Question about food and best dragon update in DA algorithm. about pymetaheuristic HOT 10 CLOSED

I have refactored the DA code in version 1.5.4. Please check which one is better for you, 1.5.3 or 1.5.4

Working way better than before, it needed more iterations as you said, but it works. Also the code is cleaner (in my opinion) than before. Thanks for your time.

from pymetaheuristic.

Ok, I will have a look. Could you also provide the function you are trying to minimize and a sample of the dataset?

from pymetaheuristic.

I'm using this function, but I have made some changes inside the algorithms to record fitness values in order to plot them. But the logic behind every algorithm is untouched.

The fitness I am using:

def fitness(weights, data, alpha=0.5, classifier='knn', n_neighbors=5):
    reduction_count = np.sum(weights == 0)
    weights[weights < 0.1] = 0.0
    classification_rate = compute_accuracy(weights, data=data, classifier=classifier, n_neighbors=n_neighbors)
    reduction_rate = reduction_count / len(weights)

    # Calculate the error as a percentage
    classification_error = 1 - classification_rate['TrainError']
    reduction_error = 1 - reduction_rate

    # Compute fitness as a combination of classification and reduction errors
    fitness_train = alpha * classification_error + (1 - alpha) * reduction_error
    classification_error = 1 - classification_rate['ValError']
    fitness_val = alpha * classification_error + (1 - alpha) * reduction_error

    return {'TrainFitness': fitness_train, 'ValFitness': fitness_val}

Accuracy function:

def compute_accuracy(weights, data, classifier='knn', n_neighbors=5):
    sample = data['data']
    labels = data['labels']

    sample_weighted = np.multiply(sample, weights)
    x_train, x_test, y_train, y_test = train_test_split(sample_weighted, labels, test_size=0.2, random_state=42)

    if (classifier == 'knn'):
        classifier = KNeighborsClassifier(n_neighbors=n_neighbors, weights='distance')
    elif (classifier == 'svc'):
        classifier = SVC(kernel='rbf')
    else:
        print('No valid classifier, using KNN by default')
        classifier = KNeighborsClassifier(n_neighbors=n_neighbors, weights='distance')

    # Train the classifier
    classifier.fit(x_train, y_train)
    y_pred = classifier.predict(x_train)
    e_in = accuracy_score(y_train, y_pred)

    y_pred = classifier.predict(x_test)
    e_out = accuracy_score(y_test, y_pred)

    return {'TrainError': e_in, 'ValError': e_out}

Sample of Ionosphere:

1,0,0.99539,-0.05889,0.85243,0.02306,0.83398,-0.37708,1,0.03760,0.85243,-0.17755,0.59755,-0.44945,0.60536,-0.38223,0.84356,-0.38542,0.58212,-0.32192,0.56971,-0.29674,0.36946,-0.47357,0.56811,-0.51171,0.41078,-0.46168,0.21266,-0.34090,0.42267,-0.54487,0.18641,-0.45300,g
1,0,1,-0.18829,0.93035,-0.36156,-0.10868,-0.93597,1,-0.04549,0.50874,-0.67743,0.34432,-0.69707,-0.51685,-0.97515,0.05499,-0.62237,0.33109,-1,-0.13151,-0.45300,-0.18056,-0.35734,-0.20332,-0.26569,-0.20468,-0.18401,-0.19040,-0.11593,-0.16626,-0.06288,-0.13738,-0.02447,b
1,0,1,-0.03365,1,0.00485,1,-0.12062,0.88965,0.01198,0.73082,0.05346,0.85443,0.00827,0.54591,0.00299,0.83775,-0.13644,0.75535,-0.08540,0.70887,-0.27502,0.43385,-0.12062,0.57528,-0.40220,0.58984,-0.22145,0.43100,-0.17365,0.60436,-0.24180,0.56045,-0.38238,g

from pymetaheuristic.

In other algorithms like GAO of GWO it works just fine, but in DA something related with how food is generated is minimizing every feature to zero.

from pymetaheuristic.

Please try using version 1.5.1 of pyMetaheuristic. I believe the issue has been resolved in this update. Should you encounter any further problems, do not hesitate to inform me.

from pymetaheuristic.

Please try using version 1.5.1 of pyMetaheuristic. I believe the issue has been resolved in this update. Should you encounter any further problems, do not hesitate to inform me.

I have tested the new version and although the values are no longer minimised to zero, the food does not update and remains stuck at the same values iteration after iteration.

I have uploaded in my TFG-Wrapped-Based-Metaheuristics-Feature-Selection repository the changes I introduced in the algorithms and everything necessary to be able to run the datasets. However, I sincerely believe that I have not altered any of the logic of the algorithm, in fact, the rest of the algorithms that I have modified work very well. I have to say that it is an initial version for a university work and it is not yet parameterised or modularised in a proper way.

from pymetaheuristic.

I would also like to know where the food and predator update operators come from, I have read the original paper and it only mentions that F_i = F_p - P, being F_i food source and F_p food position vector. Thanks for your attention and sorry if for the for the inconvenience if something I asked was bad formulated or incorrect.

from pymetaheuristic.

Kindly review the updates in pyMetaheuristic version 1.5.3. In this version, 'food position' has been designated as the reference point for the optimal solution, while 'predator position' signifies the location of the enemy. These adjustments were specifically made to accelerate the algorithm's convergence rate. However, it's important to emphasize that the fundamental logic of the algorithm remains unchanged. Despite these enhancements, it's worth noting that the Dragonfly Algorithm (DA) inherently requires a considerable number of iterations to achieve a satisfactory solution.

from pymetaheuristic.

I have refactored the DA code in version 1.5.4. Please check which one is better for you, 1.5.3 or 1.5.4

from pymetaheuristic.

I have refactored the DA code in version 1.5.4. Please check which one is better for you, 1.5.3 or 1.5.4

I will have a look in the next few hours, thanks!

from pymetaheuristic.