57 lines
2 KiB
Python
57 lines
2 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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from pathlib import Path
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import argparse
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def sample_single_act(pred_path, save_path, j):
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data = pandas.read_csv(pred_path).values
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total_data = []
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for u in range(1,6):
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act_data = data[data[:,1]==u]
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final_save_path = save_path + "/rate_" + str(j) + "_act_" + str(int(u)) + "_pred.csv"
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head = []
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for r in range(7):
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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(act_data[:,1:]).to_csv(final_save_path, header=head)
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def main():
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# parsing parameters
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parser = argparse.ArgumentParser(description='')
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parser.add_argument('--batch_size', type=int, default=8, help='batch size')
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parser.add_argument('--lr', type=float, default=1e-4, help='learning rate')
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parser.add_argument('--hidden_size', type=int, default=64, help='hidden_size')
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parser.add_argument('--model_type', type=str, default='lstmlast', help='model type')
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args = parser.parse_args()
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task = np.arange(7)
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user_num = 5
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bs = args.batch_size
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lr = args.lr # 1e-4
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hs = args.hidden_size #128
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model_type = args.model_type #'lstmlast'
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rate = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
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for i in task:
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for j in rate:
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for l in range(user_num):
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pred_path = "prediction/task" + str(i) + "/" + model_type + "_bs_" + str(bs) + "_lr_" + str(lr) + "_hidden_size_" + str(hs) + "/user" + str(l) + "_rate_" + str(j) + "_pred.csv"
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if j == 100:
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pred_path = "prediction/task" + str(i) + "/" + model_type + "_bs_" + str(bs) + "_lr_" + str(lr) + "_hidden_size_" + str(hs) + "/user" + str(l) + "_pred.csv"
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save_path = "prediction/single_act/task" + str(i) + "/" + model_type + "_bs_" + str(bs) + "_lr_" + str(lr) + "_hidden_size_" + str(hs) + "/user" + str(l)
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Path(save_path).mkdir(parents=True, exist_ok=True)
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data = sample_single_act(pred_path, save_path, j)
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if __name__ == '__main__':
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# split the prediction by action sequence id, from 10% to 90%
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main()
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