semvec/pypi-packages huggingface dataset:
efb-wechat-comwechat-slave 1.0.9
EFB Slave for WeChat on ComWeChat
Requires Python: >=3.7
Keywords: wechat

[教程地址](https://blog.honus.top/2022/10/15/580.html)

```docker
docker pull honus/efb-wechat-comwechat-slave:latest
```

部分已经支持的命令：

```
/search - 按关键字匹配好友昵称搜索联系人

/addtogroup - 按wxid添加好友到群组

/getmemberlist - 查看群组用户wxid

/at - 后面跟wxid，多个用英文,隔开，最后可用空格隔开，带内容。

/sendcard - 后面格式'wxid nickname'

/changename - 修改群组名称

/getstaticinfo - 可获取friends, groups, contacts信息
```
cisco-pyaci 1.1.2
Python Bindings for Cisco ACI REST API
Requires Python: >=3.8

PyACI: Python Bindings for Cisco ACI REST API
=============================================
.. image:: https://codecov.io/gh/datacenter/pyaci/branch/master/graph/badge.svg
   :alt: codecov status
   :target: https://codecov.io/gh/datacenter/pyaci

.. image:: https://readthedocs.org/projects/pyaci/badge/?version=latest&style=plastic
   :alt: Doc Status
   :target: http://pyaci.readthedocs.io/en/latest/

Documentation
-------------

Documentation is available at http://pyaci.readthedocs.io/en/latest/

Examples
--------

Jupyter Notebook based examples can be found in `examples <examples>`_ directory.
boyd-ml-utils 0.4.1
machine learning utilities by boy-dad
Requires Python: >=3.10.12

# boyd-ml-utils
## Overview
I just want to try publishing a package in PyPI. I'll add unit tests, more functionalities and GitHub pages in the future.

## Sample usage
`OptunaSearchCV` works like `RandomizedSearchCV` in `scikit-learn` but the parameter distributions must be specified using distribution classes in `optuna`.
```
import pandas as pd
from boyd_ml_utils.model_selection import OptunaSearchCV
from optuna.distributions import IntDistribution
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split

# Load the iris dataset
iris = load_iris()
X = pd.DataFrame(iris.data, columns=iris.feature_names)
y = X.pop("petal width (cm)")  # Treat petal width as the target variable

# Split the dataset
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Define a Random Forest Regressor model
rf = RandomForestRegressor(random_state=42)

# Define hyperparameter space for RandomizedSearchCV
param_dist = {
    "n_estimators": IntDistribution(low=10, high=200),
    "max_depth": IntDistribution(low=3, high=20),
    "min_samples_split": IntDistribution(low=2, high=10),
    "min_samples_leaf": IntDistribution(low=1, high=5),
}

# Perform Randomized Search
random_search = OptunaSearchCV(
    estimator=rf,
    param_distributions=param_dist,
    n_iter=100,
    scoring="neg_mean_squared_error",
    cv=5,
    n_jobs=5,
)

random_search.fit(X_train, y_train)

# Best parameters and model evaluation
best_model = random_search.best_estimator_
y_pred = best_model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)

print("Best Parameters:", random_search.best_params_)
print("Mean Squared Error on Test Data:", mse)
```