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‎dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

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‎dev/_downloads/642fb840f6f615a1e77f69cb707f783e/partial-dependence-plot.py

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#############################################################################
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# Optimisation using decision trees
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# =================================

‎dev/_downloads/93a88000cf87942c55fd039a68ca7e84/partial-dependence-plot-with-categorical.py

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‎dev/_downloads/scikit-optimize-docs.pdf

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‎dev/_modules/skopt/space/space.html

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

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func_vals: array([0.20718649])
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models: []
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random_state: RandomState(MT19937) at 0x7FAA32E7DD40
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random_state: RandomState(MT19937) at 0x7FDC2CDB8D40
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space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')])
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specs: {'args': {'self': <skopt.optimizer.optimizer.Optimizer object at 0x7faa1c54c7c0>, '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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specs: {'args': {'self': <skopt.optimizer.optimizer.Optimizer object at 0x7fdc1029b7c0>, '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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x: [-0.7315058981975282]
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‎dev/_sources/auto_examples/bayesian-optimization.rst.txt

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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 0x7FDC0AFD8440
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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 0x7faa0e7c3ee0>, '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 0x7fdc0aec61f0>, '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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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 0x7FAA1505AC40, '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, 'n_initial_points': 10, 'initial_point_generator': 'random', 'acq_func': 'EI', 'acq_optimizer': 'auto', 'x0': None, 'y0': None, 'random_state': RandomState(MT19937) at 0x7FDC0AFD8440, '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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.. code-block:: none
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<matplotlib.axes._subplots.AxesSubplot object at 0x7faa0e89dd90>
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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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.. code-block:: none
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<matplotlib.axes._subplots.AxesSubplot object at 0x7faa154553d0>
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<matplotlib.axes._subplots.AxesSubplot object at 0x7fdc0b357f40>
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‎dev/_sources/auto_examples/interruptible-optimization.rst.txt

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specs: {'args': {'func': <function obj_fun at 0x7fdc0afc0310>, '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, 'n_initial_points': 10, 'initial_point_generator': 'random', 'acq_func': 'LCB', 'acq_optimizer': 'auto', 'x0': [-20.0], 'y0': None, 'random_state': RandomState(MT19937) at 0x7FAA1709C540, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7faa0ea7b9d0>], '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=655685735), 'n_calls': 10, 'n_random_starts': 0, 'n_initial_points': 10, 'initial_point_generator': 'random', 'acq_func': 'LCB', 'acq_optimizer': 'auto', 'x0': [-20.0], 'y0': None, 'random_state': RandomState(MT19937) at 0x7FDC10F62540, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7fdc0875a730>], '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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specs: {'args': {'func': <function obj_fun at 0x7fdc0afc0310>, '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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0.07662367, 0.08260541, -0.13236828, -0.17524445, 0.10024491]), 'random_state': RandomState(MT19937) at 0x7FAA1712DB40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7faa0ea7b9d0>], '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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0.07662367, 0.08260541, -0.13236828, -0.17524445, 0.10024491]), 'random_state': RandomState(MT19937) at 0x7FDC11074E40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7fdc0875a730>], '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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‎dev/_sources/auto_examples/optimizer-with-different-base-estimator.rst.txt

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

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‎dev/_sources/auto_examples/plots/partial-dependence-plot-2D.rst.txt

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

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

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‎dev/_sources/auto_examples/plots/sg_execution_times.rst.txt

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Computation times
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=================
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| :ref:`sphx_glr_auto_examples_plots_visualizing-results.py` (``visualizing-results.py``) | 08:01.767 | 88.0 MB |
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| :ref:`sphx_glr_auto_examples_plots_visualizing-results.py` (``visualizing-results.py``) | 07:46.158 | 88.6 MB |
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| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot.py` (``partial-dependence-plot.py``) | 00:48.786 | 8.9 MB |
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| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot.py` (``partial-dependence-plot.py``) | 00:42.778 | 8.9 MB |
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| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-2D.py` (``partial-dependence-plot-2D.py``) | 00:13.539 | 10.6 MB |
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| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-2D.py` (``partial-dependence-plot-2D.py``) | 00:16.694 | 10.7 MB |
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| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-with-categorical.py` (``partial-dependence-plot-with-categorical.py``) | 00:12.991 | 33.9 MB |
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| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-with-categorical.py` (``partial-dependence-plot-with-categorical.py``) | 00:12.639 | 33.9 MB |
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‎dev/_sources/auto_examples/plots/visualizing-results.rst.txt

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‎dev/_sources/auto_examples/sampler/initial-sampling-method-integer.rst.txt

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‎dev/_sources/auto_examples/sampler/initial-sampling-method.rst.txt

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‎dev/_sources/auto_examples/sampler/sampling_comparison.rst.txt

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‎dev/_sources/auto_examples/sampler/sg_execution_times.rst.txt

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| :ref:`sphx_glr_auto_examples_sampler_sampling_comparison.py` (``sampling_comparison.py``) | 02:44.872 | 8.0 MB |
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| :ref:`sphx_glr_auto_examples_sampler_sampling_comparison.py` (``sampling_comparison.py``) | 02:51.735 | 8.1 MB |
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| :ref:`sphx_glr_auto_examples_sampler_initial-sampling-method.py` (``initial-sampling-method.py``) | 00:08.576 | 8.1 MB |
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| :ref:`sphx_glr_auto_examples_sampler_initial-sampling-method-integer.py` (``initial-sampling-method-integer.py``) | 00:08.095 | 8.1 MB |
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| :ref:`sphx_glr_auto_examples_sampler_initial-sampling-method.py` (``initial-sampling-method.py``) | 00:07.841 | 8.2 MB |
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