relevanceai.api.endpoints.prediction

Prediction services

Module Contents

Classes

PredictionClient

Base class for all relevanceai client utilities
class relevanceai.api.endpoints.prediction.PredictionClient(project, api_key)

Bases: relevanceai.base._Base

Base class for all relevanceai client utilities

KNN(self, dataset_id: str, vector: list, vector_field: str, target_field: str, k: int = 5, weighting: bool or list = True, impute_value: int = 0, predict_operation: str = 'most_frequent', include_search_results: bool = True)

Predict using KNN regression.

Parameters

  • dataset_id (string) – Unique name of dataset

  • vector (list) – Vector, a list/array of floats that represents a piece of data.

  • vector_field (string) – The vector field to search in. It can either be an array of strings (automatically equally weighted) (e.g. [’check_vector_’, ‘yellow_vector_’]) or it is a dictionary mapping field to float where the weighting is explicitly specified (e.g. {’check_vector_’: 0.2, ‘yellow_vector_’: 0.5})

  • target_field (string) – The field to perform regression on.

  • k (int) – The number of results for KNN.

  • weighting (bool/list) – The weighting for each prediction

  • impute_value (int) – The value used to fill in the document when the data is missing.

  • predict_operation (string) – How to predict using the vectors. One of most_frequent or `sum_scores

  • include_search_results (bool) – Whether to include search results.

KNN_from_results(self, field: str, results: list, impute_value: int = 0, predict_operation: str = 'most_frequent')

Predict using KNN regression from search results

Parameters

  • field (string) – Field in results to use for the prediction. Can be multiplied with weighting.

  • results (dict) – List of results in a dictionary

  • weighting (bool/list) – The weighting for each prediction

  • impute_value (int) – The value used to fill in the document when the data is missing.

  • predict_operation (string) – How to predict using the vectors. One of most_frequent or `sum_scores