skorch 0.1.0.post1

scikit-learn compatible neural network library for pytorch

.. image:: https://github.com/dnouri/skorch/blob/master/assets/skorch.svg
:width: 30%
======

|build| |coverage| |docs|

A scikit-learn compatible neural network library that wraps PyTorch.

.. |build| image:: https://travis-ci.org/dnouri/skorch.svg?branch=master
:alt: Build Status
:scale: 100%
:target: https://travis-ci.org/dnouri/skorch?branch=master

.. |coverage| image:: https://github.com/dnouri/skorch/blob/master/assets/coverage.svg
:alt: Test Coverage
:scale: 100%

.. |docs| image:: https://readthedocs.org/projects/skorch/badge/?version=latest
:alt: Documentation Status
:scale: 100%
:target: https://skorch.readthedocs.io/en/latest/?badge=latest

Resources:

- `Documentation <https://skorch.readthedocs.io/en/latest/?badge=latest>`_
- `Source Code <https://github.com/dnouri/skorch/>`_

Example
-------

To see a more elaborate example, look `here
<https://github.com/dnouri/skorch/tree/master/notebooks/README.md>`__.

.. code:: python

import numpy as np
from sklearn.datasets import make_classification
import torch
from torch import nn
import torch.nn.functional as F

from skorch.net import NeuralNetClassifier


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


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

self.dense0 = nn.Linear(20, num_units)
self.nonlin = nonlin
self.dropout = nn.Dropout(0.5)
self.dense1 = nn.Linear(num_units, 10)
self.output = nn.Linear(10, 2)

def forward(self, X, **kwargs):
X = self.nonlin(self.dense0(X))
X = self.dropout(X)
X = F.relu(self.dense1(X))
X = F.softmax(self.output(X), dim=-1)
return X


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

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

In an sklearn Pipeline:

.. code:: python

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler


pipe = Pipeline([
('scale', StandardScaler()),
('net', net),
])

pipe.fit(X, y)
y_proba = pipe.predict_proba(X)

With grid search

.. code:: python

from sklearn.model_selection import GridSearchCV


params = {
'lr': [0.01, 0.02],
'max_epochs': [10, 20],
'module__num_units': [10, 20],
}
gs = GridSearchCV(net, params, refit=False, cv=3, scoring='accuracy')

gs.fit(X, y)
print(gs.best_score_, gs.best_params_)

Installation
------------

pip installation
~~~~~~~~~~~~~~~~

To install with pip, run:

.. code:: bash

pip install -U skorch

We recommend to use a virtual environment for this.

>From source
~~~~~~~~~~~

If you would like to use the must recent additions to skorch or
help development, you should install skorch from source.

Using conda
^^^^^^^^^^^

You need a working conda installation. Get the correct miniconda for
your system from `here <https://conda.io/miniconda.html>`__.

If you just want to use skorch, use:

.. code:: bash

git clone https://github.com/dnouri/skorch.git
cd skorch
conda env create
source activate skorch
# install pytorch version for your system (see below)
python setup.py install

If you want to help developing, run:

.. code:: bash

git clone https://github.com/dnouri/skorch.git
cd skorch
conda env create
source activate skorch
# install pytorch version for your system (see below)
conda install --file requirements-dev.txt
python setup.py develop

py.test # unit tests
pylint skorch # static code checks

Using pip
^^^^^^^^^

If you just want to use skorch, use:

.. code:: bash

git clone https://github.com/dnouri/skorch.git
cd skorch
# create and activate a virtual environment
pip install -r requirements.txt
# install pytorch version for your system (see below)
python setup.py install

If you want to help developing, run:

.. code:: bash

git clone https://github.com/dnouri/skorch.git
cd skorch
# create and activate a virtual environment
pip install -r requirements.txt
# install pytorch version for your system (see below)
pip install -r requirements-dev.txt
python setup.py develop

py.test # unit tests
pylint skorch # static code checks

PyTorch
~~~~~~~

PyTorch is not covered by the dependencies, since the PyTorch
version you need is dependent on your system. For installation
instructions for PyTorch, visit the `PyTorch website
<http://pytorch.org/>`__.

In general, this should work (assuming CUDA 9):

.. code:: bash

# using conda:
conda install pytorch cuda90 -c pytorch
# using pip
pip install http://download.pytorch.org/whl/cu90/torch-0.3.0.post4-cp36-cp36m-linux_x86_64.whl