def normal_test(data, type):
print '----------------------------------------------------'
print 'TEST FUNCTION STARTED FOR ' + type + '!'
total_data_size = len(data)
training_size = int(round(total_data_size/2))
test_size = training_size
print 'Total Size: ' + str(total_data_size)
print 'Training Size: ' + str(training_size)
print 'Test Size: ' + str(test_size)
print 'Training Started for ' + type + '!'
classification_methods = {
#uncomment based on what classification algorithm you would like to test
'NB' : NB(train=data[:training_size], baseline=MAJORITY, method=MULTINOMIAL),
'KNN2' : KNN(train=data[:training_size], baseline=MAJORITY, k=2, distance=COSINE),
'KNN3' : KNN(train=data[:training_size], baseline=MAJORITY, k=3, distance=COSINE),
'KNN4' : KNN(train=data[:training_size], baseline=MAJORITY, k=4, distance=COSINE),
'KNN5' : KNN(train=data[:training_size], baseline=MAJORITY, k=5, distance=COSINE),
'KNN6' : KNN(train=data[:training_size], baseline=MAJORITY, k=6, distance=COSINE),
'KNN7' : KNN(train=data[:training_size], baseline=MAJORITY, k=7, distance=COSINE),
'KNN8' : KNN(train=data[:training_size], baseline=MAJORITY, k=8, distance=COSINE),
'KNN9' : KNN(train=data[:training_size], baseline=MAJORITY, k=9, distance=COSINE),
'KNN10' : KNN(train=data[:training_size], baseline=MAJORITY, k=10, distance=COSINE),
'SLP1' : SLP(train=data[:training_size], baseline=MAJORITY, iterations=1),
'SLP2' : SLP(train=data[:training_size], baseline=MAJORITY, iterations=2),
'SLP3' : SLP(train=data[:training_size], baseline=MAJORITY, iterations=3),
'SVM' : SVM(train=data[:training_size], type=CLASSIFICATION, kernel=POLYNOMIAL),
}
print 'Normal Testing Started!'
# uncomment to start the normal test
for classification in classification_methods.keys():
#measure the time it takes to classify!
start = timeit.default_timer()
#normal test
accuracy, precision, recall, f1 = classification_methods[classification].test(data[training_size:training_size+test_size])
stop = timeit.default_timer()
print '*' + classification + '*'
print 'Accuracy: ' + str(accuracy)
print 'Precision: ' + str(precision)
print 'Recall: ' + str(recall)
print 'F1-score: ' + str(f1)
print 'Time: ' + str(stop - start)
print
def report(model_file, text_file):
pl = Pipeline()
model = KNN.load(model_file)
nlp = en_core_web_sm.load()
make_doc = lambda text: nlp(unidecode(text).strip())
text = [line for line in open(text_file) if line.strip()]
docs = [make_doc(line) for line in text]
sentences = [sent.text for doc in docs for sent in doc.sents]
print('\n'.join(text))
predictions = pl.predict(model, sentences, print_pred=False)
print('\n \n ############### Soft Skills ############\n')
print(
*[sent for sent, pred in zip(sentences, predictions) if 'yes' == pred],
sep='\n')
def train(cls, train_file, model_file):
sents_dic = (json.loads(jsonl)
for jsonl in SoftSkills.load(train_file))
model = KNN()
for sent in sents_dic:
text = sent['text']
v = count([word for word, pos in tag(text)]) # {'sweet': 1}
if v:
model.train(v, type=sent['soft skill'])
model.save(model_file)
return model
# Let's test how our model performs as a classifier.
# A document can have a label (or type, or class).
# For example, in the movie reviews corpus,
# there are positive reviews (score > 0) and negative reviews (score < 0).
# A classifier uses a model as "training" data
# to predict the label (type/class) of unlabeled documents.
# In this case, it can predict whether a new movie review is positive or
# negative.
# The details are not that important right now, just observe the accuracy.
# Naturally, we want accuracy to stay the same after LSA reduction,
# and hopefully decrease the time needed to run.
t = time.time()
print("accuracy:", KNN.test(m, folds=10)[-1])
print("time:", time.time() - t)
print()
# Reduce the documents to vectors of 10 concepts (= 1/4 of 40 features).
print("LSA reduction...")
print()
m.reduce(10)
t = time.time()
print("accuracy:", KNN.test(m, folds=10)[-1])
print("time:", time.time() - t)
print()
# Accuracy is about the same, but the performance is better: 2x-3x faster,
# because each document is now a "10-word summary" of the original review.
s = tweet.text.lower() # tweet in lowercase
p = '#win' in s and 'WIN' or 'FAIL' # document labels
s = Sentence(parse(s)) # parse tree with part-of-speech tags
s = search('JJ', s) # adjectives in the tweet
s = [match[0].string for match in s] # adjectives as a list of strings
s = " ".join(s) # adjectives as string
if len(s) > 0:
m.append(Document(s, type=p, stemmer=None))
# Train k-Nearest Neighbor on the model.
# Note that this is a only simple example: to build a robust classifier
# you would need a lot more training data (e.g., tens of thousands of tweets).
# The more training data, the more statistically reliable the classifier becomes.
# The only way to really know if you're classifier is working correctly
# is to test it with testing data, see the documentation for Classifier.test().
classifier = KNN(baseline=None) # By default, baseline=MAJORITY
for document in m: # (classify unknown documents with the most frequent type).
classifier.train(document)
# These are the adjectives the classifier has learned:
print sorted(classifier.features)
print
# We can now ask it to classify documents containing these words.
# Note that you may get different results than the ones below,
# since you will be mining other (more recent) tweets.
# Again, a robust classifier needs lots and lots of training data.
# If None is returned, the word was not recognized,
# and the classifier returned the default value (see above).
print classifier.classify('sweet') # yields 'WIN'
print classifier.classify('stupid') # yields 'FAIL'
# -*- coding: utf-8 -*-
"""
Module with methods used for retreiving tweets, containing certain
search words, from Twitter.
"""
from pattern.web import Twitter
from pattern.vector import KNN
import urllib2
twitter = Twitter(license=None, throttle=0.5)
knn = KNN()
def getSearchWords():
"""Function that returns list of search words"""
# Test data, we will use synonyms for war (found in nltk word net)
# words = ['war', 'conflict', 'jihad', ]
words = []
conflict = ['feud',
'vendetta',
'class struggle',
'strife',
'countercurrent',
'discord',
'trench warfare',
'fight',
'hassle',
'beating',
'in fighting',
'single combat',
'gunfight',
def _learner(self):
return KNN()
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 25 19:37:34 2019
@author: alternatif
"""
from pattern.web import Twitter
from pattern.en import tag
from pattern.vector import KNN, count
twitter, knn = Twitter(), KNN()
for i in range(1, 3):
for tweet in twitter.search('#win OR #fail', start=i, count=100):
s = tweet.text.lower()
p = '#win' in s and 'WIN' or 'FAIL'
v = tag(s)
v = [word for word, pos in v if pos == 'JJ'] # JJ = adjective
v = count(v) # {'sweet': 1}
if v:
knn.train(v, type=p)
print(knn.classify('sweet potato burger'))
print(knn.classify('stupid autocorrect'))
from pattern.web import Twitter
from pattern.text.en import tag
from pattern.vector import KNN, count, NaiveBayes, SVM
import os, random
import file_io as fio
corp_dir = 'essays/original'
twitter, knn, nbayes, svm = Twitter(), KNN(), NaiveBayes(), SVM()
from nltk.corpus import stopwords
import lsa
cachedStopWords = stopwords.words("english")
testSet = []
def naive():
trainingSet = []
l = lsa.getMod()
dirs = [x[0] for x in os.walk(os.path.abspath(corp_dir))]
for dir in dirs:
label = 0
if 'low' in dir:
label = -1
elif 'high' in dir:
label = 1
tfiles = []
tfiles = fio.getTopLevelFiles(dir, extension='txt')
train_smpl = []
if len(tfiles) > 0:
train_smpl = [
tfiles[i] for i in random.sample(xrange(len(tfiles)), 13)
]
for file in tfiles:
s = tweet.text.lower() # tweet in lowercase
p = '#win' in s and 'WIN' or 'FAIL' # document labels
s = Sentence(parse(s)) # parse tree with part-of-speech tags
s = search('JJ', s) # adjectives in the tweet
s = [match[0].string for match in s] # adjectives as a list of strings
s = " ".join(s) # adjectives as string
if len(s) > 0:
m.append(Document(s, type=p, stemmer=None))
# Train k-Nearest Neighbor on the model.
# Note that this is a only simple example: to build a robust classifier
# you would need a lot more training data (e.g., tens of thousands of tweets).
# The more training data, the more statistically reliable the classifier becomes.
# The only way to really know if you're classifier is working correctly
# is to test it with testing data, see the documentation for Classifier.test().
classifier = KNN(baseline=None) # By default, baseline=MAJORITY
for document in m: # (classify unknown documents with the most frequent type).
classifier.train(document)
# These are the adjectives the classifier has learned:
print sorted(classifier.features)
print
# We can now ask it to classify documents containing these words.
# Note that you may get different results than the ones below,
# since you will be mining other (more recent) tweets.
# Again, a robust classifier needs lots and lots of training data.
# If None is returned, the word was not recognized,
# and the classifier returned the default value (see above).
print classifier.classify('sweet') # yields 'WIN'
print classifier.classify('stupid') # yields 'FAIL'
s = tweet.text.lower() # tweet in lowercase
p = "#win" in s and "WIN" or "FAIL" # document labels
s = Sentence(parse(s)) # parse tree with part-of-speech tags
s = search("JJ", s) # adjectives in the tweet
s = [match[0].string for match in s] # adjectives as a list of strings
s = " ".join(s) # adjectives as string
if len(s) > 0:
m.append(Document(s, type=p, stemmer=None))
# Train k-Nearest Neighbor on the model.
# Note that this is a only simple example: to build a robust classifier
# you would need a lot more training data (e.g., tens of thousands of tweets).
# The more training data, the more statistically reliable the classifier becomes.
# The only way to really know if you're classifier is working correctly
# is to test it with testing data, see the documentation for Classifier.test().
classifier = KNN(baseline=None) # By default, baseline=MAJORITY
for document in m: # (classify unknown documents with the most frequent type).
classifier.train(document)
# These are the adjectives the classifier has learned:
print sorted(classifier.features)
print
# We can now ask it to classify documents containing these words.
# Note that you may get different results than the ones below,
# since you will be mining other (more recent) tweets.
# Again, a robust classifier needs lots and lots of training data.
# If None is returned, the word was not recognized,
# and the classifier returned the default value (see above).
print classifier.classify("sweet potato burger") # yields 'WIN'
print classifier.classify("stupid autocorrect") # yields 'FAIL'
def setup():
global pages
global urlalias
global revurlalias
global knn
pages = dict()
urlalias = dict()
revurlalias = dict()
knn = KNN()
db = MySQLdb.connect(host="192.168.200.26",
user="******",
passwd="xxxsecretxxx",
db="pla")
cur = db.cursor()
cur.execute("select source, alias from url_alias")
for row in cur.fetchall():
urlalias[row[1]] = row[0]
revurlalias[row[0]] = row[1]
cur.execute("select tid, name, description, vid from taxonomy_term_data;")
for row in cur.fetchall():
url = 'taxonomy/term/' + str(row[0])
pages[url] = row[1]
if url in revurlalias:
pages[revurlalias[url]] = row[1]
url = revurlalias[url]
if row[3] == 3:
soup = bs4.BeautifulSoup(row[2])
the_text = re.sub(r'[\n\r]+', r' ', soup.get_text(' ')).lower()
knn.train(Document(the_text, stemmer=PORTER), url)
knn.train(Document(row[1].lower()), url)
cur.execute(
"select a.tid, c.body_value, d.title from taxonomy_term_data as a inner join field_data_field_practice_areas as b on (a.tid=b.field_practice_areas_tid and b.entity_type='node' and b.bundle != 'professionals' and b.deleted=0) inner join field_data_body as c on (b.entity_id=c.entity_id and b.entity_type=c.entity_type) inner join node as d on (c.entity_id=d.nid);"
)
for row in cur.fetchall():
url = 'taxonomy/term/' + str(row[0])
if url in revurlalias:
url = revurlalias[url]
soup = bs4.BeautifulSoup(row[1])
the_text = re.sub(r'[\n\r]+', r' ', soup.get_text(' ')).lower()
knn.train(Document(the_text, stemmer=PORTER), url)
knn.train(Document(row[2].lower()), url)
cur.execute("select nid, title from node where status=1;")
for row in cur.fetchall():
url = 'node/' + str(row[0])
pages[url] = row[1]
if url in revurlalias:
pages[revurlalias[url]] = row[1]
db.close()
pgcur = conn.cursor()
pgcur.execute(
"select query, target from website_queries where target is not null group by query, target"
)
for row in pgcur.fetchall():
words = re.split(r'[\n\r,;]+ *', row[1])
for word in words:
print("training on " + row[0].lower() + " for " + word)
knn.train(Document(row[0].lower()), word)
conn.commit()
pgcur.close()
print "number of documents:", len(corpus)
print "number of words:", len(corpus.vector)
print "number of words (average):", sum(
len(d.terms) for d in corpus.documents) / float(len(corpus))
print
# This may be too much words for some clustering algorithms (e.g., hierarchical).
# We'll reduce the documents to vectors of 4 concepts.
# First, let's test how the corpus would perform as a classifier.
# The details of KNN are not that important right now, just observe the numbers.
# Naturally, we want accuracy to stay the same after LSA reduction,
# and hopefully decrease the time needed to run.
t = time.time()
print "accuracy:", KNN.test(corpus, folds=10)[-1]
print "time:", time.time() - t
print
# Reduce the documents to vectors of 4 concepts (= 1/7 of 30 words).
print "LSA reduction..."
print
corpus.reduce(4)
t = time.time()
print "accuracy:", KNN.test(corpus, folds=10)[-1]
print "time:", time.time() - t
print
# Not bad, accuracy is about the same but performance is 3x faster,
# because each document is now a "4-word summary" of the original review.
s = tweet.text.lower() # tweet in lowercase
p = "#win" in s and "WIN" or "FAIL" # document labels
s = Sentence(parse(s)) # parse tree with part-of-speech tags
s = search("JJ", s) # adjectives in the tweet
s = [match[0].string for match in s] # adjectives as a list of strings
s = " ".join(s) # adjectives as string
if len(s) > 0:
corpus.append(Document(s, type=p, stemmer=None))
# Train k-nearest neighbor on the corpus.
# Note that this is a only simple example: to build a robust classifier
# you would need a lot more training data (e.g., tens of thousands of tweets).
# The more training data, the more statistically reliable the classifier becomes.
# The only way to really know if you're classifier is working correctly
# is to test it with testing data, see the documentation for Classifier.test().
classifier = KNN()
for document in corpus:
classifier.train(document)
# These are the adjectives the classifier has learned:
print sorted(classifier.terms)
print
# We can ask it to classify texts containing those words.
# Note that you may get different results than the ones indicated below,
# since you will be mining other (more recent) tweets.
# Again, a robust classifier needs lots and lots of training data.
print classifier.classify("sweet") # yields 'WIN'
print classifier.classify("stupid") # yields 'FAIL'
# "What can I do with it?"
p = '#win' in s and 'WIN' or 'FAIL' # document labels
# parse tree with part-of-speech tags
s = Sentence(parse(s))
s = search('JJ', s) # adjectives in the tweet
s = [match[0].string for match in s] # adjectives as a list of strings
s = " ".join(s) # adjectives as string
if len(s) > 0:
m.append(Document(s, type=p, stemmer=None))
# Train k-Nearest Neighbor on the model.
# Note that this is a only simple example: to build a robust classifier
# you would need a lot more training data (e.g., tens of thousands of tweets).
# The more training data, the more statistically reliable the classifier becomes.
# The only way to really know if you're classifier is working correctly
# is to test it with testing data, see the documentation for Classifier.test().
classifier = KNN(baseline=None) # By default, baseline=MAJORITY
# (classify unknown documents with the most frequent type).
for document in m:
classifier.train(document)
# These are the adjectives the classifier has learned:
print(sorted(classifier.features))
print()
# We can now ask it to classify documents containing these words.
# Note that you may get different results than the ones below,
# since you will be mining other (more recent) tweets.
# Again, a robust classifier needs lots and lots of training data.
# If None is returned, the word was not recognized,
# and the classifier returned the default value (see above).
print(classifier.classify('sweet potato burger')) # yields 'WIN'
): #searches 15*100=1500 tweets for these classes of hashtags
p = '#win' in tweet.description.lower() and 'WIN' or 'FAIL'
m = '#fail' in tweet.description.lower() and 'WIN' or 'FAIL'
s = tweet.description.lower()
s = Sentence(
parse(s)
) #parse anlayzes & gives strings that are annotated with specified tags
s = search('JJ',
s) #searches for adjectives in tweets (JJ = adjectiive)
s = [match[0].string for match in s]
s = ' '.join(s)
if len(s) > 0:
corpus.append(Document(s, type=p))
corpus.append(Document(s, type=m))
classifier = KNN() #k-nearest neighbor classifier = K-NN
objects = []
for document in corpus: #documents are an unordered bag of given sentences.
classifier.train(
document) #adjective vectors in corpus trains the classifier
objects.append(classifier.classify('awesome')) #predicts awesome as win
objects.append(classifier.classify('cool')) #predicts cool as win
objects.append(classifier.classify('damn')) #predicts damn as fail
objects.append(classifier.classify('sucks')) #predicts sucks as fail
print objects
wincounter = 0
failcounter = 0
for thing in objects:
p = '#win' in tweet.description.lower() and 'WIN' or 'FAIL'
s = tweet.description.lower() # tweet in lowercase
s = Sentence(parse(s)) # parse tree with part-of-speech tags
s = search('JJ', s) # adjectives in the tweet
s = [match[0].string for match in s] # adjectives as a list of strings
s = " ".join(s) # adjectives as string
if len(s) > 0:
corpus.append(Document(s, type=p, stemmer=None))
# Train k-nearest neighbor on the corpus.
# Note that this is a only simple example: to build a robust classifier
# you would need a lot more training data (e.g., tens of thousands of tweets).
# The more training data, the more statistically reliable the classifier becomes.
# The only way to really know if you're classifier is working correctly
# is to test it with testing data, see the documentation for Classifier.test().
classifier = KNN()
for document in corpus:
classifier.train(document)
# These are the adjectives the classifier has learned:
print sorted(classifier.terms)
print
# We can ask it to classify texts containing those words.
# Note that you may get different results than the ones indicated below,
# since you will be mining other (more recent) tweets.
# Again, a robust classifier needs lots and lots of training data.
print classifier.classify('sweet') # yields 'WIN'
print classifier.classify('stupid') # yields 'FAIL'
# "What can I do with it?"
# We'll reduce the document vectors to 10 concepts. # Let's test how our model performs as a classifier. # A document can have a label (or type, or class). # For example, in the movie reviews corpus, # there are positive reviews (score > 0) and negative reviews (score < 0). # A classifier uses a model as "training" data # to predict the label (type/class) of unlabeled documents. # In this case, it can predict whether a new movie review is positive or negative. # The details are not that important right now, just observe the accuracy. # Naturally, we want accuracy to stay the same after LSA reduction, # and hopefully decrease the time needed to run. t = time.time() print "accuracy:", KNN.test(m, folds=10)[-1] print "time:", time.time() - t print # Reduce the documents to vectors of 10 concepts (= 1/4 of 40 features). print "LSA reduction..." print m.reduce(10) t = time.time() print "accuracy:", KNN.test(m, folds=10)[-1] print "time:", time.time() - t print # Accuracy is about the same, but the performance is better: 2x-3x faster, # because each document is now a "10-word summary" of the original review.
corpus = Corpus(documents) print "number of documents:", len(corpus) print "number of words:", len(corpus.vector) print "number of words (average):", sum(len(d.terms) for d in corpus.documents) / float(len(corpus)) print # This may be too much words for some clustering algorithms (e.g., hierarchical). # We'll reduce the documents to vectors of 4 concepts. # First, let's test how the corpus would perform as a classifier. # The details of KNN are not that important right now, just observe the numbers. # Naturally, we want accuracy to stay the same after LSA reduction, # and hopefully decrease the time needed to run. t = time.time() print "accuracy:", KNN.test(corpus, folds=10)[-1] print "time:", time.time() - t print # Reduce the documents to vectors of 4 concepts (= 1/7 of 30 words). print "LSA reduction..." print corpus.reduce(4) t = time.time() print "accuracy:", KNN.test(corpus, folds=10)[-1] print "time:", time.time() - t print # Not bad, accuracy is about the same but performance is 3x faster, # because each document is now a "4-word summary" of the original review.
# We'll reduce the document vectors to 10 concepts.
# Let's test how our model performs as a classifier.
# A document can have a label (or type, or class).
# For example, in the movie reviews corpus,
# there are positive reviews (score > 0) and negative reviews (score < 0).
# A classifier uses a model as "training" data
# to predict the label (type/class) of unlabeled documents.
# In this case, it can predict whether a new movie review is positive or negative.
# The details are not that important right now, just observe the accuracy.
# Naturally, we want accuracy to stay the same after LSA reduction,
# and hopefully decrease the time needed to run.
t = time.time()
print("accuracy:", KNN.test(m, folds=10)[-1])
print("time:", time.time() - t)
print()
# Reduce the documents to vectors of 10 concepts (= 1/4 of 40 features).
print("LSA reduction...")
print()
m.reduce(10)
t = time.time()
print("accuracy:", KNN.test(m, folds=10)[-1])
print("time:", time.time() - t)
print()
# Accuracy is about the same, but the performance is better: 2x-3x faster,
# because each document is now a "10-word summary" of the original review.
x += 1 print "ERROR" print x / n print t1 print t2 #print xxx print len(corpus) print len(corpus.features) print len(corpus.documents[0].vector) from time import time t = time() print KNN.test(corpus, folds=10) print time()-t print "filter..." from time import time t = time() f = corpus.feature_selection(150, verbose=False) print f print time()-t corpus = corpus.filter(f) #corpus.reduce(300) #print len(corpus.lsa.vectors[corpus.documents[0].id]) #print corpus.lsa.vectors[corpus.documents[0].id] #print len(corpus)
s = tweet.text.lower() # tweet in lowercase
p = '#win' in s and 'WIN' or 'FAIL' # document labels
s = Sentence(parse(s)) # parse tree with part-of-speech tags
s = search('JJ', s) # adjectives in the tweet
s = [match[0].string for match in s] # adjectives as a list of strings
s = " ".join(s) # adjectives as string
if len(s) > 0:
m.append(Document(s, type=p, stemmer=None))
# Train k-Nearest Neighbor on the model.
# Note that this is a only simple example: to build a robust classifier
# you would need a lot more training data (e.g., tens of thousands of tweets).
# The more training data, the more statistically reliable the classifier becomes.
# The only way to really know if you're classifier is working correctly
# is to test it with testing data, see the documentation for Classifier.test().
classifier = KNN(baseline=None) # By default, baseline=MAJORITY
for document in m: # (classify unknown documents with the most frequent type).
classifier.train(document)
# These are the adjectives the classifier has learned:
print sorted(classifier.features)
print
# We can now ask it to classify documents containing these words.
# Note that you may get different results than the ones below,
# since you will be mining other (more recent) tweets.
# Again, a robust classifier needs lots and lots of training data.
# If None is returned, the word was not recognized,
# and the classifier returned the default value (see above).
print classifier.classify('sweet potato burger') # yields 'WIN'
print classifier.classify('stupid autocorrect') # yields 'FAIL'
print 'Number of Negative Tweets:',len(neg_lines)
print 'Number of Positive Tweets:',len(pos_lines)
documents = []
for line in neg_lines:
document = Document(line,stopword=True,stemmer=PORTER,type='0')
documents.append(document)
for line in pos_lines:
document = Document(line,stopword=True,stemmer=PORTER,type='1')
documents.append(document)
corpus = Corpus(documents,weight=TFIDF)
print "number of documents:", len(corpus)
print "number of words:", len(corpus.vector)
print "number of words (average):", sum(len(d.terms) for d in corpus.documents) / float(len(corpus))
print
#Filtering top 1000 features using Information Gain Criterion
corpus=corpus.filter(features=(corpus.feature_selection(top=1000,method=IG)))
# To test the accuracy of a classifier, Using 10-fold crossvalidation
# This yields 4 scores: Accuracy, Precision, Recall and F-score.
print 'classifying using KNN'
print '-------------------------'
print '(Accuracy, Precision,REcall,F-Measure)'
print KNN.test(corpus,k=100,folds=10,distance=COSINE)
f_neg.close()
f_pos.close()
