From ea283caa94b135e59edb32ae0ea45a1728a898b6 Mon Sep 17 00:00:00 2001
From: gospodnetic <petra.gospodnetic@gmail.com>
Date: Fri, 5 Jun 2020 09:03:16 +0200
Subject: [PATCH] Output best results to the table
---
Benchmark.py | 14 +++++------
LogContainer.py | 67 ++++++++++++++++++++++++++++++++++++++++++++-----
2 files changed, 68 insertions(+), 13 deletions(-)
diff --git a/Benchmark.py b/Benchmark.py
index f3d3233..41edbe9 100644
--- a/Benchmark.py
+++ b/Benchmark.py
@@ -57,16 +57,16 @@ class Benchmark:
tex_file.write("\n& \makecell{{{}}}".format(method))
for model in models:
- # self.log_container_per_model[model].get_best_log()
- logs = self.log_container_per_model[model].get_logs_by_method(method)
- if len(logs) == 0:
+ try:
+ best_log = self.log_container_per_model[model].get_best_log(method)
+ except:
tex_file.write(" & - & - & - & -")
continue
- VPC_count = logs[0].VPC["count"] + logs[0].VPC["discarded_count"]
- VPC_used = logs[0].VPC["count"]
- OVP = len(logs[0].optimization["OVP"])
- coverage = util.set_precision(logs[0].coverage["percent_fraction"] * 100, 2)
+ VPC_count = best_log.VPC["count"] + best_log.VPC["discarded_count"]
+ VPC_used = best_log.VPC["count"]
+ OVP = len(best_log.optimization["OVP"])
+ coverage = util.set_precision(best_log.coverage["percent_fraction"] * 100, 2)
tex_file.write(" & {} & {} & {} & {}".format(VPC_count, VPC_used, OVP, coverage))
tex_file.write("\\\\")
diff --git a/LogContainer.py b/LogContainer.py
index 00acc9c..9db4a20 100644
--- a/LogContainer.py
+++ b/LogContainer.py
@@ -1,5 +1,6 @@
import Log
import pprint
+from enum import Enum
class LogContainer:
def __init__(self, methods_per_approach):
@@ -13,12 +14,6 @@ class LogContainer:
self.__parse_methods_per_approach()
- def __parse_methods_per_approach(self):
- self.approaches_per_method = {}
- for approach in self.methods_per_approach:
- for method in self.methods_per_approach[approach]:
- self.approaches_per_method[method] = approach
-
def add_log(self, log):
method = log.VPC["method"]
if method in self.approaches_per_method:
@@ -51,3 +46,63 @@ class LogContainer:
method_logs.append(log)
return method_logs
+
+ # Log which obtains coverage over 99% with minimal number of viewpoint candidates
+ # If no log obrains coverage over 99%, the log with the greatest coverage is considered.
+ def get_best_log(self, method):
+ method_logs = self.get_logs_by_method(method)
+ if len(method_logs) == 0:
+ raise Exception("Error: No logs available for given method ({})".format(method))
+
+ # Find logs with coverage >99%.
+ high_coverage_logs = self.__filter_coverage_threshold(method_logs, 0.99, ComparisonType.GEQ)
+
+ if len(high_coverage_logs) > 0:
+ return self.max_coverage_log(high_coverage_logs)
+ else:
+ return self.max_coverage_log(method_logs)
+
+ def max_coverage_log(self, input_logs=None):
+ if not input_logs:
+ input_logs = self.logs
+ if len(input_logs) == 0:
+ raise Exception("Error: no logs available.")
+
+ max_coverage = input_logs[0].coverage["percent_fraction"]
+ max_log = input_logs[0]
+ for log in input_logs:
+ if log.coverage["percent_fraction"] > max_coverage:
+ max_log = log
+ return max_log
+
+ def __parse_methods_per_approach(self):
+ self.approaches_per_method = {}
+ for approach in self.methods_per_approach:
+ for method in self.methods_per_approach[approach]:
+ self.approaches_per_method[method] = approach
+
+ # Find logs which have coverage greater or equal to the given threshold.
+ # threshold is given as a fraction (e.g. 0.99)
+ def __filter_coverage_threshold(self, input_logs, threshold, comparison_type):
+ filtered_logs = []
+ for log in input_logs:
+ if comparison_type == ComparisonType.G:
+ if log.coverage["percent_fraction"] > threshold:
+ filtered_logs.append(log)
+ elif comparison_type == ComparisonType.GEQ:
+ if log.coverage["percent_fraction"] >= threshold:
+ filtered_logs.append(log)
+ elif comparison_type == ComparisonType.LEQ:
+ if log.coverage["percent_fraction"] <= threshold:
+ filtered_logs.append(log)
+ elif comparison_type == ComparisonType.L:
+ if log.coverage["percent_fraction"] < threshold:
+ filtered_logs.append(log)
+
+ return filtered_logs
+
+class ComparisonType(Enum):
+ G = 0 # >
+ GEQ = 1 # >=
+ LEQ = 2 # <=
+ L = 3 # <
--
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