add confidence intervals for api

.gitignore

@@ -0,0 +1 @@
+runs/api_models/__pycache__/*.pyc

runs/api_models/deepseek-r1/deepseek-reasoner-zero-shot-C1/bootstrap_confidence_intervals.csv

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+experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
+deepseek-reasoner-zero-shot-C1,2025-06-30 15:33:44,0.5065391840566376,0.42139117620280725,0.5858167517132609,0.16442557551045361,0.2215346775433057,0.15611671900668242,0.306723118942748,0.1506063999360656,0.40940278020811266,0.32520094685192064,0.49356231540594575,0.16836136855402511

runs/api_models/deepseek-r1/deepseek-reasoner-zero-shot-C2/bootstrap_confidence_intervals.csv

@@ -0,0 +1,2 @@
+experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
+deepseek-reasoner-zero-shot-C2,2025-06-30 15:34:23,0.01473848598300359,-0.014940334195692621,0.055896445993668054,0.07083678018936068,0.09512985172272563,0.020127342058186615,0.18882669143307435,0.16869934937488773,0.06962107856931386,0.023982700826140426,0.12434890820191224,0.10036620737577182

runs/api_models/deepseek-r1/deepseek-reasoner-zero-shot-C3/bootstrap_confidence_intervals.csv

@@ -0,0 +1,2 @@
+experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
+deepseek-reasoner-zero-shot-C3,2025-06-30 15:35:03,0.42427887678877213,0.29278650188137595,0.5449246220699722,0.2521381201885962,0.24709564450221505,0.18539374481613408,0.3228920638060504,0.13749831898991632,0.3378260927471412,0.2574970316843851,0.42096414890304595,0.16346711721866086

runs/api_models/deepseek-r1/deepseek-reasoner-zero-shot-C4/bootstrap_confidence_intervals.csv

@@ -0,0 +1,2 @@
+experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
+deepseek-reasoner-zero-shot-C4,2025-06-30 15:35:42,0.3815712950108138,0.2803760092271128,0.47899580849660867,0.19861979926949586,0.17144527260462014,0.1258957932112163,0.2285557981324928,0.1026600049212765,0.37175290907977915,0.2863213955266201,0.45743767424989523,0.17111627872327512

runs/api_models/deepseek-r1/deepseek-reasoner-zero-shot-C5/bootstrap_confidence_intervals.csv

@@ -0,0 +1,2 @@
+experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
+deepseek-reasoner-zero-shot-C5,2025-06-30 15:36:21,0.5064905297336298,0.37585136953931625,0.627167414884462,0.2513160453451458,0.2822318244472843,0.216820362063635,0.3513804341294417,0.1345600720658067,0.3339847771542349,0.2537047920593666,0.41843901801470434,0.16473422595533777

runs/api_models/gpt-4o/gpt-4o-2024-11-20-zero-shot-C1/bootstrap_confidence_intervals.csv

@@ -0,0 +1,2 @@
+experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
+gpt-4o-2024-11-20-zero-shot-C1,2025-06-30 15:30:28,0.4875098128776781,0.4075366331732076,0.5661412785954112,0.1586046454222036,0.22039074946734208,0.15490609880315764,0.3037138278889899,0.14880772908583223,0.4310748797592803,0.34464224298367047,0.5155400944584835,0.170897851474813

runs/api_models/gpt-4o/gpt-4o-2024-11-20-zero-shot-C2/bootstrap_confidence_intervals.csv

@@ -0,0 +1,2 @@
+experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
+gpt-4o-2024-11-20-zero-shot-C2,2025-06-30 15:31:07,0.2072455118121436,0.05778147725394796,0.34982293350689964,0.2920414562529517,0.17292647136998113,0.1110580864444951,0.24655129780363258,0.1354932113591375,0.20964837378050505,0.13828835193510916,0.28382396163802226,0.1455356097029131

runs/api_models/gpt-4o/gpt-4o-2024-11-20-zero-shot-C3/bootstrap_confidence_intervals.csv

@@ -0,0 +1,2 @@
+experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
+gpt-4o-2024-11-20-zero-shot-C3,2025-06-30 15:31:46,0.3874602413243428,0.2481470939391138,0.5176722378471288,0.26952514390801496,0.24471418829414107,0.1736233183958159,0.32888693913009015,0.15526362073427424,0.26652211762769756,0.19183379129568326,0.34306527287253047,0.1512314815768472

runs/api_models/gpt-4o/gpt-4o-2024-11-20-zero-shot-C4/bootstrap_confidence_intervals.csv

@@ -0,0 +1,2 @@
+experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
+gpt-4o-2024-11-20-zero-shot-C4,2025-06-30 15:32:25,0.5116996703669392,0.4104212578835423,0.6037155276446341,0.19329426976109182,0.2807685632339878,0.1704651875949637,0.42070727894369825,0.25024209134873454,0.3843695067989506,0.298747939199668,0.4701118175919892,0.1713638783923212

runs/api_models/gpt-4o/gpt-4o-2024-11-20-zero-shot-C5/bootstrap_confidence_intervals.csv

@@ -0,0 +1,2 @@
+experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
+gpt-4o-2024-11-20-zero-shot-C5,2025-06-30 15:33:04,0.5428095384041859,0.4066260449774681,0.6651585702907594,0.2585325253132913,0.27362099303431836,0.21423232459778377,0.3347781839450608,0.12054585934727705,0.2828746236369826,0.2096485443838562,0.3605551612749459,0.15090661689108972

runs/api_models/run_all_bootstrap_ci.py

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+#!/usr/bin/env python3
+"""
+Run bootstrap confidence interval computation for all API model experiments.
+"""
+
+import subprocess
+import sys
+from pathlib import Path
+
+def main():
+    # Base directory containing the API model results
+    base_dir = Path("/workspace/jbcs2025_experiments_report/runs/api_models")
+    
+    # List of model directories to process
+    model_dirs = ["sabia-3", "gpt-4o", "deepseek-r1"]
+    
+    # Number of bootstrap samples
+    n_bootstrap = 10000
+    
+    # Process each model directory
+    for model_dir in model_dirs:
+        model_path = base_dir / model_dir
+        
+        if not model_path.exists():
+            print(f"Warning: Directory {model_path} does not exist, skipping...")
+            break 
+        
+        print(f"\n{'='*60}")
+        print(f"Processing model: {model_dir}")
+        print(f"{'='*60}")
+        
+        # Get all first-level subdirectories
+        subdirs = [d for d in model_path.iterdir() if d.is_dir()]
+        
+        if not subdirs:
+            raise FileNotFoundError(f"No subdirectories found in {model_path}")
+        
+        # Process each subdirectory
+        for i, subdir in enumerate(subdirs, 1):
+            print(f"\n[{i}/{len(subdirs)}] Processing: {subdir.name}")
+            
+            # Construct the command
+            cmd = [
+                sys.executable,
+                "compute_bootstrap_ci.py",
+                str(subdir),
+                "--n-bootstrap", str(n_bootstrap)
+            ]
+            
+            try:
+                # Run the command without capturing output to preserve tqdm formatting
+                result = subprocess.run(cmd, check=True)
+                    
+            except subprocess.CalledProcessError as e:
+                print(f"ERROR: Command failed for {subdir.name}")
+                print(f"Exit code: {e.returncode}")
+                print(f"Error output: {e.stderr}")
+                continue
+            except Exception as e:
+                print(f"ERROR: Unexpected error for {subdir.name}: {e}")
+                continue
+    
+    print(f"\n{'='*60}")
+    print("All processing completed!")
+    print(f"{'='*60}")
+
+if __name__ == "__main__":
+    main()

runs/api_models/sabia-3/sabia-3-zero-shot-C1/bootstrap_confidence_intervals.csv

@@ -0,0 +1,2 @@
+experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
+sabia-3-zero-shot-C1,2025-06-30 15:27:09,0.6774316249330261,0.6057142857142858,0.7444916275847978,0.13877734187051205,0.3322749233375538,0.25107884260922797,0.44217262102209304,0.19109377841286507,0.6468183948171619,0.5651409581904621,0.728388842853713,0.1632478846632509

runs/api_models/sabia-3/sabia-3-zero-shot-C2/bootstrap_confidence_intervals.csv

@@ -0,0 +1,2 @@
+experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
+sabia-3-zero-shot-C2,2025-06-30 15:27:48,0.03192278656645976,-0.014177957722872423,0.07661827209946197,0.09079622982233439,0.0752924263856506,0.043123040752351106,0.11350596521980306,0.07038292446745195,0.09842797080806479,0.05146133128447315,0.15524936783957105,0.1037880365550979

runs/api_models/sabia-3/sabia-3-zero-shot-C3/bootstrap_confidence_intervals.csv

@@ -0,0 +1,2 @@
+experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
+sabia-3-zero-shot-C3,2025-06-30 15:28:28,0.3246568788369811,0.19279666995851927,0.45209812867633853,0.2593014587178193,0.21136810332780584,0.14867370449328218,0.28738962256439154,0.13871591807110936,0.285006859113549,0.21002954980503674,0.36795514373849075,0.157925593933454

runs/api_models/sabia-3/sabia-3-zero-shot-C4/bootstrap_confidence_intervals.csv

@@ -0,0 +1,2 @@
+experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
+sabia-3-zero-shot-C4,2025-06-30 15:29:08,0.4653994276483699,0.34018819834002006,0.5772301426746734,0.23704194433465337,0.26759160010047583,0.17743920345301964,0.3846598028320292,0.20722059937900958,0.5124443951217361,0.42347344832630857,0.6024732123251144,0.17899976399880585

runs/api_models/sabia-3/sabia-3-zero-shot-C5/bootstrap_confidence_intervals.csv

@@ -0,0 +1,2 @@
+experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
+sabia-3-zero-shot-C5,2025-06-30 15:29:48,0.5451644712018167,0.40711410991535146,0.6741883926545115,0.26707428273916,0.3689488072678057,0.3005606300203075,0.43848899339416186,0.1379283633738544,0.4279799537243523,0.343551042281439,0.5146665075184903,0.1711154652370513