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Mongodb document similarity

Project description

Mongodb relation finder
=======================

This is project is using to find relationship between mongodb documents

This will be the initial version of the project


---

How to use:

Install package with pip

`
pip install closeness
`

---

See the example,

.. code-block:: python

from closeness.closeness_aggregation import ClosenessAggregation
from pymongo import MongoClient
client = MongoClient()
db = client.test_database
user_collection = db.user_collection
user1 = {
'name': 'User 1',
'age': 25,
'gender': 'male',
'tags': [
"tag1",
"tag2",
"tag3",
],
'friends': [
{"user_id": "friend1", 'name': "name1"},
{"user_id": "friend2", 'name': "name2"},
{"user_id": "friend3", 'name': "name3"},
]
}
user2 = {
'name': 'User 2',
'age': 25,
'gender': 'male',
'tags': [
"tag1",
"tag2",
"tag3",
],
'friends': [
{"user_id": "friend1", 'name': "name1"},
{"user_id": "friend2", 'name': "name2"},
{"user_id": "friend3", 'name': "name3"},
]
}
user3 = {
'name': 'User 3',
'age': 30,
'gender': 'female',
'tags': [
"tag1",
],
'friends': [
{"user_id": "friend3", 'name': "name3"},
]
}
user_collection.insert([user1, user2, user3])
query_stage = {'$match': {'name': {'$ne': user1['name']}}}
ARRAY_CMP_FIELDS = [
{
'field': 'tags',
'weight': 3
}
]
ARRAY_DICT_CMP_FIELDS = [
{
'field': 'friends',
'unique': 'user_id',
'weight': .5
}
]
STRING_CMP_FIELDS = [
{
'field': 'gender',
'weight': .5
}
]
NUM_CMP_FIELDS = [
{
'field': 'age',
'from': -1,
'to': 1,
'weight': .3
}
]
OUT_PUT_FIELDS = [
'name', 'age'
]
test = ClosenessAggregation(
user1,
query_stage,
OUT_PUT_FIELDS,
10,
ARRAY_CMP_FIELDS=ARRAY_CMP_FIELDS,
STRING_CMP_FIELDS=STRING_CMP_FIELDS,
NUM_CMP_FIELDS=NUM_CMP_FIELDS,
ARRAY_DICT_CMP_FIELDS=ARRAY_DICT_CMP_FIELDS,
)


aggregation_query = closeness_obj.get_aggregation_pipeline(
mode=ClosenessAggregation.SIMPLE
)

result = user_collection.aggregate(aggregation_query)

# {u'ok': 1.0, u'result': [
# {u'age': 25,
# u'_id': ObjectId('55c478e1b67e2055605a559e'),
# u'name': u'User 2',
# u'rank': 100.00000000000001},
# {u'age': 30,
# u'_id': ObjectId('55c478e1b67e2055605a559f'),
# u'name': u'User 3',
# u'rank': 27.131782945736436}
# ]}

aggregation_query = test.get_aggregation_pipeline(
mode=ClosenessAggregation.FUZZY
)
result = user_collection.aggregate(aggregation_query)

# {u'ok': 1.0,
# u'result': [
# {u'age': 25,
# u'_id': ObjectId('55c44846b67e2028fe51c3fb'),
# u'name': u'User 2',
# u'rank': 99.99095908598945},
# {u'age': 30,
# u'_id': ObjectId('55c44846b67e2028fe51c3fc'),
# u'name': u'User 3',
# u'rank': 45.1925335646266}
# ]}

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