| 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980818283 |
- # 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 typing import Any
- import io
- import pandas as pd
- import matplotlib.pyplot as plt
- from PIL import Image
- from ...common.result import BaseTSResult
- def visualize(forecast: pd.DataFrame, actual_data: pd.DataFrame) -> Image.Image:
- """
- Visualizes both the time series forecast and actual results, returning them as a Pillow image.
- Args:
- forecast (pd.DataFrame): The DataFrame containing the forecast data.
- actual_data (pd.Series): The actual observed data for comparison.
- title (str): The title of the plot.
- Returns:
- Image.Image: The visualized result as a Pillow image.
- """
- plt.figure(figsize=(12, 6))
- forecast_columns = forecast.columns
- index_name = forecast.index.name
- actual_data = actual_data.set_index(index_name)
- actual_data.index = actual_data.index.astype(str)
- forecast.index = forecast.index.astype(str)
- length = min(len(forecast), len(actual_data))
- actual_data = actual_data.tail(length)
-
- plt.plot(actual_data.index, actual_data[forecast_columns[0]], label='Actual Data', color='blue', linestyle='--')
- plt.plot(forecast.index, forecast[forecast_columns[0]], label='Forecast', color='red')
- plt.title('Time Series Forecast')
- plt.xlabel('Time')
- plt.ylabel(forecast_columns[0])
- plt.legend()
- plt.grid(True)
- plt.xticks(ticks=range(0, 2*length, 10))
- plt.xticks(rotation=45)
- buf = io.BytesIO()
- plt.savefig(buf, bbox_inches='tight')
- buf.seek(0)
- plt.close()
- image = Image.open(buf)
- return image
- class TSFcResult(BaseTSResult):
- """A class representing the result of a time series forecasting task."""
- def _to_img(self) -> Image.Image:
- """apply"""
- forecast = self["forecast"]
- ts_input = pd.read_csv(self["input_path"])
- return {"res": visualize(forecast, ts_input)}
-
- def _to_csv(self) -> Any:
- """
- Converts the forecasting results to a CSV format.
- Returns:
- Any: The forecast data formatted for CSV output, typically a DataFrame or similar structure.
- """
- return {"res": self["forecast"]}
|