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<li><a class="reference internal" href="#">Species distribution modeling</a><ul>
<li><a class="reference internal" href="#references">References</a></li>
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<p><a class="reference internal" href="#sphx-glr-download-auto-examples-applications-plot-species-distribution-modeling-py"><span class="std std-ref">Go to the end</span></a>
to download the full example code or to run this example in your browser via JupyterLite or Binder</p>
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<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="Link 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="Link 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: 10.66s
</pre></div>
</div>
<div class="line-block">
<div class="line"><br /></div>
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<div class="highlight-Python 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">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span><span class="p">,</span> <span class="n">svm</span>
<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.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="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>
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