#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon May 14 13:04:39 2018 @author: whb17 """ import p2funcs as p2f k_list = [2e-7, 0.000002, 0.00002, 0.0002, 0.002, 0.02, 0.2, 2.0] select_k = 0.0002 k_i = k_list.index(select_k) k_list_t2=list(p2f.frange(k_list[k_i], k_list[k_i+1], k_list[k_i])) print(k_list_t2)
# Print aggregate gamma values
for i in range(len(opt_t1_gammas)):
print('\nOptimal tier 1 gamma for dataset %s found to be %s' %
(toy_dataset_list[i][0], opt_t1_gammas[i]))
# Find most frequent gamma value
t1_gamma_consensus = p2f.most_common(opt_t1_gammas)
#Just in case last value selected:
if t1_gamma_consensus == t1_gamma_list[-1]:
t1_gamma_consensus = t1_gamma_list[-2]
# Create tier 2 gamma list
gamma_i_t1 = t1_gamma_list.index(t1_gamma_consensus)
t2_gamma_list = list(
p2f.frange(t1_gamma_list[gamma_i_t1 - 1], t1_gamma_list[gamma_i_t1],
t1_gamma_list[gamma_i_t1 - 1])) + list(
p2f.frange(t1_gamma_list[gamma_i_t1],
t1_gamma_list[gamma_i_t1 + 1],
t1_gamma_list[gamma_i_t1]))
#t2_gamma_list = [t1_gamma_list[gamma_i_t1]]
#Dictionary of AUC matrices by gamma
amat_dict_list = []
# Collect optimal tier2 gammas
opt_t2_gammas = []
for toy_label, toy_X in toy_dataset_list:
print('\n##### Now running dataset %s through Tier 2 #####' % toy_label)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri May 18 11:16:11 2018
@author: whb17
"""
import p2funcs as p2f
tier1 = [0.002 / 10000, 0.2 / 1000, 0.2 / 100, 0.2 / 10, 0.2, 0.2 * 10]
tier2 = list(p2f.frange(tier1[2 - 1], tier1[2], tier1[2 - 1])) + list(
p2f.frange(tier1[2], tier1[2 + 1], tier1[2]))
print(tier2)
# Name of script to trace where images came from
scriptname = 'gs_tune_2'
#Select current toy dataset
#dataset = '018'
#mesa = pd.read_csv('../../data/mesa/MESA_Clinical_data_full_COMBI-BIO_non-verbose.csv', sep=',', header=0, index_col=1)
#List of toy datasets to test
#dataset_list = ['017', '018', '019', '020', '021', '022', '023', '024']
dataset_list = ['023']
#dataset_list = ['MESA']
# hyperparameters to test
#gamma_list = [2e-10, 2e-9, 2e-8, 2e-7, 2e-6, 2e-5, 2e-4, 2e-3, 2e-2, 0.2, 2.0]
gamma_list = list(p2f.frange(0.002, 0.02, 0.002))
for dataset in dataset_list:
print('\nRunning %s with %s:' % (scriptname, dataset))
#Create directory if directory does not exist
#filepath = '../../figs/out/%s/%s/%s/' % (scriptname, nowdate, dataset)
filepath = '../../figs/out/%s/%s/' % (scriptname, nowdate)
if not os.path.exists(filepath):
os.makedirs(filepath)
# Import dataset and target
if dataset == 'MESA':
X = pd.read_csv(