# Copyright 2008-2015 Nokia Networks
# Copyright 2016- Robot Framework Foundation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import difflib
[docs]class RecommendationFinder(object):
def __init__(self, normalizer=None):
self.normalizer = normalizer or (lambda x: x)
self.recommendations = None
[docs] def find(self, name, candidates, max_matches=10):
"""Return a list of close matches to `name` from `candidates`."""
if not name or not candidates:
return []
norm_name = self.normalizer(name)
norm_candidates = self._get_normalized_candidates(candidates)
cutoff = self._calculate_cutoff(norm_name)
norm_matches = difflib.get_close_matches(
norm_name, norm_candidates, n=max_matches, cutoff=cutoff
)
self.recommendations = self._get_original_candidates(
norm_candidates, norm_matches
)
return self.recommendations
def _get_normalized_candidates(self, candidates):
norm_candidates = {}
# sort before normalization for consistent Python/Jython ordering
for cand in sorted(candidates):
norm = self.normalizer(cand)
norm_candidates.setdefault(norm, []).append(cand)
return norm_candidates
def _get_original_candidates(self, norm_candidates, norm_matches):
candidates = []
for norm_match in norm_matches:
candidates.extend(norm_candidates[norm_match])
return candidates
def _calculate_cutoff(self, string, min_cutoff=.5, max_cutoff=.85,
step=.03):
"""Calculate a cutoff depending on string length.
Default values determined by manual tuning until the results
"look right".
"""
cutoff = min_cutoff + len(string) * step
return min(cutoff, max_cutoff)