Merge "Add command fuzzy matching"
This commit is contained in:
commit
5970766546
48
cliff/app.py
48
cliff/app.py
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@ -9,9 +9,11 @@ import logging
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import logging.handlers
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import os
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import sys
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import operator
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from .complete import CompleteCommand
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from .help import HelpAction, HelpCommand
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from .utils import damerau_levenshtein, COST
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# Make sure the cliff library has a logging handler
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# in case the app developer doesn't set up logging.
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@ -299,10 +301,56 @@ class App(object):
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self.interpreter.cmdloop()
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return 0
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def get_fuzzy_matches(self, cmd):
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"""return fuzzy matches of unknown command
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"""
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sep = '_'
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if self.command_manager.convert_underscores:
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sep = ' '
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all_cmds = [k[0] for k in self.command_manager]
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dist = []
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for candidate in sorted(all_cmds):
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prefix = candidate.split(sep)[0]
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# Give prefix match a very good score
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if candidate.startswith(cmd):
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dist.append((candidate, 0))
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continue
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# Levenshtein distance
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dist.append((candidate, damerau_levenshtein(cmd, prefix, COST)+1))
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dist = sorted(dist, key=operator.itemgetter(1, 0))
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matches = []
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i = 0
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# Find the best similarity
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while (not dist[i][1]):
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matches.append(dist[i][0])
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i += 1
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best_similarity = dist[i][1]
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while (dist[i][1] == best_similarity):
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matches.append(dist[i][0])
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i += 1
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return matches
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def run_subcommand(self, argv):
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try:
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subcommand = self.command_manager.find_command(argv)
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except ValueError as err:
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# If there was no exact match, try to find a fuzzy match
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the_cmd = argv[0]
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fuzzy_matches = self.get_fuzzy_matches(the_cmd)
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if fuzzy_matches:
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article = 'a'
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if self.NAME[0] in 'aeiou':
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article = 'an'
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self.stdout.write('%s: \'%s\' is not %s %s command. '
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'See \'%s --help\'.\n'
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% (self.NAME, the_cmd, article,
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self.NAME, self.NAME))
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self.stdout.write('Did you mean one of these?\n')
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for match in fuzzy_matches:
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self.stdout.write(' %s\n' % match)
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else:
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if self.options.debug:
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raise
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else:
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@ -3,8 +3,7 @@ from argparse import ArgumentError
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try:
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from StringIO import StringIO
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except ImportError:
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# Probably python 3, that test won't be run so ignore the error
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pass
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from io import StringIO
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import sys
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import nose
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@ -13,6 +12,7 @@ import mock
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from cliff.app import App
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from cliff.command import Command
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from cliff.commandmanager import CommandManager
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from cliff.tests import utils
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def make_app(**kwargs):
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@ -432,3 +432,19 @@ def test_unknown_cmd_debug():
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app.run(['--debug', 'hell']) == 2
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except ValueError as err:
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assert "['hell']" in ('%s' % err)
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def test_list_matching_commands():
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stdout = StringIO()
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app = App('testing', '1',
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utils.TestCommandManager(utils.TEST_NAMESPACE),
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stdout=stdout)
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app.NAME = 'test'
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try:
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assert app.run(['t']) == 2
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except SystemExit:
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pass
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output = stdout.getvalue()
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assert "test: 't' is not a test command. See 'test --help'." in output
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assert 'Did you mean one of these?' in output
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assert 'three word command\n two words\n' in output
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@ -0,0 +1,88 @@
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
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# implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Each edit operation is assigned different cost, such as:
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# 'w' means swap operation, the cost is 0;
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# 's' means substitution operation, the cost is 2;
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# 'a' means insertion operation, the cost is 1;
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# 'd' means deletion operation, the cost is 3;
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# The smaller cost results in the better similarity.
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COST = {'w': 0, 's': 2, 'a': 1, 'd': 3}
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def damerau_levenshtein(s1, s2, cost):
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"""Calculates the Damerau-Levenshtein distance between two strings.
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The Levenshtein distance says the minimum number of single-character edits
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(i.e. insertions, deletions, swap or substitution) required to change one
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string to the other.
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The idea is to reserve a matrix to hold the Levenshtein distances between
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all prefixes of the first string and all prefixes of the second, then we
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can compute the values in the matrix in a dynamic programming fashion. To
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avoid a large space complexity, only the last three rows in the matrix is
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needed.(row2 holds the current row, row1 holds the previous row, and row0
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the row before that.)
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More details:
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https://en.wikipedia.org/wiki/Levenshtein_distance
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https://github.com/git/git/commit/8af84dadb142f7321ff0ce8690385e99da8ede2f
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"""
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if s1 == s2:
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return 0
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len1 = len(s1)
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len2 = len(s2)
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if len1 == 0:
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return len2 * cost['a']
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if len2 == 0:
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return len1 * cost['d']
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row1 = [i * cost['a'] for i in range(len2 + 1)]
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row2 = row1[:]
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row0 = row1[:]
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for i in range(len1):
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row2[0] = (i + 1) * cost['d']
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for j in range(len2):
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# substitution
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sub_cost = row1[j] + (s1[i] != s2[j]) * cost['s']
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# insertion
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ins_cost = row2[j] + cost['a']
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# deletion
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del_cost = row1[j + 1] + cost['d']
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# swap
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swp_condition = ((i > 0)
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and (j > 0)
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and (s1[i - 1] == s2[j])
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and (s1[i] == s2[j - 1])
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)
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# min cost
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if swp_condition:
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swp_cost = row0[j - 1] + cost['w']
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p_cost = min(sub_cost, ins_cost, del_cost, swp_cost)
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else:
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p_cost = min(sub_cost, ins_cost, del_cost)
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row2[j + 1] = p_cost
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row0, row1, row2 = row1, row2, row0
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return row1[-1]
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