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<div class="mume markdown-preview ">
<p><strong>XPER (eXplainable PERformance)</strong> is a methodology designed to measure the specific contribution of the input features to the predictive performance of any econometric or machine learning model. XPER is built on Shapley values and interpretability tools developed in machine learning but with the distinct objective of focusing on model performance (AUC, <span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow><annotation encoding="application/x-tex">R^2</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8141em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.00773em;">R</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8141em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span></span></span></span></span></span></span></span>) and not on model predictions (<span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mover accent="true"><mi>y</mi><mo>^</mo></mover></mrow><annotation encoding="application/x-tex">\hat{y}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8889em;vertical-align:-0.1944em;"></span><span class="mord accent"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.6944em;"><span style="top:-3em;"><span class="pstrut" style="height:3em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span></span><span style="top:-3em;"><span class="pstrut" style="height:3em;"></span><span class="accent-body" style="left:-0.1944em;"><span class="mord">^</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.1944em;"><span></span></span></span></span></span></span></span></span>). XPER has as a special case the standard explainability method in Machine Learning (SHAP).</p>
<p><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License: MIT"></p>
<h2 class="mume-header" id="01-install-">01 Install 🚀</h2>
<p>The library has been tested on Linux, MacOSX and Windows. It relies on the following Python modules:</p>
<p>Pandas<br>
Numpy<br>
Scipy<br>
Scikit-learn</p>
<p>XPER can be installed from <a href="https://pypi.org/project/XPER">PyPI</a>:</p>
<pre>pip install -i https://test.pypi.org/simple/ XPER==0.0.4
</pre>
<h4 class="mume-header" id="post-installation-check">Post installation check</h4>
<p>After a correct installation, you should be able to import the module without errors:</p>
<pre data-role="codeBlock" data-info="python" class="language-python"><span class="token keyword keyword-import">import</span> XPER
</pre><h2 class="mume-header" id="02-xper-example-on-sampled-data-step-by-step-%EF%B8%8F">02 XPER example on sampled data step by step ➡️</h2>
<h4 class="mume-header" id="1%EF%B8%8F%E2%83%A3-load-the-data-">1️⃣ Load the Data 💽</h4>
<ul>
<li>Option 1</li>
</ul>
<pre data-role="codeBlock" data-info="python" class="language-python"><span class="token keyword keyword-import">import</span> XPER
<span class="token keyword keyword-from">from</span> XPER<span class="token punctuation">.</span>datasets<span class="token punctuation">.</span>sample <span class="token keyword keyword-import">import</span> sample_generation
X_train<span class="token punctuation">,</span> y_train<span class="token punctuation">,</span> X_test<span class="token punctuation">,</span> y_test<span class="token punctuation">,</span> p<span class="token punctuation">,</span> N<span class="token punctuation">,</span> seed <span class="token operator">=</span> sample_generation<span class="token punctuation">(</span>N<span class="token operator">=</span><span class="token number">500</span><span class="token punctuation">,</span>p<span class="token operator">=</span><span class="token number">6</span><span class="token punctuation">,</span>seed<span class="token operator">=</span><span class="token number">123456</span><span class="token punctuation">)</span>
</pre><p><img src="images/Sample.png" alt="sample"></p>
<ul>
<li>Option 2</li>
</ul>
<pre data-role="codeBlock" data-info="python" class="language-python"><span class="token keyword keyword-from">from</span> XPER<span class="token punctuation">.</span>datasets<span class="token punctuation">.</span>load_data <span class="token keyword keyword-import">import</span> boston
df <span class="token operator">=</span> boston<span class="token punctuation">(</span><span class="token punctuation">)</span>
df<span class="token punctuation">.</span>head<span class="token punctuation">(</span><span class="token number">3</span><span class="token punctuation">)</span>
</pre><p><img src="images/Boston.png" alt="boston"></p>
<h4 class="mume-header" id="2%EF%B8%8F%E2%83%A3-load-the-trained-model-or-train-your-model-%EF%B8%8F">2️⃣ Load the trained model or train your model ⚙️</h4>
<pre data-role="codeBlock" data-info="python" class="language-python"><span class="token keyword keyword-import">import</span> joblib
model <span class="token operator">=</span> joblib<span class="token punctuation">.</span>load<span class="token punctuation">(</span><span class="token string">'xgboost_model.joblib'</span><span class="token punctuation">)</span>
result <span class="token operator">=</span> loaded_model<span class="token punctuation">.</span>score<span class="token punctuation">(</span>X_test<span class="token punctuation">,</span> y_test<span class="token punctuation">)</span>
<span class="token keyword keyword-print">print</span><span class="token punctuation">(</span><span class="token string">"Model performance: "</span><span class="token punctuation">,</span>result<span class="token punctuation">)</span>
</pre><h4 class="mume-header" id="3%EF%B8%8F%E2%83%A3-monitor-performance-">3️⃣ Monitor Performance 📈</h4>
<pre data-role="codeBlock" data-info="python" class="language-python"><span class="token keyword keyword-from">from</span> XPER<span class="token punctuation">.</span>models<span class="token punctuation">.</span>Performance <span class="token keyword keyword-import">import</span> evaluate_model_performance
Eval_Metric <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token string">"Precision"</span><span class="token punctuation">]</span>
PM <span class="token operator">=</span> evaluate_model_performance<span class="token punctuation">(</span>Eval_Metric<span class="token punctuation">,</span> X_train<span class="token punctuation">,</span> y_train<span class="token punctuation">,</span> X_test<span class="token punctuation">,</span> y_test<span class="token punctuation">,</span> model<span class="token punctuation">)</span>
<span class="token keyword keyword-print">print</span><span class="token punctuation">(</span><span class="token string">"Performance Metrics: "</span><span class="token punctuation">,</span>PM<span class="token punctuation">)</span>
</pre><p><img src="images/Performance-Metrics.png" alt="metrics"></p>
<pre data-role="codeBlock" data-info="python" class="language-python"><span class="token keyword keyword-from">from</span> XPER<span class="token punctuation">.</span>models<span class="token punctuation">.</span>Performance <span class="token keyword keyword-import">import</span> calculate_XPER_values
CFP <span class="token operator">=</span> <span class="token boolean">None</span>
CFN <span class="token operator">=</span> <span class="token boolean">None</span>
result <span class="token operator">=</span> calculate_XPER_values<span class="token punctuation">(</span>X_test<span class="token punctuation">,</span> y_test<span class="token punctuation">,</span> model<span class="token punctuation">,</span> Eval_Metric<span class="token punctuation">,</span> CFP<span class="token punctuation">,</span> CFN<span class="token punctuation">,</span> PM<span class="token punctuation">)</span>
<span class="token keyword keyword-print">print</span><span class="token punctuation">(</span><span class="token string">"Efficiency bench XPER: "</span><span class="token punctuation">,</span> result<span class="token punctuation">[</span><span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
</pre><h2 class="mume-header" id="03-acknowledgements">03 Acknowledgements</h2>
<p>The contributors to this library are</p>
<ul>
<li><a href="https://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=4582330">Sébastien Saurin</a></li>
<li><a href="https://sites.google.com/view/christophe-hurlin/home">Christophe Hurlin</a></li>
<li><a href="https://www.hec.edu/fr/faculty-research/faculty-directory/faculty-member/perignon-christophe">Christophe Pérignon</a></li>
</ul>
<h2 class="mume-header" id="04-references">04 References</h2>
<ol>
<li><em>XPER:</em> Hué, Sullivan, Hurlin, Christophe, Pérignon, Christophe and Saurin Sébastien. "Explainable Performance (XPER): Measuring the Driving Forces of Predictive Performance". HEC Paris Research Paper No. FIN-2022-1463, Available at SSRN: <a href="https://ssrn.com/abstract=4280563">https://ssrn.com/abstract=4280563</a> or <a href="http://dx.doi.org/10.2139/ssrn.4280563">http://dx.doi.org/10.2139/ssrn.4280563</a>, 2022.</li>
</ol>
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