==================================================
TLViz — Visualising and analysing component models
==================================================
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:alt: JOSS | 10.21105/joss.04754
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:target: https://github.com/psf/black
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TLViz is a Python package for visualising component-based decomposition models like PARAFAC and PCA.
Documentation
-------------
The documentation
is available on `the TensorLy website <https://tensorly.org/viz>`_ and includes
* A `primer on tensors <http://tensorly.org/viz/stable/about_tensors.html#>`_, `tensor factorisations <http://tensorly.org/viz/stable/about_tensors.html#what-are-tensor-factorisations>`_ and the `notation we use <http://tensorly.org/viz/stable/about_tensors.html#notation>`_
* `An example gallery <http://tensorly.org/viz/stable/auto_examples/index.html>`_
* `The API reference <http://tensorly.org/viz/stable/api.html>`_
Dependencies
------------
TLViz supports Python 3.8 or above (it may also work with Python 3.6 and 3.7, though that is not officially supported).
Installation requires matplotlib, numpy, pandas, scipy, statsmodels and xarray.
Installation
------------
To install the latest stable release of TLViz and its dependencies, run:
.. code:: raw
pip install tensorly-viz
There is also functionality to create improved QQ-plots with Pingoiun.
However, this is disabled by default due to the restrictive GPL lisence.
To enable this possibility, you must manually `install Pingoiun <https://pingouin-stats.org>`_.
To install the latest development version of TLViz, you can either clone
this repo or run
.. code:: raw
pip install git+https://github.com/marieroald/tlviz.git
Some extra dependencies are needed to run the examples, tests or build the documentation.
For more information about installing these dependencies, see the `installation guide <https://tensorly.org/viz/stable/installation.html>`_.
Example
-------
.. code:: python
import tlviz
import matplotlib.pyplot as plt
from tensorly.decomposition import parafac
def fit_parafac(dataset, num_components, num_inits):
model_candidates = [
parafac(dataset.data, num_components, init="random", random_state=i)
for i in range(num_inits)
]
model = tlviz.multimodel_evaluation.get_model_with_lowest_error(
model_candidates, dataset
)
return tlviz.postprocessing.postprocess(model, dataset)
data = tlviz.data.load_aminoacids()
cp_tensor = fit_parafac(data, 3, num_inits=3)
tlviz.visualisation.components_plot(cp_tensor)
plt.show()
.. code:: raw
Loading Aminoacids dataset from:
Bro, R, PARAFAC: Tutorial and applications, Chemometrics and Intelligent Laboratory Systems, 1997, 38, 149-171
.. image:: docs/figures/readme_example.svg
:width: 800
:alt: An example figure showing the component vectors of a three component PARAFAC model fitted to a fluoresence spectroscopy dataset.
This example uses TensorLy to fit five three-component PARAFAC models to the data. Then it uses TLViz to do the following steps:
#. Select the model that gave the lowest reconstruction error.
#. Normalise the component vectors, storing their magnitude in a separate weight-vector.
#. Permute the components in descending weight (i.e. signal strength) order.
#. Flip the components so they point in a logical direction compared to the data.
#. Convert the factor matrices into Pandas DataFrames with logical indices.
#. Plot the components using matplotlib.
All these steps are described in the `API documentation <https://tensorly.org/viz/stable/api.html>`_ with references to the literature.
Testing
-------
The test suite requires an additional set of dependencies. To install these, run
.. code:: raw
pip install tlviz[test]
or
.. code:: raw
pip install -e .[test]
inside your local copy of the TLViz repository.
The tests can be run by calling ``pytest`` with no additional arguments.
All doctests are ran by default and a coverage summary will be printed on the screen.
To generate a coverage report, run ``coverage html``.
Contributing
------------
Contributions are welcome to TLViz, see the `contribution guidelines <http://tensorly.org/viz/stable/contributing.html>`_.
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"author": "Marie Roald & Yngve Mardal Moe",
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Then it uses TLViz to do the following steps:\n\n#. Select the model that gave the lowest reconstruction error.\n#. Normalise the component vectors, storing their magnitude in a separate weight-vector.\n#. Permute the components in descending weight (i.e. signal strength) order.\n#. Flip the components so they point in a logical direction compared to the data.\n#. Convert the factor matrices into Pandas DataFrames with logical indices.\n#. Plot the components using matplotlib.\n\nAll these steps are described in the `API documentation <https://tensorly.org/viz/stable/api.html>`_ with references to the literature.\n\nTesting\n-------\n\nThe test suite requires an additional set of dependencies. 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