forked from huawei/mindspore2022
234 lines
8.4 KiB
Python
234 lines
8.4 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ==============================================================================
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"""
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Testing UnicodeCharTokenizer op in DE
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"""
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import numpy as np
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import mindspore.dataset as ds
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from mindspore import log as logger
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import mindspore.dataset.text as nlp
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DATA_FILE = "../data/dataset/testTokenizerData/1.txt"
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NORMALIZE_FILE = "../data/dataset/testTokenizerData/normalize.txt"
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REGEX_REPLACE_FILE = "../data/dataset/testTokenizerData/regex_replace.txt"
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REGEX_TOKENIZER_FILE = "../data/dataset/testTokenizerData/regex_tokenizer.txt"
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def split_by_unicode_char(input_strs):
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"""
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Split utf-8 strings to unicode characters
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"""
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out = []
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for s in input_strs:
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out.append([c for c in s])
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return out
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def test_unicode_char_tokenizer():
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"""
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Test UnicodeCharTokenizer
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"""
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input_strs = ("Welcome to Beijing!", "北京欢迎您！", "我喜欢English!", " ")
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dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
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tokenizer = nlp.UnicodeCharTokenizer()
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dataset = dataset.map(operations=tokenizer)
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tokens = []
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for i in dataset.create_dict_iterator():
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text = nlp.to_str(i['text']).tolist()
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tokens.append(text)
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logger.info("The out tokens is : {}".format(tokens))
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assert split_by_unicode_char(input_strs) == tokens
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def test_whitespace_tokenizer():
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"""
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Test WhitespaceTokenizer
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"""
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whitespace_strs = [["Welcome", "to", "Beijing!"],
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["北京欢迎您！"],
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["我喜欢English!"],
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[""]]
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dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
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tokenizer = nlp.WhitespaceTokenizer()
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dataset = dataset.map(operations=tokenizer)
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tokens = []
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for i in dataset.create_dict_iterator():
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text = nlp.to_str(i['text']).tolist()
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tokens.append(text)
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logger.info("The out tokens is : {}".format(tokens))
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assert whitespace_strs == tokens
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def test_unicode_script_tokenizer():
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"""
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Test UnicodeScriptTokenizer when para keep_whitespace=False
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"""
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unicode_script_strs = [["Welcome", "to", "Beijing", "!"],
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["北京欢迎您", "！"],
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["我喜欢", "English", "!"],
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[""]]
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dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
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tokenizer = nlp.UnicodeScriptTokenizer(keep_whitespace=False)
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dataset = dataset.map(operations=tokenizer)
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tokens = []
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for i in dataset.create_dict_iterator():
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text = nlp.to_str(i['text']).tolist()
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tokens.append(text)
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logger.info("The out tokens is : {}".format(tokens))
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assert unicode_script_strs == tokens
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def test_unicode_script_tokenizer2():
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"""
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Test UnicodeScriptTokenizer when para keep_whitespace=True
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"""
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unicode_script_strs2 = [["Welcome", " ", "to", " ", "Beijing", "!"],
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["北京欢迎您", "！"],
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["我喜欢", "English", "!"],
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[" "]]
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dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
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tokenizer = nlp.UnicodeScriptTokenizer(keep_whitespace=True)
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dataset = dataset.map(operations=tokenizer)
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tokens = []
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for i in dataset.create_dict_iterator():
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text = nlp.to_str(i['text']).tolist()
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tokens.append(text)
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logger.info("The out tokens is :", tokens)
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assert unicode_script_strs2 == tokens
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def test_case_fold():
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"""
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Test CaseFold
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"""
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expect_strs = ["welcome to beijing!", "北京欢迎您!", "我喜欢english!", " "]
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dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
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op = nlp.CaseFold()
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dataset = dataset.map(operations=op)
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lower_strs = []
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for i in dataset.create_dict_iterator():
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text = nlp.to_str(i['text']).tolist()
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lower_strs.append(text)
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assert lower_strs == expect_strs
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def test_normalize_utf8():
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"""
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Test NormalizeUTF8
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"""
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def normalize(normalize_form):
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dataset = ds.TextFileDataset(NORMALIZE_FILE, shuffle=False)
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normalize = nlp.NormalizeUTF8(normalize_form=normalize_form)
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dataset = dataset.map(operations=normalize)
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out_bytes = []
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out_texts = []
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for i in dataset.create_dict_iterator():
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out_bytes.append(i['text'])
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out_texts.append(nlp.to_str(i['text']).tolist())
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logger.info("The out bytes is : ", out_bytes)
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logger.info("The out texts is: ", out_texts)
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return out_bytes
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expect_normlize_data = [
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# NFC
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[b'\xe1\xb9\xa9', b'\xe1\xb8\x8d\xcc\x87', b'q\xcc\xa3\xcc\x87',
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b'\xef\xac\x81', b'2\xe2\x81\xb5', b'\xe1\xba\x9b\xcc\xa3'],
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# NFKC
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[b'\xe1\xb9\xa9', b'\xe1\xb8\x8d\xcc\x87', b'q\xcc\xa3\xcc\x87',
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b'fi', b'25', b'\xe1\xb9\xa9'],
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# NFD
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[b's\xcc\xa3\xcc\x87', b'd\xcc\xa3\xcc\x87', b'q\xcc\xa3\xcc\x87',
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b'\xef\xac\x81', b'2\xe2\x81\xb5', b'\xc5\xbf\xcc\xa3\xcc\x87'],
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# NFKD
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[b's\xcc\xa3\xcc\x87', b'd\xcc\xa3\xcc\x87', b'q\xcc\xa3\xcc\x87',
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b'fi', b'25', b's\xcc\xa3\xcc\x87']
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]
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assert normalize(nlp.utils.NormalizeForm.NFC) == expect_normlize_data[0]
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assert normalize(nlp.utils.NormalizeForm.NFKC) == expect_normlize_data[1]
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assert normalize(nlp.utils.NormalizeForm.NFD) == expect_normlize_data[2]
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assert normalize(nlp.utils.NormalizeForm.NFKD) == expect_normlize_data[3]
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def test_regex_replace():
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"""
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Test RegexReplace
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"""
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def regex_replace(first, last, expect_str, pattern, replace):
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dataset = ds.TextFileDataset(REGEX_REPLACE_FILE, shuffle=False)
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if first > 1:
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dataset = dataset.skip(first - 1)
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if last >= first:
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dataset = dataset.take(last - first + 1)
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replace_op = nlp.RegexReplace(pattern, replace)
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dataset = dataset.map(operations=replace_op)
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out_text = []
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for i in dataset.create_dict_iterator():
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text = nlp.to_str(i['text']).tolist()
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out_text.append(text)
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logger.info("Out:", out_text)
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logger.info("Exp:", expect_str)
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assert expect_str == out_text
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regex_replace(1, 2, ['H____ W____', "L__'_ G_"], "\\p{Ll}", '_')
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regex_replace(3, 5, ['hello', 'world', '31:beijing'], "^(\\d:|b:)", "")
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regex_replace(6, 6, ["WelcometoChina!"], "\\s+", "")
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regex_replace(7, 8, ['我不想长大', 'WelcometoShenzhen!'], "\\p{Cc}|\\p{Cf}|\\s+", "")
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def test_regex_tokenizer():
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"""
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Test RegexTokenizer
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"""
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def regex_tokenizer(first, last, expect_str, delim_pattern, keep_delim_pattern):
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dataset = ds.TextFileDataset(REGEX_TOKENIZER_FILE, shuffle=False)
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if first > 1:
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dataset = dataset.skip(first - 1)
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if last >= first:
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dataset = dataset.take(last - first + 1)
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tokenizer_op = nlp.RegexTokenizer(delim_pattern, keep_delim_pattern)
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dataset = dataset.map(operations=tokenizer_op)
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out_text = []
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count = 0
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for i in dataset.create_dict_iterator():
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text = nlp.to_str(i['text']).tolist()
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np.testing.assert_array_equal(text, expect_str[count])
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count += 1
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out_text.append(text)
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logger.info("Out:", out_text)
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logger.info("Exp:", expect_str)
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regex_tokenizer(1, 1, [['Welcome', 'to', 'Shenzhen!']], "\\s+", "")
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regex_tokenizer(1, 1, [['Welcome', ' ', 'to', ' ', 'Shenzhen!']], "\\s+", "\\s+")
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regex_tokenizer(2, 2, [['北', '京', '欢', '迎', '您', '!Welcome to Beijing!']], r"\p{Han}", r"\p{Han}")
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regex_tokenizer(3, 3, [['12', '￥+', '36', '￥=?']], r"[\p{P}|\p{S}]+", r"[\p{P}|\p{S}]+")
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regex_tokenizer(3, 3, [['12', '36']], r"[\p{P}|\p{S}]+", "")
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regex_tokenizer(3, 3, [['￥+', '￥=?']], r"[\p{N}]+", "")
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if __name__ == '__main__':
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test_unicode_char_tokenizer()
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test_whitespace_tokenizer()
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test_unicode_script_tokenizer()
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test_unicode_script_tokenizer2()
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test_case_fold()
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test_normalize_utf8()
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test_regex_replace()
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test_regex_tokenizer()
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