Convert PTAModel to EnergyModel signature

outlier detection / removal is not supported at the moment.

1 files changed, 152 insertions, 0 deletions

diff --git a/bin/eval-outlier-removal.py b/bin/eval-outlier-removal.py
new file mode 100755
index 0000000..b6e8733
--- /dev/null
+++ b/bin/eval-outlier-removal.py
@@ -0,0 +1,152 @@
+#!/usr/bin/env python3
+
+import getopt
+import plotter
+import re
+import sys
+from dfatool import PTAModel, RawData, soft_cast_int, pta_trace_to_aggregate
+
+opts = {}
+
+def model_quality_table(result_lists, info_list):
+    for state_or_tran in result_lists[0].keys():
+        for key in result_lists[0][state_or_tran].keys():
+            buf = '{:20s} {:15s}'.format(state_or_tran, key)
+            for i, results in enumerate(result_lists):
+                info = info_list[i]
+                buf += '  |||  '
+                if info == None or info(state_or_tran, key):
+                    result = results[state_or_tran][key]
+                    if 'smape' in result:
+                        buf += '{:6.2f}% / {:9.0f}'.format(result['smape'], result['mae'])
+                    else:
+                        buf += '{:6}    {:9.0f}'.format('', result['mae'])
+                else:
+                    buf += '{:6}----{:9}'.format('', '')
+            print(buf)
+
+def combo_model_quality_table(result_lists, info_list):
+    for state_or_tran in result_lists[0][0].keys():
+        for key in result_lists[0][0][state_or_tran].keys():
+            for sub_result_lists in result_lists:
+                buf = '{:20s} {:15s}'.format(state_or_tran, key)
+                for i, results in enumerate(sub_result_lists):
+                    info = info_list[i]
+                    buf += '  |||  '
+                    if info == None or info(state_or_tran, key):
+                        result = results[state_or_tran][key]
+                        if 'smape' in result:
+                            buf += '{:6.2f}% / {:9.0f}'.format(result['smape'], result['mae'])
+                        else:
+                            buf += '{:6}    {:9.0f}'.format('', result['mae'])
+                    else:
+                        buf += '{:6}----{:9}'.format('', '')
+                print(buf)
+
+if __name__ == '__main__':
+
+    ignored_trace_indexes = []
+    discard_outliers = None
+
+    try:
+        raw_opts, args = getopt.getopt(sys.argv[1:], "",
+            'plot ignored-trace-indexes= discard-outliers='.split(' '))
+
+        for option, parameter in raw_opts:
+            optname = re.sub(r'^--', '', option)
+            opts[optname] = parameter
+
+            if 'ignored-trace-indexes' in opts:
+                ignored_trace_indexes = list(map(int, opts['ignored-trace-indexes'].split(',')))
+                if 0 in ignored_trace_indexes:
+                    print('[E] arguments to --ignored-trace-indexes start from 1')
+
+            if 'discard-outliers' in opts:
+                discard_outliers = float(opts['discard-outliers'])
+
+    except getopt.GetoptError as err:
+        print(err)
+        sys.exit(2)
+
+    raw_data = RawData(args)
+
+    preprocessed_data = raw_data.get_preprocessed_data()
+    by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data, ignored_trace_indexes)
+    m1 = PTAModel(by_name, parameters, arg_count,
+        traces = preprocessed_data,
+        ignore_trace_indexes = ignored_trace_indexes)
+    m2 = PTAModel(by_name, parameters, arg_count,
+        traces = preprocessed_data,
+        ignore_trace_indexes = ignored_trace_indexes,
+        discard_outliers = discard_outliers)
+
+    print('--- simple static model ---')
+    static_m1 = m1.get_static()
+    static_m2 = m2.get_static()
+    #for state in model.states():
+    #    print('{:10s}: {:.0f} µW  ({:.2f})'.format(
+    #        state,
+    #        static_model(state, 'power'),
+    #        model.generic_param_dependence_ratio(state, 'power')))
+    #    for param in model.parameters():
+    #        print('{:10s}  dependence on {:15s}: {:.2f}'.format(
+    #            '',
+    #            param,
+    #            model.param_dependence_ratio(state, 'power', param)))
+    #for trans in model.transitions():
+    #    print('{:10s}: {:.0f} / {:.0f} / {:.0f} pJ  ({:.2f} / {:.2f} / {:.2f})'.format(
+    #        trans, static_model(trans, 'energy'),
+    #        static_model(trans, 'rel_energy_prev'),
+    #        static_model(trans, 'rel_energy_next'),
+    #        model.generic_param_dependence_ratio(trans, 'energy'),
+    #        model.generic_param_dependence_ratio(trans, 'rel_energy_prev'),
+    #        model.generic_param_dependence_ratio(trans, 'rel_energy_next')))
+    #    print('{:10s}: {:.0f} µs'.format(trans, static_model(trans, 'duration')))
+    static_q1 = m1.assess(static_m1)
+    static_q2 = m2.assess(static_m2)
+    static_q12 = m1.assess(static_m2)
+
+    print('--- LUT ---')
+    lut_m1 = m1.get_param_lut()
+    lut_m2 = m2.get_param_lut()
+    lut_q1 = m1.assess(lut_m1)
+    lut_q2 = m2.assess(lut_m2)
+    lut_q12 = m1.assess(lut_m2)
+
+    print('--- param model ---')
+    param_m1, param_i1 = m1.get_fitted()
+    for state in m1.states():
+        for attribute in ['power']:
+            if param_i1(state, attribute):
+                print('{:10s}: {}'.format(state, param_i1(state, attribute)['function']._model_str))
+                print('{:10s}  {}'.format('', param_i1(state, attribute)['function']._regression_args))
+    for trans in m1.transitions():
+        for attribute in ['energy', 'rel_energy_prev', 'rel_energy_next', 'duration', 'timeout']:
+            if param_i1(trans, attribute):
+                print('{:10s}: {:10s}: {}'.format(trans, attribute, param_i1(trans, attribute)['function']._model_str))
+                print('{:10s}  {:10s}  {}'.format('', '', param_i1(trans, attribute)['function']._regression_args))
+    param_m2, param_i2 = m2.get_fitted()
+    for state in m2.states():
+        for attribute in ['power']:
+            if param_i2(state, attribute):
+                print('{:10s}: {}'.format(state, param_i2(state, attribute)['function']._model_str))
+                print('{:10s}  {}'.format('', param_i2(state, attribute)['function']._regression_args))
+    for trans in m2.transitions():
+        for attribute in ['energy', 'rel_energy_prev', 'rel_energy_next', 'duration', 'timeout']:
+            if param_i2(trans, attribute):
+                print('{:10s}: {:10s}: {}'.format(trans, attribute, param_i2(trans, attribute)['function']._model_str))
+                print('{:10s}  {:10s}  {}'.format('', '', param_i2(trans, attribute)['function']._regression_args))
+
+    analytic_q1 = m1.assess(param_m1)
+    analytic_q2 = m2.assess(param_m2)
+    analytic_q12 = m1.assess(param_m2)
+    model_quality_table([static_q1, analytic_q1, lut_q1], [None, param_i1, None])
+    model_quality_table([static_q2, analytic_q2, lut_q2], [None, param_i2, None])
+    model_quality_table([static_q12, analytic_q12, lut_q12], [None, param_i2, None])
+    combo_model_quality_table([
+        [static_q1, analytic_q1, lut_q1],
+        [static_q2, analytic_q2, lut_q2],
+        [static_q12, analytic_q12, lut_q12]],
+        [None, param_i1, None])
+
+    sys.exit(0)