diff --git a/What's In Your Prediction.txt b/What's In Your Prediction.txt index f451939..75f4030 100644 --- a/What's In Your Prediction.txt +++ b/What's In Your Prediction.txt @@ -7,7 +7,7 @@ # # What's in *your* prediction? # -# Every Featrix prediction returns is a JSON structure that includes: +# Every prediction Featrix returns is a JSON object that includes: # # 1. An ID to update Featrix on the ground truth if you discover the truth later. # 2. An echo of your query to verify the query is what you expect. @@ -19,68 +19,69 @@ # mixed with Featrix embeddings. { - prediction_featrix_id: "....", # call a feedback API later with - # the ground truth if you'd like - prediction_time: "Thu Sep 26 18:17:52 2024", # the time the query ran. + prediction_featrix_id: "....", # - Call a feedback API later with + # the ground truth if you'd like. + + prediction_time: "Thu Sep 26 18:17:52 2024", # - The time the query ran. - original_query: { # your original input query - numerical_input_1: 2, # dictionary of column: pairs + original_query: { # - Your original input query. + numerical_input_1: 2, # - Dictionary of : pairs. }, - actual_query: { # if you specified a column not - ... # present in the model, your query may - }, # be reduced before execution. + actual_query: { # - If you specified a column not + ... # present in the model, your query may + }, # be reduced before execution. - ignored_query_columns: [ # for emphasis, we let you know - ... # which parts of the original query - ], # were ignored. + ignored_query_columns: [ # - For emphasis, we let you know + ... # which parts of the original query + ], # were ignored. - results: { # your actual prediction: classifier - cat_value_1: "0.25", # with probability or a regression. + results: { # - Your actual prediction: classifier + cat_value_1: "0.25", # with probability or a regression. ... }, - column_guardrails: [ # notes if any column in the query was - { numerical_input_1: "within 1 stdev" }, # pushing the limit of the training data + query_column_guardrails: [ # - Notes if any column in the query was + { numerical_input_1: "within 1 stdev" }, # pushing the limit of the training data. { numerical_input_2: ">3 stdevs" }, ... ], - cross_column_guardrails_score: [ # a cross-column score for how far out - 0 to 1 # of training the query was, when considered - ], # against all the columns queried. + query_cross_column_guardrails_score: [ # - A cross-column score for how far out + 0 to 1 # of training the query was, when considered + ], # against all the columns queried. - nearest_neighbors_in_training_data: [ # if security policy allows, we return - { ...row 234 }, # nearest neighbors in the training space - { ...row 645 }, ... # for human evaluation of the result. + nearest_neighbors_in_training_data: [ # - If your security policy allows, we return + { ...row 234 }, # nearest neighbors in the training space + { ...row 645 }, ... # for human evaluation of the result. ], - upstream_embedding_models: { # metadata of upstream models used. - col1: "sentence-all-MiniLM-L12-v1", # these might be other models you made - col6: "featrix-upstream-52b43b29", # with Featrix or embedding models - col12: "openai-text-embedding-3-large" # from third parties. + upstream_embedding_models: { # - Metadata of upstream models used. + col1: "sentence-all-MiniLM-L12-v1", # These might be other models you made + col6: "featrix-upstream-52b43b29", # with Featrix or embedding models + col12: "openai-text-embedding-3-large" # from third parties. } - featrix_model: { # the neural function (model) - model_featrix_id: "...", # the Featrix id for this model - model_hash: "...", # the hash to identify a specific artifact - # in the Featrix system that ran this prediction - model_train_time: "...", # when this model was last trained - model_train_loops: 500, # how many passes were made over the data - model_train_featrix_version: "1.2", # the Featrix version that trained this model. - model_predict_featrix_version: "1.2", # the Featrix version that ran this model. - model_metrics: { # metrics from the validation set on this model - f1: 0.75 # MSE, loss, accuracy, precision, recall, et al. + featrix_model: { # - The neural function (model) details. + model_featrix_id: "...", # - The Featrix id for this model. + model_hash: "...", # - The hash to identify a specific artifact + # in the Featrix system that ran this prediction. + model_train_time: "...", # - When this model was last trained. + model_train_loops: 500, # - How many passes were made over the data. + model_train_featrix_version: "1.2", # - The Featrix version that trained this model. + model_predict_featrix_version: "1.2", # - The Featrix version that ran this model. + model_metrics: { # - Metrics from the validation set on this model: + f1: 0.75 # MSE, loss, accuracy, precision, recall, et al. } }, - featrix_embedding_space: { # the Featrix embedding space information - embedding_space_featrix_id: "...", # the Featrix id for this embedding space - embedding_space_hash: "...", # the hash to identify a specific artifact - embedding_space_train_time: "...", # when this model was last trained - embedding_space_train_loops: 500, # how many passes were made over the data - embedding_space_featrix_version: "1.2", # the Featrix version that trained this model. - embedding_space_run_featrix_version: "1.2", # the Featrix version that ran this model. + featrix_embedding_space: { # - The Featrix embedding space details. + embedding_space_featrix_id: "...", # - The Featrix id for this embedding space. + embedding_space_hash: "...", # - The hash to identify a specific artifact. + embedding_space_train_time: "...", # - When this model was last trained. + embedding_space_train_loops: 500, # - How many passes were made over the data. + embedding_space_featrix_version: "1.2", # - The Featrix version that trained this model. + embedding_space_run_featrix_version: "1.2", # - The Featrix version that ran this model. } }