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tabular.model.html
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tabular.model.html
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---
title: Tabular model
keywords: fastai
sidebar: home_sidebar
summary: "A basic model that can be used on tabular data"
---
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">nbdev.showdoc</span> <span class="k">import</span> <span class="o">*</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">set_device</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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<h2 id="Model">Model<a class="anchor-link" href="#Model">¶</a></h2>
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<h4 id="emb_sz_rule" class="doc_header"><code>emb_sz_rule</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/tabular/model.py#L11" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>emb_sz_rule</code>(<strong><code>n_cat</code></strong>)</p>
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<p>Rule of thumb to pick embedding size corresponding to <code>n_cat</code></p>
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<h4 id="get_emb_sz" class="doc_header"><code>get_emb_sz</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/tabular/model.py#L24" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_emb_sz</code>(<strong><code>to</code></strong>, <strong><code>sz_dict</code></strong>=<em><code>None</code></em>)</p>
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<p>Get default embedding size from <code>TabularPreprocessor</code> <code>proc</code> or the ones in <code>sz_dict</code></p>
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<h2 id="TabularModel" class="doc_header"><code>class</code> <code>TabularModel</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/tabular/model.py#L29" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>TabularModel</code>(<strong><code>emb_szs</code></strong>, <strong><code>n_cont</code></strong>, <strong><code>out_sz</code></strong>, <strong><code>layers</code></strong>, <strong><code>ps</code></strong>=<em><code>None</code></em>, <strong><code>embed_p</code></strong>=<em><code>0.0</code></em>, <strong><code>y_range</code></strong>=<em><code>None</code></em>, <strong><code>use_bn</code></strong>=<em><code>True</code></em>, <strong><code>bn_final</code></strong>=<em><code>False</code></em>) :: <a href="/torchcore.html#Module"><code>Module</code></a></p>
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<p>Basic model for tabular data.</p>
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<h2 id="Integration-example-with-training">Integration example with training<a class="anchor-link" href="#Integration-example-with-training">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">fastai2.data.all</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">fastai2.tabular.core</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">fastai2.optimizer</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">fastai2.learner</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">fastai2.metrics</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">fastai2.callback.all</span> <span class="k">import</span> <span class="o">*</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">path</span> <span class="o">=</span> <span class="n">untar_data</span><span class="p">(</span><span class="n">URLs</span><span class="o">.</span><span class="n">ADULT_SAMPLE</span><span class="p">)</span>
<span class="n">df</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="n">path</span><span class="o">/</span><span class="s1">'adult.csv'</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cat_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'workclass'</span><span class="p">,</span> <span class="s1">'education'</span><span class="p">,</span> <span class="s1">'marital-status'</span><span class="p">,</span> <span class="s1">'occupation'</span><span class="p">,</span> <span class="s1">'relationship'</span><span class="p">,</span> <span class="s1">'race'</span><span class="p">]</span>
<span class="n">cont_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'age'</span><span class="p">,</span> <span class="s1">'fnlwgt'</span><span class="p">,</span> <span class="s1">'education-num'</span><span class="p">]</span>
<span class="n">procs</span> <span class="o">=</span> <span class="p">[</span><span class="n">Categorify</span><span class="p">,</span> <span class="n">FillMissing</span><span class="p">,</span> <span class="n">Normalize</span><span class="p">]</span>
<span class="n">splits</span> <span class="o">=</span> <span class="n">RandomSplitter</span><span class="p">()(</span><span class="n">range_of</span><span class="p">(</span><span class="n">df</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">to</span> <span class="o">=</span> <span class="n">TabularPandas</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">procs</span><span class="p">,</span> <span class="n">cat_names</span><span class="p">,</span> <span class="n">cont_names</span><span class="p">,</span> <span class="n">y_names</span><span class="o">=</span><span class="s2">"salary"</span><span class="p">,</span> <span class="n">splits</span><span class="o">=</span><span class="n">splits</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">trn_dl</span> <span class="o">=</span> <span class="n">TabDataLoader</span><span class="p">(</span><span class="n">to</span><span class="o">.</span><span class="n">train</span><span class="p">,</span> <span class="n">bs</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">drop_last</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">val_dl</span> <span class="o">=</span> <span class="n">TabDataLoader</span><span class="p">(</span><span class="n">to</span><span class="o">.</span><span class="n">valid</span><span class="p">,</span> <span class="n">bs</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">dbunch</span> <span class="o">=</span> <span class="n">DataBunch</span><span class="p">(</span><span class="n">trn_dl</span><span class="p">,</span> <span class="n">val_dl</span><span class="p">)</span>
<span class="n">dbunch</span><span class="o">.</span><span class="n">show_batch</span><span class="p">()</span>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>workclass</th>
<th>education</th>
<th>marital-status</th>
<th>occupation</th>
<th>relationship</th>
<th>race</th>
<th>age_na</th>
<th>fnlwgt_na</th>
<th>education-num_na</th>
<th>age</th>
<th>fnlwgt</th>
<th>education-num</th>
<th>salary</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>Private</td>
<td>Some-college</td>
<td>Never-married</td>
<td>Transport-moving</td>
<td>Own-child</td>
<td>Black</td>
<td>False</td>
<td>False</td>
<td>False</td>
<td>21.000001</td>
<td>319163.001761</td>
<td>10.0</td>
<td><50k</td>
</tr>
<tr>
<th>1</th>
<td>?</td>
<td>Assoc-acdm</td>
<td>Married-civ-spouse</td>
<td>?</td>
<td>Wife</td>
<td>White</td>
<td>False</td>
<td>False</td>
<td>False</td>
<td>35.000000</td>
<td>144172.000973</td>
<td>12.0</td>
<td><50k</td>
</tr>
<tr>
<th>2</th>
<td>Self-emp-not-inc</td>
<td>Some-college</td>
<td>Married-civ-spouse</td>
<td>Farming-fishing</td>
<td>Husband</td>
<td>White</td>
<td>False</td>
<td>False</td>
<td>False</td>
<td>55.000000</td>
<td>184425.000136</td>
<td>10.0</td>
<td>>=50k</td>
</tr>
<tr>
<th>3</th>
<td>Private</td>
<td>Bachelors</td>
<td>Married-civ-spouse</td>
<td>Prof-specialty</td>
<td>Husband</td>
<td>White</td>
<td>False</td>
<td>False</td>
<td>False</td>
<td>36.000000</td>
<td>156667.000850</td>
<td>13.0</td>
<td>>=50k</td>
</tr>
<tr>
<th>4</th>
<td>Private</td>
<td>Some-college</td>
<td>Married-civ-spouse</td>
<td>Craft-repair</td>
<td>Husband</td>
<td>Asian-Pac-Islander</td>
<td>False</td>
<td>False</td>
<td>False</td>
<td>38.000000</td>
<td>396594.996426</td>
<td>10.0</td>
<td>>=50k</td>
</tr>
<tr>
<th>5</th>
<td>Private</td>
<td>Some-college</td>
<td>Married-civ-spouse</td>
<td>Exec-managerial</td>
<td>Husband</td>
<td>White</td>
<td>False</td>
<td>False</td>
<td>False</td>
<td>35.000000</td>
<td>226788.999137</td>
<td>10.0</td>
<td><50k</td>
</tr>
<tr>
<th>6</th>
<td>Private</td>
<td>Assoc-voc</td>
<td>Married-civ-spouse</td>
<td>Craft-repair</td>
<td>Husband</td>
<td>White</td>
<td>False</td>
<td>False</td>
<td>False</td>
<td>32.000000</td>
<td>148206.998673</td>
<td>11.0</td>
<td><50k</td>
</tr>
<tr>
<th>7</th>
<td>Private</td>
<td>10th</td>
<td>Married-civ-spouse</td>
<td>Craft-repair</td>
<td>Husband</td>
<td>White</td>
<td>False</td>
<td>False</td>
<td>False</td>
<td>31.000000</td>
<td>209538.000388</td>
<td>6.0</td>
<td><50k</td>
</tr>
<tr>
<th>8</th>
<td>State-gov</td>
<td>Bachelors</td>
<td>Married-civ-spouse</td>
<td>Prof-specialty</td>
<td>Husband</td>
<td>Asian-Pac-Islander</td>
<td>False</td>
<td>False</td>
<td>False</td>
<td>38.000000</td>
<td>125499.001839</td>
<td>13.0</td>
<td>>=50k</td>
</tr>
<tr>
<th>9</th>
<td>Private</td>
<td>HS-grad</td>
<td>Never-married</td>
<td>Adm-clerical</td>
<td>Own-child</td>
<td>White</td>
<td>False</td>
<td>False</td>
<td>False</td>
<td>25.000000</td>
<td>231637.999891</td>
<td>9.0</td>
<td><50k</td>
</tr>
</tbody>
</table>
</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">TabularModel</span><span class="p">(</span><span class="n">get_emb_sz</span><span class="p">(</span><span class="n">to</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">to</span><span class="o">.</span><span class="n">cont_names</span><span class="p">),</span> <span class="mi">2</span><span class="p">,</span> <span class="p">[</span><span class="mi">200</span><span class="p">,</span><span class="mi">100</span><span class="p">])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">opt_func</span> <span class="o">=</span> <span class="n">partial</span><span class="p">(</span><span class="n">Adam</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">)</span>
<span class="n">learn</span> <span class="o">=</span> <span class="n">Learner</span><span class="p">(</span><span class="n">dbunch</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">CrossEntropyLossFlat</span><span class="p">(),</span> <span class="n">opt_func</span><span class="o">=</span><span class="n">opt_func</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="n">accuracy</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span><span class="o">.</span><span class="n">fit_one_cycle</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
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<th>epoch</th>
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<td>0</td>
<td>0.362401</td>
<td>0.361022</td>
<td>0.827856</td>
<td>00:40</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span><span class="o">.</span><span class="n">show_results</span><span class="p">()</span>
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<pre>
<span class="ansi-red-fg">------------------------------------------------------------------------</span>
<span class="ansi-red-fg">ValueError</span> Traceback (most recent call last)
<span class="ansi-green-fg"><ipython-input-18-c3b657dcc9ae></span> in <span class="ansi-cyan-fg"><module></span>
<span class="ansi-green-fg">----> 1</span><span class="ansi-red-fg"> </span>learn<span class="ansi-blue-fg">.</span>show_results<span class="ansi-blue-fg">(</span><span class="ansi-blue-fg">)</span>
<span class="ansi-green-fg">~/git/fastai_dev/dev/local/learner.py</span> in <span class="ansi-cyan-fg">show_results</span><span class="ansi-blue-fg">(self, ds_idx, dl, max_n, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 295</span> b <span class="ansi-blue-fg">=</span> dl<span class="ansi-blue-fg">.</span>one_batch<span class="ansi-blue-fg">(</span><span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 296</span> _<span class="ansi-blue-fg">,</span>_<span class="ansi-blue-fg">,</span>preds <span class="ansi-blue-fg">=</span> self<span class="ansi-blue-fg">.</span>get_preds<span class="ansi-blue-fg">(</span>dl<span class="ansi-blue-fg">=</span><span class="ansi-blue-fg">[</span>b<span class="ansi-blue-fg">]</span><span class="ansi-blue-fg">,</span> with_decoded<span class="ansi-blue-fg">=</span><span class="ansi-green-fg">True</span><span class="ansi-blue-fg">)</span>
<span class="ansi-green-fg">--> 297</span><span class="ansi-red-fg"> </span>self<span class="ansi-blue-fg">.</span>dbunch<span class="ansi-blue-fg">.</span>show_results<span class="ansi-blue-fg">(</span>b<span class="ansi-blue-fg">,</span> preds<span class="ansi-blue-fg">,</span> max_n<span class="ansi-blue-fg">=</span>max_n<span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">**</span>kwargs<span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 298</span>
<span class="ansi-green-intense-fg ansi-bold"> 299</span> <span class="ansi-green-fg">def</span> show_training_loop<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span>
<span class="ansi-green-fg">~/git/fastai_dev/dev/local/data/core.py</span> in <span class="ansi-cyan-fg">show_results</span><span class="ansi-blue-fg">(self, b, out, max_n, ctxs, show, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 83</span> x<span class="ansi-blue-fg">,</span>y<span class="ansi-blue-fg">,</span>its <span class="ansi-blue-fg">=</span> self<span class="ansi-blue-fg">.</span>show_batch<span class="ansi-blue-fg">(</span>b<span class="ansi-blue-fg">,</span> max_n<span class="ansi-blue-fg">=</span>max_n<span class="ansi-blue-fg">,</span> show<span class="ansi-blue-fg">=</span><span class="ansi-green-fg">False</span><span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 84</span> b_out <span class="ansi-blue-fg">=</span> b<span class="ansi-blue-fg">[</span><span class="ansi-blue-fg">:</span>self<span class="ansi-blue-fg">.</span>n_inp<span class="ansi-blue-fg">]</span> <span class="ansi-blue-fg">+</span> <span class="ansi-blue-fg">(</span>tuple<span class="ansi-blue-fg">(</span>out<span class="ansi-blue-fg">)</span> <span class="ansi-green-fg">if</span> is_listy<span class="ansi-blue-fg">(</span>out<span class="ansi-blue-fg">)</span> <span class="ansi-green-fg">else</span> <span class="ansi-blue-fg">(</span>out<span class="ansi-blue-fg">,</span><span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">)</span>
<span class="ansi-green-fg">---> 85</span><span class="ansi-red-fg"> </span>x1<span class="ansi-blue-fg">,</span>y1<span class="ansi-blue-fg">,</span>outs <span class="ansi-blue-fg">=</span> self<span class="ansi-blue-fg">.</span>show_batch<span class="ansi-blue-fg">(</span>b_out<span class="ansi-blue-fg">,</span> max_n<span class="ansi-blue-fg">=</span>max_n<span class="ansi-blue-fg">,</span> show<span class="ansi-blue-fg">=</span><span class="ansi-green-fg">False</span><span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 86</span> res <span class="ansi-blue-fg">=</span> <span class="ansi-blue-fg">(</span>x<span class="ansi-blue-fg">,</span>x1<span class="ansi-blue-fg">,</span><span class="ansi-green-fg">None</span><span class="ansi-blue-fg">,</span><span class="ansi-green-fg">None</span><span class="ansi-blue-fg">)</span> <span class="ansi-green-fg">if</span> its <span class="ansi-green-fg">is</span> <span class="ansi-green-fg">None</span> <span class="ansi-green-fg">else</span> <span class="ansi-blue-fg">(</span>x<span class="ansi-blue-fg">,</span> y<span class="ansi-blue-fg">,</span> its<span class="ansi-blue-fg">,</span> outs<span class="ansi-blue-fg">.</span>itemgot<span class="ansi-blue-fg">(</span>slice<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">.</span>n_inp<span class="ansi-blue-fg">,</span><span class="ansi-green-fg">None</span><span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 87</span> <span class="ansi-green-fg">if</span> <span class="ansi-green-fg">not</span> show<span class="ansi-blue-fg">:</span> <span class="ansi-green-fg">return</span> res
<span class="ansi-green-fg">~/git/fastai_dev/dev/local/data/core.py</span> in <span class="ansi-cyan-fg">show_batch</span><span class="ansi-blue-fg">(self, b, max_n, ctxs, show, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 77</span> <span class="ansi-green-fg">def</span> show_batch<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">,</span> b<span class="ansi-blue-fg">=</span><span class="ansi-green-fg">None</span><span class="ansi-blue-fg">,</span> max_n<span class="ansi-blue-fg">=</span><span class="ansi-cyan-fg">9</span><span class="ansi-blue-fg">,</span> ctxs<span class="ansi-blue-fg">=</span><span class="ansi-green-fg">None</span><span class="ansi-blue-fg">,</span> show<span class="ansi-blue-fg">=</span><span class="ansi-green-fg">True</span><span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">**</span>kwargs<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span>
<span class="ansi-green-intense-fg ansi-bold"> 78</span> <span class="ansi-green-fg">if</span> b <span class="ansi-green-fg">is</span> <span class="ansi-green-fg">None</span><span class="ansi-blue-fg">:</span> b <span class="ansi-blue-fg">=</span> self<span class="ansi-blue-fg">.</span>one_batch<span class="ansi-blue-fg">(</span><span class="ansi-blue-fg">)</span>
<span class="ansi-green-fg">---> 79</span><span class="ansi-red-fg"> </span><span class="ansi-green-fg">if</span> <span class="ansi-green-fg">not</span> show<span class="ansi-blue-fg">:</span> <span class="ansi-green-fg">return</span> self<span class="ansi-blue-fg">.</span>_pre_show_batch<span class="ansi-blue-fg">(</span>b<span class="ansi-blue-fg">,</span> max_n<span class="ansi-blue-fg">=</span>max_n<span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 80</span> show_batch<span class="ansi-blue-fg">(</span><span class="ansi-blue-fg">*</span>self<span class="ansi-blue-fg">.</span>_pre_show_batch<span class="ansi-blue-fg">(</span>b<span class="ansi-blue-fg">,</span> max_n<span class="ansi-blue-fg">=</span>max_n<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">,</span> ctxs<span class="ansi-blue-fg">=</span>ctxs<span class="ansi-blue-fg">,</span> max_n<span class="ansi-blue-fg">=</span>max_n<span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">**</span>kwargs<span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 81</span>
<span class="ansi-green-fg">~/git/fastai_dev/dev/local/data/core.py</span> in <span class="ansi-cyan-fg">_pre_show_batch</span><span class="ansi-blue-fg">(self, b, max_n)</span>
<span class="ansi-green-intense-fg ansi-bold"> 69</span> <span class="ansi-green-fg">def</span> _pre_show_batch<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">,</span> b<span class="ansi-blue-fg">,</span> max_n<span class="ansi-blue-fg">=</span><span class="ansi-cyan-fg">9</span><span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span>
<span class="ansi-green-intense-fg ansi-bold"> 70</span> <span class="ansi-blue-fg">"Decode `b` to be ready for `show_batch`"</span>
<span class="ansi-green-fg">---> 71</span><span class="ansi-red-fg"> </span>b <span class="ansi-blue-fg">=</span> self<span class="ansi-blue-fg">.</span>decode<span class="ansi-blue-fg">(</span>b<span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 72</span> <span class="ansi-green-fg">if</span> hasattr<span class="ansi-blue-fg">(</span>b<span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">'show'</span><span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span> <span class="ansi-green-fg">return</span> b<span class="ansi-blue-fg">,</span><span class="ansi-green-fg">None</span><span class="ansi-blue-fg">,</span><span class="ansi-green-fg">None</span>
<span class="ansi-green-intense-fg ansi-bold"> 73</span> its <span class="ansi-blue-fg">=</span> self<span class="ansi-blue-fg">.</span>_decode_batch<span class="ansi-blue-fg">(</span>b<span class="ansi-blue-fg">,</span> max_n<span class="ansi-blue-fg">,</span> full<span class="ansi-blue-fg">=</span><span class="ansi-green-fg">False</span><span class="ansi-blue-fg">)</span>
<span class="ansi-green-fg">~/git/fastai_dev/dev/local/data/core.py</span> in <span class="ansi-cyan-fg">decode</span><span class="ansi-blue-fg">(self, b)</span>
<span class="ansi-green-intense-fg ansi-bold"> 59</span> <span class="ansi-green-fg">if</span> isinstance<span class="ansi-blue-fg">(</span>f<span class="ansi-blue-fg">,</span>Pipeline<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span> f<span class="ansi-blue-fg">.</span>split_idx<span class="ansi-blue-fg">=</span>split_idx
<span class="ansi-green-intense-fg ansi-bold"> 60</span>
<span class="ansi-green-fg">---> 61</span><span class="ansi-red-fg"> </span><span class="ansi-green-fg">def</span> decode<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">,</span> b<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span> <span class="ansi-green-fg">return</span> self<span class="ansi-blue-fg">.</span>before_batch<span class="ansi-blue-fg">.</span>decode<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">.</span>after_batch<span class="ansi-blue-fg">.</span>decode<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">.</span>_retain_dl<span class="ansi-blue-fg">(</span>b<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 62</span> <span class="ansi-green-fg">def</span> decode_batch<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">,</span> b<span class="ansi-blue-fg">,</span> max_n<span class="ansi-blue-fg">=</span><span class="ansi-cyan-fg">9</span><span class="ansi-blue-fg">,</span> full<span class="ansi-blue-fg">=</span><span class="ansi-green-fg">True</span><span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span> <span class="ansi-green-fg">return</span> self<span class="ansi-blue-fg">.</span>_decode_batch<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">.</span>decode<span class="ansi-blue-fg">(</span>b<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">,</span> max_n<span class="ansi-blue-fg">,</span> full<span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 63</span>
<span class="ansi-green-fg">~/git/fastai_dev/dev/local/core/transform.py</span> in <span class="ansi-cyan-fg">decode</span><span class="ansi-blue-fg">(self, o, full)</span>
<span class="ansi-green-intense-fg ansi-bold"> 207</span>
<span class="ansi-green-intense-fg ansi-bold"> 208</span> <span class="ansi-green-fg">def</span> decode <span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">,</span> o<span class="ansi-blue-fg">,</span> full<span class="ansi-blue-fg">=</span><span class="ansi-green-fg">True</span><span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span>
<span class="ansi-green-fg">--> 209</span><span class="ansi-red-fg"> </span><span class="ansi-green-fg">if</span> full<span class="ansi-blue-fg">:</span> <span class="ansi-green-fg">return</span> compose_tfms<span class="ansi-blue-fg">(</span>o<span class="ansi-blue-fg">,</span> tfms<span class="ansi-blue-fg">=</span>self<span class="ansi-blue-fg">.</span>fs<span class="ansi-blue-fg">,</span> is_enc<span class="ansi-blue-fg">=</span><span class="ansi-green-fg">False</span><span class="ansi-blue-fg">,</span> reverse<span class="ansi-blue-fg">=</span><span class="ansi-green-fg">True</span><span class="ansi-blue-fg">,</span> split_idx<span class="ansi-blue-fg">=</span>self<span class="ansi-blue-fg">.</span>split_idx<span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 210</span> <span class="ansi-red-fg">#Not full means we decode up to the point the item knows how to show itself.</span>
<span class="ansi-green-intense-fg ansi-bold"> 211</span> <span class="ansi-green-fg">for</span> f <span class="ansi-green-fg">in</span> reversed<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">.</span>fs<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span>
<span class="ansi-green-fg">~/git/fastai_dev/dev/local/core/transform.py</span> in <span class="ansi-cyan-fg">compose_tfms</span><span class="ansi-blue-fg">(x, tfms, is_enc, reverse, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 147</span> <span class="ansi-green-fg">for</span> f <span class="ansi-green-fg">in</span> tfms<span class="ansi-blue-fg">:</span>
<span class="ansi-green-intense-fg ansi-bold"> 148</span> <span class="ansi-green-fg">if</span> <span class="ansi-green-fg">not</span> is_enc<span class="ansi-blue-fg">:</span> f <span class="ansi-blue-fg">=</span> f<span class="ansi-blue-fg">.</span>decode
<span class="ansi-green-fg">--> 149</span><span class="ansi-red-fg"> </span>x <span class="ansi-blue-fg">=</span> f<span class="ansi-blue-fg">(</span>x<span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">**</span>kwargs<span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 150</span> <span class="ansi-green-fg">return</span> x
<span class="ansi-green-intense-fg ansi-bold"> 151</span>
<span class="ansi-green-fg">~/git/fastai_dev/dev/local/core/transform.py</span> in <span class="ansi-cyan-fg">decode</span><span class="ansi-blue-fg">(self, x, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 86</span> <span class="ansi-green-fg">def</span> use_as_item<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span> <span class="ansi-green-fg">return</span> ifnone<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">.</span>as_item_force<span class="ansi-blue-fg">,</span> self<span class="ansi-blue-fg">.</span>as_item<span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 87</span> <span class="ansi-green-fg">def</span> __call__<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">,</span> x<span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">**</span>kwargs<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span> <span class="ansi-green-fg">return</span> self<span class="ansi-blue-fg">.</span>_call<span class="ansi-blue-fg">(</span><span class="ansi-blue-fg">'encodes'</span><span class="ansi-blue-fg">,</span> x<span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">**</span>kwargs<span class="ansi-blue-fg">)</span>
<span class="ansi-green-fg">---> 88</span><span class="ansi-red-fg"> </span><span class="ansi-green-fg">def</span> decode <span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">,</span> x<span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">**</span>kwargs<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span> <span class="ansi-green-fg">return</span> self<span class="ansi-blue-fg">.</span>_call<span class="ansi-blue-fg">(</span><span class="ansi-blue-fg">'decodes'</span><span class="ansi-blue-fg">,</span> x<span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">**</span>kwargs<span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 89</span> <span class="ansi-green-fg">def</span> setup<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">,</span> items<span class="ansi-blue-fg">=</span><span class="ansi-green-fg">None</span><span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span> <span class="ansi-green-fg">return</span> self<span class="ansi-blue-fg">.</span>setups<span class="ansi-blue-fg">(</span>items<span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 90</span> <span class="ansi-green-fg">def</span> __repr__<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span> <span class="ansi-green-fg">return</span> <span class="ansi-blue-fg">f'{self.__class__.__name__}: {self.use_as_item} {self.encodes} {self.decodes}'</span>
<span class="ansi-green-fg">~/git/fastai_dev/dev/local/core/transform.py</span> in <span class="ansi-cyan-fg">_call</span><span class="ansi-blue-fg">(self, fn, x, split_idx, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 93</span> <span class="ansi-green-fg">if</span> split_idx<span class="ansi-blue-fg">!=</span>self<span class="ansi-blue-fg">.</span>split_idx <span class="ansi-green-fg">and</span> self<span class="ansi-blue-fg">.</span>split_idx <span class="ansi-green-fg">is</span> <span class="ansi-green-fg">not</span> <span class="ansi-green-fg">None</span><span class="ansi-blue-fg">:</span> <span class="ansi-green-fg">return</span> x
<span class="ansi-green-intense-fg ansi-bold"> 94</span> f <span class="ansi-blue-fg">=</span> getattr<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">,</span> fn<span class="ansi-blue-fg">)</span>
<span class="ansi-green-fg">---> 95</span><span class="ansi-red-fg"> </span><span class="ansi-green-fg">if</span> self<span class="ansi-blue-fg">.</span>use_as_item <span class="ansi-green-fg">or</span> <span class="ansi-green-fg">not</span> is_listy<span class="ansi-blue-fg">(</span>x<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span> <span class="ansi-green-fg">return</span> self<span class="ansi-blue-fg">.</span>_do_call<span class="ansi-blue-fg">(</span>f<span class="ansi-blue-fg">,</span> x<span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">**</span>kwargs<span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 96</span> res <span class="ansi-blue-fg">=</span> tuple<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">.</span>_do_call<span class="ansi-blue-fg">(</span>f<span class="ansi-blue-fg">,</span> x_<span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">**</span>kwargs<span class="ansi-blue-fg">)</span> <span class="ansi-green-fg">for</span> x_ <span class="ansi-green-fg">in</span> x<span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 97</span> <span class="ansi-green-fg">return</span> retain_type<span class="ansi-blue-fg">(</span>res<span class="ansi-blue-fg">,</span> x<span class="ansi-blue-fg">)</span>
<span class="ansi-green-fg">~/git/fastai_dev/dev/local/core/transform.py</span> in <span class="ansi-cyan-fg">_do_call</span><span class="ansi-blue-fg">(self, f, x, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 98</span>
<span class="ansi-green-intense-fg ansi-bold"> 99</span> <span class="ansi-green-fg">def</span> _do_call<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">,</span> f<span class="ansi-blue-fg">,</span> x<span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">**</span>kwargs<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span>
<span class="ansi-green-fg">--> 100</span><span class="ansi-red-fg"> </span><span class="ansi-green-fg">return</span> x <span class="ansi-green-fg">if</span> f <span class="ansi-green-fg">is</span> <span class="ansi-green-fg">None</span> <span class="ansi-green-fg">else</span> retain_type<span class="ansi-blue-fg">(</span>f<span class="ansi-blue-fg">(</span>x<span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">**</span>kwargs<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">,</span> x<span class="ansi-blue-fg">,</span> f<span class="ansi-blue-fg">.</span>returns_none<span class="ansi-blue-fg">(</span>x<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 101</span>
<span class="ansi-green-intense-fg ansi-bold"> 102</span> add_docs<span class="ansi-blue-fg">(</span>Transform<span class="ansi-blue-fg">,</span> decode<span class="ansi-blue-fg">=</span><span class="ansi-blue-fg">"Delegate to `decodes` to undo transform"</span><span class="ansi-blue-fg">,</span> setup<span class="ansi-blue-fg">=</span><span class="ansi-blue-fg">"Delegate to `setups` to set up transform"</span><span class="ansi-blue-fg">)</span>
<span class="ansi-green-fg">~/git/fastai_dev/dev/local/core/dispatch.py</span> in <span class="ansi-cyan-fg">__call__</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 96</span> <span class="ansi-green-fg">if</span> <span class="ansi-green-fg">not</span> f<span class="ansi-blue-fg">:</span> <span class="ansi-green-fg">return</span> args<span class="ansi-blue-fg">[</span><span class="ansi-cyan-fg">0</span><span class="ansi-blue-fg">]</span>
<span class="ansi-green-intense-fg ansi-bold"> 97</span> <span class="ansi-green-fg">if</span> self<span class="ansi-blue-fg">.</span>inst <span class="ansi-green-fg">is</span> <span class="ansi-green-fg">not</span> <span class="ansi-green-fg">None</span><span class="ansi-blue-fg">:</span> f <span class="ansi-blue-fg">=</span> types<span class="ansi-blue-fg">.</span>MethodType<span class="ansi-blue-fg">(</span>f<span class="ansi-blue-fg">,</span> self<span class="ansi-blue-fg">.</span>inst<span class="ansi-blue-fg">)</span>
<span class="ansi-green-fg">---> 98</span><span class="ansi-red-fg"> </span><span class="ansi-green-fg">return</span> f<span class="ansi-blue-fg">(</span><span class="ansi-blue-fg">*</span>args<span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">**</span>kwargs<span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 99</span>
<span class="ansi-green-intense-fg ansi-bold"> 100</span> <span class="ansi-green-fg">def</span> __get__<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">,</span> inst<span class="ansi-blue-fg">,</span> owner<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span>
<span class="ansi-green-fg">~/git/fastai_dev/dev/local/tabular/core.py</span> in <span class="ansi-cyan-fg">decodes</span><span class="ansi-blue-fg">(self, o)</span>
<span class="ansi-green-intense-fg ansi-bold"> 155</span> <span class="ansi-green-fg">def</span> decodes<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">,</span> o<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span>
<span class="ansi-green-intense-fg ansi-bold"> 156</span> cats<span class="ansi-blue-fg">,</span>conts<span class="ansi-blue-fg">,</span>targs <span class="ansi-blue-fg">=</span> to_np<span class="ansi-blue-fg">(</span>o<span class="ansi-blue-fg">)</span>
<span class="ansi-green-fg">--> 157</span><span class="ansi-red-fg"> </span>vals <span class="ansi-blue-fg">=</span> np<span class="ansi-blue-fg">.</span>concatenate<span class="ansi-blue-fg">(</span><span class="ansi-blue-fg">[</span>cats<span class="ansi-blue-fg">,</span>conts<span class="ansi-blue-fg">,</span>targs<span class="ansi-blue-fg">]</span><span class="ansi-blue-fg">,</span> axis<span class="ansi-blue-fg">=</span><span class="ansi-cyan-fg">1</span><span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 158</span> df <span class="ansi-blue-fg">=</span> pd<span class="ansi-blue-fg">.</span>DataFrame<span class="ansi-blue-fg">(</span>vals<span class="ansi-blue-fg">,</span> columns<span class="ansi-blue-fg">=</span>self<span class="ansi-blue-fg">.</span>to<span class="ansi-blue-fg">.</span>all_col_names<span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 159</span> to <span class="ansi-blue-fg">=</span> self<span class="ansi-blue-fg">.</span>to<span class="ansi-blue-fg">.</span>new<span class="ansi-blue-fg">(</span>df<span class="ansi-blue-fg">)</span>
<span class="ansi-red-fg">ValueError</span>: all the input arrays must have same number of dimensions</pre>
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