pyspark RandomForestRegressor 随机森林回归

最新推荐文章于 2024-11-21 11:06:48 发布

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最新推荐文章于 2024-11-21 11:06:48 发布

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本文详细介绍使用PySpark进行随机森林回归模型的训练过程。从数据加载到预处理，再到模型训练与评估，全面展示了如何在大数据环境下利用随机森林解决回归问题。通过实际案例，读者可以了解关键步骤和技术细节。

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jun  8 09:27:08 2018

@author: luogan
"""

from pyspark.ml import Pipeline
from pyspark.ml.regression import RandomForestRegressor
from pyspark.ml.feature import VectorIndexer
from pyspark.ml.evaluation import RegressionEvaluator

from pyspark.sql import SparkSession

spark= SparkSession\
                .builder \
                .appName("dataFrame") \
                .getOrCreate()

# Load and parse the data file, converting it to a DataFrame.
data = spark.read.format("libsvm").load("/home/luogan/lg/softinstall/spark-2.2.0-bin-hadoop2.7/data/mllib/sample_libsvm_data.txt")

# Automatically identify categorical features, and index them.
# Set maxCategories so features with > 4 distinct values are treated as continuous.
featureIndexer =\
    VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)

# Split the data into training and test sets (30% held out for testing)
(trainingData, testData) = data.randomSplit([0.7, 0.3])

# Train a RandomForest model.
rf = RandomForestRegressor(featuresCol="indexedFeatures")

# Chain indexer and forest in a Pipeline
pipeline = Pipeline(stages=[featureIndexer, rf])

# Train model.  This also runs the indexer.
model = pipeline.fit(trainingData)

# Make predictions.
predictions = model.transform(testData)

# Select example rows to display.
predictions.select("prediction", "label", "features").show(5)

# Select (prediction, true label) and compute test error
evaluator = RegressionEvaluator(
    labelCol="label", predictionCol="prediction", metricName="rmse")
rmse = evaluator.evaluate(predictions)
print("Root Mean Squared Error (RMSE) on test data = %g" % rmse)

rfModel = model.stages[1]
print(rfModel)  # summary only

　结果：

+----------+-----+--------------------+
|prediction|label|            features|
+----------+-----+--------------------+
|       0.0|  0.0|(692,[95,96,97,12...|
|       0.3|  0.0|(692,[100,101,102...|
|       0.0|  0.0|(692,[123,124,125...|
|      0.05|  0.0|(692,[124,125,126...|
|       0.0|  0.0|(692,[124,125,126...|
+----------+-----+--------------------+
only showing top 5 rows

Root Mean Squared Error (RMSE) on test data = 0.127949
RandomForestRegressionModel (uid=RandomForestRegressor_4acc9ab165e4f84f7169) with 20 trees

原文：https://blog.youkuaiyun.com/luoganttcc/article/details/80618336

PySpark 分类模型训练参考：

https://blog.youkuaiyun.com/u013719780/article/details/51792097