Import only required modules from mlpy

sklearn_rf_training_fixrank.py

@@ -4,12 +4,11 @@
 ## Requires Python >= 2.7, mlpy >= 3.5
 
 
-from __future__ import division
 import numpy as np
 import csv
 import os.path
 from scaling import norm_l2
-import mlpy
+from mlpy import bootstrap_ci, borda_count, canberra_stability
 from input_output import load_data
 import performance as perf
 import sys
@@ -233,31 +232,31 @@ ADOR_APPROX = (ASENS / (1 - ASPEC)) / ((1 - ASENS) / ASPEC)
 # confidence intervals
 NPVCI = []
 for i in range(NPV.shape[1]):
-    NPVCI.append(mlpy.bootstrap_ci(NPV[:, i]))
+    NPVCI.append(bootstrap_ci(NPV[:, i]))
 PPVCI = []
 for i in range(PPV.shape[1]):
-    PPVCI.append(mlpy.bootstrap_ci(PPV[:, i]))
+    PPVCI.append(bootstrap_ci(PPV[:, i]))
 SENSCI = []
 for i in range(SENS.shape[1]):
-    SENSCI.append(mlpy.bootstrap_ci(SENS[:, i]))
+    SENSCI.append(bootstrap_ci(SENS[:, i]))
 SPECCI = []
 for i in range(SPEC.shape[1]):
-    SPECCI.append(mlpy.bootstrap_ci(SPEC[:, i]))
+    SPECCI.append(bootstrap_ci(SPEC[:, i]))
 MCCCI = []
 for i in range(MCC.shape[1]):
-    MCCCI.append(mlpy.bootstrap_ci(MCC[:, i]))
+    MCCCI.append(bootstrap_ci(MCC[:, i]))
 AUCCI = []
 for i in range(AUC.shape[1]):
-    AUCCI.append(mlpy.bootstrap_ci(AUC[:, i]))
+    AUCCI.append(bootstrap_ci(AUC[:, i]))
 DORCI = []
 for i in range(DOR.shape[1]):
-    DORCI.append(mlpy.bootstrap_ci(DOR[:, i]))
+    DORCI.append(bootstrap_ci(DOR[:, i]))
 ACCCI = []
 for i in range(ACC.shape[1]):
-    ACCCI.append(mlpy.bootstrap_ci(ACC[:, i]))
+    ACCCI.append(bootstrap_ci(ACC[:, i]))
 
 # Borda list
-BORDA_ID, _, BORDA_POS = mlpy.borda_count(RANKING)
+BORDA_ID, _, BORDA_POS = borda_count(RANKING)
 # optimal number of features (yielding max MCC)
 opt_feats = FSTEPS[np.argmax(AMCC)]
 
@@ -265,7 +264,7 @@ opt_feats = FSTEPS[np.argmax(AMCC)]
 STABILITY = []
 PR = np.argsort( RANKING )
 for ss in FSTEPS:
-    STABILITY.append( mlpy.canberra_stability(PR, ss) )
+    STABILITY.append( canberra_stability(PR, ss) )
 
 metrics_w.writerow(["FS_WITH_BEST_MCC", opt_feats])
 metrics_w.writerow(["STEP",