C# .NET fuzzy string matching implementation of Seat Geek's well known python FuzzyWuzzy algorithm.
C# .NET fuzzy string matching implementation of Seat Geek's well known python FuzzyWuzzy algorithm.
C# .NET fuzzy string matching implementation of Seat Geek's well known python FuzzyWuzzy algorithm.
v.2.0.0
As of 2.0.0, all empty strings will return a score of 0. Prior, the partial scoring system would return a score of 100, regardless if the other input had correct value or not. This was a result of the partial scoring system returning an empty set for the matching blocks As a result, this led to incorrrect values in the composite scores; several of them (token set, token sort), relied on the prior value of empty strings.
As a result, many 1.X.X unit test may be broken with the 2.X.X upgrade, but it is within the expertise fo all the 1.X.X developers to recommednd the upgrade to the 2.X.X series regardless, should their version accommodate it or not, as it is closer to the ideal behavior of the library.
Install-Package FuzzySharp
Fuzz.Ratio("mysmilarstring","myawfullysimilarstirng")
72
Fuzz.Ratio("mysmilarstring","mysimilarstring")
97Fuzz.PartialRatio("similar", "somewhresimlrbetweenthisstring")
71Fuzz.TokenSortRatio("order words out of"," words out of order")
100
Fuzz.PartialTokenSortRatio("order words out of"," words out of order")
100Fuzz.TokenSetRatio("fuzzy was a bear", "fuzzy fuzzy fuzzy bear")
100
Fuzz.PartialTokenSetRatio("fuzzy was a bear", "fuzzy fuzzy fuzzy bear")
100Fuzz.TokenInitialismRatio("NASA", "National Aeronautics and Space Administration");
89
Fuzz.TokenInitialismRatio("NASA", "National Aeronautics Space Administration");
100
Fuzz.TokenInitialismRatio("NASA", "National Aeronautics Space Administration, Kennedy Space Center, Cape Canaveral, Florida 32899");
53
Fuzz.PartialTokenInitialismRatio("NASA", "National Aeronautics Space Administration, Kennedy Space Center, Cape Canaveral, Florida 32899");
100Fuzz.TokenAbbreviationRatio("bl 420", "Baseline section 420", PreprocessMode.Full);
40
Fuzz.PartialTokenAbbreviationRatio("bl 420", "Baseline section 420", PreprocessMode.Full);
50 Fuzz.WeightedRatio("The quick brown fox jimps ofver the small lazy dog", "the quick brown fox jumps over the small lazy dog")
95Process.ExtractOne("cowboys", new[] { "Atlanta Falcons", "New York Jets", "New York Giants", "Dallas Cowboys"})
(string: Dallas Cowboys, score: 90, index: 3)Process.ExtractTop("goolge", new[] { "google", "bing", "facebook", "linkedin", "twitter", "googleplus", "bingnews", "plexoogl" }, limit: 3);
[(string: google, score: 83, index: 0), (string: googleplus, score: 75, index: 5), (string: plexoogl, score: 43, index: 7)]…Process.ExtractSorted("goolge", new [] {"google", "bing", "facebook", "linkedin", "twitter", "googleplus", "bingnews", "plexoogl" })
[(string: google, score: 83, index: 0), (string: googleplus, score: 75, index: 5), (string: plexoogl, score: 43, index: 7), (string: facebook, score: 29, index: 2), (string: linkedin, score: 29, index: 3), (string: bingnews, score: 29, index: 6), (string: bing, score: 22, index: 1), (string: twitter, score: 15, index: 4)]Extraction will use WeightedRatio and full process by default. Override these in the method parameters to use different scorers and processing.
Here we use the Fuzz.Ratio scorer and keep the strings as is, instead of Full Process (which will .ToLowercase() before comparing)
Process.ExtractOne("cowboys", new[] { "Atlanta Falcons", "New York Jets", "New York Giants", "Dallas Cowboys" }, s => s, ScorerCache.Get());
(string: Dallas Cowboys, score: 57, index: 3)Extraction can operate on objects of similar type. Use the "process" parameter to reduce the object to the string which it should be compared on. In the following example, the object is an array that contains the matchup, the arena, the date, and the time. We are matching on the first (0 index) parameter, the matchup.
var events = new[]
{
new[] { "chicago cubs vs new york mets", "CitiField", "2011-05-11", "8pm" },
new[] { "new york yankees vs boston red sox", "Fenway Park", "2011-05-11", "8pm" },
new[] { "atlanta braves vs pittsburgh pirates", "PNC Park", "2011-05-11", "8pm" },
};
var query = new[] { "new york mets vs chicago cubs", "CitiField", "2017-03-19", "8pm" };
var best = Process.ExtractOne(query, events, strings => strings[0]);
best: (value: { "chicago cubs vs new york mets", "CitiField", "2011-05-11", "8pm" }, score: 95, index: 0)FuzzySharp was written with English in mind, and as such the Default string preprocessor only looks at English alphanumeric characters in the input strings, and will strip all others out. However, the Extract methods in the Process class do provide the option to specify your own string preprocessor. If this parameter is omitted, the Default will be used. However if you provide your own, the provided one will be used, so you are free to provide your own criteria for whatever character set you want to admit. For instance, using the parameter (s) => s will prevent the string from being altered at all before being run through the similarity algorithms.
E.g.,
var query = "strng";
var choices = new [] { "stríng", "stráng", "stréng" };
var results = Process.ExtractAll(query, choices, (s) => s);The above will run the similarity algorithm on all the choices without stripping out the accented characters.
Scoring strategies are stateless, and as such should be static. However, in order to get them to share all the code they have in common via inheritance, making them static was not possible. Currently one way around having to new up an instance everytime you want to use one is to use the cache. This will ensure only one instance of each scorer ever exists.
var ratio = ScorerCache.Get();
var partialRatio = ScorerCache.Get();
var tokenSet = ScorerCache.Get();
var partialTokenSet = ScorerCache.Get();
var tokenSort = ScorerCache.Get();
var partialTokenSort = ScorerCache.Get();
var tokenAbbreviation = ScorerCache.Get();
var partialTokenAbbreviation = ScorerCache.Get();
var weighted = ScorerCache.Get();No open issues yet, or sync has not completed.