def _add_example(self, cls, message):
"""Add a training example
"""
words = preprocessing.extract_words(message)
if not words: # Treat cases where 'paper cut' is pre-processing as negatives.
return
# Update self.documents[n] and self.vocab[n] for ngrams and cls
for n in (1,2,3):
ngrams = preprocessing.get_ngrams(n, words)
self.documents[n].append((cls,ngrams))
def _add_example(self, cls, message):
"""Add a training example
"""
words = preprocessing.extract_words(message)
if not words: # Treat cases where 'paper cut' is pre-processing as negatives.
return
# Update self.documents[n] and self.vocab[n] for ngrams and cls
for n in (1, 2, 3):
ngrams = preprocessing.get_ngrams(n, words)
self.documents[n].append((cls, ngrams))
def _add_example(self, cls, message):
"""Add a training example
"""
words = preprocessing.extract_words(message)
if not words:
return
documents = self.pos_documents if cls else self.neg_documents
for n in (1, 2, 3):
ngrams = preprocessing.get_ngrams(n, words)
documents[n].append(ngrams)
for g in ngrams:
self.vocab[n].add(g)
def _add_example(self, cls, message):
"""Add a training example
"""
words = preprocessing.extract_words(message)
if not words:
return
documents = self.pos_documents if cls else self.neg_documents
for n in (1, 2, 3):
ngrams = preprocessing.get_ngrams(n, words)
documents[n].append(ngrams)
for g in ngrams:
self.vocab[n].add(g)
def _add_example(self, cls, message):
"""Add a training example
"""
words = preprocessing.extract_words(message)
if not words: # Treat cases where 'paper cut' is pre-processing as negatives.
return
self.class_count[cls] += 1
# Update ngram_counts and ngram_keys for ngrams cls
for n in (1,2,3):
ngrams = preprocessing.get_ngrams(n, words)
ngrams = BayesClassifier.get_features(ngrams)
for g in ngrams:
count = self.ngram_counts[n].get(g, [0,0])
count[cls] += 1
self.ngram_counts[n][g] = count
self.ngram_keys[n].add(g)
def _add_example(self, cls, message):
"""Add a training example
"""
words = preprocessing.extract_words(message)
if not words: # Treat cases where 'paper cut' is pre-processing as negatives.
return
self.class_count[cls] += 1
# Update ngram_counts and ngram_keys for ngrams cls
for n in (1, 2, 3):
ngrams = preprocessing.get_ngrams(n, words)
ngrams = BayesClassifier.get_features(ngrams)
for g in ngrams:
count = self.ngram_counts[n].get(g, [0, 0])
count[cls] += 1
self.ngram_counts[n][g] = count
self.ngram_keys[n].add(g)
def classify(self, message, detailed=False):
"""
'message' is a string to classify. Return True or False classification.
Method is to calculate a log_odds from a liklihood based on
trigram, bigram and unigram (p,n) counts in the training set
For each trigram
return smoothed trigram score if trigram in training set, else
for the 2 bigrams in the trigram
return smoothed bigram score if bigram in training set, else
for the 2 unigrams in the bigram
return smoothed unigram score
get_score() shows the smoothed scoring
<n>gram_score() shows the backoff and smoothing factors
"""
words = preprocessing.extract_words(message)
if not words:
return False, 0.0
# Best intuition would be to compute back-off based on counts
ngrams = dict(
(n, preprocessing.get_ngrams(n, words)) for n in (1, 2, 3))
query_vecs = dict(
(n, RocchioClassifier.get_query_vec(ngrams[n])) for n in (1, 2, 3))
weights = RocchioClassifier.get_weights()
def get_weighted_distance(centroid):
return sum(
RocchioClassifier.get_distance(centroid[n], query_vecs[n]) *
weights[n] for n in (1, 2, 3))
pos_distance = get_weighted_distance(self.pos_centroid)
neg_distance = get_weighted_distance(self.neg_centroid)
diff = (pos_distance + EPSILON) / (neg_distance + EPSILON)
return diff > RocchioClassifier.threshold, math.log(diff)
def classify(self, message, detailed=False):
"""
'message' is a string to classify. Return True or False classification.
Method is to calculate a log_odds from a liklihood based on
trigram, bigram and unigram (p,n) counts in the training set
For each trigram
return smoothed trigram score if trigram in training set, else
for the 2 bigrams in the trigram
return smoothed bigram score if bigram in training set, else
for the 2 unigrams in the bigram
return smoothed unigram score
get_score() shows the smoothed scoring
<n>gram_score() shows the backoff and smoothing factors
"""
words = preprocessing.extract_words(message)
if not words:
return False, 0.0
# Best intuition would be to compute back-off based on counts
ngrams = dict((n, preprocessing.get_ngrams(n, words)) for n in (1, 2, 3))
query_vecs = dict((n, RocchioClassifier.get_query_vec(ngrams[n])) for n in (1, 2, 3))
weights = RocchioClassifier.get_weights()
def get_weighted_distance(centroid):
return sum(RocchioClassifier.get_distance(centroid[n], query_vecs[n]) * weights[n] for n in (1, 2, 3))
pos_distance = get_weighted_distance(self.pos_centroid)
neg_distance = get_weighted_distance(self.neg_centroid)
diff = (pos_distance + EPSILON) / (neg_distance + EPSILON)
return diff > RocchioClassifier.threshold, math.log(diff)
def classify(self, message, detailed=False):
"""
'message' is a string to classify. Return True or False classification.
Method is to calculate a log_odds from a liklihood based on
trigram, bigram and unigram (p,n) counts in the training set
For each trigram
return smoothed trigram score if trigram in training set, else
for the 2 bigrams in the trigram
return smoothed bigram score if bigram in training set, else
for the 2 unigrams in the bigram
return smoothed unigram score
get_score() shows the smoothed scoring
<n>gram_score() shows the backoff and smoothing factors
"""
def get_docs_with_terms(vectorizer, ngrams):
docs = set()
for term in ngrams:
if term in vectorizer.vocab:
docs |= set(vectorizer.tfidf[term].keys())
return docs
def get_nearest(K, documents, vectorizer, ngrams, doc_ids):
"""Return doc ids of K documents nearest query_vec
"""
# Compute scores and add to a priority queue
scores = []
for i in doc_ids:
heapq.heappush(scores, (vectorizer.get_distance(i, ngrams), i, documents[i][0]))
# Return top K scores
return [(cls,i,dist) for dist,i,cls in heapq.nlargest(K,scores)]
words = preprocessing.extract_words(message)
if not words:
return False, 0.0
ngrams = dict((n,preprocessing.get_ngrams(n, words)) for n in (1,2,3))
diffs = {}
for n in (1,2,3):
doc_ids = get_docs_with_terms(self.vectorizers[n], ngrams[n])
nearest = get_nearest(KnnClassifier.K, self.documents[n], self.vectorizers[n], ngrams[n], doc_ids )
pos = sum((1 if cls else -1) * (KnnClassifier.backoff ** k) for k,(cls,_,_) in enumerate(nearest))
max_pos = sum(KnnClassifier.backoff ** k for k in range(len(nearest)))
# pos2/max_pos2 is in range [-1,+1]
pos2 = sum((1 if cls else -1) * (KnnClassifier.backoff2 ** (2*k)) for k,(cls,_,_) in enumerate(nearest))
max_pos2 = sum(KnnClassifier.backoff2 ** (2*k) for k in range(len(nearest)))
pos *= pos2
max_pos *= max_pos2
diffs[n] = pos/max_pos if max_pos else 0.0
weights = KnnClassifier.get_weights()
diff = sum(diffs[n]*weights[n] for n in (1,2,3))
return diff > KnnClassifier.threshold, diff
def classify(self, message, detailed=False):
"""
'message' is a string to classify. Return True or False classification.
Method is to calculate a log_odds from a liklihood based on
trigram, bigram and unigram (p,n) counts in the training set
For each trigram
return smoothed trigram score if trigram in training set, else
for the 2 bigrams in the trigram
return smoothed bigram score if bigram in training set, else
for the 2 unigrams in the bigram
return smoothed unigram score
get_score() shows the smoothed scoring
<n>gram_score() shows the backoff and smoothing factors
"""
def get_docs_with_terms(vectorizer, ngrams):
docs = set()
for term in ngrams:
if term in vectorizer.vocab:
docs |= set(vectorizer.tfidf[term].keys())
return docs
def get_nearest(K, documents, vectorizer, ngrams, doc_ids):
"""Return doc ids of K documents nearest query_vec
"""
# Compute scores and add to a priority queue
scores = []
for i in doc_ids:
heapq.heappush(
scores,
(vectorizer.get_distance(i, ngrams), i, documents[i][0]))
# Return top K scores
return [(cls, i, dist)
for dist, i, cls in heapq.nlargest(K, scores)]
words = preprocessing.extract_words(message)
if not words:
return False, 0.0
ngrams = dict(
(n, preprocessing.get_ngrams(n, words)) for n in (1, 2, 3))
diffs = {}
for n in (1, 2, 3):
doc_ids = get_docs_with_terms(self.vectorizers[n], ngrams[n])
nearest = get_nearest(KnnClassifier.K, self.documents[n],
self.vectorizers[n], ngrams[n], doc_ids)
pos = sum((1 if cls else -1) * (KnnClassifier.backoff**k)
for k, (cls, _, _) in enumerate(nearest))
max_pos = sum(KnnClassifier.backoff**k
for k in range(len(nearest)))
# pos2/max_pos2 is in range [-1,+1]
pos2 = sum((1 if cls else -1) * (KnnClassifier.backoff2**(2 * k))
for k, (cls, _, _) in enumerate(nearest))
max_pos2 = sum(KnnClassifier.backoff2**(2 * k)
for k in range(len(nearest)))
pos *= pos2
max_pos *= max_pos2
diffs[n] = pos / max_pos if max_pos else 0.0
weights = KnnClassifier.get_weights()
diff = sum(diffs[n] * weights[n] for n in (1, 2, 3))
return diff > KnnClassifier.threshold, diff
