# -*- coding: utf-8 -*- import unittest import numpy as np from sklearn.metrics.pairwise import rbf_kernel from multimodal.tests.datasets.get_dataset_path import get_dataset_path from multimodal.kernels.lpMKL import MKL from multimodal.datasets.data_sample import MultiModalArray import pickle from sklearn.exceptions import NotFittedError class MKLTest(unittest.TestCase): @classmethod def setUpClass(clf): input_x = get_dataset_path("input_x_dic.pkl") f = open(input_x, "rb") kernel_dict = pickle.load(f) f.close() test_input_x = get_dataset_path("test_kernel_input_x.pkl") f = open(test_input_x, "rb") test_kernel_dict = pickle.load(f) f.close() test_input_y = get_dataset_path("test_input_y.npy") input_y = get_dataset_path("input_y.npy") y = np.load(input_y) test_y = np.load(test_input_y) clf.y = y clf.kernel_dict = kernel_dict clf.test_kernel_dict = test_kernel_dict clf.test_y = test_y def testInitMKL(self): mkl = MKL(lmbda=3, m_param = 1.0, kernel = "precomputed", kernel_params = None, use_approx = True, precision = 1E-4, n_loops = 50) self.assertEqual(mkl.m_param, 1.0) self.assertEqual(mkl.lmbda, 3) self.assertEqual(mkl.n_loops, 50) self.assertEqual(mkl.precision, 1E-4) def testFitMKLDict(self): ####################################################### # task with dict and not precomputed ####################################################### mkl = MKL(lmbda=3, m_param = 1.0, kernel=['rbf'], kernel_params=[{'gamma':50}], use_approx = True, precision = 1E-4, n_loops = 50) mkl.fit(self.kernel_dict, y=self.y, views_ind=None) self.assertEqual(mkl.C.shape, (120,)) np.testing.assert_almost_equal(mkl.weights, np.array([0.70629782, 0.70791482]), 8) def testFitMKLDictNLoop(self): ####################################################### # task with dict and not precomputed ####################################################### mkl = MKL(lmbda=3, m_param = 0.3, kernel=['rbf'], kernel_params=[{'gamma':50}], use_approx = True, precision = 1E-4, n_loops = 50) views_ind = [120, 240] mkl.fit(self.kernel_dict, y=self.y, views_ind=None) self.assertEqual(mkl.C.shape, (120,)) np.testing.assert_almost_equal(mkl.weights, np.array([0.70629782, 0.70791482]), 8) def testFitMKLMetricPrecision(self): ####################################################### # task with Metric array ####################################################### # mvml = MVML.fit(self.kernel_dict, self.y) w_expected = np.array([[0.5], [0.5]]) x_metricl = MultiModalArray(self.kernel_dict) mkl2 = MKL(lmbda=3, m_param = 0.3, kernel=['rbf'], kernel_params=[{'gamma':50}], use_approx = True, precision = 1E0, n_loops = 50) with self.assertRaises(ValueError): mkl2.fit(x_metricl, y=self.y, views_ind=None) def testFitMKLMetricPrecision2(self): ####################################################### # task with Metric array ####################################################### # mvml = MVML.fit(self.kernel_dict, self.y) w_expected = np.array([[0.5], [0.5]]) x_metricl = MultiModalArray(self.kernel_dict) mkl2 = MKL(lmbda=3, m_param = 0.3, kernel="precomputed", use_approx = True, precision = 1E-9, n_loops = 600) mkl2.fit(x_metricl, y=self.y, views_ind=None) def testPredictMVML_witoutFit(self): mkl = MKL(lmbda=3, m_param = 0.3, kernel=['rbf'], kernel_params=[{'gamma':50}], use_approx = True, precision = 1E-9, n_loops = 50) with self.assertRaises(NotFittedError): mkl.predict(self.test_kernel_dict) def testPredictMVML_witoutFit(self): x_metric = MultiModalArray(self.kernel_dict) mkl = MKL(lmbda=3, m_param = 0.3, kernel=['rbf'], kernel_params=[{'gamma':50}], use_approx = True, precision = 1E-9, n_loops = 50) mkl.fit(x_metric, y=self.y, views_ind=None) pred =mkl.predict(self.test_kernel_dict) self.assertEqual(pred.shape, (80,))