def _parallel_train(self, data, model):
px.train(data=data,
iters=100,
shared_states=False,
in_model=model,
mode=CONFIG.MODELTYPE,
opt_regularization_hook=CONFIG.REGULARIZATION)
def _aggregate(self, opt, **kwargs):
naivekl = np.zeros(self.model[0].weights.shape[0])
K = self.K
X = self.X
if opt:
logger.debug("===KL CREATE DATA===")
data = np.ascontiguousarray(X, dtype=np.uint16)
data = np.ascontiguousarray(
np.vstack((data, self.states - 1)).astype(np.uint16))
logger.debug("===KL CREATE DUMMY MODEL===")
model = px.train(data=data, graph=self.graph, iters=0)
s = np.ctypeslib.as_array(model.empirical_stats,
shape=(model.dimension, ))
s -= model.phi((self.states - 1).ravel())
model.num_instances -= 1
logger.debug("===KL TRAIN BOOTSTRAP===")
res = px.train(in_model=model,
opt_regularization_hook=CONFIG.REGULARIZATION)
logger.debug("===KL MERGE WEIGHTS===")
weights = np.ascontiguousarray(np.copy(res.weights))
states = np.ascontiguousarray(self.states)
logger.debug("===KL CREATE RESULT MODEL===")
kl_model = px.Model(weights=weights.astype(np.float64),
graph=self.graph,
states=states)
"""
else:
average_statistics = []
for i, samples in enumerate(X):
avg = np.mean([self.phi[i](x) for x in samples], axis=0)
average_statistics.append(avg)
self.average_suff_stats = average_statistics
x0 = np.zeros(self.model[0].weights.shape[0])
obj = partial(self.naive_kl, average_statistics=average_statistics,
graph=self.model[0].graph,
states=np.copy(self.model[0].states))
res = minimize(obj, x0, callback=self.callback, tol=self.eps, options={"maxiter": 50, "gtol": 1e-3})
kl_model = px.Model(weights=res.x, graph=self.model[0].graph, states=self.model[0].states)
"""
naivekl += np.copy(kl_model.weights)
# self.test(kl_model)
"""
try:
fisher_matrix = []
inverse_fisher = []
for i in range(K):
fisher_matrix.append(self.fisher_information(i, kl_m[:kl_model.weights.shape[0]], kl_model.weights))
inverse_fisher.append(np.linalg.inv(fisher_matrix[i]))
except np.linalg.LinAlgError as e:
pass
"""
return kl_model.weights
def _create_graph(self):
"""
Creates an independency structure (graph) from data. The specified mode for the independency structure is used,
when creating this object.
"""
holdout = np.ascontiguousarray(self.data_set.holdout.to_numpy().astype(
np.uint16))
self.edgelist = np.copy(
px.train(
data=holdout,
graph=CONFIG.GRAPHTYPE,
iters=1,
mode=CONFIG.MODELTYPE,
opt_regularization_hook=CONFIG.REGULARIZATION).graph.edgelist)
self.graph = self._px_create_graph()
self.weights = self.init_weights()
def run_training(self, data, obs, split, progr_train):
import pxpy as px
data = self.setup_for_cv(split, data, obs)
missing = data == HIDE_VAL
overall_loss = []
for emiter in range(self.emiters):
self.obj = sys.maxsize
loss = []
new_modelname = self.model_filename + '_{}'.format(emiter)
if not os.path.isfile(new_modelname):
if emiter != 0: # load the previous model and fill data gaps with gibbs
data[missing] = HIDE_VAL
with warnings.catch_warnings(record=True):
warnings.simplefilter('ignore')
prev_model = px.load_model(self.model_filename +
'_{}'.format(emiter - 1))
self.predict_gibbs(prev_model, data)
else:
prev_model = None
with warnings.catch_warnings(record=True):
warnings.simplefilter('ignore')
model = px.train(
data=data,
iters=sys.maxsize,
graph=px.create_graph(self.edges),
mode=getattr(px.ModelType, self.mode),
shared_states=bool(self.shared),
in_model=prev_model,
opt_regularization_hook=self.regularize,
opt_progress_hook=(
lambda x, em=emiter, loss=loss: self.
check_progress(x, progr_train, em, loss)))
model.save(new_modelname)
model.graph.delete()
model.delete()
overall_loss.append(('EM Iter ' + str(emiter), loss))
progr_train[self.split] = (100.0 / self.emiters) * (emiter + 1)
self.cons.progress(progr_train, self.split)
self.plot_convergence(overall_loss)
super().run_training(data, obs, split,
progr_train) # for final console output
def __init__(self, states, edgelist=None, seed=None):
super(Synthetic, self).__init__()
n_vars = 15
n_samples = 1000
n_states = 10
self.random_state = np.random.RandomState(seed=seed)
# Generate random cov
cov = self.random_state.randn(n_vars, n_vars)
cov = np.dot(cov, cov.T) / n_vars
# Generate data from normal
self.data = pd.DataFrame(
scipy.stats.multivariate_normal(mean=np.zeros(n_vars),
cov=np.dot(cov, cov.T) /
n_vars).rvs(n_samples))
data_disc, disc_ttt = px.discretize(data=self.data,
num_states=n_states)
# Add sample to ensure same state space for each variable
data_disc = np.concatenate([
data_disc,
np.full(shape=(1, n_vars),
fill_value=n_states - 1,
dtype=np.uint16)
])
# Generate model
self.global_model = px.train(data_disc,
graph=px.GraphType.auto_tree,
mode=px.ModelType.mrf,
iters=0)
self.global_weights = np.copy(self.global_model.weights)
# TODO: Remove the statistics for full point.
edgelist = self.global_model.graph.edgelist
stats = self.global_model.statistics
cov[a[x]:a[x + 1], a[x]:a[x + 1]] = - rhs[a[x]:a[x + 1], a[x]:a[x + 1]]
cov -= np.diag(np.diag(cov))
cov += diag + np.diag(np.full(model.weights.shape[0], eps))
return cov
if __name__ == '__main__':
data = main()
res = None
for arr in data:
res = arr if res is None else np.vstack((res, arr))
res = np.ascontiguousarray(res, dtype=np.float64)
disc, M = px.discretize(res, 10)
model = px.train(disc, graph=px.GraphType.auto_tree, iters=10000)
gen_semi_random_cov(model, 1e-1)
mu, A = model.infer()
vars = model.weights.shape[0]
mu = mu[:vars]
fi = np.outer(mu - model.statistics, mu - model.statistics)
phis = []
for d in disc:
phis.append(model.phi(d))
cov_XY = np.cov(np.array(phis).T)
EX_EY = np.outer(mu, mu)
E_XY = cov_XY + EX_EY
new_data = os.path.join(CONFIG.ROOT_DIR, "data")
os.chdir(new_data)
os.mkdir("SYNTH")
df = pd.DataFrame(disc)
def predict(self, weights=None, n_test=None):
logger.debug("===PREDICT PREPARE DATA===")
test = np.ascontiguousarray(self.data_set.test.to_numpy().astype(
np.uint16))
tmp = np.ascontiguousarray(
np.full(shape=(1, self.state_space.shape[0]),
fill_value=self.state_space,
dtype=np.uint16))
tmp_test = np.vstack((test, tmp))
test_model = px.train(data=tmp_test,
graph=self._px_create_graph(),
mode=px.ModelType.mrf,
opt_regularization_hook=CONFIG.REGULARIZATION,
iters=0,
k=4)
test_model = self.scale_phi_emp(test_model)
statistics = np.copy(test_model.statistics)
if weights is not None:
np.copyto(test_model.weights, weights)
_, a = test_model.infer()
test_ll = [a - np.inner(weights, statistics)]
test_model.delete()
else:
partitions = []
if CONFIG.MODELTYPE == px.ModelType.integer:
for mod in self.px_model_scaled:
if test_model.weights.shape[0] != mod.weights.shape[0]:
print("error")
np.copyto(test_model.weights, mod.weights)
_, a = test_model.infer()
partitions.append(a)
else:
for mod in self.px_model:
if test_model.weights.shape[0] != mod.weights.shape[0]:
print("error")
np.copyto(test_model.weights, mod.weights)
_, a = test_model.infer()
partitions.append(a)
test_model.delete()
if isinstance(self.data_set.label_column, str):
label_column_idx = self.data_set.test.columns.get_loc(
self.data_set.label_column)
test[:, label_column_idx] = -1
else:
test[:, self.data_set.label_column] = -1
if n_test is None:
n_test = test.shape[0] - 1
else:
n_test = np.min([n_test, test.shape[0] - 1])
test = np.ascontiguousarray(test[:n_test])
logger.debug("===PREDICT START PREDICTIONS===")
if weights is None:
logger.debug("===PREDICT ALL LOCAL MODELS===")
if self.trained:
if CONFIG.MODELTYPE == px.ModelType.integer:
predictions = [
px_model.predict(
np.ascontiguousarray(np.copy(test[:n_test])))
for px_model in self.px_model_scaled
]
test_ll = [
partitions[i] -
np.inner(self.px_model_scaled[i].weights, statistics)
for i in range(len(self.px_model_scaled))
]
return predictions, test_ll
else:
test_ll = [
partitions[i] -
np.inner(self.px_model[i].weights, statistics)
for i in range(len(self.px_model))
]
return [
px_model.predict(
np.ascontiguousarray(np.copy(test[:n_test])))
for px_model in self.px_model
], test_ll
else:
logger.debug("===PREDICT INPUT MODEL===")
px_model = px.Model(weights=weights,
graph=px.create_graph(self.edgelist),
states=self.state_space + 1)
return px_model.predict(test[:n_test]), test_ll
def scale_model(self, model, data):
weights = np.log(2) * np.ascontiguousarray(np.copy(model.weights))
res = px.train(data=data, graph=self.graph, iters=0)
res = self.scale_phi_emp(res)
np.copyto(res.weights, weights)
return res
def train(self,
epochs=1,
iters=100,
split=None,
n_models=None,
mode=px.ModelType.mrf):
"""
TODO
Parameters
----------
epochs : int
Number of outer iterations
iters: int
Number of inner iteration (per call of px.train)
split: Split
class:src.preprocessing.split.Split Contains the number of splits and thus the number of models to be trained.
Each split will be distributed to a single device/model.
Raises
------
RuntimeError
Returns
-------
None
"""
self.maxiter = iters
self.epoch += 1
models = []
scaled_models = []
train = np.ascontiguousarray(self.data_set.train.to_numpy().astype(
np.uint16))
# Timing
start = time.time()
update = None
iter_time = None
# Initialization for best Params
if split is None:
total_models = 1
split = [np.arange(train.shape[0])]
else:
total_models = len(split) if n_models is None else np.min(
[len(split), n_models])
self.best_weights[self.epoch] = [0] * total_models
self.best_objs[self.epoch] = [np.infty] * total_models
self.px_batch_local[self.epoch] = [0] * total_models
# Distributed Training
for i, idx in enumerate(split):
self.curr_iter = 0
if len(split) > 1:
self.n_local_data = np.int64(
np.min([
np.ceil(self.data_delta *
self.sample_func(self.epoch)), idx.shape[0]
]))
else:
self.n_local_data = idx.shape[0]
self.curr_model = i
if n_models is not None:
if i >= n_models:
break
update, _ = log_progress(start, update, iter_time, total_models, i)
logger.info("===TRAINING=== CREATE DATA===")
tmp = np.ascontiguousarray(
np.full(shape=(1, self.state_space.shape[0]),
fill_value=self.state_space,
dtype=np.uint16))
data = np.ascontiguousarray(
np.copy(train[idx[:self.n_local_data].flatten()]))
data = np.vstack((data, tmp))
model = px.train(data=data,
graph=self.graph,
mode=CONFIG.MODELTYPE,
opt_regularization_hook=CONFIG.REGULARIZATION,
iters=0,
k=4)
model = self.scale_phi_emp(model)
if self.epoch > 1 and CONFIG.FEEDBACK:
np.copyto(model.weights,
np.ascontiguousarray(self.best_aggregate))
_, A = model.infer()
logger.info("LOCAL LL WITH AGGREGATE WEIGHTS :" +
str(A - np.inner(model.weights, model.statistics)))
logger.info("===TRAINING=== START TRAINING===")
model = px.train(iters=iters,
shared_states=False,
opt_progress_hook=self.opt_progress_hook,
mode=CONFIG.MODELTYPE,
in_model=model,
opt_regularization_hook=CONFIG.REGULARIZATION,
k=4)
logger.info("===TRAINING=== FINISHED TRAINING===")
self.reset_train()
if len(split) > 1:
self.px_batch_local[self.epoch][i] = model
#model = self.merge_weights(model)
if self.epoch == 1:
models.append(model)
if CONFIG.MODELTYPE == px.ModelType.integer:
scaled_models.append(self.scale_model(model, data))
else:
self.px_model[i] = model
if CONFIG.MODELTYPE == px.ModelType.integer:
scaled_model = self.scale_model(model, data)
self.px_model_scaled[i] = scaled_model
iter_time = time.time()
if not self.px_model:
self.px_model = models
if CONFIG.MODELTYPE == px.ModelType.integer:
self.px_model_scaled = scaled_models
self.px_batch[self.epoch] = self.px_model
end = time.time()
logger.info("Finished Training Models: " +
"{:.2f} s".format(end - start))
if not self.trained:
self.trained = True
