semvec/pypi-packages huggingface dataset:
dber 0.0.5
数据库操作工具。
License: MIT
Requires Python: >=3



### 使用教程

**快速开始**

```python
import dber

db = dber.SQLite("test.db")

create_table_sql = """
    CREATE TABLE IF NOT EXISTS t_test (
        data_key varchar(255) PRIMARY KEY, 
        data_value text,
        data_int integer
    );
"""
# 创建数据表
db.execute(create_table_sql)

# 插入数据
db.replace("replace into t_test values (?,?,?)", "test_key", "test_data", 1)

# 查询数据
print(db.select("select * from t_test where data_key = ?", "test_key"))
print(db.selectOne("select * from t_test where data_key = ?", "test_key"))
print(db.selectTable("t_test", data_key="test_key"))

# 查询字段
print(db.getColumnIntValue("select data_int from t_test where data_key = ?", "test_key"))
print(db.getColumnFloatValue("select data_int from t_test where data_key = ?", "test_key"))
print(db.getColumnBoolValue("select data_value,data_int from t_test where data_key = ?", "test_key", column="data_int"))

# 批量插入数据
rows = []
for i in range(0, 100):
    row = (f"k{i}", f"v{i}", i)
    rows.append(row)

db.replaceBatch("replace into t_test values (?,?,?)", rows)
print(db.count("t_test"))
print(db.count("t_test", where="data_int > 90"))

# 更新数据
db.update("update t_test set data_int=? where data_key = ?", 100, 'test_key')

# 删除数据
db.delete("delete from t_test")
# 清空表
db.clear("t_test")
# 删除表
db.drop("t_test")

# 关闭
db.close()
```
shortcutml 0.8
Machine learning baseline prototyping tools
License: MIT
Keywords: machine learning,summary

# ShortcutML

![banner](./img/banner.png)

## Description

Sometimes, we don’t want to pay attention to detail very much in some machine learning phase. We are in a hurry and only need to prototype our ideas as fast as possible. Then, this library is for you. It’s not making any magic optimization, but it’s summarising all code that maybe you would write when in this kind of situation. This library will be fully customizable from model selection until scoring metrics in the future.

## Installation

\
Via pip

```bash
# Install via pip
$ pip install shortcutml
```

Via git clone

```bash
# Clone the repo
$ git clone https://github.com/SulthanAbiyyu/ShortcutML

# Change working dir to shortcutml
$ cd shortcutml

# Install requirements
$ python3 -m pip install -r requirements.txt

# Run one-time setup
$ python3 install.py
```

## Usage

<br />

### BaselineModel

```python
from shortcutml.model_selection import BaselineModel

bm = BaselineModel(type="regression") # other type option: "classification"

# Evaluate baseline model
bm.evaluate(X_train, X_test, y_train, y_test)

# Plot result
bm.plot_baseline()

# Result dataframe
bm.test_result

# Model lists
bm.classification_models()
bm.regression_models()

# Still not support custom model and scoring
# Default scoring for classification tasks is f1 score and RMSE for regression
```

### TextCleaningIndo

```python
from shortcutml.preprocessing import TextCleaningIndo

tci = TextCleaningIndo()

# Applying all preprocessing process
df["text"] = df["text"].apply(tci.all_preprocessing)
```

### AutoSearchCV

```python
from shortcutml.model_selection import AutoSearchCV

search = AutoSearchCV(model, type="grid") # other type option: "random"
search.search(X,y)

search.cv_results_
```

## Project Plan

- [x] BaselineModel
- [x] TextCleaningIndo
- [ ] TextCleaningEnglish
- [ ] Fully customizable component
- [x] FeatureSelection -> Pearson, Lasso, Chi Squared, ..
- [x] AutoSearchCV -> Random and Grid
- [ ] ..
      <br />

## Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

---

<br />
Sulthan Abiyyu, <br />
16 January 2022
hiddb 0.1.10
Python SDK for HIDDB
License: MIT
Requires Python: >=3.7,<4.0
Keywords: HIDDB,hiddb,vector database,similarity search

# HIDDB Python SDK

The official SDK for the [HIDDB](https://hiddb.com) vector database.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install the SDK.

```bash
pip install hiddb
```

## Usage

Create a collection within a database `<your database_id>`.

```python
from hiddb.synchronous import HIDDB

hiddb = HIDDB("<key>", "<secret>")

# Create a collection named 'wordvectors'
hiddb.create_collection(database_id="<your database_id>", collection_id="wordvectors")
```

Create an index within this collection:

```python
# Create an index on field 'vector' within the collection and dimension 300
hiddb.create_index(
    database_id="<your database_id>",
    collection_name='wordvectors',
    index_name="vector",
    dimension=300
)
```

Insert documents like that:

```python
document = {
    "vector": [0.0]*300,
    "id": "my first document"
}

hiddb.insert_document(
    database_id=database_id,
    collection_name='wordvectors',
    documents=[document]
)
```

Search for nearest documents:

```python
similar_words = hiddb.search_nearest_documents(
    database_id="<your database_id>",
    collection_name='wordvectors',
    index_name="vector",
    vectors=[[42.0]*300],
    max_neighbors=10
)
```

More examples and a more detailed documentation will follow soon 🚀

## Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.
wechat-work 0.0.3
A tool for Wechat Work
License: MIT
Keywords: wechat,wechat work,message,notification

# 企业微信工具

这是一个基于企业微信 API 开发的用于消息通知的 Python 库

## Guide

开始之前，请先跟随一下步骤创建一个企业微信应用

- [注册/登录企业微信管理后台](https://work.weixin.qq.com/)
- [创建/查看/管理应用](https://work.weixin.qq.com/wework_admin/frame#apps)
- [查看企业ID](https://work.weixin.qq.com/wework_admin/frame#profile)
- [查看企业微信成员信息](https://work.weixin.qq.com/wework_admin/frame#contacts)
- [添加插件以支持推送到个人微信](https://work.weixin.qq.com/wework_admin/frame#profile/wxPlugin)

## Installation

- 通过 `pip` 安装

```bash
pip install wechat_work
```

- 通过 `git` 和 `pip` 安装

```bash
pip install git+https://github.com/Micro-sheep/wechat_work.git
```

## Examples

```python
from wechat_work import WechatWork
corpid = '企业 ID'
appid = '企业应用 ID'
corpsecret = '企业应用 Secret'
users = ['企业微信的用户账号1', '企业微信的用户账号2']
w = WechatWork(corpid=corpid,
               appid=appid,
               corpsecret=corpsecret)
# 发送文本
w.send_text('Hello World!', users)
# 发送 Markdown
w.send_markdown('# Hello World', users)
# 发送图片
w.send_image('./hello.jpg', users)
# 发送文件
w.send_file('./hello.txt', users)

```