import json import random import numpy as np from src.utils2 import c_ex as c, get_path import src.dataloaders as d from src.logistic import fit_logistic_regression path = get_path(__file__) + '/..' D = d.trainingset_extended() Dt = d.testset_extended() cols = c('sde5', 'v11', 'e9') a = range(D.shape[0]) random.shuffle(a) X = D[:, cols] X = X[a[:320000], :] y = D[a[:320000], c('isalert')] y = y.astype(int)^1 Xt = D[:, cols] Xt = Xt[a[320000:], :] yt = D[a[320000:], c('isalert')] yt = yt.astype(int)^1 num_tests = 1
learningrate = 0.04 max_epochs = 40 continue_epochs = 6 training_size = 100000 feature_set = 'forward' if feature_set == 'winner': features = ['sde5', 'v11', 'e9'] elif feature_set == 'forward': features = ['sde1', 'v11', 'e9'] else: raise Exception('Unknown feature set') if training_size == 'all': D = d.trainingset_extended() training_size = int(D.shape[0]) else: D = get_random_subset(d.trainingset_extended(), training_size) T = d.testset_extended() cols = c(*features) Xt = T[:, cols] yt = T[:, c.isalert] bins = get_bins(T.shape[0], num_bins) neural_network = train_network(D[:, cols], D[:, c.isalert], hidden_units=hidden_units, learningrate=learningrate,