no domain class found for name race

本文介绍在使用Grails 1.3.5版本进行实体类生成时遇到的错误及解决方案。通过调整命令格式,将grails generate-all domain-class-name更改为grails generate-all project-name.domain-class-name,成功解决了nodomainclassfoundfornamerace的问题。
在学习grails的时候,按照《Grails入门指南》操作
在grails1.3.5下使用grails generate-all时出现错误[b]no domain class found for name race[/b]
解决方法是将原命名:grails generate-all domain-class-name 改为:grails generate-all project-name.domain-class-name
这块需要加上project名称,教程里的没有加,估计是版本的问题。
🚀 开始加载特征数据... ✅ 源域样本数: 124 ✅ 目标域样本数: 16 📊 源域类别统计: 0 4 1 40 2 40 3 40 Name: count, dtype: int64 🔁 正在使用 MMD 进行特征空间分布对齐... Step 0: MMD Loss = 0.4441 Step 50: MMD Loss = 0.4440 Step 100: MMD Loss = 0.4440 Step 150: MMD Loss = 0.4440 🔒 底层特征提取器已冻结,仅微调分类头... 🧪 正在微调分类头(基于对齐后的源域数据)... Epoch [50/100], Loss: 1.2004 Epoch [100/100], Loss: 1.0956 📌 使用 Top-5 高置信样本进行自训练... 🔍 高置信伪标签: J.mat → Inner Race (置信度=1.000) I.mat → Inner Race (置信度=1.000) C.mat → Inner Race (置信度=1.000) E.mat → Inner Race (置信度=1.000) H.mat → Inner Race (置信度=0.958) 🔄 正在进行自训练(Self-Training)... Epoch [50/100], Loss: 1.2323 Epoch [100/100], Loss: 1.1367 🏆 最终迁移诊断结果: Filename Predicted_Class Confidence Label_Index A.mat Inner Race 0.427 2 B.mat Inner Race 0.348 2 C.mat Inner Race 1.000 2 D.mat Inner Race 0.886 2 E.mat Inner Race 1.000 2 F.mat Inner Race 0.665 2 G.mat Ball 0.365 3 H.mat Inner Race 0.996 2 I.mat Inner Race 1.000 2 J.mat Inner Race 1.000 2 K.mat Inner Race 0.993 2 L.mat Inner Race 0.412 2 M.mat Inner Race 0.919 2 N.mat Inner Race 0.956 2 O.mat Ball 0.367 3 P.mat Inner Race 0.911 2 🎨 正在生成 t-SNE 可视化... Traceback (most recent call last): File D:\anaconda3\Lib\site-packages\spyder_kernels\py3compat.py:356 in compat_exec exec(code, globals, locals) File c:\users\1\desktop\未命名28.py:303 for i, v in enumerate(result_df['Predicted_Class'].value_counts()[class_names]): File D:\anaconda3\Lib\site-packages\pandas\core\series.py:1153 in __getitem__ return self._get_with(key) File D:\anaconda3\Lib\site-packages\pandas\core\series.py:1194 in _get_with return self.loc[key] File D:\anaconda3\Lib\site-packages\pandas\core\indexing.py:1191 in __getitem__ return self._getitem_axis(maybe_callable, axis=axis) File D:\anaconda3\Lib\site-packages\pandas\core\indexing.py:1420 in _getitem_axis return self._getitem_iterable(key, axis=axis) File D:\anaconda3\Lib\site-packages\pandas\core\indexing.py:1360 in _getitem_iterable keyarr, indexer = self._get_listlike_indexer(key, axis) File D:\anaconda3\Lib\site-packages\pandas\core\indexing.py:1558 in _get_listlike_indexer keyarr, indexer = ax._get_indexer_strict(key, axis_name) File D:\anaconda3\Lib\site-packages\pandas\core\indexes\base.py:6200 in _get_indexer_strict self._raise_if_missing(keyarr, indexer, axis_name) File D:\anaconda3\Lib\site-packages\pandas\core\indexes\base.py:6252 in _raise_if_missing raise KeyError(f"{not_found} not in index") KeyError: "['Normal', 'Outer Race'] not in index"
09-24
🚀 开始加载特征数据与任务二模型... ✅ 源域样本数: 124 ✅ 目标域样本数: 15 📦 正在加载任务二训练好的随机森林模型... ✅ 成功加载 Random Forest 模型(基于 41 维特征) 🔄 正在进行特征对齐(TCA)以减小域间差异... ⚠️ 未安装 transferlearning 库,正在使用本地实现... ✅ TCA 对齐完成!新特征维度: 10 🔮 正在使用源域模型生成目标域伪标签... 📋 初始伪标签预测结果: Filename Predicted_Class Confidence Confidence_Bin 0 A.mat Ball 0.53 Medium (0.5~0.7) 1 C.mat Inner Race 0.59 Medium (0.5~0.7) 2 D.mat Ball 0.61 Medium (0.5~0.7) 3 E.mat Ball 0.39 Low (<0.5) 4 F.mat Ball 0.48 Low (<0.5) 5 G.mat Outer Race 0.57 Medium (0.5~0.7) 6 H.mat Ball 0.39 Low (<0.5) 7 I.mat Inner Race 0.58 Medium (0.5~0.7) 8 J.mat Ball 0.36 Low (<0.5) 9 K.mat Ball 0.42 Low (<0.5) 10 L.mat Ball 0.59 Medium (0.5~0.7) 11 M.mat Ball 0.51 Medium (0.5~0.7) 12 N.mat Inner Race 0.50 Low (<0.5) 13 O.mat Ball 0.41 Low (<0.5) 14 P.mat Ball 0.70 Medium (0.5~0.7) 🔁 正在执行自训练(Self-Training)以迭代优化... ✅ 第1轮自训练完成,新增 1 个高置信样本,总训练样本: 125 ✅ 第2轮自训练完成,新增 2 个高置信样本,总训练样本: 127 ✅ 第3轮自训练完成,新增 4 个高置信样本,总训练样本: 131 Traceback (most recent call last): File D:\anaconda3\Lib\site-packages\spyder_kernels\py3compat.py:356 in compat_exec exec(code, globals, locals) File c:\users\1\desktop\未命名23.py:231 for i, v in enumerate(final_result_df['Predicted_Class'].value_counts()[class_names]): File D:\anaconda3\Lib\site-packages\pandas\core\series.py:1153 in __getitem__ return self._get_with(key) File D:\anaconda3\Lib\site-packages\pandas\core\series.py:1194 in _get_with return self.loc[key] File D:\anaconda3\Lib\site-packages\pandas\core\indexing.py:1191 in __getitem__ return self._getitem_axis(maybe_callable, axis=axis) File D:\anaconda3\Lib\site-packages\pandas\core\indexing.py:1420 in _getitem_axis return self._getitem_iterable(key, axis=axis) File D:\anaconda3\Lib\site-packages\pandas\core\indexing.py:1360 in _getitem_iterable keyarr, indexer = self._get_listlike_indexer(key, axis) File D:\anaconda3\Lib\site-packages\pandas\core\indexing.py:1558 in _get_listlike_indexer keyarr, indexer = ax._get_indexer_strict(key, axis_name) File D:\anaconda3\Lib\site-packages\pandas\core\indexes\base.py:6200 in _get_indexer_strict self._raise_if_missing(keyarr, indexer, axis_name) File D:\anaconda3\Lib\site-packages\pandas\core\indexes\base.py:6252 in _raise_if_missing raise KeyError(f"{not_found} not in index") KeyError: "['Normal'] not in index"
09-24
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