{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":84795,"databundleVersionId":10462807,"sourceType":"competition"}],"dockerImageVersionId":30822,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# 🏆 Konwinski Prize - AI GitHub Issue Resolver\n\n## Overview\nThis competition challenges us to build an AI system that can resolve real GitHub issues, evaluated on a contamination-free test set collected after submission freeze. The goal is to achieve >90% accuracy on the SWE-bench benchmark.\n\n### Competition Goals\n- Build an AI that can resolve GitHub issues automatically\n- Achieve high accuracy on a new test set collected post-submission\n- Use only open-source code and models\n\n### Evaluation Metric\n```\nscore = (a - b) / (a + b + c)\nwhere:\na = correctly resolved issues\nb = failing issues\nc = skipped issues\n```\n\n### Prizes\n- 1st Place: $50,000 (+ $775,000 if score > 90%)\n- 2nd Place: $20,000\n- 3rd-5th Place: $10,000 each\n- Additional threshold prizes at 30%, 40%, ..., 90%\n\n## 📚 Imports and Setup","metadata":{}},{"cell_type":"code","source":"!pip show kprize\n\nimport os\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom typing import Optional, Tuple\nfrom kprize.client import Client\n\n# Display settings\npd.set_option('display.max_columns', None)\npd.set_option('display.max_rows', 100)\n\n# Competition paths\nDATA_DIR = Path('../input/konwinski-prize')\nTRAIN_PATH = DATA_DIR / 'data/data.parquet'","metadata":{"execution":{"iopub.status.busy":"2025-01-05T18:23:53.255385Z","iopub.execute_input":"2025-01-05T18:23:53.255796Z","iopub.status.idle":"2025-01-05T18:23:53.940284Z","shell.execute_reply.started":"2025-01-05T18:23:53.255753Z","shell.execute_reply":"2025-01-05T18:23:53.938359Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 🔍 Data Exploration","metadata":{}},{"cell_type":"code","source":"def load_and_explore_data() -> pd.DataFrame:\n    \"\"\"Load and perform initial exploration of training data.\"\"\"\n    df = pd.read_parquet(TRAIN_PATH)\n    \n    print(f\"Dataset shape: {df.shape}\")\n    print(\"\\nColumns:\")\n    for col in df.columns:\n        print(f\"- {col}: {df[col].dtype}\")\n        \n    print(\"\\nSample repositories:\")\n    print(df['repo'].value_counts().head())\n    \n    return df\n\ndf = load_and_explore_data()","metadata":{"execution":{"iopub.status.busy":"2025-01-05T18:03:18.944532Z","iopub.execute_input":"2025-01-05T18:03:18.944961Z","iopub.status.idle":"2025-01-05T18:03:19.031449Z","shell.execute_reply.started":"2025-01-05T18:03:18.944932Z","shell.execute_reply":"2025-01-05T18:03:19.029805Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 🤖 Issue Resolver Implementation","metadata":{}},{"cell_type":"code","source":"class IssueResolver:\n    def __init__(self):\n        \"\"\"Initialize the issue resolver with any required models or resources.\"\"\"\n        # TODO: Initialize models, tokenizers, etc.\n        pass\n        \n    def preprocess_issue(self, problem_statement: str) -> str:\n        \"\"\"Clean and format the issue description.\"\"\"\n        # TODO: Implement preprocessing\n        return problem_statement\n    \n    def analyze_repo(self, repo: str) -> dict:\n        \"\"\"Extract relevant information about the repository.\"\"\"\n        # TODO: Implement repo analysis\n        return {}\n    \n    def generate_patch(self, repo_info: dict, processed_issue: str) -> str:\n        \"\"\"Generate a patch to resolve the issue.\"\"\"\n        # TODO: Implement patch generation\n        return \"\"\n    \n    def validate_patch(self, patch: str) -> bool:\n        \"\"\"Validate the generated patch for basic correctness.\"\"\"\n        # TODO: Implement validation\n        return True\n    \n    def solve_issue(self, repo: str, problem_statement: str) -> str:\n        \"\"\"Main method to resolve a GitHub issue.\n        \n        Args:\n            repo: The GitHub repository name\n            problem_statement: Description of the issue to resolve\n            \n        Returns:\n            str: The patch that resolves the issue\n        \"\"\"\n        processed_issue = self.preprocess_issue(problem_statement)\n        repo_info = self.analyze_repo(repo)\n        \n        patch = self.generate_patch(repo_info, processed_issue)\n        \n        if self.validate_patch(patch):\n            return patch\n        return \"\"\n    \n    def should_skip(self, repo: str, problem_statement: str) -> bool:\n        \"\"\"Determine if we should skip this issue.\n        \n        Args:\n            repo: The GitHub repository name\n            problem_statement: Description of the issue to resolve\n            \n        Returns:\n            bool: True if we should skip this issue, False otherwise\n        \"\"\"\n        # TODO: Implement skip logic\n        return False","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-05T18:00:56.170365Z","iopub.status.idle":"2025-01-05T18:00:56.171259Z","shell.execute_reply":"2025-01-05T18:00:56.170924Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 🔄 Submission Pipeline","metadata":{}},{"cell_type":"code","source":"class SubmissionPipeline:\n    def __init__(self):\n        \"\"\"Initialize the submission pipeline.\"\"\"\n        self.client = Client()\n        self.resolver = IssueResolver()\n        self.stats = {\n            'processed': 0,\n            'skipped': 0,\n            'errors': 0\n        }\n    \n    def log_stats(self):\n        \"\"\"Log current submission statistics.\"\"\"\n        print(\"\\nSubmission Stats:\")\n        for k, v in self.stats.items():\n            print(f\"- {k}: {v}\")\n    \n    def process_test_cases(self):\n        \"\"\"Process all test cases and submit solutions.\"\"\"\n        while True:\n            # Get next test case\n            test_case = self.client.get_next_test_case()\n            if test_case is None:\n                break\n                \n            self.stats['processed'] += 1\n            if self.stats['processed'] % 10 == 0:\n                self.log_stats()\n                \n            repo = test_case.repo\n            problem_statement = test_case.problem_statement\n            \n            try:\n                # Check if we should skip\n                if self.resolver.should_skip(repo, problem_statement):\n                    self.client.submit_skip()\n                    self.stats['skipped'] += 1\n                    continue\n                    \n                # Generate and submit patch\n                patch = self.resolver.solve_issue(repo, problem_statement)\n                self.client.submit_solution(patch)\n                \n            except Exception as e:\n                print(f\"Error processing test case: {e}\")\n                self.client.submit_skip()\n                self.stats['errors'] += 1\n        \n        self.log_stats()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-05T18:00:56.172179Z","iopub.status.idle":"2025-01-05T18:00:56.172804Z","shell.execute_reply":"2025-01-05T18:00:56.172557Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 🚀 Generate Submission","metadata":{}},{"cell_type":"code","source":"def main():\n    \"\"\"Main execution function.\"\"\"\n    print(\"Starting submission pipeline...\")\n    pipeline = SubmissionPipeline()\n    pipeline.process_test_cases()\n    print(\"\\nSubmission complete!\")\n\nif __name__ == \"__main__\":\n    main()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-05T18:00:56.173977Z","iopub.status.idle":"2025-01-05T18:00:56.174676Z","shell.execute_reply":"2025-01-05T18:00:56.174398Z"}},"outputs":[],"execution_count":null}]}