Metadata-Version: 2.1
Name: mlpath
Version: 0.0.11
Summary: A lightweight api for machine and deep learning experiment logging in the form of a python library. 
Home-page: https://mlpath.readthedocs.io/
Author: Essam W., Abullah A.
Author-email: essamwisam@outlook.com
License: GPLv3
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE

# MLPath
A lightweight api for machine and deep learning experiment logging in the form of a python library. 

### Installation
```
pip install mlpath
```

### Get started
import the mlquest module which encompasses all the logging functionality

```Python
from mlpath import mlquest as mlq
l = mlq.l

# let's try this out
def DatasetFilter(x_param, y_param, z_param, **kwargs):
    return x_param * y_param * z_param

def FeatureExtractor(p_num, k_num, l_num, **kwargs):
    return p_num**k_num + l_num

def NaiveBayes(alpha, beta_param, c=0, depth_ratio=4, **kwargs):
    return alpha + beta_param + c


mlq.start('NaiveBayes')

dataset = l(DatasetFilter)(14, 510, 4, m_num=63, g_num=3, h_num=4)
features = l(FeatureExtractor)(12, 2, 12)
accuracy = l(NaiveBayes)(alpha=1024, beta_param=7, c=12,  depth_ratio=538, mega_p=63, g_estim=3, h=43)

mlq.log_metrics(accuracy=accuracy)

mlq.end()
```


