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109 lines
3.8 KiB
Python
109 lines
3.8 KiB
Python
# Copyright (c) 2015, Zhenwen Dai
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import abc
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import numpy as np
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import GPy
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class RegressionMethod(object):
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__metaclass__ = abc.ABCMeta
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def __init__(self):
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self.preprocess = True
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def _preprocess(self, data, train):
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"""Zero-mean, unit-variance normalization by default"""
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if train:
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inputs, labels = data
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self.data_mean = inputs.mean(axis=0)
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self.data_std = inputs.std(axis=0)
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self.labels_mean = labels.mean(axis=0)
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self.labels_std = labels.std(axis=0)
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return ((inputs-self.data_mean)/self.data_std, (labels-self.labels_mean)/self.labels_std)
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else:
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return (data-self.data_mean)/self.data_std
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def _reverse_trans_labels(self, labels):
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return labels*self.labels_std+self.labels_mean
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def fit(self, train_data):
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if self.preprocess:
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train_data = self._preprocess(train_data, True)
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return self._fit(train_data)
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def predict(self, test_data):
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if self.preprocess:
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test_data = self._preprocess(test_data, False)
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labels = self._predict(test_data)
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if self.preprocess:
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labels = self._reverse_trans_labels(labels)
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return labels
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@abc.abstractmethod
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def _fit(self, train_data):
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"""Fit the model. Return True if successful"""
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return True
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@abc.abstractmethod
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def _predict(self, test_data):
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"""Predict on test data"""
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return None
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class GP_RBF(RegressionMethod):
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name = 'GP_RBF'
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def _fit(self, train_data):
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inputs, labels = train_data
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self.model = GPy.models.GPRegression(inputs, labels,kernel=GPy.kern.RBF(inputs.shape[-1],ARD=True) +GPy.kern.Linear(inputs.shape[1], ARD=True) )
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self.model.likelihood.variance[:] = labels.var()*0.01
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self.model.optimize()
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return True
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def _predict(self, test_data):
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return self.model.predict(test_data)[0]
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class SparseGP_RBF(RegressionMethod):
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name = 'SparseGP_RBF'
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def _fit(self, train_data):
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inputs, labels = train_data
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self.model = GPy.models.SparseGPRegression(inputs, labels,kernel=GPy.kern.RBF(inputs.shape[-1],ARD=True) +GPy.kern.Linear(inputs.shape[1], ARD=True) ,num_inducing=100)
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self.model.likelihood.variance[:] = labels.var()*0.01
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self.model.optimize()
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return True
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def _predict(self, test_data):
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return self.model.predict(test_data)[0]
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# class MRD_RBF(RegressionMethod):
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# name = 'MRD_RBF'
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#
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# def _fit(self, train_data):
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# inputs, labels = train_data
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# Q = 5
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# self.model = GPy.models.MRD([inputs, labels],Q,kernel=GPy.kern.RBF(Q,ARD=True),num_inducing=50)
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# self.model.Y0.likelihood.variance[:] = inputs.var()*0.01
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# self.model.Y1.likelihood.variance[:] = labels.var()*0.01
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# self.model.optimize()
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# return True
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#
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# def _predict(self, test_data):
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# return self.model.predict(self.model.Y0.infer_newX(test_data)[0])[0]
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class SVIGP_RBF(RegressionMethod):
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name = 'SVIGP_RBF'
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def _fit(self, train_data):
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X, Y = train_data
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Z = X[np.random.permutation(X.shape[0])[:100]]
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k = GPy.kern.RBF(X.shape[1], ARD=True) + GPy.kern.Linear(X.shape[1], ARD=True) + GPy.kern.White(X.shape[1],0.01)
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lik = GPy.likelihoods.StudentT(deg_free=3.)
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self.model = GPy.core.SVGP(X, Y, Z=Z, kernel=k, likelihood=lik)
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[self.model.optimize('scg', max_iters=40, gtol=0, messages=0, xtol=0, ftol=0) for i in range(10)]
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self.model.optimize('bfgs', max_iters=1000, gtol=0, messages=0)
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return True
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def _predict(self, test_data):
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return self.model.predict(test_data)[0]
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