------+-----+
    only showing top 10 rows

》自定义函数 udf 将 ？ 转换成 0

from pyspark.sql.functions import udf
    def replace_question(x):
        return ("0" if x=="?" else x)
    replace_question = udf(replace_question)
    
    
    from pyspark.sql.functions import col
    import pyspark.sql.types
    
    # 将第 4个字段开始转换成 double类型
    df = row_df.select(['url', 'alchemy_category'] + [replace_question(col(column)).cast("double").alias(column) for column in row_df.columns[4:]])
    
    df.printSchema()

》划分 训练集和测试集, 并将其 暂存到内存中

#
    train_df, test_df = df.randomSplit([0.7, 0.3])
    train_df.cache()
    test_df.cache()

》先将字符串形式转换成 数字形式

from pyspark.ml.feature import StringIndexer
    
    categoryIndexer = StringIndexer(inputCol='alchemy_category', outputCol='alchemy_category_Index')
    categoryTransfomer = categoryIndexer.fit(df)
    category_train = categoryTransfomer.transform(train_df)
    category_train.select("alchemy_category", "alchemy_category_Index").show(5)

+------------------+----------------------+
    |  alchemy_category|alchemy_category_Index|
    +------------------+----------------------+
    |arts_entertainment|                   2.0|
    |        recreation|                   1.0|
    |                 ?|                   0.0|
    |                 ?|                   0.0|
    |          business|                   3.0|
    +------------------+----------------------+
    only showing top 5 rows

》进行独热编码

from pyspark.ml.feature import OneHotEncoder
    
    onehot = OneHotEncoder(dropLast= False, inputCol="alchemy_category_Index", outputCol= "alchemy_category_vector")
    df2 = onehot.transform(category_train)
    df2.select("alchemy_category", "alchemy_category_Index", "alchemy_category_vector").show(5)
    #输出以个 14 维 的矩阵形式

+------------------+----------------------+-----------------------+
    |  alchemy_category|alchemy_category_Index|alchemy_category_vector|
    +------------------+----------------------+-----------------------+
    |arts_entertainment|                   2.0|         (14,[2],[1.0])|
    |        recreation|                   1.0|         (14,[1],[1.0])|
    |                 ?|                   0.0|         (14,[0],[1.0])|
    |                 ?|                   0.0|         (14,[0],[1.0])|
    |          business|                   3.0|         (14,[3],[1.0])|
    +------------------+----------------------+-----------------------+
    only showing top 5 rows

》将几个特征列转换成 1 列的行向量，并统一命名。在建模时，只需使用该集合特征就可以。

from pyspark.ml.feature import VectorAssembler
    
    #选择要进行转换的多个特征
    asseblerInputs = ["alchemy_category_vector"] + row_df.columns[4: -1]
    
    
    assembler = VectorAssembler(inputCols=asseblerInputs, outputCol= "features")
    df3 = assembler.transform(df2)
    print(df3.columns)

['url', 'alchemy_category', 'alchemy_category_score', 'avglinksize', 'commonlinkratio_1', 'commonlinkratio_2', 'commonlinkratio_3', 'commonlinkratio_4', 'compression_ratio', 'embed_ratio', 'framebased', 'frameTagRatio', 'hasDomainLink', 'html_ratio', 'image_ratio', 'is_news', 'lengthyLinkDomain', 'linkwordscore', 'news_front_page', 'non_markup_alphanum_characters', 'numberOfLinks', 'numwords_in_url', 'parametrizedLinkRatio', 'spelling_errors_ratio', 'label', 'alchemy_category_Index', 'alchemy_category_vector', 'features']

df3.select("features").first() #抽取得到所需要建模的特征
    Out[22]:
    Row(features=SparseVector(36, {2: 1.0, 14: 0.5811, 15: 2.5268, 16: 0.6807, 17: 0.3277, 18: 0.2689, 19: 0.2605, 20: 0.471, 23: 0.0277, 25: 0.2184, 26: 0.1053, 27: 1.0, 28: 1.0, 29: 13.0, 31: 16382.0, 32: 238.0, 33: 8.0, 34: 0.3361, 35: 0.0943}))
    In [23]:
    
    df2.select("spelling_errors_ratio").first()
    Out[23]:
    Row(spelling_errors_ratio=0.094298246)

》使用决策树进行建模

#
    from pyspark.ml.classification import DecisionTreeClassifier
    dt = DecisionTreeClassifier(labelCol="label", featuresCol="features", impurity="gini", maxDepth=10, maxBins=14)
    dt_model = dt.fit(df3)
    print(dt_model)

》由于上述同样对测试集需要进行同样操作，所以使用较为麻烦，最好是使用pipeline

#1. 建立pipeline
    from pyspark.ml import Pipeline
    from pyspark.ml.feature import StringIndexer, OneHotEncoder, VectorAssembler
    from pyspark.ml.classification import DecisionTreeClassifier
    
    stringIndex = StringIndexer(inputCol="alchemy_category", outputCol="alchemy_category_index")
    onehoter = OneHotEncoder(dropLast=False, inputCol="alchemy_category_index", outputCol="alchemy_category_vector")
    assemleInputs = ["alchemy_category_vector"] + row_df.columns[4: -1]
    assembler = VectorAssembler(inputCols=asseblerInputs, outputCol="features")
    dt = DecisionTreeClassifier(labelCol="label", featuresCol="features", impurity="gini", maxDepth=10, maxBins=14)
    pipeline = Pipeline(stages = [stringIndex, onehoter, assembler, dt])
    pipeline.getStages() #查看pipeline 的阶段

[StringIndexer_6c703f5b0925,
     OneHotEncoder_d71a3c746298,
     VectorAssembler_72028ec11847,
     DecisionTreeClassifier_ecf3417a4a68]

》使用pipeline 进行数据处理和 模型训练

pipelineModel = pipeline.fit(train_df)
    pipelineModel.stages[3]
    # 进行预测
    predicted = pipelineModel.transform(test_df)
    predicted.columns

'spelling_errors_ratio',
     'label',
     'alchemy_category_index',
     'alchemy_category_vector',
     'features',
     'rawPrediction',
     'probability',
     'prediction']

# 预测的类别，预测的概率
    predicted.select("rawPrediction", "probability", "prediction", "label").take(5)

[Row(rawPrediction=DenseVector([56.0, 123.0]), probability=DenseVector([0.3128, 0.6872]), prediction=1.0, label=1.0),
     Row(rawPrediction=DenseVector([132.0, 32.0]), probability=DenseVector([0.8049, 0.1951]), prediction=0.0, label=0.0),
     Row(rawPrediction=DenseVector([132.0, 32.0]), probability=DenseVector([0.8049, 0.1951]), prediction=0.0, label=0.0),
     Row(rawPrediction=DenseVector([28.0, 221.0]), probability=DenseVector([0.1124, 0.8876]), prediction=1.0, label=1.0),
     Row(rawPrediction=DenseVector([120.0, 46.0]), probability=DenseVector([0.7229, 0.2771]), prediction=0.0, label=0.0)]

》使用auc 对模型进行评估

from pyspark.ml.evaluation import BinaryClassificationEvaluator
    evaluator = BinaryClassificationEvaluator(rawPredictionCol="rawPrediction", labelCol="label", metricName="areaUnderROC")
    auc = evaluator.evaluate(predicted)
    auc    #0.62

》使用TrainValidationSplit找到最好参数

from pyspark.ml.tuning import ParamGridBuilder, TrainValidationSplit
    
    paramGrid = ParamGridBuilder().addGrid(dt.impurity, ["gini", "entropy"]).addGrid(dt.maxDepth, [5, 10, 15]).addGrid(dt.maxBins, [10, 15, 20]).build()
    # 在训练集找到最好的参数
    tsv = TrainValidationSplit(estimator=dt, evaluator=evaluator, estimatorParamMaps=paramGrid, trainRatio=0.8)
    tsv_pipeline = Pipeline(stages = [stringIndex, onehoter, assembler, tsv]) #将 dt 转换成 tsv 
    tsv_pipelineModel = tsv_pipeline.fit(train_df)
    bestModel = tsv_pipelineModel.stages[3].bestModel #找出最好的模型
    bestModel
    tsv_prediction = tsv_pipelineModel.transform(test_df)
    tsv_auc = evaluator.evaluate(tsv_prediction)
    tsv_auc    #0.65

》使用交叉验证方法

from pyspark.ml.tuning import CrossValidator
    
    cv = CrossValidator(estimator=dt, evaluator= evaluator, estimatorParamMaps=paramGrid, numFolds=3)
    cv_pipeline = Pipeline(stages = [stringIndex, onehoter, assembler, cv])
    cv_pipelineModel = cv_pipeline.fit(train_df)
    bestModel = cv_pipelineModel.stages[3].bestModel
    bestModel
    cv_prediction = cv_pipelineModel.transform(test_df)
    cv_auc = evaluator.evaluate(cv_prediction)
    cv_auc  # 0.67

》使用随机森林， 随机森林表现较好

from pyspark.ml.classification import RandomForestClassifier
    
    rf = RandomForestClassifier(labelCol="label", featuresCol="features", numTrees=50)
    rfpipeline = Pipeline(stages = [stringIndex, onehoter, assembler, rf])
    rfpipelineModel = rfpipeline.fit(train_df)
    rf_preddiction = rfpipelineModel.transform(test_df)
    evaluator.evaluate(rf_preddiction) # 0.73
    
    #使用交叉验证, 使用 3 折 交叉验证获取最好的参数
    paramGrid = ParamGridBuilder().addGrid(rf.impurity, ["gini", "entropy"]).addGrid(rf.maxDepth, [5, 10, 15]).addGrid(rf.maxBins, [10, 15, 20]). \
    addGrid(rf.numTrees, [10, 20, 60]).build()
    cv_rf = CrossValidator(estimator=dt, estimatorParamMaps=paramGrid, evaluator=evaluator, numFolds=3)
    rfcv_pipeline = Pipeline(stages = [stringIndex, onehoter, assembler, cv_rf])
    rfcvModel = rfcv_pipeline.fit(train_df)
    rfcv_prediction = rfcvModel.transform(test_df)
    evaluator.evaluate(rfcv_prediction)    # 0.75

### 2.多元分类 ###

预测森林的覆盖类别

》读取文件

file = r'D:\data\spark\covtype.data'
    rawData = sc.textFile(file)
    lines = rawData.map(lambda x: x.split(","))
    lines.count() #581012
    
    #查看字段数
    fieldNum = len(lines.first())
    fieldNum # 55

》创建列名和DataFrame

from pyspark.sql.types import StringType, StructField, StructType
    fields = [StructField("f" + str(i), StringType(), True) for i in range(fieldNum)]
    schema = StructType(fields)
    covtype_df = spark.createDataFrame(lines, schema)
    covtype_df.columns
    covtype_df.printSchema()

》将字符串类型转换成double 类型

#转换成double 类型, col用于读取字段的数据
    from pyspark.sql.functions import col
    covtype_df = covtype_df.select([col(column).cast("double").alias(column) for column in covtype_df.columns])
    covtype_df.show(2)
    
    # 设置label ，withColumn， 最后一行设置为类别，从0开始
    covtype_df = covtype_df.withColumn("label", covtype_df["f54"]-1).drop("f54")
    covtype_df.show(1)

+------+----+---+-----+---+-----+-----+-----+-----+------+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+-----+
    |    f0|  f1| f2|   f3| f4|   f5|   f6|   f7|   f8|    f9|f10|f11|f12|f13|f14|f15|f16|f17|f18|f19|f20|f21|f22|f23|f24|f25|f26|f27|f28|f29|f30|f31|f32|f33|f34|f35|f36|f37|f38|f39|f40|f41|f42|f43|f44|f45|f46|f47|f48|f49|f50|f51|f52|f53|label|
    +------+----+---+-----+---+-----+-----+-----+-----+------+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+-----+
    |2596.0|51.0|3.0|258.0|0.0|510.0|221.0|232.0|148.0|6279.0|1.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|1.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|  4.0|
    +------+----+---+-----+---+-----+-----+-----+-----+------+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+-----+
    only showing top 1 row

》将数据分为训练集和测试集

#将数据随机分为训练集和测试集， 并将它们存与内存中，加快后序运行速度 cache()
    train_df, test_df = covtype_df.randomSplit(weights=[0.7, 0.3], seed=666)#设立随机种子
    train_df.cache()
    test_df.cache()

》使用pipeline

#进行训练
    from pyspark.ml import Pipeline
    from pyspark.ml.feature import VectorAssembler
    from pyspark.ml.classification import DecisionTreeClassifier
    from pyspark.ml import evaluation
    featuresCols = covtype_df.columns[:54]
    # featuresCols
    vec = VectorAssembler(inputCols=featuresCols, outputCol="features")
    dt = DecisionTreeClassifier(labelCol="label", featuresCol="features", impurity="gini", seed=666, maxBins=20, maxDepth=5)
    dt_pipeline = Pipeline(stages = [vec, dt])
    dtModel = dt_pipeline.fit(train_df)
    dt_prediction = dtModel.transform(test_df)
    dt_prediction.columns
    
    dt_prediction.select(["features", "probability", "rawPrediction", "prediction", "label"]).show(2)

+--------------------+--------------------+--------------------+----------+-----+
    |            features|         probability|       rawPrediction|prediction|label|
    +--------------------+--------------------+--------------------+----------+-----+
    |(54,[0,1,2,3,4,5,...|[0.0,0.0244421380...|[0.0,471.0,12438....|       2.0|  2.0|
    |(54,[0,1,2,3,4,5,...|[0.0,0.0244421380...|[0.0,471.0,12438....|       2.0|  2.0|
    +--------------------+--------------------+--------------------+----------+-----+
    only showing top 2 rows

》对模型进行评估

#对模型准确率进行评估
    from pyspark.ml.evaluation import MulticlassClassificationEvaluator
    evaluator = MulticlassClassificationEvaluator(labelCol="label", predictionCol="prediction", metricName= "accuracy") #使用准确率进行评估
    acc = evaluator.evaluate(dt_prediction)
    acc # 0.70
    
    # 找出最佳模型, TrainValidationSplit需要传入分类器，参数，评估器
    from pyspark.ml.tuning import ParamGridBuilder, TrainValidationSplit
    
    paramGrid = ParamGridBuilder().addGrid(dt.impurity, ["gini", "entropy"]).addGrid(dt.maxDepth, [10, 15, 25]).addGrid(dt.maxBins, [30, 40, 50]).build()
    tvs = TrainValidationSplit(estimator=dt, evaluator=evaluator, estimatorParamMaps=paramGrid, trainRatio=0.8, seed=666)
    tsv_pipeline = Pipeline(stages=[vec, tvs])
    tsvMoodel = tsv_pipeline.fit(train_df)
    
    bestModel = tsvMoodel.stages[1].bestModel
    bestModel.toDebugString[:500] #对建树过程进行查看
    
    # 使用 withColumnRenamed 对列名进行重新命名
    predictions = tsvMoodel.transform(test_df)
    result = predictions.withColumnRenamed("f0", "海拔").withColumnRenamed("f1", "方位") \
    .withColumnRenamed("f2", "斜率").withColumnRenamed("f3", "垂直距离") \
    .withColumnRenamed("f4", "水平距离").withColumnRenamed("f5", "阴影")
    result.select("海拔", "方位", "斜率", "垂直距离", "水平距离",  "阴影", "label", "prediction").show(5)

+------+----+----+--------+--------+-----+-----+----------+
    |  海拔|方位|斜率|垂直距离|水平距离| 阴影|label|prediction|
    +------+----+----+--------+--------+-----+-----+----------+
    |1867.0|20.0|15.0|   108.0|    19.0|120.0|  2.0|       2.0|
    |1876.0|25.0|17.0|   124.0|    26.0|150.0|  2.0|       2.0|
    |1876.0|29.0|19.0|   124.0|    34.0| 90.0|  5.0|       5.0|
    |1877.0|27.0|24.0|    90.0|    18.0| 95.0|  5.0|       5.0|
    |1883.0|29.0|24.0|    60.0|    24.0|108.0|  5.0|       5.0|
    +------+----+----+--------+--------+-----+-----+----------+
    only showing top 5 rows

》计算准确率

#计算准确率
    acc = evaluator.evaluate(predictions)
    acc
    #计算准确率
    acc = evaluator.evaluate(predictions)
    acc
    Out[86]:
    0.9292631820660754

### 3. 回归 ###

“bike sharing” 数据集用于预测在不同情况下的单车的每一小时的租用的数量

》创建DataFrame

from pyspark.sql import SparkSession
    spark = SparkSession.builder.appName("Python Spark Regression").config("spark.some.config.option", "some-value").getOrCreate()
    # 创建 DataFrame
    file = r'D:\data\spark\hour.csv'
    hour_df = spark.read.format('csv').option("header", "true").load(file)
    hour_df.count()    # 17379
    hour_df.printSchema()

# 删除不需要的字段
    hour_df = hour_df.drop("instant").drop("yr").drop("dteday").drop("casual").drop("registered")
    
    #将字符串类型转换成 double 类型, col 读取字段数据， cast("double") 转换成double
    from pyspark.sql.functions import col
    hour_df = hour_df.select([col(column).cast("double").alias(column) for column in hour_df.columns])
    hour_df.printSchema()

hour_df.show(5)

+------+----+---+-------+-------+----------+----------+----+------+----+---------+----+
    |season|mnth| hr|holiday|weekday|workingday|weathersit|temp| atemp| hum|windspeed| cnt|
    +------+----+---+-------+-------+----------+----------+----+------+----+---------+----+
    |   1.0| 1.0|0.0|    0.0|    6.0|       0.0|       1.0|0.24|0.2879|0.81|      0.0|16.0|
    |   1.0| 1.0|1.0|    0.0|    6.0|       0.0|       1.0|0.22|0.2727| 0.8|      0.0|40.0|
    |   1.0| 1.0|2.0|    0.0|    6.0|       0.0|       1.0|0.22|0.2727| 0.8|      0.0|32.0|
    |   1.0| 1.0|3.0|    0.0|    6.0|       0.0|       1.0|0.24|0.2879|0.75|      0.0|13.0|
    |   1.0| 1.0|4.0|    0.0|    6.0|       0.0|       1.0|0.24|0.2879|0.75|      0.0| 1.0|
    +------+----+---+-------+-------+----------+----------+----+------+----+---------+----+
    only showing top 5 rows

》划分训练集和测试集

train_df, test_df = hour_df.randomSplit(weights=[0.7, 0.3], seed=666)
    train_df.cache()
    test_df.cache()

》建立的pipeline

#使用VectorIndexer 使月份，星期， 小时转换成类别
    from pyspark.ml import Pipeline
    from pyspark.ml.feature import VectorIndexer, VectorAssembler, StringIndexer
    from pyspark.ml.regression import DecisionTreeRegressor
    featuresCols = hour_df.columns[:-1]
    featuresCols
    
    vector = VectorAssembler(inputCols=featuresCols, outputCol="va_features")
    vIndexer = VectorIndexer(inputCol="va_features", outputCol="features", maxCategories=24)
    dt = DecisionTreeRegressor(featuresCol="features", labelCol="cnt", seed=1024)
    dt_pipeline = Pipeline(stages=[vector, vIndexer, dt])
    dt_pipeline.getStages()
    
    dtModel = dt_pipeline.fit(train_df)
    prediction = dtModel.transform(test_df)
    prediction.columns
    
    prediction.select("cnt", "prediction").show(5)

+----+-----------------+
    | cnt|       prediction|
    +----+-----------------+
    |39.0|           59.575|
    |10.0|38.08888888888889|
    |12.0|38.08888888888889|
    |13.0|38.08888888888889|
    |16.0|38.08888888888889|
    +----+-----------------+
    only showing top 5 rows

\#预测的很差

》对模型进行评估

from pyspark.ml.evaluation import RegressionEvaluator
    evaluator = RegressionEvaluator(labelCol="cnt", predictionCol="prediction", metricName="rmse")
    rmse = evaluator.evaluate(prediction)
    rmse # 93.2708
    
    #找出最佳模型参数
    from pyspark.ml.tuning import ParamGridBuilder, TrainValidationSplit
    
    paramGrid = ParamGridBuilder().addGrid(dt.maxDepth, [5, 10, 15, 25]).addGrid(dt.maxBins, [25, 35, 45, 50]).build() # bins 必须要大于 24
    tvs = TrainValidationSplit(estimator=dt, evaluator=evaluator, estimatorParamMaps=paramGrid, trainRatio=0.8)
    tvs_pipeline = Pipeline(stages=[vector, vIndexer, tvs])
    tvs_model = tvs_pipeline.fit(train_df)
    tvs_prediction = tvs_model.transform(test_df)
    tsv_rmse = evaluator.evaluate(tvs_prediction)
    tsv_rmse # 82.3553
    
    #使用交叉验证
    from pyspark.ml.tuning import CrossValidator
    cv = CrossValidator(estimator=dt, evaluator=evaluator, estimatorParamMaps=paramGrid, numFolds=3)
    cv_pipeline = Pipeline(stages = [vector, vIndexer, cv])
    cv_model = cv_pipeline.fit(train_df)
    cv_prediction = cv_model.transform(test_df)
    cv_rmse = evaluator.evaluate(cv_prediction)
    cv_rmse    #82.483

》使用梯度提升树（GBDT）进行训练

from pyspark.ml.regression import GBTRegressor
    gbdt = GBTRegressor(labelCol="cnt", featuresCol="features")
    gbdt_pipeline = Pipeline(stages=[vector, vIndexer, gbdt])
    gbdt_model = gbdt_pipeline.fit(train_df)
    gbdt_prediction = gbdt_model.transform(test_df)
    gbdt_rmse = evaluator.evaluate(gbdt_prediction)
    gbdt_rmse    # 75.863
    
    #使用交叉验证找出最佳的参数
    paramGrid = ParamGridBuilder().addGrid(gbdt.maxDepth, [5, 10]).addGrid(gbdt.maxBins, [25, 40]).addGrid(gbdt.maxIter, [10, 50]).build()
    cross = CrossValidator(estimator=gbdt, evaluator=evaluator, numFolds=3, estimatorParamMaps=paramGrid, seed=1024)
    gbdt_cross = Pipeline(stages=[vector, vIndexer, cross])
    
    gbdt_model = gbdt_cross.fit(train_df)
    pred = gbdt_model.transform(test_df)
    rmse = evaluator.evaluate(pred)
    rmse #  70

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[2020]: https://mp.weixin.qq.com/s?__biz=MzIyNTI3NDQ4NQ==&mid=2247487704&idx=1&sn=5f4b2127c4d49fd07ae072a0721424a2&chksm=e8036fc2df74e6d489c4aa9b06f917ef7cee6027f13e150fca53cf79d5d188d4ccc1af49e098&scene=21#wechat_redirect
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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwNTg3NTc1_size_16_color_FFFFFF_t_70_pic_center]: /images/20220121/66aa2c838c1042b3a66daa75ae814d77.png