1. 数据集介绍：
此数据集包含了一些特征如下图，主要通过这些特征来决定是否雇佣员工。
代码：
# 快速查看数据集
Hire_data=pd.read_csv('/Users/{0}/Documents/MLCourse/PastHires.csv'.format(username))
Hire_data.head()
数据集样例
2. 安装相应的packages, 并请提前安装好Anaconda3, Python3 链接可参考： Anaconda 安装。
2.1 安装 package pydotplus (used for visualizing decision tree)
安装方法：Terminal 输入 conda install pydotplus
2.2 安装 Pyspark (used for running machine learning job in Spark)
安装方法：Terminal 输入 pip install pyspark
2.3 安装 Pandas
安装方法：Terminal 输入 pip install pandas
2.4 安装 Numpy
安装方法：Terminal 输入 pip install numpy
3. 用决策树建模并且预测结果
3.1 标签和特征参数
Hired这一列作为label, 其它作为特征参数。
3.2 代码部分实现：
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.tree import DecisionTree
from pyspark import SparkConf, SparkContext
from numpy import array
import pandas as pd
import getpass
username = getpass.getuser()
# Initialize a Spark Context:
conf = SparkConf().setMaster("local").setAppName("SparkDecisionTree")
sc = SparkContext(conf = conf)
# 定义函数binary, mapEducation将一些非数字的值转换为数字，便于处理
# 定义函数createLabeledPoints 返回label列和所需特征列
def binary(YN):
if (YN == 'Y'):
return 1
else:
return 0
def mapEducation(degree):
if (degree == 'BS'):
return 1
elif (degree =='MS'):
return 2
elif (degree == 'PhD'):
return 3
else:
return 0
# Convert a list of raw fields from our CSV file to a
# LabeledPoint that MLLib can use. All data must be numerical...
def createLabeledPoints(fields):
yearsExperience = int(fields[0])
employed = binary(fields[1])
previousEmployers = int(fields[2])
educationLevel = mapEducation(fields[3])
topTier = binary(fields[4])
interned = binary(fields[5])
hired = binary(fields[6])
return LabeledPoint(hired, array([yearsExperience, employed,
previousEmployers, educationLevel, topTier, interned])
# SparkContext read local file, will create a RDD
rawData = sc.textFile('/Users/{0}/Documents/MLCourse/PastHires.csv'.format(username))
# Get all fields name, save result in a RDD
header = rawData.first()
# Get all data except for the row with fields name, save result in a RDD
rawData = rawData.filter(lambda x:x != header)
# 将数据集根据逗号分割分成不同的fields
csvData = rawData.map(lambda x: x.split(","))
# 建立训练数据集
trainingData = csvData.map(createLabeledPoints)
# 建立测试数据集，下面数字的意思可参考对应列名
testCandidates = [ array([10, 1, 3, 1, 0, 0])]
# 将测试数据集变为RDD， 如果不明白RDD， 请查看Spark RDD的说明
testData = sc.parallelize(testCandidates)
# 用训练数据集建立决策树模型，categoricalFeaturesInfo为种类mapping，例如1代表field[1],即为employed?列，2代表mapping成两个值，因为employed?列只有Y和N。剩余3:4, 4:2, 5:2的意思也可根据上述描述进行赋值。
model = DecisionTree.trainClassifier(trainingData, numClasses=2,
categoricalFeaturesInfo={1:2, 3:4, 4:2, 5:2},
impurity='gini', maxDepth=5, maxBins=32)
# 将测试数据集放入决策树模型
predictions = model.predict(testData)
# 打印最终决策后的结果
print('Hire prediction:')
results = predictions.collect()
for result in results:
print(result)
预测结果为 Hire
# 打印决策树内部是如何工作的
print('Learned classification tree model:')
print(model.toDebugString())
决策树内部分析图
参考 https://www.udemy.com/course/data-science-and-machine-learning-with-python-hands-on/
如要转载，请注明参考。最后，希望我的总结能帮到您，happy learning :)
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