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https://github.com/hmiladhia/piskle
A serialization package optimized for scikit-learn
https://github.com/hmiladhia/piskle
data-science machine-learning python scikit-learn serialization
Last synced: 26 days ago
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A serialization package optimized for scikit-learn
- Host: GitHub
- URL: https://github.com/hmiladhia/piskle
- Owner: hmiladhia
- License: mit
- Created: 2021-01-08T19:34:16.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2021-10-12T20:50:48.000Z (about 3 years ago)
- Last Synced: 2024-04-26T12:04:21.909Z (6 months ago)
- Topics: data-science, machine-learning, python, scikit-learn, serialization
- Language: Python
- Homepage:
- Size: 34.2 KB
- Stars: 5
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Piskle
   
`Piskle` allows you to selectively serialize python objects to save on memory and load times.
It has special support for exporting `scikit-learn`'s models in an optimized way,
exporting exactly what's needed to make predictions.
## Example:
To use `piskle`, you first need a model to export. You can use this as an example:
```python
from sklearn import datasets
from sklearn.neural_network import MLPClassifier
data = datasets.load_iris()
model = MLPClassifier().fit(data.data, data.target)
```
Exporting the model is then as easy as the following:
```python
import piskle
piskle.dump(model, 'model.pskl')
```
Loading it is even easier:
```python
model = piskle.load('model.pskl')
```
If you want even faster serialization, you can disable the `optimize` feature.
Note that this feature reduces the size of the exported file even further and improves loading time.
```python
piskle.dump(model, 'model.pskl', optimize=False)
```
## Future Improvements
This is still an early working version of piskle, there are still a few improvements planned:
- More thorough testing
- Version Management: Support for more versions of scikit-learn (earlier versions)
- Support for more Estimators (Feel free to contact us for a specific request)
- Support for "Nested" Estimators (Pipelines, RandomForests, etc...)
- Support for other serialization methods (such as joblib, shelve or json...)
## Contribute
As this is still a work in progress, while using piskle, you might encounter some bugs.
It would be a great help to us, if you could **report them in the github repo**.
Feel free, to share with us any potential improvements you'd like to see in piskle.
If you like the project and want to support us, you can buy us a coffee here:
## Currently Supported Models
### Predictors ( Classifiers, Regressors, ...)
| Estimator | Reference |
| :--------------------: | :--------------------: |
| LinearSVC | sklearn.svm |
| LinearRegression | sklearn.linear_model |
| LogisticRegression | sklearn.linear_model |
| Lasso | sklearn.linear_model |
| Ridge | sklearn.linear_model |
| Perceptron | sklearn.linear_model |
| GaussianNB | sklearn.naive_bayes |
| KNeighborsRegressor | sklearn.neighbors |
| KNeighborsClassifier | sklearn.neighbors |
| MLPClassifier | sklearn.neural_network |
| MLPRegressor | sklearn.neural_network |
| DecisionTreeClassifier | sklearn.tree |
| DecisionTreeRegressor | sklearn.tree |
| KMeans | sklearn.cluster |
| GaussianMixture | sklearn.mixture |
### Transformers
| Estimator | Reference |
| :-------------: | :-----------------------------: |
| PCA | sklearn.decomposition |
| FastICA | sklearn.decomposition |
| CountVectorizer | sklearn.feature_extraction.text |
| TfidfVectorizer | sklearn.feature_extraction.text |
| SimpleImputer | sklearn.impute |
| StandardScaler | sklearn.preprocessing |
| LabelEncoder | sklearn.preprocessing |
| OneHotEncoder | sklearn.preprocessing |