def classify(event_instances,train_events,test_events):
if args.median:
#generate feature_tte list
print "generating feature_tte list"
feature_tte = defaultdict(list)
for ev in train_events:
for tweet in event_instances[1][ev]:
for feature in tweet["features"]:
if re.search(r"timex_",feature):
try:
feature_tte[feature].append(int(tweet["label"]))
# print "after","_".join(feature.split("_")[:-1])
except:
continue
#calculate_median
print "calculating median"
feature_new = {}
for feature in feature_tte.keys():
if gen_functions.return_standard_deviation(feature_tte[feature]) < 2 and len(feature_tte[feature]) >= 2:
feature_new[feature] = str(int(numpy.median(feature_tte[feature]))) + "_days"
if args.median_out:
median_out.write(feature + "\t" + feature_new[feature] + "\n")
# print feature,feature_tte[feature],feature_new[feature]
else:
feature_new[feature] = feature
#convert features
print "converting features"
for ev in train_events:
for instance in event_instances[0][ev]:
new_features = []
for r,feature in enumerate(instance["features"]):
if re.search(r"timex_",feature):
featureo = "_".join(feature.split("_")[:-1])
if not re.search(r"timex_",feature_new[featureo]):
extra_reg = int(feature.split("_")[-1])
new_feature = str(int(feature_new[featureo].split("_")[0]) + extra_reg) + "_days"
new_features.append(new_feature)
else:
new_features.append(feature)
instance["features"] = new_features
for ev in test_events:
for instance in event_instances[0][ev]:
new_features = []
for r,feature in enumerate(instance["features"]):
if re.search(r"timex_",feature):
featureo = "_".join(feature.split("_")[:-1])
try:
if not re.search(r"timex_",feature_new[featureo]):
extra_reg = int(feature.split("_")[-1])
new_feature = str(int(feature_new[featureo].split("_")[0]) + extra_reg) + "_days"
new_features.append(new_feature)
# if re.search("ajaaz",ev):
# print feature,new_feature
except:
continue
else:
new_features.append(feature)
instance["features"] = new_features
train = sum([event_instances[0][x] for x in train_events],[])
test = []
for event in test_events:
print event
testdict = {}
eventparts = event.split("/") + [args.scaling]
eventdir = args.d
for part in eventparts:
eventdir = eventdir + part + "/"
if not os.path.exists(eventdir):
os.system("mkdir " + eventdir)
print eventdir
if args.majority:
eventout = eventdir + "tweet.txt"
else:
eventout = eventdir + str(args.window) + "_" + str(args.step) + ".txt"
testdict["out"] = eventout
testdict["instances"] = event_instances[0][event]
test.append(testdict)
if args.c == "median_baseline":
for td in test:
outfile = open(td["out"],"w")
instances = td["instances"]
for instance in instances:
#extract day_estimations
ests = []
labelcount = defaultdict(int)
for feature in instance["features"]:
if re.search(r"days",feature):
ests.append(feature)
if len(ests) > 0:
for est in ests:
labelcount[est] += 1
topest = [e for e in sorted(labelcount, key=labelcount.get, reverse=True)][0]
num = re.search(r"(-?\d+)_days",topest).groups()[0]
else:
num = "during"
# if re.search("ajaaz",td["out"]):
# if re.search("fall_11",td["out"]):
# print instance["features"],num
outfile.write(instance["label"] + " " + str(num) + "\n")
outfile.close()
else:
#set up classifier object
if args.jobs:
cl = Classifier(train,test,jobs=args.jobs,scaling=args.scaling)
else:
cl = Classifier(train,test,scaling=args.scaling)
if args.stdev:
cl.filter_stdev(args.stdev, "timex_")
if args.balance:
print "balancing..."
cl.balance_data()
print "counting..."
cl.count_feature_frequency()
if args.f:
print "pruning..."
cl.prune_features_topfrequency(args.f)
#generate sparse input
print "indexing..."
cl.index_features()
#generate classifiers
print "classifying..."
if args.c == "svm":
if args.cw:
cl.classify_svm(classweight="auto")
else:
cl.classify_svm()
elif args.c == "svr":
print "svr"
if args.cw:
cl.classify_svm(t="continuous",classweight="auto")
else:
cl.classify_svm(t="continuous")
