PyCaret异常检测setup()报错：AttributeError问题求助

问题描述 #

使用PyCaret执行异常检测时，只要数据集中包含分类变量，运行setup()函数就会触发以下错误：

AttributeError: 'DataFrame' object has no attribute 'unique'

复现代码 #

import pandas as pd
from pycaret.anomaly import setup

data = pd.read_csv('https://raw.githubusercontent.com/numenta/NAB/master/data/realKnownCause/nyc_taxi.csv')
data['timestamp'] = pd.to_datetime(data['timestamp'])

data.set_index('timestamp', drop=True, inplace=True)
# 按小时重采样时间序列
data = data.resample('H').sum()
# 从日期生成特征
data['day'] = [i.day for i in data.index]
data['day_name'] = [i.day_name() for i in data.index]
data['day_of_year'] = [i.dayofyear for i in data.index]
data['week_of_year'] = [i.weekofyear for i in data.index]
data['hour'] = [i.hour for i in data.index]
data['is_weekday'] = [i.isoweekday() for i in data.index]

s = setup(data, session_id = 123)

错误截图 #

解决方案 #

1. 显式指定分类特征 #

在setup()中明确声明分类列，避免PyCaret自动推断时出现逻辑错误：

s = setup(data, session_id=123, categorical_features=['day_name'])

2. 更新PyCaret至最新版本 #

该错误大概率是旧版本的已知bug，通过更新修复：

pip install --upgrade pycaret

3. 提前转换分类变量为数值型 #

如果无需保留分类标签，可提前对分类列做编码处理：

# 标签编码day_name列
data['day_name'] = pd.factorize(data['day_name'])[0]
# 执行setup
s = setup(data, session_id=123)

4. 验证数据索引合法性 #

确保数据集使用单级索引，避免PyCaret处理时混淆维度：

print(data.index.nlevels)  # 输出应为1，若大于1需重置索引

内容的提问来源于stack exchange，提问作者vraka0723