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- # 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.
- from pathlib import Path
- import numpy as np
- import pandas as pd
- from ..utils.io import TSWriter
- from .base import BaseResult
- class TSFcResult(BaseResult):
- def __init__(self, data):
- super().__init__(data)
- self._writer = TSWriter(backend="pandas")
- def save_to_csv(self, save_path):
- """write ts"""
- if not save_path.endswith(".csv"):
- save_path = Path(save_path) / f"{Path(self['ts_path']).stem}.csv"
- self._writer.write(save_path, self["forecast"])
- class TSClsResult(BaseResult):
- def __init__(self, data):
- super().__init__(
- {"ts_path": data["ts_path"], "classification": self.process_data(data)}
- )
- self._writer = TSWriter(backend="pandas")
- def process_data(self, data):
- """apply"""
- pred_ts = data["forecast"][0]
- pred_ts -= np.max(pred_ts, axis=-1, keepdims=True)
- pred_ts = np.exp(pred_ts) / np.sum(np.exp(pred_ts), axis=-1, keepdims=True)
- classid = np.argmax(pred_ts, axis=-1)
- pred_score = pred_ts[classid]
- result = {"classid": [classid], "score": [pred_score]}
- result = pd.DataFrame.from_dict(result)
- result.index.name = "sample"
- return result
- def save_to_csv(self, save_path):
- """write ts"""
- if not save_path.endswith(".csv"):
- save_path = Path(save_path) / f"{Path(self['ts_path']).stem}.csv"
- self._writer.write(save_path, self["classification"])
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