How to use reset_state method in tempest

Best Python code snippet using tempest_python

test_bess_chpp_hwt.py

Source:test_bess_chpp_hwt.py

...37    assert (model.state == [123,2,4,11,1,900,1800,2700,55,17]).all()38    model.bess.soc_min = 039    model.bess.soc_max = 140def test_get_feasible_actions():41    def reset_state(mode, dwell_time, temperature, stored_energy=0):42        model.bess.stored_energy = stored_energy43        model.demand.demand = 1144        model.chpp.mode = mode45        model.chpp.dwell_time = dwell_time46        model.chpp.min_off_time = 180047        model.chpp.min_on_time = 180048        model.hwt.temperature = temperature49        model.hwt.ambient_temperature = 1750    bess_actions  = model.bess.feasible_actions51    reset_state(1, 900, 70)52    model.eval()53    assert np.isin(model.determine_feasible_actions(), [-5500] + bess_actions).all() # must remain running54    assert len(model.feasible_actions) == len([-5500] + bess_actions)55    # top, lower boundary region56    reset_state(1, 900, 79.5)57    assert np.isin(model.determine_feasible_actions(), [-1000, -5500] + bess_actions.transpose().reshape(-1, 1).repeat(2, axis=1)).all() # may turn off58    reset_state(1, 900, 79.5)59    model.train()60    assert np.isin(model.determine_feasible_actions(), [-5500] + bess_actions).all() # must remain running61    reset_state(0, 1800, 79.5)62    assert np.isin(model.determine_feasible_actions(), [0] + bess_actions).all() # must remain stopped63    64    reset_state(0, 1800, 79.5)65    model.eval()66    assert np.isin(model.determine_feasible_actions(), [0, -4000] + bess_actions.transpose().reshape(-1, 1).repeat(2, axis=1)).all() # may turn on67    # top, upper boundary region68    reset_state(1, 900, 80.5)69    assert np.isin(model.determine_feasible_actions(), [-1000, -5500] + bess_actions.transpose().reshape(-1, 1).repeat(2, axis=1)).all() # may turn off70    reset_state(1, 900, 80.5)71    model.train()72    assert np.isin(model.determine_feasible_actions(), [-1000] + bess_actions).all() # must turn off73    74    # top, above boundary region75    reset_state(1, 900, 81.5)76    model.eval()77    assert np.isin(model.determine_feasible_actions(), [-1000] + bess_actions).all() # must turn off78    reset_state(1, 900, 80.5)79    model.train()80    assert np.isin(model.determine_feasible_actions(), [-1000] + bess_actions).all() # must turn off81    # bottom, upper boundary region82    reset_state(0, 900, 60.5)83    model.eval()84    assert np.isin(model.determine_feasible_actions(), [-4000, 0] + bess_actions.transpose().reshape(-1, 1).repeat(2, axis=1)).all() # may turn on85    assert len(model.feasible_actions) == len(np.unique([-4000, 0] + bess_actions.transpose().reshape(-1, 1).repeat(2, axis=1)))86    reset_state(0, 900, 60.5)87    model.train()88    assert np.isin(model.determine_feasible_actions(), [0] + bess_actions).all() # must remain off89    reset_state(1, 1800, 60.5)90    assert np.isin(model.determine_feasible_actions(), [-5500] + bess_actions).all() # must remain running91    92    reset_state(1, 1800, 60.5)93    model.eval()94    assert np.isin(model.determine_feasible_actions(), [-5500, -1000] + bess_actions.transpose().reshape(-1, 1).repeat(2, axis=1)).all() # may turn off95    # bottom, lower boundary region96    reset_state(0, 900, 59.5)97    assert np.isin(model.determine_feasible_actions(), [-4000, 0] + bess_actions.transpose().reshape(-1, 1).repeat(2, axis=1)).all() # may turn on98    99    reset_state(0, 900, 59.5)100    model.train()101    assert np.isin(model.determine_feasible_actions(), [-4000] + bess_actions).all() # must turn on102    # bottom, below boundary region103    reset_state(0, 900, 58.5)104    model.eval()105    assert np.isin(model.determine_feasible_actions(), [-4000] + bess_actions).all() # must turn on106    reset_state(0, 900, 58.5)107    model.train()108    assert np.isin(model.determine_feasible_actions(), [-4000] + bess_actions).all() # must turn on109    model.eval()110    # brute force111    np.random.seed(1924)112    for i in range(1000):113        state = model.sample_state()114        model.feasible_actions115    model.bess.soc_min = 0116    model.bess.soc_max = 1117def test_state_transition():118    def reset_state(mode, dwell_time, temperature, stored_energy=0):119        model.bess.stored_energy = stored_energy120        model.demand.demand = 11121        model.chpp.mode = mode122        model.chpp.dwell_time = dwell_time123        model.chpp.min_off_time = 1800124        model.chpp.min_on_time = 1800125        model.hwt.temperature = temperature126        model.hwt.ambient_temperature = 17127        model._feasible_actions = None128    model.eval()129    model.bess.soc_min = 0130    model.bess.soc_max = 1131    reset_state(0, 2700, 75, 0)132    state, interaction = model.transition(-5500) # chpp needs to run, since bess is empty133    assert model.chpp.mode == 1134    assert bess.stored_energy == 0135    assert interaction[0] == 4000136    reset_state(1, 2700, 75, 0)137    state, interaction = model.transition(-5000) # chpp needs to run, since bess is empty138    assert model.chpp.mode == 1139    assert bess.stored_energy == 500 * 900140    assert interaction[0] == 5000141    142    reset_state(1, 2700, 75, 5000*900)143    state, interaction = model.transition(-5000) # high tank temp., use bess and switch off chpp (=1000W)144    assert model.chpp.mode == 0 145    assert bess.stored_energy == 1000*900146    assert interaction[0] == 5000147    reset_state(0, 2700, 65, model.bess.capacity)148    state, interaction = model.transition(-5500) # chpp should run, since temp. is low149    assert model.chpp.mode == 1150    assert bess.stored_energy == model.bess.capacity - (5500 - 4000) * 900151    assert interaction[0] == 5500152    reset_state(1, 2700, 65, 5000*900)153    state, interaction = model.transition(-5000) # chpp should run, since temp. is low154    assert model.chpp.mode == 1155    assert bess.stored_energy == 5500 * 900156    assert interaction[0] == 5000157    # top, lower boundary region158    reset_state(1, 900, 79.5)159    state, interaction = model.transition(-1000)160    assert model.chpp.mode == 0  # may turn off161    assert bess.stored_energy == 0162    assert interaction[0] == 1000163    reset_state(1, 900, 79.5)164    model.train()165    state, interaction = model.transition(-1000)166    assert model.chpp.mode == 1 # must remain running, BESS will cover the difference167    assert bess.stored_energy == (5500-1000) * 900168    assert interaction[0] == 1000169    reset_state(1, 900, 79.5, model.bess.capacity)170    state, interaction = model.transition(-1000)171    assert model.chpp.mode == 1 # must remain running, BESS can not cover the difference172    assert np.isclose(bess.stored_energy, model.bess.capacity)173    assert interaction[0] == 5500174    reset_state(0, 1800, 79.5)175    state, interaction = model.transition(-4000)176    assert model.chpp.mode == 0 # must remain stopped177    assert bess.stored_energy == 0 178    assert interaction[0] == 0179    180    reset_state(0, 1800, 79.5)181    model.eval()182    state, interaction = model.transition(-4000)183    assert model.chpp.mode == 1 # may turn on, as battery is empty184    assert bess.stored_energy == 0 185    assert interaction[0] == 4000186    reset_state(0, 1800, 79.5, model.bess.capacity)187    model.eval()188    state, interaction = model.transition(-4000)189    assert model.chpp.mode == 0 # may turn on, but won't as battery can be discharged190    assert bess.stored_energy == model.bess.capacity - 4000 * 900191    assert interaction[0] == 4000192    # top, upper boundary region193    reset_state(1, 900, 80.5) 194    state, interaction = model.transition(-1000)195    assert model.chpp.mode == 0 # may turn off196    assert bess.stored_energy == 0197    assert interaction[0] == 1000198    reset_state(1, 900, 80.5)199    model.train() 200    state, interaction = model.transition(-5500)201    assert model.chpp.mode == 0 # must turn off202    assert bess.stored_energy == 0203    assert interaction[0] == 1000 # -1000 is closer to -5500 than 0204    205    # top, above boundary region206    reset_state(1, 900, 81.5)207    model.eval()208    state, interaction = model.transition(-5500)209    assert model.chpp.mode == 0 # must turn off210    assert bess.stored_energy == 0 211    assert interaction[0] == 1000212    reset_state(1, 900, 80.5)213    model.train()214    state, interaction = model.transition(-5500)215    assert model.chpp.mode == 0 # must turn off216    assert bess.stored_energy == 0217    assert interaction[0] == 1000 218    ###219    # bottom, upper boundary region220    reset_state(0, 900, 60.5)221    model.eval()222    state, interaction = model.transition(-4000)223    assert model.chpp.mode == 1 # may turn on224    assert bess.stored_energy == 0225    assert interaction[0] == 4000 226    reset_state(0, 900, 60.5)227    model.train()228    state, interaction = model.transition(-4000)229    assert model.chpp.mode == 0 # must remain off230    assert bess.stored_energy == 0 231    assert interaction[0] == 0232    reset_state(1, 1800, 60.5)233    state, interaction = model.transition(-1000)234    assert model.chpp.mode == 1 # must remain running, bess will cover the difference235    assert bess.stored_energy == (5500-1000)*900236    assert interaction[0] == 1000237    238    reset_state(1, 1800, 60.5, model.bess.capacity)239    state, interaction = model.transition(-1000)240    assert model.chpp.mode == 1 # must remain running, bess can not cover the difference241    assert bess.stored_energy == model.bess.capacity242    assert interaction[0] == 5500243    reset_state(1, 1800, 60.5)244    model.eval()245    state, interaction = model.transition(-1000)246    assert model.chpp.mode == 1 # may turn off, but won't as battery can charge247    assert bess.stored_energy == (5500-1000) * 900248    assert interaction[0] == 1000249    reset_state(1, 1800, 60.5, model.bess.capacity)250    state, interaction = model.transition(-1000)251    assert model.chpp.mode == 0 # may turn off, as battery is already full252    assert bess.stored_energy == model.bess.capacity253    assert interaction[0] == 1000254    # bottom, lower boundary region255    reset_state(0, 900, 59.5)256    state, interaction = model.transition(-4000)257    assert model.chpp.mode == 1 # may turn on258    assert bess.stored_energy == 0 259    assert interaction[0] == 4000260    261    reset_state(0, 900, 59.5)262    model.train()263    state, interaction = model.transition(-0)264    assert model.chpp.mode == 1 # must turn on, but bess has free capacity265    assert bess.stored_energy == 4000 * 900266    assert interaction[0] == 0267    reset_state(0, 900, 59.5, model.bess.capacity)268    state, interaction = model.transition(-0)269    assert model.chpp.mode == 1 # must turn on, and bess is full270    assert bess.stored_energy == model.bess.capacity271    assert interaction[0] == 4000272    # bottom, below boundary region273    reset_state(0, 900, 58.5)274    model.eval()275    state, interaction = model.transition(-0)276    assert model.chpp.mode == 1 # must turn on, but bess has free capacity277    assert bess.stored_energy == 4000 * 900278    assert interaction[0] == 0279    reset_state(0, 900, 58.5, model.bess.capacity)280    state, interaction = model.transition(-0)281    assert model.chpp.mode == 1 # must turn on, and bess is full282    assert bess.stored_energy == model.bess.capacity283    assert interaction[0] == 4000284    reset_state(0, 900, 58.5)285    model.train()286    state, interaction = model.transition(-0)287    assert model.chpp.mode == 1 # must turn on, but bess has free capacity288    assert bess.stored_energy == 4000 * 900289    assert interaction[0] == 0290    reset_state(0, 900, 58.5, model.bess.capacity)291    state, interaction = model.transition(-0)292    assert model.chpp.mode == 1 # must turn on, and bess is full293    assert bess.stored_energy == model.bess.capacity294    assert interaction[0] == 4000...

CS598_Mohan_Sun_HW7_RNN_model.py

Source:CS598_Mohan_Sun_HW7_RNN_model.py

...14        self.lstm = nn.LSTMCell(in_size, out_size)15        self.out_size = out_size16        self.h = None17        self.c = None18    def reset_state(self):19        self.h = None20        self.c = None21    def forward(self, x):22        batch_size = x.size(0)23        if self.h is None:24            state_size = [batch_size, self.out_size]25            if is_cuda:26                self.c = Variable(torch.zeros(state_size)).cuda()27                self.h = Variable(torch.zeros(state_size)).cuda()28            else:29                self.c = Variable(torch.zeros(state_size))30                self.h = Variable(torch.zeros(state_size))31        self.h, self.c = self.lstm(x,(self.h,self.c))32        return self.h33class LockedDropout(nn.Module):34    def __init__(self):35        super(LockedDropout,self).__init__()36        self.m = None37    def reset_state(self):38        self.m = None39    def forward(self, x, dropout=0.5, train=True):40        if not train:41            return x42        if self.m is None:43            self.m = x.data.new(x.size()).bernoulli_(1-dropout)44        if is_cuda:45            mask = Variable(self.m, requires_grad=False).cuda()/(1-dropout)46        else:47            mask = Variable(self.m, requires_grad=False)/(1-dropout)48        return mask * x49class RNN_model(nn.Module):50    def __init__(self, vocab_size, no_of_hidden_units, switches = [False, False]):51        super(RNN_model, self).__init__()52        self.switches = switches53        self.embedding = nn.Embedding(vocab_size, no_of_hidden_units)#padding_idx=0)54        self.lstm1 = StatefulLSTM(no_of_hidden_units, no_of_hidden_units)55        self.bn_lstm1 = nn.BatchNorm1d(no_of_hidden_units)56        self.dropout1 = LockedDropout() if not switches[0] else nn.Dropout(p=0.5)57        if switches[1]:58            self.lstm2 = StatefulLSTM(no_of_hidden_units, no_of_hidden_units)59            self.bn_lstm2 = nn.BatchNorm1d(no_of_hidden_units)60            self.dropout2 = LockedDropout() if not switches[0] else nn.Dropout(p=0.5)61        self.fc_output = nn.Linear(no_of_hidden_units, 1)62        self.loss = nn.BCEWithLogitsLoss()63    def reset_state(self):64        self.lstm1.reset_state()65        if not  self.switches[0]:66            self.dropout1.reset_state()67        if self.switches[1]:68            self.lstm2.reset_state()69            if not self.switches[0]:70                self.dropout2.reset_state()71    def forward(self, x, t, train=True):72        embed = self.embedding(x) #[batch_size, time_steps, features]73        no_of_timesteps = embed.shape[1]74        self.reset_state()75        outputs = []76        for i in range(no_of_timesteps):77            h = self.lstm1(embed[:,i,:])78            h = self.bn_lstm1(h)79            dargs = [h, 0.5, train] if not self.switches[0] else [h]80            h = self.dropout1(*dargs)81            if self.switches[1]:82                h = self.lstm2(h)83                h = self.bn_lstm2(h)84                dargs = [h, 0.3, train] if not self.switches[0] else [h]85                h = self.dropout2(*dargs)86            outputs.append(h)87        outputs = torch.stack(outputs) #[time_steps, batch_size, features]88        outputs = outputs.permute(1,2,0) #[batch_size, features, time_steps]89        pool = nn.MaxPool1d(no_of_timesteps)90        h = pool(outputs)91        h = h.view(h.size(0),-1)92        #h = self.dropout(h)93        h = self.fc_output(h)94        return self.loss(h[:,0],t), h[:,0]95class RNN_model_GloVe(nn.Module):96    def __init__(self, no_of_hidden_units, switches=[False, False]):97        super(RNN_model_GloVe, self).__init__()98        self.switches = switches99        self.lstm1 = StatefulLSTM(300, no_of_hidden_units)100        self.bn_lstm1 = nn.BatchNorm1d(no_of_hidden_units)101        self.dropout1 = LockedDropout() if not switches[0] else nn.Dropout(p=0.5)102        if switches[1]:103            self.lstm2 = StatefulLSTM(no_of_hidden_units, no_of_hidden_units)104            self.bn_lstm2 = nn.BatchNorm1d(no_of_hidden_units)105            self.dropout2 = LockedDropout() if not switches[0] else nn.Dropout(p=0.5)106        self.fc_output = nn.Linear(no_of_hidden_units, 1)107        self.loss = nn.BCEWithLogitsLoss()108    def reset_state(self):109        self.lstm1.reset_state()110        if not self.switches[0]:111            self.dropout1.reset_state()112        if self.switches[1]:113            self.lstm2.reset_state()114            if not self.switches[0]:115                self.dropout2.reset_state()116    def forward(self, x, t, train=True):117        no_of_timesteps = x.shape[1]118        self.reset_state()119        outputs = []120        for i in range(no_of_timesteps):121            h = self.lstm1(x[:,i,:])122            h = self.bn_lstm1(h)123            dargs = [h, 0.5, train] if not self.switches[0] else [h]124            h = self.dropout1(*dargs)125            if self.switches[1]:126                h = self.lstm2(h)127                h = self.bn_lstm2(h)128                dargs = [h, 0.3, train] if not self.switches[0] else [h]129                h = self.dropout2(*dargs)130            outputs.append(h)131        outputs = torch.stack(outputs) #[time_steps, batch_size, features]132        outputs = outputs.permute(1,2,0) #[batch_size, features, time_steps]133        pool = nn.MaxPool1d(no_of_timesteps)134        h = pool(outputs)135        h = h.view(h.size(0),-1)136        #h = self.dropout(h)137        h = self.fc_output(h)138        return self.loss(h[:,0],t), h[:,0]139class RNN_language_model(nn.Module):140    def __init__(self, vocab_size, no_of_hidden_units):141        super(RNN_language_model, self).__init__()142        self.embedding = nn.Embedding(vocab_size, no_of_hidden_units)143        self.lstm1 = StatefulLSTM(no_of_hidden_units,no_of_hidden_units)144        self.bn_lstm1 = nn.BatchNorm1d(no_of_hidden_units)145        self.dropout1 = LockedDropout()146        self.lstm2 = StatefulLSTM(no_of_hidden_units,no_of_hidden_units)147        self.bn_lstm2 = nn.BatchNorm1d(no_of_hidden_units)148        self.dropout2 = LockedDropout()149        self.lstm3 = StatefulLSTM(no_of_hidden_units,no_of_hidden_units)150        self.bn_lstm3 = nn.BatchNorm1d(no_of_hidden_units)151        self.dropout3 = LockedDropout()152        self.decoder = nn.Linear(no_of_hidden_units, vocab_size)153        self.loss = nn.CrossEntropyLoss()154        self.vocab_size = vocab_size155    def reset_state(self):156        self.lstm1.reset_state()157        self.dropout1.reset_state()158        self.lstm2.reset_state()159        self.dropout2.reset_state()160        self.lstm3.reset_state()161        self.dropout3.reset_state()162    163    def forward(self, x, train=True):164        embed = self.embedding(x)165        no_of_timesteps = embed.shape[1]-1166        self.reset_state()167        outputs = []168        for i in range(no_of_timesteps):169            h = self.lstm1(embed[:,i,:])170            h = self.bn_lstm1(h)171            h = self.dropout1(h, 0.3, train)172            173            h = self.lstm2(h)174            h = self.bn_lstm2(h)175            h = self.dropout2(h, 0.3, train)176            177            h = self.lstm3(h)178            h = self.bn_lstm3(h)179            h = self.dropout3(h, 0.3, train)180            181            h = self.decoder(h)182            outputs.append(h)183        outputs = torch.stack(outputs) # time_steps, batch_size, vocab_size184        prediction = outputs.permute(1,0,2) # batch, time, vocab185        outputs = outputs.permute(1,2,0) # batch, vocab, time186        if(train):187            prediction = prediction.contiguous().view(-1,self.vocab_size)188            target = x[:,1:].contiguous().view(-1)189            loss = self.loss(prediction, target)190            return loss, outputs191        else:192            return outputs193class RNN_model_modified(nn.Module):194    def __init__(self, vocab_size, no_of_hidden_units, switches = False):195        super(RNN_model, self).__init__()196        self.switches = switches197        self.embedding = nn.Embedding(vocab_size, no_of_hidden_units)#padding_idx=0)198        self.lstm1 = StatefulLSTM(no_of_hidden_units, no_of_hidden_units)199        self.bn_lstm1 = nn.BatchNorm1d(no_of_hidden_units)200        self.dropout1 = LockedDropout() if not switches else nn.Dropout(p=0.5)201        self.lstm2 = StatefulLSTM(no_of_hidden_units, no_of_hidden_units)202        self.bn_lstm2 = nn.BatchNorm1d(no_of_hidden_units)203        self.dropout2 = LockedDropout() if not switches else nn.Dropout(p=0.5)204        205        self.lstm3 = StatefulLSTM(no_of_hidden_units, no_of_hidden_units)206        self.bn_lstm3 = nn.BatchNorm1d(no_of_hidden_units)207        self.dropout3 = LockedDropout() if not switches else nn.Dropout(p=0.5)208        self.fc_output = nn.Linear(no_of_hidden_units, 1)209        self.loss = nn.BCEWithLogitsLoss()210    def reset_state(self):211        self.lstm1.reset_state()212        self.lstm2.reset_state()213        self.lstm3.reset_state()214        if not self.switches:215            self.dropout1.reset_state()216            self.dropout2.reset_state()217            self.dropout3.reset_state()218    def forward(self, x, t, train=True):219        embed = self.embedding(x) #[batch_size, time_steps, features]220        no_of_timesteps = embed.shape[1]221        self.reset_state()222        outputs = []223        for i in range(no_of_timesteps):224            h = self.lstm1(embed[:,i,:])225            h = self.bn_lstm1(h)226            dargs = [h, 0.5, train] if not self.switches else [h]227            h = self.dropout1(*dargs)228            h = self.lstm2(h)229            h = self.bn_lstm2(h)230            dargs = [h, 0.3, train] if not self.switches else [h]231            h = self.dropout2(*dargs)232            h = self.lstm3(h)233            h = self.bn_lstm3(h)234            dargs = [h, 0.3, train] if not self.switches else [h]235            h = self.dropout3(*dargs)...

user_profile_setname_test.py

Source:user_profile_setname_test.py

...8BASE_URL = 'http://127.0.0.1:' + str(PORT)9HEADER = {"Content-Type": "application/json"}1011@pytest.fixture(autouse=True)12def reset_state():13    requests.post(BASE_URL + "/workspace/reset", json = ())14    reg1 = json.dumps({15        'email': 'first@first.com',16        'password': 'happydays',17        'name_first': 'James',18        'name_last': 'Lu'19    }).encode('utf-8')2021    # Register a user22    req = urllib.request.Request(f"{BASE_URL}/auth/register", headers=HEADER, data=reg1)23    payload_json = json.load(urllib.request.urlopen(req))24    #assert type(payload_json) == 'dict'25    u_id = payload_json['u_id']26    token = payload_json['token']
...

pure_ranking_example.py

Source:pure_ranking_example.py

...23import tensorflow as tf24os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'25os.environ["KMP_WARNINGS"] = "FALSE"26tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)27def reset_state(name):28    tf.compat.v1.reset_default_graph()29    print("\n", "=" * 30, name, "=" * 30)30if __name__ == "__main__":31    start_time = time.perf_counter()32    data = pd.read_csv("sample_data/sample_movielens_rating.dat", sep="::",33                       names=["user", "item", "label", "time"])34    train_data, eval_data = split_by_ratio_chrono(data, test_size=0.2)35    train_data, data_info = DatasetPure.build_trainset(train_data)36    eval_data = DatasetPure.build_evalset(eval_data)37    print(data_info)38    # do negative sampling, assume the data only contains positive feedback39    train_data.build_negative_samples(data_info, item_gen_mode="random",40                                      num_neg=1, seed=2020)41    eval_data.build_negative_samples(data_info, item_gen_mode="random",42                                     num_neg=1, seed=2222)43    reset_state("SVD")44    svd = SVD("ranking", data_info, embed_size=16, n_epochs=3, lr=0.001,45              reg=None, batch_size=256, batch_sampling=False, num_neg=1)46    svd.fit(train_data, verbose=2, shuffle=True, eval_data=eval_data,47            metrics=["loss", "balanced_accuracy",48                     "roc_auc", "pr_auc", "precision",49                     "recall", "map", "ndcg"])50    print("prediction: ", svd.predict(user=1, item=2333))51    print("recommendation: ", svd.recommend_user(user=1, n_rec=7))52    reset_state("SVD++")53    svdpp = SVDpp(task="ranking", data_info=data_info, embed_size=16,54                  n_epochs=3, lr=0.001, reg=None, batch_size=256)55    svdpp.fit(train_data, verbose=2, eval_data=eval_data,56              metrics=["loss", "balanced_accuracy",57                       "roc_auc", "pr_auc", "precision",58                       "recall", "map", "ndcg"])59    print("prediction: ", svdpp.predict(user=1, item=2333))60    print("recommendation: ", svdpp.recommend_user(user=1, n_rec=7))61    reset_state("NCF")62    ncf = NCF("ranking", data_info, embed_size=16, n_epochs=1, lr=0.001,63              lr_decay=False, reg=None, batch_size=256, num_neg=1, use_bn=True,64              dropout_rate=None, hidden_units="128,64,32", tf_sess_config=None)65    ncf.fit(train_data, verbose=2, shuffle=True, eval_data=eval_data,66            metrics=["loss", "balanced_accuracy",67                     "roc_auc", "pr_auc", "precision",68                     "recall", "map", "ndcg"])69    print("prediction: ", ncf.predict(user=1, item=2333))70    print("recommendation: ", ncf.recommend_user(user=1, n_rec=7))71    reset_state("ALS")72    als = ALS(task="ranking", data_info=data_info, embed_size=16, n_epochs=2,73              reg=5.0, alpha=10, seed=42)74    als.fit(train_data, verbose=2, use_cg=True, n_threads=1,75            eval_data=eval_data, metrics=["loss", "balanced_accuracy",76                                          "roc_auc", "pr_auc", "precision",77                                          "recall", "map", "ndcg"])78    print("prediction: ", als.predict(user=1, item=2333))79    print("recommendation: ", als.recommend_user(user=1, n_rec=7))80    reset_state("BPR")81    bpr = BPR("ranking", data_info, embed_size=16, n_epochs=3, lr=3e-4,82              reg=None, batch_size=256, num_neg=1, use_tf=True)83    bpr.fit(train_data, verbose=2, num_threads=4, eval_data=eval_data,84            metrics=["loss", "balanced_accuracy", "roc_auc", "pr_auc",85                     "precision", "recall", "map", "ndcg"],86            optimizer="adam")87    reset_state("RNN4Rec")88    rnn = RNN4Rec("ranking", data_info, rnn_type="gru", loss_type="cross_entropy",89                  embed_size=16, n_epochs=2, lr=0.001, lr_decay=None,90                  hidden_units="16,16", reg=None, batch_size=256, num_neg=1,91                  dropout_rate=None, recent_num=10, tf_sess_config=None)92    rnn.fit(train_data, verbose=2, shuffle=True, eval_data=eval_data,93            metrics=["loss", "balanced_accuracy",94                     "roc_auc", "pr_auc", "precision",95                     "recall", "map", "ndcg"])96    print("prediction: ", rnn.predict(user=1, item=2333))97    print("recommendation: ", rnn.recommend_user(user=1, n_rec=7))98    reset_state("Caser")99    caser = Caser("ranking", data_info, embed_size=16, n_epochs=2, lr=1e-4,100                  lr_decay=None, reg=None, batch_size=2048, num_neg=1,101                  dropout_rate=0.0, use_bn=False, nh_filters=16, nv_filters=4,102                  recent_num=10, tf_sess_config=None)103    caser.fit(train_data, verbose=2, shuffle=True, eval_data=eval_data,104              metrics=["loss", "balanced_accuracy", "roc_auc", "pr_auc",105                       "precision", "recall", "map", "ndcg"])106    print("prediction: ", caser.predict(user=1, item=2333))107    print("recommendation: ", caser.recommend_user(user=1, n_rec=7))108    reset_state("WaveNet")109    wave = WaveNet("ranking", data_info, embed_size=16, n_epochs=2, lr=1e-4,110                   lr_decay=None, reg=None, batch_size=2048, num_neg=1,111                   dropout_rate=0.0, use_bn=False, n_filters=16, n_blocks=2,112                   n_layers_per_block=4, recent_num=10, tf_sess_config=None)113    wave.fit(train_data, verbose=2, shuffle=True, eval_data=eval_data,114             metrics=["loss", "balanced_accuracy", "roc_auc", "pr_auc",115                      "precision", "recall", "map", "ndcg"])116    print("prediction: ", wave.predict(user=1, item=2333))117    print("recommendation: ", wave.recommend_user(user=1, n_rec=7))118    reset_state("Item2Vec")119    item2vec = Item2Vec("ranking", data_info, embed_size=16, norm_embed=False,120                        window_size=3, n_epochs=2, n_threads=0)121    item2vec.fit(train_data, verbose=2, shuffle=True, eval_data=eval_data,122                 metrics=["loss", "balanced_accuracy", "roc_auc", "pr_auc",123                          "precision", "recall", "map", "ndcg"])124    print("prediction: ", item2vec.predict(user=1, item=2333))125    print("recommendation: ", item2vec.recommend_user(user=1, n_rec=7))126    reset_state("DeepWalk")127    deepwalk = DeepWalk("ranking", data_info, embed_size=16, norm_embed=False,128                        n_walks=10, walk_length=10, window_size=5, n_epochs=2,129                        n_threads=0)130    deepwalk.fit(train_data, verbose=2, shuffle=True, eval_data=eval_data,131                 metrics=["loss", "balanced_accuracy", "roc_auc", "pr_auc",132                          "precision", "recall", "map", "ndcg"])133    print("prediction: ", deepwalk.predict(user=1, item=2333))134    print("recommendation: ", deepwalk.recommend_user(user=1, n_rec=7))135    reset_state("NGCF")136    ngcf = NGCF("ranking", data_info, embed_size=16, n_epochs=2, lr=3e-4,137                lr_decay=None, reg=0.0, batch_size=2048, num_neg=1,138                node_dropout=0.0, message_dropout=0.0, hidden_units="64,64,64",139                device=torch.device("cpu"))140    ngcf.fit(train_data, verbose=2, shuffle=True, eval_data=eval_data,141             metrics=["loss", "balanced_accuracy", "roc_auc", "pr_auc",142                      "precision", "recall", "map", "ndcg"])143    print("prediction: ", ngcf.predict(user=1, item=2333))144    print("recommendation: ", ngcf.recommend_user(user=1, n_rec=7))145    reset_state("LightGCN")146    lightgcn = LightGCN("ranking", data_info, embed_size=32, n_epochs=2, lr=1e-4,147                        lr_decay=None, reg=0.0, batch_size=2048, num_neg=1,148                        dropout=0.0, n_layers=3, device=torch.device("cpu"))149    lightgcn.fit(train_data, verbose=2, shuffle=True, eval_data=eval_data,150                 metrics=["loss", "balanced_accuracy", "roc_auc", "pr_auc",151                          "precision", "recall", "map", "ndcg"])152    print("prediction: ", lightgcn.predict(user=1, item=2333))153    print("recommendation: ", lightgcn.recommend_user(user=1, n_rec=7))154    reset_state("user_cf")155    user_cf = UserCF(task="ranking", data_info=data_info, k=20, sim_type="cosine")156    user_cf.fit(train_data, verbose=2, mode="invert", num_threads=4, min_common=1,157                eval_data=eval_data, metrics=["loss", "balanced_accuracy",158                                              "roc_auc", "pr_auc", "precision",159                                              "recall", "map", "ndcg"])160    print("prediction: ", user_cf.predict(user=1, item=2333))161    print("recommendation: ", user_cf.recommend_user(user=1, n_rec=7))162    reset_state("item_cf")163    item_cf = ItemCF(task="ranking", data_info=data_info, k=20, sim_type="pearson")164    item_cf.fit(train_data, verbose=2, mode="invert", num_threads=1, min_common=1,165                eval_data=eval_data, metrics=["loss", "balanced_accuracy",166                                              "roc_auc", "pr_auc", "precision",167                                              "recall", "map", "ndcg"])168    print("prediction: ", item_cf.predict(user=1, item=2333))169    print("recommendation: ", item_cf.recommend_user(user=1, n_rec=7))...

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