chrislit /
abydos
| Conditions | 19 |
| Total Lines | 118 |
| Code Lines | 59 |
| Lines | 0 |
| Ratio | 0 % |
| Tests | 45 |
| CRAP Score | 19 |
| Changes | 0 | ||
Small methods make your code easier to understand, in particular if combined with a good name. Besides, if your method is small, finding a good name is usually much easier.
For example, if you find yourself adding comments to a method's body, this is usually a good sign to extract the commented part to a new method, and use the comment as a starting point when coming up with a good name for this new method.
Commonly applied refactorings include:
If many parameters/temporary variables are present:
Complex classes like abydos.distance._damerau_levenshtein.DamerauLevenshtein.dist_abs() often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.
Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.
| 1 | # -*- coding: utf-8 -*- |
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| 57 | 1 | def dist_abs(self, src, tar, cost=(1, 1, 1, 1)): |
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| 58 | """Return the Damerau-Levenshtein distance between two strings. |
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| 59 | |||
| 60 | Parameters |
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| 61 | ---------- |
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| 62 | src : str |
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| 63 | Source string for comparison |
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| 64 | tar : str |
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| 65 | Target string for comparison |
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| 66 | cost : tuple |
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| 67 | A 4-tuple representing the cost of the four possible edits: |
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| 68 | inserts, deletes, substitutions, and transpositions, respectively |
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| 69 | (by default: (1, 1, 1, 1)) |
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| 70 | |||
| 71 | Returns |
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| 72 | ------- |
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| 73 | int (may return a float if cost has float values) |
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| 74 | The Damerau-Levenshtein distance between src & tar |
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| 75 | |||
| 76 | Raises |
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| 77 | ------ |
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| 78 | ValueError |
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| 79 | Unsupported cost assignment; the cost of two transpositions must |
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| 80 | not be less than the cost of an insert plus a delete. |
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| 81 | |||
| 82 | Examples |
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| 83 | -------- |
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| 84 | >>> cmp = DamerauLevenshtein() |
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| 85 | >>> cmp.dist_abs('cat', 'hat') |
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| 86 | 1 |
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| 87 | >>> cmp.dist_abs('Niall', 'Neil') |
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| 88 | 3 |
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| 89 | >>> cmp.dist_abs('aluminum', 'Catalan') |
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| 90 | 7 |
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| 91 | >>> cmp.dist_abs('ATCG', 'TAGC') |
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| 92 | 2 |
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| 93 | |||
| 94 | """ |
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| 95 | 1 | ins_cost, del_cost, sub_cost, trans_cost = cost |
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| 96 | |||
| 97 | 1 | if src == tar: |
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| 98 | 1 | return 0 |
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| 99 | 1 | if not src: |
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| 100 | 1 | return len(tar) * ins_cost |
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| 101 | 1 | if not tar: |
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| 102 | 1 | return len(src) * del_cost |
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| 103 | |||
| 104 | 1 | if 2 * trans_cost < ins_cost + del_cost: |
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| 105 | 1 | raise ValueError( |
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| 106 | 'Unsupported cost assignment; the cost of two transpositions ' |
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| 107 | + 'must not be less than the cost of an insert plus a delete.' |
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| 108 | ) |
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| 109 | |||
| 110 | 1 | d_mat = np_zeros((len(src)) * (len(tar)), dtype=np_int).reshape( |
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| 111 | (len(src), len(tar)) |
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| 112 | ) |
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| 113 | |||
| 114 | 1 | if src[0] != tar[0]: |
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| 115 | 1 | d_mat[0, 0] = min(sub_cost, ins_cost + del_cost) |
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| 116 | |||
| 117 | 1 | src_index_by_character = {src[0]: 0} |
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| 118 | 1 | for i in range(1, len(src)): |
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| 119 | 1 | del_distance = d_mat[i - 1, 0] + del_cost |
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| 120 | 1 | ins_distance = (i + 1) * del_cost + ins_cost |
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| 121 | 1 | match_distance = i * del_cost + ( |
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| 122 | 0 if src[i] == tar[0] else sub_cost |
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| 123 | ) |
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| 124 | 1 | d_mat[i, 0] = min(del_distance, ins_distance, match_distance) |
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| 125 | |||
| 126 | 1 | for j in range(1, len(tar)): |
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| 127 | 1 | del_distance = (j + 1) * ins_cost + del_cost |
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| 128 | 1 | ins_distance = d_mat[0, j - 1] + ins_cost |
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| 129 | 1 | match_distance = j * ins_cost + ( |
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| 130 | 0 if src[0] == tar[j] else sub_cost |
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| 131 | ) |
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| 132 | 1 | d_mat[0, j] = min(del_distance, ins_distance, match_distance) |
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| 133 | |||
| 134 | 1 | for i in range(1, len(src)): |
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| 135 | 1 | max_src_letter_match_index = 0 if src[i] == tar[0] else -1 |
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| 136 | 1 | for j in range(1, len(tar)): |
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| 137 | 1 | candidate_swap_index = ( |
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| 138 | -1 |
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| 139 | if tar[j] not in src_index_by_character |
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| 140 | else src_index_by_character[tar[j]] |
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| 141 | ) |
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| 142 | 1 | j_swap = max_src_letter_match_index |
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| 143 | 1 | del_distance = d_mat[i - 1, j] + del_cost |
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| 144 | 1 | ins_distance = d_mat[i, j - 1] + ins_cost |
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| 145 | 1 | match_distance = d_mat[i - 1, j - 1] |
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| 146 | 1 | if src[i] != tar[j]: |
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| 147 | 1 | match_distance += sub_cost |
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| 148 | else: |
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| 149 | 1 | max_src_letter_match_index = j |
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| 150 | |||
| 151 | 1 | if candidate_swap_index != -1 and j_swap != -1: |
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| 152 | 1 | i_swap = candidate_swap_index |
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| 153 | |||
| 154 | 1 | if i_swap == 0 and j_swap == 0: |
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| 155 | 1 | pre_swap_cost = 0 |
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| 156 | else: |
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| 157 | 1 | pre_swap_cost = d_mat[ |
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| 158 | max(0, i_swap - 1), max(0, j_swap - 1) |
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| 159 | ] |
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| 160 | 1 | swap_distance = ( |
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| 161 | pre_swap_cost |
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| 162 | + (i - i_swap - 1) * del_cost |
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| 163 | + (j - j_swap - 1) * ins_cost |
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| 164 | + trans_cost |
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| 165 | ) |
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| 166 | else: |
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| 167 | 1 | swap_distance = maxsize |
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| 168 | |||
| 169 | 1 | d_mat[i, j] = min( |
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| 170 | del_distance, ins_distance, match_distance, swap_distance |
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| 171 | ) |
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| 172 | 1 | src_index_by_character[src[i]] = i |
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| 173 | |||
| 174 | 1 | return d_mat[len(src) - 1, len(tar) - 1] |
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| 175 | |||
| 337 |
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