├── run.sh ├── README.md ├── requirements.txt ├── model.py ├── utils.py ├── train.py └── CLINC_OOD ├── valid.label └── test.label /run.sh: -------------------------------------------------------------------------------- 1 | python train.py --dataset CLINC_OOD --proportion 100 --mode both --setting gda --experiment_No vallian --ind_pre_epoches 20 --supcont_pre_epoches 20 --norm_coef 0.1 --cuda #without supervised contrastive pre-training 2 | python train.py --dataset CLINC_OOD --proportion 100 --mode both --setting gda --experiment_No vallian --ind_pre_epoches 20 --supcont_pre_epoches 20 --norm_coef 0.1 --sup_cont --cuda #with supervised contrastive pre-training -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Code for our ACL 2021 paper - [Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning](https://arxiv.org/pdf/2105.14289.pdf) 2 | 3 | # Requirements 4 | 5 | ``` 6 | python 3.6.2 7 | torch 1.4.0 8 | tensorflow 1.14.0 9 | ``` 10 | For detailed dependencies, please refer to requirements.txt 11 | 12 | # Get Started 13 | 14 | prepare Glove or BERT pretrained embeddings from https://github.com/stanfordnlp/GloVe and https://github.com/google-research/bert 15 | 16 | put the embedding file glove.6B.300d.txt into ./glove_embeddings 17 | 18 | modify the script to train or test model in different modes 19 | 20 | ``` 21 | bash run.sh 22 | ``` 23 | 24 | # Citation 25 | 26 | ``` 27 | @inproceedings{Zeng2021ModelingDR, 28 | title={Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning}, 29 | author={Zhiyuan Zeng and Keqing He and Yuanmeng Yan and Zijun Liu and Yanan Wu and Hong Xu and Huixing Jiang and Weiran Xu}, 30 | booktitle={ACL/IJCNLP}, 31 | year={2021} 32 | } 33 | ``` 34 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | absl-py==0.11.0 2 | astor==0.8.1 3 | cached-property==1.5.2 4 | certifi==2020.6.20 5 | chardet==4.0.0 6 | click==7.1.2 7 | cycler==0.10.0 8 | dataclasses==0.8 9 | filelock==3.0.12 10 | gast==0.4.0 11 | google-pasta==0.2.0 12 | grpcio==1.33.2 13 | h5py==3.1.0 14 | idna==2.10 15 | importlib-metadata==2.0.0 16 | joblib==1.0.0 17 | Keras==2.1.4 18 | Keras-Applications==1.0.8 19 | Keras-Preprocessing==1.1.2 20 | kiwisolver==1.3.1 21 | Markdown==3.3.3 22 | matplotlib==3.3.2 23 | mkl-fft==1.2.0 24 | mkl-random==1.1.1 25 | mkl-service==2.3.0 26 | nltk==3.5 27 | olefile==0.46 28 | packaging==20.8 29 | pandas==0.25.3 30 | Pillow==8.0.1 31 | protobuf==3.13.0 32 | pyparsing==2.4.7 33 | python-dateutil==2.8.1 34 | pytz==2020.4 35 | PyYAML==5.3.1 36 | regex==2020.11.13 37 | requests==2.25.1 38 | sacremoses==0.0.43 39 | scikit-learn==0.21.3 40 | scipy==1.5.4 41 | six==1.15.0 42 | tensorboard==1.14.0 43 | tensorflow==1.14.0 44 | tensorflow-estimator==1.14.0 45 | termcolor==1.1.0 46 | thop==0.0.31.post2005241907 47 | threadpoolctl==2.1.0 48 | tokenizers==0.9.4 49 | torch==1.4.0 50 | torchvision==0.5.0 51 | tqdm==4.55.0 52 | transformers==4.1.1 53 | urllib3==1.26.2 54 | Werkzeug==1.0.1 55 | wrapt==1.12.1 56 | zipp==3.4.0 57 | -------------------------------------------------------------------------------- /model.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence 4 | import numpy as np 5 | from transformers import BertModel, BertTokenizer 6 | 7 | 8 | def pair_cosine_similarity(x, x_adv, eps=1e-8): 9 | n = x.norm(p=2, dim=1, keepdim=True) 10 | n_adv = x_adv.norm(p=2, dim=1, keepdim=True) 11 | return (x @ x.t()) / (n * n.t()).clamp(min=eps), (x_adv @ x_adv.t()) / (n_adv * n_adv.t()).clamp(min=eps), (x @ x_adv.t()) / (n * n_adv.t()).clamp(min=eps) 12 | 13 | 14 | def nt_xent(x, x_adv, mask, cuda=True, t=0.1): 15 | x, x_adv, x_c = pair_cosine_similarity(x, x_adv) 16 | x = torch.exp(x / t) 17 | x_adv = torch.exp(x_adv / t) 18 | x_c = torch.exp(x_c / t) 19 | mask_count = mask.sum(1) 20 | mask_reverse = (~(mask.bool())).long() 21 | if cuda: 22 | dis = (x * (mask - torch.eye(x.size(0)).long().cuda()) + x_c * mask) / (x.sum(1) + x_c.sum(1) - torch.exp(torch.tensor(1 / t))) + mask_reverse 23 | dis_adv = (x_adv * (mask - torch.eye(x.size(0)).long().cuda()) + x_c.T * mask) / (x_adv.sum(1) + x_c.sum(0) - torch.exp(torch.tensor(1 / t))) + mask_reverse 24 | else: 25 | dis = (x * (mask - torch.eye(x.size(0)).long()) + x_c * mask) / (x.sum(1) + x_c.sum(1) - torch.exp(torch.tensor(1 / t))) + mask_reverse 26 | dis_adv = (x_adv * (mask - torch.eye(x.size(0)).long()) + x_c.T * mask) / (x_adv.sum(1) + x_c.sum(0) - torch.exp(torch.tensor(1 / t))) + mask_reverse 27 | loss = (torch.log(dis).sum(1) + torch.log(dis_adv).sum(1)) / mask_count 28 | return -loss.mean() 29 | 30 | 31 | def PGD_contrastive(model, inputs, eps=8. / 255., alpha=2. / 255., iters=10): 32 | inputs = model.get_embedding(inputs) 33 | delta = torch.rand_like(inputs) * eps * 2 - eps 34 | delta = torch.nn.Parameter(delta) 35 | for i in range(iters): 36 | features = model(inputs + delta, mode='inference')[1] 37 | model.zero_grad() 38 | loss = nt_xent(features) 39 | loss.backward() 40 | delta.data = delta.data + alpha * delta.grad.sign() 41 | delta.grad = None 42 | delta.data = torch.clamp(delta.data, min=-eps, max=eps) 43 | delta.data = torch.clamp(inputs + delta.data, min=0, max=1) - inputs 44 | 45 | return (inputs + delta).detach() 46 | 47 | 48 | class BiLSTM(nn.Module): 49 | def __init__(self, embedding_matrix, BATCH_SIZE, HIDDEN_DIM, CON_DIM, NUM_LAYERS, n_class_seen, DO_NORM, ALPHA, BETA, OOD_LOSS, ADV, CONT_LOSS, norm_coef, cl_mode=1, lmcl=True, use_cuda=True, use_bert=False, sup_cont=False): 50 | super(BiLSTM, self).__init__() 51 | self.bsz = BATCH_SIZE 52 | self.hidden_dim = HIDDEN_DIM 53 | self.con_dim = CON_DIM 54 | self.num_layers = NUM_LAYERS 55 | self.output_dim = n_class_seen 56 | self.do_norm = DO_NORM 57 | self.alpha = ALPHA 58 | self.beta = BETA 59 | self.ood_loss = OOD_LOSS 60 | self.adv = ADV 61 | self.cont_loss = CONT_LOSS 62 | self.norm_coef = norm_coef 63 | self.use_bert = use_bert 64 | self.sup_cont = sup_cont 65 | self.use_cuda = 'cuda' if use_cuda else 'cpu' 66 | if self.use_bert: 67 | print('Loading Bert...') 68 | self.bert_model = BertModel.from_pretrained('bert-base-uncased').to(self.use_cuda) 69 | self.bert_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') 70 | self.rnn = nn.GRU(input_size=768, hidden_size=self.hidden_dim, 71 | num_layers=self.num_layers, 72 | batch_first=True, bidirectional=True).to(self.use_cuda) 73 | for name, param in self.bert_model.named_parameters(): 74 | if name.startswith('pooler'): 75 | continue 76 | else: 77 | param.requires_grad_(False) 78 | else: 79 | self.embedding = nn.Embedding(embedding_matrix.shape[0], embedding_matrix.shape[1], 80 | _weight=torch.from_numpy(embedding_matrix)) 81 | self.rnn = nn.GRU(input_size=embedding_matrix.shape[1], hidden_size=self.hidden_dim, num_layers=self.num_layers, 82 | batch_first=True, bidirectional=True).to(self.use_cuda) 83 | self.fc = nn.Linear(self.hidden_dim * 2, self.output_dim).to(self.use_cuda) 84 | self.cont_fc = nn.Linear(self.hidden_dim * 2, self.con_dim) 85 | self.dropout = nn.Dropout(p=0.5) 86 | self.lmcl = lmcl 87 | self.cl_mode = cl_mode 88 | 89 | def get_embedding(self, seq): 90 | seq_embed = self.embedding(seq) 91 | seq_embed = self.dropout(seq_embed) 92 | seq_embed = torch.tensor(seq_embed, dtype=torch.float32, requires_grad=True).cuda() 93 | return seq_embed 94 | 95 | def lmcl_loss(self, probs, label, margin=0.35, scale=30): 96 | probs = label * (probs - margin) + (1 - label) * probs 97 | probs = torch.softmax(probs, dim=1) 98 | return probs 99 | 100 | 101 | def forward(self, seq, adv_features=None, label=None, sim=None, mode='ind_pre'): 102 | if mode == 'ind_pre' or mode == 'finetune': 103 | if self.use_bert: 104 | seq_embed = self.bert_model(**self.bert_tokenizer(seq, return_tensors='pt', padding=True, truncation=True).to(self.use_cuda))[0] 105 | seq_embed = self.dropout(seq_embed) 106 | seq_embed = seq_embed.clone().detach().requires_grad_(True).float() 107 | else: 108 | seq_embed = self.embedding(seq) 109 | seq_embed = self.dropout(seq_embed) 110 | seq_embed = seq_embed.clone().detach().requires_grad_(True).float() 111 | _, ht = self.rnn(seq_embed) 112 | ht = torch.cat((ht[0].squeeze(0), ht[1].squeeze(0)), dim=1) 113 | logits = self.fc(ht) 114 | if self.lmcl and sim != None: 115 | probs = self.lmcl_loss(logits, label) 116 | else: 117 | probs = torch.softmax(logits, dim=1) 118 | ce_loss = torch.sum(torch.mul(-torch.log(probs), label)) 119 | if not self.sup_cont or mode == 'finetune': 120 | return ce_loss 121 | else: 122 | seq_embed.retain_grad() # we need to get gradient w.r.t embeddings 123 | ce_loss.backward(retain_graph=True) 124 | unnormalized_noise = seq_embed.grad.detach_() 125 | for p in self.parameters(): 126 | if p.grad is not None: 127 | p.grad.detach_() 128 | p.grad.zero_() 129 | norm = unnormalized_noise.norm(p=2, dim=-1) 130 | normalized_noise = unnormalized_noise / (norm.unsqueeze(dim=-1) + 1e-10) # add 1e-10 to avoid NaN 131 | noise_embedding = seq_embed + self.norm_coef * normalized_noise 132 | _, h_adv = self.rnn(noise_embedding, None) 133 | h_adv = torch.cat((h_adv[0].squeeze(0), h_adv[1].squeeze(0)), dim=1) 134 | label_mask = torch.mm(label,label.T).bool().long() 135 | sup_cont_loss = nt_xent(ht, h_adv, label_mask, cuda=self.use_cuda=='cuda') 136 | return sup_cont_loss 137 | elif mode == 'inference': 138 | _, ht = self.rnn(seq) 139 | ht = torch.cat((ht[0].squeeze(0), ht[1].squeeze(0)), dim=1) 140 | logits = self.fc(ht) 141 | probs = torch.softmax(logits, dim=1) 142 | return probs, ht 143 | elif mode == 'validation': 144 | if self.use_bert: 145 | seq_embed = self.bert_model(**self.bert_tokenizer(seq, return_tensors='pt', padding=True, truncation=True).to(self.use_cuda))[0] 146 | seq_embed = self.dropout(seq_embed) 147 | seq_embed = seq_embed.clone().detach().requires_grad_(True).float() 148 | else: 149 | seq_embed = self.embedding(seq) 150 | seq_embed = self.dropout(seq_embed) 151 | seq_embed = seq_embed.clone().detach().requires_grad_(True).float() 152 | _, ht = self.rnn(seq_embed) 153 | ht = torch.cat((ht[0].squeeze(0), ht[1].squeeze(0)), dim=1) 154 | logits = self.fc(ht) 155 | probs = torch.softmax(logits, dim=1) 156 | return torch.argmax(label, dim=1).tolist(), torch.argmax(probs, dim=1).tolist(), ht 157 | elif mode == 'test': 158 | if self.use_bert: 159 | seq_embed = self.bert_model(**self.bert_tokenizer(seq, return_tensors='pt', padding=True, truncation=True).to(self.use_cuda))[0] 160 | seq_embed = self.dropout(seq_embed) 161 | seq_embed = seq_embed.clone().detach().requires_grad_(True).float() 162 | else: 163 | seq_embed = self.embedding(seq) 164 | seq_embed = self.dropout(seq_embed) 165 | seq_embed = seq_embed.clone().detach().requires_grad_(True).float() 166 | _, ht = self.rnn(seq_embed) 167 | ht = torch.cat((ht[0].squeeze(0), ht[1].squeeze(0)), dim=1) 168 | logits = self.fc(ht) 169 | probs = torch.softmax(logits, dim=1) 170 | return probs, ht 171 | else: 172 | raise ValueError("undefined mode") 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | from typing import List 2 | import os 3 | import json 4 | import pandas as pd 5 | import itertools 6 | import matplotlib 7 | 8 | matplotlib.use('Agg') 9 | import matplotlib.pyplot as plt 10 | import tensorflow as tf 11 | from keras.backend import set_session 12 | import numpy as np 13 | import random as rn 14 | from sklearn.decomposition import PCA 15 | 16 | SEED = 123 17 | tf.random.set_random_seed(SEED) 18 | 19 | 20 | def naive_arg_topK(matrix, K, axis=0): 21 | full_sort = np.argsort(matrix, axis=axis) 22 | return full_sort.take(np.arange(K), axis=axis) 23 | 24 | 25 | def set_allow_growth(device="1"): 26 | config = tf.ConfigProto() 27 | config.gpu_options.allow_growth = True # dynamically grow the memory used on the GPU 28 | config.gpu_options.visible_device_list = device 29 | sess = tf.Session(config=config) 30 | set_session(sess) # set this TensorFlow session as the default session for Keras 31 | 32 | 33 | def load_data(dataset): 34 | texts = [] 35 | labels = [] 36 | partition_to_n_row = {} 37 | for partition in ['train', 'valid', 'test']: 38 | with open("./" + dataset + "/" + partition + ".seq.in") as fp: 39 | lines = fp.read().splitlines() 40 | texts.extend(lines) 41 | partition_to_n_row[partition] = len(lines) 42 | with open("./" + dataset + "/" + partition + ".label") as fp: 43 | labels.extend(fp.read().splitlines()) 44 | 45 | df = pd.DataFrame([texts, labels]).T 46 | df.columns = ['text', 'label'] 47 | return df, partition_to_n_row 48 | 49 | 50 | def get_score(cm): 51 | fs = [] 52 | ps = [] 53 | rs = [] 54 | n_class = cm.shape[0] 55 | correct = [] 56 | total = [] 57 | for idx in range(n_class): 58 | TP = cm[idx][idx] 59 | correct.append(TP) 60 | total.append(cm[idx].sum()) 61 | r = TP / cm[idx].sum() if cm[idx].sum() != 0 else 0 62 | p = TP / cm[:, idx].sum() if cm[:, idx].sum() != 0 else 0 63 | f = 2 * r * p / (r + p) if (r + p) != 0 else 0 64 | fs.append(f * 100) 65 | ps.append(p * 100) 66 | rs.append(r * 100) 67 | 68 | f = np.mean(fs).round(2) 69 | p_seen = np.mean(ps[:-1]).round(2) 70 | r_seen = np.mean(rs[:-1]).round(2) 71 | f_seen = np.mean(fs[:-1]).round(2) 72 | p_unseen = round(ps[-1], 2) 73 | r_unseen = round(rs[-1], 2) 74 | f_unseen = round(fs[-1], 2) 75 | acc = (sum(correct) / sum(total) * 100).round(2) 76 | acc_in = (sum(correct[:-1]) / sum(total[:-1]) * 100).round(2) 77 | acc_ood = (correct[-1] / total[-1] * 100).round(2) 78 | print(f"Overall(macro): , f:{f}, acc:{acc}, p:{p}, r:{r}") 79 | print(f"Seen(macro): , f:{f_seen}, acc:{acc_in}, p:{p_seen}, r:{r_seen}") 80 | print(f"=====> Uneen(Experiment) <=====: , f:{f_unseen}, acc:{acc_ood}, p:{p_unseen}, r:{r_unseen}") 81 | 82 | return f, acc, f_seen, acc_in, p_seen, r_seen, f_unseen, acc_ood, p_unseen, r_unseen 83 | 84 | 85 | def plot_confusion_matrix(output_dir, cm, classes, normalize=False, 86 | title='Confusion matrix', figsize=(12, 10), 87 | cmap=plt.cm.Blues): 88 | """ 89 | This function prints and plots the confusion matrix. 90 | Normalization can be applied by setting `normalize=True`. 91 | """ 92 | if normalize: 93 | cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] 94 | print("Normalized confusion matrix") 95 | else: 96 | print('Confusion matrix, without normalization') 97 | 98 | # Compute confusion matrix 99 | np.set_printoptions(precision=2) 100 | plt.figure(figsize=figsize) 101 | plt.imshow(cm, interpolation='nearest', cmap=cmap) 102 | plt.title(title) 103 | plt.colorbar() 104 | tick_marks = np.arange(len(classes)) 105 | plt.xticks(tick_marks, classes, rotation=45) 106 | plt.yticks(tick_marks, classes) 107 | 108 | fmt = '.2f' if normalize else 'd' 109 | thresh = cm.max() / 2. 110 | for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): 111 | plt.text(j, i, format(cm[i, j], fmt), 112 | horizontalalignment="center", 113 | color="white" if cm[i, j] > thresh else "black") 114 | 115 | plt.ylabel('True label') 116 | plt.xlabel('Predicted label') 117 | plt.tight_layout() 118 | plt.savefig(os.path.join(output_dir, "mat.png")) 119 | 120 | 121 | def mahalanobis_distance(x: np.ndarray, 122 | y: np.ndarray, 123 | covariance: np.ndarray) -> float: 124 | """ 125 | Calculate the mahalanobis distance. 126 | 127 | Params: 128 | - x: the sample x, shape (num_features,) 129 | - y: the sample y (or the mean of the distribution), shape (num_features,) 130 | - covariance: the covariance of the distribution, shape (num_features, num_features) 131 | 132 | Returns: 133 | - score: the mahalanobis distance in float 134 | 135 | """ 136 | num_features = x.shape[0] 137 | 138 | vec = x - y 139 | cov_inv = np.linalg.inv(covariance) 140 | bef_sqrt = np.matmul(np.matmul(vec.reshape(1, num_features), cov_inv), vec.reshape(num_features, 1)) 141 | return np.sqrt(bef_sqrt).item() 142 | 143 | 144 | def confidence(features: np.ndarray, 145 | means: np.ndarray, 146 | distance_type: str, 147 | cov: np.ndarray = None) -> np.ndarray: 148 | """ 149 | Calculate mahalanobis or euclidean based confidence score for each class. 150 | 151 | Params: 152 | - features: shape (num_samples, num_features) 153 | - means: shape (num_classes, num_features) 154 | - cov: shape (num_features, num_features) or None (if use euclidean distance) 155 | 156 | Returns: 157 | - confidence: shape (num_samples, num_classes) 158 | """ 159 | assert distance_type in ("euclidean", "mahalanobis") 160 | 161 | num_samples = features.shape[0] 162 | num_features = features.shape[1] 163 | num_classes = means.shape[0] 164 | if distance_type == "euclidean": 165 | cov = np.identity(num_features) 166 | 167 | features = features.reshape(num_samples, 1, num_features).repeat(num_classes, 168 | axis=1) # (num_samples, num_classes, num_features) 169 | means = means.reshape(1, num_classes, num_features).repeat(num_samples, 170 | axis=0) # (num_samples, num_classes, num_features) 171 | vectors = features - means # (num_samples, num_classes, num_features) 172 | cov_inv = np.linalg.inv(cov) 173 | bef_sqrt = np.matmul(np.matmul(vectors.reshape(num_samples, num_classes, 1, num_features), cov_inv), 174 | vectors.reshape(num_samples, num_classes, num_features, 1)).squeeze() 175 | result = np.sqrt(bef_sqrt) 176 | result[np.isnan(result)] = 1e12 # solve nan 177 | return result 178 | 179 | 180 | def get_test_info(texts: pd.Series, 181 | label: pd.Series, 182 | label_mask: pd.Series, 183 | softmax_prob: np.ndarray, 184 | softmax_classes: List[str], 185 | lof_result: np.ndarray = None, 186 | gda_result: np.ndarray = None, 187 | gda_classes: List[str] = None, 188 | save_to_file: bool = False, 189 | output_dir: str = None) -> pd.DataFrame: 190 | """ 191 | Return a pd.DataFrame, including the following information for each test instances: 192 | - the text of the instance 193 | - label & masked label of the sentence 194 | - the softmax probability for each seen classes (sum up to 1) 195 | - the softmax prediction 196 | - the softmax confidence (i.e. the max softmax probability among all seen classes) 197 | - (if use lof) lof prediction result (1 for in-domain and -1 for out-of-domain) 198 | - (if use gda) gda mahalanobis distance for each seen classes 199 | - (if use gda) the gda confidence (i.e. the min mahalanobis distance among all seen classes) 200 | """ 201 | df = pd.DataFrame() 202 | df['label'] = label 203 | df['label_mask'] = label_mask 204 | for idx, _class in enumerate(softmax_classes): 205 | df[f'softmax_prob_{_class}'] = softmax_prob[:, idx] 206 | df['softmax_prediction'] = [softmax_classes[idx] for idx in softmax_prob.argmax(axis=-1)] 207 | df['softmax_confidence'] = softmax_prob.max(axis=-1) 208 | if lof_result is not None: 209 | df['lof_prediction'] = lof_result 210 | if gda_result is not None: 211 | for idx, _class in enumerate(gda_classes): 212 | df[f'm_dist_{_class}'] = gda_result[:, idx] 213 | df['gda_prediction'] = [gda_classes[idx] for idx in gda_result.argmin(axis=-1)] 214 | df['gda_confidence'] = gda_result.min(axis=-1) 215 | df['text'] = [text for text in texts] 216 | 217 | if save_to_file: 218 | df.to_csv(os.path.join(output_dir, "test_info.csv")) 219 | 220 | return df 221 | 222 | 223 | def estimate_best_threshold(seen_m_dist: np.ndarray, 224 | unseen_m_dist: np.ndarray) -> float: 225 | """ 226 | Given mahalanobis distance for seen and unseen instances in valid set, estimate 227 | a best threshold (i.e. achieving best f1 in valid set) for test set. 228 | """ 229 | lst = [] 230 | for item in seen_m_dist: 231 | lst.append((item, "seen")) 232 | for item in unseen_m_dist: 233 | lst.append((item, "unseen")) 234 | # sort by m_dist: [(5.65, 'seen'), (8.33, 'seen'), ..., (854.3, 'unseen')] 235 | lst = sorted(lst, key=lambda item: item[0]) 236 | 237 | threshold = 0. 238 | tp, fp, fn = len(unseen_m_dist), len(seen_m_dist), 0 239 | 240 | def compute_f1(tp, fp, fn): 241 | p = tp / (tp + fp + 1e-10) 242 | r = tp / (tp + fn + 1e-10) 243 | return (2 * p * r) / (p + r + 1e-10) 244 | 245 | f1 = compute_f1(tp, fp, fn) 246 | 247 | for m_dist, label in lst: 248 | if label == "seen": # fp -> tn 249 | fp -= 1 250 | else: # tp -> fn 251 | tp -= 1 252 | fn += 1 253 | if compute_f1(tp, fp, fn) > f1: 254 | f1 = compute_f1(tp, fp, fn) 255 | threshold = m_dist + 1e-10 256 | 257 | print("estimated threshold:", threshold) 258 | return threshold 259 | 260 | 261 | def pca_visualization(X: np.ndarray, 262 | y: pd.Series, 263 | classes: List[str], 264 | save_path: str): 265 | """ 266 | Apply PCA visualization for features. 267 | """ 268 | red_features = PCA(n_components=2, svd_solver="full").fit_transform(X) 269 | 270 | plt.style.use("seaborn-darkgrid") 271 | fig, ax = plt.subplots() 272 | for _class in classes: 273 | if _class == "unseen": 274 | ax.scatter(red_features[y == _class, 0], red_features[y == _class, 1], 275 | label=_class, alpha=0.5, s=20, edgecolors='none', color="gray") 276 | else: 277 | ax.scatter(red_features[y == _class, 0], red_features[y == _class, 1], 278 | label=_class, alpha=0.5, s=20, edgecolors='none', zorder=10) 279 | ax.legend() 280 | ax.grid(True) 281 | plt.savefig(save_path, format="png") 282 | 283 | 284 | def log_pred_results(f: float, 285 | acc: float, 286 | f_seen: float, 287 | acc_in: float, 288 | p_seen: float, 289 | r_seen: float, 290 | f_unseen: float, 291 | acc_ood: float, 292 | p_unseen: float, 293 | r_unseen: float, 294 | classes: List[str], 295 | output_dir: str, 296 | confusion_matrix: np.ndarray, 297 | ood_loss, 298 | adv, 299 | cont_loss, 300 | threshold: float = None): 301 | with open(os.path.join(output_dir, "results.txt"), "w") as f_out: 302 | f_out.write( 303 | f"Overall: f1(macro):{f} acc:{acc} \nSeen: f1(marco):{f_seen} acc:{acc_in} p:{p_seen} r:{r_seen}\n" 304 | f"=====> Uneen(Experiment) <=====: f1(marco):{f_unseen} acc:{acc_ood} p:{p_unseen} r:{r_unseen}\n\n" 305 | f"Classes:\n{classes}\n\n" 306 | f"Threshold:\n{threshold}\n\n" 307 | f"Confusion matrix:\n{confusion_matrix}\n" 308 | f"mode:\nood_loss:{ood_loss}\nadv:{adv}\ncont_loss:{cont_loss}") 309 | with open(os.path.join(output_dir, "results.json"), "w") as f_out: 310 | json.dump({ 311 | "f1_overall": f, 312 | "acc_overall": acc, 313 | "f1_seen": f_seen, 314 | "acc_seen": acc_in, 315 | "p_seen": p_seen, 316 | "r_seen": r_seen, 317 | "f1_unseen": f_unseen, 318 | "acc_unseen": acc_ood, 319 | "p_unseen": p_unseen, 320 | "r_unseen": r_unseen, 321 | "classes": classes, 322 | "confusion_matrix": confusion_matrix.tolist(), 323 | "threshold": threshold, 324 | "ood_loss": ood_loss, 325 | "adv": adv, 326 | "cont_loss": cont_loss 327 | }, fp=f_out, ensure_ascii=False, indent=4) 328 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | # Preprocessing 2 | import sys 3 | import random 4 | import time 5 | import json 6 | import os 7 | import argparse 8 | from utils import * 9 | import numpy as np 10 | import pandas as pd 11 | from tqdm import tqdm 12 | from nltk.tokenize import word_tokenize 13 | from sklearn.preprocessing import LabelEncoder 14 | from keras.utils import to_categorical 15 | from keras.preprocessing.text import Tokenizer 16 | from keras.preprocessing.sequence import pad_sequences 17 | from sklearn import metrics 18 | from thop import profile 19 | # Modeling 20 | import torch 21 | from model import BiLSTM 22 | from model import PGD_contrastive 23 | from keras.callbacks import ModelCheckpoint, EarlyStopping 24 | from keras.models import Model, load_model 25 | from keras import backend as K 26 | 27 | # Evaluation 28 | from sklearn.metrics import confusion_matrix 29 | from sklearn.neighbors import LocalOutlierFactor 30 | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis 31 | 32 | 33 | # Parse Arguments 34 | def parse_args(): 35 | parser = argparse.ArgumentParser(add_help=True) 36 | parser.add_argument("--dataset", type=str, choices=["CLINC", "CLINC_OOD"], required=True, 37 | help="The dataset to use, ATIS or SNIPS.") 38 | parser.add_argument("--proportion", type=int, required=True, 39 | help="The proportion of seen classes, range from 0 to 100.") 40 | parser.add_argument("--seen_classes", type=str, nargs="+", default=None, 41 | help="The specific seen classes.") 42 | parser.add_argument("--mode", type=str, choices=["train", "test", "both", "find_threshold"], default="both", 43 | help="Specify running mode: only train, only test or both.") 44 | parser.add_argument("--setting", type=str, nargs="+", default=None, 45 | help="The settings to detect ood samples, e.g. 'lof' or 'gda_lsqr") 46 | parser.add_argument("--model_dir", type=str, default=None, 47 | help="The directory contains model file (.h5), requried when test only.") 48 | parser.add_argument("--seen_classes_seed", type=int, default=None, 49 | help="The random seed to randomly choose seen classes.") 50 | # default arguments 51 | parser.add_argument("--cuda", action="store_true", 52 | help="Whether to use GPU or not.") 53 | parser.add_argument("--gpu_device", type=str, default="0", 54 | help="The gpu device to use.") 55 | parser.add_argument("--output_dir", type=str, default="./experiments", 56 | help="The directory to store training models & logs.") 57 | parser.add_argument("--experiment_No", type=str, default="", 58 | help="Manually setting of experiment number.") 59 | # model hyperparameters 60 | parser.add_argument("--embedding_file", type=str, 61 | default="./glove_embeddings/glove.6B.300d.txt", 62 | help="The embedding file to use.") 63 | parser.add_argument("--hidden_dim", type=int, default=128, 64 | help="The dimension of hidden state.") 65 | parser.add_argument("--contractive_dim", type=int, default=32, 66 | help="The dimension of hidden state.") 67 | parser.add_argument("--embedding_dim", type=int, default=300, 68 | help="The dimension of word embeddings.") 69 | parser.add_argument("--max_seq_len", type=int, default=None, 70 | help="The max sequence length. When set to None, it will be implied from data.") 71 | parser.add_argument("--max_num_words", type=int, default=10000, 72 | help="The max number of words.") 73 | parser.add_argument("--num_layers", type=int, default=1, 74 | help="The layers number of lstm.") 75 | parser.add_argument("--do_normalization", type=bool, default=True, 76 | help="whether to do normalization or not.") 77 | parser.add_argument("--alpha", type=float, default=1.0, 78 | help="relative weights of classified loss.") 79 | parser.add_argument("--beta", type=float, default=1.0, 80 | help="relative weights of adversarial classified loss.") 81 | parser.add_argument("--unseen_proportion", type=int, default=100, 82 | help="proportion of unseen class examples to add in, range from 0 to 100.") 83 | parser.add_argument("--mask_proportion", type=int, default=0, 84 | help="proportion of seen class examples to mask, range from 0 to 100.") 85 | parser.add_argument("--ood_loss", action="store_true", 86 | help="whether ood examples to backpropagate loss or not.") 87 | parser.add_argument("--adv", action="store_true", 88 | help="whether to generate perturbation through adversarial attack.") 89 | parser.add_argument("--cont_loss", action="store_true", 90 | help="whether to backpropagate contractive loss or not.") 91 | parser.add_argument("--norm_coef", type=float, default=0.1, 92 | help="coefficients of the normalized adversarial vectors") 93 | parser.add_argument("--n_plus_1", action="store_true", 94 | help="treat out of distribution examples as the N+1 th class") 95 | parser.add_argument("--augment", action="store_true", 96 | help="whether to use back translation to enhance the ood data") 97 | parser.add_argument("--cl_mode", type=int, default=1, 98 | help="mode for computing contrastive loss") 99 | parser.add_argument("--lmcl", action="store_true", 100 | help="whether to use LMCL loss") 101 | parser.add_argument("--cont_proportion", type=float, default=1.0, 102 | help="coefficients of the normalized adversarial vectors") 103 | parser.add_argument("--dataset_proportion", type=float, default=100, 104 | help="proportion for each in-domain data") 105 | parser.add_argument("--use_bert", action="store_true", 106 | help="whether to use bert") 107 | parser.add_argument("--sup_cont", action="store_true", 108 | help="whether to add supervised contrastive loss") 109 | # training hyperparameters 110 | parser.add_argument("--ind_pre_epoches", type=int, default=10, 111 | help="Max epoches when in-domain pre-training.") 112 | parser.add_argument("--supcont_pre_epoches", type=int, default=100, 113 | help="Max epoches when in-domain supervised contrastive pre-training.") 114 | parser.add_argument("--aug_pre_epoches", type=int, default=100, 115 | help="Max epoches when adversarial contrastive training.") 116 | parser.add_argument("--finetune_epoches", type=int, default=20, 117 | help="Max epoches when finetune model") 118 | parser.add_argument("--patience", type=int, default=20, 119 | help="Patience when applying early stop.") 120 | parser.add_argument("--batch_size", type=int, default=50, 121 | help="Mini-batch size for train and validation") 122 | parser.add_argument("--learning_rate", type=float, default=0.001, 123 | help="learning rate") 124 | parser.add_argument("--weight_decay", type=float, default=0.0001, 125 | help="weight_decay") 126 | parser.add_argument('--clip', type=float, default=0.25, help='gradient clipping') 127 | args = parser.parse_args() 128 | return args 129 | 130 | 131 | args = parse_args() 132 | dataset = args.dataset 133 | proportion = args.proportion 134 | BETA = args.beta 135 | ALPHA = args.alpha 136 | DO_NORM = args.do_normalization 137 | NUM_LAYERS = args.num_layers 138 | HIDDEN_DIM = args.hidden_dim 139 | BATCH_SIZE = args.batch_size 140 | EMBEDDING_FILE = args.embedding_file 141 | MAX_SEQ_LEN = args.max_seq_len 142 | MAX_NUM_WORDS = args.max_num_words 143 | EMBEDDING_DIM = args.embedding_dim 144 | CON_DIM = args.contractive_dim 145 | OOD_LOSS = args.ood_loss 146 | CONT_LOSS = args.cont_loss 147 | ADV = args.adv 148 | NORM_COEF = args.norm_coef 149 | LMCL = args.lmcl 150 | CL_MODE = args.cl_mode 151 | USE_BERT = args.use_bert 152 | SUP_CONT = args.sup_cont 153 | CUDA = args.cuda 154 | df, partition_to_n_row = load_data(dataset) 155 | 156 | df['content_words'] = df['text'].apply(lambda s: word_tokenize(s)) 157 | texts = df['content_words'].apply(lambda l: " ".join(l)) 158 | 159 | # Do not filter out "," and "." 160 | tokenizer = Tokenizer(num_words=MAX_NUM_WORDS, oov_token="", filters='!"#$%&()*+-/:;<=>@[\]^_`{|}~') 161 | 162 | tokenizer.fit_on_texts(texts) 163 | word_index = tokenizer.word_index 164 | sequences = tokenizer.texts_to_sequences(texts) 165 | sequences_pad = pad_sequences(sequences, maxlen=MAX_SEQ_LEN, padding='post', truncating='post') 166 | 167 | # Train-valid-test split 168 | idx_train = (None, partition_to_n_row['train']) 169 | idx_valid = (partition_to_n_row['train'], partition_to_n_row['train'] + partition_to_n_row['valid']) 170 | idx_test = (partition_to_n_row['train'] + partition_to_n_row['valid'], partition_to_n_row['train'] + partition_to_n_row['valid'] + partition_to_n_row['test']) 171 | idx_cont = (partition_to_n_row['train'] + partition_to_n_row['valid'] + partition_to_n_row['test'], None) 172 | 173 | X_train = sequences_pad[idx_train[0]:idx_train[1]] 174 | X_valid = sequences_pad[idx_valid[0]:idx_valid[1]] 175 | X_test = sequences_pad[idx_test[0]:idx_test[1]] 176 | X_cont = sequences_pad[idx_cont[0]:idx_cont[1]] 177 | 178 | df_train = df[idx_train[0]:idx_train[1]] 179 | df_valid = df[idx_valid[0]:idx_valid[1]] 180 | df_test = df[idx_test[0]:idx_test[1]] 181 | df_cont = df[idx_cont[0]:idx_cont[1]] 182 | 183 | y_train = df_train.label.reset_index(drop=True) 184 | y_valid = df_valid.label.reset_index(drop=True) 185 | y_test = df_test.label.reset_index(drop=True) 186 | y_cont = df_cont.label.reset_index(drop=True) 187 | train_text = df_train.text.reset_index(drop=True) 188 | valid_text = df_valid.text.reset_index(drop=True) 189 | test_text = df_test.text.reset_index(drop=True) 190 | cont_text = df_cont.text.reset_index(drop=True) 191 | print("cont: %d" % (X_cont.shape[0])) 192 | 193 | n_class = y_train.unique().shape[0] 194 | if 'CLINC_OOD' in args.dataset and not args.n_plus_1: 195 | n_class -= 1 196 | if args.augment and 'oos_b' in list(y_train.unique()): 197 | n_class -= 1 198 | n_class_seen = round(n_class * proportion / 100) 199 | print(n_class_seen) 200 | 201 | if args.seen_classes is None: 202 | if args.seen_classes_seed is not None: 203 | random.seed(args.seen_classes_seed) 204 | y_cols = y_train.unique() 205 | y_cols_lst = list(y_cols) 206 | if 'oos' in y_cols_lst: 207 | y_cols_lst.remove('oos') 208 | if 'oos_b' in y_cols_lst: 209 | y_cols_lst.remove('oos_b') 210 | random.shuffle(y_cols_lst) 211 | y_cols_seen = y_cols_lst[:n_class_seen] 212 | y_cols_unseen = y_cols_lst[n_class_seen:] 213 | else: 214 | # Original implementation 215 | weighted_random_sampling = False 216 | if weighted_random_sampling: 217 | y_cols = y_train.unique() 218 | y_vc = y_train.value_counts() 219 | y_vc = y_vc / y_vc.sum() 220 | y_cols_seen = np.random.choice(y_vc.index, n_class_seen, p=y_vc.values, replace=False) 221 | y_cols_unseen = [y_col for y_col in y_cols if y_col not in y_cols_seen] 222 | else: 223 | y_cols = list(y_train.unique()) 224 | if 'oos' in y_cols and not args.n_plus_1: 225 | y_cols.remove('oos') 226 | if args.augment and 'oos_b' in y_cols: 227 | y_cols.remove('oos_b') 228 | y_cols_seen = random.sample(y_cols, n_class_seen) 229 | y_cols_unseen = [y_col for y_col in y_cols if y_col not in y_cols_seen] 230 | else: 231 | y_cols = y_train.unique() 232 | y_cols_seen = [y_col for y_col in y_cols if y_col in args.seen_classes and y_col != 'oos'] 233 | y_cols_unseen = [y_col for y_col in y_cols if y_col not in args.seen_classes] 234 | print(y_cols_seen) 235 | print(y_cols_unseen) 236 | 237 | y_cols_unseen_b = [] 238 | if 'CLINC_OOD' in args.dataset and not args.n_plus_1: 239 | y_cols_unseen = ['oos'] 240 | if args.augment: 241 | y_cols_unseen = ['oos'] 242 | y_cols_unseen_b = ['oos_b'] 243 | 244 | for i in range(len(y_cols_seen)): 245 | tmp_idx = y_train[y_train.isin([y_cols_seen[i]])] 246 | tmp_idx = tmp_idx[:int(args.dataset_proportion / 100 * len(tmp_idx))].index 247 | if not i: 248 | part_train_seen_idx = tmp_idx 249 | else: 250 | part_train_seen_idx = np.concatenate((part_train_seen_idx, tmp_idx), axis=0) 251 | 252 | train_seen_idx = y_train[y_train.isin(y_cols_seen)].index 253 | train_ood_idx = y_train[y_train.isin(y_cols_unseen)] 254 | train_ood_idx = train_ood_idx[:int(args.unseen_proportion / 100 * len(train_ood_idx))].index 255 | 256 | valid_seen_idx = y_valid[y_valid.isin(y_cols_seen)].index 257 | valid_ood_idx = y_valid[y_valid.isin(y_cols_unseen)] 258 | valid_ood_idx = valid_ood_idx[:int(args.unseen_proportion / 100 * len(valid_ood_idx))].index 259 | 260 | test_seen_idx = y_test[y_test.isin(y_cols_seen)].index 261 | test_ood_idx = y_test[y_test.isin(y_cols_unseen)].index 262 | 263 | src_cols = ['src'] 264 | bt_cols = ['bt'] 265 | src_idx = y_cont[y_cont.isin(src_cols)] 266 | ind_src_idx = src_idx[:int(args.cont_proportion * 0.8 * len(src_idx))].index 267 | ood_src_idx = src_idx[int(0.8 * len(src_idx)):int(0.8 * len(src_idx) + args.cont_proportion * 0.2 * len(src_idx))].index 268 | bt_idx = y_cont[y_cont.isin(bt_cols)] 269 | ind_bt_idx = bt_idx[:int(args.cont_proportion * 0.8 * len(bt_idx))].index 270 | ood_bt_idx = bt_idx[int(0.8 * len(bt_idx)):int(0.8 * len(bt_idx) + args.cont_proportion * 0.2 * len(bt_idx))].index 271 | 272 | X_train_seen = X_train[part_train_seen_idx] 273 | X_train_ood = X_train[train_ood_idx] 274 | y_train_seen = y_train[part_train_seen_idx] 275 | train_seen_text = list(train_text[part_train_seen_idx]) 276 | train_unseen_text = list(train_text[train_ood_idx]) 277 | X_valid_seen = X_valid[valid_seen_idx] 278 | X_valid_ood = X_valid[valid_ood_idx] 279 | y_valid_seen = y_valid[valid_seen_idx] 280 | valid_seen_text = list(valid_text[valid_seen_idx]) 281 | valid_unseen_text = list(valid_text[valid_ood_idx]) 282 | X_test_seen = X_test[test_seen_idx] 283 | X_test_ood = X_test[test_ood_idx] 284 | y_test_seen = y_test[test_seen_idx] 285 | test_seen_text = list(test_text[test_seen_idx]) 286 | test_unseen_text = list(test_text[test_ood_idx]) 287 | 288 | print("train : valid : test = %d : %d : %d" % (X_train_seen.shape[0], X_valid_seen.shape[0], X_test_seen.shape[0])) 289 | 290 | src_ind_x = X_cont[ind_src_idx] 291 | src_ind_y = y_cont[ind_src_idx] 292 | bt_ind_x = X_cont[ind_bt_idx] 293 | bt_ind_y = y_cont[ind_bt_idx] 294 | src_ood_x = X_cont[ood_src_idx] 295 | src_ood_y = y_cont[ood_src_idx] 296 | bt_ood_x = X_cont[ood_bt_idx] 297 | bt_ood_y = y_cont[ood_bt_idx] 298 | 299 | if y_cols_unseen_b: 300 | train_ood_idx_b = y_train[y_train.isin(y_cols_unseen_b)].index 301 | X_train_ood_b = X_train[train_ood_idx_b] 302 | 303 | le = LabelEncoder() 304 | le.fit(y_train_seen) 305 | y_train_idx = le.transform(y_train_seen) 306 | y_valid_idx = le.transform(y_valid_seen) 307 | y_test_idx = le.transform(y_test_seen) 308 | ood_index = y_test_idx[0] 309 | y_train_onehot = to_categorical(y_train_idx) 310 | y_valid_onehot = to_categorical(y_valid_idx) 311 | y_test_onehot = to_categorical(y_test_idx) 312 | for i in range(int(args.mask_proportion / 100 * len(y_train_onehot))): 313 | y_train_onehot[i] = [0.0] * n_class_seen 314 | for i in range(int(args.mask_proportion / 100 * len(y_valid_onehot))): 315 | y_valid_onehot[i] = [0.0] * n_class_seen 316 | y_train_ood = np.array([[0.0] * n_class_seen for _ in range(len(train_ood_idx))]) 317 | y_valid_ood = np.array([[0.0] * n_class_seen for _ in range(len(valid_ood_idx))]) 318 | y_test_ood = np.array([[0.0] * n_class_seen for _ in range(len(test_ood_idx))]) 319 | 320 | y_test_mask = y_test.copy() 321 | y_test_mask[y_test_mask.isin(y_cols_unseen)] = 'unseen' 322 | train_text = train_seen_text + train_unseen_text 323 | valid_text = valid_seen_text + valid_unseen_text 324 | test_text = list(test_text) 325 | if not args.unseen_proportion: 326 | train_data_raw = train_data = (X_train_seen, y_train_onehot) 327 | valid_data_raw = valid_data = (X_valid_seen, y_valid_onehot) 328 | else: 329 | train_data_raw = (X_train_seen, y_train_onehot) 330 | valid_data_raw = (X_valid_seen, y_valid_onehot) 331 | train_data_ood = (X_train_ood, y_train_ood) 332 | valid_data_ood = (X_valid_ood, y_valid_ood) 333 | train_data = (np.concatenate((X_train_seen,X_train_ood),axis=0), np.concatenate((y_train_onehot,y_train_ood),axis=0)) 334 | valid_data = (np.concatenate((X_valid_seen,X_valid_ood),axis=0), np.concatenate((y_valid_onehot,y_valid_ood),axis=0)) 335 | test_data = (X_test, y_test_mask) 336 | test_data_4np1 = (X_test, y_test_onehot) 337 | if args.augment: 338 | train_augment = (np.concatenate((src_ind_x,src_ood_x),axis=0),np.concatenate((bt_ind_x,bt_ood_x),axis=0)) 339 | 340 | 341 | class DataLoader(object): 342 | def __init__(self, data, batch_size, mode='train', use_bert=False, raw_text=None): 343 | self.use_bert = use_bert 344 | if self.use_bert: 345 | self.inp = list(raw_text) 346 | else: 347 | self.inp = data[0] 348 | self.tgt = data[1] 349 | self.batch_size = batch_size 350 | self.n_samples = len(data[0]) 351 | self.n_batches = self.n_samples // self.batch_size 352 | self.mode = mode 353 | self._shuffle_indices() 354 | 355 | def _shuffle_indices(self): 356 | if self.mode == 'test': 357 | self.indices = np.arange(self.n_samples) 358 | else: 359 | self.indices = np.random.permutation(self.n_samples) 360 | self.index = 0 361 | self.batch_index = 0 362 | 363 | def _create_batch(self): 364 | batch = [] 365 | n = 0 366 | while n < self.batch_size: 367 | _index = self.indices[self.index] 368 | batch.append((self.inp[_index],self.tgt[_index])) 369 | self.index += 1 370 | n += 1 371 | self.batch_index += 1 372 | seq, label = tuple(zip(*batch)) 373 | if not self.use_bert: 374 | seq = torch.LongTensor(seq) 375 | if self.mode not in ['test','augment']: 376 | label = torch.FloatTensor(label) 377 | elif self.mode == 'augment': 378 | label = torch.LongTensor(label) 379 | 380 | return seq, label 381 | 382 | def __len__(self): 383 | return self.n_batches 384 | 385 | def __iter__(self): 386 | for _ in range(self.n_batches): 387 | if self.batch_index == self.n_batches: 388 | raise StopIteration() 389 | yield self._create_batch() 390 | 391 | if args.mode in ["train", "both"]: 392 | # GPU setting 393 | os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3" 394 | set_allow_growth(device=args.gpu_device) 395 | 396 | timestamp = str(time.time()) # strftime("%m%d%H%M") 397 | if args.experiment_No: 398 | output_dir = os.path.join(args.output_dir, f"{dataset}-{proportion}-{args.experiment_No}") 399 | else: 400 | output_dir = os.path.join(args.output_dir, f"{dataset}-{proportion}-{timestamp}") 401 | if not os.path.exists(output_dir): 402 | os.mkdir(output_dir) 403 | with open(os.path.join(output_dir, "seen_classes.txt"), "w") as f_out: 404 | f_out.write("\n".join(le.classes_)) 405 | with open(os.path.join(output_dir, "unseen_classes.txt"), "w") as f_out: 406 | f_out.write("\n".join(y_cols_unseen)) 407 | 408 | if not USE_BERT: 409 | print("Load pre-trained GloVe embedding...") 410 | MAX_FEATURES = min(MAX_NUM_WORDS, len(word_index)) + 1 # +1 for PAD 411 | def get_coefs(word, *arr): 412 | return word, np.asarray(arr, dtype='float32') 413 | embeddings_index = dict(get_coefs(*o.strip().split()) for o in open(EMBEDDING_FILE)) 414 | all_embs = np.stack(embeddings_index.values()) 415 | emb_mean, emb_std = all_embs.mean(), all_embs.std() 416 | embedding_matrix = np.random.normal(emb_mean, emb_std, (MAX_FEATURES, EMBEDDING_DIM)) 417 | for word, i in word_index.items(): 418 | if i >= MAX_FEATURES: continue 419 | embedding_vector = embeddings_index.get(word) 420 | if embedding_vector is not None: embedding_matrix[i] = embedding_vector 421 | else: 422 | embedding_matrix = None 423 | 424 | filepath = os.path.join(output_dir, 'model_best.pkl') 425 | model = BiLSTM(embedding_matrix, BATCH_SIZE, HIDDEN_DIM, CON_DIM, NUM_LAYERS, n_class_seen, DO_NORM, ALPHA, BETA, OOD_LOSS, ADV, CONT_LOSS, NORM_COEF, CL_MODE, LMCL, use_bert=USE_BERT, sup_cont=SUP_CONT, use_cuda=CUDA) 426 | optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=args.learning_rate, 427 | weight_decay=args.weight_decay) 428 | if args.cuda: 429 | torch.backends.cudnn.enabled = True 430 | torch.backends.cudnn.benchmark = True 431 | model.cuda() 432 | 433 | #in-domain pre-training 434 | best_f1 = 0 435 | 436 | if args.sup_cont: 437 | for epoch in range(1,args.supcont_pre_epoches+1): 438 | global_step = 0 439 | losses = [] 440 | train_loader = DataLoader(train_data_raw, BATCH_SIZE, use_bert=USE_BERT, raw_text=train_seen_text) 441 | train_iterator = tqdm( 442 | train_loader, initial=global_step, 443 | desc="Iter (loss=X.XXX)") 444 | model.train() 445 | for j, (seq, label) in enumerate(train_iterator): 446 | if args.cuda: 447 | if not USE_BERT: 448 | seq = seq.cuda() 449 | label = label.cuda() 450 | loss = model(seq, None, label, mode='ind_pre') 451 | train_iterator.set_description('Iter (sup_cont_loss=%5.3f)' % (loss.item())) 452 | losses.append(loss) 453 | optimizer.zero_grad() 454 | loss.backward() 455 | torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip) 456 | optimizer.step() 457 | global_step += 1 458 | print('Epoch: [{0}] : Loss {loss:.4f}'.format( 459 | epoch, loss=sum(losses)/global_step)) 460 | torch.save(model, filepath) 461 | 462 | for epoch in range(1,args.ind_pre_epoches+1): 463 | global_step = 0 464 | losses = [] 465 | train_loader = DataLoader(train_data_raw, BATCH_SIZE, use_bert=USE_BERT, raw_text=train_seen_text) 466 | train_iterator = tqdm( 467 | train_loader, initial=global_step, 468 | desc="Iter (loss=X.XXX)") 469 | valid_loader = DataLoader(valid_data, BATCH_SIZE, use_bert=USE_BERT, raw_text=valid_text) 470 | model.train() 471 | for j, (seq, label) in enumerate(train_iterator): 472 | if args.cuda: 473 | if not USE_BERT: 474 | seq = seq.cuda() 475 | label = label.cuda() 476 | if epoch == 1: 477 | loss = model(seq, None, label, mode='finetune') 478 | else: 479 | loss = model(seq, None, label, sim=sim, mode='finetune') 480 | train_iterator.set_description('Iter (ce_loss=%5.3f)' % (loss.item())) 481 | losses.append(loss) 482 | optimizer.zero_grad() 483 | loss.backward() 484 | torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip) 485 | optimizer.step() 486 | global_step += 1 487 | print('Epoch: [{0}] : Loss {loss:.4f}'.format( 488 | epoch, loss=sum(losses)/global_step)) 489 | 490 | model.eval() 491 | predict = [] 492 | target = [] 493 | if args.cuda: 494 | sim = torch.zeros((n_class_seen, HIDDEN_DIM*2)).cuda() 495 | else: 496 | sim = torch.zeros((n_class_seen, HIDDEN_DIM * 2)) 497 | for j, (seq, label) in enumerate(valid_loader): 498 | if args.cuda: 499 | if not USE_BERT: 500 | seq = seq.cuda() 501 | label = label.cuda() 502 | output = model(seq, None, label, mode='validation') 503 | predict += output[0] 504 | target += output[1] 505 | sim += torch.mm(label.T, output[2]) 506 | sim = sim / len(predict) 507 | n_sim = sim.norm(p=2, dim=1, keepdim=True) 508 | sim = (sim @ sim.t()) / (n_sim * n_sim.t()).clamp(min=1e-8) 509 | if args.cuda: 510 | sim = sim - 1e4 * torch.eye(n_class_seen).cuda() 511 | else: 512 | sim = sim - 1e4 * torch.eye(n_class_seen) 513 | sim = torch.softmax(sim, dim=1) 514 | f1 = metrics.f1_score(target, predict, average='macro') 515 | if f1 > best_f1: 516 | torch.save(model, filepath) 517 | best_f1 = f1 518 | print('f1:{f1:.4f}'.format(f1=f1)) 519 | 520 | 521 | if args.mode in ["test", "both", "find_threshold"]: 522 | 523 | if args.n_plus_1: 524 | test_loader = DataLoader(test_data_4np1, BATCH_SIZE, use_bert=USE_BERT) 525 | torch.no_grad() 526 | model.eval() 527 | predict = [] 528 | target = [] 529 | for j, (seq, label) in enumerate(test_loader): 530 | if args.cuda: 531 | if not USE_BERT: 532 | seq = seq.cuda() 533 | label = label.cuda() 534 | output = model(seq, label, 'valid') 535 | predict += output[1] 536 | target += output[0] 537 | m = np.zeros((len(y_cols_seen),len(y_cols_seen))) 538 | for i in range(len(predict)): 539 | m[target[i]][predict[i]] += 1 540 | m[[ood_index, len(y_cols_seen) - 1], :] = m[[len(y_cols_seen) - 1, ood_index], :] 541 | m[:, [ood_index, len(y_cols_seen) - 1]] = m[:, [len(y_cols_seen) - 1, ood_index]] 542 | print(get_score(m)) 543 | 544 | 545 | else: 546 | if args.mode in ["test","find_threshold"]: 547 | model_dir = args.model_dir 548 | else: 549 | model_dir = output_dir 550 | if args.cuda: 551 | model = torch.load(os.path.join(model_dir, "model_best.pkl"), map_location='cuda:0') 552 | else: 553 | model = torch.load(os.path.join(model_dir, "model_best.pkl"), map_location='cpu') 554 | train_loader = DataLoader(train_data_raw, BATCH_SIZE, 'test', use_bert=USE_BERT, raw_text=train_seen_text) 555 | valid_loader = DataLoader(valid_data_raw, BATCH_SIZE, use_bert=USE_BERT, raw_text=valid_seen_text) 556 | valid_ood_loader = DataLoader(valid_data_ood, BATCH_SIZE, 'test', use_bert=USE_BERT, raw_text=valid_unseen_text) 557 | test_loader = DataLoader(test_data, BATCH_SIZE, 'test', use_bert=USE_BERT, raw_text=test_text) 558 | torch.no_grad() 559 | model.eval() 560 | predict = [] 561 | target = [] 562 | for j, (seq, label) in enumerate(valid_loader): 563 | if args.cuda: 564 | if not USE_BERT: 565 | seq = seq.cuda() 566 | label = label.cuda() 567 | output = model(seq, None, label, mode='validation') 568 | predict += output[1] 569 | target += output[0] 570 | f1 = metrics.f1_score(target, predict, average='macro') 571 | print(f"in-domain f1:{f1}") 572 | 573 | valid_loader = DataLoader(valid_data_raw, BATCH_SIZE, 'test', use_bert=USE_BERT, raw_text=valid_seen_text) 574 | classes = list(le.classes_) + ['unseen'] 575 | #print(list(le.classes_)) 576 | #classes = list(le.classes_) 577 | feature_train = None 578 | feature_valid = None 579 | feature_valid_ood = None 580 | feature_test = None 581 | prob_train = None 582 | prob_valid = None 583 | prob_valid_ood = None 584 | prob_test = None 585 | for j, (seq, label) in enumerate(train_loader): 586 | if args.cuda: 587 | if not USE_BERT: 588 | seq = seq.cuda() 589 | output = model(seq, None, None, mode='test') 590 | if feature_train != None: 591 | feature_train = torch.cat((feature_train,output[1]),dim=0) 592 | prob_train = torch.cat((prob_train,output[0]),dim=0) 593 | else: 594 | feature_train = output[1] 595 | prob_train = output[0] 596 | for j, (seq, label) in enumerate(valid_loader): 597 | if args.cuda: 598 | if not USE_BERT: 599 | seq = seq.cuda() 600 | output = model(seq, None, None, mode='test') 601 | if feature_valid != None: 602 | feature_valid = torch.cat((feature_valid,output[1]),dim=0) 603 | prob_valid = torch.cat((prob_valid,output[0]),dim=0) 604 | else: 605 | feature_valid = output[1] 606 | prob_valid = output[0] 607 | for j, (seq, label) in enumerate(valid_ood_loader): 608 | if args.cuda: 609 | if not USE_BERT: 610 | seq = seq.cuda() 611 | output = model(seq, None, None, mode='test') 612 | if feature_valid_ood != None: 613 | feature_valid_ood = torch.cat((feature_valid_ood,output[1]),dim=0) 614 | prob_valid_ood = torch.cat((prob_valid_ood,output[0]),dim=0) 615 | else: 616 | feature_valid_ood = output[1] 617 | prob_valid_ood = output[0] 618 | for j, (seq, label) in enumerate(test_loader): 619 | if args.cuda: 620 | if not USE_BERT: 621 | seq = seq.cuda() 622 | output = model(seq, None, None, mode='test') 623 | if feature_test != None: 624 | feature_test = torch.cat((feature_test,output[1]),dim=0) 625 | prob_test = torch.cat((prob_test, output[0]), dim=0) 626 | else: 627 | feature_test = output[1] 628 | prob_test = output[0] 629 | feature_train = feature_train.cpu().detach().numpy() 630 | feature_valid = feature_valid.cpu().detach().numpy() 631 | feature_valid_ood = feature_valid_ood.cpu().detach().numpy() 632 | feature_test = feature_test.cpu().detach().numpy() 633 | prob_train = prob_train.cpu().detach().numpy() 634 | prob_valid = prob_valid.cpu().detach().numpy() 635 | prob_valid_ood = prob_valid_ood.cpu().detach().numpy() 636 | prob_test = prob_test.cpu().detach().numpy() 637 | if args.mode == 'find_threshold': 638 | settings = ['gda_lsqr_'+str(10.0+1.0*(i)) for i in range(20)] 639 | else: 640 | settings = args.setting 641 | for setting in settings: 642 | pred_dir = os.path.join(model_dir, f"{setting}") 643 | if not os.path.exists(pred_dir): 644 | os.mkdir(pred_dir) 645 | setting_fields = setting.split("_") 646 | ood_method = setting_fields[0] 647 | 648 | assert ood_method in ("lof", "gda", "msp") 649 | 650 | if ood_method == "lof": 651 | method = 'LOF (LMCL)' 652 | lof = LocalOutlierFactor(n_neighbors=20, contamination=0.05, novelty=True, n_jobs=-1) 653 | lof.fit(feature_train) 654 | l = len(feature_test) 655 | y_pred_lof = pd.Series(lof.predict(feature_test)) 656 | test_info = get_test_info(texts=texts[idx_test[0]:idx_test[0]+l], 657 | label=y_test[:l], 658 | label_mask=y_test_mask[:l], 659 | softmax_prob=prob_test, 660 | softmax_classes=list(le.classes_), 661 | lof_result=y_pred_lof, 662 | save_to_file=True, 663 | output_dir=pred_dir) 664 | pca_visualization(feature_test, y_test_mask[:l], classes, os.path.join(pred_dir, "pca_test.png")) 665 | df_seen = pd.DataFrame(prob_test, columns=le.classes_) 666 | df_seen['unseen'] = 0 667 | 668 | y_pred = df_seen.idxmax(axis=1) 669 | y_pred[y_pred_lof[y_pred_lof == -1].index] = 'unseen' 670 | cm = confusion_matrix(y_test_mask[:l], y_pred, classes) 671 | 672 | f, f_seen, f_unseen, p_unseen, r_unseen = get_score(cm) 673 | plot_confusion_matrix(pred_dir, cm, classes, normalize=False, figsize=(9, 6), 674 | title=method + ' on ' + dataset + ', f1-macro=' + str(f)) 675 | print(cm) 676 | log_pred_results(f, f_seen, f_unseen, p_unseen, r_unseen, classes, pred_dir, cm, OOD_LOSS, ADV, CONT_LOSS) 677 | elif ood_method == "gda": 678 | solver = setting_fields[1] if len(setting_fields) > 1 else "lsqr" 679 | threshold = setting_fields[2] if len(setting_fields) > 2 else "auto" 680 | distance_type = setting_fields[3] if len(setting_fields) > 3 else "mahalanobis" 681 | assert solver in ("svd", "lsqr") 682 | assert distance_type in ("mahalanobis", "euclidean") 683 | l = len(feature_test) 684 | method = 'GDA (LMCL)' 685 | gda = LinearDiscriminantAnalysis(solver=solver, shrinkage=None, store_covariance=True) 686 | gda.fit(prob_train, y_train_seen[:len(prob_train)]) 687 | # print(np.max(gda.covariance_class.diagonal())) 688 | # print(np.min(gda.covariance_class.diagonal())) 689 | # print(np.mean(gda.covariance_class.diagonal())) 690 | # print(np.median(gda.covariance_class.diagonal())) 691 | # print(np.max(np.linalg.norm(gda.covariance_, axis=0))) 692 | # print(np.min(np.linalg.norm(gda.covariance_, axis=0))) 693 | # print(np.mean(np.linalg.norm(gda.covariance_, axis=0))) 694 | # print(np.median(np.linalg.norm(gda.covariance_, axis=0))) 695 | # dis_matrix = np.matmul(gda.means_, gda.means_.T) 696 | # K = [1,5,10,30,50] 697 | # for k in K: 698 | # knn = naive_arg_topK(dis_matrix, k, axis=1) 699 | # sum = 0 700 | # for i in range(knn.shape[0]): 701 | # for j in knn[i]: 702 | # sum += dis_matrix[i][j] 703 | # print(sum/(k*knn.shape[0])) 704 | if threshold == "auto": 705 | # feature_valid_seen = get_deep_feature.predict(valid_data[0]) 706 | # valid_unseen_idx = y_valid[~y_valid.isin(y_cols_seen)].index 707 | # feature_valid_ood = get_deep_feature.predict(X_valid[valid_unseen_idx]) 708 | seen_m_dist = confidence(prob_valid, gda.means_, distance_type, gda.covariance_).min(axis=1) 709 | unseen_m_dist = confidence(prob_valid_ood, gda.means_, distance_type, gda.covariance_).min(axis=1) 710 | threshold = estimate_best_threshold(seen_m_dist, unseen_m_dist) 711 | # seen_m_dist = confidence(feature_valid, gda.means_, distance_type, gda.covariance_).min(axis=1) 712 | # unseen_m_dist = confidence(feature_valid_ood, gda.means_, distance_type, gda.covariance_).min(axis=1) 713 | # threshold = estimate_best_threshold(seen_m_dist, unseen_m_dist) 714 | else: 715 | threshold = float(threshold) 716 | 717 | y_pred = pd.Series(gda.predict(prob_test)) 718 | gda_result = confidence(prob_test, gda.means_, distance_type, gda.covariance_) 719 | test_info = get_test_info(texts=texts[idx_test[0]:idx_test[0]+l], 720 | label=y_test[:l], 721 | label_mask=y_test_mask[:l], 722 | softmax_prob=prob_test, 723 | softmax_classes=list(le.classes_), 724 | gda_result=gda_result, 725 | gda_classes=gda.classes_, 726 | save_to_file=True, 727 | output_dir=pred_dir) 728 | #pca_visualization(prob_test, y_test_mask[:l], classes, os.path.join(pred_dir, "pca_test.png")) 729 | #pca_visualization(prob_train, y_train[:15000], classes, os.path.join(pred_dir, "pca_test.png")) 730 | #pca_visualization(feature_test, y_test_mask[:l], classes, os.path.join(pred_dir, "pca_test.png")) 731 | y_pred_score = pd.Series(gda_result.min(axis=1)) 732 | y_pred[y_pred_score[y_pred_score > threshold].index] = 'unseen' 733 | cm = confusion_matrix(y_test_mask[:l], y_pred, classes) 734 | f, acc_all, f_seen, acc_in, p_seen, r_seen, f_unseen, acc_ood, p_unseen, r_unseen = get_score(cm) 735 | # plot_confusion_matrix(pred_dir, cm, classes, normalize=False, figsize=(9, 6), 736 | # title=method + ' on ' + dataset + ', f1-macro=' + str(f)) 737 | print(cm) 738 | #log_pred_results(f, acc_all, f_seen, acc_in, p_seen, r_seen, f_unseen, acc_ood, p_unseen, r_unseen, classes, pred_dir, cm, OOD_LOSS, ADV, CONT_LOSS, threshold) 739 | elif ood_method == "msp": 740 | threshold = setting_fields[1] if len(setting_fields) > 1 else "auto" 741 | method = 'MSP (LMCL)' 742 | l = len(feature_test) 743 | if threshold == "auto": 744 | #prob_valid_seen = model.predict(valid_data[0]) 745 | #valid_unseen_idx = y_valid[~y_valid.isin(y_cols_seen)].index 746 | #prob_valid_unseen = model.predict(X_valid[valid_unseen_idx]) 747 | seen_conf = prob_valid.max(axis=1) * -1.0 748 | unseen_conf = prob_valid_ood.max(axis=1) * -1.0 749 | threshold = -1.0 * estimate_best_threshold(seen_conf, unseen_conf) 750 | else: 751 | threshold = float(threshold) 752 | 753 | df_seen = pd.DataFrame(prob_test, columns=le.classes_) 754 | df_seen['unseen'] = 0 755 | 756 | y_pred = df_seen.idxmax(axis=1) 757 | y_pred_score = df_seen.max(axis=1) 758 | y_pred[y_pred_score[y_pred_score < threshold].index] = 'unseen' 759 | cm = confusion_matrix(y_test_mask[:l], y_pred, classes) 760 | 761 | f, acc_all, f_seen, acc_in, p_seen, r_seen, f_unseen, acc_ood, p_unseen, r_unseen = get_score(cm) 762 | plot_confusion_matrix(pred_dir, cm, classes, normalize=False, figsize=(9, 6), 763 | title=method + ' on ' + dataset + ', f1-macro=' + str(f)) 764 | print(cm) 765 | log_pred_results(f, acc_all, f_seen, acc_in, p_seen, r_seen, f_unseen, acc_ood, p_unseen, r_unseen, 766 | classes, pred_dir, cm, OOD_LOSS, ADV, CONT_LOSS, threshold) 767 | -------------------------------------------------------------------------------- /CLINC_OOD/valid.label: -------------------------------------------------------------------------------- 1 | oos 2 | oos 3 | oos 4 | oos 5 | oos 6 | oos 7 | oos 8 | oos 9 | oos 10 | oos 11 | oos 12 | oos 13 | oos 14 | oos 15 | oos 16 | oos 17 | oos 18 | oos 19 | oos 20 | oos 21 | oos 22 | oos 23 | oos 24 | oos 25 | oos 26 | oos 27 | oos 28 | oos 29 | oos 30 | oos 31 | oos 32 | oos 33 | oos 34 | oos 35 | oos 36 | oos 37 | oos 38 | oos 39 | oos 40 | oos 41 | oos 42 | oos 43 | oos 44 | oos 45 | oos 46 | oos 47 | oos 48 | oos 49 | oos 50 | oos 51 | oos 52 | oos 53 | oos 54 | oos 55 | oos 56 | oos 57 | oos 58 | oos 59 | oos 60 | oos 61 | oos 62 | oos 63 | oos 64 | oos 65 | oos 66 | oos 67 | oos 68 | oos 69 | oos 70 | oos 71 | oos 72 | oos 73 | oos 74 | oos 75 | oos 76 | oos 77 | oos 78 | oos 79 | oos 80 | oos 81 | oos 82 | oos 83 | oos 84 | oos 85 | oos 86 | oos 87 | oos 88 | oos 89 | oos 90 | oos 91 | oos 92 | oos 93 | oos 94 | oos 95 | oos 96 | oos 97 | oos 98 | oos 99 | oos 100 | oos 101 | translate 102 | translate 103 | translate 104 | translate 105 | translate 106 | translate 107 | translate 108 | translate 109 | translate 110 | translate 111 | translate 112 | translate 113 | translate 114 | translate 115 | translate 116 | translate 117 | translate 118 | translate 119 | translate 120 | translate 121 | transfer 122 | transfer 123 | transfer 124 | transfer 125 | transfer 126 | transfer 127 | transfer 128 | transfer 129 | transfer 130 | transfer 131 | transfer 132 | transfer 133 | transfer 134 | transfer 135 | transfer 136 | transfer 137 | transfer 138 | transfer 139 | transfer 140 | transfer 141 | timer 142 | timer 143 | timer 144 | timer 145 | timer 146 | timer 147 | timer 148 | timer 149 | timer 150 | timer 151 | timer 152 | timer 153 | timer 154 | timer 155 | timer 156 | timer 157 | timer 158 | timer 159 | timer 160 | timer 161 | definition 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insurance_change 212 | insurance_change 213 | insurance_change 214 | insurance_change 215 | insurance_change 216 | insurance_change 217 | insurance_change 218 | insurance_change 219 | insurance_change 220 | insurance_change 221 | find_phone 222 | find_phone 223 | find_phone 224 | find_phone 225 | find_phone 226 | find_phone 227 | find_phone 228 | find_phone 229 | find_phone 230 | find_phone 231 | find_phone 232 | find_phone 233 | find_phone 234 | find_phone 235 | find_phone 236 | find_phone 237 | find_phone 238 | find_phone 239 | find_phone 240 | find_phone 241 | travel_alert 242 | travel_alert 243 | travel_alert 244 | travel_alert 245 | travel_alert 246 | travel_alert 247 | travel_alert 248 | travel_alert 249 | travel_alert 250 | travel_alert 251 | travel_alert 252 | travel_alert 253 | travel_alert 254 | travel_alert 255 | travel_alert 256 | travel_alert 257 | travel_alert 258 | travel_alert 259 | travel_alert 260 | travel_alert 261 | pto_request 262 | pto_request 263 | pto_request 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| fun_fact 312 | fun_fact 313 | fun_fact 314 | fun_fact 315 | fun_fact 316 | fun_fact 317 | fun_fact 318 | fun_fact 319 | fun_fact 320 | fun_fact 321 | change_language 322 | change_language 323 | change_language 324 | change_language 325 | change_language 326 | change_language 327 | change_language 328 | change_language 329 | change_language 330 | change_language 331 | change_language 332 | change_language 333 | change_language 334 | change_language 335 | change_language 336 | change_language 337 | change_language 338 | change_language 339 | change_language 340 | change_language 341 | payday 342 | payday 343 | payday 344 | payday 345 | payday 346 | payday 347 | payday 348 | payday 349 | payday 350 | payday 351 | payday 352 | payday 353 | payday 354 | payday 355 | payday 356 | payday 357 | payday 358 | payday 359 | payday 360 | payday 361 | replacement_card_duration 362 | replacement_card_duration 363 | replacement_card_duration 364 | replacement_card_duration 365 | replacement_card_duration 366 | replacement_card_duration 367 | replacement_card_duration 368 | replacement_card_duration 369 | replacement_card_duration 370 | replacement_card_duration 371 | replacement_card_duration 372 | replacement_card_duration 373 | replacement_card_duration 374 | replacement_card_duration 375 | replacement_card_duration 376 | replacement_card_duration 377 | replacement_card_duration 378 | replacement_card_duration 379 | replacement_card_duration 380 | replacement_card_duration 381 | time 382 | time 383 | time 384 | time 385 | time 386 | time 387 | time 388 | time 389 | time 390 | time 391 | time 392 | time 393 | time 394 | time 395 | time 396 | time 397 | time 398 | time 399 | time 400 | time 401 | application_status 402 | application_status 403 | application_status 404 | application_status 405 | application_status 406 | application_status 407 | application_status 408 | application_status 409 | application_status 410 | application_status 411 | application_status 412 | application_status 413 | application_status 414 | application_status 415 | application_status 416 | application_status 417 | application_status 418 | application_status 419 | application_status 420 | application_status 421 | flight_status 422 | flight_status 423 | flight_status 424 | flight_status 425 | flight_status 426 | flight_status 427 | flight_status 428 | flight_status 429 | flight_status 430 | flight_status 431 | flight_status 432 | flight_status 433 | flight_status 434 | flight_status 435 | flight_status 436 | flight_status 437 | flight_status 438 | flight_status 439 | flight_status 440 | flight_status 441 | flip_coin 442 | flip_coin 443 | flip_coin 444 | flip_coin 445 | flip_coin 446 | flip_coin 447 | flip_coin 448 | flip_coin 449 | flip_coin 450 | flip_coin 451 | flip_coin 452 | flip_coin 453 | flip_coin 454 | flip_coin 455 | flip_coin 456 | flip_coin 457 | flip_coin 458 | flip_coin 459 | flip_coin 460 | flip_coin 461 | change_user_name 462 | change_user_name 463 | change_user_name 464 | change_user_name 465 | change_user_name 466 | change_user_name 467 | change_user_name 468 | change_user_name 469 | change_user_name 470 | change_user_name 471 | change_user_name 472 | change_user_name 473 | change_user_name 474 | change_user_name 475 | change_user_name 476 | change_user_name 477 | change_user_name 478 | change_user_name 479 | change_user_name 480 | change_user_name 481 | where_are_you_from 482 | where_are_you_from 483 | where_are_you_from 484 | where_are_you_from 485 | where_are_you_from 486 | where_are_you_from 487 | where_are_you_from 488 | where_are_you_from 489 | where_are_you_from 490 | where_are_you_from 491 | where_are_you_from 492 | where_are_you_from 493 | where_are_you_from 494 | where_are_you_from 495 | where_are_you_from 496 | where_are_you_from 497 | where_are_you_from 498 | where_are_you_from 499 | where_are_you_from 500 | where_are_you_from 501 | shopping_list_update 502 | shopping_list_update 503 | shopping_list_update 504 | shopping_list_update 505 | shopping_list_update 506 | shopping_list_update 507 | shopping_list_update 508 | shopping_list_update 509 | shopping_list_update 510 | shopping_list_update 511 | shopping_list_update 512 | shopping_list_update 513 | shopping_list_update 514 | shopping_list_update 515 | shopping_list_update 516 | shopping_list_update 517 | shopping_list_update 518 | shopping_list_update 519 | shopping_list_update 520 | shopping_list_update 521 | what_can_i_ask_you 522 | what_can_i_ask_you 523 | what_can_i_ask_you 524 | what_can_i_ask_you 525 | what_can_i_ask_you 526 | what_can_i_ask_you 527 | what_can_i_ask_you 528 | what_can_i_ask_you 529 | what_can_i_ask_you 530 | what_can_i_ask_you 531 | what_can_i_ask_you 532 | what_can_i_ask_you 533 | what_can_i_ask_you 534 | what_can_i_ask_you 535 | what_can_i_ask_you 536 | what_can_i_ask_you 537 | what_can_i_ask_you 538 | what_can_i_ask_you 539 | what_can_i_ask_you 540 | what_can_i_ask_you 541 | maybe 542 | maybe 543 | maybe 544 | maybe 545 | maybe 546 | maybe 547 | maybe 548 | maybe 549 | maybe 550 | maybe 551 | maybe 552 | maybe 553 | maybe 554 | maybe 555 | maybe 556 | maybe 557 | maybe 558 | maybe 559 | maybe 560 | maybe 561 | oil_change_how 562 | oil_change_how 563 | oil_change_how 564 | oil_change_how 565 | oil_change_how 566 | oil_change_how 567 | oil_change_how 568 | oil_change_how 569 | oil_change_how 570 | oil_change_how 571 | oil_change_how 572 | oil_change_how 573 | oil_change_how 574 | oil_change_how 575 | oil_change_how 576 | oil_change_how 577 | oil_change_how 578 | oil_change_how 579 | oil_change_how 580 | oil_change_how 581 | restaurant_reservation 582 | restaurant_reservation 583 | restaurant_reservation 584 | restaurant_reservation 585 | restaurant_reservation 586 | restaurant_reservation 587 | restaurant_reservation 588 | restaurant_reservation 589 | restaurant_reservation 590 | restaurant_reservation 591 | restaurant_reservation 592 | restaurant_reservation 593 | restaurant_reservation 594 | restaurant_reservation 595 | restaurant_reservation 596 | restaurant_reservation 597 | restaurant_reservation 598 | restaurant_reservation 599 | restaurant_reservation 600 | restaurant_reservation 601 | balance 602 | balance 603 | balance 604 | balance 605 | balance 606 | balance 607 | balance 608 | balance 609 | balance 610 | balance 611 | balance 612 | balance 613 | balance 614 | balance 615 | balance 616 | balance 617 | balance 618 | balance 619 | balance 620 | balance 621 | confirm_reservation 622 | confirm_reservation 623 | confirm_reservation 624 | confirm_reservation 625 | confirm_reservation 626 | confirm_reservation 627 | confirm_reservation 628 | confirm_reservation 629 | confirm_reservation 630 | confirm_reservation 631 | confirm_reservation 632 | confirm_reservation 633 | confirm_reservation 634 | confirm_reservation 635 | confirm_reservation 636 | confirm_reservation 637 | confirm_reservation 638 | confirm_reservation 639 | confirm_reservation 640 | confirm_reservation 641 | freeze_account 642 | freeze_account 643 | freeze_account 644 | freeze_account 645 | freeze_account 646 | freeze_account 647 | freeze_account 648 | freeze_account 649 | freeze_account 650 | freeze_account 651 | freeze_account 652 | freeze_account 653 | freeze_account 654 | freeze_account 655 | freeze_account 656 | freeze_account 657 | freeze_account 658 | freeze_account 659 | freeze_account 660 | freeze_account 661 | rollover_401k 662 | rollover_401k 663 | rollover_401k 664 | rollover_401k 665 | rollover_401k 666 | rollover_401k 667 | rollover_401k 668 | rollover_401k 669 | rollover_401k 670 | rollover_401k 671 | rollover_401k 672 | rollover_401k 673 | rollover_401k 674 | rollover_401k 675 | rollover_401k 676 | rollover_401k 677 | rollover_401k 678 | rollover_401k 679 | rollover_401k 680 | rollover_401k 681 | who_made_you 682 | who_made_you 683 | who_made_you 684 | who_made_you 685 | who_made_you 686 | who_made_you 687 | who_made_you 688 | who_made_you 689 | who_made_you 690 | who_made_you 691 | who_made_you 692 | who_made_you 693 | who_made_you 694 | who_made_you 695 | who_made_you 696 | who_made_you 697 | who_made_you 698 | who_made_you 699 | who_made_you 700 | who_made_you 701 | distance 702 | distance 703 | distance 704 | distance 705 | distance 706 | distance 707 | distance 708 | distance 709 | distance 710 | distance 711 | distance 712 | distance 713 | distance 714 | distance 715 | distance 716 | distance 717 | distance 718 | distance 719 | distance 720 | distance 721 | user_name 722 | user_name 723 | user_name 724 | user_name 725 | user_name 726 | user_name 727 | user_name 728 | user_name 729 | user_name 730 | user_name 731 | user_name 732 | user_name 733 | user_name 734 | user_name 735 | user_name 736 | user_name 737 | user_name 738 | user_name 739 | user_name 740 | user_name 741 | timezone 742 | timezone 743 | timezone 744 | timezone 745 | timezone 746 | timezone 747 | timezone 748 | timezone 749 | timezone 750 | timezone 751 | timezone 752 | timezone 753 | timezone 754 | timezone 755 | timezone 756 | timezone 757 | timezone 758 | timezone 759 | timezone 760 | timezone 761 | next_song 762 | next_song 763 | next_song 764 | next_song 765 | next_song 766 | next_song 767 | next_song 768 | next_song 769 | next_song 770 | next_song 771 | next_song 772 | next_song 773 | next_song 774 | next_song 775 | next_song 776 | next_song 777 | next_song 778 | next_song 779 | next_song 780 | next_song 781 | transactions 782 | transactions 783 | transactions 784 | transactions 785 | transactions 786 | transactions 787 | transactions 788 | transactions 789 | transactions 790 | transactions 791 | transactions 792 | transactions 793 | transactions 794 | transactions 795 | transactions 796 | transactions 797 | transactions 798 | transactions 799 | transactions 800 | transactions 801 | restaurant_suggestion 802 | restaurant_suggestion 803 | restaurant_suggestion 804 | restaurant_suggestion 805 | restaurant_suggestion 806 | restaurant_suggestion 807 | restaurant_suggestion 808 | restaurant_suggestion 809 | restaurant_suggestion 810 | restaurant_suggestion 811 | restaurant_suggestion 812 | restaurant_suggestion 813 | restaurant_suggestion 814 | restaurant_suggestion 815 | restaurant_suggestion 816 | restaurant_suggestion 817 | restaurant_suggestion 818 | restaurant_suggestion 819 | restaurant_suggestion 820 | restaurant_suggestion 821 | rewards_balance 822 | rewards_balance 823 | rewards_balance 824 | rewards_balance 825 | rewards_balance 826 | rewards_balance 827 | rewards_balance 828 | rewards_balance 829 | rewards_balance 830 | rewards_balance 831 | rewards_balance 832 | rewards_balance 833 | rewards_balance 834 | rewards_balance 835 | rewards_balance 836 | rewards_balance 837 | rewards_balance 838 | rewards_balance 839 | rewards_balance 840 | rewards_balance 841 | pay_bill 842 | pay_bill 843 | pay_bill 844 | pay_bill 845 | pay_bill 846 | pay_bill 847 | pay_bill 848 | pay_bill 849 | pay_bill 850 | pay_bill 851 | pay_bill 852 | pay_bill 853 | pay_bill 854 | pay_bill 855 | pay_bill 856 | pay_bill 857 | pay_bill 858 | pay_bill 859 | pay_bill 860 | pay_bill 861 | spending_history 862 | spending_history 863 | spending_history 864 | spending_history 865 | spending_history 866 | spending_history 867 | spending_history 868 | spending_history 869 | spending_history 870 | spending_history 871 | spending_history 872 | spending_history 873 | spending_history 874 | spending_history 875 | spending_history 876 | spending_history 877 | spending_history 878 | spending_history 879 | spending_history 880 | spending_history 881 | pto_request_status 882 | pto_request_status 883 | pto_request_status 884 | pto_request_status 885 | pto_request_status 886 | pto_request_status 887 | pto_request_status 888 | pto_request_status 889 | pto_request_status 890 | pto_request_status 891 | pto_request_status 892 | pto_request_status 893 | pto_request_status 894 | pto_request_status 895 | pto_request_status 896 | pto_request_status 897 | pto_request_status 898 | pto_request_status 899 | pto_request_status 900 | pto_request_status 901 | credit_score 902 | credit_score 903 | credit_score 904 | credit_score 905 | credit_score 906 | credit_score 907 | credit_score 908 | credit_score 909 | credit_score 910 | credit_score 911 | credit_score 912 | credit_score 913 | credit_score 914 | credit_score 915 | credit_score 916 | credit_score 917 | credit_score 918 | credit_score 919 | credit_score 920 | credit_score 921 | new_card 922 | new_card 923 | new_card 924 | new_card 925 | new_card 926 | new_card 927 | new_card 928 | new_card 929 | new_card 930 | new_card 931 | new_card 932 | new_card 933 | new_card 934 | new_card 935 | new_card 936 | new_card 937 | new_card 938 | new_card 939 | new_card 940 | new_card 941 | lost_luggage 942 | lost_luggage 943 | lost_luggage 944 | lost_luggage 945 | lost_luggage 946 | lost_luggage 947 | lost_luggage 948 | lost_luggage 949 | lost_luggage 950 | lost_luggage 951 | lost_luggage 952 | lost_luggage 953 | lost_luggage 954 | lost_luggage 955 | lost_luggage 956 | lost_luggage 957 | lost_luggage 958 | lost_luggage 959 | lost_luggage 960 | lost_luggage 961 | repeat 962 | repeat 963 | repeat 964 | repeat 965 | repeat 966 | repeat 967 | repeat 968 | repeat 969 | repeat 970 | repeat 971 | repeat 972 | repeat 973 | repeat 974 | repeat 975 | repeat 976 | repeat 977 | repeat 978 | repeat 979 | repeat 980 | repeat 981 | mpg 982 | mpg 983 | mpg 984 | mpg 985 | mpg 986 | mpg 987 | mpg 988 | mpg 989 | mpg 990 | mpg 991 | mpg 992 | mpg 993 | mpg 994 | mpg 995 | mpg 996 | mpg 997 | mpg 998 | mpg 999 | mpg 1000 | mpg 1001 | oil_change_when 1002 | oil_change_when 1003 | oil_change_when 1004 | oil_change_when 1005 | oil_change_when 1006 | oil_change_when 1007 | oil_change_when 1008 | oil_change_when 1009 | oil_change_when 1010 | oil_change_when 1011 | oil_change_when 1012 | oil_change_when 1013 | oil_change_when 1014 | oil_change_when 1015 | oil_change_when 1016 | oil_change_when 1017 | oil_change_when 1018 | oil_change_when 1019 | oil_change_when 1020 | oil_change_when 1021 | yes 1022 | yes 1023 | yes 1024 | yes 1025 | yes 1026 | yes 1027 | yes 1028 | yes 1029 | yes 1030 | yes 1031 | yes 1032 | yes 1033 | yes 1034 | yes 1035 | yes 1036 | yes 1037 | yes 1038 | yes 1039 | yes 1040 | yes 1041 | travel_suggestion 1042 | travel_suggestion 1043 | travel_suggestion 1044 | travel_suggestion 1045 | travel_suggestion 1046 | travel_suggestion 1047 | travel_suggestion 1048 | travel_suggestion 1049 | travel_suggestion 1050 | travel_suggestion 1051 | travel_suggestion 1052 | travel_suggestion 1053 | travel_suggestion 1054 | travel_suggestion 1055 | travel_suggestion 1056 | travel_suggestion 1057 | travel_suggestion 1058 | travel_suggestion 1059 | travel_suggestion 1060 | travel_suggestion 1061 | insurance 1062 | insurance 1063 | insurance 1064 | insurance 1065 | insurance 1066 | insurance 1067 | insurance 1068 | insurance 1069 | insurance 1070 | insurance 1071 | insurance 1072 | insurance 1073 | insurance 1074 | insurance 1075 | insurance 1076 | insurance 1077 | insurance 1078 | insurance 1079 | insurance 1080 | insurance 1081 | todo_list_update 1082 | todo_list_update 1083 | todo_list_update 1084 | todo_list_update 1085 | todo_list_update 1086 | todo_list_update 1087 | todo_list_update 1088 | todo_list_update 1089 | todo_list_update 1090 | todo_list_update 1091 | todo_list_update 1092 | todo_list_update 1093 | todo_list_update 1094 | todo_list_update 1095 | todo_list_update 1096 | todo_list_update 1097 | todo_list_update 1098 | todo_list_update 1099 | todo_list_update 1100 | todo_list_update 1101 | reminder 1102 | reminder 1103 | reminder 1104 | reminder 1105 | reminder 1106 | reminder 1107 | reminder 1108 | reminder 1109 | reminder 1110 | reminder 1111 | reminder 1112 | reminder 1113 | reminder 1114 | reminder 1115 | reminder 1116 | reminder 1117 | reminder 1118 | reminder 1119 | reminder 1120 | reminder 1121 | change_speed 1122 | change_speed 1123 | change_speed 1124 | change_speed 1125 | change_speed 1126 | change_speed 1127 | change_speed 1128 | change_speed 1129 | change_speed 1130 | change_speed 1131 | change_speed 1132 | change_speed 1133 | change_speed 1134 | change_speed 1135 | change_speed 1136 | change_speed 1137 | change_speed 1138 | change_speed 1139 | change_speed 1140 | change_speed 1141 | tire_pressure 1142 | tire_pressure 1143 | tire_pressure 1144 | tire_pressure 1145 | tire_pressure 1146 | tire_pressure 1147 | tire_pressure 1148 | tire_pressure 1149 | tire_pressure 1150 | tire_pressure 1151 | tire_pressure 1152 | tire_pressure 1153 | tire_pressure 1154 | tire_pressure 1155 | tire_pressure 1156 | tire_pressure 1157 | tire_pressure 1158 | tire_pressure 1159 | tire_pressure 1160 | tire_pressure 1161 | no 1162 | no 1163 | no 1164 | no 1165 | no 1166 | no 1167 | no 1168 | no 1169 | no 1170 | no 1171 | no 1172 | no 1173 | no 1174 | no 1175 | no 1176 | no 1177 | no 1178 | no 1179 | no 1180 | no 1181 | apr 1182 | apr 1183 | apr 1184 | apr 1185 | apr 1186 | apr 1187 | apr 1188 | apr 1189 | apr 1190 | apr 1191 | apr 1192 | apr 1193 | apr 1194 | apr 1195 | apr 1196 | apr 1197 | apr 1198 | apr 1199 | apr 1200 | apr 1201 | nutrition_info 1202 | nutrition_info 1203 | nutrition_info 1204 | nutrition_info 1205 | nutrition_info 1206 | nutrition_info 1207 | nutrition_info 1208 | nutrition_info 1209 | nutrition_info 1210 | nutrition_info 1211 | nutrition_info 1212 | nutrition_info 1213 | nutrition_info 1214 | nutrition_info 1215 | nutrition_info 1216 | nutrition_info 1217 | nutrition_info 1218 | nutrition_info 1219 | nutrition_info 1220 | nutrition_info 1221 | calendar 1222 | calendar 1223 | calendar 1224 | calendar 1225 | calendar 1226 | calendar 1227 | calendar 1228 | calendar 1229 | calendar 1230 | calendar 1231 | calendar 1232 | calendar 1233 | calendar 1234 | calendar 1235 | calendar 1236 | calendar 1237 | calendar 1238 | calendar 1239 | calendar 1240 | calendar 1241 | uber 1242 | uber 1243 | uber 1244 | uber 1245 | uber 1246 | uber 1247 | uber 1248 | uber 1249 | uber 1250 | uber 1251 | uber 1252 | uber 1253 | uber 1254 | uber 1255 | uber 1256 | uber 1257 | uber 1258 | uber 1259 | uber 1260 | uber 1261 | calculator 1262 | calculator 1263 | calculator 1264 | calculator 1265 | calculator 1266 | calculator 1267 | calculator 1268 | calculator 1269 | calculator 1270 | calculator 1271 | calculator 1272 | calculator 1273 | calculator 1274 | calculator 1275 | calculator 1276 | calculator 1277 | calculator 1278 | calculator 1279 | calculator 1280 | calculator 1281 | date 1282 | date 1283 | date 1284 | date 1285 | date 1286 | date 1287 | date 1288 | date 1289 | date 1290 | date 1291 | date 1292 | date 1293 | date 1294 | date 1295 | date 1296 | date 1297 | date 1298 | date 1299 | date 1300 | date 1301 | carry_on 1302 | carry_on 1303 | carry_on 1304 | carry_on 1305 | carry_on 1306 | carry_on 1307 | carry_on 1308 | carry_on 1309 | carry_on 1310 | carry_on 1311 | carry_on 1312 | carry_on 1313 | carry_on 1314 | carry_on 1315 | carry_on 1316 | carry_on 1317 | carry_on 1318 | carry_on 1319 | carry_on 1320 | carry_on 1321 | pto_used 1322 | pto_used 1323 | pto_used 1324 | pto_used 1325 | pto_used 1326 | pto_used 1327 | pto_used 1328 | pto_used 1329 | pto_used 1330 | pto_used 1331 | pto_used 1332 | pto_used 1333 | pto_used 1334 | pto_used 1335 | pto_used 1336 | pto_used 1337 | pto_used 1338 | pto_used 1339 | pto_used 1340 | pto_used 1341 | schedule_maintenance 1342 | schedule_maintenance 1343 | schedule_maintenance 1344 | schedule_maintenance 1345 | schedule_maintenance 1346 | schedule_maintenance 1347 | schedule_maintenance 1348 | schedule_maintenance 1349 | schedule_maintenance 1350 | schedule_maintenance 1351 | schedule_maintenance 1352 | schedule_maintenance 1353 | schedule_maintenance 1354 | schedule_maintenance 1355 | schedule_maintenance 1356 | schedule_maintenance 1357 | schedule_maintenance 1358 | schedule_maintenance 1359 | schedule_maintenance 1360 | schedule_maintenance 1361 | travel_notification 1362 | travel_notification 1363 | travel_notification 1364 | travel_notification 1365 | travel_notification 1366 | travel_notification 1367 | travel_notification 1368 | travel_notification 1369 | travel_notification 1370 | travel_notification 1371 | travel_notification 1372 | travel_notification 1373 | travel_notification 1374 | travel_notification 1375 | travel_notification 1376 | travel_notification 1377 | travel_notification 1378 | travel_notification 1379 | travel_notification 1380 | travel_notification 1381 | sync_device 1382 | sync_device 1383 | sync_device 1384 | sync_device 1385 | sync_device 1386 | sync_device 1387 | sync_device 1388 | sync_device 1389 | sync_device 1390 | sync_device 1391 | sync_device 1392 | sync_device 1393 | sync_device 1394 | sync_device 1395 | sync_device 1396 | sync_device 1397 | sync_device 1398 | sync_device 1399 | sync_device 1400 | sync_device 1401 | thank_you 1402 | thank_you 1403 | thank_you 1404 | thank_you 1405 | thank_you 1406 | thank_you 1407 | thank_you 1408 | thank_you 1409 | thank_you 1410 | thank_you 1411 | thank_you 1412 | thank_you 1413 | thank_you 1414 | thank_you 1415 | thank_you 1416 | thank_you 1417 | thank_you 1418 | thank_you 1419 | thank_you 1420 | thank_you 1421 | roll_dice 1422 | roll_dice 1423 | roll_dice 1424 | roll_dice 1425 | roll_dice 1426 | roll_dice 1427 | roll_dice 1428 | roll_dice 1429 | roll_dice 1430 | roll_dice 1431 | roll_dice 1432 | roll_dice 1433 | roll_dice 1434 | roll_dice 1435 | roll_dice 1436 | roll_dice 1437 | roll_dice 1438 | roll_dice 1439 | roll_dice 1440 | roll_dice 1441 | food_last 1442 | food_last 1443 | food_last 1444 | food_last 1445 | food_last 1446 | food_last 1447 | food_last 1448 | food_last 1449 | food_last 1450 | food_last 1451 | food_last 1452 | food_last 1453 | food_last 1454 | food_last 1455 | food_last 1456 | food_last 1457 | food_last 1458 | food_last 1459 | food_last 1460 | food_last 1461 | cook_time 1462 | cook_time 1463 | cook_time 1464 | cook_time 1465 | cook_time 1466 | cook_time 1467 | cook_time 1468 | cook_time 1469 | cook_time 1470 | cook_time 1471 | cook_time 1472 | cook_time 1473 | cook_time 1474 | cook_time 1475 | cook_time 1476 | cook_time 1477 | cook_time 1478 | cook_time 1479 | cook_time 1480 | cook_time 1481 | reminder_update 1482 | reminder_update 1483 | reminder_update 1484 | reminder_update 1485 | reminder_update 1486 | reminder_update 1487 | reminder_update 1488 | reminder_update 1489 | reminder_update 1490 | reminder_update 1491 | reminder_update 1492 | reminder_update 1493 | reminder_update 1494 | reminder_update 1495 | reminder_update 1496 | reminder_update 1497 | reminder_update 1498 | reminder_update 1499 | reminder_update 1500 | reminder_update 1501 | report_lost_card 1502 | report_lost_card 1503 | report_lost_card 1504 | report_lost_card 1505 | report_lost_card 1506 | report_lost_card 1507 | report_lost_card 1508 | report_lost_card 1509 | report_lost_card 1510 | report_lost_card 1511 | 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| pin_change 2016 | pin_change 2017 | pin_change 2018 | pin_change 2019 | pin_change 2020 | pin_change 2021 | goodbye 2022 | goodbye 2023 | goodbye 2024 | goodbye 2025 | goodbye 2026 | goodbye 2027 | goodbye 2028 | goodbye 2029 | goodbye 2030 | goodbye 2031 | goodbye 2032 | goodbye 2033 | goodbye 2034 | goodbye 2035 | goodbye 2036 | goodbye 2037 | goodbye 2038 | goodbye 2039 | goodbye 2040 | goodbye 2041 | account_blocked 2042 | account_blocked 2043 | account_blocked 2044 | account_blocked 2045 | account_blocked 2046 | account_blocked 2047 | account_blocked 2048 | account_blocked 2049 | account_blocked 2050 | account_blocked 2051 | account_blocked 2052 | account_blocked 2053 | account_blocked 2054 | account_blocked 2055 | account_blocked 2056 | account_blocked 2057 | account_blocked 2058 | account_blocked 2059 | account_blocked 2060 | account_blocked 2061 | what_song 2062 | what_song 2063 | what_song 2064 | what_song 2065 | what_song 2066 | what_song 2067 | what_song 2068 | what_song 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| oos 216 | oos 217 | oos 218 | oos 219 | oos 220 | oos 221 | oos 222 | oos 223 | oos 224 | oos 225 | oos 226 | oos 227 | oos 228 | oos 229 | oos 230 | oos 231 | oos 232 | oos 233 | oos 234 | oos 235 | oos 236 | oos 237 | oos 238 | oos 239 | oos 240 | oos 241 | oos 242 | oos 243 | oos 244 | oos 245 | oos 246 | oos 247 | oos 248 | oos 249 | oos 250 | oos 251 | oos 252 | oos 253 | oos 254 | oos 255 | oos 256 | oos 257 | oos 258 | oos 259 | oos 260 | oos 261 | oos 262 | oos 263 | oos 264 | oos 265 | oos 266 | oos 267 | oos 268 | oos 269 | oos 270 | oos 271 | oos 272 | oos 273 | oos 274 | oos 275 | oos 276 | oos 277 | oos 278 | oos 279 | oos 280 | oos 281 | oos 282 | oos 283 | oos 284 | oos 285 | oos 286 | oos 287 | oos 288 | oos 289 | oos 290 | oos 291 | oos 292 | oos 293 | oos 294 | oos 295 | oos 296 | oos 297 | oos 298 | oos 299 | oos 300 | oos 301 | oos 302 | oos 303 | oos 304 | oos 305 | oos 306 | oos 307 | oos 308 | oos 309 | oos 310 | oos 311 | oos 312 | oos 313 | oos 314 | oos 315 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| oos 616 | oos 617 | oos 618 | oos 619 | oos 620 | oos 621 | oos 622 | oos 623 | oos 624 | oos 625 | oos 626 | oos 627 | oos 628 | oos 629 | oos 630 | oos 631 | oos 632 | oos 633 | oos 634 | oos 635 | oos 636 | oos 637 | oos 638 | oos 639 | oos 640 | oos 641 | oos 642 | oos 643 | oos 644 | oos 645 | oos 646 | oos 647 | oos 648 | oos 649 | oos 650 | oos 651 | oos 652 | oos 653 | oos 654 | oos 655 | oos 656 | oos 657 | oos 658 | oos 659 | oos 660 | oos 661 | oos 662 | oos 663 | oos 664 | oos 665 | oos 666 | oos 667 | oos 668 | oos 669 | oos 670 | oos 671 | oos 672 | oos 673 | oos 674 | oos 675 | oos 676 | oos 677 | oos 678 | oos 679 | oos 680 | oos 681 | oos 682 | oos 683 | oos 684 | oos 685 | oos 686 | oos 687 | oos 688 | oos 689 | oos 690 | oos 691 | oos 692 | oos 693 | oos 694 | oos 695 | oos 696 | oos 697 | oos 698 | oos 699 | oos 700 | oos 701 | oos 702 | oos 703 | oos 704 | oos 705 | oos 706 | oos 707 | oos 708 | oos 709 | oos 710 | oos 711 | oos 712 | oos 713 | oos 714 | oos 715 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translate 1010 | translate 1011 | translate 1012 | translate 1013 | translate 1014 | translate 1015 | translate 1016 | translate 1017 | translate 1018 | translate 1019 | translate 1020 | translate 1021 | translate 1022 | translate 1023 | translate 1024 | translate 1025 | translate 1026 | translate 1027 | translate 1028 | translate 1029 | translate 1030 | translate 1031 | transfer 1032 | transfer 1033 | transfer 1034 | transfer 1035 | transfer 1036 | transfer 1037 | transfer 1038 | transfer 1039 | transfer 1040 | transfer 1041 | transfer 1042 | transfer 1043 | transfer 1044 | transfer 1045 | transfer 1046 | transfer 1047 | transfer 1048 | transfer 1049 | transfer 1050 | transfer 1051 | transfer 1052 | transfer 1053 | transfer 1054 | transfer 1055 | transfer 1056 | transfer 1057 | transfer 1058 | transfer 1059 | transfer 1060 | transfer 1061 | timer 1062 | timer 1063 | timer 1064 | timer 1065 | timer 1066 | timer 1067 | timer 1068 | timer 1069 | timer 1070 | timer 1071 | timer 1072 | 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1315 | fun_fact 1316 | fun_fact 1317 | fun_fact 1318 | fun_fact 1319 | fun_fact 1320 | fun_fact 1321 | fun_fact 1322 | fun_fact 1323 | fun_fact 1324 | fun_fact 1325 | fun_fact 1326 | fun_fact 1327 | fun_fact 1328 | fun_fact 1329 | fun_fact 1330 | fun_fact 1331 | change_language 1332 | change_language 1333 | change_language 1334 | change_language 1335 | change_language 1336 | change_language 1337 | change_language 1338 | change_language 1339 | change_language 1340 | change_language 1341 | change_language 1342 | change_language 1343 | change_language 1344 | change_language 1345 | change_language 1346 | change_language 1347 | change_language 1348 | change_language 1349 | change_language 1350 | change_language 1351 | change_language 1352 | change_language 1353 | change_language 1354 | change_language 1355 | change_language 1356 | change_language 1357 | change_language 1358 | change_language 1359 | change_language 1360 | change_language 1361 | payday 1362 | payday 1363 | payday 1364 | 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1410 | replacement_card_duration 1411 | replacement_card_duration 1412 | replacement_card_duration 1413 | replacement_card_duration 1414 | replacement_card_duration 1415 | replacement_card_duration 1416 | replacement_card_duration 1417 | replacement_card_duration 1418 | replacement_card_duration 1419 | replacement_card_duration 1420 | replacement_card_duration 1421 | time 1422 | time 1423 | time 1424 | time 1425 | time 1426 | time 1427 | time 1428 | time 1429 | time 1430 | time 1431 | time 1432 | time 1433 | time 1434 | time 1435 | time 1436 | time 1437 | time 1438 | time 1439 | time 1440 | time 1441 | time 1442 | time 1443 | time 1444 | time 1445 | time 1446 | time 1447 | time 1448 | time 1449 | time 1450 | time 1451 | application_status 1452 | application_status 1453 | application_status 1454 | application_status 1455 | application_status 1456 | application_status 1457 | application_status 1458 | application_status 1459 | application_status 1460 | application_status 1461 | application_status 1462 | application_status 1463 | application_status 1464 | application_status 1465 | application_status 1466 | application_status 1467 | application_status 1468 | application_status 1469 | application_status 1470 | application_status 1471 | application_status 1472 | application_status 1473 | application_status 1474 | application_status 1475 | application_status 1476 | application_status 1477 | application_status 1478 | application_status 1479 | application_status 1480 | application_status 1481 | flight_status 1482 | flight_status 1483 | flight_status 1484 | flight_status 1485 | flight_status 1486 | flight_status 1487 | flight_status 1488 | flight_status 1489 | flight_status 1490 | flight_status 1491 | flight_status 1492 | flight_status 1493 | flight_status 1494 | flight_status 1495 | flight_status 1496 | flight_status 1497 | flight_status 1498 | flight_status 1499 | flight_status 1500 | flight_status 1501 | flight_status 1502 | flight_status 1503 | flight_status 1504 | flight_status 1505 | flight_status 1506 | flight_status 1507 | flight_status 1508 | flight_status 1509 | flight_status 1510 | flight_status 1511 | flip_coin 1512 | flip_coin 1513 | flip_coin 1514 | flip_coin 1515 | flip_coin 1516 | flip_coin 1517 | flip_coin 1518 | flip_coin 1519 | flip_coin 1520 | flip_coin 1521 | flip_coin 1522 | flip_coin 1523 | flip_coin 1524 | flip_coin 1525 | flip_coin 1526 | flip_coin 1527 | flip_coin 1528 | flip_coin 1529 | flip_coin 1530 | flip_coin 1531 | flip_coin 1532 | flip_coin 1533 | flip_coin 1534 | flip_coin 1535 | flip_coin 1536 | flip_coin 1537 | flip_coin 1538 | flip_coin 1539 | flip_coin 1540 | flip_coin 1541 | change_user_name 1542 | change_user_name 1543 | change_user_name 1544 | change_user_name 1545 | change_user_name 1546 | change_user_name 1547 | change_user_name 1548 | change_user_name 1549 | change_user_name 1550 | change_user_name 1551 | change_user_name 1552 | change_user_name 1553 | change_user_name 1554 | change_user_name 1555 | change_user_name 1556 | change_user_name 1557 | change_user_name 1558 | change_user_name 1559 | change_user_name 1560 | change_user_name 1561 | change_user_name 1562 | change_user_name 1563 | change_user_name 1564 | change_user_name 1565 | change_user_name 1566 | change_user_name 1567 | change_user_name 1568 | change_user_name 1569 | change_user_name 1570 | change_user_name 1571 | where_are_you_from 1572 | where_are_you_from 1573 | where_are_you_from 1574 | where_are_you_from 1575 | where_are_you_from 1576 | where_are_you_from 1577 | where_are_you_from 1578 | where_are_you_from 1579 | where_are_you_from 1580 | where_are_you_from 1581 | where_are_you_from 1582 | where_are_you_from 1583 | where_are_you_from 1584 | where_are_you_from 1585 | where_are_you_from 1586 | where_are_you_from 1587 | where_are_you_from 1588 | where_are_you_from 1589 | where_are_you_from 1590 | where_are_you_from 1591 | where_are_you_from 1592 | where_are_you_from 1593 | where_are_you_from 1594 | where_are_you_from 1595 | where_are_you_from 1596 | where_are_you_from 1597 | where_are_you_from 1598 | where_are_you_from 1599 | where_are_you_from 1600 | where_are_you_from 1601 | shopping_list_update 1602 | shopping_list_update 1603 | shopping_list_update 1604 | shopping_list_update 1605 | shopping_list_update 1606 | shopping_list_update 1607 | shopping_list_update 1608 | shopping_list_update 1609 | shopping_list_update 1610 | shopping_list_update 1611 | shopping_list_update 1612 | shopping_list_update 1613 | shopping_list_update 1614 | shopping_list_update 1615 | shopping_list_update 1616 | shopping_list_update 1617 | shopping_list_update 1618 | shopping_list_update 1619 | shopping_list_update 1620 | shopping_list_update 1621 | shopping_list_update 1622 | shopping_list_update 1623 | shopping_list_update 1624 | shopping_list_update 1625 | shopping_list_update 1626 | shopping_list_update 1627 | shopping_list_update 1628 | shopping_list_update 1629 | shopping_list_update 1630 | shopping_list_update 1631 | what_can_i_ask_you 1632 | what_can_i_ask_you 1633 | what_can_i_ask_you 1634 | what_can_i_ask_you 1635 | what_can_i_ask_you 1636 | what_can_i_ask_you 1637 | what_can_i_ask_you 1638 | what_can_i_ask_you 1639 | what_can_i_ask_you 1640 | what_can_i_ask_you 1641 | what_can_i_ask_you 1642 | what_can_i_ask_you 1643 | what_can_i_ask_you 1644 | what_can_i_ask_you 1645 | what_can_i_ask_you 1646 | what_can_i_ask_you 1647 | what_can_i_ask_you 1648 | what_can_i_ask_you 1649 | what_can_i_ask_you 1650 | what_can_i_ask_you 1651 | what_can_i_ask_you 1652 | what_can_i_ask_you 1653 | what_can_i_ask_you 1654 | what_can_i_ask_you 1655 | what_can_i_ask_you 1656 | what_can_i_ask_you 1657 | what_can_i_ask_you 1658 | what_can_i_ask_you 1659 | what_can_i_ask_you 1660 | what_can_i_ask_you 1661 | maybe 1662 | maybe 1663 | maybe 1664 | maybe 1665 | maybe 1666 | maybe 1667 | maybe 1668 | maybe 1669 | maybe 1670 | maybe 1671 | maybe 1672 | maybe 1673 | maybe 1674 | maybe 1675 | maybe 1676 | maybe 1677 | maybe 1678 | maybe 1679 | maybe 1680 | maybe 1681 | maybe 1682 | maybe 1683 | maybe 1684 | maybe 1685 | maybe 1686 | maybe 1687 | maybe 1688 | maybe 1689 | maybe 1690 | maybe 1691 | oil_change_how 1692 | oil_change_how 1693 | oil_change_how 1694 | oil_change_how 1695 | oil_change_how 1696 | oil_change_how 1697 | oil_change_how 1698 | oil_change_how 1699 | oil_change_how 1700 | oil_change_how 1701 | oil_change_how 1702 | oil_change_how 1703 | oil_change_how 1704 | oil_change_how 1705 | oil_change_how 1706 | oil_change_how 1707 | oil_change_how 1708 | oil_change_how 1709 | oil_change_how 1710 | oil_change_how 1711 | oil_change_how 1712 | oil_change_how 1713 | oil_change_how 1714 | oil_change_how 1715 | oil_change_how 1716 | oil_change_how 1717 | oil_change_how 1718 | oil_change_how 1719 | oil_change_how 1720 | oil_change_how 1721 | restaurant_reservation 1722 | restaurant_reservation 1723 | restaurant_reservation 1724 | restaurant_reservation 1725 | restaurant_reservation 1726 | restaurant_reservation 1727 | restaurant_reservation 1728 | restaurant_reservation 1729 | restaurant_reservation 1730 | restaurant_reservation 1731 | restaurant_reservation 1732 | restaurant_reservation 1733 | restaurant_reservation 1734 | restaurant_reservation 1735 | restaurant_reservation 1736 | restaurant_reservation 1737 | restaurant_reservation 1738 | restaurant_reservation 1739 | restaurant_reservation 1740 | restaurant_reservation 1741 | restaurant_reservation 1742 | restaurant_reservation 1743 | restaurant_reservation 1744 | restaurant_reservation 1745 | restaurant_reservation 1746 | restaurant_reservation 1747 | restaurant_reservation 1748 | restaurant_reservation 1749 | restaurant_reservation 1750 | restaurant_reservation 1751 | balance 1752 | balance 1753 | balance 1754 | balance 1755 | balance 1756 | balance 1757 | balance 1758 | balance 1759 | balance 1760 | balance 1761 | balance 1762 | balance 1763 | balance 1764 | balance 1765 | balance 1766 | balance 1767 | balance 1768 | balance 1769 | balance 1770 | balance 1771 | balance 1772 | balance 1773 | balance 1774 | balance 1775 | balance 1776 | balance 1777 | balance 1778 | balance 1779 | balance 1780 | balance 1781 | confirm_reservation 1782 | confirm_reservation 1783 | confirm_reservation 1784 | confirm_reservation 1785 | confirm_reservation 1786 | confirm_reservation 1787 | confirm_reservation 1788 | confirm_reservation 1789 | confirm_reservation 1790 | confirm_reservation 1791 | confirm_reservation 1792 | confirm_reservation 1793 | confirm_reservation 1794 | confirm_reservation 1795 | confirm_reservation 1796 | confirm_reservation 1797 | confirm_reservation 1798 | confirm_reservation 1799 | confirm_reservation 1800 | confirm_reservation 1801 | confirm_reservation 1802 | confirm_reservation 1803 | confirm_reservation 1804 | confirm_reservation 1805 | confirm_reservation 1806 | confirm_reservation 1807 | confirm_reservation 1808 | confirm_reservation 1809 | confirm_reservation 1810 | confirm_reservation 1811 | freeze_account 1812 | freeze_account 1813 | freeze_account 1814 | freeze_account 1815 | freeze_account 1816 | freeze_account 1817 | freeze_account 1818 | freeze_account 1819 | freeze_account 1820 | freeze_account 1821 | freeze_account 1822 | freeze_account 1823 | freeze_account 1824 | freeze_account 1825 | freeze_account 1826 | freeze_account 1827 | freeze_account 1828 | freeze_account 1829 | freeze_account 1830 | freeze_account 1831 | freeze_account 1832 | freeze_account 1833 | freeze_account 1834 | freeze_account 1835 | freeze_account 1836 | freeze_account 1837 | freeze_account 1838 | freeze_account 1839 | freeze_account 1840 | freeze_account 1841 | rollover_401k 1842 | rollover_401k 1843 | rollover_401k 1844 | rollover_401k 1845 | rollover_401k 1846 | rollover_401k 1847 | rollover_401k 1848 | rollover_401k 1849 | rollover_401k 1850 | rollover_401k 1851 | rollover_401k 1852 | rollover_401k 1853 | rollover_401k 1854 | rollover_401k 1855 | rollover_401k 1856 | rollover_401k 1857 | rollover_401k 1858 | rollover_401k 1859 | rollover_401k 1860 | rollover_401k 1861 | rollover_401k 1862 | rollover_401k 1863 | rollover_401k 1864 | rollover_401k 1865 | rollover_401k 1866 | rollover_401k 1867 | rollover_401k 1868 | rollover_401k 1869 | rollover_401k 1870 | rollover_401k 1871 | who_made_you 1872 | who_made_you 1873 | who_made_you 1874 | who_made_you 1875 | who_made_you 1876 | who_made_you 1877 | who_made_you 1878 | who_made_you 1879 | who_made_you 1880 | who_made_you 1881 | who_made_you 1882 | who_made_you 1883 | who_made_you 1884 | who_made_you 1885 | who_made_you 1886 | who_made_you 1887 | who_made_you 1888 | who_made_you 1889 | who_made_you 1890 | who_made_you 1891 | who_made_you 1892 | who_made_you 1893 | who_made_you 1894 | who_made_you 1895 | who_made_you 1896 | who_made_you 1897 | who_made_you 1898 | who_made_you 1899 | who_made_you 1900 | who_made_you 1901 | distance 1902 | distance 1903 | distance 1904 | distance 1905 | distance 1906 | distance 1907 | distance 1908 | distance 1909 | distance 1910 | distance 1911 | distance 1912 | distance 1913 | distance 1914 | distance 1915 | distance 1916 | distance 1917 | distance 1918 | distance 1919 | distance 1920 | distance 1921 | distance 1922 | distance 1923 | distance 1924 | distance 1925 | distance 1926 | distance 1927 | distance 1928 | distance 1929 | distance 1930 | distance 1931 | user_name 1932 | user_name 1933 | user_name 1934 | user_name 1935 | user_name 1936 | user_name 1937 | user_name 1938 | user_name 1939 | user_name 1940 | user_name 1941 | user_name 1942 | user_name 1943 | user_name 1944 | user_name 1945 | user_name 1946 | user_name 1947 | user_name 1948 | user_name 1949 | user_name 1950 | user_name 1951 | user_name 1952 | user_name 1953 | user_name 1954 | user_name 1955 | user_name 1956 | user_name 1957 | user_name 1958 | user_name 1959 | user_name 1960 | user_name 1961 | timezone 1962 | timezone 1963 | timezone 1964 | timezone 1965 | timezone 1966 | timezone 1967 | timezone 1968 | timezone 1969 | timezone 1970 | timezone 1971 | timezone 1972 | timezone 1973 | timezone 1974 | timezone 1975 | timezone 1976 | timezone 1977 | timezone 1978 | timezone 1979 | timezone 1980 | timezone 1981 | timezone 1982 | timezone 1983 | timezone 1984 | timezone 1985 | timezone 1986 | timezone 1987 | timezone 1988 | timezone 1989 | timezone 1990 | timezone 1991 | next_song 1992 | next_song 1993 | next_song 1994 | next_song 1995 | next_song 1996 | next_song 1997 | next_song 1998 | next_song 1999 | next_song 2000 | next_song 2001 | next_song 2002 | next_song 2003 | next_song 2004 | next_song 2005 | next_song 2006 | next_song 2007 | next_song 2008 | next_song 2009 | next_song 2010 | next_song 2011 | next_song 2012 | next_song 2013 | next_song 2014 | next_song 2015 | next_song 2016 | next_song 2017 | next_song 2018 | next_song 2019 | next_song 2020 | next_song 2021 | transactions 2022 | transactions 2023 | transactions 2024 | transactions 2025 | transactions 2026 | transactions 2027 | transactions 2028 | transactions 2029 | transactions 2030 | transactions 2031 | transactions 2032 | transactions 2033 | transactions 2034 | transactions 2035 | transactions 2036 | transactions 2037 | transactions 2038 | transactions 2039 | transactions 2040 | transactions 2041 | transactions 2042 | transactions 2043 | transactions 2044 | transactions 2045 | transactions 2046 | transactions 2047 | transactions 2048 | transactions 2049 | transactions 2050 | transactions 2051 | restaurant_suggestion 2052 | restaurant_suggestion 2053 | restaurant_suggestion 2054 | restaurant_suggestion 2055 | restaurant_suggestion 2056 | restaurant_suggestion 2057 | restaurant_suggestion 2058 | restaurant_suggestion 2059 | restaurant_suggestion 2060 | restaurant_suggestion 2061 | restaurant_suggestion 2062 | restaurant_suggestion 2063 | restaurant_suggestion 2064 | restaurant_suggestion 2065 | restaurant_suggestion 2066 | restaurant_suggestion 2067 | restaurant_suggestion 2068 | restaurant_suggestion 2069 | restaurant_suggestion 2070 | restaurant_suggestion 2071 | restaurant_suggestion 2072 | restaurant_suggestion 2073 | restaurant_suggestion 2074 | restaurant_suggestion 2075 | restaurant_suggestion 2076 | restaurant_suggestion 2077 | restaurant_suggestion 2078 | restaurant_suggestion 2079 | restaurant_suggestion 2080 | restaurant_suggestion 2081 | rewards_balance 2082 | rewards_balance 2083 | rewards_balance 2084 | rewards_balance 2085 | rewards_balance 2086 | rewards_balance 2087 | rewards_balance 2088 | rewards_balance 2089 | rewards_balance 2090 | rewards_balance 2091 | rewards_balance 2092 | rewards_balance 2093 | rewards_balance 2094 | rewards_balance 2095 | rewards_balance 2096 | rewards_balance 2097 | rewards_balance 2098 | rewards_balance 2099 | rewards_balance 2100 | rewards_balance 2101 | rewards_balance 2102 | rewards_balance 2103 | rewards_balance 2104 | rewards_balance 2105 | rewards_balance 2106 | rewards_balance 2107 | rewards_balance 2108 | rewards_balance 2109 | rewards_balance 2110 | rewards_balance 2111 | pay_bill 2112 | pay_bill 2113 | pay_bill 2114 | pay_bill 2115 | pay_bill 2116 | pay_bill 2117 | pay_bill 2118 | pay_bill 2119 | pay_bill 2120 | pay_bill 2121 | pay_bill 2122 | pay_bill 2123 | pay_bill 2124 | pay_bill 2125 | pay_bill 2126 | pay_bill 2127 | pay_bill 2128 | pay_bill 2129 | pay_bill 2130 | pay_bill 2131 | pay_bill 2132 | pay_bill 2133 | pay_bill 2134 | pay_bill 2135 | pay_bill 2136 | pay_bill 2137 | pay_bill 2138 | pay_bill 2139 | pay_bill 2140 | pay_bill 2141 | spending_history 2142 | spending_history 2143 | spending_history 2144 | spending_history 2145 | spending_history 2146 | spending_history 2147 | spending_history 2148 | spending_history 2149 | spending_history 2150 | spending_history 2151 | spending_history 2152 | spending_history 2153 | spending_history 2154 | spending_history 2155 | spending_history 2156 | spending_history 2157 | spending_history 2158 | spending_history 2159 | spending_history 2160 | spending_history 2161 | spending_history 2162 | spending_history 2163 | spending_history 2164 | spending_history 2165 | spending_history 2166 | spending_history 2167 | spending_history 2168 | spending_history 2169 | spending_history 2170 | spending_history 2171 | pto_request_status 2172 | pto_request_status 2173 | pto_request_status 2174 | pto_request_status 2175 | pto_request_status 2176 | pto_request_status 2177 | pto_request_status 2178 | pto_request_status 2179 | pto_request_status 2180 | pto_request_status 2181 | pto_request_status 2182 | pto_request_status 2183 | pto_request_status 2184 | pto_request_status 2185 | pto_request_status 2186 | pto_request_status 2187 | pto_request_status 2188 | pto_request_status 2189 | pto_request_status 2190 | pto_request_status 2191 | pto_request_status 2192 | pto_request_status 2193 | pto_request_status 2194 | pto_request_status 2195 | pto_request_status 2196 | pto_request_status 2197 | pto_request_status 2198 | pto_request_status 2199 | pto_request_status 2200 | pto_request_status 2201 | credit_score 2202 | credit_score 2203 | credit_score 2204 | credit_score 2205 | credit_score 2206 | credit_score 2207 | credit_score 2208 | credit_score 2209 | credit_score 2210 | credit_score 2211 | credit_score 2212 | credit_score 2213 | credit_score 2214 | credit_score 2215 | credit_score 2216 | credit_score 2217 | credit_score 2218 | credit_score 2219 | credit_score 2220 | credit_score 2221 | credit_score 2222 | credit_score 2223 | credit_score 2224 | credit_score 2225 | credit_score 2226 | credit_score 2227 | credit_score 2228 | credit_score 2229 | credit_score 2230 | credit_score 2231 | new_card 2232 | new_card 2233 | new_card 2234 | new_card 2235 | new_card 2236 | new_card 2237 | new_card 2238 | new_card 2239 | new_card 2240 | new_card 2241 | new_card 2242 | new_card 2243 | new_card 2244 | new_card 2245 | new_card 2246 | new_card 2247 | new_card 2248 | new_card 2249 | new_card 2250 | new_card 2251 | new_card 2252 | new_card 2253 | new_card 2254 | new_card 2255 | new_card 2256 | new_card 2257 | new_card 2258 | new_card 2259 | new_card 2260 | new_card 2261 | lost_luggage 2262 | lost_luggage 2263 | lost_luggage 2264 | lost_luggage 2265 | lost_luggage 2266 | lost_luggage 2267 | lost_luggage 2268 | lost_luggage 2269 | lost_luggage 2270 | lost_luggage 2271 | lost_luggage 2272 | lost_luggage 2273 | lost_luggage 2274 | lost_luggage 2275 | lost_luggage 2276 | lost_luggage 2277 | lost_luggage 2278 | lost_luggage 2279 | lost_luggage 2280 | lost_luggage 2281 | lost_luggage 2282 | lost_luggage 2283 | lost_luggage 2284 | lost_luggage 2285 | lost_luggage 2286 | lost_luggage 2287 | lost_luggage 2288 | lost_luggage 2289 | lost_luggage 2290 | lost_luggage 2291 | repeat 2292 | repeat 2293 | repeat 2294 | repeat 2295 | repeat 2296 | repeat 2297 | repeat 2298 | repeat 2299 | repeat 2300 | repeat 2301 | repeat 2302 | repeat 2303 | repeat 2304 | repeat 2305 | repeat 2306 | repeat 2307 | repeat 2308 | repeat 2309 | repeat 2310 | repeat 2311 | repeat 2312 | repeat 2313 | repeat 2314 | repeat 2315 | repeat 2316 | repeat 2317 | repeat 2318 | repeat 2319 | repeat 2320 | repeat 2321 | mpg 2322 | mpg 2323 | mpg 2324 | mpg 2325 | mpg 2326 | mpg 2327 | mpg 2328 | mpg 2329 | mpg 2330 | mpg 2331 | mpg 2332 | mpg 2333 | mpg 2334 | mpg 2335 | mpg 2336 | mpg 2337 | mpg 2338 | mpg 2339 | mpg 2340 | mpg 2341 | mpg 2342 | mpg 2343 | mpg 2344 | mpg 2345 | mpg 2346 | mpg 2347 | mpg 2348 | mpg 2349 | mpg 2350 | mpg 2351 | oil_change_when 2352 | oil_change_when 2353 | oil_change_when 2354 | oil_change_when 2355 | oil_change_when 2356 | oil_change_when 2357 | oil_change_when 2358 | oil_change_when 2359 | oil_change_when 2360 | oil_change_when 2361 | oil_change_when 2362 | oil_change_when 2363 | oil_change_when 2364 | oil_change_when 2365 | oil_change_when 2366 | oil_change_when 2367 | oil_change_when 2368 | oil_change_when 2369 | oil_change_when 2370 | oil_change_when 2371 | oil_change_when 2372 | oil_change_when 2373 | oil_change_when 2374 | oil_change_when 2375 | oil_change_when 2376 | oil_change_when 2377 | oil_change_when 2378 | oil_change_when 2379 | oil_change_when 2380 | oil_change_when 2381 | yes 2382 | yes 2383 | yes 2384 | yes 2385 | yes 2386 | yes 2387 | yes 2388 | yes 2389 | yes 2390 | yes 2391 | yes 2392 | yes 2393 | yes 2394 | yes 2395 | yes 2396 | yes 2397 | yes 2398 | yes 2399 | yes 2400 | yes 2401 | yes 2402 | yes 2403 | yes 2404 | yes 2405 | yes 2406 | yes 2407 | yes 2408 | yes 2409 | yes 2410 | yes 2411 | travel_suggestion 2412 | travel_suggestion 2413 | travel_suggestion 2414 | travel_suggestion 2415 | travel_suggestion 2416 | travel_suggestion 2417 | travel_suggestion 2418 | travel_suggestion 2419 | travel_suggestion 2420 | travel_suggestion 2421 | travel_suggestion 2422 | travel_suggestion 2423 | travel_suggestion 2424 | travel_suggestion 2425 | travel_suggestion 2426 | travel_suggestion 2427 | travel_suggestion 2428 | travel_suggestion 2429 | travel_suggestion 2430 | travel_suggestion 2431 | travel_suggestion 2432 | travel_suggestion 2433 | travel_suggestion 2434 | travel_suggestion 2435 | travel_suggestion 2436 | travel_suggestion 2437 | travel_suggestion 2438 | travel_suggestion 2439 | travel_suggestion 2440 | travel_suggestion 2441 | insurance 2442 | insurance 2443 | insurance 2444 | insurance 2445 | insurance 2446 | insurance 2447 | insurance 2448 | insurance 2449 | insurance 2450 | insurance 2451 | insurance 2452 | insurance 2453 | insurance 2454 | insurance 2455 | insurance 2456 | insurance 2457 | insurance 2458 | insurance 2459 | insurance 2460 | insurance 2461 | insurance 2462 | insurance 2463 | insurance 2464 | insurance 2465 | insurance 2466 | insurance 2467 | insurance 2468 | insurance 2469 | insurance 2470 | insurance 2471 | todo_list_update 2472 | todo_list_update 2473 | todo_list_update 2474 | todo_list_update 2475 | todo_list_update 2476 | todo_list_update 2477 | todo_list_update 2478 | todo_list_update 2479 | todo_list_update 2480 | todo_list_update 2481 | todo_list_update 2482 | todo_list_update 2483 | todo_list_update 2484 | todo_list_update 2485 | todo_list_update 2486 | todo_list_update 2487 | todo_list_update 2488 | todo_list_update 2489 | todo_list_update 2490 | todo_list_update 2491 | todo_list_update 2492 | todo_list_update 2493 | todo_list_update 2494 | todo_list_update 2495 | todo_list_update 2496 | todo_list_update 2497 | todo_list_update 2498 | todo_list_update 2499 | todo_list_update 2500 | todo_list_update 2501 | reminder 2502 | reminder 2503 | reminder 2504 | reminder 2505 | reminder 2506 | reminder 2507 | reminder 2508 | reminder 2509 | reminder 2510 | reminder 2511 | reminder 2512 | reminder 2513 | reminder 2514 | reminder 2515 | reminder 2516 | reminder 2517 | reminder 2518 | reminder 2519 | reminder 2520 | reminder 2521 | reminder 2522 | reminder 2523 | reminder 2524 | reminder 2525 | reminder 2526 | reminder 2527 | reminder 2528 | reminder 2529 | reminder 2530 | reminder 2531 | change_speed 2532 | change_speed 2533 | change_speed 2534 | change_speed 2535 | change_speed 2536 | change_speed 2537 | change_speed 2538 | change_speed 2539 | change_speed 2540 | change_speed 2541 | change_speed 2542 | change_speed 2543 | change_speed 2544 | change_speed 2545 | change_speed 2546 | change_speed 2547 | change_speed 2548 | change_speed 2549 | change_speed 2550 | change_speed 2551 | change_speed 2552 | change_speed 2553 | change_speed 2554 | change_speed 2555 | change_speed 2556 | change_speed 2557 | change_speed 2558 | change_speed 2559 | change_speed 2560 | change_speed 2561 | tire_pressure 2562 | tire_pressure 2563 | tire_pressure 2564 | tire_pressure 2565 | tire_pressure 2566 | tire_pressure 2567 | tire_pressure 2568 | tire_pressure 2569 | tire_pressure 2570 | tire_pressure 2571 | tire_pressure 2572 | tire_pressure 2573 | tire_pressure 2574 | tire_pressure 2575 | tire_pressure 2576 | tire_pressure 2577 | tire_pressure 2578 | tire_pressure 2579 | tire_pressure 2580 | tire_pressure 2581 | tire_pressure 2582 | tire_pressure 2583 | tire_pressure 2584 | tire_pressure 2585 | tire_pressure 2586 | tire_pressure 2587 | tire_pressure 2588 | tire_pressure 2589 | tire_pressure 2590 | tire_pressure 2591 | no 2592 | no 2593 | no 2594 | no 2595 | no 2596 | no 2597 | no 2598 | no 2599 | no 2600 | no 2601 | no 2602 | no 2603 | no 2604 | no 2605 | no 2606 | no 2607 | no 2608 | no 2609 | no 2610 | no 2611 | no 2612 | no 2613 | no 2614 | no 2615 | no 2616 | no 2617 | no 2618 | no 2619 | no 2620 | no 2621 | apr 2622 | apr 2623 | apr 2624 | apr 2625 | apr 2626 | apr 2627 | apr 2628 | apr 2629 | apr 2630 | apr 2631 | apr 2632 | apr 2633 | apr 2634 | apr 2635 | apr 2636 | apr 2637 | apr 2638 | apr 2639 | apr 2640 | apr 2641 | apr 2642 | apr 2643 | apr 2644 | apr 2645 | apr 2646 | apr 2647 | apr 2648 | apr 2649 | apr 2650 | apr 2651 | nutrition_info 2652 | nutrition_info 2653 | nutrition_info 2654 | nutrition_info 2655 | nutrition_info 2656 | nutrition_info 2657 | nutrition_info 2658 | nutrition_info 2659 | nutrition_info 2660 | nutrition_info 2661 | nutrition_info 2662 | nutrition_info 2663 | nutrition_info 2664 | nutrition_info 2665 | nutrition_info 2666 | nutrition_info 2667 | nutrition_info 2668 | nutrition_info 2669 | nutrition_info 2670 | nutrition_info 2671 | nutrition_info 2672 | nutrition_info 2673 | nutrition_info 2674 | nutrition_info 2675 | nutrition_info 2676 | nutrition_info 2677 | nutrition_info 2678 | nutrition_info 2679 | nutrition_info 2680 | nutrition_info 2681 | calendar 2682 | calendar 2683 | calendar 2684 | calendar 2685 | calendar 2686 | calendar 2687 | calendar 2688 | calendar 2689 | calendar 2690 | calendar 2691 | calendar 2692 | calendar 2693 | calendar 2694 | calendar 2695 | calendar 2696 | calendar 2697 | calendar 2698 | calendar 2699 | calendar 2700 | calendar 2701 | calendar 2702 | calendar 2703 | calendar 2704 | calendar 2705 | calendar 2706 | calendar 2707 | calendar 2708 | calendar 2709 | calendar 2710 | calendar 2711 | uber 2712 | uber 2713 | uber 2714 | uber 2715 | uber 2716 | uber 2717 | uber 2718 | uber 2719 | uber 2720 | uber 2721 | uber 2722 | uber 2723 | uber 2724 | uber 2725 | uber 2726 | uber 2727 | uber 2728 | uber 2729 | uber 2730 | uber 2731 | uber 2732 | uber 2733 | uber 2734 | uber 2735 | uber 2736 | uber 2737 | uber 2738 | uber 2739 | uber 2740 | uber 2741 | calculator 2742 | calculator 2743 | calculator 2744 | calculator 2745 | calculator 2746 | calculator 2747 | calculator 2748 | calculator 2749 | calculator 2750 | calculator 2751 | calculator 2752 | calculator 2753 | calculator 2754 | calculator 2755 | calculator 2756 | calculator 2757 | calculator 2758 | calculator 2759 | calculator 2760 | calculator 2761 | calculator 2762 | calculator 2763 | calculator 2764 | calculator 2765 | calculator 2766 | calculator 2767 | calculator 2768 | calculator 2769 | calculator 2770 | calculator 2771 | date 2772 | date 2773 | date 2774 | date 2775 | date 2776 | date 2777 | date 2778 | date 2779 | date 2780 | date 2781 | date 2782 | date 2783 | date 2784 | date 2785 | date 2786 | date 2787 | date 2788 | date 2789 | date 2790 | date 2791 | date 2792 | date 2793 | date 2794 | date 2795 | date 2796 | date 2797 | date 2798 | date 2799 | date 2800 | date 2801 | carry_on 2802 | carry_on 2803 | carry_on 2804 | carry_on 2805 | carry_on 2806 | carry_on 2807 | carry_on 2808 | carry_on 2809 | carry_on 2810 | carry_on 2811 | carry_on 2812 | carry_on 2813 | carry_on 2814 | carry_on 2815 | carry_on 2816 | carry_on 2817 | carry_on 2818 | carry_on 2819 | carry_on 2820 | carry_on 2821 | carry_on 2822 | carry_on 2823 | carry_on 2824 | carry_on 2825 | carry_on 2826 | carry_on 2827 | carry_on 2828 | carry_on 2829 | carry_on 2830 | carry_on 2831 | pto_used 2832 | pto_used 2833 | pto_used 2834 | pto_used 2835 | pto_used 2836 | pto_used 2837 | pto_used 2838 | pto_used 2839 | pto_used 2840 | pto_used 2841 | pto_used 2842 | pto_used 2843 | pto_used 2844 | pto_used 2845 | pto_used 2846 | pto_used 2847 | pto_used 2848 | pto_used 2849 | pto_used 2850 | pto_used 2851 | pto_used 2852 | pto_used 2853 | pto_used 2854 | pto_used 2855 | pto_used 2856 | pto_used 2857 | pto_used 2858 | pto_used 2859 | pto_used 2860 | pto_used 2861 | schedule_maintenance 2862 | schedule_maintenance 2863 | schedule_maintenance 2864 | schedule_maintenance 2865 | schedule_maintenance 2866 | schedule_maintenance 2867 | schedule_maintenance 2868 | schedule_maintenance 2869 | schedule_maintenance 2870 | schedule_maintenance 2871 | schedule_maintenance 2872 | schedule_maintenance 2873 | schedule_maintenance 2874 | schedule_maintenance 2875 | schedule_maintenance 2876 | schedule_maintenance 2877 | schedule_maintenance 2878 | schedule_maintenance 2879 | schedule_maintenance 2880 | schedule_maintenance 2881 | schedule_maintenance 2882 | schedule_maintenance 2883 | schedule_maintenance 2884 | schedule_maintenance 2885 | schedule_maintenance 2886 | schedule_maintenance 2887 | schedule_maintenance 2888 | schedule_maintenance 2889 | schedule_maintenance 2890 | schedule_maintenance 2891 | travel_notification 2892 | travel_notification 2893 | travel_notification 2894 | travel_notification 2895 | travel_notification 2896 | travel_notification 2897 | travel_notification 2898 | travel_notification 2899 | travel_notification 2900 | travel_notification 2901 | travel_notification 2902 | travel_notification 2903 | travel_notification 2904 | travel_notification 2905 | travel_notification 2906 | travel_notification 2907 | travel_notification 2908 | travel_notification 2909 | travel_notification 2910 | travel_notification 2911 | travel_notification 2912 | travel_notification 2913 | travel_notification 2914 | travel_notification 2915 | travel_notification 2916 | travel_notification 2917 | travel_notification 2918 | travel_notification 2919 | travel_notification 2920 | travel_notification 2921 | sync_device 2922 | sync_device 2923 | sync_device 2924 | sync_device 2925 | sync_device 2926 | sync_device 2927 | sync_device 2928 | sync_device 2929 | sync_device 2930 | sync_device 2931 | sync_device 2932 | sync_device 2933 | sync_device 2934 | sync_device 2935 | sync_device 2936 | sync_device 2937 | sync_device 2938 | sync_device 2939 | sync_device 2940 | sync_device 2941 | sync_device 2942 | sync_device 2943 | sync_device 2944 | sync_device 2945 | sync_device 2946 | sync_device 2947 | sync_device 2948 | sync_device 2949 | sync_device 2950 | sync_device 2951 | thank_you 2952 | thank_you 2953 | thank_you 2954 | thank_you 2955 | thank_you 2956 | thank_you 2957 | thank_you 2958 | thank_you 2959 | thank_you 2960 | thank_you 2961 | thank_you 2962 | thank_you 2963 | thank_you 2964 | thank_you 2965 | thank_you 2966 | thank_you 2967 | thank_you 2968 | thank_you 2969 | thank_you 2970 | thank_you 2971 | thank_you 2972 | thank_you 2973 | thank_you 2974 | thank_you 2975 | thank_you 2976 | thank_you 2977 | thank_you 2978 | thank_you 2979 | thank_you 2980 | thank_you 2981 | roll_dice 2982 | roll_dice 2983 | roll_dice 2984 | roll_dice 2985 | roll_dice 2986 | roll_dice 2987 | roll_dice 2988 | roll_dice 2989 | roll_dice 2990 | roll_dice 2991 | roll_dice 2992 | roll_dice 2993 | roll_dice 2994 | roll_dice 2995 | roll_dice 2996 | roll_dice 2997 | roll_dice 2998 | roll_dice 2999 | roll_dice 3000 | roll_dice 3001 | roll_dice 3002 | roll_dice 3003 | roll_dice 3004 | roll_dice 3005 | roll_dice 3006 | roll_dice 3007 | roll_dice 3008 | roll_dice 3009 | roll_dice 3010 | roll_dice 3011 | food_last 3012 | food_last 3013 | food_last 3014 | food_last 3015 | food_last 3016 | food_last 3017 | food_last 3018 | food_last 3019 | food_last 3020 | food_last 3021 | food_last 3022 | food_last 3023 | food_last 3024 | food_last 3025 | food_last 3026 | food_last 3027 | food_last 3028 | food_last 3029 | food_last 3030 | food_last 3031 | food_last 3032 | food_last 3033 | food_last 3034 | food_last 3035 | food_last 3036 | food_last 3037 | food_last 3038 | food_last 3039 | food_last 3040 | food_last 3041 | cook_time 3042 | cook_time 3043 | cook_time 3044 | cook_time 3045 | cook_time 3046 | cook_time 3047 | cook_time 3048 | cook_time 3049 | cook_time 3050 | cook_time 3051 | cook_time 3052 | cook_time 3053 | cook_time 3054 | cook_time 3055 | cook_time 3056 | cook_time 3057 | cook_time 3058 | cook_time 3059 | cook_time 3060 | cook_time 3061 | cook_time 3062 | cook_time 3063 | cook_time 3064 | cook_time 3065 | cook_time 3066 | cook_time 3067 | cook_time 3068 | cook_time 3069 | cook_time 3070 | cook_time 3071 | reminder_update 3072 | reminder_update 3073 | reminder_update 3074 | reminder_update 3075 | reminder_update 3076 | reminder_update 3077 | reminder_update 3078 | reminder_update 3079 | reminder_update 3080 | reminder_update 3081 | reminder_update 3082 | reminder_update 3083 | reminder_update 3084 | reminder_update 3085 | reminder_update 3086 | reminder_update 3087 | reminder_update 3088 | reminder_update 3089 | reminder_update 3090 | reminder_update 3091 | reminder_update 3092 | reminder_update 3093 | reminder_update 3094 | reminder_update 3095 | reminder_update 3096 | reminder_update 3097 | reminder_update 3098 | reminder_update 3099 | reminder_update 3100 | reminder_update 3101 | report_lost_card 3102 | report_lost_card 3103 | report_lost_card 3104 | report_lost_card 3105 | report_lost_card 3106 | report_lost_card 3107 | report_lost_card 3108 | report_lost_card 3109 | report_lost_card 3110 | report_lost_card 3111 | report_lost_card 3112 | report_lost_card 3113 | report_lost_card 3114 | report_lost_card 3115 | report_lost_card 3116 | report_lost_card 3117 | report_lost_card 3118 | report_lost_card 3119 | report_lost_card 3120 | report_lost_card 3121 | report_lost_card 3122 | report_lost_card 3123 | report_lost_card 3124 | report_lost_card 3125 | report_lost_card 3126 | report_lost_card 3127 | report_lost_card 3128 | report_lost_card 3129 | report_lost_card 3130 | report_lost_card 3131 | ingredient_substitution 3132 | ingredient_substitution 3133 | ingredient_substitution 3134 | ingredient_substitution 3135 | ingredient_substitution 3136 | ingredient_substitution 3137 | ingredient_substitution 3138 | ingredient_substitution 3139 | ingredient_substitution 3140 | ingredient_substitution 3141 | ingredient_substitution 3142 | ingredient_substitution 3143 | ingredient_substitution 3144 | ingredient_substitution 3145 | ingredient_substitution 3146 | ingredient_substitution 3147 | ingredient_substitution 3148 | ingredient_substitution 3149 | ingredient_substitution 3150 | ingredient_substitution 3151 | ingredient_substitution 3152 | ingredient_substitution 3153 | ingredient_substitution 3154 | ingredient_substitution 3155 | ingredient_substitution 3156 | ingredient_substitution 3157 | ingredient_substitution 3158 | ingredient_substitution 3159 | ingredient_substitution 3160 | ingredient_substitution 3161 | make_call 3162 | make_call 3163 | make_call 3164 | make_call 3165 | make_call 3166 | make_call 3167 | make_call 3168 | make_call 3169 | make_call 3170 | make_call 3171 | make_call 3172 | make_call 3173 | make_call 3174 | make_call 3175 | make_call 3176 | make_call 3177 | make_call 3178 | make_call 3179 | make_call 3180 | make_call 3181 | make_call 3182 | make_call 3183 | make_call 3184 | make_call 3185 | make_call 3186 | make_call 3187 | make_call 3188 | make_call 3189 | make_call 3190 | make_call 3191 | alarm 3192 | alarm 3193 | alarm 3194 | alarm 3195 | alarm 3196 | alarm 3197 | alarm 3198 | alarm 3199 | alarm 3200 | alarm 3201 | alarm 3202 | alarm 3203 | alarm 3204 | alarm 3205 | alarm 3206 | alarm 3207 | alarm 3208 | alarm 3209 | alarm 3210 | alarm 3211 | alarm 3212 | alarm 3213 | alarm 3214 | alarm 3215 | alarm 3216 | alarm 3217 | alarm 3218 | alarm 3219 | alarm 3220 | alarm 3221 | todo_list 3222 | todo_list 3223 | todo_list 3224 | todo_list 3225 | todo_list 3226 | todo_list 3227 | todo_list 3228 | todo_list 3229 | todo_list 3230 | todo_list 3231 | todo_list 3232 | todo_list 3233 | todo_list 3234 | todo_list 3235 | todo_list 3236 | todo_list 3237 | todo_list 3238 | todo_list 3239 | todo_list 3240 | todo_list 3241 | todo_list 3242 | todo_list 3243 | todo_list 3244 | todo_list 3245 | todo_list 3246 | todo_list 3247 | todo_list 3248 | todo_list 3249 | todo_list 3250 | todo_list 3251 | change_accent 3252 | change_accent 3253 | change_accent 3254 | change_accent 3255 | change_accent 3256 | change_accent 3257 | change_accent 3258 | change_accent 3259 | change_accent 3260 | change_accent 3261 | change_accent 3262 | change_accent 3263 | change_accent 3264 | change_accent 3265 | change_accent 3266 | change_accent 3267 | change_accent 3268 | change_accent 3269 | change_accent 3270 | change_accent 3271 | change_accent 3272 | change_accent 3273 | change_accent 3274 | change_accent 3275 | change_accent 3276 | change_accent 3277 | change_accent 3278 | change_accent 3279 | change_accent 3280 | change_accent 3281 | w2 3282 | w2 3283 | w2 3284 | w2 3285 | w2 3286 | w2 3287 | w2 3288 | w2 3289 | w2 3290 | w2 3291 | w2 3292 | w2 3293 | w2 3294 | w2 3295 | w2 3296 | w2 3297 | w2 3298 | w2 3299 | w2 3300 | w2 3301 | w2 3302 | w2 3303 | w2 3304 | w2 3305 | w2 3306 | w2 3307 | w2 3308 | w2 3309 | w2 3310 | w2 3311 | bill_due 3312 | bill_due 3313 | bill_due 3314 | bill_due 3315 | bill_due 3316 | bill_due 3317 | bill_due 3318 | bill_due 3319 | bill_due 3320 | bill_due 3321 | bill_due 3322 | bill_due 3323 | bill_due 3324 | bill_due 3325 | bill_due 3326 | bill_due 3327 | bill_due 3328 | bill_due 3329 | bill_due 3330 | bill_due 3331 | bill_due 3332 | bill_due 3333 | bill_due 3334 | bill_due 3335 | bill_due 3336 | bill_due 3337 | bill_due 3338 | bill_due 3339 | bill_due 3340 | bill_due 3341 | calories 3342 | calories 3343 | calories 3344 | calories 3345 | calories 3346 | calories 3347 | calories 3348 | calories 3349 | calories 3350 | calories 3351 | calories 3352 | calories 3353 | calories 3354 | calories 3355 | calories 3356 | calories 3357 | calories 3358 | calories 3359 | calories 3360 | calories 3361 | calories 3362 | calories 3363 | calories 3364 | calories 3365 | calories 3366 | calories 3367 | calories 3368 | calories 3369 | calories 3370 | calories 3371 | damaged_card 3372 | damaged_card 3373 | damaged_card 3374 | damaged_card 3375 | damaged_card 3376 | damaged_card 3377 | damaged_card 3378 | damaged_card 3379 | damaged_card 3380 | damaged_card 3381 | damaged_card 3382 | damaged_card 3383 | damaged_card 3384 | damaged_card 3385 | damaged_card 3386 | damaged_card 3387 | damaged_card 3388 | damaged_card 3389 | damaged_card 3390 | damaged_card 3391 | damaged_card 3392 | damaged_card 3393 | damaged_card 3394 | damaged_card 3395 | damaged_card 3396 | damaged_card 3397 | damaged_card 3398 | damaged_card 3399 | damaged_card 3400 | damaged_card 3401 | restaurant_reviews 3402 | restaurant_reviews 3403 | restaurant_reviews 3404 | restaurant_reviews 3405 | restaurant_reviews 3406 | restaurant_reviews 3407 | restaurant_reviews 3408 | restaurant_reviews 3409 | restaurant_reviews 3410 | restaurant_reviews 3411 | restaurant_reviews 3412 | restaurant_reviews 3413 | restaurant_reviews 3414 | restaurant_reviews 3415 | restaurant_reviews 3416 | restaurant_reviews 3417 | restaurant_reviews 3418 | restaurant_reviews 3419 | restaurant_reviews 3420 | restaurant_reviews 3421 | restaurant_reviews 3422 | restaurant_reviews 3423 | restaurant_reviews 3424 | restaurant_reviews 3425 | restaurant_reviews 3426 | restaurant_reviews 3427 | restaurant_reviews 3428 | restaurant_reviews 3429 | restaurant_reviews 3430 | restaurant_reviews 3431 | routing 3432 | routing 3433 | routing 3434 | routing 3435 | routing 3436 | routing 3437 | routing 3438 | routing 3439 | routing 3440 | routing 3441 | routing 3442 | routing 3443 | routing 3444 | routing 3445 | routing 3446 | routing 3447 | routing 3448 | routing 3449 | routing 3450 | routing 3451 | routing 3452 | routing 3453 | routing 3454 | routing 3455 | routing 3456 | routing 3457 | routing 3458 | routing 3459 | routing 3460 | routing 3461 | do_you_have_pets 3462 | do_you_have_pets 3463 | do_you_have_pets 3464 | do_you_have_pets 3465 | do_you_have_pets 3466 | do_you_have_pets 3467 | do_you_have_pets 3468 | do_you_have_pets 3469 | do_you_have_pets 3470 | do_you_have_pets 3471 | do_you_have_pets 3472 | do_you_have_pets 3473 | do_you_have_pets 3474 | do_you_have_pets 3475 | do_you_have_pets 3476 | do_you_have_pets 3477 | do_you_have_pets 3478 | do_you_have_pets 3479 | do_you_have_pets 3480 | do_you_have_pets 3481 | do_you_have_pets 3482 | do_you_have_pets 3483 | do_you_have_pets 3484 | do_you_have_pets 3485 | do_you_have_pets 3486 | do_you_have_pets 3487 | do_you_have_pets 3488 | do_you_have_pets 3489 | do_you_have_pets 3490 | do_you_have_pets 3491 | schedule_meeting 3492 | schedule_meeting 3493 | schedule_meeting 3494 | schedule_meeting 3495 | schedule_meeting 3496 | schedule_meeting 3497 | schedule_meeting 3498 | schedule_meeting 3499 | schedule_meeting 3500 | schedule_meeting 3501 | schedule_meeting 3502 | schedule_meeting 3503 | schedule_meeting 3504 | schedule_meeting 3505 | schedule_meeting 3506 | schedule_meeting 3507 | schedule_meeting 3508 | schedule_meeting 3509 | schedule_meeting 3510 | schedule_meeting 3511 | schedule_meeting 3512 | schedule_meeting 3513 | schedule_meeting 3514 | schedule_meeting 3515 | schedule_meeting 3516 | schedule_meeting 3517 | schedule_meeting 3518 | schedule_meeting 3519 | schedule_meeting 3520 | schedule_meeting 3521 | gas_type 3522 | gas_type 3523 | gas_type 3524 | gas_type 3525 | gas_type 3526 | gas_type 3527 | gas_type 3528 | gas_type 3529 | gas_type 3530 | gas_type 3531 | gas_type 3532 | gas_type 3533 | gas_type 3534 | gas_type 3535 | gas_type 3536 | gas_type 3537 | gas_type 3538 | gas_type 3539 | gas_type 3540 | gas_type 3541 | gas_type 3542 | gas_type 3543 | gas_type 3544 | gas_type 3545 | gas_type 3546 | gas_type 3547 | gas_type 3548 | gas_type 3549 | gas_type 3550 | gas_type 3551 | plug_type 3552 | plug_type 3553 | plug_type 3554 | plug_type 3555 | plug_type 3556 | plug_type 3557 | 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