pydatrie 0.3

Pure Python implementation of DARTS (Double ARray Trie System)

Pydatrie

Pydatire is a pure python implementation of DARTS (Double ARray Trie System). It was introduced by Aoe at 1989 and was used for main data structure of mecab, one of the most famous Japenese morpheme analyzer. I ported java implementation of this to python and added many features like searching keys and values by given prefix.

I started this project to make pure python Korean mecab analyzer which was named pecab. But sadly I can't find pure python implementation of DARTS, so I also made this package. You can see the pecab project here.

Installation

pip install pydatrie

Usages

The usages were inspired by datrie package, a python wrapper of libdatrie. Please check the details from the following code script.

from pydatrie import DoubleArrayTrie

# create trie
# the input dict type is Dict[str, Any]
trie = DoubleArrayTrie(
    {
        "AB": "1",
        "ABCD": "2",
        "EF": "3",
        "EFGH": "4",
    }
)

# exact matching search using `__getitem__`
trie["AB"]
# "1"
trie["EF"]
# "3"

# exact matching search using `get`, equivalent with `__getitem__`
trie.get("AB")
# "1"
trie.get("EF")
# "3"

# common prefix search using `prefixes`
trie.prefixes()
# ["AB", "ABCD", "EF", "EFGH"]
trie.prefixes("ABC")
# ["AB"]
trie.prefixes("ABCD")
# ["AB", "ABCD"]

# common prefix search with value using `prefix_items`
trie.prefix_items()
# [('AB', '1'), ('ABCD', '2'), ('EF', '3'), ('EFGH', '4')]
trie.prefix_items("ABC")
# [('AB', '1')]
trie.prefix_items("ABCD")
# [('AB', '1'), ('ABCD', '2')]

# longest prefix search using `longest_prefix`
trie.longest_prefix()
# ABCD
trie.longest_prefix("ABC")
# AB

# longest prefix search with value using `longest_prefix_item`
trie.longest_prefix_item()
# ('ABCD', '2')
trie.longest_prefix_item("ABC")
# ('AB', '1')

# shortest prefix search using `shortest_prefix`
trie.shortest_prefix()
# AB
trie.shortest_prefix("EFG")
# EF

# shortest prefix search with value using `shortest_prefix`
trie.shortest_prefix_item()
# ('AB', '1')
trie.shortest_prefix_item("EFG")
# ('EF', '3')

# check trie has the exact key using `__contains__`
"EF" in trie
# True
"EFG" in trie
# False

# check trie has any prefix using `has_prefix`
trie.has_prefix("EF")
# True
trie.has_prefix("EFG")
# True

# check trie has any key which starts with given prefix using `has_keys_with_prefix`
trie.has_keys_with_prefix("A")
# True
trie.has_keys_with_prefix("X")
# False

# search all the keys starts with given prefix using `keys`
trie.keys()
# ['AB', 'ABCD', 'EF', 'EFGH']
trie.keys("A")
# ['AB', 'ABCD']
trie.keys("ABC")
# ['ABCD']

# search all the values matched with keys starts with given prefix using `keys`
trie.values()
# ['1', '2', '3', '4']
trie.values("A")
# ['1', '2']
trie.values("ABC")
# ['2']

# search all the keys and values matched with keys starts with given prefix using `keys`
trie.items()
# [('AB', '1'), ('ABCD', '2'), ('EF', '3'), ('EFGH', '4')]
trie.items("A")
# [('AB', '1'), ('ABCD', '2')]
trie.items("ABC")
# [('ABCD', '2')]

# common suffix search using `suffixes`
trie.suffixes()
# ['AB', 'ABCD', 'EF', 'EFGH']
trie.suffixes("A")
# ['B', 'BCD']

# check the size of trie using `__len__`
len(trie)
# 4

# save trie using `save`
trie.save("file.dat")

# load trie using `load`
trie2 = DoubleArrayTrie.load("file.dat")
trie2.items()
# [('AB', '1'), ('ABCD', '2'), ('EF', '3'), ('EFGH', '4')]
# it works same with original object!

# check sameness of two tries using `__eq__`
trie == trie2
# True

# print information of trie
print(trie)
# DoubleArrayTrie(size=4, keys=['AB', 'ABCD', 'EF', ...]), values=['1', '2', '3', ...])

# for loop using `__iter__` and `__next__`
for key in trie:
    print(key)
# AB
# ABCD
# EF
# EFGH

# modify value matched with given key using `modify_value`
trie.modify_value("AB", "99")
trie.items()
# [('AB', '99'), ('ABCD', '2'), ('EF', '3'), ('EFGH', '4')]

Limitations

Current implementation doesn't support dynamical node insertion and update. So you need to add all inputs when you create the object.

License

Copyright 2022 Hyunwoong Ko.

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.