def splinter_scrape_ta_reviews(city='', state='', write_to_db=False, start_num=0, end_num=-1):
"""PURPOSE: To """
engine = cadb.connect_aws_db(write_unicode=True)
blinks = get_hotel_urls(city, state, engine)
# only do the specified hotel range
if start_num != 0:
blinks = blinks[start_num:]
if end_num != -1:
if len(blinks) < end_num:
print('end_num exceeded number of hotels. resetting to max.')
end_num = len(blinks)
blinks = blinks[:end_num]
br = Browser()
donebids = get_done_business_ids(city, engine)
for hotel_id, biz_id, link in blinks:
# check to see if there are already reviews for that hotel
if int(biz_id) not in donebids:
bigdf = scrape_hotel(link, br, engine)
bigdf['hotel_id'] = hotel_id
bigdf['business_id'] = biz_id
bigdf['biz_review_id'] = np.int64(bigdf['biz_review_id'].values)
bigdf = remove_duplicates(bigdf, city, engine)
if write_to_db:
try:
bigdf.to_sql('ta_reviews', engine, if_exists='append', index=False)
except:
print('WRITING TO DB FAILED!!!')
else:
print('business_id {} already scraped.'.format(biz_id))
def retrieve_best_hotels(city, state=''):
"""PURPOSE: To """
engine = cadb.connect_aws_db(write_unicode=True)
conn = engine.connect()
cmd = 'SELECT * FROM yelp_reviews'
yelp_reviews = pd.read_sql(cmd, engine)
cmd = 'SELECT * FROM yelp_hotels'
yelp_hotels = pd.read_sql(cmd, engine)
yelp = pd.merge(yelp_hotels, yelp_reviews, on='business_id', how='inner')
yelp_city = yelp[yelp['hotel_city'] == city.strip()]
yelp_dog_review = yelp_city[yelp_city['review_text'].str.contains('dog')].copy().reset_index()
average_dog_ratings = [np.mean(yelp_dog_review[yelp_dog_review['hotel_id'] == hotel_id]['review_rating'].values) for hotel_id in np.unique(yelp_dog_review['hotel_id'])]
unique_hotels = yelp_dog_review[yelp_dog_review['hotel_id'].isin(np.unique(yelp_dog_review['hotel_id']))].copy()
unique_hotels.drop_duplicates(cols='hotel_id', inplace=True)
unique_hotels['average_rating'] = average_dog_ratings
best_dog_hotel_names = unique_hotels.sort(columns='average_rating', ascending=False)['hotel_name'].head(10).values
best_dog_hotel_ratings = np.round(unique_hotels.sort(columns='average_rating', ascending=False)['average_rating'].head(10).values, 1)
string_ratings = [str(rat) for rat in best_dog_hotel_ratings]
#print('best dog hotels:')
#print(best_dog_hotel_names)
return best_dog_hotel_names, string_ratings
def get_biz_ids(city, engine):
cmd = "SELECT business_id FROM ta_hotels "
cmd += "where hotel_city = "
cmd += '"' + (" ").join(city.split("_")) + '"'
try:
xstng_bizs = [int(biz_id[0]) for biz_id in pd.read_sql_query(cmd, engine).values]
except:
engine = cadb.connect_aws_db(write_unicode=True)
xstng_bizs = [int(biz_id[0]) for biz_id in pd.read_sql_query(cmd, engine).values]
return xstng_bizs
def get_biz_review_ids(city, engine):
cmd = 'select biz_review_id from ta_reviews r inner join '
cmd += 'ta_hotels h on r.business_id=h.business_id '
cmd += 'where h.hotel_city = '
cmd += '"'+(' ').join(city.split('_'))+'"'
try:
xstng_revs = [int(rev_id[0]) for rev_id in pd.read_sql_query(cmd, engine).values]
except:
engine = cadb.connect_aws_db(write_unicode=True)
xstng_revs = [int(rev_id[0]) for rev_id in pd.read_sql_query(cmd, engine).values]
return xstng_revs
def retrieve_best_hotels3(city, state='',
revdb='ta',
max_revs=10):
"""
PURPOSE:
To retrieve the reviews for all hotels for a given city,
compute the hotels ratings, and return the data for the
top ten hotels for that city.
"""
# convert city input in all lower case
city = str(city).lower()
engine = cadb.connect_aws_db(write_unicode=True)
if revdb == 'yelp':
revdf = get_yelp_reviews(city, engine)
if revdb == 'ta':
revdf = get_ta_reviews(city, engine)
# get the unique hotels
unique_biz_ids = np.unique(revdf['business_id'])
unique_hotel_df = revdf[revdf['business_id'].isin(unique_biz_ids)].copy()
unique_hotel_df.drop_duplicates(subset='business_id', inplace=True)
unique_hotel_df['num_dog_reviews'] = [len(revdf[revdf['business_id'] == business_id]) for business_id in unique_hotel_df['business_id'].values]
#max_revs = unique_hotel_df['num_dog_reviews'].max()
print('max reviews is: {}'.format(max_revs))
unique_hotel_df['average_dog_rating'] = [np.mean(revdf[revdf['business_id'] == business_id]['dog_rating'].values) * 0.8 + min(1, unique_hotel_df[unique_hotel_df['business_id'] == business_id]['num_dog_reviews'].values[0]/max_revs) for business_id in unique_hotel_df['business_id'].values]
# print('dog ratings are...')
# print(unique_hotel_df['average_dog_rating'].values)
uniq_df_srtd = unique_hotel_df.sort_values(by='average_dog_rating', ascending=False).head(10).copy()
best_dog_hotel_names = uniq_df_srtd['hotel_name'].values
best_dog_hotel_ratings = np.round(uniq_df_srtd['average_dog_rating'].values, 1)
string_ratings = [str(rat) for rat in best_dog_hotel_ratings]
best_dog_hotel_imgs = uniq_df_srtd['hotel_img_url'].values
best_dog_hotel_urls = uniq_df_srtd['hotel_url'].values
best_dog_hotel_prices = [str(prc) if prc > 0 else "nan" for prc in np.int64(np.round(uniq_df_srtd['hotel_price'].values))]
# print('best dog hotel prices')
# print(best_dog_hotel_prices)
#print('best dog hotels:')
#print(best_dog_hotel_names)
return best_dog_hotel_names, string_ratings, best_dog_hotel_imgs, best_dog_hotel_urls, best_dog_hotel_prices
def classify_review_type(traindb="yelp", ntrain_dog_revs=1500, ntrain_gen_revs=1500, classdb="ta", verbose=False):
"""
PURPOSE: To classify reviews as being either pet-related or general.
:param traindb:
The data set to use for training. The default is Yelp.
:param ntrain_dog_revs:
The number of dog-related hotel reviews to use in the training.
:param ntrain_gen_revs:
The number of general hotel reviews to use in the training.
:param classdb:
The data set to classify (either yelp or ta)
:param verbose:
Set this to True to print progress shit.
"""
engine = cadb.connect_aws_db(write_unicode=True)
if verbose:
print("grabbing bf data...")
# get the bringfido reviews
bfdf = get_bf_reviews(engine)
if verbose:
print("grabbing general review data...")
if traindb == "ta":
print("using TA data for training...")
gentraindf = get_ta_reviews(engine)
if traindb == "yelp":
print("using Yelp data for training...")
gentraindf = get_yelp_reviews(engine)
train_data = np.hstack(
(bfdf["review_text"].values[:ntrain_dog_revs], gentraindf["review_text"].values[:ntrain_gen_revs])
)
labels = ["dog"] * ntrain_dog_revs
labels.extend(["general"] * ntrain_gen_revs)
y_train = labels
if verbose:
print("vectorizing...")
t0 = time()
vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5, stop_words="english")
X_train = vectorizer.fit_transform(train_data)
duration = time() - t0
print("vectorized in {:.2f} seconds.".format(duration))
penalty = "l2"
clf = LinearSVC(loss="l2", penalty=penalty, dual=False, tol=1e-3)
if verbose:
print("training model...")
clf.fit(X_train, y_train)
if classdb == "yelp":
print("categorizing Yelp data...")
classdf = get_yelp_reviews(engine, remove_shorts=False)
if classdb == "ta":
print("categorizing TA data...")
classdf = get_ta_reviews(engine, remove_shorts=False)
X_yrevs = vectorizer.transform(classdf["review_text"].values)
if verbose:
print("predicting...")
pred = clf.predict(X_yrevs)
classdf["review_category"] = pred
update_table_rev_cat(classdf, engine)
def splinter_scrape_ta_hotels(city_url="", city="new_haven", state="ct", write_to_db=False, max_pages=20):
"""PURPOSE: To """
# this only needs to be done at the very beginning
br = Browser()
if city_url == "":
city_url = get_city(city.lower() + "_" + state.lower())
print("using the following url:")
print("{}".format(city_url))
# city_url = "http://www.tripadvisor.com/Hotels-g31310-Phoenix_Arizona-Hotels.html"
#####################################################
# do not edit below this line
#####################################################
# more_pages is used to keep track if there is more
# than one page of hotel results for the given city
more_pages = True
# scraping will start on page 1 of the hotel results
page = 1
# open the URL in a browser object:
br.visit(city_url)
# find the div to enter the date range. This is needed to get pricing info:
date_bar = br.find_by_xpath('//*[contains(@class, "meta_date_wrapper")]')
# find the check in calendar span:
cin_btn = date_bar.find_by_xpath('span[contains(@class, "meta_date_field check_in")]/span')[0]
# now click the check_in span to activate it
cin_btn.click()
# select the right calendar div (next month)
rightcal = br.find_by_xpath('//div[contains(@class, "month")]')[1]
# now select the third Friday of next month as the check in date
fri_btn = rightcal.find_by_xpath("table/tbody/tr[3]/td[6]/div")
# and click it
fri_btn.click()
# now choose the next day (saturday) as the check out date
cout_btn = date_bar.find_by_xpath('span[contains(@class, "meta_date_field check_out")]/span')[0]
cout_btn.click()
leftcal = br.find_by_xpath('//div[contains(@class, "month")]')[0]
sat_btn = leftcal.find_by_xpath("table/tbody/tr[3]/td[7]/div")
sat_btn.click()
print("Dates selected.")
# wait a few seconds for ta to retrieve prices
time.sleep(5)
# get the city and state info
loclist = br.find_by_xpath('//*[contains(@id, "BREADCRUMBS")]')
locstring = loclist.text.split(u"\u203a")
hotel_city = city = locstring[2].lower()
hotel_state = re.findall("\w+ \(([A-Z][A-Z])\)", locstring[1])[0].lower()
# create a pandas dataframe that will be used for writing
# the results to the DB:
columns = [
"hotel_id",
"hotel_url",
"hotel_img_url",
"hotel_name",
"hotel_address",
"hotel_city",
"hotel_state",
"hotel_rating",
"hotel_latitude",
"hotel_longitude",
"hotel_price",
"business_id",
"review_count",
"dog_review_count",
]
bigdf = pd.DataFrame(columns=columns)
# create some lists to fill w. the results from each page
hotel_names = []
links = []
img_url = []
hotel_price = []
business_id = []
print("starting scraper loop.")
while more_pages and page <= max_pages:
print("*" * 75)
print("Now scraping page {} of {} of the hotel results".format(page, max_pages))
print("*" * 75)
# get all the review divs
print("waiting a few seconds before scraping...")
time.sleep(np.random.uniform(8, 20))
listing_div = br.find_by_xpath('//*[contains(@class, "hotels_lf_condensed")]')
xsts1 = br.is_element_present_by_xpath('//*[contains(@class, "photo_booking")]', wait_time=1)
xsts2 = br.is_element_present_by_xpath('//*[contains(@class, "property_details")]', wait_time=1)
xsts3 = br.is_element_present_by_xpath(
'//*[contains(@class, "prw_rup")]/div/div/div/div[@class="headerContents"]/div[contains(@class, "price")]',
wait_time=1,
)
while len(listing_div) < 1 or not xsts1 or not xsts2 or not xsts3:
print("now waiting for DOIs to return")
time.sleep(5)
listing_div = br.find_by_xpath('//*[contains(@class, "hotels_lf_condensed")]')
xsts1 = br.is_element_present_by_xpath('//*[contains(@class, "photo_booking")]', wait_time=1)
xsts2 = br.is_element_present_by_xpath('//*[contains(@class, "property_details")]', wait_time=1)
xsts3 = br.is_element_present_by_xpath(
'//*[contains(@class, "prw_rup")]/div/div/div/div[@class="headerContents"]/div[contains(@class, "price")]',
wait_time=1,
)
print("# of listings: {}".format(len(listing_div)))
print("photo_booking exists: {}".format(xsts1))
print("property_details exists: {}".format(xsts2))
print("prw_up exists: {}".format(xsts3))
print("Number of hotel listings on this page: {}".format(len(listing_div)))
df = pd.DataFrame(columns=columns)
for listing in listing_div:
try:
biz_id = re.findall("hotel_(\d+)", listing["id"])
if len(biz_id) > 0:
biz_id = biz_id[0]
else:
biz_id = None
print("business_id: {}".format(biz_id))
business_id.append(biz_id)
except:
print("!" * 80)
print("biz_id DOES NOT EXIST!")
print("!" * 80)
business_id.append(None)
try:
prop = listing.find_by_xpath('div/div/div/div[contains(@class, "property_details")]')
except:
print("!" * 80)
print("prop DIV DOES NOT EXIST!")
print("!" * 80)
try:
title = prop.find_by_xpath('div/div[@class="listing_title"]')
print(title.text)
hotel_names.append(title.text)
except:
print("!" * 80)
print("TITLE DIV DOES NOT EXIST!")
print("!" * 80)
hotel_names.append(None)
try:
hotel_link = title.find_by_xpath("a")["href"]
print(hotel_link)
links.append(hotel_link)
except:
print("!" * 80)
print("hotel_link DOES NOT EXIST!")
print("!" * 80)
links.append(None)
try:
hotel_img = prop.find_by_xpath('div[@class="photo_booking"]/div/div/a/img')["src"]
print("Hotel img URL: {}".format(hotel_img))
img_url.append(hotel_img)
except:
print("!" * 80)
print("hotel_img DIV DOES NOT EXIST!")
print("!" * 80)
img_url.append(None)
try:
price_text = prop.find_by_xpath(
'div[contains(@class, "prw_rup")]/div/div/div/div[@class="headerContents"]/div[contains(@class, "price")]'
).text
price = re.findall("(\d+)", price_text)[0]
print("Price: ${}".format(price))
hotel_price.append(price)
except:
print("!" * 80)
print("price DIV DOES NOT EXIST!")
print("!" * 80)
hotel_price.append(None)
print("*" * 50)
if len(hotel_names) > 0:
print("len of hotel_names: {}".format(len(hotel_names)))
print("len of hotel_price: {}".format(len(hotel_price)))
print("len of img_url: {}".format(len(img_url)))
print("len of business_id: {}".format(len(business_id)))
print("len of hotel_city: {}".format(len(hotel_city)))
print("len of hotel_state: {}".format(len(hotel_state)))
df["hotel_name"] = hotel_names
df["hotel_price"] = hotel_price
df["hotel_img_url"] = img_url
df["hotel_url"] = links
df["business_id"] = business_id
df["hotel_city"] = hotel_city
df["hotel_state"] = hotel_state
bigdf = bigdf.append(df)
# update the page number
page += 1
# if more pages are desired, look for a "next" button
if page <= max_pages:
nxt_btn = br.find_by_xpath(
'//div[contains(@class, "deckTools")]/div[contains(@class, "unified")]/a[contains(@class, "next")]'
)
# if there is a next button, click it
# else exit the while loop
if len(nxt_btn) > 0:
nxt_btn.click()
else:
more_pages = False
if write_to_db:
engine = cadb.connect_aws_db(write_unicode=True)
bigdf = remove_ad_hotels(bigdf)
remove_duplicate_hotels(bigdf, city, engine)
bigdf.to_sql("ta_hotels", engine, if_exists="append", index=False)
def retrieve_best_hotels2(city, state='',
revdb='ta',
max_revs=10):
"""PURPOSE: To """
city = str(city).lower()
engine = cadb.connect_aws_db(write_unicode=True)
# create and train the model to use to classify ratings:
clf, vectorizer = create_and_train_model(engine)
if revdb == 'yelp':
revdf = get_yelp_reviews(city, engine)
if revdb == 'ta':
revdf = get_ta_reviews(city, engine)
review_text = revdf['review_text'].values
print('Number of reviews: {}'.format(len(review_text)))
t0 = time()
X_pred = vectorizer.transform(review_text)
duration = time() - t0
print('transformed test data in {:.2f} seconds.'.format(duration))
#yelp_city = yelp[yelp['hotel_city'] == city.strip()]
#yelp_dog_review = yelp_city[yelp_city['review_text'].str.contains('dog')].copy().reset_index()
#average_dog_ratings = [np.mean(yelp_dog_review[yelp_dog_review['hotel_id'] == hotel_id]['review_rating'].values) for hotel_id in np.unique(yelp_dog_review['hotel_id'])]
#now predict the rating based on the sentiment of the review:
y_pred = clf.predict(X_pred)
revdf['dog_rating'] = y_pred
# get the unique hotels
unique_biz_ids = np.unique(revdf['business_id'])
unique_hotel_df = revdf[revdf['business_id'].isin(unique_biz_ids)].copy()
unique_hotel_df.drop_duplicates(cols='business_id', inplace=True)
unique_hotel_df['num_dog_reviews'] = [len(revdf[revdf['business_id'] == business_id]) for business_id in unique_hotel_df['business_id'].values]
#max_revs = unique_hotel_df['num_dog_reviews'].max()
print('max reviews is: {}'.format(max_revs))
unique_hotel_df['average_dog_rating'] = [np.mean(revdf[revdf['business_id'] == business_id]['dog_rating'].values) * 0.8 + min(1, unique_hotel_df[unique_hotel_df['business_id'] == business_id]['num_dog_reviews'].values[0]/max_revs) for business_id in unique_hotel_df['business_id'].values]
print('dog ratings are...')
print(unique_hotel_df['average_dog_rating'].values)
uniq_df_srtd = unique_hotel_df.sort(columns='average_dog_rating', ascending=False).head(10).copy()
best_dog_hotel_names = uniq_df_srtd['hotel_name'].values
best_dog_hotel_ratings = np.round(uniq_df_srtd['average_dog_rating'].values, 1)
string_ratings = [str(rat) for rat in best_dog_hotel_ratings]
best_dog_hotel_imgs = uniq_df_srtd['hotel_img_url'].values
best_dog_hotel_urls = uniq_df_srtd['hotel_url'].values
best_dog_hotel_prices = [str(prc) if prc > 0 else "nan" for prc in np.int64(np.round(uniq_df_srtd['hotel_price'].values))]
print('best dog hotel prices')
print(best_dog_hotel_prices)
#print('best dog hotels:')
#print(best_dog_hotel_names)
return best_dog_hotel_names, string_ratings, best_dog_hotel_imgs, best_dog_hotel_urls, best_dog_hotel_prices
def splinter_scrape_bf(city, state):
"""PURPOSE: To """
city = city.lower()
state = state.lower()
br = Browser()
citystate_string = city+'_'+state+'_us/'
url = 'http://www.bringfido.com/lodging/city/'+citystate_string
br.visit(url)
page = 1
npages = len(br.find_by_xpath('//*[@id="results_paging_controls_bottom"]/span'))
columns = ['hotel_id',
'hotel_img_url',
'hotel_url',
'hotel_name',
'hotel_address',
'hotel_city',
'hotel_state',
'hotel_rating',
'hotel_latitude',
'hotel_longitude',
'review_count',
'hotel_address',
'business_id',
'review_id',
'user_id',
'username',
'review_title',
'review_text',
'review_rating',
'review_date']
bigdf = pd.DataFrame(columns=columns)
while (page == 1 or page < npages):
print('*'*70)
print('Now on page {}'.format(page))
archive_links = br.find_by_xpath('//*[@id="results_list"]/div')
hotel_names = []
text_summaries = []
links = []
biz_ids = []
hotel_img_urls = []
df = pd.DataFrame(columns=columns)
texts = []
titles = []
authors = []
ratings = []
hnms = []
hiurls = []
bids = []
lnks = []
for lnk in archive_links:
hotel_names.append(lnk.find_by_xpath('div[2]/h1/a').value)
text_summaries.append(lnk.text)
this_link = lnk.find_by_xpath('div/h1/a')['href']
links.append(this_link)
hotel_img_urls.append(lnk.find_by_xpath('div/div[@class="photo_inner"]/a/img')['src'])
biz_ids.append(lnk['id'].split('_')[-1])
for hotel_id, link in enumerate(links):
print('*'*75)
print('Now on {}: {}'.format(hotel_id, link))
print('*'*75)
br.visit(link)
# hotel_description = br.find_by_xpath('//*[@class="body"]').text
# scrape the address details section of the page
details = br.find_by_xpath('//*[@class="address"]').text.split('\n')
# now get just the address:
address = details[0]
# and just the city, state, country, and zip code:
csczip = details[1]
# and just the phone number
# phone = details[2]
# now separate the city, state, and zip:
city, state, zipcode = csczip.strip().split(',')
zipcode = zipcode[3:]
#Now using correct Xpath we are fetching URL of archives
reviews = br.find_by_xpath('//*[@class="review_container"]')
print(reviews)
print('')
for rev in reviews:
titles.append(rev.find_by_xpath('div/div[1]').text)
authors.append(rev.find_by_xpath('div/div[2]').text)
texts.append(rev.find_by_xpath('div/div[3]').text)
ratings.append(rev.find_by_xpath('div[2]/img')['src'].split('/')[-1][0:1])
hnms.append(hotel_names[hotel_id])
hiurls.append(hotel_img_urls[hotel_id])
bids.append(biz_ids[hotel_id])
lnks.append(link)
print(rev.find_by_xpath('div[2]/img')['src'].split('/')[-1][0:1])
print('Number of new titles: {}'.format(len(titles)))
print('Number of new ratings: {}'.format(len(ratings)))
df['review_title'] = titles
df['username'] = authors
df['review_text'] = texts
df['review_rating'] = ratings
df['hotel_id'] = hotel_id
df['hotel_name'] = hnms
df['hotel_url'] = lnks
df['hotel_img_url'] = hiurls
df['hotel_address'] = address
df['hotel_city'] = city
df['hotel_state'] = state
df['hotel_rating'] = np.mean([int(rat) for rat in ratings])
df['hotel_latitude'] = None
df['hotel_longitude'] = None
df['review_count'] = len(texts)
df['review_id'] = 0
df['user_id'] = 0
df['business_id'] = bids
print('new entries from this page: {}'.format(len(df)))
bigdf = bigdf.append(df.copy())
page += 1
if page < npages:
page_timeout = True
while page_timeout:
br.visit(url)
time.sleep(1)
print('Now scraping page {} of {}'.format(page, npages))
button = br.find_by_id('page_'+str(page))
print(button)
if len(button) > 0:
button.click()
page_timeout = False
bigdf_reviews = bigdf[['hotel_id', 'review_id', 'business_id', 'user_id',
'username', 'review_title', 'review_text', 'review_rating']].copy()
bigdf_hotels = bigdf[['hotel_id', 'hotel_url', 'hotel_img_url', 'hotel_name',
'hotel_address', 'hotel_city', 'hotel_state', 'hotel_rating',
'hotel_latitude', 'hotel_longitude', 'business_id', 'review_count']].copy()
bigdf_hotels.drop_duplicates(subset='business_id', inplace=True)
bigdf_hotels['hotel_id'] = None
bigdf_reviews['review_id'] = None
print('Number of bf reviews to add: {}'.format(len(bigdf_reviews)))
engine = cadb.connect_aws_db(write_unicode=True)
bigdf_reviews.to_sql('bf_reviews', engine, if_exists='append', index=False)
bigdf_hotels.to_sql('bf_hotels', engine, if_exists='append', index=False)