sklx

Name	sklx JSON
Version	0.0.1 JSON
	download
home_page	None
Summary	A scikit-learn compatible neural network library that wraps MLX.
upload_time	2024-10-27 03:18:14
maintainer	None
docs_url	None
author	None
requires_python	>=3.10
license	BSD 3-Clause License
keywords	sklx
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # SKLX

A scikit-learn compatible neural network library that wraps MLX.
Highly inspired by [skorch](https://github.com/skorch-dev/skorch).

> [!WARNING]
> This is still under development and non of the following examples actually work.

## Examples

```python
import numpy as np
from sklearn.datasets import make_classification
from mlx import nn
from sklx import NeuralNetClassifier

X, y = make_classification(1000, 20, n_informative=10, random_state=0)
X = X.astype(np.float32)
y = y.astype(np.int64)

class MyModule(nn.Module):
    def __init__(self, num_units=10, nonlin=nn.ReLU()):
        super().__init__()

        self.dense0 = nn.Linear(20, num_units)
        self.nonlin = nonlin
        self.dropout = nn.Dropout(0.5)
        self.dense1 = nn.Linear(num_units, num_units)
        self.output = nn.Linear(num_units, 2)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, X, **kwargs):
        X = self.nonlin(self.dense0(X))
        X = self.dropout(X)
        X = self.nonlin(self.dense1(X))
        X = self.softmax(self.output(X))
        return X

net = NeuralNetClassifier(
    MyModule,
    max_epochs=10,
    lr=0.1,
)

net.fit(X, y)
y_proba = net.predict_proba(X)
```

            

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    "description": "# SKLX\n\nA scikit-learn compatible neural network library that wraps MLX.\nHighly inspired by [skorch](https://github.com/skorch-dev/skorch).\n\n> [!WARNING]\n> This is still under development and non of the following examples actually work.\n\n## Examples\n\n```python\nimport numpy as np\nfrom sklearn.datasets import make_classification\nfrom mlx import nn\nfrom sklx import NeuralNetClassifier\n\nX, y = make_classification(1000, 20, n_informative=10, random_state=0)\nX = X.astype(np.float32)\ny = y.astype(np.int64)\n\nclass MyModule(nn.Module):\n    def __init__(self, num_units=10, nonlin=nn.ReLU()):\n        super().__init__()\n\n        self.dense0 = nn.Linear(20, num_units)\n        self.nonlin = nonlin\n        self.dropout = nn.Dropout(0.5)\n        self.dense1 = nn.Linear(num_units, num_units)\n        self.output = nn.Linear(num_units, 2)\n        self.softmax = nn.Softmax(dim=-1)\n\n    def forward(self, X, **kwargs):\n        X = self.nonlin(self.dense0(X))\n        X = self.dropout(X)\n        X = self.nonlin(self.dense1(X))\n        X = self.softmax(self.output(X))\n        return X\n\nnet = NeuralNetClassifier(\n    MyModule,\n    max_epochs=10,\n    lr=0.1,\n)\n\nnet.fit(X, y)\ny_proba = net.predict_proba(X)\n```\n",
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