106 lines
4.6 KiB
Python
106 lines
4.6 KiB
Python
from pkg.dash.func.info_func import *
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from pkg.dash.app_init import app
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from dash.dependencies import Input, Output
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from dash import html
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import pandas as pd
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from datetime import timedelta
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@app.callback(
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[Output('test-info-tooltip', 'children')],
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[Input('test-button', 'n_clicks'),
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Input('test-date-input', 'date'),
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Input('test-time-input', 'value')]
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)
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def update_test_info(n_clicks, test_date, test_time):
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if n_clicks == 0:
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return [html.Div("Click 'Test' to see historical probability results.")]
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est = pytz.timezone('US/Eastern')
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# 解析测试日期和时间
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try:
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test_date = pd.to_datetime(test_date).date()
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test_datetime = pd.to_datetime(f"{test_date} {test_time}").tz_localize(est) # 使用 est
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except ValueError:
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return [html.Div("Invalid date or time format. Use YYYY-MM-DD and HH:MM:SS (e.g., 12:00:00).")]
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# 1. 计算到 test_datetime 的累计推文数（模拟当时的 tweet_count）
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data = render_data.global_agg_df.copy()
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historical_data = data[data['datetime_est'] <= test_datetime]
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if historical_data.empty:
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return [html.Div(f"No data available up to {test_datetime}")]
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tweet_count = historical_data['tweet_count'].sum()
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# 2. 计算实际最终推文数（到当天结束时的总数）
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day_end = pd.to_datetime(f"{test_date} 23:59:59").tz_localize(est) # 使用 est
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actual_data = data[(data['date'] == test_date) & (data['datetime_est'] <= day_end)]
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if actual_data.empty:
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return [html.Div(f"No data available for {test_date}")]
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actual_end_count = actual_data['tweet_count'].sum()
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# 3. 模拟 days_to_next_friday（从 test_datetime 到下周五）
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days_to_next_friday = (4 - test_date.weekday()) % 7
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next_friday = (test_datetime.replace(hour=12, minute=0, second=0, microsecond=0) +
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timedelta(days=days_to_next_friday))
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if test_datetime > next_friday:
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next_friday += timedelta(days=7)
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days_to_next_friday = (next_friday - test_datetime).total_seconds() / (24 * 60 * 60)
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# 4. 设置预测范围（基于实际最终推文数的 ±10%）
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prob_start = actual_end_count * 0.9 # 90% of actual
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prob_end = actual_end_count * 1.1 # 110% of actual
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# 5. 调用原始的 calculate_tweet_probability() 计算概率
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probability = calculate_tweet_probability(tweet_count, days_to_next_friday, prob_start, prob_end)
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prob_min, prob_max = map(float, probability.split(" - "))
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formatted_probability = f"{prob_min * 100:.2f}% - {prob_max * 100:.2f}%"
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# 6. 构建测试结果表格
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test_table_rows = [
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html.Tr([
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html.Th("Test Date and Time:", colSpan=2, style={'paddingRight': '10px'}),
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html.Td(str(test_datetime), colSpan=6, style={'paddingRight': '10px'})
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]),
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html.Tr([
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html.Th("Tweet Count at Test Time:", colSpan=2, style={'paddingRight': '10px'}),
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html.Td(str(tweet_count), colSpan=6, style={'paddingRight': '10px'})
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]),
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html.Tr([
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html.Th("Actual Final Tweet Count:", colSpan=2, style={'paddingRight': '10px'}),
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html.Td(str(actual_end_count), colSpan=6, style={'paddingRight': '10px'})
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]),
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html.Tr([
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html.Th(f"Predicted Range ({int(prob_start)}-{int(prob_end)}):", colSpan=2, style={'paddingRight': '10px'}),
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html.Td(formatted_probability, colSpan=6, style={'paddingRight': '10px'})
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]),
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html.Tr([
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html.Th("Does Actual Fall in Range?", colSpan=2, style={'paddingRight': '10px'}),
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html.Td(
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"Yes" if prob_start <= actual_end_count <= prob_end else "No",
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colSpan=6,
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style={'paddingRight': '10px', 'color': 'green' if prob_start <= actual_end_count <= prob_end else 'red'}
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)
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])
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]
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if prob_start <= actual_end_count <= prob_end:
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expected_prob = (prob_max + prob_min) / 2
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test_table_rows.append(
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html.Tr([
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html.Th("Expected Probability:", colSpan=2, style={'paddingRight': '10px'}),
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html.Td(f"~{expected_prob * 100:.2f}% (should be high if model fits)", colSpan=6, style={'paddingRight': '10px'})
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])
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)
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else:
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test_table_rows.append(
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html.Tr([
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html.Th("Note:", colSpan=2, style={'paddingRight': '10px'}),
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html.Td("Model prediction does not match actual outcome.", colSpan=6, style={'paddingRight': '10px', 'color': 'red'})
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])
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)
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test_table = html.Table(test_table_rows, style={
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'width': '100%',
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'textAlign': 'left',
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'borderCollapse': 'collapse'
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})
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return [test_table] |