def prepare_args()

in data.py [0:0]

• 36 lines of code
• 10 McCabe index (conditional complexity)

    def prepare_args(self):
        """
        Prepare args
        :return:
        """
        parser = self.setup_args()
        mode = self.args.mode
        if mode == "train":
            # mode = 'train:ordered'
            mode = "train:evalmode"
        self.parlai_opts.append("-dt {}".format(mode))
        self.parlai_opts.append("-t {}".format(self.args.data_name))
        if self.args.agent == "ir":
            # IR Baseline
            self.parlai_opts.append("--length_penalty 0.5")
            self.parlai_opts.append("--history_size 10")
            self.parlai_opts.append("--use_reply label")
        elif self.args.agent == "seq2seq":
            # Seq2Seq opts
            self.parlai_opts.append(
                "-mf zoo:convai2/seq2seq/convai2_self_seq2seq_model"
            )
            self.parlai_opts.append("-m legacy:seq2seq:0")
            self.parlai_opts.append("-opt sgd")
        elif self.args.agent == "kvmemnn":
            self.parlai_opts.append("-mf zoo:convai2/kvmemnn/model")
            # self.parlai_opts.append("-nt 40")
        elif self.args.agent == "polyencoder":
            self.parlai_opts.append("-mf zoo:pretrained_transformers/model_poly/model")
            self.parlai_opts.append("--no_cuda")
        else:
            # specific model and path
            self.parlai_opts.append("-mf {}".format(self.args.mf))
        opt = parser.parse_args(" ".join(self.parlai_opts).split(" "))
        if self.args.agent == "seq2seq":
            # FIX: temporary fix for parlai issue
            opt["override"] = {
                "model": "legacy:seq2seq:0",
                "model_file": "/private/home/koustuvs/mlp/parlai_koustuvs/data/models/convai2/seq2seq/convai2_self_seq2seq_model",
            }
        if self.args.agent == "polyencoder":
            opt["override"] = {
                "fixed_candidates_path": "/private/home/koustuvs/mlp/parlai_koustuvs/data/models/pretrained_transformers/convai_trainset_cands.txt",
                "ignore_bad_candidates": True,
            }
        return opt