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)
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 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 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 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
# -*- 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)
for t,p in lda[ [(t,1) for t in IDl] ]:
print '__with prob:{}% is N{}: {}'.format(int(p*100),t, ' '.join([w for _,w in lda.show_topic(t,10)]))
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
plt.scatter(xx,yy, marker='.')
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
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)