def gd(self, batch_size=None, **kwargs):
if self.verbose > 0:
print(self.indent + ' TSNE.gd():')
print(self.indent + ' specs:')
if batch_size is None or batch_size >= self.n_samples:
Xi = None
F = lambda embedding: self.gradient(embedding)
if self.verbose > 0:
print(self.indent + ' gradient type : full')
else:
def Xi():
indices = np.arange(self.n_samples)
np.random.shuffle(indices)
xi = {'indices': indices}
return xi
F = lambda X, indices: self.gradient(X, batch_size, indices)
if self.verbose > 0:
print(self.indent + ' gradient type : batch')
print(self.indent + ' batch size :', batch_size)
self.embedding, H = gd.single(self.embedding,
F,
Xi=Xi,
verbose=self.verbose,
indent=self.indent + ' ',
**kwargs)
self.update()
if self.verbose > 0:
print(self.indent + f' final stress : {self.cost:0.2e}')
def smart_initialize(self,max_iter=[50,30],lr=[1,0.1],
batch_size=10,**kwargs):
"""\
Computes an mds embedding (dimension embedding_dimension) of the
combined distances. Only works when self.Q_is_fixed is False (as this
is unnecessary otherwhise).
Parameters :
X0 : None or array
Optional initial embedding (used to compute mds embedding)
Q0 : None or list of arrays
Optional initial projection parameters.
"""
assert self.fixed_embedding is False
assert self.fixed_projections is False
if self.verbose > 0:
print(self.indent+' MPSE.smart_initialize():')
distances = np.sum(self.distances,axis=0)/self.n_perspectives
if self.weights is not None and self.weights[0] is not None:
weights = np.product(self.weights,axis=0)
else:
weights = None
vis = mds.MDS(distances,dim=self.embedding_dimension,min_grad=1e-4,
indent=self.indent+' ',
initial_embedding=self.embedding,
weights = weights,
verbose=self.verbose)
vis.gd(batch_size=batch_size, max_iter=max_iter[0],lr=lr[0],**kwargs)
self.embedding = vis.X
H = vis.computation_history[0]
H['fixed_embedding'] = False
H['fixed_projections'] = True
self.computation_history.append(H)
def Xi():
indices = np.arange(self.n_samples)
np.random.shuffle(indices)
xi = {
'indices' : indices
}
return xi
F = lambda Q, indices : self.gradient(self.embedding,Q,
batch_size=batch_size,
return_embedding=False)
Q0 = np.array(self.projections)
self.projections, H = gd.single(Q0,F,Xi=Xi,p=self.proj.restrict,
max_iter=max_iter[1],lr=lr[1],
verbose=self.verbose,indent=self.indent+' ',
**kwargs)
H['fixed_embedding'] = True
H['fixed_projections'] = False
self.computation_history.append(H)
return
def gd(self, batch_size=None, lr=None, fixed_projections='default',
fixed_embedding='default', **kwargs):
if fixed_projections == 'default':
fixed_projections = self.fixed_projections
if fixed_embedding == 'default':
fixed_embedding = self.fixed_embedding
assert batch_size is None or isinstance(batch_size,int)
assert fixed_embedding is False or fixed_projections is False
if lr is None:
if self.visualization_method == 'mds':
if fixed_projections:
lr = 1
elif fixed_embedding:
lr = 0.01
else:
lr = [1,0.01]
elif self.visualization_method == 'tsne':
if fixed_projections:
lr = 100
elif fixed_embedding:
lr = 10
else:
lr = [100,10]
else:
lr = [1,1]
if self.verbose > 0:
print(self.indent+' MPSE.gd():')
print(self.indent+f' initial stress : {self.cost:0.2e}')
if fixed_projections:
if self.verbose > 0:
print(self.indent+' mpse method : fixed projections')
if batch_size is None or batch_size >= self.n_samples:
Xi = None
F = lambda X : self.gradient(X,self.projections,
return_projections=False)
else:
if self.verbose > 0:
print(self.indent+' batch size :',batch_size)
def Xi():
indices = np.arange(self.n_samples)
np.random.shuffle(indices)
xi = {
'indices' : indices
}
return xi
F = lambda X, indices: self.gradient(X,self.projections,
batch_size=batch_size,
indices=indices,
return_projections=False)
self.embedding, H = gd.single(
self.embedding,F,Xi=Xi, verbose=self.verbose, lr=lr,
indent=self.indent+' ',**kwargs)
H['fixed_projections'] = True
H['fixed_embedding'] = False
self.computation_history.append(H)
elif fixed_embedding:
if self.verbose > 0:
print(self.indent+' mpse method : fixed embedding')
if batch_size is None or batch_size >= self.n_samples:
Xi = None
F = lambda Q : self.gradient(self.embedding,Q,
return_embedding=False)
else:
if self.verbose > 0:
print(self.indent+' batch size :',batch_size)
def Xi():
indices = np.arange(self.n_samples)
np.random.shuffle(indices)
xi = {
'indices' : indices
}
return xi
F = lambda Q, indices: self.gradient(self.embedding,Q,
batch_size=batch_size,
indices=indices,
return_embedding=False)
Q0 = np.array(self.projections)
self.projections, H = gd.single(Q0,F,Xi=Xi,p=self.proj.restrict,
verbose=self.verbose,lr=lr,
indent=self.indent+' ',**kwargs)
H['fixed_projections'] = False
H['fixed_embedding'] = True
self.computation_history.append(H)
else:
if self.verbose > 0:
print(self.indent+' fixed : None')
if batch_size is None or batch_size >= self.n_samples:
Xi = None
F = lambda params : self.gradient(params[0],params[1])
else:
if self.verbose > 0:
print(self.indent+' batch size :',batch_size)
def Xi():
indices = np.arange(self.n_samples)
np.random.shuffle(indices)
xi = {
'indices' : indices
}
return xi
F = lambda params, indices: self.gradient(params[0],params[1],
batch_size=batch_size,
indices=indices)
params = [self.embedding,np.array(self.projections)]
params, H = gd.multiple(params,F,Xi=Xi,p=[None,self.proj.restrict],
verbose=self.verbose,lr=lr,
indent=self.indent+' ',**kwargs)
self.embedding = params[0]
self.projections = params[1]
H['fixed_projections'] = False
H['fixed_embedding'] = False
self.computation_history.append(H)
self.update()
if self.verbose > 0:
print(self.indent+f' final cost : {self.cost:0.2f}')
costs = ', '.join(f'{x:0.2f}' for x in self.individual_cost)
print(self.indent+f' individual costs : {costs}')
