fix: raw row clustering — remove entity concept, use row index, all numeric features

analysis/views/auto.py

@@ -274,7 +274,7 @@ def run_llm_analysis_view(request):
                     _run_clustering_pipeline(
                         run=run,
                         store=store,
-                        entity_ds_id=entity_ds_id,
+                        ds_id=entity_ds_id,
                         feature_columns=None,
                         algorithm='agglomerative',
                         min_cluster_size=5,

analysis/views/clustering.py

@@ -196,16 +196,19 @@ def cluster_detail(request, display_id, cluster_label):
 
 
 @profile
-def _run_clustering_pipeline(run, store, entity_ds_id, feature_columns, algorithm,
+def _run_clustering_pipeline(run, store, ds_id, feature_columns, algorithm,
                               min_cluster_size, run_umap=True, head=None):
-    """Unified clustering pipeline: clustering → feature extraction → UMAP → ORM save.
+    """Clustering pipeline on raw rows: clustering → feature extraction → UMAP → ORM save.
+
+    Operates directly on raw data rows (no entity aggregation).
+    Per-row results stored in EntityProfile with row index as identifier.
 
     Args:
         run: AnalysisRun ORM object.
         store: SessionStore instance.
-        entity_ds_id: dataset ID in SessionStore pointing to entity-aggregated data.
-        feature_columns: list of feature column names (None = auto-detect).
-        algorithm: 'hdbscan' or 'kmeans'.
+        ds_id: dataset ID in SessionStore pointing to raw data.
+        feature_columns: list of feature column names (None = auto-detect all numeric).
+        algorithm: clustering algorithm.
         min_cluster_size: int for HDBSCAN.
         run_umap: bool, whether to compute and save UMAP-2D embeddings.
         head: optional int, downsample to at most this many rows (min 100).
@@ -217,10 +220,10 @@ def _run_clustering_pipeline(run, store, entity_ds_id, feature_columns, algorith
         from analysis.tool_registry import _handle_run_clustering, _handle_extract_features
         import polars as pl
 
-        entry = store.get_dataset(entity_ds_id)
+        entry = store.get_dataset(ds_id)
         if entry is None:
             run.status = 'failed'
-            run.error_message = 'Entity dataset not found in session store'
+            run.error_message = '数据集在会话中不存在'
             run.save(update_fields=['status', 'error_message'])
             return
 
@@ -241,11 +244,11 @@ def _run_clustering_pipeline(run, store, entity_ds_id, feature_columns, algorith
                 run.save(update_fields=['progress_msg'])
                 lf = lf.head(head)
                 store.store_dataset(
-                    entity_ds_id, lf,
+                    ds_id, lf,
                     schema=schema,
                     metadata=entry.get('metadata', {}),
                 )
-                entry = store.get_dataset(entity_ds_id)  # refresh
+                entry = store.get_dataset(ds_id)
                 row_count = head
 
         # Resolve feature columns — use actual LazyFrame dtypes (not stored schema dict
@@ -268,7 +271,7 @@ def _run_clustering_pipeline(run, store, entity_ds_id, feature_columns, algorith
             feature_cols = [
                 live_names[i] for i, dt in enumerate(live_dtypes)
                 if dt in numeric_types and not live_names[i].startswith('_')
-            ][:10]
+            ]
 
         # ── Step 1: Clustering ────────────────────────────────────────────────
         # If no numeric columns detected, pass empty list to let
@@ -279,7 +282,7 @@ def _run_clustering_pipeline(run, store, entity_ds_id, feature_columns, algorith
         run.save(update_fields=['status', 'progress_pct', 'progress_msg'])
 
         result = asyncio.run(_handle_run_clustering(
-            dataset_id=entity_ds_id,
+            dataset_id=ds_id,
             cluster_columns=feature_cols,
             algorithm=algorithm,
             params={'min_cluster_size': min_cluster_size},
@@ -307,7 +310,7 @@ def _run_clustering_pipeline(run, store, entity_ds_id, feature_columns, algorith
         run.save(update_fields=['status', 'progress_pct', 'progress_msg'])
 
         feat_result = asyncio.run(_handle_extract_features(
-            dataset_id=entity_ds_id,
+            dataset_id=ds_id,
             cluster_result_id=cluster_id,
             top_k=10, method='zscore', save_to_db=True,
             run_id=run.id,
@@ -431,10 +434,6 @@ def _run_clustering_pipeline(run, store, entity_ds_id, feature_columns, algorith
                         if coords is None:
                             raise Exception('UMAP produced no output')
 
-                        non_num = [c for c in df_umap.columns
-                                   if c not in num_cols and not c.startswith('_')]
-                        ent_col = non_num[0] if non_num else df_umap.columns[0]
-
                         cluster_entry = store.get_cluster_result(cluster_id)
                         labels_list = cluster_entry.get('labels', []) if cluster_entry else []
 
@@ -443,9 +442,9 @@ def _run_clustering_pipeline(run, store, entity_ds_id, feature_columns, algorith
 
                         profiles = []
                         cr_cache = {}
-                        for i, row in enumerate(df_umap.iter_rows(named=True)):
-                            ev = str(row.get(ent_col, '')) or 'entity'
-                            ev = f'{ev}_{i}'
+                        for i in range(len(df_umap)):
+                            # Use row index as identifier — no entity column
+                            ev = f'row_{i}'
                             lbl = labels_list[i] if i < len(labels_list) else -1
                             cache_key = f'{run.id}_{lbl}'
                             if cache_key not in cr_cache:

analysis/views/manual.py

@@ -23,9 +23,9 @@ def manual_page(request):
 
     # Get all tools metadata
     tools = get_tools_meta()
-    core_names = {'profile_data', 'build_entity_profiles', 'compute_scores',
-                  'run_clustering', 'extract_features', 'detect_anomalies',
-                  'visualize_anomalies'}
+    core_names = {'profile_data', 'run_clustering', 'extract_features',
+                  'filter_data', 'preprocess_data',
+                  'detect_anomalies', 'visualize_anomalies'}
     diag_names = {'validate_data', 'explore_distributions', 'find_outliers',
                   'diagnose_clustering', 'compare_datasets', 'export_debug_sample',
                   'repair_schema'}
@@ -165,7 +165,7 @@ def manual_run_analysis(request):
 
             # Use the unified clustering pipeline (clustering → extraction → UMAP)
             _run_clustering_pipeline(
-                run=run, store=store, entity_ds_id=ds_id,
+                run=run, store=store, ds_id=ds_id,
                 feature_columns=feature_columns,
                 algorithm=algorithm, min_cluster_size=min_cluster_size,
                 run_umap=True, head=head,