Python fuzz.ratio方法代碼示例

# 需要導入模塊: from fuzzywuzzy import fuzz [as 別名]
# 或者: from fuzzywuzzy.fuzz import ratio [as 別名]
def tieBreak(self, query, i, j):
        """
        當去除停用詞後導致兩個字串的匹配度一樣時，從原文裡挑選出更適合的

        Args:
            - query: 使用者的輸入
            - i: index 為 i 的 title
            - j: index 為 j 的 title

        Return: (target, index)
            - target: 較適合的標題
            - index : 該標題的 id
        """
        raw1 = self.titles[i]
        raw2 = self.titles[j]

        r1 = fuzz.ratio(query, raw1)
        r2 = fuzz.ratio(query, raw2)

        if r1 > r2:
            return (raw1,i)
        else:
            return (raw2,j) 

# 需要導入模塊: from fuzzywuzzy import fuzz [as 別名]
# 或者: from fuzzywuzzy.fuzz import ratio [as 別名]
def test_ratio():
    """Test Korean-specific fuzzy search."""

    assert fuzz.ratio('강', '공') == 0
    assert ratio('강', '공') == 67

    assert fuzz.ratio('안녕', '인형') == 0
    assert ratio('안녕', '인형') == 67

    assert fuzz.ratio('사당', 'ㅅㄷ') == 0
    assert ratio('사당', 'ㅅㄷ') == 57

    assert fuzz.ratio('사당', 'ㅏㅏ') == 0
    assert ratio('사당', 'ㅏㅏ') == 57

    assert fuzz.ratio('사당', 'ㅅㅏㄷㅏㅇ') == 0
    assert ratio('사당', 'ㅅㅏㄷㅏㅇ') == 80 

# 需要導入模塊: from fuzzywuzzy import fuzz [as 別名]
# 或者: from fuzzywuzzy.fuzz import ratio [as 別名]
def is_mispelling(self, token1, token2):
        mis_t1 = set(self.get_misspellings(token1))
        mis_t2 = set(self.get_misspellings(token2))
        common = mis_t1.intersection(mis_t2).difference({''})  # Difference in case '' included in tokens

        if len(common) > 0:
            return True

        # Misspellings only really make sense if the tokens are words not numbers
        if token1.isalpha() and token2.isalpha():
            if fuzz.ratio(token1, token2) > self.fuzz_ratio_threshold:
                return True

        try:
            t1f = float(token1)
            t2f = float(token2)
            if max(t1f, t2f)/min(t1f, t2f) < self.number_fuzz_threshold:
                return True

        except (ValueError, ZeroDivisionError):
            pass



        return False 

# 需要導入模塊: from fuzzywuzzy import fuzz [as 別名]
# 或者: from fuzzywuzzy.fuzz import ratio [as 別名]
def get_fixture_channels(self, events, fixture):
    chann = []
    items = []

    for item in events:
      evnt = item['event']
      comp = fuzz.ratio(fixture.competition.name, evnt['competition'])
      home = fuzz.ratio(fixture.home_team.name, evnt['home'])
      away = fuzz.ratio(fixture.away_team.name, evnt['away'])
      comb = (comp + home + away) / 3

      items.append({ 'ratio': comb, 'channels': item['channels'] })

    if items:
      sort = sorted(items, key=itemgetter('ratio'), reverse=True)[0]

      if sort['ratio'] > 70:
        chann = self.data.get_multiple('channel', 'name', sort['channels'])
        chann = [c.id for c in chann]

    return chann 

# 需要導入模塊: from fuzzywuzzy import fuzz [as 別名]
# 或者: from fuzzywuzzy.fuzz import ratio [as 別名]
def ratio(string_a, string_b):
        """At the most basic level, return a Levenshtein distance ratio via
        fuzzywuzzy.
        :param string_a: str
        :param string_b: str
        :return: float
        """
        from cltk.utils.cltk_logger import logger
        try:
            from fuzzywuzzy import fuzz

        except ImportError as imp_err:  # pragma: no cover
            message = "'fuzzywuzzy' library required for this module: %s. Install with " \
                      "`pip install fuzzywuzzy python-Levenshtein`" % imp_err
            logger.error(message)
            print(message)
            raise ImportError

        return fuzz.ratio(string_a, string_b) / 100 

# 需要導入模塊: from fuzzywuzzy import fuzz [as 別名]
# 或者: from fuzzywuzzy.fuzz import ratio [as 別名]
def similar_string_fast(first_string, second_string):
    """Determine if two strings are similar (using two most effective methods).

    Params:
    - first_string: (type: string) first string.
    - second_string: (type: string) second string.

    Returns:
    - result: (type: bool) match result.
    """
    partial_score = fuzz.ratio(first_string, second_string)
    token_score = fuzz.token_set_ratio(first_string, second_string)

    if max(partial_score, token_score) >= SCORE_THRESHOLD_FAST:
        return True

    return False 

# 需要導入模塊: from fuzzywuzzy import fuzz [as 別名]
# 或者: from fuzzywuzzy.fuzz import ratio [as 別名]
def _get_page(self, topic, request_options=None):
        topics_list = self.get_topics_list()
        if topic.startswith(':'):
            topics_list = [x for x in topics_list if x.startswith(':')]
        else:
            topics_list = [x for x in topics_list if not x.startswith(':')]

        if _USING_FUZZYWUZZY:
            possible_topics = process.extract(topic, topics_list, scorer=fuzz.ratio)[:3]
        else:
            possible_topics = process.extract(topic, topics_list, limit=3, scorer=fuzz.ratio)
        possible_topics_text = "\n".join([("    * %s %s" % x) for x in possible_topics])
        return """
Unknown topic.
Do you mean one of these topics maybe?

%s
    """ % possible_topics_text 

# 需要導入模塊: from fuzzywuzzy import fuzz [as 別名]
# 或者: from fuzzywuzzy.fuzz import ratio [as 別名]
def findItemName(self, itemDictionary, messageItem):

		bestScore = 0
		score = 0
		bestItem = None

		try:
			for itemName, itemLabel in list(itemDictionary.items()):
				score = fuzz.ratio(messageItem, itemLabel)
				if score > bestScore:
					bestScore = score
					bestItem = itemName
		except KeyError:
                    pass

		return bestItem 

# 需要導入模塊: from fuzzywuzzy import fuzz [as 別名]
# 或者: from fuzzywuzzy.fuzz import ratio [as 別名]
def normalize(self,string):
        original_string = string
        string = string.lower()
        if string[0] == 'x':
            if len(string) == 1:
                return 'tak'
            result_string = 'tak '
            string = string[1:]
        else:
            result_string = ''
        results = []
        for i in range(len(self.user)):
            total = 0
            for k in self.user[i]: total += fuzz.ratio(string, k)
            results.append(total)
        if len(np.where(np.array(results) > 60)[0]) < 1:
            return original_string
        ids = np.argmax(results)
        return result_string + self.corpus[ids] 

# 需要導入模塊: from fuzzywuzzy import fuzz [as 別名]
# 或者: from fuzzywuzzy.fuzz import ratio [as 別名]
def normalize(self, string):
        original_string = string
        string = string.lower()
        if string[0] == 'x':
            if len(string) == 1:
                return 'tak'
            result_string = 'tak '
            string = string[1:]
        else:
            result_string = ''
        results = []
        for i in range(len(self.user)):
            total = 0
            for k in self.user[i]:
                total += fuzz.ratio(string, k)
            results.append(total)
        if len(np.where(np.array(results) > 60)[0]) < 1:
            return original_string
        ids = np.argmax(results)
        return result_string + self.corpus[ids] 

# 需要導入模塊: from fuzzywuzzy import fuzz [as 別名]
# 或者: from fuzzywuzzy.fuzz import ratio [as 別名]
def _expand_df(df):
    """
    Add alternate forms to DataFrame, then sort DataFrame by alias length
    (for order of extraction from text) and similarity to original name (in
    order to select most appropriate term to associate with alias).
    """
    df = df.copy()
    df['alias'] = df['alias'].apply(uk_to_us)
    new_rows = []
    for index, row in df.iterrows():
        alias = row['alias']
        alt_forms = _gen_alt_forms(alias)
        for alt_form in alt_forms:
            temp_row = row.copy()
            temp_row['alias'] = alt_form
            new_rows.append(temp_row.tolist())
    alt_df = pd.DataFrame(columns=df.columns, data=new_rows)
    df = pd.concat((df, alt_df), axis=0)
    # Sort by name length and similarity of alternate form to preferred term
    # For example, "task switching" the concept should take priority over the
    # "task switching" version of the "task-switching" task.
    df['length'] = df['alias'].str.len()
    df['ratio'] = df[['alias', 'name']].apply(_get_ratio, axis=1)
    df = df.sort_values(by=['length', 'ratio'], ascending=[False, False])
    return df 

# 需要導入模塊: from fuzzywuzzy import fuzz [as 別名]
# 或者: from fuzzywuzzy.fuzz import ratio [as 別名]
def _create_fuzzy_wuzzy_features(self, df):
        df['fuzzy_ratio'] = df[['spn_1', 'spn_2']].apply(lambda row: fuzz.ratio(row['spn_1'], row['spn_2']), axis=1)
        df['fuzzy_set_ratio'] = df[['spn_1', 'spn_2']].apply(lambda row: fuzz.token_set_ratio(row['spn_1'], row['spn_2']), axis=1)
        df['fuzzy_partial_ratio'] = df[['spn_1', 'spn_2']].apply(lambda row: fuzz.partial_ratio(row['spn_1'], row['spn_2']), axis=1)
        df['fuzzy_token_sort_ratio'] = df[['spn_1', 'spn_2']].apply(lambda row: fuzz.token_sort_ratio(row['spn_1'], row['spn_2']), axis=1)
        df['fuzzy_qratio'] = df[['spn_1', 'spn_2']].apply(lambda row: fuzz.QRatio(row['spn_1'], row['spn_2']), axis=1)
        df['fuzzy_WRatio'] = df[['spn_1', 'spn_2']].apply(lambda row: fuzz.WRatio(row['spn_1'], row['spn_2']), axis=1)
   
        def _get_longest_substr_ratio(a, b):
            strs = list(distance.lcsubstrings(a, b))
            if len(strs) == 0:
                return 0
            else:
                return len(strs[0]) / (min(len(a), len(b)) + 1)

        df['longest_substr_ratio'] = df[['spn_1', 'spn_2']].apply(lambda row: _get_longest_substr_ratio(row['spn_1'], row['spn_2']), axis=1) 

# 需要導入模塊: from fuzzywuzzy import fuzz [as 別名]
# 或者: from fuzzywuzzy.fuzz import ratio [as 別名]
def find_near_matches(w, sentence):
    ret = []
    max_ratio = 0
    t = 0
    for word in sentence.split():
        while sentence[t] != word[0]:
            t += 1
        score = (fuzz.ratio(w, word) + fuzz.partial_ratio(w, word)) / 2
        if score > max_ratio:
            max_ratio = score
            ret = [(t, t + len(word))]
        elif score == max_ratio:
            ret.append((t, t + len(word)))
        else:
            pass
        t += len(word)
    return ret if max_ratio > 85 else [] 

# 需要導入模塊: from fuzzywuzzy import fuzz [as 別名]
# 或者: from fuzzywuzzy.fuzz import ratio [as 別名]
def check_name(self, ctx, team, name):
        """Checks the name of the team"""
        tba_parser = ctx.cog.tba_parser
        ftc_teams = ctx.cog.ftc_teams

        actual_name = ""

        if self.mode == "frc":
            # check for existence
            team_data = tba_parser.get_team(team)
            try:
                getattr(team_data, "Errors")
            except tbapi.InvalidKeyError:
                """There is no error, so do nothing"""
            else:
                return -1
            actual_name = team_data.nickname
        elif self.mode == "ftc":
            if team not in ftc_teams:
                return -1
            actual_name = ftc_teams[team]

        self.last_name = actual_name
        self.last_team = team
        return fuzz.ratio(actual_name.lower(), name.lower()) 

# 需要導入模塊: from fuzzywuzzy import fuzz [as 別名]
# 或者: from fuzzywuzzy.fuzz import ratio [as 別名]
def fitness(agents):

    for agent in agents:

        agent.fitness = fuzz.ratio(agent.string, in_str)

    return agents