156 lines
5.6 KiB
Python
Executable File
156 lines
5.6 KiB
Python
Executable File
"""
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Article : A reduced universum twin support vector machine for class imbalance learning
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Link : https://sci-hub.tw/https://www.sciencedirect.com/science/article/abs/pii/S0031320319304510
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Author : Saeed Khosravi
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"""
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import numpy as np
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from cvxopt import solvers, matrix
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import math
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class RUTSVM:
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def __init__(self, X, y, C1, C2, CU, eps):
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self.X = X
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self.y = y
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self.C1 = C1
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self.C2 = C2
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self.CU = CU
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self.eps = eps
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def fit(self):
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self.w1, self.b1 = self.plane1(self.X, self.y, self.C1, self.CU, self.eps)
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self.w2, self.b2 = self.plane2(self.X, self.y, self.C2, self.CU, self.eps)
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def predict(self, x_test):
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norm2_w1 = np.linalg.norm(self.w1)
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norm2_w2 = np.linalg.norm(self.w2)
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distance_1 = np.abs(np.dot(x_test, self.w1) + self.b1)/norm2_w1
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distance_2 = np.abs(np.dot(x_test, self.w2) + self.b2)/norm2_w2
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y_pred = np.zeros_like(distance_1).reshape((-1, 1))
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for i in range(y_pred.shape[0]):
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if (distance_1[i] < distance_2[i]):
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y_pred[i][0] = 1;
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else:
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y_pred[i][0] = -1;
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self.preds = y_pred
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def plane1(self, X, y, c1, cu, eps):
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S, T, O, T_, O_, e1, e2, eg, ed = self.split_dataset(X, y)
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m1 = S.shape[0]
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m2 = T_.shape[0]
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mg = O_.shape[0]
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STS = np.dot(S.T, S)
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I = np.eye(STS.shape[0], STS.shape[1])
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STS_inv = np.linalg.inv(1e-4*I + STS)
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_P = np.dot(np.dot(T_, STS_inv), T_.T)
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_P = np.concatenate((_P, -np.dot(np.dot(T_, STS_inv), O_.T)), axis = 1)
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_P2 = -np.dot(-np.dot(O_, STS_inv), T_.T)
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_P2 = np.concatenate((_P2, np.dot(np.dot(O_, STS_inv), O_.T)), axis = 1)
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_P = np.concatenate((_P, _P2), axis = 0) # (m1 + mg , m1 + mg)
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_q = np.concatenate((-e1.T, (1-eps)*eg.T), axis = 1).T # (m1 + mg , 1)
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_G1 = np.concatenate(( np.eye(m1, m1), np.zeros((m1, mg))), axis = 1)
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_G2 = np.concatenate((-np.eye(m1, m1), np.zeros((m1, mg))), axis = 1)
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_G3 = np.concatenate(( np.zeros((mg, m1)), np.eye(mg, mg)), axis = 1)
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_G4 = np.concatenate((np.zeros((mg, m1)), -np.eye(mg, mg)), axis = 1)
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_G = np.concatenate((_G1, _G2), axis = 0)
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_G = np.concatenate((_G , _G3), axis = 0)
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_G = np.concatenate((_G, _G4), axis = 0) # (2m1 + 2mg , m1 + mg)
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_h = np.zeros((2*m1 + 2*mg, 1))
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_h[:m1, :] = c1
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_h[2*m1:2*m1+mg, :] = cu
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_P = matrix(_P, tc= 'd')
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_q = matrix(_q, tc = 'd')
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_G = matrix(_G, tc = 'd')
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_h = matrix(_h, tc = 'd')
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qp_sol = solvers.qp(_P, _q, _G, _h, kktsolver='ldl', options={'kktreg':1e-9, 'show_progress':False})
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qp_sol = np.array(qp_sol['x'])
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alphas = qp_sol[:m1, 0]
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mus = qp_sol[m1:, 0]
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vp = -np.dot(STS_inv, np.dot(T_.T, alphas) - np.dot(O_.T, mus))
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w = vp[:-1]
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b = vp[-1]
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return w, b
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def plane2(self, X, y, c2, cu, eps):
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S, T, O, T_, O_, e1, e2, eg, ed = self.split_dataset(X, y)
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m1 = S.shape[0]
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m2 = T.shape[0]
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md = O.shape[0]
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TTT = np.dot(T.T, T)
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I = np.eye(TTT.shape[0], TTT.shape[1])
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TTT_inv = np.linalg.inv(1e-4*I + TTT)
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_P = np.dot(np.dot(S, TTT_inv), S.T)
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_P = np.concatenate((_P, -np.dot(np.dot(S, TTT_inv), O.T)), axis = 1)
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_P2 = -np.dot(-np.dot(O, TTT_inv), S.T)
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_P2 = np.concatenate((_P2, np.dot(np.dot(O, TTT_inv), O.T)), axis = 1)
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_P = np.concatenate((_P, _P2), axis = 0) # (m1 + md , m1 + md)
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_q = np.concatenate((-e1.T, (eps - 1)*ed.T), axis = 1).T # (m1 + md , 1)
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_G1 = np.concatenate(( np.eye(m1, m1), np.zeros((m1, md))), axis = 1)
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_G2 = np.concatenate((-np.eye(m1, m1), np.zeros((m1, md))), axis = 1)
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_G3 = np.concatenate(( np.zeros((md, m1)), np.eye(md, md)), axis = 1)
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_G4 = np.concatenate((np.zeros((md, m1)), -np.eye(md, md)), axis = 1)
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_G = np.concatenate((_G1, _G2), axis = 0)
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_G = np.concatenate((_G , _G3), axis = 0)
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_G = np.concatenate((_G, _G4), axis = 0) # (2m1 + 2md , m1 + md)
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_h = np.zeros((2*m1 + 2*md, 1))
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_h[:m1, :] = c2
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_h[2*m1:2*m1+md, :] = cu
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_P = matrix(_P, tc= 'd')
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_q = matrix(_q, tc = 'd')
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_G = matrix(_G, tc = 'd')
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_h = matrix(_h, tc = 'd')
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qp_sol = solvers.qp(_P, _q, _G, _h, kktsolver='ldl', options={'kktreg':1e-9, 'show_progress':False})
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qp_sol = np.array(qp_sol['x'])
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alphas = qp_sol[:m1, 0]
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mus = qp_sol[m1:, 0]
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vn = -np.dot(TTT_inv, np.dot(S.T, alphas) - np.dot(O.T, mus))
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w = vn[:-1]
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b = vn[-1]
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return w, b
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def split_dataset(self, X, y):
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X1 = X[np.ix_(y[:,0] == 1),:][0,:,:]
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X2 = X[np.ix_(y[:,0] == -1),:][0,:,:]
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r, n = X1.shape
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s, n = X2.shape
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X2_ = X2[:r, :]
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U = X2[r: , :]
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d, n = U.shape
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g = math.ceil(r/2)
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tmp = np.random.choice(np.arange(1, d), g)
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U_ = U[tmp, :]
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e1 = np.ones((X1.shape[0], 1))
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e2 = np.ones((X2.shape[0], 1))
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eg = np.ones((U_.shape[0], 1))
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ed = np.ones((U.shape[0] , 1))
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S = np.concatenate((X1 , e1), axis = 1)
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T_ = np.concatenate((X2_, e1), axis = 1)
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T = np.concatenate((X2 , e2), axis = 1)
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O_ = np.concatenate((U_ , eg), axis = 1)
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O = np.concatenate((U , ed), axis = 1)
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return S, T, O, T_, O_, e1, e2, eg, ed
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def get_params(self):
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return self.w1, self.b1, self.w2, self.b2
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def get_preds(self):
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return self.preds |