resolve conflicts
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#
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#
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# Copyright (c) 2017-18, Carnegie Mellon University Database Group
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# Copyright (c) 2017-18, Carnegie Mellon University Database Group
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#
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#
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# from: https://github.com/KqSMea8/use_default
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# from: https://github.com/KqSMea8/CDBTune
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# Zhang, Ji, et al. "An end-to-end automatic cloud database tuning system using
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# Zhang, Ji, et al. "An end-to-end automatic cloud database tuning system using
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# deep reinforcement learning." Proceedings of the 2019 International Conference
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# deep reinforcement learning." Proceedings of the 2019 International Conference
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# on Management of Data. ACM, 2019
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# on Management of Data. ACM, 2019
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@ -52,11 +52,11 @@ DEFAULT_LEARNING_RATE = 0.01
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# a small bias when using training data points as starting points.
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# a small bias when using training data points as starting points.
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GPR_EPS = 0.001
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GPR_EPS = 0.001
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DEFAULT_RIDGE = 0.01
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DEFAULT_RIDGE = 1.00
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DEFAULT_EPSILON = 1e-6
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DEFAULT_EPSILON = 1e-6
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DEFAULT_SIGMA_MULTIPLIER = 3.0
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DEFAULT_SIGMA_MULTIPLIER = 1.0
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DEFAULT_MU_MULTIPLIER = 1.0
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DEFAULT_MU_MULTIPLIER = 1.0
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@ -670,8 +670,9 @@ def configuration_recommendation(recommendation_input):
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epsilon=DEFAULT_EPSILON,
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epsilon=DEFAULT_EPSILON,
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max_iter=MAX_ITER,
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max_iter=MAX_ITER,
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sigma_multiplier=DEFAULT_SIGMA_MULTIPLIER,
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sigma_multiplier=DEFAULT_SIGMA_MULTIPLIER,
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mu_multiplier=DEFAULT_MU_MULTIPLIER)
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mu_multiplier=DEFAULT_MU_MULTIPLIER,
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model.fit(X_scaled, y_scaled, X_min, X_max, ridge=DEFAULT_RIDGE)
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ridge=DEFAULT_RIDGE)
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model.fit(X_scaled, y_scaled, X_min, X_max)
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res = model.predict(X_samples, constraint_helper=constraint_helper)
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res = model.predict(X_samples, constraint_helper=constraint_helper)
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best_config_idx = np.argmin(res.minl.ravel())
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best_config_idx = np.argmin(res.minl.ravel())
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