==================================================
TLViz — Visualising and analysing component models
==================================================
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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 <https://tlviz.readthedocs.io/en/latest/about_tensors.html#what-are-tensors-and-tensor-decompositions>`_, `tensor factorisations <https://tlviz.readthedocs.io/en/latest/about_tensors.html#what-are-tensor-factorisations>`_ and the `notation we use <https://tlviz.readthedocs.io/en/latest/about_tensors.html#notation>`_
* `An example gallery <https://tlviz.readthedocs.io/en/latest/auto_examples/index.html>`_
* `The API reference <https://tlviz.readthedocs.io/en/latest/api.html>`_
Dependencies
------------
TLViz supports Python 3.7 or above (it may also work with Python 3.6, 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 tlviz
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
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:
#. 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,
#. and plot the components using matplotlib.
All these steps are described in the `API documentation <https://tlviz.readthedocs.io/en/latest/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 <https://tlviz.readthedocs.io/en/latest/contributing.html>`_.
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