Recent Releases of pca
pca - v2.0.0
Developing new functionalities is really cool. However, when making incremental improvements over time, the code complexity also gradually increases. I took the time to refactor the entire plotting part. When using this version, you likely need to rename some input parameters in your code. But it is worth it because the plots became even more beautiful!
- scattering is now performed in scatterd library
- Many input parameters for plotting are aligned to the scatter functionality of matplotlib.
- for plotting, some parameters such as textlabel are removed because were redundant.
- for plotting, the parameter y is renamed into labels
- it is now possible to add density and gradient into the plots and keeping the plot look nice
- Changing the ordering of the density layer is possible (on top or below)
- Fix for 3d plot and the positioning of the text labels
- High improvements in plotting speed when having many data points!
- updated documentation, docstrings and readme
- Jupyter Notebook
Published by erdogant over 2 years ago
pca - v1.9.0
- set default std=3 wich is more common for outlier detection
- Multiple test corrections for the hotelling t2 test
multipletestsis set in the predict function and not during initialization anymore.y_probais the corrected Pvalue.Prawis the uncorrected Pvalue in the output dataframe
- Jupyter Notebook
Published by erdogant almost 3 years ago
pca - v1.8.6
- Font color inherits the arrow color (default).
- Font colors can be adjusted in the plots.
- Sizes of the scatter can be adjusted with parameter
s. - Colors of the scatter ben be adjusted with parameter
c. figcan be given as an input parameter to make iterative changes to the plot.
Examples can be found here.
- Jupyter Notebook
Published by erdogant almost 3 years ago
pca - v1.7.0
- Density coloring implemented with the
gradientparameter.
In this example, the cmap=Set1will be used to color the class labels. The coloring will have a continuous scale towards the borders.
pca.scatter(cmap='Set1', gradient='#ffffff')
- Jupyter Notebook
Published by erdogant almost 4 years ago
pca - 1.5.1
- Detection of outliers is now optional. If you do not want to detect outliers use the following during initialiation: detectoutliers=None. The detection of outliers can now be set by: detectoutliers='ht2', 'spe'
- Jupyter Notebook
Published by erdogant over 4 years ago
pca - 1.5.0
Many improvements thanks to Daniel Scott!
- Added alpha_transparency option & tests
- Fixed syntax in Readme
- Fix default value in docstring
- Added alpha_transparency argument & tests for all relevant plots
- Added pytest github workflow
- Fix pytest yaml syntax
- Added miniconda pandas to github workflow so that test suite can run
- Added requirements to pytest
- Removed explicit reference to tests
- Removed macosx support from tests. Colormap not supported on osx"
- Removed explicit reference to tests
- Moved tests dir
- Initialized test directory
- Added wget to dependencies
- Improved commenting in test coverage
- pytest fix for slow CI on windows
- pytest fix for slow CI on windows (contd)
- Added numpy installation to conda
- pytest fix for slow CI builds
- Disable testing for windows (only linux)
- Jupyter Notebook
Published by erdogant over 4 years ago
pca -
- Sparse matrix support with TruncatedSVD
- Sparse data support with SparsePCA
- width and heigth changed in figsize()
- Code cleaning
- Jupyter Notebook
Published by erdogant almost 6 years ago
pca - minor updates
- fit outputs 'X' instead of 'pc'
- plot_explainedvar changed into plot
- Jupyter Notebook
Published by erdogant almost 6 years ago