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csinva
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    15. 

  15. </head>
    16. 

  16. <body>
    17. 

  17. <main>
    18. 

  18. <article id="content">
    19. 

  19. <section id="section-intro">
    20. 

  20. <details class="source">
    21. 

  21. <summary>
    22. 

  22. <span>Expand source code</span>
    23. 

  23. </summary>
    24. 

  24. <pre><code class="python">from __future__ import division
    25. 

  25. 

  26. __author__ = &#39;Victor Ruiz, vmr11@pitt.edu&#39;
    27. 

  27. 

  28. from math import log
    29. 

  29. 

  30. import pandas as pd
    31. 

  31. 

  32. 

  33. def entropy(data_classes, base=2):
    34. 

  34.     &#39;&#39;&#39;
    35. 

  35.     Computes the entropy of a set of labels (class instantiations)
    36. 

  36.     :param base: logarithm base for computation
    37. 

  37.     :param data_classes: Series with labels of examples in a dataset
    38. 

  38.     :return: value of entropy
    39. 

  39.     &#39;&#39;&#39;
    40. 

  40.     if not isinstance(data_classes, pd.core.series.Series):
    41. 

  41.         raise AttributeError(&#39;input array should be a pandas series&#39;)
    42. 

  42.     classes = data_classes.unique()
    43. 

  43.     N = len(data_classes)
    44. 

  44.     ent = 0  # initialize entropy
    45. 

  45. 

  46.     # iterate over classes
    47. 

  47.     for c in classes:
    48. 

  48.         partition = data_classes[data_classes == c]  # data with class = c
    49. 

  49.         proportion = len(partition) / N
    50. 

  50.         # update entropy
    51. 

  51.         ent -= proportion * log(proportion, base)
    52. 

  52. 

  53.     return ent
    54. 

  54. 

  55. 

  56. def cut_point_information_gain(dataset, cut_point, feature_label, class_label):
    57. 

  57.     &#39;&#39;&#39;
    58. 

  58.     Return de information gain obtained by splitting a numeric attribute in two according to cut_point
    59. 

  59.     :param dataset: pandas dataframe with a column for attribute values and a column for class
    60. 

  60.     :param cut_point: threshold at which to partition the numeric attribute
    61. 

  61.     :param feature_label: column label of the numeric attribute values in data
    62. 

  62.     :param class_label: column label of the array of instance classes
    63. 

  63.     :return: information gain of partition obtained by threshold cut_point
    64. 

  64.     &#39;&#39;&#39;
    65. 

  65.     if not isinstance(dataset, pd.core.frame.DataFrame):
    66. 

  66.         raise AttributeError(&#39;input dataset should be a pandas data frame&#39;)
    67. 

  67. 

  68.     entropy_full = entropy(dataset[class_label])  # compute entropy of full dataset (w/o split)
    69. 

  69. 

  70.     # split data at cut_point
    71. 

  71.     data_left = dataset[dataset[feature_label] &lt;= cut_point]
    72. 

  72.     data_right = dataset[dataset[feature_label] &gt; cut_point]
    73. 

  73.     (N, N_left, N_right) = (len(dataset), len(data_left), len(data_right))
    74. 

  74. 

  75.     gain = entropy_full - (N_left / N) * entropy(data_left[class_label]) - \
    76. 

  76.            (N_right / N) * entropy(data_right[class_label])
    77. 

  77. 

  78.     return gain</code></pre>
    79. 

  79. </details>
    80. 

  80. </section>
    81. 

  81. <section>
    82. 

  82. </section>
    83. 

  83. <section>
    84. 

  84. </section>
    85. 

  85. <section>
    86. 

  86. <h2 class="section-title" id="header-functions">Functions</h2>
    87. 

  87. <dl>
    88. 

  88. <dt id="imodels.util.metrics.cut_point_information_gain"><code class="name flex">
    89. 

  89. <span>def <span class="ident">cut_point_information_gain</span></span>(<span>dataset, cut_point, feature_label, class_label)</span>
    90. 

  90. </code></dt>
    91. 

  91. <dd>
    92. 

  92. <div class="desc"><p>Return de information gain obtained by splitting a numeric attribute in two according to cut_point
    93. 

  93. :param dataset: pandas dataframe with a column for attribute values and a column for class
    94. 

  94. :param cut_point: threshold at which to partition the numeric attribute
    95. 

  95. :param feature_label: column label of the numeric attribute values in data
    96. 

  96. :param class_label: column label of the array of instance classes
    97. 

  97. :return: information gain of partition obtained by threshold cut_point</p></div>
    98. 

  98. <details class="source">
    99. 

  99. <summary>
    100. 

  100. <span>Expand source code</span>
    101. 

  101. </summary>
    102. 

  102. <pre><code class="python">def cut_point_information_gain(dataset, cut_point, feature_label, class_label):
    103. 

  103.     &#39;&#39;&#39;
    104. 

  104.     Return de information gain obtained by splitting a numeric attribute in two according to cut_point
    105. 

  105.     :param dataset: pandas dataframe with a column for attribute values and a column for class
    106. 

  106.     :param cut_point: threshold at which to partition the numeric attribute
    107. 

  107.     :param feature_label: column label of the numeric attribute values in data
    108. 

  108.     :param class_label: column label of the array of instance classes
    109. 

  109.     :return: information gain of partition obtained by threshold cut_point
    110. 

  110.     &#39;&#39;&#39;
    111. 

  111.     if not isinstance(dataset, pd.core.frame.DataFrame):
    112. 

  112.         raise AttributeError(&#39;input dataset should be a pandas data frame&#39;)
    113. 

  113. 

  114.     entropy_full = entropy(dataset[class_label])  # compute entropy of full dataset (w/o split)
    115. 

  115. 

  116.     # split data at cut_point
    117. 

  117.     data_left = dataset[dataset[feature_label] &lt;= cut_point]
    118. 

  118.     data_right = dataset[dataset[feature_label] &gt; cut_point]
    119. 

  119.     (N, N_left, N_right) = (len(dataset), len(data_left), len(data_right))
    120. 

  120. 

  121.     gain = entropy_full - (N_left / N) * entropy(data_left[class_label]) - \
    122. 

  122.            (N_right / N) * entropy(data_right[class_label])
    123. 

  123. 

  124.     return gain</code></pre>
    125. 

  125. </details>
    126. 

  126. </dd>
    127. 

  127. <dt id="imodels.util.metrics.entropy"><code class="name flex">
    128. 

  128. <span>def <span class="ident">entropy</span></span>(<span>data_classes, base=2)</span>
    129. 

  129. </code></dt>
    130. 

  130. <dd>
    131. 

  131. <div class="desc"><p>Computes the entropy of a set of labels (class instantiations)
    132. 

  132. :param base: logarithm base for computation
    133. 

  133. :param data_classes: Series with labels of examples in a dataset
    134. 

  134. :return: value of entropy</p></div>
    135. 

  135. <details class="source">
    136. 

  136. <summary>
    137. 

  137. <span>Expand source code</span>
    138. 

  138. </summary>
    139. 

  139. <pre><code class="python">def entropy(data_classes, base=2):
    140. 

  140.     &#39;&#39;&#39;
    141. 

  141.     Computes the entropy of a set of labels (class instantiations)
    142. 

  142.     :param base: logarithm base for computation
    143. 

  143.     :param data_classes: Series with labels of examples in a dataset
    144. 

  144.     :return: value of entropy
    145. 

  145.     &#39;&#39;&#39;
    146. 

  146.     if not isinstance(data_classes, pd.core.series.Series):
    147. 

  147.         raise AttributeError(&#39;input array should be a pandas series&#39;)
    148. 

  148.     classes = data_classes.unique()
    149. 

  149.     N = len(data_classes)
    150. 

  150.     ent = 0  # initialize entropy
    151. 

  151. 

  152.     # iterate over classes
    153. 

  153.     for c in classes:
    154. 

  154.         partition = data_classes[data_classes == c]  # data with class = c
    155. 

  155.         proportion = len(partition) / N
    156. 

  156.         # update entropy
    157. 

  157.         ent -= proportion * log(proportion, base)
    158. 

  158. 

  159.     return ent</code></pre>
    160. 

  160. </details>
    161. 

  161. </dd>
    162. 

  162. </dl>
    163. 

  163. </section>
    164. 

  164. <section>
    165. 

  165. </section>
    166. 

  166. </article>
    167. 

  167. <nav id="sidebar">
    168. 

  168. <h1>Index 🔍</h1>
    169. 

  169. <div class="toc">
    170. 

  170. <ul></ul>
    171. 

  171. </div>
    172. 

  172. <ul id="index">
    173. 

  173. <li><h3>Super-module</h3>
    174. 

  174. <ul>
    175. 

  175. <li><code><a title="imodels.util" href="index.html">imodels.util</a></code></li>
    176. 

  176. </ul>
    177. 

  177. </li>
    178. 

  178. <li><h3><a href="#header-functions">Functions</a></h3>
    179. 

  179. <ul class="">
    180. 

  180. <li><code><a title="imodels.util.metrics.cut_point_information_gain" href="#imodels.util.metrics.cut_point_information_gain">cut_point_information_gain</a></code></li>
    181. 

  181. <li><code><a title="imodels.util.metrics.entropy" href="#imodels.util.metrics.entropy">entropy</a></code></li>
    182. 

  182. </ul>
    183. 

  183. </li>
    184. 

  184. </ul>
    185. 

  185. <p><img align="center" width=100% src="https://csinva.io/imodels/img/anim.gif"> </img></p>
    186. 

  186. <!-- add wave animation -->
    187. 

  187. </nav>
    188. 

  188. </main>
    189. 

  189. <footer id="footer">
    190. 

  190. </footer>
    191. 

  191. </body>
    192. 

  192. </html>
    193. 

  193. <!-- add github corner -->
    194. 

  194. <a href="https://github.com/csinva/imodels" class="github-corner" aria-label="View source on GitHub"><svg width="120" height="120" viewBox="0 0 250 250" style="fill:#70B7FD; color:#fff; position: absolute; top: 0; border: 0; right: 0;" aria-hidden="true"><path d="M0,0 L115,115 L130,115 L142,142 L250,250 L250,0 Z"></path><path d="m128.3,109.0 c113.8,99.7 119.0,89.6 119.0,89.6 c122.0,82.7 120.5,78.6 120.5,78.6 c119.2,72.0 123.4,76.3 123.4,76.3 c127.3,80.9 125.5,87.3 125.5,87.3 c122.9,97.6 130.6,101.9 134.4,103.2" fill="currentcolor" style="transform-origin: 130px 106px;" class="octo-arm"></path><path d="M115.0,115.0 C114.9,115.1 118.7,116.5 119.8,115.4 L133.7,101.6 C136.9,99.2 139.9,98.4 142.2,98.6 C133.8,88.0 127.5,74.4 143.8,58.0 C148.5,53.4 154.0,51.2 159.7,51.0 C160.3,49.4 163.2,43.6 171.4,40.1 C171.4,40.1 176.1,42.5 178.8,56.2 C183.1,58.6 187.2,61.8 190.9,65.4 C194.5,69.0 197.7,73.2 200.1,77.6 C213.8,80.2 216.3,84.9 216.3,84.9 C212.7,93.1 206.9,96.0 205.4,96.6 C205.1,102.4 203.0,107.8 198.3,112.5 C181.9,128.9 168.3,122.5 157.7,114.1 C157.9,116.9 156.7,120.9 152.7,124.9 L141.0,136.5 C139.8,137.7 141.6,141.9 141.8,141.8 Z" fill="currentColor" class="octo-body"></path></svg></a><style>.github-corner:hover .octo-arm{animation:octocat-wave 560ms ease-in-out}@keyframes octocat-wave{0%,100%{transform:rotate(0)}20%,60%{transform:rotate(-25deg)}40%,80%{transform:rotate(10deg)}}@media (max-width:500px){.github-corner:hover .octo-arm{animation:none}.github-corner .octo-arm{animation:octocat-wave 560ms ease-in-out}}</style>
    195. 

  195. <!-- add wave animation stylesheet -->
    196. 

  196. <link rel="stylesheet" href="github.css">
    197.
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