Phase2 = phase2.EntityResolver() execution_time_list = [] accuracy_list = [] tp_count = [] eviction_parameter_recorder = [] whole_level = [] val = math.ceil(length / batch_size) for i in range(val): print(i) print("anani siki2m") # val =3 my_classifier = svm.SVM1('training.csv') #last one is the without eviction, that why i added one more. #look the defter notes to see mapping. eviction_parameter = 2 eviction_parameter_recorder.append(eviction_parameter) Phase1 = phase1.SatadishaModule() Phase2 = phase2.EntityResolver() total_time = 0 execution_time_list = [] tweets_been_processed_list = [] tweets_been_processed = 0 level_holder = [] annotated_tweet_evenly_partitioned_list = np.array_split(annotated_tweets, val)
def __init__(self): self.counter = 0 self.my_classifier = svm.SVM1('training.csv')