def runTraining(folderName):
trainingFiles = [] #Liste des fichiers d'entrainement
trainingData = [] #données étudiées
pos = 0
neg = 0
for dir in os.walk(folderName):
dir = dir[1] #get directory list
for i, f in enumerate(dir):
actualFolder = os.path.join(folderName, f)
for files in os.walk(
actualFolder): #Parcourir liste dossier dans training
trainingFiles.append(files[2])
for j, tf in enumerate(
files[2]): #Parcourir liste des fichiers dans *star
tempP, tempN = (an.analyzeFile(
os.path.join(actualFolder, tf)))
pos = (pos + tempP) / 2
neg = (neg + tempN) / 2
trainingData.append([pos, neg])
return trainingData
help="Folder with training examples")
argparser.add_argument("-f", "--fileName", help="File to analyze")
args = argparser.parse_args()
print("Analyzing training files... Please wait ... ")
data = td.runTraining(args.directoryName) #Analyse des fichiers de test
if args.fileName:
print(
"You chose to analyze a new file to determine how many stars it should have."
)
print("Analyzing " + str(args.fileName) + ". Please wait...")
filename = os.path.join(
"tests", args.fileName
) #le fichier que l'on veut analyser doit se trouver dans le dossier "tests"
pos, neg = an.analyzeFile(filename) #Analyse du fichier
print("RESULTS :")
if (max(pos, neg) == pos):
print("This review is more positive than negative")
else:
print("This review is more negative than positive")
# METHODE 1 - Non Concluante (Précision à ~0,6 étoiles)
# res=an.getBestMatch1(data,pos,neg)
# print("Method 1 : This review should have "+str(res)+" star(s)")
#METHODE 2 - MEILLEURE (Précision à ~0,5 étoiles)
res = an.getBestMatch2(data, pos, neg)
print("This review should have " + str(res) + " star(s)")
# print("Method 2 : This review should have "+str(res)+" star(s)")
