2.1 泰坦尼克—决策树模型 - 图1

  1. import pandas as pd
  2. import sklearn.tree as tree
  3. import graphviz #画图
  4. from sklearn.feature_extraction import DictVectorizer# 将字符串替换为数字

导入数据

  1. train_data=pd.read_csv('./train.csv')
  2. test_data=pd.read_csv('./test.csv')

数据加载

  1. print(train_data.info())
  1. <class 'pandas.core.frame.DataFrame'>
  2. RangeIndex: 891 entries, 0 to 890
  3. Data columns (total 12 columns):
  4. PassengerId    891 non-null int64
  5. Survived       891 non-null int64
  6. Pclass         891 non-null int64
  7. Name           891 non-null object
  8. Sex            891 non-null object
  9. Age            714 non-null float64
  10. SibSp          891 non-null int64
  11. Parch          891 non-null int64
  12. Ticket         891 non-null object
  13. Fare           891 non-null float64
  14. Cabin          204 non-null object
  15. Embarked       889 non-null object
  16. dtypes: float64(2), int64(5), object(5)
  17. memory usage: 83.7+ KB
  18. None
  1. train_data.describe()
PassengerId Survived Pclass Age SibSp Parch Fare
count 891.000000 891.000000 891.000000 714.000000 891.000000 891.000000 891.000000
mean 446.000000 0.383838 2.308642 29.699118 0.523008 0.381594 32.204208
std 257.353842 0.486592 0.836071 14.526497 1.102743 0.806057 49.693429
min 1.000000 0.000000 1.000000 0.420000 0.000000 0.000000 0.000000
25% 223.500000 0.000000 2.000000 20.125000 0.000000 0.000000 7.910400
50% 446.000000 0.000000 3.000000 28.000000 0.000000 0.000000 14.454200
75% 668.500000 1.000000 3.000000 38.000000 1.000000 0.000000 31.000000
max 891.000000 1.000000 3.000000 80.000000 8.000000 6.000000 512.329200 
  1. train_data.describe(include=['O'])
Name Sex Ticket Cabin Embarked
count 891 891 891 204 889
unique 891 2 681 147 3
top Heininen, Miss. Wendla Maria male CA. 2343 G6 S
freq 1 577 7 4 644 
  1. train_data.head()
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th… female 38.0 1 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S 
  1. train_data.tail()
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.00 NaN S
887 888 1 1 Graham, Miss. Margaret Edith female 19.0 0 0 112053 30.00 B42 S
888 889 0 3 Johnston, Miss. Catherine Helen “Carrie” female NaN 1 2 W./C. 6607 23.45 NaN S
889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.00 C148 C
890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.75 NaN Q 
  1. print(test_data.info())
  1. <class 'pandas.core.frame.DataFrame'>
  2. RangeIndex: 418 entries, 0 to 417
  3. Data columns (total 11 columns):
  4. PassengerId    418 non-null int64
  5. Pclass         418 non-null int64
  6. Name           418 non-null object
  7. Sex            418 non-null object
  8. Age            332 non-null float64
  9. SibSp          418 non-null int64
  10. Parch          418 non-null int64
  11. Ticket         418 non-null object
  12. Fare           417 non-null float64
  13. Cabin          91 non-null object
  14. Embarked       418 non-null object
  15. dtypes: float64(2), int64(4), object(5)
  16. memory usage: 36.0+ KB
  17. None

我们发现 Age、Fare 和 Cabin 这三个字段的数据有所缺失

数据清洗

利用平均值填充连续型数据段

  1. train_data['Age'].fillna(train_data['Age'].mean(),inplace=True)
  2. test_data['Age'].fillna(test_data['Age'].mean(),inplace=True)
  1. train_data['Fare'].fillna(train_data['Fare'].mean(),inplace=True)
  2. test_data['Fare'].fillna(test_data['Fare'].mean(),inplace=True)

字符型数据以众数填充

  1. train_data['Embarked'].value_counts()
  1. S    644
  2. C    168
  3. Q     77
  4. Name: Embarked, dtype: int64
  1. train_data['Embarked'].fillna('S',inplace=True)

特征值选取

cabin存在太多缺失数据无法补全，予以舍去

  1. features=['Pclass','Sex','Age','SibSp','Parch','Fare','Embarked']
  2. train_features=train_data[features]
  3. test_features=test_data[features]
  4. train_labels=train_data['Survived']
  1. # 字符串替换为数字
  2. dvec=DictVectorizer(sparse=False)
  3. train_features=dvec.fit_transform(train_features.to_dict(orient='record'))
  1. print(dvec.feature_names_)
  1. ['Age', 'Embarked=C', 'Embarked=Q', 'Embarked=S', 'Fare', 'Parch', 'Pclass', 'Sex=female', 'Sex=male', 'SibSp']
  1. train_features=pd.DataFrame(train_features,columns=[dvec.feature_names_])
  2. train_features.head()
Age Embarked=C Embarked=Q Embarked=S Fare Parch Pclass Sex=female Sex=male SibSp
0 22.0 0.0 0.0 1.0 7.2500 0.0 3.0 0.0 1.0 1.0
1 38.0 1.0 0.0 0.0 71.2833 0.0 1.0 1.0 0.0 1.0
2 26.0 0.0 0.0 1.0 7.9250 0.0 3.0 1.0 0.0 0.0
3 35.0 0.0 0.0 1.0 53.1000 0.0 1.0 1.0 0.0 1.0
4 35.0 0.0 0.0 1.0 8.0500 0.0 3.0 0.0 1.0 0.0

构建ID3决策树

  1. clf=tree.DecisionTreeClassifier(criterion='entropy')
  2. clf.fit(train_features,train_labels)
  1. DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',
  2.                        max_depth=None, max_features=None, max_leaf_nodes=None,
  3.                        min_impurity_decrease=0.0, min_impurity_split=None,
  4.                        min_samples_leaf=1, min_samples_split=2,
  5.                        min_weight_fraction_leaf=0.0, presort='deprecated',
  6.                        random_state=None, splitter='best')
  1. test_features=dvec.fit_transform(test_features.to_dict(orient='record'))
  2. clf.predict(test_features)
  1. array([0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1,
  2.        1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1,
  3.        1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,
  4.        0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0,
  5.        0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0,
  6.        0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0,
  7.        0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
  8.        0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1,
  9.        1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0,
  10.        0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,
  11.        1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,
  12.        0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,
  13.        0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
  14.        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1,
  15.        0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,
  16.        1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0,
  17.        0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0,
  18.        1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1,
  19.        0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0])
clf.score(train_features,train_labels)

0.9820426487093153

import pydotplus
from sklearn import tree
import collections
from PIL import Image

dot_data = tree.export_graphviz(clf, feature_names=['年龄', '登陆港C', '登陆港Q', '登陆港S', '船票价格', '父母子女数', '船票等级', '女性', '男性', '兄妹，配偶数'], out_file=None,filled=True, rounded=True)


# graph = pydotplus.graph_from_dot_data(dot_data)
# colors = ( 'lightblue','orange')
# edges = collections.defaultdict(list)
# for edge in graph.get_edge_list():
#     edges[edge.get_source()].append(int(edge.get_destination()))

# for edge in edges:
#     edges[edge].sort()    
#     for i in range(2):
#         dest = graph.get_node(str(edges[edge][i]))[0]

#         dest.set_fillcolor(colors[i])
# graph.write_png('tree.png')        

# from IPython.display import Image
# Image("tree.png")

graphviz.Source(dot_data)

2.1 泰坦尼克—决策树模型 - 图2

数据集

test.csv

train.csv