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
import json
from src.utils2 import get_path
root = get_path(__file__) + "/../.."
with open(root + "/sessions/18-forward-selection/data/forward-selection-results-2-for-docs.json") as f:
data = json.load(f)
num_rows = len(data["labels_chosen"])
columns_change = int(num_rows / 2)
def print_table_header():
print r"\begin{minipage}[t]{50mm}"
print r"\vspace{0pt}"
print r"\begin{tabularx}{50mm}{ l R }"
print r"Feature added & AUC \\\hline"
def print_table_footer():
print r"\end{tabularx}"
print r"\end{minipage}"
print_table_header()
for i, feature in enumerate(data["labels_chosen"]):
if i == columns_change:
print r"{\itshape continues ...} & "
print_table_footer()
import json
from src.utils2 import get_path
root = get_path(__file__) + '/../..'
with open(root+'/sessions/18-forward-selection/data/forward-selection-results-2-for-docs.json') as f:
data = json.load(f)
def print_table_header():
print r'\begin{tabularx}{40mm}{ l R }'
print r'Feature added & AUC \\\hline'
def print_table_footer():
print r'\end{tabularx}'
features = data['labels_chosen'][:5] + data['labels_chosen'][-2:]
aucs = data['max_auc'][:5] + data['max_auc'][-2:]
print_table_header()
for i, feature in enumerate(features):
print r'%s & %.04f \\' % (feature, aucs[i])
if i==4:
print r'$\vdots$ & $\vdots$ \\'
print_table_footer()
