# Large-Scale MMD-MA
[**Installation**](#installation)
| [**Command line instructions**](#commandline)
| [**Examples**](#examples)
| [**Input**](#input)
| [**Output**](#output)
| [**Citation**](#citation)
| [**Contact**](#contact)
The objective of [MMD-MA](https://pubmed.ncbi.nlm.nih.gov/34632462/) is to
match points coming from two different spaces in a lower dimensional space. To
this end, two sets of points are projected, from two different spaces endowed
with a positive definite kernel, to a shared Euclidean space of lower dimension
`low_dim`. The mappings from high to low dimensional space are
obtained using functions belonging to the respective RKHS. To obtain the
mappings, we minimise a [loss function](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/train.py#L173) that is composed of three terms:
- an MMD term between the low dimensional representations of the two views,
which encourages them to have the same distribution. The RBF kernel is used
to compute MMD.
- two non-collapsing penalty terms (corresponding to the `pen_dual` or
`pen_primal` functions), one for each view. These terms ensure that
the low dimensional representations are mutually orthogonal, preventing
collapsing.
- two distortion penalties (corresponding to the `dis_dual` or
`dis_primal` functions), one for each view. These terms encourage the
low dimensional representation to obtain the same pairwise structure as
the original views.
MMD-MA can be formulated using either the primal (when we use the linear
kernel in the input spaces) or the dual problem. Each has
advantages or disadvantages depending on the input data. For each view,
when the number of features `p` is larger than the number of samples `n`
`p >> n`, then the dual formulation is beneficial in terms
of runtime and memory, while if `n >> p`, the primal
formulation is favorable.
Additionally, in order to scale the computation of MMD to a large number of
samples, we propose either to use the
[KeOps](https://www.kernel-operations.io/keops/index.html) library which
lets you compute large kernel operations on GPUs without memory overflow.
## Installation<a id="installation"></a>
To install the latest release of lsmmdma, use the following command:
```bash
$ pip install lsmmdma
```
To install the **development** version, use the following command instead:
```bash
$ pip install git+https://github.com/google-research/large-scale-mmdma
```
Alternatively, it can be installed from sources with the following command:
```bash
$ python setup.py install
```
In Google Colab, use the following command:
```bash
$ !pip install lsmmdma
```
The KeOps library might require to be installed separately in advance, according
to the given [instructions](http://www.kernel-operations.io/keops/python/installation.html). Typically, in Google Colab one would run this command before installing `lsmmmdma`:
```bash
$ !pip install pykeops
```
## Command line instructions<a id="commandline"></a>
The algorithm can be run with a command line using:
```bash
python3 -m lsmmmda.main [flags]
```
A set of flags is available to determine the IO, the model, the hyperparameters
and the seed.
**Input/Output** It is possible to give as input either a path and filenames pointing to the
user input or to choose to generate data with the [data_pipeline.generate_data](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/data/data_pipeline.py#L13) function. In the
case one wants to generate simulation data, the input flags are:
- `--data`: str, it can be either 'branch', 'triangle' or '' (default). The
simulated data is described in the pydoc of [data_pipeline.generate_data](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/data/data_pipeline.py#L13). ''
means that simulated data is not used.
- `--n`: int (default 300), number of samples for both views.
- `--p`: int (default 1000), number of features for both views.
For data given by the user, the input flags are:
- `--input_dir`: str (default None), input directory.
- `--input_fv`: str (default None), filename of the array (n1 x p1 or n1 x n1)
that serves as first set of points.
- `--input_sv`: str (default None), filename of the array (n2 x p2 or n2 x n2)
that serves as second set of points.
- `--rd_vec`: str (default None), filename of the vector that contains the indices
of the samples from the first view that match (ground truth) the samples from
the second view. This is only used at evaluation time. If `--rd_vec` is not
used, we assume that the samples of both views follow the same ordering.
- `--labels_fv`: str (default None), filename of the vector that contains the
labels of the samples from the first view. Must match the order of the samples
in `input_fv`.
- `--labels_sv`: str (default None), filename of the vector that contains the
labels of the samples from the second view, following the same ordering.
In both cases, two other flags are also available:
- `--kernel`: bool (default False), whether the input data given by the user is
a kernel (n x n instead of n x p). This parameter can only be set to True when
`--m` is 'dual'.
- `--output_dir`: str (default None), output directory.
**Model** The flags allow you to choose between four algorithms, using either
the 'primal' or 'dual' formulation, and using KeOps or not.
- `--m`: str, either 'primal' or 'dual' (default).
- `--keops`: integer, either 1 (use keops), 0 (not not use keops) or -1 (automatic, uses keops
from 4000 samples onwards) (default).
- `--use_unbiased_mmd`: bool (default True), determines whether or not to use
the unbiased version of MMD (see [Gretton et al. 2012](https://www.jmlr.org/papers/volume13/gretton12a/gretton12a.pdf) Lemma 6).
**Seeds** The seed for the training phase, and for generating the data when
`--data` is not '', is fixed with the flag `--seed` (int, default value is 0).
If one wishes to use multipe starts when training (X seeds and selection
of the best one based on the value of the loss), it is possible to also define
the number of seeds to try with: `--ns` (int, default value is 1).
**Model hyperparameters** Several hyperparameters ought to be fixed in advance:
- `--d`: int (default 5), dimension of the latent space.
- `--init`: str (default 'uniform,0.,0.1'), initialisation for the learned
parameters. It can be sampled from a 'uniform', 'normal', 'xavier_uniform' or
'xavier_normal' distributions. The parameters of the initialisation functions
are passed to the same flag separated by a coma. See [initializers.py](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/initializers.py) and [train.py](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/train.py#L138).
- `--l1`: float (default 1e-4), hyperparameter in front of both penalty terms. Note that
the penalty terms are automatically scaled by 1/sqrt(p).
- `--l2`: float (default 1e-4), hyperparameter in front of both distortion terms. Note that
the distortion terms are automatically scaled by 1/(n*sqrt(p)).
- `--lr`: float (default 1e-5), learning rate.
- `--s`: float (default 1.0), scale parameter of the RBF kernel in MMD.
**Training and evaluation** Several flags structure the training loop:
- `--e`: int (default 5001), number of epochs for the training process.
- `--ne`: int (default 100), regular interval at which the evaluation (call to
[metrics.Evaluation](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/metrics.py))
is done, every 'ne' epochs. 0 means that the results are never evaluated.
- `--nr`: int (default 100), regular interval at which the components
of the loss are recorded, every 'ne' epochs. 0 means that they are never
recorded. The loss is recorded every epoch nonetheless.
- `--pca`: int (default 100), regular interval at which PCA is performed on the
embeddings, every 'pca' epochs. 0 means that PCA is not used on the output.
- `--short_eval`: bool (default True), whether or not to compute all the metrics
(False) or only a set of them (True) (see [metrics.py](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/metrics.py)).
- `--nn`: int (default 5), number of neighbours taken into account in
the computation of the Label Transfer Accuracy metrics.
**Stopping criterion** Two flags enable to control the [stopping criterion](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/train.py#L293):
- `--ws`: int (default 0), window size on which the loss is averaged for the stopping criterion. If set to 0, the algorithm stops at the last epoch without loss-based stopping criterion.
- `--threshold`: float (default 1e-3), threshold for the stopping criterion.
**Timing** Timing the method is possible when the flag `--time` is set to True
(default False).
## Examples
We show now a few examples of usage of the command line to run the algorithm. We
also introduce two notebooks that display the usage of the algorithm and its
runtime.
1. To run the algorithm on simulated data from [data_pipeline.py](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/data/data_pipeline.py),
a minimal set of commands is:
```bash
python3 -m lsmmdma.main --output_dir outdir \
--data branch --n 300 --p 400 \
--e 1001 --d 5 --keops False --m dual
```
2. To run the algorithm on simulated data from [data_pipeline.py](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/data/data_pipeline.py), one can
also choose when to record the intermediate results, to set the hyperparameters
and to allow for 5 multiple starts:
```bash
python3 -m lsmmdma.main --output_dir outdir \
--data branch --n 300 --p 400 \
--seed 4 --ns 5 \
--keops False --m dual \
--e 1001 --nr 100 --ne 100 --pca 100 \
--d 5 --lr 1e-5 --l1 1e-4 --l2 1e-4 --s 1.0 --init 'uniform,0,0.1'
```
3. To run the algorithm on user input data, in the form n_sample x p_feature.
`--data` should be '' (default) and `--kernel` should be False (default). The
argument `--keops` can be True or False, `--m` can be 'dual' or 'primal'.
```bash
python3 -m lsmmdma.main --input_dir datadir --output_dir outdir \
--input_fv my_data_1 --input_sv my_data_2 --kernel False \
--seed 4 --ns 5 \
--keops True --m dual \
--e 1001 --nr 100 --ne 100 --pca 100 \
--d 5 --lr 1e-5 --l1 1e-4 --l2 1e-4 --s 1.0 --init 'uniform,0,0.1'
```
4. To run the algorithm on user kernel data, in the form n_sample x n_sample.
`--data` should be '' (default) and `--kernel` should be True. The
argument `--keops` can be True or False, `--m` can only be 'dual'.
```bash
python3 -m lsmmdma.main --input_dir datadir --output_dir outdir \
--input_fv my_data_1 --input_sv my_data_2 --kernel True \
--seed 4 --ns 5 \
--keops True --m dual \
--e 1001 --nr 100 --ne 100 --pca 100 \
--d 5 --lr 1e-5 --l1 1e-4 --l2 1e-4 --s 1.0 --init 'uniform,0,0.1'
```
A [tutorial](https://github.com/google-research/large_scale_mmdma/blob/master/examples/tutorial101.ipynb) and a [benchmark](https://github.com/google-research/large_scale_mmdma/blob/master/examples/benchmark.ipynb) notebooks are also available in [examples/](https://github.com/google-research/large_scale_mmdma/tree/master/examples).
## Input<a id="input"></a>
In case the user is providing the input data, supported formats are AnnData
objects (`.h5ad`), numpy arrays (`.npy`), tab-separated arrays (`.tsv`),
coma-separated arrays (`.csv`) and white-space separated arrays.
## Output<a id="output"></a>
When one uses [main.py](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/main.py),
several files are saved in the output directory
`FLAGS.output_dir`:
- `[key:val].tsv`: results of the model at the last epoch.
- `[key:val]_tracking.json`: loss and its components during training,
evaluation metrics during training, seed, number of epochs.
- `[key:val]_model.json`: model and optimiser state dictionaries,
loss, number of epochs, seed.
- `[key:val]_pca_X.npy`: 2D representation obtained with PCA on the
embeddings during training (for the first and second views).
- `[key:val]_embeddings_X.npy`: embeddings during training (for the first and
second views).
- `generated_data_X`: first view, second view and `rd_vec`
generated by [data_pipeline.generate_data](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/data/data_pipeline.py#L13).
`[key:val]` represents a list of key:value as determined by the flags and
written in [main.py](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/main.py#L144).
## Citation
If you have found our work useful, please consider citing us:
```
@article{meng2022lsmmd,
title={LSMMD-MA: Scaling multimodal data integration for single-cell genomics data analysis},
author={Meng-Papaxanthos, Laetitia and Zhang, Ran and Li, Gang and Cuturi, Marco and Noble, William Stafford and Vert, Jean-Philippe},
journal={bioRxiv},
year={2022},
publisher={Cold Spring Harbor Laboratory}
}
```
## Contact
In case you have questions, reach out to `lpapaxanthos@google.com`.
## Disclaimer
This is not an officially supported Google product.
Raw data
{
"_id": null,
"home_page": "https://github.com/google-research/large_scale_mmdma",
"name": "lsmmdma",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "kernel,data integration",
"author": "Google LLC",
"author_email": "no-reply@google.com",
"download_url": "https://files.pythonhosted.org/packages/b5/67/c271a619ec17e8e3c1454e49cbf8b8c09c874c6119189b7597f1199311bd/lsmmdma-0.1.11.tar.gz",
"platform": null,
"description": "# Large-Scale MMD-MA\n\n[**Installation**](#installation)\n| [**Command line instructions**](#commandline)\n| [**Examples**](#examples)\n| [**Input**](#input)\n| [**Output**](#output)\n| [**Citation**](#citation)\n| [**Contact**](#contact)\n\nThe objective of [MMD-MA](https://pubmed.ncbi.nlm.nih.gov/34632462/) is to\nmatch points coming from two different spaces in a lower dimensional space. To\nthis end, two sets of points are projected, from two different spaces endowed\nwith a positive definite kernel, to a shared Euclidean space of lower dimension\n`low_dim`. The mappings from high to low dimensional space are\nobtained using functions belonging to the respective RKHS. To obtain the\nmappings, we minimise a [loss function](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/train.py#L173) that is composed of three terms:\n- an MMD term between the low dimensional representations of the two views,\nwhich encourages them to have the same distribution. The RBF kernel is used\nto compute MMD.\n- two non-collapsing penalty terms (corresponding to the `pen_dual` or\n`pen_primal` functions), one for each view. These terms ensure that\nthe low dimensional representations are mutually orthogonal, preventing\ncollapsing.\n- two distortion penalties (corresponding to the `dis_dual` or\n`dis_primal` functions), one for each view. These terms encourage the\nlow dimensional representation to obtain the same pairwise structure as\nthe original views.\n\nMMD-MA can be formulated using either the primal (when we use the linear\nkernel in the input spaces) or the dual problem. Each has\nadvantages or disadvantages depending on the input data. For each view,\nwhen the number of features `p` is larger than the number of samples `n`\n`p >> n`, then the dual formulation is beneficial in terms\nof runtime and memory, while if `n >> p`, the primal\nformulation is favorable.\n\nAdditionally, in order to scale the computation of MMD to a large number of\nsamples, we propose either to use the\n[KeOps](https://www.kernel-operations.io/keops/index.html) library which\nlets you compute large kernel operations on GPUs without memory overflow.\n\n## Installation<a id=\"installation\"></a>\n\nTo install the latest release of lsmmdma, use the following command:\n\n```bash\n$ pip install lsmmdma\n```\n\nTo install the **development** version, use the following command instead:\n\n```bash\n$ pip install git+https://github.com/google-research/large-scale-mmdma\n```\n\nAlternatively, it can be installed from sources with the following command:\n\n```bash\n$ python setup.py install\n```\n\nIn Google Colab, use the following command:\n```bash\n$ !pip install lsmmdma\n```\n\nThe KeOps library might require to be installed separately in advance, according\nto the given [instructions](http://www.kernel-operations.io/keops/python/installation.html). Typically, in Google Colab one would run this command before installing `lsmmmdma`:\n```bash\n$ !pip install pykeops\n```\n\n## Command line instructions<a id=\"commandline\"></a>\n\nThe algorithm can be run with a command line using:\n\n```bash\npython3 -m lsmmmda.main [flags]\n```\n\nA set of flags is available to determine the IO, the model, the hyperparameters\nand the seed.\n\n\n**Input/Output** It is possible to give as input either a path and filenames pointing to the\nuser input or to choose to generate data with the [data_pipeline.generate_data](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/data/data_pipeline.py#L13) function. In the\ncase one wants to generate simulation data, the input flags are:\n- `--data`: str, it can be either 'branch', 'triangle' or '' (default). The\nsimulated data is described in the pydoc of [data_pipeline.generate_data](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/data/data_pipeline.py#L13). ''\nmeans that simulated data is not used.\n- `--n`: int (default 300), number of samples for both views.\n- `--p`: int (default 1000), number of features for both views.\n\nFor data given by the user, the input flags are:\n- `--input_dir`: str (default None), input directory.\n- `--input_fv`: str (default None), filename of the array (n1 x p1 or n1 x n1)\nthat serves as first set of points.\n- `--input_sv`: str (default None), filename of the array (n2 x p2 or n2 x n2)\nthat serves as second set of points.\n- `--rd_vec`: str (default None), filename of the vector that contains the indices\nof the samples from the first view that match (ground truth) the samples from\nthe second view. This is only used at evaluation time. If `--rd_vec` is not\nused, we assume that the samples of both views follow the same ordering.\n- `--labels_fv`: str (default None), filename of the vector that contains the\nlabels of the samples from the first view. Must match the order of the samples\nin `input_fv`.\n- `--labels_sv`: str (default None), filename of the vector that contains the\nlabels of the samples from the second view, following the same ordering.\n\nIn both cases, two other flags are also available:\n- `--kernel`: bool (default False), whether the input data given by the user is\na kernel (n x n instead of n x p). This parameter can only be set to True when\n`--m` is 'dual'.\n- `--output_dir`: str (default None), output directory.\n\n**Model** The flags allow you to choose between four algorithms, using either\nthe 'primal' or 'dual' formulation, and using KeOps or not.\n- `--m`: str, either 'primal' or 'dual' (default).\n- `--keops`: integer, either 1 (use keops), 0 (not not use keops) or -1 (automatic, uses keops\nfrom 4000 samples onwards) (default).\n- `--use_unbiased_mmd`: bool (default True), determines whether or not to use\nthe unbiased version of MMD (see [Gretton et al. 2012](https://www.jmlr.org/papers/volume13/gretton12a/gretton12a.pdf) Lemma 6).\n\n**Seeds** The seed for the training phase, and for generating the data when\n`--data` is not '', is fixed with the flag `--seed` (int, default value is 0).\nIf one wishes to use multipe starts when training (X seeds and selection\nof the best one based on the value of the loss), it is possible to also define\nthe number of seeds to try with: `--ns` (int, default value is 1).\n\n**Model hyperparameters** Several hyperparameters ought to be fixed in advance:\n- `--d`: int (default 5), dimension of the latent space.\n- `--init`: str (default 'uniform,0.,0.1'), initialisation for the learned\nparameters. It can be sampled from a 'uniform', 'normal', 'xavier_uniform' or\n'xavier_normal' distributions. The parameters of the initialisation functions\nare passed to the same flag separated by a coma. See [initializers.py](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/initializers.py) and [train.py](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/train.py#L138).\n- `--l1`: float (default 1e-4), hyperparameter in front of both penalty terms. Note that\n the penalty terms are automatically scaled by 1/sqrt(p).\n- `--l2`: float (default 1e-4), hyperparameter in front of both distortion terms. Note that\n the distortion terms are automatically scaled by 1/(n*sqrt(p)).\n- `--lr`: float (default 1e-5), learning rate.\n- `--s`: float (default 1.0), scale parameter of the RBF kernel in MMD.\n\n**Training and evaluation** Several flags structure the training loop:\n- `--e`: int (default 5001), number of epochs for the training process.\n- `--ne`: int (default 100), regular interval at which the evaluation (call to\n[metrics.Evaluation](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/metrics.py))\nis done, every 'ne' epochs. 0 means that the results are never evaluated.\n- `--nr`: int (default 100), regular interval at which the components\nof the loss are recorded, every 'ne' epochs. 0 means that they are never\nrecorded. The loss is recorded every epoch nonetheless.\n- `--pca`: int (default 100), regular interval at which PCA is performed on the\nembeddings, every 'pca' epochs. 0 means that PCA is not used on the output.\n- `--short_eval`: bool (default True), whether or not to compute all the metrics\n(False) or only a set of them (True) (see [metrics.py](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/metrics.py)).\n- `--nn`: int (default 5), number of neighbours taken into account in\nthe computation of the Label Transfer Accuracy metrics.\n\n**Stopping criterion** Two flags enable to control the [stopping criterion](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/train.py#L293):\n- `--ws`: int (default 0), window size on which the loss is averaged for the stopping criterion. If set to 0, the algorithm stops at the last epoch without loss-based stopping criterion.\n- `--threshold`: float (default 1e-3), threshold for the stopping criterion.\n\n**Timing** Timing the method is possible when the flag `--time` is set to True\n(default False).\n\n\n## Examples\nWe show now a few examples of usage of the command line to run the algorithm. We\nalso introduce two notebooks that display the usage of the algorithm and its\nruntime.\n\n1. To run the algorithm on simulated data from [data_pipeline.py](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/data/data_pipeline.py),\na minimal set of commands is:\n\n```bash\npython3 -m lsmmdma.main --output_dir outdir \\\n--data branch --n 300 --p 400 \\\n--e 1001 --d 5 --keops False --m dual\n```\n\n2. To run the algorithm on simulated data from [data_pipeline.py](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/data/data_pipeline.py), one can\nalso choose when to record the intermediate results, to set the hyperparameters\nand to allow for 5 multiple starts:\n\n```bash\npython3 -m lsmmdma.main --output_dir outdir \\\n--data branch --n 300 --p 400 \\\n--seed 4 --ns 5 \\\n--keops False --m dual \\\n--e 1001 --nr 100 --ne 100 --pca 100 \\\n--d 5 --lr 1e-5 --l1 1e-4 --l2 1e-4 --s 1.0 --init 'uniform,0,0.1'\n```\n\n3. To run the algorithm on user input data, in the form n_sample x p_feature.\n`--data` should be '' (default) and `--kernel` should be False (default). The\nargument `--keops` can be True or False, `--m` can be 'dual' or 'primal'.\n\n```bash\npython3 -m lsmmdma.main --input_dir datadir --output_dir outdir \\\n--input_fv my_data_1 --input_sv my_data_2 --kernel False \\\n--seed 4 --ns 5 \\\n--keops True --m dual \\\n--e 1001 --nr 100 --ne 100 --pca 100 \\\n--d 5 --lr 1e-5 --l1 1e-4 --l2 1e-4 --s 1.0 --init 'uniform,0,0.1'\n```\n\n4. To run the algorithm on user kernel data, in the form n_sample x n_sample.\n`--data` should be '' (default) and `--kernel` should be True. The\nargument `--keops` can be True or False, `--m` can only be 'dual'.\n\n```bash\npython3 -m lsmmdma.main --input_dir datadir --output_dir outdir \\\n--input_fv my_data_1 --input_sv my_data_2 --kernel True \\\n--seed 4 --ns 5 \\\n--keops True --m dual \\\n--e 1001 --nr 100 --ne 100 --pca 100 \\\n--d 5 --lr 1e-5 --l1 1e-4 --l2 1e-4 --s 1.0 --init 'uniform,0,0.1'\n```\n\nA [tutorial](https://github.com/google-research/large_scale_mmdma/blob/master/examples/tutorial101.ipynb) and a [benchmark](https://github.com/google-research/large_scale_mmdma/blob/master/examples/benchmark.ipynb) notebooks are also available in [examples/](https://github.com/google-research/large_scale_mmdma/tree/master/examples).\n\n## Input<a id=\"input\"></a>\n\nIn case the user is providing the input data, supported formats are AnnData\nobjects (`.h5ad`), numpy arrays (`.npy`), tab-separated arrays (`.tsv`),\ncoma-separated arrays (`.csv`) and white-space separated arrays.\n\n## Output<a id=\"output\"></a>\n\nWhen one uses [main.py](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/main.py),\nseveral files are saved in the output directory\n`FLAGS.output_dir`:\n- `[key:val].tsv`: results of the model at the last epoch.\n- `[key:val]_tracking.json`: loss and its components during training,\nevaluation metrics during training, seed, number of epochs.\n- `[key:val]_model.json`: model and optimiser state dictionaries,\nloss, number of epochs, seed.\n- `[key:val]_pca_X.npy`: 2D representation obtained with PCA on the\nembeddings during training (for the first and second views).\n- `[key:val]_embeddings_X.npy`: embeddings during training (for the first and\nsecond views).\n- `generated_data_X`: first view, second view and `rd_vec`\ngenerated by [data_pipeline.generate_data](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/data/data_pipeline.py#L13).\n\n`[key:val]` represents a list of key:value as determined by the flags and\nwritten in [main.py](https://github.com/google-research/large_scale_mmdma/blob/master/lsmmdma/main.py#L144).\n\n## Citation\n\nIf you have found our work useful, please consider citing us:\n\n```\n@article{meng2022lsmmd,\n title={LSMMD-MA: Scaling multimodal data integration for single-cell genomics data analysis},\n author={Meng-Papaxanthos, Laetitia and Zhang, Ran and Li, Gang and Cuturi, Marco and Noble, William Stafford and Vert, Jean-Philippe},\n journal={bioRxiv},\n year={2022},\n publisher={Cold Spring Harbor Laboratory}\n}\n```\n\n## Contact\n\nIn case you have questions, reach out to `lpapaxanthos@google.com`.\n\n\n## Disclaimer\n\nThis is not an officially supported Google product.\n",
"bugtrack_url": null,
"license": "Apache 2.0",
"summary": "Scaling MMD-MA.",
"version": "0.1.11",
"project_urls": {
"Homepage": "https://github.com/google-research/large_scale_mmdma"
},
"split_keywords": [
"kernel",
"data integration"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "6e84f95030a1d8c2360a0ce6351c89902adea4a03be220a7bd2bb08f919c01b9",
"md5": "f2b5690c3b56ad5d4cbbb1bafad39b70",
"sha256": "76d6258b0ba733f4ded750ae21e92baff357e3bcffe8bf3f0382de03f54b0ba9"
},
"downloads": -1,
"filename": "lsmmdma-0.1.11-py3-none-any.whl",
"has_sig": false,
"md5_digest": "f2b5690c3b56ad5d4cbbb1bafad39b70",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 32085,
"upload_time": "2023-06-07T10:49:22",
"upload_time_iso_8601": "2023-06-07T10:49:22.885240Z",
"url": "https://files.pythonhosted.org/packages/6e/84/f95030a1d8c2360a0ce6351c89902adea4a03be220a7bd2bb08f919c01b9/lsmmdma-0.1.11-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "b567c271a619ec17e8e3c1454e49cbf8b8c09c874c6119189b7597f1199311bd",
"md5": "09bfdf89341b1eb62347149f8d914b31",
"sha256": "8dd50f256a63cadba6a80e3bd35c0adbe21a0bf07b1b65ed03cdc7a827bebca0"
},
"downloads": -1,
"filename": "lsmmdma-0.1.11.tar.gz",
"has_sig": false,
"md5_digest": "09bfdf89341b1eb62347149f8d914b31",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 29640,
"upload_time": "2023-06-07T10:49:24",
"upload_time_iso_8601": "2023-06-07T10:49:24.796719Z",
"url": "https://files.pythonhosted.org/packages/b5/67/c271a619ec17e8e3c1454e49cbf8b8c09c874c6119189b7597f1199311bd/lsmmdma-0.1.11.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-06-07 10:49:24",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "google-research",
"github_project": "large_scale_mmdma",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [],
"lcname": "lsmmdma"
}
JSON
