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beta_experiment.py

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+from train_TF import train_model
+from test_TF import test_model
+import numpy as np
+import argparse
+import math
+import os
+
+if __name__ == '__main__':
+
+	# Parser arguments
+	parser = argparse.ArgumentParser(description= 'Experimment for the ')
+	parser.add_argument('--root-path', '--root',
+	                    default='./',
+	                    help='path to folder that contain dataset')
+	parser.add_argument('--dataset', '--ds',
+	                    default='',
+	                    help='path to folder that contain dataset')
+	parser.add_argument('--beta', '--bt',
+	                    default='0',
+	                    help='path to folder that contain dataset')
+	args = parser.parse_args()
+
+	if not os.path.isfile('./generated_data/beta_experiment/results.npy'):
+		table = np.zeros((5,10))
+		table[:] = np.NaN
+		np.save('./generated_data/beta_experiment/results.npy', table)
+
+
+	#------------- Training and updating results in the table of experiment -------------
+
+	table = np.load('./generated_data/beta_experiment/results.npy')
+
+	datasets = ['ETH-univ','ETH-hotel', 'UCY-zara1', 'UCY-zara2', 'UCY-univ3']
+	betas = np.array([0,0.3,0.5,1,-1])
+
+	try:
+
+		test_index = int(args.dataset)
+		beta_index = int(args.beta)
+
+		training_names = datasets.copy()
+		test_name = [datasets[test_index]]
+		del training_names[i]
+		beta = betas[beta_index]
+
+		print(f"starting training for {test_name}")
+		train_model(training_names,test_name,args.root_path,beta = beta, epochs = 50)
+		ade,fde = test_model(test_name,args.root_path)
+		ade_average = np.mean(ade)
+		fde_average = np.mean(fde)
+		table[test_index,beta_index*2] = ade_average
+		table[test_index,beta_index*2+1] = fde_average
+		np.save('./generated_data/beta_experiment/results.npy', table)
+
+	except:
+
+		print("training did not start either because not demanded or because of poor parameter handling")
+
+	#--------------------- Printing results in the table of experiment --------------------
+
+	# This generates latex code for the table to be copied directly
+	print("Printing current values")
+	for i in range(5):
+		s = datasets[i]
+		for j in range(5):
+			s += " & " 
+			if math.isnan(table[i,2*j]):
+				s += "NC"
+			else:
+				s += str(table[i,2*j])
+			s += "/"
+			if math.isnan(table[i,2*j+1]):
+				s += "NC"
+			else:
+				s += str(table[i,2*j+1])
+		s += " \\\\"
+		print(s)