用户网站访问行为预测-----Nupic算法简单Demo

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/jiede1/article/details/80773838

之前介绍了两篇Nupic的技术细节—-脑皮层学习算法 —nupic的深入学习（一），脑皮层学习算法 —nupic的深入学习（二），但缺少了利用Nupic的具体实例。这篇文章会利用Nupic算法，基于已有的用户访问网站类别数据，预测用户的访问网站的类别。

---

1. 数据说明

数据，代码都在Github上。可下载。
列表中的元素就是网站类别

PAGE_CATEGORIES = [
  "frontpage", "news", "tech", "local", "opinion", "on-air", "misc", "weather",
  "msn-news", "health", "living", "business", "msn-sports", "sports", "summary",
  "bbs", "travel"
]

下面就是算法要读取的数据（msnbc990928.zip）

% Different categories found in input file:

frontpage news tech local opinion on-air misc weather msn-news health living business msn-sports sports summary bbs travel


% Sequences:

1 1 
2 
3 2 2 4 2 2 2 3 3 
5 
1 
6 
1 1 
6 
6 7 7 7 6 6 8 8 8 8 
6 9 4 4 4 10 3 10 5 10 4 4 4 
1 1 1 11 1 1 1 
12 12 

数据序列中，每行代表一个用户的点击情况，比如第一行，用户先点击了frontpage 1次，然后点击了news 1次，算法要做的工作是，基于已有的用户点击行为，预测下一刻用户的点击行为。

2.算法运行

在Github上下载源码，运行

python webdata.py

算法会完成所有操作，结果会打印在控制台。

3.算法代码解析

算法分为两个架构：1.配置神经网络各个组件的参数；2.依次读取单个用户的数据，训练算法；3. 利用算法预测
分步骤讲述如下：
(一) 配置神经网络各个组件的参数

#网页的类别
# List of page categories used in the dataset
PAGE_CATEGORIES = [
  "frontpage", "news", "tech", "local", "opinion", "on-air", "misc", "weather",
  "msn-news", "health", "living", "business", "msn-sports", "sports", "summary",
  "bbs", "travel"
]

#配置编码器，这里利用SDRCategoryEncoder
# Configure the sensor/input region using the "SDRCategoryEncoder" to encode
# the page category into SDRs suitable for processing directly by the TM
SENSOR_PARAMS = {
  "verbosity": 0,
  "encoders": {
    "page": {
      "fieldname": "page",
      "name": "page",
      "type": "SDRCategoryEncoder",
      # The output of this encoder will be passed directly to the TM region,
      # therefore the number of bits should match TM's "inputWidth" parameter
      "n": 1024,
      # Use ~2% sparsity
      "w": 21
    },
  },
}

#配置时间池组件，使算法有学习功能的组件
# Configure the temporal memory to learn a sequence of page SDRs and make
# predictions on the next page of the sequence.
TM_PARAMS = {
  "seed": 1960,
  # Use "nupic.bindings.algorithms.TemporalMemoryCPP" algorithm
  "temporalImp": "tm_cpp",
  # Should match the encoder output
  "inputWidth": 1024,
  "columnCount": 1024,
  # Use 1 cell per column for first order prediction.
  # Use more cells per column for variable order predictions.
  "cellsPerColumn": 1,
}

#配置Classifier组件，使得算法能够输出预测的网站类别
# Configure the output region with a classifier used to decode TM SDRs back
# into pages
CL_PARAMS = {
  "implementation": "cpp",
  "regionName": "SDRClassifierRegion",
  # alpha parameter controls how fast the classifier learns/forgets. Higher
  # values make it adapt faster and forget older patterns faster.
  "alpha": 0.001,
  "steps": 1,
}

#将所有的参数组合在一起，构成完成的Model
#顺序是# page => [encoder] => [TM] => [classifier] => prediction
# Create a simple HTM network that will receive the current page as input, pass
# the encoded page SDR to the temporal memory to learn the sequences and
# interpret the output SDRs from the temporary memory using the SDRClassifier
# whose output will be a list of predicted next pages and their probabilities.
MODEL_PARAMS = {
  "version": 1,
  "model": "HTMPrediction",
  "modelParams": {
    "inferenceType": "TemporalMultiStep",

    "sensorParams": SENSOR_PARAMS,

    # The purpose of the spatial pooler is to create a stable representation of
    # the input SDRs. In our case the category encoder output is already a
    # stable representation of the category therefore adding the spatial pooler
    # to this network would not help and could potentially slow down the
    # learning process
    "spEnable": False,
    "spParams": {},

    "tmEnable": True,
    "tmParams": TM_PARAMS,

    "clParams": CL_PARAMS,
  },
}

(二) 依次读取单个用户的数据，训练算法

model.enableLearning()   #开启算法学习功能
for count in xrange(LEARNING_RECORDS):  #遍历用于训练的用户
    # Learn each user session as a single sequence
    session = readUserSession(datafile)  #读出每个用户的数据
    model.resetSequenceStates()    #初始化每个用户作为第一个训练样本
    for page in session:   #对于每个用户，逐次输入序列元素，训练
        model.run({"page": page})

(三) 利用算法预测

# Infer one page of the sequence at the time
model.resetSequenceStates()   #初始化每个用户作为第一个训练样本
session = readUserSession(datafile)  #读取一个用户的点击序列
for page in session:  #遍历该序列，预测下一步用户的点击
result = model.run({"page": page})

#取得预测结果，预测结果类似inferences:  {'msn-news': 0.43472087316837504, 'misc': 0.04838564363576856, 'weather': 0.05264233343663612, …
inferences = result.inferences["multiStepPredictions"][1]  

# Print predictions ordered by probabilities
predicted = sorted(inferences.items(),   #将预测结果排序,概率大优先 
key=itemgetter(1),
                  reverse=True)
prediction_table.add_row([page, zip(*predicted)[0]])

print "User Session to Predict: ", session
print prediction_table

4.算法结果

### Sample output:
```text
The following table shows the encoded SDRs for every page category in the dataset
+---------------+-----------------------------------------------------------------------------+
| Page Category | Encoded SDR (on bit indices)                                                |
+---------------+-----------------------------------------------------------------------------+
| bbs           | [ 19  26 115 171 293 364 390 442 470 477 550 598 624 670 705 719 744 748    |
|               |  788 850 956]                                                               |
| business      | [ 48 104 144 162 213 280 305 355 376 403 435 628 694 724 780 850 854 870    |
|               |  891 930 955]                                                               |
| frontpage     | [  4   7  35  37  48  91 118 143 155 313 339 410 560 627 736 762 795 864    |
|               |  885 889 966]                                                               |
| health        | [  50   67  124  209  214  229  288  337  380  402  437  474  566  584  614 |
|               |   661  754  840  846  894 1008]                                             |
| living        | [195 198 209 219 261 317 332 348 353 369 371 375 399 495 501 556 595 758    |
|               |  799 813 920]                                                               |
| local         | [  3  48 221 275 284 457 466 516 574 626 645 688 699 761 855 867 899 925    |
|               |  942 987 997]                                                               |
| misc          | [ 40  61  90 106 127 179 202 208 217 373 417 523 577 580 722 751 865 925    |
|               |  926 928 938]                                                               |
| msn-news      | [  29   71   72   74  149  241  261  263  276  365  465  528  529  575  577 |
|               |   661  781  799  830  980 1019]                                             |
| msn-sports    | [119 138 150 164 197 263 391 454 510 581 589 614 661 700 724 742 809 886    |
|               |  889 978 989]                                                               |
| news          | [  18   44   71  109  191  322  333  337  375  402  447  587  653  660  794 |
|               |   837  853  913  936  954 1019]                                             |
| on-air        | [  27   80  134  158  187  199  214  286  374  439  445  484  490  590  670 |
|               |   771  823  934  952  965 1014]                                             |
| opinion       | [163 165 216 241 251 260 307 336 382 449 493 540 607 668 679 717 736 866    |
|               |  888 902 981]                                                               |
| sports        | [  20   39   65  141  147  230  232  248  332  361  467  476  689  847  851 |
|               |   862  866  889  936  958 1010]                                             |
| summary       | [ 32  34 106 206 302 340 414 564 566 568 596 619 645 657 761 813 879 888    |
|               |  897 944 997]                                                               |
| tech          | [108 276 327 372 411 431 479 577 592 606 650 690 747 756 763 913 936 949    |
|               |  961 981 983]                                                               |
| travel        | [149 164 179 239 316 319 365 427 437 470 632 729 739 748 787 818 821 824    |
|               |  834 906 919]                                                               |
| weather       | [  9  12  21  38  45 146 203 205 284 400 471 506 520 532 595 613 621 639    |
|               |  805 970 987]                                                               |
+---------------+-----------------------------------------------------------------------------+

Start Learning page sequences using the first 10000 user sessions
Learned 10000 Sessions
Finished Learning

Start Inference using a new user session from the dataset
User Session to Predict:  ['on-air', 'misc', 'misc', 'misc', 'on-air', 'misc', 'misc', 'misc', 'on-air', 'on-air', 'on-air', 'on-air', 'tech', 'msn-news', 'tech', 'msn-news', 'local', 'tech', 'local', 'local', 'local', 'local', 'local', 'local']
+----------+---------------------------------------------------------------------------------------+
|   Page   | Prediction                                                                            |
+----------+---------------------------------------------------------------------------------------+
|  on-air  | ('on-air', 'misc', 'frontpage', 'news', 'summary', 'msn-news', 'weather', 'local')    |
+----------+---------------------------------------------------------------------------------------+
|   misc   | ('misc', 'frontpage', 'on-air', 'local', 'msn-news', 'msn-sports', 'news', 'sports')  |
+----------+---------------------------------------------------------------------------------------+
|   misc   | ('misc', 'frontpage', 'on-air', 'local', 'msn-news', 'msn-sports', 'news', 'sports')  |
+----------+---------------------------------------------------------------------------------------+
|   misc   | ('misc', 'frontpage', 'on-air', 'local', 'msn-news', 'msn-sports', 'news', 'sports')  |
+----------+---------------------------------------------------------------------------------------+
|  on-air  | ('on-air', 'misc', 'frontpage', 'news', 'summary', 'msn-news', 'weather', 'local')    |
+----------+---------------------------------------------------------------------------------------+
|   misc   | ('misc', 'frontpage', 'on-air', 'local', 'msn-news', 'msn-sports', 'news', 'sports')  |
+----------+---------------------------------------------------------------------------------------+
|   misc   | ('misc', 'frontpage', 'on-air', 'local', 'msn-news', 'msn-sports', 'news', 'sports')  |
+----------+---------------------------------------------------------------------------------------+
|   misc   | ('misc', 'frontpage', 'on-air', 'local', 'msn-news', 'msn-sports', 'news', 'sports')  |
+----------+---------------------------------------------------------------------------------------+
|  on-air  | ('on-air', 'misc', 'frontpage', 'news', 'summary', 'msn-news', 'weather', 'local')    |
+----------+---------------------------------------------------------------------------------------+
|  on-air  | ('on-air', 'misc', 'frontpage', 'news', 'summary', 'msn-news', 'weather', 'local')    |
+----------+---------------------------------------------------------------------------------------+
|  on-air  | ('on-air', 'misc', 'frontpage', 'news', 'summary', 'msn-news', 'weather', 'local')    |
+----------+---------------------------------------------------------------------------------------+
|  on-air  | ('on-air', 'misc', 'frontpage', 'news', 'summary', 'msn-news', 'weather', 'local')    |
+----------+---------------------------------------------------------------------------------------+
|   tech   | ('tech', 'frontpage', 'news', 'msn-news', 'on-air', 'business', 'local', 'sports')    |
+----------+---------------------------------------------------------------------------------------+
| msn-news | ('msn-news', 'frontpage', 'local', 'weather', 'misc', 'on-air', 'msn-sports', 'tech') |
+----------+---------------------------------------------------------------------------------------+
|   tech   | ('tech', 'frontpage', 'news', 'msn-news', 'on-air', 'business', 'local', 'sports')    |
+----------+---------------------------------------------------------------------------------------+
| msn-news | ('msn-news', 'frontpage', 'local', 'weather', 'misc', 'on-air', 'msn-sports', 'tech') |
+----------+---------------------------------------------------------------------------------------+
|  local   | ('local', 'frontpage', 'misc', 'news', 'msn-news', 'on-air', 'weather', 'sports')     |
+----------+---------------------------------------------------------------------------------------+
|   tech   | ('tech', 'frontpage', 'news', 'msn-news', 'on-air', 'business', 'local', 'sports')    |
+----------+---------------------------------------------------------------------------------------+
|  local   | ('local', 'frontpage', 'misc', 'news', 'msn-news', 'on-air', 'weather', 'sports')     |
+----------+---------------------------------------------------------------------------------------+
|  local   | ('local', 'frontpage', 'misc', 'news', 'msn-news', 'on-air', 'weather', 'sports')     |
+----------+---------------------------------------------------------------------------------------+
|  local   | ('local', 'frontpage', 'misc', 'news', 'msn-news', 'on-air', 'weather', 'sports')     |
+----------+---------------------------------------------------------------------------------------+
|  local   | ('local', 'frontpage', 'misc', 'news', 'msn-news', 'on-air', 'weather', 'sports')     |
+----------+---------------------------------------------------------------------------------------+
|  local   | ('local', 'frontpage', 'misc', 'news', 'msn-news', 'on-air', 'weather', 'sports')     |
+----------+---------------------------------------------------------------------------------------+
|  local   | ('local', 'frontpage', 'misc', 'news', 'msn-news', 'on-air', 'weather', 'sports')     |
+----------+---------------------------------------------------------------------------------------+

Compute prediction accuracy by checking if the next page in the sequence is within the predicted pages calculated by the model:
 - Prediction Accuracy: 0.614173228346
 - Accuracy Predicting Top 3 Pages: 0.825196850394

代码中算法利用了10000条数据作为训练数据，然后训练了100条数据，这些都可以自己修改源码。从结果中看到，算法的正确率是0.6左右。如果将算法的预测扩大为3个候选（即预测下一次可能是A，B，或C，而不仅仅预测为单个），则准确率大大增加，为0.8左右。

5.算法总结

算法没有与其他预测算法做过比较，因此也无从知道优劣，有兴趣者可以基于该数据集，利用其他预测算法尝试，对比两种算法的准确率。