def process_candidates(input_tuple, association_dict, language, in_dir, out_dir, s3, s3_bucket, freq_threshold = 1, mode = ""):
threshold = input_tuple[0]
candidate_file = input_tuple[1]
print("\tStarting " + str(threshold))
Load = Loader(in_dir, out_dir, language, s3, s3_bucket)
C = Candidates(language = language, Loader = Load, association_dict = association_dict)
if mode == "candidates":
filename = str(candidate_file + ".candidates.p")
else:
filename = str(candidate_file) + ".delta." + str(threshold) + ".p"
if filename not in Load.list_output():
candidates = C.process_file(candidate_file, threshold, freq_threshold, save = False)
Load.save_file(candidates, filename)
#Clean
del association_dict
del C
return
def process_candidates(input_tuple,
association_dict,
language,
in_dir,
out_dir,
s3,
s3_bucket,
freq_threshold=1,
mode=""):
threshold = input_tuple[0]
candidate_file = input_tuple[1]
print("\tStarting " + str(threshold))
Load = Loader(in_dir, out_dir, language, s3, s3_bucket)
C = Candidates(language=language,
Loader=Load,
association_dict=association_dict)
if mode == "candidates":
filename = str(candidate_file + ".candidates.p")
else:
filename = str(candidate_file) + ".delta." + str(threshold) + ".p"
if filename not in Load.list_output():
candidates = C.process_file(candidate_file,
threshold,
freq_threshold,
save=False)
Load.save_file(candidates, filename)
#Clean
del association_dict
del C
return
class C2xG(object):
def __init__(self, data_dir, language, s3 = False, s3_bucket = "", nickname = "", model = "", zho_split = False):
#Initialize
in_dir = os.path.join(data_dir, "IN")
if nickname == "":
out_dir = os.path.join(data_dir, "OUT")
else:
out_dir = os.path.join(data_dir, "OUT", nickname)
self.language = language
self.zho_split = zho_split
self.Load = Loader(in_dir, out_dir, language = self.language, s3 = s3, s3_bucket = s3_bucket)
self.Encode = Encoder(Loader = self.Load, zho_split = self.zho_split)
self.Association = Association(Loader = self.Load)
self.Candidates = Candidates(language = self.language, Loader = self.Load)
self.Parse = Parser(self.Load, self.Encode)
self.in_dir = in_dir
self.out_dir = out_dir
self.s3 = s3
self.s3_bucket = s3_bucket
#Try to load default or specified model
if model == "":
model = self.language + ".Grammar.v1.p"
try:
modelname = os.path.join(".", "data", "models", model)
with open(modelname, "rb") as handle:
self.model = pickle.load(handle)
except:
try:
modelname = os.path.join("..", "c2xg", "c2xg", "data", "models", model)
with open(modelname, "rb") as handle:
self.model = pickle.load(handle)
except:
print("No model exists, loading empty model.")
self.model = None
self.n_features = len(self.model)
#------------------------------------------------------------------
def parse_return(self, input, mode = "files", workers = 1):
#Compatbility with idNet
if mode == "idNet":
mode = "lines"
#Make sure grammar is loaded
if self.model == None:
print("Unable to parse: No grammar model provided.")
sys.kill()
#Accepts str of filename or list of strs of filenames
if isinstance(input, str):
input = [input]
#Text as input
if mode == "lines":
lines = self.Parse.parse_idNet(input, self.model, workers)
return lines
#Filenames as input
elif mode == "files":
features = self.Parse.parse_batch(input, self.model, workers)
return features
#-------------------------------------------------------------------------------
def parse_yield(self, input, mode = "files"):
#Make sure grammar is loaded
if self.model == None:
print("Unable to parse: No grammar model provided.")
sys.kill()
#Accepts str of filename or list of strs in batch/stream modes
if isinstance(input, str):
input = [input]
#Filenames as input
if mode == "files":
for features in self.Parse.parse_stream(input, self.model):
yield features
#Texts as input
elif mode == "lines":
for line in input:
line = self.Parse.parse_line_yield(line, self.model)
yield line
#-------------------------------------------------------------------------------
def learn(self,
nickname,
cycles = 1,
cycle_size = (1, 5, 20),
freq_threshold = 10,
beam_freq_threshold = 10,
turn_limit = 10,
workers = 1,
mdl_workers = 1,
states = None
):
self.nickname = nickname
#Check learning state and resume
self.model_state_file = self.language + "." + self.nickname + ".State.p"
try:
loader_files = self.Load.list_output()
except:
loader_files = []
if self.model_state_file in loader_files:
print("Resuming learning state.")
self.progress_dict, self.data_dict = self.Load.load_file(self.model_state_file)
if states != None:
print("Manual state change!")
for state in states:
self.progress_dict[state[0]][state[1]] = state[2]
else:
print("Initializing learning state.")
self.data_dict = self.divide_data(cycles, cycle_size)
self.progress_dict = self.set_progress()
self.Load.save_file((self.progress_dict, self.data_dict), self.model_state_file)
#Learn each cycle
for cycle in self.progress_dict.keys():
if isinstance(cycle, int):
if self.progress_dict[cycle]["State"] == "Complete":
print("\t Cycle " + str(cycle) + " already complete.")
#This cycle is not yet finished
else:
#-----------------#
#BACKGROUND STAGE
#-----------------#
if self.progress_dict[cycle]["Background_State"] != "Complete":
#Check if ngram extraction is finished
if self.progress_dict[cycle]["Background_State"] == "None":
check_files = self.Load.list_output(type = "ngrams")
pop_list = []
for i in range(len(self.progress_dict[cycle]["Background"])):
if self.progress_dict[cycle]["Background"][i] + ".ngrams.p" in check_files:
pop_list.append(i)
#Pop items separately in reverse order
if len(pop_list) > 0:
for i in sorted(pop_list, reverse = True):
self.progress_dict[cycle]["Background"].pop(i)
#If remaining background files, process them
if len(self.progress_dict[cycle]["Background"]) > 0:
print("\tNow processing remaining files: " + str(len(self.progress_dict[cycle]["Background"])))
self.Association.find_ngrams(self.progress_dict[cycle]["Background"], workers)
#Change state
self.progress_dict[cycle]["Background_State"] = "Ngrams"
self.Load.save_file((self.progress_dict, self.data_dict), self.model_state_file)
#Check if ngram merging is finished
if self.progress_dict[cycle]["Background_State"] == "Ngrams":
files = [filename + ".ngrams.p" for filename in self.data_dict[cycle]["Background"]]
print("\tNow merging ngrams for files: " + str(len(files)))
ngrams = self.Association.merge_ngrams(files, freq_threshold)
#Save data and state
self.Load.save_file(ngrams, nickname + ".Cycle-" + str(cycle) + ".Merged-Grams.p")
self.progress_dict[cycle]["Background_State"] = "Merged"
self.Load.save_file((self.progress_dict, self.data_dict), self.model_state_file)
#Check if association_dict has been made
if self.progress_dict[cycle]["Background_State"] == "Merged":
ngrams = self.Load.load_file(nickname + ".Cycle-" + str(cycle) + ".Merged-Grams.p")
association_dict = self.Association.calculate_association(ngrams = ngrams, save = False)
del ngrams
self.Load.save_file(association_dict, nickname + ".Cycle-" + str(cycle) + ".Association_Dict.p")
self.progress_dict[cycle]["Background_State"] = "Complete"
self.Load.save_file((self.progress_dict, self.data_dict), self.model_state_file)
self.association_dict = association_dict
else:
print("\tLoading association_dict.")
self.association_dict = self.Load.load_file(nickname + ".Cycle-" + str(cycle) + ".Association_Dict.p")
#-----------------#
#CANDIDATE STAGE
#-----------------#
if self.progress_dict[cycle]["Candidate_State"] != "Complete":
print("Initializing Candidates module")
C = Candidates(self.language, self.Load, workers, self.association_dict)
#Find beam search threshold
if self.progress_dict["BeamSearch"] == "None" or self.progress_dict["BeamSearch"] == {}:
print("Finding Beam Search settings.")
delta_threshold = delta_grid_search(candidate_file = self.data_dict["BeamCandidates"],
test_file = self.data_dict["BeamTest"],
workers = workers,
mdl_workers = mdl_workers,
association_dict = self.association_dict,
freq_threshold = beam_freq_threshold,
language = self.language,
in_dir = self.in_dir,
out_dir = self.out_dir,
s3 = self.s3,
s3_bucket = self.s3_bucket
)
self.progress_dict["BeamSearch"] = delta_threshold
self.progress_dict[cycle]["Candidate_State"] = "Threshold"
self.Load.save_file((self.progress_dict, self.data_dict), self.model_state_file)
#If saved, load beam search threshold
else:
print("Loading Beam Search settings.")
delta_threshold = self.progress_dict["BeamSearch"]
self.progress_dict[cycle]["Candidate_State"] = "Threshold"
#Check which files have been completed
if self.progress_dict[cycle]["Candidate_State"] == "Threshold":
check_files = self.Load.list_output(type = "candidates")
pop_list = []
for i in range(len(self.progress_dict[cycle]["Candidate"])):
if self.progress_dict[cycle]["Candidate"][i] + ".candidates.p" in check_files:
pop_list.append(i)
#Pop items separately in reverse order
if len(pop_list) > 0:
for i in sorted(pop_list, reverse = True):
self.progress_dict[cycle]["Candidate"].pop(i)
#If remaining candidate files, process them
if len(self.progress_dict[cycle]["Candidate"]) > 0:
print("\n\tNow processing remaining files: " + str(len(self.progress_dict[cycle]["Candidate"])))
#Multi-process#
if workers > len(self.progress_dict[cycle]["Candidate"]):
candidate_workers = len(self.progress_dict[cycle]["Candidate"])
else:
candidate_workers = workers
pool_instance = mp.Pool(processes = candidate_workers, maxtasksperchild = 1)
distribute_list = [(delta_threshold, x) for x in self.progress_dict[cycle]["Candidate"]]
pool_instance.map(partial(process_candidates,
association_dict = self.association_dict.copy(),
language = self.language,
in_dir = self.in_dir,
out_dir = self.out_dir,
s3 = self.s3,
s3_bucket = self.s3_bucket,
mode = "candidates"
), distribute_list, chunksize = 1)
pool_instance.close()
pool_instance.join()
self.progress_dict[cycle]["Candidate_State"] = "Merge"
self.Load.save_file((self.progress_dict, self.data_dict), self.model_state_file)
#Merge and Save candidates
if self.progress_dict[cycle]["Candidate_State"] == "Merge":
output_files = [filename + ".candidates.p" for filename in self.data_dict[cycle]["Candidate"]]
candidates = self.Candidates.merge_candidates(output_files, freq_threshold)
self.Load.save_file(candidates, nickname + ".Cycle-" + str(cycle) + ".Candidates.p")
self.progress_dict[cycle]["Candidate_State"] = "Dict"
self.Load.save_file((self.progress_dict, self.data_dict), self.model_state_file)
#Make association vectors
if self.progress_dict[cycle]["Candidate_State"] == "Dict":
candidates = self.Load.load_file(nickname + ".Cycle-" + str(cycle) + ".Candidates.p")
candidate_dict = self.Candidates.get_association(candidates, self.association_dict)
self.Load.save_file(candidate_dict, nickname + ".Cycle-" + str(cycle) + ".Candidate_Dict.p")
self.progress_dict[cycle]["Candidate_State"] == "Complete"
self.Load.save_file((self.progress_dict, self.data_dict), self.model_state_file)
else:
print("\tLoading candidate_dict.")
candidate_dict = self.Load.load_file(nickname + ".Cycle-" + str(cycle) + ".Candidate_Dict.p")
candidates = self.Load.load_file(nickname + ".Cycle-" + str(cycle) + ".Candidates.p")
del self.association_dict
#-----------------#
#MDL STAGE
#-----------------#
if self.progress_dict[cycle]["MDL_State"] != "Complete":
#Prep test data for MDL
if self.progress_dict[cycle]["MDL_State"] == "None":
MDL = MDL_Learner(self.Load, self.Encode, self.Parse, freq_threshold = 1, vectors = candidate_dict, candidates = candidates)
MDL.get_mdl_data(self.progress_dict[cycle]["Test"], workers = mdl_workers)
self.Load.save_file(MDL, nickname + ".Cycle-" + str(cycle) + ".MDL.p")
self.progress_dict[cycle]["MDL_State"] = "EM"
self.Load.save_file((self.progress_dict, self.data_dict), self.model_state_file)
#Run EM-based Tabu Search
if self.progress_dict[cycle]["MDL_State"] == "EM":
try:
MDL.search_em(turn_limit, mdl_workers)
except:
MDL = self.Load.load_file(nickname + ".Cycle-" + str(cycle) + ".MDL.p")
MDL.search_em(turn_limit, mdl_workers)
self.Load.save_file(MDL, nickname + ".Cycle-" + str(cycle) + ".MDL.p")
self.progress_dict[cycle]["MDL_State"] = "Direct"
self.Load.save_file((self.progress_dict, self.data_dict), self.model_state_file)
#Run direct Tabu Search
if self.progress_dict[cycle]["MDL_State"] == "Direct":
try:
MDL.search_direct(turn_limit*3, mdl_workers)
except:
MDL = self.Load.load_file(nickname + ".Cycle-" + str(cycle) + ".MDL.p")
MDL.search_direct(turn_limit*3, mdl_workers)
#Get grammar to save
grammar_dict = defaultdict(dict)
for i in range(len(MDL.candidates)):
grammar_dict[i]["Constructions"] = MDL.candidates[i]
grammar_dict[i]["Matches"] = MDL.matches[i]
#Save grammar
self.Load.save_file(grammar_dict, nickname + ".Cycle-" + str(cycle) + ".Final_Grammar.p")
self.progress_dict[cycle]["MDL_State"] = "Complete"
self.progress_dict[cycle]["State"] = "Complete"
self.Load.save_file((self.progress_dict, self.data_dict), self.model_state_file)
del MDL
#-----------------#
#MERGING STAGE
#-----------------#
if self.progress_dict[cycle]["State"] == "Complete":
print("Starting to merge fold grammars.")
grammar_files = [nickname + ".Cycle-" + str(i) + ".Final_Grammar.p" for i in range(cycles)]
final_grammar = self.merge_grammars(grammar_files)
self.Load.save_file(final_grammar, self.language + ".Grammar.p")
#-------------------------------------------------------------------------------
def merge_grammars(self, grammar_files):
all_grammars = {}
#Load all grammar files
for file in grammar_files:
current_dict = self.Load.load_file(file)
#Iterate over constructions in current fold grammar
for key in current_dict.keys():
current_construction = current_dict[key]["Constructions"]
current_construction = current_construction.tolist()
current_matches = current_dict[key]["Matches"]
#Reformat
new_construction = []
for unit in current_construction:
new_type = unit[0]
new_index = unit[1]
if new_type != 0:
new_construction.append(tuple((new_type, new_index)))
#Make hashable
new_construction = tuple(new_construction)
#Add to dictionary
if new_construction not in all_grammars:
all_grammars[new_construction] = {}
all_grammars[new_construction]["Matches"] = current_matches
all_grammars[new_construction]["Selected"] = 1
else:
all_grammars[new_construction]["Matches"] += current_matches
all_grammars[new_construction]["Selected"] += 1
#Done loading grammars
print("Final grammar for " + self.language + " contains " + str(len(list(all_grammars.keys()))))
final_grammar = list(all_grammars.keys())
final_grammar = self.Parse.format_grammar(final_grammar)
return final_grammar
#-------------------------------------------------------------------------------
def divide_data(self, cycles, cycle_size):
input_files = self.Load.list_input()
#Get number of files to use for each purpose
num_test_files = cycle_size[0]
num_candidate_files = cycle_size[1]
num_background_files = cycle_size[2]
num_cycle_files = cycle_size[0] + cycle_size[1] + cycle_size[2]
#Get Beam Search tuning files
candidate_i = random.randint(0, len(input_files))
candidate_file = input_files.pop(candidate_i)
test_i = random.randint(0, len(input_files))
test_file = input_files.pop(test_i)
#Get and divide input data
data_dict = defaultdict(dict)
data_dict["BeamCandidates"] = candidate_file
data_dict["BeamTest"] = test_file
#Get unique data for each cycle
for cycle in range(cycles):
#Randomize remaining files
random.shuffle(input_files)
cycle_files = []
#Gather as many files as required
for segment in range(num_cycle_files):
current_file = input_files.pop()
cycle_files.append(current_file)
#Assign files as final MDL test data
random.shuffle(cycle_files)
test_files = []
for file in range(num_test_files):
current_file = cycle_files.pop()
test_files.append(current_file)
data_dict[cycle]["Test"] = test_files
#Assign files as candidate estimation data
random.shuffle(cycle_files)
candidate_files = []
for file in range(num_candidate_files):
current_file = cycle_files.pop()
candidate_files.append(current_file)
data_dict[cycle]["Candidate"] = candidate_files
#Assign files as candidate estimation data
random.shuffle(cycle_files)
background_files = []
for file in range(num_background_files):
current_file = cycle_files.pop()
background_files.append(current_file)
data_dict[cycle]["Background"] = background_files
return data_dict
#-------------------------------------------------------------------------------
def set_progress(self):
progress_dict = defaultdict(dict)
progress_dict["BeamSearch"] = "None"
for cycle in self.data_dict.keys():
if isinstance(cycle, int):
progress_dict[cycle]["State"] = "Incomplete"
progress_dict[cycle]["Background"] = self.data_dict[cycle]["Background"].copy()
progress_dict[cycle]["Background_State"] = "None"
progress_dict[cycle]["Candidate"] = self.data_dict[cycle]["Candidate"].copy()
progress_dict[cycle]["Candidate_State"] = "None"
progress_dict[cycle]["Test"] = self.data_dict[cycle]["Test"].copy()
progress_dict[cycle]["MDL_State"] = "None"
return progress_dict
class C2xG(object):
def __init__(self,
data_dir,
language,
s3=False,
s3_bucket="",
nickname="",
model="",
zho_split=False):
#Initialize
in_dir = os.path.join(data_dir, "IN")
if nickname == "":
out_dir = os.path.join(data_dir, "OUT")
else:
out_dir = os.path.join(data_dir, "OUT", nickname)
self.language = language
self.zho_split = zho_split
self.Load = Loader(in_dir,
out_dir,
language=self.language,
s3=s3,
s3_bucket=s3_bucket)
self.Encode = Encoder(Loader=self.Load, zho_split=self.zho_split)
self.Association = Association(Loader=self.Load)
self.Candidates = Candidates(language=self.language, Loader=self.Load)
self.Parse = Parser(self.Load, self.Encode)
self.in_dir = in_dir
self.out_dir = out_dir
self.s3 = s3
self.s3_bucket = s3_bucket
#Try to load default or specified model
if model == "":
model = self.language + ".Grammar.v1.p"
try:
modelname = os.path.join(".", "data", "models", model)
with open(modelname, "rb") as handle:
self.model = pickle.load(handle)
except:
try:
modelname = os.path.join("..", "c2xg", "c2xg", "data",
"models", model)
with open(modelname, "rb") as handle:
self.model = pickle.load(handle)
except:
print("No model exists, loading empty model.")
self.model = None
self.n_features = len(self.model)
#------------------------------------------------------------------
def parse_return(self, input, mode="files", workers=1):
#Compatbility with idNet
if mode == "idNet":
mode = "lines"
#Make sure grammar is loaded
if self.model == None:
print("Unable to parse: No grammar model provided.")
sys.kill()
#Accepts str of filename or list of strs of filenames
if isinstance(input, str):
input = [input]
#Text as input
if mode == "lines":
lines = self.Parse.parse_idNet(input, self.model, workers)
return lines
#Filenames as input
elif mode == "files":
features = self.Parse.parse_batch(input, self.model, workers)
return features
#-------------------------------------------------------------------------------
def parse_yield(self, input, mode="files"):
#Make sure grammar is loaded
if self.model == None:
print("Unable to parse: No grammar model provided.")
sys.kill()
#Accepts str of filename or list of strs in batch/stream modes
if isinstance(input, str):
input = [input]
#Filenames as input
if mode == "files":
for features in self.Parse.parse_stream(input, self.model):
yield features
#Texts as input
elif mode == "lines":
for line in input:
line = self.Parse.parse_line_yield(line, self.model)
yield line
#-------------------------------------------------------------------------------
def learn(self,
nickname,
cycles=1,
cycle_size=(1, 5, 20),
freq_threshold=10,
beam_freq_threshold=10,
turn_limit=10,
workers=1,
mdl_workers=1,
states=None):
self.nickname = nickname
#Check learning state and resume
self.model_state_file = self.language + "." + self.nickname + ".State.p"
try:
loader_files = self.Load.list_output()
except:
loader_files = []
if self.model_state_file in loader_files:
print("Resuming learning state.")
self.progress_dict, self.data_dict = self.Load.load_file(
self.model_state_file)
if states != None:
print("Manual state change!")
for state in states:
self.progress_dict[state[0]][state[1]] = state[2]
else:
print("Initializing learning state.")
self.data_dict = self.divide_data(cycles, cycle_size)
self.progress_dict = self.set_progress()
self.Load.save_file((self.progress_dict, self.data_dict),
self.model_state_file)
#Learn each cycle
for cycle in self.progress_dict.keys():
if isinstance(cycle, int):
if self.progress_dict[cycle]["State"] == "Complete":
print("\t Cycle " + str(cycle) + " already complete.")
#This cycle is not yet finished
else:
#-----------------#
#BACKGROUND STAGE
#-----------------#
if self.progress_dict[cycle][
"Background_State"] != "Complete":
#Check if ngram extraction is finished
if self.progress_dict[cycle][
"Background_State"] == "None":
check_files = self.Load.list_output(type="ngrams")
pop_list = []
for i in range(
len(self.progress_dict[cycle]
["Background"])):
if self.progress_dict[cycle]["Background"][
i] + ".ngrams.p" in check_files:
pop_list.append(i)
#Pop items separately in reverse order
if len(pop_list) > 0:
for i in sorted(pop_list, reverse=True):
self.progress_dict[cycle][
"Background"].pop(i)
#If remaining background files, process them
if len(self.progress_dict[cycle]
["Background"]) > 0:
print("\tNow processing remaining files: " +
str(
len(self.progress_dict[cycle]
["Background"])))
self.Association.find_ngrams(
self.progress_dict[cycle]["Background"],
workers)
#Change state
self.progress_dict[cycle][
"Background_State"] = "Ngrams"
self.Load.save_file(
(self.progress_dict, self.data_dict),
self.model_state_file)
#Check if ngram merging is finished
if self.progress_dict[cycle][
"Background_State"] == "Ngrams":
files = [
filename + ".ngrams.p" for filename in
self.data_dict[cycle]["Background"]
]
print("\tNow merging ngrams for files: " +
str(len(files)))
ngrams = self.Association.merge_ngrams(
files, freq_threshold)
#Save data and state
self.Load.save_file(
ngrams, nickname + ".Cycle-" + str(cycle) +
".Merged-Grams.p")
self.progress_dict[cycle][
"Background_State"] = "Merged"
self.Load.save_file(
(self.progress_dict, self.data_dict),
self.model_state_file)
#Check if association_dict has been made
if self.progress_dict[cycle][
"Background_State"] == "Merged":
ngrams = self.Load.load_file(nickname + ".Cycle-" +
str(cycle) +
".Merged-Grams.p")
association_dict = self.Association.calculate_association(
ngrams=ngrams, save=False)
del ngrams
self.Load.save_file(
association_dict, nickname + ".Cycle-" +
str(cycle) + ".Association_Dict.p")
self.progress_dict[cycle][
"Background_State"] = "Complete"
self.Load.save_file(
(self.progress_dict, self.data_dict),
self.model_state_file)
self.association_dict = association_dict
else:
print("\tLoading association_dict.")
self.association_dict = self.Load.load_file(
nickname + ".Cycle-" + str(cycle) +
".Association_Dict.p")
#-----------------#
#CANDIDATE STAGE
#-----------------#
if self.progress_dict[cycle][
"Candidate_State"] != "Complete":
print("Initializing Candidates module")
C = Candidates(self.language, self.Load, workers,
self.association_dict)
#Find beam search threshold
if self.progress_dict[
"BeamSearch"] == "None" or self.progress_dict[
"BeamSearch"] == {}:
print("Finding Beam Search settings.")
delta_threshold = delta_grid_search(
candidate_file=self.
data_dict["BeamCandidates"],
test_file=self.data_dict["BeamTest"],
workers=workers,
mdl_workers=mdl_workers,
association_dict=self.association_dict,
freq_threshold=beam_freq_threshold,
language=self.language,
in_dir=self.in_dir,
out_dir=self.out_dir,
s3=self.s3,
s3_bucket=self.s3_bucket)
self.progress_dict["BeamSearch"] = delta_threshold
self.progress_dict[cycle][
"Candidate_State"] = "Threshold"
self.Load.save_file(
(self.progress_dict, self.data_dict),
self.model_state_file)
#If saved, load beam search threshold
else:
print("Loading Beam Search settings.")
delta_threshold = self.progress_dict["BeamSearch"]
self.progress_dict[cycle][
"Candidate_State"] = "Threshold"
#Check which files have been completed
if self.progress_dict[cycle][
"Candidate_State"] == "Threshold":
check_files = self.Load.list_output(
type="candidates")
pop_list = []
for i in range(
len(self.progress_dict[cycle]
["Candidate"])):
if self.progress_dict[cycle]["Candidate"][
i] + ".candidates.p" in check_files:
pop_list.append(i)
#Pop items separately in reverse order
if len(pop_list) > 0:
for i in sorted(pop_list, reverse=True):
self.progress_dict[cycle]["Candidate"].pop(
i)
#If remaining candidate files, process them
if len(self.progress_dict[cycle]["Candidate"]) > 0:
print("\n\tNow processing remaining files: " +
str(
len(self.progress_dict[cycle]
["Candidate"])))
#Multi-process#
if workers > len(self.progress_dict[cycle]
["Candidate"]):
candidate_workers = len(
self.progress_dict[cycle]["Candidate"])
else:
candidate_workers = workers
pool_instance = mp.Pool(
processes=candidate_workers,
maxtasksperchild=1)
distribute_list = [
(delta_threshold, x) for x in
self.progress_dict[cycle]["Candidate"]
]
pool_instance.map(
partial(process_candidates,
association_dict=self.
association_dict.copy(),
language=self.language,
in_dir=self.in_dir,
out_dir=self.out_dir,
s3=self.s3,
s3_bucket=self.s3_bucket,
mode="candidates"),
distribute_list,
chunksize=1)
pool_instance.close()
pool_instance.join()
self.progress_dict[cycle][
"Candidate_State"] = "Merge"
self.Load.save_file(
(self.progress_dict, self.data_dict),
self.model_state_file)
#Merge and Save candidates
if self.progress_dict[cycle][
"Candidate_State"] == "Merge":
output_files = [
filename + ".candidates.p" for filename in
self.data_dict[cycle]["Candidate"]
]
candidates = self.Candidates.merge_candidates(
output_files, freq_threshold)
self.Load.save_file(
candidates, nickname + ".Cycle-" + str(cycle) +
".Candidates.p")
self.progress_dict[cycle][
"Candidate_State"] = "Dict"
self.Load.save_file(
(self.progress_dict, self.data_dict),
self.model_state_file)
#Make association vectors
if self.progress_dict[cycle][
"Candidate_State"] == "Dict":
candidates = self.Load.load_file(nickname +
".Cycle-" +
str(cycle) +
".Candidates.p")
candidate_dict = self.Candidates.get_association(
candidates, self.association_dict)
self.Load.save_file(
candidate_dict, nickname + ".Cycle-" +
str(cycle) + ".Candidate_Dict.p")
self.progress_dict[cycle][
"Candidate_State"] == "Complete"
self.Load.save_file(
(self.progress_dict, self.data_dict),
self.model_state_file)
else:
print("\tLoading candidate_dict.")
candidate_dict = self.Load.load_file(
nickname + ".Cycle-" + str(cycle) +
".Candidate_Dict.p")
candidates = self.Load.load_file(nickname + ".Cycle-" +
str(cycle) +
".Candidates.p")
del self.association_dict
#-----------------#
#MDL STAGE
#-----------------#
if self.progress_dict[cycle]["MDL_State"] != "Complete":
#Prep test data for MDL
if self.progress_dict[cycle]["MDL_State"] == "None":
MDL = MDL_Learner(self.Load,
self.Encode,
self.Parse,
freq_threshold=1,
vectors=candidate_dict,
candidates=candidates)
MDL.get_mdl_data(self.progress_dict[cycle]["Test"],
workers=mdl_workers)
self.Load.save_file(
MDL,
nickname + ".Cycle-" + str(cycle) + ".MDL.p")
self.progress_dict[cycle]["MDL_State"] = "EM"
self.Load.save_file(
(self.progress_dict, self.data_dict),
self.model_state_file)
#Run EM-based Tabu Search
if self.progress_dict[cycle]["MDL_State"] == "EM":
try:
MDL.search_em(turn_limit, mdl_workers)
except:
MDL = self.Load.load_file(nickname +
".Cycle-" +
str(cycle) +
".MDL.p")
MDL.search_em(turn_limit, mdl_workers)
self.Load.save_file(
MDL,
nickname + ".Cycle-" + str(cycle) + ".MDL.p")
self.progress_dict[cycle]["MDL_State"] = "Direct"
self.Load.save_file(
(self.progress_dict, self.data_dict),
self.model_state_file)
#Run direct Tabu Search
if self.progress_dict[cycle]["MDL_State"] == "Direct":
try:
MDL.search_direct(turn_limit * 3, mdl_workers)
except:
MDL = self.Load.load_file(nickname +
".Cycle-" +
str(cycle) +
".MDL.p")
MDL.search_direct(turn_limit * 3, mdl_workers)
#Get grammar to save
grammar_dict = defaultdict(dict)
for i in range(len(MDL.candidates)):
grammar_dict[i][
"Constructions"] = MDL.candidates[i]
grammar_dict[i]["Matches"] = MDL.matches[i]
#Save grammar
self.Load.save_file(
grammar_dict, nickname + ".Cycle-" +
str(cycle) + ".Final_Grammar.p")
self.progress_dict[cycle]["MDL_State"] = "Complete"
self.progress_dict[cycle]["State"] = "Complete"
self.Load.save_file(
(self.progress_dict, self.data_dict),
self.model_state_file)
del MDL
#-----------------#
#MERGING STAGE
#-----------------#
if self.progress_dict[cycle]["State"] == "Complete":
print("Starting to merge fold grammars.")
grammar_files = [
nickname + ".Cycle-" + str(i) + ".Final_Grammar.p"
for i in range(cycles)
]
final_grammar = self.merge_grammars(grammar_files)
self.Load.save_file(final_grammar, self.language + ".Grammar.p")
#-------------------------------------------------------------------------------
def merge_grammars(self, grammar_files):
all_grammars = {}
#Load all grammar files
for file in grammar_files:
current_dict = self.Load.load_file(file)
#Iterate over constructions in current fold grammar
for key in current_dict.keys():
current_construction = current_dict[key]["Constructions"]
current_construction = current_construction.tolist()
current_matches = current_dict[key]["Matches"]
#Reformat
new_construction = []
for unit in current_construction:
new_type = unit[0]
new_index = unit[1]
if new_type != 0:
new_construction.append(tuple((new_type, new_index)))
#Make hashable
new_construction = tuple(new_construction)
#Add to dictionary
if new_construction not in all_grammars:
all_grammars[new_construction] = {}
all_grammars[new_construction]["Matches"] = current_matches
all_grammars[new_construction]["Selected"] = 1
else:
all_grammars[new_construction][
"Matches"] += current_matches
all_grammars[new_construction]["Selected"] += 1
#Done loading grammars
print("Final grammar for " + self.language + " contains " +
str(len(list(all_grammars.keys()))))
final_grammar = list(all_grammars.keys())
final_grammar = self.Parse.format_grammar(final_grammar)
return final_grammar
#-------------------------------------------------------------------------------
def divide_data(self, cycles, cycle_size):
input_files = self.Load.list_input()
#Get number of files to use for each purpose
num_test_files = cycle_size[0]
num_candidate_files = cycle_size[1]
num_background_files = cycle_size[2]
num_cycle_files = cycle_size[0] + cycle_size[1] + cycle_size[2]
#Get Beam Search tuning files
candidate_i = random.randint(0, len(input_files))
candidate_file = input_files.pop(candidate_i)
test_i = random.randint(0, len(input_files))
test_file = input_files.pop(test_i)
#Get and divide input data
data_dict = defaultdict(dict)
data_dict["BeamCandidates"] = candidate_file
data_dict["BeamTest"] = test_file
#Get unique data for each cycle
for cycle in range(cycles):
#Randomize remaining files
random.shuffle(input_files)
cycle_files = []
#Gather as many files as required
for segment in range(num_cycle_files):
current_file = input_files.pop()
cycle_files.append(current_file)
#Assign files as final MDL test data
random.shuffle(cycle_files)
test_files = []
for file in range(num_test_files):
current_file = cycle_files.pop()
test_files.append(current_file)
data_dict[cycle]["Test"] = test_files
#Assign files as candidate estimation data
random.shuffle(cycle_files)
candidate_files = []
for file in range(num_candidate_files):
current_file = cycle_files.pop()
candidate_files.append(current_file)
data_dict[cycle]["Candidate"] = candidate_files
#Assign files as candidate estimation data
random.shuffle(cycle_files)
background_files = []
for file in range(num_background_files):
current_file = cycle_files.pop()
background_files.append(current_file)
data_dict[cycle]["Background"] = background_files
return data_dict
#-------------------------------------------------------------------------------
def set_progress(self):
progress_dict = defaultdict(dict)
progress_dict["BeamSearch"] = "None"
for cycle in self.data_dict.keys():
if isinstance(cycle, int):
progress_dict[cycle]["State"] = "Incomplete"
progress_dict[cycle]["Background"] = self.data_dict[cycle][
"Background"].copy()
progress_dict[cycle]["Background_State"] = "None"
progress_dict[cycle]["Candidate"] = self.data_dict[cycle][
"Candidate"].copy()
progress_dict[cycle]["Candidate_State"] = "None"
progress_dict[cycle]["Test"] = self.data_dict[cycle][
"Test"].copy()
progress_dict[cycle]["MDL_State"] = "None"
return progress_dict
) Vectorizer.fit([x for x in Texts]) features = Vectorizer.get_feature_names() RawVectorizer = CountVectorizer(input = "content", encoding = "utf-8", decode_error = "replace", strip_accents = None, lowercase = False, stop_words = None, ngram_range = (1, 1), analyzer = preprocessor, min_df = 1, max_features = 100000, vocabulary = features ) #-- Get correct name if pos == True: pos_name = "POS" else: pos_name = "LEX" #------------------ filename = "Vectorizer.TFIDF." + language + "." + pos_name + ".100k.Legislative.p" Load.save_file(Vectorizer, filename) filename = "Vectorizer.RAW." + language + "." + pos_name + ".100k.Legislative.p" Load.save_file(RawVectorizer, filename)
import os
import random
if __name__ == "__main__":
from modules.Encoder import Encoder
from modules.Loader import Loader
from modules.Parser import Parser
from modules.MDL_Learner import MDL_Learner
#Load association measure module
in_dir = os.path.join("..", "..", "..", "..", "Test", "In")
out_dir = os.path.join("..", "..", "..", "..", "Test", "Out")
Load = Loader(in_dir, out_dir, language="eng")
Encode = Encoder(Loader=Load)
Parse = Parser(Load, Encode)
#Initiate MDL
MDL = MDL_Learner(Load, Encode, Parse, freq_threshold=25)
#Get MDL annotated data
test_files = ["eng.test.txt"]
MDL.get_mdl_data(test_files, workers=10)
#Delete after testing
Load.save_file(MDL, "mdl.p")
#MDL = Load.load_file("mdl.p")
MDL.search(turn_limit=10, workers=16)
#Evaluate a subset of the total candidates
#total_mdl = MDL.evaluate_subset(subset)