基于RandomForestClassifier的titanic生存概率分析

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import matplotlib.pyplot as plt
%matplotlib inline

from keras.models import Sequential
from keras.layers import Dense , Dropout , Lambda, Flatten, Activation
from keras.optimizers import Adam ,RMSprop
from sklearn.model_selection import train_test_split
from keras import backend as K

train = pd.read_csv("data/titanic/train.csv")
print(train.shape)
train.head()

train.info()
train.describe()

# 加载测试数据
test= pd.read_csv("data/titanic/test.csv")
print(test.shape)
test.head()

# 对年龄进行分段处理
def simplify_ages(df):
df.Age = df.Age.fillna(-0.5)
bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)
group_names = ['Unknown', 'Baby', 'Child', 'Teenager', 'Student', 'Young Adult', 'Adult', 'Senior']
categories = pd.cut(df.Age, bins, labels=group_names)
df.Age = categories
return df

# 对家庭成员数量进行分析 以及是否独自乘船
def family_size(df):
df['family_size'] = df['SibSp'] + df['Parch'] + 1
df['is_alone'] = df.family_size.apply(lambda x: 0 if x==1 else 1)
return df

# 对仓位进行处理
def simplify_cabins(df):
df.Cabin = df.Cabin.fillna('N')
df.Cabin = df.Cabin.apply(lambda x: x[0])
return df

# 对费用进行分析 按照费用等级进行分段
def simplify_fares(df):
df.Fare = df.Fare.fillna(-0.5)
bins = (-1, 0, 8, 15, 31, 1000)
group_names = ['Unknown', '1_quartile', '2_quartile', '3_quartile', '4_quartile']
categories = pd.cut(df.Fare, bins, labels=group_names)
df.Fare = categories
return df

# 对游客姓名进行处理分析 
def format_name(df):
df['Lname'] = df.Name.apply(lambda x: x.split(' ')[0])
df['NamePrefix'] = df.Name.apply(lambda x: x.split(' ')[1])
return df 

# 处理登船港口
def convert_embmarked(df):
df =pd.get_dummies(data=df,columns=['Embarked'])
return df

# 去掉无效信息 姓名 和船票号码对于生存影响不大
def drop_features(df):
#return df.drop(['Ticket', 'Name', 'Embarked'], axis=1)
df = df.drop(['Ticket'], axis =1)
df = df.drop(['Name'], axis=1)
# df = df.drop(['Embarked'], axis =1)
return df

# 对所有的数据进行统一处理
def transform_features(df):
df = simplify_ages(df)
df = family_size(df)
df = simplify_cabins(df)
df = simplify_fares(df)
df = format_name(df)
df = convert_embmarked(df)
df = drop_features(df)
return df

data_train = transform_features(train)
data_test = transform_features(test)
data_train.head()

# 对字符串变量进行编码

from sklearn import preprocessing
def encode_features(df_train, df_test):
features = ['Fare', 'Cabin', 'Age', 'Sex', 'Lname', 'NamePrefix']
df_combined = pd.concat([df_train[features], df_test[features]])

for feature in features:
le = preprocessing.LabelEncoder()
le = le.fit(df_combined[feature])
df_train[feature] = le.transform(df_train[feature])
df_test[feature] = le.transform(df_test[feature])
return df_train, df_test

data_train, data_test = encode_features(data_train, data_test)
data_train.head()

from sklearn.model_selection import train_test_split

# 去掉无关数据
X_all = data_train.drop(['Survived', 'PassengerId'], axis=1)
y_all = data_train['Survived']

# 将数据集进行拆分 80%用于训练，剩余的用于校验
num_test = 0.20
X_train, X_test, y_train, y_test = train_test_split(X_all, y_all, test_size=num_test, random_state=23)

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import make_scorer, accuracy_score
from sklearn.model_selection import GridSearchCV

# 随机森林
model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)
#history=model.fit(X_all, y_all)
history=model.fit(X_train, y_train)
data_test = data_test.drop(['PassengerId'], axis=1)
predictions = model.predict(data_test)

# list all data in history
print(history)

RF_predictions = model.predict(X_test)
score = accuracy_score(y_test ,RF_predictions)
print(score)

from sklearn.model_selection import KFold
# 通过kfold进行交叉验证
def run_kfold(clf):
kf = KFold(10)
outcomes = []
fold = 0
fprs, tprs, scores = [], [], []
for train_index, test_index in kf.split(X_all):
fold += 1
X_train, X_test = X_all.values[train_index], X_all.values[test_index]
y_train, y_test = y_all.values[train_index], y_all.values[test_index]
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
outcomes.append(accuracy)
print("Fold {0} accuracy: {1}".format(fold, accuracy)) 
mean_outcome = np.mean(outcomes)
print("Mean Accuracy: {0}".format(mean_outcome)) 
return outcomes
outcomes = run_kfold(model)
pd.DataFrame(outcomes, columns=['AUC Train'])

plt.plot(outcomes)

由于数据集较小，因而在交叉验证的过程中精确度并不会有太大的提高，基本是属于震荡状态。

output = pd.DataFrame({'PassengerId': test.PassengerId, 'Survived': predictions})
output.to_csv('forest.csv', index=False)

最后保存文件就ok了。

另外还可以训练多层感知器来做同样的事情。

model = Sequential()
init = 'uniform'
optimizer = 'Adam'
model.add(Dense(16, input_dim=X_train.shape[1], kernel_initializer=init, activation='relu'))
model.add(Dense(8, kernel_initializer=init, activation='relu'))
model.add(Dense(4, kernel_initializer=init, activation='relu'))
model.add(Dense(1, kernel_initializer=init, activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])

history=model.fit(X_all.as_matrix(), y_all.as_matrix(), epochs=600, verbose=2)

def show_train_history(train_history, train, validation):

plt.plot(train_history.history[train])

plt.plot(train_history.history[validation])

plt.title('Train History')

plt.ylabel('train')

plt.xlabel('Epoch')

plt.legend(['loss', 'accuracy'], loc='center right')

plt.show()

show_train_history(history, 'loss', 'accuracy')

同样训练不会有太大的准确度提升。

prediction =model.predict(data_test.as_matrix())
submission = pd.DataFrame({
'PassengerId': test['PassengerId'],
'percentage': prediction[:,0],
})

# list to series
se = pd.Series(prediction.tolist())

series = []
# 将概率转换为生存状态
for val in submission.percentage:
if val >= 0.5:
series.append(1)
else:
series.append(0)
submission['Survived'] = series
submission

submission= submission.drop(['percentage'], axis=1)
submission.to_csv('p.csv', index=False, header=True)