Dimensionality reduction ( DR ) is a data transformation process which provides a low-dimensional ( attribute or variable ) representation of high dimensional data sets. This with the following purposes: noise reduction, storage space reduction, data visualization, efficient data processing and to concentrate the important information in fewer variables than the original set. A performance visual measure in DM is topology preservation. Quality curves RNX, proposed by Lee and Verleysen, evaluates performance generating a graphical representation of topology preservation. Nowadays there is a tool for topology conservation evaluation of DM algorithms, developed also by Lee and Verleysen (2009) but this tool is implemented only in Matlab. Here a problem arises because Matlab is limited and cannot be implemented in other technologies. here, we are going to implement, in python, a software evaluation module of curves RNX in order to be used in other technologies.
Use the package manager pip to install nxcurve.
pip install nxcurvefrom sklearn import manifold, datasets # datasets
from nxcurve import quality_curve
n_comp = 2 # number of components to be reduced
n_nei = 20 # nearest neighbors
nsamples = 2000 # number of points (samples)
# Creating manifold
X, color = datasets.make_swiss_roll(n_samples=nsamples)
# Performing dimensionality reduction
X_r, err = manifold.locally_linear_embedding(X, n_neighbors=n_nei, n_components=n_comp)
# Drawing RNX curve
quality_curve(X,X_r,n_nei,'r',True)
"""
input: X original data, X_r reduced data, n_neighbors, option, graph
output: _NX vector, area under the curve and plot if graph == True
Any character in the following list generates a new figure: (opt)
q: Q_NX(K)
b: N_NX(K)
r: R_NX(K)
"""- RNX curve and area under the curve
- QNX curve and area under the curve
- BNX curve and area under the curve
- Grahp for the coranking matrix
- LCMC from a coranking matrix (local continuity meta criterion)
- Error Handling
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.