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mhjensen 4f4c4e6946 update on log reg
2018-10-11 05:59:30 +02:00

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<!-- navigation toc: --> <li><a href="._LogReg-bs001.html#___sec0" style="font-size: 80%;">Logistic Regression</a></li>
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<!-- navigation toc: --> <li><a href="._LogReg-bs019.html#___sec18" style="font-size: 80%;">Logistic regression</a></li>
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<h2 id="___sec19" class="anchor">Exploring the logistic regression </h2>
<p>
The penalization factor \( \lambda \) is inverted in the case of the
logistic regression model we use. We will explore several values of
\( \lambda \) using both L1 and L2 penalization. We do this using a grid
search over different parameters and run a 3-fold cross validation for
each configuration. In other words, we fit a model 3 times for each
configuration of the hyper parameters.
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>lambdas <span style="color: #666666">=</span> np<span style="color: #666666">.</span>logspace(<span style="color: #666666">-7</span>, <span style="color: #666666">-1</span>, <span style="color: #666666">7</span>)
param_grid <span style="color: #666666">=</span> {
<span style="color: #BA2121">&quot;C&quot;</span>: <span style="color: #008000">list</span>(<span style="color: #666666">1.0/</span>lambdas),
<span style="color: #BA2121">&quot;penalty&quot;</span>: [<span style="color: #BA2121">&quot;l1&quot;</span>, <span style="color: #BA2121">&quot;l2&quot;</span>]
}
clf <span style="color: #666666">=</span> skms<span style="color: #666666">.</span>GridSearchCV(
skl<span style="color: #666666">.</span>LogisticRegression(),
param_grid<span style="color: #666666">=</span>param_grid,
n_jobs<span style="color: #666666">=-1</span>,
return_train_score<span style="color: #666666">=</span><span style="color: #008000">True</span>
)
t0 <span style="color: #666666">=</span> time<span style="color: #666666">.</span>time()
clf<span style="color: #666666">.</span>fit(X_train, y_train)
t1 <span style="color: #666666">=</span> time<span style="color: #666666">.</span>time()
<span style="color: #008000; font-weight: bold">print</span> (
<span style="color: #BA2121">&quot;Time spent fitting GridSearchCV(LogisticRegression): {0:.3f} sec&quot;</span><span style="color: #666666">.</span>format(
t1 <span style="color: #666666">-</span> t0
)
)
</pre></div>
<p>
We can see that logistic regression is quite slow and using the grid
search and cross validation results in quite a heavy
computation. Below we show the results of the different
configurations.
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>logreg_df <span style="color: #666666">=</span> pd<span style="color: #666666">.</span>DataFrame(clf<span style="color: #666666">.</span>cv_results_)
display(logreg_df)
</pre></div>
<p>
<p>
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