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csinva
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auxilin-prediction
mirror of https://github.com/Yu-Group/auxilin-prediction

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    15. 

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    16. 

  16. <body>
    17. 

  17. <main>
    18. 

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

  19. <header>
    20. 

  20. <h1 class="title">Module <code>src.interpret</code></h1>
    21. 

  21. </header>
    22. 

  22. <section id="section-intro">
    23. 

  23. <details class="source">
    24. 

  24. <summary>
    25. 

  25. <span>Expand source code</span>
    26. 

  26. </summary>
    27. 

  27. <pre><code class="python">import sys
    28. 

  28. sys.path.append(&#39;../../hierarchical-dnn-interpretations&#39;) # if pip install doesn&#39;t work
    29. 

  29. import acd
    30. 

  30. from acd.scores import cd_propagate
    31. 

  31. import numpy as np
    32. 

  32. import seaborn as sns
    33. 

  33. import matplotlib.colors
    34. 

  34. import matplotlib.pyplot as plt
    35. 

  35. import torch
    36. 

  36. import viz
    37. 

  37. 

  38. 

  39. def calc_cd_score(xtrack_t, xfeats_t, start, stop, model):
    40. 

  40.     with torch.no_grad():
    41. 

  41.         rel, irrel = cd_propagate.propagate_lstm(xtrack_t.unsqueeze(-1), model.lstm, start=start, stop=stop, my_device=&#39;cpu&#39;)
    42. 

  42.     rel = rel.squeeze(1)
    43. 

  43.     irrel = irrel.squeeze(1)
    44. 

  44.     rel, irrel = cd_propagate.propagate_conv_linear(rel, irrel, model.fc)
    45. 

  45.     #return rel.item()
    46. 

  46.     return rel.data.numpy()
    47. 

  47. 

  48. def plot_segs(track_segs, cd_scores, xtrack,
    49. 

  49.               pred=None, y=None, vabs=None, cbar=True, xticks=True, yticks=True):
    50. 

  50.     &#39;&#39;&#39;Plot a single segmentation plot
    51. 

  51.     &#39;&#39;&#39;
    52. 

  52. #     cm = sns.diverging_palette(22, 220, as_cmap=True, center=&#39;light&#39;)
    53. 

  53. #     cm = LinearSegmentedColormap.from_list(
    54. 

  54. #         name=&#39;orange-blue&#39;, 
    55. 

  55. #         colors=[(222/255, 85/255, 51/255),&#39;lightgray&#39;, (50/255, 129/255, 168/255)]
    56. 

  56. #     )
    57. 

  57.     if vabs is None:
    58. 

  58.         vabs = np.max(np.abs(cd_scores))
    59. 

  59.     norm = matplotlib.colors.Normalize(vmin=-vabs, vmax=vabs)
    60. 

  60.     #vabs = 1.2
    61. 

  61.     # plt.plot(xtrack, zorder=0, lw=2, color=&#39;#111111&#39;)
    62. 

  62.     for i in range(len(track_segs)):
    63. 

  63.         (s, e) = track_segs[i]
    64. 

  64.         cd_score = cd_scores[i]
    65. 

  65.         seq_len = e - s
    66. 

  66.         xs = np.arange(s, e)
    67. 

  67.         if seq_len &gt; 1:
    68. 

  68.             cd_score = [cd_score] * seq_len
    69. 

  69.             col = viz.cmap(norm(cd_score[0]))
    70. 

  70.             while len(col) == 1:
    71. 

  71.                 col = col[0]
    72. 

  72.             plt.plot(xs, xtrack[s: e], zorder=0, lw=2, color=col, alpha=0.5)
    73. 

  73.         plt.scatter(xs, xtrack[s: e],
    74. 

  74.                     c=cd_score, cmap=viz.cmap, vmin=-vabs, vmax=vabs, s=6)
    75. 

  75.     if pred is not None:
    76. 

  76.         plt.title(f&#34;Pred: {pred: .1f}, y: {y}&#34;, fontsize=24)
    77. 

  77.     cb = None
    78. 

  78.     if cbar:
    79. 

  79.         cb = plt.colorbar() #label=&#39;CD Score&#39;)
    80. 

  80.         cb.outline.set_visible(False)
    81. 

  81.     if not xticks:
    82. 

  82.         plt.xticks([])
    83. 

  83.     if not yticks:
    84. 

  84.         plt.yticks([])
    85. 

  85.     return cb
    86. 

  86.     
    87. 

  87.     
    88. 

  88.     
    89. 

  89. def max_abs_sum_seg(scores_list, min_length: int=1):
    90. 

  90.     &#34;&#34;&#34;
    91. 

  91.     score_list[i][j] is the score for the segment from i to j (inclusive)
    92. 

  92.     Params
    93. 

  93.     ------
    94. 

  94.     min_length
    95. 

  95.         Minimum allowable length for a segment
    96. 

  96.     &#34;&#34;&#34;
    97. 

  97.     
    98. 

  98.     n = len(scores_list[0])
    99. 

  99.     res = [0]*n
    100. 

  100.     paths = {}
    101. 

  101.     for s in range(n):
    102. 

  102.         for e in range(s, n):
    103. 

  103.             if e - s &gt;= min_length - 1:
    104. 

  104.                 scores_list[s][e] = abs(scores_list[s][e])
    105. 

  105.             else:
    106. 

  106.                 scores_list[s][e] = -10000
    107. 

  107.     paths[-1] = []
    108. 

  108.     res[0] = scores_list[0][0]
    109. 

  109.     paths[0] = [0]
    110. 

  110.     for i in (range(1, n)):
    111. 

  111.         cand = [res[j-1] + scores_list[j][i] for j in range(i + 1)]
    112. 

  112.         seg_start = np.argmax(cand)
    113. 

  113.         res[i] = max(cand)
    114. 

  114.         paths[i] = paths[seg_start - 1] + [seg_start]
    115. 

  115.     return res, paths</code></pre>
    116. 

  116. </details>
    117. 

  117. </section>
    118. 

  118. <section>
    119. 

  119. </section>
    120. 

  120. <section>
    121. 

  121. </section>
    122. 

  122. <section>
    123. 

  123. <h2 class="section-title" id="header-functions">Functions</h2>
    124. 

  124. <dl>
    125. 

  125. <dt id="src.interpret.calc_cd_score"><code class="name flex">
    126. 

  126. <span>def <span class="ident">calc_cd_score</span></span>(<span>xtrack_t, xfeats_t, start, stop, model)</span>
    127. 

  127. </code></dt>
    128. 

  128. <dd>
    129. 

  129. <section class="desc"></section>
    130. 

  130. <details class="source">
    131. 

  131. <summary>
    132. 

  132. <span>Expand source code</span>
    133. 

  133. </summary>
    134. 

  134. <pre><code class="python">def calc_cd_score(xtrack_t, xfeats_t, start, stop, model):
    135. 

  135.     with torch.no_grad():
    136. 

  136.         rel, irrel = cd_propagate.propagate_lstm(xtrack_t.unsqueeze(-1), model.lstm, start=start, stop=stop, my_device=&#39;cpu&#39;)
    137. 

  137.     rel = rel.squeeze(1)
    138. 

  138.     irrel = irrel.squeeze(1)
    139. 

  139.     rel, irrel = cd_propagate.propagate_conv_linear(rel, irrel, model.fc)
    140. 

  140.     #return rel.item()
    141. 

  141.     return rel.data.numpy()</code></pre>
    142. 

  142. </details>
    143. 

  143. </dd>
    144. 

  144. <dt id="src.interpret.max_abs_sum_seg"><code class="name flex">
    145. 

  145. <span>def <span class="ident">max_abs_sum_seg</span></span>(<span>scores_list, min_length=1)</span>
    146. 

  146. </code></dt>
    147. 

  147. <dd>
    148. 

  148. <section class="desc"><p>score_list[i][j] is the score for the segment from i to j (inclusive)
    149. 

  149. Params</p>
    150. 

  150. <hr>
    151. 

  151. <dl>
    152. 

  152. <dt><strong><code>min_length</code></strong></dt>
    153. 

  153. <dd>Minimum allowable length for a segment</dd>
    154. 

  154. </dl></section>
    155. 

  155. <details class="source">
    156. 

  156. <summary>
    157. 

  157. <span>Expand source code</span>
    158. 

  158. </summary>
    159. 

  159. <pre><code class="python">def max_abs_sum_seg(scores_list, min_length: int=1):
    160. 

  160.     &#34;&#34;&#34;
    161. 

  161.     score_list[i][j] is the score for the segment from i to j (inclusive)
    162. 

  162.     Params
    163. 

  163.     ------
    164. 

  164.     min_length
    165. 

  165.         Minimum allowable length for a segment
    166. 

  166.     &#34;&#34;&#34;
    167. 

  167.     
    168. 

  168.     n = len(scores_list[0])
    169. 

  169.     res = [0]*n
    170. 

  170.     paths = {}
    171. 

  171.     for s in range(n):
    172. 

  172.         for e in range(s, n):
    173. 

  173.             if e - s &gt;= min_length - 1:
    174. 

  174.                 scores_list[s][e] = abs(scores_list[s][e])
    175. 

  175.             else:
    176. 

  176.                 scores_list[s][e] = -10000
    177. 

  177.     paths[-1] = []
    178. 

  178.     res[0] = scores_list[0][0]
    179. 

  179.     paths[0] = [0]
    180. 

  180.     for i in (range(1, n)):
    181. 

  181.         cand = [res[j-1] + scores_list[j][i] for j in range(i + 1)]
    182. 

  182.         seg_start = np.argmax(cand)
    183. 

  183.         res[i] = max(cand)
    184. 

  184.         paths[i] = paths[seg_start - 1] + [seg_start]
    185. 

  185.     return res, paths</code></pre>
    186. 

  186. </details>
    187. 

  187. </dd>
    188. 

  188. <dt id="src.interpret.plot_segs"><code class="name flex">
    189. 

  189. <span>def <span class="ident">plot_segs</span></span>(<span>track_segs, cd_scores, xtrack, pred=None, y=None, vabs=None, cbar=True, xticks=True, yticks=True)</span>
    190. 

  190. </code></dt>
    191. 

  191. <dd>
    192. 

  192. <section class="desc"><p>Plot a single segmentation plot</p></section>
    193. 

  193. <details class="source">
    194. 

  194. <summary>
    195. 

  195. <span>Expand source code</span>
    196. 

  196. </summary>
    197. 

  197. <pre><code class="python">def plot_segs(track_segs, cd_scores, xtrack,
    198. 

  198.               pred=None, y=None, vabs=None, cbar=True, xticks=True, yticks=True):
    199. 

  199.     &#39;&#39;&#39;Plot a single segmentation plot
    200. 

  200.     &#39;&#39;&#39;
    201. 

  201. #     cm = sns.diverging_palette(22, 220, as_cmap=True, center=&#39;light&#39;)
    202. 

  202. #     cm = LinearSegmentedColormap.from_list(
    203. 

  203. #         name=&#39;orange-blue&#39;, 
    204. 

  204. #         colors=[(222/255, 85/255, 51/255),&#39;lightgray&#39;, (50/255, 129/255, 168/255)]
    205. 

  205. #     )
    206. 

  206.     if vabs is None:
    207. 

  207.         vabs = np.max(np.abs(cd_scores))
    208. 

  208.     norm = matplotlib.colors.Normalize(vmin=-vabs, vmax=vabs)
    209. 

  209.     #vabs = 1.2
    210. 

  210.     # plt.plot(xtrack, zorder=0, lw=2, color=&#39;#111111&#39;)
    211. 

  211.     for i in range(len(track_segs)):
    212. 

  212.         (s, e) = track_segs[i]
    213. 

  213.         cd_score = cd_scores[i]
    214. 

  214.         seq_len = e - s
    215. 

  215.         xs = np.arange(s, e)
    216. 

  216.         if seq_len &gt; 1:
    217. 

  217.             cd_score = [cd_score] * seq_len
    218. 

  218.             col = viz.cmap(norm(cd_score[0]))
    219. 

  219.             while len(col) == 1:
    220. 

  220.                 col = col[0]
    221. 

  221.             plt.plot(xs, xtrack[s: e], zorder=0, lw=2, color=col, alpha=0.5)
    222. 

  222.         plt.scatter(xs, xtrack[s: e],
    223. 

  223.                     c=cd_score, cmap=viz.cmap, vmin=-vabs, vmax=vabs, s=6)
    224. 

  224.     if pred is not None:
    225. 

  225.         plt.title(f&#34;Pred: {pred: .1f}, y: {y}&#34;, fontsize=24)
    226. 

  226.     cb = None
    227. 

  227.     if cbar:
    228. 

  228.         cb = plt.colorbar() #label=&#39;CD Score&#39;)
    229. 

  229.         cb.outline.set_visible(False)
    230. 

  230.     if not xticks:
    231. 

  231.         plt.xticks([])
    232. 

  232.     if not yticks:
    233. 

  233.         plt.yticks([])
    234. 

  234.     return cb</code></pre>
    235. 

  235. </details>
    236. 

  236. </dd>
    237. 

  237. </dl>
    238. 

  238. </section>
    239. 

  239. <section>
    240. 

  240. </section>
    241. 

  241. </article>
    242. 

  242. <nav id="sidebar">
    243. 

  243. <h1>Index</h1>
    244. 

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

  245. <ul></ul>
    246. 

  246. </div>
    247. 

  247. <ul id="index">
    248. 

  248. <li><h3>Super-module</h3>
    249. 

  249. <ul>
    250. 

  250. <li><code><a title="src" href="index.html">src</a></code></li>
    251. 

  251. </ul>
    252. 

  252. </li>
    253. 

  253. <li><h3><a href="#header-functions">Functions</a></h3>
    254. 

  254. <ul class="">
    255. 

  255. <li><code><a title="src.interpret.calc_cd_score" href="#src.interpret.calc_cd_score">calc_cd_score</a></code></li>
    256. 

  256. <li><code><a title="src.interpret.max_abs_sum_seg" href="#src.interpret.max_abs_sum_seg">max_abs_sum_seg</a></code></li>
    257. 

  257. <li><code><a title="src.interpret.plot_segs" href="#src.interpret.plot_segs">plot_segs</a></code></li>
    258. 

  258. </ul>
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Integrate Google Cloud Storage
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Select bucket
Upload key
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Use Google Cloud Storage!

Browsing data directories saved to Google Cloud Storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

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Service Account Key

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auxilin-prediction is now integrated with Google Cloud Storage!

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Integrate AWS S3
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Access key
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Use AWS S3 as storage!

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Specify your S3 bucket

Access key (If needed)

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auxilin-prediction is now integrated with AWS S3!

Delete Storage Key

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Integrate S3 compatible storage
Use S3 like remote
Select bucket
Access key
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Use any S3 compatible storage!

Browsing data directories saved to S3 compatible storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your S3 bucket

Access key (If needed)

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auxilin-prediction is now integrated with your S3 compatible storage!

Delete Storage Key

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Integrate Azure Cloud Storage
Use Azure Storage
Select bucket
Set key
Finish

Use Azure Cloud Storage!

Browsing data directories saved to Azure Cloud Storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your Azure Storage bucket

Access key (If needed)

Congratulations!

auxilin-prediction is now integrated with Azure Cloud Storage!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes