Ensemble_model_external.py

#import cPickle as pickle;
import pickle
import sys;
import math;
import numpy as np;
import codecs
if (len(sys.argv)<4):
    print("Parameters insufficients");
hownet_filename = sys.argv[4];
model1_filename = sys.argv[1];
model2_filename = sys.argv[2];
ratio = float(sys.argv[3]);
output_filename = sys.argv[5];
model_filename = sys.argv[6];
with codecs.open(model1_filename,'rb') as model1_file:
    with codecs.open(model2_filename,'rb') as model2_file:
        model1 = [];
        model2 = [];
        print('Loading Models...')
        while True:
            try:
                model1.append(pickle.load(model1_file,))
                model2.append(pickle.load(model2_file,))
                #model1.append(pickle.load(model1_file,encoding="iso-8859-1"));
                #model2.append(pickle.load(model2_file,encoding='iso-8859-1'));
            except EOFError: 
                break;
        print('Loading Models Complete, have read %d results from model1, %d results from model2' % (len(model1),len(model2)))
        assert(len(model1) == len(model2))
        index = 0;
        length = len(model1);
        test_words = [];
        print('Loading test files')
        with codecs.open(hownet_filename,'r') as test:
            for line in test:
                test_words.append(line.strip());
        print('Loading Complete,training beginning.')
        with codecs.open(output_filename,'w',encoding='utf8') as output:
            with open(model_filename,'wb') as model_file:
                while (index < length):
                    predict0 = model1[index];
                    predict1 = model2[index];
                    predict = [];
                    for key in predict0:
                        predict.append((key,ratio/(1+ratio)*(predict0[key])+1/(1+ratio)*predict1[key]));
                    predict.sort(key=lambda x:x[1],reverse=True);
                    result = [x[0] for x in predict];
                    target_str = test_words[index]+'\n'
                    result_str = ' '.join([x.encode("utf-8") for x in result])
                    target_str +=  result_str +'\n'
                    output.write(target_str.decode("utf-8"));
                    pickle.dump(predict,model_file)
                    index += 1;
        print('Training complete.')