## skx
scikit-learn compatible extensions for supervised learning on tabular data.
## Documentation
See [docs.sk-x.org](https://docs.sk-x.org).
## Requirements
- Python >= 3.12
- scikit-learn ~= 1.7.0
- numpy >= 2.0, scipy >= 1.13, pandas >= 2.0
## Install
```bash
pip install skx
```
## Usage
### Augmentation functions
```python
import numpy as np
from skx.augmentation import gaussian_augment
X = np.array([[1, 2], [3, 4], [5, 6]])
y = np.array([1, 2, 3])
X_aug, y_aug, sample_weight = gaussian_augment(X, y, factor=2.0, y_std=0.1)
```
### Augmentation meta-estimators
```python
from sklearn.linear_model import LinearRegression
from skx.augmentation import GaussianAugmentedRegressor
reg = GaussianAugmentedRegressor(LinearRegression(), factor=10.0, y_std=0.1)
reg.fit(X, y)
pred = reg.predict([[2, 3]])
```
### Ensembles
```python
from sklearn.linear_model import LinearRegression
from skx.ensemble import MixtureOfExpertsRegressor
moe = MixtureOfExpertsRegressor(
estimator=LinearRegression(), n_estimators=5, split="kmeans", n_clusters=3
)
moe.fit(X, y)
pred = moe.predict(X)
```
### Neural network
```python
from skx.neural_network import LinearScalingMLPRegressor
mlp = LinearScalingMLPRegressor(
n_hidden_layers=2,
hidden_layer_width=50,
shrink_factor=0.5,
max_iter=100,
)
mlp.fit(X, np.column_stack([y, y])) # multi-output example
```
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"description": "## skx\n\nscikit-learn compatible extensions for supervised learning on tabular data.\n\n## Documentation\n\nSee [docs.sk-x.org](https://docs.sk-x.org).\n\n## Requirements\n\n- Python >= 3.12\n- scikit-learn ~= 1.7.0\n- numpy >= 2.0, scipy >= 1.13, pandas >= 2.0\n\n## Install\n\n```bash\npip install skx\n```\n\n## Usage\n\n### Augmentation functions\n\n```python\nimport numpy as np\nfrom skx.augmentation import gaussian_augment\n\nX = np.array([[1, 2], [3, 4], [5, 6]])\ny = np.array([1, 2, 3])\nX_aug, y_aug, sample_weight = gaussian_augment(X, y, factor=2.0, y_std=0.1)\n```\n\n### Augmentation meta-estimators\n\n```python\nfrom sklearn.linear_model import LinearRegression\nfrom skx.augmentation import GaussianAugmentedRegressor\n\nreg = GaussianAugmentedRegressor(LinearRegression(), factor=10.0, y_std=0.1)\nreg.fit(X, y)\npred = reg.predict([[2, 3]])\n```\n\n### Ensembles\n\n```python\nfrom sklearn.linear_model import LinearRegression\nfrom skx.ensemble import MixtureOfExpertsRegressor\n\nmoe = MixtureOfExpertsRegressor(\n estimator=LinearRegression(), n_estimators=5, split=\"kmeans\", n_clusters=3\n)\nmoe.fit(X, y)\npred = moe.predict(X)\n```\n\n### Neural network\n\n```python\nfrom skx.neural_network import LinearScalingMLPRegressor\n\nmlp = LinearScalingMLPRegressor(\n n_hidden_layers=2,\n hidden_layer_width=50,\n shrink_factor=0.5,\n max_iter=100,\n)\nmlp.fit(X, np.column_stack([y, y])) # multi-output example\n```\n",
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