From 1b2823d1dcf64aa988c822a74c4751bef4de2972 Mon Sep 17 00:00:00 2001
From: "raffaele.gaetano" <raffaele.gaetano@cirad.fr>
Date: Tue, 21 May 2019 20:39:42 +0200
Subject: [PATCH] ENH: k-fold report to latex table.
---
validationFramework.py | 105 ++++++++++++++++++++++++++++++++++++++++-
1 file changed, 104 insertions(+), 1 deletion(-)
diff --git a/validationFramework.py b/validationFramework.py
index 3d98e02..c80c7c5 100644
--- a/validationFramework.py
+++ b/validationFramework.py
@@ -134,7 +134,6 @@ def formatValidationTxt(classes,cm,acc,kappa,prf,txt_out):
tid.close()
-
def genKFolds(shp,fld,k,out_fld=None):
# Read all features and store classes in a separate array
ds = ogr.Open(shp)
@@ -251,6 +250,110 @@ def kFoldReport(fscores,accs,kappas,txt_out):
tid.close()
+def readKFoldReport(fn,cln,tag = None):
+ fid = open(fn, 'rb')
+ cid = open(cln, 'rb')
+ # read report
+ classes = []
+ fsc_mean = []
+ fsc_std = []
+ oa_mean = None
+ oa_std = None
+ kc_mean = None
+ kc_std = None
+
+ clnames = {}
+ notfound = tag is not None
+ cidl = cid.read().splitlines()
+ for cl in cidl:
+ if tag is not None and notfound:
+ if cl != tag:
+ continue
+ if cl == tag and notfound:
+ notfound = False
+ continue
+
+ scl = cl.split(',')
+ if len(scl) == 2:
+ clnames[int(scl[0])] = scl[1]
+ else:
+ break
+
+ for l in fid:
+ line = l.split()
+ if len(line) == 0:
+ continue
+ if line[0] == 'Class':
+ classes.append(int(line[1]))
+ fsc_mean.append(float(line[3]))
+ fsc_std.append(float(line[5]))
+ elif line[0] == 'Overall':
+ oa_mean = float(line[2][:-1])
+ oa_std = float(line[4][:-1])
+ elif line[0] == 'Kappa':
+ kc_mean = float(line[2])
+ kc_std = float(line[4])
+
+ out = {}
+ out['PerClass'] = {}
+ out['OverallAcc'] = [oa_mean,oa_std]
+ out['Kappa'] = [kc_mean,kc_std]
+ out['ClassDict'] = clnames
+
+ for i in range(len(classes)):
+ out['PerClass'][clnames[classes[i]]] = [fsc_mean[i],fsc_std[i]]
+
+ fid.close()
+ cid.close()
+
+ return out
+
+def kFoldReportToLatexTable(fn,cln,tag=None,ofn=None,mode='vertical'):
+ dct = readKFoldReport(fn,cln,tag)
+ if ofn == None:
+ ofn = fn.replace('.txt','.tex')
+ oid = open(ofn,'wb')
+ oid.write('\\documentclass{standalone}\n')
+ oid.write('\\usepackage[dvipsnames]{xcolor}\n')
+ oid.write('\\renewcommand\\familydefault{\\sfdefault}\n')
+ oid.write('\\begin{document}\n')
+
+ pcf = [dct['ClassDict'][i] for i in sorted(dct['ClassDict'])]
+
+ #def tabular
+ if mode == 'vertical':
+ oid.write('\\begin{tabular}{|c|c|}\n')
+ oid.write('\\hline\n')
+ oid.write('\\textbf{Class} & \\textbf{F-Score} \\\\\\hline\n')
+ for c in pcf:
+ clr = 'black'
+ if dct['PerClass'][c][0] < 0.3:
+ clr = 'red'
+ elif dct['PerClass'][c][0] >= 0.3 and dct['PerClass'][c][0] < 0.5:
+ clr = 'orange'
+ elif dct['PerClass'][c][0] > 0.75:
+ clr = 'ForestGreen'
+ oid.write('\\textit{%s} & \\color{%s}%1.4f$\\pm$%1.4f \\\\\\hline\n' % (c,clr,dct['PerClass'][c][0],dct['PerClass'][c][1]))
+ oid.write('\\hline\n')
+ clr = 'black'
+ if dct['OverallAcc'][0] < 0.3:
+ clr = 'red'
+ elif dct['OverallAcc'][0] >= 0.3 and dct['OverallAcc'][0] < 0.5:
+ clr = 'orange'
+ elif dct['OverallAcc'][0] > 0.75:
+ clr = 'ForestGreen'
+ oid.write('\\textbf{Overall Acc.} & {\\color{%s}\\textbf{%2.2f}\\%% $\\pm$ \\textbf{%2.2f}\\%%} \\\\\\hline\n' % (clr,dct['OverallAcc'][0],dct['OverallAcc'][1]))
+ oid.write('\\textbf{Kappa} & \\textbf{%0.4f} $\\pm$ \\textbf{%0.4f} \\\\\\hline\n' % (dct['Kappa'][0],dct['Kappa'][1]))
+ oid.write('\\end{tabular}')
+
+ oid.write('\\end{document}\n')
+ oid.close()
+ cdir = os.getcwd()
+ os.chdir(os.path.dirname(ofn))
+ subprocess.call(['pdflatex',ofn])
+ os.chdir(cdir)
+ return
+
def getTrainingDataFromShapefile(shp,fields,code):
ds = ogr.Open(shp)
ly = ds.GetLayer(0)
--
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