TimeSeriesCloudPredictor.predict_proba_real_time#

TimeSeriesCloudPredictor.predict_proba_real_time(**kwargs) → DataFrame[source]#

Predict probability with the deployed endpoint. A deployed endpoint is required. This is intended to provide a low latency inference. If you want to inference on a large dataset, use predict_proba() instead. If your problem_type is regression, this functions identically to predict_real_time, returning the same output.

Parameters

  • test_data (Union(str, pandas.DataFrame)) – The test data to be inferenced. Can be a pandas.DataFrame, or a local path to csv file.

  • test_data_image_column (default = None) – If provided a csv file or pandas.DataFrame as the test_data and test_data involves image modality, you must specify the column name corresponding to image paths. The path MUST be an abspath

  • accept (str, default = application/x-parquet) – Type of accept output content. Valid options are application/x-parquet, text/csv, application/json

  • kwargs – Additional args that you would pass to predict calls of an AutoGluon logic

Returns

Will return a Pandas.Series when it’s a regression problem. Will return a Pandas.DataFrame otherwise

Return type

Pandas.DataFrame or Pandas.Series