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prepocessing.py
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prepocessing.py
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#!/usr/bin/env python3
import yaml
import sys
import os
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
def processing(source: str) -> str:
### alterative for pytest
file_path = "/data"
if source[:3] == "py_":
file_path = "./data"
source = source[3:]
train = pd.read_csv(f"{file_path}/train.csv")
test = pd.read_csv(f"{file_path}/test.csv")
whole_data = pd.concat([train,test], ignore_index = True)
# Name processing
#Title Feature(New)
whole_data['Title'] = whole_data['Name'].apply(lambda x:x.split(',')[1].split('.')[0].strip())
whole_data['Title'].replace(['Mr'], 'Mr', inplace=True)
whole_data['Title'].replace(['Mlle', 'Miss'], 'Miss', inplace=True)
whole_data['Title'].replace(['Mme', 'Ms', 'Mrs'],'Mrs', inplace=True)
whole_data['Title'].replace(['Capt', 'Col', 'Major', 'Dr', 'Rev'],'Officer', inplace=True)
whole_data['Title'].replace(['Don', 'Sir', 'the Countess', 'Dona', 'Lady'], 'Royalty', inplace=True)
whole_data['Title'].replace(['Master','Jonkheer'],'Master', inplace=True)
#Treatment of family characteristics
whole_data['Family'] = whole_data['SibSp'] + whole_data['Parch'] + 1
#Classfication : Family label
def Family_label(z):
if (z > 7):
return 0
elif (z >= 2) & (z <=4):
return 2
elif ((z > 4) & (z <= 7)) | (z == 1):
return 1
whole_data['Family_label'] = whole_data['Family'].apply(Family_label)
# 'Cabin' data processing
whole_data['Cabin'] = whole_data['Cabin'].fillna("Unknown")
whole_data['Deck'] = whole_data['Cabin'].str.get(0)
# 'Ticket' processing
whole_data['Ticket'].value_counts()
Ticket_Count = dict(whole_data['Ticket'].value_counts())
whole_data['Ticket_Class'] = whole_data['Ticket'].apply(lambda x:Ticket_Count[x])
##Todo: save Ticket_Class ， Survived -> ticket.csv
# The survival rate of 'tickets' from 2 to 4 is higher than all others
def Ticket_Label(t):
if (t > 8):
return 0
elif ((t > 4) & (t <= 8)) | (t == 1):
return 1
elif (t >= 2) & (t <= 4):
return 2
whole_data['Ticket_Class'] = whole_data['Ticket_Class'].apply(Ticket_Label)
#Embarked number
whole_data[whole_data['Embarked'].isnull()]
whole_data.groupby(by=["Pclass",'Embarked']).Fare.median()
whole_data['Embarked'] = whole_data['Embarked'].fillna('C')
whole_data[whole_data['Fare'].isnull()]
fare=whole_data[(whole_data['Embarked'] == "S") & (whole_data['Pclass'] == 3)].Fare.median()
whole_data['Fare']=whole_data['Fare'].fillna(fare)
agedf = whole_data[['Age', 'Pclass','Sex','Title']]
agedf = pd.get_dummies(agedf)
know_age = agedf[agedf.Age.notnull()].values
unknow_age = agedf[agedf.Age.isnull()].values
y = know_age[:, 0]
x = know_age[:, 1:]
random = RandomForestRegressor(random_state=0, n_estimators=100, n_jobs=-1)
random.fit(x, y)
predict_Ages = random.predict(unknow_age[:, 1::])
whole_data.loc[ (whole_data.Age.isnull()), 'Age' ] = predict_Ages
whole_data['Surname']=whole_data['Name'].apply(lambda x:x.split(',')[0].strip())
Surname_Count = dict(whole_data['Surname'].value_counts())
whole_data['FamilyGroup'] = whole_data['Surname'].apply(lambda x:Surname_Count[x])
Female_Child_Group=whole_data.loc[(whole_data['FamilyGroup']>=2) & ((whole_data['Age']<=12) | (whole_data['Sex']=='female'))]
Male_Adult_Group=whole_data.loc[(whole_data['FamilyGroup']>=2) & (whole_data['Age']>12) & (whole_data['Sex']=='male')]
Female_Child=pd.DataFrame(Female_Child_Group.groupby('Surname')['Survived'].mean().value_counts())
Female_Child.columns=['GroupCount']
Male_Adult=pd.DataFrame(Male_Adult_Group.groupby('Surname')['Survived'].mean().value_counts())
Male_Adult.columns=['GroupCount']
Female_Child_Group=Female_Child_Group.groupby('Surname')['Survived'].mean()
Dead_List=set(Female_Child_Group[Female_Child_Group.apply(lambda x:x==0)].index)
Male_Adult_List=Male_Adult_Group.groupby('Surname')['Survived'].mean()
Survived_List=set(Male_Adult_List[Male_Adult_List.apply(lambda x:x==1)].index)
if source == "EDA":
whole_data = whole_data[whole_data['Survived'].notnull()]
whole_data.to_csv(f"{file_path}/data_for_visual.csv")
if source == "train":
train=whole_data.loc[whole_data['Survived'].notnull()]
whole_data=whole_data[['Survived','Pclass','Sex','Age','Fare','Embarked','Title','Family_label','Deck','Ticket_Class']]
whole_data=pd.get_dummies(whole_data)
train=whole_data[whole_data['Survived'].notnull()]
train.to_csv(f"{file_path}/{source}_traindata.csv")
if source == "test":
test=whole_data.loc[whole_data['Survived'].isnull()]
test.loc[(test['Surname'].apply(lambda x:x in Dead_List)),'Sex'] = 'male'
test.loc[(test['Surname'].apply(lambda x:x in Dead_List)),'Age'] = 60
test.loc[(test['Surname'].apply(lambda x:x in Dead_List)),'Title'] = 'Mr'
test.loc[(test['Surname'].apply(lambda x:x in Survived_List)),'Sex'] = 'female'
test.loc[(test['Surname'].apply(lambda x:x in Survived_List)),'Age'] = 5
test.loc[(test['Surname'].apply(lambda x:x in Survived_List)),'Title'] = 'Miss'
# Feature conversion
whole_data=whole_data[['Survived','Pclass','Sex','Age','Fare','Embarked','Title','Family_label','Deck','Ticket_Class']]
whole_data=pd.get_dummies(whole_data)
test=whole_data[whole_data['Survived'].isnull()].drop('Survived',axis=1)
test.to_csv(f"{file_path}/{source}_testdata.csv")
return f"features was saved at {file_path}"
if __name__ == "__main__":
# Make sure that at least one argument is given, that is either 'write' or 'read'
if len(sys.argv) != 2:
print(f"Usage: {sys.argv[0]} write|read")
exit(1)
# If it checks out, call the appropriate function
command = sys.argv[1]
if command == "processing":
print(yaml.dump({ "contents": processing(os.environ["SOURCE"]) }))