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danaderp
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ds4se
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  1. ---
    2. 

  2. 

  3. title: Title
    4. 

  4. 

  5. keywords: fastai
    6. 

  6. sidebar: home_sidebar
    7. 

  7. 

  8. summary: "summary"
    9. 

  9. ---
    10. 

  10. <!--
    11. 

  11. 

  12. #################################################
    13. 

  13. ### THIS FILE WAS AUTOGENERATED! DO NOT EDIT! ###
    14. 

  14. #################################################
    15. 

  15. # file to edit: nbs/DS4SEtutorial.ipynb
    16. 

  16. # command to build the docs after a change: nbdev_build_docs
    17. 

  17. 

  18. -->
    19. 

  19. 

  20. <div class="container" id="notebook-container">
    21. 

  21.     
    22. 

  22. <div class="cell border-box-sizing code_cell rendered">
    23. 

  23. 

  24. </div>
    25. 

  25. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    26. 

  26. <div class="text_cell_render border-box-sizing rendered_html">
    27. 

  27. <h1 id="DS4SE-Tutorial">DS4SE Tutorial<a class="anchor-link" href="#DS4SE-Tutorial">&#182;</a></h1>
    28. 

  28. </div>
    29. 

  29. </div>
    30. 

  30. </div>
    31. 

  31. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    32. 

  32. <div class="text_cell_render border-box-sizing rendered_html">
    33. 

  33. <p>This quick tutorial uses <a href="https://pypi.org/project/ds4se/">DS4SE API</a> to:</p>
    34. 

  34. <ol>
    35. 

  35. <li>Calculate traceability value between one pair of artifacts.</li>
    36. 

  36. <li><p>For source and target artifact class in Libest dataset, calculate:</p>
    37. 

  37. <blockquote><p>1) the number of documents in each class</p>
    38. 

  38. <p>2) the vocab size of each class</p>
    39. 

  39. <p>3) the average number of token in each documents of each class</p>
    40. 

  40. <p>4) the top three most frequent tokens in source and target artifact classes</p>
    41. 

  41. <p>5) the top three most frequent tokens in source artifact class</p>
    42. 

  42. <p>6) the number of shared vocabulary between source and target artifact class</p>
    43. 

  43. <p>7) the cross entropy value of source and target artifact class</p>
    44. 

  44. </blockquote>
    45. 

  45. </li>
    46. 

  46. </ol>
    47. 

  47. 

  48. </div>
    49. 

  49. </div>
    50. 

  50. </div>
    51. 

  51. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    52. 

  52. <div class="text_cell_render border-box-sizing rendered_html">
    53. 

  53. <p>This is a quick introduction on how to use the DS4SE API, to follow this tutorial in Google Colab, click the right arrow button in each cell in sequence or click Runtime-&gt; Run all to run all the cells at once</p>
    54. 

  54. 

  55. </div>
    56. 

  56. </div>
    57. 

  57. </div>
    58. 

  58. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    59. 

  59. <div class="text_cell_render border-box-sizing rendered_html">
    60. 

  60. <p>Download and install dependent libraries of DS4SE.</p>
    61. 

  61. 

  62. </div>
    63. 

  63. </div>
    64. 

  64. </div>
    65. 

  65. <div class="cell border-box-sizing code_cell rendered">
    66. 

  66. <div class="input">
    67. 

  67. 

  68. <div class="inner_cell">
    69. 

  69.     <div class="input_area">
    70. 

  70. <div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>pip install --upgrade gensim
    71. 

  71. <span class="o">!</span>pip install nbdev
    72. 

  72. <span class="o">!</span>pip install sentencepiece
    73. 

  73. <span class="o">!</span>pip install dit
    74. 

  74. </pre></div>
    75. 

  75. 

  76.     </div>
    77. 

  77. </div>
    78. 

  78. </div>
    79. 

  79. 

  80. <div class="output_wrapper">
    81. 

  81. <div class="output">
    82. 

  82. 

  83. <div class="output_area">
    84. 

  84. 

  85. <div class="output_subarea output_stream output_stdout output_text">
    86. 

  86. <pre>Requirement already up-to-date: gensim in /usr/local/lib/python3.6/dist-packages (3.8.3)
    87. 

  87. Requirement already satisfied, skipping upgrade: scipy&gt;=0.18.1 in /usr/local/lib/python3.6/dist-packages (from gensim) (1.4.1)
    88. 

  88. Requirement already satisfied, skipping upgrade: six&gt;=1.5.0 in /usr/local/lib/python3.6/dist-packages (from gensim) (1.15.0)
    89. 

  89. Requirement already satisfied, skipping upgrade: numpy&gt;=1.11.3 in /usr/local/lib/python3.6/dist-packages (from gensim) (1.18.5)
    90. 

  90. Requirement already satisfied, skipping upgrade: smart-open&gt;=1.8.1 in /usr/local/lib/python3.6/dist-packages (from gensim) (3.0.0)
    91. 

  91. Requirement already satisfied, skipping upgrade: requests in /usr/local/lib/python3.6/dist-packages (from smart-open&gt;=1.8.1-&gt;gensim) (2.23.0)
    92. 

  92. Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,&lt;1.26,&gt;=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests-&gt;smart-open&gt;=1.8.1-&gt;gensim) (1.24.3)
    93. 

  93. Requirement already satisfied, skipping upgrade: idna&lt;3,&gt;=2.5 in /usr/local/lib/python3.6/dist-packages (from requests-&gt;smart-open&gt;=1.8.1-&gt;gensim) (2.10)
    94. 

  94. Requirement already satisfied, skipping upgrade: certifi&gt;=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests-&gt;smart-open&gt;=1.8.1-&gt;gensim) (2020.6.20)
    95. 

  95. Requirement already satisfied, skipping upgrade: chardet&lt;4,&gt;=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests-&gt;smart-open&gt;=1.8.1-&gt;gensim) (3.0.4)
    96. 

  96. Requirement already satisfied: nbdev in /usr/local/lib/python3.6/dist-packages (1.1.5)
    97. 

  97. Requirement already satisfied: jupyter-client in /usr/local/lib/python3.6/dist-packages (from nbdev) (5.3.5)
    98. 

  98. Requirement already satisfied: nbformat&gt;=4.4.0 in /usr/local/lib/python3.6/dist-packages (from nbdev) (5.0.8)
    99. 

  99. Requirement already satisfied: ipykernel in /usr/local/lib/python3.6/dist-packages (from nbdev) (4.10.1)
    100. 

  100. Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from nbdev) (3.13)
    101. 

  101. Requirement already satisfied: pip in /usr/local/lib/python3.6/dist-packages (from nbdev) (19.3.1)
    102. 

  102. Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from nbdev) (20.4)
    103. 

  103. Requirement already satisfied: nbconvert&lt;6 in /usr/local/lib/python3.6/dist-packages (from nbdev) (5.6.1)
    104. 

  104. Requirement already satisfied: fastcore&gt;=1.3.1 in /usr/local/lib/python3.6/dist-packages (from nbdev) (1.3.6)
    105. 

  105. Requirement already satisfied: python-dateutil&gt;=2.1 in /usr/local/lib/python3.6/dist-packages (from jupyter-client-&gt;nbdev) (2.8.1)
    106. 

  106. Requirement already satisfied: tornado&gt;=4.1 in /usr/local/lib/python3.6/dist-packages (from jupyter-client-&gt;nbdev) (5.1.1)
    107. 

  107. Requirement already satisfied: traitlets in /usr/local/lib/python3.6/dist-packages (from jupyter-client-&gt;nbdev) (4.3.3)
    108. 

  108. Requirement already satisfied: jupyter-core&gt;=4.6.0 in /usr/local/lib/python3.6/dist-packages (from jupyter-client-&gt;nbdev) (4.6.3)
    109. 

  109. Requirement already satisfied: pyzmq&gt;=13 in /usr/local/lib/python3.6/dist-packages (from jupyter-client-&gt;nbdev) (19.0.2)
    110. 

  110. Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from nbformat&gt;=4.4.0-&gt;nbdev) (0.2.0)
    111. 

  111. Requirement already satisfied: jsonschema!=2.5.0,&gt;=2.4 in /usr/local/lib/python3.6/dist-packages (from nbformat&gt;=4.4.0-&gt;nbdev) (2.6.0)
    112. 

  112. Requirement already satisfied: ipython&gt;=4.0.0 in /usr/local/lib/python3.6/dist-packages (from ipykernel-&gt;nbdev) (5.5.0)
    113. 

  113. Requirement already satisfied: pyparsing&gt;=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging-&gt;nbdev) (2.4.7)
    114. 

  114. Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from packaging-&gt;nbdev) (1.15.0)
    115. 

  115. Requirement already satisfied: entrypoints&gt;=0.2.2 in /usr/local/lib/python3.6/dist-packages (from nbconvert&lt;6-&gt;nbdev) (0.3)
    116. 

  116. Requirement already satisfied: mistune&lt;2,&gt;=0.8.1 in /usr/local/lib/python3.6/dist-packages (from nbconvert&lt;6-&gt;nbdev) (0.8.4)
    117. 

  117. Requirement already satisfied: testpath in /usr/local/lib/python3.6/dist-packages (from nbconvert&lt;6-&gt;nbdev) (0.4.4)
    118. 

  118. Requirement already satisfied: bleach in /usr/local/lib/python3.6/dist-packages (from nbconvert&lt;6-&gt;nbdev) (3.2.1)
    119. 

  119. Requirement already satisfied: defusedxml in /usr/local/lib/python3.6/dist-packages (from nbconvert&lt;6-&gt;nbdev) (0.6.0)
    120. 

  120. Requirement already satisfied: pandocfilters&gt;=1.4.1 in /usr/local/lib/python3.6/dist-packages (from nbconvert&lt;6-&gt;nbdev) (1.4.3)
    121. 

  121. Requirement already satisfied: jinja2&gt;=2.4 in /usr/local/lib/python3.6/dist-packages (from nbconvert&lt;6-&gt;nbdev) (2.11.2)
    122. 

  122. Requirement already satisfied: pygments in /usr/local/lib/python3.6/dist-packages (from nbconvert&lt;6-&gt;nbdev) (2.6.1)
    123. 

  123. Requirement already satisfied: decorator in /usr/local/lib/python3.6/dist-packages (from traitlets-&gt;jupyter-client-&gt;nbdev) (4.4.2)
    124. 

  124. Requirement already satisfied: pickleshare in /usr/local/lib/python3.6/dist-packages (from ipython&gt;=4.0.0-&gt;ipykernel-&gt;nbdev) (0.7.5)
    125. 

  125. Requirement already satisfied: setuptools&gt;=18.5 in /usr/local/lib/python3.6/dist-packages (from ipython&gt;=4.0.0-&gt;ipykernel-&gt;nbdev) (50.3.2)
    126. 

  126. Requirement already satisfied: prompt-toolkit&lt;2.0.0,&gt;=1.0.4 in /usr/local/lib/python3.6/dist-packages (from ipython&gt;=4.0.0-&gt;ipykernel-&gt;nbdev) (1.0.18)
    127. 

  127. Requirement already satisfied: simplegeneric&gt;0.8 in /usr/local/lib/python3.6/dist-packages (from ipython&gt;=4.0.0-&gt;ipykernel-&gt;nbdev) (0.8.1)
    128. 

  128. Requirement already satisfied: pexpect; sys_platform != &#34;win32&#34; in /usr/local/lib/python3.6/dist-packages (from ipython&gt;=4.0.0-&gt;ipykernel-&gt;nbdev) (4.8.0)
    129. 

  129. Requirement already satisfied: webencodings in /usr/local/lib/python3.6/dist-packages (from bleach-&gt;nbconvert&lt;6-&gt;nbdev) (0.5.1)
    130. 

  130. Requirement already satisfied: MarkupSafe&gt;=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2&gt;=2.4-&gt;nbconvert&lt;6-&gt;nbdev) (1.1.1)
    131. 

  131. Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit&lt;2.0.0,&gt;=1.0.4-&gt;ipython&gt;=4.0.0-&gt;ipykernel-&gt;nbdev) (0.2.5)
    132. 

  132. Requirement already satisfied: ptyprocess&gt;=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != &#34;win32&#34;-&gt;ipython&gt;=4.0.0-&gt;ipykernel-&gt;nbdev) (0.6.0)
    133. 

  133. Requirement already satisfied: sentencepiece in /usr/local/lib/python3.6/dist-packages (0.1.94)
    134. 

  134. Requirement already satisfied: dit in /usr/local/lib/python3.6/dist-packages (1.2.3)
    135. 

  135. Requirement already satisfied: boltons in /usr/local/lib/python3.6/dist-packages (from dit) (20.2.1)
    136. 

  136. Requirement already satisfied: debtcollector in /usr/local/lib/python3.6/dist-packages (from dit) (2.2.0)
    137. 

  137. Requirement already satisfied: numpy&gt;=1.11 in /usr/local/lib/python3.6/dist-packages (from dit) (1.18.5)
    138. 

  138. Requirement already satisfied: scipy&gt;=0.15.0 in /usr/local/lib/python3.6/dist-packages (from dit) (1.4.1)
    139. 

  139. Requirement already satisfied: prettytable in /usr/local/lib/python3.6/dist-packages (from dit) (1.0.1)
    140. 

  140. Requirement already satisfied: six&gt;=1.4.0 in /usr/local/lib/python3.6/dist-packages (from dit) (1.15.0)
    141. 

  141. Requirement already satisfied: networkx in /usr/local/lib/python3.6/dist-packages (from dit) (2.5)
    142. 

  142. Requirement already satisfied: contextlib2 in /usr/local/lib/python3.6/dist-packages (from dit) (0.5.5)
    143. 

  143. Requirement already satisfied: pbr!=2.1.0,&gt;=2.0.0 in /usr/local/lib/python3.6/dist-packages (from debtcollector-&gt;dit) (5.5.1)
    144. 

  144. Requirement already satisfied: wrapt&gt;=1.7.0 in /usr/local/lib/python3.6/dist-packages (from debtcollector-&gt;dit) (1.12.1)
    145. 

  145. Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from prettytable-&gt;dit) (50.3.2)
    146. 

  146. Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prettytable-&gt;dit) (0.2.5)
    147. 

  147. Requirement already satisfied: decorator&gt;=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx-&gt;dit) (4.4.2)
    148. 

  148. </pre>
    149. 

  149. </div>
    150. 

  150. </div>
    151. 

  151. 

  152. </div>
    153. 

  153. </div>
    154. 

  154. 

  155. </div>
    156. 

  156. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    157. 

  157. <div class="text_cell_render border-box-sizing rendered_html">
    158. 

  158. <p>Download and install DS4SE. Import TensorFlow into your program:</p>
    159. 

  159. 

  160. </div>
    161. 

  161. </div>
    162. 

  162. </div>
    163. 

  163. <div class="cell border-box-sizing code_cell rendered">
    164. 

  164. <div class="input">
    165. 

  165. 

  166. <div class="inner_cell">
    167. 

  167.     <div class="input_area">
    168. 

  168. <div class=" highlight hl-ipython3"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">ds4se</span>
    169. 

  169. </pre></div>
    170. 

  170. 

  171.     </div>
    172. 

  172. </div>
    173. 

  173. </div>
    174. 

  174. 

  175. <div class="output_wrapper">
    176. 

  176. <div class="output">
    177. 

  177. 

  178. <div class="output_area">
    179. 

  179. 

  180. <div class="output_subarea output_stream output_stdout output_text">
    181. 

  181. <pre>Requirement already satisfied: ds4se in /usr/local/lib/python3.6/dist-packages (0.2.1)
    182. 

  182. </pre>
    183. 

  183. </div>
    184. 

  184. </div>
    185. 

  185. 

  186. </div>
    187. 

  187. </div>
    188. 

  188. 

  189. </div>
    190. 

  190. <div class="cell border-box-sizing code_cell rendered">
    191. 

  191. <div class="input">
    192. 

  192. 

  193. <div class="inner_cell">
    194. 

  194.     <div class="input_area">
    195. 

  195. <div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">ds4se.facade</span> <span class="k">as</span> <span class="nn">facade</span>
    196. 

  196. </pre></div>
    197. 

  197. 

  198.     </div>
    199. 

  199. </div>
    200. 

  200. </div>
    201. 

  201. 

  202. </div>
    203. 

  203. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    204. 

  204. <div class="text_cell_render border-box-sizing rendered_html">
    205. 

  205. <p>Import other libraries needed for this tutorial:</p>
    206. 

  206. 

  207. </div>
    208. 

  208. </div>
    209. 

  209. </div>
    210. 

  210. <div class="cell border-box-sizing code_cell rendered">
    211. 

  211. <div class="input">
    212. 

  212. 

  213. <div class="inner_cell">
    214. 

  214.     <div class="input_area">
    215. 

  215. <div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
    216. 

  216. <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
    217. 

  217. </pre></div>
    218. 

  218. 

  219.     </div>
    220. 

  220. </div>
    221. 

  221. </div>
    222. 

  222. 

  223. </div>
    224. 

  224. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    225. 

  225. <div class="text_cell_render border-box-sizing rendered_html">
    226. 

  226. <p>Load and prapare <a href="https://github.com/WM-SEMERU/ds4se/tree/master/nbs/test_data">Libest dataset</a>. Convert the column name in which actual file content is stored into "contents".</p>
    227. 

  227. 

  228. </div>
    229. 

  229. </div>
    230. 

  230. </div>
    231. 

  231. <div class="cell border-box-sizing code_cell rendered">
    232. 

  232. <div class="input">
    233. 

  233. 

  234. <div class="inner_cell">
    235. 

  235.     <div class="input_area">
    236. 

  236. <div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>wget https://raw.githubusercontent.com/WM-SEMERU/ds4se/SE_Proj2_Facade/nbs/test_data/%5Blibest-pre-req%5D.csv
    237. 

  237. <span class="o">!</span>wget https://raw.githubusercontent.com/WM-SEMERU/ds4se/SE_Proj2_Facade/nbs/test_data/%5Blibest-pre-tc%5D.csv
    238. 

  238. </pre></div>
    239. 

  239. 

  240.     </div>
    241. 

  241. </div>
    242. 

  242. </div>
    243. 

  243. 

  244. <div class="output_wrapper">
    245. 

  245. <div class="output">
    246. 

  246. 

  247. <div class="output_area">
    248. 

  248. 

  249. <div class="output_subarea output_stream output_stdout output_text">
    250. 

  250. <pre>--2020-11-21 04:17:44--  https://raw.githubusercontent.com/WM-SEMERU/ds4se/SE_Proj2_Facade/nbs/test_data/%5Blibest-pre-req%5D.csv
    251. 

  251. Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...
    252. 

  252. Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.
    253. 

  253. HTTP request sent, awaiting response... 200 OK
    254. 

  254. Length: 60790 (59K) [text/plain]
    255. 

  255. Saving to: ‘[libest-pre-req].csv.1’
    256. 

  256. 

  257. [libest-pre-req].cs 100%[===================&gt;]  59.37K  --.-KB/s    in 0.01s   
    258. 

  258. 

  259. 2020-11-21 04:17:44 (4.28 MB/s) - ‘[libest-pre-req].csv.1’ saved [60790/60790]
    260. 

  260. 

  261. --2020-11-21 04:17:44--  https://raw.githubusercontent.com/WM-SEMERU/ds4se/SE_Proj2_Facade/nbs/test_data/%5Blibest-pre-tc%5D.csv
    262. 

  262. Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...
    263. 

  263. Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.
    264. 

  264. HTTP request sent, awaiting response... 200 OK
    265. 

  265. Length: 343900 (336K) [text/plain]
    266. 

  266. Saving to: ‘[libest-pre-tc].csv.1’
    267. 

  267. 

  268. [libest-pre-tc].csv 100%[===================&gt;] 335.84K  --.-KB/s    in 0.04s   
    269. 

  269. 

  270. 2020-11-21 04:17:45 (8.32 MB/s) - ‘[libest-pre-tc].csv.1’ saved [343900/343900]
    271. 

  271. 

  272. </pre>
    273. 

  273. </div>
    274. 

  274. </div>
    275. 

  275. 

  276. </div>
    277. 

  277. </div>
    278. 

  278. 

  279. </div>
    280. 

  280. <div class="cell border-box-sizing code_cell rendered">
    281. 

  281. <div class="input">
    282. 

  282. 

  283. <div class="inner_cell">
    284. 

  284.     <div class="input_area">
    285. 

  285. <div class=" highlight hl-ipython3"><pre><span></span><span class="n">source_file</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;[libest-pre-req].csv&quot;</span><span class="p">,</span><span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;ids&#39;</span><span class="p">,</span> <span class="s1">&#39;text&#39;</span><span class="p">],</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">&#39; &#39;</span><span class="p">)</span>
    286. 

  286. <span class="n">target_file</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;[libest-pre-tc].csv&quot;</span><span class="p">,</span><span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;ids&#39;</span><span class="p">,</span> <span class="s1">&#39;text&#39;</span><span class="p">],</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">&#39; &#39;</span><span class="p">)</span>
    287. 

  287. <span class="n">source_file</span> <span class="o">=</span> <span class="n">source_file</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;text&quot;</span><span class="p">:</span><span class="s2">&quot;contents&quot;</span><span class="p">})</span>
    288. 

  288. <span class="n">target_file</span> <span class="o">=</span> <span class="n">target_file</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;text&quot;</span><span class="p">:</span><span class="s2">&quot;contents&quot;</span><span class="p">})</span>
    289. 

  289. </pre></div>
    290. 

  290. 

  291.     </div>
    292. 

  292. </div>
    293. 

  293. </div>
    294. 

  294. 

  295. </div>
    296. 

  296. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    297. 

  297. <div class="text_cell_render border-box-sizing rendered_html">
    298. 

  298. <p>Create a pandas dataframe to store the result:</p>
    299. 

  299. 

  300. </div>
    301. 

  301. </div>
    302. 

  302. </div>
    303. 

  303. <div class="cell border-box-sizing code_cell rendered">
    304. 

  304. <div class="input">
    305. 

  305. 

  306. <div class="inner_cell">
    307. 

  307.     <div class="input_area">
    308. 

  308. <div class=" highlight hl-ipython3"><pre><span></span><span class="n">d</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;source&#39;</span><span class="p">:</span> <span class="p">[],</span> <span class="s1">&#39;target&#39;</span><span class="p">:</span> <span class="p">[],</span> <span class="s1">&#39;distance&#39;</span><span class="p">:[],</span><span class="s1">&#39;similarity/traceability&#39;</span><span class="p">:[]}</span>
    309. 

  309. <span class="n">output_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">d</span><span class="p">)</span>
    310. 

  310. </pre></div>
    311. 

  311. 

  312.     </div>
    313. 

  313. </div>
    314. 

  314. </div>
    315. 

  315. 

  316. </div>
    317. 

  317. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    318. 

  318. <div class="text_cell_render border-box-sizing rendered_html">
    319. 

  319. <p>Retrive one element from source artifact and one element from target artifact to calculate traceability. Store id information for reference.</p>
    320. 

  320. 

  321. </div>
    322. 

  322. </div>
    323. 

  323. </div>
    324. 

  324. <div class="cell border-box-sizing code_cell rendered">
    325. 

  325. <div class="input">
    326. 

  326. 

  327. <div class="inner_cell">
    328. 

  328.     <div class="input_area">
    329. 

  329. <div class=" highlight hl-ipython3"><pre><span></span><span class="n">source_id</span> <span class="o">=</span> <span class="n">source_file</span><span class="p">[</span><span class="s2">&quot;ids&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
    330. 

  330. <span class="n">target_id</span> <span class="o">=</span> <span class="n">target_file</span><span class="p">[</span><span class="s2">&quot;ids&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
    331. 

  331. <span class="n">source_string</span> <span class="o">=</span> <span class="n">source_file</span><span class="p">[</span><span class="s2">&quot;contents&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
    332. 

  332. <span class="n">target_string</span> <span class="o">=</span> <span class="n">target_file</span><span class="p">[</span><span class="s2">&quot;contents&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
    333. 

  333. </pre></div>
    334. 

  334. 

  335.     </div>
    336. 

  336. </div>
    337. 

  337. </div>
    338. 

  338. 

  339. </div>
    340. 

  340. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    341. 

  341. <div class="text_cell_render border-box-sizing rendered_html">
    342. 

  342. <p>Call TraceLinkValue method to calcuate the distance and traceability values of this pair. In this example we used word2vec technique.</p>
    343. 

  343. 

  344. </div>
    345. 

  345. </div>
    346. 

  346. </div>
    347. 

  347. <div class="cell border-box-sizing code_cell rendered">
    348. 

  348. <div class="input">
    349. 

  349. 

  350. <div class="inner_cell">
    351. 

  351.     <div class="input_area">
    352. 

  352. <div class=" highlight hl-ipython3"><pre><span></span><span class="n">TLV</span> <span class="o">=</span> <span class="n">facade</span><span class="o">.</span><span class="n">TraceLinkValue</span><span class="p">(</span><span class="n">source_string</span><span class="p">,</span> <span class="n">target_string</span><span class="p">,</span> <span class="s2">&quot;word2vec&quot;</span><span class="p">)</span>
    353. 

  353. <span class="n">distance</span> <span class="o">=</span> <span class="n">TLV</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    354. 

  354. <span class="n">traceability</span> <span class="o">=</span> <span class="n">TLV</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
    355. 

  355. </pre></div>
    356. 

  356. 

  357.     </div>
    358. 

  358. </div>
    359. 

  359. </div>
    360. 

  360. 

  361. <div class="output_wrapper">
    362. 

  362. <div class="output">
    363. 

  363. 

  364. <div class="output_area">
    365. 

  365. 

  366. <div class="output_subarea output_stream output_stderr output_text">
    367. 

  367. <pre>2020-11-21 04:17:45,247 : INFO : adding document #0 to Dictionary(0 unique tokens: [])
    368. 

  368. 2020-11-21 04:17:45,256 : INFO : built Dictionary(1815 unique tokens: [&#39;@return&#39;, &#39;Converts&#39;, &#39;The&#39;, &#39;a&#39;, &#39;and&#39;]...) from 153 documents (total 5769 corpus positions)
    369. 

  369. 2020-11-21 04:17:45,257 : INFO : loading Word2Vec object from /usr/local/lib/python3.6/dist-packages/ds4se/model/word2vec_libest.model
    370. 

  370. 2020-11-21 04:17:45,327 : INFO : loading wv recursively from /usr/local/lib/python3.6/dist-packages/ds4se/model/word2vec_libest.model.wv.* with mmap=None
    371. 

  371. 2020-11-21 04:17:45,330 : INFO : setting ignored attribute vectors_norm to None
    372. 

  372. 2020-11-21 04:17:45,332 : INFO : loading vocabulary recursively from /usr/local/lib/python3.6/dist-packages/ds4se/model/word2vec_libest.model.vocabulary.* with mmap=None
    373. 

  373. 2020-11-21 04:17:45,332 : INFO : loading trainables recursively from /usr/local/lib/python3.6/dist-packages/ds4se/model/word2vec_libest.model.trainables.* with mmap=None
    374. 

  374. 2020-11-21 04:17:45,333 : INFO : setting ignored attribute cum_table to None
    375. 

  375. 2020-11-21 04:17:45,334 : INFO : loaded /usr/local/lib/python3.6/dist-packages/ds4se/model/word2vec_libest.model
    376. 

  376. 2020-11-21 04:17:45,366 : INFO : precomputing L2-norms of word weight vectors
    377. 

  377. 2020-11-21 04:17:45,370 : INFO : constructing a sparse term similarity matrix using &lt;gensim.models.keyedvectors.WordEmbeddingSimilarityIndex object at 0x7f3cc1334518&gt;
    378. 

  378. 2020-11-21 04:17:45,374 : INFO : iterating over columns in dictionary order
    379. 

  379. 2020-11-21 04:17:45,379 : INFO : PROGRESS: at 0.06% columns (1 / 1815, 0.055096% density, 0.055096% projected density)
    380. 

  380. 2020-11-21 04:17:45,624 : INFO : PROGRESS: at 55.15% columns (1001 / 1815, 0.140033% density, 0.209102% projected density)
    381. 

  381. 2020-11-21 04:17:45,746 : INFO : constructed a sparse term similarity matrix with 0.173668% density
    382. 

  382. 2020-11-21 04:17:45,765 : INFO : adding document #0 to Dictionary(0 unique tokens: [])
    383. 

  383. 2020-11-21 04:17:45,768 : INFO : built Dictionary(502 unique tokens: [&#39;accompani&#39;, &#39;addit&#39;, &#39;agre&#39;, &#39;agreement&#39;, &#39;algorithm&#39;]...) from 2 documents (total 2134 corpus positions)
    384. 

  384. 2020-11-21 04:17:49,106 : INFO : Computed distances or similarities (&#39;source&#39;, &#39;target&#39;)[[0.4005029238604272, 0.7140292126228072]]
    385. 

  385. </pre>
    386. 

  386. </div>
    387. 

  387. </div>
    388. 

  388. 

  389. </div>
    390. 

  390. </div>
    391. 

  391. 

  392. </div>
    393. 

  393. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    394. 

  394. <div class="text_cell_render border-box-sizing rendered_html">
    395. 

  395. <p>Display the result:</p>
    396. 

  396. 

  397. </div>
    398. 

  398. </div>
    399. 

  399. </div>
    400. 

  400. <div class="cell border-box-sizing code_cell rendered">
    401. 

  401. <div class="input">
    402. 

  402. 

  403. <div class="inner_cell">
    404. 

  404.     <div class="input_area">
    405. 

  405. <div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The traceability value between artifacts </span><span class="si">{}</span><span class="s2"> and </span><span class="si">{}</span><span class="s2"> is </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">source_id</span><span class="p">,</span><span class="n">target_id</span><span class="p">,</span><span class="nb">format</span><span class="p">(</span><span class="n">traceability</span><span class="p">,</span><span class="s1">&#39;.2f&#39;</span><span class="p">)))</span>
    406. 

  406. </pre></div>
    407. 

  407. 

  408.     </div>
    409. 

  409. </div>
    410. 

  410. </div>
    411. 

  411. 

  412. <div class="output_wrapper">
    413. 

  413. <div class="output">
    414. 

  414. 

  415. <div class="output_area">
    416. 

  416. 

  417. <div class="output_subarea output_stream output_stdout output_text">
    418. 

  418. <pre>The traceability value between artifacts RQ46-pre.txt and us3496.c is 0.71
    419. 

  419. </pre>
    420. 

  420. </div>
    421. 

  421. </div>
    422. 

  422. 

  423. </div>
    424. 

  424. </div>
    425. 

  425. 

  426. </div>
    427. 

  427. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    428. 

  428. <div class="text_cell_render border-box-sizing rendered_html">
    429. 

  429. <p>Call <strong>NumDoc</strong> method to count the number of documents in each artifacts:</p>
    430. 

  430. 

  431. </div>
    432. 

  432. </div>
    433. 

  433. </div>
    434. 

  434. <div class="cell border-box-sizing code_cell rendered">
    435. 

  435. <div class="input">
    436. 

  436. 

  437. <div class="inner_cell">
    438. 

  438.     <div class="input_area">
    439. 

  439. <div class=" highlight hl-ipython3"><pre><span></span><span class="n">num_docs</span> <span class="o">=</span> <span class="n">facade</span><span class="o">.</span><span class="n">NumDoc</span><span class="p">(</span><span class="n">source_file</span><span class="p">,</span> <span class="n">target_file</span><span class="p">)</span>
    440. 

  440. </pre></div>
    441. 

  441. 

  442.     </div>
    443. 

  443. </div>
    444. 

  444. </div>
    445. 

  445. 

  446. </div>
    447. 

  447. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    448. 

  448. <div class="text_cell_render border-box-sizing rendered_html">
    449. 

  449. <p>Display the number of documents result:</p>
    450. 

  450. 

  451. </div>
    452. 

  452. </div>
    453. 

  453. </div>
    454. 

  454. <div class="cell border-box-sizing code_cell rendered">
    455. 

  455. <div class="input">
    456. 

  456. 

  457. <div class="inner_cell">
    458. 

  458.     <div class="input_area">
    459. 

  459. <div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Source artifacts contains </span><span class="si">{}</span><span class="s2"> documents, Target artifacts contains </span><span class="si">{}</span><span class="s2"> documents.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_docs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">num_docs</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
    460. 

  460. </pre></div>
    461. 

  461. 

  462.     </div>
    463. 

  463. </div>
    464. 

  464. </div>
    465. 

  465. 

  466. <div class="output_wrapper">
    467. 

  467. <div class="output">
    468. 

  468. 

  469. <div class="output_area">
    470. 

  470. 

  471. <div class="output_subarea output_stream output_stdout output_text">
    472. 

  472. <pre>Source artifacts contains 52 documents, Target artifacts contains 21 documents.
    473. 

  473. </pre>
    474. 

  474. </div>
    475. 

  475. </div>
    476. 

  476. 

  477. </div>
    478. 

  478. </div>
    479. 

  479. 

  480. </div>
    481. 

  481. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    482. 

  482. <div class="text_cell_render border-box-sizing rendered_html">
    483. 

  483. <p>Call <strong>VocabSize</strong> method to count the vocabulary size of each artifacts:</p>
    484. 

  484. 

  485. </div>
    486. 

  486. </div>
    487. 

  487. </div>
    488. 

  488. <div class="cell border-box-sizing code_cell rendered">
    489. 

  489. <div class="input">
    490. 

  490. 

  491. <div class="inner_cell">
    492. 

  492.     <div class="input_area">
    493. 

  493. <div class=" highlight hl-ipython3"><pre><span></span><span class="n">vocab_size</span> <span class="o">=</span> <span class="n">facade</span><span class="o">.</span><span class="n">VocabSize</span><span class="p">(</span><span class="n">source_file</span><span class="p">,</span><span class="n">target_file</span><span class="p">)</span>
    494. 

  494. </pre></div>
    495. 

  495. 

  496.     </div>
    497. 

  497. </div>
    498. 

  498. </div>
    499. 

  499. 

  500. </div>
    501. 

  501. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    502. 

  502. <div class="text_cell_render border-box-sizing rendered_html">
    503. 

  503. <p>Display the vocabulary size of each artifacts:</p>
    504. 

  504. 

  505. </div>
    506. 

  506. </div>
    507. 

  507. </div>
    508. 

  508. <div class="cell border-box-sizing code_cell rendered">
    509. 

  509. <div class="input">
    510. 

  510. 

  511. <div class="inner_cell">
    512. 

  512.     <div class="input_area">
    513. 

  513. <div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Source artifacts&#39;s vocab size is </span><span class="si">{}</span><span class="s2">. Target artifacts&#39;s vocab size is </span><span class="si">{}</span><span class="s2">.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">vocab_size</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">vocab_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
    514. 

  514. </pre></div>
    515. 

  515. 

  516.     </div>
    517. 

  517. </div>
    518. 

  518. </div>
    519. 

  519. 

  520. <div class="output_wrapper">
    521. 

  521. <div class="output">
    522. 

  522. 

  523. <div class="output_area">
    524. 

  524. 

  525. <div class="output_subarea output_stream output_stdout output_text">
    526. 

  526. <pre>Source artifacts&#39;s vocab size is 2349. Target artifacts&#39;s vocab size is 3168.
    527. 

  527. </pre>
    528. 

  528. </div>
    529. 

  529. </div>
    530. 

  530. 

  531. </div>
    532. 

  532. </div>
    533. 

  533. 

  534. </div>
    535. 

  535. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    536. 

  536. <div class="text_cell_render border-box-sizing rendered_html">
    537. 

  537. <p>Computes the average number of token in each class and also the difference between them using <strong>AverageToken</strong> method:</p>
    538. 

  538. 

  539. </div>
    540. 

  540. </div>
    541. 

  541. </div>
    542. 

  542. <div class="cell border-box-sizing code_cell rendered">
    543. 

  543. <div class="input">
    544. 

  544. 

  545. <div class="inner_cell">
    546. 

  546.     <div class="input_area">
    547. 

  547. <div class=" highlight hl-ipython3"><pre><span></span><span class="n">token</span> <span class="o">=</span> <span class="n">facade</span><span class="o">.</span><span class="n">AverageToken</span><span class="p">(</span><span class="n">source_file</span><span class="p">,</span> <span class="n">target_file</span><span class="p">)</span>
    548. 

  548. </pre></div>
    549. 

  549. 

  550.     </div>
    551. 

  551. </div>
    552. 

  552. </div>
    553. 

  553. 

  554. </div>
    555. 

  555. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    556. 

  556. <div class="text_cell_render border-box-sizing rendered_html">
    557. 

  557. <p>Display the result:</p>
    558. 

  558. 

  559. </div>
    560. 

  560. </div>
    561. 

  561. </div>
    562. 

  562. <div class="cell border-box-sizing code_cell rendered">
    563. 

  563. <div class="input">
    564. 

  564. 

  565. <div class="inner_cell">
    566. 

  566.     <div class="input_area">
    567. 

  567. <div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;On average, each document in source artifact class contains </span><span class="si">{}</span><span class="s2"> tokens and each document in target artifact class contains </span><span class="si">{}</span><span class="s2"> tokens &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">token</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">token</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
    568. 

  568. </pre></div>
    569. 

  569. 

  570.     </div>
    571. 

  571. </div>
    572. 

  572. </div>
    573. 

  573. 

  574. <div class="output_wrapper">
    575. 

  575. <div class="output">
    576. 

  576. 

  577. <div class="output_area">
    578. 

  578. 

  579. <div class="output_subarea output_stream output_stdout output_text">
    580. 

  580. <pre>On average, each document in source artifact class contains 365.21153846153845 tokens and each document in target artifact class contains 4970.476190476191 tokens 
    581. 

  581. </pre>
    582. 

  582. </div>
    583. 

  583. </div>
    584. 

  584. 

  585. </div>
    586. 

  586. </div>
    587. 

  587. 

  588. </div>
    589. 

  589. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    590. 

  590. <div class="text_cell_render border-box-sizing rendered_html">
    591. 

  591. <p>To find out the most frequent token in both source and target artifacts, use <strong>VocabShared</strong> method</p>
    592. 

  592. 

  593. </div>
    594. 

  594. </div>
    595. 

  595. </div>
    596. 

  596. <div class="cell border-box-sizing code_cell rendered">
    597. 

  597. <div class="input">
    598. 

  598. 

  599. <div class="inner_cell">
    600. 

  600.     <div class="input_area">
    601. 

  601. <div class=" highlight hl-ipython3"><pre><span></span><span class="n">vocab_shared</span> <span class="o">=</span> <span class="n">facade</span><span class="o">.</span><span class="n">VocabShared</span><span class="p">(</span><span class="n">source_file</span><span class="p">,</span> <span class="n">target_file</span><span class="p">)</span>
    602. 

  602. </pre></div>
    603. 

  603. 

  604.     </div>
    605. 

  605. </div>
    606. 

  606. </div>
    607. 

  607. 

  608. </div>
    609. 

  609. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    610. 

  610. <div class="text_cell_render border-box-sizing rendered_html">
    611. 

  611. <p>Display the result:</p>
    612. 

  612. 

  613. </div>
    614. 

  614. </div>
    615. 

  615. </div>
    616. 

  616. <div class="cell border-box-sizing code_cell rendered">
    617. 

  617. <div class="input">
    618. 

  618. 

  619. <div class="inner_cell">
    620. 

  620.     <div class="input_area">
    621. 

  621. <div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;the top three most frequent token used in two artifact classes and their corresponding count and frenquency is:&quot;</span><span class="p">)</span>
    622. 

  622. <span class="n">vocab_shared</span>
    623. 

  623. </pre></div>
    624. 

  624. 

  625.     </div>
    626. 

  626. </div>
    627. 

  627. </div>
    628. 

  628. 

  629. <div class="output_wrapper">
    630. 

  630. <div class="output">
    631. 

  631. 

  632. <div class="output_area">
    633. 

  633. 

  634. <div class="output_subarea output_stream output_stdout output_text">
    635. 

  635. <pre>the top three most frequent token used in two artifact classes and their corresponding count and frenquency is:
    636. 

  636. </pre>
    637. 

  637. </div>
    638. 

  638. </div>
    639. 

  639. 

  640. <div class="output_area">
    641. 

  641. 

  642. 

  643. 

  644. <div class="output_text output_subarea output_execute_result">
    645. 

  645. <pre>{&#39;1&#39;: [2903, 0.02353065144969239],
    646. 

  646.  &#39;8&#39;: [2241, 0.01816472266578045],
    647. 

  647.  &#39;▁&#39;: [53876, 0.43669906217830773]}</pre>
    648. 

  648. </div>
    649. 

  649. 

  650. </div>
    651. 

  651. 

  652. </div>
    653. 

  653. </div>
    654. 

  654. 

  655. </div>
    656. 

  656. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    657. 

  657. <div class="text_cell_render border-box-sizing rendered_html">
    658. 

  658. <p>Use <strong>Vocab</strong> method for the most frequent token in just source artifacts class:</p>
    659. 

  659. 

  660. </div>
    661. 

  661. </div>
    662. 

  662. </div>
    663. 

  663. <div class="cell border-box-sizing code_cell rendered">
    664. 

  664. <div class="input">
    665. 

  665. 

  666. <div class="inner_cell">
    667. 

  667.     <div class="input_area">
    668. 

  668. <div class=" highlight hl-ipython3"><pre><span></span><span class="n">vocab</span> <span class="o">=</span> <span class="n">facade</span><span class="o">.</span><span class="n">Vocab</span><span class="p">(</span><span class="n">source_file</span><span class="p">)</span>
    669. 

  669. </pre></div>
    670. 

  670. 

  671.     </div>
    672. 

  672. </div>
    673. 

  673. </div>
    674. 

  674. 

  675. </div>
    676. 

  676. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    677. 

  677. <div class="text_cell_render border-box-sizing rendered_html">
    678. 

  678. <p>Display the result:</p>
    679. 

  679. 

  680. </div>
    681. 

  681. </div>
    682. 

  682. </div>
    683. 

  683. <div class="cell border-box-sizing code_cell rendered">
    684. 

  684. <div class="input">
    685. 

  685. 

  686. <div class="inner_cell">
    687. 

  687.     <div class="input_area">
    688. 

  688. <div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;the top three most frequent token used in source artifact classes and their corresponding count and frenquency is:&quot;</span><span class="p">)</span>
    689. 

  689. <span class="n">vocab</span>
    690. 

  690. </pre></div>
    691. 

  691. 

  692.     </div>
    693. 

  693. </div>
    694. 

  694. </div>
    695. 

  695. 

  696. <div class="output_wrapper">
    697. 

  697. <div class="output">
    698. 

  698. 

  699. <div class="output_area">
    700. 

  700. 

  701. <div class="output_subarea output_stream output_stdout output_text">
    702. 

  702. <pre>the top three most frequent token used in source artifact classes and their corresponding count and frenquency is:
    703. 

  703. </pre>
    704. 

  704. </div>
    705. 

  705. </div>
    706. 

  706. 

  707. <div class="output_area">
    708. 

  708. 

  709. 

  710. 

  711. <div class="output_text output_subarea output_execute_result">
    712. 

  712. <pre>{&#39;client&#39;: [291, 0.01532304775946501],
    713. 

  713.  &#39;est&#39;: [281, 0.014796482544363119],
    714. 

  714.  &#39;▁&#39;: [8912, 0.4692749196988047]}</pre>
    715. 

  715. </div>
    716. 

  716. 

  717. </div>
    718. 

  718. 

  719. </div>
    720. 

  720. </div>
    721. 

  721. 

  722. </div>
    723. 

  723. <div class="cell border-box-sizing code_cell rendered">
    724. 

  724. <div class="input">
    725. 

  725. 

  726. <div class="inner_cell">
    727. 

  727.     <div class="input_area">
    728. 

  728. <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sharedvocabsize</span> <span class="o">=</span> <span class="n">facade</span><span class="o">.</span><span class="n">SharedVocabSize</span><span class="p">(</span><span class="n">source_file</span><span class="p">,</span> <span class="n">target_file</span><span class="p">)</span>
    729. 

  729. </pre></div>
    730. 

  730. 

  731.     </div>
    732. 

  732. </div>
    733. 

  733. </div>
    734. 

  734. 

  735. </div>
    736. 

  736. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    737. 

  737. <div class="text_cell_render border-box-sizing rendered_html">
    738. 

  738. <p>Display the result:</p>
    739. 

  739. 

  740. </div>
    741. 

  741. </div>
    742. 

  742. </div>
    743. 

  743. <div class="cell border-box-sizing code_cell rendered">
    744. 

  744. <div class="input">
    745. 

  745. 

  746. <div class="inner_cell">
    747. 

  747.     <div class="input_area">
    748. 

  748. <div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;the number of shared token between source and target artifact classes is </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">sharedvocabsize</span><span class="p">))</span>
    749. 

  749. </pre></div>
    750. 

  750. 

  751.     </div>
    752. 

  752. </div>
    753. 

  753. </div>
    754. 

  754. 

  755. <div class="output_wrapper">
    756. 

  756. <div class="output">
    757. 

  757. 

  758. <div class="output_area">
    759. 

  759. 

  760. <div class="output_subarea output_stream output_stdout output_text">
    761. 

  761. <pre>the number of shared token between source and target artifact classes is 5042
    762. 

  762. </pre>
    763. 

  763. </div>
    764. 

  764. </div>
    765. 

  765. 

  766. </div>
    767. 

  767. </div>
    768. 

  768. 

  769. </div>
    770. 

  770. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    771. 

  771. <div class="text_cell_render border-box-sizing rendered_html">
    772. 

  772. <p>Use <strong>CrossEntropy</strong> methods to calcualte cross entropy of source and target class:</p>
    773. 

  773. 

  774. </div>
    775. 

  775. </div>
    776. 

  776. </div>
    777. 

  777. <div class="cell border-box-sizing code_cell rendered">
    778. 

  778. <div class="input">
    779. 

  779. 

  780. <div class="inner_cell">
    781. 

  781.     <div class="input_area">
    782. 

  782. <div class=" highlight hl-ipython3"><pre><span></span><span class="n">entropy</span> <span class="o">=</span> <span class="n">facade</span><span class="o">.</span><span class="n">CrossEntropy</span><span class="p">(</span><span class="n">source_file</span><span class="p">,</span> <span class="n">target_file</span><span class="p">)</span>
    783. 

  783. </pre></div>
    784. 

  784. 

  785.     </div>
    786. 

  786. </div>
    787. 

  787. </div>
    788. 

  788. 

  789. </div>
    790. 

  790. <div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
    791. 

  791. <div class="text_cell_render border-box-sizing rendered_html">
    792. 

  792. <p>Display the result:</p>
    793. 

  793. 

  794. </div>
    795. 

  795. </div>
    796. 

  796. </div>
    797. 

  797. <div class="cell border-box-sizing code_cell rendered">
    798. 

  798. <div class="input">
    799. 

  799. 

  800. <div class="inner_cell">
    801. 

  801.     <div class="input_area">
    802. 

  802. <div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The cross entropy value of source and target artifacts is </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">format</span><span class="p">(</span><span class="n">entropy</span><span class="p">,</span><span class="s2">&quot;.2f&quot;</span><span class="p">)))</span>
    803. 

  803. </pre></div>
    804. 

  804. 

  805.     </div>
    806. 

  806. </div>
    807. 

  807. </div>
    808. 

  808. 

  809. <div class="output_wrapper">
    810. 

  810. <div class="output">
    811. 

  811. 

  812. <div class="output_area">
    813. 

  813. 

  814. <div class="output_subarea output_stream output_stdout output_text">
    815. 

  815. <pre>The cross entropy value of source and target artifacts is 6.16
    816. 

  816. </pre>
    817. 

  817. </div>
    818. 

  818. </div>
    819. 

  819. 

  820. </div>
    821. 

  821. </div>
    822. 

  822. 

  823. </div>
    824. 

  824. </div>
    825. 

  825.  
    826. 

  826.
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