SGD.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.ensemble import RandomForestRegressor
from sklearn.pipeline import Pipeline,make_pipeline
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
from sklearn.feature_selection import SelectKBest
#from sklearn import cross_validation, metrics
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score

#from sklearn.grid_search import GridSearchCV, RandomizedSearchCV
from sklearn.model_selection import GridSearchCV
import warnings
warnings.filterwarnings('ignore')

train = pd.read_csv('train.csv',dtype={"Age": np.float64})
test = pd.read_csv('test.csv',dtype={"Age": np.float64})
PassengerId=test['PassengerId']
all_data = pd.concat([train, test], ignore_index = True)
"""
sns.barplot(x="Sex", y="Survived", data=train, palette='Set3')
print("Percentage of females who survived:%.2f" % (train["Survived"][train["Sex"] == 'female'].value_counts(normalize = True)[1]*100))
print("Percentage of males who survived:%.2f" % (train["Survived"][train["Sex"] == 'male'].value_counts(normalize = True)[1]*100))

sns.barplot(x="Pclass", y="Survived", data=train, palette='Set3')
print("Percentage of Pclass = 1 who survived:%.2f" % (train["Survived"][train["Pclass"] == 1].value_counts(normalize = True)[1]*100))
print("Percentage of Pclass = 2 who survived:%.2f" % (train["Survived"][train["Pclass"] == 2].value_counts(normalize = True)[1]*100))
print("Percentage of Pclass = 3 who survived:%.2f" % (train["Survived"][train["Pclass"] == 3].value_counts(normalize = True)[1]*100))

sns.barplot(x="SibSp", y="Survived", data=train, palette='Set3')

sns.barplot(x="Parch", y="Survived", data=train, palette='Set3')

facet = sns.FacetGrid(train, hue="Survived",aspect=2)
facet.map(sns.kdeplot,'Age',shade= True)
facet.set(xlim=(0, train['Age'].max()))
facet.add_legend()

facet = sns.FacetGrid(train, hue="Survived",aspect=2)
facet.map(sns.kdeplot,'Fare',shade= True)
facet.set(xlim=(0, 200))
facet.add_legend()
"""
all_data['Title'] = all_data['Name'].apply(lambda x:x.split(',')[1].split('.')[0].strip())
Title_Dict = {}
Title_Dict.update(dict.fromkeys(['Capt', 'Col', 'Major', 'Dr', 'Rev'], 'Officer'))
Title_Dict.update(dict.fromkeys(['Don', 'Sir', 'the Countess', 'Dona', 'Lady'], 'Royalty'))
Title_Dict.update(dict.fromkeys(['Mme', 'Ms', 'Mrs'], 'Mrs'))
Title_Dict.update(dict.fromkeys(['Mlle', 'Miss'], 'Miss'))
Title_Dict.update(dict.fromkeys(['Mr'], 'Mr'))
Title_Dict.update(dict.fromkeys(['Master','Jonkheer'], 'Master'))
all_data['Title'] = all_data['Title'].map(Title_Dict)
#sns.barplot(x="Title", y="Survived", data=all_data, palette='Set3')

all_data['FamilySize']=all_data['SibSp']+all_data['Parch']+1
#sns.barplot(x="FamilySize", y="Survived", data=all_data, palette='Set3')

def Fam_label(s):
    if (s >= 2) & (s <= 4):
        return 2
    elif ((s > 4) & (s <= 7)) | (s == 1):
        return 1
    elif (s > 7):
        return 0
all_data['FamilyLabel']=all_data['FamilySize'].apply(Fam_label)
#sns.barplot(x="FamilyLabel", y="Survived", data=all_data, palette='Set3')

all_data['Cabin'] = all_data['Cabin'].fillna('Unknown')
all_data['Deck']=all_data['Cabin'].str.get(0)
#sns.barplot(x="Deck", y="Survived", data=all_data, palette='Set3')

Ticket_Count = dict(all_data['Ticket'].value_counts())
all_data['TicketGroup'] = all_data['Ticket'].apply(lambda x:Ticket_Count[x])
#sns.barplot(x='TicketGroup', y='Survived', data=all_data, palette='Set3')

def Ticket_Label(s):
    if (s >= 2) & (s <= 4):
        return 2
    elif ((s > 4) & (s <= 8)) | (s == 1):
        return 1
    elif (s > 8):
        return 0

all_data['TicketGroup'] = all_data['TicketGroup'].apply(Ticket_Label)
#sns.barplot(x='TicketGroup', y='Survived', data=all_data, palette='Set3')

age_df = all_data[['Age', 'Pclass','Sex','Title']]
age_df=pd.get_dummies(age_df)
known_age = age_df[age_df.Age.notnull()].as_matrix()
unknown_age = age_df[age_df.Age.isnull()].as_matrix()
y = known_age[:, 0]
X = known_age[:, 1:]
rfr = RandomForestRegressor(random_state=0, n_estimators=100, n_jobs=-1)
rfr.fit(X, y)
predictedAges = rfr.predict(unknown_age[:, 1::])
all_data.loc[ (all_data.Age.isnull()), 'Age' ] = predictedAges 

all_data[all_data['Embarked'].isnull()]

#sns.boxplot(x="Embarked", y="Fare", hue="Pclass",data=all_data, palette="Set3")

all_data['Embarked'] = all_data['Embarked'].fillna('C')

all_data[all_data['Fare'].isnull()]
fare=all_data[(all_data['Embarked'] == "S") & (all_data['Pclass'] == 3)].Fare.median()
all_data['Fare']=all_data['Fare'].fillna(fare)

all_data['Surname']=all_data['Name'].apply(lambda x:x.split(',')[0].strip())
Surname_Count = dict(all_data['Surname'].value_counts())
all_data['FamilyGroup'] = all_data['Surname'].apply(lambda x:Surname_Count[x])
Female_Child_Group=all_data.loc[(all_data['FamilyGroup']>=2) & ((all_data['Age']<=12) | (all_data['Sex']=='female'))]
Male_Adult_Group=all_data.loc[(all_data['FamilyGroup']>=2) & (all_data['Age']>12) & (all_data['Sex']=='male')]

Female_Child=pd.DataFrame(Female_Child_Group.groupby('Surname')['Survived'].mean().value_counts())
Female_Child.columns=['GroupCount']

#sns.barplot(x=Female_Child.index, y=Female_Child["GroupCount"], palette='Set3').set_xlabel('AverageSurvived')

Male_Adult=pd.DataFrame(Male_Adult_Group.groupby('Surname')['Survived'].mean().value_counts())
Male_Adult.columns=['GroupCount']
#sns.barplot(x=Male_Adult.index, y=Male_Adult['GroupCount'], palette='Set3').set_xlabel('AverageSurvived')

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)
#print(Dead_List)
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)
#print(Survived_List)

train=all_data.loc[all_data['Survived'].notnull()]
test=all_data.loc[all_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'

all_data=pd.concat([train, test])
all_data=all_data[['Survived','Pclass','Sex','Age','Fare','Embarked','Title','FamilyLabel','Deck','TicketGroup']]
all_data=pd.get_dummies(all_data)
train=all_data[all_data['Survived'].notnull()]
test=all_data[all_data['Survived'].isnull()].drop('Survived',axis=1)
X = train.as_matrix()[:,1:]
y = train.as_matrix()[:,0]

pipe=Pipeline([('select',SelectKBest(k=20)), 
               ('classify', RandomForestClassifier(random_state = 10, max_features = 'sqrt'))])

param_test = {'classify__n_estimators':list(range(20,50,2)), 
              'classify__max_depth':list(range(3,60,3))}
gsearch = GridSearchCV(estimator = pipe, param_grid = param_test, scoring='roc_auc', cv=10)
gsearch.fit(X,y)
#print(gsearch.best_params_, gsearch.best_score_)

select = SelectKBest(k = 20)
clf = RandomForestClassifier(random_state = 10, warm_start = True, 
                                  n_estimators = 26,
                                  max_depth = 6, 
                                  max_features = 'sqrt')
pipeline = make_pipeline(select, clf)
pipeline.fit(X, y)


print(cross_val_score(lasso, X, y, cv=10))  

print("CV Score : Mean - %.7g | Std - %.7g " % (np.mean(cv_score), np.std(cv_score)))

predictions = pipeline.predict(test)
submission = pd.DataFrame({"PassengerId": PassengerId, "Survived": predictions.astype(np.int32)})
submission.to_csv("submission.csv", index=False)