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‎_downloads/56061d60b433157ebf7d13e5f8a92c2a/auto_examples_python.zip

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‎_downloads/80167a555a9152802d82aab42c671ad2/auto_examples_jupyter.zip

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‎_modules/skopt/callbacks.html

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@@ -382,9 +382,11 @@ <h1>Source code for skopt.callbacks</h1><div class="highlight"><pre>
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<span class="sd"> Examples</span>
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<span class="sd"> --------</span>
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<span class="sd"> &gt;&gt;&gt; import skopt</span>
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<span class="sd"> &gt;&gt;&gt;</span>
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<span class="sd"> &gt;&gt;&gt; def obj_fun(x):</span>
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<span class="sd"> ... return x[0]**2</span>
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<span class="sd"> &gt;&gt;&gt; checkpoint_callback = skopt.callbacks.CheckpointSaver(&quot;./result.pkl&quot;)</span>
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<span class="sd"> &gt;&gt;&gt; skopt.gp_minimize(obj_fun, dims, callback=[checkpoint_callback])</span>
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<span class="sd"> &gt;&gt;&gt; skopt.gp_minimize(obj_fun, [(-2, 2)], n_calls=10,</span>
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<span class="sd"> ... callback=[checkpoint_callback]) # doctest: +SKIP</span>
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<span class="sd"> Parameters</span>
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<span class="sd"> ----------</span>

‎_modules/skopt/plots.html

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@@ -627,6 +627,9 @@ <h1>Source code for skopt.plots</h1><div class="highlight"><pre>
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<span class="c1"># calculating dependence. (Unless partial</span>
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<span class="c1"># dependence is to be used instead).</span>
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<span class="n">space</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">space</span>
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<span class="k">if</span> <span class="n">space</span><span class="o">.</span><span class="n">n_dims</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
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<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;plot_objective needs at least two&quot;</span>
632+
<span class="s2">&quot;variables. Found only one.&quot;</span><span class="p">)</span>
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<span class="n">x_vals</span> <span class="o">=</span> <span class="n">_evaluate_min_params</span><span class="p">(</span><span class="n">result</span><span class="p">,</span> <span class="n">minimum</span><span class="p">,</span> <span class="n">n_minimum_search</span><span class="p">)</span>
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<span class="k">if</span> <span class="n">sample_source</span> <span class="o">==</span> <span class="s2">&quot;random&quot;</span><span class="p">:</span>
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<span class="n">x_eval</span> <span class="o">=</span> <span class="kc">None</span>

‎_modules/skopt/utils.html

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@@ -130,7 +130,7 @@ <h1>Source code for skopt.utils</h1><div class="highlight"><pre>
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<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">GradientBoostingRegressor</span>
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<span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <span class="n">dump</span> <span class="k">as</span> <span class="n">dump_</span>
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<span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <span class="n">load</span> <span class="k">as</span> <span class="n">load_</span>
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<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>
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<span class="kn">from</span> <span class="nn">.learning</span> <span class="kn">import</span> <span class="n">ExtraTreesRegressor</span>
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<span class="kn">from</span> <span class="nn">.learning</span> <span class="kn">import</span> <span class="n">GaussianProcessRegressor</span>
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<span class="kn">from</span> <span class="nn">.learning</span> <span class="kn">import</span> <span class="n">GradientBoostingQuantileRegressor</span>
@@ -141,6 +141,7 @@ <h1>Source code for skopt.utils</h1><div class="highlight"><pre>
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<span class="kn">from</span> <span class="nn">.space</span> <span class="kn">import</span> <span class="n">Space</span><span class="p">,</span> <span class="n">Categorical</span><span class="p">,</span> <span class="n">Integer</span><span class="p">,</span> <span class="n">Real</span><span class="p">,</span> <span class="n">Dimension</span>
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144+
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<span class="n">__all__</span> <span class="o">=</span> <span class="p">(</span>
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<span class="s2">&quot;load&quot;</span><span class="p">,</span>
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<span class="s2">&quot;dump&quot;</span><span class="p">,</span>
@@ -549,8 +550,13 @@ <h1>Source code for skopt.utils</h1><div class="highlight"><pre>
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<span class="sd"> &gt;&gt;&gt; from skopt.utils import dimensions_aslist</span>
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<span class="sd"> &gt;&gt;&gt; search_space = {&#39;name1&#39;: Real(0,1),</span>
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<span class="sd"> ... &#39;name2&#39;: Integer(2,4), &#39;name3&#39;: Real(-1,1)}</span>
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<span class="sd"> &gt;&gt;&gt; dimensions_aslist(search_space)</span>
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<span class="sd"> [Real(0,1), Integer(2,4), Real(-1,1)]</span>
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<span class="sd"> &gt;&gt;&gt; dimensions_aslist(search_space)[0]</span>
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<span class="sd"> Real(low=0, high=1, prior=&#39;uniform&#39;, transform=&#39;identity&#39;)</span>
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<span class="sd"> &gt;&gt;&gt; dimensions_aslist(search_space)[1]</span>
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<span class="sd"> Integer(low=2, high=4, prior=&#39;uniform&#39;, transform=&#39;identity&#39;)</span>
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<span class="sd"> &gt;&gt;&gt; dimensions_aslist(search_space)[2]</span>
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<span class="sd"> Real(low=-1, high=1, prior=&#39;uniform&#39;, transform=&#39;identity&#39;)</span>
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<span class="sd"> &quot;&quot;&quot;</span>
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<span class="n">params_space_list</span> <span class="o">=</span> <span class="p">[</span>
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<span class="n">search_space</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">search_space</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
@@ -578,7 +584,7 @@ <h1>Source code for skopt.utils</h1><div class="highlight"><pre>
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<span class="sd"> Returns</span>
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<span class="sd"> -------</span>
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<span class="sd"> params_dict : dict</span>
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<span class="sd"> params_dict : OrderedDict</span>
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<span class="sd"> dictionary with parameter names as keys to which</span>
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<span class="sd"> corresponding parameter values are assigned.</span>
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@@ -590,11 +596,11 @@ <h1>Source code for skopt.utils</h1><div class="highlight"><pre>
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<span class="sd"> ... &#39;name2&#39;: Integer(2,4), &#39;name3&#39;: Real(-1,1)}</span>
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<span class="sd"> &gt;&gt;&gt; point_as_list = [0.66, 3, -0.15]</span>
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<span class="sd"> &gt;&gt;&gt; point_asdict(search_space, point_as_list)</span>
593-
<span class="sd"> {&#39;name1&#39;: 0.66, &#39;name2&#39;: 3, &#39;name3&#39;: -0.15}</span>
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<span class="sd"> OrderedDict([(&#39;name1&#39;, 0.66), (&#39;name2&#39;, 3), (&#39;name3&#39;, -0.15)])</span>
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<span class="sd"> &quot;&quot;&quot;</span>
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<span class="n">params_dict</span> <span class="o">=</span> <span class="p">{</span>
596-
<span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">search_space</span><span class="o">.</span><span class="n">keys</span><span class="p">()),</span> <span class="n">point_as_list</span><span class="p">)</span>
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<span class="p">}</span>
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<span class="n">params_dict</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span>
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<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">search_space</span><span class="o">.</span><span class="n">keys</span><span class="p">()),</span> <span class="n">point_as_list</span><span class="p">):</span>
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<span class="n">params_dict</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
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<span class="k">return</span> <span class="n">params_dict</span></div>
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@@ -786,8 +792,8 @@ <h1>Source code for skopt.utils</h1><div class="highlight"><pre>
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<span class="sd"> &gt;&gt;&gt; # and use this function-decorator to specify the</span>
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<span class="sd"> &gt;&gt;&gt; # search-space dimensions.</span>
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<span class="sd"> &gt;&gt;&gt; @use_named_args(dimensions=dimensions)</span>
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<span class="sd"> &gt;&gt;&gt; def my_objective_function(foo, bar, baz):</span>
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<span class="sd"> &gt;&gt;&gt; return foo ** 2 + bar ** 4 + baz ** 8</span>
795+
<span class="sd"> ... def my_objective_function(foo, bar, baz):</span>
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<span class="sd"> ... return foo ** 2 + bar ** 4 + baz ** 8</span>
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<span class="sd"> &gt;&gt;&gt;</span>
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<span class="sd"> &gt;&gt;&gt; # Not the function is callable from the outside as</span>
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<span class="sd"> &gt;&gt;&gt; # `my_objective_function(x)` where `x` is a list of unnamed arguments,</span>
@@ -806,7 +812,9 @@ <h1>Source code for skopt.utils</h1><div class="highlight"><pre>
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<span class="sd"> &gt;&gt;&gt;</span>
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<span class="sd"> &gt;&gt;&gt; # Print the best-found results.</span>
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<span class="sd"> &gt;&gt;&gt; print(&quot;Best fitness:&quot;, result.fun)</span>
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<span class="sd"> Best fitness: 0.1948080835239698</span>
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<span class="sd"> &gt;&gt;&gt; print(&quot;Best parameters:&quot;, result.x)</span>
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<span class="sd"> Best parameters: [0.44134853091052617, 0.06570954323368307, 0.17586123323419825]</span>
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<span class="sd"> Parameters</span>
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<span class="sd"> ----------</span>

‎_sources/auto_examples/ask-and-tell.rst.txt

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@@ -175,7 +175,7 @@ and report the value back to the optimizer:
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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 0x7FF586BE2A40
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random_state: RandomState(MT19937) at 0x7F0518481A40
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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]
@@ -312,7 +312,7 @@ meantime:
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 2.872 seconds)
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**Total running time of the script:** ( 0 minutes 2.757 seconds)
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.. _sphx_glr_download_auto_examples_ask-and-tell.py:

‎_sources/auto_examples/bayesian-optimization.rst.txt

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@@ -267,13 +267,13 @@ provide the following information:
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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=822569775)]
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random_state: RandomState(MT19937) at 0x7FF5D4326A40
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random_state: RandomState(MT19937) at 0x7F0565DCFA40
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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 0x7ff5d6b458b0>, '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 0x7f05683ecca0>, '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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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=822569775), 'n_calls': 15, 'n_random_starts': 5, 'acq_func': 'EI', 'acq_optimizer': 'auto', 'x0': None, 'y0': None, 'random_state': RandomState(MT19937) at 0x7FF5D4326A40, '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=822569775), 'n_calls': 15, 'n_random_starts': 5, 'acq_func': 'EI', 'acq_optimizer': 'auto', 'x0': None, 'y0': None, 'random_state': RandomState(MT19937) at 0x7F0565DCFA40, '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: [-0.35076964213550554]
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x_iters: [[-0.009345334109402526], [1.2713537644662787], [0.4484475787090836], [1.0854396754496047], [1.4426790855107496], [0.9698921802985794], [-0.4464493263345515], [-0.6474638307563569], [-0.35076964213550554], [-0.28714768066245777], [-0.2968537677230516], [-2.0], [2.0], [-1.3149517821938494], [-0.3218160845454081]]
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@@ -305,7 +305,7 @@ the last iteration:
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.. code-block:: none
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<matplotlib.axes._subplots.AxesSubplot object at 0x7ff5521050a0>
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<matplotlib.axes._subplots.AxesSubplot object at 0x7f05684473a0>
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 3.089 seconds)
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**Total running time of the script:** ( 0 minutes 2.991 seconds)
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.. _sphx_glr_download_auto_examples_bayesian-optimization.py:

‎_sources/auto_examples/exploration-vs-exploitation.rst.txt

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@@ -562,7 +562,7 @@ recalculated.
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 1 minutes 4.196 seconds)
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**Total running time of the script:** ( 0 minutes 52.811 seconds)
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.. _sphx_glr_download_auto_examples_exploration-vs-exploitation.py:

‎_sources/auto_examples/hyperparameter-optimization.rst.txt

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@@ -207,14 +207,14 @@ Convergence plot
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.. code-block:: none
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<matplotlib.axes._subplots.AxesSubplot object at 0x7ff5d6b6f3d0>
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<matplotlib.axes._subplots.AxesSubplot object at 0x7f05686a7f10>
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 1 minutes 47.417 seconds)
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**Total running time of the script:** ( 1 minutes 26.638 seconds)
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.. _sphx_glr_download_auto_examples_hyperparameter-optimization.py:

‎_sources/auto_examples/interruptible-optimization.rst.txt

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@@ -163,13 +163,13 @@ and pass it to the minimizer:
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n_restarts_optimizer=2, noise='gaussian',
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normalize_y=True, optimizer='fmin_l_bfgs_b',
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random_state=655685735)]
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random_state: RandomState(MT19937) at 0x7FF5D4326A40
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random_state: RandomState(MT19937) at 0x7F0565DCFA40
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space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
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specs: {'args': {'func': <function obj_fun at 0x7ff5d6cf1ca0>, 'dimensions': Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
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specs: {'args': {'func': <function obj_fun at 0x7f05683e6d30>, 'dimensions': Space([Real(low=-20.0, high=20.0, 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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random_state=655685735), 'n_calls': 10, 'n_random_starts': 0, 'acq_func': 'LCB', 'acq_optimizer': 'auto', 'x0': [-20.0], 'y0': None, 'random_state': RandomState(MT19937) at 0x7FF5D4326A40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7ff551ff8100>], 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
172+
random_state=655685735), 'n_calls': 10, 'n_random_starts': 0, 'acq_func': 'LCB', 'acq_optimizer': 'auto', 'x0': [-20.0], 'y0': None, 'random_state': RandomState(MT19937) at 0x7F0565DCFA40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f04e39bceb0>], '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: [20.0]
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x_iters: [[-20.0], [20.0], [20.0], [-20.0], [-20.0], [20.0], [-20.0], [20.0], [20.0], [20.0]]
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@@ -321,14 +321,14 @@ The previous results can then be used to continue the optimization process:
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random_state=655685735)]
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random_state: RandomState(MT19937) at 0x7FF5D4326A40
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random_state: RandomState(MT19937) at 0x7F0565DCFA40
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space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
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specs: {'args': {'func': <function obj_fun at 0x7ff5d6cf1ca0>, 'dimensions': Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
326+
specs: {'args': {'func': <function obj_fun at 0x7f05683e6d30>, 'dimensions': Space([Real(low=-20.0, high=20.0, 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='gaussian',
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normalize_y=True, optimizer='fmin_l_bfgs_b',
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random_state=655685735), 'n_calls': 10, 'n_random_starts': 0, 'acq_func': 'LCB', 'acq_optimizer': 'auto', 'x0': [[-20.0], [20.0], [20.0], [-20.0], [-20.0], [20.0], [-20.0], [20.0], [20.0], [20.0]], 'y0': array([-0.04682088, -0.08228249, -0.00653801, -0.07133619, 0.09063509,
331-
0.07662367, 0.08260541, -0.13236828, -0.17524445, 0.10024491]), 'random_state': RandomState(MT19937) at 0x7FF5D4326A40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7ff551ff8100>], 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
331+
0.07662367, 0.08260541, -0.13236828, -0.17524445, 0.10024491]), 'random_state': RandomState(MT19937) at 0x7F0565DCFA40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f04e39bceb0>], '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: [20.0]
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x_iters: [[-20.0], [20.0], [20.0], [-20.0], [-20.0], [20.0], [-20.0], [20.0], [20.0], [20.0], [20.0], [20.0], [-20.0], [-20.0], [-20.0], [-20.0], [-20.0], [-20.0], [-20.0], [-20.0]]
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‎_sources/auto_examples/optimizer-with-different-base-estimator.rst.txt

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‎_sources/auto_examples/parallel-optimization.rst.txt

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‎_sources/auto_examples/plots/partial-dependence-plot-with-categorical.rst.txt

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‎_sources/auto_examples/plots/partial-dependence-plot.rst.txt

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