diff --git a/.buildinfo b/.buildinfo
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# Sphinx build info version 1
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: d628a5059c7c9908d767a69c7f5c30c2
+config: cfb055ff857797bb1dc79c85df5a5e35
tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/.ipynb_checkpoints/LICENSE-checkpoint b/.ipynb_checkpoints/LICENSE-checkpoint
deleted file mode 100644
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--- a/.ipynb_checkpoints/LICENSE-checkpoint
+++ /dev/null
@@ -1,29 +0,0 @@
-BSD 3-Clause License
-
-Copyright (c) 2007-2021 The scikit-learn developers.
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-
-* Redistributions of source code must retain the above copyright notice, this
- list of conditions and the following disclaimer.
-
-* Redistributions in binary form must reproduce the above copyright notice,
- this list of conditions and the following disclaimer in the documentation
- and/or other materials provided with the distribution.
-
-* Neither the name of the copyright holder nor the names of its
- contributors may be used to endorse or promote products derived from
- this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
-FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/.ipynb_checkpoints/README-checkpoint.md b/.ipynb_checkpoints/README-checkpoint.md
deleted file mode 100644
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@@ -1,78 +0,0 @@
-# `robustica`
-
-
-
-Fully customizable robust Independent Component Analysis (ICA).
-
-[](https://pypi.python.org/pypi/robustica)
-[](https://opensource.org/licenses/BSD-3-Clause)
-
-## Description
-This package contains 3 modules:
-- `RobustICA`
-
- Defines the most important class that allows to perform and customize robust independent component analysis.
-
-- `InferComponents`
-
- Retrieves the number of components that explain a user-defined percentage of variance.
-
-- `examples`
-
- Contains handy functions to quickly create or access example datasets.
-
-## Requirements
-In brackets, versions of packages used to revelop `robustica`.
-- `numpy` (1.19.2)
-- `pandas` (1.1.2)
-- `scipy` (1.6.2)
-- `scikit-learn` (0.23.2)
-- `scikit-learn-extra` (0.2.0)
-- `joblib` (1.0.1)
-- `tqdm` (4.59.0)
-
-## Installation
-### pip
-```shell
-pip install robustica
-```
-### local
-```shell
-git clone https://github.com/MiqG/robustica.git
-cd robustica
-pip install -e .
-```
-
-## Usage
-```python
-from robustica import RobustICA
-from robustica.examples import make_sampledata
-
-X = make_sampledata(ncol=300, nrow=2000, seed=123)
-
-rica = RobustICA(n_components=10)
-S, A = rica.fit_transform(X)
-```
-
-## Tutorials
-- [Basic pipeline for exploratory analysis](https://github.com/CRG-CNAG/robustica/blob/main/tutorials/basics.ipynb)
-- [Using a custom clustering class](https://github.com/CRG-CNAG/robustica/blob/main/tutorials/customize_clustering.ipynb)
-- [Inferring the number of components](https://github.com/CRG-CNAG/robustica/blob/main/tutorials/infer_components.ipynb)
-
-
-## Contact
-This project has been fully developed at the [Centre for Genomic Regulation](https://www.crg.eu/) within the group of [Design of Biological Systems](https://www.crg.eu/en/luis_serrano)
-
-Please, report any issues that you experience through this repository's ["Issues"](https://github.com/CRG-CNAG/robustica/issues) or email:
-- [Miquel Anglada-Girotto](mailto:miquel.anglada@crg.eu)
-- [Sarah A. Head](mailto:sarah.dibartolo@crg.eu)
-- [Luis Serrano](mailto:luis.serrano@crg.eu)
-
-## License
-
-`robustica` is distributed under a BSD 3-Clause License (see [LICENSE](https://github.com/CRG-CNAG/robustica/blob/main/LICENSE)).
-
-## References
-- *Himberg, J., & Hyvarinen, A.* "Icasso: software for investigating the reliability of ICA estimates by clustering and visualization". IEEE XIII Workshop on Neural Networks for Signal Processing (2003). DOI: https://doi.org/10.1109/NNSP.2003.1318025
-- *Sastry, Anand V., et al.* "The Escherichia coli transcriptome mostly consists of independently regulated modules." Nature communications 10.1 (2019): 1-14. DOI: https://doi.org/10.1038/s41467-019-13483-w
-- *Kairov, U., Cantini, L., Greco, A. et al.* Determining the optimal number of independent components for reproducible transcriptomic data analysis. BMC Genomics 18, 712 (2017). DOI: https://doi.org/10.1186/s12864-017-4112-9
diff --git a/.ipynb_checkpoints/env-checkpoint.yml b/.ipynb_checkpoints/env-checkpoint.yml
deleted file mode 100644
index c9a6003..0000000
--- a/.ipynb_checkpoints/env-checkpoint.yml
+++ /dev/null
@@ -1,13 +0,0 @@
-name: robustica
-channels:
- - bioconda
- - conda-forge
- - anaconda
-dependencies:
- - numpy
- - pandas
- - scipy
- - scikit-learn
- - sckikit-learn-extra
- - joblib
- - tqdm
\ No newline at end of file
diff --git a/.ipynb_checkpoints/examples-checkpoint.ipynb b/.ipynb_checkpoints/examples-checkpoint.ipynb
deleted file mode 100644
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--- a/.ipynb_checkpoints/examples-checkpoint.ipynb
+++ /dev/null
@@ -1,697 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {},
- "outputs": [],
- "source": [
- "from robustica import RobustICA, InferComponents, sampledata\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import seaborn as sns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.datasets import fetch_openml\n",
- "mice = fetch_openml(name='miceprotein', version=4)\n",
- "X = mice['data']\n",
- "X[np.isnan(X)] = 0"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [],
- "source": [
- "inference = InferComponents()\n",
- "inference.fit(X)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "*E. coli* dataset"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## with Sastry method"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n"
- ]
- }
- ],
- "source": [
- "rica = RobustICA(n_components=inference.inferred_components_,\n",
- " n_jobs=10,\n",
- " robust_method='sastry', \n",
- " robust_kws={'min_samples':5, 'n_jobs':10})\n",
- "rica.fit(X)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "data": {
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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.clustermap(rica.S, row_cluster=False, cmap='coolwarm', center=0, figsize=(5,8), robust=True)\n",
- "sns.clustermap(rica.A.T, row_cluster=False, cmap='coolwarm', center=0, figsize=(10,5), robust=True)\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## with DBSCAN"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n"
- ]
- }
- ],
- "source": [
- "rica = RobustICA(n_components=10,\n",
- " n_jobs=10,\n",
- " tol=1e-3,\n",
- " robust_method='DBSCAN',\n",
- " robust_kws={'min_samples':5, 'n_jobs':10})\n",
- "rica.fit(X)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "data": {
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- " -0.044537 \n",
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- " ... \n",
- " 1.464603 \n",
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- " 0.408077 \n",
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- " 1.198529 \n",
- " \n",
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- " ... \n",
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- " ... \n",
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- " \n",
- " \n",
- " b4688 \n",
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- " -0.695325 \n",
- " ... \n",
- " -0.885297 \n",
- " -0.462485 \n",
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- " -1.726577 \n",
- " -2.734490 \n",
- " -1.189069 \n",
- " \n",
- " \n",
- " b4693 \n",
- " -0.278909 \n",
- " 0.278909 \n",
- " 1.361362 \n",
- " 1.020310 \n",
- " 0.608108 \n",
- " 0.988541 \n",
- " 2.558416 \n",
- " 2.142724 \n",
- " 3.120867 \n",
- " 3.104887 \n",
- " ... \n",
- " -0.374963 \n",
- " 0.856574 \n",
- " -1.147824 \n",
- " -0.814089 \n",
- " 2.054471 \n",
- " 1.853620 \n",
- " 1.957717 \n",
- " 1.943582 \n",
- " 2.233115 \n",
- " 2.023755 \n",
- " \n",
- " \n",
- " b4696_1 \n",
- " 0.050526 \n",
- " -0.050526 \n",
- " 1.166436 \n",
- " 1.043373 \n",
- " -0.531441 \n",
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- " 0.914055 \n",
- " 0.731165 \n",
- " -0.127269 \n",
- " -0.164046 \n",
- " ... \n",
- " 0.261604 \n",
- " 0.278426 \n",
- " 0.201089 \n",
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- " 0.122287 \n",
- " 0.504402 \n",
- " 0.425213 \n",
- " 0.629383 \n",
- " 0.805945 \n",
- " \n",
- " \n",
- " b4696_2 \n",
- " -0.031653 \n",
- " 0.031653 \n",
- " 0.785573 \n",
- " 0.881353 \n",
- " -0.477271 \n",
- " -0.916095 \n",
- " 0.837603 \n",
- " 0.801393 \n",
- " -0.071710 \n",
- " -0.000540 \n",
- " ... \n",
- " -0.499371 \n",
- " 0.398783 \n",
- " 0.096609 \n",
- " -0.103446 \n",
- " -0.519098 \n",
- " 0.615363 \n",
- " 0.343959 \n",
- " 0.580288 \n",
- " 0.366905 \n",
- " 0.702608 \n",
- " \n",
- " \n",
- " b4705 \n",
- " 0.724324 \n",
- " -0.724324 \n",
- " -4.350151 \n",
- " -4.317498 \n",
- " -0.747489 \n",
- " -1.257045 \n",
- " -3.308337 \n",
- " -4.421970 \n",
- " -2.679693 \n",
- " -1.872713 \n",
- " ... \n",
- " -1.968530 \n",
- " -1.365300 \n",
- " -5.468290 \n",
- " -2.997169 \n",
- " -3.673367 \n",
- " -3.161608 \n",
- " -3.959910 \n",
- " -4.088644 \n",
- " -5.468290 \n",
- " -5.468290 \n",
- " \n",
- " \n",
- "
\n",
- "
3923 rows × 278 columns
\n",
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, ax = plt.subplots(2,4,figsize=(10,5))\n",
- "ax = ax.ravel()\n",
- "\n",
- "for n,i in zip(x.keys(),ax):\n",
- " g = sns.histplot(ax=i, x = x[n])\n",
- " g.set_title('n components: %s'%n)\n",
- " \n",
- "plt.tight_layout()\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 159,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, ax = plt.subplots(2,4,figsize=(10,5))\n",
- "ax = ax.ravel()\n",
- "\n",
- "for n,i in zip(x.keys(),ax):\n",
- " g = sns.scatterplot(ax=i, x = np.arange(len(x[n])), y = np.cumsum(x[n]))\n",
- " g.set_title('total components: %s'%n)\n",
- " g.axhline(0.9, linestyle='dashed', color='black')\n",
- " \n",
- "plt.tight_layout()\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "It seems like if there are very few components the cumulative explained variance ratio increases lineary and if there are too many every component has the same weight as we are creating a \"synonymous matrix\".\n",
- "\n",
- "The number of components necessary to explain 90% of the variance depends on the number of components that we choose at the beginning."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## are the top components similar to each other?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 160,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "x = pd.DataFrame({n:results[n]['S'][:,0] for n in results.keys()})\n",
- "\n",
- "sns.clustermap(np.abs(x), cmap='coolwarm', robust=True)\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 161,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "x = pd.DataFrame({n:results[n]['A'][:,0] for n in results.keys()})\n",
- "\n",
- "sns.clustermap(np.abs(x), cmap='coolwarm', robust=True)\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## When does the change in explained variance ratio happen?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 124,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n",
- "/home/miquel/miniconda3/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
- " warnings.warn('FastICA did not converge. Consider increasing '\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 15min 41s, sys: 45.4 s, total: 16min 26s\n",
- "Wall time: 1min 38s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "\n",
- "n_components = [270,271,272,273,274,275,276,277,(X.shape[1])]\n",
- "\n",
- "results = {}\n",
- "for n in n_components:\n",
- " # run ICA with all components\n",
- " ica = FastICA(n_components = n)\n",
- " S = ica.fit_transform(X)\n",
- " A = ica.mixing_\n",
- "\n",
- " # sort by decreasing explained variance\n",
- " explained_variance = np.array([calculate_explained_variance(X.values, S, A, k) for k in range(S.shape[1])])\n",
- " explained_variance_ratio = explained_variance / np.sum(explained_variance)\n",
- " idx = np.argsort(explained_variance_ratio)[::-1]\n",
- "\n",
- " explained_variance = explained_variance[idx]\n",
- " explained_variance_ratio = explained_variance_ratio[idx]\n",
- " S = S[:,idx]\n",
- " A = A[:,idx]\n",
- " \n",
- " result = {'S':S,'A':A,'explained_variance':explained_variance,'explained_variance_ratio':explained_variance_ratio}\n",
- " results[n] = result"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 125,
- "metadata": {},
- "outputs": [],
- "source": [
- "x = {n:results[n]['explained_variance_ratio'] for n in results.keys()}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 126,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, ax = plt.subplots(2,4,figsize=(10,5))\n",
- "ax = ax.ravel()\n",
- "\n",
- "for n,i in zip(x.keys(),ax):\n",
- " g = sns.histplot(ax=i, x = x[n])\n",
- " g.set_title('n components: %s'%n)\n",
- " \n",
- "plt.tight_layout()\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 127,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, ax = plt.subplots(2,4,figsize=(10,5))\n",
- "ax = ax.ravel()\n",
- "\n",
- "for n,i in zip(x.keys(),ax):\n",
- " g = sns.scatterplot(ax=i, x = np.arange(len(x[n])), y = np.cumsum(x[n]))\n",
- " g.set_title('total components: %s'%n)\n",
- " g.axhline(0.9, linestyle='dashed', color='black')\n",
- " \n",
- "plt.tight_layout()\n",
- "plt.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/.ipynb_checkpoints/setup-checkpoint.py b/.ipynb_checkpoints/setup-checkpoint.py
deleted file mode 100644
index 83db99e..0000000
--- a/.ipynb_checkpoints/setup-checkpoint.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from setuptools import setup
-from pathlib import Path
-
-this_directory = Path(__file__).parent
-long_description = (this_directory / "README.md").read_text()
-
-setup(
- name="robustica",
- version="0.1.2",
- packages=["robustica"],
- python_requires=">=3.8",
- package_data={"": ["LICENSE", "*.md","*.ipynb","*.yml"]},
- install_requires=[
- "numpy",
- "pandas",
- "scipy",
- "scikit-learn",
- "scikit-learn-extra",
- "joblib",
- "tqdm",
- ],
- author="Miquel Anglada Girotto",
- author_email="miquel.anglada@crg.eu",
- description="Fully cumstomizable robust Independent Components Analysis (ICA)",
- long_description=long_description,
- long_description_content_type='text/markdown',
- url="https://github.com/CRG-CNAG/robustica",
- project_urls={
- "Issues": "https://github.com/CRG-CNAG/robustica/issues",
- "Documentation": "https://crg-cnag.github.io/robustica/"
- },
-)
diff --git a/InferComponents.html b/InferComponents.html
index e1265c5..012f2f4 100644
--- a/InferComponents.html
+++ b/InferComponents.html
@@ -4,8 +4,8 @@
InferComponents - robustica 0.1.3 documentation
+ InferComponents - robustica 0.1.4 documentation
+
@@ -398,7 +374,6 @@ InferComponents
-
diff --git a/RobustICA.html b/RobustICA.html
index 081daeb..2cf2b65 100644
--- a/RobustICA.html
+++ b/RobustICA.html
@@ -4,8 +4,8 @@