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spark.py
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spark.py
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from pyspark import SparkContext, SparkConf
from pyspark.sql.types import *
from pyspark.sql import SQLContext
import pandas as pd
from pyspark.ml.feature import RFormula, StringIndexer
from pyspark.ml.classification import LogisticRegression, DecisionTreeClassifier, GBTClassifier, RandomForestClassifier
from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.ml.tuning import ParamGridBuilder, CrossValidator
import matplotlib.pyplot as plt
"""
File to be run in pyspark :
execfile('path_to_file/file.py')
"""
def load_data(path):
# load data in pandas dataFrame
data = pd.read_csv(path)
print data.head()
# create a SQLContext with the sparkContext 'sc' in pyspark
sqlc = SQLContext(sc)
# create a pyspark dataFrame from the pandas df
df = sqlc.createDataFrame(data)
return df
def data_preparation(df, avg_age,feat_name="features",lab_name='label'):
df = df.fillna(avg_age,subset=['Age'])
"""
## unnecessary when using Rformula
df = df.replace(['male','female'],['-1','1'],'Sex')
df = df.withColumn('Sex',df.Sex.cast('int'))
df = df.replace(['S','Q','C'],['-1','0','1'],'Embarked')
df = df.withColumn('Embarked',df.Embarked.cast('int'))
df.printSchema()
"""
# Rformula automatically formats categorical data (Sex and Embarked) into numerical data
formula = RFormula(formula="Survived ~ Sex + Age + Pclass + Fare + SibSp + Parch",
featuresCol=feat_name,
labelCol=lab_name)
df = formula.fit(df).transform(df)
df.show(truncate=False)
return df
def find_avg_age(df):
df = df.drop('Cabin')
df = df.drop('Ticket')
df = df.drop('Name')
df = df.drop('PassengerId')
# filling missing value in Age with the average age
dfnoNaN = df.dropna()
avg_age = dfnoNaN.groupby().avg('Age').collect()[0][0]
print "avg(age) = ", avg_age
return avg_age
def buil_lrmodel(path):
df = load_data(path)
#-------------------- preparing the dataset -------------------------------------------
avg_age = find_avg_age(df)
df = data_preparation(df, avg_age)
print "count = " , df.count()
df = df.drop('Cabin')
df = df.drop('Ticket')
df = df.drop('Name')
#------------------ Build a model ----------------------------------------------------
lr = LogisticRegression(maxIter=10, regParam=0.01)
model = lr.fit(df)
prediction = model.transform(df)
prediction.show(truncate=False)
evaluator = BinaryClassificationEvaluator()
print "classification evaluation :" , evaluator.evaluate(prediction)
#-------------- selecting models with cross validation -----------------------------------
lr = LogisticRegression()
grid = ParamGridBuilder().addGrid(lr.maxIter, [1,10,50,150,200,500,1000])\
.addGrid(lr.regParam, [0.01, 0.05, 0.1,]).build()
cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator)
cvModel = cv.fit(df)
prediction = cvModel.transform(df)
prediction.show(truncate=False)
print "classification evaluation :" , evaluator.evaluate(prediction)
return cvModel,avg_age
def build_decisionTree(path):
df = load_data(path)
avg_age=find_avg_age(df)
df = data_preparation(df, avg_age)
df = df.drop('Cabin')
df = df.drop('Ticket')
df = df.drop('Name')
stringIndexer = StringIndexer(inputCol="Survived", outputCol="indexed")
si_model = stringIndexer.fit(df)
df = si_model.transform(df)
df.show(truncate=False)
dt = DecisionTreeClassifier(labelCol='indexed')
grid = ParamGridBuilder().addGrid(dt.maxDepth, [1,2,3,5,6,8,10]).build()
evaluator = BinaryClassificationEvaluator()
cv = CrossValidator(estimator=dt, estimatorParamMaps=grid, evaluator=evaluator)
cvModel = cv.fit(df)
prediction = cvModel.transform(df)
prediction.show(truncate=False)
print "classification evaluation :" , evaluator.evaluate(prediction)
return cvModel,avg_age
def build_randomForest(path):
df = load_data(path)
avg_age=find_avg_age(df)
df = data_preparation(df, avg_age)
df = df.drop('Cabin')
df = df.drop('Ticket')
df = df.drop('Name')
stringIndexer = StringIndexer(inputCol="Survived", outputCol="indexed")
si_model = stringIndexer.fit(df)
df = si_model.transform(df)
df.show()
rdf = RandomForestClassifier(labelCol='indexed')
grid = ParamGridBuilder().addGrid(rdf.maxDepth, [1,2,3,5,6,8,10])\
.addGrid(rdf.numTrees,[1,5,10,30,50,100,200]).build()
evaluator = BinaryClassificationEvaluator()
cv = CrossValidator(estimator=rdf, estimatorParamMaps=grid, evaluator=evaluator)
cvModel = rdf.fit(df)
prediction = cvModel.transform(df)
prediction.show()
print "classification evaluation :" , evaluator.evaluate(prediction)
return cvModel,avg_age
def apply_onTest(model,avg_age,path):
df = load_data(path)
df = df.drop('Cabin')
df = df.drop('Ticket')
df = df.drop('Name')
df = data_preparation(df, avg_age)
print "count = " , df.count()
prediction = model.transform(df)
prediction.show(truncate=False)
return prediction
if __name__ == "__main__":
path = '/home/maxime/kaggle/spark.ml-training-on-titanic-dataset/'
#model,mean_age = buil_lrmodel(path+'data/train.csv')
#model,mean_age = build_decisionTree(path+'data/train.csv')
model,mean_age = build_randomForest(path+'data/train.csv')
df = apply_onTest(model,mean_age,path+'data/test.csv')
df = df.select('PassengerId','prediction')
df = df.withColumnRenamed('prediction','Survived')
df.show()
df = df.toPandas()
df['Survived']=df['Survived'].astype('int')
df.to_csv(path+'results.csv',index=False)
result = pd.read_csv(path+'data/genderclassmodel.csv')
result.rename(columns={'Survived':'target','PassengerId':'Id'},inplace=True)
df_comp = pd.concat([df, result], axis=1, join='inner')
df_comp['diff']=df_comp.Survived - df_comp.target
print df_comp.head()
ax = df_comp.plot(kind='scatter', x='Id', y='target',color='r',s=100,label='target')
df_comp.plot(kind='scatter',x='PassengerId',y='Survived',ax=ax,color='b',label='prediction')
df_comp.plot(kind='scatter',x='PassengerId',y='diff')
plt.show()