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f936b8f
added pattern design code
RAmruthaVignesh May 1, 2025
10ff39e
pattern generation with KPI
RAmruthaVignesh May 6, 2025
94394b1
Merge pull request #1 from RAmruthaVignesh/datagen
RAmruthaVignesh May 6, 2025
a88b0cb
Added pattern inject.
AmirAbaskohi May 6, 2025
7215989
Updated pattern inject.
AmirAbaskohi May 7, 2025
c3c2cc1
Merge pull request #2 from RAmruthaVignesh/inject_pattern
RAmruthaVignesh May 7, 2025
c1e34f8
Merged from main
RAmruthaVignesh May 7, 2025
5323820
Merged from main
RAmruthaVignesh May 8, 2025
b2edfea
evaluation framework
Shailesh-05 May 12, 2025
01618d1
bleurt metric added
Shailesh-05 May 12, 2025
ded3ada
Added agent ada code
chiragoel May 13, 2025
df2fec7
Instructions to run ada
chiragoel May 13, 2025
066fc54
evaluation framework added
Shailesh-05 May 14, 2025
c779397
version 1 results generated
RAmruthaVignesh May 14, 2025
78d6dba
latest changes
RAmruthaVignesh May 15, 2025
ed1e7a4
Merge pull request #4 from RAmruthaVignesh/agent_ada
RAmruthaVignesh May 15, 2025
f8700c6
Merge branch 'datagen'
RAmruthaVignesh May 15, 2025
b77d794
Updated inject test.
AmirAbaskohi May 17, 2025
9132478
Merge pull request #6 from RAmruthaVignesh/InjectTestFix
RAmruthaVignesh May 17, 2025
eb7feca
improved to skill based pattern and question generation
RAmruthaVignesh May 17, 2025
7fae599
Merge branch 'main' into datagen
RAmruthaVignesh May 17, 2025
4916d0e
Full pipeline
RAmruthaVignesh May 18, 2025
65f3d25
aded datagen updated
RAmruthaVignesh May 18, 2025
3bcf615
checking in all changes
RAmruthaVignesh May 21, 2025
e378d4a
pushing latest code
RAmruthaVignesh Jun 5, 2025
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2 changes: 2 additions & 0 deletions .gitignore
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Expand Up @@ -12,6 +12,8 @@ build/
develop-eggs/
dist/
downloads/
results/
data/
eggs/
.eggs/
lib/
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31 changes: 31 additions & 0 deletions .gradio/certificate.pem
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26 changes: 26 additions & 0 deletions .vscode/launch.json
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{
// Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"name": "main.py with arguments",
"type": "debugpy",
"request": "launch",
"program": "main.py",
"console": "integratedTerminal",
"args": [
"----savedir_base",
"results/"
]
},
{
"name": "Python Debugger: Current File",
"type": "debugpy",
"request": "launch",
"program": "${file}",
"console": "integratedTerminal"
}
]
}
6 changes: 5 additions & 1 deletion README.md
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Expand Up @@ -69,8 +69,12 @@ print("score_summary: ", score_summary)
python main.py --openai_api_key <openai_api_key>
--savedir_base <savedir_base>
```
## 4. Running AgentADA


```bash
cd ada_insightarena
python main_insightarena.py -e insights_insightarena
```
## Citation

```bibtex
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201 changes: 201 additions & 0 deletions ada_insightarena/LICENSE
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81 changes: 81 additions & 0 deletions ada_insightarena/batch_generator.py
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import os
import shutil
import json

# Root folder containing:
# - Numbered dataset folders (e.g., "1", "2", ...)
# - Corresponding vis folders (e.g., "vis_0", "vis_1", ...)
root_datasets_dir = "results/insights_w_skills/Batch1"

# 1. Identify which indices are valid (i.e., both dataset and vis folders exist).
def filter_valid_indices(indices):
valid = []
for idx in indices:
dataset_path = os.path.join(root_datasets_dir, str(idx))
vis_path = os.path.join(root_datasets_dir, f"vis_{idx-1}")
# Check that both dataset and vis folders exist and are directories
if os.path.isdir(dataset_path) and os.path.isdir(vis_path):
valid.append(idx)
return valid

# 2. Create the pools, filtering out missing folders.
setA = filter_valid_indices(range(1, 50)) # 1..49
setB = filter_valid_indices(range(50, 66)) # 50..65
setC = filter_valid_indices(range(66, 101)) # 66..100

# 3. Determine how many full batches we can form with a 4–2–4 split.
num_batches = min(len(setA) // 4, len(setB) // 2, len(setC) // 4)

# 4. Contents for exp_dict.json (constant)
exp_dict_contents = {
"challenge": "mid",
"model": "gpt-4-0",
"eval_mode": "insights",
"with_skills": 0
}

# Helper function to create a batch folder, place exp_dict.json,
# and copy the dataset + vis folders for the given indices.
def create_batch(batch_folder_name, indices):
# Create the batch folder
os.makedirs(batch_folder_name, exist_ok=True)

# Create exp_dict.json
exp_dict_path = os.path.join(batch_folder_name, "exp_dict.json")
with open(exp_dict_path, "w") as f:
json.dump(exp_dict_contents, f, indent=4)

# Copy dataset folders and corresponding vis folders
for idx in indices:
dataset_src = os.path.join(root_datasets_dir, str(idx))
dataset_dst = os.path.join(batch_folder_name, str(idx))
shutil.copytree(dataset_src, dataset_dst)

vis_src = os.path.join(root_datasets_dir, f"vis_{idx-1}")
vis_dst = os.path.join(batch_folder_name, f"vis_{idx-1}")
shutil.copytree(vis_src, vis_dst)

# 5. Create full batches with 4–2–4.
for i in range(1, num_batches + 1):
# Slice out the next 4 from setA, 2 from setB, 4 from setC
batchA = setA[:4]
setA = setA[4:]

batchB = setB[:2]
setB = setB[2:]

batchC = setC[:4]
setC = setC[4:]

batch_indices = batchA + batchB + batchC
batch_folder = f"results/insights_w_skills_Batch{i}"

create_batch(batch_folder, batch_indices)
print(f"Created {batch_folder} with dataset indices: {batch_indices}")

# 6. Create one leftover batch if there are any indices left in setA, setB, or setC.
leftover_indices = setA + setB + setC
if leftover_indices:
leftover_batch_folder = f"results/insights_w_skills_Batch{num_batches+1}_leftover"
create_batch(leftover_batch_folder, leftover_indices)
print(f"Created {leftover_batch_folder} with leftover dataset indices: {leftover_indices}")
24 changes: 24 additions & 0 deletions ada_insightarena/convert_encoding.py
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import pandas as pd

def convert_csv_encoding(file_path):
"""
Reads a CSV file with ISO-8859-1 encoding and replaces it with UTF-8 encoding.

:param file_path: Path to the CSV file that needs encoding conversion.
"""
try:
# Load CSV with ISO-8859-1 encoding
df = pd.read_csv(file_path, encoding="ISO-8859-1")

# Save CSV with UTF-8 encoding, replacing the original file
df.to_csv(file_path, encoding="utf-8", index=False)

print(f"Successfully converted {file_path} to UTF-8 encoding.")
except Exception as e:
print(f"Error: {e}")

# Example usage
file_path = "data/csvs/142/data.csv" # Change to your actual file path
convert_csv_encoding(file_path)

#142,159,167,301,305,306,326,340
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