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')
