def main(indir=r"Z:\ermunds\results\2005 20t unbranded",
modelName="2005 20topics",
world_list_file_name="adjectives.txt"):
dirs = gslib.LDAdirs(modelName, indir)
dict1 = gensim.corpora.dictionary.Dictionary().load(dirs.dictFileName)
lda = gensim.models.ldamodel.LdaModel(id2word=dict1).load(dirs.modelFname)
brands = lists.BrandsClustered_1
adjs = words_from_file(world_list_file_name)
adjs.extend(brands) # just a check
brs = pruneWordsList(brands, lda)
wrds = pruneWordsList(adjs, lda)
sims = LDAscalarProd(lda, brs["IDs"], wrds["IDs"])
wordsWeights = LDAweights(lda, wrds["IDs"])
brandsWeights = LDAweights(lda, brs["IDs"])
sims_norm = sims / numpy.sqrt(numpy.outer(brandsWeights, wordsWeights))
sims_norm_df = pandas.DataFrame(sims_norm,
index=brs.index,
columns=wrds.index)
sims_df = pandas.DataFrame(sims, index=brs.index, columns=wrds.index)
stats = pandas.Series([indir, modelName], index=['indir', 'modelName'])
stats_df = pandas.DataFrame(stats)
writer = pandas.ExcelWriter(os.path.join(indir, modelName + '.xlsx'))
sims_norm_df.to_excel(writer, sheet_name='cosine distance')
sims_df.to_excel(writer, sheet_name='raw scalar products')
brs.to_excel(writer, sheet_name='brands')
wrds.to_excel(writer, sheet_name='words')
stats_df.to_excel(writer, sheet_name='stats')
writer.save()
def total_purge(words):
cols = list()
for year in range(2002, 2013):
print year
modelDir = 'Z:\\ermunds\\results\\%d 20topics' % year
modelName = '%d 20topics' % year
dirs = gslib.LDAdirs(modelName, modelDir)
(dict1, _, lda) = gslib.loadStuff(dirs)
words_df = getLikes.pruneWordsList(words, lda)
col = words_df["Counts"]
col.name = str(year)
cols.append(col)
stats = pandas.DataFrame(cols)
return stats
def get_divs(words, brands, indir, modelName, topics_marginal_probs):
dirs = gslib.LDAdirs(modelName, indir)
(dict1, _, lda) = gslib.loadStuff(dirs)
brands_df = pruneWordsList(brands, lda)
words_df = pruneWordsList(words, lda)
probs = ptopic_given_word(lda, topics_marginal_probs)
sims = LDA_simmetricKLdiv(probs, brands_df["IDs"], words_df["IDs"])
sims_norm_df = pandas.DataFrame(sims,
index=brands_df.index,
columns=words_df.index)
return (sims_norm_df, brands_df, words_df)
def get_likes(words, brands, indir, modelName):
dirs = gslib.LDAdirs(modelName, indir)
(dict1, _, lda) = gslib.loadStuff(dirs)
brands_df = pruneWordsList(brands, lda)
words_df = pruneWordsList(words, lda)
probs = pword_given_topic(lda)
sims = LDAscalarProd(probs, brands_df["IDs"], words_df["IDs"])
wordsWeights = LDAweights(lda, words_df["IDs"])
brandsWeights = LDAweights(lda, brands_df["IDs"])
sims_norm = sims / numpy.sqrt(numpy.outer(brandsWeights, wordsWeights))
sims_norm_df = pandas.DataFrame(sims_norm,
index=brands_df.index,
columns=words_df.index)
return (sims_norm_df, brands_df, words_df)
def main():
'''
resorts sims and saves a png copy
'''
# dirs = gsLib.LDAdirs(indir=r"Z:\ermunds\results\all\all unbranded threads",modelName="unbranded2passes_20topics")
#dirs = gsLib.LDAdirs(indir=r"Z:\ermunds\results\all branded threads", modelName="All2passes_20topics")
dirs = gsLib.LDAdirs(indir=r"Z:\ermunds\results\all unbranded threads 2",
modelName="unbranded220topics")
#dirs = gsLib.LDAdirs(indir=r"Z:\ermunds\results\sink", modelName="unbranded220topics")
CSVin = "simsN_posts"
CSVout = "simsNtweaked"
suffix = ''
figName = 'heatmap_from_posts_no whitening' + suffix
#mp.generateCSV(indir=dirs.indir,modelName=dirs.modelName,suffix = suffix)
sims, brands = mp.loadCSV(dirs, CSVin)
nbrands = BrandsClustered_1
# caps bug of may 14
del nbrands[nbrands.index('mercedes-benz')]
idx = numpy.zeros(len(nbrands), dtype=int)
for i, b in enumerate(nbrands):
idx[i] = brands.index(b)
'''
ibrand = brands.index('ram')
idx = numpy.argsort(-sims[ibrand,:])
ibrand = brands.index('jeep')
sort_a_slice(idx,sims,a=6,b=None,compare_to=ibrand)
ibrand = brands.index('nissan')
sort_a_slice(idx,sims,a=10,b=None,compare_to=ibrand)
ibrand = brands.index('chrysler')
sort_a_slice(idx,sims,a=15,b=None,compare_to=ibrand)
ibrand = brands.index('bmw')
sort_a_slice(idx,sims,a=23,b=None,compare_to=ibrand,sign=1)
'''
(sims, nbrands) = select(sims, brands, idx)
mp.saveCSV(dirs, CSVout, nbrands, sims)
draw.main(dirs, CSVout, figName)
def main(
outdir=r'Z:\ermunds\results\2005 20t unbranded',
num_passes=2,
n_repeat=10,
num_topics=20,
threadChoseStr='',
modelTag='2005+',
time_low_cutoff=time.strptime("1 Jan 2005", "%d %b %Y"),
time_hi_cutoff=time.strptime("1 Jan 2006", "%d %b %Y"),
):
'''
# time_low_cutoff, time_hi_cutoff posts are chosen between these two dates
# threadChoseStr - filter topic names by this phrase
'''
dTr = bf.notMain(threadChoseStr)
modelName = modelTag + str(num_topics) + 'topics'
dirs = gslib.LDAdirs(modelName, outdir)
with open(dirs.dataFileName, 'a') as file1:
pickle.dump(dTr, file1)
## setup logging to file and console
logger = logging.getLogger('')
logger.setLevel(logging.DEBUG)
fh = logging.FileHandler(dirs.logFileName)
fh.setLevel(logging.DEBUG)
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
formatter = logging.Formatter(
'%(asctime)s - %(name)-12s - %(levelname)s - %(message)s')
ch.setFormatter(formatter)
fh.setFormatter(formatter)
logger.addHandler(ch)
logger.addHandler(fh)
## get threads, extract post texts and save to single file
# 7min per 1GB
logging.log(logging.INFO, "building doc list")
lineCounter = 0
with open(dirs.allDocsFileName, 'a') as docDumpFile:
for Trlist in dTr.values():
for Tr in Trlist:
for p in Tr.getPosts():
if (p.msgTime > time_low_cutoff) and (p.msgTime <
time_hi_cutoff):
doc = gslib.textCleanUp(
p.msgTitle) + gslib.textCleanUp(p.msgText)
lineCounter += 1
print(doc, file=docDumpFile)
logging.log(logging.INFO, "total {} docs ".format(lineCounter))
#build dict 1.5H/GB
dict1 = gslib.build_dict(dirs)
dict1.save(dirs.dictFileName)
#dict1 = gensim.corpora.dictionary.Dictionary().load(dirs.dictFileName)
#pipe docfile to gensim corpus
#fixme - corpusAdapter missing a len() property
corpus = gslib.corpusAdapter(dirs.allDocsFileName, id2word=dict1)
gensim.corpora.MmCorpus.serialize(fname=dirs.corpusFname,
corpus=corpus,
id2word=dict1)
mm = gensim.corpora.MmCorpus(dirs.corpusFname)
## run the LDA (2h per update on 2M posts)
# first runs a small step and then update 9 times saving results to disk every time
lda = gensim.models.ldamodel.LdaModel(corpus=mm,
id2word=dict1,
num_topics=num_topics,
update_every=0,
passes=num_passes)
lda.save(dirs.modelFname + "_0")
for i in xrange(n_repeat - 1):
lda.update(mm)
# save inremediate result
lda.save(dirs.modelFname + "_" + str(i + 1))
for t in lda.show_topics(-1):
logging.info(str('all topics here') + t)
lda.save(dirs.modelFname)
logger.removeHandler(ch)
logger.removeHandler(fh)
return modelName
# -*- coding: utf-8 -*-
"""
Created on Tue May 14 23:23:59 2013
@author: Vasya
"""
import gensim
import logging
import genSimLDAlib as gslib
#import someBrandFiltering as bf
dirs = gslib.LDAdirs(indir=r"Z:\ermunds\results\all unbranded threads 2",
modelName="unbranded220topics")
logging.basicConfig(
level=logging.DEBUG,
format='%(asctime)s %(name)-20s %(levelname)-8s %(message)s',
datefmt='%m-%d %H:%M:%S',
filename=dirs.logFileName,
filemode='a')
console = logging.StreamHandler()
console.setLevel(logging.INFO)
formatter = logging.Formatter(
'%(asctime)s %(name)-12s %(levelname)-8s %(message)s')
console.setFormatter(formatter)
logging.getLogger('').addHandler(console)
logging.info("ading more from beh.py")
dict1 = gensim.corpora.dictionary.Dictionary().load(dirs.dictFileName)
mm = gensim.corpora.MmCorpus(dirs.corpusFname)
"""
import getLikes
import numpy as np
import genSimLDAlib as gslib
import mess_with_sims
indir=r"Z:\ermunds\results\2005 20topics"
modelName="2005+20topics"
dirs = gslib.LDAdirs(modelName,indir)
(dict1,_,lda)=gslib.loadStuff(dirs)
words = mess_with_sims.BrandsClustered_1
#words = lda.id2word.values()
wordsClean = getLikes.pruneWordsList(words,lda)
weights = getLikes.LDAweights(lda,wordsClean["IDs"])
import matplotlib.pyplot as plt
yy = wordsClean.Counts
xx = np.sqrt(weights)
labels = wordsClean.index
def compute_and_save(modelName="201220topics", LDAdir=r"Z:\ermunds\results\2012 20topics"):
dirs = gslib.LDAdirs(indir=LDAdir,modelName=modelName)
(_,mm,lda)=gslib.loadStuff(dirs)
agg = marginal_topic_distribution(lda,mm)
np.savetxt( os.path.join(LDAdir,"topics_marginal.csv"), agg, delimiter=",")
return agg
# -*- coding: utf-8 -*-
"""
Created on Tue May 14 23:52:27 2013
@author: Vasya
"""
import gensim
import logging
import genSimLDAlib as gslib
#import someBrandFiltering as bf
num_topics = 100
num_passes = 2
dirs = gslib.LDAdirs(indir=r"Z:\ermunds\results\sink",
modelName="unbranded220topics")
logging.basicConfig(
level=logging.DEBUG,
format='%(asctime)s %(name)-20s %(levelname)-8s %(message)s',
datefmt='%m-%d %H:%M:%S',
filename=dirs.logFileName,
filemode='a')
console = logging.StreamHandler()
console.setLevel(logging.INFO)
formatter = logging.Formatter(
'%(asctime)s %(name)-12s %(levelname)-8s %(message)s')
console.setFormatter(formatter)
logging.getLogger('').addHandler(console)
logging.info("adding more from beh2.py")
dict1 = gensim.corpora.dictionary.Dictionary().load(dirs.dictFileName)
def main(BAVfile, sheet_name, LDAdir, modelName):
BAV_raw = pandas.read_excel(BAVfile,
sheet_name,
index_col=0,
na_values=['NA'])
#Hack!!! Total_Prefer_pct is th last useless col
#idx_first = BAV_raw.columns.get_loc('Brand_Asset_C')+1
idx_first = 1
good_cols = [
col for col in BAV_raw.columns[idx_first:]
if len(col.split("_")) == 2 and col.endswith('pct')
]
BAV_filtered = BAV_raw[good_cols]
BAV_filtered.columns = map(lambda x: x.split("_")[0], BAV_filtered.columns)
# filter brands - depends onf the dictionay creation way
# ie if '-' goes to space this will work
# if '-' is dropped tnen will not
BAV_filtered = try_drop(BAV_filtered, 'General Motors (GM)')
BAV_filtered = try_drop(BAV_filtered, 'Ford Motor Company')
BAV_filtered = try_drop(BAV_filtered, 'Smart (car)')
BAV_filtered = try_drop(BAV_filtered, 'Mini Cooper')
BAV_filtered = rename_row(BAV_filtered, 'Mercedes-Benz', 'Mercedes')
BAV_filtered = rename_row(BAV_filtered, 'Mitsubishi Vehicles',
'Mitsubishi')
BAV_filtered = rename_row(BAV_filtered, 'Rolls-Royce', 'Royce')
BAV_filtered = rename_row(BAV_filtered, 'Aston Martin', 'Aston')
BAV_filtered = rename_row(BAV_filtered, 'Alfa Romeo', 'Romeo')
words = [w.encode() for w in BAV_filtered.columns]
brands = [b.encode() for b in BAV_filtered.index]
topicsPs = np.genfromtxt(os.path.join(LDAdir, 'topics_marginal.csv'))
(LDA_df, BrandsInfo, WordsInfo) = getLikes.get_likes(words=words,
brands=brands,
indir=LDAdir,
modelName=modelName)
(divs, _, _) = getLikes.get_divs(words,
brands,
indir=LDAdir,
modelName=modelName,
topics_marginal_probs=topicsPs)
BAV_filtered = BAV_filtered[LDA_df.columns]
BAV_filtered = BAV_filtered.ix[LDA_df.index]
dirs = gslib.LDAdirs(modelName, LDAdir)
(dict1, _, lda) = gslib.loadStuff(dirs)
probs = getLikes.ptopic_given_word(lda, topicsPs)
probs_df = pandas.DataFrame(probs, columns=lda.id2word.values())
alls = pandas.concat([BrandsInfo["IDs"], WordsInfo["IDs"]])
x = probs_df[alls]
x.columns = alls.index
writer = pandas.ExcelWriter(
os.path.join(LDAdir, modelName + '_BAV_comp.xlsx'))
LDA_df.to_excel(writer, sheet_name='cosine distance')
BAV_filtered.to_excel(writer, sheet_name='BAV')
divs.to_excel(writer, sheet_name='KL divs')
BrandsInfo.to_excel(writer, sheet_name='brands')
WordsInfo.to_excel(writer, sheet_name='words')
x.to_excel(writer, sheet_name='p_topic_given_word')
writer.save
return (LDA_df, BAV_filtered, divs, BrandsInfo, WordsInfo)
# -*- coding: utf-8 -*-
"""
Created on Mon May 13 09:28:54 2013
@author: Vasya
"""
import ldaModel2bradsSims_direct as mp
import sims_csv_plotter
import genSimLDAlib as gsLib
import gensim
#dirs = gsLib.LDAdirs(modelName = 'PricesStemmed20passes_20topics',indir = r"Z:\ermunds\results\1 prices paid\5-6-2013")
dirs = gsLib.LDAdirs(indir=r"Z:\ermunds\results\all branded threads",
modelName="All2passes_20topics")
dict1 = gensim.corpora.dictionary.Dictionary().load(dirs.dictFileName)
lda = gensim.models.ldamodel.LdaModel(id2word=dict1).load(dirs.modelFname)
simsR, brands = mp.corrBrands(lda)
sims = mp.normalize(simsR)
##mp.saveCSV(dirs,'simsN',brands,sims)
#sims,brands= mp.loadCSV(dirs,"simsN")
##sims_csv_plotter.main(dirs,CSVin="simsN",figName='fromTopics')
brands[brands.index('mercedes-benz')] = 'mercedesbenz'
mp.likes(sims, brands, brands[0])
print 'tada'
mp.likes2(lda, brands, word='luxury') # looks like sims cvs
# -*- coding: utf-8 -*-
"""
Created on Fri May 17 14:31:58 2013
@author: Vasya
"""
#import gensim
import genSimLDAlib as gslib
import mess_with_sims as sims
import numpy
dirs = gslib.LDAdirs(indir=r"Z:\ermunds\results\2012 20topics",
modelName="201220topics")
docsfilename = dirs.allDocsFileName
(dict1, mm, lda) = gslib.loadStuff(dirs)
brands = sims.BrandsClustered_1
# decompose a post into topics and ptint them
gslib.make_sense(1, lda, mm, docsfilename)
# guess the topic of concept list
consepts = ['cheap', 'ugly', 'unrelaible']
consepts = ['young', 'trendy', 'fast', 'macho'] # fail
consepts = ['green', 'environment', 'sustainable', 'hybrid'] #n75
consepts = ['reliable', 'safe'] # n8
consepts = 'air hot heat cool exhaust system fan coolant temp blow'.split(
) # n5
ws, IDl, ID2index = gslib.world_list2IDs(dict1,
consepts,
tokenizef=gslib.wordCleanUp)
topicsPs = np.genfromtxt(os.path.join(modelDir, 'topics_marginal.csv'))
words = getLikes.words_from_file(r"Z:\ermunds\adjectives.txt")
brands = getLikes.words_from_file(r"Z:\ermunds\brands.txt")
(divs, _, _) = getLikes.get_divs(words,
brands,
indir=modelDir,
modelName=modelName,
topics_marginal_probs=topicsPs)
(sims, b, w) = getLikes.get_likes(words,
brands,
indir=modelDir,
modelName=modelName)
dirs = gslib.LDAdirs(modelName, modelDir)
(dict1, _, lda) = gslib.loadStuff(dirs)
brands_df = getLikes.pruneWordsList(brands, lda)
words_df = getLikes.pruneWordsList(words, lda)
probs = getLikes.ptopic_given_word(lda, topicsPs)
probs_df = pd.DataFrame(probs, columns=lda.id2word.values())
alls = pd.concat([brands_df["IDs"], words_df["IDs"]])
x = probs_df[alls]
x.columns = alls.index
writer = pd.ExcelWriter(os.path.join(modelDir, modelName + '_new.xlsx'))
sims.to_excel(writer, sheet_name='cosine distance')
divs.to_excel(writer, sheet_name='KL divs')
b.to_excel(writer, sheet_name='brands')
fig.savefig(figfname)
plt.show()
def plotSims(sims,brands,dirs,figName='heatmap'):
fig = plt.figure(figsize=(11,9))
ax = fig.add_axes([.1,.1,.8,.8]) # ruined by corobar
imgplot = ax.imshow(sims,interpolation='none')
idx= xrange(len(brands))
ax.set_yticks(idx)
ax.set_yticklabels(zip(brands,idx))
ax.set_xticks(idx)
ax.set_xticklabels(brands,rotation=90)
plt.colorbar(imgplot)
ax.set_position([.1,.2,.6,.6])
figfname=dirs.indir+'\\'+figName+'.png'
fig.savefig(figfname)
plt.show()
if __name__ == '__main__':
dirs = gsLib.LDAdirs(indir=r"Z:\ermunds\results\sink"
,modelName="All2passes_20topics")
main(dirs)