Add logic parameter to recommendation endpoints. Use HTTPS as default.

Author: OndraFiedler

recombee_api_client/api_client.py

@@ -19,7 +19,7 @@ class RecombeeClient:
     """
     BATCH_MAX_SIZE = 10000
 
-    def __init__(self, database_id, token, protocol = 'http', options = {}):
+    def __init__(self, database_id, token, protocol = 'https', options = {}):
         """
         @param database_id: Name of your database_id at Recombee
         @param token: Secret token obtained from Recombee for signing requests
@@ -63,7 +63,7 @@ def send(self, request):
 
     @staticmethod
     def __get_http_headers(additional_headers=None):
-        headers = {'User-Agent': 'recombee-python-api-client/2.3.0'}
+        headers = {'User-Agent': 'recombee-python-api-client/2.4.0'}
         if additional_headers:
             headers.update(additional_headers)
         return headers

recombee_api_client/api_requests/recommend_items_to_item.py

@@ -13,7 +13,7 @@ class RecommendItemsToItem(Request):
 
     """
 
-    def __init__(self, item_id, target_user_id, count, user_impact=DEFAULT, filter=DEFAULT, booster=DEFAULT, cascade_create=DEFAULT, scenario=DEFAULT, return_properties=DEFAULT, included_properties=DEFAULT, diversity=DEFAULT, min_relevance=DEFAULT, rotation_rate=DEFAULT, rotation_time=DEFAULT, expert_settings=DEFAULT, return_ab_group=DEFAULT):
+    def __init__(self, item_id, target_user_id, count, filter=DEFAULT, booster=DEFAULT, cascade_create=DEFAULT, scenario=DEFAULT, logic=DEFAULT, return_properties=DEFAULT, included_properties=DEFAULT, user_impact=DEFAULT, diversity=DEFAULT, min_relevance=DEFAULT, rotation_rate=DEFAULT, rotation_time=DEFAULT, expert_settings=DEFAULT, return_ab_group=DEFAULT):
         """
         Required parameters:
         @param item_id: ID of the item for which the recommendations are to be generated.
@@ -53,9 +53,6 @@ def __init__(self, item_id, target_user_id, count, user_impact=DEFAULT, filter=D
         
         
         Optional parameters:
-        @param user_impact: If *targetUserId* parameter is present, the recommendations are biased towards the user given. Using *userImpact*, you may control this bias. For an extreme case of `userImpact=0.0`, the interactions made by the user are not taken into account at all (with the exception of history-based blacklisting), for `userImpact=1.0`, you'll get user-based recommendation. The default value is `0`.
-        
-        
         @param filter: Boolean-returning [ReQL](https://docs.recombee.com/reql.html) expression which allows you to filter recommended items based on the values of their attributes.
         
         @param booster: Number-returning [ReQL](https://docs.recombee.com/reql.html) expression which allows you to boost recommendation rate of some items based on the values of their attributes.
@@ -64,6 +61,14 @@ def __init__(self, item_id, target_user_id, count, user_impact=DEFAULT, filter=D
         
         @param scenario: Scenario defines a particular application of recommendations. It can be for example "homepage", "cart" or "emailing". You can see each scenario in the UI separately, so you can check how well each application performs. The AI which optimizes models in order to get the best results may optimize different scenarios separately, or even use different models in each of the scenarios.
         
+        @param logic: Logic specifies particular behavior of the recommendation models. You can pick tailored logic for your domain (e-commerce, multimedia, fashion ...) and use case.
+        
+        See [this section](https://docs.recombee.com/recommendation_logic.html) for list of available logics and other details.
+        
+        
+        The difference between `logic` and `scenario` is that `logic` specifies mainly behavior, while `scenario` specifies the place where recommendations are shown to the users.
+        
+        
         @param return_properties: With `returnProperties=true`, property values of the recommended items are returned along with their IDs in a JSON dictionary. The acquired property values can be used for easy displaying of the recommended items to the user. 
         
         
@@ -162,6 +167,9 @@ def __init__(self, item_id, target_user_id, count, user_impact=DEFAULT, filter=D
         ```
         
         
+        @param user_impact: **Expert option** If *targetUserId* parameter is present, the recommendations are biased towards the given user. Using *userImpact*, you may control this bias. For an extreme case of `userImpact=0.0`, the interactions made by the user are not taken into account at all (with the exception of history-based blacklisting), for `userImpact=1.0`, you'll get user-based recommendation. The default value is `0`.
+        
+        
         @param diversity: **Expert option** Real number from [0.0, 1.0] which determines how much mutually dissimilar should the recommended items be. The default value is 0.0, i.e., no diversification. Value 1.0 means maximal diversification.
         
         
@@ -184,13 +192,14 @@ def __init__(self, item_id, target_user_id, count, user_impact=DEFAULT, filter=D
         self.item_id = item_id
         self.target_user_id = target_user_id
         self.count = count
-        self.user_impact = user_impact
         self.filter = filter
         self.booster = booster
         self.cascade_create = cascade_create
         self.scenario = scenario
+        self.logic = logic
         self.return_properties = return_properties
         self.included_properties = included_properties
+        self.user_impact = user_impact
         self.diversity = diversity
         self.min_relevance = min_relevance
         self.rotation_rate = rotation_rate
@@ -209,8 +218,6 @@ def get_body_parameters(self):
         p = dict()
         p['targetUserId'] = self.target_user_id
         p['count'] = self.count
-        if self.user_impact is not DEFAULT:
-            p['userImpact'] = self.user_impact
         if self.filter is not DEFAULT:
             p['filter'] = self.filter
         if self.booster is not DEFAULT:
@@ -219,10 +226,14 @@ def get_body_parameters(self):
             p['cascadeCreate'] = self.cascade_create
         if self.scenario is not DEFAULT:
             p['scenario'] = self.scenario
+        if self.logic is not DEFAULT:
+            p['logic'] = self.logic
         if self.return_properties is not DEFAULT:
             p['returnProperties'] = self.return_properties
         if self.included_properties is not DEFAULT:
             p['includedProperties'] = self.included_properties
+        if self.user_impact is not DEFAULT:
+            p['userImpact'] = self.user_impact
         if self.diversity is not DEFAULT:
             p['diversity'] = self.diversity
         if self.min_relevance is not DEFAULT:

recombee_api_client/api_requests/recommend_items_to_user.py

@@ -13,7 +13,7 @@ class RecommendItemsToUser(Request):
 
     """
 
-    def __init__(self, user_id, count, filter=DEFAULT, booster=DEFAULT, cascade_create=DEFAULT, scenario=DEFAULT, return_properties=DEFAULT, included_properties=DEFAULT, diversity=DEFAULT, min_relevance=DEFAULT, rotation_rate=DEFAULT, rotation_time=DEFAULT, expert_settings=DEFAULT, return_ab_group=DEFAULT):
+    def __init__(self, user_id, count, filter=DEFAULT, booster=DEFAULT, cascade_create=DEFAULT, scenario=DEFAULT, logic=DEFAULT, return_properties=DEFAULT, included_properties=DEFAULT, diversity=DEFAULT, min_relevance=DEFAULT, rotation_rate=DEFAULT, rotation_time=DEFAULT, expert_settings=DEFAULT, return_ab_group=DEFAULT):
         """
         Required parameters:
         @param user_id: ID of the user for which personalized recommendations are to be generated.
@@ -30,6 +30,14 @@ def __init__(self, user_id, count, filter=DEFAULT, booster=DEFAULT, cascade_crea
         
         @param scenario: Scenario defines a particular application of recommendations. It can be for example "homepage", "cart" or "emailing". You can see each scenario in the UI separately, so you can check how well each application performs. The AI which optimizes models in order to get the best results may optimize different scenarios separately, or even use different models in each of the scenarios.
         
+        @param logic: Logic specifies particular behavior of the recommendation models. You can pick tailored logic for your domain (e-commerce, multimedia, fashion ...) and use case.
+        
+        See [this section](https://docs.recombee.com/recommendation_logic.html) for list of available logics and other details.
+        
+        
+        The difference between `logic` and `scenario` is that `logic` specifies mainly behavior, while `scenario` specifies the place where recommendations are shown to the users.
+        
+        
         @param return_properties: With `returnProperties=true`, property values of the recommended items are returned along with their IDs in a JSON dictionary. The acquired property values can be used for easy displaying of the recommended items to the user. 
         
         
@@ -153,6 +161,7 @@ def __init__(self, user_id, count, filter=DEFAULT, booster=DEFAULT, cascade_crea
         self.booster = booster
         self.cascade_create = cascade_create
         self.scenario = scenario
+        self.logic = logic
         self.return_properties = return_properties
         self.included_properties = included_properties
         self.diversity = diversity
@@ -180,6 +189,8 @@ def get_body_parameters(self):
             p['cascadeCreate'] = self.cascade_create
         if self.scenario is not DEFAULT:
             p['scenario'] = self.scenario
+        if self.logic is not DEFAULT:
+            p['logic'] = self.logic
         if self.return_properties is not DEFAULT:
             p['returnProperties'] = self.return_properties
         if self.included_properties is not DEFAULT:

recombee_api_client/api_requests/recommend_users_to_item.py

@@ -13,7 +13,7 @@ class RecommendUsersToItem(Request):
 
     """
 
-    def __init__(self, item_id, count, filter=DEFAULT, booster=DEFAULT, cascade_create=DEFAULT, scenario=DEFAULT, return_properties=DEFAULT, included_properties=DEFAULT, diversity=DEFAULT, expert_settings=DEFAULT, return_ab_group=DEFAULT):
+    def __init__(self, item_id, count, filter=DEFAULT, booster=DEFAULT, cascade_create=DEFAULT, scenario=DEFAULT, logic=DEFAULT, return_properties=DEFAULT, included_properties=DEFAULT, diversity=DEFAULT, expert_settings=DEFAULT, return_ab_group=DEFAULT):
         """
         Required parameters:
         @param item_id: ID of the item for which the recommendations are to be generated.
@@ -30,6 +30,14 @@ def __init__(self, item_id, count, filter=DEFAULT, booster=DEFAULT, cascade_crea
         
         @param scenario: Scenario defines a particular application of recommendations. It can be for example "homepage", "cart" or "emailing". You can see each scenario in the UI separately, so you can check how well each application performs. The AI which optimizes models in order to get the best results may optimize different scenarios separately, or even use different models in each of the scenarios.
         
+        @param logic: Logic specifies particular behavior of the recommendation models. You can pick tailored logic for your domain (e-commerce, multimedia, fashion ...) and use case.
+        
+        See [this section](https://docs.recombee.com/recommendation_logic.html) for list of available logics and other details.
+        
+        
+        The difference between `logic` and `scenario` is that `logic` specifies mainly behavior, while `scenario` specifies the place where recommendations are shown to the users.
+        
+        
         @param return_properties: With `returnProperties=true`, property values of the recommended users are returned along with their IDs in a JSON dictionary. The acquired property values can be used for easy displaying the recommended users. 
         
         
@@ -132,6 +140,7 @@ def __init__(self, item_id, count, filter=DEFAULT, booster=DEFAULT, cascade_crea
         self.booster = booster
         self.cascade_create = cascade_create
         self.scenario = scenario
+        self.logic = logic
         self.return_properties = return_properties
         self.included_properties = included_properties
         self.diversity = diversity
@@ -156,6 +165,8 @@ def get_body_parameters(self):
             p['cascadeCreate'] = self.cascade_create
         if self.scenario is not DEFAULT:
             p['scenario'] = self.scenario
+        if self.logic is not DEFAULT:
+            p['logic'] = self.logic
         if self.return_properties is not DEFAULT:
             p['returnProperties'] = self.return_properties
         if self.included_properties is not DEFAULT:

recombee_api_client/api_requests/recommend_users_to_user.py

@@ -13,7 +13,7 @@ class RecommendUsersToUser(Request):
 
     """
 
-    def __init__(self, user_id, count, filter=DEFAULT, booster=DEFAULT, cascade_create=DEFAULT, scenario=DEFAULT, return_properties=DEFAULT, included_properties=DEFAULT, diversity=DEFAULT, min_relevance=DEFAULT, rotation_rate=DEFAULT, rotation_time=DEFAULT, expert_settings=DEFAULT, return_ab_group=DEFAULT):
+    def __init__(self, user_id, count, filter=DEFAULT, booster=DEFAULT, cascade_create=DEFAULT, scenario=DEFAULT, logic=DEFAULT, return_properties=DEFAULT, included_properties=DEFAULT, diversity=DEFAULT, min_relevance=DEFAULT, rotation_rate=DEFAULT, rotation_time=DEFAULT, expert_settings=DEFAULT, return_ab_group=DEFAULT):
         """
         Required parameters:
         @param user_id: User to which we find similar users
@@ -30,6 +30,14 @@ def __init__(self, user_id, count, filter=DEFAULT, booster=DEFAULT, cascade_crea
         
         @param scenario: Scenario defines a particular application of recommendations. It can be for example "homepage", "cart" or "emailing". You can see each scenario in the UI separately, so you can check how well each application performs. The AI which optimizes models in order to get the best results may optimize different scenarios separately, or even use different models in each of the scenarios.
         
+        @param logic: Logic specifies particular behavior of the recommendation models. You can pick tailored logic for your domain (e-commerce, multimedia, fashion ...) and use case.
+        
+        See [this section](https://docs.recombee.com/recommendation_logic.html) for list of available logics and other details.
+        
+        
+        The difference between `logic` and `scenario` is that `logic` specifies mainly behavior, while `scenario` specifies the place where recommendations are shown to the users.
+        
+        
         @param return_properties: With `returnProperties=true`, property values of the recommended users are returned along with their IDs in a JSON dictionary. The acquired property values can be used for easy displaying the recommended users. 
         
         
@@ -141,6 +149,7 @@ def __init__(self, user_id, count, filter=DEFAULT, booster=DEFAULT, cascade_crea
         self.booster = booster
         self.cascade_create = cascade_create
         self.scenario = scenario
+        self.logic = logic
         self.return_properties = return_properties
         self.included_properties = included_properties
         self.diversity = diversity
@@ -168,6 +177,8 @@ def get_body_parameters(self):
             p['cascadeCreate'] = self.cascade_create
         if self.scenario is not DEFAULT:
             p['scenario'] = self.scenario
+        if self.logic is not DEFAULT:
+            p['logic'] = self.logic
         if self.return_properties is not DEFAULT:
             p['returnProperties'] = self.return_properties
         if self.included_properties is not DEFAULT:

setup.py

@@ -11,7 +11,7 @@
 setup(
     name='recombee-api-client',
 
-    version='2.3.0',
+    version='2.4.0',
 
     description='Client for Recombee recommendation API',
     long_description=long_description,

tests/test_cases/item_to_item_recommendation.py

@@ -10,7 +10,7 @@
 
 class ItemToItemRecommendationTest (RecommendationsTest ):
 
-    def create_request(self,item_id,target_user_id,count,user_impact=None,filter=None,booster=None,cascade_create=None,scenario=None,return_properties=None,included_properties=None,diversity=None,min_relevance=None,rotation_rate=None,rotation_time=None,expert_settings=None,return_ab_group=None):
+    def create_request(self,item_id,target_user_id,count,filter=None,booster=None,cascade_create=None,scenario=None,logic=None,return_properties=None,included_properties=None,user_impact=None,diversity=None,min_relevance=None,rotation_rate=None,rotation_time=None,expert_settings=None,return_ab_group=None):
         pass
 
     def test_item_to_item_recommendation(self):

tests/test_cases/recommendation.py

@@ -10,7 +10,7 @@
 
 class RecommendationTest (RecommendationsTest ):
 
-    def create_request(self,user_id,count,filter=None,booster=None,cascade_create=None,scenario=None,return_properties=None,included_properties=None,diversity=None,expert_settings=None,return_ab_group=None):
+    def create_request(self,user_id,count,filter=None,booster=None,cascade_create=None,scenario=None,logic=None,return_properties=None,included_properties=None,diversity=None,expert_settings=None,return_ab_group=None):
         pass
 
     def test_recommendation(self):

tests/test_cases/test_recommend_items_to_item.py

@@ -9,5 +9,5 @@
 
 class RecommendItemsToItemTestCase (ItemToItemRecommendationTest):
 
-    def create_request(self, item_id, target_user_id, count, user_impact=None, filter=None, booster=None, cascade_create=None, scenario=None, return_properties=None, included_properties=None, diversity=None, min_relevance=None, rotation_rate=None, rotation_time=None, expert_settings=None, return_ab_group=None):
-        return RecommendItemsToItem(item_id, target_user_id, count, user_impact=user_impact, filter=filter, booster=booster, cascade_create=cascade_create, scenario=scenario, return_properties=return_properties, included_properties=included_properties, diversity=diversity, min_relevance=min_relevance, rotation_rate=rotation_rate, rotation_time=rotation_time, expert_settings=expert_settings, return_ab_group=return_ab_group)
+    def create_request(self, item_id, target_user_id, count, filter=None, booster=None, cascade_create=None, scenario=None, logic=None, return_properties=None, included_properties=None, user_impact=None, diversity=None, min_relevance=None, rotation_rate=None, rotation_time=None, expert_settings=None, return_ab_group=None):
+        return RecommendItemsToItem(item_id, target_user_id, count, filter=filter, booster=booster, cascade_create=cascade_create, scenario=scenario, logic=logic, return_properties=return_properties, included_properties=included_properties, user_impact=user_impact, diversity=diversity, min_relevance=min_relevance, rotation_rate=rotation_rate, rotation_time=rotation_time, expert_settings=expert_settings, return_ab_group=return_ab_group)

tests/test_cases/test_recommend_items_to_user.py

@@ -9,5 +9,5 @@
 
 class RecommendItemsToUserTestCase (RecommendationTest):
 
-    def create_request(self, user_id, count, filter=None, booster=None, cascade_create=None, scenario=None, return_properties=None, included_properties=None, diversity=None, min_relevance=None, rotation_rate=None, rotation_time=None, expert_settings=None, return_ab_group=None):
-        return RecommendItemsToUser(user_id, count, filter=filter, booster=booster, cascade_create=cascade_create, scenario=scenario, return_properties=return_properties, included_properties=included_properties, diversity=diversity, min_relevance=min_relevance, rotation_rate=rotation_rate, rotation_time=rotation_time, expert_settings=expert_settings, return_ab_group=return_ab_group)
+    def create_request(self, user_id, count, filter=None, booster=None, cascade_create=None, scenario=None, logic=None, return_properties=None, included_properties=None, diversity=None, min_relevance=None, rotation_rate=None, rotation_time=None, expert_settings=None, return_ab_group=None):
+        return RecommendItemsToUser(user_id, count, filter=filter, booster=booster, cascade_create=cascade_create, scenario=scenario, logic=logic, return_properties=return_properties, included_properties=included_properties, diversity=diversity, min_relevance=min_relevance, rotation_rate=rotation_rate, rotation_time=rotation_time, expert_settings=expert_settings, return_ab_group=return_ab_group)