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Load East Timor data through pods #6

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lawrennd merged 1 commit into from
Oct 25, 2026
Merged

Load East Timor data through pods #6

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22 changes: 5 additions & 17 deletions _lectures/07-non-gaussian-likelihoods.html
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Expand Up @@ -71,23 +71,11 @@ <h1 id="non-gaussian-likelihoods">Non Gaussian Likelihoods</h1>
<p>[Gaussian processes model functions. If our observation is a corrupted version of this function and the corruption process is <em>also</em> Gaussian, it is trivial to account for this. However, there are many circumstances where our observation may be non Gaussian. In these cases we need to turn to approximate inference techniques. As a simple illustration, we’ll use a dataset of binary observations of the language that is spoken in different regions of East-Timor. First we will load the data and a couple of libraries to visualize it.}</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> matplotlib.patches <span class="im">import</span> Polygon</span>
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> matplotlib.collections <span class="im">import</span> PatchCollection</span>
<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> pathlib <span class="im">import</span> Path</span>
<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pickle</span>
<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> urllib.request</span></code></pre></div>
<div class="sourceCode" id="cb11"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Load East Timor language regions (local copy preferred; remote host is unreliable)</span></span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>data_path <span class="op">=</span> Path(<span class="st">&#39;data/EastTimor2.pickle&#39;</span>)</span>
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> <span class="kw">not</span> data_path.exists():</span>
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a> data_path <span class="op">=</span> Path(<span class="st">&#39;EastTimor2.pickle&#39;</span>)</span>
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> <span class="kw">not</span> data_path.exists():</span>
<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a> url <span class="op">=</span> <span class="st">&#39;http://staffwww.dcs.shef.ac.uk/people/M.Zwiessele/gpss/lab2/EastTimor.pickle&#39;</span></span>
<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a> urllib.request.urlretrieve(url, <span class="st">&#39;EastTimor2.pickle&#39;</span>)</span>
<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a> data_path <span class="op">=</span> Path(<span class="st">&#39;EastTimor2.pickle&#39;</span>)</span>
<span id="cb11-9"><a href="#cb11-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb11-10"><a href="#cb11-10" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> <span class="bu">open</span>(data_path, <span class="st">&#39;rb&#39;</span>) <span class="im">as</span> f:</span>
<span id="cb11-11"><a href="#cb11-11" aria-hidden="true" tabindex="-1"></a> <span class="cf">try</span>:</span>
<span id="cb11-12"><a href="#cb11-12" aria-hidden="true" tabindex="-1"></a> X, y, polygons <span class="op">=</span> pickle.load(f, encoding<span class="op">=</span><span class="st">&#39;latin1&#39;</span>)</span>
<span id="cb11-13"><a href="#cb11-13" aria-hidden="true" tabindex="-1"></a> <span class="cf">except</span> <span class="pp">TypeError</span>:</span>
<span id="cb11-14"><a href="#cb11-14" aria-hidden="true" tabindex="-1"></a> X, y, polygons <span class="op">=</span> pickle.load(f)</span></code></pre></div>
<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pods</span></code></pre></div>
<div class="sourceCode" id="cb11"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>data <span class="op">=</span> pods.datasets.east_timor()</span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>X <span class="op">=</span> data[<span class="st">&#39;X&#39;</span>]</span>
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>y <span class="op">=</span> data[<span class="st">&#39;Y&#39;</span>]</span>
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a>polygons <span class="op">=</span> data[<span class="st">&#39;polygons&#39;</span>]</span></code></pre></div>
<p>Now we will create a map of East Timor and, using GPy, plot the data on top of it. A classification model can be defined in a similar way to the regression model, but now using <code>GPy.models.GPClassification</code>. However, once we’ve define the model, we also need to update the approximation to the likelihood. This runs the Expectation propagation updates.</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> GPy</span></code></pre></div>
<div class="sourceCode" id="cb13"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Define the model</span></span>
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22 changes: 5 additions & 17 deletions _notebooks/07-non-gaussian-likelihoods.ipynb
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Expand Up @@ -278,9 +278,7 @@
"source": [
"from matplotlib.patches import Polygon\n",
"from matplotlib.collections import PatchCollection\n",
"from pathlib import Path\n",
"import pickle\n",
"import urllib.request"
"import pods"
],
"id": "bf80c131-13db-46da-b32a-977525075286"
},
Expand All @@ -290,20 +288,10 @@
"metadata": {},
"outputs": [],
"source": [
"# Load East Timor language regions (local copy preferred; remote host is unreliable)\n",
"data_path = Path('data/EastTimor2.pickle')\n",
"if not data_path.exists():\n",
" data_path = Path('EastTimor2.pickle')\n",
"if not data_path.exists():\n",
" url = 'http://staffwww.dcs.shef.ac.uk/people/M.Zwiessele/gpss/lab2/EastTimor.pickle'\n",
" urllib.request.urlretrieve(url, 'EastTimor2.pickle')\n",
" data_path = Path('EastTimor2.pickle')\n",
"\n",
"with open(data_path, 'rb') as f:\n",
" try:\n",
" X, y, polygons = pickle.load(f, encoding='latin1')\n",
" except TypeError:\n",
" X, y, polygons = pickle.load(f)"
"data = pods.datasets.east_timor()\n",
"X = data['X']\n",
"y = data['Y']\n",
"polygons = data['polygons']"
],
"id": "03afdd84-a321-4c43-8ad7-f8a49c474a3b"
},
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