scikit-learn compatible neural network library for pytorch
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A scikit-learn compatible neural network library that wraps PyTorch.
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Documentation <https://skorch.readthedocs.io/en/latest/?badge=latest>_Source Code <https://github.com/skorch-dev/skorch/>_Installation <https://github.com/skorch-dev/skorch#installation>_To see more elaborate examples, look here <https://github.com/skorch-dev/skorch/tree/master/notebooks/README.md>__.
.. code:: python
import numpy as np
from sklearn.datasets import make_classification
from torch import nn
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=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,
# Shuffle training data on each epoch
iterator_train__shuffle=True,
)
net.fit(X, y)
y_proba = net.predict_proba(X)
In an sklearn Pipeline <https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html>_:
.. 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 <https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html>_:
.. code:: python
from sklearn.model_selection import GridSearchCV
# deactivate skorch-internal train-valid split and verbose logging
net.set_params(train_split=False, verbose=0)
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', verbose=2)
gs.fit(X, y)
print("best score: {:.3f}, best params: {}".format(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/skorch-dev/skorch/tree/master/examples/cli>_Integration with GPyTorch for Gaussian Processes <https://skorch.readthedocs.io/en/latest/user/probabilistic.html>_Integration with Hugging Face 🤗 <https://skorch.readthedocs.io/en/stable/user/huggingface.html>_skorch requires Python 3.8 or higher.
You need a working conda installation. Get the correct miniconda for
your system from here <https://conda.io/miniconda.html>__.
To install skorch, you need to use the conda-forge channel:
.. code:: bash
conda install -c conda-forge skorch
We recommend to use a conda virtual environment <https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html>_.
Note: The conda channel is not managed by the skorch
maintainers. More information is available here <https://github.com/conda-forge/skorch-feedstock>__.
To install with pip, run:
.. code:: bash
python -m pip install -U skorch
Again, we recommend to use a virtual environment <https://docs.python.org/3/tutorial/venv.html>_ for this.
If you would like to use the most recent additions to skorch or help development, you should install skorch from source.
To install skorch from source using conda, proceed as follows:
.. code:: bash
git clone https://github.com/skorch-dev/skorch.git
cd skorch
conda create -n skorch-env python=3.10
conda activate skorch-env
conda install -c pytorch pytorch
python -m pip install -r requirements.txt
python -m pip install .
If you want to help developing, run:
.. code:: bash
git clone https://github.com/skorch-dev/skorch.git
cd skorch
conda create -n skorch-env python=3.10
conda activate skorch-env
conda install -c pytorch pytorch
python -m pip install -r requirements.txt
python -m pip install -r requirements-dev.txt
python -m pip install -e .
py.test # unit tests
pylint skorch # static code checks
You may adjust the Python version to any of the supported Python versions.
For pip, follow these instructions instead:
.. code:: bash
git clone https://github.com/skorch-dev/skorch.git
cd skorch
# create and activate a virtual environment
python -m pip install -r requirements.txt
# install pytorch version for your system (see below)
python -m pip install .
If you want to help developing, run:
.. code:: bash
git clone https://github.com/skorch-dev/skorch.git
cd skorch
# create and activate a virtual environment
python -m pip install -r requirements.txt
# install pytorch version for your system (see below)
python -m pip install -r requirements-dev.txt
python -m pip install -e .
py.test # unit tests
pylint skorch # static code checks
PyTorch is not covered by the dependencies, since the PyTorch version
you need is dependent on your OS and device. For installation
instructions for PyTorch, visit the PyTorch website <http://pytorch.org/>__. skorch officially supports the last four
minor PyTorch versions, which currently are:
However, that doesn't mean that older versions don't work, just that they aren't tested. Since skorch mostly relies on the stable part of the PyTorch API, older PyTorch versions should work fine.
In general, running this to install PyTorch should work:
.. code:: bash
# using conda:
conda install pytorch pytorch-cuda -c pytorch
# using pip
python -m pip install torch
blog post <https://neptune.ai/blog/model-training-libraries-pytorch-ecosystem>_
"8 Creators and Core Contributors Talk About Their Model Training
Libraries From PyTorch Ecosystem" 2020talk 1 <https://www.youtube.com/watch?v=Qbu_DCBjVEk>_ "skorch: A
scikit-learn compatible neural network library" at PyCon/PyData 2019poster <https://github.com/githubnemo/skorch-poster>_
for the PyTorch developer conference 2019talk 2 <https://www.youtube.com/watch?v=0J7FaLk0bmQ>_
"Skorch: A Union of Scikit learn and PyTorch" at SciPy 2019talk 3 <https://www.youtube.com/watch?v=yAXsxf2CQ8M>_
"Skorch - A Union of Scikit-learn and PyTorch" at PyData 2018GitHub discussions <https://github.com/skorch-dev/skorch/discussions>_:
user questions, thoughts, install issues, general discussions.
GitHub issues <https://github.com/skorch-dev/skorch/issues>_: bug
reports, feature requests, RFCs, etc.
Slack: We run the #skorch channel on the PyTorch Slack server <https://pytorch.slack.com/>, for which you can request access here <https://bit.ly/ptslack>.