semvec/pypi-packages huggingface dataset:
sksearchspace 2020.8.0.0.23.1
Defines search spaces for scikit-lean estimators
License: MIT
Requires Python: >=3.6

# sksearchspace

![.github/workflows/ci.yml](https://github.com/thomasjpfan/sksearchspace/workflows/.github/workflows/ci.yml/badge.svg) [![codecov](https://codecov.io/gh/thomasjpfan/sksearchspace/branch/master/graph/badge.svg)](https://codecov.io/gh/thomasjpfan/sksearchspace)

Scikit-learn Search Space Configurations with curated search spaces for [scikit-learn estimators](http://github.com/scikit-learn/scikit-learn).

## Usage

```py
from sksearchspace import SearchSpace
from sklearn.tree import DecisionTreeClassifier

estimator_space = SearchSpace.for_sklearn_estimator(DecisionTreeClassifier, seed=42)
estimator_space.sample()
# {'criterion': 'entropy','min_samples_leaf': 15, 'min_samples_split': 11}

estimator_space.sample()
# {'criterion': 'entropy', 'min_samples_leaf': 12, 'min_samples_split': 4}
```

`sksearchspace` uses [ConfigSpace](https://automl.github.io/ConfigSpace/master/) for sampling. The `ConfigSpace` configuration can be accessed through an attribute:

```py
estimator_space.configuration
# Configuration space object:
# Hyperparameters:
#   criterion, Type: Categorical, Choices: {gini, entropy}, Default: gini
#   min_samples_leaf, Type: UniformInteger, Range: [1, 20], Default: 1
#   min_samples_split, Type: UniformInteger, Range: [2, 20], Default: 2
```

A json file can be loaded as follows:

```py
with open("search_space.json", "r") as f:
    estimator_space = SearchSpace(f.read())
```

# License

Copyright (c) 2020 Thomas J. Fan

Distributed under the terms of the MIT license, pytest is free and open source software.
pypkgs 0.1.1
Python package that eases the pain of concatenating Pandas categoricals!
License: MIT
Requires Python: >=3.7,<4.0

## pypkgs 

![](https://github.com/TomasBeuzen/pypkgs/workflows/build/badge.svg) [![codecov](https://codecov.io/gh/TomasBeuzen/pypkgs/branch/master/graph/badge.svg)](https://codecov.io/gh/TomasBeuzen/pypkgs) ![build](https://github.com/TomasBeuzen/pypkgs/workflows/build/badge.svg) [![Documentation Status](https://readthedocs.org/projects/pypkgs/badge/?version=latest)](https://pypkgs.readthedocs.io/en/latest/?badge=latest)

Python package that eases the pain of concatenating Pandas categoricals!

### Installation

```
pip install -i https://test.pypi.org/simple/ pypkgs
```

### Dependencies

See [poetry.lock](poetry.lock) for a list of dependencies.

### Usage

```python
>>> from pypkgs import pypkgs
>>> import pandas as pd
>>> a = pd.Categorical(["character", "hits", "your", "eyeballs"])
>>> b = pd.Categorical(["but", "integer", "where it", "counts"])
>>> pypkgs.catbind(a, b)
[character, hits, your, eyeballs, but, integer, where it, counts]
Categories (8, object): [but, character, counts,
eyeballs, hits, integer, where it, your]
```

### Documentation
The official documentation is hosted on Read the Docs: https://pypkgs.readthedocs.io/en/latest/

### Credits
This package was created with Cookiecutter and the UBC-MDS/cookiecutter-ubc-mds project template, modified from the [pyOpenSci/cookiecutter-pyopensci](https://github.com/pyOpenSci/cookiecutter-pyopensci) project template and the [audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage).