Adjust safety margin in dataset shape analysis based on sample size for improved accuracy

model_training_nnn_tpu/dataset_tf.py

@@ -934,8 +934,15 @@ def analyze_dataset_shapes(dataset_tf: BrainToTextDatasetTF, sample_size: int =
         'n_features': dataset_tf.feature_dim
     }
 
-    # 5. 添加更大的安全边际（30% buffer）防止填充错误
-    safety_margin = 1.3
+    # 5. 添加适当的安全边际 - 基于分析范围调整
+    if sample_size == -1:
+        # 全数据分析：只需要很小的边际应对可能的舍入误差
+        safety_margin = 1.02  # 2% buffer for rounding errors
+        margin_reason = "minimal buffer for full dataset analysis"
+    else:
+        # 采样分析：需要更大的边际应对未采样到的极值
+        safety_margin = 1.3   # 30% buffer for sampling uncertainty
+        margin_reason = f"larger buffer due to sampling only {sample_size} trials"
 
     final_max_shapes = {
         'max_time_steps': int(original_max_shapes['max_time_steps'] * safety_margin),
@@ -949,7 +956,7 @@ def analyze_dataset_shapes(dataset_tf: BrainToTextDatasetTF, sample_size: int =
 
     print(f"✅ Parallel analysis complete in {analysis_time:.2f} seconds!")
     print(f"📊 Successfully analyzed {len(local_max_shapes)}/{total_trials_to_analyze} trials ({successful_rate:.1f}%)")
-    print(f"📏 Final max shapes (with {int((safety_margin-1)*100)}% safety margin):")
+    print(f"📏 Final max shapes (with {int((safety_margin-1)*100)}% safety margin - {margin_reason}):")
     print(f"   Time steps: {original_max_shapes['max_time_steps']} → {final_max_shapes['max_time_steps']}")
     print(f"   Phone sequence length: {original_max_shapes['max_phone_seq_len']} → {final_max_shapes['max_phone_seq_len']}")
     print(f"   Transcription length: {original_max_shapes['max_transcription_len']} → {final_max_shapes['max_transcription_len']}")