/
naive_bayes_sklearn.html
881 lines (655 loc) · 232 KB
/
naive_bayes_sklearn.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
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
<title>naive_bayes_sklearn</title>
<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
<!-- Custom stylesheet, it must be in the same directory as the html file -->
<link rel="stylesheet" href="/static/css/md_notebook-win10.css">
<!-- Loading mathjax macro -->
<!-- Load mathjax -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS_HTML"></script>
<!-- MathJax configuration -->
<script type="text/x-mathjax-config">
MathJax.Hub.Config({
tex2jax: {
inlineMath: [ ['$','$'], ["\\(","\\)"] ],
displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
processEscapes: true,
processEnvironments: true
},
// Center justify equations in code and markdown cells. Elsewhere
// we use CSS to left justify single line equations in code cells.
displayAlign: 'center',
"HTML-CSS": {
styles: {'.MathJax_Display': {"margin": 0}},
linebreaks: { automatic: true }
}
});
</script>
<!-- End of mathjax configuration --></head>
<body>
<div tabindex="-1" id="notebook" class="border-box-sizing">
<div class="container" id="notebook-container">
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="朴素贝叶斯基本概念">朴素贝叶斯基本概念<a class="anchor-link" href="#朴素贝叶斯基本概念">¶</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="简单示例">简单示例<a class="anchor-link" href="#简单示例">¶</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [1]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 用 0 代表没有下雨，而1代表下雨。</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="c1"># 过去7天是否下雨可以用数组表示 </span>
<span class="n">y</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="p">)</span>
<span class="c1"># 其他气象信息: 北风、闷热、多云、天气预报是否下雨</span>
<span class="n">X</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span>
<span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="p">])</span>
<span class="c1"># 分析下雨、不下雨时每个气象条件的频数</span>
<span class="n">counts</span><span class="o">=</span><span class="p">{}</span>
<span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
<span class="n">counts</span><span class="p">[</span><span class="n">label</span><span class="p">]</span><span class="o">=</span><span class="n">X</span><span class="p">[</span> <span class="n">y</span><span class="o">==</span><span class="n">label</span> <span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Feature counts:</span><span class="se">\n</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">counts</span><span class="p">))</span>
<span class="c1"># 没下雨时(y=0), 4天天气预报都说下雨，1天北风，2天闷热，0天多云。</span>
<span class="c1"># 下雨时(y=1)，天气预报都说没有下雨，1天北风，3天闷热，3天多云。</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>Feature counts:
{0: array([1, 2, 0, 4]), 1: array([1, 3, 3, 0])}
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="使用-伯努利贝叶斯分类器">使用 伯努利贝叶斯分类器<a class="anchor-link" href="#使用-伯努利贝叶斯分类器">¶</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [2]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">BernoulliNB</span>
<span class="c1"># 拟合数据</span>
<span class="n">clf</span><span class="o">=</span><span class="n">BernoulliNB</span><span class="p">()</span>
<span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">)</span>
<span class="c1"># 预测一下训练集</span>
<span class="nb">print</span><span class="p">(</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="p">)</span>
<span class="c1"># 打分</span>
<span class="nb">print</span><span class="p">(</span> <span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>[0 1 1 0 1 0 0]
1.0
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [3]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 如果天气预报说没有下雨，且出现多云，倾向于归类到“下雨”</span>
<span class="nb">print</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]]</span> <span class="p">))</span>
<span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]]</span> <span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>[1]
</pre>
</div>
</div>
<div class="output_area">
<div class="prompt output_prompt">Out[3]:</div>
<div class="output_text output_subarea output_execute_result">
<pre>array([[0.13848881, 0.86151119]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [4]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 如果天气预报说下雨，且北风，闷热，无云，倾向于归类到“不下雨”</span>
<span class="nb">print</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]]</span> <span class="p">))</span>
<span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]]</span> <span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>[0]
</pre>
</div>
</div>
<div class="output_area">
<div class="prompt output_prompt">Out[4]:</div>
<div class="output_text output_subarea output_execute_result">
<pre>array([[0.92340878, 0.07659122]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="朴素贝叶斯的不同方法">朴素贝叶斯的不同方法<a class="anchor-link" href="#朴素贝叶斯的不同方法">¶</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="伯努利朴素贝叶斯">伯努利朴素贝叶斯<a class="anchor-link" href="#伯努利朴素贝叶斯">¶</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [1]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_blobs</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="c1"># 生成样本，数量500，分类数5</span>
<span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="o">=</span><span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
<span class="c1"># 拆分数据</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
<span class="c1"># 使用伯努利贝叶斯拟合数据</span>
<span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">BernoulliNB</span>
<span class="n">nb</span><span class="o">=</span><span class="n">BernoulliNB</span><span class="p">()</span>
<span class="n">nb</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># 打分</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"trainning score: </span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">nb</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> <span class="p">)</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"testing score: </span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">nb</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="p">)</span> <span class="p">)</span>
<span class="c1"># 只有一半分类是正确的，很糟糕！</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>trainning score: 0.499
testing score: 0.544
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [2]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 可视化，为什么这么糟糕</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">+</span><span class="mf">0.5</span>
<span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">+</span><span class="mf">0.5</span>
<span class="c1"># 用不同背景色表示不同分类</span>
<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">),</span>
<span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">))</span>
<span class="n">z</span><span class="o">=</span><span class="n">nb</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[(</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">())])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Pastel1</span><span class="p">,</span> <span class="n">shading</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">)</span>
<span class="c1"># 画训练集和测试集散点图</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X_train</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">cool</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X_test</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y_test</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">cool</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s1">'k'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'*'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span> <span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span> <span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Classifier: BernoulliNB"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
</div>
</div>
</div>
<pre>
# 这就是简单把2条线，分为4个象限，注意有3个颜色。
# 因为使用了伯努利朴素贝叶斯的默认参数 binarize=0.0，所以模型对于数据的判断是
# 如果特征1大于或等于0，且特征2大于或等于0，归为一类；
# 如果特征1小于0，且特征2小于0，归为一类；
# 其余归为一类。
# 所以分类效果很烂。
# 对于多分类，不能使用伯努利朴素贝叶斯模型了。可以使用高斯朴素贝叶斯模型。
</pre>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="高斯朴素贝叶斯">高斯朴素贝叶斯<a class="anchor-link" href="#高斯朴素贝叶斯">¶</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [3]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 就是假设样本的特征符合高斯分布/正态分布。</span>
<span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">GaussianNB</span>
<span class="n">gnb</span><span class="o">=</span><span class="n">GaussianNB</span><span class="p">()</span>
<span class="n">gnb</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># 打分</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"trainning score: </span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">gnb</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> <span class="p">)</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"testing score: </span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">gnb</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="p">)</span> <span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>trainning score: 0.939
testing score: 0.968
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [4]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 可视化，为什么分类效果这么好</span>
<span class="c1"># 用不同背景色表示不同分类</span>
<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">),</span>
<span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">))</span>
<span class="n">z</span><span class="o">=</span><span class="n">gnb</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[(</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">())])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Pastel1</span><span class="p">,</span> <span class="n">shading</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">)</span>
<span class="c1"># 画训练集和测试集散点图</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X_train</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">cool</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X_test</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y_test</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">cool</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s1">'k'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'*'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span> <span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span> <span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Classifier: GaussianNB"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
</div>
</div>
</div># 可见，高斯NB的分类边界比伯努利NB复杂的多，且基本分类正确。
# 最常用，因为自然科学和社会科学，大量现象都符合正态分布。
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="多项式朴素贝叶斯分布">多项式朴素贝叶斯分布<a class="anchor-link" href="#多项式朴素贝叶斯分布">¶</a></h2>
</div>
</div>
</div># 二项分布通过抛硬币来理解，多项式分布可以通过掷骰子来理解。
# 均匀的6面骰子，每次投掷后朝上的一面是1-6这6个数字。如果投掷n次，则每个面朝上的次数的分布，符合多项式分布。
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [5]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">MultinomialNB</span>
<span class="n">mnb</span><span class="o">=</span><span class="n">MultinomialNB</span><span class="p">()</span>
<span class="c1">#mnb.fit(X_train, y_train) # 报错 ValueError: Negative values in data passed to MultinomialNB (input X)</span>
<span class="c1">#mnb.score(X_test, y_test)</span>
<span class="c1"># 只能传入非负数</span>
<span class="c1"># 导入数据预处理工具 MinMaxScaler，作用是把特征值全部转为0-1之间。</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span>
<span class="n">scaler</span><span class="o">=</span><span class="n">MinMaxScaler</span><span class="p">()</span>
<span class="n">scaler</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">X_train_scaled</span><span class="o">=</span><span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">X_test_scaled</span><span class="o">=</span><span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="c1"># 使用多项式朴素贝叶斯拟合经过预处理的数据</span>
<span class="n">mnb</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_scaled</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">mnb</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test_scaled</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt output_prompt">Out[5]:</div>
<div class="output_text output_subarea output_execute_result">
<pre>0.32</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [6]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 这个打分很糟糕，比伯努利NB还差。可视化</span>
<span class="c1"># 用不同背景色表示不同分类</span>
<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">),</span>
<span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">))</span>
<span class="n">z</span><span class="o">=</span><span class="n">mnb</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[(</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">())])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Pastel1</span><span class="p">,</span> <span class="n">shading</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">)</span>
<span class="c1"># 画训练集和测试集散点图</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X_train</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">cool</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X_test</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y_test</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">cool</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s1">'k'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'*'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span> <span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span> <span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Classifier: MultinomialNB"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
</div>
</div>
</div>
<pre># 大部分数据放到了错误的分类中。
# 多项式NB只适合对非负离散数值特征进行分类。典型例子是转化为向量后的文本数据进行分类。
</pre>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="真实数据---判断中流是良性还是恶性">真实数据 - 判断中流是良性还是恶性<a class="anchor-link" href="#真实数据---判断中流是良性还是恶性">¶</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="了解数据">了解数据<a class="anchor-link" href="#了解数据">¶</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [1]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_breast_cancer</span>
<span class="n">cancer</span><span class="o">=</span><span class="n">load_breast_cancer</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span> <span class="n">cancer</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [2]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># print(cancer.DESCR)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [3]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span> <span class="n">cancer</span><span class="o">.</span><span class="n">target_names</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span> <span class="n">cancer</span><span class="p">[</span><span class="s2">"feature_names"</span><span class="p">])</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>['malignant' 'benign']
['mean radius' 'mean texture' 'mean perimeter' 'mean area'
'mean smoothness' 'mean compactness' 'mean concavity'
'mean concave points' 'mean symmetry' 'mean fractal dimension'
'radius error' 'texture error' 'perimeter error' 'area error'
'smoothness error' 'compactness error' 'concavity error'
'concave points error' 'symmetry error' 'fractal dimension error'
'worst radius' 'worst texture' 'worst perimeter' 'worst area'
'worst smoothness' 'worst compactness' 'worst concavity'
'worst concave points' 'worst symmetry' 'worst fractal dimension']
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="建模">建模<a class="anchor-link" href="#建模">¶</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [4]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">cancer</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">cancer</span><span class="o">.</span><span class="n">target</span>
<span class="c1"># 拆分数据</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">38</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"train set size:"</span><span class="p">,</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"test set size:"</span><span class="p">,</span> <span class="n">X_test</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="c1"># 建模</span>
<span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">GaussianNB</span>
<span class="n">gnb</span><span class="o">=</span><span class="n">GaussianNB</span><span class="p">()</span>
<span class="n">gnb</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># 打分</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"trainning score: </span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">gnb</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> <span class="p">)</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"testing score: </span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">gnb</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="p">)</span> <span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>train set size: (426, 30)
test set size: (143, 30)
trainning score: 0.948
testing score: 0.944
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [5]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 随便预测一个</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"predict:"</span><span class="p">,</span> <span class="n">gnb</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span> <span class="p">[</span><span class="n">X</span><span class="p">[</span><span class="mi">312</span><span class="p">]]</span> <span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"real:"</span><span class="p">,</span> <span class="n">y</span><span class="p">[</span><span class="mi">312</span><span class="p">])</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>predict: [1]
real: 1
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="学习曲线-x=样本量-y=打分">学习曲线 x=样本量 y=打分<a class="anchor-link" href="#学习曲线-x=样本量-y=打分">¶</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [6]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="c1"># 导入学习曲线库</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">learning_curve</span>
<span class="c1"># 导入随机拆分工具</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">ShuffleSplit</span>
<span class="c1"># 定义一个函数绘制学习曲线</span>
<span class="k">def</span> <span class="nf">plot_learning_curve</span><span class="p">(</span><span class="n">estimator</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">ylim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">train_sizes</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">5</span><span class="p">)):</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
<span class="k">if</span> <span class="n">ylim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="o">*</span><span class="n">ylim</span><span class="p">)</span>
<span class="c1"># xlab</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Traning examples"</span><span class="p">)</span>
<span class="c1"># ylab</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Score"</span><span class="p">)</span>
<span class="n">train_sizes</span><span class="p">,</span> <span class="n">train_scores</span><span class="p">,</span> <span class="n">test_scores</span> <span class="o">=</span> <span class="n">learning_curve</span><span class="p">(</span>
<span class="n">estimator</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">cv</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">,</span> <span class="n">train_sizes</span><span class="o">=</span><span class="n">train_sizes</span><span class="p">)</span>
<span class="n">train_scores_mean</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">train_scores</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">test_scores_mean</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">test_scores</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">train_sizes</span><span class="p">,</span> <span class="n">train_scores_mean</span><span class="p">,</span> <span class="s1">'o-'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'r'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Training score"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">train_sizes</span><span class="p">,</span> <span class="n">test_scores_mean</span><span class="p">,</span> <span class="s1">'o-'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'g'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Cross-validation score"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"lower right"</span><span class="p">)</span>
<span class="k">return</span> <span class="n">plt</span>
<span class="c1"># setting</span>
<span class="n">title</span><span class="o">=</span><span class="s2">"Learning Curves (Naive Bayes)"</span>
<span class="n">cv</span><span class="o">=</span><span class="n">ShuffleSplit</span><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">estimator</span><span class="o">=</span><span class="n">GaussianNB</span><span class="p">()</span>
<span class="n">plot_learning_curve</span><span class="p">(</span><span class="n">estimator</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">ylim</span><span class="o">=</span><span class="p">(</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">1.01</span><span class="p">),</span> <span class="n">cv</span><span class="o">=</span><span class="n">cv</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
</div>
</div>
</div>
<pre># 可见，随着样本量的增大，训练集打分逐渐降低，因为要拟合的信息越来越多。
# 而测试集打分基本不变，说明高斯NB在预测方面，对样本量的要求没那么苛刻。如果样本量少，可以考虑NB建模。
</pre>
</div>
</div>
</body>
</html>