def _munge_params_tabular()

in src/autotrain/app/params.py [0:0]

• 28 lines of code
• 13 McCabe index (conditional complexity)

    def _munge_params_tabular(self):
        _params = self._munge_common_params()
        _params["model"] = self.base_model
        if not self.using_hub_dataset:
            _params["id_column"] = "autotrain_id"
            _params["valid_split"] = "validation"
            if len(self.column_mapping["label"]) == 1:
                _params["target_columns"] = ["autotrain_label"]
            else:
                _params["target_columns"] = [
                    "autotrain_label_" + str(i) for i in range(len(self.column_mapping["label"]))
                ]
        else:
            _params["id_column"] = self.column_mapping.get("id" if not self.api else "id_column", "id")
            _params["train_split"] = self.train_split
            _params["valid_split"] = self.valid_split
            _params["target_columns"] = self.column_mapping.get("label" if not self.api else "target_columns", "label")

        if len(_params["categorical_imputer"].strip()) == 0 or _params["categorical_imputer"].lower() == "none":
            _params["categorical_imputer"] = None
        if len(_params["numerical_imputer"].strip()) == 0 or _params["numerical_imputer"].lower() == "none":
            _params["numerical_imputer"] = None
        if len(_params["numeric_scaler"].strip()) == 0 or _params["numeric_scaler"].lower() == "none":
            _params["numeric_scaler"] = None

        if "classification" in self.task:
            _params["task"] = "classification"
        else:
            _params["task"] = "regression"

        return TabularParams(**_params)