pandas dataframe 做机器学习训练数据=》直接使用iloc或者as_matrix即可
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样本示意，为kdd99数据源：
0,udp,private,SF,105,146,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0.00,0.00,0.00,0.00,1.00,0.00,0.00,255,254,1.00,0.01,0.00,0.00,0.00,0.00,0.00,0.00,normal. 0,udp,private,SF,105,146,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0.00,0.00,0.00,0.00,1.00,0.00,0.00,255,254,1.00,0.01,0.00,0.00,0.00,0.00,0.00,0.00,normal. 0,udp,private,SF,105,146,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0.00,0.00,0.00,0.00,1.00,0.00,0.00,255,254,1.00,0.01,0.00,0.00,0.00,0.00,0.00,0.00,normal. 0,udp,private,SF,105,146,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0.00,0.00,0.00,0.00,1.00,0.00,0.00,255,254,1.00,0.01,0.00,0.00,0.00,0.00,0.00,0.00,snmpgetattack. 0,udp,private,SF,105,146,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0.00,0.00,0.00,0.00,1.00,0.00,0.00,255,254,1.00,0.01,0.01,0.00,0.00,0.00,0.00,0.00,snmpgetattack. 0,udp,private,SF,105,146,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0.00,0.00,0.00,0.00,1.00,0.00,0.00,255,255,1.00,0.00,0.01,0.00,0.00,0.00,0.00,0.00,snmpgetattack. 0,udp,domain_u,SF,29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0.00,0.00,0.00,0.00,0.50,1.00,0.00,10,3,0.30,0.30,0.30,0.00,0.00,0.00,0.00,0.00,normal. 0,udp,private,SF,105,146,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0.00,0.00,0.00,0.00,1.00,0.00,0.00,255,253,0.99,0.01,0.00,0.00,0.00,0.00,0.00,0.00,normal. 0,udp,private,SF,105,146,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0.00,0.00,0.00,0.00,1.00,0.00,0.00,255,254,1.00,0.01,0.00,0.00,0.00,0.00,0.00,0.00,snmpgetattack. 0,tcp,http,SF,223,185,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,4,0.00,0.00,0.00,0.00,1.00,0.00,0.00,71,255,1.00,0.00,0.01,0.01,0.00,0.00,0.00,0.00,normal. 0,udp,private,SF,105,146,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0.00,0.00,0.00,0.00,1.00,0.00,0.00,255,254,1.00,0.01,0.00,0.00,0.00,0.00,0.00,0.00,snmpgetattack. 0,tcp,http,SF,230,260,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,19,0.00,0.00,0.00,0.00,1.00,0.00,0.11,3,255,1.00,0.00,0.33,0.07,0.33,0.00,0.00,0.00,normal. 0,udp,private,SF,105,146,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0.00,0.00,0.00,0.00,1.00,0.00,0.00,255,254,1.00,0.01,0.01,0.00,0.00,0.00,0.00,0.00,normal. 0,udp,private,SF,105,146,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0.00,0.00,0.00,0.00,1.00,0.00,0.00,255,252,0.99,0.01,0.00,0.00,0.00,0.00,0.00,0.00,snmpgetattack. 1,tcp,smtp,SF,3170,329,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,2,0.00,0.00,0.00,0.00,1.00,0.00,1.00,54,39,0.72,0.11,0.02,0.00,0.02,0.00,0.09,0.13,normal. 0,tcp,http,SF,297,13787,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,2,0.00,0.00,0.00,0.00,1.00,0.00,0.00,177,255,1.00,0.00,0.01,0.01,0.00,0.00,0.00,0.00,normal. 0,tcp,http,SF,291,3542,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,12,12,0.00,0.00,0.00,0.00,1.00,0.00,0.00,187,255,1.00,0.00,0.01,0.01,0.00,0.00,0.00,0.00,normal. 0,tcp,http,SF,295,753,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,21,22,0.00,0.00,0.00,0.00,1.00,0.00,0.09,196,255,1.00,0.00,0.01,0.01,0.00,0.00,0.00,0.00,normal. 0,udp,private,SF,105,146,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0.00,0.00,0.00,0.00,1.00,0.00,0.00,255,254,1.00,0.01,0.01,0.00,0.00,0.00,0.00,0.00,snmpgetattack. 0,udp,private,SF,105,146,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0.00,0.00,0.00,0.00,1.00,0.00,0.00,255,254,1.00,0.01,0.00,0.00,0.00,0.00,0.00,0.00,snmpgetattack. 0,tcp,http,SF,268,9235,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,5,0.00,0.00,0.00,0.00,1.00,0.00,0.00,58,255,1.00,0.00,0.02,0.05,0.00,0.00,0.00,0.00,normal. 0,udp,private,SF,105,146,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0.00,0.00,0.00,0.00,1.00,0.00,0.00,255,253,0.99,0.01,0.00,0.00,0.00,0.00,0.00,0.00,snmpgetattack. 0,tcp,http,SF,223,185,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,3,0.00,0.00,0.00,0.00,1.00,0.00,0.00,255,255,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,normal. 0,tcp,http,SF,227,8841,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,13,13,0.00,0.00,0.00,0.00,1.00,0.00,0.00,255,255,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,normal. 0,tcp,http,SF,222,19564,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,22,23,0.00,0.00,0.00,0.00,1.00,0.00,0.09,255,255,1.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,normal. 0,tcp,ftp_data,SF,740,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0.00,0.00,0.00,0.00,1.00,0.00,0.00,77,33,0.34,0.08,0.34,0.06,0.00,0.00,0.00,0.00,normal. 0,udp,private,SF,105,146,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0.00,0.00,0.00,0.00,1.00,0.00,0.00,255,254,1.00,0.01,0.00,0.00,0.00,0.00,0.00,0.00,normal. 0,tcp,ftp_data,SF,35195,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,10,0.00,0.00,0.00,0.00,1.00,0.00,0.00,92,44,0.43,0.07,0.43,0.05,0.00,0.00,0.00,0.00,normal. 0,tcp,ftp_data,SF,8325,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,20,0.00,0.00,0.00,0.00,1.00,0.00,0.00,103,54,0.49,0.06,0.49,0.04,0.00,0.00,0.00,0.00,normal.
代码：
# -*- coding:utf-8 -*- import re import matplotlib.pyplot as plt import os from sklearn.feature_extraction.text import CountVectorizer from sklearn import preprocessing from sklearn import cross_validation import os from sklearn.datasets import load_iris from sklearn import tree import pydotplus from sklearn.preprocessing import LabelEncoder import numpy as np import pandas as pd from sklearn_pandas import DataFrameMapper def label(x): if x == "normal.": return 0 else: return 1 if __name__ == ‘__main__‘: data = pd.read_csv(‘../data/kddcup99/corrected‘, sep=",", header=None) print data.columns print data.iloc[0,0], data.iloc[0,1] print len(data) col_cnt = len(data.columns) normal = data.loc[data.loc[:, col_cnt-1] == "normal.", :] print "normal len:", len(normal) guess = data.loc[data.loc[:, col_cnt-1] == "guess_passwd.", :] print "normal len:", len(guess) data = pd.concat([normal, guess]) print len(data) le = preprocessing.LabelEncoder() for i in range(col_cnt-1): if isinstance(data.iloc[0,i], str): print "tranform string column only:", i data.loc[:,i] = le.fit_transform(data.loc[:,i]) data.loc[:,col_cnt-1] = data.loc[:,col_cnt-1].apply(label) print data.iloc[0,0], data.iloc[0,1] x = data.iloc[:, range(col_cnt-1)] #x = data.iloc[:, [0,4,5,6,7,8,22,23,24,25,26,27,28,29,30]] y = data.iloc[:, col_cnt-1]
‘‘‘ also OK
data = data.as_matrix()
x = data[:, range(col_cnt-1)]
y = data[:, col_cnt-1]
‘‘‘
print "x=>" print x.iloc[0:3, :] print "y=>" print y[-3:] #v=load_kdd99("../data/kddcup99/corrected") #x,y=get_guess_passwdandNormal(v) clf = tree.DecisionTreeClassifier() clf = clf.fit(x, y) print clf print cross_validation.cross_val_score(clf, x, y, n_jobs=-1, cv=10) clf = clf.fit(x, y) dot_data = tree.export_graphviz(clf, out_file=None) graph = pydotplus.graph_from_dot_data(dot_data) graph.write_pdf("../photo/6/iris-dt.pdf")
结果：
Int64Index([ 0, 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], dtype=‘int64‘) 0 udp 311029 normal len: 60593 normal len: 4367 64960 tranform string column only: 1 tranform string column only: 2 tranform string column only: 3 0 2 x=> 0 1 2 3 4 5 6 7 8 9 ... 31 32 33 34 35 0 0 2 15 7 105 146 0 0 0 0 ... 255 254 1.0 0.01 0.0 1 0 2 15 7 105 146 0 0 0 0 ... 255 254 1.0 0.01 0.0 2 0 2 15 7 105 146 0 0 0 0 ... 255 254 1.0 0.01 0.0 36 37 38 39 40 0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0.0 [3 rows x 41 columns] y=> 142098 1 142099 1 142101 1 Name: 41, dtype: int64 DecisionTreeClassifier(class_weight=None, criterion=‘gini‘, max_depth=None, max_features=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, presort=False, random_state=None, splitter=‘best‘) fg[ 0.9561336 0.99892258 0.99938433 0.99984606 0.99984606 0.99969212 1. 0.99984604 0.99969207 1. ]
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