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lawrennd merged 1 commit into from
Oct 25, 2026
Merged

Fix Deep GP notebook smoke: write_figure and MNIST ARFF parser #7

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18 changes: 10 additions & 8 deletions _lectures/12-deep-gps-II.html
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Expand Up @@ -1007,21 +1007,23 @@ <h2 id="subsample-of-the-mnist-data">Subsample of the MNIST Data</h2>
<p>The data is a modified version of a data set from the US National Institute of Standards and Technology (<a href="https://www.nist.gov/">NIST</a>.</p>
<p>First load in the MNIST data set from scikit learn. This can take a little while because it’s large to download.</p>
<div class="sourceCode" id="cb66"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb66-1"><a href="#cb66-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sklearn.datasets <span class="im">import</span> fetch_openml</span></code></pre></div>
<div class="sourceCode" id="cb67"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb67-1"><a href="#cb67-1" aria-hidden="true" tabindex="-1"></a>mnist <span class="op">=</span> fetch_openml(<span class="st">&#39;mnist_784&#39;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb67"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb67-1"><a href="#cb67-1" aria-hidden="true" tabindex="-1"></a>mnist <span class="op">=</span> fetch_openml(<span class="st">&#39;mnist_784&#39;</span>, version<span class="op">=</span><span class="dv">1</span>, as_frame<span class="op">=</span><span class="va">False</span>, parser<span class="op">=</span><span class="st">&#39;liac-arff&#39;</span>)</span></code></pre></div>
<p>Sub-sample the dataset to make the training faster.</p>
<div class="sourceCode" id="cb68"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb68-1"><a href="#cb68-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span></code></pre></div>
<div class="sourceCode" id="cb69"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb69-1"><a href="#cb69-1" aria-hidden="true" tabindex="-1"></a>np.random.seed(<span class="dv">0</span>)</span>
<span id="cb69-2"><a href="#cb69-2" aria-hidden="true" tabindex="-1"></a>digits <span class="op">=</span> [<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">2</span>,<span class="dv">3</span>,<span class="dv">4</span>]</span>
<span id="cb69-3"><a href="#cb69-3" aria-hidden="true" tabindex="-1"></a>N_per_digit <span class="op">=</span> <span class="dv">100</span></span>
<span id="cb69-4"><a href="#cb69-4" aria-hidden="true" tabindex="-1"></a>Y <span class="op">=</span> []</span>
<span id="cb69-5"><a href="#cb69-5" aria-hidden="true" tabindex="-1"></a>labels <span class="op">=</span> []</span>
<span id="cb69-6"><a href="#cb69-6" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> d <span class="kw">in</span> digits:</span>
<span id="cb69-7"><a href="#cb69-7" aria-hidden="true" tabindex="-1"></a> imgs <span class="op">=</span> mnist[<span class="st">&#39;data&#39;</span>][mnist[<span class="st">&#39;target&#39;</span>]<span class="op">==</span><span class="bu">str</span>(d)]</span>
<span id="cb69-8"><a href="#cb69-8" aria-hidden="true" tabindex="-1"></a> Y.append(imgs.loc[np.random.permutation(imgs.index)[:N_per_digit]])</span>
<span id="cb69-9"><a href="#cb69-9" aria-hidden="true" tabindex="-1"></a> labels.append(np.ones(N_per_digit)<span class="op">*</span>d)</span>
<span id="cb69-10"><a href="#cb69-10" aria-hidden="true" tabindex="-1"></a>Y <span class="op">=</span> np.vstack(Y).astype(np.float64)</span>
<span id="cb69-11"><a href="#cb69-11" aria-hidden="true" tabindex="-1"></a>labels <span class="op">=</span> np.hstack(labels)</span>
<span id="cb69-12"><a href="#cb69-12" aria-hidden="true" tabindex="-1"></a>Y <span class="op">/=</span> <span class="dv">255</span></span></code></pre></div>
<span id="cb69-6"><a href="#cb69-6" aria-hidden="true" tabindex="-1"></a>data <span class="op">=</span> np.asarray(mnist.data)</span>
<span id="cb69-7"><a href="#cb69-7" aria-hidden="true" tabindex="-1"></a>target <span class="op">=</span> np.asarray(mnist.target).astype(<span class="bu">str</span>)</span>
<span id="cb69-8"><a href="#cb69-8" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> d <span class="kw">in</span> digits:</span>
<span id="cb69-9"><a href="#cb69-9" aria-hidden="true" tabindex="-1"></a> imgs <span class="op">=</span> data[target <span class="op">==</span> <span class="bu">str</span>(d)]</span>
<span id="cb69-10"><a href="#cb69-10" aria-hidden="true" tabindex="-1"></a> Y.append(imgs[np.random.permutation(<span class="bu">len</span>(imgs))[:N_per_digit]])</span>
<span id="cb69-11"><a href="#cb69-11" aria-hidden="true" tabindex="-1"></a> labels.append(np.ones(N_per_digit)<span class="op">*</span>d)</span>
<span id="cb69-12"><a href="#cb69-12" aria-hidden="true" tabindex="-1"></a>Y <span class="op">=</span> np.vstack(Y).astype(np.float64)</span>
<span id="cb69-13"><a href="#cb69-13" aria-hidden="true" tabindex="-1"></a>labels <span class="op">=</span> np.hstack(labels)</span>
<span id="cb69-14"><a href="#cb69-14" aria-hidden="true" tabindex="-1"></a>Y <span class="op">/=</span> <span class="dv">255</span></span></code></pre></div>
<p>Now let’s visualise some examples from our subsampled dataset to get a sense of what the data looks like.</p>
<h2 id="mnist-digit-examples">MNIST Digit Examples</h2>
<div style="text-align:right">
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27 changes: 19 additions & 8 deletions _notebooks/11-deep-gps-I.ipynb
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Expand Up @@ -659,9 +659,10 @@
"outputs": [],
"source": [
"from matplotlib import rc\n",
"from mlai import plot\n",
"import mlai\n",
"\n",
"rc(\"font\", **{'family':'sans-serif','sans-serif':['Helvetica'],'size':30})\n",
"rc(\"text\", usetex=True)"
"rc(\"font\", **{'family':'sans-serif','sans-serif':['Helvetica'],'size':30})"
],
"id": "b4899adc-c510-4ced-bf32-adf9da122d91"
},
Expand All @@ -672,7 +673,9 @@
"outputs": [],
"source": [
"pgm = plot.horizontal_chain(depth=5)\n",
"pgm.render().figure.savefig(\"./deepgp/deep-markov.svg\", transparent=True)"
"mlai.write_figure('deep-markov.svg',\n",
" figure=pgm.render().figure,\n",
" directory='./deepgp')"
],
"id": "e1f06e27-f3e2-4ebc-92ce-67e9c5f3ab1a"
},
Expand All @@ -699,8 +702,10 @@
"outputs": [],
"source": [
"from matplotlib import rc\n",
"rc(\"font\", **{'family':'sans-serif','sans-serif':['Helvetica'], 'size':15})\n",
"rc(\"text\", usetex=True)"
"from mlai import plot\n",
"import mlai\n",
"\n",
"rc(\"font\", **{'family':'sans-serif','sans-serif':['Helvetica'], 'size':15})"
],
"id": "e0db8165-a2d6-4ae6-9182-88ad41625848"
},
Expand All @@ -711,7 +716,9 @@
"outputs": [],
"source": [
"pgm = plot.vertical_chain(depth=5)\n",
"pgm.render().figure.savefig(\"./deepgp/deep-markov-vertical.svg\", transparent=True)"
"mlai.write_figure('deep-markov-vertical.svg',\n",
" figure=pgm.render().figure,\n",
" directory='./deepgp')"
],
"id": "0c6d23c5-f13e-49aa-9708-48d8bfac81d9"
},
Expand Down Expand Up @@ -781,7 +788,9 @@
"metadata": {},
"outputs": [],
"source": [
"import daft"
"import daft\n",
"from mlai import plot\n",
"import mlai"
],
"id": "734eaf8a-f0bc-42b1-bc97-aa370a311d86"
},
Expand All @@ -794,7 +803,9 @@
"pgm = plot.vertical_chain(depth=5, shape=[2, 7])\n",
"pgm.add_node(daft.Node('y_2', r'$\\mathbf{y}_2$', 1.5, 3.5, observed=True))\n",
"pgm.add_edge('f_2', 'y_2')\n",
"pgm.render().figure.savefig(\"./deepgp/deep-markov-vertical-side.svg\", transparent=True)"
"mlai.write_figure('deep-markov-vertical-side.svg',\n",
" figure=pgm.render().figure,\n",
" directory='./deepgp')"
],
"id": "a4160027-6d3b-4366-8f9a-9d488835c063"
},
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8 changes: 5 additions & 3 deletions _notebooks/12-deep-gps-II.ipynb
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Expand Up @@ -2873,7 +2873,7 @@
"metadata": {},
"outputs": [],
"source": [
"mnist = fetch_openml('mnist_784')"
"mnist = fetch_openml('mnist_784', version=1, as_frame=False, parser='liac-arff')"
],
"id": "4dd1b849-4c7f-4da9-bc81-88a20da534c7"
},
Expand Down Expand Up @@ -2906,9 +2906,11 @@
"N_per_digit = 100\n",
"Y = []\n",
"labels = []\n",
"data = np.asarray(mnist.data)\n",
"target = np.asarray(mnist.target).astype(str)\n",
"for d in digits:\n",
" imgs = mnist['data'][mnist['target']==str(d)]\n",
" Y.append(imgs.loc[np.random.permutation(imgs.index)[:N_per_digit]])\n",
" imgs = data[target == str(d)]\n",
" Y.append(imgs[np.random.permutation(len(imgs))[:N_per_digit]])\n",
" labels.append(np.ones(N_per_digit)*d)\n",
"Y = np.vstack(Y).astype(np.float64)\n",
"labels = np.hstack(labels)\n",
Expand Down
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