import os

import numpy as np

import logistic

# read in data

folder = 'proteindata'

filenames = ['P', 'N', 'Q']

max_key = 24081

npy_filenames = 'pos', 'neg', 'test_pos'

if __name__=='__main__':

pos, neg, test_pos = (np.load(os.path.join(folder, 'data.%s.swissprot.npy' % d)) for d in npy_filenames)

print 'read data...'

# set up data

table = []

for cp in [1.0, 0.5, 0.1, 0.7, 0.6, 0.4, 0.3, 0.2, 0.9, 0.8]:

# split out the validation set separately

split_half = lambda a: logistic.sample_split(a, len(a) / 2)

half_pos, v_pos = split_half(pos)

half_neg, v_neg = split_half(neg)

half_test_pos, v_test_pos = split_half(test_pos)

# figure out the subset to sample (c)

u = logistic.vstack([half_neg, half_test_pos])

pos_sample, unlabeled = logistic.sample_positive(cp, half_pos, u)

# create validation set the same way

u = logistic.vstack([v_neg, v_test_pos])

v_p, v_u = logistic.sample_positive(cp, v_pos, u)

print 'set up data...'

_, estimators = logistic.calculate_estimators(pos_sample, unlabeled, v_p, v_u)

t = (cp,

len(half_pos), len(half_neg), len(half_test_pos),

estimators,

float(int(len(half_pos) * cp)) / (len(half_test_pos) + len(half_pos)),

)

table.append(t)

print t