Dropped unused imports; minor improvements

sklearn_rf_training_fixrank.py

@@ -8,25 +8,24 @@ import numpy as np
 import pandas as pd
 import csv
 import os.path
-from scaling import norm_l2
 from mlpy import borda_count, canberra_stability
 from input_output import load_data
 import performance as perf
 import sys
-import tarfile
 import glob
 import argparse
 import configparser as ConfigParser
 from sklearn.ensemble import RandomForestClassifier
+from sklearn.multiclass import OneVsRestClassifier
 from sklearn.metrics import roc_auc_score, matthews_corrcoef, accuracy_score
 from sklearn import preprocessing
 from sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit, train_test_split
 import bootstrapped.bootstrap as bs
 import bootstrapped.stats_functions as bs_stats
 
-__author__  = 'Marco Chierici, Alessandro Zandona'
-__version__ = '2.0'
-__date__    = '15 December 2016'
+__author__  = 'Marco Chierici'
+__version__ = '2.5'
+__date__    = '28 Nov 2019'
 
 class myArgumentParser(argparse.ArgumentParser):
     def __init__(self, *args, **kwargs):
@@ -68,8 +67,8 @@ RANKFEATS = args.rankFeats
 relief_k = args.reliefk
 
 BASEFILE = os.path.splitext(os.path.basename(DATAFILE))[0]
-SVM_TYPE = 'RandomForest'
-OUTFILE = os.path.join(OUTDIR, '_'.join([BASEFILE, SVM_TYPE, 'RF' if RANK_METHOD=='randomForest' else RANK_METHOD]))
+MODEL_TYPE = 'RandomForest'
+OUTFILE = os.path.join(OUTDIR, '_'.join([BASEFILE, MODEL_TYPE, 'RF' if RANK_METHOD=='randomForest' else RANK_METHOD]))
 
 # create OUTDIR if not present
 try:
@@ -82,7 +81,7 @@ except OSError:
 if RANK_METHOD == 'ReliefF':
     from relief import ReliefF
     # add ReliefF K to OUTFILE
-    OUTFILE = os.path.join(OUTDIR, '_'.join([BASEFILE, SVM_TYPE, RANK_METHOD + str(relief_k)]))
+    OUTFILE = os.path.join(OUTDIR, '_'.join([BASEFILE, MODEL_TYPE, RANK_METHOD + str(relief_k)]))
 elif RANK_METHOD == 'tree' :
     from sklearn.ensemble import ExtraTreesClassifier
 elif RANK_METHOD == 'KBest':