# Practical Naive Bayes — Classification of Amazon Reviews

If you search around the internet looking for applying Naive Bayes classification on text, you’ll find a ton of articles that talk about the intuition behind the algorithm, maybe some slides from a lecture about the math and some notation behind it, and a bunch of articles I’m not going to link here that pretty much just paste some code and call it an explanation.

So I’m going to try to do a little more here, by hopefully writing and explaining enough, is let you yourself write a working Naive Bayes classifier.

There are three sections here. First is setup, and what format I’m expecting your text to be in for the classification. Second, I’ll talk about how to run naive Bayes on your own, using slow Python data structures. Finally, we’ll use Python’s NLTK and it’s classifier so you can see how to use that, since, let’s be honest, it’s gonna be quicker. Note that you wouldn’t want to use either of these in production, so look for a follow up post about how you might go about doing that.

As always, twitter, and check out the full code on github.

### Setup

Data from this is going to be from this UCSD Amazon review data set. I swear one of the biggest issues with running these algorithms on your own is finding a data set big and varied enough to get interesting results. Otherwise you’ll spend most of your time scraping and cleaning data that by the time you get to the ML part of the project, you’re sufficiently annoyed. So big thanks that this data already exists.

You’ll notice that this set has millions of reviews for products across 24 different classes. In order to keep the complexity down here (this is a tutorial post after all), I’m sticking with two classes, and ones that are somewhat far enough different from each other to show that classification works, we’ll be classifying baby reviews against tools and home improvement reviews.

##### Preprocessing

First thing I want to do now, after unpacking the .gz file, is to get a train and test set that’s smaller than the 160,792 and 134,476 of baby and tool reviews respectively. For purposes here, I’m going to use 1000 of each, with 800 used for training, and 200 used for testing. The algorithms are able to support any number of training and test reviews, but for demonstration purposes, we’re making that number lower.

Check the github repo if you want to see the code, but I wrote a script that just takes the full file, picks 1000 random numbers, segments 800 into the training set, and 200 into the test set, and saves them to files with the names “train_CLASSNAME.json” and “test_CLASSNAME.json” where classname is either “baby” or “tool”.

Also, the files from that dataset are really nice, in that they’re already python objects. So to get them into a script, all you have to do is run “eval” on each line of the file if you want the dict object.

##### Features

There really wasn’t a good place to talk about this, so I’ll mention it here before getting into either of the self, and nltk running of the algorithm. The features we’re going to use are simply the lowercased version of all the words in the review. This means, in order to get a list of these words from the block of text, we remove punctuation, lowercase every word, split on spaces, and then remove words that are in the NLTK corpus of stopwords (basically boring words that don’t have any information about class).

```from nltk.corpus import stopwords
STOP_WORDS = set(stopwords.words('english'))
def clean_review(review):
exclude = set(string.punctuation)
review = ''.join(ch for ch in review if ch not in exclude)
split_sentence = review.lower().split(" ")
clean = [word for word in split_sentence if word not in STOP_WORDS]
return clean```

Realize here that there are tons of different ways to do this, and ways to get more sophisticated that hopefully can get you better results! Things like stemming, which takes words down to their root word (wikipedia gives the example of “stems”, “stemmer”, “stemming”, “stemmed” as based on “stem”). You might want to include n-grams, for an n larger than 1 in our case as well.

Basically, there’s tons of processing on the text that you could do here. But since this I’m just talking about how Naive Bayes works, I’m sticking with simplicity. Maybe in the future I can get fancy and see how well I can do in classifying these reviews.

Ok, on to the actual algorithm.

# Getting Song Lyrics from Genius’s API + Scraping

Genius is a great resource. At a high level, Genius has song lyrics and allows users to comment on what the artist meant. Starting as Rap Genius, where users annotated rap lyrics, the site rebranded as “Genius”, allowing all songs to be talked about. According to their website, “Genius is the world’s biggest collection of song lyrics and crowdsourced musical knowledge.” Recently even, they’ve moved to allowing annotations of pretty much anything posted online.

I’ve have used it a bunch recently while trying to figure out what the hell Frank Ocean was trying to say in his new album Blond. Users of the site explained tons of Frank’s references that went whoosh right over my head when I listened the first time and all the times after.

And recently, when I had some ideas for mini projects using song lyrics, I was pretty happy to find that Genius had a API for getting the data on their site. Whenever I’m trying to get data elsewhere, I’m much happier with an API, or at least being able to get it from JSON responses rather than parsing HTML. It’s just cleaner to look at, and with an API, I can expect good documentation that isn’t going to change with css updates.

Their API docs looked pretty good at first glance, with endpoints for artists, songs, albums, and annotations. One things I did notice was that they don’t have an artist entry point. A lot of what I want to do is artist based, meaning I need to know the artist id for everyone. And in order for me to get that, I have to search the artist, grab a song from the results, hit the song endpoint for that song’s information, and then grab the artist id from there. It’d be nice if you could specify what I’m searching for when I hit the search endpoint so I don’t have to go through that whole charade just to get the artist. But that’s a blog post for another time. Overall, they give out tons of information pretty easily.

But why, Genius, why don’t you have an endpoint for getting the raw lyrics of a song?! You have a songs endpoint on the API, and you give me a ton of information from there — the song title, album name, featured artists on the song, number of annotations, images associated with the song, album information, page views for that song, and a whole host of more data. But the one thing you don’t give me, and the one thing that people using the API probably want the most, is plain text lyrics!

Pre-Genius, I was stuck with these jankily laid out sites with super old looking css that would have the lyrics, but not necessarily correct, and definitely no annotations. Those sites are probably easily scrapeable considering their simplicity, but searching for the right song would be more difficult, and the lyrics might not be correct. Genius solved this all now for a web user, but dammit, I want the lyrics in the API!

Now you might be able to get the entire set of lyrics by using the annotations endpoint, which had information about all the annotations for a certain song or article, but that would require a song to have annotations for every word in the song. For someone like Chance the Rapper who like Frank Ocean (and most other hip hop artists uses tons of references in his lyrics, having complete annotations might not be an issue. But of Jake Owen, who’s new single “American Country Love Song” has probably the most self explanatory lyrics ever (sorry for throwing you under the bus here, Jake. Still a fan), there’s no need to annotate anything, and getting the lyrics in this manner wouldn’t work.

The lyrics are there on the internet however, and I can get at them by hitting the song endpoint, and using the web url that it returns. The rest of this article will show you how to do that using Python and it’s requests and BeautifulSoup libraries. But I don’t have to have to resort to HTML parsing, and I don’t think Genius wants users doing that either.

I’m left here wondering why they don’t want to give up the lyrics so easily, and I really don’t have much to go on. Genius’s goal seems to be wanting to annotate the internet. It has already moved on from their initial site of Rap Genius, into all music, and now into speech transcripts, as well as pretty much any other content on the web. Their value comes from those annotations themselves, not the information they’re annotating. They give away the annotations freely, but not the information (lyrics) in this case.

Enough speculation on why Genius doesn’t spit out the lyrics to a song when you get the other information. And as I’m writing this, I realize I easily could have overlooked something in their API and Genius might return the full lyrics, but I overlooked it. In that case, half of this article will be pointless and I’ll hold my head in shame from yelling at them like I did.

For purposes here, I’m going to show you how to get the song lyrics from Genius if you have the song title, and also talk through my process of getting there.

Note of clarification, just to make sure I’m not violating their terms of service, this post is for informational purposes only. Hopefully this can help programmers out there learn. Don’t do something bad with this knowledge. Code time!

First thing you’re going to need is an account set up with Genius. You can sign up from the upper right hand corner of the genius.com homepage. After that, navigate to the api docs where you’ll then see your Bearer token that you’ll need for all API requests.

I’m using the requests library here, and once you have the bearer token, here’s what all the API requests to Genius should look like if, for example, you’re searching for a song title.

```import requests

#TOKEN below should be the string that the API docs tells you
#Clearly I'm not giving mine out here on the internet. That'd be dumb
base_url = "http://api.genius.com"
#Key line below here when, this is how to authorize your request when
#using the API
search_url = base_url + "/search"
song_title = "In the Midst of It All"
params = {'q': song_title}

The response, according to the Genius API, would be a list of songs that match that string passed in, with the first result being the Tom Misch song that I was going for. By changing around the url that is passed into the request method, you can access all the information that Genius supplies from the API (pretty much everything but the lyrics).

# The Special Relationship Between Noodles and Qdoba

I’ve had a theory that for every Noodles, there’s a Qdoba that’s right next door. It might be some sort of selection bias however, since I can think of a couple locations where they’re directly next to each other. To me, Noodles and Qdoba have a special relationship, at least compared to other restaurants. I figured now was about the time I should test this, and I can use Chipotle to test.

The question is: Which restaurant is more special to Noodles, Qdoba or Chipotle?

### Finding the Noodles, Qdoba, and Chipotle locations

Initially, I went to Noodle’s website and their locations page and was planning on getting the data from there. But what I realized was that it just used the Google Maps API to get it’s data, so I might as well just go right to the Google source and use their api correctly.

Google’s docs are pretty good in this case, and after grabbing an API key, I started in on finding the Dobas. For prototyping, I just started with the latitude and longitude of Milwaukee, my home town, and a place where I know there multiple Qdobas / Noodles pairs.

```import requests
location_milwaukee = '43.0389,-87.9065' #Milwaukee
params = {}
params['type'] = 'restaurant'
params['radius'] = 50000 #in meters, and going be an issue
params['keyword'] = 'Qdoba'
params['location'] = location
r = requests.get(url, params=params)
results = r.json()['results']
print results```

Put your Google Places API key in the ‘key’ param, run those lines of code (assuming you pip installed requests) and you’ll see 20 Qdoba locations along with some extra information spit out on your console.

### Issues

Two obstacles came up with this part of the project – one simple to fix, the other decently tough. First the simple one.

In order to limit the amount of information coming across the wire, Google limits each API request to 20 results. When there are more than 20 results they find, they also pass back in the json a param named “next_page_token”. So when we see this param passed back, we need to stick with the same location, and add the param “pagetoken” and hit the same endpoint. There’s also a time aspect to this request where we need to wait a couple seconds before hitting the endpoint to grab the remaining locations. Not too bad.

Second issue here, and somewhat of an annoying one, is the radius parameter. 50 km is not quite the size of the entire US. This is actually a really interesting problem that, after talking with work colleagues, there isn’t a straightforward solution. What we really need here, is a set of latitudes and longitudes where, with the 50 km radius, will cover the entirety of the United States. Sure you could put a location every miles or so, but that would take forever to search for. So instead of finding a solution to this problem isn’t in the scope of this article (maybe later). Instead, I found this nice gist of the top 246 metro locations in the US and their latitude and longitudes and am just going to use that and hope it covers enough of the country to be useful.

Complete code for this part of the project includes writing the locations of the restaurants to a tab separated values (tsv) file. Normally would use a csv, but since the addresses have commas in them, it could get confusing.

```from major_city_list import major_cities

keyword_qdoba = 'Qdoba Mexican Eats'
keyword_noodles = 'Noodles & Company'
keyword_chipotle = 'Chipotle'
search_keywords = [keyword_qdoba, keyword_noodles, keyword_chipotle]

params = {}
params['type'] = 'restaurant'
for keyword in search_keywords:
params['keyword'] = keyword
keyword_info = {}
for city in major_cities:
print city["city"]
location = "%s,%s" % (city["latitude"], city["longitude"])
params['location'] = location
while True:
r = requests.get(url, params=params)
results = r.json()['results']
num_results = len(results)
print "results: %s" % num_results
for result in results:
lat = result["geometry"]["location"]["lat"]
lng = result["geometry"]["location"]["lng"]
key = "%s%s" % (lat, lng * -1)
keyword_info[key] = info
try:
next_page_token = r.json()['next_page_token']
params["pagetoken"] = next_page_token
time.sleep(2)
except KeyError:
params.pop("pagetoken", None)
break

filename = "%s.tsv" % keyword
filename = filename.lower().replace(" ", "_")
with open(filename, 'wb') as tsvfile:
writer = csv.writer(tsvfile, delimiter='\t')
for key, info in keyword_info.iteritems():

Final thing to point out here is about why I have this be a multi step process. I could have written a script that does this part, and then all the rest of the project at once. But you’ll find that when working on things and bugfixing, it’s better to split tasks up, save the results, and then use those results without having to go back out to the internet.

### Finding nearest companion

Step two of this process here is finding the closest Qdoba and Chipotle for each Noodles. With that information, we can figure out how far away the nearest companion is. At first, I was tempted to go right back to the Google Places API since, well, it was designed for this purpose. However first, I decided to see if I could brute force it with the n^2 loop over every location and find the shortest distance algorithm. Turns out that was a great decision because it was way quicker and more accurate.

Code steps are 1) Read in the noodles.tsv file generated above, 2) read in the chipotle and qdoba .tsv files, 3) for each Noodles, loop the entire other file and store the closest location, 4) store that information in another tsv file. In this case, code is easier to figure out than explanation.

```keywords = ['chipotle', 'qdoba']
noodles_locations = []
filename = "noodles.tsv"
with open(filename, 'rb') as tsvfile:
noodles_locations.append(row)
for keyword in keywords:
information = []
filename = "%s.tsv" % keyword
keyword_locations = []
with open(filename, 'rb') as tsvfile:
keyword_locations.append(row)
count = 0
for noodle_location in noodles_locations:
print count
test_loc = (noodle_location[0], noodle_location[1])
best_distance = 100000 #something large
for location in keyword_locations:
found_loc = (location[0], location[1])
distance = vincenty(test_loc, found_loc).miles
if distance < best_distance:
best_distance = distance
best_location = [location[0], location[1], location[2]]
info_row = [noodle_location[0], noodle_location[1], noodle_location[2], best_location[0], best_location[1], best_location[2]]
information.append(info_row)
count += 1
filename = "noodles_closest_%s.tsv" % keyword
with open(filename, 'wb') as tsvfile:
writer = csv.writer(tsvfile, delimiter='\t')
for info in information:
writer.writerow(info)
```

### Analyze!

For my dumb theory to be true, there needs to be a disproportionate number of Qdobas and Noodles within walking distance of each other, and specifically, right next to each other compared to Chipotle.

After analyzing the data, I’m totally right.

I found 418 Noodles, 790 Chipotles, and 618 Qdobas. Even with the extra 172 Chipotles, there’s a Qdoba closer to a Noodles than there is a Chipotle.

Some numbers. If you’re at a Noodles, there’s a 12.7% chance you’re within 0.1 miles of a Qdoba, 19.9% chance you’re within 0.25 miles, and 35.9% chance you’re within 1 mile. Chipotle has percentages of 6.4%, 12.7%, 30.6% respectively.

Check out the histograms:

While not much of a difference, you can see a little more action on the left side of the Qdoba histogram compared to the Chipotle one.

As a final, final test, I went through each Noodle location again, found the nearest Qdoba and nearest Chipotle and counted the number of Noodles that had a Qdoba closer, and Noodles that had Chipotle closer. Final tally, 214 had a Qdoba closer, 204 had a Chipotle closer.

### So how close are Qdobas and Chipotles from each other?

For fun, I ran the code to see how close the nearest Chipotle was from each Qdoba.

6.6% Qdobas had a Chipotle within 0.1 miles, 12.8% had one within 0.25 miles, and 28% within 1 mile. Semi-surprising that it was this high, but I guess people don’t want to go far for food.

The histogram is definitely more telling that Chipotles are further apart. Check out the y axis scaling here.

### What’s the point of this?

Knowing this kind of information really isn’t all that useful. Fun, sure, but not too particularly useful. But what it does show is how powerful knowledge of the internet and programming can be. In just a short amount of time, we went from a dumb theory about restaurants to finding an answer. Also, maybe you’re looking to open a Qdoba somewhere in the US, and want to know if there’s a lonely Noodles that needs a companion!

# Gather all the PGA Tour stats

As someone who likes writing and investigating data sets, and as a huge fan of golf (and writer of a golf blog, Golf on the Mind), when I realized that the PGA Tour website has a crap ton of stats about players on the PGA Tour going back to the early 80s, I figured there was definitely some investigating to do. And the first step, as with any data analysis, is getting the data into a standard and usable form. And in reality, you’ll find this effort takes up most of your time if you do this sort of thing.

So before I can start looking at anything interesting, I need to do some scraping. This article will take you through that process.

UPDATE — 5/25/18 — I get way too many questions about whether the data is available, so I went back through and updated the code and currently scraping it every week. I’m not going to post the link here, but shoot me an email and I can go ahead and share the links.

The usual process for scraping, is to grab the html page, extract the data from that page, store that data. Repeat for however many web pages have the data you want. In this case however I wanted to do something different. Instead of grabbing the data from a web page and storing that data, I wanted to actually store the html file itself as a first step. Only after would I deal with getting the info from that page.

Reasoning behind this was to avoid unnecessarily hitting pgatour.com’s servers. Undoubtedly, when scraping, you’ll run into errors in your code – either missing data, oddly formatted data you didn’t account for, or any other random errors that can happen. When this happens, and you have to go back and grab the web page again, you’re both wasting time by grabbing the same file over the internet, and using up resources on that server’s end. Neither a good result.

So in my case here, I wrote code to download and save the html once, and then I can extract data as I please from those files without going over the internet. Now for the specifics.

On pgatour.com, the stats base page is located at pgatour.com/stats.html. If you notice at the top. This will land you at the overview page, but you can notice at the top there are eight categories of stats: Off the Tee, Approach the Green, Around the Green, Putting, Scoring, Streaks, Money/Finishes, and Points/Rankings. Under each of these categories are a list of stats in a table. Clicking on any of those links and you’ll get the current year’s stats for all the qualifying players. On the side, you’ll notice a dropdown where you can select the year you want the stat for. Our goal is to get the pages for each of those stats, for every year offered, and save the page in a directory named for the stat, and the year as the filename.

The url pattern when you’re on a single stat is straight forward. For example the url for current Driving Distance is http://www.pgatour.com/stats/stat.101.html, and the url for Driving Distance in 2015 is http://www.pgatour.com/stats/stat.101.2015.html. Simply injecting the year into the url after the stat id will get you what you need.

In order to get the different stats from the category page, we’re going to loop the categories, yank out url and name for a stat, grab the current page, see which years the stat is offered for, generate the required urls, and loop those urls saving the page! Reading the code should make this make more sense.

The last issue with grabbing the html pages is how long it takes. In the end, we’re talking about over 100 stats, with about 15-20 years of history. At first, I wanted to play nice not overwhelm the pgatour.com servers, but then I realized that pgatour.com can probably handle the load since they need to be able to deal with the constant refreshing that people do when checking leaderboards at the end of a tournament. Thankfully, python’s Gevent library allows us to easily, in parallel, grab pages and save them. After all that explanation, take a look at the code I used to save the files.

```url_stub = "http://www.pgatour.com/stats/stat.%s.%s.html" #stat id, year
category_url_stub = 'http://www.pgatour.com/stats/categories.%s.html'
category_labels = ['RPTS_INQ', 'ROTT_INQ', 'RAPP_INQ', 'RARG_INQ', 'RPUT_INQ', 'RSCR_INQ', 'RSTR_INQ', 'RMNY_INQ']
pga_tour_base_url = "http://www.pgatour.com"
def gather_pages(url, filename):
print filename
urllib.urlretrieve(url, filename)

def gather_html():
stat_ids = []
for category in category_labels:
category_url = category_url_stub % (category)
page = requests.get(category_url)
html = BeautifulSoup(page.text.replace('\n',''), 'html.parser')
for table in html.find_all("div", class_="table-content"):
starting_year = 2015 #page in order to see which years we have info for
for stat_id in stat_ids:
url = url_stub % (stat_id, starting_year)
page = requests.get(url)
html = BeautifulSoup(page.text.replace('\n',''), 'html.parser')
stat = html.find("div", class_="parsys mainParsys").find('h3').text
print stat
directory = "stats_html/%s" % stat.replace('/', ' ') #need to replace to avoid
if not os.path.exists(directory):
os.makedirs(directory)
years = []
for option in html.find("select", class_="statistics-details-select").find_all("option"):
year = option['value']
if year not in years:
years.append(year)
url_filenames = []
for year in years:
url = url_stub % (stat_id, year)
filename = "%s/%s.html" % (directory, year)
url_filenames.append((url, filename))
jobs = [gevent.spawn(gather_pages, pair[0], pair[1]) for pair in url_filenames]
gevent.joinall(jobs)```

### Step 2 — Convert HTML to CSV

Now that I have the html files for every stat, I want to go through the process of getting the info from the tables in the html, into a consumable csv format. Luckily, the html is very nicely formatted so I can actually use the info. I saved all the html files in a directory called stats_html, and I basically want to create the same folder structure in a top level directory I’m calling stats_csv.

Steps in this task are 1) Read in the files, 2) using Beautiful Soup, extract the headers for the table, and then all of the data rows and 3) write that info as a csv file. I’ll just go right to the code since that’s easiest to understand as well.

```
for folder in os.listdir("stats_html"):
path = "stats_html/%s" % folder
if os.path.isdir(path):
for file in os.listdir(path):
if file[0] == '.':
continue #.DS_Store issues
csv_lines = []
file_path = path + "/" + file
csv_dir = "stats_csv/" + folder
if not os.path.exists(csv_dir):
os.makedirs(csv_dir)
csv_file_path = csv_dir + "/" + file.split('.')[0] + '.csv'
print csv_file_path
if os.path.isfile(csv_file_path): #pass if already done the conversion
continue
with open(file_path, 'r') as ff:
html = BeautifulSoup(f.replace('\n',''), 'html.parser')
table = html.find('table', class_='table-styled')