Basic encoder combining embedding of node id and dense feature.
| 30 | |
| 31 | |
| 32 | class ShallowEncoder(layers.Layer): |
| 33 | """ |
| 34 | Basic encoder combining embedding of node id and dense feature. |
| 35 | """ |
| 36 | |
| 37 | def __init__(self, dim=None, feature_idx='f1', feature_dim=0, max_id=-1, |
| 38 | sparse_feature_idx=-1, sparse_feature_max_id=-1, |
| 39 | embedding_dim=16, use_hash_embedding=False, combiner='concat', |
| 40 | **kwargs): |
| 41 | super(ShallowEncoder, self).__init__(**kwargs) |
| 42 | |
| 43 | if combiner not in ['add', 'concat']: |
| 44 | raise ValueError('combiner must be \'add\' or \'concat\'.') |
| 45 | if combiner == 'add' and dim is None: |
| 46 | raise ValueError('add must be used with dim provided.') |
| 47 | |
| 48 | use_feature = feature_idx != -1 |
| 49 | use_id = max_id != -1 |
| 50 | use_sparse_feature = sparse_feature_idx != -1 |
| 51 | |
| 52 | if not isinstance(feature_idx, list) and use_feature: |
| 53 | feature_idx = [feature_idx] |
| 54 | if isinstance(feature_dim, int) and use_feature: |
| 55 | feature_dim = [feature_dim] |
| 56 | if use_feature and len(feature_idx) != len(feature_dim): |
| 57 | raise ValueError('feature_dim must be the same length as feature' |
| 58 | '_idx.idx:%s, dim:%s' % (str(feature_idx), |
| 59 | str(feature_dim))) |
| 60 | |
| 61 | if isinstance(sparse_feature_idx, int) and use_sparse_feature: |
| 62 | sparse_feature_idx = [sparse_feature_idx] |
| 63 | if isinstance(sparse_feature_max_id, int) and use_sparse_feature: |
| 64 | sparse_feature_max_id = [sparse_feature_max_id] |
| 65 | if use_sparse_feature and \ |
| 66 | len(sparse_feature_idx) != len(sparse_feature_max_id): |
| 67 | |
| 68 | raise ValueError('sparse_feature_idx must be the same length as' |
| 69 | 'sparse_feature_max_id.') |
| 70 | |
| 71 | embedding_num = (1 if use_id else 0) + \ |
| 72 | (len(sparse_feature_idx) if use_sparse_feature else 0) |
| 73 | |
| 74 | if combiner == 'add': |
| 75 | embedding_dim = dim |
| 76 | if isinstance(embedding_dim, int) and embedding_num: |
| 77 | embedding_dim = [embedding_dim] * embedding_num |
| 78 | if embedding_num and len(embedding_dim) != embedding_num: |
| 79 | raise ValueError('length of embedding_num must be int(use_id) + ' |
| 80 | 'len(sparse_feature_idx)') |
| 81 | |
| 82 | if isinstance(use_hash_embedding, bool) and embedding_num: |
| 83 | use_hash_embedding = [use_hash_embedding] * embedding_num |
| 84 | if embedding_num and len(use_hash_embedding) != embedding_num: |
| 85 | raise ValueError('length of use_hash_embedding must be int(use_id)' |
| 86 | ' + len(sparse_feature_idx)') |
| 87 | |
| 88 | # model architechture |
| 89 | self.dim = dim |