From 711ddb361d5ce3b694628bab066a2537e38f1143 Mon Sep 17 00:00:00 2001 From: "Jonathan C. McKinney" Date: Wed, 6 Nov 2024 02:03:28 -0800 Subject: [PATCH] Fix typo --- openai_server/agent_prompting.py | 4 +--- src/version.py | 2 +- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/openai_server/agent_prompting.py b/openai_server/agent_prompting.py index ff832b1d0..b11c7eae1 100644 --- a/openai_server/agent_prompting.py +++ b/openai_server/agent_prompting.py @@ -724,8 +724,6 @@ def get_wolfram_alpha_helper(): def get_dai_helper(): cwd = os.path.abspath(os.getcwd()) if os.getenv('ENABLE_DAI'): - # https://wolframalpha.readthedocs.io/en/latest/?badge=latest - # https://products.wolframalpha.com/api/documentation dai = f"""\n* DriverlessAI is an advanced AutoML tool for data science model making and predictions. * If user specifically asks for a DAI model, then you should use the existing pre-built python code to query DriverlessAI, E.g.: ```sh @@ -733,7 +731,7 @@ def get_dai_helper(): # execution: true python {cwd}/openai_server/agent_tools/driverless_ai_data_science.py ``` -* usage: python {cwd}/openai_server/agent_tools/wolfram_alpha_math_science_query.py [--experiment_key EXPERIMENT_KEY] [--dataset_key DATASET_KEY] [--data-url DATA_URL] [--dataset-name DATASET_NAME] [--data-source DATA_SOURCE] [--target-column TARGET_COLUMN] [--task {{classification,regression,predict,shapley_original_features,shapley_transformed_features,transform,fit_and_transform,artifacts}}] [--scorer SCORER] [--experiment-name EXPERIMENT_NAME] [--accuracy {{1,2,3,4,5,6,7,8,9,10}}] [--time {{1,2,3,4,5,6,7,8,9,10}}] [--interpretability {{1,2,3,4,5,6,7,8,9,10}}] [--train-size TRAIN_SIZE] [--seed SEED] [--fast] [--force] +* usage: python {cwd}/openai_server/agent_tools/driverless_ai_data_science.py [--experiment_key EXPERIMENT_KEY] [--dataset_key DATASET_KEY] [--data-url DATA_URL] [--dataset-name DATASET_NAME] [--data-source DATA_SOURCE] [--target-column TARGET_COLUMN] [--task {{classification,regression,predict,shapley_original_features,shapley_transformed_features,transform,fit_and_transform,artifacts}}] [--scorer SCORER] [--experiment-name EXPERIMENT_NAME] [--accuracy {{1,2,3,4,5,6,7,8,9,10}}] [--time {{1,2,3,4,5,6,7,8,9,10}}] [--interpretability {{1,2,3,4,5,6,7,8,9,10}}] [--train-size TRAIN_SIZE] [--seed SEED] [--fast] [--force] * Typical case for creating experiment might be: python {cwd}/openai_server/agent_tools/driverless_ai_data_science.py --dataset-name "my_dataset" --data-url "https://mydata.com/mydata.csv" --target-column "target" --task "classification" --scorer "auc" --experiment-name "my_experiment" * A typical re-use of the experiment_key and dataset_key for prediction (or shapley, transform, fit_and_transform) would be like: diff --git a/src/version.py b/src/version.py index b34ee573b..0a86202e4 100644 --- a/src/version.py +++ b/src/version.py @@ -1 +1 @@ -__version__ = "263ec150c635d116ac1549842eebe7882fbf3a0b" +__version__ = "b437c5a5e6089ff42c2fb556b20df0b0a32dcece"