def plotGainsAtCost(runs, costIncrement):
sessid = str(runs[0].get_session_id())
yValueLists = [
(FiguresConfig.avgPlotName,
stats.get_average_cumulated_gains_at_cost_range(runs, costIncrement)),
('top50%',
stats.get_average_top_cumulated_gains_at_cost_range(runs,
costIncrement,
proportion=50)),
('bottom50%',
stats.get_average_top_cumulated_gains_at_cost_range(runs,
costIncrement,
proportion=50,
bottom=True)),
('avg +1SD',
stats.get_avg_cumulated_gain_plusSD_at_cost_range(
runs, costIncrement)),
('avg -1SD',
stats.get_avg_cumulated_gain_plusSD_at_cost_range(
runs, costIncrement, -1))
]
defaultPlot('cost', 'cg', stats.get_max_cost_range(runs, costIncrement),
yValueLists,
stats.get_amount_of_runs_at_cost_range(runs, costIncrement),
get_filename('gainAtCost', runs))
def plotAverageGainsAtCostAcrossSessions(sessions, costIncrement):
yValueLists = [
(FiguresConfig.avgPlotName,
stats.get_average_cross_session_cumulated_gains_at_cost_range(
sessions, costIncrement))
]
yValueLists = yValueLists + [
(get_session_id(runs),
stats.get_average_cumulated_gains_at_cost_range(runs, costIncrement))
for runs in sessions
]
runValueLists = [
(FiguresConfig.avgPlotName,
stats.get_average_amount_of_runs_at_cost_range(
sessions, costIncrement))
] + [(get_session_id(runs),
stats.get_amount_of_runs_at_cost_range(runs, costIncrement))
for runs in sessions]
defaultPlot(
'cost', 'avg cg',
stats.get_max_cross_session_cost_range(sessions, costIncrement),
yValueLists, runValueLists, FiguresConfig.outputDirectory + '/' +
get_filename_prefix() + 'X-session-gainAtCost.png', True)
def plotGainsAtCost( runs, costIncrement ):
sessid = str(runs[0].get_session_id())
yValueLists = [
(FiguresConfig.avgPlotName, stats.get_average_cumulated_gains_at_cost_range( runs, costIncrement ) ),
('top50%', stats.get_average_top_cumulated_gains_at_cost_range( runs, costIncrement, proportion = 50 ) ),
('bottom50%', stats.get_average_top_cumulated_gains_at_cost_range( runs, costIncrement, proportion = 50, bottom = True ) ),
('avg +1SD', stats.get_avg_cumulated_gain_plusSD_at_cost_range( runs, costIncrement ) ),
('avg -1SD', stats.get_avg_cumulated_gain_plusSD_at_cost_range( runs, costIncrement, -1 ) )
]
defaultPlot( 'cost', 'cg', stats.get_max_cost_range( runs, costIncrement ), yValueLists,
stats.get_amount_of_runs_at_cost_range(runs, costIncrement), get_filename('gainAtCost', runs))
def plotAverageGainsAtCostAcrossSessions( sessions, costIncrement ):
yValueLists = [
(FiguresConfig.avgPlotName, stats.get_average_cross_session_cumulated_gains_at_cost_range( sessions, costIncrement ) )
]
yValueLists = yValueLists + [(get_session_id(runs), stats.get_average_cumulated_gains_at_cost_range(runs, costIncrement)) for runs in sessions]
runValueLists = [(FiguresConfig.avgPlotName, stats.get_average_amount_of_runs_at_cost_range(sessions, costIncrement) )] + [
(get_session_id(runs), stats.get_amount_of_runs_at_cost_range(runs, costIncrement) ) for runs in sessions]
defaultPlot( 'cost', 'avg cg', stats.get_max_cross_session_cost_range( sessions, costIncrement ), yValueLists,
runValueLists, FiguresConfig.outputDirectory + '/' + get_filename_prefix() + 'X-session-gainAtCost.png', True )
def handle_cross_session_stats( sessions ):
output = cStringIO.StringIO()
writer = csv.writer( output )
def catWrite(listA,listB):
copy = listA[:]
copy.extend( listB )
writer.writerow( copy )
catWrite( [ 'rank' ], stats.get_max_cross_session_rank_range( sessions ) )
catWrite( [ 'avg amt runs' ], stats.get_average_amount_of_runs_at_total_rank_range( sessions ) )
catWrite( [ 'avg gain' ], stats.get_average_cross_session_cumulated_gains_at_total_rank_range( sessions ) )
catWrite( [ 'avg cost' ], stats.get_average_cross_session_cumulated_costs_at_total_rank_range( sessions ) )
catWrite( [ 'avg gain SD' ], stats.get_cross_session_cumulated_gain_stddevs_at_total_rank_range( sessions ) )
# Derived gains
derivedGains = config.get_default_derived_gains_dict()
for derivedGain in derivedGains.values():
catWrite( [ 'avg ' + derivedGain.id ], stats.get_average_cross_session_derived_gains_at_total_rank_range( derivedGain.id, sessions ) )
catWrite( [ 'avg ' + derivedGain.id + ' SD' ], stats.get_cross_session_derived_gain_stddevs_at_total_rank_range( derivedGain.id, sessions ) )
# Per-session averages
for runs in sessions:
sessid = str(runs[0].get_session_id())
catWrite( [ sessid + ' avg gain' ], stats.get_average_cumulated_gains_at_total_rank_range( runs ) )
writer.writerow([])
costInterval = 10
catWrite( [ 'cost' ], stats.get_max_cross_session_cost_range( sessions, costInterval ) )
catWrite( [ 'avg amt runs' ], stats.get_average_amount_of_runs_at_cost_range( sessions, costInterval ) )
catWrite( [ 'avg gain' ], stats.get_average_cross_session_cumulated_gains_at_cost_range( sessions, costInterval ) )
catWrite( [ 'avg gain SD' ], stats.get_average_cross_session_cumulated_gain_stddevs_at_cost_range( sessions, costInterval ) )
# Derived gains
for derivedGain in derivedGains.values():
catWrite( [ 'avg ' + derivedGain.id ], stats.get_average_cross_session_derived_gains_at_cost_range( derivedGain.id, sessions, costInterval ) )
catWrite( [ 'avg ' + derivedGain.id + ' SD' ], stats.get_average_cross_session_derived_gain_stddevs_at_cost_range( derivedGain.id, sessions, costInterval ) )
# Per-session averages
for runs in sessions:
sessid = str(runs[0].get_session_id())
catWrite( [ sessid + ' avg gain' ], stats.get_average_cumulated_gains_at_cost_range( runs, costInterval ) )
writer.writerow([])
return output.getvalue()
def handle_stats( runs ):
output = cStringIO.StringIO()
writer = csv.writer( output )
def catWrite(listA,listB):
copy = listA[:]
copy.extend( listB )
writer.writerow( copy )
catWrite( [ 'rank' ], stats.get_max_rank_range( runs ) )
catWrite( [ 'amt runs' ], stats.get_amount_of_runs_at_total_rank_range( runs ) )
catWrite( [ 'avg gain' ], stats.get_average_cumulated_gains_at_total_rank_range( runs ) )
catWrite( [ 'max gain' ], stats.get_max_cumulated_gains_at_total_rank_range( runs ) )
catWrite( [ 'min gain' ], stats.get_min_cumulated_gains_at_total_rank_range( runs ) )
catWrite( [ 'avg cost' ], stats.get_average_cumulated_costs_at_total_rank_range( runs ) )
catWrite( [ 'max cost' ], stats.get_max_cumulated_costs_at_total_rank_range( runs ) )
catWrite( [ 'min cost' ], stats.get_min_cumulated_costs_at_total_rank_range( runs ) )
writer.writerow([])
costInterval = 10
catWrite( [ 'cost' ], stats.get_min_cost_range( runs, costInterval ) )
catWrite( [ 'amt runs' ], stats.get_amount_of_runs_at_cost_range( runs, costInterval ) )
catWrite( [ 'avg gain' ], stats.get_average_cumulated_gains_at_cost_range( runs, costInterval ) )
catWrite( [ 'max gain' ], stats.get_max_cumulated_gains_at_cost_range( runs, costInterval ) )
catWrite( [ 'min gain' ], stats.get_min_cumulated_gains_at_cost_range( runs, costInterval ) )
writer.writerow([])
writer.writerow( [ 'avgTotalRank', stats.get_average_total_rank(runs)] )
writer.writerow( [ 'avgGain', stats.get_average_cumulated_gain(runs)] )
writer.writerow( [ 'avgCost', stats.get_average_cumulated_cost(runs)] )
writer.writerow( [ 'maxTotalRank', stats.get_max_total_rank(runs)] )
writer.writerow( [ 'maxGain', stats.get_max_cumulated_gain(runs)] )
writer.writerow( [ 'maxCost', stats.get_max_cumulated_cost(runs)] )
writer.writerow( [ 'minTotalRank', stats.get_min_total_rank(runs)] )
writer.writerow( [ 'minGain', stats.get_min_cumulated_gain(runs)] )
writer.writerow( [ 'minCost', stats.get_min_cumulated_cost(runs)] )
writer.writerow( [ 'varTotalRank', stats.get_total_rank_variance(runs)] )
writer.writerow( [ 'varGain', stats.get_cumulated_gain_variance(runs)] )
writer.writerow( [ 'varCost', stats.get_cumulated_cost_variance(runs)] )
return output.getvalue()
def handle_stats( runs ):
return 'avgGainsRank = ' + repr(stats.get_average_cumulated_gains_at_total_rank_range( runs )) + '\n' \
+ 'maxGainsRank = ' + repr(stats.get_max_cumulated_gains_at_total_rank_range( runs )) + '\n' \
+ 'minGainsRank = ' + repr(stats.get_min_cumulated_gains_at_total_rank_range( runs )) + '\n' \
+ 'avgGainsCost = ' + repr(stats.get_average_cumulated_gains_at_cost_range( runs, 10 )) + '\n' \
+ 'maxGainsCost = ' + repr(stats.get_max_cumulated_gains_at_cost_range( runs, 10 )) + '\n' \
+ 'minGainsCost = ' + repr(stats.get_min_cumulated_gains_at_cost_range( runs, 10 )) + '\n' \
+ 'avgCostsRank = ' + repr(stats.get_average_cumulated_costs_at_total_rank_range( runs )) + '\n' \
+ 'maxCostsRank = ' + repr(stats.get_max_cumulated_costs_at_total_rank_range( runs )) + '\n' \
+ 'minCostsRank = ' + repr(stats.get_min_cumulated_costs_at_total_rank_range( runs )) + '\n' \
+ 'avgTotalRank = ' + repr(stats.get_average_total_rank(runs)) + '\n' \
+ 'avgGain = ' + repr(stats.get_average_cumulated_gain(runs)) + '\n' \
+ 'avgCost = ' + repr(stats.get_average_cumulated_cost(runs)) + '\n' \
+ 'maxTotalRank = ' + repr(stats.get_max_total_rank(runs)) + '\n' \
+ 'maxGain = ' + repr(stats.get_max_cumulated_gain(runs)) + '\n' \
+ 'maxCost = ' + repr(stats.get_max_cumulated_cost(runs)) + '\n' \
+ 'minTotalRank = ' + repr(stats.get_min_total_rank(runs)) + '\n' \
+ 'minGain = ' + repr(stats.get_min_cumulated_gain(runs)) + '\n' \
+ 'minCost = ' + repr(stats.get_min_cumulated_cost(runs)) + '\n' \
+ 'varTotalRank = ' + repr(stats.get_total_rank_variance(runs)) + '\n' \
+ 'varGain = ' + repr(stats.get_cumulated_gain_variance(runs)) + '\n' \
+ 'varCost = ' + repr(stats.get_cumulated_cost_variance(runs))
def handle_stats(runs):
return 'avgGainsRank = ' + repr(stats.get_average_cumulated_gains_at_total_rank_range( runs )) + '\n' \
+ 'maxGainsRank = ' + repr(stats.get_max_cumulated_gains_at_total_rank_range( runs )) + '\n' \
+ 'minGainsRank = ' + repr(stats.get_min_cumulated_gains_at_total_rank_range( runs )) + '\n' \
+ 'avgGainsCost = ' + repr(stats.get_average_cumulated_gains_at_cost_range( runs, 10 )) + '\n' \
+ 'maxGainsCost = ' + repr(stats.get_max_cumulated_gains_at_cost_range( runs, 10 )) + '\n' \
+ 'minGainsCost = ' + repr(stats.get_min_cumulated_gains_at_cost_range( runs, 10 )) + '\n' \
+ 'avgCostsRank = ' + repr(stats.get_average_cumulated_costs_at_total_rank_range( runs )) + '\n' \
+ 'maxCostsRank = ' + repr(stats.get_max_cumulated_costs_at_total_rank_range( runs )) + '\n' \
+ 'minCostsRank = ' + repr(stats.get_min_cumulated_costs_at_total_rank_range( runs )) + '\n' \
+ 'avgTotalRank = ' + repr(stats.get_average_total_rank(runs)) + '\n' \
+ 'avgGain = ' + repr(stats.get_average_cumulated_gain(runs)) + '\n' \
+ 'avgCost = ' + repr(stats.get_average_cumulated_cost(runs)) + '\n' \
+ 'maxTotalRank = ' + repr(stats.get_max_total_rank(runs)) + '\n' \
+ 'maxGain = ' + repr(stats.get_max_cumulated_gain(runs)) + '\n' \
+ 'maxCost = ' + repr(stats.get_max_cumulated_cost(runs)) + '\n' \
+ 'minTotalRank = ' + repr(stats.get_min_total_rank(runs)) + '\n' \
+ 'minGain = ' + repr(stats.get_min_cumulated_gain(runs)) + '\n' \
+ 'minCost = ' + repr(stats.get_min_cumulated_cost(runs)) + '\n' \
+ 'varTotalRank = ' + repr(stats.get_total_rank_variance(runs)) + '\n' \
+ 'varGain = ' + repr(stats.get_cumulated_gain_variance(runs)) + '\n' \
+ 'varCost = ' + repr(stats.get_cumulated_cost_variance(runs))
