def evaluteFullFile(filename):
tempname = "tmp_" + filename
temp = open(tempname, "w")
for row in open(filename, "r").readlines():
if int(row.split()[3]) > 49:
continue
temp.write(row)
temp.flush()
evaluate(tempname, "dev2014", "__eval.csv", [])
deleteFile(tempname)
from scipy.spatial.distance import pdist, cdist from filterDataset import filterDT from collections import Counter from getParamters import getParamters from evaluation import evaluate, deleteFile ############################################################################### #####----------------------INPUT PART BEGINS-----------------------------###### ############################################################################### args = getParamters() # The name says by itself OUTFILE = args.outfile deleteFile(OUTFILE) # Options: "flickr", "oracle", "metis", "kmeans" # "simkmeans", "min-max", "multigraph", # "spectral", "agglomerative", "affinity" # "textsim" METHOD = args.method # Auxiliar method is an option only if the clustering method is multigraph AUXILIAR_METHOD = args.method_aux # Should be "dev2013", "test2013", "dev2014" or "test2014" WORKING_DATASET = args.working_dataset # CSV where the main values will be saved EVAL_CSV = args.evalcsv