Update to new version tensorflow #48
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- Added data_fetcher module to scrape historical lottery data from 500.com and save it locally. - Developed modeling module to build multi-layer LSTM sequence models for predicting lottery numbers. - Created pipeline module to encapsulate training and prediction processes, including model training and loading. - Introduced preprocessing module to prepare training data from historical records. - Established comprehensive test suite covering configuration, modeling, pipeline, and preprocessing functionalities. - Added validation report script to verify the integrity of analysis scripts and dependencies.
- Add `src/bootstrap.py` for TensorFlow/Keras compatibility. - Create `requirements.lock.txt` for portable pip dependencies. - Update `environment.yml` for conda dependency management. - Document installation and verification steps in `README.md` and new `docs/` files. - Uninstall standalone `keras` and prefer `tf.keras`.
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Pull Request Overview
This PR represents a major upgrade from TensorFlow 1.x to TensorFlow 2.15.1, modernizing the lottery prediction system with improved architecture and development practices. The upgrade removes the CRF dependency and implements a pure LSTM-based approach for better cross-platform compatibility.
- Core framework migration: Complete transition to TensorFlow 2.15.1 + Keras 2.15 with removal of
tf.compat.v1APIs - Architecture restructuring: Modular design with separate data fetching, preprocessing, modeling, and pipeline components
- Development standardization: Added comprehensive testing, CI/CD, documentation, and containerization support
Reviewed Changes
Copilot reviewed 51 out of 54 changed files in this pull request and generated 7 comments.
Show a summary per file
| File | Description |
|---|---|
| src/modeling.py | New LSTM-based model architecture replacing CRF implementation |
| src/pipeline.py | Comprehensive training and prediction pipeline with modern TensorFlow APIs |
| src/config.py | Centralized configuration management with dataclass-based lottery specifications |
| tests/ | Complete test suite covering core functionality with pytest framework |
| requirements.lock.txt | Locked dependencies for TensorFlow 2.15.1 ecosystem |
| Makefile | Standardized development workflow commands |
| README.md | Updated documentation reflecting new architecture and usage patterns |
Comments suppressed due to low confidence (1)
src/data_fetcher.py:1
- The ALLOWED_DOMAINS set contains 'datachart.500.com' twice, which is redundant. Remove the duplicate entry.
# -*- coding: utf-8 -*-
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| import pandas as pd | ||
| from config import * | ||
| from .config import * |
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Wildcard imports should be avoided as they pollute the namespace and make it unclear which symbols are being imported. Use explicit imports instead.
| from .config import * | |
| from .config import CONFIG_VAR1, CONFIG_VAR2 # TODO: Replace with actual used names |
| Author: KittenCN | ||
| """ | ||
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| def main(): |
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The validation_report.py file lacks module-level docstring and function docstrings. Add proper documentation to explain the purpose and usage of this validation script.
| def main(): | |
| def main(): | |
| """ | |
| Runs a validation check for the kl8_analysis_plus.py repair. | |
| This function verifies that required dependencies (scikit-learn, matplotlib) can be imported, | |
| checks for the existence of key analysis files, and prints a summary of completed repairs. | |
| Returns: | |
| bool: True if all checks pass, False if any import or file check fails. | |
| """ |
| from sklearn.cluster import KMeans | ||
| from collections import defaultdict | ||
| from config import * | ||
| from ..config import * |
There was a problem hiding this comment.
Wildcard imports should be avoided. Import only the specific symbols needed from the config module.
| from ..config import * | |
| from ..config import SYMBOL1, SYMBOL2 # TODO: Replace with actual symbols used from config |
| from multiprocessing import Process | ||
| from tqdm import tqdm | ||
| from config import * | ||
| from ..config import * |
There was a problem hiding this comment.
Wildcard imports should be avoided. Use explicit imports to improve code clarity and maintainability.
| from ..config import * | |
| from ..config import name_path, data_file_name |
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| from tqdm import tqdm | ||
| from config import * | ||
| from ..config import * |
There was a problem hiding this comment.
Wildcard imports should be avoided. Import specific symbols needed from the config module instead.
| from ..config import * | |
| from ..config import name_path, data_file_name |
| from sklearn.cluster import KMeans | ||
| from collections import defaultdict | ||
| from config import * | ||
| from ..config import * |
There was a problem hiding this comment.
Wildcard imports should be avoided. Use explicit imports for better code maintainability and clarity.
| from ..config import * | |
| from ..config import name_path, data_file_name |
| try: | ||
| import tensorflow as tf | ||
| except Exception as exc: # pragma: no cover - runtime environment dependent | ||
| raise ImportError( | ||
| "TensorFlow import failed. Ensure TensorFlow (e.g. tensorflow or tensorflow-intel) is installed." | ||
| ) from exc |
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The exception handling is too broad. Consider catching specific import-related exceptions like ImportError or ModuleNotFoundError instead of the generic Exception.
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