scikit-learn compatible neural network library for pytorch
Project description
.. 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%
.. |docs| image:: https://readthedocs.org/projects/skorch/badge/?version=latest
:alt: Documentation Status
:scale: 100%
:target: https://skorch.readthedocs.io/en/latest/?badge=latest
.. |powered| image:: https://github.com/dnouri/skorch/blob/master/assets/powered.svg
:alt: Powered by
:scale: 100%
:target: https://github.com/ottogroup/
=========
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)
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,
)
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-0.4.0-cp36-cp36m-linux_x86_64.whl
=============
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 daniel.nouri@gmail.com.
: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%
.. |docs| image:: https://readthedocs.org/projects/skorch/badge/?version=latest
:alt: Documentation Status
:scale: 100%
:target: https://skorch.readthedocs.io/en/latest/?badge=latest
.. |powered| image:: https://github.com/dnouri/skorch/blob/master/assets/powered.svg
:alt: Powered by
:scale: 100%
:target: https://github.com/ottogroup/
=========
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)
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,
)
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-0.4.0-cp36-cp36m-linux_x86_64.whl
=============
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 daniel.nouri@gmail.com.
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