https://github.com/amueller/patsylearn
Patsy Adaptors for Scikit-learn
Science Score: 23.0%
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Repository
Patsy Adaptors for Scikit-learn
Basic Info
- Host: GitHub
- Owner: amueller
- License: gpl-2.0
- Language: Python
- Default Branch: master
- Size: 22.5 KB
Statistics
- Stars: 48
- Watchers: 4
- Forks: 11
- Open Issues: 5
- Releases: 1
Metadata Files
README.md
Patsy-Learn
A simple patsy to scikit-learn adaptor. Be aware that the content of this package is pretty experimental. Feedback welcome.
This package provides two classes:
- A scikit-learn meta-estimator PatsyModel, that feeds the design matrix created by patsy into a scikit-learn estimator.
- A scikit-learn transformer, that uses a patsy formula to transform and select features.
Both classes work on pandas DataFrames. If you want to use PatsyModel for a supervised estimator, the input dataframe is expected to contain both the data and the target.
Example
```python
put the iris dataset into a pandas dataframe
from sklearn.datasets import load_iris import pandas as pd
iris = loadiris() names = [fname.replace(" ", "").strip("(cm)") for fname in iris.featurenames] irisdf = pd.DataFrame(iris.data, columns=names) irisdf['species'] = iris.target
create logistic regression with two features
from patsylearn import PatsyModel from sklearn.linearmodel import LogisticRegression model = PatsyModel(LogisticRegression(), "species ~ sepallength + petal_length")
model is an sklearn estimator.
from sklearn.crossvalidation import traintestsplit datatrain, datatest = traintestsplit(irisdf) model.fit(datatrain) print(model.score(datatest))
use PatsyTransformer to just select and transform features
transformer = PatsyTransformer("sepallength + np.log(petallength) + petallength:sepalwidth")
transformer is a scikit-learn transformer
transformer.fit(data) print(transformer.transform(data).shape) ```
Owner
- Name: Andreas Mueller
- Login: amueller
- Kind: user
- Location: Los Gatos
- Company: Microsoft
- Website: http://amueller.github.io
- Twitter: amuellerml
- Repositories: 194
- Profile: https://github.com/amueller
Scikit-learn core-developer, Principal Research SDE @microsoft
GitHub Events
Total
Last Year
Committers
Last synced: about 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| Andreas Mueller | a****r@n****u | 13 |
| scls19fr | s****r | 1 |
| giorgiop | g****i@n****u | 1 |
| Pierre Navaro | p****o | 1 |
| root | a****v@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: about 1 year ago
All Time
- Total issues: 9
- Total pull requests: 7
- Average time to close issues: 8 months
- Average time to close pull requests: about 1 year
- Total issue authors: 4
- Total pull request authors: 5
- Average comments per issue: 1.11
- Average comments per pull request: 2.14
- Merged pull requests: 4
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- amueller (5)
- jnothman (2)
- pnavaro (1)
- sashaostr (1)
Pull Request Authors
- sashaostr (3)
- ceholden (2)
- giorgiop (1)
- pnavaro (1)
- scls19fr (1)
Top Labels
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Dependencies
- patsy >=0.4
- scikit-learn >=0.16