Using the RNSA
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Importing the Real-Valued Negative Selection Algorithm
from aisp.nsa import RNSA
Generating dice bubbles for classes randomly
Using the make_blobs function, two sets of data are generated in the form of bubbles, in the range between 0 and 1, representing each class x and y. Then this data is separated into test and training sets.
from sklearn.datasets import make_blobs
from sklearn.model_selection import train_test_split
# Generating the samples and outputs for the training.
samples, output = make_blobs(n_samples=500 , n_features=2, cluster_std=0.07, center_box=([0.0, 1.0]), centers=[[0.25, 0.75], [0.75, 0.25]], random_state=1234)
# Separating data for training and testing.
train_x, test_x, train_y, test_y = train_test_split(samples, output, test_size=0.2)
Testing the model default-NSA
Start the model with 500 detectors, each with a radius of 0.06. Then, it presents the result of the forecast accuracy.
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score
# Starting the class.
model = RNSA(N=500, r=0.05, seed=1234)
# Carrying out the training:
model.fit(X=train_x, y=train_y)
# Previewing classes with test samples.
prev_y = model.predict(test_x)
# Showing the accuracy of predictions for actual data.
print(f"The accuracy is {accuracy_score(prev_y, test_y)}")
print(classification_report(test_y, prev_y))
Output:
✔ Non-self detectors for classes (0, 1) successfully generated: ┇██████████┇ 1000/1000 detectors
The accuracy is 1.0
precision recall f1-score support
0 1.00 1.00 1.00 55
1 1.00 1.00 1.00 45
accuracy 1.00 100
macro avg 1.00 1.00 1.00 100
weighted avg 1.00 1.00 1.00 100
Detector and sample plotting
Testing the model V-detector
Start the model with 50 detectors, where the minimum radius is 0.05 and the sample's own radius is 0.04. It then shows the forecast accuracy result.
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score
# Starting the class.
model = RNSA(N=20, r=0.02, algorithm='V-detector', r_s=0.04, seed=123)
# Carrying out the training:
model.fit(X=train_x, y=train_y)
# Previewing classes with test samples.
prev_y = model.predict(test_x)
# Showing the accuracy of predictions for actual data.
print(f"The accuracy is {accuracy_score(prev_y, test_y)}")
print(classification_report(test_y, prev_y))
Output:
✔ Non-self detectors for classes (0, 1) successfully generated: ┇██████████┇ 40/40 detectors
The accuracy is 1.0
precision recall f1-score support
0 1.00 1.00 1.00 55
1 1.00 1.00 1.00 45
accuracy 1.00 100
macro avg 1.00 1.00 1.00 100
weighted avg 1.00 1.00 1.00 100
V-Detector and sample plotting
