forked from fastai/fastai
/
tabular.html
516 lines (429 loc) · 19.4 KB
/
tabular.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
---
title: tabular
keywords: fastai
sidebar: home_sidebar
summary: "Application to tabular/structured data"
---
<!--
#################################################
### THIS FILE WAS AUTOGENERATED! DO NOT EDIT! ###
#################################################
# file to edit: docs_src/tabular.ipynb
# instructions: https://docs.fast.ai/gen_doc_main.html
-->
<div class="container" id="notebook-container">
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Tabular-data">Tabular data<a class="anchor-link" href="#Tabular-data">¶</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><a href="/tabular.html#tabular"><code>tabular</code></a> contains all the necessary classes to deal with tabular data, across two modules:</p>
<ul>
<li><a href="/tabular.transform.html#tabular.transform"><code>tabular.transform</code></a>: defines the <a href="/tabular.transform.html#TabularTransform"><code>TabularTransform</code></a> class to help with preprocessing;</li>
<li><a href="/tabular.data.html#tabular.data"><code>tabular.data</code></a>: defines the <a href="/tabular.data.html#TabularDataset"><code>TabularDataset</code></a> that handles that data, as well as the methods to quickly get a <a href="/tabular.data.html#TabularDataBunch"><code>TabularDataBunch</code></a>.</li>
</ul>
<p>To create a model, you'll need to use <a href="/tabular.html#tabular"><code>models.tabular</code></a>. See below for an end-to-end example using all these modules.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Preprocessing-tabular-data">Preprocessing tabular data<a class="anchor-link" href="#Preprocessing-tabular-data">¶</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>First, let's import everything we need for the tabular application.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">fastai.tabular</span> <span class="k">import</span> <span class="o">*</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Tabular data usually comes in the form of a delimited file (such as .csv) containing variables of different kinds: text/category, numbers, and perhaps some missing values. The example we'll work with in this section is a sample of the <a href="https://archive.ics.uci.edu/ml/datasets/adult">adult dataset</a> which has some census information on individuals. We'll use it to train a model to predict whether salary is greater than \$50k or not.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<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">path</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>PosixPath('/home/ubuntu/.fastai/data/adult_sample')</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></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>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>age</th>
<th>workclass</th>
<th>fnlwgt</th>
<th>education</th>
<th>education-num</th>
<th>marital-status</th>
<th>occupation</th>
<th>relationship</th>
<th>race</th>
<th>sex</th>
<th>capital-gain</th>
<th>capital-loss</th>
<th>hours-per-week</th>
<th>native-country</th>
<th>salary</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>49</td>
<td>Private</td>
<td>101320</td>
<td>Assoc-acdm</td>
<td>12.0</td>
<td>Married-civ-spouse</td>
<td>NaN</td>
<td>Wife</td>
<td>White</td>
<td>Female</td>
<td>0</td>
<td>1902</td>
<td>40</td>
<td>United-States</td>
<td>>=50k</td>
</tr>
<tr>
<th>1</th>
<td>44</td>
<td>Private</td>
<td>236746</td>
<td>Masters</td>
<td>14.0</td>
<td>Divorced</td>
<td>Exec-managerial</td>
<td>Not-in-family</td>
<td>White</td>
<td>Male</td>
<td>10520</td>
<td>0</td>
<td>45</td>
<td>United-States</td>
<td>>=50k</td>
</tr>
<tr>
<th>2</th>
<td>38</td>
<td>Private</td>
<td>96185</td>
<td>HS-grad</td>
<td>NaN</td>
<td>Divorced</td>
<td>NaN</td>
<td>Unmarried</td>
<td>Black</td>
<td>Female</td>
<td>0</td>
<td>0</td>
<td>32</td>
<td>United-States</td>
<td><50k</td>
</tr>
<tr>
<th>3</th>
<td>38</td>
<td>Self-emp-inc</td>
<td>112847</td>
<td>Prof-school</td>
<td>15.0</td>
<td>Married-civ-spouse</td>
<td>Prof-specialty</td>
<td>Husband</td>
<td>Asian-Pac-Islander</td>
<td>Male</td>
<td>0</td>
<td>0</td>
<td>40</td>
<td>United-States</td>
<td>>=50k</td>
</tr>
<tr>
<th>4</th>
<td>42</td>
<td>Self-emp-not-inc</td>
<td>82297</td>
<td>7th-8th</td>
<td>NaN</td>
<td>Married-civ-spouse</td>
<td>Other-service</td>
<td>Wife</td>
<td>Black</td>
<td>Female</td>
<td>0</td>
<td>0</td>
<td>50</td>
<td>United-States</td>
<td><50k</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Here all the information that will form our input is in the 14 first columns, and the dependent variable is the last column. We will split our input between two types of variables: categorical and continuous.</p>
<ul>
<li>Categorical variables will be replaced by a category - a unique id that identifies them - before they are passed through an embedding layer.</li>
<li>Continuous variables will be normalized and then directly fed to the model.</li>
</ul>
<p>Another thing we need to handle are the missing values: our model isn't going to like receiving NaNs so we should remove them in a smart way. All of this preprocessing is done by <a href="/tabular.transform.html#TabularTransform"><code>TabularTransform</code></a> objects and <a href="/tabular.data.html#TabularDataset"><code>TabularDataset</code></a>.</p>
<p>We can define a bunch of Transforms that will be applied to our variables. Here we transform all categorical variables into categories. We also replace missing values for continuous variables by the median column value and normalize those.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">procs</span> <span class="o">=</span> <span class="p">[</span><span class="n">FillMissing</span><span class="p">,</span> <span class="n">Categorify</span><span class="p">,</span> <span class="n">Normalize</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>To split our data into training and validation sets, we use valid indexes</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">valid_idx</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="o">-</span><span class="mi">2000</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Then let's manually split our variables into categorical and continuous variables (we can ignore the dependent variable at this stage). fastai will assume all variables that aren't dependent or categorical are continuous, unless we explicitly pass a list to the <code>cont_names</code> parameter when constructing our <a href="/basic_data.html#DataBunch"><code>DataBunch</code></a>.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dep_var</span> <span class="o">=</span> <span class="s1">'salary'</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="s1">'sex'</span><span class="p">,</span> <span class="s1">'native-country'</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Now we're ready to pass this information to <a href="/tabular.data.html#TabularDataBunch.from_df"><code>TabularDataBunch.from_df</code></a> to create the <a href="/basic_data.html#DataBunch"><code>DataBunch</code></a> that we'll use for training.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">TabularDataBunch</span><span class="o">.</span><span class="n">from_df</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">dep_var</span><span class="p">,</span> <span class="n">valid_idx</span><span class="o">=</span><span class="n">valid_idx</span><span class="p">,</span> <span class="n">procs</span><span class="o">=</span><span class="n">procs</span><span class="p">,</span> <span class="n">cat_names</span><span class="o">=</span><span class="n">cat_names</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">train_ds</span><span class="o">.</span><span class="n">cont_names</span><span class="p">)</span> <span class="c1"># `cont_names` defaults to: set(df)-set(cat_names)-{dep_var}</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>['capital-gain', 'fnlwgt', 'hours-per-week', 'capital-loss', 'education-num', 'age']
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>We can grab a mini-batch of data and take a look (note that <a href="/torch_core.html#to_np"><code>to_np</code></a> here converts from pytorch tensor to numpy):</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="p">(</span><span class="n">cat_x</span><span class="p">,</span><span class="n">cont_x</span><span class="p">),</span><span class="n">y</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">train_dl</span><span class="p">))</span>
<span class="k">for</span> <span class="n">o</span> <span class="ow">in</span> <span class="p">(</span><span class="n">cat_x</span><span class="p">,</span> <span class="n">cont_x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span> <span class="nb">print</span><span class="p">(</span><span class="n">to_np</span><span class="p">(</span><span class="n">o</span><span class="p">[:</span><span class="mi">5</span><span class="p">]))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>[[ 5 8 3 13 1 5 2 40 1]
[ 5 12 3 5 1 3 2 40 1]
[ 1 2 3 1 1 5 2 40 1]
[ 5 12 3 4 1 5 2 40 1]
[ 7 12 3 5 1 5 2 40 1]]
[[ 0.886658 -1.382477 -0.035789 -0.216787 0.753904 0.393667]
[-0.145922 -0.367974 1.176085 4.469497 -0.421569 0.833029]
[-0.145922 -1.454197 -2.621122 -0.216787 -1.205218 2.07789 ]
[-0.145922 0.135373 -0.035789 -0.216787 -0.421569 0.173985]
[-0.145922 -0.224013 -0.035789 -0.216787 -0.421569 2.151118]]
[1 1 0 0 1]
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>After being processed in <a href="/tabular.data.html#TabularDataset"><code>TabularDataset</code></a>, the categorical variables are replaced by ids and the continuous variables are normalized. The codes corresponding to categorical variables are all put together, as are all the continuous variables.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Defining-a-model">Defining a model<a class="anchor-link" href="#Defining-a-model">¶</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Once we have our data ready in a <a href="/basic_data.html#DataBunch"><code>DataBunch</code></a>, we just need to create a model to then define a <a href="/basic_train.html#Learner"><code>Learner</code></a> and start training. The fastai library has a flexible and powerful <a href="/tabular.models.html#TabularModel"><code>TabularModel</code></a> in <a href="/tabular.html#tabular"><code>models.tabular</code></a>. To use that function, we just need to specify the embedding sizes for each of our categorical variables.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">tabular_learner</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">layers</span><span class="o">=</span><span class="p">[</span><span class="mi">200</span><span class="p">,</span><span class="mi">100</span><span class="p">],</span> <span class="n">emb_szs</span><span class="o">=</span><span class="p">{</span><span class="s1">'native-country'</span><span class="p">:</span> <span class="mi">10</span><span class="p">},</span> <span class="n">metrics</span><span class="o">=</span><span class="n">accuracy</span><span class="p">)</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> <span class="mf">1e-2</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea ">
<table border="1" class="dataframe">
<thead>
<tr style="text-align: left;">
<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>accuracy</th>
<th>time</th>
</tr>
</thead>
<tbody>
<tr>
<td>0</td>
<td>0.321540</td>
<td>0.319863</td>
<td>0.844000</td>
<td>00:04</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>As usual, we can use the <a href="/basic_train.html#Learner.predict"><code>Learner.predict</code></a> method to get predictions. In this case, we need to pass the row of a dataframe that has the same names of categorical and continuous variables as our training or validation dataframe.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>(Category >=50k, tensor(1), tensor([0.1864, 0.8136]))</pre>
</div>
</div>
</div>
</div>
</div>
</div>