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# Author: Simon Blanke |
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# Email: [email protected] |
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# License: MIT License |
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import numpy as np |
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from hyperactive import Hyperactive |
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X, y = np.array([0]), np.array([0]) |
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memory = False |
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n_iter = 25 |
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def sphere_function(para, X_train, y_train): |
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loss = [] |
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for key in para.keys(): |
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if key == "iteration": |
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continue |
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loss.append(para[key] * para[key]) |
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return -np.array(loss).sum() |
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search_config = { |
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sphere_function: { |
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"x1": list(np.arange(-3, 3, 0.1)), |
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"x2": list(np.arange(-3, 3, 0.1)), |
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} |
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} |
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def test_skip_retrain(): |
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for skip_retrain in ["many", "some", "few", "never"]: |
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opt = Hyperactive(X, y, memory=memory) |
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opt.search( |
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search_config, |
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n_iter=n_iter, |
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optimizer={"Bayesian": {"skip_retrain": skip_retrain}}, |
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) |
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def test_start_up_evals(): |
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for start_up_evals in [1, 100]: |
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opt = Hyperactive(X, y, memory=memory) |
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opt.search( |
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search_config, |
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n_iter=n_iter, |
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optimizer={"Bayesian": {"start_up_evals": start_up_evals}}, |
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) |
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def test_warm_start_smbo(): |
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opt = Hyperactive(X, y, memory="long") |
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opt.search( |
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search_config, n_iter=n_iter, optimizer={"Bayesian": {"warm_start_smbo": True}} |
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) |
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def test_max_sample_size(): |
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for max_sample_size in [10, 100, 10000, 10000000000]: |
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opt = Hyperactive(X, y, memory=memory) |
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opt.search( |
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search_config, |
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n_iter=n_iter, |
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optimizer={"Bayesian": {"max_sample_size": True}}, |
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) |
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def test_gpr(): |
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opt = Hyperactive(X, y, memory=memory) |
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opt.search( |
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search_config, n_iter=n_iter, optimizer={"Bayesian": {"gpr": "gp_linear"}} |
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) |
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SimonBlanke /
Hyperactive
