import sys sys.path.append("..") from sklearn.datasets import load_wine from src.BaseSVDD import BaseSVDD, BananaDataset # Banana-shaped dataset generation and partitioning X, y = BananaDataset.generate(number=100, display='on') X_train, X_test, y_train, y_test = BananaDataset.split(X, y, ratio=0.3) #  svdd = BaseSVDD(C=0.9, gamma=0.3, kernel='rbf', display='on') #  svdd.fit(X_train, y_train) #  svdd.plot_boundary(X_train, y_train) # y_test_predict = svdd.predict(X_test, y_test) # radius = svdd.radius distance = svdd.get_distance(X_test) svdd.plot_distance(radius, distance)

import sys sys.path.append("..") from src.BaseSVDD import BaseSVDD, BananaDataset # Banana-shaped dataset generation and partitioning X, y = BananaDataset.generate(number=100, display='on') X_train, X_test, y_train, y_test = BananaDataset.split(X, y, ratio=0.3) # kernel list kernelList = {"1": BaseSVDD(C=0.9, kernel='rbf', gamma=0.3, display='on'), "2": BaseSVDD(C=0.9, kernel='poly',degree=2, display='on'), "3": BaseSVDD(C=0.9, kernel='linear', display='on') } #  for i in range(len(kernelList)): svdd = kernelList.get(str(i+1)) svdd.fit(X_train, y_train) svdd.plot_boundary(X_train, y_train)

import sys sys.path.append("..") import numpy as np from src.BaseSVDD import BaseSVDD from sklearn.decomposition import KernelPCA # create 100 points with 5 dimensions X = np.r_[np.random.randn(50, 5) + 1, np.random.randn(50, 5)] y = np.append(np.ones((50, 1), dtype=np.int64), -np.ones((50, 1), dtype=np.int64), axis=0) # number of the dimensionality kpca = KernelPCA(n_components=2, kernel="rbf", gamma=0.1, fit_inverse_transform=True) X_kpca = kpca.fit_transform(X) # fit the SVDD model svdd = BaseSVDD(C=0.9, gamma=10, kernel='rbf', display='on') # fit and predict svdd.fit(X_kpca, y) y_test_predict = svdd.predict(X_kpca, y) # plot the distance curve radius = svdd.radius distance = svdd.get_distance(X_kpca) svdd.plot_distance(radius, distance) # plot the boundary svdd.plot_boundary(X_kpca, y)

import sys sys.path.append("..") from src.BaseSVDD import BaseSVDD, BananaDataset from sko.PSO import PSO import matplotlib.pyplot as plt # Banana-shaped dataset generation and partitioning X, y = BananaDataset.generate(number=100, display='off') X_train, X_test, y_train, y_test = BananaDataset.split(X, y, ratio=0.3) # objective function def objective_func(x): x1, x2 = x svdd = BaseSVDD(C=x1, gamma=x2, kernel='rbf', display='off') y = 1-svdd.fit(X_train, y_train).accuracy return y # Do PSO pso = PSO(func=objective_func, n_dim=2, pop=10, max_iter=20, lb=[0.01, 0.01], ub=[1, 3], w=0.8, c1=0.5, c2=0.5) pso.run() print('best_x is', pso.gbest_x) print('best_y is', pso.gbest_y) # plot the result fig = plt.figure(figsize=(6, 4)) ax = fig.add_subplot(1, 1, 1) ax.plot(pso.gbest_y_hist) ax.yaxis.grid() plt.show()

import sys sys.path.append("..") from src.BaseSVDD import BaseSVDD, BananaDataset from sklearn.model_selection import cross_val_score # Banana-shaped dataset generation and partitioning X, y = BananaDataset.generate(number=100, display='on') X_train, X_test, y_train, y_test = BananaDataset.split(X, y, ratio=0.3) #  svdd = BaseSVDD(C=0.9, gamma=0.3, kernel='rbf', display='on') # cross validation (k-fold) k = 5 scores = cross_val_score(svdd, X_train, y_train, cv=k, scoring='accuracy') # print("Cross validation scores:") for scores_ in scores: print(scores_) print("Mean cross validation score: {:4f}".format(scores.mean()))

import sys sys.path.append("..") from sklearn.datasets import load_wine from src.BaseSVDD import BaseSVDD, BananaDataset from sklearn.model_selection import KFold, LeaveOneOut, ShuffleSplit from sklearn.model_selection import learning_curve, GridSearchCV # Banana-shaped dataset generation and partitioning X, y = BananaDataset.generate(number=100, display='off') X_train, X_test, y_train, y_test = BananaDataset.split(X, y, ratio=0.3) param_grid = [ {"kernel": ["rbf"], "gamma": [0.1, 0.2, 0.5], "C": [0.1, 0.5, 1]}, {"kernel": ["linear"], "C": [0.1, 0.5, 1]}, {"kernel": ["poly"], "C": [0.1, 0.5, 1], "degree": [2, 3, 4, 5]}, ] svdd = GridSearchCV(BaseSVDD(display='off'), param_grid, cv=5, scoring="accuracy") svdd.fit(X_train, y_train) print("best parameters:") print(svdd.best_params_) print("\n") #  best_model = svdd.best_estimator_ means = svdd.cv_results_["mean_test_score"] stds = svdd.cv_results_["std_test_score"] for mean, std, params in zip(means, stds, svdd.cv_results_["params"]): print("%0.3f (+/-%0.03f) for %r" % (mean, std * 2, params)) print()

best parameters: {'C': 0.5, 'gamma': 0.1, 'kernel': 'rbf'} 0.921 (+/-0.159) for {'C': 0.1, 'gamma': 0.1, 'kernel': 'rbf'} 0.893 (+/-0.192) for {'C': 0.1, 'gamma': 0.2, 'kernel': 'rbf'} 0.857 (+/-0.296) for {'C': 0.1, 'gamma': 0.5, 'kernel': 'rbf'} 0.950 (+/-0.086) for {'C': 0.5, 'gamma': 0.1, 'kernel': 'rbf'} 0.921 (+/-0.131) for {'C': 0.5, 'gamma': 0.2, 'kernel': 'rbf'} 0.864 (+/-0.273) for {'C': 0.5, 'gamma': 0.5, 'kernel': 'rbf'} 0.950 (+/-0.086) for {'C': 1, 'gamma': 0.1, 'kernel': 'rbf'} 0.921 (+/-0.131) for {'C': 1, 'gamma': 0.2, 'kernel': 'rbf'} 0.864 (+/-0.273) for {'C': 1, 'gamma': 0.5, 'kernel': 'rbf'} 0.807 (+/-0.246) for {'C': 0.1, 'kernel': 'linear'} 0.821 (+/-0.278) for {'C': 0.5, 'kernel': 'linear'} 0.793 (+/-0.273) for {'C': 1, 'kernel': 'linear'} 0.879 (+/-0.184) for {'C': 0.1, 'degree': 2, 'kernel': 'poly'} 0.836 (+/-0.305) for {'C': 0.1, 'degree': 3, 'kernel': 'poly'} 0.771 (+/-0.416) for {'C': 0.1, 'degree': 4, 'kernel': 'poly'} 0.757 (+/-0.448) for {'C': 0.1, 'degree': 5, 'kernel': 'poly'} 0.871 (+/-0.224) for {'C': 0.5, 'degree': 2, 'kernel': 'poly'} 0.814 (+/-0.311) for {'C': 0.5, 'degree': 3, 'kernel': 'poly'} 0.800 (+/-0.390) for {'C': 0.5, 'degree': 4, 'kernel': 'poly'} 0.764 (+/-0.432) for {'C': 0.5, 'degree': 5, 'kernel': 'poly'} 0.871 (+/-0.224) for {'C': 1, 'degree': 2, 'kernel': 'poly'} 0.850 (+/-0.294) for {'C': 1, 'degree': 3, 'kernel': 'poly'} 0.800 (+/-0.390) for {'C': 1, 'degree': 4, 'kernel': 'poly'} 0.771 (+/-0.416) for {'C': 1, 'degree': 5, 'kernel': 'poly'}