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Pushing the docs to dev/ for branch: master, commit 3a54a67b962bf0af75fc2c5d3ad4fc3475bea3ce
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dev/_downloads/642fb840f6f615a1e77f69cb707f783e/partial-dependence-plot.py

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@@ -32,7 +32,6 @@ def funny_func(x):
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s += (x[i] * i) ** 2
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return s
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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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_ = plot_objective(result, n_points=10, sample_source=[15, 4, 7, 15, 'b', 'entropy', 'e'],
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minimum=[15, 4, 7, 15, 'b', 'entropy', 'e'])
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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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fun: 0.2071864923643295
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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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x_iters: [[-0.7315058981975282]]
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 2.968 seconds)
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**Total running time of the script:** ( 0 minutes 2.791 seconds)
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**Estimated memory usage:** 11 MB
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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 0x7FAA1505AC40
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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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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=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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x: [-0.35076964188527904]
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x_iters: [[-0.009345334109402526], [1.2713537644662787], [0.4484475787090836], [1.0854396754496047], [1.4426790855107496], [0.9698921802985794], [-0.4464493263345517], [-0.6474638284799423], [-0.35076964188527904], [-0.28714767658880325], [-0.2968537755362253], [-2.0], [2.0], [-1.3149517825054502], [-0.32181607448732485]]
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.. code-block:: none
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<matplotlib.axes._subplots.AxesSubplot object at 0x7faa0e89dd90>
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<matplotlib.axes._subplots.AxesSubplot object at 0x7fdc087c7850>
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 3.333 seconds)
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**Total running time of the script:** ( 0 minutes 3.180 seconds)
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**Estimated memory usage:** 8 MB
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dev/_sources/auto_examples/exploration-vs-exploitation.rst.txt

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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 36.596 seconds)
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**Total running time of the script:** ( 0 minutes 30.331 seconds)
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**Estimated memory usage:** 8 MB
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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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**Total running time of the script:** ( 0 minutes 29.919 seconds)
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**Total running time of the script:** ( 0 minutes 30.193 seconds)
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**Estimated memory usage:** 37 MB
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**Estimated memory usage:** 36 MB
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.. _sphx_glr_download_auto_examples_hyperparameter-optimization.py:

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