TorchPairwise [](https://github.com/inspiros/torchpairwise/actions) [](https://pypi.org/project/torchpairwise/) [](https://pepy.tech/project/torchpairwise) [](LICENSE.txt)
======
This package provides highly-efficient pairwise metrics for **PyTorch**.
## News
- **v0.1.1:** Added **SNR** distance (``torchpairwise.snr_distances``) presented in https://arxiv.org/abs/1904.02616.
## Highlights
``torchpairwise`` is a collection of **general purpose** pairwise metric functions that behave similar to
``torch.cdist`` (which only implements $L_p$ distance).
Instead, we offer a lot more metrics ported from other packages such as
``scipy.spatial.distance`` and ``sklearn.metrics.pairwise``.
For task-specific metrics (e.g. for evaluation of classification, regression, clustering, ...), you should be in the
wrong place, please head to the [TorchMetrics](https://github.com/Lightning-AI/torchmetrics) repo.
Written in ``torch``'s C++ API, the main differences are that our metrics:
- are all (_except some boolean distances_) **differentiable** with backward formulas manually derived, implemented,
and verified with ``torch.autograd.gradcheck``.
- are **batched** and can exploit GPU parallelization.
- can be integrated seamlessly within **PyTorch**-based projects, all functions are ``torch.jit.script``-able.
### List of pairwise distance metrics
| ``torchpairwise`` ops | Equivalences in other libraries | Differentiable |
|:---------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------:|
| ``euclidean_distances`` | [``sklearn.metrics.pairwise.euclidean_distances``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.euclidean_distances.html) | ✔️ |
| ``haversine_distances`` | [``sklearn.metrics.pairwise.haversine_distances``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.haversine_distances.html) | ✔️ |
| ``manhattan_distances`` | [``sklearn.metrics.pairwise.manhattan_distances``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.manhattan_distances.html) | ✔️ |
| ``cosine_distances`` | [``sklearn.metrics.pairwise.cosine_distances``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.cosine_distances.html) | ✔️ |
| ``l1_distances`` | _(Alias of ``manhattan_distances``)_ | ✔️ |
| ``l2_distances`` | _(Alias of ``euclidean_distances``)_ | ✔️ |
| ``lp_distances`` | _(Alias of ``minkowski_distances``)_ | ✔️ |
| ``linf_distances`` | _(Alias of ``chebyshev_distances``)_ | ✔️ |
| ``directed_hausdorff_distances`` | [``scipy.spatial.distance.directed_hausdorff``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.directed_hausdorff.html) [^1] | ✔️ |
| ``minkowski_distances`` | [``scipy.spatial.distance.minkowski``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.minkowski.html) [^1] | ✔️ |
| ``wminkowski_distances`` | [``scipy.spatial.distance.wminkowski``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.wminkowski.html) [^1] | ✔️ |
| ``sqeuclidean_distances`` | [``scipy.spatial.distance.sqeuclidean_distances``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.sqeuclidean_distances.html) [^1] | ✔️ |
| ``correlation_distances`` | [``scipy.spatial.distance.correlation``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.correlation.html) [^1] | ✔️ |
| ``hamming_distances`` | [``scipy.spatial.distance.hamming``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.hamming.html) [^1] | ❌[^2] |
| ``jaccard_distances`` | [``scipy.spatial.distance.jaccard``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.jaccard.html) [^1] | ❌[^2] |
| ``kulsinski_distances`` | [``scipy.spatial.distance.kulsinski``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.kulsinski.html) [^1] | ❌[^2] |
| ``kulczynski1_distances`` | [``scipy.spatial.distance.kulczynski1``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.kulczynski1.html) [^1] | ❌[^2] |
| ``seuclidean_distances`` | [``scipy.spatial.distance.seuclidean``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.seuclidean.html) [^1] | ✔️ |
| ``cityblock_distances`` | [``scipy.spatial.distance.cityblock``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cityblock.html) [^1] _(Alias of ``manhattan_distances``)_ | ✔️ |
| ``mahalanobis_distances`` | [``scipy.spatial.distance.mahalanobis``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.mahalanobis.html) [^1] | ✔️ |
| ``chebyshev_distances`` | [``scipy.spatial.distance.chebyshev``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.chebyshev.html) [^1] | ✔️ |
| ``braycurtis_distances`` | [``scipy.spatial.distance.braycurtis``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.braycurtis.html) [^1] | ✔️ |
| ``canberra_distances`` | [``scipy.spatial.distance.canberra``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.canberra.html) [^1] | ✔️ |
| ``jensenshannon_distances`` | [``scipy.spatial.distance.jensenshannon``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.jensenshannon.html) [^1] | ✔️ |
| ``yule_distances`` | [``scipy.spatial.distance.yule``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.yule.html) [^1] | ❌[^2] |
| ``dice_distances`` | [``scipy.spatial.distance.dice``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.dice.html) [^1] | ❌[^2] |
| ``rogerstanimoto_distances`` | [``scipy.spatial.distance.rogerstanimoto``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.rogerstanimoto.html) [^1] | ❌[^2] |
| ``russellrao_distances`` | [``scipy.spatial.distance.russellrao``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.russellrao.html) [^1] | ❌[^2] |
| ``sokalmichener_distances`` | [``scipy.spatial.distance.sokalmichener``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.sokalmichener.html) [^1] | ❌[^2] |
| ``sokalsneath_distances`` | [``scipy.spatial.distance.sokalsneath``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.sokalsneath.html) [^1] | ❌[^2] |
| ``snr_distances`` | [``pytorch_metric_learning.distances.SNRDistance``](https://kevinmusgrave.github.io/pytorch-metric-learning/distances/#snrdistance) [^1] | ✔️ |
[^1]: These metrics are not pairwise but a pairwise form can be computed by
calling ``scipy.spatial.distance.cdist(x1, x2, metric="[metric_name_or_callable]")``.
[^2]: These are boolean distances. ``hamming_distances`` can be applied for floating point inputs but involves
comparison.
### Other pairwise metrics or kernel functions
These metrics are usually used to compute kernel for machine learning algorithms.
| ``torchpairwise`` ops | Equivalences in other libraries | Differentiable |
|:-------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------:|
| ``linear_kernel`` | [``sklearn.metrics.pairwise.linear_kernel``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.linear_kernel.html) | ✔️ |
| ``polynomial_kernel`` | [``sklearn.metrics.pairwise.polynomial_kernel``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.polynomial_kernel.html) | ✔️ |
| ``sigmoid_kernel`` | [``sklearn.metrics.pairwise.sigmoid_kernel``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.sigmoid_kernel.html) | ✔️ |
| ``rbf_kernel`` | [``sklearn.metrics.pairwise.rbf_kernel``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.rbf_kernel.html) | ✔️ |
| ``laplacian_kernel`` | [``sklearn.metrics.pairwise.laplacian_kernel``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.laplacian_kernel.html) | ✔️ |
| ``cosine_similarity`` | [``sklearn.metrics.pairwise.cosine_similarity``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.cosine_similarity.html) | ✔️ |
| ``additive_chi2_kernel`` | [``sklearn.metrics.pairwise.additive_chi2_kernel``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.additive_chi2_kernel.html) | ✔️ |
| ``chi2_kernel`` | [``sklearn.metrics.pairwise.chi2_kernel``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.chi2_kernel.html) | ✔️ |
### Custom ``cdist`` and ``pdist``
Furthermore, we provide a convenient wrapper function analoguous to ``torch.cdist`` excepts that it takes a string
``metric: str = "minkowski"`` indicating the desired metric to be used as the third argument,
and extra metric-specific arguments are passed as keywords.
```python
import torch, torchpairwise
# directed_hausdorff_distances is a pairwise 2d metric
x1 = torch.rand(10, 6, 3)
x2 = torch.rand(8, 5, 3)
generator = torch.Generator().manual_seed(1)
output = torchpairwise.cdist(x1, x2,
metric="directed_hausdorff",
shuffle=True, # kwargs exclusive to directed_hausdorff
generator=generator)
```
Note that pairwise metrics on the second table are currently not allowed keys for ``cdist``
because they are not _dist_.
We have a similar plan for ``pdist`` (which is equivalent to calling ``cdist(x1, x1)`` but avoid storing duplicated
positions).
However, that requires a total overhaul of existing C++/Cuda kernels and won't be available soon.
## Future Improvements
- Add more metrics (contact me or create a feature request issue).
- Add memory-efficient ``argkmin`` for retrieving pairwise neighbors' distances and indices without storing the whole
pairwise distance matrix.
- Add an equivalence of ``torch.pdist`` with ``metric: str = "minkowski"`` argument.
- (_Unlikely_) Support sparse layouts.
## Requirements
- `torch>=2.1.0` (`torch>=1.9.0` if compiled from source)
## Installation
#### From PyPI:
To install prebuilt wheels from [torchpairwise](https://pypi.org/project/torchpairwise), simply run:
```console
pip install torchpairwise
```
Note that the Linux and Windows wheels in **PyPI** are compiled with ``torch==2.1.0`` and **Cuda 12.1**.
We only do a non-strict version checking and a warning will be raised if ``torch``'s and ``torchpairwise``'s
Cuda versions do not match.
#### From Source:
Make sure your machine has a C++17 and a Cuda compiler installed, then clone the repo and run:
```console
pip install .
```
## Usage
The basic usecase is very straight-forward if you are familiar with
``sklearn.metrics.pairwise`` and ``scipy.spatial.distance``:
<table>
<tr>
<th>scikit-learn / SciPy</th>
<th>TorchPairwise</th>
</tr>
<tr>
<td>
<sub>
```python
import numpy as np
import sklearn.metrics.pairwise as sklearn_pairwise
x1 = np.random.rand(10, 5)
x2 = np.random.rand(12, 5)
output = sklearn_pairwise.cosine_similarity(x1, x2)
print(output)
```
</sub>
<td>
<sub>
```python
import torch
import torchpairwise
x1 = torch.rand(10, 5, device='cuda')
x2 = torch.rand(12, 5, device='cuda')
output = torchpairwise.cosine_similarity(x1, x2)
print(output)
```
</sub>
</td>
</tr>
<tr>
<td>
<sub>
```python
import numpy as np
import scipy.spatial.distance as distance
x1 = np.random.binomial(
1, p=0.6, size=(10, 5)).astype(np.bool_)
x2 = np.random.binomial(
1, p=0.7, size=(12, 5)).astype(np.bool_)
output = distance.cdist(x1, x2, metric='jaccard')
print(output)
```
</sub>
<td>
<sub>
```python
import torch
import torchpairwise
x1 = torch.bernoulli(
torch.full((10, 5), fill_value=0.6, device='cuda')).to(torch.bool)
x2 = torch.bernoulli(
torch.full((12, 5), fill_value=0.7, device='cuda')).to(torch.bool)
output = torchpairwise.jaccard_distances(x1, x2)
print(output)
```
</sub>
</td>
</tr>
</table>
Please check the [tests](tests) folder where we will add more examples.
## License
The code is released under the MIT license. See [`LICENSE.txt`](LICENSE.txt) for details.
Raw data
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"keywords": "pairwise_metric,pairwise_distance,kernel_function",
"author": "Hoang-Nhat Tran (inspiros)",
"author_email": "hnhat.tran@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/c3/a2/ab7a2ed4e179b681223ba43fb1cf4ab55b3970182f641ce49d4afe4a8594/torchpairwise-0.1.1.tar.gz",
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"description": "TorchPairwise [](https://github.com/inspiros/torchpairwise/actions) [](https://pypi.org/project/torchpairwise/) [](https://pepy.tech/project/torchpairwise) [](LICENSE.txt)\n======\n\nThis package provides highly-efficient pairwise metrics for **PyTorch**.\n\n## News\n- **v0.1.1:** Added **SNR** distance (``torchpairwise.snr_distances``) presented in https://arxiv.org/abs/1904.02616.\n\n## Highlights\n\n``torchpairwise`` is a collection of **general purpose** pairwise metric functions that behave similar to\n``torch.cdist`` (which only implements $L_p$ distance).\nInstead, we offer a lot more metrics ported from other packages such as\n``scipy.spatial.distance`` and ``sklearn.metrics.pairwise``.\nFor task-specific metrics (e.g. for evaluation of classification, regression, clustering, ...), you should be in the\nwrong place, please head to the [TorchMetrics](https://github.com/Lightning-AI/torchmetrics) repo.\n\nWritten in ``torch``'s C++ API, the main differences are that our metrics:\n\n- are all (_except some boolean distances_) **differentiable** with backward formulas manually derived, implemented,\n and verified with ``torch.autograd.gradcheck``.\n- are **batched** and can exploit GPU parallelization.\n- can be integrated seamlessly within **PyTorch**-based projects, all functions are ``torch.jit.script``-able.\n\n### List of pairwise distance metrics\n\n| ``torchpairwise`` ops | Equivalences in other libraries | Differentiable |\n|:---------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------:|\n| ``euclidean_distances`` | [``sklearn.metrics.pairwise.euclidean_distances``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.euclidean_distances.html) | \u2714\ufe0f |\n| ``haversine_distances`` | [``sklearn.metrics.pairwise.haversine_distances``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.haversine_distances.html) | \u2714\ufe0f |\n| ``manhattan_distances`` | [``sklearn.metrics.pairwise.manhattan_distances``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.manhattan_distances.html) | \u2714\ufe0f |\n| ``cosine_distances`` | [``sklearn.metrics.pairwise.cosine_distances``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.cosine_distances.html) | \u2714\ufe0f |\n| ``l1_distances`` | _(Alias of ``manhattan_distances``)_ | \u2714\ufe0f |\n| ``l2_distances`` | _(Alias of ``euclidean_distances``)_ | \u2714\ufe0f |\n| ``lp_distances`` | _(Alias of ``minkowski_distances``)_ | \u2714\ufe0f |\n| ``linf_distances`` | _(Alias of ``chebyshev_distances``)_ | \u2714\ufe0f |\n| ``directed_hausdorff_distances`` | [``scipy.spatial.distance.directed_hausdorff``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.directed_hausdorff.html) [^1] | \u2714\ufe0f |\n| ``minkowski_distances`` | [``scipy.spatial.distance.minkowski``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.minkowski.html) [^1] | \u2714\ufe0f |\n| ``wminkowski_distances`` | [``scipy.spatial.distance.wminkowski``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.wminkowski.html) [^1] | \u2714\ufe0f |\n| ``sqeuclidean_distances`` | [``scipy.spatial.distance.sqeuclidean_distances``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.sqeuclidean_distances.html) [^1] | \u2714\ufe0f |\n| ``correlation_distances`` | [``scipy.spatial.distance.correlation``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.correlation.html) [^1] | \u2714\ufe0f |\n| ``hamming_distances`` | [``scipy.spatial.distance.hamming``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.hamming.html) [^1] | \u274c[^2] |\n| ``jaccard_distances`` | [``scipy.spatial.distance.jaccard``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.jaccard.html) [^1] | \u274c[^2] |\n| ``kulsinski_distances`` | [``scipy.spatial.distance.kulsinski``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.kulsinski.html) [^1] | \u274c[^2] |\n| ``kulczynski1_distances`` | [``scipy.spatial.distance.kulczynski1``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.kulczynski1.html) [^1] | \u274c[^2] |\n| ``seuclidean_distances`` | [``scipy.spatial.distance.seuclidean``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.seuclidean.html) [^1] | \u2714\ufe0f |\n| ``cityblock_distances`` | [``scipy.spatial.distance.cityblock``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cityblock.html) [^1] _(Alias of ``manhattan_distances``)_ | \u2714\ufe0f |\n| ``mahalanobis_distances`` | [``scipy.spatial.distance.mahalanobis``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.mahalanobis.html) [^1] | \u2714\ufe0f |\n| ``chebyshev_distances`` | [``scipy.spatial.distance.chebyshev``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.chebyshev.html) [^1] | \u2714\ufe0f |\n| ``braycurtis_distances`` | [``scipy.spatial.distance.braycurtis``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.braycurtis.html) [^1] | \u2714\ufe0f |\n| ``canberra_distances`` | [``scipy.spatial.distance.canberra``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.canberra.html) [^1] | \u2714\ufe0f |\n| ``jensenshannon_distances`` | [``scipy.spatial.distance.jensenshannon``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.jensenshannon.html) [^1] | \u2714\ufe0f |\n| ``yule_distances`` | [``scipy.spatial.distance.yule``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.yule.html) [^1] | \u274c[^2] |\n| ``dice_distances`` | [``scipy.spatial.distance.dice``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.dice.html) [^1] | \u274c[^2] |\n| ``rogerstanimoto_distances`` | [``scipy.spatial.distance.rogerstanimoto``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.rogerstanimoto.html) [^1] | \u274c[^2] |\n| ``russellrao_distances`` | [``scipy.spatial.distance.russellrao``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.russellrao.html) [^1] | \u274c[^2] |\n| ``sokalmichener_distances`` | [``scipy.spatial.distance.sokalmichener``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.sokalmichener.html) [^1] | \u274c[^2] |\n| ``sokalsneath_distances`` | [``scipy.spatial.distance.sokalsneath``](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.sokalsneath.html) [^1] | \u274c[^2] |\n| ``snr_distances`` | [``pytorch_metric_learning.distances.SNRDistance``](https://kevinmusgrave.github.io/pytorch-metric-learning/distances/#snrdistance) [^1] | \u2714\ufe0f |\n\n[^1]: These metrics are not pairwise but a pairwise form can be computed by\ncalling ``scipy.spatial.distance.cdist(x1, x2, metric=\"[metric_name_or_callable]\")``.\n\n[^2]: These are boolean distances. ``hamming_distances`` can be applied for floating point inputs but involves\ncomparison.\n\n### Other pairwise metrics or kernel functions\n\nThese metrics are usually used to compute kernel for machine learning algorithms.\n\n| ``torchpairwise`` ops | Equivalences in other libraries | Differentiable |\n|:-------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------:|\n| ``linear_kernel`` | [``sklearn.metrics.pairwise.linear_kernel``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.linear_kernel.html) | \u2714\ufe0f |\n| ``polynomial_kernel`` | [``sklearn.metrics.pairwise.polynomial_kernel``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.polynomial_kernel.html) | \u2714\ufe0f |\n| ``sigmoid_kernel`` | [``sklearn.metrics.pairwise.sigmoid_kernel``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.sigmoid_kernel.html) | \u2714\ufe0f |\n| ``rbf_kernel`` | [``sklearn.metrics.pairwise.rbf_kernel``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.rbf_kernel.html) | \u2714\ufe0f |\n| ``laplacian_kernel`` | [``sklearn.metrics.pairwise.laplacian_kernel``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.laplacian_kernel.html) | \u2714\ufe0f |\n| ``cosine_similarity`` | [``sklearn.metrics.pairwise.cosine_similarity``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.cosine_similarity.html) | \u2714\ufe0f |\n| ``additive_chi2_kernel`` | [``sklearn.metrics.pairwise.additive_chi2_kernel``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.additive_chi2_kernel.html) | \u2714\ufe0f |\n| ``chi2_kernel`` | [``sklearn.metrics.pairwise.chi2_kernel``](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.chi2_kernel.html) | \u2714\ufe0f |\n\n### Custom ``cdist`` and ``pdist``\n\nFurthermore, we provide a convenient wrapper function analoguous to ``torch.cdist`` excepts that it takes a string\n``metric: str = \"minkowski\"`` indicating the desired metric to be used as the third argument,\nand extra metric-specific arguments are passed as keywords.\n\n```python\nimport torch, torchpairwise\n\n# directed_hausdorff_distances is a pairwise 2d metric\nx1 = torch.rand(10, 6, 3)\nx2 = torch.rand(8, 5, 3)\n\ngenerator = torch.Generator().manual_seed(1)\noutput = torchpairwise.cdist(x1, x2,\n metric=\"directed_hausdorff\",\n shuffle=True, # kwargs exclusive to directed_hausdorff\n generator=generator)\n```\n\nNote that pairwise metrics on the second table are currently not allowed keys for ``cdist``\nbecause they are not _dist_.\nWe have a similar plan for ``pdist`` (which is equivalent to calling ``cdist(x1, x1)`` but avoid storing duplicated\npositions).\nHowever, that requires a total overhaul of existing C++/Cuda kernels and won't be available soon.\n\n## Future Improvements\n\n- Add more metrics (contact me or create a feature request issue).\n- Add memory-efficient ``argkmin`` for retrieving pairwise neighbors' distances and indices without storing the whole\n pairwise distance matrix.\n- Add an equivalence of ``torch.pdist`` with ``metric: str = \"minkowski\"`` argument.\n- (_Unlikely_) Support sparse layouts.\n\n## Requirements\n\n- `torch>=2.1.0` (`torch>=1.9.0` if compiled from source)\n\n## Installation\n\n#### From PyPI:\n\nTo install prebuilt wheels from [torchpairwise](https://pypi.org/project/torchpairwise), simply run:\n\n```console\npip install torchpairwise\n```\n\nNote that the Linux and Windows wheels in **PyPI** are compiled with ``torch==2.1.0`` and **Cuda 12.1**.\nWe only do a non-strict version checking and a warning will be raised if ``torch``'s and ``torchpairwise``'s\nCuda versions do not match.\n\n#### From Source:\n\nMake sure your machine has a C++17 and a Cuda compiler installed, then clone the repo and run:\n\n```console\npip install .\n```\n\n## Usage\n\nThe basic usecase is very straight-forward if you are familiar with\n``sklearn.metrics.pairwise`` and ``scipy.spatial.distance``:\n\n<table>\n<tr>\n<th>scikit-learn / SciPy</th>\n<th>TorchPairwise</th>\n</tr>\n\n<tr>\n<td>\n<sub>\n\n```python\nimport numpy as np\nimport sklearn.metrics.pairwise as sklearn_pairwise\n\nx1 = np.random.rand(10, 5)\nx2 = np.random.rand(12, 5)\n\noutput = sklearn_pairwise.cosine_similarity(x1, x2)\nprint(output)\n```\n\n</sub>\n<td>\n<sub>\n\n```python\nimport torch\nimport torchpairwise\n\nx1 = torch.rand(10, 5, device='cuda')\nx2 = torch.rand(12, 5, device='cuda')\n\noutput = torchpairwise.cosine_similarity(x1, x2)\nprint(output)\n```\n\n</sub>\n</td>\n</tr>\n\n<tr>\n<td>\n<sub>\n\n```python\nimport numpy as np\nimport scipy.spatial.distance as distance\n\nx1 = np.random.binomial(\n 1, p=0.6, size=(10, 5)).astype(np.bool_)\nx2 = np.random.binomial(\n 1, p=0.7, size=(12, 5)).astype(np.bool_)\n\noutput = distance.cdist(x1, x2, metric='jaccard')\nprint(output)\n```\n\n</sub>\n<td>\n<sub>\n\n```python\nimport torch\nimport torchpairwise\n\nx1 = torch.bernoulli(\n torch.full((10, 5), fill_value=0.6, device='cuda')).to(torch.bool)\nx2 = torch.bernoulli(\n torch.full((12, 5), fill_value=0.7, device='cuda')).to(torch.bool)\n\noutput = torchpairwise.jaccard_distances(x1, x2)\nprint(output)\n```\n\n</sub>\n</td>\n</tr>\n\n</table>\n\nPlease check the [tests](tests) folder where we will add more examples.\n\n## License\n\nThe code is released under the MIT license. See [`LICENSE.txt`](LICENSE.txt) for details.\n",
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