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96 lines
2.8 KiB
96 lines
2.8 KiB
import pandas as pd
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from streamlit_echarts import st_echarts
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def draw_echarts(model_name: str, target: str, target_name: str,
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history_data: pd.DataFrame, pred_data: pd.DataFrame):
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"""
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构造 ECharts 图表的配置并在 Streamlit 应用中展示。
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Args:
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model_name (str): 模型的名称
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target (str): 目标值的列名
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target_name (str): 目标值的显示名称
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historical_data (pd.DataFrame): 历史数据
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predicted_data (pd.DataFrame): 预测数据
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Returns:
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dict: ECharts 图表的配置
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"""
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# 数据处理，将历史数据和预测数据添加 None 值以适应图表的 x 轴
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history_values = history_data[target].values.tolist() + [
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None for _ in range(len(pred_data))
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]
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pred_values = [None for _ in range(len(history_data))
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] + pred_data[target].values.tolist()
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# 定义ECharts的配置
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option = {
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"title": {
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"text": f"{model_name}模型",
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"x": "auto"
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},
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# 配置提示框组件
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"tooltip": {
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"trigger": "axis"
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},
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# 配置图例组件
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"legend": {
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"data": [f"{target_name}历史数据", f"{target_name}预测数据"],
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"left": "right"
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},
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# 配置x轴和y轴
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"xAxis": {
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"type":
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"category",
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"data":
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history_data.index.astype(str).to_list() +
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pred_data.index.astype(str).to_list()
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},
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"yAxis": {
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"type": "value"
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},
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# 配置数据区域缩放组件
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"dataZoom": [{
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"type": "inside",
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"start": 0,
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"end": 100
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}],
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"series": []
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}
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# 添加历史数据系列
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if any(history_values):
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option["series"].append({
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"name": f"{target_name}历史数据",
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"type": "line",
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"data": history_values,
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"smooth": "true"
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})
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# 添加预测数据的系列
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if any(pred_values):
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option["series"].append({
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"name": f"{target_name}预测数据",
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"type": "line",
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"data": pred_values,
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"smooth": "true",
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"lineStyle": {
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"type": "dashed"
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}
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})
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# 在Streamlit应用中展示ECharts图表
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st_echarts(options=option)
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return option
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# history_data = pd.read_csv('data/normalized_df.csv',
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# index_col="date",
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# parse_dates=["date"])
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# pred_data = pd.read_csv('data/VAR_Forecasting_df.csv',
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# index_col="date",
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# parse_dates=["date"])
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# draw_echarts('VAR_Forecasting', 'liugan_index', '流感指数', history_data,
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# pred_data)
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