PaddleX/paddlex/inference/models/ts.py

# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os

from ...utils.func_register import FuncRegister
from ...modules.ts_forecast.model_list import MODELS
from ..components import *
from ..results import TSResult
from ..utils.process_hook import batchable_method
from .base import BasicPredictor


class TSPredictor(BasicPredictor):

    entities = MODELS

    def _check_args(self, kwargs):
        pass

    def _build_components(self):
        preprocess = self._build_preprocess()
        predictor = TSPPPredictor(
            model_dir=self.model_dir,
            model_prefix=self.MODEL_FILE_PREFIX,
            option=self.pp_option,
        )
        postprocess = self._build_postprocess()
        return {**preprocess, "predictor": predictor, **postprocess}

    def _build_preprocess(self):
        if not self.config.get("info_params", None):
            raise Exception("info_params is not found in config file")

        ops = {}
        ops["ReadTS"] = ReadTS()
        ops["TSCutOff"] = TSCutOff(self.config["size"])

        if self.config.get("scale", None):
            scaler_file_path = os.path.join(self.model_dir, "scaler.pkl")
            if not os.path.exists(scaler_file_path):
                raise Exception(f"Cannot find scaler file: {scaler_file_path}")
            ops["TSNormalize"] = TSNormalize(
                scaler_file_path, self.config["info_params"]
            )

        ops["BuildTSDataset"] = BuildTSDataset(self.config["info_params"])

        if self.config.get("time_feat", None):
            ops["TimeFeature"] = TimeFeature(
                self.config["info_params"],
                self.config["size"],
                self.config["holiday"],
            )
        ops["TStoArray"] = TStoArray(self.config["input_data"])
        return ops

    def _build_postprocess(self):
        if not self.config.get("info_params", None):
            raise Exception("info_params is not found in config file")

        ops = {}
        ops["ArraytoTS"] = ArraytoTS(self.config["info_params"])
        if self.config.get("scale", None):
            scaler_file_path = os.path.join(self.model_dir, "scaler.pkl")
            if not os.path.exists(scaler_file_path):
                raise Exception(f"Cannot find scaler file: {scaler_file_path}")
            ops["TSDeNormalize"] = TSDeNormalize(
                scaler_file_path, self.config["info_params"]
            )
        return ops

    @batchable_method
    def _pack_res(self, data):
        return {
            "result": TSResult({"ts_path": data["ts_path"], "forecast": data["pred"]})
        }