Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/connor-makowski/scx
MIT's Supply Chain Python Package
https://github.com/connor-makowski/scx
optimization python
Last synced: about 1 month ago
JSON representation
MIT's Supply Chain Python Package
- Host: GitHub
- URL: https://github.com/connor-makowski/scx
- Owner: connor-makowski
- License: mit
- Created: 2022-05-31T15:49:34.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-12T18:39:32.000Z (about 2 months ago)
- Last Synced: 2024-11-12T19:32:43.464Z (about 2 months ago)
- Topics: optimization, python
- Language: Jupyter Notebook
- Homepage:
- Size: 430 KB
- Stars: 19
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
# SCx
[](https://badge.fury.io/py/scx)
[](https://opensource.org/licenses/MIT)
MIT's [Supply Chain Micromaster](https://micromasters.mit.edu/scm/) (SCx) Python Package
## Documentation
[Technical documentation](https://connor-makowski.github.io/scx/scx.html) can be found [here](https://connor-makowski.github.io/scx/scx.html).
## Examples
- [All Optimization Examples](https://github.com/connor-makowski/scx/tree/main/notebooks/optimization/)
- [Basic Examples](https://github.com/connor-makowski/scx/tree/main/notebooks/optimization/basic_examples)
- [Blinky Examples (with video walkthroughs)](https://github.com/connor-makowski/scx/tree/main/notebooks/optimization/blinky_examples)
- [Food On Wheels Examples (with video walkthroughs)](https://github.com/connor-makowski/scx/tree/main/notebooks/optimization/fow_examples)
- [Eco Pants Examples (with video walkthroughs)](https://github.com/connor-makowski/scx/tree/main/notebooks/optimization/eco_pants_examples)
- [Miscellaneous Examples](https://github.com/connor-makowski/scx/tree/main/notebooks/optimization/misc_examples)
- [All Database Examples](https://github.com/connor-makowski/scx/tree/main/notebooks/database/)
- [Transaction Database Example](https://github.com/connor-makowski/scx/tree/main/notebooks/database/Transaction.ipynb)
## Setup
### Cloud Setup (Google Colab)
- You can access google colab [here](https://colab.research.google.com/)
- Create a new notebook
- Install the `scx` package by adding the following to a new code cell at the **top** of your notebook and running it:
- `pip install scx`
### Local Setup
Make sure you have Python 3.7.x (or higher) installed on your system. You can download it [here](https://www.python.org/downloads/).
Recommended (but Optional) -> Expand this section to setup and activate a virtual environment.
- Install (or upgrade) virtualenv:
```
python3 -m pip install --upgrade virtualenv
```
- Create your virtualenv named `venv`:
```
python3 -m virtualenv venv
```
- Activate your virtual environment
- On Unix (Mac or Linux):
```
source venv/bin/activate
```
- On Windows:
```
venv\scripts\activate
```
```
pip install scx
```
## Optimization Getting Started
See all of the optimization examples [here](https://github.com/connor-makowski/scx/tree/main/notebooks/optimization/).
### Basic Usage
```py
from scx.optimize import Model
```
#### Simple Optimization
```py
from scx.optimize import Model
# Create variables
product_1_amt = Model.variable(name="product_1", lowBound=0)
product_2_amt = Model.variable(name="product_2", lowBound=0)
# Initialize the model
my_model = Model(name="Generic_Problem", sense='maximize')
# Add the Objective Fn
my_model.add_objective(
fn = (product_1_amt*1)+(product_2_amt*1)
)
# Add Constraints
my_model.add_constraint(
name = 'input_1_constraint',
fn = product_1_amt*1+product_2_amt*2 <= 100
)
my_model.add_constraint(
name = 'input_2_constraint',
fn = product_1_amt*3+product_2_amt*1 <= 200
)
# Solve the model
my_model.solve(get_duals=True, get_slacks=True)
# Show the outputs
# NOTE: outputs can be fetched directly as a dictionary with `my_model.get_outputs()`
my_model.show_outputs()
```
Outputs:
```py
{'duals': {'input_1_constraint': 0.4, 'input_2_constraint': 0.2},
'objective': 80.0,
'slacks': {'input_1_constraint': -0.0, 'input_2_constraint': -0.0},
'status': 'Optimal',
'variables': {'product_1': 60.0, 'product_2': 20.0}}
```
## Database Getting Started
See all of the database examples [here](https://github.com/connor-makowski/scx/tree/main/notebooks/database/)
### Basic Usage
```py
from scx.database import Database
# Specify the S3 path to the data
data_folder = 's3://scx-dev/databases/supermarket/'
# Create the database
db = Database(f"""
CREATE TABLE Customers AS SELECT * FROM read_parquet('{data_folder}customers.parquet');
""")
# Show the database Schema
db.show_info()
# Query the database
db.query("SELECT * FROM Customers LIMIT 5")
```