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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from __future__ import print_function
import sys
from pyspark import SparkContext
from pyspark.ml.classification import LogisticRegression
from pyspark.mllib.evaluation import MulticlassMetrics
from pyspark.ml.feature import StringIndexer
from pyspark.mllib.util import MLUtils
from pyspark.sql import SQLContext
"""
A simple example demonstrating a logistic regression with elastic net regularization Pipeline.
Run with:
bin/spark-submit examples/src/main/python/ml/logistic_regression.py
"""
if __name__ == "__main__":
if len(sys.argv) > 1:
print("Usage: logistic_regression", file=sys.stderr)
exit(-1)
sc = SparkContext(appName="PythonLogisticRegressionExample")
sqlContext = SQLContext(sc)
# Load and parse the data file into a dataframe.
df = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
# Map labels into an indexed column of labels in [0, numLabels)
stringIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel")
si_model = stringIndexer.fit(df)
td = si_model.transform(df)
[training, test] = td.randomSplit([0.7, 0.3])
lr = LogisticRegression(maxIter=100, regParam=0.3).setLabelCol("indexedLabel")
lr.setElasticNetParam(0.8)
# Fit the model
lrModel = lr.fit(training)
predictionAndLabels = lrModel.transform(test).select("prediction", "indexedLabel") \
.map(lambda x: (x.prediction, x.indexedLabel))
metrics = MulticlassMetrics(predictionAndLabels)
print("weighted f-measure %.3f" % metrics.weightedFMeasure())
print("precision %s" % metrics.precision())
print("recall %s" % metrics.recall())
sc.stop()
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