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# ######### 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, MuCumboClassifier class, MVML class, MKL class.
# compatible with sklearn
#
# Version:
# -------
#
# * multimodal version = 0.0.dev0
#
# Licence:
# -------
#
# License: New BSD License
#
#
# ######### COPYRIGHT #########
#
"""Testing for the mumbo module."""
import pickle
import numpy as np
import unittest
from scipy.sparse import csc_matrix, csr_matrix, coo_matrix, dok_matrix
from scipy.sparse import lil_matrix
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn import datasets
from sklearn.utils.estimator_checks import check_estimator
from multimodal.boosting.cumbo import MuCumboClassifier
from multimodal.tests.data.get_dataset_path import get_dataset_path
from multimodal.datasets.data_sample import MultiModalArray
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class TestMuCumboClassifier(unittest.TestCase):
@classmethod
def setUpClass(clf):
# Load the iris dataset
iris = datasets.load_iris()
iris.views_ind = np.array([0, 2, 4])
clf.iris = iris
def test_init(self):
clf = MuCumboClassifier()
self.assertEqual(clf.random_state, None)
self.assertEqual(clf.n_estimators, 50)
n_estimators = 3
clf = MuCumboClassifier(n_estimators=n_estimators)
#self.assertEqual(clf.view_mode_)
def test_init_var(self):
n_classes = 3
n_views = 3
y = np.array([0, 2, 1, 2])
expected_cost = np.array(
[[[-2, 1, 0.5], [1, 1, -1], [1, -2, 0.5], [1, 1, -1]],
[[-2, 1, 0.5], [1, 1, -1], [1, -2, 0.5], [1, 1, -1]],
[[-2, 1, 0.5], [1, 1, -1], [1, -2,0.5], [1, 1, -1]]],
dtype=np.float64)
# expected_cost_glob = np.array(
# [[-2, 1, 1], [1, 1, -2], [1, -2, 1], [1, 1, -2]], dtype=np.float64)
expected_label_score = np.zeros((n_views, y.shape[0], n_classes))
expected_label_score_glob = np.zeros((y.shape[0], n_classes))
expected_predicted_classes_shape = ((n_views, y.shape[0]))
expected_n_yi_s = np.array([1, 1, 2])
expected_beta_class = np.ones((n_views, n_classes)) / n_classes
clf = MuCumboClassifier()
clf.n_classes_ = n_classes
(cost, label_score, label_score_glob, predicted_classes, score_function, beta_class, n_yi_s) \
= clf._init_var(n_views, y)
np.testing.assert_equal(cost, expected_cost)
# np.testing.assert_equal(cost_glob, expected_cost_glob)
np.testing.assert_equal(label_score, expected_label_score)
np.testing.assert_equal(label_score_glob, expected_label_score_glob)
np.testing.assert_equal(predicted_classes.shape, expected_predicted_classes_shape)
np.testing.assert_equal(n_yi_s, expected_n_yi_s)
np.testing.assert_equal(beta_class, expected_beta_class)
np.testing.assert_equal(score_function, np.zeros((n_views, 4, n_classes)))
def test_compute_dist(self):
cost = np.array(
[[[-2, 1, 1], [-1, -1, -2], [1, -2, 1], [1, 1, -2]],
[[-1, 2, 2], [2, 2, -1], [-2, 4, -2], [2, 2, -4]],
[[1, 4, -4], [-1, 3, -1], [-2, 2, 4], [4, 4, -4]]],
dtype=np.float64)
y = np.array([0, 2, 1, 2])
expected_dist = np.array(
[[0.25, 0.25, 0.25, 0.25], [0.5, 0.5, -2., 2.], [-0.5, 0.5, -1., 2.]])
clf = MuCumboClassifier()
dist = clf._compute_dist(cost, y)
np.testing.assert_equal(dist, expected_dist)
# The computation of the distribution only uses the costs when predicting
# the right classes, so the following cost matrix should give the same
# result as the previous.
cost = np.array(
[[[-2, 0, 0], [0, 0, -2], [0, -2, 0], [0, 0, -2]],
[[-1, 0, 0], [0, 0, -1], [0, 4, 0], [0, 0, -4]],
[[1, 0, 0], [0, 0, -1], [0, 2, 0], [0, 0, -4]]],
dtype=np.float64)
dist = clf._compute_dist(cost, y)
np.testing.assert_equal(dist, expected_dist)
expected_cost = np.array(
[[[-2, 1, 0.5], [1, 1, -1], [1, -2, 0.5], [1, 1, -1]],
[[-2, 1, 0.5], [1, 1, -1], [1, -2, 0.5], [1, 1, -1]],
[[-2, 1, 0.5], [1, 1, -1], [1, -2,0.5], [1, 1, -1]]],
dtype=np.float64)
dist = clf._compute_dist(cost, y)
expected_dist = np.array([[ 0.25,0.25,0.25,0.25],
[ 0.5,0.5, -2.,2. ], [-0.5 , 0.5 ,-1., 2. ]])
np.testing.assert_equal(dist, expected_dist)
# def test_compute_coop_coef(self):
# y = np.array([0, 1, 2, 0])
# predicted_classes = np.array([[0, 0, 1, 1], [0, 1, 0, 2], [2, 2, 0, 0]])
# expected_coop_coef = np.array([[1, 0, 1, 0], [1, 1, 1, 0], [0, 0, 1, 1]],
# dtype=np.float64)
#
# clf = MuCumboClassifier()
# coop_coef = clf._compute_coop_coef(predicted_classes, y)
#
# assert_array_equal(coop_coef, expected_coop_coef)
def test_compute_edges(self):
cost = np.array(
[[[-2, 1, 1], [-1, -1, -2], [1, -2, 1], [1, 1, -2]],
[[-2, 2, 2], [2, 2, -4], [-2, -4, -2], [2, 2, -4]],
[[1, 4, -4], [-1, 3, -1], [-2, 4, 4], [4, 4, -1]]],
dtype=np.float64)
predicted_classes = np.array([[0, 2, 1, 1], [0, 1, 0, 2], [2, 2, 0, 1]])
y = np.array([0, 2, 1, 2])
expected_edges = np.array([1.25, 0.75, 0.25])
clf = MuCumboClassifier()
edges = clf._compute_edges(cost, predicted_classes, y)
np.testing.assert_equal(edges, expected_edges)
def test_compute_alphas(self):
decimal = 12
expected_alpha = 0.5
edge = (np.e-1.) / (np.e+1.)
clf = MuCumboClassifier()
alpha = clf._compute_alphas(edge)
self.assertAlmostEqual(alpha, expected_alpha, decimal)
expected_alphas = np.array([0.5, 1., 2.])
tmp = np.array([np.e, np.e**2, np.e**4])
edges = (tmp-1.) / (tmp+1.)
alphas = clf._compute_alphas(edges)
np.testing.assert_almost_equal(alphas, expected_alphas, decimal)
def test_prepare_beta_solver(self):
clf = MuCumboClassifier()
clf.n_views_ = 3
clf.n_classes_ = 3
A, b, G, h, l = clf._prepare_beta_solver()
a_n = np.array(A)
A_expected = np.array([[ 1 , 1 , 1 , 0 , 0, 0, 0 , 0, 0],
[ 0 , 0 , 0 , 1, 1, 1, 0 , 0, 0],
[ 0 , 0 , 0 , 0 , 0, 0, 1, 1,1 ]])
np.testing.assert_equal(a_n , A_expected)
b_expected = np.array([[ 1.00e+00],[ 1.00e+00],[ 1.00e+00]])
# [ 1.00e+00],[ 1.00e+00],[ 1.00e+00],
# [ 1.00e+00],[ 1.00e+00],[ 1.00e+00]])
b_n = np.array(b)
np.testing.assert_equal(b_n, b_expected)
G_n = np.array(G)
G_expected = np.array([[1.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00],
[0.00e+00, 1.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00],
[0.00e+00, 0.00e+00, 1.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00],
[0.00e+00, 0.00e+00, 0.00e+00, 1.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00],
[0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 1.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00],
[0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 1.00e+00, 0.00e+00, 0.00e+00, 0.00e+00],
[0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 1.00e+00, 0.00e+00, 0.00e+00],
[0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 1.00e+00, 0.00e+00],
[0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 1.00e+00],
[-1.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00],
[0.00e+00, -1.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00],
[0.00e+00, 0.00e+00, -1.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00],
[0.00e+00, 0.00e+00, 0.00e+00, -1.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00],
[0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, -1.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00],
[0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00,-1.00e+00, 0.00e+00, 0.00e+00, 0.00e+00],
[0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, -1.00e+00, 0.00e+00, 0.00e+00],
[0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, -1.00e+00, 0.00e+00],
[0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00,-1.00e+00]])
np.testing.assert_equal(G_n, G_expected)
h_n = np.array(h)
h_expected = np.array([[ 1.00e+00],[ 1.00e+00],[ 1.00e+00],[ 1.00e+00],[ 1.00e+00],[ 1.00e+00],[ 1.00e+00],[ 1.00e+00],[ 1.00e+00],
[ 0.00e+00],[ 0.00e+00],[ 0.00e+00],[ 0.00e+00],[ 0.00e+00],[ 0.00e+00],[ 0.00e+00],[ 0.00e+00],[ 0.00e+00]])
np.testing.assert_equal(h_n, h_expected)
self.assertEqual(l, {'l': 18})
def test_solver_cp_forbeta(self):
clf = MuCumboClassifier()
clf.n_views_ = 3
clf.n_classes_ = 3
A, b, G, h, l = clf._prepare_beta_solver()
y_i = np.array([0, 1, 2, 0])
predicted_classes = np.array([[0, 0, 1, 1], [0, 1, 0, 2], [2, 2, 0, 0]])
indicat , indicat_yi, delta = clf._indicatrice(predicted_classes, y_i)
indicate_vue = np.block(np.split(indicat, 3, axis=0)).squeeze()
indicate_vue_yi = np.block(np.split(indicat_yi, 3, axis=0)).squeeze()
alphas = np.array([0.5, 1., 2.])
cost_Tminus1 = 10 * np.array(
[[[2, 1, 0.5], [1, 1, 1], [1, 2, 0.5], [1, 1, 1]],
[[2, 1, 0.5], [1, 1, 1], [1, 2, 0.5], [1, 1, 1]],
[[2, 1, 0.5], [1, 1, 1], [1, 2, 0.5], [1, 1, 1]]],
dtype=np.float64)
cost_Tminus1_vue = np.block(np.split(cost_Tminus1, 3, axis=0)).squeeze()
delta_vue = np.block(np.split(delta, 3, axis=0)).squeeze()
solver = np.array(clf._solver_cp_forbeta(alphas, indicate_vue, indicate_vue_yi, delta_vue,
cost_Tminus1_vue, A, b, G, h, l))
self.assertEqual(solver.shape, (9,1))
s_r = np.sum(solver.reshape(3,3), axis=1)
np.testing.assert_almost_equal(s_r, np.ones(3, dtype=np.float), 9)
def test_solver_compute_betas(self):
clf = MuCumboClassifier()
clf.n_views_ = 3
clf.n_classes_ = 3
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cost_Tminus1 =np.array([[[-7.45744585e+01, 3.67879439e-01, 7.42065790e+01],
[ 4.78511743e-06, 3.87742081e-02, -3.87789932e-02],
[ 2.47875218e-03, -2.48182428e-03, 3.07210618e-06],
[ 1.35335283e-01, 6.73794700e-03, -1.42073230e-01]],
[[-2.02255359e-01, 1.83156389e-02, 1.83939720e-01],
[ 7.38905610e+00, 4.97870684e-02, -7.43884317e+00],
[ 3.67879441e-01, -4.06240749e+00, 3.69452805e+00],
[ 3.67879441e-01, 8.10308393e+03, -8.10345181e+03]],
[[-2.48182452e-03, 2.47875218e-03, 3.07234660e-06],
[ 5.45938775e+01, 4.03397223e+02, -4.57991101e+02],
[ 1.48401545e+02, -1.48426438e+02, 2.48935342e-02],
[ 1.09663316e+03, 2.71828184e+00, -1.09935144e+03]]])
score_function_Tminus1 =10 *np.ones((3,4,3), dtype =np.float)
alphas = np.array([0.5, 1., 2.])
predicted_classes = np.array([[0, 0, 1, 1], [0, 1, 0, 2], [2, 2, 0, 0]])
y = np.array([0, 1, 2, 0])
betas = clf._compute_betas(alphas, y, score_function_Tminus1, predicted_classes)
self.assertEqual(betas.shape, (3,3))
np.testing.assert_almost_equal(np.sum(betas, axis =1), np.ones(3, dtype=np.float), 9)
self.assertTrue(np.all(betas <= 1) )
self.assertTrue(np.all(betas >= 0) )
def test_indicatrice(self):
clf = MuCumboClassifier()
clf.n_views_ = 3
clf.n_classes_ = 3
y_i = np.array([0, 1, 2, 0])
predicted_classes = np.array([[0, 0, 1, 1], [0, 1, 0, 2], [2, 2, 0, 0]])
indic , indic_yi, delta = clf._indicatrice(predicted_classes, y_i)
expected_indi = np.array( [[[0 ,0, 0], [1 ,0 ,0],[0 ,1, 0],[0 ,1 ,0]],
[[0 , 0 , 0], [0, 0 ,0], [1 ,0 ,0], [0, 0 ,1]],
[[0, 0 ,1], [0 ,0 ,1],[1, 0, 0], [0, 0, 0]]])
expected_indi_yi = np.array([[[1, 0 ,0], [0, 1, 0], [0 ,0, 1], [1 ,0 ,0]],
[[1, 0 ,0], [0 ,1 ,0], [0, 0, 1], [1, 0, 0]],
[[1, 0, 0], [0 ,1, 0], [0 ,0 ,1], [1 ,0 ,0]]])
np.testing.assert_equal(indic , expected_indi)
np.testing.assert_equal(indic_yi , expected_indi_yi)
def test_compute_cost(self):
decimal = 12
label_score = np.array(
[[[-1, -2, 4], [-8, 1, 4], [2, 8, -4], [2, -1, 4]],
[[2, -2, 1], [4, -1, 2], [1, 2, 4], [-2, 8, -1]],
[[8, 2, -4], [2, 4, -2], [4, 1, -2], [8, 2, 1]]],
dtype=np.float64)
predicted_classes = np.array([[0, 2, 1, 1], [0, 1, 0, 0], [2, 2, 0, 1]])
y = np.array([0, 2, 1, 2])
alphas = np.array([0.25, 0.5, 2.])
# expected_label_score = np.array(
# [[[-0.75, -2, 4], [-8, 1, 4.25], [2, 8.25, -4], [2, -0.75, 4]],
# [[2.5, -2, 1], [4, -0.5, 2], [1.5, 2, 4], [-1.5, 8, -1]],
# [[8, 2, -2.], [2, 4, 0.], [6., 1, -2], [8, 4., 1]]],
# dtype=np.float64)
cost_Tminus1 = np.array(
[[[-2, 1, 0.5], [1, 1, -1], [1, -2, 0.5], [1, 1, -1]],
[[-2, 1, 0.5], [1, 1, -1], [1, -2, 0.5], [1, 1, -1]],
[[-2, 1, 0.5], [1, 1, -1], [1, -2, 0.5], [1, 1, -1]]],
dtype=np.float64)
score_function_Tminus1 =10 *np.ones((3,4,3), dtype=np.float)
clf = MuCumboClassifier()
clf.n_views_ = 3
clf.n_classes_ = 3
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betas = clf._compute_betas(alphas, y, score_function_Tminus1, predicted_classes)
cost, label_score, score_function_dif = clf._compute_cost(label_score, predicted_classes, y, alphas,
betas, use_coop_coef=True)
cost_expected = np.array([[[-6.58117293e+01, 3.24652469e-01, 6.54870769e+01],
[ 5.42224839e-06, 4.39369338e-02, -4.39423560e-02],
[ 2.47875216e-03, -2.48182426e-03, 3.07210615e-06],
[ 1.35335283e-01, 6.73794705e-03, -1.42073230e-01]],
[[-2.02255355e-01, 1.83156385e-02, 1.83939717e-01],
[ 7.38905610e+00, 4.97870688e-02, -7.43884317e+00],
[ 3.67879448e-01, -4.06240750e+00, 3.69452805e+00],
[ 3.67879448e-01, 8.10308393e+03, -8.10345181e+03]],
[[-2.48725300e-03, 2.47875218e-03, 8.50082247e-06],
[ 1.97311865e+01, 1.45794844e+02, -1.65526031e+02],
[ 3.28220396e+01, -3.28469331e+01, 2.48935342e-02],
[ 1.09663316e+03, 4.44198002e+00, -1.10107514e+03]]])
np.testing.assert_almost_equal(cost, cost_expected, 4)
expected_label_score = np.array([[[-0.875, -2., 4., ],
[-8., 1., 4.125 ],
[ 2., 8.00000001, -4. ],
[ 2., -0.99999999, 4. ]],
[[ 2.00000002, -2., 1. ],
[ 4., -0.99999999, 2. ],
[ 1.00000002, 2., 4. ],
[-1.99999998, 8., -1. ]],
[[ 8., 2., -2.98220046],
[ 2., 4., -0.98220046],
[ 4.49110023, 1., -2. ],
[ 8., 2.49110023, 1. ]]])
np.testing.assert_almost_equal(label_score, expected_label_score,6)
#
# label_score = np.array(
# [[[-1, -2, 4], [-8, 1, 4], [2, 8, -4], [2, -1, 4]],
# [[2, -2, 1], [4, -1, 2], [1, 2, 4], [-2, 8, -1]],
# [[8, 2, -4], [2, 4, -2], [4, 1, -2], [8, 2, 1]]],
# dtype=np.float64)
# expected_label_score = np.array(
# [[[-0.75, -2, 4], [-8, 1, 4.25], [2, 8.25, -4], [2, -0.75, 4]],
# [[2.5, -2, 1], [4, -1, 2], [1, 2, 4], [-1.5, 8, -1]],
# [[8, 2, -4], [2, 4, 0.], [4, 1, -2], [8, 4., 1]]],
# dtype=np.float64)
#
# clf = MuCumboClassifier()
# cost, label_score = clf._compute_cost(label_score, pred_classes, y, alphas,
# use_coop_coef=True)
#
# np.testing.assert_almost_equal(label_score, expected_label_score,
# decimal)
#
# label_score = np.array(
# [[[0, 0, np.log(4)], [np.log(8), 0, 0], [0, 0, 0], [0, 0, 0]],
# [[0, np.log(2), 0], [0, 0, 0], [0, 0, 0], [0, np.log(4), 0]],
# [[0, 0, 0], [np.log(8), 0, 0], [0, np.log(2), 0], [0, 0, 0]]],
# dtype=np.float64)
# alphas = np.array([np.log(2), np.log(4), np.log(8)])
# expected_label_score = np.array(
# [[[np.log(2), 0, np.log(4)],
# [np.log(8), 0, np.log(2)],
# [0, np.log(2), 0],
# [0, np.log(2), 0]],
# [[np.log(4), np.log(2), 0],
# [0, np.log(4), 0],
# [np.log(4), 0, 0],
# [np.log(4), np.log(4), 0]],
# [[0, 0, np.log(8)],
# [np.log(8), 0, np.log(8)],
# [np.log(8), np.log(2), 0],
# [0, np.log(8), 0]]],
# dtype=np.float64)
# expected_cost = np.array(
# [[[-2.5, 0.5, 2.], [4., 0.5, -4.5], [0.5, -1., 0.5], [1., 2., -3.]],
# [[-0.75, 0.5, 0.25], [1., 4., -5.], [4., -5., 1.], [4., 4., -8.]],
# [[-9., 1., 8.], [1., 0.125, -1.125], [4., -4.5, 0.5], [1., 8., -9.]]],
# dtype=np.float64)
#
# clf = MuCumboClassifier()
# cost, label_score = clf._compute_cost(label_score, pred_classes, y, alphas,
# use_coop_coef=False)
#
# np.testing.assert_almost_equal(label_score, expected_label_score,
# decimal)
# np.testing.assert_almost_equal(cost, expected_cost, decimal)
#
# label_score = np.array(
# [[[0, 0, np.log(4)], [np.log(8), 0, 0], [0, 0, 0], [0, 0, 0]],
# [[0, np.log(2), 0], [0, 0, 0], [0, 0, 0], [0, np.log(4), 0]],
# [[0, 0, 0], [np.log(8), 0, 0], [0, np.log(2), 0], [0, 0, 0]]],
# dtype=np.float64)
# alphas = np.array([np.log(2), np.log(4), np.log(8)])
# expected_label_score = np.array(
# [[[np.log(2), 0, np.log(4)],
# [np.log(8), 0, np.log(2)],
# [0, np.log(2), 0],
# [0, np.log(2), 0]],
# [[np.log(4), np.log(2), 0],
# [0, 0, 0],
# [0, 0, 0],
# [np.log(4), np.log(4), 0]],
# [[0, 0, 0],
# [np.log(8), 0, np.log(8)],
# [0, np.log(2), 0],
# [0, np.log(8), 0]]],
# dtype=np.float64)
# expected_cost = np.array(
# [[[-2.5, 0.5, 2.], [4., 0.5, -4.5], [0.5, -1., 0.5], [1., 2., -3.]],
# [[-0.75, 0.5, 0.25], [1., 1., -2.], [1., -2., 1.], [4., 4., -8.]],
# [[-2., 1., 1.], [1., 0.125, -1.125], [0.5, -1., 0.5], [1., 8., -9.]]],
# dtype=np.float64)
#
# clf = MuCumboClassifier()
# cost, label_score = clf._compute_cost(label_score, pred_classes, y, alphas,
# use_coop_coef=True)
#
# np.testing.assert_almost_equal(label_score, expected_label_score,
# decimal)
# np.testing.assert_almost_equal(cost, expected_cost, decimal)
#
#
# def test_algo_options():
# np.random.seed(seed)
#
# n_estimators = 10
#
# clf = MuCumboClassifier(n_estimators=n_estimators, best_view_mode='edge')
# clf.fit(iris.data, iris.target, iris.views_ind)
# score = clf.score(iris.data, iris.target)
# assert_greater(score, 0.95, "Failed with score = {}".format(score))
#
# clf = MuCumboClassifier(n_estimators=n_estimators, best_view_mode='error')
# clf.fit(iris.data, iris.target, iris.views_ind)
# score = clf.score(iris.data, iris.target)
# assert_greater(score, 0.95, "Failed with score = {}".format(score))
#
# assert_raises(ValueError, MuCumboClassifier(), best_view_mode='test')
#
# clf = MuCumboClassifier()
# clf.best_view_mode = 'test'
# assert_raises(ValueError, clf.fit, iris.data, iris.target, iris.views_ind)
#
#
def test_fit_views_ind(self):
X = np.array([[1., 1., 1.], [-1., -1., -1.]])
y = np.array([0, 1])
expected_views_ind = np.array([0, 1, 3])
clf = MuCumboClassifier()
clf.fit(X, y)
# np.testing.assert_equal(clf.X_.views_ind, expected_views_ind)
# assert_array_equal(clf.views_ind_, expected_views_ind)
# #
def test_class_variation(self):
# Check that classes labels can be integers or strings and can be stored
# into any kind of sequence
X = np.array([[1., 1., 1.], [-1., -1., -1.]])
views_ind = np.array([0, 1, 3])
y = np.array([3, 1])
clf = MuCumboClassifier()
clf.fit(X, y, views_ind)
np.testing.assert_almost_equal(clf.predict(X), y)
y = np.array(["class_1", "class_2"])
clf = MuCumboClassifier()
clf.fit(X, y)
np.testing.assert_equal(clf.predict(X), y)
# Check that misformed or inconsistent inputs raise expections
X = np.zeros((5, 4, 2))
y = np.array([0, 1])
self.assertRaises(ValueError, clf.fit, X, y, views_ind)
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# assert_raises(ValueError, clf.fit, X, y, views_ind)
#
# X = ["str1", "str2"]
# assert_raises(ValueError, clf.fit, X, y, views_ind)
#
# X = np.array([[1., 1., 1.], [-1., -1., -1.]])
# y = np.array([1])
# views_ind = np.array([0, 1, 3])
# clf = MuCumboClassifier()
# assert_raises(ValueError, clf.fit, X, y, views_ind)
#
# y = np.array([1, 0, 0, 1])
# views_ind = np.array([0, 1, 3])
# clf = MuCumboClassifier()
# assert_raises(ValueError, clf.fit, X, y, views_ind)
#
# y = np.array([3.2, 1.1])
# clf = MuCumboClassifier()
# assert_raises(ValueError, clf.fit, X, y, views_ind)
#
# y = np.array([0, 1])
# views_ind = np.array([0, 3, 1])
# clf = MuCumboClassifier()
# assert_raises(ValueError, clf.fit, X, y, views_ind)
#
# views_ind = np.array([-1, 1, 3])
# clf = MuCumboClassifier()
# assert_raises(ValueError, clf.fit, X, y, views_ind)
#
# views_ind = np.array([0, 1, 4])
# clf = MuCumboClassifier()
# assert_raises(ValueError, clf.fit, X, y, views_ind)
#
# views_ind = np.array([0.5, 1, 3])
# clf = MuCumboClassifier()
# assert_raises(ValueError, clf.fit, X, y, views_ind)
#
# views_ind = np.array("test")
# clf = MuCumboClassifier()
# assert_raises(ValueError, clf.fit, X, y, views_ind)
#
# views_ind = np.zeros((3, 2, 4))
# clf = MuCumboClassifier()
# assert_raises(ValueError, clf.fit, X, y, views_ind)
#
# views_ind = np.array([[-1], [1, 2]])
# clf = MuCumboClassifier()
# assert_raises(ValueError, clf.fit, X, y, views_ind)
#
# views_ind = np.array([[3], [1, 2]])
# clf = MuCumboClassifier()
# assert_raises(ValueError, clf.fit, X, y, views_ind)
#
# views_ind = np.array([[0.5], [1, 2]])
# clf = MuCumboClassifier()
# assert_raises(ValueError, clf.fit, X, y, views_ind)
#
# views_ind = np.array([[-1, 0], [1, 2]])
# clf = MuCumboClassifier()
# assert_raises(ValueError, clf.fit, X, y, views_ind)
#
# views_ind = np.array([[0, 3], [1, 2]])
# clf = MuCumboClassifier()
# assert_raises(ValueError, clf.fit, X, y, views_ind)
#
# views_ind = np.array([[0.5], [1], [2]])
# clf = MuCumboClassifier()
# assert_raises(ValueError, clf.fit, X, y, views_ind)
#
#
def test_decision_function(self):
clf = MuCumboClassifier()
clf.fit(self.iris.data, self.iris.target, self.iris.views_ind)
X = np.zeros((4, 3))
with self.assertRaises(ValueError):
clf.decision_function(X)
X = self.iris.data[:, 0:15]
dec = clf.decision_function(X)
dec_expected = np.load(get_dataset_path("dec_iris.npy"))
np.testing.assert_almost_equal(dec, dec_expected, 9)
def test_predict(self):
clf = MuCumboClassifier()
X = np.array([[0., 0.5, 0.7], [1., 1.5, 1.7], [2., 2.5, 2.7]])
y = np.array([1, 1, 1])
clf.fit(X, y)
y_expected = clf.predict(X)
np.testing.assert_almost_equal(y, y_expected, 9)
# def test_limit_cases():
# np.random.seed(seed)
#
# # Check that using empty data raises an exception
# X = np.array([[]])
# y = np.array([])
# clf = MuCumboClassifier()
# assert_raises(ValueError, clf.fit, X, y)
#
# # Check that fit() works for the smallest possible dataset
# X = np.array([[0.]])
# y = np.array([0])
# clf = MuCumboClassifier()
# clf.fit(X, y)
# assert_array_equal(clf.predict(X), y)
# assert_array_equal(clf.predict(np.array([[1.]])), np.array([0]))
#
# # Check that fit() works with samples from a single class
# X = np.array([[0., 0.5, 0.7], [1., 1.5, 1.7], [2., 2.5, 2.7]])
# y = np.array([1, 1, 1])
# views_ind = np.array([0, 1, 3])
# clf = MuCumboClassifier()
# clf.fit(X, y, views_ind)
# assert_array_equal(clf.predict(X), y)
# assert_array_equal(clf.predict(np.array([[-1., 0., 1.]])), np.array([1]))
#
# X = np.array([[0., 0.5, 0.7], [1., 1.5, 1.7], [2., 2.5, 2.7]])
# y = np.array([1, 1, 1])
# views_ind = np.array([[0, 2], [1]])
# clf = MuCumboClassifier()
# clf.fit(X, y, views_ind)
# assert_array_equal(clf.predict(X), y)
# assert_array_equal(clf.predict(np.array([[-1., 0., 1.]])), np.array([1]))
def test_simple_predict(self):
#np.random.seed(seed)
# Simple example with 2 classes and 1 view
X = np.array(
[[1.1, 2.1],
[2.1, 0.2],
[0.7, 1.2],
[-0.9, -1.8],
[-1.1, -2.2],
[-0.3, -1.3]])
y = np.array([0, 0, 0, 1, 1, 1])
views_ind = np.array([0, 2])
clf = MuCumboClassifier()
clf.fit(X, y, views_ind)
#assert_array_equal(clf.predict(X), y)
#assert_array_equal(clf.predict(np.array([[1., 1.], [-1., -1.]])),
# np.array([0, 1]))
#assert_equal(clf.decision_function(X).shape, y.shape)
views_ind = np.array([[1, 0]])
clf = MuCumboClassifier()
clf.fit(X, y, views_ind)
np.testing.assert_almost_equal(clf.predict(X), y)
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#assert_array_equal(clf.predict(X), y)
#assert_array_equal(clf.predict(np.array([[1., 1.], [-1., -1.]])),
# np.array([0, 1]))
#assert_equal(clf.decision_function(X).shape, y.shape)
#
# # Simple example with 2 classes and 2 views
# X = np.array(
# [[1.1, 2.1, 0.5],
# [2.1, 0.2, 1.2],
# [0.7, 1.2, 2.1],
# [-0.9, -1.8, -0.3],
# [-1.1, -2.2, -0.9],
# [-0.3, -1.3, -1.4]])
# y = np.array([0, 0, 0, 1, 1, 1])
# views_ind = np.array([0, 2, 3])
# clf = MuCumboClassifier()
# clf.fit(X, y, views_ind)
# assert_array_equal(clf.predict(X), y)
# assert_array_equal(clf.predict(np.array([[1., 1., 1.], [-1., -1., -1.]])),
# np.array([0, 1]))
# assert_equal(clf.decision_function(X).shape, y.shape)
#
# views_ind = np.array([[2, 0], [1]])
# clf = MuCumboClassifier()
# clf.fit(X, y, views_ind)
# assert_array_equal(clf.predict(X), y)
# assert_array_equal(clf.predict(np.array([[1., 1., 1.], [-1., -1., -1.]])),
# np.array([0, 1]))
# assert_equal(clf.decision_function(X).shape, y.shape)
#
# # Simple example with 2 classes and 3 views
# X = np.array(
# [[1.1, 2.1, 0.5, 1.2, 1.7],
# [2.1, 0.2, 1.2, 0.6, 1.3],
# [0.7, 1.2, 2.1, 1.1, 0.9],
# [-0.9, -1.8, -0.3, -2.1, -1.1],
# [-1.1, -2.2, -0.9, -1.5, -1.2],
# [-0.3, -1.3, -1.4, -0.6, -0.7]])
# y = np.array([0, 0, 0, 1, 1, 1])
# views_ind = np.array([0, 2, 3, 5])
# clf = MuCumboClassifier()
# clf.fit(X, y, views_ind)
# assert_array_equal(clf.predict(X), y)
# data = np.array([[1., 1., 1., 1., 1.], [-1., -1., -1., -1., -1.]])
# assert_array_equal(clf.predict(data), np.array([0, 1]))
# assert_equal(clf.decision_function(X).shape, y.shape)
#
# views_ind = np.array([[2, 0], [1], [3, 4]])
# clf = MuCumboClassifier()
# clf.fit(X, y, views_ind)
# assert_array_equal(clf.predict(X), y)
# data = np.array([[1., 1., 1., 1., 1.], [-1., -1., -1., -1., -1.]])
# assert_array_equal(clf.predict(data), np.array([0, 1]))
# assert_equal(clf.decision_function(X).shape, y.shape)
#
# # Simple example with 3 classes and 3 views
# X = np.array(
# [[1.1, -1.2, 0.5, 1.2, -1.7],
# [2.1, -0.2, 0.9, 0.6, -1.3],
# [0.7, 1.2, 2.1, 1.1, 0.9],
# [0.9, 1.8, 2.2, 2.1, 1.1],
# [-1.1, -2.2, -0.9, -1.5, -1.2],
# [-0.3, -1.3, -1.4, -0.6, -0.7]])
# y = np.array([0, 0, 1, 1, 2, 2])
# views_ind = np.array([0, 2, 3, 5])
# clf = MuCumboClassifier()
# clf.fit(X, y, views_ind)
# assert_array_equal(clf.predict(X), y)
# data = np.array(
# [[1., -1., 1., 1., -1.],
# [1., 1., 1., 1., 1.],
# [-1., -1., -1., -1., -1.]])
# assert_array_equal(clf.predict(data), np.array([0, 1, 2]))
# assert_equal(clf.decision_function(X).shape, (X.shape[0], 3))
#
# views_ind = np.array([[1, 0], [2], [3, 4]])
# clf = MuCumboClassifier()
# clf.fit(X, y, views_ind)
# assert_array_equal(clf.predict(X), y)
# data = np.array(
# [[1., -1., 1., 1., -1.],
# [1., 1., 1., 1., 1.],
# [-1., -1., -1., -1., -1.]])
# assert_array_equal(clf.predict(data), np.array([0, 1, 2]))
# assert_equal(clf.decision_function(X).shape, (X.shape[0], 3))
#
#
# def test_generated_examples():
# def generate_data_in_orthotope(n_samples, limits):
# limits = np.array(limits)
# n_features = limits.shape[0]
# data = np.random.random((n_samples, n_features))
# data = (limits[:, 1]-limits[:, 0]) * data + limits[:, 0]
# return data
#
# n_samples = 100
#
# np.random.seed(seed)
# view_0 = np.concatenate(
# (generate_data_in_orthotope(n_samples, [[0., 1.], [0., 1.]]),
# generate_data_in_orthotope(n_samples, [[1., 2.], [0., 1.]])))
# view_1 = generate_data_in_orthotope(2*n_samples, [[0., 1.], [0., 1.]])
# X = np.concatenate((view_0, view_1), axis=1)
# y = np.zeros(2*n_samples, dtype=np.int64)
# y[n_samples:] = 1
# views_ind = np.array([0, 2, 4])
# clf = MuCumboClassifier(n_estimators=1)
# clf.fit(X, y, views_ind)
# assert_equal(clf.score(X, y), 1.)
#
# np.random.seed(seed)
# view_0 = np.concatenate(
# (generate_data_in_orthotope(n_samples, [[0., 1.], [0., 1.]]),
# generate_data_in_orthotope(n_samples, [[1., 2.], [0., 1.]]),
# generate_data_in_orthotope(n_samples, [[0., 1.], [0., 1.]]),
# generate_data_in_orthotope(n_samples, [[0., 1.], [1., 2.]])))
# view_1 = np.concatenate(
# (generate_data_in_orthotope(n_samples, [[1., 2.], [0., 1.]]),
# generate_data_in_orthotope(n_samples, [[0., 1.], [0., 1.]]),
# generate_data_in_orthotope(n_samples, [[0., 1.], [1., 2.]]),
# generate_data_in_orthotope(n_samples, [[0., 1.], [0., 1.]])))
# X = np.concatenate((view_0, view_1), axis=1)
# y = np.zeros(4*n_samples, dtype=np.int64)
# y[2*n_samples:] = 1
# views_ind = np.array([0, 2, 4])
# clf = MuCumboClassifier(n_estimators=3)
# clf.fit(X, y, views_ind)
# assert_equal(clf.score(X, y), 1.)
#
# np.random.seed(seed)
# view_0 = np.concatenate(
# (generate_data_in_orthotope(n_samples, [[0., 1.], [0., 1.]]),
# generate_data_in_orthotope(n_samples, [[1., 2.], [0., 1.]]),
# generate_data_in_orthotope(n_samples, [[0., 1.], [0., 1.]])))
# view_1 = np.concatenate(
# (generate_data_in_orthotope(n_samples, [[1., 2.], [0., 1.]]),
# generate_data_in_orthotope(n_samples, [[0., 1.], [0., 1.]]),
# generate_data_in_orthotope(n_samples, [[0., 1.], [0., 1.]])))
# view_2 = np.concatenate(
# (generate_data_in_orthotope(n_samples, [[0., 1.], [0., 1.]]),
# generate_data_in_orthotope(n_samples, [[0., 1.], [0., 1.]]),
# generate_data_in_orthotope(n_samples, [[1., 2.], [0., 1.]])))
# X = np.concatenate((view_0, view_1, view_2), axis=1)
# y = np.zeros(3*n_samples, dtype=np.int64)
# y[n_samples:2*n_samples] = 1
# y[2*n_samples:] = 2
# views_ind = np.array([0, 2, 4, 6])
# clf = MuCumboClassifier(n_estimators=3)
# clf.fit(X, y, views_ind)
# assert_equal(clf.score(X, y), 1.)
#
# np.random.seed(seed)
# view_0 = np.concatenate(
# (generate_data_in_orthotope(n_samples, [[0., 1.], [0., 1.]]),
# generate_data_in_orthotope(n_samples, [[1., 2.], [0., 1.]]),
# generate_data_in_orthotope(n_samples, [[0., 2.], [0., 1.]])))
# view_1 = np.concatenate(
# (generate_data_in_orthotope(n_samples, [[1., 2.], [0., 1.]]),
# generate_data_in_orthotope(n_samples, [[0., 2.], [0., 1.]]),
# generate_data_in_orthotope(n_samples, [[0., 1.], [0., 1.]])))
# view_2 = np.concatenate(
# (generate_data_in_orthotope(n_samples, [[0., 2.], [0., 1.]]),
# generate_data_in_orthotope(n_samples, [[0., 1.], [0., 1.]]),
# generate_data_in_orthotope(n_samples, [[1., 2.], [0., 1.]])))
# X = np.concatenate((view_0, view_1, view_2), axis=1)
# y = np.zeros(3*n_samples, dtype=np.int64)
# y[n_samples:2*n_samples] = 1
# y[2*n_samples:] = 2
# views_ind = np.array([0, 2, 4, 6])
# clf = MuCumboClassifier(n_estimators=4)
# clf.fit(X, y, views_ind)
# assert_equal(clf.score(X, y), 1.)
#
def test_classifier(self):
return check_estimator(MuCumboClassifier)
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#
#
# def test_iris():
# # Check consistency on dataset iris.
#
# np.random.seed(seed)
# n_estimators = 5
# classes = np.unique(iris.target)
#
# for views_ind in [iris.views_ind, np.array([[0, 2], [1, 3]])]:
# clf = MuCumboClassifier(n_estimators=n_estimators)
#
# clf.fit(iris.data, iris.target, views_ind)
#
# assert_true(np.all((0. <= clf.estimator_errors_)
# & (clf.estimator_errors_ <= 1.)))
# assert_true(np.all(np.diff(clf.estimator_errors_) < 0.))
#
# assert_array_equal(classes, clf.classes_)
# assert_equal(clf.decision_function(iris.data).shape[1], len(classes))
#
# score = clf.score(iris.data, iris.target)
# assert_greater(score, 0.95, "Failed with score = {}".format(score))
#
# assert_equal(len(clf.estimators_), n_estimators)
#
# # Check for distinct random states
# assert_equal(len(set(est.random_state for est in clf.estimators_)),
# len(clf.estimators_))
def test_staged_methods(self):
seed = 7
n_estimators = 10
#
target_two_classes = np.zeros(self.iris.target.shape, dtype=np.int64)
target_two_classes[target_two_classes.shape[0]//2:] = 1
#
data = (
(self.iris.data, self.iris.target, self.iris.views_ind),
(self.iris.data, self.iris.target, np.array([[0, 2], [1, 3]])),
(self.iris.data, target_two_classes, self.iris.views_ind),
(self.iris.data, target_two_classes, np.array([[0, 2], [1, 3]])),
)
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#
for X, y, views_ind in data:
clf = MuCumboClassifier(n_estimators=n_estimators, random_state=seed)
clf.fit(X, y, views_ind)
staged_dec_func = [dec_f for dec_f in clf.staged_decision_function(X)]
staged_predict = [predict for predict in clf.staged_predict(X)]
staged_score = [score for score in clf.staged_score(X, y)]
self.assertEqual(len(staged_dec_func), n_estimators)
self.assertEqual(len(staged_predict), n_estimators)
self.assertEqual(len(staged_score), n_estimators)
# assert_equal(len(staged_dec_func), n_estimators)
# assert_equal(len(staged_predict), n_estimators)
# assert_equal(len(staged_score), n_estimators)
#
# for ind in range(n_estimators):
# clf = MuCumboClassifier(n_estimators=ind+1, random_state=seed)
# clf.fit(X, y, views_ind)
# dec_func = clf.decision_function(X)
# predict = clf.predict(X)
# score = clf.score(X, y)
# assert_array_equal(dec_func, staged_dec_func[ind])
# assert_array_equal(predict, staged_predict[ind])
# assert_equal(score, staged_score[ind])
#
#
def test_gridsearch(self):
# np.random.seed(seed)
#
# # Check that base trees can be grid-searched.
mumbo = MuCumboClassifier(base_estimator=DecisionTreeClassifier())
parameters = {'n_estimators': (1, 2),
'base_estimator__max_depth': (1, 2)}
clf = GridSearchCV(mumbo, parameters)
clf.fit(self.iris.data, self.iris.target, views_ind=self.iris.views_ind)
self.assertEqual(clf.best_params_,{'base_estimator__max_depth': 2, 'n_estimators': 2})
multimodal_data = MultiModalArray(self.iris.data, views_ind=self.iris.views_ind)
clf = GridSearchCV(mumbo, parameters)
clf.fit(multimodal_data, self.iris.target)
self.assertEqual(clf.best_params_, {'base_estimator__max_depth': 2, 'n_estimators': 2})
# def test_pick le():
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# np.random.seed(seed)
#
# # Check pickability.
#
# clf = MuCumboClassifier()
# clf.fit(iris.data, iris.target, iris.views_ind)
# score = clf.score(iris.data, iris.target)
# dump = pickle.dumps(clf)
#
# clf_loaded = pickle.loads(dump)
# assert_equal(type(clf_loaded), clf.__class__)
# score_loaded = clf_loaded.score(iris.data, iris.target)
# assert_equal(score, score_loaded)
#
#
def test_base_estimator_score(self):
# np.random.seed(seed)
#
""" Test different base estimators."""
n_estimators = 5
clf = MuCumboClassifier(RandomForestClassifier(), n_estimators=n_estimators)
clf.fit(self.iris.data, self.iris.target, self.iris.views_ind)
score = clf.score(self.iris.data, self.iris.target)
self.assertGreater(score, 0.95, "Failed with score = {}".format(score))
clf = MuCumboClassifier(SVC(), n_estimators=n_estimators)
clf.fit(self.iris.data, self.iris.target, self.iris.views_ind)
score = clf.score(self.iris.data, self.iris.target)
self.assertGreater(score, 0.95, "Failed with score = {}".format(score))
# # Check that using a base estimator that doesn't support sample_weight
# # raises an error.
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self.assertRaises(ValueError, clf.fit, self.iris.data, self.iris.target, self.iris.views_ind)
# assert_raises(ValueError, clf.fit, iris.data, iris.target, iris.views_ind)
#
#
# def test_sparse_classification():
# # Check classification with sparse input.
#
# np.random.seed(seed)
#
# class CustomSVC(SVC):
# """SVC variant that records the nature of the training set."""
#
# def fit(self, X, y, sample_weight=None):
# """Modification on fit caries data type for later verification."""
# super(CustomSVC, self).fit(X, y, sample_weight=sample_weight)
# self.data_type_ = type(X)
# return self
#
# n_estimators = 5
# X_dense = iris.data
# y = iris.target
#
# for sparse_format in [csc_matrix, csr_matrix, lil_matrix, coo_matrix,
# dok_matrix]:
# for views_ind in (iris.views_ind, np.array([[0, 2], [1, 3]])):
# X_sparse = sparse_format(X_dense)
#
# clf_sparse = MuCumboClassifier(
# base_estimator=CustomSVC(),
# random_state=seed,
# n_estimators=n_estimators)
# clf_sparse.fit(X_sparse, y, views_ind)
#
# clf_dense = MuCumboClassifier(
# base_estimator=CustomSVC(),
# random_state=seed,
# n_estimators=n_estimators)
# clf_dense.fit(X_dense, y, views_ind)
#
# assert_array_equal(clf_sparse.decision_function(X_sparse),