├── .gitattributes ├── .gitignore ├── README.md ├── predictData.txt ├── test.txt ├── testTrain.txt ├── weibo.py ├── 文件说明.txt ├── 训练说明.txt └── 说明.txt /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | 4 | # Custom for Visual Studio 5 | *.cs diff=csharp 6 | 7 | # Standard to msysgit 8 | *.doc diff=astextplain 9 | *.DOC diff=astextplain 10 | *.docx diff=astextplain 11 | *.DOCX diff=astextplain 12 | *.dot diff=astextplain 13 | *.DOT diff=astextplain 14 | *.pdf diff=astextplain 15 | *.PDF diff=astextplain 16 | *.rtf diff=astextplain 17 | *.RTF diff=astextplain 18 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | 5 | # C extensions 6 | *.so 7 | 8 | # Distribution / packaging 9 | .Python 10 | env/ 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | lib/ 17 | lib64/ 18 | parts/ 19 | sdist/ 20 | var/ 21 | *.egg-info/ 22 | .installed.cfg 23 | *.egg 24 | 25 | # PyInstaller 26 | # Usually these files are written by a python script from a template 27 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 28 | *.manifest 29 | *.spec 30 | 31 | # Installer logs 32 | pip-log.txt 33 | pip-delete-this-directory.txt 34 | 35 | # Unit test / coverage reports 36 | htmlcov/ 37 | .tox/ 38 | .coverage 39 | .cache 40 | nosetests.xml 41 | coverage.xml 42 | 43 | # Translations 44 | *.mo 45 | *.pot 46 | 47 | # Django stuff: 48 | *.log 49 | 50 | # Sphinx documentation 51 | docs/_build/ 52 | 53 | # PyBuilder 54 | target/ 55 | 56 | # ========================= 57 | # Operating System Files 58 | # ========================= 59 | 60 | # OSX 61 | # ========================= 62 | 63 | .DS_Store 64 | .AppleDouble 65 | .LSOverride 66 | 67 | # Thumbnails 68 | ._* 69 | 70 | # Files that might appear on external disk 71 | .Spotlight-V100 72 | .Trashes 73 | 74 | # Directories potentially created on remote AFP share 75 | .AppleDB 76 | .AppleDesktop 77 | Network Trash Folder 78 | Temporary Items 79 | .apdisk 80 | 81 | # Windows 82 | # ========================= 83 | 84 | # Windows image file caches 85 | Thumbs.db 86 | ehthumbs.db 87 | 88 | # Folder config file 89 | Desktop.ini 90 | 91 | # Recycle Bin used on file shares 92 | $RECYCLE.BIN/ 93 | 94 | # Windows Installer files 95 | *.cab 96 | *.msi 97 | *.msm 98 | *.msp 99 | 100 | # Windows shortcuts 101 | *.lnk 102 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # basic_of_datamining 2 | basic_of_datamining 3 | 利用python中的pandas，sklearn进行数据挖掘 4 | 5 | pandas用来对数据集进行处理 6 | sklearn中提供了一些机器学习方法的实现 7 | 8 | 要利用这两个库，首先应该安装，其中numpy，scipy是两个比较重要的依赖库 9 | 10 | 在Ubuntu下，pandas安装可以使用： 11 | sudo apt-get install python-pandas 12 | 13 | 14 | sklearn的安装可以参照： 15 | http://blog.csdn.net/wbgxx333/article/details/12168675 16 | 17 | 18 | 19 | 20 | 一个数据挖掘的整体流程，主要包括 21 | 1. 定义问题 22 | 2. 准备数据 23 | 3. 浏览数据 24 | 4. 生成模型 25 | 5. 浏览和验证模型 26 | 6. 部署和更新模型 27 | 28 | 而在实现时，通俗的讲为： 29 | 30 | 1 加载训练数据 31 | 2 对数据进行各种预处理（抽样，去噪等） 32 | 3 提取特征 33 | 4 训练模型 34 | 5 利用模型对预测数据进行预测 35 | 6 预测结果评价（评分） 36 | 不断调整2 3 4 5 6步，进行优化 37 | 38 | 下面的例子，实现了这样一个基本流程 39 | 提供 刚刚入门 数据挖掘 概念者 40 | 41 | 一个较为基础的python+pandas+sklearn实现 42 | 43 | 来自一个比赛： 44 | http://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.333.4.scNYuk&raceId=5 45 | 46 | 对问题进行了简化处理，并没有将微博的内容和时间考虑在内 47 | 48 | 49 | 是对其比较初步的处理 50 | 51 | 52 | 数据来源是 来自阿里巴巴天池平台和新浪微博 53 | 54 | 这个地址：http://tianchi.aliyun.com/datalab/index.htm?spm=5176.100067.1234.4.t8WkFa（以后会更新） 55 | 56 | 例子的地址在： 57 | 58 | 59 | 在这个地址中文件，有相应数据文件和源码，同时会有对有文件和源码的解释 60 | 源码中，因为是针对特定问题的，所以一些对数据的加载、处理 或 评分细节不必深究， 61 | 重要的是整体的流程 62 | 源码中保留了一些被注释了的代码，大多是一些调试代码 63 | 64 | 对应的测试大数据集合下载地址： 65 | 66 | http://pan.baidu.com/s/1qW49CcO 67 | http://download.csdn.net/detail/xinxing__8185/9270655 68 | -------------------------------------------------------------------------------- /test.txt: -------------------------------------------------------------------------------- 1 | 07fc721342df1a4c1992560b582992f8 5fefc2488f99aba5782baa43096b76dc 2014-11-04 0 7 2 2 | 07fc721342df1a4c1992560b582992f8 397d9abe2ebf9ba986fe5ab5a6139e11 2014-11-04 0 3 0 3 | 07fc721342df1a4c1992560b582992f8 a2ae3959e9aa21a47993506e2609a4ec 2014-11-08 0 0 0 4 | 07fc721342df1a4c1992560b582992f8 fcc63a086522040a271ba6d1fb604734 2014-11-09 0 6 0 5 | 07fc721342df1a4c1992560b582992f8 6ab0f93ab59cbdaf25f4a023642971a6 2014-11-11 0 5 1 6 | 07fc721342df1a4c1992560b582992f8 e0fc906adc95874d634897294c3eebfa 2014-11-13 0 0 0 7 | 07fc721342df1a4c1992560b582992f8 dfa0674fd2a0a7779a323c43b9d10e0c 2014-11-13 0 0 0 8 | 07fc721342df1a4c1992560b582992f8 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create time: 2015-09 7 | 8 | 实现了一个基本数据挖掘的流程，包括 9 | 1 加载训练数据 10 | 2 对数据进行各种预处理（抽样，去噪等） 11 | 3 提取特征 12 | 4 训练模型 13 | 5 利用模型对预测数据进行预测 14 | 6 预测结果评价（评分） 15 | 16 | 提供 刚刚入门 数据挖掘 概念者 17 | 18 | 一个较为基础的python+pandas+sklearn实现 19 | 20 | 注：从 __main__ 看起 21 | """ 22 | 23 | from pandas import Series, DataFrame 24 | import pandas as pd 25 | from sklearn import datasets, linear_model 26 | import numpy as np 27 | import datetime 28 | 29 | import time 30 | import math 31 | 32 | def loadData(filename): #加载数据 33 | dicData = {} 34 | uid = [] #用户id 35 | mid = [] #微博id 36 | datatime = [] #时间 37 | forward = [] #转发量 38 | comment = [] #评论量 39 | like = [] #赞 量 40 | content = [] #内容 41 | with open(filename) as f: 42 | for line in f: 43 | #print line 44 | lineSplit = line.strip().split('\t') 45 | #print lineSplit 46 | #根据需求加载相应的数据 47 | ''' 48 | if len(lineSplit) == 7: 49 | uid.append(lineSplit[0]) 50 | #mid.append(lineSplit[1]) 51 | #datatime.append(lineSplit[2]) 52 | forward.append(int(lineSplit[3])) 53 | comment.append(int(lineSplit[4])) 54 | like.append(int(lineSplit[5])) 55 | #content.append(lineSplit[6].strip()) 56 | ''' 57 | if len(lineSplit) == 6: 58 | uid.append(lineSplit[0]) 59 | #mid.append(lineSplit[1]) 60 | #datatime.append(lineSplit[2]) 61 | forward.append(int(lineSplit[3])) 62 | comment.append(int(lineSplit[4])) 63 | like.append(int(lineSplit[5])) 64 | #content.append(lineSplit[6].strip()) 65 | 66 | dicData['uid'] = np.array(uid) #转换为array 67 | #dicData['mid'] = np.array(mid) 68 | #dicData['datatime'] = np.array(datatime) 69 | dicData['forward'] = np.array(forward) 70 | dicData['comment'] = np.array(comment) 71 | dicData['like'] = np.array(like) 72 | #dicData['content'] = np.array(content) 73 | 74 | 75 | #print dicData 76 | return DataFrame(dicData) #使用DataFrame 进行数据格式化 77 | #print uid 78 | 79 | def loadPredictData(filename): #加载预测数据 80 | #预测的数据特征一部分来自训练数据(用户特征)，一部分来自本身（时间和文本） 81 | #保证 预测的特征 和 训练时的特征一一对应 82 | 83 | 84 | #获取用户特征 （保证预测集 全部在 训练集中出现） 85 | df = pd.read_csv('trainFeatures.csv') 86 | #print df 87 | FeaturesLen = len(df.values[0,:]) - 1 88 | #print len(df[0]) 89 | userFeature = {} 90 | for data in df.values: 91 | userFeature[data[0]] = data[1:] 92 | #print userFeature 93 | 94 | predictData = [] 95 | 96 | uidAndMid = [] 97 | 98 | 99 | keys = userFeature.keys() 100 | dickeys = dict(zip(keys, keys)) 101 | 102 | 103 | with open(filename) as f: #打开预测数据文件 104 | for line in f: 105 | #print line 106 | lineSplit = line.strip().split('\t') 107 | #print lineSplit 108 | if len(lineSplit) == 3: 109 | if lineSplit[0] in dickeys: 110 | data = [] 111 | uid = [] 112 | data.append(lineSplit[0]) 113 | data.append(lineSplit[1]) 114 | 115 | uid.append(lineSplit[0]) 116 | uid.append(lineSplit[1]) 117 | uidAndMid.append(uid) 118 | 119 | data.append(lineSplit[2]) 120 | 121 | data = data + list(userFeature[lineSplit[0]]) 122 | predictData.append(data) 123 | 124 | return predictData, FeaturesLen, uidAndMid 125 | 126 | 127 | def getFeatures(df): #获取训练数据特征 128 | #add one feature by column 129 | 130 | functions = ['count', 'mean', 'max', 'std', 'min', 'median'] #函数功能分别为计数，求平均，求最大，求标准差，最小，中值 131 | 132 | #functions = [ 'mean'] 133 | gourped = df.groupby('uid') 134 | 135 | re = gourped['like', 'forward', 'comment'].agg(functions) 136 | #print re 137 | #print re.unstack('uid') 138 | df = pd.merge(df, re, left_on='uid', right_index=True) #按uid合并 139 | #print df 140 | saveDf = df.copy() #拷贝 141 | 142 | del saveDf['comment'] 143 | del saveDf['forward'] 144 | del saveDf['like'] 145 | 146 | saveDf = saveDf.drop_duplicates() 147 | 148 | #存储的用户特征 149 | saveDf.to_csv('trainFeatures.csv', index=False) 150 | return df 151 | 152 | 153 | 154 | #type 1 is forward 155 | 156 | #type 2 is comment 157 | 158 | #type 3 is like 159 | def trainModel(df, Type): #训练模型 160 | print len(df) 161 | df = df.dropna() #过滤nan值 162 | print len(df) 163 | if Type == 1: 164 | train_x = np.array(df.values[:, 4:]) #特征列 165 | #print train_x 166 | #print train_x 167 | train_y = np.array(df.values[:, 1]) #目标列 168 | 169 | if Type == 2: 170 | train_x = df.values[:, 4:] 171 | #print train_x 172 | train_y = df.values[:, 0] 173 | 174 | if Type == 3: 175 | train_x = df.values[:, 4:] 176 | #print train_x 177 | train_y = df.values[:, 2] 178 | 179 | #print train_y 180 | # Create linear regression object 181 | regr = linear_model.LinearRegression() 182 | #regr = linear_model.Ridge (alpha = .5) 183 | #regr = linear_model.RidgeCV(alphas=[0.1, 1.0, 10.0]) 184 | #regr = linear_model.Lasso(alpha = 0.1) 185 | #regr = linear_model.LassoLars(alpha=.1) 186 | 187 | #regr = linear_model.BayesianRidge() 188 | # Train the model using the training sets 189 | regr.fit(train_x, train_y) 190 | 191 | # The coefficients 192 | #print('Coefficients: \n', regr.coef_) 193 | 194 | 195 | return regr 196 | 197 | def predictM(regr, predict_x, FeaturesLength, df=None): #利用模型来预测结果 198 | 199 | lensum = len(predict_x[0]) 200 | length = lensum - FeaturesLength 201 | #print predict_x 202 | predict_X = [] 203 | for data in predict_x: 204 | predict_X.append(map(float,data[length:])) 205 | 206 | #print predict_X 207 | from sklearn.preprocessing import Imputer 208 | 209 | predict_X = Imputer().fit_transform(predict_X) 210 | #predict_X = np.array(predict_X) 211 | 212 | preRe = regr.predict(predict_X) 213 | return preRe 214 | 215 | def putPrecitResult(uidAndMid, ref, rec, rel,filename): #将预测结果存入文件中 216 | 217 | result = ([a, b, c , d] for a, b, c, d in zip(uidAndMid, ref, rec, rel)) 218 | with open(filename,'w') as f: 219 | for re in result: 220 | re = str(re) 221 | re = re.replace('[',' ') 222 | re = re.replace(']',' ') 223 | re = re.replace(',',' ') 224 | re = re.replace('\'',' ') 225 | #print re 226 | f.write(re.strip()) 227 | f.write('\n') 228 | 229 | 230 | def getResult(filename): #计算预测值 评分 231 | #评分标准，不同问题会有不同的评分方式 232 | #总体是按照一定的规则，将预测出的结果与真实的结果进行比较 233 | #source file 234 | 235 | #result file 236 | #set the value 0 237 | 238 | #and change if the value is given 239 | RealRe = {} 240 | 241 | with open('testTrain.txt') as f: #打开测试集的数据 242 | for line in f: 243 | #print line 244 | lineSplit = line.strip().split() 245 | #print lineSplit 246 | if len(lineSplit) == 5: 247 | data = [] 248 | data.append(int(lineSplit[2])) 249 | data.append(int(lineSplit[3])) 250 | data.append(int(lineSplit[4])) 251 | data.append(0) 252 | data.append(0) 253 | data.append(0) 254 | 255 | 256 | #print data 257 | RealRe[lineSplit[0]+lineSplit[1]] = data 258 | 259 | #print RealRe 260 | print 'realRe finish' 261 | keys = RealRe.keys() 262 | 263 | dickeys = dict(zip(keys, keys)) 264 | with open(filename) as f2: 265 | for line in f2: 266 | #print line 267 | lineSplit = line.strip().split() 268 | #print lineSplit 269 | if len(lineSplit) == 5: 270 | if lineSplit[0]+lineSplit[1] in dickeys: 271 | RealRe[lineSplit[0]+lineSplit[1]][3] = int(lineSplit[2]) 272 | RealRe[lineSplit[0]+lineSplit[1]][4] = int(lineSplit[3]) 273 | RealRe[lineSplit[0]+lineSplit[1]][5] = int(lineSplit[4]) 274 | #print RealRe 275 | print 'open result finish' 276 | countSum = 0.0 277 | countPre = 0.0 278 | for k, v in RealRe.iteritems(): #评分标准，不同问题会有不同的评分方式 279 | counti = v[0] + v[1] +v[2] 280 | if counti > 100: 281 | counti = 100 282 | df = math.fabs(v[0]-v[3])/(v[0]+5) 283 | dc = math.fabs(v[1]-v[4])/(v[1]+3) 284 | dl = math.fabs(v[2]-v[5])/(v[2]+3) 285 | #print df,dc,dl 286 | precision = 1-0.5*df-0.25*dc-0.25*dl 287 | #print precision 288 | if precision > 0.8: 289 | countPre += counti+1 290 | countSum += counti+1 291 | 292 | preSum = countPre/countSum 293 | 294 | #print preSum 295 | return preSum 296 | 297 | def modelSave(model, filename): 298 | from sklearn.externals import joblib 299 | joblib.dump(model, filename) 300 | 301 | def modelLoad(filename): 302 | from sklearn.externals import joblib 303 | model = joblib.load(filename) 304 | return model 305 | 306 | if __name__ == "__main__": 307 | 308 | starttime = datetime.datetime.now() #用来计算时间间隔 309 | #do something 310 | df = loadData('test.txt') #加载训练数据 311 | endtime = datetime.datetime.now() 312 | interval=(endtime - starttime).seconds 313 | #print df.isnull() 314 | #df = df.dropna() 315 | 316 | print 'loadData time (seconds):' 317 | print interval 318 | 319 | 320 | starttime = datetime.datetime.now() 321 | #do something 322 | newdf = getFeatures(df) #获取训练数据特征 323 | #print newdf 324 | #newdf = newdf.dropna() 325 | #print newdf.isnull() 326 | endtime = datetime.datetime.now() 327 | interval=(endtime - starttime).seconds 328 | 329 | print 'getFeatures time (seconds):' 330 | print interval 331 | 332 | starttime = datetime.datetime.now() 333 | #do something 334 | regrf = trainModel(newdf, Type=1) #训练模型 335 | regrc = trainModel(newdf, Type=2) 336 | regrl = trainModel(newdf, Type=3) 337 | 338 | endtime = datetime.datetime.now() 339 | interval=(endtime - starttime).seconds 340 | 341 | print 'trainModel time (seconds):' 342 | print interval 343 | 344 | 345 | #modelSave(regrf, 'modelf2.pkl') 346 | #modelSave(regrc, 'modelc2.pkl') 347 | #modelSave(regrl, 'modell2.pkl') 348 | 349 | #model = modelLoad('model.pkl') 350 | #print model 351 | #print model.coef_ 352 | 353 | starttime = datetime.datetime.now() 354 | #do something 355 | predictData, FeaturesLen, uidAndMid = loadPredictData('predictData.txt') #加载预测数据 356 | #from numpy import nan 357 | #print predictData[:2] 358 | print len(predictData) 359 | 360 | resultf = predictM(regrf, predict_x=predictData, FeaturesLength=FeaturesLen) #预测 361 | resultc = predictM(regrc, predict_x=predictData, FeaturesLength=FeaturesLen) 362 | resultl = predictM(regrl, predict_x=predictData, FeaturesLength=FeaturesLen) 363 | #print (resultf) 364 | #print (resultf) 365 | #print (resultf) 366 | #ref = map(int,map(math.fabs,map(math.floor,resultf))) 367 | #rec = map(int,map(math.fabs,map(math.floor,resultc))) 368 | #rel = map(int,map(math.fabs,map(math.floor,resultl))) 369 | 370 | ref = map(int,resultf) 371 | rec = map(int,resultc) 372 | rel = map(int,resultl) 373 | 374 | #print ref, rec, rel 375 | endtime = datetime.datetime.now() 376 | interval=(endtime - starttime).seconds 377 | print 'predictM time (seconds):' 378 | print interval 379 | 380 | 381 | #print uidAndMid 382 | putPrecitResult(uidAndMid, ref, rec, rel, '2015.txt') #合并 产生结果 383 | 384 | 385 | 386 | starttime = datetime.datetime.now() 387 | 388 | #do something 389 | precision = getResult('2015.txt') #对预测值进行评分 390 | print precision 391 | 392 | 393 | 394 | endtime = datetime.datetime.now() 395 | interval=(endtime - starttime).seconds 396 | 397 | print 'getResult time (seconds):' 398 | print interval 399 | 400 | 401 | -------------------------------------------------------------------------------- /文件说明.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhangxinxing/basic_of_datamining/5527f7518db33cefadba94000dd01c88afc7ade0/文件说明.txt -------------------------------------------------------------------------------- /训练说明.txt: 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