From 468da6d7796b1c7c632647c80fbd1e960d711d33 Mon Sep 17 00:00:00 2001 From: Srideepika Jayaraman Date: Tue, 18 Nov 2025 13:55:41 -0500 Subject: [PATCH 1/2] spiral code draft --- README.md | 200 +- analysis/RQ1/cost_comparison_api_calls.pdf | Bin 0 -> 18639 bytes analysis/RQ1/cost_comparison_tokens.pdf | Bin 0 -> 18440 bytes analysis/RQ1/cot_k5/sample.txt | 0 analysis/RQ1/rq1_analysis.ipynb | 83 + analysis/RQ1/rq1_analysis_rq1_1_0729.ipynb | 2199 ++++++++++++++ analysis/RQ1/rq1_analysis_rq1_2_0729.ipynb | 2663 +++++++++++++++++ analysis/ablation/analysis_ablations.ipynb | 189 ++ data/data_library.txt | 5 + scripts/__init__.py | 0 scripts/create_residual_dataset.py | 58 + scripts/environment.yml | 208 ++ scripts/run_ablation_experiments.sh | 116 + scripts/run_ablation_experiments_daily.sh | 116 + scripts/run_all_residual.sh | 180 ++ scripts/run_all_residual_react_rafa.sh | 180 ++ scripts/run_all_residual_spiral.sh | 180 ++ scripts/run_all_residual_tot_lats.sh | 180 ++ scripts/run_experiments_final_0726.sh | 95 + scripts/run_taskbench_experiments.py | 338 +++ scripts/taskbench_cot_baseline.py | 327 ++ scripts/taskbench_lats_baseline.py | 395 +++ scripts/taskbench_rafa_baseline.py | 404 +++ scripts/taskbench_react_baseline.py | 381 +++ scripts/taskbench_react_rafa_baseline.py | 386 +++ scripts/taskbench_smriv_mcts_ablations.py | 480 +++ scripts/taskbench_smriv_mcts_revised_final.py | 645 ++++ scripts/taskbench_spiral_method_final.py | 386 +++ scripts/taskbench_tot_baseline.py | 376 +++ scripts/test_client_updated.py | 83 + scripts/utils/__init__.py | 0 .../__pycache__/__init__.cpython-312.pyc | Bin 0 -> 178 bytes scripts/utils/generic_client.py | 244 ++ scripts/utils/raw_utils.py | 284 ++ scripts/utils/ritz_client.py | 320 ++ scripts/utils/watsonx_client.py | 71 + 36 files changed, 11770 insertions(+), 2 deletions(-) create mode 100644 analysis/RQ1/cost_comparison_api_calls.pdf create mode 100644 analysis/RQ1/cost_comparison_tokens.pdf create mode 100644 analysis/RQ1/cot_k5/sample.txt create mode 100644 analysis/RQ1/rq1_analysis.ipynb create mode 100644 analysis/RQ1/rq1_analysis_rq1_1_0729.ipynb create mode 100644 analysis/RQ1/rq1_analysis_rq1_2_0729.ipynb create mode 100644 analysis/ablation/analysis_ablations.ipynb create mode 100644 data/data_library.txt create mode 100644 scripts/__init__.py create mode 100644 scripts/create_residual_dataset.py create mode 100644 scripts/environment.yml create mode 100755 scripts/run_ablation_experiments.sh create mode 100755 scripts/run_ablation_experiments_daily.sh create mode 100755 scripts/run_all_residual.sh create mode 100755 scripts/run_all_residual_react_rafa.sh create mode 100755 scripts/run_all_residual_spiral.sh create mode 100755 scripts/run_all_residual_tot_lats.sh create mode 100755 scripts/run_experiments_final_0726.sh create mode 100644 scripts/run_taskbench_experiments.py create mode 100644 scripts/taskbench_cot_baseline.py create mode 100644 scripts/taskbench_lats_baseline.py create mode 100644 scripts/taskbench_rafa_baseline.py create mode 100644 scripts/taskbench_react_baseline.py create mode 100644 scripts/taskbench_react_rafa_baseline.py create mode 100644 scripts/taskbench_smriv_mcts_ablations.py create mode 100644 scripts/taskbench_smriv_mcts_revised_final.py create mode 100644 scripts/taskbench_spiral_method_final.py create mode 100644 scripts/taskbench_tot_baseline.py create mode 100644 scripts/test_client_updated.py create mode 100644 scripts/utils/__init__.py create mode 100644 scripts/utils/__pycache__/__init__.cpython-312.pyc create mode 100644 scripts/utils/generic_client.py create mode 100644 scripts/utils/raw_utils.py create mode 100644 scripts/utils/ritz_client.py create mode 100644 scripts/utils/watsonx_client.py diff --git a/README.md b/README.md index 399c463..711d31f 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,198 @@ -# SPIRAL -This is an AAAI-2026 conference paper repo. +# SPIRAL: Symbolic LLM Planning via Grounded and Reflective Search + +This repository contains the code and analysis notebooks for **SPIRAL**, our framework that embeds a tri-agent cognitive architecture into an MCTS loop to enable more robust, grounded, and reflective planning with large language models. The full details are described in our paper and appendix. + +## Table of Contents + +- [Repository Structure](#repository-structure) +- [Getting Started](#getting-started) +- [Dependencies](#dependencies) +- [Data](#data) +- [Running Experiments](#running-experiments) +- [Scripts & Agents](#scripts--agents) +- [Analysis Notebooks](#analysis-notebooks) +- [Configuration & Hyperparameters](#configuration--hyperparameters) +- [License](#license) + +--- + +## Repository Structure + +Note: Enter SPIRAL folder to follow the next information. + +``` +├── analysis/ +│ ├── ablation/ +│ │ └── analysis_ablations.ipynb +│ ├── baseline/ +│ │ ├── cot_k1/ +│ │ ├── cot_k3/ +│ │ ├── cot_k5/ +│ │ ├── spiral/ +│ │ ├── analysis_baseline_performance.ipynb +│ │ ├── analysis_cost_benefit.ipynb +│ │ ├── cost_comparison_api_calls.pdf +│ │ └── cost_comparison_tokens.pdf +│ └── sota/ +│ ├── sota_performance/ +│ └── tot_hyper_params_performance/ +│ +├── scripts/ +│ ├── taskbench_ablation.py +│ ├── taskbench_cot_baseline.py +│ ├── taskbench_lats_baseline.py +│ ├── taskbench_rafa_baseline.py +│ ├── taskbench_react_baseline.py +│ ├── taskbench_react_rafa_baseline.py +│ ├── taskbench_spiral.py +│ └── taskbench_tot_baseline.py +│ +├── Taskbench/ +│ ├── data_dailylifeapis/ +│ └── data_huggingface/ +│ +├── utils/ +│ └── generic_client.py +│ +├── environment.yml +├── run_all_baseline_experiments.sh +├── run_all_ablation_experiments.sh +├── run_all_sota_experiments.sh +└── LICENSE +``` + +--- + +## Getting Started + +1. **Clone the repository** + ```bash + git clone https://github.com//SPIRAL.git + cd SPIRAL + ``` + +2. **Create the Conda environment** + ```bash + conda create -n spiral python=3.11 + conda activate spiral + pip install -r requirements.txt + ``` + +3. **Download TaskBench datasets** + Place the `dailylifeapis` and `huggingface` benchmark data under `Taskbench/data_dailylifeapis/` and `Taskbench/data_huggingface/`, respectively. + ``` + cd Taskbench/ + huggingface-cli download microsoft/Taskbench --local-dir . --repo-type dataset + ``` +--- + +## Dependencies + + +All required packages are listed in requirements.txt + +--- + +## Data + +We evaluate on two TaskBench tool-use benchmarks: + +- **DailyLifeAPIs** (`Taskbench/data_dailylifeapis/`) +- **HuggingFace** (`Taskbench/data_huggingface/`) + +Each dataset should be organized as in the original TaskBench release: + +``` +Taskbench/data_dailylifeapis/ +└── problems.jsonl + +Taskbench/data_huggingface/ +└── problems.jsonl +``` + +--- + +## Running Experiments + +### 1. Baseline Methods +NOTE: these won't work, they require a different library structure + +```bash +./run_all_baseline_experiments.sh +``` + +This will run Chain-of-Thought (k=1,3,5), ReAct, RAFA, ToT, LATS, etc., via the corresponding `taskbench_*_baseline.py` scripts. + +### 2. SPIRAL Agent +NOTE: relative imports need to be fixed with a proper refactor. For now, this will work. +```bash +cd scripts/ +python taskbench_spiral.py --run_name test --api_family dailylifeapis num_problems 10 --seed 50 model_namet mistral --debug_llm_output +``` + +Or run both benchmarks end-to-end: + +```bash +./run_all_sota_experiments.sh +``` + +### 3. Ablation Studies + +```bash +./run_all_ablation_experiments.sh +``` + +This will sweep over standard MCTS budgets and disable components (Planner, Simulator, Critic) to quantify their impact. + +--- + +## Scripts & Agents + +- **`scripts/taskbench_spiral.py`** + Implements the SPIRAL agent: + - **Planner**: proposes actions via LLM prompts + - **Simulator**: predicts next observation + - **Critic**: scores plan progress + +- **Baseline scripts** (`taskbench_cot_baseline.py`, `taskbench_react_baseline.py`, etc.) + Wrap existing state-of-the-art methods for fair comparison. + +- **`utils/generic_client.py`** + A drop-in replacement for internal APIs, providing a `HuggingFaceChatClient` to interface with any HF LLM. + +--- + +## Analysis Notebooks + +All result aggregation, tables, and figures are in `analysis/`: + +- **`analysis_baseline_performance.ipynb`** +- **`analysis_cost_benefit.ipynb`** +- **`analysis_ablations.ipynb`** +- **`analysis/sota_*`** + +Use these notebooks to reproduce the tables and plots in the paper and appendix. + +--- + +## Configuration & Hyperparameters + +Detailed hyperparameters are in Appendix B of the paper: + +| Component | Default Value | +|-------------------------|--------------------------| +| MCTS Budget (K) | 50 iterations | +| Exploration Constant C | 1.5 | +| Planner Temperature | 0.1 | +| Simulator Temperature | 0.0 | +| Critic Temperature | 0.0 | +| Reward Weight α | 0.5 | +| Random Seeds | [42, 101, 1234, 2024, 12345] | + +See **Appendix B** (`SPIRAL_Technical_Appendix.pdf`) for full details. + +--- + +## License + +This project is released under the **MIT License**. 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gwErHbtiwQdK5BNSnn35aYb/SK9JycnKqFWvXrl0r3NeoUSMdPHhQeXl5Zdq3bdsmSerdu3eFY3v16qWQkBBZLBZt27ZN7du3LzP2kksusV9pd6FWrVopISFBR44ccemxAPAvXxYjcXtWAACA4DN37lxlZmaWacvMzFRoaKjS0tL8FBXgP+SfyD85i8InAAAAAAgg4yYvkKnYtSv9Dh07XW477fHMCnqXFRsdoemPDZUkbZ4xUdYSk0tzF+UeKbe9cdpfnBobWidWncc979J88I1rrrlGcXFx2rBhg/bu3atFixZJOrdCSkRERKVjjx49Kkk6e/aszp49W+VcRUVF1Yo1Ojq6wn2lq4BdePXsiRMnJEkJCQkVjo2IiNBFF12k48eP2/s7O1Y6d1VhMCSeAE8ym83KyMgo82XQZ599psjISJ/M761ipCZNmujQoUNl2rg9KwAAQMUcfS4cPny40tLSPL7asy/nquhWW6wGimBF/on8k7MofAIAAACAAGIqNstUYnFpjM1WftvZY4SfV2RlLTHJWlLs9LwWq03H8sv2P5ZfrBKTSSHGqpfVt4b+XmQ14YPHZTI7P/fhvKPltsfOe9jp8bER0Xrp9qec7h9swsPDNWTIEL3//vv68MMP7VfO3XLLLVWOtVjOnXtPPfWURowY4dU4AdQujq6AHzJkiL766iuPz+XLYqQLvzDj9qwAAACV8+XKSL6cKzk52b5KzflYDRTBivwTnGX0dwAAAAAAAO+x2ayymMouo2wx5clms3p97uW7TzpsX7H7lMvHMpmLVezCj01lq71ssrk03mRxvsgqWJUmmebMmaPDhw+rXbt26ty5c5XjGjZsKKn6S4h7U/369SVJhw8frrCPyWTSqVOnyvQ///+rupouGK62AzytoivgvYFiJAAAgJrLlysj+XKuO+64w2E7RRsIZuSfyD85g7/WAQAAAKAWy8/ZWGF7TOJlXp37QJ7j2+Ltz6ve0tGoGTp37qx27drp119/lSTdeuutTo1LSUlRdna2Vq1apQcffNDleQ2Gc6uF2S5cysyDLrnkEh06dEg//vhjhX3Wrl1rX6L8/ITbJZdcoo8//ljbtm1Tfn6+w6XO9+7dW2lSC4BjFV0BX1OtGP9XWUxV3yL2zAUrSx04cECfjxmrEEPlqyOGRERo4GvTqxUjAABAIPLlykiemMvZVaxPbHBcoPHnSWMUf2nTCsexajVqM/JP5J+cQeETAAAAANRiJfnHXGr3pKS4CO06Ub7IqXlcpFfntVltMp8um1A0ny6WzWqTwYlb7MF5Dz30kD05c+ONNzo1ZtiwYfr000/166+/6v3336/wilZJKigoUGhoqMLDw+1tMTExkqTTp09XI/LKDR48WMuXL9fGjRv1/fff68orryyz32w26/XXX5cktWvXTu3atbPvGzRokJ577jkVFRVp9uzZmjBhQrnjv/baa16LHajNUlNT9d5775Vpa9KkiUvHcPZLp0Onyl4Ve+DAAd0z56Eq30fO/9LJYjLJUlz1XKfM5nJtq08cU5+69aocCwAAEIwcfS6UvHNrYk/MVbqKdVUKj55x2F5w9IxiKhlvCg1zOhYgEJF/Iv9UFW51BwAAAAC1WFh0Q5faPemaNhepX6u4Mm39WsVpYJt6Xp339JajLrXDfVdddZX+/ve/6+9//7vi4+OdGnP55ZfblymfMmWKnnnmGR04cMC+v7i4WJs2bdILL7ygq6++Wrm5uWXGX3zxxZKkb7/91mvLdQ8aNEhdu3aVJD3wwAP65JNPVFJSIulc8cOECRO0ceO51dQeeuihMmMTEhLsybTXX39dM2fO1NmzZyVJubm5mjJlipYsWaLY2FivxA74mtlsVnp6ugYMGGD/KSryzsp+oaGhSkpKKtfmCmdvnWo+U/6LpeMbDvrsVqk5ThRMAQAABCtHnwu9dWtiX84V0SDKpXYgWJB/Iv9UFVZ8AgAAAIBaLLppimw2qwqObLW3RSV0VnTTFK/PHWI0aHC7eA1u51xCwlNMxwtcaofvTZ48WSEhIfrwww81Z84czZkzR1FRUQoLC9OZM2dktVrtfQ0X3Opp6NChysjI0L59+9SvXz/Fx8crIiJCkvT++++rcePG1Y4vJCREr776qsaMGaNdu3bpoYce0qRJk1SnTh37lX5Go1GTJk3SVVddVW78ww8/rN27d+uHH37Q1KlT9corrygmJkanT5+WzWbTPffco82bN2vdunXVjhXwt4yMDGVmZpZpGzJkiL766iunj7F5xkRZS6q+JZzFaiuTqJakghOHtXHaXyodF1onVp3HPe90PBXx5ftI0/OuNEZwMpvN5f59DR8+XGlpaV75ohUAgGDj7GdQSSrKPVJu2xufQet2aSSbzaYzvxy3t8V2aqC6XRq5dBwA55B/Cp78E38hAQAAAH7ClxnwBYPBqNhmPRTbrIe/Q/GZiAZRMh3Od9iOmiE8PFz//ve/deutt+q///2vfv75Zx09elQFBQWqX7++WrVqpR49emjQoEFKSEgoM7Zly5aaO3euZs6cqS1btujUqVMy/99toswObhflroSEBH300Uf64IMP9MUXX2j37t0qLCxUkyZNdPnllystLU3JyckOx0ZERCg9PV3vv/++Fi5cqD179shms6l79+6688479cc//tErt2AA/OHCoid3WEtMspZUvcLRsl255dpOFJRUOdYa6twXWlXx1vvIX5s00/RDB+3bPWLqqldsXCUjEAzmzp1b7t9XZmamQkNDlZaW5qeoAADwrnGTF8hU7NzfdYeOnS63nfZ45Z9NY6MjNP2xoZKc/wxqsdp0LL9sv2P5xSoxmRRSyW2Q3fkMajAaVC+lseqlVL+gAgD5p2DKP/FtCgAAAOAnfJkBeAdXSHrWhAkTNGHCBJfH9ezZUzt37qy0T0pKilJSXF99rFu3bnrjjTcq7VPV3JI0b968SvdHRERo1KhRGjVqlCvhSTp3O4TU1FSlpqa6NTeA8g7keaaAyRmJwzsqO3O7fdub7yNhRqMeTGzulWMjcGVlZTls3759u8N2AABqA1OxWaYSS5X9bDarzEV5ZdrMRXkqKi6RwWCscFy4k0VV51u++6TD9hW7T2nQxRe5fDwAjpF/Iv9UHRQ+AUCAY7UQAAhcwfplhqP3rs8++0yRkZF+jAq1CVdIAkD1OXu1fd2mXXU6Z3O5dleutndWUlyEdp0ocmmMu4yhRiX9+RKfzAU4kpycrA0bNpRr79ixox+iAQCgZsnP2Vhhe0ziZR6dq6Li+/15vvlcCgCoWsUlrwCAgFDRaiHBUsELAIGsoiVqffFlhtlsVnp6ugYMGGD/SU9P9+gyvRVx9N41ZMgQr88LAACcV3q1fVU/kY1TFJXQuczYRimpVY9142r7a9pcpH6tyt4C7t8DW1TrcQI1VUVXbAfLrSoAAKhMSf4xl9qrIykuwmF78zgu4AOAmoKlQAAgwAXraiEAUBukpqbqvffeK9fuiy8z/HmbvYreuwAAQOAxGIyKbdZDsc16eH2uEKNBg9vFa3C7eK/PBQAAgJorLLqhis/kOGz3tGvaXCSrTVq15/db6/VrFaeBbep5fC4AgHsofIJP+PNWXNwGDLUdS58DQOAKDQ1VUlKSDhw4YG9LSkryyWcUfxbOVvTeBQAAAASDFeP/KovJ8W1zSn2Xe8Jh+1PDhusPF9WvdGxIRIQGvjbd7fgAAN7F91bVF900RTabVQVHttrbohI6K7ppisfnovgeAGo+bnUHn/Dnrbi4DRhqO5Y+BwC4w5+32XP03tWkSROvzwsAAADUBBaTSZbi4kp/sosKHY7NLiyscmxVRVUAAP/ie6vqK111NOGy0faf2GY9ZDDw1TcABCNe/eET/lxRgNuAobYrXS3kfL5aLQQAELj8WTjr6L2L9y0AAADgd03Cwx22N62gHQBQPWazWenp6RowYID9Jz09XWaz2eNz8b0VAACeReETfMKfKwr4c24AAICaisJZAAAAoObqHRunHjF1y7T1iKmrXrFxfooIAGo3X67CxPdWAAB4Ft9qwCdSU1P13nvvlWv3xYoC/pwbAAAEn3GTF8hU7PzVgIeOnS63nfZ4ZgW9y4qNjtD0x4ZKkjbPmChriWu3tCjKPVJue+O0vzg1NrROrDqPe96l+QAAAAA4J8RgUN+4euobV8/foQBAUPDlKkx8bwUAgGdR+BREzGazMjIyylSsDx8+XGlpaV6/sr90RYEDBw7Y23y1ooA/5wYAAMHHVGyWqcTidH+brfy2s+PDzyuwspaYZC0pdnpei9WmY/ll+x/LL1aJyaQQo6HK8dbQ34usJnzwuExm5+eWpMN5R8ttj533sFNjI0LD9eqIp12aDwAAAAAABBZffq+VnJysDRs2lGv3xipMfG8FAIBncau7IOLLZToBAABQsy3ffdJh+4rdp1w+lslcrGIXfkzFJpWcLrs6ldVqdX68i0VWAAAAAAAg8Pjye63U1FSH7azCBABAzUfhUxDx5TKdAAAAqJrNZpXFlFemzWLKk81m9frcB/Ic3xZvf16R1+c+veVouTbL2RKvzwsAAAAAAAKHL7/XKl2F6XyswgQAQGDg3TqI+HKZTgAAAFQtP2djhe0xiZd5de6kuAjtOlG+yKl5XKRX55Uk0/ECr88BAAAAAAACWyB+rzXhg8edWqn6cN7Rcttj5z1c5bjYiGi9dPtT7oYHAECtROFTEElNTdV7771Xrp1lOgHAeb68rzyA2q8k/5hL7Z50TZuLZLVJq/b8vuJUv1ZxGtimntfnjmgQJdPhfK/PAwAAACB4kcMBAp+nvtfaPGOirCWOV74uZbHadODAgTJtBw4c0M8v3acQo6HSsaF1YtV53POSJJO5WMVOFD7ZZCu37cw4U2hYlX0AAAg2fLoPIqXLdJ7/wY1lOgHANRXdVz40NFRpaWl+igpAoAqLbqjiMzkO270txGjQ4HbxGtwu3utzXahul0ay2Ww688txe1vi8Jp7tSYAAACAwOPLHA5FVoB3eOp7LWuJSdaSyouKlu3Kddj+5Y6jGnTxRZUfP7TyoqoL2aw2mU+Xjcd8ulg2q02GKoqsAABAeXziBoAaZsX4v8picu0PpfzDh8ttL7v7XqfGhkREaOBr012aL5j58r7yAGq/6KYpstmsKjiy1d4WldBZ0U1T/BiV9xmMBtVLaax6KY39HQoAAACAWsqXORwulAMC34E8xzn5/XlFHp/r9JajFbbHdUvw+HwAANR2FD4BQA1jMZlkKa56Sdvz2Wy2ctuuHgPOCcT7ygOouQwGo2Kb9VBssx7+DgUAAAAAahVf5nC4UA4IfElxEdp1onyRU/O4SI/PZTpe4FI7AACoHIVPAAC4wFP3lQcAAAAOHjyoAQMGSJJ27tzp52gAAKhdfJnD4UI5wHXjJi+QqdhcZb9Dx06X2057PLOC3r+LjY7Q9MeGOh3PNW0uktUmrdqTZ2/r1ypOA9vUc/oYzopoECXT4XyH7QAAeFKw5J4ofELAmfDB4zKZXVvJ5nDe0XLbY+c97NTYiNBwvTriaZfmA1B7eeq+8gAAAAg8WVlZWrFihRITE3XLLbf4LY7Tp0/r+++/19atW7Vt2zZt27ZNBQUFSkxM1MqVK/0WFwAANYkvczhcKAe4zlRslqnEUmW/C252IJtNTo0Ld6Ko6nwhRoMGt4vX4HbxLo1zR90ujWSz2XTml+P2tthODVS3SyOvzw0AqNnIPbmHb2nhts0zJspa4viex44U5R4pt71x2l+cGhtaJ1adxz0vSTKZi1XsYuGTTbZy264eAwAAAAAQ3LKysjRjxgxdfvnlfk0+rVu3ThMnTvTb/AAAoCwulAO8w2azymLKK9NmMeXJZrPKYDD6KarqMxgNqpfSWPVSGvs7FABADUPuyT186g5wzi4FWsrdJUFLnb80qLXEJGuJK8VDtnLbzo63hjpfYAUAAAAAwcTVvwv9KSI8VG/+8zZ/hxHwIiIi1KNHD3Xu3FmXXHKJTp06pSlTpvg7LAAAAMCj8nM2Vtgek3iZj6MBgOBF7in4BFruicKnAOfsUqCl3F0StJSrS4MCgDeYzWZlZGQoM/P3ws3hw4crLS2NK+kAAEDQcfXvQgS+P/zhD/rDH/5g3/7666/9GA0AAM4jpwPAFSX5x1xqBwB4B7mn4BNouSf+kgAABJy5c+eWSZBJUmZmpkJDQ5WWluanqAAAAOBvVqtVn332mRYvXqzt27frzJkzio+PV+vWrXXttdfqtttuU3h4uL3/unXrNGfOHG3atEl5eXmKi4tTSkqKRo0ape7du5c5dv/+/ZWdnW0f1759e/u+xMRErVy50uOPZ968eXr66acVFRWl1157Tb179/b4HAAA+JIncjqbZ0yUtcS5OwQU5R4pt71x2l8qHRNaJ1adxz0vSZrwweMymZ27a8HhvKPltsfOe7jSMRGh4Xp1xNNOHR8IRmHRDVV8JsdhOwAA/kDuqWai8AkAApzFZtNJc9nV2E6azbLYbAoxGPwUlXdlZWU5bN++fbuPIwEAAEBNkZ+frwkTJmj16tWSpIYNG6pDhw46fvy41q5dqzVr1qhv375q1qyZJGnmzJmaOnWqJCk+Pl7t27dXdna2li9fruXLl+vhhx/W3XffbT/+JZdcorCwMO3du1cxMTFq166dfV/Dhp7/4uXll1/WG2+8ofj4eL399tvq1KmTx+cAAMDXPJHTsZaYZC1xrhhJspXbrmqsNfT3oiqTuVjFThY+2S6Yyyab02MBOBbdNEU2m1UFR7ba26ISOiu6aYofowIABCtyTzUXhU8AEODWnMlz2P7jmTz1qVvPt8H4SHJysjZs2FCuvWPHjn6IBgAAADXBE088odWrV6thw4Z6/vnn1adPH/u+3NxcLVq0SFFRUZKk1atXa+rUqTIYDHrkkUc0atQoGY1GWSwWzZo1Sy+99JJefPFFderUyX6l2/Tp07Vw4UJNmjRJHTt21Lx587zyOKxWq5566inNnz9fiYmJmj17tlq2bOmVuQAA8DVyOgBcYTAYFdush2Kb9fB3KAAAkHuqwYz+DgAAUD2Hih1fOZZTQXttkJqa6rB95MiRPo4EAAAANcH27dv1+eefy2g0aubMmWUST9K5q+rGjBmj+Ph4SdKbb74pSRoyZIhGjx4to/FceiQkJERjx47VoEGDZLPZ9MYbb/j0cRQXF+uBBx7Q/Pnz1a5dO33wwQcBn3gCAOB8vszpWKw2HcsvKdN2LL9EFuuFq0BVn81qk/l02Vyc+XSxbF6YCwAAAL5H7qlmo/ApiNhsVllMZVeGsZjyZLNZ/RSRb/BHJ2q7JufdJ/Z8TStorw1CQ0OVlJRUpi0pKUmhoSxkCAAAEIy+/PJLSdIVV1xR5bLcBQUFWr9+vSTprrvuctgnLS1NkrR+/XoVFhZ6MNKKnT17VmPHjtWyZcuUkpKid999VwkJCT6ZGwAAX/FlTmf57pMO21fsPuXxuU5vOepSOwAAAAILuaeajcKnIJKfs9Gldk/y5dU1F+KPTtR2vWPj1COmbpm2HjF11Ss2zk8RAQAAAL61e/duSVJKSkqVfffv3y+LxSJJuvjiix32adeunSTJbDZr3759HoqycqmpqVqzZo369u2rjIwMxcXxeR4AgOo4kGdy2L4/r8jjc5mOF7jUDgAAgMBC7qlmY2mMIFKSf8yldk+q7OqaQRdf5NW5+aMTtV2IwaC+cfXUN66ev0MBAAAA/OLs2bOSpJiYGKf7RkVFqU6dOg77REdHKyoqSgUFBcrPz/dcoJXYv3+/JKlt27YVxgUAQE01bvICmYrNTvU9dOx0ue20xzMrHRMbHaHpjw11KaakuAjtOlG+yKl5XKRLx3FGRIMomQ6X/8wQ0SDK43MBAADA98g91Wy1svDp5MmTysjI0Ndff62DBw+qpKRE8fHxSklJ0ciRI9W9e3eH4/Lz8/XWW29p2bJlysnJUVRUlLp27arRo0erZ8+ePn4UnhcW3VDFZ3IctnubL6+uuRB/dAKoyuYZE2Utcfw65UhR7pFy2xun/cWpscawCHW9f5pL8QEAAKBypUmn0sSSM30LCgpUWFjoMNGTn5+vgoJzF8tER0d7MNKKzZo1S2PGjNHs2bNlMBj0yCOP+GReAAA8wVRslqnE4lRfm638dlVjw50sqjrfNW0uktUmrdqTZ2/r1ypOA9vUc/lYVanbpZFsNpvO/HLc3hbbqYHqdmnk8blQfWazWRkZGcrM/L3g7rPPPlNkpOeL4gAAQO1A7qlmq3W3utu7d69uuOEGzZw5U7/99pvq16+vtm3b6uzZs1q6dKn+/Oc/65133ik3Ljc3V7feeqvefPNNZWdnq02bNoqIiNCqVat011136b333vP9g/Gw6KYpikroXKYtKqGzoptWvRxbdSXFRThs98bVNReq26WRYjs1KNPGH50AzmctMclaUuz0j3ThbTptLox3vsAKAAAAzildNnzjxqpv5Z6UlKSQkBBJ0q5duxz2KW0PDQ1VixYt7O0Gg6G6oVaoa9eumjVrlmJjYzVr1iz95z//8dpcAAD4i81mlcWUV6bNYsqTzWb1+FwhRoMGt4vXC4Na2X8Gt4tXiNHz7+cGo0H1Uhor6c+X2H/qpTSWwQtzofrmzp1bpuhJkoYMGeKnaAAAQCAg91Sz1brCp3/+8586duyYWrZsqU8++UQrVqzQ4sWLtWbNGo0ePVo2m03/+c9/tHfv3jLjHn/8ce3Zs0edOnXSihUrtGjRIq1atUpTpkyRzWbT008/raysLP88KA8xGIyKbdZDCZeNtv/ENushg8H7p8E1bS5Sv1Zl7xHpratrLsQfnQAAAABQu1177bWSpB9++KHKv92jo6N12WWXSZLmzJnjsE9GRoYkqXv37mWuyouIOHdRT1GRd1Yv7tq1q2bPnq3Y2Fi9/fbbtSoBBQCAJOXnOP6iqKJ2wBsC/bseAADge+SearZaVfh09uxZrV27VpL08MMPq23btvZ9EREReuSRR9SiRQuZzWZ9//339n3bt2/XypUrZTQaNW3aNCUkJEg6V003bNgw3XTTTbJYLHr99dd9+4BqEV9eXQMAAAAACC4dOnTQ9ddfL6vVqrFjx2rNmjVl9ufm5mr27NnKzc2VJI0bN07SuVuavPPOO7Jaz60yYbVaNWvWLC1dulQGg0H33XdfmeM0b95ckvTbb7/Zj+VpXbp0KZOAevHFF70yDwAA/lCSf8yldsAbkpOT/R0CAAAIMOSearZQfwfgScXFxbL93w3CS0+I8xkMBiUlJWnfvn0ym3+/J/iyZcskSb169SqzjFipYcOG6eOPP9Y333yjgoICRUVFeekRAEDwGjd5gUzF5qo7/p9Dx06X2057PLOC3uXFRkdo+mNDne4PAABQU0WEB86f9t6MdfLkyTpx4oTWrFmjUaNGqWHDhmrcuLFOnDihw4cPy2q16tprr1V8fLz69OmjiRMnatq0aXr22Wf11ltvqWnTpsrOzrYnlR588EH16tWrzBwdO3ZUq1attGfPHg0cOFBt27ZVRESEGjRooGnTpnnssZQmoEaPHq309HRJ0kMPPVSmT8+ePe3/X5rjOHToUJn266+/Xk8++aTH4gIAoLrCohuq+EyOw3bAV1JTU/Xee++VaWvSpImfogEAoOYj93QOuaeam3ty+1nft2+fNmzYoMOHD+vkyZOqU6eOLrroIrVv314pKSmKjIz0ZJxOiY+PV+PGjXX48GFt3LhR7dq1K7O/oKBAO3bskCR17tzZ3r5p0yZJ55YRc6RLly4KDw+XyWRSVlaWfVkyAIDnmIrNMpVYnO7/f3WuZbZdGR/uQpEVAABATfbmP2/zdwg1QkxMjGbNmqUlS5Zo8eLF2rFjh3bs2KH69eurV69eGjRokBo1amTvP27cOKWkpGju3LnauHGjsrKyFBcXp4EDB2rUqFHq0aNHuTmMRqPS09M1depU/fTTT9q2bZssFosSExM9/nhKE1BjxoxRenq6DAaDHnz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+ "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "import os\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from pathlib import Path\n", + "\n", + "# --- 1. Data Parsing ---\n", + "# This part finds and processes all 'summary.json' files.\n", + "\n", + "# Define the root directory (current directory, since the notebook is in RQ1)\n", + "root_dir = Path('.') \n", + "experiment_data = []\n", + "\n", + "# Find all summary.json files within the subdirectories\n", + "summary_files = root_dir.glob('**/summary.json')\n", + "\n", + "for file_path in summary_files:\n", + " try:\n", + " # Extract metadata from the file path.\n", + " # e.g., parts = ('cot_k1', 'dailylifeapis_experiments', 'llama_4', ...)\n", + " parts = file_path.parent.parts\n", + " \n", + " # Ensure the path is long enough to contain the required parts\n", + " if len(parts) >= 4:\n", + " method = parts[-4]\n", + " dataset = parts[-3].replace('_experiments', '')\n", + " model = parts[-2]\n", + "\n", + " # Read the JSON data\n", + " with open(file_path, 'r') as f:\n", + " data = json.load(f)\n", + " \n", + " # Extract accuracy, remove '%' and convert to float\n", + " accuracy_str = data.get('final_accuracy', '0%')\n", + " accuracy = float(accuracy_str.strip('%'))\n", + " \n", + " experiment_data.append({\n", + " 'method': method,\n", + " 'dataset': dataset,\n", + " 'model': model,\n", + " 'accuracy': accuracy\n", + " })\n", + "\n", + " except (IndexError, json.JSONDecodeError, ValueError) as e:\n", + " print(f\"Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "# Create a pandas DataFrame from the collected data\n", + "df = pd.DataFrame(experiment_data)\n", + "\n", + "# --- 2. Data Visualization ---\n", + "# This part creates the plots using the processed DataFrame.\n", + "\n", + "if not df.empty:\n", + " # Set plot style and context for better aesthetics\n", + " sns.set_theme(style=\"whitegrid\")\n", + " sns.set_context(\"talk\")\n", + "\n", + " # Get the list of unique datasets\n", + " datasets = df['dataset'].unique()\n", + " \n", + " # Create subplots, one for each dataset\n", + " fig, axes = plt.subplots(1, len(datasets), figsize=(12 * len(datasets), 8), sharey=True)\n", + " if len(datasets) == 1: # Ensure axes is always iterable\n", + " axes = [axes]\n", + "\n", + " # Define a consistent order for methods for plotting\n", + " method_order = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + " \n", + " for i, dataset_name in enumerate(datasets):\n", + " ax = axes[i]\n", + " # Filter data for the current dataset\n", + " dataset_df = df[df['dataset'] == dataset_name]\n", + " \n", + " # Create the bar plot\n", + " # seaborn automatically calculates the mean and standard deviation (errorbar='sd')\n", + " sns.barplot(\n", + " data=dataset_df,\n", + " x='model',\n", + " y='accuracy',\n", + " hue='method',\n", + " hue_order=[m for m in method_order if m in dataset_df['method'].unique()],\n", + " ax=ax,\n", + " errorbar='sd', # Use standard deviation for error bars\n", + " capsize=.05\n", + " )\n", + " \n", + " # Customize the plot\n", + " dataset_title = 'DailyLifeAPIs' if 'dailylife' in dataset_name else 'HuggingFace'\n", + " ax.set_title(f'Model Performance on {dataset_title} Dataset', fontsize=18, pad=20)\n", + " ax.set_xlabel('Model', fontsize=14)\n", + " ax.set_ylabel('Final Accuracy (%)' if i == 0 else '', fontsize=14)\n", + " ax.tick_params(axis='x', rotation=45)\n", + " ax.legend(title='Method')\n", + "\n", + " # Adjust layout and display the plot\n", + " plt.suptitle('Comparison of Methods by Final Accuracy', fontsize=22, y=1.05)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "else:\n", + " print(\"No data was loaded. Please check the file paths and JSON format.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "48a42d17", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "import os\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from pathlib import Path\n", + "import numpy as np # Import numpy for nanmin\n", + "\n", + "# --- 1. Data Parsing (Same as before) ---\n", + "root_dir = Path('.') \n", + "experiment_data = []\n", + "summary_files = root_dir.glob('**/summary.json')\n", + "\n", + "for file_path in summary_files:\n", + " try:\n", + " parts = file_path.parent.parts\n", + " if len(parts) >= 4:\n", + " method = parts[-4]\n", + " dataset = parts[-3].replace('_experiments', '')\n", + " model = parts[-2]\n", + "\n", + " with open(file_path, 'r') as f:\n", + " data = json.load(f)\n", + " \n", + " accuracy_str = data.get('final_accuracy', '0%')\n", + " accuracy = float(accuracy_str.strip('%'))\n", + " \n", + " experiment_data.append({\n", + " 'method': method,\n", + " 'dataset': dataset,\n", + " 'model': model,\n", + " 'accuracy': accuracy\n", + " })\n", + " except (IndexError, json.JSONDecodeError, ValueError) as e:\n", + " print(f\"Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "df = pd.DataFrame(experiment_data)\n", + "\n", + "# --- 2. Data Filtering and Visualization ---\n", + "\n", + "# ✨ NEW: Filter for specific models\n", + "models_to_keep = ['llama_4', 'llama_3_3_70b_instruct']\n", + "df_filtered = df[df['model'].isin(models_to_keep)].copy() # Use .copy() to avoid SettingWithCopyWarning\n", + "\n", + "if not df_filtered.empty:\n", + " # ✨ NEW: Calculate the minimum accuracy to adjust the y-axis\n", + " # Using np.nanmin is safe in case of any NaN values\n", + " min_accuracy = np.nanmin(df_filtered['accuracy'])\n", + " # Set the bottom of the y-axis slightly below the minimum value\n", + " y_axis_bottom = max(0, min_accuracy - 10) # Start 10 points below min, but not less than 0\n", + "\n", + " sns.set_theme(style=\"whitegrid\")\n", + " sns.set_context(\"talk\")\n", + "\n", + " datasets = df_filtered['dataset'].unique()\n", + " \n", + " # ✨ UPDATED: Adjusted figure size for fewer models\n", + " fig, axes = plt.subplots(1, len(datasets), figsize=(18, 8), sharey=True)\n", + " if len(datasets) == 1:\n", + " axes = [axes]\n", + "\n", + " method_order = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + " \n", + " for i, dataset_name in enumerate(datasets):\n", + " ax = axes[i]\n", + " dataset_df = df_filtered[df_filtered['dataset'] == dataset_name]\n", + " \n", + " sns.barplot(\n", + " data=dataset_df,\n", + " x='model',\n", + " y='accuracy',\n", + " hue='method',\n", + " hue_order=[m for m in method_order if m in dataset_df['method'].unique()],\n", + " ax=ax,\n", + " errorbar='sd',\n", + " capsize=.05\n", + " )\n", + " \n", + " dataset_title = 'DailyLifeAPIs' if 'dailylife' in dataset_name else 'HuggingFace'\n", + " ax.set_title(f'Performance on {dataset_title} Dataset', fontsize=18, pad=20)\n", + " ax.set_xlabel('Model', fontsize=14)\n", + " ax.set_ylabel('Final Accuracy (%)' if i == 0 else '', fontsize=14)\n", + " ax.tick_params(axis='x', rotation=0) # No rotation needed for two models\n", + " \n", + " # ✨ NEW: Set the y-axis limits\n", + " ax.set_ylim(bottom=y_axis_bottom, top=100)\n", + " \n", + " # Adjust legend position\n", + " ax.legend(title='Method', loc='upper left')\n", + "\n", + " plt.suptitle('Comparison of Methods for Llama Models', fontsize=22, y=1.05)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "else:\n", + " print(\"No data found for the specified models ('llama_4', 'llama_3_3_70b_instruct').\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8e7d5b2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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kivOxxx5T9erV9ddff2n16tXpJo7WrVunhIQEI9aU4uLi9Nxzz+n48eNycHBQp06d1LRpU5UoUUImk0kHDx7UzJkzdePGDQ0ZMsQYWWRNZGSkXn75ZcXGxmrYsGFq2LChcuXKJX9//1QJoISEBFWvXl3NmjVTpUqVlD9/fplMJgUEBGjVqlXasWOHTpw4odGjR9sc3fLRRx8ZSaPy5ctr0KBBKl++vCIiIrRx40bNnz9fr776arrvu9ls1siRI43z2Lx5c7Vv314lS5aUvb29jhw5opkzZyowMFAjR47U/PnzVbVq1XS3mR6TyWQkqNq0aWMkJevUqaOiRYvq6tWrWrlyZbqJoxUrVhjzIrm4uKhXr15q3LixChYsqKioKJ0+fVqbN2/WhQsX0rT9+++/9b///c9IOLVu3VodOnRQyZIllZiYqPPnz2vXrl3auHFjpo8xo9avXy9/f395e3tr4MCBKl++vGJjY1P9f52QkCA3Nzc1bdpU9erV0+OPPy53d3fduHFDZ8+e1Zw5c3TlyhVNmzZNpUuXtpq0SUxM1PDhw405D0uXLq2+ffuqSpUqcnV1VUhIiA4fPqzff/89VbvGjRsb3xnLli1LN3FkSSDnzZs3W5KLAAAADxUzAADAQ2rv3r1mb29vs7e3t3np0qVms9lsnjZtmtnb29vctGlTc2JiorFuYmKiuWnTpmZvb2/zzz//bDabzealS5ca7ffu3Ztm+59++qmx/JtvvkmzPDEx0fzaa68Z68ydOzfNOi+//LKxfPXq1WmW375929ylSxdjHW9v7zTrxMXFmZs3b2729vY2P//88+aoqCir78fChQuNbezcuTPNcss2xo4da7V9eg4ePGhs+7XXXrvn9ne6ceOGuXr16mZvb29zo0aNzIGBgWnWOX78uLlGjRpmb29vc+PGjc1xcXGpll++fDnV+/bRRx+l2UZUVJRx3HXq1DH7+PiYZ82alWa94OBg8xNPPGH29vY2Dxs2zGrMKffVvXt3c0RERJp1li9fbqwzcuTINMsjIyPNQUFBNt+XxMRE87hx48ze3t7mGjVqmMPDw9Osk/K69fb2Ni9atMjm9sxms7lfv35mb29vc79+/dIss3wmXn755XS3cfPmzTSvpby2rcUQGxtr7t+/v7HO1q1bbcbm7e1tbt68ufnatWtp1jlw4IDZx8fH7O3tbf7ggw/SjdOWlN8V3t7e5rffftvqeuPHjzfW+e2331ItS0hIMK6l5557zua+Zs6caWzjxIkTmYrXbE76jFSuXNns7e1tnjRpUqpl27ZtS/d9nT17trH8yJEjNvfRs2dPs7e3t7lNmzZpln399ddmb29vc61atcxHjx612j4gIMDcsGFDs7e3t3n06NFplo8dO9aIo0aNGuaTJ0+me8znz59Pd/mSJUuM7e3evTvN8uPHjxvXSu/evc3R0dFp1vn9999TXQuTJ09Os47lu7Ry5crmbdu2WY3l1q1b5o4dO5q9vb3Nffr0STfuu9mwYYMRz549e1It++KLL8ze3t7mKlWqmG/dumW1fVBQkPF9Wr9+ffPp06dt7svad2337t3N3t7e5goVKpjXrFljs21oaGia9zS975eUJk+ebPP/OLM59ffrs88+a46NjbW5rRs3bpjDwsJsLo+NjTU/99xzxvdKfHx8mnV+/fVXY38jRoywub+EhIQ030vffvut8X5dvXrVarvr168bn98PP/zQZqwAAACPqvSL9QMAADxkunTpInt7e129elX79u0zXt+3b5+uXr0qe3v7NE/WWxMXF6clS5ZISnpq3Vo5OTs7O02cONEoUTd37txUy0NCQowntJs3b251hIK7u/tdS6OtXbtWV65ckYuLiz7//HO5urpaXa9Xr16qVq2apH+edM4uKcvj5c+fP8vbW7p0qaKjoyVJb775ZpqROVLSCLIhQ4ZIShrdkt7T7kWLFtUbb7yR5nVXV1ejzN2tW7dUvXp1Pfvss2nWK1iwoFq3bi0paXTX3bz//vtWywF269bNGK20ceNGY04oCzc3NxUqVMjmdu3s7DR27Fg5ODgoKipKu3fvTjeOevXqqWfPnneN1xZLGSVrZfVSurMMY8rz0bhxY6sxODs76+OPPzbK7d35+bjThAkTrM4XU6tWLVWvXl1Sxs7N3RQoUMCYI+pO48ePV758+SQpTdlDe3t79ejRQ1JS2cirV69a3cbSpUslJV2/d5aRuxdr1qyRyWSS9E+ZOouGDRsaI3Uso5JSSll2bvXq1Va3f+nSJf3111+S0o42ssxbJUmjRo2yOSKyePHiGj58uKSk+ZTuLJGW0gsvvGBzbh2Lu83H9NRTTxnvqbXvg4ULF8psNkuSPvjgA+XKlSvNOu3atTM+69aYzWZjLpr+/fvbHH2YJ08e4zvnjz/+sDqSJ6Ms57BIkSLGfE8WXbt2lZT0f9K6deustv/tt9+M79P3339f3t7eNvd153ftzp07jRKu/fv3T3cETd68ea2+p9nJ3t5eH374Yboj9fLly2fMOWSNs7OzxowZI0m6cuVKmtHFiYmJmj59uqSk9/yzzz6zuT97e/s030tPPfWU7OzslJiYqOXLl1ttt2rVKuPzS5k6AADwb0TiCAAAPFIKFy5s3HizlKZL+XO9evUyNJn5sWPHFB4eLilp3g1rE8ZLSYmf9u3bS0qalyflXC/79u0zykBZbjhbU61atXTLb1nmo6hdu7ZxU9sWSwLgzz//THe9exUZGWn8bCtxdS8s86p4enqmexM3ZULC0saa1q1b2yzPlvJmteVcpbdeWFiYce6t8fb2Tre0oOUmYXx8vPbv329zPSmpRNW1a9f0999/G/OmBAcHG4maU6dOpds+I0nQ9FiSD+vWrTNuPGfE/v37jWv76aeftrleiRIl1KBBgzRt7uTp6almzZrZ3E7lypUlySgbmRXt27e3eQ3nzp3buEbOnDmTJvH39NNPy97e3uYN42PHjsnf319S1m8WW5IJ1apVS5NQcXBwUIcOHSQlfT+knD9NSkruWt73devWpZlDTEqdULrzOjpw4IBu374tSWrbtm26cdauXVtS0rVsSUBYc2fy627MZrNCQkJ0/vx547Ph7+9vfH9b+2xYviMsZUttSW/OtLNnz+rSpUuSMn7sUua/c2/dumWUxOvUqZPs7OxSLS9fvryRLEv5f1pKlnKSJUuWvOs8SHey7FuS1aT6g1azZs17nkMpLi5OgYGBOnv2rHGdWBKIUtpr5eTJk7p27ZqkpP9j7nVOwJTfa7YSR5aHNypXrnzXhCkAAMCjiDmOAADAI6dbt27as2eP1q9fr3fffVdS0tPw0j9Pb9/NmTNnjJ8tox1sqV69ujE64cyZM8aIEssNZEl3nf+iatWqqfaZ0rFjxyQlPRnu4+Nz9+D1z0iS7JLyxtq9JBhssbw3lSpVSnc+ngIFCqh48eK6cuVKqvfzTumNVPDw8DB+LlOmTIbWi4yMtPlE+93OpWXUl5R0nHc+wW8ymbRo0SKtXLlSJ06cMJ5Kt+bmzZvp7iuj14Mt3bp10w8//KDDhw+rZcuWateunerXr68nn3wy3STlvX4+tm/frujoaF2+fNnquXrsscdkb2/7mbU8efJISp3AzKy7zSdWrVo1Y7TNnXPwFClSRI0aNdL27du1fPlyY7SNheVmsbOzc5aSemfOnDGSMLYSLl26dNGvv/6qmJgY/f7772lGfXXu3Fk7duxQSEiI9u7da9zotlizZo2kpPPz2GOPpVpm+c6RpEaNGmU47jsTbRZubm4qWbJkhraxdetWzZ8/XwcOHEj3fN/52YiNjdXFixcl/ZNotCW9ayDlsffu3TsjIUuyfex3s3btWpsjyyy6dOmikydP6vDhw7p06VKqOeZMJpPxeXzyySfTJJ7u5sSJE5KkYsWK2Zyj6kHK6HdaVFSU5syZo7Vr1+rs2bM2k9JS2mvFcszS3Udb2vL0009r165dunjxog4ePJhqO0ePHs22BDIAAMDDihFHAADgkdO6dWu5uroqIiJCmzZt0saNGxUZGSk3Nze1adMmQ9sICwszfr7bKJ8CBQpYbXcv5d1SbuNOoaGh6ba1JiYm5p7bpCdlqbLsSEpZ3qeMlL2z3LhP+d7eKb1RUCkTEumVWUq5Xno3Ie/lXKa8Biy/9+7dW++//77++uuvdJNGUtLN8PRYEiqZNXz4cKPs0o0bNzR37ly99NJLql+/vjp16qTJkydbPd+ZvbZtncO7jWKznBtrI2fu1d3iTbncWryWBM2lS5d04MAB4/W4uDgjGdOqVassnRvLaCNHR0ebpcOqVKmismXLSrI+EqVVq1bG+3pnubrjx4/r3LlzkqyPWrtx40am4rb1vZNeWTELs9mst956S0OHDtXWrVvvmiS8c18pRwne7Ts7veXZfex3YznXPj4+NpMmnTp1Mka93lmaMCwszBhdkzLJmVGWpEpm2t4PGblWAgIC1LlzZ3399dc6ffp0ut/XUtrv0ZSJpMwed6tWrZQ3b15J/5SntLD87uLikuVRoQAAAA8rRhwBAIBHTu7cudWqVSutXr1aK1euNG6qtWrVSm5ubve8vXt9gju7WW6KNWnSxOo8Pg+Cj4+PUaIr5dPaWZXT721mZCXmjz76yBhJ0qpVKz311FPy8fFR/vz55eLiYmy7WbNmunr1aqpyS9akN0onI5ycnPTxxx9r0KBBWrNmjfbu3atjx44ZoxjOnDmjmTNn6osvvlCrVq2ytK+HRVavuebNm6tAgQK6fv26li1bZpQr27hxo5Foysoog8TERCPREx8fr/r169+1zcGDBxUQEJCqxFfu3LnVsmVLrVmzRuvXr9fEiRPl4uIi6Z/RRilL3qWU8kb88uXLjXmq7qZIkSJWX7dV6jOlJUuWGPPKVaxYUc8++6yqVaumwoULy9XV1djGmDFjbJZsyw4pk5M//fRThkfhZGbut3PnzunIkSOSpNOnT2dotM2qVav08ssvP5LfnRmRkWtlzJgxCggIkJ2dnXr06KGOHTuqbNmyypcvn5ycnIz5hywl/u72PZoZzs7O6tq1q2bNmiVfX1+9/fbbcnNzU2xsrNauXSsp6SGWjCTCAAAAHkUkjgAAwCOpW7duWr16tXbt2pXqtYxKOVrgxo0b6ZY4SzkiI2W7O7dx56TktrZxJy8vLwUHB8tkMqU76fn95O7urooVK+r48eM6f/68Ll68mKa81b3IkyePQkJCMjR6yVICKquja7LL3WJOuTzlSK2IiAj9/vvvkpJGeXz55Zc2t5He6Kr7oVy5cnrllVckJT2df+jQISPxGhUVpddee00bNmwwyjCmPK57ubYfhnN4t/OXcsSJtXidnJzUtWtXTZ8+Xb6+vpowYYJy585tjDIoVqxYmrJw92LPnj0KCgq6pzZms1krV67UiBEjUr3euXNnrVmzRhEREdqyZYvatWunxMRE48Z2gwYNrCY8LCMppKTRObYSQtlp8eLFkpLKFi5YsMDm6EBbn42UN+jvNkozveUpr20PD4/7+p2bmQTY5cuXdejQIaM0Wp48eYykfmbK5VnOdWZL7VkSWHcbDRgVFZWp7d/p77//1qFDhyRJQ4cO1auvvmp1vTtHe6aU8voOCQkxRu7dq549e2rWrFmKioqSr6+vevTooY0bNxqj3yhTBwAA/s1IHAEAgEdS/fr1VbBgQeNmWKFChTL05L5FyonV//rrr3TnQbA8MX5nu5Q3HI8ePZruzfWU82rcqVKlSgoODtaxY8cUFxcnZ2fnu8Z/P/To0UPHjx+X2WzWnDlzNGHChExvy9vbWyEhITpx4oTi4+Ntjmi4ceOGAgMDjTYPg6NHj2Z4ecrr4cKFC0ZpOmujPCz+/vvvbLvJmhkuLi5q0KCBGjRoIB8fH33yySeKiYnR1q1b1atXL0lpPx/pXduWz4erq2uG57m5n44dO5ZuEtnW+UupZ8+emj59unHDuGHDhtq9e7ekpAR1VkaCWUqROTs76+OPP77rtn755RedOHFCq1atSpM4atSokfLmzaubN29qzZo1ateunfbv328kpmyV0bKM1JCkP/74I93rNbtY5ulp0aKFzaSR2Ww2RuzdycXFRaVKldKlS5dsrmOR3vftncee2Tlw7sZsNmvVqlWSkkZ0Dh069K7rjx8/XrGxsVqxYoURl5OTk8qXL6/Tp0/r4MGDMpvN9zQaqVKlSjp06JACAwN15cqVe57nyDL/XcpSgdZcuHDhnrZry9mzZ42f07su0zvHKefAOnjwoOrVq5epWMqVK6cnnnhChw8f1rJly9SjRw8jgVy8ePF7+psDAADgUcMcRwAA4JHk4OCgrl27ytnZ2Sgpcy83c6tUqWI8wb5ixQqbT1OnHEVSrlw5Y0SGJNWtW9cou7N8+XKb+zpy5IgxkbY1LVq0kCTdvn1by5Yty/AxZLennnrKmA9i7ty52r9/f4bb3jkvh+WGWnh4uNavX2+z3ZIlS4wyQw/LTTh/f/90y/VZbhw6ODioTp06xuspy39FR0fbbL9gwYJsiDJ7pLyhmnJekDp16hjX9p3ze6QUGBhoJFRStslJvr6+NuejiYqKsvl5TqlMmTLGjftly5Zp+fLlSkxMNEpnZVZkZKQ2btwoSWrYsKE6d+6sjh07pvuva9eukpJuzB8+fDjV9hwdHdW+fXtJ0rZt2xQeHm6UqXN1dbVZfrBBgwbG/EizZ8++L6W+7mT5fKSXNN20aVO6I2Ms3xEnTpwwElHW3Pl9lFLlypWNEVaLFi266zxjmbVv3z4jKd6tW7e7nudOnTqpUaNGkpKu4ZRxNW/eXFLS3D+bNm26pzgs/79I0qxZs+75OCzlEc+fP6+IiAir64SGhhrfA1mVHd+jFSpUMJLdixcvvut8Wul5+umnJSUloPbu3as9e/ZIkrp37/6vLScIAAAgkTgCAACPsDfeeENHjx7V0aNH9frrr99TW2dnZ+OGkL+/v3744Yc065jNZn3wwQfGDfVnnnkm1fJChQqpZcuWkqTNmzdr3bp1abYRGRmpd999N91Yunfvbtzk+uyzz3TgwIF01z948OA9JXUyytXVVV988YVRFmno0KHy8/NLt01gYKBGjx6tDz/8MNXrTz31lHFj+rPPPrNamuvUqVP66aefJEmFCxd+qObYefvtt63e4F69erW2bdsmKWkOo5SJh1KlShk3EpcvX271ZvzmzZs1d+7c+xR1ardu3dLmzZvTTQqkLPWYcv6clOdj+/btVhOjcXFxGj9+vDHK6s7PR04JCQnRp59+anXZp59+apSq69u3b7rb6dmzp6Skz9tvv/0mKSk5lpVRVevXrzeuq7Zt22aoTdu2bY3rylpCxDKqKC4uTqtXrzYStS1btjRGi9zJ09PTOF+HDx/Wxx9/nG4psuvXrxul5jLLUvpyy5YtVsuMXbp0Se+991662+jVq5fxXrz99ttWE4R+fn7asGGDzW3Y29sbo38uX76sMWPGKC4uzub6ERERxvm/FynL1LVp0yZDbSzXxO3bt1MliPr162fM3/fOO++k+yDCtWvXUv3eoEEDYwTOb7/9ZpQxtObmzZtp3lNLctxkMll9H0wmkyZMmGAzWXuvUpZItfUgxbx589JNoNnb2+v555+XlPR+jB071uY5TkxMTLd0ZIcOHZQ7d26ZzWa9/vrr2ZJABgAAeBRQqg4AAPxnjRgxQhs2bNDly5f13Xffyd/fXz169FDBggUVEBCg3377zUjQPPHEE+rdu3eabYwdO1a7du1SZGSkXn/9dR04cEBt27aVu7u7Tp8+rWnTpunChQuqUqWKzdI6zs7O+vbbb9W/f39FRUXp2WefVYcOHdSqVSuVKFHCmNvi+PHj2rBhg/z9/fX222+nGu2SXerXr68PP/xQ7777rqKiojRy5EjVqFFDbdu2VcWKFZUnTx7dvn1bly9f1o4dO7R582bFxcXJw8Mj1Xby5cunN954Q++//76uXbumHj16aPDgwapZs6bi4+O1e/duoxSYnZ2dPvjgAzk5OWX78WSG5Vw99dRTGjx4sLy9vXX79m35+flp4cKFkpLKN40ZMyZVu7x586pp06baunWrduzYoUGDBqlv374qVqyYbty4ofXr12v58uUqWbKkwsPD7zpPS1ZFREToxRdfVPHixdWmTRtVq1ZNxYsXl4ODg0JCQrRlyxYjGVC4cGE1a9YsVfvx48dr7969CgsL0/jx43Xo0CF16NBBnp6eOnfunGbMmKGTJ09Kktq3b6+mTZve1+PJqCpVqmj+/PkKCAhQnz59VLRoUV29elXz58/Xzp07JSWV7+rTp0+622nXrp0+/PBD3b5925g3KatzmlgSP05OTqlGgqSnaNGiqlq1qo4cOaLff/9db731VqpyljVr1lSJEiUUEBCgb7/91igpZqtMncWoUaN04MAB/fXXX5o9e7b279+vXr16qUKFCnJzc1NYWJjOnj2r3bt3a/v27fL29jaSaZnRrVs3ff755woODlbv3r2Nz1ZsbKz27t2rX3/9VXFxcapcubLNUnRVqlRRr169tHDhQh0+fFhPP/20nn/+eZUvX14RERHasGGD5s+fr2rVqhklFK2NCunbt692796tDRs2yNfXVydOnFDv3r1VrVo1eXh4KCIiQufOndP+/fu1efNmOTs7q1+/fhk+1ujoaCPpXrly5VRJ2fS0aNFCTk5OMplMWrFihVGqrWDBgnr33Xc1duxY3bhxQz179lTPnj3VpEkTFSxYUJGRkTpz5ow2b96s8+fPG6PaLL744gs9/fTTioqK0ujRo+Xr66sOHTqoZMmSSkxM1MWLF7Vr1y75+flp9erVqeJt2rSpihcvritXrmjSpEm6efOmWrduLRcXF509e1Zz5szRiRMnVKNGDf35558Zfo9sqVSpkry9veXv76+FCxcqPDxcXbt2VcGCBXXt2jWtWrVKfn5+qlmzpv744w+b23nmmWe0ZcsW7dq1Sxs2bFDnzp31v//9T1WqVJGrq6tCQkL0119/ae3aterUqZNefvllq9txc3NTx44dtWjRImM0XL169e655B8AAMCjhsQRAAD4z3J3d9esWbM0ePBgnTt3Tn5+flZH2NSsWVM//vij1TJcJUqU0I8//qgXX3xRkZGRmjdvnubNm5dqnREjRsjOzi7dORlq1KihOXPm6JVXXtHVq1e1evVqrV69Ot3Y75ennnpKJUuW1AcffCB/f3/9+eef6d4QLFasmF577bU0rz/zzDO6ffu2Jk2apOvXr+uTTz5Js46zs7M++OCDhybpIEnNmjVTs2bNNGXKFL355ptplru7u+vHH3+0ejN44sSJ+t///meUcLuzfFOxYsX0/fffa8iQIfct/jtduXJFM2fOtLm8YMGC+uGHH9KMTilSpIhmzZqloUOHKjg4WIsXL7Y66qRNmzb67LPPsj3uzHr11Vc1c+ZM7dixQzt27Eiz/PHHH9dPP/1kc94ti1y5cqlTp06aP3++JMnDwyPDo4SsuXr1qpGIrlu3rvLkyZPhtm3bttWRI0cUFhamLVu2pImjU6dO+umnn4ykUd68eY2yZ7Y4OztrxowZevPNN7V+/XqdOnVK77//vs31s/qdM2DAAO3evVs7d+7UhQsX9NZbb6VanitXLn322Wfatm1bunMYTZgwQcHBwdqyZYvOnDmjcePGpVpeokQJffXVV2rdurUkWZ0zzs7OTt98840++ugjLViwQJcuXdIXX3xhc5/58uW7l0PVhg0bjPJo7dq1y3A7Dw8PNWjQQNu2bdOuXbt0/fp1FShQQFJS4s1sNmvixImKiYnRnDlzNGfOnDTbsJbQKFu2rObMmaOXXnpJV69e1fr169MtIZqSs7OzPv/8cw0ePFhRUVGaNWtWqpJ3Dg4OGj9+vMLCwrIlcWRnZ6fPP/9czz77rMLCwvT7778b5SUtvL29NWnSJDVu3Njmduzt7fX9999r7Nix8vPz04ULF/Txxx9nKqaePXtq0aJFxu9ZTSADAAA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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from pathlib import Path\n", + "\n", + "# --- 1. Robust Data Parsing (Same as before) ---\n", + "root_dir = Path('.')\n", + "detailed_data = []\n", + "ALL_EXPECTED_METHODS = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "results_files = root_dir.glob('**/results.json')\n", + "\n", + "for file_path in results_files:\n", + " try:\n", + " parts = file_path.parts\n", + " current_method = None\n", + " for m in ALL_EXPECTED_METHODS:\n", + " if m in parts:\n", + " current_method = m\n", + " break\n", + " \n", + " if current_method:\n", + " method_index = parts.index(current_method)\n", + " dataset = parts[method_index + 1].replace('_experiments', '')\n", + " model = parts[method_index + 2]\n", + "\n", + " with open(file_path, 'r') as f:\n", + " results_list = json.load(f)\n", + "\n", + " for item in results_list:\n", + " metrics = item.get('metrics', {})\n", + " total_tokens = None\n", + "\n", + " if current_method in ['cot_k1', 'cot_k3', 'cot_k5']:\n", + " reasoning_cost = metrics.get('reasoning_cost', {})\n", + " total_tokens = reasoning_cost.get('total_llm_tokens')\n", + " elif current_method == 'spiral':\n", + " search_process = metrics.get('search_process', {})\n", + " exp_tokens = search_process.get('expansion_llm_tokens', 0)\n", + " sim_tokens = search_process.get('simulation_llm_tokens', 0)\n", + " crit_tokens = search_process.get('critic_llm_tokens', 0)\n", + " total_tokens = exp_tokens + sim_tokens + crit_tokens\n", + " \n", + " detailed_data.append({\n", + " 'method': current_method, 'dataset': dataset, 'model': model,\n", + " 'accuracy': metrics.get('accuracy'), 'plan_length': metrics.get('plan_length'),\n", + " 'total_llm_tokens': total_tokens\n", + " })\n", + " except Exception as e:\n", + " print(f\"🔴 Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "# --- 2. Diagnostics and Filtering (Same as before) ---\n", + "df_raw = pd.DataFrame(detailed_data)\n", + "df_cleaned = df_raw.dropna(subset=['accuracy', 'plan_length', 'total_llm_tokens']).copy()\n", + "models_to_keep = ['llama_4', 'llama_3_3_70b_instruct', 'phi',]\n", + "df_filtered = df_cleaned[df_cleaned['model'].isin(models_to_keep)].copy()\n", + "\n", + "\n", + "# --- 3. Generate Bar Plots ---\n", + "if not df_filtered.empty:\n", + " agg_df = df_filtered.groupby(['method', 'dataset', 'model']).agg(\n", + " avg_accuracy=('accuracy', 'mean'),\n", + " avg_tokens=('total_llm_tokens', 'mean'),\n", + " ).reset_index()\n", + " agg_df['avg_accuracy'] = agg_df['avg_accuracy'] * 100\n", + "\n", + " sns.set_theme(style=\"whitegrid\", context=\"talk\")\n", + " plot_method_order = [m for m in ALL_EXPECTED_METHODS if m in agg_df['method'].unique()]\n", + "\n", + " # ✨ Plot 1: Average Accuracy Bar Plot ✨\n", + " g_acc = sns.catplot(\n", + " data=agg_df,\n", + " kind='bar',\n", + " x='model',\n", + " y='avg_accuracy',\n", + " hue='method',\n", + " hue_order=plot_method_order,\n", + " col='dataset',\n", + " height=7,\n", + " aspect=1.1\n", + " )\n", + " g_acc.fig.suptitle('Model Comparison by Average Accuracy', y=1.03, fontsize=20)\n", + " g_acc.set_axis_labels(\"Model\", \"Average Accuracy (%)\")\n", + " g_acc.set_titles(\"Dataset: {col_name}\")\n", + " g_acc.set(ylim=(0, 105)) # Set y-axis to be 0-100%\n", + " plt.tight_layout(rect=[0, 0, 1, 0.97])\n", + " \n", + " # ✨ Plot 2: Average Cost Bar Plot ✨\n", + " g_cost = sns.catplot(\n", + " data=agg_df,\n", + " kind='bar',\n", + " x='model',\n", + " y='avg_tokens',\n", + " hue='method',\n", + " hue_order=plot_method_order,\n", + " col='dataset',\n", + " height=7,\n", + " aspect=1.1\n", + " )\n", + " g_cost.fig.suptitle('Model Comparison by Average Cost (Tokens)', y=1.03, fontsize=20)\n", + " g_cost.set_axis_labels(\"Model\", \"Average LLM Tokens per Task\")\n", + " g_cost.set_titles(\"Dataset: {col_name}\")\n", + " plt.tight_layout(rect=[0, 0, 1, 0.97])\n", + " \n", + " plt.show()\n", + "\n", + "else:\n", + " print(\"🔴 No data available for plotting after filtering.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a2d11714", + "metadata": {}, + "outputs": [], + "source": [ + "# import json\n", + "# import pandas as pd\n", + "# import matplotlib.pyplot as plt\n", + "# import seaborn as sns\n", + "# from pathlib import Path\n", + "\n", + "# # --- 1. Robust Data Parsing for All Metrics ---\n", + "# root_dir = Path('.')\n", + "# detailed_data = []\n", + "# ALL_EXPECTED_METHODS = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "# results_files = root_dir.glob('**/results.json')\n", + "\n", + "# for file_path in results_files:\n", + "# try:\n", + "# parts = file_path.parts\n", + "# current_method = None\n", + "# for m in ALL_EXPECTED_METHODS:\n", + "# if m in parts:\n", + "# current_method = m\n", + "# break\n", + " \n", + "# if current_method:\n", + "# method_index = parts.index(current_method)\n", + "# dataset = parts[method_index + 1].replace('_experiments', '')\n", + "# model = parts[method_index + 2]\n", + "\n", + "# with open(file_path, 'r') as f:\n", + "# results_list = json.load(f)\n", + "\n", + "# for item in results_list:\n", + "# metrics = item.get('metrics', {})\n", + "# total_tokens = None\n", + "# generation_time = None # ✨ Initialize time metric\n", + "\n", + "# if current_method in ['cot_k1', 'cot_k3', 'cot_k5']:\n", + "# reasoning_cost = metrics.get('reasoning_cost', {})\n", + "# total_tokens = reasoning_cost.get('total_llm_tokens')\n", + "# generation_time = metrics.get('generation_time_seconds') # ✨ Get CoT time\n", + "# elif current_method == 'spiral':\n", + "# search_process = metrics.get('search_process', {})\n", + "# exp_tokens = search_process.get('expansion_llm_tokens', 0)\n", + "# sim_tokens = search_process.get('simulation_llm_tokens', 0)\n", + "# crit_tokens = search_process.get('critic_llm_tokens', 0)\n", + "# total_tokens = exp_tokens + sim_tokens + crit_tokens\n", + "# generation_time = metrics.get('search_time_seconds') # ✨ Get Spiral time\n", + " \n", + "# detailed_data.append({\n", + "# 'method': current_method, 'dataset': dataset, 'model': model,\n", + "# 'accuracy': metrics.get('accuracy'), 'plan_length': metrics.get('plan_length'),\n", + "# 'total_llm_tokens': total_tokens,\n", + "# 'generation_time': generation_time # ✨ Add time to data\n", + "# })\n", + "# except Exception as e:\n", + "# print(f\"🔴 Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "# # --- 2. Diagnostics and Filtering ---\n", + "# df_raw = pd.DataFrame(detailed_data)\n", + "# # ✨ Update dropna to include the new time metric\n", + "# df_cleaned = df_raw.dropna(subset=['accuracy', 'plan_length', 'total_llm_tokens', 'generation_time']).copy()\n", + "# models_to_keep = ['llama_4', 'llama_3_3_70b_instruct']\n", + "# df_filtered = df_cleaned[df_cleaned['model'].isin(models_to_keep)].copy()\n", + "\n", + "\n", + "# # --- 3. Generate New Bar Plots ---\n", + "# if not df_filtered.empty:\n", + "# # ✨ Update aggregation to include the new metrics\n", + "# agg_df = df_filtered.groupby(['method', 'dataset', 'model']).agg(\n", + "# avg_time=('generation_time', 'mean'),\n", + "# avg_plan_length=('plan_length', 'mean')\n", + "# ).reset_index()\n", + "\n", + "# sns.set_theme(style=\"whitegrid\", context=\"talk\")\n", + "# plot_method_order = [m for m in ALL_EXPECTED_METHODS if m in agg_df['method'].unique()]\n", + "\n", + "# # ✨ Plot 1: Average Generation Time Bar Plot ✨\n", + "# g_time = sns.catplot(\n", + "# data=agg_df,\n", + "# kind='bar',\n", + "# x='model',\n", + "# y='avg_time',\n", + "# hue='method',\n", + "# hue_order=plot_method_order,\n", + "# col='dataset',\n", + "# height=7,\n", + "# aspect=1.1\n", + "# )\n", + "# g_time.fig.suptitle('Model Comparison by Average Generation Time (Latency)', y=1.03, fontsize=20)\n", + "# g_time.set_axis_labels(\"Model\", \"Average Time (s)\")\n", + "# g_time.set_titles(\"Dataset: {col_name}\")\n", + "# plt.tight_layout(rect=[0, 0, 1, 0.97])\n", + " \n", + "# # ✨ Plot 2: Average Plan Length Bar Plot ✨\n", + "# g_plan = sns.catplot(\n", + "# data=agg_df,\n", + "# kind='bar',\n", + "# x='model',\n", + "# y='avg_plan_length',\n", + "# hue='method',\n", + "# hue_order=plot_method_order,\n", + "# col='dataset',\n", + "# height=7,\n", + "# aspect=1.1\n", + "# )\n", + "# g_plan.fig.suptitle('Model Comparison by Average Solution Plan Length', y=1.03, fontsize=20)\n", + "# g_plan.set_axis_labels(\"Model\", \"Average Plan Length (API calls)\")\n", + "# g_plan.set_titles(\"Dataset: {col_name}\")\n", + "# plt.tight_layout(rect=[0, 0, 1, 0.97])\n", + " \n", + "# plt.show()\n", + "\n", + "# else:\n", + "# print(\"🔴 No data available for plotting after filtering.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b73ec4ab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- 📊 Aggregated Performance Results (TXT) ---\n", + "\n", + "method dataset model avg_accuracy avg_tokens\n", + "cot_k1 dailylifeapis deepseek_v2_5 66.609881 2849.763203\n", + "cot_k1 dailylifeapis llama_3_3_70b_instruct 94.867550 2577.998344\n", + "cot_k1 dailylifeapis llama_4 57.947020 2535.493377\n", + "cot_k1 dailylifeapis phi 85.666667 2521.696667\n", + "cot_k1 dailylifeapis qwen2_5_72b_instruct 89.713322 2484.738617\n", + "cot_k3 dailylifeapis deepseek_v2_5 67.892977 17128.936455\n", + "cot_k3 dailylifeapis llama_3_3_70b_instruct 94.876033 15384.796694\n", + "cot_k3 dailylifeapis llama_4 60.596026 15180.917219\n", + "cot_k3 dailylifeapis phi 86.235489 15182.059701\n", + "cot_k3 dailylifeapis qwen2_5_72b_instruct 89.643463 14836.555178\n", + "cot_k5 dailylifeapis deepseek_v2_5 68.822554 42750.237148\n", + "cot_k5 dailylifeapis llama_3_3_70b_instruct 94.380165 38354.003306\n", + "cot_k5 dailylifeapis llama_4 60.000000 37847.866116\n", + "cot_k5 dailylifeapis phi 86.446281 38055.803306\n", + "cot_k5 dailylifeapis qwen2_5_72b_instruct 89.509306 37071.497462\n", + "spiral dailylifeapis deepseek_v2_5 91.239669 28288.485950\n", + "spiral dailylifeapis llama_3_3_70b_instruct 98.347107 26498.680992\n", + "spiral dailylifeapis llama_4 83.305785 27029.013223\n", + "spiral dailylifeapis phi 91.570248 27910.905785\n", + "spiral dailylifeapis qwen2_5_72b_instruct 97.685950 32287.720661\n", + "cot_k1 huggingface deepseek_v2_5 75.772559 2555.237330\n", + "cot_k1 huggingface llama_3_3_70b_instruct 92.483923 2400.162379\n", + "cot_k1 huggingface llama_4 76.429163 2438.171500\n", + "cot_k1 huggingface phi 92.076392 2310.373019\n", + "cot_k1 huggingface qwen2_5_72b_instruct 86.779367 2247.411790\n", + "cot_k3 huggingface deepseek_v2_5 78.777671 15318.115496\n", + "cot_k3 huggingface llama_3_3_70b_instruct 92.788462 14274.810897\n", + "cot_k3 huggingface llama_4 75.645756 14382.203362\n", + "cot_k3 huggingface phi 92.845659 13935.188505\n", + "cot_k3 huggingface qwen2_5_72b_instruct 88.163621 13401.097476\n", + "cot_k5 huggingface deepseek_v2_5 78.614337 38327.207849\n", + "cot_k5 huggingface llama_3_3_70b_instruct 93.752503 35609.369243\n", + "cot_k5 huggingface llama_4 77.084189 35542.245996\n", + "cot_k5 huggingface phi 93.707415 34960.898196\n", + "cot_k5 huggingface qwen2_5_72b_instruct 88.471616 33513.159825\n", + "spiral huggingface deepseek_v2_5 96.840000 19942.803600\n", + "spiral huggingface llama_3_3_70b_instruct 97.440000 19197.837200\n", + "spiral huggingface llama_4 93.040000 25293.519200\n", + "spiral huggingface phi 95.480000 22630.710000\n", + "spiral huggingface qwen2_5_72b_instruct 97.080000 28476.893200\n", + "\n", + "==================================================\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from pathlib import Path\n", + "\n", + "# --- 1. Robust Data Parsing ---\n", + "# This section remains unchanged, as it correctly parses all found results.\n", + "root_dir = Path('.')\n", + "detailed_data = []\n", + "ALL_EXPECTED_METHODS = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "results_files = root_dir.glob('**/results.json')\n", + "\n", + "for file_path in results_files:\n", + " try:\n", + " parts = file_path.parts\n", + " current_method = None\n", + " for m in ALL_EXPECTED_METHODS:\n", + " if m in parts:\n", + " current_method = m\n", + " break\n", + " \n", + " if current_method:\n", + " method_index = parts.index(current_method)\n", + " dataset = parts[method_index + 1].replace('_experiments', '')\n", + " model = parts[method_index + 2]\n", + "\n", + " with open(file_path, 'r') as f:\n", + " results_list = json.load(f)\n", + "\n", + " for item in results_list:\n", + " metrics = item.get('metrics', {})\n", + " total_tokens = None\n", + "\n", + " if current_method in ['cot_k1', 'cot_k3', 'cot_k5']:\n", + " reasoning_cost = metrics.get('reasoning_cost', {})\n", + " total_tokens = reasoning_cost.get('total_llm_tokens')\n", + " elif current_method == 'spiral':\n", + " search_process = metrics.get('search_process', {})\n", + " exp_tokens = search_process.get('expansion_llm_tokens', 0)\n", + " sim_tokens = search_process.get('simulation_llm_tokens', 0)\n", + " crit_tokens = search_process.get('critic_llm_tokens', 0)\n", + " total_tokens = exp_tokens + sim_tokens + crit_tokens\n", + " \n", + " detailed_data.append({\n", + " 'method': current_method, 'dataset': dataset, 'model': model,\n", + " 'accuracy': metrics.get('accuracy'), 'plan_length': metrics.get('plan_length'),\n", + " 'total_llm_tokens': total_tokens\n", + " })\n", + " except Exception as e:\n", + " print(f\"🔴 Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "# --- 2. Diagnostics and Filtering ---\n", + "df_raw = pd.DataFrame(detailed_data)\n", + "df_cleaned = df_raw.dropna(subset=['accuracy', 'plan_length', 'total_llm_tokens']).copy()\n", + "\n", + "# ✨ MODIFICATION: Include all 5 models for comparison\n", + "models_to_keep = [\n", + " 'deepseek_v2_5', \n", + " 'llama_3_3_70b_instruct', \n", + " 'llama_4', \n", + " 'phi', \n", + " 'qwen2_5_72b_instruct'\n", + "]\n", + "df_filtered = df_cleaned[df_cleaned['model'].isin(models_to_keep)].copy()\n", + "\n", + "# --- 3. Aggregate Data ---\n", + "if not df_filtered.empty:\n", + " agg_df = df_filtered.groupby(['method', 'dataset', 'model']).agg(\n", + " avg_accuracy=('accuracy', 'mean'),\n", + " avg_tokens=('total_llm_tokens', 'mean'),\n", + " ).reset_index()\n", + " agg_df['avg_accuracy'] = agg_df['avg_accuracy'] * 100\n", + "\n", + " # ✨ MODIFICATION: Define the neutral (alphabetical) order for models\n", + " model_order_neutral = [\n", + " 'deepseek_v2_5', \n", + " 'llama_3_3_70b_instruct', \n", + " 'llama_4', \n", + " 'phi', \n", + " 'qwen2_5_72b_instruct'\n", + " ]\n", + " \n", + " # Ensure the 'model' column is sorted according to the neutral order\n", + " agg_df['model'] = pd.Categorical(agg_df['model'], categories=model_order_neutral, ordered=True)\n", + " agg_df = agg_df.sort_values(by=['dataset', 'method', 'model'])\n", + "\n", + " # --- 4. ✍️ Generate Text Result ---\n", + " print(\"\\n--- 📊 Aggregated Performance Results (TXT) ---\\n\")\n", + " # Set pandas display options to show all rows and columns for clarity\n", + " pd.set_option('display.max_rows', None)\n", + " pd.set_option('display.max_columns', None)\n", + " pd.set_option('display.width', 1000)\n", + " print(agg_df.to_string(index=False))\n", + " print(\"\\n\" + \"=\"*50 + \"\\n\") # Separator before plots appear\n", + "\n", + " # --- 5. 🖼️ Generate Bar Plots ---\n", + " sns.set_theme(style=\"whitegrid\", context=\"talk\")\n", + " plot_method_order = [m for m in ALL_EXPECTED_METHODS if m in agg_df['method'].unique()]\n", + "\n", + " # Plot 1: Average Accuracy Bar Plot\n", + " g_acc = sns.catplot(\n", + " data=agg_df,\n", + " kind='bar',\n", + " x='model',\n", + " y='avg_accuracy',\n", + " hue='method',\n", + " hue_order=plot_method_order,\n", + " order=model_order_neutral, # ✨ MODIFICATION: Enforce neutral order on plot\n", + " col='dataset',\n", + " height=7,\n", + " aspect=1.2,\n", + " legend_out=True\n", + " )\n", + " g_acc.fig.suptitle('Model Comparison by Average Accuracy', y=1.03, fontsize=20)\n", + " g_acc.set_axis_labels(\"Model\", \"Average Accuracy (%)\")\n", + " g_acc.set_titles(\"Dataset: {col_name}\")\n", + " g_acc.set(ylim=(0, 105))\n", + " g_acc.set_xticklabels(rotation=15) # Rotate labels slightly for readability\n", + " plt.tight_layout(rect=[0, 0, 1, 0.97])\n", + " \n", + " # Plot 2: Average Cost Bar Plot\n", + " g_cost = sns.catplot(\n", + " data=agg_df,\n", + " kind='bar',\n", + " x='model',\n", + " y='avg_tokens',\n", + " hue='method',\n", + " hue_order=plot_method_order,\n", + " order=model_order_neutral, # ✨ MODIFICATION: Enforce neutral order on plot\n", + " col='dataset',\n", + " height=7,\n", + " aspect=1.2,\n", + " legend_out=True\n", + " )\n", + " g_cost.fig.suptitle('Model Comparison by Average Cost (Tokens)', y=1.03, fontsize=20)\n", + " g_cost.set_axis_labels(\"Model\", \"Average LLM Tokens per Task\")\n", + " g_cost.set_titles(\"Dataset: {col_name}\")\n", + " g_cost.set_xticklabels(rotation=15) # Rotate labels slightly for readability\n", + " plt.tight_layout(rect=[0, 0, 1, 0.97])\n", + " \n", + " plt.show()\n", + "\n", + "else:\n", + " print(\"🔴 No data available for plotting after filtering for the 5 specified models.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "618dae79", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "RQ1: Model Performance Comparison\n", + "================================================================================\n", + "\n", + "--- 📊 RQ1 METRICS TABLE ---\n", + "dataset dailylifeapis huggingface Planning Premium\n", + "model \n", + "deepseek_v2_5 73.72 82.72 -9.00\n", + "llama_3_3_70b_instruct 95.62 94.12 1.50\n", + "llama_4 65.47 80.64 -15.17\n", + "phi 87.48 93.53 -6.05\n", + "qwen2_5_72b_instruct 91.67 90.27 1.40\n", + "\n", + "================================================================================\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_344543/3451224480.py:118: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " ax2 = sns.barplot(data=rq1_accuracy.reset_index(), x='model', y='Planning Premium',\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "RQ2: Method Performance Comparison (SPIRAL vs. cot_k5)\n", + "================================================================================\n", + "\n", + "--- 📊 RQ2 METRICS TABLE ---\n", + "method avg_accuracy avg_tokens avg_plan_length avg_invalid_steps Efficiency Score\n", + "cot_k5 85.04 36257.71 2.71 0.00 8.10\n", + "spiral 95.29 24139.99 2.39 0.39 9.44\n", + "\n", + "================================================================================\n", + "\n" + ] + }, + { + "data": { + "image/png": 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Ahg8frvDw8ASP37x5U3v37tXevXsVGBhoNzjNnz9fw4YNS/DdJ907iD9z5ozmzJmjt956SwMGDEixjjFjxmj8+PEO1Xrx4kUFBgYqMDBQHTp00GeffZbkPiIltqsdRYsWTTE42SxcuFCxsbFycXExroa0bt1aY8eOVXR0tBYtWpTkzAs2Gzdu1DvvvJPge0+697myfXf99ddfdk8qBQcHa9CgQTp06FCiZbbBIRctWqRp06YlOIGbHv7++2998cUXiomJsbv83LlzdkfWl6Tz58/r/PnzWrp0qdq0aaORI0cm+XeMjY3Vt99+q6lTp8pqtSZYduXKFV25ckWbNm3SiRMnjNDXsWNHTZkyRXFxcVqwYEGyo40fPnxYBw4ckCS9+OKLKf7eyZkzZ46GDx+e4DW5ePGi5s6dq0WLFunbb79VixYtklw/qe+i4OBgBQcHa/78+Xrttdf01ltvJVtHZGSk+vbtm2SPO2dxxn749u3b6tu3r3bv3p3gcduxx+LFi1OcRi0qKkq7du2SpAT3FjtLUFCQ3nrrrURXPW2DuwYEBKhbt276+OOPkzwmTIrVatW3336rSZMmSbp3kuaLL75I0GsxK+0vpXuvcXBwsNavX59sO29vbxUqVEiXL1/Wf//9RzBOBsEYyYp/EOjt7W23jS3M5MiRI9kzVvfLkyePnn76aQUFBenKlSs6ffp0mu/F3LZtm/H/1JwBdqZnn31W+fLl040bN7R06VJ16dIlwXJbN+pnnnkmVVcuoqKi1Lt3bx04cECurq56/vnn9eyzz6p48eKKjo7W9u3bNXnyZF29elX9+/c3rjpIUpMmTVSxYkXNmDFDf//9t6R7B473s3fQeenSJQ0cOFAuLi569913Va1aNbm5uWnnzp36+eefdfPmTY0ePVr169fXk08+mWj97777ztgZFy9eXP369VP58uUVERGh1atXa/r06bp165YGDBigOXPm2L0KNmTIEON95O/vr169eqlkyZK6evWq5s6dq8DAQH3yyScpvobpzfaa/vDDD1q1apUee+wxTZw4MUGb4sWLG+369u1rnGF/++23jTY5cuRI9XOeOHFCXbt2NYLJc889p5YtW8rHx0dxcXE6deqUNmzYkKqpw+535swZde/eXbdu3ZKnp6e6desmf39/FSlSRDdu3NDq1as1c+ZM7du3T6+//rqmT5+eZLfwo0ePasmSJSpUqJD69OmjihUrymq1KigoSL/99psiIyM1bNgw1apVy+7n4eeff9bYsWMl3ftu6Nq1q2rWrKl8+fLp5s2bOnjwoNHd1iZfvnxq2rSpFi1apCVLluijjz5S9uzZ7da3cOFC4yA3rQfd5cqVM/4/c+ZMNWrUKE3fdfHNnz/fmFPSw8NDnTp10jPPPKNChQopPDxcR44c0erVq+1O8bV27VoNHTpUVqtVOXPmVJ8+fVS7dm1ly5ZNu3bt0oQJE3T9+nWNHj1auXPnTjCo4v2WL1+uo0ePytfXV7169dKTTz6pyMjIBEFvyZIlev/992W1WuXj46Pu3burTJky8vLyUkhIiObMmaN169Zpzpw58vT0TNRrJbVsn/3UDIZnY/t+rVGjhvG95uPjoypVqmjnzp2aP39+ssF48+bN6tevn2JiYuTq6qoXXnhBjRs3VtGiRRUZGakTJ07ov//+M06mxHflyhV16dLFGGugVq1aateunXG7THBwsDZv3pwht/fs27dPAQEBKlKkiPr27auKFSsqJiZGO3bsMNrExcXJzc1N9erVU926dfXEE08ob968CgsL06lTpzRjxgwdO3ZMAQEB8vHx0Ztvvmn3uYYNG6Z///1X0r1R9bt3764qVaood+7cunbtmvbu3Zvod37iiSdUpUoV7dq1S3Pnzk02GNvmp8+WLdsDHbgfOnRIixYtkpeXlwYMGCB/f39FRkZq3bp1mjp1qqKiojRkyBAVL17c7nvujz/+0KhRoyTd62HRpUsXlSpVSrlz59apU6c0ffp07dq1S7/88ovy58+f7Pvsu+++05EjR9SoUSO1b99exYoV05UrV4x7453FGfvhwYMHG6G4atWq6tGjh0qUKKFr164pICBACxcu1PDhw5OtY+/evcZJu7R8nlPj0KFDevXVV42xbLp166ZGjRopR44cOnTokH777TcFBwdr+vTpypEjR5IDXtoTGxurjz/+2HgP9urVS0OHDk3QAysr7S9t/P39tXjxYoWGhurMmTPJ9miqVKmSVq9eneC4GXZYYUqbN2+2+vr6Wn19fa1jx4612yYmJsbatm1bo922bdsStbl48aKxvFOnTmmu47vvvjPWX7x4cZrWjY2NtXbo0MFYf9++fXbbnTt3zmjTvXv3NNdo8++//xrb2bx5s9VqtVqHDx9u9fX1tXbu3DlB21u3bln9/f2tvr6+1iVLllitVqt17Nixxvrnzp1LtP3Ro0dbfX19rdWrV0/ydwkODrbWrVvX6uvra33nnXcSLY//HCnp3r270bZhw4bWixcvJmqzbds2q5+fn9XX19f6+eefJ1p++PBha9myZa2+vr7W559/3hoWFpaozbp164w2HTp0SLR8zZo1Rh39+vWzRkdHJ2rz008/GW18fX2t//77b4q/nz3xX585c+ZYjxw5kuy/kydP2t3OBx98YLxuyWnYsKHV19fX+sEHH6SqJnvatWtn9fX1tZYtW9a6aNGiJLdz7do1a0RERJq23blzZ6uvr6+1bdu21qtXr9ptE//vN3PmzETLbb+jr6+vtV27dtZbt24larNgwQKjzeTJkxMtP3DggPEcTZs2tV64cCHJ3/P8+fMJft64caOx7YCAgCTXa9OmjdXX19fapk2bJNskZ8CAAQnegy+++KL1xx9/tK5duzbJ1+5+oaGh1sqVK1t9fX2ttWvXth45ciTJtvf/nlFRUdZ69epZfX19rU899ZT14MGDidaJ//1QuXJlu3XF/x1efvlla2RkpN3nv3r1qrVatWpWX19f64cffmj3c2m1/t/3VtmyZa0nTpxI7te368qVK0Y9P//8c6rWOXjwYILPcXwzZswwlh07dszu+nfv3jVey8qVKxvf5/bc/3ewWq3WgQMHGs/x22+/Jbnu7du3rTdu3EjwWGq/O+Lvb+ztL+J/7pL67rW5c+eONTQ0NMnlcXFx1qFDhxrvrZs3byZqs3LlSuP5OnfunOzz3f+azZ4921h3x44ddteJioqy1qxZ0+rr62t97bXXktx2cuK/Jg0bNrReunQpUZtNmzZZy5cvb3yG73fs2DFrhQoVjGOjuLi4RG1iY2Ot7733nvF63f83jn985evrax0zZoxDv098tv21vWMYZ+yHV6xYYdQ7aNAga2xsbKI2EydOTHE//NtvvxnLk/setyf+e97etl988UWrr6+vtVy5ctb169cnWn7jxg1ry5Ytje+jo0ePJmpjb58cGRmZ4DM9btw4u/Vllf1lfFu3bk31MXT846jLly8n29bMGHwLidi6hHTv3t2YdqRZs2Z279uI3wUtpW4j9sQfnfD+7mwpmTJlivbu3Svp3v0TmTEEve2s9q5du3Tu3Dnj8WXLlunu3bvKnTu3GjVqlOJ27ty5Y5ztfeutt5L8Xby9vfX6668bz3F/90ZHffzxx3avJlevXt3oDhX/CoTN33//bczR/MUXXxhz9MZXv3594wqdrZtofDNmzJAkubu76/PPP7fbje/11193+giVH330kVq3bp3sv6S6j2eUoKAgo2thjx491KpVqyTb5s+fP8mrpfZs377d6PL29ddfJ3lWun79+sb0Graz6Un56quv7I7S3bp1a+OeQ3tzk0+cOFFxcXGyWCwaM2ZMsnPYFi1aNMHPtWrVUokSJZKt78CBAzp8+LAkx7to3j+91759+/Tzzz+rf//+ql27tpo1a6bPP//c+HvZ89dffxnd5kaMGJHse/r+33PFihXGFcrXXnstwVVsG29vb73//vuSpIiIiGT/Xi4uLvriiy+SHFH/77//1q1bt1S4cGF9+umnSXavfeONN1S4cGGjq2xaxR+7IqnRau9nu30ne/bsiaZ+adGihXGVJqlBuObPn2+8loMHD062q/P9f4eTJ08avTOaNGmifv36Jblurly5MmRqmeHDh9v97rXJmTOn8fmzx2Kx6IMPPpCrq6vCw8Ptdvu1zX+aI0cO/fjjj8k+3/2vWcuWLZUrVy5JSX9G16xZYxwDPGg3akn64IMP7M4VX6tWLXXs2FHSvc/wvn37EiyfNGmSoqOjVbFiRQ0aNCjBFUMbFxcXDRs2TO7u7goPD0+2Z0CpUqXSfco8Z+yH//nnH0n3PlOfffaZ3W7IvXv3VoUKFZKtJf5AqqnpKZdae/fuNf5WHTt2VL169RK1yZs3r9HVOy4uzji2SI7tNq4VK1bIYrHok08+sTvvclbaX8YXv474x6D2xP9+TamtmRGMoXHjxsnPz8/4V7t2bfXq1Us7d+5Ujhw51Lt37yTnCI5/n0dSE4wnx7azlO7dX5daW7duNWoqUKCAPv300yTbFi9eXEeOHNGRI0ecdn+xTZUqVYyuK/EPCm3/b968earuZdy2bZtu3bolSUnO8Wdjm5IqOjo62YPw1MqTJ48aNGiQ5HLbjtDeF6lt8Jsnn3wy2fuJOnXqlGgd6V73pa1bt0qS6tatm+T9hS4uLmrbtm2S239UrV271vh/Wu7fT41Vq1ZJujcQXnKDNUn/957bv39/kvcx+vr6JjlYlMViUfny5SUlfh/FxcXpv//+k3SvW6ytXWpZLBbjgG/z5s06f/58oja2AxQ3N7c0jXocn5eXl/7++299/vnndg8OT58+rb/++kvt27fXkCFD7J60snXL9fHxSXKOzqTYPjcWi8Xu9HY2zZs3V+7cuROsY0/VqlWTvR/ddn9zgwYNkp0TOFu2bMa93bYDx7SwDVglKdmwZRMTE6NFixZJkho2bJjowDJfvnzGYIELFy40AkN8tr9Dzpw5E3w3pca6deuM+2t79eqVpnXTQ9GiRdM82FB0dLQuXryoEydO6OjRozp69KguXbpkjJNhO4lkc/36daOLbYsWLVJ9H7hNzpw5jZN6S5cuTXBPpY3tM1qoUKFUDfaYnLx58yb7+YofvO//jNjeG82aNbMbim3y5MljnNhK7n2f3MwazvKg++GYmBije21yt35ZLJYUB1e1fZ5z5Mjh1LnE45+sSe77r1q1asZtdSnd1339+nX16tVLmzZtUrZs2TRq1Ch169bNbtussr+8X/yxbWwzJyQl/kk62+B7SIx7jJGssmXLqkePHkneJxH/oMSRq5fx77NJ7ZfosWPHNGjQIMXExMjDw0M//vhjqq80pIc2bdrop59+0sKFCzVo0CBduHDBCHupDXPx5++zdyY0KSl9EaZGyZIlkx2kwvZlev89UVFRUcZ9kP7+/sk+R7ly5eTm5qbo6GgdPXrUePzs2bPGQVJK9yOl9BxplRGD4jwoW4+NYsWKJXmPv6Ns77lTp06luKO3iY6OVlhYmN3PW0rTkSX1PgoODjZOijk6mmi7du00duxYxcbGat68eQnO+EdFRRlBqnHjxg80Sqqbm5s6deqkTp06KTQ0VDt27NC+ffu0d+9e7dmzx7iHOSAgQJcuXdKkSZOMg+Lo6Ghj9P5q1aole9Btj23d4sWLJ3slxt3dXeXKlTNGH05Kcn/z2NhYIxzNnDlTM2fOTFWNjhxshYWFGf9PzdVV25gUUuI5Um3atGmjlStX6uLFi9qyZUui+8Ft91FXqFAhTff7S//3mXRzc0uXwYXSKi2f3VmzZmnBggU6ePBgokGl4ru/99bhw4eNkwGOfkY7duyoWbNm6fbt21q2bFmCfePly5eNwYPatGnj8CBuNuXKlUt2G/H3R/EHVgsJCTGC3ffff5/kBYH7Jfe+T+3fx1HO2g/fvXtXklK8IpxSzzzb5zk1J7nSwvb95+bmZre3THyVK1fWiRMndObMGUVFRdk9trx8+bK6d++u48ePK3v27Prxxx+TvUCQVfaX94v/Ots74WRvm5Jjx+tmwRVjqEuXLlq4cKEWLlyo+fPna/z48WrXrp1cXFy0a9cu9ejRI8FZ/fjiH2Q6clB09epVu9tKyrlz59SnTx+FhYXJ1dVVo0ePNs7OZRZbd+rTp09r9+7dCggIkNVqlbe3d5LTfNwv/uuQFrad2YNI6cDQFprvv/IS/4A2pRMTbm5uxpnN+OulZRuZefIjs9gOUO11CXxQSX2mU5LUztfR91H8g3BHf8/ChQsbU83MmzcvwYi5K1euNKZKc0YXzfjP2bJlS33wwQeaPn26goKCNGDAAOP33Lx5sxHIpXvvdVtdjvyett8hNZ8D2/bjf77ul9yBa1hYWJJXOpLjyPdR/B41qVnf1j06X758euaZZ+y2adiwofH72etO/SCfK9u6efPmdeoVMUelJoDcuHFDnTt31ogRIxKcwElKZGRkgp+d8Rn19/c3rrDe38V0/vz5xvvNGZ/RlD4j2bJls7s/So/9cHp3pXfGfjh+b72Uuj+ntNz2mbj/PfSgbN9/+fLlS/HEie22PqvVmmRPxKCgIB0/flzSvdtBkgvFUtbZX94v/uuc0usS/32a1MUucMUYuvdlGv9et3Llyqlhw4aqWbOmhg4dqpCQEP3vf//Tr7/+mmjdxx57TPnz59f169d15MgRxcbGpqnbkO3su5TyqNKhoaHq3bu3Ll26JIvFoq+++irJKSgyko+Pj6pWraqdO3dqwYIFxtQjbdq0SfVVodjYWOP/8+bNS/UZ8+TuxcxIab36lV7bQOrZ3nNly5Y1RmBNjbR2o8woHTt21Jo1a3Tu3Dlt27ZNNWrUkPR/B+FFihRJU2+MtMqXL5/eeecdWa1W/fbbb5KkwMBAp0+L4azPSXLf0/G/jzp27JjitEc2jhxsxT8hmlyQl6Rbt24ZXbxv3LiRqnElli9fruHDhzt0q8/DIDX72y+//NK47aZJkyZ68cUX5efnpwIFCsjDw8N4TzVo0EAXLlxINBWTs3Ts2FFffvmltm7dquDgYKMrv+0zWqVKFafMLuHoZyR+CBk4cKCaN2+eqvWSCzlpnTLoQWSFfagtON+8eVNWqzVL1GRP1apVdfbsWV25ckXjxo1T5cqVk73IklX3l7YTBlLKJ8nif7/abrdBYgRjJKldu3Zas2aNli1bptWrV2vTpk2JuqRZLBZVr15dK1asUEREhDZt2pTqg89bt24ZAwvkzJkz2e4x165dU58+fYz7LYYNG5al7jlt27atdu7cqX///dc4g5eWA+L4B4deXl5ZJvAmJy33q8TExBhf4PHXi/9FntI2HD2b/zCzvS+c0WX+frYrB+Hh4U4f2Cwt4r/3H+T3bNCggTFP49y5c1WjRg2FhoZqw4YNku59RjPiILVTp05GMD579qzxeN68eeXi4qK4uDiHfk/b3ys1PXNs23f0alX89axWa7q+P+JfgUopGC9dujTNV6LCw8O1fPnyBPuL/Pnz6+LFiw79HWzv17CwsCS7aSbHFhRSuhKUUrfI1Lp9+7aWLl0q6d6gPt99912SbZN6/Z31GY0/7/i8efP0xhtvaPfu3Tp58qQk5/XocHR/FP9+zWzZsmXq92JqOXs/nNKV0ZSW2z7PcXFxunXrltO6VNv+Njdu3FBMTEyyFw9sr4PFYkny+UuWLKkRI0aoZ8+eunbtmvr3768//vgjyV5+WWV/eb/4V8TvH/QuubbFihVLt5oednSlRrIGDx5snJEeM2aM3Tbt2rUz/p+Wwa1mzpxpdO1o1KhRkgcYt27d0iuvvGJ0e3n33XeTHCAhs7Ro0ULu7u7GQVvlypVVunTpVK8f/6TAzp07Ha4jI8/Ouru7G/NO3z/C5f3i39MWf6dSokQJYyTl+0cHvV9Kyx9FtgE4zp8/r5CQkHTZ9rlz59IleKdW8eLFjYOXB5lf0dXVVe3bt5d0b8T2O3fuaN68ecZo187sRp2c+KP/xv88urm5GfOAb9++Pc1X5WzrBgcHJ3twGh0dbdxD6+gBnLu7u/F8D/J9lBo+Pj7GFTd7czfHZ+sWXahQIY0ePTrFf7YTjPePlm177+/fvz/NAdR2D2Z0dLQxIFVa2AacTGmwyVOnTqV52/acPn3a+O5t2bJlku1OnDiR5H2H5cqVM97LKY2SmxzbvOPS/93yYJsXOWfOnGrRooXD247v8OHDyd4KcPjwYbv7Ix8fH+NKWnq/753FWfth2y0NKQ3oGX88FHvibzelz3Na2L6P4n+/JcX2OpQsWTLZE1dPPvmkpkyZovz58ys8PFz9+vVLciC1rLK/vF/819j2GiXF9p3i7u5uzOSAxAjGSFbp0qWNndWePXuMqy/xNWzY0PhArl27VosXL05xu2fOnNHPP/8s6d7BY1LT4kRERKh///7Gl/Wrr76q/v37O/S7pKc8efKoSZMmcnd3l7u7e5qvZtepU8c4OJw2bZrDXdni7wSioqIc2kZa2HoQHDt2LNmd8pw5cxKtI907K2/r8rphwwZjCpX7xcXFad68ec4o+aESf6qvKVOmpMu2rVarpk2b5tRtp4WLi4sxCu22bdsS3F6RVh06dJDFYlF4eLiWLFlivGeefvrpBzoQSMvnMf4JnPtHfW7YsKGke+HWNsppatk+N/HDhD3Lli0zRri/v4dPWtjeHydPnjQGRkoP8QexSu7k17lz54yw0qxZM7Vq1SrFf7YQtnnz5gTTyNh+t4iIiFQPLGbz7LPPGiFx6tSpaVpX+r/3xJ07d4wrpfeLiorS8uXL07xte+J3i0/uJIBtuh578uXLpypVqki6d9U+/muZVrYRhUNCQrR27VotWbJE0r2/qb2paxxx48YNY3Rpe+J/fuJ/RlxdXY3vog0bNujEiRNOqSe9OWM/bOtGvH79+iRPvFmtVgUEBCRbS/zB2Zx5MrtOnTrG/5P7/tu1a5dxESX+Oknx8/PTlClTlC9fPt25c0evvPKK3dcwq+wv72d7jd3c3FKc0cHWtnz58txjnAyCMVI0YMAA40DA3n3GLi4u+vbbb40P2tChQ42dnT2HDx9Wr169jLPTXbt2tfuBjoqK0qBBg4yDoZ49e2rw4MFprj84ONiYiqpHjx5pXj+1xowZY8yL2LVr1zStmydPHuMq+K5du/TVV18l29XuypUrmj17dqLH41+tit+NM7106dLF6J46bNiwBNN32QQFBRk7ZH9//0QjZ3bp0kXSvb/3J598kuBAzmbChAnJjrD7qKpTp45xheqvv/5K9qTT9evX0zT4Ub169Yy/xcSJE5P9zErSkSNHjPs7na1v375ycXGR1WrV4MGDE8xte7/klpUoUcI40fLjjz8aZ9Mf9Grxf//9p7feeivF0H7jxg19+eWXxs/3TxnTvXt3417XTz75JNn39P2/Z5MmTYzP9/jx4xOMpmtz4cIFffPNN5Lu3fdou4LuiJ49exq1fvjhh8aosElZu3Ztoml+Ust2MH3kyJEkT+gtWLDAOEGR0pR2NrZ298+x3KZNG+Pevx9++MGYRcCe+/8OpUuX1nPPPSfp3sBuf/zxR5LrhoeHJ+qebHt/StLkyZPtrvf1118/UPiMr0SJEsb++/6B6WxWr16t6dOnJ7sd23zNEREReuutt4yTL/Yk9xmNP+94/H2Gs3t0jBw50m7X4q1bt2rWrFmS7l39v39/1L9/f7m6uiouLk5vvvlmsr9LbGysAgICkm2TEZyxH+7cubOkewM0DR8+3O7xx+TJk1O8oly0aFFjBoWUrmCnhb+/vzGmwOzZs+1ORXfr1i198sknku4dl9qOLVJStmxZTZ48WXnz5tXt27fVp0+fRKE+K+0v47O9xk899VSC6U/vFxUVZexv6tatm+51Pcy4xxgp8vX1VaNGjbRq1Spt27ZN27dvTzRlQ/ny5fXdd99pyJAhioqK0uDBg/XPP/+odevWevzxx+Xm5qYLFy5o9erVWrRokdHNyTbAlz3vvvuugoKCJN3bmXbo0CHZA0k3N7c0dV/Oat566y1t27ZNe/bs0bRp07R161Z16tRJZcuWVc6cORUWFqbjx49r48aN+u+//+Tr66uOHTsm2IbtrL5078Dg1VdfVaFChYwDI29v7weeCiM+Pz8/9e7dWxMnTtThw4fVrl079evXT+XKlVNERITWrFmjP//8U7GxsXJzc9OIESMSbaNRo0Zq2LCh1qxZozVr1qhLly7q1auXSpYsqatXr2revHlasmSJKlasmGI3rrQIDg5O1Ujo+fPnT5dRoVNr1KhR6tChg8LDw/XOO+8oMDBQLVu2lI+Pj+Li4nTmzBlt2LBBy5Yt08KFC5Odm/Z+33//vTp27KgbN25o8ODBCggIUMuWLVWqVCm5uLjo6tWrOnTokNasWaPdu3erT58+Ca5iO0u5cuX0xhtvGGG2devW6tq1q2rVqqV8+fLp5s2bOnTokFasWCEXF5dkb9no2LGjtmzZYnR38/T0THWQSkpcXJwCAwMVGBiosmXLqkGDBqpUqZIKFSokNzc3Xb16VTt27NCsWbOMe+ErVKiQ4DYT6V4X4OHDh+uDDz7Q1atX1bFjR3Xs2FH169dXoUKFdOfOHR07dkyrV6/WqVOntHLlSmNdd3d3ff7553r11Vd1+/ZtdenSRX379lXt2rXl6uqqXbt26bfffjOe//33309xBNnkFCxYUN98843efPNNXb58WS+++KLatWun+vXrq0iRIoqJidHFixe1d+9eLVu2TOfOndP48eOTnJszOY0bN9a4ceMUHR2t7du3273SYwu2BQoUSPWUQVWrVjXuOw8ICDB6G3l4eOjbb79V3759FRERod69e6tNmzZq0qSJihQpoqioKJ08eVLr1q3T6tWrE33vDB8+XLt379alS5c0atQorV+/Xu3atdPjjz8ui8WikJAQbdmyRUuWLNHYsWMTTAtXvnx5ValSRbt27dKsWbMUHR2ttm3bKnfu3Dpz5oxmzpypzZs3G20eVP78+fXss89q7dq1Wr9+vfr06aMuXbqoWLFiunr1qpYvX6558+bJx8dHN2/eTPJqYaNGjdShQwfNmTNHu3btUsuWLdW9e3dVrVpVnp6eun79uvbv368lS5aobNmy+vrrr+1ux3Zbw5gxY4zPaMmSJZ06u0TZsmV14sQJtW/fXgMGDFClSpUUFRWl//77T1OmTDHuUR0+fHiidf38/PT+++9r5MiROn78uJ5//nl16tRJtWrVUsGCBRUZGamQkBDt3r1bgYGBunz5shYuXJip44I4Yz/ctGlT1atXT0FBQVq+fLm6deumHj16qGTJkrp27ZoCAgIUEBAgf39/I4wldetWo0aN9Oeff2rLli1OHYDriy++UMeOHRUdHa1+/fqpR48eatiwoXLkyKFDhw7pt99+M8ah6dOnT5puJSlfvrwmTZqk3r176+bNm+rbt6+mTJmS4KJNVtlf2ty+fdsI8CkNRLtt2zajG73txB7sIxgjVV599VWj69+vv/6qiRMnJmrTvHlzFSxYUMOHD9fx48e1ZcsWY4Rmezp06KBPPvkkyXtA4ncl27x5c4oTy3t7e2fIGbr04u7urkmTJunDDz/U8uXLdfjwYbs7MBt73c5KliypFi1aaOnSpQoKCjJOLNisWrUqTcEpNd577z1FRERoxowZOnv2rIYNG5aoTe7cufXDDz8kOcDad999p379+mnnzp3as2dPop4B5cuX14gRIx7oCtj9Pvroo1S169mzp/73v/857XnTqkyZMvrzzz+NObKXL1/utG6WJUqU0D///KM333xTR48eNU5OJCW5M9IP6vXXX5eLi4t++ukn3bx5U+PHj9f48eMTtYt/xc2epk2bKm/evMaVupYtW6Z5rtr75cmTRzlz5lR4eLgOHz6c4pXRunXr6vvvv7d7Eqpt27ayWq369NNPdffuXf355592g769easbNGigkSNH6pNPPtGdO3c0duxYjR07NkEbV1dXvfXWW2nutWJP06ZN9csvv+jDDz/UjRs39M8//yTZ5dbFxcXh17l8+fKqUKGCDhw4oIULFyYKxjt27DB6wDRp0iTVg6i5uLjoueee04wZM3Ts2DHt37/fuOpUq1YtjR8/Xu+++67CwsI0d+7cRNMIJaVgwYKaMWOGXn/9dR09elSbN2/W5s2bU/37fvnll+rRo4dx4u/+20T69OmjJ5980inBWJI+/fRTde3aVefPn9fGjRu1cePGBMuLFSumn3/+OcXblEaMGKHs2bNr+vTpunTpkkaPHm23XUonR+LPOy7Jqd/r0r0Tbd27d9enn35qdx/q5uamb775Jsl5qHv16qWcOXPqq6++0q1btzRx4kS7xzy2bcWfciyzOGM/PGbMGPXt21d79+7Vzp07E91nXb58eQ0fPty4up/U792pUyf9+eefunDhgrZv3+60kx7lypXT+PHj9dZbb+n27duaNGmSJk2alKhdt27d9O6776Z5+xUrVtTEiRPVu3dvhYWFqXfv3po6darxfs5K+0tJWrFihSIjI5UtWza1atUq2ba2qQOffPLJFOeBNjuCMVLF399fdevW1YYNGxQUFKS9e/fanUy+evXqCggI0LJly7Rq1Srt27dPV65cSTRJ+ZgxY5IdCMSsPD099dNPP2n79u2aP3++tm/frkuXLikyMlKenp7y8fGRv7+/nn322SRH/x41apQqVqyoZcuW6dSpU7pz506KI6A+CBcXFw0fPlytWrXSP//8ox07dujKlStyd3eXj4+Pnn32Wb388svJXr3y9PTUn3/+qX/++Ufz58/XiRMnZLFYVKJECbVs2VIvv/xylhrwIqNVrFhRgYGBmj17tlauXKljx44pLCxM7u7uKl68uKpUqaIWLVo4dNKjdOnSmj9/vpYuXarly5dr3759unbtmmJjY5UvXz6VLl1a1apV03PPPWd0604vr776qpo3b64ZM2Zo06ZNOn/+vO7evas8efLoiSeeUJ06dVK8f9/Dw0PNmzc37h11RhfNatWqadOmTdq4caO2bt2qAwcO6PTp07px44bi4uLk6ekpb29vVaxYUa1atUpwhdCedu3aqW7dupo+fbrWr1+vc+fO6c6dO/L09FTp0qVVq1atJE8EtmvXTk8//bSmTp2qDRs26MKFC4qLi9Njjz2mWrVqqXv37vLz83vg39nG1lto1qxZWrdunY4fP27MI1+wYEE9+eSTqlWrlpo1a5biqKjJ6dy5sz755BOtWLFCn332WYITpvG7Qaf16n/Tpk01Y8YMSfcG74o/xdMzzzyjlStX6u+//9aaNWt06tQp3bp1Szly5FDJkiVVvXp1Pf/883a36+Pjo/nz5ysgIECBgYE6cOCAbty4IVdXVxUpUkRPPfWUmjZtavfqdpkyZTRv3jz9+uuv+u+//3Tp0iXlzp1bFSpUUI8ePfTss8+mOqSnRtGiRTV37lz9/vvvWrVqlc6fPy8PDw95e3urSZMm6tmzZ6pGMHd1ddWwYcPUvn17zZw5U1u2bNGlS5cUHR2tfPnyyc/PT88880yKJ7ELFy6s2rVrKygoSK6urol6VjhDx44djcGVduzYoevXr8vLy0u1a9dWv3799MQTTyS7fqdOndSoUSP9888/2rBhg/HecHd312OPPSY/Pz/VqVNHTZs2faCeGc7ijP1wnjx5NGPGDE2fPl0BAQE6depUov1w/Pvik7on3NfX1+jxsHDhQqf2BqhXr55WrFihqVOnat26dTp37pyioqJUsGBBVatWTS+99FKqe5TY4+/vr4kTJ6pPnz66ceOGevXqpalTpxrfqVlpf2kLu02aNEm2V1tkZKRxMj213cvNzGJNrwnrgPusXr1aAwcOVFxcnJ588klNnz7d4elEACApL730knbt2qUnnngiVYMBIvPduXNHTZo00bVr1zRq1KgUwxUeXnFxcWrYsKEuXryo+vXr6/fff3fKdhs1aqSQkBC1a9cuya7ceDALFizQ+++/L+neFcukBjVcsmSJBg8erLx582rNmjXpfvXUbEJCQvTcc88pNjZWM2fO1FNPPZVkW9vfLF++fFq9ejV/ixQw+BYyTKNGjYz7iY8dO6bXXnstzXNSAkByTp48aXRBzagpmvDgcuXKpddff13SvcH20rOXCzLXhg0bjAGrbKNU4+FgO9Ho5eUlHx+fJNu1aNFCFSpUUFhYWIoDuyHtJkyYoNjYWD3zzDPJhuK4uDhNmDBB0r1BLgnFKSMYI0O9/PLLxujLO3bs0LvvvssBEACnsV198vDwSJcumkg/L730knx8fHT8+HEFBgZmdjlIJ7bPaKFChRKN3I7MExoamuzMBrNnz9a6desk3RsrIblBtSwWi4YMGSLp3mjWSc2RjbS7cOGC5s6dK1dXV+M1TkpgYKBOnDihYsWKqWfPnhlU4cONe4yR4f73v//J29vb+KI8evSoQyOZAsDdu3cVGhqqiIgIrVy50hjIqFOnTqkadRxZh5ubm77++mtt2rTJ7rRteDjdvn1bV69e1e3bt/Xvv/8ag3K+8sorTp0lAQ9mw4YNGjVqlFq1aqUaNWqoWLFiiouL07lz57RkyRJjlPyCBQumOFCbdG+u5I8//lg3btzQ+fPnU7yvG6lz4cIFDRgwQD4+PimOJxEbG6tBgwapVq1ayp49ewZV+HDjHmMAwENry5Ytic6EFy1aVAsWLGAMAyALmDt3rj788MMEj5UvX16zZs2Sm5ub056He4wfjL2/0/0KFSqk3377LcE0RsCjhFN1AICHnsViUaFChVSrVi1j0BcAWYeLi4uKFi2qhg0batCgQU4NxXhwDRo00KeffqqgoCAdP35c169f1507d5Q7d26VKVNGDRs21EsvvZTkaNTAo4ArxgAAAAAAU2PwLQAAAACAqRGMAQAAAACmRjAGAAAAAJgawRgAAAAAYGoEYwAAAACAqRGMAQAAAACmRjAGAAAAAJgawRgAAAAAYGoEYwAAAACAqRGMAQAAAACmRjAGAAAAAJgawRgAAAAAYGoEYwAAAACAqRGMAQAAAACmRjAGAAAAAJgawRgAAAAAYGoEYwAAAACAqRGMAQAAAACmRjAGAAAAAJgawRgAAAAAYGrZMruArGLfvn2Kjo6Wi4uLPDw8MrscAAAAAMADiIyMVFxcnNzc3FSpUqVk2xKM/7/o6GhZrVbFxsYqPDw8s8sBAAAAADhBdHR0im0Ixv+fi4uLYmNjZbFYlCNHjswuBwAAAADwACIiImS1WuXikvIdxATj/8/Dw0Ph4eHKkSOHypUrl9nlAAAAAAAewKFDhxQeHp6qW2UZfAsAAAAAYGoEYwAAAACAqRGMAQAAAACmRjAGAAAAAJgawRgAAAAAYGoEYwAAAACAqRGMAQAAAACmRjAGAAAAAJgawRgAAAAAYGoEYwAAAACAqRGMAQAAAACmRjAGAAAAAJgawRgAAAAAYGoEYwAAAACAqRGMAQAAAACmRjAGAAAAAJgawRgAAAAAYGoEYwAAAACAqRGMAQAAAACmRjAGAAAAAJgawRgAAAAAYGoEYwAAAACAqRGMAQAAAACmli2zCwAAAEiNVz+bo8iomMwuAwBMycM9m8YP75DZZaQbgjEAAHgoREbFKDI6NrPLAAA8guhKDQAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUsmV2AUm5fv26Jk+erDVr1ig4OFjR0dHy8vJSlSpV1KNHD1WvXj2zSwQAAAAAPAKy5BXj06dPq3Xr1powYYKOHz+uAgUK6IknntDt27cVGBio7t27a8qUKZldJgAAAADgEZAlg/Hw4cN1+fJllSpVSgsXLtTKlSs1f/58bdq0SX369JHVatWoUaN0+vTpzC4VAAAAAPCQy3LB+Pbt29qyZYskaciQIXriiSeMZR4eHnr//fdVsmRJxcTEKCgoKLPKBAAAAAA8IrLcPcZRUVGyWq2SpBIlSiRabrFY5OPjozNnzigmJiajy8tyXv1sjiKjeB0AIDN4uGfT+OEdMrsMAADwgLJcMPby8lKRIkV08eJF7dq1S76+vgmWh4eH6/Dhw5KkSpUqZUaJWUpkVIwio2MzuwwAAAAAeGhluWAsSe+++67ef/99ffvtt3JxcVGDBg3k6empY8eO6fvvv9eVK1fUpk0bVatWzenPbbVaFRub9YOmq6trZpcAAIjnYdh3PKzY5wFA1vKw7PNsPZFTI0sG4zZt2ih37tz69ddf9fHHHydYVqhQIX366ad66aWX0uW5IyIitHv37nTZtrO4uLioSpUqmV0GACCevXv3Ki4uLrPLeOSwzwOArOdR3OdlucG3bM6cOaOrV6/KxcVF3t7e8vPzU44cOXT58mXNmzdPx44dy+wSAQAAAACPgCx5xfizzz7TjBkzVKlSJf3xxx8qXbq0JOnu3bsaO3asJk6cqC5duiggIEDe3t5Ofe4cOXLIz8/PqdsEADz6/P39M7sEAAAyxMOyzzty5IgiIiJS1TbLBePDhw/r77//lpubm3788ccEwTd79ux6//33dfDgQW3atEkTJkzQiBEjnPr8FouFe5kAAGnGvgMAYBYPyz7PYrGkum2W60q9Y8cOWa1WlSxZMsmrwXXr1pUk7d+/PyNLAwAAAAA8grJcML5z506q20ZFRaVjJQAAAAAAM8hywdh2P/GZM2cUEhJit82GDRsStAUAAAAAwFFZLhjXrVtXBQoUUHR0tN566y2dOnXKWHb37l19++232rRpkyTphRdeyKwyAQAAAACPiCw3+FbOnDn13XffaeDAgdq3b59atmypYsWKKVeuXDp79qwxqli3bt3UpEmTTK4WAAAAAPCwy3LBWJLq1KmjgIAATZkyRRs3btT58+cVGhqqfPnyqU6dOurUqZMaNGiQ2WUCAAAAAB4BWTIYS5KPj4+GDRuW2WUAAAAAAB5xWe4eYwAAAAAAMhLBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmBrBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmBrBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmBrBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmBrBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmBrBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmBrBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmBrBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmBrBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmBrBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmBrBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmBrBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmBrBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmBrBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmBrBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmBrBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmBrBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmBrBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmBrBGAAAAABgagRjAAAAAICpEYwBAAAAAKZGMAYAAAAAmFq2B1n58uXLWr58uU6dOqWIiAh9+eWXkqRr164pODhYvr6+yp49u1MKBQAAAAAgPTh8xXj69Olq3LixPv/8c/3111+aO3eusezq1avq3LmzAgICnFIkAAAAAADpxaFgvHr1an3++efy9fXVr7/+qi5duiRY/uSTT8rPz08rV650SpEAAAAAAKQXh7pST5w4UcWKFdO0adOUM2dOHThwIFEbX19fbd++/YELBAAAAAAgPTl0xfjQoUN69tlnlTNnziTbFC5cWFevXnW4MAAAAAAAMoJDwdhqtSpbtuQvNl+9elXu7u4OFQUAAAAAQEZxKBiXLl1aO3bsSHJ5TEyMtm/fLl9fX4cLAwAAAAAgIzgUjFu3bq2DBw9q3LhxiZbFxsbqm2++0blz59S2bdsHrQ8AAAAAgHTl0OBb3bt31+rVq/Xzzz9r4cKFRpfpt956S/v371dISIjq1q2rDh06OLVYAAAAAACczaErxm5ubpo4caL69++vGzdu6NixY7JarVq2bJnCwsLUr18//frrr7JYLM6uFwAAAAAAp3LoirEkubu7a/DgwXr77bd18uRJhYWFydPTU2XKlJGrq6szawQAAAAAIN04FIwbN26s+vXra/jw4bJYLCpTpoyz6wIAAAAAIEM41JX6+vXr8vT0dHYtAAAAAABkOIeuGPv5+en06dNOLsW+devWafbs2dq9e7du3LihvHnzysfHRzVr1tQbb7yR4nzKAAAAAAAkx6Erxv369dOaNWu0efNmZ9djiImJ0ZAhQ9S/f3+tWLFC2bJlU9myZZUjRw7t379f48ePV2RkZLo9PwAAAADAHBy63Hrz5k3VrVtXffv2VePGjVWpUiUVLFjQ7ijUjs5l/OmnnyogIECVKlXSiBEjVL58eWNZRESENm7caEwTBQAAAACAoxwKxkOHDpXFYpHVatXy5cu1fPlySUoQjK1WqywWi0PBePPmzZo9e7a8vb01ZcqURPcz58iRQ40bN3akdAAAAAAAEnAoGI8cOdLZdSQwefJkSVKfPn0Y5AsAAAAAkK4cCsbt2rVzdh2GyMhIbdiwQZJUu3ZtHT9+XDNnztSJEyfk7u6ucuXKqUOHDvL29k63GgAAAAAA5pHlhnQ+fPiwoqOjJUk7duzQiBEjjJ8lac2aNfrjjz80cuRIPf/8805/fqvVqtjYWKdv19lcXV0zuwQAQDwPw77jYcU+DwCylodln2e1WlPd9oGCcXBwsBYuXKhDhw7pzp07ypUrl8qVK6fWrVurePHiDm3z8uXLxv9tg259/PHHKlu2rC5cuKAxY8Zo6dKlGjp0qB5//PEEg3I5Q0REhHbv3u3UbTqbi4uLqlSpktllAADi2bt3r+Li4jK7jEcO+zwAyHoexX2ew8F46tSpGjVqlGJjYxMk8eXLl+vnn3/WkCFD9PLLL6d5u3fu3DH+nz17dv3+++/KmzevJKlkyZIaPXq0Tp8+rUOHDmn8+PEaO3aso78CAAAAAACOBeM1a9Zo5MiRyp8/v3r16qWaNWuqUKFCunLlirZs2aLJkyfr66+/VsmSJdWgQYM0bdvDw8P4f7t27YxQbOPi4qJevXrpgw8+UFBQkOLi4uTi4tB0zHblyJFDfn5+TtseAMAc/P39M7sEAAAyxMOyzzty5IgiIiJS1dahYDx58mTlzZtX8+bNU5EiRYzHvb29VblyZbVu3Vpt27bV5MmT0xyM4wfhMmXK2G3z+OOPS7p3dfnGjRvy8vJK+y+RBIvFwr1MAIA0Y98BADCLh2WfF3864ZQ4dKn14MGDatmyZYJQHF/RokXVokULHThwIM3btoVeSXJzc7PbJv5V5UetbzsAAAAAIGM5FIyjo6OVI0eOZNvkzJkzwWjSqVW4cGFjKqZz587ZbWN73MPDQ/ny5UvzcwAAAAAAYONQMC5VqpTWrFmjmJgYu8tjYmK0du1alSpVyqGiWrRoIUlauHCh3eeYM2eOJOnpp59WtmxZbsYpAAAAAMBDxKFg3LZtW506dUp9+/bV/v37Eyzbt2+f+vXrp1OnTqldu3YOFdW3b1/lzp1bwcHBGjFihCIjIyXdm4dq2rRpWrNmjSwWi/r37+/Q9gEAAAAAsHHocmvPnj21bds2rV69Wh07dlT27NlVoEABXb16VXfv3pXValXjxo3Vs2dPh4ry8vLS2LFj9dprr2nmzJlasmSJSpUqpYsXL+ry5cuyWCwaMmSIatas6dD2AQAAAACwcSgYu7q66pdfftH8+fM1b948HT58WBcuXJCnp6cqV66stm3bqm3btg9UWJ06dbRgwQJNmDBBGzdu1OHDh+Xp6alGjRqpd+/eqlGjxgNtHwAAAAAAycFgbOOMAJycUqVKaeTIkem2fQAAAAAAHLrHGAAAAACAR4VDwXjNmjUaNGiQQkND7S4PDQ3VoEGDtG7dugcqDgAAAACA9OZQMJ4+fbrOnj2rwoUL211euHBhBQcHa8aMGQ9UHAAAAAAA6c2hYHz48GFVrlw52Tb+/v46dOiQQ0UBAAAAAJBRHArGYWFh8vLySrZN/vz5df36dYeKAgAAAAAgozgUjL28vHTq1Klk25w6dUp58+Z1qCgAAAAAADKKQ8G4evXqWrNmjQ4fPmx3+eHDh7V69WrmGgYAAAAAZHkOBeN+/fpJkrp27apx48Zp165dOn/+vHbt2qVx48apa9eucnFxUf/+/Z1aLAAAAAAAzpbNkZXKli2r7777TkOHDtXPP/+sn3/+2VhmtVqVM2dOjR49WmXLlnVaoQAAAAAApAeHgrEkNWvWTNWrV9fcuXO1b98+3b59W7lz55a/v7/atWuX4uBcAAAAAABkBQ4HY0kqUKCA0a0aAAAAAICHkUP3GAMAAAAA8KhIdTC+e/euzp07p9u3bydaFhwcrIEDB6patWqqVq2aBgwYoBMnTji1UAAAAAAA0kOqg/Gff/6ppk2bJgq8t27dUvfu3bV69WrduXNHd+7c0bp169SjRw9duXLF6QUDAAAAAOBMqQ7G27dvV9GiRVW5cuUEj//111+6ePGiqlevrpUrV2rjxo3q1auXrl27pilTpji7XgAAAAAAnCrVwfj48eOqXr16osdXrFghi8Wir776SsWLF5eXl5eGDh2qUqVKKSgoyKnFAgAAAADgbKkOxteuXVPRokUTPHb37l0dOXJEvr6+8vHxSbCsZs2aOnfunHOqBAAAAAAgnaQ6GMfGxio8PDzBY4cPH1ZsbKz8/f0Ttc+XL5+ioqIevEIAAAAAANJRqoNx0aJFdfDgwQSPbd++XRaLxW4wDgsLk5eX14NXCAAAAABAOkp1MK5du7Z27typhQsXSpIuX76sf/75Ry4uLqpfv36i9gcOHFCxYsWcVykAAAAAAOkg1cF4wIAB8vT01Pvvv6+aNWuqYcOGCg4O1gsvvKDChQsnaHvx4kXt379fNWrUcHrBAAAAAAA4U5q6Uv/555+qUaOGIiMjVbBgQfXu3Vuffvpporb//vuvPD097V5JBgAAAAAgK8mWlsZly5bV1KlTU2w3cOBADRw40OGiAAAAAADIKKm+YgwAAAAAwKOIYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1BwKxq+88opWrFih2NhYZ9cDAAAAAECGyubISkFBQdqwYYMKFCigdu3aqUOHDipZsqSzawMAAAAAIN05dMV4xYoVeuWVV2SxWPT777+refPmevnll7V48WJFRUU5u0YAAAAAANKNQ1eMfXx89O677+rtt9/WmjVrNGfOHK1fv15bt25Vnjx51LZtW3Xs2FFPPPGEs+sFAAAAAMCpHmjwLVdXVzVp0kTjx4/XmjVr9OabbypPnjyaNm2aWrdurS5dumjevHmKjIx0Vr0AAAAAADiV00alfuyxx9SvXz+98847KlSokKxWq3bt2qWPPvpI9evX1x9//KG4uDhnPR0AAAAAAE7hUFfq+506dUqzZ8/WggULdO3aNbm7u+uFF15Q27ZtdfDgQU2fPl3ff/+9rl69qg8++MAZTwkAAAAAgFM4HIwjIyO1dOlSzZkzRzt27JDVatXjjz+ufv36qV27dsqbN68kqXbt2urRo4d69+6tBQsWEIwBAAAAAFmKQ8F4xIgRWrRokW7duqVs2bKpZcuW6ty5s2rUqGG3vbu7u+rVq6edO3c+ULEAAAAAADibQ8F4xowZKlGihPr376/27dvLy8srxXVq1KihgQMHOvJ0AAAAAACkG4eC8eTJk1W7du00rVOtWjVVq1bNkacDAAAAACDdODQqdVpDMQAAAAAAWZVDwXjevHlq3769QkND7S4PDQ1V+/bttXDhwgcqDgAAAACA9OZQMJ47d67c3NxUuHBhu8sLFy4sDw8PzZkz54GKAwAAAAAgvTkUjE+cOKFy5col26ZcuXI6ceKEQ0UBAAAAAJBRHArGt27dMuYpToqnp6fCwsIcKgoAAAAAgIziUDB+7LHHdOjQoWTbHD58WAULFnSoKAAAAAAAMopDwbhOnToKCgrShg0b7C4PCgrS+vXrVa9evQcqDgAAAACA9ObQPMb9+/fXkiVL1L9/f7Vp00Z169ZV4cKFFRoaqg0bNiggIECenp7q37+/s+sFAAAAAMCpHArGPj4+mjBhgt555x3NmzdP8+fPN5ZZrVYVKVJEP/zwg3x8fJxVJwAAAAAA6cKhYCxJ1atX18qVK7Vq1Srt3btXt2/fVu7cueXv769GjRrJ3d3dmXUCAAAAAJAuHA7GkuTu7q4WLVqoRYsWzqoHAAAAAIAM5dDgWwAAAAAAPCocvmIcFRWllStXat++fbp165ZiY2MTtbFYLPrqq68eqEAAAAAAANKTQ8E4JCREffr00dmzZ2W1WpNsRzAGAAAAAGR1DgXjkSNH6syZM3rhhRf04osvqkiRInJ1dXV2bQAAAAAApDuHgvHmzZtVu3ZtffPNN86uBwAAAACADOXQ4FtxcXEqV66cs2sBAAAAACDDORSMK1eurJMnTzq7FgAAAAAAMpxDwfjdd9/V5s2bFRgY6Ox6AAAAAADIUA7dY7x27VrVrFlTgwcP1owZM1ShQgXlypUrUTuLxaKBAwc+cJEAAAAAAKQXh4LxuHHjjP9v3bpVW7dutduOYAwAAAAAyOocCsbTpk1zdh0AAAAAAGQKh4JxjRo1nF0HAAAAAACZwqHBtwAAAAAAeFQ4HIxjYmI0ZcoUdejQQVWrVlX58uWNZYcOHdKnn36qU6dOOaVIAAAAAADSi0Ndqe/evas+ffpo165dyp8/vzw9PRUREWEsL168uObOnau8efNq8ODBTisWAAAAAABnc+iK8fjx47Vz506988472rBhgzp27Jhgee7cufX0008rKCjIKUUCAAAAAJBeHArGS5cuVc2aNdWvXz9ZLBZZLJZEbXx8fHThwoUHLhAAAAAAgPTkUDA+f/68KlasmGybXLly6datWw4VBQAAAABARnEoGOfKlUvXrl1Lts25c+fk5eXlUFEAAAAAAGQUh4LxU089pdWrV+vmzZt2l1+4cEHr1q1T9erVH6g4AAAAAADSm0PBuG/fvrp586Z69eqlHTt2KCYmRpIUERGhTZs2qW/fvoqNjVXv3r2dWiwAAAAAAM7m0HRNTz/9tIYNG6avvvpK3bt3Nx6vWrWqJMnV1VXDhw9P8T5kAAAAAAAym0PBWJK6du2qmjVr6u+//9bevXsVFhamXLlyqXLlyuratauefPJJZ9YJAAAAAEC6cDgYS1KZMmX08ccfO6sWAAAAAAAynEP3GAMAAAAA8KhI1RXj8+fPS5IKFy4sV1dX4+fUKFasmGOVAQAAAACQAVIVjBs1aiSLxaIlS5aodOnSxs8psVgsOnjw4AMXCQAAAABAeklVMG7btq0sFoty586d4GcAAAAAAB52qQrGX3/9dbI/AwAAAADwsGLwLQAAAACAqTkUjC9evKhVq1bp5s2bdpeHhYVp1apVCg0NfaDiAAAAAABIbw4F419++UUfffSRsmfPbnd5jhw59NFHH2nChAkPVBwAAAAAAOnNoWC8ZcsW1a1bV+7u7naXu7u7q169etq4ceMDFQcAAAAAQHpzKBiHhobK29s72TbFihWjKzUAAAAAIMtzKBi7ubnp9u3byba5ffs2UzoBAAAAALI8h4Kxr6+v1qxZo6ioKLvLIyMjtXr1avn6+j5QcQAAAAAApDeHgnH79u118eJFvfbaazp37lyCZWfPntXrr7+uS5cuqWPHjk4pEgAAAACA9JLNkZVefPFFrVu3TsuXL1eLFi3k7e2twoULKzQ0VCEhIYqJiVHLli314osvOq3QdevWqX///pIkb29vrV692mnbBgAAAACYl0PBWJJ+/PFHTZ8+XTNmzNDJkyd15swZSdITTzyhrl27qmvXrk4r8s6dO/r000+dtj0AAAAAAGwcDsYWi0Xdu3dX9+7dFR4erlu3bil37tzKmTOnM+uTJI0ZM0bnz59X48aNtWrVKqdvHwAAAABgXg7dY3y/nDlzqnDhwukSinfv3q3p06ercePGatKkidO3DwAAAAAwN6cE4/QSHR2tYcOGKXv27Prkk08yuxwAAAAAwCMoVV2pGzduLIvFosmTJ8vHx0eNGzdO1cYtFotWrlzpcHETJkzQ0aNH9eGHH6pIkSIObwcAAAAAgKSkKhhbrVZZrdYEP6d2PUedOHFCEyZMUIUKFdSjRw+Ht5NWVqtVsbGxGfZ8jnJ1dc3sEgAA8TwM+46HFfs8AMhaHpZ9XlryaKqC8f1TI6X3VElWq1Uff/yxYmJi9Nlnn2XoDjEiIkK7d+/OsOdzhIuLi6pUqZLZZQAA4tm7d6/i4uIyu4xHDvs8AMh6HsV9XqruMR45cqSCgoKMn8+fP6/bt2+nW1EzZszQzp071a1bN1WqVCndngcAAAAAgFRdMZ46dapy586tevXqSbp3z/HAgQM1aNAgpxcUGhqq0aNHq3Dhwnr77bedvv2U5MiRQ35+fhn+vACAh5u/v39mlwAAQIZ4WPZ5R44cUURERKrapioY58yZU3fv3jV+fpB7h1Py+eef6/bt2xo5cqQ8PT3T7XmSYrFYuJcJAJBm7DsAAGbxsOzzLBZLqtumKhiXKlVKK1as0HPPPadChQpJkm7duqXz58+nuG6xYsVSXYwkHTx4UJL02Wef6bPPPkuwzBbOL1y4oLp160qSfvrpJ1WtWjVNzwEAAAAAgE2qgnGfPn00ZMgQvfTSS8Zj06ZN07Rp05Jdz2KxGEE3ra5cuZLksri4OGN5dHS0Q9sHAAAAAEBKZTB+/vnnVbx4ca1du1ahoaGaN2+e/Pz8VK5cOacXlNyI13PnztWHH34ob2/vdB8ZGwAAAABgDqkKxpL01FNP6amnnpIkzZs3T02aNEmXwbcAAAAAAMhIDk3XNHLkSDVp0iTdigIAAAAAIKOkKhhPnTpVu3fvNn7+6KOPtHLlyvSqCQAAAACADJPlpmtKTvv27dW+fftMeW4AAAAAwKMpy03XBAAAAABARsqy0zUBAAAAAJARstx0TQAAAAAAZCSmawIAAAAAmFqqg3F806ZNk7e3t7NrAQAAAAAgwzkUjGvUqJFim6ioKEVFRcnT09ORpwAAAAAAIEOkah5jSWrcuHGiwbbWr1+vkSNH2m3/22+/6emnn36w6gAAAAAASGepDsYhISG6efNmgsf27NmT4sjUAAAAAABkZakOxgAAAAAAPIoIxgAAAAAAUyMYAwAAAABMjWAMAAAAADA1gjEAAAAAwNTSNI/xwoULtWfPHuPns2fPSpL69euXqK1tGQAAAAAAWVmagvGZM2d05syZRI+vX7/ebnuLxeJYVQAAAAAAZJBUB+NVq1alZx0AAAAAAGSKVAdjb2/v9KwDAAAAAIBMweBbAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1AjGAAAAAABTIxgDAAAAAEyNYAwAAAAAMDWCMQAAAADA1LJldgH3s1qt2rVrl1avXq0dO3bo5MmTun37tnLnzq3y5curbdu2at26tSwWS2aXCgAAAAB4BGS5YLx582b16tXL+NnHx0fe3t4KCQnRhg0btGHDBi1evFg//fST3N3dM69QAAAAAMAjIct1pbZarSpevLj+97//aePGjVq5cqXmzp2rLVu26JtvvpG7u7vWrl2rH3/8MbNLBQAAAAA8ArJcMPb391dgYKB69uypAgUKJFjWtm1bDRw4UJI0Z84cxcXFZUaJAAAAAIBHSJYLxp6ennJzc0tyef369SVJN27c0LVr1zKqLAAAAADAIyrLBeOU3L171/h/9uzZM7ESAAAAAMCjIMsNvpWSxYsXS5LKli0rT09Pp2/farUqNjbW6dt1NldX18wuAQAQz8Ow73hYsc8DgKzlYdnnWa3WVLd9qILx/v379c8//0iS+vfvny7PERERod27d6fLtp3FxcVFVapUyewyAADx7N27l7Ev0gH7PADIeh7Ffd5D05X6ypUreuONNxQTE6PnnntOrVq1yuySAAAAAACPgIfiivGtW7fUr18/nT9/XhUqVNDXX3+dbs+VI0cO+fn5pdv2AQCPJn9//8wuAQCADPGw7POOHDmiiIiIVLXN8sH4zp07euWVV3Tw4EE9+eSTmjhxYrrcW2xjsVi4lwkAkGbsOwAAZvGw7PMsFkuq22bprtQREREaMGCAdu/erVKlSmny5MnKnz9/ZpcFAAAAAHiEZNlgHBkZqddee03btm2Tt7e3pkyZokKFCmV2WQAAAACAR0yWDMbR0dF64403tGnTJhUuXFhTp05V0aJFM7ssAAAAAMAjKMsF49jYWL377rtat26dChUqpKlTp8rHxyezywIAAAAAPKKy3OBbS5cu1bJlyyRJ7u7u+uijj5JsO2zYMJUvXz6jSgMAAAAAPIKyXDCOiooy/h8SEqKQkJAk2966dSsjSgIAAAAAPMKyXDBu37692rdvn9llAAAAAABMIsvdYwwAAAAAQEYiGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFPLltkFJGfz5s2aPHmy9uzZo/DwcBUrVkzNmzdX//79lTNnzswuDwAAAADwCMiyV4z//PNP9erVS2vXrpWHh4fKlCmjkJAQ/frrr+rQoYNu3LiR2SUCAAAAAB4BWTIY79+/X1999ZUkacSIEVq7dq3mzZunlStXqkKFCjpx4oSGDRuWyVUCAAAAAB4FWTIY//LLL4qLi9MLL7ygzp07y2KxSJIKFy6s0aNHy8XFRcuXL9fhw4czuVIAAAAAwMMuywXjO3fuaP369ZKkTp06JVpeqlQp1apVS5IUGBiYobUBAAAAAB49WS4YHzp0SFFRUXJ3d5e/v7/dNtWqVZMk7dmzJyNLAwAAAAA8grLcqNSnTp2SJBUrVkxubm5225QoUSJBW2eyWq2KjY11+nadzdXVVZLk4Z7l/oQAYBrxv4Mfhn3Hw4p9HgBkvodxn2e1WlPdNsvtYcLCwiRJefPmTbKNbZmtrTNERkZKkiIiIrR7926nbTe92KareqtThUyuBADM7dChQ5Kk8PDwTK7k0cU+DwCyhod1n2fLesnJcsHYVnRSV4slyd3dPUFbZ4iLi3PatjLCw/ZmBADAUezzAAAPIjVZL8sFYw8PD0lSdHR0km2ioqIStHUGNzc3RUdHy8XFxanbBQAAAABkvMjISMXFxSV70dUmywXj1HSTTk1367SqVKmS07YFAAAAAHh4ZLlRqUuVKiVJOn/+fJJXjc+ePZugLQAAAAAAjspywbhcuXJyc3NTVFSU9u7da7fNjh07JElPPfVUBlYGAAAAAHgUZblg7OnpqXr16kmSZs2alWj56dOntXnzZklS8+bNM7Q2AAAAAMCjJ8sFY0l6/fXXZbFYtGDBAs2cOdOYf+rSpUt65513FBcXpyZNmqhs2bKZXCkAAAAA4GFnsaZl1uMMNGXKFH399deyWq0qWrSo8ufPr+PHjysqKkqlS5fWjBkz5OXlldllAgAAAAAeclk2GEvSpk2bNGnSJO3du1fh4eEqVqyYmjdvrv79+ytXrlyZXR4AAAAA4BGQpYMxAAAAAADpLUveYwwAAAAAQEYhGAMAAAAATI1gDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxgIdCcHCw/Pz85Ofnl9mlAACQKnPnzpWfn5+GDh2aoc87dOhQ+fn5ae7cuRn6vMDDLFtmFwDg0XTo0CGtXLlS3t7eat++fabVMXfuXH344YfJtunXr5/ee++9DKoIAAAAWQ3BGEC6OHTokMaNG6caNWpkajC28fT0lK+vr91l3t7eGVwNAMAMcufOrdKlS6tQoUKZXQqAFBCMAZhC+fLl9eeff2Z2GQAAE3nuuef03HPPZXYZAFKBe4wBAAAAAKbGFWPApOLi4rR48WLNnz9fBw8e1K1bt+Tl5aXHH39cTZs2VYcOHeTu7m6037p1q6ZOnardu3crLCxMefPmVZUqVdSrVy9Vr149wbYbNWqkkJAQY734A2Z5e3tr9erVTv99/vzzT3355ZfKmTOnfv75Z9WuXdvpzwEAeHSEhobqt99+U1BQkM6fPy8XFxflz59fJUuWVN26ddW7d2+5ublJkrEfW7VqlYKDgzVhwgQdOHBAUVFR8vPzU8+ePdWqVatEz2Eb56Jdu3b6+uuvjceDg4PVuHFjSdKRI0e0YsUKTZs2TUeOHFFYWJjmz5+vcuXK6fLly1q+fLnWrl2rU6dO6dKlS8qWLZsef/xxtWzZUt27d0+wrwbgOIIxYEJ37tzRG2+8oQ0bNkiSChUqpLJly+rKlSvasmWLNm3apPr166t48eKSpAkTJmj06NGSJC8vL/n5+SkkJEQrVqzQihUrNGTIEL3yyivG9itWrCg3NzedPn060b296XGf1Q8//KBff/1VXl5e+uOPP1ShQoVEbc6fP6+hQ4fqwoULyp49ux5//HE1a9ZMTz31lNPrAQBkbefPn1eHDh109epVubm5qUSJEsqRI4dCQ0O1ZcsWbd68WS+99JIRjG0CAwP1/fffK1euXCpZsqRCQ0O1e/du7d69W4cOHXJoIMfff/9d3333nby8vFSiRAldvHjRWDZ79mz9+OOP8vDwUKFCheTr66sbN27o4MGD2rdvn1asWKGpU6cSjgEnIBgDJvTxxx9rw4YNKlSokL755hvVrVvXWHbt2jXNmzdPOXPmlCRt2LBBo0ePlsVi0fvvv69evXrJxcVFsbGxmjhxor7//nt99913qlChgnGVduzYscZZ8vS8tzcuLk6ffvqpZs6cKW9vb02aNEmlSpWy2zY4OFjBwcHGz2vXrtWkSZPUqlUrffnll8qRI0e61AgAyHomTZqkq1evqk6dOvr+++/l5eVlLLty5YoWLVqUKBRL907EdunSRUOHDpW7u7usVqv++ecfjRgxQr///rtq1aqlevXqpamWH3/8UZ988om6dOkiFxcXxcXFKSYmRpJUo0YNTZ48WU8//XSCei5evKjPP/9cK1eu1OTJkzVgwAAHXwkANtxjDJjMwYMHtWTJErm4uGjChAkJQrF074pw3759jYOE8ePHS5JatWqlPn36yMXl3teGq6ur+vfvr2bNmslqterXX3/N0N8jKipKb7/9tmbOnClfX1/9/fffdkNxnjx59Morr2j69OkKCgrSvn37FBgYqFdffVXZsmXT4sWLM3x+SQBA5jp16pQkqVu3bglCsSQVLFhQvXr1snvCtHTp0ho2bJhxhdZisahLly564YUXJEm//fZbmmvp1KmTunXrZuxfXVxcjO1Xr15dderUSRTSixQpou+++05ubm6aP39+mp8TQGJcMQZMZvny5ZKkOnXq2O1yHF94eLh27NghSXr55Zfttundu7eWLVumHTt2KCIiIkOuvN6+fVuDBg3Spk2bVKVKFU2YMEF58+a127ZJkyZq0qRJgsdKly6twYMHy8/PT4MHD1ZgYKC2b9+e6F5pAMCjqVixYpKkFStW6Nlnn7V7ddiebt26yWKx2H183rx52r59e5r3he3atUt2+d27d4391IULFxQRESGr1SrpXjA/deqU7t69q+zZs6f6OQEkRjAGTObEiROSpCpVqqTY9uzZs4qNjZUkPfnkk3bb2O4fjomJ0ZkzZ1S2bFknVZq0nj176sCBA6pfv77Gjh3rcBhv2bKlpkyZoj179mjFihUEYwAwiR49emj+/PmaP3++/vvvPz3zzDOqWrWqnn76aZUpUybJ9Z544olkH4+NjU3zvjC55zt27JgGDBhgDGiZlLCwMIIx8IDoSg2YzO3btyVJnp6eqW6bM2fOJMNnrly5jPuR79y546Qqk3f27FlJ9w5EHvQKte0EwZkzZx64LgDAw8F2C07Dhg11+/ZtLViwQMOHD1fLli3VunVrrVu3zu5693e7tsmRI4fD+0LbeveLjY3Vm2++qZCQENWuXVuTJ0/Wxo0btX//fh05ckRHjhxR0aJFJUnR0dFpek4AiRGMAZOxBWJb6E1N2/DwcEVERNhtc+fOHYWHh0u6F5IzwsSJE5U7d25NmjRJ33777QNty9Z9zjbQCQDAHCpWrKjx48dr27ZtmjZtmt588035+vrq6NGjeu2117R79+5E61y7ds3utiIiIpy+L9y3b59OnjypokWLavz48apTp44KFChg7LesVqvCwsKc8lwACMaA6di6RO/atSvFtj4+PnJ1dZV0rzuXPbbHs2XLppIlSxqP27sHy1kqV65shOOJEydq1KhRDm/LVn+RIkWcVR4A4CGSPXt21axZUwMHDlRAQIAaNGig2NhYzZo1K1Hb48eP292G7TYlV1dXlShRwil12WZSqFSpkt1u0kePHjXCOIAHRzAGTKZp06aSpI0bN+rQoUPJts2VK5eqVasmSZo6dardNpMnT5Z0b+TM+N2aPTw8JN0bNCQ9VK5cWZMmTVLu3Ln1xx9/OBSODx8+rPXr10tSotG5AQDmY7FYVLlyZUnSpUuXEi2fPn263fVsj1erVi3JrtFpZQvDly9ftrt84sSJTnkeAPcQjAGTKVu2rJ5//nnFxcWpf//+2rRpU4Ll165d06RJk4zuYq+++qokafHixZoyZYri4uIk3ZtDeOLEiQoMDJTFYtFrr72WYDu2M+bHjx9PsuvZg/L3908Qjr/77rsEy2/fvq23335bO3fuNEbwtFm/fr369eun2NhYlS1b1jhhAAB49H3yySdauHBhotuKTp06pXnz5kmS3ZkbTp06pS+++EJRUVGS7nVnnjVrljFlUr9+/ZxW41NPPSU3Nzft2rVLM2fONB6PiorSDz/8oICAgFSPpg0gZYxKDZjQZ599pqtXr2rTpk3q1auXChUqpCJFiujq1au6ePGi4uLi1LRpU3l5ealu3boaPHiwxowZo5EjR+q3335TsWLFFBISYgTed999V7Vq1UrwHOXLl1fp0qV16tQpNWnSRE888YQ8PDxUsGBBjRkzxmm/iy0c9+nTR7///rsk6b333pN0L7wvXbpUS5cuVa5cueTj4yN3d3edP39eV65ckXSva/mvv/5qdBkHADz69uzZo5kzZ8rV1VU+Pj7KmzevwsLCdObMGVmtVvn6+uqVV15JtN7bb7+t77//XvPnz1epUqV08eJF44punz59VL9+fafVWLBgQfXt21fjx4/XJ598onHjxumxxx7TmTNndOvWLb3xxhuaO3duiiNWA0gdgjFgQp6enpo4caICAgI0f/58HT58WIcPH1aBAgVUq1YtNWvWTI899pjR/tVXX1WVKlU0bdo07dq1S4cOHVLevHnVpEkT9erVS08//XSi53BxcdHvv/+u0aNHa9u2bdq/f79iY2Pl7e3t9N/HFo779u2r33//XRaLRe+++65y5Mih999/X7t379bRo0d1/vx5hYeHy9PTUzVr1lSzZs3UoUMHo9s3AMAcPvzwQ61Zs0bbt29XaGiogoOD5eHhoUqVKum5555Tjx497M560Lx5c1WsWFETJkzQ/v37FRUVJX9/f/Xs2VOtW7d2ep2DBw9W0aJFNX36dGO+4rJly6p79+5q3ry55s6d6/TnBMzKYr2/fyEAAAAAg5+fnyRp1apVKl68eCZXAyA9cI8xAAAAAMDUCMYAAAAAAFPjHmMAGe7NN99McvoJe/7+++90rAYAAABmRzAGkOH279/PKJoAAADIMhh8CwAAAABgatxjDAAAAAAwNYIxAAAAAMDUCMYAAAAAAFMjGAMAAAAATI1gDAAAAAAwNYIxAAAm1ahRIzVq1ChTnvunn36Sn5+ftmzZkinPDwBAfARjAAAySHBwsPz8/OTn56e6desqJibGbrsTJ04Y7R4kuBI+AQBIHYIxAAAZLFu2bLpy5YrWrVtnd/mcOXPk4uIiFxd20wAAZAT2uAAAZLAqVaood+7c+vfffxMti4mJUUBAgOrUqaNs2bJlQnUAAJgPe1wAADKYh4eHWrZsqX///VdXr15VgQIFjGVr167VlStX9OKLL2rr1q2J1rVarfr33381Z84cHT16VLGxsSpTpoy6du2qDh06GO169OhhrN+zZ0/jcW9vb61evTrBNu/cuaMxY8YoMDBQN27cUOnSpTVw4EA1b9480fNfu3ZNv/76q1atWqVLly4pd+7cqlGjhgYOHChfX99E7S9cuKBRo0YpKChI0dHRqlChgt566620v2gAAKQjgjEAAJmgQ4cOmjlzphYsWKA+ffoYj8+ZM0f58uVTkyZNEq1jtVr13nvvadGiRSpVqpSef/55ubu7a8OGDfrf//6nEydO6IMPPpAktWvXTpK0detWtWvXTt7e3pKk3LlzJ9hmdHS0+vbtq7CwMDVr1kwRERFasmSJ3n77bf3xxx+qV6+e0fbatWvq3Lmzzp49qxo1aqhVq1YKDg7WsmXLtG7dOv3xxx+qXr260f7SpUvq3LmzQkNDVa9ePVWoUEEnTpxQ7969VbNmTee9mAAAPCCCMQAAmcDf31++vr6aO3euEYwvX76s9evXq0uXLnJ3d0+0zuzZs7Vo0SK1b99eI0aMkJubmyQpKipKb775piZNmqRWrVqpYsWKat++vUJCQoxgnFQQvXTpkipVqqRp06YZz9m6dWv16tVLkydPThCMR40apbNnz2rAgAF65513jMfXrVun/v3766OPPlJgYKBxb/To0aMVGhqqt99+W6+99prRfubMmfrkk08e8BUEAMB5uMcYAIBM8uKLL+rYsWPas2ePJGnevHmKiYnRiy++aLf9X3/9pZw5c2r48OFGKJYkd3d3DR48WJK0ePHiNNfx4YcfJgjitWvXlre3t/bv3288FhUVpcWLFytfvnwJQq4kPfvss6pbt67OnDmjnTt3Gu2XLFmiAgUKJLgiLkkdO3ZUqVKl0lwnAADphSvGAABkkjZt2ui7777Tv//+q8qVK2vu3LkqX768ypUrl6htRESEjh49qscee0y///57ouW2qZ9OnjyZphry5MkjHx+fRI8XLlxYu3fvNn4+efKkIiMjVbNmTeXIkSNR+5o1a2rDhg06dOiQqlevrlOnTikyMlK1atWSh4dHgrYuLi6qWrWqTp8+naZaAQBILwRjAAAyiZeXlxo2bKjFixerefPmOnXqlIYNG2a37c2bN2W1WhUaGqpx48Yluc3w8PA01XD/Pcc22bJlU1xcnPHz7du3JUkFCxa0275QoUIJ2t26dUuSEgwsFl9SjwMAkBkIxgAAZKIOHTpo+fLlGjp0qDw8PNS6dWu77XLlyiVJqlChgubOnZuRJUqSPD09JUlXrlyxu9z2uK2dLXBfvXrVbvukHgcAIDNwjzEAAJmoXr16Kly4sEJDQ9WkSRPlzZvXbjtPT0+VKVNGJ0+e1M2bN1O1bdsgWPGv/Drq8ccfl4eHh/bt26eIiIhEy7ds2SJJRjfw0qVLy8PDQ/v371dkZGSCtnFxcca9yAAAZAUEYwAAMpGrq6t+/vln/fzzzwlGeranR48eioiI0Mcff2y3y/S5c+cUHBxs/JwvXz5J9+YSflDu7u5q1aqVrl+/rgkTJiRY9t9//ykoKEglS5ZU1apVjfYtWrTQ1atXNWnSpATtZ8+ezf3FAIAsha7UAABkskqVKqlSpUoptnvppZe0Z88ezZs3Tzt37lSdOnX02GOP6erVqzp58qT27Nmj77//XsWLF5d0b0Asi8Wi0aNH69ixY8qdO7fy5Mmj7t27O1TnkCFDtG3bNv3666/atWuXKleurJCQEAUGBipHjhz66quvjKvUkvTuu+9q06ZN+uGHH7Rjxw6VL19eJ06c0Lp161SvXj0FBQU5VAcAAM5GMAYA4CFhsVj09ddfq379+po9e7bWrl2r8PBweXl5qWTJkvrggw9Uu3Zto/0TTzyhkSNHatKkSfrrr78UFRUlb29vh4Oxl5eXZs2apV9++UWrV6/Wjh075OnpqcaNG2vQoEHy9fVN0P6xxx7TP//8o1GjRikoKEjbt29XhQoVNHnyZG3evJlgDADIMixWq9Wa2UUAAAAAAJBZuMcYAAAAAGBqBGMAAAAAgKkRjAEAAAAApkYwBgAAAACYGsEYAAAAAGBqBGMAAAAAgKkRjAEAAAAApkYwBgAAAACYGsEYAAAAAGBqBGMAAAAAgKkRjAEAAAAApkYwBgAAAACYGsEYAAAAAGBq/w+YAjYE79+2ywAAAABJRU5ErkJggg==", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from pathlib import Path\n", + "import numpy as np\n", + "\n", + "# --- 1. Robust Data Parsing ---\n", + "# This section is updated to handle both spiral and baseline metric names.\n", + "root_dir = Path('.')\n", + "detailed_data = []\n", + "ALL_EXPECTED_METHODS = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "results_files = root_dir.glob('**/results.json')\n", + "\n", + "for file_path in results_files:\n", + " try:\n", + " parts = file_path.parts\n", + " current_method = None\n", + " for m in ALL_EXPECTED_METHODS:\n", + " if m in parts:\n", + " current_method = m\n", + " break\n", + " \n", + " if current_method:\n", + " method_index = parts.index(current_method)\n", + " # Standardize dataset names by removing common suffixes\n", + " dataset = parts[method_index + 1].replace('_experiments', '').replace('_v3', '')\n", + " model = parts[method_index + 2]\n", + "\n", + " with open(file_path, 'r') as f:\n", + " results_list = json.load(f)\n", + "\n", + " for item in results_list:\n", + " metrics = item.get('metrics', {})\n", + " total_tokens = None\n", + " latency = None\n", + " invalid_steps = 0 # Default to 0 for baselines\n", + "\n", + " if current_method == 'spiral':\n", + " search_process = metrics.get('search_process', {})\n", + " exp_tokens = search_process.get('expansion_llm_tokens', 0)\n", + " sim_tokens = search_process.get('simulation_llm_tokens', 0)\n", + " crit_tokens = search_process.get('critic_llm_tokens', 0)\n", + " total_tokens = exp_tokens + sim_tokens + crit_tokens\n", + " latency = metrics.get('search_time_seconds')\n", + " invalid_steps = metrics.get('robustness', {}).get('invalid_steps_generated', 0)\n", + " else: # Baseline methods (cot_k1, etc.)\n", + " reasoning_cost = metrics.get('reasoning_cost', {})\n", + " total_tokens = reasoning_cost.get('total_llm_tokens')\n", + " latency = metrics.get('generation_time_seconds')\n", + "\n", + " detailed_data.append({\n", + " 'method': current_method, 'dataset': dataset, 'model': model,\n", + " 'accuracy': metrics.get('accuracy'), 'plan_length': metrics.get('plan_length'),\n", + " 'total_llm_tokens': total_tokens,\n", + " 'latency_seconds': latency,\n", + " 'invalid_steps': invalid_steps\n", + " })\n", + " except Exception as e:\n", + " print(f\"🔴 Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "# --- 2. Data Cleaning and Preparation ---\n", + "df_raw = pd.DataFrame(detailed_data)\n", + "df_cleaned = df_raw.dropna(subset=['accuracy', 'total_llm_tokens']).copy()\n", + "df_cleaned['accuracy'] = df_cleaned['accuracy'] * 100 # Convert to percentage\n", + "\n", + "# Define the models and methods for analysis\n", + "models_to_keep = [\n", + " 'deepseek_v2_5', 'llama_3_3_70b_instruct', 'llama_4', \n", + " 'phi', 'qwen2_5_72b_instruct'\n", + "]\n", + "methods_to_keep = ['cot_k5', 'spiral'] # Comparing spiral against the strongest baseline\n", + "\n", + "df_models = df_cleaned[df_cleaned['model'].isin(models_to_keep)].copy()\n", + "df_methods = df_cleaned[df_cleaned['method'].isin(methods_to_keep)].copy()\n", + "\n", + "\n", + "# --- 3. RQ1: Model Performance Analysis ---\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"RQ1: Model Performance Comparison\")\n", + "print(\"=\"*80 + \"\\n\")\n", + "\n", + "if not df_models.empty:\n", + " # Calculate task-specific accuracy\n", + " rq1_accuracy = df_models.groupby(['model', 'dataset'])['accuracy'].mean().unstack()\n", + " \n", + " # Calculate Planning Premium\n", + " if 'dailylifeapis' in rq1_accuracy.columns and 'huggingface' in rq1_accuracy.columns:\n", + " rq1_accuracy['Planning Premium'] = rq1_accuracy['dailylifeapis'] - rq1_accuracy['huggingface']\n", + " else:\n", + " print(\"🔴 Skipping 'Planning Premium' calculation: missing 'dailylifeapis' or 'huggingface' data.\")\n", + "\n", + " # --- Print RQ1 Table ---\n", + " print(\"--- 📊 RQ1 METRICS TABLE ---\")\n", + " print(rq1_accuracy.to_string(float_format=\"%.2f\"))\n", + " print(\"\\n\" + \"=\"*80 + \"\\n\")\n", + "\n", + " # --- Plot RQ1 Metrics ---\n", + " sns.set_theme(style=\"whitegrid\", context=\"talk\")\n", + " \n", + " # Plot 1: Task-Specific Accuracy\n", + " plot_df_rq1_acc = rq1_accuracy.reset_index().melt(id_vars='model', value_vars=['dailylifeapis', 'huggingface'],\n", + " var_name='Dataset', value_name='Average Accuracy')\n", + " plt.figure(figsize=(14, 8))\n", + " ax1 = sns.barplot(data=plot_df_rq1_acc, x='model', y='Average Accuracy', hue='Dataset',\n", + " order=sorted(models_to_keep))\n", + " ax1.set_title('RQ1: Model Accuracy by Task Complexity', fontsize=20, pad=20)\n", + " ax1.set_xlabel('Model', fontsize=14)\n", + " ax1.set_ylabel('Average Accuracy (%)', fontsize=14)\n", + " ax1.set_ylim(0, 105)\n", + " plt.xticks(rotation=10)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # Plot 2: Planning Premium\n", + " if 'Planning Premium' in rq1_accuracy.columns:\n", + " plt.figure(figsize=(14, 8))\n", + " ax2 = sns.barplot(data=rq1_accuracy.reset_index(), x='model', y='Planning Premium',\n", + " order=sorted(models_to_keep), palette='coolwarm')\n", + " ax2.set_title('RQ1: Planning Premium (Accuracy Drop on Complex Tasks)', fontsize=20, pad=20)\n", + " ax2.set_xlabel('Model', fontsize=14)\n", + " ax2.set_ylabel('Accuracy Difference (dailylifeapis - huggingface)', fontsize=14)\n", + " ax2.axhline(0, color='black', linewidth=0.8, linestyle='--')\n", + " plt.xticks(rotation=10)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "else:\n", + " print(\"🔴 No data available for RQ1 analysis.\")\n", + "\n", + "\n", + "# --- 4. RQ2: Method Performance Analysis (SPIRAL vs. Baseline) ---\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"RQ2: Method Performance Comparison (SPIRAL vs. cot_k5)\")\n", + "print(\"=\"*80 + \"\\n\")\n", + "\n", + "if not df_methods.empty:\n", + " # Aggregate data by method\n", + " rq2_agg = df_methods.groupby('method').agg(\n", + " avg_accuracy=('accuracy', 'mean'),\n", + " avg_tokens=('total_llm_tokens', 'mean'),\n", + " avg_plan_length=('plan_length', 'mean'),\n", + " avg_invalid_steps=('invalid_steps', 'mean')\n", + " ).reset_index()\n", + "\n", + " # Calculate Efficiency Score\n", + " # Adding a small epsilon to avoid log(0) if tokens are 0\n", + " rq2_agg['Efficiency Score'] = rq2_agg['avg_accuracy'] / np.log(rq2_agg['avg_tokens'] + 1e-9)\n", + "\n", + " # --- Print RQ2 Table ---\n", + " print(\"--- 📊 RQ2 METRICS TABLE ---\")\n", + " print(rq2_agg.to_string(index=False, float_format=\"%.2f\"))\n", + " print(\"\\n\" + \"=\"*80 + \"\\n\")\n", + " \n", + " # --- Plot RQ2 Metrics ---\n", + " # Plot 3: Efficiency Score\n", + " plt.figure(figsize=(10, 7))\n", + " ax3 = sns.barplot(data=rq2_agg, x='method', y='Efficiency Score', order=methods_to_keep)\n", + " ax3.set_title('RQ2: Method Efficiency Score (Accuracy per log(Token))', fontsize=20, pad=20)\n", + " ax3.set_xlabel('Method', fontsize=14)\n", + " ax3.set_ylabel('Efficiency Score', fontsize=14)\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " # Plot 4: Method Robustness\n", + " plt.figure(figsize=(10, 7))\n", + " ax4 = sns.barplot(data=rq2_agg, x='method', y='avg_invalid_steps', order=methods_to_keep)\n", + " ax4.set_title('RQ2: Method Robustness (Lower is Better)', fontsize=20, pad=20)\n", + " ax4.set_xlabel('Method', fontsize=14)\n", + " ax4.set_ylabel('Average Invalid Steps per Task', fontsize=14)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # Plot 5: Solution Conciseness\n", + " plt.figure(figsize=(10, 7))\n", + " ax5 = sns.barplot(data=rq2_agg, x='method', y='avg_plan_length', order=methods_to_keep)\n", + " ax5.set_title('RQ2: Solution Conciseness (Lower is Better)', fontsize=20, pad=20)\n", + " ax5.set_xlabel('Method', fontsize=14)\n", + " ax5.set_ylabel('Average Plan Length', fontsize=14)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "else:\n", + " print(\"🔴 No data available for RQ2 analysis.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e1e1f1fb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================================\n", + "📊 Results for Dataset: DAILYLIFEAPIS\n", + "====================================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral \n", + " Accuracy Plan Length Tokens Accuracy Plan Length Tokens Accuracy Plan Length Tokens Accuracy Plan Length Tokens\n", + "model \n", + "deepseek_v2_5 66.61% 2.82 2,850 67.89% 2.84 17,129 68.82% 2.82 42,750 91.24% 2.74 28,288\n", + "llama_3_3_70b_instruct 94.87% 3.04 2,578 94.88% 3.10 15,385 94.38% 3.09 38,354 98.35% 2.94 26,499\n", + "llama_4 57.95% 2.89 2,535 60.60% 2.89 15,181 60.00% 2.92 37,848 83.31% 2.84 27,029\n", + "phi 85.67% 2.77 2,522 86.24% 2.80 15,182 86.45% 2.81 38,056 91.57% 2.69 27,911\n", + "qwen2_5_72b_instruct 89.71% 2.88 2,485 89.64% 2.87 14,837 89.51% 2.91 37,071 97.69% 2.73 32,288\n", + "\n", + "\n", + "\n", + "====================================================================================================\n", + "📊 Results for Dataset: HUGGINGFACE\n", + "====================================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral \n", + " Accuracy Plan Length Tokens Accuracy Plan Length Tokens Accuracy Plan Length Tokens Accuracy Plan Length Tokens\n", + "model \n", + "deepseek_v2_5 75.77% 2.71 2,555 78.78% 2.67 15,318 78.61% 2.70 38,327 96.84% 2.30 19,943\n", + "llama_3_3_70b_instruct 92.48% 2.77 2,400 92.79% 2.80 14,275 93.75% 2.78 35,609 97.44% 2.28 19,198\n", + "llama_4 76.43% 2.57 2,438 75.65% 2.58 14,382 77.08% 2.54 35,542 93.04% 2.35 25,294\n", + "phi 92.08% 2.53 2,310 92.85% 2.57 13,935 93.71% 2.59 34,961 95.48% 2.25 22,631\n", + "qwen2_5_72b_instruct 86.78% 2.68 2,247 88.16% 2.68 13,401 88.47% 2.71 33,513 97.08% 2.25 28,477\n", + "\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_344543/4072946406.py:79: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n", + "/tmp/ipykernel_344543/4072946406.py:86: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n", + " pivoted = agg_df.pivot_table(\n" + ] + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "import numpy as np\n", + "\n", + "# --- 1. Robust Data Parsing ---\n", + "# This section remains largely the same, ensuring all required metrics are captured.\n", + "root_dir = Path('.')\n", + "detailed_data = []\n", + "ALL_EXPECTED_METHODS = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "results_files = root_dir.glob('**/results.json')\n", + "\n", + "for file_path in results_files:\n", + " try:\n", + " parts = file_path.parts\n", + " current_method = None\n", + " for m in ALL_EXPECTED_METHODS:\n", + " if m in parts:\n", + " current_method = m\n", + " break\n", + " \n", + " if current_method:\n", + " method_index = parts.index(current_method)\n", + " # Standardize dataset names\n", + " dataset = parts[method_index + 1].replace('_experiments', '').replace('_v3', '')\n", + " model = parts[method_index + 2]\n", + "\n", + " with open(file_path, 'r') as f:\n", + " results_list = json.load(f)\n", + "\n", + " for item in results_list:\n", + " metrics = item.get('metrics', {})\n", + " total_tokens = None\n", + "\n", + " if current_method == 'spiral':\n", + " search_process = metrics.get('search_process', {})\n", + " exp_tokens = search_process.get('expansion_llm_tokens', 0)\n", + " sim_tokens = search_process.get('simulation_llm_tokens', 0)\n", + " crit_tokens = search_process.get('critic_llm_tokens', 0)\n", + " total_tokens = exp_tokens + sim_tokens + crit_tokens\n", + " else: # Baseline methods\n", + " reasoning_cost = metrics.get('reasoning_cost', {})\n", + " total_tokens = reasoning_cost.get('total_llm_tokens')\n", + "\n", + " detailed_data.append({\n", + " 'method': current_method, 'dataset': dataset, 'model': model,\n", + " 'Accuracy': metrics.get('accuracy'),\n", + " 'Plan Length': metrics.get('plan_length'),\n", + " 'Tokens': total_tokens\n", + " })\n", + " except Exception as e:\n", + " print(f\"🔴 Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "# --- 2. Data Cleaning and Preparation ---\n", + "df_raw = pd.DataFrame(detailed_data)\n", + "# Drop rows where any of the essential metrics are missing\n", + "df_cleaned = df_raw.dropna(subset=['Accuracy', 'Plan Length', 'Tokens']).copy()\n", + "df_cleaned['Accuracy'] = df_cleaned['Accuracy'] * 100 # Convert to percentage\n", + "\n", + "# Define the models and methods for the final table\n", + "models_to_keep = [\n", + " 'deepseek_v2_5', 'llama_3_3_70b_instruct', 'llama_4', \n", + " 'phi', 'qwen2_5_72b_instruct'\n", + "]\n", + "methods_to_keep = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "\n", + "df_filtered = df_cleaned[\n", + " df_cleaned['model'].isin(models_to_keep) & \n", + " df_cleaned['method'].isin(methods_to_keep)\n", + "].copy()\n", + "\n", + "# --- 3. Aggregate and Restructure Data for Table ---\n", + "if not df_filtered.empty:\n", + " # Set categorical types to enforce a specific order in the final table\n", + " df_filtered['model'] = pd.Categorical(df_filtered['model'], categories=sorted(models_to_keep), ordered=True)\n", + " df_filtered['method'] = pd.Categorical(df_filtered['method'], categories=methods_to_keep, ordered=True)\n", + "\n", + " # Group by all necessary fields and calculate the mean for our metrics\n", + " agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n", + " 'Accuracy': 'mean',\n", + " 'Plan Length': 'mean',\n", + " 'Tokens': 'mean'\n", + " }).reset_index()\n", + "\n", + " # Pivot the table to create the desired multi-level column structure\n", + " pivoted = agg_df.pivot_table(\n", + " index=['dataset', 'model'],\n", + " columns='method',\n", + " values=['Accuracy', 'Plan Length', 'Tokens']\n", + " )\n", + "\n", + " # Reorder columns to group by method, then by metric\n", + " pivoted = pivoted.swaplevel(0, 1, axis=1).sort_index(axis=1)\n", + "\n", + " # --- 4. Print Formatted Tables ---\n", + " pd.set_option('display.max_columns', None)\n", + " pd.set_option('display.width', 200) # Adjust width for better console display\n", + "\n", + " datasets = pivoted.index.get_level_values('dataset').unique()\n", + " \n", + " for dataset_name in datasets:\n", + " print(\"\\n\" + \"=\"*100)\n", + " print(f\"📊 Results for Dataset: {dataset_name.upper()}\")\n", + " print(\"=\"*100)\n", + " \n", + " dataset_table = pivoted.loc[dataset_name]\n", + " \n", + " # Define formatting for each column to improve readability\n", + " formatters = {\n", + " ('cot_k1', 'Accuracy'): \"{:.2f}%\".format,\n", + " ('cot_k3', 'Accuracy'): \"{:.2f}%\".format,\n", + " ('cot_k5', 'Accuracy'): \"{:.2f}%\".format,\n", + " ('spiral', 'Accuracy'): \"{:.2f}%\".format,\n", + " ('cot_k1', 'Plan Length'): \"{:.2f}\".format,\n", + " ('cot_k3', 'Plan Length'): \"{:.2f}\".format,\n", + " ('cot_k5', 'Plan Length'): \"{:.2f}\".format,\n", + " ('spiral', 'Plan Length'): \"{:.2f}\".format,\n", + " ('cot_k1', 'Tokens'): \"{:,.0f}\".format,\n", + " ('cot_k3', 'Tokens'): \"{:,.0f}\".format,\n", + " ('cot_k5', 'Tokens'): \"{:,.0f}\".format,\n", + " ('spiral', 'Tokens'): \"{:,.0f}\".format,\n", + " }\n", + " \n", + " # Create a dictionary of formatters that actually exist in the table\n", + " valid_formatters = {col: fmt for col, fmt in formatters.items() if col in dataset_table.columns}\n", + "\n", + " print(dataset_table.to_string(formatters=valid_formatters))\n", + " print(\"\\n\")\n", + "\n", + "else:\n", + " print(\"🔴 No data available for table generation after filtering.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6768ab95", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "========================================================================================================================\n", + "📊 Results for Dataset: DAILYLIFEAPIS\n", + "========================================================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral \n", + " Accuracy LLM Calls Plan Length Accuracy LLM Calls Plan Length Accuracy LLM Calls Plan Length Accuracy LLM Calls Plan Length\n", + "model \n", + "deepseek_v2_5 66.61% 1.0 2.82 67.89% 3.0 2.84 68.82% 5.0 2.82 91.24% 6.5 2.74\n", + "llama_3_3_70b_instruct 94.87% 1.0 3.04 94.88% 3.0 3.10 94.38% 5.0 3.09 98.35% 7.0 2.94\n", + "llama_4 57.95% 1.0 2.89 60.60% 3.0 2.89 60.00% 5.0 2.92 83.31% 6.9 2.84\n", + "phi 85.67% 1.0 2.77 86.24% 3.0 2.80 86.45% 5.0 2.81 91.57% 6.8 2.69\n", + "qwen2_5_72b_instruct 89.71% 1.0 2.88 89.64% 3.0 2.87 89.51% 5.0 2.91 97.69% 7.1 2.73\n", + "\n", + "\n", + "\n", + "========================================================================================================================\n", + "📊 Results for Dataset: HUGGINGFACE\n", + "========================================================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral \n", + " Accuracy LLM Calls Plan Length Accuracy LLM Calls Plan Length Accuracy LLM Calls Plan Length Accuracy LLM Calls Plan Length\n", + "model \n", + "deepseek_v2_5 75.77% 1.0 2.71 78.78% 3.0 2.67 78.61% 5.0 2.70 96.84% 5.6 2.30\n", + "llama_3_3_70b_instruct 92.48% 1.0 2.77 92.79% 3.0 2.80 93.75% 5.0 2.78 97.44% 5.7 2.28\n", + "llama_4 76.43% 1.0 2.57 75.65% 3.0 2.58 77.08% 5.0 2.54 93.04% 6.4 2.35\n", + "phi 92.08% 1.0 2.53 92.85% 3.0 2.57 93.71% 5.0 2.59 95.48% 6.0 2.25\n", + "qwen2_5_72b_instruct 86.78% 1.0 2.68 88.16% 3.0 2.68 88.47% 5.0 2.71 97.08% 6.5 2.25\n", + "\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_344543/1905152413.py:79: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n", + "/tmp/ipykernel_344543/1905152413.py:86: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n", + " pivoted = agg_df.pivot_table(\n" + ] + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "import numpy as np\n", + "\n", + "# --- 1. Robust Data Parsing ---\n", + "# Updated to capture LLM calls for all methods.\n", + "root_dir = Path('.')\n", + "detailed_data = []\n", + "ALL_EXPECTED_METHODS = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "results_files = root_dir.glob('**/results.json')\n", + "\n", + "for file_path in results_files:\n", + " try:\n", + " parts = file_path.parts\n", + " current_method = None\n", + " for m in ALL_EXPECTED_METHODS:\n", + " if m in parts:\n", + " current_method = m\n", + " break\n", + " \n", + " if current_method:\n", + " method_index = parts.index(current_method)\n", + " # Standardize dataset names\n", + " dataset = parts[method_index + 1].replace('_experiments', '').replace('_v3', '')\n", + " model = parts[method_index + 2]\n", + "\n", + " with open(file_path, 'r') as f:\n", + " results_list = json.load(f)\n", + "\n", + " for item in results_list:\n", + " metrics = item.get('metrics', {})\n", + " llm_calls = None\n", + "\n", + " if current_method == 'spiral':\n", + " search_process = metrics.get('search_process', {})\n", + " exp_calls = search_process.get('expansion_llm_calls', 0)\n", + " sim_calls = search_process.get('simulation_llm_calls', 0)\n", + " crit_calls = search_process.get('critic_llm_calls', 0)\n", + " llm_calls = exp_calls + sim_calls + crit_calls\n", + " else: # Baseline methods\n", + " reasoning_cost = metrics.get('reasoning_cost', {})\n", + " llm_calls = reasoning_cost.get('llm_calls')\n", + "\n", + " detailed_data.append({\n", + " 'method': current_method, 'dataset': dataset, 'model': model,\n", + " 'Accuracy': metrics.get('accuracy'),\n", + " 'Plan Length': metrics.get('plan_length'),\n", + " 'LLM Calls': llm_calls\n", + " })\n", + " except Exception as e:\n", + " print(f\"🔴 Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "# --- 2. Data Cleaning and Preparation ---\n", + "df_raw = pd.DataFrame(detailed_data)\n", + "# Drop rows where any of the essential metrics are missing\n", + "df_cleaned = df_raw.dropna(subset=['Accuracy', 'Plan Length', 'LLM Calls']).copy()\n", + "df_cleaned['Accuracy'] = df_cleaned['Accuracy'] * 100 # Convert to percentage\n", + "\n", + "# Define the models and methods for the final table\n", + "models_to_keep = [\n", + " 'deepseek_v2_5', 'llama_3_3_70b_instruct', 'llama_4', \n", + " 'phi', 'qwen2_5_72b_instruct'\n", + "]\n", + "methods_to_keep = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "\n", + "df_filtered = df_cleaned[\n", + " df_cleaned['model'].isin(models_to_keep) & \n", + " df_cleaned['method'].isin(methods_to_keep)\n", + "].copy()\n", + "\n", + "# --- 3. Aggregate and Restructure Data for Table ---\n", + "if not df_filtered.empty:\n", + " # Set categorical types to enforce a specific order in the final table\n", + " df_filtered['model'] = pd.Categorical(df_filtered['model'], categories=sorted(models_to_keep), ordered=True)\n", + " df_filtered['method'] = pd.Categorical(df_filtered['method'], categories=methods_to_keep, ordered=True)\n", + "\n", + " # Group by all necessary fields and calculate the mean for our metrics\n", + " agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n", + " 'Accuracy': 'mean',\n", + " 'Plan Length': 'mean',\n", + " 'LLM Calls': 'mean'\n", + " }).reset_index()\n", + "\n", + " # Pivot the table to create the desired multi-level column structure\n", + " pivoted = agg_df.pivot_table(\n", + " index=['dataset', 'model'],\n", + " columns='method',\n", + " values=['Accuracy', 'Plan Length', 'LLM Calls']\n", + " )\n", + "\n", + " # Reorder columns to group by method, then by metric\n", + " pivoted = pivoted.swaplevel(0, 1, axis=1).sort_index(axis=1)\n", + "\n", + " # --- 4. Print Formatted Tables ---\n", + " pd.set_option('display.max_columns', None)\n", + " pd.set_option('display.width', 200) # Adjust width for better console display\n", + "\n", + " datasets = sorted(pivoted.index.get_level_values('dataset').unique())\n", + " \n", + " for dataset_name in datasets:\n", + " print(\"\\n\" + \"=\"*120)\n", + " print(f\"📊 Results for Dataset: {dataset_name.upper()}\")\n", + " print(\"=\"*120)\n", + " \n", + " dataset_table = pivoted.loc[dataset_name]\n", + " \n", + " # Define formatting for each column to improve readability\n", + " formatters = {\n", + " ('cot_k1', 'Accuracy'): \"{:.2f}%\".format,\n", + " ('cot_k3', 'Accuracy'): \"{:.2f}%\".format,\n", + " ('cot_k5', 'Accuracy'): \"{:.2f}%\".format,\n", + " ('spiral', 'Accuracy'): \"{:.2f}%\".format,\n", + " ('cot_k1', 'Plan Length'): \"{:.2f}\".format,\n", + " ('cot_k3', 'Plan Length'): \"{:.2f}\".format,\n", + " ('cot_k5', 'Plan Length'): \"{:.2f}\".format,\n", + " ('spiral', 'Plan Length'): \"{:.2f}\".format,\n", + " ('cot_k1', 'LLM Calls'): \"{:.1f}\".format,\n", + " ('cot_k3', 'LLM Calls'): \"{:.1f}\".format,\n", + " ('cot_k5', 'LLM Calls'): \"{:.1f}\".format,\n", + " ('spiral', 'LLM Calls'): \"{:.1f}\".format,\n", + " }\n", + " \n", + " # Create a dictionary of formatters that actually exist in the table\n", + " valid_formatters = {col: fmt for col, fmt in formatters.items() if col in dataset_table.columns}\n", + "\n", + " print(dataset_table.to_string(formatters=valid_formatters))\n", + " print(\"\\n\")\n", + "\n", + "else:\n", + " print(\"🔴 No data available for table generation after filtering.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f3216c49", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "========================================================================================================================\n", + "📊 Results for Dataset: DAILYLIFEAPIS\n", + "========================================================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral \n", + " Accuracy Latency (s) Solution Conciseness Accuracy Latency (s) Solution Conciseness Accuracy Latency (s) Solution Conciseness Accuracy Latency (s) Solution Conciseness\n", + "model \n", + "deepseek_v2_5 66.61% ± 47.20 48.30 ± 11.97 2.82 ± 1.58 67.89% ± 46.73 241.31 ± 81.91 2.84 ± 1.59 68.82% ± 46.36 329.26 ± 118.05 2.82 ± 1.59 91.24% ± 28.30 83.03 ± 66.50 2.74 ± 1.52\n", + "llama_3_3_70b_instruct 94.87% ± 22.08 44.29 ± 11.16 3.04 ± 1.74 94.88% ± 22.07 89.53 ± 25.70 3.10 ± 1.78 94.38% ± 23.05 417.48 ± 407.56 3.09 ± 1.81 98.35% ± 12.76 52.34 ± 23.43 2.94 ± 1.42\n", + "llama_4 57.95% ± 49.41 20.35 ± 2.78 2.89 ± 1.63 60.60% ± 48.90 42.98 ± 8.64 2.89 ± 1.61 60.00% ± 49.03 70.24 ± 20.21 2.92 ± 1.61 83.31% ± 37.32 32.15 ± 15.92 2.84 ± 1.57\n", + "phi 85.67% ± 35.07 28.49 ± 6.49 2.77 ± 1.61 86.24% ± 34.48 76.26 ± 17.89 2.80 ± 1.62 86.45% ± 34.26 174.65 ± 102.13 2.81 ± 1.61 91.57% ± 27.81 52.29 ± 49.74 2.69 ± 1.47\n", + "qwen2_5_72b_instruct 89.71% ± 30.40 34.20 ± 5.01 2.88 ± 1.59 89.64% ± 30.50 119.40 ± 25.20 2.87 ± 1.57 89.51% ± 30.67 505.60 ± 127.96 2.91 ± 1.62 97.69% ± 15.05 217.78 ± 140.42 2.73 ± 1.58\n", + "\n", + "\n", + "\n", + "========================================================================================================================\n", + "📊 Results for Dataset: HUGGINGFACE\n", + "========================================================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral \n", + " Accuracy Latency (s) Solution Conciseness Accuracy Latency (s) Solution Conciseness Accuracy Latency (s) Solution Conciseness Accuracy Latency (s) Solution Conciseness\n", + "model \n", + "deepseek_v2_5 75.77% ± 42.85 42.61 ± 14.16 2.71 ± 1.53 78.78% ± 40.90 392.80 ± 103.52 2.67 ± 1.58 78.61% ± 41.01 516.54 ± 406.35 2.70 ± 1.61 96.84% ± 17.50 331.72 ± 215.55 2.30 ± 1.37\n", + "llama_3_3_70b_instruct 92.48% ± 26.37 68.08 ± 38.37 2.77 ± 1.62 92.79% ± 25.87 97.38 ± 41.86 2.80 ± 1.65 93.75% ± 24.21 224.37 ± 99.04 2.78 ± 1.64 97.44% ± 15.80 76.09 ± 53.07 2.28 ± 1.35\n", + "llama_4 76.43% ± 42.45 16.35 ± 11.23 2.57 ± 1.54 75.65% ± 42.93 43.75 ± 21.05 2.58 ± 1.56 77.08% ± 42.04 66.83 ± 35.89 2.54 ± 1.58 93.04% ± 25.45 42.14 ± 35.72 2.35 ± 1.44\n", + "phi 92.08% ± 27.02 20.76 ± 10.47 2.53 ± 1.49 92.85% ± 25.78 85.07 ± 35.77 2.57 ± 1.54 93.71% ± 24.29 189.32 ± 61.59 2.59 ± 1.53 95.48% ± 20.78 79.60 ± 97.10 2.25 ± 1.40\n", + "qwen2_5_72b_instruct 86.78% ± 33.88 28.42 ± 13.26 2.68 ± 1.57 88.16% ± 32.31 128.14 ± 47.45 2.68 ± 1.56 88.47% ± 31.94 494.95 ± 154.22 2.71 ± 1.57 97.08% ± 16.84 154.00 ± 121.25 2.25 ± 1.41\n", + "\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_344543/3861063636.py:74: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n", + "/tmp/ipykernel_344543/3861063636.py:100: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n", + " final_pivot = pivoted.pivot_table(\n" + ] + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "import numpy as np\n", + "\n", + "# --- 1. Robust Data Parsing ---\n", + "# Updated to capture latency and prepare for std deviation calculation.\n", + "root_dir = Path('.')\n", + "detailed_data = []\n", + "ALL_EXPECTED_METHODS = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "results_files = root_dir.glob('**/results.json')\n", + "\n", + "for file_path in results_files:\n", + " try:\n", + " parts = file_path.parts\n", + " current_method = None\n", + " for m in ALL_EXPECTED_METHODS:\n", + " if m in parts:\n", + " current_method = m\n", + " break\n", + " \n", + " if current_method:\n", + " method_index = parts.index(current_method)\n", + " # Standardize dataset names\n", + " dataset = parts[method_index + 1].replace('_experiments', '').replace('_v3', '')\n", + " model = parts[method_index + 2]\n", + "\n", + " with open(file_path, 'r') as f:\n", + " results_list = json.load(f)\n", + "\n", + " for item in results_list:\n", + " metrics = item.get('metrics', {})\n", + " latency = None\n", + "\n", + " if current_method == 'spiral':\n", + " latency = metrics.get('search_time_seconds')\n", + " else: # Baseline methods\n", + " latency = metrics.get('generation_time_seconds')\n", + "\n", + " detailed_data.append({\n", + " 'method': current_method, 'dataset': dataset, 'model': model,\n", + " 'Accuracy': metrics.get('accuracy'),\n", + " 'Solution Conciseness': metrics.get('plan_length'),\n", + " 'Latency (s)': latency\n", + " })\n", + " except Exception as e:\n", + " print(f\"🔴 Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "# --- 2. Data Cleaning and Preparation ---\n", + "df_raw = pd.DataFrame(detailed_data)\n", + "# Drop rows where any of the essential metrics are missing\n", + "df_cleaned = df_raw.dropna(subset=['Accuracy', 'Solution Conciseness', 'Latency (s)']).copy()\n", + "df_cleaned['Accuracy'] = df_cleaned['Accuracy'] * 100 # Convert to percentage\n", + "\n", + "# Define the models and methods for the final table\n", + "models_to_keep = [\n", + " 'deepseek_v2_5', 'llama_3_3_70b_instruct', 'llama_4', \n", + " 'phi', 'qwen2_5_72b_instruct'\n", + "]\n", + "methods_to_keep = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "\n", + "df_filtered = df_cleaned[\n", + " df_cleaned['model'].isin(models_to_keep) & \n", + " df_cleaned['method'].isin(methods_to_keep)\n", + "].copy()\n", + "\n", + "# --- 3. Aggregate and Restructure Data for Table ---\n", + "if not df_filtered.empty:\n", + " # Set categorical types to enforce a specific order in the final table\n", + " df_filtered['model'] = pd.Categorical(df_filtered['model'], categories=sorted(models_to_keep), ordered=True)\n", + " df_filtered['method'] = pd.Categorical(df_filtered['method'], categories=methods_to_keep, ordered=True)\n", + "\n", + " # Group by all necessary fields and calculate both mean and std\n", + " agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n", + " 'Accuracy': ['mean', 'std'],\n", + " 'Solution Conciseness': ['mean', 'std'],\n", + " 'Latency (s)': ['mean', 'std']\n", + " })\n", + " \n", + " # Create formatted strings (e.g., \"mean ± std\") for each metric\n", + " metrics_to_format = ['Accuracy', 'Solution Conciseness', 'Latency (s)']\n", + " for metric in metrics_to_format:\n", + " mean_col = (metric, 'mean')\n", + " std_col = (metric, 'std')\n", + " \n", + " # Format Accuracy with a '%' sign\n", + " if metric == 'Accuracy':\n", + " agg_df[(metric, 'formatted')] = agg_df[mean_col].map('{:.2f}%'.format) + ' ± ' + agg_df[std_col].map('{:.2f}'.format)\n", + " else:\n", + " agg_df[(metric, 'formatted')] = agg_df[mean_col].map('{:.2f}'.format) + ' ± ' + agg_df[std_col].map('{:.2f}'.format)\n", + "\n", + " # Extract only the formatted columns for the final table\n", + " formatted_cols = [(metric, 'formatted') for metric in metrics_to_format]\n", + " pivoted = agg_df[formatted_cols].reset_index()\n", + " \n", + " # Clean up column names for pivoting\n", + " pivoted.columns = ['dataset', 'model', 'method'] + metrics_to_format\n", + " \n", + " # Pivot the formatted strings into the final table structure\n", + " final_pivot = pivoted.pivot_table(\n", + " index=['dataset', 'model'],\n", + " columns='method',\n", + " values=metrics_to_format,\n", + " aggfunc='first' # Use 'first' since values are already unique strings\n", + " )\n", + "\n", + " # Reorder columns to group by method, then by metric\n", + " final_pivot = final_pivot.swaplevel(0, 1, axis=1).sort_index(axis=1)\n", + "\n", + " # --- 4. Print Formatted Tables ---\n", + " pd.set_option('display.max_columns', None)\n", + " pd.set_option('display.width', 200)\n", + "\n", + " datasets = sorted(final_pivot.index.get_level_values('dataset').unique())\n", + " \n", + " for dataset_name in datasets:\n", + " print(\"\\n\" + \"=\"*120)\n", + " print(f\"📊 Results for Dataset: {dataset_name.upper()}\")\n", + " print(\"=\"*120)\n", + " \n", + " dataset_table = final_pivot.loc[dataset_name]\n", + " print(dataset_table.to_string())\n", + " print(\"\\n\")\n", + "\n", + "else:\n", + " print(\"🔴 No data available for table generation after filtering.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4a9ef33c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "========================================================================================================================\n", + "📊 Results for Dataset: DAILYLIFEAPIS\n", + "========================================================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral \n", + " Accuracy Robust Success Rate Solution Conciseness Accuracy Robust Success Rate Solution Conciseness Accuracy Robust Success Rate Solution Conciseness Accuracy Robust Success Rate Solution Conciseness\n", + "model \n", + "deepseek_v2_5 66.61% ± 47.20 66.61% ± 47.20 2.82 ± 1.58 67.89% ± 46.73 67.89% ± 46.73 2.84 ± 1.59 68.82% ± 46.36 68.82% ± 46.36 2.82 ± 1.59 91.24% ± 28.30 91.24% ± 28.30 2.74 ± 1.52\n", + "llama_3_3_70b_instruct 94.87% ± 22.08 94.87% ± 22.08 3.04 ± 1.74 94.88% ± 22.07 94.88% ± 22.07 3.10 ± 1.78 94.38% ± 23.05 94.38% ± 23.05 3.09 ± 1.81 98.35% ± 12.76 98.02% ± 13.95 2.94 ± 1.42\n", + "llama_4 57.95% ± 49.41 57.95% ± 49.41 2.89 ± 1.63 60.60% ± 48.90 60.60% ± 48.90 2.89 ± 1.61 60.00% ± 49.03 60.00% ± 49.03 2.92 ± 1.61 83.31% ± 37.32 83.14% ± 37.47 2.84 ± 1.57\n", + "phi 85.67% ± 35.07 85.67% ± 35.07 2.77 ± 1.61 86.24% ± 34.48 86.24% ± 34.48 2.80 ± 1.62 86.45% ± 34.26 86.45% ± 34.26 2.81 ± 1.61 91.57% ± 27.81 90.74% ± 29.01 2.69 ± 1.47\n", + "qwen2_5_72b_instruct 89.71% ± 30.40 89.71% ± 30.40 2.88 ± 1.59 89.64% ± 30.50 89.64% ± 30.50 2.87 ± 1.57 89.51% ± 30.67 89.51% ± 30.67 2.91 ± 1.62 97.69% ± 15.05 97.69% ± 15.05 2.73 ± 1.58\n", + "\n", + "\n", + "\n", + "========================================================================================================================\n", + "📊 Results for Dataset: HUGGINGFACE\n", + "========================================================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral \n", + " Accuracy Robust Success Rate Solution Conciseness Accuracy Robust Success Rate Solution Conciseness Accuracy Robust Success Rate Solution Conciseness Accuracy Robust Success Rate Solution Conciseness\n", + "model \n", + "deepseek_v2_5 75.77% ± 42.85 75.77% ± 42.85 2.71 ± 1.53 78.78% ± 40.90 78.78% ± 40.90 2.67 ± 1.58 78.61% ± 41.01 78.61% ± 41.01 2.70 ± 1.61 96.84% ± 17.50 96.56% ± 18.23 2.30 ± 1.37\n", + "llama_3_3_70b_instruct 92.48% ± 26.37 92.48% ± 26.37 2.77 ± 1.62 92.79% ± 25.87 92.79% ± 25.87 2.80 ± 1.65 93.75% ± 24.21 93.75% ± 24.21 2.78 ± 1.64 97.44% ± 15.80 96.12% ± 19.32 2.28 ± 1.35\n", + "llama_4 76.43% ± 42.45 76.43% ± 42.45 2.57 ± 1.54 75.65% ± 42.93 75.65% ± 42.93 2.58 ± 1.56 77.08% ± 42.04 77.08% ± 42.04 2.54 ± 1.58 93.04% ± 25.45 89.36% ± 30.84 2.35 ± 1.44\n", + "phi 92.08% ± 27.02 92.08% ± 27.02 2.53 ± 1.49 92.85% ± 25.78 92.85% ± 25.78 2.57 ± 1.54 93.71% ± 24.29 93.71% ± 24.29 2.59 ± 1.53 95.48% ± 20.78 94.52% ± 22.76 2.25 ± 1.40\n", + "qwen2_5_72b_instruct 86.78% ± 33.88 86.78% ± 33.88 2.68 ± 1.57 88.16% ± 32.31 88.16% ± 32.31 2.68 ± 1.56 88.47% ± 31.94 88.47% ± 31.94 2.71 ± 1.57 97.08% ± 16.84 96.88% ± 17.39 2.25 ± 1.41\n", + "\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_344543/257763819.py:80: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n", + "/tmp/ipykernel_344543/257763819.py:109: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n", + " final_pivot = pivoted.pivot_table(\n" + ] + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "import numpy as np\n", + "\n", + "# --- 1. Robust Data Parsing ---\n", + "# Updated to capture robustness metrics for the new calculation.\n", + "root_dir = Path('.')\n", + "detailed_data = []\n", + "ALL_EXPECTED_METHODS = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "results_files = root_dir.glob('**/results.json')\n", + "\n", + "for file_path in results_files:\n", + " try:\n", + " parts = file_path.parts\n", + " current_method = None\n", + " for m in ALL_EXPECTED_METHODS:\n", + " if m in parts:\n", + " current_method = m\n", + " break\n", + " \n", + " if current_method:\n", + " method_index = parts.index(current_method)\n", + " # Standardize dataset names\n", + " dataset = parts[method_index + 1].replace('_experiments', '').replace('_v3', '')\n", + " model = parts[method_index + 2]\n", + "\n", + " with open(file_path, 'r') as f:\n", + " results_list = json.load(f)\n", + "\n", + " for item in results_list:\n", + " metrics = item.get('metrics', {})\n", + " invalid_steps = 0 # Default to 0 for baselines that don't track this\n", + "\n", + " if current_method == 'spiral':\n", + " invalid_steps = metrics.get('robustness', {}).get('invalid_steps_generated', 0)\n", + "\n", + " detailed_data.append({\n", + " 'method': current_method, 'dataset': dataset, 'model': model,\n", + " 'Accuracy': metrics.get('accuracy'),\n", + " 'Solution Conciseness': metrics.get('plan_length'),\n", + " 'invalid_steps': invalid_steps\n", + " })\n", + " except Exception as e:\n", + " print(f\"🔴 Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "# --- 2. Data Cleaning and Preparation ---\n", + "df_raw = pd.DataFrame(detailed_data)\n", + "# Drop rows where any of the essential metrics are missing\n", + "df_cleaned = df_raw.dropna(subset=['Accuracy', 'Solution Conciseness', 'invalid_steps']).copy()\n", + "\n", + "# --- 3. Calculate New Metric and Aggregate ---\n", + "# Calculate Robust Success Rate: 1 if accurate AND no errors, else 0\n", + "df_cleaned['Robust Success Rate'] = np.where(\n", + " (df_cleaned['Accuracy'] == 1.0) & (df_cleaned['invalid_steps'] == 0), \n", + " 100.0, \n", + " 0.0\n", + ")\n", + "df_cleaned['Accuracy'] = df_cleaned['Accuracy'] * 100 # Convert to percentage\n", + "\n", + "# Define the models and methods for the final table\n", + "models_to_keep = [\n", + " 'deepseek_v2_5', 'llama_3_3_70b_instruct', 'llama_4', \n", + " 'phi', 'qwen2_5_72b_instruct'\n", + "]\n", + "methods_to_keep = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "\n", + "df_filtered = df_cleaned[\n", + " df_cleaned['model'].isin(models_to_keep) & \n", + " df_cleaned['method'].isin(methods_to_keep)\n", + "].copy()\n", + "\n", + "# --- 4. Restructure Data for Table ---\n", + "if not df_filtered.empty:\n", + " # Set categorical types to enforce a specific order in the final table\n", + " df_filtered['model'] = pd.Categorical(df_filtered['model'], categories=sorted(models_to_keep), ordered=True)\n", + " df_filtered['method'] = pd.Categorical(df_filtered['method'], categories=methods_to_keep, ordered=True)\n", + "\n", + " # Group by all necessary fields and calculate both mean and std\n", + " agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n", + " 'Accuracy': ['mean', 'std'],\n", + " 'Solution Conciseness': ['mean', 'std'],\n", + " 'Robust Success Rate': ['mean', 'std']\n", + " })\n", + " \n", + " # Create formatted strings (e.g., \"mean ± std\") for each metric\n", + " metrics_to_format = ['Accuracy', 'Solution Conciseness', 'Robust Success Rate']\n", + " for metric in metrics_to_format:\n", + " mean_col = (metric, 'mean')\n", + " std_col = (metric, 'std')\n", + " \n", + " # Fill NaN std values with 0 for formatting\n", + " agg_df[std_col] = agg_df[std_col].fillna(0)\n", + " \n", + " # Format metrics with a '%' sign where appropriate\n", + " if 'Accuracy' in metric or 'Rate' in metric:\n", + " agg_df[(metric, 'formatted')] = agg_df[mean_col].map('{:.2f}%'.format) + ' ± ' + agg_df[std_col].map('{:.2f}'.format)\n", + " else:\n", + " agg_df[(metric, 'formatted')] = agg_df[mean_col].map('{:.2f}'.format) + ' ± ' + agg_df[std_col].map('{:.2f}'.format)\n", + "\n", + " # Extract only the formatted columns for the final table\n", + " formatted_cols = [(metric, 'formatted') for metric in metrics_to_format]\n", + " pivoted = agg_df[formatted_cols].reset_index()\n", + " \n", + " # Clean up column names for pivoting\n", + " pivoted.columns = ['dataset', 'model', 'method'] + metrics_to_format\n", + " \n", + " # Pivot the formatted strings into the final table structure\n", + " final_pivot = pivoted.pivot_table(\n", + " index=['dataset', 'model'],\n", + " columns='method',\n", + " values=metrics_to_format,\n", + " aggfunc='first' # Use 'first' since values are already unique strings\n", + " )\n", + "\n", + " # Reorder columns to group by method, then by metric\n", + " final_pivot = final_pivot.swaplevel(0, 1, axis=1).sort_index(axis=1)\n", + "\n", + " # --- 5. Print Formatted Tables ---\n", + " pd.set_option('display.max_columns', None)\n", + " pd.set_option('display.width', 200)\n", + "\n", + " datasets = sorted(final_pivot.index.get_level_values('dataset').unique())\n", + " \n", + " for dataset_name in datasets:\n", + " print(\"\\n\" + \"=\"*120)\n", + " print(f\"📊 Results for Dataset: {dataset_name.upper()}\")\n", + " print(\"=\"*120)\n", + " \n", + " dataset_table = final_pivot.loc[dataset_name]\n", + " print(dataset_table.to_string())\n", + " print(\"\\n\")\n", + "\n", + "else:\n", + " print(\"🔴 No data available for table generation after filtering.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "827e5551", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "========================================================================================================================\n", + "📊 Results for Dataset: DAILYLIFEAPIS\n", + "========================================================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral \n", + " Accuracy Robust Success Rate Solution Conciseness Accuracy Robust Success Rate Solution Conciseness Accuracy Robust Success Rate Solution Conciseness Accuracy Robust Success Rate Solution Conciseness\n", + "model \n", + "deepseek_v2_5 66.60% ± 4.90 66.60% ± 4.90 2.82 ± 0.17 67.90% ± 3.87 67.90% ± 3.87 2.84 ± 0.15 68.83% ± 4.19 68.83% ± 4.19 2.82 ± 0.15 91.24% ± 2.65 91.24% ± 2.65 2.74 ± 0.15\n", + "llama_3_3_70b_instruct 94.87% ± 1.80 94.87% ± 1.80 3.04 ± 0.17 94.88% ± 1.36 94.88% ± 1.36 3.10 ± 0.21 94.38% ± 1.88 94.38% ± 1.88 3.09 ± 0.21 98.35% ± 0.83 98.02% ± 0.45 2.94 ± 0.13\n", + "llama_4 57.95% ± 5.59 57.95% ± 5.59 2.89 ± 0.18 60.60% ± 4.16 60.60% ± 4.16 2.89 ± 0.18 60.00% ± 5.31 60.00% ± 5.31 2.92 ± 0.20 83.31% ± 4.11 83.14% ± 3.99 2.84 ± 0.13\n", + "phi 85.67% ± 2.67 85.67% ± 2.67 2.77 ± 0.19 86.24% ± 3.61 86.24% ± 3.61 2.80 ± 0.19 86.45% ± 3.44 86.45% ± 3.44 2.81 ± 0.18 91.57% ± 2.44 90.74% ± 3.27 2.69 ± 0.14\n", + "qwen2_5_72b_instruct 89.70% ± 2.03 89.70% ± 2.03 2.88 ± 0.19 89.64% ± 1.14 89.64% ± 1.14 2.87 ± 0.21 89.50% ± 1.37 89.50% ± 1.37 2.91 ± 0.20 97.69% ± 1.23 97.69% ± 1.23 2.73 ± 0.16\n", + "\n", + "\n", + "\n", + "========================================================================================================================\n", + "📊 Results for Dataset: HUGGINGFACE\n", + "========================================================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral \n", + " Accuracy Robust Success Rate Solution Conciseness Accuracy Robust Success Rate Solution Conciseness Accuracy Robust Success Rate Solution Conciseness Accuracy Robust Success Rate Solution Conciseness\n", + "model \n", + "deepseek_v2_5 75.77% ± 1.57 75.77% ± 1.57 2.71 ± 0.08 79.34% ± 2.42 79.34% ± 2.42 2.60 ± 0.19 78.61% ± 1.40 78.61% ± 1.40 2.70 ± 0.07 96.84% ± 0.65 96.56% ± 0.70 2.30 ± 0.05\n", + "llama_3_3_70b_instruct 92.48% ± 1.29 92.48% ± 1.29 2.77 ± 0.05 92.79% ± 1.24 92.79% ± 1.24 2.80 ± 0.10 93.75% ± 0.68 93.75% ± 0.68 2.78 ± 0.05 97.44% ± 0.89 96.12% ± 1.11 2.28 ± 0.06\n", + "llama_4 76.43% ± 0.97 76.43% ± 0.97 2.57 ± 0.06 75.65% ± 1.31 75.65% ± 1.31 2.58 ± 0.07 77.09% ± 0.76 77.09% ± 0.76 2.54 ± 0.09 93.04% ± 0.89 89.36% ± 1.38 2.35 ± 0.04\n", + "phi 92.08% ± 0.26 92.08% ± 0.26 2.53 ± 0.06 92.84% ± 0.95 92.84% ± 0.95 2.57 ± 0.08 93.71% ± 1.13 93.71% ± 1.13 2.59 ± 0.06 95.48% ± 0.86 94.52% ± 1.15 2.25 ± 0.06\n", + "qwen2_5_72b_instruct 86.78% ± 1.58 86.78% ± 1.58 2.68 ± 0.05 88.16% ± 1.11 88.16% ± 1.11 2.68 ± 0.04 88.47% ± 1.65 88.47% ± 1.65 2.71 ± 0.05 97.08% ± 0.54 96.88% ± 0.41 2.25 ± 0.05\n", + "\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_344543/718077589.py:93: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id']).agg({\n", + "/tmp/ipykernel_344543/718077589.py:100: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " agg_df = run_means.groupby(['dataset', 'model', 'method']).agg({\n", + "/tmp/ipykernel_344543/718077589.py:129: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n", + " final_pivot = pivoted.pivot_table(\n" + ] + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "import numpy as np\n", + "import re\n", + "\n", + "# --- 1. Robust Data Parsing ---\n", + "# Updated to capture a unique run_id for each experiment run.\n", + "root_dir = Path('.')\n", + "detailed_data = []\n", + "ALL_EXPECTED_METHODS = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "results_files = root_dir.glob('**/results.json')\n", + "\n", + "for file_path in results_files:\n", + " try:\n", + " parts = file_path.parts\n", + " current_method = None\n", + " for m in ALL_EXPECTED_METHODS:\n", + " if m in parts:\n", + " current_method = m\n", + " break\n", + " \n", + " if current_method:\n", + " method_index = parts.index(current_method)\n", + " # Standardize dataset names\n", + " dataset = parts[method_index + 1].replace('_experiments', '').replace('_v3', '')\n", + " model = parts[method_index + 2]\n", + " \n", + " # --- FIX: Ensure run_id is always a string ---\n", + " # This assumes a directory structure like .../run_seed_42/results.json\n", + " # It finds the part of the path that indicates the run seed.\n", + " run_id_match = re.search(r'run_seed_(\\d+)', str(file_path))\n", + " if run_id_match:\n", + " run_id = run_id_match.group(1) # Keep as a string (e.g., '42')\n", + " else:\n", + " # Fallback if no seed is found in the path, uses the parent directory name\n", + " run_id = file_path.parent.name # This is already a string\n", + "\n", + " with open(file_path, 'r') as f:\n", + " results_list = json.load(f)\n", + "\n", + " for item in results_list:\n", + " metrics = item.get('metrics', {})\n", + " invalid_steps = 0 # Default to 0 for baselines that don't track this\n", + "\n", + " if current_method == 'spiral':\n", + " invalid_steps = metrics.get('robustness', {}).get('invalid_steps_generated', 0)\n", + "\n", + " detailed_data.append({\n", + " 'run_id': run_id, # Add run identifier\n", + " 'method': current_method, 'dataset': dataset, 'model': model,\n", + " 'Accuracy': metrics.get('accuracy'),\n", + " 'Solution Conciseness': metrics.get('plan_length'),\n", + " 'invalid_steps': invalid_steps\n", + " })\n", + " except Exception as e:\n", + " print(f\"🔴 Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "# --- 2. Data Cleaning and Preparation ---\n", + "df_raw = pd.DataFrame(detailed_data)\n", + "# Drop rows where any of the essential metrics are missing\n", + "df_cleaned = df_raw.dropna(subset=['Accuracy', 'Solution Conciseness', 'invalid_steps']).copy()\n", + "\n", + "# --- 3. Calculate New Metric and Aggregate ---\n", + "# Calculate Robust Success Rate: 1 if accurate AND no errors, else 0\n", + "df_cleaned['Robust Success Rate'] = np.where(\n", + " (df_cleaned['Accuracy'] == 1.0) & (df_cleaned['invalid_steps'] == 0), \n", + " 100.0, \n", + " 0.0\n", + ")\n", + "df_cleaned['Accuracy'] = df_cleaned['Accuracy'] * 100 # Convert to percentage\n", + "\n", + "# Define the models and methods for the final table\n", + "models_to_keep = [\n", + " 'deepseek_v2_5', 'llama_3_3_70b_instruct', 'llama_4', \n", + " 'phi', 'qwen2_5_72b_instruct'\n", + "]\n", + "methods_to_keep = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "\n", + "df_filtered = df_cleaned[\n", + " df_cleaned['model'].isin(models_to_keep) & \n", + " df_cleaned['method'].isin(methods_to_keep)\n", + "].copy()\n", + "\n", + "# --- 4. Restructure Data for Table ---\n", + "if not df_filtered.empty:\n", + " # Set categorical types to enforce a specific order in the final table\n", + " df_filtered['model'] = pd.Categorical(df_filtered['model'], categories=sorted(models_to_keep), ordered=True)\n", + " df_filtered['method'] = pd.Categorical(df_filtered['method'], categories=methods_to_keep, ordered=True)\n", + "\n", + " # --- MODIFICATION: Correct Standard Deviation Calculation ---\n", + " # First, calculate the mean for each metric within each run.\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id']).agg({\n", + " 'Accuracy': 'mean',\n", + " 'Solution Conciseness': 'mean',\n", + " 'Robust Success Rate': 'mean'\n", + " }).reset_index()\n", + "\n", + " # Now, calculate the mean and std of those per-run means.\n", + " agg_df = run_means.groupby(['dataset', 'model', 'method']).agg({\n", + " 'Accuracy': ['mean', 'std'],\n", + " 'Solution Conciseness': ['mean', 'std'],\n", + " 'Robust Success Rate': ['mean', 'std']\n", + " })\n", + " \n", + " # Create formatted strings (e.g., \"mean ± std\") for each metric\n", + " metrics_to_format = ['Accuracy', 'Solution Conciseness', 'Robust Success Rate']\n", + " for metric in metrics_to_format:\n", + " mean_col = (metric, 'mean')\n", + " std_col = (metric, 'std')\n", + " \n", + " # Fill NaN std values with 0 for formatting\n", + " agg_df[std_col] = agg_df[std_col].fillna(0)\n", + " \n", + " # Format metrics with a '%' sign where appropriate\n", + " if 'Accuracy' in metric or 'Rate' in metric:\n", + " agg_df[(metric, 'formatted')] = agg_df[mean_col].map('{:.2f}%'.format) + ' ± ' + agg_df[std_col].map('{:.2f}'.format)\n", + " else:\n", + " agg_df[(metric, 'formatted')] = agg_df[mean_col].map('{:.2f}'.format) + ' ± ' + agg_df[std_col].map('{:.2f}'.format)\n", + "\n", + " # Extract only the formatted columns for the final table\n", + " formatted_cols = [(metric, 'formatted') for metric in metrics_to_format]\n", + " pivoted = agg_df[formatted_cols].reset_index()\n", + " \n", + " # Clean up column names for pivoting\n", + " pivoted.columns = ['dataset', 'model', 'method'] + metrics_to_format\n", + " \n", + " # Pivot the formatted strings into the final table structure\n", + " final_pivot = pivoted.pivot_table(\n", + " index=['dataset', 'model'],\n", + " columns='method',\n", + " values=metrics_to_format,\n", + " aggfunc='first' # Use 'first' since values are already unique strings\n", + " )\n", + "\n", + " # Reorder columns to group by method, then by metric\n", + " final_pivot = final_pivot.swaplevel(0, 1, axis=1).sort_index(axis=1)\n", + "\n", + " # --- 5. Print Formatted Tables ---\n", + " pd.set_option('display.max_columns', None)\n", + " pd.set_option('display.width', 200)\n", + "\n", + " datasets = sorted(final_pivot.index.get_level_values('dataset').unique())\n", + " \n", + " for dataset_name in datasets:\n", + " print(\"\\n\" + \"=\"*120)\n", + " print(f\"📊 Results for Dataset: {dataset_name.upper()}\")\n", + " print(\"=\"*120)\n", + " \n", + " dataset_table = final_pivot.loc[dataset_name]\n", + " print(dataset_table.to_string())\n", + " print(\"\\n\")\n", + "\n", + "else:\n", + " print(\"🔴 No data available for table generation after filtering.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "06a0139d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "========================================================================================================================\n", + "📊 Results for Dataset: DAILYLIFEAPIS\n", + "========================================================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral \n", + " Accuracy Complex Task Accuracy Simple Task Accuracy Accuracy Complex Task Accuracy Simple Task Accuracy Accuracy Complex Task Accuracy Simple Task Accuracy Accuracy Complex Task Accuracy Simple Task Accuracy\n", + "model \n", + "deepseek_v2_5 66.60% ± 4.90 59.22% ± 5.86 85.12% ± 6.42 67.90% ± 3.87 60.91% ± 3.83 86.04% ± 4.09 68.83% ± 4.19 62.41% ± 5.34 84.81% ± 2.06 91.24% ± 2.65 89.89% ± 3.57 94.33% ± 3.95\n", + "llama_3_3_70b_instruct 94.87% ± 1.80 94.68% ± 2.67 94.97% ± 1.85 94.88% ± 1.36 94.82% ± 1.58 95.26% ± 1.73 94.38% ± 1.88 94.06% ± 2.08 95.54% ± 3.63 98.35% ± 0.83 98.82% ± 0.82 95.79% ± 2.44\n", + "llama_4 57.95% ± 5.59 47.59% ± 4.73 84.43% ± 6.19 60.60% ± 4.16 50.21% ± 5.35 87.72% ± 5.09 60.00% ± 5.31 48.86% ± 6.53 89.21% ± 3.95 83.31% ± 4.11 79.53% ± 4.44 93.64% ± 2.96\n", + "phi 85.67% ± 2.67 83.88% ± 2.54 89.51% ± 3.34 86.24% ± 3.61 85.19% ± 4.00 89.81% ± 4.86 86.45% ± 3.44 84.27% ± 4.38 91.63% ± 1.65 91.57% ± 2.44 90.53% ± 2.25 95.48% ± 3.63\n", + "qwen2_5_72b_instruct 89.70% ± 2.03 89.25% ± 2.10 90.45% ± 4.30 89.64% ± 1.14 89.02% ± 1.80 91.01% ± 3.33 89.50% ± 1.37 88.47% ± 1.21 92.10% ± 3.39 97.69% ± 1.23 100.00% ± 0.00 94.09% ± 3.94\n", + "\n", + "\n", + "\n", + "========================================================================================================================\n", + "📊 Results for Dataset: HUGGINGFACE\n", + "========================================================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral \n", + " Accuracy Complex Task Accuracy Simple Task Accuracy Accuracy Complex Task Accuracy Simple Task Accuracy Accuracy Complex Task Accuracy Simple Task Accuracy Accuracy Complex Task Accuracy Simple Task Accuracy\n", + "model \n", + "deepseek_v2_5 75.77% ± 1.57 67.92% ± 1.97 93.84% ± 0.97 79.34% ± 2.42 71.94% ± 3.06 94.98% ± 1.35 78.61% ± 1.40 71.16% ± 1.46 94.44% ± 1.34 96.84% ± 0.65 95.81% ± 1.23 98.89% ± 0.91\n", + "llama_3_3_70b_instruct 92.48% ± 1.29 92.18% ± 1.67 93.10% ± 0.71 92.79% ± 1.24 92.29% ± 1.41 93.85% ± 1.40 93.75% ± 0.68 93.75% ± 0.67 93.77% ± 0.97 97.44% ± 0.89 96.76% ± 1.25 99.08% ± 0.90\n", + "llama_4 76.43% ± 0.97 67.78% ± 1.80 91.81% ± 1.15 75.65% ± 1.31 66.24% ± 1.82 91.67% ± 2.40 77.09% ± 0.76 68.34% ± 0.83 90.88% ± 2.17 93.04% ± 0.89 92.94% ± 0.56 93.63% ± 1.92\n", + "phi 92.08% ± 0.26 90.38% ± 0.75 95.27% ± 1.20 92.84% ± 0.95 90.72% ± 1.59 96.86% ± 1.55 93.71% ± 1.13 91.72% ± 2.10 97.49% ± 1.06 95.48% ± 0.86 94.95% ± 0.82 96.67% ± 1.83\n", + "qwen2_5_72b_instruct 86.78% ± 1.58 85.05% ± 1.85 90.51% ± 2.33 88.16% ± 1.11 86.57% ± 1.80 91.45% ± 3.79 88.47% ± 1.65 87.09% ± 2.00 91.50% ± 2.61 97.08% ± 0.54 98.52% ± 0.49 97.73% ± 1.06\n", + "\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_344543/3042241539.py:67: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " acc_overall = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Accuracy'].mean().reset_index()\n", + "/tmp/ipykernel_344543/3042241539.py:71: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " acc_simple = simple_tasks.groupby(['dataset', 'model', 'method', 'run_id'])['Accuracy'].mean().reset_index()\n", + "/tmp/ipykernel_344543/3042241539.py:76: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " acc_complex = complex_tasks.groupby(['dataset', 'model', 'method', 'run_id'])['Accuracy'].mean().reset_index()\n", + "/tmp/ipykernel_344543/3042241539.py:89: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " agg_df = run_means.groupby(['dataset', 'model', 'method']).agg({\n", + "/tmp/ipykernel_344543/3042241539.py:113: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n", + " final_pivot = pivoted.pivot_table(\n" + ] + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "import numpy as np\n", + "import re\n", + "\n", + "# --- 1. Robust Data Parsing ---\n", + "# Captures run_id to correctly calculate std dev across runs.\n", + "root_dir = Path('.')\n", + "detailed_data = []\n", + "ALL_EXPECTED_METHODS = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "results_files = root_dir.glob('**/results.json')\n", + "\n", + "for file_path in results_files:\n", + " try:\n", + " parts = file_path.parts\n", + " current_method = None\n", + " for m in ALL_EXPECTED_METHODS:\n", + " if m in parts:\n", + " current_method = m\n", + " break\n", + " \n", + " if current_method:\n", + " method_index = parts.index(current_method)\n", + " dataset = parts[method_index + 1].replace('_experiments', '').replace('_v3', '')\n", + " model = parts[method_index + 2]\n", + " \n", + " run_id_match = re.search(r'run_seed_(\\d+)', str(file_path))\n", + " run_id = run_id_match.group(1) if run_id_match else file_path.parent.name\n", + "\n", + " with open(file_path, 'r') as f:\n", + " results_list = json.load(f)\n", + "\n", + " for item in results_list:\n", + " metrics = item.get('metrics', {})\n", + " detailed_data.append({\n", + " 'run_id': str(run_id), # Ensure run_id is a string\n", + " 'method': current_method, 'dataset': dataset, 'model': model,\n", + " 'Accuracy': metrics.get('accuracy'),\n", + " 'Plan Length': metrics.get('plan_length')\n", + " })\n", + " except Exception as e:\n", + " print(f\"🔴 Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "# --- 2. Data Cleaning and Preparation ---\n", + "df_raw = pd.DataFrame(detailed_data)\n", + "df_cleaned = df_raw.dropna(subset=['Accuracy', 'Plan Length']).copy()\n", + "\n", + "models_to_keep = [\n", + " 'deepseek_v2_5', 'llama_3_3_70b_instruct', 'llama_4', \n", + " 'phi', 'qwen2_5_72b_instruct'\n", + "]\n", + "methods_to_keep = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "\n", + "df_filtered = df_cleaned[\n", + " df_cleaned['model'].isin(models_to_keep) & \n", + " df_cleaned['method'].isin(methods_to_keep)\n", + "].copy()\n", + "\n", + "# --- 3. Aggregate and Restructure Data for Table ---\n", + "if not df_filtered.empty:\n", + " df_filtered['model'] = pd.Categorical(df_filtered['model'], categories=sorted(models_to_keep), ordered=True)\n", + " df_filtered['method'] = pd.Categorical(df_filtered['method'], categories=methods_to_keep, ordered=True)\n", + "\n", + " # --- MODIFICATION: Calculate metrics based on task complexity ---\n", + " # 1. Overall Accuracy\n", + " acc_overall = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Accuracy'].mean().reset_index()\n", + " \n", + " # 2. Simple Task Accuracy (Plan Length == 1)\n", + " simple_tasks = df_filtered[df_filtered['Plan Length'] == 1]\n", + " acc_simple = simple_tasks.groupby(['dataset', 'model', 'method', 'run_id'])['Accuracy'].mean().reset_index()\n", + " acc_simple.rename(columns={'Accuracy': 'Simple Task Accuracy'}, inplace=True)\n", + "\n", + " # 3. Complex Task Accuracy (Plan Length > 1)\n", + " complex_tasks = df_filtered[df_filtered['Plan Length'] > 1]\n", + " acc_complex = complex_tasks.groupby(['dataset', 'model', 'method', 'run_id'])['Accuracy'].mean().reset_index()\n", + " acc_complex.rename(columns={'Accuracy': 'Complex Task Accuracy'}, inplace=True)\n", + "\n", + " # Merge the new metrics together\n", + " run_means = pd.merge(acc_overall, acc_simple, on=['dataset', 'model', 'method', 'run_id'], how='left')\n", + " run_means = pd.merge(run_means, acc_complex, on=['dataset', 'model', 'method', 'run_id'], how='left')\n", + " \n", + " # Convert all accuracies to percentages\n", + " for col in ['Accuracy', 'Simple Task Accuracy', 'Complex Task Accuracy']:\n", + " if col in run_means.columns:\n", + " run_means[col] *= 100\n", + "\n", + " # Calculate the final mean and std of the per-run means\n", + " agg_df = run_means.groupby(['dataset', 'model', 'method']).agg({\n", + " 'Accuracy': ['mean', 'std'],\n", + " 'Simple Task Accuracy': ['mean', 'std'],\n", + " 'Complex Task Accuracy': ['mean', 'std']\n", + " })\n", + " \n", + " # Create formatted strings (e.g., \"mean ± std\")\n", + " metrics_to_format = ['Accuracy', 'Simple Task Accuracy', 'Complex Task Accuracy']\n", + " for metric in metrics_to_format:\n", + " mean_col = (metric, 'mean')\n", + " std_col = (metric, 'std')\n", + " \n", + " agg_df[mean_col] = agg_df[mean_col].fillna(0)\n", + " agg_df[std_col] = agg_df[std_col].fillna(0)\n", + " \n", + " agg_df[(metric, 'formatted')] = agg_df.apply(\n", + " lambda row: f\"{row[mean_col]:.2f}% ± {row[std_col]:.2f}\" if row[mean_col] > 0 else \"N/A\", axis=1\n", + " )\n", + "\n", + " # Pivot the formatted strings into the final table structure\n", + " formatted_cols = [(metric, 'formatted') for metric in metrics_to_format]\n", + " pivoted = agg_df[formatted_cols].reset_index()\n", + " pivoted.columns = ['dataset', 'model', 'method'] + metrics_to_format\n", + " \n", + " final_pivot = pivoted.pivot_table(\n", + " index=['dataset', 'model'], columns='method', values=metrics_to_format, aggfunc='first'\n", + " )\n", + "\n", + " # Reorder columns to group by method, then by metric\n", + " final_pivot = final_pivot.swaplevel(0, 1, axis=1).sort_index(axis=1)\n", + "\n", + " # --- 4. Print Formatted Tables ---\n", + " pd.set_option('display.max_columns', None)\n", + " pd.set_option('display.width', 200)\n", + "\n", + " datasets = sorted(final_pivot.index.get_level_values('dataset').unique())\n", + " \n", + " for dataset_name in datasets:\n", + " print(\"\\n\" + \"=\"*120)\n", + " print(f\"📊 Results for Dataset: {dataset_name.upper()}\")\n", + " print(\"=\"*120)\n", + " \n", + " dataset_table = final_pivot.loc[dataset_name]\n", + " print(dataset_table.to_string())\n", + " print(\"\\n\")\n", + "\n", + "else:\n", + " print(\"🔴 No data available for table generation after filtering.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e1b1484", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/analysis/RQ1/rq1_analysis_rq1_2_0729.ipynb b/analysis/RQ1/rq1_analysis_rq1_2_0729.ipynb new file mode 100644 index 0000000..74f108b --- /dev/null +++ b/analysis/RQ1/rq1_analysis_rq1_2_0729.ipynb @@ -0,0 +1,2663 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "16dff438", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1124385/1081677035.py:89: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Solution Conciseness'].mean().reset_index()\n", + "/tmp/ipykernel_1124385/1081677035.py:92: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " agg_df_conciseness = run_means.groupby(['dataset', 'model', 'method'])['Solution Conciseness'].agg(['mean', 'std']).reset_index()\n", + "/tmp/ipykernel_1124385/1081677035.py:100: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n", + " conciseness_table = agg_df_conciseness.pivot_table(\n", + "/tmp/ipykernel_1124385/1081677035.py:116: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " plot_agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "📊 Solution Conciseness (Average Plan Length)\n", + "================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral\n", + "dataset model \n", + "dailylifeapis deepseek_v2_5 2.82 ± 0.17 2.84 ± 0.15 2.82 ± 0.15 2.74 ± 0.15\n", + " llama_3_3_70b_instruct 3.04 ± 0.17 3.10 ± 0.21 3.09 ± 0.21 2.94 ± 0.13\n", + " llama_4 2.89 ± 0.18 2.89 ± 0.18 2.92 ± 0.20 2.84 ± 0.13\n", + " phi 2.77 ± 0.19 2.80 ± 0.19 2.81 ± 0.18 2.69 ± 0.14\n", + " qwen2_5_72b_instruct 2.88 ± 0.19 2.87 ± 0.21 2.91 ± 0.20 2.73 ± 0.16\n", + "huggingface deepseek_v2_5 2.71 ± 0.08 2.60 ± 0.19 2.70 ± 0.07 2.30 ± 0.05\n", + " llama_3_3_70b_instruct 2.77 ± 0.05 2.80 ± 0.10 2.78 ± 0.05 2.28 ± 0.06\n", + " llama_4 2.57 ± 0.06 2.58 ± 0.07 2.54 ± 0.09 2.35 ± 0.04\n", + " phi 2.53 ± 0.06 2.57 ± 0.08 2.59 ± 0.06 2.25 ± 0.06\n", + " qwen2_5_72b_instruct 2.68 ± 0.05 2.68 ± 0.04 2.71 ± 0.05 2.25 ± 0.05\n", + "\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "import numpy as np\n", + "import re\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# --- 1. Robust Data Parsing ---\n", + "# Captures all necessary metrics for both the table and the plots.\n", + "root_dir = Path('.')\n", + "detailed_data = []\n", + "ALL_EXPECTED_METHODS = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "results_files = root_dir.glob('**/results.json')\n", + "\n", + "for file_path in results_files:\n", + " try:\n", + " parts = file_path.parts\n", + " current_method = None\n", + " for m in ALL_EXPECTED_METHODS:\n", + " if m in parts:\n", + " current_method = m\n", + " break\n", + " \n", + " if current_method:\n", + " method_index = parts.index(current_method)\n", + " dataset = parts[method_index + 1].replace('_experiments', '').replace('_v3', '')\n", + " model = parts[method_index + 2]\n", + " \n", + " run_id_match = re.search(r'run_seed_(\\d+)', str(file_path))\n", + " run_id = run_id_match.group(1) if run_id_match else file_path.parent.name\n", + "\n", + " with open(file_path, 'r') as f:\n", + " results_list = json.load(f)\n", + "\n", + " for item in results_list:\n", + " metrics = item.get('metrics', {})\n", + " llm_calls = None\n", + " total_tokens = None\n", + "\n", + " if current_method == 'spiral':\n", + " search_process = metrics.get('search_process', {})\n", + " exp_calls = search_process.get('expansion_llm_calls', 0)\n", + " sim_calls = search_process.get('simulation_llm_calls', 0)\n", + " crit_calls = search_process.get('critic_llm_calls', 0)\n", + " llm_calls = exp_calls + sim_calls + crit_calls\n", + " \n", + " exp_tokens = search_process.get('expansion_llm_tokens', 0)\n", + " sim_tokens = search_process.get('simulation_llm_tokens', 0)\n", + " crit_tokens = search_process.get('critic_llm_tokens', 0)\n", + " total_tokens = exp_tokens + sim_tokens + crit_tokens\n", + " else: # Baseline methods\n", + " reasoning_cost = metrics.get('reasoning_cost', {})\n", + " llm_calls = reasoning_cost.get('llm_calls')\n", + " total_tokens = reasoning_cost.get('total_llm_tokens')\n", + "\n", + " detailed_data.append({\n", + " 'run_id': str(run_id),\n", + " 'method': current_method, 'dataset': dataset, 'model': model,\n", + " 'Solution Conciseness': metrics.get('plan_length'),\n", + " 'Tokens': total_tokens,\n", + " 'API Calls': llm_calls\n", + " })\n", + " except Exception as e:\n", + " print(f\"🔴 Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "# --- 2. Data Cleaning and Preparation ---\n", + "df_raw = pd.DataFrame(detailed_data)\n", + "df_cleaned = df_raw.dropna().copy()\n", + "\n", + "models_to_keep = [\n", + " 'deepseek_v2_5', 'llama_3_3_70b_instruct', 'llama_4', \n", + " 'phi', 'qwen2_5_72b_instruct'\n", + "]\n", + "methods_to_keep = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "\n", + "df_filtered = df_cleaned[\n", + " df_cleaned['model'].isin(models_to_keep) & \n", + " df_cleaned['method'].isin(methods_to_keep)\n", + "].copy()\n", + "\n", + "# --- 3. Generate and Print Solution Conciseness Table ---\n", + "if not df_filtered.empty:\n", + " # Set categorical types to enforce order\n", + " df_filtered['model'] = pd.Categorical(df_filtered['model'], categories=sorted(models_to_keep), ordered=True)\n", + " df_filtered['method'] = pd.Categorical(df_filtered['method'], categories=methods_to_keep, ordered=True)\n", + "\n", + " # Calculate mean per run\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Solution Conciseness'].mean().reset_index()\n", + " \n", + " # Calculate final mean and std across runs\n", + " agg_df_conciseness = run_means.groupby(['dataset', 'model', 'method'])['Solution Conciseness'].agg(['mean', 'std']).reset_index()\n", + " \n", + " # Format the string for printing\n", + " agg_df_conciseness['Formatted'] = agg_df_conciseness.apply(\n", + " lambda row: f\"{row['mean']:.2f} ± {row['std']:.2f}\", axis=1\n", + " )\n", + "\n", + " # Pivot to create the final table structure\n", + " conciseness_table = agg_df_conciseness.pivot_table(\n", + " index=['dataset', 'model'],\n", + " columns='method',\n", + " values='Formatted',\n", + " aggfunc='first'\n", + " )\n", + " \n", + " print(\"\\n\" + \"=\"*80)\n", + " print(\"📊 Solution Conciseness (Average Plan Length)\")\n", + " print(\"=\"*80)\n", + " print(conciseness_table.to_string())\n", + " print(\"\\n\")\n", + "\n", + " # --- 4. Generate Bar Plots for Average Cost ---\n", + " \n", + " # Aggregate data for plotting\n", + " plot_agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n", + " 'Tokens': 'mean',\n", + " 'API Calls': 'mean'\n", + " }).reset_index()\n", + "\n", + " sns.set_theme(style=\"whitegrid\", context=\"talk\")\n", + "\n", + " # Plot 1: Average Tokens\n", + " g_tokens = sns.catplot(\n", + " data=plot_agg_df,\n", + " kind='bar',\n", + " x='model',\n", + " y='Tokens',\n", + " hue='method',\n", + " col='dataset',\n", + " hue_order=methods_to_keep,\n", + " order=sorted(models_to_keep),\n", + " height=7,\n", + " aspect=1.2,\n", + " sharey=False # Allow y-axes to have different scales\n", + " )\n", + " g_tokens.fig.suptitle('Model Comparison by Average Cost (Tokens)', y=1.03, fontsize=20)\n", + " g_tokens.set_axis_labels(\"Model\", \"Average Tokens per Task\")\n", + " g_tokens.set_titles(\"Dataset: {col_name}\")\n", + " g_tokens.set_xticklabels(rotation=15)\n", + " plt.tight_layout(rect=[0, 0, 1, 0.97])\n", + " plt.show()\n", + "\n", + " # Plot 2: Average API Calls\n", + " g_calls = sns.catplot(\n", + " data=plot_agg_df,\n", + " kind='bar',\n", + " x='model',\n", + " y='API Calls',\n", + " hue='method',\n", + " col='dataset',\n", + " hue_order=methods_to_keep,\n", + " order=sorted(models_to_keep),\n", + " height=7,\n", + " aspect=1.2,\n", + " sharey=False # Allow y-axes to have different scales\n", + " )\n", + " g_calls.fig.suptitle('Model Comparison by Average Cost (API Calls)', y=1.03, fontsize=20)\n", + " g_calls.set_axis_labels(\"Model\", \"Average API Calls per Task\")\n", + " g_calls.set_titles(\"Dataset: {col_name}\")\n", + " g_calls.set_xticklabels(rotation=15)\n", + " plt.tight_layout(rect=[0, 0, 1, 0.97])\n", + " plt.show()\n", + "\n", + "else:\n", + " print(\"🔴 No data available for analysis after filtering.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b37645f3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1124385/473271306.py:89: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Solution Conciseness'].mean().reset_index()\n", + "/tmp/ipykernel_1124385/473271306.py:92: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " agg_df_conciseness = run_means.groupby(['dataset', 'model', 'method'])['Solution Conciseness'].agg(['mean', 'std']).reset_index()\n", + "/tmp/ipykernel_1124385/473271306.py:100: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n", + " conciseness_table = agg_df_conciseness.pivot_table(\n", + "/tmp/ipykernel_1124385/473271306.py:116: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " plot_agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "📊 Solution Conciseness (Average Plan Length)\n", + "================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral\n", + "dataset model \n", + "dailylifeapis deepseek_v2_5 2.82 ± 0.17 2.84 ± 0.15 2.82 ± 0.15 2.74 ± 0.15\n", + " llama_3_3_70b_instruct 3.04 ± 0.17 3.10 ± 0.21 3.09 ± 0.21 2.94 ± 0.13\n", + " llama_4 2.89 ± 0.18 2.89 ± 0.18 2.92 ± 0.20 2.84 ± 0.13\n", + " phi 2.77 ± 0.19 2.80 ± 0.19 2.81 ± 0.18 2.69 ± 0.14\n", + " qwen2_5_72b_instruct 2.88 ± 0.19 2.87 ± 0.21 2.91 ± 0.20 2.73 ± 0.16\n", + "huggingface deepseek_v2_5 2.71 ± 0.08 2.60 ± 0.19 2.70 ± 0.07 2.30 ± 0.05\n", + " llama_3_3_70b_instruct 2.77 ± 0.05 2.80 ± 0.10 2.78 ± 0.05 2.28 ± 0.06\n", + " llama_4 2.57 ± 0.06 2.58 ± 0.07 2.54 ± 0.09 2.35 ± 0.04\n", + " phi 2.53 ± 0.06 2.57 ± 0.08 2.59 ± 0.06 2.25 ± 0.06\n", + " qwen2_5_72b_instruct 2.68 ± 0.05 2.68 ± 0.04 2.71 ± 0.05 2.25 ± 0.05\n", + "\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "import numpy as np\n", + "import re\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# --- 1. Robust Data Parsing ---\n", + "# Captures all necessary metrics for both the table and the plots.\n", + "root_dir = Path('.')\n", + "detailed_data = []\n", + "ALL_EXPECTED_METHODS = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "results_files = root_dir.glob('**/results.json')\n", + "\n", + "for file_path in results_files:\n", + " try:\n", + " parts = file_path.parts\n", + " current_method = None\n", + " for m in ALL_EXPECTED_METHODS:\n", + " if m in parts:\n", + " current_method = m\n", + " break\n", + " \n", + " if current_method:\n", + " method_index = parts.index(current_method)\n", + " dataset = parts[method_index + 1].replace('_experiments', '').replace('_v3', '')\n", + " model = parts[method_index + 2]\n", + " \n", + " run_id_match = re.search(r'run_seed_(\\d+)', str(file_path))\n", + " run_id = run_id_match.group(1) if run_id_match else file_path.parent.name\n", + "\n", + " with open(file_path, 'r') as f:\n", + " results_list = json.load(f)\n", + "\n", + " for item in results_list:\n", + " metrics = item.get('metrics', {})\n", + " llm_calls = None\n", + " total_tokens = None\n", + "\n", + " if current_method == 'spiral':\n", + " search_process = metrics.get('search_process', {})\n", + " exp_calls = search_process.get('expansion_llm_calls', 0)\n", + " sim_calls = search_process.get('simulation_llm_calls', 0)\n", + " crit_calls = search_process.get('critic_llm_calls', 0)\n", + " llm_calls = exp_calls + sim_calls + crit_calls\n", + " \n", + " exp_tokens = search_process.get('expansion_llm_tokens', 0)\n", + " sim_tokens = search_process.get('simulation_llm_tokens', 0)\n", + " crit_tokens = search_process.get('critic_llm_tokens', 0)\n", + " total_tokens = exp_tokens + sim_tokens + crit_tokens\n", + " else: # Baseline methods\n", + " reasoning_cost = metrics.get('reasoning_cost', {})\n", + " llm_calls = reasoning_cost.get('llm_calls')\n", + " total_tokens = reasoning_cost.get('total_llm_tokens')\n", + "\n", + " detailed_data.append({\n", + " 'run_id': str(run_id),\n", + " 'method': current_method, 'dataset': dataset, 'model': model,\n", + " 'Solution Conciseness': metrics.get('plan_length'),\n", + " 'Tokens': total_tokens,\n", + " 'API Calls': llm_calls\n", + " })\n", + " except Exception as e:\n", + " print(f\"🔴 Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "# --- 2. Data Cleaning and Preparation ---\n", + "df_raw = pd.DataFrame(detailed_data)\n", + "df_cleaned = df_raw.dropna().copy()\n", + "\n", + "models_to_keep = [\n", + " 'deepseek_v2_5', 'llama_3_3_70b_instruct', 'llama_4', \n", + " 'phi', 'qwen2_5_72b_instruct'\n", + "]\n", + "methods_to_keep = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "\n", + "df_filtered = df_cleaned[\n", + " df_cleaned['model'].isin(models_to_keep) & \n", + " df_cleaned['method'].isin(methods_to_keep)\n", + "].copy()\n", + "\n", + "# --- 3. Generate and Print Solution Conciseness Table ---\n", + "if not df_filtered.empty:\n", + " # Set categorical types to enforce order\n", + " df_filtered['model'] = pd.Categorical(df_filtered['model'], categories=sorted(models_to_keep), ordered=True)\n", + " df_filtered['method'] = pd.Categorical(df_filtered['method'], categories=methods_to_keep, ordered=True)\n", + "\n", + " # Calculate mean per run\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Solution Conciseness'].mean().reset_index()\n", + " \n", + " # Calculate final mean and std across runs\n", + " agg_df_conciseness = run_means.groupby(['dataset', 'model', 'method'])['Solution Conciseness'].agg(['mean', 'std']).reset_index()\n", + " \n", + " # Format the string for printing\n", + " agg_df_conciseness['Formatted'] = agg_df_conciseness.apply(\n", + " lambda row: f\"{row['mean']:.2f} ± {row['std']:.2f}\", axis=1\n", + " )\n", + "\n", + " # Pivot to create the final table structure\n", + " conciseness_table = agg_df_conciseness.pivot_table(\n", + " index=['dataset', 'model'],\n", + " columns='method',\n", + " values='Formatted',\n", + " aggfunc='first'\n", + " )\n", + " \n", + " print(\"\\n\" + \"=\"*80)\n", + " print(\"📊 Solution Conciseness (Average Plan Length)\")\n", + " print(\"=\"*80)\n", + " print(conciseness_table.to_string())\n", + " print(\"\\n\")\n", + "\n", + " # --- 4. Generate Bar Plots for Average Cost ---\n", + " \n", + " # Aggregate data for plotting\n", + " plot_agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n", + " 'Tokens': 'mean',\n", + " 'API Calls': 'mean'\n", + " }).reset_index()\n", + "\n", + " # --- MODIFICATION: Beautify plots ---\n", + " sns.set_theme(style=\"darkgrid\", context=\"talk\", palette=\"plasma\")\n", + "\n", + " # Plot 1: Average Tokens\n", + " g_tokens = sns.catplot(\n", + " data=plot_agg_df,\n", + " kind='bar',\n", + " x='model',\n", + " y='Tokens',\n", + " hue='method',\n", + " col='dataset',\n", + " hue_order=methods_to_keep,\n", + " order=sorted(models_to_keep),\n", + " height=7,\n", + " aspect=1.2,\n", + " sharey=False\n", + " )\n", + " g_tokens.fig.suptitle('Model Comparison by Average Cost (Tokens)', y=1.12, fontsize=22)\n", + " sns.move_legend(\n", + " g_tokens, \"upper center\",\n", + " bbox_to_anchor=(.5, 1.02), ncol=len(methods_to_keep), title='Method', frameon=True\n", + " )\n", + " g_tokens.set_axis_labels(\"Model\", \"Average Tokens per Task\", fontsize=16)\n", + " g_tokens.set_titles(\"Dataset: {col_name}\", size=18)\n", + " g_tokens.set_xticklabels(rotation=15, ha='right')\n", + " plt.tight_layout(rect=[0, 0, 1, 0.95])\n", + " plt.show()\n", + "\n", + " # Plot 2: Average API Calls\n", + " g_calls = sns.catplot(\n", + " data=plot_agg_df,\n", + " kind='bar',\n", + " x='model',\n", + " y='API Calls',\n", + " hue='method',\n", + " col='dataset',\n", + " hue_order=methods_to_keep,\n", + " order=sorted(models_to_keep),\n", + " height=7,\n", + " aspect=1.2,\n", + " sharey=False\n", + " )\n", + " g_calls.fig.suptitle('Model Comparison by Average Cost (API Calls)', y=1.12, fontsize=22)\n", + " sns.move_legend(\n", + " g_calls, \"upper center\",\n", + " bbox_to_anchor=(.5, 1.02), ncol=len(methods_to_keep), title='Method', frameon=True\n", + " )\n", + " g_calls.set_axis_labels(\"Model\", \"Average API Calls per Task\", fontsize=16)\n", + " g_calls.set_titles(\"Dataset: {col_name}\", size=18)\n", + " g_calls.set_xticklabels(rotation=15, ha='right')\n", + " plt.tight_layout(rect=[0, 0, 1, 0.95])\n", + " plt.show()\n", + "\n", + "else:\n", + " print(\"🔴 No data available for analysis after filtering.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3fa5e7df", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1124385/1006895860.py:89: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Solution Conciseness'].mean().reset_index()\n", + "/tmp/ipykernel_1124385/1006895860.py:92: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " agg_df_conciseness = run_means.groupby(['dataset', 'model', 'method'])['Solution Conciseness'].agg(['mean', 'std']).reset_index()\n", + "/tmp/ipykernel_1124385/1006895860.py:100: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n", + " conciseness_table = agg_df_conciseness.pivot_table(\n", + "/tmp/ipykernel_1124385/1006895860.py:116: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " plot_agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "📊 Solution Conciseness (Average Plan Length)\n", + "================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral\n", + "dataset model \n", + "dailylifeapis deepseek_v2_5 2.82 ± 0.17 2.84 ± 0.15 2.82 ± 0.15 2.74 ± 0.15\n", + " llama_3_3_70b_instruct 3.04 ± 0.17 3.10 ± 0.21 3.09 ± 0.21 2.94 ± 0.13\n", + " llama_4 2.89 ± 0.18 2.89 ± 0.18 2.92 ± 0.20 2.84 ± 0.13\n", + " phi 2.77 ± 0.19 2.80 ± 0.19 2.81 ± 0.18 2.69 ± 0.14\n", + " qwen2_5_72b_instruct 2.88 ± 0.19 2.87 ± 0.21 2.91 ± 0.20 2.73 ± 0.16\n", + "huggingface deepseek_v2_5 2.71 ± 0.08 2.60 ± 0.19 2.70 ± 0.07 2.30 ± 0.05\n", + " llama_3_3_70b_instruct 2.77 ± 0.05 2.80 ± 0.10 2.78 ± 0.05 2.28 ± 0.06\n", + " llama_4 2.57 ± 0.06 2.58 ± 0.07 2.54 ± 0.09 2.35 ± 0.04\n", + " phi 2.53 ± 0.06 2.57 ± 0.08 2.59 ± 0.06 2.25 ± 0.06\n", + " qwen2_5_72b_instruct 2.68 ± 0.05 2.68 ± 0.04 2.71 ± 0.05 2.25 ± 0.05\n", + "\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "import numpy as np\n", + "import re\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# --- 1. Robust Data Parsing ---\n", + "# Captures all necessary metrics for both the table and the plots.\n", + "root_dir = Path('.')\n", + "detailed_data = []\n", + "ALL_EXPECTED_METHODS = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "results_files = root_dir.glob('**/results.json')\n", + "\n", + "for file_path in results_files:\n", + " try:\n", + " parts = file_path.parts\n", + " current_method = None\n", + " for m in ALL_EXPECTED_METHODS:\n", + " if m in parts:\n", + " current_method = m\n", + " break\n", + " \n", + " if current_method:\n", + " method_index = parts.index(current_method)\n", + " dataset = parts[method_index + 1].replace('_experiments', '').replace('_v3', '')\n", + " model = parts[method_index + 2]\n", + " \n", + " run_id_match = re.search(r'run_seed_(\\d+)', str(file_path))\n", + " run_id = run_id_match.group(1) if run_id_match else file_path.parent.name\n", + "\n", + " with open(file_path, 'r') as f:\n", + " results_list = json.load(f)\n", + "\n", + " for item in results_list:\n", + " metrics = item.get('metrics', {})\n", + " llm_calls = None\n", + " total_tokens = None\n", + "\n", + " if current_method == 'spiral':\n", + " search_process = metrics.get('search_process', {})\n", + " exp_calls = search_process.get('expansion_llm_calls', 0)\n", + " sim_calls = search_process.get('simulation_llm_calls', 0)\n", + " crit_calls = search_process.get('critic_llm_calls', 0)\n", + " llm_calls = exp_calls + sim_calls + crit_calls\n", + " \n", + " exp_tokens = search_process.get('expansion_llm_tokens', 0)\n", + " sim_tokens = search_process.get('simulation_llm_tokens', 0)\n", + " crit_tokens = search_process.get('critic_llm_tokens', 0)\n", + " total_tokens = exp_tokens + sim_tokens + crit_tokens\n", + " else: # Baseline methods\n", + " reasoning_cost = metrics.get('reasoning_cost', {})\n", + " llm_calls = reasoning_cost.get('llm_calls')\n", + " total_tokens = reasoning_cost.get('total_llm_tokens')\n", + "\n", + " detailed_data.append({\n", + " 'run_id': str(run_id),\n", + " 'method': current_method, 'dataset': dataset, 'model': model,\n", + " 'Solution Conciseness': metrics.get('plan_length'),\n", + " 'Tokens': total_tokens,\n", + " 'API Calls': llm_calls\n", + " })\n", + " except Exception as e:\n", + " print(f\"🔴 Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "# --- 2. Data Cleaning and Preparation ---\n", + "df_raw = pd.DataFrame(detailed_data)\n", + "df_cleaned = df_raw.dropna().copy()\n", + "\n", + "models_to_keep = [\n", + " 'deepseek_v2_5', 'llama_3_3_70b_instruct', 'llama_4', \n", + " 'phi', 'qwen2_5_72b_instruct'\n", + "]\n", + "methods_to_keep = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "\n", + "df_filtered = df_cleaned[\n", + " df_cleaned['model'].isin(models_to_keep) & \n", + " df_cleaned['method'].isin(methods_to_keep)\n", + "].copy()\n", + "\n", + "# --- 3. Generate and Print Solution Conciseness Table ---\n", + "if not df_filtered.empty:\n", + " # Set categorical types to enforce order\n", + " df_filtered['model'] = pd.Categorical(df_filtered['model'], categories=sorted(models_to_keep), ordered=True)\n", + " df_filtered['method'] = pd.Categorical(df_filtered['method'], categories=methods_to_keep, ordered=True)\n", + "\n", + " # Calculate mean per run\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Solution Conciseness'].mean().reset_index()\n", + " \n", + " # Calculate final mean and std across runs\n", + " agg_df_conciseness = run_means.groupby(['dataset', 'model', 'method'])['Solution Conciseness'].agg(['mean', 'std']).reset_index()\n", + " \n", + " # Format the string for printing\n", + " agg_df_conciseness['Formatted'] = agg_df_conciseness.apply(\n", + " lambda row: f\"{row['mean']:.2f} ± {row['std']:.2f}\", axis=1\n", + " )\n", + "\n", + " # Pivot to create the final table structure\n", + " conciseness_table = agg_df_conciseness.pivot_table(\n", + " index=['dataset', 'model'],\n", + " columns='method',\n", + " values='Formatted',\n", + " aggfunc='first'\n", + " )\n", + " \n", + " print(\"\\n\" + \"=\"*80)\n", + " print(\"📊 Solution Conciseness (Average Plan Length)\")\n", + " print(\"=\"*80)\n", + " print(conciseness_table.to_string())\n", + " print(\"\\n\")\n", + "\n", + " # --- 4. Generate Bar Plots for Average Cost ---\n", + " \n", + " # Aggregate data for plotting\n", + " plot_agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n", + " 'Tokens': 'mean',\n", + " 'API Calls': 'mean'\n", + " }).reset_index()\n", + "\n", + " # --- MODIFICATION: Beautify plots ---\n", + " sns.set_theme(style=\"whitegrid\", context=\"talk\") # Reverted to a clean, standard theme\n", + "\n", + " # Map for aligned model names\n", + " model_name_map = {\n", + " 'deepseek_v2_5': 'DeepSeek-V2.5',\n", + " 'llama_3_3_70b_instruct': 'Llama 3.3 70B',\n", + " 'llama_4': 'Llama 4 Maverick 17B',\n", + " 'phi': 'Phi 4 14B',\n", + " 'qwen2_5_72b_instruct': 'Qwen 2.5 72B'\n", + " }\n", + " plot_agg_df['model_long_name'] = plot_agg_df['model'].map(model_name_map)\n", + " model_order = [model_name_map[m] for m in sorted(models_to_keep)]\n", + "\n", + "\n", + " # Plot 1: Average Tokens\n", + " g_tokens = sns.catplot(\n", + " data=plot_agg_df,\n", + " kind='bar',\n", + " x='model_long_name',\n", + " y='Tokens',\n", + " hue='method',\n", + " col='dataset',\n", + " hue_order=methods_to_keep,\n", + " order=model_order,\n", + " height=7,\n", + " aspect=1.2,\n", + " sharey=False\n", + " )\n", + " g_tokens.fig.suptitle('Model Comparison by Average Cost (Tokens)', y=1.12, fontsize=22)\n", + " sns.move_legend(\n", + " g_tokens, \"upper center\",\n", + " bbox_to_anchor=(.5, 1.02), ncol=len(methods_to_keep), title=None, frameon=False\n", + " )\n", + " g_tokens.set_axis_labels(\"Model\", \"Average Tokens per Task\", fontsize=16)\n", + " g_tokens.set_titles(\"Dataset: {col_name}\", size=18)\n", + " g_tokens.set_xticklabels(rotation=15, ha='right')\n", + " plt.tight_layout(rect=[0, 0, 1, 0.95])\n", + " plt.show()\n", + "\n", + " # Plot 2: Average API Calls\n", + " g_calls = sns.catplot(\n", + " data=plot_agg_df,\n", + " kind='bar',\n", + " x='model_long_name',\n", + " y='API Calls',\n", + " hue='method',\n", + " col='dataset',\n", + " hue_order=methods_to_keep,\n", + " order=model_order,\n", + " height=7,\n", + " aspect=1.2,\n", + " sharey=False\n", + " )\n", + " g_calls.fig.suptitle('Model Comparison by Average Cost (API Calls)', y=1.12, fontsize=22)\n", + " sns.move_legend(\n", + " g_calls, \"upper center\",\n", + " bbox_to_anchor=(.5, 1.02), ncol=len(methods_to_keep), title=None, frameon=False\n", + " )\n", + " g_calls.set_axis_labels(\"Model\", \"Average API Calls per Task\", fontsize=16)\n", + " g_calls.set_titles(\"Dataset: {col_name}\", size=18)\n", + " g_calls.set_xticklabels(rotation=15, ha='right')\n", + " plt.tight_layout(rect=[0, 0, 1, 0.95])\n", + " plt.show()\n", + "\n", + "else:\n", + " print(\"🔴 No data available for analysis after filtering.\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1124385/2693839281.py:89: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Solution Conciseness'].mean().reset_index()\n", + "/tmp/ipykernel_1124385/2693839281.py:92: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " agg_df_conciseness = run_means.groupby(['dataset', 'model', 'method'])['Solution Conciseness'].agg(['mean', 'std']).reset_index()\n", + "/tmp/ipykernel_1124385/2693839281.py:100: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n", + " conciseness_table = agg_df_conciseness.pivot_table(\n", + "/tmp/ipykernel_1124385/2693839281.py:116: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " plot_agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "📊 Solution Conciseness (Average Plan Length)\n", + "================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral\n", + "dataset model \n", + "dailylifeapis deepseek_v2_5 2.82 ± 0.17 2.84 ± 0.15 2.82 ± 0.15 2.74 ± 0.15\n", + " llama_3_3_70b_instruct 3.04 ± 0.17 3.10 ± 0.21 3.09 ± 0.21 2.94 ± 0.13\n", + " llama_4 2.89 ± 0.18 2.89 ± 0.18 2.92 ± 0.20 2.84 ± 0.13\n", + " phi 2.77 ± 0.19 2.80 ± 0.19 2.81 ± 0.18 2.69 ± 0.14\n", + " qwen2_5_72b_instruct 2.88 ± 0.19 2.87 ± 0.21 2.91 ± 0.20 2.73 ± 0.16\n", + "huggingface deepseek_v2_5 2.71 ± 0.08 2.60 ± 0.19 2.70 ± 0.07 2.30 ± 0.05\n", + " llama_3_3_70b_instruct 2.77 ± 0.05 2.80 ± 0.10 2.78 ± 0.05 2.28 ± 0.06\n", + " llama_4 2.57 ± 0.06 2.58 ± 0.07 2.54 ± 0.09 2.35 ± 0.04\n", + " phi 2.53 ± 0.06 2.57 ± 0.08 2.59 ± 0.06 2.25 ± 0.06\n", + " qwen2_5_72b_instruct 2.68 ± 0.05 2.68 ± 0.04 2.71 ± 0.05 2.25 ± 0.05\n", + "\n", + "\n" + ] + }, + { + "data": { + "image/png": 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1xx9/WE+uOnbsqMcff1wNGza0++8D57vWEh9/durUKb311lvavXu3WrRooZ49e6pKlSr65ptvFB0draeeekoLFixQUFCQtU+9evUUHBysAwcOKCoqSvfee6/y8/Pl4eEhDw8PJSUlacOGDapYsaL+9re/ydvbu9x/ZzjeX03EkpOTFR4err1791q31a9f33plaMuWLfX0008rIyNDLVu2lHTlhKigoEAeHh4KCQnR7t27dezYMZ09e1b169e3tpGkpUuX6siRI+rZs6eCg4ML7cPN7c/vYdcjNTVV27ZtU8eOHbVv3z4lJCQoISHBOsYkWZeP6dy5syIjI3Xw4EHdeeedqlChgvWE/bffftMPP/ygwMBATZw4Uf7+/iwF4qKys7P17bff6ssvv9SZM2dUtWpVtW3bVtWrV1dCQoJiY2M1depUJSQkaOzYsdYvEYxGo/XL1R07dkj633uUZRxt3bpV+/btU/369dW5c2cn/HYAbhTMCWFPzAfhCMwHUZ6YE+JGwpzw1kaxEA5X0klUZmamdu3apeXLl2vDhg3Wq/Dc3d1lNps1dOhQDR8+XI0aNfrLDyTL8WvWrKmmTZvql19+0X//+1/rFVZNmjSRm5ubjh49qmPHjmn79u1644031KVLl3L4jeFIlpMoy5XIVy/xER8fr4iICO3bt0+zZs0q0ve///2vdu/erQcffFDTpk2zjrH+/ftr8uTJ2rRpk2bNmqVnn31WDRo0kCRVrVpVoaGhOnDggHbs2KF7771XHh4eOnbsmCIiIrRmzRrFx8erZ8+eqlmzpuP+ECh313qeyZ/VqFFD1apVU/fu3dW2bVu1bdtWLVu2tF55nJ2dLX9/f2VkZFhPxq++Uq9Nmzby9PTUqVOndOnSJev2mJgYrVmzRmvWrFHNmjXVp08f1a1b196/Kpzo6vewy5cvq0KFCqXuu3XrVqWnp2v48OGqWLGiNm/erOjo6EITQ8s469mzpyIjI3XkyBHl5+frxIkT2rhxoyIiIrR3717l5+crICBAmzdvVmBgoOrUqWPfXxQ3lGPHjmnlypVq2LChBgwYoPz8fLm5uWnx4sV655131LBhQz3//PPq16+f9cuqY8eO6bPPPtPy5cv1xRdfaPjw4dbnhBkMBuvzKJKTk/XHH39YH1YfHx+vDRs26IcfflClSpX06KOPWt8buSIeuHUxJ0R5YT4IR2E+CEdhTghnYE7omigWwuEs/wdPSEiQ0WhU3bp1lZaWpoULF+qbb75RWlqamjZtqttuu01169bV77//rhUrVuibb77RiRMn9MUXX5T6yhU3Nzc1adJE9evXV+3atTVw4ED16dNHvr6++uOPP7Rnzx59++232rVrl+bMmaNatWqpcePGvBHdxK4+iUpOTtbWrVu1ceNG7dixQxcvXrS2S0lJkZ+fn/V1Zmamfv31V0lSWFiY3NzclJubK4PBoJo1a+rxxx9XQkKCIiIiFBISolGjRslsNsvLy0utW7eWJK1evVqZmZnaunVrkWdRrF+/Xrt371bnzp01adKkQg+9x83J8h5x8uRJ/frrr/L19VX//v2Lbevh4aFp06apatWqhbZbvsjYu3evzp07p9q1axc74WzdurX1GRRz585VRkaGdu3apezs7ELHe/PNN/Xjjz9q7Nix6tSpEw8cv8llZWVZvzCNiorS+PHj9eijj/7lZ6DlavYtW7aoSpUqatmypdq2bavNmzcrKipKjzzyiLWtZYzcfvvtkq5c4derVy9lZGRIujJ2/fz8VKNGDeXk5GjRokWaP3++PvjgA911113XfXcHbkxnz561fl5GRUVZPy979OihAQMGyMPDQwUFBapUqZLq1KmjqVOnFrrSMz8/X02aNNEzzzyjXbt26fTp04qOjlb37t2t73N+fn5q1aqVkpOT9e6778rDw0ObNm2yLr9l8f7772vDhg0KCwvTo48+6tC/AwDHYU6I8sJ8EI7CfBCOwJwQjsKcEBLFQjhBUlKSpkyZoq1bt+rll1/WyJEj9euvv2rWrFlq06aNXn/9dd11113WD6ucnBzdfvvteuWVV7Rt2zbt2LGjVLciX/2MiuHDh6tbt25q0qSJJFkfNH7fffcpKChIQ4cO1f79+7V+/XqNGTOGSeFNKjc3V9u2bVNERIS2bt1qXTNburLWda1atRQfH6+uXbta12u3fGDFxcXp5MmTatq0qXVd7KufJ9GmTRuFhYVp7ty5+umnnzRq1CjrlapBQUGqXLmyLl68qJUrV1of2tu+fXu1aNFCQUFB2rhxoxYsWKBVq1bJ09NT4eHh1itocPNJTU3Vjh079MMPP2jTpk2SpD59+qhbt27Wq5/+zDIxLO45PJUqVVJaWprq1q2rpk2bWvtY9tevX19NmjTR1q1btXLlSusVWSEhIQoNDVVISIhMJpM+++wzbdy4US+88IKmTJmie+65h6VBbkLZ2dnatm2bVqxYoXXr1lm3nzhxQpmZmfL19b1mfw8PD506dUo7duxQaGio6tatq7Zt20qS9u7dq7S0NOt4vHqM1axZU+fOnZOPj4/uvvtu3XbbbWrTpo2aNm0qHx8fpaamaubMmfrqq6/04YcfytPTU3fddVe5/A1Qvi5fvqzt27dr48aN+vXXX5WUlGTdV69ePZnNZuXm5hZ6LpObm5t69eqlfv36Wa8OtZxreXh4yGw2q3bt2mrVqpVOnz6t2NjYQhNDHx8fhYSEaMOGDVq1apU1VteuXdWmTRs1b95clStX1pdffqmffvpJu3btUo0aNQqdEwK4dTAnRHlgPghHYT6I8sacEOWNOSGKQ7EQZXa9a2e7u7tr+/btqlq1qrp37y5JatGihUJDQzVp0iR17NhR0pUTKJPJJC8vL/3tb3/T0qVLtWPHDu3fv79UE0PLh12LFi0K3V5vycGSe/Pmza3Hj4mJUUZGhipXrlzq3x83jk8//VRz5syRdOWhuHfeeafatGmj0NBQdevWTd9//73++c9/yt3dXQEBASooKLCOk2rVqiknJ0epqamqV69ekWNXrFjReozDhw/rt99+U7NmzSRdeYBvcHCwdu3apccee0yTJ08u0n/QoEGqX7++hg8frl9//VUdOnTQQw89VI5/DZSXpKQk60T/3Llzqly5si5duqTk5GQlJiaqevXq17wS/eqJmqXNxYsX5e7urkuXLlmfnWNhOalq3bq1tm3bpvr162vChAkKCwsr9AWGJAUEBKhy5cr68ccf9e233+qee+7hi66bUEREhKZPn67U1FS1aNFCAQEB1mcsnTt3Tr6+vn95t8Pp06d17tw59e3bV5IUHBysqlWr6ty5czp69Kj1s1b631Wnt99+u1asWKGePXvqzTffLDRW8/LyVL16dY0cOVJHjhzRrl27tG7dOiaGN6FLly7pkUceUVxcnKQry/P16NFDbdq0UZs2bdShQwc9+uijOnTokPWZTJbxVqNGDRkMBuvrq8egwWDQxYsXrXdSVKxYUZKsS2l5enoWmmjOnj1bbdu2LXKF/Xvvvaf8/HytXr1a33//vZo3b65GjRqV158DgJ0wJ8SNgPkgHIH5IByBOSHKE3NClIRiIcrs6mU+SsNkMsnHx0dpaWnWh3vXr19fS5YsKdTOzc1Nbm5u1gfttm3bVjt27FBCQoL1OKWJa3nTutaHaHBwsNzc3HT+/HmlpKQwMbzJWMZC79695e7urubNm6tVq1ZF1ur/448/rO2log+3r1Klis6fP6/k5GQFBAQUGTNBQUFq1aqVIiMjtWnTJjVr1kxms1m+vr667bbbtGvXLuuVOPn5+dYPTct/oaGhatasmY4ePar9+/frwQcftH5g4sZnGQ/Hjh3TDz/8oIoVK+rZZ59Vs2bN9OGHH+rkyZNKSEhQ69atSz0hsxzzwIEDMplMat26tbKysqwnVFdr166djEajzGaz6tSpI6PRaB3L0pUvvGrVqqUePXroxx9/1K5du3Tu3DmejeIEf35WTmlZ+lgeJP/II49o2LBhkqTdu3crISFBiYmJaty4cYnHtYyppUuXSpI6dOggSapevbrat2+vDRs2aPfu3erYsWORHPv06aMVK1bo6NGj+uOPPxQQEGB9f7UsLePn56dmzZpp9+7dOnDgwHU/MwPOZTab5e3trX79+lmv3mzdunWhz8v09HTre9Dly5et/a6eCJY0/vLy8rRz505VqFDB+uX/1QIDA9WoUSMlJCSoZs2aqlq1qvXZF9KV/w94eHjorrvu0tq1a3XkyBEdPnyYiSFwE2BOCGdiPghHYD6I68GcEDcq5oS4Fu5DR5mlpqZqxIgRat++vTZt2lToIczFiYuLk5ubmxo3bqysrCxJsn7gWJZjuJrl6qrTp09L+t/SDdd7+3Fxb2KWEys/Pz+ZTCbl5OSoSpUq13VclJ3lJKq4f//SsIyFNm3a6B//+Ifuuece64ec5WpkSdYrZkJCQpSbm1voGHl5edaHMx86dMia19V8fX0VGhoqSYqKirJuNxqNatWqlSRp27Ztkq5cNePu7m496SooKFCFChXk5+cns9kss9msnJwcm35fOFdISIiefPJJvfXWWxo5cqTuvPNONWzYUBcvXlR8fLzy8/NLfSzLmLeM4UqVKqlmzZqF/r9gOWFq2bKlAgIClJycrP3791v7Wf6zvK5atapq1qyp/Pz8QstIwHEsX5gaDAbrEld/9dko/e/f+o477tDChQs1ZcoUBQUFKSgoSE2aNNGFCxd07Nixa44xg8Ggc+fOKS4uTt26dVPdunWtsTt16iRJ2r59e6HliCz/a9l/+PBh67JdV3/W5ufnW690NpvNql69epHn8eDGZjkXeuqpp/TSSy8pLCzM+nlpGVfJyclKSEhQtWrVrF8ulXbpqm+++UYFBQXq1KmT9TP1ajVq1LB+jv7888/W7ZZiwNWf5wUFBcrKyrL+fwjAjY05IcqC+SBuJswHURrMCXGjYk6Ia6FYiDKrVq2a8vPzlZWVpRkzZujIkSOSik7yLK9zc3OVlpamatWqWddAtrxRFffGY9l/9cPG7eHqD8WrrzCsUaOGXY6Pv2Y5WTEYDNYPhby8PJ0+fdp65YotxzSZTNZjX32FlGUM1qhRo8hVeL6+vtYPsYMHDxZ7bKPRaH1+xdGjR61Xi0pSo0aNFBAQoOzsbGv//Px86+TUzc1NWVlZ1hwqVqyoSpUqlepkEWWXl5dX5AuB62X5t65evbqefPJJdevWTRUrVpSHh4datGghSfrtt9907tw5SaWbCLi7uys3N1eZmZmSSr7KWboybps1a6a8vDwdO3bM+rBpyxcrlt/v5MmTunDhgmrXrn1dE1XYz++//67XXntNAwYM0McffyypdOPB4ur3GstJcZs2bSRdmbRZ/u3/zBLj6NGjOnbsmJo3b64KFSpYj2FZru3AgQNKT0/XH3/8obi4OGu/qlWrqmnTpsrLy9ORI0eUl5dnfU+VrnzpdenSJR0/flzSlavra9euzfuYk+Tm5mrr1q164403dPjwYUnFf8Fekj9/XlrubLDcVXHx4kXr8mqlkZycbH2eyoMPPqgKFSoUyadixYrWiaHlvM4yGbz6S+LU1FT5+PgoMzOTK0iBmwRzQtiC+SDzQUdhPghHY04IR2BOCHujWIgyMZlMMhgMeumll9SlSxfFxsbqq6++Uk5Ojtzc3Ap9WPz5apWMjAxVrFjxmrfjW54hsHDhQqWnp6tjx47WB9KXJWdLHpYHmc+ePVuSNHTo0DIdG9fH8m9/7NgxzZs3T4888oh69+6tJ554QhMnTtTHH3983Se3V1+9ZeHm5qZz585Zj2U5ib76BLxGjRpq2LChJCkmJqbIfosqVapYr/Q7ceKEdXvNmjWt625bHnDu4eFR6KqYb775Rtu3b5eXl5fuu+++Qn8DlI/ExET9/e9/13333VfipN/iek9uCwoKrGOqVatWqlKlio4fP65Tp05d13GMRqMOHDgg6cryV1d/aXF1LOl/k4Pff//demW95b3MaDTq7NmzWrNmjUwmk0JCQnTbbbdx0u5gly9f1kcffaQlS5boyJEj+u233ySV/iq8P7P0a9++vSTpyJEjOnv2bLFtLe8nGzdulCQNHjxYkqzPMrFcZZydna2HH35YPXv21BtvvGG9YlSSdZmQmJgY6xezlvewhIQEvffee9q6dauqVKmiPn36FIqL8nfo0CF98sknGjJkiEJDQzV69Gh988032rFjh6Tr+7co7vNSuvL+Ur16dXl5eZXqi1rLe9ayZct0/Phxde/eXf369bN+MXo1y9JwRqNR8fHxOn/+fKHigOULs08//VSZmZnq1KmT6tevX+rfCYBzMCeErZgPch5V3pgPMh90BuaEKE/MCVGeWBwdSk1Nla+vr01r5Vs+LEJDQ/XUU08pKipKv/zyixo1aqTRo0cX+wZ18OBBubm5qWnTpsrKylKlSpVKPL6bm5tSUlK0Zs0aSdLAgQPl4+NT4rMmCgoKZDabi12OJi0tTRUrVrTeLn/q1ClFRUXpq6++Umpqqm6//XYeyutA+fn52r59u1asWKG1a9cqLy9P7u7uqlSpkoxGozZs2KANGzYoJSVF48ePl7+//18+vPlaPD09FRMTI6PRqODgYEmFP0C9vLzUsmVL+fr66uDBg4qPj1dQUJD1amNLbMtyMhUqVLAumSRdWS4kNDRUv/zyi7Zu3apx48YpOTlZR44c0Z49e7Ru3TolJCSobt26mjBhgtq1a1e2PyBKxcfHR8nJyTpz5ozOnDlTbBtbniMgFb5KuXnz5qpbt64SEhL0+++/q2PHjqU6niW2ZfLn7+8vd3f3Qle5X61NmzaqUqWKkpKSrFesJiYmKjY2Vlu2bNGqVauUmZmpO++8Uy+88IIkTtodyWw2Kz8/X1u2bFHdunWtS8ScOnVK9evXt+k9zDIOWrduLX9/f504cUKnTp2yvo8VF3/z5s1q27atKlWqpNjYWG3evFnbt29XTEyMsrOzZTQadeLECVWqVEm33XabvL29rbn17NlT8+fP18GDBxUbG6vMzEzt3btX0dHR2rFjh0wmk5o2bapJkybpzjvvtMvfDdd24MABTZs2TceOHVNGRoakK1djtm3bVq1bt1abNm2sywWV5f/vlved06dPKzU1VR06dLA+R+xa3N3dlZSUpPnz58vDw0Ph4eHW7cWpW7eumjdvrgMHDig2NlZ33HGH9fNy+/btWrlypc6ePas2bdpo4sSJqlatms2/E4DSY07InNDRmA8yH3QE5oPMBx2NOSHKA3NC5oSOQrHQhe3atUsvvfSSfHx89N5776l58+bFfmhdvTTItXTt2lXDhw/XN998o08++US9evVS48aNrfstD8S9ePGiCgoKVK9ePeuyG8Ud27L9hx9+UFxcnNq2basHH3zwmh+sJV2lk5mZqU8//VRHjx6Vj4+PEhMTlZKSYj25GjRokP7xj39Ynx/AyVT5y87O1kcffaRDhw6pWbNm6t+/v7p27aqQkBBlZmbq888/13//+18tW7ZMrVq10tChQ1VQUHDdzyWxsDwgNzc3V7Vq1ZL0vzFm+d8WLVqoefPm2rVrlzZu3KigoKAiV+FVqVJFycnJqlu3rvXKU+nK5LNFixZyd3fX/v37NX78eMXFxVmv9pOk22+/XYMGDdLdd99t0++A61elShV17NhR33//veLi4tSrV69CJzpms1lubm4ymUzat2+ffH191axZs1K/D1ja1KlTR0FBQYqNjdXRo0dL/YBvNze3QlfwWa5y/nNsy3tb8+bN1aBBA8XFxWnBggX66quvdPjwYaWkpEi6crI4ZMgQPfzww9YlS+AYljHz008/KS8vT127dtXhw4cVFxenAwcOqH79+ja9h1nGgr+/v4KDg7Vp0yYdOXJEPXr0sH7ReXXbmJgY5efn6/jx43rooYesY0O6cjVp06ZNdfToUQUFBRV6PoBF+/bt5enpqYSEBI0bN07nz5+37mvcuLHuvfde9e3bt9iJKezLMlHLy8tTdHS0PDw81L9/f3Xo0EFt2rRRUFCQ9fle9mR577p06dJffjFryfH9999XRkaGhgwZ8v/au/O4qOr9j+OvGTbZQUFWQUEWARERxH3DfU1NbTGvmi223Mz7y7p1u9fM6rZZ3VYrK0vNTHMt09wVFHBDZZd9EUQRcWGf+f3B4xwZQUVzjc/zn2zmzJkzM4fv+b7P+Z7PV72rQpl/6vJ93s7Ojk6dOnH06FE+/fRT1q9fz4EDB9S2UKPRMGTIECZPnqyOnhdC3DqSCSUT3imSB8XtIHlQ3E6SCcXNJplQMuHtJhcLmyHlj9jMzAyNRsPZs2c5ceIEfn5+jS7flA6S0mg89thjZGdns3PnThYuXMhTTz2Fp6en2jDo9Xq1EVMm875Sg6PRaDhx4gTLly8H4KWXXrrm9hQUFPDjjz9y8eJFJkyYoB64rKyscHZ2ZunSpRgbG2NsbIyLiwtDhw5l6NChdOrUCRMTkyuO3hKN+zMh2szMjP79+zNhwgQeeOABg+dsbGyYOnUqp0+fZuXKlRw8eJBJkyb9qd8mLS0NY2Nj7OzsDEaAwqV9qm3btgwdOpS4uDg2bNjAyJEjcXFxobq6Wr0VXjlwWVpaYm5ubrAeDw8P7OzsOH36NFu2bKFly5aMGDGC/v3706NHD1q2bHnD2y+un3IyqlOnTvzyyy8kJiZSUlJiMGJOo9Fw7NgxJk6ciJ2dHR9//DFwfSOxlHYjICCAX3/9lbS0NAoLC2nbtm2T/kY0Gg1JSUlYW1urddivdLLMwsKC9u3bc/ToUaKjowFwdXXl/vvvZ8CAAXTv3r3BfimapqioiJ9//hl7e3seeOCBGz4RdeTIEZycnLjvvvs4c+YMCQkJxMfHM3z48BtuL5V9uXPnzmowLC0txcnJSV1G2dfs7OzQaDTqJPOBgYH06dOHvn37EhISQmlpKd26deP48eOkp6fTrl079W+htrYWY2NjXF1dyc7ORqPRMHToUAYMGEDPnj1l/qbbTDnmhYaGqnM1zJw5s0HpPaX81Y3cDXT5++n1erV0lo2NjbrvNUY5ubZnzx527dqFra2twfG8frmi+lq0aKHO63P48GEOHz6Mvb09w4YNIzIykp49e8rIUSFuA8mEkglvBsmDkgfvZpIHxfWSTCiZ8G4jmVDcbnKxsBlSGhovLy98fX3ZtWsXqamp9OvXz+CgpRxk8vPziYuLIywsDHd390bXqdFo0Ol0ODo6MnXqVJKSkli/fj02Nja88sor6nIXL14kJSUFExMTXF1dDbanvpqaGoyNjfnqq6/Iz89n7NixBAcHqyVlwPBWZmVb8/Ly+Oqrr3B0dKRz5874+/urz40bNw4/Pz+srKzw9PRstNGRUHh1er3eoFPd2IjjpnZ8TE1N+dvf/qZ2ZGtqatRa+wD29vZYW1uj0WiwsbG54dCuvK6yspLCwkJ8fX3VWteXb6uJiQljxozhiy++IDU1lQULFvDPf/5TDXU6nY6vvvoKqJvLxNTU1OAzt2zZkqlTp6LVahk4cKDBSFNxY4qKinBwcGhyJ73+qHfldwkODsbOzo709HROnDiBu7u7+lx5eTkLFixAp9PxyiuvqHMA3IjAwEBatmxJTk4OOTk5TR7JaWJiQk1NDRUVFbi5uV1xOeUEW0REhDpKsVevXgbhQNy46OhoPvnkE8LDwxk4cOB1l7rSaDSUlJSwefNmQkJCCAsLU+eJSEhIUI9rN0LZhtDQUIyNjUlNTaWwsNDgt1eW8fT05Mknn6RVq1ZEREQ0OFlgZmZGUFAQx44d4+jRowYhQ/n7ee+997CwsPjT80GJP08JZmFhYezYsYPo6GiD36Wpo9abQtnfjx8/DtS1aVeb40aj0VBdXc2yZcs4d+4cTz75JAEBAerz+fn5HDlyhKSkJMaMGaNut3LnxsCBAwkPD6dv374N2kulvyH9MiFuHcmEkglvhORByYO3m+TBSyQP3nqSCSUT3o0kE0q/7HaSi4XNmJWVFb6+vmzfvp20tDRKS0uxs7MDLjUOy5YtY968eQQHB9OjR4+rrq/+hLuzZs3i5ZdfZvny5QwZMkTtcFlaWnLy5Emqq6vx9fU1eC+FUh7kwIEDbNiwAXNzcx588MFGG4eqqip1kl6om1RamdMiMzPTYN329vbqJL3K+yoTrUrD0zT1O9xFRUUkJiZy8eJFgoODcXZ2vu6RuNbW1uq/jYyM1AlxtVotZ86cYe/evej1evr27funJ4JW/ltSUnLFzrROp8PKyoqXX36Zt956i/Xr13PmzBl69erFmTNn2LJlC+np6QwYMIAhQ4ao34nCysqKxx577Ia2Uxg6cuQIs2bNws7OjgULFlx1VKay31w+KbPym3t7e9OuXTsOHTpEZmYmYWFh6nIbNmwgOjqa+++/n2HDht3QttZ/nzZt2nDkyBHS09Pp06dPk0JFYmIiVlZWWFtbq7XnG6ME5Pvuu4/77rvvhrZVNKTsV35+fvj7+1NSUsLJkydxcnK67lGfhw4d4sKFC4wePRoAHx8fbG1tycrK4vjx4/j7+9/QiS5l+YCAANzd3cnLyyMzM1Mtx1F/ncbGxowfP159rU6nQ6fTqSP6zM3N6dChA8eOHWPv3r0G+5ISXDt27Hhd2yeu7vTp05w4cQIvL68mzfdQnxLM+vfvz44dO9i3bx/9+vVj3759JCQkcO7cORwdHWnZsiUTJkz4U6Mvlb8F5WSCVqvF2Nj4qiNJd+7cyc6dO3Fzc2PcuHFERUVx6NAh4uPjSUpKUkv7ubm54eXlpf5NBQQE8Mknnxisq6amRt1PGzsBLYS4NSQTSia8HpIHJQ/eLpIHG5I8eOtIJrxPXVYy4a0hmVAy4b1CLhY2U/XLJNjY2JCenk5+fj52dnbqc7m5uXz22Wc4OTnxn//8R63rf6X1QV0jYmpqyrhx49i8eTM7duzg3Xff5a233sLLy4sTJ05ga2sL0KD0h0K55XnZsmWUlZXx1FNPERwcDMDZs2dJSkpSb1EeMWIEI0aMUCeEtrCw4M0338TNzY3w8PBG119/JOSfvT27OampqSE5OZktW7awefNmMjIygLoOq5WVFZ07d+all1664br49X+PEydO8OWXX1JZWclrr72mBvo/U+bm2LFjGBsb4+3tTUlJSaMlYJSO1bBhw7CwsODjjz9mz5497NmzR11m2LBhPPnkkzg4ONzQdoirU37jFi1aYG9vz6lTpygoKLjqflW/w5KSkkJRUREuLi54eXlhZGSEqakpQUFBHDp0iKSkJEpLS7G3tyczM5MPP/wQJycnHn30UbUdudETES1btsTPz4+DBw+Smpqqnmy70uhB5b0qKio4deoUPj4+6ihncfNcq91QnvPw8OCJJ57Azs5OLYfRVMpvuXXrVoyNjdXyQW3btsXNzY2kpCSOHDmi3tlwo5/DysqKwMBAsrKySE5OpqysDBsbm0b3WWWbGjv5OXnyZHr16iWT0d8i1dXVJCQksGHDBnbu3ElhYSE2NjY4OTnRs2dPJk2adMW7ci6ntG/Kyfno6Giee+45EhMTgbowr5ScWbp0Kf/973+JiIi4oXZMq9VSUlJCUVGRQUC82kj+xYsXU1tbi16vZ8aMGeTk5KjPubq68sADD9CzZ0/69u3b6N0nSuiUPpkQd4ZkQsmE10PyIOpzkgdvHcmDkgdvBcmEkglvN8mEdSQT3nvkF/gLU/5QLx9dBZcOhD4+Pri6upKfn092djaBgYHq5M4LFy7k1KlTzJ49W73tWHmdMkGpVqtFo9EYNEDKco8//jgXLlwgLi6OpUuX8uqrr3L27FmysrJwdXVV61w3dsDevXs3W7duxcLCgoCAAJYvX87+/fs5duwYWVlZ6nL+/v5UVlZibm6ubsO1RljJqITrd/bsWb744gt+++03ioqKsLKyIiIiAjc3N/R6PatXr2bHjh2UlJTwzTffYGVldUPvsXjxYrZt20ZycrL6+NatWzE3N2fAgAE3tF5lPy0tLaWmpgYPDw9atmx5zc6iUss9KSmJY8eO4ebmRlhYGI6Ojte9DaLplN9EObmTkJCgTtjd2O9VXl7O3r17WbNmDdHR0Zw/fx6oK7sxb948IiIiAAgJCVEnfy8rK8Pe3p6FCxdy+vRp5s+fT7t27f5UeQNlP+vQoQNmZmZkZGRw7tw57OzsMDY2prq6ukHHR3mvixcv0qJFC7p162YwulrcmNjYWGbNmsWgQYN47bXX1DkXrsXKyupPjSYuLi4mJiaGkJAQ9WSmh4cH7dq1IzExkUOHDjFx4sQbDobV1dWYmpoSGhrKr7/+SmZmpvo3cfDgQYqKigy2/2r7sp+f3xXnpBJ/Tl5eHosXL2blypWUl5djb2+Ph4cHZmZmJCYmkpiYyOHDh5k/fz6enp5NPnHRpk0b7OzsKC0tpbKykieeeILevXtjampKVFQU27dv58iRI7z22mvMmTOHAQMGXHX055VYW1uTlpaGXq+/5gmSlJQUdd6mgoICbGxsGDhwIAMGDKBXr15XvaCgfDYJg0LcepIJGyeZ8PpIHpQ8eLtIHpQ8eDNIH0yY9wAAZEZJREFUJry0TVcimfDWkUwomfBeJr/GX1j9xuDy+sVKQ+Pu7o63tzcpKSmkpqYyePBgjI2N2bt3LytXrqRXr148/PDDDdat3BKsrDs1NZWEhATc3Nzo06cPer2e0NBQZs6cqU4O3qFDB/r378/Zs2epra3Fx8enwXqVBnLlypVUVFRga2vLCy+8QHl5OVBXsqZfv37qZKfKHBeXk0npby6tVssPP/yAu7s7L730EgMGDMDDw0N9Pjw8nC+++IIjR44QExNDZGTkdR+QKisrWbJkCTU1Nfj6+uLk5ISxsTG7d+9m165d9O/fnzfffPO6b6dX9gOlNNHFixeBpo1KtbW1pVu3bnTr1u263lP8eZaWljzzzDPXPCGwYsUKPvroIy5evIiXlxddu3bFxcUFExMTg5HqQUFBODk5kZWVxcmTJ0lISGDNmjX06tWL4cOHAzd+0qj+vDk9evTA2dmZ3NxctmzZgqmpKX/88QcFBQV89NFHjXa0RowYwdixY2/ovZujy+vWX/63fO7cOUpKSti9ezfQcC4jZa6PxhQXF7Nw4UKKi4t5/vnnr1ruqP46NRoN2dnZ5Ofnqycn9Xo9NjY2tGvXDiMjI7Xm/7VKeFyJ0oZ17doVCwsLjh49ytNPP01GRoZa1kNOYN1ZRUVFvPHGG2zfvh1/f38mTpxInz59cHd3p7a2lo0bNzJ37lwOHz7MokWLmDdvXpPaHWU0+siRI9FoNDz99NNqiUCom4dnwIABvPvuu+zZs4fly5czYMCAG+oHlZeXY2lpCaCOIr28T6Xs846Ojjz44IPo9XoiIyMbzGeijBKt32cUQtx+kgklE94MkgclD95ukgfF1UgmlEx4t5JMKJnwXicXC/9ilD/WqqoqDh48yB9//EFycjJmZmaEhYXRt29fAgMD1WVNTU3x9/fn999/JyUlhdLSUqysrHjzzTcxMzPjySefxNLSskGjoNTrV8q/pKamUlNTQ79+/Qzqsvfo0YMpU6awbNky3n33XfR6Pa1atcLIyIjz58+rjY9Co9Fw/vx5zpw5A9SVpQkKCqJv3770798ff3//Jn0PEgpvHr1ej7W1NfPmzSMkJAQvLy/1OWV+kMjISOLi4sjJyeHgwYNERkZed0e7devWLFiwACcnJ9q1a4exsTFVVVVs376dhQsXsn37dlavXs306dOv+zNUVVWRkpKCqampepu/7CN3FyVg1e88WFlZUVlZybFjx2jTpg2tW7c26KSvWLGCt956CycnJ95880169eqlhkmlDJbC09OT9u3bs2fPHn777TdiYmLQarXMmjULCwuLP3UySXldUVER0dHRVFVVUVpayttvv22w3NmzZxt9ff05dkRDShAE1DsXlH1A+d3q7xchISHY29tTUFBAYWEhzs7Oasf6Sh1U5fVnzpxhz549nD59mgkTJjSpjFb9+U4A+vXrpz5ubGxMly5daNmyJRkZGbz55pskJyfTvXt3Zs6c2eTv4MKFC+zevZuYmBiio6O5ePEiFy9eJDY2Fqir9R8eHq6OuhZ3hlarpaioiIEDB/LSSy81KCszcuRI0tLS+Pbbbzlw4AAnTpzAxcXlmicflH322WefxcLCAhMTE8DwpIiPjw/Tp09nz549REdHU1xcfEMnCdLS0qioqKB169YG5QTrU96zZcuWDeZlUuaYUP5WZZSoEHeGZMI60t+/OSQPittB8qC4GsmEkgnvFZIJJRPe6+TXugOUSWVvZNRSU25Nzs7O5v3332fz5s3q4+bm5kRHR7No0SLmzp3LqFGj1PX4+/tjb29Pbm4uubm5xMbGkpGRwVNPPUVYWBjQsFE4evQor776KlA3IiYkJIS+ffuqk3zX9/jjj3P8+HGio6NZsGABZ86coV+/fgajWuszNzfnueeeo7q6mq5duzY4iNef9FzKx9x6ync8btw4wHAEl3JwsrS0pKKiAkAdRXIjHW1lLgqoO7iYmpoyZMgQSktL+c9//sPOnTsZN26cweiZa1FOgKSmplJVVdXo6GVx++l0Ompra9V9qP7+kpWVhYuLC+fPn2fWrFnExcXx/vvvM2LECHWZM2fO8Omnn2JmZsbMmTMZOnQocGnUUv3OiBIegoKCiI6OZs2aNZSXl2NkZMSmTZuwtLRU5xO4kXlQkpKSeOWVV9R68Yp27drRt29f+vXrR2hoqITAG1T/eFlZWUlMTAwHDhygsLAQT09PgoOD6dq1K6ampurJx+DgYHbu3El0dDTjxo1T94fMzEz279+PTqeje/fuBiPioe7Oim7durFy5UqOHz9Or169rrk/KPvcoUOH6Ny5szoRfG5uLseOHWPt2rWcPXuWqqoqvv/+e6DuhNWTTz7Z5H1t9erVzJ8/X/1/Z2dnunfvTmRkJN26dbuhklzi5nN0dGTu3Ln4+flhZmZm0N9T2rhOnTphbW1NRUUFBQUFuLi4XHO9yn6izO91+eNQ14Z27NiRtm3bkpWVRVpaGo6Ojk0+8aUsZ2JiQkFBAWZmZk2ep0UZKarVaiUICnEdJBNKJrxXSB4Ut4LkQXE9JBNKJrxXSCaUTHivk1/vNrj89vjr6TTrdDr19vimhMmcnBz+85//sG/fPrp27cq4cePo0qULGo2GZcuW8c033/Dee++h1+sZPXo0AO3bt8fd3Z2UlBSWLVvGli1bcHZ2ZurUqVd8H29vb2bNmkXnzp3p2rXrFbdLp9Ph4ODA3/72N3JycsjLywPqSgLY2to22mAZGRmpgRQuzbOhTMgrty3fWRqNRh3Rpfzuu3fvZseOHfj6+tKzZ88/tX6lc25sbKzuH+Hh4UDdCYnrPegoI8PGjx+Pubm5OsJL3FmXT7B9+vRpvvnmG1asWMG5c+dYvHgxXbp0wd/fn8OHD6s10JV9Ljk5mbNnz9KqVStGjRoFGO47jQkJCcHS0hJzc3NGjBhBdHQ0X331FevXr+fBBx/kiSeeQKPRXPeo0osXL1JcXIy1tTUDBw6kT58+dO/e/bpOYogrO3bsGNu3b2fHjh0kJCQ0uswjjzzC1KlTcXNzAyAiIoKdO3eyY8cOxo0bR1JSEm+//Tb79u1TX2Nvb8+sWbOYNGmSul9ZWFioJ5BSUlI4d+7cVecNUfa5ffv2kZKSQkREBO+88w5RUVGkpKQ0WL5t27bMnz+f4ODgJoVCZV9s3749Dz/8MH5+fvTu3btJYULcGcrcJJfPeaPsK9bW1pw+fZp27drRpk0b4ObOm+Xl5UVWVhbJycn06NGjya9TtrWqqoqBAwfSq1evBkH0SqRfJkTTSCaUTPhXIXlQ3AySB8X1kEwomfBeIplQ3MvkYuFtUD/QpaamcuzYMQoLC7GwsFBHGl1J/Q5UaWmpOsLKycnJYDnl4LF582b27dvHiBEjeO+99wwamzlz5uDi4sIbb7zBjz/+qAZDFxcXfHx8OHLkCFu2bKG8vJza2lq+++47JkyYoB6A6o+wcnNz48knn1TXrYQ3JcDW335Avb3+lVdewcLCgs6dOxs83xjlM0mDc/dRfresrCy2bt3KypUradeuHX//+9/VffNGRuRB4wfIoqIiLC0t0Wg0nDt37rpHTNnb2/P0009f97aIG3f5CbHLlZeXM3v2bHJzc/nyyy/54osvWLFiBe3atSMwMBBTU1OMjY3x9/dHq9WSmJhISUkJLVu2BCAxMZGKigpCQ0PVdV5pf1MeDwwMpFWrVpw6dYqBAwfy3HPP8d5777FlyxY++OAD1q1bxz//+U+DEc1NERgYyJo1a2jVqtV1vU5c2/fff8+bb74JgJmZGV26dKFDhw4EBQVhaWnJhg0b2LRpEz/99BN6vZ5//etfAHTp0gWAffv2UVZWxiuvvEJqair9+/enbdu2ZGZmsmfPHv7zn//Qrl07unbtqrZZXl5eODg4kJqayokTJ7C2tr5me6Z08GNiYoiJiQHAwcGBnj17MmDAADIyMvjiiy8wMTGhY8eOmJqaNukkhPK8zJVz77l8f1F+y+TkZKDuDoxrTfbeVMr+WV5erpYZdHBwMHjfpgoLCzM4OS+EuHkkE0om/CuRPCiuRfKguFkkE0omvFdJJhT3IrlYeItVV1dz8OBB1q1bx5YtWxrUKG/bti0BAQENaggrf+T5+fls3LiRdevWkZqaikajwcPDg9DQUGbMmIG3t7c6KW5JSQkrV67E3NycWbNmNWiU8vPzMTIywtramkOHDpGVlaXW3vbz88Pc3BxPT08CAgI4dOgQn332GWvWrGH69OlMmDBBvX26/gTCSufvWuHN1NSU8ePH07FjR3x9fZv03ckcAjePUqZH+Z1uNLgp4uLi+M9//kNGRobB46+99hpRUVGMGjWK4ODgJo/Iq7899W9d12q1VFVV8cMPP3DhwgUmTpyoHuzE3af+ROHXGvVubm5OVFSU+vtu27aNF154gYceekidQBnqRkQ5Oztz/Phx8vPz1XCo1H1XThxcbZ9WHnd0dMTPz4+srCyOHDlCjx49ePvtt4mKiuKHH35g586dzJgxg5EjR/Loo482udxCixYtrlhCS9wY5fcMDAykdevWVFZW8uSTTzJt2jSD5Xr37o2xsTG//vorv/zyC3PmzMHU1JR27drh4uLCiRMnmDdvHiYmJixbtkwd4afT6XjnnXf47rvv+Pnnn/H19VVH/np4eODl5cWxY8fIzs6+6jFL2bc8PT1xdnbG09OTwYMH06dPHzUsAuzdu5fWrVtTUFBAdHQ0/fv3v8nfmLhZlBPdt6J0SlVVlTqPyeTJk29oHcrfhtLe1i9n88cff3Dy5EksLCyIiIi44e1srHyXEOLPkUxYRzLhnSN5UNwOkgfFzSSZUNwpkgklEzZn8ovfIsof7h9//MH7779Pfn4+tra2DB8+HB8fH9q1a0dFRQX5+flUVlYavFbpUBcVFfHWW2+xZcsWte6wjY0NOTk5rF69mujoaL777ju1trpGo6GgoAAnJyecnJw4f/48R44cIT4+nsOHD5OQkMCpU6fU90lISFCDYYcOHbC2tubixYuMGjWKZ599lo8//pj169czf/58Vq1axT/+8Q+DEVY3MseGcoCtra2V+SVusfqd5frBvaysDBsbmz+1Tmtra5ycnPDw8CAoKAgvLy/y8/P56aef+OGHH9i1axfLli1rdGSdciKjvvr7gfJcVVUVSUlJLFu2jG3btuHi4sKwYcPUOQ3E3UejuTRReHJyMseOHaO6uppevXrh4uKidjKUicVHjBjB6tWr+fbbbxkxYgSPPvooYNgpcXNzw9vbm71795KZmanW/lfKghw5coTz589fc3Sxst+FhISwadMmUlNTOX36NC4uLvTs2ZPw8HA2b97MwoUL2bBhAxs2bGDs2LG89dZbt+rrElehtAkdOnSgdevWJCYmUl1drT6vdN5btGjByJEjOXToEAUFBRw8eJBu3bphY2NDSEgIJ06cYMOGDTz77LMEBwdTW1tLbW0tpqamjB07lt9//53Y2FjS09PVkaeOjo74+vqyd+9e0tLSGDRo0FWPVUq7uGPHjgbPVVVVYWpqiqurK25ubuTm5hIVFSXB8C5yeX+k/vHpzJkzWFlZ/enjjrKPbN26lfj4ePz9/dVyald7TWOj8ZXtrN/eVlVVsXPnThYuXEhNTQ0zZsxocLfR9bha+S4hxPWRTNg4yYS3h+RBcbtJHhQ3k2RCcbtIJmxIMmHzJb/6LaLRaNi8eTMvvvgiWq2WF154gfvvv79JtX6V0XP/+Mc/2L9/PxMmTGDy5Mn4+fkBdaU+lFIJb7/9NvPmzaN169ZkZ2fj6OhIWVkZzz77LNnZ2WRlZanrdXV15f777ycyMpKuXbtiaWmpdpi8vLzw8vIiNjaW5ORkIiIimD9/PsOHD+err75i//79zJgxg9GjR/Pss8/i7u7+p0KdlJG59ZTf5/z58+zbt48tW7aQkpKCsbExQUFBDB48mO7duwNNH1mqLOPn58eHH37YYH8eO3Ys06ZNIy0tjbVr1/Lwww9jZmZmsExjv31eXh5r1qzB3t6eCxcukJmZyfHjxzl69CgA4eHh/N///R+dOnW6/i9C3FQ1NTUN5pdQlJSUsGrVKn7++WdycnLUx42NjRk3bhwzZszAw8MDnU4HQJ8+fVi9ejXW1tZqOQ3lxJjSKXFwcMDPz49t27aRlpamBksPDw910uY9e/aoE9or6nf2Kioq1JGenTp1wsbGhoyMDIqLi3FxcVGDwsiRI4mMjOS7775TQ6O4c/R6PRYWFgQEBHDs2DESEhIoLi7G0dHRoCPfunVrbGxsKCgoIDExUd2XunfvzsaNG3FxcaF3795AXfujtEFeXl6EhYWxYcMGjh8/TmhoKBqNBlNTU3x9fTEzMyM5Odmg3FFj6redNTU16qg+ZV0ATk5OdOjQASMjI3XOALlT4u5Q/w4LvV7P/v37Wbt2LXFxcWg0GiIiInjooYfw9/e/4ffQaDScP3+eFStWADB+/Hjc3NyuerfFlU6+b9myhZycHNq0aUNRURGpqakcOXKE5ORk9S6iGTNm3PC2CiFuLsmEVyeZ8NaSPChuBcmD4naSTChuB8mEQlwiFwtvkcrKSnbt2kV1dTX33XefOkIKDMtqAI02DJs3b+bo0aN07dqV559/3uCg1LZtW1588UWKiorYs2cPMTExjBo1Cq1Wi62tLfn5+ezcuRNra2sGDBhAZGQkvXr1anREgdIg2tvb4+vrS3R0NGlpaerIrB49ehAeHs7q1av5+eefWbduHevWreO5555j5syZt+KrE02g3Gp+tZG4Fy5c4LfffmPp0qVqPWxjY2OsrKw4evQo69at45lnnmHatGnXXYZGo9GowVCn06nb4uDgwJAhQ0hLS+PQoUOMGTPGIByWl5ezefNmiouLmTZtmrr9tra2xMTEEBcXpy5rbGxMWFgYI0eOpG/fvri4uPzpcjniz1NCm3JSSa/XA3Ud4kWLFvHNN9+g0Wjo168fwcHB1NTUsGbNGlasWEFWVhZffPEFFhYWAGoHuby8HHd390ZHTGm1Wnx8fLCysiIlJYVTp07h7OyMsbExQ4YMYeHChfz444+4ubnRsWNHNTwqbVtMTAxJSUlMnToVqBvJ3rJlSzIyMkhOTiY4ONjghIW5ubm0bXcJ5e89JCSElStXkpWVRX5+Po6OjtTW1qLT6TA1NVXLs7Vo0cJglLwyf0lhYWGD459er8fExITAwEA2bNhAQkIC586dU1/v7e2tljvKy8ujZcuWTWp/rjTyrkWLFrz44ot/5usQt0h0dDTbtm3jX//6F3v37mXOnDnqHTfW1tasWLGCqKgo3nvvPTp37nzDx6Fdu3axd+9eOnfuzLhx44CrnxwoLS1l586ddOjQAV9fX3VEckVFBd98843BXUFGRkb07t2bsWPH0rt3bznpIMRdRDKhuFUkD4o7RfKguJ0kE4rbQTKhEJfIxcJb5MKFC8TFxdGiRQv69OkDXDrIabVatUMFhg2D0snet28flZWVPPjggwahsLCwkKSkJGJjYzl9+rTa8Ro1ahRubm44OTmRmJjIxIkTmTdvnsE26fV6qqur0Wg0mJiYUFVVpf4b6m7tt7GxITMzkxMnTuDj46M2RBMnTqRfv37s3r0bT09Pmej0DlH2ofq3ml/+nGLnzp28+uqr2NraMm7cOPr3709QUBCOjo5ER0fz2muv8eGHHzJkyBBcXV1veJuUUYXKCMOQkBAAMjMzsbW1NdiugwcPqp2jwYMH4+HhAdQdfF966SUyMjKora3F09NTndS8PgmGt5bSLl3te/7jjz945ZVXmDRpEv/4xz+oqanBxMSEdevWsWjRIkJCQnj33XcNavNPmDCBRx99lNjYWFasWMHkyZMxNjbG1tYWHx8f0tLSKCkpafC+9ScXd3NzIz09ndzcXJydnQEYPXo0sbGxxMTE8MYbb/DYY48RGRlJSUkJx44dUyc6DwsL4+GHH8bExAQrKytGjhzJxYsXZaToXU45Nnbs2BEHBwdOnDjB8ePHCQkJMQhg69ev59y5c9jb29O3b1/1cQ8PD3x9fUlNTSUzMxMnJyf1GKvsW/7+/rRs2ZKkpCROnTqlBkM3Nzfat29PVFQUGRkZBAcHS/tzD1FOWtbfTxoLdFVVVbzyyiucOHECX19fVq1ahaOjI3PnzqVLly7k5eXx1VdfsXnzZn744Qf8/f0N5tBpqnPnzvHee+8B8Mgjj6jz6ijbevnx/NSpU7z77rusXbuWCRMm8Prrr6vP9ezZk7///e+cOHECOzs7AgIC6NSpU4PjpRDi7iCZUNxskgelP3YrSR4UdxvJhOJGSSYU4sbIxcIbdPlkp5c3OMqIk4qKCk6fPg0Y1hS+UidIq9VSUVFBRUUFxsbGlJaWEh8fz759+4iPjycxMZHCwkL1dT4+PnTt2hWAVq1aERYWRnR0NElJSRQWFuLs7GwQAJWG4+jRo2zatInIyEg6d+4M1I2wMjU15cCBA6Snp+Pj42PQ0LRu3Zrx48ff7K9SXIEySW39fUX5d0FBATExMRQWFhIWFkZ4eHiDfcrR0ZHp06fz7LPPGhzIKisr8ff3x8fHh/z8fNasWcO0adNu6GCnUEp8AKSlpQGXRlPV3y5XV1ccHBw4deoU+/fvV8MhQGBgIIGBgTe8DeLPu1bHt7Kykt27d1NWVqbOP2JiYkJ1dTWffvop5ubmPPfccwbBEMDFxYXx48fzySefsHPnTvr27avOq9OnTx/S0tI4fPgwI0aMaHR7XFxc8PHxYdOmTWRkZKh13b29vZk/fz5PPvkkhw8f5plnnqFVq1ZUV1dz9uxZAEJCQpgxY4ZBB/GZZ575E9+SuN28vb1p27YtsbGx5OXlUV5eTn5+Pvv372fDhg3s378fJycnnn32WRwdHdXwZ2pqSteuXUlNTSUqKopu3bo1GIHv5eVF27ZtSUtLIz8/Hy8vL6DueOrn58fWrVtJT09XRyiLe0P90lgnT57k9OnT+Pr6GpSXUUoCDR06lG+//ZZ33nmH9u3b88MPP6hz3tjb2/PSSy8RExNDdHQ0R44cuaFJ4hcvXkxBQQH9+vVTLxY0Nh+Gsl2Wlpa4ubkBsHbtWl5//XW1P2Zvb8/999/fYKSoTqdTR+PLSQwhbh/JhOJWkjwobjfJg+JuJZlQXC/JhJIJxY2RVq6JLi8LU/8PWRlpqVAOIN26dSMnJ4cVK1ZQXV1N9+7dcXJy4tSpU2RnZ6PVavH09MTS0tKgpEZtbS0VFRXU1tby2WefUVxcrK7b0dGRMWPGEBkZSffu3bG2tjbYzsGDB7N792727dvHN998w5QpU3B3d1c/w/Hjx9m0aRNLlizBzMzMYCRV27ZtmTJlCo6Ojmot78tdaXJVcfMo+1pj33FNTQ1vv/02y5cvVyd2Njc3Z+zYsfzzn/80mHC3U6dOdOjQAXNzc0pKSoiPjyc2NpZDhw6RkpJCeXk5APv372fUqFG0adPmum6lr/83ofw9HD16lC+++AKAqVOnYmxsbLBOR0dHpkyZQlVVlcFoL3H9lLIv1+tqv3FhYSHHjh0jNDS00Xr8ZmZmpKamApcmlIe6EwKVlZX4+fmp855UVVWRkZHBoUOHOHbsGLGxsZSXlxMTE0N8fLwaDvv168eiRYuIiYmhsrKywZwmALa2tvj5+fHbb7+RkpKizjmh0+nw9vbmp59+YsmSJaSkpJCamoq5uTndunVjyJAh9OjRAzs7u+v+nsTdQWlngoKCiI2NZf369ezevZusrCwuXLgA1M2ZM2XKFO677z7A8M6Mrl27smTJEnbu3Mk//vEP9Tnlb8DJyQkfHx8OHjxIWloa3bt3x9jYGI1Gg6+vL5aWlhw6dIjCwkK1NJJ0uu88ZZ6by4+TyshMZST51q1b1dGW3t7eREZGMmXKFDQajbpvdenShW+//Zaqqip69OiBlZUVNTU1GBkZodFocHV1ZeDAgaxatYojR44QGhp6XZPbp6ens3TpUiwsLJg8ebIaOqHuRO+xY8coKirikUceUfctc3NzfH196devH126dGnQ3tcvWaj8v/TLhLg9JBPWkUx4a0keFE0heVDyYHMhmVA0RjKhZEJxa8jFwiaq/wdXXV1NXFycOkG4h4cHffr0oWvXrrRq1UptmEaPHs2hQ4dIS0vjnXfewd3dnZMnT6pB0tramjNnzuDg4MDo0aN5+umnMTc3x9LSEltbW4yMjDhz5gw9e/Zk6NChdO/eXQ15V9KmTRueeuop8vPz+f777/n1118ZOHAgZWVlarmaiooKfHx8ePrpp9WOHNQ1RI8//vhV13+lyVXFjbt8dJJWq6W2tpb4+HjS09Pp2LEjfn5+aDQa3n//fX744Qe6d+9OQEAAp0+fZuvWrSxbtgx/f38mTJig/j7KqOEzZ87wxRdfsGbNGsrKyoC6Eg6dOnUiKiqKxMRE8vLyaNOmTZN+26qqKs6cOaPWey8pKSExMZEtW7awYcMGqqqqeOCBB4iMjAQMRydaWVldcx8TTaN0EtLT0zEyMqJt27aNBkZlPhPlNcrvcXkHt6qqinHjxlFSUsKjjz7Kc889Z3DC6/KTFvXbxIKCAk6fPk379u35+eef1cmVU1NT1ZMQ5ubm9OrVi8GDB9O/f3/1tZ07d0aj0ZCamkpubi7t27dvsP0ajQYfHx9atWrF8ePHKSoqwtPTU12mZcuW/P3vf6eyspKysjIcHR1v/IsVdxVlHw0ODlbnX8rPzycwMJBevXoxcOBAOnbseMXXBwYGYmJiQmpqKmfPnlXn1oFL+1ZgYKA6cX1ZWZl6YsTNzQ0XFxd1TpX62yPurKtNAB8VFcXcuXPJzc3FxsYGPz8/TE1NiY2NVSd9f+utt9R1BAUF0aJFCyoqKtQ5TZRjstLude7cmbVr15KQkGAwkv5a9Ho9K1as4MyZM4wbN46uXbuye/dujh49yuHDh0lMTFTnmYiMjMTV1VXdL4cOHcrQoUNv6HsQQtw6kgnrSCa8uSQPihsheVDyYHMhmVA0RjLh1b8HIW6UXCy8BuUPNCoqCo1GQ48ePfj888/57LPP1GUOHDjA6tWrGThwIK+++qraaQ4LC+O9995j7ty5nD9/Xi3H4ezsjLW1Nfn5+eh0Ok6fPs3XX39NZWUl06ZNw9XVlcDAQNatW0fHjh3573//q3Z2lDkmlHI3RkZG5OXlodfr1VIPXbt25auvvuLzzz9n3759rF27loqKCrUW9/Dhwxk0aBBt27Zt9DPf6Ag1cWOUA1B5eTnm5uYsXbqUL7/8kqKiIgAsLCx46KGH6NWrF3v37uXll19mypQp6uu//PJLFixYwJo1awgODsbf31/db8+cOcMTTzzBkSNHCA8PZ9y4cQwcOFAdfTxnzhzWrVtHeno6Xbt2bdLvfuTIEZYsWUJhYSHnz5/n7Nmz6khnBwcHpk+fzuTJkxuMcBY3z7lz5/j+++9ZvHgxZWVl9OnThy+//NJg3hvF5fOZZGRkUF1djZ+fn/pYbW0tpqam/Pvf/+b9999n0aJFREREqKURoK4DUlBQAGDQuQbUNu/gwYPExMSoj3fs2JG+ffvSr18/goKCGmxbbW0txsbGhIWFERcXR3x8fINwqGjTpg3W1tbExsaSlZWFp6dng06RmZmZBMO/GCWIdezYETs7O8rLy5k9ezZTp041WK6xMl1QV7IoJCSEuLg4YmNjGTRokHqMq3/iwcjIiF27djF16lQ1GPr7+7Nq1Sqp+3+H1NbWAjR6XMrNzWX79u0EBAQQFham/pZJSUk88cQTWFpa8uKLLzJ48GC1dMumTZuYN28eq1evZsCAAURGRqLVamndujWdOnUiJiZGPZZd3g/y9fXFycmJlJQUTp482eRgmJWVxYoVK4C6eZtGjRpFdna2+ryrqysTJ06kb9++ajtafx/W6/VqOymEuLMkE4pbSfKguF6SByUPNieSCZsvyYSSCcXt16z3NGWy06uNrtJoNOzevZvHHnuMNm3a8PTTT/PZZ58RGRnJyJEjad++Pfv37+eTTz5hy5Yt1NbW8sEHH9CiRQv0ej1+fn78+OOPZGRkUFpaSuvWrampqaGsrAxLS0vOnDnDt99+y9atW/n999/x9fVlwoQJhIWFERQUxJEjR/juu+944YUXDGpuKxISEnjnnXfo168fU6ZMwcjICJ1OR9u2bXnrrbc4e/YsycnJmJiY4O/vb3Cr85VIKLyyoqIifvjhB9q3b8999913zZrlV7otvr4vvviCDz/8kFmzZuHo6Mh///tfXF1dGT58OLW1tWzfvp3Fixfz008/ERISwpQpU6itrVU79CNHjmTdunUkJSVx9OhR/P391X14586dJCYmEhAQwNy5c/H29gYulUlSfuvU1FTKysqwt7e/4nYqfxvu7u5YWVlx8uRJqqursbW1JTw8nH79+tGrV69Gy5WIa1NGfDbl7+/ChQssX75cHRmslIJpbF8sKSkhOjqaDRs2cPDgQS5cuICTkxPt2rXj8ccfJyIiQn3PoUOHkpmZyUcffcTXX39N69at8ff3N+gkpaamYmZmhq+vr/oeAQEB6lwVgwYNYtKkSXTt2rVBh/ryvxclzPbr14+4uDji4uIazIGj7Muurq7MmDEDKysrg9Hvonlwc3PDy8uL7OxscnJyOHfuHNbW1uq+eaU2VqvVEhERQVxcHLt372bQoEHqfqe8xtvbm+nTp+Pu7q62kdD435O4fjdapudKbWF5eTmvvfYae/bs4YMPPgAutROfffYZNTU1PProo0ybNs3gdUOGDKGoqIg333yT1atX4+fnh4eHB1qtlm7duhETE8PevXsZO3Zsg32kXbt2eHt7s3fvXrKysujQoUOTPvOGDRvU0fSHDh3C2tqagQMH0r9/f3r16qWGwSvRaDSyHwpxG0gmbJxkwsZJHpQ8eKtIHpQ8KK5OMuG9SzKhZEJx72jWe1v9EgplZWVUV1c3OjIgLCwMqKvf/vHHHzNmzBjefvtt9XkfHx9sbW356KOP2L59Ozt27GDo0KEGDaEyQW5jXFxcyMnJUSd1njBhAt7e3syYMYOnn36aRYsWYWlpyfDhw/H09CQrK4v4+Hg2btzI7t27MTU15YEHHlAbUa1Wq84hYW9vb9CRUsKw1DK+MQcOHODrr7+mc+fO3HfffY0euJQJZa/WWYFLo1SU/SQuLo4LFy4wYsQIXn/9dbX+9apVq3jllVeoqalR908jIyP1vV1dXQkLC+P48eMkJyer9fsBoqOjqampYfLkyXh7e6sHK+VAY2lpCdR1+IuLi68aDpXtdHZ2Zvbs2UybNg0rK6trHthE09Qf8Zmeno6zs7P6+1zOxsaGixcv4uTkxNmzZyksLCQ3N7fBPCMHDx7k448/Zu/evWg0Gjw8PPDy8kKn0xEVFUVeXh7PP/88Q4cOpbq6GhMTE+6//35iYmKIiYnhq6++4v3331e3y9LSUh3FruzbSuDr2bMnO3bsIDw8nF69egF1JyHqTxptbGxMbm4uCQkJDB48WF1Hnz59ePfdd9m+fTtlZWXY2Ng0+Mzm5uaMGzfu5n7p4p6g7EPBwcFs376dtLQ0Tp48ibW1dZOOY126dAHgl19+Yd68eQ062jY2NjzzzDO3ZNubs1OnTqHRaAz6VZePzqypqWm0P6LX69myZQtRUVG4ubnx2GOPqY+bm5urJ16V449Op6O4uJjU1FTc3NzUuUoUGRkZZGRkEB8fj0ajIS4ujiNHjuDh4QFc6udFR0cDDU8KWFtb4+/vz65du0hOTqZ///7qcbYxyom+zp07ExkZSWhoKH369DGY20f5PLW1tQ1G/Ashbi/JhJIJr4fkQcmDt4rkQcmD4sokE96bJBNKJhT3nmZ7sbC2tpaEhAQ2btzInj17KC0tpV27dvj6+vL444/TunVrdTlzc3MCAgJITEwkPz+f+fPnA3UdH6WTPnz4cNLT0/n000/59ddf6d27d4OO3eUjKZT/d3Nzo23bthw/fpyLFy+q5UciIyOZNWsWP//8M//73/9YvHgxGo2GixcvUlVVBUB4eDh/+9vfDGq+Q8Ma2vXDoATCGxcaGoqVlRWZmZkNap0r6n+/8fHxJCQkYGZmRufOnWnTpg0mJiYG+0KfPn345JNPiIqKwt7ens8++wwTExP1wDd+/Hi+//57UlJSaN26tcGk38p6AgICMDMzIy0tjcLCQrWckFKGSLnFvaKiQh1Bmp6ezrZt24C6g2Z+fr7B6MCradmypYwYvclyc3OZO3cuBw8epLy8nP/7v/9j2rRpjXYWMjIyMDExoXPnzpSWlrJv3z6ioqJ44IEH1PIERUVFfPnll8TGxjJx4kQGDRpE586d1ZHk+/btY+rUqXz++ecMHTpUPRnh6OjICy+8wPTp0/n1118ZPXo0vXr1wsjIiOTkZKytrXFxceHixYsGJxNGjx7N3r17WbRoEba2towZM6bBSNL09HQ++ugj9uzZQ9euXdV9yMfHBysrKxwdHSkvL280HAoREhKCpaUleXl55OTkGIz4vBpfX19CQ0Px9fVVR9GLm+/cuXPs3buXP/74g6NHj1JZWYm7uzsODg707duX4cOHY2pqanD8u9IISaW0UHV1NY6OjvTu3Rt/f3+qq6sxNTXFwcEBgPz8fEJDQ9FqteTk5JCTk0OXLl0oLy9n+/btxMfHEx8fT1JSEqWlper6fXx8cHZ2Nvh/BwcHTp06RXZ2Np6enup2Kv0nf39/zM3N1XXVf/3llHa7V69e6skyRU1NDRqNBq1WK6NEhbgLSCYU10vyYB3Jgzef5EHJg+LaJBPe3SQTXiKZUNyr7sq98UZvT4ZLAehaFi9ezKeffsqFCxfUCeRTUlKIjY0lOjqauXPn0rVrV3UkQN++fUlMTFRHjALqwUXZ3mHDhvHpp59y6NAhTpw40aDW+uWhUBlxaGxsTEVFBXq9Hnd3d3WEhFar5cknn6RHjx5s3LiRrKws8vLysLCwICQkhIEDBxIaGtqkkQcSBm8OZ2dnOnTooNbT79Onj8GomJqaGo4ePcratWvZsmWLOkkt1I1CGTNmDHPmzMHU1FTdHzp06KCOJO7QoYPaYdZqtWpA7NevHykpKRQUFHD+/PkG4dDPz4/WrVuTnZ1Ndna2Gg6VkTGbNm1i8uTJav3+8vJyli5dyrlz5xgxYgS//vorhw8fpmfPntJpukMuXLigBkNjY2O++uorAgMDDUaBK793cXExpaWl2Nvb06dPH/bt28eOHTt44IEH1FIJNjY2jBkzhueee86gPEJZWRlJSUlkZWVhb29PSkoKhw4donPnzkBdGxoYGMgjjzzCJ598wv/+9z9atGhBREQEZ8+e5dSpU/j7++Pm5mZQJqd3795MmzaNL774gnfffZfExERGjx7NmTNnSElJITo6mqioKFq0aMH999+Pubm5+n5arZZ9+/ZJB0k0Smkr/f39cXV1JTMzk+PHj9O/f/8m9RVatWrFsmXLbvVmNmuxsbF89NFHHDhwAKibx8bCwoKcnBzi4uLYtm0bq1at4s0331RPWkLdyN7Fixczd+5cOnfurLZxJ06cwN/fn8TERIqLi1m+fDlz587F1NSUyspKrKys1BPlCjc3N/R6PUePHmX69Onk5+erz3l6ejJy5Ej69+9P165d1ZNhCjs7O0JDQ9m8eTN79uzB09NTPdGm7GNKiZrExERyc3OvGgzrU0aKKifmpZ0ToukkE0omvBtJHhS3iuRByYPiyiQT3v0kE16ZZEJxL7kr9k69Xm9QxuB6QqESsJSr8U0JQF9//TXvvfce7u7uzJ49mx49etCuXTuSk5P59ttvWbt2LZ9//jmurq64u7sD0L9/fz7//HMuXLjQoPOsbG/79u1p1aoVp06d4tSpU41OzFx/W5VO1dKlS9mzZw9GRkaEh4cDhkEuODiY4OBgzp07h7Gxsdqpqv8d3GiQFnUTtJ88eZJu3bo1af4Opdb5vn37DCb81ul0/PHHH3zwwQfk5OTg6urKqFGj8PLywtLSkuXLl7NkyRLs7e2ZMWMGZmZmasmOzp07k5OTg7u7e6NlN0JDQzExMeH48eMUFxert/DXr53dtm1b9u7dS3p6On379gWgW7duapmi6dOn07dvX06fPk1sbCz5+fm8/vrr6HQ69uzZg6WlpRpGxY1pypwkV+Li4kKPHj3YunUr7u7unDp1is8//xw3Nzc8PDzUyboBtQ0wMzNTR9IpE8krHR5zc3MGDhyIiYkJFy9eZP/+/ezatYvY2FjS0tLUEAmwfft2NRwqxo8fz4kTJ1i1ahXffvstERERWFtbA1BZWdngc1pbWzNr1izKy8vVzt7ixYsN1hkSEsKkSZMYNGiQ+hmUdUhnSVyLvb09gYGBpKWlcfz4cc6cOXPVUlni9tiwYQMLFiygqKiIQYMGMXbsWDp27IijoyMZGRns3LmTb7/9lri4OF588UX+9a9/ERAQAEBSUhIpKSksXrwYNzc39Q4evV5PWVkZDg4OdOrUiQ0bNhAZGUnv3r2BupNpWq1WbZOgrvxMy5YtKSkp4fz584wcOZJ+/frRo0ePK975UP9iQkREBJs3b2bHjh08/PDDanur/Nfd3R1HR0fy8/Opqalp8vcjI0WFaDrJhJIJ7xTJg5IHbxbJg5IHxa0lmfDuJJnw6iQTinvJHdlTL5+4WaPRqH94BQUFlJaW0qpVK5ycnK4ZeuoHrDNnzpCQkMCFCxcIDw9XG4L668jNzWXp0qXY29vz+uuvG4zS8vf357///S95eXkcOHCALVu2MHXqVKAunJmamlJQUMC5c+cabIcymrBjx47s2LFDHb1QW1tLTU0NmZmZ2NjY4OrqCtSVAYmPj2fDhg3s2rULOzs7pk6dSrdu3Rr9nDqdTm0ALx+RIKHwxi1ZsoT58+fj6enJBx98QEBAwDX3OSW8x8XFAZduLddqtaxbt45WrVrx/PPP07t3b4OwOWTIEGbOnMmSJUvo0qUL3bp1U8NE9+7dWbt2LdnZ2Wo5Ibh0QAoICMDNzY3s7Gxyc3Px9/c32CYbGxt8fX3ZvXs3aWlpasDUaDS88cYbvPPOOxw9epS0tDQAHBwceOaZZxg7dixarZZJkyb92a9SYBiWLp+8/VosLS3p1KkTW7duxdramnHjxrFgwQK+/PJL5s+fb7DurKwsAHVEp7u7O3l5eSQkJBAYGKh2dpSguGbNGr777jtycnJo0aIFPXr0YPjw4ZiYmDBnzhxiY2MbTAru4uLC3//+d37//Xd27NjBzz//TGVlJSYmJrRt29ag/FF9//znPxk1ahSpqakkJCRQU1ODn58fPXv2xNPT80a+ViHUdlk5GZKRkaGOphZ3TlFREd988w0FBQU88cQTPP/88+pzOp0OLy8vvLy8cHV15ZNPPuHgwYMsWrSI119/HQsLCx5++GGOHj3Krl27CAsLY/LkyUBd+3P69Gnc3NyYMGEC27ZtY+nSpYSGhmJpacnFixfVkoBwqQ8WERHBxo0befzxx3n00UcNtkUJcyYmJlRXV5ORkYG/v7+6b4WGhgINj+0KMzMz3n777UbnMRNC3BjJhJIJ7waSByUP3kySByUPiltHMuHdSTKhEH8td+RiYf0wl5eXx549e4iOjiY1NZXS0lLMzMwwNzenoqKC+++/n2HDhl2xDnV5eTmbNm3i559/Vm911mg0ODg4qJ3xVq1aqR21lJQUTpw4wZAhQwxCIdSF0sTERLRaLVVVVezatYvRo0erAbNbt27s2rWLvXv3EhgYaDCa1MjIiPPnz6sdKyU8GhkZkZGRwX333Yefnx/GxsYUFhZSXl6u3irt4+PDAw88wIMPPnjFEWj1H5cRCX+eciAIDAykVatWVFZWkpubq45suRpfX1/s7e1JSkqioKAAV1dXdf966qmnaN26tTrBbllZGQkJCRw5coSEhARyc3M5d+4c27Zto1u3bmr4CwsLU/fPU6dOqbW3lecdHR3x8/MjKyuL1NRU+vTpo3bMlSAQEBCAra0tmZmZnDhxAhsbG3Q6HWFhYSxcuJDDhw+TlZVFYGAgoaGhsg/dZNXV1ezatYtly5ZRUlLCM888Q2RkZJPLYBkbG6v7X2ZmJmPHjmXdunWsXLmSoUOH0rNnT4MTXAAWFhbqKKu8vDx27dplEA4BVq9ezbx582jVqhWvv/46w4YNU09a6PV6XnrpJY4dO0ZBQYFBKQi9Xo+TkxN///vf+fjjj/n6669xcHCguroaJycnzMzMGv1ser2eoKAggoKCZAJ6cdONHDmSkJAQQkJCGpQNEbePEsSWLl1KYmIiffr04emnnwYunRjTarXqsXbQoEEAPPfcc+zZs4dt27YxcuRIPD09mThxIv/+979ZsmQJ9913H1ZWVpiammJpaYmNjQ3h4eH079+fP/74gx07djBixAi1X1ZUVARcmjx++PDhbNy4kbVr19KpUyfCwsLUOUnq99l+++033n77bXbs2KEeSz08PLC2tubcuXOkpqY2OmeTEgqb2q4LIa5OMqFkwjtJ8qDkwZtN8uCl10keFLeSZMK7g2RCyYTir+mO9A7379/PL7/8QlRUlPpHDdC6dWscHBxwdXUlIyODU6dO8cknn7Bq1Spmz57NqFGjGqxr1apVLFiwgIsXLxIUFISfnx8mJiZs2LCBpUuXcvz4cb755hu1I3z06FEAunTpwqlTp0hLS+Pw4cPqxOPFxcXqui0sLLh48aLaAPXt25ddu3axZ88eevXqRXBwMJWVlRgZGakTSO/fvx8TExO6du2qrsfHx4egoCBqamo4f/48JiYmODk5ERoaSmRkJF26dJF5AW6z+vWmXVxcSExMJC0tjcGDB19zVK69vT2dO3dm27ZtHDx4UB0ZDNCxY0egrizHypUr+f333zl06JA6esXOzg64NEpF6di4u7vj6+tLYmIiqamp+Pn5qduhHIA6derEpk2b1Il0lQCqLOfj44O5uTmHDx8mLS0NPz8/9cBlb29P//79//T3Jq4sKyuLl19+mbNnz9KiRQsyMzOB6yuh5enpibe3N+np6Zw5c4YXXniBF154gffeew9LS0u1NIyyTqV0TFhYGL/++is7d+5k5syZ6vNVVVV88cUXmJiY8H//93+MHj0aIyMjdXS7RqPBw8ODrKwsjh07ZhAOFffffz/Z2dksW7ZMnXPlwoULV/wMMqpd3ArKfuXi4oKLi8sd3hphZGREUVERsbGxAGo/RqfTGZx4VH43rVbLkCFD8PLyIiMjg6ioKHr37o2trS1jxoxh7dq1xMTEsH79eh588EFKSkrUuyFatGjBAw88QFRUFMuWLTO4S0g5wV5/8vh+/fqxY8cO3njjDZ599ln69OlDdXU16enpxMbG8ssvv5CcnIyzszMnT55U2z1LS0u++eYbXF1dadWq1VXvKpFQKMTNIZlQMuGdJHlQ3GySB7nuzyvE9ZBMeHeRTCiZUPw13dGLhRqNhh49etCrVy86dOiAv7+/evt4cXExR44cYdGiRRw8eJAPP/wQnU7HmDFj1NELe/bsYf78+Xh7ezN37ly1HAjAQw89xKxZs9i3bx+rVq1i9OjRmJmZqZ2aVatWsWzZMrV8A9QdcMaPH8+AAQOIiIhQR1spnfNevXoBdeHynXfe4fPPP1fLwOh0OlauXElZWRnh4eFqeQWlYfnpp584c+YMZWVlODo6GtRUVpYD6VjdTnq9HgsLCwICAjh27BjHjx/n9OnT6ijOq+nWrRvbtm1j3759jBw50uAgUVpayoIFC1i/fj21tbWEhYUxaNAg+vbti5OTEwMHDiQ5OZns7Gw8PT3VES7h4eEkJiZy5MgRBg8eTIsWLQzes1OnTlhbW5ORkUFRUVGDcOjp6cmkSZOwsbFRa3iL2ycvL4+zZ8/i4+NDeno6SUlJVFdXX9dIN3t7e4KDg0lPT2fTpk08++yzPPbYY7z//vt8/fXXfPrpp5SXl3PixAksLCzw8/MD6kpiARw+fFgtB6PX66mqqqKmpga9Xk9ERARGRkZqm2RmZsbRo0fVExd79+5l2LBh6rZoNBr0ej2WlpZMmzaN3bt3k5ubS+vWrQkJCQGkcyREc6bVajl8+DDGxsbqvEhXahOUftvQoUP57LPPOHbsGJmZmYSEhGBkZMQDDzxAbGwsP/30E507d8bR0ZGKigrKysqAujbugQce4Pvvv2ft2rWcP38ejUaj9qWU9zU3N2f+/PnMnj2b2NhYnnrqKdzc3KisrOTcuXPqyfxx48Yxffr0BifElBO8Mu+XELeHZELJhHea5EFxM0keFEI0N5IJhfjruSMXCwcOHMhXX32Fg4MDTz/9NF26dFGfU+aucHR0JDIyks6dOzN58mQyMzNZuHAhI0eOVEcLfPTRRwA88cQTBqEQ6kqDPPTQQ7z55pts3ryZTp064evrq5auSUlJwdLSkgEDBtC/f3969+6Ns7Nzo9urNDienp7qRKn79+9n2rRphISEUFNTQ0xMDJmZmbRr147Zs2erk4MrrzUyMsLBwUENHsocE0r5HWmAbj+l4Q8JCWHlypVkZmaSl5eHg4PDNQ8KYWFhQN1JDjA8GP7666+sWLECPz8/Xn75ZSIiItTnzp8/T/v27SkqKiIuLs6gZn/37t1ZvHgx8fHx6mjE+uv28/NTJwdOSkpSA4HCzMyMmTNn/slvRVwvZV9ZtmwZzs7OhIaGUlBQQGZmJrm5uXh5eTW5k2FhYUFwcDCrV69m165dPPvsszz44IPs2bOHrVu3snTpUh5++GFycnK4ePGiWpvd3d2dDh06kJSUxP79+9USNRcuXMDV1ZXS0lJSU1PVEklKYN2yZQt5eXlotVr279/fYN4JZZvbtGnDhx9+SIsWLa5Y/ksI0bwUFhbi6OhIcXGxenL7Sm2d8lh4eDgWFhacPn2a7Oxs9URTjx49uO+++1i9ejU//fQT//nPfygvL0er1arzcz388MOsXr2adevWYWdnh16vV9sy5X31ej0ODg4sXLiQDRs2EBUVRU5ODufOncPT05Pu3bszaNAg9cTalUifTIjbQzKhZMI7TfKguBkkDwohmivJhEL89dyRYUBt2rTBzs6O4uJiUlJSgLoRBmA4d4VOp6Nly5b8/e9/p3Xr1mRkZLBt2zYA0tLSOH36NIGBgQwZMkRdd1FREVu2bOF///sf69evR6fTER0drZaa6dGjB1A3Afjq1av57LPPmDBhAs7OzurIq8rKSqCudIgygkEZbaXMaTFmzBg6duzI+vXrWb58OTk5OXTv3p2XXnqpSaOslDkmLp8sVdw+yu/TsWNHHBwcOHHiBBkZGcC1Dwrt2rWjTZs2ZGVlqfuwso9s3boVgKlTpxIREaGW+ADUCXihbuRe/e1QRoqmp6dTUFBg8H56vR4rKysGDhzIxIkTDUoaiTtLo9FQUFBAWloa/fr1Y/DgwVhYWFBYWEhaWhpwaZT4tWi1Wvz8/GjRogWJiYmcPn0aKysrnnvuOdzc3HjnnXc4cOAAVlZWuLu7q+UWrK2t1RNsO3bsUNdnaWlJcHAwFy5c4Oeff6akpAQTExNKS0tZvnw5y5YtY/r06Wr7qmxvYwIDAyUYCiFU586dw8zMDGNjY7XM1pXaOuU416FDB0xMTDh//jznz59Xn7e1teXBBx/Ezs6OH3/8kfT0dBwcHLC2tlZLAbq5uTF+/HiysrKIj48HDOengEvHbnNzcyZMmMCHH37IZ599xm+//cbSpUt55pln1FDY1HZZCHHrSCaUTHinSR4UN4PkQSFEcyWZUIi/njtysdDMzIzAwEAuXrxIeno65eXljQYkpSHp3r27Okp0y5YtAJw5c4aCggJMTEw4cOAACxcu5Omnn+aBBx7gmWee4bPPPiM+Ph4vLy/+9re/qaP5PD09CQkJoaysjLVr16olaKqqqtBoNJiamqojqd59910WLVpksE2RkZEAnDp1in//+9/8/PPP/Pjjj8TFxfHtt9/St29fGX1wj/H29qZt27aUlZVx/Phxqqqqrvkac3PzBqNJlTlKdDodtra2jZav0Wq1HDhwAI1GQ3x8vFrLW6/XY29vj7+/PxcvXuTgwYNqiIRLB7vZs2czb9482rVrdzM+uviTlM5IcnIyhYWFdOjQgZCQENzd3SkrKyMpKQm4vvIsbm5u+Pv7U1tby4EDBwAIDQ1l6tSpVFZW8sorr3Ds2DGcnJxwc3NDr9ej1WoJDQ0FYNeuXcClEwoTJ07E3NycrVu3MnXqVCZOnMj999/P3LlzCQgIYM6cOQwfPpyBAweqc6gIIcS1tG7dGhMTE7RaLdnZ2cDVT6zq9Xrs7Oyws7OjqqrKoLSaXq8nODiY4cOHA/D+++9TUVGBubm5QemuMWPG0KVLF6qqqjAxMVH7cFc6ya7T6XB2dsbExASdTkdNTU2DECmEuHMkE4q7heRBcaMkDwohmjPJhEL89dyxAuPKyM/09HQKCwuBK1/Rt7W1VWvuHzx4kOLiYjw8PAA4duwYM2bM4IMPPmDr1q1UV1czevRoPvroI2JjY/ntt9+YM2cO7u7uamPw4IMP4uLiwqeffsqSJUsoLi5WJ5NPT09n8eLFjBkzhiVLlpCbmwtcanS6deumbkdhYSEeHh507twZCwsLdDqdQYde3P2UskBBQUFA3e+vjFi51ggTZV/Yt2+f+piNjQ12dnZcuHCBnJwcoG7fUSb3/fTTT3F0dKRly5bk5+eTmJgIQHV1NQA9e/bE39/fYDJ6cfdSfqM//vgDExMTIiMjsbS0xMfHh9raWlJTU686+XtjbG1t1RrpUVFR6uMjRoxg8uTJZGVlce7cOSoqKjA1NVU7N/7+/pibm5Odnc2pU6fQaDTU1tbi6enJW2+9Ra9evcjLy+PIkSOUl5fz8MMP8+9//xuAOXPm8Mknn+Du7n4zvhYhRDPg4eGBg4MDVVVVJCUlqSfYr0Sj0XD27Fm1vJ9yIlan06nH27Fjx9K+fXu2b99OVlYWZ86coWXLlmr/zdvbm/Hjx6uv8/b2vuqxuv5xVKvVYmxsLMdWIe4ykgnFnSZ5UPwZkgeFEM2ZZEIh/nruyJyFAJ07d8bc3JycnBxycnKuOTIuMDAQIyMjcnNzOXHiBEFBQbRu3ZqTJ08SEhLC+PHj6datW4OJSevPEaEYPnw4VVVV/Pvf/+aDDz5gxYoVah33oqIiysrKsLCw4PHHH2fKlCnApcmdW7ZsiZeXlzpPgIuLi/oe0tjce5SDWEhICKampmRnZ5OTk4Obm9s1X6uUFoqPj1dr+ysjTHfu3MnHH3+Mg4MDISEhpKWl8dtvv/HLL7/wwgsvkJKSwrp164iJiSEoKEg98fDkk0/y5JNP3rLPK26+iooK4uLi6Ny5szoCXSkdk52dTXZ2NgEBAY22RY0xMzNTw2FMTIz6eMuWLXnqqac4fPgwtbW1jBkzhqqqKvWklouLCyEhIezdu5fY2FiGDx+OTqdTJ5Du3bs3ycnJ2NnZSfkYIcSfZmpqSmhoKPHx8Rw+fJjk5GSCg4MbbeuUx/Ly8khOTsbGxkZth+ov26FDB8aNG8f777+PXq+ntLTUoJ3TarUMGTIEHx8f/P39b9+HFULcMpIJxZ0meVD8WZIHhRDNlWRCIf567liScXJywtXVleLiYtLT04Gr3/5raWlJ+/btAcjJyUGr1aoj+SIjI5kwYQJt2rRBp9NRVVVFVVUVNTU1aLVazpw5w+bNm9VJUU1MTJgwYQIrV66kT58+2NrakpycTH5+Ps7OzjzxxBMsXbqU2bNnG5QOUUaI9unTB4C4uDjg+kpKiLuLss8FBQXh7OxMUVFRk+epcHNzIyAggJMnT6rznwAMHTqUoUOHUlpayvPPP0/fvn2ZMWMGv/zyCxMmTGDq1KlMmzaNDz/8kMmTJwNXvl1e3Bq1tbUGI77/TJ3yPXv2kJeXR58+fbC2tgbqOjeOjo6cPHlSLT3T1PfQaDT4+Phgb29PVlaWOiK5pqaGli1bsmDBAr777jumTJmidpagrhSSUnd98+bNAOoIZqhrQ7t06SLBUAhx03Tv3p327dtz8uRJ1q9fD6D2tRT1J7hPSkri7NmzuLu7qydY6zM2NmbChAm0bt0anU5Hx44dKS0tNVjG1NRUDYX130cIcW+STCjuNMmDzZPkQSGEuDkkEwrx13LH7iw0MjKia9eupKenk5aWRllZGTY2NldcvqamBnt7ewC1DvKIESNYt24dy5cvx9PTk8GDB6PVag06TGVlZSxatIivv/6a/fv3Y2VlBdQ1VIGBgXz55ZecPHmS0tJS3NzcsLS0vOI2KA1bv379+O6774iJiTEY3SDuXa6urnh7e5OTk0NaWhoXLlxosC8oYUIJcsbGxkRERJCYmEhcXBxhYWFUV1fj4ODA7Nmz8fPzY9u2bZw8eRJ/f3+GDBlC7969MTIyokOHDnTo0OG2f87mrH7npH4Yv1bbcyXKqKjDhw+j1WrVCeWhrhRDmzZtyMvLIysrq8F7XouTkxMBAQFERUURExODh4eHuu1Kua3GjB07loCAAPr37w9I/XUhxK0VHBzMmDFjSExM5Ndff8XX15cJEyag1Wqpra3FyMhIbYf279/PggULMDU1ZcaMGY32nXQ6HdbW1nz++efY2tri4uJy1feXE/NC3PskE4q7heTBvz7Jg0IIcfNJJhTir+WOXSyEutGfP/74I5mZmRQUFGBjY2PQgavP1NSU5ORkLCwscHR0BOpGL0yaNImffvqJN954g6ysLIYPH05FRQVJSUlER0ezceNGKisr6du3r8FE5fXfo3Xr1rRu3Rqoa5SuVEJG6dwpE0cnJiZSXFzcpBIl4u6l7HPBwcFs376djIwMCgsL8fb2VifM1Wg0Bp378+fPY2VlRUREBN9++y07duxg5syZ6sg9R0dHpk6dytixY7G1tb1TH03Uo/zNnz9/nn379rFlyxZSUlIwNjYmKCiIwYMH0717d4ArtkP1abVazp8/z8aNG2nTpg2BgYHqcw4ODgQFBbF3716ioqIoLy9n27ZtvPPOO4SFhakdpiuxsrKiU6dOREVFsXfvXiZMmNCkz+jn56eOJhVCiFvNwsKCRx55hM2bN3Pw4EFeffVVqqqq6N69O15eXgCcOHGCqKgoli1bRklJCY888giDBg1qdH1Kv0sZJarX69Hr9RIAhfiLk0wo7jTJg82D5EEhhLj5JBMK8ddyRy8WBgQEYGtrS15eHpmZmVetNZyfn09lZSXl5eV07doVqAuLr776KjqdjjVr1rBgwQI++ugjg3ISbm5uTJo0iXHjxtGyZcsrrl/pDF5rngmdToepqSkrVqzA19eXFi1a3MAnF3ejkJAQLC0tyc3NJS8vD29vb3WUS0lJCQcPHmT37t3s37+f4OBg3nrrLTp06ICRkRF6vZ7y8nLMzc3V9en1egmGt4ler1dP6Fwp1F24cIHffvuNpUuXkpycDNSNBraysuLo0aOsW7eOZ555hmnTpjUpHAIcP36coqIiXnjhBUxNTamurubYsWPEx8ezZcsW9Ho9iYmJJCYmAhAbG0tYWNg1R5WampqqYfP3339nwYIFUppICHHXUULbW2+9xYcffshvv/3G66+/jqOjI76+vpw5c4bi4mKKi4sB8PLyYsiQIZiYmDRp3RqNRkbEC9EMSCYUdwvJg/cuyYNCCHFnSCYU4q/ljl4stLe3x8PDg4SEBNLT0w06ZEpnT+kQLV68mPLycoYMGYKTk5O6jLGxMa+//jqjR4/m0KFDJCUlUVFRQbt27ejduzfh4eEGddqvpKkNj1arRa/XExwcfIOfWtxtlN/e398fV1dXsrKySExMxMLCQh3Jd+TIEYM5Btq3b09paSlOTk7ExcVhYWFxxfWKW6d+x+Hy8HR5wNu5cyevvvoqtra2jBs3jv79+xMUFISjoyPR0dG89tprfPjhhwwZMgRXV9cmve8ff/xBTU0NycnJ/N///R/79u3j1KlT6nLKiaaBAwfy8ssv4+Tk1OTg2bZtW7p27YqHh4c6clkIIe4mGo0GnU6Hh4cHr7/+OkOHDmXx4sWUlZVx9OhRysvLcXJyYsSIEaSkpHD8+HEmT57M5MmTeeyxx9T+3JXWLYRoHiQTijtN8uC9S/KgEELcWZIJhfhruaMXCzUaDf369ePo0aOkpaVx8uRJtZFQOntVVVW89dZbbNq0CScnJ5544gl1tF79RiM8PJzw8PBG54vQ6XQ3dSSCNFZ/Tfb29gQEBJCZmcknn3xiMBrZ0tKSiIgI+vfvT/fu3XF3d1efaywYipuvsb9j5d8FBQXExMRQWFhIWFgY4eHhDf5OHR0dmT59Os8++6zBiN/Kykr8/f3x8fEhPz+fNWvWMG3aNINlrkQJkatXr1YfCwgIYMCAAYSHh7N06VL++OMPHB0dDdq2pvD29ub7779v0rJCCHGnKHfeWFpaMnjwYAYPHkxRUREnT57E2dlZLRNYUFDAN998w9atW1m3bh1WVlY8+OCDVw2HQojmQTKhuFtIHry7SR4UQoi7k2RCIf467ujFQoDevXvz6aefkpOTQ1lZmdpA5ObmsmfPHn7++WeSkpJo3749Tz/9NAEBAVdcl16vx9TUVB2B2pQSMkLApZGBXl5e1NTUANClSxf69u1Lnz59rloOSdxaV5ovBqCmpoa3336b5cuXU11dDYC5uTljx47ln//8p0FZg06dOtGhQwfMzc0pKSkhPj6e2NhYDh06REpKCuXl5UDdhMujRo2iTZs2Vxz1qTzWqVMnHB0d6d+/P5GRkXTt2tUgVMbFxbFjxw6SkpLIzc296jqFEOKvwsnJySDwVVZW4urqyr/+9S8efPBBampqcHV1lRHyQgiVZEJxp0kevHtJHhRCiHuPZEIh7k13/GKht7c3Dg4O5Ofns3r1aqytrTlw4AAJCQmcOXMGU1NTxo4dy+TJk68aCuFSh62xEhRCNMWoUaPo0qULnTp1alL9bHHz1dTUGJSJ0mq11NbWEh8fT3p6Oh07dsTPzw+NRsP777/PDz/8QPfu3QkICOD06dNs3bqVZcuW4e/vz4QJE9R2wcTEBFNTU86cOcMXX3zBmjVrKCsrA6Bjx47qBPKJiYnk5eXRpk2ba4a4oKAgdu/ebfCYTqejpqYGU1NT/P39MTExIT4+nuTkZNq0aXOTvy0hhLg71T8RZmZmpj7u7e19pzZJCHEXk0wo7haSB+88yYNCCPHXIJlQiHvPHb9YaGlpia+vL3v27OGbb75RH2/bti3jx49n0KBBBAYGYmxsLCOwxC2j7Feurq7XnJ9A3FpKMCwvL8fc3JylS5fy5ZdfUlRUBNSV+XnooYfo1asXe/fu5eWXX2bKlCnq67/88ksWLFjAmjVrCA4Oxt/fX207zpw5wxNPPMGRI0cIDw9n3LhxDBw4EGtrawDmzJnDunXrSE9Pp2vXrk0+wVRTU6OekNJqteqJBW9vbyIiInB2dsbX1xeQklVCiOZB2johxPWQTCjuNMmDdw/Jg0II8dcg7Z0Q9547frEQoFevXiQnJxMaGsqAAQPo2bOnWs+4PmlkhLi7FBUV8cMPP9C+fXvuu+++BqNAL6fT6QCuWgbqiy++4MMPP2TWrFk4Ojry3//+F1dXV4YPH05tbS3bt29n8eLF/PTTT4SEhDBlyhRqa2upra3F1NSUkSNHsm7dOpKSkjh69Cj+/v5q27Fz504SExMJCAhg7ty56mgmZV4bJQympqZSVlaGvb19k76Hyz+z8n7t2rXjs88+a9I6hBBCCCGaM8mEQtx7JA/WkTwohBBCiL+Cu+Ji4UMPPcTUqVMNHtPr9dTW1sr8EkLcxQ4cOMDXX39N586due+++xodeanT6dDr9eooyyupra3FyMhIDVZxcXFcuHCBESNG8Prrr6ujM1etWsUrr7xCTU0NrVq1AsDIyEh9b1dXV8LCwjh+/DjJyclUVFTQokULAKKjo6mpqWHy5Ml4e3urI0yVcGdpaQnUhcPi4uImh0MhhBBCCPHnSCYU4t4jeVAIIYQQ4q/jrkhcpqamQF3phtraWoMOm4RCIe5eoaGhWFlZkZmZydmzZxsd6a3VatXgFh8fz7Jly1i1ahUZGRnqJPT1y0n16dMHExMToqKiyM7OZs6cOZiYmKDT6dDpdIwfPx4/Pz/0ej2tW7emsrJSfS+9Xg9AQEAAZmZmpKWlUVhYqD6vzBGRnZ0NQEVFhXoCKj09nW3btgGQkZFBfn7+zf66hBBCCCHEFUgmFOLeI3lQCCGEEOKv465KXcbGxgYjyYQQdzdnZ2c6dOhAaWkp8fHxQN2IUEVNTQ2HDh1i7ty59OrVi0mTJjFv3jxeeeUVJk2axNtvv01VVRUajUY9CdShQwdcXFzUf7ds2RIwLFXTr18/AAoKCjh//rz6uBIO/fz8aN26NdnZ2WoQBAgLCwNg06ZNFBcXY25ujpGREeXl5SxdupRz584xYsQIysrKOHz4MFVVVTf7KxNCCCGEEFchmVCIe4fkQSGEEEKIv467ogypEOLuceTIEU6ePEm3bt2wsrK65vIRERHExcWxb98++vTpoz6u0+n4448/+OCDD8jJycHV1ZVRo0bh5eWFpaUly5cvZ8mSJdjb2zNjxgzMzMzUOS46d+5MTk4O7u7ulJWVYWNjY/CeoaGhmJiYcPz4cYqLi9XyM0qAbNeuHW3btmXv3r2kp6fTt29fALp164anpydZWVlMnz6dvn37cvr0aWJjY8nPz+f1119Hp9OxZ88eLC0t1Tk1hBBCCCGEEKI5kDwoeVAIIYQQzZNcLBRCqJYsWcL8+fPx9PTkgw8+ICAgwKAkTGPCw8OBujklALXEjFarZd26dbRq1Yrnn3+e3r17G4TNIUOGMHPmTJYsWUKXLl3o1q2bGsa6d+/O2rVryc7ONhjNqWxHQEAAbm5uZGdnk5ubi7+/v8E22djY4Ovry+7du0lLS1MDpkaj4Y033uCdd97h6NGjpKWlAeDg4MAzzzzD2LFj0Wq1TJo06c9+lUIIIYQQQghxT5E8KHlQCCGEEM2XXCwUQqgBMDAwkFatWlFZWUlubi4BAQHXfK2vry/29vYkJSVRUFCAq6urOiL0qaeeonXr1jg5OQFQVlZGQkICR44cISEhgdzcXM6dO8e2bdvo1q2bGv7CwsIwNjYmJSWFU6dO4eDgAFwKh46Ojvj5+ZGVlUVqaip9+vTBzMwMqBvBqtVqCQgIwNbWlszMTE6cOIGNjQ06nY6wsDAWLlzI4cOHycrKIjAwkNDQUHVSeyGEEEIIIYRoTiQPSh4UQgghhJDekBBCDV1+fn64uLiQmJhIWloagwcPvuZ8Mfb29nTu3Jlt27Zx8OBBXF1d1ec6duwIQGVlJStXruT333/n0KFD1NTUAGBnZwdcGoVqYmICgLu7O76+viQmJpKamoqfn5+6HUr469SpE5s2bSIpKYnS0lI1gCrL+fj4YG5uzuHDh0lLS8PPz08tS2Nvb0///v3/9PcmhBBCCCGEEPc6yYNCCCGEEEJ77UWEEM2BXq/HwsKCgIAAdDodx48f5/Tp0016bbdu3QDYt28fYDj5fGlpKW+88Qbvvfce8fHxhIWF8eqrr7JlyxZ2796Nk5MTycnJ6sTzSpkZpZzNkSNHqKysbPCenTp1wtramoyMDIqKitTHlXDo6enJpEmTePXVV+ndu/f1fh1CCCGEEEII0WxIHhRCCCGEaN7kYqEQAqgLhwAhISFotVoyMzPJy8szeO5KwsLCANi/fz9gGA5//fVXVqxYgYeHB1999RXfffcdDz/8MO7u7lRWVtK+fXv0er06mlTRvXt3AOLj4zl79qz6uLJuPz8/HB0dycjIICkpqcE2mZmZMXPmTB5++GFsbW2v67sQQgghhBBCiOZE8qAQQgghRPMmFwuFEMCl0NWxY0ccHBw4ceIEGRkZANcsPdOuXTvatGlDVlYWKSkpAGppma1btwIwdepUIiIiqK2tVZ+7ePEitbW1AOzdu9dgO5SRounp6RQUFBi8n16vx8rKioEDBzJx4kS6du36pz+/EEIIIYQQQjRXkgeFEEIIIZo3uVgohDDg7e1N27ZtKSsr4/jx42oZmKsxNzdvMJrU2NiYoqIidDodtra26qT09Wm1Wg4cOIBGoyE+Ph6dToexsTF6vR57e3v8/f25ePEiBw8eVEMkXAqrs2fPZt68ebRr1+5mfHQhhBBCCCGEaNYkDwohhBBCNE9ysVAIoVImiw8KCgIgPT2d4uJi4NqlZy6fpwLAxsYGOzs7Lly4QE5ODgBGRkYYGxsD8Omnn+Lo6EjLli3Jz88nMTERgOrqagB69uyJv7+/wWT0QgghhBBCCCFuPsmDQgghhBDNl/S2hBAqZYRmSEgIpqamZGdnq6HuWkJCQoC6OSWUCeiVEaYmJiZ8/PHHbNq0iaKiIvbs2cPLL7/Mjz/+yMMPP0zPnj3R6/XExMQAdQES4Mknn2TNmjX06tXrmqVvhBBCCCGEEELcOMmDQgghhBDNl/Gd3gAhxN1DCWBBQUE4OztTVFRERkYG3bt3v2Y4c3NzIyAggMTERI4ePaqWoRk6dCgJCQmsXr2a559/Hp1Op75mwoQJTJ06ldTUVAYMGED//v2BS+FQCCGEEEIIIcTtIXlQCCGEEKL5kouFQogGXF1d8fb2Jicnh7S0NC5cuIClpaXBMsqcEUqQMzY2JiIigsTEROLi4ggLC6O6uhoHBwdmz56Nn58f27Zt4+TJk/j7+zNkyBB69+6NkZERHTp0oEOHDrf9cwohhBBCCCGEMCR5UAghhBCi+ZEypEIIA8pcFMHBwQBkZGRQWFgIQFVVlfq8kZGRGgzPnz8PQEREBAA7duwAUOeicHR0ZOrUqXz88cf8/vvvfPjhhwwbNgwrK6vb86GEEEIIIYQQQlyT5EEhhBBCiOZJ7iwUQjQqJCQES0tLcnNzycvLw9vbG1NTUwBKSko4ePAgu3fvZv/+/QQHB/PWW2/RoUMHjIyM0Ov1lJeXY25urq5Pr9dja2t7pz6OEEIIIYQQQogmkjwohBBCCNG8yMVCIYQBZS4Kf39/XF1dycrKIjExEQsLC6Kioti7dy9HjhxRR5QCtG/fntLSUpycnIiLi8PCwuKK6xVCCCGEEEIIcXeSPCiEEEII0Txp9PV7eEIIUc+LL77Ir7/+il6vV+ekALC0tCQiIoL+/fvTvXt33N3d7+BWCiGEEEIIIYS42SQPCiGEEEI0H3JnoRCiAb1ej0ajwcvLi5qaGgC6dOlC37596dOnD/7+/nd4C4UQQgghhBBC3AqSB4UQQgghmh+5s1AI0YASDgsKCigoKKBTp06YmJjc6c0SQgghhBBCCHGLSR4UQgghhGh+5GKhEEIIIYQQQgghhBBCCCGEEM2U9k5vgBBCCCGEEEIIIYQQQgghhBDizpCLhUIIIYQQQgghhBBCCCGEEEI0U3KxUAghhBBCCCGEEEIIIYQQQohmSi4WCiGEEEIIIYQQQgghhBBCCNFMycVCIYQQQgghhBBCCCGEEEIIIZopuVgohBBCCCGEEEIIIYQQQgghRDMlFwuFEEIIIYQQQgghhBBCCCGEaKbkYqEQQgghhBBCCCGEEEIIIYQQzZRcLBRCCCGEEEIIIYQQQgghhBCimZKLhUIIIcRtNGDAAPz8/PDz82P+/PlXXfbrr79Wlw0ICLjl25aXl4efnx8DBgy4Kev75Zdf8PPz46WXXrop6xNCCCGEEEKIe5nkQSGEEHcruVgohBBC3CHr16+nqqrqis+vWrXqNm6NEEIIIYQQQojbRfKgEEKIu4lcLBRCCCHugKCgIEpLS9m6dWujzx88eJCMjAw6dux4m7dMCCGEEEIIIcStJHlQCCHE3UYuFgohhBB3wPjx44ErjxZduXKlwXJCCCGEEEIIIf4aJA8KIYS42xjf6Q0QQgghmiNfX1+CgoKIioqiqKgIJycn9bkLFy6wceNGnJ2d6dWr1xXXUVpayjfffMPWrVvJy8tDq9XSrl07hg0bxiOPPEKLFi0afd327dtZtGgRCQkJaLVa/Pz8mD59Ov7+/lfd5rNnz7J48WK2bt1KTk4OOp0ODw8Phg0bxrRp0zA3N7+xL0MIIYQQQgghmhHJg0IIIe42cmehEEIIcYeMHz8enU7HL7/8YvD4xo0buXjxIvfddx8ajabR1+bm5jJu3DgWLlxISUkJffv2pVu3bmRlZfHee+/x0EMPcfbs2Qav++6773jyySeJi4ujffv29OvXj8rKSp5++mmWLFlyxW09fvw4Y8aM4dNPP+X06dN06dKF7t27U1JSwkcffcSDDz7IuXPn/twXIoQQQgghhBDNhORBIYQQdxO5s1AIIYS4Q0aNGsXbb7/N6tWrmTlzpvr4qlWr0Gg03H///Vd87T/+8Q/y8/MZMGAA77//PhYWFgCUlJQwY8YMEhISmDdvHu+//776muTkZN555x20Wi0ffPABQ4cOVZ9bt24dc+bMafS9KioqmDlzJidOnGDmzJk89dRTmJqaAlBeXs6//vUvNmzYwJtvvslbb731p74TIYQQQgghhGgOJA8KIYS4m8idhUIIIcQdYm1tzaBBg8jOziY2NhaAjIwMDh48SHh4OG3atGn0dfv37yc+Ph5zc3Nef/11NRgCtGzZknnz5gHw22+/UVhYqD63ZMkSamtrGTp0qEEwBBg9ejQDBgxo9P1Wr15NTk4O/fv3Z9asWWowBDA3N2fevHm0atWKdevWNTp6VQghhBBCCCGEIcmDQggh7iZysVAIIYS4gy6f2F7579UmsleCZO/evXFwcGjwfFBQEP7+/uh0OnXZ+q8bPXp0o+sdO3Zso4/v3LkTgGHDhjX6vKWlJUFBQdTU1HD06NErbrcQQgghhBBCiEskDwohhLhbSBlSIYQQ4g7q1q0b7u7ubNq0iZdffpm1a9diZWXVYKRnfUVFRQC4u7tfcRkPDw+Sk5PVZQF1VOmVXnelx3NzcwGYM2fOFUvTKEpKSq76vBBCCCGEEEKIOpIHhRBC3C3kYqEQQghxB2k0GsaOHcvHH3/Miy++SHFxMZMmTaJFixZ3etNUOp0OuPLI1fpcXV1vxyYJIYQQQgghxD1P8qAQQoi7hVwsFEIIIe6wcePG8emnn7J9+3bg6iVnAJycnIBLIzwbozynLKv8Oycnh/z8fHx8fBq8Jj8/v9F1ubi4kJGRwf3333/VEa5CCCGEEEIIIa6P5EEhhBB3A5mzUAghhLjDXF1diYyMxM7OjpCQEDp16nTV5bt27QrA7t27OXXqVIPnExMTSUpKQqvVEh4erj6u/Hv9+vWNrnfNmjWNPt6nTx8ANm7ceM3PIoQQQgghhBCi6SQPCiGEuBvIxUIhhBDiLvDJJ58QExPDTz/9dM1lw8LC6NSpExUVFfz73/+mvLxcfa6kpIR///vfAAwfPhwXFxf1uUceeQQjIyM2btzIH3/8YbDOX3/9lS1btjT6fhMnTsTNzY3ff/+dd999l/PnzzdYpri4mBUrVjTpswohhBBCCCGEuETyoBBCiDtNypAKIYQQ96D333+fv/3tb2zdupXIyEjCwsKoqakhJiaG8+fPExgYqIZERYcOHZg9ezbvvvsuzzzzDJ06daJNmzZkZ2dz9OhRpk6dynfffdfgvSwsLFi4cCFPPPEEX3/9NStWrMDPzw8nJycqKirIysoiPT2dVq1aMXHixNv0DQghhBBCCCFE8yR5UAghxM0mFwuFEEKIe1CbNm345Zdf+Oabb9iyZQs7duxAq9XSrl07hg0bxpQpU2jRokWD182YMYN27dqxaNEikpKSSEtLw8/Pj//9738EBgY2Gg4BfHx8WLduHcuXL2fLli2kpKRw+PBh7OzscHZ2Zvr06QwaNOgWf2ohhBBCCCGEEJIHhRBC3GwavV6vv9MbIYQQQgghhBBCCCGEEEIIIYS4/WTOQiGEEEIIIYQQQgghhBBCCCGaKblYKIQQQgghhBBCCCGEEEIIIUQzJRcLhRBCCCGEEEIIIYQQQgghhGim5GKhEEIIIYQQQgghhBBCCCGEEM2UXCwUQgghhBBCCCGEEEIIIYQQopmSi4VCCCGEEEIIIYQQQgghhBBCNFNysVAIIYQQQgghhBBCCCGEEEKIZkouFgohhBBCCCGEEEIIIYQQQgjRTMnFQiGEEEIIIYQQQgghhBBCCCGaKblYKIQQQgghhBBCCCGEEEIIIUQzJRcLhRBCCCGEEEIIIYQQQgghhGim5GKhEEIIIYQQQgghhBBCCCGEEM2UXCwUQgghhBBCCCGEEEIIIYQQopn6fzJVqKtyP3CJAAAAAElFTkSuQmCC", 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5wEwmkzGfREZGhg4fPmxMML97924tXbpU8+bNk4eHh+666y5jf8mV74BjoEaELVAjwtaoEWFr1IiwJ2rEmosGIa6I9Q80NTVVZrNZQUFBysnJ0bRp0/TDDz8oJydHLVu2VIcOHRQUFKQ9e/Zo/vz5+uGHH7Rv3z598803l33ViZOTk5o3b67g4GA1aNBAw4cPV3R0tLy8vHT48GFt2rRJM2fO1IYNGzR58mTVr19fISEh7EgcwNkHRRkZGYqJidHy5cu1bt06HT9+3FguMzNT/v7+xuO8vDytXr1akjRgwAA5OTmpsLBQJpNJ9erV03333afU1FQtW7ZM4eHhuv/++2WxWOTm5qZ27dpJkn7//Xfl5eUpJibmnHkklixZoo0bN6pr1656+umny0xSj+rNus/Yv3+/Vq9eLS8vLw0ePPi8y7q4uOjNN9+Uj49PmeetJyw2b96srKwsNWjQ4LwFZbt27Yz5I/7zn//oxIkT2rBhg/Lz88us74033tAvv/yixx57TF26dGFicAdy8uRJ44Tp2rVr9cQTT+iuu+665Pej9ar1v/76S97e3mrTpo2ioqK0atUqrV27VnfeeaexrDVfevbsKenMFXvXXnutTpw4IelMHvv7+6tu3boqKCjQt99+qylTpuiDDz7Qddddd8V3dKBqO3LkiPFdunbtWuO7tHfv3rr55pvl4uKi0tJSeXh4qGHDhnr99dfLXMFZXFys5s2ba8yYMdqwYYMOHjyorVu3qlevXsa+z9/fX23btlVGRobeffddubi4aOXKlcbQWlbvv/++li5dqgEDBuiuu+6y6c8BQOWgRoQtUCPC1qgRYUvUiLA1akRY0SDEFUlPT9crr7yimJgYPf/88xo9erRWr16tL774QpGRkXrttdd03XXXGV86BQUF6tmzp1544QXFxsZq3bp1l3XL8NlzTIwaNUo9evRQ8+bNJcmYEHzQoEFq0aKFbrvtNm3btk1LlizRww8/TOFXzRUWFio2NlbLli1TTEyMMa61dGY86vr162v37t3q3r27Mba69YsnMTFR+/fvV8uWLY2xq8+eCyIyMlIDBgzQf/7zH/3666+6//77jatPW7RooTp16uj48eNauHChMZlux44d1bp1a7Vo0ULLly/X1KlT9dtvv8nV1VVPPfWUcfULqq/s7GytW7dO8+bN08qVKyVJ0dHR6tGjh3EF099ZC7/zzavj4eGhnJwcBQUFqWXLlsZ7rK8HBwerefPmiomJ0cKFC42rqsLDwxUREaHw8HCVlJTov//9r5YvX65nnnlGr7zyim644QaG9qjm8vPzFRsbq/nz52vx4sXG8/v27VNeXp68vLwu+n4XFxcdOHBA69atU0REhIKCghQVFSVJ2rx5s3JycozcPDvf6tWrp6ysLHl6eur6669Xhw4dFBkZqZYtW8rT01PZ2dn6/PPP9d133+nDDz+Uq6urrrvuukr5GcA2Tp8+rTVr1mj58uVavXq10tPTjdcaNWoki8WiwsLCMvMtOTk56dprr9XAgQONqz6tx2MuLi6yWCxq0KCB2rZtq4MHDyo+Pr5M8efp6anw8HAtXbpUv/32m7Gt7t27KzIyUqGhoapTp46mT5+uX3/9VRs2bFDdunXLHDcCqJ6oEVHZqBFha9SIsBVqRNgKNSIuhAZhDXel41s7OztrzZo18vHxUa9evSRJrVu3VkREhJ5++ml17txZ0pkDopKSErm5uemmm27SrFmztG7dOm3btu2yij/rl1br1q3L3A5vjcEae2hoqLH+7du368SJE6pTp85lf35UPZ999pkmT54s6cxktX369FFkZKQiIiLUo0cPzZkzRy+//LKcnZ0VGBio0tJSI198fX1VUFCg7OxsNWrU6Jx1u7u7G+tISEjQrl271KpVK0lnJtYNCwvThg0bdM899+ill1465/0jRoxQcHCwRo0apdWrV6tTp0665ZZbKvGngcqWnp5uFPRZWVmqU6eOTp06pYyMDKWlpcnPz++iV5yfXYhZlzl+/LicnZ116tQpYy4cK+tBUrt27RQbG6vg4GCNHTtWAwYMKHOiQpICAwNVp04d/fLLL5o5c6ZuuOEGTm5Vc8uWLdNbb72l7OxstW7dWoGBgcb8SVlZWfLy8rrkHQ4HDx5UVlaW+vfvL0kKCwuTj4+PsrKylJycbHwPS/+7mrRnz56aP3+++vbtqzfeeKNM3hYVFcnPz0+jR49WUlKSNmzYoMWLF1P8VWOnTp3SnXfeqcTERElnhuPr3bu3IiMjFRkZqU6dOumuu+5SXFycMdeSNe/q1q0rk8lkPD47F00mk44fP27cOeHu7i5JxjBZrq6uZYrJL7/8UlFRUedcSf/ee++puLhYv//+u+bMmaPQ0FA1a9assn4cAMqBGhFVDTUibIkaEbZEjQhboEbExdAgrOHOHqbjcpSUlMjT01M5OTnGJNzBwcH66aefyizn5OQkJycnYwLcqKgorVu3TqmpqcZ6Lme71p3Oxb4Mw8LC5OTkpKNHjyozM5Pir5qy5kS/fv3k7Oys0NBQtW3b9pzx9Q8fPmwsL507Gb23t7eOHj2qjIwMBQYGnpM7LVq0UNu2bbVixQqtXLlSrVq1ksVikZeXlzp06KANGzYYV9EUFxcbX37W/yIiItSqVSslJydr27ZtGjZsmPHFh+rDmhcpKSmaN2+e3N3d9eSTT6pVq1b68MMPtX//fqWmpqpdu3aXXXBZ17ljxw6VlJSoXbt2OnnypHGAdLb27dvLbDbLYrGoYcOGMpvNRk5LZ05y1a9fX71799Yvv/yiDRs2KCsri/lN7Ozvc99cLut7rJO/33nnnbrjjjskSRs3blRqaqrS0tIUEhJywfVa82vWrFmSpE6dOkmS/Pz81LFjRy1dulQbN25U586dz4kxOjpa8+fPV3Jysg4fPqzAwEBjn2sdJsbf31+tWrXSxo0btWPHjiue8wJVg8ViUe3atTVw4EDjqsx27dqV+S7Nzc019kunT5823nd2sXehPCwqKtL69etVq1YtowlwtqZNm6pZs2ZKTU1VvXr15OPjY8xdIZ35W3BxcdF1112nP/74Q0lJSUpISKD4A6oYakRUFdSIsCVqRJQHNSKqOmpEXAr3oNdw2dnZuvfee9WxY0etXLmyzCTJ55OYmCgnJyeFhITo5MmTkmR8cViHUTib9QqpgwcPSvrfkAtXepvw+XZC1gMlf39/lZSUqKCgQN7e3le0XlQc60HR+fLgclhzIjIyUv/4xz90ww03GF9W1quNJRlXu4SHh6uwsLDMOoqKiozJk+Pi4oy4zubl5aWIiAhJ0tq1a43nzWaz2rZtK0mKjY2VdOaKF2dnZ+MgqrS0VLVq1ZK/v78sFossFosKCgrK9XlRNYSHh+uhhx7SxIkTNXr0aPXp00dNmjTR8ePHtXv3bhUXF1/2uqy5b81lDw8P1atXr8zfhPUAqE2bNgoMDFRGRoa2bdtmvM/6n/Wxj4+P6tWrp+Li4jLDP8A+rCdMTSaTMXzVpb43pf/93q+55hpNmzZNr7zyilq0aKEWLVqoefPmOnbsmFJSUi6abyaTSVlZWUpMTFSPHj0UFBRkbLtLly6SpDVr1pQZYsj6f+vrCQkJxpBcZ38PFxcXG1cyWywW+fn5nTO/DqoH6/HSI488oueee04DBgwwvkut+ZWRkaHU1FT5+voaJ5Qud1iqH374QaWlperSpYvxfXu2unXrGt+xCxYsMJ63NgXO/q4vLS3VyZMnjb8lAFUHNSIqCjUiqiNqRFwJakRUddSIuBQahDWcr6+viouLdfLkSU2aNElJSUmSzi3krI8LCwuVk5MjX19fY3xi647mfDsO6+tnTwpeEc7+cjv7asG6detWyPpx+awHHyaTydi5FxUV6eDBg8ZVJ+VZZ0lJibHus69ysuZi3bp1z7mizsvLy/gy2rlz53nXbTabjbknkpOTjStAJalZs2YKDAxUfn6+8f7i4mKj+HRyctLJkyeNGNzd3eXh4XFZB3+oOEVFRecU/lfK+jv38/PTQw89pB49esjd3V0uLi5q3bq1JGnXrl3KysqSdHkH+M7OziosLFReXp6kC1/BLJ3J31atWqmoqEgpKSnGZNDWEyjWz7d//34dO3ZMDRo0uKJCFJVjz549evXVV3XzzTfr448/lnR5uWF19v7HesAbGRkp6UxhZs2Dv7NuIzk5WSkpKQoNDVWtWrWMdViHZduxY4dyc3N1+PBhJSYmGu/z8fFRy5YtVVRUpKSkJBUVFRn7WenMia5Tp05p7969ks5cRd+gQQP2bXZWWFiomJgYTZgwQQkJCZLOf6L9Qv7+XWq9k8F6F8Xx48eNIdQuR0ZGhjEvyrBhw1SrVq1z4nF3dzeKP+uxn7XgO/sEcXZ2tjw9PZWXl8eVoUAVRI2Iq0WNSI1oa9SIsBdqRNgSNSIqAw3CGqykpEQmk0nPPfecunXrpvj4eH333XcqKCiQk5NTmZ3+3680OXHihNzd3S96+7x13P9p06YpNzdXnTt3NiaRv5qYrXFYJxz/8ssvJUm33XbbVa0b5WPNgZSUFH311Ve688471a9fPz344IMaP368Pv744ys+aD37CiwrJycnZWVlGeuyHhyffWBdt25dNWnSRJK0ffv2c1638vb2Nq7a27dvn/F8vXr1jLGxrRORu7i4lLmi5YcfftCaNWvk5uamQYMGlfkZoHKlpaXp7rvv1qBBgy5Y3Ftd6UFraWmpkVtt27aVt7e39u7dqwMHDlzResxms3bs2CHpzNBWZ5+cOHtb0v8O+vfs2WNcQW/dt5nNZh05ckSLFi1SSUmJwsPD1aFDBw7G7ej06dP66KOP9NNPPykpKUm7du2SdPlX1f2d9X0dO3aUJCUlJenIkSPnXda6j1m+fLkkaeTIkZJkzElivYo4Pz9ft99+u/r27asJEyYYV4JKMob62L59u3Fi1rpfS01N1XvvvaeYmBh5e3srOjq6zHZhO3Fxcfr000916623KiIiQg888IB++OEHrVu3TtKV/U7O910qndnn+Pn5yc3N7bJO0lr3Y7Nnz9bevXvVq1cvDRw40Dgpejbr8G9ms1m7d+/W0aNHyzQJrCfJPvvsM+Xl5alLly4KDg6+7M8EoPJRI6IiUCNyHGUr1IjUiPZEjQhboEZEZWNQ9GouOztbXl5e5Rrf3rrTj4iI0COPPKK1a9fqzz//VLNmzfTAAw+cdwezc+dOOTk5qWXLljp58qQ8PDwuuH4nJydlZmZq0aJFkqThw4fL09PzgnNFlJaWymKxnHdomZycHLm7uxu3tx84cEBr167Vd999p+zsbPXs2ZPJcu2guLhYa9as0fz58/XHH3+oqKhIzs7O8vDwkNls1tKlS7V06VJlZmbqiSeeUEBAwCUnV74YV1dXbd++XWazWWFhYZLKfhG6ubmpTZs28vLy0s6dO7V79261aNHCuJrYum3rsDC1atUyhkGSzgz3ERERoT///FMxMTF6/PHHlZGRoaSkJG3atEmLFy9WamqqgoKCNHbsWLVv3/7qfoC4Ip6ensrIyNChQ4d06NCh8y5TnrH/pbJXIIeGhiooKEipqanas2ePOnfufFnrs27bWtwFBATI2dm5zNXsZ4uMjJS3t7fS09ONq1DT0tIUHx+vv/76S7/99pvy8vLUp08fPfPMM5I4GLcXi8Wi4uJi/fXXXwoKCjKGezlw4ICCg4PLtV+z5kS7du0UEBCgffv26cCBA8a+7XzbX7VqlaKiouTh4aH4+HitWrVKa9as0fbt25Wfny+z2ax9+/bJw8NDHTp0UO3atY3Y+vbtqylTpmjnzp2Kj49XXl6eNm/erK1bt2rdunUqKSlRy5Yt9fTTT6tPnz4V8nPD5dmxY4fefPNNpaSk6MSJE5LOXGUZFRWldu3aKTIy0hgC6Gr2AdZ90cGDB5Wdna1OnToZc4VdjLOzs9LT0zVlyhS5uLjoqaeeMp4/n6CgIIWGhmrHjh2Kj4/XNddcY3yXrlmzRgsXLtSRI0cUGRmp8ePHy9fXt9yfCcD5USNSI9oTNSI1oi1RI1Ij2gs1IioTNSI1oi3RIKymNmzYoOeee06enp567733FBoaet4vn7OH9riY7t27a9SoUfrhhx/06aef6tprr1VISIjxunWi2uPHj6u0tFSNGjUyhs0437qtz8+bN0+JiYmKiorSsGHDLvoFeaErbPLy8vTZZ58pOTlZnp6eSktLU2ZmpnGwNGLECP3jH/8wxvzn4Mh28vPz9dFHHykuLk6tWrXS4MGD1b17d4WHhysvL09ff/21ZsyYodmzZ6tt27a67bbbVFpaesXzi1hZJ64tLCxU/fr1Jf0v16z/b926tUJDQ7VhwwYtX75cLVq0OOeKOm9vb2VkZCgoKMi4mlQ6U1y2bt1azs7O2rZtm5544gklJiYaV+5JUs+ePTVixAhdf/315foMKD9vb2917txZc+bMUWJioq699toyBy4Wi0VOTk4qKSnRli1b5OXlpVatWl32fsG6TMOGDdWiRQvFx8crOTn5sifidnJyKnM1nvUK5r9v27qvCw0NVePGjZWYmKipU6fqu+++U0JCgjIzMyWdOfi79dZbdfvttxtDjsD2rPnz66+/qqioSN27d1dCQoISExO1Y8cOBQcHl2u/Zs2LgIAAhYWFaeXKlUpKSlLv3r2NE51nL7t9+3YVFxdr7969uuWWW4w8kc5cJdqyZUslJyerRYsWZcb1t+rYsaNcXV2Vmpqqxx9/XEePHjVeCwkJ0Y033qj+/fuft/hE5bAWY0VFRdq6datcXFw0ePBgderUSZGRkWrRooUxh1dFsu7PTp06dcmTstYY33//fZ04cUK33nqrcReFdV6pv+e+j4+PIiMjtWPHDn322Wf69ddftWnTJmP/aDKZdMMNN+juu+82rpIHUDGoEakRqwJqRNgSNSLsgRoRlYUakRrRHmgQVjPWP0I3NzeZTCbl5ubq0KFDCg0NPe/yl3PAY/2jf+ihh7Rv3z6tXLlSkydP1mOPPaYmTZoYf9gWi8XYCVkn3b7QDsNkMunQoUOaOXOmJOn555+/ZDzp6en64YcfdOrUKd1yyy3GF5Cnp6caNGig77//Xi4uLnJxcVHDhg01YMAADRgwQJGRkXJ1db3gFVi4uKspmN3c3HTttdfqlltu0e23317mNS8vL40ePVpHjx7VrFmztHnzZt12221X9TtKTk6Wi4uLfHx8ylzVKf0vt5o2baoBAwZow4YNWrBggQYPHqyGDRuqqKjIuHXd+gXk4eEhd3f3Mutp3LixfHx8dPToUS1ZskR+fn4aNGiQrr32WvXo0UN+fn7ljh/lZz0BFRkZqTlz5ig+Pl7Z2dllrn4zmUzauXOnbr31Vvn4+OiTTz6RdGVXU1n3I23atNHChQuVnJysw4cPq2nTppf1t2IymZSQkKA6deoYY6Zf6ARZ7dq11aJFC+3YsUOxsbGSpMDAQI0cOVL9+vVT9+7dz8lPXLmMjAz9/PPP8vX11e23317uk0/bt29XQECAhg4dqmPHjikuLk7btm3TjTfeWO59qDWv27dvbxR/OTk5CggIMJax5p2Pj49MJpMxMXx4eLh69+6tPn36KCoqSjk5OerWrZt2796tlJQUNWvWzPi7KCkpkYuLiwIDA7Vv3z6ZTCYNGDBA/fr1U8+ePZmbyU6s34cdOnQw5loYM2bMOUPtWYe2Ks+dQH/fnsViMYbF8vLyMnLwfKwn1FavXq1Vq1bJ29u7zHf92UMQna1WrVrGPD1bt27V1q1b5evrq4EDByo6Olo9e/bkilCgglEjUiNWNGpEasTqgBoR5UWNSI1YVVEjwh5oEFYz1h1FSEiIWrVqpVWrVmnXrl3q27dvmS8f65dFWlqaNmzYoE6dOqlRo0bnXafJZFJpaan8/f01evRoJSQk6Ndff5WXl5deeuklY7lTp04pKSlJrq6uCgwMLBPP2YqLi+Xi4qKvvvpKaWlpGjZsmCIiIozhYaSytxxbYz148KC++uor+fv7q3379goLCzNeGz58uEJDQ+Xp6akmTZqcd6dB4Xd5LBZLmYPl811RfLkHMmazWffee69xgFpcXGyMjy9Jvr6+qlOnjkwmk7y8vMpdoFvfV1BQoMOHD6tVq1bGeNR/j9XV1VU333yzvvzyS+3atUsffPCBXnjhBaNoKy0t1VdffSXpzJwkZrO5zGf28/PT6NGj5eTkpP79+5e5ehRXJyMjQ/Xq1bvsg++zr263/n4iIiLk4+OjlJQUHTp0SI0aNTJey8/P1wcffKDS0lK99NJLxrj95REeHi4/Pz/t379f+/fvv+yrM11dXVVcXKzTp08rKCjogstZT6p17drVuOKwV69eZQ76UTFiY2P16aefqnPnzurfv/8VD2NlMpmUnZ2txYsXKyoqSp06dTLmeYiLizO+88rDGkOHDh3k4uKiXbt26fDhw2XywLpMkyZN9Oijj6pu3brq2rXrOScG3Nzc1LZtW+3cuVM7duwoU0BY/5b+/e9/q3bt2lc91xMqjrX46tSpk1asWKHY2Ngyv5/LvTr9cljzfvfu3ZLO7OcuNmeNyWRSUVGRZsyYoRMnTujRRx9VmzZtjNfT0tK0fft2JSQk6Oabbzbitt6p0b9/f3Xu3Fl9+vQ5Zx9qPRbh2A2oGNSI1IhXixqRGtFeqBH/hxrRdqgRqRGrMmpEjt1sjQZhNeXp6alWrVpp+fLlSk5OVk5Ojnx8fCT97497xowZev311xUREaEePXpcdH1nT4Q7btw4vfjii5o5c6ZuuOEG4wDKw8NDR44cUVFRkVq1alVmW1bW4T02bdqkBQsWyN3dXXfcccd5/7gLCwuNyXOlM5M+W+ekSE1NLbNuX19fY/Jc63atE5+y47gyZx9IZ2RkKD4+XqdOnVJERIQaNGhwxVfa1qlTx/i3s7OzMVGtk5OTjh07pjVr1shisahPnz5XPVGz9f/Z2dkXPEguLS2Vp6enXnzxRU2cOFG//vqrjh07pl69eunYsWNasmSJUlJS1K9fP91www3Gz8TK09NTDz30ULnixPlt375d48aNk4+Pjz744IOLXmlpzZ+/T5ps/d03b95czZo105YtW5SamqpOnToZyy1YsECxsbEaOXKkBg4cWK5Yz95OcHCwtm/frpSUFPXu3fuyioX4+Hh5enqqTp06xjjx52MtgIcOHaqhQ4eWK1ZcnDXHQkNDFRYWpuzsbB05ckQBAQFXfDXnli1bdPLkSQ0ZMkSS1LJlS3l7e2vv3r3avXu3wsLCynVyy7p8mzZt1KhRIx08eFCpqanGkBpnr9PFxUUjRoww3ltaWqrS0lLjCj13d3e1bt1aO3fu1Jo1a8rklbU4bdeu3RXFh8tz9OhRHTp0SCEhIZc1X8PZrMXXtddeqxUrVmjt2rXq27ev1q5dq7i4OJ04cUL+/v7y8/PTLbfcclVXVVr/JqwnDpycnOTi4nLRK0RXrlyplStXKigoSMOHD1dMTIy2bNmibdu2KSEhwRjKLygoSCEhIcbfVps2bfTpp5+WWVdxcbGRr+c7+Qzg6lEjUiOWFzUiNaKtUSOeixqx8lEjDjWWpUasXNSI1IjVCQ3Caujs4Q28vLyUkpKitLQ0+fj4GK8dOHBAn3/+uQICAvTPf/7TGIv/QuuTzuwEzGazhg8frsWLF2vFihV67733NHHiRIWEhOjQoUPy9vaWpHOG7rCy3po8Y8YMHT9+XI899pgiIiIkSbm5uUpISDBuJR40aJAGDRpkTNhcu3ZtvfXWWwoKClLnzp3Pu/6zr2q82tuoa6Li4mIlJiZqyZIlWrx4sfbs2SPpzIGop6en2rdvr+eff77cY9mf/Xs5dOiQ/vOf/6igoEATJkwwiverGa5m586dcnFxUfPmzZWdnX3eoVysB0oDBw5U7dq19cknn2j16tVavXq1sczAgQP16KOPql69euWKA5fH+ruuVauWfH19lZWVpfT09Ivm19kHIElJScrIyFDDhg0VEhIiZ2dnmc1mtW3bVlu2bFFCQoJycnLk6+ur1NRUffTRRwoICNADDzxg7FfKe8LBz89PoaGh2rx5s3bt2mWcYLvQlYDWbZ0+fVpZWVlq2bKlcQUzKsel9iXW1xo3bqxHHnlEPj4+xpAWl8v6e126dKlcXFyMIYGaNm2qoKAgJSQkaPv27cbdDOX9HJ6engoPD9fevXuVmJio48ePy8vL67z5a43pfCc/7777bvXq1YsJ5CtZUVGR4uLitGDBAq1cuVKHDx+Wl5eXAgIC1LNnT912220XvCPn76z7POtJ+tjYWD355JOKj4+XdKZwtw4f8/333+vtt99W165dy7Vvc3JyUnZ2tjIyMsoUgRe7Yn/atGkqKSmRxWLRgw8+qP379xuvBQYG6vbbb1fPnj3Vp0+f895tYi0sOW4DKh81IjVieVEjyniNGrHyUSNSI1YmakRqRHuhRjyDGrF64rdQRVn/0P5+hZT0vy+0li1bKjAwUGlpadq3b5/Cw8ONyZcnT56srKws/d///Z9xe7D1fdYJQ52cnGQymcrsQKzLPfzwwzp58qQ2bNig77//Xq+88opyc3O1d+9eBQYGGmNRn++L96+//tLSpUtVu3ZttWnTRjNnztTGjRu1c+dO7d2711guLCxMBQUFcnd3N2K41FVSXElQfrm5ufryyy/122+/KSMjQ56enuratauCgoJksVg0d+5crVixQtnZ2frmm2/k6elZrm1MmzZNy5YtU2JiovH80qVL5e7urn79+pVrvdZ8zcnJUXFxsRo3biw/P79LHvxZx11PSEjQzp07FRQUpE6dOsnf3/+KY8CVs/5urCd04uLijIm1z/d7y8/P15o1azRv3jzFxsYqLy9P0plhM15//XV17dpVkhQVFWVM1n78+HH5+vpq8uTJOnr0qP71r3+pWbNmVzUsgTXfWrduLTc3N+3Zs0cnTpyQj4+PXFxcVFRUdM6BjHVbp06dUq1atdStW7cyV07j6q1fv17jxo3TddddpwkTJhhzJlyKp6fnVV0tnJmZqXXr1ikqKso4mdm4cWM1a9ZM8fHx2rJli2699dZyF39FRUUym83q0KGDFi5cqNTUVOPvY/PmzcrIyCgT/8XyOjQ09ILzTaFiHDx4UNOmTdOsWbOUn58vX19fNW7cWG5uboqPj1d8fLy2bt2qf/3rX2rSpMlln6QIDg6Wj4+PcnJyVFBQoEceeUTXXHONzGazYmJitHz5cm3fvl0TJkzQs88+q379+l30qs4LqVOnjpKTk2WxWC55MiQpKcmYjyk9PV1eXl7q37+/+vXrp169el20sWD9bBR8QMWiRjw/asTyo0akRrQ1akRqxIpEjfi/mC6EGrHyUSNSI1Z3/EaqqLP/mP8+trB1R9GoUSM1b95cSUlJ2rVrl66//nq5uLhozZo1mjVrlnr16qW77rrrnHVbb921rnvXrl2Ki4tTUFCQevfuLYvFog4dOmjMmDHGJN6tW7fWtddeq9zcXJWUlKhly5bnrNe6g5s1a5ZOnz4tb29vPfPMM8rPz5d0ZviZvn37GpOPWueo+Dsmkq8cTk5O+vbbb9WoUSM9//zz6tevnxo3bmy83rlzZ3355Zfavn271q1bp+jo6Cv+YikoKNB3332n4uJitWrVSgEBAXJxcdFff/2lVatW6dprr9Vbb711xbe/W/PBOtzQqVOnJF3elabe3t7q1q2bunXrdkXbRMXx8PDQE088ccnC/6efftKkSZN06tQphYSEqEuXLmrYsKFcXV3LXJHetm1bBQQEaO/evTpy5Iji4uI0b9489erVSzfeeKOk8p8oOnsenB49eqhBgwY6cOCAlixZIrPZrD///FPp6emaNGnSeQ+cBg0apGHDhpVr2zXd38eb//vf94kTJ5Sdna2//vpL0rnzFFnn7DifzMxMTZ48WZmZmXrqqacuOoTR2es0mUzat2+f0tLSjJOTFotFXl5eatasmZydnY2x+i81DMeFWPdrXbp0Ue3atbVjxw49/vjj2rNnjzE0ByetqoaMjAy9+eabWr58ucLCwnTrrbeqd+/eatSokUpKSvT777/rtdde09atW/X111/r9ddfv6x9kfWq88GDB8tkMunxxx83hgSUzsyr069fP7333ntavXq1Zs6cqX79+pXrWCk/P18eHh6SZFwd+vfjLmvu+/v764477pDFYlF0dPQ585JYr/48+7gSQOWiRqRGrGjUiNSI9kKNiMtBjUiNWNVRI1IjOgIahFWI9Y+tsLBQmzdv1p9//qnExES5ubmpU6dO6tOnj8LDw41lzWazwsLCtGjRIiUlJSknJ0eenp5666235ObmpkcffVQeHh7n/FFbx9i3DuWya9cuFRcXq2/fvmXGUO/Ro4dGjRqlGTNm6L333pPFYlHdunXl7OysvLw8Y+dhZTKZlJeXp2PHjkk6M8RM27Zt1adPH1177bUKCwu7rJ8DhV/Fs1gsqlOnjl5//XVFRUUpJCTEeM06z0d0dLQ2bNig/fv3a/PmzYqOjr7iA+j69evrgw8+UEBAgJo1ayYXFxcVFhZq+fLlmjx5spYvX665c+fq/vvvv+LPUFhYqKSkJJnNZuO2fHKlarIWUGcfDHh6eqqgoEA7d+5UcHCw6tevX+bg+6efftLEiRMVEBCgt956S7169TKKResQV1ZNmjRRixYttHr1av32229at26dnJycNG7cONWuXfuqTiBZ35eRkaHY2FgVFhYqJydH77zzTpnlcnNzz/v+s+fMwcVZiz1Jxt0K1nyw/g7PzpGoqCj5+voqPT1dhw8fVoMGDYyD5gsdfFrff+zYMa1evVpHjx7VLbfccllDZJ09b4kk9e3b13jexcVFHTt2lJ+fn/bs2aO33npLiYmJ6t69u8aMGXPZP4OTJ0/qr7/+0rp16xQbG6tTp07p1KlTWr9+vaQzY/R37tzZuKoa9uXk5KSMjAz1799fzz///DlDxAwePFjJycmaMmWKNm3apEOHDqlhw4aXPNFgzd2xY8eqdu3acnV1lVT2BEjLli11//33a/Xq1YqNjVVmZma5TggkJyfr9OnTql+/fpnhA89m3aafn9858y1Z54iw/s1y9SdQ+agRz+C4v+JRI8KWqBFxOagRqRGrG2pEakRHwG/sClkney3PlUeXcwvxvn379P7772vx4sXG8+7u7oqNjdXXX3+t1157TTfddJOxnrCwMPn6+urAgQM6cOCA1q9frz179uixxx5Tp06dJJ37R71jxw698sorks5czRIVFaU+ffoYk3Gf7eGHH9bu3bsVGxurDz74QMeOHVPfvn3LXK16Nnd3dz355JMqKipSly5dzvkyPntycoaCsR3rz3r48OGSyl6FZf2S8fDw0OnTpyXJuAKkPAfQ1nkkpDNfEmazWTfccINycnL0z3/+UytXrtTw4cPLXPlyKdaTHbt27VJhYeF5r06G/ZSWlqqkpMTIpbPzZu/evWrYsKHy8vI0btw4bdiwQe+//74GDRpkLHPs2DF99tlncnNz05gxYzRgwABJ/7vy6OyDC2tR0LZtW8XGxmrevHnKz8+Xs7Oz/vjjD3l4eBhzAJRnLpOEhAS99NJLxtjuVs2aNVOfPn3Ut29fdejQgSKvApz9XVpQUKB169Zp06ZNOnz4sJo0aaKIiAh16dJFZrPZOPkYERGhlStXKjY2VsOHDzdyIzU1VRs3blRpaam6d+9e5sp36czdFN26ddOsWbO0e/du9erV65K5Yc2/LVu2qH379sbk7QcOHNDOnTs1f/585ebmqrCwUNOnT5d05iTVo48+etl5N3fuXP3rX/8yHjdo0EDdu3dXdHS0unXrVq7htlB5/P399dprryk0NFRubm5ljgmt+73IyEjVqVNHp0+fVnp6uho2bHjJ9VrzxTqH19+fl87sV9u1a6emTZtq7969Sk5Olr+//2Wf7LIu5+rqqvT0dLm5uV32fCvWK0CtE9YDOBc1IjVidUSNiMpEjYjyoEakRqxuqBGpER0Bv8FL+Pvt7FdyMFxaWmrczn45BeP+/fv1z3/+U2vXrlWXLl00fPhwdezYUSaTSTNmzNA333yjf//737JYLBoyZIgkqUWLFmrUqJGSkpI0Y8YMLVmyRA0aNNDo0f+vvfuOj6LO/zj+2k0jnUB6QgIJ6SEkIYVQQglNqoCABTlA7mzYuBPv9PTHAerZ8RQ7KgqoKAIRRXoNkIQWSirpBUIghFDSd39/5DFDFhJ65/P8R9wyO7uZnfm+Zz77+U5s8XW8vb15/vnnCQsLIyoqqsX10ul02Nvb85e//IWCggKKioqAxp/w29raNrvDMTIyUkMnnJsnQ5koV35efHvQaDRqVZby99+yZQsbN27E19eX7t27X9PylUG3sbGxup1ERkYCjScfrvTgoVR3jR49GnNzc7VKS9wezp8I+/jx43z99dcsXryYU6dOMX/+fLp06YK/vz979+5V+5Ur2156ejonT56kbdu2DBs2DDDchpoTGhqKpaUl5ubmDBkyhG3btvHll1/y22+/8dBDD/H444+j0WiuuFL07NmzlJWVYW1tTb9+/YiNjSUmJuaKTlaIy3PgwAE2bNjAxo0bOXjwYLOPefTRR5k4cSJubm4AREdHs2nTJjZu3MioUaNIS0vjrbfeYseOHepz7OzseP755xk3bpy6jVlYWKgnjTIyMjh16tRF5/9Qtr8dO3aQkZFBdHQ0b7/9NgkJCWRkZFzw+Pbt2zN79mxCQkIuK/gp22XHjh155JFH8PPzo2fPnpcVFMStpcwxcv4cNso2Y21tzfHjx+nQoQPt2rUDru/cWF5eXuTl5ZGenq5OWn85lHWtra2lX79+9OjR44Kw2RIZuwlxIcmIkhHvRpIRxfUkGVFcDcmIkhHvRJIRxZ1OLhBeQtPQlpmZyYEDBzhy5AgWFhZqtVBLmg6IKioq1CopJycng8cpB4HVq1ezY8cOhgwZwrvvvmuws5g+fTouLi68/vrr/PDDD2r4c3FxwcfHh3379rF27VqqqqpoaGjg22+/ZcyYMeqBpGmVlJubG0888YS67JYmu1fWXfk5/CuvvIKFhQVhYWEG9zdHeU+yw7h9KX+/vLw81q1bxy+//EKHDh149tln1W30aqrroPkDXWlpKZaWlmg0Gk6dOnXFVU92dnY8/fTTV7wu4tqdfxLsfFVVVUybNo3CwkK++OILPvvsMxYvXkyHDh0ICgrC1NQUY2Nj/P390Wq1pKamUl5eTps2bQBITU2lurqa8PBwdZktbXfK7UFBQbRt25Zjx47Rr18/nnvuOd59913Wrl3LBx98QHx8PP/6178MqpUvR1BQEMuWLaNt27ZX9DxxZb777jveeOMNAMzMzOjSpQsBAQEEBwdjaWnJihUrWLVqFT/99BN6vZ5///vfAHTp0gWAHTt2UFlZySuvvEJmZiZ9+vShffv25ObmsnXrVv7v//6PDh06EBUVpe7HvLy8sLe3JzMzk8OHD2NtbX3JfZwyeE9MTCQxMREAe3t7unfvTt++fcnJyeGzzz7DxMSETp06YWpqelknHJT7Ze6bO9f5243yN01PTwcaf3FxqQnaL5eynVZVValtBe3t7Q1e93JFREQYnKQXQlwdyYiSEe9WkhHF5ZKMKK43yYiSEe90khHFnUouEF5EXV0du3fvJj4+nrVr117QT7x9+/YEBgZe0N9X+ZIWFxezcuVK4uPjyczMRKPR4OHhQXh4OFOmTMHb21udrLa8vJxffvkFc3Nznn/++Qt2KsXFxRgZGWFtbc2ePXvIy8tT+2P7+flhbm6Op6cngYGB7Nmzh08++YRly5YxefJkxowZo/7MuenEvspg7lIBzdTUlNGjR9OpUyd8fX0v67OTvv/Xn9J6R/l7XW0wUyQnJ/N///d/5OTkGNz+n//8h4SEBIYNG0ZISMhlV9c1XZ+mPzXXarXU1tby/fffc+bMGcaOHasetMTtq+mE3peqbjc3NychIUH9O69fv54XX3yRhx9+WJ3gGBqrmpydnTl06BDFxcVq+FN6tCsnCC62bSu3Ozg44OfnR15eHvv27aNbt2689dZbJCQk8P3337Np0yamTJnC0KFDeeyxxy67TUKrVq1abI8lrp3ytw0KCsLR0ZGamhqeeOIJJk2aZPC4nj17YmxszO+//86vv/7K9OnTMTU1pUOHDri4uHD48GFmzpyJiYkJixYtUiv2dDodb7/9Nt9++y0///wzvr6+amWvh4cHXl5eHDhwgPz8/Isez5TtzNPTE2dnZzw9PRkwYACxsbFqIATYvn07jo6OlJSUsG3bNvr06XOdPzFxvSknvG9EG5Ta2lp1PpLx48df1TKU74iyD27ammbNmjUcPXoUCwsLoqOjr3o9m2vNJYS4fJIRG0lGvD1IRhQ3k2REcSNIRhS3mmREyYj3OvmrN0P54q1Zs4b33nuP4uJibG1tGTx4MD4+PnTo0IHq6mqKi4upqakxeK4yUC4tLeXNN99k7dq1ak9gGxsbCgoKWLp0Kdu2bePbb79V+6BrNBpKSkpwcnLCycmJ06dPs2/fPlJSUti7dy8HDx7k2LFj6uscPHhQDX8BAQFYW1tz9uxZhg0bxjPPPMNHH33Eb7/9xuzZs1myZAl///vfDaqkrmaODOVA2dDQIPND3CRNB8FNQ3plZSU2NjbXtExra2ucnJzw8PAgODgYLy8viouL+emnn/j+++/ZvHkzixYtarZKTjlp0VTT7UG5r7a2lrS0NBYtWsT69etxcXHhvvvuU+chELcvjebchN7p6ekcOHCAuro6evTogYuLizpoUCYAHzJkCEuXLuWbb75hyJAhPPbYY4DhIMPNzQ1vb2+2b99Obm6u2q9faeuxb98+Tp8+fcnKYWX7Cw0NZdWqVWRmZnL8+HFcXFzo3r07kZGRrF69ms8//5wVK1awYsUKRo4cyZtvvnmjPi5xmZT9REBAAI6OjqSmplJXV6ferwzMW7VqxdChQ9mzZw8lJSXs3r2brl27YmNjQ2hoKIcPH2bFihU888wzhISE0NDQQENDA6ampowcOZI///yTpKQksrOz1YpSBwcHfH192b59O1lZWfTv3/+ixzFlX7lx48YL7qutrcXU1BRXV1fc3NwoLCwkISFBwt9t6PwxS9Nj14kTJ7CysrrmY5Kyraxbt46UlBT8/f3VlmkXe05zVffKejbdB9fW1rJp0yY+//xz6uvrmTJlygW/NLoSF2vNJYRomWTE5klGvPkkI4pbRTKiuBEkI4qbTTLihSQj3tvkL98MjUbD6tWreemll9Bqtbz44os88MADl9WHV6mE+/vf/87OnTsZM2YM48ePx8/PD2hs1aG0OHjrrbeYOXMmjo6O5Ofn4+DgQGVlJc888wz5+fnk5eWpy3V1deWBBx4gLi6OqKgoLC0t1QGQl5cXXl5eJCUlkZ6eTnR0NLNnz2bw4MF8+eWX7Ny5kylTpjB8+HCeeeYZ3N3drym4SUuYm0f5O50+fZodO3awdu1aMjIyMDY2Jjg4mAEDBhATEwNcfrWo8hg/Pz/mzJlzwXY9cuRIJk2aRFZWD82rvwAAUrdJREFUFsuXL+eRRx7BzMzM4DHNbQNFRUUsW7YMOzs7zpw5Q25uLocOHWL//v0AREZG8o9//IPOnTtf+Qchboj6+voL5oZQlJeXs2TJEn7++WcKCgrU242NjRk1ahRTpkzBw8MDnU4HQGxsLEuXLsXa2lpth6GcDFMGGfb29vj5+bF+/XqysrLU4Ojh4aFOqrx161Z1AnpF08FbdXW1Wr3ZuXNnbGxsyMnJoaysDBcXFzUADB06lLi4OL799ls1FIrbg16vx8LCgsDAQA4cOMDBgwcpKyvDwcHBYJDu6OiIjY0NJSUlpKamqttVTEwMK1euxMXFhZ49ewKN+yRlv+Tl5UVERAQrVqzg0KFDhIeHo9FoMDU1xdfXFzMzM9LT0w1aGDWn6f60vr5erdJTlgXg5OREQEAARkZGaq9/+XXE7aXpLyr0ej07d+5k+fLlJCcno9FoiI6O5uGHH8bf3/+qX0Oj0XD69GkWL14MwOjRo3Fzc7vorytaOgm/du1aCgoKaNeuHaWlpWRmZrJv3z7S09PVXxBNmTLlqtdVCHH1JCNenGTEm0cyoriRJCOKW0EyoriZJCMKYUguEDajpqaGzZs3U1dXx/33369WOYFhWwyg2S/26tWr2b9/P1FRUbzwwgsGB5f27dvz0ksvUVpaytatW0lMTGTYsGFotVpsbW0pLi5m06ZNWFtb07dvX+Li4ujRo0ezVQDKDs3Ozg5fX1+2bdtGVlaWWl3VrVs3IiMjWbp0KT///DPx8fHEx8fz3HPP8eSTT96Ij05cAeWn4RertD1z5gx//PEHCxcuVHtWGxsbY2Vlxf79+4mPj2fq1KlMmjTpitvJaDQaNfjpdDp1Xezt7Rk4cCBZWVns2bOHESNGGIS/qqoqVq9eTVlZGZMmTVLX39bWlsTERJKTk9XHGhsbExERwdChQ+nVqxcuLi7X3PZGXD9KKFNOJOn1eqBxoDtv3jy+/vprNBoNvXv3JiQkhPr6epYtW8bixYvJy8vjs88+w8LCAkAd+FZVVeHu7t5s1ZNWq8XHxwcrKysyMjI4duwYzs7OGBsbM3DgQD7//HN++OEH3Nzc6NSpkxoOlX1dYmIiaWlpTJw4EWisWG/Tpg05OTmkp6cTEhJicGLC3Nxc9nW3IWUfEBoayi+//EJeXh7FxcU4ODjQ0NCATqfD1NRUbcPWqlUrg2p4ZR6SI0eOXHBs1Ov1mJiYEBQUxIoVKzh48CCnTp1Sn+/t7a22MCoqKqJNmzaXtU9qqZKuVatWvPTSS9fycYgbbNu2baxfv55///vfbN++nenTp6u/trG2tmbx4sUkJCTw7rvvEhYWdtXHqM2bN7N9+3bCwsIYNWoUcPETARUVFWzatImAgAB8fX3ViuPq6mq+/vprg18EGRkZ0bNnT0aOHEnPnj3lBIMQt4hkRHEzSEYUt5pkRHErSEYUN5NkRCEMyQXCZpw5c4bk5GRatWpFbGwscO5gpdVq1QESGH6xlcHzjh07qKmp4aGHHjIIfkeOHCEtLY2kpCSOHz+uDqSGDRuGm5sbTk5OpKamMnbsWGbOnGmwTnq9nrq6OjQaDSYmJtTW1qr/hsaf4tvY2JCbm8vhw4fx8fFRdyRjx46ld+/ebNmyBU9PT5l49BZTtqWmPw0//z7Fpk2bePXVV7G1tWXUqFH06dOH4OBgHBwc2LZtG//5z3+YM2cOAwcOxNXV9arXSakQVKoFQ0NDAcjNzcXW1tZgvXbv3q0OdgYMGICHhwfQeBD95z//SU5ODg0NDXh6eqqTjzclwe/mUPZTF/u816xZwyuvvMK4ceP4+9//Tn19PSYmJsTHxzNv3jxCQ0N55513DPrpjxkzhscee4ykpCQWL17M+PHjMTY2xtbWFh8fH7KysigvL7/gdZtOAu7m5kZ2djaFhYU4OzsDMHz4cJKSkkhMTOT111/nr3/9K3FxcZSXl3PgwAF1QvKIiAgeeeQRTExMsLKyYujQoZw9e1aqP+8gynGzU6dO2Nvbc/jwYQ4dOkRoaKhByPrtt984deoUdnZ29OrVS73dw8MDX19fMjMzyc3NxcnJST3+KtuZv78/bdq0IS0tjWPHjqnhz83NjY4dO5KQkEBOTg4hISGyT7oDKScsm24vzYW22tpaXnnlFQ4fPoyvry9LlizBwcGBGTNm0KVLF4qKivjyyy9ZvXo133//Pf7+/gZz4lyuU6dO8e677wLw6KOPqvPkKOt6/rH+2LFjvPPOOyxfvpwxY8Ywa9Ys9b7u3bvz7LPPcvjwYVq3bk1gYCCdO3e+4FgqhLj5JCOKG0kyoozHbgbJiOJ2JRlRXCvJiEJcvXvyAuH5k4+ev8NQqkWqq6s5fvw4YNjvt6VBjVarpbq6murqaoyNjamoqCAlJYUdO3aQkpJCamoqR44cUZ/n4+NDVFQUAG3btiUiIoJt27aRlpbGkSNHcHZ2Ngh5yhd///79rFq1iri4OMLCwoDGKilTU1N27dpFdnY2Pj4+BjsKR0dHRo8efb0/SnEJyuSxTbcZ5d8lJSUkJiZy5MgRIiIiiIyMvGDbcnBwYPLkyTzzzDMGB6Samhr8/f3x8fGhuLiYZcuWMWnSpKs6aCmUFh0AWVlZwLmKqKbr5erqir29PceOHWPnzp1q+AMICgoiKCjoqtdBXD+XGtDW1NSwZcsWKisr1TlETExMqKurY+7cuZibm/Pcc88ZBD8AFxcXRo8ezccff8ymTZvo1auXOk9ObGwsWVlZ7N27lyFDhjS7Pi4uLvj4+LBq1SpycnLUHuze3t7Mnj2bJ554gr179zJ16lTatm1LXV0dJ0+eBCA0NJQpU6YYDPimTp16DZ+SuJW8vb1p3749SUlJFBUVUVVVRXFxMTt37mTFihXs3LkTJycnnnnmGRwcHNSAZ2pqSlRUFJmZmSQkJNC1a9cLKu29vLxo3749WVlZFBcX4+XlBTQea/38/Fi3bh3Z2dlqBbK4szRte3X06FGOHz+Or6+vQasYpc3PoEGD+Oabb3j77bfp2LEj33//vTqHjZ2dHf/85z9JTExk27Zt7Nu376omdp8/fz4lJSX07t1bvWjQ3HwWynpZWlri5uYGwPLly5k1a5Y6ZrOzs+OBBx64oAJUp9OpVfdywkKIG0MyorhZJCOKW0UyorjdSUYUV0syomREcfXuiT3e+S1emn4RlQpKhXIg6Nq1KwUFBSxevJi6ujpiYmJwcnLi2LFj5Ofno9Vq8fT0xNLS0qAlRkNDA9XV1TQ0NPDJJ59QVlamLtvBwYERI0YQFxdHTEwM1tbWBus5YMAAtmzZwo4dO/j666+ZMGEC7u7u6ns4dOgQq1atYsGCBZiZmRlUQ7Vv354JEybg4OCg9ts+X0uTnYrrT9nmmvus6+vreeutt/jxxx/ViZfNzc0ZOXIk//rXvwwmwu3cuTMBAQGYm5tTXl5OSkoKSUlJ7Nmzh4yMDKqqqgDYuXMnw4YNo127dlf00/em3w3le7F//34+++wzACZOnIixsbHBMh0cHJgwYQK1tbUGFVvi6intW67Uxf7WR44c4cCBA4SHhzfbQ9/MzIzMzEzg3ATw0Bj8a2pq8PPzU+cuqa2tJScnhz179nDgwAGSkpKoqqoiMTGRlJQUNfz17t2befPmkZiYSE1NzQXzkgDY2tri5+fHH3/8QUZGhjpfhE6nw9vbm59++okFCxaQkZFBZmYm5ubmdO3alYEDB9KtWzdat259xZ+TuP0o+57g4GCSkpL47bff2LJlC3l5eZw5cwZonANnwoQJ3H///YDhrzGioqJYsGABmzZt4u9//7t6n/J9cHJywsfHh927d5OVlUVMTAzGxsZoNBp8fX2xtLRkz549HDlyRG13JAPq24cyb835x1Cl4lKpGF+3bp1aRent7U1cXBwTJkxAo9Go21iXLl345ptvqK2tpVu3blhZWVFfX4+RkREajQZXV1f69evHkiVL2LdvH+Hh4Vc0IX12djYLFy7EwsKC8ePHq8ESGk/yHjhwgNLSUh599FF1GzM3N8fX15fevXvTpUuXC44BTVsUKv8vYzchrj/JiI0kI948khHFlZCMKBnxXiMZUVyMZETJiOLGuScuEDb9wtTV1ZGcnKxO5O3h4UFsbCxRUVG0bdtW3bEMHz6cPXv2kJWVxdtvv427uztHjx5Vw6K1tTUnTpzA3t6e4cOH8/TTT2Nubo6lpSW2trYYGRlx4sQJunfvzqBBg4iJiVGDXEvatWvHU089RXFxMd999x2///47/fr1o7KyUm09U11djY+PD08//bQ6MIPGHcnf/va3iy6/pclOxbU7v8JIq9XS0NBASkoK2dnZdOrUCT8/PzQaDe+99x7ff/89MTExBAYGcvz4cdatW8eiRYvw9/dnzJgx6t9JqQo+ceIEn332GcuWLaOyshJobL3QuXNnEhISSE1NpaioiHbt2l3W37i2tpYTJ06ovdnLy8tJTU1l7dq1rFixgtraWh588EHi4uIAw0pDKyurS25r4sooB/3s7GyMjIxo3759s4FQmZNEeY7ydzl/4FpbW8uoUaMoLy/nscce47nnnjM4yXX+yYmm+8iSkhKOHz9Ox44d+fnnn9XJjzMzM9WTDebm5vTo0YMBAwbQp08f9blhYWFoNBoyMzMpLCykY8eOF6y/RqPBx8eHtm3bcujQIUpLS/H09FQf06ZNG5599llqamqorKzEwcHh6j9YcdtStteQkBB1bqXi4mKCgoLo0aMH/fr1o1OnTi0+PygoCBMTEzIzMzl58qQ6Vw6c286CgoLUyeYrKyvVkyBubm64uLioc6M0XR9xe7jYpO0JCQnMmDGDwsJCbGxs8PPzw9TUlKSkJHWi9jfffFNdRnBwMK1ataK6ulqdm0Q5Xiv7wrCwMJYvX87BgwcNKuYvRa/Xs3jxYk6cOMGoUaOIiopiy5Yt7N+/n71795KamqrOExEXF4erq6u6fQ4aNIhBgwZd1ecghLg+JCM2kox440hGFNdCMqJkxHuNZERxMZIRL/45CHEt7uoLhMoXLCEhAY1GQ7du3fj000/55JNP1Mfs2rWLpUuX0q9fP1599VV1MBwREcG7777LjBkzOH36tNpOw9nZGWtra4qLi9HpdBw/fpyvvvqKmpoaJk2ahKurK0FBQcTHx9OpUyf++9//qoMXZY4IpXWNkZERRUVF6PV6tUVDVFQUX375JZ9++ik7duxg+fLlVFdXq/2yBw8eTP/+/Wnfvn2z7/lqq8zEtVEOJFVVVZibm7Nw4UK++OILSktLAbCwsODhhx+mR48ebN++nZdffpkJEyaoz//iiy94//33WbZsGSEhIfj7+6vb74kTJ3j88cfZt28fkZGRjBo1in79+qnVxdOnTyc+Pp7s7GyioqIu6++/b98+FixYwJEjRzh9+jQnT55UK5nt7e2ZPHky48ePv6CCWVx/p06d4rvvvmP+/PlUVlYSGxvLF198YTCPjeL8OUlycnKoq6vDz89Pva2hoQFTU1Nee+013nvvPebNm0d0dLTa0gAaBxQlJSUABoNmQN0H7t69m8TERPX2Tp060atXL3r37k1wcPAF69bQ0ICxsTEREREkJyeTkpJyQfhTtGvXDmtra5KSksjLy8PT0/OCQY6ZmZkEv7uYErY6depE69atqaqqYtq0aUycONHgcc214ILGNkShoaEkJyeTlJRE//791eNf05MMRkZGbN68mYkTJ6rhz9/fnyVLlki//lusoaEBoNljVmFhIRs2bCAwMJCIiAj1b5qWlsbjjz+OpaUlL730EgMGDFDbsKxatYqZM2eydOlS+vbtS1xcHFqtFkdHRzp37kxiYqJ6nDt/rOTr64uTkxMZGRkcPXr0ssNfXl4eixcvBhrnYxo2bBj5+fnq/a6urowdO5ZevXqp+9am27Jer1f3nUKIm0cyorhZJCOKqyUZUTLivUgyopCMKBlR3Bp37NamTD56sQopjUbDli1b+Otf/0q7du14+umn+eSTT4iLi2Po0KF07NiRnTt38vHHH7N27VoaGhr44IMPaNWqFXq9Hj8/P3744QdycnKoqKjA0dGR+vp6KisrsbS05MSJE3zzzTesW7eOP//8E19fX8aMGUNERATBwcHs27ePb7/9lhdffNGgL7bi4MGDvP322/Tu3ZsJEyZgZGSETqejffv2vPnmm5w8eZL09HRMTEzw9/c3+ElySyT4XVppaSnff/89HTt25P77779kf/GWfsbe1GeffcacOXN4/vnncXBw4L///S+urq4MHjyYhoYGNmzYwPz58/npp58IDQ1lwoQJNDQ0qAP1oUOHEh8fT1paGvv378ff31/dljdt2kRqaiqBgYHMmDEDb29v4FzrI+VvnpmZSWVlJXZ2di2up/IdcXd3x8rKiqNHj1JXV4etrS2RkZH07t2bHj16NNtuRFw+pYrzcr6PZ86c4ccff1SrfpWWLs1tk+Xl5Wzbto0VK1awe/duzpw5g5OTEx06dOBvf/sb0dHR6msOGjSI3NxcPvzwQ7766iscHR3x9/c3GPRkZmZiZmaGr6+v+hqBgYHqPBP9+/dn3LhxREVFXTBQPv97o4TV3r17k5ycTHJy8gVz2ijbtKurK1OmTMHKysqgyl3ce9zc3PDy8iI/P5+CggJOnTqFtbW1up22tN/VarVER0eTnJzMli1b6N+/v7oNKs/x9vZm8uTJuLu7q/tNaP67Ja7e1bbeaWn/WFVVxX/+8x+2bt3KBx98AJzbd3zyySfU19fz2GOPMWnSJIPnDRw4kNLSUt544w2WLl2Kn58fHh4eaLVaunbtSmJiItu3b2fkyJEXbCsdOnTA29ub7du3k5eXR0BAwGW95xUrVqhV83v27MHa2pp+/frRp08fevTooQa+lmg0GtkehbjOJCM2TzLipUlGlIx4o0lGlIwoLo9kxDufZETJiOLOcsducU1bH1RWVlJXV9fs1fyIiAigsdf6Rx99xIgRI3jrrbfU+318fLC1teXDDz9kw4YNbNy4kUGDBhnsyJSJa5vj4uJCQUGBOunymDFj8Pb2ZsqUKTz99NPMmzcPS0tLBg8ejKenJ3l5eaSkpLBy5Uq2bNmCqakpDz74oLoT1Gq16hwQdnZ2BgMjJfBKn+Frs2vXLr766ivCwsK4//77mz0AKRO9XmzwAecqTJTtJTk5mTNnzjBkyBBmzZql9qhesmQJr7zyCvX19ep2amRkpL62q6srERERHDp0iPT0dLXnPsC2bduor69n/PjxeHt7qwcd5YBhaWkJNA7ky8rKLhr+lPV0dnZm2rRpTJo0CSsrq0seoMSVaVrFmZ2djbOzs/p3Op+NjQ1nz57FycmJkydPcuTIEQoLCy+YK2T37t189NFHbN++HY1Gg4eHB15eXuh0OhISEigqKuKFF15g0KBB1NXVYWJiwgMPPEBiYiKJiYl8+eWXvPfee+p6WVpaqtXqyjauBLru3buzceNGIiMj6dGjB9B4sqHppM7GxsYUFhZy8OBBBgwYoC4jNjaWd955hw0bNlBZWYmNjc0F79nc3JxRo0Zd3w9d3HGU7SkkJIQNGzaQlZXF0aNHsba2vqxjXJcuXQD49ddfmTlz5gWDaBsbG6ZOnXpD1l3AsWPH0Gg0BmOv86su6+vrmx2z6PV61q5dS0JCAm5ubvz1r39Vbzc3N1dPuirHJp1OR1lZGZmZmbi5ualzjihycnLIyckhJSUFjUZDcnIy+/btw8PDAzg3Fty2bRtw4QkAa2tr/P392bx5M+np6fTp00c9BjdHObkXFhZGXFwc4eHhxMbGGszVo7yfhoaGCyr7hRA3jmREyYhXSzKiZMQbTTKiZERxaZIR72ySESUjijvTHXmBsKGhgYMHD7Jy5Uq2bt1KRUUFHTp0wNfXl7/97W84OjqqjzM3NycwMJDU1FSKi4uZPXs20DiQUQbfgwcPJjs7m7lz5/L777/Ts2fPCwZq51c/KP/v5uZG+/btOXToEGfPnlXbh8TFxfH888/z888/87///Y/58+ej0Wg4e/YstbW1AERGRvKXv/zFoD87XNjnumngk9B37cLDw7GysiI3N/eCvuSKpp9zSkoKBw8exMzMjLCwMNq1a4eJiYnBNhEbG8vHH39MQkICdnZ2fPLJJ5iYmKgHsNGjR/Pdd9+RkZGBo6OjweTcynICAwMxMzMjKyuLI0eOqC2ClNZCyk/Sq6ur1arQ7Oxs1q9fDzQe/IqLiw0q/S6mTZs2UgV6gxQWFjJjxgx2795NVVUV//jHP5g0aVKzB/+cnBxMTEwICwujoqKCHTt2kJCQwIMPPqi2FSgtLeWLL74gKSmJsWPH0r9/f8LCwtSK8R07djBx4kQ+/fRTBg0apJ50cHBw4MUXX2Ty5Mn8/vvvDB8+nB49emBkZER6ejrW1ta4uLhw9uxZg5MGw4cPZ/v27cybNw9bW1tGjBhxQXVodnY2H374IVu3biUqKkrdlnx8fLCyssLBwYGqqqpmw58QTYWGhmJpaUlRUREFBQUGlZwX4+vrS3h4OL6+vmq1vLhxTp06xfbt21mzZg379++npqYGd3d37O3t6dWrF4MHD8bU1NTg2NhS5aPSLqiurg4HBwd69uyJv78/dXV1mJqaYm9vD0BxcTHh4eFotVoKCgooKCigS5cuVFVVsWHDBlJSUkhJSSEtLY2Kigp1+T4+Pjg7Oxv8v729PceOHSM/Px9PT091PZUxlr+/P+bm5uqymj7/fMq+vEePHuoJMkV9fT0ajQatVivVn0LcZJIRxbWQjNhIMuKNIxlRMqK4fJIR7wySEc+RjCjuZNd9i7zanxHDuZBzKfPnz2fu3LmcOXNGnfQ9IyODpKQktm3bxowZM4iKilKv3vfq1YvU1FS1EhRQDxLK+t53333MnTuXPXv2cPjw4Qv6op8f/JTqQWNjY6qrq9Hr9bi7u6tVDVqtlieeeIJu3bqxcuVK8vLyKCoqwsLCgtDQUPr160d4ePhlVQtI4Lu+nJ2dCQgIUHvgx8bGGlS01NfXs3//fpYvX87atWvVyWOhsYJkxIgRTJ8+HVNTU3W7CAgIUCuFAwIC1IGwVqtVA2Dv3r3JyMigpKSE06dPXxD+/Pz8cHR0JD8/n/z8fDX8KVUtq1atYvz48WrP/aqqKhYuXMipU6cYMmQIv//+O3v37qV79+4yCLrFzpw5owY/Y2NjvvzyS4KCggyqvZW/e1lZGRUVFdjZ2REbG8uOHTvYuHEjDz74oNriwMbGhhEjRvDcc88ZtDWorKwkLS2NvLw87OzsyMjIYM+ePYSFhQGN+9SgoCAeffRRPv74Y/73v//RqlUroqOjOXnyJMeOHcPf3x83NzeDdjc9e/Zk0qRJfPbZZ7zzzjukpqYyfPhwTpw4QUZGBtu2bSMhIYFWrVrxwAMPYG5urr6eVqtlx44dMuARl6TsP/39/XF1dSU3N5dDhw7Rp0+fyxpHtG3blkWLFt3o1RRAUlISH374Ibt27QIa56WxsLCgoKCA5ORk1q9fz5IlS3jjjTfUE5bQWLk7f/58ZsyYQVhYmLrfO3z4MP7+/qSmplJWVsaPP/7IjBkzMDU1paamBisrK/WEucLNzQ29Xs/+/fuZPHkyxcXF6n2enp4MHTqUPn36EBUVpZ4AU7Ru3Zrw8HBWr17N1q1b8fT0VE+uKdua0m4mNTWVwsLCi4a/ppQKUOUEvez7hGieZETJiLc7yYjiRpOMKBlRXJpkxDuHZMSWSUYUd5pr3kL1er1B+4ErCX5KiFKuoF9OyPnqq6949913cXd3Z9q0aXTr1o0OHTqQnp7ON998w/Lly/n0009xdXXF3d0dgD59+vDpp59y5syZCwbFyvp27NiRtm3bcuzYMY4dO9bsxMlN11UZJC1cuJCtW7diZGREZGQkYBjWQkJCCAkJ4dSpUxgbG6uDpKafwdWGZXHOvn37OHr0KF27dr2seTiUvuQ7duwwmJhbp9OxZs0aPvjgAwoKCnB1dWXYsGF4eXlhaWnJjz/+yIIFC7Czs2PKlCmYmZmpLTfCwsIoKCjA3d292bYZ4eHhmJiYcOjQIcrKytSf3Dftb92+fXu2b99OdnY2vXr1AqBr165q66HJkyfTq1cvjh8/TlJSEsXFxcyaNQudTsfWrVuxtLRUw6a4Npczr0hLXFxc6NatG+vWrcPd3Z1jx47x6aef4ubmhoeHhzqpNqDuE8zMzNSqOGXid2UAY25uTr9+/TAxMeHs2bPs3LmTzZs3k5SURFZWlsFk9Rs2bFDDn2L06NEcPnyYJUuW8M033xAdHY21tTUANTU1F7xPa2trnn/+eaqqqtTB2/z58w2WGRoayrhx4+jfv7/6HpRlyOBHXAk7OzuCgoLIysri0KFDnDhx4qJtsMTNtWLFCt5//31KS0vp378/I0eOpFOnTjg4OJCTk8OmTZv45ptvSE5O5qWXXuLf//43gYGBAKSlpZGRkcH8+fNxc3NTf72j1+uprKzE3t6ezp07s2LFCuLi4ujZsyfQeAJNq9Wq+ylobCXTpk0bysvLOX36NEOHDqV3795069atxV86NL2oEB0dzerVq9m4cSOPPPKIug9W/uvu7o6DgwPFxcXU19df9ucjFaBCNE8yomTE24FkRMmI15tkRMmI4uaQjHh7k4x4cZIRxZ3mirfW8ydW1mg06henpKSEiooK2rZti5OT0yWDTdMQdeLECQ4ePMiZM2eIjIxUv8hNl1FYWMjChQuxs7Nj1qxZBpVW/v7+/Pe//6WoqIhdu3axdu1aJk6cCDQGMFNTU0pKSjh16tQF66FUBnbq1ImNGzeqFQcNDQ3U19eTm5uLjY0Nrq6uQGMbj5SUFFasWMHmzZtp3bo1EydOpGvXrs2+T51Op+7Azq8ikOB37RYsWMDs2bPx9PTkgw8+IDAw8JLbnhLUk5OTgXM/BddqtcTHx9O2bVteeOEFevbsaRAmBw4cyJNPPsmCBQvo0qULXbt2VUNCTEwMy5cvJz8/X20RBOcOLIGBgbi5uZGfn09hYSH+/v4G62RjY4Ovry9btmwhKytLDZAajYbXX3+dt99+m/3795OVlQWAvb09U6dOZeTIkWi1WsaNG3etH6VoomkYOn+y9UuxtLSkc+fOrFu3Dmtra0aNGsX777/PF198wezZsw2WnZeXB6BWabq7u1NUVMTBgwcJCgpSBy9KEFy2bBnffvstBQUFtGrVim7dujF48GBMTEyYPn06SUlJF0ze7eLiwrPPPsuff/7Jxo0b+fnnn6mpqcHExIT27dsbtDNq6l//+hfDhg0jMzOTgwcPUl9fj5+fH927d8fT0/NqPlYhDCj7auXER05OjlotLW690tJSvv76a0pKSnj88cd54YUX1Pt0Oh1eXl54eXnh6urKxx9/zO7du5k3bx6zZs3CwsKCRx55hP3797N582YiIiIYP3480LhPOn78OG5ubowZM4b169ezcOFCwsPDsbS05OzZs2oLQDg3TouOjmblypX87W9/47HHHjNYFyWwmZiYUFdXR05ODv7+/uo2Fh4eDlx43FeYmZnx1ltvNTtXmRDi0iQjSka83UhGlIx4I0hGlIwobjzJiLc3yYhC3H2u+AJh08BWVFTE1q1b2bZtG5mZmVRUVGBmZoa5uTnV1dU88MAD3HfffS32iq6qqmLVqlX8/PPP6k+SNRoN9vb26iC7bdu26sArIyODw4cPM3DgQIPgB43BMzU1Fa1WS21tLZs3b2b48OFqiOzatSubN29m+/btBAUFGVSJGhkZcfr0aXWgpAREIyMjcnJyuP/++/Hz88PY2JgjR45QVVWl/qTZx8eHBx98kIceeqjFKrKmt0sVwfWj7NCDgoJo27YtNTU1FBYWqlUpF+Pr64udnR1paWmUlJTg6uqqbmdPPfUUjo6O6sS3lZWVHDx4kH379nHw4EEKCws5deoU69evp2vXrmq4i4iIULfTY8eOqf2xlfsdHBzw8/MjLy+PzMxMYmNj1QG3MsAPDAzE1taW3NxcDh8+jI2NDTqdjoiICD7//HP27t1LXl4eQUFBhIeHy7Z0g9TV1bF582YWLVpEeXk5U6dOJS4u7rJbXBkbG6vbYW5uLiNHjiQ+Pp5ffvmFQYMG0b17d4OTWgAWFhZqpVRRURGbN282CH8AS5cuZebMmbRt25ZZs2Zx3333qScn9Ho9//znPzlw4AAlJSUGLRz0ej1OTk48++yzfPTRR3z11VfY29tTV1eHk5MTZmZmzb43vV5PcHAwwcHBMmG8uKGGDh1KaGgooaGhF7T+EDefErYWLlxIamoqsbGxPP3008C5k2FarVY9Dvfv3x+A5557jq1bt7J+/XqGDh2Kp6cnY8eO5bXXXmPBggXcf//9WFlZYWpqiqWlJTY2NkRGRtKnTx/WrFnDxo0bGTJkiDp2Ky0tBc5N+D548GBWrlzJ8uXL6dy5MxEREercIk3HdX/88QdvvfUWGzduVI+zHh4eWFtbc+rUKTIzM5udi0kJfpe7rxdCnCMZUTLi7UIyomTEG0Uy4rnnSUYUN4NkxNuLZETJiOLudcUjx507d/Lrr7+SkJCgfikBHB0dsbe3x9XVlZycHI4dO8bHH3/MkiVLmDZtGsOGDbtgWUuWLOH999/n7NmzBAcH4+fnh4mJCStWrGDhwoUcOnSIr7/+Wh3g7t+/H4AuXbpw7NgxsrKy2Lt3rzpBeFlZmbpsCwsLzp49q+5AevXqxebNm9m6dSs9evQgJCSEmpoajIyM1Amed+7ciYmJCVFRUepyfHx8CA4Opr6+ntOnT2NiYoKTkxPh4eHExcXRpUsX6eV/izTtCe3i4kJqaipZWVkMGDDgklW3dnZ2hIWFsX79enbv3q1W/gJ06tQJaGyr8csvv/Dnn3+yZ88etfKkdevWwLkKE2Wg4u7ujq+vL6mpqWRmZuLn56euh3Ig6dy5M6tWrVInuFUCpvI4Hx8fzM3N2bt3L1lZWfj5+akHIDs7O/r06XPNn5u4tLy8PF5++WVOnjxJq1atyM3NBa6sPZanpyfe3t5kZ2dz4sQJXnzxRV588UXeffddLC0t1RYvyjKVFjARERH8/vvvbNq0iSeffFK9v7a2ls8++wwTExP+8Y9/MHz4cIyMjNQqdo1Gg4eHB3l5eRw4cMAg/CkeeOAB8vPzWbRokTpvypkzZ1p8D1K9Lm40ZRtzcXHBxcXlFq+NUBgZGVFaWkpSUhKAOtbR6XQGJx2Vv59Wq2XgwIF4eXmRk5NDQkICPXv2xNbWlhEjRrB8+XISExP57bffeOihhygvL1d//dCqVSsefPBBEhISWLRokcEvhJQT7U0nfO/duzcbN27k9ddf55lnniE2Npa6ujqys7NJSkri119/JT09HWdnZ44eParuCy0tLfn6669xdXWlbdu2F/0ViQQ/Ia6cZETJiLcLyYjiRpGMyBW/XyGuhmTE25NkRMmI4u511RcINRoN3bp1o0ePHgQEBODv76/+3LusrIx9+/Yxb948du/ezZw5c9DpdIwYMUKtONi6dSuzZ8/G29ubGTNmqO08AB5++GGef/55duzYwZIlSxg+fDhmZmbqIGXJkiUsWrRIbbsAjQeO0aNH07dvX6Kjo9WKKWXQ3aNHD6AxQL799tt8+umnaksXnU7HL7/8QmVlJZGRkWpbBGXH8NNPP3HixAkqKytxcHAw6HesPA5koHQr6PV6LCwsCAwM5MCBAxw6dIjjx4+rlZkX07VrV9avX8+OHTsYOnSowc6+oqKC999/n99++42GhgYiIiLo378/vXr1wsnJiX79+pGenk5+fj6enp5qdUpkZCSpqans27ePAQMG0KpVK4PX7Ny5M9bW1uTk5FBaWnpB+PP09GTcuHHY2NiofbbFzVdUVMTJkyfx8fEhOzubtLQ06urqrqhqzc7OjpCQELKzs1m1ahXPPPMMf/3rX3nvvff46quvmDt3LlVVVRw+fBgLCwv8/PyAxnZXAHv37lXbuuj1empra6mvr0ev1xMdHY2RkZG6jzIzM2P//v3qCYrt27dz3333qeui0WjQ6/VYWloyadIktmzZQmFhIY6OjoSGhgIy2BFCGNJqtezduxdjY2N1vqOW9hPK2G7QoEF88sknHDhwgNzcXEJDQzEyMuLBBx8kKSmJn376ibCwMBwcHKiurqayshJo3O89+OCDfPfddyxfvpzTp0+j0WjU8Zbyuubm5syePZtp06aRlJTEU089hZubGzU1NZw6dUo9qT9q1CgmT558wUkw5eSuzO0lxPUnGVEy4u1EMqK4ESQjCiHudZIRhbg7XfEFwn79+vHll19ib2/P008/TZcuXdT7lLknHBwciIuLIywsjPHjx5Obm8vnn3/O0KFD1Sv8H374IQCPP/64QfCDxtYeDz/8MG+88QarV6+mc+fO+Pr6qm1oMjIysLS0pG/fvvTp04eePXvi7Ozc7PoqOwxPT0914tKdO3cyadIkQkNDqa+vJzExkdzcXDp06MC0adPUSbyV5xoZGWFvb68GCmWOCKWVjuxAbh1lBx4aGsovv/xCbm4uRUVF2NvbX3LnHhERATSe0ADDg9rvv//O4sWL8fPz4+WXXyY6Olq97/Tp03Ts2JHS0lKSk5MN+uzHxMQwf/58UlJS1MrCpsv28/NTJ+1NS0tTB/oKMzMznnzyyWv8VMTVUraZRYsW4ezsTHh4OCUlJeTm5lJYWIiXl9dlDxosLCwICQlh6dKlbN68mWeeeYaHHnqIrVu3sm7dOhYuXMgjjzxCQUEBZ8+eVfuou7u7ExAQQFpaGjt37lRbzZw5cwZXV1cqKirIzMxUWx4pgXTt2rUUFRWh1WrZuXPnBXNGKOvcrl075syZQ6tWrVps7SWEEEeOHMHBwYGysjL1JHdL+z/ltsjISCwsLDh+/Dj5+fnqyaVu3bpx//33s3TpUn766Sf+7//+j6qqKrRarToH1yOPPMLSpUuJj4+ndevW6PV6df+mvK5er8fe3p7PP/+cFStWkJCQQEFBAadOncLT05OYmBj69++vnkxriYzbhLj+JCNKRrydSEYU15NkRCGEaCQZUYi70xWXA7Vr147WrVtTVlZGRkYG0FgVAIZzT+h0Otq0acOzzz6Lo6MjOTk5rF+/HoCsrCyOHz9OUFAQAwcOVJddWlrK2rVr+d///sdvv/2GTqdj27ZtatuYbt26AY0TdS9dupRPPvmEMWPG4OzsrFZP1dTUAI2tP5SqA6ViSpmTYsSIEXTq1InffvuNH3/8kYKCAmJiYvjnP/95WZVSyhwR509eKm4+5e/UqVMn7O3tOXz4MDk5OcCld+4dOnSgXbt25OXlqduysq2sW7cOgIkTJxIdHa226ADUiXGhsQqv6Xoo1Z/Z2dmUlJQYvJ5er8fKyop+/foxduxYgzZF4vag0WgoKSkhKyuL3r17M2DAACwsLDhy5AhZWVnAuWrwS9Fqtfj5+dGqVStSU1M5fvw4VlZWPPfcc7i5ufH222+za9curKyscHd3V9skWFtbqyfVNm7cqC7P0tKSkJAQzpw5w88//0x5eTkmJiZUVFTw448/smjRIiZPnqzub5X1bU5QUJAEPyHERZ06dQozMzOMjY3VFlot7f+UY2BAQAAmJiacPn2a06dPq/fb2try0EMP0bp1a3744Qeys7Oxt7fH2tpabf3n5ubG6NGjycvLIyUlBTCcXwLOHdfNzc0ZM2YMc+bM4ZNPPuGPP/5g4cKFTJ06VQ1+l7uvFkJcH5IRJSPeTiQjiutJMqIQQjSSjCjE3emKLxCamZkRFBTE2bNnyc7OpqqqqtkQpOwIYmJi1OrPtWvXAnDixAlKSkowMTFh165dfP755zz99NM8+OCDTJ06lU8++YSUlBS8vLz4y1/+olbmeXp6EhoaSmVlJcuXL1fbydTW1qLRaDA1NVWrod555x3mzZtnsE5xcXEAHDt2jNdee42ff/6ZH374geTkZL755ht69eolFQN3KG9vb9q3b09lZSWHDh2itrb2ks8xNze/oEJUmWtEp9Nha2vbbBsarVbLrl270Gg0pKSkqP229Xo9dnZ2+Pv7c/bsWXbv3q2GRDh30Jo2bRozZ86kQ4cO1+Oti+tEGVykp6dz5MgRAgICCA0Nxd3dncrKStLS0oAra7Pi5uaGv78/DQ0N7Nq1C4Dw8HAmTpxITU0Nr7zyCgcOHMDJyQk3Nzf0ej1arZbw8HAANm/eDJw7cTB27FjMzc1Zt24dEydOZOzYsTzwwAPMmDGDwMBApk+fzuDBg+nXr586D4oQQlwNR0dHTExM0Gq15OfnAxc/qarX62ndujWtW7emtrbWoH2aXq8nJCSEwYMHA/Dee+9RXV2Nubm5QVuuESNG0KVLF2prazExMVHHeS2dbNfpdDg7O2NiYoJOp6O+vv6CoCiEuDkkI4rbkWREca0kIwohxDmSEYW4O11VQ3GlojM7O5sjR44ALV+Ft7W1Vfvk7969m7KyMjw8PAA4cOAAU6ZM4YMPPmDdunXU1dUxfPhwPvzwQ5KSkvjjjz+YPn067u7u6pf5oYcewsXFhblz57JgwQLKysrUCeCzs7OZP38+I0aMYMGCBRQWFgLndhpdu3ZV1+PIkSN4eHgQFhaGhYUFOp3OYKAu7hxKq5/g4GCgcTtQqk0uVR2ibBM7duxQb7OxsaF169acOXOGgoICoHEbUibdnTt3Lg4ODrRp04bi4mJSU1MBqKurA6B79+74+/sbTB4vbn/K32rNmjWYmJgQFxeHpaUlPj4+NDQ0kJmZedHJ2ptja2ur9jNPSEhQbx8yZAjjx48nLy+PU6dOUV1djampqTpY8ff3x9zcnPz8fI4dO4ZGo6GhoQFPT0/efPNNevToQVFREfv27aOqqopHHnmE1157DYDp06fz8ccf4+7ufj0+FiHEPcrDwwN7e3tqa2tJS0tTT7S3RKPRcPLkSbWdn3ISVqfTqcfikSNH0rFjRzZs2EBeXh4nTpygTZs26hjP29ub0aNHq8/z9va+6HG86TFWq9VibGwsx10hbiHJiOJ2IhlRXA+SEYUQ4hzJiELcna54DkKAsLAwzM3NKSgooKCg4JJVbkFBQRgZGVFYWMjhw4cJDg7G0dGRo0ePEhoayujRo+natesFE4U2neNBMXjwYGpra3nttdf44IMPWLx4sdpzvbS0lMrKSiwsLPjb3/7GhAkTgHOTL7dp0wYvLy+1t7+Li4v6GrKzuHMpB6PQ0FBMTU3Jz8+noKAANze3Sz5XaReUkpKi9uNXqkY3bdrERx99hL29PaGhoWRlZfHHH3/w66+/8uKLL5KRkUF8fDyJiYkEBwerJxmeeOIJnnjiiRv2fsWNU11dTXJyMmFhYWqludICJj8/n/z8fAIDA5vdNzXHzMxMDX+JiYnq7W3atOGpp55i7969NDQ0MGLECGpra9UTWS4uLoSGhrJ9+3aSkpIYPHgwOp1OneC5Z8+epKen07p1a2kDI4S4IUxNTQkPDyclJYW9e/eSnp5OSEhIs/s/5baioiLS09OxsbFR901NHxsQEMCoUaN477330Ov1VFRUGOz7tFotAwcOxMfHB39//5v3ZoUQ14VkRHE7kYworhfJiEII0UgyohB3p6tKPE5OTri6ulJWVkZ2djZw8Z/pWlpa0rFjRwAKCgrQarVqVV5cXBxjxoyhXbt26HQ6amtrqa2tpb6+Hq1Wy4kTJ1i9erU6SamJiQljxozhl19+ITY2FltbW9LT0ykuLsbZ2ZnHH3+chQsXMm3aNIPWH0rlZ2xsLADJycmNH4CEvjuesu0FBwfj7OxMaWnpZc8x4ebmRmBgIEePHlXnMQEYNGgQgwYNoqKighdeeIFevXoxZcoUfv31V8aMGcPEiROZNGkSc+bMYfz48UDLP28XN1ZDQ4NBZfe19BTfunUrRUVFxMbGYm1tDTQOVhwcHDh69KjaQuZyX0Oj0eDj44OdnR15eXlqtXF9fT1t2rTh/fff59tvv2XChAnq4AcaWxspPdJXr14NoFYnQ+M+tUuXLhL8hBA3VExMDB07duTo0aP89ttvAOp4TNF0Uvq0tDROnjyJu7u7enK1KWNjY8aMGYOjoyM6nY5OnTpRUVFh8BhTU1M1+DV9HSHE7U8yoridSEa8t0lGFEKIG0MyohB3n6v6BaGRkRFRUVFkZ2eTlZVFZWUlNjY2LT6+vr4eOzs7ALVH8ZAhQ4iPj+fHH3/E09OTAQMGoNVqDQZAlZWVzJs3j6+++oqdO3diZWUFNO5ogoKC+OKLLzh69CgVFRW4ublhaWnZ4jooO6bevXvz7bffkpiYaFCRIO58rq6ueHt7U1BQQFZWFmfOnLlgm1BCghLUjI2NiY6OJjU1leTkZCIiIqirq8Pe3p5p06bh5+fH+vXrOXr0KP7+/gwcOJCePXtiZGREQEAAAQEBN/19CsPBRtPQfal9UUuUyqa9e/ei1WrVCeChsYVCu3btKCoqIi8v74LXvBQnJycCAwNJSEggMTERDw8Pdd2VVlrNGTlyJIGBgfTp0weQXulCiJsvJCSEESNGkJqayu+//46vry9jxoxBq9XS0NCAkZGRum/auXMn77//PqampkyZMqXZ8ZVOp8Pa2ppPP/0UW1tbXFxcLvr6coJeiDuLZERxO5KMeO+QjCiEEDeeZEQh7j5XdYEQGqs6f/jhB3JzcykpKcHGxsZgQNaUqakp6enpWFhY4ODgADRWHIwbN46ffvqJ119/nby8PAYPHkx1dTVpaWls27aNlStXUlNTQ69evQwmFG/6Go6Ojjg6OgKNO5WW2sEogzVlYufU1FTKysouq8WIuP0p215ISAgbNmwgJyeHI0eO4O3trU5kq9FoDAbtp0+fxsrKiujoaL755hs2btzIk08+qVbhOTg4MHHiREaOHImtre2temuiGco+4PTp0+zYsYO1a9eSkZGBsbExwcHBDBgwgJiYGIAW90tNabVaTp8+zcqVK2nXrh1BQUHqffb29gQHB7N9+3YSEhKoqqpi/fr1vP3220RERKgDoJZYWVnRuXNnEhIS2L59O2PGjLms9+jn56dWiAohxK1gYWHBo48+yurVq9m9ezevvvoqtbW1xMTE4OXlBcDhw4dJSEhg0aJFlJeX8+ijj9K/f/9ml6eMzZTqT71ej16vl5AnxF1EMqK4nUhGvLdIRhRCiBtPMqIQd5+rvkAYGBiIra0tRUVF5ObmXrQPcHFxMTU1NVRVVREVFQU0BsJXX30VnU7HsmXLeP/99/nwww8N2kC4ubkxbtw4Ro0aRZs2bVpcvjK4u9Q8ETqdDlNTUxYvXoyvry+tWrW6incubmehoaFYWlpSWFhIUVER3t7eaoVKeXk5u3fvZsuWLezcuZOQkBDefPNNAgICMDIyQq/XU1VVhbm5ubo8vV4vwe8m0+v16kmclkLbmTNn+OOPP1i4cCHp6elAY6WvlZUV+/fvJz4+nqlTpzJp0qTLCn8Ahw4dorS0lBdffBFTU1Pq6uo4cOAAKSkprF27Fr1eT2pqKqmpqQAkJSURERFxyUpRU1NTNUz++eefvP/++9JqSAhxR1CC2ZtvvsmcOXP4448/mDVrFg4ODvj6+nLixAnKysooKysDwMvLi4EDB2JiYnJZy9ZoNFL5LsRdRjKiuB1JRrzzSUYUQojbg2REIe4+V32B0M7ODg8PDw4ePEh2drbBAEsZvCkDnPnz51NVVcXAgQNxcnJSH2NsbMysWbMYPnw4e/bsIS0tjerqajp06EDPnj2JjIw06KneksvdcWi1WvR6PSEhIVf5rsXtStkG/P39cXV1JS8vj9TUVCwsLNSqvH379hnMC9CxY0cqKipwcnIiOTkZCwuLFpcrbrymA4Hzw9H5AW7Tpk28+uqr2NraMmrUKPr06UNwcDAODg5s27aN//znP8yZM4eBAwfi6up6Wa+7Zs0a6uvrSU9P5x//+Ac7duzg2LFj6uOUk0v9+vXj5ZdfxsnJ6bKDZfv27YmKisLDw0OtShZCiNudRqNBp9Ph4eHBrFmzGDRoEPPnz6eyspL9+/dTVVWFk5MTQ4YMISMjg0OHDjF+/HjGjx/PX//6V3XM19KyhRB3H8mI4nYiGfHOJxlRCCFuL5IRhbj7XPUFQo1GQ+/evdm/fz9ZWVkcPXpU/ZIrg7fa2lrefPNNVq1ahZOTE48//rhaedf0Sx8ZGUlkZGSz8z3odLrrWj0gO5u7m52dHYGBgeTm5vLxxx8bVBtbWloSHR1Nnz59iImJwd3dXb2vueAnbpzmvtfKv0tKSkhMTOTIkSNEREQQGRl5wffWwcGByZMn88wzzxhU89bU1ODv74+Pjw/FxcUsW7aMSZMmGTymJUpIXLp0qXpbYGAgffv2JTIykoULF7JmzRocHBwM9nWXw9vbm+++++6yHiuEELcT5Vc3lpaWDBgwgAEDBlBaWsrRo0dxdnZW2wKWlJTw9ddfs27dOuLj47GysuKhhx66aAAUQtx9JCOK25FkxDuDZEQhhLgzSEYU4u5y1RcIAXr27MncuXMpKCigsrJS/YIXFhaydetWfv75Z9LS0ujYsSNPP/00gYGBLS5Lr9djamqqVpZeTjsYIZpSKvW8vLyor68HoEuXLvTq1YvY2NiLtjgSN0dL878A1NfX89Zbb/Hjjz9SV1cHgLm5OSNHjuRf//qXQTuCzp07ExAQgLm5OeXl5aSkpJCUlMSePXvIyMigqqoKaJwQediwYbRr167FSk7lts6dO+Pg4ECfPn2Ii4sjKirKIDQmJyezceNG0tLSKCwsvOgyhRDibubk5GQQ6mpqanB1deXf//43Dz30EPX19bi6ukolvBD3KMmI4nYiGfH2JxlRCCHufJIRhbhzXdMFQm9vb+zt7SkuLmbp0qVYW1uza9cuDh48yIkTJzA1NWXkyJGMHz/+osEPzg3AmmsdIcSVGDZsGF26dKFz586X1eNa3Dj19fUGLaC0Wi0NDQ2kpKSQnZ1Np06d8PPzQ6PR8N577/H9998TExNDYGAgx48fZ926dSxatAh/f3/GjBmj7idMTEwwNTXlxIkTfPbZZyxbtozKykoAOnXqpE74npqaSlFREe3atbtkSAsODmbLli0Gt+l0Ourr6zE1NcXf3x8TExNSUlJIT0+nXbt21/nTEkKIO0fTk19mZmbq7d7e3rdqlYQQtwnJiOJ2JBnx9iEZUQgh7k6SEYW4M13TBUJLS0t8fX3ZunUrX3/9tXp7+/btGT16NP379ycoKAhjY2OpohI3nLJ9ubq6XnJOAXFzKMGvqqoKc3NzFi5cyBdffEFpaSnQ2Lbn4YcfpkePHmzfvp2XX36ZCRMmqM//4osveP/991m2bBkhISH4+/ur+5ITJ07w+OOPs2/fPiIjIxk1ahT9+vXD2toagOnTpxMfH092djZRUVGXfVKpvr5ePQml1WrVEwje3t5ER0fj7OyMr68vIO2ohBD3Ltn/CSFaIhlR3E4kI95+JCMKIcTdSfZ/QtyZrukCIUCPHj1IT08nPDycvn370r17d7XXcFOykxDi9lZaWsr3339Px44duf/++y+o7DyfTqcDuGiLp88++4w5c+bw/PPP4+DgwH//+19cXV0ZPHgwDQ0NbNiwgfnz5/PTTz8RGhrKhAkTaGhooKGhAVNTU4YOHUp8fDxpaWns378ff39/dV+yadMmUlNTCQwMZMaMGWpFkjJPjRL2MjMzqaysxM7O7rI+h/Pfs/J6HTp04JNPPrmsZQghhBBC3MskIwpxd5CM2EgyohBCCCHuVtd8gfDhhx9m4sSJBrfp9XoaGhpkfggh7iC7du3iq6++IiwsjPvvv7/ZakqdToder1crJ1vS0NCAkZGRGpySk5M5c+YMQ4YMYdasWWrF5ZIlS3jllVeor6+nbdu2ABgZGamv7erqSkREBIcOHSI9PZ3q6mpatWoFwLZt26ivr2f8+PF4e3urVaNKeLO0tAQaw19ZWdllhz8hhBBCCHFtJCMKcXeQjCiEEEIIcXe75mRmamoKNLZcaGhoMBiASfAT4s4RHh6OlZUVubm5nDx5stmKbq1WqwazlJQUFi1axJIlS8jJyVEnjW/aKio2NhYTExMSEhLIz89n+vTpmJiYoNPp0Ol0jB49Gj8/P/R6PY6OjtTU1KivpdfrAQgMDMTMzIysrCyOHDmi3q/M75Cfnw9AdXW1etIpOzub9evXA5CTk0NxcfH1/riEEEIIIUQLJCMKcXeQjCiEEEIIcXe7bunM2NjYoBpMCHFncXZ2JiAggIqKClJSUoDGKk9FfX09e/bsYcaMGfTo0YNx48Yxc+ZMXnnlFcaNG8dbb71FbW0tGo1GPfETEBCAi4uL+u82bdoAhi1nevfuDUBJSQmnT59Wb1fCn5+fH46OjuTn56tBDyAiIgKAVatWUVZWhrm5OUZGRlRVVbFw4UJOnTrFkCFDqKysZO/evdTW1l7vj0wIIYQQQlyEZEQh7mySEYUQQggh7m7X3GJUCHH72rdvH0ePHqVr165YWVld8vHR0dEkJyezY8cOYmNj1dt1Oh1r1qzhgw8+oKCgAFdXV4YNG4aXlxeWlpb8+OOPLFiwADs7O6ZMmYKZmZk6P0VYWBgFBQW4u7tTWVmJjY2NwWuGh4djYmLCoUOHKCsrU9vIKAGxQ4cOtG/fnu3bt5OdnU2vXr0A6Nq1K56enuTl5TF58mR69erF8ePHSUpKori4mFmzZqHT6di6dSuWlpbqfBhCCCGEEEIIca+SjCgZUQghhBBCIRcIhbhLLViwgNmzZ+Pp6ckHH3xAYGCgQWuX5kRGRgKN80EAaqsYrVZLfHw8bdu25YUXXqBnz54GYXLgwIE8+eSTLFiwgC5dutC1a1c1bMXExLB8+XLy8/MNKjSV9QgMDMTNzY38/HwKCwvx9/c3WCcbGxt8fX3ZsmULWVlZaoDUaDS8/vrrvP322+zfv5+srCwA7O3tmTp1KiNHjkSr1TJu3Lhr/SiFEEIIIYQQ4o4nGVEyohBCCCFEU3KBUIi7jBLwgoKCaNu2LTU1NRQWFhIYGHjJ5/r6+mJnZ0daWholJSW4urqqVZ5PPfUUjo6OODk5AVBZWcnBgwfZt28fBw8epLCwkFOnTrF+/Xq6du2qhruIiAiMjY3JyMjg2LFj2NvbA+fCn4ODA35+fuTl5ZGZmUlsbCxmZmZAY1WqVqslMDAQW1tbcnNzOXz4MDY2Nuh0OiIiIvj888/Zu3cveXl5BAUFER4erk5CL4QQQgghhBD3OsmIkhGFEEIIIZojIyQh7jJKqPLz88PFxYXU1FSysrIYMGDAJed/sbOzIywsjPXr17N7925cXV3V+zp16gRATU0Nv/zyC3/++Sd79uyhvr4egNatWwPnKktNTEwAcHd3x9fXl9TUVDIzM/Hz81PXQwl3nTt3ZtWqVaSlpVFRUaEGTOVxPj4+mJubs3fvXrKysvDz81Pby9jZ2dGnT59r/tyEEEIIIYQQ4m4kGVEIIYQQQjRHe+mHCCHuNHq9HgsLCwIDA9HpdBw6dIjjx49f1nO7du0KwI4dOwDDyeIrKip4/fXXeffdd0lJSSEiIoJXX32VtWvXsmXLFpycnEhPT1cnilfaxShtafbt20dNTc0Fr9m5c2esra3JycmhtLRUvV0Jf56enowbN45XX32Vnj17XunHIYQQQgghhBD3NMmIQgghhBDifHKBUIi7kF6vByA0NBStVktubi5FRUUG97UkIiICgJ07dwKG4e/3339n8eLFeHh48OWXX/Ltt9/yyCOP4O7uTk1NDR07dkSv16sVooqYmBgAUlJSOHnypHq7smw/Pz8cHBzIyckhLS3tgnUyMzPjySef5JFHHsHW1vaKPgshhBBCCCGEuNdJRhRCCCGEEOeTC4RC3IWUUNWpUyfs7e05fPgwOTk5AJdsIdOhQwfatWtHXl4eGRkZAGqLmHXr1gEwceJEoqOjaWhoUO87e/YsDQ0NAGzfvt1gPZTqz+zsbEpKSgxeT6/XY2VlRb9+/Rg7dixRUVHX/P6FEEIIIYQQQpwjGVEIIYQQQpxPLhAKcRfz9vamffv2VFZWcujQIbWdy8WYm5tfUCFqbGxMaWkpOp0OW1tbdRL5prRaLbt27UKj0ZCSkoJOp8PY2Bi9Xo+dnR3+/v6cPXuW3bt3qyERzoXRadOmMXPmTDp06HA93roQQgghhBBCiPNIRhRCCCGEEAq5QCjEXUqZ3D04OBiA7OxsysrKgEu3kDl/jgkAGxsbWrduzZkzZygoKADAyMgIY2NjAObOnYuDgwNt2rShuLiY1NRUAOrq6gDo3r07/v7+BpPHCyGEEEIIIYS4OSQjCiGEEEKIpmQEJsRdSqm6DA0NxdTUlPz8fDW0XUpoaCjQOB+EMmG8UjVqYmLCRx99xKpVqygtLWXr1q28/PLL/PDDDzzyyCN0794dvV5PYmIi0BgQAZ544gmWLVtGjx49LtnCRgghhBBCCCHE9SUZUQghhBBCNGV8q1dACHFjKAErODgYZ2dnSktLycnJISYm5pLhy83NjcDAQFJTU9m/f7/aTmbQoEEcPHiQpUuX8sILL6DT6dTnjBkzhokTJ5KZmUnfvn3p06cPcC78CSGEEEIIIYS4dSQjCiGEEEKIpuQCoRB3OVdXV7y9vSkoKCArK4szZ85gaWlp8BhlvgclqBkbGxMdHU1qairJyclERERQV1eHvb0906ZNw8/Pj/Xr13P06FH8/f0ZOHAgPXv2xMjIiICAAAICAm76+xRCCCGEEEIIcWmSEYUQQgghBEiLUSHuaso8EiEhIQDk5ORw5MgRAGpra9X7jYyM1OB3+vRpAKKjowHYuHEjgDqPhIODAxMnTuSjjz7izz//ZM6cOdx3331YWVndnDclhBBCCCGEEOKqSEYUQgghhBAK+QWhEPeA0NBQLC0tKSwspKioCG9vb0xNTQEoLy9n9+7dbNmyhZ07dxISEsKbb75JQEAARkZG6PV6qqqqMDc3V5en1+uxtbW9VW9HCCGEEEIIIcQ1kIwohBBCCCHkAqEQdzFlHgl/f39cXV3Jy8sjNTUVCwsLEhIS2L59O/v27VOrRAE6duxIRUUFTk5OJCcnY2Fh0eJyhRBCCCGEEELcOSQjCiGEEEIIhUbfdNQnhLhrvfTSS/z+++/o9Xp1PgkAS0tLoqOj6dOnDzExMbi7u9/CtRRCCCGEEEIIcTNIRhRCCCGEuLfJLwiFuMvp9Xo0Gg1eXl7U19cD0KVLF3r16kVsbCz+/v63eA2FEEIIIYQQQtwskhGFEEIIIQTILwiFuOsp4a+kpISSkhI6d+6MiYnJrV4tIYQQQgghhBC3gGREIYQQQggBcoFQCCGEEEIIIYQQQgghhBBCiHuK9lavgBBCCCGEEEIIIYQQQgghhBDi5pELhEIIIYQQQgghhBBCCCGEEELcQ+QCoRBCCCGEEEIIIYQQQgghhBD3ELlAKIQQQgghhBBCCCGEEEIIIcQ9RC4QCiGEEEIIIYQQQgghhBBCCHEPkQuEQgghhBBCCCGEEEIIIYQQQtxD5AKhEEIIIYQQQgghhBBCCCGEEPcQuUAohBBCCCGEEEIIIYQQQgghxD1ELhAKIYQQQgghhBBCCCGEEEIIcQ+RC4RCCCHEVerbty9+fn74+fkxe/bsiz72q6++Uh8bGBh4w9etqKgIPz8/+vbte12W9+uvv+Ln58c///nP67I8IYQQQgghhLjbSEYUQghxJ5ELhEIIIcR18Ntvv1FbW9vi/UuWLLmJayOEEEIIIYQQ4laSjCiEEOJ2JxcIhRBCiGsUHBxMRUUF69ata/b+3bt3k5OTQ6dOnW7ymgkhhBBCCCGEuNkkIwohhLgTyAVCIYQQ4hqNHj0aaLkC9JdffjF4nBBCCCGEEEKIu5dkRCGEEHcC41u9AkIIIcSdztfXl+DgYBISEigtLcXJyUm978yZM6xcuRJnZ2d69OjR4jIqKir4+uuvWbduHUVFRWi1Wjp06MB9993Ho48+SqtWrZp93oYNG5g3bx4HDx5Eq9Xi5+fH5MmT8ff3v+g6nzx5kvnz57Nu3ToKCgrQ6XR4eHhw3333MWnSJMzNza/uwxBCCCGEEEKIe5xkRCGEEHcC+QWhEEIIcR2MHj0anU7Hr7/+anD7ypUrOXv2LPfffz8ajabZ5xYWFjJq1Cg+//xzysvL6dWrF127diUvL493332Xhx9+mJMnT17wvG+//ZYnnniC5ORkOnbsSO/evampqeHpp59mwYIFLa7roUOHGDFiBHPnzuX48eN06dKFmJgYysvL+fDDD3nooYc4derUtX0gQgghhBBCCHEPk4wohBDidie/IBRCCCGug2HDhvHWW2+xdOlSnnzySfX2JUuWoNFoeOCBB1p87t///neKi4vp27cv7733HhYWFgCUl5czZcoUDh48yMyZM3nvvffU56Snp/P222+j1Wr54IMPGDRokHpffHw806dPb/a1qqurefLJJzl8+DBPPvkkTz31FKampgBUVVXx73//mxUrVvDGG2/w5ptvXtNnIoQQQgghhBD3KsmIQgghbnfyC0IhhBDiOrC2tqZ///7k5+eTlJQEQE5ODrt37yYyMpJ27do1+7ydO3eSkpKCubk5s2bNUoMfQJs2bZg5cyYAf/zxB0eOHFHvW7BgAQ0NDQwaNMgg+AEMHz6cvn37Nvt6S5cupaCggD59+vD888+rwQ/A3NycmTNn0rZtW+Lj45utSBVCCCGEEEIIcWmSEYUQQtzu5AKhEEIIcZ2cPxG98t+LTTyvBMWePXtib29/wf3BwcH4+/uj0+nUxzZ93vDhw5td7siRI5u9fdOmTQDcd999zd5vaWlJcHAw9fX17N+/v8X1FkIIIYQQQghxcZIRhRBC3M6kxagQQghxnXTt2hV3d3dWrVrFyy+/zPLly7GysrqgerOp0tJSANzd3Vt8jIeHB+np6epjAbVStKXntXR7YWEhANOnT2+xxYyivLz8ovcLIYQQQgghhGiZZEQhhBC3M7lAKIQQQlwnGo2GkSNH8tFHH/HSSy9RVlbGuHHjaNWq1a1eNZVOpwNarkZtytXV9WaskhBCCCGEEELclSQjCiGEuJ3JBUIhhBDiOho1ahRz585lw4YNwMVbxwA4OTkB56o2m6PcpzxW+XdBQQHFxcX4+Phc8Jzi4uJml+Xi4kJOTg4PPPDARatWhRBCCCGEEEJcO8mIQgghblcyB6EQQghxHbm6uhIXF0fr1q0JDQ2lc+fOF318VFQUAFu2bOHYsWMX3J+amkpaWhparZbIyEj1duXfv/32W7PLXbZsWbO3x8bGArBy5cpLvhchhBBCCCGEENdGMqIQQojblVwgFEIIIa6zjz/+mMTERH766adLPjYiIoLOnTtTXV3Na6+9RlVVlXpfeXk5r732GgCDBw/GxcVFve/RRx/FyMiIlStXsmbNGoNl/v7776xdu7bZ1xs7dixubm78+eefvPPOO5w+ffqCx5SVlbF48eLLeq9CCCGEEEIIIS5OMqIQQojbkbQYFUIIIW6x9957j7/85S+sW7eOuLg4IiIiqK+vJzExkdOnTxMUFKSGQEVAQADTpk3jnXfeYerUqXTu3Jl27dqRn5/P/v37mThxIt9+++0Fr2VhYcHnn3/O448/zldffcXixYvx8/PDycmJ6upq8vLyyM7Opm3btowdO/YmfQJCCCGEEEIIIRSSEYUQQtwMcoFQCCGEuMXatWvHr7/+ytdff83atWvZuHEjWq2WDh06cN999zFhwoRmJ7GfMmUKHTp0YN68eaSlpZGVlYWfnx//+9//CAoKajb8Afj4+BAfH8+PP/7I2rVrycjIYO/evbRu3RpnZ2cmT55M//79b/C7FkIIIYQQQgjRHMmIQgghbgaNXq/X3+qVEEIIIYQQQgghhBBCCCGEEELcHDIHoRBCCCGEEEIIIYQQQgghhBD3ELlAKIQQQgghhBBCCCGEEEIIIcQ9RC4QCiGEEEIIIYQQQgghhBBCCHEPkQuEQgghhBBCCCGEEEIIIYQQQtxD5AKhEEIIIYQQQgghhBBCCCGEEPcQuUAohBBCCCGEEEIIIYQQQgghxD1ELhAKIYQQQgghhBBCCCGEEEIIcQ+RC4RCCCGEEEIIIYQQQgghhBBC3EPkAqEQQgghhBBCCCGEEEIIIYQQ9xC5QCiEEEIIIYQQQgghhBBCCCHEPUQuEAohhBBCCCGEEEIIIYQQQghxD5ELhEIIIYQQQgghhBBCCCGEEELcQ+QCoRBCCCGEEEIIIYQQQgghhBD3kP8HH+marwq6fwUAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "import numpy as np\n", + "import re\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# --- 1. Robust Data Parsing ---\n", + "# Captures all necessary metrics for both the table and the plots.\n", + "root_dir = Path('.')\n", + "detailed_data = []\n", + "ALL_EXPECTED_METHODS = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "results_files = root_dir.glob('**/results.json')\n", + "\n", + "for file_path in results_files:\n", + " try:\n", + " parts = file_path.parts\n", + " current_method = None\n", + " for m in ALL_EXPECTED_METHODS:\n", + " if m in parts:\n", + " current_method = m\n", + " break\n", + " \n", + " if current_method:\n", + " method_index = parts.index(current_method)\n", + " dataset = parts[method_index + 1].replace('_experiments', '').replace('_v3', '')\n", + " model = parts[method_index + 2]\n", + " \n", + " run_id_match = re.search(r'run_seed_(\\d+)', str(file_path))\n", + " run_id = run_id_match.group(1) if run_id_match else file_path.parent.name\n", + "\n", + " with open(file_path, 'r') as f:\n", + " results_list = json.load(f)\n", + "\n", + " for item in results_list:\n", + " metrics = item.get('metrics', {})\n", + " llm_calls = None\n", + " total_tokens = None\n", + "\n", + " if current_method == 'spiral':\n", + " search_process = metrics.get('search_process', {})\n", + " exp_calls = search_process.get('expansion_llm_calls', 0)\n", + " sim_calls = search_process.get('simulation_llm_calls', 0)\n", + " crit_calls = search_process.get('critic_llm_calls', 0)\n", + " llm_calls = exp_calls + sim_calls + crit_calls\n", + " \n", + " exp_tokens = search_process.get('expansion_llm_tokens', 0)\n", + " sim_tokens = search_process.get('simulation_llm_tokens', 0)\n", + " crit_tokens = search_process.get('critic_llm_tokens', 0)\n", + " total_tokens = exp_tokens + sim_tokens + crit_tokens\n", + " else: # Baseline methods\n", + " reasoning_cost = metrics.get('reasoning_cost', {})\n", + " llm_calls = reasoning_cost.get('llm_calls')\n", + " total_tokens = reasoning_cost.get('total_llm_tokens')\n", + "\n", + " detailed_data.append({\n", + " 'run_id': str(run_id),\n", + " 'method': current_method, 'dataset': dataset, 'model': model,\n", + " 'Solution Conciseness': metrics.get('plan_length'),\n", + " 'Tokens': total_tokens,\n", + " 'API Calls': llm_calls\n", + " })\n", + " except Exception as e:\n", + " print(f\"🔴 Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "# --- 2. Data Cleaning and Preparation ---\n", + "df_raw = pd.DataFrame(detailed_data)\n", + "df_cleaned = df_raw.dropna().copy()\n", + "\n", + "models_to_keep = [\n", + " 'deepseek_v2_5', 'llama_3_3_70b_instruct', 'llama_4', \n", + " 'phi', 'qwen2_5_72b_instruct'\n", + "]\n", + "methods_to_keep = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "\n", + "df_filtered = df_cleaned[\n", + " df_cleaned['model'].isin(models_to_keep) & \n", + " df_cleaned['method'].isin(methods_to_keep)\n", + "].copy()\n", + "\n", + "# --- 3. Generate and Print Solution Conciseness Table ---\n", + "if not df_filtered.empty:\n", + " # Set categorical types to enforce order\n", + " df_filtered['model'] = pd.Categorical(df_filtered['model'], categories=sorted(models_to_keep), ordered=True)\n", + " df_filtered['method'] = pd.Categorical(df_filtered['method'], categories=methods_to_keep, ordered=True)\n", + "\n", + " # Calculate mean per run\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Solution Conciseness'].mean().reset_index()\n", + " \n", + " # Calculate final mean and std across runs\n", + " agg_df_conciseness = run_means.groupby(['dataset', 'model', 'method'])['Solution Conciseness'].agg(['mean', 'std']).reset_index()\n", + " \n", + " # Format the string for printing\n", + " agg_df_conciseness['Formatted'] = agg_df_conciseness.apply(\n", + " lambda row: f\"{row['mean']:.2f} ± {row['std']:.2f}\", axis=1\n", + " )\n", + "\n", + " # Pivot to create the final table structure\n", + " conciseness_table = agg_df_conciseness.pivot_table(\n", + " index=['dataset', 'model'],\n", + " columns='method',\n", + " values='Formatted',\n", + " aggfunc='first'\n", + " )\n", + " \n", + " print(\"\\n\" + \"=\"*80)\n", + " print(\"📊 Solution Conciseness (Average Plan Length)\")\n", + " print(\"=\"*80)\n", + " print(conciseness_table.to_string())\n", + " print(\"\\n\")\n", + "\n", + " # --- 4. Generate Bar Plots for Average Cost ---\n", + " \n", + " # Aggregate data for plotting\n", + " plot_agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n", + " 'Tokens': 'mean',\n", + " 'API Calls': 'mean'\n", + " }).reset_index()\n", + "\n", + " # --- MODIFICATION: Beautify plots ---\n", + " sns.set_theme(style=\"darkgrid\", context=\"talk\") # Set dark theme, use default palette\n", + "\n", + " # Map for aligned model names\n", + " model_name_map = {\n", + " 'deepseek_v2_5': 'DeepSeek-V2.5',\n", + " 'llama_3_3_70b_instruct': 'Llama 3.3 70B',\n", + " 'llama_4': 'Llama 4 Maverick 17B',\n", + " 'phi': 'Phi 4 14B',\n", + " 'qwen2_5_72b_instruct': 'Qwen 2.5 72B'\n", + " }\n", + " plot_agg_df['model_long_name'] = plot_agg_df['model'].map(model_name_map)\n", + " model_order = [model_name_map[m] for m in sorted(models_to_keep)]\n", + "\n", + "\n", + " # Plot 1: Average Tokens\n", + " g_tokens = sns.catplot(\n", + " data=plot_agg_df,\n", + " kind='bar',\n", + " x='model_long_name',\n", + " y='Tokens',\n", + " hue='method',\n", + " col='dataset',\n", + " hue_order=methods_to_keep,\n", + " order=model_order,\n", + " height=7,\n", + " aspect=1.2,\n", + " sharey=False\n", + " )\n", + " g_tokens.fig.suptitle('Model Comparison by Average Cost (Tokens)', y=1.12, fontsize=22)\n", + " sns.move_legend(\n", + " g_tokens, \"upper center\",\n", + " bbox_to_anchor=(.5, 1.02), ncol=len(methods_to_keep), title=None, frameon=False\n", + " )\n", + " g_tokens.set_axis_labels(\"Model\", \"Average Tokens per Task\", fontsize=16)\n", + " g_tokens.set_titles(\"Dataset: {col_name}\", size=18)\n", + " g_tokens.set_xticklabels(rotation=15, ha='right')\n", + " plt.tight_layout(rect=[0, 0, 1, 0.95])\n", + " plt.show()\n", + "\n", + " # Plot 2: Average API Calls\n", + " g_calls = sns.catplot(\n", + " data=plot_agg_df,\n", + " kind='bar',\n", + " x='model_long_name',\n", + " y='API Calls',\n", + " hue='method',\n", + " col='dataset',\n", + " hue_order=methods_to_keep,\n", + " order=model_order,\n", + " height=7,\n", + " aspect=1.2,\n", + " sharey=False\n", + " )\n", + " g_calls.fig.suptitle('Model Comparison by Average Cost (API Calls)', y=1.12, fontsize=22)\n", + " sns.move_legend(\n", + " g_calls, \"upper center\",\n", + " bbox_to_anchor=(.5, 1.02), ncol=len(methods_to_keep), title=None, frameon=False\n", + " )\n", + " g_calls.set_axis_labels(\"Model\", \"Average API Calls per Task\", fontsize=16)\n", + " g_calls.set_titles(\"Dataset: {col_name}\", size=18)\n", + " g_calls.set_xticklabels(rotation=15, ha='right')\n", + " plt.tight_layout(rect=[0, 0, 1, 0.95])\n", + " plt.show()\n", + "\n", + "else:\n", + " print(\"🔴 No data available for analysis after filtering.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1124385/2159591203.py:89: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Solution Conciseness'].mean().reset_index()\n", + "/tmp/ipykernel_1124385/2159591203.py:92: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " agg_df_conciseness = run_means.groupby(['dataset', 'model', 'method'])['Solution Conciseness'].agg(['mean', 'std']).reset_index()\n", + "/tmp/ipykernel_1124385/2159591203.py:100: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n", + " conciseness_table = agg_df_conciseness.pivot_table(\n", + "/tmp/ipykernel_1124385/2159591203.py:116: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " plot_agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "📊 Solution Conciseness (Average Plan Length)\n", + "================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral\n", + "dataset model \n", + "dailylifeapis deepseek_v2_5 2.82 ± 0.17 2.84 ± 0.15 2.82 ± 0.15 2.74 ± 0.15\n", + " llama_3_3_70b_instruct 3.04 ± 0.17 3.10 ± 0.21 3.09 ± 0.21 2.94 ± 0.13\n", + " llama_4 2.89 ± 0.18 2.89 ± 0.18 2.92 ± 0.20 2.84 ± 0.13\n", + " phi 2.77 ± 0.19 2.80 ± 0.19 2.81 ± 0.18 2.69 ± 0.14\n", + " qwen2_5_72b_instruct 2.88 ± 0.19 2.87 ± 0.21 2.91 ± 0.20 2.73 ± 0.16\n", + "huggingface deepseek_v2_5 2.71 ± 0.08 2.60 ± 0.19 2.70 ± 0.07 2.30 ± 0.05\n", + " llama_3_3_70b_instruct 2.77 ± 0.05 2.80 ± 0.10 2.78 ± 0.05 2.28 ± 0.06\n", + " llama_4 2.57 ± 0.06 2.58 ± 0.07 2.54 ± 0.09 2.35 ± 0.04\n", + " phi 2.53 ± 0.06 2.57 ± 0.08 2.59 ± 0.06 2.25 ± 0.06\n", + " qwen2_5_72b_instruct 2.68 ± 0.05 2.68 ± 0.04 2.71 ± 0.05 2.25 ± 0.05\n", + "\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "import numpy as np\n", + "import re\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# --- 1. Robust Data Parsing ---\n", + "# Captures all necessary metrics for both the table and the plots.\n", + "root_dir = Path('.')\n", + "detailed_data = []\n", + "ALL_EXPECTED_METHODS = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "results_files = root_dir.glob('**/results.json')\n", + "\n", + "for file_path in results_files:\n", + " try:\n", + " parts = file_path.parts\n", + " current_method = None\n", + " for m in ALL_EXPECTED_METHODS:\n", + " if m in parts:\n", + " current_method = m\n", + " break\n", + " \n", + " if current_method:\n", + " method_index = parts.index(current_method)\n", + " dataset = parts[method_index + 1].replace('_experiments', '').replace('_v3', '')\n", + " model = parts[method_index + 2]\n", + " \n", + " run_id_match = re.search(r'run_seed_(\\d+)', str(file_path))\n", + " run_id = run_id_match.group(1) if run_id_match else file_path.parent.name\n", + "\n", + " with open(file_path, 'r') as f:\n", + " results_list = json.load(f)\n", + "\n", + " for item in results_list:\n", + " metrics = item.get('metrics', {})\n", + " llm_calls = None\n", + " total_tokens = None\n", + "\n", + " if current_method == 'spiral':\n", + " search_process = metrics.get('search_process', {})\n", + " exp_calls = search_process.get('expansion_llm_calls', 0)\n", + " sim_calls = search_process.get('simulation_llm_calls', 0)\n", + " crit_calls = search_process.get('critic_llm_calls', 0)\n", + " llm_calls = exp_calls + sim_calls + crit_calls\n", + " \n", + " exp_tokens = search_process.get('expansion_llm_tokens', 0)\n", + " sim_tokens = search_process.get('simulation_llm_tokens', 0)\n", + " crit_tokens = search_process.get('critic_llm_tokens', 0)\n", + " total_tokens = exp_tokens + sim_tokens + crit_tokens\n", + " else: # Baseline methods\n", + " reasoning_cost = metrics.get('reasoning_cost', {})\n", + " llm_calls = reasoning_cost.get('llm_calls')\n", + " total_tokens = reasoning_cost.get('total_llm_tokens')\n", + "\n", + " detailed_data.append({\n", + " 'run_id': str(run_id),\n", + " 'method': current_method, 'dataset': dataset, 'model': model,\n", + " 'Solution Conciseness': metrics.get('plan_length'),\n", + " 'Tokens': total_tokens,\n", + " 'API Calls': llm_calls\n", + " })\n", + " except Exception as e:\n", + " print(f\"🔴 Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "# --- 2. Data Cleaning and Preparation ---\n", + "df_raw = pd.DataFrame(detailed_data)\n", + "df_cleaned = df_raw.dropna().copy()\n", + "\n", + "models_to_keep = [\n", + " 'deepseek_v2_5', 'llama_3_3_70b_instruct', 'llama_4', \n", + " 'phi', 'qwen2_5_72b_instruct'\n", + "]\n", + "methods_to_keep = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "\n", + "df_filtered = df_cleaned[\n", + " df_cleaned['model'].isin(models_to_keep) & \n", + " df_cleaned['method'].isin(methods_to_keep)\n", + "].copy()\n", + "\n", + "# --- 3. Generate and Print Solution Conciseness Table ---\n", + "if not df_filtered.empty:\n", + " # Set categorical types to enforce order\n", + " df_filtered['model'] = pd.Categorical(df_filtered['model'], categories=sorted(models_to_keep), ordered=True)\n", + " df_filtered['method'] = pd.Categorical(df_filtered['method'], categories=methods_to_keep, ordered=True)\n", + "\n", + " # Calculate mean per run\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Solution Conciseness'].mean().reset_index()\n", + " \n", + " # Calculate final mean and std across runs\n", + " agg_df_conciseness = run_means.groupby(['dataset', 'model', 'method'])['Solution Conciseness'].agg(['mean', 'std']).reset_index()\n", + " \n", + " # Format the string for printing\n", + " agg_df_conciseness['Formatted'] = agg_df_conciseness.apply(\n", + " lambda row: f\"{row['mean']:.2f} ± {row['std']:.2f}\", axis=1\n", + " )\n", + "\n", + " # Pivot to create the final table structure\n", + " conciseness_table = agg_df_conciseness.pivot_table(\n", + " index=['dataset', 'model'],\n", + " columns='method',\n", + " values='Formatted',\n", + " aggfunc='first'\n", + " )\n", + " \n", + " print(\"\\n\" + \"=\"*80)\n", + " print(\"📊 Solution Conciseness (Average Plan Length)\")\n", + " print(\"=\"*80)\n", + " print(conciseness_table.to_string())\n", + " print(\"\\n\")\n", + "\n", + " # --- 4. Generate Bar Plots for Average Cost ---\n", + " \n", + " # Aggregate data for plotting\n", + " plot_agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n", + " 'Tokens': 'mean',\n", + " 'API Calls': 'mean'\n", + " }).reset_index()\n", + "\n", + " # --- MODIFICATION: Beautify and compact plots ---\n", + " sns.set_theme(style=\"darkgrid\", context=\"talk\") \n", + "\n", + " # Map for aligned model names\n", + " model_name_map = {\n", + " 'deepseek_v2_5': 'DeepSeek-V2.5',\n", + " 'llama_3_3_70b_instruct': 'Llama 3.3 70B',\n", + " 'llama_4': 'Llama 4 Maverick 17B',\n", + " 'phi': 'Phi 4 14B',\n", + " 'qwen2_5_72b_instruct': 'Qwen 2.5 72B'\n", + " }\n", + " plot_agg_df['model_long_name'] = plot_agg_df['model'].map(model_name_map)\n", + " model_order = [model_name_map[m] for m in sorted(models_to_keep)]\n", + "\n", + "\n", + " # Plot 1: Average Tokens\n", + " g_tokens = sns.catplot(\n", + " data=plot_agg_df,\n", + " kind='bar',\n", + " x='model_long_name',\n", + " y='Tokens',\n", + " hue='method',\n", + " col='dataset',\n", + " hue_order=methods_to_keep,\n", + " order=model_order,\n", + " height=6, # Reduced height\n", + " aspect=1.1, # Reduced aspect ratio\n", + " sharey=False\n", + " )\n", + " # Adjust title and legend position for compactness\n", + " g_tokens.fig.suptitle('Model Comparison by Average Cost (Tokens)', y=1.08, fontsize=22)\n", + " sns.move_legend(\n", + " g_tokens, \"upper center\",\n", + " bbox_to_anchor=(.5, 0.98), # Moved legend down\n", + " ncol=len(methods_to_keep), \n", + " title=None, \n", + " frameon=False\n", + " )\n", + " g_tokens.set_axis_labels(\"Model\", \"Average Tokens per Task\", fontsize=16)\n", + " g_tokens.set_titles(\"Dataset: {col_name}\", size=18)\n", + " g_tokens.set_xticklabels(rotation=15, ha='right')\n", + " plt.tight_layout(rect=[0, 0, 1, 0.92]) # Adjust rect to prevent cutoff\n", + " plt.show()\n", + "\n", + " # Plot 2: Average API Calls\n", + " g_calls = sns.catplot(\n", + " data=plot_agg_df,\n", + " kind='bar',\n", + " x='model_long_name',\n", + " y='API Calls',\n", + " hue='method',\n", + " col='dataset',\n", + " hue_order=methods_to_keep,\n", + " order=model_order,\n", + " height=6, # Reduced height\n", + " aspect=1.1, # Reduced aspect ratio\n", + " sharey=False\n", + " )\n", + " # Adjust title and legend position for compactness\n", + " g_calls.fig.suptitle('Model Comparison by Average Cost (API Calls)', y=1.08, fontsize=22)\n", + " sns.move_legend(\n", + " g_calls, \"upper center\",\n", + " bbox_to_anchor=(.5, 0.98), # Moved legend down\n", + " ncol=len(methods_to_keep), \n", + " title=None, \n", + " frameon=False\n", + " )\n", + " g_calls.set_axis_labels(\"Model\", \"Average API Calls per Task\", fontsize=16)\n", + " g_calls.set_titles(\"Dataset: {col_name}\", size=18)\n", + " g_calls.set_xticklabels(rotation=15, ha='right')\n", + " plt.tight_layout(rect=[0, 0, 1, 0.92]) # Adjust rect to prevent cutoff\n", + " plt.show()\n", + "\n", + "else:\n", + " print(\"🔴 No data available for analysis after filtering.\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f0041ed4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1124385/2515839475.py:89: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Solution Conciseness'].mean().reset_index()\n", + "/tmp/ipykernel_1124385/2515839475.py:92: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " agg_df_conciseness = run_means.groupby(['dataset', 'model', 'method'])['Solution Conciseness'].agg(['mean', 'std']).reset_index()\n", + "/tmp/ipykernel_1124385/2515839475.py:100: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n", + " conciseness_table = agg_df_conciseness.pivot_table(\n", + "/tmp/ipykernel_1124385/2515839475.py:116: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " plot_agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "📊 Solution Conciseness (Average Plan Length)\n", + "================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral\n", + "dataset model \n", + "dailylifeapis deepseek_v2_5 2.82 ± 0.17 2.84 ± 0.15 2.82 ± 0.15 2.74 ± 0.15\n", + " llama_3_3_70b_instruct 3.04 ± 0.17 3.10 ± 0.21 3.09 ± 0.21 2.94 ± 0.13\n", + " llama_4 2.89 ± 0.18 2.89 ± 0.18 2.92 ± 0.20 2.84 ± 0.13\n", + " phi 2.77 ± 0.19 2.80 ± 0.19 2.81 ± 0.18 2.69 ± 0.14\n", + " qwen2_5_72b_instruct 2.88 ± 0.19 2.87 ± 0.21 2.91 ± 0.20 2.73 ± 0.16\n", + "huggingface deepseek_v2_5 2.71 ± 0.08 2.60 ± 0.19 2.70 ± 0.07 2.30 ± 0.05\n", + " llama_3_3_70b_instruct 2.77 ± 0.05 2.80 ± 0.10 2.78 ± 0.05 2.28 ± 0.06\n", + " llama_4 2.57 ± 0.06 2.58 ± 0.07 2.54 ± 0.09 2.35 ± 0.04\n", + " phi 2.53 ± 0.06 2.57 ± 0.08 2.59 ± 0.06 2.25 ± 0.06\n", + " qwen2_5_72b_instruct 2.68 ± 0.05 2.68 ± 0.04 2.71 ± 0.05 2.25 ± 0.05\n", + "\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "import numpy as np\n", + "import re\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# --- 1. Robust Data Parsing ---\n", + "# Captures all necessary metrics for both the table and the plots.\n", + "root_dir = Path('.')\n", + "detailed_data = []\n", + "ALL_EXPECTED_METHODS = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "results_files = root_dir.glob('**/results.json')\n", + "\n", + "for file_path in results_files:\n", + " try:\n", + " parts = file_path.parts\n", + " current_method = None\n", + " for m in ALL_EXPECTED_METHODS:\n", + " if m in parts:\n", + " current_method = m\n", + " break\n", + " \n", + " if current_method:\n", + " method_index = parts.index(current_method)\n", + " dataset = parts[method_index + 1].replace('_experiments', '').replace('_v3', '')\n", + " model = parts[method_index + 2]\n", + " \n", + " run_id_match = re.search(r'run_seed_(\\d+)', str(file_path))\n", + " run_id = run_id_match.group(1) if run_id_match else file_path.parent.name\n", + "\n", + " with open(file_path, 'r') as f:\n", + " results_list = json.load(f)\n", + "\n", + " for item in results_list:\n", + " metrics = item.get('metrics', {})\n", + " llm_calls = None\n", + " total_tokens = None\n", + "\n", + " if current_method == 'spiral':\n", + " search_process = metrics.get('search_process', {})\n", + " exp_calls = search_process.get('expansion_llm_calls', 0)\n", + " sim_calls = search_process.get('simulation_llm_calls', 0)\n", + " crit_calls = search_process.get('critic_llm_calls', 0)\n", + " llm_calls = exp_calls + sim_calls + crit_calls\n", + " \n", + " exp_tokens = search_process.get('expansion_llm_tokens', 0)\n", + " sim_tokens = search_process.get('simulation_llm_tokens', 0)\n", + " crit_tokens = search_process.get('critic_llm_tokens', 0)\n", + " total_tokens = exp_tokens + sim_tokens + crit_tokens\n", + " else: # Baseline methods\n", + " reasoning_cost = metrics.get('reasoning_cost', {})\n", + " llm_calls = reasoning_cost.get('llm_calls')\n", + " total_tokens = reasoning_cost.get('total_llm_tokens')\n", + "\n", + " detailed_data.append({\n", + " 'run_id': str(run_id),\n", + " 'method': current_method, 'dataset': dataset, 'model': model,\n", + " 'Solution Conciseness': metrics.get('plan_length'),\n", + " 'Tokens': total_tokens,\n", + " 'API Calls': llm_calls\n", + " })\n", + " except Exception as e:\n", + " print(f\"🔴 Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "# --- 2. Data Cleaning and Preparation ---\n", + "df_raw = pd.DataFrame(detailed_data)\n", + "df_cleaned = df_raw.dropna().copy()\n", + "\n", + "models_to_keep = [\n", + " 'deepseek_v2_5', 'llama_3_3_70b_instruct', 'llama_4', \n", + " 'phi', 'qwen2_5_72b_instruct'\n", + "]\n", + "methods_to_keep = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "\n", + "df_filtered = df_cleaned[\n", + " df_cleaned['model'].isin(models_to_keep) & \n", + " df_cleaned['method'].isin(methods_to_keep)\n", + "].copy()\n", + "\n", + "# --- 3. Generate and Print Solution Conciseness Table ---\n", + "if not df_filtered.empty:\n", + " # Set categorical types to enforce order\n", + " df_filtered['model'] = pd.Categorical(df_filtered['model'], categories=sorted(models_to_keep), ordered=True)\n", + " df_filtered['method'] = pd.Categorical(df_filtered['method'], categories=methods_to_keep, ordered=True)\n", + "\n", + " # Calculate mean per run\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Solution Conciseness'].mean().reset_index()\n", + " \n", + " # Calculate final mean and std across runs\n", + " agg_df_conciseness = run_means.groupby(['dataset', 'model', 'method'])['Solution Conciseness'].agg(['mean', 'std']).reset_index()\n", + " \n", + " # Format the string for printing\n", + " agg_df_conciseness['Formatted'] = agg_df_conciseness.apply(\n", + " lambda row: f\"{row['mean']:.2f} ± {row['std']:.2f}\", axis=1\n", + " )\n", + "\n", + " # Pivot to create the final table structure\n", + " conciseness_table = agg_df_conciseness.pivot_table(\n", + " index=['dataset', 'model'],\n", + " columns='method',\n", + " values='Formatted',\n", + " aggfunc='first'\n", + " )\n", + " \n", + " print(\"\\n\" + \"=\"*80)\n", + " print(\"📊 Solution Conciseness (Average Plan Length)\")\n", + " print(\"=\"*80)\n", + " print(conciseness_table.to_string())\n", + " print(\"\\n\")\n", + "\n", + " # --- 4. Generate Bar Plots for Average Cost ---\n", + " \n", + " # Aggregate data for plotting\n", + " plot_agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n", + " 'Tokens': 'mean',\n", + " 'API Calls': 'mean'\n", + " }).reset_index()\n", + "\n", + " # --- MODIFICATION: Beautify and compact plots ---\n", + " sns.set_theme(style=\"darkgrid\", context=\"talk\") \n", + "\n", + " # Map for aligned model names\n", + " model_name_map = {\n", + " 'deepseek_v2_5': 'DeepSeek-V2.5',\n", + " 'llama_3_3_70b_instruct': 'Llama 3.3 70B',\n", + " 'llama_4': 'Llama 4 Maverick 17B',\n", + " 'phi': 'Phi 4 14B',\n", + " 'qwen2_5_72b_instruct': 'Qwen 2.5 72B'\n", + " }\n", + " plot_agg_df['model_long_name'] = plot_agg_df['model'].map(model_name_map)\n", + " model_order = [model_name_map[m] for m in sorted(models_to_keep)]\n", + "\n", + "\n", + " # Plot 1: Average Tokens\n", + " g_tokens = sns.catplot(\n", + " data=plot_agg_df,\n", + " kind='bar',\n", + " x='model_long_name',\n", + " y='Tokens',\n", + " hue='method',\n", + " col='dataset',\n", + " hue_order=methods_to_keep,\n", + " order=model_order,\n", + " height=6, \n", + " aspect=1.1,\n", + " sharey=False\n", + " )\n", + " # Adjust legend position and remove plot title\n", + " sns.move_legend(\n", + " g_tokens, \"upper center\",\n", + " bbox_to_anchor=(.5, 1.0), # Position legend closer to plots\n", + " ncol=len(methods_to_keep), \n", + " title=None, \n", + " frameon=False\n", + " )\n", + " g_tokens.set_axis_labels(\"Model\", \"Average Tokens per Task\", fontsize=16)\n", + " g_tokens.set_titles(\"Dataset: {col_name}\", size=18)\n", + " g_tokens.set_xticklabels(rotation=15, ha='right')\n", + " plt.tight_layout(rect=[0, 0, 1, 0.95]) # Adjust rect to make space for legend\n", + " plt.show()\n", + "\n", + " # Plot 2: Average API Calls\n", + " g_calls = sns.catplot(\n", + " data=plot_agg_df,\n", + " kind='bar',\n", + " x='model_long_name',\n", + " y='API Calls',\n", + " hue='method',\n", + " col='dataset',\n", + " hue_order=methods_to_keep,\n", + " order=model_order,\n", + " height=6,\n", + " aspect=1.1,\n", + " sharey=False\n", + " )\n", + " # Adjust legend position and remove plot title\n", + " sns.move_legend(\n", + " g_calls, \"upper center\",\n", + " bbox_to_anchor=(.5, 1.0), # Position legend closer to plots\n", + " ncol=len(methods_to_keep), \n", + " title=None, \n", + " frameon=False\n", + " )\n", + " g_calls.set_axis_labels(\"Model\", \"Average API Calls per Task\", fontsize=16)\n", + " g_calls.set_titles(\"Dataset: {col_name}\", size=18)\n", + " g_calls.set_xticklabels(rotation=15, ha='right')\n", + " plt.tight_layout(rect=[0, 0, 1, 0.95]) # Adjust rect to make space for legend\n", + " plt.show()\n", + "\n", + "else:\n", + " print(\"🔴 No data available for analysis after filtering.\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0eaced87", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1124385/499172073.py:89: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Solution Conciseness'].mean().reset_index()\n", + "/tmp/ipykernel_1124385/499172073.py:92: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " agg_df_conciseness = run_means.groupby(['dataset', 'model', 'method'])['Solution Conciseness'].agg(['mean', 'std']).reset_index()\n", + "/tmp/ipykernel_1124385/499172073.py:100: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n", + " conciseness_table = agg_df_conciseness.pivot_table(\n", + "/tmp/ipykernel_1124385/499172073.py:116: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " plot_agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "📊 Solution Conciseness (Average Plan Length)\n", + "================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral\n", + "dataset model \n", + "dailylifeapis deepseek_v2_5 2.82 ± 0.17 2.84 ± 0.15 2.82 ± 0.15 2.74 ± 0.15\n", + " llama_3_3_70b_instruct 3.04 ± 0.17 3.10 ± 0.21 3.09 ± 0.21 2.94 ± 0.13\n", + " llama_4 2.89 ± 0.18 2.89 ± 0.18 2.92 ± 0.20 2.84 ± 0.13\n", + " phi 2.77 ± 0.19 2.80 ± 0.19 2.81 ± 0.18 2.69 ± 0.14\n", + " qwen2_5_72b_instruct 2.88 ± 0.19 2.87 ± 0.21 2.91 ± 0.20 2.73 ± 0.16\n", + "huggingface deepseek_v2_5 2.71 ± 0.08 2.60 ± 0.19 2.70 ± 0.07 2.30 ± 0.05\n", + " llama_3_3_70b_instruct 2.77 ± 0.05 2.80 ± 0.10 2.78 ± 0.05 2.28 ± 0.06\n", + " llama_4 2.57 ± 0.06 2.58 ± 0.07 2.54 ± 0.09 2.35 ± 0.04\n", + " phi 2.53 ± 0.06 2.57 ± 0.08 2.59 ± 0.06 2.25 ± 0.06\n", + " qwen2_5_72b_instruct 2.68 ± 0.05 2.68 ± 0.04 2.71 ± 0.05 2.25 ± 0.05\n", + "\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "import numpy as np\n", + "import re\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# --- 1. Robust Data Parsing ---\n", + "# Captures all necessary metrics for both the table and the plots.\n", + "root_dir = Path('.')\n", + "detailed_data = []\n", + "ALL_EXPECTED_METHODS = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "results_files = root_dir.glob('**/results.json')\n", + "\n", + "for file_path in results_files:\n", + " try:\n", + " parts = file_path.parts\n", + " current_method = None\n", + " for m in ALL_EXPECTED_METHODS:\n", + " if m in parts:\n", + " current_method = m\n", + " break\n", + " \n", + " if current_method:\n", + " method_index = parts.index(current_method)\n", + " dataset = parts[method_index + 1].replace('_experiments', '').replace('_v3', '')\n", + " model = parts[method_index + 2]\n", + " \n", + " run_id_match = re.search(r'run_seed_(\\d+)', str(file_path))\n", + " run_id = run_id_match.group(1) if run_id_match else file_path.parent.name\n", + "\n", + " with open(file_path, 'r') as f:\n", + " results_list = json.load(f)\n", + "\n", + " for item in results_list:\n", + " metrics = item.get('metrics', {})\n", + " llm_calls = None\n", + " total_tokens = None\n", + "\n", + " if current_method == 'spiral':\n", + " search_process = metrics.get('search_process', {})\n", + " exp_calls = search_process.get('expansion_llm_calls', 0)\n", + " sim_calls = search_process.get('simulation_llm_calls', 0)\n", + " crit_calls = search_process.get('critic_llm_calls', 0)\n", + " llm_calls = exp_calls + sim_calls + crit_calls\n", + " \n", + " exp_tokens = search_process.get('expansion_llm_tokens', 0)\n", + " sim_tokens = search_process.get('simulation_llm_tokens', 0)\n", + " crit_tokens = search_process.get('critic_llm_tokens', 0)\n", + " total_tokens = exp_tokens + sim_tokens + crit_tokens\n", + " else: # Baseline methods\n", + " reasoning_cost = metrics.get('reasoning_cost', {})\n", + " llm_calls = reasoning_cost.get('llm_calls')\n", + " total_tokens = reasoning_cost.get('total_llm_tokens')\n", + "\n", + " detailed_data.append({\n", + " 'run_id': str(run_id),\n", + " 'method': current_method, 'dataset': dataset, 'model': model,\n", + " 'Solution Conciseness': metrics.get('plan_length'),\n", + " 'Tokens': total_tokens,\n", + " 'API Calls': llm_calls\n", + " })\n", + " except Exception as e:\n", + " print(f\"🔴 Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "# --- 2. Data Cleaning and Preparation ---\n", + "df_raw = pd.DataFrame(detailed_data)\n", + "df_cleaned = df_raw.dropna().copy()\n", + "\n", + "models_to_keep = [\n", + " 'deepseek_v2_5', 'llama_3_3_70b_instruct', 'llama_4', \n", + " 'phi', 'qwen2_5_72b_instruct'\n", + "]\n", + "methods_to_keep = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "\n", + "df_filtered = df_cleaned[\n", + " df_cleaned['model'].isin(models_to_keep) & \n", + " df_cleaned['method'].isin(methods_to_keep)\n", + "].copy()\n", + "\n", + "# --- 3. Generate and Print Solution Conciseness Table ---\n", + "if not df_filtered.empty:\n", + " # Set categorical types to enforce order\n", + " df_filtered['model'] = pd.Categorical(df_filtered['model'], categories=sorted(models_to_keep), ordered=True)\n", + " df_filtered['method'] = pd.Categorical(df_filtered['method'], categories=methods_to_keep, ordered=True)\n", + "\n", + " # Calculate mean per run\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Solution Conciseness'].mean().reset_index()\n", + " \n", + " # Calculate final mean and std across runs\n", + " agg_df_conciseness = run_means.groupby(['dataset', 'model', 'method'])['Solution Conciseness'].agg(['mean', 'std']).reset_index()\n", + " \n", + " # Format the string for printing\n", + " agg_df_conciseness['Formatted'] = agg_df_conciseness.apply(\n", + " lambda row: f\"{row['mean']:.2f} ± {row['std']:.2f}\", axis=1\n", + " )\n", + "\n", + " # Pivot to create the final table structure\n", + " conciseness_table = agg_df_conciseness.pivot_table(\n", + " index=['dataset', 'model'],\n", + " columns='method',\n", + " values='Formatted',\n", + " aggfunc='first'\n", + " )\n", + " \n", + " print(\"\\n\" + \"=\"*80)\n", + " print(\"📊 Solution Conciseness (Average Plan Length)\")\n", + " print(\"=\"*80)\n", + " print(conciseness_table.to_string())\n", + " print(\"\\n\")\n", + "\n", + " # --- 4. Generate Bar Plots for Average Cost ---\n", + " \n", + " # Aggregate data for plotting\n", + " plot_agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n", + " 'Tokens': 'mean',\n", + " 'API Calls': 'mean'\n", + " }).reset_index()\n", + " \n", + " # --- MODIFICATION: Use scientific notation for tokens ---\n", + " plot_agg_df['Tokens (in 10k)'] = plot_agg_df['Tokens'] / 10000\n", + "\n", + " # --- MODIFICATION: Beautify and compact plots ---\n", + " sns.set_theme(style=\"darkgrid\", context=\"talk\") \n", + "\n", + " # Map for aligned model and method names\n", + " model_name_map = {\n", + " 'deepseek_v2_5': 'DeepSeek-V2.5',\n", + " 'llama_3_3_70b_instruct': 'Llama 3.3 70B',\n", + " 'llama_4': 'Llama 4 Maverick 17B',\n", + " 'phi': 'Phi 4 14B',\n", + " 'qwen2_5_72b_instruct': 'Qwen 2.5 72B'\n", + " }\n", + " method_name_map = {\n", + " 'cot_k1': 'CoT (k=1)',\n", + " 'cot_k3': 'CoT (k=3)',\n", + " 'cot_k5': 'CoT (k=5)',\n", + " 'spiral': 'SPIRAL'\n", + " }\n", + " plot_agg_df['model_long_name'] = plot_agg_df['model'].map(model_name_map)\n", + " plot_agg_df['method_long_name'] = plot_agg_df['method'].map(method_name_map)\n", + " \n", + " model_order = [model_name_map[m] for m in sorted(models_to_keep)]\n", + " method_order = [method_name_map[m] for m in methods_to_keep]\n", + "\n", + "\n", + " # Plot 1: Average Tokens\n", + " g_tokens = sns.catplot(\n", + " data=plot_agg_df,\n", + " kind='bar',\n", + " x='model_long_name',\n", + " y='Tokens (in 10k)', # Use scaled values\n", + " hue='method_long_name',\n", + " col='dataset',\n", + " hue_order=method_order,\n", + " order=model_order,\n", + " height=5, \n", + " aspect=1.3,\n", + " sharey=False\n", + " )\n", + " sns.move_legend(\n", + " g_tokens, \"upper center\",\n", + " bbox_to_anchor=(.5, 1.05), \n", + " ncol=len(methods_to_keep), \n", + " title=None, \n", + " frameon=False\n", + " )\n", + " g_tokens.set_axis_labels(\"Model\", \"Average Tokens per Task (in 10k)\", fontsize=16)\n", + " g_tokens.set_titles(\"Dataset: {col_name}\", size=18)\n", + " g_tokens.set_xticklabels(rotation=15, ha='right')\n", + " plt.tight_layout(rect=[0, 0, 1, 0.98])\n", + " plt.show()\n", + "\n", + " # Plot 2: Average API Calls\n", + " g_calls = sns.catplot(\n", + " data=plot_agg_df,\n", + " kind='bar',\n", + " x='model_long_name',\n", + " y='API Calls',\n", + " hue='method_long_name',\n", + " col='dataset',\n", + " hue_order=method_order,\n", + " order=model_order,\n", + " height=5,\n", + " aspect=1.3,\n", + " sharey=False\n", + " )\n", + " sns.move_legend(\n", + " g_calls, \"upper center\",\n", + " bbox_to_anchor=(.5, 1.05),\n", + " ncol=len(methods_to_keep), \n", + " title=None, \n", + " frameon=False\n", + " )\n", + " g_calls.set_axis_labels(\"Model\", \"Average API Calls per Task\", fontsize=16)\n", + " g_calls.set_titles(\"Dataset: {col_name}\", size=18)\n", + " g_calls.set_xticklabels(rotation=15, ha='right')\n", + " plt.tight_layout(rect=[0, 0, 1, 0.98])\n", + " plt.show()\n", + "\n", + "else:\n", + " print(\"🔴 No data available for analysis after filtering.\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3f107521", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1677407/3799365949.py:89: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Solution Conciseness'].mean().reset_index()\n", + "/tmp/ipykernel_1677407/3799365949.py:92: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " agg_df_conciseness = run_means.groupby(['dataset', 'model', 'method'])['Solution Conciseness'].agg(['mean', 'std']).reset_index()\n", + "/tmp/ipykernel_1677407/3799365949.py:100: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n", + " conciseness_table = agg_df_conciseness.pivot_table(\n", + "/tmp/ipykernel_1677407/3799365949.py:116: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " plot_agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "📊 Solution Conciseness (Average Plan Length)\n", + "================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral\n", + "dataset model \n", + "dailylifeapis deepseek_v2_5 2.82 ± 0.17 2.84 ± 0.15 2.82 ± 0.15 2.74 ± 0.15\n", + " llama_3_3_70b_instruct 3.04 ± 0.17 3.10 ± 0.21 3.09 ± 0.21 2.94 ± 0.13\n", + " llama_4 2.89 ± 0.18 2.89 ± 0.18 2.92 ± 0.20 2.84 ± 0.13\n", + " phi 2.77 ± 0.19 2.80 ± 0.19 2.81 ± 0.18 2.69 ± 0.14\n", + " qwen2_5_72b_instruct 2.88 ± 0.19 2.87 ± 0.21 2.91 ± 0.20 2.73 ± 0.16\n", + "huggingface deepseek_v2_5 2.71 ± 0.08 2.60 ± 0.19 2.70 ± 0.07 2.30 ± 0.05\n", + " llama_3_3_70b_instruct 2.77 ± 0.05 2.80 ± 0.10 2.78 ± 0.05 2.28 ± 0.06\n", + " llama_4 2.57 ± 0.06 2.58 ± 0.07 2.54 ± 0.09 2.35 ± 0.04\n", + " phi 2.53 ± 0.06 2.57 ± 0.08 2.59 ± 0.06 2.25 ± 0.06\n", + " qwen2_5_72b_instruct 2.68 ± 0.05 2.68 ± 0.04 2.71 ± 0.05 2.25 ± 0.05\n", + "\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "import numpy as np\n", + "import re\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# --- 1. Robust Data Parsing ---\n", + "# Captures all necessary metrics for both the table and the plots.\n", + "root_dir = Path('.')\n", + "detailed_data = []\n", + "ALL_EXPECTED_METHODS = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "results_files = root_dir.glob('**/results.json')\n", + "\n", + "for file_path in results_files:\n", + " try:\n", + " parts = file_path.parts\n", + " current_method = None\n", + " for m in ALL_EXPECTED_METHODS:\n", + " if m in parts:\n", + " current_method = m\n", + " break\n", + " \n", + " if current_method:\n", + " method_index = parts.index(current_method)\n", + " dataset = parts[method_index + 1].replace('_experiments', '').replace('_v3', '')\n", + " model = parts[method_index + 2]\n", + " \n", + " run_id_match = re.search(r'run_seed_(\\d+)', str(file_path))\n", + " run_id = run_id_match.group(1) if run_id_match else file_path.parent.name\n", + "\n", + " with open(file_path, 'r') as f:\n", + " results_list = json.load(f)\n", + "\n", + " for item in results_list:\n", + " metrics = item.get('metrics', {})\n", + " llm_calls = None\n", + " total_tokens = None\n", + "\n", + " if current_method == 'spiral':\n", + " search_process = metrics.get('search_process', {})\n", + " exp_calls = search_process.get('expansion_llm_calls', 0)\n", + " sim_calls = search_process.get('simulation_llm_calls', 0)\n", + " crit_calls = search_process.get('critic_llm_calls', 0)\n", + " llm_calls = exp_calls + sim_calls + crit_calls\n", + " \n", + " exp_tokens = search_process.get('expansion_llm_tokens', 0)\n", + " sim_tokens = search_process.get('simulation_llm_tokens', 0)\n", + " crit_tokens = search_process.get('critic_llm_tokens', 0)\n", + " total_tokens = exp_tokens + sim_tokens + crit_tokens\n", + " else: # Baseline methods\n", + " reasoning_cost = metrics.get('reasoning_cost', {})\n", + " llm_calls = reasoning_cost.get('llm_calls')\n", + " total_tokens = reasoning_cost.get('total_llm_tokens')\n", + "\n", + " detailed_data.append({\n", + " 'run_id': str(run_id),\n", + " 'method': current_method, 'dataset': dataset, 'model': model,\n", + " 'Solution Conciseness': metrics.get('plan_length'),\n", + " 'Tokens': total_tokens,\n", + " 'API Calls': llm_calls\n", + " })\n", + " except Exception as e:\n", + " print(f\"🔴 Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "# --- 2. Data Cleaning and Preparation ---\n", + "df_raw = pd.DataFrame(detailed_data)\n", + "df_cleaned = df_raw.dropna().copy()\n", + "\n", + "models_to_keep = [\n", + " 'deepseek_v2_5', 'llama_3_3_70b_instruct', 'llama_4', \n", + " 'phi', 'qwen2_5_72b_instruct'\n", + "]\n", + "methods_to_keep = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "\n", + "df_filtered = df_cleaned[\n", + " df_cleaned['model'].isin(models_to_keep) & \n", + " df_cleaned['method'].isin(methods_to_keep)\n", + "].copy()\n", + "\n", + "# --- 3. Generate and Print Solution Conciseness Table ---\n", + "if not df_filtered.empty:\n", + " # Set categorical types to enforce order\n", + " df_filtered['model'] = pd.Categorical(df_filtered['model'], categories=sorted(models_to_keep), ordered=True)\n", + " df_filtered['method'] = pd.Categorical(df_filtered['method'], categories=methods_to_keep, ordered=True)\n", + "\n", + " # Calculate mean per run\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Solution Conciseness'].mean().reset_index()\n", + " \n", + " # Calculate final mean and std across runs\n", + " agg_df_conciseness = run_means.groupby(['dataset', 'model', 'method'])['Solution Conciseness'].agg(['mean', 'std']).reset_index()\n", + " \n", + " # Format the string for printing\n", + " agg_df_conciseness['Formatted'] = agg_df_conciseness.apply(\n", + " lambda row: f\"{row['mean']:.2f} ± {row['std']:.2f}\", axis=1\n", + " )\n", + "\n", + " # Pivot to create the final table structure\n", + " conciseness_table = agg_df_conciseness.pivot_table(\n", + " index=['dataset', 'model'],\n", + " columns='method',\n", + " values='Formatted',\n", + " aggfunc='first'\n", + " )\n", + " \n", + " print(\"\\n\" + \"=\"*80)\n", + " print(\"📊 Solution Conciseness (Average Plan Length)\")\n", + " print(\"=\"*80)\n", + " print(conciseness_table.to_string())\n", + " print(\"\\n\")\n", + "\n", + " # --- 4. Generate Bar Plots for Average Cost ---\n", + " \n", + " # Aggregate data for plotting\n", + " plot_agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n", + " 'Tokens': 'mean',\n", + " 'API Calls': 'mean'\n", + " }).reset_index()\n", + " \n", + " # Use scientific notation for tokens\n", + " plot_agg_df['Tokens (in 10k)'] = plot_agg_df['Tokens'] / 10000\n", + "\n", + " # Set the plot theme\n", + " sns.set_theme(style=\"darkgrid\", context=\"talk\") \n", + "\n", + " # Map for aligned model and method names\n", + " model_name_map = {\n", + " 'deepseek_v2_5': 'DeepSeek-V2.5',\n", + " 'llama_3_3_70b_instruct': 'Llama 3.3 70B',\n", + " 'llama_4': 'Llama 4 Maverick 17B',\n", + " 'phi': 'Phi 4 14B',\n", + " 'qwen2_5_72b_instruct': 'Qwen 2.5 72B'\n", + " }\n", + " method_name_map = {\n", + " 'cot_k1': 'CoT (k=1)',\n", + " 'cot_k3': 'CoT (k=3)',\n", + " 'cot_k5': 'CoT (k=5)',\n", + " 'spiral': 'SPIRAL'\n", + " }\n", + " plot_agg_df['model_long_name'] = plot_agg_df['model'].map(model_name_map)\n", + " plot_agg_df['method_long_name'] = plot_agg_df['method'].map(method_name_map)\n", + " \n", + " model_order = [model_name_map[m] for m in sorted(models_to_keep)]\n", + " method_order = [method_name_map[m] for m in methods_to_keep]\n", + "\n", + "\n", + " # Plot 1: Average Tokens\n", + " g_tokens = sns.catplot(\n", + " data=plot_agg_df,\n", + " kind='bar',\n", + " x='model_long_name',\n", + " y='Tokens (in 10k)', \n", + " hue='method_long_name',\n", + " col='dataset',\n", + " hue_order=method_order,\n", + " order=model_order,\n", + " height=5, \n", + " aspect=1.3,\n", + " sharey=False\n", + " )\n", + " sns.move_legend(\n", + " g_tokens, \"upper center\",\n", + " bbox_to_anchor=(.5, 1.05), \n", + " ncol=len(methods_to_keep), \n", + " title=None, \n", + " frameon=False\n", + " )\n", + " g_tokens.set_axis_labels(\"Model\", \"Average Tokens per Task (in 10k)\", fontsize=16)\n", + " g_tokens.set_titles(\"Dataset: {col_name}\", size=18)\n", + " g_tokens.set_xticklabels(rotation=15, ha='right')\n", + " plt.tight_layout(rect=[0, 0, 1, 0.98])\n", + " \n", + " # --- MODIFICATION: Save the plot as a high-resolution PDF ---\n", + " plt.savefig(\"cost_comparison_tokens.pdf\", dpi=300, bbox_inches='tight')\n", + " plt.show()\n", + "\n", + " # Plot 2: Average API Calls\n", + " g_calls = sns.catplot(\n", + " data=plot_agg_df,\n", + " kind='bar',\n", + " x='model_long_name',\n", + " y='API Calls',\n", + " hue='method_long_name',\n", + " col='dataset',\n", + " hue_order=method_order,\n", + " order=model_order,\n", + " height=5,\n", + " aspect=1.3,\n", + " sharey=False\n", + " )\n", + " sns.move_legend(\n", + " g_calls, \"upper center\",\n", + " bbox_to_anchor=(.5, 1.05),\n", + " ncol=len(methods_to_keep), \n", + " title=None, \n", + " frameon=False\n", + " )\n", + " g_calls.set_axis_labels(\"Model\", \"Average API Calls per Task\", fontsize=16)\n", + " g_calls.set_titles(\"Dataset: {col_name}\", size=18)\n", + " g_calls.set_xticklabels(rotation=15, ha='right')\n", + " plt.tight_layout(rect=[0, 0, 1, 0.98])\n", + " \n", + " # --- MODIFICATION: Save the plot as a high-resolution PDF ---\n", + " plt.savefig(\"cost_comparison_api_calls.pdf\", dpi=300, bbox_inches='tight')\n", + " plt.show()\n", + "\n", + "else:\n", + " print(\"🔴 No data available for analysis after filtering.\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4e18acda", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1677407/753936016.py:89: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Solution Conciseness'].mean().reset_index()\n", + "/tmp/ipykernel_1677407/753936016.py:92: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " agg_df_conciseness = run_means.groupby(['dataset', 'model', 'method'])['Solution Conciseness'].agg(['mean', 'std']).reset_index()\n", + "/tmp/ipykernel_1677407/753936016.py:100: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n", + " conciseness_table = agg_df_conciseness.pivot_table(\n", + "/tmp/ipykernel_1677407/753936016.py:116: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " plot_agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "📊 Solution Conciseness (Average Plan Length)\n", + "================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral\n", + "dataset model \n", + "dailylifeapis deepseek_v2_5 2.82 ± 0.17 2.84 ± 0.15 2.82 ± 0.15 2.74 ± 0.15\n", + " llama_3_3_70b_instruct 3.04 ± 0.17 3.10 ± 0.21 3.09 ± 0.21 2.94 ± 0.13\n", + " llama_4 2.89 ± 0.18 2.89 ± 0.18 2.92 ± 0.20 2.84 ± 0.13\n", + " phi 2.77 ± 0.19 2.80 ± 0.19 2.81 ± 0.18 2.69 ± 0.14\n", + " qwen2_5_72b_instruct 2.88 ± 0.19 2.87 ± 0.21 2.91 ± 0.20 2.73 ± 0.16\n", + "huggingface deepseek_v2_5 2.71 ± 0.08 2.60 ± 0.19 2.70 ± 0.07 2.30 ± 0.05\n", + " llama_3_3_70b_instruct 2.77 ± 0.05 2.80 ± 0.10 2.78 ± 0.05 2.28 ± 0.06\n", + " llama_4 2.57 ± 0.06 2.58 ± 0.07 2.54 ± 0.09 2.35 ± 0.04\n", + " phi 2.53 ± 0.06 2.57 ± 0.08 2.59 ± 0.06 2.25 ± 0.06\n", + " qwen2_5_72b_instruct 2.68 ± 0.05 2.68 ± 0.04 2.71 ± 0.05 2.25 ± 0.05\n", + "\n", + "\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "import numpy as np\n", + "import re\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# --- 1. Robust Data Parsing ---\n", + "# Captures all necessary metrics for both the table and the plots.\n", + "root_dir = Path('.')\n", + "detailed_data = []\n", + "ALL_EXPECTED_METHODS = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "results_files = root_dir.glob('**/results.json')\n", + "\n", + "for file_path in results_files:\n", + " try:\n", + " parts = file_path.parts\n", + " current_method = None\n", + " for m in ALL_EXPECTED_METHODS:\n", + " if m in parts:\n", + " current_method = m\n", + " break\n", + " \n", + " if current_method:\n", + " method_index = parts.index(current_method)\n", + " dataset = parts[method_index + 1].replace('_experiments', '').replace('_v3', '')\n", + " model = parts[method_index + 2]\n", + " \n", + " run_id_match = re.search(r'run_seed_(\\d+)', str(file_path))\n", + " run_id = run_id_match.group(1) if run_id_match else file_path.parent.name\n", + "\n", + " with open(file_path, 'r') as f:\n", + " results_list = json.load(f)\n", + "\n", + " for item in results_list:\n", + " metrics = item.get('metrics', {})\n", + " llm_calls = None\n", + " total_tokens = None\n", + "\n", + " if current_method == 'spiral':\n", + " search_process = metrics.get('search_process', {})\n", + " exp_calls = search_process.get('expansion_llm_calls', 0)\n", + " sim_calls = search_process.get('simulation_llm_calls', 0)\n", + " crit_calls = search_process.get('critic_llm_calls', 0)\n", + " llm_calls = exp_calls + sim_calls + crit_calls\n", + " \n", + " exp_tokens = search_process.get('expansion_llm_tokens', 0)\n", + " sim_tokens = search_process.get('simulation_llm_tokens', 0)\n", + " crit_tokens = search_process.get('critic_llm_tokens', 0)\n", + " total_tokens = exp_tokens + sim_tokens + crit_tokens\n", + " else: # Baseline methods\n", + " reasoning_cost = metrics.get('reasoning_cost', {})\n", + " llm_calls = reasoning_cost.get('llm_calls')\n", + " total_tokens = reasoning_cost.get('total_llm_tokens')\n", + "\n", + " detailed_data.append({\n", + " 'run_id': str(run_id),\n", + " 'method': current_method, 'dataset': dataset, 'model': model,\n", + " 'Solution Conciseness': metrics.get('plan_length'),\n", + " 'Tokens': total_tokens,\n", + " 'API Calls': llm_calls\n", + " })\n", + " except Exception as e:\n", + " print(f\"🔴 Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "# --- 2. Data Cleaning and Preparation ---\n", + "df_raw = pd.DataFrame(detailed_data)\n", + "df_cleaned = df_raw.dropna().copy()\n", + "\n", + "models_to_keep = [\n", + " 'deepseek_v2_5', 'llama_3_3_70b_instruct', 'llama_4', \n", + " 'phi', 'qwen2_5_72b_instruct'\n", + "]\n", + "methods_to_keep = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "\n", + "df_filtered = df_cleaned[\n", + " df_cleaned['model'].isin(models_to_keep) & \n", + " df_cleaned['method'].isin(methods_to_keep)\n", + "].copy()\n", + "\n", + "# --- 3. Generate and Print Solution Conciseness Table ---\n", + "if not df_filtered.empty:\n", + " # Set categorical types to enforce order\n", + " df_filtered['model'] = pd.Categorical(df_filtered['model'], categories=sorted(models_to_keep), ordered=True)\n", + " df_filtered['method'] = pd.Categorical(df_filtered['method'], categories=methods_to_keep, ordered=True)\n", + "\n", + " # Calculate mean per run\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Solution Conciseness'].mean().reset_index()\n", + " \n", + " # Calculate final mean and std across runs\n", + " agg_df_conciseness = run_means.groupby(['dataset', 'model', 'method'])['Solution Conciseness'].agg(['mean', 'std']).reset_index()\n", + " \n", + " # Format the string for printing\n", + " agg_df_conciseness['Formatted'] = agg_df_conciseness.apply(\n", + " lambda row: f\"{row['mean']:.2f} ± {row['std']:.2f}\", axis=1\n", + " )\n", + "\n", + " # Pivot to create the final table structure\n", + " conciseness_table = agg_df_conciseness.pivot_table(\n", + " index=['dataset', 'model'],\n", + " columns='method',\n", + " values='Formatted',\n", + " aggfunc='first'\n", + " )\n", + " \n", + " print(\"\\n\" + \"=\"*80)\n", + " print(\"📊 Solution Conciseness (Average Plan Length)\")\n", + " print(\"=\"*80)\n", + " print(conciseness_table.to_string())\n", + " print(\"\\n\")\n", + "\n", + " # --- 4. Generate Bar Plots for Average Cost ---\n", + " \n", + " # Aggregate data for plotting\n", + " plot_agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n", + " 'Tokens': 'mean',\n", + " 'API Calls': 'mean'\n", + " }).reset_index()\n", + " \n", + " # Use scientific notation for tokens\n", + " plot_agg_df['Tokens (in 10k)'] = plot_agg_df['Tokens'] / 10000\n", + "\n", + " # Set the plot theme\n", + " sns.set_theme(style=\"darkgrid\", context=\"talk\") \n", + "\n", + " # Map for aligned model and method names\n", + " model_name_map = {\n", + " 'deepseek_v2_5': 'DeepSeek-V2.5',\n", + " 'llama_3_3_70b_instruct': 'Llama 3.3 70B',\n", + " 'llama_4': 'Llama 4 Maverick 17B',\n", + " 'phi': 'Phi 4 14B',\n", + " 'qwen2_5_72b_instruct': 'Qwen 2.5 72B'\n", + " }\n", + " method_name_map = {\n", + " 'cot_k1': 'CoT (k=1)',\n", + " 'cot_k3': 'CoT (k=3)',\n", + " 'cot_k5': 'CoT (k=5)',\n", + " 'spiral': 'SPIRAL'\n", + " }\n", + " plot_agg_df['model_long_name'] = plot_agg_df['model'].map(model_name_map)\n", + " plot_agg_df['method_long_name'] = plot_agg_df['method'].map(method_name_map)\n", + " \n", + " model_order = [model_name_map[m] for m in sorted(models_to_keep)]\n", + " method_order = [method_name_map[m] for m in methods_to_keep]\n", + "\n", + "\n", + " # Plot 1: Average Tokens\n", + " g_tokens = sns.catplot(\n", + " data=plot_agg_df,\n", + " kind='bar',\n", + " x='model_long_name',\n", + " y='Tokens (in 10k)', \n", + " hue='method_long_name',\n", + " col='dataset',\n", + " hue_order=method_order,\n", + " order=model_order,\n", + " height=5, \n", + " aspect=1.3,\n", + " sharey=False\n", + " )\n", + " # --- MODIFICATION: Increase all font sizes ---\n", + " sns.move_legend(\n", + " g_tokens, \"upper center\",\n", + " bbox_to_anchor=(.5, 1.05), \n", + " ncol=len(methods_to_keep), \n", + " title=None, \n", + " frameon=False\n", + " )\n", + " plt.setp(g_tokens.legend.get_texts(), fontsize='20') # Legend font size\n", + " g_tokens.set_axis_labels(\"Model\", \"Average Tokens per Task (in 10k)\", fontsize=20) # Axis label font size\n", + " g_tokens.set_titles(\"Dataset: {col_name}\", size=22) # Title font size\n", + " g_tokens.set_xticklabels(rotation=15, ha='right', fontsize=16) # X-tick label font size\n", + " for ax in g_tokens.axes.flat:\n", + " ax.tick_params(axis='y', labelsize=16) # Y-tick label font size\n", + "\n", + " plt.tight_layout(rect=[0, 0, 1, 0.98])\n", + " plt.savefig(\"cost_comparison_tokens.pdf\", dpi=300, bbox_inches='tight')\n", + " plt.show()\n", + "\n", + " # Plot 2: Average API Calls\n", + " g_calls = sns.catplot(\n", + " data=plot_agg_df,\n", + " kind='bar',\n", + " x='model_long_name',\n", + " y='API Calls',\n", + " hue='method_long_name',\n", + " col='dataset',\n", + " hue_order=method_order,\n", + " order=model_order,\n", + " height=5,\n", + " aspect=1.3,\n", + " sharey=False\n", + " )\n", + " # --- MODIFICATION: Increase all font sizes ---\n", + " sns.move_legend(\n", + " g_calls, \"upper center\",\n", + " bbox_to_anchor=(.5, 1.05),\n", + " ncol=len(methods_to_keep), \n", + " title=None, \n", + " frameon=False\n", + " )\n", + " plt.setp(g_calls.legend.get_texts(), fontsize='20') # Legend font size\n", + " g_calls.set_axis_labels(\"Model\", \"Average API Calls per Task\", fontsize=20) # Axis label font size\n", + " g_calls.set_titles(\"Dataset: {col_name}\", size=22) # Title font size\n", + " g_calls.set_xticklabels(rotation=15, ha='right', fontsize=16) # X-tick label font size\n", + " for ax in g_calls.axes.flat:\n", + " ax.tick_params(axis='y', labelsize=16) # Y-tick label font size\n", + "\n", + " plt.tight_layout(rect=[0, 0, 1, 0.98])\n", + " plt.savefig(\"cost_comparison_api_calls.pdf\", dpi=300, bbox_inches='tight')\n", + " plt.show()\n", + "\n", + "else:\n", + " print(\"🔴 No data available for analysis after filtering.\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cfad2e71", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1677407/318525849.py:89: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Solution Conciseness'].mean().reset_index()\n", + "/tmp/ipykernel_1677407/318525849.py:92: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " agg_df_conciseness = run_means.groupby(['dataset', 'model', 'method'])['Solution Conciseness'].agg(['mean', 'std']).reset_index()\n", + "/tmp/ipykernel_1677407/318525849.py:100: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n", + " conciseness_table = agg_df_conciseness.pivot_table(\n", + "/tmp/ipykernel_1677407/318525849.py:116: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " plot_agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "📊 Solution Conciseness (Average Plan Length)\n", + "================================================================================\n", + "method cot_k1 cot_k3 cot_k5 spiral\n", + "dataset model \n", + "dailylifeapis deepseek_v2_5 2.82 ± 0.17 2.84 ± 0.15 2.82 ± 0.15 2.74 ± 0.15\n", + " llama_3_3_70b_instruct 3.04 ± 0.17 3.10 ± 0.21 3.09 ± 0.21 2.94 ± 0.13\n", + " llama_4 2.89 ± 0.18 2.89 ± 0.18 2.92 ± 0.20 2.84 ± 0.13\n", + " phi 2.77 ± 0.19 2.80 ± 0.19 2.81 ± 0.18 2.69 ± 0.14\n", + " qwen2_5_72b_instruct 2.88 ± 0.19 2.87 ± 0.21 2.91 ± 0.20 2.73 ± 0.16\n", + "huggingface deepseek_v2_5 2.71 ± 0.08 2.60 ± 0.19 2.70 ± 0.07 2.30 ± 0.05\n", + " llama_3_3_70b_instruct 2.77 ± 0.05 2.80 ± 0.10 2.78 ± 0.05 2.28 ± 0.06\n", + " llama_4 2.57 ± 0.06 2.58 ± 0.07 2.54 ± 0.09 2.35 ± 0.04\n", + " phi 2.53 ± 0.06 2.57 ± 0.08 2.59 ± 0.06 2.25 ± 0.06\n", + " qwen2_5_72b_instruct 2.68 ± 0.05 2.68 ± 0.04 2.71 ± 0.05 2.25 ± 0.05\n", + "\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "import numpy as np\n", + "import re\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# --- 1. Robust Data Parsing ---\n", + "# Captures all necessary metrics for both the table and the plots.\n", + "root_dir = Path('.')\n", + "detailed_data = []\n", + "ALL_EXPECTED_METHODS = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "results_files = root_dir.glob('**/results.json')\n", + "\n", + "for file_path in results_files:\n", + " try:\n", + " parts = file_path.parts\n", + " current_method = None\n", + " for m in ALL_EXPECTED_METHODS:\n", + " if m in parts:\n", + " current_method = m\n", + " break\n", + " \n", + " if current_method:\n", + " method_index = parts.index(current_method)\n", + " dataset = parts[method_index + 1].replace('_experiments', '').replace('_v3', '')\n", + " model = parts[method_index + 2]\n", + " \n", + " run_id_match = re.search(r'run_seed_(\\d+)', str(file_path))\n", + " run_id = run_id_match.group(1) if run_id_match else file_path.parent.name\n", + "\n", + " with open(file_path, 'r') as f:\n", + " results_list = json.load(f)\n", + "\n", + " for item in results_list:\n", + " metrics = item.get('metrics', {})\n", + " llm_calls = None\n", + " total_tokens = None\n", + "\n", + " if current_method == 'spiral':\n", + " search_process = metrics.get('search_process', {})\n", + " exp_calls = search_process.get('expansion_llm_calls', 0)\n", + " sim_calls = search_process.get('simulation_llm_calls', 0)\n", + " crit_calls = search_process.get('critic_llm_calls', 0)\n", + " llm_calls = exp_calls + sim_calls + crit_calls\n", + " \n", + " exp_tokens = search_process.get('expansion_llm_tokens', 0)\n", + " sim_tokens = search_process.get('simulation_llm_tokens', 0)\n", + " crit_tokens = search_process.get('critic_llm_tokens', 0)\n", + " total_tokens = exp_tokens + sim_tokens + crit_tokens\n", + " else: # Baseline methods\n", + " reasoning_cost = metrics.get('reasoning_cost', {})\n", + " llm_calls = reasoning_cost.get('llm_calls')\n", + " total_tokens = reasoning_cost.get('total_llm_tokens')\n", + "\n", + " detailed_data.append({\n", + " 'run_id': str(run_id),\n", + " 'method': current_method, 'dataset': dataset, 'model': model,\n", + " 'Solution Conciseness': metrics.get('plan_length'),\n", + " 'Tokens': total_tokens,\n", + " 'API Calls': llm_calls\n", + " })\n", + " except Exception as e:\n", + " print(f\"🔴 Skipping file due to error: {file_path} -> {e}\")\n", + "\n", + "# --- 2. Data Cleaning and Preparation ---\n", + "df_raw = pd.DataFrame(detailed_data)\n", + "df_cleaned = df_raw.dropna().copy()\n", + "\n", + "models_to_keep = [\n", + " 'deepseek_v2_5', 'llama_3_3_70b_instruct', 'llama_4', \n", + " 'phi', 'qwen2_5_72b_instruct'\n", + "]\n", + "methods_to_keep = ['cot_k1', 'cot_k3', 'cot_k5', 'spiral']\n", + "\n", + "df_filtered = df_cleaned[\n", + " df_cleaned['model'].isin(models_to_keep) & \n", + " df_cleaned['method'].isin(methods_to_keep)\n", + "].copy()\n", + "\n", + "# --- 3. Generate and Print Solution Conciseness Table ---\n", + "if not df_filtered.empty:\n", + " # Set categorical types to enforce order\n", + " df_filtered['model'] = pd.Categorical(df_filtered['model'], categories=sorted(models_to_keep), ordered=True)\n", + " df_filtered['method'] = pd.Categorical(df_filtered['method'], categories=methods_to_keep, ordered=True)\n", + "\n", + " # Calculate mean per run\n", + " run_means = df_filtered.groupby(['dataset', 'model', 'method', 'run_id'])['Solution Conciseness'].mean().reset_index()\n", + " \n", + " # Calculate final mean and std across runs\n", + " agg_df_conciseness = run_means.groupby(['dataset', 'model', 'method'])['Solution Conciseness'].agg(['mean', 'std']).reset_index()\n", + " \n", + " # Format the string for printing\n", + " agg_df_conciseness['Formatted'] = agg_df_conciseness.apply(\n", + " lambda row: f\"{row['mean']:.2f} ± {row['std']:.2f}\", axis=1\n", + " )\n", + "\n", + " # Pivot to create the final table structure\n", + " conciseness_table = agg_df_conciseness.pivot_table(\n", + " index=['dataset', 'model'],\n", + " columns='method',\n", + " values='Formatted',\n", + " aggfunc='first'\n", + " )\n", + " \n", + " print(\"\\n\" + \"=\"*80)\n", + " print(\"📊 Solution Conciseness (Average Plan Length)\")\n", + " print(\"=\"*80)\n", + " print(conciseness_table.to_string())\n", + " print(\"\\n\")\n", + "\n", + " # --- 4. Generate Bar Plots for Average Cost ---\n", + " \n", + " # Aggregate data for plotting\n", + " plot_agg_df = df_filtered.groupby(['dataset', 'model', 'method']).agg({\n", + " 'Tokens': 'mean',\n", + " 'API Calls': 'mean'\n", + " }).reset_index()\n", + " \n", + " # Use scientific notation for tokens\n", + " plot_agg_df['Tokens (in 10k)'] = plot_agg_df['Tokens'] / 10000\n", + "\n", + " # Set the plot theme\n", + " sns.set_theme(style=\"darkgrid\", context=\"talk\") \n", + "\n", + " # Map for aligned model and method names\n", + " model_name_map = {\n", + " 'deepseek_v2_5': 'DeepSeek-V2.5',\n", + " 'llama_3_3_70b_instruct': 'Llama 3.3 70B',\n", + " 'llama_4': 'Llama 4 Maverick 17B',\n", + " 'phi': 'Phi 4 14B',\n", + " 'qwen2_5_72b_instruct': 'Qwen 2.5 72B'\n", + " }\n", + " method_name_map = {\n", + " 'cot_k1': 'CoT (k=1)',\n", + " 'cot_k3': 'CoT (k=3)',\n", + " 'cot_k5': 'CoT (k=5)',\n", + " 'spiral': 'SPIRAL'\n", + " }\n", + " plot_agg_df['model_long_name'] = plot_agg_df['model'].map(model_name_map)\n", + " plot_agg_df['method_long_name'] = plot_agg_df['method'].map(method_name_map)\n", + " \n", + " model_order = [model_name_map[m] for m in sorted(models_to_keep)]\n", + " method_order = [method_name_map[m] for m in methods_to_keep]\n", + "\n", + "\n", + " # Plot 1: Average Tokens\n", + " g_tokens = sns.catplot(\n", + " data=plot_agg_df,\n", + " kind='bar',\n", + " x='model_long_name',\n", + " y='Tokens (in 10k)', \n", + " hue='method_long_name',\n", + " col='dataset',\n", + " hue_order=method_order,\n", + " order=model_order,\n", + " height=5, \n", + " aspect=1.3,\n", + " sharey=False\n", + " )\n", + " sns.move_legend(\n", + " g_tokens, \"upper center\",\n", + " bbox_to_anchor=(.5, 1.05), \n", + " ncol=len(methods_to_keep), \n", + " title=None, \n", + " frameon=False\n", + " )\n", + " # --- MODIFICATION: Abbreviate Y-axis label ---\n", + " g_tokens.set_axis_labels(\"Model\", \"Avg. Tokens (10k)\", fontsize=20) \n", + " g_tokens.set_titles(\"Dataset: {col_name}\", size=22) \n", + " g_tokens.set_xticklabels(rotation=15, ha='right', fontsize=16) \n", + " plt.setp(g_tokens.legend.get_texts(), fontsize='20') \n", + " for ax in g_tokens.axes.flat:\n", + " ax.tick_params(axis='y', labelsize=16) \n", + "\n", + " plt.tight_layout(rect=[0.02, 0, 1, 0.98]) # Give a little more left padding\n", + " plt.savefig(\"cost_comparison_tokens.pdf\", dpi=300, bbox_inches='tight')\n", + " plt.show()\n", + "\n", + " # Plot 2: Average API Calls\n", + " g_calls = sns.catplot(\n", + " data=plot_agg_df,\n", + " kind='bar',\n", + " x='model_long_name',\n", + " y='API Calls',\n", + " hue='method_long_name',\n", + " col='dataset',\n", + " hue_order=method_order,\n", + " order=model_order,\n", + " height=5,\n", + " aspect=1.3,\n", + " sharey=False\n", + " )\n", + " sns.move_legend(\n", + " g_calls, \"upper center\",\n", + " bbox_to_anchor=(.5, 1.05),\n", + " ncol=len(methods_to_keep), \n", + " title=None, \n", + " frameon=False\n", + " )\n", + " # --- MODIFICATION: Abbreviate Y-axis label ---\n", + " g_calls.set_axis_labels(\"Model\", \"Avg. API Calls\", fontsize=20)\n", + " g_calls.set_titles(\"Dataset: {col_name}\", size=22)\n", + " g_calls.set_xticklabels(rotation=15, ha='right', fontsize=16)\n", + " plt.setp(g_calls.legend.get_texts(), fontsize='20')\n", + " for ax in g_calls.axes.flat:\n", + " ax.tick_params(axis='y', labelsize=16)\n", + "\n", + " plt.tight_layout(rect=[0.02, 0, 1, 0.98]) # Give a little more left padding\n", + " plt.savefig(\"cost_comparison_api_calls.pdf\", dpi=300, bbox_inches='tight')\n", + " plt.show()\n", + "\n", + "else:\n", + " print(\"🔴 No data available for analysis after filtering.\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "73d7fc0c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/analysis/ablation/analysis_ablations.ipynb b/analysis/ablation/analysis_ablations.ipynb new file mode 100644 index 0000000..d2de443 --- /dev/null +++ b/analysis/ablation/analysis_ablations.ipynb @@ -0,0 +1,189 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "679167cd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting analysis for RQ3 (Ablation Study)...\n", + "\n", + "Processing Dataset: DAILYLIFEAPIS\n", + " Processing Model: llama_3_3_70b_instruct\n", + " Processing Model: llama_4\n", + "\n", + "Processing Dataset: HUGGINGFACE\n", + " Processing Model: llama_3_3_70b_instruct\n", + " Processing Model: llama_4\n", + "\n", + "================================================================================\n", + "📊 RQ3: Ablation Study on SPIRAL Components (Overall Accuracy)\n", + "================================================================================\n", + "dataset dailylifeapis huggingface\n", + "model method \n", + "llama_3_3_70b_instruct Baseline MCTS (Light) 87.60% ± 1.31 89.68% ± 1.43\n", + " Baseline MCTS (Medium) 87.11% ± 1.72 89.12% ± 0.95\n", + " Baseline MCTS (Heavy) 86.28% ± 1.71 89.44% ± 1.03\n", + " Greedy (w/o Plan History) 94.71% ± 1.90 87.48% ± 1.42\n", + " SPIRAL w/o Simulator 92.89% ± 1.11 69.48% ± 3.02\n", + " SPIRAL w/o Validator 87.27% ± 2.38 90.08% ± 1.30\n", + " SPIRAL w/ Uniform Rewards 88.27% ± 1.79 89.12% ± 1.06\n", + " SPIRAL (Full) 98.35% ± 0.83 97.44% ± 0.89\n", + "llama_4 Baseline MCTS (Light) 79.84% ± 4.66 73.80% ± 1.39\n", + " Baseline MCTS (Medium) 79.83% ± 4.77 73.64% ± 1.51\n", + " Baseline MCTS (Heavy) 80.16% ± 4.21 73.44% ± 2.85\n", + " Greedy (w/o Plan History) 81.82% ± 2.41 91.08% ± 1.46\n", + " SPIRAL w/o Simulator 80.99% ± 4.89 55.28% ± 2.87\n", + " SPIRAL w/o Validator 80.00% ± 4.31 75.16% ± 2.46\n", + " SPIRAL w/ Uniform Rewards 79.01% ± 5.31 74.44% ± 2.06\n", + " SPIRAL (Full) 83.30% ± 4.11 93.04% ± 0.89\n", + "\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1831368/136290419.py:84: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n", + " final_table = summary_table.pivot_table(\n" + ] + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "import re\n", + "\n", + "# --- Configuration ---\n", + "# This script assumes it is run from a directory containing the dataset folders.\n", + "ROOT_DIR = Path('.') \n", + "MODELS = ['llama_3_3_70b_instruct', 'llama_4']\n", + "SEEDS = [42, 101, 1234, 2024, 12345]\n", + "DATASETS = ['dailylifeapis', 'huggingface']\n", + "\n", + "# --- MODIFICATION: Updated method mapping and added SPIRAL ---\n", + "ABLATION_METHODS_MAP = {\n", + " 'Baseline MCTS (Light)': 'light',\n", + " 'Baseline MCTS (Medium)': 'medium',\n", + " 'Baseline MCTS (Heavy)': 'heavy',\n", + " 'Greedy (w/o Plan History)': 'no_mcts',\n", + " 'SPIRAL w/o Simulator': 'no_sim_feedback',\n", + " 'SPIRAL w/o Validator': 'no_validator',\n", + " 'SPIRAL w/ Uniform Rewards': 'uniform_rewards',\n", + " 'SPIRAL (Full)': 'spiral' \n", + "}\n", + "\n", + "# --- Data Parsing ---\n", + "all_results = []\n", + "\n", + "print(\"Starting analysis for RQ3 (Ablation Study)...\")\n", + "\n", + "for dataset in DATASETS:\n", + " print(f\"\\nProcessing Dataset: {dataset.upper()}\")\n", + " \n", + " for model in MODELS:\n", + " print(f\" Processing Model: {model}\")\n", + "\n", + " for method_name, method_folder in ABLATION_METHODS_MAP.items():\n", + " \n", + " for seed in SEEDS:\n", + " try:\n", + " # Construct the path based on the corrected folder structure\n", + " summary_path = ROOT_DIR / dataset / model / method_folder / f'run_seed_{seed}' / 'summary.json'\n", + "\n", + " if not summary_path.exists():\n", + " raise StopIteration # Handle missing files gracefully\n", + "\n", + " with open(summary_path, 'r') as f:\n", + " summary = json.load(f)\n", + " accuracy = float(summary['final_accuracy'].strip('%'))\n", + " \n", + " all_results.append({\n", + " 'dataset': dataset,\n", + " 'model': model,\n", + " 'method': method_name,\n", + " 'seed': seed,\n", + " 'accuracy': accuracy\n", + " })\n", + "\n", + " except StopIteration:\n", + " print(f\" - ⚠️ WARNING: Could not find summary for {dataset}/{model}/{method_folder}, seed {seed}. Skipping.\")\n", + " continue\n", + " except Exception as e:\n", + " print(f\" - 🔴 ERROR: Parsing failed for {dataset}/{model}/{method_folder}, seed {seed} -> {e}\")\n", + " continue\n", + "\n", + "# --- Aggregate and Print Table ---\n", + "if all_results:\n", + " df = pd.DataFrame(all_results)\n", + " \n", + " # Calculate mean and std across seeds\n", + " summary_table = df.groupby(['dataset', 'model', 'method'])['accuracy'].agg(['mean', 'std']).reset_index()\n", + " \n", + " # Format for display\n", + " summary_table['std'] = summary_table['std'].fillna(0)\n", + " summary_table['Final Accuracy'] = summary_table.apply(\n", + " lambda row: f\"{row['mean']:.2f}% ± {row['std']:.2f}\", axis=1\n", + " )\n", + " \n", + " # Reorder methods for logical presentation\n", + " method_order = list(ABLATION_METHODS_MAP.keys())\n", + " summary_table['method'] = pd.Categorical(summary_table['method'], categories=method_order, ordered=True)\n", + " summary_table = summary_table.sort_values(['dataset', 'model', 'method'])\n", + "\n", + " # Pivot for final presentation\n", + " final_table = summary_table.pivot_table(\n", + " index=['model', 'method'],\n", + " columns='dataset',\n", + " values='Final Accuracy',\n", + " aggfunc='first'\n", + " )\n", + " \n", + " print(\"\\n\" + \"=\"*80)\n", + " print(\"📊 RQ3: Ablation Study on SPIRAL Components (Overall Accuracy)\")\n", + " print(\"=\"*80)\n", + " print(final_table.to_string())\n", + " print(\"\\n\")\n", + "\n", + "else:\n", + " print(\"🔴 No results were generated. Please check the file paths and error messages.\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02c222ae", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/data/data_library.txt b/data/data_library.txt new file mode 100644 index 0000000..85ffb9d --- /dev/null +++ b/data/data_library.txt @@ -0,0 +1,5 @@ +from datasets import load_dataset + + +ds_humaneval = load_dataset("openai/openai_humaneval") +ds_math500 = load_dataset("HuggingFaceH4/MATH-500") \ No newline at end of file diff --git a/scripts/__init__.py b/scripts/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/scripts/create_residual_dataset.py b/scripts/create_residual_dataset.py new file mode 100644 index 0000000..af69691 --- /dev/null +++ b/scripts/create_residual_dataset.py @@ -0,0 +1,58 @@ +import json +import argparse +import shutil +from pathlib import Path +from datasets import load_dataset + +def main(): + parser = argparse.ArgumentParser(description="Filter TaskBench dataset and create a local residual data folder.") + parser.add_argument('--api_family', required=True, type=str, help="The original API family (e.g., huggingface).") + parser.add_argument('--results_path', required=True, type=Path, help="Path to the results.json from the CoT run.") + args = parser.parse_args() + + # 1. Define paths + original_data_dir = Path("Taskbench") / f"data_{args.api_family}" + residual_api_family = f"{args.api_family}_residual" + residual_data_dir = Path("Taskbench") / f"data_{residual_api_family}" + + # Clean up previous residual directory if it exists + if residual_data_dir.exists(): + shutil.rmtree(residual_data_dir) + residual_data_dir.mkdir(parents=True) + + # 2. Load results and find failed IDs + if not args.results_path.exists(): + print(f"Error: Results file not found at {args.results_path}"); return + with open(args.results_path, 'r', encoding='utf-8') as f: + results_data = json.load(f) + failed_ids = {res['id'] for res in results_data if res.get('metrics', {}).get('accuracy', 0.0) < 1.0} + + if not failed_ids: + print(f"No failed problems found. Residual directory '{residual_data_dir}' is empty.") + return + + # 3. Load original dataset and filter + try: + full_dataset = load_dataset('microsoft/Taskbench', name=args.api_family, split='test') + except Exception as e: + print(f"Error loading dataset '{args.api_family}': {e}"); return + + # 4. Write filtered records to the new residual directory + output_path = residual_data_dir / "user_requests.jsonl" + count = 0 + with open(output_path, 'w', encoding='utf-8') as f: + for record in full_dataset: + if record['id'] in failed_ids: + out_record = {'id': record['id'], 'instruction': record['instruction'], 'input': record.get('input', ''), 'tool_steps': record.get('tool_steps', [])} + f.write(json.dumps(out_record) + '\n') + count += 1 + + # 5. Copy tool and graph descriptions to the new directory + for desc_file in ["tool_desc.json", "graph_desc.json"]: + if (original_data_dir / desc_file).exists(): + shutil.copy(original_data_dir / desc_file, residual_data_dir / desc_file) + + print(f"✅ Created residual dataset with {count} problems at '{residual_data_dir}'") + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/scripts/environment.yml b/scripts/environment.yml new file mode 100644 index 0000000..915836b --- /dev/null +++ b/scripts/environment.yml @@ -0,0 +1,208 @@ +name: base +channels: + - defaults +dependencies: + - _libgcc_mutex=0.1=main + - _openmp_mutex=5.1=1_gnu + - anaconda-anon-usage=0.7.1=py313hfc0e8ea_100 + - annotated-types=0.6.0=py313h06a4308_0 + - anyio=4.7.0=py313h06a4308_0 + - archspec=0.2.3=pyhd3eb1b0_0 + - blas=1.0=mkl + - boltons=24.1.0=py313h06a4308_0 + - brotli-python=1.0.9=py313h6a678d5_9 + - bzip2=1.0.8=h5eee18b_6 + - c-ares=1.19.1=h5eee18b_0 + - ca-certificates=2025.2.25=h06a4308_0 + - certifi=2025.6.15=py313h06a4308_0 + - cffi=1.17.1=py313h1fdaa30_1 + - charset-normalizer=3.3.2=pyhd3eb1b0_0 + - conda=25.5.1=py313h06a4308_0 + - conda-anaconda-telemetry=0.1.2=py313h06a4308_1 + - conda-anaconda-tos=0.2.0=py313h06a4308_0 + - conda-content-trust=0.2.0=py313h06a4308_1 + - conda-libmamba-solver=25.4.0=pyhd3eb1b0_0 + - conda-package-handling=2.4.0=py313h06a4308_0 + - conda-package-streaming=0.11.0=py313h06a4308_0 + - cpp-expected=1.1.0=hdb19cb5_0 + - cryptography=45.0.3=py313h2ccb017_0 + - deprecated=1.2.13=py313h06a4308_0 + - distro=1.9.0=py313h06a4308_0 + - expat=2.7.1=h6a678d5_0 + - filelock=3.17.0=py313h06a4308_0 + - fmt=9.1.0=hdb19cb5_1 + - frozendict=2.4.2=py313h06a4308_0 + - gmp=6.3.0=h6a678d5_0 + - gmpy2=2.2.1=py313h5eee18b_0 + - h11=0.16.0=py313h06a4308_0 + - httpcore=1.0.9=py313h06a4308_0 + - httpx=0.28.1=py313h06a4308_0 + - icu=73.1=h6a678d5_0 + - idna=3.7=py313h06a4308_0 + - importlib-metadata=8.5.0=py313h06a4308_0 + - intel-openmp=2023.1.0=hdb19cb5_46306 + - jinja2=3.1.6=py313h06a4308_0 + - jsonpatch=1.33=py313h06a4308_1 + - jsonpointer=2.1=pyhd3eb1b0_0 + - krb5=1.20.1=h143b758_1 + - ld_impl_linux-64=2.40=h12ee557_0 + - libabseil=20250127.0=cxx17_h6a678d5_0 + - libarchive=3.7.7=hfab0078_0 + - libcurl=8.12.1=hc9e6f67_0 + - libedit=3.1.20230828=h5eee18b_0 + - libev=4.33=h7f8727e_1 + - libffi=3.4.4=h6a678d5_1 + - libgcc-ng=11.2.0=h1234567_1 + - libgomp=11.2.0=h1234567_1 + - libmamba=2.0.5=haf1ee3a_1 + - libmambapy=2.0.5=py313hdb19cb5_1 + - libmpdec=4.0.0=h5eee18b_0 + - libnghttp2=1.57.0=h2d74bed_0 + - libprotobuf=5.29.3=h3cdef7c_1 + - libsolv=0.7.30=he621ea3_1 + - libssh2=1.11.1=h251f7ec_0 + - libstdcxx-ng=11.2.0=h1234567_1 + - libtorch=2.6.0=cpu_mkl_h881e62d_106 + - libuuid=1.41.5=h5eee18b_0 + - libuv=1.48.0=h5eee18b_0 + - libxcb=1.17.0=h9b100fa_0 + - libxml2=2.13.8=hfdd30dd_0 + - lz4-c=1.9.4=h6a678d5_1 + - markdown-it-py=2.2.0=py313h06a4308_1 + - markupsafe=3.0.2=py313h5eee18b_0 + - mdurl=0.1.0=py313h06a4308_0 + - menuinst=2.2.0=py313h06a4308_1 + - mkl=2023.1.0=h213fc3f_46344 + - mkl-service=2.4.0=py313h5eee18b_2 + - mkl_fft=1.3.11=py313h5eee18b_0 + - mkl_random=1.2.8=py313h06d7b56_0 + - mpc=1.3.1=h5eee18b_0 + - mpfr=4.2.1=h5eee18b_0 + - mpmath=1.3.0=py313h06a4308_0 + - ncurses=6.4=h6a678d5_0 + - networkx=3.4.2=py313h06a4308_0 + - nlohmann_json=3.11.2=h6a678d5_0 + - numpy=2.3.1=py313h8d96ed3_0 + - numpy-base=2.3.1=py313h8e760e0_0 + - openssl=3.0.16=h5eee18b_0 + - opentelemetry-api=1.30.0=py313h06a4308_0 + - packaging=24.2=py313h06a4308_0 + - pcre2=10.42=hebb0a14_1 + - pip=25.1=pyhc872135_2 + - platformdirs=4.3.7=py313h06a4308_0 + - pluggy=1.5.0=py313h06a4308_0 + - pthread-stubs=0.3=h0ce48e5_1 + - pybind11-abi=5=hd3eb1b0_0 + - pycosat=0.6.6=py313h5eee18b_2 + - pycparser=2.21=pyhd3eb1b0_0 + - pydantic=2.10.3=py313h06a4308_0 + - pydantic-core=2.27.1=py313h4aa5aa6_0 + - pygments=2.19.1=py313h06a4308_0 + - pysocks=1.7.1=py313h06a4308_0 + - python=3.13.5=h4612cfd_100_cp313 + - python_abi=3.13=0_cp313 + - pytorch=2.6.0=cpu_mkl_py313hfd6889d_106 + - readline=8.2=h5eee18b_0 + - reproc=14.2.4=h6a678d5_2 + - reproc-cpp=14.2.4=h6a678d5_2 + - rich=13.9.4=py313h06a4308_0 + - ruamel.yaml=0.18.10=py313h5eee18b_0 + - ruamel.yaml.clib=0.2.12=py313h5eee18b_0 + - setuptools=72.1.0=py313h06a4308_0 + - simdjson=3.10.1=hdb19cb5_0 + - sleef=3.5.1=h5eee18b_2 + - sniffio=1.3.0=py313h06a4308_0 + - spdlog=1.11.0=hdb19cb5_0 + - sqlite=3.45.3=h5eee18b_0 + - sympy=1.13.3=py313h06a4308_1 + - tbb=2021.8.0=hdb19cb5_0 + - tk=8.6.14=h993c535_1 + - tqdm=4.67.1=py313h7040dfc_0 + - truststore=0.10.0=py313h06a4308_0 + - typing-extensions=4.12.2=py313h06a4308_0 + - typing_extensions=4.12.2=py313h06a4308_0 + - urllib3=2.3.0=py313h06a4308_0 + - wheel=0.45.1=py313h06a4308_0 + - wrapt=1.17.0=py313h5eee18b_0 + - xorg-libx11=1.8.12=h9b100fa_1 + - xorg-libxau=1.0.12=h9b100fa_0 + - xorg-libxdmcp=1.1.5=h9b100fa_0 + - xorg-xorgproto=2024.1=h5eee18b_1 + - xz=5.6.4=h5eee18b_1 + - yaml-cpp=0.8.0=h6a678d5_1 + - zipp=3.21.0=py313h06a4308_0 + - zlib=1.2.13=h5eee18b_1 + - zstandard=0.23.0=py313h2c38b39_1 + - zstd=1.5.6=hc292b87_0 + - pip: + - aiohappyeyeballs==2.6.1 + - aiohttp==3.12.14 + - aiosignal==1.4.0 + - attrs==25.3.0 + - cachetools==6.1.0 + - click==8.2.1 + - dataclasses==0.6 + - dataclasses-json==0.6.7 + - datasets==4.0.0 + - dill==0.3.8 + - frozenlist==1.7.0 + - fsspec==2025.3.0 + - greenlet==3.2.3 + - hf-xet==1.1.5 + - httpx-sse==0.4.1 + - huggingface-hub==0.33.2 + - ibm-cos-sdk==2.14.2 + - ibm-cos-sdk-core==2.14.2 + - ibm-cos-sdk-s3transfer==2.14.2 + - ibm-watsonx-ai==1.3.30 + - jiter==0.10.0 + - jmespath==1.0.1 + - joblib==1.5.1 + - jsonschema==4.24.0 + - jsonschema-specifications==2025.4.1 + - langchain==0.3.26 + - langchain-community==0.3.27 + - langchain-core==0.3.68 + - langchain-ibm==0.3.15 + - langchain-text-splitters==0.3.8 + - langsmith==0.4.4 + - litellm==1.74.1 + - lomond==0.3.3 + - marshmallow==3.26.1 + - multidict==6.6.3 + - multiprocess==0.70.16 + - mypy-extensions==1.1.0 + - openai==1.94.0 + - orjson==3.10.18 + - pandas==2.2.3 + - pillow==11.3.0 + - propcache==0.3.2 + - pyarrow==20.0.0 + - pydantic-settings==2.10.1 + - python-dateutil==2.9.0.post0 + - python-dotenv==1.1.1 + - pytz==2025.2 + - pyyaml==6.0.2 + - referencing==0.36.2 + - regex==2024.11.6 + - requests==2.32.4 + - requests-toolbelt==1.0.0 + - rpds-py==0.26.0 + - safetensors==0.5.3 + - scikit-learn==1.7.0 + - scipy==1.16.0 + - sentence-transformers==5.0.0 + - six==1.17.0 + - sqlalchemy==2.0.41 + - tabulate==0.9.0 + - tenacity==9.1.2 + - threadpoolctl==3.6.0 + - tiktoken==0.9.0 + - tokenizers==0.21.2 + - transformers==4.53.1 + - typing-inspect==0.9.0 + - typing-inspection==0.4.1 + - tzdata==2025.2 + - xxhash==3.5.0 + - yarl==1.20.1 +prefix: /u/coderdoge/miniconda3 diff --git a/scripts/run_ablation_experiments.sh b/scripts/run_ablation_experiments.sh new file mode 100755 index 0000000..1266176 --- /dev/null +++ b/scripts/run_ablation_experiments.sh @@ -0,0 +1,116 @@ +#!/bin/bash + +# ───────────────────────────────────────────────────────────────────────────── +# Experiment Configuration +# ───────────────────────────────────────────────────────────────────────────── + +# An array of the API families (subtasks) to run. +API_FAMILIES=( + "huggingface" + "dailylifeapis" +) + +# An array of the model checkpoints to evaluate. +MODELS_TO_RUN=( + "llama_4" + "llama_3_3_70b_instruct" + "deepseek_v2_5" + "qwen2_5_72b_instruct" + "phi" +) + +# Seeds for reproducibility. +SEEDS=(42 101 1234 2024 12345) + +# Baseline MCTS configurations (light, medium, heavy search budget). +BASELINE_CONFIGS=( + "light" + "medium" + "heavy" +) + +# Ablation modes for the main method. +ABLATION_MODES=( + "no_mcts" + "no_sim_feedback" + "no_plan_history" + "uniform_rewards" + "no_validator" +) + +# ───────────────────────────────────────────────────────────────────────────── +# Main Execution Logic +# ───────────────────────────────────────────────────────────────────────────── + +echo "✅ Starting comprehensive Baseline and Ablation experiment batch." +start_time=$(date +%s) + +# Iterate through each API family. +for api_family in "${API_FAMILIES[@]}"; do + echo "==================================================================" + echo "📦 Starting Subtask: $api_family" + echo "==================================================================" + + # Dynamically set the number of problems for the dataset. + if [ "$api_family" == "huggingface" ]; then + NUM_PROBLEMS=500 + elif [ "$api_family" == "dailylifeapis" ]; then + NUM_PROBLEMS=121 + elif [ "$api_family" == "multimedia" ]; then + NUM_PROBLEMS=222 + else + echo "⚠️ Warning: Unknown API family '$api_family'. Defaulting to 50 problems." + NUM_PROBLEMS=50 + fi + echo " (Dataset size: $NUM_PROBLEMS problems)" + + # Loop through each model checkpoint. + for model in "${MODELS_TO_RUN[@]}"; do + echo " 🚀 Processing Model: $model" + echo " ----------------------------------------------------------------" + + # --- GROUP 1: BASELINE MCTS EXPERIMENTS --- + echo " 📊 Processing Baseline MCTS Group" + for config in "${BASELINE_CONFIGS[@]}"; do + echo " - Configuration: $config" + for seed in "${SEEDS[@]}"; do + RUN_NAME="baselines/${api_family}/${model}/${config}/run_seed_${seed}" + echo " - Starting Run (Seed: $seed)..." + python run_taskbench_experiments.py \ + --api_family "$api_family" \ + --model_name "$model" \ + --baseline_mcts_config "$config" \ + --num_problems "$NUM_PROBLEMS" \ + --seed "$seed" \ + --run_name "$RUN_NAME" + echo " - Run complete." + done + done + echo " ✅ Finished Baseline MCTS group for model: $model" + + # --- GROUP 2: ABLATION EXPERIMENTS --- + echo " 🔬 Processing Ablation Group" + for ablation in "${ABLATION_MODES[@]}"; do + echo " - Ablation: $ablation" + for seed in "${SEEDS[@]}"; do + RUN_NAME="ablations/${api_family}/${model}/${ablation}/run_seed_${seed}" + echo " - Starting Run (Seed: $seed)..." + python run_taskbench_experiments.py \ + --api_family "$api_family" \ + --model_name "$model" \ + --ablation_mode "$ablation" \ + --num_problems "$NUM_PROBLEMS" \ + --seed "$seed" \ + --run_name "$RUN_NAME" + echo " - Run complete." + done + done + echo " ✅ Finished Ablation group for model: $model" + echo " ----------------------------------------------------------------" + done +done + +end_time=$(date +%s) +duration=$((end_time - start_time)) + +echo "🎉 All baseline and ablation experiments completed in $(($duration / 3600))h $(($duration % 3600 / 60))m $(($duration % 60))s." \ No newline at end of file diff --git a/scripts/run_ablation_experiments_daily.sh b/scripts/run_ablation_experiments_daily.sh new file mode 100755 index 0000000..6ef26db --- /dev/null +++ b/scripts/run_ablation_experiments_daily.sh @@ -0,0 +1,116 @@ +#!/bin/bash + +# ───────────────────────────────────────────────────────────────────────────── +# Experiment Configuration +# ───────────────────────────────────────────────────────────────────────────── + +# An array of the API families (subtasks) to run. +API_FAMILIES=( + # "huggingface" + "dailylifeapis" +) + +# An array of the model checkpoints to evaluate. +MODELS_TO_RUN=( + "llama_4" + "llama_3_3_70b_instruct" + # "deepseek_v2_5" + # "qwen2_5_72b_instruct" + # "phi" +) + +# Seeds for reproducibility. +SEEDS=(42 101 1234 2024 12345) + +# Baseline MCTS configurations (light, medium, heavy search budget). +BASELINE_CONFIGS=( + "light" + "medium" + "heavy" +) + +# Ablation modes for the main method. +ABLATION_MODES=( + "no_mcts" + "no_sim_feedback" + "no_plan_history" + "uniform_rewards" + "no_validator" +) + +# ───────────────────────────────────────────────────────────────────────────── +# Main Execution Logic +# ───────────────────────────────────────────────────────────────────────────── + +echo "✅ Starting comprehensive Baseline and Ablation experiment batch." +start_time=$(date +%s) + +# Iterate through each API family. +for api_family in "${API_FAMILIES[@]}"; do + echo "==================================================================" + echo "📦 Starting Subtask: $api_family" + echo "==================================================================" + + # Dynamically set the number of problems for the dataset. + if [ "$api_family" == "huggingface" ]; then + NUM_PROBLEMS=500 + elif [ "$api_family" == "dailylifeapis" ]; then + NUM_PROBLEMS=121 + elif [ "$api_family" == "multimedia" ]; then + NUM_PROBLEMS=222 + else + echo "⚠️ Warning: Unknown API family '$api_family'. Defaulting to 50 problems." + NUM_PROBLEMS=50 + fi + echo " (Dataset size: $NUM_PROBLEMS problems)" + + # Loop through each model checkpoint. + for model in "${MODELS_TO_RUN[@]}"; do + echo " 🚀 Processing Model: $model" + echo " ----------------------------------------------------------------" + + # --- GROUP 1: BASELINE MCTS EXPERIMENTS --- + echo " 📊 Processing Baseline MCTS Group" + for config in "${BASELINE_CONFIGS[@]}"; do + echo " - Configuration: $config" + for seed in "${SEEDS[@]}"; do + RUN_NAME="baselines/${api_family}/${model}/${config}/run_seed_${seed}" + echo " - Starting Run (Seed: $seed)..." + python run_taskbench_experiments.py \ + --api_family "$api_family" \ + --model_name "$model" \ + --baseline_mcts_config "$config" \ + --num_problems "$NUM_PROBLEMS" \ + --seed "$seed" \ + --run_name "$RUN_NAME" + echo " - Run complete." + done + done + echo " ✅ Finished Baseline MCTS group for model: $model" + + # --- GROUP 2: ABLATION EXPERIMENTS --- + echo " 🔬 Processing Ablation Group" + for ablation in "${ABLATION_MODES[@]}"; do + echo " - Ablation: $ablation" + for seed in "${SEEDS[@]}"; do + RUN_NAME="ablations/${api_family}/${model}/${ablation}/run_seed_${seed}" + echo " - Starting Run (Seed: $seed)..." + python run_taskbench_experiments.py \ + --api_family "$api_family" \ + --model_name "$model" \ + --ablation_mode "$ablation" \ + --num_problems "$NUM_PROBLEMS" \ + --seed "$seed" \ + --run_name "$RUN_NAME" + echo " - Run complete." + done + done + echo " ✅ Finished Ablation group for model: $model" + echo " ----------------------------------------------------------------" + done +done + +end_time=$(date +%s) +duration=$((end_time - start_time)) + +echo "🎉 All baseline and ablation experiments completed in $(($duration / 3600))h $(($duration % 3600 / 60))m $(($duration % 60))s." \ No newline at end of file diff --git a/scripts/run_all_residual.sh b/scripts/run_all_residual.sh new file mode 100755 index 0000000..3b210e0 --- /dev/null +++ b/scripts/run_all_residual.sh @@ -0,0 +1,180 @@ +#!/bin/bash +set -e +set -o pipefail + +# ============================================================================== +# MASTER SCRIPT FOR RESIDUAL LEARNING EXPERIMENTS (USING PRE-COMPUTED CoT) +# ============================================================================== +# This script uses existing CoT (k=1) results to create a residual dataset, +# then runs ToT, LATS, ReAct, RAFA, the ReAct+RAFA Hybrid, and SPIRAL on the +# problems that CoT failed. +# ============================================================================== + +# ───────────────────────────────────────────────────────────────────────────── +# Global Experiment Configuration +# ───────────────────────────────────────────────────────────────────────────── + +# An array of the API families (subtasks) to run. +API_FAMILIES=( + "huggingface" + "dailylifeapis" +) + +# An array of the model checkpoints to evaluate. +MODELS_TO_RUN=( + "llama_4" + "llama_3_3_70b_instruct" + "deepseek_v2_5" + "qwen2_5_72b_instruct" + "phi" +) + +# An array of specific, common seeds for reproducibility. +SEEDS=(42 101 1234 2024 12345) + +# Path to the pre-computed CoT k=1 results +COT_BACKUP_DIR="predictions/predictions_cot_k1_backup" + +# ───────────────────────────────────────────────────────────────────────────── +# Method-Specific Hyperparameters +# ───────────────────────────────────────────────────────────────────────────── +# Note: CoT params are now only used to find the correct results path. +COT_K=1 +COT_TEMPERATURE=0.7 + +# 2. ToT (Runs on CoT's failures) +TOT_MAX_STEPS=4 +TOT_SEARCH_BREADTH=3 +TOT_CANDIDATES_PER_STATE=2 + +# 3. LATS (Runs on CoT's failures) +LATS_MCTS_ITERATIONS=25 +LATS_EXPLORATION_WEIGHT=1.0 +LATS_CANDIDATES_PER_STATE=2 + +# 4. ReAct (Runs on CoT's failures) +REACT_MAX_STEPS=8 + +# 5. RAFA (Runs on CoT's failures) +RAFA_MAX_REAL_STEPS=4 +RAFA_SEARCH_BREADTH=3 +RAFA_SEARCH_DEPTH=2 + +# 6. ReAct+RAFA Hybrid (Runs on CoT's failures) +HYBRID_MAX_REAL_STEPS=4 +HYBRID_SEARCH_BREADTH=3 +HYBRID_SEARCH_DEPTH=2 + +# 7. SPIRAL (Your Method, runs on CoT's failures) +SPIRAL_MCTS_ITERATIONS=50 +SPIRAL_MAX_DEPTH=8 + +# ============================================================================== +# Main Execution Logic +# ============================================================================== + +echo "✅ Starting comprehensive experiment batch using pre-computed CoT results from '${COT_BACKUP_DIR}'." +main_start_time=$(date +%s) + +for api_family in "${API_FAMILIES[@]}"; do + echo "##################################################################" + echo "📦 API Family: $api_family" + + for model in "${MODELS_TO_RUN[@]}"; do + echo "==================================================================" + echo "🚀 Model: $model" + + for seed in "${SEEDS[@]}"; do + echo " ----------------------------------------------------------------" + echo " 🌱 Starting run for Seed: $seed" + + # --- STEP 1: Locate Pre-computed CoT results and Create Residual Dataset --- + cot_extra_args="--consistency_level ${COT_K} --temperature ${COT_TEMPERATURE}" + cot_run_name_suffix=$(echo "$cot_extra_args" | tr -d '[:space:]' | tr -c '[:alnum:]' '_') + cot_results_path="${COT_BACKUP_DIR}/${api_family}_experiments/${model}/run${cot_run_name_suffix}_seed_${seed}/results.json" + + residual_api_family="${api_family}_residual" + residual_data_dir="Taskbench/data_${residual_api_family}" + residual_dataset_file="${residual_data_dir}/user_requests.jsonl" + + echo " [1/7] Creating residual dataset from CoT failures..." + if [ ! -f "$cot_results_path" ]; then + echo " ⚠️ Pre-computed CoT results not found at '$cot_results_path'. Skipping this run." + continue + fi + + python create_residual_dataset.py --api_family "$api_family" --results_path "$cot_results_path" + + if [ ! -f "$residual_dataset_file" ] || [ ! -s "$residual_dataset_file" ]; then + echo " ✅ CoT solved all problems in the pre-computed run. No residual experiments needed." + rm -rf "$residual_data_dir" + continue + fi + + residual_problems=$(wc -l < "$residual_dataset_file") + echo " -> CoT failed on ${residual_problems} problems. Continuing with advanced methods." + + # --- STEP 2: Run ToT on the RESIDUAL dataset --- + tot_run_name="predictions_tot_residual/${model}/${api_family}_seed${seed}" + echo " [2/7] Running Tree of Thoughts (ToT) on residual dataset..." + python taskbench_tot_baseline.py \ + --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + --run_name "$tot_run_name" --max_steps ${TOT_MAX_STEPS} \ + --search_breadth ${TOT_SEARCH_BREADTH} --candidates_per_state ${TOT_CANDIDATES_PER_STATE} + + # --- STEP 3: Run LATS on the RESIDUAL dataset --- + lats_run_name="predictions_lats_residual/${model}/${api_family}_seed${seed}" + echo " [3/7] Running Language Agent Tree Search (LATS) on residual dataset..." + python taskbench_lats_baseline.py \ + --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + --run_name "$lats_run_name" --mcts_iterations ${LATS_MCTS_ITERATIONS} \ + --exploration_weight ${LATS_EXPLORATION_WEIGHT} --candidates_per_state ${LATS_CANDIDATES_PER_STATE} + + # --- STEP 4: Run ReAct on the RESIDUAL dataset --- + react_run_name="predictions_react_residual/${model}/${api_family}_seed${seed}" + echo " [4/7] Running ReAct on residual dataset..." + python taskbench_react_baseline.py \ + --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + --run_name "$react_run_name" --max_steps ${REACT_MAX_STEPS} + + # --- STEP 5: Run RAFA on the RESIDUAL dataset --- + rafa_run_name="predictions_rafa_residual/${model}/${api_family}_seed${seed}" + echo " [5/7] Running RAFA on residual dataset..." + python taskbench_rafa_baseline.py \ + --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + --run_name "$rafa_run_name" --max_real_steps ${RAFA_MAX_REAL_STEPS} \ + --search_breadth ${RAFA_SEARCH_BREADTH} --search_depth ${RAFA_SEARCH_DEPTH} + + # --- STEP 6: Run ReAct+RAFA Hybrid on the RESIDUAL dataset --- + hybrid_run_name="predictions_react_rafa_residual/${model}/${api_family}_seed${seed}" + echo " [6/7] Running ReAct+RAFA Hybrid on residual dataset..." + python taskbench_react_rafa_baseline.py \ + --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + --run_name "$hybrid_run_name" --max_real_steps ${HYBRID_MAX_REAL_STEPS} \ + --search_breadth ${HYBRID_SEARCH_BREADTH} --search_depth ${HYBRID_SEARCH_DEPTH} + + # --- STEP 7: Run SPIRAL (Your Method) on the RESIDUAL dataset --- + spiral_run_name="predictions_spiral_residual/${model}/${api_family}_seed${seed}" + echo " [7/7] Running SPIRAL on residual dataset..." + python taskbench_spiral_method_final.py \ + --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + --run_name "$spiral_run_name" --mcts_iterations ${SPIRAL_MCTS_ITERATIONS} --max_depth ${SPIRAL_MAX_DEPTH} + + # --- Clean up this run's residual data --- + rm -rf "$residual_data_dir" + echo " -> Cleaned up residual data for seed $seed." + echo " ----------------------------------------------------------------" + done # seed loop + done # model loop +done # api_family loop + +# --- Final Summary --- +main_end_time=$(date +%s) +duration=$((main_end_time - main_start_time)) + +echo "" +echo "##################################################################" +echo "🎉 ALL RESIDUAL EXPERIMENTS COMPLETED!" +echo "Total execution time: $(($duration / 3600))h $(($duration % 3600 / 60))m $(($duration % 60))s." +echo "Check the 'predictions_*' directories for results." +echo "##################################################################" \ No newline at end of file diff --git a/scripts/run_all_residual_react_rafa.sh b/scripts/run_all_residual_react_rafa.sh new file mode 100755 index 0000000..1d34d66 --- /dev/null +++ b/scripts/run_all_residual_react_rafa.sh @@ -0,0 +1,180 @@ +#!/bin/bash +set -e +set -o pipefail + +# ============================================================================== +# MASTER SCRIPT FOR RESIDUAL LEARNING EXPERIMENTS (USING PRE-COMPUTED CoT) +# ============================================================================== +# This script uses existing CoT (k=1) results to create a residual dataset, +# then runs ToT, LATS, ReAct, RAFA, the ReAct+RAFA Hybrid, and SPIRAL on the +# problems that CoT failed. +# ============================================================================== + +# ───────────────────────────────────────────────────────────────────────────── +# Global Experiment Configuration +# ───────────────────────────────────────────────────────────────────────────── + +# An array of the API families (subtasks) to run. +API_FAMILIES=( + "huggingface" + "dailylifeapis" +) + +# An array of the model checkpoints to evaluate. +MODELS_TO_RUN=( + "llama_4" + "llama_3_3_70b_instruct" + "deepseek_v2_5" + "qwen2_5_72b_instruct" + "phi" +) + +# An array of specific, common seeds for reproducibility. +SEEDS=(42 101 1234 2024 12345) + +# Path to the pre-computed CoT k=1 results +COT_BACKUP_DIR="predictions/predictions_cot_k1_backup" + +# ───────────────────────────────────────────────────────────────────────────── +# Method-Specific Hyperparameters +# ───────────────────────────────────────────────────────────────────────────── +# Note: CoT params are now only used to find the correct results path. +COT_K=1 +COT_TEMPERATURE=0.7 + +# 2. ToT (Runs on CoT's failures) +TOT_MAX_STEPS=4 +TOT_SEARCH_BREADTH=3 +TOT_CANDIDATES_PER_STATE=2 + +# 3. LATS (Runs on CoT's failures) +LATS_MCTS_ITERATIONS=25 +LATS_EXPLORATION_WEIGHT=1.0 +LATS_CANDIDATES_PER_STATE=2 + +# 4. ReAct (Runs on CoT's failures) +REACT_MAX_STEPS=8 + +# 5. RAFA (Runs on CoT's failures) +RAFA_MAX_REAL_STEPS=4 +RAFA_SEARCH_BREADTH=3 +RAFA_SEARCH_DEPTH=2 + +# 6. ReAct+RAFA Hybrid (Runs on CoT's failures) +HYBRID_MAX_REAL_STEPS=4 +HYBRID_SEARCH_BREADTH=3 +HYBRID_SEARCH_DEPTH=2 + +# 7. SPIRAL (Your Method, runs on CoT's failures) +SPIRAL_MCTS_ITERATIONS=50 +SPIRAL_MAX_DEPTH=8 + +# ============================================================================== +# Main Execution Logic +# ============================================================================== + +echo "✅ Starting comprehensive experiment batch using pre-computed CoT results from '${COT_BACKUP_DIR}'." +main_start_time=$(date +%s) + +for api_family in "${API_FAMILIES[@]}"; do + echo "##################################################################" + echo "📦 API Family: $api_family" + + for model in "${MODELS_TO_RUN[@]}"; do + echo "==================================================================" + echo "🚀 Model: $model" + + for seed in "${SEEDS[@]}"; do + echo " ----------------------------------------------------------------" + echo " 🌱 Starting run for Seed: $seed" + + # --- STEP 1: Locate Pre-computed CoT results and Create Residual Dataset --- + cot_extra_args="--consistency_level ${COT_K} --temperature ${COT_TEMPERATURE}" + cot_run_name_suffix=$(echo "$cot_extra_args" | tr -d '[:space:]' | tr -c '[:alnum:]' '_') + cot_results_path="${COT_BACKUP_DIR}/${api_family}_experiments/${model}/run${cot_run_name_suffix}_seed_${seed}/results.json" + + residual_api_family="${api_family}_residual" + residual_data_dir="Taskbench/data_${residual_api_family}" + residual_dataset_file="${residual_data_dir}/user_requests.jsonl" + + echo " [1/7] Creating residual dataset from CoT failures..." + if [ ! -f "$cot_results_path" ]; then + echo " ⚠️ Pre-computed CoT results not found at '$cot_results_path'. Skipping this run." + continue + fi + + python create_residual_dataset.py --api_family "$api_family" --results_path "$cot_results_path" + + if [ ! -f "$residual_dataset_file" ] || [ ! -s "$residual_dataset_file" ]; then + echo " ✅ CoT solved all problems in the pre-computed run. No residual experiments needed." + rm -rf "$residual_data_dir" + continue + fi + + residual_problems=$(wc -l < "$residual_dataset_file") + echo " -> CoT failed on ${residual_problems} problems. Continuing with advanced methods." + + # # --- STEP 2: Run ToT on the RESIDUAL dataset --- + # tot_run_name="predictions_tot_residual/${model}/${api_family}_seed${seed}" + # echo " [2/7] Running Tree of Thoughts (ToT) on residual dataset..." + # python taskbench_tot_baseline.py \ + # --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + # --run_name "$tot_run_name" --max_steps ${TOT_MAX_STEPS} \ + # --search_breadth ${TOT_SEARCH_BREADTH} --candidates_per_state ${TOT_CANDIDATES_PER_STATE} + + # # --- STEP 3: Run LATS on the RESIDUAL dataset --- + # lats_run_name="predictions_lats_residual/${model}/${api_family}_seed${seed}" + # echo " [3/7] Running Language Agent Tree Search (LATS) on residual dataset..." + # python taskbench_lats_baseline.py \ + # --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + # --run_name "$lats_run_name" --mcts_iterations ${LATS_MCTS_ITERATIONS} \ + # --exploration_weight ${LATS_EXPLORATION_WEIGHT} --candidates_per_state ${LATS_CANDIDATES_PER_STATE} + + # --- STEP 4: Run ReAct on the RESIDUAL dataset --- + react_run_name="predictions_react_residual/${model}/${api_family}_seed${seed}" + echo " [4/7] Running ReAct on residual dataset..." + python taskbench_react_baseline.py \ + --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + --run_name "$react_run_name" --max_steps ${REACT_MAX_STEPS} + + # --- STEP 5: Run RAFA on the RESIDUAL dataset --- + rafa_run_name="predictions_rafa_residual/${model}/${api_family}_seed${seed}" + echo " [5/7] Running RAFA on residual dataset..." + python taskbench_rafa_baseline.py \ + --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + --run_name "$rafa_run_name" --max_real_steps ${RAFA_MAX_REAL_STEPS} \ + --search_breadth ${RAFA_SEARCH_BREADTH} --search_depth ${RAFA_SEARCH_DEPTH} + + # --- STEP 6: Run ReAct+RAFA Hybrid on the RESIDUAL dataset --- + hybrid_run_name="predictions_react_rafa_residual/${model}/${api_family}_seed${seed}" + echo " [6/7] Running ReAct+RAFA Hybrid on residual dataset..." + python taskbench_react_rafa_baseline.py \ + --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + --run_name "$hybrid_run_name" --max_real_steps ${HYBRID_MAX_REAL_STEPS} \ + --search_breadth ${HYBRID_SEARCH_BREADTH} --search_depth ${HYBRID_SEARCH_DEPTH} + + # # --- STEP 7: Run SPIRAL (Your Method) on the RESIDUAL dataset --- + # spiral_run_name="predictions_spiral_residual/${model}/${api_family}_seed${seed}" + # echo " [7/7] Running SPIRAL on residual dataset..." + # python taskbench_spiral_method_v4_0725.py \ + # --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + # --run_name "$spiral_run_name" --mcts_iterations ${SPIRAL_MCTS_ITERATIONS} --max_depth ${SPIRAL_MAX_DEPTH} + + # --- Clean up this run's residual data --- + rm -rf "$residual_data_dir" + echo " -> Cleaned up residual data for seed $seed." + echo " ----------------------------------------------------------------" + done # seed loop + done # model loop +done # api_family loop + +# --- Final Summary --- +main_end_time=$(date +%s) +duration=$((main_end_time - main_start_time)) + +echo "" +echo "##################################################################" +echo "🎉 ALL RESIDUAL EXPERIMENTS COMPLETED!" +echo "Total execution time: $(($duration / 3600))h $(($duration % 3600 / 60))m $(($duration % 60))s." +echo "Check the 'predictions_*' directories for results." +echo "##################################################################" \ No newline at end of file diff --git a/scripts/run_all_residual_spiral.sh b/scripts/run_all_residual_spiral.sh new file mode 100755 index 0000000..1182009 --- /dev/null +++ b/scripts/run_all_residual_spiral.sh @@ -0,0 +1,180 @@ +#!/bin/bash +set -e +set -o pipefail + +# ============================================================================== +# MASTER SCRIPT FOR RESIDUAL LEARNING EXPERIMENTS (USING PRE-COMPUTED CoT) +# ============================================================================== +# This script uses existing CoT (k=1) results to create a residual dataset, +# then runs ToT, LATS, ReAct, RAFA, the ReAct+RAFA Hybrid, and SPIRAL on the +# problems that CoT failed. +# ============================================================================== + +# ───────────────────────────────────────────────────────────────────────────── +# Global Experiment Configuration +# ───────────────────────────────────────────────────────────────────────────── + +# An array of the API families (subtasks) to run. +API_FAMILIES=( + "huggingface" + "dailylifeapis" +) + +# An array of the model checkpoints to evaluate. +MODELS_TO_RUN=( + "llama_4" + "llama_3_3_70b_instruct" + "deepseek_v2_5" + "qwen2_5_72b_instruct" + "phi" +) + +# An array of specific, common seeds for reproducibility. +SEEDS=(42 101 1234 2024 12345) + +# Path to the pre-computed CoT k=1 results +COT_BACKUP_DIR="predictions/predictions_cot_k1_backup" + +# ───────────────────────────────────────────────────────────────────────────── +# Method-Specific Hyperparameters +# ───────────────────────────────────────────────────────────────────────────── +# Note: CoT params are now only used to find the correct results path. +COT_K=1 +COT_TEMPERATURE=0.7 + +# 2. ToT (Runs on CoT's failures) +TOT_MAX_STEPS=4 +TOT_SEARCH_BREADTH=3 +TOT_CANDIDATES_PER_STATE=2 + +# 3. LATS (Runs on CoT's failures) +LATS_MCTS_ITERATIONS=25 +LATS_EXPLORATION_WEIGHT=1.0 +LATS_CANDIDATES_PER_STATE=2 + +# 4. ReAct (Runs on CoT's failures) +REACT_MAX_STEPS=8 + +# 5. RAFA (Runs on CoT's failures) +RAFA_MAX_REAL_STEPS=4 +RAFA_SEARCH_BREADTH=3 +RAFA_SEARCH_DEPTH=2 + +# 6. ReAct+RAFA Hybrid (Runs on CoT's failures) +HYBRID_MAX_REAL_STEPS=4 +HYBRID_SEARCH_BREADTH=3 +HYBRID_SEARCH_DEPTH=2 + +# 7. SPIRAL (Your Method, runs on CoT's failures) +SPIRAL_MCTS_ITERATIONS=50 +SPIRAL_MAX_DEPTH=8 + +# ============================================================================== +# Main Execution Logic +# ============================================================================== + +echo "✅ Starting comprehensive experiment batch using pre-computed CoT results from '${COT_BACKUP_DIR}'." +main_start_time=$(date +%s) + +for api_family in "${API_FAMILIES[@]}"; do + echo "##################################################################" + echo "📦 API Family: $api_family" + + for model in "${MODELS_TO_RUN[@]}"; do + echo "==================================================================" + echo "🚀 Model: $model" + + for seed in "${SEEDS[@]}"; do + echo " ----------------------------------------------------------------" + echo " 🌱 Starting run for Seed: $seed" + + # --- STEP 1: Locate Pre-computed CoT results and Create Residual Dataset --- + cot_extra_args="--consistency_level ${COT_K} --temperature ${COT_TEMPERATURE}" + cot_run_name_suffix=$(echo "$cot_extra_args" | tr -d '[:space:]' | tr -c '[:alnum:]' '_') + cot_results_path="${COT_BACKUP_DIR}/${api_family}_experiments/${model}/run${cot_run_name_suffix}_seed_${seed}/results.json" + + residual_api_family="${api_family}_residual" + residual_data_dir="Taskbench/data_${residual_api_family}" + residual_dataset_file="${residual_data_dir}/user_requests.jsonl" + + echo " [1/7] Creating residual dataset from CoT failures..." + if [ ! -f "$cot_results_path" ]; then + echo " ⚠️ Pre-computed CoT results not found at '$cot_results_path'. Skipping this run." + continue + fi + + python create_residual_dataset.py --api_family "$api_family" --results_path "$cot_results_path" + + if [ ! -f "$residual_dataset_file" ] || [ ! -s "$residual_dataset_file" ]; then + echo " ✅ CoT solved all problems in the pre-computed run. No residual experiments needed." + rm -rf "$residual_data_dir" + continue + fi + + residual_problems=$(wc -l < "$residual_dataset_file") + echo " -> CoT failed on ${residual_problems} problems. Continuing with advanced methods." + + # # --- STEP 2: Run ToT on the RESIDUAL dataset --- + # tot_run_name="predictions_tot_residual/${model}/${api_family}_seed${seed}" + # echo " [2/7] Running Tree of Thoughts (ToT) on residual dataset..." + # python taskbench_tot_baseline.py \ + # --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + # --run_name "$tot_run_name" --max_steps ${TOT_MAX_STEPS} \ + # --search_breadth ${TOT_SEARCH_BREADTH} --candidates_per_state ${TOT_CANDIDATES_PER_STATE} + + # # --- STEP 3: Run LATS on the RESIDUAL dataset --- + # lats_run_name="predictions_lats_residual/${model}/${api_family}_seed${seed}" + # echo " [3/7] Running Language Agent Tree Search (LATS) on residual dataset..." + # python taskbench_lats_baseline.py \ + # --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + # --run_name "$lats_run_name" --mcts_iterations ${LATS_MCTS_ITERATIONS} \ + # --exploration_weight ${LATS_EXPLORATION_WEIGHT} --candidates_per_state ${LATS_CANDIDATES_PER_STATE} + + # # --- STEP 4: Run ReAct on the RESIDUAL dataset --- + # react_run_name="predictions_react_residual/${model}/${api_family}_seed${seed}" + # echo " [4/7] Running ReAct on residual dataset..." + # python taskbench_react_baseline.py \ + # --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + # --run_name "$react_run_name" --max_steps ${REACT_MAX_STEPS} + + # # --- STEP 5: Run RAFA on the RESIDUAL dataset --- + # rafa_run_name="predictions_rafa_residual/${model}/${api_family}_seed${seed}" + # echo " [5/7] Running RAFA on residual dataset..." + # python taskbench_rafa_baseline.py \ + # --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + # --run_name "$rafa_run_name" --max_real_steps ${RAFA_MAX_REAL_STEPS} \ + # --search_breadth ${RAFA_SEARCH_BREADTH} --search_depth ${RAFA_SEARCH_DEPTH} + + # # --- STEP 6: Run ReAct+RAFA Hybrid on the RESIDUAL dataset --- + # hybrid_run_name="predictions_react_rafa_residual/${model}/${api_family}_seed${seed}" + # echo " [6/7] Running ReAct+RAFA Hybrid on residual dataset..." + # python taskbench_react_rafa_baseline.py \ + # --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + # --run_name "$hybrid_run_name" --max_real_steps ${HYBRID_MAX_REAL_STEPS} \ + # --search_breadth ${HYBRID_SEARCH_BREADTH} --search_depth ${HYBRID_SEARCH_DEPTH} + + # --- STEP 7: Run SPIRAL (Your Method) on the RESIDUAL dataset --- + spiral_run_name="predictions_spiral_residual/${model}/${api_family}_seed${seed}" + echo " [7/7] Running SPIRAL on residual dataset..." + python taskbench_spiral_method_final.py \ + --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + --run_name "$spiral_run_name" --mcts_iterations ${SPIRAL_MCTS_ITERATIONS} --max_depth ${SPIRAL_MAX_DEPTH} + + # --- Clean up this run's residual data --- + rm -rf "$residual_data_dir" + echo " -> Cleaned up residual data for seed $seed." + echo " ----------------------------------------------------------------" + done # seed loop + done # model loop +done # api_family loop + +# --- Final Summary --- +main_end_time=$(date +%s) +duration=$((main_end_time - main_start_time)) + +echo "" +echo "##################################################################" +echo "🎉 ALL RESIDUAL EXPERIMENTS COMPLETED!" +echo "Total execution time: $(($duration / 3600))h $(($duration % 3600 / 60))m $(($duration % 60))s." +echo "Check the 'predictions_*' directories for results." +echo "##################################################################" \ No newline at end of file diff --git a/scripts/run_all_residual_tot_lats.sh b/scripts/run_all_residual_tot_lats.sh new file mode 100755 index 0000000..4acccea --- /dev/null +++ b/scripts/run_all_residual_tot_lats.sh @@ -0,0 +1,180 @@ +#!/bin/bash +set -e +set -o pipefail + +# ============================================================================== +# MASTER SCRIPT FOR RESIDUAL LEARNING EXPERIMENTS (USING PRE-COMPUTED CoT) +# ============================================================================== +# This script uses existing CoT (k=1) results to create a residual dataset, +# then runs ToT, LATS, ReAct, RAFA, the ReAct+RAFA Hybrid, and SPIRAL on the +# problems that CoT failed. +# ============================================================================== + +# ───────────────────────────────────────────────────────────────────────────── +# Global Experiment Configuration +# ───────────────────────────────────────────────────────────────────────────── + +# An array of the API families (subtasks) to run. +API_FAMILIES=( + "huggingface" + "dailylifeapis" +) + +# An array of the model checkpoints to evaluate. +MODELS_TO_RUN=( + # "llama_4" + # "llama_3_3_70b_instruct" + "deepseek_v2_5" + "qwen2_5_72b_instruct" + "phi" +) + +# An array of specific, common seeds for reproducibility. +SEEDS=(42 101 1234 2024 12345) + +# Path to the pre-computed CoT k=1 results +COT_BACKUP_DIR="predictions/predictions_cot_k1_backup" + +# ───────────────────────────────────────────────────────────────────────────── +# Method-Specific Hyperparameters +# ───────────────────────────────────────────────────────────────────────────── +# Note: CoT params are now only used to find the correct results path. +COT_K=1 +COT_TEMPERATURE=0.7 + +# 2. ToT (Runs on CoT's failures) +TOT_MAX_STEPS=4 +TOT_SEARCH_BREADTH=3 +TOT_CANDIDATES_PER_STATE=2 + +# 3. LATS (Runs on CoT's failures) +LATS_MCTS_ITERATIONS=25 +LATS_EXPLORATION_WEIGHT=1.0 +LATS_CANDIDATES_PER_STATE=2 + +# 4. ReAct (Runs on CoT's failures) +REACT_MAX_STEPS=8 + +# 5. RAFA (Runs on CoT's failures) +RAFA_MAX_REAL_STEPS=4 +RAFA_SEARCH_BREADTH=3 +RAFA_SEARCH_DEPTH=2 + +# 6. ReAct+RAFA Hybrid (Runs on CoT's failures) +HYBRID_MAX_REAL_STEPS=4 +HYBRID_SEARCH_BREADTH=3 +HYBRID_SEARCH_DEPTH=2 + +# 7. SPIRAL (Your Method, runs on CoT's failures) +SPIRAL_MCTS_ITERATIONS=50 +SPIRAL_MAX_DEPTH=8 + +# ============================================================================== +# Main Execution Logic +# ============================================================================== + +echo "✅ Starting comprehensive experiment batch using pre-computed CoT results from '${COT_BACKUP_DIR}'." +main_start_time=$(date +%s) + +for api_family in "${API_FAMILIES[@]}"; do + echo "##################################################################" + echo "📦 API Family: $api_family" + + for model in "${MODELS_TO_RUN[@]}"; do + echo "==================================================================" + echo "🚀 Model: $model" + + for seed in "${SEEDS[@]}"; do + echo " ----------------------------------------------------------------" + echo " 🌱 Starting run for Seed: $seed" + + # --- STEP 1: Locate Pre-computed CoT results and Create Residual Dataset --- + cot_extra_args="--consistency_level ${COT_K} --temperature ${COT_TEMPERATURE}" + cot_run_name_suffix=$(echo "$cot_extra_args" | tr -d '[:space:]' | tr -c '[:alnum:]' '_') + cot_results_path="${COT_BACKUP_DIR}/${api_family}_experiments/${model}/run${cot_run_name_suffix}_seed_${seed}/results.json" + + residual_api_family="${api_family}_residual" + residual_data_dir="Taskbench/data_${residual_api_family}" + residual_dataset_file="${residual_data_dir}/user_requests.jsonl" + + echo " [1/7] Creating residual dataset from CoT failures..." + if [ ! -f "$cot_results_path" ]; then + echo " ⚠️ Pre-computed CoT results not found at '$cot_results_path'. Skipping this run." + continue + fi + + python create_residual_dataset.py --api_family "$api_family" --results_path "$cot_results_path" + + if [ ! -f "$residual_dataset_file" ] || [ ! -s "$residual_dataset_file" ]; then + echo " ✅ CoT solved all problems in the pre-computed run. No residual experiments needed." + rm -rf "$residual_data_dir" + continue + fi + + residual_problems=$(wc -l < "$residual_dataset_file") + echo " -> CoT failed on ${residual_problems} problems. Continuing with advanced methods." + + # --- STEP 2: Run ToT on the RESIDUAL dataset --- + tot_run_name="predictions_tot_residual/${model}/${api_family}_seed${seed}" + echo " [2/7] Running Tree of Thoughts (ToT) on residual dataset..." + python taskbench_tot_baseline.py \ + --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + --run_name "$tot_run_name" --max_steps ${TOT_MAX_STEPS} \ + --search_breadth ${TOT_SEARCH_BREADTH} --candidates_per_state ${TOT_CANDIDATES_PER_STATE} + + # --- STEP 3: Run LATS on the RESIDUAL dataset --- + lats_run_name="predictions_lats_residual/${model}/${api_family}_seed${seed}" + echo " [3/7] Running Language Agent Tree Search (LATS) on residual dataset..." + python taskbench_lats_baseline.py \ + --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + --run_name "$lats_run_name" --mcts_iterations ${LATS_MCTS_ITERATIONS} \ + --exploration_weight ${LATS_EXPLORATION_WEIGHT} --candidates_per_state ${LATS_CANDIDATES_PER_STATE} + + # # --- STEP 4: Run ReAct on the RESIDUAL dataset --- + # react_run_name="predictions_react_residual/${model}/${api_family}_seed${seed}" + # echo " [4/7] Running ReAct on residual dataset..." + # python taskbench_react_baseline.py \ + # --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + # --run_name "$react_run_name" --max_steps ${REACT_MAX_STEPS} + + # # --- STEP 5: Run RAFA on the RESIDUAL dataset --- + # rafa_run_name="predictions_rafa_residual/${model}/${api_family}_seed${seed}" + # echo " [5/7] Running RAFA on residual dataset..." + # python taskbench_rafa_baseline.py \ + # --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + # --run_name "$rafa_run_name" --max_real_steps ${RAFA_MAX_REAL_STEPS} \ + # --search_breadth ${RAFA_SEARCH_BREADTH} --search_depth ${RAFA_SEARCH_DEPTH} + + # # --- STEP 6: Run ReAct+RAFA Hybrid on the RESIDUAL dataset --- + # hybrid_run_name="predictions_react_rafa_residual/${model}/${api_family}_seed${seed}" + # echo " [6/7] Running ReAct+RAFA Hybrid on residual dataset..." + # python taskbench_react_rafa_baseline.py \ + # --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + # --run_name "$hybrid_run_name" --max_real_steps ${HYBRID_MAX_REAL_STEPS} \ + # --search_breadth ${HYBRID_SEARCH_BREADTH} --search_depth ${HYBRID_SEARCH_DEPTH} + + # # --- STEP 7: Run SPIRAL (Your Method) on the RESIDUAL dataset --- + # spiral_run_name="predictions_spiral_residual/${model}/${api_family}_seed${seed}" + # echo " [7/7] Running SPIRAL on residual dataset..." + # python taskbench_spiral_method_v4_0725.py \ + # --api_family "$residual_api_family" --model_name "$model" --num_problems "$residual_problems" --seed "$seed" \ + # --run_name "$spiral_run_name" --mcts_iterations ${SPIRAL_MCTS_ITERATIONS} --max_depth ${SPIRAL_MAX_DEPTH} + + # --- Clean up this run's residual data --- + rm -rf "$residual_data_dir" + echo " -> Cleaned up residual data for seed $seed." + echo " ----------------------------------------------------------------" + done # seed loop + done # model loop +done # api_family loop + +# --- Final Summary --- +main_end_time=$(date +%s) +duration=$((main_end_time - main_start_time)) + +echo "" +echo "##################################################################" +echo "🎉 ALL RESIDUAL EXPERIMENTS COMPLETED!" +echo "Total execution time: $(($duration / 3600))h $(($duration % 3600 / 60))m $(($duration % 60))s." +echo "Check the 'predictions_*' directories for results." +echo "##################################################################" \ No newline at end of file diff --git a/scripts/run_experiments_final_0726.sh b/scripts/run_experiments_final_0726.sh new file mode 100755 index 0000000..115544b --- /dev/null +++ b/scripts/run_experiments_final_0726.sh @@ -0,0 +1,95 @@ +#!/bin/bash + +# ───────────────────────────────────────────────────────────────────────────── +# Experiment Configuration +# ───────────────────────────────────────────────────────────────────────────── + +# An array of the API families (subtasks) to run. +API_FAMILIES=( + # + "dailylifeapis" + "huggingface" + # "multimedia" +) + +# An array of the model checkpoints to evaluate. +MODELS_TO_RUN=( + # "llama_4" + # "llama_3" + # "llama_4_scout_17b_16e_instruct" + # "deepseek_v3_h200" + # "qwen3_8b" # Not working + # + "llama_3_3_70b_instruct" + "qwen2_5_72b_instruct" + "phi" + "deepseek_v2_5" +) + +# An array of specific, common seeds for reproducibility. +SEEDS=(42 101 1234 2024 12345) + + +# ───────────────────────────────────────────────────────────────────────────── +# Main Execution Logic +# ───────────────────────────────────────────────────────────────────────────── + +echo "✅ Starting comprehensive experiment batch for all API families." +start_time=$(date +%s) + +# NEW: Outermost loop to iterate through each API family. +for api_family in "${API_FAMILIES[@]}"; do + echo "==================================================================" + echo "📦 Starting Subtask: $api_family" + echo "==================================================================" + + # Dynamically set the number of problems for the full dataset of each subtask. + if [ "$api_family" == "huggingface" ]; then + NUM_PROBLEMS=500 + elif [ "$api_family" == "dailylifeapis" ]; then + NUM_PROBLEMS=121 + elif [ "$api_family" == "multimedia" ]; then + NUM_PROBLEMS=222 + else + echo "⚠️ Warning: Unknown API family '$api_family'. Defaulting to 50 problems." + NUM_PROBLEMS=50 + fi + echo " (Full dataset size: $NUM_PROBLEMS problems)" + + # Define the main output directory for this subtask. + MAIN_OUTPUT_DIR="predictions/${api_family}_experiments" + mkdir -p "$MAIN_OUTPUT_DIR" + + # Loop through each model checkpoint. + for model in "${MODELS_TO_RUN[@]}"; do + echo " 🚀 Processing Model: $model" + MODEL_DIR="${MAIN_OUTPUT_DIR}/${model}" + mkdir -p "$MODEL_DIR" + + # Loop through the predefined array of seeds. + for seed in "${SEEDS[@]}"; do + RUN_NAME="${api_family}_experiments/${model}/run_seed_${seed}" + + echo " - Starting Run (Seed: $seed)..." + + # Execute the Python script with all the specified arguments. + python taskbench_smriv_mcts_revised_final.py \ + --api_family "$api_family" \ + --model_name "$model" \ + --num_problems "$NUM_PROBLEMS" \ + --seed "$seed" \ + --run_name "$RUN_NAME" \ + --max_workers 16 \ + --debug_llm_output + + echo " - Run with seed $seed complete." + done + echo " ✅ Finished all runs for model: $model" + echo " ----------------------------------------------------------------" + done +done + +end_time=$(date +%s) +duration=$((end_time - start_time)) + +echo "🎉 All experiments for all subtasks completed in $(($duration / 3600))h $(($duration % 3600 / 60))m $(($duration % 60))s." diff --git a/scripts/run_taskbench_experiments.py b/scripts/run_taskbench_experiments.py new file mode 100644 index 0000000..1401341 --- /dev/null +++ b/scripts/run_taskbench_experiments.py @@ -0,0 +1,338 @@ +#!/usr/bin/env python3 +# run_taskbench_experiments.py + +import os +import sys +import json +import time +import math +import shutil +import tempfile +import argparse +import subprocess +from pathlib import Path +from typing import List, Optional, Dict, Any, Set, Tuple +from dataclasses import dataclass, field +from concurrent.futures import ProcessPoolExecutor, as_completed +from multiprocessing import Manager +import collections +import ast +import re +from datetime import datetime + +import random + +from SPIRAL.scripts.utils.ritz_client import MODELMAP, MODEL_ID_MAP + +import numpy as np +import torch +from sentence_transformers import SentenceTransformer, util + +from datasets import load_dataset +from tqdm import tqdm +from SPIRAL.scripts.utils.ritz_client import RitsChatClient, MODELMAP, MODEL_ID_MAP + +# --- Unchanged Helper functions and constants from original script --- +def make_value_hashable(value: Any) -> Any: + if isinstance(value, dict): return frozenset((k, make_value_hashable(v)) for k, v in value.items()) + if isinstance(value, list): return tuple(make_value_hashable(v) for v in value) + return value +CORRECTED_TOOL_PARAMETERS = { "Token Classification": {"text": "string"}, "Translation": {"text": "string", "source_lang": "string", "target_lang": "string"}, "Summarization": {"text": "string"}, "Question Answering": {"context": "string", "question": "string"}, "Conversational": {"prompt": "string", "history": "list"}, "Text Generation": {"prompt": "string"}, "Sentence Similarity": {"sentence1": "string", "sentence2": "string"}, "Tabular Classification": {"table_image_path": "string"}, "Object Detection": {"image_path": "string"}, "Image Classification": {"image_path": "string"}, "Image-to-Image": {"image_path": "string", "target_image_path": "string"}, "Image-to-Text": {"image_path": "string"}, "Text-to-Image": {"prompt": "string"}, "Text-to-Video": {"prompt": "string"}, "Visual Question Answering": {"image_path": "string", "question": "string"}, "Document Question Answering": {"document_image_path": "string", "question": "string"}, "Image Segmentation": {"image_path": "string"}, "Depth Estimation": {"image_path": "string"}, "Text-to-Speech": {"text": "string"}, "Automatic Speech Recognition": {"audio_path": "string"}, "Audio-to-Audio": {"audio_path": "string"}, "Audio Classification": {"audio_path": "string"}, "Image Editing": {"image_path": "string", "edits": "dict"}, "get_weather": {"location": "string", "date": "string"}, "get_news_for_topic": {"topic": "string"}, "stock_operation": {"stock": "string", "operation": "string"}, "book_flight": {"date": "string", "from": "string", "to": "string"}, "book_hotel": {"date": "string", "name": "string"}, "book_restaurant": {"date": "string", "name": "string"}, "book_car": {"date": "string", "location": "string"}, "online_shopping": {"website": "string", "product": "string"}, "send_email": {"email_address": "string", "content": "string"}, "send_sms": {"phone_number": "string", "content": "string"}, "share_by_social_network": {"content": "string", "social_network": "string"}, "search_by_engine": {"query": "string", "engine": "string"}, "apply_for_job": {"job": "string"}, "see_doctor_online": {"disease": "string", "doctor": "string"}, "consult_lawyer_online": {"issue": "string", "lawyer": "string"}, "enroll_in_course": {"course": "string", "university": "string"}, "buy_insurance": {"insurance": "string", "company": "string"}, "online_banking": {"instruction": "string", "bank": "string"}, "daily_bill_payment": {"bill": "string"}, "sell_item_online": {"item": "string", "store": "string"}, "do_tax_return": {"year": "string"}, "apply_for_passport": {"country": "string"}, "pay_for_credit_card": {"credit_card": "string"}, "auto_housework_by_robot": {"instruction": "string"}, "auto_driving_to_destination": {"destination": "string"}, "deliver_package": {"package": "string", "destination": "string"}, "order_food_delivery": {"food": "string", "location": "string", "platform": "string"}, "order_taxi": {"location": "string", "platform": "string"}, "play_music_by_title": {"title": "string"}, "play_movie_by_title": {"title": "string"}, "take_note": {"content": "string"}, "borrow_book_online": {"book": "string", "library": "string"}, "recording_audio": {"content": "string"}, "make_video_call": {"phone_number": "string"}, "make_voice_call": {"phone_number": "string"}, "organize_meeting_online": {"topic": "string"}, "attend_meeting_online": {"topic": "string"}, "software_management": {"software": "string", "instruction": "string"}, "print_document": {"document": "string"}, "set_alarm": {"time": "string"}, } +SENTENCE_MODEL = None +def get_sentence_model(): + global SENTENCE_MODEL + if SENTENCE_MODEL is None: SENTENCE_MODEL = SentenceTransformer('all-MiniLM-L6-v2') + return SENTENCE_MODEL +def parse_tool_code(text: str) -> str: + match = re.search(r"```(?:python\n)?(.*?)\n?```", text, re.DOTALL) + return match.group(1).strip() if match else text.strip() +def load_tool_descriptions_from_file(api_family_data_dir: Path) -> str: + tool_desc_path = api_family_data_dir / "tool_desc.json" + if not tool_desc_path.exists(): raise FileNotFoundError(f"Tool description file not found: {tool_desc_path}.") + with open(tool_desc_path, 'r', encoding='utf-8') as f: tool_data_root = json.load(f) + description_parts = ["Available tools (use the `api_call` function to invoke them):"] + tool_nodes = tool_data_root.get("nodes", []) + for tool_node in tool_nodes: + if not isinstance(tool_node, dict): continue + tool_id, tool_desc, params = tool_node.get("id"), tool_node.get("desc"), tool_node.get("parameters", []) + if not tool_id or not tool_desc: continue + args_list, example_args_dict = [], {} + effective_params = [{"name": n, "type": t} for n, t in CORRECTED_TOOL_PARAMETERS.get(tool_id, {}).items()] or params + for param in effective_params: + param_name, param_type = param.get("name"), param.get("type", "Any") + if param_name: args_list.append(f"`{param_name}` ({param_type})"); example_args_dict[param_name] = f"<{param_name}_value>" + example_call_str = f"api_call(\"{tool_id}\", {json.dumps(example_args_dict)})" + description_parts.extend([f"\n`{example_call_str}`", f" Description: {tool_desc}"]) + if args_list: description_parts.append(f" Parameters: {'; '.join(args_list)}") + return "\n".join(description_parts) +def load_graph_descriptions_from_file(api_family_data_dir: Path) -> str: + graph_desc_path = api_family_data_dir / "graph_desc.json" + if not graph_desc_path.exists(): return "" + with open(graph_desc_path, 'r', encoding='utf-8') as f: graph_data = json.load(f) + description_parts = ["\n--- Tool Dependencies ---"] + for dep_type, deps in graph_data.items(): + if isinstance(deps, list) and deps: + description_parts.append(f"{dep_type.replace('_', ' ').title()}:") + for dep in deps: + if not isinstance(dep, dict): continue + pre, post = dep.get("pre_tool"), dep.get("post_tool") + if "resource" in dep_type: + res = ", ".join(dep.get("resources", [])); description_parts.append(f" - `{post}` requires resource(s) `{res}` from `{pre}`.") + elif "temporal" in dep_type: + cond = dep.get("condition", "completion"); description_parts.append(f" - `{post}` can only be called after `{pre}` upon its {cond}.") + return "\n".join(description_parts) if len(description_parts) > 1 else "" +class ToolValidator: + def __init__(self, parsed_tool_data_root: Dict, debug_llm_output: bool = False): + self.tool_signatures = collections.defaultdict(dict) + if not isinstance(parsed_tool_data_root, dict) or "nodes" not in parsed_tool_data_root: return + for tool_node in parsed_tool_data_root["nodes"]: + if not isinstance(tool_node, dict): continue + tool_id, parameters = tool_node.get("id"), tool_node.get("parameters", []) + if tool_id: + effective_params = [{"name": n, "type": t} for n, t in CORRECTED_TOOL_PARAMETERS.get(tool_id, {}).items()] or parameters + self.tool_signatures[tool_id] = {"parameters": {p.get("name"): p.get("type") for p in effective_params if isinstance(p, dict)}} + def validate_api_call(self, code_str: str) -> bool: + match = re.search(r'api_call\("([^"]+)",\s*({.*?})\)', code_str, re.DOTALL) + if not match: return False + tool_id, args_str = match.group(1), match.group(2) + if tool_id not in self.tool_signatures: return False + try: + parsed_args = ast.literal_eval(args_str) + if not isinstance(parsed_args, dict): return False + for arg_name in parsed_args: + if arg_name not in self.tool_signatures[tool_id]["parameters"]: return False + return True + except (ValueError, SyntaxError): return False +class SimulatedToolExecutor: + def __init__(self, user_request: str, debug_llm_output: bool = False): + self.client = RitsChatClient(temperature=0.2, max_tokens=150); self.user_request = user_request; self.debug_llm_output = debug_llm_output + def execute(self, api_call_str: str, ablation_mode: str) -> Tuple[str, int]: + if ablation_mode == 'no_sim_feedback': return 'Observation: tool_output = "OK"', 0 + prompt_template = """You are a simulated API tool. Your role is to provide a realistic, one-line observation for the given tool call, based on the user's overall goal. + ### Rules: + 1. Your entire response MUST be a single line starting with `Observation: tool_output = `. + 2. The value part should be a plausible result. For tools that create files (like image editing or generation), the value should be a new, unique filename string (e.g., `"edited_image.png"`). For analysis tools, it should be a short, descriptive string or the direct answer (e.g., `"a red sports car"`). + 3. The observation must be grounded in the user's request. + ### User's Goal: + "{user_request}" + ### Tool Call to Simulate: + `{api_call_str}` + ### Your Single-Line Response: + """ + prompt = prompt_template.format(user_request=self.user_request, api_call_str=api_call_str) + try: + response_text, tokens_used = self.client.send(prompt) + if response_text and response_text.strip().startswith("Observation: tool_output ="): return response_text.strip().split('\n')[0], tokens_used + if self.debug_llm_output: print(f" Executor LLM failed format. Response: {response_text}", file=sys.stderr) + return 'Observation: tool_output = "Error: Tool simulation failed."', tokens_used + except Exception as e: + if self.debug_llm_output: print(f" Executor LLM call failed: {e}", file=sys.stderr) + return 'Observation: tool_output = "Error: Tool simulation encountered an exception."', 0 +@dataclass +class Node: + chain: List[str]; parent: Optional["Node"] = None; children: List["Node"] = field(default_factory=list) + visits: int = 0; value_sum: float = 0.0; _id: int = field(default_factory=lambda: id(Node)) + def __post_init__(self): self._id = id(self) + def __hash__(self): return hash(self._id) + def __eq__(self, other): return isinstance(other, Node) and self._id == other._id + @property + def depth(self) -> int: return 0 if self.parent is None else self.parent.depth + 1 + def backpropagate(self, reward: float): + current = self + while current is not None: current.visits += 1; current.value_sum += reward; current = current.parent + def uct_score(self, exploration_constant: float = 1.0) -> float: + if self.visits == 0: return float('inf') + if self.parent is None or self.parent.visits == 0: return self.value_sum / self.visits + exploitation = self.value_sum / self.visits + exploration = exploration_constant * math.sqrt(math.log(self.parent.visits) / self.visits) + return exploitation + exploration + +def process_taskbench_problem(problem_info: Dict) -> Optional[Dict]: + # Unpack all arguments + idx, example, api_family, debug_llm, parsed_tool_data, log_path, log_lock, ablation_mode, baseline_config = ( + problem_info['dataset_index'], problem_info['example'], problem_info['api_family_for_tools'], + problem_info['debug_llm_output'], problem_info['parsed_tool_data'], problem_info['log_path'], problem_info['log_lock'], + problem_info['ablation_mode'], problem_info['baseline_mcts_config'] + ) + + run_mode_str = ablation_mode if ablation_mode != 'none' else f"baseline_mcts_{baseline_config}" + def write_log(message: str): + with log_lock: + with log_path.open("a", encoding="utf-8") as f: f.write(f"--- P{idx} ({example['id']}) | Mode: {run_mode_str} ---\n{message}\n" + "="*40 + "\n\n") + + # --- Experiment Configuration --- + user_request_text = example['instruction'] + tool_validator = ToolValidator(parsed_tool_data, debug_llm) + simulated_executor = SimulatedToolExecutor(user_request=user_request_text, debug_llm_output=debug_llm) + planner_client = RitsChatClient(temperature=0.0, max_tokens=1024) + + # Define search budget based on experiment type + MAX_DEPTH = 8 + BUDGET_MAP = {'light': 15, 'medium': 30, 'heavy': 50} + BUDGET_ITERATIONS = BUDGET_MAP[baseline_config] if baseline_config != 'none' else 50 + + is_uniform_rewards = (ablation_mode == 'uniform_rewards' or baseline_config != 'none') + is_no_mcts = (ablation_mode == 'no_mcts') + + # --- Metric counters --- + start_time = time.time(); expansion_llm_calls, expansion_llm_tokens = 0, 0 + simulation_llm_calls, simulation_llm_tokens = 0, 0; invalid_steps_generated = 0 + + try: + tools_description = load_tool_descriptions_from_file(Path("Taskbench") / f"data_{api_family}") + graph_description = load_graph_descriptions_from_file(Path("Taskbench") / f"data_{api_family}") + except (FileNotFoundError, ValueError) as e: + write_log(f"CRITICAL ERROR: Could not load descriptions. Error: {e}"); return None + + base_prompt_parts = [ "You are an expert assistant...", "## RULES:", "1. Generate ONLY the single next `api_call(...)`...", "\n## TOOLS:", tools_description, graph_description, '## FINISH ACTION:\n`finish(reason="")`...'] # Truncated for brevity + + final_chain = []; final_best_node = None; total_nodes_explored = 0 + + # --- Main Logic: Greedy Search or MCTS --- + if is_no_mcts: + current_chain = [f"Instruction: {example['instruction']}"] + for _ in range(MAX_DEPTH): + prompt_list = list(base_prompt_parts) + if ablation_mode != 'no_plan_history': prompt_list.append(f"## CURRENT PLAN:\n" + "\n".join(current_chain)) + prompt_list.append("\nRespond with ONLY the next line of code:") + prompt_expand = "\n".join(filter(None, prompt_list)) + response, tokens_used = planner_client.send(prompt_expand); expansion_llm_calls += 1; expansion_llm_tokens += tokens_used + extracted_code = parse_tool_code(response.strip()) + if extracted_code.startswith("finish("): current_chain.append(extracted_code); break + if ablation_mode == 'no_validator' or tool_validator.validate_api_call(extracted_code): + observation, sim_tokens = simulated_executor.execute(extracted_code, ablation_mode) + simulation_llm_calls += 1; simulation_llm_tokens += sim_tokens; current_chain.extend([extracted_code, observation]) + else: invalid_steps_generated += 1; break + final_chain = current_chain; total_nodes_explored = 1 + else: # MCTS Run (Baseline or Ablation) + root = Node(chain=[f"Instruction: {example['instruction']}"]); terminal_nodes = [] + try: + for i in range(BUDGET_ITERATIONS): + current_node = root + while current_node.children: current_node = max(current_node.children, key=lambda n: n.uct_score()) + if current_node.depth >= MAX_DEPTH or any("finish(" in step for step in current_node.chain): + current_node.backpropagate(-0.5); continue + prompt_list = list(base_prompt_parts) + if ablation_mode != 'no_plan_history': prompt_list.append(f"## CURRENT PLAN:\n" + "\n".join(current_node.chain)) + prompt_list.append("\nRespond with ONLY the next line of code:") + prompt_expand = "\n".join(filter(None, prompt_list)) + response, tokens_used = planner_client.send(prompt_expand); expansion_llm_calls += 1; expansion_llm_tokens += tokens_used + extracted_code = parse_tool_code(response.strip()) + if extracted_code.startswith("finish("): + new_node = Node(chain=current_node.chain + [extracted_code], parent=current_node) + current_node.children.append(new_node); terminal_nodes.append(new_node); new_node.backpropagate(1.0) + elif ablation_mode == 'no_validator' or tool_validator.validate_api_call(extracted_code): + observation, sim_tokens = simulated_executor.execute(extracted_code, ablation_mode) + simulation_llm_calls += 1; simulation_llm_tokens += sim_tokens + reward = 0.0 if is_uniform_rewards else 0.1 + new_node = Node(chain=current_node.chain + [extracted_code, observation], parent=current_node) + current_node.children.append(new_node); new_node.backpropagate(reward) + else: invalid_steps_generated += 1; current_node.backpropagate(-1.0) + except Exception as e: import traceback; write_log(f"MCTS ERROR: {e}\n{traceback.format_exc()}") + + final_best_node = root + if terminal_nodes: final_best_node = max(terminal_nodes, key=lambda n: n.value_sum / n.visits if n.visits > 0 else -1) + else: + q, all_nodes = collections.deque([root]), {root} + while q: + n = q.popleft() + for child in n.children: + if child not in all_nodes: all_nodes.add(child); q.append(child) + if all_nodes: final_best_node = max(list(all_nodes), key=lambda n: (n.value_sum / n.visits if n.visits > 0 else -1, n.depth)) + q_explore, explored = collections.deque([root]), {root} + while q_explore: + n = q_explore.popleft() + for child in n.children: + if child not in explored: explored.add(child); q_explore.append(child) + total_nodes_explored, final_chain = len(explored), final_best_node.chain + + search_time_seconds = time.time() - start_time + task_steps = [parse_tool_code(s) for s in final_chain[1:]] + plan_length = sum(1 for s in task_steps if s.startswith("api_call")) + final_reward_score = 0.0 + EVALUATION_PROMPT = """Did the 'Generated Plan' successfully solve the 'User Request'? Answer with only "Yes" or "No".\n[User Request]:\n{user_request}\n\n[Generated Plan]:\n{generated_plan}\n\n[Answer (Yes/No)]:""" + try: + eval_client = RitsChatClient(temperature=0.0, max_tokens=10) + eval_prompt = EVALUATION_PROMPT.format(user_request=user_request_text, generated_plan="\n".join(task_steps)) + verdict, _ = eval_client.send(eval_prompt) + if verdict.strip().lower().startswith("yes"): final_reward_score = 1.0 + except Exception as e: write_log(f"Warning: LLM-based evaluation failed. Error: {e}") + + return {"record": { "id": example['id'], "result": {"task_steps": task_steps}, "metrics": { "accuracy": final_reward_score, "final_plan_reward": (final_best_node.value_sum / final_best_node.visits if final_best_node and final_best_node.visits > 0 else 0), "search_time_seconds": round(search_time_seconds, 2), "plan_length": plan_length, "search_process": { "total_nodes_explored": total_nodes_explored, "mcts_iterations": BUDGET_ITERATIONS if not is_no_mcts else 0, "expansion_llm_calls": expansion_llm_calls, "expansion_llm_tokens": expansion_llm_tokens, "simulation_llm_calls": simulation_llm_calls, "simulation_llm_tokens": simulation_llm_tokens, }, "robustness": {"invalid_steps_generated": invalid_steps_generated} } } } + +# --- Data loading helpers --- +def load_local(data_dir: Path, split: str): + path = data_dir / 'user_requests.json' + if not path.exists(): path = data_dir / 'user_requests.jsonl' + if not path.exists(): raise FileNotFoundError(f"Missing {data_dir}/user_requests.json or .jsonl") + with path.open() as f: + for ln in f: + data = json.loads(ln) + yield {'id': data['id'], 'instruction': data.get('user_request',''), 'input': data.get('input',''), 'tool_steps': data.get('tool_steps',[])} +def load_hf(config_name: str): + try: + ds = load_dataset('microsoft/Taskbench', name=config_name, split='test') + for ex in ds: yield {'id': ex['id'], 'instruction': ex['instruction'], 'input': ex.get('input',''), 'tool_steps': ex.get('tool_steps',[])} + except Exception as e: print(f"\n❌ HF Load Error: {e}", file=sys.stderr); sys.exit(1) + +def main(): + ap = argparse.ArgumentParser(description="Run Baseline and Ablation Experiments on TaskBench.") + ap.add_argument('--run_name', type=str, default=None); ap.add_argument('--api_family', type=str, default='huggingface') + ap.add_argument('--num_problems', type=int, default=50); ap.add_argument('--seed', type=int, default=42) + ap.add_argument('--model_name', type=str, default='llama_4'); ap.add_argument('--max_workers', type=int, default=os.cpu_count()) + ap.add_argument('--debug_llm_output', action='store_true') + + group = ap.add_mutually_exclusive_group(required=True) + group.add_argument('--ablation_mode', type=str, default='none', choices=['none', 'no_mcts', 'no_sim_feedback', 'no_plan_history', 'uniform_rewards', 'no_validator']) + group.add_argument('--baseline_mcts_config', type=str, default='none', choices=['none', 'light', 'medium', 'heavy']) + args = ap.parse_args() + + run_mode = args.ablation_mode if args.ablation_mode != 'none' else f"baseline_{args.baseline_mcts_config}" + MODELMAP.set_model('generate_model', args.model_name) + print(f"✅ Model: {MODELMAP.generate_model} | Experiment: {run_mode}") + + random.seed(args.seed); np.random.seed(args.seed); torch.manual_seed(args.seed) + if torch.cuda.is_available(): torch.cuda.manual_seed_all(args.seed) + + run_name = args.run_name or f"{run_mode}_{args.api_family}_{args.model_name}_{datetime.now():%Y%m%d_%H%M%S}" + run_dir = Path('predictions') / run_name; run_dir.mkdir(parents=True, exist_ok=True) + log_path = run_dir / 'debug_log.txt'; log_path.unlink(missing_ok=True) + print(f"✅ Outputs -> {run_dir}") + + api_data_path = Path("Taskbench") / f"data_{args.api_family}" + if not api_data_path.is_dir(): print(f"❌ Error: '{api_data_path}' not found.", file=sys.stderr); sys.exit(1) + with open(api_data_path / "tool_desc.json", 'r', encoding='utf-8') as f: parsed_tool_data = json.load(f) + + all_records = list(load_hf(config_name=args.api_family)); random.shuffle(all_records) + records_to_process = all_records[:args.num_problems] + + with Manager() as manager: + log_lock = manager.Lock() + problems = [{"dataset_index": j, "example": ex, "api_family_for_tools": args.api_family, "debug_llm_output": args.debug_llm_output, + "parsed_tool_data": parsed_tool_data, "log_path": log_path, "log_lock": log_lock, + "ablation_mode": args.ablation_mode, "baseline_mcts_config": args.baseline_mcts_config} + for j, ex in enumerate(records_to_process)] + + results = [] + with ProcessPoolExecutor(max_workers=args.max_workers) as executor: + futures = {executor.submit(process_taskbench_problem, p): p['dataset_index'] for p in problems} + desc = f"Running '{run_mode}' on {args.api_family}" + for future in tqdm(as_completed(futures), total=len(problems), desc=desc): + try: + if res := future.result(): results.append(res['record']) + except Exception as e: print(f"Problem {futures[future]} failed: {e}", file=sys.stderr) + + with (run_dir / 'results.json').open("w", encoding="utf-8") as f: json.dump(results, f, indent=2) + + total_correct = sum(1 for r in results if r.get('metrics', {}).get('accuracy', 0.0) > 0.9) + accuracy = (total_correct / len(results)) * 100 if results else 0 + + summary = {"run_name": run_name, "model_name": args.model_name, "api_family": args.api_family, "experiment_mode": run_mode, + "num_problems": len(records_to_process), "seed": args.seed, "final_accuracy": f"{accuracy:.2f}%"} + with (run_dir / 'summary.json').open("w", encoding="utf-8") as f: json.dump(summary, f, indent=2) + + print(f"📊 Final Accuracy for '{run_mode}': {accuracy:.2f}%") + print(f"✅ Summary saved to {run_dir / 'summary.json'}") + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/scripts/taskbench_cot_baseline.py b/scripts/taskbench_cot_baseline.py new file mode 100644 index 0000000..3d1d6b0 --- /dev/null +++ b/scripts/taskbench_cot_baseline.py @@ -0,0 +1,327 @@ +#!/usr/bin/env python3 +# taskbench_cot_baseline.py + +import os +import sys +import json +import time +import argparse +from pathlib import Path +from typing import List, Optional, Dict, Tuple +from concurrent.futures import ProcessPoolExecutor, as_completed +from multiprocessing import Manager +import collections +import ast +import re +from datetime import datetime +import random +import numpy as np +import torch + +from datasets import load_dataset +from tqdm import tqdm +from SPIRAL.scripts.utils.ritz_client import RitsChatClient, MODELMAP, MODEL_ID_MAP + +# ───────────────────────────────────────────────────────────────────────────── +# NOTE: The following helper functions and constants are copied from the +# MCTS script to ensure a fair and consistent experimental setup. +# ───────────────────────────────────────────────────────────────────────────── + +CORRECTED_TOOL_PARAMETERS = { + "Token Classification": {"text": "string"}, "Translation": {"text": "string", "source_lang": "string", "target_lang": "string"}, "Summarization": {"text": "string"}, "Question Answering": {"context": "string", "question": "string"}, "Conversational": {"prompt": "string", "history": "list"}, "Text Generation": {"prompt": "string"}, "Sentence Similarity": {"sentence1": "string", "sentence2": "string"}, "Tabular Classification": {"table_image_path": "string"}, "Object Detection": {"image_path": "string"}, "Image Classification": {"image_path": "string"}, "Image-to-Image": {"image_path": "string", "target_image_path": "string"}, "Image-to-Text": {"image_path": "string"}, "Text-to-Image": {"prompt": "string"}, "Text-to-Video": {"prompt": "string"}, "Visual Question Answering": {"image_path": "string", "question": "string"}, "Document Question Answering": {"document_image_path": "string", "question": "string"}, "Image Segmentation": {"image_path": "string"}, "Depth Estimation": {"image_path": "string"}, "Text-to-Speech": {"text": "string"}, "Automatic Speech Recognition": {"audio_path": "string"}, "Audio-to-Audio": {"audio_path": "string"}, "Audio Classification": {"audio_path": "string"}, "Image Editing": {"image_path": "string", "edits": "dict"}, "get_weather": {"location": "string", "date": "string"}, "get_news_for_topic": {"topic": "string"}, "stock_operation": {"stock": "string", "operation": "string"}, "book_flight": {"date": "string", "from": "string", "to": "string"}, "book_hotel": {"date": "string", "name": "string"}, "book_restaurant": {"date": "string", "name": "string"}, "book_car": {"date": "string", "location": "string"}, "online_shopping": {"website": "string", "product": "string"}, "send_email": {"email_address": "string", "content": "string"}, "send_sms": {"phone_number": "string", "content": "string"}, "share_by_social_network": {"content": "string", "social_network": "string"}, "search_by_engine": {"query": "string", "engine": "string"}, "apply_for_job": {"job": "string"}, "see_doctor_online": {"disease": "string", "doctor": "string"}, "consult_lawyer_online": {"issue": "string", "lawyer": "string"}, "enroll_in_course": {"course": "string", "university": "string"}, "buy_insurance": {"insurance": "string", "company": "string"}, "online_banking": {"instruction": "string", "bank": "string"}, "daily_bill_payment": {"bill": "string"}, "sell_item_online": {"item": "string", "store": "string"}, "do_tax_return": {"year": "string"}, "apply_for_passport": {"country": "string"}, "pay_for_credit_card": {"credit_card": "string"}, "auto_housework_by_robot": {"instruction": "string"}, "auto_driving_to_destination": {"destination": "string"}, "deliver_package": {"package": "string", "destination": "string"}, "order_food_delivery": {"food": "string", "location": "string", "platform": "string"}, "order_taxi": {"location": "string", "platform": "string"}, "play_music_by_title": {"title": "string"}, "play_movie_by_title": {"title": "string"}, "take_note": {"content": "string"}, "borrow_book_online": {"book": "string", "library": "string"}, "recording_audio": {"content": "string"}, "make_video_call": {"phone_number": "string"}, "make_voice_call": {"phone_number": "string"}, "organize_meeting_online": {"topic": "string"}, "attend_meeting_online": {"topic": "string"}, "software_management": {"software": "string", "instruction": "string"}, "print_document": {"document": "string"}, "set_alarm": {"time": "string"}, +} + +def load_tool_descriptions_from_file(api_family_data_dir: Path) -> str: + tool_desc_path = api_family_data_dir / "tool_desc.json" + if not tool_desc_path.exists(): + raise FileNotFoundError(f"Tool description file not found: {tool_desc_path}.") + try: + with open(tool_desc_path, 'r', encoding='utf-8') as f: tool_data_root = json.load(f) + except json.JSONDecodeError as e: + raise ValueError(f"Invalid JSON in {tool_desc_path}: {e}") from e + description_parts = ["Available tools (use the `api_call` function to invoke them):"] + if not isinstance(tool_data_root, dict) or "nodes" not in tool_data_root: + raise ValueError("Expected tool_desc.json to have a root dict with a 'nodes' key.") + tool_nodes = tool_data_root["nodes"] + for tool_node in tool_nodes: + if not isinstance(tool_node, dict): continue + tool_id, tool_desc = tool_node.get("id"), tool_node.get("desc") + parameters = tool_node.get("parameters", []) + if not tool_id or not tool_desc: continue + args_list, example_args_dict = [], {} + effective_parameters = [{"name": n, "type": t} for n, t in CORRECTED_TOOL_PARAMETERS.get(tool_id, {}).items()] or parameters + for param in effective_parameters: + param_name, param_type = param.get("name"), param.get("type", "Any") + if param_name: + args_list.append(f"`{param_name}` ({param_type})") + example_args_dict[param_name] = f"<{param_name}_value>" + example_call_str = f"api_call(\"{tool_id}\", {json.dumps(example_args_dict)})" + description_parts.append(f"\n`{example_call_str}`") + description_parts.append(f" Description: {tool_desc}") + if args_list: description_parts.append(f" Parameters: {'; '.join(args_list)}") + return "\n".join(description_parts) + +def load_graph_descriptions_from_file(api_family_data_dir: Path) -> str: + graph_desc_path = api_family_data_dir / "graph_desc.json" + if not graph_desc_path.exists(): return "" + try: + with open(graph_desc_path, 'r', encoding='utf-8') as f: graph_data = json.load(f) + except (json.JSONDecodeError, Exception) as e: + print(f"Warning: Could not read {graph_desc_path}: {e}", file=sys.stderr); return "" + description_parts = ["\n--- Tool Dependencies ---"] + for dep_type, deps in graph_data.items(): + if isinstance(deps, list) and deps: + description_parts.append(f"{dep_type.replace('_', ' ').title()}:") + for dep in deps: + if not isinstance(dep, dict): continue + pre, post = dep.get("pre_tool"), dep.get("post_tool") + if "resource" in dep_type: + res = ", ".join(dep.get("resources", [])); description_parts.append(f" - `{post}` requires resource(s) `{res}` from `{pre}`.") + elif "temporal" in dep_type: + cond = dep.get("condition", "completion"); description_parts.append(f" - `{post}` can only be called after `{pre}` upon its {cond}.") + return "\n".join(description_parts) if len(description_parts) > 1 else "" + +# ───────────────────────────────────────────────────────────────────────────── +# NEW: Core CoT Logic +# ───────────────────────────────────────────────────────────────────────────── + +def parse_plan_from_response(text: str) -> List[str]: + """Extracts a Python list of strings from a markdown code block.""" + match = re.search(r"```(?:python)?\s*(\[.*?\])\s*```", text, re.DOTALL) + if not match: + return [] + try: + plan = ast.literal_eval(match.group(1).strip()) + if isinstance(plan, list) and all(isinstance(item, str) for item in plan): + return plan + except (ValueError, SyntaxError): + return [] + return [] + +def make_plan_hashable(plan: List[str]) -> Tuple[str, ...]: + """Converts a list of strings to a hashable tuple for voting.""" + return tuple(plan) + +def process_problem_with_cot(problem_info: Dict) -> Optional[Dict]: + """ + Generates and evaluates a plan for a given problem using a Chain-of-Thought + approach with optional self-consistency. + """ + idx, example, api_family, log_path, log_lock, consistency_level, temperature = ( + problem_info['dataset_index'], problem_info['example'], problem_info['api_family_for_tools'], + problem_info['log_path'], problem_info['log_lock'], + problem_info['consistency_level'], problem_info['temperature'] + ) + + def write_log(message: str): + with log_lock: + with log_path.open("a", encoding="utf-8") as f: + f.write(f"--- Problem {idx} ({example['id']}) ---\n{message}\n" + "="*80 + "\n\n") + + user_request_text = example['instruction'] + + # Use a RitsChatClient with the specified temperature for diverse sampling + client = RitsChatClient(temperature=temperature, max_tokens=2048) + + # Load tool descriptions for the prompt + try: + tools_description = load_tool_descriptions_from_file(Path("Taskbench") / f"data_{api_family}") + graph_description = load_graph_descriptions_from_file(Path("Taskbench") / f"data_{api_family}") + except (FileNotFoundError, ValueError) as e: + write_log(f"CRITICAL ERROR: Could not load descriptions. Error: {e}"); return None + + # Construct the CoT prompt + prompt_template = """You are an expert planner. Your task is to create a complete step-by-step plan to solve the user's request using the available tools. + +### RULES +1. **Think Step-by-Step**: First, write your reasoning within the 'Thought' section. Analyze the request, break it down, and formulate a high-level plan. +2. **Generate Final Plan**: After your reasoning, provide the final, complete plan as a Python list of strings inside a python markdown block. +3. **Tool Calls**: Each string in the list must be a valid `api_call(...)` for one of the available tools. +4. **Finish Call**: The last step in your plan MUST be `finish(reason=\"\")`. + +### AVAILABLE TOOLS +{tools_description} +{graph_description} + +### USER REQUEST +{user_request} + +### YOUR RESPONSE + +#### Thought +(Your step-by-step reasoning and logic goes here. Break down the problem and map it to the available tools.) + +#### Plan +```python +[ + "api_call(\"tool_name_1\", {{\"param1\": \"value1\"}})", + "api_call(\"tool_name_2\", {{\"param1\": \"value2\"}})", + "finish(reason=\"The plan is complete.\")" +] +```""" + prompt = prompt_template.format( + tools_description=tools_description, + graph_description=graph_description, + user_request=user_request_text + ) + + start_time = time.time() + generated_plans = [] + total_llm_tokens = 0 + + # Generate 'k' plans for self-consistency + for _ in range(consistency_level): + response, tokens_used = client.send(prompt) + total_llm_tokens += tokens_used + plan = parse_plan_from_response(response) + if plan: + generated_plans.append(plan) + + if not generated_plans: + write_log("Failed to generate any valid plans from the LLM.") + return None + + # Self-consistency: vote for the most frequent plan + plan_counts = collections.Counter(make_plan_hashable(p) for p in generated_plans) + best_plan_tuple = plan_counts.most_common(1)[0][0] + final_plan = list(best_plan_tuple) + + generation_time_seconds = time.time() - start_time + + # Evaluate the final plan using the same LLM-based evaluator + final_reward_score = 0.0 + EVALUATION_PROMPT = """Did the 'Generated Plan' successfully solve the 'User Request'? Answer with only "Yes" or "No".\n[User Request]:\n{user_request}\n\n[Generated Plan]:\n{generated_plan}\n\n[Answer (Yes/No)]:""" + try: + eval_client = RitsChatClient(temperature=0.0, max_tokens=10) + eval_prompt = EVALUATION_PROMPT.format(user_request=user_request_text, generated_plan="\n".join(final_plan)) + verdict, _ = eval_client.send(eval_prompt) + if verdict.strip().lower().startswith("yes"): final_reward_score = 1.0 + except Exception as e: + write_log(f"Warning: LLM-based evaluation failed. Error: {e}") + + # Structure the final output with comparable metrics + final_output = { + "id": example['id'], + "result": { + "task_steps": final_plan + }, + "metrics": { + "accuracy": final_reward_score, + "generation_time_seconds": round(generation_time_seconds, 2), + "plan_length": sum(1 for step in final_plan if step.startswith("api_call")), + "reasoning_cost": { + "llm_calls": consistency_level, + "total_llm_tokens": total_llm_tokens, + } + } + } + return {"record": final_output} + +# --- Data loading helper --- +def load_hf(config_name: str): + try: + ds = load_dataset('microsoft/Taskbench', name=config_name, split='test') + for ex in ds: + yield {'id': ex['id'], 'instruction': ex['instruction'], 'input': ex.get('input',''), 'tool_steps': ex.get('tool_steps',[])} + except Exception as e: + print(f"\n❌ Failed to load '{config_name}' from Hugging Face.", file=sys.stderr) + print(f"Error: {e}", file=sys.stderr) + sys.exit(1) + +# ───────────────────────────────────────────────────────────────────────────── +# Main Orchestrator +# ───────────────────────────────────────────────────────────────────────────── +def main(): + ap = argparse.ArgumentParser(description="Run CoT Baseline on TaskBench.") + + # --- Experiment Configuration --- + ap.add_argument('--run_name', type=str, default=None, help="Optional name for the output directory.") + ap.add_argument('--api_family', type=str, default='huggingface', help="API family to test.") + ap.add_argument('--num_problems', type=int, default=50, help="Number of problems to sample.") + ap.add_argument('--seed', type=int, default=42, help="Random seed for reproducibility.") + ap.add_argument('--model_name', type=str, default='llama_4', help="The model checkpoint to use.") + + # --- CoT Hyperparameters --- + ap.add_argument('--consistency_level', type=int, default=1, choices=[1, 3, 5], help="Number of plans for self-consistency (k). 1=Standard CoT.") + ap.add_argument('--temperature', type=float, default=0.7, help="Temperature for LLM sampling. Should be >0 for self-consistency.") + + # --- Execution Settings --- + ap.add_argument('--max_workers', type=int, default=os.cpu_count(), help="Maximum parallel processes.") + args = ap.parse_args() + + # Validate model name + valid_rits_models = list(MODEL_ID_MAP["rits"].keys()) + if args.model_name not in valid_rits_models: + print(f"❌ Error: Invalid model name '{args.model_name}'. Choose from: {valid_rits_models}", file=sys.stderr) + sys.exit(1) + + MODELMAP.set_model('generate_model', args.model_name) + print(f"✅ Configured to use model: {MODELMAP.generate_model}") + + # Set seeds + random.seed(args.seed); np.random.seed(args.seed); torch.manual_seed(args.seed) + if torch.cuda.is_available(): torch.cuda.manual_seed_all(args.seed) + + # Setup run directory + run_name = args.run_name + if run_name is None: + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + run_name = f"cot_k{args.consistency_level}_{args.api_family}_{args.model_name}_{timestamp}" + print(f"✅ No run name provided. Using auto-generated name: {run_name}") + + run_dir = Path('predictions') / run_name; run_dir.mkdir(parents=True, exist_ok=True) + log_path = run_dir / 'debug_log.txt' + if log_path.exists(): log_path.unlink() + print(f"✅ Outputs will be saved in: {run_dir}") + + # Load and sample data + all_records = list(load_hf(config_name=args.api_family)) + random.shuffle(all_records) + records_to_process = all_records[:args.num_problems] + print(f"✅ Loaded and sampled {len(records_to_process)} problems.") + + # Process problems in parallel + with Manager() as manager: + log_lock = manager.Lock() + + problems_to_submit = [{ + "dataset_index": j, "example": ex, "api_family_for_tools": args.api_family, + "log_path": log_path, "log_lock": log_lock, + "consistency_level": args.consistency_level, "temperature": args.temperature + } for j, ex in enumerate(records_to_process)] + + run_results = [] + desc = f"CoT (k={args.consistency_level}) on {args.api_family}" + with ProcessPoolExecutor(max_workers=args.max_workers) as executor: + futures = {executor.submit(process_problem_with_cot, prob): prob['dataset_index'] for prob in problems_to_submit} + for future in tqdm(as_completed(futures), total=len(records_to_process), desc=desc): + try: + result = future.result() + if result: run_results.append(result['record']) + except Exception as e: + print(f"Problem {futures[future]} failed: {e}", file=sys.stderr) + + # Save results + run_output_path = run_dir / 'results.json' + with run_output_path.open("w", encoding="utf-8") as f: json.dump(run_results, f, indent=2) + + total_correct = sum(1 for r in run_results if r.get('metrics', {}).get('accuracy', 0.0) > 0.9) + total_problems = len(run_results) + accuracy = (total_correct / total_problems) * 100 if total_problems > 0 else 0 + print(f"📈 Accuracy for this run: {accuracy:.2f}% | Results saved to {run_output_path}") + + # Save summary + summary = { + "run_name": run_name, "model_name": args.model_name, "api_family": args.api_family, + "num_problems_processed": len(records_to_process), "seed": args.seed, + "consistency_level": args.consistency_level, "temperature": args.temperature, + "final_accuracy": f"{accuracy:.2f}%" + } + summary_path = run_dir / 'summary.json' + with summary_path.open("w", encoding="utf-8") as f: json.dump(summary, f, indent=2) + + print(f"📊 Final Accuracy: {accuracy:.2f}%") + print(f"✅ Final summary saved to {summary_path}") + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/scripts/taskbench_lats_baseline.py b/scripts/taskbench_lats_baseline.py new file mode 100644 index 0000000..c0c3033 --- /dev/null +++ b/scripts/taskbench_lats_baseline.py @@ -0,0 +1,395 @@ +#!/usr/bin/env python3 +# taskbench_lats_baseline.py + +import os +import sys +import json +import time +import argparse +from pathlib import Path +from typing import List, Optional, Dict, Tuple +from concurrent.futures import ProcessPoolExecutor, as_completed +from multiprocessing import Manager +import ast +import re +from datetime import datetime +import random +import numpy as np +import torch +import math + +from datasets import load_dataset +from tqdm import tqdm +from SPIRAL.scripts.utils.ritz_client import RitsChatClient, MODELMAP, MODEL_ID_MAP + +# ───────────────────────────────────────────────────────────────────────────── +# NOTE: The following helper functions and constants are copied from the +# other scripts to ensure a fair and consistent experimental setup. +# ───────────────────────────────────────────────────────────────────────────── + +CORRECTED_TOOL_PARAMETERS = { + "Token Classification": {"text": "string"}, "Translation": {"text": "string", "source_lang": "string", "target_lang": "string"}, "Summarization": {"text": "string"}, "Question Answering": {"context": "string", "question": "string"}, "Conversational": {"prompt": "string", "history": "list"}, "Text Generation": {"prompt": "string"}, "Sentence Similarity": {"sentence1": "string", "sentence2": "string"}, "Tabular Classification": {"table_image_path": "string"}, "Object Detection": {"image_path": "string"}, "Image Classification": {"image_path": "string"}, "Image-to-Image": {"image_path": "string", "target_image_path": "string"}, "Image-to-Text": {"image_path": "string"}, "Text-to-Image": {"prompt": "string"}, "Text-to-Video": {"prompt": "string"}, "Visual Question Answering": {"image_path": "string", "question": "string"}, "Document Question Answering": {"document_image_path": "string", "question": "string"}, "Image Segmentation": {"image_path": "string"}, "Depth Estimation": {"image_path": "string"}, "Text-to-Speech": {"text": "string"}, "Automatic Speech Recognition": {"audio_path": "string"}, "Audio-to-Audio": {"audio_path": "string"}, "Audio Classification": {"audio_path": "string"}, "Image Editing": {"image_path": "string", "edits": "dict"}, "get_weather": {"location": "string", "date": "string"}, "get_news_for_topic": {"topic": "string"}, "stock_operation": {"stock": "string", "operation": "string"}, "book_flight": {"date": "string", "from": "string", "to": "string"}, "book_hotel": {"date": "string", "name": "string"}, "book_restaurant": {"date": "string", "name": "string"}, "book_car": {"date": "string", "location": "string"}, "online_shopping": {"website": "string", "product": "string"}, "send_email": {"email_address": "string", "content": "string"}, "send_sms": {"phone_number": "string", "content": "string"}, "share_by_social_network": {"content": "string", "social_network": "string"}, "search_by_engine": {"query": "string", "engine": "string"}, "apply_for_job": {"job": "string"}, "see_doctor_online": {"disease": "string", "doctor": "string"}, "consult_lawyer_online": {"issue": "string", "lawyer": "string"}, "enroll_in_course": {"course": "string", "university": "string"}, "buy_insurance": {"insurance": "string", "company": "string"}, "online_banking": {"instruction": "string", "bank": "string"}, "daily_bill_payment": {"bill": "string"}, "sell_item_online": {"item": "string", "store": "string"}, "do_tax_return": {"year": "string"}, "apply_for_passport": {"country": "string"}, "pay_for_credit_card": {"credit_card": "string"}, "auto_housework_by_robot": {"instruction": "string"}, "auto_driving_to_destination": {"destination": "string"}, "deliver_package": {"package": "string", "destination": "string"}, "order_food_delivery": {"food": "string", "location": "string", "platform": "string"}, "order_taxi": {"location": "string", "platform": "string"}, "play_music_by_title": {"title": "string"}, "play_movie_by_title": {"title": "string"}, "take_note": {"content": "string"}, "borrow_book_online": {"book": "string", "library": "string"}, "recording_audio": {"content": "string"}, "make_video_call": {"phone_number": "string"}, "make_voice_call": {"phone_number": "string"}, "organize_meeting_online": {"topic": "string"}, "attend_meeting_online": {"topic": "string"}, "software_management": {"software": "string", "instruction": "string"}, "print_document": {"document": "string"}, "set_alarm": {"time": "string"}, +} + +def load_tool_descriptions_from_file(api_family_data_dir: Path) -> str: + # (Implementation is identical to other scripts) + tool_desc_path = api_family_data_dir / "tool_desc.json" + if not tool_desc_path.exists(): raise FileNotFoundError(f"Tool description file not found: {tool_desc_path}.") + with open(tool_desc_path, 'r', encoding='utf-8') as f: tool_data_root = json.load(f) + description_parts = ["Available tools (use the `api_call` function to invoke them):"] + tool_nodes = tool_data_root.get("nodes", []) + for tool_node in tool_nodes: + tool_id, tool_desc = tool_node.get("id"), tool_node.get("desc") + parameters = tool_node.get("parameters", []) + if not tool_id or not tool_desc: continue + args_list, example_args_dict = [], {} + effective_parameters = [{"name": n, "type": t} for n, t in CORRECTED_TOOL_PARAMETERS.get(tool_id, {}).items()] or parameters + for param in effective_parameters: + param_name, param_type = param.get("name"), param.get("type", "Any") + if param_name: args_list.append(f"`{param_name}` ({param_type})"); example_args_dict[param_name] = f"<{param_name}_value>" + example_call_str = f"api_call(\"{tool_id}\", {json.dumps(example_args_dict)})" + description_parts.append(f"\n`{example_call_str}`\n Description: {tool_desc}") + if args_list: description_parts.append(f" Parameters: {'; '.join(args_list)}") + return "\n".join(description_parts) + +def load_graph_descriptions_from_file(api_family_data_dir: Path) -> str: + # (Implementation is identical to other scripts) + graph_desc_path = api_family_data_dir / "graph_desc.json" + if not graph_desc_path.exists(): return "" + with open(graph_desc_path, 'r', encoding='utf-8') as f: graph_data = json.load(f) + description_parts = ["\n--- Tool Dependencies ---"] + for dep_type, deps in graph_data.items(): + if isinstance(deps, list) and deps: + description_parts.append(f"{dep_type.replace('_', ' ').title()}:") + for dep in deps: + pre, post = dep.get("pre_tool"), dep.get("post_tool") + if "resource" in dep_type: + res = ", ".join(dep.get("resources", [])); description_parts.append(f" - `{post}` requires resource(s) `{res}` from `{pre}`.") + elif "temporal" in dep_type: + cond = dep.get("condition", "completion"); description_parts.append(f" - `{post}` can only be called after `{pre}` upon its {cond}.") + return "\n".join(description_parts) if len(description_parts) > 1 else "" + +class SimulatedToolExecutor: + # (Implementation is identical to other scripts) + def __init__(self, user_request: str): + self.client = RitsChatClient(temperature=0.2, max_tokens=150) + self.user_request = user_request + + def execute(self, api_call_str: str) -> Tuple[str, int]: + prompt_template = """You are a simulated API tool. Provide a realistic, one-line observation for the given tool call. +### User's Goal: +"{user_request}" +### Tool Call to Simulate: +`{api_call_str}` +### Your Single-Line Response (must start with `Observation: tool_output = `): +""" + prompt = prompt_template.format(user_request=self.user_request, api_call_str=api_call_str) + try: + response_text, tokens_used = self.client.send(prompt) + if response_text and response_text.strip().startswith("Observation: tool_output ="): + return response_text.strip().split('\n')[0], tokens_used + return 'Observation: tool_output = "Error: Tool simulation failed."', tokens_used + except Exception: + return 'Observation: tool_output = "Error: Tool simulation encountered an exception."', 0 + +# ───────────────────────────────────────────────────────────────────────────── +# Core LATS Logic +# ───────────────────────────────────────────────────────────────────────────── + +class LATS_Node: + """A node in the MCTS tree for LATS.""" + def __init__(self, state: List[str], parent: Optional['LATS_Node'] = None, action: Optional[str] = None): + self.state = state # The sequence of (Thought, Action, Observation) strings + self.parent = parent + self.action = action # The action that led to this state + self.children: List['LATS_Node'] = [] + self.visits = 0 + self.value = 0.0 + + def is_terminal(self) -> bool: + return self.state and self.state[-1].startswith("Action: finish(") + + def is_fully_expanded(self, num_candidates: int) -> bool: + return len(self.children) >= num_candidates + +class LATS_Agent: + """The core agent for LATS, responsible for proposing actions, evaluating states, and reflecting.""" + def __init__(self, user_request: str, tools_desc: str, graph_desc: str): + self.user_request = user_request + self.tools_desc = tools_desc + self.graph_desc = graph_desc + self.agent_client = RitsChatClient(temperature=0.5, max_tokens=512) + self.value_client = RitsChatClient(temperature=0.0, max_tokens=256) + self.reflect_client = RitsChatClient(temperature=0.1, max_tokens=512) + + def _format_reflections(self, reflections: List[str]) -> str: + if not reflections: return "" + formatted = "\n".join(f"- {r}" for r in reflections) + return f"\n### PREVIOUS MISTAKES (Reflections)\n{formatted}\n" + + def propose_actions(self, state_history: str, num_candidates: int, reflections: List[str]) -> Tuple[List[str], int]: + prompt = f"""As an expert assistant, you solve tasks by thinking and acting. +### AVAILABLE TOOLS +{self.tools_desc} +{self.graph_desc} +{self._format_reflections(reflections)} +### TASK +User Request: {self.user_request} + +### CURRENT TRAJECTORY +{state_history} + +### INSTRUCTION +Based on the trajectory, generate a Python list of {num_candidates} diverse and promising `api_call(...)` or `finish(...)` actions to try next. +Your response MUST be ONLY a Python list of strings in a markdown block. +```python +[ ... ] +```""" + response, tokens = self.agent_client.send(prompt) + match = re.search(r"```(?:python)?\s*(\[.*?\])\s*```", response, re.DOTALL) + if match: + try: return ast.literal_eval(match.group(1).strip()), tokens + except: pass + return [], tokens + + def evaluate_state(self, state_history: str, reflections: List[str]) -> Tuple[float, int]: + prompt = f"""As a state evaluator, assess the potential of the current trajectory to solve the user's request. +### TASK +User Request: {self.user_request} +{self._format_reflections(reflections)} +### CURRENT TRAJECTORY +{state_history} + +### INSTRUCTION +Evaluate the trajectory's progress and likelihood of success. +Respond with ONLY a single line: `Score: | Justification: `""" + response, tokens = self.value_client.send(prompt) + match = re.search(r"Score:\s*([0-9.]+)", response) + return (float(match.group(1)) if match else 0.0), tokens + + def reflect(self, failed_trajectory: str) -> Tuple[str, int]: + prompt = f"""You are a reasoning agent reflecting on a failed attempt. +### TASK +User Request: {self.user_request} + +### FAILED TRAJECTORY +{failed_trajectory} + +### INSTRUCTION +You were unsuccessful. In a few sentences, diagnose the primary reason for failure and devise a concise, high-level plan to mitigate this specific failure in the future. +Your response must be a short paragraph.""" + reflection, tokens = self.reflect_client.send(prompt) + return reflection.strip(), tokens + +def process_problem_with_lats(problem_info: Dict) -> Optional[Dict]: + idx, example, api_family, log_path, log_lock, args = ( + problem_info['dataset_index'], problem_info['example'], problem_info['api_family_for_tools'], + problem_info['log_path'], problem_info['log_lock'], problem_info['args'] + ) + + def write_log(message: str): + with log_lock: + with log_path.open("a", encoding="utf-8") as f: + f.write(f"--- Problem {idx} ({example['id']}) ---\n{message}\n" + "="*80 + "\n\n") + + user_request_text = example['instruction'] + try: + tools_desc = load_tool_descriptions_from_file(Path("Taskbench") / f"data_{api_family}") + graph_desc = load_graph_descriptions_from_file(Path("Taskbench") / f"data_{api_family}") + except (FileNotFoundError, ValueError) as e: + write_log(f"CRITICAL ERROR: Could not load descriptions. Error: {e}"); return None + + # Initialize LATS components + agent = LATS_Agent(user_request_text, tools_desc, graph_desc) + sim_executor = SimulatedToolExecutor(user_request_text) + + start_time = time.time() + total_llm_tokens = 0 + reflections: List[str] = [] + root = LATS_Node(state=[f"User Request: {user_request_text}"]) + + for i in range(args.mcts_iterations): + # 1. SELECTION + leaf = root + while leaf.children: + leaf = max(leaf.children, key=lambda n: (n.value / (n.visits + 1e-6)) + args.exploration_weight * math.sqrt(math.log(leaf.visits + 1) / (n.visits + 1e-6))) + + # 2. EXPANSION + state_history_str = "\n".join(leaf.state) + if not leaf.is_terminal(): + actions, prop_tokens = agent.propose_actions(state_history_str, args.candidates_per_state, reflections) + total_llm_tokens += prop_tokens + for act in actions: + # Add action and observation to create child state + new_state = leaf.state + [f"Action: {act}"] + if not act.startswith("finish("): + obs, exec_tokens = sim_executor.execute(act) + total_llm_tokens += exec_tokens + new_state.append(obs) + child_node = LATS_Node(state=new_state, parent=leaf, action=act) + leaf.children.append(child_node) + + # 3. EVALUATION & 4. SIMULATION + node_to_simulate = leaf.children[0] if leaf.children else leaf + + # Simple simulation: just evaluate the current node's potential + sim_state_str = "\n".join(node_to_simulate.state) + sim_score, eval_tokens = agent.evaluate_state(sim_state_str, reflections) + total_llm_tokens += eval_tokens + + # A more complete simulation would run a greedy rollout here. + # For simplicity in this baseline, we use the evaluated score as the simulation result. + reward = sim_score + + # 5. BACKPROPAGATION + temp_node = node_to_simulate + while temp_node is not None: + temp_node.visits += 1 + temp_node.value = ((temp_node.value * (temp_node.visits - 1)) + reward) / temp_node.visits + temp_node = temp_node.parent + + # 6. REFLECTION (if simulation ended in failure) + if node_to_simulate.is_terminal() and reward < 0.5: # Heuristic for failure + reflection, reflect_tokens = agent.reflect(sim_state_str) + total_llm_tokens += reflect_tokens + if reflection: reflections.append(reflection) + + generation_time_seconds = time.time() - start_time + + # Final plan selection: traverse from root, choosing the most visited child + best_plan_node = root + final_plan_steps = [] + while best_plan_node.children: + best_plan_node = max(best_plan_node.children, key=lambda n: n.visits) + if best_plan_node.action: + final_plan_steps.append(best_plan_node.action) + + # Final evaluation of the chosen plan + final_reward_score = 0.0 + EVALUATION_PROMPT = """Did the 'Generated Plan' successfully solve the 'User Request'? Answer with only "Yes" or "No".\n[User Request]:\n{user_request}\n\n[Generated Plan]:\n{generated_plan}\n\n[Answer (Yes/No)]:""" + try: + eval_client = RitsChatClient(temperature=0.0, max_tokens=10) + eval_prompt = EVALUATION_PROMPT.format(user_request=user_request_text, generated_plan="\n".join(final_plan_steps)) + verdict, eval_tokens = eval_client.send(eval_prompt) + total_llm_tokens += eval_tokens + if verdict.strip().lower().startswith("yes"): final_reward_score = 1.0 + except Exception as e: + write_log(f"Warning: LLM-based evaluation failed. Error: {e}") + + final_output = { + "id": example['id'], + "result": {"task_steps": final_plan_steps}, + "metrics": { + "accuracy": final_reward_score, + "generation_time_seconds": round(generation_time_seconds, 2), + "plan_length": sum(1 for s in final_plan_steps if s.startswith("api_call")), + "reasoning_cost": {"total_llm_tokens": total_llm_tokens} + } + } + return {"record": final_output} + +def load_hf(config_name: str): + # (Implementation is identical to other scripts) + try: + ds = load_dataset('microsoft/Taskbench', name=config_name, split='test') + for ex in ds: yield {'id': ex['id'], 'instruction': ex['instruction'], 'input': ex.get('input',''), 'tool_steps': ex.get('tool_steps',[])} + except Exception as e: + print(f"\n❌ Failed to load '{config_name}' from Hugging Face.", file=sys.stderr); sys.exit(1) + +# ───────────────────────────────────────────────────────────────────────────── +# Main Orchestrator +# ───────────────────────────────────────────────────────────────────────────── +def main(): + ap = argparse.ArgumentParser(description="Run Language Agent Tree Search (LATS) Baseline on TaskBench.") + + ap.add_argument('--run_name', type=str, default=None) + ap.add_argument('--api_family', type=str, default='huggingface') + ap.add_argument('--num_problems', type=int, default=50) + ap.add_argument('--seed', type=int, default=42) + ap.add_argument('--model_name', type=str, default='llama_4') + ap.add_argument('--mcts_iterations', type=int, default=10, help="Number of iterations for the MCTS loop (k).") + ap.add_argument('--exploration_weight', type=float, default=1.0, help="Exploration weight (w) for UCT.") + ap.add_argument('--candidates_per_state', type=int, default=2, help="Number of actions to expand from a node (n).") + ap.add_argument('--max_workers', type=int, default=os.cpu_count()) + args = ap.parse_args() + + valid_rits_models = list(MODEL_ID_MAP["rits"].keys()) + if args.model_name not in valid_rits_models: + print(f"❌ Error: Invalid model name '{args.model_name}'. Choose from: {valid_rits_models}", file=sys.stderr); sys.exit(1) + + MODELMAP.set_model('generate_model', args.model_name) + print(f"✅ Configured to use model: {MODELMAP.generate_model}") + + random.seed(args.seed); np.random.seed(args.seed); torch.manual_seed(args.seed) + if torch.cuda.is_available(): torch.cuda.manual_seed_all(args.seed) + + run_name = args.run_name or f"lats_k{args.mcts_iterations}_{args.api_family}_{args.model_name}_{datetime.now().strftime('%Y%m%d_%H%M%S')}" + run_dir = Path('predictions') / run_name; run_dir.mkdir(parents=True, exist_ok=True) + log_path = run_dir / 'debug_log.txt' + if log_path.exists(): log_path.unlink() + print(f"✅ Outputs will be saved in: {run_dir}") + + # --- Load Data (with local override) --- + local_data_dir = Path("Taskbench") / f"data_{args.api_family}" + all_records = [] + + def load_local(data_dir: Path): + path = data_dir / 'user_requests.jsonl'; + if not path.exists(): path = data_dir / 'user_requests.json' + with path.open('r', encoding='utf-8') as f: + for line in f: yield json.loads(line) + + if local_data_dir.is_dir(): + print(f"✅ Found local dataset at '{local_data_dir}'. Loading...") + all_records = list(load_local(local_data_dir)) + else: + print(f"✅ No local dataset found. Loading '{args.api_family}' from Hugging Face...") + all_records = list(load_hf(config_name=args.api_family)) + + if not all_records: + print(f"❌ No problems loaded for API family '{args.api_family}'. Exiting.", file=sys.stderr); sys.exit(1) + + num_to_process = min(args.num_problems, len(all_records)) + random.shuffle(all_records) + records_to_process = all_records[:num_to_process] + print(f"✅ Loaded {len(all_records)} problems, processing {len(records_to_process)}.") + + with Manager() as manager: + log_lock = manager.Lock() + + problems_to_submit = [{"dataset_index": j, "example": ex, "api_family_for_tools": args.api_family, "log_path": log_path, "log_lock": log_lock, "args": args} for j, ex in enumerate(records_to_process)] + + run_results = [] + with ProcessPoolExecutor(max_workers=args.max_workers) as executor: + futures = {executor.submit(process_problem_with_lats, prob): prob['dataset_index'] for prob in problems_to_submit} + for future in tqdm(as_completed(futures), total=len(records_to_process), desc=f"LATS on {args.api_family}"): + try: + result = future.result() + if result: run_results.append(result['record']) + except Exception as e: + print(f"\nProblem {futures[future]} failed: {e}", file=sys.stderr) + + run_output_path = run_dir / 'results.json' + with run_output_path.open("w", encoding="utf-8") as f: json.dump(run_results, f, indent=2) + + total_correct = sum(1 for r in run_results if r.get('metrics', {}).get('accuracy', 0.0) > 0.9) + accuracy = (total_correct / len(run_results)) * 100 if run_results else 0 + + summary = { + "run_name": run_name, "model_name": args.model_name, "api_family": args.api_family, + "num_problems_processed": len(records_to_process), "seed": args.seed, + "mcts_iterations": args.mcts_iterations, + "exploration_weight": args.exploration_weight, + "candidates_per_state": args.candidates_per_state, + "final_accuracy": f"{accuracy:.2f}%" + } + summary_path = run_dir / 'summary.json' + with summary_path.open("w", encoding="utf-8") as f: json.dump(summary, f, indent=2) + + print(f"\n{'='*25} Experiment Complete {'='*25}") + print(f"📊 Final Accuracy: {accuracy:.2f}%") + print(f"✅ Results saved to {run_output_path}") + print(f"✅ Final summary saved to {summary_path}") + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/scripts/taskbench_rafa_baseline.py b/scripts/taskbench_rafa_baseline.py new file mode 100644 index 0000000..957cb44 --- /dev/null +++ b/scripts/taskbench_rafa_baseline.py @@ -0,0 +1,404 @@ +#!/usr/bin/env python3 +# taskbench_rafa_baseline.py + +import os +import sys +import json +import time +import argparse +from pathlib import Path +from typing import List, Optional, Dict, Tuple +from concurrent.futures import ProcessPoolExecutor, as_completed +from multiprocessing import Manager +import ast +import re +from datetime import datetime +import random +import numpy as np +import torch +import copy + +from datasets import load_dataset +from tqdm import tqdm +from SPIRAL.scripts.utils.ritz_client import RitsChatClient, MODELMAP, MODEL_ID_MAP + +# ───────────────────────────────────────────────────────────────────────────── +# NOTE: The following helper functions and constants are copied from the +# MCTS and CoT scripts to ensure a fair and consistent experimental setup. +# ───────────────────────────────────────────────────────────────────────────── + +CORRECTED_TOOL_PARAMETERS = { + "Token Classification": {"text": "string"}, "Translation": {"text": "string", "source_lang": "string", "target_lang": "string"}, "Summarization": {"text": "string"}, "Question Answering": {"context": "string", "question": "string"}, "Conversational": {"prompt": "string", "history": "list"}, "Text Generation": {"prompt": "string"}, "Sentence Similarity": {"sentence1": "string", "sentence2": "string"}, "Tabular Classification": {"table_image_path": "string"}, "Object Detection": {"image_path": "string"}, "Image Classification": {"image_path": "string"}, "Image-to-Image": {"image_path": "string", "target_image_path": "string"}, "Image-to-Text": {"image_path": "string"}, "Text-to-Image": {"prompt": "string"}, "Text-to-Video": {"prompt": "string"}, "Visual Question Answering": {"image_path": "string", "question": "string"}, "Document Question Answering": {"document_image_path": "string", "question": "string"}, "Image Segmentation": {"image_path": "string"}, "Depth Estimation": {"image_path": "string"}, "Text-to-Speech": {"text": "string"}, "Automatic Speech Recognition": {"audio_path": "string"}, "Audio-to-Audio": {"audio_path": "string"}, "Audio Classification": {"audio_path": "string"}, "Image Editing": {"image_path": "string", "edits": "dict"}, "get_weather": {"location": "string", "date": "string"}, "get_news_for_topic": {"topic": "string"}, "stock_operation": {"stock": "string", "operation": "string"}, "book_flight": {"date": "string", "from": "string", "to": "string"}, "book_hotel": {"date": "string", "name": "string"}, "book_restaurant": {"date": "string", "name": "string"}, "book_car": {"date": "string", "location": "string"}, "online_shopping": {"website": "string", "product": "string"}, "send_email": {"email_address": "string", "content": "string"}, "send_sms": {"phone_number": "string", "content": "string"}, "share_by_social_network": {"content": "string", "social_network": "string"}, "search_by_engine": {"query": "string", "engine": "string"}, "apply_for_job": {"job": "string"}, "see_doctor_online": {"disease": "string", "doctor": "string"}, "consult_lawyer_online": {"issue": "string", "lawyer": "string"}, "enroll_in_course": {"course": "string", "university": "string"}, "buy_insurance": {"insurance": "string", "company": "string"}, "online_banking": {"instruction": "string", "bank": "string"}, "daily_bill_payment": {"bill": "string"}, "sell_item_online": {"item": "string", "store": "string"}, "do_tax_return": {"year": "string"}, "apply_for_passport": {"country": "string"}, "pay_for_credit_card": {"credit_card": "string"}, "auto_housework_by_robot": {"instruction": "string"}, "auto_driving_to_destination": {"destination": "string"}, "deliver_package": {"package": "string", "destination": "string"}, "order_food_delivery": {"food": "string", "location": "string", "platform": "string"}, "order_taxi": {"location": "string", "platform": "string"}, "play_music_by_title": {"title": "string"}, "play_movie_by_title": {"title": "string"}, "take_note": {"content": "string"}, "borrow_book_online": {"book": "string", "library": "string"}, "recording_audio": {"content": "string"}, "make_video_call": {"phone_number": "string"}, "make_voice_call": {"phone_number": "string"}, "organize_meeting_online": {"topic": "string"}, "attend_meeting_online": {"topic": "string"}, "software_management": {"software": "string", "instruction": "string"}, "print_document": {"document": "string"}, "set_alarm": {"time": "string"}, +} + +def load_tool_descriptions_from_file(api_family_data_dir: Path) -> str: + # (Implementation is identical to ReAct and MCTS scripts) + tool_desc_path = api_family_data_dir / "tool_desc.json" + if not tool_desc_path.exists(): + raise FileNotFoundError(f"Tool description file not found: {tool_desc_path}.") + with open(tool_desc_path, 'r', encoding='utf-8') as f: + tool_data_root = json.load(f) + description_parts = ["Available tools (use the `api_call` function to invoke them):"] + tool_nodes = tool_data_root.get("nodes", []) + for tool_node in tool_nodes: + tool_id, tool_desc = tool_node.get("id"), tool_node.get("desc") + parameters = tool_node.get("parameters", []) + if not tool_id or not tool_desc: continue + args_list, example_args_dict = [], {} + effective_parameters = [{"name": n, "type": t} for n, t in CORRECTED_TOOL_PARAMETERS.get(tool_id, {}).items()] or parameters + for param in effective_parameters: + param_name, param_type = param.get("name"), param.get("type", "Any") + if param_name: + args_list.append(f"`{param_name}` ({param_type})") + example_args_dict[param_name] = f"<{param_name}_value>" + example_call_str = f"api_call(\"{tool_id}\", {json.dumps(example_args_dict)})" + description_parts.append(f"\n`{example_call_str}`\n Description: {tool_desc}") + if args_list: description_parts.append(f" Parameters: {'; '.join(args_list)}") + return "\n".join(description_parts) + + +def load_graph_descriptions_from_file(api_family_data_dir: Path) -> str: + # (Implementation is identical to ReAct and MCTS scripts) + graph_desc_path = api_family_data_dir / "graph_desc.json" + if not graph_desc_path.exists(): return "" + with open(graph_desc_path, 'r', encoding='utf-8') as f: + graph_data = json.load(f) + description_parts = ["\n--- Tool Dependencies ---"] + for dep_type, deps in graph_data.items(): + if isinstance(deps, list) and deps: + description_parts.append(f"{dep_type.replace('_', ' ').title()}:") + for dep in deps: + pre, post = dep.get("pre_tool"), dep.get("post_tool") + if "resource" in dep_type: + res = ", ".join(dep.get("resources", [])); description_parts.append(f" - `{post}` requires resource(s) `{res}` from `{pre}`.") + elif "temporal" in dep_type: + cond = dep.get("condition", "completion"); description_parts.append(f" - `{post}` can only be called after `{pre}` upon its {cond}.") + return "\n".join(description_parts) if len(description_parts) > 1 else "" + +class SimulatedToolExecutor: + # (Implementation is identical to ReAct and MCTS scripts) + def __init__(self, user_request: str): + self.client = RitsChatClient(temperature=0.2, max_tokens=150) + self.user_request = user_request + + def execute(self, api_call_str: str) -> Tuple[str, int]: + prompt_template = """You are a simulated API tool. Your role is to provide a realistic, one-line observation for the given tool call, based on the user's overall goal. +### Rules: +1. Your entire response MUST be a single line starting with `Observation: tool_output = `. +2. The value part should be a plausible result. +### User's Goal: +"{user_request}" +### Tool Call to Simulate: +`{api_call_str}` +### Your Single-Line Response: +""" + prompt = prompt_template.format(user_request=self.user_request, api_call_str=api_call_str) + try: + response_text, tokens_used = self.client.send(prompt) + if response_text and response_text.strip().startswith("Observation: tool_output ="): + return response_text.strip().split('\n')[0], tokens_used + return 'Observation: tool_output = "Error: Tool simulation failed."', tokens_used + except Exception: + return 'Observation: tool_output = "Error: Tool simulation encountered an exception."', 0 + +# ───────────────────────────────────────────────────────────────────────────── +# Core RAFA Logic +# ───────────────────────────────────────────────────────────────────────────── + +# --- RAFA Agent Definitions --- +class RAFA_Elite: + def __init__(self, user_request: str, tools_description: str, graph_description: str, breadth: int): + self.client = RitsChatClient(temperature=0.4, max_tokens=1024) + self.prompt_template = """You are an Elite Planner. Your goal is to propose a diverse set of candidate next actions to solve the user's request, based on the history of what has been tried. + +### AVAILABLE TOOLS +{tools_description} +{graph_description} + +### TASK +User Request: {user_request} + +### CURRENT PLAN HISTORY +{history} + +### INSTRUCTION +Based on the history, generate a Python list of {breadth} distinct and promising `api_call(...)` or `finish(...)` actions to take next. +Your response MUST be ONLY a Python list of strings in a markdown block. +```python +[ + "action_string_1", + "action_string_2", + ... +] +```""" + self.user_request = user_request + self.tools_description = tools_description + self.graph_description = graph_description + self.breadth = breadth + + def propose(self, history: str) -> Tuple[List[str], int]: + prompt = self.prompt_template.format( + tools_description=self.tools_description, + graph_description=self.graph_description, + user_request=self.user_request, + history=history, + breadth=self.breadth + ) + response, tokens = self.client.send(prompt) + match = re.search(r"```(?:python)?\s*(\[.*?\])\s*```", response, re.DOTALL) + if match: + try: + plan = ast.literal_eval(match.group(1).strip()) + if isinstance(plan, list) and all(isinstance(p, str) for p in plan): + return plan, tokens + except: + return [], tokens + return [], tokens + +class RAFA_Critic: + def __init__(self, user_request: str): + self.client = RitsChatClient(temperature=0.0, max_tokens=100) + self.prompt_template = """You are a Critic. Your task is to evaluate a proposed plan trajectory based on its likelihood of success in solving the user's request. + +### TASK +User Request: {user_request} + +### PROPOSED PLAN TRAJECTORY +{trajectory} + +### INSTRUCTION +Evaluate the plan. Is it coherent? Is it making progress? Is it likely to succeed? +Respond with ONLY a single line in the format: `Score: | Justification: `""" + self.user_request = user_request + + def evaluate(self, trajectory: str) -> Tuple[float, int]: + prompt = self.prompt_template.format(user_request=self.user_request, trajectory=trajectory) + response, tokens = self.client.send(prompt) + match = re.search(r"Score:\s*([0-9.]+)", response) + score = float(match.group(1)) if match else 0.0 + return score, tokens + +def process_problem_with_rafa(problem_info: Dict) -> Optional[Dict]: + idx, example, api_family, log_path, log_lock, args = ( + problem_info['dataset_index'], problem_info['example'], problem_info['api_family_for_tools'], + problem_info['log_path'], problem_info['log_lock'], problem_info['args'] + ) + + def write_log(message: str): + with log_lock: + with log_path.open("a", encoding="utf-8") as f: + f.write(f"--- Problem {idx} ({example['id']}) ---\n{message}\n" + "="*80 + "\n\n") + + user_request_text = example['instruction'] + try: + tools_desc = load_tool_descriptions_from_file(Path("Taskbench") / f"data_{api_family}") + graph_desc = load_graph_descriptions_from_file(Path("Taskbench") / f"data_{api_family}") + except (FileNotFoundError, ValueError) as e: + write_log(f"CRITICAL ERROR: Could not load descriptions. Error: {e}"); return None + + # --- Initialize RAFA components and environment --- + elite_agent = RAFA_Elite(user_request_text, tools_desc, graph_desc, args.search_breadth) + model_agent = SimulatedToolExecutor(user_request_text) # Internal model for planning + critic_agent = RAFA_Critic(user_request_text) + real_environment = SimulatedToolExecutor(user_request_text) # External environment for acting + + start_time = time.time() + memory_buffer = ["User Request: " + user_request_text] + final_plan_steps = [] + total_tokens = 0 + + for real_step in range(args.max_real_steps): + # --- 1. REASON FOR FUTURE (Planning Phase) --- + planned_trajectories = [] + + # Propose initial set of actions from the current state + history_str = "\n".join(memory_buffer) + candidate_actions, elite_tokens = elite_agent.propose(history_str) + total_tokens += elite_tokens + + # Expand each candidate action into a full trajectory + for action in candidate_actions: + trajectory = [action] + sim_history = copy.deepcopy(memory_buffer) + sim_history.append(action) + + # Lookahead for `search_depth` steps + for _ in range(args.search_depth - 1): + # NOTE: For simplicity, this RAFA baseline uses a single-beam lookahead. + # A more complex version could re-invoke the Elite agent at each depth. + next_action_proposals, next_elite_tokens = elite_agent.propose("\n".join(sim_history)) + total_tokens += next_elite_tokens + if not next_action_proposals: break + + next_action = next_action_proposals[0] # Take the top proposal for the beam + trajectory.append(next_action) + if next_action.startswith("finish("): break + + obs, model_tokens = model_agent.execute(next_action) + total_tokens += model_tokens + sim_history.extend([next_action, obs]) + + planned_trajectories.append(trajectory) + + # Evaluate all planned trajectories + best_trajectory = None + max_score = -1.0 + + for trajectory in planned_trajectories: + traj_str = "\n".join(trajectory) + score, critic_tokens = critic_agent.evaluate(traj_str) + total_tokens += critic_tokens + if score > max_score: + max_score = score + best_trajectory = trajectory + + if not best_trajectory: + write_log("RAFA planning failed to produce a valid trajectory.") + break + + # --- 2. ACT FOR NOW (Execution Phase) --- + action_to_execute = best_trajectory[0] + final_plan_steps.append(action_to_execute) + + if action_to_execute.startswith("finish("): + break + + observation, env_tokens = real_environment.execute(action_to_execute) + total_tokens += env_tokens + + # Update memory buffer with the real interaction + memory_buffer.append(action_to_execute) + memory_buffer.append(observation) + + generation_time_seconds = time.time() - start_time + + # Evaluate the final executed plan + final_reward_score = 0.0 + EVALUATION_PROMPT = """Did the 'Generated Plan' successfully solve the 'User Request'? Answer with only "Yes" or "No".\n[User Request]:\n{user_request}\n\n[Generated Plan]:\n{generated_plan}\n\n[Answer (Yes/No)]:""" + try: + eval_client = RitsChatClient(temperature=0.0, max_tokens=10) + eval_prompt = EVALUATION_PROMPT.format(user_request=user_request_text, generated_plan="\n".join(final_plan_steps)) + verdict, _ = eval_client.send(eval_prompt) + if verdict.strip().lower().startswith("yes"): final_reward_score = 1.0 + except Exception as e: + write_log(f"Warning: LLM-based evaluation failed. Error: {e}") + + final_output = { + "id": example['id'], + "result": {"task_steps": final_plan_steps}, + "metrics": { + "accuracy": final_reward_score, + "generation_time_seconds": round(generation_time_seconds, 2), + "plan_length": sum(1 for s in final_plan_steps if s.startswith("api_call")), + "reasoning_cost": {"total_llm_tokens": total_tokens} + } + } + return {"record": final_output} + +def load_hf(config_name: str): + try: + ds = load_dataset('microsoft/Taskbench', name=config_name, split='test') + for ex in ds: + yield {'id': ex['id'], 'instruction': ex['instruction'], 'input': ex.get('input',''), 'tool_steps': ex.get('tool_steps',[])} + except Exception as e: + print(f"\n❌ Failed to load '{config_name}' from Hugging Face.", file=sys.stderr); sys.exit(1) + +def main(): + ap = argparse.ArgumentParser(description="Run RAFA Baseline on TaskBench.") + + ap.add_argument('--run_name', type=str, default=None) + ap.add_argument('--api_family', type=str, default='huggingface') + ap.add_argument('--num_problems', type=int, default=50) + ap.add_argument('--seed', type=int, default=42) + ap.add_argument('--model_name', type=str, default='llama_4') + ap.add_argument('--max_real_steps', type=int, default=8, help="Max steps to execute in the 'real' env.") + ap.add_argument('--search_breadth', type=int, default=3, help="Number of candidate actions to explore at each step (B).") + ap.add_argument('--search_depth', type=int, default=2, help="Lookahead steps for each candidate trajectory (U).") + ap.add_argument('--max_workers', type=int, default=os.cpu_count()) + args = ap.parse_args() + + valid_rits_models = list(MODEL_ID_MAP["rits"].keys()) + if args.model_name not in valid_rits_models: + print(f"❌ Error: Invalid model name '{args.model_name}'. Choose from: {valid_rits_models}", file=sys.stderr); sys.exit(1) + + MODELMAP.set_model('generate_model', args.model_name) + print(f"✅ Configured to use model: {MODELMAP.generate_model}") + + random.seed(args.seed); np.random.seed(args.seed); torch.manual_seed(args.seed) + if torch.cuda.is_available(): torch.cuda.manual_seed_all(args.seed) + + run_name = args.run_name or f"rafa_b{args.search_breadth}d{args.search_depth}_{args.api_family}_{args.model_name}_{datetime.now().strftime('%Y%m%d_%H%M%S')}" + run_dir = Path('predictions') / run_name; run_dir.mkdir(parents=True, exist_ok=True) + log_path = run_dir / 'debug_log.txt' + if log_path.exists(): log_path.unlink() + print(f"✅ Outputs will be saved in: {run_dir}") + + # --- Load Data (with local override) --- + local_data_dir = Path("Taskbench") / f"data_{args.api_family}" + all_records = [] + + # The 'load_local' function needs to be present in each script + def load_local(data_dir: Path): + path = data_dir / 'user_requests.jsonl' + if not path.exists(): path = data_dir / 'user_requests.json' + with path.open('r', encoding='utf-8') as f: + for line in f: + yield json.loads(line) + + # Check if a local directory for the api_family exists + if local_data_dir.is_dir(): + print(f"✅ Found local dataset at '{local_data_dir}'. Loading...") + all_records = list(load_local(local_data_dir)) + else: + # Fallback to Hugging Face if no local data is found + print(f"✅ No local dataset found. Loading '{args.api_family}' from Hugging Face...") + all_records = list(load_hf(config_name=args.api_family)) + + if not all_records: + print(f"❌ No problems loaded for API family '{args.api_family}'. Exiting.", file=sys.stderr) + sys.exit(1) + + # Shuffle and select the specified number of problems + num_to_process = min(args.num_problems, len(all_records)) + random.shuffle(all_records) + records_to_process = all_records[:num_to_process] + print(f"✅ Loaded {len(all_records)} problems, processing {len(records_to_process)}.") + + with Manager() as manager: + log_lock = manager.Lock() + + problems_to_submit = [{"dataset_index": j, "example": ex, "api_family_for_tools": args.api_family, "log_path": log_path, "log_lock": log_lock, "args": args} for j, ex in enumerate(records_to_process)] + + run_results = [] + desc = f"RAFA (B={args.search_breadth}, D={args.search_depth}) on {args.api_family}" + with ProcessPoolExecutor(max_workers=args.max_workers) as executor: + futures = {executor.submit(process_problem_with_rafa, prob): prob['dataset_index'] for prob in problems_to_submit} + for future in tqdm(as_completed(futures), total=len(records_to_process), desc=desc): + try: + result = future.result() + if result: run_results.append(result['record']) + except Exception as e: + print(f"Problem {futures[future]} failed: {e}", file=sys.stderr) + + run_output_path = run_dir / 'results.json' + with run_output_path.open("w", encoding="utf-8") as f: json.dump(run_results, f, indent=2) + + total_correct = sum(1 for r in run_results if r.get('metrics', {}).get('accuracy', 0.0) > 0.9) + accuracy = (total_correct / len(run_results)) * 100 if run_results else 0 + + summary = { + "run_name": run_name, "model_name": args.model_name, "api_family": args.api_family, + "num_problems_processed": len(records_to_process), "seed": args.seed, + "search_breadth": args.search_breadth, "search_depth": args.search_depth, + "final_accuracy": f"{accuracy:.2f}%" + } + summary_path = run_dir / 'summary.json' + with summary_path.open("w", encoding="utf-8") as f: json.dump(summary, f, indent=2) + + print(f"\n{'='*25} Experiment Complete {'='*25}") + print(f"📊 Final Accuracy: {accuracy:.2f}%") + print(f"✅ Results saved to {run_output_path}") + print(f"✅ Final summary saved to {summary_path}") + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/scripts/taskbench_react_baseline.py b/scripts/taskbench_react_baseline.py new file mode 100644 index 0000000..a2b662d --- /dev/null +++ b/scripts/taskbench_react_baseline.py @@ -0,0 +1,381 @@ +#!/usr/bin/env python3 +# taskbench_react_baseline.py + +import os +import sys +import json +import time +import argparse +from pathlib import Path +from typing import List, Optional, Dict, Tuple +from concurrent.futures import ProcessPoolExecutor, as_completed +from multiprocessing import Manager +import collections +import ast +import re +from datetime import datetime +import random +import numpy as np +import torch + +from datasets import load_dataset +from tqdm import tqdm +from SPIRAL.scripts.utils.ritz_client import RitsChatClient, MODELMAP, MODEL_ID_MAP + +# ───────────────────────────────────────────────────────────────────────────── +# NOTE: The following helper functions and constants are copied from the +# MCTS and CoT scripts to ensure a fair and consistent experimental setup. +# ───────────────────────────────────────────────────────────────────────────── + +CORRECTED_TOOL_PARAMETERS = { + "Token Classification": {"text": "string"}, "Translation": {"text": "string", "source_lang": "string", "target_lang": "string"}, "Summarization": {"text": "string"}, "Question Answering": {"context": "string", "question": "string"}, "Conversational": {"prompt": "string", "history": "list"}, "Text Generation": {"prompt": "string"}, "Sentence Similarity": {"sentence1": "string", "sentence2": "string"}, "Tabular Classification": {"table_image_path": "string"}, "Object Detection": {"image_path": "string"}, "Image Classification": {"image_path": "string"}, "Image-to-Image": {"image_path": "string", "target_image_path": "string"}, "Image-to-Text": {"image_path": "string"}, "Text-to-Image": {"prompt": "string"}, "Text-to-Video": {"prompt": "string"}, "Visual Question Answering": {"image_path": "string", "question": "string"}, "Document Question Answering": {"document_image_path": "string", "question": "string"}, "Image Segmentation": {"image_path": "string"}, "Depth Estimation": {"image_path": "string"}, "Text-to-Speech": {"text": "string"}, "Automatic Speech Recognition": {"audio_path": "string"}, "Audio-to-Audio": {"audio_path": "string"}, "Audio Classification": {"audio_path": "string"}, "Image Editing": {"image_path": "string", "edits": "dict"}, "get_weather": {"location": "string", "date": "string"}, "get_news_for_topic": {"topic": "string"}, "stock_operation": {"stock": "string", "operation": "string"}, "book_flight": {"date": "string", "from": "string", "to": "string"}, "book_hotel": {"date": "string", "name": "string"}, "book_restaurant": {"date": "string", "name": "string"}, "book_car": {"date": "string", "location": "string"}, "online_shopping": {"website": "string", "product": "string"}, "send_email": {"email_address": "string", "content": "string"}, "send_sms": {"phone_number": "string", "content": "string"}, "share_by_social_network": {"content": "string", "social_network": "string"}, "search_by_engine": {"query": "string", "engine": "string"}, "apply_for_job": {"job": "string"}, "see_doctor_online": {"disease": "string", "doctor": "string"}, "consult_lawyer_online": {"issue": "string", "lawyer": "string"}, "enroll_in_course": {"course": "string", "university": "string"}, "buy_insurance": {"insurance": "string", "company": "string"}, "online_banking": {"instruction": "string", "bank": "string"}, "daily_bill_payment": {"bill": "string"}, "sell_item_online": {"item": "string", "store": "string"}, "do_tax_return": {"year": "string"}, "apply_for_passport": {"country": "string"}, "pay_for_credit_card": {"credit_card": "string"}, "auto_housework_by_robot": {"instruction": "string"}, "auto_driving_to_destination": {"destination": "string"}, "deliver_package": {"package": "string", "destination": "string"}, "order_food_delivery": {"food": "string", "location": "string", "platform": "string"}, "order_taxi": {"location": "string", "platform": "string"}, "play_music_by_title": {"title": "string"}, "play_movie_by_title": {"title": "string"}, "take_note": {"content": "string"}, "borrow_book_online": {"book": "string", "library": "string"}, "recording_audio": {"content": "string"}, "make_video_call": {"phone_number": "string"}, "make_voice_call": {"phone_number": "string"}, "organize_meeting_online": {"topic": "string"}, "attend_meeting_online": {"topic": "string"}, "software_management": {"software": "string", "instruction": "string"}, "print_document": {"document": "string"}, "set_alarm": {"time": "string"}, +} + +def parse_tool_code(text: str) -> str: + """Extracts Python code from a markdown block if present, otherwise returns the text.""" + match = re.search(r"```(?:python\n)?(.*?)\n?```", text, re.DOTALL) + return match.group(1).strip() if match else text.strip() + +def load_tool_descriptions_from_file(api_family_data_dir: Path) -> str: + tool_desc_path = api_family_data_dir / "tool_desc.json" + if not tool_desc_path.exists(): + raise FileNotFoundError(f"Tool description file not found: {tool_desc_path}.") + with open(tool_desc_path, 'r', encoding='utf-8') as f: + tool_data_root = json.load(f) + description_parts = ["Available tools (use the `api_call` function to invoke them):"] + tool_nodes = tool_data_root.get("nodes", []) + for tool_node in tool_nodes: + tool_id, tool_desc = tool_node.get("id"), tool_node.get("desc") + parameters = tool_node.get("parameters", []) + if not tool_id or not tool_desc: continue + args_list, example_args_dict = [], {} + effective_parameters = [{"name": n, "type": t} for n, t in CORRECTED_TOOL_PARAMETERS.get(tool_id, {}).items()] or parameters + for param in effective_parameters: + param_name, param_type = param.get("name"), param.get("type", "Any") + if param_name: + args_list.append(f"`{param_name}` ({param_type})") + example_args_dict[param_name] = f"<{param_name}_value>" + example_call_str = f"api_call(\"{tool_id}\", {json.dumps(example_args_dict)})" + description_parts.append(f"\n`{example_call_str}`\n Description: {tool_desc}") + if args_list: description_parts.append(f" Parameters: {'; '.join(args_list)}") + return "\n".join(description_parts) + +def load_graph_descriptions_from_file(api_family_data_dir: Path) -> str: + graph_desc_path = api_family_data_dir / "graph_desc.json" + if not graph_desc_path.exists(): return "" + with open(graph_desc_path, 'r', encoding='utf-8') as f: + graph_data = json.load(f) + description_parts = ["\n--- Tool Dependencies ---"] + for dep_type, deps in graph_data.items(): + if isinstance(deps, list) and deps: + description_parts.append(f"{dep_type.replace('_', ' ').title()}:") + for dep in deps: + pre, post = dep.get("pre_tool"), dep.get("post_tool") + if "resource" in dep_type: + res = ", ".join(dep.get("resources", [])); description_parts.append(f" - `{post}` requires resource(s) `{res}` from `{pre}`.") + elif "temporal" in dep_type: + cond = dep.get("condition", "completion"); description_parts.append(f" - `{post}` can only be called after `{pre}` upon its {cond}.") + return "\n".join(description_parts) if len(description_parts) > 1 else "" + +class ToolValidator: + def __init__(self, parsed_tool_data_root: Dict): + self.tool_signatures = collections.defaultdict(dict) + tool_nodes = parsed_tool_data_root.get("nodes", []) + for tool_node in tool_nodes: + tool_id, parameters = tool_node.get("id"), tool_node.get("parameters", []) + if tool_id: + effective_params = [{"name": n, "type": t} for n, t in CORRECTED_TOOL_PARAMETERS.get(tool_id, {}).items()] or parameters + self.tool_signatures[tool_id] = {"parameters": {p.get("name"): p.get("type") for p in effective_params if isinstance(p, dict)}} + + def validate_api_call(self, code_str: str) -> bool: + match = re.search(r'api_call\("([^"]+)",\s*({.*?})\)', code_str, re.DOTALL) + if not match: return False + tool_id, args_str = match.group(1), match.group(2) + if tool_id not in self.tool_signatures: return False + expected_params = self.tool_signatures[tool_id]["parameters"] + try: + parsed_args = ast.literal_eval(args_str) + return isinstance(parsed_args, dict) and all(arg_name in expected_params for arg_name in parsed_args) + except (ValueError, SyntaxError): + return False + +class SimulatedToolExecutor: + def __init__(self, user_request: str): + self.client = RitsChatClient(temperature=0.2, max_tokens=150) + self.user_request = user_request + + def execute(self, api_call_str: str) -> Tuple[str, int]: + prompt_template = """You are a simulated API tool. Your role is to provide a realistic, one-line observation for the given tool call, based on the user's overall goal. +### Rules: +1. Your entire response MUST be a single line starting with `Observation: tool_output = `. +2. The value part should be a plausible result. For tools that create files (like image editing or generation), the value should be a new, unique filename string (e.g., `"edited_image.png"`). For analysis tools, it should be a short, descriptive string or the direct answer (e.g., `"a red sports car"`). +3. The observation must be grounded in the user's request. +### User's Goal: +"{user_request}" +### Tool Call to Simulate: +`{api_call_str}` +### Your Single-Line Response: +""" + prompt = prompt_template.format(user_request=self.user_request, api_call_str=api_call_str) + try: + response_text, tokens_used = self.client.send(prompt) + if response_text and response_text.strip().startswith("Observation: tool_output ="): + return response_text.strip().split('\n')[0], tokens_used + return 'Observation: tool_output = "Error: Tool simulation failed."', tokens_used + except Exception: + return 'Observation: tool_output = "Error: Tool simulation encountered an exception."', 0 + +# ───────────────────────────────────────────────────────────────────────────── +# Core ReAct Logic +# ───────────────────────────────────────────────────────────────────────────── + +def parse_react_response(text: str) -> Tuple[Optional[str], Optional[str]]: + """Extracts Thought and Action from the LLM's response.""" + thought_match = re.search(r"Thought:\s*(.*)", text, re.DOTALL) + action_match = re.search(r"Action:\s*(.*)", text, re.DOTALL) + + thought = thought_match.group(1).strip() if thought_match else None + action = action_match.group(1).strip() if action_match else None + + return thought, action + +def process_problem_with_react(problem_info: Dict) -> Optional[Dict]: + """ + Generates and evaluates a plan for a given problem using the ReAct methodology. + """ + idx, example, api_family, log_path, log_lock, max_steps, parsed_tool_data = ( + problem_info['dataset_index'], problem_info['example'], problem_info['api_family_for_tools'], + problem_info['log_path'], problem_info['log_lock'], problem_info['max_steps'], + problem_info['parsed_tool_data'] + ) + + def write_log(message: str): + with log_lock: + with log_path.open("a", encoding="utf-8") as f: + f.write(f"--- Problem {idx} ({example['id']}) ---\n{message}\n" + "="*80 + "\n\n") + + user_request_text = example['instruction'] + client = RitsChatClient(temperature=0.1, max_tokens=1024) + tool_validator = ToolValidator(parsed_tool_data) + simulated_executor = SimulatedToolExecutor(user_request=user_request_text) + + try: + tools_description = load_tool_descriptions_from_file(Path("Taskbench") / f"data_{api_family}") + graph_description = load_graph_descriptions_from_file(Path("Taskbench") / f"data_{api_family}") + except (FileNotFoundError, ValueError) as e: + write_log(f"CRITICAL ERROR: Could not load descriptions. Error: {e}"); return None + + # Construct the ReAct prompt + prompt_template = """You are an expert assistant that reasons and acts to solve a user's request. You operate in a Thought-Action-Observation cycle. + +### RULES +1. **Always** use the following format for your response: + Thought: (Your reasoning about the current state and what to do next) + Action: (A single `api_call(...)` or the final `finish(...)` call) +2. The `finish(reason="...")` action is used ONLY when the user's request is fully satisfied. +3. Analyze the observation from the previous step to inform your next thought. + +### AVAILABLE TOOLS +{tools_description} +{graph_description} + +### TASK +User Request: {user_request} + +--- START OF TRAJECTORY --- +{history} +""" + + start_time = time.time() + history: List[str] = [] + task_steps: List[str] = [] + total_llm_tokens = 0 + llm_calls = 0 + + for step in range(max_steps): + # Construct the prompt for the current step + history_str = "\n".join(history) + prompt = prompt_template.format( + tools_description=tools_description, + graph_description=graph_description, + user_request=user_request_text, + history=history_str + ) + + response, tokens_used = client.send(prompt) + total_llm_tokens += tokens_used + llm_calls += 1 + + thought, action = parse_react_response(response) + + if not thought or not action: + write_log(f"Step {step+1}: Failed to parse Thought/Action from response:\n{response}") + break # End trajectory if format is broken + + history.append(f"Thought: {thought}") + history.append(f"Action: {action}") + task_steps.append(action) + + if action.startswith("finish("): + break # Task is complete + + if tool_validator.validate_api_call(action): + observation, sim_tokens = simulated_executor.execute(action) + total_llm_tokens += sim_tokens + history.append(observation) + else: + history.append("Observation: Error: Invalid tool call or syntax. Please check the tool's description and parameters.") + + generation_time_seconds = time.time() - start_time + + # Evaluate the final plan + final_reward_score = 0.0 + EVALUATION_PROMPT = """Did the 'Generated Plan' successfully solve the 'User Request'? Answer with only "Yes" or "No".\n[User Request]:\n{user_request}\n\n[Generated Plan]:\n{generated_plan}\n\n[Answer (Yes/No)]:""" + try: + eval_client = RitsChatClient(temperature=0.0, max_tokens=10) + eval_prompt = EVALUATION_PROMPT.format(user_request=user_request_text, generated_plan="\n".join(task_steps)) + verdict, _ = eval_client.send(eval_prompt) + if verdict.strip().lower().startswith("yes"): final_reward_score = 1.0 + except Exception as e: + write_log(f"Warning: LLM-based evaluation failed. Error: {e}") + + final_output = { + "id": example['id'], + "result": { + "task_steps": task_steps + }, + "metrics": { + "accuracy": final_reward_score, + "generation_time_seconds": round(generation_time_seconds, 2), + "plan_length": sum(1 for s in task_steps if s.startswith("api_call")), + "reasoning_cost": { + "llm_calls": llm_calls, + "total_llm_tokens": total_llm_tokens, + } + } + } + return {"record": final_output} + +def load_hf(config_name: str): + try: + ds = load_dataset('microsoft/Taskbench', name=config_name, split='test') + for ex in ds: + yield {'id': ex['id'], 'instruction': ex['instruction'], 'input': ex.get('input',''), 'tool_steps': ex.get('tool_steps',[])} + except Exception as e: + print(f"\n❌ Failed to load '{config_name}' from Hugging Face.", file=sys.stderr); sys.exit(1) + +# ───────────────────────────────────────────────────────────────────────────── +# Main Orchestrator +# ───────────────────────────────────────────────────────────────────────────── +def main(): + ap = argparse.ArgumentParser(description="Run ReAct Baseline on TaskBench.") + + ap.add_argument('--run_name', type=str, default=None, help="Optional name for the output directory.") + ap.add_argument('--api_family', type=str, default='huggingface', help="API family to test.") + ap.add_argument('--num_problems', type=int, default=50, help="Number of problems to sample.") + ap.add_argument('--seed', type=int, default=42, help="Random seed for reproducibility.") + ap.add_argument('--model_name', type=str, default='llama_4', help="The model checkpoint to use.") + ap.add_argument('--max_steps', type=int, default=10, help="Maximum number of steps in a ReAct trajectory.") + ap.add_argument('--max_workers', type=int, default=os.cpu_count(), help="Maximum parallel processes.") + args = ap.parse_args() + + valid_rits_models = list(MODEL_ID_MAP["rits"].keys()) + if args.model_name not in valid_rits_models: + print(f"❌ Error: Invalid model name '{args.model_name}'. Choose from: {valid_rits_models}", file=sys.stderr); sys.exit(1) + + MODELMAP.set_model('generate_model', args.model_name) + print(f"✅ Configured to use model: {MODELMAP.generate_model}") + + random.seed(args.seed); np.random.seed(args.seed); torch.manual_seed(args.seed) + if torch.cuda.is_available(): torch.cuda.manual_seed_all(args.seed) + + run_name = args.run_name + if run_name is None: + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + run_name = f"react_{args.api_family}_{args.model_name}_{timestamp}" + print(f"✅ No run name provided. Using auto-generated name: {run_name}") + + run_dir = Path('predictions') / run_name; run_dir.mkdir(parents=True, exist_ok=True) + log_path = run_dir / 'debug_log.txt' + if log_path.exists(): log_path.unlink() + print(f"✅ Outputs will be saved in: {run_dir}") + + api_family_data_path = Path("Taskbench") / f"data_{args.api_family}" + try: + with open(api_family_data_path / "tool_desc.json", 'r', encoding='utf-8') as f: + parsed_tool_data = json.load(f) + except Exception as e: + print(f"⚠️ Warning: Could not parse tool_desc.json: {e}", file=sys.stderr); parsed_tool_data = {"nodes": []} + + # --- Load Data (with local override) --- + local_data_dir = Path("Taskbench") / f"data_{args.api_family}" + all_records = [] + + # The 'load_local' function needs to be present in each script + def load_local(data_dir: Path): + path = data_dir / 'user_requests.jsonl' + if not path.exists(): path = data_dir / 'user_requests.json' + with path.open('r', encoding='utf-8') as f: + for line in f: + yield json.loads(line) + + # Check if a local directory for the api_family exists + if local_data_dir.is_dir(): + print(f"✅ Found local dataset at '{local_data_dir}'. Loading...") + all_records = list(load_local(local_data_dir)) + else: + # Fallback to Hugging Face if no local data is found + print(f"✅ No local dataset found. Loading '{args.api_family}' from Hugging Face...") + all_records = list(load_hf(config_name=args.api_family)) + + if not all_records: + print(f"❌ No problems loaded for API family '{args.api_family}'. Exiting.", file=sys.stderr) + sys.exit(1) + + # Shuffle and select the specified number of problems + num_to_process = min(args.num_problems, len(all_records)) + random.shuffle(all_records) + records_to_process = all_records[:num_to_process] + print(f"✅ Loaded {len(all_records)} problems, processing {len(records_to_process)}.") + + with Manager() as manager: + log_lock = manager.Lock() + + problems_to_submit = [{ + "dataset_index": j, "example": ex, "api_family_for_tools": args.api_family, + "log_path": log_path, "log_lock": log_lock, "max_steps": args.max_steps, + "parsed_tool_data": parsed_tool_data + } for j, ex in enumerate(records_to_process)] + + run_results = [] + with ProcessPoolExecutor(max_workers=args.max_workers) as executor: + futures = {executor.submit(process_problem_with_react, prob): prob['dataset_index'] for prob in problems_to_submit} + for future in tqdm(as_completed(futures), total=len(records_to_process), desc=f"ReAct on {args.api_family}"): + try: + result = future.result() + if result: run_results.append(result['record']) + except Exception as e: + print(f"Problem {futures[future]} failed: {e}", file=sys.stderr) + + run_output_path = run_dir / 'results.json' + with run_output_path.open("w", encoding="utf-8") as f: json.dump(run_results, f, indent=2) + + total_correct = sum(1 for r in run_results if r.get('metrics', {}).get('accuracy', 0.0) > 0.9) + total_problems = len(run_results) + accuracy = (total_correct / total_problems) * 100 if total_problems > 0 else 0 + + summary = { + "run_name": run_name, "model_name": args.model_name, "api_family": args.api_family, + "num_problems_processed": len(records_to_process), "seed": args.seed, + "final_accuracy": f"{accuracy:.2f}%" + } + summary_path = run_dir / 'summary.json' + with summary_path.open("w", encoding="utf-8") as f: json.dump(summary, f, indent=2) + + print(f"\n{'='*25} Experiment Complete {'='*25}") + print(f"📊 Final Accuracy: {accuracy:.2f}%") + print(f"✅ Results saved to {run_output_path}") + print(f"✅ Final summary saved to {summary_path}") + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/scripts/taskbench_react_rafa_baseline.py b/scripts/taskbench_react_rafa_baseline.py new file mode 100644 index 0000000..10322b6 --- /dev/null +++ b/scripts/taskbench_react_rafa_baseline.py @@ -0,0 +1,386 @@ +#!/usr/bin/env python3 +# taskbench_react_rafa_baseline.py + +import os +import sys +import json +import time +import argparse +from pathlib import Path +from typing import List, Optional, Dict, Tuple +from concurrent.futures import ProcessPoolExecutor, as_completed +from multiprocessing import Manager +import ast +import re +from datetime import datetime +import random +import numpy as np +import torch + +from datasets import load_dataset +from tqdm import tqdm +from SPIRAL.scripts.utils.ritz_client import RitsChatClient, MODELMAP, MODEL_ID_MAP + +# ───────────────────────────────────────────────────────────────────────────── +# NOTE: The following helper functions and constants are copied from the +# other scripts to ensure a fair and consistent experimental setup. +# ───────────────────────────────────────────────────────────────────────────── + +CORRECTED_TOOL_PARAMETERS = { + "Token Classification": {"text": "string"}, "Translation": {"text": "string", "source_lang": "string", "target_lang": "string"}, "Summarization": {"text": "string"}, "Question Answering": {"context": "string", "question": "string"}, "Conversational": {"prompt": "string", "history": "list"}, "Text Generation": {"prompt": "string"}, "Sentence Similarity": {"sentence1": "string", "sentence2": "string"}, "Tabular Classification": {"table_image_path": "string"}, "Object Detection": {"image_path": "string"}, "Image Classification": {"image_path": "string"}, "Image-to-Image": {"image_path": "string", "target_image_path": "string"}, "Image-to-Text": {"image_path": "string"}, "Text-to-Image": {"prompt": "string"}, "Text-to-Video": {"prompt": "string"}, "Visual Question Answering": {"image_path": "string", "question": "string"}, "Document Question Answering": {"document_image_path": "string", "question": "string"}, "Image Segmentation": {"image_path": "string"}, "Depth Estimation": {"image_path": "string"}, "Text-to-Speech": {"text": "string"}, "Automatic Speech Recognition": {"audio_path": "string"}, "Audio-to-Audio": {"audio_path": "string"}, "Audio Classification": {"audio_path": "string"}, "Image Editing": {"image_path": "string", "edits": "dict"}, "get_weather": {"location": "string", "date": "string"}, "get_news_for_topic": {"topic": "string"}, "stock_operation": {"stock": "string", "operation": "string"}, "book_flight": {"date": "string", "from": "string", "to": "string"}, "book_hotel": {"date": "string", "name": "string"}, "book_restaurant": {"date": "string", "name": "string"}, "book_car": {"date": "string", "location": "string"}, "online_shopping": {"website": "string", "product": "string"}, "send_email": {"email_address": "string", "content": "string"}, "send_sms": {"phone_number": "string", "content": "string"}, "share_by_social_network": {"content": "string", "social_network": "string"}, "search_by_engine": {"query": "string", "engine": "string"}, "apply_for_job": {"job": "string"}, "see_doctor_online": {"disease": "string", "doctor": "string"}, "consult_lawyer_online": {"issue": "string", "lawyer": "string"}, "enroll_in_course": {"course": "string", "university": "string"}, "buy_insurance": {"insurance": "string", "company": "string"}, "online_banking": {"instruction": "string", "bank": "string"}, "daily_bill_payment": {"bill": "string"}, "sell_item_online": {"item": "string", "store": "string"}, "do_tax_return": {"year": "string"}, "apply_for_passport": {"country": "string"}, "pay_for_credit_card": {"credit_card": "string"}, "auto_housework_by_robot": {"instruction": "string"}, "auto_driving_to_destination": {"destination": "string"}, "deliver_package": {"package": "string", "destination": "string"}, "order_food_delivery": {"food": "string", "location": "string", "platform": "string"}, "order_taxi": {"location": "string", "platform": "string"}, "play_music_by_title": {"title": "string"}, "play_movie_by_title": {"title": "string"}, "take_note": {"content": "string"}, "borrow_book_online": {"book": "string", "library": "string"}, "recording_audio": {"content": "string"}, "make_video_call": {"phone_number": "string"}, "make_voice_call": {"phone_number": "string"}, "organize_meeting_online": {"topic": "string"}, "attend_meeting_online": {"topic": "string"}, "software_management": {"software": "string", "instruction": "string"}, "print_document": {"document": "string"}, "set_alarm": {"time": "string"}, +} + +def load_tool_descriptions_from_file(api_family_data_dir: Path) -> str: + # (Implementation is identical to previous scripts) + tool_desc_path = api_family_data_dir / "tool_desc.json" + if not tool_desc_path.exists(): + raise FileNotFoundError(f"Tool description file not found: {tool_desc_path}.") + with open(tool_desc_path, 'r', encoding='utf-8') as f: tool_data_root = json.load(f) + description_parts = ["Available tools (use the `api_call` function to invoke them):"] + tool_nodes = tool_data_root.get("nodes", []) + for tool_node in tool_nodes: + tool_id, tool_desc = tool_node.get("id"), tool_node.get("desc") + parameters = tool_node.get("parameters", []) + if not tool_id or not tool_desc: continue + args_list, example_args_dict = [], {} + effective_parameters = [{"name": n, "type": t} for n, t in CORRECTED_TOOL_PARAMETERS.get(tool_id, {}).items()] or parameters + for param in effective_parameters: + param_name, param_type = param.get("name"), param.get("type", "Any") + if param_name: + args_list.append(f"`{param_name}` ({param_type})") + example_args_dict[param_name] = f"<{param_name}_value>" + example_call_str = f"api_call(\"{tool_id}\", {json.dumps(example_args_dict)})" + description_parts.append(f"\n`{example_call_str}`\n Description: {tool_desc}") + if args_list: description_parts.append(f" Parameters: {'; '.join(args_list)}") + return "\n".join(description_parts) + +def load_graph_descriptions_from_file(api_family_data_dir: Path) -> str: + # (Implementation is identical to previous scripts) + graph_desc_path = api_family_data_dir / "graph_desc.json" + if not graph_desc_path.exists(): return "" + with open(graph_desc_path, 'r', encoding='utf-8') as f: graph_data = json.load(f) + description_parts = ["\n--- Tool Dependencies ---"] + for dep_type, deps in graph_data.items(): + if isinstance(deps, list) and deps: + description_parts.append(f"{dep_type.replace('_', ' ').title()}:") + for dep in deps: + pre, post = dep.get("pre_tool"), dep.get("post_tool") + if "resource" in dep_type: + res = ", ".join(dep.get("resources", [])); description_parts.append(f" - `{post}` requires resource(s) `{res}` from `{pre}`.") + elif "temporal" in dep_type: + cond = dep.get("condition", "completion"); description_parts.append(f" - `{post}` can only be called after `{pre}` upon its {cond}.") + return "\n".join(description_parts) if len(description_parts) > 1 else "" + +class SimulatedToolExecutor: + # (Implementation is identical to previous scripts) + def __init__(self, user_request: str): + self.client = RitsChatClient(temperature=0.2, max_tokens=150) + self.user_request = user_request + + def execute(self, api_call_str: str) -> Tuple[str, int]: + prompt_template = """You are a simulated API tool. Provide a realistic, one-line observation for the given tool call. +### User's Goal: +"{user_request}" +### Tool Call to Simulate: +`{api_call_str}` +### Your Single-Line Response (must start with `Observation: tool_output = `): +""" + prompt = prompt_template.format(user_request=self.user_request, api_call_str=api_call_str) + try: + response_text, tokens_used = self.client.send(prompt) + if response_text and response_text.strip().startswith("Observation: tool_output ="): + return response_text.strip().split('\n')[0], tokens_used + return 'Observation: tool_output = "Error: Tool simulation failed."', tokens_used + except Exception: + return 'Observation: tool_output = "Error: Tool simulation encountered an exception."', 0 + +# ───────────────────────────────────────────────────────────────────────────── +# Core ReAct+RAFA Logic +# ───────────────────────────────────────────────────────────────────────────── + +class HybridElite: + def __init__(self, user_request: str, tools_description: str, graph_description: str, breadth: int): + self.client = RitsChatClient(temperature=0.4, max_tokens=1024) + self.prompt_template = """As an Elite Planner, propose diverse next actions to solve the user's request, based on the history. + +### TOOLS +{tools_description} +{graph_description} + +### TASK +User Request: {user_request} + +### CURRENT TRAJECTORY +{history} + +### INSTRUCTION +Generate a Python list of {breadth} distinct `api_call(...)` or `finish(...)` actions to take next. +Respond with ONLY a Python list of strings in a markdown block. +```python +["action_1", "action_2"] +```""" + self.user_request, self.tools_desc, self.graph_desc, self.breadth = user_request, tools_description, graph_description, breadth + + def propose(self, history: str) -> Tuple[List[str], int]: + prompt = self.prompt_template.format(tools_description=self.tools_desc, graph_description=self.graph_desc, user_request=self.user_request, history=history, breadth=self.breadth) + response, tokens = self.client.send(prompt) + match = re.search(r"```(?:python)?\s*(\[.*?\])\s*```", response, re.DOTALL) + if match: + try: + plan = ast.literal_eval(match.group(1).strip()) + if isinstance(plan, list) and all(isinstance(p, str) for p in plan): return plan, tokens + except: pass + return [], tokens + +class HybridCritic: + def __init__(self, user_request: str): + self.client = RitsChatClient(temperature=0.0, max_tokens=100) + self.prompt_template = """As a Critic, evaluate the following plan's likelihood of success. + +### TASK +User Request: {user_request} + +### PROPOSED PLAN +{trajectory} + +### INSTRUCTION +Respond with ONLY a single line: `Score: | Justification: `""" + self.user_request = user_request + + def evaluate(self, trajectory: str) -> Tuple[float, str, int]: + prompt = self.prompt_template.format(user_request=self.user_request, trajectory=trajectory) + response, tokens = self.client.send(prompt) + score_match = re.search(r"Score:\s*([0-9.]+)", response) + just_match = re.search(r"Justification:\s*(.*)", response) + score = float(score_match.group(1)) if score_match else 0.0 + justification = just_match.group(1).strip() if just_match else "No justification provided." + return score, justification, tokens + +def process_problem_with_react_rafa(problem_info: Dict) -> Optional[Dict]: + idx, example, api_family, log_path, log_lock, args = ( + problem_info['dataset_index'], problem_info['example'], problem_info['api_family_for_tools'], + problem_info['log_path'], problem_info['log_lock'], problem_info['args'] + ) + + def write_log(message: str): + with log_lock: + with log_path.open("a", encoding="utf-8") as f: + f.write(f"--- Problem {idx} ({example['id']}) ---\n{message}\n" + "="*80 + "\n\n") + + user_request_text = example['instruction'] + try: + tools_desc = load_tool_descriptions_from_file(Path("Taskbench") / f"data_{api_family}") + graph_desc = load_graph_descriptions_from_file(Path("Taskbench") / f"data_{api_family}") + except Exception as e: + write_log(f"CRITICAL ERROR: Could not load descriptions. Error: {e}"); return None + + elite_agent = HybridElite(user_request_text, tools_desc, graph_desc, args.search_breadth) + model_agent = SimulatedToolExecutor(user_request_text) # Internal model for planning + critic_agent = HybridCritic(user_request_text) + real_environment = SimulatedToolExecutor(user_request_text) # External environment + + start_time = time.time() + # Memory buffer now stores the full ReAct-style trajectory + memory_buffer = ["User Request: " + user_request_text] + final_plan_steps = [] + total_tokens = 0 + + for real_step in range(args.max_real_steps): + # --- 1. REASON FOR FUTURE (RAFA Planning) --- + history_str = "\n".join(memory_buffer) + candidate_actions, elite_tokens = elite_agent.propose(history_str) + total_tokens += elite_tokens + + planned_trajectories = [] + for action in candidate_actions: + trajectory = [action] + sim_history_list = memory_buffer + [f"Action: {action}"] # Use a temporary history for simulation + if not action.startswith("finish("): + obs, model_tokens = model_agent.execute(action) + total_tokens += model_tokens + sim_history_list.append(obs) + + # Lookahead for `search_depth` steps + current_beam = [ (trajectory, sim_history_list) ] + for depth in range(args.search_depth - 1): + next_beam = [] + for traj, hist in current_beam: + next_actions, next_elite_tokens = elite_agent.propose("\n".join(hist)) + total_tokens += next_elite_tokens + if not next_actions: continue + + next_action = next_actions[0] # Single beam for simplicity + new_traj = traj + [next_action] + new_hist = hist + [f"Action: {next_action}"] + if next_action.startswith("finish("): + next_beam.append( (new_traj, new_hist) ) + break + + obs, model_tokens = model_agent.execute(next_action) + total_tokens += model_tokens + new_hist.append(obs) + next_beam.append( (new_traj, new_hist) ) + current_beam = next_beam + if not current_beam: break + + if current_beam: + planned_trajectories.extend([traj for traj, hist in current_beam]) + + # Evaluate trajectories and select the best one + best_trajectory, best_justification, max_score = None, "No valid plan was found.", -1.0 + for trajectory in planned_trajectories: + score, justification, critic_tokens = critic_agent.evaluate("\n".join(trajectory)) + total_tokens += critic_tokens + if score > max_score: + max_score, best_trajectory, best_justification = score, trajectory, justification + + if not best_trajectory: + write_log("ReAct+RAFA planning failed to produce a trajectory.") + break + + # --- 2. ACT FOR NOW (ReAct-style Execution) --- + thought = best_justification + action_to_execute = best_trajectory[0] + + final_plan_steps.append(action_to_execute) + memory_buffer.append(f"Thought: {thought}") + memory_buffer.append(f"Action: {action_to_execute}") + + if action_to_execute.startswith("finish("): + break + + observation, env_tokens = real_environment.execute(action_to_execute) + total_tokens += env_tokens + memory_buffer.append(observation) + + generation_time_seconds = time.time() - start_time + + # Evaluate the final executed plan + final_reward_score = 0.0 + EVALUATION_PROMPT = """Did the 'Generated Plan' successfully solve the 'User Request'? Answer with only "Yes" or "No".\n[User Request]:\n{user_request}\n\n[Generated Plan]:\n{generated_plan}\n\n[Answer (Yes/No)]:""" + try: + eval_client = RitsChatClient(temperature=0.0, max_tokens=10) + eval_prompt = EVALUATION_PROMPT.format(user_request=user_request_text, generated_plan="\n".join(final_plan_steps)) + verdict, _ = eval_client.send(eval_prompt) + if verdict.strip().lower().startswith("yes"): final_reward_score = 1.0 + except Exception as e: + write_log(f"Warning: LLM-based evaluation failed. Error: {e}") + + final_output = { + "id": example['id'], + "result": {"task_steps": final_plan_steps}, + "metrics": { + "accuracy": final_reward_score, + "generation_time_seconds": round(generation_time_seconds, 2), + "plan_length": sum(1 for s in final_plan_steps if s.startswith("api_call")), + "reasoning_cost": {"total_llm_tokens": total_tokens} + } + } + return {"record": final_output} + +def load_hf(config_name: str): + try: + ds = load_dataset('microsoft/Taskbench', name=config_name, split='test') + for ex in ds: yield {'id': ex['id'], 'instruction': ex['instruction'], 'input': ex.get('input',''), 'tool_steps': ex.get('tool_steps',[])} + except Exception as e: + print(f"\n❌ Failed to load '{config_name}' from Hugging Face.", file=sys.stderr); sys.exit(1) + +def main(): + ap = argparse.ArgumentParser(description="Run ReAct+RAFA Hybrid Baseline on TaskBench.") + + ap.add_argument('--run_name', type=str, default=None) + ap.add_argument('--api_family', type=str, default='huggingface') + ap.add_argument('--num_problems', type=int, default=50) + ap.add_argument('--seed', type=int, default=42) + ap.add_argument('--model_name', type=str, default='llama_4') + ap.add_argument('--max_real_steps', type=int, default=8, help="Max steps in the ReAct loop.") + ap.add_argument('--search_breadth', type=int, default=3, help="RAFA planner breadth (B).") + ap.add_argument('--search_depth', type=int, default=2, help="RAFA planner depth (U).") + ap.add_argument('--max_workers', type=int, default=os.cpu_count()) + args = ap.parse_args() + + valid_rits_models = list(MODEL_ID_MAP["rits"].keys()) + if args.model_name not in valid_rits_models: + print(f"❌ Error: Invalid model name '{args.model_name}'. Choose from: {valid_rits_models}", file=sys.stderr); sys.exit(1) + + MODELMAP.set_model('generate_model', args.model_name) + print(f"✅ Configured to use model: {MODELMAP.generate_model}") + + random.seed(args.seed); np.random.seed(args.seed); torch.manual_seed(args.seed) + if torch.cuda.is_available(): torch.cuda.manual_seed_all(args.seed) + + run_name = args.run_name or f"react_rafa_b{args.search_breadth}d{args.search_depth}_{args.api_family}_{args.model_name}_{datetime.now().strftime('%Y%m%d_%H%M%S')}" + run_dir = Path('predictions') / run_name; run_dir.mkdir(parents=True, exist_ok=True) + log_path = run_dir / 'debug_log.txt' + if log_path.exists(): log_path.unlink() + print(f"✅ Outputs will be saved in: {run_dir}") + + # --- Load Data (with local override) --- + local_data_dir = Path("Taskbench") / f"data_{args.api_family}" + all_records = [] + + # The 'load_local' function needs to be present in each script + def load_local(data_dir: Path): + path = data_dir / 'user_requests.jsonl' + if not path.exists(): path = data_dir / 'user_requests.json' + with path.open('r', encoding='utf-8') as f: + for line in f: + yield json.loads(line) + + # Check if a local directory for the api_family exists + if local_data_dir.is_dir(): + print(f"✅ Found local dataset at '{local_data_dir}'. Loading...") + all_records = list(load_local(local_data_dir)) + else: + # Fallback to Hugging Face if no local data is found + print(f"✅ No local dataset found. Loading '{args.api_family}' from Hugging Face...") + all_records = list(load_hf(config_name=args.api_family)) + + if not all_records: + print(f"❌ No problems loaded for API family '{args.api_family}'. Exiting.", file=sys.stderr) + sys.exit(1) + + # Shuffle and select the specified number of problems + num_to_process = min(args.num_problems, len(all_records)) + random.shuffle(all_records) + records_to_process = all_records[:num_to_process] + print(f"✅ Loaded {len(all_records)} problems, processing {len(records_to_process)}.") + + with Manager() as manager: + log_lock = manager.Lock() + + problems_to_submit = [{"dataset_index": j, "example": ex, "api_family_for_tools": args.api_family, "log_path": log_path, "log_lock": log_lock, "args": args} for j, ex in enumerate(records_to_process)] + + run_results = [] + desc = f"ReAct+RAFA (B={args.search_breadth}, D={args.search_depth}) on {args.api_family}" + with ProcessPoolExecutor(max_workers=args.max_workers) as executor: + futures = {executor.submit(process_problem_with_react_rafa, prob): prob['dataset_index'] for prob in problems_to_submit} + for future in tqdm(as_completed(futures), total=len(records_to_process), desc=desc): + try: + result = future.result() + if result: run_results.append(result['record']) + except Exception as e: + print(f"Problem {futures[future]} failed: {e}", file=sys.stderr) + + run_output_path = run_dir / 'results.json' + with run_output_path.open("w", encoding="utf-8") as f: json.dump(run_results, f, indent=2) + + total_correct = sum(1 for r in run_results if r.get('metrics', {}).get('accuracy', 0.0) > 0.9) + accuracy = (total_correct / len(run_results)) * 100 if run_results else 0 + + summary = { + "run_name": run_name, "model_name": args.model_name, "api_family": args.api_family, + "num_problems_processed": len(records_to_process), "seed": args.seed, + "search_breadth": args.search_breadth, "search_depth": args.search_depth, + "final_accuracy": f"{accuracy:.2f}%" + } + summary_path = run_dir / 'summary.json' + with summary_path.open("w", encoding="utf-8") as f: json.dump(summary, f, indent=2) + + print(f"\n{'='*25} Experiment Complete {'='*25}") + print(f"📊 Final Accuracy: {accuracy:.2f}%") + print(f"✅ Results saved to {run_output_path}") + print(f"✅ Final summary saved to {summary_path}") + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/scripts/taskbench_smriv_mcts_ablations.py b/scripts/taskbench_smriv_mcts_ablations.py new file mode 100644 index 0000000..8c5fce2 --- /dev/null +++ b/scripts/taskbench_smriv_mcts_ablations.py @@ -0,0 +1,480 @@ +#!/usr/bin/env python3 +# taskbench_smriv_mcts_ablations.py + +import os +import sys +import json +import time +import math +import argparse +from pathlib import Path +from typing import List, Optional, Dict, Any, Tuple +from dataclasses import dataclass, field +from concurrent.futures import ProcessPoolExecutor, as_completed +from multiprocessing import Manager +import collections +import ast +import re +from datetime import datetime + +import random + +# New dependencies for semantic matching and rule evolution +import numpy as np +import torch +from sentence_transformers import SentenceTransformer + +from datasets import load_dataset +from tqdm import tqdm +from SPIRAL.scripts.utils.ritz_client import RitsChatClient, MODELMAP, MODEL_ID_MAP + +def make_value_hashable(value: Any) -> Any: + """Recursively converts lists to tuples and dicts to frozensets of items.""" + if isinstance(value, dict): + return frozenset((k, make_value_hashable(v)) for k, v in value.items()) + if isinstance(value, list): + return tuple(make_value_hashable(v) for v in value) + return value + +# ───────────────────────────────────────────────────────────────────────────── +# Manually corrected/enriched parameter definitions for common tools +# ───────────────────────────────────────────────────────────────────────────── +CORRECTED_TOOL_PARAMETERS = { + "Token Classification": {"text": "string"}, "Translation": {"text": "string", "source_lang": "string", "target_lang": "string"}, "Summarization": {"text": "string"}, "Question Answering": {"context": "string", "question": "string"}, "Conversational": {"prompt": "string", "history": "list"}, "Text Generation": {"prompt": "string"}, "Sentence Similarity": {"sentence1": "string", "sentence2": "string"}, "Tabular Classification": {"table_image_path": "string"}, "Object Detection": {"image_path": "string"}, "Image Classification": {"image_path": "string"}, "Image-to-Image": {"image_path": "string", "target_image_path": "string"}, "Image-to-Text": {"image_path": "string"}, "Text-to-Image": {"prompt": "string"}, "Text-to-Video": {"prompt": "string"}, "Visual Question Answering": {"image_path": "string", "question": "string"}, "Document Question Answering": {"document_image_path": "string", "question": "string"}, "Image Segmentation": {"image_path": "string"}, "Depth Estimation": {"image_path": "string"}, "Text-to-Speech": {"text": "string"}, "Automatic Speech Recognition": {"audio_path": "string"}, "Audio-to-Audio": {"audio_path": "string"}, "Audio Classification": {"audio_path": "string"}, "Image Editing": {"image_path": "string", "edits": "dict"}, "get_weather": {"location": "string", "date": "string"}, "get_news_for_topic": {"topic": "string"}, "stock_operation": {"stock": "string", "operation": "string"}, "book_flight": {"date": "string", "from": "string", "to": "string"}, "book_hotel": {"date": "string", "name": "string"}, "book_restaurant": {"date": "string", "name": "string"}, "book_car": {"date": "string", "location": "string"}, "online_shopping": {"website": "string", "product": "string"}, "send_email": {"email_address": "string", "content": "string"}, "send_sms": {"phone_number": "string", "content": "string"}, "share_by_social_network": {"content": "string", "social_network": "string"}, "search_by_engine": {"query": "string", "engine": "string"}, "apply_for_job": {"job": "string"}, "see_doctor_online": {"disease": "string", "doctor": "string"}, "consult_lawyer_online": {"issue": "string", "lawyer": "string"}, "enroll_in_course": {"course": "string", "university": "string"}, "buy_insurance": {"insurance": "string", "company": "string"}, "online_banking": {"instruction": "string", "bank": "string"}, "daily_bill_payment": {"bill": "string"}, "sell_item_online": {"item": "string", "store": "string"}, "do_tax_return": {"year": "string"}, "apply_for_passport": {"country": "string"}, "pay_for_credit_card": {"credit_card": "string"}, "auto_housework_by_robot": {"instruction": "string"}, "auto_driving_to_destination": {"destination": "string"}, "deliver_package": {"package": "string", "destination": "string"}, "order_food_delivery": {"food": "string", "location": "string", "platform": "string"}, "order_taxi": {"location": "string", "platform": "string"}, "play_music_by_title": {"title": "string"}, "play_movie_by_title": {"title": "string"}, "take_note": {"content": "string"}, "borrow_book_online": {"book": "string", "library": "string"}, "recording_audio": {"content": "string"}, "make_video_call": {"phone_number": "string"}, "make_voice_call": {"phone_number": "string"}, "organize_meeting_online": {"topic": "string"}, "attend_meeting_online": {"topic": "string"}, "software_management": {"software": "string", "instruction": "string"}, "print_document": {"document": "string"}, "set_alarm": {"time": "string"}, +} + +# --- Global Sentence Transformer Model (Preserved from original) --- +SENTENCE_MODEL = None +def get_sentence_model(): + """Initializes and returns the sentence transformer model as a singleton.""" + global SENTENCE_MODEL + if SENTENCE_MODEL is None: + SENTENCE_MODEL = SentenceTransformer('all-MiniLM-L6-v2') + return SENTENCE_MODEL + +def parse_tool_code(text: str) -> str: + """Extracts Python code from a markdown block.""" + match = re.search(r"```(?:python\n)?(.*?)\n?```", text, re.DOTALL) + return match.group(1).strip() if match else text.strip() + +# ───────────────────────────────────────────────────────────────────────────── +# Functions to load and format tool & graph descriptions dynamically +# ───────────────────────────────────────────────────────────────────────────── +def load_tool_descriptions_from_file(api_family_data_dir: Path) -> str: + """Loads and formats tool descriptions from the specified tool_desc.json file.""" + tool_desc_path = api_family_data_dir / "tool_desc.json" + if not tool_desc_path.exists(): + raise FileNotFoundError(f"Tool description file not found: {tool_desc_path}.") + try: + with open(tool_desc_path, 'r', encoding='utf-8') as f: + tool_data_root = json.load(f) + except json.JSONDecodeError as e: + raise ValueError(f"Invalid JSON in {tool_desc_path}: {e}") from e + + description_parts = ["Available tools (use the `api_call` function to invoke them):"] + if not isinstance(tool_data_root, dict) or "nodes" not in tool_data_root: + raise ValueError("Expected tool_desc.json to have a root dict with a 'nodes' key.") + tool_nodes = tool_data_root["nodes"] + + for tool_node in tool_nodes: + if not isinstance(tool_node, dict): continue + tool_id, tool_desc = tool_node.get("id"), tool_node.get("desc") + parameters = tool_node.get("parameters", []) + if not tool_id or not tool_desc: continue + + args_list, example_args_dict = [], {} + effective_parameters = [{"name": n, "type": t} for n, t in CORRECTED_TOOL_PARAMETERS.get(tool_id, {}).items()] or parameters + + for param in effective_parameters: + param_name, param_type = param.get("name"), param.get("type", "Any") + if param_name: + args_list.append(f"`{param_name}` ({param_type})") + example_args_dict[param_name] = f"<{param_name}_value>" + + example_call_str = f"api_call(\"{tool_id}\", {json.dumps(example_args_dict)})" + description_parts.append(f"\n`{example_call_str}`") + description_parts.append(f" Description: {tool_desc}") + if args_list: description_parts.append(f" Parameters: {'; '.join(args_list)}") + return "\n".join(description_parts) + +def load_graph_descriptions_from_file(api_family_data_dir: Path) -> str: + """Loads and formats graph (dependency) descriptions for the LLM prompt.""" + graph_desc_path = api_family_data_dir / "graph_desc.json" + if not graph_desc_path.exists(): return "" + try: + with open(graph_desc_path, 'r', encoding='utf-8') as f: + graph_data = json.load(f) + except (json.JSONDecodeError, Exception) as e: + print(f"Warning: Could not read {graph_desc_path}: {e}", file=sys.stderr) + return "" + + description_parts = ["\n--- Tool Dependencies ---"] + for dep_type, deps in graph_data.items(): + if isinstance(deps, list) and deps: + description_parts.append(f"{dep_type.replace('_', ' ').title()}:") + for dep in deps: + if not isinstance(dep, dict): continue + pre, post = dep.get("pre_tool"), dep.get("post_tool") + if "resource" in dep_type: + res = ", ".join(dep.get("resources", [])) + description_parts.append(f" - `{post}` requires resource(s) `{res}` from `{pre}`.") + elif "temporal" in dep_type: + cond = dep.get("condition", "completion") + description_parts.append(f" - `{post}` can only be called after `{pre}` upon its {cond}.") + + return "\n".join(description_parts) if len(description_parts) > 1 else "" + +# ───────────────────────────────────────────────────────────────────────────── +# Tool Validator (Frontend Compiler) +# ───────────────────────────────────────────────────────────────────────────── +class ToolValidator: + def __init__(self, parsed_tool_data_root: Dict, debug_llm_output: bool = False): + self.tool_signatures = collections.defaultdict(dict) + self.debug_llm_output = debug_llm_output + if not isinstance(parsed_tool_data_root, dict) or "nodes" not in parsed_tool_data_root: + return + tool_nodes = parsed_tool_data_root["nodes"] + for tool_node in tool_nodes: + if not isinstance(tool_node, dict): continue + tool_id, parameters = tool_node.get("id"), tool_node.get("parameters", []) + if tool_id: + effective_params = [{"name": n, "type": t} for n, t in CORRECTED_TOOL_PARAMETERS.get(tool_id, {}).items()] or parameters + self.tool_signatures[tool_id] = { + "parameters": {p.get("name"): p.get("type") for p in effective_params if isinstance(p, dict)} + } + + def validate_api_call(self, code_str: str) -> bool: + """Performs basic syntax and semantic validation of an api_call string.""" + match = re.search(r'api_call\("([^"]+)",\s*({.*?})\)', code_str, re.DOTALL) + if not match: return False + tool_id, args_str = match.group(1), match.group(2) + if tool_id not in self.tool_signatures: return False + + expected_params = self.tool_signatures[tool_id]["parameters"] + try: + parsed_args = ast.literal_eval(args_str) + if not isinstance(parsed_args, dict): return False + for arg_name in parsed_args: + if arg_name not in expected_params: return False + return True + except (ValueError, SyntaxError): + return False + +# ───────────────────────────────────────────────────────────────────────────── +# Simulated Tool Executor +# ───────────────────────────────────────────────────────────────────────────── +class SimulatedToolExecutor: + def __init__(self, user_request: str, debug_llm_output: bool = False): + self.client = RitsChatClient(temperature=0.2, max_tokens=150) + self.user_request = user_request + self.debug_llm_output = debug_llm_output + + def execute(self, api_call_str: str, ablation_mode: str) -> Tuple[str, int]: + # ABLATION: No Rich Simulation Feedback + if ablation_mode == 'no_sim_feedback': + return 'Observation: tool_output = "OK"', 0 + + prompt_template = """You are a simulated API tool. Your role is to provide a realistic, one-line observation for the given tool call, based on the user's overall goal. + ### Rules: + 1. Your entire response MUST be a single line starting with `Observation: tool_output = `. + 2. The value part should be a plausible result. For tools that create files (like image editing or generation), the value should be a new, unique filename string (e.g., `"edited_image.png"`). For analysis tools, it should be a short, descriptive string or the direct answer (e.g., `"a red sports car"`). + 3. The observation must be grounded in the user's request. + ### User's Goal: + "{user_request}" + ### Tool Call to Simulate: + `{api_call_str}` + ### Your Single-Line Response: + """ + prompt = prompt_template.format(user_request=self.user_request, api_call_str=api_call_str) + try: + response_text, tokens_used = self.client.send(prompt) + if response_text and response_text.strip().startswith("Observation: tool_output ="): + return response_text.strip().split('\n')[0], tokens_used + else: + if self.debug_llm_output: print(f" Executor LLM failed format. Response: {response_text}", file=sys.stderr) + return 'Observation: tool_output = "Error: Tool simulation failed."', tokens_used + except Exception as e: + if self.debug_llm_output: print(f" Executor LLM call failed: {e}", file=sys.stderr) + return 'Observation: tool_output = "Error: Tool simulation encountered an exception."', 0 + +# ───────────────────────────────────────────────────────────────────────────── +# MCTS Node +# ───────────────────────────────────────────────────────────────────────────── +@dataclass +class Node: + chain: List[str] + parent: Optional["Node"] = None + children: List["Node"] = field(default_factory=list) + visits: int = 0 + value_sum: float = 0.0 + _id: int = field(default_factory=lambda: id(Node)) + + def __post_init__(self): self._id = id(self) + def __hash__(self): return hash(self._id) + def __eq__(self, other): + if not isinstance(other, Node): return NotImplemented + return self._id == other._id + @property + def depth(self) -> int: return 0 if self.parent is None else self.parent.depth + 1 + def backpropagate(self, reward: float): + current = self + while current is not None: + current.visits += 1; current.value_sum += reward; current = current.parent + def uct_score(self, exploration_constant: float = 1.0) -> float: + if self.visits == 0: return float('inf') + if self.parent is None or self.parent.visits == 0: return self.value_sum / self.visits + exploitation = self.value_sum / self.visits + exploration = exploration_constant * math.sqrt(math.log(self.parent.visits) / self.visits) + return exploitation + exploration + +# ───────────────────────────────────────────────────────────────────────────── +# Core Logic for a Single Problem +# ───────────────────────────────────────────────────────────────────────────── +def process_taskbench_problem(problem_info: Dict) -> Optional[Dict]: + idx, example, api_family, debug_llm, parsed_tool_data, log_path, log_lock, ablation_mode = ( + problem_info['dataset_index'], problem_info['example'], problem_info['api_family_for_tools'], + problem_info['debug_llm_output'], problem_info['parsed_tool_data'], + problem_info['log_path'], problem_info['log_lock'], problem_info['ablation_mode'] + ) + + def write_log(message: str): + with log_lock: + with log_path.open("a", encoding="utf-8") as f: + f.write(f"--- Problem {idx} ({example['id']}) | Ablation: {ablation_mode} ---\n{message}\n" + "="*40 + "\n\n") + + user_request_text = example['instruction'] + tool_validator = ToolValidator(parsed_tool_data, debug_llm) + simulated_executor = SimulatedToolExecutor(user_request=user_request_text, debug_llm_output=debug_llm) + planner_client = RitsChatClient(temperature=0.0, max_tokens=1024) + + initial_prompt = f"Instruction: {example['instruction']}" + (f" | Input: {example['input']}" if example.get('input') else "") + BUDGET_ITERATIONS, MAX_DEPTH = 50, 8 + + # --- Initialize metric counters + start_time = time.time() + expansion_llm_calls, expansion_llm_tokens = 0, 0 + simulation_llm_calls, simulation_llm_tokens = 0, 0 + invalid_steps_generated = 0 + + try: + tools_description = load_tool_descriptions_from_file(Path("Taskbench") / f"data_{api_family}") + graph_description = load_graph_descriptions_from_file(Path("Taskbench") / f"data_{api_family}") + except (FileNotFoundError, ValueError) as e: + write_log(f"CRITICAL ERROR: Could not load descriptions. Error: {e}"); return None + + # --- Base Prompt Construction --- + base_prompt_parts = [ + "You are an expert assistant that only responds with code.", "Your task is to create a plan to solve the user's request by generating a sequence of tool calls.", "## RULES:", + "1. Generate ONLY the single next `api_call(...)` or the final `finish(...)` call.", + "2. If a previous step produced an observation `tool_output = `, you MUST use that exact `` in the arguments of the next tool.", + "3. When the user's request is fully satisfied, you MUST call `finish(reason=\"\")`.", + "\n## TOOLS:", tools_description, graph_description, '## FINISH ACTION:\n`finish(reason="")`: Call this ONLY when the task is complete.', + ] + + final_chain = [] + final_best_node = None + total_nodes_explored = 0 + + # --- ABLATION 1: No MCTS (Greedy Search) --- + if ablation_mode == 'no_mcts': + current_chain = [initial_prompt] + for _ in range(MAX_DEPTH): + prompt_list = list(base_prompt_parts) + # ABLATION 3 (Planner): Conditionally add plan history + if ablation_mode != 'no_plan_history': + prompt_list.append(f"## CURRENT PLAN:\n" + "\n".join(current_chain)) + prompt_list.append("\nRespond with ONLY the next line of code:") + prompt_expand = "\n".join(filter(None, prompt_list)) + + response, tokens_used = planner_client.send(prompt_expand); expansion_llm_calls += 1; expansion_llm_tokens += tokens_used + extracted_code = parse_tool_code(response.strip()) + + if extracted_code.startswith("finish("): + current_chain.append(extracted_code); break + + # ABLATION 5 (Validator): Bypass validator if mode is 'no_validator' + if ablation_mode == 'no_validator' or tool_validator.validate_api_call(extracted_code): + observation, sim_tokens = simulated_executor.execute(extracted_code, ablation_mode) + simulation_llm_calls += 1; simulation_llm_tokens += sim_tokens + current_chain.extend([extracted_code, observation]) + else: + invalid_steps_generated += 1; break # End greedy search on invalid step + final_chain = current_chain; total_nodes_explored = 1 + + # --- DEFAULT: Run MCTS (with other potential ablations) --- + else: + root = Node(chain=[initial_prompt]); terminal_nodes = [] + try: + for _ in range(BUDGET_ITERATIONS): + current_node = root + while current_node.children: + current_node = max(current_node.children, key=lambda n: n.uct_score()) + if current_node.depth >= MAX_DEPTH or any("finish(" in step for step in current_node.chain): + current_node.backpropagate(-0.5); continue + + prompt_list = list(base_prompt_parts) + # ABLATION 3 (Planner): Conditionally add plan history + if ablation_mode != 'no_plan_history': + prompt_list.append(f"## CURRENT PLAN:\n" + "\n".join(current_node.chain)) + prompt_list.append("\nRespond with ONLY the next line of code:") + prompt_expand = "\n".join(filter(None, prompt_list)) + + response, tokens_used = planner_client.send(prompt_expand); expansion_llm_calls += 1; expansion_llm_tokens += tokens_used + extracted_code = parse_tool_code(response.strip()) + + if extracted_code.startswith("finish("): + new_node = Node(chain=current_node.chain + [extracted_code], parent=current_node) + current_node.children.append(new_node); terminal_nodes.append(new_node) + new_node.backpropagate(1.0) + # ABLATION 5 (Validator): Bypass validator if mode is 'no_validator' + elif ablation_mode == 'no_validator' or tool_validator.validate_api_call(extracted_code): + observation, sim_tokens = simulated_executor.execute(extracted_code, ablation_mode) + simulation_llm_calls += 1; simulation_llm_tokens += sim_tokens + + # ABLATION 4 (Critic): Use uniform rewards + reward = 0.0 if ablation_mode == 'uniform_rewards' else 0.1 + + new_node = Node(chain=current_node.chain + [extracted_code, observation], parent=current_node) + current_node.children.append(new_node); new_node.backpropagate(reward) + else: + invalid_steps_generated += 1; current_node.backpropagate(-1.0) + except Exception as e: + import traceback; write_log(f"CRITICAL ERROR in MCTS loop: {e}\n{traceback.format_exc()}") + + # Find best node from MCTS + final_best_node = root + if terminal_nodes: + final_best_node = max(terminal_nodes, key=lambda n: n.value_sum / n.visits if n.visits > 0 else -1) + else: + all_nodes_q, all_nodes_set = collections.deque([root]), {root} + while all_nodes_q: + node = all_nodes_q.popleft() + for child in node.children: + if child not in all_nodes_set: all_nodes_set.add(child); all_nodes_q.append(child) + if all_nodes_set: + final_best_node = max(list(all_nodes_set), key=lambda n: (n.value_sum / n.visits if n.visits > 0 else -1, n.depth)) + + nodes_q, explored_nodes = collections.deque([root]), {root} + while nodes_q: + node = nodes_q.popleft() + for child in node.children: + if child not in explored_nodes: explored_nodes.add(child); nodes_q.append(child) + total_nodes_explored, final_chain = len(explored_nodes), final_best_node.chain + + search_time_seconds = time.time() - start_time + task_steps = [parse_tool_code(step) for step in final_chain[1:]] + plan_length = sum(1 for step in task_steps if step.startswith("api_call")) + + final_reward_score = 0.0 + EVALUATION_PROMPT = """Did the 'Generated Plan' successfully solve the 'User Request'? Answer with only "Yes" or "No".\n[User Request]:\n{user_request}\n\n[Generated Plan]:\n{generated_plan}\n\n[Answer (Yes/No)]:""" + try: + eval_client = RitsChatClient(temperature=0.0, max_tokens=10) + eval_prompt = EVALUATION_PROMPT.format(user_request=user_request_text, generated_plan="\n".join(task_steps)) + verdict, _ = eval_client.send(eval_prompt) + if verdict.strip().lower().startswith("yes"): final_reward_score = 1.0 + except Exception as e: + write_log(f"Warning: LLM-based evaluation failed. Error: {e}") + + final_output = { + "id": example['id'], "result": {"task_steps": task_steps}, + "metrics": { + "accuracy": final_reward_score, + "final_plan_reward": (final_best_node.value_sum / final_best_node.visits if final_best_node and final_best_node.visits > 0 else 0), + "search_time_seconds": round(search_time_seconds, 2), "plan_length": plan_length, + "search_process": { "total_nodes_explored": total_nodes_explored, "mcts_iterations": BUDGET_ITERATIONS if ablation_mode != 'no_mcts' else 0, + "expansion_llm_calls": expansion_llm_calls, "expansion_llm_tokens": expansion_llm_tokens, + "simulation_llm_calls": simulation_llm_calls, "simulation_llm_tokens": simulation_llm_tokens, + }, "robustness": {"invalid_steps_generated": invalid_steps_generated} + } + } + return {"record": final_output} + +# --- Data loading helpers (Preserved from original) --- +def load_local(data_dir: Path, split: str): + path = data_dir / 'user_requests.json' + if not path.exists(): path = data_dir / 'user_requests.jsonl' + if not path.exists(): raise FileNotFoundError(f"Missing {data_dir}/user_requests.json or .jsonl") + with path.open() as f: + for ln in f: + data = json.loads(ln) + yield {'id': data['id'], 'instruction': data.get('user_request',''), 'input': data.get('input',''), 'tool_steps': data.get('tool_steps',[])} + +def load_hf(config_name: str): + """Loads a specific configuration from the microsoft/Taskbench dataset.""" + try: + ds = load_dataset('microsoft/Taskbench', name=config_name, split='test') + for ex in ds: + yield {'id': ex['id'], 'instruction': ex['instruction'], 'input': ex.get('input',''), 'tool_steps': ex.get('tool_steps',[])} + except Exception as e: + print(f"\n❌ Failed to load '{config_name}' from Hugging Face.", file=sys.stderr) + print(f"Error: {e}", file=sys.stderr) + sys.exit(1) + +def main(): + ap = argparse.ArgumentParser(description="Run Ablation Studies for SMR-IV MCTS on TaskBench.") + ap.add_argument('--run_name', type=str, default=None, help="Optional name for the output directory.") + ap.add_argument('--api_family', type=str, default='huggingface', help="API family to test.") + ap.add_argument('--num_problems', type=int, default=50, help="Number of problems to sample.") + ap.add_argument('--seed', type=int, default=42, help="Random seed for reproducibility.") + ap.add_argument('--model_name', type=str, default='llama_4', help="The model checkpoint to use.") + + # REVISED: Argument for ablation mode selection + ap.add_argument('--ablation_mode', type=str, required=True, + choices=['no_mcts', 'no_sim_feedback', 'no_plan_history', 'uniform_rewards', 'no_validator'], + help="Specify the ablation mode to run.") + + ap.add_argument('--max_workers', type=int, default=os.cpu_count(), help="Max parallel processes.") + ap.add_argument('--debug_llm_output', action='store_true', help="Print detailed LLM IO for debugging.") + args = ap.parse_args() + + valid_rits_models = list(MODEL_ID_MAP["rits"].keys()) + if args.model_name not in valid_rits_models: + print(f"❌ Error: Invalid model name '{args.model_name}'. Choose from: {valid_rits_models}", file=sys.stderr); sys.exit(1) + MODELMAP.set_model('generate_model', args.model_name) + print(f"✅ Configured to use model: {MODELMAP.generate_model} | Ablation Mode: {args.ablation_mode}") + + random.seed(args.seed); np.random.seed(args.seed); torch.manual_seed(args.seed) + if torch.cuda.is_available(): torch.cuda.manual_seed_all(args.seed) + + run_name = args.run_name or f"{args.ablation_mode}_{args.api_family}_{args.model_name}_{datetime.now():%Y%m%d_%H%M%S}" + run_dir = Path('predictions') / run_name; run_dir.mkdir(parents=True, exist_ok=True) + log_path = run_dir / 'debug_log.txt'; log_path.unlink(missing_ok=True) + print(f"✅ Outputs will be saved in: {run_dir}") + + api_family_data_path = Path("Taskbench") / f"data_{args.api_family}" + if not api_family_data_path.is_dir(): print(f"❌ Error: 'Taskbench/data_{args.api_family}' not found.", file=sys.stderr); sys.exit(1) + with open(api_family_data_path / "tool_desc.json", 'r', encoding='utf-8') as f: parsed_tool_data = json.load(f) + + all_records = list(load_hf(config_name=args.api_family)); random.shuffle(all_records) + records_to_process = all_records[:args.num_problems] + + with Manager() as manager: + log_lock = manager.Lock() + problems_to_submit = [{"dataset_index": j, "example": ex, "api_family_for_tools": args.api_family, "debug_llm_output": args.debug_llm_output, + "parsed_tool_data": parsed_tool_data, "log_path": log_path, "log_lock": log_lock, "ablation_mode": args.ablation_mode} + for j, ex in enumerate(records_to_process)] + + run_results = [] + with ProcessPoolExecutor(max_workers=args.max_workers) as executor: + futures = {executor.submit(process_taskbench_problem, p): p['dataset_index'] for p in problems_to_submit} + desc = f"Ablating '{args.ablation_mode}' on {args.api_family}" + for future in tqdm(as_completed(futures), total=len(problems_to_submit), desc=desc): + try: + if result := future.result(): run_results.append(result['record']) + except Exception as e: print(f"Problem {futures[future]} failed: {e}", file=sys.stderr) + + with (run_dir / 'results.json').open("w", encoding="utf-8") as f: json.dump(run_results, f, indent=2) + + total_correct = sum(1 for r in run_results if r.get('metrics', {}).get('accuracy', 0.0) > 0.9) + total_problems = len(run_results) + accuracy = (total_correct / total_problems) * 100 if total_problems > 0 else 0 + + summary = {"run_name": run_name, "model_name": args.model_name, "api_family": args.api_family, "ablation_mode": args.ablation_mode, + "num_problems_processed": len(records_to_process), "seed": args.seed, "final_accuracy": f"{accuracy:.2f}%"} + with (run_dir / 'summary.json').open("w", encoding="utf-8") as f: json.dump(summary, f, indent=2) + + print(f"📊 Final Accuracy for '{args.ablation_mode}': {accuracy:.2f}%") + print(f"✅ Summary saved to {run_dir / 'summary.json'}") + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/scripts/taskbench_smriv_mcts_revised_final.py b/scripts/taskbench_smriv_mcts_revised_final.py new file mode 100644 index 0000000..2f65982 --- /dev/null +++ b/scripts/taskbench_smriv_mcts_revised_final.py @@ -0,0 +1,645 @@ +#!/usr/bin/env python3 +# taskbench_smriv_mcts_revised.py + +import os +import sys +import json +import time +import math +import shutil +import tempfile +import argparse +import subprocess +import traceback +from pathlib import Path +from typing import List, Optional, Dict, Any, Set, Tuple +from dataclasses import dataclass, field +from concurrent.futures import ProcessPoolExecutor, as_completed +from multiprocessing import Manager +import collections +import ast +import re +from datetime import datetime + +import random +import dotenv +from SPIRAL.scripts.utils.ritz_client import MODELMAP, MODEL_ID_MAP + +# New dependencies for semantic matching and rule evolution +import numpy as np +import torch +from sentence_transformers import SentenceTransformer, util +from datasets import load_dataset + +from tqdm import tqdm +from SPIRAL.scripts.utils.ritz_client import RitsChatClient, MODELMAP, MODEL_ID_MAP + +def make_value_hashable(value: Any) -> Any: + """Recursively converts lists to tuples and dicts to frozensets of items.""" + if isinstance(value, dict): + return frozenset((k, make_value_hashable(v)) for k, v in value.items()) + if isinstance(value, list): + return tuple(make_value_hashable(v) for v in value) + return value + +# ───────────────────────────────────────────────────────────────────────────── +# Manually corrected/enriched parameter definitions for common tools +# ───────────────────────────────────────────────────────────────────────────── +CORRECTED_TOOL_PARAMETERS = { + "Token Classification": {"text": "string"}, "Translation": {"text": "string", "source_lang": "string", "target_lang": "string"}, "Summarization": {"text": "string"}, "Question Answering": {"context": "string", "question": "string"}, "Conversational": {"prompt": "string", "history": "list"}, "Text Generation": {"prompt": "string"}, "Sentence Similarity": {"sentence1": "string", "sentence2": "string"}, "Tabular Classification": {"table_image_path": "string"}, "Object Detection": {"image_path": "string"}, "Image Classification": {"image_path": "string"}, "Image-to-Image": {"image_path": "string", "target_image_path": "string"}, "Image-to-Text": {"image_path": "string"}, "Text-to-Image": {"prompt": "string"}, "Text-to-Video": {"prompt": "string"}, "Visual Question Answering": {"image_path": "string", "question": "string"}, "Document Question Answering": {"document_image_path": "string", "question": "string"}, "Image Segmentation": {"image_path": "string"}, "Depth Estimation": {"image_path": "string"}, "Text-to-Speech": {"text": "string"}, "Automatic Speech Recognition": {"audio_path": "string"}, "Audio-to-Audio": {"audio_path": "string"}, "Audio Classification": {"audio_path": "string"}, "Image Editing": {"image_path": "string", "edits": "dict"}, "get_weather": {"location": "string", "date": "string"}, "get_news_for_topic": {"topic": "string"}, "stock_operation": {"stock": "string", "operation": "string"}, "book_flight": {"date": "string", "from": "string", "to": "string"}, "book_hotel": {"date": "string", "name": "string"}, "book_restaurant": {"date": "string", "name": "string"}, "book_car": {"date": "string", "location": "string"}, "online_shopping": {"website": "string", "product": "string"}, "send_email": {"email_address": "string", "content": "string"}, "send_sms": {"phone_number": "string", "content": "string"}, "share_by_social_network": {"content": "string", "social_network": "string"}, "search_by_engine": {"query": "string", "engine": "string"}, "apply_for_job": {"job": "string"}, "see_doctor_online": {"disease": "string", "doctor": "string"}, "consult_lawyer_online": {"issue": "string", "lawyer": "string"}, "enroll_in_course": {"course": "string", "university": "string"}, "buy_insurance": {"insurance": "string", "company": "string"}, "online_banking": {"instruction": "string", "bank": "string"}, "daily_bill_payment": {"bill": "string"}, "sell_item_online": {"item": "string", "store": "string"}, "do_tax_return": {"year": "string"}, "apply_for_passport": {"country": "string"}, "pay_for_credit_card": {"credit_card": "string"}, "auto_housework_by_robot": {"instruction": "string"}, "auto_driving_to_destination": {"destination": "string"}, "deliver_package": {"package": "string", "destination": "string"}, "order_food_delivery": {"food": "string", "location": "string", "platform": "string"}, "order_taxi": {"location": "string", "platform": "string"}, "play_music_by_title": {"title": "string"}, "play_movie_by_title": {"title": "string"}, "take_note": {"content": "string"}, "borrow_book_online": {"book": "string", "library": "string"}, "recording_audio": {"content": "string"}, "make_video_call": {"phone_number": "string"}, "make_voice_call": {"phone_number": "string"}, "organize_meeting_online": {"topic": "string"}, "attend_meeting_online": {"topic": "string"}, "software_management": {"software": "string", "instruction": "string"}, "print_document": {"document": "string"}, "set_alarm": {"time": "string"}, +} + +# --- Global Sentence Transformer Model --- +SENTENCE_MODEL = None +def get_sentence_model(): + """Initializes and returns the sentence transformer model as a singleton.""" + global SENTENCE_MODEL + if SENTENCE_MODEL is None: + SENTENCE_MODEL = SentenceTransformer('all-MiniLM-L6-v2') + return SENTENCE_MODEL + +def parse_tool_code(text: str) -> str: + """Extracts Python code from a markdown block.""" + match = re.search(r"```(?:python\n)?(.*?)\n?```", text, re.DOTALL) + return match.group(1).strip() if match else text.strip() + +def parse_api_calls(steps:List[str]) -> List[Dict]: + api_calls = [] + for step in steps: + step = step.strip() + api_call_prefix = "api_call(" + api_call_suffix = ")" + if step.startswith(api_call_prefix) and step.endswith(api_call_suffix): + api_call_text = step[len(api_call_prefix):][:-len(api_call_suffix)] + api_call_text = api_call_text.strip() + if "," in api_call_text: + api_name, _, arg_text = api_call_text.partition(",") + api_name = api_name.strip().strip('"') + arg_text = arg_text.strip() + args = json.loads(arg_text) + arguments = [] + for name, value in args.items(): + arguments.append({ + "name": name, + "value": value + }) + api_calls.append({ + "arguments": arguments, + "task": api_name + }) + + return api_calls + +def to_task_steps(api_calls: List[Dict]) -> List[str]: + steps = [] + for index, api_call in enumerate(api_calls): + name = api_call["task"] + args = api_call["arguments"] + step = f"Step {index+1}: Call {name} API" + arg_value_pairs = [] + for arg in args: + arg_value_pairs.append(f"{arg['name']}: {arg['value']}") + if len(arg_value_pairs)==1: + step += f" with {arg_value_pairs[0]}" + elif len(arg_value_pairs) > 1: + step += f" with "+", ".join(arg_value_pairs[:-1]) + step += f" and {arg_value_pairs[-1]}" + steps.append(step) + return steps + +def to_task_links(api_calls: List[Dict]) -> List[Dict]: + """ + + """ + task_links = [] + if len(api_calls) > 1: + for index, api_call in enumerate(api_calls): + if index == 0: + continue + task_links.append({ + "source": api_calls[index-1]["task"], + "target": api_call["task"] + }) + + return task_links +# ───────────────────────────────────────────────────────────────────────────── +# Functions to load and format tool & graph descriptions dynamically +# ───────────────────────────────────────────────────────────────────────────── +def load_tool_descriptions_from_file(api_family_data_dir: Path) -> str: + """Loads and formats tool descriptions from the specified tool_desc.json file.""" + tool_desc_path = api_family_data_dir / "tool_desc.json" + if not tool_desc_path.exists(): + raise FileNotFoundError(f"Tool description file not found: {tool_desc_path}.") + try: + with open(tool_desc_path, 'r', encoding='utf-8') as f: + tool_data_root = json.load(f) + except json.JSONDecodeError as e: + raise ValueError(f"Invalid JSON in {tool_desc_path}: {e}") from e + + description_parts = ["Available tools (use the `api_call` function to invoke them):"] + if not isinstance(tool_data_root, dict) or "nodes" not in tool_data_root: + raise ValueError("Expected tool_desc.json to have a root dict with a 'nodes' key.") + tool_nodes = tool_data_root["nodes"] + + for tool_node in tool_nodes: + if not isinstance(tool_node, dict): continue + tool_id, tool_desc = tool_node.get("id"), tool_node.get("desc") + parameters = tool_node.get("parameters", []) + if not tool_id or not tool_desc: continue + + args_list, example_args_dict = [], {} + effective_parameters = [{"name": n, "type": t} for n, t in CORRECTED_TOOL_PARAMETERS.get(tool_id, {}).items()] or parameters + + for param in effective_parameters: + param_name, param_type = param.get("name"), param.get("type", "Any") + if param_name: + args_list.append(f"`{param_name}` ({param_type})") + example_args_dict[param_name] = f"<{param_name}_value>" + + example_call_str = f"api_call(\"{tool_id}\", {json.dumps(example_args_dict)})" + description_parts.append(f"\n`{example_call_str}`") + description_parts.append(f" Description: {tool_desc}") + if args_list: description_parts.append(f" Parameters: {'; '.join(args_list)}") + return "\n".join(description_parts) + +def load_graph_descriptions_from_file(api_family_data_dir: Path) -> str: + """Loads and formats graph (dependency) descriptions for the LLM prompt.""" + graph_desc_path = api_family_data_dir / "graph_desc.json" + if not graph_desc_path.exists(): return "" + try: + with open(graph_desc_path, 'r', encoding='utf-8') as f: + graph_data = json.load(f) + except (json.JSONDecodeError, Exception) as e: + print(f"Warning: Could not read {graph_desc_path}: {e}", file=sys.stderr) + return "" + + description_parts = ["\n--- Tool Dependencies ---"] + for dep_type, deps in graph_data.items(): + if isinstance(deps, list) and deps: + description_parts.append(f"{dep_type.replace('_', ' ').title()}:") + for dep in deps: + if not isinstance(dep, dict): continue + pre, post = dep.get("pre_tool"), dep.get("post_tool") + if "resource" in dep_type: + res = ", ".join(dep.get("resources", [])) + description_parts.append(f" - `{post}` requires resource(s) `{res}` from `{pre}`.") + elif "temporal" in dep_type: + cond = dep.get("condition", "completion") + description_parts.append(f" - `{post}` can only be called after `{pre}` upon its {cond}.") + + return "\n".join(description_parts) if len(description_parts) > 1 else "" + +# ───────────────────────────────────────────────────────────────────────────── +# Tool Validator (Frontend Compiler) +# ───────────────────────────────────────────────────────────────────────────── +class ToolValidator: + def __init__(self, parsed_tool_data_root: Dict, debug_llm_output: bool = False): + self.tool_signatures = collections.defaultdict(dict) + self.debug_llm_output = debug_llm_output + if not isinstance(parsed_tool_data_root, dict) or "nodes" not in parsed_tool_data_root: + return + tool_nodes = parsed_tool_data_root["nodes"] + for tool_node in tool_nodes: + if not isinstance(tool_node, dict): continue + tool_id, parameters = tool_node.get("id"), tool_node.get("parameters", []) + if tool_id: + effective_params = [{"name": n, "type": t} for n, t in CORRECTED_TOOL_PARAMETERS.get(tool_id, {}).items()] or parameters + self.tool_signatures[tool_id] = { + "parameters": {p.get("name"): p.get("type") for p in effective_params if isinstance(p, dict)} + } + + def validate_api_call(self, code_str: str) -> bool: + """Performs basic syntax and semantic validation of an api_call string.""" + match = re.search(r'api_call\("([^"]+)",\s*({.*?})\)', code_str, re.DOTALL) + if not match: return False + tool_id, args_str = match.group(1), match.group(2) + if tool_id not in self.tool_signatures: return False + + expected_params = self.tool_signatures[tool_id]["parameters"] + try: + parsed_args = ast.literal_eval(args_str) + if not isinstance(parsed_args, dict): return False + for arg_name in parsed_args: + if arg_name not in expected_params: return False + return True + except (ValueError, SyntaxError): + return False + +# ───────────────────────────────────────────────────────────────────────────── +# Simulated Tool Executor +# ───────────────────────────────────────────────────────────────────────────── +class SimulatedToolExecutor: + def __init__(self, user_request: str, debug_llm_output: bool = False): + self.client = RitsChatClient(temperature=0.2, max_tokens=150) + self.user_request = user_request + self.debug_llm_output = debug_llm_output + + def execute(self, api_call_str: str) -> Tuple[str, int]: # MODIFIED: Added token count to return + """Simulates API execution and returns the observation and token count.""" + prompt_template = """You are a simulated API tool. Your role is to provide a realistic, one-line observation for the given tool call, based on the user's overall goal. + ### Rules: + 1. Your entire response MUST be a single line starting with `Observation: tool_output = `. + 2. The value part should be a plausible result. For tools that create files (like image editing or generation), the value should be a new, unique filename string (e.g., `"edited_image.png"`). For analysis tools, it should be a short, descriptive string or the direct answer (e.g., `"a red sports car"`). + 3. The observation must be grounded in the user's request. + ### User's Goal: + "{user_request}" + ### Tool Call to Simulate: + `{api_call_str}` + ### Your Single-Line Response: + """ + prompt = prompt_template.format(user_request=self.user_request, api_call_str=api_call_str) + try: + # MODIFIED: Capture token count from the send call + response_text, tokens_used = self.client.send(prompt) + prefix = "Assistant:" + if response_text and response_text.strip().startswith(prefix): + response_text = response_text.strip()[len(prefix):] + if response_text and response_text.strip().startswith("Observation: tool_output ="): + return response_text.strip().split('\n')[0], tokens_used + else: + if self.debug_llm_output: print(f" Executor LLM failed format. Response: {response_text}", file=sys.stderr) + return 'Observation: tool_output = "Error: Tool simulation failed."', tokens_used + except Exception as e: + if self.debug_llm_output: print(f" Executor LLM call failed: {e}", file=sys.stderr) + return 'Observation: tool_output = "Error: Tool simulation encountered an exception."', 0 + +# ───────────────────────────────────────────────────────────────────────────── +# MCTS Node +# ───────────────────────────────────────────────────────────────────────────── +@dataclass +class Node: + chain: List[str] + parent: Optional["Node"] = None + children: List["Node"] = field(default_factory=list) + visits: int = 0 + value_sum: float = 0.0 + _id: int = field(default_factory=lambda: id(Node)) + + def __post_init__(self): self._id = id(self) + def __hash__(self): return hash(self._id) + def __eq__(self, other): + if not isinstance(other, Node): return NotImplemented + return self._id == other._id + @property + def depth(self) -> int: return 0 if self.parent is None else self.parent.depth + 1 + def backpropagate(self, reward: float): + current = self + while current is not None: + current.visits += 1; current.value_sum += reward; current = current.parent + def uct_score(self, exploration_constant: float = 1.0) -> float: + if self.visits == 0: return float('inf') + if self.parent is None or self.parent.visits == 0: return self.value_sum / self.visits + exploitation = self.value_sum / self.visits + exploration = exploration_constant * math.sqrt(math.log(self.parent.visits) / self.visits) + return exploitation + exploration + def __str__(self): + return f"Node: {self.chain}" +# ───────────────────────────────────────────────────────────────────────────── +# Core MCTS Logic for a Single Problem +# ───────────────────────────────────────────────────────────────────────────── +def process_taskbench_problem(problem_info: Dict) -> Optional[Dict]: + idx, example, api_family, debug_llm, parsed_tool_data, log_path, log_lock = ( + problem_info['dataset_index'], problem_info['example'], problem_info['api_family_for_tools'], + problem_info['debug_llm_output'], problem_info['parsed_tool_data'], + problem_info['log_path'], problem_info['log_lock'] + ) + + def write_log(message: str): + with log_lock: + with log_path.open("a", encoding="utf-8") as f: + f.write(f"--- Problem {idx} ({example['id']}) ---\n{message}\n" + "="*40 + "\n\n") + + user_request_text = example['instruction'] + tool_validator = ToolValidator(parsed_tool_data, debug_llm) + simulated_executor = SimulatedToolExecutor(user_request=user_request_text, debug_llm_output=debug_llm) + client = RitsChatClient(temperature=0.0, max_tokens=1024) + + initial_prompt = f"Instruction: {example['instruction']}" + (f" | Input: {example['input']}" if example.get('input') else "") + root = Node(chain=[initial_prompt]) + BUDGET_ITERATIONS, MAX_DEPTH = 50, 8 + terminal_nodes = [] + + # NEW: Initialize metric counters + start_time = time.time() + expansion_llm_calls, expansion_llm_tokens = 0, 0 + simulation_llm_calls, simulation_llm_tokens = 0, 0 + invalid_steps_generated = 0 + + try: + tools_description = load_tool_descriptions_from_file(Path("Taskbench") / f"data_{api_family}") + graph_description = load_graph_descriptions_from_file(Path("Taskbench") / f"data_{api_family}") + except (FileNotFoundError, ValueError) as e: + write_log(f"CRITICAL ERROR: Could not load descriptions. Error: {e}"); return None + + try: + for _ in range(BUDGET_ITERATIONS): + current_node = root + while current_node.children: + current_node = max(current_node.children, key=lambda n: n.uct_score()) + if current_node.depth >= MAX_DEPTH or any("finish(" in step for step in current_node.chain): + current_node.backpropagate(-0.5); continue + + prompt_parts = [ + "You are an expert assistant that only responds with code.", "Your task is to create a plan to solve the user's request by generating a sequence of tool calls.", "## RULES:", + "1. Generate ONLY the single next `api_call(...)` or the final `finish(...)` call.", + "2. If a previous step produced an observation `tool_output = `, you MUST use that exact `` in the arguments of the next tool.", + "3. When the user's request is fully satisfied, you MUST call `finish(reason=\"\")`.", + "\n## TOOLS:", tools_description, graph_description, '## FINISH ACTION:\n`finish(reason="")`: Call this ONLY when the task is complete.', + f"## CURRENT PLAN:\n" + "\n".join(current_node.chain), "\nRespond with ONLY the next line of code:" + ] + prompt_expand = "\n".join(filter(None, prompt_parts)) + + # MODIFIED: Capture token usage for Planner + response, tokens_used = client.send(prompt_expand, max_tokens=1024) + expansion_llm_calls += 1 + expansion_llm_tokens += tokens_used + + extracted_code = parse_tool_code(response.strip()) + + if extracted_code.startswith("finish("): + new_node = Node(chain=current_node.chain + [extracted_code], parent=current_node) + current_node.children.append(new_node) + terminal_nodes.append(new_node) + new_node.backpropagate(1.0) + elif tool_validator.validate_api_call(extracted_code): + # MODIFIED: Capture token usage for Simulator + observation, sim_tokens = simulated_executor.execute(extracted_code) + simulation_llm_calls += 1 + simulation_llm_tokens += sim_tokens + + new_node = Node(chain=current_node.chain + [extracted_code, observation], parent=current_node) + current_node.children.append(new_node) + new_node.backpropagate(0.1) + else: + # NEW: Track invalid steps + invalid_steps_generated += 1 + current_node.backpropagate(-1.0) + except Exception as e: + import traceback + write_log(f"CRITICAL ERROR in MCTS loop: {e}\n{traceback.format_exc()}") + + # NEW: Finalize metrics after search + search_time_seconds = time.time() - start_time + + final_best_node = root + if terminal_nodes: + final_best_node = max(terminal_nodes, key=lambda n: n.value_sum / n.visits if n.visits > 0 else -1) + else: + all_nodes_q = collections.deque([root]) + all_nodes_set = {root} + while all_nodes_q: + node = all_nodes_q.popleft() + for child in node.children: + if child not in all_nodes_set: + all_nodes_set.add(child) + all_nodes_q.append(child) + if all_nodes_set: + final_best_node = max(list(all_nodes_set), key=lambda n: (n.value_sum / n.visits if n.visits > 0 else -1, n.depth)) + + # NEW: Calculate total nodes explored + nodes_q = collections.deque([root]) + explored_nodes = {root} + while nodes_q: + node = nodes_q.popleft() + for child in node.children: + if child not in explored_nodes: + explored_nodes.add(child) + nodes_q.append(child) + total_nodes_explored = len(explored_nodes) + + task_steps = [parse_tool_code(step) for step in final_best_node.chain[1:]] + plan_length = sum(1 for step in task_steps if step.startswith("api_call")) + + terminal_nodes_info = [] + for tn in terminal_nodes: + tn_steps = [parse_tool_code(step) for step in tn.chain[1:] if step.startswith("api_call")] + tn_steps = "|".join(tn_steps) + tn_value = tn.value_sum / tn.visits if tn.visits > 0 else -1 + terminal_nodes_info.append({ + "steps":tn_steps, + "avg_value":tn_value, + "sum_value":tn.value_sum, + "visits":tn.visits + }) + + final_reward_score = 0.0 + EVALUATION_PROMPT = """Did the 'Generated Plan' successfully solve the 'User Request'? Answer with only "Yes" or "No".\n[User Request]:\n{user_request}\n\n[Generated Plan]:\n{generated_plan}\n\n[Answer (Yes/No)]:""" + try: + eval_client = RitsChatClient(temperature=0.0, max_tokens=10) + eval_prompt = EVALUATION_PROMPT.format(user_request=user_request_text, generated_plan="\n".join(task_steps)) + verdict, _ = eval_client.send(eval_prompt) + if verdict.strip().lower().startswith("yes"): final_reward_score = 1.0 + except Exception as e: + write_log(f"Warning: LLM-based evaluation failed. Error: {e}") + + # MODIFIED: Structure the final output with the new metrics dictionary + task_nodes = parse_api_calls(task_steps) + final_output = { + "id": example['id'], + "user_utterance": example['instruction'], + "result": { + "task_steps": to_task_steps(task_nodes), + "task_nodes": task_nodes, + "task_links": to_task_links(task_nodes), + "task_steps_with_observations": task_steps + }, + "ground_truth": example['tool_nodes'], + "terminal_nodes_info":terminal_nodes_info, + "metrics": { + "accuracy": final_reward_score, + "final_plan_reward": final_best_node.value_sum / final_best_node.visits if final_best_node.visits > 0 else 0, + "search_time_seconds": round(search_time_seconds, 2), + "plan_length": plan_length, + "search_process": { + "total_nodes_explored": total_nodes_explored, + "mcts_iterations": BUDGET_ITERATIONS, + "num_terminal_nodes": len(terminal_nodes), + "invalid_steps_generated": invalid_steps_generated, + "expansion_llm_calls": expansion_llm_calls, + "expansion_llm_tokens": expansion_llm_tokens, + "simulation_llm_calls": simulation_llm_calls, + "simulation_llm_tokens": simulation_llm_tokens, + }, + "robustness": { + "invalid_steps_generated": invalid_steps_generated, + } + } + } + + # The 'record' key is preserved to match the expected format for your main loop + return {"record": final_output} + +# --- Data loading helpers --- +def load_local(data_dir: Path, split: str): + path = data_dir / 'user_requests.json' + if not path.exists(): path = data_dir / 'user_requests.jsonl' + if not path.exists(): raise FileNotFoundError(f"Missing {data_dir}/user_requests.json or .jsonl") + with path.open() as f: + for ln in f: + data = json.loads(ln) + yield {'id': data['id'], 'instruction': data.get('user_request',''), + 'input': data.get('input',''), + 'tool_steps': data.get('tool_steps',[]), + 'tool_nodes': data.get("task_nodes", [])} + +def load_hf(config_name: str): + """Loads a specific configuration from the microsoft/Taskbench dataset.""" + try: + # The 'name' parameter specifies the dataset configuration (e.g., 'huggingface', 'dailylifeapis') + # The 'split' should be 'test', as this is the only split available. + ds = load_dataset('microsoft/Taskbench', name=config_name, split='test') + print(f"config_name: {config_name}") + for index, ex in enumerate(ds): + if index ==0: print(f"Example: {ex}") + tool_nodes = ex.get("tool_nodes", []) + if isinstance(tool_nodes, str): + tool_nodes = json.loads(tool_nodes) + yield {'id': ex['id'], 'instruction': ex['instruction'], + 'input': ex.get('input',''), + 'tool_steps': ex.get('tool_steps',[]), + 'tool_nodes': tool_nodes} + except Exception as e: + print(f"\n❌ Failed to load '{config_name}' from Hugging Face.", file=sys.stderr) + print(f"Error: {e}", file=sys.stderr) + print("Please ensure the API family name is correct and you have an internet connection.", file=sys.stderr) + sys.exit(1) + +def main(): + #dotenv.load_dotenv() + ap = argparse.ArgumentParser(description="Run Revised SMR-IV MCTS on TaskBench.") + + # --- Experiment Configuration --- + ap.add_argument('--run_name', type=str, default=None, help="Optional name for the output directory.") + ap.add_argument('--api_family', type=str, default='huggingface', help="API family to test (e.g., 'huggingface', 'dailylifeapis', 'multimedia').") + ap.add_argument('--num_problems', type=int, default=50, help="Number of problems to sample from the dataset.") + ap.add_argument('--seed', type=int, default=42, help="Random seed for reproducibility.") + + # NEW: Argument for model selection + ap.add_argument('--model_name', type=str, default='llama_4', help="The model checkpoint to use for the agent.") + + # --- Execution Settings --- + ap.add_argument('--max_workers', type=int, default=os.cpu_count(), help="Maximum number of parallel processes.") + ap.add_argument('--debug_llm_output', action='store_true', help="Print detailed LLM prompts and responses for debugging.") + #ap.add_argument('--llm_platform', type=str, choices=['watsonx', 'rits', 'hf'], default='rits', help="The platform to retrieve models from or to send model requests") + ap.add_argument('--env', type=str, default=None, help="Absolute or relative path to the .env file to use") + args = ap.parse_args() + dotenv.load_dotenv(dotenv_path=args.env) + #os.environ["USE_WATSONX"] = "True" if args.llm_platform.lower() == "watsonx" else "False" + + # NEW: Validate and set the model from the command-line argument + valid_rits_models = list(MODEL_ID_MAP["rits"].keys()) + if args.model_name not in valid_rits_models: + print(f"❌ Error: Invalid model name '{args.model_name}'.", file=sys.stderr) + print(f" Please choose from the following available models: {valid_rits_models}", file=sys.stderr) + sys.exit(1) + + MODELMAP.set_model('generate_model', args.model_name) + print(f"✅ Configured to use model: {MODELMAP.generate_model}") + + # Set random seeds for reproducibility + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(args.seed) + + run_name = args.run_name + if run_name is None: + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + run_name = f"smriv_mcts_{args.api_family}_{args.model_name}_{timestamp}" + print(f"✅ No run name provided. Using auto-generated name: {run_name}") + + # ... (the rest of the main function remains the same) ... + + run_dir = Path('predictions') / run_name + run_dir.mkdir(parents=True, exist_ok=True) + log_path = run_dir / 'debug_log.txt' + if log_path.exists(): log_path.unlink() + print(f"✅ Outputs will be saved in: {run_dir}") + + api_family_data_path = Path("Taskbench") / f"data_{args.api_family}" + if not api_family_data_path.is_dir(): + print(f"❌ Error: 'Taskbench/data_{args.api_family}' not found. Make sure the directory exists.", file=sys.stderr) + sys.exit(1) + + print(f"Pre-parsing tool descriptions from {api_family_data_path}...") + try: + with open(api_family_data_path / "tool_desc.json", 'r', encoding='utf-8') as f: + parsed_tool_data = json.load(f) + except Exception as e: + print(f"⚠️ Warning: Could not parse tool_desc.json: {e}", file=sys.stderr); parsed_tool_data = {"nodes": []} + + print(f"Loading data from Hugging Face for API family: '{args.api_family}'...") + all_records = list(load_hf(config_name=args.api_family)) + + random.shuffle(all_records) + records_to_process = all_records[:args.num_problems] + print(f"✅ Loaded and sampled {len(records_to_process)} problems.") + + with Manager() as manager: + log_lock = manager.Lock() + + print(f"\n{'─'*25} Starting Run {'─'*25}") + + problems_to_submit = [ + {"dataset_index": j, "example": ex, "api_family_for_tools": args.api_family, + "debug_llm_output": args.debug_llm_output, "parsed_tool_data": parsed_tool_data, + "log_path": log_path, "log_lock": log_lock} + for j, ex in enumerate(records_to_process) + ] + + run_results = [] + with ProcessPoolExecutor(max_workers=args.max_workers) as executor: + futures = {executor.submit(process_taskbench_problem, prob): prob['dataset_index'] for prob in problems_to_submit} + for future in tqdm(as_completed(futures), total=len(records_to_process), desc=f"SMR-IV MCTS ({args.api_family})"): + try: + result = future.result() + if result: run_results.append(result['record']) + if len(run_results) > 0 and len(run_results) % 10 == 0: + total_correct = sum(1 for r in run_results if r.get('metrics', {}).get('accuracy', 0.0) > 0.9) + total_problems = len(run_results) + accuracy = (total_correct / total_problems) * 100 if total_problems > 0 else 0 + print(f"Current accuracy: {accuracy:.2f}%", file=sys.stderr) + + except Exception as e: + traceback.print_exc() + print(f"Problem {futures[future]} failed: {e}", file=sys.stderr) + + #run_output_path = run_dir / f'{os.path.basename(MODELMAP.get_model_id("generate_model"))}.json' + model_info = MODEL_ID_MAP["rits"].get(MODELMAP.generate_model ) #"model_id"] + if model_info is None: + model_info = MODEL_ID_MAP["watsonx"].get(MODELMAP.generate_model) + if model_info is None: + print(f"WARNING: Model info not found for model: {MODELMAP.generate_model}") + file_name = "results" + else: + model_id = model_info["model_id"] + file_name = f"{os.path.basename(model_id)}" + + run_output_path = run_dir / f'{file_name}.json' + with run_output_path.open("w", encoding="utf-8") as f: + json.dump(run_results, f, indent=2) + + total_correct = sum(1 for r in run_results if r.get('metrics', {}).get('accuracy', 0.0) > 0.9) + total_problems = len(run_results) + accuracy = (total_correct / total_problems) * 100 if total_problems > 0 else 0 + print(f"📈 Accuracy for this run: {accuracy:.2f}% | Results saved to {run_output_path}") + + print(f"\n{'='*25} Experiment Complete {'='*25}") + summary = { + "run_name": run_name, + "model_name": args.model_name, + "api_family": args.api_family, + "num_problems_processed": len(records_to_process), + "seed": args.seed, + "final_accuracy": f"{accuracy:.2f}%", + } + summary_path = run_dir / 'summary.json' + with summary_path.open("w", encoding="utf-8") as f: + json.dump(summary, f, indent=2) + + print(f"📊 Final Accuracy: {accuracy:.2f}%") + print(f"✅ Final summary saved to {summary_path}") + +if __name__ == '__main__': + main() diff --git a/scripts/taskbench_spiral_method_final.py b/scripts/taskbench_spiral_method_final.py new file mode 100644 index 0000000..0113093 --- /dev/null +++ b/scripts/taskbench_spiral_method_final.py @@ -0,0 +1,386 @@ +#!/usr/bin/env python3 +# taskbench_spiral_baseline.py + +import os +import sys +import json +import time +import math +import argparse +import traceback +from pathlib import Path +from typing import List, Optional, Dict, Tuple +from dataclasses import dataclass, field +from concurrent.futures import ProcessPoolExecutor, as_completed +from multiprocessing import Manager +import collections +import ast +import re +from datetime import datetime +import random +import numpy as np +import torch + +from datasets import load_dataset +from tqdm import tqdm +from utils.ritz_client import RitsChatClient, MODELMAP, MODEL_ID_MAP +import dotenv + +# ───────────────────────────────────────────────────────────────────────────── +# NOTE: The following helper functions and constants are copied from the +# other scripts to ensure a fair and consistent experimental setup. +# ───────────────────────────────────────────────────────────────────────────── + +CORRECTED_TOOL_PARAMETERS = { + "Token Classification": {"text": "string"}, "Translation": {"text": "string", "source_lang": "string", "target_lang": "string"}, "Summarization": {"text": "string"}, "Question Answering": {"context": "string", "question": "string"}, "Conversational": {"prompt": "string", "history": "list"}, "Text Generation": {"prompt": "string"}, "Sentence Similarity": {"sentence1": "string", "sentence2": "string"}, "Tabular Classification": {"table_image_path": "string"}, "Object Detection": {"image_path": "string"}, "Image Classification": {"image_path": "string"}, "Image-to-Image": {"image_path": "string", "target_image_path": "string"}, "Image-to-Text": {"image_path": "string"}, "Text-to-Image": {"prompt": "string"}, "Text-to-Video": {"prompt": "string"}, "Visual Question Answering": {"image_path": "string", "question": "string"}, "Document Question Answering": {"document_image_path": "string", "question": "string"}, "Image Segmentation": {"image_path": "string"}, "Depth Estimation": {"image_path": "string"}, "Text-to-Speech": {"text": "string"}, "Automatic Speech Recognition": {"audio_path": "string"}, "Audio-to-Audio": {"audio_path": "string"}, "Audio Classification": {"audio_path": "string"}, "Image Editing": {"image_path": "string", "edits": "dict"}, "get_weather": {"location": "string", "date": "string"}, "get_news_for_topic": {"topic": "string"}, "stock_operation": {"stock": "string", "operation": "string"}, "book_flight": {"date": "string", "from": "string", "to": "string"}, "book_hotel": {"date": "string", "name": "string"}, "book_restaurant": {"date": "string", "name": "string"}, "book_car": {"date": "string", "location": "string"}, "online_shopping": {"website": "string", "product": "string"}, "send_email": {"email_address": "string", "content": "string"}, "send_sms": {"phone_number": "string", "content": "string"}, "share_by_social_network": {"content": "string", "social_network": "string"}, "search_by_engine": {"query": "string", "engine": "string"}, "apply_for_job": {"job": "string"}, "see_doctor_online": {"disease": "string", "doctor": "string"}, "consult_lawyer_online": {"issue": "string", "lawyer": "string"}, "enroll_in_course": {"course": "string", "university": "string"}, "buy_insurance": {"insurance": "string", "company": "string"}, "online_banking": {"instruction": "string", "bank": "string"}, "daily_bill_payment": {"bill": "string"}, "sell_item_online": {"item": "string", "store": "string"}, "do_tax_return": {"year": "string"}, "apply_for_passport": {"country": "string"}, "pay_for_credit_card": {"credit_card": "string"}, "auto_housework_by_robot": {"instruction": "string"}, "auto_driving_to_destination": {"destination": "string"}, "deliver_package": {"package": "string", "destination": "string"}, "order_food_delivery": {"food": "string", "location": "string", "platform": "string"}, "order_taxi": {"location": "string", "platform": "string"}, "play_music_by_title": {"title": "string"}, "play_movie_by_title": {"title": "string"}, "take_note": {"content": "string"}, "borrow_book_online": {"book": "string", "library": "string"}, "recording_audio": {"content": "string"}, "make_video_call": {"phone_number": "string"}, "make_voice_call": {"phone_number": "string"}, "organize_meeting_online": {"topic": "string"}, "attend_meeting_online": {"topic": "string"}, "software_management": {"software": "string", "instruction": "string"}, "print_document": {"document": "string"}, "set_alarm": {"time": "string"}, +} + +def parse_tool_code(text: str) -> str: + match = re.search(r"```(?:python\n)?(.*?)\n?```", text, re.DOTALL) + return match.group(1).strip() if match else text.strip() + +def load_tool_descriptions_from_file(api_family_data_dir: Path) -> str: + tool_desc_path = api_family_data_dir / "tool_desc.json" + if not tool_desc_path.exists(): raise FileNotFoundError(f"Tool description file not found: {tool_desc_path}.") + with open(tool_desc_path, 'r', encoding='utf-8') as f: tool_data_root = json.load(f) + description_parts = ["Available tools (use the `api_call` function to invoke them):"] + tool_nodes = tool_data_root.get("nodes", []) + for tool_node in tool_nodes: + tool_id, tool_desc = tool_node.get("id"), tool_node.get("desc") + parameters = tool_node.get("parameters", []) + if not tool_id or not tool_desc: continue + args_list, example_args_dict = [], {} + effective_parameters = [{"name": n, "type": t} for n, t in CORRECTED_TOOL_PARAMETERS.get(tool_id, {}).items()] or parameters + for param in effective_parameters: + param_name, param_type = param.get("name"), param.get("type", "Any") + if param_name: args_list.append(f"`{param_name}` ({param_type})"); example_args_dict[param_name] = f"<{param_name}_value>" + example_call_str = f"api_call(\"{tool_id}\", {json.dumps(example_args_dict)})" + description_parts.append(f"\n`{example_call_str}`\n Description: {tool_desc}") + if args_list: description_parts.append(f" Parameters: {'; '.join(args_list)}") + return "\n".join(description_parts) + +def load_graph_descriptions_from_file(api_family_data_dir: Path) -> str: + graph_desc_path = api_family_data_dir / "graph_desc.json" + if not graph_desc_path.exists(): return "" + with open(graph_desc_path, 'r', encoding='utf-8') as f: graph_data = json.load(f) + description_parts = ["\n--- Tool Dependencies ---"] + for dep_type, deps in graph_data.items(): + if isinstance(deps, list) and deps: + description_parts.append(f"{dep_type.replace('_', ' ').title()}:") + for dep in deps: + pre, post = dep.get("pre_tool"), dep.get("post_tool") + if "resource" in dep_type: + res = ", ".join(dep.get("resources", [])); description_parts.append(f" - `{post}` requires resource(s) `{res}` from `{pre}`.") + elif "temporal" in dep_type: + cond = dep.get("condition", "completion"); description_parts.append(f" - `{post}` can only be called after `{pre}` upon its {cond}.") + return "\n".join(description_parts) if len(description_parts) > 1 else "" + +class ToolValidator: + def __init__(self, parsed_tool_data_root: Dict): + self.tool_signatures = collections.defaultdict(dict) + tool_nodes = parsed_tool_data_root.get("nodes", []) + for tool_node in tool_nodes: + tool_id, parameters = tool_node.get("id"), tool_node.get("parameters", []) + if tool_id: + effective_params = [{"name": n, "type": t} for n, t in CORRECTED_TOOL_PARAMETERS.get(tool_id, {}).items()] or parameters + self.tool_signatures[tool_id] = {"parameters": {p.get("name"): p.get("type") for p in effective_params if isinstance(p, dict)}} + + def validate_api_call(self, code_str: str) -> bool: + match = re.search(r'api_call\("([^"]+)",\s*({.*?})\)', code_str, re.DOTALL) + if not match: return False + tool_id, args_str = match.group(1), match.group(2) + if tool_id not in self.tool_signatures: return False + expected_params = self.tool_signatures[tool_id]["parameters"] + try: + parsed_args = ast.literal_eval(args_str) + return isinstance(parsed_args, dict) and all(arg_name in expected_params for arg_name in parsed_args) + except (ValueError, SyntaxError): return False + +class SimulatedToolExecutor: + def __init__(self, user_request: str): + self.client = RitsChatClient(temperature=0.2, max_tokens=150) + self.user_request = user_request + + def execute(self, api_call_str: str) -> Tuple[str, int]: + prompt_template = """You are a simulated API tool. Provide a realistic, one-line observation for the given tool call. +### User's Goal: +"{user_request}" +### Tool Call to Simulate: +`{api_call_str}` +### Your Single-Line Response (must start with `Observation: tool_output = `): +""" + prompt = prompt_template.format(user_request=self.user_request, api_call_str=api_call_str) + try: + response_text, tokens_used = self.client.send(prompt) + if response_text and response_text.strip().startswith("Observation: tool_output ="): + return response_text.strip().split('\n')[0], tokens_used + return 'Observation: tool_output = "Error: Tool simulation failed."', tokens_used + except Exception: return 'Observation: tool_output = "Error: Tool simulation encountered an exception."', 0 + +# ───────────────────────────────────────────────────────────────────────────── +# MCTS Node +# ───────────────────────────────────────────────────────────────────────────── +@dataclass +class Node: + chain: List[str] + parent: Optional["Node"] = None + children: List["Node"] = field(default_factory=list) + visits: int = 0 + value_sum: float = 0.0 + _id: int = field(default_factory=lambda: id(Node)) + + def __post_init__(self): self._id = id(self) + def __hash__(self): return hash(self._id) + def __eq__(self, other): + if not isinstance(other, Node): return NotImplemented + return self._id == other._id + @property + def depth(self) -> int: return 0 if self.parent is None else self.parent.depth + 1 + def backpropagate(self, reward: float): + current = self + while current is not None: + current.visits += 1; current.value_sum += reward; current = current.parent + def uct_score(self, exploration_constant: float = 1.0) -> float: + if self.visits == 0: return float('inf') + if self.parent is None or self.parent.visits == 0: return self.value_sum / self.visits + exploitation = self.value_sum / self.visits + exploration = exploration_constant * math.sqrt(math.log(self.parent.visits) / self.visits) + return exploitation + exploration + +# ───────────────────────────────────────────────────────────────────────────── +# Core SPIRAL MCTS Logic +# ───────────────────────────────────────────────────────────────────────────── +def process_problem_with_spiral(problem_info: Dict) -> Optional[Dict]: + idx, example, api_family, log_path, log_lock, args, parsed_tool_data = ( + problem_info['dataset_index'], problem_info['example'], problem_info['api_family_for_tools'], + problem_info['log_path'], problem_info['log_lock'], problem_info['args'], + problem_info['parsed_tool_data'] + ) + #print(f"Processing example: {example}") + def write_log(message: str): + with log_lock: + with log_path.open("a", encoding="utf-8") as f: + f.write(f"--- Problem {idx} ({example['id']}) ---\n{message}\n" + "="*80 + "\n\n") + + user_request_text = example['instruction'] + #print(f"User request: {user_request_text}") + tool_validator = ToolValidator(parsed_tool_data) + simulated_executor = SimulatedToolExecutor(user_request=user_request_text) + client = RitsChatClient(temperature=0.0, max_tokens=1024) + + initial_prompt = f"Instruction: {example['instruction']}" + (f" | Input: {example['input']}" if example.get('input') else "") + root = Node(chain=[initial_prompt]) + terminal_nodes = [] + + start_time = time.time() + total_llm_tokens = 0 + llm_calls = 0 + + try: + tools_description = load_tool_descriptions_from_file(Path("Taskbench") / f"data_{api_family}") + graph_description = load_graph_descriptions_from_file(Path("Taskbench") / f"data_{api_family}") + except (FileNotFoundError, ValueError) as e: + write_log(f"CRITICAL ERROR: Could not load descriptions. Error: {e}"); return None + + try: + for _ in range(args.mcts_iterations): + current_node = root + while current_node.children: + current_node = max(current_node.children, key=lambda n: n.uct_score()) + if current_node.depth >= args.max_depth or any("finish(" in step for step in current_node.chain): + current_node.backpropagate(-0.5); continue + + prompt_parts = [ + "You are an expert assistant that only responds with code.", "Your task is to create a plan to solve the user's request by generating a sequence of tool calls.", "## RULES:", + "1. Generate ONLY the single next `api_call(...)` or the final `finish(...)` call.", "2. When the user's request is fully satisfied, you MUST call `finish(reason=\"\")`.", + "\n## TOOLS:", tools_description, graph_description, '## FINISH ACTION:\n`finish(reason="")`', + f"## CURRENT PLAN:\n" + "\n".join(current_node.chain), "\nRespond with ONLY the next line of code:" + ] + prompt_expand = "\n".join(filter(None, prompt_parts)) + + response, tokens_used = client.send(prompt_expand, max_tokens=1024) + total_llm_tokens += tokens_used + llm_calls += 1 + extracted_code = parse_tool_code(response.strip()) + + if extracted_code.startswith("finish("): + new_node = Node(chain=current_node.chain + [extracted_code], parent=current_node) + current_node.children.append(new_node) + terminal_nodes.append(new_node) + new_node.backpropagate(1.0) + elif tool_validator.validate_api_call(extracted_code): + observation, sim_tokens = simulated_executor.execute(extracted_code) + total_llm_tokens += sim_tokens + new_node = Node(chain=current_node.chain + [extracted_code, observation], parent=current_node) + current_node.children.append(new_node) + new_node.backpropagate(0.1) # Small reward for valid, non-terminal steps + else: + current_node.backpropagate(-1.0) # Penalty for invalid steps + except Exception as e: + import traceback + write_log(f"CRITICAL ERROR in MCTS loop: {e}\n{traceback.format_exc()}") + + generation_time_seconds = time.time() - start_time + + final_best_node = root + if terminal_nodes: + final_best_node = max(terminal_nodes, key=lambda n: n.value_sum / n.visits if n.visits > 0 else -1) + else: # If no terminal node was reached, pick the best non-terminal node + all_nodes = [n for n in (collections.deque([root])) if n.children or (q.extend(n.children) for q in [collections.deque([root])])] + if all_nodes: final_best_node = max(all_nodes, key=lambda n: (n.value_sum / n.visits if n.visits > 0 else -1, n.depth)) + + task_steps = [parse_tool_code(step) for step in final_best_node.chain[1:]] + plan_length = sum(1 for step in task_steps if step.startswith("api_call")) + + final_reward_score = 0.0 + EVALUATION_PROMPT = """Did the 'Generated Plan' successfully solve the 'User Request'? Answer with only "Yes" or "No".\n[User Request]:\n{user_request}\n\n[Generated Plan]:\n{generated_plan}\n\n[Answer (Yes/No)]:""" + try: + eval_client = RitsChatClient(temperature=0.0, max_tokens=10) + eval_prompt = EVALUATION_PROMPT.format(user_request=user_request_text, generated_plan="\n".join(task_steps)) + verdict, eval_tokens = eval_client.send(eval_prompt) + total_llm_tokens += eval_tokens + llm_calls += 1 + if verdict.strip().lower().startswith("yes"): final_reward_score = 1.0 + except Exception as e: write_log(f"Warning: LLM-based evaluation failed. Error: {e}") + + final_output = { + "id": example['id'], "result": {"task_steps": task_steps}, + "metrics": { + "accuracy": final_reward_score, "generation_time_seconds": round(generation_time_seconds, 2), + "plan_length": plan_length, "reasoning_cost": {"llm_calls": llm_calls, "total_llm_tokens": total_llm_tokens,} + } + } + return {"record": final_output} + +def load_hf(config_name: str): + try: + ds = load_dataset('microsoft/Taskbench', name=config_name, split='test') + for ex in ds: yield {'id': ex['id'], 'instruction': ex['instruction'], 'input': ex.get('input',''), 'tool_steps': ex.get('tool_steps',[])} + except Exception as e: print(f"\n❌ Failed to load '{config_name}' from Hugging Face.", file=sys.stderr); sys.exit(1) + +# ───────────────────────────────────────────────────────────────────────────── +# Main Orchestrator +# ───────────────────────────────────────────────────────────────────────────── +def load_local(data_dir: Path): + path = data_dir / 'user_requests.json' + if not path.exists(): path = data_dir / 'user_requests.jsonl' + if not path.exists(): raise FileNotFoundError(f"Missing {data_dir}/user_requests.json or .jsonl") + with path.open() as f: + for ln in f: + data = json.loads(ln) + yield {'id': data['id'], 'instruction': data.get('user_request',''), + 'input': data.get('input',''), + 'tool_steps': data.get('tool_steps',[]), + 'tool_nodes': data.get("task_nodes", [])} +def main(): + dotenv.load_dotenv() + ap = argparse.ArgumentParser(description="Run SPIRAL MCTS on TaskBench.") + + ap.add_argument('--run_name', type=str, default=None) + ap.add_argument('--api_family', type=str, default='huggingface') + ap.add_argument('--num_problems', type=int, default=50) + ap.add_argument('--seed', type=int, default=42) + ap.add_argument('--model_name', type=str, default='llama_4') + # Method-specific hyperparameters now as arguments + ap.add_argument('--mcts_iterations', type=int, default=50, help="Number of iterations for the MCTS loop.") + ap.add_argument('--max_depth', type=int, default=8, help="Maximum depth of the search tree.") + ap.add_argument('--max_workers', type=int, default=os.cpu_count()) + args = ap.parse_args() + + valid_rits_models = list(MODEL_ID_MAP["rits"].keys()) + if args.model_name not in valid_rits_models: + print(f"❌ Error: Invalid model name '{args.model_name}'. Choose from: {valid_rits_models}", file=sys.stderr); sys.exit(1) + + MODELMAP.set_model('generate_model', args.model_name) + print(f"✅ Configured to use model: {MODELMAP.generate_model}") + + random.seed(args.seed); np.random.seed(args.seed); torch.manual_seed(args.seed) + if torch.cuda.is_available(): torch.cuda.manual_seed_all(args.seed) + + run_name = args.run_name + if run_name is None: + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + run_name = f"spiral_{args.api_family}_{args.model_name}_{timestamp}" + print(f"✅ No run name provided. Using auto-generated name: {run_name}") + + run_dir = Path('predictions') / run_name; run_dir.mkdir(parents=True, exist_ok=True) + log_path = run_dir / 'debug_log.txt' + if log_path.exists(): log_path.unlink() + print(f"✅ Outputs will be saved in: {run_dir}") + + api_family_data_path = Path("Taskbench") / f"data_{args.api_family}" + try: + with open(api_family_data_path / "tool_desc.json", 'r', encoding='utf-8') as f: + parsed_tool_data = json.load(f) + except Exception as e: + print(f"⚠️ Warning: Could not parse tool_desc.json: {e}", file=sys.stderr); parsed_tool_data = {"nodes": []} + + local_data_dir = Path("Taskbench") / f"data_{args.api_family}" + all_records = [] + + '''def load_local(data_dir: Path): + path = data_dir / 'user_requests.jsonl' + if not path.exists(): path = data_dir / 'user_requests.json' + with path.open('r', encoding='utf-8') as f: + for line in f: + yield json.loads(line) + ''' + if local_data_dir.is_dir(): + print(f"✅ Found local dataset at '{local_data_dir}'. Loading...") + all_records = list(load_local(local_data_dir)) + else: + print(f"✅ No local dataset found. Loading '{args.api_family}' from Hugging Face...") + all_records = list(load_hf(config_name=args.api_family)) + + if not all_records: + print(f"❌ No problems loaded for API family '{args.api_family}'. Exiting.", file=sys.stderr) + sys.exit(1) + + num_to_process = min(args.num_problems, len(all_records)) + random.shuffle(all_records) + records_to_process = all_records[:num_to_process] + print(f"✅ Loaded {len(all_records)} problems, processing {len(records_to_process)}.") + + with Manager() as manager: + log_lock = manager.Lock() + + problems_to_submit = [{ + "dataset_index": j, "example": ex, "api_family_for_tools": args.api_family, + "log_path": log_path, "log_lock": log_lock, "args": args, + "parsed_tool_data": parsed_tool_data + } for j, ex in enumerate(records_to_process)] + + run_results = [] + with ProcessPoolExecutor(max_workers=args.max_workers) as executor: + futures = {executor.submit(process_problem_with_spiral, prob): prob['dataset_index'] for prob in problems_to_submit} + for future in tqdm(as_completed(futures), total=len(records_to_process), desc=f"SPIRAL on {args.api_family}"): + try: + result = future.result() + if result: run_results.append(result['record']) + except Exception as e: + traceback.print_exc() + print(f"Problem {futures[future]} failed: {e}", file=sys.stderr) + + run_output_path = run_dir / 'results.json' + with run_output_path.open("w", encoding="utf-8") as f: json.dump(run_results, f, indent=2) + + total_correct = sum(1 for r in run_results if r.get('metrics', {}).get('accuracy', 0.0) > 0.9) + total_problems = len(run_results) + accuracy = (total_correct / total_problems) * 100 if total_problems > 0 else 0 + + summary = { + "run_name": run_name, "model_name": args.model_name, "api_family": args.api_family, + "num_problems_processed": len(records_to_process), "seed": args.seed, + "mcts_iterations": args.mcts_iterations, "max_depth": args.max_depth, + "final_accuracy": f"{accuracy:.2f}%" + } + summary_path = run_dir / 'summary.json' + with summary_path.open("w", encoding="utf-8") as f: json.dump(summary, f, indent=2) + + print(f"\n{'='*25} Experiment Complete {'='*25}") + print(f"📊 Final Accuracy: {accuracy:.2f}%") + print(f"✅ Results saved to {run_output_path}") + print(f"✅ Final summary saved to {summary_path}") + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/scripts/taskbench_tot_baseline.py b/scripts/taskbench_tot_baseline.py new file mode 100644 index 0000000..fb854fc --- /dev/null +++ b/scripts/taskbench_tot_baseline.py @@ -0,0 +1,376 @@ +#!/usr/bin/env python3 +# taskbench_tot_baseline.py + +import os +import sys +import json +import time +import argparse +from pathlib import Path +from typing import List, Optional, Dict, Tuple +from concurrent.futures import ProcessPoolExecutor, as_completed +from multiprocessing import Manager +import ast +import re +from datetime import datetime +import random +import numpy as np +import torch + +from datasets import load_dataset +from tqdm import tqdm +from SPIRAL.scripts.utils.ritz_client import RitsChatClient, MODELMAP, MODEL_ID_MAP + +# ───────────────────────────────────────────────────────────────────────────── +# NOTE: The following helper functions and constants are copied from the +# other scripts to ensure a fair and consistent experimental setup. +# ───────────────────────────────────────────────────────────────────────────── + +CORRECTED_TOOL_PARAMETERS = { + "Token Classification": {"text": "string"}, "Translation": {"text": "string", "source_lang": "string", "target_lang": "string"}, "Summarization": {"text": "string"}, "Question Answering": {"context": "string", "question": "string"}, "Conversational": {"prompt": "string", "history": "list"}, "Text Generation": {"prompt": "string"}, "Sentence Similarity": {"sentence1": "string", "sentence2": "string"}, "Tabular Classification": {"table_image_path": "string"}, "Object Detection": {"image_path": "string"}, "Image Classification": {"image_path": "string"}, "Image-to-Image": {"image_path": "string", "target_image_path": "string"}, "Image-to-Text": {"image_path": "string"}, "Text-to-Image": {"prompt": "string"}, "Text-to-Video": {"prompt": "string"}, "Visual Question Answering": {"image_path": "string", "question": "string"}, "Document Question Answering": {"document_image_path": "string", "question": "string"}, "Image Segmentation": {"image_path": "string"}, "Depth Estimation": {"image_path": "string"}, "Text-to-Speech": {"text": "string"}, "Automatic Speech Recognition": {"audio_path": "string"}, "Audio-to-Audio": {"audio_path": "string"}, "Audio Classification": {"audio_path": "string"}, "Image Editing": {"image_path": "string", "edits": "dict"}, "get_weather": {"location": "string", "date": "string"}, "get_news_for_topic": {"topic": "string"}, "stock_operation": {"stock": "string", "operation": "string"}, "book_flight": {"date": "string", "from": "string", "to": "string"}, "book_hotel": {"date": "string", "name": "string"}, "book_restaurant": {"date": "string", "name": "string"}, "book_car": {"date": "string", "location": "string"}, "online_shopping": {"website": "string", "product": "string"}, "send_email": {"email_address": "string", "content": "string"}, "send_sms": {"phone_number": "string", "content": "string"}, "share_by_social_network": {"content": "string", "social_network": "string"}, "search_by_engine": {"query": "string", "engine": "string"}, "apply_for_job": {"job": "string"}, "see_doctor_online": {"disease": "string", "doctor": "string"}, "consult_lawyer_online": {"issue": "string", "lawyer": "string"}, "enroll_in_course": {"course": "string", "university": "string"}, "buy_insurance": {"insurance": "string", "company": "string"}, "online_banking": {"instruction": "string", "bank": "string"}, "daily_bill_payment": {"bill": "string"}, "sell_item_online": {"item": "string", "store": "string"}, "do_tax_return": {"year": "string"}, "apply_for_passport": {"country": "string"}, "pay_for_credit_card": {"credit_card": "string"}, "auto_housework_by_robot": {"instruction": "string"}, "auto_driving_to_destination": {"destination": "string"}, "deliver_package": {"package": "string", "destination": "string"}, "order_food_delivery": {"food": "string", "location": "string", "platform": "string"}, "order_taxi": {"location": "string", "platform": "string"}, "play_music_by_title": {"title": "string"}, "play_movie_by_title": {"title": "string"}, "take_note": {"content": "string"}, "borrow_book_online": {"book": "string", "library": "string"}, "recording_audio": {"content": "string"}, "make_video_call": {"phone_number": "string"}, "make_voice_call": {"phone_number": "string"}, "organize_meeting_online": {"topic": "string"}, "attend_meeting_online": {"topic": "string"}, "software_management": {"software": "string", "instruction": "string"}, "print_document": {"document": "string"}, "set_alarm": {"time": "string"}, +} + +def load_tool_descriptions_from_file(api_family_data_dir: Path) -> str: + tool_desc_path = api_family_data_dir / "tool_desc.json" + if not tool_desc_path.exists(): + raise FileNotFoundError(f"Tool description file not found: {tool_desc_path}.") + with open(tool_desc_path, 'r', encoding='utf-8') as f: + tool_data_root = json.load(f) + description_parts = ["Available tools (use the `api_call` function to invoke them):"] + tool_nodes = tool_data_root.get("nodes", []) + for tool_node in tool_nodes: + tool_id, tool_desc = tool_node.get("id"), tool_node.get("desc") + parameters = tool_node.get("parameters", []) + if not tool_id or not tool_desc: continue + args_list, example_args_dict = [], {} + effective_parameters = [{"name": n, "type": t} for n, t in CORRECTED_TOOL_PARAMETERS.get(tool_id, {}).items()] or parameters + for param in effective_parameters: + param_name, param_type = param.get("name"), param.get("type", "Any") + if param_name: + args_list.append(f"`{param_name}` ({param_type})") + example_args_dict[param_name] = f"<{param_name}_value>" + example_call_str = f"api_call(\"{tool_id}\", {json.dumps(example_args_dict)})" + description_parts.append(f"\n`{example_call_str}`\n Description: {tool_desc}") + if args_list: description_parts.append(f" Parameters: {'; '.join(args_list)}") + return "\n".join(description_parts) + +def load_graph_descriptions_from_file(api_family_data_dir: Path) -> str: + graph_desc_path = api_family_data_dir / "graph_desc.json" + if not graph_desc_path.exists(): return "" + with open(graph_desc_path, 'r', encoding='utf-8') as f: + graph_data = json.load(f) + description_parts = ["\n--- Tool Dependencies ---"] + for dep_type, deps in graph_data.items(): + if isinstance(deps, list) and deps: + description_parts.append(f"{dep_type.replace('_', ' ').title()}:") + for dep in deps: + pre, post = dep.get("pre_tool"), dep.get("post_tool") + if "resource" in dep_type: + res = ", ".join(dep.get("resources", [])); description_parts.append(f" - `{post}` requires resource(s) `{res}` from `{pre}`.") + elif "temporal" in dep_type: + cond = dep.get("condition", "completion"); description_parts.append(f" - `{post}` can only be called after `{pre}` upon its {cond}.") + return "\n".join(description_parts) if len(description_parts) > 1 else "" + +# ───────────────────────────────────────────────────────────────────────────── +# Core ToT Logic +# ───────────────────────────────────────────────────────────────────────────── + +class ToT_Proposer: + """Generates candidate thoughts for the ToT search.""" + def __init__(self, user_request: str, tools_description: str, graph_description: str, num_candidates: int): + self.client = RitsChatClient(temperature=0.5, max_tokens=1024) + self.user_request = user_request + self.tools_desc = tools_description + self.graph_desc = graph_description + self.num_candidates = num_candidates + self.prompt_template = """As an expert planner, your goal is to generate diverse and promising next steps to solve a user's request. + +### AVAILABLE TOOLS +{tools_description} +{graph_description} + +### TASK +User Request: {user_request} + +### CURRENT PLAN +{plan_history} + +### INSTRUCTION +Based on the current plan, generate a Python list of {num_candidates} distinct `api_call(...)` or `finish(...)` actions to take next. +Focus on variety and relevance. Your response MUST be ONLY a Python list of strings in a markdown block. +```python +[ + "action_string_1", + "action_string_2", + ... +] +```""" + + def propose(self, plan_history_str: str) -> Tuple[List[str], int]: + prompt = self.prompt_template.format( + tools_description=self.tools_desc, + graph_description=self.graph_desc, + user_request=self.user_request, + plan_history=plan_history_str, + num_candidates=self.num_candidates + ) + response, tokens = self.client.send(prompt) + match = re.search(r"```(?:python)?\s*(\[.*?\])\s*```", response, re.DOTALL) + if match: + try: + # Use literal_eval for safe evaluation of the list string + proposals = ast.literal_eval(match.group(1).strip()) + if isinstance(proposals, list) and all(isinstance(p, str) for p in proposals): + return proposals, tokens + except (ValueError, SyntaxError): + # Fallback if parsing fails + return [], tokens + return [], tokens + +class ToT_Evaluator: + """Evaluates the quality of a partial plan (a state in the ToT).""" + def __init__(self, user_request: str): + self.client = RitsChatClient(temperature=0.0, max_tokens=150) + self.user_request = user_request + self.prompt_template = """As a meticulous evaluator, assess the following partial plan for its potential to solve the user's request. + +### TASK +User Request: {user_request} + +### PARTIAL PLAN +{plan_to_evaluate} + +### INSTRUCTION +Evaluate the plan's progress and likelihood of success. Is it on a good path? Is it coherent and logical? +Respond with ONLY a single line in the format: `Score: | Justification: `""" + + def evaluate(self, plan_str: str) -> Tuple[float, int]: + prompt = self.prompt_template.format(user_request=self.user_request, plan_to_evaluate=plan_str) + response, tokens = self.client.send(prompt) + score_match = re.search(r"Score:\s*([0-9.]+)", response) + score = float(score_match.group(1)) if score_match else 0.0 + return score, tokens + +def process_problem_with_tot(problem_info: Dict) -> Optional[Dict]: + """ + Generates and evaluates a plan for a given problem using the Tree of Thoughts (ToT) + methodology with Breadth-First Search (BFS). + """ + idx, example, api_family, log_path, log_lock, args = ( + problem_info['dataset_index'], problem_info['example'], problem_info['api_family_for_tools'], + problem_info['log_path'], problem_info['log_lock'], problem_info['args'] + ) + + def write_log(message: str): + with log_lock: + with log_path.open("a", encoding="utf-8") as f: + f.write(f"--- Problem {idx} ({example['id']}) ---\n{message}\n" + "="*80 + "\n\n") + + user_request_text = example['instruction'] + try: + tools_description = load_tool_descriptions_from_file(Path("Taskbench") / f"data_{api_family}") + graph_description = load_graph_descriptions_from_file(Path("Taskbench") / f"data_{api_family}") + except (FileNotFoundError, ValueError) as e: + write_log(f"CRITICAL ERROR: Could not load descriptions. Error: {e}"); return None + + # Initialize ToT components + proposer = ToT_Proposer(user_request_text, tools_description, graph_description, args.candidates_per_state) + evaluator = ToT_Evaluator(user_request_text) + + start_time = time.time() + total_llm_tokens = 0 + llm_calls = 0 + + # ToT with BFS state representation: A list of (plan_steps, score) tuples + # Start with an empty plan + active_states = [ ([], 1.0) ] + + for step in range(args.max_steps): + all_new_candidates = [] + for plan_steps, _ in active_states: + plan_history_str = "\n".join(plan_steps) if plan_steps else "No steps taken yet." + + # 1. GENERATE thoughts for the current state + proposals, prop_tokens = proposer.propose(plan_history_str) + total_llm_tokens += prop_tokens + llm_calls += 1 + + for p in proposals: + # A new candidate is the old plan plus the new proposal + new_plan = plan_steps + [p] + all_new_candidates.append(new_plan) + if p.startswith("finish("): # If a plan is finished, keep it for evaluation + continue + + if not all_new_candidates: + break # Stop if no new ideas are generated + + # 2. EVALUATE all generated candidate plans + evaluated_candidates = [] + for plan in all_new_candidates: + plan_str = "\n".join(plan) + score, eval_tokens = evaluator.evaluate(plan_str) + total_llm_tokens += eval_tokens + llm_calls += 1 + evaluated_candidates.append( (plan, score) ) + + # 3. SELECT the best `b` (breadth) states for the next step + evaluated_candidates.sort(key=lambda x: x[1], reverse=True) + active_states = evaluated_candidates[:args.search_breadth] + + # Check if the top state is a finished plan + if active_states and active_states[0][0][-1].startswith("finish("): + break + + generation_time_seconds = time.time() - start_time + + # Final selection: choose the best plan from the final set of active states + final_plan_steps = active_states[0][0] if active_states else [] + + # Final evaluation of the chosen plan + final_reward_score = 0.0 + EVALUATION_PROMPT = """Did the 'Generated Plan' successfully solve the 'User Request'? Answer with only "Yes" or "No".\n[User Request]:\n{user_request}\n\n[Generated Plan]:\n{generated_plan}\n\n[Answer (Yes/No)]:""" + try: + eval_client = RitsChatClient(temperature=0.0, max_tokens=10) + eval_prompt = EVALUATION_PROMPT.format(user_request=user_request_text, generated_plan="\n".join(final_plan_steps)) + verdict, eval_tokens = eval_client.send(eval_prompt) + total_llm_tokens += eval_tokens + llm_calls +=1 + if verdict.strip().lower().startswith("yes"): final_reward_score = 1.0 + except Exception as e: + write_log(f"Warning: LLM-based evaluation failed. Error: {e}") + + final_output = { + "id": example['id'], + "result": { + "task_steps": final_plan_steps + }, + "metrics": { + "accuracy": final_reward_score, + "generation_time_seconds": round(generation_time_seconds, 2), + "plan_length": sum(1 for s in final_plan_steps if s.startswith("api_call")), + "reasoning_cost": { + "llm_calls": llm_calls, + "total_llm_tokens": total_llm_tokens, + } + } + } + return {"record": final_output} + + +def load_hf(config_name: str): + try: + ds = load_dataset('microsoft/Taskbench', name=config_name, split='test') + for ex in ds: + yield {'id': ex['id'], 'instruction': ex['instruction'], 'input': ex.get('input',''), 'tool_steps': ex.get('tool_steps',[])} + except Exception as e: + print(f"\n❌ Failed to load '{config_name}' from Hugging Face.", file=sys.stderr); sys.exit(1) + +# ───────────────────────────────────────────────────────────────────────────── +# Main Orchestrator +# ───────────────────────────────────────────────────────────────────────────── +def main(): + ap = argparse.ArgumentParser(description="Run Tree of Thoughts (ToT) Baseline on TaskBench.") + + ap.add_argument('--run_name', type=str, default=None, help="Optional name for the output directory.") + ap.add_argument('--api_family', type=str, default='huggingface', help="API family to test.") + ap.add_argument('--num_problems', type=int, default=50, help="Number of problems to sample.") + ap.add_argument('--seed', type=int, default=42, help="Random seed for reproducibility.") + ap.add_argument('--model_name', type=str, default='llama_4', help="The model checkpoint to use.") + ap.add_argument('--max_steps', type=int, default=5, help="Maximum number of steps (depth) in the ToT search.") + ap.add_argument('--search_breadth', type=int, default=5, help="Beam width for BFS (b).") + ap.add_argument('--candidates_per_state', type=int, default=3, help="Number of new thoughts to propose per state (k).") + ap.add_argument('--max_workers', type=int, default=os.cpu_count(), help="Maximum parallel processes.") + args = ap.parse_args() + + valid_rits_models = list(MODEL_ID_MAP["rits"].keys()) + if args.model_name not in valid_rits_models: + print(f"❌ Error: Invalid model name '{args.model_name}'. Choose from: {valid_rits_models}", file=sys.stderr); sys.exit(1) + + MODELMAP.set_model('generate_model', args.model_name) + print(f"✅ Configured to use model: {MODELMAP.generate_model}") + + random.seed(args.seed); np.random.seed(args.seed); torch.manual_seed(args.seed) + if torch.cuda.is_available(): torch.cuda.manual_seed_all(args.seed) + + run_name = args.run_name + if run_name is None: + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + run_name = f"tot_b{args.search_breadth}_{args.api_family}_{args.model_name}_{timestamp}" + print(f"✅ No run name provided. Using auto-generated name: {run_name}") + + run_dir = Path('predictions') / run_name; run_dir.mkdir(parents=True, exist_ok=True) + log_path = run_dir / 'debug_log.txt' + if log_path.exists(): log_path.unlink() + print(f"✅ Outputs will be saved in: {run_dir}") + + # --- Load Data (with local override) --- + local_data_dir = Path("Taskbench") / f"data_{args.api_family}" + all_records = [] + + def load_local(data_dir: Path): + path = data_dir / 'user_requests.jsonl' + if not path.exists(): path = data_dir / 'user_requests.json' + with path.open('r', encoding='utf-8') as f: + for line in f: + yield json.loads(line) + + if local_data_dir.is_dir(): + print(f"✅ Found local dataset at '{local_data_dir}'. Loading...") + all_records = list(load_local(local_data_dir)) + else: + print(f"✅ No local dataset found. Loading '{args.api_family}' from Hugging Face...") + all_records = list(load_hf(config_name=args.api_family)) + + if not all_records: + print(f"❌ No problems loaded for API family '{args.api_family}'. Exiting.", file=sys.stderr) + sys.exit(1) + + num_to_process = min(args.num_problems, len(all_records)) + random.shuffle(all_records) + records_to_process = all_records[:num_to_process] + print(f"✅ Loaded {len(all_records)} problems, processing {len(records_to_process)}.") + + with Manager() as manager: + log_lock = manager.Lock() + + problems_to_submit = [{ + "dataset_index": j, "example": ex, "api_family_for_tools": args.api_family, + "log_path": log_path, "log_lock": log_lock, "args": args + } for j, ex in enumerate(records_to_process)] + + run_results = [] + with ProcessPoolExecutor(max_workers=args.max_workers) as executor: + futures = {executor.submit(process_problem_with_tot, prob): prob['dataset_index'] for prob in problems_to_submit} + for future in tqdm(as_completed(futures), total=len(records_to_process), desc=f"ToT on {args.api_family}"): + try: + result = future.result() + if result: run_results.append(result['record']) + except Exception as e: + print(f"Problem {futures[future]} failed: {e}", file=sys.stderr) + + run_output_path = run_dir / 'results.json' + with run_output_path.open("w", encoding="utf-8") as f: json.dump(run_results, f, indent=2) + + total_correct = sum(1 for r in run_results if r.get('metrics', {}).get('accuracy', 0.0) > 0.9) + total_problems = len(run_results) + accuracy = (total_correct / total_problems) * 100 if total_problems > 0 else 0 + + summary = { + "run_name": run_name, "model_name": args.model_name, "api_family": args.api_family, + "num_problems_processed": len(records_to_process), "seed": args.seed, + "search_breadth": args.search_breadth, + "candidates_per_state": args.candidates_per_state, + "max_steps": args.max_steps, + "final_accuracy": f"{accuracy:.2f}%" + } + summary_path = run_dir / 'summary.json' + with summary_path.open("w", encoding="utf-8") as f: json.dump(summary, f, indent=2) + + print(f"\n{'='*25} Experiment Complete {'='*25}") + print(f"📊 Final Accuracy: {accuracy:.2f}%") + print(f"✅ Results saved to {run_output_path}") + print(f"✅ Final summary saved to {summary_path}") + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/scripts/test_client_updated.py b/scripts/test_client_updated.py new file mode 100644 index 0000000..d612feb --- /dev/null +++ b/scripts/test_client_updated.py @@ -0,0 +1,83 @@ +import time +import os +# Correctly import all necessary components from the single ritz_client.py file +from SPIRAL.scripts.utils.ritz_client import RitsChatClient, MODELMAP, MODEL_ID_MAP + +# Ensure we are testing the RITS platform, not Watsonx +os.environ["USE_WATSONX"] = "False" + +print("--- Starting RITS Client Full Model Check ---") + +# Get the list of all available RITS models from the configuration +models_to_test = list(MODEL_ID_MAP['rits'].keys()) +passed_models = [] +failed_models = {} # Using a dict to store model and failure reason + +# Loop through each model and perform a sanity check +for model_name in models_to_test: + print(f"\n" + "="*50) + print(f"--- 🧪 Testing Model: {model_name} ---") + print("="*50) + + try: + # 1. Set the current model to be tested + # This tells the next RitsChatClient instance which model to use + MODELMAP.set_model('generate_model', model_name) + print(f"Active model set to '{model_name}'.") + + # 2. Initialize a new client instance for this specific model + start_time = time.time() + client = RitsChatClient(temperature=0.7) + init_time = time.time() - start_time + print(f"Client for '{model_name}' initialized in {init_time:.2f} seconds.") + + # 3. Send the test prompt to the specific model endpoint + print("Sending test prompt...") + prompt = "Hello! Please respond with just the word 'OK'." + start_time = time.time() + response, tokens = client.send(prompt, max_tokens=10) + send_time = time.time() - start_time + + print(f"Received response in {send_time:.2f} seconds.") + response_text = response.strip() + print(f"LLM Response: '{response_text}'") + print(f"Tokens used: {tokens}") + + # 4. Validate the response + if response and "ok" in response_text.lower(): + print(f"\n✅ PASSED: Model '{model_name}' is responding correctly.") + passed_models.append(model_name) + else: + error_message = f"Received an unexpected response: '{response_text}'" + print(f"\n❌ FAILED: {error_message}") + failed_models[model_name] = error_message + + except Exception as e: + error_message = f"An exception occurred: {e}" + print(f"\n❌ FAILED: {error_message}") + failed_models[model_name] = str(e) + print("This could indicate a problem with the model endpoint, your API key, or network connection.") + +# --- Final Summary --- +print("\n\n" + "#"*60) +print("--- Full Model Check Summary ---") +print("#"*60) + +total_models = len(models_to_test) +print(f"\nTested {total_models} models.") + +print(f"\n✅ Passed Models ({len(passed_models)}/{total_models}):") +if passed_models: + for m in sorted(passed_models): + print(f"- {m}") +else: + print("None") + +print(f"\n❌ Failed Models ({len(failed_models)}/{total_models}):") +if failed_models: + for model, reason in sorted(failed_models.items()): + print(f"- {model}: {reason}") +else: + print("None") + +print("\n--- Model Check Complete ---") \ No newline at end of file diff --git a/scripts/utils/__init__.py b/scripts/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/scripts/utils/__pycache__/__init__.cpython-312.pyc b/scripts/utils/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..33ea49b229a69f0e89736b4b25ecef8dab326e27 GIT binary patch literal 178 zcmX@j%ge<81SXx*nIQTxh(HIQS%4zb87dhx8U0o=6fpsLpFwJV1?qKEC6r#ErkF8 literal 0 HcmV?d00001 diff --git a/scripts/utils/generic_client.py b/scripts/utils/generic_client.py new file mode 100644 index 0000000..e7c9a31 --- /dev/null +++ b/scripts/utils/generic_client.py @@ -0,0 +1,244 @@ +#!/usr/bin/env python3 +# utils/generic_client.py +# +# Refactored for public submission with generic Hugging Face implementations. +# This file provides all necessary components and helper functions used by the +# TaskBench experiment scripts, using only publicly available names. + +import re +import json +import torch +from pathlib import Path +from typing import List, Optional, Dict, Any, Tuple, Union +import ast +import collections + +# Hugging Face Transformers for generic LLM implementation +from transformers import pipeline, AutoTokenizer + +from .ritz_client import RitsChatClient +from .watsonx_client import WatsonxChatClient + +# ───────────────────────────────────────────────────────────────────────────── +# 1. GENERIC MODEL CONFIGURATION +# ───────────────────────────────────────────────────────────────────────────── + +MODEL_ID_MAP = { + # This key is used by the experiment scripts to validate model names. + "taskbench_models": { + + # Meta Llama Models (Public versions as proxies) + "llama_3": "meta-llama/Meta-Llama-3-8B-Instruct", + # Using a powerful model as a proxy for non-public Llama-4 + "llama_4": "mistralai/Mixtral-8x7B-Instruct-v0.1", + "llama_3_3_70b_instruct": "meta-llama/Meta-Llama-3-70B-Instruct", + + # Mistral Models + "mistral": "mistralai/Mistral-7B-Instruct-v0.3", + "codestral": "mistralai/Codestral-22B-v0.1", + "mixtral_8_22b": "mistralai/Mixtral-8x22B-v0.1", + + # Other Models (Public versions as proxies) + "phi": "microsoft/Phi-3-mini-4k-instruct", + "deepseek_v2_5": "deepseek-ai/DeepSeek-V2-Lite", + "qwen2_5_72b_instruct": "Qwen/Qwen2-72B-Instruct", + } +} + + +class MODELMAP: + """ + Class to configure which model to use for different task types. + """ + er_model = "llama_4" + generate_model = "llama_4" + review_model = "llama_4" + explain_model = "phi" + + @classmethod + def set_model(cls, model_type: str, model_name: str): + VALID_TYPES = ["er_model", "generate_model", "review_model", "explain_model"] + VALID_MODELS = list(MODEL_ID_MAP["taskbench_models"].keys()) + if model_name not in VALID_MODELS: + raise ValueError(f"Invalid model: {model_name}. Choose from {VALID_MODELS}") + if model_type not in VALID_TYPES: + raise ValueError(f"Invalid model type: {model_type}. Choose from {VALID_TYPES}") + setattr(cls, model_type, model_name) + + @classmethod + def get_model_id(cls, model_type: str) -> str: + model_name = getattr(cls, model_type) + return MODEL_ID_MAP["taskbench_models"][model_name] + +# ───────────────────────────────────────────────────────────────────────────── +# 2. CORE LLM CLIENT (Hugging Face Implementation) +# ───────────────────────────────────────────────────────────────────────────── + +class HFPipelineManager: + """ + A generic wrapper for Hugging Face models using the transformers pipeline. + """ + def __init__(self, model_id: str, temperature: float, max_new_tokens: int): + device = 0 if torch.cuda.is_available() else -1 + self.tokenizer = AutoTokenizer.from_pretrained(model_id) + self.pipe = pipeline( + "text-generation", + model=model_id, + tokenizer=self.tokenizer, + device=device, + torch_dtype=torch.bfloat16, + ) + self.default_params = { + "temperature": temperature, + "max_new_tokens": max_new_tokens, + "do_sample": True if temperature > 0 else False, + "return_full_text": False, + "pad_token_id": self.pipe.tokenizer.eos_token_id, + } + + def generate(self, prompt: str, **kwargs) -> str: + gen_params = self.default_params.copy() + if "temperature" in kwargs: + gen_params["do_sample"] = True if kwargs["temperature"] > 0 else False + gen_params.update(kwargs) + + response = self.pipe(prompt, **gen_params) + return response[0]['generated_text'].strip() + + +class HuggingFaceChatClient: + """ + Generic chat client that maintains conversation history. + """ + def __init__(self, temperature: float = 0.5, max_tokens: int = 1024): + model_id = MODELMAP.get_model_id("generate_model") + self.manager = HFPipelineManager( + model_id=model_id, + temperature=temperature, + max_new_tokens=max_tokens, + ) + + def send(self, user_message: str, **kwargs) -> Tuple[str, int]: + """ + Sends a message to the LLM and returns the response and token count. + """ + prompt = user_message + input_tokens = len(self.manager.tokenizer.encode(prompt)) + response_text = self.manager.generate(prompt, **kwargs) + generated_tokens = len(self.manager.tokenizer.encode(response_text)) + total_tokens = input_tokens + generated_tokens + return response_text, total_tokens + +# ───────────────────────────────────────────────────────────────────────────── +# 3. TASKBENCH HELPER FUNCTIONS & CLASSES +# ───────────────────────────────────────────────────────────────────────────── + +CORRECTED_TOOL_PARAMETERS = { + "Token Classification": {"text": "string"}, "Translation": {"text": "string", "source_lang": "string", "target_lang": "string"}, "Summarization": {"text": "string"}, "Question Answering": {"context": "string", "question": "string"}, "Conversational": {"prompt": "string", "history": "list"}, "Text Generation": {"prompt": "string"}, "Sentence Similarity": {"sentence1": "string", "sentence2": "string"}, "Tabular Classification": {"table_image_path": "string"}, "Object Detection": {"image_path": "string"}, "Image Classification": {"image_path": "string"}, "Image-to-Image": {"image_path": "string", "target_image_path": "string"}, "Image-to-Text": {"image_path": "string"}, "Text-to-Image": {"prompt": "string"}, "Text-to-Video": {"prompt": "string"}, "Visual Question Answering": {"image_path": "string", "question": "string"}, "Document Question Answering": {"document_image_path": "string", "question": "string"}, "Image Segmentation": {"image_path": "string"}, "Depth Estimation": {"image_path": "string"}, "Text-to-Speech": {"text": "string"}, "Automatic Speech Recognition": {"audio_path": "string"}, "Audio-to-Audio": {"audio_path": "string"}, "Audio Classification": {"audio_path": "string"}, "Image Editing": {"image_path": "string", "edits": "dict"}, "get_weather": {"location": "string", "date": "string"}, "get_news_for_topic": {"topic": "string"}, "stock_operation": {"stock": "string", "operation": "string"}, "book_flight": {"date": "string", "from": "string", "to": "string"}, "book_hotel": {"date": "string", "name": "string"}, "book_restaurant": {"date": "string", "name": "string"}, "book_car": {"date": "string", "location": "string"}, "online_shopping": {"website": "string", "product": "string"}, "send_email": {"email_address": "string", "content": "string"}, "send_sms": {"phone_number": "string", "content": "string"}, "share_by_social_network": {"content": "string", "social_network": "string"}, "search_by_engine": {"query": "string", "engine": "string"}, "apply_for_job": {"job": "string"}, "see_doctor_online": {"disease": "string", "doctor": "string"}, "consult_lawyer_online": {"issue": "string", "lawyer": "string"}, "enroll_in_course": {"course": "string", "university": "string"}, "buy_insurance": {"insurance": "string", "company": "string"}, "online_banking": {"instruction": "string", "bank": "string"}, "daily_bill_payment": {"bill": "string"}, "sell_item_online": {"item": "string", "store": "string"}, "do_tax_return": {"year": "string"}, "apply_for_passport": {"country": "string"}, "pay_for_credit_card": {"credit_card": "string"}, "auto_housework_by_robot": {"instruction": "string"}, "auto_driving_to_destination": {"destination": "string"}, "deliver_package": {"package": "string", "destination": "string"}, "order_food_delivery": {"food": "string", "location": "string", "platform": "string"}, "order_taxi": {"location": "string", "platform": "string"}, "play_music_by_title": {"title": "string"}, "play_movie_by_title": {"title": "string"}, "take_note": {"content": "string"}, "borrow_book_online": {"book": "string", "library": "string"}, "recording_audio": {"content": "string"}, "make_video_call": {"phone_number": "string"}, "make_voice_call": {"phone_number": "string"}, "organize_meeting_online": {"topic": "string"}, "attend_meeting_online": {"topic": "string"}, "software_management": {"software": "string", "instruction": "string"}, "print_document": {"document": "string"}, "set_alarm": {"time": "string"}, +} + +def parse_tool_code(text: str) -> str: + match = re.search(r"```(?:python\n)?(.*?)\n?```", text, re.DOTALL) + return match.group(1).strip() if match else text.strip() + +def load_tool_descriptions_from_file(api_family_data_dir: Path) -> str: + tool_desc_path = api_family_data_dir / "tool_desc.json" + if not tool_desc_path.exists(): + raise FileNotFoundError(f"Tool description file not found: {tool_desc_path}.") + with open(tool_desc_path, 'r', encoding='utf-8') as f: + tool_data_root = json.load(f) + description_parts = ["Available tools (use the `api_call` function to invoke them):"] + tool_nodes = tool_data_root.get("nodes", []) + for tool_node in tool_nodes: + tool_id, tool_desc = tool_node.get("id"), tool_node.get("desc") + parameters = tool_node.get("parameters", []) + if not tool_id or not tool_desc: continue + args_list, example_args_dict = [], {} + effective_parameters = [{"name": n, "type": t} for n, t in CORRECTED_TOOL_PARAMETERS.get(tool_id, {}).items()] or parameters + for param in effective_parameters: + param_name, param_type = param.get("name"), param.get("type", "Any") + if param_name: + args_list.append(f"`{param_name}` ({param_type})") + example_args_dict[param_name] = f"<{param_name}_value>" + example_call_str = f"api_call(\"{tool_id}\", {json.dumps(example_args_dict)})" + description_parts.append(f"\n`{example_call_str}`\n Description: {tool_desc}") + if args_list: description_parts.append(f" Parameters: {'; '.join(args_list)}") + return "\n".join(description_parts) + +def load_graph_descriptions_from_file(api_family_data_dir: Path) -> str: + graph_desc_path = api_family_data_dir / "graph_desc.json" + if not graph_desc_path.exists(): return "" + with open(graph_desc_path, 'r', encoding='utf-8') as f: + graph_data = json.load(f) + description_parts = ["\n--- Tool Dependencies ---"] + for dep_type, deps in graph_data.items(): + if isinstance(deps, list) and deps: + description_parts.append(f"{dep_type.replace('_', ' ').title()}:") + for dep in deps: + pre, post = dep.get("pre_tool"), dep.get("post_tool") + if "resource" in dep_type: + res = ", ".join(dep.get("resources", [])); description_parts.append(f" - `{post}` requires resource(s) `{res}` from `{pre}`.") + elif "temporal" in dep_type: + cond = dep.get("condition", "completion"); description_parts.append(f" - `{post}` can only be called after `{pre}` upon its {cond}.") + return "\n".join(description_parts) if len(description_parts) > 1 else "" + +class ToolValidator: + def __init__(self, parsed_tool_data_root: Dict): + self.tool_signatures = collections.defaultdict(dict) + tool_nodes = parsed_tool_data_root.get("nodes", []) + for tool_node in tool_nodes: + tool_id, parameters = tool_node.get("id"), tool_node.get("parameters", []) + if tool_id: + effective_params = [{"name": n, "type": t} for n, t in CORRECTED_TOOL_PARAMETERS.get(tool_id, {}).items()] or parameters + self.tool_signatures[tool_id] = {"parameters": {p.get("name"): p.get("type") for p in effective_params if isinstance(p, dict)}} + + def validate_api_call(self, code_str: str) -> bool: + match = re.search(r'api_call\("([^"]+)",\s*({.*?})\)', code_str, re.DOTALL) + if not match: return False + tool_id, args_str = match.group(1), match.group(2) + if tool_id not in self.tool_signatures: return False + expected_params = self.tool_signatures[tool_id]["parameters"] + try: + parsed_args = ast.literal_eval(args_str) + return isinstance(parsed_args, dict) and all(arg_name in expected_params for arg_name in parsed_args) + except (ValueError, SyntaxError): + return False + +class SimulatedToolExecutor: + def __init__(self, user_request: str): + self.client = HuggingFaceChatClient(temperature=0.2, max_tokens=150) + self.user_request = user_request + + def execute(self, api_call_str: str) -> Tuple[str, int]: + prompt_template = """You are a simulated API tool. Provide a realistic, one-line observation for the given tool call. +### User's Goal: "{user_request}" +### Tool Call: `{api_call_str}` +### Your Response (one line starting with `Observation: tool_output = `): +""" + prompt = prompt_template.format(user_request=self.user_request, api_call_str=api_call_str) + try: + response_text, tokens_used = self.client.send(prompt) + if response_text and response_text.strip().startswith("Observation: tool_output ="): + return response_text.strip().split('\n')[0], tokens_used + return 'Observation: tool_output = "Error: Tool simulation failed."', tokens_used + except Exception: + return 'Observation: tool_output = "Error: Tool simulation encountered an exception."', 0 + +# ───────────────────────────────────────────────────────────────────────────── +# 4. CORE LLM CLIENT (Hugging, RITS, or WATSONX ) +# ───────────────────────────────────────────────────────────────────────────── + +def getLLMChatClient(llm_platform:str, **kwargs) -> Union[RitsChatClient, WatsonxChatClient, HuggingFaceChatClient]: + """ + llm_platform: the llm platform to use. It must be "watsonx", "rits" or "hf" + """ + llm_platform = llm_platform.lower() + if llm_platform == "watsonx".lower(): + model_id = MODELMAP.get_model_id("generate_model") + return WatsonxChatClient(model_id, **kwargs) + elif llm_platform == "rits".lower(): + return RitsChatClient(**kwargs) + elif llm_platform == "hf".lower(): + return HuggingFaceChatClient(**kwargs) + else: + raise Exception(f"Unknown or Unsupported LLM Platform: {llm_platform}.") \ No newline at end of file diff --git a/scripts/utils/raw_utils.py b/scripts/utils/raw_utils.py new file mode 100644 index 0000000..73c7d48 --- /dev/null +++ b/scripts/utils/raw_utils.py @@ -0,0 +1,284 @@ +import os +from typing import Any, List, Optional +from langchain_core.callbacks.manager import CallbackManagerForLLMRun +from langchain_core.outputs import LLMResult +from langchain_core.messages import BaseMessage +from langchain_ibm import WatsonxLLM +from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams +from ibm_watsonx_ai.foundation_models.utils.enums import DecodingMethods + + + +MODEL_ID_MAP = { + "watsonx": { + "granite": { + "model_id": "ibm/granite-3-2-8b-instruct", + "model_url_id": None, + }, + "llama_4": { + "model_id": "meta-llama/llama-4-maverick-17b-128e-instruct-fp8", + "model_url_id": None, + }, + "mistral": { + "model_id": "mistralai/mistral-large", + "model_url_id": None, + }, + }, + "rits": { + "granite": { + "model_id": "ibm-granite/granite-3.3-8b-instruct", + "model_url_id": "granite-3-3-8b-instruct", + }, + "llama_3": { + "model_id": "meta-llama/llama-3-1-405b-instruct-fp8", + "model_url_id": "llama-3-1-405b-instruct-fp8", + }, + "llama_4": { + "model_id": "meta-llama/llama-4-maverick-17b-128e-instruct-fp8", + "model_url_id": "llama-4-mvk-17b-128e-fp8", + }, + "phi": {"model_id": "microsoft/phi-4", "model_url_id": "microsoft-phi-4"}, + }, +} + +def parsebool(val): + bool_map = { + "y": True, + "true": True, + "t": True, + "yes": True, + "n": False, + "false": False, + "f": False, + "no": False, + } + + return bool_map.get(str(val).lower(), False) + +class MODELMAP: + er_model = "granite" + generate_model = "granite" + review_model = "granite" + explain_model = "granite" + + @classmethod + def set_model(cls, model_type: str, model_name: str): + is_watsonx = parsebool(os.environ.get("USE_WATSONX", False)) + VALID_TYPES = ["er_model", "generate_model", "review_model", "explain_model"] + VALID_MODELS = list(MODEL_ID_MAP["watsonx"].keys()) + if not is_watsonx: + VALID_MODELS = list(MODEL_ID_MAP["rits"].keys()) + + if model_name not in VALID_MODELS: + raise ValueError( + f"Invalid model name: {model_name}. Choose from {VALID_MODELS}" + ) + if model_type not in VALID_TYPES: + raise ValueError( + f"Invalid model type: {model_type}. Choose from {VALID_TYPES}" + ) + setattr(cls, model_type, model_name) + + @classmethod + def get_wpa_model_details(cls): + is_watsonx = parsebool(os.environ.get("USE_WATSONX", False)) + if is_watsonx: + return ( + MODEL_ID_MAP["watsonx"][cls.generate_model]["model_id"], + MODEL_ID_MAP["watsonx"][cls.generate_model]["model_url_id"], + ) + else: + return ( + MODEL_ID_MAP["rits"][cls.generate_model]["model_id"], + MODEL_ID_MAP["rits"][cls.generate_model]["model_url_id"], + ) + + + + +class LLMSelector: + def __init__( + self, + model_id="ibm/granite-20b-code-instruct", + print_prompt=False, + model_url_id=None, + temperature=0, # default is greedy sampling + top_p=None, + n=1, # this should always be 1 for langchain's chain. + min_tokens=1, + max_tokens=None, + max_retries=2, + ): + is_watsonx = parsebool(os.environ.get("USE_WATSONX", "False")) + if is_watsonx: + api_url = "{}".format(os.environ.get("WATSONX_URL")) + api_key = os.environ.get("WATSONX_APIKEY") + project_id = os.environ.get("WATSONX_PROJECT_ID") + + decoding_method = DecodingMethods.SAMPLE.value + if temperature == 0: + decoding_method = DecodingMethods.GREEDY.value + + parameters = { + GenParams.DECODING_METHOD: decoding_method, + GenParams.MAX_NEW_TOKENS: max_tokens, + GenParams.MIN_NEW_TOKENS: min_tokens, + GenParams.TEMPERATURE: temperature, + GenParams.TOP_K: n, + GenParams.TOP_P: top_p, + } + + self.model = WatsonxLLM( + model_id=model_id, + url=api_url, + apikey=api_key, + project_id=project_id, + params=parameters, + ) + else: + self.model = lc_lite_llm( + model_id=model_id, + print_prompt=print_prompt, + model_url_id=model_url_id, + temperature=temperature, # default is greedy sampling + top_p=top_p, + n=n, # this should always be 1 for langchain's chain. + min_tokens=min_tokens, + max_tokens=max_tokens, + max_retries=max_retries, + ) + + def generate(self, input): + result = { + "llm_response": "", + "token_usage": { + "input_token_count": 0, + "generated_token_count": 0, + "total_token_count": 0, + }, + } + is_watsonx = parsebool(os.environ.get("USE_WATSONX", "False")) + if is_watsonx: + response = self.model.generate(input) + result["llm_response"] = response.generations[0][0].text + result["token_usage"]["input_token_count"] = int( + response.llm_output["token_usage"]["input_token_count"] + ) + result["token_usage"]["generated_token_count"] = int( + response.llm_output["token_usage"]["generated_token_count"] + ) + result["token_usage"]["total_token_count"] = ( + result["token_usage"]["input_token_count"] + + result["token_usage"]["generated_token_count"] + ) + else: + response = self.model.invoke(input) + result["llm_response"] = response.content + result["token_usage"]["input_token_count"] = int( + response.response_metadata["token_usage"]["prompt_tokens"] + ) + result["token_usage"]["generated_token_count"] = int( + response.response_metadata["token_usage"]["completion_tokens"] + ) + result["token_usage"]["total_token_count"] = int( + response.response_metadata["token_usage"]["total_tokens"] + ) + + return result + + +# if not os.environ.get("USE_WATSONX", True): +import litellm +from langchain_community.chat_models import ChatLiteLLM +from langchain_community.chat_models.litellm import _create_retry_decorator + + +class LCLITELLM(ChatLiteLLM): + litellm.set_verbose = True + print_mcac_prompt: bool = False + + def completion_with_retry( + self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any + ) -> Any: + """Use tenacity to retry the completion call.""" + retry_decorator = _create_retry_decorator(self, run_manager=run_manager) + # os.environ["LITELLM_LOG"] = "True" + + @retry_decorator + def _completion_with_retry(**kwargs: Any) -> Any: + is_watsonx = parsebool(os.environ.get("USE_WATSONX", "False")) + if is_watsonx: + if os.environ.get("WATSONX_APIKEY", None) is None: + raise Exception( + "env variable WATSONX_APIKEY must be provided to use RITS service." + ) + kwargs["api_key"] = os.environ.get("WATSONX_APIKEY") + kwargs["project_id"] = os.environ.get("WATSONX_PROJECT_ID") + + else: + if os.environ.get("RITS_API_KEY", None) is None: + raise Exception( + "env variable RITS_API_KEY must be provided to use RITS service." + ) + kwargs["extra_headers"] = {"RITS_API_KEY": os.environ["RITS_API_KEY"]} + kwargs["api_key"] = os.environ.get("RITS_API_KEY") + + return self.client.completion(**kwargs) + + return _completion_with_retry(**kwargs) + + def _generate( + self, + messages: List[BaseMessage], + stop: Optional[list[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> LLMResult: + + if self.print_mcac_prompt: + + print("#" * 100) + # print(type(messages[0].content)) + # print(messages[0].content) + print("#" * 100) + + return super(LCLITELLM, self)._generate( + messages=messages, stop=stop, run_manager=run_manager, **kwargs + ) + + +def lc_lite_llm( + model_id="ibm/granite-20b-code-instruct", + print_prompt=False, + model_url_id=None, + temperature=0, # default is greedy sampling + top_p=None, + n=1, # this should always be 1 for langchain's chain. + min_tokens=1, + max_tokens=None, + max_retries=2, +): + is_watsonx = parsebool(os.environ.get("USE_WATSONX", "False")) + model = "openai/{}".format(model_id) + if is_watsonx: + api_url = "{}".format(os.environ.get("WATSONX_URL")) + model = "watsonx/{}".format(model_id) + else: + api_url = "{}/{}/v1".format(os.environ.get("RITS_URL"), model_url_id) + if model_url_id is None: + model_url_id = model_id.split("/")[-1] + model_url_id = model_url_id.replace(".", "-") + + model = LCLITELLM( + model=model, + api_base=api_url, + temperature=temperature, + top_p=top_p, + n=n, + min_tokens=min_tokens, + max_tokens=max_tokens, + max_retries=max_retries, + ) + if print_prompt: + model.print_mcac_prompt = True + return model \ No newline at end of file diff --git a/scripts/utils/ritz_client.py b/scripts/utils/ritz_client.py new file mode 100644 index 0000000..8dbfc4c --- /dev/null +++ b/scripts/utils/ritz_client.py @@ -0,0 +1,320 @@ +import os +import re +import time +import logging +from typing import List, Optional, Any +from langchain_core.callbacks.manager import CallbackManagerForLLMRun +from langchain_core.outputs import LLMResult +from langchain_core.messages import BaseMessage +from langchain_ibm import WatsonxLLM +from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams +from ibm_watsonx_ai.foundation_models.utils.enums import DecodingMethods + + +# ───────────────────────────────────────────────────────────────────────────── +# DISABLE LITELLM LOGGING ENTIRELY +# ───────────────────────────────────────────────────────────────────────────── +logging.disable(logging.CRITICAL) + +import litellm +from langchain_community.chat_models import ChatLiteLLM +from langchain_community.chat_models.litellm import _create_retry_decorator + +# ───────────────────────────────────────────────────────────────────────────── +# MODEL ID MAPS +# ───────────────────────────────────────────────────────────────────────────── +MODEL_ID_MAP = { + "watsonx": { + "granite": {"model_id": "ibm/granite-3-2-8b-instruct", "model_url_id": None}, + "llama_4": {"model_id": "meta-llama/llama-4-maverick-17b-128e-instruct-fp8", "model_url_id": None}, + "mistral": {"model_id": "mistralai/mistral-large", "model_url_id": None}, + }, + "rits": { + "granite": {"model_id": "ibm-granite/granite-3.3-8b-instruct", "model_url_id": "granite-3-3-8b-instruct"}, + "llama_3": {"model_id": "meta-llama/llama-3-1-405b-instruct-fp8", "model_url_id": "llama-3-1-405b-instruct-fp8"}, + "llama_4": {"model_id": "meta-llama/llama-4-maverick-17b-128e-instruct-fp8", "model_url_id": "llama-4-mvk-17b-128e-fp8"}, + "phi": {"model_id": "microsoft/phi-4", "model_url_id": "microsoft-phi-4"}, + "codestral": {"model_id": "mistralai/Codestral-22B-v0.1", "model_url_id": "codestral-22b-v01"}, + "mixtral_8_22b": {"model_id": "mistralai/mixtral-8x22B-instruct-v0.1", "model_url_id": "mixtral-8x22b-instruct-a100"}, + "granite_34b": {"model_id": "ibm-granite/granite-34b-code-instruct-8k", "model_url_id": "granite-34b-code-instruct-8k"}, + "granite_20b": {"model_id": "ibm-granite/granite-20b-code-instruct-8k", "model_url_id": "granite-20b-code-instruct-8k"}, + "deepseek_v3_h200": { + "model_id": "deepseek-ai/DeepSeek-V3", + "model_url_id": "deepseek-v3-h200" + }, + "deepseek_v2_5": { + "model_id": "deepseek-ai/DeepSeek-V2.5", + "model_url_id": "deepseek-v2-5" + }, + "llama_4_scout_17b_16e_instruct": { + "model_id": "meta-llama/Llama-4-Scout-17B-16E-Instruct", + "model_url_id": "llama-4-scout-17b-16e-instruct" + }, + "llama_3_3_70b_instruct": { + "model_id": "meta-llama/llama-3-3-70b-instruct", + "model_url_id": "llama-3-3-70b-instruct" + }, + "qwen3_8b": { + "model_id": "Qwen/Qwen3-8B", + "model_url_id": "qwen3-8b" + }, + "qwen2_5_72b_instruct": { + "model_id": "Qwen/Qwen2.5-72B-Instruct", + "model_url_id": "qwen2-5-72b-instruct" + }, + }, +} + + +def parsebool(val): + bool_map = {"y": True, "true": True, "t": True, "yes": True, "n": False, "false": False, "f": False, "no": False} + return bool_map.get(str(val).lower(), False) + + +class MODELMAP: + er_model = "llama_4" + generate_model = "llama_4" + review_model = "llama_4" + explain_model = "llama_4" + + @classmethod + def set_model(cls, model_type: str, model_name: str): + is_watsonx = parsebool(os.environ.get("USE_WATSONX", False)) + VALID_TYPES = ["er_model", "generate_model", "review_model", "explain_model"] + VALID_MODELS = list(MODEL_ID_MAP["watsonx"].keys()) + if not is_watsonx: + VALID_MODELS = list(MODEL_ID_MAP["rits"].keys()) + + if model_name not in VALID_MODELS: + raise ValueError(f"Invalid model name: {model_name}. Choose from {VALID_MODELS}") + if model_type not in VALID_TYPES: + raise ValueError(f"Invalid model type: {model_type}. Choose from {VALID_TYPES}") + setattr(cls, model_type, model_name) + + @classmethod + def get_wpa_model_details(cls): + is_watsonx = parsebool(os.environ.get("USE_WATSONX", False)) + if is_watsonx: + return ( + MODEL_ID_MAP["watsonx"][cls.generate_model]["model_id"], + MODEL_ID_MAP["watsonx"][cls.generate_model]["model_url_id"], + ) + else: + return ( + MODEL_ID_MAP["rits"][cls.generate_model]["model_id"], + MODEL_ID_MAP["rits"][cls.generate_model]["model_url_id"], + ) + + +class LLMSelector: + def __init__( + self, + model_id="ibm/granite-20b-code-instruct", + print_prompt=False, + model_url_id=None, + temperature=0, + top_p=None, + n=1, + min_tokens=1, + max_tokens=None, + max_retries=3, + ): + is_watsonx = parsebool(os.environ.get("USE_WATSONX", "False")) + if is_watsonx: + api_url = os.environ["WATSONX_URL"] + api_key = os.environ["WATSONX_APIKEY"] + project_id = os.environ["WATSONX_PROJECT_ID"] + decoding_method = DecodingMethods.SAMPLE.value + if temperature == 0: + decoding_method = DecodingMethods.GREEDY.value + parameters = { + GenParams.DECODING_METHOD: decoding_method, + GenParams.MAX_NEW_TOKENS: max_tokens, + GenParams.MIN_NEW_TOKENS: min_tokens, + GenParams.TEMPERATURE: temperature, + GenParams.TOP_K: n, + GenParams.TOP_P: top_p, + } + self.model = WatsonxLLM( + model_id=model_id, url=api_url, apikey=api_key, project_id=project_id, params=parameters + ) + else: + self.model = lc_lite_llm( + model_id=model_id, + print_prompt=print_prompt, + model_url_id=model_url_id, + temperature=temperature, + top_p=top_p, + n=n, + min_tokens=min_tokens, + max_tokens=max_tokens, + max_retries=max_retries, + ) + + def generate(self, input: List[Any]) -> dict: + result = { + "llm_response": "", + "token_usage": {"input_token_count": 0, "generated_token_count": 0, "total_token_count": 0}, + } + is_watsonx = parsebool(os.environ.get("USE_WATSONX", "False")) + if is_watsonx: + response = self.model.generate(input) + result["llm_response"] = response.generations[0][0].text + result["token_usage"]["input_token_count"] = int( + response.llm_output["token_usage"]["input_token_count"] + ) + result["token_usage"]["generated_token_count"] = int( + response.llm_output["token_usage"]["generated_token_count"] + ) + result["token_usage"]["total_token_count"] = ( + result["token_usage"]["input_token_count"] + + result["token_usage"]["generated_token_count"] + ) + else: + response = self.model.invoke(input) + result["llm_response"] = response.content + result["token_usage"]["input_token_count"] = int( + response.response_metadata["token_usage"]["prompt_tokens"] + ) + result["token_usage"]["generated_token_count"] = int( + response.response_metadata["token_usage"]["completion_tokens"] + ) + result["token_usage"]["total_token_count"] = int( + response.response_metadata["token_usage"]["total_tokens"] + ) + return result + + +class LCLITELLM(ChatLiteLLM): + print_mcac_prompt: bool = False + + def completion_with_retry( + self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any + ) -> Any: + retry_decorator = _create_retry_decorator(self, run_manager=run_manager) + + @retry_decorator + def _completion_with_retry(**kwargs: Any) -> Any: + is_watsonx = parsebool(os.environ.get("USE_WATSONX", "False")) + if is_watsonx: + if os.environ.get("WATSONX_APIKEY", None) is None: + raise Exception("WATSONX_APIKEY must be provided.") + kwargs["api_key"] = os.environ["WATSONX_APIKEY"] + kwargs["project_id"] = os.environ["WATSONX_PROJECT_ID"] + else: + if os.environ.get("RITS_API_KEY", None) is None: + raise Exception("RITS_API_KEY must be provided.") + kwargs["extra_headers"] = {"RITS_API_KEY": os.environ["RITS_API_KEY"]} + kwargs["api_key"] = os.environ["RITS_API_KEY"] + return self.client.completion(**kwargs) + + return _completion_with_retry(**kwargs) + + def _generate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> LLMResult: + if self.print_mcac_prompt: + print("#" * 60) + print(messages[0].content) + print("#" * 60) + return super(LCLITELLM, self)._generate(messages=messages, stop=stop, run_manager=run_manager, **kwargs) + + +def lc_lite_llm( + model_id="ibm/granite-20b-code-instruct", + print_prompt=False, + model_url_id=None, + temperature=0, + top_p=None, + n=1, + min_tokens=1, + max_tokens=None, + max_retries=2, +): + is_watsonx = parsebool(os.environ.get("USE_WATSONX", "False")) + if is_watsonx: + api_url = os.environ["WATSONX_URL"] + model = "watsonx/{}".format(model_id) + else: + if model_url_id is None: + model_url_id = model_id.split("/")[-1].replace(".", "-") + api_url = "{}/{}/v1".format(os.environ["RITS_URL"], model_url_id) + model = "openai/{}".format(model_id) + + rits_model = LCLITELLM( + model=model, + api_base=api_url, + temperature=temperature, + top_p=top_p, + n=n, + min_tokens=min_tokens, + max_tokens=max_tokens, + max_retries=max_retries, + ) + if print_prompt: + rits_model.print_mcac_prompt = True + return rits_model + + +# ───────────────────────────────────────────────────────────────────────────── +# RitsChatClient: preserves conversation history across multiple `send(...)` calls +# ───────────────────────────────────────────────────────────────────────────── +class RitsChatClient: + def __init__(self, temperature=0.5, top_p=1.0, max_tokens: int = 256): + model_id, model_url_id = MODELMAP.get_wpa_model_details() + self.selector = LLMSelector( + model_id=model_id, + model_url_id=model_url_id, + temperature=temperature, + top_p=top_p, + max_tokens=max_tokens, + ) + self.history: List[str] = [] + + def reset(self): + """Clear conversation history.""" + self.history = [] + + # START OF CORRECTION + def send( + self, + user_message: str, + max_tokens: Optional[int] = None, + temperature: Optional[float] = None + ) -> (str, int): + """ + Sends a message to the LLM, managing history and allowing temporary + overrides for temperature and max_tokens. + """ + if len(self.history) > 10: + self.history = self.history[-10:] + + self.history.append(f"User: {user_message}") + combined = "\n\n".join(self.history) + + # Store original model parameters + original_max_tokens = self.selector.model.max_tokens + original_temperature = self.selector.model.temperature + + try: + # Temporarily override parameters if new values are provided + if max_tokens is not None: + self.selector.model.max_tokens = max_tokens + if temperature is not None: + self.selector.model.temperature = temperature + + # Generate the response + out = self.selector.generate([combined]) + text = out["llm_response"] + tok = out["token_usage"]["total_token_count"] + self.history.append(f"Assistant: {text}") + return text, tok + finally: + # IMPORTANT: Restore original parameters to avoid affecting subsequent calls + self.selector.model.max_tokens = original_max_tokens + self.selector.model.temperature = original_temperature + # END OF CORRECTION \ No newline at end of file diff --git a/scripts/utils/watsonx_client.py b/scripts/utils/watsonx_client.py new file mode 100644 index 0000000..b3138a7 --- /dev/null +++ b/scripts/utils/watsonx_client.py @@ -0,0 +1,71 @@ +# File: utilities/watsonx_client.py + +import os +from ibm_watsonx_ai.foundation_models.schema import TextChatParameters +from langchain_ibm import ChatWatsonx +from langchain.schema import SystemMessage, HumanMessage, AIMessage + + +class WatsonxChatClient: + """ + A simple wrapper around ChatWatsonx that keeps track of conversation history. + """ + + # Hardcoded Watsonx.ai endpoint, project ID, and API key + _URL = os.getenv("WATSONX_URL") + _PROJECT_ID = os.getenv("WATSONX_PROJECT_ID") + _APIKEY = os.getenv("WATSONX_APIKEY") # Replace with your actual key + + def __init__( + self, + model_id: str, + system_prompt: str = "You are a helpful assistant.", + max_tokens: int = 200, + temperature: float = 0.5, + top_p: float = 1.0, + ): + """ + Initializes the WatsonxChatClient. + + Parameters: + - model_id: The Watsonx.ai model ID (e.g. "ibm/granite-3-8b-instruct"). + - system_prompt: The initial “system” message for every conversation. + - max_tokens: Max tokens to produce per response. + - temperature: Sampling temperature. + - top_p: Nucleus sampling parameter. + """ + parameters = TextChatParameters( + max_tokens=max_tokens, + temperature=temperature, + top_p=top_p, + ) + + self._chat = ChatWatsonx( + model_id=model_id, + url=self._URL, + project_id=self._PROJECT_ID, + apikey=self._APIKEY, + params=parameters, + ) + + self._conversation = [SystemMessage(content=system_prompt)] + + def send(self, user_text: str) -> str: + """ + Appends the given user_text as a HumanMessage to the conversation, + calls Watsonx.invoke(...), and returns the assistant’s reply. + """ + self._conversation.append(HumanMessage(content=user_text)) + ai_message: AIMessage = self._chat.invoke(input=self._conversation) + self._conversation.append(ai_message) + return ai_message.content + + def reset(self, system_prompt: str = None): + """ + Clears the current conversation history. Optionally override the system prompt. + """ + if system_prompt is not None: + self._conversation = [SystemMessage(content=system_prompt)] + else: + original_system = self._conversation[0].content + self._conversation = [SystemMessage(content=original_system)] From 58a2fb846e71d28651b9da7e19810918fa8bcff4 Mon Sep 17 00:00:00 2001 From: Srideepika Jayaraman Date: Tue, 18 Nov 2025 14:19:46 -0500 Subject: [PATCH 2/2] remove cache files --- .../utils/__pycache__/__init__.cpython-312.pyc | Bin 178 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 scripts/utils/__pycache__/__init__.cpython-312.pyc diff --git a/scripts/utils/__pycache__/__init__.cpython-312.pyc b/scripts/utils/__pycache__/__init__.cpython-312.pyc deleted file mode 100644 index 33ea49b229a69f0e89736b4b25ecef8dab326e27..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 178 zcmX@j%ge<81SXx*nIQTxh(HIQS%4zb87dhx8U0o=6fpsLpFwJV1?qKEC6r#ErkF8