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111 lines
3.6 KiB
111 lines
3.6 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import OpTest
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def Levenshtein(hyp, ref):
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""" Compute the Levenshtein distance between two strings.
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:param hyp: hypothesis string in index
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:type hyp: list
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:param ref: reference string in index
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:type ref: list
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"""
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m = len(hyp)
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n = len(ref)
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if m == 0:
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return n
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if n == 0:
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return m
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dist = np.zeros((m + 1, n + 1)).astype("float32")
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for i in range(0, m + 1):
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dist[i][0] = i
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for j in range(0, n + 1):
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dist[0][j] = j
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for i in range(1, m + 1):
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for j in range(1, n + 1):
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cost = 0 if hyp[i - 1] == ref[j - 1] else 1
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deletion = dist[i - 1][j] + 1
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insertion = dist[i][j - 1] + 1
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substitution = dist[i - 1][j - 1] + cost
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dist[i][j] = min(deletion, insertion, substitution)
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return dist[m][n]
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class TestEditDistanceOp(OpTest):
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def setUp(self):
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self.op_type = "edit_distance"
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normalized = False
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x1 = np.array([[0, 12, 3, 5, 8, 2]]).astype("int64")
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x2 = np.array([[0, 12, 4, 7, 8]]).astype("int64")
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x1 = np.transpose(x1)
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x2 = np.transpose(x2)
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x1_lod = [0, 1, 5]
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x2_lod = [0, 3, 4]
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num_strs = len(x1_lod) - 1
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distance = np.zeros((num_strs, 1)).astype("float32")
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sequence_num = np.array(2).astype("int64")
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for i in range(0, num_strs):
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distance[i] = Levenshtein(
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hyp=x1[x1_lod[i]:x1_lod[i + 1]],
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ref=x2[x2_lod[i]:x2_lod[i + 1]])
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if normalized is True:
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len_ref = x2_lod[i + 1] - x2_lod[i]
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distance[i] = distance[i] / len_ref
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self.attrs = {'normalized': normalized}
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self.inputs = {'Hyps': (x1, [x1_lod]), 'Refs': (x2, [x2_lod])}
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self.outputs = {'Out': distance, 'SequenceNum': sequence_num}
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def test_check_output(self):
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self.check_output()
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class TestEditDistanceOpNormalized(OpTest):
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def setUp(self):
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self.op_type = "edit_distance"
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normalized = True
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x1 = np.array([[0, 10, 3, 6, 5, 8, 2]]).astype("int64")
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x2 = np.array([[0, 10, 4, 6, 7, 8]]).astype("int64")
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x1 = np.transpose(x1)
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x2 = np.transpose(x2)
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x1_lod = [0, 1, 3, 6]
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x2_lod = [0, 2, 3, 5]
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num_strs = len(x1_lod) - 1
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distance = np.zeros((num_strs, 1)).astype("float32")
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sequence_num = np.array(3).astype("int64")
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for i in range(0, num_strs):
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distance[i] = Levenshtein(
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hyp=x1[x1_lod[i]:x1_lod[i + 1]],
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ref=x2[x2_lod[i]:x2_lod[i + 1]])
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if normalized is True:
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len_ref = x2_lod[i + 1] - x2_lod[i]
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distance[i] = distance[i] / len_ref
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self.attrs = {'normalized': normalized}
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self.inputs = {'Hyps': (x1, [x1_lod]), 'Refs': (x2, [x2_lod])}
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self.outputs = {'Out': distance, 'SequenceNum': sequence_num}
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def test_check_output(self):
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self.check_output()
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if __name__ == '__main__':
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unittest.main()
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