semvec/pypi-packages huggingface dataset:
citebang 0.1.0
open-source tool used for academic literature searches
License: MIT
Requires Python: >=3
Keywords: science research academics citation citations literature search

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citebang (0.1.0)
================================================================================

'citebang' is an open-source tool used for academic literature searches.


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Installation
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Users
^^^^^

All stable releases of this project are available through PyPi.
To install this project via pip, use the following command:

    $ python3 -m pip install --upgrade citebang

To check whether or not the package was installed, try the following command:

    $ python3 -c "import citebang"

Installing the package this way will also add the 'citebang' CLI command:

    $ citebang --help


Developers
^^^^^^^^^^

If you would like to contribute to this project, it is recommended that you
clone this repository and install all its dependencies and developer tools. An
easy-to-use makefile is provided to make this process fast and easy.

    1. clone this git repository
    2. run **make install** to install a local virtual environment, all
       dependencies, and used developer tools
    3. run **make help** for more information
    4. done

Note that 'make install' does not touch your global installation. All changes
are local, and thus removing the cloned repository's directory also cleans up
the project.


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Tests
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To run all tests, simply use the command:

    $ make test


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License
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This project is licensed under the MIT License.
feature-selector 1.0.0
FeatureSelector is a class for removing features for a dataset intended for machine learning
License: MIT
Requires Python: >=3.5
Keywords: 特征选择,Feature Selector,Feature

# Feature Selector: Simple Feature Selection in Python

Feature selector is a tool for dimensionality reduction of machine learning datasets.
---

# Install
```
pip install feature_selector
```

# Methods

There are five methods used to identify features to remove:

1. Missing Values
2. Single Unique Values
3. Collinear Features
4. Zero Importance Features
5. Low Importance Features 

## Usage

Refer to the [Feature Selector Usage notebook](https://github.com/WillKoehrsen/feature-selector/blob/master/Feature%20Selector%20Usage.ipynb) for how to use

## Visualizations

The `FeatureSelector` also includes a number of visualization methods to inspect 
characteristics of a dataset. 

__Correlation Heatmap__

![](images/example_collinear_heatmap.png)

__Most Important Features__

![](images/example_top_feature_importances.png)

Requires:

```
python==3.6+
lightgbm==2.1.1
matplotlib==2.1.2
seaborn==0.8.1
numpy==1.14.5
pandas==0.23.1
scikit-learn==0.19.1

```

## Contact

Any questions can be directed to wjk68@case.edu!