skorch 0.6.0

scikit-learn compatible neural network library for pytorch

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

------------

|build| |coverage| |docs| |powered|

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%

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:alt: Documentation Status
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:target: https://skorch.readthedocs.io/en/latest/?badge=latest

.. |powered| image:: https://github.com/dnouri/skorch/blob/master/assets/powered.svg
:alt: Powered by
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=========
Resources
=========

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

========
Examples
========

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

.. code:: python

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

from skorch 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=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,
# Shuffle training data on each epoch
iterator_train__shuffle=True,
)

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_)

skorch also provides many convenient features, among others:

- `Learning rate schedulers <https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler>`_ (Warm restarts, cyclic LR and many more)
- `Scoring using sklearn (and custom) scoring functions <https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring>`_
- `Early stopping <https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EarlyStopping>`_
- `Checkpointing <https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.Checkpoint>`_
- `Parameter freezing/unfreezing <https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.Freezer>`_
- `Progress bar <https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.ProgressBar>`_ (for CLI as well as jupyter)
- `Automatic inference of CLI parameters <https://github.com/dnouri/skorch/tree/master/examples/cli>`_

============
Installation
============

skorch requires Python 3.5 or higher.

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 -c conda-forge --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/>`__. The
current version of skorch assumes PyTorch >= 1.1.0.

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

.. code:: bash

# using conda:
conda install pytorch -c pytorch
# using pip
pip install torch

=============
Communication
=============

- `GitHub issues <https://github.com/dnouri/skorch/issues>`_: bug
reports, feature requests, install issues, RFCs, thoughts, etc.

- Slack: We run the #skorch channel on the `PyTorch Slack server
<https://pytorch.slack.com/>`_. If you need an invite, send an
email to [e-mail].