99 lines
3.2 KiB
Python
99 lines
3.2 KiB
Python
import numpy as np
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from scipy.stats import norm
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import sys
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def UCB(incumbent, mu, stddev, support, return_score=False, beta=0.5):
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return mu + beta * stddev
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def PI(incumbent, mu, stddev, support, return_score=False, eps=0.):
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with np.errstate(divide='warn'):
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imp = mu - incumbent - eps
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Z = imp / stddev
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score = norm.cdf(Z)
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if not return_score:
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return np.argmax(score)
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else:
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return score
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def EI(incumbent, mu, stddev, support, return_score=False, eps=0.):
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with np.errstate(divide='warn'):
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imp = mu - incumbent - eps
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Z = imp / stddev
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score = imp * norm.cdf(Z) + stddev * norm.pdf(Z)
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if not return_score:
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import pdb; pdb.set_trace()
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score[support] = 0
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return np.argmax(score)
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else:
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# score[support] = 0
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return score
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# Different acquisition functions
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def acq_fn(predictions, ytrain=None, stds=None, explore_type='its'):
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predictions = np.array(predictions)
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if stds is None:
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stds = np.sqrt(np.var(predictions, axis=0))
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# Upper confidence bound (UCB) acquisition function
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if explore_type == 'ucb':
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explore_factor = 0.5
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mean = np.mean(predictions, axis=0)
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ucb = mean - explore_factor * stds
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sorted_indices = np.argsort(ucb)
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# Expected improvement (EI) acquisition function
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elif explore_type == 'ei':
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ei_calibration_factor = 5.
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mean = list(np.mean(predictions, axis=0))
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factored_stds = list(stds / ei_calibration_factor)
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min_y = ytrain.min()
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gam = [(min_y - mean[i]) / factored_stds[i] for i in range(len(mean))]
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ei = [-1 * factored_stds[i] * (gam[i] * norm.cdf(gam[i]) + norm.pdf(gam[i]))
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for i in range(len(mean))]
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sorted_indices = np.argsort(ei)
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# Probability of improvement (PI) acquisition function
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elif explore_type == 'pi':
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mean = list(np.mean(predictions, axis=0))
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stds = list(stds)
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min_y = ytrain.min()
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pi = [-1 * norm.cdf(min_y, loc=mean[i], scale=stds[i]) for i in range(len(mean))]
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sorted_indices = np.argsort(pi)
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# Thompson sampling (TS) acquisition function
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elif explore_type == 'ts':
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rand_ind = np.random.randint(predictions.shape[0])
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ts = predictions[rand_ind,:]
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sorted_indices = np.argsort(ts)
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# Top exploitation
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elif explore_type == 'percentile':
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min_prediction = np.min(predictions, axis=0)
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sorted_indices = np.argsort(min_prediction)
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# Top mean
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elif explore_type == 'mean':
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mean = np.mean(predictions, axis=0)
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sorted_indices = np.argsort(mean)
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elif explore_type == 'confidence':
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confidence_factor = 2
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mean = np.mean(predictions, axis=0)
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conf = mean + confidence_factor * stds
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sorted_indices = np.argsort(conf)
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# Independent Thompson sampling (ITS) acquisition function
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elif explore_type == 'its':
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# predictions: (5, 119), mean: (119,), stds: (119,)
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mean = np.mean(predictions, axis=0)
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samples = np.random.normal(mean, stds)
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sorted_indices = np.argsort(samples)
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else:
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print('{} is not a valid exploration type'.format(explore_type))
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raise NotImplementedError()
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return sorted_indices |