def _build_naml()

in recommenders/models/newsrec/models/naml.py [0:0]
91 lines of code
1 McCabe index (conditional complexity)

    def _build_naml(self):
        """The main function to create NAML's logic. The core of NAML
        is a user encoder and a news encoder.

        Returns:
            object: a model used to train.
            object: a model used to evaluate and predict.
        """
        hparams = self.hparams

        his_input_title = keras.Input(
            shape=(hparams.his_size, hparams.title_size), dtype="int32"
        )
        his_input_body = keras.Input(
            shape=(hparams.his_size, hparams.body_size), dtype="int32"
        )
        his_input_vert = keras.Input(shape=(hparams.his_size, 1), dtype="int32")
        his_input_subvert = keras.Input(shape=(hparams.his_size, 1), dtype="int32")

        pred_input_title = keras.Input(
            shape=(hparams.npratio + 1, hparams.title_size), dtype="int32"
        )
        pred_input_body = keras.Input(
            shape=(hparams.npratio + 1, hparams.body_size), dtype="int32"
        )
        pred_input_vert = keras.Input(shape=(hparams.npratio + 1, 1), dtype="int32")
        pred_input_subvert = keras.Input(shape=(hparams.npratio + 1, 1), dtype="int32")

        pred_input_title_one = keras.Input(
            shape=(
                1,
                hparams.title_size,
            ),
            dtype="int32",
        )
        pred_input_body_one = keras.Input(
            shape=(
                1,
                hparams.body_size,
            ),
            dtype="int32",
        )
        pred_input_vert_one = keras.Input(shape=(1, 1), dtype="int32")
        pred_input_subvert_one = keras.Input(shape=(1, 1), dtype="int32")

        his_title_body_verts = layers.Concatenate(axis=-1)(
            [his_input_title, his_input_body, his_input_vert, his_input_subvert]
        )

        pred_title_body_verts = layers.Concatenate(axis=-1)(
            [pred_input_title, pred_input_body, pred_input_vert, pred_input_subvert]
        )

        pred_title_body_verts_one = layers.Concatenate(axis=-1)(
            [
                pred_input_title_one,
                pred_input_body_one,
                pred_input_vert_one,
                pred_input_subvert_one,
            ]
        )
        pred_title_body_verts_one = layers.Reshape((-1,))(pred_title_body_verts_one)

        embedding_layer = layers.Embedding(
            self.word2vec_embedding.shape[0],
            hparams.word_emb_dim,
            weights=[self.word2vec_embedding],
            trainable=True,
        )

        self.newsencoder = self._build_newsencoder(embedding_layer)
        self.userencoder = self._build_userencoder(self.newsencoder)

        user_present = self.userencoder(his_title_body_verts)
        news_present = layers.TimeDistributed(self.newsencoder)(pred_title_body_verts)
        news_present_one = self.newsencoder(pred_title_body_verts_one)

        preds = layers.Dot(axes=-1)([news_present, user_present])
        preds = layers.Activation(activation="softmax")(preds)

        pred_one = layers.Dot(axes=-1)([news_present_one, user_present])
        pred_one = layers.Activation(activation="sigmoid")(pred_one)

        model = keras.Model(
            [
                his_input_title,
                his_input_body,
                his_input_vert,
                his_input_subvert,
                pred_input_title,
                pred_input_body,
                pred_input_vert,
                pred_input_subvert,
            ],
            preds,
        )

        scorer = keras.Model(
            [
                his_input_title,
                his_input_body,
                his_input_vert,
                his_input_subvert,
                pred_input_title_one,
                pred_input_body_one,
                pred_input_vert_one,
                pred_input_subvert_one,
            ],
            pred_one,
        )

        return model, scorer