# -*- coding: utf-8 -*-
# ######### COPYRIGHT #########
#
# Copyright(c) 2020
# -----------------
#
# * Université d'Aix Marseille (AMU) -
# * Centre National de la Recherche Scientifique (CNRS) -
# * Université de Toulon (UTLN).
# * Copyright © 2019-2020 AMU, CNRS, UTLN
#
# Contributors:
# ------------
#
# * Sokol Koço <sokol.koco_AT_lis-lab.fr>
# * Cécile Capponi <cecile.capponi_AT_univ-amu.fr>
# * Florent Jaillet <florent.jaillet_AT_math.cnrs.fr>
# * Dominique Benielli <dominique.benielli_AT_univ-amu.fr>
# * Riikka Huusari <rikka.huusari_AT_univ-amu.fr>
# * Baptiste Bauvin <baptiste.bauvin_AT_univ-amu.fr>
# * Hachem Kadri <hachem.kadri_AT_lis-lab.fr>
#
# Description:
# -----------
#
# The multimodal package implement classifiers multiview,
# MumboClassifier class, MuComboClassifier class, MVML class, MKL class.
# compatible with sklearn
#
# Version:
# -------
#
# * multimodal version = 0.0.dev0
#
# Licence:
# -------
#
# License: New BSD License
#
#
# ######### COPYRIGHT #########
import pickle
import unittest
import numpy as np
from sklearn.exceptions import NotFittedError
from multimodal.datasets.data_sample import MultiModalArray
from multimodal.kernels.mvml import MVML
from multimodal.tests.datasets.get_dataset_path import get_dataset_path
from sklearn.utils.estimator_checks import check_estimator
class MVMLTest(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 testInitMVML(self):
mvml = MVML(lmbda=0.1, eta=1, nystrom_param=0.2)
self.assertEqual(mvml.nystrom_param, 0.2)
self.assertEqual(mvml.learn_A, 1)
self.assertEqual(mvml.learn_w, 0)
self.assertEqual(mvml.n_loops, 6)
self.assertEqual(mvml.lmbda, 0.1)
self.assertEqual(mvml.eta, 1)
def testFitMVMLDict(self):
#######################################################
# task with dict and not precomputed
#######################################################
mvml = MVML(lmbda=0.1, eta=1,
kernel=['rbf'], kernel_params=[{'gamma':50}],
nystrom_param=0.2)
views_ind = [120, 240]
mvml.fit(self.kernel_dict, y=self.y, views_ind=None)
self.assertEqual(mvml.A.shape, (48, 48))
self.assertEqual(mvml.g.shape,(48, 1))
w_expected = np.array([[0.5],[0.5]])
np.testing.assert_almost_equal(mvml.w, w_expected, 8)
def testFitMVMLPrecision(self):
#######################################################
# task with dict and not precomputed
#######################################################
mvml = MVML(lmbda=0.1, eta=1,
kernel=['rbf'], kernel_params=[{'gamma':50}],
nystrom_param=0.2, precision=1E-0)
views_ind = [120, 240]
mvml.fit(self.kernel_dict, y=self.y, views_ind=None)
self.assertEqual(mvml.A.shape, (48, 48))
self.assertEqual(mvml.g.shape,(48, 1))
w_expected = np.array([[0.5],[0.5]])
np.testing.assert_almost_equal(mvml.w, w_expected, 8)
def testFitMVMLDictNLoop(self):
#######################################################
# task with dict and not precomputed
#######################################################
mvml = MVML(lmbda=0.1, eta=1,
kernel=['rbf'], kernel_params=[{'gamma':50}],
nystrom_param=0.2, n_loops=2, learn_w=1)
views_ind = [120, 240]
mvml.fit(self.kernel_dict, y=self.y, views_ind=None)
self.assertEqual(mvml.A.shape, (48, 48))
self.assertEqual(mvml.g.shape,(48, 1))
w_expected = np.array([0.655, 0.65 ])
np.testing.assert_almost_equal(mvml.w, w_expected, 3)
def testFitMVMLMetric(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)
mvml2 = MVML(lmbda=0.1, eta=1, nystrom_param=1.0)
mvml2.fit(x_metricl, y=self.y, views_ind=None)
self.assertEqual(mvml2.A.shape, (240, 240))
self.assertEqual(mvml2.g.shape,(240, 1))
np.testing.assert_almost_equal(mvml2.w, w_expected, 8)
with self.assertRaises(ValueError):
mvml2.fit([1, 2, 3])
def testFitMVMLMetric_learA4(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)
mvml2 = MVML(lmbda=0.1, eta=1, nystrom_param=1.0, learn_A=4)
mvml2.fit(x_metricl, y=self.y, views_ind=None)
self.assertEqual(mvml2.A.shape, (240, 240))
self.assertEqual(mvml2.g.shape,(240, 1))
np.testing.assert_almost_equal(mvml2.w, w_expected, 8)
with self.assertRaises(ValueError):
mvml2.fit([1, 2, 3])
def testFitMVMLMetric_learA3(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)
mvml2 = MVML(lmbda=0.1, eta=1, nystrom_param=1.0, learn_A=3)
mvml2.fit(x_metricl, y=self.y, views_ind=None)
self.assertEqual(mvml2.A.shape, (240, 240))
self.assertEqual(mvml2.g.shape,(240, 1))
np.testing.assert_almost_equal(mvml2.w, w_expected, 8)
with self.assertRaises(ValueError):
mvml2.fit([1, 2, 3])
def testFitMVMLMetric_PredictA2(self):
#######################################################
# task with Metric array
#######################################################
w_expected = np.array([0.2, 0.1]) # [0.94836083 , 0.94175933] [ 0.7182, 0.7388]
x_metricl = MultiModalArray(self.kernel_dict)
mvml2 = MVML(lmbda=0.1, eta=1, nystrom_param=0.6,
learn_A=2, learn_w=1)
mvml2.fit(x_metricl, y=self.y, views_ind=None)
self.assertEqual(mvml2.A.shape, (144, 144))
self.assertEqual(mvml2.g.shape,(144, 1))
np.testing.assert_almost_equal(mvml2.w, w_expected, 0)
pred = mvml2.predict(self.test_kernel_dict)
self.assertEqual(pred.shape, (80,))
def testFitMVMLMetric_PredictA1(self):
#######################################################
# task with Metric array
#######################################################
w_expected = np.array([1.3, 1.4]) # [0.94836083 , 0.94175933] [ 0.7182, 0.7388]
x_metricl = MultiModalArray(self.kernel_dict)
mvml2 = MVML(lmbda=0.1, eta=1, nystrom_param=0.6,
learn_A=1, learn_w=1)
mvml2.fit(x_metricl, y=self.y, views_ind=None)
self.assertEqual(mvml2.A.shape, (144, 144))
self.assertEqual(mvml2.g.shape,(144, 1))
np.testing.assert_almost_equal(mvml2.w, w_expected, 0)
pred = mvml2.predict(self.test_kernel_dict)
self.assertEqual(pred.shape, (80,))
def testFitMVMLArray_2d(self):
#######################################################
# task with nparray 2d
#######################################################
w_expected = np.array([[0.5], [0.5]])
x_metricl = MultiModalArray(self.kernel_dict)
x_array = np.asarray(x_metricl)
mvml3 = MVML(lmbda=0.1, eta=1, nystrom_param=1.0)
mvml3.fit(x_array, y=self.y, views_ind=[0, 120, 240])
self.assertEqual(mvml3.A.shape, (240, 240))
self.assertEqual(mvml3.g.shape,(240, 1))
np.testing.assert_almost_equal(mvml3.w, w_expected, 8)
def testFitMVMLArray_1d(self):
#######################################################
# task with nparray 1d
#######################################################
w_expected = np.array([[0.5], [0.5]])
n_views = len(self.kernel_dict)
x_array_1d = np.ndarray((n_views), dtype=object)
for v in range(n_views):
x_array_1d[v] = self.kernel_dict[v]
mvml4 = MVML(lmbda=0.1, eta=1, learn_A=3, nystrom_param=0.6,
kernel=['rbf'], kernel_params=[{'gamma':50}])
mvml4.fit(x_array_1d, y=self.y)
self.assertEqual(mvml4.A.shape, (144, 144))
self.assertEqual(mvml4.g.shape,(144, 1))
np.testing.assert_almost_equal(mvml4.w, w_expected, 8)
def testPredictMVML_witoutFit(self):
mvml = MVML(lmbda=0.1, eta=1,
kernel=['rbf'], kernel_params=[{'gamma':50}],
nystrom_param=0.2)
with self.assertRaises(NotFittedError):
mvml.predict(self.test_kernel_dict)
def testPredictMVMLKernel(self):
mvml = MVML(lmbda=0.1, eta=1,
kernel=['rbf'], kernel_params={'gamma':50},
nystrom_param=0.2, learn_A=4)
mvml.fit(self.kernel_dict, y=self.y)
pred =mvml.predict(self.test_kernel_dict)
self.assertEqual(pred.shape, (80,))
def testPredictMVML(self):
mvml = MVML(lmbda=0.1, eta=1,
nystrom_param=1.0, learn_A=4)
mvml.fit(self.kernel_dict, y=self.y)
pred = mvml.predict(self.test_kernel_dict)
self.assertEqual(pred.shape, (80,))
# print(pred.shape)
def test_classifier(self):
pass
# return check_estimator(MVML)
if __name__ == "__main__":
# import sys;sys.argv = ['', 'Test.testName']
unittest.main()
# MVMLTest.testFitMVMLMetric_PredictA2()