def gen_feature_vec(review, useful_bools, useful_ratios, biz_review_counts,
top100, threshold):
text = review['text']
words = set(flesch_kincaid.words(text.lower()))
useful = useful_bools[review['id']]
length = len(text)
reading_ease, reading_level = flesch_kincaid.results(text)
rating = review['stars']
useful_ratio = useful_ratios[
review['user_id']] if review['user_id'] in useful_ratios else 0
biz_review_count = biz_review_counts[review['business_id']]
features = [
useful, length, reading_ease, reading_level, rating, useful_ratio,
biz_review_count
]
for word in top100:
features.append(1 if word in words else 0)
#f = features
#interaction_features = [f[i] * f[j] for i in range(len(f)) for j in range(i + 1, len(f))]
#polynomial_features = [x ** y for x in features for y in range(2, 5)]
#features.extend(interaction_features)
#features.extend(polynomial_features)
return [str(x) for x in features]
def gen_feature_vec(review,
useful_bools,
useful_ratios,
biz_review_counts,
top100,
threshold):
text = review['text']
words = set(flesch_kincaid.words(text.lower()))
useful = useful_bools[review['id']]
length = len(text)
reading_ease, reading_level = flesch_kincaid.results(text)
rating = review['stars']
useful_ratio = useful_ratios[review['user_id']] if review['user_id'] in useful_ratios else 0
biz_review_count = biz_review_counts[review['business_id']]
features = [
useful,
length,
reading_ease,
reading_level,
rating,
useful_ratio,
biz_review_count
]
for word in top100:
features.append(1 if word in words else 0)
#f = features
#interaction_features = [f[i] * f[j] for i in range(len(f)) for j in range(i + 1, len(f))]
#polynomial_features = [x ** y for x in features for y in range(2, 5)]
#features.extend(interaction_features)
#features.extend(polynomial_features)
return [str(x) for x in features]
''').fetchall()
biz_review_counts = {k: v for k, v in biz_review_counts}
reviews = c.execute('SELECT * FROM review').fetchall()
for USEFUL_THRESHOLD in [1, 3, 5]:
freq = defaultdict(int)
useful_reviews = c.execute(
'''SELECT r.text
FROM review AS r
WHERE r.useful >= ?
''', (USEFUL_THRESHOLD, )).fetchall()
for review in useful_reviews:
for word in flesch_kincaid.words(review['text'].lower()):
freq[word] += 1
top100 = sorted(freq.iteritems(), key=lambda item: -item[1])
top100 = [k for k, v in top100 if k not in common_words.WORDS][:100]
print(top100, len(top100))
useful_bools = c.execute(
'''SELECT r.id, r.useful >= ?
FROM review AS r
''', (USEFUL_THRESHOLD, )).fetchall()
useful_bools = {k: v for k, v in useful_bools}
useful_ratios = c.execute(
'''SELECT
u.user_id,
GROUP BY b.business_id
''').fetchall()
biz_review_counts = {k:v for k,v in biz_review_counts}
reviews = c.execute('SELECT * FROM review').fetchall()
for USEFUL_THRESHOLD in [1, 3, 5]:
freq = defaultdict(int)
useful_reviews = c.execute('''SELECT r.text
FROM review AS r
WHERE r.useful >= ?
''', (USEFUL_THRESHOLD,)).fetchall()
for review in useful_reviews:
for word in flesch_kincaid.words(review['text'].lower()):
freq[word] += 1
top100 = sorted(freq.iteritems(), key=lambda item: -item[1])
top100 = [k for k, v in top100 if k not in common_words.WORDS][:100]
print(top100, len(top100))
useful_bools = c.execute('''SELECT r.id, r.useful >= ?
FROM review AS r
''', (USEFUL_THRESHOLD,)).fetchall()
useful_bools = {k:v for k,v in useful_bools}
useful_ratios = c.execute('''SELECT
u.user_id,
1.0 * SUM(CASE WHEN r.useful >= ? THEN 1 ELSE 0 END) / COUNT(*)
FROM user AS u, review AS r
