def resolve_certainty(certainty_info):
'''Resolve certainty with Naive Bayes'''
if certainty_info == '':
return 'No certainty info.'
else:
nb = NB()
for observation, certainty in csv(
'library/templatetags/c_training_data.csv'):
v = Document(observation, type=int(certainty), stopwords=True)
nb.train(v)
return nb.classify(Document(certainty_info))
def extractSentiment(characterSentences):
"""
Trains a Naive Bayes classifier object with the reviews.csv file, analyzes
the sentence, and returns the tone.
"""
nb = NB()
characterTones = defaultdict(list)
for review, rating in csv("reviews.csv"):
nb.train(Document(review, type=int(rating), stopwords=True))
for key, value in characterSentences.items():
for x in value:
characterTones[key].append(nb.classify(str(x)))
return characterTones
def extractSentiment(characterSentences):
"""
Trains a Naive Bayes classifier object with the reviews.csv file, analyzes
the sentence, and returns the tone.
"""
nb = NB()
characterTones = defaultdict(list)
for review, rating in csv("reviews.csv"):
nb.train(Document(review, type=int(rating), stopwords=True))
for key, value in characterSentences.iteritems():
for x in value:
characterTones[key].append(nb.classify(str(x)))
return characterTones
def classifyTweets(filename, trainingSet):
print('Classifying {}...\n'.format(filename))
data = open('{}/processed{}'.format(INPUT_PATH, filename.capitalize()), 'rb')
reader = csv.reader(data)
info = list(reader)
classifier = NB(train = trainingSet, alpha = 0.0001)
tweets = []
for i in range(len(info)):
tweet = info[i][0]
result = classifier.classify(Document(tweet))
tweets.append([tweet, result])
# Write all tweets to file
with open('{}/results.csv'.format(OUTPUT_PATH), 'wb+') as f:
writer = csv.writer(f)
writer.writerows(tweets)
class NBModel:
def __init__(self):
self.nb = NB()
self.stats = Statistics()
try:
# self.nb = self.nb.load("./nb_training.p")
self.new_nb_model = True
except IOError:
self.new_nb_model = False
print("Creating new NB model")
def naive_bayes_train(self, reviews):
for review in reviews:
if review.rating is not None and review.rating < 10 and review.rating > 1:
v = Document(review.text, type=int(review.rating), stopwords=True)
self.nb.train(v)
#self.nb.save("./nb_training.p")
# print self.nb.classes
def nb_test_imdb(self, reviews):
arr = []
for review in reviews:
if review.rating is not None:
v = Document(review.text, type=int(review.rating), stopwords=True)
arr.append(v)
print self.nb.test(arr, target=None)
def nb_classify_tweets(self, tvshow, tweets):
ratingSum = 0
tweet_docs = [(self.nb.classify(Document(tweet)), tweet) for tweet in tweets]
for tweet in tweet_docs:
ratingSum += tweet[0]
self.nb_stats()
Statistics().printStats(tvshow, ratingSum, len(tweet_docs))
print self.nb.distribution
def nb_stats(self):
print('----------- Classifier stats -----------')
# print("Features: ", self.nb.features)
print("Classes: ", self.nb.classes)
print("Skewness: ", self.nb.skewness)
print("Distribution: ", self.nb.distribution)
print("Majority: ", self.nb.majority)
print("Minority: ", self.nb.minority)
d1 = Document('Cats are independent pets.', name='cat')
d2 = Document('Dogs are trustworthy pets.', name='dog')
d3 = Document('Boxes are made of cardboard.', name='box')
m = Model((d1, d2, d3))
print m.cluster(method=HIERARCHICAL, k=2)
# hierarchical clustering
cluster = Cluster((1, Cluster((2, Cluster((3, 4))))))
print cluster.depth
print cluster.flatten(1)
# training a classifier
nb = NB()
for review, rating in csv('data/input/reviews.csv'):
v = Document(review, type=int(rating), stopwords=True)
nb.train(v)
print nb.classes
print nb.classify(Document('A good movie!'))
# testing a classifier
data = csv('data/input/reviews.csv')
data = [(review, int(rating)) for review, rating in data]
data = [
Document(review, type=rating, stopwords=True) for review, rating in data
]
nb = NB(train=data[:500])
accuracy, precision, recall, f1 = nb.test(data[500:])
print accuracy
# binary classification
data = csv('data/input/reviews.csv')
data = [(review, int(rating) >= 3) for review, rating in data]
data = [
Document(review, type=rating, stopwords=True) for review, rating in data
]
class Emoticon_Sentiment:
"""
Use Emoticon sentiment to classify and predict comments.
This class provides a binary sentiment analyzer (1 = positive, -1 =
negative). It is automatically created using emoticons from thousands of
comments.
"""
def __init__(self, basefile):
"""
Initialize the emoticon sentiment. Setup variables.
Args:
basefile (str): The location of the comments file that should serve
as the basis for the classifier.
"""
self.base = basefile
self.comments = []
self.nb = NB()
self.stop = stopwords.words('english')
self.positive = [':-)', ':)', ':D', ':o)', ':]', ':3', ':c)', ':>',
'=]', '8)', '=)', ':}', ':^)', ':っ)', ':-D', '8-D',
'8D', 'x-D', 'xD', 'X-D', 'XD', '=-D', '=D', '=-3',
'=3', 'B^D', ':-))', ':*', ':^*', ';-)', ';)', '*-)',
'*)', ';-]', ';]', ';D', ';^)']
self.negative = ['>:[', ':-(', ':(', ':-c', ':c', ':-<', ':っC',
':<', ':-[', ':[', ':{', ':-||', ':@', '>:(', 'D:<',
'D:', 'D8', 'D;', 'D=', 'DX', 'v.v', '>:)', '>;)',
'>:-)', '}:-)', '}:)', '3:-)', '3:)', ':-###..',
':###..', '>:/', ':-/', ':-.', ':/']
def read_base_comments(self):
"""
Read the base comments.
Read the file that loads the comments that serve as the base for the
classifier.
Returns:
list: list of comments from the text file
"""
f = open(self.base, 'r')
self.comments = json.load(f)
f.close()
return self.comments
def preprocess(self, comment):
"""
Preprocess comment.
Take a comment and pre-processes it (all to lowercase, remove links,
delete punctuation, remove stopwords and numbers)
Args:
comment (str): The text string to be processed
Returns:
str: the processed text string
"""
h = HTMLParser.HTMLParser()
text = h.unescape(comment.lower())
p = re.compile(r"(\b(?:(?:https?|ftp|file)://|www\.|ftp\.)"
"[-A-Za-z0-9+&@#/%?=~_()|!:,.;]*"
"[-A-Za-z0-9+&@#/%=~_()|])")
text = re.sub(p, '', text)
exclude = set(string.punctuation)
tokens = word_tokenize(text)
cleaned = [token for token in tokens if token not in exclude]
text = ' '.join([w.encode('ascii', errors='ignore') for w in cleaned
if w.lower() not in self.stop and not w.isdigit()])
return text
def parse_comments(self):
"""
Parse comment, check for emoticon, remove neutrals.
Parse a list of comments, by searching for emoticons. Once an emoticon
is found, it flags it positive or negative.
With multiple finds, it sums their value; neutrals are thrown out.
Returns:
list: 2-dimensional list of parsed comments and their sentiment flags
"""
extended_comments = [[self.comments[i], 0, False]
for i in range(len(self.comments))]
# loop through comments and search for emoticons: +1 if positive, -1
# if negative, and remove emoticons from text
for comment in extended_comments:
for n in self.negative:
if comment[0].encode('utf-8').find(n) > -1:
comment[1] = comment[1]-1
comment[0] = comment[0].replace(n, '')
comment[2] = True
for p in self.positive:
if comment[0].encode('utf-8').find(p) > -1:
comment[1] = comment[1]+1
comment[0] = comment[0].replace(p, '')
comment[2] = True
# throw out comments that have no emoticons
parsed_comments = [[comment[0], comment[1]] for comment
in extended_comments if comment[2]]
# normalize the values by converting all positives (1,2,3,etc) to 1,
# and all negatives (-1,-2,-3 etc) to -1
for item in parsed_comments:
if item[1] > 1:
item[1] = 1
elif item[1] < -1:
item[1] = -1
# throw out neutrals
parsed_comments = [[self.preprocess(i[0]), i[1]]
for i in parsed_comments if i[1] != 0]
return parsed_comments
def train_classifier(self, comments):
"""
take the flagged comments and trains a Naive Bayes classifier.
Args:
comments (list): The list of flagged comments to be trained
"""
shuffle(comments)
for comment in comments:
v = Document(comment[0], type=int(comment[1]))
self.nb.train(v)
def predict(self, text):
"""
Take a comment and makes a sentiment prediction.
Args:
text (str): The text string to be analyzed for sentiment
Returns:
int: 1 if positive sentiment, -1 if negative
"""
return self.nb.classify(text)
from pattern.db import csv
from collections import Counter
nb = NB()
wordStats = Counter()
opinionStats = Counter({'positive': 0, 'negative': 0, 'overall': 0})
for grade, opinion in csv('trainData.csv', separator = '\t'):
comment = Document(opinion, type=int(grade), stopwords = True)
nb.train(comment)
tree = xmlTree.parse("Posts.xml")
root = tree.getroot()
for row in root:
doc = Document(plaintext(row.attrib['Body']),
filter = lambda w: w.strip("'").isalpha() and len(w) > 1,
stopwords = False)
opinion = nb.classify(doc)
opinionStats['overall'] +=1
if opinion > 0:
opinionStats['positive'] += 1
else:
opinionStats['negative'] += 1
wordStats += Counter(doc.words)
print wordStats.most_common(10)
print opinionStats
# of known things. For these known things, we already know the type,
# and we have a description of each thing, called a "vector".
# A vector is a just Python dictionary of features and feature weights.
# ------------------------------------------------------------------------------------
# The simplest classification algorithm is Naive Bayes,
# but it works quite well with text.
# A trivial example with animal features:
nb = NB()
nb.train({"swim": 1, "fin": 2, "legs": 0, "wings": 0}, type="fish")
nb.train({"swim": 0, "fin": 0, "legs": 1, "wings": 2}, type="bird")
nb.train({"swim": 1, "fin": 0, "legs": 4, "wings": 0}, type="mammal")
print nb.classify({"legs": 4})
print
# ------------------------------------------------------------------------------------
# For text, usually the word order is discarded in favor of word count.
# This is called a "bag-of-words" model, i.e., we count each word in a
# document. We can then compare it to other documents to see if they
# have frequent words in common. If so, they probably belong to the
# same class or type.
# For example, if we have 10,000 product reviews + star rating,
# we can transform each review to a vector of adjective => count,
# use the star rating as vector type (e.g., * = -1, ***** = +1),
# and use this training set to predict the star rating of other reviews.
print "number of words:", len(m.vector)
print "number of words (average):", sum(len(d.features)
for d in m.documents) / float(len(m))
print
# Train Naive Bayes on all documents.
# Each document has a type: True for actual e-mail, False for spam.
# This results in a "binary" classifier that either answers True or False
# for unknown documents.
classifier = NB()
for document in m:
classifier.train(document)
# We can now ask it questions about unknown e-mails:
print classifier.classify("win money") # False: most likely spam.
print classifier.classify("fix bug") # True: most likely a real message.
print
print classifier.classify(
"customer") # False: people don't talk like this on developer lists...
print classifier.classify(
"guys") # True: because most likely everyone knows everyone.
print
# To test the accuracy of a classifier,
# we typically use 10-fold cross validation.
# This means that 10 individual tests are performed,
# each with 90% of the corpus as training data and 10% as testing data.
from pattern.vector import k_fold_cv
print k_fold_cv(Bayes, documents=m, folds=10)
class NBClassifier:
"""
This class interfaces a pattern corpus with a pattern.vector nb classifier
"""
def __init__(self, corpus, **kargs):
"""
Initializes the NBClassifier class with a corpus and a NB instance
(input): corpus = a corpus of pattern Documents constructed from Grams
"""
self.corpus = corpus
self.documents = self.corpus.documents
self.model = Model(documents=self.documents, weight='TF-IDF')
#self.documents.words = self.documents.keywords
self.split_idx = len(self) / 4
self.nb = NB()
def __len__(self):
return len(self.documents)
def classify_document(self, document):
"""
classify document with nb instance
(input):
document = Document instance with same format as classifier train set
(output): classification result
"""
return (
self.nb.classify(Document(document, stemmer=PORTER), discrete=True)
)
def nb_train(self):
"""
This function trains the classifier with (3/4) of the documents
(input): None
(outpu): trains self.nb
"""
train_start = datetime.datetime.now()
print "training with {0} docs".format(len(self) - self.split_idx)
documents = self.documents[:-self.split_idx]
random.shuffle(documents)
[self.nb.train(doc) for doc in documents]
train_end = datetime.datetime.now()
print "training {0} docs took {1} seconds".format(len(documents), (train_end - train_start).seconds)
def nb_test(self, gold_standard=None):
"""
Evaluates the classifier on (1/4) of the documents
(input): None
(output): accuracy, precision, recall, f1
"""
if not gold_standard:
test_docs = self.documents[-self.split_idx:]
conf_matrix = self.nb.confusion_matrix(test_docs)
print conf_matrix.table
return self.nb.test(test_docs)
else:
return self.nb.test(gold_standard)
def get_documentlabel_distribution(self):
"""
Gets the label distribution of the document set passed into
self.nb classifier
(input): None
(output):
distribution = dictionary representation of the label:frequency distribution
rankdistribution = a ranked list of label keys by frequency
"""
distribution = self.nb.distribution
rankdistribution = sorted(
distribution,
key=lambda x: distribution[x],
reverse=True)
for each, key in enumerate(rankdistribution[:10]):
print "{0}:\t{1} ({2})".format(each, key, distribution[key])
return distribution, rankdistribution
def run(self):
"""
executes training / testing of classifier
"""
print "training and testing {0} total documents...".format(len(self))
self.nb_train()
print "testing"
result = self.nb_test()
print "finalizing"
self.nb.finalize()
return result
recall
#test SVM
testsvm = SVM(train=datadocs[:500])
print 'svm features = ', testsvm.features
saccuracy, sprecision, srecall, sf1 = testsvm.test(datadocs[500:])
print 'svm accuracy =', saccuracy
#classifier training example with test classificaiton
nb2 = NB()
for review, rating in data:
v = Document(review, type=int(rating))
#print v.vector
nb2.train(v)
print 'nb2 classes', nb2.classes
print 'test classification', nb2.classify(Document('A poor movie!'))
#cosine similarity example
from pattern.vector import Vector, distance
v1 = Vector({"curiosity": 1, "kill": 1, "cat": 1})
v2 = Vector({"curiosity": 1, "explore": 1, "mars": 1})
print 'cosine similarity between two vectors', 1 - distance(v1, v2)
# a model is a collection of Document objects
#todo now that we've built SVM, needs to create documents out of dummy tweets
#todo then add them to the SVM classifier as a training and test set
#todo build db schema to save tweets and relationships to db
#todo get some sample twitter data in db
class NBModel:
def __init__(self):
self.nb = NB()
self.stats = Statistics()
try:
print("dir: " + os.getcwd())
if os.getcwd().endswith("tv_ratings_frontend"):
print("Working in django")
self.nb = self.nb.load("ratings_frontend/backend/pattern_ml/nb_training.p")
else:
print("Not working in django")
self.nb = self.nb.load("./nb_training.p")
self.new_nb_model = True
print("Using existing pickled model")
except IOError:
self.new_nb_model = False
print("Creating new NB model")
def nb_train_text(self, reviews):
for review in reviews:
if review.rating is not None:# and review.rating < 10 and review.rating > 1:
v = Document(review.text, type=int(review.rating), stopwords=True)
self.nb.train(v)
self.nb.save("./nb_training.p")
# print self.nb.classes
def nb_train_summary(self, reviews):
for review in reviews:
if review.rating is not None:# and review.rating < 10 and review.rating > 1:
v = Document(review.summary, type=int(review.rating), stopwords=True)
self.nb.train(v)
def nb_train_all_text(self, review_set):
for review_list in review_set:
self.nb_train_text(review_list)
self.nb.save_model()
def save_model(self):
# print ""
self.nb.save('./nb_training.p')
def nb_test_imdb(self, reviews):
arr = []
for review in reviews:
if review.rating is not None:
v = Document(self.review_to_words(review.text), type=int(review.rating), stopwords=True)
arr.append(v)
print self.nb.test(arr, target=None)
def nb_classify_tweets(self, tvshow, tweets):
ratingSum = 0
tweet_docs = [(self.nb.classify(Document(self.review_to_words(tweet))), self.review_to_words(tweet)) for tweet in tweets]
for tweet in tweet_docs:
ratingSum += tweet[0]
#print tweet
# print tweet
self.nb_stats()
Statistics().printStats(tvshow, ratingSum, len(tweet_docs))
print self.nb.distribution
return Statistics().get_stats(tvshow, ratingSum, len(tweet_docs))
def nb_stats(self):
print('----------- Classifier stats -----------')
# print("Features: ", self.nb.features)
print("Classes: ", self.nb.classes)
print("Skewness: ", self.nb.skewness)
print("Distribution: ", self.nb.distribution)
print("Majority: ", self.nb.majority)
print("Minority: ", self.nb.minority)
def review_to_words(self, raw_review):
no_url = re.sub("http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+", "", raw_review)
# Remove numerics
letters_only = re.sub("[^a-zA-Z]", " ", no_url)
# to lowercase
words = letters_only.lower().split()
# remove stop words - the, of , a ....
stops = set(stopwords.words("english"))
meaningful_words = [w for w in words if not w in stops]
return (" ".join(meaningful_words))
print("number of words:", len(m.vector))
print("number of words (average):", sum(len(d.features)
for d in m.documents) / float(len(m)))
print()
# Train Naive Bayes on all documents.
# Each document has a type: True for actual e-mail, False for spam.
# This results in a "binary" classifier that either answers True or False
# for unknown documents.
classifier = NB()
for document in m:
classifier.train(document)
# We can now ask it questions about unknown e-mails:
print(classifier.classify("win money")) # False: most likely spam.
print(classifier.classify("fix bug")) # True: most likely a real message.
print()
# False: people don't talk like this on developer lists...
print(classifier.classify("customer"))
# True: because most likely everyone knows everyone.
print(classifier.classify("guys"))
print()
# To test the accuracy of a classifier,
# we typically use 10-fold cross validation.
# This means that 10 individual tests are performed,
# each with 90% of the corpus as training data and 10% as testing data.
from pattern.vector import k_fold_cv
print(k_fold_cv(NB, documents=m, folds=10))
# of known things. For these known things, we already know the type,
# and we have a description of each thing, called a "vector".
# A vector is a just Python dictionary of features and feature weights.
# ------------------------------------------------------------------------------------
# The simplest classification algorithm is Naive Bayes,
# but it works quite well with text.
# A trivial example with animal features:
nb = NB()
nb.train({"swim": 1, "fin": 2, "legs": 0, "wings": 0}, type="fish")
nb.train({"swim": 0, "fin": 0, "legs": 1, "wings": 2}, type="bird")
nb.train({"swim": 1, "fin": 0, "legs": 4, "wings": 0}, type="mammal")
print nb.classify({"legs": 4})
print
# ------------------------------------------------------------------------------------
# For text, usually the word order is discarded in favor of word count.
# This is called a "bag-of-words" model, i.e., we count each word in a
# document. We can then compare it to other documents to see if they
# have frequent words in common. If so, they probably belong to the
# same class or type.
# For example, if we have 10,000 product reviews + star rating,
# we can transform each review to a vector of adjective => count,
# use the star rating as vector type (e.g., * = -1, ***** = +1),
# and use this training set to predict the star rating of other reviews.
print "number of documents:", len(m)
print "number of words:", len(m.vector)
print "number of words (average):", sum(len(d.features) for d in m.documents) / float(len(m))
print
# Train Naive Bayes on all documents.
# Each document has a type: True for actual e-mail, False for spam.
# This results in a "binary" classifier that either answers True or False
# for unknown documents.
classifier = NB()
for document in m:
classifier.train(document)
# We can now ask it questions about unknown e-mails:
print classifier.classify("win money") # False: most likely spam.
print classifier.classify("fix bug") # True: most likely a real message.
print
print classifier.classify("customer") # False: people don't talk like this on developer lists...
print classifier.classify("guys") # True: because most likely everyone knows everyone.
print
# To test the accuracy of a classifier,
# we typically use 10-fold cross validation.
# This means that 10 individual tests are performed,
# each with 90% of the corpus as training data and 10% as testing data.
from pattern.vector import k_fold_cv
print k_fold_cv(Bayes, documents=m, folds=10)
# This yields 4 scores: (Accuracy, Precision, Recall, F-score).
print("number of words:", len(m.vector))
print("number of words (average):",
sum(len(d.features) for d in m.documents) / float(len(m)))
print()
# Train Naive Bayes on all documents.
# Each document has a type: True for actual e-mail, False for spam.
# This results in a "binary" classifier that either answers True or False
# for unknown documents.
classifier = NB()
for document in m:
classifier.train(document)
# We can now ask it questions about unknown e-mails:
print(classifier.classify("win money")) # False: most likely spam.
print(classifier.classify("fix bug")) # True: most likely a real message.
print()
# False: people don't talk like this on developer lists...
print(classifier.classify("customer"))
# True: because most likely everyone knows everyone.
print(classifier.classify("guys"))
print()
# To test the accuracy of a classifier,
# we typically use 10-fold cross validation.
# This means that 10 individual tests are performed,
# each with 90% of the corpus as training data and 10% as testing data.
from pattern.vector import k_fold_cv
documents=[]
for linea in result.readlines():
document = Document(linea.split("/")[1], type=linea.split("/")[0])
documents.append(document)
m = Model(documents)
nb=NB()
for document in m:
nb.train(document)
print nb.classify("opinion")
print nb.classify("noticia")
from pattern.vector import k_fold_cv
print k_fold_cv(NB, documents=m, folds=10)
#for palabra in lines:
# print d.get(palabra)
"""
1. Sacar bien los resultados
#HuracanPatricia
#AguaEnMarte
2. Modificar poster de acuerdo a los resultados y enfocarse en el desarrollo
"""