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brendanator / atari-rl

Atari - Deep Reinforcement Learning algorithms in TensorFlow
MIT License
135 stars 31 forks source link

Sourcery refactored main branch #25

Closed sourcery-ai[bot] closed 2 years ago

sourcery-ai[bot] commented 2 years ago

Branch main refactored by Sourcery.

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sourcery-ai[bot] commented 2 years ago

Sourcery Code Quality Report

✅  Merging this PR will increase code quality in the affected files by 0.29%.

Quality metrics Before After Change
Complexity 3.41 ⭐ 3.18 ⭐ -0.23 👍
Method Length 46.66 ⭐ 46.50 ⭐ -0.16 👍
Working memory 7.21 🙂 7.17 🙂 -0.04 👍
Quality 76.62% ⭐ 76.91% ⭐ 0.29% 👍
Other metrics Before After Change
Lines 604 593 -11
Changed files Quality Before Quality After Quality Change
agents/agent.py 86.72% ⭐ 87.07% ⭐ 0.35% 👍
agents/exploration_bonus.py 80.72% ⭐ 80.91% ⭐ 0.19% 👍
agents/training.py 71.66% 🙂 71.58% 🙂 -0.08% 👎
atari/atari.py 78.28% ⭐ 78.28% ⭐ 0.00%
networks/inputs.py 70.24% 🙂 71.02% 🙂 0.78% 👍
util/util.py 80.39% ⭐ 80.39% ⭐ 0.00%

Here are some functions in these files that still need a tune-up:

File Function Complexity Length Working Memory Quality Recommendation
networks/inputs.py Inputs.__init__ 0 ⭐ 189 😞 15 😞 51.40% 🙂 Try splitting into smaller methods. Extract out complex expressions
agents/training.py Trainer.train_agent 11 🙂 126 😞 11 😞 53.13% 🙂 Try splitting into smaller methods. Extract out complex expressions
util/util.py run_directory 10 🙂 125 😞 8 🙂 60.32% 🙂 Try splitting into smaller methods
atari/atari.py Atari.reset 6 ⭐ 116 🙂 10 😞 60.95% 🙂 Extract out complex expressions
networks/inputs.py auto_placeholder 4 ⭐ 135 😞 9 🙂 62.66% 🙂 Try splitting into smaller methods

Legend and Explanation

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Help us improve this quality report!