def addMultiWords(self, listOfMultiWords):
"""
Updates the internal regex with a
list of multiwords
"""
buff = StringIO.StringIO()
for multiWord in listOfMultiWords:
if multiWord not in self.multiWordsRegex and multiWord not in buff.getvalue(
):
buff.write(self.regexTemplate.format(multiWord))
#add a normalized (no accents) version
if multiWord != Preprocessor.normalize(multiWord):
buff.write(
self.regexTemplate.format(
Preprocessor.normalize(multiWord)))
if len(self.multiWordsRegex) == 0:
self.multiWordsRegex = buff.getvalue().strip('|')
else:
self.multiWordsRegex += "|" + buff.getvalue().strip('|')
buff.close()
def Predict():
"""
Predicts the data from DATA/test.csv
:return:
"""
#get user input from file test.csv
csvhand = Handler.CSVHandler("DATA/test.csv")
pred_data = csvhand.getData()
pred_data_pro = Preprocessor.preprocess(pred_data, train=False)
fieldsInData = list(Preprocessor.getFI()) + list(
Preprocessor.getSubjects()) # fields are FI_X and S_X
predictions = Model.Predict(pred_data_pro)
C = Model.getClusterCenters() #cluster centers
for pred_i in range(len(predictions)):
pred = predictions[pred_i] #get the prediciton vlaue form the list
Ctarget = C[pred] # get the corresponding cluster
student = pred_data_pro.values[
pred_i] #get the row of values (for a student) for which predicition is done
print("Needed ", end=" ")
for att_i in range(len(fieldsInData)):
Catt = Ctarget[att_i] #Cluster attribute value
studentAtt = student[att_i] # attribute value present in thhe
field = fieldsInData[att_i] # column name
if (Catt >
0.5): #if the value is high . Value might me changed later
print(field, end=" "
) # print the field or append it to the result array
print()
return
def spectrograms_augmentation(self, class_to_augment, many, version, methods):
spec_aug = []
if(version == 1):
aug_dir = self.augmented_spec_v1_dir + class_to_augment + "/"
elif(version == 2):
aug_dir = self.augmented_spec_v2_dir + class_to_augment + "/"
if not os.path.exists(aug_dir):
os.makedirs(aug_dir)
files = os.listdir(aug_dir)
if(len(files) >= many):
for file in files:
spec = np.load(file)
spec_aug.append(spec)
if(len(spec_aug) >= many):
break
return np.asarray(spec_aug, dtype=np.float32)
preprocessor = Preprocessor(aug_dir, version=version, dump=True)
shutil.rmtree(aug_dir)
os.makedirs(aug_dir)
audio_signals_aug = self.audio_signals_augmentation(
class_to_augment, many, methods)
print("creating new spectrograms...")
name = 0
for sig_aug in tqdm(audio_signals_aug):
spec = preprocessor.compute_spectrogram(
sig_aug, str(name) + ".npy")
spec_aug.append(spec)
name += 1
if(len(spec_aug) >= many):
break
return np.asarray(spec_aug, dtype=np.float32)
def Run(source: str, debug: bool):
try:
prog = Program(source)
preproc = Preprocessor()
preproc.preprocess(prog)
# for l in prog.preprocessed:
# print(l)
assembler = Assembler()
assembler.assemble(prog)
# for l in prog.labels:
# print(l, f" Position: {l.position}")
# for i in prog.instructions:
# print(i, f" Position: {i.position} Label: {i.labelName}")
# for p in i.parameters:
# print(" ", p, end = "")
# if p.labelName != None:
# print(f" {p.labelName}")
# else:
# print("")
# for b in prog.binary:
# print("%04X " % b, end = "")
# print("")
computer = Computer()
computer.loadProgram(prog)
if debug == False:
computer.run()
for l in prog.labels:
if l.size > 0:
print("%13s (%6s[%3d]): " % (l.name, l.datatype, l.size), end ="")
for i in range(l.position, l.position + l.size):
print("%d " % computer.memory[i], end = "")
print("")
else:
debugger = Debugger(computer, prog)
debugger.run()
except PreprocessorError as e:
print(e)
except AssemblerError as e:
print(e)
except CompilerError as e:
print(e)
except Exception as e:
raise e
def __init__(self, sample_rate=32000, dim_to_conform=3000):
self.class_id_mapping = {
'Hi-hat': 0,
'Saxophone': 1,
'Trumpet': 2,
'Glockenspiel': 3,
'Cello': 4,
'Knock': 5,
'Gunshot_or_gunfire': 6,
'Clarinet': 7,
'Computer_keyboard': 8,
'Keys_jangling': 9,
'Snare_drum': 10,
'Writing': 11,
'Laughter': 12,
'Tearing': 13,
'Fart': 14,
'Oboe': 15,
'Flute': 16,
'Cough': 17,
'Telephone': 18,
'Bark': 19,
'Chime': 20,
'Bass_drum': 21,
'Bus': 22,
'Squeak': 23,
'Scissors': 24,
'Harmonica': 25,
'Gong': 26,
'Microwave_oven': 27,
'Burping_or_eructation': 28,
'Double_bass': 29,
'Shatter': 30,
'Fireworks': 31,
'Tambourine': 32,
'Cowbell': 33,
'Electric_piano': 34,
'Meow': 35,
'Drawer_open_or_close': 36,
'Applause': 37,
'Acoustic_guitar': 38,
'Violin_or_fiddle': 39,
'Finger_snapping': 40
}
self.classes_frequency = {}
self.classes_percent = {}
self.classes_verified = {}
self.files = []
self.labels = []
self.verified = []
self.files_loaded = set()
self.train_csv = False
self.sample_rate = sample_rate
self.preprocessor = Preprocessor(dump=True)
self.dim_to_conform = dim_to_conform
def preprocess(sentence): # Uses functions in Preprocessor.py to format sentence, including accounting for spelling errors
normalized_sentence = Preprocessor.sentence_normalizer(sentence)
formatted_sentence = Preprocessor.sentence_formatter(normalized_sentence) # remove punctuation
lemmatized_sentence = Preprocessor.sentence_lemmatizer(formatted_sentence) # lemmatize words
cleaned_sentence = Preprocessor.sentence_cleaner(lemmatized_sentence) # remove stopwords
preprocessed_sentence = cleaned_sentence
#print(preprocessed_sentence)
return preprocessed_sentence
def regularize_data():
#Check if the unregularized data exists. If not, construct it
if not os.path.exists('TrainingSet.pkl'):
Preprocessor.process_mnist()
maxEdgeness = 0
maxCornerness = 0
edgeness, cornerness, answers = pickle.load(open('TrainingSet.pkl', 'r'))
for i in xrange(len(edgeness)):
for x in xrange(28):
for y in xrange(28):
if maxEdgeness < edgeness[i][x][y]:
maxEdgeness = edgeness[i][x][y]
if maxCornerness < cornerness[i][x][y]:
maxCornerness = cornerness[i][x][y]
edgeness, cornerness, answers = pickle.load(open('ValidationSet.pkl', 'r'))
for i in xrange(len(edgeness)):
for x in xrange(28):
for y in xrange(28):
if maxEdgeness < edgeness[i][x][y]:
maxEdgeness = edgeness[i][x][y]
if maxCornerness < cornerness[i][x][y]:
maxCornerness = cornerness[i][x][y]
edgeness, cornerness, answers = pickle.load(open('TestSet.pkl', 'r'))
for i in xrange(len(edgeness)):
for x in xrange(28):
for y in xrange(28):
if maxEdgeness < edgeness[i][x][y]:
maxEdgeness = edgeness[i][x][y]
if maxCornerness < cornerness[i][x][y]:
maxCornerness = cornerness[i][x][y]
edgeness, cornerness, answers = pickle.load(open('TrainingSet.pkl', 'r'))
for i in xrange(len(edgeness)):
for x in xrange(28):
for y in xrange(28):
edgeness[i][x][y] /= maxEdgeness
cornerness[i][x][y] /= maxCornerness
pickle.dump([edgeness, cornerness, answers],
open('TrainingSetRegularized.pkl', mode='w'))
edgeness, cornerness, answers = pickle.load(open('ValidationSet.pkl', 'r'))
for i in xrange(len(edgeness)):
for x in xrange(28):
for y in xrange(28):
edgeness[i][x][y] /= maxEdgeness
cornerness[i][x][y] /= maxCornerness
pickle.dump([edgeness, cornerness, answers],
open('ValidationSetRegularized.pkl', mode='w'))
edgeness, cornerness, answers = pickle.load(open('TestSet.pkl', 'r'))
for i in xrange(len(edgeness)):
for x in xrange(28):
for y in xrange(28):
edgeness[i][x][y] /= maxEdgeness
cornerness[i][x][y] /= maxCornerness
pickle.dump([edgeness, cornerness, answers],
open('TestSetRegularized.pkl', mode='w'))
def load_spectrogram(self, version):
if (len(self.files) == 0):
raise NameError("load the file name list before from csv file")
if (version == 1):
data_path = "./dataset/spec/ver1/"
elif (version == 2):
data_path = "./dataset/spec/ver2/"
preprocessor = Preprocessor(spectrogram_path=data_path,
version=version,
test=False,
dump=True)
if (self.train_csv):
audio_path = "./dataset/audio_train/"
else:
audio_path = "./dataset/audio_test/"
print("loading spectrograms...")
for file_name in tqdm(self.files):
spec_file_name = file_name.replace(".wav", ".npy")
spec_file_name = os.path.join(data_path, spec_file_name)
try:
spec = np.load(spec_file_name)
except FileNotFoundError:
print(
file_name,
" spectrogram not exist, compute spectrogram from the original file"
)
audio_file_name = os.path.join(audio_path, file_name)
signal, sample_rate = librosa.load(audio_file_name,
sr=self.sample_rate,
mono=True)
spec = preprocessor.compute_spectrogram(
signal, os.path.basename(spec_file_name))
self.spectrograms.append(spec)
# compute statistics
Xk = spec.shape[1]
if (self.spec_statistic["max"] == None
or Xk > self.spec_statistic["max"]):
self.spec_statistic["max"] = Xk
if (self.spec_statistic["min"] == None
or Xk < self.spec_statistic["min"]):
self.spec_statistic["min"] = Xk
k = len(self.spectrograms)
delta = (Xk - self.spec_statistic["average"]) / k
self.spec_statistic[
"average"] = self.spec_statistic["average"] + delta
self.spec_statistic["variance"] = (
((k - 1) * self.spec_statistic["variance"]) /
k) + (delta * (Xk - self.spec_statistic["average"]))
self.spec_statistic["len_hist"][
Xk] = self.spec_statistic["len_hist"].setdefault(Xk, 0) + 1
return self.spectrograms, self.labels
def CoarseClassify(trainfile, testfile):
fulltrainX, fulltrainFY, fulltrainCY = Preprocessor.getdataset(trainfile)
fulltestX, fulltestFY, fulltestCY = Preprocessor.getdataset(testfile)
vec = TfidfVectorizer(binary=True, use_idf=True, decode_error='ignore')
tfidf_train_data = vec.fit_transform(fulltrainX)
tfidf_test_data = vec.transform(fulltestX)
trainX = tfidf_train_data.toarray().tolist()
testX = tfidf_test_data.toarray().tolist()
return classify(trainX, fulltrainCY, testX, fulltestCY, fulltrainFY,
fulltestFY)
def preprocess(
sentence): # Uses functions in Preprocessor.py to format sentence
formatted_sentence = Preprocessor.sentence_formatter(
sentence) # remove punctuation
lemmatized_sentence = Preprocessor.sentence_lemmatizer(
formatted_sentence) # lemmatize words
cleaned_sentence = Preprocessor.sentence_cleaner(
lemmatized_sentence) # remove stopwords
preprocessed_sentence = cleaned_sentence
return preprocessed_sentence
def CoarseClassifytext(trainfile, text):
process = "dummy:dummy " + text
textdf = pd.DataFrame(data=[process])
fulltrainX, fulltrainFY, fulltrainCY = Preprocessor.getdataset(trainfile)
fulltestX, Trash1, Trash2 = Preprocessor.preprocess(textdf)
vec = TfidfVectorizer(binary=True, use_idf=True, decode_error='ignore')
tfidf_train_data = vec.fit_transform(fulltrainX)
tfidf_test_data = vec.transform(fulltestX)
trainX = tfidf_train_data.toarray().tolist()
testX = tfidf_test_data.toarray().tolist()
return predict(testX), testX, trainX, fulltrainCY, fulltrainFY
def testPreProcessor():
csvhand = han.CSVHandler("../DATA/input.csv")
csvhand.open()
data = csvhand.getData()
data_train = pr.preprocess(data)
csvhand = han.CSVHandler("../DATA/input.csv")
csvhand.open()
data_test = csvhand.getData()
data_test = pr.preprocess(data_test, train=False)
import pdb
pdb.set_trace()
return
def main():
"""Drives the entire translation process."""
"""Preprocess the file"""
pp = Preprocessor()
pp.remove_white_spaces_comments()
pp.write_temp_file()
"""First pass - adds labels to the symbol table."""
parser = Parser(sys.argv[1] + '.tmp')
symbol_table = SymbolTable()
pc = -1
while parser.has_more_commands():
parser.advance()
command_type = parser.command_type()
if command_type == 'A_COMMAND' or command_type == 'C_COMMAND':
pc += 1
elif command_type == 'L_COMMAND':
label = parser.symbol()
symbol_table.addEntry(label, pc + 1)
"""Second pass - handles variables names and writes the *.hack file."""
ram_address = 16
parser = Parser(sys.argv[1] + '.tmp')
code = Code()
file_name = parser.get_file_name()
hack_file = open(file_name + '.hack', 'w')
while parser.has_more_commands():
parser.advance()
command_type = parser.command_type()
if command_type == 'A_COMMAND':
a_symbol = parser.symbol()
if a_symbol[0] in '0123456789':
a_symbol_binary = code.convert_to_binary(a_symbol)
hack_file.write('0' + a_symbol_binary + '\n')
else:
if symbol_table.contains(a_symbol) is False:
symbol_table.addEntry(a_symbol, ram_address)
ram_address += 1
address = symbol_table.GetAddress(a_symbol)
address_binary = code.convert_to_binary(address)
hack_file.write('0' + address_binary + '\n')
elif command_type == 'C_COMMAND':
comp = code.comp(parser.comp())
dest = code.dest(parser.dest())
jump = code.jump(parser.jump())
hack_file.write('111' + comp + dest + jump + '\n')
hack_file.close()
def __call__(self, data, targetClass, num_of_rules ):
if self.dataOK(data): # Checks weather targetClass is discrete
original_data = data
data = Preprocessor.generateFeatures(data, targetClass)
# initialization of beams
beam = [SDRule(data=data, targetClass=targetClass, g=self.g)] * self.beamWidth
newBeam = [SDRule(data=data, targetClass=targetClass, g=self.g)] * self.beamWidth
worstRuleIndex = 0
improvements = true
while improvements:
improvements = false
for rule in beam:
# for f in features:
for feature in data.domain.attributes:
newRule = rule.cloneAndAddCondition(feature, 'True')
if newRule.support > self.minSupport and self.betterThanWorstRule(newRule, newBeam, worstRuleIndex) and self.isRelevant(newRule, newBeam):
worstRuleIndex = self.replaceWorstRule(newRule, newBeam, worstRuleIndex)
improvements = true
beam = newBeam
# perform rule subset selection
if num_of_rules != 0:
beam = self.ruleSubsetSelection(beam, num_of_rules, data)
targetClassRule = SDRule(original_data, targetClass, conditions=[], g=1)
# change beam so the rules apply to original data
fixedBeam = [rule.getFixed(original_data) for rule in beam]
return SDRules(fixedBeam, targetClassRule)
def testPreProcessor():
csvHandler = han.CSVHandler("../DATA/input.csv")
csvHandler.open() # todo remove this kind of useless code
data = csvHandler.getData()
data=pr.preprocess(data)
print(data)
return
def train(samples_proportion=0.7):
global words_in_ham, ham_word_count, words_in_spam, spam_word_count, raw_ham_prob, raw_spam_prob
ham, spam = read_spam_ham()
print("Spam size: " + str(len(spam)) + " Ham size: " + str(len(ham)))
all_emails = append_ham_and_spam(ham, spam)
random.shuffle(all_emails)
print('Corpus size = ' + str(len(all_emails)) + ' emails')
features = [(Preprocessor.get_features(email, ' '), label)
for (email, label) in all_emails]
print('Collected ' + str(len(features)) + ' feature sets')
'''
# define Support value in %
support = 10
spam_support_count = (spam_size * 10) / 100;
ham_support_count = (ham_size * 10) / 100;
print('Spam support count:' + str(spam_support_count))
print('Ham support count:' + str(ham_support_count))
# get the spam frequent itemset and ham frequent itemset
# spam_frequent, ham_frequent = get_frequent(all_features, spam_support_count, ham_support_count)
# train the our own naivebayes classifier and collect dictionary of raw probabilities of words
'''
train_size = int(len(features) * samples_proportion)
train_set, test_set = features[:train_size], features[train_size:]
ham_mail_count, spam_mail_count = mails_in_ham_spam(train_set)
spam_prior = 1.0 * spam_mail_count / len(train_set)
ham_prior = 1.0 * ham_mail_count / len(train_set)
words_in_ham, words_in_spam = frequency_in_ham_spam(train_set)
spam_vocab = len(spam_word_count)
ham_vocab = len(ham_word_count)
t = get_probabilities_in_each_class(ham_prior, words_in_ham, ham_vocab,
ham_word_count, raw_ham_prob,
raw_spam_prob, spam_prior,
words_in_spam, spam_vocab,
spam_word_count, test_set, train_set)
ham_prior, words_in_ham, ham_vocab, raw_ham_prob, raw_spam_prob, spam_prior, words_in_spam, spam_vocab, test_set, train_set = get_parameters(
t)
#print("Train Size:" + str(len(train_set)) + str(' Test size:') + str(len(test_set)))
#evaluate(train_set, test_set, raw_spam_prob, raw_ham_prob, words_in_spam, words_in_ham, spam_vocab, ham_vocab,
# spam_prior,
# ham_prior)
classifier = NaiveBayesClassifier(list(spam_word_count),
list(ham_word_count))
t = classifier.prob_classify(classifier, train_set).max()
def inferTarget(self,opinion):
"""
Tries to identify mentions of the targets in a message
Params: opinion -> Opinion object
Returns: tuple(inferred target, algorithm metadata)
"""
info = u"Targets: "
sentence = Preprocessor.separateSpecialSymbols(opinion.sentence.lower())
#print sentence
matches = []
for target in self.targets:
for name in target.names:
if sentence.find(" "+name+" ") != -1:
matches.append(name)
for nickName in target.nicknames:
if sentence.find(" "+nickName+" ") != -1:
matches.append(nickName)
for ergo in target.ergos:
if sentence.find(" "+ergo+" ") != -1:
matches.append(ergo)
targets = {}
for mention in matches:
targetId = self.getTargetByMention(mention)
if targetId != None and not self.isFalsePositive(mention, sentence):
if mention not in info:
info += mention + ","
targets[targetId] = mention
if len(targets) > 0:
results = []
info = info.strip(',')
for target,mention in targets.items():
results.append(opinion.clone(target=target,mention=mention,metadata=info))
return results
else:
return None
def get_prediction(filename):
preprocessor = Preprocessor.Preprocessor()
# trainer = Trainer.Trainer(preprocessor)
predictor = Predictor.Predictor(preprocessor)
# image_name_list = os.listdir(os.path.join(os.getcwd(), "img"))
# for image_name in image_name_list:
# image_path = os.path.join(os.path.join(os.getcwd(), "img"), image_name)
return predictor.predict([filename])
def menu():
PreProcessor = Preprocessor.PreProcessor()
opcao = input("Preparar base de treino e teste yes/No?")
if opcao.lower() in ["yes","y"]:
prepararBasesTreinoTeste(PreProcessor)
opcao = input("Preparar base de treino e teste sem stemming yes/No?")
if opcao.lower() in ["yes","y"]:
prepararBasesTreinoTesteWithoutStemming(PreProcessor)
PreProcessor = Preprocessor.PreProcessor()
opcao = input("Preparar base de treino e teste da pasta de janelas Yes/no?")
if opcao.lower() in ["","yes","y"]:
prepararBasesTreinoTestePorJanela(PreProcessor)
opcao = input("Preparar base Yes/no (não uso, pre-processo a mensagem antes de classificar):")
if opcao.lower() in ["","yes","y"]:
prepararBase(PreProcessor)
def generate_inverted_list(self, docs_dict):
inv_list = dict()
for (doc_id, doc) in docs_dict.items():
term_list = Preprocessor.preprocessor_tokenizer(doc)
for term in term_list:
if term not in inv_list:
inv_list[term] = []
inv_list[term].append(doc_id)
return inv_list
def test_Response(self):
questions, responses = Preprocessor.load_corpus()
question_list = Processor.vectorizer(questions)
self.assertEqual(
Processor.process("what is a star?", question_list, responses),
"Stars are mostly made of hydrogen and helium\n")
self.assertEqual(Processor.process("Hello", question_list, responses),
"Hello! I am Nova.\n")
self.assertEqual(
Processor.process("What is your job?", question_list, responses),
"I teach you about astronomy and geography!\n")
def preProcess(self):
try:
# preprocess the data and update df with the processed data
preprocessor = Preprocessor(self.df)
self.df = preprocessor.df
self.hasProcessed=True
except KeyError:
tkMessageBox.showinfo("K Means Clustering", "The selected file is invalid")
return
#pop message to user
tkMessageBox.showinfo("K Means Clustering", "Preprocessing completed successfully!")
def getTopNFormulae(text, n):
global model, multiWordDict, thresholdFormulaConf
words = pp.parse(text, multiWordDict)
nameList, valueList = getFormulaConfidence(getConceptConfidence(words))
result = {}
index = 0
for value in valueList:
result[nameList[index]] = value
index = index + 1
return sorted(result, key=result.get, reverse=True)[0:n]
def Train():
"""
Trains the model with data from DATA/input.csv
:return:
"""
# initialize source -csv source
csvhand = Handler.CSVHandler("DATA/input.csv")
train_data = csvhand.getData()
train_data_pro = Preprocessor.preprocess(train_data)
Model.Init() #intialize the model
labels_ = Model.Train(train_data_pro)
return
def getTopNFormulae(text, n):
global model, multiWordDict, thresholdFormulaConf
words = pp.parse(text, multiWordDict)
nameList, valueList = getFormulaConfidence(getConceptConfidence(words))
result = {}
index = 0
for value in valueList:
result[nameList[index]] = value
index = index + 1
return sorted(result, key=result.get, reverse=True)[0:n]
def train_structured_perceptron():
model = pos.StructuredPerceptron()
fail = Preprocessor.load_sp_data('data/train_dev_data/PT.5000.train')
train_data = []
train_labels = []
for _, (d, l) in enumerate(fail):
train_data.append(d)
train_labels.append(l)
train_model(train_data, train_labels, model, 'models/sp-pt.model',
(5, 0.2))
def process_image(self, img_path):
self._img_path = img_path
image_array = image.img_to_array(
image.load_img(img_path, target_size=self._target_size))
average_edgeness = np.zeros(
(image_array.shape[0], image_array.shape[1]))
average_cornerness = np.zeros(
(image_array.shape[0], image_array.shape[1]))
#On a channel by channel basis computes the features values of all points
for color_channel in xrange(image_array.shape[2]):
channel_array = np.zeros(
(image_array.shape[0], image_array.shape[1]))
for x in xrange(len(channel_array)):
for y in xrange(len(channel_array[0])):
channel_array[x][y] = image_array[x][y][color_channel]
#Could potentially crash if not given a 56 x 56 image as input for this method
channel_edgeness, channel_cornerness = Preprocessor.process_image_sd2(
channel_array)
for x in xrange(len(channel_array)):
for y in xrange(len(channel_array[0])):
#Next 2 lines utilize "magic numbers" that are the maximum that was
#found over the MNIST dataset, effectively regularizing the data
average_edgeness[x][y] += channel_edgeness[x][
y] / 0.634789974395 / image_array.shape[2]
average_cornerness[x][y] += channel_cornerness[x][
y] / 0.16428281162 / image_array.shape[2]
#Scales image size down to 28 by 28 to fit the model
ensmalled_edgeness = np.zeros(
(image_array.shape[0] / 2, image_array.shape[1] / 2))
ensmalled_cornerness = np.zeros(
(image_array.shape[0] / 2, image_array.shape[1] / 2))
for x in xrange(len(ensmalled_edgeness)):
for y in xrange(len(ensmalled_edgeness[0])):
ensmalled_edgeness[x][y] += average_edgeness[2 * x][2 * y]
ensmalled_edgeness[x][y] += average_edgeness[2 * x + 1][2 * y]
ensmalled_edgeness[x][y] += average_edgeness[2 * x][2 * y + 1]
ensmalled_edgeness[x][y] += average_edgeness[2 * x + 1][2 * y +
1]
ensmalled_edgeness[x][y] /= 4.0
ensmalled_cornerness[x][y] += average_cornerness[2 * x][2 * y]
ensmalled_cornerness[x][y] += average_cornerness[2 * x + 1][2 *
y]
ensmalled_cornerness[x][y] += average_cornerness[2 * x][2 * y +
1]
ensmalled_cornerness[x][y] += average_cornerness[2 * x +
1][2 * y + 1]
ensmalled_cornerness[x][y] /= 4.0
self._image_array = np.expand_dims(np.asarray(
[ensmalled_edgeness, ensmalled_cornerness]),
axis=0)
def test_structured_perceptron():
model = pos.StructuredPerceptron()
fail = Preprocessor.load_sp_data('data/train_dev_data/EN.dev')
test_data = []
test_labels = []
for _, (d, l) in enumerate(fail):
test_data.append(d)
test_labels.append(l)
acc_en = test_model(test_data, test_labels, model,
'models/postagger.model')
print "Accuracy on english:", acc_en
model = pos.StructuredPerceptron()
fail = Preprocessor.load_sp_data('data/train_dev_data/NL.dev')
test_data = []
test_labels = []
for _, (d, l) in enumerate(fail):
test_data.append(d)
test_labels.append(l)
acc_nl = test_model(test_data, test_labels, model, 'models/sp-nl.model')
print "Accuracy on dutch:", acc_nl
model = pos.StructuredPerceptron()
fail = Preprocessor.load_sp_data('data/train_dev_data/PT.dev')
test_data = []
test_labels = []
for _, (d, l) in enumerate(fail):
test_data.append(d)
test_labels.append(l)
acc_pt = test_model(test_data, test_labels, model, 'models/sp-pt.model')
print "Accuracy on portuguese:", acc_pt
print "Total accuracy:", (acc_en + acc_nl + acc_pt) / 3.0
def run():
# 创建预处理器对象
dpre = Preprocessor.Preprocessor()
# 执行数据预处理
dpre.preprocessing()
# 创建LDA处理模型
lda = LDAModel.LDAModel(dpre)
# 执行参数估算
lda.est()
def plot_NYISO_forecast_error(true_path, pred_path):
""" Plots and give metrics regarding the NYISO forecasts
Args :
true_path : (file / folder string) : True NYISO load data file / folder
pred_path : (file / folder string) : NYISO forecasts load data file / folder
"""
import Loader as ld
import Preprocessor as pre
import matplotlib.pyplot as plt
true_ld = ld.NY_Loader(true_path)
nyiso_ld = ld.NY_Loader(pred_path)
true_pre = pre.NY_Preprocessor(true_ld.data, 'Integrated Load', year_range=list(range(2008, 2018)))
nyiso_pre = pre.NY_Preprocessor(nyiso_ld.data, 'Integrated Load', year_range=list(range(2008, 2018)), fix_duplicates='keep_last')
results = mlu.get_results(true_pre.get_data().values, nyiso_pre.get_data(), true_pre.get_data().index)
import Plotter
Plotter.plot_results(results, groupby='month')
print("Global error : {0}".format(mlu.get_measures(results, 'global', 'MAPE')))
print("Global error : {0}".format(mlu.get_measures(results, 'global', 'RMSE')))
plt.show()
def getTopNConcepts(text, n):
global model, multiWordDict
words = pp.parse(text, multiWordDict)
conceptConf = getConceptConfidence(words)
count = 0
result = []
for w in sorted(conceptConf, key=conceptConf.get, reverse=True):
result.append(w)
count = count + 1
if count == n:
break
return result
def preprocess(type):
pp = Preprocessor.Preprocessor()
df = pp.getDataFrame('./data/original/' + type)
pp.toNumericData(df, save=type)
df = pd.read_csv('./csv/' + type + '.csv', sep="\t", header=None)
df, label = pp.toAutoEncoderData(df)
print('df {} Label {}'.format(df.shape, label.shape))
print(df.head)
df.to_csv('./data/processed/' + type + '.csv',
sep="\t",
header=None,
index=False)
np.save('./data/processed/' + type + '_label.npy', label)
def __init__(self, filename='./corpus/train.csv'):
if os.path.exists(filename):
data = pd.read_csv(filename)
self.data = shuffle(data)
X_data = pd.DataFrame(data.drop('sentiment', axis=1))
Y_data = column_or_1d(data[:]['sentiment'], warn=True)
self.X_train, self.X_val,\
self.y_train, self.y_val = train_test_split(X_data, Y_data, test_size=0.3, random_state=1)
self.model = None
self.load_model()
self.preprocessor = Preprocessor.Preprocessor()
else:
print('No Source!')
self.preprocessor.process_data()
def getTopNConcepts(text, n):
global model, multiWordDict
words = pp.parse(text, multiWordDict)
conceptConf = getConceptConfidence(words)
count = 0
result = []
for w in sorted(conceptConf, key=conceptConf.get, reverse=True):
result.append(w)
count = count + 1
if count == n:
break
return result
def addMultiWords(self, listOfMultiWords):
"""
Updates the internal regex with a
list of multiwords
"""
buff = StringIO.StringIO()
for multiWord in listOfMultiWords:
if multiWord not in self.multiWordsRegex and multiWord not in buff.getvalue():
buff.write(self.regexTemplate.format(multiWord))
# add a normalized (no accents) version
if multiWord != Preprocessor.normalize(multiWord):
buff.write(self.regexTemplate.format(Preprocessor.normalize(multiWord)))
if len(self.multiWordsRegex) == 0:
self.multiWordsRegex = buff.getvalue().strip("|")
else:
self.multiWordsRegex += "|" + buff.getvalue().strip("|")
buff.close()
def PredictRaw(studentRecord):
"""
Predicts the data from DATA/test.csv
:studentRecord: data of the format [ID,FI,E1...E2] ID and FI are must
:return: list of subjects he should have studied
"""
studentRecord = formatStudent(studentRecord)
pred_data = pd.DataFrame().from_records([studentRecord])
pred_data.columns = ["ID", "FI", "E", "E", "E"]
pred_data_pro = Preprocessor.preprocess(pred_data, train=False)
FI = list(Preprocessor.getFI())
subjects = list(Preprocessor.getSubjects())
fieldsInData = FI + subjects # fields are FI_X and S_X
predictions = Model.Predict(pred_data_pro)
C = Model.getClusterCenters() #cluster centers
pred = predictions[
0] #get the prediciton value which is the only value present
Ctarget = C[pred] # get the corresponding cluster
student = pred_data_pro.values[
0] #get the row of values (for a student) for which predicition is done
print("Needed ", end=" ")
result = {"FI": None, "S": []}
for att_i in range(len(fieldsInData)):
Catt = Ctarget[att_i] #Cluster attribute value
studentAtt = student[att_i] # attribute value present in thhe
field = fieldsInData[att_i] # column name
if (Catt > 0.5): #if the value is high . Value might me changed later
print(field,
end=" ") # print the field or append it to the result array
if (field in FI):
result["FI"] = field
else:
result["S"].append(field)
print()
return result
def predict(self, predictedLength):
"""
Predict probabilities throughout the day where the given block-duration is likely to be available
:param predictedLength: The duration of the predicted block
:return: A list containing the predicted probabilities
"""
predicted_times = []
current_prediction = 0
for x in p.decimal_range(8, 17, 0.25):
current_prediction = (self.wknn.predict_proba(
[[x, predictedLength]])) * 100
if (current_prediction[0][1] > 0):
predicted_times.append([x, current_prediction[0][1]])
return predicted_times
def _process_xml_query(self, xml_node):
query_id = xml_node.getElementsByTagName("QueryNumber")[0].firstChild.nodeValue
query = xml_node.getElementsByTagName("QueryText")[0].firstChild.nodeValue
processed_query = " ".join(Preprocessor.preprocessor_tokenizer(query))
self.query_dict[query_id] = processed_query
records = xml_node.getElementsByTagName("Records")[0]
relevant_documents_list = list()
for item in records.getElementsByTagName("Item"):
doc_id = item.firstChild.nodeValue
scores = item.getAttribute("score")
votes = 0
for i in range(len(scores)):
if scores[i]!='0':
votes += 1
relevant_documents_list.append((doc_id, votes))
self.expected_docs_by_query[query_id] = relevant_documents_list
def query_vector(self, query):
terms = Preprocessor.preprocessor_tokenizer(query)
counter = collections.Counter(terms)
query_vector = dict()
query_vector_magnitude = 0
for term in terms:
if term not in self.document_frequency:
continue
max_tf = counter.most_common(1)[0][1] # it returns an list of item + frequency
tf = counter[term]
df = self.document_frequency[term]
val = (0.5 + 0.5*tf/max_tf)*math.log10(self.n_terms/df)
query_vector[term] = val
query_vector_magnitude += val*val
# normalizing step
query_vector_magnitude = math.sqrt(query_vector_magnitude)
for term in query_vector:
query_vector[term] /= query_vector_magnitude
return query_vector
def loadTargetsFromFile(filename):
# load targets, their names, nicknames and ergonyms from given file into an array of three lists.
# returns the array.
# names with accents and cedilla are duplicated after normalization, except if escaped with ^.
#
NAMES = 0
NICKNAMES = 1
ERGOS = 2
f = codecs.open(filename,"r", "utf-8")
targets = []
for fileLine in f:
line = fileLine.lower()
# lines starting with "#" are skipped -- mjs 2011.10.27
if line[0] == '#':
if debug: print "skipped: ", line
continue
sepIndex = line.find(":")
id = line[0:sepIndex]
names = []
nicknames = []
ergos = []
if id != None and id != '':
mentions = line[sepIndex+1:].split(';')
try:
namesTokens = mentions[NAMES]
for name in namesTokens.split(','):
cleanName = name.replace("\n","").strip(' ').rstrip(' ')
if debug: print "cleanName = ", cleanName
if cleanName != '':
if cleanName[0] != '^':
names.append(cleanName)
if cleanName != Preprocessor.normalize(cleanName):
names.append(Preprocessor.normalize(cleanName))
if debug: print "appended clean name", cleanName
else:
names.append(cleanName[1:])
if debug: print "appended unclean name", cleanName, cleanName[1:]
except IndexError:
None
try:
nicknamesTokens = mentions[NICKNAMES]
for name in nicknamesTokens.split(','):
cleanName = name.replace("\n","").strip(' ').rstrip(' ')
if debug: print "cleanName (nickname) = ", cleanName
if cleanName != '':
if cleanName[0] != '^':
nicknames.append(cleanName)
if cleanName != Preprocessor.normalize(cleanName):
nicknames.append(Preprocessor.normalize(cleanName))
if debug: print "appended clean nickname", cleanName, Preprocessor.normalize(cleanName)
else:
nicknames.append(cleanName[1:])
if debug: print "appended uncleaned nickname ", cleanName, cleanName[1:]
except IndexError:
None
try:
ergoTokens = mentions[ERGOS]
for name in ergoTokens.split(','):
cleanName = name.replace("\n","").strip(' ').rstrip(' ')
if debug: print "cleanName (ergonym) = ", cleanName
if cleanName != '':
if cleanName[0] != '^':
ergos.append(cleanName)
if cleanName != Preprocessor.normalize(cleanName):
ergos.append(Preprocessor.normalize(cleanName))
if debug: print "appended clean ergonym", cleanName, Preprocessor.normalize(cleanName)
else:
ergos.append(cleanName[1:])
if debug: print "appended uncleaned ergonym", cleanName, cleanName[1:],
except IndexError:
None
targets.append(Person(id,names,nicknames,ergos))
f.close()
return targets
def newGetTweets(beginDate,endDate,proxy):
"""
Gets tweets from a service for a certain period
Params: begin date
end date
proxy
Returns: list of Opinion instances
"""
print "Getting new tweets..."
username = "******"
password = "******"
top_level_url = "http://pattie.fe.up.pt/solr/portugal/select"
requestTweets = "http://pattie.fe.up.pt/solr/portugal/select?q=created_at:[{0}%20TO%20{1}]&indent=on&wt=json"
#requestTweets = "http://pattie.fe.up.pt/solr/portugal/select?q=created_at:[2012-05-25T13:00:00Z%20TO%202012-05-25T15:00:00Z]&indent=on&wt=json"
#Password manager because the service requires authentication
password_mgr = urllib2.HTTPPasswordMgrWithDefaultRealm()
password_mgr.add_password(None, top_level_url, username, password)
auth_handler = urllib2.HTTPBasicAuthHandler(password_mgr)
opener = None
if proxy != None:
proxy_handler = urllib2.ProxyHandler({'http': proxy})
opener = urllib2.build_opener(auth_handler,proxy_handler)
else:
#opener = urllib2.build_opener(auth_handler)
opener = urllib2.build_opener()
if beginDate.strftime('%Y') == "1900":
print "Getting Tweets from STDIN ..."
twitterData = sys.stdin;
else:
"""
print "Requesting: " + requestTweets.format(beginDate.strftime('%Y-%m-%dT%H:%M:%Sz'),
endDate.strftime('%Y-%m-%dT%H:%M:%Sz'))
twitterData = opener.open(requestTweets.format(beginDate.strftime('%Y-%m-%dT%H:%M:%Sz'),
endDate.strftime('%Y-%m-%dT%H:%M:%Sz')));
"""
print "Requesting: " + requestTweets.format(urllib.quote(beginDate.strftime('%Y-%m-%dT%H:%M:%SZ')),
urllib.quote(endDate.strftime('%Y-%m-%dT%H:%M:%SZ')))
twitterData = opener.open(requestTweets.format(urllib.quote(beginDate.strftime('%Y-%m-%dT%H:%M:%SZ')),
urllib.quote(endDate.strftime('%Y-%m-%dT%H:%M:%SZ'))));
#Read the JSON response
jsonTwitter = simplejson.loads(unicode(twitterData.read().decode("utf-8")))
print jsonTwitter["response"]
politicians = getPoliticians()
sentiTokens = getSentiTokens()
multiWordTokenizer = getMultiWordsTokenizer(politicians, sentiTokens)
listOfTweets = []
#Build a dictionary
for tweet in jsonTwitter["response"]["docs"]:
id = str(tweet["user_id"]) + "_" + str(tweet["id"])
userId = unicode(tweet["user_id"])
#print userId
date = datetime.strptime(tweet["created_at"], '%Y-%m-%dT%H:%M:%Sz')
taggedSentence = multiWordTokenizer.tokenizeMultiWords(unicode(tweet["text"]))
taggedSentence = Preprocessor.removeURLs(taggedSentence)
taggedSentence = Preprocessor.removeUsernames(taggedSentence)
listOfTweets.append(Opinionizers.Opinion(tweet["id"],unicode(tweet["text"]),user=userId,date=date,processedSentence = taggedSentence))
print len(listOfTweets), " tweets loaded\n"
return listOfTweets
def testOldProcessWithDiagnostics(sourceFile):
results = {"numOf-1":0,"correct-1":0,"numOf0":0,"correct0":0,"numOf1":0,"correct1":0}
#corpus = csv.reader(codecs.open(sourceFile,"r","utf-8"),delimiter='|')
corpus = codecs.open(sourceFile,"r","utf-8")
listOfTweets = []
i=0
politicians = getFromCache(PERSONS_CACHE) #getPoliticians()
sentiTokens = getFromCache(SENTI_CACHE) #getSentiTokens()
rulesClassifier = EuroOpinionizers.Rules(politicians,sentiTokens)
naiveClassifier = EuroOpinionizers.Naive(politicians,sentiTokens)
multiWordTokenizer = getFromCache(MULTIWORD_CACHE) #getMultiWordsTokenizer(politicians, sentiTokens)
print "loading tweets..."
for line in corpus:
tweet = line.split('|')
#skip the first line
if tweet[0] != 'ID':
#print tweet
"""
fullSentence = ''
#in some cases the message spawns across several fields so we are concatenating them...
for block in tweet[TEXT:]:
fullSentence = fullSentence + block
"""
fullSentence = tweet[TEXT]
tokenizedSentence = multiWordTokenizer.tokenizeMultiWords(unicode(fullSentence))
tokenizedSentence = Preprocessor.removeURLs(tokenizedSentence)
tokenizedSentence = Preprocessor.removeUsernames(tokenizedSentence)
o = Opinion(id = tweet[ID],
user = u"Teste",
sentence = unicode(fullSentence),
processedSentence = unicode(tokenizedSentence),
target = unicode(tweet[TARGET]),
mention = unicode(tweet[MENTION]),
polarity = int(tweet[SENTIMENT_POLARITY]))
listOfTweets.append(o)
i = i+1
"""
if i!=0 and i%10 == 0:
break
"""
print "tweets loaded..."
falseNeg = []
falsePos = []
falseNeut = []
totalList = []
for tweet in listOfTweets:
rulesTweet = rulesClassifier.inferPolarity(tweet,True)
naiveTweet = naiveClassifier.inferPolarity(tweet,True)
regex = ur"score:(.*);"
sentiScore = re.search(regex, naiveTweet.metadata).group(1)
tweetScore = int(sentiScore) + int(rulesTweet.polarity)
if tweetScore > 0:
tweet.polarity = 1
elif tweetScore < 0:
tweet.polarity = -1
else:
tweet.polarity = 0
tweet.metadata = rulesTweet.metadata + ";" + naiveTweet.metadata
totalList.append(tweet)
"""
if tweet.polarity == -1:
results["numOf-1"] += 1
if tweet.polarity == classifiedTweet.polarity:
results["correct-1"] +=1
else:
matchedRules = rulesDiagnosticHelper(rulesClassifier.generateFeatureSet(tweet,True))
classifiedTweet.metadata += " " + matchedRules
falseNeg.append(classifiedTweet)
elif tweet.polarity == 0:
results["numOf0"] += 1
if tweet.polarity == classifiedTweet.polarity:
results["correct0"] +=1
else:
matchedRules = rulesDiagnosticHelper(rulesClassifier.generateFeatureSet(tweet,True))
classifiedTweet.metadata += " " + matchedRules
falseNeut.append(classifiedTweet)
if tweet.polarity == 1:
results["numOf1"] += 1
if tweet.polarity == classifiedTweet.polarity:
results["correct1"] +=1
else:
matchedRules = rulesDiagnosticHelper(rulesClassifier.generateFeatureSet(tweet,True))
classifiedTweet.metadata += " " + matchedRules
falsePos.append(classifiedTweet)
logTweets(falseNeg,"./falseNegs.csv")
logTweets(falseNeut,"./falseNeut.csv")
logTweets(falsePos,"./falsePos.csv")
"""
logTweets(totalList,"./newProcess.csv")
"""
def testSubjectivity(sourceFile):
corpus = codecs.open(sourceFile,"r","utf-8")
listOfTweets = []
rejectList = []
i=0
politicians = getPoliticians()
sentiTokens = getSentiTokens()
#rulesClassifier = EuroOpinionizers.Rules(politicians,sentiTokens)
naiveClassifier = EuroOpinionizers.Naive(politicians,sentiTokens)
#multiWordTokenizer = getMultiWordsTokenizer(politicians, sentiTokens)
print "loading tweets..."
for line in corpus:
tweet = line.replace("\"","\'").split('|')
#skip the first line
if tweet[0] != 'PERIOD':
#print tweet
fullSentence = ''
#in some cases the message spawns across several fields so we are concatenating them...
for block in tweet[TEXT:]:
fullSentence = fullSentence + block
tokenizedSentence = Preprocessor.separateSpecialSymbols(unicode(fullSentence)) #multiWordTokenizer.tokenizeMultiWords(unicode(fullSentence))
#tokenizedSentence = Preprocessor.removeURLs(tokenizedSentence)
#tokenizedSentence = Preprocessor.removeUsernames(tokenizedSentence)
print tokenizedSentence
o = Opinion(id = tweet[ID],
user = u"Teste",
sentence = unicode(fullSentence),
processedSentence = tokenizedSentence,
target = unicode(tweet[TARGET]),
mention = unicode(tweet[MENTION]),
polarity = int(tweet[SENTIMENT_POLARITY]))
matches = re.findall(naiveClassifier.sentiTokensRegex ,tokenizedSentence)
if matches != None and len(matches) > 0:
listOfTweets.append(o)
else:
rejectList.append(o)
i = i+1
"""
if i!=0 and i%30 == 0:
break
"""
logTweets(listOfTweets,"./listOfTweets.csv")
logTweets(rejectList,"./rejectList.csv")
def genFeatsWithSubjectivity(isGoldStandard,sourceFile, destinyFile):
corpus = codecs.open(sourceFile,"r","utf-8")
politicians = getPoliticians()
sentiTokens = getSentiTokens()
rulesClassifier = EuroOpinionizers.Rules(politicians,sentiTokens)
naiveClassifier = EuroOpinionizers.Naive(politicians,sentiTokens)
multiWordTokenizer = getMultiWordsTokenizer(politicians, sentiTokens)
listOfTweets = []
i=0
for line in corpus:
tweet = line.replace("\"","\'").split('|')
#skip the first line
if tweet[0] != 'PERIOD':
sentence = ''
for block in tweet[TEXT:]:
sentence = sentence + block
tokenizedSentence = multiWordTokenizer.tokenizeMultiWords(sentence)
tokenizedSentence = Preprocessor.removeURLs(tokenizedSentence)
tokenizedSentence = Preprocessor.removeUsernames(tokenizedSentence)
#tokenizedSentence = separateSpecialSymbols(tokenizedSentence)
o = Opinion(id = tweet[ID],
sentence = unicode(sentence),
processedSentence = unicode(tokenizedSentence),
target = unicode(tweet[TARGET]),
mention = unicode(tweet[MENTION]),
polarity = int(tweet[SENTIMENT_POLARITY]))
listOfTweets.append(o)
i = i+1
"""
if i!=0 and i%50 == 0:
break
"""
print "tweets loaded..."
featuresFile = open(destinyFile,"w")
featuresFile.write(ARFF_HEADERS)
unknownSentiFile = codecs.open("./unknownSentiment.csv","w","utf-8")
featurama = cStringIO.StringIO()
unknownSentiment = cStringIO.StringIO()
tmp = cStringIO.StringIO()
for tweet in listOfTweets:
#used to verify if an instance matches any rule and\or has any sentiToken
#if after processing this var still has value 0 then we don't add it to
#the training set...
sentiCounter = 0
tmp = cStringIO.StringIO()
tmp.write(str(tweet.id)+ ",")
featureSet = rulesClassifier.genConditionalFeatureSet(tweet,True)
for feature in featureSet:
tmp.write(str(feature) + ",")
sentiCounter += feature
naiveClassification = naiveClassifier.inferPolarity(tweet,True)
tmp.write(str(naiveClassification.polarity) + ",")
sentiCounter+= naiveClassification.polarity
pos = naiveClassification.metadata.find("score:")
score = naiveClassification.metadata[pos+6:].replace(";","")
tmp.write(str(score) + ",")
sentiCounter+= int(score)
isNews = isNewsSource(tweet.sentence)
tmp.write(str(isNews) + ",")
sentiCounter+= isNews
if sentiCounter != 0:
if isGoldStandard:
featurama.write(unicode(tmp.getvalue())+unicode(tweet.polarity))
else:
featurama.write(tmp.getvalue()+"?\n")
else:
unknownSentiment.write(str(tweet.id)+"|"+str(tweet.polarity)+"|"+tweet.sentence+"\n")
featuresFile.write(featurama.getvalue())
featuresFile.close()
unknownSentiFile.write(unknownSentiment.getvalue())
unknownSentiFile.close()
if i=='m' or i=='M':
corpus='mr'
binary_classification=True
else:
corpus='reuters'
#Forming Prepare the cat_num_docs dictionary, where the number of documents in the training set for each
##category are stored
##also forming the training set and test set
start_time = time.time()
#Here, apart from the naive bayes classifier, everything is done by nltk
#2) refer the comments for the function get_testset_trainset()
li = Preprocessor.get_testset_trainset(corpus)
testset = li[1]
trainset = li[0]
li = Preprocessor.startup()
cat_num_docs = li[1]
###--------------------DEBUG STATEMENTS----------------------
#for f in trainset:
# print f , FilenameCategoriesDict[f]
#print "Freedom\n"
#for f in testset:
# print f
###--------------------DEBUG STATEMENTS----------------------
def __init__(self):
self.stack = Stack()
self.preprocessor = Preprocessor()
self.memory = {}
self.usedWrite = False
def init():
global model, stdFormulaList, drvFormulaList, concepts, multiWordDict
# Get Data
print "Loading Physics questions from default dataset ..."
train_data = readFile('./Datasets/Training1.csv')
validation_data = readFile('./Datasets/Validation1.csv')
test_data = readFile('./Datasets/Testing1.csv')
print "Done!"
# Get Domain Knowledge
print "Loading Domain Knowledge ..."
DM.init()
stdFormulaList = DM.getStdFormulaList()
drvFormulaList = DM.getDrvFormulaList()
concepts = DM.getConcepts()
multiWordDict = DM.getMultiWordDict()
print "Done!"
# Generate Matrix
print "Generating feature matrix for training ..."
words = pp.parse(train_data[0,0], multiWordDict)
X_Train = getFeatureVector(words)
for i in range(1, train_data.shape[0]):
words = pp.parse(train_data[i,0], multiWordDict)
X_Train = np.vstack([X_Train, getFeatureVector(words)])
words = pp.parse(validation_data[0,0], multiWordDict)
X_Validation = getFeatureVector(words)
for i in range(1, validation_data.shape[0]):
words = pp.parse(validation_data[i,0], multiWordDict)
X_Validation = np.vstack([X_Validation, getFeatureVector(words)])
words = pp.parse(test_data[0,0], multiWordDict)
X_Test = getFeatureVector(words)
for i in range(1, test_data.shape[0]):
words = pp.parse(test_data[i,0], multiWordDict)
X_Test = np.vstack([X_Test, getFeatureVector(words)])
Y_Train = train_data[:,1]
Y_Validation = validation_data[:,1]
Y_Test = test_data[:,1]
print "Done!"
# Train the model
print "Training ..."
model.fit(X_Train, Y_Train)
print "Done!"
# Predict the result
print "\n Training Data:"
print "Prediction " + str(model.predict(X_Train))
print "Actual " + str(Y_Train)
print "Accuracy " + str(model.score(X_Train,Y_Train)*100) + "%"
print "\n Vaildation Data:"
print "Prediction " + str(model.predict(X_Validation))
print "Actual " + str(Y_Validation)
print "Accuracy " + str(model.score(X_Validation,Y_Validation)*100) + "%"
print "\n Test Data:"
print "Prediction " + str(model.predict(X_Test))
print "Actual " + str(Y_Test)
print "Accuracy " + str(model.score(X_Test,Y_Test)*100) + "%"
def getCategory(text):
global model, multiWordDict
words = pp.parse(text, multiWordDict)
return getClassName(model.predict([getFeatureVector(words)]))
class Interpreter:
stack = None
preprocessor = None
memory = None
Keywords = [';', ':', 'DO', 'LOOP', 'BEGIN', 'UNTIL', 'EXPECT', 'LEAVE','DUP', 'SWAP', 'DROP', 'EMIT', 'MOD', 'KEY', 'DEPTH', 'ROLL', 'PICK']
def __init__(self):
self.stack = Stack()
self.preprocessor = Preprocessor()
self.memory = {}
self.usedWrite = False
def interact(self):
while True:
try:
line = raw_input('>>> ')
line = self.preprocessor.preprocess(line)
self.process(line)
if self.usedWrite:
sys.stdout.write('\n')
self.usedWrite = False
except Exception as e:
print e.message
def push(self, statement):
try:
print '>>>', statement
statement = self.preprocessor.preprocess(statement)
self.process(statement)
if self.usedWrite:
sys.stdout.write('\n')
self.usedWrite = False
except Exception as e:
print e.message
def process(self, line, is_inside_until=False):
statement = Tokenizer(line.strip(' ').strip('\n'), ' ')
while statement.has_next():
token = statement.next()
if token in ['.', '.s', 'CR']:
if token == '.':
sys.stdout.write(self.stack.pop())
self.usedWrite = True
elif token == 'CR':
print
else:
print self.stack
elif token in self.memory:
self.process(self.memory[token])
elif token in ['DUP', '+', '-', '*', '/', 'SWAP', 'DROP', '<', '>', '<=', '>=', '=', 'EMIT', 'MOD', 'KEY',
'DEPTH', 'ROLL', 'PICK']:
import Operators
Operators.Op[token](self.stack)
elif token == ':':
self.function_definition(statement)
elif token == 'IF':
self.handle_if(statement)
elif token == 'DO':
self.do_loop(statement)
elif token == 'BEGIN':
self.until_loop(statement)
elif token == 'LEAVE':
if is_inside_until and self.stack.pop() != '0':
return False
elif token == 'EXPECT':
self.expect()
elif token.startswith('."'):
self.print_string(token, statement)
elif token.isdigit():
self.stack.push(token)
elif token.strip() == '':
pass
else:
raise NameError('Invalid Input: ' + token)
return True
def print_string(self, current_token, statement):
if current_token.endswith('"'):
print current_token[2:-1]
else:
# If the string contains white space then we need to connect its parts.
string = current_token
while statement.has_next():
token = statement.next()
string = string + ' ' + token
if string.endswith('"'):
break
print string[2:-1]
def expect(self):
count = int(self.stack.pop())
string = ''
while len(string) < count:
string = string + raw_input('... ')
for index in xrange(count):
self.stack.push(string[index])
def until_loop(self, statement):
loop_body = ''
pair = 1
while statement.has_next():
token = statement.next()
if token == 'UNTIL' and pair == 1:
pair -= 1
break
elif token == 'BEGIN':
pair += 1
elif token == 'UNTIL' and pair > 1:
pair -= 1
loop_body = loop_body + ' ' + token
if pair != 0:
raise NameError('Unmatched UNTIL Block')
while True:
# If not means if LEAVE occurred
if not self.process(loop_body, is_inside_until=True):
break
elif self.stack.top() == '0':
break
def do_loop(self, statement):
loop_body = ''
pair = 1
while statement.has_next():
token = statement.next()
if token == 'DO':
pair += 1
elif token == 'LOOP' and pair > 1:
pair -= 1
elif token == 'LOOP' and pair == 1:
pair -= 1
break
loop_body = loop_body + ' ' + token
if pair != 0:
raise NameError('Unmatched DO Block')
start = int(self.stack.pop())
end = int(self.stack.pop())
for i in xrange(start, end):
self.process(loop_body)
def handle_if(self, statement):
condition = bool(int(self.stack.pop())) #if the top was !=0 its True otherwise its False
if_body = ''
pair = 1
if condition:
while statement.has_next():
token = statement.next()
if token == 'IF':
pair += 1
elif token == 'ELSE' and pair == 1: # if pair was one ELSE is for the current if
break
elif token == 'THEN' and pair > 1: # means that the THEN is not for the currect if but in its body
pair -= 1
elif token == 'THEN' and pair == 1: # THEN is for the current if
pair -= 1
break
if_body = if_body + ' ' + token
if pair != 0:
while statement.has_next():
token = statement.next()
if token == 'IF': #if it was breaked with else it will ignore everything till THEN of the current if
pair += 1
elif token == 'THEN':
pair -= 1
elif token == 'THEN' and pair == 1:
pair -= 1
break
else:
collect = False
while statement.has_next():
token = statement.next()
if token == 'IF':
pair += 1
elif token == 'ELSE' and pair == 1:
collect = True
continue
elif token == 'THEN' and pair > 1:
pair -= 1
elif token == 'THEN' and pair == 1:
pair -= 1
break
if collect:
if_body = if_body + ' ' + token
if pair != 0:
raise NameError('Unmatched IF Block')
self.process(if_body)
def function_definition(self, statement):
name = statement.next()
self.memory[name] = ''
while statement.has_next():
token = statement.next()
# Return when we reach end of the function definition.
if token == ';':
return
self.memory[name] = self.memory[name] + ' ' + token
raise NameError('Syntax Error')
def loadSentiTokens(path,pathExceptions):
f = codecs.open(path,"r", "utf-8")
adjectives = []
firstLine = f.next()
exceptions = unicode(loadExceptionTokens(pathExceptions))
lemmaRegex = ",(.*?)\."
flexRegex = "^(.*?),"
polarityRegex = "POL:..=(-1|0|1)(?:;|$)"
posRegex = "PoS=(.*?);"
currentLemma = re.search(lemmaRegex,firstLine).group(1)
pol = re.findall(polarityRegex,firstLine)
pol1 = int(pol[0])
try:
pol2 = int(pol[1])
except IndexError:
pol2 = 0
currentPolarity = str(pol1 + pol2)
currentPos = re.search(posRegex,firstLine).group(1).lower()
currentFlex = re.search(flexRegex,firstLine).group(1)
currentFlexions = []
currentFlexions.append(currentFlex)
if currentFlex not in exceptions and currentFlex != Preprocessor.normalize(currentFlex):
currentFlexions.append(Preprocessor.normalize(currentFlex))
for line in f:
try:
if "REV=Amb" not in line:
lemma = re.search(lemmaRegex,line).group(1)
#print re.search(lemmaRegex,line).groups()
if lemma != currentLemma:
if currentLemma not in exceptions and currentLemma != Preprocessor.normalize(currentLemma):
currentFlexions.append(Preprocessor.normalize(currentLemma))
adjectives.append(SentiToken(currentLemma,currentPolarity,currentPos,currentFlexions))
currentLemma = lemma
#currentPolarity = re.search(polarityRegex,line).group(1)
pol = re.findall(polarityRegex,line)
pol1 = int(pol[0])
try:
pol2 = int(pol[1])
except IndexError:
pol2 = 0
currentPolarity = str(pol1 + pol2)
currentPos = re.search(posRegex,line).group(1).lower()
currentFlexions = []
currentFlex = re.search(flexRegex,line).group(1)
currentFlexions.append(currentFlex)
#print "L:", currentLemma,"P:",currentPolarity,"POS:",currentPos,"F:",currentFlex
if currentFlex not in exceptions and currentFlex != Preprocessor.normalize(currentFlex):
currentFlexions.append(Preprocessor.normalize(currentFlex))
else:
currentFlex = re.search(flexRegex,line).group(1)
currentFlexions.append(currentFlex)
#print "l:", lemma, "f:", currentFlex, "p:", currentPos
if currentFlex not in exceptions and currentFlex != Preprocessor.normalize(currentFlex):
currentFlexions.append(Preprocessor.normalize(currentFlex))
except:
None
f.close()
return adjectives
def generateTextFeatures(sourceFile,destinyFile):
corpus = codecs.open(sourceFile,"r","utf-8")
politicians = getPoliticians()
sentiTokens = getSentiTokens()
#rulesClassifier = EuroOpinionizers.Rules(politicians,sentiTokens)
#naiveClassifier = EuroOpinionizers.Naive(politicians,sentiTokens)
multiWordTokenizer = getMultiWordsTokenizer(politicians, sentiTokens)
listOfTweets = []
i=0
headers = """
@relation twitometro
@ATTRIBUTE ID NUMERIC
@ATTRIBUTE tweet STRING
@ATTRIBUTE polarity? {-1,0,1}
@data
"""
featuresFile = open(destinyFile,"w")
featuresFile.write(headers)
for line in corpus:
tweet = line.replace("\"","\'").split('|')
#skip the first line
if tweet[0] != 'PERIOD':
sentence = ''
for block in tweet[TEXT:]:
sentence = sentence + block
tokenizedSentence = multiWordTokenizer.tokenizeMultiWords(sentence)
tokenizedSentence = Preprocessor.removeURLs(tokenizedSentence)
tokenizedSentence = Preprocessor.removeUsernames(tokenizedSentence)
tokenizedSentence = tokenizedSentence.replace(tweet[MENTION]," <TARGET> ")
tokenizedSentence = Preprocessor.removeStopWords(tokenizedSentence)
tokenizedSentence = Preprocessor.separateSpecialSymbols(tokenizedSentence)
tokenizedSentence = tokenizedSentence.replace("\n","")
tokenizedSentence = tokenizedSentence.replace(","," ")
featuresFile.write(tweet[ID]+",\""+tokenizedSentence+"\","+tweet[SENTIMENT_POLARITY]+"\n")
print sentence + " --> " + tokenizedSentence
"""
o = Opinion(id = tweet[ID],
sentence = unicode(sentence),
processedSentence = unicode(tokenizedSentence),
target = unicode(tweet[TARGET]),
mention = unicode(tweet[MENTION]),
polarity = int(tweet[SENTIMENT_POLARITY]))
listOfTweets.append(o)
"""
i = i+1
"""
if i!=0 and i%20 == 0:
break
"""
featuresFile.close()
