Kaggle Titanic问题

发表于

Kaggle Titanic问题

[技能get点]

  • Pandas基本操作
  • [√] 随机森林算法 ——> 数学原理、算法思想
  • [√] kaggle竞赛的一个项目完成参与流程
  • [√] 特征功能 ——> 特征提取

随机森林算法

kaggle竞赛参与流程

特征提取

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
#! /usr/bin/python
# -*- coding:utf-8 -*-
"""
@author: abc
@file: xyzforest.py
@date: 2017-01-06
"""
__author__ = "abc"

import numpy as np
import pandas as pd
import csv
from sklearn.ensemble import RandomForestClassifier


class Titanic(object):
    """
    Titanic
    """
    def __init__(self):
        """
        __init__
        """
        self.train_path = "/home/abc/Projects/kaggle/Titanic/train.csv"
        self.test_path = "/home/abc/Projects/kaggle/Titanic/test.csv"

    def load_data(self, path):
        """
        加载数据
        :param path:
        :return:
        """
        return pd.read_csv(path, header=0)

    def wash_train_data(self, train_data):
        """
        清洗训练数据
        :param train_data:
        :return:
        """
        # PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
        # 性别数值化
        train_data.Sex = train_data.Sex.map({'female': 0, 'male': 1}).astype(int)

        # Embarked 数值化, 补充众数
        if len(train_data.Embarked[train_data.Embarked.isnull()]) > 0:
            train_data.Embarked[train_data.Embarked.isnull()] = train_data.Embarked.dropna().mode().values
        ports_dict = {name: index for index, name in enumerate(set(train_data.Embarked))}
        train_data.Embarked = train_data.Embarked.map(ports_dict).astype(int)

        # 年龄补充平均数
        if len(train_data.Age[train_data.Age.isnull()]) > 0:
            train_data.Age[train_data.Age.isnull()] = train_data.Age.dropna().median()

        # 删除多余字段
        train_data = train_data.drop(self.drop_label(), axis=1)

        self.data_concat(train_data)

        return train_data.values

    def wash_test_data(self, test_data):
        """
        清洗测试数据
        :param test_data:
        :return:
        """
        # PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
        test_data.Sex = test_data.Sex.map({'female': 0, 'male': 1}).astype(int)

        if len(test_data.Embarked[test_data.Embarked.isnull()]) > 0:
            test_data.Embarked[test_data.Embarked.isnull()] = test_data.Embarked.dropna().mode().values
        ports_dict = {name: index for index, name in enumerate(np.unique(test_data.Embarked))}
        test_data.Embarked = test_data.Embarked.map(ports_dict).astype(int)

        if len(test_data.Age[test_data.Age.isnull()]) > 0:
            test_data.Age[test_data.Age.isnull()] = test_data.Age.dropna().median()

        if len(test_data.Fare[test_data.Fare.isnull()]) > 0:
            median_fare = np.zeros(3)
            for f in range(0, 3):  # loop 0 to 2
                median_fare[f] = test_data[test_data.Pclass == f + 1]['Fare'].dropna().median()
            for f in range(0, 3):  # loop 0 to 2
                test_data.loc[(test_data.Fare.isnull()) & (test_data.Pclass == f + 1), 'Fare'] = median_fare[f]

        test_data = test_data.drop(self.drop_label(), axis=1)

        self.data_concat(test_data)

        return test_data.values

    def drop_label(self):
        """
        drop_label
        :return:
        """
        return ['Name', 'Ticket', 'PassengerId']

    def data_concat(self, raw_data):
        """
        data_concat
        :param raw_data:
        :return:
        """
        # 根据年龄优化
        raw_data.loc[raw_data.Age < 18, "Age"] = 0
        raw_data.loc[raw_data.Age >= 18, "Age"][raw_data.Age < 45] = 1
        raw_data.loc[raw_data.Age >= 45, "Age"] = 2
        # 根据父母孩子数量优化
        raw_data.loc[raw_data.Parch > 0, "Parch"] = 1
        raw_data.loc[raw_data.Cabin.isnull(), "Cabin"] = 1
        raw_data.loc[raw_data.Cabin.notnull(), "Cabin"] = 0

    def rediction_model(self, train_data, label_data, test_data):
        """
        预测模型
        :param train_data:
        :param test_data:
        :return:
        """
        forest = RandomForestClassifier(n_estimators=2000)
        forest = forest.fit(train_data, label_data)
        return forest.predict(test_data).astype(int)

    def save_result(self, path, head, data):
        """
        save_result
        :param path:
        :param head:
        :param data:
        :return:
        """
        with open(path, "wb") as wbf:
            csv_obj = csv.writer(wbf)
            csv_obj.writerow(head)
            for item in data:
                csv_obj.writerow(list(item))

    def run(self):
        """
        运行
        :return:
        """
        # 加载数据
        train_data = self.load_data(self.train_path)
        test_data = self.load_data(self.test_path)
        passenger_id = test_data.PassengerId.values
        # 清洗数据
        train_data = self.wash_train_data(train_data)
        test_data = self.wash_test_data(test_data)
        train_data, label_data = train_data[0::, 1::], [val for val in train_data[0::, 0]]
        head = ["PassengerId", "Survived"]
        data = zip(passenger_id, self.rediction_model(train_data, label_data, test_data))
        self.save_result("xyzforest.csv", head, data)

if __name__ == "__main__":
    tt = Titanic()
    print tt.run()

参考

pandas documentation

【原】十分钟搞定pandas

机器学习系列(3)_逻辑回归应用之Kaggle泰坦尼克之灾

Kaggle系列——Titanic 80%＋精确度纪录