diff options
| -rwxr-xr-x | bin/dfatool | 5 | ||||
| -rwxr-xr-x | bin/merge.py | 107 | ||||
| -rw-r--r-- | lib/Kratos/DFADriver.pm | 48 |
3 files changed, 2 insertions, 158 deletions
diff --git a/bin/dfatool b/bin/dfatool index 0c57b7c..6de30d9 100755 --- a/bin/dfatool +++ b/bin/dfatool @@ -178,9 +178,6 @@ my %action = ( $driver->update_model; } }, - validate => sub { - $driver->validate_model(@data_files); - }, crossvalidate => sub { printf("./dfatool crossvalidate %s %s\n", $xml_file, @@ -340,7 +337,7 @@ test programs to assess a device's energy usage. B<dfatool> [I<options>] enable|disable|maketest|rmtest|log|loop I<driver.xml> -B<dfatool> [I<options>] analyze|validate|crossvalidate|ls|list|show +B<dfatool> [I<options>] analyze|crossvalidate|ls|list|show I<driver.xml> I<data.tar> [I<moredata.tar ...>] =head1 VERSION diff --git a/bin/merge.py b/bin/merge.py index 8cf4dca..e5365b2 100755 --- a/bin/merge.py +++ b/bin/merge.py @@ -841,109 +841,6 @@ def crossvalidate(by_name, by_param, by_trace, model, parameters): print('estimate function %s timeout by %s: MAE %.f µs, SMAPE %.2f%%' % ( name, param, np.mean(estimate_mae[name]), np.mean(estimate_smape[name]))) -def validate(by_name, by_param, parameters): - aggdata = { - 'state' : {}, - 'transition' : {}, - } - for key, val in by_name.items(): - name = key - isa = val['isa'] - model = data['model'][isa][name] - - if isa == 'state': - aggdata[isa][name] = { - 'power' : { - 'goodness' : aggregate_measures(model['power']['static'], val['means']), - 'median' : np.median(val['means']), - 'mean' : np.mean(val['means']), - 'std_inner' : np.std(val['means']), - 'function' : {}, - }, - 'online_power' : { - 'goodness' : regression_measures(np.array(val['online_means']), np.array(val['means'])), - 'median' : np.median(val['online_means']), - 'mean' : np.mean(val['online_means']), - 'std_inner' : np.std(val['online_means']), - 'function' : {}, - }, - 'online_duration' : { - 'goodness' : regression_measures(np.array(val['online_durations']), np.array(val['durations'])), - 'median' : np.median(val['online_durations']), - 'mean' : np.mean(val['online_durations']), - 'std_inner' : np.std(val['online_durations']), - 'function' : {}, - }, - 'clip' : { - 'mean' : np.mean(val['clip_rate']), - 'max' : max(val['clip_rate']), - }, - 'timeout' : {}, - } - if 'function' in model['power']: - aggdata[isa][name]['power']['function'] = { - 'estimate' : { - 'fit' : assess_function(model['power']['function']['estimate'], - name, 'means', parameters, by_param), - }, - 'user': { - 'fit' : assess_function(model['power']['function']['user'], - name, 'means', parameters, by_param), - }, - } - if isa == 'transition': - aggdata[isa][name] = { - 'duration' : { - 'goodness' : aggregate_measures(model['duration']['static'], val['durations']), - 'median' : np.median(val['durations']), - 'mean' : np.mean(val['durations']), - 'std_inner' : np.std(val['durations']), - 'function' : {}, - }, - 'energy' : { - 'goodness' : aggregate_measures(model['energy']['static'], val['energies']), - 'median' : np.median(val['energies']), - 'mean' : np.mean(val['energies']), - 'std_inner' : np.std(val['energies']), - 'function' : {}, - }, - 'rel_energy_prev' : { - 'goodness' : aggregate_measures(model['rel_energy_prev']['static'], val['rel_energies_prev']), - 'median' : np.median(val['rel_energies_prev']), - 'mean' : np.mean(val['rel_energies_prev']), - 'std_inner' : np.std(val['rel_energies_prev']), - 'function' : {}, - }, - 'rel_energy_next' : { - 'goodness' : aggregate_measures(model['rel_energy_next']['static'], val['rel_energies_next']), - 'median' : np.median(val['rel_energies_next']), - 'mean' : np.mean(val['rel_energies_next']), - 'std_inner' : np.std(val['rel_energies_next']), - 'function' : {}, - }, - 'clip' : { - 'mean' : np.mean(val['clip_rate']), - 'max' : max(val['clip_rate']), - }, - 'timeout' : {}, - } - if 'function' in model['timeout']: - aggdata[isa][name]['timeout'] = { - 'median' : np.median(val['timeouts']), - 'mean' : np.mean(val['timeouts']), - 'function': { - 'estimate' : { - 'fit' : assess_function(model['timeout']['function']['estimate'], - name, 'timeouts', parameters, by_param), - }, - 'user': { - 'fit' : assess_function(model['timeout']['function']['user'], - name, 'timeouts', parameters, by_param), - }, - }, - } - return aggdata - def analyze_by_param(aggval, by_param, allvalues, name, key1, key2, param, param_idx): aggval[key1]['std_by_param'][param] = mean_std_by_param( by_param, allvalues, name, key2, param_idx) @@ -1129,9 +1026,7 @@ if 'substates' in opts: else: plotter.plot_substate_thresholds(data['model'], by_name) -if 'validate' in opts: - data['aggregate'] = validate(by_name, by_param, parameters) -elif 'crossvalidate' in opts: +if 'crossvalidate' in opts: crossvalidate(by_name, by_param, by_trace, data['model'], parameters) else: data['aggregate'] = analyze(by_name, by_arg, by_param, by_trace, parameters) diff --git a/lib/Kratos/DFADriver.pm b/lib/Kratos/DFADriver.pm index 432d0e5..481dc67 100644 --- a/lib/Kratos/DFADriver.pm +++ b/lib/Kratos/DFADriver.pm @@ -108,13 +108,6 @@ sub analyze { $self->log->analyze( @{$json_files} ); } -sub validate_model { - my ( $self, @files ) = @_; - my ( $logs, $json_files ) = $self->preprocess(@files); - $self->log->validate( @{$json_files} ); - $self->assess_validation; -} - sub crossvalidate_model { my ( $self, @files ) = @_; my ( $logs, $json_files ) = $self->preprocess(@files); @@ -561,47 +554,6 @@ sub assess_model_tex { say '\end{tabular}'; } -sub assess_validation { - my ($self) = @_; - - for my $name ( sort keys %{ $self->{log}{aggregate}{state} } ) { - my $state = $self->{log}{aggregate}{state}{$name}; - - printf( "Validating %s:\n", $name ); - $self->printf_clip($state); - $self->printf_goodness( $self->model->get_state_power($name), - $state, 'power', 'µW' ); - $self->printf_fit( $state, 'power', 'µW' ); - $self->printf_online_goodness( - $state, 'online_power', 'µW' ); - $self->printf_online_goodness( - $state, 'online_duration', 'µs' ); - } - for my $name ( sort keys %{ $self->{log}{aggregate}{transition} } ) { - my $transition = $self->{log}{aggregate}{transition}{$name}; - - printf( "Validating %s:\n", $name ); - $self->printf_clip($transition); - $self->printf_goodness( - $self->model->get_transition_by_name($name)->{duration}{static}, - $transition, 'duration', 'µs' ); - $self->printf_goodness( - $self->model->get_transition_by_name($name)->{energy}{static}, - $transition, 'energy', 'pJ' ); - $self->printf_goodness( - $self->model->get_transition_by_name($name)->{rel_energy_prev}{static}, - $transition, 'rel_energy_prev', 'pJ' ); - if ( exists $transition->{rel_energy_next}{median} ) { - $self->printf_goodness( - $self->model->get_transition_by_name($name)->{rel_energy_next}{static}, - $transition, 'rel_energy_next', 'pJ' ); - } - if ( exists $transition->{timeout}{median} ) { - $self->printf_fit( $transition, 'timeout', 'µs' ); - } - } -} - sub update_model { my ($self) = @_; |