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minor bugfixes in plotting: quantiles are now computed using predict_kw

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correctly
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James Hensman 2015-08-18 08:00:47 +01:00
parent 097b048100
commit ea3bfbb597
4 changed files with 9 additions and 5 deletions
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6
GPy/core/gp.py
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@ -249,7 +249,7 @@ class GP(Model):
mean, var = self.likelihood.predictive_values(mu, var, full_cov, Y_metadata=Y_metadata) mean, var = self.likelihood.predictive_values(mu, var, full_cov, Y_metadata=Y_metadata)
return mean, var return mean, var
def predict_quantiles(self, X, quantiles=(2.5, 97.5), Y_metadata=None): def predict_quantiles(self, X, quantiles=(2.5, 97.5), Y_metadata=None, kern=None):
""" """
Get the predictive quantiles around the prediction at X Get the predictive quantiles around the prediction at X
@ -257,10 +257,12 @@ class GP(Model):
:type X: np.ndarray (Xnew x self.input_dim) :type X: np.ndarray (Xnew x self.input_dim)
:param quantiles: tuple of quantiles, default is (2.5, 97.5) which is the 95% interval :param quantiles: tuple of quantiles, default is (2.5, 97.5) which is the 95% interval
:type quantiles: tuple :type quantiles: tuple
:param kern: optional kernel to use for prediction
:type predict_kw: dict
:returns: list of quantiles for each X and predictive quantiles for interval combination :returns: list of quantiles for each X and predictive quantiles for interval combination
:rtype: [np.ndarray (Xnew x self.output_dim), np.ndarray (Xnew x self.output_dim)] :rtype: [np.ndarray (Xnew x self.output_dim), np.ndarray (Xnew x self.output_dim)]
""" """
m, v = self._raw_predict(X, full_cov=False) m, v = self._raw_predict(X, full_cov=False, kern=kern)
if self.normalizer is not None: if self.normalizer is not None:
m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v) m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v)
return self.likelihood.predictive_quantiles(m, v, quantiles, Y_metadata=Y_metadata) return self.likelihood.predictive_quantiles(m, v, quantiles, Y_metadata=Y_metadata)

3
GPy/models/gp_var_gauss.py
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@ -60,7 +60,8 @@ class GPVariationalGaussianApproximation(Model):
var = np.diag(Sigma).reshape(-1,1) var = np.diag(Sigma).reshape(-1,1)
F, dF_dm, dF_dv, dF_dthetaL = self.likelihood.variational_expectations(self.Y, m, var, Y_metadata=self.Y_metadata) F, dF_dm, dF_dv, dF_dthetaL = self.likelihood.variational_expectations(self.Y, m, var, Y_metadata=self.Y_metadata)
self.likelihood.gradient = dF_dthetaL.sum(1).sum(1) if dF_dthetaL is not None:
self.likelihood.gradient = dF_dthetaL.sum(1).sum(1)
dF_da = np.dot(K, dF_dm) dF_da = np.dot(K, dF_dm)
SigmaB = Sigma*self.beta SigmaB = Sigma*self.beta
dF_db = -np.diag(Sigma.dot(np.diag(dF_dv.flatten())).dot(SigmaB))*2 dF_db = -np.diag(Sigma.dot(np.diag(dF_dv.flatten())).dot(SigmaB))*2

3
GPy/plotting/matplot_dep/models_plots.py
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@ -110,7 +110,8 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
else: else:
Y_metadata['output_index'] = extra_data Y_metadata['output_index'] = extra_data
m, v = model.predict(Xgrid, full_cov=False, Y_metadata=Y_metadata, **predict_kw) m, v = model.predict(Xgrid, full_cov=False, Y_metadata=Y_metadata, **predict_kw)
lower, upper = model.predict_quantiles(Xgrid, Y_metadata=Y_metadata) fmu, fv = model._raw_predict(Xgrid, full_cov=False, **predict_kw)
lower, upper = model.likelihood.predictive_quantiles(fmu, fv, (2.5, 97.5), Y_metadata=Y_metadata)
for d in which_data_ycols: for d in which_data_ycols:

2
GPy/testing/inference_tests.py
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@ -8,7 +8,7 @@ The test cases for various inference algorithms
import unittest, itertools import unittest, itertools
import numpy as np import numpy as np
import GPy import GPy
#np.seterr(invalid='raise')
class InferenceXTestCase(unittest.TestCase): class InferenceXTestCase(unittest.TestCase):