def test(limit=100):
# Get filenames.
indir = '/Users/jmenick/Desktop/alife_refactor/output/lda_model_200'
name = 'lda_200'
pnofn = '/'.join([indir, 'pnos.p'])
ldafn = '/'.join([indir, name+'.lda'])
corpusfn = '/'.join([indir, 'corpus_'+name+'.svmlight'])
vocabfn = '/'.join([indir, 'vocab_'+name+'.dict'])
# Load persisted data from disk.
print "loading data..."
vocab = load_vocab(vocabfn)
corpus = load_corpus(corpusfn)
lda = load_lda(ldafn)
pnos = load_obj(pnofn)
pno2id = {p:i for i,p in enumerate(pnos)}
#produce visualization... commented out for now. keeps crashing the machine.
# print "producing visualization..."
# visfn = '/'.join([indir, 'vis.html'])
# vis_data = prepare(lda, corpus, vocab)
# print "saving visualization..."
# pyLDAvis.save_html(vis_data, visfn)
# put doc topics in db.
print "Getting doc topics..."
assert(len(corpus) == len(pnos))
db = MongoClient().patents
def partfunc(doc):
topics = lda[corpus[pno2id[doc['_id']]]]
return {'$set': {'lda_topics': topics}}
pats_test = db.traits.find().limit(limit)
for p in pats_test:
pprint(partfunc(p))
print "\nDone."
def load_pops(start_time, limit = None):
date_str = dt_as_str(start_time)
popfn = '/'.join([_pop_dir, date_str+'.p'])
doc = load_obj(popfn)
if limit is not None:
return doc['new_ancestors'], doc['descendants']
else:
return doc['new_ancestors'][:limit], doc['descendants'][:limit]
def load_pops(start_time, limit=None):
date_str = dt_as_str(start_time)
popfn = '/'.join([_pop_dir, date_str + '.p'])
doc = load_obj(popfn)
if limit is not None:
return doc['new_ancestors'], doc['descendants']
else:
return doc['new_ancestors'][:limit], doc['descendants'][:limit]
def main():
db = MongoClient().patents
family_names = [
"stents",
"zeolites",
"bubblejet",
"cellphone",
"pcr",
"microarrays",
"semiconductors",
"nonwovenwebs",
"rsa",
"browser",
]
family_pnos = [4655771, 4061724, 4723129, 5103459, 4683202, 5143854, 4064521, 4340563, 4405829, 5572643]
family_thresholds = [350, 60, 75, 225, 150, 175, 125, 100, 400, 250]
lilfriend_names = [
"skate",
"murphybed",
"hummingbirdfeeder",
"telescopicumbrella",
"hybridengine",
"minesweeper",
"humanoidrobot",
"recumbentbike",
"hangglider",
"ziplock",
]
lilfriend_pnos = [6000704, 4766623, 5454348, 4880023, 5191766, 3938459, 6377014, 5284351, 4417707, 6004032]
lilfriend_thresholds = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
bigfriend_names = [
"dentallaser",
"ballisticvest",
"hungryhippos",
"sharkprod",
"gatlinggun",
"nuclearwastetreatment",
"gfp",
"roughterrainchasis",
"bowflex",
"radaraltimeter",
]
bigfriend_pnos = [5616141, 4287607, 4119312, 4667431, 4154143, 4274976, 5491084, 4061199, 4725057, 4945360]
bigfriend_thresholds = [25, 25, 10, 12, 8, 9, 25, 30, 15, 10]
names = family_names + lilfriend_names + bigfriend_names
pnos = family_pnos + lilfriend_pnos + bigfriend_pnos
thresholds = family_thresholds + lilfriend_thresholds + bigfriend_thresholds
for pno, threshold, name in zip(pnos, thresholds, names):
print "getting lineage for patent {} ({}), with threhold {}.".format(pno, name, threshold)
lineage = crawl_lineage(
db, pno, n_generations=5, enforce_func=lambda x: len(x.get("citedby", [])) >= threshold, flatten=True
)
adj = subnet_adj_dict(lineage)
dict_fn = "/Users/jmenick/Desktop/sandbox/jacobs_pca_dicts/{}_pca_dict.p".format(name)
colordict = load_obj(dict_fn)
savefn = "{}_{}_force_pca_test.pdf".format(pno, name)
network_plot(pno, adj, colordict, False, savefn)
print "done with {}".format(name)
def test2():
db = MongoClient().patents
family_names = [
'stents', 'zeolites', 'bubblejet', 'cellphone', 'pcr', 'microarrays',
'semiconductors', 'nonwovenwebs', 'rsa', 'browser'
]
family_pnos = [
4655771, 4061724, 4723129, 5103459, 4683202, 5143854, 4064521, 4340563,
4405829, 5572643
]
family_thresholds = [350, 60, 75, 225, 150, 175, 125, 100, 400, 250]
lilfriend_names = [
'skate', 'murphybed', 'hummingbirdfeeder', 'telescopicumbrella',
'hybridengine', 'minesweeper', 'humanoidrobot', 'recumbentbike',
'hangglider', 'ziplock'
]
lilfriend_pnos = [
6000704, 4766623, 5454348, 4880023, 5191766, 3938459, 6377014, 5284351,
4417707, 6004032
]
lilfriend_thresholds = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
bigfriend_names = [
'dentallaser', 'ballisticvest', 'hungryhippos', 'sharkprod',
'gatlinggun', 'nuclearwastetreatment', 'gfp', 'roughterrainchasis',
'bowflex', 'radaraltimeter'
]
bigfriend_pnos = [
5616141, 4287607, 4119312, 4667431, 4154143, 4274976, 5491084, 4061199,
4725057, 4945360
]
bigfriend_thresholds = [25, 25, 10, 12, 8, 9, 25, 30, 15, 10]
names = family_names + lilfriend_names + bigfriend_names
pnos = family_pnos + lilfriend_pnos + bigfriend_pnos
thresholds = family_thresholds + lilfriend_thresholds + bigfriend_thresholds
names = names[:1]
pnos = pnos[:1]
thresholds = thresholds[:1]
for pno, threshold, name in zip(pnos, thresholds, names):
print "getting lineage for patent {} ({}), with threhold {}.".format(
pno, name, threshold)
lineage = crawl_lineage(
db,
pno,
n_generations=5,
enforce_func=lambda x: len(x.get('citedby', [])) >= threshold,
flatten=True)
adj = subnet_adj_dict(lineage)
dict_fn = '/Users/jmenick/Desktop/sandbox/jacobs_pca_dicts/{}_pca_dict.p'.format(
name)
colordict = load_obj(dict_fn)
savefn = '{}_{}_force_pca_test.pdf'.format(pno, name)
print "getting plot..."
network_plot(pno, adj, colordict, False, savefn)
print "done with {}".format(name)
def _load_df():
""" Loads a dictionary of document frequencies from disk. Assumes It lives at a particular location on disk.
TODO: throw this function (and all such functions) into alife.data
"""
_data_dir = '/Users/jmenick/Desktop/alife_refactor/output/aggregate_stats'
_df_fn = '/'.join([_data_dir, 'tfidf_doc_freq.p'])
try:
df_dict = load_obj(_df_fn)
except:
raise RuntimeError("A document frequency dictionary is not stored in {}.".format(_df_fn))
return sorted(df_dict.items() ,key = lambda x: x[1])
def main():
# Get filenames.
indir = '/Users/jmenick/Desktop/alife_refactor/output/lda_model_200'
name = 'lda_200'
pnofn = '/'.join([indir, 'pnos.p'])
ldafn = '/'.join([indir, name + '.lda'])
corpusfn = '/'.join([indir, 'corpus_' + name + '.svmlight'])
vocabfn = '/'.join([indir, 'vocab_' + name + '.dict'])
# Load persisted data from disk.
print "loading data..."
vocab = load_vocab(vocabfn)
corpus = load_corpus(corpusfn)
lda = load_lda(ldafn)
pnos = load_obj(pnofn)
pno2id = {p: i for i, p in enumerate(pnos)}
#produce visualization... commented out for now due to crashing. Ugh PCA again...
# visfn = '/'.join([indir, 'vis.html'])
# vis_data = prepare(lda, corpus, vocab)
# pyLDAvis.save_html(vis_data, visfn)
# put doc topics in db.
print "inserting doc topics..."
db = MongoClient().patents
print "len(corpus): {}, len(pnos): {}".format(len(pnos), len(corpus))
def partfunc(doc):
pno = doc['_id']
try:
corpus_idx = pno2id[pno]
bow = corpus[corpus_idx]
topics = lda[bow]
return {'$set': {'lda_topics': topics}}
except:
logging.warning("no topics for {}".format(pno))
return {'$set': {'no_topics': True}}
parallelMap(partfunc,
in_collection=db.traits,
out_collection=db.traits,
findArgs={
'spec': {},
'fields': {
'_id': 1
}
},
bSize=1000,
updateFreq=500)
def test():
db = MongoClient().patents
pno = 4723129
threshold = 75
print "getting lineage..."
lineage = crawl_lineage(
db, pno, n_generations=5, enforce_func=lambda x: len(x.get("citedby", [])) >= threshold, flatten=True
)
adj = subnet_adj_dict(lineage)
bubblejet_color_dict_fn = "/Users/jmenick/Desktop/sandbox/jacobs_pca_dicts/bubblejet_pca_dict.p"
bubblejet_colors = load_obj(bubblejet_color_dict_fn)
savefn = "{}_force_pca_test.pdf".format(pno)
print "making plot..."
network_plot(pno, adj, bubblejet_colors, True, savefn)
return adj, bubblejet_colors
def main():
# Get filenames.
indir = '/Users/jmenick/Desktop/alife_refactor/output/lda_model_200'
name = 'lda_200'
pnofn = '/'.join([indir, 'pnos.p'])
ldafn = '/'.join([indir, name+'.lda'])
corpusfn = '/'.join([indir, 'corpus_'+name+'.svmlight'])
vocabfn = '/'.join([indir, 'vocab_'+name+'.dict'])
# Load persisted data from disk.
print "loading data..."
vocab = load_vocab(vocabfn)
corpus = load_corpus(corpusfn)
lda = load_lda(ldafn)
pnos = load_obj(pnofn)
pno2id = {p:i for i,p in enumerate(pnos)}
#produce visualization... commented out for now due to crashing. Ugh PCA again...
# visfn = '/'.join([indir, 'vis.html'])
# vis_data = prepare(lda, corpus, vocab)
# pyLDAvis.save_html(vis_data, visfn)
# put doc topics in db.
print "inserting doc topics..."
db = MongoClient().patents
print "len(corpus): {}, len(pnos): {}".format(len(pnos), len(corpus))
def partfunc(doc):
pno = doc['_id']
try:
corpus_idx = pno2id[pno]
bow = corpus[corpus_idx]
topics = lda[bow]
return {'$set': {'lda_topics': topics}}
except:
logging.warning("no topics for {}".format(pno))
return {'$set': {'no_topics': True}}
parallelMap(
partfunc,
in_collection = db.traits,
out_collection = db.traits,
findArgs = {
'spec': {},
'fields': {'_id':1}
},
bSize = 1000,
updateFreq = 500
)
def test():
db = MongoClient().patents
pno = 4723129
threshold = 75
print "getting lineage..."
lineage = crawl_lineage(
db,
pno,
n_generations=5,
enforce_func=lambda x: len(x.get('citedby', [])) >= threshold,
flatten=True)
adj = subnet_adj_dict(lineage)
bubblejet_color_dict_fn = '/Users/jmenick/Desktop/sandbox/jacobs_pca_dicts/bubblejet_pca_dict.p'
bubblejet_colors = load_obj(bubblejet_color_dict_fn)
savefn = '{}_force_pca_test.pdf'.format(pno)
print "making plot..."
network_plot(pno, adj, bubblejet_colors, True, savefn)
return adj, bubblejet_colors
def test(limit=100):
# Get filenames.
indir = '/Users/jmenick/Desktop/alife_refactor/output/lda_model_200'
name = 'lda_200'
pnofn = '/'.join([indir, 'pnos.p'])
ldafn = '/'.join([indir, name + '.lda'])
corpusfn = '/'.join([indir, 'corpus_' + name + '.svmlight'])
vocabfn = '/'.join([indir, 'vocab_' + name + '.dict'])
# Load persisted data from disk.
print "loading data..."
vocab = load_vocab(vocabfn)
corpus = load_corpus(corpusfn)
lda = load_lda(ldafn)
pnos = load_obj(pnofn)
pno2id = {p: i for i, p in enumerate(pnos)}
#produce visualization... commented out for now. keeps crashing the machine.
# print "producing visualization..."
# visfn = '/'.join([indir, 'vis.html'])
# vis_data = prepare(lda, corpus, vocab)
# print "saving visualization..."
# pyLDAvis.save_html(vis_data, visfn)
# put doc topics in db.
print "Getting doc topics..."
assert (len(corpus) == len(pnos))
db = MongoClient().patents
def partfunc(doc):
topics = lda[corpus[pno2id[doc['_id']]]]
return {'$set': {'lda_topics': topics}}
pats_test = db.traits.find().limit(limit)
for p in pats_test:
pprint(partfunc(p))
print "\nDone."
def test():
gpes = load_obj('gpes_tfidf.p')
plot_gpe(gpes, savefn='gpes_tfidf_test.pdf')
def test():
gpes = load_obj('gpes_tfidf.p')
plot_gpe(gpes, savefn='gpes_tfidf_test.pdf')
def load_anc_dec(start_date, indir):
""" Load two lists of patents; ancestral and descendant poulation respectively. """
filename = '/'.join([indir, dt_as_str(start_date)+'.p'])
pop_dict = load_obj(filename)
assert(start_date == pop_dict['start']) # Make sure the date we think we're loading matches the stored date.
return pop_dict['ancestors'], pop_dict['descendants']
def load_pop(start_date):
""" Load a list of patents (dictionaries) occuring in the month following the given start_date."""
filename = '/'.join([_pop_dir, dt_as_str(start_date)+'.p'])
pop_dict = load_obj(filename)
assert(start_date == pop_dict['start']) # Make sure the date we think we're loading matches the stored date.
return pop_dict['descendants']
from alife.util.general import load_obj
import sys
import os
import csv
if __name__ == '__main__':
if len(sys.argv) != 2:
sys.exit("Usage: python {} <path to .p file>".format(sys.argv[0]))
else:
infn = sys.argv[1]
inbase = os.path.basename(infn)
outfn = inbase.split('.')[0] + '.csv'
gpes = load_obj(infn)
with open(outfn, 'wb') as outfile:
writer = csv.writer(outfile)
writer.writerow(['trait', 'time_step', 't1', 't2', 't3', 'total'])
for trait, series in gpes.items():
for step,term_list in enumerate(series):
writer.writerow([trait, step]+list(term_list))
