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small changes
This commit is contained in:
Nicolò Fusi
parent
dc1e747702
commit
4182d60513
1 changed files with 63 additions and 48 deletions
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@ -97,52 +97,67 @@ class opt_SGD(Optimizer):
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return subset
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def shift_constraints(self, j):
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# back them up
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bounded_i = copy.deepcopy(self.model.constrained_bounded_indices)
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bounded_l = copy.deepcopy(self.model.constrained_bounded_lowers)
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bounded_u = copy.deepcopy(self.model.constrained_bounded_uppers)
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for b in range(len(bounded_i)): # for each group of constraints
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for bc in range(len(bounded_i[b])):
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pos = np.where(j == bounded_i[b][bc])[0]
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constrained_indices = copy.deepcopy(self.model.constrained_indices)
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for c, constraint in enumerate(constrained_indices):
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mask = (np.ones_like(constrained_indices[c]) == 1)
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for i in range(len(constrained_indices[c])):
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pos = np.where(j == constrained_indices[c][i])[0]
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if len(pos) == 1:
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pos2 = np.where(self.model.constrained_bounded_indices[b] == bounded_i[b][bc])[0][0]
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self.model.constrained_bounded_indices[b][pos2] = pos[0]
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self.model.constrained_indices[c][i] = pos
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else:
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if len(self.model.constrained_bounded_indices[b]) == 1:
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# if it's the last index to be removed
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# the logic here is just a mess. If we remove the last one, then all the
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# b-indices change and we have to iterate through everything to find our
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# current index. Can't deal with this right now.
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raise NotImplementedError
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mask[i] = False
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else: # just remove it from the indices
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mask = self.model.constrained_bounded_indices[b] != bc
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self.model.constrained_bounded_indices[b] = self.model.constrained_bounded_indices[b][mask]
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self.model.constrained_indices[c] = self.model.constrained_indices[c][mask]
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return constrained_indices
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# back them up
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# bounded_i = copy.deepcopy(self.model.constrained_bounded_indices)
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# bounded_l = copy.deepcopy(self.model.constrained_bounded_lowers)
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# bounded_u = copy.deepcopy(self.model.constrained_bounded_uppers)
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# for b in range(len(bounded_i)): # for each group of constraints
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# for bc in range(len(bounded_i[b])):
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# pos = np.where(j == bounded_i[b][bc])[0]
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# if len(pos) == 1:
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# pos2 = np.where(self.model.constrained_bounded_indices[b] == bounded_i[b][bc])[0][0]
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# self.model.constrained_bounded_indices[b][pos2] = pos[0]
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# else:
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# if len(self.model.constrained_bounded_indices[b]) == 1:
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# # if it's the last index to be removed
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# # the logic here is just a mess. If we remove the last one, then all the
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# # b-indices change and we have to iterate through everything to find our
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# # current index. Can't deal with this right now.
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# raise NotImplementedError
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# else: # just remove it from the indices
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# mask = self.model.constrained_bounded_indices[b] != bc
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# self.model.constrained_bounded_indices[b] = self.model.constrained_bounded_indices[b][mask]
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# here we shif the positive constraints. We cycle through each positive
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# constraint
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positive = self.model.constrained_positive_indices.copy()
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mask = (np.ones_like(positive) == 1)
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for p in range(len(positive)):
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# we now check whether the constrained index appears in the j vector
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# (the vector of the "active" indices)
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pos = np.where(j == self.model.constrained_positive_indices[p])[0]
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if len(pos) == 1:
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self.model.constrained_positive_indices[p] = pos
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else:
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mask[p] = False
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self.model.constrained_positive_indices = self.model.constrained_positive_indices[mask]
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# # here we shif the positive constraints. We cycle through each positive
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# # constraint
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# positive = self.model.constrained_positive_indices.copy()
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# mask = (np.ones_like(positive) == 1)
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# for p in range(len(positive)):
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# # we now check whether the constrained index appears in the j vector
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# # (the vector of the "active" indices)
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# pos = np.where(j == self.model.constrained_positive_indices[p])[0]
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# if len(pos) == 1:
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# self.model.constrained_positive_indices[p] = pos
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# else:
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# mask[p] = False
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# self.model.constrained_positive_indices = self.model.constrained_positive_indices[mask]
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return (bounded_i, bounded_l, bounded_u), positive
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def restore_constraints(self, b, p):
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self.model.constrained_bounded_indices = b[0]
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self.model.constrained_bounded_lowers = b[1]
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self.model.constrained_bounded_uppers = b[2]
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self.model.constrained_positive_indices = p
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# return (bounded_i, bounded_l, bounded_u), positive
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def restore_constraints(self, c):#b, p):
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# self.model.constrained_bounded_indices = b[0]
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# self.model.constrained_bounded_lowers = b[1]
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# self.model.constrained_bounded_uppers = b[2]
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# self.model.constrained_positive_indices = p
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self.model.constrained_indices = c
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def get_param_shapes(self, N = None, Q = None):
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model_name = self.model.__class__.__name__
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if model_name == 'GPLVM':
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@ -168,8 +183,8 @@ 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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self.model.likelihood._mean = Y.mean()
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self.model.likelihood._std = Y.std()
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self.model.likelihood._bias = Y.mean()
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self.model.likelihood._scale = Y.std()
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self.model.likelihood.set_data(Y)
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j = self.subset_parameter_vector(self.x_opt, samples, shapes)
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@ -181,16 +196,16 @@ class opt_SGD(Optimizer):
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self.model.likelihood.YYT = np.dot(self.model.likelihood.Y, self.model.likelihood.Y.T)
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self.model.likelihood.trYYT = np.trace(self.model.likelihood.YYT)
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b, p = self.shift_constraints(j)
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ci = self.shift_constraints(j)
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f, fp = f_fp(self.x_opt[j])
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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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self.restore_constraints(ci)
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# restore likelihood _bias and _scale, 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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self.model.likelihood._bias = 0
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self.model.likelihood._scale = 1
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return f, step, self.model.N
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@ -200,8 +215,8 @@ class opt_SGD(Optimizer):
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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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self.model.likelihood._bias = 0.0
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self.model.likelihood._scale = 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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@ -217,6 +232,7 @@ class opt_SGD(Optimizer):
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else:
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features = np.argsort(NLL)
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import pdb; pdb.set_trace()
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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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@ -250,7 +266,6 @@ class opt_SGD(Optimizer):
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# plt.clf()
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# plt.plot(self.param_traces['noise'])
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# import pdb; pdb.set_trace()
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# for k in self.param_traces.keys():
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# self.param_traces[k].append(self.model.get(k)[0])
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