: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:: predict_raw(deployment_token, deployment_id, **kwargs)

      Raw interface for returning predictions from Plug and Play deployments.

      :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 \*\*kwargs: Arbitrary key/value pairs may be passed in and is sent as part of the request body.
      :type \*\*kwargs: dict


   .. py:method:: lookup_features(deployment_token, deployment_id, query_data = {}, limit_results = None, result_columns = None)

      Returns the feature group deployed in the feature store project.

      :param deployment_token: A deployment token used to authenticate access to created deployments. This token only authorizes predictions on deployments in this project, so it can be safely embedded inside an application or website.
      :type deployment_token: str
      :param deployment_id: A unique identifier for a deployment created under the project.
      :type deployment_id: str
      :param query_data: A dictionary where the key is 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 the value is the unique value of the same entity.
      :type query_data: dict
      :param limit_results: If provided, will limit the number of results to the value specified.
      :type limit_results: int
      :param result_columns: If provided, will limit the columns present in each result to the columns specified in this list.
      :type result_columns: list


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

      Returns a prediction for Predictive Modeling

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


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

      Returns a list of predictions for predictive modeling.

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


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

      Returns a list of predictions for Predictive Modeling.

      :param deployment_token: The deployment token used 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 for a deployment created under the project.
      :type deployment_id: str
      :param query_data: A dictionary where the 'key' is the source dataset name, and the 'value' is a list of records corresponding to the dataset rows.
      :type query_data: dict


   .. py:method:: predict_lead(deployment_token, deployment_id, query_data, explain_predictions = False, explainer_type = None)

      Returns the probability of a user being a lead based on their interaction with the service/product and their own attributes (e.g. income, assets, credit score, etc.). Note that the inputs to this method, wherever applicable, should be the column names in the 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: A dictionary containing user attributes and/or user's interaction data with the product/service (e.g. number of clicks, items in cart, etc.).
      :type query_data: dict
      :param explain_predictions: Will explain predictions for leads
      :type explain_predictions: bool
      :param explainer_type: Type of explainer to use for explanations
      :type explainer_type: str


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

      Returns the probability of a user to churn out in response to their 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 the '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 the 'value' will be the unique value of the same entity.
      :type query_data: dict


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

      Returns the probability of a transaction performed under a specific account being fraudulent or not. Note that the inputs to this method, wherever applicable, should 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: A 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: A unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param query_data: A dictionary containing transaction attributes (e.g. credit card type, transaction location, transaction amount, etc.).
      :type query_data: dict


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

      Returns a classification prediction

      :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 within an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier for a deployment created under the project.
      :type deployment_id: str
      :param query_data: A dictionary where the 'Key' is the column name (e.g. a column with the 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 the 'Value' is the unique value of the same entity.
      :type query_data: dict
      :param threshold: A float value that is applied on the popular class label.
      :type threshold: float
      :param threshold_class: The label upon which the threshold is added (binary labels only).
      :type threshold_class: str
      :param thresholds: Maps labels to thresholds (multi-label classification only). Defaults to F1 optimal threshold if computed for the given class, else uses 0.5.
      :type thresholds: list
      :param explain_predictions: If True, returns the SHAP explanations for all input features.
      :type explain_predictions: bool
      :param fixed_features: A 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
      :param explainer_type: The type of explainer to use.
      :type explainer_type: str


   .. py:method:: predict_target(deployment_token, deployment_id, query_data = {}, explain_predictions = False, fixed_features = None, nested = None, explainer_type = 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 of a deployment created under the project.
      :type deployment_id: str
      :param query_data: A dictionary where the 'key' is 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 the 'value' is 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
      :param explainer_type: The type of explainer to use.
      :type explainer_type: str


   .. py:method:: get_anomalies(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(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, should be the column names in the 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(deployment_token, deployment_id, query_data, future_data = None, num_predictions = None, prediction_start = None, explain_predictions = False, explainer_type = 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 list of values known ahead of time that are relevant for forecasting (e.g. State Holidays, National Holidays, etc.). Each element is a dictionary, where the key and the value both will be of type 'str'. For example future data entered for a Store may be [{"Holiday":"No", "Promo":"Yes", "Date": "2015-07-31 00:00:00"}].
      :type future_data: list
      :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
      :param explain_predictions: Will explain predictions for forecasting
      :type explain_predictions: bool
      :param explainer_type: Type of explainer to use for explanations
      :type explainer_type: str


   .. py:method:: get_k_nearest(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(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(deployment_token, deployment_id, query_data, threshold = None)

      Returns a list of scored labels from a deployment.

      :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: This argument is deprecated and will be ignored.
      :type threshold: None


   .. py:method:: get_entities_from_pdf(deployment_token, deployment_id, pdf = None, doc_id = None, return_extracted_features = False)

      Extracts text from the provided PDF and returns a list of recognized labels and their scores.

      :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 pdf: (Optional) The pdf to predict on. One of pdf or docId must be specified.
      :type pdf: io.TextIOBase
      :param doc_id: (Optional) The pdf to predict on. One of pdf or docId must be specified.
      :type doc_id: str
      :param return_extracted_features: (Optional) If True, will return all extracted features (e.g. all tokens in a page) from the PDF. Default is False.
      :type return_extracted_features: bool


   .. py:method:: get_recommendations(deployment_token, deployment_id, query_data, num_items = 50, page = 1, exclude_item_ids = None, score_field = '', scaling_factors = None, restrict_items = None, exclude_items = None, 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 restriction 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
      :type exclude_items: list
      :param explore_fraction: Explore fraction.
      :type explore_fraction: float


   .. py:method:: get_personalized_ranking(deployment_token, deployment_id, query_data, preserve_ranks = None, preserve_unknown_items = False, scaling_factors = None)

      Returns a list of items with personalized promotions for a given user under the specified project deployment. Note that the inputs to this method, wherever applicable, should be the column names in the 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 in 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 should 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 the 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 preserve_unknown_items: If true, any items that are unknown to the model, will not be reranked, and the original position in the query will be preserved.
      :type preserve_unknown_items: bool
      :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(deployment_token, deployment_id, query_data, preserve_ranks = None, preserve_unknown_items = False, scaling_factors = None)

      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 preserve_unknown_items: If true, any items that are unknown to the model, will not be reranked, and the original position in the query will be preserved
      :type preserve_unknown_items: bool
      :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 is a type of item that might be less popular but you want to promote it or there is an item that always comes up and you want to demote it.
      :type scaling_factors: list


   .. py:method:: get_related_items(deployment_token, deployment_id, query_data, num_items = 50, page = 1, scaling_factors = None, restrict_items = None, exclude_items = None)

      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 the '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 the '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 restriction 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_chat_response(deployment_token, deployment_id, messages, search_results = None)

      Return a chat response which continues the conversation based on the input messages and search results.

      :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 messages: A list of chronologically ordered messages, starting with a user message and alternating sources. A message is a dict with attributes:     is_user (bool): Whether the message is from the user.      text (str): The message's text.
      :type messages: list
      :param search_results: A list of chronologically ordered retrieved search results using the deployment. A retrieved search result is a dict with attributes:     msg_id (int): The corresponding message's index.      result (list): List of NlpSearchPrediction objects.
      :type search_results: list


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

      Return the most relevant search results to the search query from the uploaded documents.

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


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

      Predicts sentiment on a document

      :param deployment_token: A token used to authenticate access to deployments created in this project. 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: A unique string identifier for a deployment created under this project.
      :type deployment_id: str
      :param document: The document to be analyzed for sentiment.
      :type document: str


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

      Predicts the classification of the document

      :param deployment_token: The deployment token used 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: A unique string identifier for the deployment created under the project.
      :type deployment_id: str
      :param document: The document to be classified.
      :type document: str


   .. py:method:: get_classification(deployment_token, deployment_id, document)

      Predicts the classification of the document

      :param deployment_token: The deployment token used 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: A unique string identifier for the deployment created under the project.
      :type deployment_id: str
      :param document: The document to be classified.
      :type document: str


   .. py:method:: get_summary(deployment_token, deployment_id, query_data)

      Returns a JSON of the predicted summary for the given document. 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 'text' mapped to mapping 'DOCUMENT' 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: Raw data dictionary containing the required document data - must have a key 'document' corresponding to a DOCUMENT type text as value.
      :type query_data: dict


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

      Predicts the language of the text

      :param deployment_token: The deployment token used to authenticate access to created deployments. This token is only authorized to predict on deployments within this project, making it safe to embed this model in an application or website.
      :type deployment_token: str
      :param deployment_id: A unique string identifier for a deployment created under the project.
      :type deployment_id: str
      :param query_data: The input string to detect.
      :type query_data: str


   .. py:method:: get_assignments(deployment_token, deployment_id, query_data, forced_assignments = None)

      Get all positive assignments that match a query.

      :param deployment_token: The deployment token used to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it can be safely embedded in an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier of a deployment created under the project.
      :type deployment_id: str
      :param query_data: Specifies the set of assignments being requested.
      :type query_data: dict
      :param forced_assignments: Set of assignments to force and resolve before returning query results.
      :type forced_assignments: dict


   .. py:method:: check_constraints(deployment_token, deployment_id, query_data)

      Check for any constraints violated by the overrides.

      :param deployment_token: The deployment token used 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 within an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier for a deployment created under the project.
      :type deployment_id: str
      :param query_data: Assignment overrides to the solution.
      :type query_data: dict


   .. py:method:: predict_with_binary_data(deployment_token, deployment_id, blob, blob_key_name = 'blob')

      Make predictions for a given blob, e.g. image, audio

      :param deployment_token: A token used 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 in an application or website.
      :type deployment_token: str
      :param deployment_id: A unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param blob: The multipart/form-data of the data.
      :type blob: io.TextIOBase
      :param blob_key_name: The key to access the blob data in the model query data.
      :type blob_key_name: str


   .. py:method:: describe_image(deployment_token, deployment_id, image, categories, top_n = None)

      Describe the similarity between an image and a list of categories.

      :param deployment_token: Authentication token to access created deployments. This token is only authorized to predict on deployments in the current project, and can be safely embedded in an application or website.
      :type deployment_token: str
      :param deployment_id: Unique identifier of a deployment created under the project.
      :type deployment_id: str
      :param image: Image to describe.
      :type image: io.TextIOBase
      :param categories: List of candidate categories to compare with the image.
      :type categories: list
      :param top_n: Return the N most similar categories.
      :type top_n: int


   .. py:method:: transcribe_audio(deployment_token, deployment_id, audio)

      Transcribe the audio

      :param deployment_token: The deployment token to authenticate access to created deployments. This token is only authorized to make predictions on deployments in this project, so it can be safely embedded in an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier of a deployment created under the project.
      :type deployment_id: str
      :param audio: The audio to transcribe.
      :type audio: io.TextIOBase


   .. py:method:: classify_image(deployment_token, deployment_id, image)

      Classify an image.

      :param deployment_token: A 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: A unique string identifier to a deployment created under the project.
      :type deployment_id: str
      :param image: The binary data of the image to classify.
      :type image: io.TextIOBase


   .. py:method:: classify_pdf(deployment_token, deployment_id, pdf = None)

      Returns a classification prediction from a PDF

      :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 within an application or website.
      :type deployment_token: str
      :param deployment_id: The unique identifier for a deployment created under the project.
      :type deployment_id: str
      :param pdf: (Optional) The pdf to predict on. One of pdf or docId must be specified.
      :type pdf: io.TextIOBase


   .. py:method:: get_objects_from_image(deployment_token, deployment_id, image)

      Classify an image.

      :param deployment_token: A 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: A unique string identifier to a deployment created under the project.
      :type deployment_id: str
      :param image: The binary data of the image to detect objects from.
      :type image: io.TextIOBase


   .. py:method:: transfer_style(deployment_token, deployment_id, source_image, style_image)

      Change the source image to adopt the visual style from the style image.

      :param deployment_token: A token used 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 in an application or website.
      :type deployment_token: str
      :param deployment_id: A unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param source_image: The source image to apply the makeup.
      :type source_image: io.TextIOBase
      :param style_image: The image that has the style as a reference.
      :type style_image: io.TextIOBase


   .. py:method:: generate_image(deployment_token, deployment_id, query_data)

      Generate an image from text prompt.

      :param deployment_token: The deployment token used 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 within an application or website.
      :type deployment_token: str
      :param deployment_id: A unique identifier to a deployment created under the project.
      :type deployment_id: str
      :param query_data: Specifies the text prompt. For example, {'prompt': 'a cat'}
      :type query_data: dict



