dcf583f15d08415d4e1972d44c250a5f13f6b10c,tests/models/DIEN_test.py,,get_xy_fd,#,10

Before Change

                       SparseFeat("item_gender", 2+1,hash_flag),
                       DenseFeat("score", 1)]

    feature_columns += [VarLenSparseFeat("hist_item", maxlen=4,vocabulary_size=3+1, embedding_name="item"),
                        VarLenSparseFeat("hist_item_gender", maxlen=4,vocabulary_size=3+1, embedding_name="item_gender")]

    behavior_feature_list = ["item","item_gender"]
    uid = np.array([0, 1, 2])
    ugender = np.array([0, 1, 0])
    iid = np.array([1, 2, 3])/ǖ is mask value
    igender = np.array([1, 2, 1])// 0 is mask value
    score = np.array([0.1, 0.2, 0.3])

    hist_iid = np.array([[ 1, 2, 3,0], [ 1, 2, 3,0], [ 1, 2, 0,0]])
    hist_igender = np.array([[1, 1, 2,0 ], [2, 1, 1, 0], [2, 1, 0, 0]])

    behavior_length = np.array([3,3,2])

    feature_dict = {"user": uid, "gender": ugender, "item": iid, "item_gender": igender,
                    "hist_item": hist_iid, "hist_item_gender": hist_igender,
                    "score": score}


    if use_neg:
        feature_dict["neg_hist_item"] = np.array([[1, 2, 3, 0], [1, 2, 3, 0], [1, 2, 0, 0]])
        feature_dict["neg_hist_item_gender"] = np.array([[1, 1, 2, 0], [2, 1, 1, 0], [2, 1, 0, 0]])
        feature_columns += [VarLenSparseFeat("neg_hist_item", maxlen=4,vocabulary_size=3+1, embedding_name="item"),
                        VarLenSparseFeat("neg_hist_item_gender", maxlen=4,vocabulary_size=3+1, embedding_name="item_gender")]


    feature_names = get_feature_names(feature_columns)
    x = {name:feature_dict[name] for name in feature_names}
    x["seq_length"] = behavior_length
    y = [1, 0, 1]

After Change

                       SparseFeat("item_gender", 2+1,hash_flag),
                       DenseFeat("score", 1)]

    feature_columns += [
        VarLenSparseFeat(SparseFeat("hist_item", vocabulary_size=3 + 1, embedding_dim=8, embedding_name="item"),
                         maxlen=4),
        VarLenSparseFeat(SparseFeat("hist_item_gender", 2 + 1, embedding_dim=4, embedding_name="item_gender"), maxlen=4)]

    behavior_feature_list = ["item","item_gender"]
    uid = np.array([0, 1, 2])
    ugender = np.array([0, 1, 0])
    iid = np.array([1, 2, 3])/ǖ is mask value
    igender = np.array([1, 2, 1])// 0 is mask value
    score = np.array([0.1, 0.2, 0.3])

    hist_iid = np.array([[ 1, 2, 3,0], [ 1, 2, 3,0], [ 1, 2, 0,0]])
    hist_igender = np.array([[1, 1, 2,0 ], [2, 1, 1, 0], [2, 1, 0, 0]])

    behavior_length = np.array([3,3,2])

    feature_dict = {"user": uid, "gender": ugender, "item": iid, "item_gender": igender,
                    "hist_item": hist_iid, "hist_item_gender": hist_igender,
                    "score": score}


    if use_neg:
        feature_dict["neg_hist_item"] = np.array([[1, 2, 3, 0], [1, 2, 3, 0], [1, 2, 0, 0]])
        feature_dict["neg_hist_item_gender"] = np.array([[1, 1, 2, 0], [2, 1, 1, 0], [2, 1, 0, 0]])
        feature_columns += [
            VarLenSparseFeat(SparseFeat("neg_hist_item", vocabulary_size=3 + 1, embedding_dim=8, embedding_name="item"),
                             maxlen=4),
            VarLenSparseFeat(SparseFeat("neg_hist_item_gender", 2 + 1, embedding_dim=4, embedding_name="item_gender"),
                             maxlen=4)]


    feature_names = get_feature_names(feature_columns)
    x = {name:feature_dict[name] for name in feature_names}
    x["seq_length"] = behavior_length
    y = [1, 0, 1]

Instances