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random_forest.py
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random_forest.py
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import numpy
import os
import parse_tweets_sheffield as pts
from nltk import TweetTokenizer
from sklearn.ensemble import RandomForestClassifier
from evaluation_metrics import semeval_f1_taskA
def load_data(fname,pos):
tid,tweets,sentiments = [],[],[]
tknzr = TweetTokenizer(reduce_len=True)
n_not_available = 0
with open(fname) as f:
for line in f:
splits = line.split('\t')
tweet = splits[pos + 1]
sentiment = convertSentiment(splits[pos])
tid.append(splits[0])
tweet = pts.preprocess_tweet(tweet)
tweet_tok = tknzr.tokenize(tweet.decode('utf-8'))
tweets.append(tweet_tok)
sentiments.append(int(sentiment))
return tid,tweets,sentiments
def convertSentiment(sentiment):
return {
"positive": 2,
"negative": 0,
"neutral": 1,
"objective-OR-neutral": 2,
"objective": 2
}.get(sentiment,0)
def load_senvecs(sub_dirs,train_files):
data_dir = "predictions/taskB"
X_train = {}
for sub_dir in sub_dirs:
spath = os.path.join(data_dir,sub_dir)
id = 0
for file in train_files:
fname = os.path.join(spath,file)
for line in open(fname,'r'):
vec = line.split(' ')[1:]
if not id in X_train.keys():
X_train[id] = vec
else:
X_train[id].extend(vec)
id += 1
return X_train
def load_pred_prob(fname):
f = open(fname,'r')
data = f.readlines()
data = map(lambda x: x.split('\t')[0:3],data)
return numpy.asarray(data)
def load_pred_pred(fname):
f = open(fname,'r')
data = f.readlines()
data = map(lambda x: convertSentiment(x.replace('\n','')),data)
out = numpy.asarray(data)
return out.reshape(out.shape[0],1)
def getX(ids,sen_vecs):
ndim = len(sen_vecs[sen_vecs.keys()[0]])
X = numpy.zeros((len(ids), ndim),dtype='float32')
counter = 0
for id in ids:
vec = sen_vecs[id]
X[counter] = vec
counter += 1
return X
def extract_data(data_dir,files,models):
pred_prob = 'predictions_probs'
pred_pred = 'predictions_pred'
pred_model = {}
X_train = None
for file in files:
X_train_file = None
for model in models:
ddir = os.path.join(data_dir,model)
pred_dir = os.path.join(ddir,pred_pred)
prob_dir = os.path.join(ddir,pred_prob)
fname_prob = os.path.join(prob_dir,file.replace('tsv','txt'))
fname_pred = os.path.join(pred_dir,file.replace('tsv','txt'))
probs = load_pred_prob(fname_prob)
preds = load_pred_pred(fname_pred)
prob_pred = numpy.concatenate((probs,preds),axis=1)
pred_model[model] = preds
if X_train_file is None:
X_train_file = prob_pred
else:
X_train_file = numpy.concatenate((X_train_file,prob_pred),axis=1)
if X_train is None:
X_train = X_train_file
else:
X_train = numpy.concatenate((X_train,X_train_file),axis=0)
return X_train,pred_model
def extract_labels(data_dir,files,sent_pos):
y = []
for file,spos in zip(files,sent_pos):
ddir = os.path.join(data_dir,file)
_,_,sentiments = load_data(ddir,spos)
y.extend(sentiments)
y = numpy.asarray(y)
return y
def main():
semeval_dir = 'semeval'
train2013 = "task-B-train.20140221.tsv"
dev2013 = "task-B-dev.20140225.tsv"
test2013_sms = "task-B-test2013-sms.tsv"
test2013_twitter = "task-B-test2013-twitter.tsv"
test2014_twitter = "task-B-test2014-twitter.tsv"
test2014_livejournal = "task-B-test2014-livejournal.tsv"
test2014_sarcasm = "test_2014_sarcasm.tsv"
test15 = "task-B-test2015-twitter.tsv"
train16 = "task-A-train-2016.tsv"
dev2016 = "task-A-dev-2016.tsv"
devtest2016 = "task-A-devtest-2016.tsv"
test2016 = "SemEval2016-task4-test.subtask-A.tsv"
data_dir = 'ACL/ep15params'
models = ['L1A','L2A','L2B','L3A','L3B','L3C','L3E','L3F','L3G','L3H']
pred_prob = 'predictions_probs'
pred_pred = 'predictions_pred'
files_training = [train2013,dev2013,train16,dev2016,devtest2016]
sentpos_train = [2,2,1,1,1]
files_test = [test2016,test15,test2014_twitter,test2013_twitter,test2014_livejournal,test2014_sarcasm]
sentpos_test = [1,2,2,2,2,2]
X_train,_ = extract_data(data_dir,files_training,models)
y_train = extract_labels(semeval_dir,files_training,sentpos_train)
testSets = {}
goldSets = {}
predModel = {}
for file,spos in zip(files_test,sentpos_test):
X_test,y_pred_model = extract_data(data_dir,[file],models)
y_test = extract_labels(semeval_dir,[file],[spos])
testSets[file] = X_test
goldSets[file] = y_test
predModel[file] = y_pred_model
print X_train.shape
print y_train.shape
print 'Fit model'
model = RandomForestClassifier(n_estimators=300,max_depth=3,max_features=15,bootstrap=True,n_jobs=4)
model.fit(X_train,y_train)
print 'Compute score'
for file in files_test:
X_test = testSets[file]
y_test = goldSets[file]
y_pred = model.predict(X_test)
print 'Set:\t{}\tRF\tScore:\t\t{}'.format(file,semeval_f1_taskA(y_test,y_pred))
for m in models:
y_pred = predModel[file][m]
print 'Set:\t{}\t{}\tScore:\t\t{}'.format(file,m,semeval_f1_taskA(y_test,y_pred))
print '\n'
if __name__ == '__main__':
main()