| Name | scikit-lego JSON |
| Version |
0.8.2
JSON |
| download |
| home_page | None |
| Summary | A collection of lego bricks for scikit-learn pipelines |
| upload_time | 2024-04-16 12:18:13 |
| maintainer | Francesco Bruzzesi |
| docs_url | None |
| author | Vincent D. Warmerdam, Matthijs Brouns |
| requires_python | >=3.6 |
| license | MIT License Copyright (c) 2019 vincent d warmerdam Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
|
| Travis-CI |
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[](https://www.pepy.tech/projects/scikit-lego)
[](https://pypi.org/project/scikit-lego/)
[](https://anaconda.org/conda-forge/scikit-lego)
[](https://github.com/astral-sh/ruff)
[](https://zenodo.org/badge/latestdoi/166836939)
# scikit-lego
<a href="https://koaning.github.io/scikit-lego/"><img src="images/logo.png" width="35%" height="35%" align="right" /></a>
We love scikit learn but very often we find ourselves writing
custom transformers, metrics and models. The goal of this project
is to attempt to consolidate these into a package that offers
code quality/testing. This project started as a collaboration between
multiple companies in the Netherlands but has since received contributions
from around the globe. It was initiated by [Matthijs Brouns](https://www.mbrouns.com/)
and [Vincent D. Warmerdam](https://koaning.io) as a tool to teach people how
to contribute to open source.
Note that we're not formally affiliated with the scikit-learn project at all,
but we aim to strictly adhere to their standards.
The same holds with lego. LEGO® is a trademark of the LEGO Group of companies which does not sponsor, authorize or endorse this project.
## Installation
Install `scikit-lego` via pip with
```bash
python -m pip install scikit-lego
```
Via [conda](https://conda.io/projects/conda/en/latest/) with
```bash
conda install -c conda-forge scikit-lego
```
Alternatively, to edit and contribute you can fork/clone and run:
```bash
python -m pip install -e ".[dev]"
python setup.py develop
```
## Documentation
The documentation can be found [here](https://koaning.github.io/scikit-lego/).
## Usage
We offer custom metrics, models and transformers. You can import them just like you would
in scikit-learn.
```python
# the scikit learn stuff we love
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
# from scikit lego stuff we add
from sklego.preprocessing import RandomAdder
from sklego.mixture import GMMClassifier
...
mod = Pipeline([
("scale", StandardScaler()),
("random_noise", RandomAdder()),
("model", GMMClassifier())
])
...
```
## Features
Here's a list of features that this library currently offers:
- `sklego.datasets.load_abalone` loads in the abalone dataset
- `sklego.datasets.load_arrests` loads in a dataset with fairness concerns
- `sklego.datasets.load_chicken` loads in the joyful chickweight dataset
- `sklego.datasets.load_heroes` loads a heroes of the storm dataset
- `sklego.datasets.load_hearts` loads a dataset about hearts
- `sklego.datasets.load_penguins` loads a lovely dataset about penguins
- `sklego.datasets.fetch_creditcard` fetch a fraud dataset from openml
- `sklego.datasets.make_simpleseries` make a simulated timeseries
- `sklego.pandas_utils.add_lags` adds lag values in a pandas dataframe
- `sklego.pandas_utils.log_step` a useful decorator to log your pipeline steps
- `sklego.dummy.RandomRegressor` dummy benchmark that predicts random values
- `sklego.linear_model.DeadZoneRegressor` experimental feature that has a deadzone in the cost function
- `sklego.linear_model.DemographicParityClassifier` logistic classifier constrained on demographic parity
- `sklego.linear_model.EqualOpportunityClassifier` logistic classifier constrained on equal opportunity
- `sklego.linear_model.ProbWeightRegression` linear model that treats coefficients as probabilistic weights
- `sklego.linear_model.LowessRegression` locally weighted linear regression
- `sklego.linear_model.LADRegression` least absolute deviation regression
- `sklego.linear_model.QuantileRegression` linear quantile regression, generalizes LADRegression
- `sklego.linear_model.ImbalancedLinearRegression` punish over/under-estimation of a model directly
- `sklego.naive_bayes.GaussianMixtureNB` classifies by training a 1D GMM per column per class
- `sklego.naive_bayes.BayesianGaussianMixtureNB` classifies by training a bayesian 1D GMM per class
- `sklego.mixture.BayesianGMMClassifier` classifies by training a bayesian GMM per class
- `sklego.mixture.BayesianGMMOutlierDetector` detects outliers based on a trained bayesian GMM
- `sklego.mixture.GMMClassifier` classifies by training a GMM per class
- `sklego.mixture.GMMOutlierDetector` detects outliers based on a trained GMM
- `sklego.meta.ConfusionBalancer` experimental feature that allows you to balance the confusion matrix
- `sklego.meta.DecayEstimator` adds decay to the sample_weight that the model accepts
- `sklego.meta.EstimatorTransformer` adds a model output as a feature
- `sklego.meta.OutlierClassifier` turns outlier models into classifiers for gridsearch
- `sklego.meta.GroupedPredictor` can split the data into runs and run a model on each
- `sklego.meta.GroupedTransformer` can split the data into runs and run a transformer on each
- `sklego.meta.SubjectiveClassifier` experimental feature to add a prior to your classifier
- `sklego.meta.Thresholder` meta model that allows you to gridsearch over the threshold
- `sklego.meta.RegressionOutlierDetector` meta model that finds outliers by adding a threshold to regression
- `sklego.meta.ZeroInflatedRegressor` predicts zero or applies a regression based on a classifier
- `sklego.preprocessing.ColumnCapper` limits extreme values of the model features
- `sklego.preprocessing.ColumnDropper` drops a column from pandas
- `sklego.preprocessing.ColumnSelector` selects columns based on column name
- `sklego.preprocessing.InformationFilter` transformer that can de-correlate features
- `sklego.preprocessing.IdentityTransformer` returns the same data, allows for concatenating pipelines
- `sklego.preprocessing.OrthogonalTransformer` makes all features linearly independent
- `sklego.preprocessing.PandasTypeSelector` selects columns based on pandas type
- `sklego.preprocessing.RandomAdder` adds randomness in training
- `sklego.preprocessing.RepeatingBasisFunction` repeating feature engineering, useful for timeseries
- `sklego.preprocessing.DictMapper` assign numeric values on categorical columns
- `sklego.preprocessing.OutlierRemover` experimental method to remove outliers during training
- `sklego.model_selection.GroupTimeSeriesSplit` timeseries Kfold for groups with different amount of observations per group
- `sklego.model_selection.KlusterFoldValidation` experimental feature that does K folds based on clustering
- `sklego.model_selection.TimeGapSplit` timeseries Kfold with a gap between train/test
- `sklego.pipeline.DebugPipeline` adds debug information to make debugging easier
- `sklego.pipeline.make_debug_pipeline` shorthand function to create a debugable pipeline
- `sklego.metrics.correlation_score` calculates correlation between model output and feature
- `sklego.metrics.equal_opportunity_score` calculates equal opportunity metric
- `sklego.metrics.p_percent_score` proxy for model fairness with regards to sensitive attribute
- `sklego.metrics.subset_score` calculate a score on a subset of your data (meant for fairness tracking)
## New Features
We want to be rather open here in what we accept but we do demand three
things before they become added to the project:
1. any new feature contributes towards a demonstrable real-world usecase
2. any new feature passes standard unit tests (we use the ones from scikit-learn)
3. the feature has been discussed in the issue list beforehand
We automate all of our testing and use pre-commit hooks to keep the code working.
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"description": "[](https://www.pepy.tech/projects/scikit-lego)\n[](https://pypi.org/project/scikit-lego/)\n[](https://anaconda.org/conda-forge/scikit-lego)\n\n\n\n[](https://github.com/astral-sh/ruff)\n[](https://zenodo.org/badge/latestdoi/166836939)\n\n# scikit-lego\n\n<a href=\"https://koaning.github.io/scikit-lego/\"><img src=\"images/logo.png\" width=\"35%\" height=\"35%\" align=\"right\" /></a>\n\nWe love scikit learn but very often we find ourselves writing\ncustom transformers, metrics and models. The goal of this project\nis to attempt to consolidate these into a package that offers\ncode quality/testing. This project started as a collaboration between\nmultiple companies in the Netherlands but has since received contributions\nfrom around the globe. It was initiated by [Matthijs Brouns](https://www.mbrouns.com/)\nand [Vincent D. Warmerdam](https://koaning.io) as a tool to teach people how\nto contribute to open source.\n\nNote that we're not formally affiliated with the scikit-learn project at all,\nbut we aim to strictly adhere to their standards.\n\nThe same holds with lego. LEGO\u00ae is a trademark of the LEGO Group of companies which does not sponsor, authorize or endorse this project.\n\n## Installation\n\nInstall `scikit-lego` via pip with\n\n```bash\npython -m pip install scikit-lego\n```\n\nVia [conda](https://conda.io/projects/conda/en/latest/) with\n\n```bash\nconda install -c conda-forge scikit-lego\n```\n\nAlternatively, to edit and contribute you can fork/clone and run:\n\n```bash\npython -m pip install -e \".[dev]\"\npython setup.py develop\n```\n\n## Documentation\n\nThe documentation can be found [here](https://koaning.github.io/scikit-lego/).\n\n## Usage\n\nWe offer custom metrics, models and transformers. You can import them just like you would\nin scikit-learn.\n\n```python\n# the scikit learn stuff we love\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.pipeline import Pipeline\n\n# from scikit lego stuff we add\nfrom sklego.preprocessing import RandomAdder\nfrom sklego.mixture import GMMClassifier\n\n...\n\nmod = Pipeline([\n (\"scale\", StandardScaler()),\n (\"random_noise\", RandomAdder()),\n (\"model\", GMMClassifier())\n])\n\n...\n```\n\n## Features\n\nHere's a list of features that this library currently offers:\n\n- `sklego.datasets.load_abalone` loads in the abalone dataset\n- `sklego.datasets.load_arrests` loads in a dataset with fairness concerns\n- `sklego.datasets.load_chicken` loads in the joyful chickweight dataset\n- `sklego.datasets.load_heroes` loads a heroes of the storm dataset\n- `sklego.datasets.load_hearts` loads a dataset about hearts\n- `sklego.datasets.load_penguins` loads a lovely dataset about penguins\n- `sklego.datasets.fetch_creditcard` 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