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features1.py
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import sklearn.preprocessing as pp
import numpy as np
import pandas as pd
import re
def get_title(name):
title_search = re.search(' ([A-Za-z]+)\.', name)
# If the title exists, extract and return it.
if title_search:
return title_search.group(1)
return ""
def add_title(df):
df['Title'] = df['Name'].apply(get_title)
df['Title'] = df['Title'].replace(['Lady', 'Dona', 'the Countess', 'Sir', 'Don', 'Jonkheer'], 'Royalty')
df['Title'] = df['Title'].replace(['Capt','Col','Major','Dr','Rev'], 'Officer')
df['Title'] = df['Title'].replace(['Mlle','Ms'], 'Miss')
df['Title'] = df['Title'].replace('Mme', 'Mrs')
return df
def fillAges(row, grouped_median):
if row['Sex']=='female' and row['Pclass'] == 1:
if row['Title'] == 'Miss':
return grouped_median.loc['female', 1, 'Miss']['Age']
elif row['Title'] == 'Mrs':
return grouped_median.loc['female', 1, 'Mrs']['Age']
elif row['Title'] == 'Officer':
return grouped_median.loc['female', 1, 'Officer']['Age']
elif row['Title'] == 'Royalty':
return grouped_median.loc['female', 1, 'Royalty']['Age']
elif row['Sex']=='female' and row['Pclass'] == 2:
if row['Title'] == 'Miss':
return grouped_median.loc['female', 2, 'Miss']['Age']
elif row['Title'] == 'Mrs':
return grouped_median.loc['female', 2, 'Mrs']['Age']
elif row['Sex']=='female' and row['Pclass'] == 3:
if row['Title'] == 'Miss':
return grouped_median.loc['female', 3, 'Miss']['Age']
elif row['Title'] == 'Mrs':
return grouped_median.loc['female', 3, 'Mrs']['Age']
elif row['Sex']=='male' and row['Pclass'] == 1:
if row['Title'] == 'Master':
return grouped_median.loc['male', 1, 'Master']['Age']
elif row['Title'] == 'Mr':
return grouped_median.loc['male', 1, 'Mr']['Age']
elif row['Title'] == 'Officer':
return grouped_median.loc['male', 1, 'Officer']['Age']
elif row['Title'] == 'Royalty':
return grouped_median.loc['male', 1, 'Royalty']['Age']
elif row['Sex']=='male' and row['Pclass'] == 2:
if row['Title'] == 'Master':
return grouped_median.loc['male', 2, 'Master']['Age']
elif row['Title'] == 'Mr':
return grouped_median.loc['male', 2, 'Mr']['Age']
elif row['Title'] == 'Officer':
return grouped_median.loc['male', 2, 'Officer']['Age']
elif row['Sex']=='male' and row['Pclass'] == 3:
if row['Title'] == 'Master':
return grouped_median.loc['male', 3, 'Master']['Age']
elif row['Title'] == 'Mr':
return grouped_median.loc['male', 3, 'Mr']['Age']
#buggy
def fill_age(df):
grouped_train = df.iloc[:891].groupby(['Sex','Pclass','Title'])
grouped_test = df.iloc[891:].groupby(['Sex','Pclass','Title'])
grouped_median_train = grouped_train.median()
grouped_median_test = grouped_test.median()
df.info()
df.loc[:891,('Age')] = df.iloc[:891].apply(lambda r : r['Age'] if isinstance(r['Age'], float) else 999.0, axis=1)
df.loc[891:,('Age')] = df.iloc[891:].apply(lambda r : fillAges(r, grouped_median_test) if np.isnan(r['Age']) else r['Age'], axis=1)
df.info()
return df
def fill_age_1(df):
grouped_train = df.groupby(['Sex','Pclass','Title'])
grouped_median_train = grouped_train.median()
df['Age'] = df.apply(lambda r : fillAges(r, grouped_median_train) if np.isnan(r['Age']) else r['Age'],axis=1)
return df
def fill_fare(df):
df.iloc[:891].Fare.fillna(df.iloc[:891].Fare.mean(), inplace=True)
df.iloc[891:].Fare.fillna(df.iloc[891:].Fare.mean(), inplace=True)
return df
def fill_embarked(df):
df['Embarked'].fillna('S', inplace=True)
return df
def fill_cabin(df):
df.Cabin.fillna('U', inplace=True)
df['Cabin'] = df['Cabin'].apply(lambda c : 1 if c[0]=='U' else 0)
return df
def ticketAlpha(ticket):
ticket = ticket.replace('.','')
ticket = ticket.replace('/','')
ticket = ticket.split()
ticket = map(lambda t : t.strip(), ticket)
ticket = filter(lambda t : not t.isdigit(), ticket)
if len(ticket) > 0:
return ticket[0]
else:
return 'XXX'
def ticketNum(ticket):
ticket = ticket.replace('.','')
ticket = ticket.replace('/','')
ticket = ticket.replace('[a-zA-Z]','')
ticket = ticket.split()
return str(ticket[len(ticket)-1])[0]
def fill_ticket(df):
df['TicketAlpha'] = df['Ticket'].map(ticketAlpha)
df['TicketNum'] = df['Ticket'].map(ticketNum)
df['TicketNum'] = df['TicketNum'].convert_objects(convert_numeric=True)
return df
def add_family(df):
df['FamilySize'] = df['Parch'] + df['SibSp'] + 1
df['Singleton'] = df['FamilySize'].map(lambda s: 1 if s == 1 else 0)
df['SmallFamily'] = df['FamilySize'].map(lambda s: 1 if 2<=s<=4 else 0)
df['LargeFamily'] = df['FamilySize'].map(lambda s: 1 if 5<=s else 0)
return df
def simplify_ages_intuition(df):
bins=(-1,0,5,12,18,25,35,60,120)
tags = ['Unknown', 'Baby', 'Child', 'Teenager', 'Student', 'Young Adult', 'Adult', 'Senior']
df['Age'] = pd.cut(df['Age'],bins , labels=tags)
return df
def simplify_ages_bins(df,b):
tags=range(b)
df['Age'] = pd.cut(df['Age'],bins=b,labels=tags)
return df
def add_share(total):
FareFreq=total.Fare.value_counts()
TicketFreq=total.Ticket.value_counts()
CabinFreq=total.Cabin.value_counts()
total['ShareFare'] = total['Fare'].apply(lambda x: FareFreq[x] if FareFreq[x]>1 else 0)
total['ShareTicket'] = total['Ticket'].apply(lambda x: TicketFreq[x] if TicketFreq[x]>1 else 0)
total['ShareCabin'] = total['Cabin'].apply(lambda x: CabinFreq[x] if CabinFreq[x]>1 else 0)