pca 0.1.7

pca is a python package that performs the principal component analysis and to make insightful plots.

pca

• pca is a python package that performs the principal component analysis and creates insightful plots.
• Biplot to plot the loadings
• Explained variance
• Scatter plot with the loadings

Method overview

# fit
model=pca.fit(X)
# biplot
ax=pca.biplot(model)
ax=pca.biplot3d(model)
# plot explained variance
ax = pca.plot(model)
# Normalize out components from your dataset
Xnorm=pca.norm(X)

Contents

• Installation
• Requirements
• Quick Start
• Contribute
• Citation
• Maintainers
• License

Installation

• Install pca from PyPI (recommended). pca is compatible with Python 3.6+ and runs on Linux, MacOS X and Windows.
• It is distributed under the MIT license.

Requirements

• Creation of a new environment is not necessarily.

conda create -n env_pca python=3.6
conda activate env_pca
pip install numpy matplotlib sklearn

Quick Start

pip install pca

• Alternatively, install pca from the GitHub source:

git clone https://github.com/erdogant/pca.git
cd pca
python setup.py install

Import pca package

import pca as pca

Load example data

import numpy as np
from sklearn.datasets import load_iris
X = load_iris().data
label=iris.feature_names
labx=iris.target

X looks like this:

X=array([[5.1, 3.5, 1.4, 0.2],
         [4.9, 3. , 1.4, 0.2],
         [4.7, 3.2, 1.3, 0.2],
         [4.6, 3.1, 1.5, 0.2],
         ...
         [5. , 3.6, 1.4, 0.2],
         [5.4, 3.9, 1.7, 0.4],
         [4.6, 3.4, 1.4, 0.3],
         [5. , 3.4, 1.5, 0.2],

labx=[0, 0, 0, 0,...,2, 2, 2, 2, 2]
label=['label1','label2','label3','label4']

PCA reduce dimensions and plot explained variance

# Fit
model = pca.fit(X)
# Plot the explained variance. The total of captured variance is 1 and PC1 captures more then 90% of it.
ax = pca.plot(model)
# Biplot in 2D with shows the directions of features and weights of influence
ax  = pca.biplot(model)
# Biplot in 3D
ax  = pca.biplot3d(model)

Reduce dimensions as above but now plot with labx and label names

model = pca.fit(X, labx=labx, feat=feat)
ax  = pca.biplot(model)
ax  = pca.biplot3d(model)

Reduce dimensions to the number of components that capture 95% of the explained variance

# Fit model and determine the number of required components that captures 95% of the explained variance.
model = pca.fit(X, n_components=0.95)
# Plot the explained variance. The required number of components is 2 to capture 95% of the variance.
ax = pca.plot(model)

Reduce dimensions to exactly 2d and 3d

# Set components=2 to reduce to 2d
model = pca.fit(X, n_components=2)
# Set components=3 to reduce to 3d
model = pca.fit(X, n_components=3)

PCA normalization.

# Normalizing out the 1st and more components from the data. 
# This is usefull if the data is seperated in its first component(s) by unwanted or biased variance. Such as sex or experiment location etc. 

print(X.shape)
(150, 4)

# Normalize out 1st component and return data
Xnorm = pca.norm(X, pcexclude=[1])

print(Xnorm.shape)
(150, 4)

# In this case, PC1 is "removed" and the PC2 has become PC1 etc
ax = pca.biplot(model)

Citation

Please cite pca in your publications if this is useful for your research. Here is an example BibTeX entry:

@misc{erdogant2019pca,
  title={pca},
  author={Erdogan Taskesen},
  year={2019},
  howpublished={\url{https://github.com/erdogant/pca}},
}

Maintainers

• Erdogan Taskesen, github: erdogant

Contribute

• Contributions are welcome.

Licence

See LICENSE for details.

TODO

• Add feature importance in the output.

Donation

• This work is created and maintained in my free time. If you wish to buy me a Coffee for this work, it is very appreciated.