-
Notifications
You must be signed in to change notification settings - Fork 81
/
Copy pathplot_species_distribution_modeling.html
568 lines (490 loc) · 57 KB
/
plot_species_distribution_modeling.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
<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
<meta charset="utf-8">
<meta name="generator" content="Docutils 0.19: https://docutils.sourceforge.io/" />
<meta property="og:title" content="Species distribution modeling" />
<meta property="og:type" content="website" />
<meta property="og:url" content="https://scikit-learn/stable/auto_examples/applications/plot_species_distribution_modeling.html" />
<meta property="og:site_name" content="scikit-learn" />
<meta property="og:description" content="Modeling species’ geographic distributions is an important problem in conservation biology. In this example, we model the geographic distribution of two South American mammals given past observatio..." />
<meta property="og:image" content="https://scikit-learn.org/stable/_static/scikit-learn-logo-small.png" />
<meta property="og:image:alt" content="scikit-learn" />
<meta name="description" content="Modeling species’ geographic distributions is an important problem in conservation biology. In this example, we model the geographic distribution of two South American mammals given past observatio..." />
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Species distribution modeling — scikit-learn 1.2.2 documentation</title>
<link rel="canonical" href="http://scikit-learn.org/stable/auto_examples/applications/plot_species_distribution_modeling.html" />
<link rel="shortcut icon" href="../../_static/favicon.ico"/>
<link rel="stylesheet" href="../../_static/css/vendor/bootstrap.min.css" type="text/css" />
<link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
<link rel="stylesheet" href="../../_static/plot_directive.css" type="text/css" />
<link rel="stylesheet" href="../../_static/sg_gallery.css" type="text/css" />
<link rel="stylesheet" href="../../_static/sg_gallery-binder.css" type="text/css" />
<link rel="stylesheet" href="../../_static/sg_gallery-dataframe.css" type="text/css" />
<link rel="stylesheet" href="../../_static/sg_gallery-rendered-html.css" type="text/css" />
<link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
<script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
<script src="../../_static/js/vendor/jquery-3.6.3.slim.min.js"></script>
</head>
<body>
<nav id="navbar" class="sk-docs-navbar navbar navbar-expand-md navbar-light bg-light py-0">
<div class="container-fluid sk-docs-container px-0">
<a class="navbar-brand py-0" href="../../index.html">
<img
class="sk-brand-img"
src="../../_static/scikit-learn-logo-small.png"
alt="logo"/>
</a>
<button
id="sk-navbar-toggler"
class="navbar-toggler"
type="button"
data-toggle="collapse"
data-target="#navbarSupportedContent"
aria-controls="navbarSupportedContent"
aria-expanded="false"
aria-label="Toggle navigation"
>
<span class="navbar-toggler-icon"></span>
</button>
<div class="sk-navbar-collapse collapse navbar-collapse" id="navbarSupportedContent">
<ul class="navbar-nav mr-auto">
<li class="nav-item">
<a class="sk-nav-link nav-link" href="../../install.html">Install</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link" href="../../user_guide.html">User Guide</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link" href="../../modules/classes.html">API</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link" href="../index.html">Examples</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link" target="_blank" rel="noopener noreferrer" href="https://blog.scikit-learn.org/">Community</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../getting_started.html" >Getting Started</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../tutorial/index.html" >Tutorial</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../whats_new/v1.2.html" >What's new</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../glossary.html" >Glossary</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="https://scikit-learn.org/dev/developers/index.html" target="_blank" rel="noopener noreferrer">Development</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../faq.html" >FAQ</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../support.html" >Support</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../related_projects.html" >Related packages</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../roadmap.html" >Roadmap</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../governance.html" >Governance</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../about.html" >About us</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="https://github.com/scikit-learn/scikit-learn" >GitHub</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="https://scikit-learn.org/dev/versions.html" >Other Versions and Download</a>
</li>
<li class="nav-item dropdown nav-more-item-dropdown">
<a class="sk-nav-link nav-link dropdown-toggle" href="#" id="navbarDropdown" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">More</a>
<div class="dropdown-menu" aria-labelledby="navbarDropdown">
<a class="sk-nav-dropdown-item dropdown-item" href="../../getting_started.html" >Getting Started</a>
<a class="sk-nav-dropdown-item dropdown-item" href="../../tutorial/index.html" >Tutorial</a>
<a class="sk-nav-dropdown-item dropdown-item" href="../../whats_new/v1.2.html" >What's new</a>
<a class="sk-nav-dropdown-item dropdown-item" href="../../glossary.html" >Glossary</a>
<a class="sk-nav-dropdown-item dropdown-item" href="https://scikit-learn.org/dev/developers/index.html" target="_blank" rel="noopener noreferrer">Development</a>
<a class="sk-nav-dropdown-item dropdown-item" href="../../faq.html" >FAQ</a>
<a class="sk-nav-dropdown-item dropdown-item" href="../../support.html" >Support</a>
<a class="sk-nav-dropdown-item dropdown-item" href="../../related_projects.html" >Related packages</a>
<a class="sk-nav-dropdown-item dropdown-item" href="../../roadmap.html" >Roadmap</a>
<a class="sk-nav-dropdown-item dropdown-item" href="../../governance.html" >Governance</a>
<a class="sk-nav-dropdown-item dropdown-item" href="../../about.html" >About us</a>
<a class="sk-nav-dropdown-item dropdown-item" href="https://github.com/scikit-learn/scikit-learn" >GitHub</a>
<a class="sk-nav-dropdown-item dropdown-item" href="https://scikit-learn.org/dev/versions.html" >Other Versions and Download</a>
</div>
</li>
</ul>
<div id="searchbox" role="search">
<div class="searchformwrapper">
<form class="search" action="../../search.html" method="get">
<input class="sk-search-text-input" type="text" name="q" aria-labelledby="searchlabel" />
<input class="sk-search-text-btn" type="submit" value="Go" />
</form>
</div>
</div>
</div>
</div>
</nav>
<div class="d-flex" id="sk-doc-wrapper">
<input type="checkbox" name="sk-toggle-checkbox" id="sk-toggle-checkbox">
<label id="sk-sidemenu-toggle" class="sk-btn-toggle-toc btn sk-btn-primary" for="sk-toggle-checkbox">Toggle Menu</label>
<div id="sk-sidebar-wrapper" class="border-right">
<div class="sk-sidebar-toc-wrapper">
<div class="btn-group w-100 mb-2" role="group" aria-label="rellinks">
<a href="plot_prediction_latency.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="Prediction Latency">Prev</a><a href="index.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="Examples based on real world datasets">Up</a>
<a href="plot_cyclical_feature_engineering.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="Time-related feature engineering">Next</a>
</div>
<div class="alert alert-danger p-1 mb-2" role="alert">
<p class="text-center mb-0">
<strong>scikit-learn 1.2.2</strong><br/>
<a href="http://scikit-learn.org/dev/versions.html">Other versions</a>
</p>
</div>
<div class="alert alert-warning p-1 mb-2" role="alert">
<p class="text-center mb-0">
Please <a class="font-weight-bold" href="../../about.html#citing-scikit-learn"><string>cite us</string></a> if you use the software.
</p>
</div>
<div class="sk-sidebar-toc">
<ul>
<li><a class="reference internal" href="#">Species distribution modeling</a><ul>
<li><a class="reference internal" href="#references">References</a></li>
</ul>
</li>
</ul>
</div>
</div>
</div>
<div id="sk-page-content-wrapper">
<div class="sk-page-content container-fluid body px-md-3" role="main">
<div class="sphx-glr-download-link-note admonition note">
<p class="admonition-title">Note</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-applications-plot-species-distribution-modeling-py"><span class="std std-ref">here</span></a>
to download the full example code or to run this example in your browser via Binder</p>
</div>
<section class="sphx-glr-example-title" id="species-distribution-modeling">
<span id="sphx-glr-auto-examples-applications-plot-species-distribution-modeling-py"></span><h1>Species distribution modeling<a class="headerlink" href="#species-distribution-modeling" title="Permalink to this heading">¶</a></h1>
<p>Modeling species’ geographic distributions is an important
problem in conservation biology. In this example, we
model the geographic distribution of two South American
mammals given past observations and 14 environmental
variables. Since we have only positive examples (there are
no unsuccessful observations), we cast this problem as a
density estimation problem and use the <a class="reference internal" href="../../modules/generated/sklearn.svm.OneClassSVM.html#sklearn.svm.OneClassSVM" title="sklearn.svm.OneClassSVM"><code class="xref py py-class docutils literal notranslate"><span class="pre">OneClassSVM</span></code></a>
as our modeling tool. The dataset is provided by Phillips et. al. (2006).
If available, the example uses
<a class="reference external" href="https://matplotlib.org/basemap/">basemap</a>
to plot the coast lines and national boundaries of South America.</p>
<p>The two species are:</p>
<blockquote>
<div><ul class="simple">
<li><p><a class="reference external" href="http://www.iucnredlist.org/details/3038/0">“Bradypus variegatus”</a> ,
the Brown-throated Sloth.</p></li>
<li><p><a class="reference external" href="http://www.iucnredlist.org/details/13408/0">“Microryzomys minutus”</a> ,
also known as the Forest Small Rice Rat, a rodent that lives in Peru,
Colombia, Ecuador, Peru, and Venezuela.</p></li>
</ul>
</div></blockquote>
<section id="references">
<h2>References<a class="headerlink" href="#references" title="Permalink to this heading">¶</a></h2>
<blockquote>
<div><ul class="simple">
<li><p><a class="reference external" href="http://rob.schapire.net/papers/ecolmod.pdf">“Maximum entropy modeling of species geographic distributions”</a>
S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,
190:231-259, 2006.</p></li>
</ul>
</div></blockquote>
<img src="../../_images/sphx_glr_plot_species_distribution_modeling_001.png" srcset="../../_images/sphx_glr_plot_species_distribution_modeling_001.png" alt="bradypus variegatus, microryzomys minutus" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>________________________________________________________________________________
Modeling distribution of species 'bradypus variegatus'
- fit OneClassSVM ... done.
- plot coastlines from coverage
- predict species distribution
Area under the ROC curve : 0.868443
________________________________________________________________________________
Modeling distribution of species 'microryzomys minutus'
- fit OneClassSVM ... done.
- plot coastlines from coverage
- predict species distribution
Area under the ROC curve : 0.993919
time elapsed: 11.32s
</pre></div>
</div>
<div class="line-block">
<div class="line"><br /></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Peter Prettenhofer <[email protected]></span>
<span class="c1"># Jake Vanderplas <[email protected]></span>
<span class="c1">#</span>
<span class="c1"># License: BSD 3 clause</span>
<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span></a>
<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="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.utils.Bunch.html#sklearn.utils.Bunch" title="sklearn.utils.Bunch" class="sphx-glr-backref-module-sklearn-utils sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Bunch</span></a>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.datasets.fetch_species_distributions.html#sklearn.datasets.fetch_species_distributions" title="sklearn.datasets.fetch_species_distributions" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">fetch_species_distributions</span></a>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">svm</span><span class="p">,</span> <span class="n">metrics</span>
<span class="c1"># if basemap is available, we'll use it.</span>
<span class="c1"># otherwise, we'll improvise later...</span>
<span class="k">try</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">mpl_toolkits.basemap</span> <span class="kn">import</span> <span class="n">Basemap</span>
<span class="n">basemap</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
<span class="n">basemap</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">def</span> <span class="nf">construct_grids</span><span class="p">(</span><span class="n">batch</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Construct the map grid from the batch object</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> batch : Batch object</span>
<span class="sd"> The object returned by :func:`fetch_species_distributions`</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> (xgrid, ygrid) : 1-D arrays</span>
<span class="sd"> The grid corresponding to the values in batch.coverages</span>
<span class="sd"> """</span>
<span class="c1"># x,y coordinates for corner cells</span>
<span class="n">xmin</span> <span class="o">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">x_left_lower_corner</span> <span class="o">+</span> <span class="n">batch</span><span class="o">.</span><span class="n">grid_size</span>
<span class="n">xmax</span> <span class="o">=</span> <span class="n">xmin</span> <span class="o">+</span> <span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">Nx</span> <span class="o">*</span> <span class="n">batch</span><span class="o">.</span><span class="n">grid_size</span><span class="p">)</span>
<span class="n">ymin</span> <span class="o">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">y_left_lower_corner</span> <span class="o">+</span> <span class="n">batch</span><span class="o">.</span><span class="n">grid_size</span>
<span class="n">ymax</span> <span class="o">=</span> <span class="n">ymin</span> <span class="o">+</span> <span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">Ny</span> <span class="o">*</span> <span class="n">batch</span><span class="o">.</span><span class="n">grid_size</span><span class="p">)</span>
<span class="c1"># x coordinates of the grid cells</span>
<span class="n">xgrid</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="n">xmin</span><span class="p">,</span> <span class="n">xmax</span><span class="p">,</span> <span class="n">batch</span><span class="o">.</span><span class="n">grid_size</span><span class="p">)</span>
<span class="c1"># y coordinates of the grid cells</span>
<span class="n">ygrid</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="n">ymin</span><span class="p">,</span> <span class="n">ymax</span><span class="p">,</span> <span class="n">batch</span><span class="o">.</span><span class="n">grid_size</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="n">xgrid</span><span class="p">,</span> <span class="n">ygrid</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">create_species_bunch</span><span class="p">(</span><span class="n">species_name</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">coverages</span><span class="p">,</span> <span class="n">xgrid</span><span class="p">,</span> <span class="n">ygrid</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Create a bunch with information about a particular organism</span>
<span class="sd"> This will use the test/train record arrays to extract the</span>
<span class="sd"> data specific to the given species name.</span>
<span class="sd"> """</span>
<span class="n">bunch</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.utils.Bunch.html#sklearn.utils.Bunch" title="sklearn.utils.Bunch" class="sphx-glr-backref-module-sklearn-utils sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Bunch</span></a><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">" "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">species_name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"_"</span><span class="p">)[:</span><span class="mi">2</span><span class="p">]))</span>
<span class="n">species_name</span> <span class="o">=</span> <span class="n">species_name</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s2">"ascii"</span><span class="p">)</span>
<span class="n">points</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">test</span><span class="o">=</span><span class="n">test</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="n">train</span><span class="p">)</span>
<span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pts</span> <span class="ow">in</span> <span class="n">points</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="c1"># choose points associated with the desired species</span>
<span class="n">pts</span> <span class="o">=</span> <span class="n">pts</span><span class="p">[</span><span class="n">pts</span><span class="p">[</span><span class="s2">"species"</span><span class="p">]</span> <span class="o">==</span> <span class="n">species_name</span><span class="p">]</span>
<span class="n">bunch</span><span class="p">[</span><span class="s2">"pts_</span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="n">label</span><span class="p">]</span> <span class="o">=</span> <span class="n">pts</span>
<span class="c1"># determine coverage values for each of the training & testing points</span>
<span class="n">ix</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.searchsorted.html#numpy.searchsorted" title="numpy.searchsorted" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">searchsorted</span></a><span class="p">(</span><span class="n">xgrid</span><span class="p">,</span> <span class="n">pts</span><span class="p">[</span><span class="s2">"dd long"</span><span class="p">])</span>
<span class="n">iy</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.searchsorted.html#numpy.searchsorted" title="numpy.searchsorted" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">searchsorted</span></a><span class="p">(</span><span class="n">ygrid</span><span class="p">,</span> <span class="n">pts</span><span class="p">[</span><span class="s2">"dd lat"</span><span class="p">])</span>
<span class="n">bunch</span><span class="p">[</span><span class="s2">"cov_</span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="n">label</span><span class="p">]</span> <span class="o">=</span> <span class="n">coverages</span><span class="p">[:,</span> <span class="o">-</span><span class="n">iy</span><span class="p">,</span> <span class="n">ix</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
<span class="k">return</span> <span class="n">bunch</span>
<span class="k">def</span> <span class="nf">plot_species_distribution</span><span class="p">(</span>
<span class="n">species</span><span class="o">=</span><span class="p">(</span><span class="s2">"bradypus_variegatus_0"</span><span class="p">,</span> <span class="s2">"microryzomys_minutus_0"</span><span class="p">)</span>
<span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Plot the species distribution.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">species</span><span class="p">)</span> <span class="o">></span> <span class="mi">2</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span>
<span class="s2">"Note: when more than two species are provided,"</span>
<span class="s2">" only the first two will be used"</span>
<span class="p">)</span>
<span class="n">t0</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span></a><span class="p">()</span>
<span class="c1"># Load the compressed data</span>
<span class="n">data</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.fetch_species_distributions.html#sklearn.datasets.fetch_species_distributions" title="sklearn.datasets.fetch_species_distributions" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">fetch_species_distributions</span></a><span class="p">()</span>
<span class="c1"># Set up the data grid</span>
<span class="n">xgrid</span><span class="p">,</span> <span class="n">ygrid</span> <span class="o">=</span> <span class="n">construct_grids</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="c1"># The grid in x,y coordinates</span>
<span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html#numpy.meshgrid" title="numpy.meshgrid" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span></a><span class="p">(</span><span class="n">xgrid</span><span class="p">,</span> <span class="n">ygrid</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="c1"># create a bunch for each species</span>
<span class="n">BV_bunch</span> <span class="o">=</span> <span class="n">create_species_bunch</span><span class="p">(</span>
<span class="n">species</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">data</span><span class="o">.</span><span class="n">train</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">test</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">coverages</span><span class="p">,</span> <span class="n">xgrid</span><span class="p">,</span> <span class="n">ygrid</span>
<span class="p">)</span>
<span class="n">MM_bunch</span> <span class="o">=</span> <span class="n">create_species_bunch</span><span class="p">(</span>
<span class="n">species</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">data</span><span class="o">.</span><span class="n">train</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">test</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">coverages</span><span class="p">,</span> <span class="n">xgrid</span><span class="p">,</span> <span class="n">ygrid</span>
<span class="p">)</span>
<span class="c1"># background points (grid coordinates) for evaluation</span>
<a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.seed.html#numpy.random.seed" title="numpy.random.seed" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span></a><span class="p">(</span><span class="mi">13</span><span class="p">)</span>
<span class="n">background_points</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.c_.html#numpy.c_" title="numpy.c_" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">c_</span></a><span class="p">[</span>
<a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.randint.html#numpy.random.randint" title="numpy.random.randint" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span></a><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">Ny</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">10000</span><span class="p">),</span>
<a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.randint.html#numpy.random.randint" title="numpy.random.randint" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span></a><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">Nx</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">10000</span><span class="p">),</span>
<span class="p">]</span><span class="o">.</span><span class="n">T</span>
<span class="c1"># We'll make use of the fact that coverages[6] has measurements at all</span>
<span class="c1"># land points. This will help us decide between land and water.</span>
<span class="n">land_reference</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">coverages</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span>
<span class="c1"># Fit, predict, and plot for each species.</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">species</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">([</span><span class="n">BV_bunch</span><span class="p">,</span> <span class="n">MM_bunch</span><span class="p">]):</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"_"</span> <span class="o">*</span> <span class="mi">80</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Modeling distribution of species '</span><span class="si">%s</span><span class="s2">'"</span> <span class="o">%</span> <span class="n">species</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
<span class="c1"># Standardize features</span>
<span class="n">mean</span> <span class="o">=</span> <span class="n">species</span><span class="o">.</span><span class="n">cov_train</span><span class="o">.</span><span class="n">mean</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="n">std</span> <span class="o">=</span> <span class="n">species</span><span class="o">.</span><span class="n">cov_train</span><span class="o">.</span><span class="n">std</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="n">train_cover_std</span> <span class="o">=</span> <span class="p">(</span><span class="n">species</span><span class="o">.</span><span class="n">cov_train</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span> <span class="o">/</span> <span class="n">std</span>
<span class="c1"># Fit OneClassSVM</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">" - fit OneClassSVM ... "</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s2">""</span><span class="p">)</span>
<span class="n">clf</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.svm.OneClassSVM.html#sklearn.svm.OneClassSVM" title="sklearn.svm.OneClassSVM" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">svm</span><span class="o">.</span><span class="n">OneClassSVM</span></a><span class="p">(</span><span class="n">nu</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="s2">"rbf"</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">0.5</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">train_cover_std</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"done."</span><span class="p">)</span>
<span class="c1"># Plot map of South America</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot" title="matplotlib.pyplot.subplot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="n">basemap</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">" - plot coastlines using basemap"</span><span class="p">)</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">Basemap</span><span class="p">(</span>
<span class="n">projection</span><span class="o">=</span><span class="s2">"cyl"</span><span class="p">,</span>
<span class="n">llcrnrlat</span><span class="o">=</span><span class="n">Y</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span>
<span class="n">urcrnrlat</span><span class="o">=</span><span class="n">Y</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span>
<span class="n">llcrnrlon</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span>
<span class="n">urcrnrlon</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span>
<span class="n">resolution</span><span class="o">=</span><span class="s2">"c"</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">m</span><span class="o">.</span><span class="n">drawcoastlines</span><span class="p">()</span>
<span class="n">m</span><span class="o">.</span><span class="n">drawcountries</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">" - plot coastlines from coverage"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contour.html#matplotlib.pyplot.contour" title="matplotlib.pyplot.contour" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">contour</span></a><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">land_reference</span><span class="p">,</span> <span class="n">levels</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">9998</span><span class="p">],</span> <span class="n">colors</span><span class="o">=</span><span class="s2">"k"</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s2">"solid"</span>
<span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xticks.html#matplotlib.pyplot.xticks" title="matplotlib.pyplot.xticks" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">xticks</span></a><span class="p">([])</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.yticks.html#matplotlib.pyplot.yticks" title="matplotlib.pyplot.yticks" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">yticks</span></a><span class="p">([])</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">" - predict species distribution"</span><span class="p">)</span>
<span class="c1"># Predict species distribution using the training data</span>
<span class="n">Z</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ones.html#numpy.ones" title="numpy.ones" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">ones</span></a><span class="p">((</span><span class="n">data</span><span class="o">.</span><span class="n">Ny</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">Nx</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">np</span><span class="o">.</span><span class="n">float64</span></a><span class="p">)</span>
<span class="c1"># We'll predict only for the land points.</span>
<span class="n">idx</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.where.html#numpy.where" title="numpy.where" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">where</span></a><span class="p">(</span><span class="n">land_reference</span> <span class="o">></span> <span class="o">-</span><span class="mi">9999</span><span class="p">)</span>
<span class="n">coverages_land</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">coverages</span><span class="p">[:,</span> <span class="n">idx</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">idx</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">T</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">decision_function</span><span class="p">((</span><span class="n">coverages_land</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span> <span class="o">/</span> <span class="n">std</span><span class="p">)</span>
<span class="n">Z</span> <span class="o">*=</span> <span class="n">pred</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
<span class="n">Z</span><span class="p">[</span><span class="n">idx</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">idx</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span> <span class="o">=</span> <span class="n">pred</span>
<span class="n">levels</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="n">Z</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">Z</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="mi">25</span><span class="p">)</span>
<span class="n">Z</span><span class="p">[</span><span class="n">land_reference</span> <span class="o">==</span> <span class="o">-</span><span class="mi">9999</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">9999</span>
<span class="c1"># plot contours of the prediction</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contourf.html#matplotlib.pyplot.contourf" title="matplotlib.pyplot.contourf" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">contourf</span></a><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">Z</span><span class="p">,</span> <span class="n">levels</span><span class="o">=</span><span class="n">levels</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">Reds</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.colorbar.html#matplotlib.pyplot.colorbar" title="matplotlib.pyplot.colorbar" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span></a><span class="p">(</span><span class="nb">format</span><span class="o">=</span><span class="s2">"</span><span class="si">%.2f</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># scatter training/testing points</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html#matplotlib.pyplot.scatter" title="matplotlib.pyplot.scatter" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span></a><span class="p">(</span>
<span class="n">species</span><span class="o">.</span><span class="n">pts_train</span><span class="p">[</span><span class="s2">"dd long"</span><span class="p">],</span>
<span class="n">species</span><span class="o">.</span><span class="n">pts_train</span><span class="p">[</span><span class="s2">"dd lat"</span><span class="p">],</span>
<span class="n">s</span><span class="o">=</span><span class="mi">2</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span>
<span class="n">c</span><span class="o">=</span><span class="s2">"black"</span><span class="p">,</span>
<span class="n">marker</span><span class="o">=</span><span class="s2">"^"</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s2">"train"</span><span class="p">,</span>
<span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html#matplotlib.pyplot.scatter" title="matplotlib.pyplot.scatter" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span></a><span class="p">(</span>
<span class="n">species</span><span class="o">.</span><span class="n">pts_test</span><span class="p">[</span><span class="s2">"dd long"</span><span class="p">],</span>
<span class="n">species</span><span class="o">.</span><span class="n">pts_test</span><span class="p">[</span><span class="s2">"dd lat"</span><span class="p">],</span>
<span class="n">s</span><span class="o">=</span><span class="mi">2</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span>
<span class="n">c</span><span class="o">=</span><span class="s2">"black"</span><span class="p">,</span>
<span class="n">marker</span><span class="o">=</span><span class="s2">"x"</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s2">"test"</span><span class="p">,</span>
<span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">()</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="matplotlib.pyplot.title" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">title</span></a><span class="p">(</span><span class="n">species</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.axis.html#matplotlib.pyplot.axis" title="matplotlib.pyplot.axis" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">axis</span></a><span class="p">(</span><span class="s2">"equal"</span><span class="p">)</span>
<span class="c1"># Compute AUC with regards to background points</span>
<span class="n">pred_background</span> <span class="o">=</span> <span class="n">Z</span><span class="p">[</span><span class="n">background_points</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">background_points</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
<span class="n">pred_test</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">decision_function</span><span class="p">((</span><span class="n">species</span><span class="o">.</span><span class="n">cov_test</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span> <span class="o">/</span> <span class="n">std</span><span class="p">)</span>
<span class="n">scores</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.r_.html#numpy.r_" title="numpy.r_" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">r_</span></a><span class="p">[</span><span class="n">pred_test</span><span class="p">,</span> <span class="n">pred_background</span><span class="p">]</span>
<span class="n">y</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.r_.html#numpy.r_" title="numpy.r_" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">r_</span></a><span class="p">[</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ones.html#numpy.ones" title="numpy.ones" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">ones</span></a><span class="p">(</span><span class="n">pred_test</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.zeros.html#numpy.zeros" title="numpy.zeros" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">zeros</span></a><span class="p">(</span><span class="n">pred_background</span><span class="o">.</span><span class="n">shape</span><span class="p">)]</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve" title="sklearn.metrics.roc_curve" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">metrics</span><span class="o">.</span><span class="n">roc_curve</span></a><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">scores</span><span class="p">)</span>
<span class="n">roc_auc</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.metrics.auc.html#sklearn.metrics.auc" title="sklearn.metrics.auc" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">metrics</span><span class="o">.</span><span class="n">auc</span></a><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.text.html#matplotlib.pyplot.text" title="matplotlib.pyplot.text" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">text</span></a><span class="p">(</span><span class="o">-</span><span class="mi">35</span><span class="p">,</span> <span class="o">-</span><span class="mi">70</span><span class="p">,</span> <span class="s2">"AUC: </span><span class="si">%.3f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">roc_auc</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s2">"right"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2"> Area under the ROC curve : </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">roc_auc</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">time elapsed: </span><span class="si">%.2f</span><span class="s2">s"</span> <span class="o">%</span> <span class="p">(</span><a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span></a><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>
<span class="n">plot_species_distribution</span><span class="p">()</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 11.511 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-applications-plot-species-distribution-modeling-py">
<div class="binder-badge docutils container">
<a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.2.X?urlpath=lab/tree/notebooks/auto_examples/applications/plot_species_distribution_modeling.ipynb"><img alt="Launch binder" src="../../_images/binder_badge_logo8.svg" width="150px" /></a>
</div>
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/69878e8e2864920aa874c5a68cecf1d3/plot_species_distribution_modeling.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_species_distribution_modeling.py</span></code></a></p>
</div>
<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/b5d1ec88ae06ced89813c50d00effe51/plot_species_distribution_modeling.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_species_distribution_modeling.ipynb</span></code></a></p>
</div>
</div>
<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
</section>
</section>
</div>
<div class="container">
<footer class="sk-content-footer">
© 2007 - 2023, scikit-learn developers (BSD License).
<a href="../../_sources/auto_examples/applications/plot_species_distribution_modeling.rst.txt" rel="nofollow">Show this page source</a>
</footer>
</div>
</div>
</div>
<script src="../../_static/js/vendor/bootstrap.min.js"></script>
<script>
window.ga=window.ga||function(){(ga.q=ga.q||[]).push(arguments)};ga.l=+new Date;
ga('create', 'UA-22606712-2', 'auto');
ga('set', 'anonymizeIp', true);
ga('send', 'pageview');
</script>
<script async src='https://www.google-analytics.com/analytics.js'></script>
<script defer data-domain="scikit-learn.org" src="https://views.scientific-python.org/js/script.js">
</script>
<script>
$(document).ready(function() {
/* Add a [>>>] button on the top-right corner of code samples to hide
* the >>> and ... prompts and the output and thus make the code
* copyable. */
var div = $('.highlight-python .highlight,' +
'.highlight-python3 .highlight,' +
'.highlight-pycon .highlight,' +
'.highlight-default .highlight')
var pre = div.find('pre');
// get the styles from the current theme
pre.parent().parent().css('position', 'relative');
var hide_text = 'Hide prompts and outputs';
var show_text = 'Show prompts and outputs';
// create and add the button to all the code blocks that contain >>>
div.each(function(index) {
var jthis = $(this);
if (jthis.find('.gp').length > 0) {
var button = $('<span class="copybutton">>>></span>');
button.attr('title', hide_text);
button.data('hidden', 'false');
jthis.prepend(button);
}
// tracebacks (.gt) contain bare text elements that need to be
// wrapped in a span to work with .nextUntil() (see later)
jthis.find('pre:has(.gt)').contents().filter(function() {
return ((this.nodeType == 3) && (this.data.trim().length > 0));
}).wrap('<span>');
});
// define the behavior of the button when it's clicked
$('.copybutton').click(function(e){
e.preventDefault();
var button = $(this);
if (button.data('hidden') === 'false') {
// hide the code output
button.parent().find('.go, .gp, .gt').hide();
button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden');
button.css('text-decoration', 'line-through');
button.attr('title', show_text);
button.data('hidden', 'true');
} else {
// show the code output
button.parent().find('.go, .gp, .gt').show();
button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible');
button.css('text-decoration', 'none');
button.attr('title', hide_text);
button.data('hidden', 'false');
}
});
/*** Add permalink buttons next to glossary terms ***/
$('dl.glossary > dt[id]').append(function() {
return ('<a class="headerlink" href="#' +
this.getAttribute('id') +
'" title="Permalink to this term">¶</a>');
});
});
</script>
<script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js"></script>
<script src="https://scikit-learn.org/versionwarning.js"></script>
</body>
</html>