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#---设备配置---#
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device: cpu
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# device: cuda
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#---训练配置---#
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n_epochs: 150
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batch_size: 16
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learning_rate: 0.001
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nc: 4
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#data_train: train_val # train: 只用train训练，val做验证， infer做测试；train_val: 用train和val做训练，infer做验证， infer做测试；all: 全部训练，全部验证，全部测试（数据先1/5作为infer，剩下的再1/5作为val，剩下的4/5作为训练）
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data_train: train_val
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early_stop_patience: 50
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gamma: 0.98
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step_size: 10
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experimental_mode: false
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experiments_count: 50
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replace_model: true # 是否替换现有模型
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#---检测和推理配置---#
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# 检测和推理使用模型路径
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model_path: /data/model/psychology.pth
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default_model_path: model/psychology.pth
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#---样本特征---#
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# 标签名称
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label_name: 类别
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# 特征名称
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feature_names:
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- "强迫症状数字化"
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- "人际关系敏感数字化"
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- "抑郁数字化"
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- "多因子症状"
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- "母亲教养方式数字化"
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- "父亲教养方式数字化"
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- "自评家庭经济条件数字化"
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- "有无心理治疗（咨询）史数字化"
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- "学业情况数字化"
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- "出勤情况数字化"
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# 定义特征权重列表
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feature_weights:
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- 0.135
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- 0.085
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- 0.08
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- 0.2
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- 0.09
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- 0.09
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- 0.06
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- 0.06
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- 0.08
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- 0.12
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#---网络结构---#
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# MLP configuration
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mlp:
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input_dim: 10 # Number of input features
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layers:
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- output_dim: 32
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activation: relu
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- output_dim: 128
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activation: relu
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- output_dim: 32
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activation: relu
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output_dim: 4 # Number of classes
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# Transformer configuration
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transformer:
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d_model: 32 # Reduced embedding dimension
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nhead: 4 # Reduced number of attention heads
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num_encoder_layers: 2 # Reduced number of encoder layers
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num_decoder_layers: 2 # Reduced number of decoder layers
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dim_feedforward: 128 # Reduced feedforward network dimension
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dropout: 0.1 # Dropout probability
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input_dim: 10 # Number of input features
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output_dim: 4 # Number of classes
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#---训练配置备份---#
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# MLP good train param 1
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# #---训练配置---#
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# n_epochs: 150
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# batch_size: 16
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# learning_rate: 0.001
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# nc: 4
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# #data_train: train_val # train: 只用train训练，val做验证， infer做测试；train_val: 用train和val做训练，infer做验证， infer做测试；all: 全部训练，全部验证，全部测试（数据先1/5作为infer，剩下的再1/5作为val，剩下的4/5作为训练）
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# data_train: train_val
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# early_stop_patience: 50
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# gamma: 0.98
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# step_size: 10
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# experiments_count: 1
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# MLP good train param 2
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# #---训练配置---#
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# n_epochs: 300
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# batch_size: 8
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# learning_rate: 0.0005
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# nc: 4
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# #data_train: train_val # train: 只用train训练，val做验证， infer做测试；train_val: 用train和val做训练，infer做验证， infer做测试；all: 全部训练，全部验证，全部测试（数据先1/5作为infer，剩下的再1/5作为val，剩下的4/5作为训练）
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# data_train: train_val
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# early_stop_patience: 50
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# gamma: 0.98
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# step_size: 10
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# experiments_count: 1
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# Transformer good train param 1
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# #---训练配置---#
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# n_epochs: 150
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# batch_size: 64
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# learning_rate: 0.001
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# nc: 4
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# #data_train: train_val # train: 只用train训练，val做验证， infer做测试；train_val: 用train和val做训练，infer做验证， infer做测试；all: 全部训练，全部验证，全部测试（数据先1/5作为infer，剩下的再1/5作为val，剩下的4/5作为训练）
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# data_train: train_val
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# early_stop_patience: 50
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# gamma: 0.98
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# step_size: 10
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# experiments_count: 1
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# Transformer good train param 2
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# #---训练配置---#
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# n_epochs: 300
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# batch_size: 8
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# learning_rate: 0.0005
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# nc: 4
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# #data_train: train_val # train: 只用train训练，val做验证， infer做测试；train_val: 用train和val做训练，infer做验证， infer做测试；all: 全部训练，全部验证，全部测试（数据先1/5作为infer，剩下的再1/5作为val，剩下的4/5作为训练）
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# data_train: train_val
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# early_stop_patience: 50
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# gamma: 0.98
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# step_size: 10
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# experiments_count: 1 |