Note
Click here to download the full example code
FRFS preprocessingΒΆ
Sample usage of FRFS preprocessing, demonstrated in combination with (strict) FRNN classification.
The figures contain the training instances within a section of the selected feature space. The training instances are coloured according to their true labels, while the feature space is coloured according to predictions on the basis of the training instances, making the decision boundaries visible.
Two subfigures are displayed: the first represents simple selection of the first two features, while the second represents selection of two features by FRFS.

Out:
/home/oliver/code/scikit-learn-contrib/fuzzy-rough-learn/examples/neighbours/plot_frfs_preprocessing.py:83: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
plt.show()
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import datasets
from frlearn.base import select_class
from frlearn.neighbours import FRFS, FRNN
from frlearn.utils.owa_operators import strict
# Import example data.
iris = datasets.load_iris()
X_orig = iris.data
y_orig = iris.target
# Define color maps.
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
# Initialise figure with wide aspect for two side-by-side subfigures.
plt.figure(figsize=(8, 4))
for i, use_frfs in enumerate([False, True]):
axes = plt.subplot(1, 2, i + 1)
if use_frfs:
# Create an instance of the FRFS preprocessor and process the data.
preprocessor = FRFS(n_features=2)
X, y = preprocessor.process(X_orig, y_orig)
else:
# Select first two features.
X = X_orig[:, :2]
y = y_orig
# Create an instance of the FRNN classifier and construct the model.
clf = FRNN(upper_weights=strict(), lower_weights=strict(), upper_k=1, lower_k=1)
model = clf.construct(X, y)
# Create a mesh of points in the attribute space.
step_size = .02
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, step_size), np.arange(y_min, y_max, step_size))
# Query mesh points to obtain class values and select highest valued class.
Z = model.query(np.c_[xx.ravel(), yy.ravel()])
Z = select_class(Z, labels=model.classes)
# Plot mesh.
Z = Z.reshape(xx.shape)
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
# Plot training instances.
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor='k', s=20)
# Set subplot aspect to standard aspect ratio.
axes.set_aspect(1.0 / axes.get_data_ratio() * .75)
# Set plot dimensions.
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
# Describe the subfigures.
plt.title('...two features selected by FRFS' if use_frfs else '...first two features')
plt.suptitle('FRNN applied to iris dataset with ...', fontsize=14)
plt.show()
Total running time of the script: ( 0 minutes 0.923 seconds)