65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
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import numpy as np
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from numpy import genfromtxt
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import csv
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import pandas
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import argparse
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def sample_predciton(path, rate):
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data = pandas.read_csv(path).values
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task_list = [0, 1, 2]
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start = 0
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stop = 0
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num_unique = np.unique(data[:,1])
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#print('unique number', num_unique)
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samples = []
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for j in task_list:
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for i in num_unique:
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inx = np.where((data[:,1] == i) & (data[:,-2] == j))
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samples.append(data[inx])
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for i in range(len(samples)):
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n = int(len(samples[i])*(100-rate)/100)
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samples[i] = samples[i][:-n]
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return np.vstack(samples)
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def main():
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parser = argparse.ArgumentParser(description='')
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parser.add_argument('--LOSS', type=str, default='ce')
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parser.add_argument('--MODEL_TYPE', type=str, default="lstmlast_cross_entropy_bs_32_iter_2000_train_task_prob" )
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parser.add_argument('--EPOCHS', type=int, default=50)
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parser.add_argument('--TASK', type=str, default='test_task')
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args = parser.parse_args()
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task = ['put_fridge', 'put_dishwasher', 'read_book']
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sets = [args.TASK]
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rate = [10, 20, 30, 40, 50, 60, 70, 80, 90]
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for i in task:
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for j in rate:
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for k in sets:
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if k == 'test_task':
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user_num = 92
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if k == 'new_test_task':
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user_num = 9
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for l in range(user_num):
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pred_path = "prediction/" + k + "/" + "user" + str(user_num) + "/ce/" + i + "/" + "loss_weight_" + args.MODEL_TYPE + "_prediction_" + i + "_user" + str(l) + ".csv"
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save_path = "prediction/" + k + "/" + "user" + str(user_num) + "/ce/" + i + "/" + "loss_weight_" + args.MODEL_TYPE + "_prediction_" + i + "_user" + str(l) + "_rate_" + str(j) + ".csv"
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data = sample_predciton(pred_path, j)
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head = []
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for r in range(79):
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head.append('act'+str(r+1))
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head.append('task_name')
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head.append('gt')
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head.insert(0,'action_id')
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pandas.DataFrame(data[:,1:]).to_csv(save_path, header=head)
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if __name__ == '__main__':
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main()
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