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minor changes
This commit is contained in:
Nicolo Fusi
parent
ffa1879cfc
commit
c8d64a4a69
2 changed files with 19 additions and 36 deletions
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@ -75,7 +75,10 @@ class opt_SGD(Optimizer):
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return (np.isnan(data).sum(axis=1) == 0)
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def check_for_missing(self, data):
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return np.isnan(data).sum() > 0
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if sp.sparse.issparse(self.model.likelihood.Y):
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return True
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else:
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return np.isnan(data).sum() > 0
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def subset_parameter_vector(self, x, samples, param_shapes):
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subset = np.array([], dtype = int)
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@ -149,10 +152,10 @@ class opt_SGD(Optimizer):
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else:
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raise NotImplementedError
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def step_with_missing_data(self, f_fp, X, step, shapes, sparse_matrix):
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def step_with_missing_data(self, f_fp, X, step, shapes):
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N, Q = X.shape
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if not sparse_matrix:
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if not sp.sparse.issparse(self.model.likelihood.Y):
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Y = self.model.likelihood.Y
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samples = self.non_null_samples(self.model.likelihood.Y)
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self.model.N = samples.sum()
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@ -165,7 +168,6 @@ class opt_SGD(Optimizer):
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if self.model.N == 0 or Y.std() == 0.0:
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return 0, step, self.model.N
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# FIXME: get rid of self.center, everything should be centered by default
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self.model.likelihood._mean = Y.mean()
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self.model.likelihood._std = Y.std()
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self.model.likelihood.set_data(Y)
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@ -173,10 +175,6 @@ class opt_SGD(Optimizer):
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j = self.subset_parameter_vector(self.x_opt, samples, shapes)
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self.model.X = X[samples]
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# if self.center:
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# self.model.likelihood.Y -= self.model.likelihood.Y.mean()
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# self.model.likelihood.Y /= self.model.likelihood.Y.std()
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model_name = self.model.__class__.__name__
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if model_name == 'Bayesian_GPLVM':
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@ -185,33 +183,31 @@ class opt_SGD(Optimizer):
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b, p = self.shift_constraints(j)
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f, fp = f_fp(self.x_opt[j])
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# momentum_term = self.momentum * step[j]
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# step[j] = self.learning_rate[j] * fp
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# self.x_opt[j] -= step[j] + momentum_term
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step[j] = self.momentum * step[j] + self.learning_rate[j] * fp
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self.x_opt[j] -= step[j]
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self.restore_constraints(b, p)
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# restore likelihood _mean and _std, otherwise when we call set_data(y) on
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# the next feature, it will get normalized with the mean and std of this one.
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self.model.likelihood._mean = 0
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self.model.likelihood._std = 1
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return f, step, self.model.N
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def opt(self, f_fp=None, f=None, fp=None):
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self.x_opt = self.model._get_params_transformed()
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X, Y = self.model.X.copy(), self.model.likelihood.Y.copy()
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N, Q = self.model.X.shape
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D = self.model.likelihood.Y.shape[1]
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self.trace = []
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sparse_matrix = sp.sparse.issparse(self.model.likelihood.Y)
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missing_data = True
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if not sparse_matrix:
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missing_data = self.check_for_missing(self.model.likelihood.Y)
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self.model.likelihood.YYT = None
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self.model.likelihood.trYYT = None
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self.model.likelihood._mean = 0.0
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self.model.likelihood._std = 1.0
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N, Q = self.model.X.shape
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D = self.model.likelihood.Y.shape[1]
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num_params = self.model._get_params()
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self.trace = []
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missing_data = self.check_for_missing(self.model.likelihood.Y)
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step = np.zeros_like(num_params)
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for it in range(self.iterations):
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@ -224,34 +220,26 @@ class opt_SGD(Optimizer):
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b = len(features)/self.batch_size
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features = [features[i::b] for i in range(b)]
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NLL = []
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count = 0
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last_printed_count = -1
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for j in features:
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count += 1
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for count, j in enumerate(features):
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self.model.D = len(j)
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self.model.likelihood.D = len(j)
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self.model.likelihood.set_data(Y[:, j])
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if missing_data or sparse_matrix:
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if missing_data:
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shapes = self.get_param_shapes(N, Q)
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f, step, Nj = self.step_with_missing_data(f_fp, X, step, shapes, sparse_matrix)
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f, step, Nj = self.step_with_missing_data(f_fp, X, step, shapes)
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else:
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Nj = N
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f, fp = f_fp(self.x_opt)
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# momentum_term = self.momentum * step # compute momentum using update(t-1)
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# step = self.learning_rate * fp # compute update(t)
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# self.x_opt -= step + momentum_term
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step = self.momentum * step + self.learning_rate * fp
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self.x_opt -= step
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if self.messages == 2:
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noise = self.model.likelihood._variance
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status = "evaluating {feature: 5d}/{tot: 5d} \t f: {f: 2.3f} \t non-missing: {nm: 4d}\t noise: {noise: 2.4f}\r".format(feature = count, tot = len(features), f = f, nm = Nj, noise = noise)
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sys.stdout.write(status)
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sys.stdout.flush()
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last_printed_count = count
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self.param_traces['noise'].append(noise)
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NLL.append(f)
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@ -269,7 +257,6 @@ class opt_SGD(Optimizer):
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self.model.likelihood.D = D
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self.model.likelihood.Y = Y
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# self.model.Youter = np.dot(Y, Y.T)
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self.trace.append(self.f_opt)
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if self.iteration_file is not None:
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f = open(self.iteration_file + "iteration%d.pickle" % it, 'w')
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@ -282,7 +269,3 @@ class opt_SGD(Optimizer):
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status = "SGD Iteration: {0: 3d}/{1: 3d} f: {2: 2.3f}\n".format(it+1, self.iterations, self.f_opt)
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sys.stdout.write(status)
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sys.stdout.flush()
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