<img src="dogfail.jpg", width="500px">
The following notebook contains our original attempt to predict box office gross incomes using text sentiment analysis. It represents a large portion of our work and many hours of gathering, cleaning, and doing preliminary analysis on data, which is why we felt it necessary to include here. This notebook contains our original line of thought and shows data science process. We consider it a fail not in the sense that we were doing poor statistics, but in the sense that the data we obtained was not fit for the task at hand. Our preliminary analysis helped to illuminate this. We ultimately decided that we needed more data, and have included the process for obtaining and analysis this better data in another notebook, Final_Project_vFINAL. Please see that notebook after this one.
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
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
For our project we will be using data from 3 different sources
First we import the requests and BeautifulSoup libraries to make working with HTTP requests easier, and then easily transfer HTML content to Python data structures.
In [2]:
from bs4 import BeautifulSoup
# The "requests" library makes working with HTTP requests easier
# than the built-in urllib libraries.
import requests
Secondly, we prepare the data frame movie_df to store the data that we will scrape from BOM. We give this dataframe 9 columns:
* ranking: the ranking of the movie in its release year by gross
* title: movie title
* gross: how much the movie grossed while in theatres
* Total_theaters: the total number of theaters that showed this movie
* opening_gross: how much the movie grossed in the opening weekend (Fri-Sun)
* opening_theaters: the total number of theaters that showed this movie in the opening weekend (Fri-Sun)
* open_date: date of opening
* close_date: date of closing
* year: year of release
In [3]:
bom_df = pd.DataFrame(columns=['close_date', 'gross', 'open_date', 'opening_gross', 'opening_theaters','ranking','title','total_theaters','year'])
Now we write a function rowInfoGrabber that we will call in a loop over the table on the BOM webpage to grab the attributes and save them into the corresponding columns in movie_df.
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
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
<img src="fantasia.png", width="700px">
In [5]:
fields = ["ranking", "title", "gross", "total_theaters", "opening_gross", "opening_theaters", "open_date", "close_date"]
Finally we're ready to scrape!
Because IMDB was created in 1990, we will scrape that far back in BOM. So we're scraping the past 26 years (1990 - 2015). Also note that because the HTML was changed starting in 2001, our scraping will be a little different before and after then.
In [6]:
%%time
years = [1990 + i for i in range(26)]
for year in years:
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)
time.sleep(1)
CPU times: user 12.9 s, sys: 210 ms, total: 13.1 s
Wall time: 46.4 s
In [43]:
print bom_df.shape
bom_df.head()
(2600, 9)
Out[43]:
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
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 [44]:
# splitting the close_date and open_date into the respective month and day
bom_df['close_month'] = bom_df['close_date'].map(lambda x: '0' if x=='-' else x[:x.find('/')])
bom_df['close_day'] = bom_df['close_date'].map(lambda x: '0' if x=='-' else x[x.find('/')+1:len(x)])
bom_df['open_month'] = bom_df['open_date'].map(lambda x: x[:x.find('/')])
bom_df['open_day'] = bom_df['open_date'].map(lambda x: x[x.find('/')+1:len(x)])
# dropping the old close_date and open_date
bom_df = bom_df.drop('close_date', 1)
bom_df = bom_df.drop('open_date', 1)
# creating an open_year by turning the year column into a string and getting rid of trailing bits
bom_df['open_year'] = bom_df.year.astype(str)
bom_df['open_year'] = bom_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 = bom_df['close_month'].astype(int)
open_month = bom_df['open_month'].astype(int)
year = bom_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)
bom_df['close_year'] = close_year
bom_df['close_year'] = bom_df['close_year'].astype(str)
In [45]:
# making close_date and open_date by concatenating the year, month and day
import datetime
close_date = []
for index, row in bom_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)
bom_df['close_date'] = close_date
bom_df['open_date']=bom_df.open_year + '-' + bom_df.open_month + '-' + bom_df.open_day
bom_df['open_date']=bom_df['open_date'].apply(pd.datetools.parse)
Let's take a look at the data, now!
In [46]:
bom_df.head()
Out[46]:
gross
opening_gross
opening_theaters
ranking
title
total_theaters
year
close_month
close_day
open_month
open_day
open_year
close_year
close_date
open_date
0
285761243
17081997
1202
1
Home Alone
2173
1990
0
0
11
16
1990
1991
None
1990-11-16
1
217631306
12191540
1101
2
Ghost
1766
1990
0
0
7
13
1990
1991
None
1990-07-13
2
184208848
598257
14
3
Dances with Wolves
1636
1990
0
0
11
9
1990
1991
None
1990-11-09
3
178406268
11280591
1325
4
Pretty Woman
1811
1990
0
0
3
23
1990
1991
None
1990-03-23
4
135265915
25398367
2006
5
Teenage Mutant Ninja Turtles
2377
1990
0
0
3
30
1990
1991
None
1990-03-30
Let's take a look at if we can get the run times for each movie!
In [47]:
run_time=[]
for index, row in bom_df.iterrows():
if row.close_date != None:
run_time.append(row['close_date']-row['open_date'])
else:
run_time.append('N/A')
Looks like the data is ready for us to use! Let's save this data so we're ready to use it next time.
In [12]:
! pip install pymongo
Requirement already satisfied (use --upgrade to upgrade): pymongo in /Users/alpkaancelik/anaconda/lib/python2.7/site-packages
In [48]:
# Save the movie Dictionaries corresponding to each row of the BoxOfficeMojo table.
import json
import pymongo
from bson import json_util
# Make a dictionary out of the dataset for storage in JSON format.
movieSaved = {feature: bom_df[feature].values.tolist() for feature in bom_df.columns.values}
fp = open("allMovies.json","w")
json.dump(movieSaved, fp, default=json_util.default)
fp.close()
We have cleaned up our IMDB review dataset in the ipython notebook IMDB_reviews.ipynb. From that notebook we have been able to save dictionaries of our data, which we will now call.
In [49]:
with open("train_df_dict.json", "r") as fd:
train_df_dict = json.load(fd)
with open("test_df_dict.json", "r") as fd:
test_df_dict = json.load(fd)
In [50]:
train_df = pd.DataFrame(train_df_dict)
test_df = pd.DataFrame(test_df_dict)
The Stanford group distinguishes between train and test because this was relevant for their project. This may prove to be useful later, so we will keep two separate dataframes. However, for our purposes at the moment, we can combine them since the BOM data will serve as our true data set.
In [51]:
IMDB_df = train_df.append(test_df)
We want to figure out which movies from IMDB_df are also present in our BOM DF. So let's get all the movie titles in our BOM table.
In [52]:
BOM_movie_list = bom_df.title.values.tolist()
Now let's create a mask over IMDB_df, the boolean values of which indicate whether or not a movie is in the BOM list.
In [53]:
movie_mask = [(movie in BOM_movie_list) for movie in IMDB_df.movie_name]
In [54]:
sum(movie_mask)
Out[54]:
4111
We can now create our IMDB data frame with only those movies that also appear in the BOM tables from 1990 - 2015.
In [55]:
IMDB_dftouse=IMDB_df[movie_mask]
Finally we want to save our dictionary of IMDB_dftouse into a JSON file for storage.
In [56]:
IMDB_dftouse_dict = {feature: IMDB_dftouse[feature].values.tolist() for feature in IMDB_dftouse.columns.values}
fp = open("IMDB_dftouse_dict.json","w")
json.dump(IMDB_dftouse_dict, fp)
fp.close()
In [57]:
# Reopen
with open("IMDB_dftouse_dict.json", "r") as fd:
IMDB_dftouse_dict = json.load(fd)
IMDB_dftouse = pd.DataFrame(IMDB_dftouse_dict)
Note: We originally intended to use the AFINN word list prepared by Finn Årup Nielsen to obtain word valences (http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=60100), but ultimately decided to use labMT.
labMTs word list contains around 10,000 scored words, whereas AFINN's only contains 2477.
Now let's download 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 [58]:
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 [59]:
labmt.head()
Out[59]:
happiness_rank
happiness_average
happiness_standard_deviation
twitter_rank
google_rank
nyt_rank
lyrics_rank
word
laughter
1
8.50
0.9313
3600
--
--
1728
happiness
2
8.44
0.9723
1853
2458
--
1230
love
3
8.42
1.1082
25
317
328
23
happy
4
8.30
0.9949
65
1372
1313
375
laughed
5
8.26
1.1572
3334
3542
--
2332
Now let's create a happiness dictionary of (word, valence) pairs where each valence is that word's original valence minus the average valence.
In [60]:
average = labmt.happiness_average.mean()
happiness = (labmt.happiness_average - average).to_dict()
In [61]:
print "Score(happy): ", happiness['happy']
print "Score(miserable): ", happiness['miserable']
print "Best score: ", max(happiness.values())
print "Worst score: ", min(happiness.values())
Score(happy): 2.92476032088
Score(miserable): -2.83523967912
Best score: 3.12476032088
Worst score: -4.07523967912
In [62]:
# Save to disc
# fp = open("happiness.json","w")
# json.dump(happiness, fp)
# fp.close()
In [63]:
# Reopen
with open("happiness.json", "r") as fp:
happiness = json.load(fp)
Now let's collect several attributes from a given review's text body, and save all valuable information into a new data frame. First we define a function that removes stop words (all non important words from a valence perspective) from a text body.
In [64]:
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
Now we'll write a function that returns total happiness, average happiness, total scorable words, and percentage of scorable words in a given review text.
In [65]:
'''
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():
if word in valenceDict.keys():
count_relevant += 1
happiness_total += valenceDict[word]
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
Now we'll write a function that, given a data frame, returns a new data frame with the concatenation of valence (happiness) info in 4 new columns: valence sum, valence average, # of scorable words, % of scorable words.
In [66]:
'''
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():
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)
Now let's create a new dataframe valence_df with the valence statistics run on our IMDB_df. This code takes a few minutes to run.
In [67]:
%%time
valence_df = getAllInfo(IMDB_dftouse, happiness, stopwords)
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-67-65c3726ef4dc> in <module>()
----> 1 get_ipython().run_cell_magic(u'time', u'', u'valence_df = getAllInfo(IMDB_dftouse, happiness, stopwords)')
/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>()
<ipython-input-66-b22b5564e667> in getAllInfo(df, valenceDict, stopwords)
8 for i, row in df.iterrows():
9 cleaned_review = removeStopWords(row['text'], stopwords)
---> 10 valence_sum, valence_avg, review_len, review_fraction = getValenceInfo(cleaned_review, valenceDict)
11 valence_suml.append(valence_sum)
12 valence_avgl.append(valence_avg)
<ipython-input-65-882715705a0c> in getValenceInfo(text, valenceDict)
9 for word in text.split():
10 if word in valenceDict.keys():
---> 11 count_relevant += 1
12 happiness_total += valenceDict[word]
13 if count_relevant != 0:
KeyboardInterrupt:
In [ ]:
valence_df.head()
Let's convert True/False to 1/0. It is needed to make valence_df JSON serializable, also better practice.
In [143]:
valence_df.positive = 1.0*valence_df.positive
In [144]:
# Save to disc
fp = open("valence_df_dict.json","w")
json.dump(valence_df.to_dict(), fp)
fp.close()
In [68]:
# Reopen
with open("valence_df_dict.json", "r") as fp:
valence_df_dict = json.load(fp)
valence_df = pd.DataFrame(valence_df_dict)
Let's see how well our valence summaries correlate with the reviews stars. This will serve as a sanity check to see if our valence estimates are working.
In [69]:
stars_val_avg = ols('stars ~ valence_avg',valence_df).fit()
stars_val_avg.summary()
sns.regplot(y="valence_avg", x="stars", data=valence_df)
Out[69]:
<matplotlib.axes._subplots.AxesSubplot at 0x10ea4ff50>
Yes it does, to some extent. The explanatory power is not high though.
Now, let's have a look at the valences of our positive and negative reviews and see how each is distributed to get a better sense of our data.
In [70]:
graph_val_avg = plt.hist(valence_df.valence_sum[valence_df.stars >=7],bins=40,alpha=0.7,label="Positive reviews")
graph_val_avg = plt.hist(valence_df.valence_sum[valence_df.stars <=4],bins=40,alpha=0.7,label="Negative reviews")
graph_val_avg = plt.title("Valence sum for positive and negative reviews")
graph_val_avg = plt.xlabel("Sum of valence")
graph_val_avg = plt.ylabel("Frequency")
graph_val_avg = plt.legend(loc='upper right')
In [71]:
graph_val_avg = plt.hist(valence_df.valence_avg[valence_df.stars >=7],bins=40,alpha=0.7,label="Positive reviews")
graph_val_avg = plt.hist(valence_df.valence_avg[valence_df.stars <=4],bins=40,alpha=0.7,label="Negative reviews")
graph_val_avg = plt.title("Valence average for positive and negative reviews")
graph_val_avg = plt.xlabel("Average valence")
graph_val_avg = plt.ylabel("Frequency")
graph23 = plt.legend(loc='upper left')
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
Let's make the dataframe a little bit cleaner.
In [74]:
# making close_date and open_date by concatenating the year, month and day
import datetime
close_date = []
for index, row in bom_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)
bom_df['close_date'] = close_date
bom_df['open_date']=bom_df.open_year + '-' + bom_df.open_month + '-' + bom_df.open_day
bom_df['open_date']=bom_df['open_date'].apply(pd.datetools.parse)
# dropping unnecessary columns
bom_df = bom_df.drop('close_day', 1)
bom_df = bom_df.drop('close_month', 1)
bom_df = bom_df.drop('open_day', 1)
bom_df = bom_df.drop('open_month', 1)
Great! Let's take a look at all the dataframes we've created
In [75]:
bom_df.head()
Out[75]:
close_date
close_year
gross
open_date
open_year
opening_gross
opening_theaters
ranking
title
total_theaters
year
0
None
1991
285761243
1990-11-16
1990
17081997
1202
1
Home Alone
2173
1990
1
None
1991
217631306
1990-07-13
1990
12191540
1101
2
Ghost
1766
1990
2
None
1991
184208848
1990-11-09
1990
598257
14
3
Dances with Wolves
1636
1990
3
None
1991
178406268
1990-03-23
1990
11280591
1325
4
Pretty Woman
1811
1990
4
None
1991
135265915
1990-03-30
1990
25398367
2006
5
Teenage Mutant Ninja Turtles
2377
1990
5
None
1991
122012643
1990-03-02
1990
17161835
1225
6
The Hunt for Red October
1817
1990
6
None
1991
119394840
1990-06-01
1990
25533700
2060
7
Total Recall
2131
1990
7
None
1991
117540947
1990-07-06
1990
21744661
2507
8
Die Hard 2: Die Harder
2507
1990
8
None
1991
103738726
1990-06-15
1990
22543911
2332
9
Dick Tracy
2332
1990
9
None
1991
91457688
1990-12-22
1990
7918560
1833
10
Kindergarten Cop
1937
1990
10
None
1991
87727583
1990-05-25
1990
19089645
2019
11
Back to the Future Part III
2070
1990
11
None
1991
86303188
1990-07-27
1990
11718981
1349
12
Presumed Innocent
1451
1990
12
None
1991
82670733
1990-06-29
1990
15490445
2307
13
Days of Thunder
2307
1990
13
None
1991
80818974
1990-06-08
1990
19475559
2721
14
Another 48 HRS.
2721
1990
14
None
1991
71609321
1990-11-21
1990
13774642
1281
15
Three Men and a Little Lady
1614
1990
15
None
1991
70978012
1990-05-18
1990
15338160
1944
16
Bird on a Wire
2008
1990
16
None
1991
66666062
1990-12-25
1990
19558558
1901
17
The Godfather Part III
1922
1990
17
None
1991
61489265
1990-08-10
1990
10034685
1319
18
Flatliners
1483
1990
18
None
1991
61276872
1990-11-30
1990
10076834
1244
19
Misery
1370
1990
19
None
1991
56362352
1990-12-07
1990
159622
2
20
Edward Scissorhands
1372
1990
20
None
1991
53470891
1990-07-27
1990
10026900
1714
21
Problem Child
1769
1990
21
None
1991
53208180
1990-07-20
1990
8045760
1479
22
Arachnophobia
2005
1990
22
None
1991
52096475
1990-12-22
1990
417076
12
23
Awakenings
1330
1990
23
None
1991
47789074
1990-12-14
1990
8100640
1576
24
Look Who's Talking Too
1647
1990
24
None
1991
47410827
1990-02-09
1990
9213631
1301
25
Hard to Kill
1508
1990
25
None
1991
46836214
1990-09-19
1990
6368901
1070
26
Goodfellas
1328
1990
26
None
1991
46044396
1990-10-05
1990
11790047
1968
27
Marked for Death
1974
1990
27
None
1991
45681173
1990-06-22
1990
14145411
1768
28
Robocop 2
1806
1990
28
None
1991
44645619
1990-07-13
1990
7708029
1901
29
The Jungle Book (re-issue) (1990)
1923
1990
29
None
1991
44143410
1990-08-03
1990
8017438
1770
30
Young Guns II
1770
1990
...
...
...
...
...
...
...
...
...
...
...
...
2570
None
2016
31090320
2015-10-16
2015
13143310
2984
71
Crimson Peak
2991
2015
2571
None
2016
29476394
2015-09-02
2015
8246267
1960
72
A Walk in the Woods
2158
2015
2572
2015-10-29 00:00:00
2015
27740955
2015-08-21
2015
10542116
2766
73
Sinister 2
2799
2015
2573
2015-11-26 00:00:00
2015
27288872
2015-08-26
2015
8111264
3355
74
No Escape
3415
2015
2574
None
2016
27121602
2015-10-23
2015
10812861
3082
75
The Last Witch Hunter
3082
2015
2575
2015-10-11 00:00:00
2015
26822144
2015-08-07
2015
6610961
1603
76
Ricki and the Flash
2064
2015
2576
2015-03-19 00:00:00
2015
26501323
2015-01-02
2015
15027415
2602
77
The Woman in Black 2: Angel of Death
2602
2015
2577
2015-05-07 00:00:00
2015
26461644
2015-03-13
2015
11012305
3171
78
Run All Night
3171
2015
2578
2015-06-11 00:00:00
2015
25801047
2015-02-27
2015
10203437
2666
79
The Lazarus Effect
2666
2015
2579
2015-09-03 00:00:00
2015
25442958
2015-04-10
2015
237264
4
80
Ex Machina
2004
2015
2580
None
2016
22822987
2015-11-13
2015
8317545
2603
81
Love the Coopers
2603
2015
2581
2015-09-17 00:00:00
2015
22764410
2015-07-10
2015
9808463
2720
82
The Gallows
2720
2015
2582
2015-10-15 00:00:00
2015
22467450
2015-08-21
2015
8326530
3261
83
Hitman: Agent 47
3273
2015
2583
2015-03-26 00:00:00
2015
22348241
2015-01-30
2015
8310252
2893
84
Project Almanac
2900
2015
2584
2015-05-14 00:00:00
2015
21571189
2015-01-30
2015
6213362
1823
85
Black or White
1823
2015
2585
2015-07-30 00:00:00
2015
21067116
2015-05-29
2015
9670235
2815
86
Aloha
2815
2015
2586
2015-10-22 00:00:00
2015
19375982
2015-08-05
2015
4038962
2320
87
Shaun the Sheep Movie
2360
2015
2587
2015-05-21 00:00:00
2015
18754371
2015-01-16
2015
197000
12
88
Still Alice
1318
2015
2588
None
2016
18300124
2015-10-23
2015
8070493
1656
89
Paranormal Activity: The Ghost Dimension
1656
2015
2589
None
2016
17753943
2015-10-09
2015
521522
4
90
Steve Jobs
2493
2015
2590
2015-11-05 00:00:00
2015
17737646
2015-07-17
2015
2434908
361
91
Mr. Holmes
898
2015
2591
2015-09-17 00:00:00
2015
17506470
2015-06-19
2015
6100010
2002
92
Dope
2002
2015
2592
None
2016
17402274
2015-11-20
2015
6652996
2392
93
The Secret in their Eyes (2015)
2392
2015
2593
None
2016
17265460
2015-12-04
2015
16293325
2902
94
Krampus
2902
2015
2594
2015-03-19 00:00:00
2015
17223265
2015-02-06
2015
7217640
2875
95
Seventh Son
2875
2015
2595
None
2016
16792305
2015-11-06
2015
295009
5
96
Spotlight
1089
2015
2596
2015-07-23 00:00:00
2015
16432322
2015-04-17
2015
4577861
2012
97
Monkey Kingdom
2012
2015
2597
2015-11-19 00:00:00
2015
16029670
2015-09-04
2015
7355622
3434
98
The Transporter Refueled
3434
2015
2598
2015-06-25 00:00:00
2015
14674076
2015-03-13
2015
160089
4
99
It Follows
1655
2015
2599
2015-10-08 00:00:00
2015
14440985
2015-08-21
2015
5454284
2778
100
American Ultra
2778
2015
2600 rows × 11 columns
Now, we want to make a new dataframe flattened_df that we can use to run our regressions on. This dataframe will include all the columns in movie_df and the extra columns
In [76]:
# set index to title
indexed_df = bom_df.set_index("title")
In [77]:
# use groupby to get the review_count, star_avg, valence_avg, valence_sum, pct_scorables, n_scorables
gold = valence_df.groupby("movie_name")
n_scorables = gold.n_scorables.mean()
pct_scorables = gold.pct_scorables.mean()
review_count = gold.movie_name.count()
star_avg = gold.stars.mean()
positive = gold.positive.mean()
valence_avg = gold.valence_avg.mean()
valence_sum = gold.valence_sum.mean()
In [78]:
# concatenate the two dfs into our final dataframe flattened_df
flattened_df = pd.concat([indexed_df, review_count], axis=1, join_axes=[indexed_df.index])
flattened_df.rename(columns={'movie_name': 'review_count'}, inplace=True)
flattened_df = pd.concat([flattened_df, star_avg], axis=1, join_axes=[indexed_df.index])
flattened_df.rename(columns={'stars': 'star_avg'}, inplace=True)
flattened_df = pd.concat([flattened_df, positive], axis=1, join_axes=[indexed_df.index])
flattened_df = pd.concat([flattened_df, valence_avg], axis=1, join_axes=[indexed_df.index])
# flattened_df.rename(columns={'movie_name': 'review_count'}, inplace=True)
flattened_df = pd.concat([flattened_df, valence_sum], axis=1, join_axes=[indexed_df.index])
# flattened_df.rename(columns={'movie_name': 'review_count'}, inplace=True)
flattened_df = pd.concat([flattened_df, n_scorables], axis=1, join_axes=[indexed_df.index])
flattened_df = pd.concat([flattened_df, pct_scorables], axis=1, join_axes=[indexed_df.index])
# flattened_df.rename(columns={'absolute_stars': 'abs_star_avg'}, inplace=True)
In [79]:
flattened_df.head()
Out[79]:
close_date
close_year
gross
open_date
open_year
opening_gross
opening_theaters
ranking
total_theaters
year
review_count
star_avg
positive
valence_avg
valence_sum
n_scorables
pct_scorables
title
Home Alone
None
1991
285761243
1990-11-16
1990
17081997
1202
1
2173
1990
NaN
NaN
NaN
NaN
NaN
NaN
NaN
Ghost
None
1991
217631306
1990-07-13
1990
12191540
1101
2
1766
1990
NaN
NaN
NaN
NaN
NaN
NaN
NaN
Dances with Wolves
None
1991
184208848
1990-11-09
1990
598257
14
3
1636
1990
NaN
NaN
NaN
NaN
NaN
NaN
NaN
Pretty Woman
None
1991
178406268
1990-03-23
1990
11280591
1325
4
1811
1990
2
1
0
0.407409
84.055626
338
0.613324
Teenage Mutant Ninja Turtles
None
1991
135265915
1990-03-30
1990
25398367
2006
5
2377
1990
NaN
NaN
NaN
NaN
NaN
NaN
NaN
Let's create a new column for the absolute value of the valence averages of the reviews for a particular movie. This will eventually test the hypothesis of whether or not polarizing movies make a lot of money.
In [46]:
valence_df['abs_valence_avg'] = np.abs(valence_df['valence_avg']-np.mean(flattened_df['valence_avg']))
abs_valence_avg = gold.abs_valence_avg.mean()
flattened_df = pd.concat([flattened_df, abs_valence_avg], axis=1, join_axes=[indexed_df.index])
In [47]:
flattened_df.head()
Out[47]:
close_date
close_year
gross
open_date
open_year
opening_gross
opening_theaters
ranking
total_theaters
year
review_count
star_avg
positive
valence_avg
valence_sum
n_scorables
pct_scorables
abs_valence_avg
title
Home Alone
None
1991
285761243
1990-11-16
1990
17081997
1202
1
2173
1990
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
Ghost
None
1991
217631306
1990-07-13
1990
12191540
1101
2
1766
1990
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
Dances with Wolves
None
1991
184208848
1990-11-09
1990
598257
14
3
1636
1990
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
Pretty Woman
None
1991
178406268
1990-03-23
1990
11280591
1325
4
1811
1990
2
1
0
0.407409
84.055626
338
0.613324
0.129283
Teenage Mutant Ninja Turtles
None
1991
135265915
1990-03-30
1990
25398367
2006
5
2377
1990
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
Now let's take only those movies that appear in the IMDB data set, i.e. those that have a review count.
In [48]:
flattened_df = flattened_df[~flattened_df['review_count'].map(np.isnan)]
print "Num. overlapping movies: ", flattened_df.shape[0]
Out[48]:
(334, 18)
The purchasing power of individuals change over time due to the changes in the value of money. Thus, to compare the true value of movies that came out in different years, we need yearly U.S. inflation data.
For instance, Avatar looks like highest grossing movie of all time, but when adjusted for inflation, turns out it's actually Gone with the Wind.
Hence, we need to adjust the gross incomes of movies for inflation over the past 26 years to get better insight into how successful they truly were in the box office.
In [80]:
inflation = pd.read_csv("inf.csv")
In [81]:
years_90 = range(1990,2015)
infdict = {}
infindex, infvalue = 0, 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 [82]:
infdict
Out[82]:
{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}
For movies that are released this year, we use the inflation data of 2014, since the 2015 numbers will be released in 2016
In [89]:
newgross, newopengross = [], []
for gross, opengross, openyr in zip(flattened_df.gross, flattened_df.opening_gross, flattened_df.open_year):
if int(openyr) == 2015:
openyr = 2014
newgross.append(infdict[int(openyr)]*gross)
newopengross.append(infdict[int(openyr)]*opengross)
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-89-60ecfe487ae8> in <module>()
1 newgross, newopengross = [], []
2 for gross, opengross, openyr in zip(flattened_df.gross, flattened_df.opening_gross, flattened_df.open_year):
----> 3 newgross.append(infdict[int(openyr)]*gross)
4 newopengross.append(infdict[int(openyr)]*opengross)
KeyError: 2015
In [54]:
dftouse = flattened_df
dftouse['adj_gross'] = newgross
dftouse['adj_opening_gross'] = newopengross
In [55]:
# 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 [56]:
dftouse.head()
Out[56]:
close_date
close_year
gross
open_date
open_year
opening_gross
opening_theaters
ranking
total_theaters
year
review_count
star_avg
positive
valence_avg
valence_sum
n_scorables
pct_scorables
abs_valence_avg
adj_gross
adj_opening_gross
adj_gross_bin
adj_opening_gross_bin
title
Pretty Woman
None
1991
178406268
1990-03-23
1990
11280591
1325
4
1811
1990
2
1.000000
0.0
0.407409
84.055626
338.000000
0.613324
0.129283
1.880366e+08
11889512.388355
1
0
Dick Tracy
None
1991
103738726
1990-06-15
1990
22543911
2332
9
2332
1990
24
8.875000
1.0
0.619329
46.075866
142.750000
0.580769
0.294604
1.093385e+08
23760821.495652
1
1
Flatliners
None
1991
61489265
1990-08-10
1990
10034685
1319
18
1483
1990
30
6.966667
0.8
0.341229
27.310654
145.866667
0.616311
0.164876
6.480843e+07
10576352.925191
0
0
Problem Child
None
1991
53470891
1990-07-27
1990
10026900
1714
21
1769
1990
8
2.500000
0.0
0.181693
15.963458
120.000000
0.607353
0.192622
5.635723e+07
10568147.694282
0
0
Marked for Death
None
1991
46044396
1990-10-05
1990
11790047
1968
27
1974
1990
3
4.000000
0.0
0.052945
10.623149
376.000000
0.564525
0.277000
4.852985e+07
12426468.601316
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 [57]:
keep = ['ranking','total_theaters', 'year', 'review_count', 'star_avg','positive','valence_avg','valence_sum',
'n_scorables','pct_scorables','abs_valence_avg','adj_gross', 'adj_opening_gross', 'adj_gross_bin','adj_opening_gross_bin']
dftouse = dftouse[keep]
In [58]:
dftouse.head()
Out[58]:
ranking
total_theaters
year
review_count
star_avg
positive
valence_avg
valence_sum
n_scorables
pct_scorables
abs_valence_avg
adj_gross
adj_opening_gross
adj_gross_bin
adj_opening_gross_bin
title
Pretty Woman
4
1811
1990
2
1.000000
0.0
0.407409
84.055626
338.000000
0.613324
0.129283
1.880366e+08
11889512.388355
1
0
Dick Tracy
9
2332
1990
24
8.875000
1.0
0.619329
46.075866
142.750000
0.580769
0.294604
1.093385e+08
23760821.495652
1
1
Flatliners
18
1483
1990
30
6.966667
0.8
0.341229
27.310654
145.866667
0.616311
0.164876
6.480843e+07
10576352.925191
0
0
Problem Child
21
1769
1990
8
2.500000
0.0
0.181693
15.963458
120.000000
0.607353
0.192622
5.635723e+07
10568147.694282
0
0
Marked for Death
27
1974
1990
3
4.000000
0.0
0.052945
10.623149
376.000000
0.564525
0.277000
4.852985e+07
12426468.601316
0
0
In [60]:
# Let's save
fp = open("dftouse.json","w")
json.dump(dftouse.to_dict(), fp)
fp.close()
In [8]:
with open("complete_valence.json", "r") as fp:
complete_df_dict = json.load(fp)
completedf = pd.DataFrame(complete_df_dict)
In [14]:
completedf = completedf.reset_index().drop('index',1)
dftouse = completedf
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
In [34]:
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, m*x + b, '-')
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'
The above scatterplots seem to show that there is little correlation between opening gross and all three predictors.
However, we foresee a limitation to our analysis. The reviews dataset we have was constructed by taking the most polarizing reviews that did not represent the realistic viewership response. For example, very successful movies that have bad reviews might be predicted as lowly grossing because they have a few poor reviews and it is in our dataset. To curb this problem, we are going to divide our dataset into movies that that received poor reviews and movies receivin good reviews. This is defined as the reviewer giving less than 4 stars and the reviewer giving more than 7 stars. We then conduct our regression analysis for each of these sub-datasets. Our hypothesis is that good, higher grossing movies that received poor reviews will receive less poorer reviews on average. In contrast, the good movies that received good reviews should receive higher reviews on average. We create these separate datasets below.
In [20]:
dftouse_small = dftouse[dftouse['valence_avg'] <= np.mean(dftouse['valence_avg'])]
dftouse_large = dftouse[dftouse['valence_avg'] >= np.mean(dftouse['valence_avg'])]
In [21]:
# And now, regressions
reg_list = []
og_val_avg = ols('adj_opening_gross ~ valence_avg',dftouse).fit()
og_val_avg_small = ols('adj_opening_gross ~ valence_avg',dftouse_small).fit()
og_val_avg_large = ols('adj_opening_gross ~ valence_avg',dftouse_large).fit()
og_abs_val_avg_large = ols('adj_opening_gross ~ abs_valence_avg',dftouse).fit()
og_val_sum = ols('adj_opening_gross ~ valence_sum',dftouse).fit()
og_val_sum_small = ols('adj_opening_gross ~ valence_sum',dftouse_small).fit()
og_val_sum_large = ols('adj_opening_gross ~ valence_sum',dftouse_large).fit()
reg_list.append(og_val_avg)
reg_list.append(og_val_avg)
reg_list.append(og_val_avg_small)
reg_list.append(og_val_avg_large)
reg_list.append(og_abs_val_avg_large)
reg_list.append(og_val_sum)
reg_list.append(og_val_sum_small)
reg_list.append(og_val_sum_large)
og_val_avg.summary()
og_val_avg_small.summary()
og_val_avg_large.summary()
og_abs_val_avg_large.summary()
og_val_sum.summary()
og_val_sum_small.summary()
og_val_sum_large.summary()
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-21-e535dc2ef9cf> in <module>()
2 reg_list = []
3
----> 4 og_val_avg = ols('adj_opening_gross ~ valence_avg',dftouse).fit()
5 og_val_avg_small = ols('adj_opening_gross ~ valence_avg',dftouse_small).fit()
6 og_val_avg_large = ols('adj_opening_gross ~ valence_avg',dftouse_large).fit()
/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/statsmodels/base/model.pyc in from_formula(cls, formula, data, subset, *args, **kwargs)
145 (endog, exog), missing_idx = handle_formula_data(data, None, formula,
146 depth=eval_env,
--> 147 missing=missing)
148 kwargs.update({'missing_idx': missing_idx,
149 'missing': missing})
/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/statsmodels/formula/formulatools.pyc in handle_formula_data(Y, X, formula, depth, missing)
63 if data_util._is_using_pandas(Y, None):
64 result = dmatrices(formula, Y, depth, return_type='dataframe',
---> 65 NA_action=na_action)
66 else:
67 result = dmatrices(formula, Y, depth, return_type='dataframe',
/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/patsy/highlevel.pyc in dmatrices(formula_like, data, eval_env, NA_action, return_type)
295 eval_env = EvalEnvironment.capture(eval_env, reference=1)
296 (lhs, rhs) = _do_highlevel_design(formula_like, data, eval_env,
--> 297 NA_action, return_type)
298 if lhs.shape[1] == 0:
299 raise PatsyError("model is missing required outcome variables")
/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/patsy/highlevel.pyc in _do_highlevel_design(formula_like, data, eval_env, NA_action, return_type)
150 return iter([data])
151 builders = _try_incr_builders(formula_like, data_iter_maker, eval_env,
--> 152 NA_action)
153 if builders is not None:
154 return build_design_matrices(builders, data,
/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/patsy/highlevel.pyc in _try_incr_builders(formula_like, data_iter_maker, eval_env, NA_action)
55 formula_like.rhs_termlist],
56 data_iter_maker,
---> 57 NA_action)
58 else:
59 return None
/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/patsy/build.pyc in design_matrix_builders(termlists, data_iter_maker, NA_action)
658 factor_states,
659 data_iter_maker,
--> 660 NA_action)
661 # Now we need the factor evaluators, which encapsulate the knowledge of
662 # how to turn any given factor into a chunk of data:
/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/patsy/build.pyc in _examine_factor_types(factors, factor_states, data_iter_maker, NA_action)
422 for data in data_iter_maker():
423 for factor in list(examine_needed):
--> 424 value = factor.eval(factor_states[factor], data)
425 if factor in cat_sniffers or guess_categorical(value):
426 if factor not in cat_sniffers:
/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/patsy/eval.pyc in eval(self, memorize_state, data)
483 # http://nedbatchelder.com/blog/200711/rethrowing_exceptions_in_python.html
484 def eval(self, memorize_state, data):
--> 485 return self._eval(memorize_state["eval_code"], memorize_state, data)
486
487 def test_EvalFactor_basics():
/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/patsy/eval.pyc in _eval(self, code, memorize_state, data)
466 self,
467 self._eval_env.eval,
--> 468 code, inner_namespace=inner_namespace)
469
470 def memorize_chunk(self, state, which_pass, data):
/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/patsy/compat.pyc in call_and_wrap_exc(msg, origin, f, *args, **kwargs)
115 def call_and_wrap_exc(msg, origin, f, *args, **kwargs):
116 try:
--> 117 return f(*args, **kwargs)
118 except Exception as e:
119 if sys.version_info[0] >= 3:
/Users/alpkaancelik/anaconda/lib/python2.7/site-packages/patsy/eval.pyc in eval(self, expr, source_name, inner_namespace)
125 code = compile(expr, source_name, "eval", self.flags, False)
126 return eval(code, {}, VarLookupDict([inner_namespace]
--> 127 + self._namespaces))
128
129 @classmethod
<string> in <module>()
NameError: name 'adj_opening_gross' is not defined
Unfortunately, our regressions above didn't give us significant results, meaning that there might not a strong relationship between our valance variables and gross box office success.
Now, we attempt to fit a logistic regression on our data to see if we'll get a better result.
In [69]:
logit_model = logit('adj_gross_bin ~ abs_valence_avg',dftouse).fit()
print logit_model.summary()
Optimization terminated successfully.
Current function value: 0.620079
Iterations 5
Logit Regression Results
==============================================================================
Dep. Variable: adj_gross_bin No. Observations: 331
Model: Logit Df Residuals: 329
Method: MLE Df Model: 1
Date: Sun, 06 Dec 2015 Pseudo R-squ.: 0.01112
Time: 21:28:47 Log-Likelihood: -205.25
converged: True LL-Null: -207.55
LLR p-value: 0.03168
===================================================================================
coef std err z P>|z| [95.0% Conf. Int.]
-----------------------------------------------------------------------------------
Intercept -0.1770 0.292 -0.605 0.545 -0.750 0.396
abs_valence_avg -2.8592 1.357 -2.107 0.035 -5.518 -0.200
===================================================================================
Clearly not the way to go.
Looking at the regression results above, we can see our valence data is not strongly correlated with gross box office numbers. However, this does not imply that there is not a relationship between movie reviews and box office success. It is certainly possible that there are different underlying structures for various groups of movies in the world.
A neat way to discover these structures is to implement a Clustering Algorithm. Using the sklearn.mixture.GMM, we can determine if there are underlying Gaussian distributions in the population that is creating the patches of data we see in our sample. Thus, we can determine which subsets of our data we should be running our regressions on.
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, so we should take this into account when doing further analysis.
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)
In [147]:
# with open("IMDB_dftouse_dict.json", "r") as fd:
# IMDB_dftouse_dict = json.load(fd)
# maas_IMDB = pd.DataFrame(IMDB_dftouse_dict)
with open("valence_df_dict.json", "r") as fp:
valence_df_dict = json.load(fp)
maas_IMDB = pd.DataFrame(valence_df_dict)
In [148]:
maas_IMDB.head()
Out[148]:
movie_id
movie_name
n_scorables
pct_scorables
positive
stars
text
url
valence_avg
valence_sum
0
10027
Titanic
120
0.666667
1
7
Sure, Titanic was a good movie, the first time...
http://www.imdb.com/title/tt0120338/usercommen...
0.479760
38.380826
1
10028
Titanic
65
0.538462
1
10
When I saw this movie I was stunned by what a ...
http://www.imdb.com/title/tt0120338/usercommen...
0.508760
17.806611
10
10037
Titanic
95
0.589474
1
9
The ship may have sunk but the movie didn't!!!...
http://www.imdb.com/title/tt0120338/usercommen...
0.700832
39.246578
100
11015
Cliffhanger
84
0.500000
1
9
This movie is directed by Renny Harlin the fin...
http://www.imdb.com/title/tt0106582/usercommen...
0.794284
33.359933
1000
10544
The First Power
194
0.618557
0
4
When childhood memory tells you this was a sca...
http://www.imdb.com/title/tt0099578/usercommen...
0.132427
15.891239
In [149]:
maas_IMDB.shape
Out[149]:
(4111, 10)
In [150]:
with open("happiness.json", "r") as fp:
happiness = json.load(fp)
In [151]:
len(happiness)
Out[151]:
10222
This will take the form of a few steps.
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
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']])
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']],
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u'fan',
u'jump',
u'stint'],
[u'character', u'attribute'],
[u'anal-retentive', u'character', u'offset', u'demeanor'],
[u'movie', u'laugh', u'time', u'bit', u'movie']],
[[u'flick', u'pro'],
[u'comedy',
u'vehicle',
u'character',
u'sequel',
u'movie',
u'border',
u'cult',
u'obsession'],
[u'group',
u'grandson',
u'role',
u'movie',
u'board.<br',
u'fun',
u'romp',
u'company.<br',
u'movie',
u'entertainment']],
[[u'scripting', u'comedy', u'movie', u'year'],
[u'character', u'bit'],
[u'timing', u'script', u'underdog', u'plot'],
[u'age', u'set', u'reference', u'galley', u'scene'],
[u'movie']],
[[u'plot', u'world', u'comedy', u'movie', u'rule'],
[u'movie'],
[u'guy', u'segment'],
[u'actor', u'movie', u'character', u'movie'],
[u'actor', u'shot', u'character', u'movie', u'year', u'sub'],
[u'chance', u'brand', u'sub'],
[u'crew', u'misfit'],
[u'series'],
[u'thing', u'officer', u'officer', u'woman'],
[u'commander',
u'position',
u'movie',
u'fair',
u'fact',
u'movie',
u'theater'],
[u'silence',
u'fart',
u'reaction',
u'smell',
u'movie',
u'fun',
u'movie',
u'hate',
u'movie']],
[[u'comedy', u'heart', u'well.<br'],
[u'piece', u'art'],
[u'enterprise', u'ship', u'diesel', u'too.<br', u'scene', u'stingray'],
[u'matter', u'sir'],
[u'problem.<br', u'story'],
[u'sub', u'task', u'diesel', u'sub', u'attack'],
[u'eye', u'star', u'grudge', u'crew', u'screw'],
[u'crew', u'working', u'team', u'date', u'sub']],
[[u'intelligence', u'fleet'],
[u'friend', u'yard', u'sale', u'diesel', u'fleet'],
[u'country', u'hand', u'baby', u'warhead'],
[u'year',
u'command',
u'diesel',
u'sub',
u'exercise',
u'surface',
u'fleet',
u'line',
u'attack',
u'sub'],
[u'command',
u'time',
u'whip',
u'news',
u'bear',
u'err',
u'group',
u'submariner',
u'warrior'],
[u'penis', u'commander', u'favorite', u'delegate', u'authority', u'manner'],
[u'piece', u'recruitment', u'propaganda', u'gem', u'creator']],
[[u'adventure',
u'lead',
u'role',
u'feature',
u'sub',
u'commander',
u'sub',
u'series',
u'war-game',
u'crew',
u'man',
u'woman',
u'too).<br',
u'writer',
u'comedy',
u'film'],
[u'character', u'crew', u'plenty', u'movie', u'humor', u'sword'],
[u'member',
u'scene',
u'gesture',
u'impersonation',
u'personalities.<br',
u'version'],
[u'film', u'job', u'way']],
[[u'movie', u'family'],
[u'eye', u'mention', u'movie', u'movie'],
[u'movie'],
[u'bit', u'sheep', u'navy'],
[u'kind', u'guy', u'golf', u'sub', u'ball', u'golf', u'course'],
[u'kind', u'guy', u'wake', u'hangover', u'tattoo', u'dongle'],
[u'application',
u'command',
u'sub',
u'time',
u'sub',
u'time',
u'desk',
u'job',
u'life'],
[u'admiralty', u'boat', u'sub'],
[u'diesel', u'sub', u'relic'],
[u'mission',
u'war',
u'game.<br',
u'bit',
u'boat',
u'point',
u'talk',
u'torn'],
[u'sub', u'world', u'country'],
[u'renegade', u'captain', u'diesel', u'sub', u'warhead', u'basis'],
[u'admiralty', u'war', u'game'],
[u'mission', u'sea', u'navy', u'basis'],
[u'battle', u'course.<br', u'plot'],
[u'movie', u'gag'],
[u'laugh', u'night', u'look']],
[[u'documentary', u'movie', u'list', u'actor', u'director', u'movie'],
[u'theme',
u'script',
u'cast',
u'director',
u'lot',
u'money',
u'movie.<br',
u'proof',
u'corollary',
u'theory'],
[u'screenplay'],
[u'actor', u'unknown', u'movie'],
[u'movie', u'case'],
[u'character',
u'actor',
u'enjoyability',
u'film.<br',
u'dynamic',
u'movie',
u'magic',
u'film',
u'transition',
u'line',
u'line',
u'dialog.<br',
u'premise',
u'lot',
u'lot',
u'inaccuracy',
u'thing',
u'world',
u'navy'],
[u'merchant',
u'marine',
u'feel',
u'movie',
u'mark',
u'life',
u'sea',
u'goes.<br',
u'movie',
u'family',
u'film',
u'fan',
u'lot',
u'wit',
u'sarcasm']],
[[u'video',
u'tape',
u'year',
u'watch',
u'year.<br',
u'wit',
u'movie',
u'success'],
[u'formula'],
[u'ratings.<br', u'comedy'],
[u'fun', u'flick']],
[[u'actor', u'movie', u'way'],
[u'detective', u'mob', u'boss', u'city'],
[u'time', u'boy', u'man', u'city', u'singer', u'girlfriend', u'eye'],
[u'face'],
[u'villian', u'book', u'collection.<br', u'movie'],
[u'movie', u'age', u'fun', u'movie', u'family']],
[[u'role', u'sexpot', u'brain'],
[u'line', u'catch', u'phrase'],
[u'movie', u'plot', u'script', u'surprise', u'time'],
[u'phrase',
u'term',
u'head',
u'obstacle',
u'sheer',
u'accuracy',
u'realism',
u'movie',
u'character']],
[[u'movie', u'cast', u'character', u'script', u'synergy'],
[u'character', u'truth', u'person', u'team', u'goal', u'morality'],
[u'situation', u'movie', u'opening', u'premise'],
[u'tension', u'humor'],
[u'movie', u'movie', u'term', u'entertaining'],
[u'music',
u'copy',
u'music',
u'post',
u'war',
u'navy',
u'movie',
u'humor',
u'point',
u'greatness',
u'eye',
u'beholder'],
[u'scene', u'credit', u'music', u'video', u'screen', u'feature'],
[u'scene', u'cut', u'casting', u'acting', u'movie', u'pretension'],
[u'guy', u'gal.<br', u'day', u'thought', u'film'],
[u'fortune'],
[u'wife', u'night', u'screen'],
[u'movie', u'level', u'budget', u'film', u'winner'],
[u'home',
u'video',
u'movie',
u'vault',
u'shame',
u'shot',
u'color',
u'year',
u'release'],
[u'copy'],
[u'eye',
u'candy',
u'film',
u'credit',
u'sequence',
u'screen',
u'music',
u'video',
u'feature']],
[[u'woman',
u'enchanted',
u'castle',
u'coast',
u'novel',
u'film',
u'virtue',
u'script',
u'ensemble'],
[u'element',
u'movie',
u'feeling',
u'appreciation',
u'story',
u'resolution.<br',
u'actress'],
[u'wife', u'longing', u'wisteria', u'tranquillity'],
[u'housewife', u'responsibility', u'hostess', u'castle'],
[u'noblewoman', u'throng', u'male', u'admirer'],
[u'scene', u'lady', u'memory', u'past.<br', u'man', u'story'],
[u'husband', u'role', u'researcher', u'personality', u'writer'],
[u'spouse',
u'man',
u'attempt',
u'bath',
u'bathtub',
u'assistance',
u'castle',
u'servant'],
[u'decade',
u'actor',
u'lady',
u'landlord.<br',
u'maven',
u'proprietress',
u'housewife']],
[[u'kind', u'movie', u'poster', u'trailer'],
[u'kind',
u'movie',
u'waitress',
u'cross',
u'country',
u'adventure',
u'crook',
u'fact',
u'film',
u'tale',
u'person',
u'dream'],
[u'unpredictability'],
[u'violence', u'dream', u'reality', u'drama', u'comedy']],
[[u'summer', u'retread', u'disappointment', u'breath', u'air'],
[u'picture', u'man', u'woman', u'sex', u'film', u'brillant', u'company'],
[u'performance',
u'career',
u'waitress',
u'death',
u'husband',
u'mainstay',
u'fantasy',
u'world'],
[u'trick', u'film', u'character'],
[u'love', u'image', u'soap', u'opera'],
[u'film', u'life', u'humor', u'love', u'violence']],
[[u'movie'],
[u'moment'],
[u'rest', u'cast', u'character'],
[u'story', u'twist'],
[u'formula', u'movie']],
[[u'film', u'trip', u'town'],
[u'soap', u'groupie'],
[u'character', u'forgiveness', u'goof'],
[u'fantasy', u'fact', u'speak', u'devil', u'role'],
[u'wish', u'wish', u'chair'],
[u'friendship'],
[u'cold', u'winter', u'day']],
[[u'writing', u'acting', u'plot-twister', u'film', u'year'],
[u'casting', u'direction', u'film', u'performance', u'soul'],
[u'time'],
[u'tongue', u'cheek', u'performance'],
[u'spoof', u'soap', u'opera', u'movie']],
[[u'question',
u'film',
u'people?<br',
u'answer',
u'film',
u'whiney',
u'note',
u'voice',
u'voice'],
[u'fact', u'reality', u'time', u'film', u'task.<br'],
[u'sublimate',
u'energy',
u'surface',
u'most.<br',
u'allusion',
u'time',
u'film'],
[u'point',
u'song',
u'sera',
u'sera',
u'soundtrack',
u'thing',
u'film',
u'performance',
u'cast.<br',
u'predictable.<br',
u'reason',
u'film',
u'master',
u'voice']],
[[u'goof', u'error<br', u'room', u'suicide', u'gun', u'silencer'],
[u'second', u'bang', u'gun']],
[[u'book',
u'movie',
u'villain',
u'life',
u'screen',
u'smile',
u'line',
u'dialog'],
[u'manner'],
[u'homage',
u'gangster',
u'picture',
u'time',
u'meant',
u'kid',
u'archetype',
u'color',
u'criminal',
u'tommy',
u'gun',
u'personality'],
[u'film',
u'continuation',
u'sight',
u'gag',
u'seriously.<br',
u'time',
u'movie',
u'depiction',
u'reality',
u'comic-book',
u'outline',
u'old-school'],
[u'story',
u'cop',
u'crook',
u'cop',
u'detective',
u'bust',
u'boy',
u'performance',
u'date',
u'sense',
u'sense',
u'style',
u'luck'],
[u'kid', u'spunk', u'kid'],
[u'nightclub',
u'dame',
u'kind',
u'performance',
u'suit',
u'role',
u'fine',
u'feeling'],
[u'dealing',
u'figure',
u'voice.<br',
u'impersonation',
u'film',
u'splash',
u'effect',
u'explosion',
u'comedy',
u'action',
u'syrup',
u'stack',
u'pancake'],
[u'world',
u'shot',
u'thrust',
u'plot',
u'line',
u'editing',
u'montage',
u'camera',
u'angle',
u'panel',
u'comic'],
[u'sequence', u'story', u'music', u'punch', u'gun-shot', u'way'],
[u'heap-load',
u'dialog',
u'script',
u'favorite',
u'enemy',
u'enemy',
u'enemy',
u'reference',
u'figure',
u'quotes).<br',
u'time',
u'movie',
u'theater',
u'bit',
u'fantasy',
u'degree'],
[u'cheer',
u'kid',
u'action',
u'intent',
u'cousin',
u'comic-book',
u'movie',
u'pg-13',
u'fare',
u'adult',
u'throw-back',
u'panache',
u'feeling'],
[u'ham', u'word']],
[[u'nurse',
u'september',
u'film',
u'festival',
u'film',
u'sea',
u'vlissingen'],
[u'day', u'time', u'evening', u'movie', u'taste', u'festival'],
[u'nurse', u'ren\xe9e', u'zellweger', u'girl', u'love', u'soap-opera-star'],
[u'role', u'freeman', u'rock', u'\xe0nd', u'ren\xe9e', u'zellweger'],
[u'mix', u'romance', u'violence', u'roadmovie'],
[u'soap-opera-lover', u'nurse', u'movie', u'character'],
[u'friend',
u'cause',
u'story',
u'line',
u'freeman',
u'rock',
u'scene',
u'thriller-aspect',
u'picture'],
[u'end', u'nurse', u'charm']],
[[u'combination', u'plot', u'character'],
[u'way', u'job', u'work', u'villain'],
[u'standout'],
[u'head'],
[u'sort',
u'sparkle',
u'performance',
u'part.<br',
u'road',
u'movie',
u'synopsis'],
[u'road']],
[[u'film'],
[u'profanity', u'violence', u'innuendo', u'movie', u'child'],
[u'waitress', u'trance', u'life', u'character', u'soap', u'opera'],
[u'life', u'hitman', u'drug'],
[u'situation', u'hospital', u'set', u'soap', u'opera'],
[u'problem'],
[u'vote', u'actress', u'oscar', u'nominee'],
[u'performance', u'biography'],
[u'diagnosis']],
[[u'movie', u'crime', u'drama', u'comedy', u'way', u'movie', u'end'],
[u'theater', u'scalping', u'scene'],
[u'soap', u'opera', u'actor'],
[u'ability', u'soap', u'scene', u'expression', u'voice', u'intonation'],
[u'scene', u'sleepless', u'comedy'],
[u'fascination', u'grandfather', u'scene', u'photograph', u'laugh'],
[u'performance', u'moment'],
[u'waitress', u'crude', u'husband', u'dirt'],
[u'job', u'hospital', u'credential', u'movie'],
[u'fan', u'movie'],
[u'style', u'argument', u'sheriff', u'soap', u'opera'],
[u'blood', u'violence', u'lot', u'language', u'watch', u'plenty', u'laugh']],
[[u'spoiler',
u'spoiler',
u'spoiler',
u'film',
u'amusing.it',
u'lot',
u'line',
u'film.however',
u'problem',
u'plot',
u'simple.betty(renee',
u'life',
u'house',
u'wife',
u'husband',
u'love',
u'soap',
u'series',
u'character,dr'],
[u'minute', u'film'],
[u'film'],
[u'reference',
u'end',
u'film',
u'conclusion',
u'place',
u'home',
u'conclusion',
u'end',
u'film',
u'wonder',
u'experience',
u'answer',
u'all.<br',
u'film',
u'reality',
u'fantasy'],
[u'scene',
u'subject',
u'joke',
u'plot',
u'line',
u'film',
u'way',
u'subject',
u'way).<br',
u'film',
u'soap',
u'series',
u'obsession',
u'form',
u'sympathy',
u'obsession',
u'one.<br',
u'film',
u'scene',
u'subject',
u'impact',
u'characters.<br',
u'make',
u'before.it',
u'shame',
u'film',
u'concentrate',
u'subjects.as',
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]:
[[(0, 1),
(1, 1),
(2, 1),
(3, 1),
(4, 1),
(5, 1),
(6, 1),
(7, 1),
(8, 1),
(9, 1),
(10, 1),
(11, 1),
(12, 1),
(13, 1),
(14, 1),
(15, 1),
(16, 1),
(17, 1),
(18, 1),
(19, 1),
(20, 1),
(21, 1)],
[(1, 1),
(10, 1),
(22, 1),
(23, 1),
(24, 1),
(25, 1),
(26, 1),
(27, 1),
(28, 1)],
[(1, 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),
(48, 1),
(49, 1),
(50, 1)],
[(64, 1),
(1, 1),
(66, 1),
(67, 1),
(4, 1),
(69, 1),
(68, 1),
(65, 1),
(51, 1),
(52, 1),
(53, 1),
(54, 1),
(55, 1),
(56, 1),
(57, 1),
(58, 1),
(59, 1),
(60, 1),
(61, 1),
(62, 1),
(63, 1)],
[(1, 1),
(2, 1),
(25, 1),
(30, 1),
(48, 1),
(55, 1),
(61, 1),
(70, 1),
(71, 1),
(72, 1),
(73, 1),
(74, 1),
(75, 1),
(76, 1),
(77, 1),
(78, 1),
(79, 1),
(80, 1),
(81, 1),
(82, 1),
(83, 1),
(84, 1),
(85, 1),
(86, 1),
(87, 1),
(88, 1),
(89, 1),
(90, 1),
(91, 1),
(92, 1),
(93, 1),
(94, 1),
(95, 1),
(96, 1),
(97, 1),
(98, 1),
(99, 1),
(100, 1),
(101, 1),
(102, 1),
(103, 1),
(104, 1),
(105, 1),
(106, 1),
(107, 1),
(108, 1),
(109, 1),
(110, 1),
(111, 1),
(112, 1),
(113, 1),
(114, 1)],
[(1, 1),
(73, 1),
(42, 1),
(115, 1),
(116, 1),
(117, 1),
(118, 1),
(119, 1),
(94, 1)],
[(128, 1),
(1, 1),
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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 "=========================================="
[(0, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1), (18, 1), (19, 1), (20, 1), (21, 1)]
[(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
==========================================
[(1, 1), (4, 1), (342, 1), (135, 1), (267, 1), (12, 1), (13, 1), (15, 1), (24, 1), (150, 1), (151, 1), (280, 1), (26, 1), (346, 1), (176, 1), (214, 1), (304, 1), (294, 1), (295, 1), (296, 1), (297, 1), (298, 1), (299, 1), (300, 1), (301, 1), (302, 1), (303, 1), (48, 1), (305, 1), (306, 1), (307, 1), (308, 1), (309, 1), (310, 1), (311, 1), (312, 1), (313, 1), (314, 1), (315, 1), (316, 1), (317, 1), (318, 1), (319, 1), (320, 1), (321, 1), (322, 1), (323, 1), (324, 1), (325, 1), (326, 1), (327, 1), (328, 1), (329, 1), (330, 1), (55, 1), (332, 1), (333, 1), (78, 1), (335, 1), (336, 1), (337, 1), (338, 1), (339, 1), (340, 1), (334, 1), (203, 1), (343, 1), (7, 1), (345, 1), (331, 1), (94, 1), (344, 1), (103, 1), (106, 1), (39, 1), (114, 1), (244, 1), (245, 1), (120, 1), (341, 1)]
[(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
==========================================
[(1, 1), (262, 1), (649, 1), (651, 1), (652, 1), (653, 1), (654, 1), (655, 1), (656, 1), (657, 1), (658, 1), (659, 1), (660, 1), (34, 1), (560, 1), (564, 1), (565, 1), (567, 1), (440, 1), (327, 1), (584, 1), (204, 1), (589, 1), (602, 1), (93, 1), (613, 1), (234, 1), (109, 1), (499, 1), (117, 1), (632, 1)]
[(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
==========================================
[(1, 1), (4, 1), (13, 1), (14, 1), (557, 1), (20, 1), (30, 1), (800, 1), (801, 1), (802, 1), (803, 1), (804, 1), (805, 1), (806, 1), (807, 1), (808, 1), (809, 1), (810, 1), (811, 1), (812, 1), (813, 1), (814, 1), (815, 1), (48, 1), (817, 1), (818, 1), (819, 1), (820, 1), (821, 1), (822, 1), (823, 1), (824, 1), (825, 1), (826, 1), (827, 1), (828, 1), (317, 1), (830, 1), (816, 1), (73, 1), (207, 1), (733, 1), (50, 1), (829, 1), (121, 1), (42, 1)]
[(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
==========================================
[(128, 1), (1, 1), (2, 1), (10, 1), (529, 1), (23, 1), (29, 1), (709, 1), (176, 1), (36, 1), (173, 1), (48, 1), (50, 1), (1097, 1), (439, 1), (443, 1), (60, 1), (573, 1), (1087, 1), (1088, 1), (1089, 1), (1090, 1), (1091, 1), (1092, 1), (1093, 1), (1094, 1), (1095, 1), (1096, 1), (73, 1), (330, 1), (1099, 1), (1100, 1), (1101, 1), (1098, 1), (348, 1), (102, 1), (161, 1), (121, 1), (127, 1)]
[(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
==========================================
[(1, 1), (2, 1), (262, 1), (7, 1), (10, 1), (13, 1), (142, 1), (527, 1), (1179, 1), (1180, 1), (283, 1), (169, 1), (50, 1), (180, 1), (60, 1), (61, 1), (77, 1), (1237, 1), (1238, 1), (1239, 1), (1240, 1), (1241, 1), (1242, 1), (1243, 1), (1244, 1), (1245, 1), (1246, 1), (1247, 1), (1248, 1), (1249, 1), (226, 1), (872, 1), (111, 1), (573, 1)]
[(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
==========================================
[(1, 1), (4, 1), (10, 1), (13, 1), (1552, 1), (1557, 1), (1558, 1), (1559, 1), (1560, 1), (1561, 1), (1434, 1), (1563, 1), (1564, 1), (1562, 1), (30, 1), (1567, 1), (1568, 1), (1569, 1), (1570, 1), (1187, 1), (1193, 1), (1565, 1), (307, 1), (1566, 1), (182, 1), (1206, 1), (73, 1), (459, 1), (80, 1), (210, 1), (1571, 1), (484, 1), (1511, 1), (1106, 1), (117, 1), (21, 1)]
[(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
==========================================
[(128, 1), (1, 1), (2, 1), (774, 1), (140, 1), (13, 1), (150, 1), (294, 1), (1447, 1), (557, 1), (562, 1), (188, 1), (1471, 1), (326, 1), (1738, 1), (1739, 1), (1740, 1), (1741, 1), (78, 1), (93, 1), (234, 1), (755, 1), (759, 1)]
[(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
==========================================
[(1, 1), (370, 1), (635, 1), (4, 1), (71, 1), (169, 1), (458, 1), (299, 1), (13, 1), (14, 1), (114, 1), (334, 1), (822, 1), (55, 1), (283, 1), (60, 1), (1962, 1), (30, 1)]
[(0, 0.95378325642061546), (1, 0.046216743579384635)]
movie plenty term film memory year lover genre character performance fan hallmark form action direction man support scene
==========================================
[(512, 1), (1, 1), (150, 1), (2065, 1), (2066, 1), (2067, 1), (2068, 1), (2069, 1), (2070, 1), (2071, 1), (24, 1), (1314, 1), (419, 1), (1572, 1), (421, 1), (55, 1), (699, 1), (61, 1), (446, 1), (1475, 1), (1862, 1), (341, 1), (966, 1), (1515, 1), (1267, 1), (119, 1)]
[(0, 0.46619251483444163), (1, 0.53380748516555832)]
bunch movie plot schtick soldier victim you!<br village cleansing slaughter <br attack row town rescue action seat guy night chopper money.<br body count explosions.<br battle child
==========================================
[(0, 1), (2, 1), (131, 1), (2186, 1), (2187, 1), (2188, 1), (2189, 1), (2190, 1), (2191, 1), (2192, 1), (2193, 1), (2194, 1), (2195, 1), (2196, 1), (1045, 1), (2198, 1), (2199, 1), (2200, 1), (25, 1), (2202, 1), (29, 1), (927, 1), (551, 1), (169, 1), (176, 1), (2201, 1), (446, 1), (2197, 1), (523, 1), (73, 1), (1485, 1), (1746, 1), (106, 1), (752, 1), (759, 1), (1428, 1), (123, 1), (234, 1), (383, 1)]
[(0, 0.021890103615292478), (1, 0.97810989638470747)]
sure time stage panhandler id prepaid casino bankroll risk spending sock contact yahoo drug reality poker hiding center day security picture cause cash year fact sleeping night housing sign person smoke bank work month game convention lot money tv
==========================================
[(128, 1), (4, 1), (397, 1), (14, 1), (16, 1), (529, 1), (150, 1), (25, 1), (920, 1), (153, 1), (1190, 1), (680, 1), (2218, 1), (299, 1), (45, 1), (2222, 1), (176, 1), (307, 1), (1987, 1), (2256, 1), (2299, 1), (486, 1), (1895, 1), (1130, 1), (2288, 1), (2294, 1), (2295, 1), (2296, 1), (2297, 1), (2298, 1), (123, 1), (2300, 1), (106, 1)]
[(0, 0.97082768569047551), (1, 0.02917231430952455)]
way film start performance director bit plot day help sort prize meantime boxing genre husband boxer fact today round ending.<br departure style rage fight ranking customer champ see.<br mystery-suspense silent lot punch work
==========================================
[(1, 1), (1508, 1), (262, 1), (294, 1), (104, 1), (169, 1), (2410, 1), (2411, 1), (2412, 1), (2413, 1), (2414, 1), (2415, 1), (2416, 1), (113, 1), (24, 1), (4, 1), (602, 1), (795, 1), (29, 1), (1548, 1)]
[(0, 0.96577961440552562), (1, 0.034220385594474316)]
movie generation minute comment reviewer year fogey etc.<br courtesy doberman et al pint pleasure <br film life episode picture travesty
==========================================
[(1, 1), (1387, 1), (37, 1), (262, 1), (2502, 1), (2507, 1), (492, 1), (14, 1), (1298, 1), (142, 1), (183, 1)]
[(0, 0.34225805562206801), (1, 0.6577419443779321)]
movie army woman minute right propaganda credit performance hell cliché course
==========================================
[(1536, 1), (150, 1), (103, 1), (136, 1), (2582, 1), (204, 1), (109, 1), (718, 1), (141, 1), (616, 1), (2578, 1), (2579, 1), (2580, 1), (2581, 1), (726, 1), (1083, 1), (446, 1), (2165, 1)]
[(0, 0.5148238371301902), (1, 0.48517616286980991)]
arm plot problem hope jinks need beer entertainment humour fun bag popcorn bouyant muppet chair song night tale
==========================================
[(147, 1), (1, 1), (739, 1), (4, 1), (758, 1), (33, 1), (2024, 1), (841, 1), (74, 1), (13, 1), (173, 1), (141, 1), (403, 1), (117, 1), (150, 1), (567, 1), (2008, 1), (1956, 1), (2650, 1), (1338, 1), (30, 1)]
[(0, 0.44219681356040169), (1, 0.55780318643959825)]
laugh movie impact film trap family home boring villain character role humour car comedy plot boy subplot slapstick holiday feeling scene
==========================================
[(128, 1), (1, 1), (10, 1), (591, 1), (48, 1), (50, 1), (2739, 1), (2740, 1), (150, 1), (1399, 1), (1368, 1), (1017, 1), (1354, 1)]
[(0, 0.5884511343437524), (1, 0.41154886565624754)]
way movie acting fashion story actor disgust.<br sub plot portrayal flop attention subject
==========================================
[(1, 1), (2, 1), (1474, 1), (848, 1), (499, 1), (121, 1)]
[(0, 0.68977779831876362), (1, 0.31022220168123638)]
movie time manner member hour cast
==========================================
[(1, 1), (2875, 1), (1956, 1), (101, 1), (2502, 1), (73, 1), (140, 1), (1133, 1), (558, 1), (13, 1), (720, 1), (529, 1), (691, 1), (150, 1), (857, 1), (153, 1), (100, 1), (2874, 1), (184, 1), (120, 1)]
[(0, 0.0788344973346434), (1, 0.92116550266535646)]
movie summation slapstick lead right person sense truth watch character movie.<br bit message plot jerk sort idea member.<br theme premise
==========================================
[(263, 1), (2465, 1), (2375, 1), (4, 1), (103, 1), (2024, 1), (138, 1), (939, 1), (492, 1), (13, 1), (2958, 1), (2959, 1), (48, 1), (81, 1), (114, 1), (2870, 1), (2960, 1), (26, 1), (2954, 1), (94, 1), (447, 1)]
[(0, 0.16116198545853039), (1, 0.83883801454146967)]
matter nose painting film problem home caricature group credit character them.<br ears story run fan time.<br strike effect lifestyle kind storyline
==========================================
[(3065, 1), (1, 1), (671, 1)]
[(0, 0.23147976236506002), (1, 0.76852023763494004)]
fetish movie punk
==========================================
[(1, 1), (2, 1), (4, 1), (2597, 1), (902, 1), (1505, 1), (968, 1), (73, 1), (343, 1), (204, 1), (878, 1), (143, 1), (1751, 1), (153, 1), (858, 1), (1983, 1), (92, 1), (3167, 1)]
[(0, 0.91651274728454457), (1, 0.083487252715455526)]
movie time film stupidity explain sheer theater person crap need pile joke bucket sort monster toilet trash cheesiest
==========================================
[(1, 1), (2, 1), (388, 1), (529, 1), (150, 1), (24, 1), (4, 1), (538, 1), (30, 1), (3253, 1), (932, 1), (1830, 1), (3250, 1), (3251, 1), (3252, 1), (3230, 1), (182, 1), (187, 1), (1852, 1), (74, 1), (1397, 1), (3254, 1), (73, 1), (330, 1), (1483, 1), (599, 1), (1008, 1), (3217, 1), (316, 1), (240, 1), (499, 1), (116, 1), (245, 1), (105, 1), (2301, 1)]
[(0, 0.77381423681525741), (1, 0.22618576318474265)]
movie time expectation bit plot <br film reviews scene krypyonite hero rock think hospital movie?<br continent thing screen henchman villain budget half-wit person point head son mistake kryptonite graphic half hour writer question doctor fear
==========================================
[(1, 1), (3, 1), (388, 1), (1797, 1), (10, 1), (140, 1), (13, 1), (150, 1), (4, 1), (670, 1), (288, 1), (33, 1), (1116, 1), (3372, 1), (477, 1), (48, 1), (1073, 1), (50, 1), (180, 1), (94, 1), (1338, 1), (60, 1), (1345, 1), (3138, 1), (196, 1), (455, 1), (1865, 1), (1100, 1), (3427, 1), (2398, 1), (3418, 1), (3419, 1), (3420, 1), (3421, 1), (3422, 1), (3423, 1), (3424, 1), (3425, 1), (3426, 1), (99, 1), (3428, 1), (2284, 1), (114, 1), (755, 1), (123, 1), (382, 1)]
[(0, 0.77293641662866819), (1, 0.22706358337133187)]
movie opinion expectation violence acting sense character plot film school production family force adamantium maker story motivation actor moment kind feeling man death skeleton nature relationship project up.<br vù mystery arch.<br screw comics.<br xmen wolverine one.<br marvel hero.<br dejá flick back-story revenge fan million lot enemy
==========================================
[(1, 1), (34, 1), (1659, 1), (1348, 1), (262, 1), (13, 1), (2287, 1), (50, 1), (153, 1), (571, 1)]
[(0, 0.56305953035012957), (1, 0.43694046964987049)]
movie friend rate development minute character favourite actor sort cinema
==========================================
[(128, 1), (1, 1), (4, 1), (2182, 1), (395, 1), (13, 1), (146, 1), (2844, 1), (2846, 1), (1116, 1), (1068, 1), (48, 1), (3637, 1), (3638, 1), (3639, 1), (3640, 1), (3641, 1), (3642, 1), (3643, 1), (3644, 1), (2758, 1), (455, 1), (2124, 1), (207, 1), (2640, 1), (1080, 1), (3540, 1), (1500, 1), (2024, 1), (1001, 1), (2925, 1), (1648, 1)]
[(0, 0.031649095312947115), (1, 0.96835090468705287)]
way movie film boss mile character world colonial indochine force scope story geography underpinning subjects.<br link remembrance colonialism ty relationships.<br ride relationship thousand end confusion black dignity struggle home tension peer beginning
==========================================
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[(0, 0.30452911279758349), (1, 0.69547088720241645)]
movie film office character hand surface sympathy scene employment story weakness predicament song casting crown return villain course attitude lord father offense kind screen usher problem effort musical voice highlight actress closing stage majority jewel on-screen factor lacking resonance svengali-like stand-point trio year devotion diva unprofessionalism role implication motherhood mothering unemployment talent gap moment cabaret belting melange strut singer edge seat puddle slack version set energy era end department singing impact rendition charge girl position desperation event production glitz record point member title piece portion rage emotion mother winner signature start daughter contestant behavior producer card disco charisma group technique act number result depth business score manager selling tune
==========================================
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[(0, 0.47125127414944118), (1, 0.52874872585055888)]
pocket film minute fall character <br role actor lack edge man sequence prequel idea fight lava tax film-maker film-maker.<br darkness sabre trilogy
==========================================
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[(0, 0.26195177240191797), (1, 0.73804822759808197)]
film eye character director victim <br effect disdain dinner table family society woman room stature stand scope husband mountain boy colonialism man guy household perspective childhood memory sky bond father poem truth pace country interaction friendship teacher government hiding space business year fact theme glance nature official audience plate servant food crew coach mirror dress replacement kitchen pain form longing sequence public chocolat emphasis manservant bronze bedroom ant command propeller passenger compound visitor plantation balance body confrontation manservant.<br isolation oppressor phrase mother tension shoulder spite house attraction conversation gesture land coffee rule shower adult mistress limit relationship dignity plane image emotion home walk tone upset heart owner taunt
==========================================
[(1, 1), (2, 1), (3971, 1), (3972, 1), (3973, 1), (262, 1), (903, 1), (3976, 1), (30, 1), (1165, 1), (557, 1), (146, 1), (920, 1), (4, 1), (900, 1), (28, 1), (670, 1), (931, 1), (932, 1), (3974, 1), (3975, 1), (173, 1), (1583, 1), (50, 1), (180, 1), (2485, 1), (187, 1), (60, 1), (317, 1), (1219, 1), (327, 1), (1225, 1), (1612, 1), (461, 1), (3469, 1), (1490, 1), (347, 1), (93, 1), (738, 1), (483, 1), (769, 1), (2165, 1), (119, 1), (2424, 1), (126, 1)]
[(0, 0.13239594886230058), (1, 0.86760405113769945)]
movie time crapfest bastardization.<br asininity minute film.<br badger scene teacher music world help film house rent school loser hero biker spittle.<br role wind actor moment vacation screen man sequence here.<br skill appearance fringe dog bout dance robot kid bomb series crew tale child military element
==========================================
[(128, 1), (48, 1), (2, 1), (4, 1), (649, 1), (13, 1), (1133, 1), (1519, 1), (240, 1), (54, 1), (100, 1), (4026, 1), (1615, 1), (447, 1)]
[(0, 0.43758482647249103), (1, 0.56241517352750892)]
way story time film waste character truth rubbish half place idea robinson brother storyline
==========================================
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[(0, 0.92613176169210099), (1, 0.073868238307898904)]
notoriety movie appeal story doubt recipe charisma script movie.<br actor parent title thing <br script.<br lot guy scene
==========================================
[(1, 1), (264, 1), (4105, 1), (4106, 1), (4107, 1), (141, 1), (14, 1), (4056, 1), (2834, 1), (562, 1), (4104, 1), (50, 1), (182, 1), (187, 1), (335, 1), (3540, 1), (600, 1), (93, 1), (94, 1), (110, 1), (243, 1), (117, 1), (119, 1)]
[(0, 0.96329238429017527), (1, 0.036707615709824802)]
movie attempt onslaught crudity lowlight humour performance beggar sentimentality level kudo actor thing screen belief dignity father kid kind experience mouth comedy child
==========================================
[(1, 1), (259, 1), (4, 1), (449, 1), (780, 1), (14, 1), (16, 1), (788, 1), (22, 1), (153, 1), (24, 1), (25, 1), (538, 1), (90, 1), (670, 1), (33, 1), (550, 1), (1193, 1), (4141, 1), (302, 1), (182, 1), (827, 1), (573, 1), (62, 1), (1345, 1), (1477, 1), (455, 1), (72, 1), (73, 1), (330, 1), (3151, 1), (725, 1), (169, 1), (602, 1), (3291, 1), (1830, 1), (103, 1), (12, 1), (125, 1), (241, 1), (140, 1), (117, 1), (2934, 1), (759, 1), (248, 1), (509, 1)]
[(0, 0.083891531674858258), (1, 0.9161084683251417)]
movie coach film conclusion share performance director event rating sort <br day reviews mix school family deal heart butt critic thing field reason star death football relationship touch person point bond issue year life priority rock problem eye drama men sense comedy dramas game team building
==========================================
[(128, 1), (1346, 1), (4, 1), (73, 1), (13, 1), (14, 1), (150, 1), (55, 1), (602, 1), (2714, 1)]
[(0, 0.11019210305266998), (1, 0.88980789694732998)]
way slow film person character performance plot action life buff
==========================================
[(34, 1), (3, 1), (613, 1), (4235, 1), (2, 1), (1615, 1), (3250, 1), (4244, 1), (759, 1), (1497, 1)]
[(0, 0.89678545309651791), (1, 0.10321454690348218)]
friend opinion player mode time brother think multi-player game video
==========================================
[(1, 1), (2, 1), (3, 1), (1381, 1), (4329, 1), (4012, 1), (1560, 1), (1305, 1)]
[(0, 0.12830898295225734), (1, 0.87169101704774266)]
movie time opinion everybody tuxedo quote wedding dress
==========================================
[(1, 1), (264, 1), (10, 1), (1938, 1), (1173, 1), (23, 1), (33, 1), (1704, 1), (557, 1), (176, 1), (691, 1), (183, 1), (4412, 1), (4413, 1), (2502, 1), (78, 1), (207, 1), (4180, 1), (463, 1), (103, 1), (60, 1), (2809, 1)]
[(0, 0.16335428339310781), (1, 0.83664571660689224)]
movie attempt acting parent set-up job family spoiler music fact message course fiance pigeon right hole end acceptance rest problem man mannerism
==========================================
[(1, 1), (2891, 1), (116, 1), (830, 1)]
[(0, 0.84363820610123841), (1, 0.15636179389876159)]
movie stroke writer genius
==========================================
[(2, 1), (4, 1), (261, 1), (435, 1), (280, 1), (29, 1), (800, 1), (4513, 1), (4514, 1), (4515, 1), (4516, 1), (4517, 1), (1446, 1), (3495, 1), (42, 1), (3143, 1), (307, 1), (1353, 1), (571, 1), (573, 1), (708, 1), (1607, 1), (73, 1), (2770, 1), (1500, 1), (677, 1), (1122, 1), (2155, 1), (1391, 1), (4465, 1)]
[(0, 0.053795388924666225), (1, 0.94620461107533382)]
time film motion number aspect picture magnolia sceptic mainstream sell-out spears.<br revenue approach celebrity stuff return today artist cinema reason industry sale person recognition struggle magic language consumer faith pornography
==========================================
[(1281, 1), (900, 1), (4069, 1), (262, 1), (71, 1), (937, 1), (619, 1), (1132, 1), (1646, 1), (239, 1), (48, 1), (2258, 1), (307, 1), (84, 1), (1, 1), (23, 1), (538, 1), (151, 1), (1857, 1)]
[(0, 0.057144291807999958), (1, 0.94285570819199993)]
choice house babysitter minute memory situation cell trouble teen girl story phone today killer movie job reviews line crack
==========================================
[(1, 1), (4614, 1), (4615, 1), (4616, 1), (13, 1), (1567, 1), (169, 1), (42, 1), (690, 1), (302, 1), (48, 1), (946, 1), (182, 1), (60, 1), (446, 1), (1348, 1), (214, 1), (345, 1), (99, 1), (4069, 1), (616, 1), (2027, 1)]
[(0, 0.045135899483411429), (1, 0.95486410051658854)]
movie mofo willies.<br minority character criticism year stuff folk critic story taste thing man night development audience light flick babysitter fun dude
==========================================
[(128, 1), (1345, 1), (4, 1), (4641, 1), (330, 1), (207, 1), (2928, 1), (627, 1), (1076, 1), (725, 1), (4664, 1)]
[(0, 0.06568644161749583), (1, 0.93431355838250418)]
way death film penalty point end killing tear murder issue forgiveness
==========================================
[(1, 1), (1508, 1), (1254, 1), (230, 1), (1345, 1), (298, 1), (50, 1), (654, 1), (385, 1), (4690, 1), (182, 1), (60, 1), (991, 1)]
[(0, 0.49642056981395066), (1, 0.50357943018604934)]
movie generation doubt soundtrack death history actor guess spite fotography thing man credibility
==========================================
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[(0, 0.84904543179960401), (1, 0.15095456820039599)]
spotlight movie time performer film film.<br grease stain true-to-life lounge character performance theme.<br farce reality plot contrast treat slime lizard scenery skill role rest actor host nomination theme man tooth milieu type version professional person merit uncle result polyester craft pathetic.<br farce.<br study life ability kind tier rendition character.<br age humor shine example cast
==========================================
[(1, 1), (1222, 1), (1286, 1), (1953, 1), (74, 1), (207, 1), (4788, 1), (61, 1)]
[(0, 0.20231685169719016), (1, 0.79768314830280984)]
movie trailer screening tonight villain end scariest guy
==========================================
[(128, 1), (1, 1), (2, 1), (1092, 1), (22, 1), (520, 1), (937, 1), (4810, 1), (2066, 1), (340, 1), (53, 1), (150, 1), (55, 1), (61, 1), (670, 1)]
[(0, 0.92470798480953864), (1, 0.075292015190461345)]
way movie time period rating surprise situation douchebag soldier title terrorist plot action guy school
==========================================
[(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
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)
Let's explore how many of our movies made gross sales above average, and how many had a value below average
In [59]:
plt.hist(dftouse.adj_gross_bin)
plt.title("Gross Above Mean (1) vs. Gross Below Mean (0)")
Out[59]:
<matplotlib.text.Text at 0x10de09f50>
Turns out we have more movies below the mean than above, which makes a lot of sense, since there are only very few blockbusters, and they bring the mean to a higher value than it would have been without them.
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
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
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2284.0,
11813.0,
2285.0,
2286.0,
2287.0,
2288.0,
2289.0,
2290.0,
2291.0,
2292.0,
2293.0,
2294.0,
11814.0,
2295.0,
2296.0,
2307.0,
2308.0,
2309.0,
2310.0,
2311.0,
2312.0,
2313.0,
2314.0,
11815.0,
2315.0,
2316.0,
2317.0,
2318.0,
2319.0,
2320.0,
2321.0,
2322.0,
2323.0,
2324.0,
11816.0,
2325.0,
2326.0,
2327.0,
2328.0,
2329.0,
2330.0,
2331.0,
2332.0,
2333.0,
2334.0,
11817.0,
2335.0,
2336.0,
2378.0,
2837.0,
2838.0,
2839.0,
2840.0,
2841.0,
2842.0,
2843.0,
11818.0,
2844.0,
2845.0,
2846.0,
2847.0,
2848.0,
2849.0,
2850.0,
2851.0,
2852.0,
...]
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)
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
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
Here, we attempt to see if we can get better predictive power using some of the classifiers we learnt in class.
List of classifiers we are going to be trying:
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]:
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)
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 [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 [90]:
plt.hist(clflog1.predict_proba(Xtest)[:,1]*100);
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: 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############# 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]
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)
At the end of our analysis, we reached the following conclusions:
We are going to talk about these in more detail in the beginning of our final report.
Content source: starbro/BeastMode
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