- XGBoost是使用梯度提升框架实现的高效、灵活、可移植的机器学习库，全称是eXtreme Gradient Boosting,是GBDT(GBM)的一个C++实现。它将树的生成并行完成，从而提高学习速度。
- 一般地说，XGBoost的速度和性能优于sklearn.ensemble.GradientBoostingClassifier类。
- XGBoost的作者为华盛顿大学陈天奇，并封装了Python接口，随着在机器学习竞赛中的优异表现，其他学者封装完成了R/Julia等接口。
- 在实际的工作中，效果不错。
代码：
数据
训练
预测
2 Kaggle简介Kaggle是一个数据分析的竞赛平台。
3 代码实现泰坦尼克号的生死预测
数据说明：
pclass:舱位
sex:性别。使用0、1来标记男性和女性。
age:年龄，存在部分缺失值。使用决策树or随机森林or根据舱位，将舱位相同的均值作为该舱位存在缺失值的人的年龄等等。
fare票价，与舱位有直接的联系。
cabin房间号：缺失值严重。
embarked:起始城市，有三个C、Q、S，外加缺失值Unknow,所以人为将embarked变成四列，
S——1000 C——0100 Q——0010
数据预处理：
代码：
使用logistic回归/随机森林/XGBoost
import xgboost as xgb
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import pandas as pd
import csv
def show_accuracy(a, b, tip):
acc = a.ravel() == b.ravel()
acc_rate = 100 * float(acc.sum()) / a.size
print '%s正确率：%.3f%%' % (tip, acc_rate)
return acc_rate
def load_data(file_name, is_train):
data = pd.read_csv(file_name) # 数据文件路径
# print 'data.describe() = n', data.describe()
# 性别
data['Sex'] = data['Sex'].map({'female': 0, 'male': 1}).astype(int)
# 补齐船票价格缺失值
if len(data.Fare[data.Fare.isnull()]) > 0:
fare = np.zeros(3)
for f in range(0, 3):
fare[f] = data[data.Pclass == f + 1]['Fare'].dropna().median()
for f in range(0, 3): # loop 0 to 2
data.loc[(data.Fare.isnull()) & (data.Pclass == f + 1), 'Fare'] = fare[f]
# 年龄：使用均值代替缺失值
# mean_age = data['Age'].dropna().mean()
# data.loc[(data.Age.isnull()), 'Age'] = mean_age
if is_train:
# 年龄：使用随机森林预测年龄缺失值
print '随机森林预测缺失年龄：--start--'
data_for_age = data[['Age', 'Survived', 'Fare', 'Parch', 'SibSp', 'Pclass']]
age_exist = data_for_age.loc[(data.Age.notnull())] # 年龄不缺失的数据
age_null = data_for_age.loc[(data.Age.isnull())]
# print age_exist
x = age_exist.values[:, 1:]
y = age_exist.values[:, 0]
rfr = RandomForestRegressor(n_estimators=1000)
rfr.fit(x, y)
age_hat = rfr.predict(age_null.values[:, 1:])
# print age_hat
data.loc[(data.Age.isnull()), 'Age'] = age_hat
print '随机森林预测缺失年龄：--over--'
else:
print '随机森林预测缺失年龄2：--start--'
data_for_age = data[['Age', 'Fare', 'Parch', 'SibSp', 'Pclass']]
age_exist = data_for_age.loc[(data.Age.notnull())] # 年龄不缺失的数据
age_null = data_for_age.loc[(data.Age.isnull())]
# print age_exist
x = age_exist.values[:, 1:]
y = age_exist.values[:, 0]
rfr = RandomForestRegressor(n_estimators=1000)
rfr.fit(x, y)
age_hat = rfr.predict(age_null.values[:, 1:])
# print age_hat
data.loc[(data.Age.isnull()), 'Age'] = age_hat
print '随机森林预测缺失年龄2：--over--'
# 起始城市
data.loc[(data.Embarked.isnull()), 'Embarked'] = 'S' # 保留缺失出发城市
# data['Embarked'] = data['Embarked'].map({'S': 0, 'C': 1, 'Q': 2, 'U': 0}).astype(int)
# print data['Embarked']
embarked_data = pd.get_dummies(data.Embarked)
print embarked_data
# embarked_data = embarked_data.rename(columns={'S': 'Southampton', 'C': 'Cherbourg', 'Q': 'Queenstown', 'U': 'UnknownCity'})
embarked_data = embarked_data.rename(columns=lambda x: 'Embarked_' + str(x))
data = pd.concat([data, embarked_data], axis=1)
print data.describe()
data.to_csv('New_Data.csv')
x = data[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked_C', 'Embarked_Q', 'Embarked_S']]
# x = data[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']]
y = None
if 'Survived' in data:
y = data['Survived']
x = np.array(x)
y = np.array(y)
# 思考：这样做，其实发生了什么？
x = np.tile(x, (5, 1))
y = np.tile(y, (5, ))
if is_train:
return x, y
return x, data['PassengerId']
def write_result(c, c_type):
file_name = 'Titanic.test.csv'
x, passenger_id = load_data(file_name, False)
if type == 3:
x = xgb.DMatrix(x)
y = c.predict(x)
y[y > 0.5] = 1
y[~(y > 0.5)] = 0
predictions_file = open("Prediction_%d.csv" % c_type, "wb")
open_file_object = csv.writer(predictions_file)
open_file_object.writerow(["PassengerId", "Survived"])
open_file_object.writerows(zip(passenger_id, y))
predictions_file.close()
if __name__ == "__main__":
x, y = load_data('Titanic.train.csv', True)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=1)
#
lr = LogisticRegression(penalty='l2')
lr.fit(x_train, y_train)
y_hat = lr.predict(x_test)
lr_acc = accuracy_score(y_test, y_hat)
# write_result(lr, 1)
rfc = RandomForestClassifier(n_estimators=100)
rfc.fit(x_train, y_train)
y_hat = rfc.predict(x_test)
rfc_acc = accuracy_score(y_test, y_hat)
# write_result(rfc, 2)
# XGBoost
data_train = xgb.DMatrix(x_train, label=y_train)
data_test = xgb.DMatrix(x_test, label=y_test)
watch_list = [(data_test, 'eval'), (data_train, 'train')]
param = {'max_depth': 6, 'eta': 0.8, 'silent': 1, 'objective': 'binary:logistic'}
# 'subsample': 1, 'alpha': 0, 'lambda': 0, 'min_child_weight': 1}
bst = xgb.train(param, data_train, num_boost_round=100, evals=watch_list)
y_hat = bst.predict(data_test)
# write_result(bst, 3)
y_hat[y_hat > 0.5] = 1
y_hat[~(y_hat > 0.5)] = 0
xgb_acc = accuracy_score(y_test, y_hat)
print 'Logistic回归：%.3f%%' % lr_acc
print '随机森林：%.3f%%' % rfc_acc
print 'XGBoost：%.3f%%' % xgb_acc
