def execute(self, data):
rt = RT.RT()
movie = data["movie"]
Ebert = False
pred = False
proba = 0
count, title, date_string, fresh_score, id_list = le.get_movie(rt, str(movie))
if count > 0:
total, last_page, review_list = le.get_reviews(rt, id_list[0], fresh_score)
entries, A = le.build_matrix(review_list, id_list, critic_list, fillzeros=True)
if entries > 0:
X_test, y_test = A[:, :-1], A[:, -1]
Ebert = y_test[0] > 0
pred = RF.predict(X_test)[0] == 1
proba = RF.predict_proba(X_test)[0, 1]
result = []
result.append({"Count": count})
result.append({"info": title + " " + date_string})
result.append({"EbertReviewed": Ebert})
result.append({"great": pred})
result.append({"prob": proba})
return result
def scrape_reviews(df, collection, starting_row=0):
'''
Iterate over movie ids in dataframe and write reviews to a Mongo collection
Dataframe will be updated in place with progress on the scraping
Input/Output: df (Pandas Dataframe)
collection (Mongo collection)
starting_row - if restarting set to first unscraped row (int)
'''
rt = RT.RT()
for index, row in df[starting_row:].iterrows():
if index%50 == 0:
print 'Scraping movie:', index
m_id = df['movie_id'][index]
m_fresh = df['freshness'][index]
m_fresh = m_fresh.strip().strip('%')
total_stated, final_page, review_list = le.get_reviews(rt, m_id, m_fresh)
add_to_mongo(review_list, collection)
total_items = len(review_list)
if total_stated < total_items-1:
print 'For', m_id, 'found', total_items, 'out of', total_stated
df['total'][index] = total_stated
df['found'][index] = total_items
df['pages'][index] = final_page