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- </head>
- <body>
- <main>
- <article id="content">
- <header>
- <h1 class="title">Module <code>src.interpret</code></h1>
- </header>
- <section id="section-intro">
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">import sys
- sys.path.append('../../hierarchical-dnn-interpretations') # if pip install doesn't work
- import acd
- from acd.scores import cd_propagate
- import numpy as np
- import seaborn as sns
- import matplotlib.colors
- import matplotlib.pyplot as plt
- import torch
- import viz
- def calc_cd_score(xtrack_t, xfeats_t, start, stop, model):
- with torch.no_grad():
- rel, irrel = cd_propagate.propagate_lstm(xtrack_t.unsqueeze(-1), model.lstm, start=start, stop=stop, my_device='cpu')
- rel = rel.squeeze(1)
- irrel = irrel.squeeze(1)
- rel, irrel = cd_propagate.propagate_conv_linear(rel, irrel, model.fc)
- #return rel.item()
- return rel.data.numpy()
- def plot_segs(track_segs, cd_scores, xtrack,
- pred=None, y=None, vabs=None, cbar=True, xticks=True, yticks=True):
- '''Plot a single segmentation plot
- '''
- # cm = sns.diverging_palette(22, 220, as_cmap=True, center='light')
- # cm = LinearSegmentedColormap.from_list(
- # name='orange-blue',
- # colors=[(222/255, 85/255, 51/255),'lightgray', (50/255, 129/255, 168/255)]
- # )
- if vabs is None:
- vabs = np.max(np.abs(cd_scores))
- norm = matplotlib.colors.Normalize(vmin=-vabs, vmax=vabs)
- #vabs = 1.2
- # plt.plot(xtrack, zorder=0, lw=2, color='#111111')
- for i in range(len(track_segs)):
- (s, e) = track_segs[i]
- cd_score = cd_scores[i]
- seq_len = e - s
- xs = np.arange(s, e)
- if seq_len > 1:
- cd_score = [cd_score] * seq_len
- col = viz.cmap(norm(cd_score[0]))
- while len(col) == 1:
- col = col[0]
- plt.plot(xs, xtrack[s: e], zorder=0, lw=2, color=col, alpha=0.5)
- plt.scatter(xs, xtrack[s: e],
- c=cd_score, cmap=viz.cmap, vmin=-vabs, vmax=vabs, s=6)
- if pred is not None:
- plt.title(f"Pred: {pred: .1f}, y: {y}", fontsize=24)
- cb = None
- if cbar:
- cb = plt.colorbar() #label='CD Score')
- cb.outline.set_visible(False)
- if not xticks:
- plt.xticks([])
- if not yticks:
- plt.yticks([])
- return cb
-
-
-
- def max_abs_sum_seg(scores_list, min_length: int=1):
- """
- score_list[i][j] is the score for the segment from i to j (inclusive)
- Params
- ------
- min_length
- Minimum allowable length for a segment
- """
-
- n = len(scores_list[0])
- res = [0]*n
- paths = {}
- for s in range(n):
- for e in range(s, n):
- if e - s >= min_length - 1:
- scores_list[s][e] = abs(scores_list[s][e])
- else:
- scores_list[s][e] = -10000
- paths[-1] = []
- res[0] = scores_list[0][0]
- paths[0] = [0]
- for i in (range(1, n)):
- cand = [res[j-1] + scores_list[j][i] for j in range(i + 1)]
- seg_start = np.argmax(cand)
- res[i] = max(cand)
- paths[i] = paths[seg_start - 1] + [seg_start]
- return res, paths</code></pre>
- </details>
- </section>
- <section>
- </section>
- <section>
- </section>
- <section>
- <h2 class="section-title" id="header-functions">Functions</h2>
- <dl>
- <dt id="src.interpret.calc_cd_score"><code class="name flex">
- <span>def <span class="ident">calc_cd_score</span></span>(<span>xtrack_t, xfeats_t, start, stop, model)</span>
- </code></dt>
- <dd>
- <section class="desc"></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def calc_cd_score(xtrack_t, xfeats_t, start, stop, model):
- with torch.no_grad():
- rel, irrel = cd_propagate.propagate_lstm(xtrack_t.unsqueeze(-1), model.lstm, start=start, stop=stop, my_device='cpu')
- rel = rel.squeeze(1)
- irrel = irrel.squeeze(1)
- rel, irrel = cd_propagate.propagate_conv_linear(rel, irrel, model.fc)
- #return rel.item()
- return rel.data.numpy()</code></pre>
- </details>
- </dd>
- <dt id="src.interpret.max_abs_sum_seg"><code class="name flex">
- <span>def <span class="ident">max_abs_sum_seg</span></span>(<span>scores_list, min_length=1)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>score_list[i][j] is the score for the segment from i to j (inclusive)
- Params</p>
- <hr>
- <dl>
- <dt><strong><code>min_length</code></strong></dt>
- <dd>Minimum allowable length for a segment</dd>
- </dl></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def max_abs_sum_seg(scores_list, min_length: int=1):
- """
- score_list[i][j] is the score for the segment from i to j (inclusive)
- Params
- ------
- min_length
- Minimum allowable length for a segment
- """
-
- n = len(scores_list[0])
- res = [0]*n
- paths = {}
- for s in range(n):
- for e in range(s, n):
- if e - s >= min_length - 1:
- scores_list[s][e] = abs(scores_list[s][e])
- else:
- scores_list[s][e] = -10000
- paths[-1] = []
- res[0] = scores_list[0][0]
- paths[0] = [0]
- for i in (range(1, n)):
- cand = [res[j-1] + scores_list[j][i] for j in range(i + 1)]
- seg_start = np.argmax(cand)
- res[i] = max(cand)
- paths[i] = paths[seg_start - 1] + [seg_start]
- return res, paths</code></pre>
- </details>
- </dd>
- <dt id="src.interpret.plot_segs"><code class="name flex">
- <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>
- </code></dt>
- <dd>
- <section class="desc"><p>Plot a single segmentation plot</p></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def plot_segs(track_segs, cd_scores, xtrack,
- pred=None, y=None, vabs=None, cbar=True, xticks=True, yticks=True):
- '''Plot a single segmentation plot
- '''
- # cm = sns.diverging_palette(22, 220, as_cmap=True, center='light')
- # cm = LinearSegmentedColormap.from_list(
- # name='orange-blue',
- # colors=[(222/255, 85/255, 51/255),'lightgray', (50/255, 129/255, 168/255)]
- # )
- if vabs is None:
- vabs = np.max(np.abs(cd_scores))
- norm = matplotlib.colors.Normalize(vmin=-vabs, vmax=vabs)
- #vabs = 1.2
- # plt.plot(xtrack, zorder=0, lw=2, color='#111111')
- for i in range(len(track_segs)):
- (s, e) = track_segs[i]
- cd_score = cd_scores[i]
- seq_len = e - s
- xs = np.arange(s, e)
- if seq_len > 1:
- cd_score = [cd_score] * seq_len
- col = viz.cmap(norm(cd_score[0]))
- while len(col) == 1:
- col = col[0]
- plt.plot(xs, xtrack[s: e], zorder=0, lw=2, color=col, alpha=0.5)
- plt.scatter(xs, xtrack[s: e],
- c=cd_score, cmap=viz.cmap, vmin=-vabs, vmax=vabs, s=6)
- if pred is not None:
- plt.title(f"Pred: {pred: .1f}, y: {y}", fontsize=24)
- cb = None
- if cbar:
- cb = plt.colorbar() #label='CD Score')
- cb.outline.set_visible(False)
- if not xticks:
- plt.xticks([])
- if not yticks:
- plt.yticks([])
- return cb</code></pre>
- </details>
- </dd>
- </dl>
- </section>
- <section>
- </section>
- </article>
- <nav id="sidebar">
- <h1>Index</h1>
- <div class="toc">
- <ul></ul>
- </div>
- <ul id="index">
- <li><h3>Super-module</h3>
- <ul>
- <li><code><a title="src" href="index.html">src</a></code></li>
- </ul>
- </li>
- <li><h3><a href="#header-functions">Functions</a></h3>
- <ul class="">
- <li><code><a title="src.interpret.calc_cd_score" href="#src.interpret.calc_cd_score">calc_cd_score</a></code></li>
- <li><code><a title="src.interpret.max_abs_sum_seg" href="#src.interpret.max_abs_sum_seg">max_abs_sum_seg</a></code></li>
- <li><code><a title="src.interpret.plot_segs" href="#src.interpret.plot_segs">plot_segs</a></code></li>
- </ul>
- </li>
- </ul>
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