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creating_varthresh_testing_files.py
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creating_varthresh_testing_files.py
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import numpy as np
all_api_calls_file = open('mixed_dataset/all_api_calls.txt')
all_api_calls = []
#column_names = []
for lines in all_api_calls_file.readlines():
all_api_calls.append(lines[:-1])
all_api_calls_file.close()
api_index = dict((c,i+1) for i,c in enumerate(all_api_calls))
l1_file = open('tree.txt','r')
features_selected_by_l1 = []
features_selected_by_l1_index = []
for line in l1_file.readlines():
#print(line[:-1])
features_selected_by_l1.append(line[:-1])
features_selected_by_l1_index.append(int(api_index[line[:-1]]))
#print(api_index[line[:-1]])
l1_file.close()
processed_and_indexed_training_examples = []
outputs = []
indexed_file = open('testing_file_index','r')
l1_indexed_files = open('testing_file_index_varthresh','w')
count = 0
for row in indexed_file.readlines():
#print(row)
count = count + 1
#print(count)
components = row.split('-')
name = components[0]
indexes = components[1]
indexes = indexes.split(',')[:-1]
if name[-1] == 'e':
outputs.append([0,1])
else:
outputs.append([1,0])
#print(name)
#print(indexes)
xlist = []
xlist_str = name+'-'
for i in indexes:
if int(i) not in features_selected_by_l1_index:
xlist.append(0)
#print('Got zero')
xlist_str = xlist_str + '0'+','
continue
#print('Got one')
xlist_str = xlist_str + str(int(i))+','
xlist.append(int(i))
xlist_str = xlist_str+'\n'
print(len(xlist))
l1_indexed_files.write(xlist_str)
processed_and_indexed_training_examples.append(xlist)
print(count)
#break
indexed_file.close()
l1_indexed_files.close()
#print(processed_and_indexed_training_examples)
#print(outputs)
#print(processed_and_indexed_training_examples)
processed_and_indexed_training_examples = np.array(processed_and_indexed_training_examples)
outputs = np.array(outputs)