使用级联预测模型完整代码

最新推荐文章于 2025-05-21 08:17:43 发布

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最新推荐文章于 2025-05-21 08:17:43 发布 · 2.2k 阅读

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本文详细介绍了如何使用级联预测模型进行数据分析，并提供了一段完整的代码实现。通过实例，阐述了该模型在处理复杂预测问题时的优势，包括预处理步骤、模型训练、评估和优化等关键环节。

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import pandas as pd
import numpy as np
import re
import sklearn
import seaborn as sns
import matplotlib.pyplot as plt
import xgboost as xgb
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.tools as tls
 
import warnings
warnings.filterwarnings('ignore')
 
# Going to use these 5 base models for the stacking
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier
from sklearn.svm import SVC
from sklearn.cross_validation import KFold;
# Load in the train and test datasets
train = pd.read_csv('titanic_train.csv')
test = pd.read_csv('test.csv')
# Store our passenger ID for easy access
PassengerId = test['PassengerId']
# print(train.head(3))
full_data = [train, test]
 
# 额外添加一些需要从已有数据中计算得到的其他特征
# 例如姓名的长度
train['Name_length'] = train['Name'].apply(len)
test['Name_length'] = test['Name'].apply(len)
# 是否有Cabin
train['Has_Cabin'] = train["Cabin"].apply(lambda x: 0 if type(x) == float else 1)
test['Has_Cabin'] = test["Cabin"].apply(lambda x: 0 if type(x) == float else 1)
 
# 计算全部家人的数目
for dataset in full_data:
    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1
# 是否是一个人
for dataset in full_data:
    dataset['IsAlone'] = 0
    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1
# 对Embarked列的空数据进行处理
for dataset in full_data:
    dataset['Embarked'] = dataset['Embarked'].fillna('S')
# 用训练集数据的Fare的中值来填充所有Fare为空的数据
for dataset in full_data:
    dataset['Fare'] = dataset['Fare'].fillna(train['Fare'].median())
train['CategoricalFare'] = pd.qcut(train['Fare'], 4)
# 创建一个新的特征CategoricalAge
for dataset in full_data:
    age_avg = dataset['Age'].mean()
    age_std = dataset['Age'].std()
    age_null_count = dataset['Age'].isnull().sum()
    age_null_random_list = np.random.randint(age_avg - age_std, age_avg + age_std, size=age_null_count)
    dataset['Age'][np.isnan(dataset['Age'])] = age_null_random_list
    dataset['Age'] = dataset['Age'].astype(int)
train['CategoricalAge'] = pd.cut(train['Age'], 5)
# 定义一个函数，用于寻找姓名中的Title
def get_title(name):
    title_search = re.search(' ([A-Za-z]+)\.', name)
    # If the title exists, extract and return it.
    if title_search:
        return title_search.group(1)
    return ""
# 创建一个新的变量Title
for dataset in full_data:
    dataset['Title'] = dataset['Name'].apply(get_title)
# 将一些不常见的Title转换为一些对应的常见Title种类
for dataset in full_data:
    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
 
    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
 
for dataset in full_data:
    # 将性别映射至0,1
    dataset['Sex'] = dataset['Sex'].map( {'female': 0, 'male': 1} ).astype(int)
    
    # 将Title映射至0-5。
    title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Rare": 5}
    dataset['Title'] = dataset['Title'].map(title_mapping)
    dataset['Title'] = dataset['Titl