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
 
from robot.utils import seq2str
 
 
class RecommendationFinder:
 
    def __init__(self, normalizer=None):
        self.normalizernormalizer = normalizer or (lambda x: x)
 
    def find_and_format(self, name, candidates, message, max_matches=10,
                        check_missing_argument_separator=False):
        recommendations = self.findfind(name, candidates, max_matches)
        if recommendations:
            return self.formatformat(message, recommendations)
        if check_missing_argument_separator and name:
            recommendation = self._check_missing_argument_separator_check_missing_argument_separator(name, candidates)
            if recommendation:
                return f'{message} {recommendation}'
        return message
 
    
    def find(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_matches, norm_candidates)
 
    
    def format(self, message, recommendations):
        if recommendations:
            message += " Did you mean:"
            for rec in recommendations:
                message += "\n    %s" % rec
        return message
 
    def _get_normalized_candidates(self, candidates):
        norm_candidates = {}
        for cand in sorted(candidates):
            norm = self.normalizernormalizer(cand)
            norm_candidates.setdefault(norm, []).append(cand)
        return norm_candidates
 
    def _get_original_candidates(self, norm_matches, norm_candidates):
        candidates = []
        for match in norm_matches:
            candidates.extend(norm_candidates[match])
        return candidates
 
    
    def _calculate_cutoff(self, string, min_cutoff=0.5, max_cutoff=0.85, step=0.03):
        cutoff = min_cutoff + len(string) * step
        return min(cutoff, max_cutoff)
 
    def _check_missing_argument_separator(self, name, candidates):
        name = self.normalizernormalizer(name)
        candidates = self._get_normalized_candidates_get_normalized_candidates(candidates)
        matches = [c for c in candidates if name.startswith(c)]
        if not matches:
            return None
        candidates = self._get_original_candidates_get_original_candidates(matches, candidates)
        return (f"Did you try using keyword {seq2str(candidates, lastsep=' or ')} "
                f"and forgot to use enough whitespace between keyword and arguments?")