test/test_alignment.py
author Simon Chabot <simon.chabot@logilab.fr>
Thu, 21 Mar 2019 16:45:25 +0100
changeset 550 4d286f4b8027
parent 541 78ef292acda7
permissions -rw-r--r--
[aligner] Use the processings' weight to compute the distance matrix All the processing objects have a `weight` attribute so they can be mixed each other with their own weighting. Let's use it ! This attribute was unsed until now. Differential Revision: https://phab.logilab.fr/D2066

# -*- coding:utf-8 -*-
#
# copyright 2012 LOGILAB S.A. (Paris, FRANCE), all rights reserved.
# contact http://www.logilab.fr -- mailto:contact@logilab.fr
#
# This program 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 2.1 of the License, or (at your option)
# any later version.
#
# This program 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 this program. If not, see <http://www.gnu.org/licenses/>.

from __future__ import absolute_import
import sys
import six
if sys.version_info >= (2, 7):
    import unittest
else:
    import unittest2 as unittest
import random
random.seed(6)  # Make sure tests are repeatable
from os import path

import numpy as np

from nazca.utils.normalize import simplify
import nazca.rl.aligner as alig
import nazca.rl.blocking as blo
from nazca.utils.distances import LevenshteinProcessing, GeographicalProcessing


TESTDIR = path.dirname(__file__)


class AlignerTestCase(unittest.TestCase):

    def test_align(self):
        refset = [['V1', 'label1', (6.14194444444, 48.67)],
                  ['V2', 'label2', (6.2, 49)],
                  ['V3', 'label3', (5.1, 48)],
                  ['V4', 'label4', (5.2, 48.1)],
                  ]
        targetset = [['T1', 'labelt1', (6.17, 48.7)],
                     ['T2', 'labelt2', (5.3, 48.2)],
                     ['T3', 'labelt3', (6.25, 48.91)],
                     ]
        # Creation of the aligner object
        processings = (GeographicalProcessing(2, 2, units='km'),)
        aligner = alig.BaseAligner(threshold=30, processings=processings)
        mat, matched = aligner.align(refset, targetset)
        true_matched = [(0, 0), (0, 2), (1, 2), (3, 1)]
        for k, values in six.iteritems(matched):
            for v, distance in values:
                self.assertIn((k, v), true_matched)

    def test_align_with_weight(self):
        refset = [
            ['V1', 'l1', (6.14194444444, 48.67)],
            ['V2', 'l2', (6.2, 49)],
            ['V3', 'l3', (5.1, 48)],
            ['V4', 'l4', (5.2, 48.1)],
        ]
        targetset = [
            ['T1', 'l1', (6.17, 48.7)],
            ['T2', 'l1', (6.14, 48.73)],
            ['T3', 'l4', (6.25, 48.91)],
        ]

        ref_indexes = range(len(refset))
        target_indexes = range(len(targetset))

        # Compute the distance matrix on the label only (weight=1)
        aligner = alig.BaseAligner(
            threshold=0.2,
            processings=(LevenshteinProcessing(1, 1), ),
        )
        mat_difflib = aligner.compute_distance_matrix(
            refset,
            targetset,
            ref_indexes,
            target_indexes,
        )

        # Compute the distance matrix on the location only (weight=1)
        aligner = alig.BaseAligner(
            threshold=0.2,
            processings=(GeographicalProcessing(2, 2), )
        )
        mat_geo = aligner.compute_distance_matrix(
            refset,
            targetset,
            ref_indexes,
            target_indexes,
        )

        # Compute the distance matrix with label and location (same weight)
        aligner = alig.BaseAligner(
            threshold=0.2,
            processings=(
                LevenshteinProcessing(1, 1),
                GeographicalProcessing(2, 2),
            )
        )
        mat_difflib_geo = aligner.compute_distance_matrix(
            refset,
            targetset,
            ref_indexes,
            target_indexes,
        )

        np.testing.assert_almost_equal(mat_difflib_geo, mat_geo + mat_difflib)

        # Same aligner, but the processings have ≠ weights, to show that the
        # weight is taken into account in the final result
        aligner = alig.BaseAligner(
            threshold=0.2,
            processings=(
                LevenshteinProcessing(1, 1, weight=0.8),
                GeographicalProcessing(2, 2, weight=0.2),
            )
        )
        mat_difflib_geo = aligner.compute_distance_matrix(
            refset,
            targetset,
            ref_indexes,
            target_indexes,
        )

        np.testing.assert_almost_equal(
            mat_difflib_geo,
            0.2*mat_geo + 0.8*mat_difflib
        )


    def test_blocking_align(self):
        refset = [['V1', 'label1', (6.14194444444, 48.67)],
                  ['V2', 'label2', (6.2, 49)],
                  ['V3', 'label3', (5.1, 48)],
                  ['V4', 'label4', (5.2, 48.1)],
                  ]
        targetset = [['T1', 'labelt1', (6.17, 48.7)],
                     ['T2', 'labelt2', (5.3, 48.2)],
                     ['T3', 'labelt3', (6.25, 48.91)],
                     ]
        # Creation of the aligner object
        true_matched = set([(0, 0), (0, 2), (1, 2), (3, 1)])
        processings = (GeographicalProcessing(2, 2, units='km'),)
        aligner = alig.BaseAligner(threshold=30, processings=processings)
        blocking = blo.KdTreeBlocking(ref_attr_index=2,
                                      target_attr_index=2,
                                      threshold=0.3)
        blocking.fit(refset, targetset)
        predict_matched = set()
        for alignind, targetind in blocking.iter_indice_blocks():
            mat, matched = aligner._get_match(refset, targetset, alignind, targetind)
            for k, values in six.iteritems(matched):
                for v, distance in values:
                    predict_matched.add((k, v))
        self.assertEqual(true_matched, predict_matched)

    def test_blocking_align_2(self):
        refset = [['V1', 'label1', (6.14194444444, 48.67)],
                  ['V2', 'label2', (6.2, 49)],
                  ['V3', 'label3', (5.1, 48)],
                  ['V4', 'label4', (5.2, 48.1)],
                  ]
        targetset = [['T1', 'labelt1', (6.17, 48.7)],
                     ['T2', 'labelt2', (5.3, 48.2)],
                     ['T3', 'labelt3', (6.25, 48.91)],
                     ]
        # Creation of the aligner object
        true_matched = set([(0, 0), (0, 2), (1, 2), (3, 1)])
        processings = (GeographicalProcessing(2, 2, units='km'),)
        aligner = alig.BaseAligner(threshold=30, processings=processings)
        aligner.register_blocking(blo.KdTreeBlocking(ref_attr_index=2,
                                                     target_attr_index=2,
                                                     threshold=0.3))
        global_mat, global_matched = aligner.align(refset, targetset)
        predict_matched = set()
        for k, values in six.iteritems(global_matched):
            for v, distance in values:
                predict_matched.add((k, v))
        self.assertEqual(true_matched, predict_matched)

    def test_unique_align(self):
        refset = [['V1', 'label1', (6.14194444444, 48.67)],
                  ['V2', 'label2', (6.2, 49)],
                  ['V3', 'label3', (5.1, 48)],
                  ['V4', 'label4', (5.2, 48.1)],
                  ]
        targetset = [['T1', 'labelt1', (6.17, 48.7)],
                     ['T2', 'labelt2', (5.3, 48.2)],
                     ['T3', 'labelt3', (6.25, 48.91)],
                     ]
        all_matched = [(('V1', 0), ('T3', 2)), (('V1', 0), ('T1', 0)),
                       (('V2', 1), ('T3', 2)), (('V4', 3), ('T2', 1))]
        uniq_matched = [(('V1', 0), ('T1', 0)), (('V2', 1), ('T3', 2)), (('V4', 3), ('T2', 1))]
        processings = (GeographicalProcessing(2, 2, units='km'),)
        aligner = alig.BaseAligner(threshold=30, processings=processings)
        aligner.register_blocking(blo.KdTreeBlocking(ref_attr_index=2,
                                                     target_attr_index=2,
                                                     threshold=0.3))
        unimatched = list(aligner.get_aligned_pairs(refset, targetset, unique=True))
        unimatched_wo_distance = [r[:2] for r in unimatched]
        matched = list(aligner.get_aligned_pairs(refset, targetset, unique=False))
        matched_wo_distance = [r[:2] for r in matched]
        self.assertEqual(len(matched), len(all_matched))
        for m in all_matched:
            self.assertIn(m, matched_wo_distance)
        self.assertEqual(len(unimatched), len(uniq_matched))
        for m in uniq_matched:
            self.assertIn(m, unimatched_wo_distance)

    def test_align_from_file(self):
        uniq_matched = [(('V1', 0), ('T1', 0)), (('V2', 1), ('T3', 2)), (('V4', 3), ('T2', 1))]
        processings = (GeographicalProcessing(2, 2, units='km'),)
        aligner = alig.BaseAligner(threshold=30, processings=processings)
        aligner.register_blocking(blo.KdTreeBlocking(ref_attr_index=2,
                                                     target_attr_index=2,
                                                     threshold=0.3))
        matched = list(aligner.get_aligned_pairs_from_files(path.join(TESTDIR, 'data',
                                                                      'alignfile.csv'),
                                                            path.join(TESTDIR, 'data',
                                                                      'targetfile.csv'),
                                                            ref_indexes=[0, 1, (2, 3)],
                                                            target_indexes=[0, 1, (2, 3)],))
        matched_wo_distance = [r[:2] for r in matched]
        self.assertEqual(len(matched), len(uniq_matched))
        for m in uniq_matched:
            self.assertIn(m, matched_wo_distance)


class PipelineAlignerTestCase(unittest.TestCase):

    def test_pipeline_align_pairs(self):
        refset = [['V1', 'aaa', (6.14194444444, 48.67)],
                  ['V2', 'bbb', (6.2, 49)],
                  ['V3', 'ccc', (5.1, 48)],
                  ['V4', 'ddd', (5.2, 48.1)],
                  ]
        targetset = [['T1', 'zzz', (6.17, 48.7)],
                     ['T2', 'eec', (5.3, 48.2)],
                     ['T3', 'fff', (6.25, 48.91)],
                     ['T4', 'ccd', (0, 0)],
                     ]
        # Creation of the aligner object
        processings = (GeographicalProcessing(2, 2, units='km'),)
        aligner_1 = alig.BaseAligner(threshold=30, processings=processings)
        processings = (LevenshteinProcessing(1, 1),)
        aligner_2 = alig.BaseAligner(threshold=1, processings=processings)
        pipeline = alig.PipelineAligner((aligner_1, aligner_2))
        uniq_matched = [(('V1', 0), ('T1', 0)), (('V2', 1), ('T3', 2)),
                        (('V4', 3), ('T2', 1)), (('V3', 2), ('T4', 3))]
        matched = list(pipeline.get_aligned_pairs(refset, targetset, unique=True))
        matched_wo_distance = [r[:2] for r in matched]
        self.assertEqual(len(matched), len(uniq_matched))
        for m in uniq_matched:
            self.assertIn(m, matched_wo_distance)


if __name__ == '__main__':
    unittest.main()