recommendations.py

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#  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
 
 
class RecommendationFinder():
 
    def __init__(self, normalizer=None):
        self.normalizernormalizer = normalizer or (lambda x: x)
 
    
    def find_recommendations(self, name, candidates, max_matches=10):
        if not name or not candidates:
            return []
        norm_name = self.normalizernormalizer(name)
        norm_candidates = self._get_normalized_candidates_get_normalized_candidates(candidates)
        cutoff = self._calculate_cutoff_calculate_cutoff(norm_name)
        norm_matches = difflib.get_close_matches(norm_name,
                                                 norm_candidates,
                                                 n=max_matches,
                                                 cutoff=cutoff)
        return self._get_original_candidates_get_original_candidates(norm_candidates, norm_matches)
 
    @staticmethod
    
    def format_recommendations(msg, recommendations):
        if recommendations:
            msg += " Did you mean:"
            for rec in recommendations:
                msg += "\n    %s" % rec
        return msg
 
    def _get_normalized_candidates(self, candidates):
        norm_candidates = {}
        # sort before normalization for consistent Python/Jython ordering
        for cand in sorted(candidates):
            norm = self.normalizernormalizer(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):
        cutoff = min_cutoff + len(string) * step
        return min(cutoff, max_cutoff)