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‎notebooks/ask-and-tell.html

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<div class="codehilite"><pre><span></span><span class="err"> fun: -0.032758350111535384</span>
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<span class="err"> func_vals: array([-0.03275835])</span>
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<span class="err"> models: []</span>
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<span class="err"> random_state: RandomState(MT19937) at 0x7F45D42E4380</span>
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<span class="err"> random_state: RandomState(MT19937) at 0x7F759419C380</span>
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<span class="err"> space: Space([Real(low=-2.0, high=2.0, prior=&#39;uniform&#39;, transform=&#39;identity&#39;)])</span>
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<span class="err"> specs: None</span>
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<span class="err"> x: [-1.7121321838148869]</span>

‎notebooks/ask-and-tell.md

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fun: -0.032758350111535384
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func_vals: array([-0.03275835])
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models: []
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random_state: RandomState(MT19937) at 0x7F45D42E4380
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random_state: RandomState(MT19937) at 0x7F759419C380
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space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='identity')])
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specs: None
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x: [-1.7121321838148869]

‎notebooks/bayesian-optimization.html

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<span class="err"> n_restarts_optimizer=2, noise=0.010000000000000002,</span>
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<span class="err"> normalize_y=True, optimizer=&#39;fmin_l_bfgs_b&#39;,</span>
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<span class="err"> random_state=843828734)]</span>
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<span class="err"> random_state: RandomState(MT19937) at 0x7FD119B579E0</span>
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<span class="err"> random_state: RandomState(MT19937) at 0x7F912DB179E0</span>
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<span class="err"> space: Space([Real(low=-2.0, high=2.0, prior=&#39;uniform&#39;, transform=&#39;normalize&#39;)])</span>
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<span class="err"> specs: {&#39;args&#39;: {&#39;func&#39;: &lt;function f at 0x7fd119aa4ef0&gt;, &#39;dimensions&#39;: Space([Real(low=-2.0, high=2.0, prior=&#39;uniform&#39;, transform=&#39;normalize&#39;)]), &#39;base_estimator&#39;: GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,</span>
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<span class="err"> specs: {&#39;args&#39;: {&#39;func&#39;: &lt;function f at 0x7f912da64ef0&gt;, &#39;dimensions&#39;: Space([Real(low=-2.0, high=2.0, prior=&#39;uniform&#39;, transform=&#39;normalize&#39;)]), &#39;base_estimator&#39;: GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,</span>
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<span class="err"> kernel=1**2 * Matern(length_scale=1, nu=2.5),</span>
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<span class="err"> n_restarts_optimizer=2, noise=0.010000000000000002,</span>
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<span class="err"> normalize_y=True, optimizer=&#39;fmin_l_bfgs_b&#39;,</span>
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<span class="err"> random_state=843828734), &#39;n_calls&#39;: 15, &#39;n_random_starts&#39;: 5, &#39;acq_func&#39;: &#39;EI&#39;, &#39;acq_optimizer&#39;: &#39;auto&#39;, &#39;x0&#39;: None, &#39;y0&#39;: None, &#39;random_state&#39;: RandomState(MT19937) at 0x7FD119B579E0, &#39;verbose&#39;: False, &#39;callback&#39;: None, &#39;n_points&#39;: 10000, &#39;n_restarts_optimizer&#39;: 5, &#39;xi&#39;: 0.01, &#39;kappa&#39;: 1.96, &#39;n_jobs&#39;: 1, &#39;model_queue_size&#39;: None}, &#39;function&#39;: &#39;base_minimize&#39;}</span>
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<span class="err"> random_state=843828734), &#39;n_calls&#39;: 15, &#39;n_random_starts&#39;: 5, &#39;acq_func&#39;: &#39;EI&#39;, &#39;acq_optimizer&#39;: &#39;auto&#39;, &#39;x0&#39;: None, &#39;y0&#39;: None, &#39;random_state&#39;: RandomState(MT19937) at 0x7F912DB179E0, &#39;verbose&#39;: False, &#39;callback&#39;: None, &#39;n_points&#39;: 10000, &#39;n_restarts_optimizer&#39;: 5, &#39;xi&#39;: 0.01, &#39;kappa&#39;: 1.96, &#39;n_jobs&#39;: 1, &#39;model_queue_size&#39;: None}, &#39;function&#39;: &#39;base_minimize&#39;}</span>
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<span class="err"> x: [-0.25728223389301497]</span>
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<span class="err"> x_iters: [[0.8518212820929092], [-0.2861162952526968], [0.7635394201074472], [0.8766012406190926], [-0.03552426626961047], [-0.30626086081540116], [-0.25728223389301497], [-0.22378966711457093], [1.0553495077507762], [-0.25562401712808525], [-0.5244265431906165], [-1.9992867763849413], [1.999866281741757], [-1.2339044799440428], [0.45960222774783643]]</span>
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</pre></div>

‎notebooks/bayesian-optimization.md

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n_restarts_optimizer=2, noise=0.010000000000000002,
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normalize_y=True, optimizer='fmin_l_bfgs_b',
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random_state=843828734)]
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random_state: RandomState(MT19937) at 0x7FD119B579E0
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random_state: RandomState(MT19937) at 0x7F912DB179E0
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space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')])
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specs: {'args': {'func': <function f at 0x7fd119aa4ef0>, 'dimensions': Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
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specs: {'args': {'func': <function f at 0x7f912da64ef0>, 'dimensions': Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
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kernel=1**2 * Matern(length_scale=1, nu=2.5),
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n_restarts_optimizer=2, noise=0.010000000000000002,
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normalize_y=True, optimizer='fmin_l_bfgs_b',
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random_state=843828734), 'n_calls': 15, 'n_random_starts': 5, 'acq_func': 'EI', 'acq_optimizer': 'auto', 'x0': None, 'y0': None, 'random_state': RandomState(MT19937) at 0x7FD119B579E0, 'verbose': False, 'callback': None, 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
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random_state=843828734), 'n_calls': 15, 'n_random_starts': 5, 'acq_func': 'EI', 'acq_optimizer': 'auto', 'x0': None, 'y0': None, 'random_state': RandomState(MT19937) at 0x7F912DB179E0, 'verbose': False, 'callback': None, 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
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x: [-0.25728223389301497]
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x_iters: [[0.8518212820929092], [-0.2861162952526968], [0.7635394201074472], [0.8766012406190926], [-0.03552426626961047], [-0.30626086081540116], [-0.25728223389301497], [-0.22378966711457093], [1.0553495077507762], [-0.25562401712808525], [-0.5244265431906165], [-1.9992867763849413], [1.999866281741757], [-1.2339044799440428], [0.45960222774783643]]
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‎notebooks/hyperparameter-optimization.html

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</pre></div>
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<div class="codehilite"><pre><span></span><span class="err">&lt;matplotlib.axes._subplots.AxesSubplot at 0x7fe4f1618d10&gt;</span>
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<div class="codehilite"><pre><span></span><span class="err">&lt;matplotlib.axes._subplots.AxesSubplot at 0x7fd50bf68610&gt;</span>
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</pre></div>
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‎notebooks/hyperparameter-optimization.md

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‎notebooks/sklearn-gridsearchcv-replacement.html

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<span class="ss">&quot;the number of iterations.&quot;</span><span class="p">,</span> <span class="n">ConvergenceWarning</span><span class="p">)</span>
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<span class="n">val</span><span class="p">.</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9844097995545658</span>
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<span class="n">test</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9844444444444445</span>
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<span class="n">val</span><span class="p">.</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9851521900519673</span>
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<span class="n">test</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9822222222222222</span>
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<div class="codehilite"><pre><span></span><span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">96</span>
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<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">96</span>
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<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9666666666666667</span>
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<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9666666666666667</span>
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<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9666666666666667</span>
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<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9733333333333334</span>
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<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9733333333333334</span>
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<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9733333333333334</span>
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<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9733333333333334</span>
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<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9733333333333334</span>
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<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9733333333333334</span>
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<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9733333333333334</span>
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<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9733333333333334</span>
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<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9733333333333334</span>
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<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">98</span>
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<span class="n">Interrupting</span><span class="o">!</span>
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‎notebooks/sklearn-gridsearchcv-replacement.md

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best score: 0.9666666666666667
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best score: 0.98
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Interrupting!
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