Improve _try_fits performance and fairness

1 files changed, 17 insertions, 25 deletions

diff --git a/lib/dfatool.py b/lib/dfatool.py
index ecd3051..853eb13 100755
--- a/lib/dfatool.py
+++ b/lib/dfatool.py
@@ -15,7 +15,7 @@ from multiprocessing import Pool
 from automata import PTA
 from functions import analytic
 from functions import AnalyticFunction
-from utils import vprint, is_numeric, soft_cast_int, param_slice_eq, compute_param_statistics
+from utils import vprint, is_numeric, soft_cast_int, param_slice_eq, compute_param_statistics, remove_index_from_tuple
 
 arg_support_enabled = True
 
@@ -927,27 +927,18 @@ def _try_fits(by_param, state_or_tran, model_attribute, param_index, safe_functi
     median_rmsd -- mean Root Mean Square Deviation of a reference model using the median of its respective input data as model value
     results -- mean goodness-of-fit measures for the individual functions. See `analytic.functions` for keys and `aggregate_measures` for values
 
-    arguments
-    ---
-
-    by_param: measurements partitioned by state/transition/... name and parameter values.
-    Example: `{('foo', (0, 2)): {'bar': [2]}, ('foo', (0, 4)): {'bar': [4]}, ('foo', (0, 6)): {'bar': [6]}}`
-
-    state_or_tran: state/transition/... name for which goodness-of-fit will be calculated (first element of by_param key tuple).
-    Example: `'foo'`
-
-    model_attribute: attribute for which goodness-of-fit will be calculated.
-    Example: `'bar'`
-    
-    param_index -- index of the parameter used as model input
-    safe_functions_enabled -- Include "safe" variants of functions with limited argument range.
+    :param by_param: measurements partitioned by state/transition/... name and parameter values.
+        Example: `{('foo', (0, 2)): {'bar': [2]}, ('foo', (0, 4)): {'bar': [4]}, ('foo', (0, 6)): {'bar': [6]}}`
+    :param state_or_tran: state/transition/... name for which goodness-of-fit will be calculated (first element of by_param key tuple).
+        Example: `'foo'`
+    :param model_attribute: attribute for which goodness-of-fit will be calculated.
+        Example: `'bar'`
+    :param param_index: index of the parameter used as model input
+    :param safe_functions_enabled: Include "safe" variants of functions with limited argument range.
     """
 
     functions = analytic.functions(safe_functions_enabled = safe_functions_enabled)
 
-    #print('_try_fits(..., {}, {}, {})'.format(state_or_tran, model_attribute, param_index))
-
-
     for param_key in filter(lambda x: x[0] == state_or_tran, by_param.keys()):
         # We might remove elements from 'functions' while iterating over
         # its keys. A generator will not allow this, so we need to
@@ -966,18 +957,19 @@ def _try_fits(by_param, state_or_tran, model_attribute, param_index, safe_functi
     results = {}
     results_by_param = {}
 
-    # TODO diese Funktion ist unfair, wenn ein Parameter in einer Variante deutlich mehr unterschiedliche Werte
-    # aufweist als bei der Kombination mit anderen Parametern. Gibt es z.B. die Parameterkombinationen
-    # (0,2), (0, 4), (0,6), (0,8), (0, 10), 0,12), (2, 2), (2, 4), (2, 6) und wird der Parameter mit Index 1 bestimmt,
-    # so haben die Messwerte für Parameter-Index 0 == 0 mehr Gewicht als die für Parameter-Index 0 == 2.
-    # Bei klassischen AEMR-generierten Benchmarks macht das nichts, weil für alle Kombinationen die gleichen Parameterwerte
-    # genutzt werden, das kann sich aber noch ändern...
+    seen_parameter_combinations = set()
+
     # for each parameter combination:
-    for param_key in filter(lambda x: x[0] == state_or_tran, by_param.keys()):
+    for param_key in filter(lambda x: x[0] == state_or_tran and remove_index_from_tuple(x[1], param_index) not in seen_parameter_combinations, by_param.keys()):
         X = []
         Y = []
         num_valid = 0
         num_total = 0
+
+        # Ensure that each parameter combination is only optimized once. Otherwise, with parameters (1, 2, 5), (1, 3, 5), (1, 4, 5) and param_index == 1,
+        # the parameter combination (1, *, 5) would be optimized three times
+        seen_parameter_combinations.add(remove_index_from_tuple(param_key[1], param_index))
+
         # for each value of the parameter denoted by param_index (all other parameters remain the same):
         for k, v in filter(lambda kv: param_slice_eq(kv[0], param_key, param_index), by_param.items()):
             num_total += 1