def loadSubsetData(data, RS, subset_sizes, clientNum):
N_rows = data['X'].shape[0]
partitions = []
countPre = 0
for i in range (0,clientNum):
# Count = (i*subset_sizes)/N_rows
# if Count> countPre:
# data = dictslice(data, RS.permutation(N_rows))
# countPre = Count
startNum = (i*subset_sizes)%N_rows
print ("current startNum " +str(startNum))
if (startNum+subset_sizes) > N_rows:
idxs = slice(startNum, N_rows)
idxs1 = slice(0, (startNum+subset_sizes-N_rows))
part1 = dictslice(data, idxs)
part2 = dictslice(data,idxs1)
subset = part1
subset['X']=np.concatenate((part1['X'] , part2['X']), axis=0)
subset['T']=np.concatenate((part1['T'] ,part2['T']), axis=0)
else:
idxs = slice(startNum, startNum + subset_sizes)
subset = dictslice(data, idxs)
# subset = random_partition(subset, RS, [subset_sizes]).__getitem__(0)
partitions.append(subset)
partitions = partitions
return partitions
def loadSubsetData(data, RS, subset_sizes, clientNum):
N_rows = data['X'].shape[0]
partitions = []
countPre = 0
for i in range(0, clientNum):
# Count = (i*subset_sizes)/N_rows
# if Count> countPre:
# data = dictslice(data, RS.permutation(N_rows))
# countPre = Count
startNum = (i * subset_sizes) % N_rows
print("current startNum " + str(startNum))
if (startNum + subset_sizes) > N_rows:
idxs = slice(startNum, N_rows)
idxs1 = slice(0, (startNum + subset_sizes - N_rows))
part1 = dictslice(data, idxs)
part2 = dictslice(data, idxs1)
subset = part1
subset['X'] = np.concatenate((part1['X'], part2['X']), axis=0)
subset['T'] = np.concatenate((part1['T'], part2['T']), axis=0)
else:
idxs = slice(startNum, startNum + subset_sizes)
subset = dictslice(data, idxs)
# subset = random_partition(subset, RS, [subset_sizes]).__getitem__(0)
partitions.append(subset)
partitions = partitions
return partitions
def hyperloss(hyperparam_vect,
i_hyper,
alphabets,
verbose=True,
report_train_loss=False):
RS = RandomState((seed, i_hyper, "hyperloss"))
alphabet = shuffle_alphabet(RS.choice(alphabets), RS)
N_train = alphabet['X'].shape[0] - N_valid_dpts
train_data = dictslice(alphabet, slice(None, N_train))
if report_train_loss:
valid_data = dictslice(alphabet, slice(None, N_valid_dpts))
else:
valid_data = dictslice(alphabet, slice(N_train, None))
def primal_loss(W, hyperparam_vect, i_primal, reg_penalty=True):
RS = RandomState((seed, i_hyper, i_primal))
idxs = RS.permutation(N_train)[:batch_size]
minibatch = dictslice(train_data, idxs)
loss = reg_loss_fun(W, minibatch, hyperparam_vect, reg_penalty)
if verbose and i_primal % 10 == 0:
print "Iter {0}, loss, {1}".format(i_primal, getval(loss))
return loss
W0 = RS.randn(N_weights) * initialization_scale
W_final = sgd(grad(primal_loss),
hyperparam_vect,
W0,
alpha,
beta,
N_iters,
callback=None)
return reg_loss_fun(W_final,
valid_data,
hyperparam_vect,
reg_penalty=False)
def random_partition(data, RS, subset_sizes):
N_rows = data['X'].shape[0]
shuffled_data = dictslice(data, RS.permutation(N_rows))
partitions = []
start = 0
for N in subset_sizes:
idxs = slice(start, start + N)
partitions.append(dictslice(shuffled_data, idxs))
start += N
return partitions
def random_partition(data, RS, subset_sizes):
N_rows = data["X"].shape[0]
shuffled_data = dictslice(data, RS.permutation(N_rows))
partitions = []
start = 0
for N in subset_sizes:
idxs = slice(start, start + N)
partitions.append(dictslice(shuffled_data, idxs))
start += N
return partitions
def primal_loss(W, hyperparam_vect, i_primal, reg_penalty=True):
RS = RandomState((seed, i_hyper, i_primal))
idxs = RS.permutation(N_train)[:batch_size]
minibatch = dictslice(train_data, idxs)
loss = reg_loss_fun(W, minibatch, hyperparam_vect, reg_penalty)
if verbose and i_primal % 10 == 0: print "Iter {0}, loss, {1}".format(i_primal, getval(loss))
return loss
def primal_loss(W, hyperparam_vect, i_primal, reg_penalty=True):
RS = RandomState((seed, i_hyper, i_primal))
idxs = RS.permutation(N_train)[:batch_size]
minibatch = dictslice(train_data, idxs)
loss = reg_loss_fun(W, minibatch, hyperparam_vect, reg_penalty)
if verbose and i_primal % 10 == 0: print "Iter {0}, loss, {1}".format(i_primal, getval(loss))
return loss
def primal_loss(z_vect, transform, i_primal, record_results=False):
RS = RandomState((seed, i_primal, "primal"))
idxs = RS.randint(N_data, size=batch_size)
minibatch = dictslice(data, idxs)
w_vect = transform_weights(z_vect, transform)
loss = loss_fun(w_vect, **minibatch)
reg = regularization(z_vect)
return loss + reg
def primal_loss(w_vect, reg, i_primal, record_results=False):
RS = RandomState((seed, i_primal, "primal"))
idxs = RS.randint(N_data, size=batch_size)
minibatch = dictslice(data, idxs)
loss = loss_fun(w_vect, **minibatch)
reg = regularization(w_vect, reg)
if record_results and i_primal % N_thin == 0:
print "Iter {0}: train: {1}".format(i_primal, getval(loss))
return loss + reg
def primal_loss(w_vect, reg, i_primal, record_results=False):
RS = RandomState((seed, i_primal, "primal"))
idxs = RS.randint(N_data, size=batch_size)
minibatch = dictslice(data, idxs)
loss = loss_fun(w_vect, **minibatch)
reg = regularization(w_vect, reg)
if record_results and i_primal % N_thin == 0:
print "Iter {0}: train: {1}".format(i_primal, getval(loss))
return loss + reg
def hyperloss(hyperparam_vect, i_hyper, alphabets, verbose=True, report_train_loss=False):
RS = RandomState((seed, i_hyper, "hyperloss"))
alphabet = shuffle_alphabet(RS.choice(alphabets), RS)
N_train = alphabet['X'].shape[0] - N_valid_dpts
train_data = dictslice(alphabet, slice(None, N_train))
if report_train_loss:
valid_data = dictslice(alphabet, slice(None, N_valid_dpts))
else:
valid_data = dictslice(alphabet, slice(N_train, None))
def primal_loss(W, hyperparam_vect, i_primal, reg_penalty=True):
RS = RandomState((seed, i_hyper, i_primal))
idxs = RS.permutation(N_train)[:batch_size]
minibatch = dictslice(train_data, idxs)
loss = reg_loss_fun(W, minibatch, hyperparam_vect, reg_penalty)
if verbose and i_primal % 10 == 0: print "Iter {0}, loss, {1}".format(i_primal, getval(loss))
return loss
W0 = RS.randn(N_weights) * initialization_scale
W_final = sgd(grad(primal_loss), hyperparam_vect, W0, alpha, beta, N_iters, callback=None)
return reg_loss_fun(W_final, valid_data, hyperparam_vect, reg_penalty=False)
def sub_primal_stochastic_loss(z_vect, transform_vect, i_primal, i_script):
RS = RandomState((seed, i_hyper, i_primal, i_script))
N_train = train_data[i_script]['X'].shape[0]
idxs = RS.permutation(N_train)[:batch_size]
minibatch = dictslice(train_data[i_script], idxs)
loss = loss_from_latents(z_vect, transform_vect, i_script, minibatch)
reg = regularization(z_vect) if i_script == 0 else 0.0
if i_primal % N_thin == 0 and i_script == 0:
print "Iter {0}, full losses: train: {1}, valid: {2}, reg: {3}".format(
i_primal,
total_loss(train_data, getval(z_vect)),
total_loss(valid_data, getval(z_vect)),
getval(reg) / N_scripts_per_iter)
return loss + reg
def primal_loss(z_vect, transform, i_primal, record_results=False):
RS = RandomState((seed, i_primal, "primal"))
idxs = RS.randint(N_data, size=batch_size)
minibatch = dictslice(data, idxs)
w_vect = transform_weights(z_vect, transform) #TODO: this is a scale transformation, not regularization!
loss = loss_fun(w_vect, **minibatch) #use new scale for prediction
reg = regularization(z_vect) #regularize original scale
#TODO: should be equivalent: w = z*e^transform, so
# f(z*e^transform) + e^\lambda||z||^2 = f(w) + e^\lambda||z||^2 = f(w) + e^(\lambda)||e^-2transform w||^2
# see process_transform
#if record_results and i_primal % N_thin == 0:
#print "Iter {0}: train: {1}".format(i_primal, getval(loss))
return loss + reg
def sub_primal_stochastic_loss(z_vect, transform_vect, i_primal,
i_script):
RS = RandomState((seed, i_hyper, i_primal, i_script))
N_train = train_data[i_script]['X'].shape[0]
idxs = RS.permutation(N_train)[:batch_size]
minibatch = dictslice(train_data[i_script], idxs)
loss = loss_from_latents(z_vect, transform_vect, i_script,
minibatch)
reg = regularization(z_vect) if i_script == 0 else 0.0
if i_primal % N_thin == 0 and i_script == 0:
print "Iter {0}, full losses: train: {1}, valid: {2}, reg: {3}".format(
i_primal, total_loss(train_data, getval(z_vect)),
total_loss(valid_data, getval(z_vect)),
getval(reg) / N_scripts_per_iter)
return loss + reg
def primal_stochastic_loss(z_vect, transform_vect, i_primal):
RS = RandomState((seed, i_hyper, i_primal))
loss = 0.0
for _ in range(N_scripts_per_iter):
i_script = RS.randint(N_scripts)
N_train = train_data[i_script]['X'].shape[0]
idxs = RS.permutation(N_train)[:batch_size]
minibatch = dictslice(train_data[i_script], idxs)
loss += loss_from_latents(z_vect, transform_vect, i_script, minibatch)
reg = regularization(z_vect)
if i_primal % 1 == 0:
print "Iter {0}, loss {1}, reg {2}".format(i_primal, getval(loss), getval(reg))
print "Full losses: train: {0}, valid: {1}".format(
total_loss(train_data, getval(z_vect)),
total_loss(valid_data, getval(z_vect)))
return loss + reg
def primal_stochastic_loss(z_vect, transform_vect, i_primal):
RS = RandomState((seed, i_hyper, i_primal))
loss = 0.0
for _ in range(N_scripts_per_iter):
i_script = RS.randint(N_scripts)
N_train = train_data[i_script]['X'].shape[0]
idxs = RS.permutation(N_train)[:batch_size]
minibatch = dictslice(train_data[i_script], idxs)
loss += loss_from_latents(z_vect, transform_vect, i_script, minibatch)
reg = regularization(z_vect)
if i_primal % 20 == 0:
print "Iter {0}, loss {1}, reg {2}".format(i_primal, getval(loss), getval(reg))
print "Full losses: train: {0}, valid: {1}".format(
total_loss(train_data, getval(z_vect)),
total_loss(valid_data, getval(z_vect)))
return loss + reg
def split(alphabet, num_chars):
cum_chars = np.cumsum(num_chars)
def select_dataset(count):
for i, N in enumerate(cum_chars):
if count < N: return i
labels = np.argmax(alphabet['T'], axis=1)
label_counts = [0] * NUM_CHARS
split_idxs = [[] for n in num_chars]
for i_dpt, label in enumerate(labels):
i_dataset = select_dataset(label_counts[label])
split_idxs[i_dataset].append(i_dpt)
label_counts[label] += 1
data_splits = []
for n, idxs in zip(num_chars, split_idxs):
data_splits.append(dictslice(alphabet, idxs))
totals = np.sum(data_splits[-1]['T'], axis=0)
assert np.all(np.logical_or(totals == 0, totals == n))
return data_splits
def split(alphabet, num_chars):
cum_chars = np.cumsum(num_chars)
def select_dataset(count):
for i, N in enumerate(cum_chars):
if count < N: return i
labels = np.argmax(alphabet['T'], axis=1)
label_counts = [0] * NUM_CHARS
split_idxs = [[] for n in num_chars]
for i_dpt, label in enumerate(labels):
i_dataset = select_dataset(label_counts[label])
split_idxs[i_dataset].append(i_dpt)
label_counts[label] += 1
data_splits = []
for n, idxs in zip(num_chars, split_idxs):
data_splits.append(dictslice(alphabet, idxs))
totals = np.sum(data_splits[-1]['T'], axis=0)
assert np.all(np.logical_or(totals == 0, totals == n))
return data_splits
def shuffle_alphabet(alphabet, RS):
# Shuffles both data and label indices
N_rows, N_cols = alphabet['T'].shape
alphabet['T'] = alphabet['T'][:, RS.permutation(N_cols)]
return dictslice(alphabet, RS.permutation(N_rows))
def shuffle_rows(alphabet, RS):
N_rows, N_cols = alphabet['T'].shape
return dictslice(alphabet, RS.permutation(N_rows))
def shuffle_rows(alphabet, RS):
N_rows, N_cols = alphabet['T'].shape
return dictslice(alphabet, RS.permutation(N_rows))
def shuffle_alphabet(alphabet, RS):
# Shuffles both data and label indices
N_rows, N_cols = alphabet['T'].shape
alphabet['T'] = alphabet['T'][:, RS.permutation(N_cols)]
return dictslice(alphabet, RS.permutation(N_rows))