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import numpy as np |
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from hyperactive import Hyperactive |
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from hyperactive.dashboards import ProgressBoard |
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from hyperactive.dashboards.progress_board.streamlit_backend import StreamlitBackend |
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from .config import n_iter, n_jobs |
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search_ids = ["objective_function"] |
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backend = StreamlitBackend(search_ids) |
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def objective_function(opt): |
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score = -(opt["x1"] * opt["x1"]) |
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for search_id in search_ids: |
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pyplot_fig, plotly_fig = backend.create_plots(search_id) |
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return score |
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search_space = { |
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"x1": np.arange(-100, 101, 1), |
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"x2": np.arange(-100, 101, 1), |
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"x3": np.arange(-100, 101, 1), |
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"x4": np.arange(-100, 101, 1), |
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"x5": np.arange(-100, 101, 1), |
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"x6": np.arange(-100, 101, 1), |
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"x7": np.arange(-100, 101, 1), |
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"x8": np.arange(-100, 101, 1), |
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"x9": np.arange(-100, 101, 1), |
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} |
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def test_progress_board__1(): |
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prog = ProgressBoard() |
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hyper = Hyperactive() |
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hyper.add_search( |
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objective_function, |
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search_space, |
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n_iter=n_iter, |
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n_jobs=n_jobs, |
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progress_board=prog, |
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) |
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hyper.run(_test_st_backend=True) |
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SimonBlanke /
Hyperactive
