From 8a2614b07331427ebf55c8d9b5b01cec7ff829d7 Mon Sep 17 00:00:00 2001 From: NY Date: Fri, 14 Mar 2025 15:55:44 +0800 Subject: [PATCH] +probability(debug&test) --- pkg/dash/app_html.py | 23 +++--- pkg/dash/func/info_test.py | 140 ++++++++++++++++++++----------------- 2 files changed, 89 insertions(+), 74 deletions(-) diff --git a/pkg/dash/app_html.py b/pkg/dash/app_html.py index 91565ee..a9bce85 100644 --- a/pkg/dash/app_html.py +++ b/pkg/dash/app_html.py @@ -247,13 +247,20 @@ def layout_config(app): html.Tr([ html.Td("Test Time:", style={'paddingRight': '10px'}), html.Td( - dcc.Input( - id='test-time-input', - type='text', - placeholder='HH:MM:SS (e.g., 12:00:00)', - value='12:00:00', - style={'width': '100%'} - ) + html.Div([ + dcc.Input( + id='test-time-input', + type='text', + placeholder='HH:MM:SS (e.g., 12:00:00)', # 增强提示 + value='12:00:00', + pattern='[0-2][0-9]:[0-5][0-9]:[0-5][0-9]', # 限制格式 + style={'width': '100%'} + ), + html.Span( + "Enter time in HH:MM:SS format (e.g., 12:00:00)", + style={'fontSize': '12px', 'color': 'gray', 'marginTop': '5px', 'display': 'block'} + ) + ]) ) ]), html.Tr([ @@ -267,7 +274,7 @@ def layout_config(app): 'width': '50%', 'marginTop': '10px', 'borderCollapse': 'collapse' - }) + }), ], style={'marginLeft': '50px'}), dcc.Interval(id='clock-interval', interval=1000, n_intervals=0) diff --git a/pkg/dash/func/info_test.py b/pkg/dash/func/info_test.py index f045c2e..92c7669 100644 --- a/pkg/dash/func/info_test.py +++ b/pkg/dash/func/info_test.py @@ -3,6 +3,7 @@ from pkg.dash.app_init import app from dash.dependencies import Input, Output from dash import html import pandas as pd +import re from datetime import timedelta @app.callback( @@ -16,91 +17,98 @@ def update_test_info(n_clicks, test_date, test_time): return [html.Div("Click 'Test' to see historical probability results.")] est = pytz.timezone('US/Eastern') + data = render_data.global_agg_df.copy() + + # 调试：打印输入值 + print(f"test_date: {test_date}, test_time: {test_time}") + + # 检查输入是否为空 + if not test_date or not test_time: + return [html.Div("Date or time input is empty. Please provide both date (YYYY-MM-DD) and time (HH:MM:SS).")] + + # 验证时间格式 + time_pattern = r'^(?:[01]\d|2[0-3]):[0-5]\d:[0-5]\d$' # HH:MM:SS (00:00:00 to 23:59:59) + if not re.match(time_pattern, test_time): + return [html.Div("Invalid time format. Use HH:MM:SS (e.g., 12:00:00) with hours 00-23, minutes 00-59, seconds 00-59.")] + + # 重构 datetime_est，处理夏令时模糊时间 + data['hours'] = data['minute_of_day'] // 60 + data['minutes'] = data['minute_of_day'] % 60 + data['datetime_est'] = pd.to_datetime( + data['date'].astype(str) + ' ' + + data['hours'].astype(str) + ':' + + data['minutes'].astype(str) + ':00', + errors='coerce' + ).dt.tz_localize(est, ambiguous='NaT') + + if data['datetime_est'].isna().any(): + print("Warning: Some datetime_est values are NaT due to ambiguous time handling") # 解析测试日期和时间 try: - test_date = pd.to_datetime(test_date).date() - test_datetime = pd.to_datetime(f"{test_date} {test_time}").tz_localize(est) # 使用 est - except ValueError: + test_date = pd.to_datetime(test_date, format='%Y-%m-%d').date() + test_datetime = pd.to_datetime(f"{test_date} {test_time}", format='%Y-%m-%d %H:%M:%S').tz_localize(est, ambiguous=True) + except ValueError as e: + print(f"Error parsing date/time: {e}") return [html.Div("Invalid date or time format. Use YYYY-MM-DD and HH:MM:SS (e.g., 12:00:00).")] - # 1. 计算到 test_datetime 的累计推文数（模拟当时的 tweet_count） - data = render_data.global_agg_df.copy() - historical_data = data[data['datetime_est'] <= test_datetime] - if historical_data.empty: - return [html.Div(f"No data available up to {test_datetime}")] - tweet_count = historical_data['tweet_count'].sum() + # 计算周期开始时间（上一个周五 12:00 PM） + test_date_only = test_datetime.replace(hour=0, minute=0, second=0, microsecond=0) # 只考虑日期部分 + days_to_last_friday = (test_date_only.weekday() - 4) % 7 # 4 表示周五 + cycle_start = test_date_only - timedelta(days=days_to_last_friday) + cycle_start = cycle_start.replace(hour=12, minute=0, second=0, microsecond=0) # 已经是 tz-aware，直接调整时间 - # 2. 计算实际最终推文数（到当天结束时的总数） - day_end = pd.to_datetime(f"{test_date} 23:59:59").tz_localize(est) # 使用 est - actual_data = data[(data['date'] == test_date) & (data['datetime_est'] <= day_end)] + # 确保周期结束时间（下周五 12:00 PM EDT）考虑夏令时 + cycle_end = cycle_start + timedelta(days=7) + if cycle_end.month == 3 and 8 <= cycle_end.day <= 14: # 粗略检查夏令时开始（3月第二个星期日） + cycle_end = cycle_end.tz_convert(est) # 转换为 EDT + else: + cycle_end = cycle_end.tz_convert(est) # 保持一致 + + # 调试：打印周期信息 + print(f"Cycle Start: {cycle_start}, Cycle End: {cycle_end}") + + # 过滤周期内的数据 + cycle_data = data[(data['datetime_est'] >= cycle_start) & (data['datetime_est'] <= test_datetime)] + if cycle_data.empty: + return [html.Div(f"No data available in cycle from {cycle_start} to {test_datetime}")] + tweet_count = cycle_data['tweet_count'].sum() + + # 计算实际最终推文数（周期结束时的总数） + actual_data = data[(data['datetime_est'] >= cycle_start) & (data['datetime_est'] <= cycle_end)] if actual_data.empty: - return [html.Div(f"No data available for {test_date}")] + return [html.Div(f"No data available for cycle ending {cycle_end}")] actual_end_count = actual_data['tweet_count'].sum() - # 3. 模拟 days_to_next_friday（从 test_datetime 到下周五） - days_to_next_friday = (4 - test_date.weekday()) % 7 - next_friday = (test_datetime.replace(hour=12, minute=0, second=0, microsecond=0) + - timedelta(days=days_to_next_friday)) - if test_datetime > next_friday: - next_friday += timedelta(days=7) - days_to_next_friday = (next_friday - test_datetime).total_seconds() / (24 * 60 * 60) + # 计算 days_to_next_friday（从 test_datetime 到周期结束） + days_to_next_friday = (cycle_end - test_datetime).total_seconds() / (24 * 60 * 60) - # 4. 设置预测范围（基于实际最终推文数的 ±10%） - prob_start = actual_end_count * 0.9 # 90% of actual - prob_end = actual_end_count * 1.1 # 110% of actual + # 设置预测范围 + prob_start = actual_end_count * 0.9 + prob_end = actual_end_count * 1.1 - # 5. 调用原始的 calculate_tweet_probability() 计算概率 + # 计算概率 probability = calculate_tweet_probability(tweet_count, days_to_next_friday, prob_start, prob_end) prob_min, prob_max = map(float, probability.split(" - ")) formatted_probability = f"{prob_min * 100:.2f}% - {prob_max * 100:.2f}%" - # 6. 构建测试结果表格 + # 构建测试结果表格 test_table_rows = [ - html.Tr([ - html.Th("Test Date and Time:", colSpan=2, style={'paddingRight': '10px'}), - html.Td(str(test_datetime), colSpan=6, style={'paddingRight': '10px'}) - ]), - html.Tr([ - html.Th("Tweet Count at Test Time:", colSpan=2, style={'paddingRight': '10px'}), - html.Td(str(tweet_count), colSpan=6, style={'paddingRight': '10px'}) - ]), - html.Tr([ - html.Th("Actual Final Tweet Count:", colSpan=2, style={'paddingRight': '10px'}), - html.Td(str(actual_end_count), colSpan=6, style={'paddingRight': '10px'}) - ]), - html.Tr([ - html.Th(f"Predicted Range ({int(prob_start)}-{int(prob_end)}):", colSpan=2, style={'paddingRight': '10px'}), - html.Td(formatted_probability, colSpan=6, style={'paddingRight': '10px'}) - ]), - html.Tr([ - html.Th("Does Actual Fall in Range?", colSpan=2, style={'paddingRight': '10px'}), - html.Td( - "Yes" if prob_start <= actual_end_count <= prob_end else "No", - colSpan=6, - style={'paddingRight': '10px', 'color': 'green' if prob_start <= actual_end_count <= prob_end else 'red'} - ) - ]) + html.Tr([html.Th("Test Date and Time:", colSpan=2), html.Td(str(test_datetime), colSpan=6)]), + html.Tr([html.Th("Tweet Count at Test Time:", colSpan=2), html.Td(str(tweet_count), colSpan=6)]), + html.Tr([html.Th("Actual Final Tweet Count:", colSpan=2), html.Td(str(actual_end_count), colSpan=6)]), + html.Tr([html.Th(f"Predicted Range ({int(prob_start)}-{int(prob_end)}):", colSpan=2), html.Td(formatted_probability, colSpan=6)]), + html.Tr([html.Th("Does Actual Fall in Range?", colSpan=2), + html.Td("Yes" if prob_start <= actual_end_count <= prob_end else "No", + colSpan=6, style={'color': 'green' if prob_start <= actual_end_count <= prob_end else 'red'})]) ] if prob_start <= actual_end_count <= prob_end: expected_prob = (prob_max + prob_min) / 2 - test_table_rows.append( - html.Tr([ - html.Th("Expected Probability:", colSpan=2, style={'paddingRight': '10px'}), - html.Td(f"~{expected_prob * 100:.2f}% (should be high if model fits)", colSpan=6, style={'paddingRight': '10px'}) - ]) - ) + test_table_rows.append(html.Tr([html.Th("Expected Probability:", colSpan=2), + html.Td(f"~{expected_prob * 100:.2f}% (should be high if model fits)", colSpan=6)])) else: - test_table_rows.append( - html.Tr([ - html.Th("Note:", colSpan=2, style={'paddingRight': '10px'}), - html.Td("Model prediction does not match actual outcome.", colSpan=6, style={'paddingRight': '10px', 'color': 'red'}) - ]) - ) + test_table_rows.append(html.Tr([html.Th("Note:", colSpan=2), + html.Td("Model prediction does not match actual outcome.", colSpan=6, style={'color': 'red'})])) - test_table = html.Table(test_table_rows, style={ - 'width': '100%', - 'textAlign': 'left', - 'borderCollapse': 'collapse' - }) + test_table = html.Table(test_table_rows, style={'width': '100%', 'textAlign': 'left', 'borderCollapse': 'collapse'}) return [test_table] \ No newline at end of file