#!/usr/bin/env python
# coding: utf-8

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import pandas as pd #数据分析
import numpy as np #科学计算
from pandas import Series,DataFrame
 
data_train = pd.read_csv("../input/train.csv")
data_train.info()


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import matplotlib.pyplot as plt
fig = plt.figure()
fig.set(alpha=0.2)  # 设定图表颜色alpha参数
 
plt.subplot2grid((2,3),(0,0))             # 在一张大图里分列几个小图
data_train.Survived.value_counts().plot(kind='bar')# 柱状图 
plt.title("获救情况 (1为获救)") # 标题
plt.ylabel(u"人数")  
 
plt.subplot2grid((2,3),(0,1))
data_train.Pclass.value_counts().plot(kind="bar")
plt.ylabel(u"人数")
plt.title(u"乘客等级分布")
 
plt.subplot2grid((2,3),(0,2))
plt.scatter(data_train.Survived, data_train.Age)
plt.ylabel(u"年龄")                         # 设定纵坐标名称
plt.grid(b=True, which='major', axis='y') 
plt.title(u"按年龄看获救分布 (1为获救)")
 
 
plt.subplot2grid((2,3),(1,0), colspan=2)
data_train.Age[data_train.Pclass == 1].plot(kind='kde')   
data_train.Age[data_train.Pclass == 2].plot(kind='kde')
data_train.Age[data_train.Pclass == 3].plot(kind='kde')
plt.xlabel(u"年龄")# plots an axis lable
plt.ylabel(u"密度") 
plt.title(u"各等级的乘客年龄分布")
plt.legend((u'头等舱', u'2等舱',u'3等舱'),loc='best') # sets our legend for our graph.
 
 
plt.subplot2grid((2,3),(1,2))
data_train.Embarked.value_counts().plot(kind='bar')
plt.title(u"各登船口岸上船人数")
plt.ylabel(u"人数")  
plt.show()


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#看看各乘客等级的获救情况
fig = plt.figure()
fig.set(alpha=0.2)  # 设定图表颜色alpha参数
 
Survived_0 = data_train.Pclass[data_train.Survived == 0].value_counts()
Survived_1 = data_train.Pclass[data_train.Survived == 1].value_counts()
df=pd.DataFrame({u'获救':Survived_1, u'未获救':Survived_0})
df.plot(kind='bar', stacked=True)
plt.title(u"各乘客等级的获救情况")
plt.xlabel(u"乘客等级") 
plt.ylabel(u"人数") 
plt.show()


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#看看各性别的获救情况
fig = plt.figure()
fig.set(alpha=0.2)  # 设定图表颜色alpha参数
 
Survived_m = data_train.Survived[data_train.Sex == 'male'].value_counts()
Survived_f = data_train.Survived[data_train.Sex == 'female'].value_counts()
df=pd.DataFrame({u'男性':Survived_m, u'女性':Survived_f})
df.plot(kind='bar', stacked=True)
plt.title(u"按性别看获救情况")
plt.xlabel(u"性别") 
plt.ylabel(u"人数")
plt.show()


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#然后我们再来看看各种舱级别情况下各性别的获救情况
fig=plt.figure()
fig.set(alpha=0.65) # 设置图像透明度，无所谓
plt.title(u"根据舱等级和性别的获救情况")

ax1=fig.add_subplot(141)
data_train.Survived[data_train.Sex == 'female'][data_train.Pclass != 3].value_counts().plot(kind='bar', label="female highclass", color='#FA2479')
ax1.set_xticklabels([u"获救", u"未获救"], rotation=0)
ax1.legend([u"女性/高级舱"], loc='best')

ax2=fig.add_subplot(142, sharey=ax1)
data_train.Survived[data_train.Sex == 'female'][data_train.Pclass == 3].value_counts().plot(kind='bar', label='female, low class', color='pink')
ax2.set_xticklabels([u"未获救", u"获救"], rotation=0)
plt.legend([u"女性/低级舱"], loc='best')

ax3=fig.add_subplot(143, sharey=ax1)
data_train.Survived[data_train.Sex == 'male'][data_train.Pclass != 3].value_counts().plot(kind='bar', label='male, high class',color='lightblue')
ax3.set_xticklabels([u"未获救", u"获救"], rotation=0)
plt.legend([u"男性/高级舱"], loc='best')

ax4=fig.add_subplot(144, sharey=ax1)
data_train.Survived[data_train.Sex == 'male'][data_train.Pclass == 3].value_counts().plot(kind='bar', label='male low class', color='steelblue')
ax4.set_xticklabels([u"未获救", u"获救"], rotation=0)
plt.legend([u"男性/低级舱"], loc='best')

plt.show()


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fig = plt.figure()
fig.set(alpha=0.2)  # 设定图表颜色alpha参数
 
Survived_0 = data_train.Embarked[data_train.Survived == 0].value_counts()
Survived_1 = data_train.Embarked[data_train.Survived == 1].value_counts()
df=pd.DataFrame({u'获救':Survived_1, u'未获救':Survived_0})
df.plot(kind='bar', stacked=True)
plt.title(u"各登录港口乘客的获救情况")
plt.xlabel(u"登录港口") 
plt.ylabel(u"人数") 
 
plt.show()


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g = data_train.groupby(['SibSp','Survived'])
df = pd.DataFrame(g.count()['PassengerId'])
print(df)



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fig = plt.figure()
fig.set(alpha=0.2)  # 设定图表颜色alpha参数

Survived_cabin = data_train.Survived[pd.notnull(data_train.Cabin)].value_counts()
Survived_nocabin = data_train.Survived[pd.isnull(data_train.Cabin)].value_counts()
df=pd.DataFrame({u'有':Survived_cabin, u'无':Survived_nocabin}).transpose()
df.plot(kind='bar', stacked=True)
plt.title(u"按Cabin有无看获救情况")
plt.xlabel(u"Cabin有无") 
plt.ylabel(u"人数")
plt.show()


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from sklearn.ensemble import RandomForestRegressor
 
### 使用 RandomForestClassifier 填补缺失的年龄属性
def set_missing_ages(df):
 
    # 把已有的数值型特征取出来丢进Random Forest Regressor中
    age_df = df[['Age','Fare', 'Parch', 'SibSp', 'Pclass']]
 
    # 乘客分成已知年龄和未知年龄两部分
    known_age = age_df[age_df.Age.notnull()].as_matrix()
    unknown_age = age_df[age_df.Age.isnull()].as_matrix()
 
    # y即目标年龄
    y = known_age[:, 0]
 
    # X即特征属性值
    X = known_age[:, 1:]
 
    # fit到RandomForestRegressor之中
    rfr = RandomForestRegressor(random_state=0, n_estimators=2000, n_jobs=-1)
    rfr.fit(X, y)
 
    # 用得到的模型进行未知年龄结果预测
    predictedAges = rfr.predict(unknown_age[:, 1::])
 
    # 用得到的预测结果填补原缺失数据
    df.loc[ (df.Age.isnull()), 'Age' ] = predictedAges 
 
    return df, rfr
 
def set_Cabin_type(df):
    df.loc[ (df.Cabin.notnull()), 'Cabin' ] = "Yes"
    df.loc[ (df.Cabin.isnull()), 'Cabin' ] = "No"
    return df
 
data_train, rfr = set_missing_ages(data_train)
data_train = set_Cabin_type(data_train)


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dummies_Cabin = pd.get_dummies(data_train['Cabin'], prefix= 'Cabin')
 
dummies_Embarked = pd.get_dummies(data_train['Embarked'], prefix= 'Embarked')
 
dummies_Sex = pd.get_dummies(data_train['Sex'], prefix= 'Sex')
 
dummies_Pclass = pd.get_dummies(data_train['Pclass'], prefix= 'Pclass')
 
df = pd.concat([data_train, dummies_Cabin, dummies_Embarked, dummies_Sex, dummies_Pclass], axis=1)
df.drop(['Pclass', 'Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], axis=1, inplace=True)
df


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import sklearn.preprocessing as preprocessing
scaler = preprocessing.StandardScaler()
age_scale_param = scaler.fit(df[['Age']])
df['Age_scaled'] = age_scale_param.transform(df[['Age']])
fare_scale_param = scaler.fit(df[['Fare']])
df['Fare_scaled'] = scaler.transform(df[['Fare']])
df


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from sklearn import linear_model
 
# 用正则取出我们要的属性值
train_df = df.filter(regex='Survived|Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass_.*')
train_np = train_df.as_matrix()
 
# y即Survival结果
y = train_np[:, 0]
 
# X即特征属性值
X = train_np[:, 1:]
 
# fit到RandomForestRegressor之中
clf = linear_model.LogisticRegression(C=1.0, penalty='l1', tol=1e-6)
clf.fit(X, y)
 
clf


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data_test = pd.read_csv("../input/test.csv")
data_test.loc[ (data_test.Fare.isnull()), 'Fare' ] = 0
# 接着我们对test_data做和train_data中一致的特征变换
# 首先用同样的RandomForestRegressor模型填上丢失的年龄
tmp_df = data_test[['Age','Fare', 'Parch', 'SibSp', 'Pclass']]
null_age = tmp_df[data_test.Age.isnull()].as_matrix()
# 根据特征属性X预测年龄并补上
X = null_age[:, 1:]
predictedAges = rfr.predict(X)
data_test.loc[ (data_test.Age.isnull()), 'Age' ] = predictedAges
 
data_test = set_Cabin_type(data_test)
dummies_Cabin = pd.get_dummies(data_test['Cabin'], prefix= 'Cabin')
dummies_Embarked = pd.get_dummies(data_test['Embarked'], prefix= 'Embarked')
dummies_Sex = pd.get_dummies(data_test['Sex'], prefix= 'Sex')
dummies_Pclass = pd.get_dummies(data_test['Pclass'], prefix= 'Pclass')
 
 
df_test = pd.concat([data_test, dummies_Cabin, dummies_Embarked, dummies_Sex, dummies_Pclass], axis=1)
df_test.drop(['Pclass', 'Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], axis=1, inplace=True)
df_test['Age_scaled'] = scaler.fit_transform(df_test[['Age']], age_scale_param)
df_test['Fare_scaled']= scaler.fit_transform(df_test[['Fare']], fare_scale_param)


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test = df_test.filter(regex='Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass_.*')
predictions = clf.predict(test)
result = pd.DataFrame({'PassengerId':data_test['PassengerId'].as_matrix(), 'Survived':predictions.astype(np.int32)})
result.to_csv("logistic_regression_predictions.csv", index=False)

