1 files changed, 126 insertions, 0 deletions
diff --git a/bin/eval-online-model-accuracy.py b/bin/eval-online-model-accuracy.py
new file mode 100755
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+++ b/bin/eval-online-model-accuracy.py
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+#!/usr/bin/env python3
+"""
+Evaluate accuracy of online model for DFA/PTA traces.
+
+Usage:
+PYTHONPATH=lib bin/eval-online-model-accuracy.py [options] <pta/dfa definition>
+
+Options:
+--accounting=static_state|static_state_immediate|static_statetransition|static_statetransition_immedate
+    Select accounting method
+
+--depth=<depth> (default: 3)
+    Maximum number of function calls per run
+
+--sleep=<ms> (default: 0)
+    How long to sleep between simulated function calls.
+
+--trace-filter=<transition,transition,transition,...>[ <transition,transition,transition,...> ...]
+    Only consider traces whose beginning matches one of the provided transition sequences.
+    E.g. --trace-filter='init,foo init,bar' will only consider traces with init as first and foo or bar as second transition,
+    and --trace-filter='init,foo,$ init,bar,$' will only consider the traces init -> foo and init -> bar.
+"""
+
+import getopt
+import json
+import re
+import runner
+import sys
+import time
+import io
+import yaml
+from aspectc import Repo
+from automata import PTA
+from codegen import *
+from harness import OnboardTimerHarness
+from dfatool import regression_measures
+
+opt = dict()
+
+if __name__ == '__main__':
+
+    try:
+        optspec = (
+            'accounting= '
+            'arch= '
+            'app= '
+            'depth= '
+            'dummy= '
+            'instance= '
+            'repeat= '
+            'run= '
+            'sleep= '
+            'timer-pin= '
+            'trace-filter= '
+        )
+        raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(' '))
+
+        for option, parameter in raw_opts:
+            optname = re.sub(r'^--', '', option)
+            opt[optname] = parameter
+
+        if 'depth' in opt:
+            opt['depth'] = int(opt['depth'])
+        else:
+            opt['depth'] = 3
+
+        if 'sleep' in opt:
+            opt['sleep'] = int(opt['sleep'])
+        else:
+            opt['sleep'] = 0
+
+        if 'trace-filter' in opt:
+            trace_filter = []
+            for trace in opt['trace-filter'].split():
+                trace_filter.append(trace.split(','))
+            opt['trace-filter'] = trace_filter
+        else:
+            opt['trace-filter'] = None
+
+    except getopt.GetoptError as err:
+        print(err)
+        sys.exit(2)
+
+    modelfile = args[0]
+
+    with open(modelfile, 'r') as f:
+        if '.json' in modelfile:
+            pta = PTA.from_json(json.load(f))
+        else:
+            pta = PTA.from_yaml(yaml.safe_load(f))
+
+    enum = dict()
+    if '.json' not in modelfile:
+        with open(modelfile, 'r') as f:
+            driver_definition = yaml.safe_load(f)
+        if 'dummygen' in driver_definition and 'enum' in driver_definition['dummygen']:
+            enum = driver_definition['dummygen']['enum']
+
+    repo = Repo('/home/derf/var/projects/multipass/build/repo.acp')
+
+    pta.set_random_energy_model()
+
+    runs = list(pta.dfs(opt['depth'], with_arguments = True, with_parameters = True, trace_filter = opt['trace-filter'], sleep = opt['sleep']))
+
+    num_transitions = len(runs)
+
+    if len(runs) == 0:
+        print('DFS returned no traces -- perhaps your trace-filter is too restrictive?', file=sys.stderr)
+        sys.exit(1)
+
+    real_energies = list()
+    real_durations = list()
+    model_energies = list()
+    for run in runs:
+        accounting_method = get_simulated_accountingmethod(opt['accounting'])(pta, 1e6, 'uint32_t', 'uint32_t', 'uint32_t', 'uint32_t')
+        real_energy, real_duration, _, _ = pta.simulate(run, accounting = accounting_method)
+        model_energy = accounting_method.get_energy()
+        real_energies.append(real_energy)
+        real_durations.append(real_duration)
+        model_energies.append(model_energy)
+        print('actual energy {:.0f} µJ, modeled energy {:.0f} µJ'.format(real_energy / 1e6, model_energy / 1e6))
+
+    measures = regression_measures(np.array(model_energies), np.array(real_energies))
+    print('SMAPE {:.0f}%, MAE {}'.format(measures['smape'], measures['mae']))
+
+    sys.exit(0)