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How to use test_precedence method in pyshould

Best Python code snippet using pyshould_python

test.py

Source:test.py

...17        self.assertListEqual(rpn('4-8'), [Decimal('4'), Decimal('8'), '-'])18        self.assertListEqual(rpn('5*3'), [Decimal('5'), Decimal('3'), '*'])19        self.assertListEqual(rpn('6/4'), [Decimal('6'), Decimal('4'), '/'])20        self.assertListEqual(rpn('2^3'), [Decimal('2'), Decimal('3'), '^'])21    def test_precedence(self):22        self.assertListEqual(rpn('2+3*4'), [Decimal('2'), Decimal('3'), Decimal('4'), '*', '+'])23        self.assertListEqual(rpn('5*6+7'), [Decimal('5'), Decimal('6'), '*', Decimal('7'), '+'])24    def test_brackets(self):25        self.assertListEqual(rpn('(2+3)-5'),[Decimal('2'), Decimal('3'), '+', Decimal('5'), '-'])26        self.assertListEqual(rpn('4*(6-7)'), [Decimal('4'), Decimal('6'), Decimal('7'), '-', '*'])27        self.assertListEqual(rpn('(2.78+3)*(7-8)'), [Decimal('2.78'), Decimal('3'), '+',28                                                 Decimal('7'), Decimal('8'), '-',29                                                 '*'])30    def test_constants(self):31        self.assertListEqual(rpn('e'), [Decimal(math.e)])32        self.assertListEqual(rpn('e+pi'), [Decimal(math.e), Decimal(math.pi), '+'])33        self.assertListEqual(rpn('e*(3 + 7 - e)'), [Decimal(math.e), Decimal('3'),34                                                    Decimal('7'), Decimal(math.e),35                                                    '-', '+', '*'])36    def test_functions(self):37        self.assertListEqual(rpn('sin(3)'), [Decimal('3'), 'sin'])38        self.assertListEqual(rpn('arcsin(0.5)'), [Decimal('0.5'), 'arcsin'])39        self.assertListEqual(rpn('cos( 8 ) + tan(7)'), [Decimal('8'), 'cos', Decimal('7'), 'tan', '+'])40        self.assertListEqual(rpn('cos(arcsin(1/e))'), [Decimal('1'), Decimal(math.e),41                                                      '/', 'arcsin', 'cos'])42    def test_precedence(self):43        self.assertListEqual(rpn('3 * 4 +  6'), [Decimal('3'), Decimal('4'), '*', Decimal('6'), '+'])44        self.assertListEqual(rpn('1-2*5'), [Decimal('1'), Decimal('2'), Decimal('5'), '*', '-'])45        self.assertListEqual(rpn('1/2*4'), [Decimal('1'), Decimal('2'), '/', Decimal('4'), '*'])46    def test_unary_minus(self):47        self.assertListEqual(rpn('-6'), [Decimal('6'), '--'])48        self.assertListEqual(rpn('-4+3'), [Decimal('4'), '--', Decimal('3'), '+'])49        self.assertListEqual(rpn('2^-2'), [Decimal('2'), Decimal('2'), '--', '^'])50        self.assertListEqual(rpn('-5^2'), [Decimal('5'), Decimal('2'), '^', '--'])51class Test_computing_rpn(TestCase):52    def test_simple_algebra(self):53        self.assertEqual(compute_rpn([Decimal('2'), Decimal('3'), '+']), Decimal('5'))54        self.assertEqual(compute_rpn([Decimal('4'), Decimal('8'), '-']), Decimal('-4'))55        self.assertEqual(compute_rpn([Decimal('5'), Decimal('3'), '*']), Decimal('15'))56        self.assertEqual(compute_rpn([Decimal('6'), Decimal('4'), '/']), Decimal('1.5'))57        self.assertEqual(compute_rpn([Decimal('2'), Decimal('3'), '^']), Decimal('8'))58    def test_brackets(self):59        self.assertEqual(compute_rpn([Decimal('2'), Decimal('3'), '+', Decimal('5'), '-']), Decimal('0'))60        self.assertEqual(compute_rpn([Decimal('4'), Decimal('6'), Decimal('7'), '-', '*']), Decimal('-4'))61        self.assertEqual(compute_rpn([Decimal('2.78'), Decimal('3'), '+',62                                      Decimal('7'), Decimal('8'), '-', '*']), Decimal('-5.78'))63    def test_constants(self):64        self.assertEqual(compute_rpn([Decimal(math.e)]), Decimal(math.e))65        self.assertEqual(compute_rpn([Decimal(math.e), Decimal(math.pi), '+']), Decimal(math.e)+Decimal(math.pi))66        self.assertEqual(compute_rpn([Decimal(math.e), Decimal('3'), Decimal('7'), Decimal(math.e),67                                      '-', '+', '*']),68                         Decimal(math.e)*(Decimal('3') + Decimal('7')-Decimal(math.e)))69    def test_functions(self):70        self.assertEqual(compute_rpn([Decimal('3'), 'sin']), Decimal(math.sin(3)))71        self.assertEqual(compute_rpn([Decimal('0.5'), 'arcsin']), Decimal(math.asin(0.5)))72        self.assertEqual(compute_rpn([Decimal('8'), 'cos', Decimal('7'), 'tan', '+']),73                         Decimal(math.cos(8)+math.tan(7)))74        self.assertEqual(compute_rpn([Decimal('1'), Decimal(math.e), '/', 'arcsin', 'cos']),75                         Decimal(math.cos(math.asin(1/math.e))))76    def test_precedence(self):77        self.assertEqual(compute_rpn([Decimal('2'), Decimal('3'), Decimal('4'), '*', '+']), Decimal('14'))78        self.assertEqual(compute_rpn([Decimal('5'), Decimal('6'), '*', Decimal('7'), '+']), Decimal('37'))79    def test_unary_minus(self):80        self.assertEqual(compute_rpn([Decimal('6'), '--']), Decimal('-6'))81        self.assertEqual(compute_rpn([Decimal('4'), '--', Decimal('3'), '+']), Decimal('-1'))82        self.assertEqual(compute_rpn([Decimal('2'), Decimal('2'), '--', '^']), Decimal('0.25'))83        self.assertEqual(compute_rpn([Decimal('5'), Decimal('2'), '^', '--']), Decimal('-25'))84class TestGraphingCalc(TestCase):85    def test_implementable_function(self):86        pass87if __name__ == '__main__':...

train_overfit.py

Source:train_overfit.py

1import time2import numpy as np3import torch4from torch import optim5from utils import *6from Envs.JobShopMultiGymEnv import *7from Models.actorcritic import *89embeddim = 12810actorLR = 1e-411criticLR = 1e-41213device = torch.device("cuda" if torch.cuda.is_available() else "cpu")1415jobs = 1516ops = 1517macs = ops18maxTime = 10019i = 020size_agnostic = True21resume_run = True22seed = 023BS = 1282425start = time.time()26########################################################################2728print('ta{}'.format(i + 1))29test_precedence, test_timepre_ = read_instances('./data/ta/ta{}'.format(i + 1))30test_timepre = test_timepre_ / maxTime3132# Create batch33test_precedence = np.repeat(test_precedence, BS, axis=0)34test_timepre = np.repeat(test_timepre, BS, axis=0)3536test_venv = JobShopMultiGymEnv(BS, jobs, ops, macs)37test_venv.setGame(test_precedence, test_timepre)38testSamples = [i for i in range(BS)]3940filedir = './Results/%dx%d/' % (jobs, macs)41if size_agnostic:42    filedir = './Results/size_agnostic/'4344model_name = 'A2C_Seed%d_BS%d.tar' % (seed, BS)45if size_agnostic:46    model_name = '6_6_100.tar'4748# Instanitate actor-critic49actor = Actor(embeddim, jobs, ops, macs, device).to(device)50critic = Critic(embeddim, jobs, ops, macs, device).to(device)5152# Environment training5354actor_opt = optim.Adam(actor.parameters(), lr=actorLR)55critic_opt = optim.Adam(critic.parameters(), lr=criticLR)5657if resume_run:58    checkpoint = torch.load(filedir + model_name, map_location=device)59    actor.load_state_dict(checkpoint['actor_state_dict'])60    critic.load_state_dict(checkpoint['critic_state_dict'])61    actor_opt.load_state_dict(checkpoint['actor_opt_state_dict'])62    critic_opt.load_state_dict(checkpoint['critic_opt_state_dict'])6364for epi in range(10000):6566    if epi%10==1 or epi==0:67        epi_st = time.time()6869    test_venv.reset(testSamples)7071    train_rollout = Rollout(test_venv, actor, critic, device)72    ite, total_reward, States, Log_Prob, Prob, Action, Value, tr_reward, tr_entropy = train_rollout.play(BS,testSamples)73    Log_Prob = torch.stack(Log_Prob)7475    tr_reward.reverse()7677    #Compute Advantage: Qvalue - Value78    Qvalue = np.zeros((ite, BS))79    q = 0.080    for i in range(ite):81        q += np.array(tr_reward[i])  # q.shape=[BS]82        Qvalue[ite - i - 1] = q8384    Advantage = torch.tensor(Qvalue, device=device) - torch.stack(Value)  # advantage.shape=[ite,BS]8586    # Zero grad87    actor.zero_grad()88    critic.zero_grad()8990    # Compute loss fucntion91    # actor_loss = torch.mean(torch.sum(Log_Prob, dim=1)*torch.tensor(total_reward).to(device))92    actor_loss = torch.mean(-Log_Prob * Advantage.detach())93    critic_loss = 0.5 * torch.mean(Advantage ** 2)9495    # Update weights Actor and critic96    actor_loss.backward()97    critic_loss.backward()98    actor_opt.step()99    critic_opt.step()100101    if epi % 10 == 0:102103        epi_et = time.time()104        105        print('epi: %d, al: %.2e, cl: %.2e, trSpan: %.4f, trEntropy: %.2f, time: %.2f' \106              % (epi, actor_loss.data.item(), critic_loss.data.item(), np.mean(total_reward), tr_entropy, epi_et-epi_st))
...

test_instances.py

Source:test_instances.py

1from Envs.JobShopMultiGymEnv import *2from utils import *3from Models.actorcritic import *4from torch import optim5import time6embeddim = 1287device = torch.device("cuda" if torch.cuda.is_available() else "cpu")8# Change9instances = 'ta'            # Select: ta or dmu10size_agnostic = True11testsize = 112jobs = 1513macs = 1514ops = macs 15maxTime = 100               # maxTime: ta -> 100, for dmu -> 20016seed = 017BS = 12818beam_size = 219model_jobs = 620model_ops = 621start = time.time()22########################################################################23for i in range(0,10):24    25    print('{}{}'.format(instances,i+1))26    if instances=='ta':27        test_precedence,test_timepre_ = read_instances('./data/ta/ta{}'.format(i+1))28    else:29        test_precedence,test_timepre_ = read_instances('./data/dmu/dmu{}.txt'.format(i+1))30    test_timepre = test_timepre_ / maxTime31    test_venv = JobShopMultiGymEnv(testsize,jobs,ops,macs)32    test_venv.setGame(test_precedence,test_timepre)33    testSamples = [i for i in range(testsize)]34    test_venv.reset(testSamples)35    # Instanitate actor-critic36    actor = Actor(embeddim,jobs,ops,macs,device).to(device)37    critic = Critic(embeddim,jobs,ops,macs,device).to(device)38    # Environment training39    actor_opt = optim.Adam(actor.parameters())40    critic_opt = optim.Adam(critic.parameters())41    filedir = './Results/%dx%d/'%(jobs,macs)42    if size_agnostic:43        filedir = './Results/size_agnostic/%dx%d_%d/'%(model_jobs,model_ops,maxTime)44    model_name = 'A2C_Seed%d_BS%d.tar'%(seed,BS)45    checkpoint = torch.load(filedir+model_name, map_location=torch.device('cpu'))46    actor.load_state_dict(checkpoint['actor_state_dict'])47    critic.load_state_dict(checkpoint['critic_state_dict'])48    actor_opt.load_state_dict(checkpoint['actor_opt_state_dict'])49    critic_opt.load_state_dict(checkpoint['critic_opt_state_dict'])50    test_rollout = Rollout(test_venv,actor,critic,device)51    te_ite,te_total_reward,te_States,te_Log_Prob,te_Prob,te_Action,te_Value,te_reward, te_entropy = test_rollout.play(testsize,testSamples,False,beam_size)52    print(te_total_reward)53end = time.time()...

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