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---
title: USPTO 1K TPL confusion matrices
keywords: fastai
sidebar: home_sidebar
summary: "Analysis of the predictions on the USPTO 1K TPL data set for the approaches"
description: "Analysis of the predictions on the USPTO 1K TPL data set for the approaches"
nb_path: "nbs/10_results_uspto_1k_tpl.ipynb"
---
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">import</span> <span class="nn">faiss</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">Counter</span>
<span class="kn">from</span> <span class="nn">pycm</span> <span class="kn">import</span> <span class="n">ConfusionMatrix</span><span class="p">,</span> <span class="n">Compare</span>
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<h3 id="BERT-classifier-and-k-NN-classifier-predictions">BERT classifier and k-NN classifier predictions<a class="anchor-link" href="#BERT-classifier-and-k-NN-classifier-predictions"> </a></h3><p>To compute the evaluation metrics we use <a href="https://www.pycm.ir">PyCM</a>, which can be installed using <code>pip install pycm==2.7</code>. The nearest neighbours for the different fingerprint approaches are computed using <a href="https://github.com/facebookresearch/faiss">faiss</a> (version 1.5.3).</p>
<p>We have already computed confusion matrix, you can download them (together with the data set and the fingerprints) from <a href="https://ibm.box.com/v/MappingChemicalReactions">MappingChemicalReactions on Box</a>.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_nearest_neighbours_prediction</span><span class="p">(</span><span class="n">train_X</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="n">train_y</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">,</span>
<span class="n">eval_X</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="n">n_neighbours</span><span class="p">:</span> <span class="nb">int</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span> <span class="o">-></span> <span class="nb">list</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Use faiss to make a K-nearest neighbour prediction</span>
<span class="sd"> """</span>
<span class="c1"># Indexing</span>
<span class="n">index</span> <span class="o">=</span> <span class="n">faiss</span><span class="o">.</span><span class="n">IndexFlatL2</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">train_X</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
<span class="n">index</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">train_X</span><span class="p">)</span>
<span class="c1"># Querying</span>
<span class="n">_</span><span class="p">,</span> <span class="n">results</span> <span class="o">=</span> <span class="n">index</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="n">eval_X</span><span class="p">,</span> <span class="n">n_neighbours</span><span class="p">)</span>
<span class="c1"># Scoring</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">get_pred</span><span class="p">(</span><span class="n">train_y</span><span class="p">,</span> <span class="n">results</span><span class="p">)</span>
<span class="k">return</span> <span class="n">y_pred</span>
<span class="k">def</span> <span class="nf">get_pred</span><span class="p">(</span><span class="n">y</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">results</span><span class="p">:</span> <span class="nb">list</span><span class="p">)</span> <span class="o">-></span> <span class="nb">list</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Get most common label from nearest neighbour list</span>
<span class="sd"> """</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">results</span><span class="p">):</span>
<span class="n">y_pred</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Counter</span><span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="n">r</span><span class="p">])</span><span class="o">.</span><span class="n">most_common</span><span class="p">(</span><span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
<span class="k">return</span> <span class="n">y_pred</span>
<span class="k">def</span> <span class="nf">get_cache_confusion_matrix</span><span class="p">(</span>
<span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">actual_vector</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">predict_vector</span><span class="p">:</span> <span class="nb">list</span>
<span class="p">)</span> <span class="o">-></span> <span class="n">ConfusionMatrix</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Make confusion matrix and save it. </span>
<span class="sd"> """</span>
<span class="n">cm_cached</span> <span class="o">=</span> <span class="n">load_confusion_matrix</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">.pickle"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">cm_cached</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">cm_cached</span>
<span class="n">cm</span> <span class="o">=</span> <span class="n">ConfusionMatrix</span><span class="p">(</span><span class="n">actual_vector</span><span class="o">=</span><span class="n">actual_vector</span><span class="p">,</span> <span class="n">predict_vector</span><span class="o">=</span><span class="n">predict_vector</span><span class="p">)</span>
<span class="n">cm</span><span class="o">.</span><span class="n">save_html</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">.pickle"</span><span class="p">,</span> <span class="s2">"wb"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">cm</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
<span class="k">return</span> <span class="n">cm</span>
<span class="k">def</span> <span class="nf">load_confusion_matrix</span><span class="p">(</span><span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="n">Optional</span><span class="p">[</span><span class="n">ConfusionMatrix</span><span class="p">]:</span>
<span class="sd">"""</span>
<span class="sd"> Load confusion matrix if existing.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">Path</span><span class="p">(</span><span class="n">path</span><span class="p">)</span><span class="o">.</span><span class="n">is_file</span><span class="p">():</span>
<span class="k">return</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s2">"rb"</span><span class="p">))</span>
<span class="k">return</span> <span class="kc">None</span>
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<h2 id="Generate-confusion-matrices">Generate confusion matrices<a class="anchor-link" href="#Generate-confusion-matrices"> </a></h2><p>The precomputed fingerprints and confusion matrices can be downloaded from (include link)</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">'../data/uspto_1k_TPL/individual_files/test_labels.txt'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">labels_true</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">())</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">f</span><span class="o">.</span><span class="n">readlines</span><span class="p">()]</span>
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<h3 id="BERT-classifier-predictions">BERT classifier predictions<a class="anchor-link" href="#BERT-classifier-predictions"> </a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">'../data/uspto_1k_TPL/individual_files/predictions_bert_classifier.txt'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">labels_bert_class</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">())</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">f</span><span class="o">.</span><span class="n">readlines</span><span class="p">()]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cm_bert_classifier</span> <span class="o">=</span> <span class="n">get_cache_confusion_matrix</span><span class="p">(</span><span class="s1">'../data/uspto_1k_TPL/results/cm_bert_classifier'</span><span class="p">,</span> <span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_bert_class</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Accuracy : </span><span class="si">{</span><span class="n">cm_bert_classifier</span><span class="o">.</span><span class="n">overall_stat</span><span class="p">[</span><span class="s1">'Overall ACC'</span><span class="p">]</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"MCC : </span><span class="si">{</span><span class="n">cm_bert_classifier</span><span class="o">.</span><span class="n">overall_stat</span><span class="p">[</span><span class="s1">'Overall MCC'</span><span class="p">]</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"CEN : </span><span class="si">{</span><span class="n">cm_bert_classifier</span><span class="o">.</span><span class="n">overall_stat</span><span class="p">[</span><span class="s1">'Overall CEN'</span><span class="p">]</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Accuracy : 0.9893
MCC : 0.9893
CEN : 0.0056
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<h3 id="AP3_folded_256_plus_agent_features-fingerprint,-5-NN">AP3_folded_256_plus_agent_features fingerprint, 5-NN<a class="anchor-link" href="#AP3_folded_256_plus_agent_features-fingerprint,-5-NN"> </a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Get confusion matrix for the Schneider FP</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">Path</span><span class="p">(</span><span class="s1">'../data/uspto_1k_TPL/results/cm_schneider_fp_5NN.pickle'</span><span class="p">)</span><span class="o">.</span><span class="n">is_file</span><span class="p">():</span>
<span class="n">train_X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span>
<span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s2">"../data/uspto_1k_TPL/fingerprints/schneider_AP3_folded_256_plus_agent_features_total_df_1000_role.pkl"</span><span class="p">,</span> <span class="s2">"rb"</span><span class="p">))[</span>
<span class="s2">"train_valid"</span>
<span class="p">]</span>
<span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"float32"</span><span class="p">)</span>
<span class="n">train_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"../data/uspto_1k_TPL/individual_files/train_valid_labels.txt"</span><span class="p">)])</span>
<span class="n">eval_X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span>
<span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s2">"../data/uspto_1k_TPL/fingerprints/schneider_AP3_folded_256_plus_agent_features_total_df_1000_role.pkl"</span><span class="p">,</span> <span class="s2">"rb"</span><span class="p">))[</span>
<span class="s2">"test"</span>
<span class="p">]</span>
<span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"float32"</span><span class="p">)</span>
<span class="n">labels_predicted</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">get_nearest_neighbours_prediction</span><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">eval_X</span><span class="p">)]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cm_schneider_fp</span> <span class="o">=</span> <span class="n">cm</span> <span class="o">=</span> <span class="n">get_cache_confusion_matrix</span><span class="p">(</span><span class="s1">'../data/uspto_1k_TPL/results/cm_schneider_fp_5NN'</span><span class="p">,</span> <span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_predicted</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Accuracy : </span><span class="si">{</span><span class="n">cm_schneider_fp</span><span class="o">.</span><span class="n">overall_stat</span><span class="p">[</span><span class="s1">'Overall ACC'</span><span class="p">]</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"MCC : </span><span class="si">{</span><span class="n">cm_schneider_fp</span><span class="o">.</span><span class="n">overall_stat</span><span class="p">[</span><span class="s1">'Overall MCC'</span><span class="p">]</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"CEN : </span><span class="si">{</span><span class="n">cm_schneider_fp</span><span class="o">.</span><span class="n">overall_stat</span><span class="p">[</span><span class="s1">'Overall CEN'</span><span class="p">]</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Accuracy : 0.295
MCC : 0.292
CEN : 0.424
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<h3 id="BERT-fingerprint-pretrained-with-MLM">BERT fingerprint pretrained with MLM<a class="anchor-link" href="#BERT-fingerprint-pretrained-with-MLM"> </a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Get confusion matrix for the pretrained BERT fingerprint</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">Path</span><span class="p">(</span><span class="s1">'../data/uspto_1k_TPL/results/cm_bert_mlm_fp_5NN.pickle'</span><span class="p">)</span><span class="o">.</span><span class="n">is_file</span><span class="p">():</span>
<span class="n">train_X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span>
<span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s2">"../data/uspto_1k_TPL/fingerprints/USPTO_1k_TPL_bert_mlm.pkl"</span><span class="p">,</span> <span class="s2">"rb"</span><span class="p">))[</span>
<span class="s2">"train_valid"</span>
<span class="p">]</span>
<span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"float32"</span><span class="p">)</span>
<span class="n">train_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"../data/uspto_1k_TPL/individual_files/train_valid_labels.txt"</span><span class="p">)])</span>
<span class="n">eval_X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span>
<span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s2">"../data/uspto_1k_TPL/fingerprints/USPTO_1k_TPL_bert_mlm.pkl"</span><span class="p">,</span> <span class="s2">"rb"</span><span class="p">))[</span>
<span class="s2">"test"</span>
<span class="p">]</span>
<span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"float32"</span><span class="p">)</span>
<span class="n">labels_predicted</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">get_nearest_neighbours_prediction</span><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">eval_X</span><span class="p">)]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cm_bert_mlm_fp</span> <span class="o">=</span> <span class="n">cm</span> <span class="o">=</span> <span class="n">get_cache_confusion_matrix</span><span class="p">(</span><span class="s1">'../data/uspto_1k_TPL/results/cm_bert_mlm_fp_5NN'</span><span class="p">,</span> <span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_predicted</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Accuracy : </span><span class="si">{</span><span class="n">cm_bert_mlm_fp</span><span class="o">.</span><span class="n">overall_stat</span><span class="p">[</span><span class="s1">'Overall ACC'</span><span class="p">]</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"MCC : </span><span class="si">{</span><span class="n">cm_bert_mlm_fp</span><span class="o">.</span><span class="n">overall_stat</span><span class="p">[</span><span class="s1">'Overall MCC'</span><span class="p">]</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"CEN : </span><span class="si">{</span><span class="n">cm_bert_mlm_fp</span><span class="o">.</span><span class="n">overall_stat</span><span class="p">[</span><span class="s1">'Overall CEN'</span><span class="p">]</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Accuracy : 0.340
MCC : 0.337
CEN : 0.392
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<h3 id="BERT-fingerprint-after-fine-tuning-on-template-labels">BERT fingerprint after fine-tuning on template labels<a class="anchor-link" href="#BERT-fingerprint-after-fine-tuning-on-template-labels"> </a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Get confusion matrix for the fine-tuned BERT fingerprint</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">Path</span><span class="p">(</span><span class="s1">'../data/uspto_1k_TPL/results/cm_bert_class_fp_5NN.pickle'</span><span class="p">)</span><span class="o">.</span><span class="n">is_file</span><span class="p">():</span>
<span class="n">train_X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span>
<span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s2">"../data/uspto_1k_TPL/fingerprints/USPTO_1k_TPL_bert_class.pkl"</span><span class="p">,</span> <span class="s2">"rb"</span><span class="p">))[</span>
<span class="s2">"train_valid"</span>
<span class="p">]</span>
<span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"float32"</span><span class="p">)</span>
<span class="n">train_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"../data/uspto_1k_TPL/individual_files/train_valid_labels.txt"</span><span class="p">)])</span>
<span class="n">eval_X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span>
<span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s2">"../data/uspto_1k_TPL/fingerprints/USPTO_1k_TPL_bert_class.pkl"</span><span class="p">,</span> <span class="s2">"rb"</span><span class="p">))[</span>
<span class="s2">"test"</span>
<span class="p">]</span>
<span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"float32"</span><span class="p">)</span>
<span class="n">labels_predicted</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">get_nearest_neighbours_prediction</span><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">eval_X</span><span class="p">)]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cm_bert_class_fp</span> <span class="o">=</span> <span class="n">cm</span> <span class="o">=</span> <span class="n">get_cache_confusion_matrix</span><span class="p">(</span><span class="s1">'../data/uspto_1k_TPL/results/cm_bert_class_fp_5NN'</span><span class="p">,</span> <span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_predicted</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Accuracy : </span><span class="si">{</span><span class="n">cm_bert_class_fp</span><span class="o">.</span><span class="n">overall_stat</span><span class="p">[</span><span class="s1">'Overall ACC'</span><span class="p">]</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"MCC : </span><span class="si">{</span><span class="n">cm_bert_class_fp</span><span class="o">.</span><span class="n">overall_stat</span><span class="p">[</span><span class="s1">'Overall MCC'</span><span class="p">]</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"CEN : </span><span class="si">{</span><span class="n">cm_bert_class_fp</span><span class="o">.</span><span class="n">overall_stat</span><span class="p">[</span><span class="s1">'Overall CEN'</span><span class="p">]</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Accuracy : 0.989
MCC : 0.989
CEN : 0.006
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