Automl Skill

AutoML 自动化机器学习技能 | Automated Machine Learning Skill. 基于 PyCaret 进行低代码机器学习建模，支持分类、回归、聚类、异常检测、时间序列预测、自然语言处理和关联规则挖掘等任务。 未来将集成更多 AutoML 库（如 AutoGluon、FLAML 等）。 当...

Security Scan

The skill is a documentation/instruction-only AutoML guide based on PyCaret and the requested/declared surface (no env vars, no installers, no unusual binaries) matches its stated purpose.

Purpose & Capability

Name/description (PyCaret AutoML) align with the provided SKILL.md, README and extensive reference docs: classification, regression, clustering, anomaly, time-series, NLP, association rules, statsmodels examples and deployment guidance. The capabilities described are consistent with the content.

Instruction Scope

SKILL.md contains runnable Python examples (reading CSVs, calling pycaret.setup/compare_models/tune_model, statsmodels snippets). These are all within the ML automation scope. The skill does show examples that read local files (e.g., 'train.csv') and may perform model deployment, but it does not instruct the agent to read unrelated system files, collect credentials, or send data to unexpected external endpoints.

Install Mechanism

This is instruction-only and has no install spec or code. However, the skill assumes Python libraries (pycaret, statsmodels, optuna/hyperopt, shap, etc.) but does not declare dependencies or provide an installer. An agent executing these instructions may attempt to pip-install packages from PyPI — normal but worth noting because it results in network downloads and execution of third-party code.

Credentials

The skill requests no environment variables, credentials, or config paths. README mentions cloud deployment targets (AWS/GCP/Azure) conceptually but the skill does not require or attempt to access cloud credentials or unrelated secrets.

Persistence & Privilege

Skill flags show default behavior (always:false, agent invocation allowed). The skill does not request permanent presence, nor does it modify other skills or system-wide settings based on the provided materials.

Assessment

This skill is documentation-only and appears coherent for the AutoML use case. Before running it in an agent that executes code: (1) confirm your runtime has the required Python libraries or be prepared for the agent to pip-install them (which downloads and executes third‑party packages from the internet); (2) avoid supplying cloud credentials unless you intend to deploy models to cloud services — the skill mentions deployment but does not require credentials by itself; (3) review any generated or suggested code before execution, and do not run it on sensitive or production data without standard safeguards (data minimization, sandboxing, backups); (4) if you want stronger assurance, ask the skill author to include an explicit dependency list and reproducible install instructions (requirements.txt or pinned packages) and to document any cloud-deploy steps that would need credentials.

PyCaret AutoML 技能指南 | PyCaret AutoML Skill Guide

本技能帮助用户使用 PyCaret 快速构建端到端的机器学习工作流。PyCaret 是一个开源的低代码机器学习库，可以将数百行代码简化为几行。

This skill helps users build end-to-end machine learning workflows using PyCaret, an open-source low-code ML library that simplifies hundreds of lines of code into just a few lines.

核心功能 | Core Capabilities

• 自动化模型选择 - 自动比较多个模型并选择最佳模型
• 自动化超参数调优 - 使用 Optuna/Hyperopt 自动优化模型参数
• 自动化特征工程 - 自动进行数据预处理、特征转换和特征选择
• 模型集成 - 支持 Bagging、Boosting、Stacking、Blending
• 模型可解释性 - 支持 SHAP、Permutation Importance 等解释方法
• 模型部署就绪 - 生成可复现的生产级 Pipeline
• 统计推断增强 - 支持置信区间、假设检验、统计显著性分析

---

统计推断增强 | Statistical Enhancement (statsmodels)

当需要统计推断、假设检验、置信区间时，可以使用 statsmodels 补充 PyCaret：

线性回归模型

import statsmodels.api as sm

# OLS 回归（带统计显著性）
X = sm.add_constant(X)  # 添加截距
model = sm.OLS(y, X).fit()
print(model.summary())  # R², F检验, P值, 置信区间

广义线性模型 (GLM)

# 二项分布 GLM (Logistic 回归)
glm_model = sm.GLM(y, X, family=sm.families.Binomial()).fit()

# 泊松回归 (计数数据)
poisson_model = sm.GLM(y, X, family=sm.families.Poisson()).fit()

假设检验

from scipy import stats

# t 检验
t_stat, p_value = stats.ttest_ind(group1, group2)

# 卡方检验
chi2, p_value, dof, expected = stats.chi2_contingency(contingency_table)

# ANOVA
f_stat, p_value = stats.f_oneway(*groups)

时间序列分析

from statsmodels.tsa.arima.model import ARIMA
from statsmodels.tsa.statespace.sarimax import SARIMAX

# ARIMA 模型
arima_model = ARIMA(train_data, order=(1,1,1)).fit()
forecast = arima_model.forecast(steps=12)

# 季节性 SARIMAX
sarimax_model = SARIMAX(data, order=(1,1,1), seasonal_order=(1,1,1,12)).fit()

统计诊断

# 残差自相关检验 (Durbin-Watson)
from statsmodels.stats.stattools import durbin_watson
dw = durbin_watson(model.resid)

# 异方差检验
from statsmodels.stats.diagnostic import het_breuschpagan
bp_test = het_breuschpagan(model.resid, model.model.exog)

# 正态性检验
from scipy import stats
shapiro_stat, shapiro_p = stats.shapiro(model.resid)

混合效应模型 (随机效应)

# 混合线性模型 (Panel Data / 多层次数据)
from statsmodels.regression.mixed_linear_model import MixedLM
mixed_model = MixedLM(y, X, groups=group_var).fit()

PyCaret + statsmodels 组合使用

# 1. 用 PyCaret 快速建模和选择模型
from pycaret.classification import *
clf = setup(data, target='target')
best = compare_models()
tuned = tune_model(best)

# 2. 用 statsmodels 做统计推断
import statsmodels.api as sm
# 获取 PyCaret 模型的特征和预测
X_with_const = sm.add_constant(X_test)
sm_model = sm.Logit(y_test, X_with_const).fit(disp=0)
print(sm_model.summary())  # 系数显著性 P值

---

支持的机器学习任务 | Supported ML Tasks

模块	Module	任务类型	Task Type	参考文档
pycaret.classification	Classification	二分类、多分类	Binary, Multi-class	classification.md
pycaret.regression	Regression	回归预测	Regression	regression.md
pycaret.clustering	Clustering	无监督聚类	Unsupervised Clustering	clustering.md
pycaret.anomaly	Anomaly Detection	异常检测	Outlier Detection	anomaly.md
pycaret.time_series	Time Series	时间序列预测	Time Series Forecasting	time_series.md
pycaret.nlp	NLP	文本分类、主题建模	Text Classification, Topic Modeling	nlp.md
pycaret.arules	Association Rules	关联规则挖掘	Market Basket Analysis	association_rules.md

---

快速开始 | Quick Start

1. 选择您的任务类型

根据您的机器学习任务，选择相应的模块：

• 分类问题 → 使用 pycaret.classification
• 回归问题 → 使用 pycaret.regression
• 客户分群 → 使用 pycaret.clustering
• 异常检测 → 使用 pycaret.anomaly
• 时间预测 → 使用 pycaret.time_series
• 文本分析 → 使用 pycaret.nlp
• 购物篮分析 → 使用 pycaret.arules

2. 标准 AutoML 工作流 | Standard AutoML Workflow

完整的 AutoML 工作流程包含以下步骤：

Step 1: 数据收集与加载 | Data Collection & Loading

# 数据加载
import pandas as pd
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')

# 或使用 PyCaret 内置数据集
from pycaret.classification import get_data
data = get_data('breast_cancer')

Step 2: 数据理解与探索 | Data Understanding & EDA

# 基本信息
print(f"数据形状: {data.shape}")
print(f"数据类型:\n{data.dtypes}")

# 缺失值分析
missing = data.isnull().sum()
missing_pct = (missing / len(data) * 100).round(2)
print(f"缺失值比例:\n{pd.concat([missing, missing_pct], axis=1)}")

# 目标变量分布
data['target'].value_counts()

# 数值特征统计
data.describe()

Step 3: 数据预处理 | Data Preprocessing (setup 中自动完成)

# 初始化环境 - 数据预处理配置
clf = setup(
    data,
    target='target',

    # ===== 缺失值处理 =====
    numeric_imputation='mean',       # 数值型: mean/median/mode/knn/iterative
    categorical_imputation='mode',   # 类别型: mode/constant

    # ===== 异常值处理 =====
    remove_outliers=True,           # 移除异常值
    outliers_method='iforest',      # iforest/ee/lof
    outliers_threshold=0.05,        # 异常值比例

    # ===== 类别不平衡处理 =====
    fix_imbalance=True,             # 处理类别不平衡
    fix_imbalance_method='SMOTE',  # SMOTE/ADASYN/RandomOverSampler

    # ===== 数据类型指定 =====
    numeric_features=['age', 'income', 'score'],
    categorical_features=['city', 'gender', 'occupation'],
    date_features=['Date', 'created_at'],

    session_id=42
)

Step 4: 特征工程 | Feature Engineering (setup 中自动完成)

clf = setup(
    data,
    target='target',

    # ===== 特征缩放 =====
    normalize=True,                 # 归一化
    normalize_method='zscore',     # zscore/minmax/maxabs/robust

    # ===== 特征变换 =====
    transformation=True,            # 变换使数据更接近正态分布
    transformation_method='yeo-johnson',  # yeo-johnson/quantile

    # ===== 特征选择 =====
    feature_selection=True,         # 特征选择
    feature_selection_method='classic',      # classic/univariate/sequential
    n_features_to_select=0.2,     # 选择20%最重要特征

    # ===== 降维 =====
    pca=True,                      # PCA降维
    pca_method='linear',           # linear/kernel/incremental
    pca_components=0.95,           # 保留95%方差

    # ===== 多重共线性处理 =====
    remove_multicollinearity=True,
    multicollinearity_threshold=0.9,

    # ===== 特征编码 =====
    ordinal_features={'education': ['high_school', 'bachelor', 'master', 'phd']},
    high_cardinality_features='frequency',  # 处理高基数类别特征

    # ===== 特征交互 =====
    polynomial_features=True,
    polynomial_degree=2,

    # ===== 分箱（离散化） =====
    bin_numeric_features=['age', 'income'],

    session_id=42
)

Step 5: 模型选择 | Model Selection

# 比较所有模型
best_model = compare_models()

# 指定模型列表比较
best_model = compare_models(include=['lr', 'rf', 'xgboost', 'catboost', 'lightgbm'])

# 快速模式（排除耗时模型）
best_model = compare_models(turbo=True)

# 按特定指标排序
best_model = compare_models(sort='F1')  # 对于不平衡数据

Step 6: 模型训练 | Model Training

# 创建模型
model = create_model('rf')

# 指定模型参数
model = create_model('xgboost', n_estimators=100, max_depth=5)

Step 7: 超参数调优 | Hyperparameter Tuning

# 自动调优
tuned_model = tune_model(model)

# 自定义调优
tuned_model = tune_model(
    model,
    custom_grid={
        'n_estimators': [100, 200, 300],
        'max_depth': [3, 5, 7, None],
        'learning_rate': [0.01, 0.1, 0.3]
    },
    optimize='Accuracy',           # 分类: Accuracy/AUC/Recall/Precision/F1/MCC
                                  # 回归: RMSE/MSE/MAE/R2/RMSLE/MAPE
    choose_better=True,            # 返回更好的模型
    n_iter=50                      # 迭代次数
)

Step 8: 模型评估 | Model Evaluation

# 交互式评估
evaluate_model(tuned_model)

# 各种评估图表
plot_model(tuned_model, plot='auc')                # ROC曲线
plot_model(tuned_model, plot='confusion_matrix')   # 混淆矩阵
plot_model(tuned_model, plot='classification_report')  # 分类报告
plot_model(tuned_model, plot='learning_curve')    # 学习曲线
plot_model(tuned_model, plot='feature')            # 特征重要性
plot_model(tuned_model, plot='residuals')          # 残差图（回归）
plot_model(tuned_model, plot='error')              # 预测误差

# 交叉验证结果
results = pull()  # 获取当前实验结果

Step 9: 模型解释 | Model Interpretation

# SHAP 解释
interpret_model(tuned_model)

# Permutation Importance
interpret_model(tuned_model, plot='correlation')

# 局部解释
interpret_model(tuned_model, plot='reason', observation=0)

Step 10: 模型集成 | Model Ensemble

# Bagging
bagged = ensemble_model(tuned_model, method='Bagging')

# Boosting
boosted = ensemble_model(tuned_model, method='Boosting')

# 融合多个模型
blended = blend_models(
    estimator_list=['lr', 'dt', 'rf', 'xgboost'],
    method='soft',                  # soft/hard
    weights=[1, 2, 3, 2]           # 各模型权重
)

# 堆叠
stacked = stack_models(
    estimator_list=['lr', 'dt', 'rf'],
    meta_model='xgboost',
    restack=False                   # 是否允许基础模型使用原始特征
)

Step 11: 最终模型训练与预测 | Final Model Training & Prediction

# 在全部数据上训练最终模型
final_model = finalize_model(tuned_model)

# 预测
predictions = predict_model(final_model, data=test)

# 预测概率（分类）
predictions = predict_model(
    final_model,
    data=test,
    probability_threshold=0.7       # 自定义阈值
)

Step 12: 模型保存与部署 | Model Save & Deployment

# 保存模型（包含完整Pipeline）
save_model(final_model, 'my_model')

# 保存实验配置
save_experiment('my_experiment')

# 加载模型
loaded_model = load_model('my_model')

# 部署到云平台
deploy_model(
    final_model,
    platform='aws',                 # aws/gcp/azure
    authentication={
        'bucket': 'my-bucket'
    }
)

# 创建Web应用
create_app(final_model, app_path='app.py')

# 创建REST API
create_api(final_model, api_name='predict', api_file='predict.py')

# 创建Docker
create_docker('my_model', docker_path='Dockerfile')

---

AutoML 完整流程示例 | Complete AutoML Pipeline Example

from pycaret.classification import *
import pandas as pd

# ========== Step 1: 数据加载 ==========
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')

# ========== Step 2: 数据探索 ==========
print(f"训练集: {train.shape}, 测试集: {test.shape}")
print(f"缺失值:\n{train.isnull().sum()}")
print(f"目标分布:\n{train['target'].value_counts()}")

# ========== Step 3-4: 数据预处理 + 特征工程 ==========
clf = setup(
    train,
    target='target',

    # 数据预处理
    numeric_imputation='median',
    categorical_imputation='mode',
    remove_outliers=True,
    outliers_method='iforest',
    fix_imbalance=True,
    fix_imbalance_method='SMOTE',

    # 特征工程
    normalize=True,
    normalize_method='zscore',
    feature_selection=True,
    n_features_to_select=0.3,
    remove_multicollinearity=True,
    polynomial_features=True,
    polynomial_degree=2,

    # 划分配置
    train_size=0.8,
    fold_strategy='stratifiedkfold',
    fold=5,

    session_id=42
)

# ========== Step 5: 模型选择 ==========
best = compare_models(sort='AUC')

# ========== Step 6-7: 训练与调优 ==========
tuned = tune_model(best, optimize='AUC', n_iter=30)

# ========== Step 8-9: 评估与解释 ==========
evaluate_model(tuned)
interpret_model(tuned)

# ========== Step 10: 集成（可选） ==========
# ensemble = ensemble_model(tuned)

# ========== Step 11: 最终预测 ==========
final = finalize_model(tuned)
predictions = predict_model(final, data=test)

# ========== Step 12: 保存 ==========
save_model(final, 'best_model')

---

通用 API 参考 | Common API Reference

详细内容请参考 utilities.md

数据加载

from pycaret.classification import get_data

# 列出数据集
all_datasets = get_data('index')

# 加载数据集
data = get_data('breast_cancer')

配置管理

from pycaret.classification import get_config, set_config

# 获取配置
X_train = get_config('X_train')

# 设置配置
set_config('seed', 123)

模型操作

# 比较模型
best = compare_models()

# 创建模型
model = create_model('rf')

# 调优模型
tuned = tune_model(model)

# 集成
ensemble = ensemble_model(model)

# 预测
predictions = predict_model(model, data=new_data)

# 保存/加载
save_model(model, 'my_model')
loaded = load_model('my_model')

---

详细文档索引 | Detailed Documentation Index

模块	包含内容	文件
参数深度分析	setup参数选择指南、决策树、实战配置	setup_parameters_deep_dive.md
Classification	setup 参数、模型列表、评估指标、工作流	classification.md
Regression	setup 参数、回归模型、评估指标、工作流	regression.md
Time Series	时间序列特有参数、预测、季节性	time_series.md
Clustering	聚类算法、轮廓系数、分配标签	clustering.md
Anomaly	异常检测算法、可视化	anomaly.md
NLP	主题模型、文本处理、词云	nlp.md
Association Rules	关联规则、支持度、置信度	association_rules.md
Utilities	通用函数、部署、应用生成	utilities.md

---

代码模板 | Code Templates

分类任务模板

from pycaret.classification import *

data = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')

clf = setup(data, target='target', train_size=0.8)
best = compare_models()
tuned = tune_model(best)
ensemble = ensemble_model(tuned)
predictions = predict_model(ensemble, data=test)
save_model(ensemble, 'classifier')

回归任务模板

from pycaret.regression import *

data = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')

reg = setup(data, target='price', normalize=True)
best = compare_models()
tuned = tune_model(best, optimize='RMSE')
predictions = predict_model(tuned, data=test)
save_model(tuned, 'regressor')

时间序列模板

from pycaret.time_series import *

data = get_data('airline')
ts = setup(data, fh=12, seasonal_period=12)
best = compare_models()
model = create_model('arima')
predictions = predict_model(model, fh=24)

---

最佳实践 | Best Practices

1. 数据预处理: 使用 normalize=True, remove_outliers=True 等参数
2. 模型选择: 用 compare_models(turbo=True) 快速验证
3. 超参数调优: 根据时间预算设置 n_iter
4. 模型集成: 复杂任务使用 ensemble_model 或 stack_models
5. 生产部署: 使用 finalize_model() 在全量数据上训练