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‎dev/_downloads/0291a47bd3f63e064c434aea964d8e66/bayesian-optimization.ipynb

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‎dev/_downloads/227fbc11d592826e298cb4f679fc86b8/optimizer-with-different-base-estimator.ipynb

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‎dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

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‎dev/_downloads/659de5944f8dc1f0424c48f86a240d84/hyperparameter-optimization.ipynb

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"\n# Tuning a scikit-learn estimator with `skopt`\n\nGilles Louppe, July 2016\nKatie Malone, August 2016\nReformatted by Holger Nahrstaedt 2020\n\n.. currentmodule:: skopt\n\nIf you are looking for a :obj:`sklearn.model_selection.GridSearchCV` replacement checkout\n`sphx_glr_auto_examples_sklearn-gridsearchcv-replacement.py` instead.\n\n## Problem statement\n\nTuning the hyper-parameters of a machine learning model is often carried out\nusing an exhaustive exploration of (a subset of) the space all hyper-parameter\nconfigurations (e.g., using :obj:`sklearn.model_selection.GridSearchCV`), which\noften results in a very time consuming operation.\n\nIn this notebook, we illustrate how to couple :class:`gp_minimize` with sklearn's\nestimators to tune hyper-parameters using sequential model-based optimisation,\nhopefully resulting in equivalent or better solutions, but within less\nevaluations.\n\nNote: scikit-optimize provides a dedicated interface for estimator tuning via\n:class:`BayesSearchCV` class which has a similar interface to those of\n:obj:`sklearn.model_selection.GridSearchCV`. This class uses functions of skopt to perform hyperparameter\nsearch efficiently. For example usage of this class, see\n`sphx_glr_auto_examples_sklearn-gridsearchcv-replacement.py`\nexample notebook.\n"
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"\n# Tuning a scikit-learn estimator with `skopt`\n\nGilles Louppe, July 2016\nKatie Malone, August 2016\nReformatted by Holger Nahrstaedt 2020\n\n.. currentmodule:: skopt\n\nIf you are looking for a :obj:`sklearn.model_selection.GridSearchCV` replacement checkout\n`sphx_glr_auto_examples_sklearn-gridsearchcv-replacement.py` instead.\n\n## Problem statement\n\nTuning the hyper-parameters of a machine learning model is often carried out\nusing an exhaustive exploration of (a subset of) the space all hyper-parameter\nconfigurations (e.g., using :obj:`sklearn.model_selection.GridSearchCV`), which\noften results in a very time consuming operation.\n\nIn this notebook, we illustrate how to couple :class:`gp_minimize` with sklearn's\nestimators to tune hyper-parameters using sequential model-based optimisation,\nhopefully resulting in equivalent or better solutions, but within fewer\nevaluations.\n\nNote: scikit-optimize provides a dedicated interface for estimator tuning via\n:class:`BayesSearchCV` class which has a similar interface to those of\n:obj:`sklearn.model_selection.GridSearchCV`. This class uses functions of skopt to perform hyperparameter\nsearch efficiently. For example usage of this class, see\n`sphx_glr_auto_examples_sklearn-gridsearchcv-replacement.py`\nexample notebook.\n"
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‎dev/_downloads/b4f910a92676697b8c1c26c50df6d7af/strategy-comparison.ipynb

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‎dev/_modules/skopt/plots.html

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<span class="k">for</span> <span class="n">fx_i</span> <span class="ow">in</span> <span class="n">fx</span><span class="p">],</span>
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<span class="p">[</span><span class="n">fx_i</span> <span class="o">+</span> <span class="mf">1.9600</span> <span class="o">*</span> <span class="n">noise_level</span>
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<span class="n">alpha</span><span class="o">=.</span><span class="mi">2</span><span class="p">,</span> <span class="n">fc</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">ec</span><span class="o">=</span><span class="s2">&quot;None&quot;</span><span class="p">)</span>
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<span class="n">alpha</span><span class="o">=</span><span class="mf">.2</span><span class="p">,</span> <span class="n">fc</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">ec</span><span class="o">=</span><span class="s2">&quot;None&quot;</span><span class="p">)</span>
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<span class="n">ax</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]]),</span>
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<span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">y_pred</span> <span class="o">-</span> <span class="mf">1.9600</span> <span class="o">*</span> <span class="n">sigma</span><span class="p">,</span>
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<span class="n">alpha</span><span class="o">=</span><span class="mf">.2</span><span class="p">,</span> <span class="n">fc</span><span class="o">=</span><span class="s2">&quot;g&quot;</span><span class="p">,</span> <span class="n">ec</span><span class="o">=</span><span class="s2">&quot;None&quot;</span><span class="p">)</span>
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‎dev/_sources/auto_examples/ask-and-tell.rst.txt

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fun: 0.2071864923643295
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random_state: RandomState(MT19937) at 0x7F2A6E77FE40
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specs: {'args': {'dimensions': [(-2.0, 2.0)], 'base_estimator': 'GP', 'n_random_starts': None, 'n_initial_points': 10, 'initial_point_generator': 'lhs', 'n_jobs': 1, 'acq_func': 'EI', 'acq_optimizer': 'sampling', 'random_state': None, 'model_queue_size': None, 'acq_func_kwargs': None, 'acq_optimizer_kwargs': None}, 'function': 'Optimizer'}
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‎dev/_sources/auto_examples/bayesian-optimization.rst.txt

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specs: {'args': {'func': <function f at 0x7f6bb40a9940>, 'dimensions': Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5),
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specs: {'args': {'func': <function f at 0x7f2a49aafee0>, 'dimensions': Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5),
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normalize_y=True, random_state=822569775), 'n_calls': 15, 'n_random_starts': 5, 'n_initial_points': 10, 'initial_point_generator': 'random', 'acq_func': 'EI', 'acq_optimizer': 'auto', 'x0': None, 'y0': None, 'random_state': RandomState(MT19937) at 0x7F6BB4060C40, '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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normalize_y=True, random_state=822569775), 'n_calls': 15, 'n_random_starts': 5, 'n_initial_points': 10, 'initial_point_generator': 'random', 'acq_func': 'EI', 'acq_optimizer': 'auto', 'x0': None, 'y0': None, 'random_state': RandomState(MT19937) at 0x7F2A4993B140, '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_iters: [[-0.009345334109402526], [1.2713537644662787], [0.4484475787090836], [1.0854396754496047], [1.4426790855107496], [0.9579248468740373], [-0.45158087416842263], [-0.685948113064442], [-0.35518416232959327], [-0.29315379225502536], [-0.3209941608705478], [-2.0], [2.0], [-1.3373742014126968], [-0.2478422942435552]]
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‎dev/_sources/auto_examples/exploration-vs-exploitation.rst.txt

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‎dev/_sources/auto_examples/hyperparameter-optimization.rst.txt

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