chore(main): release 0.4.0 (#454)

Release-As: 0.4.0

.commitlintrc.js

@@ -0,0 +1,21 @@
+module.exports = {
+    extends: ["@commitlint/config-conventional"],
+    rules: {
+        // Configuration Format: [level, applicability, value]
+        // level: Error level, usually expressed as a number:
+        //     0 - disable rule
+        //     1 - Warning (does not prevent commits)
+        //     2 - Error (will block the commit)
+        // applicability: the conditions under which the rule applies, commonly used values:
+        //     “always” - always apply the rule
+        //     “never” - never apply the rule
+        // value: the specific value of the rule, e.g. a maximum length of 100.
+        // Refs: https://commitlint.js.org/reference/rules-configuration.html
+      "header-max-length": [2, "always", 100],
+      "type-enum": [
+        2,
+        "always",
+        ["build", "chore", "ci", "docs", "feat", "fix", "perf", "refactor", "revert", "style", "test", "Release-As"]
+      ]
+    }
+  };

.env.example

@@ -9,7 +9,8 @@ For more information about configuration options, please refer to the documentat
 
 # Global configs:
 USE_AZURE=False
-USE_AZURE_TOKEN_PROVIDER=False
+CHAT_USE_AZURE_TOKEN_PROVIDER=False
+EMBEDDING_USE_AZURE_TOKEN_PROVIDER=False
 MAX_RETRY=10
 RETRY_WAIT_SECONDS=20
 

.github/PULL_REQUEST_TEMPLATE.md

@@ -20,14 +20,12 @@
 
 ## How Has This Been Tested?
 <!---  Put an `x` in all the boxes that apply: --->
-- [ ] Pass the test by running: `pytest qlib/tests/test_all_pipeline.py` under upper directory of `qlib`.
 - [ ] If you are adding a new feature, test on your own test scripts.
 
 <!--- **ATTENTION**: If you are adding a new feature, please make sure your codes are **correctly tested**. If our test scripts do not cover your cases, please provide your own test scripts under the `tests` folder and test them. More information about test scripts can be found [here](https://docs.python.org/3/library/unittest.html#basic-example), or you could refer to those we provide under the `tests` folder. -->
 
 ## Screenshots of Test Results (if appropriate):
-1. Pipeline test:
-2. Your own tests:
+1. Your own tests:
 
 ## Types of changes
 <!--- What types of changes does your code introduce? Put an `x` in all the boxes that apply: -->

.github/workflows/pr.yml

@@ -1,18 +1,5 @@
-concurrency:
-  cancel-in-progress: true
-  group: ${{ github.workflow }}-${{ github.ref }}
-jobs:
-  lint-title:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Check PR Title for Conventional Commit Format
-        run: |
-          if ! echo "${{ github.event.pull_request.title }}" | grep -Pq '^(build|chore|ci|docs|feat|fix|perf|refactor|revert|style|test|Release-As)(\(\w+\))?!?:\s.*'; then
-            echo 'The title does not conform to the Conventional Commit.'
-            echo 'Please refer to "https://www.conventionalcommits.org/"'
-            exit 1
-          fi
 name: Lint pull request title
+
 on:
   pull_request:
     types:
@@ -20,3 +7,29 @@ on:
       - synchronize
       - reopened
       - edited
+
+concurrency:
+  cancel-in-progress: true
+  group: ${{ github.workflow }}-${{ github.ref }}
+
+jobs:
+  lint-title:
+    runs-on: ubuntu-latest
+    steps:
+      # This step is necessary because the lint title uses the .commitlintrc.js file in the project root directory.
+      - name: Checkout Repository
+        uses: actions/checkout@v4
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: '16'
+
+      - name: Install commitlint
+        run: npm install --save-dev @commitlint/{config-conventional,cli}
+
+      - name: Validate PR Title with commitlint
+        env:
+          BODY: ${{ github.event.pull_request.title }}
+        run: |
+          echo "$BODY" | npx commitlint --config .commitlintrc.js

.gitignore

@@ -4,6 +4,7 @@
 Pipfile
 public
 release-notes.md
+typescript*
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -64,7 +65,7 @@ coverage.xml
 
 # Django stuff:
 *.log
-/log/
+/log*/
 local_settings.py
 db.sqlite3
 db.sqlite3-journal
@@ -110,7 +111,7 @@ celerybeat.pid
 *.sage.py
 
 # Environments
-.env
+.env*
 .venv
 ^env/
 venv/
@@ -169,4 +170,6 @@ mlruns/
 
 # shell script
 *.out
-*.sh
+/*.sh
+.aider*
+rdagent/app/benchmark/factor/example.json

.readthedocs.yaml

@@ -10,6 +10,18 @@ build:
   os: ubuntu-22.04
   tools:
     python: "3.10"
+  # During the build process, you need to fetch tags, and since the default command to read the docs only pulls shallow code, it will cause an error.
+  # So we added the `git fetch --tags --unshallow || true` command to fetch the full tag record.
+  # Adding this command overrides the default command, so we copied it over to make sure the build was successful.
+  commands:
+    - python -mvirtualenv $READTHEDOCS_VIRTUALENV_PATH 
+    - python -m pip install --upgrade --no-cache-dir pip setuptools 
+    - python -m pip install --upgrade --no-cache-dir sphinx 
+    - python -m pip install --exists-action=w --no-cache-dir -r requirements/docs.txt 
+    - python -m pip install --upgrade --upgrade-strategy only-if-needed --no-cache-dir . 
+    - git fetch --tags --unshallow || true
+    - mkdir -p $READTHEDOCS_OUTPUT/html/
+    - python -m sphinx -T -b html -d _build/doctrees -D language=en ./docs $READTHEDOCS_OUTPUT/html
 
 # Build documentation in the docs/ directory with Sphinx
 sphinx:

CHANGELOG.md

@@ -1,5 +1,262 @@
 # Changelog
 
+## [0.4.0](https://github.com/microsoft/RD-Agent/compare/v0.3.0...v0.4.0) (2025-04-04)
+
+
+### Features
+
+* (Kaggle) add base template for competition: tabular-playground-series-may-2022 ([#481](https://github.com/microsoft/RD-Agent/issues/481)) ([f3405ca](https://github.com/microsoft/RD-Agent/commit/f3405ca732eb0ddca8e18ea72f69cbd86055c4ab))
+* a unified CoSTEER to fit more scenarios ([#491](https://github.com/microsoft/RD-Agent/issues/491)) ([cddbd02](https://github.com/microsoft/RD-Agent/commit/cddbd02e3ad3ccf6ad01443777319dc5c7eb08a7))
+* add a new competition ([#474](https://github.com/microsoft/RD-Agent/issues/474)) ([2fc0d77](https://github.com/microsoft/RD-Agent/commit/2fc0d77c485a31f647e21f4578e2e326f7032964))
+* add a tool to enable saving workspace files into a specific folder ([#728](https://github.com/microsoft/RD-Agent/issues/728)) ([bca864b](https://github.com/microsoft/RD-Agent/commit/bca864b7edeafe3f88405efb695ca8acad6252f8))
+* add baseline score stat ([#590](https://github.com/microsoft/RD-Agent/issues/590)) ([2948026](https://github.com/microsoft/RD-Agent/commit/2948026c390d067b643f8c8247c1447f1dc023e4))
+* add configurable volume mode for Docker volumes in env.py ([#537](https://github.com/microsoft/RD-Agent/issues/537)) ([642a022](https://github.com/microsoft/RD-Agent/commit/642a02239431411b91959f23e69b454997ca75d5))
+* add constraint labels for semantic search ([#680](https://github.com/microsoft/RD-Agent/issues/680)) ([0584cfc](https://github.com/microsoft/RD-Agent/commit/0584cfcd13ca1a62c85390ea2ee7574370748d31))
+* add cross validation to workflow ([#700](https://github.com/microsoft/RD-Agent/issues/700)) ([82e9b00](https://github.com/microsoft/RD-Agent/commit/82e9b00be62b01673353a7aaa3ab0e2e3ecaf3ca))
+* add describe_data_folder_v2 ([#738](https://github.com/microsoft/RD-Agent/issues/738)) ([bc8e846](https://github.com/microsoft/RD-Agent/commit/bc8e8460e0246321792ff3347b1b8905416ad075))
+* add do_truncate control for the load function ([#656](https://github.com/microsoft/RD-Agent/issues/656)) ([2b960a5](https://github.com/microsoft/RD-Agent/commit/2b960a58dfdeba69522a0f72ecf0975bb6ae87ee))
+* add do_truncate control for the load function ([#656](https://github.com/microsoft/RD-Agent/issues/656)) ([2b960a5](https://github.com/microsoft/RD-Agent/commit/2b960a58dfdeba69522a0f72ecf0975bb6ae87ee))
+* add eda to data science scenario ([#639](https://github.com/microsoft/RD-Agent/issues/639)) ([35aa479](https://github.com/microsoft/RD-Agent/commit/35aa479f00edf118d43ec228e0a84c155332957a))
+* add hypothesis guidelines and rule-based ranking ([#746](https://github.com/microsoft/RD-Agent/issues/746)) ([c077b82](https://github.com/microsoft/RD-Agent/commit/c077b8239cc72904c4bc450845ed2a11aa5445f0))
+* Add line length limit to shrink_text function and settings ([#715](https://github.com/microsoft/RD-Agent/issues/715)) ([75ed5e1](https://github.com/microsoft/RD-Agent/commit/75ed5e1c2ce1bf20bb55190c10a4134e04694d2b))
+* add loop_n parameter to the main loop ([#611](https://github.com/microsoft/RD-Agent/issues/611)) ([778c166](https://github.com/microsoft/RD-Agent/commit/778c166962250e3b9e7ad85de37f62297d370b45))
+* add max time config to costeer in data science ([#645](https://github.com/microsoft/RD-Agent/issues/645)) ([534686c](https://github.com/microsoft/RD-Agent/commit/534686c2ba7d9fa979c0762ad3177c36f6d7f4cb))
+* add mlebench submission validitor ([#545](https://github.com/microsoft/RD-Agent/issues/545)) ([712d94a](https://github.com/microsoft/RD-Agent/commit/712d94a7d6f22187fc3d18bd434e71ec6997aa9f))
+* add model removal and adjust some framework logic ([#681](https://github.com/microsoft/RD-Agent/issues/681)) ([1edf881](https://github.com/microsoft/RD-Agent/commit/1edf881c63512d351c0dd074d7a1c0965ff3119b))
+* add output_path to load function of LoopBase ([#628](https://github.com/microsoft/RD-Agent/issues/628)) ([dd33726](https://github.com/microsoft/RD-Agent/commit/dd33726ac5de75dc2030d193d457d59490b3361e))
+* add pipeline coder ([#742](https://github.com/microsoft/RD-Agent/issues/742)) ([759f295](https://github.com/microsoft/RD-Agent/commit/759f295dbf1224e177006e72d694e42dd6f372b6))
+* add rank into report (mle_summary) ([#665](https://github.com/microsoft/RD-Agent/issues/665)) ([13f7922](https://github.com/microsoft/RD-Agent/commit/13f7922aaae9e4143aac4ad08ec1c556c2faf04e))
+* add restart and fix unzip ([#538](https://github.com/microsoft/RD-Agent/issues/538)) ([ed2c7d1](https://github.com/microsoft/RD-Agent/commit/ed2c7d175f1f44ca06ad7a63b08da12f6c4df9ab))
+* add retry mechanism with wait_retry decorator and refactor diff generation ([#572](https://github.com/microsoft/RD-Agent/issues/572)) ([de1cd72](https://github.com/microsoft/RD-Agent/commit/de1cd72f068ebd1e1bd5bc2ad2b12ae484d54831))
+* add the shape of the CSV to the dataset description ([#561](https://github.com/microsoft/RD-Agent/issues/561)) ([a10c881](https://github.com/microsoft/RD-Agent/commit/a10c881bd86796e6167257ad26dd165f7e46d813))
+* add timeout settings and cleanup step in data science runner ([#539](https://github.com/microsoft/RD-Agent/issues/539)) ([295abd5](https://github.com/microsoft/RD-Agent/commit/295abd56f7b58055bd27b247dfed47eb85e9b0cd))
+* add type checker to api backend & align litellm and old backend ([#647](https://github.com/microsoft/RD-Agent/issues/647)) ([d38eae9](https://github.com/microsoft/RD-Agent/commit/d38eae986a0ba69d71288fa09fcc21e227551a02))
+* align mlebench data and evaluation & several fix on kaggle workflow ([#477](https://github.com/microsoft/RD-Agent/issues/477)) ([f6c522b](https://github.com/microsoft/RD-Agent/commit/f6c522b651db3c1f6af6815347589917f46e433a))
+* **backend:** integrate LiteLLM API Backend ([#564](https://github.com/microsoft/RD-Agent/issues/564)) ([f477687](https://github.com/microsoft/RD-Agent/commit/f4776879c76a213d53875b307c94be1ea5cfd9ba))
+* base data science scenario UI ([#525](https://github.com/microsoft/RD-Agent/issues/525)) ([39917b3](https://github.com/microsoft/RD-Agent/commit/39917b354b22a8488a17396fe2245cb41e3def03))
+* condaenv & full docker env ([#668](https://github.com/microsoft/RD-Agent/issues/668)) ([084dd6d](https://github.com/microsoft/RD-Agent/commit/084dd6d748a89492ea0888acb316b9bb9efeb62f))
+* diff mode fix ([#569](https://github.com/microsoft/RD-Agent/issues/569)) ([0c509f5](https://github.com/microsoft/RD-Agent/commit/0c509f599ce19303b44d8192ec3eb634c24992d6))
+* display LLM prompt ([#676](https://github.com/microsoft/RD-Agent/issues/676)) ([8c93bba](https://github.com/microsoft/RD-Agent/commit/8c93bba82e185edcf4204cc574df5f41bcdfa9d2))
+* Dynamically find and use sample submission file in eval tests ([#542](https://github.com/microsoft/RD-Agent/issues/542)) ([5f12b44](https://github.com/microsoft/RD-Agent/commit/5f12b44c89dd26b250e914192f9beb2da38fb3ab))
+* end-to-end optimization ([#473](https://github.com/microsoft/RD-Agent/issues/473)) ([d41343a](https://github.com/microsoft/RD-Agent/commit/d41343a63d87bf3479f5ec30745ea788580495bf))
+* Enhance eval script with file cleanup and detailed submission checks ([#529](https://github.com/microsoft/RD-Agent/issues/529)) ([cf2ff92](https://github.com/microsoft/RD-Agent/commit/cf2ff9213d3a8b0fad64df7cae0c35f996d72e27))
+* exclude invalid session log folder ([#554](https://github.com/microsoft/RD-Agent/issues/554)) ([fa86e4d](https://github.com/microsoft/RD-Agent/commit/fa86e4d1805000e0e5779c662ccbb5273fda623c))
+* improve the framework's ability to adaptively adjust the model ([#629](https://github.com/microsoft/RD-Agent/issues/629)) ([93806f3](https://github.com/microsoft/RD-Agent/commit/93806f33a1e0f29a125e29303d4b984a9817c3c0))
+* independent use_azure_token_provider on chat and embedding ([#452](https://github.com/microsoft/RD-Agent/issues/452)) ([d223004](https://github.com/microsoft/RD-Agent/commit/d223004917692e231b251330cbc8676081d5a10d))
+* integrate azure deepseek r1 ([#591](https://github.com/microsoft/RD-Agent/issues/591)) ([e79ce5c](https://github.com/microsoft/RD-Agent/commit/e79ce5c38539138abe04eb9809fbde437e97bbb7))
+* kaggle refactor ([#489](https://github.com/microsoft/RD-Agent/issues/489)) ([1b057d0](https://github.com/microsoft/RD-Agent/commit/1b057d0d63a861fba4b3cb59c6c5fc1a0e3da383))
+* **kaggle:** several update in kaggle scenarios ([#476](https://github.com/microsoft/RD-Agent/issues/476)) ([245d211](https://github.com/microsoft/RD-Agent/commit/245d211dcbfb18ebcc554247a0e3a8dbecf6f3bd))
+* loader prompt &  simplify YAML loading and update data loader specifications ([#736](https://github.com/microsoft/RD-Agent/issues/736)) ([86f8bbf](https://github.com/microsoft/RD-Agent/commit/86f8bbf15895e7c198f9bc395d055ca5f02a5bb6))
+* make spec optional ([#719](https://github.com/microsoft/RD-Agent/issues/719)) ([a16b70f](https://github.com/microsoft/RD-Agent/commit/a16b70ff34c66d7e1c4c7ff5236eca8e7d8abea9))
+* Make system prompt role customizable in LLM settings ([#632](https://github.com/microsoft/RD-Agent/issues/632)) ([e4acd92](https://github.com/microsoft/RD-Agent/commit/e4acd92cc5eec6db5c29cb2d4788020fb89099b7))
+* multi log folder, replace "epxx" in workspace path ([#555](https://github.com/microsoft/RD-Agent/issues/555)) ([8a69c9c](https://github.com/microsoft/RD-Agent/commit/8a69c9c9630860c9b644356e1f71654aea222328))
+* new exp gen v2 implementation ([#725](https://github.com/microsoft/RD-Agent/issues/725)) ([5dcc2d5](https://github.com/microsoft/RD-Agent/commit/5dcc2d5fa63bbe9ae8c4817d9b40b77600440edb))
+* new-york-city-taxi-fare-prediction_template ([#488](https://github.com/microsoft/RD-Agent/issues/488)) ([a9caab7](https://github.com/microsoft/RD-Agent/commit/a9caab7bc5dc86f395a008e523355922137aef17))
+* out spec change for o1-preview ([#666](https://github.com/microsoft/RD-Agent/issues/666)) ([22894bd](https://github.com/microsoft/RD-Agent/commit/22894bdbee26b9cad73646d2975857787e515f75))
+* refactor for general data science ([#498](https://github.com/microsoft/RD-Agent/issues/498)) ([7002dc4](https://github.com/microsoft/RD-Agent/commit/7002dc4981a4f72096b438d2fe4fd9ff268c54f3))
+* refine logic for qlib_factor_from_report ([#463](https://github.com/microsoft/RD-Agent/issues/463)) ([21348d8](https://github.com/microsoft/RD-Agent/commit/21348d89e0e0eec1b4fab4e7a497f1eb34b8fe72))
+* run benchmark on gpt-4o & llama 3.1 ([#497](https://github.com/microsoft/RD-Agent/issues/497)) ([64af0b5](https://github.com/microsoft/RD-Agent/commit/64af0b5529b687cce8b5b7a1893946e15edca626))
+* summary and UI update ([#581](https://github.com/microsoft/RD-Agent/issues/581)) ([efa51f9](https://github.com/microsoft/RD-Agent/commit/efa51f9c259a06fe219f3137f0a1005e50d2bfdd))
+* template changes for some kaggle competitions ([#484](https://github.com/microsoft/RD-Agent/issues/484)) ([2e38000](https://github.com/microsoft/RD-Agent/commit/2e38000091030811fc081d72016c7bbadf7efd50))
+* track and log accumulated completion cost in LiteLLMAPIBackend ([#727](https://github.com/microsoft/RD-Agent/issues/727)) ([b294a95](https://github.com/microsoft/RD-Agent/commit/b294a95e0b7b2ef96af355cebac92d9c87f3acab))
+* update prompts and descriptions for data science components ([#731](https://github.com/microsoft/RD-Agent/issues/731)) ([c20e226](https://github.com/microsoft/RD-Agent/commit/c20e226c3e7771c9fcd1c879a8937e4694dc03eb))
+* variable printing tool of data_science coder testing ([#658](https://github.com/microsoft/RD-Agent/issues/658)) ([116c061](https://github.com/microsoft/RD-Agent/commit/116c06190b01f0b621c021726a1be23458ab1154))
+
+
+### Bug Fixes
+
+* a default conf in scen qlib ([#503](https://github.com/microsoft/RD-Agent/issues/503)) ([d64a228](https://github.com/microsoft/RD-Agent/commit/d64a228525cbedd7687c1e06132eacd0d0647697))
+* a small bug in exp_gen ([#606](https://github.com/microsoft/RD-Agent/issues/606)) ([f734dde](https://github.com/microsoft/RD-Agent/commit/f734dde0b0101e13f38151468c8ddf9e23af26ac))
+* add check when retrying gen model codes ([#699](https://github.com/microsoft/RD-Agent/issues/699)) ([3b82f15](https://github.com/microsoft/RD-Agent/commit/3b82f159474087902d3c6007d370e3282b549015))
+* add DSExperiment type check and directory validation in log proc… ([#535](https://github.com/microsoft/RD-Agent/issues/535)) ([f59b12c](https://github.com/microsoft/RD-Agent/commit/f59b12c9cc9afde82b74bc133797ff1396678627))
+* add ensemble test, change to "use cross-validation if possible" in workflow spec ([#634](https://github.com/microsoft/RD-Agent/issues/634)) ([acc97a8](https://github.com/microsoft/RD-Agent/commit/acc97a8217253497afedcfa829902b4432e1031e))
+* add force parameter for cache_with_pickle & using cache when get kaggle leaderboard ([#687](https://github.com/microsoft/RD-Agent/issues/687)) ([c8841e5](https://github.com/microsoft/RD-Agent/commit/c8841e590a925200859acba9fda4a17d4c3aa1c7))
+* add metric name check for valid scores ([#724](https://github.com/microsoft/RD-Agent/issues/724)) ([acc2ffb](https://github.com/microsoft/RD-Agent/commit/acc2ffbde4df3b53654559d14cd035ee6be6b35e))
+* add retry mechanism for GPU device check in DockerEnv ([#573](https://github.com/microsoft/RD-Agent/issues/573)) ([a780cfb](https://github.com/microsoft/RD-Agent/commit/a780cfb621dc487cc17072bfd4aedd7d581249ab))
+* add scores.csv checking in ensemble_test ([#567](https://github.com/microsoft/RD-Agent/issues/567)) ([01808b4](https://github.com/microsoft/RD-Agent/commit/01808b47c314d1daffacc0a65e0ab934a1c41d65))
+* add stdout context length setting and improve text shrinking logic ([#559](https://github.com/microsoft/RD-Agent/issues/559)) ([4ac26a6](https://github.com/microsoft/RD-Agent/commit/4ac26a65c1f18f7513480dd562566c8a96298aa7))
+* align components' name ([#701](https://github.com/microsoft/RD-Agent/issues/701)) ([295a114](https://github.com/microsoft/RD-Agent/commit/295a1148c53d00b716b2d540573a7f43e7e2d762))
+* auto continue small bug ([#598](https://github.com/microsoft/RD-Agent/issues/598)) ([75eaecf](https://github.com/microsoft/RD-Agent/commit/75eaecf36b9f70dfc2d7fedd35836acdb05f89d6))
+* avoid try-except in ensemble eval prompts ([#637](https://github.com/microsoft/RD-Agent/issues/637)) ([5c58d6e](https://github.com/microsoft/RD-Agent/commit/5c58d6e524ef848024578033ab6d47bc9b220822))
+* avoid warning for missing llama installation when not in use ([#509](https://github.com/microsoft/RD-Agent/issues/509)) ([5ec3422](https://github.com/microsoft/RD-Agent/commit/5ec342224c2c8c4cf591f1eae673e25b14218726))
+* change devault to default ([#688](https://github.com/microsoft/RD-Agent/issues/688)) ([7f401cd](https://github.com/microsoft/RD-Agent/commit/7f401cd1c3b333285acf6d6e57654f4b9f0cb6c5))
+* change ensemble test ([#622](https://github.com/microsoft/RD-Agent/issues/622)) ([5de3595](https://github.com/microsoft/RD-Agent/commit/5de35953ed0d3e2e1f4dff0e0522f2d6475079ec))
+* change summary info of log folder ([#552](https://github.com/microsoft/RD-Agent/issues/552)) ([0eb258d](https://github.com/microsoft/RD-Agent/commit/0eb258d734e9a1280a238b9a6f63eb33047ee0a7))
+* clarify an ambiguous explanation ([#705](https://github.com/microsoft/RD-Agent/issues/705)) ([5dbfc68](https://github.com/microsoft/RD-Agent/commit/5dbfc6859cbf6cc31932dae30cf05506108fc871))
+* clarify cross_validation ([#644](https://github.com/microsoft/RD-Agent/issues/644)) ([906993e](https://github.com/microsoft/RD-Agent/commit/906993ef6482f88131d1af46f5bc66a77034b549))
+* coder prompt & model test text ([#583](https://github.com/microsoft/RD-Agent/issues/583)) ([0a41227](https://github.com/microsoft/RD-Agent/commit/0a41227f267050feaeeb47ddd4d749643eb9f198))
+* correct the configuration inheritance relationship ([#671](https://github.com/microsoft/RD-Agent/issues/671)) ([30b1ff8](https://github.com/microsoft/RD-Agent/commit/30b1ff8e1ce59b741e0b81481962063014641c0b))
+* default emb model ([#702](https://github.com/microsoft/RD-Agent/issues/702)) ([4329a72](https://github.com/microsoft/RD-Agent/commit/4329a722832a201b3fa6f9d8f9d8d46f78110410))
+* direct_exp_gen to json_target_type in DSExpGen class ([#661](https://github.com/microsoft/RD-Agent/issues/661)) ([428b74a](https://github.com/microsoft/RD-Agent/commit/428b74a988157ea864ebb40e828bd9f67589c863))
+* docker error will trigger retry and data science runner loop set to 3 ([#602](https://github.com/microsoft/RD-Agent/issues/602)) ([ad785e0](https://github.com/microsoft/RD-Agent/commit/ad785e03d5db05d9191d5e772e184532835a787b))
+* ensure expected type ([#593](https://github.com/microsoft/RD-Agent/issues/593)) ([098a9a6](https://github.com/microsoft/RD-Agent/commit/098a9a6618f70fa8dd276b9014b9e7ba9621553b))
+* filter empty log traces in ds UI ([#533](https://github.com/microsoft/RD-Agent/issues/533)) ([1a2057c](https://github.com/microsoft/RD-Agent/commit/1a2057c9fc11edc4637f0baaa6dd226eb049c36e))
+* fix a bug in cross validation ([#618](https://github.com/microsoft/RD-Agent/issues/618)) ([05a4f10](https://github.com/microsoft/RD-Agent/commit/05a4f101e0b64b860ad03294619b2350004657e8))
+* fix a bug in ensemble test script ([#713](https://github.com/microsoft/RD-Agent/issues/713)) ([ad32100](https://github.com/microsoft/RD-Agent/commit/ad321000acbd9291d22fe03a9c60e57c70511c73))
+* fix a bug in initial tasks ([#635](https://github.com/microsoft/RD-Agent/issues/635)) ([edb552e](https://github.com/microsoft/RD-Agent/commit/edb552ed283119444f357fbd0b6170b2ad97712a))
+* fix a bug in kaggle conf ([#459](https://github.com/microsoft/RD-Agent/issues/459)) ([b4ed32b](https://github.com/microsoft/RD-Agent/commit/b4ed32b17ef07d8557450063765585a48d5fcd32))
+* fix a bug in progress_bar filter ([#712](https://github.com/microsoft/RD-Agent/issues/712)) ([ba5a84d](https://github.com/microsoft/RD-Agent/commit/ba5a84dee59c39cc2a8c0d428a82da1f899ce537))
+* fix a bug in proposal (add last loop's exception to last task desc) ([#596](https://github.com/microsoft/RD-Agent/issues/596)) ([419186f](https://github.com/microsoft/RD-Agent/commit/419186ffb985fe5a0aa0f7fe59c7a223e355492e))
+* fix a bug in regular expression exception processing ([#734](https://github.com/microsoft/RD-Agent/issues/734)) ([67d3702](https://github.com/microsoft/RD-Agent/commit/67d37027bbcd7294a5890a350fe16fe78e0dfa77))
+* fix a bug in threshold score display ([#592](https://github.com/microsoft/RD-Agent/issues/592)) ([0b0a2dc](https://github.com/microsoft/RD-Agent/commit/0b0a2dc512a5560a66464ad49de25d362d0dc17e))
+* fix a bug related to model_name in ensemble ([#692](https://github.com/microsoft/RD-Agent/issues/692)) ([c6ce473](https://github.com/microsoft/RD-Agent/commit/c6ce4733f32578298abe0b60f9d82611b793cc09))
+* fix a minor bug ([#694](https://github.com/microsoft/RD-Agent/issues/694)) ([1405d8d](https://github.com/microsoft/RD-Agent/commit/1405d8dafd99ecde6f3ba9dd76133d8830d03b47))
+* fix an error in model_coder prompt ([#690](https://github.com/microsoft/RD-Agent/issues/690)) ([4528826](https://github.com/microsoft/RD-Agent/commit/452882674e915dbd9e3399c26c70ce5bb86d012c))
+* fix combined_factors_df.pkl not loading in docker ([#697](https://github.com/microsoft/RD-Agent/issues/697)) ([3984b99](https://github.com/microsoft/RD-Agent/commit/3984b995aa74318b40de7712e100d4de5cc95b11))
+* fix docs build error ([#711](https://github.com/microsoft/RD-Agent/issues/711)) ([c9e1d32](https://github.com/microsoft/RD-Agent/commit/c9e1d32d6b63560350cc7cb799c3a908e2c04e42))
+* fix ExtendedSettingsConfigDict does not work ([#660](https://github.com/microsoft/RD-Agent/issues/660)) ([3a877f3](https://github.com/microsoft/RD-Agent/commit/3a877f383b908da8d027560714030b201946bb76))
+* fix kaggle templates path error ([#747](https://github.com/microsoft/RD-Agent/issues/747)) ([3b3f504](https://github.com/microsoft/RD-Agent/commit/3b3f5041514baf741fe2d4613fa651fb5d9c002d))
+* fix KeyError direct_exp_gen ([#735](https://github.com/microsoft/RD-Agent/issues/735)) ([7200682](https://github.com/microsoft/RD-Agent/commit/7200682ac4e60d3910c29a4f7c4a37b3d24e4224))
+* fix some bugs (ensemble output, HPO, model tuning) ([#648](https://github.com/microsoft/RD-Agent/issues/648)) ([818ee29](https://github.com/microsoft/RD-Agent/commit/818ee29f8e5d4765b9801463b85b42ee9516ec33))
+* fix some bugs in the ensemble component ([#595](https://github.com/microsoft/RD-Agent/issues/595)) ([c0990ab](https://github.com/microsoft/RD-Agent/commit/c0990abb06c73ae062d9a50f50cdfd6d04aded22))
+* fix some bugs in workflow unit test ([#624](https://github.com/microsoft/RD-Agent/issues/624)) ([f845dcc](https://github.com/microsoft/RD-Agent/commit/f845dcc0ee1b059b8b32485ad46bb90c7ae0fa78))
+* fix some description errors in direct_exp_gen ([#698](https://github.com/microsoft/RD-Agent/issues/698)) ([dfaacb6](https://github.com/microsoft/RD-Agent/commit/dfaacb6d06e5d5f55e950d7177570d1efebf958f))
+* fix some minor bugs and add AutoML & cross-validation ([#604](https://github.com/microsoft/RD-Agent/issues/604)) ([18c5ef2](https://github.com/microsoft/RD-Agent/commit/18c5ef268d40efe7bb9ee18aa0d250732bdda6fa))
+* fix submission file search and add TODO in env.py ([#544](https://github.com/microsoft/RD-Agent/issues/544)) ([54d930e](https://github.com/microsoft/RD-Agent/commit/54d930e91e629f0fc2f8bdd0d0d62fcad1e99a9c))
+* fix task return dict with wrong format ([#558](https://github.com/microsoft/RD-Agent/issues/558)) ([2008244](https://github.com/microsoft/RD-Agent/commit/20082440a249dd0e5a7026c2d98c9de0288dd400))
+* fix the errors in the coder and evaluator of the five components ([#576](https://github.com/microsoft/RD-Agent/issues/576)) ([c487f83](https://github.com/microsoft/RD-Agent/commit/c487f835b651cdc40b95bbbe4efcb9a617be9e40))
+* handle division by zero in percentage calculations ([#550](https://github.com/microsoft/RD-Agent/issues/550)) ([de16c91](https://github.com/microsoft/RD-Agent/commit/de16c915e1716ef8cee43ce41069ea1a09cf1f24))
+* handle invalid regex patterns in filter_progress_bar function ([#579](https://github.com/microsoft/RD-Agent/issues/579)) ([b0daee0](https://github.com/microsoft/RD-Agent/commit/b0daee0d90e193ca1d028e01c31ebf368af89601))
+* Handle ValueError when resolving relative path for uri ([#585](https://github.com/microsoft/RD-Agent/issues/585)) ([4c7765a](https://github.com/microsoft/RD-Agent/commit/4c7765a12bda5dcfd9af72b292853d9bc28c5baf))
+* include data information in cache key generation ([#566](https://github.com/microsoft/RD-Agent/issues/566)) ([26dda46](https://github.com/microsoft/RD-Agent/commit/26dda4682b7b643c164589057cb568a4d9e55e17))
+* keep some txt files ([#557](https://github.com/microsoft/RD-Agent/issues/557)) ([54aba85](https://github.com/microsoft/RD-Agent/commit/54aba851c9fa194e318d37700307df59e06c6c84))
+* mle_score save problem ([#674](https://github.com/microsoft/RD-Agent/issues/674)) ([ca2e478](https://github.com/microsoft/RD-Agent/commit/ca2e478cf25c2c8511d5f027e32f8a98fc8e3a07))
+* move docker timeout message to __run() ([#620](https://github.com/microsoft/RD-Agent/issues/620)) ([585f4f9](https://github.com/microsoft/RD-Agent/commit/585f4f96e09f70d00eb397c10bf49c09973111df))
+* move mlebench check into runner ([#556](https://github.com/microsoft/RD-Agent/issues/556)) ([b0f7965](https://github.com/microsoft/RD-Agent/commit/b0f7965f650638273710302efee2e5da037368a2))
+* move next_component_required logic to DSTrace class and accurate implement ([#612](https://github.com/microsoft/RD-Agent/issues/612)) ([c20d311](https://github.com/microsoft/RD-Agent/commit/c20d311792f33b2ccccb466c6ec3155ff8be3213))
+* patching weird azure deployment ([#494](https://github.com/microsoft/RD-Agent/issues/494)) ([89c50ae](https://github.com/microsoft/RD-Agent/commit/89c50aee2ec8bfd1cb23767ddf7dcdd023daac8b))
+* qlib and other scenario bugs ([#636](https://github.com/microsoft/RD-Agent/issues/636)) ([98de31d](https://github.com/microsoft/RD-Agent/commit/98de31d4e577c8c450c9694f73a755c19af571f7))
+* refine prompt to generate the most simple task in init stage ([#546](https://github.com/microsoft/RD-Agent/issues/546)) ([9d6feed](https://github.com/microsoft/RD-Agent/commit/9d6feed28ce034db48482d8d9741ef8c72f4bddc))
+* replace API call with build_cls_from_json_with_retry function ([#548](https://github.com/microsoft/RD-Agent/issues/548)) ([eb72a47](https://github.com/microsoft/RD-Agent/commit/eb72a47fbf9c88dacea9691b8d7e92610492d190))
+* replace func "len()" in ensemble test code to support various data type ([#739](https://github.com/microsoft/RD-Agent/issues/739)) ([ab9c7b9](https://github.com/microsoft/RD-Agent/commit/ab9c7b955f78c5de7ec08a6c1a012a76badbdd0e))
+* return 1D embedding if create_embedding receive a string input ([#670](https://github.com/microsoft/RD-Agent/issues/670)) ([4a9c318](https://github.com/microsoft/RD-Agent/commit/4a9c3180ae4a4b043b1b4a89f51ee69cb6843142))
+* rich.print error when some control char in output ([#684](https://github.com/microsoft/RD-Agent/issues/684)) ([ec0cb2a](https://github.com/microsoft/RD-Agent/commit/ec0cb2a032824023dcd04a3acc93202471d1f90a))
+* Runnable on first complete & Rename method to next_incomplete_component for clarity ([#615](https://github.com/microsoft/RD-Agent/issues/615)) ([93d9f63](https://github.com/microsoft/RD-Agent/commit/93d9f63369a78f78e1a67ab548923bb994d1d3b4))
+* runner COSTEER evaluator ([#693](https://github.com/microsoft/RD-Agent/issues/693)) ([6a379ec](https://github.com/microsoft/RD-Agent/commit/6a379ec9b84d4e4944f1e412347aae4f5a93d476))
+* save only one mle_score pkl for a running exp ([#675](https://github.com/microsoft/RD-Agent/issues/675)) ([f87ab67](https://github.com/microsoft/RD-Agent/commit/f87ab676b73cce82bd9f997ac779e31c571b53c4))
+* Set default value for 'entry' parameter in Env.run method ([#643](https://github.com/microsoft/RD-Agent/issues/643)) ([e50d242](https://github.com/microsoft/RD-Agent/commit/e50d2424b849e4181d6ca02e9cace90236665924))
+* sort file name for cache reproduction ([#588](https://github.com/microsoft/RD-Agent/issues/588)) ([7158410](https://github.com/microsoft/RD-Agent/commit/7158410fbfdd84052f9a69cf1e04e09ac07ca598))
+* sota comparison logic ([#608](https://github.com/microsoft/RD-Agent/issues/608)) ([3575372](https://github.com/microsoft/RD-Agent/commit/35753722c0800d62855faeab996d513e62cfe7de))
+* target json type & round ([#662](https://github.com/microsoft/RD-Agent/issues/662)) ([58cb58f](https://github.com/microsoft/RD-Agent/commit/58cb58f966a1db26f5ea9662a54ba12bc921ee24))
+* templates bug ([#456](https://github.com/microsoft/RD-Agent/issues/456)) ([434a868](https://github.com/microsoft/RD-Agent/commit/434a8687eeda77e27b4938fb19694c15858ee446))
+* trace summary df showing in dsapp ([#551](https://github.com/microsoft/RD-Agent/issues/551)) ([177096d](https://github.com/microsoft/RD-Agent/commit/177096d55fecb8c7dab9650ef8f5a31024cd4c1c))
+* unzip kaggle data ([#464](https://github.com/microsoft/RD-Agent/issues/464)) ([3a9fc8e](https://github.com/microsoft/RD-Agent/commit/3a9fc8e73337d3757267b6f4482499499a1b6792))
+
+## [0.3.0](https://github.com/microsoft/RD-Agent/compare/v0.2.1...v0.3.0) (2024-10-21)
+
+
+### Features
+
+* add a new template for kaggle ([#289](https://github.com/microsoft/RD-Agent/issues/289)) ([eee3ab5](https://github.com/microsoft/RD-Agent/commit/eee3ab5b25198224826cb7a8a17eab28bd5d1f7d))
+* add download submission.csv button for kaggle scenario ([#317](https://github.com/microsoft/RD-Agent/issues/317)) ([dcdcbe4](https://github.com/microsoft/RD-Agent/commit/dcdcbe46b4858bfb133ae3cca056e7f602d5cf63))
+* add kaggle command ([#271](https://github.com/microsoft/RD-Agent/issues/271)) ([0938394](https://github.com/microsoft/RD-Agent/commit/0938394b7084ffbf3294d8c23d2d34bf7322ca0b))
+* add kaggle tpl: feedback-prize ([#331](https://github.com/microsoft/RD-Agent/issues/331)) ([a288e39](https://github.com/microsoft/RD-Agent/commit/a288e399e6b0beec62729bd7d46b98a55de5ab79))
+* add more templates for kaggle ([#291](https://github.com/microsoft/RD-Agent/issues/291)) ([da752ec](https://github.com/microsoft/RD-Agent/commit/da752ec806e6f5f5679bc27ac1c072ed9a319251))
+* add normal rag into framework ([#360](https://github.com/microsoft/RD-Agent/issues/360)) ([91b0b1f](https://github.com/microsoft/RD-Agent/commit/91b0b1f66c3c1bf757cb64c4cfbdcaafe59eab74))
+* add qlib_factor_strategy ([#307](https://github.com/microsoft/RD-Agent/issues/307)) ([f8f59ff](https://github.com/microsoft/RD-Agent/commit/f8f59ff0a1be4428a68c8c27f220aabad0b6c9f0))
+* Add ranking in kaggle scenario ([#401](https://github.com/microsoft/RD-Agent/issues/401)) ([b16b4be](https://github.com/microsoft/RD-Agent/commit/b16b4beb402e0c27dfb39ee9d2a120f1b56d447c))
+* Add runtime measurement for each step and loop in RDLoop. ([#281](https://github.com/microsoft/RD-Agent/issues/281)) ([83058c8](https://github.com/microsoft/RD-Agent/commit/83058c864ceeec413dd29bf501030d5a7bd34679))
+* add s3e11 kaggle template ([#324](https://github.com/microsoft/RD-Agent/issues/324)) ([8c57524](https://github.com/microsoft/RD-Agent/commit/8c57524bead1c8f655a08763d608eb7a6dd5975e))
+* Added RepoAnalyzer to empower auto-summary of a workspace ([#264](https://github.com/microsoft/RD-Agent/issues/264)) ([0bd349a](https://github.com/microsoft/RD-Agent/commit/0bd349af50b9b881ba1774bdeb4d723529ef2aa9))
+* Added support for loading and storing RAG in Kaggle scenarios. ([#269](https://github.com/microsoft/RD-Agent/issues/269)) ([c4895de](https://github.com/microsoft/RD-Agent/commit/c4895de83f1ed000e563d42b3468a6bd9e5a4965))
+* announce Discord and WeChat ([#367](https://github.com/microsoft/RD-Agent/issues/367)) ([acac507](https://github.com/microsoft/RD-Agent/commit/acac5078a103b71afa6bd6c053b0766a6a7e609d))
+* auto submit result after one kaggle RDLoop ([#345](https://github.com/microsoft/RD-Agent/issues/345)) ([ab55d70](https://github.com/microsoft/RD-Agent/commit/ab55d7052b53a928b84dc5d5d0d2999d90ca9056))
+* better feedback & evaluation ([#346](https://github.com/microsoft/RD-Agent/issues/346)) ([cc9a8c1](https://github.com/microsoft/RD-Agent/commit/cc9a8c1eab3ca89f8c1e5de4a2bb4e7fcc0cc615))
+* Dynamic scenario based on task ([#392](https://github.com/microsoft/RD-Agent/issues/392)) ([665a037](https://github.com/microsoft/RD-Agent/commit/665a037e4fd7326c450e3fa0d0605eea26fd9ef3))
+* Factor Implement Search Enhancement ([#294](https://github.com/microsoft/RD-Agent/issues/294)) ([4ecf25f](https://github.com/microsoft/RD-Agent/commit/4ecf25f0acf2389a172b14d3dab20895daf2ab89))
+* Feature selection v3 to support all actions  ([#280](https://github.com/microsoft/RD-Agent/issues/280)) ([0047641](https://github.com/microsoft/RD-Agent/commit/00476413fbf00e36e71ab3ccb48d4e766b6ccf4d))
+* fix some bugs and add original features' description ([#259](https://github.com/microsoft/RD-Agent/issues/259)) ([1a5f45a](https://github.com/microsoft/RD-Agent/commit/1a5f45a40d821c017bdba14af8c93710707c5ea5))
+* get kaggle notebooks & disscussion text for RAG ([#371](https://github.com/microsoft/RD-Agent/issues/371)) ([cead345](https://github.com/microsoft/RD-Agent/commit/cead3450a14bf4b142ac988c27fa098c7656a95c))
+* Iceberge competition ([#372](https://github.com/microsoft/RD-Agent/issues/372)) ([c10ea4f](https://github.com/microsoft/RD-Agent/commit/c10ea4f5d4cc56a75b47cf23c7084ee189ba1a25))
+* implement isolated model feature selection loop ([#370](https://github.com/microsoft/RD-Agent/issues/370)) ([cf1292d](https://github.com/microsoft/RD-Agent/commit/cf1292de1a0153ca14ea64971e73a1c93f7d89e3))
+* Initial version if Graph RAG in KAGGLE scenario ([#301](https://github.com/microsoft/RD-Agent/issues/301)) ([fd3c0fd](https://github.com/microsoft/RD-Agent/commit/fd3c0fd26eff7d3be72fa4f2a234e33b9f796627))
+* Integrate RAG into the Kaggle scenarios. ([#262](https://github.com/microsoft/RD-Agent/issues/262)) ([be0e48a](https://github.com/microsoft/RD-Agent/commit/be0e48a7dfbee2b5d2947d09115db5db2e5266f1))
+* Kaggle loop update (Feature & Model) ([#241](https://github.com/microsoft/RD-Agent/issues/241)) ([4cf22a6](https://github.com/microsoft/RD-Agent/commit/4cf22a65c964123b4267569ee02c0c7094c54ca4))
+* kaggle templates related ([#287](https://github.com/microsoft/RD-Agent/issues/287)) ([785fdc1](https://github.com/microsoft/RD-Agent/commit/785fdc144d16fa8454b7c9d2e53e78fe7f22a29a))
+* Model context for tuning and selection ([#284](https://github.com/microsoft/RD-Agent/issues/284)) ([f2831e7](https://github.com/microsoft/RD-Agent/commit/f2831e7442510668b0ca75953b3359894803ef3c))
+* Modify FactorRowCountEvaluator and FactorIndexEvaluator to return the ratio ([#328](https://github.com/microsoft/RD-Agent/issues/328)) ([8f43f8e](https://github.com/microsoft/RD-Agent/commit/8f43f8e87a92e05b541e925910608606ec8f6c4b))
+* New competition - Optiver ([#356](https://github.com/microsoft/RD-Agent/issues/356)) ([3705efe](https://github.com/microsoft/RD-Agent/commit/3705efe3b923748655a57d76b7a236e54d361831))
+* random forest for s3e11 ([#347](https://github.com/microsoft/RD-Agent/issues/347)) ([b57846d](https://github.com/microsoft/RD-Agent/commit/b57846d29314e9a5967945d1b4895f0f48c0f5ce))
+* refine the code in model description and fix some bugs in feedback.py ([#288](https://github.com/microsoft/RD-Agent/issues/288)) ([5b124d7](https://github.com/microsoft/RD-Agent/commit/5b124d7372137e4c613eb2749ddcc773922cc7b6))
+* refine the template in several Kaggle competitions ([#343](https://github.com/microsoft/RD-Agent/issues/343)) ([034f238](https://github.com/microsoft/RD-Agent/commit/034f238ed5ec351486b21250eabc75114961936c))
+* Revise to support better hypothesis proposal  ([#390](https://github.com/microsoft/RD-Agent/issues/390)) ([c55ec0a](https://github.com/microsoft/RD-Agent/commit/c55ec0a0f577bbf7fc6228f7b87d2089ded83b31))
+* show workspace in demo ([#348](https://github.com/microsoft/RD-Agent/issues/348)) ([ddf567c](https://github.com/microsoft/RD-Agent/commit/ddf567c551b553788be022e9312c209ef6137d64))
+* support Multi output ([#330](https://github.com/microsoft/RD-Agent/issues/330)) ([3d36c45](https://github.com/microsoft/RD-Agent/commit/3d36c452ff0983800e5343834cc69f24a508ea70))
+* Supporting COVID-19 competition ([#374](https://github.com/microsoft/RD-Agent/issues/374)) ([a1b63db](https://github.com/microsoft/RD-Agent/commit/a1b63db79600edc9a74ba713c9d0be290214a592))
+* supporting Mnist competition ([#375](https://github.com/microsoft/RD-Agent/issues/375)) ([e958a34](https://github.com/microsoft/RD-Agent/commit/e958a34f5632a46ac43bff8e0d07d6ed020fdfc2))
+* Supporting Model Specifications ([#319](https://github.com/microsoft/RD-Agent/issues/319)) ([e126471](https://github.com/microsoft/RD-Agent/commit/e1264719e10b76158a91cd0ef331848e7c2de7c7))
+* supporting various Kaggle competitions & scenarios for RD-Agent ([#409](https://github.com/microsoft/RD-Agent/issues/409)) ([75eea22](https://github.com/microsoft/RD-Agent/commit/75eea22cc3d4e6f5a94c88cce915e27c507f8c50))
+* template for kaggle ([#308](https://github.com/microsoft/RD-Agent/issues/308)) ([ff97cf0](https://github.com/microsoft/RD-Agent/commit/ff97cf0155ab6941e4b5cf7d103575f934b70dc9))
+* use auto gen seed when using LLM cache ([#441](https://github.com/microsoft/RD-Agent/issues/441)) ([ca15365](https://github.com/microsoft/RD-Agent/commit/ca15365d23eeb094f42cf3dc8f5269b2f1c42bd3))
+* use unified pickle cacher & move llm config into a isolated config ([#424](https://github.com/microsoft/RD-Agent/issues/424)) ([2879ecf](https://github.com/microsoft/RD-Agent/commit/2879ecff816d97688b60909a79c7e568d42608a1))
+* xgboost gpu accelerate ([#359](https://github.com/microsoft/RD-Agent/issues/359)) ([56a5b8f](https://github.com/microsoft/RD-Agent/commit/56a5b8f9b2c6726cc64ec5b04b4ce7935d59b572))
+
+
+### Bug Fixes
+
+* a bug of developer& edit s4e8 template ([#338](https://github.com/microsoft/RD-Agent/issues/338)) ([f12ce72](https://github.com/microsoft/RD-Agent/commit/f12ce726e7de96d478a232a3c27f92439820f8b4))
+* actively raised errors aer also considered as negative feedback. ([#268](https://github.com/microsoft/RD-Agent/issues/268)) ([46ec908](https://github.com/microsoft/RD-Agent/commit/46ec908e3594ac5e4cdc4057268e2f8800f5ed1f))
+* bug of saving preprocess cache files ([#310](https://github.com/microsoft/RD-Agent/issues/310)) ([5fb0608](https://github.com/microsoft/RD-Agent/commit/5fb0608f39f113cc9807fb1f381284a0bd4da318))
+* cache ([#383](https://github.com/microsoft/RD-Agent/issues/383)) ([f2a6e75](https://github.com/microsoft/RD-Agent/commit/f2a6e75b36ca96f7733b9c2a7154ac67bd2d7c6f))
+* change css tag of kaggle competition info crawler ([#306](https://github.com/microsoft/RD-Agent/issues/306)) ([1e3d38b](https://github.com/microsoft/RD-Agent/commit/1e3d38bf1ca3654f3a90ff392ecba1dbb4e80224))
+* debug dsagent ([#387](https://github.com/microsoft/RD-Agent/issues/387)) ([8fe9511](https://github.com/microsoft/RD-Agent/commit/8fe9511e606ba148c66f384add6ab94857079541))
+* eval_method cannot catch run factor error ([#260](https://github.com/microsoft/RD-Agent/issues/260)) ([2aaab31](https://github.com/microsoft/RD-Agent/commit/2aaab317ccb7a0121063bcd85fc36c21c7b8a391))
+* fix a bug in competition metric evaluation ([#407](https://github.com/microsoft/RD-Agent/issues/407)) ([94c47d6](https://github.com/microsoft/RD-Agent/commit/94c47d6fd5c3e38fc786a83e6d0d05e8d04498f3))
+* fix a bug in mini case ([#389](https://github.com/microsoft/RD-Agent/issues/389)) ([e75bb57](https://github.com/microsoft/RD-Agent/commit/e75bb5746f63933b750406bbd34ee63c5ba76b9f))
+* fix a bug in model tuning feedback ([#316](https://github.com/microsoft/RD-Agent/issues/316)) ([8aa088d](https://github.com/microsoft/RD-Agent/commit/8aa088da2dc7525a3970c01d01987246f47d6238))
+* fix a bug in scenario.py ([#388](https://github.com/microsoft/RD-Agent/issues/388)) ([999a1eb](https://github.com/microsoft/RD-Agent/commit/999a1eb0eff9088e1b02419db741db4acf8d9ff7))
+* fix a bug in the format of the model input ([#327](https://github.com/microsoft/RD-Agent/issues/327)) ([8f0574e](https://github.com/microsoft/RD-Agent/commit/8f0574eaaadb245b8c38e09ad4821306996d926f))
+* fix a small bug in cache using module name and function name as unique folder name ([#429](https://github.com/microsoft/RD-Agent/issues/429)) ([4f8134a](https://github.com/microsoft/RD-Agent/commit/4f8134a697d952f7ac824d7ebeec64bbc4545ab3))
+* fix a typo ([#362](https://github.com/microsoft/RD-Agent/issues/362)) ([9fafabd](https://github.com/microsoft/RD-Agent/commit/9fafabdf321b818bdd2211a2324d50cd0ebe1c1f))
+* fix cache result logic ([#430](https://github.com/microsoft/RD-Agent/issues/430)) ([5e34263](https://github.com/microsoft/RD-Agent/commit/5e342637dcc862679fd0642c6ba9ef048c984845))
+* fix command injection ([#421](https://github.com/microsoft/RD-Agent/issues/421)) ([52f30a6](https://github.com/microsoft/RD-Agent/commit/52f30a6184af1295be15e855a80b84bc424fc75d))
+* fix json load error ([#386](https://github.com/microsoft/RD-Agent/issues/386)) ([bba55fb](https://github.com/microsoft/RD-Agent/commit/bba55fb48fe105f4847c1b9c476eedc80835f523))
+* fix some bugs in feedback.py and refine the prompt ([#292](https://github.com/microsoft/RD-Agent/issues/292)) ([d834052](https://github.com/microsoft/RD-Agent/commit/d8340527f133dcc649d599d90d6402eddd37859e))
+* fix some bugs in knowledge base ([#378](https://github.com/microsoft/RD-Agent/issues/378)) ([fa6ff8e](https://github.com/microsoft/RD-Agent/commit/fa6ff8e591cf1847df77d73116649c5623161573))
+* fix some bugs in rag ([#399](https://github.com/microsoft/RD-Agent/issues/399)) ([194215c](https://github.com/microsoft/RD-Agent/commit/194215c4559aee5b6ece18d65c95fb30968e2db6))
+* fix some bugs in the entire loop ([#274](https://github.com/microsoft/RD-Agent/issues/274)) ([8a564ec](https://github.com/microsoft/RD-Agent/commit/8a564ece1d87b27ee98b76db317935e802468965))
+* fix some errors in scenario.py, proposal.py and runner.py and several complex competition scenarios([#365](https://github.com/microsoft/RD-Agent/issues/365)) ([2e383b1](https://github.com/microsoft/RD-Agent/commit/2e383b175d8448a67cb470f4e3ae8977d8ec6b5b))
+* improve_execution_time_in_kaggle_loop ([#279](https://github.com/microsoft/RD-Agent/issues/279)) ([4c8f998](https://github.com/microsoft/RD-Agent/commit/4c8f998c76f1e983a5687d2c65d3251750f2a9a0))
+* kaggle data mount problem ([#297](https://github.com/microsoft/RD-Agent/issues/297)) ([795df31](https://github.com/microsoft/RD-Agent/commit/795df311e3f93cd2f3fb51ba5698adaf10f6bd62))
+* Optiver fixes ([#357](https://github.com/microsoft/RD-Agent/issues/357)) ([b054017](https://github.com/microsoft/RD-Agent/commit/b054017463af0d1784407030f2477d212118f341))
+* partial bug in bench ([#368](https://github.com/microsoft/RD-Agent/issues/368)) ([af9808f](https://github.com/microsoft/RD-Agent/commit/af9808f98736a2df07e121c2f6d7bfeb7b7d3581))
+* preprocess output format & some mistake in spelling ([#358](https://github.com/microsoft/RD-Agent/issues/358)) ([b8b2cd6](https://github.com/microsoft/RD-Agent/commit/b8b2cd6ccd3b27aa73de847e50899a8a53b71b8f))
+* rag save file ([#385](https://github.com/microsoft/RD-Agent/issues/385)) ([1cb01dd](https://github.com/microsoft/RD-Agent/commit/1cb01dd6fe595f2f5fb86487601326611dd1a57a))
+* raise error in demo when no Metric in a Loop ([#313](https://github.com/microsoft/RD-Agent/issues/313)) ([e46a78e](https://github.com/microsoft/RD-Agent/commit/e46a78eb69271cb19978aab2f3b976c2870ca082))
+* refactor Bench ([#302](https://github.com/microsoft/RD-Agent/issues/302)) ([78a87f6](https://github.com/microsoft/RD-Agent/commit/78a87f624780ff67c0fa995ae4692678a120f99c))
+* refine some codes ([#353](https://github.com/microsoft/RD-Agent/issues/353)) ([866c2e6](https://github.com/microsoft/RD-Agent/commit/866c2e63ffa3876a3d16ad37f96da41d0558b714))
+* refine the prompt ([#286](https://github.com/microsoft/RD-Agent/issues/286)) ([77966c4](https://github.com/microsoft/RD-Agent/commit/77966c4f5e9f492c437c5b4b78d89c0f875ef0d8))
+* refine the ucb algorithm ([#406](https://github.com/microsoft/RD-Agent/issues/406)) ([14f7d97](https://github.com/microsoft/RD-Agent/commit/14f7d976e03c92d6e727524e0cdad8a03b585016))
+* revert model and make SOTA model available to COSTEER ([#351](https://github.com/microsoft/RD-Agent/issues/351)) ([3b7437b](https://github.com/microsoft/RD-Agent/commit/3b7437b87e685188259779cd85a78a0b592de9de))
+* stop using markup in docker env print ([#336](https://github.com/microsoft/RD-Agent/issues/336)) ([3009889](https://github.com/microsoft/RD-Agent/commit/3009889b5e2605b5427c76f3084e0e58026bb5ae))
+* support seed and fix absolute path ([#278](https://github.com/microsoft/RD-Agent/issues/278)) ([26352e1](https://github.com/microsoft/RD-Agent/commit/26352e13121cad5be95c0de78bb9f5dda4330614))
+* template for kaggle foreset & s4e9 ([#334](https://github.com/microsoft/RD-Agent/issues/334)) ([2393a41](https://github.com/microsoft/RD-Agent/commit/2393a41e7237615ced2c3fdd5c49308236b9f276))
+* test kaggle method ([#296](https://github.com/microsoft/RD-Agent/issues/296)) ([91a6196](https://github.com/microsoft/RD-Agent/commit/91a619618be1d7db660ea2b413a78dfaba9417a1))
+* update code to fix a small bug in model cache md5 hash ([#303](https://github.com/microsoft/RD-Agent/issues/303)) ([b00e4dc](https://github.com/microsoft/RD-Agent/commit/b00e4dc2eff5b16029a2a12a6589eadac5cfd148))
+* update new feature engineering code format ([#272](https://github.com/microsoft/RD-Agent/issues/272)) ([7850b80](https://github.com/microsoft/RD-Agent/commit/7850b8006a7c89d22629b345b4f361b0f35bc60d))
+* Update prompts.yaml to constrain only one model type   ([#341](https://github.com/microsoft/RD-Agent/issues/341)) ([5b5dfee](https://github.com/microsoft/RD-Agent/commit/5b5dfeefbc7eb9dcbd9923544005c5d281262c03))
+* Update runner.py to fix a small bug ([#282](https://github.com/microsoft/RD-Agent/issues/282)) ([8aef3ab](https://github.com/microsoft/RD-Agent/commit/8aef3abcecd6002bd4bfeedcbe2c786d8bbfe2be))
+* Use fixed file name in model costeer & fixing cache ([#311](https://github.com/microsoft/RD-Agent/issues/311)) ([1f910a5](https://github.com/microsoft/RD-Agent/commit/1f910a5248bc576895ed66c2f7b2c3e046a2bc28))
+
+
+### Performance Improvements
+
+* some small upgrade to factor costeer to improve the performance ([#420](https://github.com/microsoft/RD-Agent/issues/420)) ([9eb931f](https://github.com/microsoft/RD-Agent/commit/9eb931ffd971f252380dbd33ad1db259a4f229fd))
+
+
+### Reverts
+
+* Revert feat: Factor Implement Search Enhancement ([#294](https://github.com/microsoft/RD-Agent/issues/294)) ([#305](https://github.com/microsoft/RD-Agent/issues/305)) ([f663cf4](https://github.com/microsoft/RD-Agent/commit/f663cf42a2f75cd52aef1c6b18be7c27f0641fed))
+
+## [0.2.1](https://github.com/microsoft/RD-Agent/compare/v0.2.0...v0.2.1) (2024-09-10)
+
+
+### Bug Fixes
+
+* default model value in config ([#256](https://github.com/microsoft/RD-Agent/issues/256)) ([c097585](https://github.com/microsoft/RD-Agent/commit/c097585f631f401c2c0966f6ad4c17286924f011))
+* fix_dotenv_error ([#257](https://github.com/microsoft/RD-Agent/issues/257)) ([923063c](https://github.com/microsoft/RD-Agent/commit/923063c1fd957c4ed42e97272c72b5e9545451dc))
+* readme ([#248](https://github.com/microsoft/RD-Agent/issues/248)) ([8cede22](https://github.com/microsoft/RD-Agent/commit/8cede2209922876490148459e1134da828e1fda0))
+
 ## [0.2.0](https://github.com/microsoft/RD-Agent/compare/v0.1.0...v0.2.0) (2024-09-07)
 
 

CONTRIBUTING.md

@@ -0,0 +1,50 @@
+# Contributing to RD-Agent
+
+We welcome contributions and suggestions to improve RD-Agent. Whether it's solving an issue, addressing a bug, enhancing documentation, or even correcting a typo, every contribution is valuable and helps improve the project.
+
+## Getting Started
+
+To get started, you can explore the issues list or search for `TODO:` comments in the codebase by running the command:
+```sh
+grep -r "TODO:"
+```
+
+## How to Contribute
+
+1. **Fork the Repository**: Create a fork of the repository on GitHub.
+2. **Clone the Repository**: Clone your forked repository to your local machine.
+   ```sh
+   git clone https://github.com/your-username/RD-Agent.git
+   ```
+3. **Create a Branch**: Create a new branch for your changes.
+   ```sh
+   git checkout -b feature/your-feature-name
+   ```
+4. **Make Changes**: Make your changes to the codebase.
+5. **Commit Changes**: Commit your changes with a descriptive commit message.
+   ```sh
+   git commit -m "Description of your changes"
+   ```
+6. **Push Changes**: Push your changes to your forked repository.
+   ```sh
+   git push origin feature/your-feature-name
+   ```
+7. **Ensure CI Passes**: Make sure your code passes the automatic CI checks on GitHub.
+8. **Create a Pull Request**: Create a pull request from your forked repository to the main repository.
+
+## Code of Conduct
+
+Please adhere to the [Code of Conduct](CODE_OF_CONDUCT.md) in all your interactions with the project.
+
+## Reporting Issues
+
+If you encounter any issues or have suggestions for improvements, please open an issue on GitHub.
+
+## Guidelines
+
+- Ensure your code follows the project's coding standards.
+- Write clear and concise commit messages.
+- Update documentation as needed.
+- Test your changes thoroughly before submitting a pull request.
+
+Thank you for contributing to RD-Agent!

Makefile

@@ -68,6 +68,7 @@ init-qlib-env:
 
 dev:
 	$(PIPRUN) pip install -e .[docs,lint,package,test] -c $(CONSTRAINTS_FILE)
+	$(PIPRUN) pip install -U kaggle
 	if [ "$(CI)" != "true" ] && command -v pre-commit > /dev/null 2>&1; then pre-commit install --hook-type pre-push; fi
 
 # Generate constraints for current Python version.
@@ -97,7 +98,7 @@ mypy:
 # First deal with the core folder, and then gradually increase the scope of detection,
 # and eventually realize the detection of the complete project.
 ruff:
-	$(PIPRUN) ruff check rdagent/core --ignore FBT001,FBT002   # --exclude rdagent/scripts,git_ignore_folder
+	$(PIPRUN) ruff check rdagent/core --ignore FBT001,FBT002,I001   # --exclude rdagent/scripts,git_ignore_folder
 
 # Check lint with toml-sort.
 toml-sort:

README.md

@@ -1,14 +1,14 @@
-<h2 align="center">
+<h4 align="center">
   <img src="docs/_static/logo.png" alt="RA-Agent logo" style="width:70%; ">
+  
+  <a href="https://rdagent.azurewebsites.net" target="_blank">🖥️ Live Demo</a> | <a href="https://rdagent.azurewebsites.net/factor_loop" target="_blank">🎥 Demo Video</a> <a href="https://www.youtube.com/watch?v=JJ4JYO3HscM&list=PLALmKB0_N3_i52fhUmPQiL4jsO354uopR" target="_blank">▶️YouTube</a>   | <a href="https://rdagent.readthedocs.io/en/latest/index.html" target="_blank">📖 Documentation</a> | <a href="#-paperwork-list"> 📃 Papers </a>
+</h3>
 
-  <a href="https://rdagent.azurewebsites.net">🖥️ Live Demo</a> | <a href="https://rdagent.azurewebsites.net/factor_loop">🎥 Demo Video</a>  | <a href="https://rdagent.readthedocs.io/en/latest/index.html">📖 Documentation</a>
-</h2>
 
 [![CI](https://github.com/microsoft/RD-Agent/actions/workflows/ci.yml/badge.svg)](https://github.com/microsoft/RD-Agent/actions/workflows/ci.yml)
 [![CodeQL](https://github.com/microsoft/RD-Agent/actions/workflows/github-code-scanning/codeql/badge.svg)](https://github.com/microsoft/RD-Agent/actions/workflows/github-code-scanning/codeql)
 [![Dependabot Updates](https://github.com/microsoft/RD-Agent/actions/workflows/dependabot/dependabot-updates/badge.svg)](https://github.com/microsoft/RD-Agent/actions/workflows/dependabot/dependabot-updates)
 [![Lint PR Title](https://github.com/microsoft/RD-Agent/actions/workflows/pr.yml/badge.svg)](https://github.com/microsoft/RD-Agent/actions/workflows/pr.yml)
-[![Readthedocs Preview](https://github.com/microsoft/RD-Agent/actions/workflows/readthedocs-preview.yml/badge.svg)](https://github.com/microsoft/RD-Agent/actions/workflows/readthedocs-preview.yml)
 [![Release.yml](https://github.com/microsoft/RD-Agent/actions/workflows/release.yml/badge.svg)](https://github.com/microsoft/RD-Agent/actions/workflows/release.yml)
 [![Platform](https://img.shields.io/badge/platform-Linux-blue)](https://pypi.org/project/rdagent/#files)
 [![PyPI](https://img.shields.io/pypi/v/rdagent)](https://pypi.org/project/rdagent/)
@@ -18,11 +18,24 @@
 [![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit)](https://github.com/pre-commit/pre-commit)
 [![Checked with mypy](https://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)
 [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
+[![Chat](https://img.shields.io/badge/chat-discord-blue)](https://discord.gg/ybQ97B6Jjy)
+[![Documentation Status](https://readthedocs.org/projects/rdagent/badge/?version=latest)](https://rdagent.readthedocs.io/en/latest/?badge=latest)
+[![Readthedocs Preview](https://github.com/microsoft/RD-Agent/actions/workflows/readthedocs-preview.yml/badge.svg)](https://github.com/microsoft/RD-Agent/actions/workflows/readthedocs-preview.yml) <!-- this badge is too long, please place it in the last one to make it pretty --> 
+
+# Data Science Agent Preview
+Check out our demo video showcasing the current progress of our Data Science Agent under development:
+
+https://github.com/user-attachments/assets/3eccbecb-34a4-4c81-bce4-d3f8862f7305
 
 # 📰 News
 | 🗞️ News        | 📝 Description                 |
-| --            | ------                        |
-| First release | **RDAgent** is released on Github |
+| --            | ------      |
+| Support LiteLLM Backend | We now fully support **[LiteLLM](https://github.com/BerriAI/litellm)** as a backend for integration with multiple LLM providers. |
+| More General Data Science Agent | 🚀Coming soon! |
+| Kaggle Scenario release | We release **[Kaggle Agent](https://rdagent.readthedocs.io/en/latest/scens/kaggle_agent.html)**, try the new features!                  |
+| Official WeChat group release  | We created a WeChat group, welcome to join! (🗪[QR Code](docs/WeChat_QR_code.jpg)) |
+| Official Discord release  | We launch our first chatting channel in Discord (🗪[![Chat](https://img.shields.io/badge/chat-discord-blue)](https://discord.gg/ybQ97B6Jjy)) |
+| First release | **RDAgent** is released on GitHub |
 
 
 # 🌟 Introduction
@@ -37,9 +50,10 @@ We believe that the automatic evolution of R&D will lead to solutions of signifi
 
 <!-- Tag Cloud -->
 R&D is a very general scenario. The advent of RDAgent can be your
-- 💰 **Automatic Quant Factory** [(🎥Demo Video)](https://rdagent.azurewebsites.net/factor_loop)
-- 🤖 **Data Mining Agent:** Iteratively proposing data [(🎥Demo Video)](https://rdagent.azurewebsites.net/dmm) & models [(🎥Demo Video)](https://rdagent.azurewebsites.net/model_loop) and implementing them by gaining knowledge from data.
-- 🦾 **Research Copilot:** Auto read research papers [(🎥Demo Video)](https://rdagent.azurewebsites.net/report_model) / financial reports [(🎥Demo Video)](https://rdagent.azurewebsites.net/report_factor) and implement model structures or building datasets.
+- 💰 **Automatic Quant Factory** ([🎥Demo Video](https://rdagent.azurewebsites.net/factor_loop)|[▶️YouTube](https://www.youtube.com/watch?v=X4DK2QZKaKY&t=6s))
+- 🤖 **Data Mining Agent:** Iteratively proposing data & models ([🎥Demo Video 1](https://rdagent.azurewebsites.net/model_loop)|[▶️YouTube](https://www.youtube.com/watch?v=dm0dWL49Bc0&t=104s)) ([🎥Demo Video 2](https://rdagent.azurewebsites.net/dmm)|[▶️YouTube](https://www.youtube.com/watch?v=VIaSTZuoZg4))  and implementing them by gaining knowledge from data.
+- 🦾 **Research Copilot:** Auto read research papers ([🎥Demo Video](https://rdagent.azurewebsites.net/report_model)|[▶️YouTube](https://www.youtube.com/watch?v=BiA2SfdKQ7o)) / financial reports ([🎥Demo Video](https://rdagent.azurewebsites.net/report_factor)|[▶️YouTube](https://www.youtube.com/watch?v=ECLTXVcSx-c)) and implement model structures or building datasets.
+- 🤖 **Kaggle Agent:** Auto Model Tuning and Feature Engineering([🎥Demo Video Coming Soon...]()) and implementing them to achieve more in competitions.
 - ...
 
 You can click the links above to view the demo. We're continuously adding more methods and scenarios to the project to enhance your R&D processes and boost productivity. 
@@ -59,6 +73,7 @@ You can try above demos by running the following command:
 
 ### 🐳 Docker installation.
 Users must ensure Docker is installed before attempting most scenarios. Please refer to the [official 🐳Docker page](https://docs.docker.com/engine/install/) for installation instructions.
+Ensure the current user can run Docker commands **without using sudo**. You can verify this by executing `docker run hello-world`.
 
 ### 🐍 Create a Conda Environment
 - Create a new conda environment with Python (3.10 and 3.11 are well-tested in our CI):
@@ -76,36 +91,85 @@ Users must ensure Docker is installed before attempting most scenarios. Please r
   pip install rdagent
   ```
 
+### 💊 Health check
+- rdagent provides a health check that currently checks two things.
+  - whether the docker installation was successful.
+  - whether the default port used by the [rdagent ui](https://github.com/microsoft/RD-Agent?tab=readme-ov-file#%EF%B8%8F-monitor-the-application-results) is occupied.
+  ```sh
+  rdagent health_check
+  ```
+
+
 ### ⚙️ Configuration
-- You have to config your GPT model in the `.env`
+- The demos requires following ability:
+  - ChatCompletion
+  - json_mode
+  - embedding query
+
+- For example: If you are using the `OpenAI API`, you have to configure your GPT model in the `.env` file like this.
   ```bash
   cat << EOF  > .env
-  OPENAI_API_KEY=<your_api_key>
+  OPENAI_API_KEY=<replace_with_your_openai_api_key>
   # EMBEDDING_MODEL=text-embedding-3-small
   CHAT_MODEL=gpt-4-turbo
   EOF
   ```
+- However, not every API services support these features by default. For example: `AZURE OpenAI`, you have to configure your GPT model in the `.env` file like this.
+  ```bash
+  cat << EOF  > .env
+  USE_AZURE=True
+  EMBEDDING_OPENAI_API_KEY=<replace_with_your_azure_openai_api_key>
+  EMBEDDING_AZURE_API_BASE=<replace_with_your_azure_endpoint>
+  EMBEDDING_AZURE_API_VERSION=<replace_with_the_version_of_your_azure_openai_api>
+  EMBEDDING_MODEL=text-embedding-3-small
+  CHAT_OPENAI_API_KEY=<replace_with_your_azure_openai_api_key>
+  CHAT_AZURE_API_BASE=<replace_with_your_azure_endpoint>
+  CHAT_AZURE_API_VERSION=<replace_with_the_version_of_your_azure_openai_api>
+  CHAT_MODEL=<replace_it_with_the_name_of_your_azure_chat_model>
+  EOF
+  ```
+
+- We now support LiteLLM as a backend for integration with multiple LLM providers. If you use LiteLLM Backend to use models, you can configure as follows:
+  ```bash
+  cat << EOF  > .env
+  BACKEND=rdagent.oai.backend.LiteLLMAPIBackend
+  # It can be modified to any model supported by LiteLLM.
+  CHAT_MODEL=gpt-4o
+  EMBEDDING_MODEL=text-embedding-3-small
+  # The backend api_key fully follow the convention of litellm.
+  OPENAI_API_KEY=<replace_with_your_openai_api_key>
+  ```
+  
+- For more configuration information, please refer to the [documentation](https://rdagent.readthedocs.io/en/latest/installation_and_configuration.html).
 
 ### 🚀 Run the Application
 
 The **[🖥️ Live Demo](https://rdagent.azurewebsites.net/)** is implemented by the following commands(each item represents one demo, you can select the one you prefer):
 
-- Run the **Automated Quantitative Trading & Iterative Factors Evolution**:  Qlib self-loop factor proposal and implementation application
+- Run the **Automated Quantitative Trading & Iterative Factors Evolution**:  [Qlib](http://github.com/microsoft/qlib) self-loop factor proposal and implementation application
   ```sh
   rdagent fin_factor
   ```
 
-- Run the **Automated Quantitative Trading & Iterative Model Evolution**: Qlib self-loop model proposal and implementation application
+- Run the **Automated Quantitative Trading & Iterative Model Evolution**: [Qlib](http://github.com/microsoft/qlib) self-loop model proposal and implementation application
   ```sh
   rdagent fin_model
   ```
 
 - Run the **Automated Medical Prediction Model Evolution**: Medical self-loop model proposal and implementation application
+  >(1) Apply for an account at [PhysioNet](https://physionet.org/). <br /> (2) Request access to FIDDLE preprocessed data: [FIDDLE Dataset](https://physionet.org/content/mimic-eicu-fiddle-feature/1.0.0/). <br />
+  (3) Place your username and password in `.env`.
+  ```bash
+  cat << EOF  >> .env
+  DM_USERNAME=<your_username>
+  DM_PASSWORD=<your_password>
+  EOF
+  ```
   ```sh
   rdagent med_model
   ```
 
-- Run the **Automated Quantitative Trading & Factors Extraction from Financial Reports**:  Run the Qlib factor extraction and implementation application based on financial reports
+- Run the **Automated Quantitative Trading & Factors Extraction from Financial Reports**:  Run the [Qlib](http://github.com/microsoft/qlib) factor extraction and implementation application based on financial reports
   ```sh
   # 1. Generally, you can run this scenario using the following command:
   rdagent fin_factor_report --report_folder=<Your financial reports folder path>
@@ -125,10 +189,48 @@ The **[🖥️ Live Demo](https://rdagent.azurewebsites.net/)** is implemented b
   rdagent general_model  "https://arxiv.org/pdf/2210.09789"
   ```
 
+- Run the **Automated Kaggle Model Tuning & Feature Engineering**:  self-loop model proposal and feature engineering implementation application <br />
+  > Using **sf-crime** *(San Francisco Crime Classification)* as an example. <br />
+  > 1. Register and login on the [Kaggle](https://www.kaggle.com/) website. <br />
+  > 2. Configuring the Kaggle API. <br />
+  > (1) Click on the avatar (usually in the top right corner of the page) -> `Settings` -> `Create New Token`, A file called `kaggle.json` will be downloaded. <br />
+  > (2) Move `kaggle.json` to `~/.config/kaggle/` <br />
+  > (3) Modify the permissions of the kaggle.json file. Reference command: `chmod 600 ~/.config/kaggle/kaggle.json` <br />
+  > 3. Join the competition: Click `Join the competition` -> `I Understand and Accept` at the bottom of the [competition details page](https://www.kaggle.com/competitions/sf-crime/data).
+  ```bash
+  # Generally, you can run the Kaggle competition program with the following command:
+  rdagent kaggle --competition <your competition name>
+  
+  # Specifically, you will need to first prepare some competition description files and configure the competition description file path, which you can follow for this specific example:
+  
+  # 1. Prepare the competition description files
+  wget https://github.com/SunsetWolf/rdagent_resource/releases/download/kaggle_data/kaggle_data.zip
+  unzip kaggle_data.zip -d git_ignore_folder/kaggle_data
+
+  # 2. Add the competition description file path to the `.env` file.
+  dotenv set KG_LOCAL_DATA_PATH "$(pwd)/git_ignore_folder/kaggle_data"
+
+  # 3. run the application
+  rdagent kaggle --competition sf-crime
+  ```
+  > **Description of the above example:** <br />
+  > - Kaggle competition data, contains two parts: competition description file (json file) and competition dataset (zip file). We prepare the competition description file for you, the competition dataset will be downloaded automatically when you run the program, as in the example. <br />
+  > - If you want to download the competition description file automatically, you need to install chromedriver, The instructions for installing chromedriver can be found in the [documentation](https://rdagent.readthedocs.io/en/latest/scens/kaggle_agent.html#example-guide). <br />
+  > - The **Competition List Available** can be found [here](https://rdagent.readthedocs.io/en/latest/scens/kaggle_agent.html#competition-list-available). <br />
+
 ### 🖥️ Monitor the Application Results
-- You can serve our demo app to monitor the RD loop by running the following command:
+- You can run the following command for our demo program to see the run logs.
+
   ```sh
-  rdagent ui --port 80 --log_dir <your log folder like "log/">
+  rdagent ui --port 19899 --log_dir <your log folder like "log/">
+  ```
+
+  **Note:** Although port 19899 is not commonly used, but before you run this demo, you need to check if port 19899 is occupied. If it is, please change it to another port that is not occupied.
+
+  You can check if a port is occupied by running the following command.
+
+  ```sh
+  rdagent health_check
   ```
 
 # 🏭 Scenarios
@@ -156,17 +258,15 @@ The supported scenarios are listed below:
 
 | Scenario/Target | Model Implementation                   | Data Building                                                                      |
 | --              | --                                     | --                                                                                 |
-| **💹 Finance**      | 🤖 [Iteratively Proposing Ideas & Evolving](https://rdagent.azurewebsites.net/model_loop) |  🤖 [Iteratively Proposing Ideas & Evolving](https://rdagent.azurewebsites.net/factor_loop)  <br/>   🦾 [Auto reports reading & implementation](https://rdagent.azurewebsites.net/report_factor)  |
-| **🩺 Medical**      | 🤖 [Iteratively Proposing Ideas & Evolving](https://rdagent.azurewebsites.net/dmm) | -                                                                                  |
-| **🏭 General**      | 🦾 [Auto paper reading & implementation](https://rdagent.azurewebsites.net/report_model)    | -                                                                                  |
+| **💹 Finance**      | 🤖 [Iteratively Proposing Ideas & Evolving](https://rdagent.azurewebsites.net/model_loop)[▶️YouTube](https://www.youtube.com/watch?v=dm0dWL49Bc0&t=104s) |  🤖 [Iteratively Proposing Ideas & Evolving](https://rdagent.azurewebsites.net/factor_loop) [▶️YouTube](https://www.youtube.com/watch?v=X4DK2QZKaKY&t=6s) <br/>   🦾 [Auto reports reading & implementation](https://rdagent.azurewebsites.net/report_factor)[▶️YouTube](https://www.youtube.com/watch?v=ECLTXVcSx-c)  |
+| **🩺 Medical**      | 🤖 [Iteratively Proposing Ideas & Evolving](https://rdagent.azurewebsites.net/dmm)[▶️YouTube](https://www.youtube.com/watch?v=VIaSTZuoZg4) | -                                                                                  |
+| **🏭 General**      | 🦾 [Auto paper reading & implementation](https://rdagent.azurewebsites.net/report_model)[▶️YouTube](https://www.youtube.com/watch?v=BiA2SfdKQ7o) <br/> 🤖 Auto Kaggle Model Tuning   | 🤖Auto Kaggle feature Engineering |
+
+- **[RoadMap](https://rdagent.readthedocs.io/en/latest/scens/kaggle_agent.html#roadmap)**: Currently, we are working hard to add new features to the Kaggle scenario.
 
 Different scenarios vary in entrance and configuration. Please check the detailed setup tutorial in the scenarios documents.
 
-Here is a gallery of successful explorations. You can download the source code and view the execution trace using the command below:
-
-```bash
-rdagent ui --port 80 --log_dir gallary/
-```
+Here is a gallery of [successful explorations](https://github.com/SunsetWolf/rdagent_resource/releases/download/demo_traces/demo_traces.zip) (5 traces showed in **[🖥️ Live Demo](https://rdagent.azurewebsites.net/)**). You can download and view the execution trace using [this command](https://github.com/microsoft/RD-Agent?tab=readme-ov-file#%EF%B8%8F-monitor-the-application-results) from the documentation.
 
 Please refer to **[📖readthedocs_scen](https://rdagent.readthedocs.io/en/latest/scens/catalog.html)** for more details of the scenarios.
 
@@ -232,6 +332,10 @@ For more detail, please refer to our **[🖥️ Live Demo page](https://rdagent.
 
 # 🤝 Contributing
 
+We welcome contributions and suggestions to improve RD-Agent. Please refer to the [Contributing Guide](CONTRIBUTING.md) for more details on how to contribute.
+
+Before submitting a pull request, ensure that your code passes the automatic CI checks.
+
 ## 📝 Guidelines
 This project welcomes contributions and suggestions.
 Contributing to this project is straightforward and rewarding. Whether it's solving an issue, addressing a bug, enhancing documentation, or even correcting a typo, every contribution is valuable and helps improve RDAgent.

constraints/3.10.txt

@@ -1,266 +1,7 @@
-aiohttp==3.9.1
-aiosignal==1.3.1
-alabaster==0.7.13
-annotated-types==0.6.0
-anyio==4.2.0
-appdirs==1.4.4
-argon2-cffi==23.1.0
-argon2-cffi-bindings==21.2.0
-arrow==1.3.0
-asttokens==2.4.1
-async-lru==2.0.4
-async-timeout==4.0.3
-attrs==23.2.0
-autodoc-pydantic==2.0.1
-azure-ai-formrecognizer==3.3.2
-azure-common==1.1.28
-azure-core==1.29.6
 azure-identity==1.17.1
-Babel==2.14.0
-beautifulsoup4==4.12.2
-black==23.12.1
-bleach==6.1.0
-blosc2==2.7.1
-build==1.0.3
-certifi==2023.11.17
-cffi==1.16.0
-charset-normalizer==3.3.2
-click==8.1.7
-colorama==0.4.6
-comm==0.2.2
-contourpy==1.2.1
-coverage==7.4.0
-cryptography==41.0.7
-cycler==0.12.1
-Cython==3.0.7
-dataclasses-json==0.6.3
-debugpy==1.8.2
-decorator==5.1.1
-defusedxml==0.7.1
-dill==0.3.8
-distro==1.9.0
-docker==7.1.0
-docutils==0.20.1
-exceptiongroup==1.2.0
-executing==2.0.1
-fastjsonschema==2.20.0
-feedparser==6.0.11
-filelock==3.13.1
-fire==0.5.0
-fonttools==4.53.1
-fqdn==1.5.1
-frozenlist==1.4.1
-fsspec==2023.12.2
-furo==2023.9.10
-fuzzywuzzy==0.18.0
-git-changelog==2.4.0
-greenlet==3.0.3
-h11==0.14.0
-httpcore==1.0.2
-httpx==0.26.0
-idna==3.6
-imagesize==1.4.1
-importlib-metadata==7.0.1
-iniconfig==2.0.0
-ipykernel==6.29.5
-ipython==8.26.0
-ipywidgets==8.1.3
-isodate==0.6.1
-isoduration==20.11.0
-isort==5.13.2
-jaraco.classes==3.3.0
-jedi==0.19.1
-jeepney==0.8.0
-Jinja2==3.1.2
-joblib==1.4.2
-json5==0.9.25
-jsonpatch==1.33
-jsonpointer==2.4
-jsonschema==4.23.0
-jsonschema-specifications==2023.12.1
-jupyter==1.0.0
-jupyter-console==6.6.3
-jupyter-events==0.10.0
-jupyter-lsp==2.2.5
-jupyter_client==8.6.2
-jupyter_core==5.7.2
-jupyter_server==2.14.2
-jupyter_server_terminals==0.5.3
-jupyterlab==4.2.4
-jupyterlab_pygments==0.3.0
-jupyterlab_server==2.27.3
-jupyterlab_widgets==3.0.11
-keyring==24.3.0
-kiwisolver==1.4.5
-langchain==0.0.353
-langchain-community==0.0.7
-langchain-core==0.1.4
-langsmith==0.0.75
-Levenshtein==0.25.1
-livereload==2.6.3
-loguru==0.7.2
-loguru-mypy==0.0.4
-lxml==5.0.0
-markdown-it-py==3.0.0
-MarkupSafe==2.1.3
-marshmallow==3.20.1
-matplotlib==3.9.1
-matplotlib-inline==0.1.7
-mdit-py-plugins==0.4.0
-mdurl==0.1.2
-mistune==3.0.2
-more-itertools==10.1.0
-mpmath==1.3.0
-msal==1.30.0
-msal-extensions==1.2.0
-msgpack==1.0.8
-msrest==0.7.1
-multidict==6.0.4
-mypy==1.10.0
-mypy-extensions==1.0.0
-myst-parser==2.0.0
-nbclient==0.10.0
-nbconvert==7.16.4
-nbformat==5.10.4
-ndindex==1.8
-nest-asyncio==1.6.0
-networkx==3.2.1
-nh3==0.2.15
-notebook==7.2.1
-notebook_shim==0.2.4
-numexpr==2.10.1
-numpy==1.26.2
-nvidia-cublas-cu12==12.1.3.1
-nvidia-cuda-cupti-cu12==12.1.105
-nvidia-cuda-nvrtc-cu12==12.1.105
-nvidia-cuda-runtime-cu12==12.1.105
-nvidia-cudnn-cu12==8.9.2.26
-nvidia-cufft-cu12==11.0.2.54
-nvidia-curand-cu12==10.3.2.106
-nvidia-cusolver-cu12==11.4.5.107
-nvidia-cusparse-cu12==12.1.0.106
-nvidia-nccl-cu12==2.18.1
-nvidia-nvjitlink-cu12==12.3.101
-nvidia-nvtx-cu12==12.1.105
-oauthlib==3.2.2
-openai==1.6.1
-overrides==7.7.0
-packaging==23.2
-pandarallel==1.6.5
-pandas==2.1.4
-pandocfilters==1.5.1
-parso==0.8.4
-pathspec==0.12.1
-patsy==0.5.6
-pexpect==4.9.0
+dill==0.3.9
 pillow==10.4.0
-pkginfo==1.9.6
-platformdirs==4.1.0
-pluggy==1.3.0
-portalocker==2.10.1
-prometheus_client==0.20.0
-prompt_toolkit==3.0.47
-psutil==6.0.0
-ptyprocess==0.7.0
-pure_eval==0.2.3
-py-cpuinfo==9.0.0
-pycparser==2.21
-pydantic==2.5.3
-pydantic-settings==2.1.0
-pydantic_core==2.14.6
-Pygments==2.17.2
-PyJWT==2.8.0
-PyMuPDF==1.24.9
-PyMuPDFb==1.24.9
-pyparsing==3.1.2
-pypdf==3.17.4
-pyproject_hooks==1.0.0
-pytest==7.4.4
-python-dateutil==2.8.2
-python-dotenv==1.0.0
-python-json-logger==2.0.7
-python-Levenshtein==0.25.1
-pytz==2023.3.post1
-PyYAML==6.0.1
-pyzmq==26.0.3
-qtconsole==5.5.2
-QtPy==2.4.1
-rapidfuzz==3.9.5
-readme-renderer==42.0
-referencing==0.35.1
-regex==2024.7.24
-requests==2.31.0
-requests-oauthlib==1.3.1
-requests-toolbelt==1.0.0
-rfc3339-validator==0.1.4
-rfc3986==2.0.0
-rfc3986-validator==0.1.1
-rich==13.7.0
-rpds-py==0.19.1
-ruamel.yaml==0.18.5
-ruamel.yaml.clib==0.2.8
-ruff==0.4.5
-scikit-learn==1.5.1
-scipy==1.11.4
-SecretStorage==3.3.3
-semver==3.0.2
-Send2Trash==1.8.3
-setuptools-scm==8.0.4
-sgmllib3k==1.0.0
-shellingham==1.5.4
-six==1.16.0
-sniffio==1.3.0
-snowballstemmer==2.2.0
-soupsieve==2.5
-Sphinx==7.2.6
-sphinx-autobuild==2021.3.14
-sphinx-basic-ng==1.0.0b2
-sphinx-click==5.1.0
-sphinx-togglebutton==0.3.2
-sphinxcontrib-applehelp==1.0.7
-sphinxcontrib-devhelp==1.0.5
-sphinxcontrib-htmlhelp==2.0.4
-sphinxcontrib-jsmath==1.0.1
-sphinxcontrib-qthelp==1.0.6
-sphinxcontrib-serializinghtml==1.1.9
-SQLAlchemy==2.0.24
-stack-data==0.6.3
-statsmodels==0.14.2
-sympy==1.12
-tables==3.9.2
-tabulate==0.9.0
-tenacity==8.2.3
-termcolor==2.4.0
-terminado==0.18.1
-threadpoolctl==3.5.0
-tiktoken==0.7.0
-tinycss2==1.3.0
-toml-sort==0.23.1
-tomli==2.0.1
-tomlkit==0.12.3
-torch==2.1.2
-torch_geometric==2.5.3
-tornado==6.4
-tqdm==4.66.1
-traitlets==5.14.3
-tree-sitter==0.22.3
-tree-sitter-python==0.21.0
-triton==2.1.0
-twine==4.0.2
-typer==0.9.0
-types-psutil==6.0.0.20240621
-types-python-dateutil==2.9.0.20240316
-types-PyYAML==6.0.12.20240724
-types-tqdm==4.66.0.20240417
-typing-inspect==0.9.0
-typing_extensions==4.9.0
-tzdata==2023.4
-uri-template==1.3.0
-urllib3==2.1.0
-wcwidth==0.2.13
-webcolors==24.6.0
-webencodings==0.5.1
-websocket-client==1.8.0
-widgetsnbextension==4.0.11
-yarl==1.9.4
-zipp==3.17.0
+psutil==6.1.0
+rich==13.9.2
+scipy==1.14.1
+tqdm==4.66.5

constraints/3.11.txt

@@ -1,263 +1,7 @@
-aiohttp==3.9.1
-aiosignal==1.3.1
-alabaster==0.7.13
-annotated-types==0.6.0
-anyio==4.2.0
-appdirs==1.4.4
-argon2-cffi==23.1.0
-argon2-cffi-bindings==21.2.0
-arrow==1.3.0
-asttokens==2.4.1
-async-lru==2.0.4
-attrs==23.2.0
-autodoc-pydantic==2.0.1
-azure-ai-formrecognizer==3.3.2
-azure-common==1.1.28
-azure-core==1.29.6
 azure-identity==1.17.1
-Babel==2.14.0
-beautifulsoup4==4.12.2
-black==23.12.1
-bleach==6.1.0
-blosc2==2.7.1
-build==1.0.3
-certifi==2023.11.17
-cffi==1.16.0
-charset-normalizer==3.3.2
-click==8.1.7
-colorama==0.4.6
-comm==0.2.2
-contourpy==1.2.1
-coverage==7.4.0
-cryptography==41.0.7
-cycler==0.12.1
-Cython==3.0.7
-dataclasses-json==0.6.3
-debugpy==1.8.2
-decorator==5.1.1
-defusedxml==0.7.1
-dill==0.3.8
-distro==1.9.0
-docker==7.1.0
-docutils==0.20.1
-executing==2.0.1
-fastjsonschema==2.20.0
-feedparser==6.0.11
-filelock==3.13.1
-fire==0.5.0
-fonttools==4.53.1
-fqdn==1.5.1
-frozenlist==1.4.1
-fsspec==2023.12.2
-furo==2023.9.10
-fuzzywuzzy==0.18.0
-git-changelog==2.4.0
-greenlet==3.0.3
-h11==0.14.0
-httpcore==1.0.2
-httpx==0.26.0
-idna==3.6
-imagesize==1.4.1
-importlib-metadata==7.0.1
-iniconfig==2.0.0
-ipykernel==6.29.5
-ipython==8.26.0
-ipywidgets==8.1.3
-isodate==0.6.1
-isoduration==20.11.0
-isort==5.13.2
-jaraco.classes==3.3.0
-jedi==0.19.1
-jeepney==0.8.0
-Jinja2==3.1.2
-joblib==1.4.2
-json5==0.9.25
-jsonpatch==1.33
-jsonpointer==2.4
-jsonschema==4.23.0
-jsonschema-specifications==2023.12.1
-jupyter==1.0.0
-jupyter-console==6.6.3
-jupyter-events==0.10.0
-jupyter-lsp==2.2.5
-jupyter_client==8.6.2
-jupyter_core==5.7.2
-jupyter_server==2.14.2
-jupyter_server_terminals==0.5.3
-jupyterlab==4.2.4
-jupyterlab_pygments==0.3.0
-jupyterlab_server==2.27.3
-jupyterlab_widgets==3.0.11
-keyring==24.3.0
-kiwisolver==1.4.5
-langchain==0.0.353
-langchain-community==0.0.7
-langchain-core==0.1.4
-langsmith==0.0.75
-Levenshtein==0.25.1
-livereload==2.6.3
-loguru==0.7.2
-loguru-mypy==0.0.4
-lxml==5.0.0
-markdown-it-py==3.0.0
-MarkupSafe==2.1.3
-marshmallow==3.20.1
-matplotlib==3.9.1
-matplotlib-inline==0.1.7
-mdit-py-plugins==0.4.0
-mdurl==0.1.2
-mistune==3.0.2
-more-itertools==10.1.0
-mpmath==1.3.0
-msal==1.30.0
-msal-extensions==1.2.0
-msgpack==1.0.8
-msrest==0.7.1
-multidict==6.0.4
-mypy==1.10.0
-mypy-extensions==1.0.0
-myst-parser==2.0.0
-nbclient==0.10.0
-nbconvert==7.16.4
-nbformat==5.10.4
-ndindex==1.8
-nest-asyncio==1.6.0
-networkx==3.2.1
-nh3==0.2.15
-notebook==7.2.1
-notebook_shim==0.2.4
-numexpr==2.10.1
-numpy==1.26.2
-nvidia-cublas-cu12==12.1.3.1
-nvidia-cuda-cupti-cu12==12.1.105
-nvidia-cuda-nvrtc-cu12==12.1.105
-nvidia-cuda-runtime-cu12==12.1.105
-nvidia-cudnn-cu12==8.9.2.26
-nvidia-cufft-cu12==11.0.2.54
-nvidia-curand-cu12==10.3.2.106
-nvidia-cusolver-cu12==11.4.5.107
-nvidia-cusparse-cu12==12.1.0.106
-nvidia-nccl-cu12==2.18.1
-nvidia-nvjitlink-cu12==12.3.101
-nvidia-nvtx-cu12==12.1.105
-oauthlib==3.2.2
-openai==1.6.1
-overrides==7.7.0
-packaging==23.2
-pandarallel==1.6.5
-pandas==2.1.4
-pandocfilters==1.5.1
-parso==0.8.4
-pathspec==0.12.1
-patsy==0.5.6
-pexpect==4.9.0
+dill==0.3.9
 pillow==10.4.0
-pkginfo==1.9.6
-platformdirs==4.1.0
-pluggy==1.3.0
-portalocker==2.10.1
-prometheus_client==0.20.0
-prompt_toolkit==3.0.47
-psutil==6.0.0
-ptyprocess==0.7.0
-pure_eval==0.2.3
-py-cpuinfo==9.0.0
-pycparser==2.21
-pydantic==2.5.3
-pydantic-settings==2.1.0
-pydantic_core==2.14.6
-Pygments==2.17.2
-PyJWT==2.9.0
-PyMuPDF==1.24.9
-PyMuPDFb==1.24.9
-pyparsing==3.1.2
-pypdf==3.17.4
-pyproject_hooks==1.0.0
-pytest==7.4.4
-python-dateutil==2.8.2
-python-dotenv==1.0.0
-python-json-logger==2.0.7
-python-Levenshtein==0.25.1
-pytz==2023.3.post1
-PyYAML==6.0.1
-pyzmq==26.0.3
-qtconsole==5.5.2
-QtPy==2.4.1
-rapidfuzz==3.9.5
-readme-renderer==42.0
-referencing==0.35.1
-regex==2024.7.24
-requests==2.31.0
-requests-oauthlib==1.3.1
-requests-toolbelt==1.0.0
-rfc3339-validator==0.1.4
-rfc3986==2.0.0
-rfc3986-validator==0.1.1
-rich==13.7.0
-rpds-py==0.19.1
-ruamel.yaml==0.18.5
-ruamel.yaml.clib==0.2.8
-ruff==0.4.5
-scikit-learn==1.5.1
-scipy==1.11.4
-SecretStorage==3.3.3
-semver==3.0.2
-Send2Trash==1.8.3
-setuptools-scm==8.0.4
-sgmllib3k==1.0.0
-shellingham==1.5.4
-six==1.16.0
-sniffio==1.3.0
-snowballstemmer==2.2.0
-soupsieve==2.5
-Sphinx==7.2.6
-sphinx-autobuild==2021.3.14
-sphinx-basic-ng==1.0.0b2
-sphinx-click==5.1.0
-sphinx-togglebutton==0.3.2
-sphinxcontrib-applehelp==1.0.7
-sphinxcontrib-devhelp==1.0.5
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-sphinxcontrib-serializinghtml==1.1.9
-SQLAlchemy==2.0.24
-stack-data==0.6.3
-statsmodels==0.14.2
-sympy==1.12
-tables==3.9.2
-tabulate==0.9.0
-tenacity==8.2.3
-termcolor==2.4.0
-terminado==0.18.1
-threadpoolctl==3.5.0
-tiktoken==0.7.0
-tinycss2==1.3.0
-toml-sort==0.23.1
-tomlkit==0.12.3
-torch==2.1.2
-torch_geometric==2.5.3
-tornado==6.4
-tqdm==4.66.1
-traitlets==5.14.3
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-types-python-dateutil==2.9.0.20240316
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-widgetsnbextension==4.0.11
-yarl==1.9.4
-zipp==3.17.0
+psutil==6.1.0
+rich==13.9.2
+scipy==1.14.1
+tqdm==4.66.5

docs/_static/RD2bench.json

@@ -0,0 +1,332 @@
+{
+  "alpha053_15": {
+    "description": "Reversal class factor, negative delta of a ratio involving close, low, and high prices over 15 days.",
+    "formulation": "-1 times Deltaleft(frac{(text{close} - text{low}) - (text{high} - text{close})}{text{close} - text{low}}, 15right)",
+    "variables": {
+      "Delta(x, d)": "Change in 'x' over 'd' days.",
+      "text{close}": "Closing price of the stock.",
+      "text{low}": "Lowest price of the stock for the day.",
+      "text{high}": "Highest price of the stock for the day."
+    },
+    "Category": "Volume&Price",
+    "Difficulty": "Easy",
+    "gt_code": "import pandas as pd\ndata_pv = pd.read_hdf('daily_pv.h5')\nnew_df= data_pv.reset_index()\n# Calculate Alpha053\nnew_df['ratio'] =  (new_df['$close'] - new_df['$low'] - (new_df['$high'] - new_df['$close'])) / (new_df['$close'] - new_df['$low'])\n# the change of ratio in new_df over the 15 days\nnew_df['result']=-new_df['ratio'].diff(15)\n# transfer the result to series\nresult=pd.DataFrame(new_df['result']).set_index(data_pv.index)\nresult=result['result']\nresult.to_hdf('result.h5', key='data')"
+  },
+  "liquidity_imbalance": {
+    "description": "liquidity_imbalance=std(minute trading liquidity_imbalance)/mean(minute trading liquidity_imbalance).",
+    "formulation": "liquidity_imbalance = frac{text{std}(text{minute trading liquidity_imbalance})}{text{mean}(text{minute liquidity_imbalance})}",
+    "variables": {
+      "std(minute liquidity_imbalance)": "Standard deviation of trading liquidity_imbalance for each minute of the trading day.",
+      "mean(minute liquidity_imbalance)": "Mean of trading liquidity_imbalance for each minute of the trading day.",
+      "liquidity_imbalance": "(bid_size-ask_size)/(bid_size+ask_size), we use something like bidV for the size"
+    },
+    "Category": "High-Frequency",
+    "Difficulty": "Medium",
+    "gt_code": "import pandas as pd\ndata_hf = pd.read_hdf('high_freq.h5')\nsample_df= data_hf.reset_index()\n# Convert 'datetime' column to datetime and extract date for grouping\nsample_df['date'] = sample_df['datetime'].dt.date\nsample_df['liquidity_imbalance']=(sample_df['bidV']-sample_df['askV'])/(sample_df['bidV']+sample_df['askV'])\n# Group by instrument and date\ngrouped = sample_df.groupby(['date','instrument'])['liquidity_imbalance']\n# Calculate mean and standard deviation of the volume for each group\nstats = grouped.agg(['mean', 'std'])\n# Calculate Z value for each instrument per day\nstats['liquidity_imbalance'] = stats['std'] / stats['mean']\n# Display the calculated Z values\nresult=stats['liquidity_imbalance']\nresult.index.names = ['datetime','instrument']\n# result = result.swaplevel().sort_index()\nresult.to_hdf('result.h5', key='data')"
+  },
+  "liquidity_imbalance_2": {
+    "description": "liquidity_imbalance=std(minute trading liquidity_imbalance)/mean(minute trading liquidity_imbalance).",
+    "formulation": "liquidity_imbalance = frac{text{std}(text{minute trading liquidity_imbalance})}{text{mean}(text{minute liquidity_imbalance})}",
+    "variables": {
+      "std(minute liquidity_imbalance)": "Standard deviation of trading liquidity_imbalance for each minute of the trading day.",
+      "mean(minute liquidity_imbalance)": "Mean of trading liquidity_imbalance for each minute of the trading day.",
+      "liquidity_imbalance": "(bid_size-ask_size)/2*(bid_size+ask_size), we use something like bidV for the size"
+    },
+    "Category": "High-Frequency",
+    "Difficulty": "Medium",
+    "gt_code": "import pandas as pd\ndata_hf = pd.read_hdf('high_freq.h5')\nsample_df= data_hf.reset_index()\n# Convert 'datetime' column to datetime and extract date for grouping\nsample_df['date'] = sample_df['datetime'].dt.date\nsample_df['liquidity_imbalance']=(sample_df['bidV']-sample_df['askV'])/((sample_df['bidV']+sample_df['askV'])*2)\n# Group by instrument and date\ngrouped = sample_df.groupby(['date','instrument'])['liquidity_imbalance']\n# Calculate mean and standard deviation of the volume for each group\nstats = grouped.agg(['mean', 'std'])\n# Calculate Z value for each instrument per day\nstats['liquidity_imbalance'] = stats['std'] / stats['mean']\n# Display the calculated Z values\nresult=stats['liquidity_imbalance']\nresult.index.names = ['datetime','instrument']\n# result = result.swaplevel().sort_index()\nresult.to_hdf('result.h5', key='data')"
+  },
+  "liquidity_imbalance_3": {
+    "description": "liquidity_imbalance=std(minute trading liquidity_imbalance)/mean(minute trading liquidity_imbalance).",
+    "formulation": "liquidity_imbalance = frac{text{std}(text{minute trading liquidity_imbalance})}{text{mean}(text{minute liquidity_imbalance})}",
+    "variables": {
+      "std(minute liquidity_imbalance)": "Standard deviation of trading liquidity_imbalance for each minute of the trading day.",
+      "mean(minute liquidity_imbalance)": "Mean of trading liquidity_imbalance for each minute of the trading day.",
+      "liquidity_imbalance": "(bid_size-ask_size)/3*(bid_size+ask_size), we use something like bidV for the size"
+    },
+    "Category": "High-Frequency",
+    "Difficulty": "Medium",
+    "gt_code": "import pandas as pd\ndata_hf = pd.read_hdf('high_freq.h5')\nsample_df= data_hf.reset_index()\n# Convert 'datetime' column to datetime and extract date for grouping\nsample_df['date'] = sample_df['datetime'].dt.date\nsample_df['liquidity_imbalance']=(sample_df['bidV']-sample_df['askV'])/((sample_df['bidV']+sample_df['askV'])*3)\n# Group by instrument and date\ngrouped = sample_df.groupby(['date','instrument'])['liquidity_imbalance']\n# Calculate mean and standard deviation of the volume for each group\nstats = grouped.agg(['mean', 'std'])\n# Calculate Z value for each instrument per day\nstats['liquidity_imbalance'] = stats['std'] / stats['mean']\n# Display the calculated Z values\nresult=stats['liquidity_imbalance']\nresult.index.names = ['datetime','instrument']\n# result = result.swaplevel().sort_index()\nresult.to_hdf('result.h5', key='data')"
+  },
+  "micro_price": {
+    "description": "micro_price=std(minute trading micro_price)/mean(minute trading micro_price).",
+    "formulation": "micro_price = frac{text{std}(text{minute trading micro_price})}{text{mean}(text{minute micro_price})}",
+    "variables": {
+      "std(minute micro_price)": "Standard deviation of trading micro_price for each minute of the trading day.",
+      "mean(minute micro_price)": "Mean of trading micro_price for each minute of the trading day.",
+      "micro_price": "((df['bid_price'] * df['ask_size']) + (df['ask_price'] * df['bid_size'])) / (df['bid_size'] + df['ask_size'])"
+    },
+    "Category": "High-Frequency",
+    "Difficulty": "Hard",
+    "gt_code": "import pandas as pd\ndata_hf = pd.read_hdf('high_freq.h5')\nsample_df= data_hf.reset_index()\n# Convert 'datetime' column to datetime and extract date for grouping\nsample_df['date'] = sample_df['datetime'].dt.date\nsample_df['micro_price']=(sample_df['bid']*sample_df['askV']+sample_df['ask']*sample_df['bidV'])/(sample_df['bidV']+sample_df['askV'])\n# Group by instrument and date\ngrouped = sample_df.groupby(['date','instrument'])['micro_price']\n# Calculate mean and standard deviation of the volume for each group\nstats = grouped.agg(['mean', 'std'])\n# Calculate Z value for each instrument per day\nstats['micro_price'] = stats['std'] / stats['mean']\n# Display the calculated Z values\nresult=stats['micro_price']\nresult.index.names = ['datetime','instrument']\n# result = result.swaplevel().sort_index()\nresult.to_hdf('result.h5', key='data')"
+  },
+  "micro_price_2": {
+    "description": "micro_price_2=std(minute trading micro_price)/mean(minute trading micro_price).",
+    "formulation": "micro_price_2 = frac{text{std}(text{minute trading micro_price})}{text{mean}(text{minute micro_price})}",
+    "variables": {
+      "std(minute micro_price)": "Standard deviation of trading micro_price for each minute of the trading day.",
+      "mean(minute micro_price)": "Mean of trading micro_price for each minute of the trading day.",
+      "micro_price": "((df['bid_price'] * df['ask_size']) + (df['ask_price'] * df['bid_size'])) / 2*(df['bid_size'] + df['ask_size']), we use something like bidV for the size"
+    },
+    "Category": "High-Frequency",
+    "Difficulty": "Hard",
+    "gt_code": "import pandas as pd\ndata_hf = pd.read_hdf('high_freq.h5')\nsample_df= data_hf.reset_index()\n# Convert 'datetime' column to datetime and extract date for grouping\nsample_df['date'] = sample_df['datetime'].dt.date\nsample_df['micro_price']=(sample_df['bid']*sample_df['askV']+sample_df['ask']*sample_df['bidV'])/((sample_df['bidV']+sample_df['askV'])*2)\n# Group by instrument and date\ngrouped = sample_df.groupby(['date','instrument'])['micro_price']\n# Calculate mean and standard deviation of the volume for each group\nstats = grouped.agg(['mean', 'std'])\n# Calculate Z value for each instrument per day\nstats['micro_price'] = stats['std'] / stats['mean']\n# Display the calculated Z values\nresult=stats['micro_price']\nresult.index.names = ['datetime','instrument']\n# result = result.swaplevel().sort_index()\nresult.to_hdf('result.h5', key='data')"
+  },
+  "micro_price_3": {
+    "description": "micro_price_3=std(minute trading micro_price)/mean(minute trading micro_price).",
+    "formulation": "micro_price_3 = frac{text{std}(text{minute trading micro_price})}{text{mean}(text{minute micro_price})}",
+    "variables": {
+      "std(minute micro_price)": "Standard deviation of trading micro_price for each minute of the trading day.",
+      "mean(minute micro_price)": "Mean of trading micro_price for each minute of the trading day.",
+      "micro_price": "((df['bid_price'] * df['ask_size']) + (df['ask_price'] * df['bid_size'])) / 3*(df['bid_size'] + df['ask_size']), we use something like bidV for the size"
+    },
+    "Category": "High-Frequency",
+    "Difficulty": "Hard",
+    "gt_code": "import pandas as pd\ndata_hf = pd.read_hdf('high_freq.h5')\nsample_df= data_hf.reset_index()\n# Convert 'datetime' column to datetime and extract date for grouping\nsample_df['date'] = sample_df['datetime'].dt.date\nsample_df['micro_price']=(sample_df['bid']*sample_df['askV']+sample_df['ask']*sample_df['bidV'])/((sample_df['bidV']+sample_df['askV'])*3)\n# Group by instrument and date\ngrouped = sample_df.groupby(['date','instrument'])['micro_price']\n# Calculate mean and standard deviation of the volume for each group\nstats = grouped.agg(['mean', 'std'])\n# Calculate Z value for each instrument per day\nstats['micro_price'] = stats['std'] / stats['mean']\n# Display the calculated Z values\nresult=stats['micro_price']\nresult.index.names = ['datetime','instrument']\n# result = result.swaplevel().sort_index()\nresult.to_hdf('result.h5', key='data')"
+  },
+  "mid_price": {
+    "description": "mid_price=std(minute trading mid_price)/mean(minute trading mid_price).",
+    "formulation": "mid_price = frac{text{std}(text{minute trading mid price})}{text{mean}(text{minute mid price})}",
+    "variables": {
+      "std(minute mid_price)": "Standard deviation of trading mid_price for each minute of the trading day.",
+      "mean(minute mid_price)": "Mean of trading mid_price for each minute of the trading day.",
+      "mid_price": "The average of the bid and ask prices."
+    },
+    "Category": "High-Frequency",
+    "Difficulty": "Easy",
+    "gt_code": "import pandas as pd\ndata_hf = pd.read_hdf('high_freq.h5')\nsample_df= data_hf.reset_index()\n# Convert 'datetime' column to datetime and extract date for grouping\nsample_df['date'] = sample_df['datetime'].dt.date\nsample_df['mid_price']=(sample_df['bid']+sample_df['ask'])/2\n# Group by instrument and date\ngrouped = sample_df.groupby(['date','instrument'])['mid_price']\n# Calculate mean and standard deviation of the volume for each group\nstats = grouped.agg(['mean', 'std'])\nstats['mid_price'] = stats['std'] / stats['mean']\nresult=stats['mid_price']\nresult.index.names = ['datetime','instrument']\n# result = result.swaplevel().sort_index()\nresult.to_hdf('result.h5', key='data')"
+  },
+  "mid_price_2": {
+    "description": "mid_price=std(minute trading mid_price)/mean(minute trading mid_price).",
+    "formulation": "mid_price = frac{text{std}(text{minute trading mid price})}{text{mean}(text{minute mid price})}",
+    "variables": {
+      "std(minute mid_price)": "Standard deviation of trading mid_price for each minute of the trading day.",
+      "mean(minute mid_price)": "Mean of trading mid_price for each minute of the trading day.",
+      "mid_price_2": "the average of the bid and ask prices plus the the average of the bid and ask size (bidV and askV)."
+    },
+    "Category": "High-Frequency",
+    "Difficulty": "Easy",
+    "gt_code": "import pandas as pd\ndata_hf = pd.read_hdf('high_freq.h5')\nsample_df= data_hf.reset_index()\n# Convert 'datetime' column to datetime and extract date for grouping\nsample_df['date'] = sample_df['datetime'].dt.date\nsample_df['mid_price']=(sample_df['bid']+sample_df['ask'])/2+(sample_df['bidV']+sample_df['askV'])/2\n# Group by instrument and date\ngrouped = sample_df.groupby(['date','instrument'])['mid_price']\n# Calculate mean and standard deviation of the volume for each group\nstats = grouped.agg(['mean', 'std'])\nstats['mid_price'] = stats['std'] / stats['mean']\nresult=stats['mid_price']\nresult.index.names = ['datetime','instrument']\n# result = result.swaplevel().sort_index()\nresult.to_hdf('result.h5', key='data')"
+  },
+  "mid_price_3": {
+    "description": "mid_price=std(minute trading mid_price)/mean(minute trading mid_price).",
+    "formulation": "mid_price = frac{text{std}(text{minute trading mid price})}{text{mean}(text{minute mid price})}",
+    "variables": {
+      "std(minute mid_price)": "Standard deviation of trading mid_price for each minute of the trading day.",
+      "mean(minute mid_price)": "Mean of trading mid_price for each minute of the trading day.",
+      "mid_price_3": "The coefficient of variation (CV) of the mid-price for each minute of the trading day, calculated as the standard deviation of the mid-price divided by the mean mid-price."
+    },
+    "Category": "High-Frequency",
+    "Difficulty": "Easy",
+    "gt_code": "import pandas as pd\ndata_hf = pd.read_hdf('high_freq.h5')\nsample_df= data_hf.reset_index()\n# Convert 'datetime' column to datetime and extract date for grouping\nsample_df['date'] = sample_df['datetime'].dt.date\nsample_df['mid_price']=(sample_df['bid']+sample_df['ask'])/3\n# Group by instrument and date\ngrouped = sample_df.groupby(['date','instrument'])['mid_price']\n# Calculate mean and standard deviation of the volume for each group\nstats = grouped.agg(['mean', 'std'])\nstats['mid_price'] = stats['std'] / stats['mean']\nresult=stats['mid_price']\nresult.index.names = ['datetime','instrument']\n# result = result.swaplevel().sort_index()\nresult.to_hdf('result.h5', key='data')"
+  },
+  "PB_ROE": {
+    "description": "Constructed using the ranking difference between PB and ROE, with regression versions of PB and ROE replacing original PB and ROE to obtain reconstructed factor values.",
+    "formulation": "text{rank}(PB_t) - rank(ROE_t)",
+    "variables": {
+      "text{rank}(PB_t)": "Ranking of regression version PB on cross-section at time t.",
+      "text{rank}(ROE_t)": "Ranking of regression version single-quarter ROE on cross-section at time t."
+    },
+    "Category": "Fundamentals",
+    "Difficulty": "Easy",
+    "gt_code": "import pandas as pd\ndata_f = pd.read_hdf('daily_f.h5')\ndata = data_f.reset_index()\n# Calculate the rank of PB and ROE\ndata['PB_rank'] = data.groupby('datetime')['B/P'].rank()\ndata['ROE_rank'] = data.groupby('datetime')['ROE'].rank()\n# Calculate the difference between the ranks\ndata['PB_ROE'] = data['PB_rank'] - data['ROE_rank']\n# set the datetime and instrument as index and drop the original index\nresult=pd.DataFrame(data['PB_ROE']).set_index(data_f.index)\n# transfer the result to series\nresult=result['PB_ROE']\nresult.to_hdf('result.h5', key='data')"
+  },
+  "PB_ROE_2": {
+    "description": "Constructed using the ranking difference between PB/2 and ROE, with regression versions of PB and ROE replacing original PB and ROE to obtain reconstructed factor values.",
+    "formulation": "text{rank}(PB_t)/2 - rank(ROE_t)",
+    "variables": {
+      "text{rank}(PB_t)": "Ranking of regression version PB on cross-section at time t.",
+      "text{rank}(ROE_t)": "Ranking of regression version single-quarter ROE on cross-section at time t."
+    },
+    "Category": "Fundamentals",
+    "Difficulty": "Easy",
+    "gt_code": "import pandas as pd\ndata_f = pd.read_hdf('daily_f.h5')\ndata = data_f.reset_index()\n# Calculate the rank of PB and ROE\ndata['PB_rank'] = data.groupby('datetime')['B/P'].rank()\ndata['ROE_rank'] = data.groupby('datetime')['ROE'].rank()\n# Calculate the difference between the ranks\ndata['PB_ROE'] = data['PB_rank']/2 - data['ROE_rank']\n# set the datetime and instrument as index and drop the original index\nresult=pd.DataFrame(data['PB_ROE']).set_index(data_f.index)\n# transfer the result to series\nresult=result['PB_ROE']\nresult.to_hdf('result.h5', key='data')"
+  },
+  "PB_ROE_3": {
+    "description": "Constructed using the ranking difference between PB/3 and ROE, with regression versions of PB and ROE replacing original PB and ROE to obtain reconstructed factor values.",
+    "formulation": "text{rank}(PB_t)/3 - rank(ROE_t)",
+    "variables": {
+      "text{rank}(PB_t)": "Ranking of regression version PB on cross-section at time t.",
+      "text{rank}(ROE_t)": "Ranking of regression version single-quarter ROE on cross-section at time t."
+    },
+    "Category": "Fundamentals",
+    "Difficulty": "Easy",
+    "gt_code": "import pandas as pd\ndata_f = pd.read_hdf('daily_f.h5')\ndata = data_f.reset_index()\n# Calculate the rank of PB and ROE\ndata['PB_rank'] = data.groupby('datetime')['B/P'].rank()\ndata['ROE_rank'] = data.groupby('datetime')['ROE'].rank()\n# Calculate the difference between the ranks\ndata['PB_ROE'] = data['PB_rank']/3 - data['ROE_rank']\n# set the datetime and instrument as index and drop the original index\nresult=pd.DataFrame(data['PB_ROE']).set_index(data_f.index)\n# transfer the result to series\nresult=result['PB_ROE']\nresult.to_hdf('result.h5', key='data')"
+  },
+  "PB_ROE_movement": {
+    "description": "PB_ROE_movement=five day PB_ROE movement indicator(-1 and 1 or 0).",
+    "formulation": "PB_ROE_movement = 5_day_movement(PB_ROE), PB_ROE = text{rank}(PB_t) - rank(ROE_t)",
+    "variables": {
+      "PB_ROE": "the ranking difference between PB and ROE.",
+      "5_day_PB_ROE_movement": "1 if PB_ROE is higher than the PB_ROE 5 days ago, -1 if PB_ROE is lower than the PB_ROE 5 days ago, 0 if PB_ROE is the same as the PB_ROE 5 days ago.",
+      "text{rank}(PB_t)": "Ranking of regression version PB on cross-section at time t.",
+      "text{rank}(ROE_t)": "Ranking of regression version single-quarter ROE on cross-section at time t."
+    },
+    "Category": "Fundamentals",
+    "Difficulty": "Hard",
+    "gt_code": "import pandas as pd\ndata_f = pd.read_hdf('daily_f.h5')\nsample_df = data_f.reset_index()\n# Calculate the rank of PB and ROE\nsample_df['PB_rank'] = sample_df.groupby('datetime')['B/P'].rank()\nsample_df['ROE_rank'] = sample_df.groupby('datetime')['ROE'].rank()\nsample_df['PB_ROE'] = sample_df['PB_rank'] - sample_df['ROE_rank']\n# Group by instrument and date\nsample_df['PB_ROE_movement'] = sample_df['PB_ROE'].diff(periods=5).apply(lambda x: 1 if x > 0 else (-1 if x < 0 else 0))\n#calculate the mid_price_movement ratio for each day\n# set the datetime and instrument as index and drop the original index\nresult=pd.DataFrame(sample_df['PB_ROE_movement']).set_index(data_f.index)\n# transfer the result to series\nresult=result['PB_ROE_movement']\nresult.to_hdf('result.h5', key='data')"
+  },
+  "PB_ROE_movement_10": {
+    "description": "PB_ROE_movement=10 days PB_ROE movement indicator(-1 and 1 or 0).",
+    "formulation": "PB_ROE_movement = 10_day_movement(PB_ROE), PB_ROE = text{rank}(PB_t) - rank(ROE_t)",
+    "variables": {
+      "PB_ROE": "the ranking difference between PB and ROE.",
+      "10_day_PB_ROE_movement": "1 if PB_ROE is higher than the PB_ROE 10 days ago, -1 if PB_ROE is lower than the PB_ROE 10 days ago, 0 if PB_ROE is the same as the PB_ROE 10 days ago.",
+      "text{rank}(PB_t)": "Ranking of regression version PB on cross-section at time t.",
+      "text{rank}(ROE_t)": "Ranking of regression version single-quarter ROE on cross-section at time t."
+    },
+    "Category": "Fundamentals",
+    "Difficulty": "Hard",
+    "gt_code": "import pandas as pd\ndata_f = pd.read_hdf('daily_f.h5')\nsample_df = data_f.reset_index()\n# Calculate the rank of PB and ROE\nsample_df['PB_rank'] = sample_df.groupby('datetime')['B/P'].rank()\nsample_df['ROE_rank'] = sample_df.groupby('datetime')['ROE'].rank()\nsample_df['PB_ROE'] = sample_df['PB_rank'] - sample_df['ROE_rank']\n# Group by instrument and date\nsample_df['PB_ROE_movement'] = sample_df['PB_ROE'].diff(periods=10).apply(lambda x: 1 if x > 0 else (-1 if x < 0 else 0))\n#calculate the mid_price_movement ratio for each day\n# set the datetime and instrument as index and drop the original index\nresult=pd.DataFrame(sample_df['PB_ROE_movement']).set_index(data_f.index)\n# transfer the result to series\nresult=result['PB_ROE_movement']\nresult.to_hdf('result.h5', key='data')"
+  },
+  "PB_ROE_movement_20": {
+    "description": "PB_ROE_movement=20 days PB_ROE movement indicator(-1 and 1 or 0).",
+    "formulation": "PB_ROE_movement = 20_day_movement(PB_ROE), PB_ROE = text{rank}(PB_t) - rank(ROE_t)",
+    "variables": {
+      "PB_ROE": "the ranking difference between PB and ROE.",
+      "20_day_PB_ROE_movement": "1 if PB_ROE is higher than the PB_ROE 20 days ago, -1 if PB_ROE is lower than the PB_ROE 20 days ago, 0 if PB_ROE is the same as the PB_ROE 20 days ago.",
+      "text{rank}(PB_t)": "Ranking of regression version PB on cross-section at time t.",
+      "text{rank}(ROE_t)": "Ranking of regression version single-quarter ROE on cross-section at time t."
+    },
+    "Category": "Fundamentals",
+    "Difficulty": "Hard",
+    "gt_code": "import pandas as pd\ndata_f = pd.read_hdf('daily_f.h5')\nsample_df = data_f.reset_index()\n# Calculate the rank of PB and ROE\nsample_df['PB_rank'] = sample_df.groupby('datetime')['B/P'].rank()\nsample_df['ROE_rank'] = sample_df.groupby('datetime')['ROE'].rank()\nsample_df['PB_ROE'] = sample_df['PB_rank'] - sample_df['ROE_rank']\n# Group by instrument and date\nsample_df['PB_ROE_movement'] = sample_df['PB_ROE'].diff(periods=20).apply(lambda x: 1 if x > 0 else (-1 if x < 0 else 0))\n#calculate the mid_price_movement ratio for each day\n# set the datetime and instrument as index and drop the original index\nresult=pd.DataFrame(sample_df['PB_ROE_movement']).set_index(data_f.index)\n# transfer the result to series\nresult=result['PB_ROE_movement']\nresult.to_hdf('result.h5', key='data')"
+  },
+  "ROE_movement": {
+    "description": "ROE_movement=five day ROE movement indicator(-1 and 1 or 0).",
+    "formulation": "ROE_movement = 5_day_movement(ROE)",
+    "variables": {
+      "ROE": "ROE in fundamental statistics.",
+      "5_day_ROE_movement": "1 if ROE is higher than the ROE 5 days ago, -1 if ROE is lower than the ROE 5 days ago, 0 if ROE is the same as the ROE 5 days ago."
+    },
+    "Category": "Fundamentals",
+    "Difficulty": "Medium",
+    "gt_code": "import pandas as pd\ndata_f = pd.read_hdf('daily_f.h5')\nsample_df = data_f.reset_index()\n# Group by instrument and date\nsample_df['ROE_movement'] = sample_df['ROE'].diff(periods=5).apply(lambda x: 1 if x > 0 else (-1 if x < 0 else 0))\n#calculate the mid_price_movement ratio for each day\n# set the datetime and instrument as index and drop the original index\nresult=pd.DataFrame(sample_df['ROE_movement']).set_index(data_f.index)\n# transfer the result to series\nresult=result['ROE_movement']\nresult.to_hdf('result.h5', key='data')"
+  },
+  "ROE_movement_10": {
+    "description": "ROE_movement_10=ten day ROE movement indicator(-1 and 1 or 0).",
+    "formulation": "ROE_movement = 10_day_movement(ROE)",
+    "variables": {
+      "ROE": "ROE in fundamental statistics.",
+      "10_day_ROE_movement": "1 if ROE is higher than the ROE 10 days ago, -1 if ROE is lower than the ROE 10 days ago, 0 if ROE is the same as the ROE 10 days ago."
+    },
+    "Category": "Fundamentals",
+    "Difficulty": "Medium",
+    "gt_code": "import pandas as pd\ndata_f = pd.read_hdf('daily_f.h5')\nsample_df = data_f.reset_index()\n# Group by instrument and date\nsample_df['ROE_movement'] = sample_df['ROE'].diff(periods=10).apply(lambda x: 1 if x > 0 else (-1 if x < 0 else 0))\n#calculate the mid_price_movement ratio for each day\n# set the datetime and instrument as index and drop the original index\nresult=pd.DataFrame(sample_df['ROE_movement']).set_index(data_f.index)\n# transfer the result to series\nresult=result['ROE_movement']\nresult.to_hdf('result.h5', key='data')"
+  },
+  "ROE_movement_20": {
+    "description": "ROE_movement_20=20 day ROE movement indicator(-1 and 1 or 0).",
+    "formulation": "ROE_movement_20 = 20_day_movement(ROE)",
+    "variables": {
+      "ROE": "ROE in fundamental statistics.",
+      "20_day_ROE_movement": "1 if ROE is higher than the ROE 20 days ago, -1 if ROE is lower than the ROE 20 days ago, 0 if ROE is the same as the ROE 20 days ago."
+    },
+    "Category": "Fundamentals",
+    "Difficulty": "Medium",
+    "gt_code": "import pandas as pd\ndata_f = pd.read_hdf('daily_f.h5')\nsample_df = data_f.reset_index()\n# Group by instrument and date\nsample_df['ROE_movement'] = sample_df['ROE'].diff(periods=20).apply(lambda x: 1 if x > 0 else (-1 if x < 0 else 0))\n#calculate the mid_price_movement ratio for each day\n# set the datetime and instrument as index and drop the original index\nresult=pd.DataFrame(sample_df['ROE_movement']).set_index(data_f.index)\n# transfer the result to series\nresult=result['ROE_movement']\nresult.to_hdf('result.h5', key='data')"
+  },
+  "alpha_pv_diff": {
+    "description": "alpha_pv_diff is defined as the ratio of the difference between close prices 10 days change and open prices 10 days change to the sum of the highest minus lowest prices plus a small constant.",
+    "formulation": "frac{(text{close_diff10} - text{open_diff10})}{(text{high} - text{low} + 0.001)}",
+    "variables": {
+      "close": "Closing price of the stock",
+      "open": "Opening price of the stock",
+      "high": "Highest price of the stock during the day",
+      "low": "Lowest price of the stock during the day"
+    },
+    "Category": "Volume&Price",
+    "Difficulty": "Medium",
+    "gt_code": "import pandas as pd\ndata_pv = pd.read_hdf('daily_pv.h5')\nnew_df= data_pv.reset_index()\n# Calculate Alpha101\nnew_df['result'] = (new_df['$close'].diff(10) - new_df['$open'].diff(10)) / (new_df['$high'] - new_df['$low'] + 0.001)\n# keep the index of the original dataframe\nresult=pd.DataFrame(new_df['result']).set_index(data_pv.index)\n# transfer the result to series\nresult=result['result']\nresult.to_hdf('result.h5', key='data')"
+  },
+  "alpha_pv_diff_15": {
+    "description": "alpha_pv_diff is defined as the ratio of the difference between close prices 15 days change and open prices 15 days change to the sum of the highest minus lowest prices plus a small constant.",
+    "formulation": "frac{(text{close_diff15} - text{open_diff15})}{(text{high} - text{low} + 0.001)}",
+    "variables": {
+      "close": "Closing price of the stock",
+      "open": "Opening price of the stock",
+      "high": "Highest price of the stock during the day",
+      "low": "Lowest price of the stock during the day"
+    },
+    "Category": "Volume&Price",
+    "Difficulty": "Medium",
+    "gt_code": "import pandas as pd\ndata_pv = pd.read_hdf('daily_pv.h5')\nnew_df= data_pv.reset_index()\n# Calculate Alpha101\nnew_df['result'] = (new_df['$close'].diff(15) - new_df['$open'].diff(15)) / (new_df['$high'] - new_df['$low'] + 0.001)\n# keep the index of the original dataframe\nresult=pd.DataFrame(new_df['result']).set_index(data_pv.index)\n# transfer the result to series\nresult=result['result']\nresult.to_hdf('result.h5', key='data')"
+  },
+  "alpha_pv_diff_20": {
+    "description": "alpha_pv_diff is defined as the ratio of the difference between close prices 20 days change and open prices 20 days change to the sum of the highest minus lowest prices plus a small constant.",
+    "formulation": "frac{(text{close_diff20} - text{open_diff20})}{(text{high} - text{low} + 0.001)}",
+    "variables": {
+      "close": "Closing price of the stock",
+      "open": "Opening price of the stock",
+      "high": "Highest price of the stock during the day",
+      "low": "Lowest price of the stock during the day"
+    },
+    "Category": "Volume&Price",
+    "Difficulty": "Medium",
+    "gt_code": "import pandas as pd\ndata_pv = pd.read_hdf('daily_pv.h5')\nnew_df= data_pv.reset_index()\n# Calculate Alpha101\nnew_df['result'] = (new_df['$close'].diff(20) - new_df['$open'].diff(20)) / (new_df['$high'] - new_df['$low'] + 0.001)\n# keep the index of the original dataframe\nresult=pd.DataFrame(new_df['result']).set_index(data_pv.index)\n# transfer the result to series\nresult=result['result']\nresult.to_hdf('result.h5', key='data')"
+  },
+  "alpha_pv_diff_pct": {
+    "description": "alpha_pv is defined as the ratio of the difference between close prices 10 days change and open prices 10 days change to the sum of the highest prices 10 days change ratio minus lowest prices 10 days change ratio plus a small constant.",
+    "formulation": "frac{(text{close_diff10} - text{open_diff10})}{(text{high_pct10} - text{low_pct10} + 0.001)}",
+    "variables": {
+      "close": "Closing price of the stock",
+      "open": "Opening price of the stock",
+      "high": "Highest price of the stock during the day",
+      "low": "Lowest price of the stock during the day"
+    },
+    "Category": "Volume&Price",
+    "Difficulty": "Hard",
+    "gt_code": "import pandas as pd\ndata_pv = pd.read_hdf('daily_pv.h5')\nnew_df= data_pv.reset_index()\n# Calculate Alpha101\nnew_df['result'] = (new_df['$close'].diff(10) - new_df['$open'].diff(10)) / (new_df['$high'].pct_change(10) - new_df['$low'].pct_change(10) + 0.001)\n# keep the index of the original dataframe\nresult=pd.DataFrame(new_df['result']).set_index(data_pv.index)\n# transfer the result to series\nresult=result['result']\nresult.to_hdf('result.h5', key='data')"
+  },
+  "alpha_pv_diff_pct_15": {
+    "description": "alpha_pv is defined as the ratio of the difference between close prices 15 days change and open prices 15 days change to the sum of the highest prices 10 days change ratio minus lowest prices 10 days change ratio plus a small constant.",
+    "formulation": "frac{(text{close_diff15} - text{open_diff15})}{(text{high_pct10} - text{low_pct10} + 0.001)}",
+    "variables": {
+      "close": "Closing price of the stock",
+      "open": "Opening price of the stock",
+      "high": "Highest price of the stock during the day",
+      "low": "Lowest price of the stock during the day"
+    },
+    "Category": "Volume&Price",
+    "Difficulty": "Hard",
+    "gt_code": "import pandas as pd\ndata_pv = pd.read_hdf('daily_pv.h5')\nnew_df= data_pv.reset_index()\n# Calculate Alpha101\nnew_df['result'] = (new_df['$close'].diff(15) - new_df['$open'].diff(15)) / (new_df['$high'].pct_change(10) - new_df['$low'].pct_change(10) + 0.001)\n# keep the index of the original dataframe\nresult=pd.DataFrame(new_df['result']).set_index(data_pv.index)\n# transfer the result to series\nresult=result['result']\nresult.to_hdf('result.h5', key='data')"
+  },
+  "alpha_pv_diff_pct_20": {
+    "description": "alpha_pv is defined as the ratio of the difference between close prices 20 days change and open prices 20 days change to the sum of the highest prices 10 days change ratio minus lowest prices 10 days change ratio plus a small constant.",
+    "formulation": "frac{(text{close_diff20} - text{open_diff20})}{(text{high_pct10} - text{low_pct10} + 0.001)}",
+    "variables": {
+      "close": "Closing price of the stock",
+      "open": "Opening price of the stock",
+      "high": "Highest price of the stock during the day",
+      "low": "Lowest price of the stock during the day"
+    },
+    "Category": "Volume&Price",
+    "Difficulty": "Hard",
+    "gt_code": "import pandas as pd\ndata_pv = pd.read_hdf('daily_pv.h5')\nnew_df= data_pv.reset_index()\n# Calculate Alpha101\nnew_df['result'] = (new_df['$close'].diff(20) - new_df['$open'].diff(20)) / (new_df['$high'].pct_change(10) - new_df['$low'].pct_change(10) + 0.001)\n# keep the index of the original dataframe\nresult=pd.DataFrame(new_df['result']).set_index(data_pv.index)\n# transfer the result to series\nresult=result['result']\nresult.to_hdf('result.h5', key='data')"
+  },
+  "alpha053": {
+    "description": "Reversal class factor, negative delta of a ratio involving close, low, and high prices over 9 days.",
+    "formulation": "-1 times Deltaleft(frac{(text{close} - text{low}) - (text{high} - text{close})}{text{close} - text{low}}, 9right)",
+    "variables": {
+      "Delta(x, d)": "Change in 'x' over 'd' days.",
+      "text{close}": "Closing price of the stock.",
+      "text{low}": "Lowest price of the stock for the day.",
+      "text{high}": "Highest price of the stock for the day."
+    },
+    "Category": "Volume&Price",
+    "Difficulty": "Easy",
+    "gt_code": "import pandas as pd\ndata_pv = pd.read_hdf('daily_pv.h5')\nnew_df= data_pv.reset_index()\n# Calculate Alpha053\nnew_df['ratio'] =  (new_df['$close'] - new_df['$low'] - (new_df['$high'] - new_df['$close'])) / (new_df['$close'] - new_df['$low'])\n# the change of ratio in new_df over the 9 days\nnew_df['result']=-new_df['ratio'].diff(9)\n# transfer the result to series\nresult=pd.DataFrame(new_df['result']).set_index(data_pv.index)\nresult=result['result']\nresult.to_hdf('result.h5', key='data')"
+  },
+  "alpha053_5": {
+    "description": "Reversal class factor, negative delta of a ratio involving close, low, and high prices over 5 days.",
+    "formulation": "-1 times Deltaleft(frac{(text{close} - text{low}) - (text{high} - text{close})}{text{close} - text{low}}, 5right)",
+    "variables": {
+      "Delta(x, d)": "Change in 'x' over 'd' days.",
+      "text{close}": "Closing price of the stock.",
+      "text{low}": "Lowest price of the stock for the day.",
+      "text{high}": "Highest price of the stock for the day."
+    },
+    "Category": "Volume&Price",
+    "Difficulty": "Easy",
+    "gt_code": "import pandas as pd\ndata_pv = pd.read_hdf('daily_pv.h5')\nnew_df= data_pv.reset_index()\n# Calculate Alpha053\nnew_df['ratio'] =  (new_df['$close'] - new_df['$low'] - (new_df['$high'] - new_df['$close'])) / (new_df['$close'] - new_df['$low'])\n# the change of ratio in new_df over the 5 days\nnew_df['result']=-new_df['ratio'].diff(5)\n# transfer the result to series\nresult=pd.DataFrame(new_df['result']).set_index(data_pv.index)\nresult=result['result']\nresult.to_hdf('result.h5', key='data')"
+  }
+}

docs/installation_and_configuration.rst

@@ -11,9 +11,51 @@ Installation
 - for dev users: `See development <development.html>`_
 
 **Install Docker**: RDAgent is designed for research and development, acting like a human researcher and developer. It can write and run code in various environments, primarily using Docker for code execution. This keeps the remaining dependencies simple. Users must ensure Docker is installed before attempting most scenarios. Please refer to the `official 🐳Docker page <https://docs.docker.com/engine/install/>`_ for installation instructions.
+Ensure the current user can run Docker commands **without using sudo**. You can verify this by executing `docker run hello-world`.
 
-Configuration
-=============
+LiteLLM Backend Configuration
+=============================
+
+Please create a `.env` file in the root directory of the project and add environment variables.
+
+Here is a sample configuration for using OpenAI's gpt-4o via LiteLLM. 
+
+   .. code-block:: Properties
+
+      BACKEND=rdagent.oai.backend.LiteLLMAPIBackend
+      # It can be modified to any model supported by LiteLLM.
+      CHAT_MODEL=gpt-4o
+      EMBEDDING_MODEL=text-embedding-3-small
+      # The backend api_key fully follows the convention of litellm.
+      OPENAI_API_KEY=<replace_with_your_openai_api_key>
+
+Necessary parameters include:
+
+- `BACKEND`: The backend to use. The default is `rdagent.oai.backend.DeprecBackend`. To use the LiteLLM backend, set it to `rdagent.oai.backend.LiteLLMAPIBackend`.
+
+- `CHAT_MODEL`: The model name of the chat model. 
+
+- `EMBEDDING_MODEL`: The model name of the embedding model.
+
+The `CHAT_MODEL` and `EMBEDDING_MODEL` parameters will be passed into LiteLLM's completion function. 
+
+Therefore, when utilizing models provided by different providers, first review the interface configuration of LiteLLM. The model names must match those allowed by LiteLLM.
+
+Additionally, you need to set up the the additional parameters for the respective model provider, and the parameter names must align with those required by LiteLLM.
+
+For example, if you are using a DeepSeek model, you need to set as follows:
+
+   .. code-block:: Properties
+
+      # For some models LiteLLM requires a prefix to the model name.
+      CHAT_MODEL=deepseek/deepseek-chat
+      DEEPSEEK_API_KEY=<replace_with_your_deepseek_api_key>
+
+For more details on LiteLLM requirements, refer to the `official LiteLLM documentation <https://docs.litellm.ai/docs>`_.
+
+
+Configuration(deprecated)
+=========================
 
 To run the application, please create a `.env` file in the root directory of the project and add environment variables according to your requirements.
 
@@ -38,22 +80,23 @@ Azure OpenAI
 The following environment variables are standard configuration options for the user using the OpenAI API.
 
    .. code-block:: Properties
-      
+
       USE_AZURE=True
 
-      OPENAI_API_KEY=<replace_with_your_openai_api_key>
-      
+      EMBEDDING_OPENAI_API_KEY=<replace_with_your_azure_openai_api_key>
+      EMBEDDING_AZURE_API_BASE=  # The endpoint for the Azure OpenAI API.
+      EMBEDDING_AZURE_API_VERSION=  # The version of the Azure OpenAI API.
       EMBEDDING_MODEL=text-embedding-3-small
-      EMBEDDING_AZURE_API_BASE= # The base URL for the Azure OpenAI API.
-      EMBEDDING_AZURE_API_VERSION = # The version of the Azure OpenAI API.
 
-      CHAT_MODEL=gpt-4-turbo
-      CHAT_AZURE_API_VERSION = # The version of the Azure OpenAI API.
+      CHAT_OPENAI_API_KEY=<replace_with_your_azure_openai_api_key>
+      CHAT_AZURE_API_BASE=  # The endpoint for the Azure OpenAI API.
+      CHAT_AZURE_API_VERSION=  # The version of the Azure OpenAI API.
+      CHAT_MODEL=  # The model name of the Azure OpenAI API.
 
 Use Azure Token Provider
 ------------------------
 
-If you are using the Azure token provider, you need to set the `USE_AZURE_TOKEN_PROVIDER` environment variable to `True`. then 
+If you are using the Azure token provider, you need to set the `CHAT_USE_AZURE_TOKEN_PROVIDER` and `EMBEDDING_USE_AZURE_TOKEN_PROVIDER` environment variable to `True`. then 
 use the environment variables provided in the `Azure Configuration section <installation_and_configuration.html#azure-openai>`_.
 
 
@@ -80,31 +123,33 @@ Configuration List
 
 - OpenAI API Setting
 
-+-----------------------------+--------------------------------------------------+-------------------------+
-| Configuration Option        | Meaning                                          | Default Value           |
-+=============================+==================================================+=========================+
-| OPENAI_API_KEY              | API key for both chat and embedding models       | None                    |
-+-----------------------------+--------------------------------------------------+-------------------------+
-| EMBEDDING_OPENAI_API_KEY    | Use a different API key for embedding model      | None                    |
-+-----------------------------+--------------------------------------------------+-------------------------+
-| CHAT_OPENAI_API_KEY         | Set to use a different API key for chat model    | None                    |
-+-----------------------------+--------------------------------------------------+-------------------------+
-| EMBEDDING_MODEL             | Name of the embedding model                      | text-embedding-3-small  |
-+-----------------------------+--------------------------------------------------+-------------------------+
-| CHAT_MODEL                  | Name of the chat model                           | gpt-4-turbo             |
-+-----------------------------+--------------------------------------------------+-------------------------+
-| EMBEDDING_AZURE_API_BASE    | Base URL for the Azure OpenAI API                | None                    |
-+-----------------------------+--------------------------------------------------+-------------------------+
-| EMBEDDING_AZURE_API_VERSION | Version of the Azure OpenAI API                  | None                    |
-+-----------------------------+--------------------------------------------------+-------------------------+
-| CHAT_AZURE_API_BASE         | Base URL for the Azure OpenAI API                | None                    |
-+-----------------------------+--------------------------------------------------+-------------------------+
-| CHAT_AZURE_API_VERSION      | Version of the Azure OpenAI API                  | None                    |
-+-----------------------------+--------------------------------------------------+-------------------------+
-| USE_AZURE                   | True if you are using Azure OpenAI               | False                   |
-+-----------------------------+--------------------------------------------------+-------------------------+
-| USE_AZURE_TOKEN_PROVIDER    | True if you are using a Azure Token Provider     | False                   |
-+-----------------------------+--------------------------------------------------+-------------------------+
++-----------------------------------+-----------------------------------------------------------------+-------------------------+
+| Configuration Option              | Meaning                                                         | Default Value           |
++===================================+=================================================================+=========================+
+| OPENAI_API_KEY                    | API key for both chat and embedding models                      | None                    |
++-----------------------------------+-----------------------------------------------------------------+-------------------------+
+| EMBEDDING_OPENAI_API_KEY          | Use a different API key for embedding model                     | None                    |
++-----------------------------------+-----------------------------------------------------------------+-------------------------+
+| CHAT_OPENAI_API_KEY               | Set to use a different API key for chat model                   | None                    |
++-----------------------------------+-----------------------------------------------------------------+-------------------------+
+| EMBEDDING_MODEL                   | Name of the embedding model                                     | text-embedding-3-small  |
++-----------------------------------+-----------------------------------------------------------------+-------------------------+
+| CHAT_MODEL                        | Name of the chat model                                          | gpt-4-turbo             |
++-----------------------------------+-----------------------------------------------------------------+-------------------------+
+| EMBEDDING_AZURE_API_BASE          | Base URL for the Azure OpenAI API                               | None                    |
++-----------------------------------+-----------------------------------------------------------------+-------------------------+
+| EMBEDDING_AZURE_API_VERSION       | Version of the Azure OpenAI API                                 | None                    |
++-----------------------------------+-----------------------------------------------------------------+-------------------------+
+| CHAT_AZURE_API_BASE               | Base URL for the Azure OpenAI API                               | None                    |
++-----------------------------------+-----------------------------------------------------------------+-------------------------+
+| CHAT_AZURE_API_VERSION            | Version of the Azure OpenAI API                                 | None                    |
++-----------------------------------+-----------------------------------------------------------------+-------------------------+
+| USE_AZURE                         | True if you are using Azure OpenAI                              | False                   |
++-----------------------------------+-----------------------------------------------------------------+-------------------------+
+| CHAT_USE_AZURE_TOKEN_PROVIDER     | True if you are using an Azure Token Provider in chat model     | False                   |
++-----------------------------------+-----------------------------------------------------------------+-------------------------+
+| EMBEDDING_USE_AZURE_TOKEN_PROVIDER| True if you are using an Azure Token Provider in embedding model| False                   |
++-----------------------------------+-----------------------------------------------------------------+-------------------------+
 
 - Globol Setting
 
@@ -138,8 +183,6 @@ Configuration List
 +------------------------------+--------------------------------------------------+-------------------------+
 | prompt_cache_path            | Path to prompt cache                             | ./prompt_cache.db       |
 +------------------------------+--------------------------------------------------+-------------------------+
-| session_cache_folder_location| Path to session cache                            | ./session_cache_folder  |
-+------------------------------+--------------------------------------------------+-------------------------+
 | max_past_message_include     | Maximum number of past messages to include       | 10                      |
 +------------------------------+--------------------------------------------------+-------------------------+
 

docs/research/benchmark.rst

@@ -5,21 +5,12 @@ Benchmark
 Introduction
 =============
 
-
-Benchmarking the capabilities of the R&D is a very important research problem of the research area.
-
-Currently we are continuously exploring how to benchmark them.
-
-The current benchmarks are listed in this page
-
+Benchmarking the capabilities of R&D is a crucial research problem in this area. We are continuously exploring methods to benchmark these capabilities. The current benchmarks are listed on this page.
 
 Development Capability Benchmarking
 ===================================
 
-
-Benchmark is used to evaluate the effectiveness of factors with fixed data.
-
-It mainly includes the following steps:
+Benchmarking is used to evaluate the effectiveness of factors with fixed data. It mainly includes the following steps:
 
 1. :ref:`read and prepare the eval_data <data>`
 
@@ -27,34 +18,31 @@ It mainly includes the following steps:
 
 3. :ref:`declare the eval method and pass the arguments <config>`
 
-4. :ref:`run the eval <run>` 
+4. :ref:`run the eval <run>`
 
-5. :ref:`save and show the result <show>` 
+5. :ref:`save and show the result <show>`
 
-Configuration 
+Configuration
 -------------
 .. _config:
 
 .. autopydantic_settings:: rdagent.components.benchmark.conf.BenchmarkSettings
 
 Example
-++++++++
++++++++
 .. _example:
 
-The default value for ``bench_test_round`` is 10, and it will take about 2 hours to run 10 rounds.
-To modify it from ``10`` to ``2`` you can adjust this by adding environment variables in the .env file as shown below.
+The default value for ``bench_test_round`` is 10, which takes about 2 hours to run. To modify it from ``10`` to ``2``, adjust the environment variables in the .env file as shown below.
 
 .. code-block:: Properties
 
-      BENCHMARK_BENCH_TEST_ROUND=1
+      BENCHMARK_BENCH_TEST_ROUND=2
 
 Data Format
 -------------
 .. _data:
 
-The sample data in ``bench_data_path`` is a dictionary where each key represents a factor name. 
-
-The value associated with each key is factor data containing the following information:
+The sample data in ``bench_data_path`` is a dictionary where each key represents a factor name. The value associated with each key is factor data containing the following information:
 
 - **description**: A textual description of the factor.
 - **formulation**: A LaTeX formula representing the model's formulation.
@@ -63,22 +51,24 @@ The value associated with each key is factor data containing the following infor
 - **Difficulty**: The difficulty level of implementing or understanding the factor.
 - **gt_code**: A piece of code associated with the factor.
 
-Here is the example of this data format:
+Here is an example of this data format:
 
 .. literalinclude:: ../../rdagent/components/benchmark/example.json
    :language: json
 
+Ensure the data is placed in the ``FACTOR_COSTEER_SETTINGS.data_folder_debug``. The data files should be in ``.h5`` or ``.md`` format and must not be stored in any subfolders. LLM-Agents will review the file content and implement the tasks.
+
+.. TODO: Add a script to automatically generate the data in the `rdagent/app/quant_factor_benchmark/data` folder.
+
 Run Benchmark
 -------------
 .. _run:
 
-Start benchmark after finishing the :doc:`../installation_and_configuration`.
+Start the benchmark after completing the :doc:`../installation_and_configuration`.
 
 .. code-block:: Properties
 
-      python rdagent/app/quant_factor_benchmark/eval.py
-
-
+      dotenv run -- python rdagent/app/benchmark/factor/eval.py
 
 Once completed, a pkl file will be generated, and its path will be printed on the last line of the console.
 
@@ -86,18 +76,16 @@ Show Result
 -------------
 .. _show:
 
-The ``analysis.py`` script is used to read data from pkl and convert it to an image.
-Modify the python code in ``rdagent/app/quant_factor_benchmark/analysis.py`` to specify the path to the pkl file and the output path for the png file.
+The ``analysis.py`` script reads data from the pkl file and converts it to an image. Modify the Python code in ``rdagent/app/quant_factor_benchmark/analysis.py`` to specify the path to the pkl file and the output path for the png file.
 
 .. code-block:: Properties
 
-      python rdagent/app/quant_factor_benchmark/analysis.py
+      dotenv run -- python rdagent/app/benchmark/factor/analysis.py <log/path to.pkl>
 
 A png file will be saved to the designated path as shown below.
 
 .. image:: ../_static/benchmark.png
 
-
 Related Paper
 -------------
 
@@ -116,3 +104,6 @@ Related Paper
     }
 
 .. image:: https://github.com/user-attachments/assets/494f55d3-de9e-4e73-ba3d-a787e8f9e841
+
+To replicate the benchmark detailed in the paper, please consult the factors listed in the following file: `RD2bench.json <../_static/RD2bench.json>`_.
+Please note use ``only_correct_format=False`` when evaluating the results.

docs/scens/catalog.rst

@@ -30,17 +30,20 @@ The supported scenarios are listed below:
       - 
     * - 🏭 General
       - :ref:`🦾Auto paper reading & implementation <model_copilot_general>`
-      - 
+
+        :ref:`🤖Auto Kaggle Model Tuning <kaggle_agent>`
+      - :ref:`🤖Auto Kaggle feature Engineering <kaggle_agent>`
 
 
 .. toctree::
-   :maxdepth: 1
-   :caption: Doctree:
-   :hidden:
+    :maxdepth: 1
+    :caption: Doctree:
+    :hidden:
 
-   data_agent_fin
-   data_copilot_fin
-   model_agent_fin
-   model_agent_med
-   model_copilot_general
+    data_agent_fin
+    data_copilot_fin
+    model_agent_fin
+    model_agent_med
+    model_copilot_general
+    kaggle_agent
 

docs/scens/data_agent_fin.rst

@@ -131,8 +131,8 @@ The following environment variables can be set in the `.env` file to customize t
     :settings-show-field-summary: False
     :exclude-members: Config
 
-.. autopydantic_settings:: rdagent.components.coder.factor_coder.config.FactorImplementSettings
+.. autopydantic_settings:: rdagent.components.coder.factor_coder.config.FactorCoSTEERSettings
     :settings-show-field-summary: False
-    :members: coder_use_cache, data_folder, data_folder_debug, cache_location, enable_execution_cache, file_based_execution_timeout, select_method, select_threshold, max_loop, knowledge_base_path, new_knowledge_base_path
+    :members: coder_use_cache, data_folder, data_folder_debug, file_based_execution_timeout, select_method, max_loop, knowledge_base_path, new_knowledge_base_path
     :exclude-members: Config, fail_task_trial_limit, v1_query_former_trace_limit, v1_query_similar_success_limit, v2_query_component_limit, v2_query_error_limit, v2_query_former_trace_limit, v2_error_summary, v2_knowledge_sampler
     :no-index:

docs/scens/data_copilot_fin.rst

@@ -27,7 +27,7 @@ And this is where the **Finance Data Copilot** steps in.
 
     <div style="display: flex; justify-content: center; align-items: center;">
       <video width="600" controls>
-        <source src="https://rdagent.azurewebsites.net/media/65bb598f1372c1857ccbf09b2acf5d55830911625048c03102291098.mp4" type="video/mp4">
+        <source src="https://rdagent.azurewebsites.net/media/7b14b2bd3d8771da9cf7eb799b6d96729cec3d35c8d4f68060f3e2fd.mp4" type="video/mp4">
         Your browser does not support the video tag.
       </video>
     </div>
@@ -157,8 +157,8 @@ The following environment variables can be set in the `.env` file to customize t
     :show-inheritance:
     :exclude-members: Config
 
-.. autopydantic_settings:: rdagent.components.coder.factor_coder.config.FactorImplementSettings
+.. autopydantic_settings:: rdagent.components.coder.factor_coder.config.FactorCoSTEERSettings
     :settings-show-field-summary: False
-    :members: coder_use_cache, data_folder, data_folder_debug, cache_location, enable_execution_cache, file_based_execution_timeout, select_method, select_threshold, max_loop, knowledge_base_path, new_knowledge_base_path
+    :members: coder_use_cache, data_folder, data_folder_debug, file_based_execution_timeout, select_method, max_loop, knowledge_base_path, new_knowledge_base_path
     :exclude-members: Config, python_bin, fail_task_trial_limit, v1_query_former_trace_limit, v1_query_similar_success_limit, v2_query_component_limit, v2_query_error_limit, v2_query_former_trace_limit, v2_error_summary, v2_knowledge_sampler
     :no-index:

docs/scens/kaggle_agent.rst

@@ -0,0 +1,272 @@
+.. _kaggle_agent:
+
+=======================
+Kaggle Agent
+=======================
+
+**🤖 Automated Feature Engineering & Model Tuning Evolution**
+------------------------------------------------------------------------------------------
+
+🎨 Design
+~~~~~~~~~~~
+
+.. image:: kaggle_design.png
+   :alt: Design of Kaggle Agent
+   :align: center
+
+📖 Background
+~~~~~~~~~~~~~~
+In the landscape of data science competitions, Kaggle serves as the ultimate arena where data enthusiasts harness the power of algorithms to tackle real-world challenges.
+The Kaggle Agent stands as a pivotal tool, empowering participants to seamlessly integrate cutting-edge models and datasets, transforming raw data into actionable insights.
+
+By utilizing the **Kaggle Agent**, data scientists can craft innovative solutions that not only uncover hidden patterns but also drive significant advancements in predictive accuracy and model robustness.
+
+
+🌟 Introduction
+~~~~~~~~~~~~~~~~
+
+In this scenario, our automated system proposes hypothesis, choose action, implements code, conducts validation, and utilizes feedback in a continuous, iterative process.
+
+The goal is to automatically optimize performance metrics within the validation set or Kaggle Leaderboard, ultimately discovering the most efficient features and models through autonomous research and development.
+
+Here's an enhanced outline of the steps:
+
+**Step 1 : Hypothesis Generation 🔍**
+
+- Generate and propose initial hypotheses based on previous experiment analysis and domain expertise, with thorough reasoning and financial justification.
+
+**Step 2 : Experiment Creation ✨**
+
+- Transform the hypothesis into a task.
+- Choose a specific action within feature engineering or model tuning.
+- Develop, define, and implement a new feature or model, including its name, description, and formulation.
+
+**Step 3 : Model/Feature Implementation 👨‍💻**
+
+- Implement the model code based on the detailed description.
+- Evolve the model iteratively as a developer would, ensuring accuracy and efficiency.
+
+**Step 4 : Validation on Test Set or Kaggle 📉**
+
+- Validate the newly developed model using the test set or Kaggle dataset.
+- Assess the model's effectiveness and performance based on the validation results.
+
+**Step 5: Feedback Analysis 🔍**
+
+- Analyze validation results to assess performance.
+- Use insights to refine hypotheses and enhance the model.
+
+**Step 6: Hypothesis Refinement ♻️**
+
+- Adjust hypotheses based on validation feedback.
+- Iterate the process to continuously improve the model.
+
+🧭 Example Guide
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+- 🔧 **Set up RD-Agent Environment**
+
+  - Before you start, please make sure you have installed RD-Agent and configured the environment for RD-Agent correctly. If you want to know how to install and configure the RD-Agent, please refer to the `documentation <../installation_and_configuration.html>`_.
+
+- 🔨 **Configuring the Kaggle API**
+  
+  - Register and login on the `Kaggle <https://www.kaggle.com/>`_ website.
+  - Click on the avatar (usually in the top right corner of the page) -> ``Settings`` -> ``Create New Token``, A file called ``kaggle.json`` will be downloaded.
+  - Move ``kaggle.json`` to ``~/.config/kaggle/``
+  - Modify the permissions of the ``kaggle.json`` file.
+
+    .. code-block:: sh
+
+      chmod 600 ~/.config/kaggle/kaggle.json
+
+  - For more information about Kaggle API Settings, refer to the `Kaggle API <https://github.com/Kaggle/kaggle-api>`_.
+
+- 🔩 **Setting the Environment variables at .env file**
+
+  - Determine the path where the data will be stored and add it to the ``.env`` file.
+
+  .. code-block:: sh
+
+    dotenv set KG_LOCAL_DATA_PATH <your local directory>/kaggle_data
+
+- 📥 **Download Competition Data**
+
+  - Kaggle competition data, contains two parts: competition description file (json file) and competition dataset (zip file).
+
+    - **How to get the competition description file**
+
+      - *Manual Download (General User Suggestions):*
+
+        - Download the competition description file prepared in advance, and extract it to the specified directory.
+
+          .. code-block:: sh
+
+            wget https://github.com/SunsetWolf/rdagent_resource/releases/download/kaggle_data/kaggle_data.zip
+            unzip kaggle_data.zip -d <your local directory>/kaggle_data
+
+      - *Automatic Download (Developer Suggestions):*
+      
+        - Alternatively, you can choose to download the competition description file automatically when you run the program, but it requires ``chromedriver`` to be installed, as follows:
+
+          .. code-block:: sh
+
+            # install chrome
+            wget https://dl.google.com/linux/direct/google-chrome-stable_current_amd64.deb
+            sudo apt install ./google-chrome-stable_current_amd64.deb
+            google-chrome --version
+
+            # install chromedriver
+            wget "https://storage.googleapis.com/chrome-for-testing-public/$(google-chrome --version | grep -oP '\d+\.\d+\.\d+\.\d+')/linux64/chromedriver-linux64.zip"
+            unzip chromedriver-linux64.zip
+            cd chromedriver-linux64
+            sudo mv chromedriver /usr/local/bin
+            sudo chmod +x /usr/local/bin/chromedriver
+            chromedriver --version
+
+    - **How to get the competition dataset**
+
+      - The competition dataset is downloaded and extracted automatically when the program is run. If the zip file exists, the download will be skipped, if the unzip folder exists, the unzip will be skipped.
+
+    - **Correct directory structure (Here is an example of competition data with id sf-crime)**
+
+      .. code-block:: text
+
+        kaggle_data
+        └── zip_files
+        | └── sf-crime.zip
+        ├── sf-crime.json
+        └── sf-crime
+          └── ...
+        
+      - ``kaggle_data/zip_files/sf-crime.zip:`` Competition dataset zip files downloaded from the Kaggle website.
+
+      - ``kaggle_data/sf-crime.json:`` Competition description file.
+
+      - ``kaggle_data/sf-crime:`` The target folder for unzipping the competition dataset.
+
+- 🗳️ **Join the competition**
+
+  - If your Kaggle API account has not joined a competition, you will need to join the competition before running the program.
+    
+    - At the bottom of the competition details page, you can find the ``Join the competition`` button, click on it and select ``I Understand and Accept`` to join the competition.
+    
+    - In the **Competition List Available** below, you can jump to the competition details page.
+
+- 🚀 **Run the Application**
+
+  - You can directly run the application by using the following command:
+    
+    .. code-block:: sh
+
+        rdagent kaggle --competition <Competition ID>
+
+- 📤 **Submit the Result Automatically or Manually**
+
+  - If Auto: You need to set ``KG_AUTO_SUBMIT`` to ``true`` in the ``.env`` file.
+
+    .. code-block:: sh
+
+      dotenv set KG_AUTO_SUBMIT true
+  
+  - Else: You can download the prediction results from the UI interface and submit them manually. For more details, refer to the :doc:`UI guide <../ui>`.
+
+📋 Competition List Available
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
++-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
+| **index** | **Competition Name**              | **Task**         | **Modal** | **ID**                                                                                                  |
++===========+===================================+==================+===========+=========================================================================================================+
+| 01        | Media Campaign Cost Dataset       | Regression       | Tabular   | `playground-series-s3e11 <https://www.kaggle.com/competitions/playground-series-s3e11/data>`_           |
++-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
+| 02        | Wild Blueberry Yield Dataset      | Regression       | Tabular   | `playground-series-s3e14 <https://www.kaggle.com/competitions/playground-series-s3e14/data>`_           |
++-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
+| 03        | Crab Age Dataset                  | Regression       | Tabular   | `playground-series-s3e16 <https://www.kaggle.com/competitions/playground-series-s3e16/data>`_           |
++-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
+| 04        | Flood Prediction Dataset          | Regression       | Tabular   | `playground-series-s4e5 <https://www.kaggle.com/competitions/playground-series-s4e5/data>`_             |
++-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
+| 05        | Used Car Prices Dataset           | Regression       | Tabular   | `playground-series-s4e9 <https://www.kaggle.com/competitions/playground-series-s4e9/data>`_             |
++-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
+| 06        | Cirrhosis Outcomes Dataset        | Multi-Class      | Tabular   | `playground-series-s3e26 <https://www.kaggle.com/competitions/playground-series-s3e26/data>`_           |
++-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
+| 07        | San Francisco Crime Classification| Multi-Class      | Tabular   | `sf-crime <https://www.kaggle.com/competitions/sf-crime/data>`_                                         |
++-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
+| 08        | Poisonous Mushrooms Dataset       | Classification   | Tabular   | `playground-series-s4e8 <https://www.kaggle.com/competitions/playground-series-s4e8/data>`_             |
++-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
+| 09        | Spaceship Titanic                 | Classification   | Tabular   | `spaceship-titanic <https://www.kaggle.com/competitions/spaceship-titanic/data>`_                       |
++-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
+| 10        | Forest Cover Type Prediction      | Classification   | Tabular   | `forest-cover-type-prediction <https://www.kaggle.com/competitions/forest-cover-type-prediction/data>`_ |
++-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
+| 11        | Digit Recognizer                  | Classification   | Image     | `digit-recognizer <https://www.kaggle.com/competitions/digit-recognizer>`_                              |
++-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
+| To be continued ...                                                                                                                                                                    |
++-----------+-----------------------------------+------------------+-----------+---------------------------------------------------------------------------------------------------------+
+
+
+
+🎨 Customize one template for a new competition
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+In order to facilitate RD-Agent to generate competition codes, we have specified a competition code structure:
+
+.. image:: kaggle_template.png
+   :alt: Design of Kaggle Code Template
+   :align: center
+
+- **feature directory** contains the feature engineering code. Generally no modification is required.
+- **model directory** contains the model codes.
+  select_xx.py is used to select different features according to different models.
+  model_xx.py is the basic code of different models. Generally, only some initial parameters need to be adjusted.
+- **fea_share_preprocess.py** is some basic preprocessing code shared by different models. The degree of customization here is high, but the preprocess_script() function needs to be retained, which will be called by train.py
+- **train.py** is the main code, which connects all the codes and is also the code called during the final execution.
+
+**We will soon provide a tool for automatic/semi-automatic template generation.**
+If you want to try a different competition now, you can refer to our current template structure and content to write a new template.
+
+
+🎯 Roadmap
+~~~~~~~~~~~
+
+**Completed:**
+
+- **Kaggle Project Schema Design** ✅
+
+- **RD-Agent Integration with kaggle schema** ✅
+
+**Ongoing:**
+
+- **Template auto generation**
+
+- **Bench Optimization**
+
+  - **Online Bench**
+
+    - **RealMLBench**
+
+      - Ongoing integration
+
+      - Auto online submission
+
+      - Batch Evaluation
+
+  - **Offline Bench**
+  
+    - MLE-Bench
+
+
+🛠️ Usage of modules
+~~~~~~~~~~~~~~~~~~~~~
+
+.. _Env Config: 
+
+- **Env Config**
+
+The following environment variables can be set in the `.env` file to customize the application's behavior:
+
+.. autopydantic_settings:: rdagent.app.kaggle.conf.KaggleBasePropSetting
+    :settings-show-field-summary: False
+    :exclude-members: Config
+
+.. autopydantic_settings:: rdagent.components.coder.factor_coder.config.FactorCoSTEERSettings
+    :settings-show-field-summary: False
+    :members: coder_use_cache, file_based_execution_timeout, select_method, max_loop
+    :exclude-members: Config, fail_task_trial_limit, v1_query_former_trace_limit, v1_query_similar_success_limit, v2_query_component_limit, v2_query_error_limit, v2_query_former_trace_limit, v2_error_summary, v2_knowledge_sampler, v2_add_fail_attempt_to_latest_successful_execution, new_knowledge_base_path, knowledge_base_path, data_folder, data_folder_debug
+    :no-index:

docs/scens/model_agent_fin.rst

@@ -24,7 +24,7 @@ And this is where the **Finance Model Agent** steps in.
 
     <div style="display: flex; justify-content: center; align-items: center;">
       <video width="600" controls>
-        <source src="https://rdagent.azurewebsites.net/media/65bb598f1372c1857ccbf09b2acf5d55830911625048c03102291098.mp4" type="video/mp4">
+        <source src="https://rdagent.azurewebsites.net/media/d85e8cab1da1cd3501d69ce837452f53a971a24911eae7bfa9237137.mp4" type="video/mp4">
         Your browser does not support the video tag.
       </video>
     </div>

docs/scens/model_agent_med.rst

@@ -19,7 +19,7 @@ In this task, we aim at predicting the whether the patients will suffer from Acu
 
     <div style="display: flex; justify-content: center; align-items: center;">
       <video width="600" controls>
-        <source src="https://rdagent.azurewebsites.net/media/65bb598f1372c1857ccbf09b2acf5d55830911625048c03102291098.mp4" type="video/mp4">
+        <source src="https://rdagent.azurewebsites.net/media/1653542fc1b9fa14a306c35c1b1fc48288f980793f38abe82b023af9.mp4" type="video/mp4">
         Your browser does not support the video tag.
       </video>
     </div>
@@ -96,7 +96,14 @@ You can try our demo by running the following command:
     
   - Apply for an account at `PhysioNet <https://physionet.org/>`_.
   - Request access to FIDDLE preprocessed data: `FIDDLE Dataset <https://physionet.org/content/mimic-eicu-fiddle-feature/1.0.0/>`_.
-  - Place your username and password in `.rdagent.app.data_mining.conf`.
+  - Place your username and password in `.env`.
+
+    .. code-block:: bash
+
+        cat << EOF  >> .env
+        DM_USERNAME=<your_username>
+        DM_PASSWORD=<your_password>
+        EOF
 
 
 - 🚀 Run the Application
@@ -116,6 +123,6 @@ You can try our demo by running the following command:
 
 The following environment variables can be set in the `.env` file to customize the application's behavior:
 
-.. autopydantic_settings:: rdagent.app.data_mining.conf.PropSetting
+.. autopydantic_settings:: rdagent.app.data_mining.conf.MedBasePropSetting
     :settings-show-field-summary: False
     :exclude-members: Config

docs/scens/model_copilot_general.rst

@@ -22,7 +22,7 @@ And this is where the **General Model Copilot** steps in.
 
     <div style="display: flex; justify-content: center; align-items: center;">
       <video width="600" controls>
-        <source src="https://rdagent.azurewebsites.net/media/65bb598f1372c1857ccbf09b2acf5d55830911625048c03102291098.mp4" type="video/mp4">
+        <source src="https://rdagent.azurewebsites.net/media/b35f904765b05099b0fcddbebe041a04f4d7bde239657e5fc24bf0cc.mp4" type="video/mp4">
         Your browser does not support the video tag.
       </video>
     </div>

docs/ui.rst

@@ -38,6 +38,7 @@ Use Web App
     - Qlib Factor
     - Data Mining
     - Model from Paper
+    - Kaggle
 
 3. Click the `Config⚙️` button and input the log path (if you set the log_dir parameter, you can select a log_path in the dropdown list).
 

pyproject.toml

@@ -61,6 +61,10 @@ explicit_package_bases = true
 warn_return_any = true
 warn_unused_ignores = true
 
+[[tool.mypy.overrides]]
+ignore_missing_imports = true
+module = "llama"
+
 [tool.pytest.ini_options]
 addopts = "-l -s --durations=0"
 log_cli = true
@@ -77,10 +81,10 @@ src = ["rdagent"]
 [tool.ruff.lint]
 ignore = [
   # https://docs.astral.sh/ruff/rules/#pydocstyle-d
-  "ANN101",
   "ANN401",
   "D",
   "ERA001",
+  "EXE002",
   "FIX",
   "INP001",
   "PGH",
@@ -88,7 +92,7 @@ ignore = [
   "S101",
   "S301",
   "T20",
-  "TCH003",
+  "TC003",
   "TD",
 ]
 select = ["ALL"]

rdagent/app/benchmark/factor/analysis.py

@@ -2,7 +2,9 @@ import json
 import pickle
 from pathlib import Path
 
+import fire
 import matplotlib.pyplot as plt
+import numpy as np
 import pandas as pd
 import seaborn as sns
 
@@ -11,9 +13,10 @@ from rdagent.components.benchmark.eval_method import FactorImplementEval
 
 
 class BenchmarkAnalyzer:
-    def __init__(self, settings):
+    def __init__(self, settings, only_correct_format=False):
         self.settings = settings
         self.index_map = self.load_index_map()
+        self.only_correct_format = only_correct_format
 
     def load_index_map(self):
         index_map = {}
@@ -42,7 +45,24 @@ class BenchmarkAnalyzer:
             final_res[experiment] = processed_data.iloc[-1, :]
         return final_res
 
-    def reformat_succ_rate(self, display_df):
+    def reformat_index(self, display_df):
+        """
+        reform the results from
+
+        .. code-block:: python
+
+                              success rate
+            High_Beta_Factor           0.2
+
+        to
+
+        .. code-block:: python
+
+                                                    success rate
+            Category Difficulty Factor
+            量价       Hard       High_Beta_Factor           0.2
+
+        """
         new_idx = []
         display_df = display_df[display_df.index.isin(self.index_map.keys())]
         for idx in display_df.index:
@@ -63,12 +83,12 @@ class BenchmarkAnalyzer:
         for i in x:
             order_v.append(
                 {
-                    "avg. Run successful rate": 0,
-                    "avg. Format successful rate": 1,
-                    "avg. Correlation (value only)": 2,
-                    "max. Correlation": 3,
-                    "max. accuracy": 4,
-                    "avg. accuracy": 5,
+                    "Avg Run SR": 0,
+                    "Avg Format SR": 1,
+                    "Avg Correlation": 2,
+                    "Max Correlation": 3,
+                    "Max Accuracy": 4,
+                    "Avg Accuracy": 5,
                 }.get(i, i),
             )
         return order_v
@@ -76,11 +96,9 @@ class BenchmarkAnalyzer:
     def analyze_data(self, sum_df):
         index = [
             "FactorSingleColumnEvaluator",
-            "FactorOutputFormatEvaluator",
             "FactorRowCountEvaluator",
             "FactorIndexEvaluator",
-            "FactorMissingValuesEvaluator",
-            "FactorEqualValueCountEvaluator",
+            "FactorEqualValueRatioEvaluator",
             "FactorCorrelationEvaluator",
             "run factor error",
         ]
@@ -91,49 +109,59 @@ class BenchmarkAnalyzer:
         succ_rate = ~run_error
         succ_rate = succ_rate.mean(axis=0).to_frame("success rate")
 
-        succ_rate_f = self.reformat_succ_rate(succ_rate)
-        succ_rate_f
+        succ_rate_f = self.reformat_index(succ_rate)
 
-        sum_df_clean["FactorRowCountEvaluator"]
+        # if it rasis Error when running the evaluator, we will get NaN
+        # Running failures are reguarded to zero score.
+        format_issue = sum_df_clean[["FactorRowCountEvaluator", "FactorIndexEvaluator"]].apply(
+            lambda x: np.mean(x.fillna(0.0)), axis=1
+        )
+        format_succ_rate = format_issue.unstack().T.mean(axis=0).to_frame("success rate")
+        format_succ_rate_f = self.reformat_index(format_succ_rate)
 
-        format_issue = sum_df_clean["FactorRowCountEvaluator"] & sum_df_clean["FactorIndexEvaluator"]
-        eval_series = format_issue.unstack()
-        succ_rate = eval_series.T.fillna(False).astype(bool)  # false indicate failure
-        format_succ_rate = succ_rate.mean(axis=0).to_frame("success rate")
-        format_succ_rate_f = self.reformat_succ_rate(format_succ_rate)
+        corr = sum_df_clean["FactorCorrelationEvaluator"].fillna(0.0)
+        if self.only_correct_format:
+            corr = corr.loc[format_issue == 1.0]
 
-        corr = sum_df_clean["FactorCorrelationEvaluator"] * format_issue
-        corr = corr.unstack().T.mean(axis=0).to_frame("corr(only success)")
-        corr_res = self.reformat_succ_rate(corr)
-        corr_max = sum_df_clean["FactorCorrelationEvaluator"] * format_issue
+        corr_res = corr.unstack().T.mean(axis=0).to_frame("corr(only success)")
+        corr_res = self.reformat_index(corr_res)
 
-        corr_max = corr_max.unstack().T.max(axis=0).to_frame("corr(only success)")
-        corr_max_res = self.reformat_succ_rate(corr_max)
+        corr_max = corr.unstack().T.max(axis=0).to_frame("corr(only success)")
+        corr_max_res = self.reformat_index(corr_max)
 
-        value_max = sum_df_clean["FactorMissingValuesEvaluator"] * format_issue
+        value_max = sum_df_clean["FactorEqualValueRatioEvaluator"]
         value_max = value_max.unstack().T.max(axis=0).to_frame("max_value")
-        value_max_res = self.reformat_succ_rate(value_max)
+        value_max_res = self.reformat_index(value_max)
 
         value_avg = (
-            (sum_df_clean["FactorMissingValuesEvaluator"] * format_issue).unstack().T.mean(axis=0).to_frame("avg_value")
+            (sum_df_clean["FactorEqualValueRatioEvaluator"] * format_issue)
+            .unstack()
+            .T.mean(axis=0)
+            .to_frame("avg_value")
         )
-        value_avg_res = self.reformat_succ_rate(value_avg)
+        value_avg_res = self.reformat_index(value_avg)
 
         result_all = pd.concat(
             {
-                "avg. Correlation (value only)": corr_res.iloc[:, 0],
-                "avg. Format successful rate": format_succ_rate_f.iloc[:, 0],
-                "avg. Run successful rate": succ_rate_f.iloc[:, 0],
-                "max. Correlation": corr_max_res.iloc[:, 0],
-                "max. accuracy": value_max_res.iloc[:, 0],
-                "avg. accuracy": value_avg_res.iloc[:, 0],
+                "Avg Correlation": corr_res.iloc[:, 0],
+                "Avg Format SR": format_succ_rate_f.iloc[:, 0],
+                "Avg Run SR": succ_rate_f.iloc[:, 0],
+                "Max Correlation": corr_max_res.iloc[:, 0],
+                "Max Accuracy": value_max_res.iloc[:, 0],
+                "Avg Accuracy": value_avg_res.iloc[:, 0],
             },
             axis=1,
         )
 
-        df = result_all.sort_index(axis=1, key=self.result_all_key_order)
+        df = result_all.sort_index(axis=1, key=self.result_all_key_order).sort_index(axis=0)
         print(df)
 
+        print()
+        print(df.groupby("Category").mean())
+
+        print()
+        print(df.mean())
+
         # Calculate the mean of each column
         mean_values = df.fillna(0.0).mean()
         mean_df = pd.DataFrame(mean_values).T
@@ -159,25 +187,39 @@ class Plotter:
         plt.rc("figure", titlesize=font_size)
 
     @staticmethod
-    def plot_data(data, file_name):
-        plt.figure(figsize=(10, 6))
-        sns.barplot(x="index", y="b", hue="a", data=data)
-        plt.xlabel("Method")
+    def plot_data(data, file_name, title):
+        plt.figure(figsize=(10, 10))
         plt.ylabel("Value")
-        plt.title("Comparison of Different Methods")
+        colors = ["#3274A1", "#E1812C", "#3A923A", "#C03D3E"]
+        plt.bar(data["a"], data["b"], color=colors, capsize=5)
+        for idx, row in data.iterrows():
+            plt.text(idx, row["b"] + 0.01, f"{row['b']:.2f}", ha="center", va="bottom")
+        plt.suptitle(title, y=0.98)
+        plt.xticks(rotation=45)
+        plt.ylim(0, 1)
+        plt.tight_layout()
         plt.savefig(file_name)
 
 
-if __name__ == "__main__":
+def main(
+    path="git_ignore_folder/eval_results/res_promptV220240724-060037.pkl",
+    round=1,
+    title="Comparison of Different Methods",
+    only_correct_format=False,
+):
     settings = BenchmarkSettings()
-    benchmark = BenchmarkAnalyzer(settings)
+    benchmark = BenchmarkAnalyzer(settings, only_correct_format=only_correct_format)
     results = {
-        "1 round experiment": "git_ignore_folder/eval_results/res_promptV220240724-060037.pkl",
+        f"{round} round experiment": path,
     }
     final_results = benchmark.process_results(results)
     final_results_df = pd.DataFrame(final_results)
 
     Plotter.change_fs(20)
-    plot_data = final_results_df.drop(["max. accuracy", "avg. accuracy"], axis=0).T
+    plot_data = final_results_df.drop(["Max Accuracy", "Avg Accuracy"], axis=0).T
     plot_data = plot_data.reset_index().melt("index", var_name="a", value_name="b")
-    Plotter.plot_data(plot_data, "rdagent/app/quant_factor_benchmark/comparison_plot.png")
+    Plotter.plot_data(plot_data, "./comparison_plot.png", title)
+
+
+if __name__ == "__main__":
+    fire.Fire(main)

rdagent/app/benchmark/factor/eval.py

@@ -1,16 +1,9 @@
-import os
-import pickle
-import time
-from pathlib import Path
-from pprint import pprint
-
 from rdagent.app.qlib_rd_loop.conf import FACTOR_PROP_SETTING
 from rdagent.components.benchmark.conf import BenchmarkSettings
 from rdagent.components.benchmark.eval_method import FactorImplementEval
 from rdagent.core.scenario import Scenario
 from rdagent.core.utils import import_class
 from rdagent.log import rdagent_logger as logger
-from rdagent.scenarios.qlib.experiment.factor_experiment import QlibFactorScenario
 from rdagent.scenarios.qlib.factor_experiment_loader.json_loader import (
     FactorTestCaseLoaderFromJsonFile,
 )
@@ -25,7 +18,7 @@ if __name__ == "__main__":
     # 3.declare the method to be tested and pass the arguments.
 
     scen: Scenario = import_class(FACTOR_PROP_SETTING.scen)()
-    generate_method = import_class(bs.bench_method_cls)(scen=scen)
+    generate_method = import_class(bs.bench_method_cls)(scen=scen, **bs.bench_method_extra_kwargs)
     # 4.declare the eval method and pass the arguments.
     eval_method = FactorImplementEval(
         method=generate_method,
@@ -36,7 +29,7 @@ if __name__ == "__main__":
     )
 
     # 5.run the eval
-    res = eval_method.eval()
+    res = eval_method.eval(eval_method.develop())
 
     # 6.save the result
     logger.log_object(res)

rdagent/app/benchmark/model/eval.py

@@ -1,6 +1,6 @@
 from pathlib import Path
 
-from rdagent.components.coder.model_coder.CoSTEER import ModelCoSTEER
+from rdagent.components.coder.model_coder import ModelCoSTEER
 from rdagent.components.loader.task_loader import ModelTaskLoaderJson, ModelWsLoader
 from rdagent.scenarios.qlib.experiment.model_experiment import (
     QlibModelExperiment,
@@ -13,7 +13,7 @@ if __name__ == "__main__":
     from rdagent.components.coder.model_coder.benchmark.eval import ModelImpValEval
     from rdagent.components.coder.model_coder.one_shot import ModelCodeWriter
 
-    bench_folder = DIRNAME.parent.parent / "components" / "coder" / "model_coder" / "benchmark"
+    bench_folder = DIRNAME.parent.parent.parent / "components" / "coder" / "model_coder" / "benchmark"
     mtl = ModelTaskLoaderJson(str(bench_folder / "model_dict.json"))
 
     task_l = mtl.load()

rdagent/app/cli.py

@@ -1,29 +1,35 @@
 """
 CLI entrance for all rdagent application.
 
-This will 
+This will
 - make rdagent a nice entry and
 - autoamtically load dotenv
 """
+
+from dotenv import load_dotenv
+
+load_dotenv(".env")
+# 1) Make sure it is at the beginning of the script so that it will load dotenv before initializing BaseSettings.
+# 2) The ".env" argument is necessary to make sure it loads `.env` from the current directory.
+
 import subprocess
 from importlib.resources import path as rpath
 
 import fire
-from dotenv import load_dotenv
 
 from rdagent.app.data_mining.model import main as med_model
 from rdagent.app.general_model.general_model import (
     extract_models_and_implement as general_model,
 )
+from rdagent.app.kaggle.loop import main as kaggle_main
 from rdagent.app.qlib_rd_loop.factor import main as fin_factor
 from rdagent.app.qlib_rd_loop.factor_from_report import main as fin_factor_report
 from rdagent.app.qlib_rd_loop.model import main as fin_model
+from rdagent.app.utils.health_check import health_check
 from rdagent.app.utils.info import collect_info
 
-load_dotenv()
 
-
-def ui(port=80, log_dir="", debug=False):
+def ui(port=19899, log_dir="", debug=False):
     """
     start web app to show the log traces.
     """
@@ -47,6 +53,8 @@ def app():
             "med_model": med_model,
             "general_model": general_model,
             "ui": ui,
+            "health_check": health_check,
             "collect_info": collect_info,
+            "kaggle": kaggle_main,
         }
     )

rdagent/app/data_mining/conf.py

@@ -1,16 +1,12 @@
 from pathlib import Path
 
-from pydantic_settings import BaseSettings
+from pydantic_settings import SettingsConfigDict
 
 from rdagent.components.workflow.conf import BasePropSetting
 
 
-class PropSetting(BasePropSetting):
-    class Config:
-        env_prefix = "DM_"
-        """Use `DM_` as prefix for environment variables"""
-        protected_namespaces = ()
-        """Add 'model_' to the protected namespaces"""
+class MedBasePropSetting(BasePropSetting):
+    model_config = SettingsConfigDict(env_prefix="DM_", protected_namespaces=())
 
     # 1) overriding the default
     scen: str = "rdagent.scenarios.data_mining.experiment.model_experiment.DMModelScenario"
@@ -28,7 +24,7 @@ class PropSetting(BasePropSetting):
     runner: str = "rdagent.scenarios.data_mining.developer.model_runner.DMModelRunner"
     """Runner class"""
 
-    summarizer: str = "rdagent.scenarios.data_mining.developer.feedback.DMModelHypothesisExperiment2Feedback"
+    summarizer: str = "rdagent.scenarios.data_mining.developer.feedback.DMModelExperiment2Feedback"
     """Summarizer class"""
 
     evolving_n: int = 10
@@ -46,4 +42,4 @@ class PropSetting(BasePropSetting):
     """Physionet account password"""
 
 
-PROP_SETTING = PropSetting()
+MED_PROP_SETTING = MedBasePropSetting()

rdagent/app/data_mining/model.py

@@ -1,6 +1,6 @@
 import fire
 
-from rdagent.app.data_mining.conf import PROP_SETTING
+from rdagent.app.data_mining.conf import MED_PROP_SETTING
 from rdagent.components.workflow.rd_loop import RDLoop
 from rdagent.core.exception import ModelEmptyError
 
@@ -21,7 +21,7 @@ def main(path=None, step_n=None):
 
     """
     if path is None:
-        model_loop = ModelRDLoop(PROP_SETTING)
+        model_loop = ModelRDLoop(MED_PROP_SETTING)
     else:
         model_loop = ModelRDLoop.load(path)
     model_loop.run(step_n=step_n)

rdagent/app/data_science/conf.py

@@ -0,0 +1,34 @@
+from pydantic_settings import SettingsConfigDict
+
+from rdagent.app.kaggle.conf import KaggleBasePropSetting
+
+
+class DataScienceBasePropSetting(KaggleBasePropSetting):
+    model_config = SettingsConfigDict(env_prefix="DS_", protected_namespaces=())
+
+    # Main components
+    ## Scen
+    scen: str = "rdagent.scenarios.data_science.scen.KaggleScen"
+    """Scenario class for data mining model"""
+
+    ## Workflow Related
+    consecutive_errors: int = 5
+
+    debug_timeout: int = 600
+    """The timeout limit for running on debugging data"""
+    full_timeout: int = 3600
+    """The timeout limit for running on full data"""
+
+    ### specific feature
+
+    #### enable specification
+    spec_enabled: bool = True
+
+    proposal_version: str = "v1"
+    coder_on_whole_pipeline: bool = False
+
+    coder_max_loop: int = 10
+    runner_max_loop: int = 3
+
+
+DS_RD_SETTING = DataScienceBasePropSetting()

rdagent/app/data_science/debug.py

@@ -0,0 +1,6 @@
+import fire
+
+from rdagent.scenarios.data_science.debug.data import create_debug_data
+
+if __name__ == "__main__":
+    fire.Fire(create_debug_data)

rdagent/app/data_science/loop.py

@@ -0,0 +1,204 @@
+from pathlib import Path
+from typing import Any
+
+import fire
+
+from rdagent.app.data_science.conf import DS_RD_SETTING
+from rdagent.components.coder.data_science.ensemble import EnsembleCoSTEER
+from rdagent.components.coder.data_science.ensemble.exp import EnsembleTask
+from rdagent.components.coder.data_science.feature import FeatureCoSTEER
+from rdagent.components.coder.data_science.feature.exp import FeatureTask
+from rdagent.components.coder.data_science.model import ModelCoSTEER
+from rdagent.components.coder.data_science.model.exp import ModelTask
+from rdagent.components.coder.data_science.pipeline import PipelineCoSTEER
+from rdagent.components.coder.data_science.pipeline.exp import PipelineTask
+from rdagent.components.coder.data_science.raw_data_loader import DataLoaderCoSTEER
+from rdagent.components.coder.data_science.raw_data_loader.exp import DataLoaderTask
+from rdagent.components.coder.data_science.workflow import WorkflowCoSTEER
+from rdagent.components.coder.data_science.workflow.exp import WorkflowTask
+from rdagent.components.workflow.conf import BasePropSetting
+from rdagent.components.workflow.rd_loop import RDLoop
+from rdagent.core.exception import CoderError, RunnerError
+from rdagent.core.proposal import ExperimentFeedback
+from rdagent.core.scenario import Scenario
+from rdagent.core.utils import import_class
+from rdagent.log import rdagent_logger as logger
+from rdagent.scenarios.data_science.dev.feedback import DSExperiment2Feedback
+from rdagent.scenarios.data_science.dev.runner import DSCoSTEERRunner
+from rdagent.scenarios.data_science.experiment.experiment import DSExperiment
+from rdagent.scenarios.data_science.proposal.exp_gen import DSExpGen, DSTrace
+from rdagent.scenarios.kaggle.kaggle_crawler import download_data
+
+
+class DataScienceRDLoop(RDLoop):
+    skip_loop_error = (CoderError, RunnerError)
+
+    def __init__(self, PROP_SETTING: BasePropSetting):
+        logger.log_object(PROP_SETTING.competition, tag="competition")
+        scen: Scenario = import_class(PROP_SETTING.scen)(PROP_SETTING.competition)
+
+        ### shared components in the workflow  # TODO: check if
+        knowledge_base = (
+            import_class(PROP_SETTING.knowledge_base)(PROP_SETTING.knowledge_base_path, scen)
+            if PROP_SETTING.knowledge_base != ""
+            else None
+        )
+
+        # 1) task generation from scratch
+        # self.scratch_gen: tuple[HypothesisGen, Hypothesis2Experiment] = DummyHypothesisGen(scen),
+
+        # 2) task generation from a complete solution
+        # self.exp_gen: ExpGen = import_class(PROP_SETTING.exp_gen)(scen)
+        self.exp_gen = DSExpGen(scen)
+        self.data_loader_coder = DataLoaderCoSTEER(scen)
+        self.feature_coder = FeatureCoSTEER(scen)
+        self.model_coder = ModelCoSTEER(scen)
+        self.ensemble_coder = EnsembleCoSTEER(scen)
+        self.workflow_coder = WorkflowCoSTEER(scen)
+
+        self.pipeline_coder = PipelineCoSTEER(scen)
+
+        self.runner = DSCoSTEERRunner(scen)
+        # self.summarizer: Experiment2Feedback = import_class(PROP_SETTING.summarizer)(scen)
+        # logger.log_object(self.summarizer, tag="summarizer")
+
+        # self.trace = KGTrace(scen=scen, knowledge_base=knowledge_base)
+        self.trace = DSTrace(scen=scen)
+        self.summarizer = DSExperiment2Feedback(scen)
+        super(RDLoop, self).__init__()
+
+    def direct_exp_gen(self, prev_out: dict[str, Any]):
+        exp = self.exp_gen.gen(self.trace)
+        logger.log_object(exp)
+
+        # FIXME: this is for LLM debug webapp, remove this when the debugging is done.
+        logger.log_object(exp, tag="debug_exp_gen")
+        return exp
+
+    def coding(self, prev_out: dict[str, Any]):
+        exp = prev_out["direct_exp_gen"]
+        for tasks in exp.pending_tasks_list:
+            exp.sub_tasks = tasks
+            with logger.tag(f"{exp.sub_tasks[0].__class__.__name__}"):
+                if isinstance(exp.sub_tasks[0], DataLoaderTask):
+                    exp = self.data_loader_coder.develop(exp)
+                elif isinstance(exp.sub_tasks[0], FeatureTask):
+                    exp = self.feature_coder.develop(exp)
+                elif isinstance(exp.sub_tasks[0], ModelTask):
+                    exp = self.model_coder.develop(exp)
+                elif isinstance(exp.sub_tasks[0], EnsembleTask):
+                    exp = self.ensemble_coder.develop(exp)
+                elif isinstance(exp.sub_tasks[0], WorkflowTask):
+                    exp = self.workflow_coder.develop(exp)
+                elif isinstance(exp.sub_tasks[0], PipelineTask):
+                    exp = self.pipeline_coder.develop(exp)
+                else:
+                    raise NotImplementedError(f"Unsupported component in DataScienceRDLoop: {exp.hypothesis.component}")
+            exp.sub_tasks = []
+        logger.log_object(exp)
+        return exp
+
+    def running(self, prev_out: dict[str, Any]):
+        exp: DSExperiment = prev_out["coding"]
+        if exp.is_ready_to_run():
+            new_exp = self.runner.develop(exp)
+            logger.log_object(new_exp)
+            return new_exp
+        return exp
+
+    def feedback(self, prev_out: dict[str, Any]) -> ExperimentFeedback:
+        """
+        Assumption:
+        - If we come to feedback phase, the previous development steps are successful.
+        """
+        exp: DSExperiment = prev_out["running"]
+        if self.trace.next_incomplete_component() is None or DS_RD_SETTING.coder_on_whole_pipeline:
+            # we have alreadly completed components in previous trace. So current loop is focusing on a new proposed idea.
+            # So we need feedback for the proposal.
+            feedback = self.summarizer.generate_feedback(exp, self.trace)
+        else:
+            # Otherwise, it is on drafting stage, don't need complicated feedbacks.
+            feedback = ExperimentFeedback(
+                reason=f"{exp.hypothesis.component} is completed.",
+                decision=True,
+            )
+        logger.log_object(feedback)
+        return feedback
+
+    def record(self, prev_out: dict[str, Any]):
+        e = prev_out.get(self.EXCEPTION_KEY, None)
+        if e is None:
+            self.trace.hist.append((prev_out["running"], prev_out["feedback"]))
+        else:
+            self.trace.hist.append(
+                (
+                    prev_out["direct_exp_gen"] if isinstance(e, CoderError) else prev_out["coding"],
+                    ExperimentFeedback.from_exception(e),
+                )
+            )
+            if (
+                self.trace.sota_experiment() is None
+                and len(self.trace.hist) >= DS_RD_SETTING.consecutive_errors
+                and not DS_RD_SETTING.coder_on_whole_pipeline
+            ):
+                # if {in inital/drafting stage} and {tried enough times}
+                for _, fb in self.trace.hist[-DS_RD_SETTING.consecutive_errors :]:
+                    if fb:
+                        break  # any success will stop restarting.
+                else:  # otherwise restart it
+                    logger.error("Consecutive errors reached the limit. Dumping trace.")
+                    logger.log_object(self.trace, tag="trace before restart")
+                    self.trace = DSTrace(scen=self.trace.scen, knowledge_base=self.trace.knowledge_base)
+        logger.log_object(self.trace, tag="trace")
+        logger.log_object(self.trace.sota_experiment(), tag="SOTA experiment")
+
+
+def main(
+    path=None, output_path=None, step_n=None, loop_n=None, competition="bms-molecular-translation", do_truncate=True
+):
+    """
+
+    Parameters
+    ----------
+    path :
+        path like `$LOG_PATH/__session__/1/0_propose`. It indicates that we restore the state that after finish the step 0 in loop 1
+    output_path :
+        path like `$LOG_PATH`. It indicates that where we want to save our session and log information.
+    step_n :
+        How many steps to run; if None, it will run forever until error or KeyboardInterrupt
+    loop_n :
+        How many loops to run; if None, it will run forever until error or KeyboardInterrupt
+        - if current loop is incomplete, it will be counted as the first loop for completion.
+        - if both step_n and loop_n are provided, the process will stop as soon as either condition is met.
+    competition :
+    do_truncate :
+        If set to True, the logger will truncate the future log messages by calling `logger.storage.truncate`.
+
+
+    Auto R&D Evolving loop for models in a Kaggle scenario.
+    You can continue running session by
+    .. code-block:: bash
+        dotenv run -- python rdagent/app/data_science/loop.py [--competition titanic] $LOG_PATH/__session__/1/0_propose  --step_n 1   # `step_n` is a optional parameter
+        rdagent kaggle --competition playground-series-s4e8  # You are encouraged to use this one.
+    """
+    if competition is not None:
+        DS_RD_SETTING.competition = competition
+
+    if DS_RD_SETTING.competition:
+        if DS_RD_SETTING.scen.endswith("KaggleScen"):
+            download_data(competition=DS_RD_SETTING.competition, settings=DS_RD_SETTING)
+        else:
+            if not Path(f"{DS_RD_SETTING.local_data_path}/{competition}").exists():
+                logger.error(f"Please prepare data for competition {competition} first.")
+                return
+    else:
+        logger.error("Please specify competition name.")
+    if path is None:
+        kaggle_loop = DataScienceRDLoop(DS_RD_SETTING)
+    else:
+        kaggle_loop = DataScienceRDLoop.load(path, output_path, do_truncate)
+    kaggle_loop.run(step_n=step_n, loop_n=loop_n)
+
+
+if __name__ == "__main__":
+    fire.Fire(main)

rdagent/app/general_model/general_model.py

@@ -1,9 +1,3 @@
-from dotenv import load_dotenv
-
-from rdagent.scenarios.general_model.scenario import GeneralModelScenario
-
-load_dotenv(override=True)
-
 import fire
 
 from rdagent.components.coder.model_coder.task_loader import (
@@ -13,6 +7,7 @@ from rdagent.components.document_reader.document_reader import (
     extract_first_page_screenshot_from_pdf,
 )
 from rdagent.log import rdagent_logger as logger
+from rdagent.scenarios.general_model.scenario import GeneralModelScenario
 from rdagent.scenarios.qlib.developer.model_coder import QlibModelCoSTEER
 
 
@@ -47,7 +42,6 @@ def extract_models_and_implement(report_file_path: str) -> None:
     with logger.tag("d"):
         exp = QlibModelCoSTEER(scenario).develop(exp)
         logger.log_object(exp, tag="developed_experiment")
-    return exp
 
 
 if __name__ == "__main__":

rdagent/app/kaggle/conf.py

@@ -1,42 +1,77 @@
-from pathlib import Path
+from pydantic_settings import SettingsConfigDict
 
-from pydantic_settings import BaseSettings
-
-from rdagent.components.workflow.conf import BasePropSetting
+from rdagent.core.conf import ExtendedBaseSettings
 
 
-class PropSetting(BasePropSetting):
-    class Config:
-        env_prefix = "KG_"
-        """Use `KG_` as prefix for environment variables"""
-        protected_namespaces = ()
-        """Add 'model_' to the protected namespaces"""
+class KaggleBasePropSetting(ExtendedBaseSettings):
+    model_config = SettingsConfigDict(env_prefix="KG_", protected_namespaces=())
 
     # 1) overriding the default
-    scen: str = "rdagent.scenarios.kaggle.experiment.model_experiment.KGModelScenario"
+    scen: str = "rdagent.scenarios.kaggle.experiment.scenario.KGScenario"
     """Scenario class for data mining model"""
 
-    hypothesis_gen: str = "rdagent.scenarios.kaggle.proposal.model_proposal.KGModelHypothesisGen"
+    hypothesis_gen: str = "rdagent.scenarios.kaggle.proposal.proposal.KGHypothesisGen"
     """Hypothesis generation class"""
 
-    hypothesis2experiment: str = "rdagent.scenarios.kaggle.proposal.model_proposal.KGModelHypothesis2Experiment"
+    hypothesis2experiment: str = "rdagent.scenarios.kaggle.proposal.proposal.KGHypothesis2Experiment"
     """Hypothesis to experiment class"""
 
-    coder: str = "rdagent.scenarios.kaggle.developer.model_coder.KGModelCoSTEER"
-    """Coder class"""
+    feature_coder: str = "rdagent.scenarios.kaggle.developer.coder.KGFactorCoSTEER"
+    """Feature Coder class"""
 
-    runner: str = "rdagent.scenarios.kaggle.developer.model_runner.KGModelRunner"
-    """Runner class"""
+    model_feature_selection_coder: str = "rdagent.scenarios.kaggle.developer.coder.KGModelFeatureSelectionCoder"
+    """Model Feature Selection Coder class"""
 
-    summarizer: str = "rdagent.scenarios.kaggle.developer.feedback.KGModelHypothesisExperiment2Feedback"
+    model_coder: str = "rdagent.scenarios.kaggle.developer.coder.KGModelCoSTEER"
+    """Model Coder class"""
+
+    feature_runner: str = "rdagent.scenarios.kaggle.developer.runner.KGFactorRunner"
+    """Feature Runner class"""
+
+    model_runner: str = "rdagent.scenarios.kaggle.developer.runner.KGModelRunner"
+    """Model Runner class"""
+
+    summarizer: str = "rdagent.scenarios.kaggle.developer.feedback.KGExperiment2Feedback"
     """Summarizer class"""
 
     evolving_n: int = 10
     """Number of evolutions"""
 
-    evolving_n: int = 10
-
     competition: str = ""
+    """Kaggle competition name, e.g., 'sf-crime'"""
+
+    template_path: str = "rdagent/scenarios/kaggle/experiment/templates"
+    """Kaggle competition base templates path"""
+
+    local_data_path: str = ""
+    """Folder storing Kaggle competition data"""
+
+    if_using_mle_data: bool = False
+    auto_submit: bool = False
+    """Automatically upload and submit each experiment result to Kaggle platform"""
+    # Conditionally set the knowledge_base based on the use of graph RAG
+    knowledge_base: str = ""
+    """Knowledge base class, uses 'KGKnowledgeGraph' when advanced graph-based RAG is enabled, otherwise empty."""
+    if_action_choosing_based_on_UCB: bool = False
+    """Enable decision mechanism based on UCB algorithm"""
+
+    domain_knowledge_path: str = "/data/userdata/share/kaggle/domain_knowledge"
+    """Folder storing domain knowledge files in .case format"""
+
+    knowledge_base_path: str = "kg_graph.pkl"
+    """Advanced version of graph-based RAG"""
+
+    rag_path: str = "git_ignore_folder/kaggle_vector_base.pkl"
+    """Base version of vector-based RAG"""
+
+    if_using_vector_rag: bool = False
+    """Enable basic vector-based RAG"""
+
+    if_using_graph_rag: bool = False
+    """Enable advanced graph-based RAG"""
+
+    mini_case: bool = False
+    """Enable mini-case study for experiments"""
 
 
-PROP_SETTING = PropSetting()
+KAGGLE_IMPLEMENT_SETTING = KaggleBasePropSetting()

rdagent/app/kaggle/loop.py

@@ -0,0 +1,146 @@
+import subprocess
+from typing import Any
+
+import fire
+
+from rdagent.app.kaggle.conf import KAGGLE_IMPLEMENT_SETTING
+from rdagent.components.workflow.conf import BasePropSetting
+from rdagent.components.workflow.rd_loop import RDLoop
+from rdagent.core.developer import Developer
+from rdagent.core.exception import CoderError, FactorEmptyError, ModelEmptyError
+from rdagent.core.proposal import (
+    Experiment2Feedback,
+    Hypothesis2Experiment,
+    HypothesisGen,
+)
+from rdagent.core.scenario import Scenario
+from rdagent.core.utils import import_class
+from rdagent.log import rdagent_logger as logger
+from rdagent.scenarios.kaggle.experiment.scenario import (
+    KG_ACTION_FEATURE_ENGINEERING,
+    KG_ACTION_FEATURE_PROCESSING,
+    KG_ACTION_MODEL_FEATURE_SELECTION,
+)
+from rdagent.scenarios.kaggle.experiment.utils import python_files_to_notebook
+from rdagent.scenarios.kaggle.kaggle_crawler import download_data
+from rdagent.scenarios.kaggle.proposal.proposal import KGTrace
+
+
+class KaggleRDLoop(RDLoop):
+    def __init__(self, PROP_SETTING: BasePropSetting):
+        with logger.tag("init"):
+            scen: Scenario = import_class(PROP_SETTING.scen)(PROP_SETTING.competition)
+            logger.log_object(scen, tag="scenario")
+            knowledge_base = (
+                import_class(PROP_SETTING.knowledge_base)(PROP_SETTING.knowledge_base_path, scen)
+                if PROP_SETTING.knowledge_base != ""
+                else None
+            )
+            logger.log_object(knowledge_base, tag="knowledge_base")
+            self.hypothesis_gen: HypothesisGen = import_class(PROP_SETTING.hypothesis_gen)(scen)
+            logger.log_object(self.hypothesis_gen, tag="hypothesis generator")
+            self.hypothesis2experiment: Hypothesis2Experiment = import_class(PROP_SETTING.hypothesis2experiment)()
+            logger.log_object(self.hypothesis2experiment, tag="hypothesis2experiment")
+            self.feature_coder: Developer = import_class(PROP_SETTING.feature_coder)(scen)
+            logger.log_object(self.feature_coder, tag="feature coder")
+            self.model_feature_selection_coder: Developer = import_class(PROP_SETTING.model_feature_selection_coder)(
+                scen
+            )
+            logger.log_object(self.model_feature_selection_coder, tag="model feature selection coder")
+            self.model_coder: Developer = import_class(PROP_SETTING.model_coder)(scen)
+            logger.log_object(self.model_coder, tag="model coder")
+            self.feature_runner: Developer = import_class(PROP_SETTING.feature_runner)(scen)
+            logger.log_object(self.feature_runner, tag="feature runner")
+            self.model_runner: Developer = import_class(PROP_SETTING.model_runner)(scen)
+            logger.log_object(self.model_runner, tag="model runner")
+            self.summarizer: Experiment2Feedback = import_class(PROP_SETTING.summarizer)(scen)
+            logger.log_object(self.summarizer, tag="summarizer")
+            self.trace = KGTrace(scen=scen, knowledge_base=knowledge_base)
+            super(RDLoop, self).__init__()
+
+    def coding(self, prev_out: dict[str, Any]):
+        with logger.tag("d"):  # develop
+            if prev_out["direct_exp_gen"]["propose"].action in [
+                KG_ACTION_FEATURE_ENGINEERING,
+                KG_ACTION_FEATURE_PROCESSING,
+            ]:
+                exp = self.feature_coder.develop(prev_out["direct_exp_gen"]["exp_gen"])
+            elif prev_out["direct_exp_gen"]["propose"].action == KG_ACTION_MODEL_FEATURE_SELECTION:
+                exp = self.model_feature_selection_coder.develop(prev_out["direct_exp_gen"]["exp_gen"])
+            else:
+                exp = self.model_coder.develop(prev_out["direct_exp_gen"]["exp_gen"])
+            logger.log_object(exp.sub_workspace_list, tag="coder result")
+        return exp
+
+    def running(self, prev_out: dict[str, Any]):
+        with logger.tag("ef"):  # evaluate and feedback
+            if prev_out["direct_exp_gen"]["propose"].action in [
+                KG_ACTION_FEATURE_ENGINEERING,
+                KG_ACTION_FEATURE_PROCESSING,
+            ]:
+                exp = self.feature_runner.develop(prev_out["coding"])
+            else:
+                exp = self.model_runner.develop(prev_out["coding"])
+            logger.log_object(exp, tag="runner result")
+            if KAGGLE_IMPLEMENT_SETTING.competition in [
+                "optiver-realized-volatility-prediction",
+                "covid19-global-forecasting-week-1",
+            ]:
+                try:
+                    python_files_to_notebook(
+                        KAGGLE_IMPLEMENT_SETTING.competition, exp.experiment_workspace.workspace_path
+                    )
+                except Exception as e:
+                    logger.error(f"Merge python files to one file failed: {e}")
+            if KAGGLE_IMPLEMENT_SETTING.auto_submit:
+                csv_path = exp.experiment_workspace.workspace_path / "submission.csv"
+                try:
+                    subprocess.run(
+                        [
+                            "kaggle",
+                            "competitions",
+                            "submit",
+                            "-f",
+                            str(csv_path.absolute()),
+                            "-m",
+                            str(csv_path.parent.absolute()),
+                            KAGGLE_IMPLEMENT_SETTING.competition,
+                        ],
+                        check=True,
+                    )
+                except subprocess.CalledProcessError as e:
+                    logger.error(f"Auto submission failed: \n{e}")
+                except Exception as e:
+                    logger.error(f"Other exception when use kaggle api:\n{e}")
+
+        return exp
+
+    skip_loop_error = (ModelEmptyError, FactorEmptyError, CoderError)
+
+
+def main(path=None, step_n=None, competition=None):
+    """
+    Auto R&D Evolving loop for models in a kaggle{} scenario.
+    You can continue running session by
+    .. code-block:: bash
+        dotenv run -- python rdagent/app/kaggle/loop.py [--competition titanic] $LOG_PATH/__session__/1/0_propose  --step_n 1   # `step_n` is a optional parameter
+        rdagent kaggle --competition playground-series-s4e8  # You are encouraged to use this one.
+    """
+    if competition:
+        KAGGLE_IMPLEMENT_SETTING.competition = competition
+        download_data(competition=competition, settings=KAGGLE_IMPLEMENT_SETTING)
+        if KAGGLE_IMPLEMENT_SETTING.if_using_graph_rag:
+            KAGGLE_IMPLEMENT_SETTING.knowledge_base = (
+                "rdagent.scenarios.kaggle.knowledge_management.graph.KGKnowledgeGraph"
+            )
+    else:
+        logger.error("Please specify competition name.")
+    if path is None:
+        kaggle_loop = KaggleRDLoop(KAGGLE_IMPLEMENT_SETTING)
+    else:
+        kaggle_loop = KaggleRDLoop.load(path)
+    kaggle_loop.run(step_n=step_n)
+
+
+if __name__ == "__main__":
+    fire.Fire(main)

rdagent/app/kaggle/model.py

@@ -1,65 +0,0 @@
-from collections import defaultdict
-
-import fire
-
-from rdagent.app.kaggle.conf import PROP_SETTING
-from rdagent.components.workflow.conf import BasePropSetting
-from rdagent.components.workflow.rd_loop import RDLoop
-from rdagent.core.exception import ModelEmptyError
-from rdagent.core.proposal import (
-    Hypothesis2Experiment,
-    HypothesisExperiment2Feedback,
-    HypothesisGen,
-    Trace,
-)
-from rdagent.core.utils import import_class
-from rdagent.log import rdagent_logger as logger
-
-
-class ModelRDLoop(RDLoop):
-    def __init__(self, PROP_SETTING: BasePropSetting):
-        with logger.tag("init"):
-            scen: Scenario = import_class(PROP_SETTING.scen)(PROP_SETTING.competition)
-            logger.log_object(scen, tag="scenario")
-
-            self.hypothesis_gen: HypothesisGen = import_class(PROP_SETTING.hypothesis_gen)(scen)
-            logger.log_object(self.hypothesis_gen, tag="hypothesis generator")
-
-            self.hypothesis2experiment: Hypothesis2Experiment = import_class(PROP_SETTING.hypothesis2experiment)()
-            logger.log_object(self.hypothesis2experiment, tag="hypothesis2experiment")
-
-            self.coder: Developer = import_class(PROP_SETTING.coder)(scen)
-            logger.log_object(self.coder, tag="coder")
-            self.runner: Developer = import_class(PROP_SETTING.runner)(scen)
-            logger.log_object(self.runner, tag="runner")
-
-            self.summarizer: HypothesisExperiment2Feedback = import_class(PROP_SETTING.summarizer)(scen)
-            logger.log_object(self.summarizer, tag="summarizer")
-            self.trace = Trace(scen=scen)
-            super(RDLoop, self).__init__()
-
-    skip_loop_error = (ModelEmptyError,)
-
-
-def main(path=None, step_n=None, competition=None):
-    """
-    Auto R&D Evolving loop for models in a kaggle{} scenario.
-
-    You can continue running session by
-
-    .. code-block:: python
-
-        dotenv run -- python rdagent/app/kaggle/model.py [--competition titanic] $LOG_PATH/__session__/1/0_propose  --step_n 1   # `step_n` is a optional paramter
-
-    """
-    if competition:
-        PROP_SETTING.competition = competition
-    if path is None:
-        model_loop = ModelRDLoop(PROP_SETTING)
-    else:
-        model_loop = ModelRDLoop.load(path)
-    model_loop.run(step_n=step_n)
-
-
-if __name__ == "__main__":
-    fire.Fire(main)

rdagent/app/qlib_rd_loop/conf.py

@@ -1,14 +1,10 @@
-from pydantic_settings import BaseSettings
+from pydantic_settings import SettingsConfigDict
 
 from rdagent.components.workflow.conf import BasePropSetting
 
 
 class ModelBasePropSetting(BasePropSetting):
-    class Config:
-        env_prefix = "QLIB_MODEL_"
-        """Use `QLIB_MODEL_` as prefix for environment variables"""
-        protected_namespaces = ()
-        """Add 'model_' to the protected namespaces"""
+    model_config = SettingsConfigDict(env_prefix="QLIB_MODEL_", protected_namespaces=())
 
     # 1) override base settings
     scen: str = "rdagent.scenarios.qlib.experiment.model_experiment.QlibModelScenario"
@@ -26,7 +22,7 @@ class ModelBasePropSetting(BasePropSetting):
     runner: str = "rdagent.scenarios.qlib.developer.model_runner.QlibModelRunner"
     """Runner class"""
 
-    summarizer: str = "rdagent.scenarios.qlib.developer.feedback.QlibModelHypothesisExperiment2Feedback"
+    summarizer: str = "rdagent.scenarios.qlib.developer.feedback.QlibModelExperiment2Feedback"
     """Summarizer class"""
 
     evolving_n: int = 10
@@ -34,11 +30,7 @@ class ModelBasePropSetting(BasePropSetting):
 
 
 class FactorBasePropSetting(BasePropSetting):
-    class Config:
-        env_prefix = "QLIB_FACTOR_"
-        """Use `QLIB_FACTOR_` as prefix for environment variables"""
-        protected_namespaces = ()
-        """Add 'factor_' to the protected namespaces"""
+    model_config = SettingsConfigDict(env_prefix="QLIB_FACTOR_", protected_namespaces=())
 
     # 1) override base settings
     scen: str = "rdagent.scenarios.qlib.experiment.factor_experiment.QlibFactorScenario"
@@ -56,7 +48,7 @@ class FactorBasePropSetting(BasePropSetting):
     runner: str = "rdagent.scenarios.qlib.developer.factor_runner.QlibFactorRunner"
     """Runner class"""
 
-    summarizer: str = "rdagent.scenarios.qlib.developer.feedback.QlibFactorHypothesisExperiment2Feedback"
+    summarizer: str = "rdagent.scenarios.qlib.developer.feedback.QlibFactorExperiment2Feedback"
     """Summarizer class"""
 
     evolving_n: int = 10
@@ -75,6 +67,9 @@ class FactorFromReportPropSetting(FactorBasePropSetting):
     max_factors_per_exp: int = 10000
     """Maximum number of factors implemented per experiment"""
 
+    is_report_limit_enabled: bool = False
+    """Limits report processing count if True; processes all if False"""
+
 
 FACTOR_PROP_SETTING = FactorBasePropSetting()
 FACTOR_FROM_REPORT_PROP_SETTING = FactorFromReportPropSetting()

rdagent/app/qlib_rd_loop/factor_from_report.py

@@ -1,6 +1,6 @@
 import json
 from pathlib import Path
-from typing import Any, Tuple
+from typing import Any, Dict, Tuple
 
 import fire
 from jinja2 import Environment, StrictUndefined
@@ -11,8 +11,6 @@ from rdagent.components.document_reader.document_reader import (
     extract_first_page_screenshot_from_pdf,
     load_and_process_pdfs_by_langchain,
 )
-from rdagent.components.workflow.rd_loop import RDLoop
-from rdagent.core.exception import FactorEmptyError
 from rdagent.core.prompts import Prompts
 from rdagent.core.proposal import Hypothesis
 from rdagent.log import rdagent_logger as logger
@@ -51,6 +49,7 @@ def generate_hypothesis(factor_result: dict, report_content: str) -> str:
         user_prompt=user_prompt,
         system_prompt=system_prompt,
         json_mode=True,
+        json_target_type=Dict[str, str],
     )
 
     response_json = json.loads(response)
@@ -99,6 +98,7 @@ def extract_hypothesis_and_exp_from_reports(report_file_path: str) -> Tuple[Qlib
 
     report_content = "\n".join(docs_dict.values())
     hypothesis = generate_hypothesis(factor_result, report_content)
+    exp.hypothesis = hypothesis
     return exp, hypothesis
 
 
@@ -116,12 +116,12 @@ class FactorReportLoop(FactorRDLoop, metaclass=LoopMeta):
         self.valid_pdf_file_count = 0
         self.current_loop_hypothesis = None
         self.current_loop_exp = None
-        self.steps = ["propose_hypo_exp", "propose", "exp_gen", "coding", "running", "feedback"]
+        self.steps = ["propose_hypo_exp", "propose", "direct_exp_gen", "coding", "running", "feedback"]
 
     def propose_hypo_exp(self, prev_out: dict[str, Any]):
         with logger.tag("r"):
             while True:
-                if self.valid_pdf_file_count > 15:
+                if FACTOR_FROM_REPORT_PROP_SETTING.is_report_limit_enabled and self.valid_pdf_file_count > 15:
                     break
                 report_file_path = self.judge_pdf_data_items[self.pdf_file_index]
                 logger.info(f"Processing number {self.pdf_file_index} report: {report_file_path}")
@@ -130,7 +130,9 @@ class FactorReportLoop(FactorRDLoop, metaclass=LoopMeta):
                 if exp is None:
                     continue
                 self.valid_pdf_file_count += 1
-                exp.based_experiments = [QlibFactorExperiment(sub_tasks=[])] + [t[1] for t in self.trace.hist if t[2]]
+                exp.based_experiments = [QlibFactorExperiment(sub_tasks=[], hypothesis=hypothesis)] + [
+                    t[0] for t in self.trace.hist if t[1]
+                ]
                 exp.sub_workspace_list = exp.sub_workspace_list[: FACTOR_FROM_REPORT_PROP_SETTING.max_factors_per_exp]
                 exp.sub_tasks = exp.sub_tasks[: FACTOR_FROM_REPORT_PROP_SETTING.max_factors_per_exp]
                 logger.log_object(hypothesis, tag="hypothesis generation")
@@ -142,8 +144,14 @@ class FactorReportLoop(FactorRDLoop, metaclass=LoopMeta):
     def propose(self, prev_out: dict[str, Any]):
         return self.current_loop_hypothesis
 
-    def exp_gen(self, prev_out: dict[str, Any]):
-        return self.current_loop_exp
+    def direct_exp_gen(self, prev_out: dict[str, Any]):
+        return {"propose": self.current_loop_hypothesis, "exp_gen": self.current_loop_exp}
+
+    def coding(self, prev_out: dict[str, Any]):
+        with logger.tag("d"):  # develop
+            exp = self.coder.develop(prev_out["direct_exp_gen"]["exp_gen"])
+            logger.log_object(exp.sub_workspace_list, tag="coder result")
+        return exp
 
 
 def main(report_folder=None, path=None, step_n=None):

rdagent/app/utils/ape.py

@@ -0,0 +1,49 @@
+"""
+This is the preliminary version of the APE (Automated Prompt Engineering)
+"""
+
+import pickle
+from pathlib import Path
+
+from rdagent.core.conf import RD_AGENT_SETTINGS
+
+
+def get_llm_qa(file_path):
+    data_flt = []
+    with open(file_path, "rb") as f:
+        data = pickle.load(f)
+        print(len(data))
+        for item in data:
+            if "debug_llm" in item["tag"]:
+                data_flt.append(item)
+    return data_flt
+
+
+# Example usage
+# use
+file_path = Path(RD_AGENT_SETTINGS.log_trace_path) / "debug_llm.pkl"
+llm_qa = get_llm_qa(file_path)
+print(len(llm_qa))
+
+print(llm_qa[0])
+
+# Initialize APE backend
+from rdagent.oai.llm_utils import APIBackend
+from rdagent.utils.agent.tpl import T
+
+api = APIBackend()
+
+# Analyze test data and generate improved prompts
+for qa in llm_qa:
+    # Generate system prompt for APE
+    system_prompt = T(".prompts:ape.system").r()
+
+    # Generate user prompt with context from LLM QA
+    user_prompt = T(".prompts:ape.user").r(
+        system=qa["obj"].get("system", ""), user=qa["obj"]["user"], answer=qa["obj"]["resp"]
+    )
+    analysis_result = api.build_messages_and_create_chat_completion(
+        system_prompt=system_prompt, user_prompt=user_prompt
+    )
+    print(f"█" * 60)
+    yes = input("Do you want to continue? (y/n)")

rdagent/app/utils/health_check.py

@@ -0,0 +1,49 @@
+import socket
+
+import docker
+
+from rdagent.log import rdagent_logger as logger
+
+
+def check_docker() -> None:
+    try:
+        client = docker.from_env()
+        client.images.pull("hello-world")
+        container = client.containers.run("hello-world", detach=True)
+        logs = container.logs().decode("utf-8")
+        print(logs)
+        container.remove()
+        logger.info(f"The docker status is normal")
+    except docker.errors.DockerException as e:
+        logger.error(f"An error occurred: {e}")
+        logger.warning(
+            f"Docker status is exception, please check the docker configuration or reinstall it. Refs: https://docs.docker.com/engine/install/ubuntu/."
+        )
+
+
+def is_port_in_use(port):
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        return s.connect_ex(("127.0.0.1", port)) == 0
+
+
+def check_and_list_free_ports(start_port=19899, max_ports=10) -> None:
+    is_occupied = is_port_in_use(port=start_port)
+    if is_occupied:
+        free_ports = []
+        for port in range(start_port, start_port + max_ports):
+            if not is_port_in_use(port):
+                free_ports.append(port)
+        logger.warning(
+            f"Port 19899 is occupied, please replace it with an available port when running the `rdagent ui` command. Available ports: {free_ports}"
+        )
+    else:
+        logger.info(f"Port 19899 is not occupied, you can run the `rdagent ui` command")
+
+
+def health_check():
+    """
+    Check that docker is installed correctly,
+    and that the ports used in the sample README are not occupied.
+    """
+    check_docker()
+    check_and_list_free_ports()

rdagent/app/utils/prompts.yaml

@@ -0,0 +1,119 @@
+ape:
+  system: |-
+    We'll provide you with a pair of Chat QA about data science.
+    We are creating solutions for a Kaggle Competition based on the answers.
+    Good questions are crucial for getting good answers.
+    Please suggest how to improve the question.
+    You can analyze based on these aspects:
+    - Is the question complete (is all the information needed to answer the question provided?)
+
+    The conversation will be provided in the following format:
+
+    <question>
+      <part1>
+      ...text to describe the question...
+      </part1>
+      <part2>
+      ...text to describe the question...
+      </part2>
+    </question>
+
+    <answer>
+      ...text to describe the answer.
+    </answer>
+
+    You response should be very concorete and concise(less than 20 words) and focuse on the mentioned aspects, like
+    ```
+    Info Missing: the question ask for changing code, but it does not provide the description of current code.
+    ```
+    Please be very conversatiive when you propose improvements. Only propose improvements when it becomes impossible to give the answer.
+
+    Don't propose conerete modifications
+
+  user: |-
+    <question>
+      <part1>
+      {{system}}
+      </part1>
+      <part2>
+      {{user}}
+      </part2>
+    </question>
+
+    <answer>
+      {{answer}}
+    </answer>
+
+  optional: |-
+    If you want to suggest modification on  the question. Please follow the *SEARCH/REPLACE block* Rules!!!! It is optional.
+    Please make it concise and less than 20 lines!!!
+
+    # *SEARCH/REPLACE block* Rules:
+
+    Every *SEARCH/REPLACE block* must use this format:
+    1. The *FULL* file path alone on a line, verbatim. No bold asterisks, no quotes around it, no escaping of characters, etc.
+    2. The opening fence and code language, eg: ```python
+    3. The start of search block: <<<<<<< SEARCH
+    4. A contiguous chunk of lines to search for in the existing source code
+    5. The dividing line: =======
+    6. The lines to replace into the source code
+    7. The end of the replace block: >>>>>>> REPLACE
+    8. The closing fence: ```
+
+    Use the *FULL* file path, as shown to you by the user.
+
+    Every *SEARCH* section must *EXACTLY MATCH* the existing file content, character for character, including all comments, docstrings, etc.
+    If the file contains code or other data wrapped/escaped in json/xml/quotes or other containers, you need to propose edits to the literal contents of the file, including the container markup.
+
+    *SEARCH/REPLACE* blocks will *only* replace the first match occurrence.
+    Including multiple unique *SEARCH/REPLACE* blocks if needed.
+    Include enough lines in each SEARCH section to uniquely match each set of lines that need to change.
+
+    Keep *SEARCH/REPLACE* blocks concise.
+    Break large *SEARCH/REPLACE* blocks into a series of smaller blocks that each change a small portion of the file.
+    Include just the changing lines, and a few surrounding lines if needed for uniqueness.
+    Do not include long runs of unchanging lines in *SEARCH/REPLACE* blocks.
+
+    Only create *SEARCH/REPLACE* blocks for files that the user has added to the chat!
+
+    To move code within a file, use 2 *SEARCH/REPLACE* blocks: 1 to delete it from its current location, 1 to insert it in the new location.
+
+    Pay attention to which filenames the user wants you to edit, especially if they are asking you to create a new file.
+
+    If you want to put code in a new file, use a *SEARCH/REPLACE block* with:
+    - A new file path, including dir name if needed
+    - An empty `SEARCH` section
+    - The new file's contents in the `REPLACE` section
+
+    To rename files which have been added to the chat, use shell commands at the end of your response.
+
+    If the user just says something like "ok" or "go ahead" or "do that" they probably want you to make SEARCH/REPLACE blocks for the code changes you just proposed.
+    The user will say when they've applied your edits. If they haven't explicitly confirmed the edits have been applied, they probably want proper SEARCH/REPLACE blocks.
+
+    You are diligent and tireless!
+    You NEVER leave comments describing code without implementing it!
+    You always COMPLETELY IMPLEMENT the needed code!
+
+
+    ONLY EVER RETURN CODE IN A *SEARCH/REPLACE BLOCK*!
+    Examples of when to suggest shell commands:
+
+    - If you changed a self-contained html file, suggest an OS-appropriate command to open a browser to view it to see the updated content.
+    - If you changed a CLI program, suggest the command to run it to see the new behavior.
+    - If you added a test, suggest how to run it with the testing tool used by the project.
+    - Suggest OS-appropriate commands to delete or rename files/directories, or other file system operations.
+    - If your code changes add new dependencies, suggest the command to install them.
+    - Etc.
+
+    Here is a example of SEARCH/REPLACE BLOCK to change a function implementation to import.
+
+    <<<<<<< SEARCH
+    def hello():
+        "print a greeting"
+
+        print("hello")
+    =======
+    from hello import hello
+
+    >>>>>>> REPLACE
+# - Is there any ambiguity in the question?

rdagent/components/benchmark/conf.py

@@ -2,24 +2,16 @@ from dataclasses import field
 from pathlib import Path
 from typing import Optional
 
-from dotenv import load_dotenv
-from pydantic_settings import BaseSettings
-
-# Load environment variables
-load_dotenv(verbose=True, override=True)
-
+from rdagent.core.conf import ExtendedBaseSettings
 
 DIRNAME = Path("./")
 
 
-class BenchmarkSettings(BaseSettings):
+class BenchmarkSettings(ExtendedBaseSettings):
     class Config:
         env_prefix = "BENCHMARK_"
         """Use `BENCHMARK_` as prefix for environment variables"""
 
-    ground_truth_dir: Path = DIRNAME / "ground_truth"
-    """ground truth dir"""
-
     bench_data_path: Path = DIRNAME / "example.json"
     """data for benchmark"""
 
@@ -29,7 +21,7 @@ class BenchmarkSettings(BaseSettings):
     bench_test_case_n: Optional[int] = None
     """how many test cases to run; If not given, all test cases will be run"""
 
-    bench_method_cls: str = "rdagent.components.coder.factor_coder.CoSTEER.FactorCoSTEER"
+    bench_method_cls: str = "rdagent.components.coder.factor_coder.FactorCoSTEER"
     """method to be used for test cases"""
 
     bench_method_extra_kwargs: dict = field(

rdagent/components/benchmark/eval_method.py

@@ -5,14 +5,12 @@ from typing import Dict, List, Tuple, Union
 import pandas as pd
 from tqdm import tqdm
 
-from rdagent.components.coder.factor_coder.config import FACTOR_IMPLEMENT_SETTINGS
-from rdagent.components.coder.factor_coder.CoSTEER.evaluators import (
+from rdagent.components.coder.factor_coder.config import FACTOR_COSTEER_SETTINGS
+from rdagent.components.coder.factor_coder.eva_utils import (
     FactorCorrelationEvaluator,
-    FactorEqualValueCountEvaluator,
+    FactorEqualValueRatioEvaluator,
     FactorEvaluator,
     FactorIndexEvaluator,
-    FactorMissingValuesEvaluator,
-    FactorOutputFormatEvaluator,
     FactorRowCountEvaluator,
     FactorSingleColumnEvaluator,
 )
@@ -20,7 +18,7 @@ from rdagent.components.coder.factor_coder.factor import FactorFBWorkspace
 from rdagent.core.conf import RD_AGENT_SETTINGS
 from rdagent.core.developer import Developer
 from rdagent.core.exception import CoderError
-from rdagent.core.experiment import Task, Workspace
+from rdagent.core.experiment import Experiment, Task, Workspace
 from rdagent.core.scenario import Scenario
 from rdagent.core.utils import multiprocessing_wrapper
 
@@ -33,11 +31,34 @@ EVAL_RES = Dict[
 class TestCase:
     def __init__(
         self,
-        target_task: list[Task] = [],
-        ground_truth: list[Workspace] = [],
+        target_task: Task,
+        ground_truth: Workspace,
     ):
-        self.ground_truth = ground_truth
         self.target_task = target_task
+        self.ground_truth = ground_truth
+
+
+class TestCases:
+    def __init__(self, test_case_l: list[TestCase] = []):
+        # self.test_case_l = [TestCase(task, gt) for task, gt in zip(target_task, ground_truth)]
+        self.test_case_l = test_case_l
+
+    def __getitem__(self, item):
+        return self.test_case_l[item]
+
+    def __len__(self):
+        return len(self.test_case_l)
+
+    def get_exp(self):
+        return Experiment([case.target_task for case in self.test_case_l])
+
+    @property
+    def target_task(self):
+        return [case.target_task for case in self.test_case_l]
+
+    @property
+    def ground_truth(self):
+        return [case.ground_truth for case in self.test_case_l]
 
 
 class BaseEval:
@@ -48,13 +69,13 @@ class BaseEval:
     def __init__(
         self,
         evaluator_l: List[FactorEvaluator],
-        test_cases: List[TestCase],
+        test_cases: TestCases,
         generate_method: Developer,
         catch_eval_except: bool = True,
     ):
         """Parameters
         ----------
-        test_cases : List[TestCase]
+        test_cases : TestCases
             cases to be evaluated, ground truth are included in the test cases.
         evaluator_l : List[FactorEvaluator]
             A list of evaluators to evaluate the generated code.
@@ -102,6 +123,7 @@ class BaseEval:
         eval_res = []
         for ev in self.evaluator_l:
             try:
+                case_gen.raise_exception = True
                 eval_res.append((ev, ev.evaluate(implementation=case_gen, gt_implementation=case_gt)))
                 # if the corr ev is successfully evaluated and achieve the best performance, then break
             except CoderError as e:
@@ -118,7 +140,7 @@ class BaseEval:
 class FactorImplementEval(BaseEval):
     def __init__(
         self,
-        test_cases: TestCase,
+        test_cases: TestCases,
         method: Developer,
         *args,
         scen: Scenario,
@@ -127,26 +149,22 @@ class FactorImplementEval(BaseEval):
     ):
         online_evaluator_l = [
             FactorSingleColumnEvaluator(scen),
-            FactorOutputFormatEvaluator(scen),
             FactorRowCountEvaluator(scen),
             FactorIndexEvaluator(scen),
-            FactorMissingValuesEvaluator(scen),
-            FactorEqualValueCountEvaluator(scen),
+            FactorEqualValueRatioEvaluator(scen),
             FactorCorrelationEvaluator(hard_check=False, scen=scen),
         ]
         super().__init__(online_evaluator_l, test_cases, method, *args, **kwargs)
         self.test_round = test_round
 
-    def eval(self):
+    def develop(self):
         gen_factor_l_all_rounds = []
-        test_cases_all_rounds = []
-        res = defaultdict(list)
         for _ in tqdm(range(self.test_round), desc="Rounds of Eval"):
             print("\n========================================================")
             print(f"Eval {_}-th times...")
             print("========================================================\n")
             try:
-                gen_factor_l = self.generate_method.develop(self.test_cases.target_task)
+                gen_factor_l = self.generate_method.develop(self.test_cases.get_exp())
             except KeyboardInterrupt:
                 # TODO: Why still need to save result after KeyboardInterrupt?
                 print("Manually interrupted the evaluation. Saving existing results")
@@ -157,8 +175,14 @@ class FactorImplementEval(BaseEval):
                     "The number of cases to eval should be equal to the number of test cases.",
                 )
             gen_factor_l_all_rounds.extend(gen_factor_l.sub_workspace_list)
-            test_cases_all_rounds.extend(self.test_cases.ground_truth)
 
+        return gen_factor_l_all_rounds
+
+    def eval(self, gen_factor_l_all_rounds):
+        test_cases_all_rounds = []
+        res = defaultdict(list)
+        for _ in range(self.test_round):
+            test_cases_all_rounds.extend(self.test_cases.ground_truth)
         eval_res_list = multiprocessing_wrapper(
             [
                 (self.eval_case, (gt_case, gen_factor))

rdagent/components/coder/CoSTEER/__init__.py

@@ -0,0 +1,143 @@
+import pickle
+from datetime import datetime
+from pathlib import Path
+
+from rdagent.components.coder.CoSTEER.config import CoSTEERSettings
+from rdagent.components.coder.CoSTEER.evaluators import CoSTEERMultiFeedback
+from rdagent.components.coder.CoSTEER.evolvable_subjects import EvolvingItem
+from rdagent.components.coder.CoSTEER.knowledge_management import (
+    CoSTEERKnowledgeBaseV1,
+    CoSTEERKnowledgeBaseV2,
+    CoSTEERRAGStrategyV1,
+    CoSTEERRAGStrategyV2,
+)
+from rdagent.core.developer import Developer
+from rdagent.core.evaluation import Evaluator, Feedback
+from rdagent.core.evolving_agent import EvolvingStrategy, RAGEvoAgent
+from rdagent.core.exception import CoderError
+from rdagent.core.experiment import Experiment
+from rdagent.log import rdagent_logger as logger
+
+
+class CoSTEER(Developer[Experiment]):
+    def __init__(
+        self,
+        settings: CoSTEERSettings,
+        eva: Evaluator,
+        es: EvolvingStrategy,
+        evolving_version: int,
+        *args,
+        with_knowledge: bool = True,
+        with_feedback: bool = True,
+        knowledge_self_gen: bool = True,
+        filter_final_evo: bool = True,
+        max_loop: int | None = None,
+        **kwargs,
+    ) -> None:
+        super().__init__(*args, **kwargs)
+        self.max_loop = settings.max_loop if max_loop is None else max_loop
+        self.max_seconds = settings.max_seconds
+        self.knowledge_base_path = (
+            Path(settings.knowledge_base_path) if settings.knowledge_base_path is not None else None
+        )
+        self.new_knowledge_base_path = (
+            Path(settings.new_knowledge_base_path) if settings.new_knowledge_base_path is not None else None
+        )
+
+        self.with_knowledge = with_knowledge
+        self.with_feedback = with_feedback
+        self.knowledge_self_gen = knowledge_self_gen
+        self.filter_final_evo = filter_final_evo
+        self.evolving_strategy = es
+        self.evaluator = eva
+        self.evolving_version = evolving_version
+
+        # init knowledge base
+        self.knowledge_base = self.load_or_init_knowledge_base(
+            former_knowledge_base_path=self.knowledge_base_path,
+            component_init_list=[],
+        )
+        # init rag method
+        self.rag = (
+            CoSTEERRAGStrategyV2(self.knowledge_base, settings=settings)
+            if self.evolving_version == 2
+            else CoSTEERRAGStrategyV1(self.knowledge_base, settings=settings)
+        )
+
+    def load_or_init_knowledge_base(self, former_knowledge_base_path: Path = None, component_init_list: list = []):
+        if former_knowledge_base_path is not None and former_knowledge_base_path.exists():
+            knowledge_base = pickle.load(open(former_knowledge_base_path, "rb"))
+            if self.evolving_version == 1 and not isinstance(knowledge_base, CoSTEERKnowledgeBaseV1):
+                raise ValueError("The former knowledge base is not compatible with the current version")
+            elif self.evolving_version == 2 and not isinstance(
+                knowledge_base,
+                CoSTEERKnowledgeBaseV2,
+            ):
+                raise ValueError("The former knowledge base is not compatible with the current version")
+        else:
+            knowledge_base = (
+                CoSTEERKnowledgeBaseV2(
+                    init_component_list=component_init_list,
+                )
+                if self.evolving_version == 2
+                else CoSTEERKnowledgeBaseV1()
+            )
+        return knowledge_base
+
+    def develop(self, exp: Experiment) -> Experiment:
+
+        # init intermediate items
+        evo_exp = EvolvingItem.from_experiment(exp)
+
+        self.evolve_agent = RAGEvoAgent(
+            max_loop=self.max_loop,
+            evolving_strategy=self.evolving_strategy,
+            rag=self.rag,
+            with_knowledge=self.with_knowledge,
+            with_feedback=self.with_feedback,
+            knowledge_self_gen=self.knowledge_self_gen,
+        )
+
+        start_datetime = datetime.now()
+        for evo_exp in self.evolve_agent.multistep_evolve(evo_exp, self.evaluator):
+            assert isinstance(evo_exp, Experiment)  # multiple inheritance
+            logger.log_object(evo_exp.sub_workspace_list, tag="evolving code")
+            for sw in evo_exp.sub_workspace_list:
+                logger.info(f"evolving code workspace: {sw}")
+            if (datetime.now() - start_datetime).seconds > self.max_seconds:
+                break
+
+        if self.with_feedback and self.filter_final_evo:
+            evo_exp = self._exp_postprocess_by_feedback(evo_exp, self.evolve_agent.evolving_trace[-1].feedback)
+
+        # save new knowledge base
+        if self.new_knowledge_base_path is not None:
+            with self.new_knowledge_base_path.open("wb") as f:
+                pickle.dump(self.knowledge_base, f)
+            logger.info(f"New knowledge base saved to {self.new_knowledge_base_path}")
+        exp.sub_workspace_list = evo_exp.sub_workspace_list
+        exp.experiment_workspace = evo_exp.experiment_workspace
+        return exp
+
+    def _exp_postprocess_by_feedback(self, evo: Experiment, feedback: CoSTEERMultiFeedback) -> Experiment:
+        """
+        Responsibility:
+        - Raise Error if it failed to handle the develop task
+        -
+        """
+        assert isinstance(evo, Experiment)
+        assert isinstance(feedback, CoSTEERMultiFeedback)
+        assert len(evo.sub_workspace_list) == len(feedback)
+
+        # FIXME: when whould the feedback be None?
+        failed_feedbacks = [
+            f"- feedback{index + 1:02d}:\n  - execution: {f.execution}\n  - return_checking: {f.return_checking}\n  - code: {f.code}"
+            for index, f in enumerate(feedback)
+            if f is not None and not f.final_decision
+        ]
+
+        if len(failed_feedbacks) == len(feedback):
+            feedback_summary = "\n".join(failed_feedbacks)
+            raise CoderError(f"All tasks are failed:\n{feedback_summary}")
+
+        return evo

rdagent/components/coder/CoSTEER/config.py

@@ -0,0 +1,39 @@
+from typing import Union
+
+from rdagent.core.conf import ExtendedBaseSettings
+
+
+class CoSTEERSettings(ExtendedBaseSettings):
+    """CoSTEER settings, this setting is supposed not to be used directly!!!"""
+
+    class Config:
+        env_prefix = "CoSTEER_"
+
+    coder_use_cache: bool = False
+    """Indicates whether to use cache for the coder"""
+
+    max_loop: int = 10
+    """Maximum number of task implementation loops"""
+
+    fail_task_trial_limit: int = 20
+
+    v1_query_former_trace_limit: int = 5
+    v1_query_similar_success_limit: int = 5
+
+    v2_query_component_limit: int = 1
+    v2_query_error_limit: int = 1
+    v2_query_former_trace_limit: int = 1
+    v2_add_fail_attempt_to_latest_successful_execution: bool = False
+    v2_error_summary: bool = False
+    v2_knowledge_sampler: float = 1.0
+
+    knowledge_base_path: Union[str, None] = None
+    """Path to the knowledge base"""
+
+    new_knowledge_base_path: Union[str, None] = None
+    """Path to the new knowledge base"""
+
+    max_seconds: int = 10**6
+
+
+CoSTEER_SETTINGS = CoSTEERSettings()

rdagent/components/coder/CoSTEER/evaluators.py

@@ -0,0 +1,236 @@
+from abc import abstractmethod
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, List
+
+from rdagent.components.coder.CoSTEER.evolvable_subjects import EvolvingItem
+from rdagent.core.conf import RD_AGENT_SETTINGS
+from rdagent.core.evaluation import Evaluator, Feedback
+from rdagent.core.evolving_framework import QueriedKnowledge
+from rdagent.core.experiment import Task, Workspace
+from rdagent.core.utils import multiprocessing_wrapper
+from rdagent.log import rdagent_logger as logger
+
+if TYPE_CHECKING:
+    from rdagent.core.scenario import Scenario
+
+# TODO:
+# 1. It seems logically sound, but we currently lack a scenario to apply it.
+# 2. If it proves to be useful, relocate it to a more general location.
+#
+# class FBWorkspaceExeFeedback(Feedback):
+#     """
+#     It pairs with FBWorkspace in the abstract level.
+#     """
+#     # ws: FBWorkspace   # potential
+#     stdout: str
+
+
+@dataclass
+class CoSTEERSingleFeedback(Feedback):
+    # TODO: (xiao)
+    # it should be more general class for FBWorkspaceExeFeedback
+    # A better name of it may be NormalFeedback
+    # TODO: It should be a general feeddback for CoSTEERR
+    """
+    The feedback for the data loader evaluation.
+    It is design align the phases of the implemented code
+    - Execution -> Return Value -> Code -> Final Decision
+    """
+    execution: str
+    # execution_feedback
+    return_checking: str | None  # including every check in the testing (constraints about the generated value)
+    # value_feedback, shape_feedback, value_generated_flag
+    code: str
+    final_decision: bool
+
+    @staticmethod
+    def val_and_update_init_dict(data: dict) -> dict:
+        # TODO: (bowen) use a more general method to validate and update the data dictionary before init, like pydantic
+        """
+        Validates and converts the 'final_decision' field in the given data dictionary.
+
+        Args:
+            data (dict): The data dictionary containing the 'final_decision' field.
+
+        Returns:
+            dict: The updated data dictionary with 'final_decision' as a boolean.
+
+        Raises:
+            ValueError: If 'final_decision' is not present or not a boolean.
+        """
+        if "final_decision" not in data:
+            raise ValueError("'final_decision' is required")
+
+        if isinstance(data["final_decision"], str):
+            if data["final_decision"] == "false" or data["final_decision"] == "False":
+                data["final_decision"] = False
+            elif data["final_decision"] == "true" or data["final_decision"] == "True":
+                data["final_decision"] = True
+
+        if not isinstance(data["final_decision"], bool):
+            raise ValueError(f"'final_decision' must be a boolean, not {type(data['final_decision'])}")
+        return data
+
+    def __str__(self) -> str:
+        return f"""------------------Execution------------------
+{self.execution}
+------------------Return Checking------------------
+{self.return_checking if self.return_checking is not None else 'No return checking'}
+------------------Code------------------
+{self.code}
+------------------Final Decision------------------
+This implementation is {'SUCCESS' if self.final_decision else 'FAIL'}.
+"""
+
+    def __bool__(self):
+        return self.final_decision
+
+
+class CoSTEERSingleFeedbackDeprecated(CoSTEERSingleFeedback):
+    """This class is a base class for all code generator feedback to single implementation"""
+
+    def __init__(
+        self,
+        execution_feedback: str = None,
+        shape_feedback: str = None,
+        code_feedback: str = None,
+        value_feedback: str = None,
+        final_decision: bool = None,
+        final_feedback: str = None,
+        value_generated_flag: bool = None,
+        final_decision_based_on_gt: bool = None,
+    ) -> None:
+        self.execution_feedback = execution_feedback
+        self.code_feedback = code_feedback
+        self.value_feedback = value_feedback
+        self.final_decision = final_decision
+        self.final_feedback = final_feedback
+        self.value_generated_flag = value_generated_flag
+        self.final_decision_based_on_gt = final_decision_based_on_gt
+
+        # TODO:
+        # Not general enough. So we should not put them in the general costeer feedback
+        # Instead, we should create subclass for it.
+        self.shape_feedback = shape_feedback  # Not general enough. So
+
+    # TODO: @property
+    @property
+    def execution(self):
+        return self.execution_feedback
+
+    @property
+    def return_checking(self):
+        if self.value_generated_flag:
+            return f"value feedback: {self.value_feedback}\n\nshape feedback: {self.shape_feedback}"
+        return None
+
+    @property
+    def code(self):
+        return self.code_feedback
+
+    def __str__(self) -> str:
+        return f"""------------------Execution Feedback------------------
+{self.execution_feedback if self.execution_feedback is not None else 'No execution feedback'}
+------------------Shape Feedback------------------
+{self.shape_feedback if self.shape_feedback is not None else 'No shape feedback'}
+------------------Code Feedback------------------
+{self.code_feedback if self.code_feedback is not None else 'No code feedback'}
+------------------Value Feedback------------------
+{self.value_feedback if self.value_feedback is not None else 'No value feedback'}
+------------------Final Feedback------------------
+{self.final_feedback if self.final_feedback is not None else 'No final feedback'}
+------------------Final Decision------------------
+This implementation is {'SUCCESS' if self.final_decision else 'FAIL'}.
+"""
+
+
+class CoSTEERMultiFeedback(Feedback):
+    """Feedback contains a list, each element is the corresponding feedback for each factor implementation."""
+
+    def __init__(self, feedback_list: List[CoSTEERSingleFeedback]) -> None:
+        self.feedback_list = feedback_list
+
+    def __getitem__(self, index: int) -> CoSTEERSingleFeedback:
+        return self.feedback_list[index]
+
+    def __len__(self) -> int:
+        return len(self.feedback_list)
+
+    def append(self, feedback: CoSTEERSingleFeedback) -> None:
+        self.feedback_list.append(feedback)
+
+    def __iter__(self):
+        return iter(self.feedback_list)
+
+    def finished(self) -> bool:
+        """
+        In some implementations, tasks may fail multiple times, leading agents to skip the implementation.
+        This results in None feedback. However, we want to accept the correct parts and ignore None feedback.
+        """
+        return all(feedback.final_decision for feedback in self.feedback_list if feedback is not None)
+
+    def __bool__(self) -> bool:
+        return all(feedback.final_decision for feedback in self.feedback_list)
+
+
+class CoSTEEREvaluator(Evaluator):
+    def __init__(
+        self,
+        scen: "Scenario",
+    ) -> None:
+        self.scen = scen
+
+    # TODO:
+    # I think we should have unified interface for all evaluates, for examples.
+    # So we should adjust the interface of other factors
+    @abstractmethod
+    def evaluate(
+        self,
+        target_task: Task,
+        implementation: Workspace,
+        gt_implementation: Workspace,
+        **kwargs,
+    ) -> CoSTEERSingleFeedback:
+        raise NotImplementedError("Please implement the `evaluator` method")
+
+
+class CoSTEERMultiEvaluator(CoSTEEREvaluator):
+    """This is for evaluation of experiment. Due to we have multiple tasks, so we will return a list of evaluation feebacks"""
+
+    def __init__(self, single_evaluator: CoSTEEREvaluator, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        self.single_evaluator = single_evaluator
+
+    def evaluate(
+        self,
+        evo: EvolvingItem,
+        queried_knowledge: QueriedKnowledge = None,
+        **kwargs,
+    ) -> CoSTEERMultiFeedback:
+        multi_implementation_feedback = multiprocessing_wrapper(
+            [
+                (
+                    self.single_evaluator.evaluate,
+                    (
+                        evo.sub_tasks[index],
+                        evo.sub_workspace_list[index],
+                        evo.sub_gt_implementations[index] if evo.sub_gt_implementations is not None else None,
+                        queried_knowledge,
+                    ),
+                )
+                for index in range(len(evo.sub_tasks))
+            ],
+            n=RD_AGENT_SETTINGS.multi_proc_n,
+        )
+
+        final_decision = [
+            None if single_feedback is None else single_feedback.final_decision
+            for single_feedback in multi_implementation_feedback
+        ]
+        logger.info(f"Final decisions: {final_decision} True count: {final_decision.count(True)}")
+
+        for index in range(len(evo.sub_tasks)):
+            if final_decision[index]:
+                evo.sub_tasks[index].factor_implementation = True
+
+        return CoSTEERMultiFeedback(multi_implementation_feedback)

rdagent/components/coder/CoSTEER/evolvable_subjects.py

@@ -1,24 +1,19 @@
-from rdagent.components.coder.factor_coder.factor import (
-    FactorExperiment,
-    FactorFBWorkspace,
-    FactorTask,
-)
 from rdagent.core.evolving_framework import EvolvableSubjects
+from rdagent.core.experiment import Experiment, FBWorkspace, Task
 from rdagent.log import rdagent_logger as logger
 
 
-class FactorEvolvingItem(FactorExperiment, EvolvableSubjects):
+class EvolvingItem(Experiment, EvolvableSubjects):
     """
     Intermediate item of factor implementation.
     """
 
     def __init__(
         self,
-        sub_tasks: list[FactorTask],
-        sub_gt_implementations: list[FactorFBWorkspace] = None,
+        sub_tasks: list[Task],
+        sub_gt_implementations: list[FBWorkspace] = None,
     ):
-        FactorExperiment.__init__(self, sub_tasks=sub_tasks)
-        self.corresponding_selection: list = None
+        Experiment.__init__(self, sub_tasks=sub_tasks)
         if sub_gt_implementations is not None and len(
             sub_gt_implementations,
         ) != len(self.sub_tasks):
@@ -28,3 +23,10 @@ class FactorEvolvingItem(FactorExperiment, EvolvableSubjects):
             )
         else:
             self.sub_gt_implementations = sub_gt_implementations
+
+    @classmethod
+    def from_experiment(cls, exp: Experiment) -> Experiment:
+        ei = cls(sub_tasks=exp.sub_tasks)
+        ei.based_experiments = exp.based_experiments
+        ei.experiment_workspace = exp.experiment_workspace
+        return ei

rdagent/components/coder/CoSTEER/evolving_strategy.py

@@ -0,0 +1,124 @@
+from __future__ import annotations
+
+from abc import abstractmethod
+from pathlib import Path
+
+from rdagent.components.coder.CoSTEER.config import CoSTEERSettings
+from rdagent.components.coder.CoSTEER.evaluators import (
+    CoSTEERMultiFeedback,
+    CoSTEERSingleFeedback,
+)
+from rdagent.components.coder.CoSTEER.evolvable_subjects import EvolvingItem
+from rdagent.components.coder.CoSTEER.knowledge_management import (
+    CoSTEERQueriedKnowledge,
+)
+from rdagent.core.conf import RD_AGENT_SETTINGS
+from rdagent.core.evolving_framework import EvolvingStrategy, EvoStep, QueriedKnowledge
+from rdagent.core.experiment import FBWorkspace, Task
+from rdagent.core.prompts import Prompts
+from rdagent.core.scenario import Scenario
+from rdagent.core.utils import multiprocessing_wrapper
+
+implement_prompts = Prompts(file_path=Path(__file__).parent / "prompts.yaml")
+
+
+class MultiProcessEvolvingStrategy(EvolvingStrategy):
+    def __init__(self, scen: Scenario, settings: CoSTEERSettings):
+        super().__init__(scen)
+        self.settings = settings
+
+    @abstractmethod
+    def implement_one_task(
+        self,
+        target_task: Task,
+        queried_knowledge: QueriedKnowledge | None = None,
+        workspace: FBWorkspace | None = None,
+        prev_task_feedback: CoSTEERSingleFeedback | None = None,
+    ) -> dict[str, str]:  # FIXME: fix interface of previous implement
+        """
+        This method will input the task & current workspace,
+        and output the modification to applied to the workspace.
+        (i.e. replace the content <filename> with <content>)
+
+        Parameters
+        ----------
+        target_task : Task
+
+        queried_knowledge : QueriedKnowledge | None
+
+        workspace : FBWorkspace | None
+
+        prev_task_feedback : CoSTEERSingleFeedback | None
+            task feedback for previous evolving step
+            None indicate it is the first loop.
+
+        Return
+        ------
+        The new files {<filename>: <content>} to update the workspace.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def assign_code_list_to_evo(self, code_list: list[dict], evo: EvolvingItem) -> None:
+        """
+        Assign the code list to the evolving item.
+
+        Due to the implement_one_task take `workspace` as input and output the `modification`.
+        We should apply implementation to evo
+
+        The code list is aligned with the evolving item's sub-tasks.
+        If a task is not implemented, put a None in the list.
+        """
+        raise NotImplementedError
+
+    def evolve(
+        self,
+        *,
+        evo: EvolvingItem,
+        queried_knowledge: CoSTEERQueriedKnowledge | None = None,
+        evolving_trace: list[EvoStep] = [],
+        **kwargs,
+    ) -> EvolvingItem:
+        # 1.找出需要evolve的task
+        to_be_finished_task_index: list[int] = []
+        for index, target_task in enumerate(evo.sub_tasks):
+            target_task_desc = target_task.get_task_information()
+            if target_task_desc in queried_knowledge.success_task_to_knowledge_dict:
+                # NOTE: very weird logic:
+                # it depends on the knowledge to set the already finished task
+                evo.sub_workspace_list[index] = queried_knowledge.success_task_to_knowledge_dict[
+                    target_task_desc
+                ].implementation
+            elif (
+                target_task_desc not in queried_knowledge.success_task_to_knowledge_dict
+                and target_task_desc not in queried_knowledge.failed_task_info_set
+            ):
+                to_be_finished_task_index.append(index)
+
+        last_feedback = None
+        if len(evolving_trace) > 0:
+            last_feedback = evolving_trace[-1].feedback
+            assert isinstance(last_feedback, CoSTEERMultiFeedback)
+
+        result = multiprocessing_wrapper(
+            [
+                (
+                    self.implement_one_task,
+                    (
+                        evo.sub_tasks[target_index],
+                        queried_knowledge,
+                        evo.experiment_workspace,
+                        None if last_feedback is None else last_feedback[target_index],
+                    ),
+                )
+                for target_index in to_be_finished_task_index
+            ],
+            n=RD_AGENT_SETTINGS.multi_proc_n,
+        )
+        code_list = [None for _ in range(len(evo.sub_tasks))]
+        for index, target_index in enumerate(to_be_finished_task_index):
+            code_list[target_index] = result[index]
+
+        evo = self.assign_code_list_to_evo(code_list, evo)
+
+        return evo

rdagent/components/coder/CoSTEER/knowledge_management.py

@@ -6,28 +6,26 @@ import random
 import re
 from itertools import combinations
 from pathlib import Path
-from typing import Union
+from typing import List, Union
 
 from jinja2 import Environment, StrictUndefined
 
-from rdagent.components.coder.factor_coder.config import FACTOR_IMPLEMENT_SETTINGS
-from rdagent.components.coder.factor_coder.CoSTEER.evaluators import (
-    FactorSingleFeedback,
-)
-from rdagent.components.coder.factor_coder.factor import FactorTask
+from rdagent.components.coder.CoSTEER.config import CoSTEERSettings
+from rdagent.components.coder.CoSTEER.evaluators import CoSTEERSingleFeedback
 from rdagent.components.knowledge_management.graph import (
     UndirectedGraph,
     UndirectedNode,
 )
+from rdagent.core.evolving_agent import Feedback
 from rdagent.core.evolving_framework import (
     EvolvableSubjects,
+    EvolvingKnowledgeBase,
     EvoStep,
     Knowledge,
-    KnowledgeBase,
     QueriedKnowledge,
     RAGStrategy,
 )
-from rdagent.core.experiment import Workspace
+from rdagent.core.experiment import FBWorkspace, Task
 from rdagent.core.prompts import Prompts
 from rdagent.log import rdagent_logger as logger
 from rdagent.oai.llm_utils import (
@@ -36,65 +34,64 @@ from rdagent.oai.llm_utils import (
 )
 
 
-class FactorKnowledge(Knowledge):
+class CoSTEERKnowledge(Knowledge):
     def __init__(
         self,
-        target_task: FactorTask,
-        implementation: Workspace,
-        feedback: FactorSingleFeedback,
+        target_task: Task,
+        implementation: FBWorkspace,
+        feedback: Feedback,
     ) -> None:
-        """
-        Initialize a FactorKnowledge object. The FactorKnowledge object is used to store a factor implementation without the ground truth code and value.
-
-        Args:
-            factor (Factor): The factor object associated with the KnowledgeManagement.
-
-        Returns:
-            None
-        """
         self.target_task = target_task
         self.implementation = implementation.copy()
         self.feedback = feedback
 
     def get_implementation_and_feedback_str(self) -> str:
-        return f"""------------------Factor implementation code:------------------
-{self.implementation.code}
-------------------Factor implementation feedback:------------------
+        return f"""------------------implementation code:------------------
+{self.implementation.all_codes}
+------------------implementation feedback:------------------
 {self.feedback!s}
 """
 
 
-class FactorQueriedKnowledge(QueriedKnowledge):
+class CoSTEERQueriedKnowledge(QueriedKnowledge):
     def __init__(self, success_task_to_knowledge_dict: dict = {}, failed_task_info_set: set = set()) -> None:
         self.success_task_to_knowledge_dict = success_task_to_knowledge_dict
         self.failed_task_info_set = failed_task_info_set
 
 
-class FactorKnowledgeBaseV1(KnowledgeBase):
-    def __init__(self) -> None:
-        self.implementation_trace: dict[str, FactorKnowledge] = dict()
+class CoSTEERKnowledgeBaseV1(EvolvingKnowledgeBase):
+    def __init__(self, path: str | Path = None) -> None:
+        self.implementation_trace: dict[str, CoSTEERKnowledge] = dict()
         self.success_task_info_set: set[str] = set()
 
         self.task_to_embedding = dict()
+        super().__init__(path)
 
-    def query(self) -> QueriedKnowledge | None:
+    def query(self) -> CoSTEERQueriedKnowledge | None:
         """
         Query the knowledge base to get the queried knowledge. So far is handled in RAG strategy.
         """
         raise NotImplementedError
 
 
-class FactorQueriedKnowledgeV1(FactorQueriedKnowledge):
-    def __init__(self) -> None:
-        self.working_task_to_former_failed_knowledge_dict = dict()
-        self.working_task_to_similar_successful_knowledge_dict = dict()
-        super().__init__()
+class CoSTEERQueriedKnowledgeV1(CoSTEERQueriedKnowledge):
+    def __init__(
+        self,
+        *args,
+        task_to_former_failed_traces: dict = {},
+        task_to_similar_task_successful_knowledge: dict = {},
+        **kwargs,
+    ) -> None:
+        self.task_to_former_failed_traces = task_to_former_failed_traces
+        self.task_to_similar_task_successful_knowledge = task_to_similar_task_successful_knowledge
+        super().__init__(*args, **kwargs)
 
 
-class FactorRAGStrategyV1(RAGStrategy):
-    def __init__(self, knowledgebase: FactorKnowledgeBaseV1) -> None:
+class CoSTEERRAGStrategyV1(RAGStrategy):
+    def __init__(self, knowledgebase: CoSTEERKnowledgeBaseV1, settings: CoSTEERSettings) -> None:
         super().__init__(knowledgebase)
         self.current_generated_trace_count = 0
+        self.settings = settings
 
     def generate_knowledge(
         self,
@@ -102,6 +99,9 @@ class FactorRAGStrategyV1(RAGStrategy):
         *,
         return_knowledge: bool = False,
     ) -> Knowledge | None:
+        raise NotImplementedError(
+            "This method should be considered as an un-implemented method because we encourage everyone to use v2."
+        )
         if len(evolving_trace) == self.current_generated_trace_count:
             return
         else:
@@ -119,7 +119,7 @@ class FactorRAGStrategyV1(RAGStrategy):
                     single_feedback = feedback[task_index]
                     if single_feedback is None:
                         continue
-                    single_knowledge = FactorKnowledge(
+                    single_knowledge = CoSTEERKnowledge(
                         target_task=target_task,
                         implementation=implementation,
                         feedback=single_feedback,
@@ -140,43 +140,44 @@ class FactorRAGStrategyV1(RAGStrategy):
         self,
         evo: EvolvableSubjects,
         evolving_trace: list[EvoStep],
-    ) -> QueriedKnowledge | None:
-        v1_query_former_trace_limit = FACTOR_IMPLEMENT_SETTINGS.v1_query_former_trace_limit
-        v1_query_similar_success_limit = FACTOR_IMPLEMENT_SETTINGS.v1_query_similar_success_limit
-        fail_task_trial_limit = FACTOR_IMPLEMENT_SETTINGS.fail_task_trial_limit
+    ) -> CoSTEERQueriedKnowledge | None:
+        raise NotImplementedError(
+            "This method should be considered as an un-implemented method because we encourage everyone to use v2."
+        )
+        v1_query_former_trace_limit = self.settings.v1_query_former_trace_limit
+        v1_query_similar_success_limit = self.settings.v1_query_similar_success_limit
+        fail_task_trial_limit = self.settings.fail_task_trial_limit
 
-        queried_knowledge = FactorQueriedKnowledgeV1()
-        for target_factor_task in evo.sub_tasks:
-            target_factor_task_information = target_factor_task.get_task_information()
-            if target_factor_task_information in self.knowledgebase.success_task_info_set:
-                queried_knowledge.success_task_to_knowledge_dict[
-                    target_factor_task_information
-                ] = self.knowledgebase.implementation_trace[target_factor_task_information][-1]
+        queried_knowledge = CoSTEERQueriedKnowledgeV1()
+        for target_task in evo.sub_tasks:
+            target_task_information = target_task.get_task_information()
+            if target_task_information in self.knowledgebase.success_task_info_set:
+                queried_knowledge.success_task_to_knowledge_dict[target_task_information] = (
+                    self.knowledgebase.implementation_trace[target_task_information][-1]
+                )
             elif (
                 len(
                     self.knowledgebase.implementation_trace.setdefault(
-                        target_factor_task_information,
+                        target_task_information,
                         [],
                     ),
                 )
                 >= fail_task_trial_limit
             ):
-                queried_knowledge.failed_task_info_set.add(target_factor_task_information)
+                queried_knowledge.failed_task_info_set.add(target_task_information)
             else:
-                queried_knowledge.working_task_to_former_failed_knowledge_dict[
-                    target_factor_task_information
-                ] = self.knowledgebase.implementation_trace.setdefault(
-                    target_factor_task_information,
-                    [],
-                )[
-                    -v1_query_former_trace_limit:
-                ]
+                queried_knowledge.task_to_former_failed_traces[target_task_information] = (
+                    self.knowledgebase.implementation_trace.setdefault(
+                        target_task_information,
+                        [],
+                    )[-v1_query_former_trace_limit:]
+                )
 
                 knowledge_base_success_task_list = list(
                     self.knowledgebase.success_task_info_set,
                 )
                 similarity = calculate_embedding_distance_between_str_list(
-                    [target_factor_task_information],
+                    [target_task_information],
                     knowledge_base_success_task_list,
                 )[0]
                 similar_indexes = sorted(
@@ -191,33 +192,36 @@ class FactorRAGStrategyV1(RAGStrategy):
                     )[-1]
                     for index in similar_indexes
                 ]
-                queried_knowledge.working_task_to_similar_successful_knowledge_dict[
-                    target_factor_task_information
-                ] = similar_successful_knowledge
+                queried_knowledge.task_to_similar_task_successful_knowledge[target_task_information] = (
+                    similar_successful_knowledge
+                )
         return queried_knowledge
 
 
-class FactorQueriedGraphKnowledge(FactorQueriedKnowledge):
+class CoSTEERQueriedKnowledgeV2(CoSTEERQueriedKnowledgeV1):
     # Aggregation of knowledge
     def __init__(
         self,
-        former_traces: dict = {},
-        component_with_success_task: dict = {},
-        error_with_success_task: dict = {},
+        task_to_former_failed_traces: dict = {},
+        task_to_similar_task_successful_knowledge: dict = {},
+        task_to_similar_error_successful_knowledge: dict = {},
         **kwargs,
     ) -> None:
-        self.former_traces = former_traces
-        self.component_with_success_task = component_with_success_task
-        self.error_with_success_task = error_with_success_task
-        super().__init__(**kwargs)
+        self.task_to_similar_error_successful_knowledge = task_to_similar_error_successful_knowledge
+        super().__init__(
+            task_to_former_failed_traces=task_to_former_failed_traces,
+            task_to_similar_task_successful_knowledge=task_to_similar_task_successful_knowledge,
+            **kwargs,
+        )
 
 
-class FactorGraphRAGStrategy(RAGStrategy):
-    prompt = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")
+class CoSTEERRAGStrategyV2(RAGStrategy):
+    prompt = Prompts(file_path=Path(__file__).parent / "prompts.yaml")
 
-    def __init__(self, knowledgebase: FactorGraphKnowledgeBase) -> None:
+    def __init__(self, knowledgebase: CoSTEERKnowledgeBaseV2, settings: CoSTEERSettings) -> None:
         super().__init__(knowledgebase)
         self.current_generated_trace_count = 0
+        self.settings = settings
 
     def generate_knowledge(
         self,
@@ -234,14 +238,13 @@ class FactorGraphRAGStrategy(RAGStrategy):
                 implementations = evo_step.evolvable_subjects
                 feedback = evo_step.feedback
                 for task_index in range(len(implementations.sub_tasks)):
-                    single_feedback = feedback[task_index]
                     target_task = implementations.sub_tasks[task_index]
                     target_task_information = target_task.get_task_information()
                     implementation = implementations.sub_workspace_list[task_index]
-                    single_feedback = feedback[task_index]
-                    if single_feedback is None:
+                    single_feedback: CoSTEERSingleFeedback = feedback[task_index]
+                    if implementation is None or single_feedback is None:
                         continue
-                    single_knowledge = FactorKnowledge(
+                    single_knowledge = CoSTEERKnowledge(
                         target_task=target_task,
                         implementation=implementation,
                         feedback=single_feedback,
@@ -265,15 +268,15 @@ class FactorGraphRAGStrategy(RAGStrategy):
                         else:
                             # generate error node and store into knowledge base
                             error_analysis_result = []
-                            if not single_feedback.value_generated_flag:
+                            if single_feedback.return_checking:
                                 error_analysis_result = self.analyze_error(
-                                    single_feedback.execution_feedback,
-                                    feedback_type="execution",
+                                    single_feedback.return_checking,
+                                    feedback_type="value",
                                 )
                             else:
                                 error_analysis_result = self.analyze_error(
-                                    single_feedback.factor_value_feedback,
-                                    feedback_type="value",
+                                    single_feedback.execution,
+                                    feedback_type="execution",
                                 )
                             self.knowledgebase.working_trace_error_analysis.setdefault(
                                 target_task_information,
@@ -285,34 +288,35 @@ class FactorGraphRAGStrategy(RAGStrategy):
             self.current_generated_trace_count = len(evolving_trace)
             return None
 
-    def query(self, evo: EvolvableSubjects, evolving_trace: list[EvoStep]) -> QueriedKnowledge | None:
-        conf_knowledge_sampler = FACTOR_IMPLEMENT_SETTINGS.v2_knowledge_sampler
-        factor_implementation_queried_graph_knowledge = FactorQueriedGraphKnowledge(
+    def query(self, evo: EvolvableSubjects, evolving_trace: list[EvoStep]) -> CoSTEERQueriedKnowledge | None:
+        conf_knowledge_sampler = self.settings.v2_knowledge_sampler
+        queried_knowledge_v2 = CoSTEERQueriedKnowledgeV2(
             success_task_to_knowledge_dict=self.knowledgebase.success_task_to_knowledge_dict,
         )
 
-        factor_implementation_queried_graph_knowledge = self.former_trace_query(
+        queried_knowledge_v2 = self.former_trace_query(
             evo,
-            factor_implementation_queried_graph_knowledge,
-            FACTOR_IMPLEMENT_SETTINGS.v2_query_former_trace_limit,
+            queried_knowledge_v2,
+            self.settings.v2_query_former_trace_limit,
+            self.settings.v2_add_fail_attempt_to_latest_successful_execution,
         )
-        factor_implementation_queried_graph_knowledge = self.component_query(
+        queried_knowledge_v2 = self.component_query(
             evo,
-            factor_implementation_queried_graph_knowledge,
-            FACTOR_IMPLEMENT_SETTINGS.v2_query_component_limit,
+            queried_knowledge_v2,
+            self.settings.v2_query_component_limit,
             knowledge_sampler=conf_knowledge_sampler,
         )
-        factor_implementation_queried_graph_knowledge = self.error_query(
+        queried_knowledge_v2 = self.error_query(
             evo,
-            factor_implementation_queried_graph_knowledge,
-            FACTOR_IMPLEMENT_SETTINGS.v2_query_error_limit,
+            queried_knowledge_v2,
+            self.settings.v2_query_error_limit,
             knowledge_sampler=conf_knowledge_sampler,
         )
-        return factor_implementation_queried_graph_knowledge
+        return queried_knowledge_v2
 
     def analyze_component(
         self,
-        target_factor_task_information,
+        target_task_information,
     ) -> list[UndirectedNode]:  # Hardcode: certain component nodes
         all_component_nodes = self.knowledgebase.graph.get_all_nodes_by_label_list(["component"])
         if not len(all_component_nodes):
@@ -328,13 +332,14 @@ class FactorGraphRAGStrategy(RAGStrategy):
             )
         )
 
-        analyze_component_user_prompt = target_factor_task_information
+        analyze_component_user_prompt = target_task_information
         try:
             component_no_list = json.loads(
                 APIBackend().build_messages_and_create_chat_completion(
                     system_prompt=analyze_component_system_prompt,
                     user_prompt=analyze_component_user_prompt,
                     json_mode=True,
+                    json_target_type=List[int],
                 ),
             )["component_no_list"]
             return [all_component_nodes[index - 1] for index in sorted(list(set(component_no_list)))]
@@ -389,77 +394,87 @@ class FactorGraphRAGStrategy(RAGStrategy):
     def former_trace_query(
         self,
         evo: EvolvableSubjects,
-        factor_implementation_queried_graph_knowledge: FactorQueriedGraphKnowledge,
+        queried_knowledge_v2: CoSTEERQueriedKnowledgeV2,
         v2_query_former_trace_limit: int = 5,
-    ) -> Union[QueriedKnowledge, set]:
+        v2_add_fail_attempt_to_latest_successful_execution: bool = False,
+    ) -> Union[CoSTEERQueriedKnowledge, set]:
         """
         Query the former trace knowledge of the working trace, and find all the failed task information which tried more than fail_task_trial_limit times
         """
-        fail_task_trial_limit = FACTOR_IMPLEMENT_SETTINGS.fail_task_trial_limit
+        fail_task_trial_limit = self.settings.fail_task_trial_limit
 
-        for target_factor_task in evo.sub_tasks:
-            target_factor_task_information = target_factor_task.get_task_information()
+        for target_task in evo.sub_tasks:
+            target_task_information = target_task.get_task_information()
             if (
-                target_factor_task_information not in self.knowledgebase.success_task_to_knowledge_dict
-                and target_factor_task_information in self.knowledgebase.working_trace_knowledge
-                and len(self.knowledgebase.working_trace_knowledge[target_factor_task_information])
-                >= fail_task_trial_limit
+                target_task_information not in self.knowledgebase.success_task_to_knowledge_dict
+                and target_task_information in self.knowledgebase.working_trace_knowledge
+                and len(self.knowledgebase.working_trace_knowledge[target_task_information]) >= fail_task_trial_limit
             ):
-                factor_implementation_queried_graph_knowledge.failed_task_info_set.add(target_factor_task_information)
+                queried_knowledge_v2.failed_task_info_set.add(target_task_information)
 
             if (
-                target_factor_task_information not in self.knowledgebase.success_task_to_knowledge_dict
-                and target_factor_task_information
-                not in factor_implementation_queried_graph_knowledge.failed_task_info_set
-                and target_factor_task_information in self.knowledgebase.working_trace_knowledge
+                target_task_information not in self.knowledgebase.success_task_to_knowledge_dict
+                and target_task_information not in queried_knowledge_v2.failed_task_info_set
+                and target_task_information in self.knowledgebase.working_trace_knowledge
             ):
                 former_trace_knowledge = copy.copy(
-                    self.knowledgebase.working_trace_knowledge[target_factor_task_information],
+                    self.knowledgebase.working_trace_knowledge[target_task_information],
                 )
                 # in former trace query we will delete the right trace in the following order:[..., value_generated_flag is True, value_generated_flag is False, ...]
                 # because we think this order means a deterioration of the trial (like a wrong gradient descent)
                 current_index = 1
                 while current_index < len(former_trace_knowledge):
                     if (
-                        not former_trace_knowledge[current_index].feedback.value_generated_flag
-                        and former_trace_knowledge[current_index - 1].feedback.value_generated_flag
+                        not former_trace_knowledge[current_index].feedback.return_checking
+                        and former_trace_knowledge[current_index - 1].feedback.return_checking
                     ):
                         former_trace_knowledge.pop(current_index)
                     else:
                         current_index += 1
 
-                factor_implementation_queried_graph_knowledge.former_traces[
-                    target_factor_task_information
-                ] = former_trace_knowledge[-v2_query_former_trace_limit:]
-            else:
-                factor_implementation_queried_graph_knowledge.former_traces[target_factor_task_information] = []
+                latest_attempt = None
+                if v2_add_fail_attempt_to_latest_successful_execution:
+                    # When the last successful execution is not the last one in the working trace, it means we have tried to correct it. We should tell the agent this fail trial to avoid endless loop in the future.
+                    if (
+                        len(former_trace_knowledge) > 0
+                        and len(self.knowledgebase.working_trace_knowledge[target_task_information]) > 1
+                        and self.knowledgebase.working_trace_knowledge[target_task_information].index(
+                            former_trace_knowledge[-1]
+                        )
+                        < len(self.knowledgebase.working_trace_knowledge[target_task_information]) - 1
+                    ):
+                        latest_attempt = self.knowledgebase.working_trace_knowledge[target_task_information][-1]
 
-        return factor_implementation_queried_graph_knowledge
+                queried_knowledge_v2.task_to_former_failed_traces[target_task_information] = (
+                    former_trace_knowledge[-v2_query_former_trace_limit:],
+                    latest_attempt,
+                )
+            else:
+                queried_knowledge_v2.task_to_former_failed_traces[target_task_information] = ([], None)
+
+        return queried_knowledge_v2
 
     def component_query(
         self,
         evo: EvolvableSubjects,
-        factor_implementation_queried_graph_knowledge: FactorQueriedGraphKnowledge,
+        queried_knowledge_v2: CoSTEERQueriedKnowledgeV2,
         v2_query_component_limit: int = 5,
         knowledge_sampler: float = 1.0,
-    ) -> QueriedKnowledge | None:
-        # queried_component_knowledge = FactorQueriedGraphComponentKnowledge()
-        for target_factor_task in evo.sub_tasks:
-            target_factor_task_information = target_factor_task.get_task_information()
+    ) -> CoSTEERQueriedKnowledge | None:
+        for target_task in evo.sub_tasks:
+            target_task_information = target_task.get_task_information()
             if (
-                target_factor_task_information in self.knowledgebase.success_task_to_knowledge_dict
-                or target_factor_task_information in factor_implementation_queried_graph_knowledge.failed_task_info_set
+                target_task_information in self.knowledgebase.success_task_to_knowledge_dict
+                or target_task_information in queried_knowledge_v2.failed_task_info_set
             ):
-                factor_implementation_queried_graph_knowledge.component_with_success_task[
-                    target_factor_task_information
-                ] = []
+                queried_knowledge_v2.task_to_similar_task_successful_knowledge[target_task_information] = []
             else:
-                if target_factor_task_information not in self.knowledgebase.task_to_component_nodes:
-                    self.knowledgebase.task_to_component_nodes[target_factor_task_information] = self.analyze_component(
-                        target_factor_task_information,
+                if target_task_information not in self.knowledgebase.task_to_component_nodes:
+                    self.knowledgebase.task_to_component_nodes[target_task_information] = self.analyze_component(
+                        target_task_information,
                     )
 
-                component_analysis_result = self.knowledgebase.task_to_component_nodes[target_factor_task_information]
+                component_analysis_result = self.knowledgebase.task_to_component_nodes[target_task_information]
 
                 if len(component_analysis_result) > 1:
                     task_des_node_list = self.knowledgebase.graph_query_by_intersection(
@@ -470,9 +485,7 @@ class FactorGraphRAGStrategy(RAGStrategy):
                 else:
                     task_des_node_list = []
                     single_component_constraint = v2_query_component_limit
-                factor_implementation_queried_graph_knowledge.component_with_success_task[
-                    target_factor_task_information
-                ] = []
+                queried_knowledge_v2.task_to_similar_task_successful_knowledge[target_task_information] = []
                 for component_node in component_analysis_result:
                     # Reverse iterate, a trade-off with intersection search
                     count = 0
@@ -503,19 +516,19 @@ class FactorGraphRAGStrategy(RAGStrategy):
                             ]
                         if (
                             target_knowledge
-                            not in factor_implementation_queried_graph_knowledge.component_with_success_task[
-                                target_factor_task_information
+                            not in queried_knowledge_v2.task_to_similar_task_successful_knowledge[
+                                target_task_information
                             ]
                         ):
-                            factor_implementation_queried_graph_knowledge.component_with_success_task[
-                                target_factor_task_information
+                            queried_knowledge_v2.task_to_similar_task_successful_knowledge[
+                                target_task_information
                             ].append(target_knowledge)
 
                 # finally add embedding related knowledge
                 knowledge_base_success_task_list = list(self.knowledgebase.success_task_to_knowledge_dict)
 
                 similarity = calculate_embedding_distance_between_str_list(
-                    [target_factor_task_information],
+                    [target_task_information],
                     knowledge_base_success_task_list,
                 )[0]
                 similar_indexes = sorted(
@@ -530,28 +543,24 @@ class FactorGraphRAGStrategy(RAGStrategy):
                 for knowledge in embedding_similar_successful_knowledge:
                     if (
                         knowledge
-                        not in factor_implementation_queried_graph_knowledge.component_with_success_task[
-                            target_factor_task_information
-                        ]
+                        not in queried_knowledge_v2.task_to_similar_task_successful_knowledge[target_task_information]
                     ):
-                        factor_implementation_queried_graph_knowledge.component_with_success_task[
-                            target_factor_task_information
-                        ].append(knowledge)
+                        queried_knowledge_v2.task_to_similar_task_successful_knowledge[target_task_information].append(
+                            knowledge
+                        )
 
                 if knowledge_sampler > 0:
-                    factor_implementation_queried_graph_knowledge.component_with_success_task[
-                        target_factor_task_information
-                    ] = [
+                    queried_knowledge_v2.task_to_similar_task_successful_knowledge[target_task_information] = [
                         knowledge
-                        for knowledge in factor_implementation_queried_graph_knowledge.component_with_success_task[
-                            target_factor_task_information
+                        for knowledge in queried_knowledge_v2.task_to_similar_task_successful_knowledge[
+                            target_task_information
                         ]
                         if random.uniform(0, 1) <= knowledge_sampler
                     ]
 
                 # Make sure no less than half of the knowledge are from GT
-                queried_knowledge_list = factor_implementation_queried_graph_knowledge.component_with_success_task[
-                    target_factor_task_information
+                queried_knowledge_list = queried_knowledge_v2.task_to_similar_task_successful_knowledge[
+                    target_task_information
                 ]
                 queried_from_gt_knowledge_list = [
                     knowledge
@@ -564,54 +573,46 @@ class FactorGraphRAGStrategy(RAGStrategy):
                     if knowledge.feedback is not None and knowledge.feedback.final_decision_based_on_gt == False
                 ]
                 queried_from_gt_knowledge_count = max(
-                    min(v2_query_component_limit // 2, len(queried_from_gt_knowledge_list)),
+                    min((v2_query_component_limit // 2 + 1), len(queried_from_gt_knowledge_list)),
                     v2_query_component_limit - len(queried_without_gt_knowledge_list),
                 )
-                factor_implementation_queried_graph_knowledge.component_with_success_task[
-                    target_factor_task_information
-                ] = (
+                queried_knowledge_v2.task_to_similar_task_successful_knowledge[target_task_information] = (
                     queried_from_gt_knowledge_list[:queried_from_gt_knowledge_count]
                     + queried_without_gt_knowledge_list[: v2_query_component_limit - queried_from_gt_knowledge_count]
                 )
 
-        return factor_implementation_queried_graph_knowledge
+        return queried_knowledge_v2
 
     def error_query(
         self,
         evo: EvolvableSubjects,
-        factor_implementation_queried_graph_knowledge: FactorQueriedGraphKnowledge,
+        queried_knowledge_v2: CoSTEERQueriedKnowledgeV2,
         v2_query_error_limit: int = 5,
         knowledge_sampler: float = 1.0,
-    ) -> QueriedKnowledge | None:
-        # queried_error_knowledge = FactorQueriedGraphErrorKnowledge()
-        for task_index, target_factor_task in enumerate(evo.sub_tasks):
-            target_factor_task_information = target_factor_task.get_task_information()
-            factor_implementation_queried_graph_knowledge.error_with_success_task[target_factor_task_information] = {}
+    ) -> CoSTEERQueriedKnowledge | None:
+        for task_index, target_task in enumerate(evo.sub_tasks):
+            target_task_information = target_task.get_task_information()
+            queried_knowledge_v2.task_to_similar_error_successful_knowledge[target_task_information] = []
             if (
-                target_factor_task_information in self.knowledgebase.success_task_to_knowledge_dict
-                or target_factor_task_information in factor_implementation_queried_graph_knowledge.failed_task_info_set
+                target_task_information in self.knowledgebase.success_task_to_knowledge_dict
+                or target_task_information in queried_knowledge_v2.failed_task_info_set
             ):
-                factor_implementation_queried_graph_knowledge.error_with_success_task[
-                    target_factor_task_information
-                ] = []
+                queried_knowledge_v2.task_to_similar_error_successful_knowledge[target_task_information] = []
             else:
-                factor_implementation_queried_graph_knowledge.error_with_success_task[
-                    target_factor_task_information
-                ] = []
+                queried_knowledge_v2.task_to_similar_error_successful_knowledge[target_task_information] = []
                 if (
-                    target_factor_task_information in self.knowledgebase.working_trace_error_analysis
-                    and len(self.knowledgebase.working_trace_error_analysis[target_factor_task_information]) > 0
-                    and len(factor_implementation_queried_graph_knowledge.former_traces[target_factor_task_information])
-                    > 0
+                    target_task_information in self.knowledgebase.working_trace_error_analysis
+                    and len(self.knowledgebase.working_trace_error_analysis[target_task_information]) > 0
+                    and len(queried_knowledge_v2.task_to_former_failed_traces[target_task_information]) > 0
                 ):
-                    queried_last_trace = factor_implementation_queried_graph_knowledge.former_traces[
-                        target_factor_task_information
-                    ][-1]
-                    target_index = self.knowledgebase.working_trace_knowledge[target_factor_task_information].index(
+                    queried_last_trace = queried_knowledge_v2.task_to_former_failed_traces[target_task_information][0][
+                        -1
+                    ]
+                    target_index = self.knowledgebase.working_trace_knowledge[target_task_information].index(
                         queried_last_trace,
                     )
                     last_knowledge_error_analysis_result = self.knowledgebase.working_trace_error_analysis[
-                        target_factor_task_information
+                        target_task_information
                     ][target_index]
                 else:
                     last_knowledge_error_analysis_result = []
@@ -704,20 +705,20 @@ class FactorGraphRAGStrategy(RAGStrategy):
                     ]
 
                 same_error_success_knowledge_pair_list = same_error_success_knowledge_pair_list[:v2_query_error_limit]
-                factor_implementation_queried_graph_knowledge.error_with_success_task[
-                    target_factor_task_information
-                ] = same_error_success_knowledge_pair_list
+                queried_knowledge_v2.task_to_similar_error_successful_knowledge[target_task_information] = (
+                    same_error_success_knowledge_pair_list
+                )
 
-        return factor_implementation_queried_graph_knowledge
+        return queried_knowledge_v2
 
 
-class FactorGraphKnowledgeBase(KnowledgeBase):
-    def __init__(self, init_component_list=None) -> None:
+class CoSTEERKnowledgeBaseV2(EvolvingKnowledgeBase):
+    def __init__(self, init_component_list=None, path: str | Path = None) -> None:
         """
         Load knowledge, offer brief information of knowledge and common handle interfaces
         """
-        self.graph: UndirectedGraph = UndirectedGraph.load(Path.cwd() / "graph.pkl")
-        logger.info(f"Knowledge Graph loaded, size={self.graph.size()}")
+        self.graph: UndirectedGraph = UndirectedGraph(Path.cwd() / "graph.pkl")
+        logger.info(f"CoSTEER Knowledge Graph loaded, size={self.graph.size()}")
 
         if init_component_list:
             for component in init_component_list:
@@ -734,7 +735,7 @@ class FactorGraphKnowledgeBase(KnowledgeBase):
         # Add already success task
         self.success_task_to_knowledge_dict = {}
 
-        # key:node_id(for task trace and success implement), value:knowledge instance(aka 'FactorKnowledge')
+        # key:node_id(for task trace and success implement), value:knowledge instance(aka 'CoSTEERKnowledge')
         self.node_to_implementation_knowledge_dict = {}
 
         # store the task description to component nodes

rdagent/components/coder/CoSTEER/prompts.yaml

@@ -0,0 +1,10 @@
+
+analyze_component_prompt_v1_system: |-
+  User is getting a new task that might consist of the components below (given in component_index: component_description):
+  {{all_component_content}}
+
+  You should find out what components does the new task have, and put their indices in a list.
+  Please response the critic in the json format. Here is an example structure for the JSON output, please strictly follow the format:
+  {
+      "component_no_list": the list containing indices of components.
+  }

rdagent/components/coder/CoSTEER/task.py

@@ -0,0 +1,9 @@
+from rdagent.core.experiment import Task
+
+
+class CoSTEERTask(Task):
+    def __init__(self, base_code: str = None, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        # TODO: we may upgrade the base_code into a workspace-like thing to know previous.
+        # NOTE: (xiao) think we don't need the base_code anymore. The information should be retrieved from the workspace.
+        self.base_code = base_code

rdagent/components/coder/data_science/conf.py

@@ -0,0 +1,50 @@
+from typing import Literal
+
+from rdagent.components.coder.CoSTEER.config import CoSTEERSettings
+from rdagent.utils.env import (
+    CondaConf,
+    DockerEnv,
+    DSDockerConf,
+    Env,
+    LocalEnv,
+    MLEBDockerConf,
+    MLECondaConf,
+)
+
+
+class DSCoderCoSTEERSettings(CoSTEERSettings):
+    """Data Science CoSTEER settings"""
+
+    class Config:
+        env_prefix = "DS_Coder_CoSTEER_"
+
+    max_seconds: int = 2400
+    env_type: str = "docker"
+    # TODO: extract a function for env and conf.
+
+
+def get_ds_env(conf_type: Literal["kaggle", "mlebench"] = "kaggle") -> Env:
+    """
+    Retrieve the appropriate environment configuration based on the env_type setting.
+
+    Returns:
+        Env: An instance of the environment configured either as DockerEnv or LocalEnv.
+
+    Raises:
+        ValueError: If the env_type is not recognized.
+    """
+    conf = DSCoderCoSTEERSettings()
+    assert conf_type in ["kaggle", "mlebench"], f"Unknown conf_type: {conf_type}"
+
+    if conf.env_type == "docker":
+        env_conf = DSDockerConf() if conf_type == "kaggle" else MLEBDockerConf()
+        env = DockerEnv(conf=env_conf)
+    elif conf.env_type == "conda":
+        env = LocalEnv(
+            conf=(
+                CondaConf(conda_env_name=conf_type) if conf_type == "kaggle" else MLECondaConf(conda_env_name=conf_type)
+            )
+        )
+    else:
+        raise ValueError(f"Unknown env type: {conf.env_type}")
+    return env

rdagent/components/coder/data_science/ensemble/__init__.py

@@ -0,0 +1,166 @@
+"""
+File structure
+- ___init__.py: the entrance/agent of coder
+- evaluator.py
+- conf.py
+- exp.py: everything under the experiment, e.g.
+    - Task
+    - Experiment
+    - Workspace
+- test.py
+    - Each coder could be tested.
+"""
+
+import json
+from pathlib import Path
+from typing import Dict
+
+from jinja2 import Environment, StrictUndefined
+
+from rdagent.app.data_science.conf import DS_RD_SETTING
+from rdagent.components.coder.CoSTEER import CoSTEER
+from rdagent.components.coder.CoSTEER.evaluators import (
+    CoSTEERMultiEvaluator,
+    CoSTEERSingleFeedback,
+)
+from rdagent.components.coder.CoSTEER.evolving_strategy import (
+    MultiProcessEvolvingStrategy,
+)
+from rdagent.components.coder.CoSTEER.knowledge_management import (
+    CoSTEERQueriedKnowledge,
+)
+from rdagent.components.coder.data_science.conf import DSCoderCoSTEERSettings
+from rdagent.components.coder.data_science.ensemble.eval import EnsembleCoSTEEREvaluator
+from rdagent.components.coder.data_science.ensemble.exp import EnsembleTask
+from rdagent.core.exception import CoderError
+from rdagent.core.experiment import FBWorkspace
+from rdagent.core.scenario import Scenario
+from rdagent.oai.llm_utils import APIBackend
+from rdagent.utils.agent.ret import PythonAgentOut
+from rdagent.utils.agent.tpl import T
+
+DIRNAME = Path(__file__).absolute().resolve().parent
+
+
+class EnsembleMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
+    def implement_one_task(
+        self,
+        target_task: EnsembleTask,
+        queried_knowledge: CoSTEERQueriedKnowledge | None = None,
+        workspace: FBWorkspace | None = None,
+        prev_task_feedback: CoSTEERSingleFeedback | None = None,
+    ) -> dict[str, str]:
+        # Get task information for knowledge querying
+        ensemble_information_str = target_task.get_task_information()
+
+        # Query knowledge
+        queried_similar_successful_knowledge = (
+            queried_knowledge.task_to_similar_task_successful_knowledge[ensemble_information_str]
+            if queried_knowledge is not None
+            else []
+        )
+        queried_former_failed_knowledge = (
+            queried_knowledge.task_to_former_failed_traces[ensemble_information_str]
+            if queried_knowledge is not None
+            else []
+        )
+        queried_former_failed_knowledge = (
+            [
+                knowledge
+                for knowledge in queried_former_failed_knowledge[0]
+                if knowledge.implementation.file_dict.get("ensemble.py") != workspace.file_dict.get("ensemble.py")
+            ],
+            queried_former_failed_knowledge[1],
+        )
+
+        # Generate code with knowledge integration
+        competition_info = self.scen.get_scenario_all_desc()
+        system_prompt = T(".prompts:ensemble_coder.system").r(
+            task_desc=ensemble_information_str,
+            competition_info=competition_info,
+            queried_similar_successful_knowledge=queried_similar_successful_knowledge,
+            queried_former_failed_knowledge=(
+                queried_former_failed_knowledge[0] if queried_former_failed_knowledge else None
+            ),
+            all_code=workspace.all_codes,
+            out_spec=PythonAgentOut.get_spec(),
+        )
+
+        if DS_RD_SETTING.spec_enabled:
+            code_spec = workspace.file_dict["spec/ensemble.md"]
+        else:
+            test_code = (
+                Environment(undefined=StrictUndefined)
+                .from_string((DIRNAME / "eval_tests" / "ensemble_test.txt").read_text())
+                .render(
+                    model_names=[
+                        fn[:-3] for fn in workspace.file_dict.keys() if fn.startswith("model_") and "test" not in fn
+                    ],
+                    metric_name=self.scen.metric_name,
+                )
+            )
+            code_spec = T("scenarios.data_science.share:component_spec.general").r(
+                spec=T("scenarios.data_science.share:component_spec.Ensemble").r(), test_code=test_code
+            )
+        user_prompt = T(".prompts:ensemble_coder.user").r(
+            code_spec=code_spec,
+            latest_code=workspace.file_dict.get("ensemble.py"),
+            latest_code_feedback=prev_task_feedback,
+        )
+
+        for _ in range(5):
+            ensemble_code = PythonAgentOut.extract_output(
+                APIBackend().build_messages_and_create_chat_completion(
+                    user_prompt=user_prompt,
+                    system_prompt=system_prompt,
+                )
+            )
+            if ensemble_code != workspace.file_dict.get("ensemble.py"):
+                break
+            else:
+                user_prompt = user_prompt + "\nPlease avoid generating same code to former code!"
+        else:
+            raise CoderError("Failed to generate a new ensemble code.")
+
+        return {
+            "ensemble.py": ensemble_code,
+        }
+
+    def assign_code_list_to_evo(self, code_list: list[dict[str, str]], evo):
+        """
+        Assign the code list to the evolving item.
+
+        The code list is aligned with the evolving item's sub-tasks.
+        If a task is not implemented, put a None in the list.
+        """
+        for index in range(len(evo.sub_tasks)):
+            if code_list[index] is None:
+                continue
+            if evo.sub_workspace_list[index] is None:
+                # evo.sub_workspace_list[index] = FBWorkspace(target_task=evo.sub_tasks[index])
+                evo.sub_workspace_list[index] = evo.experiment_workspace
+            evo.sub_workspace_list[index].inject_files(**code_list[index])
+        return evo
+
+
+class EnsembleCoSTEER(CoSTEER):
+    def __init__(
+        self,
+        scen: Scenario,
+        *args,
+        **kwargs,
+    ) -> None:
+        settings = DSCoderCoSTEERSettings()
+        eva = CoSTEERMultiEvaluator(EnsembleCoSTEEREvaluator(scen=scen), scen=scen)
+        es = EnsembleMultiProcessEvolvingStrategy(scen=scen, settings=settings)
+
+        super().__init__(
+            *args,
+            settings=settings,
+            eva=eva,
+            es=es,
+            evolving_version=2,
+            scen=scen,
+            max_loop=DS_RD_SETTING.coder_max_loop,
+            **kwargs,
+        )

rdagent/components/coder/data_science/ensemble/conf.py

@@ -0,0 +1,2 @@
+# Configuration file for ensemble component
+# Currently empty as no specific configuration is needed

rdagent/components/coder/data_science/ensemble/eval.py

@@ -0,0 +1,95 @@
+import json
+import re
+from pathlib import Path
+
+from jinja2 import Environment, StrictUndefined
+
+from rdagent.app.data_science.conf import DS_RD_SETTING
+from rdagent.components.coder.CoSTEER.evaluators import (
+    CoSTEEREvaluator,
+    CoSTEERSingleFeedback,
+)
+from rdagent.components.coder.data_science.conf import get_ds_env
+from rdagent.core.evolving_framework import QueriedKnowledge
+from rdagent.core.experiment import FBWorkspace, Task
+from rdagent.utils.agent.tpl import T
+from rdagent.utils.agent.workflow import build_cls_from_json_with_retry
+
+DIRNAME = Path(__file__).absolute().resolve().parent
+
+EnsembleEvalFeedback = CoSTEERSingleFeedback
+
+
+class EnsembleCoSTEEREvaluator(CoSTEEREvaluator):
+    def evaluate(
+        self,
+        target_task: Task,
+        implementation: FBWorkspace,
+        gt_implementation: FBWorkspace,
+        queried_knowledge: QueriedKnowledge = None,
+        **kwargs,
+    ) -> EnsembleEvalFeedback:
+
+        target_task_information = target_task.get_task_information()
+        metric_name = self.scen.metric_name
+
+        if (
+            queried_knowledge is not None
+            and target_task_information in queried_knowledge.success_task_to_knowledge_dict
+        ):
+            return queried_knowledge.success_task_to_knowledge_dict[target_task_information].feedback
+        elif queried_knowledge is not None and target_task_information in queried_knowledge.failed_task_info_set:
+            return EnsembleEvalFeedback(
+                execution="This task has failed too many times, skip implementation.",
+                code="This task has failed too many times, skip implementation.",
+                return_checking="This task has failed too many times, skip implementation.",
+                final_decision=False,
+            )
+
+        env = get_ds_env()
+        env.conf.extra_volumes = {f"{DS_RD_SETTING.local_data_path}/sample/{self.scen.competition}": "/kaggle/input"}
+
+        fname = "test/ensemble_test.txt"
+        test_code = (DIRNAME / "eval_tests" / "ensemble_test.txt").read_text()
+        test_code = (
+            Environment(undefined=StrictUndefined)
+            .from_string(test_code)
+            .render(
+                model_names=[
+                    fn[:-3] for fn in implementation.file_dict.keys() if fn.startswith("model_") and "test" not in fn
+                ],
+                metric_name=metric_name,
+            )
+        )
+
+        implementation.inject_files(**{fname: test_code})
+        stdout, ret_code = implementation.execute_ret_code(env=env, entry=f"python {fname}")
+
+        stdout += f"\nNOTE: the above scripts run with return code {ret_code}"
+
+        if "main.py" in implementation.file_dict and ret_code == 0:
+            workflow_stdout = implementation.execute(env=env, entry="python main.py")
+            workflow_stdout = re.sub(r"=== Start of EDA part ===(.*)=== End of EDA part ===", "", workflow_stdout)
+        else:
+            workflow_stdout = None
+
+        system_prompt = T(".prompts:ensemble_eval.system").r(
+            task_desc=target_task_information,
+            test_code=test_code,
+            metric_name=metric_name,
+            code=implementation.file_dict["ensemble.py"],
+            workflow_stdout=workflow_stdout,
+            workflow_code=implementation.all_codes,
+        )
+        user_prompt = T(".prompts:ensemble_eval.user").r(
+            stdout=stdout,
+            workflow_stdout=workflow_stdout,
+        )
+        efb = build_cls_from_json_with_retry(
+            EnsembleEvalFeedback,
+            system_prompt=system_prompt,
+            user_prompt=user_prompt,
+            init_kwargs_update_func=EnsembleEvalFeedback.val_and_update_init_dict,
+        )
+        efb.final_decision = efb.final_decision and ret_code == 0
+        return efb

rdagent/components/coder/data_science/ensemble/eval_tests/ensemble_test.txt

@@ -0,0 +1,132 @@
+"""
+Tests for `ensemble_workflow` in ensemble.py
+
+A qualified ensemble_workflow implementation should:
+- Return predictions
+- Have correct shapes for inputs and outputs
+- Use validation data appropriately
+- Generate a scores.csv file
+"""
+
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from sklearn.model_selection import train_test_split
+import torch
+import tensorflow as tf
+from load_data import load_data
+from feature import feat_eng
+from ensemble import ensemble_workflow
+
+def print_preds_info(model_name, data_type, preds):
+    if preds is None:
+        print(f"Model {model_name} {data_type} predictions: None")
+    else:
+        print(f"Model {model_name} {data_type} predictions shape: {preds.shape}")
+
+        print("Showing a preview of the predictions (first few entries only):")
+        if isinstance(preds, (pd.DataFrame, pd.Series)):
+            print(preds.head())
+        elif isinstance(preds, (np.ndarray, torch.Tensor, tf.Tensor)):
+            print(preds[:2])
+        elif isinstance(preds, list):
+            print(pd.DataFrame(preds[:5]))
+        else:
+            print(f"Unknown prediction type: {type(preds)}")
+
+def get_length(data):
+    return data.shape[0] if hasattr(data, 'shape') else len(data)
+
+X, y, test_X, test_ids = load_data()
+X, y, test_X = feat_eng(X, y, test_X)
+train_X, val_X, train_y, val_y = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Print the types of train_y and val_y
+print(f"train_y type: {type(train_y)}, val_y type: {type(val_y)}")
+
+test_preds_dict = {}
+val_preds_dict = {}
+{% for mn in model_names %}
+from {{mn}} import model_workflow as {{mn}}_workflow
+val_preds_dict["{{mn}}"], test_preds_dict["{{mn}}"], _ = {{mn}}_workflow(
+    X=train_X,
+    y=train_y,
+    val_X=val_X,
+    val_y=val_y,
+    test_X=test_X
+)
+
+print_preds_info("{{mn}}", "test", test_preds_dict["{{mn}}"])
+{% endfor %}
+
+for key in val_preds_dict.keys():
+    if val_preds_dict[key] is None: 
+        print(f"Model {key} validation predictions (val_preds_dict[key]) is None.")
+    elif isinstance(val_preds_dict[key], list):
+        print(f"Model {key} validation predictions (val_preds_dict[key]) (list type) length: {len(val_preds_dict[key])}")
+    else:
+        print(f"Model {key} validation predictions (val_preds_dict[key]) shape: {val_preds_dict[key].shape}")
+
+    if test_preds_dict[key] is None: 
+        print(f"Model {key} test predictions (test_preds_dict[key]) is None.")
+    elif isinstance(test_preds_dict[key], list):
+        print(f"Model {key} test predictions (test_preds_dict[key]) (list type) length: {len(test_preds_dict[key])}")
+    else:
+        print(f"Model {key} test predictions (test_preds_dict[key]) shape: {test_preds_dict[key].shape}")
+
+print(f"val_y.shape: {val_y.shape}" if not isinstance(val_y, list) else f"val_y(list)'s length: {len(val_y)}")
+
+import sys
+import reprlib
+def debug_info_print(func):
+    aRepr = reprlib.Repr()
+    aRepr.maxother=300
+    def wrapper(*args, **kwargs):
+        def local_trace(frame, event, arg):
+            if event == "return" and frame.f_code == func.__code__:
+                print("\n" + "="*20 + "Running ensemble code, local variable values:" + "="*20)
+                for k, v in frame.f_locals.items():
+                    printed = aRepr.repr(v)
+                    print(f"{k}:\n {printed}")
+                print("="*20 + "Local variable values end" + "="*20)
+            return local_trace
+        
+        sys.settrace(local_trace)
+        try:
+            return func(*args, **kwargs)
+        finally:
+            sys.settrace(None)
+    return wrapper
+
+
+# Run ensemble
+final_pred = debug_info_print(ensemble_workflow)(test_preds_dict, val_preds_dict, val_y)
+
+print_preds_info("ensemble", "test", final_pred)
+
+# Check type
+pred_type = type(next(iter(test_preds_dict.values())))
+assert isinstance(final_pred, pred_type), (
+    f"Type mismatch: 'final_pred' is of type {type(final_pred)}, but expected {pred_type} "
+)
+
+# Check shape
+if isinstance(final_pred, (list, np.ndarray, pd.DataFrame, torch.Tensor, tf.Tensor)):
+    assert get_length(final_pred) == get_length(test_X), (
+        f"Wrong output sample size: get_length(final_pred)={get_length(final_pred)} "
+        f"vs. get_length(test_X)={get_length(test_X)}"
+    )
+
+# check scores.csv
+assert Path("scores.csv").exists(), "scores.csv is not generated"
+score_df = pd.read_csv("scores.csv", index_col=0)
+model_set_in_scores = set(score_df.index)
+
+assert model_set_in_scores == set({{model_names}}).union({"ensemble"}), (
+    f"The scores dataframe does not contain the correct model names as index.\ncorrect model names are: {{model_names}} + ['ensemble']\nscore_df is:\n{score_df}"
+)
+assert score_df.index.is_unique, "The scores dataframe has duplicate model names."
+assert score_df.columns.tolist() == ["{{metric_name}}"], f"The column names of the scores dataframe should be ['{{metric_name}}'], but is '{score_df.columns.tolist()}'"
+
+
+print("Ensemble test end.")

rdagent/components/coder/data_science/ensemble/exp.py

@@ -0,0 +1,13 @@
+import pickle
+import site
+import traceback
+from pathlib import Path
+from typing import Dict, Optional
+
+from rdagent.components.coder.CoSTEER.task import CoSTEERTask
+from rdagent.core.utils import cache_with_pickle
+
+
+# Because we use isinstance to distinguish between different types of tasks, we need to use sub classes to represent different types of tasks
+class EnsembleTask(CoSTEERTask):
+    pass

rdagent/components/coder/data_science/ensemble/prompts.yaml

@@ -0,0 +1,123 @@
+ensemble_coder:
+  system: |-
+    You are a world-class data scientist and machine learning engineer with deep expertise in statistics, mathematics, and computer science.
+    Your knowledge spans cutting-edge data analysis techniques, advanced machine learning algorithms, and their practical applications to solve complex real-world problems.
+
+    ## Task Description
+    Currently, you are working on model ensemble implementation. Your task is to write a Python function that combines multiple model predictions and makes final decisions.
+
+    Your specific task as follows:
+    {{ task_desc }}
+
+    ## Competition Information for This Task
+    {{ competition_info }}
+
+    {% if queried_similar_successful_knowledge|length != 0 or queried_former_failed_knowledge|length != 0 %}
+    ## Relevant Information for This Task
+    {% endif %}
+
+    {% if queried_similar_successful_knowledge|length != 0 %}
+    --------- Successful Implementations for Similar Models ---------
+    ====={% for similar_successful_knowledge in queried_similar_successful_knowledge %} Model {{ loop.index }}:=====
+    {{ similar_successful_knowledge.target_task.get_task_information() }}
+    =====Code:=====
+    {{ similar_successful_knowledge.implementation.file_dict["ensemble.py"] }}
+    {% endfor %} 
+    {% endif %}
+
+    {% if queried_former_failed_knowledge|length != 0 %}
+    --------- Previous Failed Attempts ---------
+    {% for former_failed_knowledge in queried_former_failed_knowledge %} Attempt {{ loop.index }}:
+    =====Code:=====
+    {{ former_failed_knowledge.implementation.file_dict["ensemble.py"] }}
+    =====Feedback:=====
+    {{ former_failed_knowledge.feedback }}
+    {% endfor %}
+    {% endif %}
+
+    ## Guidelines
+    1. The function's code is associated with several other functions including a data loader, feature engineering, and model training. all codes are as follows:
+    {{ all_code }}
+    2. You should avoid using logging module to output information in your generated code, and instead use the print() function.
+
+    ## Output Format
+    {% if out_spec %}
+    {{ out_spec }}
+    {% else %}
+    Please response the code in the following json format. Here is an example structure for the JSON output:
+    {
+        "code": "The Python code as a string."
+    }
+    {% endif %}
+
+  user: |-
+    --------- Code Specification ---------
+    {{ code_spec }}
+
+    {% if latest_code %}
+    --------- Former code ---------
+    {{ latest_code }}
+    {% if latest_code_feedback is not none %}
+    --------- Feedback to former code ---------
+    {{ latest_code_feedback }}
+    {% endif %}
+    The former code contains errors. You should correct the code based on the provided information, ensuring you do not repeat the same mistakes.
+    {% endif %}
+
+
+ensemble_eval:
+  system: |-
+    You are a data scientist responsible for evaluating ensemble implementation code generation.
+
+    ## Task Description
+    {{ task_desc }}
+
+    ## Ensemble Code
+    ```python
+    {{ code }}
+    ```
+
+    ## Testing Process
+    The ensemble code is tested using the following script:
+    ```python
+    {{ test_code }}
+    ```
+    You will analyze the execution results based on the test output provided.
+    
+    {% if workflow_stdout is not none %}
+    ### Whole Workflow Consideration
+    The ensemble code is part of the whole workflow. The user has executed the entire pipeline and provided additional stdout.
+
+    **Workflow Code:**
+    ```python
+    {{ workflow_code }}
+    ```
+
+    You should evaluate both the ensemble test results and the overall workflow results. **Approve the code only if both tests pass.**
+    {% endif %}
+
+    The metric used for scoring the predictions:
+    **{{ metric_name }}**
+
+    ## Evaluation Criteria
+    - You will be given the standard output (`stdout`) from the ensemble test and, if applicable, the workflow test.
+    - Code should have no try-except blocks because they can hide errors.
+    - Check whether the code implement the scoring process using the given metric.
+    - The stdout includes the local variable values from the ensemble code execution. Check whether the validation score is calculated correctly.
+    
+    Please respond with your feedback in the following JSON format and order
+    ```json
+    {
+        "execution": "Describe how well the ensemble executed, including any errors or issues encountered. Append all error messages and full traceback details without summarizing or omitting any information.",
+        "return_checking": "Detail the checks performed on the ensemble results, including shape and value validation.",
+        "code": "Assess code quality, readability, and adherence to specifications.",
+        "final_decision": <true/false>
+    }
+    ```
+  user: |-    
+    --------- Ensemble test stdout ---------
+    {{ stdout }}   
+    {% if workflow_stdout is not none %}
+    --------- Whole workflow test stdout ---------
+    {{ workflow_stdout }}
+    {% endif %}

rdagent/components/coder/data_science/ensemble/test.py

@@ -0,0 +1,58 @@
+"""
+Helper functions for testing the ensemble coder(CoSTEER-based) component.
+"""
+
+import sys
+from pathlib import Path
+
+from rdagent.components.coder.data_science.ensemble import EnsembleCoSTEER
+from rdagent.components.coder.data_science.ensemble.exp import EnsembleTask
+from rdagent.scenarios.data_science.experiment.experiment import DSExperiment
+from rdagent.scenarios.data_science.scen import KaggleScen
+
+# Add the competition folder to path
+COMPETITION_PATH = (
+    Path(__file__).parent.parent.parent.parent.parent
+    / "scenarios"
+    / "kaggle"
+    / "tpl_ex"
+    / "aerial-cactus-identification"
+)
+sys.path.append(str(COMPETITION_PATH))
+
+EnsembleExperiment = DSExperiment
+
+
+def load_ensemble_spec():
+    spec_path = COMPETITION_PATH / "spec" / "ensemble.md"
+    with open(spec_path, "r") as f:
+        return f.read()
+
+
+def develop_one_competition(competition: str):
+    # Initialize scenario and coder
+    scen = KaggleScen(competition=competition)
+    ensemble_coder = EnsembleCoSTEER(scen)
+    # Load ensemble specification
+    ensemble_spec = load_ensemble_spec()
+
+    # Create the ensemble task with actual data context and specification
+    task = EnsembleTask(
+        name="EnsembleTask",
+        description="""
+        Implement ensemble and decision making for model predictions.
+        """,
+    )
+
+    exp = EnsembleExperiment(pending_tasks_list=[task])
+
+    # Injecting the corresponding specification
+    exp.experiment_workspace.inject_files(**{"spec/ensemble.md": ensemble_spec})
+
+    # Develop the experiment
+    exp = ensemble_coder.develop(exp)
+    return exp
+
+
+if __name__ == "__main__":
+    develop_one_competition("aerial-cactus-identification")

rdagent/components/coder/data_science/feature/__init__.py

@@ -0,0 +1,142 @@
+import json
+from pathlib import Path
+from typing import Dict
+
+from rdagent.app.data_science.conf import DS_RD_SETTING
+from rdagent.components.coder.CoSTEER import CoSTEER
+from rdagent.components.coder.CoSTEER.evaluators import (
+    CoSTEERMultiEvaluator,
+    CoSTEERSingleFeedback,
+)
+from rdagent.components.coder.CoSTEER.evolving_strategy import (
+    MultiProcessEvolvingStrategy,
+)
+from rdagent.components.coder.CoSTEER.knowledge_management import (
+    CoSTEERQueriedKnowledge,
+)
+from rdagent.components.coder.data_science.conf import DSCoderCoSTEERSettings
+from rdagent.components.coder.data_science.feature.eval import FeatureCoSTEEREvaluator
+from rdagent.components.coder.data_science.feature.exp import FeatureTask
+from rdagent.core.exception import CoderError
+from rdagent.core.experiment import FBWorkspace
+from rdagent.core.scenario import Scenario
+from rdagent.oai.llm_utils import APIBackend
+from rdagent.utils.agent.ret import PythonAgentOut
+from rdagent.utils.agent.tpl import T
+
+DIRNAME = Path(__file__).absolute().resolve().parent
+
+
+class FeatureMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
+    def implement_one_task(
+        self,
+        target_task: FeatureTask,
+        queried_knowledge: CoSTEERQueriedKnowledge | None = None,
+        workspace: FBWorkspace | None = None,
+        prev_task_feedback: CoSTEERSingleFeedback | None = None,
+    ) -> dict[str, str]:
+        # return a workspace with "load_data.py", "spec/load_data.md" inside
+        # assign the implemented code to the new workspace.
+        feature_information_str = target_task.get_task_information()
+
+        # 1. query
+        queried_similar_successful_knowledge = (
+            queried_knowledge.task_to_similar_task_successful_knowledge[feature_information_str]
+            if queried_knowledge is not None
+            else []
+        )
+        queried_former_failed_knowledge = (
+            queried_knowledge.task_to_former_failed_traces[feature_information_str]
+            if queried_knowledge is not None
+            else []
+        )
+        queried_former_failed_knowledge = (
+            [
+                knowledge
+                for knowledge in queried_former_failed_knowledge[0]
+                if knowledge.implementation.file_dict.get("feature.py") != workspace.file_dict.get("feature.py")
+            ],
+            queried_former_failed_knowledge[1],
+        )
+
+        # 2. code
+        system_prompt = T(".prompts:feature_coder.system").r(
+            competition_info=self.scen.get_scenario_all_desc(),
+            task_desc=feature_information_str,
+            data_loader_code=workspace.file_dict.get("load_data.py"),
+            queried_similar_successful_knowledge=queried_similar_successful_knowledge,
+            queried_former_failed_knowledge=queried_former_failed_knowledge[0],
+            out_spec=PythonAgentOut.get_spec(),
+        )
+        code_spec = (
+            workspace.file_dict["spec/feature.md"]
+            if DS_RD_SETTING.spec_enabled
+            else T("scenarios.data_science.share:component_spec.general").r(
+                spec=T("scenarios.data_science.share:component_spec.FeatureEng").r(),
+                test_code=(DIRNAME / "eval_tests" / "feature_test.txt").read_text(),
+            )
+        )
+        user_prompt = T(".prompts:feature_coder.user").r(
+            code_spec=code_spec,
+            latest_code=workspace.file_dict.get("feature.py"),
+            latest_code_feedback=prev_task_feedback,
+        )
+
+        for _ in range(5):
+            feature_code = PythonAgentOut.extract_output(
+                APIBackend().build_messages_and_create_chat_completion(
+                    user_prompt=user_prompt,
+                    system_prompt=system_prompt,
+                )
+            )
+            if feature_code != workspace.file_dict.get("feature.py"):
+                break
+            else:
+                user_prompt = user_prompt + "\nPlease avoid generating same code to former code!"
+        else:
+            raise CoderError("Failed to generate a new feature code.")
+
+        return {
+            "feature.py": feature_code,
+        }
+
+    def assign_code_list_to_evo(self, code_list: list[dict[str, str]], evo):
+        """
+        Assign the code list to the evolving item.
+
+        The code list is aligned with the evolving item's sub-tasks.
+        If a task is not implemented, put a None in the list.
+        """
+        for index in range(len(evo.sub_tasks)):
+            if code_list[index] is None:
+                continue
+            if evo.sub_workspace_list[index] is None:
+                # evo.sub_workspace_list[index] = FBWorkspace(target_task=evo.sub_tasks[index])
+                evo.sub_workspace_list[index] = evo.experiment_workspace
+            evo.sub_workspace_list[index].inject_files(**code_list[index])
+        return evo
+
+
+class FeatureCoSTEER(CoSTEER):
+    def __init__(
+        self,
+        scen: Scenario,
+        *args,
+        **kwargs,
+    ) -> None:
+        settings = DSCoderCoSTEERSettings()
+        eva = CoSTEERMultiEvaluator(
+            FeatureCoSTEEREvaluator(scen=scen), scen=scen
+        )  # Please specify whether you agree running your eva in parallel or not
+        es = FeatureMultiProcessEvolvingStrategy(scen=scen, settings=settings)
+
+        super().__init__(
+            *args,
+            settings=settings,
+            eva=eva,
+            es=es,
+            evolving_version=2,
+            scen=scen,
+            max_loop=DS_RD_SETTING.coder_max_loop,
+            **kwargs,
+        )

rdagent/components/coder/data_science/feature/eval.py

@@ -0,0 +1,80 @@
+import json
+import re
+from pathlib import Path
+
+from rdagent.app.data_science.conf import DS_RD_SETTING
+from rdagent.components.coder.CoSTEER.evaluators import (
+    CoSTEEREvaluator,
+    CoSTEERSingleFeedback,
+)
+from rdagent.components.coder.data_science.conf import get_ds_env
+from rdagent.core.evolving_framework import QueriedKnowledge
+from rdagent.core.experiment import FBWorkspace, Task
+from rdagent.utils.agent.tpl import T
+from rdagent.utils.agent.workflow import build_cls_from_json_with_retry
+from rdagent.utils.fmt import shrink_text
+
+DIRNAME = Path(__file__).absolute().resolve().parent
+
+FeatureEvalFeedback = CoSTEERSingleFeedback
+
+
+class FeatureCoSTEEREvaluator(CoSTEEREvaluator):
+
+    def evaluate(
+        self,
+        target_task: Task,
+        implementation: FBWorkspace,
+        gt_implementation: FBWorkspace,
+        queried_knowledge: QueriedKnowledge = None,
+        **kwargs,
+    ) -> FeatureEvalFeedback:
+
+        target_task_information = target_task.get_task_information()
+        if (
+            queried_knowledge is not None
+            and target_task_information in queried_knowledge.success_task_to_knowledge_dict
+        ):
+            return queried_knowledge.success_task_to_knowledge_dict[target_task_information].feedback
+        elif queried_knowledge is not None and target_task_information in queried_knowledge.failed_task_info_set:
+            return FeatureEvalFeedback(
+                execution="This task has failed too many times, skip implementation.",
+                return_checking="This task has failed too many times, skip implementation.",
+                code="This task has failed too many times, skip implementation.",
+                final_decision=False,
+            )
+
+        env = get_ds_env()
+        env.conf.extra_volumes = {f"{DS_RD_SETTING.local_data_path}/sample/{self.scen.competition}": "/kaggle/input"}
+
+        # TODO: do we need to clean the generated temporary content?
+        fname = "test/feature_test.py"
+        test_code = (DIRNAME / "eval_tests" / "feature_test.txt").read_text()
+        implementation.inject_files(**{fname: test_code})
+
+        stdout, ret_code = implementation.execute_ret_code(env=env, entry=f"python {fname}")
+
+        if "main.py" in implementation.file_dict and ret_code == 0:
+            workflow_stdout = implementation.execute(env=env, entry="python main.py")
+            workflow_stdout = re.sub(r"=== Start of EDA part ===(.*)=== End of EDA part ===", "", workflow_stdout)
+        else:
+            workflow_stdout = None
+
+        system_prompt = T(".prompts:feature_eval.system").r(
+            task_desc=target_task.get_task_information(),
+            test_code=test_code,
+            code=implementation.file_dict["feature.py"],
+            workflow_stdout=workflow_stdout,
+            workflow_code=implementation.all_codes,
+        )
+        user_prompt = T(".prompts:feature_eval.user").r(
+            stdout=shrink_text(stdout),
+            workflow_stdout=workflow_stdout,
+        )
+
+        return build_cls_from_json_with_retry(
+            FeatureEvalFeedback,
+            system_prompt=system_prompt,
+            user_prompt=user_prompt,
+            init_kwargs_update_func=FeatureEvalFeedback.val_and_update_init_dict,
+        )

rdagent/components/coder/data_science/feature/eval_tests/feature_test.txt

@@ -0,0 +1,102 @@
+"""
+Tests for `feat_eng` in feature.py
+"""
+
+
+from copy import deepcopy
+import sys
+import numpy as np
+import pandas as pd
+from feature import feat_eng
+from load_data import load_data
+import reprlib
+aRepr = reprlib.Repr()
+aRepr.maxother=300
+
+X, y, X_test, test_ids = load_data()
+print("X:", aRepr.repr(X))
+print("y:", aRepr.repr(y))
+print("X_test:", aRepr.repr(X_test))
+print("test_ids", aRepr.repr(test_ids))
+
+print(f"X.shape: {X.shape}" if hasattr(X, 'shape') else f"X length: {len(X)}")
+print(f"y.shape: {y.shape}" if hasattr(y, 'shape') else f"y length: {len(y)}")
+print(f"X_test.shape: {X_test.shape}" if hasattr(X_test, 'shape') else f"X_test length: {len(X_test)}")
+print(f"test_ids length: {len(test_ids)}")
+
+X_loaded = deepcopy(X)
+y_loaded = deepcopy(y)
+X_test_loaded = deepcopy(X_test)
+
+def debug_info_print(func):
+    def wrapper(*args, **kwargs):
+        def local_trace(frame, event, arg):
+            if event == "return" and frame.f_code == func.__code__:
+                print("\n" + "="*20 + "Running feat_eng code, local variable values:" + "="*20)
+                for k, v in frame.f_locals.items():
+                    printed = aRepr.repr(v)
+                    print(f"{k}:\n {printed}")
+                print("="*20 + "Local variable values end" + "="*20)
+            return local_trace
+        
+        sys.settrace(local_trace)
+        try:
+            return func(*args, **kwargs)
+        finally:
+            sys.settrace(None)
+    return wrapper
+X, y, X_test = debug_info_print(feat_eng)(X, y, X_test)
+
+
+def get_length(data):
+    return data.shape[0] if hasattr(data, 'shape') else len(data)
+
+
+def get_width(data):
+    return 1 if isinstance(data, list) else data.shape[1:]
+
+
+def get_column_list(data):
+    return data.columns.tolist() if isinstance(data, pd.DataFrame) else None
+
+
+assert X is not None, "The feature engineering function returned None for X."
+assert y is not None, "The feature engineering function returned None for y."
+assert X_test is not None, "The feature engineering function returned None for X_test."
+
+assert get_length(X_test) == get_length(
+    test_ids
+), f"Mismatch in length of test images and test IDs: X_test ({get_length(X_test)}) and test_ids ({get_length(test_ids)})"
+assert get_length(X) == get_length(
+    y
+), f"Mismatch in length of training images and labels: X ({get_length(X)}) and y ({get_length(y)})"
+
+assert get_length(X) != 0, f"Training data is empty."
+assert get_length(y) != 0, f"Training labels are empty."
+assert get_length(X_test) != 0, f"Test data is empty."
+
+assert get_width(X) == get_width(
+    X_test
+), "Mismatch in width of training and test data. Width means the number of features."
+
+if isinstance(X, pd.DataFrame) and isinstance(X_test, pd.DataFrame):
+    assert get_column_list(X) == get_column_list(X_test), "Mismatch in column names of training and test data."
+
+if isinstance(X, pd.DataFrame):
+    def normalize_dtype(dtype):
+        return "numeric" if np.issubdtype(dtype, np.number) else str(dtype)
+
+    X_dtypes_unique_sorted = sorted(set(normalize_dtype(dt) for dt in X.dtypes.unique()))
+    X_loaded_dtypes_unique_sorted = sorted(set(normalize_dtype(dt) for dt in X_loaded.dtypes.unique()))
+
+    X_dtypes_unique_sorted_new = [
+        dt for dt in X_dtypes_unique_sorted if dt not in X_loaded_dtypes_unique_sorted and dt != "object"
+    ]
+    assert (
+        np.dtypes.ObjectDType in X_loaded_dtypes_unique_sorted or len(X_dtypes_unique_sorted_new) == 0
+    ), f"feature engineering has produced new data types which is not allowed, data loader data types are {X_loaded_dtypes_unique_sorted} and feature engineering data types are {X_dtypes_unique_sorted}"
+
+
+print(
+    "Feature Engineering test passed successfully. All checks including length, width, and data types have been validated."
+)

rdagent/components/coder/data_science/feature/exp.py

@@ -0,0 +1,13 @@
+import pickle
+import site
+import traceback
+from pathlib import Path
+from typing import Dict, Optional
+
+from rdagent.components.coder.CoSTEER.task import CoSTEERTask
+from rdagent.core.utils import cache_with_pickle
+
+
+# Because we use isinstance to distinguish between different types of tasks, we need to use sub classes to represent different types of tasks
+class FeatureTask(CoSTEERTask):
+    pass

rdagent/components/coder/data_science/feature/prompts.yaml

@@ -0,0 +1,122 @@
+feature_coder:
+  system: |-
+    You are a world-class data scientist and machine learning engineer with deep expertise in statistics, mathematics, and computer science.
+    Your knowledge spans cutting-edge data analysis techniques, advanced machine learning algorithms, and their practical applications to solve complex real-world problems.
+
+    ## Task Description
+    {{ task_desc }}
+    
+    ## Competition Information for This Task
+    {{ competition_info }}
+
+    {% if queried_similar_successful_knowledge|length != 0 or queried_former_failed_knowledge|length != 0 %}
+    ## Relevant Information for This Task
+    {% endif %}
+    
+    {% if queried_similar_successful_knowledge|length != 0 %}
+    --------- Successful Implementations for Similar Models ---------
+    ====={% for similar_successful_knowledge in queried_similar_successful_knowledge %} Model {{ loop.index }}:=====
+    {{ similar_successful_knowledge.target_task.get_task_information() }}
+    =====Code:=====
+    {{ similar_successful_knowledge.implementation.file_dict["feature.py"] }}
+    {% endfor %} 
+    {% endif %}
+
+    {% if queried_former_failed_knowledge|length != 0 %}
+    --------- Previous Failed Attempts ---------
+    {% for former_failed_knowledge in queried_former_failed_knowledge %} Attempt {{ loop.index }}:
+    =====Code:=====
+    {{ former_failed_knowledge.implementation.file_dict["feature.py"] }}
+    =====Feedback:=====
+    {{ former_failed_knowledge.feedback }}
+    {% endfor %}
+    {% endif %}
+
+    ## Guidelines
+    1. If feature engineering is unnecessary or should be combined with model training, you may skip this step.
+    2. Be cautious of any column drop in the code. Dropping a column easily without any more attempts, it may not be a good practice.
+    3. The function input is the output of the following data loader:
+    ```python
+    {{ data_loader_code }}
+    ```
+    3. **Additional Guidance:**
+      - If a previous attempt exists, improve upon it without repeating mistakes.
+      - If errors indicate a missing file, find a way to download it or implement an alternative solution.
+      - You should avoid using logging module to output information in your generated code, and instead use the print() function.
+
+    ## Output Format
+    {% if out_spec %}
+    {{ out_spec }}
+    {% else %}
+    Please response the code in the following json format. Here is an example structure for the JSON output:
+    {
+        "code": "The Python code as a string."
+    }
+    {% endif %}
+  
+  user: |-
+    --------- Code Specification ---------
+    {{ code_spec }}
+
+    {% if latest_code %}
+    --------- Former code ---------
+    {{ latest_code }}
+    {% if latest_code_feedback is not none %}
+    --------- Feedback to former code ---------
+    {{ latest_code_feedback }}
+    {% endif %}
+    The former code contains errors. You should correct the code based on the provided information, ensuring you do not repeat the same mistakes.
+    {% endif %}
+
+
+feature_eval:
+  system: |-
+    You are a data scientist responsible for evaluating feature engineering code generation.
+
+    ## Task Description
+    {{ task_desc }}
+
+    ## Feature Engineering Code
+    ```python
+    {{ code }}
+    ```
+
+    ## Testing Process
+    The feature engineering code is tested using the following script:
+    ```python
+    {{ test_code }}
+    ```
+    You will analyze the execution results based on the test output provided.
+
+    {% if workflow_stdout is not none %}
+    ### Whole Workflow Consideration
+    The feature engineering code is part of the whole workflow. The user has executed the entire pipeline and provided additional stdout.
+
+    **Workflow Code:**
+    ```python
+    {{ workflow_code }}
+    ```
+
+    You should evaluate both the feature engineering test results and the overall workflow results. **Approve the code only if both tests pass.**
+    {% endif %}
+    
+    ## Evaluation Criteria
+    You will be given the standard output (`stdout`) from the feature engineering test and, if applicable, the workflow test.
+    
+    Please respond with your feedback in the following JSON format and order
+    ```json
+    {
+        "execution": "Describe how well the feature engineering executed, including any errors or issues encountered. Append all error messages and full traceback details without summarizing or omitting any information.",
+        "return_checking": "Evaluate the correctness and integrity of processed data, checking for missing values, incorrect transformations, and data consistency.",
+        "code": "Assess code quality, readability, and adherence to specifications. Consider efficiency, including whether the code utilizes multi-threading or GPU acceleration for optimization.",
+        "final_decision": <true/false>
+    }
+    ```
+  
+  user: |-
+    --------- Feature engineering test stdout ---------
+    {{ stdout }}   
+    {% if workflow_stdout is not none %}
+    --------- Whole workflow test stdout ---------
+    {{ workflow_stdout }}
+    {% endif %}

rdagent/components/coder/data_science/feature/test.py

@@ -0,0 +1,37 @@
+"""
+Helper functions for testing the feature coder(CoSTEER-based) component.
+- Does the developer loop work correctly
+
+It is NOT:
+- it is not interface unittest(i.e. workspace evaluator in the CoSTEER Loop)
+"""
+
+from rdagent.components.coder.data_science.feature import FeatureCoSTEER
+from rdagent.components.coder.data_science.feature.exp import FeatureTask
+from rdagent.scenarios.data_science.experiment.experiment import DSExperiment
+from rdagent.scenarios.data_science.scen import KaggleScen
+
+
+def develop_one_competition(competition: str):  # -> experiment
+    scen = KaggleScen(competition=competition)
+    feature_coder = FeatureCoSTEER(scen)
+
+    with open("./rdagent/scenarios/kaggle/tpl_ex/aerial-cactus-identification/spec/feature.md", "r") as file:
+        feat_spec = file.read()
+
+    # Create the experiment
+    ft = FeatureTask(name="FeatureTask", description=scen.get_competition_full_desc())
+    exp = DSExperiment(
+        sub_tasks=[ft],
+    )
+
+    with open("./rdagent/scenarios/kaggle/tpl_ex/aerial-cactus-identification/load_data.py", "r") as file:
+        load_data_code = file.read()
+    exp.experiment_workspace.inject_files(**{"load_data.py": load_data_code, "spec/feature.md": feat_spec})
+
+    # Develop the experiment
+    exp = feature_coder.develop(exp)
+
+
+if __name__ == "__main__":
+    develop_one_competition("aerial-cactus-identification")

rdagent/components/coder/data_science/model/__init__.py

@@ -0,0 +1,174 @@
+from pathlib import Path
+from typing import Dict
+
+from rdagent.app.data_science.conf import DS_RD_SETTING
+from rdagent.components.coder.CoSTEER import CoSTEER
+from rdagent.components.coder.CoSTEER.evaluators import (
+    CoSTEERMultiEvaluator,
+    CoSTEERSingleFeedback,
+)
+from rdagent.components.coder.CoSTEER.evolving_strategy import (
+    MultiProcessEvolvingStrategy,
+)
+from rdagent.components.coder.CoSTEER.knowledge_management import (
+    CoSTEERQueriedKnowledge,
+)
+from rdagent.components.coder.data_science.conf import DSCoderCoSTEERSettings
+from rdagent.components.coder.data_science.model.eval import (
+    ModelGeneralCaseSpecEvaluator,
+)
+from rdagent.components.coder.data_science.model.exp import ModelTask
+from rdagent.core.exception import CoderError
+from rdagent.core.experiment import FBWorkspace
+from rdagent.core.scenario import Scenario
+from rdagent.oai.llm_utils import APIBackend
+from rdagent.utils.agent.ret import PythonBatchEditOut
+from rdagent.utils.agent.tpl import T
+
+DIRNAME = Path(__file__).absolute().resolve().parent
+
+
+class ModelMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
+    def implement_one_task(
+        self,
+        target_task: ModelTask,
+        queried_knowledge: CoSTEERQueriedKnowledge | None = None,
+        workspace: FBWorkspace | None = None,
+        prev_task_feedback: CoSTEERSingleFeedback | None = None,
+    ) -> dict[str, str]:
+        model_information_str = target_task.get_task_information()
+
+        # 1. query
+        queried_similar_successful_knowledge = (
+            queried_knowledge.task_to_similar_task_successful_knowledge[model_information_str]
+            if queried_knowledge is not None
+            else []
+        )
+        queried_former_failed_knowledge = (
+            queried_knowledge.task_to_former_failed_traces[model_information_str]
+            if queried_knowledge is not None
+            else []
+        )
+        queried_former_failed_knowledge = (
+            [
+                knowledge
+                for knowledge in queried_former_failed_knowledge[0]
+                if knowledge.implementation.file_dict.get(f"{target_task.name}.py")
+                != workspace.file_dict.get(f"{target_task.name}.py")
+            ],
+            queried_former_failed_knowledge[1],
+        )
+
+        # 2. code
+        system_prompt = T(".prompts:model_coder.system").r(
+            task_desc=model_information_str,
+            competition_info=self.scen.get_scenario_all_desc(),
+            data_loader_code=workspace.file_dict.get("load_data.py"),
+            feature_code=workspace.file_dict["feature.py"],
+            queried_similar_successful_knowledge=queried_similar_successful_knowledge,
+            queried_former_failed_knowledge=queried_former_failed_knowledge[0],
+            out_spec=PythonBatchEditOut.get_spec(),
+        )
+        # user_prompt = T(".prompts:model_coder.user").r(
+        #     model_spec=workspace.file_dict["spec/model.md"],
+        #     feature_code=workspace.file_dict["feature.py"],
+        #     latest_code=workspace.file_dict.get(f"{target_task.name}.py", None),
+        # )
+        # We want to use a simpler way to
+        code_spec = (
+            workspace.file_dict["spec/model.md"]
+            if DS_RD_SETTING.spec_enabled
+            else T("scenarios.data_science.share:component_spec.general").r(
+                spec=T("scenarios.data_science.share:component_spec.Model").r(),
+                test_code=(DIRNAME / "eval_tests" / "model_test.txt").read_text().replace("model01", target_task.name),
+            )
+        )
+        user_prompt = T(".prompts:model_coder.user_general").r(
+            code_spec=code_spec,
+            latest_model_code=workspace.get_codes(
+                r"^model_(?!test)\w+\.py$"
+            ),  # TODO: If we have high failure rate here, we should clean this step with less information.
+            latest_code_feedback=prev_task_feedback,
+        )
+
+        for _ in range(5):
+            batch_edit = PythonBatchEditOut.extract_output(
+                APIBackend().build_messages_and_create_chat_completion(
+                    user_prompt=user_prompt,
+                    system_prompt=system_prompt,
+                )
+            )
+
+            if not all(i.startswith("model_") for i in batch_edit.keys()):
+                user_prompt += "\nYou should only update model codes!"
+                continue
+
+            # 3. post process to align file name to the task name
+            # we assumpt batch_edit only contains one model file update.
+            batch_edit = {
+                (f"{target_task.name}.py" if value != "__DEL__" and key != f"{target_task.name}.py" else key): value
+                for key, value in batch_edit.items()
+            }
+
+            user_prompt = user_prompt + "\nPlease avoid generating same code to former code!"
+            # TODO: besides same code problem, we should also consider other problems lead to retry.
+            if f"{target_task.name}.py" not in batch_edit:
+                continue
+
+            if batch_edit and max(len(i.encode("utf-8")) for i in batch_edit.keys()) > 255:
+                continue
+
+            if batch_edit[f"{target_task.name}.py"] != "__DEL__" and batch_edit[
+                f"{target_task.name}.py"
+            ] != workspace.file_dict.get(f"{target_task.name}.py"):
+                break
+
+            # If the task involves model removal, assume it can only process one model at a time.
+            if len(batch_edit) == 1 and batch_edit[f"{target_task.name}.py"] == "__DEL__":
+                break
+        else:
+            raise CoderError("Failed to generate a new model code.")
+
+        return batch_edit
+
+    def assign_code_list_to_evo(self, code_list: list[dict[str, str]], evo):
+        """
+        Assign the code list to the evolving item.
+
+        The code list is aligned with the evolving item's sub-tasks.
+        If a task is not implemented, put a None in the list.
+        """
+        for index in range(len(evo.sub_tasks)):
+            if code_list[index] is None:
+                continue
+            if evo.sub_workspace_list[index] is None:
+                # evo.sub_workspace_list[index] = FBWorkspace(target_task=evo.sub_tasks[index])
+                evo.sub_workspace_list[index] = evo.experiment_workspace
+            evo.sub_workspace_list[index].inject_files(**code_list[index])
+        return evo
+
+
+class ModelCoSTEER(CoSTEER):
+    def __init__(
+        self,
+        scen: Scenario,
+        *args,
+        **kwargs,
+    ) -> None:
+        settings = DSCoderCoSTEERSettings()
+        eva = CoSTEERMultiEvaluator(
+            ModelGeneralCaseSpecEvaluator(scen=scen), scen=scen
+        )  # Please specify whether you agree running your eva in parallel or not
+        # eva = ModelGeneralCaseSpecEvaluator(scen=scen)
+        es = ModelMultiProcessEvolvingStrategy(scen=scen, settings=settings)
+
+        super().__init__(
+            *args,
+            settings=settings,
+            eva=eva,
+            es=es,
+            evolving_version=2,
+            scen=scen,
+            max_loop=DS_RD_SETTING.coder_max_loop,
+            **kwargs,
+        )

rdagent/components/coder/data_science/model/eval.py

@@ -0,0 +1,115 @@
+"""
+Beyond previous tests
+-
+"""
+
+import json
+import re
+from pathlib import Path
+
+from rdagent.app.data_science.conf import DS_RD_SETTING
+from rdagent.components.coder.CoSTEER.evaluators import (
+    CoSTEEREvaluator,
+    CoSTEERSingleFeedback,
+)
+from rdagent.components.coder.data_science.conf import get_ds_env
+from rdagent.core.evolving_framework import QueriedKnowledge
+from rdagent.core.exception import CoderError
+from rdagent.core.experiment import FBWorkspace, Task
+from rdagent.oai.llm_utils import APIBackend
+from rdagent.utils.agent.tpl import T
+from rdagent.utils.agent.workflow import build_cls_from_json_with_retry
+
+DIRNAME = Path(__file__).absolute().resolve().parent
+ModelSingleFeedback = CoSTEERSingleFeedback
+
+
+# Below are unit tests for testing the specification of the implemented model ------------------
+class ModelGeneralCaseSpecEvaluator(CoSTEEREvaluator):
+    """
+    Motivation case:
+    - Simplest case, we already split the data into train_data, valid_data, and test_data. We require the model to learn (optionally validate on valid data), and infer on test data.
+
+    Test workflow:
+    - Build train, valid, and test data to run it, and test the output (e.g., shape, etc.)
+    """
+
+    def evaluate(
+        self,
+        target_task: Task,
+        implementation: FBWorkspace,
+        gt_implementation: FBWorkspace,
+        queried_knowledge: QueriedKnowledge = None,
+        **kwargs,
+    ) -> ModelSingleFeedback:
+        target_task_information = target_task.get_task_information()
+        if (
+            queried_knowledge is not None
+            and target_task_information in queried_knowledge.success_task_to_knowledge_dict
+        ):
+            return queried_knowledge.success_task_to_knowledge_dict[target_task_information].feedback
+        elif queried_knowledge is not None and target_task_information in queried_knowledge.failed_task_info_set:
+            return ModelSingleFeedback(
+                execution="This task has failed too many times, skip implementation.",
+                return_checking="This task has failed too many times, skip implementation.",
+                code="This task has failed too many times, skip implementation.",
+                final_decision=False,
+            )
+
+        env = get_ds_env()
+        env.conf.extra_volumes = {f"{DS_RD_SETTING.local_data_path}/sample/{self.scen.competition}": "/kaggle/input"}
+
+        if_model_removed = False
+
+        if f"{target_task.name}.py" in implementation.file_dict:
+            fname = "test/model_test.py"
+            test_code = (
+                (DIRNAME / "eval_tests" / "model_test.txt").read_text().replace("model01", target_task.name)
+            )  # only check the model changed this time
+            implementation.inject_files(**{fname: test_code})
+            stdout, ret_code = implementation.execute_ret_code(env=env, entry=f"python {fname}")
+
+            if stdout is None:
+                raise CoderError(
+                    "The execution output contains too many progress bars and results in the LLM's token size exceeding the limit."
+                )
+        else:
+            ret_code = 0
+            if_model_removed = True
+            stdout = f"Model {target_task.name} removal succeeded."
+
+        if "main.py" in implementation.file_dict and ret_code == 0:
+            workflow_stdout = implementation.execute(env=env, entry="python main.py")
+            workflow_stdout = re.sub(r"=== Start of EDA part ===(.*)=== End of EDA part ===", "", workflow_stdout)
+        else:
+            workflow_stdout = None
+
+        if if_model_removed:
+            system_prompt = T(".prompts:model_eval_rm.system").r(
+                task_desc=target_task.get_task_information(),
+                workflow_stdout=workflow_stdout,
+                workflow_code=implementation.all_codes,
+            )
+            user_prompt = T(".prompts:model_eval_rm.user").r(
+                stdout=stdout,
+                workflow_stdout=workflow_stdout,
+            )
+        else:
+            system_prompt = T(".prompts:model_eval.system").r(
+                task_desc=target_task.get_task_information(),
+                test_code=test_code,
+                code=implementation.file_dict[f"{target_task.name}.py"],
+                workflow_stdout=workflow_stdout,
+                workflow_code=implementation.all_codes,
+            )
+            user_prompt = T(".prompts:model_eval.user").r(
+                stdout=stdout,
+                workflow_stdout=workflow_stdout,
+            )
+
+        return build_cls_from_json_with_retry(
+            ModelSingleFeedback,
+            system_prompt=system_prompt,
+            user_prompt=user_prompt,
+            init_kwargs_update_func=ModelSingleFeedback.val_and_update_init_dict,
+        )

rdagent/components/coder/data_science/model/eval_tests/model_test.txt

@@ -0,0 +1,92 @@
+"""
+Tests for `model_workflow` in model01.py
+"""
+import sys
+import time
+
+from feature import feat_eng
+from load_data import load_data
+from model01 import model_workflow
+from sklearn.model_selection import train_test_split
+
+
+def log_execution_results(start_time, val_pred, test_pred, hypers, execution_label):
+    """Log the results of a single model execution."""
+    feedback_str = f"{execution_label} end.\n"
+    feedback_str += f"Validation predictions shape: {val_pred.shape if val_pred is not None else 'None'}\n"
+    feedback_str += f"Test predictions shape: {test_pred.shape if test_pred is not None else 'None'}\n"
+    feedback_str += f"Hyperparameters: {hypers if hypers is not None else 'None'}\n"
+    feedback_str += f"Execution time: {time.time() - start_time:.2f} seconds.\n"
+    print(feedback_str)
+
+
+import reprlib
+aRepr = reprlib.Repr()
+aRepr.maxother=300
+
+# Load and preprocess data
+X, y, test_X, test_ids = load_data()
+X, y, test_X = feat_eng(X, y, test_X)
+
+print(f"X.shape: {X.shape}" if hasattr(X, 'shape') else f"X length: {len(X)}")
+print(f"y.shape: {y.shape}" if hasattr(y, 'shape') else f"y length: {len(y)}")
+print(f"test_X.shape: {test_X.shape}" if hasattr(test_X, 'shape') else f"test_X length: {len(test_X)}")
+print(f"test_ids length: {len(test_ids)}")
+
+train_X, val_X, train_y, val_y = train_test_split(X, y, test_size=0.8, random_state=42)
+
+print("train_X:", aRepr.repr(train_X))
+print("train_y:", aRepr.repr(train_y))
+print("val_X:", aRepr.repr(val_X))
+print("val_y:", aRepr.repr(val_y))
+
+print(f"train_X.shape: {train_X.shape}" if hasattr(train_X, 'shape') else f"train_X length: {len(train_X)}")
+print(f"train_y.shape: {train_y.shape}" if hasattr(train_y, 'shape') else f"train_y length: {len(train_y)}")
+print(f"val_X.shape: {val_X.shape}" if hasattr(val_X, 'shape') else f"val_X length: {len(val_X)}")
+print(f"val_y.shape: {val_y.shape}" if hasattr(val_y, 'shape') else f"val_y length: {len(val_y)}")
+
+
+def debug_info_print(func):
+    def wrapper(*args, **kwargs):
+        def local_trace(frame, event, arg):
+            if event == "return" and frame.f_code == func.__code__:
+                print("\n" + "="*20 + "Running model training code, local variable values:" + "="*20)
+                for k, v in frame.f_locals.items():
+                    printed = aRepr.repr(v)
+                    print(f"{k}:\n {printed}")
+                print("="*20 + "Local variable values end" + "="*20)
+            return local_trace
+        
+        sys.settrace(local_trace)
+        try:
+            return func(*args, **kwargs)
+        finally:
+            sys.settrace(None)
+    return wrapper
+
+# First execution
+print("The first execution begins.\n")
+start_time = time.time()
+val_pred, test_pred, hypers = debug_info_print(model_workflow)(
+    X=train_X,
+    y=train_y,
+    val_X=val_X,
+    val_y=val_y,
+    test_X=None,
+)
+log_execution_results(start_time, val_pred, test_pred, hypers, "The first execution")
+
+# Second execution
+print("The second execution begins.\n")
+start_time = time.time()
+val_pred, test_pred, final_hypers = debug_info_print(model_workflow)(
+    X=train_X,
+    y=train_y,
+    val_X=None,
+    val_y=None,
+    test_X=test_X,
+    hyper_params=hypers,
+)
+log_execution_results(start_time, val_pred, test_pred, final_hypers, "The second execution")
+
+print("Model code test end.")

rdagent/components/coder/data_science/model/exp.py

@@ -0,0 +1,21 @@
+from typing import Dict, Optional
+
+from rdagent.components.coder.CoSTEER.task import CoSTEERTask
+
+
+# Because we use isinstance to distinguish between different types of tasks, we need to use sub classes to represent different types of tasks
+class ModelTask(CoSTEERTask):
+    def __init__(
+        self,
+        name: str,
+        description: str,
+        *args,
+        **kwargs,
+    ) -> None:
+        super().__init__(name=name, description=description, *args, **kwargs)
+
+    def get_task_information(self):
+        task_desc = f"""name: {self.name}
+description: {self.description}
+"""
+        return task_desc

rdagent/components/coder/data_science/model/prompts.yaml

@@ -0,0 +1,180 @@
+model_coder:
+  system: |-
+    You are a world-class data scientist and machine learning engineer with deep expertise in statistics, mathematics, and computer science.
+    Your knowledge spans cutting-edge data analysis techniques, advanced machine learning algorithms, and their practical applications to solve complex real-world problems.
+
+    ## Task Description
+    {{ task_desc }}
+    
+    ## Competition Information for This Task
+    {{ competition_info }}
+
+    {% if queried_similar_successful_knowledge|length != 0 or queried_former_failed_knowledge|length != 0 %}
+    ## Relevant Information for This Task
+    {% endif %}
+    
+    {% if queried_similar_successful_knowledge|length != 0 %}
+    --------- Successful Implementations for Similar Models ---------
+    ====={% for similar_successful_knowledge in queried_similar_successful_knowledge %} Model {{ loop.index }}:=====
+    {{ similar_successful_knowledge.target_task.get_task_information() }}
+    =====Code:=====
+    {{ similar_successful_knowledge.implementation.file_dict[similar_successful_knowledge.target_task.name ~ '.py'] }}
+    {% endfor %} 
+    {% endif %}
+
+    {% if queried_former_failed_knowledge|length != 0 %}
+    --------- Previous Failed Attempts ---------
+    {% for former_failed_knowledge in queried_former_failed_knowledge %} Attempt {{ loop.index }}:
+    =====Code:=====
+    {{ former_failed_knowledge.implementation.file_dict[former_failed_knowledge.target_task.name ~ '.py'] }}
+    =====Feedback:=====
+    {{ former_failed_knowledge.feedback }}
+    {% endfor %}
+    {% endif %}
+
+    ## Guidelines
+    1. The function's input is from the output of a feature engineering function whose input is the output of a data loading function. The data loader function and feature engineering function code is as follows:
+    --------- Data Loader Code ---------
+    {{ data_loader_code }}
+    --------- Feature Engineering Code ---------
+    {{ feature_code }}
+    2. You should avoid using logging module to output information in your generated code, and instead use the print() function.
+    3. If the model can both be implemented by PyTorch and Tensorflow, please use pytorch for broader compatibility.
+
+    ## Output Format
+    {% if out_spec %}
+    {{ out_spec }}
+    The file name should be the model name described in the model task in the format "{task_name}.py". You should always follow this name format.
+    {% else %}
+    Please response the code in the following json format. Here is an example structure for the JSON output:
+    {
+        "code": "The Python code as a string."
+    }
+    {% endif %}
+
+  user_general: |-
+    --------- Code Specification ---------
+    {{ code_spec }}
+
+    --------- Former model code ---------
+    {% if latest_model_code|length == 0 %}
+    So far the workspace is empty. No model code has been implemented yet.
+    {% else %}
+    {{ latest_model_code }}
+    {% if latest_code_feedback is not none %}
+    --------- Feedback to former code ---------
+    {{ latest_code_feedback }}
+    {% endif %}
+    {% endif %}
+
+model_eval:
+  system: |-
+    You are a data scientist responsible for evaluating model building code generation.
+
+    ## Task Description
+    {{ task_desc }}
+
+    ## Model Building Code
+    ```python
+    {{ code }}
+    ```
+
+    ## Testing Process
+    The model building code is tested using the following script:
+    ```python
+    {{ test_code }}
+    ```
+
+    ### Execution Phases
+    The model is tested in two phases:
+
+    1. Initial Training Phase:
+       - The model receives **train and valid inputs** with **empty hyperparameters**.
+       - The focus is on verifying whether the model successfully trains and produces **valid outputs and hyperparameter outputs**.
+
+    2. Retraining Phase:
+       - The model receives **train and test inputs** (without valid inputs).
+       - The hyperparameters generated from the first phase are passed back for **retraining**.
+
+
+    ### Key Requirements for Approval
+    A model can only be approved if it meets all of the following conditions:
+    1. Hyperparameter Handling
+      - If hyperparameters are returned, they must include an early stop round.
+      - The hyperparameters must be correctly utilized in the model for retraining.
+      - If the early stop round is provided, it must be used in the model implementation.
+    2. The model output shape must strictly match the specifications in `spec.md`.
+
+    {% if workflow_stdout is not none %}
+    ### Whole Workflow Consideration
+    The model building code is part of the whole workflow. The user has executed the entire pipeline and provided additional stdout.
+
+    **Workflow Code:**
+    ```python
+    {{ workflow_code }}
+    ```
+
+    You should evaluate both the model building test results and the overall workflow results. **Approve the code only if both tests pass.**
+    {% endif %}
+    
+    ## Evaluation Criteria
+    You will be given the standard output (`stdout`) from the model building test and, if applicable, the workflow test.
+    [Note] If no stdout for model buidling test is provided, the model failed due to a timeout or out-of-memory error. You should analyze potential optimizations.
+
+    Please respond with your feedback in the following JSON format and order
+    ```json
+    {
+        "execution": "Describe how well the model building executed, including any errors or issues encountered. Append all error messages and full traceback details without summarizing or omitting any information.",
+        "return_checking": "Check the generated value, including whether the value is generated and comparing the shape of the model output with the requirement in spec.md. You also need to check whether the hyperparameters used for retraining are correctly returned during the test execution of the model.",
+        "code": "Assess code quality, readability, and adherence to specifications. Consider efficiency, including whether the code utilizes multi-threading or GPU acceleration for optimization.",
+        "final_decision": <true/false>
+    }
+    ```
+
+  user: |-
+    --------- Model building test stdout ---------
+    {{ stdout }}   
+    {% if workflow_stdout is not none %}
+    --------- Whole workflow test stdout ---------
+    {{ workflow_stdout }}
+    {% endif %}
+
+model_eval_rm:
+  system: |-
+    You are a data scientist responsible for evaluating model removal process.
+
+    ## Task Description
+    {{ task_desc }}
+
+    {% if workflow_stdout is not none %}
+    ## Whole Workflow Consideration
+    The model building code is part of the whole workflow. The user has executed the entire pipeline and provided additional stdout.
+
+    **Workflow Code:**
+    ```python
+    {{ workflow_code }}
+    ```
+
+    You should evaluate both the model removal test results and the overall workflow results. **Approve the code only if both tests pass.**
+    {% endif %}
+    
+    ## Evaluation Criteria
+    You will be given the standard output (`stdout`) from the model removal test and, if applicable, the workflow test.
+
+    Please respond with your feedback in the following JSON format and order
+    ```json
+    {
+        "execution": "Describe how well the model removal executed, including any errors or issues encountered. Append all error messages and full traceback details without summarizing or omitting any information.",
+        "return_checking": "Check the generated value, including whether the value is generated and comparing the shape of the model output with the requirement in spec.md.",
+        "code": "Assess code quality, readability, and adherence to specifications.",
+        "final_decision": <true/false>
+    }
+    ```
+
+  user: |-
+    --------- Model removal test stdout ---------
+    {{ stdout }}   
+    {% if workflow_stdout is not none %}
+    --------- Whole workflow test stdout ---------
+    {{ workflow_stdout }}
+    {% endif %}

rdagent/components/coder/data_science/model/test.py

@@ -0,0 +1,67 @@
+"""
+Generate dataset to test the model workflow output
+"""
+
+from pathlib import Path
+
+from rdagent.components.coder.CoSTEER.config import CoSTEER_SETTINGS
+from rdagent.components.coder.data_science.model import ModelCoSTEER
+from rdagent.components.coder.data_science.model.eval import (
+    ModelGeneralCaseSpecEvaluator,
+)
+from rdagent.components.coder.data_science.model.exp import ModelTask
+from rdagent.core.experiment import FBWorkspace
+from rdagent.scenarios.data_science.experiment.experiment import DSExperiment
+from rdagent.scenarios.data_science.scen import KaggleScen
+
+
+# Take tasks, spec.md and feat as input, generate a feedback as output
+def develop_one_competition(competition: str):
+    scen = KaggleScen(competition=competition)
+    model_coder = ModelCoSTEER(scen)
+
+    # Create the task
+    mt = ModelTask(
+        name="ModelTask",
+        description="A CNN Model",
+        model_type="CNN",
+        architecture="\hat{y}_u = CNN(X_u)",
+        # variables="variables: {'\\hat{y}_u': 'The predicted output for node u', 'X_u': 'The input features for node u'}",
+        hyperparameters="...",
+        base_code="",
+    )
+
+    tpl_ex_path = Path(__file__).resolve() / Path("rdagent/scenarios/kaggle/tpl_ex").resolve() / competition
+    injected_file_names = ["spec/model.md", "load_data.py", "feature.py", "model01.py"]
+
+    modelexp = FBWorkspace()
+    for file_name in injected_file_names:
+        file_path = tpl_ex_path / file_name
+        modelexp.inject_files(**{file_name: file_path.read_text()})
+
+    mt.base_code += modelexp.file_dict["model01.py"]
+    exp = DSExperiment(
+        sub_tasks=[mt],
+    )
+
+    # Test the evaluator:
+    """eva = ModelGeneralCaseSpecEvaluator(scen=scen)
+    exp.feedback = eva.evaluate(target_task=mt, queried_knowledge=None, implementation=modelexp, gt_implementation=None)
+    print(exp.feedback)"""
+
+    # Test the evolving strategy:
+    """es = ModelMultiProcessEvolvingStrategy(scen=scen, settings=CoSTEER_SETTINGS)
+    new_code = es.implement_one_task(target_task=mt, queried_knowledge=None, workspace=modelexp)
+    print(new_code)"""
+
+    # Run the experiment
+    for file_name in injected_file_names:
+        file_path = tpl_ex_path / file_name
+        exp.experiment_workspace.inject_files(**{file_name: file_path.read_text()})
+
+    exp = model_coder.develop(exp)
+
+
+if __name__ == "__main__":
+    develop_one_competition("aerial-cactus-identification")
+    # dotenv run -- python rdagent/components/coder/data_science/model/test.py

rdagent/components/coder/data_science/pipeline/__init__.py

@@ -0,0 +1,163 @@
+"""
+
+Loop should not large change exclude
+- Action Choice[current data loader & spec]
+- other should share
+    - Propose[choice] => Task[Choice] => CoSTEER =>
+        -
+
+Extra feature:
+- cache
+
+
+File structure
+- ___init__.py: the entrance/agent of coder
+- evaluator.py
+- conf.py
+- exp.py: everything under the experiment, e.g.
+    - Task
+    - Experiment
+    - Workspace
+- test.py
+    - Each coder could be tested.
+"""
+
+import json
+import re
+from pathlib import Path
+from typing import Dict
+
+from rdagent.app.data_science.conf import DS_RD_SETTING
+from rdagent.components.coder.CoSTEER import CoSTEER
+from rdagent.components.coder.CoSTEER.evaluators import (
+    CoSTEERMultiEvaluator,
+    CoSTEERSingleFeedback,
+)
+from rdagent.components.coder.CoSTEER.evolving_strategy import (
+    MultiProcessEvolvingStrategy,
+)
+from rdagent.components.coder.CoSTEER.knowledge_management import (
+    CoSTEERQueriedKnowledge,
+)
+from rdagent.components.coder.data_science.conf import (
+    DSCoderCoSTEERSettings,
+    get_ds_env,
+)
+from rdagent.components.coder.data_science.pipeline.eval import PipelineCoSTEEREvaluator
+from rdagent.components.coder.data_science.raw_data_loader.eval import (
+    DataLoaderCoSTEEREvaluator,
+)
+from rdagent.components.coder.data_science.raw_data_loader.exp import DataLoaderTask
+from rdagent.core.exception import CoderError
+from rdagent.core.experiment import FBWorkspace
+from rdagent.core.scenario import Scenario
+from rdagent.oai.llm_utils import APIBackend
+from rdagent.utils.agent.ret import PythonAgentOut
+from rdagent.utils.agent.tpl import T
+
+DIRNAME = Path(__file__).absolute().resolve().parent
+
+
+class PipelineMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
+    def implement_one_task(
+        self,
+        target_task: DataLoaderTask,
+        queried_knowledge: CoSTEERQueriedKnowledge | None = None,
+        workspace: FBWorkspace | None = None,
+        prev_task_feedback: CoSTEERSingleFeedback | None = None,
+    ) -> dict[str, str]:
+        competition_info = self.scen.get_scenario_all_desc()
+        runtime_environment = self.scen.get_runtime_environment()
+        data_folder_info = self.scen.processed_data_folder_description
+        pipeline_task_info = target_task.get_task_information()
+
+        queried_similar_successful_knowledge = (
+            queried_knowledge.task_to_similar_task_successful_knowledge[pipeline_task_info]
+            if queried_knowledge is not None
+            else []
+        )
+        queried_former_failed_knowledge = (
+            queried_knowledge.task_to_former_failed_traces[pipeline_task_info] if queried_knowledge is not None else []
+        )
+        queried_former_failed_knowledge = (
+            [
+                knowledge
+                for knowledge in queried_former_failed_knowledge[0]
+                if knowledge.implementation.file_dict.get("main.py") != workspace.file_dict.get("main.py")
+            ],
+            queried_former_failed_knowledge[1],
+        )
+
+        system_prompt = T(".prompts:pipeline_coder.system").r(
+            task_desc=pipeline_task_info,
+            queried_similar_successful_knowledge=queried_similar_successful_knowledge,
+            queried_former_failed_knowledge=queried_former_failed_knowledge[0],
+            out_spec=PythonAgentOut.get_spec(),
+            runtime_environment=runtime_environment,
+            spec=T("scenarios.data_science.share:component_spec.Pipeline").r(),
+        )
+        user_prompt = T(".prompts:pipeline_coder.user").r(
+            competition_info=competition_info,
+            folder_spec=data_folder_info,
+            latest_code=workspace.file_dict.get("main.py"),
+            latest_code_feedback=prev_task_feedback,
+        )
+
+        for _ in range(5):
+            pipeline_code = PythonAgentOut.extract_output(
+                APIBackend().build_messages_and_create_chat_completion(
+                    user_prompt=user_prompt,
+                    system_prompt=system_prompt,
+                )
+            )
+            if pipeline_code != workspace.file_dict.get("main.py"):
+                break
+            else:
+                user_prompt = user_prompt + "\nPlease avoid generating same code to former code!"
+        else:
+            raise CoderError("Failed to generate a new pipeline code.")
+
+        return {
+            "main.py": pipeline_code,
+        }
+
+    def assign_code_list_to_evo(self, code_list: list[dict[str, str]], evo):
+        """
+        Assign the code list to the evolving item.
+
+        The code list is aligned with the evolving item's sub-tasks.
+        If a task is not implemented, put a None in the list.
+        """
+        for index in range(len(evo.sub_tasks)):
+            if code_list[index] is None:
+                continue
+            if evo.sub_workspace_list[index] is None:
+                # evo.sub_workspace_list[index] = FBWorkspace(target_task=evo.sub_tasks[index])
+                evo.sub_workspace_list[index] = evo.experiment_workspace
+            evo.sub_workspace_list[index].inject_files(**code_list[index])
+        return evo
+
+
+class PipelineCoSTEER(CoSTEER):
+    def __init__(
+        self,
+        scen: Scenario,
+        *args,
+        **kwargs,
+    ) -> None:
+        settings = DSCoderCoSTEERSettings()
+        eva = CoSTEERMultiEvaluator(
+            PipelineCoSTEEREvaluator(scen=scen), scen=scen
+        )  # Please specify whether you agree running your eva in parallel or not
+        es = PipelineMultiProcessEvolvingStrategy(scen=scen, settings=settings)
+
+        super().__init__(
+            *args,
+            settings=settings,
+            eva=eva,
+            es=es,
+            evolving_version=2,
+            scen=scen,
+            max_loop=DS_RD_SETTING.coder_max_loop,
+            **kwargs,
+        )

rdagent/components/coder/data_science/pipeline/eval.py

@@ -0,0 +1,119 @@
+# tess successfully running.
+# (GPT) if it aligns with the spec & rationality of the spec.
+import json
+import re
+from pathlib import Path
+
+import pandas as pd
+
+from rdagent.app.data_science.conf import DS_RD_SETTING
+from rdagent.components.coder.CoSTEER import CoSTEERMultiFeedback
+from rdagent.components.coder.CoSTEER.evaluators import (
+    CoSTEEREvaluator,
+    CoSTEERSingleFeedback,
+)
+from rdagent.components.coder.CoSTEER.knowledge_management import (
+    CoSTEERQueriedKnowledgeV2,
+)
+from rdagent.components.coder.data_science.conf import get_ds_env
+from rdagent.core.experiment import FBWorkspace, Task
+from rdagent.utils.agent.tpl import T
+from rdagent.utils.agent.workflow import build_cls_from_json_with_retry
+
+DIRNAME = Path(__file__).absolute().resolve().parent
+
+PipelineSingleFeedback = CoSTEERSingleFeedback
+PipelineMultiFeedback = CoSTEERMultiFeedback
+
+
+class PipelineCoSTEEREvaluator(CoSTEEREvaluator):
+
+    def evaluate(
+        self,
+        target_task: Task,
+        implementation: FBWorkspace,
+        gt_implementation: FBWorkspace,
+        queried_knowledge: CoSTEERQueriedKnowledgeV2 = None,
+        **kwargs,
+    ) -> PipelineSingleFeedback:
+
+        target_task_information = target_task.get_task_information()
+        if (
+            queried_knowledge is not None
+            and target_task_information in queried_knowledge.success_task_to_knowledge_dict
+        ):
+            return queried_knowledge.success_task_to_knowledge_dict[target_task_information].feedback
+        elif queried_knowledge is not None and target_task_information in queried_knowledge.failed_task_info_set:
+            return PipelineSingleFeedback(
+                execution="This task has failed too many times, skip implementation.",
+                return_checking="This task has failed too many times, skip implementation.",
+                code="This task has failed too many times, skip implementation.",
+                final_decision=False,
+            )
+
+        env = get_ds_env()
+        env.conf.extra_volumes = {f"{DS_RD_SETTING.local_data_path}/sample/{self.scen.competition}": "/kaggle/input"}
+
+        # Clean the scores.csv & submission.csv.
+        implementation.execute(env=env, entry=f"rm submission.csv scores.csv")
+        stdout = implementation.execute(env=env, entry=f"python main.py")
+        stdout = re.sub(r"=== Start of EDA part ===(.*)=== End of EDA part ===", "", stdout)
+
+        score_fp = implementation.workspace_path / "scores.csv"
+        score_ret_code = 0
+        score_check_text = ""
+        if not score_fp.exists():
+            score_check_text = "[Error] Metrics file (scores.csv) is not generated!"
+            score_ret_code = 1
+        else:
+            try:
+                score_df = pd.read_csv(score_fp, index_col=0)
+                model_set_in_scores = set(score_df.index)
+
+                # Check model names (index)
+                if "ensemble" not in model_set_in_scores:
+                    score_check_text += (
+                        f"\n[Error] The score dataframe doesn't contain the ensemble model.\nscore_df is:\n{score_df}"
+                    )
+                    score_ret_code = 1
+
+                # Check metric name (columns)
+                if score_df.columns.tolist() != [self.scen.metric_name]:
+                    score_check_text += f"\n[Error] The scores dataframe does not contain the correct column names.\nCorrect columns is: ['{self.scen.metric_name}']\nBut got: {score_df.columns.tolist()}"
+                    score_ret_code = 1
+
+            except Exception as e:
+                score_check_text += f"\n[Error] in checking the scores.csv file: {e}\nscores.csv's content:\n-----\n{score_fp.read_text()}\n-----"
+                score_ret_code = 1
+
+        # Check submission file
+        base_check_code = (DIRNAME / "eval_tests" / "submission_format_test.txt").read_text()
+        implementation.inject_files(**{"test/submission_format_test.py": base_check_code})
+        # stdout += "----Submission Check 1-----\n"
+        submission_check_out, submission_ret_code = implementation.execute_ret_code(
+            env=env, entry="python test/submission_format_test.py"
+        )
+        stdout += "\n" + submission_check_out
+
+        system_prompt = T(".prompts:pipeline_eval.system").r(
+            scenario=self.scen.get_scenario_all_desc(),
+            task_desc=target_task.get_task_information(),
+            spec=T("scenarios.data_science.share:component_spec.Pipeline").r(),
+        )
+        user_prompt = T(".prompts:pipeline_eval.user").r(
+            stdout=stdout.strip(),
+            code=implementation.file_dict["main.py"],
+        )
+        wfb = build_cls_from_json_with_retry(
+            PipelineSingleFeedback,
+            system_prompt=system_prompt,
+            user_prompt=user_prompt,
+            init_kwargs_update_func=PipelineSingleFeedback.val_and_update_init_dict,
+        )
+        if score_ret_code != 0:
+            wfb.final_decision = False
+            wfb.return_checking += "\n" + score_check_text
+        if submission_ret_code != 0:
+            wfb.final_decision = False
+            wfb.return_checking += "\nSubmission file check failed."
+        return wfb

rdagent/components/coder/data_science/pipeline/eval_tests/submission_format_test.txt

@@ -0,0 +1,77 @@
+from pathlib import Path
+import pandas as pd
+import hashlib
+
+def calculate_md5(file_path):
+    with open(file_path, "rb") as f:
+        file_hash = hashlib.md5(f.read()).hexdigest()
+    return file_hash
+
+file_md5 = calculate_md5("scores.csv")
+
+"""
+find . | grep -i sample | grep -i submission | grep -v sample_submission.csv | grep -v zip_files  | grep -v 'sample/'
+./denoising-dirty-documents/sampleSubmission.csv
+./the-icml-2013-whale-challenge-right-whale-redux/sampleSubmission.csv
+./text-normalization-challenge-russian-language/ru_sample_submission_2.csv.zip
+./text-normalization-challenge-russian-language/ru_sample_submission_2.csv
+./random-acts-of-pizza/sampleSubmission.csv
+./text-normalization-challenge-english-language/en_sample_submission_2.csv.zip
+./text-normalization-challenge-english-language/en_sample_submission_2.csv
+./detecting-insults-in-social-commentary/sample_submission_null.csv
+"""
+
+# Find sample submission file dynamically
+input_dir = Path("/kaggle/input")
+# Look for common variations of sample submission filenames
+sample_submission_files = list(input_dir.glob("*sample_submission*.csv")) + \
+                         list(input_dir.glob("*sampleSubmission*.csv"))
+
+assert sample_submission_files, "Error: No sample submission file found in /kaggle/input/"
+
+# Use first matching file
+sample_submission_name = sample_submission_files[0].name
+SAMPLE_SUBMISSION_PATH = str(sample_submission_files[0])
+print(f"Using sample submission file: {sample_submission_name}")
+
+# Check if the sample submission file exists
+assert Path(SAMPLE_SUBMISSION_PATH).exists(), f"Error: {sample_submission_name} not found at {SAMPLE_SUBMISSION_PATH}"
+
+# Check if our submission file exists
+assert Path('submission.csv').exists(), "Error: submission.csv not found"
+
+sample_submission = pd.read_csv(SAMPLE_SUBMISSION_PATH)
+our_submission = pd.read_csv('submission.csv')
+
+success = True
+# Print the columns of the sample submission file
+print(f"Columns in {sample_submission_name}:", sample_submission.columns)
+print("Columns in our_submission.csv:", our_submission.columns)
+
+for col in sample_submission.columns:
+    if col not in our_submission.columns:
+        success = False
+        print(f'Column {col} not found in submission.csv')
+
+if success:
+    print(f'submission.csv\'s columns aligns with {sample_submission_name} .')
+
+
+# Print the first 5 rows of the two submission files, with columns separated by commas.
+def print_first_rows(file_path, file_name, num_rows=5):
+    print(f"\nFirst {num_rows} rows of {file_name}:")
+    try:
+        with open(file_path, 'r') as file:
+            for i, line in enumerate(file):
+                if i < num_rows:
+                    print(line.strip())
+                else:
+                    break
+    except FileNotFoundError:
+        print(f"Error: {file_name} not found.")
+
+print_first_rows(SAMPLE_SUBMISSION_PATH, sample_submission_name)
+print_first_rows('submission.csv', 'submission.csv')
+
+assert calculate_md5("scores.csv") == file_md5, "scores.csv should not be rewritten"
+print(f"\nPlease Checked the content of the submission file(submission.csv should has the same format with {sample_submission_name} but might not the same index with {sample_submission_name}). ")

rdagent/components/coder/data_science/pipeline/exp.py

@@ -0,0 +1,6 @@
+from rdagent.components.coder.CoSTEER.task import CoSTEERTask
+
+
+# Because we use isinstance to distinguish between different types of tasks, we need to use sub classes to represent different types of tasks
+class PipelineTask(CoSTEERTask):
+    pass

rdagent/components/coder/data_science/pipeline/prompts.yaml

@@ -0,0 +1,134 @@
+pipeline_coder:
+  system: |-
+    You are a world-class data scientist and machine learning engineer with deep expertise in statistics, mathematics, and computer science.
+    Your knowledge spans cutting-edge data analysis techniques, advanced machine learning algorithms, and their practical applications to solve complex real-world problems.
+
+    ## Task Description
+    {{ task_desc }}
+
+    ## The runtime environment your code will running on
+    {{ runtime_environment }}
+
+    ## Specification your code should follow
+    {{ spec }}
+
+    {% if queried_similar_successful_knowledge|length != 0 or queried_former_failed_knowledge|length != 0 %}
+    ## Relevant Information for This Task
+    {% endif %}
+    
+    {% if queried_similar_successful_knowledge|length != 0 %}
+    --------- Successful Implementations for Similar Models ---------
+    ====={% for similar_successful_knowledge in queried_similar_successful_knowledge %} Model {{ loop.index }}:=====
+    {{ similar_successful_knowledge.target_task.get_task_information() }}
+    =====Code:=====
+    {{ similar_successful_knowledge.implementation.all_codes }}
+    {% endfor %} 
+    {% endif %}
+
+    {% if queried_former_failed_knowledge|length != 0 %}
+    --------- Previous Failed Attempts ---------
+    {% for former_failed_knowledge in queried_former_failed_knowledge %} Attempt {{ loop.index }}:
+    =====Code:=====
+    {{ former_failed_knowledge.implementation.all_codes }}
+    =====Feedback:=====
+    {{ former_failed_knowledge.feedback }}
+    {% endfor %}
+    {% endif %}
+
+
+    ## Guidelines
+    1. Ensure that the dataset is loaded strictly from `/kaggle/input/`, following the exact folder structure described in the **Data Folder Description**, and do not attempt to load data from the current directory (`./`).
+    2. You should avoid using logging module to output information in your generated code, and instead use the print() function.
+    
+    ## Exploratory Data Analysis (EDA) part(Required):
+    - Before returning the data, you should always add an EDA part describing the data to help the following steps understand the data better.
+    - The EDA part should include but not limited in the following information in plain text:
+      - The shape of the data.
+      - The first 5 rows of the data.
+      - The data types of each column.
+      - The number of missing values in each column.
+      - The number of unique values in each column.
+      - The distribution of the target variable.
+      - Any other information that you think is important for the following steps.
+    - The EDA part should be drafted in plain text sending to standard output with command print or other similar functions with no more than ten thousand characters in the following schema: 
+      === Start of EDA part ===
+      { You EDA output content }
+      === End of EDA part ===
+      User will use the following code to match: re.search(r"(.*?)=== Start of EDA part ===(.*)=== End of EDA part ===", stdout, re.DOTALL).groups()[1]
+    - An evaluation agent will help to check whether the EDA part is added correctly.
+    - During the EDA part, you should try to avoid any irrelevant information sending to the standard output.
+
+    ## Output Format
+    {% if out_spec %}
+    {{ out_spec }}
+    {% else %}
+    Please response the code in the following json format. Here is an example structure for the JSON output:
+    {
+        "code": "The Python code as a string."
+    }
+    {% endif %}
+
+  user: |-
+    --------- Competition Information ---------
+    {{ competition_info }}
+
+    --------- Data Folder Description (All path are relative to the data folder) ---------
+    {{ folder_spec }}
+    
+    {% if latest_code %}
+    --------- Former code ---------
+    {{ latest_code }}
+    {% if latest_code_feedback is not none %}
+    --------- Feedback to former code ---------
+    {{ latest_code_feedback }}
+    The former code contains errors. You should correct the code based on the provided information, ensuring you do not repeat the same mistakes.
+    {% else %}
+    The former code is correct. You should try to improve the code based on the provided task while not changing the irrelevant parts.
+    {% endif %}
+    {% endif %} 
+
+    You should strictly follow the code specifications provided by the specification to implement the function.
+
+
+pipeline_eval:
+  system: |-
+    You are a data scientist responsible for evaluating code generation.
+
+    ## Task Description
+    The user is trying to build a code in the following scenario:
+    {{ scenario }}
+
+    The main code generation task is as follows:
+    {{ task_desc }}
+
+    The details on how to structure the code are given in the specification:
+    {{ spec }}
+
+    ## Evaluation Scope
+    Your focus is to check whether the workflow code:
+    1. Executes successfully, correctly generating a final submission.
+    2. Generates predictions in the correct format, ensuring they align with the submission structure!
+
+    ## Evaluation Criteria
+    You will be given the execution output (`stdout`) to determine correctness.  
+
+    [Note] 
+    1. Model performance is NOT a concern in this evaluation—only correct execution and formatting matter.
+    2. You only check the format of the submission since we only feed you part of the data, so the submission might has different index to the sample submission data.
+    
+    Please respond with your feedback in the following JSON format and order
+    ```json
+    {
+        "execution": "Describe whether the code executed successfully, correctly integrating all components and generating the final submission. Include any errors or issues encountered, and append all error messages and full traceback details without summarizing or omitting any information.",
+        "return_checking": "Verify the generated files, particularly the submission file. Ensure that its format matches the sample submission, checking the index, column names, and CSV content.",
+        "code": "Provide feedback on code quality, readability, and adherence to the given specifications.",
+        "final_decision": <true/false>
+    }
+    ```
+  
+  user: |-
+    --------- code generated by user ---------
+    {{ code }}
+
+    --------- code running stdout ---------
+    {{ stdout }}

rdagent/components/coder/data_science/raw_data_loader/README.md

@@ -0,0 +1,15 @@
+# CoSTEER
+
+- subworkspace使用主experiment_workspace `RD-Agent/rdagent/scenarios/data_science/experiment/experiment.py`
+
+## evolving_strategy ( implement_one_task() )
+
+1. xxxTask (in exp.py)
+    - spec
+    - description
+2. 
+
+## evaluator
+
+1. queried_knowledge部分 共用
+2. eval_test脚本

rdagent/components/coder/data_science/raw_data_loader/__init__.py

@@ -0,0 +1,235 @@
+"""
+
+Loop should not large change exclude
+- Action Choice[current data loader & spec]
+- other should share
+    - Propose[choice] => Task[Choice] => CoSTEER =>
+        -
+
+Extra feature:
+- cache
+
+
+File structure
+- ___init__.py: the entrance/agent of coder
+- evaluator.py
+- conf.py
+- exp.py: everything under the experiment, e.g.
+    - Task
+    - Experiment
+    - Workspace
+- test.py
+    - Each coder could be tested.
+"""
+
+import json
+import re
+from pathlib import Path
+from typing import Dict
+
+from rdagent.app.data_science.conf import DS_RD_SETTING
+from rdagent.components.coder.CoSTEER import CoSTEER
+from rdagent.components.coder.CoSTEER.evaluators import (
+    CoSTEERMultiEvaluator,
+    CoSTEERSingleFeedback,
+)
+from rdagent.components.coder.CoSTEER.evolving_strategy import (
+    MultiProcessEvolvingStrategy,
+)
+from rdagent.components.coder.CoSTEER.knowledge_management import (
+    CoSTEERQueriedKnowledge,
+)
+from rdagent.components.coder.data_science.conf import (
+    DSCoderCoSTEERSettings,
+    get_ds_env,
+)
+from rdagent.components.coder.data_science.raw_data_loader.eval import (
+    DataLoaderCoSTEEREvaluator,
+)
+from rdagent.components.coder.data_science.raw_data_loader.exp import DataLoaderTask
+from rdagent.core.exception import CoderError
+from rdagent.core.experiment import FBWorkspace
+from rdagent.core.scenario import Scenario
+from rdagent.oai.llm_utils import APIBackend
+from rdagent.utils.agent.ret import PythonAgentOut
+from rdagent.utils.agent.tpl import T
+
+DIRNAME = Path(__file__).absolute().resolve().parent
+
+
+class DataLoaderMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
+    def implement_one_task(
+        self,
+        target_task: DataLoaderTask,
+        queried_knowledge: CoSTEERQueriedKnowledge | None = None,
+        workspace: FBWorkspace | None = None,
+        prev_task_feedback: CoSTEERSingleFeedback | None = None,
+    ) -> dict[str, str]:
+        # return a workspace with "load_data.py", "spec/load_data.md" inside
+        # assign the implemented code to the new workspace.
+        competition_info = self.scen.get_scenario_all_desc()
+        runtime_environment = self.scen.get_runtime_environment()
+        data_folder_info = self.scen.processed_data_folder_description
+        data_loader_task_info = target_task.get_task_information()
+
+        queried_similar_successful_knowledge = (
+            queried_knowledge.task_to_similar_task_successful_knowledge[data_loader_task_info]
+            if queried_knowledge is not None
+            else []
+        )
+        queried_former_failed_knowledge = (
+            queried_knowledge.task_to_former_failed_traces[data_loader_task_info]
+            if queried_knowledge is not None
+            else []
+        )
+        queried_former_failed_knowledge = (
+            [
+                knowledge
+                for knowledge in queried_former_failed_knowledge[0]
+                if knowledge.implementation.file_dict.get("load_data.py") != workspace.file_dict.get("load_data.py")
+            ],
+            queried_former_failed_knowledge[1],
+        )
+
+        # 1. specifications
+        # TODO: We may move spec into a separated COSTEER task
+        if DS_RD_SETTING.spec_enabled:
+            if "spec/data_loader.md" not in workspace.file_dict:  # Only generate the spec once
+                system_prompt = T(".prompts:spec.system").r(
+                    runtime_environment=runtime_environment,
+                    task_desc=data_loader_task_info,
+                    competition_info=competition_info,
+                    folder_spec=data_folder_info,
+                )
+                data_loader_prompt = T(".prompts:spec.user.data_loader").r(
+                    latest_spec=workspace.file_dict.get("spec/data_loader.md")
+                )
+                feature_prompt = T(".prompts:spec.user.feature").r(
+                    latest_spec=workspace.file_dict.get("spec/feature.md")
+                )
+                model_prompt = T(".prompts:spec.user.model").r(latest_spec=workspace.file_dict.get("spec/model.md"))
+                ensemble_prompt = T(".prompts:spec.user.ensemble").r(
+                    latest_spec=workspace.file_dict.get("spec/ensemble.md")
+                )
+                workflow_prompt = T(".prompts:spec.user.workflow").r(
+                    latest_spec=workspace.file_dict.get("spec/workflow.md")
+                )
+
+                spec_session = APIBackend().build_chat_session(session_system_prompt=system_prompt)
+
+                data_loader_spec = spec_session.build_chat_completion(user_prompt=data_loader_prompt)
+                feature_spec = spec_session.build_chat_completion(user_prompt=feature_prompt)
+                model_spec = spec_session.build_chat_completion(user_prompt=model_prompt)
+                ensemble_spec = spec_session.build_chat_completion(user_prompt=ensemble_prompt)
+                workflow_spec = spec_session.build_chat_completion(user_prompt=workflow_prompt)
+            else:
+                data_loader_spec = workspace.file_dict["spec/data_loader.md"]
+                feature_spec = workspace.file_dict["spec/feature.md"]
+                model_spec = workspace.file_dict["spec/model.md"]
+                ensemble_spec = workspace.file_dict["spec/ensemble.md"]
+                workflow_spec = workspace.file_dict["spec/workflow.md"]
+
+        # 2. code
+        system_prompt = T(".prompts:data_loader_coder.system").r(
+            task_desc=data_loader_task_info,
+            queried_similar_successful_knowledge=queried_similar_successful_knowledge,
+            queried_former_failed_knowledge=queried_former_failed_knowledge[0],
+            out_spec=PythonAgentOut.get_spec(),
+        )
+        code_spec = (
+            data_loader_spec
+            if DS_RD_SETTING.spec_enabled
+            else T("scenarios.data_science.share:component_spec.general").r(
+                spec=T("scenarios.data_science.share:component_spec.DataLoadSpec").r(),
+                test_code=(DIRNAME / "eval_tests" / "data_loader_test.txt").read_text(),
+            )
+        )
+        user_prompt = T(".prompts:data_loader_coder.user").r(
+            competition_info=competition_info,
+            code_spec=code_spec,
+            folder_spec=data_folder_info,
+            latest_code=workspace.file_dict.get("load_data.py"),
+            latest_code_feedback=prev_task_feedback,
+        )
+
+        for _ in range(5):
+            data_loader_code = PythonAgentOut.extract_output(
+                APIBackend().build_messages_and_create_chat_completion(
+                    user_prompt=user_prompt,
+                    system_prompt=system_prompt,
+                )
+            )
+            if data_loader_code != workspace.file_dict.get("load_data.py"):
+                break
+            else:
+                user_prompt = user_prompt + "\nPlease avoid generating same code to former code!"
+        else:
+            raise CoderError("Failed to generate a new data loader code.")
+
+        return (
+            {
+                "spec/data_loader.md": data_loader_spec,
+                "spec/feature.md": feature_spec,
+                "spec/model.md": model_spec,
+                "spec/ensemble.md": ensemble_spec,
+                "spec/workflow.md": workflow_spec,
+                "load_data.py": data_loader_code,
+            }
+            if DS_RD_SETTING.spec_enabled
+            else {
+                "load_data.py": data_loader_code,
+            }
+        )
+
+    def assign_code_list_to_evo(self, code_list: list[dict[str, str]], evo):
+        """
+        Assign the code list to the evolving item.
+
+        The code list is aligned with the evolving item's sub-tasks.
+        If a task is not implemented, put a None in the list.
+        """
+        for index in range(len(evo.sub_tasks)):
+            if code_list[index] is None:
+                continue
+            if evo.sub_workspace_list[index] is None:
+                # evo.sub_workspace_list[index] = FBWorkspace(target_task=evo.sub_tasks[index])
+                evo.sub_workspace_list[index] = evo.experiment_workspace
+            evo.sub_workspace_list[index].inject_files(**code_list[index])
+        return evo
+
+
+class DataLoaderCoSTEER(CoSTEER):
+    def __init__(
+        self,
+        scen: Scenario,
+        *args,
+        **kwargs,
+    ) -> None:
+        settings = DSCoderCoSTEERSettings()
+        eva = CoSTEERMultiEvaluator(
+            DataLoaderCoSTEEREvaluator(scen=scen), scen=scen
+        )  # Please specify whether you agree running your eva in parallel or not
+        es = DataLoaderMultiProcessEvolvingStrategy(scen=scen, settings=settings)
+
+        super().__init__(
+            *args,
+            settings=settings,
+            eva=eva,
+            es=es,
+            evolving_version=2,
+            scen=scen,
+            max_loop=DS_RD_SETTING.coder_max_loop,
+            **kwargs,
+        )
+
+    def develop(self, exp):
+        new_exp = super().develop(exp)
+
+        env = get_ds_env()
+        env.conf.extra_volumes = {f"{DS_RD_SETTING.local_data_path}/{self.scen.competition}": "/kaggle/input"}
+
+        stdout = new_exp.experiment_workspace.execute(env=env, entry=f"python test/data_loader_test.py")
+        match = re.search(r"(.*?)=== Start of EDA part ===(.*)=== End of EDA part ===", stdout, re.DOTALL)
+        eda_output = match.groups()[1] if match else None
+        self.scen.eda_output = eda_output
+        return new_exp

rdagent/components/coder/data_science/raw_data_loader/eval.py

@@ -0,0 +1,88 @@
+# tess successfully running.
+# (GPT) if it aligns with the spec & rationality of the spec.
+import json
+import re
+from pathlib import Path
+
+from rdagent.app.data_science.conf import DS_RD_SETTING
+from rdagent.components.coder.CoSTEER.evaluators import (
+    CoSTEEREvaluator,
+    CoSTEERSingleFeedback,
+)
+from rdagent.components.coder.CoSTEER.knowledge_management import (
+    CoSTEERQueriedKnowledgeV2,
+)
+from rdagent.components.coder.data_science.conf import get_ds_env
+from rdagent.core.experiment import FBWorkspace, Task
+from rdagent.utils.agent.tpl import T
+from rdagent.utils.agent.workflow import build_cls_from_json_with_retry
+
+DIRNAME = Path(__file__).absolute().resolve().parent
+
+DataLoaderEvalFeedback = CoSTEERSingleFeedback
+
+
+class DataLoaderCoSTEEREvaluator(CoSTEEREvaluator):
+
+    def evaluate(
+        self,
+        target_task: Task,
+        implementation: FBWorkspace,
+        gt_implementation: FBWorkspace,
+        queried_knowledge: CoSTEERQueriedKnowledgeV2 = None,
+        **kwargs,
+    ) -> DataLoaderEvalFeedback:
+
+        target_task_information = target_task.get_task_information()
+        if (
+            queried_knowledge is not None
+            and target_task_information in queried_knowledge.success_task_to_knowledge_dict
+        ):
+            return queried_knowledge.success_task_to_knowledge_dict[target_task_information].feedback
+        elif queried_knowledge is not None and target_task_information in queried_knowledge.failed_task_info_set:
+            return DataLoaderEvalFeedback(
+                execution="This task has failed too many times, skip implementation.",
+                return_checking="This task has failed too many times, skip implementation.",
+                code="This task has failed too many times, skip implementation.",
+                final_decision=False,
+            )
+
+        env = get_ds_env()
+        env.conf.extra_volumes = {f"{DS_RD_SETTING.local_data_path}/sample/{self.scen.competition}": "/kaggle/input"}
+
+        # TODO: do we need to clean the generated temporary content?
+        fname = "test/data_loader_test.py"
+        test_code = (DIRNAME / "eval_tests" / "data_loader_test.txt").read_text()
+        implementation.inject_files(**{fname: test_code})
+        stdout, ret_code = implementation.execute_ret_code(env=env, entry=f"python {fname}")
+        match = re.search(r"(.*?)=== Start of EDA part ===(.*)=== End of EDA part ===(.*)", stdout, re.DOTALL)
+        stdout_part_1, eda_output, stdout_part_2 = match.groups() if match else (stdout, None, "")
+        stdout = stdout_part_1 + stdout_part_2
+        if eda_output is not None and len(eda_output.split(" ")) > 10000:
+            eda_output += "Length of EDA output is too long, truncated. Please reject this implementation and motivate it to reduce the length of EDA output."
+
+        if "main.py" in implementation.file_dict and ret_code == 0:
+            workflow_stdout = implementation.execute(env=env, entry="python main.py")
+            workflow_stdout = re.sub(r"=== Start of EDA part ===(.*)=== End of EDA part ===", "", workflow_stdout)
+        else:
+            workflow_stdout = None
+
+        system_prompt = T(".prompts:data_loader_eval.system").r(
+            task_desc=target_task.get_task_information(),
+            test_code=test_code,
+            code=implementation.file_dict["load_data.py"],
+            workflow_stdout=workflow_stdout,
+            workflow_code=implementation.all_codes,
+        )
+        user_prompt = T(".prompts:data_loader_eval.user").r(
+            stdout=stdout,
+            eda_output=eda_output,
+            workflow_stdout=workflow_stdout,
+        )
+
+        return build_cls_from_json_with_retry(
+            DataLoaderEvalFeedback,
+            system_prompt=system_prompt,
+            user_prompt=user_prompt,
+            init_kwargs_update_func=DataLoaderEvalFeedback.val_and_update_init_dict,
+        )

rdagent/components/coder/data_science/raw_data_loader/eval_tests/data_loader_test.txt

@@ -0,0 +1,73 @@
+"""
+Tests for `load_data` in load_data.py
+"""
+
+import pickle
+
+import pandas as pd
+from load_data import load_data
+
+import sys
+import reprlib
+def debug_info_print(func):
+    aRepr = reprlib.Repr()
+    aRepr.maxother=300
+    def wrapper(*args, **kwargs):
+        def local_trace(frame, event, arg):
+            if event == "return" and frame.f_code == func.__code__:
+                print("\n" + "="*20 + "Running data_load code, local variable values:" + "="*20)
+                for k, v in frame.f_locals.items():
+                    printed = aRepr.repr(v)
+                    print(f"{k}:\n {printed}")
+                print("="*20 + "Local variable values end" + "="*20)
+            return local_trace
+        
+        sys.settrace(local_trace)
+        try:
+            return func(*args, **kwargs)
+        finally:
+            sys.settrace(None)
+    return wrapper
+
+X, y, X_test, test_ids = debug_info_print(load_data)()
+
+
+def get_length(data):
+    return data.shape[0] if hasattr(data, 'shape') else len(data)
+
+
+def get_width(data):
+    return data.shape[1:] if hasattr(data, 'shape') else 1
+
+
+def get_column_list(data):
+    return data.columns.tolist() if isinstance(data, pd.DataFrame) else None
+
+assert X is not None, "Training data (X) is None."
+assert y is not None, "Training labels (y) are None."
+assert X_test is not None, "Test data (X_test) is None."
+assert test_ids is not None, "Test IDs (test_ids) are None."
+
+assert get_length(X_test) == get_length(
+    test_ids
+), f"Mismatch in length of test images and test IDs: X_test ({get_length(X_test)}) and test_ids ({get_length(test_ids)})"
+assert get_length(X) == get_length(
+    y
+), f"Mismatch in length of training images and labels: X ({get_length(X)}) and y ({get_length(y)})"
+
+assert get_length(X) != 0, f"Training data is empty."
+assert get_length(y) != 0, f"Training labels are empty."
+assert get_length(X_test) != 0, f"Test data is empty."
+
+assert get_width(X) == get_width(
+    X_test
+), "Mismatch in width of training and test data. Width means the number of features."
+
+if isinstance(X, pd.DataFrame) and isinstance(X_test, pd.DataFrame):
+    assert get_column_list(X) == get_column_list(X_test), "Mismatch in column names of training and test data."
+
+assert get_width(X) == get_width(
+    X_test
+), "Mismatch in width of training and test data. Width means the number of features."
+
+print("Data loader test passed successfully. Length of test images matches length of test IDs.")

rdagent/components/coder/data_science/raw_data_loader/exp.py

@@ -0,0 +1,6 @@
+from rdagent.components.coder.CoSTEER.task import CoSTEERTask
+
+
+# Because we use isinstance to distinguish between different types of tasks, we need to use sub classes to represent different types of tasks
+class DataLoaderTask(CoSTEERTask):
+    pass

rdagent/components/coder/data_science/raw_data_loader/prompts.yaml

@@ -0,0 +1,460 @@
+
+spec:
+  system: |-
+    You are a world-class data scientist and machine learning engineer with deep expertise in statistics, mathematics, and computer science.
+    Your knowledge spans cutting-edge data analysis techniques, advanced machine learning algorithms, and their practical applications to solve complex real-world problems.
+
+    Currently, you are working on a Kaggle competition project. 
+    This project involves analyzing data and building models to beat other competitors, with the code being generated by large language models.
+
+    The runtime environment you are working in includes the following libraries and their respective versions:
+    {{ runtime_environment }}
+
+    Your overall task is provided below:
+    {{ task_desc }}
+    
+    Your task is to write five specification texts (in markdown format) for the following tasks, based on the competition information provided
+    - Data loading (and preprocessing)
+    - Feature Engineering
+    - Model Building
+    - Ensemble
+    - The overall workflow
+
+    The specifications for each step should be tailored to the competition information provided. 
+    
+    Your specification should consists two parts:
+    1. The function definition in code format, including type annotations and a clear, complete docstring that describes the function's purpose, input parameters, return value, and any relevant exceptions.
+    2. Additional information or notes that the coder should consider while implementing the function.
+    
+    Your specifications should include only the function definition and docstring, without any code implementation or inline comments.
+
+    ## Competition Information for This Task
+    {{ competition_info }}
+
+    ----------- Folder Description (All path are relative to the data folder) ---------
+    - Ensure that all columns in sample_submission can be generated.
+    {{ folder_spec }}
+
+  user:
+    data_loader: |-
+      Data loader specification text should follow these detailed requirements:
+      1. Function Interface:
+        - Function Name: `load_data`
+        - Input: No input arguments.
+        - Output:
+          - `X` (DT, define based on competition information): Feature matrix for training data.
+          - `y` (DT): Target vector for training data.
+          - `X_test` (DT): Feature matrix for test data.
+          - `test_ids` (DT): Identifiers for the test data.
+        - Docstring Requirements:
+          - Describe the purpose of the function.
+          - Specify the data source location (`/kaggle/input/`).
+          - Clearly define the structure and type of the output.
+          - Inferred data shape to each input and output data variables. To uncertain dimension, use -1.
+      2. Notes:
+        - Update `DT` (data type) based on the specific competition dataset. This can include `pd.DataFrame`, `np.array`, `torch.Tensor`, etc.
+        - Only set the DT of variables without inferring the shape of these variables since you don't know the shape of the data.
+
+      Responsibilities and notes of an implemented data loader that aligns with the generated specification.
+      {% include "scenarios.data_science.share:component_spec.DataLoadSpec" %}
+
+      {% if latest_spec %}
+      6. Former Specification:
+        {{ latest_spec }}
+        You should follow the provided specifications to improve this task.
+      {% endif %}
+
+      ## Output Format
+      You should return the specification in markdown format directly, while the **function definition** within it should be in code format, tailored to the Competition Information, with detailed explanations provided in the docstring.
+
+    feature: |-
+      Feature engineering specification text should adhere to the following requirements:
+      1. Function Interface:
+        - Function Name: `feat_eng`
+        - Parameters:
+          - `X` (DT): Train data to be transformed.
+          - `y` (DT): Train label data.
+          - `X_test` (DT): Test data.
+        - Output:
+          - `X_transformed` (DT): Transformed train data.
+          - `y_transformed` (DT): Transformed train label data.
+          - `X_test_transformed` (DT): Transformed test data.
+        - Docstring Requirements:
+          - Describe the purpose of the function.
+          - Clarify the input parameters and their data types.
+          - Define the structure and format of the output.
+          - Inferred data shape to each input and output data variables. To uncertain dimension, use -1.
+
+      2. Precautions for Feature Engineering:
+        - Well handle the shape of the data:
+          - The sample size of the train data and the test data should be the same in all scenarios.
+          - To some tabular or time-series data, you may add or remove some columns so your inferred column number may be unsure.
+          - For scenarios where each dimension does not have a special meaning (like image, audio, and so on), the input shape and the output shape should be exactly the same in most cases unless there is a compelling reason to change them.
+        - Integration with the Model Pipeline:
+          - If feature engineering is deferred to the model pipeline for better overall performance, state explicitly that it will be handled at the model stage.
+            - Model-related operations should not be implemented in this step. (e.g., it uses tools combined with models like torch.Dataset with rich data transformation/augmentation)
+          - Otherwise, ensure this function applies all required transformations while avoiding data leakage.
+        - General Considerations:
+          - Ensure scalability for large datasets.
+          - Handle missing values and outliers appropriately (e.g., impute, remove, or replace).
+          - Ensure consistency between feature data types and transformations.
+          - Prevent data leakage: Do not use information derived from the test set when transforming training data.
+        - Domain-Specific Features:
+          - Apply logic for competition-specific features (e.g., text vectorization, image augmentations, categorical encoding).
+
+      3. Code Standards:
+        - Avoid using progress bars (e.g., `tqdm`) in the implementation.          
+
+      4. Notes:
+        - Align `DT` (data type) definitions with those in the Data Loader specification.
+        - GPU and multiprocessing are available and are encouraged to use for accelerating transformations.
+        - Only set the DT of variables without inferring the shape of these variables since you don't know the shape of the data.
+      
+      {% if latest_spec %}
+      5. Former Specification:
+        {{ latest_spec }}
+        You should follow the provided specifications to improve this task.
+      {% endif %}
+
+      ## Output Format
+      You should return the specification in markdown format directly, while the **function definition** within it should be in code format, tailored to the Competition Information, with detailed explanations provided in the docstring.
+
+    model: |-
+      Model building specification text should adhere to the following requirements:
+
+      1. Function Interface:
+        - Function Name: `model_workflow`
+        - Parameters:
+          - `X` (DT): Training feature data.
+          - `y` (DT): Training label data.
+          - `val_X` (Optional[DT]): Validation feature data.
+          - `val_y` (Optional[DT]): Validation label data.
+          - `test_X` (Optional[DT]): Test feature data.
+          - `hyper_params` (dict): Dictionary of hyperparameters for model configuration.
+        - Output:
+          - `pred_val` (Optional[DT]): Predictions on validation data.
+          - `pred_test` (Optional[DT]): Predictions on test data.
+          - `hyper_params` (dict): Updated dictionary of hyperparameters after training.
+        - Docstring Requirements:
+          - Describe the purpose of the function.
+          - Clarify the input parameters and their data types.
+          - Define the structure and format of the output.
+          - Inferred data shape to each input and output data variables. To uncertain dimension, use -1.
+
+      2. Code Standards:
+        - Do not use progress bars (e.g., `tqdm`) in the implementation.
+
+      3. Precautions:
+        - Ensure input arrays (`X`, `y`, `val_X`, `val_y`, `test_X`) have consistent dimensions and shapes.
+        - Use default values for hyperparameters if `hyper_params` is not provided.
+        - Train the model on `X` and `y`.
+        - Evaluate the model using `val_X` and `val_y` if validation data is available.
+        - If `test_X` is provided, generate predictions for it.
+
+      4. Notes:
+        - Align `DT` (data type) with the definitions used in Feature Engineering specifications.
+        - The device has GPU support, so you are encouraged to use it for training if necessary to accelerate the process.
+        - Some data transformations/augmentations can be included in this step (e.g., data tools provided by TensorFlow and Torch)
+
+      {% if latest_spec %}
+      5. Former Specification:
+        {{ latest_spec }}
+        You should follow the provided specifications to improve this task.
+      {% endif %}
+
+      ## Output Format
+      You should return the specification in markdown format directly, while the **function definition** within it should be in code format, tailored to the Competition Information, with detailed explanations provided in the docstring.
+
+    ensemble: |-
+      Ensemble specification text adhere to the following requirements:
+      1. Function Interface:
+        - Function Name: `ensemble_workflow`
+        - Parameters:
+          - `test_preds_dict` (Dict[str, DT]): A dictionary of test predictions from different models. The key is the model file name.
+          - `val_preds_dict` (Dict[str, DT]): A dictionary of validation predictions from different models. The key is the model file name.
+          - `val_label` (DT): Validation label.
+        - Output:
+          - `final_pred` (DT): Ensemble prediction for the test data.
+        - Docstring Requirements:
+          - Describe the purpose of the function.
+          - Clarify the input parameters and their data types.
+          - Define the structure and format of the output.
+          - Inferred data shape to each input and output data variables. To uncertain dimension, use -1.
+
+      2. Precautions:
+        - Input Validation:
+          - Ensure all predictions in `test_preds_dict` and `val_preds_dict` have consistent shapes and dimensions.
+          - Verify that `val_label` is provided and matches the length of `val_preds_dict` predictions.
+          - Handle empty or invalid inputs gracefully with appropriate error messages.
+        - Metric Calculation and Storage:
+          - Calculate the metric (mentioned in the evaluation section of the competition information) for each model and ensemble strategy on valid, and save the results in `scores.csv`, e.g.:
+            ```python
+            scores = {}
+            for model_name, val_pred in val_preds_dict.items():
+                scores[model_name] = calculate_metric(val_label, val_pred)
+            
+            ...
+            some code about ensemble strategy
+            ...
+            ensemble_val_pred = ...
+
+            ensemble_score = calculate_metric(val_label, ensemble_val_pred)
+            scores["ensemble"] = ensemble_score  # Ensure "ensemble" is explicitly stored
+            
+            scores_df = pd.DataFrame(scores.items(), columns=["Model", <metric_name>])
+            scores_df.to_csv("scores.csv", index=False)
+            ```
+          - Even if only one model is present, compute the ensemble score and store it under `"ensemble"`.
+        
+      3. Code Standards:
+        - Do not use progress bars (e.g., tqdm) in the code.
+
+      4. Notes:
+        - Align `DT` (data type) definitions with those used in model specifications.
+        - Ensure flexibility to handle multiple ensemble strategies based on competition requirements.
+        - Only set the DT of variables without inferring the shape of these variables since you don't know the shape of the data.
+
+      {% if latest_spec %}
+      5. Former Specification:
+        {{ latest_spec }}
+        You should follow the provided specifications to improve this task.
+      {% endif %}
+
+      ## Output Format
+      You should return the specification in markdown format directly, while the **function definition** within it should be in code format, tailored to the Competition Information, with detailed explanations provided in the docstring.
+
+    workflow: |-
+      Your task is to implement the main workflow script (`main.py`) for a Kaggle-style machine learning competition project. 
+      Follow the provided project structure and specifications to ensure consistency and maintainability:
+      1. Workflow Integration:
+        - Integrate the following components into the workflow:
+          - Data loading (`load_data.py`).
+          - Feature engineering (`feature.py`).
+          - Model workflow for training and testing (`model_*.py`). 
+          - Ensemble workflow that combines results from the model workflow to obtain the final prediction (`ensemble.py`).
+        - Treat each component as a modular and callable Python function.
+        - The workflow script should be flexible enough to handle either a single model or multiple models, with filenames (model_*.py) that are not determined at the outset.
+          For multiple model selection, utilize Python code to identify eligible models based on filenames, for example:
+          ```python
+          available_models = [f for f in os.listdir('.') if f.startswith('model_') and 'test' not in f]
+          ```
+      2. Feature Engineering
+        - The feature engineering should be called only once. For example:
+          `X_transformed, y_transformed, X_test_transformed = feat_eng(X, y, X_test)`
+        - It should be called before dataset splitting.
+
+      3. Dataset Splitting
+        - The dataset returned by `load_data` is not pre-split. After calling `feat_eng`, split the data into training and test sets.
+        - [Notice] If feasible, apply cross-validation on the training set (`X_transformed`, `y_transformed`) to ensure a reliable assessment of model performance.
+        - Keep the test set (`X_test_transformed`) unchanged, as it is only used for generating the final predictions.
+        - Pseudocode logic for reference:
+          ```
+          Set number of splits and initialize KFold cross-validator.
+          Create dictionaries for validation and test predictions.
+          For each model file:
+              Import the model dynamically.
+              Initialize arrays for out-of-fold (OOF) and test predictions.
+              For each fold in KFold:
+                  Split data into training and validation sets.
+                  Run model workflow to get validation and test predictions.
+                  Validate shapes.
+                  Store validation and test predictions.
+              Compute average test predictions across folds.
+              Save OOF and averaged test predictions.
+          Ensemble predictions from all models and print the final shape.
+          ```
+
+      4. Submission File:
+        - Save the final predictions as `submission.csv`, ensuring the format matches the competition requirements (refer to `sample_submission` in the Folder Description for the correct structure).
+        - Present the required submission format explicitly and ensure the output adheres to it.
+
+      5. Code Standards:
+        - Do not use progress bars (e.g., tqdm) in the code.
+
+      6. Ensemble Strategy:
+        Consolidate all model outputs into a dictionary, where each key is the model's filename (excluding the .py extension) and its corresponding value is the model's output.
+        Sample code:
+        {% raw %}
+        {% for model_name in model_names %}
+        model_module = __import__(model_name.replace('.py', ''))
+        val_pred, test_pred, _ = model_module.model_workflow(
+          X=train_X,
+          y=train_y,
+          val_X=val_X,
+          val_y=val_y,
+          test_X=X_test_transformed
+        )
+        val_preds_dict[model_module.__name__] = val_pred
+        test_preds_dict[model_module.__name__] = test_pred
+        {% endfor %}
+        final_pred = ensemble_workflow(test_preds_dict, val_preds_dict, val_y)
+        {% endraw %}
+
+      {% if latest_spec %}
+      7. Former Specification:
+        {{ latest_spec }}
+        You should follow the provided specifications to improve this task.
+      {% endif %}
+
+      ## Output Format
+      You should return the specification in markdown format directly.
+      You should create the rules based on the competition information instead of copying the requirements.
+
+data_loader_coder:
+  system: |-
+    You are a world-class data scientist and machine learning engineer with deep expertise in statistics, mathematics, and computer science.
+    Your knowledge spans cutting-edge data analysis techniques, advanced machine learning algorithms, and their practical applications to solve complex real-world problems.
+
+    ## Task Description
+    {{ task_desc }}
+
+    {% if queried_similar_successful_knowledge|length != 0 or queried_former_failed_knowledge|length != 0 %}
+    ## Relevant Information for This Task
+    {% endif %}
+    
+    {% if queried_similar_successful_knowledge|length != 0 %}
+    --------- Successful Implementations for Similar Models ---------
+    ====={% for similar_successful_knowledge in queried_similar_successful_knowledge %} Model {{ loop.index }}:=====
+    {{ similar_successful_knowledge.target_task.get_task_information() }}
+    =====Code:=====
+    {{ similar_successful_knowledge.implementation.all_codes }}
+    {% endfor %} 
+    {% endif %}
+
+    {% if queried_former_failed_knowledge|length != 0 %}
+    --------- Previous Failed Attempts ---------
+    {% for former_failed_knowledge in queried_former_failed_knowledge %} Attempt {{ loop.index }}:
+    =====Code:=====
+    {{ former_failed_knowledge.implementation.all_codes }}
+    =====Feedback:=====
+    {{ former_failed_knowledge.feedback }}
+    {% endfor %}
+    {% endif %}
+
+    ## Guidelines
+    1. Ensure that the dataset is loaded strictly from `/kaggle/input/`, following the exact folder structure described in the **Data Folder Description**, and do not attempt to load data from the current directory (`./`).
+    2. You should avoid using logging module to output information in your generated code, and instead use the print() function.
+    
+    ## Exploratory Data Analysis (EDA) part(Required):
+    - Before returning the data, you should always add an EDA part describing the data to help the following steps understand the data better.
+    - The EDA part should include but not limited in the following information in plain text:
+      - The shape of the data.
+      - The first 5 rows of the data.
+      - The data types of each column.
+      - The number of missing values in each column.
+      - The number of unique values in each column.
+      - The distribution of the target variable.
+      - Any other information that you think is important for the following steps.
+    - The EDA part should be drafted in plain text sending to standard output with command print or other similar functions with no more than ten thousand characters in the following schema: 
+      === Start of EDA part ===
+      { You EDA output content }
+      === End of EDA part ===
+      User will use the following code to match: re.search(r"(.*?)=== Start of EDA part ===(.*)=== End of EDA part ===", stdout, re.DOTALL).groups()[1]
+    - An evaluation agent will help to check whether the EDA part is added correctly.
+    - During the EDA part, you should try to avoid any irrelevant information sending to the standard output.
+
+    ## Output Format
+    {% if out_spec %}
+    {{ out_spec }}
+    {% else %}
+    Please response the code in the following json format. Here is an example structure for the JSON output:
+    {
+        "code": "The Python code as a string."
+    }
+    {% endif %}
+
+  user: |-
+    --------- Competition Information ---------
+    {{ competition_info }}
+
+    --------- Code Specification ---------
+    {{ code_spec }}
+
+    --------- Data Folder Description (All path are relative to the data folder) ---------
+    {{ folder_spec }}
+    
+    {% if latest_code %}
+    --------- Former code ---------
+    {{ latest_code }}
+    {% if latest_code_feedback is not none %}
+    --------- Feedback to former code ---------
+    {{ latest_code_feedback }}
+    {% endif %}
+    The former code contains errors. You should correct the code based on the provided information, ensuring you do not repeat the same mistakes.
+    {% endif %} 
+
+    You should strictly follow the code specifications provided by the specification to implement the function.
+
+
+data_loader_eval:
+  system: |-
+    You are a data scientist responsible for evaluating data loader code for a Kaggle-style machine learning competition project.
+    
+    ## Task Description
+    {{ task_desc }}
+
+    ## Data Loader Code
+    The data loader code is located in `load_data.py`:
+    ```python
+    {{ code }}
+    ```
+
+    ## Testing Process
+    The data loader is tested using the following script:
+    ```python
+    {{ test_code }}
+    ```
+
+    {% if workflow_stdout is not none %}
+    ### Whole Workflow Consideration
+    The data loader is part of the whole workflow. The user has executed the entire pipeline and provided additional stdout.
+
+    **Workflow Code:**
+    {{ workflow_code }}
+
+    You should evaluate both the data loader test results and the overall workflow execution. **Approve the code only if both tests pass.**
+    {% endif %}
+    
+    ## Evaluation Criteria
+    You will be given the standard output (`stdout`) from the data loader test and, if applicable, the workflow test.
+
+    ## Exploratory Data Analysis (EDA) Part evaluation
+    - The code has also generated some EDA output to help understand the data better. 
+    - The EDA part should be drafted in plain text sending to standard output with command print or other similar functions with no more than ten thousand characters in the following schema: 
+      === Start of EDA part ===
+      { You EDA output content }
+      === End of EDA part ===
+      User will use the following code to match: re.search(r"(.*?)=== Start of EDA part ===(.*)=== End of EDA part ===", stdout, re.DOTALL).groups()[1]
+    - The EDA part should include but not limited in the following information in plain text:
+      - The shape of the data.
+      - The first 5 rows of the data.
+      - The data types of each column.
+      - The number of missing values in each column.
+      - The number of unique values in each column.
+      - The distribution of the target variable.
+      - Any other information that you think is important for the following steps.
+    You will be given the EDA output, your job is to check whether the output contains the required and sufficient information. If no EDA output is provided, you should consider it as a failure. Put this evaluation result in the return_checking part.
+    
+    Your response must follow this structured JSON format:
+    ```json
+    {
+        "execution": "Describe how well the data loader executed, including any errors or issues encountered. Append all error messages and full traceback details without summarizing or omitting any information.",
+        "return_checking": "Evaluate the correctness and integrity of the loaded data. Check for issues like missing values, incorrect data types, outliers, or formatting inconsistencies.",
+        "code": "Assess code quality, readability, and adherence to best practices. Consider efficiency, including whether the code utilizes multi-threading or GPU acceleration for faster data loading.",
+        "final_decision": <true/false>
+    }
+    ```
+
+  user: |-
+    --------- Data loader test stdout ---------
+    {{ stdout }}   
+    --------- Data loader EDA stdout ---------
+    {% if eda_output is not none %}
+    {{ eda_output }}
+    {% else %}
+    No EDA output is provided.
+    {% endif %}
+    {% if workflow_stdout is not none %}
+    --------- Whole workflow test stdout ---------
+    {{ workflow_stdout }}
+    {% endif %}

rdagent/components/coder/data_science/raw_data_loader/test.py

@@ -0,0 +1,30 @@
+"""
+Helper functions for testing the raw_data_loader coder(CoSTEER-based) component.
+- Does the developer loop work correctly
+
+It is NOT:
+- it is not interface unittest(i.e. workspace evaluator in the CoSTEER Loop)
+"""
+
+from rdagent.components.coder.data_science.raw_data_loader import DataLoaderCoSTEER
+from rdagent.components.coder.data_science.raw_data_loader.exp import DataLoaderTask
+from rdagent.scenarios.data_science.experiment.experiment import DSExperiment
+from rdagent.scenarios.data_science.scen import KaggleScen
+
+
+def develop_one_competition(competition: str):  # -> experiment
+    scen = KaggleScen(competition=competition)
+    data_loader_coder = DataLoaderCoSTEER(scen)
+
+    # Create the experiment
+    dlt = DataLoaderTask(name="DataLoaderTask", description="")
+    exp = DSExperiment(
+        sub_tasks=[dlt],
+    )
+
+    # Develop the experiment
+    exp = data_loader_coder.develop(exp)
+
+
+if __name__ == "__main__":
+    develop_one_competition("aerial-cactus-identification")

rdagent/components/coder/data_science/workflow/__init__.py

@@ -0,0 +1,135 @@
+import json
+from typing import Dict
+
+from rdagent.app.data_science.conf import DS_RD_SETTING
+from rdagent.components.coder.CoSTEER import CoSTEER
+from rdagent.components.coder.CoSTEER.evaluators import (
+    CoSTEERMultiEvaluator,
+    CoSTEERSingleFeedback,
+)
+from rdagent.components.coder.CoSTEER.evolving_strategy import (
+    MultiProcessEvolvingStrategy,
+)
+from rdagent.components.coder.CoSTEER.knowledge_management import (
+    CoSTEERQueriedKnowledge,
+)
+from rdagent.components.coder.data_science.conf import DSCoderCoSTEERSettings
+from rdagent.components.coder.data_science.workflow.eval import (
+    WorkflowGeneralCaseSpecEvaluator,
+)
+from rdagent.components.coder.data_science.workflow.exp import WorkflowTask
+from rdagent.core.exception import CoderError
+from rdagent.core.experiment import FBWorkspace
+from rdagent.core.scenario import Scenario
+from rdagent.oai.llm_utils import APIBackend
+from rdagent.utils.agent.ret import PythonAgentOut
+from rdagent.utils.agent.tpl import T
+
+
+class WorkflowMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
+    def implement_one_task(
+        self,
+        target_task: WorkflowTask,
+        queried_knowledge: CoSTEERQueriedKnowledge | None = None,
+        workspace: FBWorkspace | None = None,
+        prev_task_feedback: CoSTEERSingleFeedback | None = None,
+    ) -> dict[str, str]:
+        workflow_information_str = target_task.get_task_information()
+
+        # 1. query
+        queried_similar_successful_knowledge = (
+            queried_knowledge.task_to_similar_task_successful_knowledge[workflow_information_str]
+            if queried_knowledge is not None
+            else []
+        )
+        queried_former_failed_knowledge = (
+            queried_knowledge.task_to_former_failed_traces[workflow_information_str]
+            if queried_knowledge is not None
+            else []
+        )
+        queried_former_failed_knowledge = (
+            [
+                knowledge
+                for knowledge in queried_former_failed_knowledge[0]
+                if knowledge.implementation.file_dict.get("main.py") != workspace.file_dict.get("main.py")
+            ],
+            queried_former_failed_knowledge[1],
+        )
+
+        # 2. code
+        system_prompt = T(".prompts:workflow_coder.system").r(
+            task_desc=workflow_information_str,
+            competition_info=self.scen.get_scenario_all_desc(),
+            queried_similar_successful_knowledge=queried_similar_successful_knowledge,
+            queried_former_failed_knowledge=queried_former_failed_knowledge[0],
+            out_spec=PythonAgentOut.get_spec(),
+        )
+        user_prompt = T(".prompts:workflow_coder.user").r(
+            load_data_code=workspace.file_dict["load_data.py"],
+            feature_code=workspace.file_dict["feature.py"],
+            model_codes=workspace.get_codes(r"^model_(?!test)\w+\.py$"),
+            ensemble_code=workspace.file_dict["ensemble.py"],
+            latest_code=workspace.file_dict.get("main.py"),
+            code_spec=(
+                workspace.file_dict["spec/workflow.md"]
+                if DS_RD_SETTING.spec_enabled
+                else T("scenarios.data_science.share:component_spec.Workflow").r()
+            ),
+            latest_code_feedback=prev_task_feedback,
+        )
+
+        for _ in range(5):
+            workflow_code = PythonAgentOut.extract_output(
+                APIBackend().build_messages_and_create_chat_completion(
+                    user_prompt=user_prompt,
+                    system_prompt=system_prompt,
+                )
+            )
+            if workflow_code != workspace.file_dict.get("main.py"):
+                break
+            else:
+                user_prompt = user_prompt + "\nPlease avoid generating same code to former code!"
+        else:
+            raise CoderError("Failed to generate a new workflow code.")
+
+        return {"main.py": workflow_code}
+
+    def assign_code_list_to_evo(self, code_list: list[dict[str, str]], evo):
+        """
+        Assign the code list to the evolving item.
+
+        The code list is aligned with the evolving item's sub-tasks.
+        If a task is not implemented, put a None in the list.
+        """
+        for index in range(len(evo.sub_tasks)):
+            if code_list[index] is None:
+                continue
+            if evo.sub_workspace_list[index] is None:
+                # evo.sub_workspace_list[index] = FBWorkspace(target_task=evo.sub_tasks[index])
+                evo.sub_workspace_list[index] = evo.experiment_workspace
+            evo.sub_workspace_list[index].inject_files(**code_list[index])
+        return evo
+
+
+class WorkflowCoSTEER(CoSTEER):
+    def __init__(
+        self,
+        scen: Scenario,
+        *args,
+        **kwargs,
+    ) -> None:
+        settings = DSCoderCoSTEERSettings()
+        eva = CoSTEERMultiEvaluator(
+            WorkflowGeneralCaseSpecEvaluator(scen=scen), scen=scen
+        )  # Please specify whether you agree running your eva in parallel or not
+        es = WorkflowMultiProcessEvolvingStrategy(scen=scen, settings=settings)
+        super().__init__(
+            *args,
+            settings=settings,
+            eva=eva,
+            es=es,
+            evolving_version=2,
+            scen=scen,
+            max_loop=DS_RD_SETTING.coder_max_loop,
+            **kwargs,
+        )

rdagent/components/coder/data_science/workflow/eval.py

@@ -0,0 +1,139 @@
+import json
+import re
+from pathlib import Path
+
+import pandas as pd
+
+from rdagent.app.data_science.conf import DS_RD_SETTING
+from rdagent.components.coder.CoSTEER.evaluators import (
+    CoSTEEREvaluator,
+    CoSTEERMultiFeedback,
+    CoSTEERSingleFeedback,
+)
+from rdagent.components.coder.data_science.conf import get_ds_env
+from rdagent.core.evolving_framework import QueriedKnowledge
+from rdagent.core.experiment import FBWorkspace, Task
+from rdagent.utils.agent.tpl import T
+from rdagent.utils.agent.workflow import build_cls_from_json_with_retry
+
+DIRNAME = Path(__file__).absolute().resolve().parent
+
+WorkflowSingleFeedback = CoSTEERSingleFeedback
+WorkflowMultiFeedback = CoSTEERMultiFeedback
+
+
+class WorkflowGeneralCaseSpecEvaluator(CoSTEEREvaluator):
+    """
+    Motivation case:
+    - Simplest case, we already split the data into train_data, valid_data, and test_data. We require the model to learn (optionally validate on valid data), and infer on test data.
+
+    Test workflow:
+    - Build train, valid, and test data to run it, and test the output (e.g., shape, etc.)
+    """
+
+    def evaluate(
+        self,
+        target_task: Task,
+        implementation: FBWorkspace,
+        gt_implementation: FBWorkspace,
+        queried_knowledge: QueriedKnowledge = None,
+        **kwargs,
+    ) -> CoSTEERSingleFeedback:
+        target_task_information = target_task.get_task_information()
+        if (
+            queried_knowledge is not None
+            and target_task_information in queried_knowledge.success_task_to_knowledge_dict
+        ):
+            return queried_knowledge.success_task_to_knowledge_dict[target_task_information].feedback
+        elif queried_knowledge is not None and target_task_information in queried_knowledge.failed_task_info_set:
+            return WorkflowSingleFeedback(
+                execution="This task has failed too many times, skip implementation.",
+                return_checking="This task has failed too many times, skip implementation.",
+                code="This task has failed too many times, skip implementation.",
+                final_decision=False,
+            )
+
+        env = get_ds_env()
+        env.conf.extra_volumes = {f"{DS_RD_SETTING.local_data_path}/sample/{self.scen.competition}": "/kaggle/input"}
+
+        # # DockerEnv for MLEBench submission validation
+        # mle_de_conf = MLEBDockerConf()
+        # mle_de_conf.extra_volumes = {
+        #     f"{DS_RD_SETTING.local_data_path}/zip_files": "/mle/data",
+        # }
+        # mde = DockerEnv(conf=mle_de_conf)
+        # mde.prepare()
+
+        # Clean the scores.csv & submission.csv.
+        implementation.execute(env=env, entry=f"rm submission.csv scores.csv")
+
+        stdout = implementation.execute(env=env, entry=f"python main.py")
+
+        # remove EDA part
+        stdout = re.sub(r"=== Start of EDA part ===(.*)=== End of EDA part ===", "", stdout)
+
+        # Check score file
+        score_fp = implementation.workspace_path / "scores.csv"
+        score_ret_code = 0
+        score_check_text = ""
+        if not score_fp.exists():
+            score_check_text = "[Error] Metrics file (scores.csv) is not generated!"
+            score_ret_code = 1
+        else:
+            try:
+                score_df = pd.read_csv(score_fp, index_col=0)
+                model_set_in_scores = set(score_df.index)
+                # We assume that model names in `score_df` are stored without the '.py' file extension.
+                model_set_in_folder = set(
+                    f[:-3] for f in implementation.file_dict.keys() if re.match(r"^model_(?!test)\w+\.py$", f)
+                )
+
+                # Check model names (index)
+                if model_set_in_scores != model_set_in_folder.union({"ensemble"}):
+                    score_check_text += f"\n[Error] The scores dataframe does not contain the correct model names as index.\ncorrect model names are: {model_set_in_folder.union({'ensemble'})}\nscore_df is:\n{score_df}"
+                    score_ret_code = 1
+
+                # Check metric name (columns)
+                if score_df.columns.tolist() != [self.scen.metric_name]:
+                    score_check_text += f"\n[Error] The scores dataframe does not contain the correct column names.\nCorrect columns is: ['{self.scen.metric_name}']\nBut got: {score_df.columns.tolist()}"
+                    score_ret_code = 1
+
+            except Exception as e:
+                score_check_text += f"\n[Error] in checking the scores.csv file: {e}\nscores.csv's content:\n-----\n{score_fp.read_text()}\n-----"
+                score_ret_code = 1
+
+        # Check submission file
+        base_check_code = (DIRNAME / "eval_tests" / "submission_format_test.txt").read_text()
+        implementation.inject_files(**{"test/submission_format_test.py": base_check_code})
+        # stdout += "----Submission Check 1-----\n"
+        submission_check_out, submission_ret_code = implementation.execute_ret_code(
+            env=env, entry="python test/submission_format_test.py"
+        )
+        stdout += "\n" + submission_check_out
+
+        system_prompt = T(".prompts:workflow_eval.system").r(
+            scenario=self.scen.get_scenario_all_desc(),
+            task_desc=target_task.get_task_information(),
+            spec=(
+                implementation.file_dict["spec/workflow.md"]
+                if DS_RD_SETTING.spec_enabled
+                else T("scenarios.data_science.share:component_spec.Workflow").r()
+            ),
+        )
+        user_prompt = T(".prompts:workflow_eval.user").r(
+            stdout=stdout.strip(),
+            code=implementation.file_dict["main.py"],
+        )
+        wfb = build_cls_from_json_with_retry(
+            WorkflowSingleFeedback,
+            system_prompt=system_prompt,
+            user_prompt=user_prompt,
+            init_kwargs_update_func=WorkflowSingleFeedback.val_and_update_init_dict,
+        )
+        if score_ret_code != 0:
+            wfb.final_decision = False
+            wfb.return_checking += "\n" + score_check_text
+        if submission_ret_code != 0:
+            wfb.final_decision = False
+            wfb.return_checking += "\nSubmission file check failed."
+        return wfb

rdagent/components/coder/data_science/workflow/eval_tests/submission_format_test.txt

@@ -0,0 +1,77 @@
+from pathlib import Path
+import pandas as pd
+import hashlib
+
+def calculate_md5(file_path):
+    with open(file_path, "rb") as f:
+        file_hash = hashlib.md5(f.read()).hexdigest()
+    return file_hash
+
+file_md5 = calculate_md5("scores.csv")
+
+"""
+find . | grep -i sample | grep -i submission | grep -v sample_submission.csv | grep -v zip_files  | grep -v 'sample/'
+./denoising-dirty-documents/sampleSubmission.csv
+./the-icml-2013-whale-challenge-right-whale-redux/sampleSubmission.csv
+./text-normalization-challenge-russian-language/ru_sample_submission_2.csv.zip
+./text-normalization-challenge-russian-language/ru_sample_submission_2.csv
+./random-acts-of-pizza/sampleSubmission.csv
+./text-normalization-challenge-english-language/en_sample_submission_2.csv.zip
+./text-normalization-challenge-english-language/en_sample_submission_2.csv
+./detecting-insults-in-social-commentary/sample_submission_null.csv
+"""
+
+# Find sample submission file dynamically
+input_dir = Path("/kaggle/input")
+# Look for common variations of sample submission filenames
+sample_submission_files = list(input_dir.glob("*sample_submission*.csv")) + \
+                         list(input_dir.glob("*sampleSubmission*.csv"))
+
+assert sample_submission_files, "Error: No sample submission file found in /kaggle/input/"
+
+# Use first matching file
+sample_submission_name = sample_submission_files[0].name
+SAMPLE_SUBMISSION_PATH = str(sample_submission_files[0])
+print(f"Using sample submission file: {sample_submission_name}")
+
+# Check if the sample submission file exists
+assert Path(SAMPLE_SUBMISSION_PATH).exists(), f"Error: {sample_submission_name} not found at {SAMPLE_SUBMISSION_PATH}"
+
+# Check if our submission file exists
+assert Path('submission.csv').exists(), "Error: submission.csv not found"
+
+sample_submission = pd.read_csv(SAMPLE_SUBMISSION_PATH)
+our_submission = pd.read_csv('submission.csv')
+
+success = True
+# Print the columns of the sample submission file
+print(f"Columns in {sample_submission_name}:", sample_submission.columns)
+print("Columns in our_submission.csv:", our_submission.columns)
+
+for col in sample_submission.columns:
+    if col not in our_submission.columns:
+        success = False
+        print(f'Column {col} not found in submission.csv')
+
+if success:
+    print(f'submission.csv\'s columns aligns with {sample_submission_name} .')
+
+
+# Print the first 5 rows of the two submission files, with columns separated by commas.
+def print_first_rows(file_path, file_name, num_rows=5):
+    print(f"\nFirst {num_rows} rows of {file_name}:")
+    try:
+        with open(file_path, 'r') as file:
+            for i, line in enumerate(file):
+                if i < num_rows:
+                    print(line.strip())
+                else:
+                    break
+    except FileNotFoundError:
+        print(f"Error: {file_name} not found.")
+
+print_first_rows(SAMPLE_SUBMISSION_PATH, sample_submission_name)
+print_first_rows('submission.csv', 'submission.csv')
+
+assert calculate_md5("scores.csv") == file_md5, "scores.csv should not be rewritten"
+print(f"\nPlease Checked the content of the submission file(submission.csv should align with {sample_submission_name}). ")

rdagent/components/coder/data_science/workflow/exp.py

@@ -0,0 +1,13 @@
+import pickle
+import site
+import traceback
+from pathlib import Path
+from typing import Dict, Optional
+
+from rdagent.components.coder.CoSTEER.task import CoSTEERTask
+from rdagent.core.utils import cache_with_pickle
+
+
+# Because we use isinstance to distinguish between different types of tasks, we need to use sub classes to represent different types of tasks
+class WorkflowTask(CoSTEERTask):
+    pass

rdagent/components/coder/data_science/workflow/prompts.yaml

@@ -0,0 +1,136 @@
+workflow_coder:
+  system: |-
+    You are a world-class data scientist and machine learning engineer with deep expertise in statistics, mathematics, and computer science.
+    Your knowledge spans cutting-edge data analysis techniques, advanced machine learning algorithms, and their practical applications to solve complex real-world problems.
+
+    ## Task Description
+    {{ task_desc }}
+
+    Here is the competition information for this task:
+    {{ competition_info }}
+
+    {% if queried_similar_successful_knowledge|length != 0 or queried_former_failed_knowledge|length != 0 %}
+    ## Relevant Information for This Task
+    {% endif %}
+
+    {% if queried_similar_successful_knowledge|length != 0 %}
+    --------- Successful Implementations for Similar Models ---------
+    ====={% for similar_successful_knowledge in queried_similar_successful_knowledge %} Model {{ loop.index }}:=====
+    {{ similar_successful_knowledge.target_task.get_task_information() }}
+    =====Code:=====
+    {{ similar_successful_knowledge.implementation.file_dict["main.py"] }}
+    {% endfor %} 
+    {% endif %}
+
+    {% if queried_former_failed_knowledge|length != 0 %}
+    --------- Previous Failed Attempts ---------
+    {% for former_failed_knowledge in queried_former_failed_knowledge %} Attempt {{ loop.index }}:
+    =====Code:=====
+    {{ former_failed_knowledge.implementation.file_dict["main.py"] }}
+    =====Feedback:=====
+    {{ former_failed_knowledge.feedback }}
+    {% endfor %}
+    {% endif %}
+
+    ## Guidelines
+    1. Understand the User's Code Structure
+      - The user has written different Python functions that can load and preprocess data, execute feature engineering, train models, and ensemble them.
+      - Each functionality is in a separate Python file.
+    2. Your task is only to integrate the existing processes of load_data, feature, model, and ensemble into a complete workflow. Do not edit or modify the existing Python files. The final step should output the predictions in the required format.
+    3. The user may provide specific code organization rules and instructions. Ensure that the integration follows the given framework and structure.
+    4. After predicting the output, print the shape and other information of the output to stdout to help the evaluator assess the code.
+    5. You should avoid using logging module to output information in your generated code, and instead use the print() function.
+
+    ## Output Format
+    {% if out_spec %}
+    {{ out_spec }}
+    {% else %}
+    Please response the code in the following json format. Here is an example structure for the JSON output:
+    {
+        "code": "The Python code as a string."
+    }
+    {% endif %}
+  
+  user: |-
+    --------- Code Specification ---------
+    {{ code_spec }}
+
+    --------- load data code ---------
+    file: load_data.py
+    {{ load_data_code }}
+
+    --------- feature engineering code ---------
+    file: feature.py
+    {{ feature_code }}
+
+    --------- model training code ---------
+    Attention: The input and output of the model function is flexible. Training dataset is necessary, but validation and test dateset might be optional. The hyperparameters can either be passed as arguments or be set as default values in the function. You need to use the function correctly.
+    All model files share the same function name. Please import the model files with their name like: from {file_name} import {function_name}
+    {{ model_codes }}
+
+    --------- ensemble code ---------
+    Note, we will check the index of the score.csv, so please use the model name as the index to feed into ensemble function.
+    file: ensemble.py
+    {{ ensemble_code }}
+
+    {% if latest_code %}
+    --------- Former code ---------
+    {{ latest_code }}
+    {% if latest_code_feedback is not none %}
+    --------- Feedback to former code ---------
+    {{ latest_code_feedback }}
+    {% endif %}
+    The former code contains errors. You should correct the code based on the provided information, ensuring you do not repeat the same mistakes.
+    {% endif %}
+
+workflow_eval:
+  system: |-
+    You are a data scientist responsible for evaluating workflow code generation.
+
+    ## Task Description
+    The user is trying to build a workflow in the following scenario:
+    {{ scenario }}
+
+    The main code generation task is as follows:
+    {{ task_desc }}
+
+    The user provides workflow information and its components.
+    The details on how to structure the workflow are given in the specification file:
+    ```markdown
+    {{ spec }}
+    ```
+
+    This workflow integrates multiple stages, including:
+    - Data loading
+    - Feature engineering
+    - Model training
+    - Ensembling
+
+    ## Evaluation Scope
+    Your focus is to check whether the workflow code:
+    1. Executes successfully, correctly organizing components and generating a final submission.
+    2. Generates predictions in the correct format, ensuring they align with the **sample submission** structure!
+
+    [Note] 
+    1. The individual components (data loading, feature engineering, model tuning, etc.) have already been evaluated by the user. You should only evaluate and improve the workflow code, unless there are critical issues in the components.
+    2. Model performance is NOT a concern in this evaluation—only correct execution and formatting matter.
+    3. As long as the execution does not exceed the time limit, ensure that the code uses cross-validation to split the training data and train the model. If cross-validation is not used, mention it in the execution section and set `final_decision` to `false`.
+
+    ## Evaluation Criteria
+    You will be given the workflow execution output (`stdout`) to determine correctness.  
+    
+    Please respond with your feedback in the following JSON format and order
+    ```json
+    {
+        "execution": "Describe whether the main workflow executed successfully, correctly integrating all components and generating the final submission. Include any errors or issues encountered, and append all error messages and full traceback details without summarizing or omitting any information.",
+        "return_checking": "Verify the generated files, particularly the submission file. Ensure that its format matches the sample submission, checking the index, column names, and CSV content.",
+        "code": "Provide feedback on code quality, readability, and adherence to the given specifications.",
+        "final_decision": <true/false>
+    }
+    ```
+  
+  user: |-
+    --------- Workflow test stdout ---------
+    {{ stdout }}
+    --------- Workflow code generated by user ---------
+    {{ code }}

rdagent/components/coder/data_science/workflow/test.py

@@ -0,0 +1,59 @@
+"""
+Generate dataset to test the workflow output
+"""
+
+from pathlib import Path
+
+from rdagent.components.coder.CoSTEER.config import CoSTEER_SETTINGS
+from rdagent.components.coder.data_science.workflow import WorkflowCoSTEER
+from rdagent.components.coder.data_science.workflow.eval import (
+    WorkflowGeneralCaseSpecEvaluator,
+)
+from rdagent.components.coder.data_science.workflow.exp import WorkflowTask
+from rdagent.core.experiment import FBWorkspace
+from rdagent.scenarios.data_science.experiment.experiment import DSExperiment
+from rdagent.scenarios.data_science.scen import KaggleScen
+
+
+def develop_one_competition(competition: str):
+    scen = KaggleScen(competition=competition)
+    workflow_coder = WorkflowCoSTEER(scen)
+
+    wt = WorkflowTask(
+        name="WorkflowTask",
+        description="Integrate the existing processes of load_data, feature, model, and ensemble into a complete workflow.",
+        base_code="",
+    )
+
+    tpl_ex_path = Path(__file__).resolve() / Path("rdagent/scenarios/kaggle/tpl_ex").resolve() / competition
+    injected_file_names = ["spec/workflow.md", "load_data.py", "feature.py", "model01.py", "ensemble.py", "main.py"]
+
+    workflowexp = FBWorkspace()
+    for file_name in injected_file_names:
+        file_path = tpl_ex_path / file_name
+        workflowexp.inject_files(**{file_name: file_path.read_text()})
+
+    wt.base_code += workflowexp.file_dict["main.py"]
+    exp = DSExperiment(
+        sub_tasks=[wt],
+    )
+
+    """es = WorkflowMultiProcessEvolvingStrategy(scen=scen, settings=CoSTEER_SETTINGS)
+    new_code = es.implement_one_task(target_task=wt, queried_knowledge=None, workspace = workflowexp)
+    print(new_code)"""
+
+    """eva = WorkflowGeneralCaseSpecEvaluator(scen=scen)
+    exp.feedback = eva.evaluate(target_task=wt, queried_knowledge=None, implementation=workflowexp, gt_implementation=None)
+    print(exp.feedback)"""
+
+    # Run the experiment
+    for file_name in injected_file_names:
+        file_path = tpl_ex_path / file_name
+        exp.experiment_workspace.inject_files(**{file_name: file_path.read_text()})
+
+    exp = workflow_coder.develop(exp)
+
+
+if __name__ == "__main__":
+    develop_one_competition("aerial-cactus-identification")
+    # dotenv run -- python rdagent/components/coder/data_science/workflow/test.py

rdagent/components/coder/factor_coder/CoSTEER/__init__.py

@@ -1,113 +0,0 @@
-import pickle
-from pathlib import Path
-
-from rdagent.components.coder.factor_coder.config import FACTOR_IMPLEMENT_SETTINGS
-from rdagent.components.coder.factor_coder.CoSTEER.evaluators import (
-    FactorEvaluatorForCoder,
-    FactorMultiEvaluator,
-)
-from rdagent.components.coder.factor_coder.CoSTEER.evolvable_subjects import (
-    FactorEvolvingItem,
-)
-from rdagent.components.coder.factor_coder.CoSTEER.evolving_agent import (
-    FactorRAGEvoAgent,
-)
-from rdagent.components.coder.factor_coder.CoSTEER.evolving_strategy import (
-    FactorEvolvingStrategyWithGraph,
-)
-from rdagent.components.coder.factor_coder.CoSTEER.knowledge_management import (
-    FactorGraphKnowledgeBase,
-    FactorGraphRAGStrategy,
-    FactorKnowledgeBaseV1,
-)
-from rdagent.components.coder.factor_coder.factor import FactorExperiment
-from rdagent.core.developer import Developer
-from rdagent.core.evolving_agent import RAGEvoAgent
-from rdagent.core.scenario import Scenario
-from rdagent.log import rdagent_logger as logger
-
-
-class FactorCoSTEER(Developer[FactorExperiment]):
-    def __init__(
-        self,
-        *args,
-        with_knowledge: bool = True,
-        with_feedback: bool = True,
-        knowledge_self_gen: bool = True,
-        filter_final_evo: bool = True,
-        **kwargs,
-    ) -> None:
-        super().__init__(*args, **kwargs)
-        self.max_loop = FACTOR_IMPLEMENT_SETTINGS.max_loop
-        self.knowledge_base_path = (
-            Path(FACTOR_IMPLEMENT_SETTINGS.knowledge_base_path)
-            if FACTOR_IMPLEMENT_SETTINGS.knowledge_base_path is not None
-            else None
-        )
-        self.new_knowledge_base_path = (
-            Path(FACTOR_IMPLEMENT_SETTINGS.new_knowledge_base_path)
-            if FACTOR_IMPLEMENT_SETTINGS.new_knowledge_base_path is not None
-            else None
-        )
-        self.with_knowledge = with_knowledge
-        self.with_feedback = with_feedback
-        self.knowledge_self_gen = knowledge_self_gen
-        self.filter_final_evo = filter_final_evo
-        self.evolving_strategy = FactorEvolvingStrategyWithGraph(scen=self.scen)
-        # declare the factor evaluator
-        self.factor_evaluator = FactorMultiEvaluator(FactorEvaluatorForCoder(scen=self.scen), scen=self.scen)
-        self.evolving_version = 2
-
-    def load_or_init_knowledge_base(self, former_knowledge_base_path: Path = None, component_init_list: list = []):
-        if former_knowledge_base_path is not None and former_knowledge_base_path.exists():
-            factor_knowledge_base = pickle.load(open(former_knowledge_base_path, "rb"))
-            if self.evolving_version == 1 and not isinstance(factor_knowledge_base, FactorKnowledgeBaseV1):
-                raise ValueError("The former knowledge base is not compatible with the current version")
-            elif self.evolving_version == 2 and not isinstance(
-                factor_knowledge_base,
-                FactorGraphKnowledgeBase,
-            ):
-                raise ValueError("The former knowledge base is not compatible with the current version")
-        else:
-            factor_knowledge_base = (
-                FactorGraphKnowledgeBase(
-                    init_component_list=component_init_list,
-                )
-                if self.evolving_version == 2
-                else FactorKnowledgeBaseV1()
-            )
-        return factor_knowledge_base
-
-    def develop(self, exp: FactorExperiment) -> FactorExperiment:
-        # init knowledge base
-        factor_knowledge_base = self.load_or_init_knowledge_base(
-            former_knowledge_base_path=self.knowledge_base_path,
-            component_init_list=[],
-        )
-        # init rag method
-        self.rag = FactorGraphRAGStrategy(factor_knowledge_base)
-
-        # init intermediate items
-        factor_experiment = FactorEvolvingItem(sub_tasks=exp.sub_tasks)
-
-        self.evolve_agent = FactorRAGEvoAgent(
-            max_loop=self.max_loop,
-            evolving_strategy=self.evolving_strategy,
-            rag=self.rag,
-            with_knowledge=self.with_knowledge,
-            with_feedback=self.with_feedback,
-            knowledge_self_gen=self.knowledge_self_gen,
-        )
-
-        factor_experiment = self.evolve_agent.multistep_evolve(
-            factor_experiment,
-            self.factor_evaluator,
-            filter_final_evo=self.filter_final_evo,
-        )
-
-        # save new knowledge base
-        if self.new_knowledge_base_path is not None:
-            pickle.dump(factor_knowledge_base, open(self.new_knowledge_base_path, "wb"))
-            logger.info(f"New knowledge base saved to {self.new_knowledge_base_path}")
-        exp.sub_workspace_list = factor_experiment.sub_workspace_list
-        return exp

rdagent/components/coder/factor_coder/CoSTEER/evolving_agent.py

@@ -1,19 +0,0 @@
-from rdagent.components.coder.factor_coder.CoSTEER.evaluators import FactorMultiFeedback
-from rdagent.components.coder.factor_coder.CoSTEER.evolvable_subjects import (
-    FactorEvolvingItem,
-)
-from rdagent.core.evaluation import Feedback
-from rdagent.core.evolving_agent import RAGEvoAgent
-from rdagent.core.evolving_framework import EvolvableSubjects
-
-
-class FactorRAGEvoAgent(RAGEvoAgent):
-    def filter_evolvable_subjects_by_feedback(self, evo: EvolvableSubjects, feedback: Feedback) -> EvolvableSubjects:
-        assert isinstance(evo, FactorEvolvingItem)
-        assert isinstance(feedback, list)
-        assert len(evo.sub_workspace_list) == len(feedback)
-
-        for index in range(len(evo.sub_workspace_list)):
-            if feedback[index] and not feedback[index].final_decision:
-                evo.sub_workspace_list[index].clear()
-        return evo

rdagent/components/coder/factor_coder/CoSTEER/evolving_strategy.py

@@ -1,322 +0,0 @@
-from __future__ import annotations
-
-import json
-from abc import abstractmethod
-from copy import deepcopy
-from pathlib import Path
-from typing import TYPE_CHECKING
-
-from jinja2 import Environment, StrictUndefined
-
-from rdagent.components.coder.factor_coder.config import FACTOR_IMPLEMENT_SETTINGS
-from rdagent.components.coder.factor_coder.CoSTEER.evolvable_subjects import (
-    FactorEvolvingItem,
-)
-from rdagent.components.coder.factor_coder.CoSTEER.scheduler import (
-    LLMSelect,
-    RandomSelect,
-)
-from rdagent.components.coder.factor_coder.factor import FactorFBWorkspace, FactorTask
-from rdagent.core.conf import RD_AGENT_SETTINGS
-from rdagent.core.evolving_framework import EvolvingStrategy, QueriedKnowledge
-from rdagent.core.experiment import Workspace
-from rdagent.core.prompts import Prompts
-from rdagent.core.utils import multiprocessing_wrapper
-from rdagent.oai.llm_utils import APIBackend
-
-if TYPE_CHECKING:
-    from rdagent.components.coder.factor_coder.CoSTEER.knowledge_management import (
-        FactorQueriedKnowledge,
-        FactorQueriedKnowledgeV1,
-    )
-
-implement_prompts = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")
-
-
-class MultiProcessEvolvingStrategy(EvolvingStrategy):
-    @abstractmethod
-    def implement_one_factor(
-        self,
-        target_task: FactorTask,
-        queried_knowledge: QueriedKnowledge = None,
-    ) -> Workspace:
-        raise NotImplementedError
-
-    def evolve(
-        self,
-        *,
-        evo: FactorEvolvingItem,
-        queried_knowledge: FactorQueriedKnowledge | None = None,
-        **kwargs,
-    ) -> FactorEvolvingItem:
-        # 1.找出需要evolve的factor
-        to_be_finished_task_index = []
-        for index, target_factor_task in enumerate(evo.sub_tasks):
-            target_factor_task_desc = target_factor_task.get_task_information()
-            if target_factor_task_desc in queried_knowledge.success_task_to_knowledge_dict:
-                evo.sub_workspace_list[index] = queried_knowledge.success_task_to_knowledge_dict[
-                    target_factor_task_desc
-                ].implementation
-            elif (
-                target_factor_task_desc not in queried_knowledge.success_task_to_knowledge_dict
-                and target_factor_task_desc not in queried_knowledge.failed_task_info_set
-            ):
-                to_be_finished_task_index.append(index)
-
-        # 2. 选择selection方法
-        # if the number of factors to be implemented is larger than the limit, we need to select some of them
-
-        if FACTOR_IMPLEMENT_SETTINGS.select_threshold < len(to_be_finished_task_index):
-            # Select a fixed number of factors if the total exceeds the threshold
-            if FACTOR_IMPLEMENT_SETTINGS.select_method == "random":
-                to_be_finished_task_index = RandomSelect(
-                    to_be_finished_task_index,
-                    FACTOR_IMPLEMENT_SETTINGS.select_threshold,
-                )
-
-            if FACTOR_IMPLEMENT_SETTINGS.select_method == "scheduler":
-                to_be_finished_task_index = LLMSelect(
-                    to_be_finished_task_index,
-                    FACTOR_IMPLEMENT_SETTINGS.select_threshold,
-                    evo,
-                    queried_knowledge.former_traces,
-                    self.scen,
-                )
-
-        result = multiprocessing_wrapper(
-            [
-                (self.implement_one_factor, (evo.sub_tasks[target_index], queried_knowledge))
-                for target_index in to_be_finished_task_index
-            ],
-            n=RD_AGENT_SETTINGS.multi_proc_n,
-        )
-
-        for index, target_index in enumerate(to_be_finished_task_index):
-            if evo.sub_workspace_list[target_index] is None:
-                evo.sub_workspace_list[target_index] = FactorFBWorkspace(target_task=evo.sub_tasks[target_index])
-            evo.sub_workspace_list[target_index].inject_code(**{"factor.py": result[index]})
-
-        evo.corresponding_selection = to_be_finished_task_index
-
-        return evo
-
-
-class FactorEvolvingStrategy(MultiProcessEvolvingStrategy):
-    def implement_one_factor(
-        self,
-        target_task: FactorTask,
-        queried_knowledge: FactorQueriedKnowledgeV1 = None,
-    ) -> str:
-        factor_information_str = target_task.get_task_information()
-
-        if queried_knowledge is not None and factor_information_str in queried_knowledge.success_task_to_knowledge_dict:
-            return queried_knowledge.success_task_to_knowledge_dict[factor_information_str].implementation
-        elif queried_knowledge is not None and factor_information_str in queried_knowledge.failed_task_info_set:
-            return None
-        else:
-            queried_similar_successful_knowledge = (
-                queried_knowledge.working_task_to_similar_successful_knowledge_dict[factor_information_str]
-                if queried_knowledge is not None
-                else []
-            )
-            queried_former_failed_knowledge = (
-                queried_knowledge.working_task_to_former_failed_knowledge_dict[factor_information_str]
-                if queried_knowledge is not None
-                else []
-            )
-
-            queried_former_failed_knowledge_to_render = queried_former_failed_knowledge
-
-            system_prompt = (
-                Environment(undefined=StrictUndefined)
-                .from_string(
-                    implement_prompts["evolving_strategy_factor_implementation_v1_system"],
-                )
-                .render(
-                    scenario=self.scen.get_scenario_all_desc(),
-                    queried_former_failed_knowledge=queried_former_failed_knowledge_to_render,
-                )
-            )
-            session = APIBackend(use_chat_cache=FACTOR_IMPLEMENT_SETTINGS.coder_use_cache).build_chat_session(
-                session_system_prompt=system_prompt,
-            )
-
-            queried_similar_successful_knowledge_to_render = queried_similar_successful_knowledge
-            for _ in range(10):  # max attempt to reduce the length of user_prompt
-                user_prompt = (
-                    Environment(undefined=StrictUndefined)
-                    .from_string(
-                        implement_prompts["evolving_strategy_factor_implementation_v1_user"],
-                    )
-                    .render(
-                        factor_information_str=factor_information_str,
-                        queried_similar_successful_knowledge=queried_similar_successful_knowledge_to_render,
-                        queried_former_failed_knowledge=queried_former_failed_knowledge_to_render,
-                    )
-                    .strip("\n")
-                )
-                if (
-                    session.build_chat_completion_message_and_calculate_token(
-                        user_prompt,
-                    )
-                    < RD_AGENT_SETTINGS.chat_token_limit
-                ):
-                    break
-                elif len(queried_former_failed_knowledge_to_render) > 1:
-                    queried_former_failed_knowledge_to_render = queried_former_failed_knowledge_to_render[1:]
-                elif len(queried_similar_successful_knowledge_to_render) > 1:
-                    queried_similar_successful_knowledge_to_render = queried_similar_successful_knowledge_to_render[1:]
-
-            code = json.loads(
-                session.build_chat_completion(
-                    user_prompt=user_prompt,
-                    json_mode=True,
-                ),
-            )["code"]
-
-            return code
-
-
-class FactorEvolvingStrategyWithGraph(MultiProcessEvolvingStrategy):
-    def __init__(self, *args, **kwargs) -> None:
-        super().__init__(*args, **kwargs)
-        self.num_loop = 0
-        self.haveSelected = False
-
-    def implement_one_factor(
-        self,
-        target_task: FactorTask,
-        queried_knowledge,
-    ) -> str:
-        error_summary = FACTOR_IMPLEMENT_SETTINGS.v2_error_summary
-        # 1. 提取因子的背景信息
-        target_factor_task_information = target_task.get_task_information()
-
-        # 2. 检查该因子是否需要继续做（是否已经作对，是否做错太多）
-        if (
-            queried_knowledge is not None
-            and target_factor_task_information in queried_knowledge.success_task_to_knowledge_dict
-        ):
-            return queried_knowledge.success_task_to_knowledge_dict[target_factor_task_information].implementation
-        elif queried_knowledge is not None and target_factor_task_information in queried_knowledge.failed_task_info_set:
-            return None
-        else:
-            # 3. 取出knowledge里面的经验数据（similar success、similar error、former_trace）
-            queried_similar_component_knowledge = (
-                queried_knowledge.component_with_success_task[target_factor_task_information]
-                if queried_knowledge is not None
-                else []
-            )  # A list, [success task implement knowledge]
-
-            queried_similar_error_knowledge = (
-                queried_knowledge.error_with_success_task[target_factor_task_information]
-                if queried_knowledge is not None
-                else {}
-            )  # A dict, {{error_type:[[error_imp_knowledge, success_imp_knowledge],...]},...}
-
-            queried_former_failed_knowledge = (
-                queried_knowledge.former_traces[target_factor_task_information] if queried_knowledge is not None else []
-            )
-
-            queried_former_failed_knowledge_to_render = queried_former_failed_knowledge
-
-            system_prompt = (
-                Environment(undefined=StrictUndefined)
-                .from_string(
-                    implement_prompts["evolving_strategy_factor_implementation_v1_system"],
-                )
-                .render(
-                    scenario=self.scen.get_scenario_all_desc(),
-                    queried_former_failed_knowledge=queried_former_failed_knowledge_to_render,
-                )
-            )
-
-            session = APIBackend(use_chat_cache=FACTOR_IMPLEMENT_SETTINGS.coder_use_cache).build_chat_session(
-                session_system_prompt=system_prompt,
-            )
-
-            queried_similar_component_knowledge_to_render = queried_similar_component_knowledge
-            queried_similar_error_knowledge_to_render = queried_similar_error_knowledge
-            error_summary_critics = ""
-            # 动态地防止prompt超长
-            for _ in range(10):  # max attempt to reduce the length of user_prompt
-                # 总结error（可选）
-                if (
-                    error_summary
-                    and len(queried_similar_error_knowledge_to_render) != 0
-                    and len(queried_former_failed_knowledge_to_render) != 0
-                ):
-                    error_summary_system_prompt = (
-                        Environment(undefined=StrictUndefined)
-                        .from_string(implement_prompts["evolving_strategy_error_summary_v2_system"])
-                        .render(
-                            scenario=self.scen.get_scenario_all_desc(),
-                            factor_information_str=target_factor_task_information,
-                            code_and_feedback=queried_former_failed_knowledge_to_render[
-                                -1
-                            ].get_implementation_and_feedback_str(),
-                        )
-                        .strip("\n")
-                    )
-                    session_summary = APIBackend(
-                        use_chat_cache=FACTOR_IMPLEMENT_SETTINGS.coder_use_cache
-                    ).build_chat_session(
-                        session_system_prompt=error_summary_system_prompt,
-                    )
-                    for _ in range(10):  # max attempt to reduce the length of error_summary_user_prompt
-                        error_summary_user_prompt = (
-                            Environment(undefined=StrictUndefined)
-                            .from_string(implement_prompts["evolving_strategy_error_summary_v2_user"])
-                            .render(
-                                queried_similar_component_knowledge=queried_similar_component_knowledge_to_render,
-                            )
-                            .strip("\n")
-                        )
-                        if (
-                            session_summary.build_chat_completion_message_and_calculate_token(error_summary_user_prompt)
-                            < RD_AGENT_SETTINGS.chat_token_limit
-                        ):
-                            break
-                        elif len(queried_similar_error_knowledge_to_render) > 0:
-                            queried_similar_error_knowledge_to_render = queried_similar_error_knowledge_to_render[:-1]
-                    error_summary_critics = session_summary.build_chat_completion(
-                        user_prompt=error_summary_user_prompt,
-                        json_mode=False,
-                    )
-                # 构建user_prompt。开始写代码
-                user_prompt = (
-                    Environment(undefined=StrictUndefined)
-                    .from_string(
-                        implement_prompts["evolving_strategy_factor_implementation_v2_user"],
-                    )
-                    .render(
-                        factor_information_str=target_factor_task_information,
-                        queried_similar_component_knowledge=queried_similar_component_knowledge_to_render,
-                        queried_similar_error_knowledge=queried_similar_error_knowledge_to_render,
-                        error_summary=error_summary,
-                        error_summary_critics=error_summary_critics,
-                    )
-                    .strip("\n")
-                )
-                if (
-                    session.build_chat_completion_message_and_calculate_token(
-                        user_prompt,
-                    )
-                    < RD_AGENT_SETTINGS.chat_token_limit
-                ):
-                    break
-                elif len(queried_former_failed_knowledge_to_render) > 1:
-                    queried_former_failed_knowledge_to_render = queried_former_failed_knowledge_to_render[1:]
-                elif len(queried_similar_component_knowledge_to_render) > len(
-                    queried_similar_error_knowledge_to_render,
-                ):
-                    queried_similar_component_knowledge_to_render = queried_similar_component_knowledge_to_render[:-1]
-                elif len(queried_similar_error_knowledge_to_render) > 0:
-                    queried_similar_error_knowledge_to_render = queried_similar_error_knowledge_to_render[:-1]
-
-            response = session.build_chat_completion(
-                user_prompt=user_prompt,
-                json_mode=True,
-            )
-            code = json.loads(response)["code"]
-            return code

rdagent/components/coder/factor_coder/CoSTEER/scheduler.py

@@ -1,88 +0,0 @@
-import json
-from pathlib import Path
-from typing import Dict
-
-from jinja2 import Environment, StrictUndefined
-
-from rdagent.components.coder.factor_coder.CoSTEER.evolvable_subjects import (
-    FactorEvolvingItem,
-)
-from rdagent.core.conf import RD_AGENT_SETTINGS
-from rdagent.core.prompts import Prompts
-from rdagent.core.scenario import Scenario
-from rdagent.log import rdagent_logger as logger
-from rdagent.oai.llm_utils import APIBackend
-
-scheduler_prompts = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")
-
-
-def RandomSelect(to_be_finished_task_index, implementation_factors_per_round):
-    import random
-
-    to_be_finished_task_index = random.sample(
-        to_be_finished_task_index,
-        implementation_factors_per_round,
-    )
-
-    logger.info(f"The random selection is: {to_be_finished_task_index}")
-    return to_be_finished_task_index
-
-
-def LLMSelect(
-    to_be_finished_task_index,
-    implementation_factors_per_round,
-    evo: FactorEvolvingItem,
-    former_trace: Dict,
-    scen: Scenario,
-):
-    tasks = []
-    for i in to_be_finished_task_index:
-        # find corresponding former trace for each task
-        target_factor_task_information = evo.sub_tasks[i].get_task_information()
-        if target_factor_task_information in former_trace:
-            tasks.append((i, evo.sub_tasks[i], former_trace[target_factor_task_information]))
-
-    system_prompt = (
-        Environment(undefined=StrictUndefined)
-        .from_string(
-            scheduler_prompts["select_implementable_factor_system"],
-        )
-        .render(
-            scenario=scen.get_scenario_all_desc(),
-        )
-    )
-
-    for _ in range(10):  # max attempt to reduce the length of user_prompt
-        user_prompt = (
-            Environment(undefined=StrictUndefined)
-            .from_string(
-                scheduler_prompts["select_implementable_factor_user"],
-            )
-            .render(
-                factor_num=implementation_factors_per_round,
-                sub_tasks=tasks,
-            )
-        )
-        if (
-            APIBackend().build_messages_and_calculate_token(
-                user_prompt=user_prompt,
-                system_prompt=system_prompt,
-            )
-            < RD_AGENT_SETTINGS.chat_token_limit
-        ):
-            break
-
-    response = APIBackend().build_messages_and_create_chat_completion(
-        user_prompt=user_prompt,
-        system_prompt=system_prompt,
-        json_mode=True,
-    )
-    try:
-        selection = json.loads(response)["selected_factor"]
-        if not isinstance(selection, list):
-            return to_be_finished_task_index
-        selection_index = [x for x in selection if isinstance(x, int)]
-    except:
-        return to_be_finished_task_index
-
-    return selection_index

rdagent/components/coder/factor_coder/__init__.py

@@ -0,0 +1,31 @@
+from rdagent.components.coder.CoSTEER import CoSTEER
+from rdagent.components.coder.CoSTEER.evaluators import CoSTEERMultiEvaluator
+from rdagent.components.coder.factor_coder.config import FACTOR_COSTEER_SETTINGS
+from rdagent.components.coder.factor_coder.evaluators import FactorEvaluatorForCoder
+from rdagent.components.coder.factor_coder.evolving_strategy import (
+    FactorMultiProcessEvolvingStrategy,
+)
+from rdagent.core.experiment import Experiment
+from rdagent.core.scenario import Scenario
+
+
+class FactorCoSTEER(CoSTEER):
+    def __init__(
+        self,
+        scen: Scenario,
+        *args,
+        **kwargs,
+    ) -> None:
+        setting = FACTOR_COSTEER_SETTINGS
+        eva = CoSTEERMultiEvaluator(FactorEvaluatorForCoder(scen=scen), scen=scen)
+        es = FactorMultiProcessEvolvingStrategy(scen=scen, settings=FACTOR_COSTEER_SETTINGS)
+
+        super().__init__(*args, settings=setting, eva=eva, es=es, evolving_version=2, scen=scen, **kwargs)
+
+    def develop(self, exp: Experiment) -> Experiment:
+        try:
+            exp = super().develop(exp)
+        finally:
+            es = self.evolve_agent.evolving_trace[-1]
+            exp.prop_dev_feedback = es.feedback
+        return exp

rdagent/components/coder/factor_coder/config.py

@@ -1,18 +1,10 @@
-from pathlib import Path
-from typing import Literal, Union
+from pydantic_settings import SettingsConfigDict
 
-from pydantic_settings import BaseSettings
-
-SELECT_METHOD = Literal["random", "scheduler"]
+from rdagent.components.coder.CoSTEER.config import CoSTEERSettings
 
 
-class FactorImplementSettings(BaseSettings):
-    class Config:
-        env_prefix = "FACTOR_CODER_"
-        """Use `FACTOR_CODER_` as prefix for environment variables"""
-
-    coder_use_cache: bool = False
-    """Indicates whether to use cache for the coder"""
+class FactorCoSTEERSettings(CoSTEERSettings):
+    model_config = SettingsConfigDict(env_prefix="FACTOR_CoSTEER_")
 
     data_folder: str = "git_ignore_folder/factor_implementation_source_data"
     """Path to the folder containing financial data (default is fundamental data in Qlib)"""
@@ -20,25 +12,8 @@ class FactorImplementSettings(BaseSettings):
     data_folder_debug: str = "git_ignore_folder/factor_implementation_source_data_debug"
     """Path to the folder containing partial financial data (for debugging)"""
 
-    cache_location: str = "git_ignore_folder/factor_implementation_execution_cache"
-    """Path to the cache location"""
-
-    enable_execution_cache: bool = True
-    """Indicates whether to enable the execution cache"""
-
-    # TODO: the factor implement specific settings should not appear in this settings
-    # Evolving should have a method specific settings
-    # evolving related config
-    fail_task_trial_limit: int = 20
-
-    v1_query_former_trace_limit: int = 5
-    v1_query_similar_success_limit: int = 5
-
-    v2_query_component_limit: int = 1
-    v2_query_error_limit: int = 1
-    v2_query_former_trace_limit: int = 1
-    v2_error_summary: bool = False
-    v2_knowledge_sampler: float = 1.0
+    simple_background: bool = False
+    """Whether to use simple background information for code feedback"""
 
     file_based_execution_timeout: int = 120
     """Timeout in seconds for each factor implementation execution"""
@@ -46,20 +21,8 @@ class FactorImplementSettings(BaseSettings):
     select_method: str = "random"
     """Method for the selection of factors implementation"""
 
-    select_threshold: int = 10
-    """Threshold for the number of factor selections"""
-
-    max_loop: int = 10
-    """Maximum number of task implementation loops"""
-
-    knowledge_base_path: Union[str, None] = None
-    """Path to the knowledge base"""
-
-    new_knowledge_base_path: Union[str, None] = None
-    """Path to the new knowledge base"""
-
     python_bin: str = "python"
     """Path to the Python binary"""
 
 
-FACTOR_IMPLEMENT_SETTINGS = FactorImplementSettings()
+FACTOR_COSTEER_SETTINGS = FactorCoSTEERSettings()