license + minor fixes

LICENSE.md

@@ -0,0 +1,201 @@
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app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 from executors import benchmark_executor, synth_executor
-
 from gradio_rangeslider import RangeSlider
+
 class UI:
 
     def __init__(self):
@@ -211,8 +211,9 @@ class UI:
         self.ui.queue().launch(show_error=True)
 
 
-# if __name__ == "__main__":
-ui = UI()
-# ui.run()
-demo = ui.layout()
-demo.launch()
+if __name__ == "__main__":
+    ui = UI()
+    ui.run()
+
+#demo = ui.layout()
+#demo.launch()

executors.py

@@ -12,6 +12,9 @@ from itertools import zip_longest
 from uuid import uuid4
 import csv, os
 from functools import partial
+import logging
+
+logger = logging.getLogger(__name__)
 
 
 def generate_data(max_acc, min_acc, nmodels, nanswers, nquestions) -> tuple[pd.DataFrame, list]:
@@ -68,7 +71,10 @@ def generate_data(max_acc, min_acc, nmodels, nanswers, nquestions) -> tuple[pd.D
 
 def synth_executor(acc_range: tuple[float, float], nmodels, nanswers, nquestions, noise, method) -> tuple[str, dict]:
 
+
     min_acc, max_acc = acc_range
+    logger.info(f"Synth experiment: min_acc:{min_acc}, max_acc:{max_acc}, nmodels: {nmodels}, nanswers: {nanswers}, nquestions: {nquestions}, noise:{noise}, method:{method}.")
+
     df, true_ranking = generate_data(max_acc, min_acc, nmodels, nanswers, nquestions)
 
     if noise == 0.:
@@ -130,6 +136,7 @@ def synth_executor(acc_range: tuple[float, float], nmodels, nanswers, nquestions
         )
 
     out_plot = ranker.plot("synth")
+    plt.close(out_plot)
 
     return "synth.png", eval_metrics
 
@@ -139,7 +146,7 @@ def benchmark_executor(data, mmlu_subject, evaluation, nmodels, nrows, method
     ) -> tuple[pd.DataFrame, plt.figure]:
         """Main execution flow for benchmarks"""
 
-        # gr.Info(f"Loaded run config: {data}, {evaluation}, {nmodels}.")
+        logger.info(f"Benchmark experiment: benchmark:{data}, mmlu subject: {mmlu_subject}, evaluation:{evaluation}, nmodels:{nmodels}, nquestions: {nrows}, method: {method}.")
         seed = 40
         np.random.seed(seed)
 
@@ -268,5 +275,6 @@ def benchmark_executor(data, mmlu_subject, evaluation, nmodels, nrows, method
         )
 
         out_plot = ranker.plot()
+        plt.close(out_plot)
 
         return out_df, "output.png", eval_metrics