/
models.html
450 lines (415 loc) · 20.8 KB
/
models.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
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.10.0" />
<title>ktrain.tabular.models API documentation</title>
<meta name="description" content="" />
<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/sanitize.min.css" integrity="sha256-PK9q560IAAa6WVRRh76LtCaI8pjTJ2z11v0miyNNjrs=" crossorigin>
<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/typography.min.css" integrity="sha256-7l/o7C8jubJiy74VsKTidCy1yBkRtiUGbVkYBylBqUg=" crossorigin>
<link rel="stylesheet preload" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/styles/github.min.css" crossorigin>
<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
</head>
<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>ktrain.tabular.models</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from .. import utils as U
from ..imports import *
from ..models import bn_drop_lin
MLP = "mlp"
TABULAR_MODELS = {
MLP: "a configurable multilayer perceptron with categorical variable embeddings [https://arxiv.org/abs/1604.06737]",
}
def print_tabular_classifiers():
for k, v in TABULAR_MODELS.items():
print("%s: %s" % (k, v))
def print_tabular_regression_models():
for k, v in TABULAR_MODELS.items():
print("%s: %s" % (k, v))
def _tabular_model(
name,
train_data,
multilabel=None,
is_regression=False,
metrics=None,
hidden_layers=[1000, 500],
hidden_dropouts=[0.0, 0.5],
bn=False,
verbose=1,
):
"""
```
Build and return a classification or regression model for tabular data
Args:
name (string): currently accepts 'mlp' for multilayer perceptron
train_data (TabularDataset): TabularDataset instance returned from one of the tabular_from_* functions
multilabel (bool): If True, multilabel model will be returned.
If false, binary/multiclass model will be returned.
If None, multilabel will be inferred from data.
is_regression(bool): If True, will build a regression model, else classification model.
metrics(list): List of metrics to use. If None: 'accuracy' is used for binar/multiclass,
'binary_accuracy' is used for multilabel classification, and 'mae' is used for regressio
hidden_layers(list): number of units in each hidden layer of NN
hidden_dropouts(list): Dropout values after each hidden layer of NN
bn(bool): If True, BatchNormalization will be used before each fully-connected layer in NN
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
```
"""
# check arguments
if not U.is_tabular_from_data(train_data):
err = """
Please pass training data in the form of data returned from a ktrain tabular_from* function.
"""
raise Exception(err)
if len(hidden_layers) != len(hidden_dropouts):
raise ValueError("len(hidden_layers) must equal len(hidden_dropouts)")
# reformat dropouts for each of construction
output_dropout = hidden_dropouts[1]
hidden_dropouts[1] = hidden_dropouts[0]
hidden_dropouts[0] = 0.0
# set model configuration values
if is_regression: # regression
if metrics is None or metrics == ["accuracy"]:
metrics = ["mae"]
num_classes = 1
multilabel = False
loss_func = "mse"
activation = "linear"
else: # classification
# set metrics
if multilabel and metrics is None:
metrics = ["binary_accuracy"]
elif metrics is None:
metrics = ["accuracy"]
# set number of classes and multilabel flag
num_classes = U.nclasses_from_data(train_data)
# determine multilabel
if multilabel is None:
multilabel = U.is_multilabel(train_data)
U.vprint("Is Multi-Label? %s" % (multilabel), verbose=verbose)
# set loss and activations
loss_func = "categorical_crossentropy"
activation = "softmax"
if multilabel:
loss_func = "binary_crossentropy"
activation = "sigmoid"
# construct model
ilayers = []
n_cat = len(train_data.cat_columns)
n_cont = len(train_data.cont_columns)
if n_cat == 0 and n_cont == 0:
raise ValueError("There are zero continuous and cateorical variables.")
# categorical inputs and embeddings
if n_cat > 0:
emblayers = []
num_uniques = [
max(c.cat.codes.values + 1) + 1
for n, c in train_data.df[train_data.cat_columns].items()
]
for i in range(n_cat):
inp = keras.layers.Input(shape=(1,))
ilayers.append(inp)
emb_size = min(50, (num_uniques[i] // 2) + 1)
# emb_size = min(600, round(1.6 * num_uniques[i]**0.56))
emb = keras.layers.Embedding(num_uniques[i], emb_size, input_length=1)(inp)
emblayers.append(emb)
x = keras.layers.concatenate(emblayers) if len(emblayers) > 1 else emblayers[0]
x = keras.layers.Flatten()(x)
# continuous inputs
if n_cont > 0:
x_cont = keras.layers.Input(shape=(n_cont,))
ilayers.append(x_cont)
x = keras.layers.concatenate([x, x_cont]) if n_cat > 0 else x_cont
# hidden layers
output = x
for i, n_out in enumerate(hidden_layers):
output = bn_drop_lin(output, n_out, bn=bn, p=hidden_dropouts[i], actn="relu")
# output layer
output = bn_drop_lin(output, num_classes, bn=bn, p=output_dropout, actn=activation)
# construct and compile model
model = keras.Model(inputs=ilayers, outputs=output)
model.compile(optimizer=U.DEFAULT_OPT, loss=loss_func, metrics=metrics)
U.vprint("done.", verbose=verbose)
return model
def tabular_classifier(
name,
train_data,
multilabel=None,
metrics=None,
hidden_layers=[1000, 500],
hidden_dropouts=[0.0, 0.5],
bn=False,
verbose=1,
):
"""
```
Build and return a classification model for tabular data
Args:
name (string): currently accepts 'mlp' for multilayer perceptron
train_data (TabularDataset): TabularDataset instance returned from one of the tabular_from_* functions
multilabel (bool): If True, multilabel model will be returned.
If false, binary/multiclass model will be returned.
If None, multilabel will be inferred from data.
metrics(list): List of metrics to use. If None: 'accuracy' is used for binar/multiclass,
'binary_accuracy' is used for multilabel classification, and 'mae' is used for regressio
hidden_layers(list): number of units in each hidden layer of NN
hidden_dropouts(list): Dropout values after each hidden layer of NN
bn(bool): If True, BatchNormalization will be used before each fully-connected layer in NN
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
```
"""
return _tabular_model(
name,
train_data,
multilabel=multilabel,
metrics=metrics,
hidden_layers=hidden_layers,
hidden_dropouts=hidden_dropouts,
bn=bn,
verbose=verbose,
is_regression=False,
)
def tabular_regression_model(
name,
train_data,
metrics=["mae"],
hidden_layers=[1000, 500],
hidden_dropouts=[0.0, 0.5],
bn=False,
verbose=1,
):
"""
```
Build and return a regression model for tabular data
Args:
name (string): currently accepts 'mlp' for multilayer perceptron
train_data (TabularDataset): TabularDataset instance returned from one of the tabular_from_* functions
metrics(list): list of metrics to use
hidden_layers(list): number of units in each hidden layer of NN
hidden_dropouts(list): Dropout values after each hidden layer of NN
bn(bool): If True, BatchNormalization will be before used each fully-connected layer in NN
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
```
"""
return _tabular_model(
name,
train_data,
multilabel=None,
metrics=metrics,
hidden_layers=hidden_layers,
hidden_dropouts=hidden_dropouts,
bn=bn,
verbose=verbose,
is_regression=True,
)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="ktrain.tabular.models.print_tabular_classifiers"><code class="name flex">
<span>def <span class="ident">print_tabular_classifiers</span></span>(<span>)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def print_tabular_classifiers():
for k, v in TABULAR_MODELS.items():
print("%s: %s" % (k, v))</code></pre>
</details>
</dd>
<dt id="ktrain.tabular.models.print_tabular_regression_models"><code class="name flex">
<span>def <span class="ident">print_tabular_regression_models</span></span>(<span>)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def print_tabular_regression_models():
for k, v in TABULAR_MODELS.items():
print("%s: %s" % (k, v))</code></pre>
</details>
</dd>
<dt id="ktrain.tabular.models.tabular_classifier"><code class="name flex">
<span>def <span class="ident">tabular_classifier</span></span>(<span>name, train_data, multilabel=None, metrics=None, hidden_layers=[1000, 500], hidden_dropouts=[0.0, 0.5], bn=False, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Build and return a classification model for tabular data
Args:
name (string): currently accepts 'mlp' for multilayer perceptron
train_data (TabularDataset): TabularDataset instance returned from one of the tabular_from_* functions
multilabel (bool): If True, multilabel model will be returned.
If false, binary/multiclass model will be returned.
If None, multilabel will be inferred from data.
metrics(list): List of metrics to use. If None: 'accuracy' is used for binar/multiclass,
'binary_accuracy' is used for multilabel classification, and 'mae' is used for regressio
hidden_layers(list): number of units in each hidden layer of NN
hidden_dropouts(list): Dropout values after each hidden layer of NN
bn(bool): If True, BatchNormalization will be used before each fully-connected layer in NN
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def tabular_classifier(
name,
train_data,
multilabel=None,
metrics=None,
hidden_layers=[1000, 500],
hidden_dropouts=[0.0, 0.5],
bn=False,
verbose=1,
):
"""
```
Build and return a classification model for tabular data
Args:
name (string): currently accepts 'mlp' for multilayer perceptron
train_data (TabularDataset): TabularDataset instance returned from one of the tabular_from_* functions
multilabel (bool): If True, multilabel model will be returned.
If false, binary/multiclass model will be returned.
If None, multilabel will be inferred from data.
metrics(list): List of metrics to use. If None: 'accuracy' is used for binar/multiclass,
'binary_accuracy' is used for multilabel classification, and 'mae' is used for regressio
hidden_layers(list): number of units in each hidden layer of NN
hidden_dropouts(list): Dropout values after each hidden layer of NN
bn(bool): If True, BatchNormalization will be used before each fully-connected layer in NN
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
```
"""
return _tabular_model(
name,
train_data,
multilabel=multilabel,
metrics=metrics,
hidden_layers=hidden_layers,
hidden_dropouts=hidden_dropouts,
bn=bn,
verbose=verbose,
is_regression=False,
)</code></pre>
</details>
</dd>
<dt id="ktrain.tabular.models.tabular_regression_model"><code class="name flex">
<span>def <span class="ident">tabular_regression_model</span></span>(<span>name, train_data, metrics=['mae'], hidden_layers=[1000, 500], hidden_dropouts=[0.0, 0.5], bn=False, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Build and return a regression model for tabular data
Args:
name (string): currently accepts 'mlp' for multilayer perceptron
train_data (TabularDataset): TabularDataset instance returned from one of the tabular_from_* functions
metrics(list): list of metrics to use
hidden_layers(list): number of units in each hidden layer of NN
hidden_dropouts(list): Dropout values after each hidden layer of NN
bn(bool): If True, BatchNormalization will be before used each fully-connected layer in NN
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def tabular_regression_model(
name,
train_data,
metrics=["mae"],
hidden_layers=[1000, 500],
hidden_dropouts=[0.0, 0.5],
bn=False,
verbose=1,
):
"""
```
Build and return a regression model for tabular data
Args:
name (string): currently accepts 'mlp' for multilayer perceptron
train_data (TabularDataset): TabularDataset instance returned from one of the tabular_from_* functions
metrics(list): list of metrics to use
hidden_layers(list): number of units in each hidden layer of NN
hidden_dropouts(list): Dropout values after each hidden layer of NN
bn(bool): If True, BatchNormalization will be before used each fully-connected layer in NN
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
```
"""
return _tabular_model(
name,
train_data,
multilabel=None,
metrics=metrics,
hidden_layers=hidden_layers,
hidden_dropouts=hidden_dropouts,
bn=bn,
verbose=verbose,
is_regression=True,
)</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="ktrain.tabular" href="index.html">ktrain.tabular</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="ktrain.tabular.models.print_tabular_classifiers" href="#ktrain.tabular.models.print_tabular_classifiers">print_tabular_classifiers</a></code></li>
<li><code><a title="ktrain.tabular.models.print_tabular_regression_models" href="#ktrain.tabular.models.print_tabular_regression_models">print_tabular_regression_models</a></code></li>
<li><code><a title="ktrain.tabular.models.tabular_classifier" href="#ktrain.tabular.models.tabular_classifier">tabular_classifier</a></code></li>
<li><code><a title="ktrain.tabular.models.tabular_regression_model" href="#ktrain.tabular.models.tabular_regression_model">tabular_regression_model</a></code></li>
</ul>
</li>
</ul>
</nav>
</main>
<footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
</footer>
</body>
</html>