Commit 3827b663 authored by Marco Chierici's avatar Marco Chierici
Browse files

Create list_distances.py

parent bf9488e8
import argparse
import pandas as pd
import numpy as np
from mlpy import canberra_stability
from itertools import combinations
parser = argparse.ArgumentParser()
parser.add_argument('--resultsdir', type=str, help='Results folder')
parser.add_argument('--dataset', type=str, help='Dataset name')
parser.add_argument('--model', type=str, default='randomForest', help='Model (default: %default)')
parser.add_argument('--target', type=str, help='Clinical endpoint')
parser.add_argument('--layers', type=str, nargs='+', help='')
args = parser.parse_args()
RESULTSDIR = args.resultsdir # top-level results directory
DATASET = args.dataset # 'tcga_breast'
TARGET = args.target # 'ER'
MODEL = args.model
LAYERS = args.layers
N_LAYERS = len(LAYERS)
MODE = 'rSNF'
for k in range(2, N_LAYERS+1):
for comb in combinations(LAYERS, k):
layers_concat = '_'.join(comb)
bordas = []
for datatype in [DATASET, f'{DATASET}_SNFdap']:
bordaf = f'{RESULTSDIR}/{datatype}/{TARGET}/{MODEL}/Borda_splits_50-60_{MODE}_{layers_concat}.txt'
bordas.append(pd.read_csv(bordaf, sep='\t', index_col=None))
# prepare ranks for canberra_stability
ranks = pd.concat([np.argsort(bordas[0]['FEATURE_ID']),
np.argsort(bordas[1]['FEATURE_ID'])], axis=1).transpose().values
for nf in np.arange(10, 60, 10):
cs = canberra_stability(ranks, nf)
print(f'{MODE} - {layers_concat} - stability({nf}) = {cs:.3f}')
print()
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment