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Sourcery refactored master branch #1

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@sourcery-ai sourcery-ai bot commented Oct 31, 2020

Branch master refactored by Sourcery.

If you're happy with these changes, merge this Pull Request using the Squash and merge strategy.

See our documentation here.

Run Sourcery locally

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Review changes via command line

To manually merge these changes, make sure you're on the master branch, then run:

git fetch origin sourcery/master
git merge --ff-only FETCH_HEAD
git reset HEAD^

@sourcery-ai sourcery-ai bot requested a review from andife October 31, 2020 06:59
sourcery-ai[bot]
sourcery-ai bot commented Oct 31, 2020
View reviewed changes
examples/evolvegcnh_example.py
model.train()

for epoch in range(epochs):
for _ in range(epochs):
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Lines 66-66 refactored with the following changes:

  • Replace unused for index with underscore (for-index-underscore)

examples/evolvegcno_example.py
model.train()

for epoch in range(epochs):
for _ in range(epochs):
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Lines 64-64 refactored with the following changes:

  • Replace unused for index with underscore (for-index-underscore)

examples/gclstm_example.py
model.train()

for epoch in range(epochs):
for _ in range(epochs):
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Lines 62-62 refactored with the following changes:

  • Replace unused for index with underscore (for-index-underscore)

examples/gconvgru_example.py
model.train()

for epoch in range(epochs):
for _ in range(epochs):
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Lines 63-63 refactored with the following changes:

  • Replace unused for index with underscore (for-index-underscore)

examples/gconvlstm_example.py
model.train()

for epoch in range(epochs):
for _ in range(epochs):
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Lines 62-62 refactored with the following changes:

  • Replace unused for index with underscore (for-index-underscore)

torch_geometric_temporal/nn/recurrent/gconv_gru.py
def _calculate_hidden_state(self, Z, H, H_tilde):
H = Z*H + (1-Z)*H_tilde
return H
return Z*H + (1-Z)*H_tilde
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Function GConvGRU._calculate_hidden_state refactored with the following changes:

  • Inline variable that is immediately returned (inline-immediately-returned-variable)

torch_geometric_temporal/nn/recurrent/gconv_lstm.py
T = torch.tanh(T)
C = F*C + I*T
return C
return F*C + I*T
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Function GConvLSTM._calculate_cell_state refactored with the following changes:

  • Inline variable that is immediately returned (inline-immediately-returned-variable)

torch_geometric_temporal/nn/recurrent/gconv_lstm.py
Comment on lines -188 to +187
H = O * torch.tanh(C)
return H
return O * torch.tanh(C)
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Function GConvLSTM._calculate_hidden_state refactored with the following changes:

  • Inline variable that is immediately returned (inline-immediately-returned-variable)

torch_geometric_temporal/nn/recurrent/lrgcn.py
T = torch.tanh(T)
C = F*C + I*T
return C
return F*C + I*T
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Function LRGCN._calculate_cell_state refactored with the following changes:

  • Inline variable that is immediately returned (inline-immediately-returned-variable)

torch_geometric_temporal/nn/recurrent/lrgcn.py
Comment on lines -130 to +129
H = O * torch.tanh(C)
return H
return O * torch.tanh(C)
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Function LRGCN._calculate_hidden_state refactored with the following changes:

  • Inline variable that is immediately returned (inline-immediately-returned-variable)

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sourcery-ai bot commented Oct 31, 2020

Sourcery Code Quality Report

✅  Merging this PR will increase code quality in the affected files by 0.12%.

Quality metrics Before After Change
Complexity 0.12 ⭐ 0.12 ⭐ 0.00
Method Length 39.81 ⭐ 39.56 ⭐ -0.25 👍
Working memory 7.32 🙂 7.29 🙂 -0.03 👍
Quality 83.80% ⭐ 83.92% ⭐ 0.12% 👍
Other metrics Before After Change
Lines 855 847 -8
Changed files Quality Before Quality After Quality Change
examples/evolvegcnh_example.py 81.57% ⭐ 81.57% ⭐ 0.00%
examples/evolvegcno_example.py 82.03% ⭐ 82.03% ⭐ 0.00%
examples/gclstm_example.py 82.18% ⭐ 82.18% ⭐ 0.00%
examples/gconvgru_example.py 81.59% ⭐ 81.59% ⭐ 0.00%
examples/gconvlstm_example.py 82.18% ⭐ 82.18% ⭐ 0.00%
torch_geometric_temporal/nn/recurrent/evolvegcno.py 84.51% ⭐ 84.63% ⭐ 0.12% 👍
torch_geometric_temporal/nn/recurrent/gc_lstm.py 84.38% ⭐ 84.60% ⭐ 0.22% 👍
torch_geometric_temporal/nn/recurrent/gconv_gru.py 86.11% ⭐ 86.15% ⭐ 0.04% 👍
torch_geometric_temporal/nn/recurrent/gconv_lstm.py 83.60% ⭐ 83.82% ⭐ 0.22% 👍
torch_geometric_temporal/nn/recurrent/lrgcn.py 85.79% ⭐ 86.03% ⭐ 0.24% 👍

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

File Function Complexity Length Working Memory Quality Recommendation
torch_geometric_temporal/nn/recurrent/gc_lstm.py GCLSTM._calculate_cell_state 0 49 ⭐ 12 😞 73.02% 🙂 Extract out complex expressions
examples/evolvegcnh_example.py create_mock_data 0 49 ⭐ 11 😞 74.75% 🙂 Extract out complex expressions
examples/evolvegcno_example.py create_mock_data 0 49 ⭐ 11 😞 74.75% 🙂 Extract out complex expressions
examples/gclstm_example.py create_mock_data 0 49 ⭐ 11 😞 74.75% 🙂 Extract out complex expressions
examples/gconvgru_example.py create_mock_data 0 49 ⭐ 11 😞 74.75% 🙂 Extract out complex expressions

Legend and Explanation

The emojis denote the absolute quality of the code:

  • ⭐ excellent
  • 🙂 good
  • 😞 poor
  • ⛔ very poor

The 👍 and 👎 indicate whether the quality has improved or gotten worse with this pull request.

Please see our documentation here for details on how these metrics are calculated.

We are actively working on this report - lots more documentation and extra metrics to come!

Let us know what you think of it by mentioning @sourcery-ai in a comment.

@sonarqubecloud
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Kudos, SonarCloud Quality Gate passed!

Bug A 0 Bugs
Vulnerability A 0 Vulnerabilities (and Security Hotspot 0 Security Hotspots to review)
Code Smell A 0 Code Smells

No Coverage information No Coverage information
46.2% 46.2% Duplication

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