:py:mod:`abacusai.prediction_client`
====================================

.. py:module:: abacusai.prediction_client


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   abacusai.prediction_client.PredictionClient




.. py:class:: PredictionClient(client_options = None)

   Bases: :py:obj:`abacusai.client.BaseApiClient`

   Abacus.AI Prediction API Client. Does not utilize authentication and only contains public prediction methods

   :param client_options: Optional API client configurations
   :type client_options: ClientOptions

   .. py:method:: lookup_features(self, deployment_token, deployment_id, query_data = {})

      Returns the feature group deployed in the feature store project.

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param query_data: This will be a dictionary where 'Key' will be the column name (e.g. a column with name 'user_id' in your dataset) mapped to the column mapping USER_ID that uniquely identifies the entity against which a prediction is performed and 'Value' will be the unique value of the same entity.
      :type query_data: dict


   .. py:method:: predict(self, deployment_token, deployment_id, query_data = {})

      Returns a prediction for Predictive Modeling

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param query_data: This will be a dictionary where 'Key' will be the column name (e.g. a column with name 'user_id' in your dataset) mapped to the column mapping USER_ID that uniquely identifies the entity against which a prediction is performed and 'Value' will be the unique value of the same entity.
      :type query_data: dict


   .. py:method:: predict_multiple(self, deployment_token, deployment_id, query_data = {})

      Returns a list of predictions for Predictive Modeling

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param query_data: This will be a list of dictionaries where 'Key' will be the column name (e.g. a column with name 'user_id' in your dataset) mapped to the column mapping USER_ID that uniquely identifies the entity against which a prediction is performed and 'Value' will be the unique value of the same entity.
      :type query_data: list


   .. py:method:: predict_from_datasets(self, deployment_token, deployment_id, query_data = {})

      Returns a list of predictions for Predictive Modeling

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param query_data: This will be a dictionary where 'Key' will be the source dataset name and 'Value' will be a list of records corresponding to the dataset rows
      :type query_data: dict


   .. py:method:: predict_lead(self, deployment_token, deployment_id, query_data)

      Returns the probability of a user to be a lead on the basis of his/her interaction with the service/product and user's own attributes (e.g. income, assets, credit score, etc.). Note that the inputs to this method, wherever applicable, will be the column names in your dataset mapped to the column mappings in our system (e.g. column 'user_id' mapped to mapping 'LEAD_ID' in our system).

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param query_data: This will be a dictionary containing user attributes and/or user's interaction data with the product/service (e.g. number of click, items in cart, etc.).
      :type query_data: dict


   .. py:method:: predict_churn(self, deployment_token, deployment_id, query_data)

      Returns a probability of a user to churn out in response to his/her interactions with the item/product/service. Note that the inputs to this method, wherever applicable, will be the column names in your dataset mapped to the column mappings in our system (e.g. column 'churn_result' mapped to mapping 'CHURNED_YN' in our system).

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param query_data: This will be a dictionary where 'Key' will be the column name (e.g. a column with name 'user_id' in your dataset) mapped to the column mapping USER_ID that uniquely identifies the entity against which a prediction is performed and 'Value' will be the unique value of the same entity.
      :type query_data: dict


   .. py:method:: predict_takeover(self, deployment_token, deployment_id, query_data)

      Returns a probability for each class label associated with the types of fraud or a 'yes' or 'no' type label for the possibility of fraud. Note that the inputs to this method, wherever applicable, will be the column names in your dataset mapped to the column mappings in our system (e.g. column 'account_name' mapped to mapping 'ACCOUNT_ID' in our system).

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param query_data: This will be a dictionary containing account activity characteristics (e.g. login id, login duration, login type, ip address, etc.).
      :type query_data: dict


   .. py:method:: predict_fraud(self, deployment_token, deployment_id, query_data)

      Returns a probability of a transaction performed under a specific account as being a fraud or not. Note that the inputs to this method, wherever applicable, will be the column names in your dataset mapped to the column mappings in our system (e.g. column 'account_number' mapped to the mapping 'ACCOUNT_ID' in our system).

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param query_data: This will be a dictionary containing transaction attributes (e.g. credit card type, transaction location, transaction amount, etc.).
      :type query_data: dict


   .. py:method:: predict_class(self, deployment_token, deployment_id, query_data = {}, threshold = None, threshold_class = None, explain_predictions = False, fixed_features = None, nested = None)

      Returns a prediction for regression classification

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param query_data: This will be a dictionary where 'Key' will be the column name (e.g. a column with name 'user_id' in your dataset) mapped to the column mapping USER_ID that uniquely identifies the entity against which a prediction is performed and 'Value' will be the unique value of the same entity.
      :type query_data: dict
      :param threshold: float value that is applied on the popular class label.
      :type threshold: float
      :param threshold_class: label upon which the threshold is added (Binary labels only)
      :type threshold_class: str
      :param explain_predictions: If true, returns the SHAP explanations for all input features.
      :type explain_predictions: bool
      :param fixed_features: Set of input features to treat as constant for explanations.
      :type fixed_features: list
      :param nested: If specified generates prediction delta for each index of the specified nested feature.
      :type nested: str


   .. py:method:: predict_target(self, deployment_token, deployment_id, query_data = {}, explain_predictions = False, fixed_features = None, nested = None)

      Returns a prediction from a classification or regression model. Optionally, includes explanations.

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param query_data: This will be a dictionary where 'Key' will be the column name (e.g. a column with name 'user_id' in your dataset) mapped to the column mapping USER_ID that uniquely identifies the entity against which a prediction is performed and 'Value' will be the unique value of the same entity.
      :type query_data: dict
      :param explain_predictions: If true, returns the SHAP explanations for all input features.
      :type explain_predictions: bool
      :param fixed_features: Set of input features to treat as constant for explanations.
      :type fixed_features: list
      :param nested: If specified generates prediction delta for each index of the specified nested feature.
      :type nested: str


   .. py:method:: get_anomalies(self, deployment_token, deployment_id, threshold = None, histogram = False)

      Returns a list of anomalies from the training dataset

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param threshold: The threshold score of what is an anomaly. Valid values are between 0.8 and 0.99.
      :type threshold: float
      :param histogram: If True, will return a histogram of the distribution of all points
      :type histogram: bool


   .. py:method:: is_anomaly(self, deployment_token, deployment_id, query_data = None)

      Returns a list of anomaly attributes based on login information for a specified account. Note that the inputs to this method, wherever applicable, will be the column names in your dataset mapped to the column mappings in our system (e.g. column 'account_name' mapped to mapping 'ACCOUNT_ID' in our system).

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param query_data: The input data for the prediction.
      :type query_data: dict


   .. py:method:: get_forecast(self, deployment_token, deployment_id, query_data, future_data = None, num_predictions = None, prediction_start = None)

      Returns a list of forecasts for a given entity under the specified project deployment. Note that the inputs to the deployed model will be the column names in your dataset mapped to the column mappings in our system (e.g. column 'holiday_yn' mapped to mapping 'FUTURE' in our system).

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param query_data: This will be a dictionary where 'Key' will be the column name (e.g. a column with name 'store_id' in your dataset) mapped to the column mapping ITEM_ID that uniquely identifies the entity against which forecasting is performed and 'Value' will be the unique value of the same entity.
      :type query_data: dict
      :param future_data: This will be a dictionary of values known ahead of time that are relevant for forecasting (e.g. State Holidays, National Holidays, etc.). The key and the value both will be of type 'String'. For example future data entered for a Store may be {"Holiday":"No", "Promo":"Yes"}.
      :type future_data: dict
      :param num_predictions: The number of timestamps to predict in the future.
      :type num_predictions: int
      :param prediction_start: The start date for predictions (e.g., "2015-08-01T00:00:00" as input for mid-night of 2015-08-01).
      :type prediction_start: str


   .. py:method:: get_k_nearest(self, deployment_token, deployment_id, vector, k = None, distance = None, include_score = False)

      Returns the k nearest neighbors for the provided embedding vector.

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param vector: Input vector to perform the k nearest neighbors with.
      :type vector: list
      :param k: Overrideable number of items to return
      :type k: int
      :param distance: Specify the distance function to use when finding nearest neighbors
      :type distance: str
      :param include_score: If True, will return the score alongside the resulting embedding value
      :type include_score: bool


   .. py:method:: get_multiple_k_nearest(self, deployment_token, deployment_id, queries)

      Returns the k nearest neighbors for the queries provided

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param queries: List of Mappings of format {"catalogId": "cat0", "vectors": [...], "k": 20, "distance": "euclidean"}. See getKNearest for additional information about the supported parameters
      :type queries: list


   .. py:method:: get_labels(self, deployment_token, deployment_id, query_data, threshold = 0.5)

      Returns a list of scored labels from

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param query_data: Dictionary where key is "Content" and value is the text from which entities are to be extracted.
      :type query_data: dict
      :param threshold: The minimum output probability that a predicted label must have in order for us to report it.
      :type threshold: float


   .. py:method:: get_recommendations(self, deployment_token, deployment_id, query_data, num_items = 50, page = 1, exclude_item_ids = [], score_field = '', scaling_factors = [], restrict_items = [], exclude_items = [], explore_fraction = 0.0)

      Returns a list of recommendations for a given user under the specified project deployment. Note that the inputs to this method, wherever applicable, will be the column names in your dataset mapped to the column mappings in our system (e.g. column 'time' mapped to mapping 'TIMESTAMP' in our system).

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param query_data: This will be a dictionary where 'Key' will be the column name (e.g. a column with name 'user_name' in your dataset) mapped to the column mapping USER_ID that uniquely identifies the user against which recommendations are made and 'Value' will be the unique value of the same item. For example, if you have the column name 'user_name' mapped to the column mapping 'USER_ID', then the query must have the exact same column name (user_name) as key and the name of the user (John Doe) as value.
      :type query_data: dict
      :param num_items: The number of items to recommend on one page. By default, it is set to 50 items per page.
      :type num_items: int
      :param page: The page number to be displayed. For example, let's say that the num_items is set to 10 with the total recommendations list size of 50 recommended items, then an input value of 2 in the 'page' variable will display a list of items that rank from 11th to 20th.
      :type page: int
      :param exclude_item_ids: [DEPRECATED]
      :type exclude_item_ids: list
      :param score_field: The relative item scores are returned in a separate field named with the same name as the key (score_field) for this argument.
      :type score_field: str
      :param scaling_factors: It allows you to bias the model towards certain items. The input to this argument is a list of dictionaries where the format of each dictionary is as follows: {"column": "col0", "values": ["value0", "value1"], "factor": 1.1}. The key, "column" takes the name of the column, "col0"; the key, "values" takes the list of items, "["value0", "value1"]" in reference to which the model recommendations need to be biased; and the key, "factor" takes the factor by which the item scores are adjusted.  Let's take an example where the input to scaling_factors is [{"column": "VehicleType", "values": ["SUV", "Sedan"], "factor": 1.4}]. After we apply the model to get item probabilities, for every SUV and Sedan in the list, we will multiply the respective probability by 1.1 before sorting. This is particularly useful if there's a type of item that might be less popular but you want to promote it or there's an item that always comes up and you want to demote it.
      :type scaling_factors: list
      :param restrict_items: It allows you to restrict the recommendations to certain items. The input to this argument is a list of dictionaries where the format of each dictionary is as follows: {"column": "col0", "values": ["value0", "value1", "value3", ...]}. The key, "column" takes the name of the column, "col0"; the key, "values" takes the list of items, "["value0", "value1", "value3", ...]" to which to restrict the recommendations to. Let's take an example where the input to restrict_items is [{"column": "VehicleType", "values": ["SUV", "Sedan"]}]. This input will restrict the recommendations to SUVs and Sedans. This type of restrition is particularly useful if there's a list of items that you know is of use in some particular scenario and you want to restrict the recommendations only to that list.
      :type restrict_items: list
      :param exclude_items: It allows you to exclude certain items from the list of recommendations. The input to this argument is a list of dictionaries where the format of each dictionary is as follows: {"column": "col0", "values": ["value0", "value1", ...]}. The key, "column" takes the name of the column, "col0"; the key, "values" takes the list of items, "["value0", "value1"]" to exclude from the recommendations. Let's take an example where the input to exclude_items is [{"column": "VehicleType", "values": ["SUV", "Sedan"]}]. The resulting recommendation list will exclude all SUVs and Sedans. This is particularly useful if there's a list of items that you know is of no use in some particular scenario and you don't want to show those items present in that list.
      :type exclude_items: list
      :param explore_fraction: The fraction of recommendations that is to be new items.
      :type explore_fraction: float


   .. py:method:: get_personalized_ranking(self, deployment_token, deployment_id, query_data, preserve_ranks = [], scaling_factors = [])

      Returns a list of items with personalized promotions on them for a given user under the specified project deployment. Note that the inputs to this method, wherever applicable, will be the column names in your dataset mapped to the column mappings in our system (e.g. column 'item_code' mapped to mapping 'ITEM_ID' in our system).

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param query_data: This will be a dictionary with two key-value pairs. The first pair represents a 'Key' where the column name (e.g. a column with name 'user_id' in your dataset) mapped to the column mapping USER_ID uniquely identifies the user against whom a prediction is made and a 'Value' which is the identifier value for that user. The second pair will have a 'Key' which will be the name of the column name (e.g. movie_name) mapped to ITEM_ID (unique item identifier) and a 'Value' which will be a list of identifiers that uniquely identifies those items.
      :type query_data: dict
      :param preserve_ranks: List of dictionaries of format {"column": "col0", "values": ["value0, value1"]}, where the ranks of items in query_data is preserved for all the items in "col0" with values, "value0" and "value1". This option is useful when the desired items are being recommended in the desired order and the ranks for those items need to be kept unchanged during recommendation generation.
      :type preserve_ranks: list
      :param scaling_factors: It allows you to bias the model towards certain items. The input to this argument is a list of dictionaries where the format of each dictionary is as follows: {"column": "col0", "values": ["value0", "value1"], "factor": 1.1}. The key, "column" takes the name of the column, "col0"; the key, "values" takes the list of items, "["value0", "value1"]" in reference to which the model recommendations need to be biased; and the key, "factor" takes the factor by which the item scores are adjusted.  Let's take an example where the input to scaling_factors is [{"column": "VehicleType", "values": ["SUV", "Sedan"], "factor": 1.4}]. After we apply the model to get item probabilities, for every SUV and Sedan in the list, we will multiply the respective probability by 1.1 before sorting. This is particularly useful if there's a type of item that might be less popular but you want to promote it or there's an item that always comes up and you want to demote it.
      :type scaling_factors: list


   .. py:method:: get_ranked_items(self, deployment_token, deployment_id, query_data, preserve_ranks = [], scaling_factors = [])

      Returns a list of re-ranked items for a selected user when a list of items is required to be reranked according to the user's preferences. Note that the inputs to this method, wherever applicable, will be the column names in your dataset mapped to the column mappings in our system (e.g. column 'item_code' mapped to mapping 'ITEM_ID' in our system).

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param query_data: This will be a dictionary with two key-value pairs. The first pair represents a 'Key' where the column name (e.g. a column with name 'user_id' in your dataset) mapped to the column mapping USER_ID uniquely identifies the user against whom a prediction is made and a 'Value' which is the identifier value for that user. The second pair will have a 'Key' which will be the name of the column name (e.g. movie_name) mapped to ITEM_ID (unique item identifier) and a 'Value' which will be a list of identifiers that uniquely identifies those items.
      :type query_data: dict
      :param preserve_ranks: List of dictionaries of format {"column": "col0", "values": ["value0, value1"]}, where the ranks of items in query_data is preserved for all the items in "col0" with values, "value0" and "value1". This option is useful when the desired items are being recommended in the desired order and the ranks for those items need to be kept unchanged during recommendation generation.
      :type preserve_ranks: list
      :param scaling_factors: It allows you to bias the model towards certain items. The input to this argument is a list of dictionaries where the format of each dictionary is as follows: {"column": "col0", "values": ["value0", "value1"], "factor": 1.1}. The key, "column" takes the name of the column, "col0"; the key, "values" takes the list of items, "["value0", "value1"]" in reference to which the model recommendations need to be biased; and the key, "factor" takes the factor by which the item scores are adjusted.  Let's take an example where the input to scaling_factors is [{"column": "VehicleType", "values": ["SUV", "Sedan"], "factor": 1.4}]. After we apply the model to get item probabilities, for every SUV and Sedan in the list, we will multiply the respective probability by 1.1 before sorting. This is particularly useful if there's a type of item that might be less popular but you want to promote it or there's an item that always comes up and you want to demote it.
      :type scaling_factors: list


   .. py:method:: get_related_items(self, deployment_token, deployment_id, query_data, num_items = 50, page = 1, scaling_factors = [], restrict_items = [], exclude_items = [])

      Returns a list of related items for a given item under the specified project deployment. Note that the inputs to this method, wherever applicable, will be the column names in your dataset mapped to the column mappings in our system (e.g. column 'item_code' mapped to mapping 'ITEM_ID' in our system).

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param query_data: This will be a dictionary where 'Key' will be the column name (e.g. a column with name 'user_name' in your dataset) mapped to the column mapping USER_ID that uniquely identifies the user against which related items are determined and 'Value' will be the unique value of the same item. For example, if you have the column name 'user_name' mapped to the column mapping 'USER_ID', then the query must have the exact same column name (user_name) as key and the name of the user (John Doe) as value.
      :type query_data: dict
      :param num_items: The number of items to recommend on one page. By default, it is set to 50 items per page.
      :type num_items: int
      :param page: The page number to be displayed. For example, let's say that the num_items is set to 10 with the total recommendations list size of 50 recommended items, then an input value of 2 in the 'page' variable will display a list of items that rank from 11th to 20th.
      :type page: int
      :param scaling_factors: It allows you to bias the model towards certain items. The input to this argument is a list of dictionaries where the format of each dictionary is as follows: {"column": "col0", "values": ["value0", "value1"], "factor": 1.1}. The key, "column" takes the name of the column, "col0"; the key, "values" takes the list of items, "["value0", "value1"]" in reference to which the model recommendations need to be biased; and the key, "factor" takes the factor by which the item scores are adjusted.  Let's take an example where the input to scaling_factors is [{"column": "VehicleType", "values": ["SUV", "Sedan"], "factor": 1.4}]. After we apply the model to get item probabilities, for every SUV and Sedan in the list, we will multiply the respective probability by 1.1 before sorting. This is particularly useful if there's a type of item that might be less popular but you want to promote it or there's an item that always comes up and you want to demote it.
      :type scaling_factors: list
      :param restrict_items: It allows you to restrict the recommendations to certain items. The input to this argument is a list of dictionaries where the format of each dictionary is as follows: {"column": "col0", "values": ["value0", "value1", "value3", ...]}. The key, "column" takes the name of the column, "col0"; the key, "values" takes the list of items, "["value0", "value1", "value3", ...]" to which to restrict the recommendations to. Let's take an example where the input to restrict_items is [{"column": "VehicleType", "values": ["SUV", "Sedan"]}]. This input will restrict the recommendations to SUVs and Sedans. This type of restrition is particularly useful if there's a list of items that you know is of use in some particular scenario and you want to restrict the recommendations only to that list.
      :type restrict_items: list
      :param exclude_items: It allows you to exclude certain items from the list of recommendations. The input to this argument is a list of dictionaries where the format of each dictionary is as follows: {"column": "col0", "values": ["value0", "value1", ...]}. The key, "column" takes the name of the column, "col0"; the key, "values" takes the list of items, "["value0", "value1"]" to exclude from the recommendations. Let's take an example where the input to exclude_items is [{"column": "VehicleType", "values": ["SUV", "Sedan"]}]. The resulting recommendation list will exclude all SUVs and Sedans. This is particularly useful if there's a list of items that you know is of no use in some particular scenario and you don't want to show those items present in that list.
      :type exclude_items: list


   .. py:method:: get_feature_group_rows(self, deployment_token, deployment_id, query_data)

      :param deployment_token:
      :type deployment_token: str
      :param deployment_id:
      :type deployment_id: str
      :param query_data:
      :type query_data: dict


   .. py:method:: get_search_results(self, deployment_token, deployment_id, query_data)

      TODO

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param query_data: Dictionary where key is "Content" and value is the text from which entities are to be extracted.
      :type query_data: dict


   .. py:method:: get_sentiment(self, deployment_token, deployment_id, document)

      TODO

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param document: # TODO
      :type document: str


   .. py:method:: get_entailment(self, deployment_token, deployment_id, document)

      TODO

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param document: # TODO
      :type document: str


   .. py:method:: predict_language(self, deployment_token, deployment_id, query_data)

      TODO

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param query_data: # TODO
      :type query_data: str



