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style: format code with Autopep8, isort and Ruff Formatter #11

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style: format code with Autopep8, isort and Ruff Formatter #11

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14 changes: 4 additions & 10 deletions src/everyai/classfier/classfy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -175,8 +175,7 @@ def __init__(
)
self.model_config = None
self.model_path = (
MODEL_PATH
/ f"{self.model_name}_{self.tokenizer_name}_{self.data_name}.pkl"
MODEL_PATH / f"{self.model_name}_{self.tokenizer_name}_{self.data_name}.pkl"
)
self.pipeline = pipeline if pipeline is not None else None

Expand All @@ -186,9 +185,7 @@ def load_data(self, texts, labels, data_name):
raise ValueError("Length of texts and labels should be same")
self.texts = texts
self.labels = labels
logging.info(
f"Loading data: {data_name} to classfier {self.model_name}"
)
logging.info(f"Loading data: {data_name} to classfier {self.model_name}")
self.data_name = data_name
self.classfier_name = (
f"{self.model_name}_{self.tokenizer_name}_{self.data_name}"
Expand Down Expand Up @@ -288,9 +285,7 @@ def __init__(self, **classfiy_config):
else:
logging.warning("Split size not provided or not valid")
if self.texts is None or self.labels is None or self.data_name is None:
logging.warning(
"Data not provided, please use the load_data method"
)
logging.warning("Data not provided, please use the load_data method")
if "device" in classfiy_config and classfiy_config["device"] == "cuda":
logging.warning(
"Cuda is not supported in sklearn and setting device to cpu"
Expand Down Expand Up @@ -359,8 +354,7 @@ def _split_data(self, x, y):
x_train,
y_train,
train_indices,
test_size=self.valid_size
/ (self.train_size + self.valid_size),
test_size=self.valid_size / (self.train_size + self.valid_size),
random_state=42,
)
)
Expand Down
10 changes: 7 additions & 3 deletions src/everyai/classfier/multi_feature/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,11 +5,15 @@
class CrossAttentionFusion(nn.Module):
def __init__(self, d1, d2, d3, hidden_dim, output_dim, num_heads):
super(CrossAttentionFusion, self).__init__()
self.cross_attention = nn.MultiheadAttention(embed_dim=hidden_dim, num_heads=num_heads)
self.cross_attention = nn.MultiheadAttention(
embed_dim=hidden_dim, num_heads=num_heads
)
self.fc1 = nn.Linear(hidden_dim, output_dim)

def forward(self, f1, f2, f3):
# 将所有特征拼接后输入注意力模块
merged_features = torch.cat((f1, f2, f3), dim=-1).unsqueeze(0) # 添加batch维度
attn_output, _ = self.cross_attention(merged_features, merged_features, merged_features)
attn_output, _ = self.cross_attention(
merged_features, merged_features, merged_features
)
return torch.relu(self.fc1(attn_output.squeeze(0)))