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added support for sparse matrices

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Nicolo Fusi 2013-02-06 14:46:23 +00:00
parent 6959751149
commit 71e461a780
1 changed files with 22 additions and 10 deletions
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32
GPy/inference/SGD.py
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@ -1,4 +1,6 @@
import numpy as np import numpy as np
import scipy as sp
import scipy.sparse
from optimization import Optimizer from optimization import Optimizer
from scipy import linalg, optimize from scipy import linalg, optimize
import copy import copy
@ -123,13 +125,19 @@ class opt_SGD(Optimizer):
else: else:
raise NotImplementedError raise NotImplementedError
def step_with_missing_data(self, f_fp, X, step, shapes): def step_with_missing_data(self, f_fp, X, step, shapes, sparse_matrix):
N, Q = X.shape N, Q = X.shape
samples = self.non_null_samples(self.model.Y) if not sparse_matrix:
samples = self.non_null_samples(self.model.Y)
self.model.N = samples.sum()
self.model.Y = self.model.Y[samples]
else:
samples = self.model.Y.nonzero()[0]
self.model.N = len(samples)
self.model.Y = np.asarray(self.model.Y[samples].todense(), dtype = np.float64)
j = self.subset_parameter_vector(self.x_opt, samples, shapes) j = self.subset_parameter_vector(self.x_opt, samples, shapes)
self.model.N = samples.sum()
self.model.X = X[samples] self.model.X = X[samples]
self.model.Y = self.model.Y[samples]
# self.model.Y -= self.model.Y.mean() # <----------------- WARNING!!!! # self.model.Y -= self.model.Y.mean() # <----------------- WARNING!!!!
# self.model.Y /= self.model.Y.std() # self.model.Y /= self.model.Y.std()
model_name = self.model.__class__.__name__ model_name = self.model.__class__.__name__
@ -158,7 +166,10 @@ class opt_SGD(Optimizer):
N, Q = self.model.X.shape N, Q = self.model.X.shape
D = self.model.Y.shape[1] D = self.model.Y.shape[1]
self.trace = [] self.trace = []
missing_data = self.check_for_missing(self.model.Y) sparse_matrix = sp.sparse.issparse(self.model.Y)
missing_data = True
if not sparse_matrix:
missing_data = self.check_for_missing(self.model.Y)
self.model.Youter = None # this is probably not very efficient self.model.Youter = None # this is probably not very efficient
self.model.YYT = None self.model.YYT = None
step = np.zeros_like(self.model._get_params()) step = np.zeros_like(self.model._get_params())
@ -178,13 +189,14 @@ class opt_SGD(Optimizer):
for j in features: for j in features:
count += 1 count += 1
self.model.D = len(j) self.model.D = len(j)
self.model.Y = Y[:, j] self.model.Y = Y[:, j:j+1]
# self.model.trYYT = np.sum(np.square(self.model.Y)) # self.model.trYYT = np.sum(np.square(self.model.Y))
if missing_data: if missing_data or sparse_matrix:
if self.model.Y.std() == 0.0 or self.model.Y.shape[0] == 0: # if self.model.Y.std() == 0.0 or self.model.Y.shape[0] == 0: <--- not sure about this
continue # continue
shapes = self.get_param_shapes(N, Q) shapes = self.get_param_shapes(N, Q)
f, step, Nj = self.step_with_missing_data(f_fp, X, step, shapes) f, step, Nj = self.step_with_missing_data(f_fp, X, step, shapes, sparse_matrix)
else: else:
Nj = N Nj = N
momentum_term = self.momentum * step # compute momentum using update(t-1) momentum_term = self.momentum * step # compute momentum using update(t-1)