AL-main

main.py

(X, y) = download()
(X_train_full, y_train_full, X_test, y_test) = split(trainset_size)
print ('train:', X_train_full.shape, y_train_full.shape)
print ('test :', X_test.shape, y_test.shape)
classes = len(np.unique(y))
print ('unique classes', classes)

def pickle_save(fname, data):
  filehandler = open(fname,"wb")
  pickle.dump(data,filehandler)
  filehandler.close() 
  print('saved', fname, os.getcwd(), os.listdir())

def pickle_load(fname):
  print(os.getcwd(), os.listdir())
  file = open(fname,'rb')
  data = pickle.load(file)
  file.close()
  print(data)
  return data
  
def experiment(d, models, selection_functions, Ks, repeats, contfrom):
    algos_temp = []
    print ('stopping at:', max_queried)
    count = 0
    for model_object in models:
      if model_object.__name__ not in d:
          d[model_object.__name__] = {}
      
      for selection_function in selection_functions:
        if selection_function.__name__ not in d[model_object.__name__]:
            d[model_object.__name__][selection_function.__name__] = {}
        
        for k in Ks:
            d[model_object.__name__][selection_function.__name__][str(k)] = []           
            
            for i in range(0, repeats):
                count+=1
                if count >= contfrom:
                    print ('Count = %s, using model = %s, selection_function = %s, k = %s, iteration = %s.' % (count, model_object.__name__, selection_function.__name__, k, i))
                    alg = TheAlgorithm(k, 
                                       model_object, 
                                       selection_function
                                       )
                    alg.run(X_train_full, y_train_full, X_test, y_test)
                    d[model_object.__name__][selection_function.__name__][str(k)].append(alg.clf_model.accuracies)
                    fname = 'Active-learning-experiment-' + str(count) + '.pkl'
                    pickle_save(fname, d)
                    if count % 5 == 0:
                        print(json.dumps(d, indent=2, sort_keys=True))
                    print ()
                    print ('---------------------------- FINISHED ---------------------------')
                    print ()
    return d


max_queried = 500 

repeats = 1

models = [SvmModel, RfModel, LogModel] 

selection_functions = [RandomSelection, MarginSamplingSelection, EntropySelection] 

Ks = [250,125,50,25,10] 

d = {}
stopped_at = -1 

# print('directory dump including pickle files:', os.getcwd(), np.sort(os.listdir()))  
# d = pickle_load('Active-learning-experiment-' + str(stopped_at) + '.pkl')  
# print(json.dumps(d, indent=2, sort_keys=True))

d = experiment(d, models, selection_functions, Ks, repeats, stopped_at+1)
print (d)
results = json.loads(json.dumps(d, indent=2, sort_keys=True))
print(results)