imperio/setup.py at main · SigmoidAI/imperio · GitHub

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from setuptools import setup
long_description = '''
# imperio

Imperio is a python sci-kit learn inspired package for feature engineering. It contains a some feature transformers to make your data more easy to learn from for Machine Learning Algorithms.

This version of imperio has the next methods of feature selection:
1. Box-Cox (BoxCoxTransformer).
2. Clusterize (ClusterizeTransformer).
3. Combinator (CombinatorTransformer).
4. Frequency Imputation Transformer (FrequencyImputationTransformer).
5. log Transformer (LogTransformer).
6. Smoothing (SmoothingTransformer).
7. Spatial-Sign Transformer (SpatialSignTransformer).
8. Target Imputation Transformer (TargetImputationTransformer).
9. Whitening (WhiteningTransformer).
10. Yeo-Johnson Transformer (YeoJohnsonTransformer).
11. ZCA (ZCATransformer).

All these methods work like normal sklearn transformers. They have fit, transform and fit_transform functions implemented.

Additionally every imperio transformer has an apply function which allows to apply an transformation on a pandas Data Frame.

# How to use imperio

To use a transformer from imperio you should just import the transformer from imperio in the following framework:
```python
from imperio import BoxCoxTransformer
```

class names are written above in parantheses.

Next create a object of this algorithm (Box-Cox is used as an example).

```python
method = BoxCoxTransformer()
```

Firstly you should fit the transformer, passing to it a feature matrix (X) and the target array (y).
NOTE: y argument is really used only by the Target-Imputation.

```python
method.fit(X, y)
```

After you fit the model, you can use it for transforming new data, using the transform function. To transform function you should pass only the feature matrix (X).

```python
X_transformed = method.transform(X)
```

Also you can fit and transform the data at the same time using the `fit_transform` function.

```python
X_transformed = method.fit_transform(X)
```

Also you can apply a transformation directly on a pandas DataFrame, choosing the columns that you want to change.

```python
new_df = method.apply(df, 'target', ['col1', 'col2']
```

Some advice:
1. Use `FrequencyImputationTransformer` or `TargetImputationTransformer` for categorical features.
2. Use `BoxCoxTransformer` or `YeoJohnsonTransformer` for numerical features to normalize a feature distribution.
3. Use `SpatialSignTransformer` on normalized data to bring outliers to normal samples.
4. Use `CombinatorTransformer` on tombine different transformers on categorical and numerical columns separately.

With <3 from Sigmoid!

We are open for feedback. Please send your impressions to vladimir.stojoc@gmail.com
'''
setup(
  name = 'imperio',
  packages = ['imperio'],
  version = '0.1.5',
  license='MIT',
  description = 'Imperio is a python sci-kit learn inspired package for feature engineering.',
  long_description=long_description,
  long_description_content_type='text/markdown',
  author = 'SigmoidAI - Păpăluță Vasile, Stojoc Vladimir',
  author_email = 'vladimir.stojoc@gmail.com',
  url = 'https://github.com/SigmoidAI/imperio',
  download_url = 'https://github.com/ScienceKot/kydavra/archive/v1.0.tar.gz',    # I explain this later on
  keywords = ['ml', 'machine learning', 'feature engineering', 'python', 'data science'],   # Keywords that define your package best
  install_requires=[            # I get to this in a second
          'numpy',
          'pandas',
          'scikit-learn'
      ],
  classifiers=[
    'Development Status :: 3 - Alpha',
    'Framework :: Jupyter',
    'Intended Audience :: Science/Research',
    'Topic :: Scientific/Engineering :: Artificial Intelligence',
    'Topic :: Scientific/Engineering :: Mathematics',
    'Topic :: Software Development :: Libraries :: Python Modules',
    'Topic :: Software Development :: Build Tools',
    'License :: OSI Approved :: MIT License',
    'Programming Language :: Python :: 3',
    'Programming Language :: Python :: 3.4',
    'Programming Language :: Python :: 3.5',
    'Programming Language :: Python :: 3.6',
    'Programming Language :: Python :: 3.7',
    'Programming Language :: Python :: 3.8',
  ],
)