From 1435279c7a4c8d7c967e84c3ceb5e264b5dfd886 Mon Sep 17 00:00:00 2001
From: Xie Yi <xiey@lamda.nju.edu.cn>
Date: Sat, 9 Dec 2023 14:58:58 +0800
Subject: [PATCH] [DOC] homo search doc

---
 docs/workflows/search.rst | 37 ++++++++++++++++++++++++++++++++++++-
 1 file changed, 36 insertions(+), 1 deletion(-)

diff --git a/docs/workflows/search.rst b/docs/workflows/search.rst
index d9b48d9..609e534 100644
--- a/docs/workflows/search.rst
+++ b/docs/workflows/search.rst
@@ -10,7 +10,42 @@ All the searchers are implemented as a subclass of ``BaseSearcher``. When initia
 Homo Search
 ======================
 
-The homogeneous search of helpful learnwares can be divided into two stages: semantic specification search and statistical specification search. Both of them needs ``BaseUserInfo`` as input.
+The homogeneous search of helpful learnwares can be divided into two stages: semantic specification search and statistical specification search. Both of them needs ``BaseUserInfo`` as input. The following codes shows how to use the searcher to search for helpful learnwares from a market ``easy_market`` for a user. The introduction of ``EasyMarket`` is in `COMPONENTS: Market <../components/market.html>`_.
+
+.. code-block:: python
+
+    # generate BaseUserInfo(semantic_spec + stat_info)
+    user_semantic = {
+        "Data": {"Values": ["Table"], "Type": "Class"},
+        "Task": {"Values": ["Regression"], "Type": "Class"},
+        "Library": {"Values": ["Scikit-learn"], "Type": "Class"},
+        "Scenario": {"Values": ["Business"], "Type": "Tag"},
+        "Description": {"Values": "", "Type": "String"},
+        "Name": {"Values": "", "Type": "String"},
+        "Input": {"Dimension": 82, "Description": {},},
+        "Output": {"Dimension": 1, "Description": {},}, 
+        "License": {"Values": ["MIT"], "Type": "Class"},
+    }
+    user_spec = generate_rkme_table_spec(X=x)
+    user_info = BaseUserInfo(
+        semantic_spec=user_semantic, 
+        stat_info={"RKMETableSpecification": user_spec}
+    )
+
+    # search the market for the user
+    search_result = easy_market.search_learnware(user_info)
+
+    # search result: single_result
+    single_result = search_result.get_single_results()
+    print(f"single model num: {len(single_result)}, 
+        max_score: {single_result[0].score}, 
+        min_score: {single_result[-1].score}"
+    )
+    
+    # search result: multiple_result
+    multiple_result = search_result.get_multiple_results()
+    mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares])
+    print(f"mixture_score: {multiple_result[0].score}, mixture_learnwares: {mixture_id}")
 
 
 Hetero Search