*****************
RecombeeApiClient
*****************
A Python 3 client for easy use of the `Recombee <https://www.recombee.com/>`_ recommendation API.
If you don't have an account at Recombee yet, you can create a free account `here <https://www.recombee.com/>`_.
Documentation of the API can be found at `docs.recombee.com <https://docs.recombee.com/>`_.
=============
Installation
=============
Install the client with pip:
.. code-block:: bash
$ pip install recombee-api-client
========
Examples
========
-------------
Basic example
-------------
.. code-block:: python
from recombee_api_client.api_client import RecombeeClient, Region
from recombee_api_client.exceptions import APIException
from recombee_api_client.api_requests import *
import random
client = RecombeeClient('--my-database-id--', '--db-private-token--', region=Region.US_WEST)
#Generate some random purchases of items by users
PROBABILITY_PURCHASED = 0.1
NUM = 100
purchase_requests = []
for user_id in ["user-%s" % i for i in range(NUM) ]:
for item_id in ["item-%s" % i for i in range(NUM) ]:
if random.random() < PROBABILITY_PURCHASED:
request = AddPurchase(user_id, item_id, cascade_create=True)
purchase_requests.append(request)
try:
# Send the data to Recombee, use Batch for faster processing of larger data
print('Send purchases')
client.send(Batch(purchase_requests))
# Get recommendations for user 'user-25'
response = client.send(RecommendItemsToUser('user-25', 5))
print("Recommended items: %s" % response)
# User scrolled down - get next 3 recommended items
response = client.send(RecommendNextItems(response['recommId'], 3))
print("Next recommended items: %s" % response)
except APIException as e:
print(e)
---------------------
Using property values
---------------------
.. code-block:: python
from recombee_api_client.api_client import RecombeeClient, Region
from recombee_api_client.api_requests import AddItemProperty, SetItemValues, AddPurchase
from recombee_api_client.api_requests import RecommendItemsToItem, SearchItems, Batch, ResetDatabase
import random
NUM = 100
PROBABILITY_PURCHASED = 0.1
client = RecombeeClient('--my-database-id--', '--db-private-token--', region=Region.AP_SE)
# Clear the entire database
client.send(ResetDatabase())
# We will use computers as items in this example
# Computers have four properties
# - price (floating point number)
# - number of processor cores (integer number)
# - description (string)
# - image (url of computer's photo)
# Add properties of items
client.send(AddItemProperty('price', 'double'))
client.send(AddItemProperty('num-cores', 'int'))
client.send(AddItemProperty('description', 'string'))
client.send(AddItemProperty('image', 'image'))
# Prepare requests for setting a catalog of computers
requests = [SetItemValues(
"computer-%s" % i, #itemId
#values:
{
'price': random.uniform(500, 2000),
'num-cores': random.randrange(1,9),
'description': 'Great computer',
'image': 'http://examplesite.com/products/computer-%s.jpg' % i
},
cascade_create=True # Use cascadeCreate for creating item
# with given itemId if it doesn't exist
) for i in range(NUM)]
# Send catalog to the recommender system
client.send(Batch(requests))
# Prepare some purchases of items by users
requests = []
items = ["computer-%s" % i for i in range(NUM)]
users = ["user-%s" % i for i in range(NUM)]
for item_id in items:
#Use cascadeCreate to create unexisting users
purchasing_users = [user_id for user_id in users if random.random() < PROBABILITY_PURCHASED]
requests += [AddPurchase(user_id, item_id, cascade_create=True) for user_id in purchasing_users]
# Send purchases to the recommender system
client.send(Batch(requests))
# Get 5 recommendations for user-42, who is currently viewing computer-6
# Recommend only computers that have at least 3 cores
recommended = client.send(
RecommendItemsToItem('computer-6', 'user-42', 5, filter="'num-cores'>=3")
)
print("Recommended items with at least 3 processor cores: %s" % recommended)
# Recommend only items that are more expensive then currently viewed item (up-sell)
recommended = client.send(
RecommendItemsToItem('computer-6', 'user-42', 5, filter="'price' > context_item[\"price\"]")
)
print("Recommended up-sell items: %s" % recommended)
# Filters, boosters and other settings can be also set in the Admin UI (admin.recombee.com)
# when scenario is specified
recommended = client.send(
RecommendItemsToItem('computer-6', 'user-42', 5, scenario='product_detail')
)
# Perform personalized full-text search with a user's search query (e.g. 'computers').
matches = client.send(SearchItems('user-42', 'computers', 5, scenario='search_top'))
print("Matched items: %s" % matches)
------------------
Exception handling
------------------
For the sake of brevity, the above examples omit exception handling. However, various exceptions can occur while processing request, for example because of adding an already existing item, submitting interaction of nonexistent user or because of timeout.
We are doing our best to provide the fastest and most reliable service, but production-level applications must implement a fallback solution since errors can always happen. The fallback might be, for example, showing the most popular items from the current category, or not displaying recommendations at all.
Example:
.. code-block:: python
from recombee_api_client.exceptions import *
try:
recommended = client.send(
RecommendItemsToItem('computer-6', 'user-42', 5, filter="'price' > context_item[\"price\"]")
)
except ResponseException as e:
#Handle errorneous request => use fallback
except ApiTimeoutException as e:
#Handle timeout => use fallback
except APIException as e:
#APIException is parent of both ResponseException and ApiTimeoutException
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"description": "*****************\nRecombeeApiClient\n*****************\n\nA Python 3 client for easy use of the `Recombee <https://www.recombee.com/>`_ recommendation API.\n\nIf you don't have an account at Recombee yet, you can create a free account `here <https://www.recombee.com/>`_.\n\nDocumentation of the API can be found at `docs.recombee.com <https://docs.recombee.com/>`_.\n\n=============\nInstallation\n=============\n\nInstall the client with pip:\n\n.. code-block:: bash\n\n $ pip install recombee-api-client\n\n========\nExamples\n========\n\n-------------\nBasic example\n-------------\n\n.. code-block:: python\n\n from recombee_api_client.api_client import RecombeeClient, Region\n from recombee_api_client.exceptions import APIException\n from recombee_api_client.api_requests import *\n import random\n\n client = RecombeeClient('--my-database-id--', '--db-private-token--', region=Region.US_WEST)\n\n #Generate some random purchases of items by users\n PROBABILITY_PURCHASED = 0.1\n NUM = 100\n purchase_requests = []\n\n for user_id in [\"user-%s\" % i for i in range(NUM) ]:\n for item_id in [\"item-%s\" % i for i in range(NUM) ]:\n if random.random() < PROBABILITY_PURCHASED:\n\n request = AddPurchase(user_id, item_id, cascade_create=True)\n purchase_requests.append(request)\n\n try:\n # Send the data to Recombee, use Batch for faster processing of larger data\n print('Send purchases')\n client.send(Batch(purchase_requests))\n\n # Get recommendations for user 'user-25'\n response = client.send(RecommendItemsToUser('user-25', 5))\n print(\"Recommended items: %s\" % response)\n\n # User scrolled down - get next 3 recommended items\n response = client.send(RecommendNextItems(response['recommId'], 3))\n print(\"Next recommended items: %s\" % response)\n\n except APIException as e:\n print(e)\n\n\n\n---------------------\nUsing property values\n---------------------\n\n.. code-block:: python\n\n from recombee_api_client.api_client import RecombeeClient, Region\n from recombee_api_client.api_requests import AddItemProperty, SetItemValues, AddPurchase\n from recombee_api_client.api_requests import RecommendItemsToItem, SearchItems, Batch, ResetDatabase\n import random\n\n NUM = 100\n PROBABILITY_PURCHASED = 0.1\n\n client = RecombeeClient('--my-database-id--', '--db-private-token--', region=Region.AP_SE)\n\n # Clear the entire database\n client.send(ResetDatabase())\n\n # We will use computers as items in this example\n # Computers have four properties \n # - price (floating point number)\n # - number of processor cores (integer number)\n # - description (string)\n # - image (url of computer's photo)\n\n # Add properties of items\n client.send(AddItemProperty('price', 'double'))\n client.send(AddItemProperty('num-cores', 'int'))\n client.send(AddItemProperty('description', 'string'))\n client.send(AddItemProperty('image', 'image'))\n\n # Prepare requests for setting a catalog of computers\n requests = [SetItemValues(\n \"computer-%s\" % i, #itemId\n #values:\n { \n 'price': random.uniform(500, 2000),\n 'num-cores': random.randrange(1,9),\n 'description': 'Great computer',\n 'image': 'http://examplesite.com/products/computer-%s.jpg' % i\n },\n cascade_create=True # Use cascadeCreate for creating item\n # with given itemId if it doesn't exist\n ) for i in range(NUM)]\n\n\n # Send catalog to the recommender system\n client.send(Batch(requests))\n\n # Prepare some purchases of items by users\n requests = []\n items = [\"computer-%s\" % i for i in range(NUM)]\n users = [\"user-%s\" % i for i in range(NUM)]\n\n for item_id in items:\n #Use cascadeCreate to create unexisting users\n purchasing_users = [user_id for user_id in users if random.random() < PROBABILITY_PURCHASED]\n requests += [AddPurchase(user_id, item_id, cascade_create=True) for user_id in purchasing_users]\n\n # Send purchases to the recommender system\n client.send(Batch(requests))\n\n # Get 5 recommendations for user-42, who is currently viewing computer-6\n # Recommend only computers that have at least 3 cores\n recommended = client.send(\n RecommendItemsToItem('computer-6', 'user-42', 5, filter=\"'num-cores'>=3\")\n )\n print(\"Recommended items with at least 3 processor cores: %s\" % recommended)\n\n # Recommend only items that are more expensive then currently viewed item (up-sell)\n recommended = client.send(\n RecommendItemsToItem('computer-6', 'user-42', 5, filter=\"'price' > context_item[\\\"price\\\"]\")\n )\n print(\"Recommended up-sell items: %s\" % recommended)\n\n # Filters, boosters and other settings can be also set in the Admin UI (admin.recombee.com)\n # when scenario is specified\n recommended = client.send(\n RecommendItemsToItem('computer-6', 'user-42', 5, scenario='product_detail')\n )\n\n # Perform personalized full-text search with a user's search query (e.g. 'computers').\n matches = client.send(SearchItems('user-42', 'computers', 5, scenario='search_top'))\n print(\"Matched items: %s\" % matches)\n\n------------------\nException handling\n------------------\n\nFor the sake of brevity, the above examples omit exception handling. 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