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Dinu23
requested review from
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Copilot and
jacobdenobel
and removed request for
Copilot and
jacobdenobel
| }) | ||
| evals = [1, 2] | ||
| result = align_data(df, evals, group_cols=("exp_id",), x_col="evaluations", y_col="raw_y", output="long", maximization=False) | ||
| self.assertTrue(set(result["exp_id"].to_list()) == {1, 2}) |
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assertTrue(a == b) cannot provide an informative message. Using assertEqual(a, b) instead will give more informative messages.
| }) | ||
| evals = [10, 20, 30] | ||
| result = align_data(df, evals, group_cols=("data_id",), x_col="steps", y_col="score", output="long", maximization=False) | ||
| self.assertTrue(result["steps"].to_list() == [10, 20, 30]) |
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assertTrue(a == b) cannot provide an informative message. Using assertEqual(a, b) instead will give more informative messages.
| }) | ||
| evals = [10, 20, 30] | ||
| result = align_data(df, evals, group_cols=("data_id",), x_col="steps", y_col="score", output="long", maximization=False) | ||
| self.assertTrue(result["steps"].to_list() == [10, 20, 30]) |
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assertTrue(a == b) cannot provide an informative message. Using assertEqual(a, b) instead will give more informative messages.
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| self.assertIn("AOCC", result.columns) | ||
| aocc_val = result["AOCC"][0] | ||
| self.assertTrue(aocc_val == 6.5) |
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assertTrue(a == b) cannot provide an informative message. Using assertEqual(a, b) instead will give more informative messages.
| self.assertIn("AOCC", result.columns) | ||
| aocc_f1_val = result[result["function_name"] == "f1"]["AOCC"].iloc[0] | ||
| aocc_f2_val = result[result["function_name"] == "f2"]["AOCC"].iloc[0] | ||
| self.assertTrue(aocc_f1_val == 5.5) |
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assertTrue(a == b) cannot provide an informative message. Using assertEqual(a, b) instead will give more informative messages.
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| def test_with_f_min_f_max(self): | ||
| result = aggregate_running_time(self.df, f_min=0.2, f_max=0.5, return_as_pandas=False) | ||
| self.assertTrue(result["raw_y"].min() >= 0.2) |
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assertTrue(a >= b) cannot provide an informative message. Using assertGreaterEqual(a, b) instead will give more informative messages.
| def test_with_f_min_f_max(self): | ||
| result = aggregate_running_time(self.df, f_min=0.2, f_max=0.5, return_as_pandas=False) | ||
| self.assertTrue(result["raw_y"].min() >= 0.2) | ||
| self.assertTrue(result["raw_y"].max() <= 0.5) |
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assertTrue(a <= b) cannot provide an informative message. Using assertLessEqual(a, b) instead will give more informative messages.
| }) | ||
| result = get_tournament_ratings(data, nrounds=25) | ||
| self.assertEqual(len(result), 2) | ||
| self.assertTrue(set(result["algorithm_name"]) == {"A", "B"}) |
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assertTrue(a == b) cannot provide an informative message. Using assertEqual(a, b) instead will give more informative messages.
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| def test_basic(self): | ||
| evals = [1, 10, 100] | ||
| comparison, benchmark = get_robustrank_over_time( |
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Variable comparison is not used.
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| def test_basic(self): | ||
| evals = [1, 10, 100] | ||
| comparison, benchmark = get_robustrank_over_time( |
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Variable benchmark is not used.
.vscode/settings.json
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please remove .vscode settings from repo
3 participants
-> update structure of the project
-> added plot arguments
-> new functionalities