@@ 56-69 (lines=14) @@ | ||
53 | assert "_x1_" in list(opt.search_data.columns) |
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54 | ||
55 | ||
56 | def test_obj_func_return_dictionary_1(): |
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57 | def objective_function(para): |
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58 | score = -para["x1"] * para["x1"] |
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59 | return score, {"_x1_": para["x1"], "_x1_*2": para["x1"] * 2} |
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60 | ||
61 | search_space = { |
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62 | "x1": np.arange(-100, 101, 1), |
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63 | } |
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64 | ||
65 | opt = RandomSearchOptimizer(search_space) |
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66 | opt.search(objective_function, n_iter=30) |
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67 | ||
68 | assert "_x1_" in list(opt.search_data.columns) |
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69 | assert "_x1_*2" in list(opt.search_data.columns) |
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70 | ||
@@ 41-53 (lines=13) @@ | ||
38 | opt.search(model, n_iter=30) |
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39 | ||
40 | ||
41 | def test_obj_func_return_dictionary_0(): |
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42 | def objective_function(para): |
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43 | score = -para["x1"] * para["x1"] |
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44 | return score, {"_x1_": para["x1"]} |
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45 | ||
46 | search_space = { |
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47 | "x1": np.arange(-100, 101, 1), |
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48 | } |
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49 | ||
50 | opt = RandomSearchOptimizer(search_space) |
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51 | opt.search(objective_function, n_iter=30) |
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52 | ||
53 | assert "_x1_" in list(opt.search_data.columns) |
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54 | ||
55 | ||
56 | def test_obj_func_return_dictionary_1(): |