PersonalizeRuntime
******************


Client
======

class PersonalizeRuntime.Client

   A low-level client representing Amazon Personalize Runtime

      import boto3

      client = boto3.client('personalize-runtime')

These are the available methods:

* can_paginate

* close

* get_action_recommendations

* get_paginator

* get_personalized_ranking

* get_recommendations

* get_waiter
PersonalizeRuntime / Client / get_paginator


get_paginator
*************

PersonalizeRuntime.Client.get_paginator(operation_name)

   Create a paginator for an operation.

   Parameters:
      **operation_name** (*string*) -- The operation name.  This is
      the same name as the method name on the client.  For example, if
      the method name is "create_foo", and you'd normally invoke the
      operation as "client.create_foo(**kwargs)", if the "create_foo"
      operation can be paginated, you can use the call
      "client.get_paginator("create_foo")".

   Raises:
      **OperationNotPageableError** -- Raised if the operation is not
      pageable.  You can use the "client.can_paginate" method to check
      if an operation is pageable.

   Return type:
      "botocore.paginate.Paginator"

   Returns:
      A paginator object.
PersonalizeRuntime / Client / can_paginate


can_paginate
************

PersonalizeRuntime.Client.can_paginate(operation_name)

   Check if an operation can be paginated.

   Parameters:
      **operation_name** (*string*) -- The operation name.  This is
      the same name as the method name on the client.  For example, if
      the method name is "create_foo", and you'd normally invoke the
      operation as "client.create_foo(**kwargs)", if the "create_foo"
      operation can be paginated, you can use the call
      "client.get_paginator("create_foo")".

   Returns:
      "True" if the operation can be paginated, "False" otherwise.
PersonalizeRuntime / Client / get_action_recommendations


get_action_recommendations
**************************

PersonalizeRuntime.Client.get_action_recommendations(**kwargs)

   Returns a list of recommended actions in sorted in descending order
   by prediction score. Use the "GetActionRecommendations" API if you
   have a custom campaign that deploys a solution version trained with
   a PERSONALIZED_ACTIONS recipe.

   For more information about PERSONALIZED_ACTIONS recipes, see
   PERSONALIZED_ACTIONS recipes. For more information about getting
   action recommendations, see Getting action recommendations.

   See also: AWS API Documentation

   **Request Syntax**

      response = client.get_action_recommendations(
          campaignArn='string',
          userId='string',
          numResults=123,
          filterArn='string',
          filterValues={
              'string': 'string'
          }
      )

   Parameters:
      * **campaignArn** (*string*) -- The Amazon Resource Name (ARN)
        of the campaign to use for getting action recommendations.
        This campaign must deploy a solution version trained with a
        PERSONALIZED_ACTIONS recipe.

      * **userId** (*string*) -- The user ID of the user to provide
        action recommendations for.

      * **numResults** (*integer*) -- The number of results to return.
        The default is 5. The maximum is 100.

      * **filterArn** (*string*) --

        The ARN of the filter to apply to the returned
        recommendations. For more information, see Filtering
        Recommendations.

        When using this parameter, be sure the filter resource is
        "ACTIVE".

      * **filterValues** (*dict*) --

        The values to use when filtering recommendations. For each
        placeholder parameter in your filter expression, provide the
        parameter name (in matching case) as a key and the filter
        value(s) as the corresponding value. Separate multiple values
        for one parameter with a comma.

        For filter expressions that use an "INCLUDE" element to
        include actions, you must provide values for all parameters
        that are defined in the expression. For filters with
        expressions that use an "EXCLUDE" element to exclude actions,
        you can omit the "filter-values". In this case, Amazon
        Personalize doesn't use that portion of the expression to
        filter recommendations.

        For more information, see Filtering recommendations and user
        segments.

        * *(string) --*

          * *(string) --*

   Return type:
      dict

   Returns:
      **Response Syntax**

         {
             'actionList': [
                 {
                     'actionId': 'string',
                     'score': 123.0
                 },
             ],
             'recommendationId': 'string'
         }

      **Response Structure**

      * *(dict) --*

        * **actionList** *(list) --*

          A list of action recommendations sorted in descending order
          by prediction score. There can be a maximum of 100 actions
          in the list. For information about action scores, see How
          action recommendation scoring works.

          * *(dict) --*

            An object that identifies an action.

            The API returns a list of >>``<<PredictedAction``s.

            * **actionId** *(string) --*

              The ID of the recommended action.

            * **score** *(float) --*

              The score of the recommended action. For information
              about action scores, see How action recommendation
              scoring works.

        * **recommendationId** *(string) --*

          The ID of the recommendation.

   **Exceptions**

   * "PersonalizeRuntime.Client.exceptions.InvalidInputException"

   * "PersonalizeRuntime.Client.exceptions.ResourceNotFoundException"
PersonalizeRuntime / Client / get_waiter


get_waiter
**********

PersonalizeRuntime.Client.get_waiter(waiter_name)

   Returns an object that can wait for some condition.

   Parameters:
      **waiter_name** (*str*) -- The name of the waiter to get. See
      the waiters section of the service docs for a list of available
      waiters.

   Returns:
      The specified waiter object.

   Return type:
      "botocore.waiter.Waiter"
PersonalizeRuntime / Client / close


close
*****

PersonalizeRuntime.Client.close()

   Closes underlying endpoint connections.
PersonalizeRuntime / Client / get_recommendations


get_recommendations
*******************

PersonalizeRuntime.Client.get_recommendations(**kwargs)

   Returns a list of recommended items. For campaigns, the campaign's
   Amazon Resource Name (ARN) is required and the required user and
   item input depends on the recipe type used to create the solution
   backing the campaign as follows:

   * USER_PERSONALIZATION - "userId" required, "itemId" not used

   * RELATED_ITEMS - "itemId" required, "userId" not used

   Note:

     Campaigns that are backed by a solution created using a recipe of
     type PERSONALIZED_RANKING use the API.

   For recommenders, the recommender's ARN is required and the
   required item and user input depends on the use case (domain-based
   recipe) backing the recommender. For information on use case
   requirements see Choosing recommender use cases.

   See also: AWS API Documentation

   **Request Syntax**

      response = client.get_recommendations(
          campaignArn='string',
          itemId='string',
          userId='string',
          numResults=123,
          context={
              'string': 'string'
          },
          filterArn='string',
          filterValues={
              'string': 'string'
          },
          recommenderArn='string',
          promotions=[
              {
                  'name': 'string',
                  'percentPromotedItems': 123,
                  'filterArn': 'string',
                  'filterValues': {
                      'string': 'string'
                  }
              },
          ],
          metadataColumns={
              'string': [
                  'string',
              ]
          }
      )

   Parameters:
      * **campaignArn** (*string*) -- The Amazon Resource Name (ARN)
        of the campaign to use for getting recommendations.

      * **itemId** (*string*) --

        The item ID to provide recommendations for.

        Required for "RELATED_ITEMS" recipe type.

      * **userId** (*string*) --

        The user ID to provide recommendations for.

        Required for "USER_PERSONALIZATION" recipe type.

      * **numResults** (*integer*) -- The number of results to return.
        The default is 25. If you are including metadata in
        recommendations, the maximum is 50. Otherwise, the maximum is
        500.

      * **context** (*dict*) --

        The contextual metadata to use when getting recommendations.
        Contextual metadata includes any interaction information that
        might be relevant when getting a user's recommendations, such
        as the user's current location or device type.

        * *(string) --*

          * *(string) --*

      * **filterArn** (*string*) --

        The ARN of the filter to apply to the returned
        recommendations. For more information, see Filtering
        Recommendations.

        When using this parameter, be sure the filter resource is
        "ACTIVE".

      * **filterValues** (*dict*) --

        The values to use when filtering recommendations. For each
        placeholder parameter in your filter expression, provide the
        parameter name (in matching case) as a key and the filter
        value(s) as the corresponding value. Separate multiple values
        for one parameter with a comma.

        For filter expressions that use an "INCLUDE" element to
        include items, you must provide values for all parameters that
        are defined in the expression. For filters with expressions
        that use an "EXCLUDE" element to exclude items, you can omit
        the "filter-values".In this case, Amazon Personalize doesn't
        use that portion of the expression to filter recommendations.

        For more information, see Filtering recommendations and user
        segments.

        * *(string) --*

          * *(string) --*

      * **recommenderArn** (*string*) -- The Amazon Resource Name
        (ARN) of the recommender to use to get recommendations.
        Provide a recommender ARN if you created a Domain dataset
        group with a recommender for a domain use case.

      * **promotions** (*list*) --

        The promotions to apply to the recommendation request. A
        promotion defines additional business rules that apply to a
        configurable subset of recommended items.

        * *(dict) --*

          Contains information on a promotion. A promotion defines
          additional business rules that apply to a configurable
          subset of recommended items.

          * **name** *(string) --*

            The name of the promotion.

          * **percentPromotedItems** *(integer) --*

            The percentage of recommended items to apply the promotion
            to.

          * **filterArn** *(string) --*

            The Amazon Resource Name (ARN) of the filter used by the
            promotion. This filter defines the criteria for promoted
            items. For more information, see Promotion filters.

          * **filterValues** *(dict) --*

            The values to use when promoting items. For each
            placeholder parameter in your promotion's filter
            expression, provide the parameter name (in matching case)
            as a key and the filter value(s) as the corresponding
            value. Separate multiple values for one parameter with a
            comma.

            For filter expressions that use an "INCLUDE" element to
            include items, you must provide values for all parameters
            that are defined in the expression. For filters with
            expressions that use an "EXCLUDE" element to exclude
            items, you can omit the "filter-values". In this case,
            Amazon Personalize doesn't use that portion of the
            expression to filter recommendations.

            For more information on creating filters, see Filtering
            recommendations and user segments.

            * *(string) --*

              * *(string) --*

      * **metadataColumns** (*dict*) --

        If you enabled metadata in recommendations when you created or
        updated the campaign or recommender, specify the metadata
        columns from your Items dataset to include in item
        recommendations. The map key is "ITEMS" and the value is a
        list of column names from your Items dataset. The maximum
        number of columns you can provide is 10.

        For information about enabling metadata for a campaign, see
        Enabling metadata in recommendations for a campaign. For
        information about enabling metadata for a recommender, see
        Enabling metadata in recommendations for a recommender.

        * *(string) --*

          * *(list) --*

            * *(string) --*

   Return type:
      dict

   Returns:
      **Response Syntax**

         {
             'itemList': [
                 {
                     'itemId': 'string',
                     'score': 123.0,
                     'promotionName': 'string',
                     'metadata': {
                         'string': 'string'
                     },
                     'reason': [
                         'string',
                     ]
                 },
             ],
             'recommendationId': 'string'
         }

      **Response Structure**

      * *(dict) --*

        * **itemList** *(list) --*

          A list of recommendations sorted in descending order by
          prediction score. There can be a maximum of 500 items in the
          list.

          * *(dict) --*

            An object that identifies an item.

            The and APIs return a list of >>``<<PredictedItem``s.

            * **itemId** *(string) --*

              The recommended item ID.

            * **score** *(float) --*

              A numeric representation of the model's certainty that
              the item will be the next user selection. For more
              information on scoring logic, see  how-scores-work.

            * **promotionName** *(string) --*

              The name of the promotion that included the predicted
              item.

            * **metadata** *(dict) --*

              Metadata about the item from your Items dataset.

              * *(string) --*

                * *(string) --*

            * **reason** *(list) --*

              If you use User-Personalization-v2, a list of reasons
              for why the item was included in recommendations.
              Possible reasons include the following:

              * Promoted item - Indicates the item was included as
                part of a promotion that you applied in your
                recommendation request.

              * Exploration - Indicates the item was included with
                exploration. With exploration, recommendations include
                items with less interactions data or relevance for the
                user. For more information about exploration, see
                Exploration.

              * Popular item - Indicates the item was included as a
                placeholder popular item. If you use a filter,
                depending on how many recommendations the filter
                removes, Amazon Personalize might add placeholder
                items to meet the "numResults" for your recommendation
                request. These items are popular items, based on
                interactions data, that satisfy your filter criteria.
                They don't have a relevance score for the user.

              * *(string) --*

        * **recommendationId** *(string) --*

          The ID of the recommendation.

   **Exceptions**

   * "PersonalizeRuntime.Client.exceptions.InvalidInputException"

   * "PersonalizeRuntime.Client.exceptions.ResourceNotFoundException"
PersonalizeRuntime / Client / get_personalized_ranking


get_personalized_ranking
************************

PersonalizeRuntime.Client.get_personalized_ranking(**kwargs)

   Re-ranks a list of recommended items for the given user. The first
   item in the list is deemed the most likely item to be of interest
   to the user.

   Note:

     The solution backing the campaign must have been created using a
     recipe of type PERSONALIZED_RANKING.

   See also: AWS API Documentation

   **Request Syntax**

      response = client.get_personalized_ranking(
          campaignArn='string',
          inputList=[
              'string',
          ],
          userId='string',
          context={
              'string': 'string'
          },
          filterArn='string',
          filterValues={
              'string': 'string'
          },
          metadataColumns={
              'string': [
                  'string',
              ]
          }
      )

   Parameters:
      * **campaignArn** (*string*) --

        **[REQUIRED]**

        The Amazon Resource Name (ARN) of the campaign to use for
        generating the personalized ranking.

      * **inputList** (*list*) --

        **[REQUIRED]**

        A list of items (by "itemId") to rank. If an item was not
        included in the training dataset, the item is appended to the
        end of the reranked list. If you are including metadata in
        recommendations, the maximum is 50. Otherwise, the maximum is
        500.

        * *(string) --*

      * **userId** (*string*) --

        **[REQUIRED]**

        The user for which you want the campaign to provide a
        personalized ranking.

      * **context** (*dict*) --

        The contextual metadata to use when getting recommendations.
        Contextual metadata includes any interaction information that
        might be relevant when getting a user's recommendations, such
        as the user's current location or device type.

        * *(string) --*

          * *(string) --*

      * **filterArn** (*string*) -- The Amazon Resource Name (ARN) of
        a filter you created to include items or exclude items from
        recommendations for a given user. For more information, see
        Filtering Recommendations.

      * **filterValues** (*dict*) --

        The values to use when filtering recommendations. For each
        placeholder parameter in your filter expression, provide the
        parameter name (in matching case) as a key and the filter
        value(s) as the corresponding value. Separate multiple values
        for one parameter with a comma.

        For filter expressions that use an "INCLUDE" element to
        include items, you must provide values for all parameters that
        are defined in the expression. For filters with expressions
        that use an "EXCLUDE" element to exclude items, you can omit
        the "filter-values".In this case, Amazon Personalize doesn't
        use that portion of the expression to filter recommendations.

        For more information, see Filtering Recommendations.

        * *(string) --*

          * *(string) --*

      * **metadataColumns** (*dict*) --

        If you enabled metadata in recommendations when you created or
        updated the campaign, specify metadata columns from your Items
        dataset to include in the personalized ranking. The map key is
        "ITEMS" and the value is a list of column names from your
        Items dataset. The maximum number of columns you can provide
        is 10.

        For information about enabling metadata for a campaign, see
        Enabling metadata in recommendations for a campaign.

        * *(string) --*

          * *(list) --*

            * *(string) --*

   Return type:
      dict

   Returns:
      **Response Syntax**

         {
             'personalizedRanking': [
                 {
                     'itemId': 'string',
                     'score': 123.0,
                     'promotionName': 'string',
                     'metadata': {
                         'string': 'string'
                     },
                     'reason': [
                         'string',
                     ]
                 },
             ],
             'recommendationId': 'string'
         }

      **Response Structure**

      * *(dict) --*

        * **personalizedRanking** *(list) --*

          A list of items in order of most likely interest to the
          user. The maximum is 500.

          * *(dict) --*

            An object that identifies an item.

            The and APIs return a list of >>``<<PredictedItem``s.

            * **itemId** *(string) --*

              The recommended item ID.

            * **score** *(float) --*

              A numeric representation of the model's certainty that
              the item will be the next user selection. For more
              information on scoring logic, see  how-scores-work.

            * **promotionName** *(string) --*

              The name of the promotion that included the predicted
              item.

            * **metadata** *(dict) --*

              Metadata about the item from your Items dataset.

              * *(string) --*

                * *(string) --*

            * **reason** *(list) --*

              If you use User-Personalization-v2, a list of reasons
              for why the item was included in recommendations.
              Possible reasons include the following:

              * Promoted item - Indicates the item was included as
                part of a promotion that you applied in your
                recommendation request.

              * Exploration - Indicates the item was included with
                exploration. With exploration, recommendations include
                items with less interactions data or relevance for the
                user. For more information about exploration, see
                Exploration.

              * Popular item - Indicates the item was included as a
                placeholder popular item. If you use a filter,
                depending on how many recommendations the filter
                removes, Amazon Personalize might add placeholder
                items to meet the "numResults" for your recommendation
                request. These items are popular items, based on
                interactions data, that satisfy your filter criteria.
                They don't have a relevance score for the user.

              * *(string) --*

        * **recommendationId** *(string) --*

          The ID of the recommendation.

   **Exceptions**

   * "PersonalizeRuntime.Client.exceptions.InvalidInputException"

   * "PersonalizeRuntime.Client.exceptions.ResourceNotFoundException"