PySpark MLlib：逻辑回归模型训练过程（训练、评估、编解码、保存、读取）_spark mllib 训练好的模型 保存

没有做训练测试集划分，直接全量训练，全量测试

一、引入 Spark 环境

from pyspark.sql import SparkSession
spark = SparkSession.builder.master("local[*]").getOrCreate()
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二、设置模型评估方法

# 评估
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
def check(train_eval):
    f1_score = MulticlassClassificationEvaluator(predictionCol='prediction', labelCol='Type_idx', metricName='f1').evaluate(train_eval)
    acc_score = MulticlassClassificationEvaluator(predictionCol='prediction', labelCol='Type_idx', metricName='accuracy').evaluate(train_eval)
    loss = MulticlassClassificationEvaluator(predictionCol='prediction', labelCol='Type_idx', metricName='logLoss').evaluate(train_eval)
    precision = MulticlassClassificationEvaluator(predictionCol='prediction', labelCol='Type_idx', metricName='weightedPrecision').evaluate(train_eval)
    recall = MulticlassClassificationEvaluator(predictionCol='prediction', labelCol='Type_idx', metricName='weightedRecall').evaluate(train_eval)
    return pd.DataFrame({
        'F1': [f1_score],
        'Recall': [recall],
        'Precision': [precision],
        'Accuracy': [acc_score],
        'Loss': [loss],
    })
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三、读取/修改 数据

• 这里用的是 Pandas 来读数据，然后转成 Spark 的 DataFrame
• 数据格式同 Python 模型训练：逻辑回归、KNN、朴素贝叶斯、LDA、支持向量机、GBDT、决策树

# 用 pandas 读取数据并修改异常列名，挑选训练列
import pandas as pd
df = spark.createDataFrame(pd.read_excel('data.xlsx', sheet_name='training dataset'))
# '.' 在后面会报错，这里直接换掉
df = df.withColumnRenamed('DBE.C', 'DBEC').withColumnRenamed('DBE.O', 'DBEO')
# 选择使用到的列
train_df = df.select(['C', 'H', 'O', 'N', 'S', 'group', 'AImod', 'DBE', 'MZ', 'OC', 'HC', 'SC', 'NC', 'NOSC', 'DBEC', 'DBEO', 'location', 'sample', 'Type'])
train_df
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四、编码、合并列

# 编码、合并列
from pyspark.ml.feature import StringIndexer
from pyspark.ml.feature import IndexToString
from pyspark.ml import PipelineModel
from pyspark.ml.feature import VectorAssembler
# 将 string、负数 列换成数字
indexer = StringIndexer(inputCols = ['group', 'NOSC', 'location', 'sample', 'Type'], outputCols = ['group_idx', 'NOSC_idx', 'location_idx', 'sample_idx', 'Type_idx'])
encoder = indexer.fit(train_df)
decoder = IndexToString(inputCol = 'prediction', outputCol = 'result', labels = encoder.labelsArray[4])
# 将这些列合并成一列
assembler = VectorAssembler(inputCols = ['C', 'H', 'O', 'N', 'S', 'group_idx', 'AImod', 'DBE', 'MZ',
                                         'OC', 'HC', 'SC', 'NC', 'NOSC_idx', 'DBEC', 'DBEO', 'location_idx', 'sample_idx']
                            , outputCol = 'features')
train_data = assembler.transform(encoder.transform(train_df)).select('features', 'Type_idx')
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五、模型训练

逻辑回归

• 啥也没调，指标难看

from pyspark.ml.classification import LogisticRegression
lr = LogisticRegression(featuresCol = 'features', labelCol = 'Type_idx')
model = lr.fit(train_data)
# 指标检测
check(model.transform(assembler.transform(encoder.transform(train_df))))
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朴素贝叶斯

• 这个指标更看不了，懒得调了

from pyspark.ml.classification import NaiveBayes
nb = NaiveBayes(featuresCol = 'features', labelCol = 'Type_idx')
model = nb.fit(train_data)
# 指标检测
check(model.transform(assembler.transform(encoder.transform(train_df))))
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六、模型保存

• Pipeline 会按照列表的顺序一个一个执行 transform，上一个结果传给下一个

# 流水线保存
pipeline = PipelineModel(stages = [encoder, assembler, model, decoder])
pipeline.write().overwrite().save('./output/model')
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七、读取模型测试数据

# 读取模型测试数据
import pandas as pd
df = spark.createDataFrame(pd.read_excel('data.xlsx', sheet_name='validation dataset'))
df = df.withColumnRenamed('DBE.C', 'DBEC').withColumnRenamed('DBE.O', 'DBEO')
test_df = df.select(['C', 'H', 'O', 'N', 'S', 'group', 'AImod', 'DBE', 'MZ', 'OC', 'HC', 'SC', 'NC', 'NOSC', 'DBEC', 'DBEO', 'location', 'sample'])

from pyspark.ml import PipelineModel
model = PipelineModel.load('./output/model')
test_res = model.transform(test_df)
test_res
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