From 5f09dd0025e7e4d120015b19c1bbf9fe947ad080 Mon Sep 17 00:00:00 2001
From: Franck Dary <franck.dary@lis-lab.fr>
Date: Tue, 3 Dec 2019 09:45:40 +0100
Subject: [PATCH] fixed script to print results
---
UD_any/print_results.py | 54 ++++++++++++++++++++++-------------------
1 file changed, 29 insertions(+), 25 deletions(-)
diff --git a/UD_any/print_results.py b/UD_any/print_results.py
index 39ffe17..69cdb4b 100755
--- a/UD_any/print_results.py
+++ b/UD_any/print_results.py
@@ -21,6 +21,9 @@ if __name__ == "__main__" :
model = pathToFile.split("_UD_")[0]
corpus = pathToFile.split("_UD_")[1].split('.')[0]
+ if corpus not in outputByModelScore :
+ outputByModelScore[corpus] = dict()
+
for line in open(pathToFile, "r") :
for metric in metrics :
if metric in line and metric[0] == line[0]:
@@ -28,31 +31,32 @@ if __name__ == "__main__" :
model = model.split('.')[0]
- if model not in outputByModelScore :
- outputByModelScore[model] = dict()
- if splited[0] not in outputByModelScore[model] :
- outputByModelScore[model][splited[0]] = []
-
- outputByModelScore[model][splited[0]].append([corpus, splited[0], splited[3], model])
-
- for model in outputByModelScore :
- for metric in outputByModelScore[model] :
- score = 0.0
- for exp in outputByModelScore[model][metric] :
- score += float(exp[2])
- score /= len(outputByModelScore[model][metric])
- standardDeviation = 0.0
- if len(outputByModelScore[model][metric]) > 1 :
- for exp in outputByModelScore[model][metric] :
- standardDeviation += (float(exp[2])-score)**2
- standardDeviation /= len(outputByModelScore[model][metric])-1
- standardDeviation = math.sqrt(standardDeviation)
- if standardDeviation > 0 :
- score = "%.2f[±%.2f]%%"%(score,standardDeviation)
- else :
- score = "%.2f%%"%score
- output.append(outputByModelScore[model][metric][0])
- output[-1][2] = score
+ if model not in outputByModelScore[corpus] :
+ outputByModelScore[corpus][model] = dict()
+ if splited[0] not in outputByModelScore[corpus][model] :
+ outputByModelScore[corpus][model][splited[0]] = []
+
+ outputByModelScore[corpus][model][splited[0]].append([corpus, splited[0], splited[3], model])
+
+ for corpus in outputByModelScore :
+ for model in outputByModelScore[corpus] :
+ for metric in outputByModelScore[corpus][model] :
+ score = 0.0
+ for exp in outputByModelScore[corpus][model][metric] :
+ score += float(exp[2])
+ score /= len(outputByModelScore[corpus][model][metric])
+ standardDeviation = 0.0
+ if len(outputByModelScore[corpus][model][metric]) > 1 :
+ for exp in outputByModelScore[corpus][model][metric] :
+ standardDeviation += (float(exp[2])-score)**2
+ standardDeviation /= len(outputByModelScore[corpus][model][metric])-1
+ standardDeviation = math.sqrt(standardDeviation)
+ if standardDeviation > 0 :
+ score = "%.2f[±%.2f]%%"%(score,standardDeviation)
+ else :
+ score = "%.2f%%"%score
+ output.append(outputByModelScore[corpus][model][metric][0])
+ output[-1][2] = score
maxColLens = [0 for _ in range(len(output[0]))]
--
GitLab