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# -*- coding: utf-8 -*-
"""Testing for the mumbo module."""
# Université d'Aix Marseille (AMU) -
# Centre National de la Recherche Scientifique (CNRS) -
# Université de Toulon (UTLN).
# Copyright © 2017-2018 AMU, CNRS, UTLN
#
# This file is part of multimodalboost.
#
# multimodalboost is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# multimodalboost is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with multimodalboost. If not, see <http://www.gnu.org/licenses/>.
#
# Author: Florent JAILLET - Laboratoire d'Informatique et Systèmes - UMR 7020
import pickle
import unittest
import numpy as np
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.utils.estimator_checks import check_estimator
from sklearn.ensemble import RandomForestClassifier
from sklearn.cluster import KMeans
from sklearn.tree import DecisionTreeClassifier
from sklearn import datasets
from multimodal.boosting.mumbo import MumboClassifier
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_sparse(self):
rng = np.random.RandomState(0)
X = rng.rand(40, 10)
X[X < .8] = 0
X_csr = csr_matrix(X)
clf = MumboClassifier()
y = (4 * rng.rand(40)).astype(np.int)
clf.fit(X_csr, y)
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def test_init_var(self):
n_classes = 3
n_views = 3
y = np.array([0, 2, 1, 2])
expected_cost = np.array(
[[[-2, 1, 1], [1, 1, -2], [1, -2, 1], [1, 1, -2]],
[[-2, 1, 1], [1, 1, -2], [1, -2, 1], [1, 1, -2]],
[[-2, 1, 1], [1, 1, -2], [1, -2, 1], [1, 1, -2]]],
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]))
clf = MumboClassifier()
clf.n_classes_ = n_classes
(cost, cost_glob, label_score, label_score_glob,
predicted_classes) = 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)
self.assertEqual(predicted_classes.shape, expected_predicted_classes_shape)
def test_compute_edge_global(self):
cost_global = np.array([[-2, 1, 1], [1, 1, -2], [1, -2, 1], [1, 1, -2]],
dtype=np.float64)
predicted_classes = np.array([[0, 0, 1, 1], [0, 1, 0, 2], [2, 2, 0, 0]])
y = np.array([0, 2, 1, 2])
expected_edge_global = np.array([0.25, 0.25, -0.125])
clf = MumboClassifier()
edge_global = clf._compute_edge_global(cost_global, predicted_classes, y)
np.testing.assert_equal(edge_global, expected_edge_global)
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 = MumboClassifier()
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)
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 = MumboClassifier()
coop_coef = clf._compute_coop_coef(predicted_classes, y)
np.testing.assert_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 = MumboClassifier()
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 = MumboClassifier()
alpha = clf._compute_alphas(edge)
np.testing.assert_almost_equal(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_compute_cost_global(self):
decimal = 12
label_score_glob = np.array(
[[-1, -2, 4], [-8, 1, 4], [2, 8, -4], [2, -1, 4]],
dtype=np.float64)
best_pred_classes = np.array([0, 1, 0, 2])
y = np.array([0, 2, 1, 2])
alpha = 0.5
expected_label_score_glob = np.array(
[[-0.5, -2, 4], [-8, 1.5, 4], [2.5, 8, -4], [2, -1, 4.5]],
dtype=np.float64)
clf = MumboClassifier()
cost_glob, label_score_glob = clf._compute_cost_global(
label_score_glob, best_pred_classes, y, alpha)
np.testing.assert_almost_equal(label_score_glob, expected_label_score_glob,
decimal)
label_score_glob = np.zeros((4, 3), dtype=np.float64)
alpha = 0.
expected_label_score_glob = np.zeros((4, 3), dtype=np.float64)
expected_cost_glob = np.array(
[[-2, 1, 1], [1, 1, -2], [1, -2, 1], [1, 1, -2]],
dtype=np.float64)
cost_glob, label_score_glob = clf._compute_cost_global(
label_score_glob, best_pred_classes, y, alpha)
np.testing.assert_equal(label_score_glob, expected_label_score_glob)
np.testing.assert_equal(cost_glob, expected_cost_glob, decimal)
label_score_glob = np.array(
[[0, 0, np.log(4)], [np.log(8), 0, 0], [0, 0, 0], [0, 0, 0]],
dtype=np.float64)
alpha = np.log(2)
expected_label_score_glob = np.array(
[[alpha, 0, np.log(4)],
[np.log(8), alpha, 0],
[alpha, 0, 0],
[0, 0, alpha]],
dtype=np.float64)
expected_cost_glob = np.array(
[[-2.5, 0.5, 2.], [8., 2., -10.], [2., -3., 1.], [0.5, 0.5, -1.]],
dtype=np.float64)
cost_glob, label_score_glob = clf._compute_cost_global(
label_score_glob, best_pred_classes, y, alpha)
np.testing.assert_almost_equal(label_score_glob, expected_label_score_glob,
decimal)
np.testing.assert_almost_equal(cost_glob, expected_cost_glob, decimal)
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)
pred_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)
clf = MumboClassifier()
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)
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 = MumboClassifier()
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 = MumboClassifier()
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 = MumboClassifier()
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(self):
seed = 7
np.random.seed(seed)
n_estimators = 10
#print("iris views ind", self.iris.views_ind)
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clf = MumboClassifier(n_estimators=n_estimators, best_view_mode='edge')
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 = MumboClassifier(n_estimators=n_estimators, best_view_mode='error')
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))
self.assertRaises(ValueError, MumboClassifier, best_view_mode='test')
clf = MumboClassifier()
clf.best_view_mode = 'test'
self.assertRaises(ValueError, clf.fit, self.iris.data,
self.iris.target, self.iris.views_ind)
def test_fit_arg(self):
seed = 7
np.random.seed(seed)
# Check that using the default value for views_ind corresponds to using 2
# views
X = np.array([[1., 1., 1.], [-1., -1., -1.]])
y = np.array([0, 1])
expected_views_ind = np.array([0, 1, 3])
clf = MumboClassifier()
clf.fit(X, y)
np.testing.assert_equal(clf.X_.views_ind, expected_views_ind)
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# Check that classes labels can be integers or strings and can be stored
# into any kind of sequence
views_ind = np.array([0, 1, 3])
y = np.array([3, 1])
clf = MumboClassifier()
clf.fit(X, y, views_ind)
np.testing.assert_equal(clf.predict(X), y)
y = np.array(["class_1", "class_2"])
clf = MumboClassifier()
clf.fit(X, y)
np.testing.assert_equal(clf.predict(X), y)
y = [1, 0]
clf = MumboClassifier()
clf.fit(X, y, views_ind)
np.testing.assert_equal(clf.predict(X), y)
y = (2, 1)
clf = MumboClassifier()
clf.fit(X, y, views_ind)
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)
X = ["str1", "str2"]
self.assertRaises(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 = MumboClassifier()
self.assertRaises(ValueError, clf.fit, X, y, views_ind)
y = np.array([1, 0, 0, 1])
views_ind = np.array([0, 1, 3])
clf = MumboClassifier()
self.assertRaises(ValueError, clf.fit, X, y, views_ind)
y = np.array([3.2, 1.1])
clf = MumboClassifier()
self.assertRaises(ValueError, clf.fit, X, y, views_ind)
y = np.array([0, 1])
views_ind = np.array([0, 3, 1])
clf = MumboClassifier()
self.assertRaises(ValueError, clf.fit, X, y, views_ind)
views_ind = np.array([-1, 1, 3])
clf = MumboClassifier()
self.assertRaises(ValueError, clf.fit, X, y, views_ind)
views_ind = np.array([0, 1, 4])
clf = MumboClassifier()
self.assertRaises(ValueError, clf.fit, X, y, views_ind)
views_ind = np.array([0.5, 1, 3])
clf = MumboClassifier()
self.assertRaises(ValueError, clf.fit, X, y, views_ind)
views_ind = np.array("test")
clf = MumboClassifier()
self.assertRaises(ValueError, clf.fit, X, y, views_ind)
views_ind = np.zeros((3, 2, 4))
clf = MumboClassifier()
self.assertRaises(ValueError, clf.fit, X, y, views_ind)
views_ind = np.array([[-1], [1, 2]])
clf = MumboClassifier()
self.assertRaises(ValueError, clf.fit, X, y, views_ind)
views_ind = np.array([[3], [1, 2]])
clf = MumboClassifier()
self.assertRaises(ValueError, clf.fit, X, y, views_ind)
views_ind = np.array([[0.5], [1, 2]])
clf = MumboClassifier()
self.assertRaises(ValueError, clf.fit, X, y, views_ind)
views_ind = np.array([[-1, 0], [1, 2]])
clf = MumboClassifier()
self.assertRaises(ValueError, clf.fit, X, y, views_ind)
views_ind = np.array([[0, 3], [1, 2]])
clf = MumboClassifier()
self.assertRaises(ValueError, clf.fit, X, y, views_ind)
views_ind = np.array([[0.5], [1], [2]])
clf = MumboClassifier()
self.assertRaises(ValueError, clf.fit, X, y, views_ind)
def test_decision_function_arg(self):
# Test that decision_function() gives proper exception on deficient input.
seed = 7
np.random.seed(seed)
clf = MumboClassifier()
clf.fit(self.iris.data, self.iris.target, self.iris.views_ind)
X = np.zeros((4, 3))
self.assertRaises(ValueError, clf.decision_function, X)
X = np.zeros((4, 5))
self.assertRaises(ValueError, clf.decision_function, X)
X = np.zeros((5, 4, 2))
self.assertRaises(ValueError, clf.decision_function, X)
X = ["str1", "str2"]
self.assertRaises(ValueError, clf.decision_function, X)
def test_limit_cases(self):
seed = 7
np.random.seed(seed)
# Check that using empty data raises an exception
X = np.array([[]])
y = np.array([])
clf = MumboClassifier()
self.assertRaises(ValueError, clf.fit, X, y)
# Check that fit() works for the smallest possible dataset
X = np.array([[0.]])
y = np.array([0])
clf = MumboClassifier()
clf.fit(X, y)
np.testing.assert_equal(clf.predict(X), y)
np.testing.assert_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 = MumboClassifier()
clf.fit(X, y, views_ind)
np.testing.assert_equal(clf.predict(X), y)
np.testing.assert_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 = MumboClassifier()
clf.fit(X, y, views_ind)
np.testing.assert_equal(clf.predict(X), y)
np.testing.assert_equal(clf.predict(np.array([[-1., 0., 1.]])), np.array([1]))
def test_simple_examples(self):
seed =7
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 = MumboClassifier()
clf.fit(X, y, views_ind)
np.testing.assert_equal(clf.predict(X), y)
np.testing.assert_equal(clf.predict(np.array([[1., 1.], [-1., -1.]])),
np.array([0, 1]))
X = clf._global_X_transform(X, clf.X_.views_ind)
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self.assertEqual(clf.decision_function(X).shape, y.shape)
views_ind = np.array([[1, 0]])
clf = MumboClassifier()
clf.fit(X, y, views_ind)
np.testing.assert_equal(clf.predict(X), y)
np.testing.assert_equal(clf.predict(np.array([[1., 1.], [-1., -1.]])),
np.array([0, 1]))
self.assertEqual(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 = MumboClassifier()
clf.fit(X, y, views_ind)
np.testing.assert_equal(clf.predict(X), y)
np.testing.assert_equal(clf.predict(np.array([[1., 1., 1.], [-1., -1., -1.]])),
np.array([0, 1]))
self.assertEqual(clf.decision_function(X).shape, y.shape)
views_ind = np.array([[2, 0], [1]])
clf = MumboClassifier()
clf.fit(X, y, views_ind)
np.testing.assert_equal(clf.predict(X), y)
np.testing.assert_equal(clf.predict(np.array([[1., 1., 1.], [-1., -1., -1.]])),
np.array([0, 1]))
self.assertEqual(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 = MumboClassifier()
clf.fit(X, y, views_ind)
np.testing.assert_equal(clf.predict(X), y)
data = np.array([[1., 1., 1., 1., 1.], [-1., -1., -1., -1., -1.]])
np.testing.assert_equal(clf.predict(data), np.array([0, 1]))
self.assertEqual(clf.decision_function(X).shape, y.shape)
views_ind = np.array([[2, 0], [1], [3, 4]])
clf = MumboClassifier()
clf.fit(X, y, views_ind)
np.testing.assert_equal(clf.predict(X), y)
data = np.array([[1., 1., 1., 1., 1.], [-1., -1., -1., -1., -1.]])
np.testing.assert_equal(clf.predict(data), np.array([0, 1]))
self.assertEqual(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 = MumboClassifier()
clf.fit(X, y, views_ind)
np.testing.assert_equal(clf.predict(X), y)
data = np.array(
[[1., -1., 1., 1., -1.],
[1., 1., 1., 1., 1.],
[-1., -1., -1., -1., -1.]])
np.testing.assert_equal(clf.predict(data), np.array([0, 1, 2]))
self.assertEqual(clf.decision_function(X).shape, (X.shape[0], 3))
views_ind = np.array([[1, 0], [2], [3, 4]])
clf = MumboClassifier()
clf.fit(X, y, views_ind)
np.testing.assert_equal(clf.predict(X), y)
data = np.array(
[[1., -1., 1., 1., -1.],
[1., 1., 1., 1., 1.],
[-1., -1., -1., -1., -1.]])
np.testing.assert_equal(clf.predict(data), np.array([0, 1, 2]))
self.assertEqual(clf.decision_function(X).shape, (X.shape[0], 3))
def test_generated_examples(self):
seed = 7
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 = MumboClassifier(n_estimators=1)
clf.fit(X, y, views_ind)
self.assertEqual(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 = MumboClassifier(n_estimators=3)
clf.fit(X, y, views_ind)
self.assertEqual(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 = MumboClassifier(n_estimators=3)
clf.fit(X, y, views_ind)
self.assertEqual(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 = MumboClassifier(n_estimators=4)
clf.fit(X, y, views_ind)
self.assertEqual(clf.score(X, y), 1.)
def test_classifier(self):
# X_zero_features = np.empty(0).reshape(3, 0)
# y = np.array([1, 0, 1])
# e = MumboClassifier()
# e.fit(X_zero_features, y)
# print(e.predict(X_zero_features))
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return check_estimator(MumboClassifier)
def test_iris(self):
# Check consistency on dataset iris.
seed = 7
np.random.seed(seed)
n_estimators = 5
classes = np.unique(self.iris.target)
for views_ind in [self.iris.views_ind, np.array([[0, 2], [1, 3]])]:
clf = MumboClassifier(n_estimators=n_estimators)
clf.fit(self.iris.data, self.iris.target, views_ind)
self.assertTrue(np.all((0. <= clf.estimator_errors_)
& (clf.estimator_errors_ <= 1.)))
self.assertTrue(np.all(np.diff(clf.estimator_errors_) < 0.))
np.testing.assert_equal(classes, clf.classes_)
self.assertEqual(clf.decision_function(self.iris.data).shape[1], len(classes))
score = clf.score(self.iris.data, self.iris.target)
self.assertGreater(score, 0.95, "Failed with score = {}".format(score))
self.assertEqual(len(clf.estimators_), n_estimators)
# Check for distinct random states
self.assertEqual(len(set(est.random_state for est in clf.estimators_)),
len(clf.estimators_))
def test_staged_methods(self):
n_estimators = 10
seed = 7
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]])),
)
for X, y, views_ind in data:
clf = MumboClassifier(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)
for ind in range(n_estimators):
clf = MumboClassifier(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)
np.testing.assert_equal(dec_func, staged_dec_func[ind])
np.testing.assert_equal(predict, staged_predict[ind])
self.assertEqual(score, staged_score[ind])
def test_gridsearch(self):
seed = 7
np.random.seed(seed)
# Check that base trees can be grid-searched.
mumbo = MumboClassifier(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)
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def test_pickle(self):
seed = 7
np.random.seed(seed)
# Check pickability.
clf = MumboClassifier()
clf.fit(self.iris.data, self.iris.target, self.iris.views_ind)
score = clf.score(self.iris.data, self.iris.target)
dump = pickle.dumps(clf)
clf_loaded = pickle.loads(dump)
self.assertEqual(type(clf_loaded), clf.__class__)
score_loaded = clf_loaded.score(self.iris.data, self.iris.target)
self.assertEqual(score, score_loaded)
def test_base_estimator(self):
seed = 7
np.random.seed(seed)
# Test different base estimators.
n_estimators = 5
clf = MumboClassifier(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 = MumboClassifier(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.
clf = MumboClassifier(KMeans())
self.assertRaises(ValueError, clf.fit, self.iris.data, self.iris.target, self.iris.views_ind)
def test_sparse_classification(self):
# Check classification with sparse input.
seed = 7
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 = self.iris.data
y = self.iris.target
for sparse_format in [csc_matrix, csr_matrix]: #, lil_matrix, coo_matrix,dok_matrix]:
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for views_ind in (self.iris.views_ind, np.array([[0, 2], [1, 3]])):
X_sparse = sparse_format(X_dense)
clf_sparse = MumboClassifier(
base_estimator=CustomSVC(),
random_state=seed,
n_estimators=n_estimators)
clf_sparse.fit(X_sparse, y, views_ind)
clf_dense = MumboClassifier(
base_estimator=CustomSVC(),
random_state=seed,
n_estimators=n_estimators)
clf_dense.fit(X_dense, y, views_ind)
np.testing.assert_equal(clf_sparse.decision_function(X_sparse),
clf_dense.decision_function(X_dense))
np.testing.assert_equal(clf_sparse.predict(X_sparse),
clf_dense.predict(X_dense))
self.assertEqual(clf_sparse.score(X_sparse, y),
clf_dense.score(X_dense, y))
for res_sparse, res_dense in \
zip(clf_sparse.staged_decision_function(X_sparse),
clf_dense.staged_decision_function(X_dense)):
np.testing.assert_equal(res_sparse, res_dense)
for res_sparse, res_dense in \
zip(clf_sparse.staged_predict(X_sparse),
clf_dense.staged_predict(X_dense)):
np.testing.assert_equal(res_sparse, res_dense)
for res_sparse, res_dense in \
zip(clf_sparse.staged_score(X_sparse, y),
clf_dense.staged_score(X_dense, y)):
np.testing.assert_equal(res_sparse, res_dense)
# Check that sparsity of data is maintained during training
types = [clf.data_type_ for clf in clf_sparse.estimators_]
if sparse_format == csc_matrix:
self.assertTrue(all([issubclass(type_, csc_matrix) for type_ in types]))
self.assertTrue(all([issubclass(type_, csr_matrix) for type_ in types]))