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fix

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Pi 2025-03-16 17:08:51 +08:00
parent 70c1da09ef
commit 8f5050ab31
3 changed files with 123 additions and 107 deletions
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4
pkg/dash/func/info_func.py
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@ -163,8 +163,10 @@ def calculate_avg_tweets_per_day(target, now, remain):
def calculate_tweet_probability(tweet_count, days_to_next_friday, prob_start, prob_end, peak_percentile=75):
remaining_hours = days_to_next_friday * 24
hourly_weights = get_dynamic_hourly_weights()
if remaining_hours <= 0:
return "0.0000 - 0.0000" # 测试时间超出周期结束，返回默认值
hourly_weights = get_dynamic_hourly_weights()
data = get_last_7_days_data()
if data.empty:
recent_tweets = [70] * 7

223
pkg/dash/func/info_test.py
View File

@ -7,6 +7,26 @@ import csv
import pandas as pd
import re
from datetime import timedelta
from tqdm import tqdm
from concurrent.futures import ProcessPoolExecutor, as_completed
import multiprocessing as mp
# 全局数据，避免重复加载
global_data = None
def initialize_global_data():
global global_data
if global_data is None:
global_data = render_data.global_agg_df.copy()
# 预计算常用列，避免重复操作
global_data['hours'] = global_data['minute_of_day'] // 60
global_data['minutes'] = global_data['minute_of_day'] % 60
global_data['datetime_est'] = pd.to_datetime(
global_data['date'].astype(str) + ' ' +
global_data['hours'].astype(str) + ':' +
global_data['minutes'].astype(str) + ':00',
errors='coerce'
).dt.tz_localize('US/Eastern', ambiguous='NaT')
@app.callback(
[Output('test-info-tooltip', 'children')],
@ -14,94 +34,73 @@ from datetime import timedelta
Input('test-date-input', 'date'),
Input('test-time-input', 'value')]
)
def update_test_info(n_clicks, test_date, test_time):
def update_test_info(n_clicks, test_date, test_time, data=None):
if n_clicks == 0:
return [html.Div("Click 'Test' to see historical probability results.")]
est = pytz.timezone('US/Eastern')
data = render_data.global_agg_df.copy()
data = data if data is not None else 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$' # HH:MM:SS (00:00:00 to 23:59:59)
time_pattern = r'^(?:[01]\d|2[0-3]):[0-5]\d:[0-5]\d$'
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.")]
return [html.Div("Invalid time format. Use HH:MM:SS (e.g., 12:00:00).")]
# 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, 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}")
except ValueError:
return [html.Div("Invalid date or time format. Use YYYY-MM-DD and HH:MM:SS (e.g., 12:00:00).")]
# 计算周期开始时间（上一个或当前周五 12:00 PM）
test_day_of_week = test_datetime.weekday() # 0 = Monday, 4 = Friday
test_day_of_week = test_datetime.weekday()
test_hour = test_datetime.hour
days_since_last_friday = (test_day_of_week - 4) % 7 # 4 表示周五
if test_hour < 12 and test_day_of_week == 4: # Before 12 PM on Friday
cycle_start = test_datetime - timedelta(days=days_since_last_friday + 7) # Previous Friday
days_since_last_friday = (test_day_of_week - 4) % 7
if test_hour < 12 and test_day_of_week == 4:
cycle_start = test_datetime - timedelta(days=days_since_last_friday + 7)
else:
cycle_start = test_datetime - timedelta(days=days_since_last_friday) # Current or next Friday
cycle_start = test_datetime - timedelta(days=days_since_last_friday)
cycle_start = cycle_start.replace(hour=12, minute=0, second=0, microsecond=0)
# 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)
# 精确计算夏令时开始（2025年3月9日，第二个星期日）
first_day = cycle_end.replace(day=1)
second_sunday = first_day + timedelta(days=((6 - first_day.weekday()) % 7) + 7)
if cycle_end.month == 3 and cycle_end >= second_sunday.replace(hour=2):
cycle_end = cycle_end.tz_convert(est) # EDT
cycle_end = cycle_end.tz_convert(est)
else:
cycle_end = cycle_end.tz_convert(est) # EST 或 EDT
cycle_end = cycle_end.tz_convert(est)
# 调试：打印周期信息
print(f"Cycle Start: {cycle_start}, Cycle End: {cycle_end}")
days_to_next_friday = (cycle_end - test_datetime).total_seconds() / (24 * 60 * 60)
if days_to_next_friday <= 0:
return [html.Div(f"Test time {test_datetime} is at or past cycle end {cycle_end}.")]
# 过滤周期内的数据（从周期开始到测试时间）
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}")]
return [html.Div(f"No data available 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 cycle ending {cycle_end}")]
actual_end_count = actual_data['tweet_count'].sum()
# 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)
days_elapsed = (test_datetime - cycle_start).total_seconds() / (24 * 60 * 60)
if days_elapsed <= 0:
return [html.Div(f"Test time {test_datetime} is before cycle start {cycle_start}.")]
# 设置预测范围
prob_start = actual_end_count * 0.9
prob_end = actual_end_count * 1.1
daily_avg = tweet_count / days_elapsed
predicted_end_count = daily_avg * 7
prob_start = predicted_end_count * 0.9
prob_end = predicted_end_count * 1.1
# 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}%"
try:
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}%"
except Exception as e:
return [html.Div(f"Error calculating probability: {str(e)}")]
# 构建测试结果表格（包含 Cycle End）
test_table_rows = [
html.Tr([html.Th("Cycle Start:", colSpan=4), html.Td(str(cycle_start), colSpan=6)]),
html.Tr([html.Th("Test Date and Time:", colSpan=4), html.Td(str(test_datetime), colSpan=6)]),
@ -116,19 +115,55 @@ def update_test_info(n_clicks, test_date, test_time):
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=4),
html.Td(f"~{expected_prob * 100:.2f}% (should be high if model fits)", colSpan=6)]))
html.Td(f"~{expected_prob * 100:.2f}%", colSpan=6)]))
else:
test_table_rows.append(html.Tr([html.Th("Note:", colSpan=4),
html.Td("Model prediction does not match actual outcome.", colSpan=6, style={'color': 'red'})]))
html.Td("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'})
return [test_table]
def run_loop_test(start_date="2024-10-01", end_date="2025-03-12", interval_hours=1, output_file="test_results.csv"):
est = pytz.timezone('US/Eastern')
def process_test_case(args):
test_datetime, data = args
test_date = test_datetime.date().strftime('%Y-%m-%d')
test_time = test_datetime.time().strftime('%H:%M:%S')
n_clicks = 1
# 生成测试时间序列
result = update_test_info(n_clicks, test_date, test_time, data)
if isinstance(result[0], html.Table):
table = result[0]
rows = table.children
cycle_start = str(rows[0].children[1].children)
test_dt = str(rows[1].children[1].children)
cycle_end = str(rows[2].children[1].children)
tweet_count = int(rows[3].children[1].children)
actual_end_count = int(rows[4].children[1].children)
prob_range = rows[5].children[1].children
prob_min, prob_max = [float(x.strip('%')) for x in prob_range.split(" - ")]
prob_start, prob_end = map(int, rows[5].children[0].children.split("(")[1].split(")")[0].split("-"))
in_range = rows[6].children[1].children == "Yes"
expected_prob = None
note = ""
if len(rows) > 7:
if "Expected" in rows[7].children[0].children:
expected_prob = float(rows[7].children[1].children.split()[0][1:-1])
elif "Note" in rows[7].children[0].children:
note = rows[7].children[1].children
return [
test_date, test_time, cycle_start, cycle_end, tweet_count,
actual_end_count, prob_start, prob_end, prob_min, prob_max,
"Yes" if in_range else "No", expected_prob if expected_prob is not None else "", note
]
else:
return [test_date, test_time, "", "", "", "", "", "", "", "", "", "", result[0].children]
def run_loop_test(start_date="2024-10-01", end_date="2025-03-12", interval_hours=1, output_file="test_results.csv", max_workers=None, chunk_size=1000):
est = pytz.timezone('US/Eastern')
start_dt = pd.to_datetime(start_date).tz_localize(est)
end_dt = pd.to_datetime(end_date).tz_localize(est)
time_points = []
@ -137,71 +172,49 @@ def run_loop_test(start_date="2024-10-01", end_date="2025-03-12", interval_hours
time_points.append(current_dt)
current_dt += timedelta(hours=interval_hours)
# 准备 CSV 文件
headers = [
"Test Date", "Test Time", "Cycle Start", "Cycle End", "Tweet Count at Test Time",
"Actual Final Tweet Count", "Predicted Range Start", "Predicted Range End",
"Probability Min (%)", "Probability Max (%)", "Actual in Range", "Expected Probability (%)", "Note"
]
# 如果文件不存在，写入表头
if not os.path.exists(output_file):
with open(output_file, 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(headers)
# 循环测试
for test_datetime in time_points:
test_date = test_datetime.date().strftime('%Y-%m-%d')
test_time = test_datetime.time().strftime('%H:%M:%S')
n_clicks = 1 # 假设已点击
# 预加载数据
initialize_global_data()
data = global_data
# 调用原始函数
result = update_test_info(n_clicks, test_date, test_time)
total_steps = len(time_points)
max_workers = max_workers or os.cpu_count() or 4
chunk_size = min(chunk_size, total_steps) # 确保 chunk_size 不超过总任务数
# 解析结果
if isinstance(result[0], html.Table):
table = result[0]
rows = table.children
# 分块处理时间点
chunks = [time_points[i:i + chunk_size] for i in range(0, total_steps, chunk_size)]
# 提取数据
cycle_start = str(rows[0].children[1].children)
test_dt = str(rows[1].children[1].children)
cycle_end = str(rows[2].children[1].children)
tweet_count = int(rows[3].children[1].children)
actual_end_count = int(rows[4].children[1].children)
prob_range = rows[5].children[1].children # 例如 "2.74% - 3.25%"
# 移除 % 符号并转换为浮点数
prob_min, prob_max = [float(x.strip('%')) for x in prob_range.split(" - ")]
# 从表头提取预测范围
prob_start, prob_end = map(int, rows[5].children[0].children.split("(")[1].split(")")[0].split("-"))
in_range = rows[6].children[1].children == "Yes"
# 检查是否有 Expected Probability 或 Note
expected_prob = None
note = ""
if len(rows) > 7:
if "Expected" in rows[7].children[0].children:
expected_prob = float(rows[7].children[1].children.split()[0][1:-1]) # 移除 "~" 和 "%"
elif "Note" in rows[7].children[0].children:
note = rows[7].children[1].children
with tqdm(total=total_steps, desc="Processing Test Cases", unit="step") as pbar:
for chunk in chunks:
results = []
with ProcessPoolExecutor(max_workers=max_workers) as executor:
futures = {executor.submit(process_test_case, (test_datetime, data)): test_datetime for test_datetime in chunk}
for future in as_completed(futures):
try:
result = future.result()
results.append(result)
pbar.update(1)
except Exception as e:
test_datetime = futures[future]
results.append([test_datetime.date().strftime('%Y-%m-%d'),
test_datetime.time().strftime('%H:%M:%S'),
"", "", "", "", "", "", "", "", "", "", f"Error: {str(e)}"])
pbar.update(1)
# 写入 CSV
# 每处理完一个块写入 CSV
with open(output_file, 'a', newline='') as f:
writer = csv.writer(f)
writer.writerow([
test_date, test_time, cycle_start, cycle_end, tweet_count,
actual_end_count, prob_start, prob_end, prob_min, prob_max,
"Yes" if in_range else "No", expected_prob if expected_prob is not None else "", note
])
else:
# 如果返回错误信息，也记录
with open(output_file, 'a', newline='') as f:
writer = csv.writer(f)
writer.writerow([test_date, test_time, "", "", "", "", "", "", "", "", "", "", result[0].children])
writer.writerows(results)
print(f"Processed: {test_date} {test_time}")
# 运行测试
if __name__ == "__main__":
run_loop_test(start_date="2024-10-01", end_date="2025-03-12", interval_hours=1, output_file="test_results.csv")
run_loop_test(start_date="2024-10-01", end_date="2025-03-12", interval_hours=1, output_file="test_results.csv", chunk_size=1000)

3
requirements.txt
View File

@ -10,4 +10,5 @@ matplotlib~=3.10.1
numpy~=2.2.3
scipy~=1.15.2
ipython~=8.32.0
Flask~=3.0.3
Flask~=3.0.3
tqdm~=4.67.1