add generic monte carlo cross validation

1 files changed, 26 insertions, 6 deletions

diff --git a/bin/analyze-archive.py b/bin/analyze-archive.py
index 21f9ceb..007ec25 100755
--- a/bin/analyze-archive.py
+++ b/bin/analyze-archive.py
@@ -41,6 +41,10 @@ Options:
 --discard-outliers=
     not supported at the moment
 
+--cross-validate
+    Perform cross validation when computing model quality.
+    Only works with --show-quality=table at the moment.
+
 --function-override=<name attribute function>[;<name> <attribute> <function>;...]
     Manually specify the function to fit for <name> <attribute>. A function
     specified this way bypasses parameter detection: It is always assigned,
@@ -65,6 +69,7 @@ import re
 import sys
 from dfatool import PTAModel, RawData, pta_trace_to_aggregate
 from dfatool import soft_cast_int, is_numeric, gplearn_to_function
+from dfatool import CrossValidator
 
 opts = {}
 
@@ -86,14 +91,14 @@ def format_quality_measures(result):
         return '{:6}    {:9.0f}'.format('', result['mae'])
 
 def model_quality_table(result_lists, info_list):
-    for state_or_tran in result_lists[0]['by_dfa_component'].keys():
-        for key in result_lists[0]['by_dfa_component'][state_or_tran].keys():
+    for state_or_tran in result_lists[0]['by_name'].keys():
+        for key in result_lists[0]['by_name'][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['by_dfa_component'][state_or_tran][key]
+                    result = results['by_name'][state_or_tran][key]
                     buf += format_quality_measures(result)
                 else:
                     buf += '{:6}----{:9}'.format('', '')
@@ -164,6 +169,7 @@ if __name__ == '__main__':
         optspec = (
             'plot-unparam= plot-param= show-models= show-quality= '
             'ignored-trace-indexes= discard-outliers= function-override= '
+            'cross-validate '
             'with-safe-functions hwmodel= export-energymodel='
         )
         raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(' '))
@@ -212,6 +218,8 @@ if __name__ == '__main__':
         function_override = function_override,
         hwmodel = hwmodel)
 
+    if 'cross-validate' in opts:
+        xv = CrossValidator(PTAModel, by_name, parameters, arg_count)
 
     if 'plot-unparam' in opts:
         for kv in opts['plot-unparam'].split(';'):
@@ -242,12 +250,20 @@ if __name__ == '__main__':
                 model.stats.generic_param_dependence_ratio(trans, 'rel_energy_prev'),
                 model.stats.generic_param_dependence_ratio(trans, 'rel_energy_next')))
             print('{:10s}: {:.0f} µs'.format(trans, static_model(trans, 'duration')))
-    static_quality = model.assess(static_model)
+
+    if 'cross-validate' in opts:
+        static_quality = xv.montecarlo(lambda m: m.get_static())
+    else:
+        static_quality = model.assess(static_model)
 
     if len(show_models):
         print('--- LUT ---')
     lut_model = model.get_param_lut()
-    lut_quality = model.assess(lut_model)
+
+    if 'cross-validate' in opts:
+        lut_quality = xv.montecarlo(lambda m: m.get_param_lut())
+    else:
+        lut_quality = model.assess(lut_model)
 
     if len(show_models):
         print('--- param model ---')
@@ -288,7 +304,11 @@ if __name__ == '__main__':
                 if param_info(trans, attribute):
                     print('{:10s}: {:10s}: {}'.format(trans, attribute, param_info(trans, attribute)['function']._model_str))
                     print('{:10s}  {:10s}  {}'.format('', '', param_info(trans, attribute)['function']._regression_args))
-    analytic_quality = model.assess(param_model)
+
+    if 'cross-validate' in opts:
+        analytic_quality = xv.montecarlo(lambda m: m.get_fitted()[0])
+    else:
+        analytic_quality = model.assess(param_model)
 
     if 'tex' in show_models or 'tex' in show_quality:
         print_text_model_data(model, static_model, static_quality, lut_model, lut_quality, param_model, param_info, analytic_quality)