my_chronoentities = []
my_chrono_ID_counter = 1
## parse out the doctime
## parse out reference tokens
text, tokens, spans, tags = utils.gettext, tokens, spans, tags = utils.getWhitespaceTokens(
infiles[f] + args.x)
nlp = spacy.load('en_core_web_sm')
doc = nlp(text)
chunks = []
for chunk in doc.noun_chunks:
chunks.append(chunk.text)
#my_refToks = referenceToken.convertToRefTokens(tok_list=tokens, span=spans, pos=tags, remove_stopwords="./Chrono/dosestopwords.txt")
my_refToks = referenceToken.convertToRefTokens(tok_list=tokens,
span=spans,
pos=tags)
if (debug):
print("REFERENCE TOKENS:\n")
for tok in my_refToks:
print(tok)
## mark all ref tokens if they are numeric or temporal
chroList = utils.markTemporal(my_refToks)
chroList = utils.markDose(my_refToks)
dosePhrases = utils.getDosePhrases(chroList)
for dose in dosePhrases:
for chunk in chunks:
if dose.getText() in chunk and ("bw" in chunk.lower()
for f in range(0, len(infiles)):
print("Parsing " + infiles[f] + " ...")
## Init the ChronoEntity list
my_chronoentities = []
my_chrono_ID_counter = 1
## parse out the doctime
doctime = utils.getDocTime(infiles[f] + ".dct")
if (debug): print(doctime)
## parse out reference tokens
text, tokens, spans, tags, sents = utils.getWhitespaceTokens(
infiles[f] + args.x)
#my_refToks = referenceToken.convertToRefTokens(tok_list=tokens, span=spans, remove_stopwords="./Chrono/stopwords_short2.txt")
my_refToks = referenceToken.convertToRefTokens(tok_list=tokens,
span=spans,
pos=tags,
sent_boundaries=sents)
## mark all ref tokens if they are numeric or temporal
chroList = utils.markTemporal(my_refToks)
if (debug):
print("REFERENCE TOKENS:\n")
for tok in chroList:
print(tok)
tempPhrases = utils.getTemporalPhrases(chroList, doctime)
# for c in tempPhrases:
# print(c)
def createMLTrainingMatrix(infiles,
gold_folder,
ext="",
save=False,
output="aquaint_train",
window=3):
### Algorithm
## For each input file:
## 1) parse text to refTokens list
## 2) parse SUTime to identify temporal tokens
## 3) Import gold standard file
## 4) Get list of periods and intervals with start and end coords
## 5) For each period/interval:
## - Create feature vector
## - Save features to global list
## 6) Write gold features to a csv file for import by other scripts to train ML methods.
## define list of dictionary feature vectors
obs_list = [] ### This is the list of features for each observation
category = [
] ### This is the category of the observation. 1 for period, 0 otherwise. Note that the unknowns are being grouped in with the calendar-interval category. probably need to parse that out later or change up the algorithm to not be a binary classifier.
features = {
'feat_numeric': 0,
'feat_temp_context': 0,
'feat_temp_self': 0
} ### This is the full list of features. I will use the key values to get the individual feature vectors.
if (save):
outfile = open("./gold-standard-parsing.txt", 'w')
## Loop through each file and parse
for f in range(0, len(infiles)):
print("ML Parsing " + infiles[f] + " ...")
## parse out the doctime
doctime = utils.getDocTime(infiles[f] + ".dct")
if (debug): print(doctime)
## parse out reference tokens
text, tokens, spans, tags = utils.getWhitespaceTokens(infiles[f] + ext)
my_refToks = referenceToken.convertToRefTokens(tok_list=tokens,
span=spans,
pos=tags)
## mark all ref tokens if they are numeric or temporal
chroList = utils.markTemporal(my_refToks)
## import gold standard data
gold_file = os.path.join(gold_folder,
os.path.split(infiles[f])[1],
"period-interval.gold.csv")
gold_list = []
if not os.path.exists(gold_file):
print(gold_file + " DOES NOT EXISTS")
break
if os.path.exists(gold_file):
if (save):
outfile.write("\n$$$$$$$$$$$\nProcessing: " + gold_file)
with open(gold_file) as file:
reader = csv.DictReader(file)
for row in reader:
gold_list.append({
'type': row['type'],
'start': row['start'],
'end': row['end'],
'value': row['value']
})
if (save):
outfile.write("\n" + str(row))
## loop through each reftoken term and see if it overlaps with a gold token
for r in range(0, len(chroList)):
reftok = chroList[r]
ref_s, ref_e = reftok.getSpan()
# loop through each gold instance and find the one that overlaps with the current reftok.
for g in gold_list:
# print(str(g))
if utils.overlap(
[ref_s, ref_e],
[int(g['start']), int(g['end'])]):
this_obs = {}
# if the gold token overlaps with the current reftok we need to extract the features from the reftok and add it to the list
if (save):
outfile.write("\nPrevious Token: " +
str(chroList[max(r - 1, 0)]))
outfile.write("\nTarget Token: " + str(reftok))
#print("Length: "+ str(len(my_refToks)) + "Last: "+str(min(r+1, len(my_refToks))))
outfile.write(
"\nNext Token: " +
str(chroList[min(r + 1,
len(my_refToks) - 1)]) + "\n")
### Identify Temporal features
this_obs = extract_temp_features(
chroList, r, 3, this_obs)
### Extract all words within a N-word window
this_obs, observations = extract_bow_features(
chroList, r, window, features, this_obs)
### Determine if there is a numeric before or after the target word.
this_obs = extract_numeric_feature(
chroList, r, this_obs)
### Stem and extract the actual word
this_obs, observations = extract_stem_feature(
chroList[r], features, this_obs)
### Get the correct type
if (g['type'] == 'Period'):
category.append(1)
else:
category.append(0)
obs_list.append(this_obs)
## Ok, I have all the features. Now I just need to put them all together in a matrix.
print("features length: " + str(len(features.keys())))
print("obs_list length: " + str(len(obs_list)))
print("category length: " + str(len(category)))
## Now I need to loop through the obs_list to create a list of features that contain all feature elements.
full_obs_list = [] # a list of tuples
for i in range(0, len(obs_list)):
feats = deepcopy(features)
feats.update(obs_list[i])
#full_obs_list.append((feats, category[i]))
full_obs_list.append(feats)
## Now print the list of tuples to a file, then return the list.
keys = full_obs_list[0].keys()
with open(output + '_data.csv', 'w') as output_file:
dict_writer = csv.DictWriter(output_file, keys)
dict_writer.writeheader()
dict_writer.writerows(full_obs_list)
with open(output + '_class.csv', 'w') as output_file:
for c in category:
output_file.write("%s\n" % c)
### Now return the feature list and the categories
return (full_obs_list, category)