notebook.community

Edit and run

Take 1: A story of david and goliath

Our first attempt can be read about and accessed at this notebook: LINK HERE

Take 2: Gathering and analyzing better data

About halfway into our project, we realized that our data was less than ideal for a few reasons. First, our Box Office Mojo data originally only scraped the top 100 ranked movies for each year. This represented only the upper eschelon of movie incomes. The low range of incomes, we presume, had less power to pick up a trend or correlation with valence statistics. We realized that Box Office Mojo actually includes a lot more movies! Rather than just the top 100, Box Office Mojo actually provides grossing information on all of the major movies for the year at hand, which is usally above 500. Second, the overlap between our IMDB data set from Stanford and our Box Office Mojo was just over 300 movies representing 4,111 overlapping reviews. We decided that we needed more data to better determine predictive power and the presence of trends. Third, our IMDB data represented only polarized reviews, i.e. those with stars below 4 or above 7. We decided that this would decrease ability to test one particuar hypothesis: that polarizing movies earn more money. As a result, we needed to scrape a balanced sample of reviews from IMDB for each movie, not discriminating based on number of stars. We want reviews in in the 4-7 star range.

Our new approach, as charted below, was to scrape all data from Box Office Mojo from 1990-2015, resulting in about 11,000 movies. For each of those movies, we scraped a max of 100 reviews from IMDB's website. This resulted in a final data set containing over half a million reviews, which were no longer only polarized. We feel that the new data is more fit to truly convey the presence or absence of trends and predictive power of gross income on review sentiment.

Table of Contents





In [1]:



    


%matplotlib inline
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import pandas as pd
import time
import json
import statsmodels.api as sm
from statsmodels.formula.api import logit, glm, ols
from random import random
pd.set_option('display.width', 500)
pd.set_option('display.max_columns', 100)
pd.set_option('display.notebook_repr_html', True)
import seaborn as sns
sns.set_style("whitegrid")
sns.set_context("poster")
import math
from sklearn.svm import LinearSVC

Scraping and cleaning the data

As mentioned above, we now scrape all data from Box Office Mojo from 1990-2015, resulting in about 11,000 movies. For each of those movies, we scraped a max of 100 reviews from IMDB's website. This resulted in a final data set containing over half a million reviews, which were no longer only polarized. We feel that the new data is more fit to truly convey the presence or absence of trends and predictive power of gross income on review sentiment.

Scraping BOM for box office data





In [2]:



    


from bs4 import BeautifulSoup
import requests

Let's create a data frame bom_df to contain the scraped Box Office Mojo data.





In [3]:



    


bom_df = pd.DataFrame(columns=['close_date', 'gross', 'open_date', 'opening_gross', 'opening_theaters','ranking','title','total_theaters','year'])

The function rowInfoGrabber retrieves relevant values from each row in a Box Office Mojo table.





In [4]:



    


def rowInfoGrabber(r):
    info = []
    # Ranking
    info.append(int(r.find("font").get_text()))
    # Title
    info.append(r.find("a").get_text())
    # Gross
    info.append(int(r.find("td", attrs={"align":"right"}).find("b").get_text().strip("$").replace(",","")))
    '''
    For the next 3 categories, we need to deal with the 2000 Anomaly "Fantasia" where there are missing numbers.
    In this case I have chosen to replace the missing values 'N/A' with the values from 'Final Destination', which
    if right above it in the movie table and differs in gross income by about $1 million, which is a small 
    difference. See the picture below for a snapshot of the anomaly in the movie table from 2000.
    '''
    # Total number of theaters
    if r.find_all("td",attrs={"align":"right"})[1].find("font").get_text().replace(",","") == 'N/A':
        info.append(2587)
    else:
        info.append(int(r.find_all("td",attrs={"align":"right"})[1].find("font").get_text().replace(",","")))
    # Opening Gross
    if r.find_all("td", attrs={"align":"right"})[2].find("font").get_text().strip("$").replace(",","") == 'N/A':
        info.append(10015822)
    else: 
        info.append(int(r.find_all("td", attrs={"align":"right"})[2].find("font").get_text().strip("$").replace(",","")))
    # Opening Number of Theaters
    if r.find_all("td", attrs={"align":"right"})[3].find("font").get_text().replace(",","") == 'N/A':
        info.append(2587)
    else:
        info.append(int(r.find_all("td", attrs={"align":"right"})[3].find("font").get_text().replace(",","")))
    # Date of Opening
    if not r.find_all("td", attrs={"align":"right"})[4].find("a"):
        info.append("-")
    else:
        info.append(r.find_all("td", attrs={"align":"right"})[4].find("a").get_text())
    # Date of Closing: Before 2002 they didn't have a "closing" date in their tables. We must account for this.
    if (len(r.find_all("td", attrs={"align":"right"})) <= 5):
        info.append('-')
    else:
        info.append(r.find_all("td", attrs={"align":"right"})[5].find("font").get_text())
    return info

Let's scrape all movies from Box Office Mojo from 1990-2015. This takes about 4 minutes.





In [5]:



    


%%time
fields = ["ranking", "title", "gross", "total_theaters", "opening_gross", "opening_theaters", "open_date", "close_date"]
years = [1990 + i for i in range(26)]
for year in years:
    print year
    pageText = requests.get("http://www.boxofficemojo.com/yearly/chart/?yr=%(yr)d&p=.htm" % {'yr':year})
    soup = BeautifulSoup(pageText.text, "html.parser")
    movieTable = soup.find("td", attrs={"colspan":"3"})
    movieRows = movieTable.find("table").find_all("tr")[2:102]
    movie_dicts = [dict(zip(fields, rowInfoGrabber(row))) for row in movieRows]
    year_df = pd.DataFrame(movie_dicts)
    year_df['year'] = year
    bom_df = bom_df.append(year_df, ignore_index=True)
    # See how many pages of movies there are for this particular year
    extraPageLinks = soup.find("center").find("font", attrs={"face":"Verdana"}).find("b").find_all("a")
    time.sleep(1)
    for p in extraPageLinks:
        pageText = requests.get('http://www.boxofficemojo.com' + p['href'])
        soup = BeautifulSoup(pageText.text, "html.parser")
        movieTable = soup.find("td", attrs={"colspan":"3"})
        movieRows = movieTable.find("table").find_all("tr")
        movieRows = movieRows[2:len(movieRows)-4]
        movie_dicts = [dict(zip(fields, rowInfoGrabber(row))) for row in movieRows]
        year_df = pd.DataFrame(movie_dicts)
        year_df['year'] = year
        bom_df = bom_df.append(year_df, ignore_index=True)
        time.sleep(1)



    


















    






1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
CPU times: user 58.8 s, sys: 893 ms, total: 59.7 s
Wall time: 3min 57s

Let's see how it looks.





In [6]:



    


bom_df.head()



    


















    
Out[6]:










  
    

      
      close_date
      gross
      open_date
      opening_gross
      opening_theaters
      ranking
      title
      total_theaters
      year
    

  
  
    

      0
       -
       285761243
       11/16
       17081997
       1202
       1
                         Home Alone
       2173
       1990
    

    

      1
       -
       217631306
        7/13
       12191540
       1101
       2
                              Ghost
       1766
       1990
    

    

      2
       -
       184208848
        11/9
         598257
         14
       3
                 Dances with Wolves
       1636
       1990
    

    

      3
       -
       178406268
        3/23
       11280591
       1325
       4
                       Pretty Woman
       1811
       1990
    

    

      4
       -
       135265915
        3/30
       25398367
       2006
       5
       Teenage Mutant Ninja Turtles
       2377
       1990

Now we'll save and reload so we don't have to do that again.





In [7]:



    


# with open('newBOM.json', 'w') as fp:
#     json.dump(bom_df.to_dict(), fp)
    
with open('newBOM.json','r') as fp:
    bom_dict = json.load(fp)
bom_df = pd.DataFrame(bom_dict)

Scraping IMDB for reviews

We want a less polarized review set in order to more accurately determine the effect of review polarity on gross income. For this we'll have to part with Andrew Maas's Large Movie Review Data Set and scrape IMDB ourselves for a mixture of polarized and non-polarized reviews for each movie. Unfortunately, IMDB's URLs contain a movie ID rather than a movie title, so we cannot request each page's HTML using the mere title. Fortunately, the IMDB API allows us to obtain a movie's IMDB ID based on its title by returning a JSON object.

Let's create a helper function that returns the relevant url used to query the OMDB API given the movie's title.
An example url is http://www.omdbapi.com/?t=Jurassic%20World.





In [8]:



    


'''
Name: urlOMDB
Input: Movie name
Output: url used to Query OMDB API
'''
def urlOMDB(title):
    base = "http://www.omdbapi.com/?t="
    base += title.split()[0]
    for word in title.split()[1:]:
        base += "%20" + word
    return base

Let's test our function by getting the IMDB ID for "Jurassic World".





In [9]:



    


requests.get(urlOMDB("Jurassic World")).json()['imdbID']



    


















    
Out[9]:








u'tt0369610'

Perfect. Now we'll use these IDs to scrape IMDB for each movie in our Box Office Mojo page. First, let's write a function reviewsGrabber which returns a list of (title of review, stars, text of review) tuples for a given IMDB webpage. The function cleans the review texts upon collecting them by removing punctuation and converting to lower case.





In [10]:



    


'''
Name: reviewsGrabber
Input: url (webpage)
Output: [(titleOfReview1, stars1, review1), (titleOfReview2, stars2, review2), ... (titleofReviewn, starsn, reviewn)]
'''
punctuation = set('.,;:!?()[]{}`''\"@#$%^&*+-|-=~_')

def reviewsGrabber(url):
    pageText = requests.get(url)
    soup = BeautifulSoup(pageText.text, "html.parser")
    reviewTable = soup.find("div", attrs={"id":"tn15content"})
    divs = []
    for rev in reviewTable.find_all("div", attrs={"class": None}):
        if len(rev.find_all("img")) > 1: 
            divs.append((rev.find("h2").get_text(), int(rev.find_all("img")[1]['alt'].split("/")[0])))
        else: 
            divs.append((rev.find("h2").get_text(), 'N/A'))
    reviews = []
    for rev in reviewTable.find_all("p")[:len(reviewTable.find_all("p"))-1]:
        if len(rev.find_all("b")) == 0:
            text = rev.get_text()
            text = ''.join(ch for ch in text if ch not in punctuation).replace("\n", " ").lower()
            reviews.append(text)
    return [(title,stars,text) for ((title,stars), text) in zip(divs,reviews)]

Now let's scrape each web page based on the movies in bom_df. We'll take a max of 100 movies reviews for each movie. Some movies have less than 100 reviews. IMDB lists reviews by default from best to worst. We don't want that. Fortunately, they offer the option to sort in chronological order, this works particularly well for our project, because we want to see whether the earliest reviews are a good indicator for box office performance over the opening weekend.

The following code took about 24 hours to run on my computer. Do not run it again.

I would frequenty see an error, account for it by tweaking the code (maybe throw in a try catch here and there), then continue where the code left off by changing the variable "count". (You can see that the most recent stopping point was 11,011.) Every once in awhile, the code spits out my progress as a percentage. Babysitting my computer was quite the experience.





In [11]:



    


# fields = ['title','text','stars','close_date','open_date','opening_gross','gross','rank','total_theaters','year']
# count = 11011
# for index, row in bom_df[count:].iterrows():
#     close, gross, op, op_gross, open_th, r, title, thea, yr = tuple([row[i] for i in range(9)])  
#     if random() < 0.02:
#         print str(count*100.0 / 11858) + "%"
#     try:
#         omdb = requests.get(urlOMDB(title)).json()
#     except ValueError:
#         continue
        
#     omdbJSON = dict(omdb)
#     if 'Error' in omdbJSON.keys():
#         continue
#     else:
#         imdbID = str(omdb['imdbID'])
#     time.sleep(0.1)
#     reviews = []
#     baseURL = 'http://www.imdb.com/title/%(imdbID)s/reviews?filter=chrono' % {'imdbID':imdbID}
#     pageText = requests.get(baseURL)
#     soup = BeautifulSoup(pageText.text, "html.parser")
#     if soup.find("div", attrs={'class':'error_code_404'}):
#         continue
#     try:
#         reviews = reviews + reviewsGrabber(baseURL)
#     except AttributeError:
#         continue
#     # Let's scrape the number of reviews for this given movie.
#     table = soup.find("div", attrs={"id":"tn15content"}).find_all("table", attrs={'border':'0'})
#     if table == []:
#         continue
#     td = table[1].find("td", attrs = {'align':'right'})
#     if td == None:
#         continue
#     else:
#         text = td.get_text()
#     n_reviews = int(text.split("reviews")[0].split("\n")[1])
#     # This number represents the page: page p is represented in the url as the integer 10(p-1)
#     p = 10
#     while len(reviews) < min(n_reviews, 100):
#         url = baseURL + ";filter=chrono" + str(";start=%(page)d" % {'page': p})
#         reviews = list(reviews + reviewsGrabber(url))
#         p += 10
        
#     reviews = reviews[:100]
# #     review_dict = [dict(zip(fields, [[] for i in range(len(fields))]))]
#     for review in reviews:
#         review_dict = {'title':[title], 'text': [review[2]], 'review_title': [review[0]], 'stars': [review[1]],
#                        'close_date': [close],'gross': [gross], 'open_date': [op], 'opening_gross':[op_gross],
#                        'opening_theaters': [open_th], 'rank':[r], 'theaters': [thea], 'year': [yr]}
        
#         combined_df = combined_df.append(pd.DataFrame(review_dict), ignore_index=True)
#     count += 1

Let's save our finally scraped movie reviews.





In [ ]:



    


# fp = open("complete.json","w")
# json.dump(combined_df.to_dict(), fp)
# fp.close()

with open("complete.json", "r") as fd:
    combined_dict = json.load(fd)
combined_df = pd.DataFrame(combined_dict)



    











In [ ]:



    


print "Length of dataframe: ", len(combined_df.gross.values)

Because some films do not have a close date, we will have to be careful with the close date! Next, we combine the close_date, open_date and year columns into two columns close_date and open_date that are time series. This will make it easier for us to work with the data in the future.





In [ ]:



    


def lam(x, month=False):
    if x == '-' or x =='':
        return '1'
    try:
        dummy = int(x)
    except ValueError:
        return '1'
    else:
        return x[:x.find('/')]
    
# splitting the close_date and open_date into the respective month and day
combined_df['close_month'] = combined_df['close_date'].map(lam)
combined_df['close_day'] = combined_df['close_date'].map(lambda x: '0' if x=='-' else x[x.find('/')+1:len(x)])
combined_df['open_month'] = combined_df['open_date'].map(lam)
combined_df['open_day'] = combined_df['open_date'].map(lambda x: x[x.find('/')+1:len(x)])

# dropping the old close_date and open_date
combined_df = combined_df.drop('close_date', 1)
combined_df = combined_df.drop('open_date', 1)

# creating an open_year by turning the year column into a string and getting rid of trailing bits
combined_df['open_year'] = combined_df.year.astype(str)
combined_df['open_year'] = combined_df.open_year.map(lambda x: x[:x.find('.')])

# creating a close_year column, by looking at whether the close month is earlier/later than the open month in the year
close_month = combined_df.close_month.astype(int)
open_month = combined_df.open_month.astype(int)
year = combined_df['year'].astype(int)
close_year=[]
for i in range (0, len(year)):
    if close_month[i] >= open_month[i]:
        close_year.append(year[i])
    else:
        close_year.append(year[i]+1) 
combined_df['close_year'] = close_year
combined_df['close_year'] = combined_df['close_year'].astype(str)



    











In [ ]:



    


# making close_date and open_date by concatenating the year, month and day
import datetime
close_date = []
for index, row in combined_df.iterrows():
    if row.close_day != '0':
        close_date.append(datetime.datetime(int(row.close_year), int(row.close_month), int(row.close_day)))
    else: 
        close_date.append(None)
combined_df['close_date'] = close_date
combined_df['open_date']=combined_df.open_year + '-' + combined_df.open_month + '-' + combined_df.open_day
def lam2(x):
    if x[7] == '-' or x[7:10] == 'Jan':
        return x[:7] + "1"
    else:
        return x
combined_df['open_date']=combined_df['open_date'].map(lam2).apply(pd.datetools.parse)

Let's drop obselete columns.





In [ ]:



    


combined_df = combined_df.drop('close_day', 1)
combined_df = combined_df.drop('close_month', 1)
combined_df = combined_df.drop('open_day', 1)
combined_df = combined_df.drop('open_month', 1)

And now let's save our results again.





In [ ]:



    


# import pymongo
# from bson import json_util

# fp = open("complete_dates.json","w")
# json.dump(combined_df.to_dict(), fp, default=json_util.default)
# fp.close()

with open("complete_dates.json", "r") as fd:
    complete_dates = json.load(fd)
combined_df = pd.DataFrame(complete_dates)

Datatime objects are not JSON serializable. We had to make them such, now we need to map them back to normal.





In [ ]:



    


def getdate(dic):
    if dic != None:
        return int(dic["$date"])
    else: 
        return None
    
combined_df.open_date = combined_df.open_date.map(getdate)
combined_df.close_date = combined_df.close_date.map(getdate)

Computing valence statistics by review

It's now time to collect valence statistics on our reviews in order to do sentiment analysis. We'll be using labMT (language assessment by Mechanical Turk), a word score list for sentiment analysis containing over 10,000 words available in the qdap Dictionaries package in R (http://www.inside-r.org/packages/cran/qdapDictionaries/docs/labMT). The file contains a "happiness" value, and ranks words by their happiness. It also includes mean and standard deviation, Twitter rank and Google rank.





In [ ]:



    


url = 'http://www.plosone.org/article/fetchSingleRepresentation.action?uri=info:doi/10.1371/journal.pone.0026752.s001'
labmt = pd.read_csv(url, skiprows=2, sep='\t', index_col=0)



    











In [ ]:



    


labmt.head()

Let's make a happiness dictionary consisting of the word's happiness minus the average of all words.





In [ ]:



    


average = labmt.happiness_average.mean()
happiness = (labmt.happiness_average - average).to_dict()

Let's look at the happiness values to find a narrow middle range. We'll want to disregard all words in this middle range so they do not overpower the polarized words.





In [ ]:



    


plt.hist(happiness.values())
plt.axvline(np.percentile(happiness.values(),35), color='r')
plt.axvline(np.percentile(happiness.values(),65), color='r')

We'll consider words below the 35th and above the 65th percentiles.





In [ ]:



    


lower_bound = np.percentile(happiness.values(),35)
upper_bound = np.percentile(happiness.values(),65)
print lower_bound, upper_bound

Now we'll write a function to remove stop words, words that have little to do with sentiment analysis. We use the sklearn package to obtain these words.





In [ ]:



    


from sklearn.feature_extraction import text
stopwords = text.ENGLISH_STOP_WORDS
punctuation = list('.,;:!?()[]{}`''\"@#$%^&*+-|-=~_')

def removeStopWords(text, stopwords = stopwords):
    new_text = ""
    for word in text.split():
        if word not in stopwords:
            while len(word) != 0 and word[-1] in punctuation:
                word = word[:len(word)-1]
            new_text += word + ' '
    return new_text

Finally we're ready to analyze a particular review given our valence dictionary. So let's put it in a function.





In [10]:



    


'''
Name: getValenceInfo()
Inputs: review text, dictionary of happiness
Returns: a 4-tuple of (happiness total, happiness average, total # of scorable words, % of scorable words)
'''
def getValenceInfo(text, valenceDict):
    total_words = len(text.split())
    happiness_total, count_relevant = 0, 0
    for word in text.split():
        try:
            # Only consider words below the 35th and above the 65th percentile
            if valenceDict[word] <= lower_bound or valenceDict[word] >= upper_bound:
                happiness_total += valenceDict[word]
                count_relevant += 1
        except KeyError:
            continue

    if count_relevant != 0: 
        avg_valence = 1.*happiness_total/count_relevant
    else: 
        avg_valence = 0
    return happiness_total, avg_valence, total_words, 1.*count_relevant / total_words

Before we run this through our data frame, let's reset the index.





In [ ]:



    


'''
Name: getAllInfo
Input: data frame, happiness dictionary, list of stop words
Returns: a new data frame with 4 new columns: valence_sum, valence_avg, n_scorables, pct_scorables
'''
def getAllInfo(df, valenceDict, stopwords): 
    valence_suml, valence_avgl, review_lenl, review_fractionl = [], [], [], []
    for i, row in df.iterrows():
        if random() < 0.00001:
            print i
        cleaned_review = removeStopWords(row['text'], stopwords)
        valence_sum, valence_avg, review_len, review_fraction = getValenceInfo(cleaned_review, valenceDict)
        valence_suml.append(valence_sum)
        valence_avgl.append(valence_avg)
        review_lenl.append(review_len)
        review_fractionl.append(review_fraction)
    conc = pd.DataFrame({'valence_sum': valence_suml, 'valence_avg':valence_avgl ,'n_scorables': review_lenl, 
                         'pct_scorables': review_fractionl})
    return pd.concat([df, conc], axis=1)

The code takes about 5 minutes to run. We save the results below.





In [ ]:



    


%%time
valence_df = getAllInfo(combined_df, happiness, stopwords)

Let's take a look, then save again. The file is almost 1GB in size, so it takes a minute to save.





In [ ]:



    


valence_df.head()



    











In [ ]:



    


# import pymongo
# from bson import json_util

# fp = open("complete_valence.json","w")
# json.dump(valence_df.to_dict(), fp)
# fp.close()

Adjusting for inflation





In [40]:



    


#Loading the latest complete dataset
with open("complete_valence.json", "r") as fp:
    valence_df_dict = json.load(fp)
dftouse = pd.DataFrame(valence_df_dict)



    











In [41]:



    


dftouse = dftouse.reset_index().drop('index',1)



    











In [42]:



    


inflation = pd.read_csv("inf.csv")



    











In [43]:



    


years_90 = range(1990,2015)
infdict = {}

infindex = 0
infvalue = 1

testlist = []
for row in inflation.values:
    currentval = 1 + (row[1]/100)
    cuminf = infvalue*currentval
    infdict[years_90[infindex]] = cuminf
    infindex += 1
    infvalue = cuminf
    testlist.append(cuminf)

inframe = pd.DataFrame(data=testlist, index=range(1990,2015))



    











In [44]:



    


infdict



    


















    
Out[44]:








{1990: 1.0539795644,
 1991: 1.098615219150804,
 1992: 1.1318902930939463,
 1993: 1.1652998117775315,
 1994: 1.1956843237413164,
 1995: 1.229228287177842,
 1996: 1.2652594783591868,
 1997: 1.2948373218617284,
 1998: 1.3149368109750392,
 1999: 1.3437079860225376,
 2000: 1.3890830868495474,
 2001: 1.4283409518690031,
 2002: 1.4509948911070383,
 2003: 1.4839338531885458,
 2004: 1.5236622748059583,
 2005: 1.5753562785628925,
 2006: 1.626176391500925,
 2007: 1.6725658779300527,
 2008: 1.7367773595171863,
 2009: 1.7306023124666896,
 2010: 1.7589849421993997,
 2011: 1.814513310047201,
 2012: 1.852061710150393,
 2013: 1.8791913148899477,
 2014: 1.909675988181881}

















In [45]:



    


#Creating two new lists of adjusted gross values. 
#If the open year of the movie was 2015, we use the 2014 inflation data, since 2015 data is not released until 2016
newgross, newopengross = [], []
for gross, opengross, openyr in zip(dftouse.gross, dftouse.opening_gross, dftouse.open_year):
    if int(openyr) == 2015:
        openyr = 2014
    newgross.append(infdict[int(openyr)]*gross)
    newopengross.append(infdict[int(openyr)]*opengross)



    











In [46]:



    


#dftouse = flattened_df
dftouse['adj_gross'] = newgross
dftouse['adj_opening_gross'] = newopengross



    











In [47]:



    


# creating binary variables of adjusted grossing for classifications
dftouse['adj_gross_bin'] = 1*(dftouse.adj_gross>np.mean(dftouse.adj_gross))
dftouse['adj_opening_gross_bin'] = 1*(dftouse.adj_opening_gross>np.mean(dftouse.adj_opening_gross))



    











In [48]:



    


dftouse.head()



    


















    
Out[48]:










  
    

      
      close_date
      close_year
      gross
      n_scorables
      open_date
      open_year
      opening_gross
      opening_theaters
      pct_scorables
      rank
      review_title
      stars
      text
      theaters
      title
      valence_avg
      valence_sum
      year
      adj_gross
      adj_opening_gross
      adj_gross_bin
      adj_opening_gross_bin
    

  
  
    

      0
      NaN
       1990
       285761243
       144
       632448000000
       1990
       17081997
       1202
       0.701389
         1
                           My favourite Christmas movie!
        10
        it just isn't christmas if i don't watch home...
       2173
                Home Alone
       0.758622
       76.620792
       1990
       3.011865e+08
       18004075.757142
       1
       1
    

    

      1
      NaN
       1990
       285761243
        65
       632448000000
       1990
       17081997
       1202
       0.738462
         1
                                 Very funny family movie
         8
        this is a very funny movie for kids and peopl...
       2173
                Home Alone
       1.408094
       67.588495
       1990
       3.011865e+08
       18004075.757142
       1
       1
    

    

      2
      NaN
       2001
           55835
        82
       978739200000
       2001
           9134
          7
       0.719512
       304
       The worst book to screen adaption I have ever ...
         1
        i've read all of these books and perhaps that...
         12
       Beautiful Creatures
       0.446455
       26.340859
       2001
       7.975142e+04
          13046.466254
       0
       0
    

    

      3
      NaN
       1996
         2491989
        23
       822960000000
       1996
        1593929
       1292
       0.695652
       174
       Cheesy! Cameos! And learn how to be your own g...
         8
         this is one of those movies which is so bad ...
       1292
               The Stupids
       0.292260
        4.676165
       1996
       3.153013e+06
        2016733.775082
       0
       0
    

    

      4
      NaN
       1996
         1408575
        30
       821318400000
       1996
          33843
          2
       0.533333
       189
                    Not a good induction to Shakespeare.
       N/A
         the film would have been better if the actor...
         81
       Looking for Richard
       1.055385
       16.886165
       1996
       1.782213e+06
          42820.176526
       0
       0

Now that we have adjusted for inflation, we can remove the previous gross and all columns pertaining to date (except perhaps year, since it's interesting).





In [49]:



    


keep = ['close_date','close_year', 'adj_gross','adj_gross_bin','adj_opening_gross_bin', 'n_scorables','open_date','open_year',
        'adj_opening_gross','opening_theaters','pct_scorables', 'rank', 'review_title','stars',
       'text','theaters','title','valence_avg','valence_sum','year']
dftouse = dftouse[keep]

Let's save





In [50]:



    


fp = open("dftouse.json","w")
json.dump(dftouse.to_dict(), fp)
fp.close()

# with open("dftouse.json","r") as fp:
#     dftouse_dict = json.load(fp)
# dftouse = pd.DataFrame(dftouse_dict)

Natural language processing

Now we have a dataframe of all the reviews, we want to see if we can use sentiment analysis to better describe the text. Namely we want to emulate the method used in HW5 to see what each review has to say on different topics about movies.

Create a list of nouns and adjectives for each review





In [2]:



    


with open("dftouse.json", "r") as fp:
    dftouse = json.load(fp)
maas_IMDB = pd.DataFrame(dftouse)



    











In [3]:



    


maas_IMDB=maas_IMDB.reset_index()
maas_IMDB.drop('index',1)



    


















    
Out[3]:










  
    

      
      adj_gross
      adj_gross_bin
      adj_opening_gross
      adj_opening_gross_bin
      close_date
      close_year
      n_scorables
      open_date
      open_year
      opening_theaters
      pct_scorables
      rank
      review_title
      stars
      text
      theaters
      title
      valence_avg
      valence_sum
      year
    

  
  
    

      0     
       3.011865e+08
       1
       18004075.757142
       1
                NaN
       1990
       144
        632448000000
       1990
       1202
       0.701389
         1
                           My favourite Christmas movie!
        10
        it just isn't christmas if i don't watch home...
       2173
                                 Home Alone
       0.758622
       76.620792
       1990
    

    

      1     
       3.011865e+08
       1
       18004075.757142
       1
                NaN
       1990
        65
        632448000000
       1990
       1202
       0.738462
         1
                                 Very funny family movie
         8
        this is a very funny movie for kids and peopl...
       2173
                                 Home Alone
       1.408094
       67.588495
       1990
    

    

      2     
       8.113545e+06
       0
          61087.601573
       0
                NaN
       1990
       364
        633052800000
       1990
          2
       0.434066
       109
                               Emasculation Proclamation
         5
        almost  but not quite  benniferidiotically ta...
        240
                          Mr. & Mrs. Bridge
       0.237925
       37.592131
       1990
    

    

      3     
       1.209626e+06
       0
         111846.786031
       0
       1.137542e+12
       2006
        87
       1136851200000
       2006
          7
       0.482759
       251
                           Dusting off great LA memoirs!
        10
        john fante wrote a great book called ask the ...
        111
                               Ask the Dust
       0.469998
       19.739933
       2006
    

    

      4     
       9.251838e+05
       0
          37086.578785
       0
       1.136851e+12
       2006
        66
       1136419200000
       2006
          3
       0.575758
       263
                                Incredibly disappointing
       N/A
        for the first hour i absolutely loved this fi...
         28
                         Down in the Valley
       0.338971
       12.880892
       2006
    

    

      5     
       1.063991e+05
       0
          20125.559021
       0
       1.137024e+12
       2006
       183
       1137715200000
       2006
          3
       0.677596
       404
       Touching, inspiring, hits close to home, no ma...
        10
        i knew i would love this movie and the charac...
         10
               The Real Dirt on Farmer John
       0.597825
       74.130280
       2006
    

    

      6     
       1.190422e+05
       0
          62728.924671
       0
       1.199578e+12
       2008
        98
       1199923200000
       2008
         20
       0.714286
       398
                            Showing of Talento DE Barrio
        10
        i seen the movie last night 7/23/2008 at the ...
         20
                          Talento de Barrio
       0.908189
       63.573222
       2008
    

    

      7     
       3.153013e+06
       0
        2016733.775082
       0
                NaN
       1996
        51
        822960000000
       1996
       1292
       0.745098
       174
                                 Awesome and funny movie
        10
        it doesn't seem like this movie would work bu...
       1292
                                The Stupids
       0.310023
       11.780892
       1996
    

    

      8     
       3.048295e+06
       0
        1807623.075834
       0
                NaN
       1996
        80
        821404800000
       1996
       1589
       0.487500
       176
       If It Looks Like Canadian Television And Stink...
         1
        ferhad mann must have directed it mann best k...
       1589
         Lawnmower Man 2: Beyond Cyberspace
      -0.071650
       -2.794347
       1996
    

    

      9     
       1.274503e+06
       0
         261058.457651
       0
                NaN
       1996
       133
        822009600000
       1996
         26
       0.661654
       204
                        The best comedy show of all time
        10
        but you have to be in the audience to agree f...
         26
               Mystery Science Theater 3000
       0.352147
       30.988908
       1996
    

    

      10    
       2.750256e+05
       0
          28507.721162
       0
       1.073002e+12
       2004
       372
       1074124800000
       2004
          3
       0.577957
       319
       Okay, but it never manages to fit everything t...
         5
        your enjoyment level with this movie will dep...
         17
                                       P.S.
       0.431970
       92.873469
       2004
    

    

      11    
       1.175190e+05
       0
          47104.875545
       0
       1.199750e+12
       2008
       125
       1201478400000
       2008
          7
       0.608000
       399
                          Briiliant gripping crime drama
         8
        i just watched 5 of the 6 episodes of this sh...
          7
                               Happy Valley
       0.207523
       15.771784
       2008
    

    

      12    
       1.175190e+05
       0
          47104.875545
       0
       1.199750e+12
       2008
       174
       1201478400000
       2008
          7
       0.459770
       399
       Happy Valley - Nothing Happy About it and Utte...
         8
        for some reason when i was watching this bbc ...
          7
                               Happy Valley
       0.170010
       13.600826
       2008
    

    

      13    
       4.145045e+06
       0
         157117.504706
       0
                NaN
       2001
       102
        979430400000
       2001
         44
       0.529412
       168
       a family breaks up, hrithik comes along and re...
         2
        it has all the touches of a yash johar movie ...
         44
                   Kabhi Khushi Kabhie Gham
       0.456612
       24.657057
       2001
    

    

      14    
       1.980952e+07
       0
        8330289.805463
       0
       1.073434e+12
       2004
       105
       1073088000000
       2004
       2598
       0.552381
       132
                              An underrated masterpiece.
         9
        exceeded my large expectations one of the bes...
       2598
                                Wicker Park
       0.798898
       46.336099
       2004
    

    

      15    
       2.637072e+04
       0
          17685.509327
       0
       1.390522e+12
       2014
        52
       1390003200000
       2014
         45
       0.615385
       539
                                    better than expected
         7
        this movie ended up being better than i thoug...
         45
                                    13 Sins
       0.531010
       16.992330
       2014
    

    

      16    
       1.063991e+05
       0
          20125.559021
       0
       1.137024e+12
       2006
        58
       1137715200000
       2006
          3
       0.706897
       404
                                fabulous and informative
        10
        this movie is absolutely delicious as someone...
         10
               The Real Dirt on Farmer John
       1.096468
       44.955173
       2006
    

    

      17    
       1.175190e+05
       0
          47104.875545
       0
       1.199750e+12
       2008
        94
       1201478400000
       2008
          7
       0.457447
       399
       Externally strong, internally fragile policewo...
         8
        like scandinavians the will and power of the ...
          7
                               Happy Valley
       0.131272
        5.644694
       2008
    

    

      18    
       1.175190e+05
       0
          47104.875545
       0
       1.199750e+12
       2008
        59
       1201478400000
       2008
          7
       0.508475
       399
                    Still Another Crime Detective Series
         4
        a fargo borrowing of a female cop with all th...
          7
                               Happy Valley
      -0.354573
      -10.637190
       2008
    

    

      19    
       1.035824e+08
       1
       20966886.480285
       1
       1.388794e+12
       2014
       167
       1389139200000
       2014
       2023
       0.580838
        59
       Some good performances, but agonizingly predic...
         5
        while i feel some of the criticism is a bit h...
       2167
                   The Hundred-Foot Journey
       0.469915
       45.581751
       2014
    

    

      20    
       2.553812e+07
       0
        9114301.073923
       0
       1.010275e+12
       2002
       180
       1010275200000
       2002
        867
       0.655556
       114
                                    Empire State of Mind
         9
        i love empire the music is great and better t...
        869
                                     Empire
       0.764082
       90.161718
       2002
    

    

      21    
       1.175190e+05
       0
          47104.875545
       0
       1.199750e+12
       2008
        52
       1201478400000
       2008
          7
       0.596154
       399
                         Riveting - Compelling - Intense
         9
        i don't know where to start from but if you a...
          7
                               Happy Valley
       1.029276
       31.907570
       2008
    

    

      22    
       2.750256e+05
       0
          28507.721162
       0
       1.073002e+12
       2004
       257
       1074124800000
       2004
          3
       0.599222
       319
                        This is  all too mystical for me
         6
        spoilers very similar to the back from the de...
         17
                                       P.S.
       0.183332
       28.233089
       2004
    

    

      23    
       1.175190e+05
       0
          47104.875545
       0
       1.199750e+12
       2008
        56
       1201478400000
       2008
          7
       0.642857
       399
       Point-on-point representation of life in West ...
        10
        i saw the trailer for this series thinking it...
          7
                               Happy Valley
       0.413371
       14.881372
       2008
    

    

      24    
       1.175190e+05
       0
          47104.875545
       0
       1.199750e+12
       2008
        78
       1201478400000
       2008
          7
       0.512821
       399
                                 Gripping and Compelling
         7
        happy valley is a well developed drama set in...
          7
                               Happy Valley
       0.601010
       24.040413
       2008
    

    

      25    
       6.535640e+07
       1
       13613801.396214
       0
                NaN
       1991
        59
        664934400000
       1991
       2080
       0.525424
        22
                        What planet are these kids from?
         3
         when roger ebert gave this film 35 stars i n...
       2089
                                    My Girl
       0.884115
       27.407570
       1991
    

    

      26    
       1.175190e+05
       0
          47104.875545
       0
       1.199750e+12
       2008
        78
       1201478400000
       2008
          7
       0.474359
       399
                Go to Hollywood James Norton - right now
         8
        happy valley  violent nasty confronting small...
          7
                               Happy Valley
       0.306382
       11.336132
       2008
    

    

      27    
       1.073674e+07
       0
        5061596.785981
       0
                NaN
       1998
        62
        884304000000
       1998
       2037
       0.612903
       136
                                       put this fire out
       N/A
        hokey instantly forgettable action movie whic...
       2057
                                  Firestorm
       0.340550
       12.940892
       1998
    

    

      28    
       1.175190e+05
       0
          47104.875545
       0
       1.199750e+12
       2008
        55
       1201478400000
       2008
          7
       0.600000
       399
                                       Excellent Series!
        10
        i know this genre is overplayed by the brits ...
          7
                               Happy Valley
       0.560215
       18.487091
       2008
    

    

      29    
       2.637072e+04
       0
          17685.509327
       0
       1.390522e+12
       2014
        78
       1390003200000
       2014
         45
       0.666667
       539
                            Okay but nothing impressive.
         4
        the film has its moments but the whole do tas...
         45
                                    13 Sins
       0.373222
       19.407537
       2014
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      537520
       1.202197e+05
       0
           6698.750276
       0
       1.201478e+12
       2008
       365
       1200787200000
       2008
          1
       0.536986
       397
       Shane Jacobson hits a clear home run with "Kenny"
         7
        it's pretty amazing that a film like kenny a ...
          6
                                      Kenny
       0.446597
       87.533023
       2008
    

    

      537521
       1.202197e+05
       0
           6698.750276
       0
       1.201478e+12
       2008
       106
       1200787200000
       2008
          1
       0.528302
       397
                                             Top quality
        10
        kenny is truly a quality movie the likes and ...
          6
                                      Kenny
       0.747617
       41.866578
       2008
    

    

      537522
       1.202197e+05
       0
           6698.750276
       0
       1.201478e+12
       2008
       285
       1200787200000
       2008
          1
       0.442105
       397
       Kenny shows the Australian Film Institute how ...
         8
        i saw kenny at the australian film institute ...
          6
                                      Kenny
       0.490871
       61.849800
       2008
    

    

      537523
       1.202197e+05
       0
           6698.750276
       0
       1.201478e+12
       2008
       129
       1200787200000
       2008
          1
       0.604651
       397
                                    Absolutely Fantastic
        10
        finally an aussie comedy that lives up to the...
          6
                                      Kenny
       0.922581
       71.961305
       2008
    

    

      537524
       1.202197e+05
       0
           6698.750276
       0
       1.201478e+12
       2008
        71
       1200787200000
       2008
          1
       0.619718
       397
                                               Fantastic
        10
        the best movie i have seen all year and it is...
          6
                                      Kenny
       0.155669
        6.849454
       2008
    

    

      537525
       1.202197e+05
       0
           6698.750276
       0
       1.201478e+12
       2008
        62
       1200787200000
       2008
          1
       0.596774
       397
       A surprising film, a timely reminder for every...
         9
        i enjoyed this film very interesting use of d...
          6
                                      Kenny
       1.024220
       37.896132
       2008
    

    

      537526
       1.202197e+05
       0
           6698.750276
       0
       1.201478e+12
       2008
       153
       1200787200000
       2008
          1
       0.529412
       397
                 Better than most, still not quite great
         7
        after the media hype about this film comparin...
          6
                                      Kenny
       0.653279
       52.915586
       2008
    

    

      537527
       2.750256e+05
       0
          28507.721162
       0
       1.073002e+12
       2004
       243
       1074124800000
       2004
          3
       0.646091
       319
                                  P.S. It ain't half bad
       N/A
        ps is a story about having the opportunity to...
         17
                                       P.S.
       0.432786
       67.947370
       2004
    

    

      537528
       1.063991e+05
       0
          20125.559021
       0
       1.137024e+12
       2006
        53
       1137715200000
       2006
          3
       0.566038
       404
                           Great and factual documentary
         9
        this film was just shown at the beloit film f...
         10
               The Real Dirt on Farmer John
       1.002760
       30.082810
       2006
    

    

      537529
       2.637072e+04
       0
          17685.509327
       0
       1.390522e+12
       2014
       194
       1390003200000
       2014
         45
       0.639175
       539
       Perfectly crafted; perhaps the best thriller o...
        10
        contrary to some mildly previous review this ...
         45
                                    13 Sins
       0.415728
       51.550280
       2014
    

    

      537530
       2.553812e+07
       0
        9114301.073923
       0
       1.010275e+12
       2002
        49
       1010275200000
       2002
        867
       0.571429
       114
                                 What is the fuss about?
         2
        i have never written a review before ever whi...
        869
                                     Empire
       0.012260
        0.343289
       2002
    

    

      537531
       6.535640e+07
       1
       13613801.396214
       0
                NaN
       1991
       102
        664934400000
       1991
       2080
       0.676471
        22
                                     Touching and Sweet!
       N/A
         vada sultenfuss is a young girl who's mother...
       2089
                                    My Girl
       0.422007
       29.118462
       1991
    

    

      537532
       1.202197e+05
       0
           6698.750276
       0
       1.201478e+12
       2008
        55
       1200787200000
       2008
          1
       0.545455
       397
                          Funnier than bum full of m&m's
        10
        this mockumentary is great for a laugh it's t...
          6
                                      Kenny
       0.636427
       19.092810
       2008
    

    

      537533
       1.202197e+05
       0
           6698.750276
       0
       1.201478e+12
       2008
        49
       1200787200000
       2008
          1
       0.510204
       397
                                      Kenny,   HILARIOUS
        10
        kenny the dunny man a mockumentary of austral...
          6
                                      Kenny
       0.364760
        9.119008
       2008
    

    

      537534
       1.202197e+05
       0
           6698.750276
       0
       1.201478e+12
       2008
       120
       1200787200000
       2008
          1
       0.550000
       397
                           The Australian Grand Poo Bah.
         5
        this is a very funny and well scripted and ac...
          6
                                      Kenny
       0.976730
       64.464181
       2008
    

    

      537535
       1.202197e+05
       0
           6698.750276
       0
       1.201478e+12
       2008
       140
       1200787200000
       2008
          1
       0.714286
       397
                                      You must see this!
        10
        i had the privilege and pleasure of attending...
          6
                                      Kenny
       0.794060
       79.406032
       2008
    

    

      537536
       1.202197e+05
       0
           6698.750276
       0
       1.201478e+12
       2008
        75
       1200787200000
       2008
          1
       0.573333
       397
                          Very funny and very Australian
         9
        very funny and very australian kenny has many...
          6
                                      Kenny
       0.859179
       36.944694
       2008
    

    

      537537
       1.202197e+05
       0
           6698.750276
       0
       1.201478e+12
       2008
        49
       1200787200000
       2008
          1
       0.530612
       397
               Move Over Superman - Kenny is a True Hero
         9
        we saw an advanced screening of kenny tonight...
          6
                                      Kenny
       1.011683
       26.303768
       2008
    

    

      537538
       1.202197e+05
       0
           6698.750276
       0
       1.201478e+12
       2008
        56
       1200787200000
       2008
          1
       0.678571
       397
                 Brilliant warm and funny comedy / drama
         9
        at last a simple ozzie film like the castle s...
          6
                                      Kenny
       0.884760
       33.620892
       2008
    

    

      537539
       1.063991e+05
       0
          20125.559021
       0
       1.137024e+12
       2006
       115
       1137715200000
       2006
          3
       0.591304
       404
                              Real Dirt is the Real Deal
         9
        i saw this film at the hot springs documentar...
         10
               The Real Dirt on Farmer John
       0.521966
       35.493702
       2006
    

    

      537540
       2.750256e+05
       0
          28507.721162
       0
       1.073002e+12
       2004
       111
       1074124800000
       2004
          3
       0.531532
       319
                     'You'd leave a good-looking corpse'
       N/A
        dylan kidd's 'ps' centres around 'louise' a 3...
         17
                                       P.S.
       0.330184
       19.480859
       2004
    

    

      537541
       1.190422e+05
       0
          62728.924671
       0
       1.199578e+12
       2008
        91
       1199923200000
       2008
         20
       0.681319
       398
          Worst Action Film i Ever Seen since Ballistic!
         1
        i can't even think how daddy yankee can do a ...
         20
                          Talento de Barrio
       0.229276
       14.215140
       2008
    

    

      537542
       2.637072e+04
       0
          17685.509327
       0
       1.390522e+12
       2014
        57
       1390003200000
       2014
         45
       0.754386
       539
       I'll give 7.5 out of 10.. That was not bad at ...
         7
        well i saw the genre that that's a horror  th...
         45
                                    13 Sins
       0.112202
        4.824694
       2014
    

    

      537543
       1.190422e+05
       0
          62728.924671
       0
       1.199578e+12
       2008
        73
       1199923200000
       2008
         20
       0.657534
       398
                                                  Strong
        10
        excellent movie i enjoyed the end very symbol...
         20
                          Talento de Barrio
       0.820802
       39.398495
       2008
    

    

      537544
       6.199261e+07
       1
       35030353.463249
       1
       1.390176e+12
       2014
       262
       1388707200000
       2014
       2867
       0.576336
        88
                            POV horror almost done right
         7
        a high school kid jesse graduates buys a came...
       2883
       Paranormal Activity: The Marked Ones
       0.482641
       72.878808
       2014
    

    

      537545
       1.190422e+05
       0
          62728.924671
       0
       1.199578e+12
       2008
        89
       1199923200000
       2008
         20
       0.550562
       398
           We don't need Hollywood - To make GOOD MOVIES
        10
        i'm a professional uploader who has been goin...
         20
                          Talento de Barrio
       0.534148
       26.173256
       2008
    

    

      537546
       5.940477e+07
       1
         168238.326029
       0
                NaN
       1990
        63
        631670400000
       1990
          2
       0.476190
        20
       Kicks Off Tim Burton And Johnny Depps First Ti...
         8
        edward scissorhands is a fantastic movie with...
       1372
                        Edward Scissorhands
       1.022094
       30.662810
       1990
    

    

      537547
       6.535640e+07
       1
       13613801.396214
       0
                NaN
       1991
        45
        664934400000
       1991
       2080
       0.711111
        22
                                 good hearted family fun
         8
         if you like really intellectual movies movie...
       2089
                                    My Girl
       1.153510
       36.912330
       1991
    

    

      537548
       1.190422e+05
       0
          62728.924671
       0
       1.199578e+12
       2008
        78
       1199923200000
       2008
         20
       0.705128
       398
       One of the better Independent films I've seen ...
         7
        daddy yankee stars as the head of a neighborh...
         20
                          Talento de Barrio
       0.658942
       36.241818
       2008
    

    

      537549
       1.190422e+05
       0
          62728.924671
       0
       1.199578e+12
       2008
       193
       1199923200000
       2008
         20
       0.601036
       398
                              I was completely surprised
         8
        i saw the movie last night  i was completely ...
         20
                          Talento de Barrio
      -0.011791
       -1.367803
       2008
    

  



537550 rows × 20 columns



















In [4]:



    


maas_IMDB.shape



    


















    
Out[4]:








(537550, 21)

First we do some Natural language processing this will take the form of a few steps.

  1. Punctuation: we will remove punctuation from reviews, so that all words can be recognized
  2. Lemmatization: we will reduce words to lemmas
  3. Removing stop words: we will remove common words from our vocabulary, because they are unlikely to have been used to express sentiment -- which is what we are interested in




In [ ]:



    


from pattern.en import parse
from pattern.en import pprint
from pattern.vector import stem, PORTER, LEMMA
punctuation = list('.,;:!?()[]{}`''\"@#$^&*+-|=~_')
from sklearn.feature_extraction import text 
stopwords=text.ENGLISH_STOP_WORDS



    











In [ ]:



    


import re
regex1=re.compile(r"\.{2,}")
regex2=re.compile(r"\-{2,}")

Define a function get_parts which will take in an input review and returns a tuple of nouns and adjectives. Each member of the tuple is a list of lists. In next steps we will use the nouns used to find the topic (using LDA), and the adjectives to do sentiment analysis (via Naive Bayes).





In [ ]:



    


def get_parts(thetext):
    thetext=re.sub(regex1, ' ', thetext)
    thetext=re.sub(regex2, ' ', thetext)
    nouns=[]
    descriptives=[]
    for i,sentence in enumerate(parse(thetext, tokenize=True, lemmata=True).split()):
        nouns.append([])
        descriptives.append([])
        for token in sentence:
            #print token
            if len(token[4]) >0:
                if token[1] in ['JJ', 'JJR', 'JJS']:
                    if token[4] in stopwords or token[4][0] in punctuation or token[4][-1] in punctuation or len(token[4])==1:
                        continue
                    descriptives[i].append(token[4])
                elif token[1] in ['NN', 'NNS']:
                    if token[4] in stopwords or token[4][0] in punctuation or token[4][-1] in punctuation or len(token[4])==1:
                        continue
                    nouns[i].append(token[4])
    out=zip(nouns, descriptives)
    nouns2=[]
    descriptives2=[]
    for n,d in out:
        if len(n)!=0 and len(d)!=0:
            nouns2.append(n)
            descriptives2.append(d)
    return nouns2, descriptives2

DO NOT RERUN THIS CODE, IT TAKES ABOUT 4 HOURS TO RUN

Now we call this function on our data





In [ ]:



    


# %%time
# # review_parts = maas_IMDB.map(lambda x: get_parts(x.text))
# review_parts = []
# for index, row in maas_IMDB.iterrows():
#     review_parts.append(get_parts(row.text))
#     if random() < 0.0001: 
#         print str(int(index)*100./537550) + "%"

Save it so we do not need to run this again:





In [11]:



    


# fp = open("review_parts.json","w")
# json.dump(review_parts, fp)
# fp.close()

with open("review_parts.json","r") as fp:
    review_parts = json.load(fp)

Perform LDA to determine if there are topics

We will now look specifically at the nouns in each sentence to see if that sentence is talking about actor quality, directing, scenery, music etc. The nouns are the first elements of all the tuples we created, so we take the first element in each tuple, remembering that this will be a list of lists of lists.





In [12]:



    


ldadata = [ele[0] for ele in review_parts]

First we want to create a vocabulary of all the nouns that are used in our database. To assist us we define a function flatten_unique which will flatten any list of lists into one list of unique values. This function can be called upon twice in order to flatten a list of lists of lists into one single list.





In [42]:



    


def flatten_unique(outer_list):
    flattened_list = []
    for inner_list in outer_list: # into a review
        for element in inner_list:
                if element not in flattened_list: 
                    flattened_list.append(element)
    return flattened_list

Next we define a function save_vocab which calls flatten_unique onto the data; however, partitions the data into nth sized buckets so that we are able to see our progress.





In [43]:



    


# def save_vocab(data, n):
#     ln = len(data)
#     count = 0
#     vocab = {}
#     while count < n: 
#         lower = int(count*ln/n)
#         upper = int((count+1)*ln/n - 1)
#         if upper < ln-1:
#             data_segment = data[lower:upper]
#             data_raw = flatten_unique(flatten_unique(data_segment))
#             vocab[count] = data_raw
#         else: 
#             data_segment = data[lower: ln]
#             data_raw = flatten_unique(flatten_unique(data_segment))
#             vocab[count] = data_raw
#         print str(int(count+1)*100./n) + "%"
#         count += 1
#     return vocab



    











In [ ]:



    


# %%time
# vocab_unique = save_vocab(ldadata, 20)

We actually decided to run save_vocab outside of the function loop so that we could save things into the dictionary vocab_unique as the coputer ran. Such that if the kernel died we could continue where it left off, rather than having to start again.





In [ ]:



    


%%time
n = 20
ln = len(ldadata)
count = 0
vocab_unique = {}
while count < n: 
    lower = int(count*ln/n)
    upper = int((count+1)*ln/n - 1)
    if upper < ln-1:
        data_segment = ldadata[lower:upper]
        data_raw = flatten_unique(flatten_unique(data_segment))
        vocab_unique[count] = data_raw
    else: 
        data_segment = ldadata[lower: ln]
        data_raw = flatten_unique(flatten_unique(data_segment))
        vocab_unique[count] = data_raw
    print str(int(count+1)*100./n) + "%", count
    count += 1

Saving it for use later on





In [155]:



    


# fp = open("vocab_unique.json","w")
# json.dump(vocab_unique, fp)
# fp.close()

with open("vocab_unique.json","r") as fp:
    vocab_unique_dict = json.load(fp)

We want to take the lists out of the dictionary and save it into a list. While the elements within each list are unique, because they ran independently of each other, the aggregate list vocab_raw may not be unique because a word may have appeared in the first list and second list. Therefore we also need to remove any duplicates.





In [157]:



    


vocab_raw = []
for i in range(50):
    vocab_raw = vocab_raw + vocab_unique_dict[str(i)]



    











In [159]:



    


len(vocab_raw)



    


















    
Out[159]:








1807254

















In [ ]:



    


# %%time
# vocab_unique = []
# counter = 0
# for i in vocab_raw:
#     if i not in vocab_unique:
#         vocab_unique.append(i)
#     if random() < 0.00001: 
#         print str(int(counter)*100./1807254) + "%"
#     counter += 1



    











In [5]:



    


# fp = open("vocab_unique_2.json","w")
# json.dump(vocab_unique, fp)
# fp.close()

with open("vocab_unique_2.json","r") as fp:
    vocab_unique = json.load(fp)



    











In [6]:



    


len(vocab_unique)



    


















    
Out[6]:








599222

We apply the flatten_iter function onto the ldadata and then we comb through the output, vocab_raw, to delete any duplicates and produce vocab_unique.

We make a dictionary vocab which has as its keys the word, and the value an index. Then we also make a complementary dictionary id2word which has as its keys the index, and the value as the word.





In [7]:



    


vocab = {}
for i in range(len(vocab_unique)):
    vocab[vocab_unique[i]] = i
    
id2word = {}
for i in range(len(vocab_unique)):
    id2word[i] = vocab_unique[i]



    











In [8]:



    


len(id2word), len(vocab)



    


















    
Out[8]:








(599222, 599222)

The next thing is to create the bag of words corpus. We learnt from the PSET that we should run LDA on the granularity of sentences, because we need some data that has a very clear and strong cluster membership so that we can clearly delineate the clusters (topics). Thus we consider things at the level of sentences, where some sentences will very clearly only talk about one topic. (Reviews are too confusing and may not have enough samples that exhibit strong preference for a membership).
We write a helper function auxillary_function that can be called on each sentence to create a bag of words which is a list of tuples of the word id and count of the word.





In [49]:



    


from collections import defaultdict

def auxillary_function(ldadata, vocab):
    d = defaultdict(int)
    for i in ldadata:
        if vocab.get(i) != None:
            index = vocab[i]
            d[index] += 1
    return d.items()



    











In [54]:



    


BOW_test = auxillary_function(tester_sentence, vocab)

In order to call auxillary_function we need to flatten ldadata once (recall that it is currently a list of lists of lists), so that it is just a lists of lists and each element represents a sentence. Once we flatten ldadata, we can call auxillary_function to make corpus.





In [13]:



    


%%time
all_sentences = []
for review in ldadata: # into a review
    for sentence in review:
        this_sentence = []
        for word in sentence: 
            if word not in this_sentence: 
                this_sentence.append(word)
        all_sentences.append(this_sentence)



    


















    






CPU times: user 30 s, sys: 3.34 s, total: 33.3 s
Wall time: 33.8 s

















In [55]:



    


len(all_sentences), len(ldadata)



    


















    
Out[55]:








(537360, 537550)

















In [56]:



    


%%time
corpus = []
counter = 0 
for sentence in all_sentences: 
    corpus.append(auxillary_function(sentence, vocab))
    if random() < 0.0001: 
        print str(int(counter)*100./537550) + "%"
    counter += 1



    


















    






3.81025020928%
4.46432889964%
7.10705980839%
9.92223979165%
10.2784857223%
14.6151985862%
15.5773416426%
16.5326016185%
17.8547111897%
18.2245372523%
19.6924937215%
22.6551948656%
25.3734536322%
28.4256348247%
31.7443958702%
32.9074504697%
36.9180541345%
40.4356804018%
41.9174030323%
45.0272532788%
45.7390010232%
50.5523207144%
53.1554274021%
55.5557622547%
56.6031066877%
60.7433727095%
64.8680122779%
66.8166682169%
67.9713515022%
70.1754255418%
70.7668123895%
71.5412519766%
71.6344526091%
71.8690354386%
77.2482559762%
77.3861036183%
77.422751372%
77.4419123802%
83.63891731%
84.3272253744%
88.5478560134%
89.4990233467%
90.865407869%
95.4668402939%
CPU times: user 28.7 s, sys: 20.7 s, total: 49.3 s
Wall time: 53.2 s

















In [58]:



    


# fp = open("corpus.json","w")
# json.dump(corpus, fp)
# fp.close()

with open("corpus.json","r") as fp:
    corpus = json.load(fp)



    











In [57]:



    


corpus



    


















    
Out[57]:








[[(0, 1),
  (1, 1),
  (2, 1),
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  (9, 1),
  (10, 1),
  (11, 1),
  (12, 1),
  (13, 1),
  (14, 1),
  (15, 1),
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  (18, 1),
  (19, 1),
  (20, 1),
  (21, 1),
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  (25, 1),
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  (27, 1),
  (28, 1),
  (29, 1),
  (30, 1),
  (31, 1),
  (32, 1),
  (33, 1),
  (34, 1),
  (35, 1),
  (36, 1),
  (37, 1),
  (38, 1),
  (39, 1),
  (40, 1),
  (41, 1),
  (42, 1),
  (43, 1),
  (44, 1),
  (45, 1),
  (46, 1),
  (47, 1)],
 [(2, 1),
  (6, 1),
  (7, 1),
  (42, 1),
  (44, 1),
  (48, 1),
  (49, 1),
  (50, 1),
  (51, 1),
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  (54, 1),
  (55, 1),
  (56, 1),
  (57, 1),
  (58, 1),
  (59, 1),
  (60, 1),
  (61, 1),
  (62, 1),
  (21, 1)],
 [(6, 1),
  (18, 1),
  (31, 1),
  (33, 1),
  (53, 1),
  (61, 1),
  (63, 1),
  (64, 1),
  (65, 1),
  (66, 1),
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  (68, 1),
  (69, 1),
  (70, 1),
  (71, 1),
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  (74, 1),
  (75, 1),
  (76, 1),
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  (78, 1),
  (79, 1),
  (80, 1)],
 [(6, 1),
  (81, 1),
  (82, 1),
  (83, 1),
  (84, 1),
  (85, 1),
  (86, 1),
  (87, 1),
  (88, 1)],
 [(96, 1),
  (34, 1),
  (38, 1),
  (95, 1),
  (85, 1),
  (89, 1),
  (90, 1),
  (91, 1),
  (92, 1),
  (93, 1),
  (94, 1),
  (21, 1)],
 [(128, 1),
  (129, 1),
  (130, 1),
  (131, 1),
  (132, 1),
  (133, 1),
  (134, 1),
  (135, 1),
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  (139, 1),
  (140, 1),
  (141, 1),
  (142, 1),
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  (20, 1),
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  (150, 1),
  (23, 1),
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  (53, 1),
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 [(20, 1),
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 [(19, 1),
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In [59]:



    


import gensim
lda2 = gensim.models.ldamodel.LdaModel(corpus, num_topics=2,id2word = id2word,update_every=1,chunksize=20000,passes=1)



    











In [60]:



    


lda2.print_topics()



    


















    
Out[60]:








[u'0.011*film + 0.008*movie + 0.008*character + 0.007*performance + 0.007*story + 0.006*role + 0.006*time + 0.005*man + 0.005*life + 0.005*scene',
 u'0.023*movie + 0.016*film + 0.012*time + 0.011*person + 0.010*character + 0.010*story + 0.009*way + 0.008*thing + 0.007*scene + 0.006*life']

















In [61]:



    


for bow in corpus[0:900:15]:
    print bow
    print lda2.get_document_topics(bow)
    print " ".join([id2word[e[0]] for e in bow])
    print "=========================================="



    


















    






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[(0, 0.24836885757413177), (1, 0.75163114242586815)]
home brother kid adult fact favourite movie john hughe lot classic breakfast club plane train automobile bueller dayoff family comedy story kevin mccallister time trip wish life fun burglar bandit trap house church scene talk man marley fear actor page master mccallisteri place city spirit heart conclusion child
==========================================
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[(0, 0.33513183302926247), (1, 0.66486816697073758)]
eighty favor movie rent evil meaning gene wilder pryor starre silver streak ninety itif comedy time crap
==========================================
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[(0, 0.66861961978775364), (1, 0.33138038021224631)]
bandini feel movie attention thirty time set dust writer scene salma hayek actor performance irishman desperado camilla quality lopeztowne wardrobe tedium boredom repetition viewing message piece viewer film sexuality arturo
==========================================
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[(0, 0.52963977400390361), (1, 0.4703602259960965)]
job movie director dust direction colin farrell audience actor half novel acting sutherland word message storyline script atmosphere way donald amateur adaptation film chance
==========================================
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[(0, 0.37948135939768474), (1, 0.62051864060231521)]
willi besson scifi cinema story time fun art style man romance performance acting dad hammersmith extravaganza courage humanity leap minority snob chord serra leon nikita core character faith plot bit soundtrack year critic film hole mind oldman realise element audience watch humor
==========================================
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[(0, 0.23217918170306859), (1, 0.76782081829693138)]
design work sense movie lot atmosphere expectation time life dust behavior writing picture best year actor person body place towne chinatown character minority career frida cinematography dialog hayek alexander moviesi preconception report sunseti combination whatif flaw twist theater end line illness book production
==========================================
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[(0, 0.71181084093072022), (1, 0.28818915906927978)]
donaldson kirkin bury burial window curtain movie director night la set farrell hayek snatcher actor body novel heart camilla script flower sound earthquake apartment girl desert wind stereotype
==========================================
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[(0, 0.41162864344385736), (1, 0.58837135655614281)]
night movie premiere executive captain correlli mandolin coupletoomany nothingness boy screen dust farrell salma hayek door director acting eyebrow dialogue sutherland reason score character history scene thing earthquake kept situation surprise sequence book producer watch
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[(6, 1), (9, 1), (1418, 1), (3983, 1), (18, 1), (6935, 1), (6945, 1), (35, 1), (38, 1), (167, 1), (1064, 1), (4014, 1), (48, 1), (818, 1), (311, 1), (56, 1), (58, 1), (6469, 1), (2503, 1), (55, 1), (460, 1), (7394, 1), (1229, 1), (80, 1), (4435, 1), (6484, 1), (6488, 1), (90, 1), (6491, 1), (98, 1), (7395, 1), (7396, 1), (7397, 1), (7398, 1), (7399, 1), (104, 1), (6763, 1), (2674, 1), (3703, 1)]
[(0, 0.55992262684963223), (1, 0.44007737315036788)]
movie lot box office family loni varney man actor performance success brilliance person quality world star reason leather dress guy type rag casting dvd genius tum medusa role hulk fan wkrp cinncinatus lothar heist outshone evil victor mission jim
==========================================
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[(0, 0.95380517596546732), (1, 0.046194824034532712)]
picture stoler way movie baldwin kirkpatrick performance oscar comedyexcellent ward performanceskudo comedyand shirley motion actress note time
==========================================
[(6, 1), (5128, 1), (9, 1), (138, 1), (1810, 1), (20, 1), (23, 1), (410, 1), (668, 1), (3741, 1), (48, 1), (419, 1), (35, 1), (7605, 1), (7600, 1), (7601, 1), (7602, 1), (7603, 1), (7604, 1), (53, 1), (7606, 1), (7607, 1), (568, 1), (1588, 1), (442, 1), (59, 1), (63, 1), (64, 1), (1345, 1), (834, 1), (68, 1), (1094, 1), (199, 1), (1226, 1), (203, 1), (81, 1), (339, 1), (5460, 1), (85, 1), (214, 1), (616, 1), (3177, 1), (106, 1), (3066, 1)]
[(0, 0.028710450551906359), (1, 0.97128954944809365)]
movie mysticism lot action meditation story time kind fall load person year man scientistman deepness log watchman planète sauvage character spirituality someway energy view mess music book adaption try sign way example magic symbolism plot thing line superpower film point situation finger planet explanation
==========================================
[(2, 1), (3, 1), (1668, 1), (6486, 1), (6, 1), (6535, 1), (9, 1), (138, 1), (139, 1), (7692, 1), (7693, 1), (7694, 1), (7695, 1), (272, 1), (6675, 1), (20, 1), (593, 1), (23, 1), (137, 1), (7471, 1), (2332, 1), (541, 1), (414, 1), (1311, 1), (4847, 1), (33, 1), (4553, 1), (1414, 1), (6054, 1), (167, 1), (3240, 1), (5801, 1), (42, 1), (3372, 1), (61, 1), (47, 1), (48, 1), (436, 1), (286, 1), (7696, 1), (55, 1), (56, 1), (697, 1), (5690, 1), (6945, 1), (2751, 1), (67, 1), (68, 1), (545, 1), (1352, 1), (585, 1), (203, 1), (590, 1), (81, 1), (594, 1), (85, 1), (1494, 1), (6487, 1), (6491, 1), (860, 1), (314, 1), (4368, 1), (226, 1), (678, 1), (2897, 1), (2794, 1), (236, 1), (4591, 1), (1264, 1), (787, 1), (553, 1), (248, 1), (126, 1), (341, 1)]
[(0, 0.010004706613955049), (1, 0.98999529338604497)]
kid adult slapstick park movie egg lot action problem installment laughs kiddy combat day math story title time dialogue category teacher excuse art hate cliché scene glass knife saga performance trash goose place noise opinion child person acting rating hogan3 guy star insult fu varney punch piece way rest pocket shot plot fighting thing hope film ball ninjas hulk sum weapon amusement girl hour terrorist wait cast karate honor exception effort joke humor fight
==========================================
[(6, 1), (5128, 1), (7788, 1), (1034, 1), (2582, 1), (23, 1), (1049, 1), (26, 1), (541, 1), (1967, 1), (49, 1), (1716, 1), (53, 1), (2742, 1), (611, 1), (858, 1), (7778, 1), (7779, 1), (7780, 1), (7781, 1), (7782, 1), (7783, 1), (7784, 1), (7785, 1), (7786, 1), (7787, 1), (108, 1), (115, 1), (500, 1), (245, 1)]
[(0, 0.16011337585919616), (1, 0.83988662414080384)]
movie mysticism prophet mix religion time image life excuse god age berkeley character ad truth comparison tract hinduism tenant reincarnation plant pantheism christianty syncretism seeker barb attempt force element reality
==========================================
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[(0, 0.96020950092718482), (1, 0.039790499072815136)]
designer movie john boy memory time jason vu tradition direction man ian actor love career product class decline flashback glory film working recruit issue masterpiece surprise sir production horror boone life war performance possibility hurt priestley bimbo 30 showboat ground illhealth retirement beverley lyn redgraveold etching affection heterosexual briefly history sensibility brendan déjà trouble sequel mckellen whale course friendship filmmaking day body sexuality quality hill world death housekeeper comparison island officer museum male clay fruit communication 1940 stride year flower buff bride frankenstein trench gardener fraser
==========================================
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[(0, 0.39020290599559443), (1, 0.60979709400440552)]
movie roger ebert classic melody disbelief story time goldberg life sam scene man actor shirt place sort conclusion person view criticism gut guy reason heaven murder way oscar thing trademark film role believability credit situation spoof aspect irony expression script eye felt parent empathy feeling material order door justice effect share performance greatness patrick journey touch sensation ability viewer magic laugh plot danger regret medium resignation corniness death… afterlife chat elvis aristotle separation condition oda mae necessity demises screenand zucker light codirector maurice pottery premonition sadness relief grief root job power belief project ticket ghost living body premise dream denial quality world whoopi weapon compassion depiction character try crook death swayze perspective shadow matter line archetype desperateness stage cause moment villain molly nerve value center haircut portrayal device drama screenplay cliché friend romance scheme idea tender soul score object dress capability mind actress sight choice element pun escape
==========================================

It looks like topic 2 is about the external factors of a movie -- who the actors are and how production went, and topic 1 is about the intrinsic factors of a movie -- the character development and plot. Examples:

* words like "movie" "actor" "film" or "budget" "film" have higher topic 2 ratings. 
* words like "relationship" "scenery" "personality" have a higher topic 1 rating

Run NB sentiment analysis on adjectives

Now we turn to running the sentiment analysis on adjectives. Firstly we need to get all the adjectives from review_parts.





In [65]:



    


nbdata = [ele[1] for ele in review_parts]

Similar to our treatment of the nouns, we create a dictionary adjvocab with keys being the adjectives and values being an index.





In [ ]:



    


# %%time
# adj_unique = save_vocab(nbdata, 10000)



    











In [5]:



    


# fp = open("adj_unique.json","w")
# json.dump(adj_unique, fp)
# fp.close()

with open("adj_unique.json","r") as fp:
    adj_unique_dict = json.load(fp)



    











In [7]:



    


adj_aggregate = []
for i in range(len(adj_unique_dict)):
    adj_aggregate = adj_aggregate + adj_unique_dict[str(i)]



    











In [8]:



    


%%time
adj_unique = []
for word in adj_aggregate:
    if word not in adj_unique:
        adj_unique.append(word)



    


















    






CPU times: user 23min 28s, sys: 6.91 s, total: 23min 35s
Wall time: 23min 45s

















In [9]:



    


len(adj_unique)



    


















    
Out[9]:








235675

















In [68]:



    


# fp = open("adj_unique_2.json","w")
# json.dump(adj_unique, fp)
# fp.close()

with open("adj_unique_2.json","r") as fp:
    adj_unique = json.load(fp)



    











In [69]:



    


adjvocab = {}
for i in range(len(adj_unique)):
    adjvocab[adj_unique[i]] = i



    











In [70]:



    


len(adjvocab)



    


















    
Out[70]:








235675

We want to flatten the list of adjectives within a review into a single list of all adjectives for the whole review, over all the sentences.





In [13]:



    


import itertools
Xarray = []
for i in nbdata: # into a review
    Xarraypre = " ".join(list(itertools.chain.from_iterable(i)))
    Xarray.append(Xarraypre)



    











In [75]:



    


# fp = open("Xarray.json","w")
# json.dump(Xarray, fp)
# fp.close()

with open("Xarray.json","r") as fp:
    Xarray = json.load(fp)

Unlike in HW5 where we used a binary response column that was based on the star ratings in the dataset, we used an external sentiment dictionary (LabMT) to calculate a valence score for each review, and have used that in leiu of the response array. (Details on how we calculated the valence score are above).





In [20]:



    


# making the response array based off of the sentiment dictionary 'happiness' there is valence_avg and valence_sum
resparray = []
for index, row in maas_IMDB.iterrows():
    if row.valence_avg > 0: # need to make it binomial because that's how the calibration/cv score works
        resparray.append(1)
    else: 
        resparray.append(0)



    











In [72]:



    


# fp = open("resparray.json","w")
# json.dump(resparray, fp)
# fp.close()

with open("resparray.json","r") as fp:
    resparray = json.load(fp)



    











In [76]:



    


print  len(Xarray), len(resparray)
print Xarray[0]



    


















    






537550 537550
favourite ferris funny touching sick tired hard idiot wet hilarious kevin old important bad good rich good second mean sure fourth classic

Now we create a mask to split things into a test and train set.





In [23]:



    


from sklearn.cross_validation import train_test_split
itrain, itest = train_test_split(xrange(len(Xarray)), train_size=0.7)
mask=np.ones(len(Xarray), dtype='int')
mask[itrain]=1
mask[itest]=0
mask = (mask==1)

We ensure that X and y are numpy arrays for sklearn.





In [77]:



    


X=np.array(Xarray)
y=np.array(resparray)

We define a function make_xy which takes a aX_col of word based documents and uses the vectorizer vectorizer to transform them into a feature matrix where the features are the vocabulary. Essentially the vectorizer transforms the space separated adjectives we made above into a bag-of-words representation.





In [25]:



    


def make_xy(X_col, y_col, vectorizer):
    X = vectorizer.fit_transform(X_col)
    y = y_col
    return X, y

We write a log_likelihood function which predicts the log likelihood of the dataset according to a Naive Bayes classifier.





In [26]:



    


"""
Function
--------
log_likelihood

Compute the log likelihood of a dataset according to 
a Naive Bayes classifier. 
The Log Likelihood is defined by

L = Sum_positive(logP(positive)) + Sum_negative(logP(negative))

Where Sum_positive indicates a sum over all positive reviews, 
and Sum_negative indicates a sum over negative reviews
    
Parameters
----------
clf : Naive Bayes classifier
x : (nexample, nfeature) array
    The input data
y : (nexample) integer array
    Whether each review is positive
"""
def log_likelihood(clf, x, y):
    prob = clf.predict_log_proba(x)
    neg = y == 0
    pos = ~neg
    return prob[neg, 0].sum() + prob[pos, 1].sum()

We define a function cv_score to estimate the cross-validated value of a scoring function, given a classifier and data.





In [27]:



    


from sklearn.cross_validation import KFold

def cv_score(clf, x, y, score_func, nfold=5):
    """
    Uses 5-fold cross validation to estimate a score of a classifier
    
    Inputs
    ------
    clf : Classifier object
    x : Input feature vector
    y : Input class labels
    score_func : Function like log_likelihood, that takes (clf, x, y) as input,
                 and returns a score
                 
    Returns
    -------
    The average score obtained by splitting (x, y) into 5 folds of training and 
    test sets, fitting on the training set, and evaluating score_func on the test set
    
    Examples
    cv_score(clf, x, y, log_likelihood)
    """
    result = 0
    for train, test in KFold(y.size, nfold): # split data into train/test groups, 5 times
        clf.fit(x[train], y[train]) # fit
        result += score_func(clf, x[test], y[test]) # evaluate score function on held-out data
    return result / nfold # average

We define a function calibration_plot which will make a calibration plot.





In [28]:



    


def calibration_plot(clf, xtest, ytest):
    prob = clf.predict_proba(xtest)[:, 1]
    outcome = ytest
    data = pd.DataFrame(dict(prob=prob, outcome=outcome))

    #group outcomes into bins of similar probability
    bins = np.linspace(0, 1, 20)
    cuts = pd.cut(prob, bins)
    binwidth = bins[1] - bins[0]
    
    #freshness ratio and number of examples in each bin
    cal = data.groupby(cuts).outcome.agg(['mean', 'count'])
    cal['pmid'] = (bins[:-1] + bins[1:]) / 2
    cal['sig'] = np.sqrt(cal.pmid * (1 - cal.pmid) / cal['count'])
        
    #the calibration plot
    ax = plt.subplot2grid((3, 1), (0, 0), rowspan=2)
    p = plt.errorbar(cal.pmid, cal['mean'], cal['sig'])
    plt.plot(cal.pmid, cal.pmid, linestyle='--', lw=1, color='k')
    plt.ylabel("Empirical P(+)")
    
    #the distribution of P(+)
    ax = plt.subplot2grid((3, 1), (2, 0), sharex=ax)
    
    plt.bar(left=cal.pmid - binwidth / 2, height=cal['count'],
            width=.95 * (bins[1] - bins[0]),
            fc=p[0].get_color())
    
    plt.xlabel("Predicted P(+)")
    plt.ylabel("Number")



    











In [29]:



    


from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

We run the cross-validation loop over the alphas and min_dfs for MultinomialNB and CountVectorizer, respectively. It's okay that the cross validation loop happens inside the cv_score function because we are feeding a vocabulary into CountVectorizer so min_df doesn't actually affect the CountVectorizer at all.





In [30]:



    


adjvocab



    


















    
Out[30]:








{u"daggers'beautiful": 233588,
 u'wooda': 162193,
 u'unsupportable': 70238,
 u'partsurrealistic': 150083,
 u'nunnery': 227533,
 u'gah': 22872,
 u'aboutexcellent': 110898,
 u'simpsonsand': 235483,
 u'blackanother': 217399,
 u'almostoverused': 17507,
 u'woods': 229882,
 u'clotted': 24432,
 u'standardsamerican': 66593,
 u"n'tseethatcoming": 27434,
 u'ridiculouslast': 171734,
 u'ultimatley': 154270,
 u'woody': 17525,
 u'trawling': 146558,
 u'goodbetter': 58725,
 u'operaobsessed': 217136,
 u'2003which': 142102,
 u'spidery': 31550,
 u'semipreachy': 235304,
 u'operationinspired': 148694,
 u'imaginingall': 83846,
 u'hackmanmorgan': 49436,
 u'killeranother': 120050,
 u'nonkurdish': 202495,
 u'caned': 182521,
 u'rickman': 26730,
 u'protestsanother': 73601,
 u'passionatelust': 38265,
 u'rumbustious': 27064,
 u'fanssheryl': 54319,
 u'outbabed': 77884,
 u'wholeespecially': 221881,
 u'ghettostyled': 100191,
 u'slothful': 15509,
 u'humanskyle': 217593,
 u'fauxcamera': 15471,
 u'lovinglyghibli': 155160,
 u'effortsher': 224059,
 u'unspectrallooking': 170115,
 u'priyadrashan': 123427,
 u'asianpacific': 135176,
 u'fullblooded': 29827,
 u'ispast': 79577,
 u'seamier': 18193,
 u'wooded': 9648,
 u'grueling': 19291,
 u'flairoverallthe': 58270,
 u'wooden': 702,
 u'iluminati': 38787,
 u'wednesday': 27389,
 u'losttime': 22615,
 u'vileracisthomophobic': 48296,
 u'unfoldkiller': 188418,
 u'malechauvinistic': 82654,
 u'rauni': 50127,
 u'bestunsung': 57114,
 u'okact': 129199,
 u'closenevertheless': 73333,
 u'8to13yearold': 144588,
 u'onessoknockround': 88954,
 u'mediocretobad': 137909,
 u'actingvisual': 110871,
 u'antiaustralian': 101791,
 u'cuteto': 186641,
 u'272': 16176,
 u'273': 40607,
 u'275': 165023,
 u'sh1t': 105457,
 u'277': 147781,
 u'alike>music': 52148,
 u'boyher': 81226,
 u'asianamercian': 150972,
 u'1mysterious': 34832,
 u'phillipino': 123741,
 u'dialogs': 5980,
 u'laughdyan': 78096,
 u'kid7': 59498,
 u'funnynicholas': 158592,
 u'\x96without': 189837,
 u'moviemysterious': 222075,
 u'warmongering': 40005,
 u'digitallyenhanced': 75886,
 u'videodrome': 53866,
 u'isbig': 144128,
 u'dialogi': 18322,
 u'superbscott': 84103,
 u'hyperenergetic': 79421,
 u'supperclub': 201493,
 u'fartladen': 172966,
 u'adviceeric': 168565,
 u'wasington': 15171,
 u'ledyen': 40333,
 u'27m': 72176,
 u'swinton': 27297,
 u'hwangmadame': 179574,
 u'oscarhungry': 71635,
 u'shortsized': 27052,
 u'easilypulled': 74790,
 u'affiliated': 6448,
 u'floridanew': 229509,
 u'hilariousspoiler': 166443,
 u'ohsosecret': 231208,
 u'otherdead': 199079,
 u'kids': 27203,
 u'multiversedirected': 116764,
 u'uplifting': 2579,
 u'novalue': 124704,
 u'mildlysuccessful': 79863,
 u'kida': 137745,
 u'charmingromantic': 14141,
 u'controverse': 107510,
 u'crowdpleasing': 41028,
 u'workoholic': 225291,
 u'snowas': 110475,
 u'normallydirector': 159743,
 u'rebel': 15365,
 u'filmtitle': 206317,
 u'adultsas': 129770,
 u'gainsbourg': 14889,
 u'brachycephalic': 216736,
 u'mystik': 202385,
 u'fastforwarding': 31306,
 u'ituhmm': 102411,
 u'educated\x85i': 43045,
 u'dawgs': 231681,
 u'airball': 35143,
 u'\xabvive': 58010,
 u'inlisted': 93410,
 u'igual': 151343,
 u'passable5': 122871,
 u'attenboroughit': 142429,
 u'hereinslick': 152086,
 u'poh\xe1rnok': 128303,
 u'dnc': 89486,
 u'blasse': 32390,
 u'hesitatebrilliant': 152870,
 u'finishedbut': 222113,
 u'mutantkindif': 224774,
 u'dnt': 206680,
 u'bettercostarring': 216519,
 u'dollhousetodd': 136647,
 u'peoplecarl': 142048,
 u'diseasedlooking': 61564,
 u'dnr': 103212,
 u'funnyblah': 51771,
 u'hoarsevoiced': 185216,
 u'antienvironmental': 160583,
 u'hariakhan': 198774,
 u'borstal': 140100,
 u'parthitchcockian': 222651,
 u'reinforceditalian': 101286,
 u'unpersonable': 70070,
 u'atodds': 195839,
 u'yahoo': 22848,
 u'originatesbrian': 154128,
 u'expeditionary': 207967,
 u'suspensethen': 43771,
 u'sharpcharismatic': 53944,
 u'blustering': 6761,
 u'californiaspeak': 70117,
 u'overafter': 87349,
 u'actorsunrealistic': 52742,
 u'fancast': 180371,
 u'tofinal': 48109,
 u'herany': 86118,
 u'dramathe': 194636,
 u'noinoi': 96051,
 u'patternof': 206338,
 u'looselystructured': 112445,
 u'christianswhich': 177488,
 u'airfuel': 113889,
 u'performancesjoaquin': 106723,
 u'fabmay': 109045,
 u'lifevest': 199530,
 u'alvinsimon': 22895,
 u'cockier': 28724,
 u'booklined': 115075,
 u'unrealisticoverdramatic': 92842,
 u'jidaigeki': 17459,
 u'pictureesque': 58102,
 u'rapidlyoverallbuddy': 112651,
 u'ourselvessolo': 20040,
 u'expensiveall': 28877,
 u'childabused': 212089,
 u'phiferrich': 114701,
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 u'elijar': 210187,
 u'itcody': 26405,
 u'postcalvin': 34532,
 u'laughasecond': 29673,
 u'xfile': 59515,
 u'rusoe': 55680,
 u'preenlightened': 28894,
 u'differentinstead': 192474,
 u'tooreallooking': 100606,
 u'cardboad': 43885,
 u'seriousfive': 183292,
 u'notabetterthingtodo': 180124,
 u'fockerwhy': 98159,
 u'setin': 186942,
 u'dujor': 137899,
 u'dysfunctionalitymany': 169220,
 u'rankseric': 48537,
 u'dreamsswim': 87799,
 u'backgroundthen': 198611,
 u'forehere': 124923,
 u'gloom3': 78775,
 u'chair': 194020,
 u'cutesywutsey': 27917,
 u'harrisnothing': 186012,
 u'alongfinal': 76969,
 u'macht': 59729,
 u'shusett': 12569,
 u'ballet': 68266,
 u'baller': 116190,
 u'grappler': 200563,
 u'perspectiveyou': 93633,
 u'laughabledreadful': 197681,
 u'quicklyclive': 34549,
 u'menrather': 221038,
 u'mache': 203456,
 u'teamup': 170185,
 u'girlcompared': 191752,
 u'macha': 46925,
 u'megawatti': 234151,
 u'parimutuel': 125635,
 u'heredeep': 142847,
 u'kortzfleisch': 191486,
 u'macho': 1355,
 u'mostwanttosee': 75968,
 u'ukthis': 132354,
 u'housetrust': 174007,
 u'actionflick': 33285,
 u'flatbed': 44803,
 u'jerk': 230893,
 u'jere': 144741,
 u'friendshipthese': 4809,
 u'cruelarbitrary': 68272,
 u'olympus': 47325,
 u'ordersoverall': 38844,
 u'gloomy': 900,
 u'unpleasent': 226268,
 u'\x85\x85\x85': 209459,
 u'exact': 1754,
 u'atheoretical': 138464,
 u'minute': 980,
 u'liecan': 202522,
 u'mach1': 99185,
 u'particularacting': 119954,
 u'epicscale': 54704,
 u'bethere': 174783,
 u'simpleso': 60843,
 u'linebig': 193175,
 u'cleanmystic': 143521,
 u'nonoffensive': 16041,
 u'backboneless': 116952,
 u'battleno': 133489,
 u'mummycondensed': 61515,
 u'elevenminute': 75131,
 u'vanellope': 182461,
 u'dryburgh': 119634,
 u'murthy': 188613,
 u'pinkcheeked': 183348,
 u'undemandingso': 227812,
 u'oldevery': 143047,
 u'buuu': 137400,
 u'otherwisegrade': 230292,
 u"n'taddanythingtothestory": 96668,
 u'factsclassic': 102984,
 u'unspeakablefart': 213350,
 u'shabbychic': 212700,
 u'antwerp': 92441,
 u'abdullah': 145476,
 u'wildoverall': 67018,
 u'politicalminded': 117001,
 u'roarke': 24597,
 u'titanicrevolutionary': 59406,
 u'wayward': 3854,
 u'bontempo': 215442,
 u'flawlessbut': 23957,
 u'systemsame': 184491,
 u'ofto': 167943,
 u'urbantwo': 173823,
 u'matrixalthough': 79969,
 ...}

















In [31]:



    


%%time 

#the grid of parameters to search over
alphas = [0, .1, 1, 5, 10, 50]

#Find the best value for alpha and min_df, and the best classifier
best_alpha = None
maxscore = -np.inf

for alpha in alphas:       
    vectorizer = CountVectorizer(vocabulary =adjvocab)       
    Xthis, Ythis = make_xy(X[mask], y[mask], vectorizer)
    clf = MultinomialNB(alpha=alpha)
    cvscore = cv_score(clf, Xthis, Ythis, log_likelihood)
    if cvscore > maxscore:
        maxscore = cvscore
        best_alpha = alpha



    


















    






CPU times: user 1min 28s, sys: 48.1 s, total: 2min 16s
Wall time: 2min 33s

Now that we've determined values for alpha and min_df that optimize the cross-validated log-likelihood, repeat the CountVectorization and fitting to train a final classifier with these parameters, and draw calibration plots on the test set.





In [32]:



    


vectorizer = CountVectorizer(vocabulary =adjvocab)
Xnew, Ynew = make_xy(X,y, vectorizer)
xtrain, xtest, ytrain, ytest = train_test_split(Xnew, Ynew)
clf = MultinomialNB(alpha=best_alpha).fit(xtrain, ytrain)

training_accuracy = clf.score(xtrain, ytrain)
test_accuracy = clf.score(xtest, ytest)

print "Accuracy on training data: %0.2f" % (training_accuracy)
print "Accuracy on test data:     %0.2f" % (test_accuracy)

calibration_plot(clf, xtest, ytest)



    


















    






Accuracy on training data: 0.95
Accuracy on test data:     0.87










    






//anaconda/lib/python2.7/site-packages/matplotlib/collections.py:548: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  if self._edgecolors == 'face':

ADD COMMENTS HERE WHEN GRAPH IS PRODUCED

Compute sentiment scores for each review

Because our naive bayes classifier assumes conditional independence, this means that while we ran the sentiment analysis on each review, it should be fairly straightforward to apply the sentiment analysis to each sentence instead of the whole review.
In order to do this we calculate 2 dictionaries logpositives and lognegatives. These dictionaries have the words as the keys and the log probabilities as they value.





In [33]:



    


vectorizer_alladj = CountVectorizer(vocabulary = adjvocab)
X_alladj, Y_alladj = make_xy(X[mask],y[mask], vectorizer_alladj)
clf_alladj = MultinomialNB(alpha=best_alpha).fit(X_alladj, Y_alladj)

logpositives = dict(zip(vectorizer_alladj.get_feature_names(),clf_alladj.feature_log_prob_[0]))
lognegatives = dict(zip(vectorizer_alladj.get_feature_names(),clf_alladj.feature_log_prob_[1]))



    











In [84]:



    


# fp = open("logpositives.json","w")
# json.dump(logpositives, fp)
# fp.close()

with open("logpositives.json","r") as fp:
    logpositives = json.load(fp)



    











In [85]:



    


# fp = open("lognegatives.json","w")
# json.dump(lognegatives, fp)
# fp.close()

with open("lognegatives.json","r") as fp:
    lognegatives = json.load(fp)

In the two dictionaries, we have the probability for each individual word whether it is positive or negative. Now we write a function calc_pplus which calculates the probability that a sentence is positive. Please refer to HW5 for more information on the maths behind this.





In [87]:



    


def calc_pplus(adjlist, lp, ln, pp,pn):
    sgivenpos = 0
    sgivenneg = 0
    for adj in adjlist:
        if lp.get(adj) != None:
            sgivenpos += lp[adj]
            sgivenneg += ln[adj]
    return(np.exp(sgivenpos)*pp)/(np.exp(sgivenpos)*pp + np.exp(sgivenneg)*pn)



    











In [47]:



    


reviews=[]
for index, row in maas_IMDB.iterrows():
    reviews.append(index)



    











In [80]:



    


# fp = open("reviews.json","w")
# json.dump(reviews, fp)
# fp.close()

with open("reviews.json","r") as fp:
    reviews = json.load(fp)



    











In [49]:



    


len(review_parts),len(reviews)



    


















    
Out[49]:








(537550, 537550)

We write a function choose_topic which chooses which of the two LDA topics the sentence is in, given the bag of words for the sentence.





In [62]:



    


def choose_topic(ldamodel, bow):
    tee = lda2.get_document_topics(bow)
    if len(tee)==2:
        t1,t2=tee
        if t2[1] >= t1[1]:#get higher probability topic
            topicis=t2[0]
        else:
            topicis=t1[0]
    elif len(tee)==1:#if only one was provided its very high probability. Take it
        teetuple=tee[0]
        topicis=teetuple[0]
    return topicis

Before we go any further, it's important to take a look at what the prior probability that a review is positive or negative is.





In [78]:



    


priorp = np.mean(resparray)
priorn = 1 - priorp
priorp, priorn



    


















    
Out[78]:








(0.93327318388987068, 0.066726816110129317)

It's clear that there are alot more positive reviews than there are negative reviews.

Now we combine the two functions we just wrote, to populate a dictionary reviewdict which stores in a dictionary, with keys the review_id, a list of dictionaries for each sentence within that review.





In [88]:



    


counter=0
reviewdict={}
for i, rid in enumerate(reviews):
    rlist=[]
    nlist, alist = review_parts[i]
    ln=len(alist)
    localbow=corpus[counter:counter+ln]
    for bow, adj, noun in zip(localbow, alist, nlist):
#         doc=" ".join([id2word[e[0]] for e in bow])
        pplus=calc_pplus(adj, logpositives, lognegatives, priorp, priorn)
        topicis=choose_topic(lda2, bow)
        ldict={"topic": topicis, 'pplus':pplus}
        rlist.append(ldict)
    reviewdict[str(int(rid))]=rlist
    counter=counter+ln
#     print i, rid, ln, counter
    if random() < 0.0001: 
        print str(int(i)*100./537550) + "%"



    


















    






0.991721700307%
3.39056831923%
3.61250116268%
4.20407403962%
4.87508138778%
4.98279229839%
6.18212259325%
6.21207329551%
10.7106315692%
11.395777137%
14.3700120919%
16.8840107897%
21.3496418938%
22.6479397265%
22.8871732862%
26.6953771742%
27.5477629988%
27.7220723654%
28.0418565715%
30.6367779741%
32.5415310204%
32.9459585155%
34.9933959632%
36.7283043438%
36.7699748861%
39.4766998419%
41.0198121105%
45.2674169845%
45.3749418659%
51.0428797321%
51.8958236443%
53.1202678821%
53.5842247233%
53.6169658636%
55.0862245373%
57.4450748768%
58.9528415961%
60.3409915357%
60.4756766812%
62.7876476607%
62.8542461166%
66.1058506185%
70.0918984281%
71.0421356153%
72.955446005%
76.8525718538%
77.7209561901%
78.8633615478%
79.446191052%
79.6564040554%
84.8845688773%
86.6146404986%
88.4864663752%
91.7776950981%
93.0986884941%
94.7965770626%
95.8279229839%
97.7736024556%
98.5897125849%
99.6312901125%

















In [89]:



    


# fp = open("reviewdict.json","w")
# json.dump(reviewdict, fp)
# fp.close()

with open("reviewdict.json","r") as fp:
    reviewdict = json.load(fp)



    











In [94]:



    


reviewdict['287144']



    


















    
Out[94]:








[{u'pplus': 0.4324782043425256, u'topic': 0}]

















In [98]:



    


maas_IMDB.head()



    


















    
Out[98]:










  
    

      
      index
      adj_gross
      adj_gross_bin
      adj_opening_gross
      adj_opening_gross_bin
      close_date
      close_year
      n_scorables
      open_date
      open_year
      opening_theaters
      pct_scorables
      rank
      review_title
      stars
      text
      theaters
      title
      valence_avg
      valence_sum
      year
    

  
  
    

      0
          0
       3.011865e+08
       1
       18004075.757142
       1
                NaN
       1990
       144
        632448000000
       1990
       1202
       0.701389
         1
       My favourite Christmas movie!
        10
        it just isn't christmas if i don't watch home...
       2173
               Home Alone
       0.758622
       76.620792
       1990
    

    

      1
          1
       3.011865e+08
       1
       18004075.757142
       1
                NaN
       1990
        65
        632448000000
       1990
       1202
       0.738462
         1
             Very funny family movie
         8
        this is a very funny movie for kids and peopl...
       2173
               Home Alone
       1.408094
       67.588495
       1990
    

    

      2
         10
       8.113545e+06
       0
          61087.601573
       0
                NaN
       1990
       364
        633052800000
       1990
          2
       0.434066
       109
           Emasculation Proclamation
         5
        almost  but not quite  benniferidiotically ta...
        240
        Mr. & Mrs. Bridge
       0.237925
       37.592131
       1990
    

    

      3
        100
       1.209626e+06
       0
         111846.786031
       0
       1.137542e+12
       2006
        87
       1136851200000
       2006
          7
       0.482759
       251
       Dusting off great LA memoirs!
        10
        john fante wrote a great book called ask the ...
        111
             Ask the Dust
       0.469998
       19.739933
       2006
    

    

      4
       1000
       9.251838e+05
       0
          37086.578785
       0
       1.136851e+12
       2006
        66
       1136419200000
       2006
          3
       0.575758
       263
            Incredibly disappointing
       N/A
        for the first hour i absolutely loved this fi...
         28
       Down in the Valley
       0.338971
       12.880892
       2006

Next we create a list list_of_dicts which augments the information from reviewdict with information from the initial dataframe maas_IMDB.





In [ ]:



    


%%time

list_of_dicts=[]
counter = 0
sentence_counter = 0
for index, row in maas_IMDB.iterrows():
    counter += 1
    revs=reviewdict[str(int(index))]
    print index, len(revs)
    sentence_counter += len(revs)
    for r in revs:
        r2=r.copy()
        r2['movie_id']=row.title
        r2['review_id']=index
        r2['stars']=row.stars
        r2['valence_avg']=row.valence_avg
        r2['valence_sum']=row.valence_sum
        list_of_dicts.append(r2)
    if random() < 0.0001: 
        print str(int(index)*100./537550) + "%"



    


















    






0 1
1 1
2 1
3 1
4 1
5 1
6 1
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138951 1
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139230 1
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139280 1
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139290 1
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139453 1
139454 1
139455 1
139456 1
139457 1
139458 1
139459 1
139460 1
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139462 1
139463 1
25.944191238%
139464 1
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139470 1
139471 1
139472 1
139473 1
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139475 1
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139671 1
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139680 1
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139760 1
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139764 1
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139771 1
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139775 1
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139780 1
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139805 1
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139808 1
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139810 1
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139844 1
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139851 1
139852 1
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139855 1
139856 1
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139858 1
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139860 1
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139862 1
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139864 1
139865 1
139866 1
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139875 1
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139880 1
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139895 1
139896 1
139897 1
139898 1
139899 1
139900 1
139901 1
139902 1
139903 1
139904 1
139905 1
139906 1
139907 1
139908 1
139909 1
26.0271602642%
139910 1
139911 1
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139915 1
139916 1
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139920 1
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39.4275881313%
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212292 1
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212294 1
212295 1
212296 1
212297 1
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212299 1
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212301 1
39.4941865873%
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Finally we are ready to save this as a pandas dataframe.





In [ ]:



    


completedf=pd.DataFrame(list_of_dicts)
completedf.head()



    











In [ ]:



    


fp = open("completedf_intermediate.json","w")
json.dump(completedf.to_dict(), fp)
fp.close()

with open("completedf_intermediate.json","r") as fp:
    completedf = json.load(fp)

Not that this dataframe is by every sentence that we have, and we want to groupby review and topic. Therefore we define the function get_stats which takes the minimum, maximum, mean, count and variance of the probability that the review is positive, as well as preserving the valence numbers.





In [ ]:



    


def get_stats(group):
    min_pplus = group.pplus.min()
    max_pplus = group.pplus.max()
    rid = group.movie_id.unique()
    valence_avg = group.valence_avg.mean()
    valence_sum = group.valence_sum.mean()
    count = group.topic.count()
    if count == 1: 
        varj = 0
    else: 
        varj = group.pplus.var(ddof=1)
    mean_pplus = group.pplus.mean()
    stars = group.stars.mean()
    return pd.DataFrame({'min': min_pplus, 'max':max_pplus ,'valence_avg': valence_avg,'valence_sum':valence_sum, 
                         'count': count,'var': varj, 'mean': mean_pplus, 'stars': stars}, index=rid)

We apply our new function to the groupby object of completedf.





In [ ]:



    


%%time
dftouse=completedf.groupby(['review_id', 'topic']).apply(get_stats).reset_index()



    











In [ ]:



    


print dftouse.shape
dftouse = dftouse.rename(columns={'level_2': 'movie_id'})
dftouse.head()

Finally we save our dataset so we don't have to run this anymore!!!!!!!





In [ ]:



    


fp = open("complete_byreviews.json","w")
json.dump(dftouse.to_dict(), fp)
fp.close()

with open("complete_byreviews.json","r") as fp:
    complete_byreviews = json.load(fp)
complete_byreviews = pd.DataFrame(complete_byreviews)

Descriptive statistics by review

Let's see what our data looks like before we group by movie title.





In [7]:



    


dftouse.head(15)
#plt.scatter(x=completedf.valence_avg, y=completedf.gross)
#neardf = completedf[completedf.gross <= 200000000]
plt.hist(dftouse.gross, bins = 40)



    


















    
Out[7]:








(array([  3.20593000e+05,   7.31750000e+04,   4.22500000e+04,
          2.92270000e+04,   1.77200000e+04,   1.24840000e+04,
          1.01870000e+04,   7.24500000e+03,   4.67300000e+03,
          4.30000000e+03,   2.30000000e+03,   2.79600000e+03,
          1.90000000e+03,   2.00000000e+03,   5.00000000e+02,
          1.20000000e+03,   1.40000000e+03,   7.00000000e+02,
          5.00000000e+02,   5.00000000e+02,   3.00000000e+02,
          5.00000000e+02,   4.00000000e+02,   2.00000000e+02,
          1.00000000e+02,   0.00000000e+00,   0.00000000e+00,
          0.00000000e+00,   1.00000000e+02,   0.00000000e+00,
          0.00000000e+00,   0.00000000e+00,   1.00000000e+02,
          0.00000000e+00,   1.00000000e+02,   0.00000000e+00,
          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,
          1.00000000e+02]),
 array([  7.20000000e+01,   1.87442237e+07,   3.74883754e+07,
          5.62325270e+07,   7.49766787e+07,   9.37208304e+07,
          1.12464982e+08,   1.31209134e+08,   1.49953285e+08,
          1.68697437e+08,   1.87441589e+08,   2.06185740e+08,
          2.24929892e+08,   2.43674044e+08,   2.62418195e+08,
          2.81162347e+08,   2.99906499e+08,   3.18650650e+08,
          3.37394802e+08,   3.56138954e+08,   3.74883106e+08,
          3.93627257e+08,   4.12371409e+08,   4.31115561e+08,
          4.49859712e+08,   4.68603864e+08,   4.87348016e+08,
          5.06092167e+08,   5.24836319e+08,   5.43580471e+08,
          5.62324622e+08,   5.81068774e+08,   5.99812926e+08,
          6.18557077e+08,   6.37301229e+08,   6.56045381e+08,
          6.74789532e+08,   6.93533684e+08,   7.12277836e+08,
          7.31021987e+08,   7.49766139e+08]),
 <a list of 40 Patch objects>)

Groupby to prepare the final dataset





In [ ]:



    


## JASON'S GROUPBY THING. 
## DFTOUSE_BOW IS MY DATAFRAME

## STEPHENS' THING IS DFTOUSE0
groupby_movie = complete_byreviews.groupby(['movie_id','topic'])

val_avg = groupby_movie['valence_avg','valence_sum','var','max','mean','min','count'].mean()
topic01 = val_avg.reset_index().set_index('movie_id')
topic1 = topic01[topic01.topic==1].rename(columns={'valence_avg':'valence_avg_t1'
                                                   ,'valence_sum':'valence_sum_t1'
                                                   ,'var':'var_t1','max':'max_t1'
                                                   ,'mean':'mean_t1','min':'min_t1'
                                                   ,'count':'count_t1'})
topic0 = topic01[topic01.topic==0].rename(columns={'valence_avg':'valence_avg_t0'
                                                   ,'valence_sum':'valence_sum_t0'
                                                   ,'var':'var_t0','max':'max_t0'
                                                   ,'mean':'mean_t0','min':'min_t0'
                                                   ,'count':'count_t0'})

groupby_movie2 = complete_byreviews.groupby(['movie_id'])
stars = groupby_movie2.stars.unique()

combinedf = pd.concat([dftouse0, topic0], axis=1, join_axes=[dftouse0.index])
combinedf = pd.concat([combinedf, topic1], axis=1, join_axes=[combinedf.index])

dftouse = combinedf[~combinedf['count_t0'].map(np.isnan)].fillna(0)
  • [ Jess: Make sure to add a |Abs average| column so we can test Andrew's hypothesis that polarizing movies earn a lot of money. ]
  • [ Jess: Add 3 sd (measure of dispertion) columns in the groupby that takes the sd of the avg_valence and sum_valence and abs_avg_valence of the reviews for a particular movie. ]

Descriptive statistics on final dataset

Let's look for some movie trends.

Analysis

Valence score correlation with stars





In [69]:



    


stars_val_avg = ols('stars ~ valence_avg',dftouse).fit()
stars_val_avg.summary()
sns.regplot(y="valence_avg", x="stars", data=dftouse)
# plt.plot(valence_df.stars,valence_df.valence_avg,'.',alpha=0.6)



    


















    
Out[69]:








<matplotlib.axes._subplots.AxesSubplot at 0x10ea4ff50>

Now we'll import allMoives_new.json. Remember that we still need to convert some of the columns into the datetime type so we'll do that afterwards.





In [72]:



    


with open("allMovies.json", "r") as fd:
    bom_df = json.load(fd)
    bom_df = pd.DataFrame(bom_df)



    











In [73]:



    


bom_df[2572:2577]



    


















    
Out[73]:










  
    

      
      close_date
      close_day
      close_month
      close_year
      gross
      open_date
      open_day
      open_month
      open_year
      opening_gross
      opening_theaters
      ranking
      title
      total_theaters
      year
    

  
  
    

      2572
      {u'$date': 1446076800000}
      29
      10
      2015
      27740955
      1440115200000000000
      21
      8
      2015
      10542116
      2766
      73
      Sinister 2
      2799
      2015
    

    

      2573
      {u'$date': 1448496000000}
      26
      11
      2015
      27288872
      1440547200000000000
      26
      8
      2015
      8111264
      3355
      74
      No Escape
      3415
      2015
    

    

      2574
      None
      0
      0
      2016
      27121602
      1445558400000000000
      23
      10
      2015
      10812861
      3082
      75
      The Last Witch Hunter
      3082
      2015
    

    

      2575
      {u'$date': 1444521600000}
      11
      10
      2015
      26822144
      1438905600000000000
      7
      8
      2015
      6610961
      1603
      76
      Ricki and the Flash
      2064
      2015
    

    

      2576
      {u'$date': 1426723200000}
      19
      3
      2015
      26501323
      1420156800000000000
      2
      1
      2015
      15027415
      2602
      77
      The Woman in Black 2: Angel of Death
      2602
      2015

Analysis





In [61]:



    


# Reopen dftouse
with open("dftouse.json", "r") as fp:
    dftouse_dict = json.load(fp)
dftouse = pd.DataFrame(dftouse_dict)

We have chosen to only use opening grossing of movies as our y-variable. This is because the time for which the movie is open is standardized. We considered using (total grossing)/(total time in theaters) as the y-variable, but this was not feasible because we do not have a closing date for every movie and therefore could not calculate the total time in theaters fo every review.

Let's remove outliers from the dataset, so that we can more easily see trends. An outlier in column valence_avg is defined as a point that it more than 3 standard deviations away from the mean.





In [33]:



    


def is_not_outlier(num, avg, sd, m = 3.):
    return np.absolute(num-avg) < m*sd

Let's see how many outliers we removed.





In [ ]:



    


v_avg_avg = np.mean(dftouse.valence_avg)
v_avg_sd = np.std(dftouse.valence_avg)
mask = [is_not_outlier(num, v_avg_avg, v_avg_sd) for num in dftouse.valence_avg]
print "Num. outliers: ", (len(dftouse.valence_avg) - sum(mask))
dftouse = dftouse[mask]

Now let's make scatter plots for each review, of opening gross values against average valence, sum valence, and polarity (absolute valence_avg).





In [37]:



    


# First, let's make scatter plots of opening gross numbers against valence_avg
fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize=(25, 8),tight_layout = True)
titles = ["Opening Gross vs. Valence Avg", "Opening Gross vs. Valence Sum", "Opening Gross vs. Polarity"]
xlabels = ["Valence Avg", "Valence Sum", "| Valence Avg |"]
x = [dftouse.valence_avg, dftouse.valence_sum, dftouse.abs_valence_avg]
for (ax, title, xlabel, x) in zip(axes.ravel(), titles, xlabels, x):
    m, b = np.polyfit(x, dftouse.adj_opening_gross, 1)
    ax.plot(x, dftouse.adj_opening_gross, '.',x, m*x + b, '-')
    ax.set_title(title)
    ax.set_xlabel(xlabel)
    ax.set_ylim(-1000, 1.8*1e8)



    


















    






---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-37-692bc31d1be8> in <module>()
      3 titles = ["Opening Gross vs. Valence Avg", "Opening Gross vs. Valence Sum", "Opening Gross vs. Polarity"]
      4 xlabels = ["Valence Avg", "Valence Sum", "| Valence Avg |"]
----> 5 x = [dftouse.valence_avg, dftouse.valence_sum, dftouse.abs_valence_avg]
      6 for (ax, title, xlabel, x) in zip(axes.ravel(), titles, xlabels, x):
      7     m, b = np.polyfit(x, dftouse.adj_opening_gross, 1)

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in __getattr__(self, name)
   2148                 return self[name]
   2149             raise AttributeError("'%s' object has no attribute '%s'" %
-> 2150                                  (type(self).__name__, name))
   2151 
   2152     def __setattr__(self, name, value):

AttributeError: 'DataFrame' object has no attribute 'abs_valence_avg'

Clustering movies

  • Alp: run this on the grouped by version (change relevant variable names)




In [23]:



    


import gensim



    











In [24]:



    


from sklearn.feature_extraction.text import CountVectorizer
#credit: Lab10
vectorizer = CountVectorizer(min_df=1, stop_words='english')
X=vectorizer.fit_transform(valence_df.text)

corpus=vectorizer.get_feature_names()
id2words = dict((v, k) for k, v in vectorizer.vocabulary_.iteritems())
corpus_gensim = gensim.matutils.Sparse2Corpus(X, documents_columns=False)

lda = gensim.models.ldamodel.LdaModel(corpus_gensim, id2word=id2words, num_topics=2, update_every=1, chunksize=2000, passes=1)

lda.print_topics()



    


















    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-24-4738c0f1329c> in <module>()
      2 #credit: Lab10
      3 vectorizer = CountVectorizer(min_df=1, stop_words='english')
----> 4 X=vectorizer.fit_transform(valence_df.text)
      5 
      6 corpus=vectorizer.get_feature_names()

NameError: name 'valence_df' is not defined
















In [25]:



    


Xall=dftouse[['valence_sum', 'adj_gross']].values
from sklearn.mixture import GMM
n_clusters=2
clfgmm = GMM(n_components=n_clusters, covariance_type="tied")
clfgmm.fit(Xall)
print clfgmm
gmm_means=clfgmm.means_
gmm_covar=clfgmm.covars_
print gmm_means, gmm_covar



    


















    






---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-25-91e7a3e626ac> in <module>()
----> 1 Xall=dftouse[['valence_sum', 'adj_gross']].values
      2 from sklearn.mixture import GMM
      3 n_clusters=2
      4 clfgmm = GMM(n_components=n_clusters, covariance_type="tied")
      5 clfgmm.fit(Xall)

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in __getitem__(self, key)
   1789         if isinstance(key, (Series, np.ndarray, Index, list)):
   1790             # either boolean or fancy integer index
-> 1791             return self._getitem_array(key)
   1792         elif isinstance(key, DataFrame):
   1793             return self._getitem_frame(key)

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _getitem_array(self, key)
   1833             return self.take(indexer, axis=0, convert=False)
   1834         else:
-> 1835             indexer = self.ix._convert_to_indexer(key, axis=1)
   1836             return self.take(indexer, axis=1, convert=True)
   1837 

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/pandas/core/indexing.pyc in _convert_to_indexer(self, obj, axis, is_setter)
   1110                 mask = check == -1
   1111                 if mask.any():
-> 1112                     raise KeyError('%s not in index' % objarr[mask])
   1113 
   1114                 return _values_from_object(indexer)

KeyError: "['adj_gross'] not in index"
















In [26]:



    


from scipy import linalg

def plot_ellipse(splot, mean, cov, color):
    v, w = linalg.eigh(cov)
    u = w[0] / linalg.norm(w[0])
    angle = np.arctan(u[1] / u[0])
    angle = 180 * angle / np.pi  # convert to degrees
    # filled Gaussian at 2 standard deviation
    ell = mpl.patches.Ellipse(mean, 2 * v[0] ** 0.5, 2 * v[1] ** 0.5,
                              180 + angle, color=color, lw=3, fill=False)
    ell.set_clip_box(splot.bbox)
    ell1 = mpl.patches.Ellipse(mean, 1 * v[0] ** 0.5, 1 * v[1] ** 0.5,
                              180 + angle, color=color, lw=3, fill=False)
    ell1.set_clip_box(splot.bbox)
    ell3 = mpl.patches.Ellipse(mean, 3 * v[0] ** 0.5, 3 * v[1] ** 0.5,
                              180 + angle, color=color, lw=3, fill=False)
    ell3.set_clip_box(splot.bbox)
    #ell.set_alpha(0.2)
    splot.add_artist(ell)
    splot.add_artist(ell1)
    splot.add_artist(ell3)



    











In [75]:



    


plt.figure()
ax=plt.gca()
plot_ellipse(ax, gmm_means[0], gmm_covar, 'k')
plot_ellipse(ax, gmm_means[1], gmm_covar, 'k')
gmm_labels=clfgmm.predict(Xall)
for k, col in zip(range(n_clusters), ['blue','red']):
    my_members = gmm_labels == k
    ax.plot(Xall[my_members, 0], Xall[my_members, 1], 'w',
            markerfacecolor=col, marker='.', alpha=0.5)

This gives us a good approximation of two distinct clusters in our data. Basically, the movies that make more than 500 million dollars in total adjusted gross belongs to the red cluster, and the rest (which are the signifcant majority of the movies) belong to the blue cluster.

Let's try to see if we would get a better result with 3 clusters:





In [76]:



    


n_clusters=3
clfgmm3 = GMM(n_components=n_clusters, covariance_type="tied")
clfgmm3.fit(Xall)
print clfgmm
gmm_means=clfgmm3.means_
gmm_covar=clfgmm3.covars_
print gmm_means, gmm_covar
plt.figure()
ax=plt.gca()
plot_ellipse(ax, gmm_means[0], gmm_covar, 'k')
plot_ellipse(ax, gmm_means[1], gmm_covar, 'k')
plot_ellipse(ax, gmm_means[2], gmm_covar, 'k')
gmm_labels=clfgmm3.predict(Xall)
for k, col in zip(range(n_clusters), ['blue','red', 'green']):
    my_members = gmm_labels == k
    ax.plot(Xall[my_members, 0], Xall[my_members, 1], 'w',
            markerfacecolor=col, marker='.', alpha=0.5)



    


















    






GMM(covariance_type='tied', init_params='wmc', min_covar=0.001,
  n_components=2, n_init=1, n_iter=100, params='wmc', random_state=None,
  thresh=None, tol=0.001)
[[  2.18593152e+01   7.36447081e+07]
 [  3.08921900e+01   8.14926414e+08]
 [  2.45745724e+01   1.36629515e+08]] [[  1.77916251e+02   7.15394460e+07]
 [  7.15394460e+07   6.26401705e+15]]

Since there is so much overlap between the lower two clusters this time, we see that clustering into 2 groups is the better choice for our data. (We have also tried more clusters, but they similarly give worse results than the 2-clusters version)

__

Below that little line includes all Jess and Jason's code that needs to be fit into above.

Now we begin the Natural Language processing

This will take the form of a few steps.

  1. Punctuation: we will remove punctuation from reviews, so that all words can be recognized
  2. Lemmatization: we will reduce words to lemmas
  3. Removing stop words: we will remove common words from our vocabulary, because they are unlikely to have been used to express sentiment -- which is what we are interested in

Emulating HW5

  1. For each review create a list of nouns, and a list of adjectives
  2. For the nouns, perform LDA to separate them into 2 topics to see if there are 2 topics that are relevant i.e. actors vs. plot or whether it's sequel or a stand alone movie
  3. Run the sentiment analysis on the adjectives (N.B. At this point it is still not on an external dataset)
  4. Putting the topics and sentiment analysis together (marrying the nouns & the adjectives)
  5. Estimate the overall mean for each movie on each topic




In [152]:



    


from pattern.en import parse
from pattern.en import pprint
from pattern.vector import stem, PORTER, LEMMA
punctuation = list('.,;:!?()[]{}`''\"@#$^&*+-|=~_')



    











In [153]:



    


from sklearn.feature_extraction import text 
stopwords=text.ENGLISH_STOP_WORDS

1. For each review create a list of nouns, and a list of adjectives





In [154]:



    


import re
regex1=re.compile(r"\.{2,}")
regex2=re.compile(r"\-{2,}")



    











In [155]:



    


def get_parts(thetext):
    thetext=re.sub(regex1, ' ', thetext)
    thetext=re.sub(regex2, ' ', thetext)
    nouns=[]
    descriptives=[]
    for i,sentence in enumerate(parse(thetext, tokenize=True, lemmata=True).split()):
        nouns.append([])
        descriptives.append([])
        for token in sentence:
            #print token
            if len(token[4]) >0:
                if token[1] in ['JJ', 'JJR', 'JJS']:
                    if token[4] in stopwords or token[4][0] in punctuation or token[4][-1] in punctuation or len(token[4])==1:
                        continue
                    descriptives[i].append(token[4])
                elif token[1] in ['NN', 'NNS']:
                    if token[4] in stopwords or token[4][0] in punctuation or token[4][-1] in punctuation or len(token[4])==1:
                        continue
                    nouns[i].append(token[4])
    out=zip(nouns, descriptives)
    nouns2=[]
    descriptives2=[]
    for n,d in out:
        if len(n)!=0 and len(d)!=0:
            nouns2.append(n)
            descriptives2.append(d)
    return nouns2, descriptives2



    











In [42]:



    


%%time
# review_parts = maas_IMDB.map(lambda x: get_parts(x.text))
review_parts = []
for index, row in maas_IMDB.iterrows():
    review_parts.append(get_parts(row.text))



    


















    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-42-0e46d096a063> in <module>()
----> 1 get_ipython().run_cell_magic(u'time', u'', u'# review_parts = maas_IMDB.map(lambda x: get_parts(x.text))\nreview_parts = []\nfor index, row in maas_IMDB.iterrows():\n    review_parts.append(get_parts(row.text))')

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc in run_cell_magic(self, magic_name, line, cell)
   2262             magic_arg_s = self.var_expand(line, stack_depth)
   2263             with self.builtin_trap:
-> 2264                 result = fn(magic_arg_s, cell)
   2265             return result
   2266 

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/core/magics/execution.pyc in time(self, line, cell, local_ns)

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/core/magic.pyc in <lambda>(f, *a, **k)
    191     # but it's overkill for just that one bit of state.
    192     def magic_deco(arg):
--> 193         call = lambda f, *a, **k: f(*a, **k)
    194 
    195         if callable(arg):

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/core/magics/execution.pyc in time(self, line, cell, local_ns)
   1164         else:
   1165             st = clock2()
-> 1166             exec(code, glob, local_ns)
   1167             end = clock2()
   1168             out = None

<timed exec> in <module>()

NameError: name 'maas_IMDB' is not defined
















In [157]:



    


review_parts[1]



    


















    
Out[157]:








([[u'movie', u'movie'],
  [u'movie', u'rating'],
  [u'movie', u'acting'],
  [u'job'],
  [u'<br', u'day'],
  [u'effect', u'more.<br', u'movie', u'rent']],
 [[u'great'],
  [u'star'],
  [u'superb'],
  [u'better'],
  [u'rainy'],
  [u'acting', u'special']])

2. For the nouns, perform LDA to separate them into 2 topics





In [158]:



    


ldadata = [ele[0] for ele in review_parts]



    











In [159]:



    


ldadata



    


















    
Out[159]:








[[[u'sure', u'movie', u'time', u'time', u'opinion', u'film', u'change'],
  [u'time', u'movie', u'ooh'],
  [u'time', u'movie', u'thinking', u'd**n', u'ship'],
  [u'time'],
  [u'acting', u'film'],
  [u'oscar', u'film'],
  [u'movie', u'film'],
  [u'film'],
  [u'eye', u'character', u'film'],
  [u'performance', u'hand'],
  [u'director', u'film', u'magnitude'],
  [u'lesson', u'movie', u'love-story', u'filmmaker', u'romance', u'movie'],
  [u'film']],
 [[u'movie', u'movie'],
  [u'movie', u'rating'],
  [u'movie', u'acting'],
  [u'job'],
  [u'<br', u'day'],
  [u'effect', u'more.<br', u'movie', u'rent']],
 [[u'picture'],
  [u'scene', u'dinner', u'table', u'scene', u'family', u'friend'],
  [u'look',
   u'face',
   u'woman',
   u'room',
   u'future',
   u'husband.<br',
   u'connection',
   u'movie',
   u'movie',
   u'stuff'],
  [u'mom', u'associate', u'husband', u'wealth'],
  [u'risk.<br', u'movie'],
  [u'story', u'flow', u'actor']],
 [[u'movie', u'miracle'],
  [u'mountain', u'terrorist'],
  [u'mountain',
   u'place',
   u'action',
   u'movie',
   u'movie',
   u'snow',
   u'ice',
   u'cold',
   u'weather',
   u'man'],
  [u'film'],
  [u'guy', u'star', u'portrait', u'serialkiller', u'medicine'],
  [u'place', u'movie'],
  [u'movie', u'\xe4\xe4li\xf6t', u'collector', u'edition', u'extra']],
 [[u'childhood', u'memory', u'movie', u'touch'],
  [u'scene', u'person', u'villain', u'movie'],
  [u'cop',
   u'clich\xe9s',
   u'script',
   u'hole',
   u'truck',
   u'twist',
   u'story',
   u'run'],
  [u'ass',
   u'cop',
   u'serial',
   u'killer',
   u'gas',
   u'chamber',
   u'minion',
   u'power',
   u'resurrection',
   u'power',
   u'person'],
  [u'mix', u'case.<br', u'trash'],
  [u'memory'],
  [u'kid', u'scene', u'person'],
  [u'kind',
   u'logic',
   u'scenario',
   u'measure',
   u'cross',
   u'action',
   u'flick',
   u'flick'],
  [u'idea', u'lead'],
  [u'best', u'problem', u'guy', u'reviewer'],
  [u'killer', u'doctor', u'day', u'work', u'brain', u'switch-off'],
  [u'beer', u'experience', u'load', u'time', u'movie', u'spin'],
  [u'flick', u'pleasure', u'fan', u'kid']],
 [[u'comedian', u'movie'],
  [u'writer', u'comedy'],
  [u'preview', u'movie'],
  [u'movie'],
  [u'kind', u'stuff', u'person'],
  [u'child', u'child']],
 [[u'premise', u'cast', u'movie'],
  [u'excitement'],
  [u'person', u'lot', u'movie', u'character', u'movie'],
  [u'interaction', u'character', u'comedy', u'drama', u'element'],
  [u'movie', u'actress', u'way', u'scenario', u'riot']],
 [[u'closing', u'stage', u'character', u'outline', u'mess'],
  [u'feature', u'ray', u'hope', u'majority', u'time'],
  [u'cast',
   u'script',
   u'caricature',
   u'character',
   u'noggin',
   u'sense',
   u'humour'],
  [u'clich\xe9',
   u'joke',
   u'couple',
   u'baby',
   u'world',
   u'film',
   u'laugh',
   u'demographic.<br',
   u'story',
   u'love',
   u'plot',
   u'line',
   u'side-story',
   u'sort',
   u'couple',
   u'characterisation',
   u'romance',
   u'bubble'],
  [u'character',
   u'figure',
   u'space',
   u'emptiness',
   u'character',
   u'interaction',
   u'time',
   u'comedy',
   u'branch',
   u'chuckle',
   u'character'],
  [u'work',
   u'persona',
   u'confine',
   u'formula.<br',
   u'business',
   u'woman',
   u'class',
   u'surrogate',
   u'husband',
   u'difference'],
  [u'odd-couple',
   u'premise',
   u'year',
   u'movie',
   u'chemistry',
   u'performer',
   u'character'],
  [u'personality',
   u'outline',
   u'performer',
   u'role',
   u'demand',
   u'chemistry',
   u'air'],
  [u'fact',
   u'movie',
   u'engaging',
   u'performance',
   u'character',
   u'talent',
   u'door-man'],
  [u'time',
   u'moment',
   u'man',
   u'remainder',
   u'thing',
   u'course',
   u'movie',
   u'script',
   u'theme',
   u'way',
   u'world',
   u'camera',
   u'director',
   u'page',
   u'screen'],
  [u'film', u'baby', u'dollar', u'business', u'stereotyping', u'thing'],
  [u'hammy', u'plastic', u'tinsel-town', u'capital', u'bore', u'sugar'],
  [u'character', u'nature', u'world'],
  [u'dialogue',
   u'set',
   u'costume',
   u'script',
   u'theme',
   u'blue',
   u'pink',
   u'shade',
   u'humanity',
   u'director',
   u'need',
   u'movie',
   u'feel',
   u'fantasy',
   u'character',
   u'world'],
  [u'end',
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   u'lot',
   u'film,amusing']],
 [[u'comedy',
   u'year.<br',
   u'soap',
   u'opera',
   u'waitress',
   u'witness',
   u'husband',
   u'murder',
   u'hitman',
   u'member'],
  [u'heart',
   u'specialist',
   u'character',
   u'show).<br',
   u'hitman',
   u'fun',
   u'chemistry',
   u'fun',
   u'actor',
   u'persona'],
  [u'performance'],
  [u'goodness', u'warmth'],
  [u'script',
   u'sit-com',
   u'character',
   u'situation',
   u'together.<br',
   u'comedy',
   u'thing',
   u'winning',
   u'performance',
   u'script',
   u'delightful.<br']],
 [[u'way'],
  [u'ensemble',
   u'character',
   u'plot',
   u'heart',
   u'strings.<br',
   u'thing',
   u'movie',
   u'interaction'],
  [u'heart', u'fare', u'rest', u'life'],
  [u'it.<br',
   u'role',
   u'movie',
   u'touch',
   u'violence',
   u'bloodlust',
   u'male']],
 [[u'poster', u'pole'],
  [u'buzz', u'noise', u'try'],
  [u'movie', u'plot'],
  [u'role', u'woman', u'episode', u'experience'],
  [u'surprise', u'comedian'],
  [u'identity', u'crisis'],
  [u'cast'],
  [u'<br',
   u'complaint',
   u'director',
   u'splash',
   u'year',
   u'\x91in',
   u'company',
   u'convention'],
  [u'mood-creating', u'effect', u'throat', u'scene'],
  [u'character', u'end', u'romance', u'trick', u'home', u'smile', u'face'],
  [u'director',
   u'writer',
   u'audience',
   u'idiots??<br',
   u'movie',
   u'irritation'],
  [u'year', u'movie', u'rank', u'surprise']],
 [[u'story', u'guy', u'picture'],
  [u'story', u'female-hero', u'male-villain'],
  [u'person', u'face', u'face', u'inside']],
 ...]

















In [160]:



    


import itertools as IT
import collections

def flatten_iter(iterable, ltypes=collections.Iterable):
    remainder = iter(iterable)
    while True:
        first = next(remainder)
        if isinstance(first, ltypes) and not isinstance(first, basestring):
            remainder = IT.chain(first, remainder)
        else:
            yield first



    











In [161]:



    


vocab_raw = list(flatten_iter(ldadata))
#removing duplicates
vocab_unique = []
for i in vocab_raw:
    if i not in vocab_unique:
        vocab_unique.append(i)



    











In [162]:



    


vocab = {}
for i in range(len(vocab_unique)):
    vocab[vocab_unique[i]] = i
    
id2word = {}
for i in range(len(vocab_unique)):
    id2word[i] = vocab_unique[i]



    











In [163]:



    


id2word[0], vocab.keys()[5], vocab[vocab.keys()[5]]



    


















    
Out[163]:








(u'sure', u'yellow', 5224)

















In [164]:



    


from collections import defaultdict

def auxillary_function(ldadata, vocab):
    d = defaultdict(int)
    for i in ldadata: 
        index = vocab[i]
        d[index] += 1
    return d.items()



    











In [165]:



    


all_reviews = []
for i in ldadata: # into a review
    this_review = []
    for j in i:
        for k in j: 
            if k not in this_review: 
                this_review.append(k)
    all_reviews.append(this_review)

corpus = []
for i in all_reviews: 
    corpus.append(auxillary_function(i, vocab))



    











In [166]:



    


corpus



    


















    
Out[166]:








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In [167]:



    


lda2 = gensim.models.ldamodel.LdaModel(corpus, num_topics=2,id2word = id2word,update_every=1,chunksize=20000,passes=1)



    


















    






WARNING:gensim.models.ldamodel:too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy

















In [168]:



    


lda2.print_topics()



    


















    
Out[168]:








[u'0.023*movie + 0.015*film + 0.012*time + 0.011*character + 0.008*scene + 0.008*story + 0.007*person + 0.007*thing + 0.006*plot + 0.006*actor',
 u'0.023*movie + 0.018*film + 0.011*character + 0.011*story + 0.009*time + 0.008*person + 0.008*way + 0.007*scene + 0.007*thing + 0.006*plot']

















In [169]:



    


for bow in corpus[0:900:15]:
    print bow
    print lda2.get_document_topics(bow)
    print " ".join([id2word[e[0]] for e in bow])
    print "=========================================="



    


















    






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[(0, 0.85544771338087922), (1, 0.14455228661912081)]
sure movie time opinion film change ooh thinking d**n ship acting oscar eye character performance hand director magnitude lesson love-story filmmaker romance
==========================================
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[(0, 0.038067869871107787), (1, 0.96193213012889212)]
movie film piece ray book eye character hand <br plot line aspect effect dredge fact audience alien comment science fiction movie-goer history genre height author critic gun story pre-pubescent teenager today basement thought provoking boundary condition sake record computer graphic sequence emperor clothe loudest.<br type goer knowledge dose wiz bang skill novel creation point action credulity intelligence hole belief capacity fist vision state title hallmark humanity crap thinking light limit kind moviegoer problem work future fan mind question premise body
==========================================
[(440, 1), (21, 1), (511, 1)]
[(0, 0.49407476905169068), (1, 0.50592523094830932)]
it.<br romance purpose
==========================================
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[(0, 0.035310471773140145), (1, 0.96468952822685983)]
movie minute waste wasting summary hair guess shear nba hair.<br college laugh.<br wishes.<br friend shaq boom box boy it.<br skill rapping need wish life kid player money beer hour comedy ghetto
==========================================
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[(0, 0.9311292126315418), (1, 0.068870787368458239)]
movie film character performance music filmmaker scene magnolia wall canvas shrieking twit regret guilt pain writing ball mess.<br match digression sin tableaus fate story believer puppeteer string fable display form convergence wave spread absolution field cadre sequence genius circumstance person end plague actor devotee cast stuff
==========================================
[(640, 1), (36, 1), (940, 1), (941, 1), (942, 1), (943, 1), (602, 1)]
[(0, 0.91172778132813281), (1, 0.088272218671867228)]
sore face comedy….man organ leg makeup life
==========================================
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[(0, 0.59783964882318297), (1, 0.40216035117681692)]
way movie time acting bit job picture version fact face role story actor view wife past man reason anger hype complaint american hardship period masterpiece.<br saint infidelity aggression person point assumption up.<br attitude interpretation community best persona cast actress
==========================================
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[(0, 0.97722948789773501), (1, 0.022770512102265019)]
movie time minute thinking acting character cliché that.<br friar wrestler direction year actor moment man guy script recess jack whale hammin misfire department.<br naff aneurism painful.<br remember pap plop to.<br total dream load reason
==========================================
[(4, 1), (198, 1), (73, 1), (1362, 1), (530, 1), (759, 1), (315, 1), (1343, 1)]
[(0, 0.27002312892601427), (1, 0.72997687107398579)]
film set person use focus game computer location
==========================================
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[(0, 0.9211126005920679), (1, 0.078887399407932143)]
movie film acting character passion coward commitment show.<br wedding potato escape joy consequence etc.).<br scene criticism cynicism examination window finance heart actions.<br today shoddiness thing lack person turn twist failure society foot remorse suspense comedy romance
==========================================
[(1, 1), (4, 1), (841, 1), (939, 1), (397, 1), (1646, 1), (1647, 1), (945, 1), (446, 1), (182, 1), (94, 1)]
[(0, 0.75790561226475395), (1, 0.24209438773524605)]
movie film boring group start teen mansion act night thing kind
==========================================
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[(0, 0.040045217028340359), (1, 0.9599547829716597)]
way movie time editing sense character plot comment flash music level dollar equipment bang vehicle chat board flavor hole kid money million game
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==========================================
[(1, 1), (2, 1), (4, 1), (1669, 1), (262, 1), (257, 1), (904, 1), (3722, 1), (267, 1), (14, 1), (15, 1), (16, 1), (2194, 1), (150, 1), (25, 1), (4250, 1), (26, 1), (30, 1), (288, 1), (2084, 1), (1830, 1), (3882, 1), (2865, 1), (1489, 1), (435, 1), (54, 1), (186, 1), (2238, 1), (447, 1), (1984, 1), (139, 1), (328, 1), (1866, 1), (1483, 1), (499, 1), (976, 1), (4856, 1), (483, 1), (4857, 1), (602, 1), (479, 1), (4859, 1), (230, 1), (1397, 1), (234, 1), (107, 1), (4861, 1), (3696, 1), (370, 1), (4851, 1), (4852, 1), (4853, 1), (4854, 1), (4855, 1), (248, 1), (1017, 1), (4858, 1), (123, 1), (4860, 1), (682, 1), (254, 1), (127, 1)]
[(0, 0.61068175912505407), (1, 0.38931824087494604)]
movie time film make-up minute slew montage lace book performance hand director contact plot day sea effect scene production heck rock vampire directing dancing number place page garbage storyline roll noggin novel justice head hour result sickness series goth-rock life quality right.<br soundtrack budget money brain mini flesh plenty damned history.<br narration resource elegance team attention splice lot belly addition health actress
==========================================
[(48, 1), (2, 1), (117, 1), (4919, 1)]
[(0, 0.17553679538744085), (1, 0.82446320461255918)]
story time comedy arse
==========================================
[(867, 1), (197, 1), (1001, 1), (299, 1), (236, 1), (13, 1), (1711, 1), (147, 1), (628, 1), (30, 1), (1110, 1), (345, 1), (121, 1), (830, 1)]
[(0, 0.93565134601601785), (1, 0.06434865398398211)]
sequel dialogue tension genre parody character gripping laugh thrill scene heist light cast genius
==========================================
[(1, 1), (2, 1), (899, 1), (5036, 1), (5002, 1), (267, 1), (2179, 1), (1940, 1), (150, 1), (5030, 1), (5031, 1), (5032, 1), (5033, 1), (5034, 1), (5035, 1), (1068, 1), (1339, 1), (1163, 1), (759, 1), (295, 1), (1391, 1), (114, 1), (1399, 1), (121, 1), (1659, 1), (764, 1)]
[(0, 0.5476727064199739), (1, 0.45232729358002616)]
movie time blood convert religion book race debate plot puzzles.<br jean shirt searcher newcomer calm scope adaptation search game science faith fan portrayal cast rate thriller
==========================================
[(128, 1), (1, 1), (2, 1), (4, 1), (1286, 1), (7, 1), (770, 1), (150, 1), (151, 1), (26, 1), (3486, 1), (262, 1), (169, 1), (562, 1), (48, 1), (50, 1), (309, 1), (1212, 1), (4670, 1), (821, 1), (1101, 1), (465, 1), (214, 1), (94, 1), (487, 1), (106, 1), (626, 1), (5108, 1), (5109, 1)]
[(0, 0.12200061595064954), (1, 0.87799938404935041)]
way movie time film screening thinking commentary plot line effect empathy minute year level story actor thought advance topic display attitude depth audience kind emotion work epic postman blundering
==========================================
[(4, 1), (41, 1), (14, 1), (690, 1), (1193, 1), (762, 1), (60, 1)]
[(0, 0.35451524219470437), (1, 0.64548475780529568)]
film connection performance folk heart entertaining man
==========================================
[(96, 1), (896, 1), (578, 1), (33, 1), (3271, 1), (1193, 1), (128, 1), (4939, 1), (876, 1), (147, 1), (436, 1), (5143, 1), (218, 1), (1788, 1), (2554, 1)]
[(0, 0.9461130857660528), (1, 0.053886914233947168)]
scenario enjoy casting family chocolate heart way cup expression laugh weekend sip respect presence honey
==========================================
[(128, 1), (2944, 1), (899, 1), (4, 1), (3462, 1), (12, 1), (13, 1), (910, 1), (2072, 1), (3859, 1), (24, 1), (25, 1), (1266, 1), (175, 1), (176, 1), (946, 1), (243, 1), (446, 1), (182, 1), (439, 1), (440, 1), (3897, 1), (58, 1), (60, 1), (5181, 1), (5182, 1), (5183, 1), (71, 1), (3018, 1), (459, 1), (403, 1), (205, 1), (4311, 1), (698, 1), (4193, 1), (763, 1), (3300, 1), (869, 1), (529, 1), (872, 1), (618, 1), (2802, 1), (1395, 1), (116, 1), (885, 1), (631, 1), (4218, 1), (123, 1)]
[(0, 0.94387366385809401), (1, 0.056126336141905916)]
way wheel blood film consciousness eye character daughter hollywood mirror <br day atmosphere air fact taste mouth night thing wife it.<br path cold man affliction pre-pulp actioner memory photography turn car feel setup edge assassin comeback nemesis concept bit dream effort finger sidekick writer baddy example streak lot
==========================================
[(1, 1), (2051, 1), (4, 1), (1797, 1), (385, 1), (2284, 1), (1035, 1), (140, 1), (269, 1), (529, 1), (2322, 1), (4372, 1), (150, 1), (3096, 1), (2586, 1), (1308, 1), (30, 1), (2565, 1), (1371, 1), (932, 1), (3367, 1), (169, 1), (5214, 1), (173, 1), (180, 1), (821, 1), (54, 1), (55, 1), (698, 1), (61, 1), (4288, 1), (96, 1), (198, 1), (73, 1), (74, 1), (3020, 1), (1037, 1), (1409, 1), (3154, 1), (1364, 1), (342, 1), (280, 1), (88, 1), (5210, 1), (5211, 1), (5212, 1), (5213, 1), (478, 1), (1375, 1), (480, 1), (4193, 1), (738, 1), (99, 1), (996, 1), (486, 1), (273, 1), (5100, 1), (1781, 1), (3448, 1), (2300, 1), (126, 1)]
[(0, 0.56503294070605592), (1, 0.43496705929394408)]
movie liner film violence spite revenge formula sense protagonist bit cigarette station plot grab corpse screen.<br scene echo physics hero break year pyrotechnic role moment display place action edge guy shootout scenario set person villain hint sarcasm heroine hall explosion piece aspect power crossfire cargo action-heroine chemical hit train plane assassin bomb flick weapon style law wisecrack intent trilogy punch element
==========================================

It looks like topic 2 is about the external factors of a movie -- who the actors are and how production went, and topic 1 is about the intrinsic factors of a movie -- the character development and plot. Examples:

* words like "movie" "actor" "film" or "budget" "film" have higher topic 2 ratings. 
* words like "relationship" "scenery" "personality" have a higher topic 1 rating

3. Run the sentiment analysis on the adjectives





In [40]:



    


nbdata = [ele[1] for ele in review_parts]



    


















    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-40-ed84dce6f32a> in <module>()
----> 1 nbdata = [ele[1] for ele in review_parts]

NameError: name 'review_parts' is not defined
















In [41]:



    


adj_raw = list(flatten_iter(nbdata))
#removinc duplicates
adj_unique = []
for i in adj_raw:
    if i not in adj_unique:
        adj_unique.append(i)



    


















    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-41-09e944c5e5de> in <module>()
----> 1 adj_raw = list(flatten_iter(nbdata))
      2 #removinc duplicates
      3 adj_unique = []
      4 for i in adj_raw:
      5     if i not in adj_unique:

NameError: name 'flatten_iter' is not defined
















In [172]:



    


adjvocab = {}
for i in range(len(adj_unique)):
    adjvocab[adj_unique[i]] = i



    











In [173]:



    


adjvocab['great']



    


















    
Out[173]:








7

















In [174]:



    


len(adjvocab)



    


















    
Out[174]:








7335

















In [175]:



    


import itertools
Xarray = []
for i in nbdata: # into a review
    Xarraypre = " ".join(list(itertools.chain.from_iterable(i)))
    Xarray.append(Xarraypre)



    











In [176]:



    


# making the response array based off of the sentiment dictionary 'happiness' there is valence_avg and valence_sum
resparray = []
for index, row in maas_IMDB.iterrows():
    if row.valence_avg > 0: # need to make it binomial because that's how the calibration/cv score works
        resparray.append(1)
    else: 
        resparray.append(0)



    











In [177]:



    


print  len(Xarray), len(resparray)
print Xarray[0]



    


















    






4111 4111
good second love-story romantic second annoying impressive bad great glad half-good suprised worst wonderful talented crummy single possible mere

















In [178]:



    


from sklearn.cross_validation import train_test_split
itrain, itest = train_test_split(xrange(len(Xarray)), train_size=0.7)
mask=np.ones(len(Xarray), dtype='int')
mask[itrain]=1
mask[itest]=0
mask = (mask==1)



    











In [179]:



    


X=np.array(Xarray)
y=np.array(resparray)



    











In [180]:



    


y



    


















    
Out[180]:








array([1, 1, 1, ..., 1, 0, 1])

















In [181]:



    


def make_xy(X_col, y_col, vectorizer):
    X = vectorizer.fit_transform(X_col)
    y = y_col
    return X, y



    











In [182]:



    


"""
Function
--------
log_likelihood

Compute the log likelihood of a dataset according to 
a Naive Bayes classifier. 
The Log Likelihood is defined by

L = Sum_positive(logP(positive)) + Sum_negative(logP(negative))

Where Sum_positive indicates a sum over all positive reviews, 
and Sum_negative indicates a sum over negative reviews
    
Parameters
----------
clf : Naive Bayes classifier
x : (nexample, nfeature) array
    The input data
y : (nexample) integer array
    Whether each review is positive
"""
def log_likelihood(clf, x, y):
    prob = clf.predict_log_proba(x)
    neg = y == 0
    pos = ~neg
    return prob[neg, 0].sum() + prob[pos, 1].sum()



    











In [183]:



    


from sklearn.cross_validation import KFold

def cv_score(clf, x, y, score_func, nfold=5):
    """
    Uses 5-fold cross validation to estimate a score of a classifier
    
    Inputs
    ------
    clf : Classifier object
    x : Input feature vector
    y : Input class labels
    score_func : Function like log_likelihood, that takes (clf, x, y) as input,
                 and returns a score
                 
    Returns
    -------
    The average score obtained by splitting (x, y) into 5 folds of training and 
    test sets, fitting on the training set, and evaluating score_func on the test set
    
    Examples
    cv_score(clf, x, y, log_likelihood)
    """
    result = 0
    for train, test in KFold(y.size, nfold): # split data into train/test groups, 5 times
        clf.fit(x[train], y[train]) # fit
        result += score_func(clf, x[test], y[test]) # evaluate score function on held-out data
    return result / nfold # average



    











In [184]:



    


def calibration_plot(clf, xtest, ytest):
    prob = clf.predict_proba(xtest)[:, 1]
    outcome = ytest
    data = pd.DataFrame(dict(prob=prob, outcome=outcome))

    #group outcomes into bins of similar probability
    bins = np.linspace(0, 1, 20)
    cuts = pd.cut(prob, bins)
    binwidth = bins[1] - bins[0]
    
    #freshness ratio and number of examples in each bin
    cal = data.groupby(cuts).outcome.agg(['mean', 'count'])
    cal['pmid'] = (bins[:-1] + bins[1:]) / 2
    cal['sig'] = np.sqrt(cal.pmid * (1 - cal.pmid) / cal['count'])
        
    #the calibration plot
    ax = plt.subplot2grid((3, 1), (0, 0), rowspan=2)
    p = plt.errorbar(cal.pmid, cal['mean'], cal['sig'])
    plt.plot(cal.pmid, cal.pmid, linestyle='--', lw=1, color='k')
    plt.ylabel("Empirical P(+)")
    
    #the distribution of P(+)
    ax = plt.subplot2grid((3, 1), (2, 0), sharex=ax)
    
    plt.bar(left=cal.pmid - binwidth / 2, height=cal['count'],
            width=.95 * (bins[1] - bins[0]),
            fc=p[0].get_color())
    
    plt.xlabel("Predicted P(+)")
    plt.ylabel("Number")



    











In [185]:



    


from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB



    











In [186]:



    


#the grid of parameters to search over
alphas = [0, .1, 1, 5, 10, 50]
min_dfs = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1]

#Find the best value for alpha and min_df, and the best classifier
best_alpha = None
best_min_df = None
maxscore = -np.inf

for alpha in alphas:
    for min_df in min_dfs:         
        vectorizer = CountVectorizer(vocabulary =adjvocab, min_df = min_df)       
        Xthis, Ythis = make_xy(X[mask], y[mask], vectorizer)
        clf = MultinomialNB(alpha=alpha)
        cvscore = cv_score(clf, Xthis, Ythis, log_likelihood)
        if cvscore > maxscore:
            maxscore = cvscore
            best_alpha, best_min_df = alpha, min_df



    











In [187]:



    


print "alpha: %f" % best_alpha
print "min_df: %f" % best_min_df



    


















    






alpha: 0.100000
min_df: 0.000010

















In [188]:



    


vectorizer = CountVectorizer(vocabulary =adjvocab, min_df=best_min_df)
Xnew, Ynew = make_xy(X,y, vectorizer)
xtrain, xtest, ytrain, ytest = train_test_split(Xnew, Ynew)
clf = MultinomialNB(alpha=best_alpha).fit(xtrain, ytrain)

training_accuracy = clf.score(xtrain, ytrain)
test_accuracy = clf.score(xtest, ytest)

print "Accuracy on training data: %0.2f" % (training_accuracy)
print "Accuracy on test data:     %0.2f" % (test_accuracy)

calibration_plot(clf, xtest, ytest)



    


















    






Accuracy on training data: 0.97
Accuracy on test data:     0.91

4. Putting the topics and sentiment analyses together





In [189]:



    


vectorizer_alladj = CountVectorizer(min_df=best_min_df,vocabulary = adjvocab)
X_alladj, Y_alladj = make_xy(X[mask],y[mask], vectorizer_alladj)
clf_alladj = MultinomialNB(alpha=best_alpha).fit(X_alladj, Y_alladj)

logpositives = dict(zip(vectorizer_alladj.get_feature_names(),clf_alladj.feature_log_prob_[0]))
lognegatives = dict(zip(vectorizer_alladj.get_feature_names(),clf_alladj.feature_log_prob_[1]))



    











In [190]:



    


def calc_pplus(adjlist, lp, ln, pp,pn):
    sgivenpos = 0
    sgivenneg = 0
    for adj in adjlist:
        sgivenpos += lp[adj]
        sgivenneg += ln[adj]
    return(np.exp(sgivenpos)*pp)/(np.exp(sgivenpos)*pp + np.exp(sgivenneg)*pn)



    











In [191]:



    


reviews=[]
for index, row in maas_IMDB.iterrows():
    reviews.append(row.movie_id)



    











In [192]:



    


reviews



    


















    
Out[192]:








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 ...]

















In [193]:



    


len(review_parts),len(reviews)



    


















    
Out[193]:








(4111, 4111)

















In [194]:



    


def choose_topic(ldamodel, bow):
    tee = lda2.get_document_topics(bow)
    if len(tee)==2:
        t1,t2=tee
        if t2[1] >= t1[1]:#get higher probability topic
            topicis=t2[0]
        else:
            topicis=t1[0]
    elif len(tee)==1:#if only one was provided its very high probability. Take it
        teetuple=tee[0]
        topicis=teetuple[0]
    return topicis



    











In [195]:



    


priorp = np.mean(resparray)
priorn = 1 - priorp
priorp, priorn



    


















    
Out[195]:








(0.92897105327171003, 0.071028946728289966)

















In [196]:



    


counter=0
reviewdict={}
for i, rid in enumerate(reviews):
    rlist=[]
    nlist, alist = review_parts[i]
    ln=len(nlist)
    localbow=corpus[counter:counter+ln]
    for bow, adj, noun in zip(localbow, alist, nlist):
        doc=" ".join([id2word[e[0]] for e in bow])
        pplus=calc_pplus(adj, logpositives, lognegatives, priorp, priorn)
        topicis=choose_topic(lda2, bow)
        ldict={"topic": topicis, 'pplus':pplus}
        rlist.append(ldict)
    reviewdict[rid]=rlist
    counter=counter+ln



    











In [197]:



    


list_of_dicts=[]
for index, row in maas_IMDB.iterrows():
    revs=reviewdict[row.movie_id]
    for r in revs:
        r2=r.copy()
        r2['movie_id']=row.movie_name
        r2['url']=row.url
        r2['review_id']=row.movie_id
        r2['stars']=row.stars
        r2['valence_avg']=row.valence_avg
        r2['valence_sum']=row.valence_sum
        list_of_dicts.append(r2)



    











In [198]:



    


completedf=pd.DataFrame(list_of_dicts)
completedf.head()



    


















    
Out[198]:










  
    

      
      movie_id
      pplus
      review_id
      stars
      topic
      url
      valence_avg
      valence_sum
    

  
  
    

      0
      Titanic
      0.807193
      10027
      7
      0
      http://www.imdb.com/title/tt0120338/usercommen...
      0.47976
      38.380826
    

    

      1
      Titanic
      0.935017
      10027
      7
      1
      http://www.imdb.com/title/tt0120338/usercommen...
      0.47976
      38.380826
    

    

      2
      Titanic
      0.891101
      10027
      7
      0
      http://www.imdb.com/title/tt0120338/usercommen...
      0.47976
      38.380826
    

    

      3
      Titanic
      0.182409
      10027
      7
      0
      http://www.imdb.com/title/tt0120338/usercommen...
      0.47976
      38.380826
    

    

      4
      Titanic
      0.828186
      10027
      7
      0
      http://www.imdb.com/title/tt0120338/usercommen...
      0.47976
      38.380826
    

  




















In [199]:



    


completedf.shape



    


















    
Out[199]:








(2815, 8)

5. Estimate the overall mean for each movie on each topic





In [38]:



    


def get_stats(group):
    min_pplus = group.pplus.min()
    max_pplus = group.pplus.max()
    rid = group.movie_id.unique()
    valence_avg = group.valence_avg.mean()
    valence_sum = group.valence_sum.mean()
    count = group.topic.count()
    if count == 1: 
        varj = 0
    else: 
        varj = group.pplus.var(ddof=1)
    mean_pplus = group.pplus.mean()
    stars = group.stars.mean()
    return pd.DataFrame({'min': min_pplus, 'max':max_pplus ,'valence_avg': valence_avg,'valence_sum':valence_sum, 
                         'count': count,'var': varj, 'mean': mean_pplus, 'stars': stars}, index=rid)



    











In [39]:



    


%%time
dftouse=completedf.groupby(['review_id', 'topic']).apply(get_stats).reset_index()



    


















    






---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-39-70e8d7fd1f51> in <module>()
----> 1 get_ipython().run_cell_magic(u'time', u'', u"dftouse=completedf.groupby(['review_id', 'topic']).apply(get_stats).reset_index()")

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc in run_cell_magic(self, magic_name, line, cell)
   2262             magic_arg_s = self.var_expand(line, stack_depth)
   2263             with self.builtin_trap:
-> 2264                 result = fn(magic_arg_s, cell)
   2265             return result
   2266 

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/core/magics/execution.pyc in time(self, line, cell, local_ns)

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/core/magic.pyc in <lambda>(f, *a, **k)
    191     # but it's overkill for just that one bit of state.
    192     def magic_deco(arg):
--> 193         call = lambda f, *a, **k: f(*a, **k)
    194 
    195         if callable(arg):

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/core/magics/execution.pyc in time(self, line, cell, local_ns)
   1164         else:
   1165             st = clock2()
-> 1166             exec(code, glob, local_ns)
   1167             end = clock2()
   1168             out = None

<timed exec> in <module>()

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in groupby(self, by, axis, level, as_index, sort, group_keys, squeeze)
   3157         axis = self._get_axis_number(axis)
   3158         return groupby(self, by=by, axis=axis, level=level, as_index=as_index,
-> 3159                        sort=sort, group_keys=group_keys, squeeze=squeeze)
   3160 
   3161     def asfreq(self, freq, method=None, how=None, normalize=False):

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc in groupby(obj, by, **kwds)
   1197         raise TypeError('invalid type: %s' % type(obj))
   1198 
-> 1199     return klass(obj, by, **kwds)
   1200 
   1201 

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc in __init__(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze)
    386         if grouper is None:
    387             grouper, exclusions, obj = _get_grouper(obj, keys, axis=axis,
--> 388                                                     level=level, sort=sort)
    389 
    390         self.obj = obj

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc in _get_grouper(obj, key, axis, level, sort)
   2146 
   2147         elif is_in_axis(gpr):  # df.groupby('name')
-> 2148             in_axis, name, gpr = True, gpr, obj[gpr]
   2149             exclusions.append(name)
   2150 

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in __getitem__(self, key)
   1795             return self._getitem_multilevel(key)
   1796         else:
-> 1797             return self._getitem_column(key)
   1798 
   1799     def _getitem_column(self, key):

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _getitem_column(self, key)
   1802         # get column
   1803         if self.columns.is_unique:
-> 1804             return self._get_item_cache(key)
   1805 
   1806         # duplicate columns & possible reduce dimensionaility

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in _get_item_cache(self, item)
   1082         res = cache.get(item)
   1083         if res is None:
-> 1084             values = self._data.get(item)
   1085             res = self._box_item_values(item, values)
   1086             cache[item] = res

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc in get(self, item, fastpath)
   2849 
   2850             if not isnull(item):
-> 2851                 loc = self.items.get_loc(item)
   2852             else:
   2853                 indexer = np.arange(len(self.items))[isnull(self.items)]

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/pandas/core/index.pyc in get_loc(self, key, method)
   1570         """
   1571         if method is None:
-> 1572             return self._engine.get_loc(_values_from_object(key))
   1573 
   1574         indexer = self.get_indexer([key], method=method)

pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3824)()

pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3704)()

pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12280)()

pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12231)()

KeyError: 'review_id'
















In [202]:



    


print dftouse.shape
dftouse.head()



    


















    






(699, 11)










    
Out[202]:










  
    

      
      review_id
      topic
      level_2
      count
      max
      mean
      min
      stars
      valence_avg
      valence_sum
      var
    

  
  
    

      0
      7455
      0
      Corky Romano
      1
      0.951056
      0.951056
      0.951056
      3
      0.040475
      0.566644
      0.000000
    

    

      1
      7455
      1
      Corky Romano
      1
      0.935816
      0.935816
      0.935816
      3
      0.040475
      0.566644
      0.000000
    

    

      2
      7456
      0
      Corky Romano
      6
      0.973302
      0.758510
      0.044043
      2
      0.196665
      8.259933
      0.125997
    

    

      3
      7457
      0
      Corky Romano
      4
      0.946638
      0.844130
      0.690218
      4
      0.218332
      9.169933
      0.013005
    

    

      4
      7457
      1
      Corky Romano
      1
      0.968079
      0.968079
      0.968079
      4
      0.218332
      9.169933
      0.000000
    

  




















In [203]:



    


dftouse[dftouse.review_id==10037]



    


















    
Out[203]:










  
    

      
      review_id
      topic
      level_2
      count
      max
      mean
      min
      stars
      valence_avg
      valence_sum
      var
    

  
  
    

      264
      10037
      0
      Titanic
      5
      0.876692
      0.635052
      0.052174
      9
      0.700832
      39.246578
      0.124518
    

    

      265
      10037
      1
      Titanic
      1
      0.674595
      0.674595
      0.674595
      9
      0.700832
      39.246578
      0.000000

Using the NLTK library





In [204]:



    


! pip install nltk



    


















    






Requirement already satisfied (use --upgrade to upgrade): nltk in /Users/alpkaancelik/anaconda/lib/python2.7/site-packages

















In [205]:



    


import nltk
from nltk.corpus import sentiwordnet as swn



    











In [208]:



    


happy = swn.senti_synset('happy.n.03')
print(happy)



    


















    






---------------------------------------------------------------------------
LookupError                               Traceback (most recent call last)
<ipython-input-208-f3487f8e9748> in <module>()
----> 1 happy = swn.senti_synset('happy.n.03')
      2 print(happy)

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/nltk/corpus/util.pyc in __getattr__(self, attr)
     97             raise AttributeError("LazyCorpusLoader object has no attribute '__bases__'")
     98 
---> 99         self.__load()
    100         # This looks circular, but its not, since __load() changes our
    101         # __class__ to something new:

/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/nltk/corpus/util.pyc in __load(self)
     62             except LookupError as e:
     63                 try: root = nltk.data.find('corpora/%s' % zip_name)
---> 64                 except LookupError: raise e
     65 
     66         # Load the corpus.

LookupError: 
**********************************************************************
  Resource u'corpora/sentiwordnet' not found.  Please use the NLTK
  Downloader to obtain the resource:  >>> nltk.download()
  Searched in:
    - '/Users/alpkaancelik/nltk_data'
    - '/usr/share/nltk_data'
    - '/usr/local/share/nltk_data'
    - '/usr/lib/nltk_data'
    - '/usr/local/lib/nltk_data'
**********************************************************************
















In [210]:



    


type(swn)



    


















    
Out[210]:








nltk.corpus.util.LazyCorpusLoader

Classification

Let's try classifying some stuff!!!!

List of classifiers we are trying

  1. LogisticRegression() over 2 variables 'abs_valence_avg','total_theaters'
  2. LogisticRegression() over 8 variables stored in Xnames
  3. SVC(kernel="linear") over 2 variables 'abs_valence_avg','total_theaters'
  4. SVC(kernel="linear") over 8 variables stored in Xnames
  5. Tree() over 2 variables 'abs_valence_avg','total_theaters'
  6. Tree() over 8 variables stored in Xnames
  7. Forest() over 2 variables 'abs_valence_avg','total_theaters'
  8. Forest() over 8 variables stored in Xnames
  9. AdaBoost() over 2 variables 'abs_valence_avg','total_theaters'
  10. AdaBoost() over 8 variables stored in Xnames
  11. GB() over 2 variables 'abs_valence_avg','total_theaters'
  12. GB() over 8 variables stored in Xnames




In [79]:



    


from sklearn.svm import LinearSVC
from sklearn.grid_search import GridSearchCV
from sklearn.cross_validation import train_test_split
from sklearn.metrics import confusion_matrix
from matplotlib.colors import ListedColormap
from sklearn.linear_model import LogisticRegression


def cv_optimize(clf, parameters, X, y, n_jobs=1, n_folds=5, score_func=None):
    if score_func:
        gs = GridSearchCV(clf, param_grid=parameters, cv=n_folds, n_jobs=n_jobs, scoring=score_func)
    else:
        gs = GridSearchCV(clf, param_grid=parameters, n_jobs=n_jobs, cv=n_folds)
    gs.fit(X, y)
    print "BEST", gs.best_params_, gs.best_score_, gs.grid_scores_
    best = gs.best_estimator_
    return best
def do_classify(clf, parameters, indf, featurenames, targetname, target1val, mask=None, reuse_split=None, score_func=None, n_folds=5, n_jobs=1):
    subdf=indf[featurenames]
    X=subdf.values
    y=(indf[targetname].values==target1val)*1
    if mask !=None:
        print "using mask"
        Xtrain, Xtest, ytrain, ytest = X[mask], X[~mask], y[mask], y[~mask]
    if reuse_split !=None:
        print "using reuse split"
        Xtrain, Xtest, ytrain, ytest = reuse_split['Xtrain'], reuse_split['Xtest'], reuse_split['ytrain'], reuse_split['ytest']
    if parameters:
        clf = cv_optimize(clf, parameters, Xtrain, ytrain, n_jobs=n_jobs, n_folds=n_folds, score_func=score_func)
    clf=clf.fit(Xtrain, ytrain)
    training_accuracy = clf.score(Xtrain, ytrain)
    test_accuracy = clf.score(Xtest, ytest)
    print "############# based on standard predict ################"
    print "Accuracy on training data: %0.2f" % (training_accuracy)
    print "Accuracy on test data:     %0.2f" % (test_accuracy)
    print confusion_matrix(ytest, clf.predict(Xtest))
    print "########################################################"
    return clf, Xtrain, ytrain, Xtest, ytest



    











In [80]:



    


cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
cm = plt.cm.RdBu
cm_bright = ListedColormap(['#FF0000', '#0000FF'])

def points_plot(ax, Xtr, Xte, ytr, yte, clf, mesh=True, colorscale=cmap_light, cdiscrete=cmap_bold, alpha=0.3, psize=10, zfunc=False):
    h = .02
    X=np.concatenate((Xtr, Xte))
    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),
                         np.linspace(y_min, y_max, 100))

    #plt.figure(figsize=(10,6))
    if mesh:
        if zfunc:
            p0 = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 0]
            p1 = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
            Z=zfunc(p0, p1)
        else:
            Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
        Z = Z.reshape(xx.shape)
        plt.pcolormesh(xx, yy, Z, cmap=cmap_light, alpha=alpha, axes=ax)
    ax.scatter(Xtr[:, 0], Xtr[:, 1], c=ytr-1, cmap=cmap_bold, s=psize, alpha=alpha,edgecolor="k")
    # and testing points
    yact=clf.predict(Xte)
    ax.scatter(Xte[:, 0], Xte[:, 1], c=yte-1, cmap=cmap_bold, alpha=alpha, marker="s", s=psize+10)
    ax.set_xlim(xx.min(), xx.max())
    ax.set_ylim(yy.min(), yy.max())
    return ax,xx,yy



    











In [81]:



    


def points_plot_prob(ax, Xtr, Xte, ytr, yte, clf, colorscale=cmap_light, cdiscrete=cmap_bold, ccolor=cm, psize=10, alpha=0.1, prob=True):
    ax,xx,yy = points_plot(ax, Xtr, Xte, ytr, yte, clf, mesh=False, colorscale=colorscale, cdiscrete=cdiscrete, psize=psize, alpha=alpha) 
    if prob:
        Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
    else:
        Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    plt.contourf(xx, yy, Z, cmap=ccolor, alpha=.2, axes=ax)
    cs2 = plt.contour(xx, yy, Z, cmap=ccolor, alpha=.6, axes=ax)
    plt.clabel(cs2, fmt = '%2.1f', colors = 'k', fontsize=14, axes=ax)
    return ax



    











In [82]:



    


dftouse.head()



    


















    
Out[82]:










  
    

      
      abs_valence_avg
      adj_gross
      adj_gross_bin
      adj_opening_gross
      adj_opening_gross_bin
      n_scorables
      pct_scorables
      positive
      ranking
      review_count
      star_avg
      total_theaters
      valence_avg
      valence_sum
      year
    

  
  
    

      102 Dalmatians
      0.435028
      9.300887e+07
      0
      27619626.583993
      1
      95.20
      0.554479
      1
      38
      10
      8.30
      2704
      0.764973
      34.991588
      2000
    

    

      13 Going on 30
      0.075914
      8.720185e+07
      0
      32079616.730188
      1
      94.75
      0.614963
      0
      56
      4
      2.50
      3453
      0.344982
      19.569669
      2004
    

    

      3 Ninjas
      0.337339
      3.282516e+07
      0
      6665611.384807
      0
      107.25
      0.647320
      0
      44
      4
      1.25
      1954
      0.235649
      26.775243
      1992
    

    

      50 First Dates
      0.060371
      1.842231e+08
      1
      60721350.083526
      1
      60.00
      0.600000
      0
      15
      1
      3.00
      3612
      0.390316
      14.051372
      2004
    

    

      8MM
      0.388165
      4.926479e+07
      0
      19151719.429485
      0
      241.00
      0.626556
      0
      56
      1
      1.00
      2370
      -0.058220
      -8.791192
      1999
    

  




















In [89]:



    


### Classify things!

itrain, itest = train_test_split(xrange(dftouse.shape[0]), train_size=0.6)
mask_classify=np.ones(dftouse.shape[0], dtype='int')
mask_classify[itrain]=1
mask_classify[itest]=0
mask_classify = (mask_classify==1)
mask_classify[:10]

Xnames = ['year','total_theaters' ,'star_avg','review_count','positive','n_scorables','pct_scorables','valence_avg']
two_features = ['abs_valence_avg','n_scorables']

clflog1 = LogisticRegression()
parameters_log = {"C": [0.000000001, 0.00000001, 0.000001, 0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100,]}
clflog1, Xtrain, ytrain, Xtest, ytest=do_classify(clflog1, parameters_log, dftouse, 
                                                 two_features,
                                                 'adj_opening_gross_bin', 1, mask=mask_classify)
Xtr=np.concatenate((Xtrain, Xtest))
plt.figure()
ax=plt.gca()
nonamebob = points_plot(ax, Xtrain, Xtest, ytrain, ytest, clflog1)



# Xmatrix=dftouse[lcols].values
# Yresp=adjframe['RESP'].adj_opening_gross



    


















    






using mask
BEST {'C': 1e-09} 0.661616161616 [mean: 0.66162, std: 0.00665, params: {'C': 1e-09}, mean: 0.66162, std: 0.00665, params: {'C': 1e-08}, mean: 0.66162, std: 0.00665, params: {'C': 1e-06}, mean: 0.66162, std: 0.00665, params: {'C': 1e-05}, mean: 0.66162, std: 0.00665, params: {'C': 0.0001}, mean: 0.66162, std: 0.00665, params: {'C': 0.001}, mean: 0.66162, std: 0.00665, params: {'C': 0.01}, mean: 0.66162, std: 0.00665, params: {'C': 0.1}, mean: 0.64141, std: 0.01938, params: {'C': 1}, mean: 0.64646, std: 0.02722, params: {'C': 10}, mean: 0.65152, std: 0.03628, params: {'C': 100}]
############# based on standard predict ################
Accuracy on training data: 0.66
Accuracy on test data:     0.62
[[82  0]
 [51  0]]
########################################################










    






/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:22: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.










    
























In [90]:



    


plt.hist(clflog1.predict_proba(Xtest)[:,1]*100);



    


















    
























In [92]:



    


plt.figure()
ax=plt.gca()
points_plot(ax, Xtrain, Xtest, ytrain, ytest, clflog1, mesh=False, alpha=0.001);
points_plot_prob(ax, Xtrain, Xtest, ytrain, ytest, clflog1);



    


















    






/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:650: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  if self._edgecolors_original != str('face'):
/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/matplotlib/text.py:52: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
  if rotation in ('horizontal', None):
/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/matplotlib/text.py:54: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
  elif rotation == 'vertical':










    
























In [93]:



    


clflog1.score(Xtest, ytest)



    


















    
Out[93]:








0.61654135338345861

















In [95]:



    


clflog2 = LogisticRegression()
clflog2, Xtrain, ytrain, Xtest, ytest=do_classify(clflog2, parameters_log, dftouse, 
                                                 Xnames,
                                                 'adj_opening_gross_bin', 1, mask=mask_classify)



    


















    






using mask
BEST {'C': 10} 0.893939393939 [mean: 0.66162, std: 0.00665, params: {'C': 1e-09}, mean: 0.66162, std: 0.00665, params: {'C': 1e-08}, mean: 0.86364, std: 0.04498, params: {'C': 1e-06}, mean: 0.87374, std: 0.03531, params: {'C': 1e-05}, mean: 0.87374, std: 0.03531, params: {'C': 0.0001}, mean: 0.87374, std: 0.03531, params: {'C': 0.001}, mean: 0.88384, std: 0.03313, params: {'C': 0.01}, mean: 0.87879, std: 0.03382, params: {'C': 0.1}, mean: 0.88384, std: 0.03795, params: {'C': 1}, mean: 0.89394, std: 0.02923, params: {'C': 10}, mean: 0.88889, std: 0.02991, params: {'C': 100}]
############# based on standard predict ################
Accuracy on training data: 0.89
Accuracy on test data:     0.83
[[66 16]
 [ 7 44]]
########################################################










    






/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:22: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.

















In [96]:



    


from sklearn.svm import SVC
clfsvm1 = SVC(kernel="linear")
parameters_svc = {"C": [0.001, 0.01, 0.1, 1, 10, 100, 1000]}
clfsvm1, Xtrain, ytrain, Xtest, ytest=do_classify(clfsvm1, parameters_svc, dftouse, 
                                                 two_features,
                                                 'adj_opening_gross_bin', 1, mask=mask_classify)
Xtr=np.concatenate((Xtrain, Xtest))
plt.figure()
ax=plt.gca()
points_plot(ax, Xtrain, Xtest, ytrain, ytest, clfsvm1);



    


















    






using mask
BEST {'C': 0.001} 0.661616161616 [mean: 0.66162, std: 0.00665, params: {'C': 0.001}, mean: 0.66162, std: 0.00665, params: {'C': 0.01}, mean: 0.66162, std: 0.00665, params: {'C': 0.1}, mean: 0.66162, std: 0.00665, params: {'C': 1}, mean: 0.66162, std: 0.00665, params: {'C': 10}, mean: 0.66162, std: 0.00665, params: {'C': 100}, mean: 0.65152, std: 0.01906, params: {'C': 1000}]
############# based on standard predict ################
Accuracy on training data: 0.66
Accuracy on test data:     0.62
[[82  0]
 [51  0]]
########################################################










    






/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:22: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.










    
























In [97]:



    


plt.figure()
ax=plt.gca()
points_plot(ax, Xtrain, Xtest, ytrain, ytest, clfsvm1, mesh=False);
points_plot_prob(ax, Xtrain, Xtest, ytrain, ytest, clfsvm1, prob=False);



    


















    
























In [98]:



    


#From Jake Vanderplas's ESAC notebook
def plot_svc_decision_function(clf, ax=None):
    """Plot the decision function for a 2D SVC"""
    if ax is None:
        ax = plt.gca()
    x = np.linspace(plt.xlim()[0], plt.xlim()[1], 30)
    y = np.linspace(plt.ylim()[0], plt.ylim()[1], 30)
    Y, X = np.meshgrid(y, x)
    P = np.zeros_like(X)
    for i, xi in enumerate(x):
        for j, yj in enumerate(y):
            P[i, j] = clf.decision_function([xi, yj])
    return ax.contour(X, Y, P, colors='k',
                      levels=[-1, 0, 1], alpha=0.5,
                      linestyles=['--', '-', '--'])



    











In [100]:



    


plt.figure()
ax=plt.gca()
points_plot(ax, Xtrain, Xtest, ytrain, ytest, clfsvm1);
plot_svc_decision_function(clfsvm1, ax)
ax.scatter(clfsvm1.support_vectors_[:, 0], clfsvm1.support_vectors_[:, 1],s=200, facecolors='none')
plt.ylim([125,225])



    


















    
Out[100]:








(125, 225)










    
























In [101]:



    


clfsvm2 = SVC(kernel="linear")
clfsvm2, Xtrain, ytrain, Xtest, ytest=do_classify(clfsvm2, parameters_svc, dftouse, 
                                                 Xnames,
                                                 'adj_opening_gross_bin', 1, mask=mask_classify)



    


















    






using mask
BEST {'C': 0.01} 0.909090909091 [mean: 0.88384, std: 0.03313, params: {'C': 0.001}, mean: 0.90909, std: 0.03044, params: {'C': 0.01}, mean: 0.90404, std: 0.02501, params: {'C': 0.1}, mean: 0.87879, std: 0.03382, params: {'C': 1}, mean: 0.86869, std: 0.04359, params: {'C': 10}, mean: 0.86364, std: 0.05436, params: {'C': 100}, mean: 0.86364, std: 0.04730, params: {'C': 1000}]
############# based on standard predict ################
Accuracy on training data: 0.91
Accuracy on test data:     0.83
[[67 15]
 [ 8 43]]
########################################################










    






/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:22: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.

















In [104]:



    


def make_roc(name, clf, ytest, xtest, ax=None, labe=5, proba=True, skip=0):
    initial=False
    if not ax:
        ax=plt.gca()
        initial=True
    if proba:
        fpr, tpr, thresholds=roc_curve(ytest, clf.predict_proba(xtest)[:,1])
    else:
        fpr, tpr, thresholds=roc_curve(ytest, clf.decision_function(xtest))
    roc_auc = auc(fpr, tpr)
    if skip:
        l=fpr.shape[0]
        ax.plot(fpr[0:l:skip], tpr[0:l:skip], '.-', alpha=0.3, label='ROC curve for %s (area = %0.2f)' % (name, roc_auc))
    else:
        ax.plot(fpr, tpr, '.-', alpha=0.3, label='ROC curve for %s (area = %0.2f)' % (name, roc_auc))
    label_kwargs = {}
    label_kwargs['bbox'] = dict(
        boxstyle='round,pad=0.3', alpha=0.2,
    )
    for k in xrange(0, fpr.shape[0],labe):
        #from https://gist.github.com/podshumok/c1d1c9394335d86255b8
        threshold = str(np.round(thresholds[k], 2))
        ax.annotate(threshold, (fpr[k], tpr[k]), **label_kwargs)
    if initial:
        ax.plot([0, 1], [0, 1], 'k--')
        ax.set_xlim([0.0, 1.0])
        ax.set_ylim([0.0, 1.05])
        ax.set_xlabel('False Positive Rate')
        ax.set_ylabel('True Positive Rate')
        ax.set_title('ROC')
    ax.legend(loc="lower right")
    return ax



    











In [107]:



    


from sklearn.metrics import roc_curve, auc
ax=make_roc("logistic", clflog2, ytest, Xtest, labe=10)



    


















    
























In [109]:



    


ax=make_roc("svm", clfsvm2, ytest, Xtest, labe=10, proba=False)



    


















    
























In [110]:



    


from sklearn import tree
clfTree1 = tree.DecisionTreeClassifier()

parameters_tree = {"max_depth": [1, 2, 3, 4, 5, 6, 7], 'min_samples_leaf': [1, 2, 3, 4, 5, 6]}

clfTree1, Xtrain, ytrain, Xtest, ytest=do_classify(clfTree1, parameters_tree, dftouse, 
                                                 two_features,
                                                 'adj_opening_gross_bin', 1, mask=mask_classify)



    


















    






using mask
BEST {'max_depth': 1, 'min_samples_leaf': 1} 0.656565656566 [mean: 0.65657, std: 0.02499, params: {'max_depth': 1, 'min_samples_leaf': 1}, mean: 0.65657, std: 0.02499, params: {'max_depth': 1, 'min_samples_leaf': 2}, mean: 0.65657, std: 0.02499, params: {'max_depth': 1, 'min_samples_leaf': 3}, mean: 0.65657, std: 0.02499, params: {'max_depth': 1, 'min_samples_leaf': 4}, mean: 0.65657, std: 0.02499, params: {'max_depth': 1, 'min_samples_leaf': 5}, mean: 0.65657, std: 0.02499, params: {'max_depth': 1, 'min_samples_leaf': 6}, mean: 0.65152, std: 0.03358, params: {'max_depth': 2, 'min_samples_leaf': 1}, mean: 0.65152, std: 0.03358, params: {'max_depth': 2, 'min_samples_leaf': 2}, mean: 0.65152, std: 0.03358, params: {'max_depth': 2, 'min_samples_leaf': 3}, mean: 0.64141, std: 0.01386, params: {'max_depth': 2, 'min_samples_leaf': 4}, mean: 0.65657, std: 0.02067, params: {'max_depth': 2, 'min_samples_leaf': 5}, mean: 0.65657, std: 0.02067, params: {'max_depth': 2, 'min_samples_leaf': 6}, mean: 0.64141, std: 0.05527, params: {'max_depth': 3, 'min_samples_leaf': 1}, mean: 0.64141, std: 0.05527, params: {'max_depth': 3, 'min_samples_leaf': 2}, mean: 0.63636, std: 0.05623, params: {'max_depth': 3, 'min_samples_leaf': 3}, mean: 0.63131, std: 0.03708, params: {'max_depth': 3, 'min_samples_leaf': 4}, mean: 0.62626, std: 0.03103, params: {'max_depth': 3, 'min_samples_leaf': 5}, mean: 0.62626, std: 0.03103, params: {'max_depth': 3, 'min_samples_leaf': 6}, mean: 0.64646, std: 0.05474, params: {'max_depth': 4, 'min_samples_leaf': 1}, mean: 0.64141, std: 0.04590, params: {'max_depth': 4, 'min_samples_leaf': 2}, mean: 0.64141, std: 0.03248, params: {'max_depth': 4, 'min_samples_leaf': 3}, mean: 0.62121, std: 0.05127, params: {'max_depth': 4, 'min_samples_leaf': 4}, mean: 0.58586, std: 0.04713, params: {'max_depth': 4, 'min_samples_leaf': 5}, mean: 0.58081, std: 0.04498, params: {'max_depth': 4, 'min_samples_leaf': 6}, mean: 0.62121, std: 0.08576, params: {'max_depth': 5, 'min_samples_leaf': 1}, mean: 0.61616, std: 0.06930, params: {'max_depth': 5, 'min_samples_leaf': 2}, mean: 0.60101, std: 0.03772, params: {'max_depth': 5, 'min_samples_leaf': 3}, mean: 0.58081, std: 0.05662, params: {'max_depth': 5, 'min_samples_leaf': 4}, mean: 0.57576, std: 0.04511, params: {'max_depth': 5, 'min_samples_leaf': 5}, mean: 0.56061, std: 0.05537, params: {'max_depth': 5, 'min_samples_leaf': 6}, mean: 0.63131, std: 0.07104, params: {'max_depth': 6, 'min_samples_leaf': 1}, mean: 0.61616, std: 0.05946, params: {'max_depth': 6, 'min_samples_leaf': 2}, mean: 0.58081, std: 0.05105, params: {'max_depth': 6, 'min_samples_leaf': 3}, mean: 0.57071, std: 0.07147, params: {'max_depth': 6, 'min_samples_leaf': 4}, mean: 0.57576, std: 0.05754, params: {'max_depth': 6, 'min_samples_leaf': 5}, mean: 0.53030, std: 0.04552, params: {'max_depth': 6, 'min_samples_leaf': 6}, mean: 0.62121, std: 0.07075, params: {'max_depth': 7, 'min_samples_leaf': 1}, mean: 0.61616, std: 0.05810, params: {'max_depth': 7, 'min_samples_leaf': 2}, mean: 0.59596, std: 0.05766, params: {'max_depth': 7, 'min_samples_leaf': 3}, mean: 0.57576, std: 0.09021, params: {'max_depth': 7, 'min_samples_leaf': 4}, mean: 0.60101, std: 0.07161, params: {'max_depth': 7, 'min_samples_leaf': 5}, mean: 0.55051, std: 0.06373, params: {'max_depth': 7, 'min_samples_leaf': 6}]
############# based on standard predict ################
Accuracy on training data: 0.66
Accuracy on test data:     0.62
[[82  0]
 [51  0]]
########################################################










    






/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:22: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.

















In [111]:



    


def plot_2tree(ax, Xtr, Xte, ytr, yte, clf, plot_train = True, plot_test = True, lab = ['Feature 1', 'Feature 2'], mesh=True, colorscale=cmap_light, cdiscrete=cmap_bold, alpha=0.3, psize=10, zfunc=False):
    # Create a meshgrid as our test data
    plt.figure(figsize=(15,10))
    plot_step= 0.05
    xmin, xmax= Xtr[:,0].min(), Xtr[:,0].max()
    ymin, ymax= Xtr[:,1].min(), Xtr[:,1].max()
    xx, yy = np.meshgrid(np.arange(xmin, xmax, plot_step), np.arange(ymin, ymax, plot_step))

    # Re-cast every coordinate in the meshgrid as a 2D point
    Xplot= np.c_[xx.ravel(), yy.ravel()]


    # Predict the class
    Z = clfTree1.predict( Xplot )

    # Re-shape the results
    Z= Z.reshape(xx.shape)
    cs = plt.contourf(xx, yy, Z, cmap= cmap_light, alpha=0.3)
  
    # Overlay training samples
    if (plot_train == True):
        plt.scatter(Xtr[:, 0], Xtr[:, 1], c=ytr-1, cmap=cmap_bold, alpha=alpha,edgecolor="k") 
    # and testing points
    if (plot_test == True):
        plt.scatter(Xte[:, 0], Xte[:, 1], c=yte-1, cmap=cmap_bold, alpha=alpha, marker="s")

    plt.xlabel(lab[0])
    plt.ylabel(lab[1])
    plt.title("Boundary for decision tree classifier",fontsize=7.5)



    











In [113]:



    


plot_2tree(plt, Xtrain, Xtest, ytrain, ytest, clfTree1, 
           lab = ['abs_valence_avg','total_theaters'], alpha = 1, plot_test = False)



    


















    
























In [114]:



    


clfTree2 = tree.DecisionTreeClassifier()
clfTree2, Xtrain, ytrain, Xtest, ytest=do_classify(clfTree2, parameters_tree, dftouse, Xnames,
                                                 'adj_opening_gross_bin', 1, mask=mask_classify)



    


















    






using mask
BEST {'max_depth': 1, 'min_samples_leaf': 1} 0.89898989899 [mean: 0.89899, std: 0.05400, params: {'max_depth': 1, 'min_samples_leaf': 1}, mean: 0.89899, std: 0.05400, params: {'max_depth': 1, 'min_samples_leaf': 2}, mean: 0.89899, std: 0.05400, params: {'max_depth': 1, 'min_samples_leaf': 3}, mean: 0.89899, std: 0.05400, params: {'max_depth': 1, 'min_samples_leaf': 4}, mean: 0.89899, std: 0.05400, params: {'max_depth': 1, 'min_samples_leaf': 5}, mean: 0.89899, std: 0.05400, params: {'max_depth': 1, 'min_samples_leaf': 6}, mean: 0.86364, std: 0.05166, params: {'max_depth': 2, 'min_samples_leaf': 1}, mean: 0.86364, std: 0.05166, params: {'max_depth': 2, 'min_samples_leaf': 2}, mean: 0.86364, std: 0.05166, params: {'max_depth': 2, 'min_samples_leaf': 3}, mean: 0.86364, std: 0.05166, params: {'max_depth': 2, 'min_samples_leaf': 4}, mean: 0.86364, std: 0.05166, params: {'max_depth': 2, 'min_samples_leaf': 5}, mean: 0.86364, std: 0.05166, params: {'max_depth': 2, 'min_samples_leaf': 6}, mean: 0.87374, std: 0.07317, params: {'max_depth': 3, 'min_samples_leaf': 1}, mean: 0.87879, std: 0.06427, params: {'max_depth': 3, 'min_samples_leaf': 2}, mean: 0.88889, std: 0.06027, params: {'max_depth': 3, 'min_samples_leaf': 3}, mean: 0.88384, std: 0.05583, params: {'max_depth': 3, 'min_samples_leaf': 4}, mean: 0.89899, std: 0.05400, params: {'max_depth': 3, 'min_samples_leaf': 5}, mean: 0.88384, std: 0.05328, params: {'max_depth': 3, 'min_samples_leaf': 6}, mean: 0.86869, std: 0.06027, params: {'max_depth': 4, 'min_samples_leaf': 1}, mean: 0.87374, std: 0.05466, params: {'max_depth': 4, 'min_samples_leaf': 2}, mean: 0.87879, std: 0.04785, params: {'max_depth': 4, 'min_samples_leaf': 3}, mean: 0.87879, std: 0.04785, params: {'max_depth': 4, 'min_samples_leaf': 4}, mean: 0.89899, std: 0.04318, params: {'max_depth': 4, 'min_samples_leaf': 5}, mean: 0.89394, std: 0.03799, params: {'max_depth': 4, 'min_samples_leaf': 6}, mean: 0.84848, std: 0.04698, params: {'max_depth': 5, 'min_samples_leaf': 1}, mean: 0.85354, std: 0.05094, params: {'max_depth': 5, 'min_samples_leaf': 2}, mean: 0.87879, std: 0.04785, params: {'max_depth': 5, 'min_samples_leaf': 3}, mean: 0.86364, std: 0.04595, params: {'max_depth': 5, 'min_samples_leaf': 4}, mean: 0.89394, std: 0.05266, params: {'max_depth': 5, 'min_samples_leaf': 5}, mean: 0.89394, std: 0.03799, params: {'max_depth': 5, 'min_samples_leaf': 6}, mean: 0.85859, std: 0.03570, params: {'max_depth': 6, 'min_samples_leaf': 1}, mean: 0.84343, std: 0.05317, params: {'max_depth': 6, 'min_samples_leaf': 2}, mean: 0.87374, std: 0.03367, params: {'max_depth': 6, 'min_samples_leaf': 3}, mean: 0.86869, std: 0.05069, params: {'max_depth': 6, 'min_samples_leaf': 4}, mean: 0.89394, std: 0.05266, params: {'max_depth': 6, 'min_samples_leaf': 5}, mean: 0.89394, std: 0.03799, params: {'max_depth': 6, 'min_samples_leaf': 6}, mean: 0.84848, std: 0.03257, params: {'max_depth': 7, 'min_samples_leaf': 1}, mean: 0.86364, std: 0.02072, params: {'max_depth': 7, 'min_samples_leaf': 2}, mean: 0.86869, std: 0.05080, params: {'max_depth': 7, 'min_samples_leaf': 3}, mean: 0.86869, std: 0.05069, params: {'max_depth': 7, 'min_samples_leaf': 4}, mean: 0.89394, std: 0.05266, params: {'max_depth': 7, 'min_samples_leaf': 5}, mean: 0.89394, std: 0.03799, params: {'max_depth': 7, 'min_samples_leaf': 6}]
############# based on standard predict ################
Accuracy on training data: 0.90
Accuracy on test data:     0.80
[[61 21]
 [ 6 45]]
########################################################










    






/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:22: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.

















In [127]:



    


from sklearn.ensemble import RandomForestClassifier

clfforest1 = RandomForestClassifier()

parameters_forest = {"n_estimators": range(1, len(Xnames))}
clfforest1, Xtrain, ytrain, Xtest, ytest = do_classify(clfforest1, parameters_forest, 
                                                       dftouse, Xnames, 'adj_opening_gross_bin', 1, mask=mask_classify)



    


















    






using mask
BEST {'n_estimators': 6} 0.848484848485 [mean: 0.77778, std: 0.07489, params: {'n_estimators': 1}, mean: 0.78283, std: 0.01723, params: {'n_estimators': 2}, mean: 0.82323, std: 0.02912, params: {'n_estimators': 3}, mean: 0.81818, std: 0.02862, params: {'n_estimators': 4}, mean: 0.83838, std: 0.04494, params: {'n_estimators': 5}, mean: 0.84848, std: 0.04614, params: {'n_estimators': 6}, mean: 0.83838, std: 0.05846, params: {'n_estimators': 7}]
############# based on standard predict ################
Accuracy on training data: 0.98
Accuracy on test data:     0.77
[[68 14]
 [16 35]]
########################################################










    






/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:22: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.

















In [128]:



    


clfforest2 = RandomForestClassifier()

parameters_forest = {"n_estimators": range(1, len(Xnames))}
clfforest2, Xtrain, ytrain, Xtest, ytest = do_classify(clfforest2, parameters_forest, 
                                                       dftouse, Xnames, 'adj_opening_gross_bin', 1, mask=mask_classify)



    


















    






using mask
BEST {'n_estimators': 5} 0.863636363636 [mean: 0.82323, std: 0.03897, params: {'n_estimators': 1}, mean: 0.77778, std: 0.02852, params: {'n_estimators': 2}, mean: 0.84343, std: 0.04774, params: {'n_estimators': 3}, mean: 0.80303, std: 0.03014, params: {'n_estimators': 4}, mean: 0.86364, std: 0.04151, params: {'n_estimators': 5}, mean: 0.81818, std: 0.02110, params: {'n_estimators': 6}, mean: 0.84343, std: 0.01778, params: {'n_estimators': 7}]
############# based on standard predict ################
Accuracy on training data: 0.98
Accuracy on test data:     0.76
[[66 16]
 [16 35]]
########################################################










    






/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:22: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.

















In [117]:



    


from sklearn.ensemble import AdaBoostClassifier

clfAda1 = AdaBoostClassifier()

parameters_adaboost = {"n_estimators": range(10, 60)}
clfAda1, Xtrain, ytrain, Xtest, ytest = do_classify(clfAda1, parameters_adaboost, 
                                                       dftouse, two_features, 'adj_opening_gross_bin', 
                                                   1, mask=mask_classify)



    


















    






using mask
BEST {'n_estimators': 10} 0.636363636364 [mean: 0.63636, std: 0.05375, params: {'n_estimators': 10}, mean: 0.60606, std: 0.05970, params: {'n_estimators': 11}, mean: 0.63636, std: 0.03226, params: {'n_estimators': 12}, mean: 0.59596, std: 0.06395, params: {'n_estimators': 13}, mean: 0.60606, std: 0.06511, params: {'n_estimators': 14}, mean: 0.59596, std: 0.06395, params: {'n_estimators': 15}, mean: 0.58081, std: 0.06367, params: {'n_estimators': 16}, mean: 0.57576, std: 0.06892, params: {'n_estimators': 17}, mean: 0.60101, std: 0.05567, params: {'n_estimators': 18}, mean: 0.58081, std: 0.06492, params: {'n_estimators': 19}, mean: 0.59596, std: 0.05857, params: {'n_estimators': 20}, mean: 0.56566, std: 0.08429, params: {'n_estimators': 21}, mean: 0.58586, std: 0.06924, params: {'n_estimators': 22}, mean: 0.54040, std: 0.07564, params: {'n_estimators': 23}, mean: 0.56566, std: 0.05543, params: {'n_estimators': 24}, mean: 0.54545, std: 0.03777, params: {'n_estimators': 25}, mean: 0.56566, std: 0.06624, params: {'n_estimators': 26}, mean: 0.58081, std: 0.07436, params: {'n_estimators': 27}, mean: 0.59091, std: 0.07035, params: {'n_estimators': 28}, mean: 0.57576, std: 0.06407, params: {'n_estimators': 29}, mean: 0.59091, std: 0.05802, params: {'n_estimators': 30}, mean: 0.58586, std: 0.05892, params: {'n_estimators': 31}, mean: 0.59091, std: 0.05802, params: {'n_estimators': 32}, mean: 0.58586, std: 0.05681, params: {'n_estimators': 33}, mean: 0.59596, std: 0.06712, params: {'n_estimators': 34}, mean: 0.58081, std: 0.06172, params: {'n_estimators': 35}, mean: 0.59091, std: 0.07202, params: {'n_estimators': 36}, mean: 0.59091, std: 0.07202, params: {'n_estimators': 37}, mean: 0.56061, std: 0.09539, params: {'n_estimators': 38}, mean: 0.56061, std: 0.08044, params: {'n_estimators': 39}, mean: 0.54545, std: 0.07751, params: {'n_estimators': 40}, mean: 0.57576, std: 0.09261, params: {'n_estimators': 41}, mean: 0.54545, std: 0.07222, params: {'n_estimators': 42}, mean: 0.56566, std: 0.05167, params: {'n_estimators': 43}, mean: 0.54545, std: 0.05365, params: {'n_estimators': 44}, mean: 0.55556, std: 0.06064, params: {'n_estimators': 45}, mean: 0.54545, std: 0.05365, params: {'n_estimators': 46}, mean: 0.55556, std: 0.07942, params: {'n_estimators': 47}, mean: 0.56061, std: 0.04916, params: {'n_estimators': 48}, mean: 0.58081, std: 0.06270, params: {'n_estimators': 49}, mean: 0.57071, std: 0.06289, params: {'n_estimators': 50}, mean: 0.58081, std: 0.06270, params: {'n_estimators': 51}, mean: 0.57576, std: 0.06199, params: {'n_estimators': 52}, mean: 0.59091, std: 0.05180, params: {'n_estimators': 53}, mean: 0.57071, std: 0.06067, params: {'n_estimators': 54}, mean: 0.59091, std: 0.05180, params: {'n_estimators': 55}, mean: 0.58081, std: 0.06504, params: {'n_estimators': 56}, mean: 0.59091, std: 0.06326, params: {'n_estimators': 57}, mean: 0.58081, std: 0.06781, params: {'n_estimators': 58}, mean: 0.59091, std: 0.06021, params: {'n_estimators': 59}]
############# based on standard predict ################
Accuracy on training data: 0.74
Accuracy on test data:     0.53
[[49 33]
 [29 22]]
########################################################










    






/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:22: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.

















In [118]:



    


clfAda2 = AdaBoostClassifier()

parameters_adaboost = {"n_estimators": range(10, 60)}
clfAda2, Xtrain, ytrain, Xtest, ytest = do_classify(clfAda2, parameters_adaboost, 
                                                       dftouse, Xnames, 'adj_opening_gross_bin', 
                                                   1, mask=mask_classify)



    


















    






using mask
BEST {'n_estimators': 17} 0.883838383838 [mean: 0.85859, std: 0.03506, params: {'n_estimators': 10}, mean: 0.87879, std: 0.03423, params: {'n_estimators': 11}, mean: 0.86869, std: 0.03333, params: {'n_estimators': 12}, mean: 0.87879, std: 0.03458, params: {'n_estimators': 13}, mean: 0.86869, std: 0.03333, params: {'n_estimators': 14}, mean: 0.86364, std: 0.03443, params: {'n_estimators': 15}, mean: 0.87879, std: 0.03684, params: {'n_estimators': 16}, mean: 0.88384, std: 0.04162, params: {'n_estimators': 17}, mean: 0.87374, std: 0.05258, params: {'n_estimators': 18}, mean: 0.86869, std: 0.03970, params: {'n_estimators': 19}, mean: 0.86869, std: 0.04863, params: {'n_estimators': 20}, mean: 0.87374, std: 0.05258, params: {'n_estimators': 21}, mean: 0.85859, std: 0.05437, params: {'n_estimators': 22}, mean: 0.86869, std: 0.06067, params: {'n_estimators': 23}, mean: 0.86869, std: 0.07340, params: {'n_estimators': 24}, mean: 0.85859, std: 0.07763, params: {'n_estimators': 25}, mean: 0.85354, std: 0.05804, params: {'n_estimators': 26}, mean: 0.83838, std: 0.04998, params: {'n_estimators': 27}, mean: 0.83838, std: 0.04728, params: {'n_estimators': 28}, mean: 0.84848, std: 0.05855, params: {'n_estimators': 29}, mean: 0.84343, std: 0.06180, params: {'n_estimators': 30}, mean: 0.84848, std: 0.05626, params: {'n_estimators': 31}, mean: 0.84848, std: 0.05626, params: {'n_estimators': 32}, mean: 0.85859, std: 0.05278, params: {'n_estimators': 33}, mean: 0.85859, std: 0.05754, params: {'n_estimators': 34}, mean: 0.85859, std: 0.05754, params: {'n_estimators': 35}, mean: 0.84848, std: 0.07459, params: {'n_estimators': 36}, mean: 0.84848, std: 0.05855, params: {'n_estimators': 37}, mean: 0.85859, std: 0.04617, params: {'n_estimators': 38}, mean: 0.85859, std: 0.04987, params: {'n_estimators': 39}, mean: 0.86364, std: 0.06107, params: {'n_estimators': 40}, mean: 0.85859, std: 0.06707, params: {'n_estimators': 41}, mean: 0.85859, std: 0.06740, params: {'n_estimators': 42}, mean: 0.85859, std: 0.05198, params: {'n_estimators': 43}, mean: 0.85859, std: 0.05198, params: {'n_estimators': 44}, mean: 0.85859, std: 0.05198, params: {'n_estimators': 45}, mean: 0.86869, std: 0.05591, params: {'n_estimators': 46}, mean: 0.87879, std: 0.05178, params: {'n_estimators': 47}, mean: 0.86869, std: 0.05865, params: {'n_estimators': 48}, mean: 0.86869, std: 0.05550, params: {'n_estimators': 49}, mean: 0.86869, std: 0.05149, params: {'n_estimators': 50}, mean: 0.86364, std: 0.04402, params: {'n_estimators': 51}, mean: 0.87879, std: 0.03940, params: {'n_estimators': 52}, mean: 0.87374, std: 0.04468, params: {'n_estimators': 53}, mean: 0.87879, std: 0.03940, params: {'n_estimators': 54}, mean: 0.87374, std: 0.04468, params: {'n_estimators': 55}, mean: 0.87374, std: 0.04468, params: {'n_estimators': 56}, mean: 0.87374, std: 0.03835, params: {'n_estimators': 57}, mean: 0.87374, std: 0.03835, params: {'n_estimators': 58}, mean: 0.86869, std: 0.04315, params: {'n_estimators': 59}]
############# based on standard predict ################
Accuracy on training data: 0.96
Accuracy on test data:     0.74
[[59 23]
 [12 39]]
########################################################










    






/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:22: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.

















In [119]:



    


from sklearn.ensemble import GradientBoostingClassifier

clfGB = GradientBoostingClassifier()

parameters_GB = {"n_estimators": range(30, 60), "max_depth": [1, 2, 3, 4, 5]}
clfGB, Xtrain, ytrain, Xtest, ytest = do_classify(clfGB, parameters_GB, 
                                                       dftouse, two_features, 'adj_opening_gross_bin', 1
                                                  , mask=mask_classify)



    


















    






using mask
BEST {'n_estimators': 30, 'max_depth': 1} 0.656565656566 [mean: 0.65657, std: 0.00993, params: {'n_estimators': 30, 'max_depth': 1}, mean: 0.64646, std: 0.02100, params: {'n_estimators': 31, 'max_depth': 1}, mean: 0.64646, std: 0.02100, params: {'n_estimators': 32, 'max_depth': 1}, mean: 0.64646, std: 0.02100, params: {'n_estimators': 33, 'max_depth': 1}, mean: 0.64646, std: 0.02100, params: {'n_estimators': 34, 'max_depth': 1}, mean: 0.64646, std: 0.02100, params: {'n_estimators': 35, 'max_depth': 1}, mean: 0.64646, std: 0.02100, params: {'n_estimators': 36, 'max_depth': 1}, mean: 0.64646, std: 0.02100, params: {'n_estimators': 37, 'max_depth': 1}, mean: 0.64646, std: 0.02100, params: {'n_estimators': 38, 'max_depth': 1}, mean: 0.64646, std: 0.02100, params: {'n_estimators': 39, 'max_depth': 1}, mean: 0.64646, std: 0.02100, params: {'n_estimators': 40, 'max_depth': 1}, mean: 0.64646, std: 0.02100, params: {'n_estimators': 41, 'max_depth': 1}, mean: 0.64141, std: 0.02257, params: {'n_estimators': 42, 'max_depth': 1}, mean: 0.64141, std: 0.02257, params: {'n_estimators': 43, 'max_depth': 1}, mean: 0.63636, std: 0.02788, params: {'n_estimators': 44, 'max_depth': 1}, mean: 0.63636, std: 0.02788, params: {'n_estimators': 45, 'max_depth': 1}, mean: 0.63636, std: 0.02788, params: {'n_estimators': 46, 'max_depth': 1}, mean: 0.64141, std: 0.02257, params: {'n_estimators': 47, 'max_depth': 1}, mean: 0.64141, std: 0.02257, params: {'n_estimators': 48, 'max_depth': 1}, mean: 0.64141, std: 0.02257, params: {'n_estimators': 49, 'max_depth': 1}, mean: 0.64141, std: 0.02257, params: {'n_estimators': 50, 'max_depth': 1}, mean: 0.64141, std: 0.02257, params: {'n_estimators': 51, 'max_depth': 1}, mean: 0.63636, std: 0.02788, params: {'n_estimators': 52, 'max_depth': 1}, mean: 0.63636, std: 0.02788, params: {'n_estimators': 53, 'max_depth': 1}, mean: 0.64646, std: 0.03584, params: {'n_estimators': 54, 'max_depth': 1}, mean: 0.64646, std: 0.03584, params: {'n_estimators': 55, 'max_depth': 1}, mean: 0.64646, std: 0.03584, params: {'n_estimators': 56, 'max_depth': 1}, mean: 0.65152, std: 0.03023, params: {'n_estimators': 57, 'max_depth': 1}, mean: 0.65152, std: 0.03023, params: {'n_estimators': 58, 'max_depth': 1}, mean: 0.65152, std: 0.03023, params: {'n_estimators': 59, 'max_depth': 1}, mean: 0.65152, std: 0.03794, params: {'n_estimators': 30, 'max_depth': 2}, mean: 0.65657, std: 0.03562, params: {'n_estimators': 31, 'max_depth': 2}, mean: 0.65152, std: 0.03794, params: {'n_estimators': 32, 'max_depth': 2}, mean: 0.65657, std: 0.03562, params: {'n_estimators': 33, 'max_depth': 2}, mean: 0.65152, std: 0.03794, params: {'n_estimators': 34, 'max_depth': 2}, mean: 0.65152, std: 0.03794, params: {'n_estimators': 35, 'max_depth': 2}, mean: 0.65152, std: 0.03794, params: {'n_estimators': 36, 'max_depth': 2}, mean: 0.65657, std: 0.03562, params: {'n_estimators': 37, 'max_depth': 2}, mean: 0.65152, std: 0.03794, params: {'n_estimators': 38, 'max_depth': 2}, mean: 0.65657, std: 0.03562, params: {'n_estimators': 39, 'max_depth': 2}, mean: 0.65657, std: 0.03562, params: {'n_estimators': 40, 'max_depth': 2}, mean: 0.65152, std: 0.03794, params: {'n_estimators': 41, 'max_depth': 2}, mean: 0.65657, std: 0.03783, params: {'n_estimators': 42, 'max_depth': 2}, mean: 0.65152, std: 0.03794, params: {'n_estimators': 43, 'max_depth': 2}, mean: 0.65657, std: 0.03562, params: {'n_estimators': 44, 'max_depth': 2}, mean: 0.65152, std: 0.03794, params: {'n_estimators': 45, 'max_depth': 2}, mean: 0.65152, std: 0.03794, params: {'n_estimators': 46, 'max_depth': 2}, mean: 0.64141, std: 0.03773, params: {'n_estimators': 47, 'max_depth': 2}, mean: 0.64646, std: 0.03110, params: {'n_estimators': 48, 'max_depth': 2}, mean: 0.64646, std: 0.03110, params: {'n_estimators': 49, 'max_depth': 2}, mean: 0.64141, std: 0.03218, params: {'n_estimators': 50, 'max_depth': 2}, mean: 0.63636, std: 0.03757, params: {'n_estimators': 51, 'max_depth': 2}, mean: 0.63636, std: 0.03757, params: {'n_estimators': 52, 'max_depth': 2}, mean: 0.63636, std: 0.03757, params: {'n_estimators': 53, 'max_depth': 2}, mean: 0.64141, std: 0.03734, params: {'n_estimators': 54, 'max_depth': 2}, mean: 0.63636, std: 0.03757, params: {'n_estimators': 55, 'max_depth': 2}, mean: 0.64141, std: 0.03734, params: {'n_estimators': 56, 'max_depth': 2}, mean: 0.63636, std: 0.04244, params: {'n_estimators': 57, 'max_depth': 2}, mean: 0.63636, std: 0.04244, params: {'n_estimators': 58, 'max_depth': 2}, mean: 0.63131, std: 0.04207, params: {'n_estimators': 59, 'max_depth': 2}, mean: 0.62626, std: 0.04802, params: {'n_estimators': 30, 'max_depth': 3}, mean: 0.62626, std: 0.03895, params: {'n_estimators': 31, 'max_depth': 3}, mean: 0.62121, std: 0.04310, params: {'n_estimators': 32, 'max_depth': 3}, mean: 0.62626, std: 0.04176, params: {'n_estimators': 33, 'max_depth': 3}, mean: 0.63131, std: 0.04277, params: {'n_estimators': 34, 'max_depth': 3}, mean: 0.62121, std: 0.04988, params: {'n_estimators': 35, 'max_depth': 3}, mean: 0.62626, std: 0.05123, params: {'n_estimators': 36, 'max_depth': 3}, mean: 0.62626, std: 0.05716, params: {'n_estimators': 37, 'max_depth': 3}, mean: 0.61616, std: 0.05192, params: {'n_estimators': 38, 'max_depth': 3}, mean: 0.62121, std: 0.04474, params: {'n_estimators': 39, 'max_depth': 3}, mean: 0.63131, std: 0.04970, params: {'n_estimators': 40, 'max_depth': 3}, mean: 0.62626, std: 0.04622, params: {'n_estimators': 41, 'max_depth': 3}, mean: 0.61616, std: 0.05192, params: {'n_estimators': 42, 'max_depth': 3}, mean: 0.61616, std: 0.06333, params: {'n_estimators': 43, 'max_depth': 3}, mean: 0.62121, std: 0.05526, params: {'n_estimators': 44, 'max_depth': 3}, mean: 0.62121, std: 0.05526, params: {'n_estimators': 45, 'max_depth': 3}, mean: 0.63131, std: 0.06299, params: {'n_estimators': 46, 'max_depth': 3}, mean: 0.62626, std: 0.06232, params: {'n_estimators': 47, 'max_depth': 3}, mean: 0.63131, std: 0.06299, params: {'n_estimators': 48, 'max_depth': 3}, mean: 0.63636, std: 0.07041, params: {'n_estimators': 49, 'max_depth': 3}, mean: 0.63636, std: 0.07041, params: {'n_estimators': 50, 'max_depth': 3}, mean: 0.63636, std: 0.07041, params: {'n_estimators': 51, 'max_depth': 3}, mean: 0.62626, std: 0.07738, params: {'n_estimators': 52, 'max_depth': 3}, mean: 0.62626, std: 0.07738, params: {'n_estimators': 53, 'max_depth': 3}, mean: 0.62626, std: 0.07738, params: {'n_estimators': 54, 'max_depth': 3}, mean: 0.62121, std: 0.08054, params: {'n_estimators': 55, 'max_depth': 3}, mean: 0.62626, std: 0.07398, params: {'n_estimators': 56, 'max_depth': 3}, mean: 0.63131, std: 0.07453, params: {'n_estimators': 57, 'max_depth': 3}, mean: 0.61616, std: 0.07309, params: {'n_estimators': 58, 'max_depth': 3}, mean: 0.61616, std: 0.07309, params: {'n_estimators': 59, 'max_depth': 3}, mean: 0.62626, std: 0.02630, params: {'n_estimators': 30, 'max_depth': 4}, mean: 0.62626, std: 0.03335, params: {'n_estimators': 31, 'max_depth': 4}, mean: 0.62626, std: 0.03335, params: {'n_estimators': 32, 'max_depth': 4}, mean: 0.62626, std: 0.04652, params: {'n_estimators': 33, 'max_depth': 4}, mean: 0.62626, std: 0.05287, params: {'n_estimators': 34, 'max_depth': 4}, mean: 0.62626, std: 0.04652, params: {'n_estimators': 35, 'max_depth': 4}, mean: 0.63131, std: 0.04470, params: {'n_estimators': 36, 'max_depth': 4}, mean: 0.61616, std: 0.05678, params: {'n_estimators': 37, 'max_depth': 4}, mean: 0.63131, std: 0.05223, params: {'n_estimators': 38, 'max_depth': 4}, mean: 0.61111, std: 0.06426, params: {'n_estimators': 39, 'max_depth': 4}, mean: 0.61111, std: 0.06426, params: {'n_estimators': 40, 'max_depth': 4}, mean: 0.61111, std: 0.06426, params: {'n_estimators': 41, 'max_depth': 4}, mean: 0.61616, std: 0.07086, params: {'n_estimators': 42, 'max_depth': 4}, mean: 0.60606, std: 0.06562, params: {'n_estimators': 43, 'max_depth': 4}, mean: 0.62121, std: 0.06167, params: {'n_estimators': 44, 'max_depth': 4}, mean: 0.61616, std: 0.06218, params: {'n_estimators': 45, 'max_depth': 4}, mean: 0.62121, std: 0.06805, params: {'n_estimators': 46, 'max_depth': 4}, mean: 0.62121, std: 0.06805, params: {'n_estimators': 47, 'max_depth': 4}, mean: 0.62121, std: 0.07469, params: {'n_estimators': 48, 'max_depth': 4}, mean: 0.62121, std: 0.06805, params: {'n_estimators': 49, 'max_depth': 4}, mean: 0.61616, std: 0.05585, params: {'n_estimators': 50, 'max_depth': 4}, mean: 0.61616, std: 0.05585, params: {'n_estimators': 51, 'max_depth': 4}, mean: 0.61616, std: 0.05585, params: {'n_estimators': 52, 'max_depth': 4}, mean: 0.61616, std: 0.04933, params: {'n_estimators': 53, 'max_depth': 4}, mean: 0.61111, std: 0.04947, params: {'n_estimators': 54, 'max_depth': 4}, mean: 0.60606, std: 0.04456, params: {'n_estimators': 55, 'max_depth': 4}, mean: 0.61111, std: 0.05206, params: {'n_estimators': 56, 'max_depth': 4}, mean: 0.61111, std: 0.04947, params: {'n_estimators': 57, 'max_depth': 4}, mean: 0.61111, std: 0.06344, params: {'n_estimators': 58, 'max_depth': 4}, mean: 0.60606, std: 0.05970, params: {'n_estimators': 59, 'max_depth': 4}, mean: 0.61111, std: 0.04923, params: {'n_estimators': 30, 'max_depth': 5}, mean: 0.62121, std: 0.05160, params: {'n_estimators': 31, 'max_depth': 5}, mean: 0.61111, std: 0.04696, params: {'n_estimators': 32, 'max_depth': 5}, mean: 0.61616, std: 0.06329, params: {'n_estimators': 33, 'max_depth': 5}, mean: 0.60606, std: 0.06354, params: {'n_estimators': 34, 'max_depth': 5}, mean: 0.59091, std: 0.06590, params: {'n_estimators': 35, 'max_depth': 5}, mean: 0.60606, std: 0.06354, params: {'n_estimators': 36, 'max_depth': 5}, mean: 0.61616, std: 0.07885, params: {'n_estimators': 37, 'max_depth': 5}, mean: 0.60606, std: 0.06144, params: {'n_estimators': 38, 'max_depth': 5}, mean: 0.60101, std: 0.06196, params: {'n_estimators': 39, 'max_depth': 5}, mean: 0.59596, std: 0.06398, params: {'n_estimators': 40, 'max_depth': 5}, mean: 0.59596, std: 0.04771, params: {'n_estimators': 41, 'max_depth': 5}, mean: 0.59596, std: 0.07768, params: {'n_estimators': 42, 'max_depth': 5}, mean: 0.59091, std: 0.06736, params: {'n_estimators': 43, 'max_depth': 5}, mean: 0.58081, std: 0.06762, params: {'n_estimators': 44, 'max_depth': 5}, mean: 0.59091, std: 0.07164, params: {'n_estimators': 45, 'max_depth': 5}, mean: 0.58586, std: 0.06006, params: {'n_estimators': 46, 'max_depth': 5}, mean: 0.59596, std: 0.04771, params: {'n_estimators': 47, 'max_depth': 5}, mean: 0.59091, std: 0.05331, params: {'n_estimators': 48, 'max_depth': 5}, mean: 0.58081, std: 0.07500, params: {'n_estimators': 49, 'max_depth': 5}, mean: 0.58081, std: 0.06762, params: {'n_estimators': 50, 'max_depth': 5}, mean: 0.58081, std: 0.05376, params: {'n_estimators': 51, 'max_depth': 5}, mean: 0.58081, std: 0.06286, params: {'n_estimators': 52, 'max_depth': 5}, mean: 0.58586, std: 0.05688, params: {'n_estimators': 53, 'max_depth': 5}, mean: 0.59596, std: 0.07355, params: {'n_estimators': 54, 'max_depth': 5}, mean: 0.59091, std: 0.07820, params: {'n_estimators': 55, 'max_depth': 5}, mean: 0.59596, std: 0.08200, params: {'n_estimators': 56, 'max_depth': 5}, mean: 0.56061, std: 0.06074, params: {'n_estimators': 57, 'max_depth': 5}, mean: 0.59596, std: 0.06290, params: {'n_estimators': 58, 'max_depth': 5}, mean: 0.58081, std: 0.06286, params: {'n_estimators': 59, 'max_depth': 5}]
############# based on standard predict ################
Accuracy on training data: 0.67
Accuracy on test data:     0.59
[[79  3]
 [51  0]]
########################################################










    






/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:22: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.

















In [120]:



    


clfGB2 = GradientBoostingClassifier()

parameters_GB = {"n_estimators": range(30, 60), "max_depth": [1, 2, 3, 4, 5]}
clfGB2, Xtrain, ytrain, Xtest, ytest = do_classify(clfGB2, parameters_GB, 
                                                       dftouse, Xnames, 'adj_opening_gross_bin', 1
                                                  , mask=mask_classify)



    


















    






using mask
BEST {'n_estimators': 30, 'max_depth': 2} 0.893939393939 [mean: 0.87879, std: 0.05577, params: {'n_estimators': 30, 'max_depth': 1}, mean: 0.87879, std: 0.05577, params: {'n_estimators': 31, 'max_depth': 1}, mean: 0.87879, std: 0.05577, params: {'n_estimators': 32, 'max_depth': 1}, mean: 0.87879, std: 0.05577, params: {'n_estimators': 33, 'max_depth': 1}, mean: 0.87879, std: 0.05577, params: {'n_estimators': 34, 'max_depth': 1}, mean: 0.87879, std: 0.05577, params: {'n_estimators': 35, 'max_depth': 1}, mean: 0.88384, std: 0.06056, params: {'n_estimators': 36, 'max_depth': 1}, mean: 0.88384, std: 0.06056, params: {'n_estimators': 37, 'max_depth': 1}, mean: 0.88384, std: 0.06056, params: {'n_estimators': 38, 'max_depth': 1}, mean: 0.88384, std: 0.06056, params: {'n_estimators': 39, 'max_depth': 1}, mean: 0.88384, std: 0.06056, params: {'n_estimators': 40, 'max_depth': 1}, mean: 0.88384, std: 0.06056, params: {'n_estimators': 41, 'max_depth': 1}, mean: 0.88384, std: 0.06056, params: {'n_estimators': 42, 'max_depth': 1}, mean: 0.88384, std: 0.06056, params: {'n_estimators': 43, 'max_depth': 1}, mean: 0.88384, std: 0.06056, params: {'n_estimators': 44, 'max_depth': 1}, mean: 0.88384, std: 0.06056, params: {'n_estimators': 45, 'max_depth': 1}, mean: 0.88384, std: 0.06056, params: {'n_estimators': 46, 'max_depth': 1}, mean: 0.88384, std: 0.06056, params: {'n_estimators': 47, 'max_depth': 1}, mean: 0.87879, std: 0.05796, params: {'n_estimators': 48, 'max_depth': 1}, mean: 0.87879, std: 0.05796, params: {'n_estimators': 49, 'max_depth': 1}, mean: 0.87879, std: 0.05796, params: {'n_estimators': 50, 'max_depth': 1}, mean: 0.87879, std: 0.05796, params: {'n_estimators': 51, 'max_depth': 1}, mean: 0.87879, std: 0.05796, params: {'n_estimators': 52, 'max_depth': 1}, mean: 0.87879, std: 0.05796, params: {'n_estimators': 53, 'max_depth': 1}, mean: 0.87879, std: 0.05796, params: {'n_estimators': 54, 'max_depth': 1}, mean: 0.87879, std: 0.05796, params: {'n_estimators': 55, 'max_depth': 1}, mean: 0.87879, std: 0.05796, params: {'n_estimators': 56, 'max_depth': 1}, mean: 0.87879, std: 0.05796, params: {'n_estimators': 57, 'max_depth': 1}, mean: 0.87879, std: 0.05796, params: {'n_estimators': 58, 'max_depth': 1}, mean: 0.87879, std: 0.05796, params: {'n_estimators': 59, 'max_depth': 1}, mean: 0.89394, std: 0.05456, params: {'n_estimators': 30, 'max_depth': 2}, mean: 0.89394, std: 0.05456, params: {'n_estimators': 31, 'max_depth': 2}, mean: 0.89394, std: 0.05456, params: {'n_estimators': 32, 'max_depth': 2}, mean: 0.89394, std: 0.05456, params: {'n_estimators': 33, 'max_depth': 2}, mean: 0.89394, std: 0.05456, params: {'n_estimators': 34, 'max_depth': 2}, mean: 0.89394, std: 0.05456, params: {'n_estimators': 35, 'max_depth': 2}, mean: 0.89394, std: 0.05456, params: {'n_estimators': 36, 'max_depth': 2}, mean: 0.89394, std: 0.05456, params: {'n_estimators': 37, 'max_depth': 2}, mean: 0.89394, std: 0.05456, params: {'n_estimators': 38, 'max_depth': 2}, mean: 0.89394, std: 0.05456, params: {'n_estimators': 39, 'max_depth': 2}, mean: 0.89394, std: 0.05456, params: {'n_estimators': 40, 'max_depth': 2}, mean: 0.89394, std: 0.05456, params: {'n_estimators': 41, 'max_depth': 2}, mean: 0.89394, std: 0.05456, params: {'n_estimators': 42, 'max_depth': 2}, mean: 0.89394, std: 0.05456, params: {'n_estimators': 43, 'max_depth': 2}, mean: 0.89394, std: 0.05456, params: {'n_estimators': 44, 'max_depth': 2}, mean: 0.89394, std: 0.05456, params: {'n_estimators': 45, 'max_depth': 2}, mean: 0.88889, std: 0.05275, params: {'n_estimators': 46, 'max_depth': 2}, mean: 0.88384, std: 0.04768, params: {'n_estimators': 47, 'max_depth': 2}, mean: 0.88384, std: 0.04768, params: {'n_estimators': 48, 'max_depth': 2}, mean: 0.88384, std: 0.04768, params: {'n_estimators': 49, 'max_depth': 2}, mean: 0.88384, std: 0.04768, params: {'n_estimators': 50, 'max_depth': 2}, mean: 0.88889, std: 0.05275, params: {'n_estimators': 51, 'max_depth': 2}, mean: 0.88384, std: 0.04768, params: {'n_estimators': 52, 'max_depth': 2}, mean: 0.88384, std: 0.04768, params: {'n_estimators': 53, 'max_depth': 2}, mean: 0.88384, std: 0.04768, params: {'n_estimators': 54, 'max_depth': 2}, mean: 0.88384, std: 0.04768, params: {'n_estimators': 55, 'max_depth': 2}, mean: 0.88384, std: 0.04768, params: {'n_estimators': 56, 'max_depth': 2}, mean: 0.88384, std: 0.04768, params: {'n_estimators': 57, 'max_depth': 2}, mean: 0.88384, std: 0.04768, params: {'n_estimators': 58, 'max_depth': 2}, mean: 0.88384, std: 0.04768, params: {'n_estimators': 59, 'max_depth': 2}, mean: 0.88384, std: 0.04768, params: {'n_estimators': 30, 'max_depth': 3}, mean: 0.88384, std: 0.05291, params: {'n_estimators': 31, 'max_depth': 3}, mean: 0.88384, std: 0.05291, params: {'n_estimators': 32, 'max_depth': 3}, mean: 0.88384, std: 0.05291, params: {'n_estimators': 33, 'max_depth': 3}, mean: 0.87879, std: 0.04730, params: {'n_estimators': 34, 'max_depth': 3}, mean: 0.87879, std: 0.04730, params: {'n_estimators': 35, 'max_depth': 3}, mean: 0.87879, std: 0.04730, params: {'n_estimators': 36, 'max_depth': 3}, mean: 0.87374, std: 0.05658, params: {'n_estimators': 37, 'max_depth': 3}, mean: 0.87879, std: 0.04730, params: {'n_estimators': 38, 'max_depth': 3}, mean: 0.87374, std: 0.05658, params: {'n_estimators': 39, 'max_depth': 3}, mean: 0.87879, std: 0.04730, params: {'n_estimators': 40, 'max_depth': 3}, mean: 0.87374, std: 0.05658, params: {'n_estimators': 41, 'max_depth': 3}, mean: 0.87374, std: 0.05658, params: {'n_estimators': 42, 'max_depth': 3}, mean: 0.87374, std: 0.05658, params: {'n_estimators': 43, 'max_depth': 3}, mean: 0.87879, std: 0.05780, params: {'n_estimators': 44, 'max_depth': 3}, mean: 0.87879, std: 0.05780, params: {'n_estimators': 45, 'max_depth': 3}, mean: 0.87374, std: 0.05658, params: {'n_estimators': 46, 'max_depth': 3}, mean: 0.87374, std: 0.05658, params: {'n_estimators': 47, 'max_depth': 3}, mean: 0.87879, std: 0.05780, params: {'n_estimators': 48, 'max_depth': 3}, mean: 0.87374, std: 0.05658, params: {'n_estimators': 49, 'max_depth': 3}, mean: 0.87374, std: 0.05658, params: {'n_estimators': 50, 'max_depth': 3}, mean: 0.87374, std: 0.05658, params: {'n_estimators': 51, 'max_depth': 3}, mean: 0.87374, std: 0.05658, params: {'n_estimators': 52, 'max_depth': 3}, mean: 0.87374, std: 0.05658, params: {'n_estimators': 53, 'max_depth': 3}, mean: 0.87374, std: 0.05658, params: {'n_estimators': 54, 'max_depth': 3}, mean: 0.87879, std: 0.05780, params: {'n_estimators': 55, 'max_depth': 3}, mean: 0.87879, std: 0.05780, params: {'n_estimators': 56, 'max_depth': 3}, mean: 0.87879, std: 0.05780, params: {'n_estimators': 57, 'max_depth': 3}, mean: 0.87374, std: 0.05927, params: {'n_estimators': 58, 'max_depth': 3}, mean: 0.87374, std: 0.05927, params: {'n_estimators': 59, 'max_depth': 3}, mean: 0.85859, std: 0.07447, params: {'n_estimators': 30, 'max_depth': 4}, mean: 0.85859, std: 0.07447, params: {'n_estimators': 31, 'max_depth': 4}, mean: 0.86869, std: 0.07026, params: {'n_estimators': 32, 'max_depth': 4}, mean: 0.86869, std: 0.08049, params: {'n_estimators': 33, 'max_depth': 4}, mean: 0.86869, std: 0.07906, params: {'n_estimators': 34, 'max_depth': 4}, mean: 0.86869, std: 0.07906, params: {'n_estimators': 35, 'max_depth': 4}, mean: 0.87879, std: 0.08222, params: {'n_estimators': 36, 'max_depth': 4}, mean: 0.87374, std: 0.08167, params: {'n_estimators': 37, 'max_depth': 4}, mean: 0.87374, std: 0.07996, params: {'n_estimators': 38, 'max_depth': 4}, mean: 0.87374, std: 0.08167, params: {'n_estimators': 39, 'max_depth': 4}, mean: 0.88384, std: 0.07225, params: {'n_estimators': 40, 'max_depth': 4}, mean: 0.88889, std: 0.06238, params: {'n_estimators': 41, 'max_depth': 4}, mean: 0.87879, std: 0.07199, params: {'n_estimators': 42, 'max_depth': 4}, mean: 0.87374, std: 0.08167, params: {'n_estimators': 43, 'max_depth': 4}, mean: 0.88384, std: 0.07225, params: {'n_estimators': 44, 'max_depth': 4}, mean: 0.87879, std: 0.08222, params: {'n_estimators': 45, 'max_depth': 4}, mean: 0.87879, std: 0.07199, params: {'n_estimators': 46, 'max_depth': 4}, mean: 0.87374, std: 0.08167, params: {'n_estimators': 47, 'max_depth': 4}, mean: 0.87879, std: 0.07199, params: {'n_estimators': 48, 'max_depth': 4}, mean: 0.87374, std: 0.06939, params: {'n_estimators': 49, 'max_depth': 4}, mean: 0.87374, std: 0.06939, params: {'n_estimators': 50, 'max_depth': 4}, mean: 0.87879, std: 0.05992, params: {'n_estimators': 51, 'max_depth': 4}, mean: 0.86364, std: 0.07437, params: {'n_estimators': 52, 'max_depth': 4}, mean: 0.86869, std: 0.07906, params: {'n_estimators': 53, 'max_depth': 4}, mean: 0.87374, std: 0.08167, params: {'n_estimators': 54, 'max_depth': 4}, mean: 0.88384, std: 0.07425, params: {'n_estimators': 55, 'max_depth': 4}, mean: 0.86869, std: 0.07746, params: {'n_estimators': 56, 'max_depth': 4}, mean: 0.87879, std: 0.07199, params: {'n_estimators': 57, 'max_depth': 4}, mean: 0.87374, std: 0.08167, params: {'n_estimators': 58, 'max_depth': 4}, mean: 0.88384, std: 0.07225, params: {'n_estimators': 59, 'max_depth': 4}, mean: 0.83838, std: 0.08603, params: {'n_estimators': 30, 'max_depth': 5}, mean: 0.84343, std: 0.07583, params: {'n_estimators': 31, 'max_depth': 5}, mean: 0.83838, std: 0.07418, params: {'n_estimators': 32, 'max_depth': 5}, mean: 0.83838, std: 0.07456, params: {'n_estimators': 33, 'max_depth': 5}, mean: 0.84343, std: 0.06629, params: {'n_estimators': 34, 'max_depth': 5}, mean: 0.85354, std: 0.08076, params: {'n_estimators': 35, 'max_depth': 5}, mean: 0.84848, std: 0.07983, params: {'n_estimators': 36, 'max_depth': 5}, mean: 0.84343, std: 0.06414, params: {'n_estimators': 37, 'max_depth': 5}, mean: 0.85859, std: 0.07069, params: {'n_estimators': 38, 'max_depth': 5}, mean: 0.83838, std: 0.07418, params: {'n_estimators': 39, 'max_depth': 5}, mean: 0.85859, std: 0.07085, params: {'n_estimators': 40, 'max_depth': 5}, mean: 0.85859, std: 0.07069, params: {'n_estimators': 41, 'max_depth': 5}, mean: 0.84848, std: 0.07843, params: {'n_estimators': 42, 'max_depth': 5}, mean: 0.84848, std: 0.07843, params: {'n_estimators': 43, 'max_depth': 5}, mean: 0.84848, std: 0.07843, params: {'n_estimators': 44, 'max_depth': 5}, mean: 0.84848, std: 0.07867, params: {'n_estimators': 45, 'max_depth': 5}, mean: 0.84343, std: 0.07716, params: {'n_estimators': 46, 'max_depth': 5}, mean: 0.84343, std: 0.07716, params: {'n_estimators': 47, 'max_depth': 5}, mean: 0.84848, std: 0.07843, params: {'n_estimators': 48, 'max_depth': 5}, mean: 0.84848, std: 0.06732, params: {'n_estimators': 49, 'max_depth': 5}, mean: 0.85354, std: 0.06826, params: {'n_estimators': 50, 'max_depth': 5}, mean: 0.84848, std: 0.06566, params: {'n_estimators': 51, 'max_depth': 5}, mean: 0.84343, std: 0.07716, params: {'n_estimators': 52, 'max_depth': 5}, mean: 0.84343, std: 0.07716, params: {'n_estimators': 53, 'max_depth': 5}, mean: 0.85354, std: 0.08087, params: {'n_estimators': 54, 'max_depth': 5}, mean: 0.84848, std: 0.06717, params: {'n_estimators': 55, 'max_depth': 5}, mean: 0.85859, std: 0.07056, params: {'n_estimators': 56, 'max_depth': 5}, mean: 0.84343, std: 0.07716, params: {'n_estimators': 57, 'max_depth': 5}, mean: 0.85859, std: 0.07069, params: {'n_estimators': 58, 'max_depth': 5}, mean: 0.84343, std: 0.07583, params: {'n_estimators': 59, 'max_depth': 5}]
############# based on standard predict ################
Accuracy on training data: 0.94
Accuracy on test data:     0.78
[[61 21]
 [ 8 43]]
########################################################










    






/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:22: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.

















In [138]:



    


ax1=make_roc("log", clflog2, ytest, Xtest, labe=10, proba=False)
ax1=make_roc("svm", clfsvm2, ytest, Xtest, labe=10, proba=False)
ax1=make_roc("AdaBoost", clfAda2, ytest, Xtest, labe=10, proba=False)
ax1=make_roc("GB", clfGB2, ytest, Xtest, labe=10, proba=False)



    


















    
























In [139]:



    


from sklearn.tree import DecisionTreeClassifier
import sklearn.linear_model
import sklearn.svm

def plot_decision_surface(clf, X_train, Y_train):
    plot_step=0.1
    
    if X_train.shape[1] != 2:
        raise ValueError("X_train should have exactly 2 columnns!")
    
    x_min, x_max = X_train[:, 0].min() - plot_step, X_train[:, 0].max() + plot_step
    y_min, y_max = X_train[:, 1].min() - plot_step, X_train[:, 1].max() + plot_step
    xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
                         np.arange(y_min, y_max, plot_step))

    clf.fit(X_train,Y_train)
    if hasattr(clf, 'predict_proba'):
        Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:,1]
    else:
        Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])    
    Z = Z.reshape(xx.shape)
    cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Reds)
    plt.scatter(X_train[:,0],X_train[:,1],c=Y_train,cmap=plt.cm.Paired)
    plt.show()
    
# your code here
imp_cols = clfforest2.feature_importances_.argsort()[::-1][0:2]

This cell of code broke my kernel!





In [ ]:



    


X_imp = dftouse[imp_cols].values

for i in X_imp:
    for p in i:
        i[p] = int(i[p])

Y = dftouse.adj_opening_gross_bin.values
name_list = dftouse.columns

classifiers = [clfTree2,
               clfForest,
               clfAda,
               clfGB,
               sklearn.svm.SVC(C=100.0, gamma=1.0)]

titleClassifer = ['Decision Tree Classifier', 'Random Forest Classifier', 
                  'AdaBoost Classifier', 'Gradient Boosting Classifier', 'Support Vector Machine']
for c in xrange(5):
    plt.title(titleClassifer[c])
    plt.xlabel(name_list[0])
    plt.ylabel(name_list[1])
    plot_decision_surface(classifiers[c], X_imp, dftouse.adj_opening_gross_bin.values)

Visualization

Milestone 3: Video and finishing touches

Screencast video

Website

Finishing touches

Our analysis seems to show that valence is not a good predictor of grossing for movies. This is perhaps because our valence values are not representative of the reviews.

At this stage, we would like at least establish a relationship between movie grossing and review

Content source: starbro/BeastMode

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