65 lines
2.4 KiB
Python
65 lines
2.4 KiB
Python
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# OtterTune - analysis/optimize.py
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#
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# Copyright (c) 2017-18, Carnegie Mellon University Database Group
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#
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# Author: Dana Van Aken
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import numpy as np
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import tensorflow as tf
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from gpflow import settings
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from sklearn.utils import assert_all_finite, check_array
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from sklearn.utils.validation import FLOAT_DTYPES
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from analysis.util import get_analysis_logger
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LOG = get_analysis_logger(__name__)
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def tf_optimize(model, Xnew_arr, learning_rate=0.01, maxiter=100, ucb_beta=3.,
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active_dims=None, bounds=None):
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Xnew_arr = check_array(Xnew_arr, copy=False, warn_on_dtype=True, dtype=FLOAT_DTYPES)
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Xnew = tf.Variable(Xnew_arr, name='Xnew', dtype=settings.float_type)
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if bounds is None:
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lower_bound = tf.constant(-np.infty, dtype=settings.float_type)
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upper_bound = tf.constant(np.infty, dtype=settings.float_type)
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else:
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lower_bound = tf.constant(bounds[0], dtype=settings.float_type)
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upper_bound = tf.constant(bounds[1], dtype=settings.float_type)
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Xnew_bounded = tf.minimum(tf.maximum(Xnew, lower_bound), upper_bound)
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if active_dims:
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indices = []
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updates = []
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n_rows = Xnew_arr.shape[0]
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for c in active_dims:
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for r in range(n_rows):
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indices.append([r, c])
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updates.append(Xnew_bounded[r, c])
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part_X = tf.scatter_nd(indices, updates, Xnew_arr.shape)
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Xin = part_X + tf.stop_gradient(-part_X + Xnew_bounded)
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else:
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Xin = Xnew_bounded
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beta_t = tf.constant(ucb_beta, name='ucb_beta', dtype=settings.float_type)
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y_mean_var = model.likelihood.predict_mean_and_var(*model._build_predict(Xin))
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loss = tf.subtract(y_mean_var[0], tf.multiply(beta_t, y_mean_var[1]), name='loss_fn')
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opt = tf.train.AdamOptimizer(learning_rate)
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train_op = opt.minimize(loss)
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variables = opt.variables()
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init_op = tf.variables_initializer([Xnew] + variables)
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session = model.enquire_session(session=None)
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with session.as_default():
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session.run(init_op)
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for i in range(maxiter):
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session.run(train_op)
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Xnew_value = session.run(Xnew_bounded)
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y_mean_value, y_var_value = session.run(y_mean_var)
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loss_value = session.run(loss)
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assert_all_finite(Xnew_value)
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assert_all_finite(y_mean_value)
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assert_all_finite(y_var_value)
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assert_all_finite(loss_value)
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return Xnew_value, y_mean_value, y_var_value, loss_value
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