semvec/pypi-packages huggingface dataset:
py_clean 0.2.1
python clean architecture toolkit
Keywords: clean architecture toolkit

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Python Clean Architecture Toolkit
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        :target: https://python-clean-architecture-toolkit.readthedocs.io/en/latest/?badge=latest
        :alt: Documentation Status




This ptoolkit contains classes that may be helpful for implementing clean principles to your project. It may eliminate the boilerplate code and always will be open for options.



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History
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0.1.0 (2018-06-07)
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* First release on PyPI.
cavemanstatistics 0.5
Exhaustive-Search for best R^2 in Linear Regression Models

![Kiku](Images/spongegar.png)

# cavemanstatistics 1.0

This package contains unnecessarily slow, brute-force search methods for finding highest R^2 (of linear regression models with specified or unspecified dependant variable) in a dataset. This project is mainly intended to  get to know packaging with pypi.org and to develope my workflow. In the future I'd like to vectorize the loops and maybe add more search options and better search methods. Be careful about combinatorial explosion and set the bounds appropriately. 

### Dependancies

* NumPy
* Pandas
* SciPy
* scikit-learn
* Tabulate

### Installing

Install with:

```
pip install cavemanstatistics
```

## Quick documentation

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

After installation, import:

```python
from cavemanstatistics import ExhaustiveSearch, BruteForce
```


#### Search for highest R^2 (unspecified dependant variable):

```python
model, results = ExhaustiveSearch(data = pd.dataframe, remove = list, lowerbound = int, upperbound = int, adjusted_R2 = bool).solve()
y, x = model
print(model)

('depedant variable', [list of explanatory variables])

```

ExhaustiveSearch().solve() returns a touple containing a string (dependant variable) and a list (explanatory variables) and a sorted dictionary with all results.

Parameters:
* data:  has to be a pandas dataframe
* remove: place list of variable names that you would like to exclude as depedant variables (or place an empty list)
* lowerbound: smallest integer number of explanatory variables in solution set
* upperbound: largest integer number of exoplanatory variables in solution set
* adjusted_R2: True for adjusted R^2, false for R^2


#### Search for highest R^2 (specified dependant variable):

```python
model, results = BruteForce(data = pd.dataframe, Y = str, lowerbound = int, upperbound = int, adjusted_R2 = bool).solve()
y, x = model
print(model)

('depedant variable', [list of explanatory variables])

```

BruteForce().solve() returns a touple containing a string (dependant) and a list (explanatory variables) and a sorted dictionary with all results.

Parameters:
* data:  has to be a pandas dataframe
* Y: the name of the variable that you would like to specify as dependant variable
* lowerbound: smallest integer number of explanatory variables in solution set
* upperbound: largest integer number of exoplanatory variables in solution set
* adjusted_R2: True for adjusted R^2, false for R^2


## Authors

* **Geoffrey Kasenbacher** - [kgeoffrey](https://github.com/kgeoffrey)

## License

This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details

## Acknowledgments

* This is a spawn of the frustration caused by the R-Package 'leaps'
* Tip fedora to the ascii art creators