from lexicon_convert import lexicon_convert
from stats import stats, stats_dash
from reweight import reweight, reweight_dash
from bayes import posterior, combine, save_readable
import pickle
GE = 'german'
PC = 'GermanPC'
WS = 'GermanSentiWS'
SP = 'GermanSentiSpin'
full_lex = lexicon_convert('../../data/merged_lex_hash.pm')
# Remove SentiSpin
lex = {}
for item, vals in full_lex.items():
if SP in vals:
if len(vals) == 1:
continue
else:
del vals[SP]
lex[item] = vals
rms = stats(lex, (GE, PC, WS))
reweight(lex, rms)
new = stats_dash(lex, (GE, WS))
reweight_dash(lex, new)
inf = posterior(lex, (GE, PC, WS), tol=10**-9,
bounds = ((0.001,10),(0.001,5),(0.001,5),(0.001,5)),
from lexicon_convert import lexicon_convert
from stats import stats, stats_dash
from reweight import reweight, reweight_dash
from bayes import posterior, combine, save_readable
import pickle
GE = 'german'
PC = 'GermanPC'
WS = 'GermanSentiWS'
SP = 'GermanSentiSpin'
lex = lexicon_convert('../../data/merged_lex_hash.pm', verbose=False)
rms = stats(lex, (GE, PC, WS, SP), verbose=False)
reweight(lex, rms)
new = stats_dash(lex, (GE, WS, SP), verbose=False)
reweight_dash(lex, new)
with open('../../data/premerge.pk', 'wb') as f:
pickle.dump(lex, f)
inf = posterior(lex, (GE, PC, WS, SP), tol=10**-9, initial=[ 0.52847201, 0.32771543, 0.31697963, 0.44575393, 0.60947068])
deviation = {'prior':inf['x'][0],
GE:inf['x'][1],
PC:inf['x'][2],
WS:inf['x'][3],
SP:inf['x'][4]}
new_lex, weight = combine(lex, deviation)