def lemma_tokenize(paragraph):
lmtzr = WordNetLemmatizer()
try:
return [lmtzr.lemmatize(word).lower() for sentence in tokenize(paragraph) for word in sentence]
except LookupError:
nltk.download('wordnet')
return [lmtzr.lemmatize(word).lower() for sentence in tokenize(paragraph) for word in sentence]
def parse_stories(lines, only_supporting=False):
'''Parse stories provided in the bAbi tasks format
If only_supporting is true, only the sentences that support the answer are kept.
'''
data = []
story = []
for line in lines:
line = line.decode('utf-8').strip()
nid, line = line.split(' ', 1)
nid = int(nid)
if nid == 1:
story = []
if '\t' in line:
q, a, supporting = line.split('\t')
q = tokenize(q)
substory = None
if only_supporting:
# Only select the related substory
supporting = map(int, supporting.split())
substory = [story[i - 1] for i in supporting]
else:
# Provide all the substories
substory = [x for x in story if x]
data.append((substory, q, a))
story.append('')
else:
sent = tokenize(line)
story.append(sent)
return data
def generation(cluster_matrix):
# Alternative distance metric:
# for each storyline-word, find most similar words;
# filter for words that are reasonably close to other storyline-words
allpool = []
for cluster in cluster_matrix:
pool = []
try:
for word in cluster:
# find candidate words
try:
thisword_tk = nltk.tokenize(word)
thistag_tk = nltk.pos_tag(thisword_tk)
if (thisword_tk[1] not in tagdict):
continue
except:
continue
cand = [
tup[0] for tup in glove_model.most_similar(word, topn=200)
]
# calculate distances from candidate words to other storyline-words
cand2clust_dists = np.sum([
glove_model.distances(x, cand)
for x in cluster if x != word
],
axis=0)
# indexes of qualified words in cand (comparing among themselves)
indexes = cand2clust_dists.argsort()[:200] # get top 25
keep = set()
tagdict = [
'NN', 'NNS', 'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ', 'JJ',
'JJR', 'JJS', 'RB', 'RBR', 'RBS'
]
print(cand)
smallest = len(cand)
if smallest > 200:
smallest = 200
for i in range(0, smallest):
try:
word_tk = nltk.tokenize(cand[i])
tag_tk = nltk.pos_tag(word_tk)
if (tag_tk[1] in tagdict):
keep.add(cand[i])
except:
continue
if len(keep) == 25:
break
# OR, comparing with all vocab
# indexes of words whose total distance to other storyline-words is among top 1% of all vocab
#top_dist = np.percentile(np.sum([glove_model.distances(x) for x in cluster if x!=word], axis=0),1)
#keep = [cand[i] for i in range(len(cand2clust_dists)) if cand2clust_dists[i] <= top_dist]
pool = pool + keep
print(pool)
allpool = allpool + pool
except:
print("Sad!!")
return None
return allpool
def jaccard_similarity(a, b, threshold=0.5):
"""Check if a and b are matches."""
tokens_a = [token.lower().strip(string.punctuation) for token in tokenize(a) \
if token.lower().strip(string.punctuation) not in stopwords]
tokens_b = [token.lower().strip(string.punctuation) for token in tokenize(b) \
if token.lower().strip(string.punctuation) not in stopwords]
# Calculate Jaccard similarity
ratio = len(set(tokens_a).intersection(tokens_b)) / float(
len(set(tokens_a).union(tokens_b)))
return ratio
def compute_similarity(text1, text2):
w1, w2 = tokenize(text1), tokenize(text2)
# create a sorted common vocabulary for the two lists of words
common_vocab = sorted(set(w1) | set(w2))
# create count vectors of the same lenght for the two lists of words
v1 = count_vectorize(w1, common_vocab)
v2 = count_vectorize(w2, common_vocab)
distance = compute_distance(v1, v2)
return distance
def chat_with_robo():
parser = Parser()
flag = True
print("The instructions for talk with me: \n",
"If you want finish the conversation, please type thanks or bye.\n")
print("ROBO: Hi, my name is Robo.")
while flag == True:
message = input()
message = message.lower()
if message != 'bye':
# Analyzing the input
print('\nvocabulary: ', nltk.tokenize(message))
print('\nword frequency: ' +
nltk.FreqDist(nltk.tokenize(message)).most_common(10))
# -----------
# add part-of-speech tags to text
# -----------
# Tagging message with basic nltk tokenize
print(nltk.pos_tag(nltk.word_tokenize(message)))
# Tiene problemas con la identificación del pronombre 'I', lo pone como noun (sustantivo)
# Tagging message
# trace = 1: then the parser will report the steps that it takes as it parses a text.
# rd_parser = nltk.RecursiveDescentParser(, trace = 1)
# Review grammar
# rd_parser = nltk.RecursiveDescentParser(nltk.ChartParser)
rd_parser = parser.parse(message)
i = 1
for tree_struc in rd_parser:
print(str(i) + 'tree_struc: ', tree_struc)
wrong_syntax = 1
s = tree_struc
wrong_syntax = 0
print("\n Correct Grammar")
i += 1
if wrong_syntax == 1:
print("\n Wrong Grammar")
# write_output_file(...
else:
flag = False
print("ROBO: Bye! take care..")
def is_text_initial(term, text, start_within=5, ignore_case=True):
if type(term) != type(list()):
if ignore_case: term = term.lower()
term_tokens = tokenize(term)
else:
term_tokens = [token.lower() for token in term] if ignore_case else term
if type(text) != type(list()):
if ignore_case: text = text.lower()
text_tokens = tokenize(text)
else:
text_tokens = [token.lower() for token in text] if ignore_case else text
spacey_text = ' '+(' '.join(text_tokens[:start_within-1+len(term_tokens)]))+' '
spacey_term = ' '+(' '.join(term_tokens))+' '
return spacey_term in spacey_text
def predict(text):
tokens = tokenize(text)
i = 0
prep, origin = prepare_tokens(tokens[i:i + 200])
pred = model.predict(prep)
origin = fix_holes(origin, pred)
while i * 200 < len(origin):
p, o = prepare_tokens(tokens[i:i + 200])
i += 1
pred = model.predict(p)
o = fix_holes(o, pred)
origin = origin + o
return origin, avg(origin)
#print(prepare_text(a), model.predict(pa))
def process_line(num, line):
global docs
docs.append(doc2vec.LabeledSentence(words=nltk.tokenize(line), labels=["SENT_" + str(num)]))
if (len(docs) > 100):
doc2vec_model.build_vocab(docs)
doc2vec_model.train(random.shuffle(docs))
docs = []
def load_lists():
truelist = set()
phrase_truelist = defaultdict(set)
module_file = inspect.getfile(inspect.currentframe())
module_dir = os.path.dirname(os.path.abspath(module_file))
truelist_file = os.path.join(module_dir, "truelist")
for line in open(truelist_file):
line = line.split("#")[0].strip()
if line == "":
continue
assert not any(
is_hyphen(c) for c in
line), f'Truelist entries should not contain hyphens: {line}'
if ' ' not in line:
truelist.add(line)
else:
toks = tuple(tokenize(line))
phrase_truelist[len(toks)].add(
toks) # group phrases by number of tokens
phrase_truelist = sorted(phrase_truelist.items(),
reverse=True) # bins sorted by phrase length
special_file = os.path.join(module_dir, "special-case-titles")
with open(special_file) as inF:
special_titles = {
line.strip().lower(): line.strip()
for line in inF if line.strip()
}
amodifiers = (
'North',
'South',
'East',
'West',
'Northeast',
'Northwest',
'Southeast',
'Southwest',
'Central',
'Northern',
'Southern',
'Eastern',
'Western',
'Northeastern',
'Northwestern',
'Southeastern',
'Southwestern',
'Modern',
'Ancient',
) # use subsequent word to determine fixed-case. will miss hyphenated modifiers (e.g. South-East)
ndescriptors = (
'Bay',
'Coast',
'Gulf',
'Island',
'Isle',
'Lake',
'Republic',
'University',
) # use preceding word to determine fixed-case
return truelist, phrase_truelist, special_titles, amodifiers, ndescriptors
def junk_count(html):
tokens = tokenize(BS(html).get_text())
tokens = [
token for token in tokens
if not token.isalpha() and token not in string.punctuation
]
return len(tokens)
def predict(text):
tokens = tokenize(text)
i = 0
prep, origin = prepare_tokens(tokens[i:i + 200])
pred = model.predict(prep)
origin = fix_holes(origin, pred)
score = overall_model.predict(prep)[0][0]
cnt = 1
while i * 200 < len(origin):
p, o = prepare_tokens(tokens[i:i + 200])
i += 1
pred = model.predict(p)
o = fix_holes(o, pred)
origin = origin + o
score += overall_model.predict(prep)[0][0]
cnt += 1
return origin, score / cnt
#print(prepare_text(a), model.predict(pa))
def prepare_text(text):
tokens = tokenize(text)
original = []
filteredTokens = []
for x in tokens:
if x in w2v.vocab:
filteredTokens.append(x)
original.append(0.0)
else:
original.append(None)
# for word in filteredTokens:
#
#
# output.append(w2v.word_vec(word))
output = list(map(lambda word: w2v.word_vec(word), filteredTokens))
if len(output) > 200:
output = output[:200]
else:
while len(output) < 200:
output.append([0.0] * 300)
return np.array([output]), original
def get_tokenized_dialog_lines(iterable_dialog_lines):
"""
Tokenizes with nltk tokenizer, adds START_TOKEND, EOS_SYMBOL
:param iterable_dialog_lines: IterableSentences
:return: IterableSentences
"""
return iterable_dialog_lines.add_postprocessing(lambda x: [START_TOKEN] + tokenize(x) + [EOS_SYMBOL])
def rank_sentences( tagdict, tags, topK, cooccurances, probs):
stop_lst = set(filter_reviews.get_stop_lst())
punctuation = set(['.',',','?','!','\'','\"','`','``','*','-','/','+'])
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
tokenizer2 = RegexpTokenizer(r'(\w|\')+')
tag_sentence_rank = {}
stemmer = PorterStemmer()
tokenize = tokenizer2.tokenize
stem = stemmer.stem
for tag in tags:
candidates = tagdict[tag]
if len(candidates) <= topK:
tag_sentence_rank[tag] = candidates
continue
scores = []
for (reviewIdx, sentence) in candidates:
score = 0
tokens = tokenize(sentence)
clean_line = [stem(token) for token in tokens if token not in stop_lst and token not in punctuation and token.isalpha()]
score = ( score + score_sentence_tag(clean_line, tag, cooccurances, probs) + 1) / (1.0*len(clean_line) + 1.0)
scores.append((score, (reviewIdx, sentence) ))
#max(scores)
ret = sorted(scores, key=lambda score_sent: score_sent[0], reverse=True)[:topK]
tag_sentence_rank[tag] = ret
#for i,pair in enumerate(candidates):
# if scores[i] >= lowest_score
return tag_sentence_rank
def post(self):
text = self.get_argument("rawtext")
relations = []
entities = []
tokens = []
IN = re.compile(r'.*\bin\b')
doc.headline = ['a']
def tokenize(text):
for sentence in nltk.sent_tokenize(text):
for chunk in nltk.ne_chunk(nltk.pos_tag(nltk.word_tokenize(sentence))):
if hasattr(chunk, 'node'):
if chunk.node != 'GPE':
tmp_tree = nltk.Tree(chunk.node, [(' '.join(c[0] for c in chunk.leaves()))])
else:
tmp_tree = nltk.Tree('LOCATION', [(' '.join(c[0] for c in chunk.leaves()))])
tokens.append(tmp_tree)
entities.append(tmp_tree)
else:
tokens.append(chunk[0])
return tokens
def extract_people_in_locations():
for rel in nltk.sem.extract_rels('PERSON' , 'LOCATION', doc, corpus='ieer', pattern=IN):
filler_tokens = dict(nltk.pos_tag(nltk.word_tokenize(rel['filler'])))
tmp = rel['subjtext'] + " is in " + rel['objtext']
relations.append(tmp)
doc.text = tokenize(text)
#print doc.text
extract_people_in_locations()
self.render("extractor_post.html", text=text, entities=entities, relations=relations)
def comparison(p, plist):
# print(p,plist)
plisttok = tokenize_sents(plist)
ptok = tokenize(p)
# print(plisttok)
data = rank_one.article2queries(plisttok, ptok, 1)
return data[0]['driver']
def load_file(path):
text = ''
with open(path, 'r') as f:
text = f.read()
tokens = tokenize(text)
filteredTokens = filter(lambda x: x in w2v.vocab, tokens)
filteredTokens = list(filteredTokens)
output = []
# for word in filteredTokens:
#
#
# output.append(w2v.word_vec(word))
output = list(map(lambda word: w2v.word_vec(word), filteredTokens))
if len(output) > timesteps:
output = output[:timesteps]
else:
while len(output) < timesteps:
output.append([0.0] * 300)
return np.array(output)
def __iter__(self):
with open(self.file,'r') as fp:
line = fp.readline()
while line :
if line != '':
tockenLine = ''.join(tokenize(line))
word_sentences = [word for word in tockenLine.split()]
yield word_sentences
def preProcess(df):
df.sentence = tokenize(df)
df.sentence = removePunctuation(df)
df.sentence = textNormalize(df)
df.sentence = toLower(df)
df.sentence = stemming(df)
# return tfidf(df)
return df.sentence
def __iter__(self):
with open(self.file, 'r') as fp:
line = fp.readline()
while line:
if line != '':
tockenLine = ''.join(tokenize(line))
word_sentences = [word for word in tockenLine.split()]
yield word_sentences
def train(self):
with open("../../LSTM/data/sentiment/trainsentence_and_label_binary.txt", 'r') as filedata:
data = filedata.readlines()
tokenized_sentences_with_labels = []
for sent in data:
tokenized = nltk.tokenize(sent.lower())
tokenized_sentences_with_labels.append((int(tokenized[0]), tokenized[1:]))
def generateTextObservations(text):
X = []
text_tokens = nltk.tokenize(text)
for word in text_tokens:
X.append(vec[word])
return X
def process_text():
try:
for words in tokenized[:5]:
tokenized_words=nltk.tokenize(words)
part_of_speech_tag = nltk.pos_tag(tokenized_words)
print(part_of_speech_tag)
except Exception as e:
print(str(e))
def shinglize(s, n):
"""
return size n shingles for the string s
"""
shingles = set()
tokens = tokenize(s)
for i in range(len(tokens) - n + 1):
shingles.add('_'.join(tokens[i:i+n]))
return shingles
def shinglize(s, n):
"""
return size n shingles for the string s
"""
shingles = set()
tokens = tokenize(s)
for i in range(len(tokens) - n + 1):
shingles.add('_'.join(tokens[i:i + n]))
return shingles
def word_counts(html):
lem = WordNetLemmatizer()
tokens = tokenize(BS(html).get_text())
tokens = [
lem.lemmatizer(token.lowercase) for token in tokens
if token not in string.punctuation
]
return make_dict(tokens)
def get_tokens(string):
global stop_words
rtn = []
tokens = tokenize(string)
for token in tokens:
if token.isalnum() and token not in stop_words:
rtn.append(token)
return rtn
def _processline(self,line):
tokens=["__START"]+tokenize(line)+["__END"]
previous="__END"
for token in tokens:
self.unigram[token]=self.unigram.get(token,0)+1
current=self.bigram.get(previous,{})
current[token]=current.get(token,0)+1
self.bigram[previous]=current
previous=token
def getBagOfWords(categories, stars, maxNumberOfReviewToUse):
reviews = getSetOfReviews(categories, stars, maxNumberOfReviewToUse)
output = {} #string:int
for currentReview in reviews:
for token in nltk.tokenize(currentReview):
if token in output.keys():
output[token]+=1
else:
output[token] = 1
return output
def compute_prob_line(self,line,methodparams={}):
#this will add _start to the beginning of a line of text
#compute the probability of the line according to the desired model
#and returns probability together with number of tokens
tokens=["__START"]+tokenize(line)+["__END"]
acc=0
for i,token in enumerate(tokens[1:]):
acc+=math.log(self.get_prob(token,tokens[:i+1],methodparams))
return acc,len(tokens[1:])
def count_tokens_in_chunk(idx, chunk):
print("Processing chunk", idx)
counts = Counter()
for dialog in chunk:
for utterance in dialog:
tokens = tokenize(utterance["text"])
counts += Counter(tokens)
return counts
def word_tokens(text_or_stream):
def tokenize(text):
hold_back = None
skip = False
for word in nltk.tokenize.word_tokenize(text):
if hold_back is not None:
if word == hold_back[0]:
yield Token(hold_back[0])
yield Token(hold_back[1])
yield Token(word)
skip = True
else:
yield Token(hold_back[0] + hold_back[1])
hold_back = None
if not skip:
if word.startswith(Token.APOSTROPHE):
# Use hold_back to fix tokenization errors of the form:
# | input | output | expected |
# | ------ | ------- | -------- |
# | 'word' | 'word ' | ' word ' |
hold_back = (word[0], word[1:])
else:
hold_back = None
if hold_back is None:
yield Token(word)
skip = False
if hold_back is not None:
yield Token(hold_back[0] + hold_back[1])
if isinstance(text_or_stream, str):
for token in tokenize(text_or_stream):
yield token
else:
for text in text_or_stream:
for token in tokenize(text):
yield token
def punc_count(html):
tokens = tokenize(BS(html.get_text()))
count = 0
for token in tokens:
if len(token) > 1:
for char in token:
if char in string.punctuation:
count += 1
break
return count
def compute_yules_k_for_text(sentence):
tokens = tokenize(sentence)
counter = Counter(token.upper() for token in tokens)
#compute number of word forms in a given sentence/text
m1 = sum(counter.values())
m2 = sum([frequency ** 2 for frequency in counter.values()])
#compute yules k measure and return the value
yules_k = 10000/((m1 * m1) / (m2 - m1))
return yules_k
def tfidf(documents):
tokenized_documents = [tokenize(d) for d in documents]
idf = inverse_document_frequencies(tokenized_documents)
tfidf_documents = []
for document in tokenized_documents:
doc_tfidf = []
for term in idf.keys():
tf = sublinear_term_frequency(term, document)
doc_tfidf.append(tf * idf[term])
tfidf_documents.append(doc_tfidf)
return tfidf_documents
def is_match(a, b):
"""Check if a and b are matches."""
pos_a = map(get_wordnet_pos, nltk.pos_tag(tokenize(a)))
pos_b = map(get_wordnet_pos, nltk.pos_tag(tokenize(b)))
lemmae_a = [
lemmatizer.lemmatize(token.lower().strip(string.punctuation), pos)
for token, pos in pos_a
if pos == wordnet.NOUN and token.lower().strip(string.punctuation)
not in stopwords
]
lemmae_b = [
lemmatizer.lemmatize(token.lower().strip(string.punctuation), pos)
for token, pos in pos_b
if pos == wordnet.NOUN and token.lower().strip(string.punctuation)
not in stopwords
]
# Calculate Jaccard similarity
intersect = set(lemmae_a).intersection(lemmae_b)
union = set(lemmae_a).union(lemmae_b)
return len(intersect) / float(len(union))
def get_lines_for_validation(validation_set_path, index_to_token):
with codecs.open(validation_set_path, 'r', 'utf-8') as dataset_fh:
lines = dataset_fh.readlines()
lines = [tokenize(line.strip()) for line in lines]
screened_lines = get_transformed_dialog_lines(lines, index_to_token.values())
# return true array, not iterator
lines_for_validation = []
for line in screened_lines:
lines_for_validation.append(line)
return lines_for_validation
def parse_text_to_stems(language, text, min_length=3):
""" Parse a text attribute performing cleanup, tokenization, stemmization and removal of stop-words.
:param language: The text language, relevant for stemmization.
:param text: The text to be stemmized.
:param min_length: The minimum number of characters that a word must have; otherwise it is discarded.
:returns: A list of terms.
"""
text = re.sub(" +", " ", text).lower()
tokens = tokenize(text)
stems = get_stems(tokens, language)
return remove_stopwords(stems, language, min_length)
def file2tokenized_list(files, lower=True, encoding="utf-8"):
"""
Takes a filepath, or a list of filepaths and returns lists of tokenized
strings.
You can actually also feed it a dictionary of file path lists, where each
key of the dictionary represents some category. If you chose to feed it a
dictionary, then the output will be a tuple with 3 values.
tokenized_list = the usual lists of tokenized strings.
labels = a list containing integer labels corresponding to the
category that each element of tokenized_list belongs to
cats = A list of the unique category names. Indices of the
names correspond to the integer values used in labels
such that cats[labels[i]] gives you the original name
for the category that the ith training example belongs
to.
:param files: (str, or list of strings or dict)
String of a single file path, or a list of file path strings.
:param lower: (bool)(default = True)
Convert all text to lowercase?
:param encoding: (str)(default = "utf-8")
Encoding used in the text files
:return:
A list of lists of tokenized strings (if files is a string or a list of
strings)
A tuple of 3 items if files is a dictionary containing lists of strings.
(see the description section for more details about the 3 elements
returned)
"""
# ==========================================================================
#TODO: add argument replacements, which is a dictionary of regex replacements.
# whenever it encounters some pattern, replarce it with some other text.
print("Generating a tokenised list from files")
if isinstance(files, dict):
return dict_file2tokenized_list(files, lower, encoding)
if isinstance(files, str):
files = [files]
num_items = len(files)
tokenized_list = ["MISSING"] * num_items # Will store the tokenised text
for i in range(num_items):
with open(files[i], "r") as textFile:
text = textFile.read()
text = text.decode(encoding)
if lower:
text = text.lower()
tokenized_list[i] = tokenize(text)
print("---Done!")
return tokenized_list
def main():
data = "data/top_100_entities.txt"
pathToData = "data/funnyReviews/"
fileName = "rev_data_"
suffix = ".txt"
lines = []
with open(data, 'r') as f:
lines = f.readlines()
entity_weight = []
wn_entities = []
for line in lines:
key = line.split(",")[0].split(":")[1].strip()
value = line.split(",")[1].split(":")[1].strip()
#print wn.synsets(key, pos=wn.NOUN)
wn_entities.append(wn.synsets(key, pos=wn.NOUN)[0])
entity_weight.append( (key, value) )
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
tokenizer2 = RegexpTokenizer(r'(\w|\')+')
stemmer = PorterStemmer()
tokenize = tokenizer2.tokenize
stem = stemmer.stem
stop_lst = get_stop_lst()
punctuation = set(['.',',','?','!','\'','\"','`','``','*','-','/','+'])
review = ""
with open(pathToData + fileName + str(1) + suffix, 'r') as f:
review = f.read()
review_lines = tokenizer.tokenize(review.lower())
scores = []
for sentence in review_lines:
tokens = tokenize(sentence)
clean_line = [stem(token) for token in tokens if token not in stop_lst and token not in punctuation and token.isalpha()]
#print clean_line
for item in clean_line:
word1 = wn.synsets(item, pos=wn.NOUN)
if len(word1) > 0:
if word1[0] in wn_entities:
print word1[0]
# score = ( score + score_sentence_tag(clean_line, tag, cooccurances, probs) + 1) / (1.0*len(clean_line) + 1.0)
return 0
def mark_possible_duplicates(dict_list, key):
"""
Marks the possible duplicates strings
"""
number_of_titles = len(dict_list)
full_match_criteria = 0.9
# Build a list of token sets for the strings in the key
token_list = []
for i in range(number_of_titles):
# Removes stop words to create token set
value = tokenize(dict_list[i][key])
value = remove_stop_words(value)
token_list.append(value)
# Dict of objects with the structure {(i:j) : score}
similarity_map = {}
score_threshold = 0.5
# Indexes of tokens will match indexes in dict_list
for i in range(number_of_titles):
# Trying brute forces comparison of all items (O(n^2)/2), seems fast enough for this
for j in range(i+1, number_of_titles):
score = get_token_set_match_ratio(token_list[i], token_list[j])
if score >= score_threshold:
similarity_map[(i, j)] = score
partial_matches = 0
full_matches = 0
for k, val in sorted(similarity_map.iteritems()):
# print(k)
# print(val)
if (val < full_match_criteria):
# print(dict_list[k[0]][key]).encode('utf-8')
# print(dict_list[k[1]][key]).encode('utf-8')
# print('Similarity score: ' + str(val))
partial_matches +=1
else:
mark_database_for_full_match(k, dict_list)
full_matches += 1
add_match_clusters(similarity_map, dict_list)
remove_match(similarity_map, dict_list, threshold = full_match_criteria)
remove_ids_for_corrected_clusters(dict_list)
print('Partial matches: ' + str(partial_matches))
print('Full matches: ' + str(full_matches))
return dict_list
def dict_file2tokenized_list(files, lower=True, encoding="utf-8"):
"""
Takes a dictionary of file path lists, where each key of the dictionary
represents some category.
The output will be a tuple with 3 values.
tokenized_list = lists of tokenized strings.
labels = a list containing integer labels corresponding to the
category that each element of tokenized_list belongs to
cats = A list of the unique category names. Indices of the
names correspond to the integer values used in labels
such that cats[labels[i]] gives you the original name
for the category that the ith training example belongs
to.
:param files: (str, or list of strings or dict)
String of a single file path, or a list of file path strings.
:param lower: (bool)(default = True)
Convert all text to lowercase?
:param encoding: (str)(default = "utf-8")
Encoding used in the text files
:return:
A tuple of 3 items if files is a dictionary containing lists of strings.
(see the description section for more details about the 3 elements
returned)
"""
# ==========================================================================
cats = files.keys()
num_per_category = {cat: len(files[cat]) for cat in cats}
num_items = sum(num_per_category.values())
tokenized_list = ["MISSING"] * num_items # Will store the tokenised text
labels = ["MISSING"] * num_items # Will store the labels
running_index = 0
for cat_i, cat in enumerate(cats):
#num_items_for_cat =
for example_i in range(num_per_category[cat]):
with open(files[cat][example_i], "r") as textFile:
text = textFile.read()
text = text.decode(encoding)
if lower:
text = text.lower()
tokenized_list[running_index] = tokenize(text)
labels[running_index] = cat_i
running_index += 1
print("---Done!")
return (tokenized_list, labels, cats)
def get_prediction(inp):
tokens = tokenize(inp)
if len(tokens) > timesteps:
import sys
sys.stderr.write("Exceeding allowed input length. "
"Cutting off after {} tokens.".format(timesteps))
tokens = tokens[:timesteps] # cut off after max model timesteps
vecs = vectorizer.transform([tokens]) # list of seqs
vecs = vecs.repeat(batch_size, axis=0)
input_lengths = np.array([len(tokens)] * batch_size)
pred, _ = model.step(session, task, vecs, input_lengths, mode="decode")
pred = pred.reshape([batch_size, timesteps, -1])
# Find all candidate words per timestep (all words from top k clusters)
candidates = {}
for t in range(timesteps):
if np.argmax(pred[0][t]) == dio.PAD_ID:
break
candidates[t] = set()
# get the top k clusters
# (http://stackoverflow.com/questions/6910641/)
topclusters = np.argpartition(pred[0][t],
-k_clusters)[-k_clusters:]
for c in topclusters:
candidates[t].update(task.i2l[c]) # expand with this cluster
# Find the optimal sequence from the candidate words using beam search
global lm
if not lm:
lm = kenlm.Model('data/lms/en-70k-0.2-pruned.lm')
hypos = ["<s>"]
for t in range(len(candidates)):
t_hypos = []
content_words = [
w for w, t in nltk.pos_tag(tokens) if t in CONTENT_POS
]
if prune:
copies = candidates[t].intersection(set(content_words))
if copies:
candidates[t] = copies
for cand in candidates[t]:
for h in hypos:
cand_t = h + " " + cand
score = lm.score(cand_t) # get language model score
t_hypos.append((cand_t, score))
# get beam_width highest scoring hypotheses
hypos = [
h for h, s in sorted(t_hypos, key=lambda x: x[1])[-beam_width:]
]
return hypos[:-k_best:-1] # k highest scoring hypos, revert list
def dict_file2tokenized_list(files, lower=True, encoding="utf-8"):
"""
Takes a dictionary of file path lists, where each key of the dictionary
represents some category.
The output will be a tuple with 3 values.
tokenized_list = lists of tokenized strings.
labels = a list containing integer labels corresponding to the
category that each element of tokenized_list belongs to
cats = A list of the unique category names. Indices of the
names correspond to the integer values used in labels
such that cats[labels[i]] gives you the original name
for the category that the ith training example belongs
to.
:param files: (str, or list of strings or dict)
String of a single file path, or a list of file path strings.
:param lower: (bool)(default = True)
Convert all text to lowercase?
:param encoding: (str)(default = "utf-8")
Encoding used in the text files
:return:
A tuple of 3 items if files is a dictionary containing lists of strings.
(see the description section for more details about the 3 elements
returned)
"""
# ==========================================================================
cats = files.keys()
num_per_category = {cat: len(files[cat]) for cat in cats}
num_items = sum(num_per_category.values())
tokenized_list = ["MISSING"] * num_items # Will store the tokenised text
labels = ["MISSING"] * num_items # Will store the labels
running_index = 0
for cat_i, cat in enumerate(cats):
#num_items_for_cat =
for example_i in range(num_per_category[cat]):
with open(files[cat][example_i], "r") as textFile:
text = textFile.read()
text = text.decode(encoding)
if lower:
text = text.lower()
tokenized_list[running_index] = tokenize(text)
labels[running_index] = cat_i
running_index += 1
print("---Done!")
return (tokenized_list, labels, cats)
def get_input_sequence(sentence):
"""
Prepare chatbot's input by tokenizing the sentence and adding necessary punctuation marks.
Input: "So what's up, buddy"
Output: ["so", "what", "'", "s", "up", ",", "buddy", ".", "$$$"]
"""
if not sentence:
return [START_TOKEN, EOS_SYMBOL]
# add a dot to the end of the sent in case there is no punctuation mark
if sentence[-1] not in _PUNKT_MARKS:
sentence += '.'
sequence = [START_TOKEN] + tokenize(sentence) + [EOS_SYMBOL]
return sequence
def json_converter_dfd_orig():
file_list=make_filelist(raw_doc_path_dfd_orig);
for filepath in file_list:
dfd_orig_one_doc_map={}
#ラベルの分解処理
label_list=(os.path.basename(filepath)).split('_')[:-1];
#tokenized_documentはリスト型
tokenized_document=tokenize(filepath);
#オランダ語はわからんが，一応すべて小文字化はしておく
tokenized_document=[t.lower() for t in tokenized_document];
dfd_orig_one_doc_map['labels']=label_list;
dfd_orig_one_doc_map['doc_str']=tokenized_document;
print filepath
with codecs.open(json_doc_path_dfd_orig+os.path.basename(filepath),'w','utf-8') as json_content:
json.dump(dfd_orig_one_doc_map,json_content,ensure_ascii=False,indent=4);
def train(self):
for line in self:
self.i += 1
self.minimum = int(round(math.log(self.i, 10)))
i = 0
for line in self:
tokens = tokenize(decode(line).lower())
targets = self.get_targets(tokens)
for gram in targets:
self.posterior[gram] += 1
self.grams_by_line[i].add(gram)
for token in gram:
self.prior[token] += 1
i += 1
# print self.prior.most_common(10)
# print self.posterior.most_common(10)
self.crunch()
def calculate_tf(lang="", doc=""):
""" Returns a map with all non-stopwords and its respective frequencies.
Ex: {"work": 1, "going": 1}
"""
tf_by_stem = {}
# Cleaning document
doc = re.sub(" +", " ", doc).lower()
tokens = remove_stopwords(tokenize(doc), lang, min_len=3, max_len=30)
stems = get_stems(tokens, lang)
for stem in stems:
tf_by_stem[stem] = tf_by_stem.get(stem, 0) + 1
return tf_by_stem
def main():
try:
reader = csv.reader(open(args.files[0]))
next(reader) # skip header row
except:
print "Error: could not read ", args.files[0]
try:
writer = open(args.files[1], "w")
except:
print "Error: could not write to file", args.files[1]
# stopwords = nltk.corpus.stopwords.words('english')
POS_TAGS = ['CC','CD', 'DT','EX','FW','IN', 'JJ','JJR', 'LS', 'MD',
'NN','NNS', 'NNP','NNPS', 'PDT', 'POS', 'PRP', 'PRP$', 'RB','RBR', 'RBS', 'RP', 'TO',
'UH', 'VB', 'VBG', 'VBN','VBP', 'VBZ', 'WDT','WP', 'WP$', 'WRB']
# First column (label) for each input line should be either 0 or 1 (train) or row number (test)
for line in reader:
if line[0] < 0: # Labels in test and train should be at least 0
next
else:
label = line[0]
if label == "0":
label = "-1"
lineout = label # Initialize lineout to be the label
if NAMESPACE == "C": # No parts of speech
tokens = tokenize(line[2])
lineout = lineout + " |C " + (''.join( str(x) for x in tokens))
elif NAMESPACE == "POS": # Use each part of speech as a namespace for VW
tokens = tokenize_pos(line[2])
for key in tokens:
if key not in POS_TAGS:
next
else:
lineout = lineout + " |" + key + " " + (''.join( str(x) for x in tokens[key])) + " "
else:
print "ERROR - only two namespace options defined"
exit
writer.write(lineout + "\n")
writer.close()
def find_sentences_from_reviews(tag_review_dict):
d = {}
print "looking for sentences"
stop_lst = set(get_stop_lst())
tokenize=tokenizer2.tokenize
stem = stemmer.stem
for tag, reviews in tag_review_dict.iteritems():
print "current tag: ", tag
sentences = []
for (idx, review) in reviews:
lines = tokenizer.tokenize(review)
for line in lines:
#tokens = nltk.wordpunct_tokenize(line)
tokens = tokenize(line)
clean_line = [stem(token) for token in tokens if token not in stop_lst and token not in punctuation and token.isalpha()]
if tag in clean_line:
sentences.append((idx,line))
d[tag] = sentences
return d
# <codecell>
nltk.download()
# <markdowncell>
# Density
# =======
# <codecell>
from nltk import word_tokenize as tokenize
# <codecell>
nltk.pos_tag(tokenize("The quick brown fox jumps over the lazy dog."))
# <codecell>
nltk.pos_tag(tokenize("If I were you I wouldn't do that with these."))
# <markdowncell>
# Create a density checker
# <codecell>
import re
matches = lambda x, re_parts: any([re.findall(y, x) for y in re_parts])
def get_tokenized_dialog_lines(iterable_dialog_lines):
for line in iterable_dialog_lines:
tokenized_dialog_line = tokenize(line)
tokenized_dialog_line = [START_TOKEN] + tokenized_dialog_line + [EOS_SYMBOL]
yield tokenized_dialog_line
thisTweet['minute_utc'] = float(created_utc.minute) thisTweet['day_time_utc'] = thisTweet['day_of_week_utc'] + float(created_utc.hour) / 24.0 created_est = created_utc.replace(tzinfo=pytz.utc).astimezone(localTz) thisTweet['date_est'] = str(created_est.date()) thisTweet['day_of_week_est'] = created_est.weekday() thisTweet['weekend_est'] = thisTweet['day_of_week_est'] >= 5 thisTweet['time_est'] = float(created_est.hour) + float(created_est.minute)/60 thisTweet['minute_est'] = float(created_est.minute) thisTweet['day_time_est'] = thisTweet['day_of_week_est'] + float(created_est.hour) / 24.0 # Org features. thisTweet['org'] = org thisTweet['org_category'] = orgData['category'].lower() thisTweet['social_flow_user'] = orgData['socialFlow'] thisTweet['followers_count'] = twAccounts[org]['followers_count'] # Sentiment features. thisTweet['word_count'] = len([t for t in tokenize(cleanMessage(tweet['text'])) if t not in skipTokens]) sent = sentiment.get(str(tweet['id'])) if sent: thisTweet['sentiment_class'] = sent['class'] thisTweet['sentiment_score_positive'] = sent['meanScorePosSig'] thisTweet['sentiment_score_negative'] = sent['meanScoreNegSig'] else: thisTweet['sentiment_class'] = None thisTweet['sentiment_score_positive'] = None thisTweet['sentiment_score_negative'] = None # Tweet outcomes. thisTweet['favorites'] = tweet['favorite_count'] thisTweet['retweets'] = tweet['retweet_count'] # Bitly features. This is going to be tougher.
# coding: utf-8
import sys; print('Python %s on %s' % (sys.version, sys.platform))
sys.path.extend(['/home/rharriso/Code/Python/NLTKWorkspace'])
# Page 79 Natural language processing in python
from __future__ import division
import nltk, re, pprint
f = open('ASOIAF/A Clash of Kings A Song of Ice and Fire Book 2_nodrm.txt')
txt = f.read()
text = nltk.text(text)
text = nltk.text(nltk.tokenize(text))
text = nltk.text(nltk.word_tokenize(text))
nltk.word_tokenize(txt)
tokens = nltk.word_tokenize(txt)
text = nltk.Text(tokens)
text.concordance("Arya")
#
# Load a feed
#
import feedparser
import feedparser
llog = feedparser.parse("http://rharriso.github.io/feed.xml")
llog['feed']
llog['feed']['title']
len(llog.entries)
post = llog[2]
llog.entries[2]
post = llog.entries[2]
post.content
