The function `evaluate` returns the litteral *distance* between two strings (a value of 0 being identical). While some distances are bounded by 1, other distances like `Hamming`, `Levenshtein`, `Damerau-Levenshtein`, `Jaccard` can be higher than 1.
The higher level function `compare` returns *a similarity score* between two strings. The similarity score is always between 0 and 1. A value of 0 being completely different and a value of 1 being completely similar. The output of compare is generally 1 - normalized distance, with some care for `NaN` values.
- The Python library [fuzzywuzzy](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) defines a few modifiers for the `RatcliffObershelp` similarity score. This package replicates them and extends them to any string distance:
- [Partial](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) returns the maximal similarity score between the shorter string and substrings of the longer string.
- [TokenSort](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) adjusts for differences in word orders by reording words alphabetically.
- [TokenSet](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) adjusts for differences in word orders and word numbers by comparing the intersection of two strings with each string.
- [TokenMax](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) combines scores using the base distance, the `Partial`, `TokenSort` and `TokenSet` modifiers, with penalty terms depending on string lengths.
- Each distance is tailored to a specific problem. Edit distances works well with local spelling errors, the Ratcliff-Obsershelp distance works well with edited texts, the Jaro Winkler distance was invented for short strings such as person names, the QGrams distances works well with strings composed of multiple words and fluctuating orderings.
- While word order is mostly irrelevant in this situation, edit distances heavily penalize different orderings. Instead, either use a distance robust to word order (like QGram distances), or compose a distance with `TokenSort`, which reorders the words alphabetically.
- General words (like "bank", "company") may appear in one string but no the other. One solution is to abbreviate these common names to diminish their importance (ie "bank" -> "bk", "company" -> "co"). Another solution is to use the `Overlap` distance, which compares the number of common qgrams with the length of the shorter strings. Another solution is to use the `Partial` or `TokenSet` modifiers.
`TokenMax(RatcliffObershelp())`, corresponding to the `WRatio` function in the Python library `fuzzywuzzy`, solves these two issues and may work best in this situation.
