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Sourcery refactored master branch #1

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2 changes: 1 addition & 1 deletion examples/evolvegcnh_example.py
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Original file line number Diff line number Diff line change
Expand Up @@ -63,7 +63,7 @@ def forward(self, x, edge_index, edge_weight):

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)

optimizer.zero_grad()
x, edge_index = create_mock_data(node_count, edge_per_node, node_features)
edge_weight = create_mock_edge_weight(edge_index)
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2 changes: 1 addition & 1 deletion examples/evolvegcno_example.py
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Expand Up @@ -61,7 +61,7 @@ def forward(self, x, edge_index, edge_weight):

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)

optimizer.zero_grad()
x, edge_index = create_mock_data(node_count, edge_per_node, node_features)
edge_weight = create_mock_edge_weight(edge_index)
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2 changes: 1 addition & 1 deletion examples/gclstm_example.py
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Expand Up @@ -59,7 +59,7 @@ def forward(self, x, edge_index, edge_weight):

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)

optimizer.zero_grad()
x, edge_index = create_mock_data(node_count, edge_per_node, node_features)
edge_weight = create_mock_edge_weight(edge_index)
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2 changes: 1 addition & 1 deletion examples/gconvgru_example.py
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Expand Up @@ -60,7 +60,7 @@ def forward(self, x, edge_index, edge_weight):

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)

optimizer.zero_grad()
x, edge_index = create_mock_data(node_count, edge_per_node, node_features)
edge_weight = create_mock_edge_weight(edge_index)
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2 changes: 1 addition & 1 deletion examples/gconvlstm_example.py
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Original file line number Diff line number Diff line change
Expand Up @@ -59,7 +59,7 @@ def forward(self, x, edge_index, edge_weight):

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)

optimizer.zero_grad()
x, edge_index = create_mock_data(node_count, edge_per_node, node_features)
edge_weight = create_mock_edge_weight(edge_index)
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3 changes: 1 addition & 2 deletions torch_geometric_temporal/nn/recurrent/evolvegcno.py
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Expand Up @@ -67,5 +67,4 @@ def forward(self, X: torch.FloatTensor, edge_index: torch.LongTensor,
W = self.conv_layer.weight[None, :, :]
W, _ = self.recurrent_layer(W)
self.conv_layer.weight = torch.nn.Parameter(W.squeeze())
X = self.conv_layer(X, edge_index, edge_weight)
return X
return self.conv_layer(X, edge_index, edge_weight)
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Function EvolveGCNO.forward refactored with the following changes:

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

6 changes: 2 additions & 4 deletions torch_geometric_temporal/nn/recurrent/gc_lstm.py
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Expand Up @@ -148,8 +148,7 @@ def _calculate_cell_state(self, X, edge_index, edge_weight, H, C, I, F):
T = T + self.conv_c(H, edge_index, edge_weight)
T = T + self.b_c
T = torch.tanh(T)
C = F*C + I*T
return C
return F*C + I*T
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Function GCLSTM._calculate_cell_state refactored with the following changes:

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


def _calculate_output_gate(self, X, edge_index, edge_weight, H, C):
O = torch.matmul(X, self.W_o)
Expand All @@ -160,8 +159,7 @@ def _calculate_output_gate(self, X, edge_index, edge_weight, H, C):


def _calculate_hidden_state(self, O, C):
H = O * torch.tanh(C)
return H
return O * torch.tanh(C)
Comment on lines -163 to +162
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Function GCLSTM._calculate_hidden_state refactored with the following changes:

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



def forward(self, X: torch.FloatTensor, edge_index: torch.LongTensor, edge_weight: torch.FloatTensor=None,
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3 changes: 1 addition & 2 deletions torch_geometric_temporal/nn/recurrent/gconv_gru.py
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Expand Up @@ -125,8 +125,7 @@ def _calculate_candidate_state(self, X, edge_index, edge_weight, H, R):


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)



def forward(self, X: torch.FloatTensor, edge_index: torch.LongTensor,
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6 changes: 2 additions & 4 deletions torch_geometric_temporal/nn/recurrent/gconv_lstm.py
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Expand Up @@ -172,8 +172,7 @@ def _calculate_cell_state(self, X, edge_index, edge_weight, H, C, I, F):
T = T + self.conv_h_c(H, edge_index, edge_weight)
T = T + self.b_c
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)


def _calculate_output_gate(self, X, edge_index, edge_weight, H, C):
O = self.conv_x_o(X, edge_index, edge_weight)
Expand All @@ -185,8 +184,7 @@ def _calculate_output_gate(self, X, edge_index, edge_weight, H, C):


def _calculate_hidden_state(self, O, C):
H = O * torch.tanh(C)
return H
return O * torch.tanh(C)
Comment on lines -188 to +187
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Function GConvLSTM._calculate_hidden_state refactored with the following changes:

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



def forward(self, X: torch.FloatTensor, edge_index: torch.LongTensor, edge_weight: torch.FloatTensor=None,
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6 changes: 2 additions & 4 deletions torch_geometric_temporal/nn/recurrent/lrgcn.py
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Expand Up @@ -115,8 +115,7 @@ def _calculate_cell_state(self, X, edge_index, edge_type, H, C, I, F):
T = self.conv_x_c(X, edge_index, edge_type)
T = T + self.conv_h_c(H, edge_index, edge_type)
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)



def _calculate_output_gate(self, X, edge_index, edge_type, H, C):
Expand All @@ -127,8 +126,7 @@ def _calculate_output_gate(self, X, edge_index, edge_type, H, C):


def _calculate_hidden_state(self, O, C):
H = O * torch.tanh(C)
return H
return O * torch.tanh(C)
Comment on lines -130 to +129
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Function LRGCN._calculate_hidden_state refactored with the following changes:

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



def forward(self, X: torch.FloatTensor, edge_index: torch.LongTensor, edge_type: torch.LongTensor,
Expand Down