Supervised Learning Model-Classification Learning
Author: Xie Zhong-zhao
1. Logisitic Regression
#导入pandas与numpy工具包
import pandas as pd
import numpy as np
#创建特征列表
column_names = ['sample code number','clump thickness','uniformity of cell size','uniformity of cell shape',
'marginal adhesion','single epithelial cell size','bare nuclei','bland chromatin','normal nucleoli',
'mitoses','class']
'''
#使用pandas.read_csv函数从互联网读取指定数据
'''
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data'
, names = column_names)
#打印完整数据的行列数
print(data.shape)
#将？替换为标准缺失值表示
data = data.replace(to_replace= '?', value=np.nan)
#丢弃带有缺失值的数据（只要有一个维度有缺失）
data = data.dropna(how = 'any')
#输出data的数据量和维度
print(data.shape)
'''
#准备训练数据和测试数据，通常情况下，25%的数据会做测试集，其余75%的数据用作训练集
'''
#使用sklearn.cross_validation里面的train_test_split模块用于数据分割
from sklearn.cross_validation import train_test_split
#随机采样25%的数据用于测试，剩下75%用于构建训练集合
X_train, X_test, y_train, y_test = train_test_split(data[column_names[1:10]],data[column_names[10]],test_size=0.25,random_state=33)
#查验训练样本的数量和类别分布
print(y_train.value_counts())
#查验测试样本的数量和类别分布
print(y_test.value_counts())
#从sklearn.preprocessing里导入standarscaler
from sklearn.preprocessing import StandardScaler
#从sklearn.linear_model里导入LogisticRegression与SGDClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGDClassifier
'''
#标准化数据，保证每一个维度的特征数据的方差为1，均值为0，使得预测结果不会被某些维度过大的特征值而主导
'''
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.fit_transform(X_test)
'''
初始化LogisticRegression与SGDClassifier
'''
lr = LogisticRegression()
sgdc = SGDClassifier()
#调用LogisticRegression中的fit函数/模块用来训练参数模型参数
lr.fit(X_train,y_train)
#使用训练好的模型lr对X_test进行预测，结果存储在变量lr_y_predict中
lr_y_predict = lr.predict(X_test)
#调用SGDClassifier中的fit函数/模块来训练参数模型参数
sgdc.fit(X_train,y_train)
#使用训练好的模型sgdc对X_test进行预测，结果储存在变量sgdc_y_predict中
sgdc_y_predict = sgdc.predict(X_test)
'''
使用线性分类模型对预测任务进行性能分析
'''
#从sklearn.metrics里导入classification_report模块
from sklearn.metrics import classification_report
#使用logisticRegression模型自带的评分函数score来获得模型在测试集上准确性结果
print('Accuracy of LR Classifier:', lr.score(X_test,y_test))
#使用classification_report模块来获得LogisticRegression其他三个指标的结果
print(classification_report(y_test,lr_y_predict,target_names=['benign','malignant']))
#使用SGD模型自带的评分函数score来获得模型在测试集上准确性结果
print('Accuracy of SGD Classifier:', sgdc.score(X_test,y_test))
#使用classification_report模块来获得SGD Classifier其他三个指标的结果
print(classification_report(y_test,sgdc_y_predict,target_names=['benign','malignant']))
(699, 11)
(683, 11)
2 344
4 168
Name: class, dtype: int64
2 100
4 71
Name: class, dtype: int64
Accuracy of LR Classifier: 0.970760233918
precision recall f1-score support
benign 0.96 0.99 0.98 100
malignant 0.99 0.94 0.96 71
avg / total 0.97 0.97 0.97 171
Accuracy of SGD Classifier: 0.976608187135
precision recall f1-score support
benign 0.97 0.99 0.98 100
malignant 0.99 0.96 0.97 71
avg / total 0.98 0.98 0.98 171
2. Support Vector Classification
#从sklearn.datasets里面导入手写体数字加载器
from sklearn.datasets import load_digits
#将加载的图像数据存储在digits上
digit = load_digits()
#检查数据的规模和特征维度
print(digit.data.shape)
'''
手写体数据分割代码样例
'''
#从sklearn.cross_validation中导入train_test_split用于数据分割
from sklearn.cross_validation import train_test_split
#随机选取75%数据作为训练样本，其余的25%作为测试样本
X_train,X_test,y_train,y_test = train_test_split(digit.data,digit.target,test_size=0.25,random_state=33)
#分别检验训练和测试数据的规模
print(y_train.shape)
print(y_test.shape)
'''
使用支持向量机（分类）对手写数字图像进行识别
'''
#从sklearn.precessing里导入数据标准化模块
from sklearn.preprocessing import StandardScaler
#从sklearn.svm里面导入基于线性假设的支持向量分类器LinearSVC
from sklearn.svm import LinearSVC
'''
对训练和测试的特征数据进行标准化
'''
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)
'''
支持向量机分类器LinearSVC初始化和模型训练
'''
#初始化线性假设支持向量机分类器LinearSVC
lsvc = LinearSVC()
#进行模型训练
lsvc.fit(X_train,y_train)
#利用训练好的模型对测试样本的数字类别进行预测，预测结果存储在变量y_predict中
y_predict = lsvc.predict(X_test)
'''
使用模型自带的评估函数进行准确性测评
'''
print('The Accuracy of Linear SVC is',lsvc.score(X_test,y_test))
#依然使用sklearn.metrics里面的classification_report模块对预测结果做更详细的分析
from sklearn.metrics import classification_report
print(classification_report(y_test,y_predict,target_names=digit.target_names.astype(str)))
(1797, 64)
(1347,)
(450,)
The Accuracy of Linear SVC is 0.953333333333
precision recall f1-score support
0 0.92 1.00 0.96 35
1 0.96 0.98 0.97 54
2 0.98 1.00 0.99 44
3 0.93 0.93 0.93 46
4 0.97 1.00 0.99 35
5 0.94 0.94 0.94 48
6 0.96 0.98 0.97 51
7 0.92 1.00 0.96 35
8 0.98 0.84 0.91 58
9 0.95 0.91 0.93 44
avg / total 0.95 0.95 0.95 450
3. Naive Bayes
#从sklearn.datasets里面导入新闻数据抓取器fetch_20newsgroups
from sklearn.datasets import fetch_20newsgroups
#与之前预存的数据不同，fetch_20newsgroups需要即时从互联网下载数据
news = fetch_20newsgroups(subset='all')
#查验数据的规模和细节
print(len((news.data)))
print(news.data[1])
'''
20类新闻文本数据分割
'''
#从sklearn.cross_validation导入train_test_split
from sklearn.cross_validation import train_test_split
X_train,X_test,y_train,y_test = train_test_split(news.data,news.target,test_size=0.25,random_state=33)
'''
用朴素贝叶斯分类器对新闻文本数据进行类别预测
'''
#从sklearn.feature_extraction.text里导入用于文本特征向量转化模块
from sklearn.feature_extraction.text import CountVectorizer
vec = CountVectorizer()
X_train = vec.fit_transform(X_train)
X_test = vec.transform(X_test)
#从sklearn.naive_bayes里导入朴素贝叶斯模型
from sklearn.naive_bayes import MultinomialNB
#使用默认初始化朴素贝叶斯模型
mnb = MultinomialNB()
#利用训练数据对模型的参数进行估计
mnb.fit(X_train,y_train)
#对测试的样本进行类别预测，结果存储在变量y_predict
y_predict = mnb.predict(X_test)
'''
对朴素贝叶斯分类器在新闻文本数据上的表现性能进行评估
'''
#从sklearn.metrics里面导入classification_report用来详细的分类性能报告
from sklearn.metrics import classification_report
print('The accuracy of naive bayes classifier is',mnb.score(X_test,y_test))
print(classification_report(y_test,y_predict,target_names=news.target_names))
18846
From: mblawson@midway.ecn.uoknor.edu (Matthew B Lawson)
Subject: Which high-performance VLB video card?
Summary: Seek recommendations for VLB video card
Nntp-Posting-Host: midway.ecn.uoknor.edu
Organization: Engineering Computer Network, University of Oklahoma, Norman, OK, USA
Keywords: orchid, stealth, vlb
Lines: 21
My brother is in the market for a high-performance video card that supports
VESA local bus with 1-2MB RAM. Does anyone have suggestions/ideas on:
- Diamond Stealth Pro Local Bus
- Orchid Farenheit 1280
- ATI Graphics Ultra Pro
- Any other high-performance VLB card
Please post or email. Thank you!
- Matt
--
| Matthew B. Lawson <------------> (mblawson@essex.ecn.uoknor.edu) |
--+-- "Now I, Nebuchadnezzar, praise and exalt and glorify the King --+--
| of heaven, because everything he does is right and all his ways |
| are just." - Nebuchadnezzar, king of Babylon, 562 B.C. |
The accuracy of naive bayes classifier is 0.839770797963
precision recall f1-score support
alt.atheism 0.86 0.86 0.86 201
comp.graphics 0.59 0.86 0.70 250
comp.os.ms-windows.misc 0.89 0.10 0.17 248
comp.sys.ibm.pc.hardware 0.60 0.88 0.72 240
comp.sys.mac.hardware 0.93 0.78 0.85 242
comp.windows.x 0.82 0.84 0.83 263
misc.forsale 0.91 0.70 0.79 257
rec.autos 0.89 0.89 0.89 238
rec.motorcycles 0.98 0.92 0.95 276
rec.sport.baseball 0.98 0.91 0.95 251
rec.sport.hockey 0.93 0.99 0.96 233
sci.crypt 0.86 0.98 0.91 238
sci.electronics 0.85 0.88 0.86 249
sci.med 0.92 0.94 0.93 245
sci.space 0.89 0.96 0.92 221
soc.religion.christian 0.78 0.96 0.86 232
talk.politics.guns 0.88 0.96 0.92 251
talk.politics.mideast 0.90 0.98 0.94 231
talk.politics.misc 0.79 0.89 0.84 188
talk.religion.misc 0.93 0.44 0.60 158
avg / total 0.86 0.84 0.82 4712
4. KNN
#读取Iris数据集细节资料
#从sklearn.datasets导入iris数据加载器
from sklearn.datasets import load_iris
#s使用加载器读取数据并且存入变量iris
iris = load_iris()
#查看数据规模
print(iris.data.shape)
#查看数据说明，对于一个机器学习实践者来讲，这是一个好习惯
print(iris.DESCR)
'''
对iris数据集进行分割
'''
from sklearn.cross_validation import train_test_split
X_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=0.25,random_state=33)
'''
用KNN对iris数据进行类别预测
'''
#从sklearn.precessing里导入数据标准化模块
from sklearn.preprocessing import StandardScaler
#从sklearn.neighbors里选择导入KNeighborsClassifier,K近领分类器
from sklearn.neighbors import KNeighborsClassifier
#对训练和测试集进行标准化
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)
#使用K近邻分类器对测试的数据进行类别预测，预测结果存储在y_predict中
knc = KNeighborsClassifier()
knc.fit(X_train,y_train)
y_predict = knc.predict(X_test)
'''
对KNN在iris数据上的预测性能进行评估
'''
#使用模型自带的评估函数进行准确性测评
print('the accuracy of K-Nearest Neighbor Classifier is',knc.score(X_train,y_train))
#依然sklearn.metrics里面的classification_report模块对预测结果进行详细的分析
#依然使用sklearn.metrics里面的classification_report模块对预测的结果做出更加详细分析
from sklearn.metrics import classification_report
print(classification_report(y_test,y_predict,target_names=iris.target_names))
(150, 4)
Iris Plants Database
Notes
-----
Data Set Characteristics:
:Number of Instances: 150 (50 in each of three classes)
:Number of Attributes: 4 numeric, predictive attributes and the class
:Attribute Information:
- sepal length in cm
- sepal width in cm
- petal length in cm
- petal width in cm
- class:
- Iris-Setosa
- Iris-Versicolour
- Iris-Virginica
:Summary Statistics:
============== ==== ==== ======= ===== ====================
Min Max Mean SD Class Correlation
============== ==== ==== ======= ===== ====================
sepal length: 4.3 7.9 5.84 0.83 0.7826
sepal width: 2.0 4.4 3.05 0.43 -0.4194
petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
============== ==== ==== ======= ===== ====================
:Missing Attribute Values: None
:Class Distribution: 33.3% for each of 3 classes.
:Creator: R.A. Fisher
:Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
:Date: July, 1988
This is a copy of UCI ML iris datasets.
http://archive.ics.uci.edu/ml/datasets/Iris
The famous Iris database, first used by Sir R.A Fisher
This is perhaps the best known database to be found in the
pattern recognition literature. Fisher's paper is a classic in the field and
is referenced frequently to this day. (See Duda & Hart, for example.) The
data set contains 3 classes of 50 instances each, where each class refers to a
type of iris plant. One class is linearly separable from the other 2; the
latter are NOT linearly separable from each other.
References
----------
- Fisher,R.A. "The use of multiple measurements in taxonomic problems"
Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
Mathematical Statistics" (John Wiley, NY, 1950).
- Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
(Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
- Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
Structure and Classification Rule for Recognition in Partially Exposed
Environments". IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. PAMI-2, No. 1, 67-71.
- Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions
on Information Theory, May 1972, 431-433.
- See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II
conceptual clustering system finds 3 classes in the data.
- Many, many more ...
the accuracy of K-Nearest Neighbor Classifier is 0.973214285714
precision recall f1-score support
setosa 1.00 1.00 1.00 8
versicolor 0.73 1.00 0.85 11
virginica 1.00 0.79 0.88 19
avg / total 0.92 0.89 0.90 38
5. Decision Tree
#泰坦尼克号乘客数据查验
import pandas as pd
#利用pandas的read_csv模块直接从互联网收集泰坦尼克号乘客数据
titanic = pd.read_csv('http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt')
#观察前几行数据，可以发现数据种类各异，数值型，类别型，甚至还有缺失数据
print(titanic.head())
#使用pandas,数据都转入pandas独有的dataframe格式（二维数据表格），直接使用info,查看数据的统计特性
print(titanic.info())
#相当重要的环节- 特征选择，基于一些背景知识，根据对这场故事的了解，sex,age,pclass这些特征都很有可能决定幸免的关键因素
X = titanic[['pclass','age','sex']]
y = titanic['survived']
#对当前选择的特征进行探查
print(X.info())
'''
借助上面的输出，我们设计如下几个数据处理的任务
(1)age这个数据列，只有633个，需要补充完整-用均值替代
(2)sex与pclass两个数据列的值都是类别型的，需要转化为数值特征，用0/1替代
'''
#首先我们补充age里的数据，使用平均数或者中位数都是对模型偏离造成最小影响的策略
print(X['age'].fillna(X['age'].mean(),inplace=True))
#对补完的数据重新探查
print(X.info())
print(X['age'])
print(X['sex'])
print(X['pclass'])
'''
进行数据分割和特征抽取
'''
#数据分割
from sklearn.cross_validation import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=33)
#s使用scikit-learn.feature_extraction中特征转化器
from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer(sparse=False)
#转换特征后，我们发现凡是类别型的特征都单独剥离出来，独成一列特征，数值型的则保持不变
X_train = vec.fit_transform(X_train.to_dict(orient='record'))
print(vec.feature_names_)
#同样需要对测试数据的特征进行转换
X_test = vec.transform(X_test.to_dict(orient='record'))
print(X_train)
print('training samples is',X_train.shape)
print('training samples is',X_test.shape)
'''
导入决策树分类器，使用分割到的训练数据进行模型学习
用训练好的决策树模型对测试特征数据进行预测
'''
#从sklearn.tree中导入决策树分类器
from sklearn.tree import DecisionTreeClassifier
#使用默认配置初始化决策树分类器
dtc = DecisionTreeClassifier()
#使用分割的训练数据进行模型学习
dtc.fit(X_train,y_train)
#用训练好的决策树模型对测试特征数据进行预测
y_predict = dtc.predict(X_test)
print(y_predict)
print(y_test)
'''
决策树模型对泰坦尼克号乘客是否生还的预测功能
'''
#从sklearn.metrics导入classification_report
from sklearn.metrics import classification_report
#输出预测准确性
print('the accuracy of Desion Tree is',dtc.score(X_test,y_test))
#输出更加详细的分类性能
print(classification_report(y_predict,y_test,target_names=['died','survived']))
row.names pclass survived \
0 1 1st 1
1 2 1st 0
2 3 1st 0
3 4 1st 0
4 5 1st 1
name age embarked \
0 Allen, Miss Elisabeth Walton 29.0000 Southampton
1 Allison, Miss Helen Loraine 2.0000 Southampton
2 Allison, Mr Hudson Joshua Creighton 30.0000 Southampton
3 Allison, Mrs Hudson J.C. (Bessie Waldo Daniels) 25.0000 Southampton
4 Allison, Master Hudson Trevor 0.9167 Southampton
home.dest room ticket boat sex
0 St Louis, MO B-5 24160 L221 2 female
1 Montreal, PQ / Chesterville, ON C26 NaN NaN female
2 Montreal, PQ / Chesterville, ON C26 NaN (135) male
3 Montreal, PQ / Chesterville, ON C26 NaN NaN female
4 Montreal, PQ / Chesterville, ON C22 NaN 11 male
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1313 entries, 0 to 1312
Data columns (total 11 columns):
row.names 1313 non-null int64
pclass 1313 non-null object
survived 1313 non-null int64
name 1313 non-null object
age 633 non-null float64
embarked 821 non-null object
home.dest 754 non-null object
room 77 non-null object
ticket 69 non-null object
boat 347 non-null object
sex 1313 non-null object
dtypes: float64(1), int64(2), object(8)
memory usage: 112.9+ KB
None
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1313 entries, 0 to 1312
Data columns (total 3 columns):
pclass 1313 non-null object
age 633 non-null float64
sex 1313 non-null object
dtypes: float64(1), object(2)
memory usage: 30.9+ KB
None
C:\Users\xxz\Anaconda3\lib\site-packages\pandas\core\generic.py:3191: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
self._update_inplace(new_data)
None
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1313 entries, 0 to 1312
Data columns (total 3 columns):
pclass 1313 non-null object
age 1313 non-null float64
sex 1313 non-null object
dtypes: float64(1), object(2)
memory usage: 30.9+ KB
None
0 29.000000
1 2.000000
2 30.000000
3 25.000000
4 0.916700
5 47.000000
6 63.000000
7 39.000000
8 58.000000
9 71.000000
10 47.000000
11 19.000000
12 31.194181
13 31.194181
14 31.194181
15 50.000000
16 24.000000
17 36.000000
18 37.000000
19 47.000000
20 26.000000
21 25.000000
22 25.000000
23 19.000000
24 28.000000
25 45.000000
26 39.000000
27 30.000000
28 58.000000
29 31.194181
...
1283 31.194181
1284 31.194181
1285 31.194181
1286 31.194181
1287 31.194181
1288 31.194181
1289 31.194181
1290 31.194181
1291 31.194181
1292 31.194181
1293 31.194181
1294 31.194181
1295 31.194181
1296 31.194181
1297 31.194181
1298 31.194181
1299 31.194181
1300 31.194181
1301 31.194181
1302 31.194181
1303 31.194181
1304 31.194181
1305 31.194181
1306 31.194181
1307 31.194181
1308 31.194181
1309 31.194181
1310 31.194181
1311 31.194181
1312 31.194181
Name: age, dtype: float64
0 female
1 female
2 male
3 female
4 male
5 male
6 female
7 male
8 female
9 male
10 male
11 female
12 female
13 male
14 male
15 female
16 male
17 male
18 male
19 female
20 male
21 male
22 male
23 female
24 male
25 male
26 male
27 female
28 female
29 male
...
1283 female
1284 male
1285 male
1286 male
1287 male
1288 male
1289 male
1290 male
1291 male
1292 male
1293 female
1294 male
1295 male
1296 male
1297 male
1298 male
1299 male
1300 male
1301 male
1302 male
1303 male
1304 female
1305 male
1306 female
1307 female
1308 male
1309 male
1310 male
1311 female
1312 male
Name: sex, dtype: object
0 1st
1 1st
2 1st
3 1st
4 1st
5 1st
6 1st
7 1st
8 1st
9 1st
10 1st
11 1st
12 1st
13 1st
14 1st
15 1st
16 1st
17 1st
18 1st
19 1st
20 1st
21 1st
22 1st
23 1st
24 1st
25 1st
26 1st
27 1st
28 1st
29 1st
...
1283 3rd
1284 3rd
1285 3rd
1286 3rd
1287 3rd
1288 3rd
1289 3rd
1290 3rd
1291 3rd
1292 3rd
1293 3rd
1294 3rd
1295 3rd
1296 3rd
1297 3rd
1298 3rd
1299 3rd
1300 3rd
1301 3rd
1302 3rd
1303 3rd
1304 3rd
1305 3rd
1306 3rd
1307 3rd
1308 3rd
1309 3rd
1310 3rd
1311 3rd
1312 3rd
Name: pclass, dtype: object
['age', 'pclass=1st', 'pclass=2nd', 'pclass=3rd', 'sex=female', 'sex=male']
[[ 31.19418104 0. 0. 1. 0. 1. ]
[ 31.19418104 1. 0. 0. 1. 0. ]
[ 31.19418104 0. 0. 1. 0. 1. ]
...,
[ 12. 0. 1. 0. 1. 0. ]
[ 18. 0. 1. 0. 0. 1. ]
[ 31.19418104 0. 0. 1. 1. 0. ]]
training samples is (984, 6)
training samples is (329, 6)
[0 1 0 0 0 1 1 0 0 1 0 1 1 0 0 1 0 1 0 1 1 1 0 0 0 0 1 1 0 0 1 0 0 0 1 1 1
0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0
0 1 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0
1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 1
1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0
0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 1 1 1 0
0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 1 1 1 0 0 0 1 1 0 0
1 1 0 0 0 1 0 0 0 1 0 0 0 1 1 0 0 0 0 1 0 1 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0
1 0 0 0 1 1 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0]
386 0
89 1
183 1
746 0
1211 0
384 1
729 1
1076 0
1290 0
385 1
722 1
169 1
275 0
485 0
790 0
159 1
302 0
296 1
1208 0
428 1
745 0
109 1
1300 0
906 1
1291 0
32 1
584 1
585 1
1250 0
44 0
..
414 0
241 1
31 1
764 0
932 0
1226 1
657 1
256 0
803 0
677 0
1279 1
396 1
462 1
693 0
927 0
628 1
506 0
371 0
1198 0
232 1
709 0
508 0
282 1
698 0
1049 0
1048 0
106 1
618 0
175 0
937 0
Name: survived, dtype: int64
the accuracy of Desion Tree is 0.781155015198
precision recall f1-score support
died 0.91 0.78 0.84 236
survived 0.58 0.80 0.67 93
avg / total 0.81 0.78 0.79 329
6. Ensemble Classification
import pandas as pd
'''
通过互联网读取泰坦尼克号乘客文档，并存储在变量titanic中
'''
titanic = pd.read_csv('http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt')
'''
人工选取pclass,age以及sex作为乘客生还的特征
'''
X = titanic[['pclass','age','sex']]
y = titanic['survived']
'''
对于缺失的年龄信息，我们使用全体乘客的平均年龄替代，这样可以在保证顺序训练模型的同时，尽可能不影响预测任务
'''
X['age'].fillna(X['age'].mean(),inplace=True)
'''
对原始数据进行分割，25%的乘客数据用于测试
'''
from sklearn.cross_validation import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=33)
'''
对类别型特征进行转化，成为特征向量
'''
from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer(sparse=False)
X_train = vec.fit_transform(X_train.to_dict(orient='record'))
X_test = vec.transform(X_test.to_dict(orient='record'))
'''
使用单一决策树进行模型训练以及预测分析
'''
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier()
dtc.fit(X_train,y_train)
dtc_y_pred = dtc.predict(X_test)
'''
使用随机森林分类器进行集成模型的训练以及预测分析
'''
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier()
rfc.fit(X_train,y_train)
rfc_y_pred = rfc.predict(X_test)
'''
使用梯度提升决策树进行集成模型训练和预测
'''
from sklearn.ensemble import GradientBoostingClassifier
gbc = GradientBoostingClassifier()
gbc.fit(X_train,y_train)
gbc_y_pred = gbc.predict(X_test)
'''
集成模型对泰坦尼克号乘客是否生还的预测性能
'''
#从sklearn.metrics导入classification_report
from sklearn.metrics import classification_report
#输出单一决策在测试集上的准确率，以及详细的精确率，召回率，F1指标
print('the accuracy of decision tree is',dtc.score(X_test,y_test))
print(classification_report(dtc_y_pred,y_test,target_names=['died','survived']))
#输出随机森林分类器在测试集上的准确率，以及详细的精确率，召回率，F1指标
print('the accuracy of random forest classifier is',rfc.score(X_test,y_test))
print(classification_report(rfc_y_pred,y_test,target_names=['died','survived']))
#输出梯度提升决策树在测试集上的准确率，以及详细的精确率，召回率，F1指标
print('the accuracy of decision tree is',gbc.score(X_test,y_test))
print(classification_report(gbc_y_pred,y_test,target_names=['died','survived']))
C:\Users\xxz\Anaconda3\lib\site-packages\pandas\core\generic.py:3191: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
self._update_inplace(new_data)
the accuracy of decision tree is 0.781155015198
precision recall f1-score support
died 0.91 0.78 0.84 236
survived 0.58 0.80 0.67 93
avg / total 0.81 0.78 0.79 329
the accuracy of random forest classifier is 0.775075987842
precision recall f1-score support
died 0.90 0.77 0.83 236
survived 0.57 0.78 0.66 93
avg / total 0.81 0.78 0.78 329
the accuracy of decision tree is 0.790273556231
precision recall f1-score support
died 0.92 0.78 0.84 239
survived 0.58 0.82 0.68 90
avg / total 0.83 0.79 0.80 329
