def demo2():
"""
You unexpectedly became very interested in Deep Belief Networks. As a first
stab at some background reading, you want to:
1. Find all NIPS publications with Deep in title
2. open them in the browser
Pre-requisites:
- Assumes 'pubs_nips' exists. This can be obtained by running
nips_download_parse.py or by downloading it from site.
(https://sites.google.com/site/researchpooler/home)
Side-effects:
- will use os call to open a pdf with default program
"""
print "loading the NIPS publications dataset..."
pubs = loadPubs('pubs_nips')
# get urls that correspond to publications with deep in title
p = [x['pdf'] for x in pubs if 'deep' in x['title'].lower()]
if len(p)>5:
print "oops too many (%d) results! Only opening random 5." % (len(p),)
p=p[:5]
openPDFs(p)
def demo1():
"""
You wrote an algorithm and benchmarked it on the MNIST dataset. You are
wondering how your results compare with those in the literature:
1. Finds all publications that mention mnist
2. Print out their titles
3. Open the three latest publications that mention it at least twice
Pre-requisites:
- Assumes 'pubs_nips' exists and that pdf text is present.
This can be obtained by running
nips_download_parse.py and then nips_add_pdftext.py, or by downloading it
from site (https://sites.google.com/site/researchpooler/home)
Side-effects:
- will use os call to open a pdf with default program
"""
print "loading the NIPS publications dataset..."
pubs = loadPubs('pubs_nips')
# get all papers that mention mnist
p = [x for x in pubs if 'mnist' in x.get('pdf_text',{})]
print "titles of papers that mention MNIST dataset:"
for x in p:
print x['title']
print "total of %d publications mention MNIST." %(len(p),)
# sort by number of occurences
occ = [(x['year'], x['pdf']) for i,x in enumerate(p) if x['pdf_text']['mnist']>1]
occ.sort(reverse = True)
# open the top 3 latest in browser
print "opening the top 3..."
openPDFs([x for year,x in occ[:3]])
def demo3():
"""
You found a cool paper online and you want to find similar papers:
1. Download and parse the pdf
2. Compare to text of all publications in pubs_ database
3. Open the top 3 matches in browser (but note that current matching alg is
very basic and could be much improved)
Pre-requisites:
- Assumes 'pubs_nips' exists and contains pdf text inside
(under key 'pdf_text'). This can be obtained by running
nips_download_parse.py and then nips_add_pdftext.py
or by downloading it from site.
(https://sites.google.com/site/researchpooler/home)
Side-effects:
- will use os call to open a pdf with default program
"""
# fetch this pdf from website, parse it, and make a publication dict from it
# here is a random pdf from Andrew's website
url = 'http://ai.stanford.edu/~ang/papers/icml11-DeepEnergyModels.pdf'
print "downloading %s..." % (url, )
text = convertPDF(url) #extract the text
bow = stringToWordDictionary(
text) #extract the bag of words representation
p = {'pdf_text': bow} #create a dummy publication dict
# calculate similarities to our publications
print "loading database..."
pubs = loadPubs('pubs_nips')
print "computing similarities. (may take while with current implementation)"
scores = publicationSimilarityNaive(pubs, p)
# find highest scoring pubs
lst = [(s, i) for i, s in enumerate(scores) if s >= 0]
lst.sort(reverse=True)
# display top 50 matches
m = min(50, len(lst))
for s, i in lst[:m]:
print "%.2f is similarity to %s." % (s, pubs[i]['title'])
#open the top 3 in browser
print "opening the top 3..."
openPDFs([pubs[i]['pdf'] for s, i in lst[:3]])
def demo3():
"""
You found a cool paper online and you want to find similar papers:
1. Download and parse the pdf
2. Compare to text of all publications in pubs_ database
3. Open the top 3 matches in browser (but note that current matching alg is
very basic and could be much improved)
Pre-requisites:
- Assumes 'pubs_nips' exists and contains pdf text inside
(under key 'pdf_text'). This can be obtained by running
nips_download_parse.py and then nips_add_pdftext.py
or by downloading it from site.
(https://sites.google.com/site/researchpooler/home)
Side-effects:
- will use os call to open a pdf with default program
"""
# fetch this pdf from website, parse it, and make a publication dict from it
# here is a random pdf from Andrew's website
url = 'http://ai.stanford.edu/~ang/papers/icml11-DeepEnergyModels.pdf'
print "downloading %s..." % (url,)
text = convertPDF(url) #extract the text
bow = stringToWordDictionary(text) #extract the bag of words representation
p = {'pdf_text' : bow} #create a dummy publication dict
# calculate similarities to our publications
print "loading database..."
pubs = loadPubs('pubs_nips')
print "computing similarities. (may take while with current implementation)"
scores = publicationSimilarityNaive(pubs, p)
# find highest scoring pubs
lst = [(s, i) for i,s in enumerate(scores) if s>=0]
lst.sort(reverse = True)
# display top 50 matches
m = min(50, len(lst))
for s, i in lst[:m]:
print "%.2f is similarity to %s." % (s, pubs[i]['title'])
#open the top 3 in browser
print "opening the top 3..."
openPDFs([pubs[i]['pdf'] for s,i in lst[:3]])
def demo1():
"""
You wrote an algorithm and benchmarked it on the MNIST dataset. You are
wondering how your results compare with those in the literature:
1. Finds all publications that mention mnist
2. Print out their titles
3. Open the three latest publications that mention it at least twice
Pre-requisites:
- Assumes 'pubs_nips' exists and that pdf text is present.
This can be obtained by running
nips_download_parse.py and then nips_add_pdftext.py, or by downloading it
from site (https://sites.google.com/site/researchpooler/home)
Side-effects:
- will use os call to open a pdf with default program
"""
print "loading the NIPS publications dataset..."
pubs = loadPubs('pubs_nips')
# get all papers that mention mnist
p = [x for x in pubs if 'mnist' in x.get('pdf_text', {})]
print "titles of papers that mention MNIST dataset:"
for x in p:
print x['title']
print "total of %d publications mention MNIST." % (len(p), )
# sort by number of occurences
occ = [(x['year'], x['pdf']) for i, x in enumerate(p)
if x['pdf_text']['mnist'] > 1]
occ.sort(reverse=True)
# open the top 3 latest in browser
print "opening the top 3..."
openPDFs([x for year, x in occ[:3]])
