#!/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
+----------+-----+--------------------+|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
