__author__ = 'NLP-PC'

import numpy as np

from sklearn import cross_validation

from fusion_model import linear_fusion, linear_fusion_sqr, nonlinear_max_fusion, linear_fusion_tf, linear_fusion_tfidf, \

linear_fusion_geo, linear_fusion_geo_tf, linear_fusion_geo_tfidf

from load_data import load_corpus, load_lexicon, load_mark

from file_name import get_file_path

from regression import linear_regression, linear_regression_multivariant

from positive_negative_split import get_pos_neg_va

from load_data import combine_lexicon

def cv(data, target, multivariant=False):

linear_regression_multivariant(X_train, X_test, Y_train, Y_test, cost_fun='KNN_Reg')

if __name__ == '__main__':

normalize = True

corpus = load_corpus(get_file_path('cn_corpus'))

# lexicon = load_lexicon(get_file_path('lexicon'))

mark = load_mark(get_file_path('mark'))

lexicon = combine_lexicon(get_file_path('lexicon'), get_file_path('neural_cand'))

# # the following could use to check the same words in corpus and lexicon

# from visualization import show_common_term

# show_common_term(corpus, lexicon)

# exit()

valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion(corpus, lexicon, mark)

print('start.....')

cv(valence_mean, valence_true, multivariant=False)

cv(arousal_mean, arousal_true, multivariant=False)

print('OK')

valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion_sqr(corpus, lexicon, mark)

print('start.....')

cv(valence_mean, valence_true, multivariant=False)

cv(arousal_mean, arousal_true, multivariant=False)

print('OK')

valence_mean, valence_true, arousal_mean, arousal_true = nonlinear_max_fusion(corpus, lexicon, mark)

print('start.....')

cv(valence_mean, valence_true, multivariant=False)

cv(arousal_mean, arousal_true, multivariant=False)

print('OK')

valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion_tf(corpus, lexicon, mark)

print('start.....')

cv(valence_mean, valence_true, multivariant=False)

cv(arousal_mean, arousal_true, multivariant=False)

print('OK')

valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion_tfidf(corpus, lexicon, mark)

print('start.....')

cv(valence_mean, valence_true, multivariant=False)

cv(arousal_mean, arousal_true, multivariant=False)

print('OK')

valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion_geo(corpus, lexicon, mark)

print('start.....')

cv(valence_mean, valence_true, multivariant=False)

cv(arousal_mean, arousal_true, multivariant=False)

print('OK')

valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion_geo_tf(corpus, lexicon, mark)

print('start.....')

cv(valence_mean, valence_true, multivariant=False)

cv(arousal_mean, arousal_true, multivariant=False)

print('OK')

valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion_geo_tfidf(corpus, lexicon, mark)

print('start.....')

cv(valence_mean, valence_true, multivariant=False)

cv(arousal_mean, arousal_true, multivariant=False)

print('OK')

# Note: change the avg, when changing using normalized data or not

pos_valence_mean, neg_valence_mean, valence_true, pos_arousal_mean, neg_arousal_mean, arousal_true = get_pos_neg_va(

corpus, lexicon, mark)

valence_mean = np.array([pos_valence_mean, neg_valence_mean]).T

arousal_mean = np.array([pos_arousal_mean, neg_arousal_mean]).T

print('start.....')

cv(valence_mean, valence_true, multivariant=True)

cv(arousal_mean, arousal_true, multivariant=True)

print('OK')