Titanic Xgboost版代码分析

ゝ一世哀愁。 2021-06-24 14:37 413阅读 0赞

[代码来源][Link 1]  
[关于xbgboost的调参可参考这篇文章][xbgboost]  
[或官网][Link 2]  
流程如下：  
1. 读取测试集，训练集  
2. 根据相关性手动选取特征  
3. 选取测试集  
4. 填充缺失值  
5. 处理非数字型数据  
6. 训练算法  
7. 存储数据

# 引入
    import pandas as pd
    import xgboost as xgb
    from sklearn.preprocessing import LabelEncoder
    import numpy as np
    
    
    from sklearn.base import TransformerMixin
    class DataFrameImputer(TransformerMixin):
        def fit(self, X, y=None):
            # 遍历X的列生成数组，如果c的数据类型为np.dtype('O')直接返回X[c].value_counts().index[0]（X[c]中最多的那位数），否则返回X[c].median()（X[c]的中值）。
            # 生成的Series键值用X.columns的列names表示
            self.fill = pd.Series([X[c].value_counts().index[0]
                if X[c].dtype == np.dtype('O') else X[c].median() for c in X],
                index=X.columns)
            return self
            # 填补缺失值，按列对应
        def transform(self, X, y=None):
            return X.fillna(self.fill)
    
    
    # 读取数据
    train_df = pd.read_csv('train.csv', header=0)
    test_df = pd.read_csv('test.csv', header=0)
    
    
    # 手动特征选择
    feature_columns_to_use = ['Pclass','Sex','Age','Fare','Parch']
    # csv文件中sex列非数字项
    nonnumeric_columns = ['Sex']
    
    
    # 将训练集和测试集按特征选择组合起来
    # 二者的分布稍不同
    big_X = train_df[feature_columns_to_use].append(test_df[feature_columns_to_use])
    
    
    # fit_transform 是合并了fit函数和transform函数对数据的缺失值进行填充
    big_X_imputed = DataFrameImputer().fit_transform(big_X)
    
    
    
    # XGBoost无法处理非数字数据，需要转化，将字符串转化为整型
    le = LabelEncoder()
    for feature in nonnumeric_columns:
        big_X_imputed[feature] = le.fit_transform(big_X_imputed[feature])
    
    
    # 准备训练数据：训练集，测试集，训练集label
    train_X = big_X_imputed[0:train_df.shape[0]].as_matrix()
    test_X = big_X_imputed[train_df.shape[0]::].as_matrix()
    train_y = train_df['Survived']
    
    # 训练
    gbm = xgb.XGBClassifier(max_depth=3, n_estimators=300, learning_rate=0.05).fit(train_X, train_y)
    # 预测
    predictions = gbm.predict(test_X)
    
    
    
    # 存为csv格式
    submission = pd.DataFrame({ 'PassengerId': test_df['PassengerId'],
                                'Survived': predictions })
    submission.to_csv("submission.csv", index=False)

[Link 1]: https://www.kaggle.com/datacanary/titanic/xgboost-example-python/code
[xbgboost]: http://www.2cto.com/kf/201607/528771.html
[Link 2]: https://github.com/dmlc/xgboost/blob/master/doc/parameter.md