def __init__(self, trainpath, validpaths, max_trans=0, vnodes=False, hypotest=None, saveplots=False):
assert isinstance(trainpath, str)
assert len(trainpath)
assert isinstance(validpaths, list)
for path in validpaths:
assert isinstance(path, str)
assert len(path)
assert isinstance(max_trans, int)
assert max_trans >= 0
assert isinstance(vnodes, bool)
assert isinstance(saveplots, bool)
self._modname = 'base'
if vnodes:
self._modname = 'vnode'
if hypotest:
self._modname += '+hypo'
self._savepathbase = None
if saveplots:
self._savepathbase = "./%s-plots-%s-maxtrans%d/" % (trainpath, self._modname, max_trans)
os.mkdir(self._savepathbase)
# load datasets
self._trainset = ObservationSequence(loadpath=trainpath, max_trans=max_trans, \
vnodes=vnodes)
if hypotest:
#statespace, transitions = self._trainset.admissible_statespace_transitions()
#print "orig:\n", statespace, transitions
statespace, transitions = self._trainset.hypotest_admissible_statespace_transitions(hypotest)
#print "hypotest:\n", statespace, transitions
self._trainset = ObservationSequence(loadpath=trainpath, max_trans=max_trans, vnodes=vnodes, \
admissible_statespace=statespace, \
admissible_transitions=transitions)
statespace, transitions = self._trainset.admissible_statespace_transitions()
#self._trainset.dump()
#print "trainset:\n", statespace, transitions
if not self._trainset.outseq_len():
print "warning, TransitionModel.__init__, empty trainset at path %s" % trainpath
_FILTER_TRANS = 1
self._validsets = []
for validpath in validpaths:
if _FILTER_TRANS:
# filter trans
dataset = ObservationSequence(loadpath=validpath, max_trans=max_trans, vnodes=vnodes, \
admissible_statespace=statespace, \
admissible_transitions=transitions)
else:
dataset = ObservationSequence(loadpath=validpath, max_trans=max_trans, vnodes=vnodes)
if not dataset.outseq_len():
print "TransitionModel, skipping empty validation dataset at path %s" % validpath
continue
self._validsets.append(dataset)
# define the initial set of states and the set of output symbols
self._init_states = sorted(self._trainset.states())
self._N = len(self._init_states)
self._symbols = sorted(self._trainset.symbols())
self._M = len(self._symbols)
self._symbols_ext = copy.copy(self._symbols)
if not _FILTER_TRANS:
symbols = set()
for dataset in self._validsets:
symbols.update(dataset.symbols())
print "symbols", symbols
for sym in symbols:
if sym not in self._symbols_ext:
print "sym", sym
print "TransitionalModel: appending sym=\"%s\" in valset but not in trainset" % sym
self._symbols_ext.append(sym)
self._M_ext = len(self._symbols_ext)
print "self._symbols_ext", self._symbols_ext
camsymbols = {}
for sym in self._symbols_ext:
print "sym = ", sym
for _id in sym.split('-'):
print " _id = ", _id
__id = int(_id.strip('abcdef'))
try:
camsymbols[__id].add(sym)
except:
print "new cam id", _id
camsymbols[__id] = set([sym])
for camid in camsymbols.keys():
symbols = camsymbols[camid]
camsymbols[camid] = sorted(symbols)
symbol2cam = {}
for sym in self._symbols_ext:
for _id in sym.split('-'):
__id = int(_id.strip('abcdef'))
try:
symbol2cam[sym].append(__id)
except:
symbol2cam[sym] = [__id]
symbol2cam_enc = {}
for sym,camids in symbol2cam.iteritems():
symenc = []
symbol2cam_enc[self._symbol_enc(sym)] = camids
print "camsymbols=",camsymbols
print "symbol2cam=",symbol2cam
print "symbol2cam_enc=",symbol2cam_enc
print "symbols_ext", self._symbols_ext
# define the initial A, B matrices
A, B = self._build_initial_model()
# compute the node to be split
self._compute_node_splits()
# encode datasets (bijective mapping symobols <-> integers)
self._trainset_enc = []
for observ in self._trainset.outseq():
self._trainset_enc.append(self._symbol_enc(observ))
self._validsets_enc = []
for dataset in self._validsets:
encoded = []
for observ in dataset.outseq():
encoded.append(self._symbol_enc(observ))
self._validsets_enc.append(encoded)
# print some info
self.print_info()
# create the HMM model with node splitting
self._hmm = HiddenMarkovModel(A, B, copy.copy(self._init_states), \
self._trainset_enc, validsets=self._validsets_enc, symnames=copy.copy(self._symbols_ext), symbol2cam=symbol2cam_enc)
self._transition_graph_threshold = 0.01
self._hmm.plot_transition_graph(0., savepathbase=self._savepathbase, savetag='coverage-model')
class TransitionalModel():
def __init__(self, trainpath, validpaths, max_trans=0, vnodes=False, hypotest=None, saveplots=False):
assert isinstance(trainpath, str)
assert len(trainpath)
assert isinstance(validpaths, list)
for path in validpaths:
assert isinstance(path, str)
assert len(path)
assert isinstance(max_trans, int)
assert max_trans >= 0
assert isinstance(vnodes, bool)
assert isinstance(saveplots, bool)
self._modname = 'base'
if vnodes:
self._modname = 'vnode'
if hypotest:
self._modname += '+hypo'
self._savepathbase = None
if saveplots:
self._savepathbase = "./%s-plots-%s-maxtrans%d/" % (trainpath, self._modname, max_trans)
os.mkdir(self._savepathbase)
# load datasets
self._trainset = ObservationSequence(loadpath=trainpath, max_trans=max_trans, \
vnodes=vnodes)
if hypotest:
#statespace, transitions = self._trainset.admissible_statespace_transitions()
#print "orig:\n", statespace, transitions
statespace, transitions = self._trainset.hypotest_admissible_statespace_transitions(hypotest)
#print "hypotest:\n", statespace, transitions
self._trainset = ObservationSequence(loadpath=trainpath, max_trans=max_trans, vnodes=vnodes, \
admissible_statespace=statespace, \
admissible_transitions=transitions)
statespace, transitions = self._trainset.admissible_statespace_transitions()
#self._trainset.dump()
#print "trainset:\n", statespace, transitions
if not self._trainset.outseq_len():
print "warning, TransitionModel.__init__, empty trainset at path %s" % trainpath
_FILTER_TRANS = 1
self._validsets = []
for validpath in validpaths:
if _FILTER_TRANS:
# filter trans
dataset = ObservationSequence(loadpath=validpath, max_trans=max_trans, vnodes=vnodes, \
admissible_statespace=statespace, \
admissible_transitions=transitions)
else:
dataset = ObservationSequence(loadpath=validpath, max_trans=max_trans, vnodes=vnodes)
if not dataset.outseq_len():
print "TransitionModel, skipping empty validation dataset at path %s" % validpath
continue
self._validsets.append(dataset)
# define the initial set of states and the set of output symbols
self._init_states = sorted(self._trainset.states())
self._N = len(self._init_states)
self._symbols = sorted(self._trainset.symbols())
self._M = len(self._symbols)
self._symbols_ext = copy.copy(self._symbols)
if not _FILTER_TRANS:
symbols = set()
for dataset in self._validsets:
symbols.update(dataset.symbols())
print "symbols", symbols
for sym in symbols:
if sym not in self._symbols_ext:
print "sym", sym
print "TransitionalModel: appending sym=\"%s\" in valset but not in trainset" % sym
self._symbols_ext.append(sym)
self._M_ext = len(self._symbols_ext)
print "self._symbols_ext", self._symbols_ext
camsymbols = {}
for sym in self._symbols_ext:
print "sym = ", sym
for _id in sym.split('-'):
print " _id = ", _id
__id = int(_id.strip('abcdef'))
try:
camsymbols[__id].add(sym)
except:
print "new cam id", _id
camsymbols[__id] = set([sym])
for camid in camsymbols.keys():
symbols = camsymbols[camid]
camsymbols[camid] = sorted(symbols)
symbol2cam = {}
for sym in self._symbols_ext:
for _id in sym.split('-'):
__id = int(_id.strip('abcdef'))
try:
symbol2cam[sym].append(__id)
except:
symbol2cam[sym] = [__id]
symbol2cam_enc = {}
for sym,camids in symbol2cam.iteritems():
symenc = []
symbol2cam_enc[self._symbol_enc(sym)] = camids
print "camsymbols=",camsymbols
print "symbol2cam=",symbol2cam
print "symbol2cam_enc=",symbol2cam_enc
print "symbols_ext", self._symbols_ext
# define the initial A, B matrices
A, B = self._build_initial_model()
# compute the node to be split
self._compute_node_splits()
# encode datasets (bijective mapping symobols <-> integers)
self._trainset_enc = []
for observ in self._trainset.outseq():
self._trainset_enc.append(self._symbol_enc(observ))
self._validsets_enc = []
for dataset in self._validsets:
encoded = []
for observ in dataset.outseq():
encoded.append(self._symbol_enc(observ))
self._validsets_enc.append(encoded)
# print some info
self.print_info()
# create the HMM model with node splitting
self._hmm = HiddenMarkovModel(A, B, copy.copy(self._init_states), \
self._trainset_enc, validsets=self._validsets_enc, symnames=copy.copy(self._symbols_ext), symbol2cam=symbol2cam_enc)
self._transition_graph_threshold = 0.01
self._hmm.plot_transition_graph(0., savepathbase=self._savepathbase, savetag='coverage-model')
def optimize(self, nu=0.01):
# plot coverage
self._hmm.plot_transition_graph(self._transition_graph_threshold, \
savepathbase=self._savepathbase, savetag='coverage-model')
# run BW before the first node-split
while True:
done = self._hmm.baumwelch(nu)
self._hmm.plot_perf(savepathbase=self._savepathbase)
self._hmm.plot_transition_graph(self._transition_graph_threshold, \
savepathbase=self._savepathbase)
if done:
break
while True:
keys = sorted(self._pending_splits.keys())
print "\n%d node-splits left" % len(keys)
if not len(keys):
break
key = keys[0]
splits = sorted(self._pending_splits[key], key=lambda split_data: split_data[0])
#print "splits for node ", key, " (", len(splits), "):", splits
assert len(splits)
split = splits.pop()
if not len(splits):
self._pending_splits.pop(key)
else:
self._pending_splits[key] = splits
# per ogni nodo corrente
# guarda alla backward star
# guarda alla forward star
# costruisci matrice rettangolare transizioni
# identifica la riga piu' ortogonale a tutte le altre
#
# se lo trovi splitta il nodo ribalanciando le probabilita'
#(inner, bsym, bstar_syms, fstar_syms, row)
#print split
_inner = split[0]
_bsym = split[1]
_bstar_sym = split[2]
_fstar_sym = split[3]
rates = split[4]
assert numpy.isclose(rates.sum(), 1)
#print " splitting node %s : inner=%.2f bsym=%s _bstar=%s _fstar=%s, rates=%s" % (key, _inner, _bsym, _bstar_sym, _fstar_sym, rates)
state = self._state_enc(key)
bstate = self._state_enc(_bsym)
bstar = []
for i in _bstar_sym:
bstar.append(self._state_enc(i))
fstar = []
for i in _fstar_sym:
fstar.append(self._state_enc(i))
self._hmm.split_state(state, bstate, bstar, fstar, rates)
self._hmm.plot_transition_graph(self._transition_graph_threshold, \
savepathbase=self._savepathbase, savetag='node-splitting')
while True:
done = self._hmm.baumwelch(nu)
self._hmm.plot_perf(savepathbase=self._savepathbase)
self._hmm.plot_transition_graph(self._transition_graph_threshold, \
savepathbase=self._savepathbase)
if done:
break
def _symbol_enc(self, sym):
#print sym
#print self._symbols
assert sym in self._symbols_ext
return self._symbols_ext.index(sym)
def _state_enc(self, state):
assert state in self._init_states
return self._init_states.index(state)
def name(self):
return self._modname
def nr_training_transitions(self):
return max(0, self._trainset.outseq_len() -1)
def nr_validation_transitions(self, setid):
assert isinstance(setid, int)
assert setid >= 0
assert setid < self._validsets.outseq_len()
return max(0, self._validsets[setid].outseq_len() -1)
def _build_initial_model(self):
dataset = self._trainset
A = numpy.zeros((self._N, self._N))
for src in self._init_states:
nr_from = dataset.count_transitions_from(src)
#print "nr_from: %s = %d" % (src, nr_from)
assert nr_from >= 0
if nr_from == 0:
continue
src_idx = self._state_enc(src)
for dest in self._init_states:
nr_to = dataset.count_transitions_from_to(src, dest)
#print "nr_from_to: %s->%s = %d" % (src, dest, nr_from)
assert nr_to >= 0
if nr_to == 0:
continue
dest_idx = self._state_enc(dest)
A[src_idx,dest_idx] = nr_to/nr_from
for i in xrange(self._N):
# potremmo non avere transizioni uscenti da un certo
# nodo ed in quel caso le righe di A non sommano tutte all'unita'
# usa una distribuzione uniforme
if not numpy.isclose(A[i,:].sum(), 1.):
A[i,:] = numpy.ones(self._N)/self._N
B = numpy.zeros((self._M,self._N))
for state in self._init_states:
if state in self._symbols:
B[self._symbol_enc(state), self._state_enc(state)] = 1.
else:
# this must be one of the vnull states
assert '~0~' in state
B[self._symbol_enc('0'), self._state_enc(state)] = 1.
return A, B
def _compute_node_splits(self):
triplets = self._trainset.state_triplets_dict()
fstar = self._trainset.state_fstar_dict()
bstar = self._trainset.state_bstar_dict()
#print "fstar: ", fstar
#print "bstar: ", bstar
splits = {} # computed splits keyed by the id of the splitted node
# compute splits for each state
states = tuple(set(self._trainset.states()))
for state in states:
if (state not in fstar.keys()) or (state not in bstar.keys()):
# this can happen for symbols in the first and last triplet
continue
fstar_states = sorted(fstar[state])
bstar_states = sorted(bstar[state])
nr_fstar_states = len(fstar_states)
nr_bstar_states = len(bstar_states)
if nr_fstar_states < 2 or nr_bstar_states < 2:
# the 'transition rate' table must have be at least 2x2
continue
# build the table (using a matrix)
mat = numpy.zeros((nr_bstar_states, nr_fstar_states))
for i in xrange(nr_bstar_states):
for j in xrange(nr_fstar_states):
bstate = bstar_states[i]
fstate = fstar_states[j]
try:
mat[i,j] = triplets[(bstate,state,fstate)]
except:
mat[i,j] = 0.
# we have just filled the i-th row, normalize it to unit norm
mat[i,:] /= numpy.linalg.norm(mat[i,:])
# Evaluate the inner products
matt = mat.transpose()
for i in xrange(nr_bstar_states-1):
bstate = bstar_states[i]
row = mat[i,:]
# We do all the inner products at once and then select just the smallest one
# ! inners that have been evaluted already in past iterations are
# ! skipped by setting them to 1.
inners = row.dot(matt)
inners[0:i] = 1.
idx = numpy.argmin(inners)
inner = inners[idx]
if inner > 0.2:
#print "splitting: discarding split with to high inner for node %s : from=%s inner=%.2f" % (state, bstate, inner)
continue
# add a new entry to the list of splits for the node with id 'sym'
entry = (inner, bstate, bstar_states, fstar_states, row/row.sum())
try:
#print "splitting: new split for node %s : %s" % (sym, entry)
splits[state].append(entry)
except:
splits[state] = [entry]
nr_splits = 0
print "\nNode splitting:"
for state, _splits in splits.iteritems():
#print " splits for node ", key
nr_splits += len(_splits)
print " Nr. of splits for node %s: %d" % (state,len(_splits))
#for entry in item:
#print key, entry
print " Tot. nr. of splits %d" % nr_splits
self._pending_splits = splits
def print_info(self):
print "transitional model dump: fill me"
print "Symbols: "
for i in xrange(len(self._symbols)):
print " ", i, self._symbols[i]
print "States: "
for i in xrange(len(self._init_states)):
print " ", i, self._init_states[i]
