def iterCalculatingBayesianEstimate():
reviewsavg = average(JCR.getColumnArray('reviews_avgnote'))
rows = JCR.iterRowSelectingColumns(['reviews_avgnote', 'reviews_all'], \
filterfunc=filterfieldsunder(['reviews_all'], filterunder))
global reviewmaxnum
for row in rows:
coef = float(row['reviews_all']) / (row['reviews_all'] + reviews_min_number)
bayesian_estimate = coef * row['reviews_avgnote'] + (1-coef) * reviewsavg
if row['reviews_all'] > reviewmaxnum: reviewmaxnum = row['reviews_all']
yield dict(row.items() + [('bayesian_estimate', bayesian_estimate)])
global subplot
subplot +=1
plt.subplot(subplot)
plt.title( '%s: \nmean=%s std=%s ' % (field, round(mean(column), 3), round(std(column), 3)), fontsize=11 )
plt.axhspan(0, 2000, color='none')
plt.hist( column, bins=bins, range=(0,1))
print '%s: mean=%s std=%s items_under_0.3=%s' % (field, round(mean(column), 3), round(std(column), 3), sum([1 for c in column if c<0.3]) )
JCR = JamendoCsvReader('stats_reviewAVGs.csv')
subplot = 220
columns = JCR.getColumns(['reviews_avgnote', 'avg_agreed_note', 'weighted_avg_note', 'weighted_avg_agreed_note', 'weighted_avg_agreed_note2', 'reviews_all'], \
filterfunc=filterfieldsunder(['reviews_all'],4))
plt.figure(figsize=(12,8))
for key in ['avg_agreed_note', 'weighted_avg_note', 'weighted_avg_agreed_note', 'weighted_avg_agreed_note2']:
plotAvgHist(columns[key], key)
plt.figure()
plt.title( '%s: \nmean=%s std=%s ' % ('reviews_avgnote', round(mean(columns['reviews_avgnote']), 3), \
round(std(columns['reviews_avgnote']), 3)), fontsize=11 )
plt.axhspan(0, 2000, color='none')
plt.hist( columns['reviews_avgnote'], bins=40, range=(0,1))