semvec/pypi-packages huggingface dataset:
ConnectWrapper 0.0.5
An easy-to-use wrapper for connect.det.wa.edu.au implemented in Python3
Requires Python: >=3.6

# ConnectWrapper
Written by Mitch Naake (Year 11 ATAR Computer Science, Butler College)

### _What is ConnectWrapper?_  
ConnectWrapper is an easy way for Western-Australian students to create their own apps around the teacher-student communication platform, https://connect.det.wa.edu.au/  


### _What are the benefits?_
ConnectWrapper handles all requests and responses to and from the Connect webserver. Responses from the webserver are parsed and compiled in a way that makes it easier to get information from your request, saving you endless time with trying to find specific data from your own request, it's all there in front of you, in an easy-to-read format!  


### _What can I do with ConnectWrapper?_
Upon it's initial release, ConnectWrapper has the following functions:

- Mobile
  - Authentication
    - `get_access_token` (Used for authenticating future requests)
    - `check_access_token` (Used to check whether an `access_token` is valid/invalid/expired)
    
  - Notices
    - `get_feed` (Returns posts depending on user's kwargs, default returns the 10 most-recent posts)
    - `view_notice` (Used to view the details about the notice, adds a view to notice view counter)
    - `add_comment` (Posts a comment to the specified `notice`)

*Please Note: There are more functions coming soon...*
model-clerk 1.0.0
Module to record/document your model changes

### What is model-clerk?
Model-clerk, at its core, is a lightweight logging library that helps you keep track of performance, model parameters, timing (or whatever) throughout your data science experiment. 

### I'm a data scientist. Why should I care?
As a part of your job you're constantly running new experiments. As you run new experiments, you are recoring your results (if not, you should be recording your results). Model clerk does exactly that. It reduces the inertia by automatically recording the results of your experiment so you don't have to worry about keeping track.

### Ok now that I'm convinced, where does model-clerk log stuff?
Model clerk has two places where it logs things - to excel or to google sheets. Excel setup is easier, so start with that. For google sheets usage, see the link below.

### Train a model
```python
# Imports
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression

dataset = load_iris()

# Define data
X = dataset['data']
y = dataset['target']

# Train the model
logistic_regression_classifier = LogisticRegression()
logistic_regression_classifier.fit(X, y)

predictions = logistic_regression_classifier.predict(X)
probabilities = logistic_regression_classifier.predict_proba(X)
```

### Excel Usage
```python
# Record artifacts to excel
from model_clerk import Clerk

# Set up the clerk with excel mode
clerk = Clerk(excel_mode=True)

# file_path refers to the excel workbook you want to record in, and the sheet name referes to the sheet
clerk.set_up(filepath='hello_world.xlsx', sheet_name='Sheet1')

# Record all the artifacts in one go
clerk.record(predictions=predictions, probabilities=probabilities, model_parameters=logistic_regression_classifier.get_params())
```

Figure out google sheets permissions prior to using the google sheets mode. Follow directions below if you need any help.
### Google Sheets Usage
```python
# Record artifacts to excel
from model_clerk import Clerk

# Set up the clerk with google sheets mode
clerk = Clerk(google_sheets_mode=True)

# file_path refers to the excel workbook you want to record in, and the sheet name referes to the sheet
clerk.set_up(file_path='your_google_sheets_url', sheet_name='Sheet1')

# Record all the artifacts in one go
clerk.record(predictions=predictions, probabilities=probabilities, model_parameters=logistic_regression_classifier.get_params())
```

### Google sheets permissions
1. Follow this link for how to permission your Google account - https://erikrood.com/Posts/py_gsheets.html
2. Make sure you enable the Google sheets API under the project in Google console. 
3. Update the `.env` with the path to token.json credentials file.