def plotGainsAtRank( runs ): sessid = str(runs[0].get_session_id()) yValueLists = [ (FiguresConfig.avgPlotName, stats.get_average_cumulated_gains_at_total_rank_range( runs ) ), ('top50%', stats.get_average_top_cumulated_gains_at_total_rank_range( runs, proportion = 50 ) ), ('bottom50%', stats.get_average_top_cumulated_gains_at_total_rank_range( runs, proportion = 50, bottom = True ) ), ('avg +1SD', stats.get_avg_cumulated_gain_plusSD_at_total_rank_range( runs ) ), ('avg -1SD', stats.get_avg_cumulated_gain_plusSD_at_total_rank_range( runs, -1 ) ) ] defaultPlot( 'rank', 'cg', stats.get_max_rank_range( runs ), yValueLists, stats.get_amount_of_runs_at_total_rank_range(runs), get_filename('gainAtRank', runs))
def plotAverageGainsAtRankAcrossSessions( sessions ): yValueLists = [ (FiguresConfig.avgPlotName, stats.get_average_cross_session_cumulated_gains_at_total_rank_range( sessions ) ) ] yValueLists = yValueLists + [(get_session_id(runs), stats.get_average_cumulated_gains_at_total_rank_range(runs)) for runs in sessions] runValueLists = [(FiguresConfig.avgPlotName, stats.get_average_amount_of_runs_at_total_rank_range(sessions) )] + [ (get_session_id(runs), stats.get_amount_of_runs_at_total_rank_range(runs) ) for runs in sessions] defaultPlot( 'rank', 'avg cg', stats.get_max_cross_session_rank_range( sessions ), yValueLists, runValueLists, FiguresConfig.outputDirectory + '/' + get_filename_prefix() + 'X-session-gainAtRank.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 plotGainsAtRank(runs): sessid = str(runs[0].get_session_id()) yValueLists = [ (FiguresConfig.avgPlotName, stats.get_average_cumulated_gains_at_total_rank_range(runs)), ('top50%', stats.get_average_top_cumulated_gains_at_total_rank_range( runs, proportion=50)), ('bottom50%', stats.get_average_top_cumulated_gains_at_total_rank_range( runs, proportion=50, bottom=True)), ('avg +1SD', stats.get_avg_cumulated_gain_plusSD_at_total_rank_range(runs)), ('avg -1SD', stats.get_avg_cumulated_gain_plusSD_at_total_rank_range(runs, -1)) ] defaultPlot('rank', 'cg', stats.get_max_rank_range(runs), yValueLists, stats.get_amount_of_runs_at_total_rank_range(runs), get_filename('gainAtRank', runs))
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))
def plotAverageGainsAtRankAcrossSessions(sessions): yValueLists = [ (FiguresConfig.avgPlotName, stats.get_average_cross_session_cumulated_gains_at_total_rank_range( sessions)) ] yValueLists = yValueLists + [ (get_session_id(runs), stats.get_average_cumulated_gains_at_total_rank_range(runs)) for runs in sessions ] runValueLists = [ (FiguresConfig.avgPlotName, stats.get_average_amount_of_runs_at_total_rank_range(sessions)) ] + [(get_session_id(runs), stats.get_amount_of_runs_at_total_rank_range(runs)) for runs in sessions] defaultPlot( 'rank', 'avg cg', stats.get_max_cross_session_rank_range(sessions), yValueLists, runValueLists, FiguresConfig.outputDirectory + '/' + get_filename_prefix() + 'X-session-gainAtRank.png', True)