semvec/pypi-packages huggingface dataset:
scuCourseDownloader 0.0.2
api to download scu courses

# 四川大学课表器api
<p>
<img src='https://img.shields.io/badge/author-%E5%B0%8F%E5%B7%9D-ff69b4.svg'>
<img src='https://img.shields.io/github/license/2239559319/courseDownload.svg?style=flat'>
<img src='https://img.shields.io/badge/python-3.0%2B-blue.svg'>
<img src='https://img.shields.io/badge/python-3.6-blue.svg'>
</p>

-------------

## 运行环境:python3.6
```bash
# 需要第三方模块，包：requests,lxml,openpyxl
#如果是anaconda环境用conda命令
pip install lxml
pip install requests
pip install openpyxl
```
## 使用
```bash
# 安装
pip install scuCourseDownloader
# 卸载
pip uninstall scuCourseDownloader
```
```python
#使用示例
import scuCourseDownloader

scuCourseDownloader.save_to_excel("2018-2019-2-1")
```

## 注意:

 - save_to_excel,query函数参数的格式为 2018-2019-2-1，表示18年到19年第二学期
 格式必须严格按以上格式
 - 程序运行完后会在当前目录在生成一个course.xlsx文件，就是课表文件了
 - 由于没有采用并发且请求频率限制问题，程序的运行时间大概在10分钟左右，建议做点其他事哦
 -----------------
 bug,程序交流请发邮件到:w2239559319@outlook.com
MLStacker 0.0.1
A package which supports the implementation of stacking of machine learning models

# ModelStacker
ModelStacker implements the stacking of machine learning models and very often, the stacked model is able to perform better than any of its base models. This technique is said to be the most effective when there are vast differences present amongst the base models. More information on this concepts can be found at:

1. [A Kaggler's Guide to Model Stacking in Practice](http://blog.kaggle.com/2016/12/27/a-kagglers-guide-to-model-stacking-in-practice/)

2. [Stacking Models for Improved Predictions](https://www.kdnuggets.com/2017/02/stacking-models-imropved-predictions.html)

## Dependencies
Currently, ModelStacker depends on:
- numpy==1.14.0
- pandas==0.22.0

## Installation
```
pip install -r requirements.txt
```

## Usage
### Initalize ModelStacker
```
from ModelStacker import ModelStacker
stacker = ModelStacker()
```

### Initialize and Add Base Models
```
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC

dtclf = DecisionTreeClassifier()
knnclf = KNeighborsClassifier()
svmclf = SVC()

stacker.add_base_model(dtclf)
stacker.add_base_model(knnclf)
stacker.add_base_model(svmclf)
```

### Initalize and Add Stacked Model
```
from sklearn.linear_model import LogisticRegression
lgclf = LogisticRegression()
stacker.add_stacked_model(lgclf)
```

### Fitting and Predicting
```
stacker.fit(X_train, y_train) # X_train and y_train belongs to training set
predictions = stacker.predict(X_test)
```