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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/utils.html

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@@ -698,6 +698,59 @@ <h1>Source code for skopt.utils</h1><div class="highlight"><pre>
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<span class="k">return</span> <span class="n">Space</span><span class="p">(</span><span class="n">transformed_dimensions</span><span class="p">)</span>
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<span class="k">def</span> <span class="nf">check_list_types</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">types</span><span class="p">):</span>
702+
<span class="sd">&quot;&quot;&quot;</span>
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<span class="sd"> Check whether all elements of a list `x` are of the correct type(s)</span>
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<span class="sd"> and raise a ValueError if they are not.</span>
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<span class="sd"> Note that `types` can be either a single object-type or a tuple</span>
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<span class="sd"> of object-types.</span>
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<span class="sd"> Raises `ValueError`, If one or more element in the list `x` is</span>
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<span class="sd"> not of the correct type(s).</span>
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<span class="sd"> Parameters</span>
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<span class="sd"> ----------</span>
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<span class="sd"> x : list</span>
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<span class="sd"> List of objects.</span>
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<span class="sd"> types : object or list(object)</span>
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<span class="sd"> Either a single object-type or a tuple of object-types.</span>
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<span class="sd"> &quot;&quot;&quot;</span>
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<span class="c1"># List of the elements in the list that are incorrectly typed.</span>
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<span class="n">err</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">a</span><span class="p">:</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">types</span><span class="p">),</span> <span class="n">x</span><span class="p">))</span>
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<span class="c1"># If the list is non-empty then raise an exception.</span>
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<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">err</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
727+
<span class="n">msg</span> <span class="o">=</span> <span class="s2">&quot;All elements in list must be instances of </span><span class="si">{}</span><span class="s2">, but found: </span><span class="si">{}</span><span class="s2">&quot;</span>
728+
<span class="n">msg</span> <span class="o">=</span> <span class="n">msg</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">types</span><span class="p">,</span> <span class="n">err</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="n">msg</span><span class="p">)</span>
730+
731+
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<span class="k">def</span> <span class="nf">check_dimension_names</span><span class="p">(</span><span class="n">dimensions</span><span class="p">):</span>
733+
<span class="sd">&quot;&quot;&quot;</span>
734+
<span class="sd"> Check whether all dimensions have names. Raises `ValueError`,</span>
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<span class="sd"> if one or more dimensions are unnamed.</span>
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<span class="sd"> Parameters</span>
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<span class="sd"> ----------</span>
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<span class="sd"> dimensions : list(Dimension)</span>
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<span class="sd"> List of Dimension-objects.</span>
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<span class="sd"> &quot;&quot;&quot;</span>
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<span class="c1"># List of the dimensions that have no names.</span>
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<span class="n">err_dims</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">dim</span><span class="p">:</span> <span class="n">dim</span><span class="o">.</span><span class="n">name</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">,</span> <span class="n">dimensions</span><span class="p">))</span>
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<span class="c1"># If the list is non-empty then raise an exception.</span>
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<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">err_dims</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
749+
<span class="n">msg</span> <span class="o">=</span> <span class="s2">&quot;All dimensions must have names, but found: </span><span class="si">{}</span><span class="s2">&quot;</span>
750+
<span class="n">msg</span> <span class="o">=</span> <span class="n">msg</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">err_dims</span><span class="p">)</span>
751+
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
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<div class="viewcode-block" id="use_named_args"><a class="viewcode-back" href="../../modules/generated/skopt.utils.use_named_args.html#skopt.use_named_args">[docs]</a><span class="k">def</span> <span class="nf">use_named_args</span><span class="p">(</span><span class="n">dimensions</span><span class="p">):</span>
702755
<span class="sd">&quot;&quot;&quot;</span>
703756
<span class="sd"> Wrapper / decorator for an objective function that uses named arguments</span>
@@ -783,25 +836,10 @@ <h1>Source code for skopt.utils</h1><div class="highlight"><pre>
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<span class="sd"> &quot;&quot;&quot;</span>
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785838
<span class="c1"># Ensure all dimensions are correctly typed.</span>
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<span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="n">Dimension</span><span class="p">)</span> <span class="k">for</span> <span class="n">dim</span> <span class="ow">in</span> <span class="n">dimensions</span><span class="p">):</span>
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<span class="c1"># List of the dimensions that are incorrectly typed.</span>
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<span class="n">err_dims</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">dim</span><span class="p">:</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="n">Dimension</span><span class="p">),</span>
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<span class="n">dimensions</span><span class="p">))</span>
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<span class="c1"># Error message.</span>
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<span class="n">msg</span> <span class="o">=</span> <span class="s2">&quot;All dimensions must be instances of the Dimension-class, but found: </span><span class="si">{}</span><span class="s2">&quot;</span>
793-
<span class="n">msg</span> <span class="o">=</span> <span class="n">msg</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">err_dims</span><span class="p">)</span>
794-
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
839+
<span class="n">check_list_types</span><span class="p">(</span><span class="n">dimensions</span><span class="p">,</span> <span class="n">Dimension</span><span class="p">)</span>
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<span class="c1"># Ensure all dimensions have names.</span>
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<span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">dim</span><span class="o">.</span><span class="n">name</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">for</span> <span class="n">dim</span> <span class="ow">in</span> <span class="n">dimensions</span><span class="p">):</span>
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<span class="c1"># List of the dimensions that have no names.</span>
799-
<span class="n">err_dims</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">dim</span><span class="p">:</span> <span class="n">dim</span><span class="o">.</span><span class="n">name</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">,</span> <span class="n">dimensions</span><span class="p">))</span>
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<span class="c1"># Error message.</span>
802-
<span class="n">msg</span> <span class="o">=</span> <span class="s2">&quot;All dimensions must have names, but found: </span><span class="si">{}</span><span class="s2">&quot;</span>
803-
<span class="n">msg</span> <span class="o">=</span> <span class="n">msg</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">err_dims</span><span class="p">)</span>
804-
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
842+
<span class="n">check_dimension_names</span><span class="p">(</span><span class="n">dimensions</span><span class="p">)</span>
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<span class="nd">@wraps</span><span class="p">(</span><span class="n">func</span><span class="p">)</span>
807845
<span class="k">def</span> <span class="nf">wrapper</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>

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

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -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 0x7FE82C8B5A40
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random_state: RandomState(MT19937) at 0x7FF586BE2A40
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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 2.964 seconds)
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**Total running time of the script:** ( 0 minutes 2.872 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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Original file line numberDiff line numberDiff line change
@@ -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 0x7FE8779C3A40
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random_state: RandomState(MT19937) at 0x7FF5D4326A40
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space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')])
272-
specs: {'args': {'func': <function f at 0x7fe877a6cc10>, 'dimensions': Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
272+
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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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 0x7FE8779C3A40, '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'}
276+
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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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 0x7fe87cadea60>
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<matplotlib.axes._subplots.AxesSubplot object at 0x7ff5521050a0>
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 3.256 seconds)
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**Total running time of the script:** ( 0 minutes 3.089 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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**Total running time of the script:** ( 1 minutes 4.196 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 0x7fe87cac1940>
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<matplotlib.axes._subplots.AxesSubplot object at 0x7ff5d6b6f3d0>
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**Total running time of the script:** ( 1 minutes 47.417 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 0x7FE8779C3A40
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random_state: RandomState(MT19937) at 0x7FF5D4326A40
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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 0x7fe87c8a20d0>, 'dimensions': Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
168+
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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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 0x7FE8779C3A40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7fe7f8011e20>], '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 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'}
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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 0x7FE8779C3A40
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random_state: RandomState(MT19937) at 0x7FF5D4326A40
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specs: {'args': {'func': <function obj_fun at 0x7fe87c8a20d0>, '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 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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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 0x7FE8779C3A40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7fe7f8011e20>], '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 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'}
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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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@@ -350,7 +350,7 @@ for more information on how the results get saved and possible caveats
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.. rst-class:: sphx-glr-timing
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.. _sphx_glr_download_auto_examples_interruptible-optimization.py:

‎_sources/auto_examples/optimizer-with-different-base-estimator.rst.txt

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.. _sphx_glr_download_auto_examples_optimizer-with-different-base-estimator.py:

‎_sources/auto_examples/parallel-optimization.rst.txt

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.. rst-class:: sphx-glr-timing
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