#!/usr/bin/python
# -*- coding: latin-1 *-*

import argparse
import csv
import tempfile
import os

# SCRIPT PARAMETERS
parser = argparse.ArgumentParser(description='Cross validation script.')
parser.add_argument('--training', required=True, help='File in CSV format containing training examples.')
parser.add_argument('--script', required=True, help='The script that performs classification.')
parser.add_argument('--options', required=False, help='Other options to pass to the classifier script.')
opt = vars(parser.parse_args())

# DETERMINE TARGET CLASS
targetClass=''
with open(opt['training']) as f:
	reader = csv.DictReader(f, delimiter='\t')
	# LOAD ATTRIBUTE TYPES (continuous, discrete, ignore)
	attributeClass = reader.next()
	# DETERMINE TARGET CLASS
	classLine = reader.next()
	for i in attributeClass:
		if classLine[i]=='class':
			targetClass = i

# LOAD KNOWN DATA
with open(opt['training']) as f: data = f.readlines()
header = data.pop(0)
attributeClassLine = data.pop(0)
classLine = data.pop(0)

# ITERATE OVER DATA TO GENERATE TRAINING AND TEST SETS, AND RUN CASSIFIER
trainingFileName = tempfile.mktemp()
testFileName = tempfile.mktemp()
outputFileName = tempfile.mktemp()
tests=0
errors=0
TP=0; FP=0; PP=0; P=0; TN=0; FN=0; PN=0; N=0
for i in range(len(data)):
	trainingFile=open(trainingFileName, 'w')
	trainingFile.write(header)
	trainingFile.write(attributeClassLine)
	trainingFile.write(classLine)
	testFile=open(testFileName, 'w')
	testFile.write(header)
	for j in range(len(data)):
		if i==j: testFile.write(data[j])
		else: trainingFile.write(data[j])
	trainingFile.close()
	testFile.close()
	# RUN CLASSIFIER
	cmd=opt['script']+' --training '+trainingFileName+' --sample '+testFileName+opt['options']+' > '+outputFileName
	#~ print cmd
	os.system(cmd)
	with open(outputFileName) as f:
		reader=csv.DictReader(f, delimiter='\t')
		for row in reader:
			tests+=1
			# TO DO: update PP, FP, TP, ...
			#~ if row[target] == row['prediction']
			#~ ...

os.remove(trainingFileName)
os.remove(testFileName)
os.remove(outputFileName)

#~ print 'Tests:', tests, ', Errors:', errors, ', Error rate:', round(float(errors)/tests*100,1),'%'

# TO DO
# compute and print sensitivity, specificity, precision & accuracy