SYMBOL INDEX (420 symbols across 36 files)

FILE: preprocessing/dualgnn-gen-u-u-matrix.py
  function gen_user_matrix (line 14) | def gen_user_matrix(all_edge, no_users):

FILE: src/common/abstract_recommender.py
  class AbstractRecommender (line 10) | class AbstractRecommender(nn.Module):
    method pre_epoch_processing (line 13) | def pre_epoch_processing(self):
    method post_epoch_processing (line 16) | def post_epoch_processing(self):
    method calculate_loss (line 19) | def calculate_loss(self, interaction):
    method predict (line 30) | def predict(self, interaction):
    method full_sort_predict (line 41) | def full_sort_predict(self, interaction):
    method __str__ (line 62) | def __str__(self):
  class GeneralRecommender (line 71) | class GeneralRecommender(AbstractRecommender):
    method __init__ (line 75) | def __init__(self, config, dataloader):

FILE: src/common/encoders.py
  class LightGCN_Encoder (line 11) | class LightGCN_Encoder(GeneralRecommender):
    method __init__ (line 12) | def __init__(self, config, dataset):
    method _init_model (line 30) | def _init_model(self):
    method get_norm_adj_mat (line 39) | def get_norm_adj_mat(self):
    method sparse_dropout (line 77) | def sparse_dropout(self, x, rate, noise_shape):
    method forward (line 90) | def forward(self, inputs):
    method get_embedding (line 115) | def get_embedding(self):

FILE: src/common/init.py
  function xavier_normal_initialization (line 8) | def xavier_normal_initialization(module):
  function xavier_uniform_initialization (line 27) | def xavier_uniform_initialization(module):

FILE: src/common/loss.py
  class BPRLoss (line 9) | class BPRLoss(nn.Module):
    method __init__ (line 29) | def __init__(self, gamma=1e-10):
    method forward (line 33) | def forward(self, pos_score, neg_score):
  class EmbLoss (line 38) | class EmbLoss(nn.Module):
    method __init__ (line 42) | def __init__(self, norm=2):
    method forward (line 46) | def forward(self, *embeddings):
  class L2Loss (line 54) | class L2Loss(nn.Module):
    method __init__ (line 55) | def __init__(self):
    method forward (line 58) | def forward(self, *embeddings):

FILE: src/common/trainer.py
  class AbstractTrainer (line 23) | class AbstractTrainer(object):
    method __init__ (line 29) | def __init__(self, config, model):
    method fit (line 33) | def fit(self, train_data):
    method evaluate (line 39) | def evaluate(self, eval_data):
  class Trainer (line 47) | class Trainer(AbstractTrainer):
    method __init__ (line 62) | def __init__(self, config, model, mg=False):
    method _build_optimizer (line 111) | def _build_optimizer(self):
    method _train_epoch (line 130) | def _train_epoch(self, train_data, epoch_idx, loss_func=None):
    method _valid_epoch (line 196) | def _valid_epoch(self, valid_data):
    method _check_nan (line 210) | def _check_nan(self, loss):
    method _generate_train_loss_output (line 215) | def _generate_train_loss_output(self, epoch_idx, s_time, e_time, losses):
    method fit (line 223) | def fit(self, train_data, valid_data=None, test_data=None, saved=False...
    method evaluate (line 293) | def evaluate(self, eval_data, is_test=False, idx=0):
    method plot_train_loss (line 313) | def plot_train_loss(self, show=True, save_path=None):

FILE: src/models/bm3.py
  class BM3 (line 23) | class BM3(GeneralRecommender):
    method __init__ (line 24) | def __init__(self, config, dataset):
    method get_norm_adj_mat (line 58) | def get_norm_adj_mat(self, interaction_matrix):
    method forward (line 84) | def forward(self):
    method calculate_loss (line 97) | def calculate_loss(self, interactions):
    method full_sort_predict (line 149) | def full_sort_predict(self, interaction):

FILE: src/models/bpr.py
  class BPR (line 20) | class BPR(GeneralRecommender):
    method __init__ (line 24) | def __init__(self, config, dataset):
    method get_user_embedding (line 40) | def get_user_embedding(self, user):
    method get_item_embedding (line 51) | def get_item_embedding(self, item):
    method forward (line 62) | def forward(self, dropout=0.0):
    method calculate_loss (line 67) | def calculate_loss(self, interaction):
    method full_sort_predict (line 89) | def full_sort_predict(self, interaction):

FILE: src/models/damrs.py
  class DAMRS (line 14) | class DAMRS(GeneralRecommender):
    method __init__ (line 15) | def __init__(self, config, dataset):
    method get_knn_adj_mat (line 59) | def get_knn_adj_mat(self, v_embeddings, t_embeddings):
    method compute_normalized_laplacian (line 110) | def compute_normalized_laplacian(self, indices, adj_size):
    method get_session_adj (line 119) | def get_session_adj(self):
    method label_prediction (line 141) | def label_prediction(self, emb, aug_emb):
    method generate_pesudo_labels (line 149) | def generate_pesudo_labels(self, prob1, prob2, prob3):
    method neighbor_discrimination (line 157) | def neighbor_discrimination(self, mm_positive, s_positive, emb, aug_em...
    method KL (line 182) | def KL(self, p1, p2):
    method get_norm_adj_mat (line 186) | def get_norm_adj_mat(self):
    method forward (line 212) | def forward(self):
    method calculate_loss (line 242) | def calculate_loss(self, interaction):
    method full_sort_predict (line 295) | def full_sort_predict(self, interaction):
    method get_weight_modal (line 305) | def get_weight_modal(self, users, pos_items, neg_items, user_embedding...
    method bpr_loss (line 338) | def bpr_loss(self, users, pos_items, neg_items, p_weight, n_weight):

FILE: src/models/dragon.py
  class DRAGON (line 20) | class DRAGON(GeneralRecommender):
    method __init__ (line 21) | def __init__(self, config, dataset):
    method get_knn_adj_mat (line 158) | def get_knn_adj_mat(self, mm_embeddings):
    method compute_normalized_laplacian (line 172) | def compute_normalized_laplacian(self, indices, adj_size):
    method pre_epoch_processing (line 181) | def pre_epoch_processing(self):
    method pack_edge_index (line 185) | def pack_edge_index(self, inter_mat):
    method forward (line 191) | def forward(self, interaction):
    method calculate_loss (line 262) | def calculate_loss(self, interaction):
    method full_sort_predict (line 279) | def full_sort_predict(self, interaction):
    method topk_sample (line 287) | def topk_sample(self, k):
  class User_Graph_sample (line 327) | class User_Graph_sample(torch.nn.Module):
    method __init__ (line 328) | def __init__(self, num_user, aggr_mode, dim_latent):
    method forward (line 334) | def forward(self, features, user_graph, user_matrix):
  class GCN (line 344) | class GCN(torch.nn.Module):
    method __init__ (line 345) | def __init__(self, datasets, batch_size, num_user, num_item, dim_id, a...
    method forward (line 375) | def forward(self, edge_index_drop, edge_index, features):
  class Base_gcn (line 386) | class Base_gcn(MessagePassing):
    method __init__ (line 387) | def __init__(self, in_channels, out_channels, normalize=True, bias=Tru...
    method forward (line 393) | def forward(self, x, edge_index, size=None):
    method message (line 402) | def message(self, x_j, edge_index, size):
    method update (line 412) | def update(self, aggr_out):
    method __repr (line 415) | def __repr(self):

FILE: src/models/dualgnn.py
  class DualGNN (line 21) | class DualGNN(GeneralRecommender):
    method __init__ (line 22) | def __init__(self, config, dataset):
    method pre_epoch_processing (line 131) | def pre_epoch_processing(self):
    method pack_edge_index (line 135) | def pack_edge_index(self, inter_mat):
    method forward (line 141) | def forward(self, interaction):
    method calculate_loss (line 182) | def calculate_loss(self, interaction):
    method full_sort_predict (line 199) | def full_sort_predict(self, interaction):
    method topk_sample (line 207) | def topk_sample(self, k):
  class User_Graph_sample (line 252) | class User_Graph_sample(torch.nn.Module):
    method __init__ (line 253) | def __init__(self, num_user, aggr_mode,dim_latent):
    method forward (line 259) | def forward(self, features,user_graph,user_matrix):
  class GCN (line 269) | class GCN(torch.nn.Module):
    method __init__ (line 270) | def __init__(self,datasets, batch_size, num_user, num_item, dim_id, ag...
    method forward (line 304) | def forward(self, edge_index_drop,edge_index,features):
  class Base_gcn (line 318) | class Base_gcn(MessagePassing):
    method __init__ (line 319) | def __init__(self, in_channels, out_channels, normalize=True, bias=Tru...
    method forward (line 325) | def forward(self, x, edge_index, size=None):
    method message (line 334) | def message(self, x_j, edge_index, size):
    method update (line 344) | def update(self, aggr_out):
    method __repr (line 347) | def __repr(self):

FILE: src/models/freedom.py
  class FREEDOM (line 22) | class FREEDOM(GeneralRecommender):
    method __init__ (line 23) | def __init__(self, config, dataset):
    method get_knn_adj_mat (line 79) | def get_knn_adj_mat(self, mm_embeddings):
    method compute_normalized_laplacian (line 93) | def compute_normalized_laplacian(self, indices, adj_size):
    method get_norm_adj_mat (line 102) | def get_norm_adj_mat(self):
    method pre_epoch_processing (line 128) | def pre_epoch_processing(self):
    method _normalize_adj_m (line 145) | def _normalize_adj_m(self, indices, adj_size):
    method get_edge_info (line 156) | def get_edge_info(self):
    method forward (line 164) | def forward(self, adj):
    method bpr_loss (line 180) | def bpr_loss(self, users, pos_items, neg_items):
    method calculate_loss (line 189) | def calculate_loss(self, interaction):
    method full_sort_predict (line 212) | def full_sort_predict(self, interaction):

FILE: src/models/grcn.py
  class SAGEConv (line 26) | class SAGEConv(MessagePassing):
    method __init__ (line 27) | def __init__(self, in_channels, out_channels, normalize=True, bias=Tru...
    method forward (line 32) | def forward(self, x, edge_index, weight_vector, size=None):
    method message (line 36) | def message(self, x_j):
    method update (line 39) | def update(self, aggr_out):
    method __repr__ (line 42) | def __repr__(self):
  class GATConv (line 46) | class GATConv(MessagePassing):
    method __init__ (line 47) | def __init__(self, in_channels, out_channels, self_loops=False):
    method forward (line 53) | def forward(self, x, edge_index, size=None):
    method message (line 61) | def message(self,  x_i, x_j, size_i ,edge_index_i):
    method update (line 75) | def update(self, aggr_out):
  class EGCN (line 80) | class EGCN(torch.nn.Module):
    method __init__ (line 81) | def __init__(self, num_user, num_item, dim_E, aggr_mode, has_act, has_...
    method forward (line 93) | def forward(self, edge_index, weight_vector):
  class CGCN (line 112) | class CGCN(torch.nn.Module):
    method __init__ (line 113) | def __init__(self, features, num_user, num_item, dim_C, aggr_mode, num...
    method forward (line 139) | def forward(self, edge_index):
  class GRCN (line 169) | class GRCN(GeneralRecommender):
    method __init__ (line 170) | def __init__(self,  config, dataset):
    method pack_edge_index (line 217) | def pack_edge_index(self, inter_mat):
    method forward (line 224) | def forward(self):
    method calculate_loss (line 300) | def calculate_loss(self, interaction):
    method full_sort_predict (line 335) | def full_sort_predict(self, interaction):

FILE: src/models/itemknncbf.py
  class ItemKNNCBF (line 25) | class ItemKNNCBF(GeneralRecommender):
    method __init__ (line 26) | def __init__(self, config, dataset):
    method build_item_sim_matrix (line 56) | def build_item_sim_matrix(self, features):
    method build_item_sim_matrix_with_blocks (line 67) | def build_item_sim_matrix_with_blocks(self, features, block_size=1000):
    method calculate_loss (line 103) | def calculate_loss(self, interaction):
    method full_sort_predict (line 107) | def full_sort_predict(self, interaction):

FILE: src/models/lattice.py
  class LATTICE (line 26) | class LATTICE(GeneralRecommender):
    method __init__ (line 27) | def __init__(self, config, dataset):
    method pre_epoch_processing (line 97) | def pre_epoch_processing(self):
    method get_adj_mat (line 100) | def get_adj_mat(self):
    method sparse_mx_to_torch_sparse_tensor (line 124) | def sparse_mx_to_torch_sparse_tensor(self, sparse_mx):
    method forward (line 132) | def forward(self, adj, build_item_graph=False):
    method bpr_loss (line 199) | def bpr_loss(self, users, pos_items, neg_items):
    method calculate_loss (line 213) | def calculate_loss(self, interaction):
    method full_sort_predict (line 229) | def full_sort_predict(self, interaction):

FILE: src/models/layergcn.py
  class LayerGCN (line 15) | class LayerGCN(GeneralRecommender):
    method __init__ (line 16) | def __init__(self, config, dataset):
    method pre_epoch_processing (line 51) | def pre_epoch_processing(self):
    method _normalize_adj_m (line 72) | def _normalize_adj_m(self, indices, adj_size):
    method get_edge_info (line 83) | def get_edge_info(self):
    method get_norm_adj_mat (line 91) | def get_norm_adj_mat(self):
    method get_ego_embeddings (line 117) | def get_ego_embeddings(self):
    method forward (line 125) | def forward(self):
    method bpr_loss (line 140) | def bpr_loss(self, u_embeddings, i_embeddings, user, pos_item, neg_item):
    method emb_loss (line 154) | def emb_loss(self, user, pos_item, neg_item):
    method calculate_loss (line 163) | def calculate_loss(self, interaction):
    method full_sort_predict (line 177) | def full_sort_predict(self, interaction):

FILE: src/models/lgmrec.py
  class LGMRec (line 18) | class LGMRec(GeneralRecommender):
    method __init__ (line 19) | def __init__(self, config, dataset):
    method scipy_matrix_to_sparse_tenser (line 63) | def scipy_matrix_to_sparse_tenser(self, matrix, shape):
    method get_norm_adj_mat (line 70) | def get_norm_adj_mat(self):
    method cge (line 89) | def cge(self):
    method mge (line 103) | def mge(self, str='v'):
    method forward (line 115) | def forward(self):
    method bpr_loss (line 153) | def bpr_loss(self, users, pos_items, neg_items):
    method ssl_triple_loss (line 159) | def ssl_triple_loss(self, emb1, emb2, all_emb):
    method reg_loss (line 168) | def reg_loss(self, *embs):
    method calculate_loss (line 175) | def calculate_loss(self, interaction):
    method full_sort_predict (line 196) | def full_sort_predict(self, interaction):
  class HGNNLayer (line 202) | class HGNNLayer(nn.Module):
    method __init__ (line 203) | def __init__(self, n_hyper_layer):
    method forward (line 208) | def forward(self, i_hyper, u_hyper, embeds):

FILE: src/models/lightgcn.py
  class LightGCN (line 23) | class LightGCN(GeneralRecommender):
    method __init__ (line 33) | def __init__(self, config, dataset):
    method _init_model (line 56) | def _init_model(self):
    method get_norm_adj_mat (line 65) | def get_norm_adj_mat(self):
    method get_ego_embeddings (line 103) | def get_ego_embeddings(self):
    method forward (line 115) | def forward(self):
    method calculate_loss (line 130) | def calculate_loss(self, interaction):
    method full_sort_predict (line 156) | def full_sort_predict(self, interaction):

FILE: src/models/mgcn.py
  class MGCN (line 22) | class MGCN(GeneralRecommender):
    method __init__ (line 23) | def __init__(self, config, dataset):
    method pre_epoch_processing (line 106) | def pre_epoch_processing(self):
    method get_adj_mat (line 109) | def get_adj_mat(self):
    method sparse_mx_to_torch_sparse_tensor (line 138) | def sparse_mx_to_torch_sparse_tensor(self, sparse_mx):
    method forward (line 146) | def forward(self, adj, train=False):
    method bpr_loss (line 210) | def bpr_loss(self, users, pos_items, neg_items):
    method InfoNCE (line 224) | def InfoNCE(self, view1, view2, temperature):
    method calculate_loss (line 233) | def calculate_loss(self, interaction):
    method full_sort_predict (line 255) | def full_sort_predict(self, interaction):

FILE: src/models/mmgcn.py
  class MMGCN (line 22) | class MMGCN(GeneralRecommender):
    method __init__ (line 23) | def __init__(self, config, dataset):
    method pack_edge_index (line 58) | def pack_edge_index(self, inter_mat):
    method forward (line 64) | def forward(self):
    method calculate_loss (line 79) | def calculate_loss(self, interaction):
    method full_sort_predict (line 99) | def full_sort_predict(self, interaction):
  class GCN (line 108) | class GCN(torch.nn.Module):
    method __init__ (line 109) | def __init__(self, edge_index, batch_size, num_user, num_item, dim_fea...
    method forward (line 164) | def forward(self, features, id_embedding):
  class BaseModel (line 191) | class BaseModel(MessagePassing):
    method __init__ (line 192) | def __init__(self, in_channels, out_channels, normalize=True, bias=Tru...
    method reset_parameters (line 202) | def reset_parameters(self):
    method forward (line 205) | def forward(self, x, edge_index, size=None):
    method message (line 209) | def message(self, x_j, edge_index, size):
    method update (line 212) | def update(self, aggr_out):
    method __repr (line 215) | def __repr(self):

FILE: src/models/mvgae.py
  class MVGAE (line 27) | class MVGAE(GeneralRecommender):
    method __init__ (line 28) | def __init__(self, config, dataset):
    method pack_edge_index (line 60) | def pack_edge_index(self, inter_mat):
    method reparametrize (line 66) | def reparametrize(self, mu, logvar):
    method dot_product_decode_neg (line 73) | def dot_product_decode_neg(self, z, user, neg_items, sigmoid=True):
    method dot_product_decode (line 87) | def dot_product_decode(self, z, edge_index, sigmoid=True):
    method forward (line 91) | def forward(self):
    method recon_loss (line 121) | def recon_loss(self, z, pos_edge_index, user, neg_items):
    method kl_loss (line 138) | def kl_loss(self, mu, logvar):
    method calculate_loss (line 153) | def calculate_loss(self, interaction):
    method full_sort_predict (line 174) | def full_sort_predict(self, interaction):
  class GCN (line 183) | class GCN(torch.nn.Module):
    method __init__ (line 184) | def __init__(self, device, features, edge_index, batch_size, num_user,...
    method forward (line 247) | def forward(self):
  class ProductOfExperts (line 285) | class ProductOfExperts(torch.nn.Module):
    method __init__ (line 286) | def __init__(self):
    method forward (line 294) | def forward(self, mu, logvar, eps=1e-8):
  class BaseModel (line 304) | class BaseModel(MessagePassing):
    method __init__ (line 305) | def __init__(self, in_channels, out_channels, normalize=True, bias=Tru...
    method reset_parameters (line 318) | def reset_parameters(self):
    method forward (line 322) | def forward(self, x, edge_index, size=None):
    method message (line 331) | def message(self, x_j, edge_index, size):
    method update (line 340) | def update(self, aggr_out):
    method __repr (line 347) | def __repr(self):

FILE: src/models/pgl.py
  class PGL (line 22) | class PGL(GeneralRecommender):
    method __init__ (line 23) | def __init__(self, config, dataset):
    method sparse_mx_to_torch_sparse_tensor (line 78) | def sparse_mx_to_torch_sparse_tensor(self, sparse_mx):
    method get_knn_adj_mat (line 86) | def get_knn_adj_mat(self, mm_embeddings):
    method compute_normalized_laplacian (line 100) | def compute_normalized_laplacian(self, indices, adj_size):
    method get_norm_adj_mat (line 109) | def get_norm_adj_mat(self):
    method global_subgraph_extraction (line 138) | def global_subgraph_extraction(self, adj):
    method alignment (line 156) | def alignment(self, x, y):
    method uniformity (line 161) | def uniformity(self, x, t=2):
    method save (line 165) | def save(self):
    method pre_epoch_processing (line 168) | def pre_epoch_processing(self):
    method _normalize_adj_m (line 183) | def _normalize_adj_m(self, indices, adj_size):
    method get_edge_info (line 194) | def get_edge_info(self):
    method forward (line 202) | def forward(self, adj):
    method bpr_loss (line 227) | def bpr_loss(self, users, pos_items, neg_items):
    method InfoNCE (line 236) | def InfoNCE(self, view1, view2, temperature):
    method calculate_loss (line 245) | def calculate_loss(self, interaction):
    method full_sort_predict (line 261) | def full_sort_predict(self, interaction):

FILE: src/models/selfcfed_lgn.py
  class SELFCFED_LGN (line 28) | class SELFCFED_LGN(GeneralRecommender):
    method __init__ (line 29) | def __init__(self, config, dataset):
    method forward (line 41) | def forward(self, inputs):
    method get_embedding (line 53) | def get_embedding(self):
    method loss_fn (line 57) | def loss_fn(self, p, z):  # negative cosine similarity
    method calculate_loss (line 60) | def calculate_loss(self, interaction):
    method full_sort_predict (line 71) | def full_sort_predict(self, interaction):

FILE: src/models/slmrec.py
  class SLMRec (line 20) | class SLMRec(GeneralRecommender):
    method __init__ (line 21) | def __init__(self, config, dataset):
    method __init_weight (line 28) | def __init_weight(self, dataset):
    method compute (line 73) | def compute(self):
    method feature_dropout (line 120) | def feature_dropout(self, users_idx, items_idx):
    method feature_masking (line 192) | def feature_masking(self, users_idx, items_idx, dropout=False):
    method fac (line 278) | def fac(self, idx):
    method full_sort_predict (line 307) | def full_sort_predict(self, interaction, candidate_items=None):
    method getEmbedding (line 317) | def getEmbedding(self, users, pos_items, neg_items):
    method calculate_loss (line 332) | def calculate_loss(self, interaction):
    method ssl_loss (line 339) | def ssl_loss(self, users, pos):
    method compute_ssl (line 344) | def compute_ssl(self, users, items):
    method forward (line 354) | def forward(self, users, items):
    method mm_fusion (line 362) | def mm_fusion(self, reps: list):
    method infonce (line 369) | def infonce(self, users, pos):
    method create_u_embeding_i (line 380) | def create_u_embeding_i(self):
    method create_adj_mat (line 434) | def create_adj_mat(self, interaction_csr):

FILE: src/models/smore.py
  class SMORE (line 24) | class SMORE(GeneralRecommender):
    method __init__ (line 25) | def __init__(self, config, dataset):
    method pre_epoch_processing (line 129) | def pre_epoch_processing(self):
    method max_pool_fusion (line 132) | def max_pool_fusion(self):
    method get_adj_mat (line 155) | def get_adj_mat(self):
    method sparse_mx_to_torch_sparse_tensor (line 180) | def sparse_mx_to_torch_sparse_tensor(self, sparse_mx):
    method spectrum_convolution (line 188) | def spectrum_convolution(self, image_embeds, text_embeds):
    method forward (line 208) | def forward(self, adj, train=False):
    method bpr_loss (line 293) | def bpr_loss(self, users, pos_items, neg_items):
    method InfoNCE (line 307) | def InfoNCE(self, view1, view2, temperature):
    method calculate_loss (line 316) | def calculate_loss(self, interaction):
    method full_sort_predict (line 338) | def full_sort_predict(self, interaction):

FILE: src/models/vbpr.py
  class VBPR (line 20) | class VBPR(GeneralRecommender):
    method __init__ (line 23) | def __init__(self, config, dataloader):
    method get_user_embedding (line 47) | def get_user_embedding(self, user):
    method get_item_embedding (line 58) | def get_item_embedding(self, item):
    method forward (line 69) | def forward(self, dropout=0.0):
    method calculate_loss (line 77) | def calculate_loss(self, interaction):
    method full_sort_predict (line 100) | def full_sort_predict(self, interaction):

FILE: src/utils/configurator.py
  class Config (line 15) | class Config(object):
    method __init__ (line 46) | def __init__(self, model=None, dataset=None, config_dict=None, mg=False):
    method _load_dataset_model_config (line 68) | def _load_dataset_model_config(self, config_dict, mg):
    method _build_yaml_loader (line 92) | def _build_yaml_loader(self):
    method _set_default_parameters (line 106) | def _set_default_parameters(self):
    method _init_device (line 114) | def _init_device(self):
    method __setitem__ (line 120) | def __setitem__(self, key, value):
    method __getitem__ (line 125) | def __getitem__(self, item):
    method __contains__ (line 131) | def __contains__(self, key):
    method __str__ (line 136) | def __str__(self):
    method __repr__ (line 142) | def __repr__(self):

FILE: src/utils/data_utils.py
  function flat_list_of_lists (line 22) | def flat_list_of_lists(l):
  function mask_batch_text_tokens (line 27) | def mask_batch_text_tokens(
  function image_to_tensor (line 77) | def image_to_tensor(image: np.ndarray, keepdim: bool = True) -> torch.Te...
  function get_padding (line 116) | def get_padding(image, max_w, max_h, pad_all=False):
  class ImagePad (line 140) | class ImagePad(object):
    method __init__ (line 141) | def __init__(self, max_w, max_h, fill=0, padding_mode='constant'):
    method __call__ (line 149) | def __call__(self, img):
    method __repr__ (line 166) | def __repr__(self):
  function get_resize_size (line 171) | def get_resize_size(image, max_size):
  class ImageResize (line 206) | class ImageResize(object):
    method __init__ (line 219) | def __init__(self, max_size, interpolation=Image.BILINEAR):
    method __call__ (line 224) | def __call__(self, img):
    method __repr__ (line 240) | def __repr__(self):
  function get_imagenet_transform (line 246) | def get_imagenet_transform(min_size=600, max_size=1000):
  class ImageNorm (line 260) | class ImageNorm(object):
    method __init__ (line 263) | def __init__(self, mean, std):
    method __call__ (line 270) | def __call__(self, img):
  function chunk_list (line 283) | def chunk_list(examples, chunk_size=2, pad_to_divisible=True):
  function mk_input_group (line 311) | def mk_input_group(key_grouped_examples, max_n_example_per_group=2, is_t...
  function repeat_tensor_rows (line 348) | def repeat_tensor_rows(raw_tensor, row_repeats):
  function load_decompress_img_from_lmdb_value (line 367) | def load_decompress_img_from_lmdb_value(lmdb_value):

FILE: src/utils/dataloader.py
  class AbstractDataLoader (line 15) | class AbstractDataLoader(object):
    method __init__ (line 37) | def __init__(self, config, dataset, additional_dataset=None,
    method pretrain_setup (line 59) | def pretrain_setup(self):
    method data_preprocess (line 65) | def data_preprocess(self):
    method __len__ (line 71) | def __len__(self):
    method __iter__ (line 74) | def __iter__(self):
    method __next__ (line 79) | def __next__(self):
    method pr_end (line 87) | def pr_end(self):
    method _shuffle (line 91) | def _shuffle(self):
    method _next_batch_data (line 96) | def _next_batch_data(self):
  class TrainDataLoader (line 105) | class TrainDataLoader(AbstractDataLoader):
    method __init__ (line 109) | def __init__(self, config, dataset, batch_size=1, shuffle=False):
    method pretrain_setup (line 140) | def pretrain_setup(self):
    method inter_matrix (line 155) | def inter_matrix(self, form='coo', value_field=None):
    method _create_sparse_matrix (line 176) | def _create_sparse_matrix(self, df_feat, source_field, target_field, f...
    method pr_end (line 213) | def pr_end(self):
    method _shuffle (line 218) | def _shuffle(self):
    method _next_batch_data (line 223) | def _next_batch_data(self):
    method _get_neg_sample (line 226) | def _get_neg_sample(self):
    method _get_non_neg_sample (line 252) | def _get_non_neg_sample(self):
    method _get_full_uids_sample (line 262) | def _get_full_uids_sample(self):
    method _sample_neg_ids (line 267) | def _sample_neg_ids(self, u_ids):
    method _get_my_neighbors (line 277) | def _get_my_neighbors(self, id_str):
    method _get_neighborhood_samples (line 289) | def _get_neighborhood_samples(self, ids, id_str):
    method _random (line 307) | def _random(self):
    method _get_history_items_u (line 311) | def _get_history_items_u(self):
    method _get_history_users_i (line 320) | def _get_history_users_i(self):
  class EvalDataLoader (line 330) | class EvalDataLoader(AbstractDataLoader):
    method __init__ (line 334) | def __init__(self, config, dataset, additional_dataset=None,
    method pr_end (line 353) | def pr_end(self):
    method _shuffle (line 356) | def _shuffle(self):
    method _next_batch_data (line 359) | def _next_batch_data(self):
    method _get_pos_items_per_u (line 370) | def _get_pos_items_per_u(self, eval_users):
    method _get_eval_items_per_u (line 393) | def _get_eval_items_per_u(self, eval_users):
    method get_eval_items (line 409) | def get_eval_items(self):
    method get_eval_len_list (line 412) | def get_eval_len_list(self):
    method get_eval_users (line 415) | def get_eval_users(self):

FILE: src/utils/dataset.py
  class RecDataset (line 21) | class RecDataset(object):
    method __init__ (line 22) | def __init__(self, config, df=None):
    method load_inter_graph (line 50) | def load_inter_graph(self, file_name):
    method split (line 57) | def split(self):
    method copy (line 76) | def copy(self, new_df):
    method get_user_num (line 92) | def get_user_num(self):
    method get_item_num (line 95) | def get_item_num(self):
    method shuffle (line 98) | def shuffle(self):
    method __len__ (line 103) | def __len__(self):
    method __getitem__ (line 106) | def __getitem__(self, idx):
    method __repr__ (line 110) | def __repr__(self):
    method __str__ (line 113) | def __str__(self):

FILE: src/utils/logger.py
  function init_logger (line 13) | def init_logger(config):

FILE: src/utils/metrics.py
  function recall_ (line 12) | def recall_(pos_index, pos_len):
  function recall2_ (line 18) | def recall2_(pos_index, pos_len):
  function ndcg_ (line 30) | def ndcg_(pos_index, pos_len):
  function map_ (line 66) | def map_(pos_index, pos_len):
  function precision_ (line 92) | def precision_(pos_index, pos_len):

FILE: src/utils/misc.py
  class NoOp (line 14) | class NoOp(object):
    method __getattr__ (line 16) | def __getattr__(self, name):
    method noop (line 19) | def noop(self, *args, **kwargs):
  function set_random_seed (line 23) | def set_random_seed(seed):
  function zero_none_grad (line 30) | def zero_none_grad(model):

FILE: src/utils/quick_start.py
  function quick_start (line 19) | def quick_start(model, dataset, config_dict, save_model=True, mg=False):

FILE: src/utils/topk_evaluator.py
  class TopKEvaluator (line 19) | class TopKEvaluator(object):
    method __init__ (line 29) | def __init__(self, config):
    method collect (line 36) | def collect(self, interaction, scores_tensor, full=False):
    method evaluate (line 58) | def evaluate(self, batch_matrix_list, eval_data, is_test=False, idx=0):
    method _check_args (line 104) | def _check_args(self):
    method _calculate_metrics (line 129) | def _calculate_metrics(self, pos_len_list, topk_index):
    method __str__ (line 145) | def __str__(self):

FILE: src/utils/utils.py
  function get_local_time (line 16) | def get_local_time():
  function get_model (line 28) | def get_model(model_name):
  function get_trainer (line 44) | def get_trainer():
  function init_seed (line 48) | def init_seed(seed):
  function early_stopping (line 57) | def early_stopping(value, best, cur_step, max_step, bigger=True):
  function dict2str (line 101) | def dict2str(result_dict):
  function build_knn_neighbourhood (line 119) | def build_knn_neighbourhood(adj, topk):
  function compute_normalized_laplacian (line 125) | def compute_normalized_laplacian(adj):
  function build_sim (line 134) | def build_sim(context):
  function get_sparse_laplacian (line 139) | def get_sparse_laplacian(edge_index, edge_weight, num_nodes, normalizati...
  function get_dense_laplacian (line 154) | def get_dense_laplacian(adj, normalization='none'):
  function build_knn_normalized_graph (line 171) | def build_knn_normalized_graph(adj, topk, is_sparse, norm_type):