本文总结了常见的AutoML库，可供大家选择。
LightAutoML
项目链接：https://github.com/sberbank-ai-lab/LightAutoML
推荐指数：⭐⭐⭐
LightAutoML是基于Python环境下的结构化自动机器学习库，现在支持的任务有：
- 二分类
- 多分类
- 回归任务
LightAutoML现在只支持单表单记录的形式，即每一行由样本的特征和标签组成。
import pandas as pd
from sklearn.metrics import f1_score
from lightautoml.automl.presets.tabular_presets import TabularAutoML
from lightautoml.tasks import Task
df_train = pd.read_csv('../input/titanic/train.csv')
df_test = pd.read_csv('../input/titanic/test.csv')
automl = TabularAutoML(
task = Task(
name = 'binary',
metric = lambda y_true, y_pred: f1_score(y_true, (y_pred > 0.5)*1))
)
oof_pred = automl.fit_predict(
df_train,
roles = {'target': 'Survived', 'drop': ['PassengerId']}
)
test_pred = automl.predict(df_test)
pd.DataFrame({
'PassengerId':df_test.PassengerId,
'Survived': (test_pred.data[:, 0] > 0.5)*1
}).to_csv('submit.csv', index = False)H2O AutoML
项目链接：https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html
推荐指数：⭐⭐⭐⭐
H2O AutoML是基于Python环境和R环境下的结构化自动机器学习库，支持分布式部署，对模型调参、模型选择和特征筛选支持比较完备，但使用起来比较复杂。
import h2o
from h2o.automl import H2OAutoML
h2o.init()
train_hf = h2o.H2OFrame(train_df.copy())
test_hf = h2o.H2OFrame(test_df.copy())
train_hf[target_column] = train_hf[target_column].asfactor()
aml = H2OAutoML(
seed=2021,
max_runtime_secs=100,
nfolds = 3,
exclude_algos = ["DeepLearning"]
)
aml.train(
x=list(feature_columns),
y=target_column,
training_frame=train_hf
)
preds = aml.predict(h2o.H2OFrame(test_df[feature_columns].copy()))
preds_df = h2o.as_list(preds)
preds_df
submission[['Class_1', 'Class_2', 'Class_3', 'Class_4']] = preds_df[['Class_1', 'Class_2', 'Class_3', 'Class_4']]
submission.to_csv('h2o_automl_300s.csv', index=False)
submission.head()MLJAR AutoML
项目链接：https://github.com/mljar/mljar-supervised
推荐指数：⭐⭐⭐⭐
MLJAR AutoML是基于Python环境下的结构化自动机器学习库，所支持的机器学习模型非常多，且对模型可视化支持的非常好。
from supervised.automl import AutoML # mljar-supervised
automl = AutoML(
mode="Compete",
eval_metric="f1",
total_time_limit=300,
features_selection=False # switch off feature selection
)
automl.fit(
train[feature_cols],
train[target_column]
)
preds = automl.predict(test[feature_cols])
submission['Survived'] = preds
submission.to_csv('mljar_automl_300s_f1_metric.csv', index=False)
submission.head()PyCaret
项目链接：https://pycaret.org/
推荐指数：⭐⭐⭐⭐⭐
PyCaret是基于Python环境下的结构化自动机器学习库，支持的任务包括：
- 分类
- 回归
- 聚类
- 异常检测
- NLP
- 关联规则
PyCaret支持的模型比较多，项目也比较活跃，但对模型的可视化做的不够。
from pycaret.classification import *
from category_encoders.cat_boost import CatBoostEncoder
cat_train_df = train_df.copy()
cat_test_df = test_df.copy()
ce = CatBoostEncoder()
cols_to_encode = ['name', 'sex', 'ticket', 'cabin', 'embarked']
cat_train_df[pure_cat_cols] = ce.fit_transform(cat_train_df[pure_cat_cols], cat_train_df[target_column])
cat_test_df[pure_cat_cols] = ce.transform(cat_test_df[pure_cat_cols])
setup(
data = cat_train_df[feature_cols.to_list() + [target_column]],
target = target_column,
fold = 3,
silent = True,
)
best_models = compare_models(
sort='F1',
n_select=3,
budget_time=300,
) # we will use it later
best = automl(optimize = 'F1')EvalML: AutoML
项目链接：https://evalml.alteryx.com/en/latest/
推荐指数：⭐⭐⭐
EvalML是一款比较模块比较完备的自动机器学习框架，支持分类、回归和时间序列任务。但提出的时间稍晚，所以使用的人很少。
from evalml.automl import AutoMLSearch
X = train_df.drop(columns=[target_column, 'passengerid'])
y = train_df[target_column]
X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.2)
automl = AutoMLSearch(
X_train=X_train,
y_train=y_train,
problem_type='binary',
random_seed=2021,
max_time=300,
)
automl.search()
pipeline = automl.best_pipeline
pipeline.fit(X, y)TPOT: Genetic Approach
项目链接：http://epistasislab.github.io/tpot/
推荐指数：⭐⭐⭐
TPOT是一款非常轻量级的自动机器学习框架，利用遗传算法可以快读完成特征的构造。但TPOT所支持的功能较少，所以场景有限。
from tpot import TPOTClassifier
from sklearn.model_selection import train_test_split
tpot = TPOTClassifier(generations=5, population_size=50, verbosity=2, random_state=42)
tpot.fit(X_train, y_train)
print(tpot.score(X_test, y_test))
tpot.export('tpot_digits_pipeline.py')FLAML
项目链接：https://github.com/microsoft/FLAML
推荐指数：⭐⭐⭐⭐
FLAML是由微软提出的自动机器学习库，支持分类和回归任务。FLAML对特征的构造和搜索支持的比较好，非常轻量。
from flaml import AutoML
from sklearn.datasets import load_boston
automl = AutoML()
# Specify automl goal and constraint
automl_settings = {
"time_budget": 300, # in seconds
"metric": 'accuracy',
"task": 'classification',
}
automl.fit(
X_train=train_df[feature_cols],
y_train=train_df[target_column],
**automl_settings
)
print(automl.predict_proba(train_df[feature_cols]))
