Bump webpack from 5.94.0 to 5.105.0 in /website (#1515)

* Initial plan

* Fix transformers API compatibility issues

Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>

* Add backward compatibility for transformers v4.26+ by version check

Support both tokenizer (v4.26-4.43) and processing_class (v4.44+) parameters based on installed transformers version. Fallback to tokenizer if version check fails.

Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>

* Improve exception handling specificity

Use specific exception types (ImportError, AttributeError, ValueError) instead of broad Exception catch for better error handling.

Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>

* Run pre-commit formatting on all files

Applied black formatting to fix code style across the repository.

Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>

.coveragerc

@@ -1,5 +1,7 @@
 [run]
 branch = True
-source = flaml
+source =
+  flaml
 omit =
-  *test*
+  */test/*
+  */flaml/autogen/*

.github/copilot-instructions.md

@@ -0,0 +1,243 @@
+# GitHub Copilot Instructions for FLAML
+
+## Project Overview
+
+FLAML (Fast Library for Automated Machine Learning & Tuning) is a lightweight Python library for efficient automation of machine learning and AI operations. It automates workflow based on large language models, machine learning models, etc. and optimizes their performance.
+
+**Key Components:**
+
+- `flaml/automl/`: AutoML functionality for classification and regression
+- `flaml/tune/`: Generic hyperparameter tuning
+- `flaml/default/`: Zero-shot AutoML with default configurations
+- `flaml/autogen/`: Legacy autogen code (note: AutoGen has moved to a separate repository)
+- `flaml/fabric/`: Microsoft Fabric integration
+- `test/`: Comprehensive test suite
+
+## Build and Test Commands
+
+### Installation
+
+```bash
+# Basic installation
+pip install -e .
+
+# Install with test dependencies
+pip install -e .[test]
+
+# Install with automl dependencies
+pip install -e .[automl]
+
+# Install with forecast dependencies (Linux only)
+pip install -e .[forecast]
+```
+
+### Running Tests
+
+```bash
+# Run all tests (excluding autogen)
+pytest test/ --ignore=test/autogen --reruns 2 --reruns-delay 10
+
+# Run tests with coverage
+coverage run -a -m pytest test --ignore=test/autogen --reruns 2 --reruns-delay 10
+coverage xml
+
+# Check dependencies
+python test/check_dependency.py
+```
+
+### Linting and Formatting
+
+```bash
+# Run pre-commit hooks
+pre-commit run --all-files
+
+# Format with black (line length: 120)
+black . --line-length 120
+
+# Run ruff for linting and auto-fix
+ruff check . --fix
+```
+
+## Code Style and Formatting
+
+### Python Style
+
+- **Line length:** 120 characters (configured in both Black and Ruff)
+- **Formatter:** Black (v23.3.0+)
+- **Linter:** Ruff with Pyflakes and pycodestyle rules
+- **Import sorting:** Use isort (via Ruff)
+- **Python version:** Supports Python >= 3.10 (full support for 3.10, 3.11, 3.12 and 3.13)
+
+### Code Quality Rules
+
+- Follow Black formatting conventions
+- Keep imports sorted and organized
+- Avoid unused imports (F401) - these are flagged but not auto-fixed
+- Avoid wildcard imports (F403) where possible
+- Complexity: Max McCabe complexity of 10
+- Use type hints where appropriate
+- Write clear docstrings for public APIs
+
+### Pre-commit Hooks
+
+The repository uses pre-commit hooks for:
+
+- Checking for large files, AST syntax, YAML/TOML/JSON validity
+- Detecting merge conflicts and private keys
+- Trailing whitespace and end-of-file fixes
+- pyupgrade for Python 3.8+ syntax
+- Black formatting
+- Markdown formatting (mdformat with GFM and frontmatter support)
+- Ruff linting with auto-fix
+
+## Testing Strategy
+
+### Test Organization
+
+- Tests are in the `test/` directory, organized by module
+- `test/automl/`: AutoML feature tests
+- `test/tune/`: Hyperparameter tuning tests
+- `test/default/`: Zero-shot AutoML tests
+- `test/nlp/`: NLP-related tests
+- `test/spark/`: Spark integration tests
+
+### Test Requirements
+
+- Write tests for new functionality
+- Ensure tests pass on multiple Python versions (3.10, 3.11, 3.12 and 3.13)
+- Tests should work on both Ubuntu and Windows
+- Use pytest markers for platform-specific tests (e.g., `@pytest.mark.spark`)
+- Tests should be idempotent and not depend on external state
+- Use `--reruns 2 --reruns-delay 10` for flaky tests
+
+### Coverage
+
+- Aim for good test coverage on new code
+- Coverage reports are generated for Python 3.11 builds
+- Coverage reports are uploaded to Codecov
+
+## Git Workflow and Best Practices
+
+### Branching
+
+- Main branch: `main`
+- Create feature branches from `main`
+- PR reviews are required before merging
+
+### Commit Messages
+
+- Use clear, descriptive commit messages
+- Reference issue numbers when applicable
+- ALWAYS run `pre-commit run --all-files` before each commit to avoid formatting issues
+
+### Pull Requests
+
+- Ensure all tests pass before requesting review
+- Update documentation if adding new features
+- Follow the PR template in `.github/PULL_REQUEST_TEMPLATE.md`
+- ALWAYS run `pre-commit run --all-files` before each commit to avoid formatting issues
+
+## Project Structure
+
+```
+flaml/
+├── automl/         # AutoML functionality
+├── tune/           # Hyperparameter tuning
+├── default/        # Zero-shot AutoML
+├── autogen/        # Legacy autogen (deprecated, moved to separate repo)
+├── fabric/         # Microsoft Fabric integration
+├── onlineml/       # Online learning
+└── version.py      # Version information
+
+test/               # Test suite
+├── automl/
+├── tune/
+├── default/
+├── nlp/
+└── spark/
+
+notebook/           # Example notebooks
+website/            # Documentation website
+```
+
+## Dependencies and Package Management
+
+### Core Dependencies
+
+- NumPy >= 1.17
+- Python >= 3.10 (officially supported: 3.10, 3.11, 3.12 and 3.13)
+
+### Optional Dependencies
+
+- `[automl]`: lightgbm, xgboost, scipy, pandas, scikit-learn
+- `[test]`: Full test suite dependencies
+- `[spark]`: PySpark and joblib dependencies
+- `[forecast]`: holidays, prophet, statsmodels, hcrystalball, pytorch-forecasting, pytorch-lightning, tensorboardX
+- `[hf]`: Hugging Face transformers and datasets
+- See `setup.py` for complete list
+
+### Version Constraints
+
+- Be mindful of Python version-specific dependencies (check setup.py)
+- XGBoost versions differ based on Python version
+- NumPy 2.0+ only for Python >= 3.13
+- Some features (like vowpalwabbit) only work with older Python versions
+
+## Boundaries and Restrictions
+
+### Do NOT Modify
+
+- `.git/` directory and Git configuration
+- `LICENSE` file
+- Version information in `flaml/version.py` (unless explicitly updating version)
+- GitHub Actions workflows without careful consideration
+- Existing test files unless fixing bugs or adding coverage
+
+### Be Cautious With
+
+- `setup.py`: Changes to dependencies should be carefully reviewed
+- `pyproject.toml`: Linting and testing configuration
+- `.pre-commit-config.yaml`: Pre-commit hook configuration
+- Backward compatibility: FLAML is a library with external users
+
+### Security Considerations
+
+- Never commit secrets or API keys
+- Be careful with external data sources in tests
+- Validate user inputs in public APIs
+- Follow secure coding practices for ML operations
+
+## Special Notes
+
+### AutoGen Migration
+
+- AutoGen has moved to a separate repository: https://github.com/microsoft/autogen
+- The `flaml/autogen/` directory contains legacy code
+- Tests in `test/autogen/` are ignored in the main test suite
+- Direct users to the new AutoGen repository for AutoGen-related issues
+
+### Platform-Specific Considerations
+
+- Some tests only run on Linux (e.g., forecast tests with prophet)
+- Windows and Ubuntu are the primary supported platforms
+- macOS support exists but requires special libomp setup for lgbm/xgboost
+
+### Performance
+
+- FLAML focuses on efficient automation and tuning
+- Consider computational cost when adding new features
+- Optimize for low resource usage where possible
+
+## Documentation
+
+- Main documentation: https://microsoft.github.io/FLAML/
+- Update documentation when adding new features
+- Provide clear examples in docstrings
+- Add notebook examples for significant new features
+
+## Contributing
+
+- Follow the contributing guide: https://microsoft.github.io/FLAML/docs/Contribute
+- Sign the Microsoft CLA when making your first contribution
+- Be respectful and follow the Microsoft Open Source Code of Conduct
+- Join the Discord community for discussions: https://discord.gg/Cppx2vSPVP

.github/workflows/CD.yml

@@ -12,26 +12,17 @@ jobs:
   deploy:
     strategy:
       matrix:
-        os: ['ubuntu-latest']
-        python-version: [3.8]
+        os: ["ubuntu-latest"]
+        python-version: ["3.12"]
     runs-on: ${{ matrix.os }}
     environment: package
     steps:
       - name: Checkout
-        uses: actions/checkout@v3
-      - name: Cache conda
-        uses: actions/cache@v3
+        uses: actions/checkout@v4
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
         with:
-          path: ~/conda_pkgs_dir
-          key: conda-${{ matrix.os }}-python-${{ matrix.python-version }}-${{ hashFiles('environment.yml') }}
-      - name: Setup Miniconda
-        uses: conda-incubator/setup-miniconda@v2
-        with:
-          auto-update-conda: true
-          auto-activate-base: false
-          activate-environment: hcrystalball
           python-version: ${{ matrix.python-version }}
-          use-only-tar-bz2: true
       - name: Install from source
         # This is required for the pre-commit tests
         shell: pwsh
@@ -42,7 +33,7 @@ jobs:
       - name: Build
         shell: pwsh
         run: |
-          pip install twine
+          pip install twine wheel setuptools
           python setup.py sdist bdist_wheel
       - name: Publish to PyPI
         env:

.github/workflows/deploy-website.yml

@@ -37,11 +37,11 @@ jobs:
       - name: setup python
         uses: actions/setup-python@v4
         with:
-          python-version: "3.8"
+          python-version: "3.12"
       - name: pydoc-markdown install
         run: |
           python -m pip install --upgrade pip
-          pip install pydoc-markdown==4.5.0
+          pip install pydoc-markdown==4.7.0 setuptools
       - name: pydoc-markdown run
         run: |
           pydoc-markdown
@@ -73,11 +73,11 @@ jobs:
       - name: setup python
         uses: actions/setup-python@v4
         with:
-          python-version: "3.8"
+          python-version: "3.12"
       - name: pydoc-markdown install
         run: |
           python -m pip install --upgrade pip
-          pip install pydoc-markdown==4.5.0
+          pip install pydoc-markdown==4.7.0 setuptools
       - name: pydoc-markdown run
         run: |
           pydoc-markdown

.github/workflows/openai.yml

@@ -4,14 +4,15 @@
 name: OpenAI
 
 on:
-  pull_request:
-    branches: ['main']
-    paths:
-      - 'flaml/autogen/**'
-      - 'test/autogen/**'
-      - 'notebook/autogen_openai_completion.ipynb'
-      - 'notebook/autogen_chatgpt_gpt4.ipynb'
-      - '.github/workflows/openai.yml'
+  workflow_dispatch:
+#   pull_request:
+#     branches: ['main']
+#     paths:
+#       - 'flaml/autogen/**'
+#       - 'test/autogen/**'
+#       - 'notebook/autogen_openai_completion.ipynb'
+#       - 'notebook/autogen_chatgpt_gpt4.ipynb'
+#       - '.github/workflows/openai.yml'
 
 permissions: {}
 

.github/workflows/pre-commit.yml

@@ -1,9 +1,7 @@
 name: Code formatting
 
 # see: https://help.github.com/en/actions/reference/events-that-trigger-workflows
-on:  # Trigger the workflow on push or pull request, but only for the main branch
-  push:
-    branches: [main]
+on:
   pull_request: {}
 
 defaults:

.github/workflows/python-package.yml

@@ -14,10 +14,20 @@ on:
       - 'setup.py'
   pull_request:
     branches: ['main']
+    paths:
+      - 'flaml/**'
+      - 'test/**'
+      - 'notebook/**'
+      - '.github/workflows/python-package.yml'
+      - 'setup.py'
   merge_group:
     types: [checks_requested]
+  schedule:
+    # Every other day at 02:00 UTC
+    - cron: '0 2 */2 * *'
 
-permissions: {}
+permissions:
+  contents: write
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref }}
   cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
@@ -29,8 +39,8 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        os: [ubuntu-latest, macos-latest, windows-2019]
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        os: [ubuntu-latest, windows-latest]
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
@@ -38,7 +48,7 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
       - name: On mac, install libomp to facilitate lgbm and xgboost install
-        if: matrix.os == 'macOS-latest'
+        if: matrix.os == 'macos-latest'
         run: |
           brew update
           brew install libomp
@@ -50,76 +60,82 @@ jobs:
           export LDFLAGS="$LDFLAGS -Wl,-rpath,/usr/local/opt/libomp/lib -L/usr/local/opt/libomp/lib -lomp"
       - name: Install packages and dependencies
         run: |
-          python -m pip install --upgrade pip wheel
+          python -m pip install --upgrade pip wheel setuptools
           pip install -e .
           python -c "import flaml"
           pip install -e .[test]
-      - name: On Ubuntu python 3.10, install pyspark 3.4.1
-        if: matrix.python-version == '3.10' && matrix.os == 'ubuntu-latest'
-        run: |
-          pip install pyspark==3.4.1
-          pip list | grep "pyspark"
       - name: On Ubuntu python 3.11, install pyspark 3.5.1
         if: matrix.python-version == '3.11' && matrix.os == 'ubuntu-latest'
         run: |
           pip install pyspark==3.5.1
           pip list | grep "pyspark"
-      - name: If linux and python<3.11, install ray 2
-        if: matrix.os == 'ubuntu-latest' && matrix.python-version != '3.11'
+      - name: On Ubuntu python 3.12, install pyspark 4.0.1
+        if: matrix.python-version == '3.12' && matrix.os == 'ubuntu-latest'
         run: |
-          pip install "ray[tune]<2.5.0"
-      - name: If mac and python 3.10, install ray and xgboost 1
-        if: matrix.os == 'macOS-latest' && matrix.python-version == '3.10'
+          pip install pyspark==4.0.1
+          pip list | grep "pyspark"
+      - name: On Ubuntu python 3.13, install pyspark 4.1.0
+        if: matrix.python-version == '3.13' && matrix.os == 'ubuntu-latest'
         run: |
-          pip install -e .[ray]
-          # use macOS to test xgboost 1, but macOS also supports xgboost 2
-          pip install "xgboost<2"
-      - name: If linux, install prophet on python < 3.9
-        if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.8'
+          pip install pyspark==4.1.0
+          pip list | grep "pyspark"
+      # # TODO: support ray
+      # - name: If linux and python<3.11, install ray 2
+      #   if: matrix.os == 'ubuntu-latest' && matrix.python-version < '3.11'
+      #   run: |
+      #     pip install "ray[tune]<2.5.0"
+      - name: Install prophet when on linux
+        if: matrix.os == 'ubuntu-latest'
         run: |
           pip install -e .[forecast]
-      - name: Install vw on python < 3.10
-        if: matrix.python-version == '3.8' || matrix.python-version == '3.9'
+      # TODO: support vw for python 3.10+
+      - name:  If linux and python<3.10, install vw
+        if: matrix.os == 'ubuntu-latest' && matrix.python-version < '3.10'
         run: |
           pip install -e .[vw]
-      - name: Test with pytest
-        if: matrix.python-version != '3.10'
+      - name: Pip freeze
         run: |
-          pytest test/
+          pip freeze
+      - name: Check dependencies
+        run: |
+          python test/check_dependency.py
+      - name: Clear pip cache
+        run: |
+          pip cache purge
+      - name: Test with pytest
+        timeout-minutes: 120
+        if: matrix.python-version != '3.11'
+        run: |
+          pytest test/ --ignore=test/autogen --reruns 2 --reruns-delay 10
       - name: Coverage
-        if: matrix.python-version == '3.10'
+        timeout-minutes: 120
+        if: matrix.python-version == '3.11'
         run: |
           pip install coverage
-          coverage run -a -m pytest test
+          coverage run -a -m pytest test --ignore=test/autogen --reruns 2 --reruns-delay 10
           coverage xml
       - name: Upload coverage to Codecov
-        if: matrix.python-version == '3.10'
+        if: matrix.python-version == '3.11'
         uses: codecov/codecov-action@v3
         with:
           file: ./coverage.xml
           flags: unittests
+      - name: Save dependencies
+        if: github.ref == 'refs/heads/main'
+        shell: bash
+        run: |
+          git config --global user.name 'github-actions[bot]'
+          git config --global user.email 'github-actions[bot]@users.noreply.github.com'
+          git config advice.addIgnoredFile false
 
-  # docs:
+          BRANCH=unit-tests-installed-dependencies
+          git fetch origin
+          git checkout -B "$BRANCH" "origin/$BRANCH"
 
-  #   runs-on: ubuntu-latest
-
-  #   steps:
-  #     - uses: actions/checkout@v3
-  #     - name: Setup Python
-  #       uses: actions/setup-python@v4
-  #       with:
-  #         python-version: '3.8'
-  #     - name: Compile documentation
-  #       run: |
-  #           pip install -e .
-  #           python -m pip install sphinx sphinx_rtd_theme
-  #           cd docs
-  #           make html
-  #     - name: Deploy to GitHub pages
-  #       if: ${{ github.ref == 'refs/heads/main' }}
-  #       uses: JamesIves/github-pages-deploy-action@3.6.2
-  #       with:
-  #         GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-  #         BRANCH: gh-pages
-  #         FOLDER: docs/_build/html
-  #         CLEAN: true
+          pip freeze > installed_all_dependencies_${{ matrix.python-version }}_${{ matrix.os }}.txt
+          python test/check_dependency.py > installed_first_tier_dependencies_${{ matrix.python-version }}_${{ matrix.os }}.txt
+          git add installed_*dependencies*.txt
+          mv coverage.xml ./coverage_${{ matrix.python-version }}_${{ matrix.os }}.xml || true
+          git add -f ./coverage_${{ matrix.python-version }}_${{ matrix.os }}.xml || true
+          git commit -m "Update installed dependencies for Python ${{ matrix.python-version }} on ${{ matrix.os }}" || exit 0
+          git push origin "$BRANCH" --force

.gitignore

@@ -60,6 +60,7 @@ coverage.xml
 .hypothesis/
 .pytest_cache/
 cover/
+junit
 
 # Translations
 *.mo
@@ -172,7 +173,7 @@ test/default
 test/housing.json
 test/nlp/default/transformer_ms/seq-classification.json
 
-flaml/fabric/fanova/_fanova.c
+flaml/fabric/fanova/*fanova.c
 # local config files
 *.config.local
 
@@ -184,3 +185,7 @@ notebook/lightning_logs/
 lightning_logs/
 flaml/autogen/extensions/tmp/
 test/autogen/my_tmp/
+catboost_*
+
+# Internal configs
+.pypirc

.pre-commit-config.yaml

@@ -36,7 +36,7 @@ repos:
     - id: black
 
   - repo: https://github.com/executablebooks/mdformat
-    rev: 0.7.17
+    rev: 0.7.22
     hooks:
       - id: mdformat
         additional_dependencies:

Dockerfile

@@ -1,5 +1,5 @@
 # basic setup
-FROM mcr.microsoft.com/devcontainers/python:3.8
+FROM mcr.microsoft.com/devcontainers/python:3.10
 RUN apt-get update && apt-get -y update
 RUN apt-get install -y sudo git npm
 

NOTICE.md

@@ -4,8 +4,8 @@ This repository incorporates material as listed below or described in the code.
 
 ## Component. Ray.
 
-Code in tune/\[analysis.py, sample.py, trial.py, result.py\],
-searcher/\[suggestion.py, variant_generator.py\], and scheduler/trial_scheduler.py is adapted from
+Code in tune/[analysis.py, sample.py, trial.py, result.py],
+searcher/[suggestion.py, variant_generator.py], and scheduler/trial_scheduler.py is adapted from
 https://github.com/ray-project/ray/blob/master/python/ray/tune/
 
 ## Open Source License/Copyright Notice.

README.md

@@ -14,15 +14,9 @@
     <br>
 </p>
 
-:fire: FLAML supports AutoML and Hyperparameter Tuning in [Microsoft Fabric Data Science](https://learn.microsoft.com/en-us/fabric/data-science/automated-machine-learning-fabric). In addition, we've introduced Python 3.11 support, along with a range of new estimators, and comprehensive integration with MLflow—thanks to contributions from the Microsoft Fabric product team.
+:fire: FLAML supports AutoML and Hyperparameter Tuning in [Microsoft Fabric Data Science](https://learn.microsoft.com/en-us/fabric/data-science/automated-machine-learning-fabric). In addition, we've introduced Python 3.11 and 3.12 support, along with a range of new estimators, and comprehensive integration with MLflow—thanks to contributions from the Microsoft Fabric product team.
 
-:fire: Heads-up: We have migrated [AutoGen](https://microsoft.github.io/autogen/) into a dedicated [github repository](https://github.com/microsoft/autogen). Alongside this move, we have also launched a dedicated [Discord](https://discord.gg/pAbnFJrkgZ) server and a [website](https://microsoft.github.io/autogen/) for comprehensive documentation.
-
-:fire: The automated multi-agent chat framework in [AutoGen](https://microsoft.github.io/autogen/) is in preview from v2.0.0.
-
-:fire: FLAML is highlighted in OpenAI's [cookbook](https://github.com/openai/openai-cookbook#related-resources-from-around-the-web).
-
-:fire: [autogen](https://microsoft.github.io/autogen/) is released with support for ChatGPT and GPT-4, based on [Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference](https://arxiv.org/abs/2303.04673).
+:fire: Heads-up: [AutoGen](https://microsoft.github.io/autogen/) has moved to a dedicated [GitHub repository](https://github.com/microsoft/autogen). FLAML no longer includes the `autogen` module—please use AutoGen directly.
 
 ## What is FLAML
 
@@ -30,7 +24,7 @@ FLAML is a lightweight Python library for efficient automation of machine
 learning and AI operations. It automates workflow based on large language models, machine learning models, etc.
 and optimizes their performance.
 
-- FLAML enables building next-gen GPT-X applications based on multi-agent conversations with minimal effort. It simplifies the orchestration, automation and optimization of a complex GPT-X workflow. It maximizes the performance of GPT-X models and augments their weakness.
+- FLAML enables economical automation and tuning for ML/AI workflows, including model selection and hyperparameter optimization under resource constraints.
 - For common machine learning tasks like classification and regression, it quickly finds quality models for user-provided data with low computational resources. It is easy to customize or extend. Users can find their desired customizability from a smooth range.
 - It supports fast and economical automatic tuning (e.g., inference hyperparameters for foundation models, configurations in MLOps/LMOps workflows, pipelines, mathematical/statistical models, algorithms, computing experiments, software configurations), capable of handling large search space with heterogeneous evaluation cost and complex constraints/guidance/early stopping.
 
@@ -40,16 +34,16 @@ FLAML has a .NET implementation in [ML.NET](http://dot.net/ml), an open-source,
 
 ## Installation
 
-FLAML requires **Python version >= 3.8**. It can be installed from pip:
+The latest version of FLAML requires **Python >= 3.10 and < 3.14**. While other Python versions may work for core components, full model support is not guaranteed. FLAML can be installed via `pip`:
 
 ```bash
 pip install flaml
 ```
 
-Minimal dependencies are installed without extra options. You can install extra options based on the feature you need. For example, use the following to install the dependencies needed by the [`autogen`](https://microsoft.github.io/autogen/) package.
+Minimal dependencies are installed without extra options. You can install extra options based on the feature you need. For example, use the following to install the dependencies needed by the [`automl`](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML) module.
 
 ```bash
-pip install "flaml[autogen]"
+pip install "flaml[automl]"
 ```
 
 Find more options in [Installation](https://microsoft.github.io/FLAML/docs/Installation).
@@ -57,39 +51,6 @@ Each of the [`notebook examples`](https://github.com/microsoft/FLAML/tree/main/n
 
 ## Quickstart
 
-- (New) The [autogen](https://microsoft.github.io/autogen/) package enables the next-gen GPT-X applications with a generic multi-agent conversation framework.
-  It offers customizable and conversable agents which integrate LLMs, tools and human.
-  By automating chat among multiple capable agents, one can easily make them collectively perform tasks autonomously or with human feedback, including tasks that require using tools via code. For example,
-
-```python
-from flaml import autogen
-
-assistant = autogen.AssistantAgent("assistant")
-user_proxy = autogen.UserProxyAgent("user_proxy")
-user_proxy.initiate_chat(
-    assistant,
-    message="Show me the YTD gain of 10 largest technology companies as of today.",
-)
-# This initiates an automated chat between the two agents to solve the task
-```
-
-Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers a drop-in replacement of `openai.Completion` or `openai.ChatCompletion` with powerful functionalites like tuning, caching, templating, filtering. For example, you can optimize generations by LLM with your own tuning data, success metrics and budgets.
-
-```python
-# perform tuning
-config, analysis = autogen.Completion.tune(
-    data=tune_data,
-    metric="success",
-    mode="max",
-    eval_func=eval_func,
-    inference_budget=0.05,
-    optimization_budget=3,
-    num_samples=-1,
-)
-# perform inference for a test instance
-response = autogen.Completion.create(context=test_instance, **config)
-```
-
 - With three lines of code, you can start using this economical and fast
   AutoML engine as a [scikit-learn style estimator](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML).
 
@@ -111,7 +72,10 @@ automl.fit(X_train, y_train, task="classification", estimator_list=["lgbm"])
 
 ```python
 from flaml import tune
-tune.run(evaluation_function, config={…}, low_cost_partial_config={…}, time_budget_s=3600)
+
+tune.run(
+    evaluation_function, config={…}, low_cost_partial_config={…}, time_budget_s=3600
+)
 ```
 
 - [Zero-shot AutoML](https://microsoft.github.io/FLAML/docs/Use-Cases/Zero-Shot-AutoML) allows using the existing training API from lightgbm, xgboost etc. while getting the benefit of AutoML in choosing high-performance hyperparameter configurations per task.

SECURITY.md

@@ -12,7 +12,7 @@ If you believe you have found a security vulnerability in any Microsoft-owned re
 
 Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://msrc.microsoft.com/create-report).
 
-If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com).  If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://www.microsoft.com/en-us/msrc/pgp-key-msrc).
+If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://www.microsoft.com/en-us/msrc/pgp-key-msrc).
 
 You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://www.microsoft.com/msrc).
 

flaml/autogen/__init__.py

@@ -1,3 +1,12 @@
+import warnings
+
 from .agentchat import *
 from .code_utils import DEFAULT_MODEL, FAST_MODEL
 from .oai import *
+
+warnings.warn(
+    "The `flaml.autogen` module is deprecated and will be removed in a future release. "
+    "Please refer to `https://github.com/microsoft/autogen` for latest usage.",
+    DeprecationWarning,
+    stacklevel=2,
+)

flaml/autogen/agentchat/contrib/math_user_proxy_agent.py

@@ -156,7 +156,7 @@ class MathUserProxyAgent(UserProxyAgent):
                     when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True.
             default_auto_reply (str or dict or None): the default auto reply message when no code execution or llm based reply is generated.
             max_invalid_q_per_step (int): (ADDED) the maximum number of invalid queries per step.
-            **kwargs (dict): other kwargs in [UserProxyAgent](user_proxy_agent#__init__).
+            **kwargs (dict): other kwargs in [UserProxyAgent](../user_proxy_agent#__init__).
         """
         super().__init__(
             name=name,

flaml/autogen/agentchat/contrib/retrieve_user_proxy_agent.py

@@ -123,7 +123,7 @@ class RetrieveUserProxyAgent(UserProxyAgent):
                     can be found at `https://www.sbert.net/docs/pretrained_models.html`. The default model is a
                     fast model. If you want to use a high performance model, `all-mpnet-base-v2` is recommended.
                 - customized_prompt (Optional, str): the customized prompt for the retrieve chat. Default is None.
-            **kwargs (dict): other kwargs in [UserProxyAgent](user_proxy_agent#__init__).
+            **kwargs (dict): other kwargs in [UserProxyAgent](../user_proxy_agent#__init__).
         """
         super().__init__(
             name=name,

flaml/automl/contrib/histgb.py

@@ -1,7 +1,7 @@
 try:
     from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor
-except ImportError:
-    pass
+except ImportError as e:
+    print(f"scikit-learn is required for HistGradientBoostingEstimator. Please install it; error: {e}")
 
 from flaml import tune
 from flaml.automl.model import SKLearnEstimator

flaml/automl/data.py

@@ -2,13 +2,18 @@
 #  * Copyright (c) Microsoft Corporation. All rights reserved.
 #  * Licensed under the MIT License. See LICENSE file in the
 #  * project root for license information.
+import json
 import os
-from datetime import datetime
+import random
+import re
+import uuid
+from datetime import datetime, timedelta
+from decimal import ROUND_HALF_UP, Decimal
 from typing import TYPE_CHECKING, Union
 
 import numpy as np
 
-from flaml.automl.spark import DataFrame, Series, pd, ps, psDataFrame, psSeries
+from flaml.automl.spark import DataFrame, F, Series, T, pd, ps, psDataFrame, psSeries
 from flaml.automl.training_log import training_log_reader
 
 try:
@@ -19,6 +24,7 @@ except ImportError:
 if TYPE_CHECKING:
     from flaml.automl.task import Task
 
+
 TS_TIMESTAMP_COL = "ds"
 TS_VALUE_COL = "y"
 
@@ -45,7 +51,10 @@ def load_openml_dataset(dataset_id, data_dir=None, random_state=0, dataset_forma
     """
     import pickle
 
-    import openml
+    try:
+        import openml
+    except ImportError:
+        openml = None
     from sklearn.model_selection import train_test_split
 
     filename = "openml_ds" + str(dataset_id) + ".pkl"
@@ -56,15 +65,15 @@ def load_openml_dataset(dataset_id, data_dir=None, random_state=0, dataset_forma
             dataset = pickle.load(f)
     else:
         print("download dataset from openml")
-        dataset = openml.datasets.get_dataset(dataset_id)
+        dataset = openml.datasets.get_dataset(dataset_id) if openml else None
         if not os.path.exists(data_dir):
             os.makedirs(data_dir)
         with open(filepath, "wb") as f:
             pickle.dump(dataset, f, pickle.HIGHEST_PROTOCOL)
-    print("Dataset name:", dataset.name)
+    print("Dataset name:", dataset.name) if dataset else None
     try:
         X, y, *__ = dataset.get_data(target=dataset.default_target_attribute, dataset_format=dataset_format)
-    except ValueError:
+    except (ValueError, AttributeError, TypeError):
         from sklearn.datasets import fetch_openml
 
         X, y = fetch_openml(data_id=dataset_id, return_X_y=True)
@@ -445,3 +454,343 @@ class DataTransformer:
 def group_counts(groups):
     _, i, c = np.unique(groups, return_counts=True, return_index=True)
     return c[np.argsort(i)]
+
+
+def get_random_dataframe(n_rows: int = 200, ratio_none: float = 0.1, seed: int = 42) -> DataFrame:
+    """Generate a random pandas DataFrame with various data types for testing.
+    This function creates a DataFrame with multiple column types including:
+    - Timestamps
+    - Integers
+    - Floats
+    - Categorical values
+    - Booleans
+    - Lists (tags)
+    - Decimal strings
+    - UUIDs
+    - Binary data (as hex strings)
+    - JSON blobs
+    - Nullable text fields
+    Parameters
+    ----------
+    n_rows : int, default=200
+        Number of rows in the generated DataFrame
+    ratio_none : float, default=0.1
+        Probability of generating None values in applicable columns
+    seed : int, default=42
+        Random seed for reproducibility
+    Returns
+    -------
+    pd.DataFrame
+        A DataFrame with 14 columns of various data types
+    Examples
+    --------
+    >>> df = get_random_dataframe(100, 0.05, 123)
+    >>> df.shape
+    (100, 14)
+    >>> df.dtypes
+    timestamp       datetime64[ns]
+    id                       int64
+    score                  float64
+    status                  object
+    flag                    object
+    count                   object
+    value                   object
+    tags                    object
+    rating                  object
+    uuid                    object
+    binary                  object
+    json_blob               object
+    category              category
+    nullable_text           object
+    dtype: object
+    """
+
+    np.random.seed(seed)
+    random.seed(seed)
+
+    def random_tags():
+        tags = ["AI", "ML", "data", "robotics", "vision"]
+        return random.sample(tags, k=random.randint(1, 3)) if random.random() > ratio_none else None
+
+    def random_decimal():
+        return (
+            str(Decimal(random.uniform(1, 5)).quantize(Decimal("0.01"), rounding=ROUND_HALF_UP))
+            if random.random() > ratio_none
+            else None
+        )
+
+    def random_json_blob():
+        blob = {"a": random.randint(1, 10), "b": random.random()}
+        return json.dumps(blob) if random.random() > ratio_none else None
+
+    def random_binary():
+        return bytes(random.randint(0, 255) for _ in range(4)).hex() if random.random() > ratio_none else None
+
+    data = {
+        "timestamp": [
+            datetime(2020, 1, 1) + timedelta(days=np.random.randint(0, 1000)) if np.random.rand() > ratio_none else None
+            for _ in range(n_rows)
+        ],
+        "id": range(1, n_rows + 1),
+        "score": np.random.uniform(0, 100, n_rows),
+        "status": np.random.choice(
+            ["active", "inactive", "pending", None],
+            size=n_rows,
+            p=[(1 - ratio_none) / 3, (1 - ratio_none) / 3, (1 - ratio_none) / 3, ratio_none],
+        ),
+        "flag": np.random.choice(
+            [True, False, None], size=n_rows, p=[(1 - ratio_none) / 2, (1 - ratio_none) / 2, ratio_none]
+        ),
+        "count": [np.random.randint(0, 100) if np.random.rand() > ratio_none else None for _ in range(n_rows)],
+        "value": [round(np.random.normal(50, 15), 2) if np.random.rand() > ratio_none else None for _ in range(n_rows)],
+        "tags": [random_tags() for _ in range(n_rows)],
+        "rating": [random_decimal() for _ in range(n_rows)],
+        "uuid": [str(uuid.uuid4()) if np.random.rand() > ratio_none else None for _ in range(n_rows)],
+        "binary": [random_binary() for _ in range(n_rows)],
+        "json_blob": [random_json_blob() for _ in range(n_rows)],
+        "category": pd.Categorical(
+            np.random.choice(
+                ["A", "B", "C", None],
+                size=n_rows,
+                p=[(1 - ratio_none) / 3, (1 - ratio_none) / 3, (1 - ratio_none) / 3, ratio_none],
+            )
+        ),
+        "nullable_text": [random.choice(["Good", "Bad", "Average", None]) for _ in range(n_rows)],
+    }
+
+    return pd.DataFrame(data)
+
+
+def auto_convert_dtypes_spark(
+    df: psDataFrame,
+    na_values: list = None,
+    category_threshold: float = 0.3,
+    convert_threshold: float = 0.6,
+    sample_ratio: float = 0.1,
+) -> tuple[psDataFrame, dict]:
+    """Automatically convert data types in a PySpark DataFrame using heuristics.
+
+    This function analyzes a sample of the DataFrame to infer appropriate data types
+    and applies the conversions. It handles timestamps, numeric values, booleans,
+    and categorical fields.
+
+    Args:
+        df: A PySpark DataFrame to convert.
+        na_values: List of strings to be considered as NA/NaN. Defaults to
+            ['NA', 'na', 'NULL', 'null', ''].
+        category_threshold: Maximum ratio of unique values to total values
+            to consider a column categorical. Defaults to 0.3.
+        convert_threshold: Minimum ratio of successfully converted values required
+            to apply a type conversion. Defaults to 0.6.
+        sample_ratio: Fraction of data to sample for type inference. Defaults to 0.1.
+
+    Returns:
+        tuple: (The DataFrame with converted types, A dictionary mapping column names to
+                their inferred types as strings)
+
+    Note:
+        - 'category' in the schema dict is conceptual as PySpark doesn't have a true
+            category type like pandas
+        - The function uses sampling for efficiency with large datasets
+    """
+    n_rows = df.count()
+    if na_values is None:
+        na_values = ["NA", "na", "NULL", "null", ""]
+
+    # Normalize NA-like values
+    for colname, coltype in df.dtypes:
+        if coltype == "string":
+            df = df.withColumn(
+                colname,
+                F.when(F.trim(F.lower(F.col(colname))).isin([v.lower() for v in na_values]), None).otherwise(
+                    F.col(colname)
+                ),
+            )
+
+    schema = {}
+    for colname in df.columns:
+        # Sample once at an appropriate ratio
+        sample_ratio_to_use = min(1.0, sample_ratio if n_rows * sample_ratio > 100 else 100 / n_rows)
+        col_sample = df.select(colname).sample(withReplacement=False, fraction=sample_ratio_to_use).dropna()
+        sample_count = col_sample.count()
+
+        inferred_type = "string"  # Default
+
+        if col_sample.dtypes[0][1] != "string":
+            schema[colname] = col_sample.dtypes[0][1]
+            continue
+
+        if sample_count == 0:
+            schema[colname] = "string"
+            continue
+
+        # Check if timestamp
+        ts_col = col_sample.withColumn("parsed", F.to_timestamp(F.col(colname)))
+
+        # Check numeric
+        if (
+            col_sample.withColumn("n", F.col(colname).cast("double")).filter("n is not null").count()
+            >= sample_count * convert_threshold
+        ):
+            # All whole numbers?
+            all_whole = (
+                col_sample.withColumn("n", F.col(colname).cast("double"))
+                .filter("n is not null")
+                .withColumn("frac", F.abs(F.col("n") % 1))
+                .filter("frac > 0.000001")
+                .count()
+                == 0
+            )
+            inferred_type = "int" if all_whole else "double"
+
+        # Check low-cardinality (category-like)
+        elif (
+            sample_count > 0
+            and col_sample.select(F.countDistinct(F.col(colname))).collect()[0][0] / sample_count <= category_threshold
+        ):
+            inferred_type = "category"  # Will just be string, but marked as such
+
+        # Check if timestamp
+        elif ts_col.filter(F.col("parsed").isNotNull()).count() >= sample_count * convert_threshold:
+            inferred_type = "timestamp"
+
+        schema[colname] = inferred_type
+
+    # Apply inferred schema
+    for colname, inferred_type in schema.items():
+        if inferred_type == "int":
+            df = df.withColumn(colname, F.col(colname).cast(T.IntegerType()))
+        elif inferred_type == "double":
+            df = df.withColumn(colname, F.col(colname).cast(T.DoubleType()))
+        elif inferred_type == "boolean":
+            df = df.withColumn(
+                colname,
+                F.when(F.lower(F.col(colname)).isin("true", "yes", "1"), True)
+                .when(F.lower(F.col(colname)).isin("false", "no", "0"), False)
+                .otherwise(None),
+            )
+        elif inferred_type == "timestamp":
+            df = df.withColumn(colname, F.to_timestamp(F.col(colname)))
+        elif inferred_type == "category":
+            df = df.withColumn(colname, F.col(colname).cast(T.StringType()))  # Marked conceptually
+
+        # otherwise keep as string (or original type)
+
+    return df, schema
+
+
+def auto_convert_dtypes_pandas(
+    df: DataFrame,
+    na_values: list = None,
+    category_threshold: float = 0.3,
+    convert_threshold: float = 0.6,
+    sample_ratio: float = 1.0,
+) -> tuple[DataFrame, dict]:
+    """Automatically convert data types in a pandas DataFrame using heuristics.
+
+    This function analyzes the DataFrame to infer appropriate data types
+    and applies the conversions. It handles timestamps, timedeltas, numeric values,
+    and categorical fields.
+
+    Args:
+        df: A pandas DataFrame to convert.
+        na_values: List of strings to be considered as NA/NaN. Defaults to
+            ['NA', 'na', 'NULL', 'null', ''].
+        category_threshold: Maximum ratio of unique values to total values
+            to consider a column categorical. Defaults to 0.3.
+        convert_threshold: Minimum ratio of successfully converted values required
+            to apply a type conversion. Defaults to 0.6.
+        sample_ratio: Fraction of data to sample for type inference. Not used in pandas version
+            but included for API compatibility. Defaults to 1.0.
+
+    Returns:
+        tuple: (The DataFrame with converted types, A dictionary mapping column names to
+                their inferred types as strings)
+    """
+    if na_values is None:
+        na_values = {"NA", "na", "NULL", "null", ""}
+    # Remove the empty string separately (handled by the regex `^\s*$`)
+    vals = [re.escape(v) for v in na_values if v != ""]
+    # Build inner alternation group
+    inner = "|".join(vals) if vals else ""
+    if inner:
+        pattern = re.compile(rf"^\s*(?:{inner})?\s*$")
+    else:
+        pattern = re.compile(r"^\s*$")
+
+    df_converted = df.convert_dtypes()
+    schema = {}
+
+    # Sample if needed (for API compatibility)
+    if sample_ratio < 1.0:
+        df = df.sample(frac=sample_ratio)
+
+    n_rows = len(df)
+
+    for col in df.columns:
+        series = df[col]
+        # Replace NA-like values if string
+        if series.dtype == object:
+            mask = series.astype(str).str.match(pattern)
+            series_cleaned = series.where(~mask, np.nan)
+        else:
+            series_cleaned = series
+
+        # Skip conversion if already non-object data type, except bool which can potentially be categorical
+        if (
+            not isinstance(series_cleaned.dtype, pd.BooleanDtype)
+            and not isinstance(series_cleaned.dtype, pd.StringDtype)
+            and series_cleaned.dtype != "object"
+        ):
+            # Keep the original data type for non-object dtypes
+            df_converted[col] = series
+            schema[col] = str(series_cleaned.dtype)
+            continue
+
+        # print(f"type: {series_cleaned.dtype}, column: {series_cleaned.name}")
+
+        if not isinstance(series_cleaned.dtype, pd.BooleanDtype):
+            # Try numeric (int or float)
+            numeric = pd.to_numeric(series_cleaned, errors="coerce")
+            if numeric.notna().sum() >= n_rows * convert_threshold:
+                if (numeric.dropna() % 1 == 0).all():
+                    try:
+                        df_converted[col] = numeric.astype("int")  # Nullable integer
+                        schema[col] = "int"
+                        continue
+                    except Exception:
+                        pass
+                df_converted[col] = numeric.astype("double")
+                schema[col] = "double"
+                continue
+
+            # Try datetime
+            datetime_converted = pd.to_datetime(series_cleaned, errors="coerce")
+            if datetime_converted.notna().sum() >= n_rows * convert_threshold:
+                df_converted[col] = datetime_converted
+                schema[col] = "timestamp"
+                continue
+
+            # Try timedelta
+            try:
+                timedelta_converted = pd.to_timedelta(series_cleaned, errors="coerce")
+                if timedelta_converted.notna().sum() >= n_rows * convert_threshold:
+                    df_converted[col] = timedelta_converted
+                    schema[col] = "timedelta"
+                    continue
+            except TypeError:
+                pass
+
+        # Try category
+        try:
+            unique_ratio = series_cleaned.nunique(dropna=True) / n_rows if n_rows > 0 else 1.0
+            if unique_ratio <= category_threshold:
+                df_converted[col] = series_cleaned.astype("category")
+                schema[col] = "category"
+                continue
+        except Exception:
+            pass
+        df_converted[col] = series_cleaned.astype("string")
+        schema[col] = "string"
+
+    return df_converted, schema

flaml/automl/logger.py

@@ -1,7 +1,37 @@
 import logging
+import os
+
+
+class ColoredFormatter(logging.Formatter):
+    # ANSI escape codes for colors
+    COLORS = {
+        # logging.DEBUG: "\033[36m",  # Cyan
+        # logging.INFO: "\033[32m",   # Green
+        logging.WARNING: "\033[33m",  # Yellow
+        logging.ERROR: "\033[31m",  # Red
+        logging.CRITICAL: "\033[1;31m",  # Bright Red
+    }
+    RESET = "\033[0m"  # Reset to default
+
+    def __init__(self, fmt, datefmt, use_color=True):
+        super().__init__(fmt, datefmt)
+        self.use_color = use_color
+
+    def format(self, record):
+        formatted = super().format(record)
+        if self.use_color:
+            color = self.COLORS.get(record.levelno, "")
+            if color:
+                return f"{color}{formatted}{self.RESET}"
+        return formatted
+
 
 logger = logging.getLogger(__name__)
-logger_formatter = logging.Formatter(
-    "[%(name)s: %(asctime)s] {%(lineno)d} %(levelname)s - %(message)s", "%m-%d %H:%M:%S"
+use_color = True
+if os.getenv("FLAML_LOG_NO_COLOR"):
+    use_color = False
+
+logger_formatter = ColoredFormatter(
+    "[%(name)s: %(asctime)s] {%(lineno)d} %(levelname)s - %(message)s", "%m-%d %H:%M:%S", use_color
 )
 logger.propagate = False

flaml/automl/ml.py

@@ -127,9 +127,21 @@ def metric_loss_score(
             import datasets
 
             datasets_metric_name = huggingface_submetric_to_metric.get(metric_name, metric_name.split(":")[0])
-            metric = datasets.load_metric(datasets_metric_name, trust_remote_code=True)
             metric_mode = huggingface_metric_to_mode[datasets_metric_name]
 
+            # datasets>=3 removed load_metric; prefer evaluate if available
+            try:
+                import evaluate
+
+                metric = evaluate.load(datasets_metric_name, trust_remote_code=True)
+            except Exception:
+                if hasattr(datasets, "load_metric"):
+                    metric = datasets.load_metric(datasets_metric_name, trust_remote_code=True)
+                else:
+                    from datasets import load_metric as _load_metric  # older datasets
+
+                    metric = _load_metric(datasets_metric_name, trust_remote_code=True)
+
             if metric_name.startswith("seqeval"):
                 y_processed_true = [[labels[tr] for tr in each_list] for each_list in y_processed_true]
             elif metric in ("pearsonr", "spearmanr"):
@@ -299,14 +311,14 @@ def get_y_pred(estimator, X, eval_metric, task: Task):
     else:
         y_pred = estimator.predict(X)
 
-    if isinstance(y_pred, Series) or isinstance(y_pred, DataFrame):
+    if isinstance(y_pred, (Series, DataFrame)):
         y_pred = y_pred.values
 
     return y_pred
 
 
 def to_numpy(x):
-    if isinstance(x, Series or isinstance(x, DataFrame)):
+    if isinstance(x, (Series, DataFrame)):
         x = x.values
     else:
         x = np.ndarray(x)
@@ -574,7 +586,7 @@ def _eval_estimator(
 
         # TODO: why are integer labels being cast to str in the first place?
 
-        if isinstance(val_pred_y, Series) or isinstance(val_pred_y, DataFrame) or isinstance(val_pred_y, np.ndarray):
+        if isinstance(val_pred_y, (Series, DataFrame, np.ndarray)):
             test = val_pred_y if isinstance(val_pred_y, np.ndarray) else val_pred_y.values
             if not np.issubdtype(test.dtype, np.number):
                 # some NLP models return a list
@@ -604,7 +616,12 @@ def _eval_estimator(
             logger.warning(f"ValueError {e} happened in `metric_loss_score`, set `val_loss` to `np.inf`")
         metric_for_logging = {"pred_time": pred_time}
         if log_training_metric:
-            train_pred_y = get_y_pred(estimator, X_train, eval_metric, task)
+            # For time series forecasting, X_train may be a sampled dataset whose
+            # test partition can be empty. Use the training partition from X_val
+            # (which is the dataset used to define y_train above) to keep shapes
+            # aligned and avoid empty prediction inputs.
+            X_train_for_metric = X_val.X_train if isinstance(X_val, TimeSeriesDataset) else X_train
+            train_pred_y = get_y_pred(estimator, X_train_for_metric, eval_metric, task)
             metric_for_logging["train_loss"] = metric_loss_score(
                 eval_metric,
                 train_pred_y,

flaml/automl/model.py

@@ -26,6 +26,13 @@ from sklearn.preprocessing import Normalizer
 from sklearn.svm import LinearSVC
 from xgboost import __version__ as xgboost_version
 
+try:
+    from sklearn.utils._tags import ClassifierTags, RegressorTags
+
+    SKLEARN_TAGS_AVAILABLE = True
+except ImportError:
+    SKLEARN_TAGS_AVAILABLE = False
+
 from flaml import tune
 from flaml.automl.data import group_counts
 from flaml.automl.spark import ERROR as SPARK_ERROR
@@ -111,7 +118,7 @@ def limit_resource(memory_limit, time_limit):
                 pass
 
 
-class BaseEstimator:
+class BaseEstimator(sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator):
     """The abstract class for all learners.
 
     Typical examples:
@@ -135,6 +142,7 @@ class BaseEstimator:
         self._task = task if isinstance(task, Task) else task_factory(task, None, None)
         self.params = self.config2params(config)
         self.estimator_class = self._model = None
+        self.estimator_baseclass = "sklearn"
         if "_estimator_type" in self.params:
             self._estimator_type = self.params.pop("_estimator_type")
         else:
@@ -147,6 +155,25 @@ class BaseEstimator:
             params["_estimator_type"] = self._estimator_type
         return params
 
+    def __sklearn_tags__(self):
+        """Override sklearn tags to respect the _estimator_type attribute.
+
+        This is needed for sklearn 1.7+ which uses get_tags() instead of
+        checking _estimator_type directly. Since BaseEstimator inherits from
+        ClassifierMixin, it would otherwise always be tagged as a classifier.
+        """
+        tags = super().__sklearn_tags__()
+        if hasattr(self, "_estimator_type") and SKLEARN_TAGS_AVAILABLE:
+            if self._estimator_type == "regressor":
+                tags.estimator_type = "regressor"
+                tags.regressor_tags = RegressorTags()
+                tags.classifier_tags = None
+            elif self._estimator_type == "classifier":
+                tags.estimator_type = "classifier"
+                tags.classifier_tags = ClassifierTags()
+                tags.regressor_tags = None
+        return tags
+
     @property
     def classes_(self):
         return self._model.classes_
@@ -294,6 +321,35 @@ class BaseEstimator:
             train_time = self._fit(X_train, y_train, **kwargs)
         return train_time
 
+    def preprocess(self, X):
+        """Preprocess data using estimator-level preprocessing.
+
+        This method applies estimator-specific preprocessing transformations to the input data.
+        This is the second level of preprocessing that should be applied after task-level
+        preprocessing (automl.preprocess()). Different estimator types may apply different
+        preprocessing steps (e.g., sparse matrix conversion, dataframe handling).
+
+        Args:
+            X: A numpy array or a dataframe of featurized instances, shape n*m.
+
+        Returns:
+            Preprocessed data ready for the estimator's predict/fit methods.
+
+        Example:
+            ```python
+            automl = AutoML()
+            automl.fit(X_train, y_train, task="classification")
+
+            # First apply task-level preprocessing
+            X_test_task = automl.preprocess(X_test)
+
+            # Then apply estimator-level preprocessing
+            estimator = automl.model
+            X_test_estimator = estimator.preprocess(X_test_task)
+            ```
+        """
+        return self._preprocess(X)
+
     def predict(self, X, **kwargs):
         """Predict label from features.
 
@@ -439,6 +495,7 @@ class SparkEstimator(BaseEstimator):
             raise SPARK_ERROR
         super().__init__(task, **config)
         self.df_train = None
+        self.estimator_baseclass = "spark"
 
     def _preprocess(
         self,
@@ -974,7 +1031,7 @@ class TransformersEstimator(BaseEstimator):
         from .nlp.huggingface.utils import tokenize_text
         from .nlp.utils import is_a_list_of_str
 
-        is_str = str(X.dtypes[0]) in ("string", "str")
+        is_str = str(X.dtypes.iloc[0]) in ("string", "str")
         is_list_of_str = is_a_list_of_str(X[list(X.keys())[0]].to_list()[0])
 
         if is_str or is_list_of_str:
@@ -1139,16 +1196,31 @@ class TransformersEstimator(BaseEstimator):
                     control.should_save = True
                     control.should_evaluate = True
 
-        self._trainer = TrainerForAuto(
-            args=self._training_args,
-            model_init=self._model_init,
-            train_dataset=train_dataset,
-            eval_dataset=eval_dataset,
-            tokenizer=self.tokenizer,
-            data_collator=self.data_collator,
-            compute_metrics=self._compute_metrics_by_dataset_name,
-            callbacks=[EarlyStoppingCallbackForAuto],
-        )
+        # Use processing_class for transformers >= 4.44.0, tokenizer for older versions
+        trainer_kwargs = {
+            "args": self._training_args,
+            "model_init": self._model_init,
+            "train_dataset": train_dataset,
+            "eval_dataset": eval_dataset,
+            "data_collator": self.data_collator,
+            "compute_metrics": self._compute_metrics_by_dataset_name,
+            "callbacks": [EarlyStoppingCallbackForAuto],
+        }
+
+        # Check if processing_class parameter is supported (transformers >= 4.44.0)
+        try:
+            import transformers
+            from packaging import version
+
+            if version.parse(transformers.__version__) >= version.parse("4.44.0"):
+                trainer_kwargs["processing_class"] = self.tokenizer
+            else:
+                trainer_kwargs["tokenizer"] = self.tokenizer
+        except (ImportError, AttributeError, ValueError):
+            # Fallback to tokenizer if version check fails
+            trainer_kwargs["tokenizer"] = self.tokenizer
+
+        self._trainer = TrainerForAuto(**trainer_kwargs)
 
         if self._task in NLG_TASKS:
             setattr(self._trainer, "_is_seq2seq", True)
@@ -2347,8 +2419,11 @@ class SGDEstimator(SKLearnEstimator):
         params = super().config2params(config)
         params["tol"] = params.get("tol", 0.0001)
         params["loss"] = params.get("loss", None)
-        if params["loss"] is None and self._task.is_classification():
-            params["loss"] = "log_loss" if SKLEARN_VERSION >= "1.1" else "log"
+        if params["loss"] is None:
+            if self._task.is_classification():
+                params["loss"] = "log_loss" if SKLEARN_VERSION >= "1.1" else "log"
+            else:
+                params["loss"] = "squared_error"
         if not self._task.is_classification() and "n_jobs" in params:
             params.pop("n_jobs")
 
@@ -2820,7 +2895,7 @@ class suppress_stdout_stderr:
         # Open a pair of null files
         self.null_fds = [os.open(os.devnull, os.O_RDWR) for x in range(2)]
         # Save the actual stdout (1) and stderr (2) file descriptors.
-        self.save_fds = (os.dup(1), os.dup(2))
+        self.save_fds = [os.dup(1), os.dup(2)]
 
     def __enter__(self):
         # Assign the null pointers to stdout and stderr.
@@ -2832,5 +2907,5 @@ class suppress_stdout_stderr:
         os.dup2(self.save_fds[0], 1)
         os.dup2(self.save_fds[1], 2)
         # Close the null files
-        os.close(self.null_fds[0])
-        os.close(self.null_fds[1])
+        for fd in self.null_fds + self.save_fds:
+            os.close(fd)

flaml/automl/nlp/huggingface/training_args.py

@@ -5,7 +5,7 @@ from typing import List, Optional
 from flaml.automl.task.task import NLG_TASKS
 
 try:
-    from transformers import TrainingArguments
+    from transformers import Seq2SeqTrainingArguments as TrainingArguments
 except ImportError:
     TrainingArguments = object
 
@@ -77,6 +77,14 @@ class TrainingArgumentsForAuto(TrainingArguments):
 
     logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."})
 
+    # Newer versions of HuggingFace Transformers may access `TrainingArguments.generation_config`
+    # (e.g., in generation-aware trainers/callbacks). Keep this attribute to remain compatible
+    # while defaulting to None for non-generation tasks.
+    generation_config: Optional[object] = field(
+        default=None,
+        metadata={"help": "Optional generation config (or path) used by generation-aware trainers."},
+    )
+
     @staticmethod
     def load_args_from_console():
         from dataclasses import fields

flaml/automl/nlp/huggingface/utils.py

@@ -211,29 +211,28 @@ def tokenize_onedataframe(
     hf_args=None,
     prefix_str=None,
 ):
-    with tokenizer.as_target_tokenizer():
-        _, tokenized_column_names = tokenize_row(
-            dict(X.iloc[0]),
+    _, tokenized_column_names = tokenize_row(
+        dict(X.iloc[0]),
+        tokenizer,
+        prefix=(prefix_str,) if task is SUMMARIZATION else None,
+        task=task,
+        hf_args=hf_args,
+        return_column_name=True,
+    )
+    d = X.apply(
+        lambda x: tokenize_row(
+            x,
             tokenizer,
             prefix=(prefix_str,) if task is SUMMARIZATION else None,
             task=task,
             hf_args=hf_args,
-            return_column_name=True,
-        )
-        d = X.apply(
-            lambda x: tokenize_row(
-                x,
-                tokenizer,
-                prefix=(prefix_str,) if task is SUMMARIZATION else None,
-                task=task,
-                hf_args=hf_args,
-            ),
-            axis=1,
-            result_type="expand",
-        )
-        X_tokenized = pd.DataFrame(columns=tokenized_column_names)
-        X_tokenized[tokenized_column_names] = d
-        return X_tokenized
+        ),
+        axis=1,
+        result_type="expand",
+    )
+    X_tokenized = pd.DataFrame(columns=tokenized_column_names)
+    X_tokenized[tokenized_column_names] = d
+    return X_tokenized
 
 
 def tokenize_row(
@@ -396,7 +395,7 @@ def load_model(checkpoint_path, task, num_labels=None):
 
         if task in (SEQCLASSIFICATION, SEQREGRESSION):
             return AutoModelForSequenceClassification.from_pretrained(
-                checkpoint_path, config=model_config, ignore_mismatched_sizes=True
+                checkpoint_path, config=model_config, ignore_mismatched_sizes=True, trust_remote_code=True
             )
         elif task == TOKENCLASSIFICATION:
             return AutoModelForTokenClassification.from_pretrained(checkpoint_path, config=model_config)

flaml/automl/nlp/utils.py

@@ -25,9 +25,7 @@ def load_default_huggingface_metric_for_task(task):
 
 
 def is_a_list_of_str(this_obj):
-    return (isinstance(this_obj, list) or isinstance(this_obj, np.ndarray)) and all(
-        isinstance(x, str) for x in this_obj
-    )
+    return isinstance(this_obj, (list, np.ndarray)) and all(isinstance(x, str) for x in this_obj)
 
 
 def _clean_value(value: Any) -> str:

flaml/automl/spark/__init__.py

@@ -1,3 +1,5 @@
+import atexit
+import logging
 import os
 
 os.environ["PYARROW_IGNORE_TIMEZONE"] = "1"
@@ -10,13 +12,14 @@ try:
     from pyspark.pandas import Series as psSeries
     from pyspark.pandas import set_option
     from pyspark.sql import DataFrame as sparkDataFrame
+    from pyspark.sql import SparkSession
     from pyspark.util import VersionUtils
 except ImportError:
 
     class psDataFrame:
         pass
 
-    F = T = ps = sparkDataFrame = psSeries = psDataFrame
+    F = T = ps = sparkDataFrame = SparkSession = psSeries = psDataFrame
     _spark_major_minor_version = set_option = None
     ERROR = ImportError(
         """Please run pip install flaml[spark]
@@ -32,3 +35,60 @@ try:
     from pandas import DataFrame, Series
 except ImportError:
     DataFrame = Series = pd = None
+
+
+logger = logging.getLogger(__name__)
+
+
+def disable_spark_ansi_mode():
+    """Disable Spark ANSI mode if it is enabled."""
+    spark = SparkSession.getActiveSession() if hasattr(SparkSession, "getActiveSession") else None
+    adjusted = False
+    try:
+        ps_conf = ps.get_option("compute.fail_on_ansi_mode")
+    except Exception:
+        ps_conf = None
+    ansi_conf = [None, ps_conf]  # ansi_conf and ps_conf original values
+    # Spark may store the config as string 'true'/'false' (or boolean in some contexts)
+    if spark is not None:
+        ansi_conf[0] = spark.conf.get("spark.sql.ansi.enabled")
+        ansi_enabled = (
+            (isinstance(ansi_conf[0], str) and ansi_conf[0].lower() == "true")
+            or (isinstance(ansi_conf[0], bool) and ansi_conf[0] is True)
+            or ansi_conf[0] is None
+        )
+        try:
+            if ansi_enabled:
+                logger.debug("Adjusting spark.sql.ansi.enabled to false")
+                spark.conf.set("spark.sql.ansi.enabled", "false")
+                adjusted = True
+        except Exception:
+            # If reading/setting options fail for some reason, keep going and let
+            # pandas-on-Spark raise a meaningful error later.
+            logger.exception("Failed to set spark.sql.ansi.enabled")
+
+    if ansi_conf[1]:
+        logger.debug("Adjusting pandas-on-Spark compute.fail_on_ansi_mode to False")
+        ps.set_option("compute.fail_on_ansi_mode", False)
+        adjusted = True
+
+    return spark, ansi_conf, adjusted
+
+
+def restore_spark_ansi_mode(spark, ansi_conf, adjusted):
+    """Restore Spark ANSI mode to its original setting."""
+    # Restore the original spark.sql.ansi.enabled to avoid persistent side-effects.
+    if adjusted and spark and ansi_conf[0] is not None:
+        try:
+            logger.debug(f"Restoring spark.sql.ansi.enabled to {ansi_conf[0]}")
+            spark.conf.set("spark.sql.ansi.enabled", ansi_conf[0])
+        except Exception:
+            logger.exception("Failed to restore spark.sql.ansi.enabled")
+
+    if adjusted and ansi_conf[1]:
+        logger.debug(f"Restoring pandas-on-Spark compute.fail_on_ansi_mode to {ansi_conf[1]}")
+        ps.set_option("compute.fail_on_ansi_mode", ansi_conf[1])
+
+
+spark, ansi_conf, adjusted = disable_spark_ansi_mode()
+atexit.register(restore_spark_ansi_mode, spark, ansi_conf, adjusted)

flaml/automl/spark/utils.py

@@ -59,17 +59,29 @@ def to_pandas_on_spark(
     ```
     """
     set_option("compute.default_index_type", default_index_type)
-    if isinstance(df, (DataFrame, Series)):
-        return ps.from_pandas(df)
-    elif isinstance(df, sparkDataFrame):
-        if _spark_major_minor_version[0] == 3 and _spark_major_minor_version[1] < 3:
-            return df.to_pandas_on_spark(index_col=index_col)
+    try:
+        orig_ps_conf = ps.get_option("compute.fail_on_ansi_mode")
+    except Exception:
+        orig_ps_conf = None
+    if orig_ps_conf:
+        ps.set_option("compute.fail_on_ansi_mode", False)
+
+    try:
+        if isinstance(df, (DataFrame, Series)):
+            return ps.from_pandas(df)
+        elif isinstance(df, sparkDataFrame):
+            if _spark_major_minor_version[0] == 3 and _spark_major_minor_version[1] < 3:
+                return df.to_pandas_on_spark(index_col=index_col)
+            else:
+                return df.pandas_api(index_col=index_col)
+        elif isinstance(df, (psDataFrame, psSeries)):
+            return df
         else:
-            return df.pandas_api(index_col=index_col)
-    elif isinstance(df, (psDataFrame, psSeries)):
-        return df
-    else:
-        raise TypeError(f"{type(df)} is not one of pandas.DataFrame, pandas.Series and pyspark.sql.DataFrame")
+            raise TypeError(f"{type(df)} is not one of pandas.DataFrame, pandas.Series and pyspark.sql.DataFrame")
+    finally:
+        # Restore original config
+        if orig_ps_conf:
+            ps.set_option("compute.fail_on_ansi_mode", orig_ps_conf)
 
 
 def train_test_split_pyspark(

flaml/automl/state.py

@@ -37,10 +37,9 @@ class SearchState:
         if isinstance(domain_one_dim, sample.Domain):
             renamed_type = list(inspect.signature(domain_one_dim.is_valid).parameters.values())[0].annotation
             type_match = (
-                renamed_type == Any
+                renamed_type is Any
                 or isinstance(value_one_dim, renamed_type)
-                or isinstance(value_one_dim, int)
-                and renamed_type is float
+                or (renamed_type is float and isinstance(value_one_dim, int))
             )
             if not (type_match and domain_one_dim.is_valid(value_one_dim)):
                 return False

flaml/automl/task/generic_task.py

@@ -365,6 +365,465 @@ class GenericTask(Task):
             X_train, X_val, y_train, y_val = GenericTask._split_pyspark(state, X, y, split_ratio, stratify)
         return X_train, X_val, y_train, y_val
 
+    def _handle_missing_labels_fast(
+        self,
+        state,
+        X_train,
+        X_val,
+        y_train,
+        y_val,
+        X_train_all,
+        y_train_all,
+        is_spark_dataframe,
+        data_is_df,
+    ):
+        """Handle missing labels by adding first instance to the set with missing label.
+
+        This is the faster version that may create some overlap but ensures all labels
+        are present in both sets. If a label is missing from train, it adds the first
+        instance to train. If a label is missing from val, it adds the first instance to val.
+        If no labels are missing, no instances are duplicated.
+
+        Args:
+            state: The state object containing fit parameters
+            X_train, X_val: Training and validation features
+            y_train, y_val: Training and validation labels
+            X_train_all, y_train_all: Complete dataset
+            is_spark_dataframe: Whether data is pandas_on_spark
+            data_is_df: Whether data is DataFrame/Series
+
+        Returns:
+            Tuple of (X_train, X_val, y_train, y_val) with missing labels added
+        """
+        # Check which labels are present in train and val sets
+        if is_spark_dataframe:
+            label_set_train, _ = unique_pandas_on_spark(y_train)
+            label_set_val, _ = unique_pandas_on_spark(y_val)
+            label_set_all, first = unique_value_first_index(y_train_all)
+        else:
+            label_set_all, first = unique_value_first_index(y_train_all)
+            label_set_train = np.unique(y_train)
+            label_set_val = np.unique(y_val)
+
+        # Find missing labels
+        missing_in_train = np.setdiff1d(label_set_all, label_set_train)
+        missing_in_val = np.setdiff1d(label_set_all, label_set_val)
+
+        # Add first instance of missing labels to train set
+        if len(missing_in_train) > 0:
+            missing_train_indices = []
+            for label in missing_in_train:
+                label_matches = np.where(label_set_all == label)[0]
+                if len(label_matches) > 0 and label_matches[0] < len(first):
+                    missing_train_indices.append(first[label_matches[0]])
+
+            if len(missing_train_indices) > 0:
+                X_missing_train = (
+                    iloc_pandas_on_spark(X_train_all, missing_train_indices)
+                    if is_spark_dataframe
+                    else X_train_all.iloc[missing_train_indices]
+                    if data_is_df
+                    else X_train_all[missing_train_indices]
+                )
+                y_missing_train = (
+                    iloc_pandas_on_spark(y_train_all, missing_train_indices)
+                    if is_spark_dataframe
+                    else y_train_all.iloc[missing_train_indices]
+                    if isinstance(y_train_all, (pd.Series, psSeries))
+                    else y_train_all[missing_train_indices]
+                )
+                X_train = concat(X_missing_train, X_train)
+                y_train = concat(y_missing_train, y_train) if data_is_df else np.concatenate([y_missing_train, y_train])
+
+                # Handle sample_weight if present
+                if "sample_weight" in state.fit_kwargs:
+                    sample_weight_source = (
+                        state.sample_weight_all
+                        if hasattr(state, "sample_weight_all")
+                        else state.fit_kwargs.get("sample_weight")
+                    )
+                    if sample_weight_source is not None and max(missing_train_indices) < len(sample_weight_source):
+                        missing_weights = (
+                            sample_weight_source[missing_train_indices]
+                            if isinstance(sample_weight_source, np.ndarray)
+                            else sample_weight_source.iloc[missing_train_indices]
+                        )
+                        state.fit_kwargs["sample_weight"] = concat(missing_weights, state.fit_kwargs["sample_weight"])
+
+        # Add first instance of missing labels to val set
+        if len(missing_in_val) > 0:
+            missing_val_indices = []
+            for label in missing_in_val:
+                label_matches = np.where(label_set_all == label)[0]
+                if len(label_matches) > 0 and label_matches[0] < len(first):
+                    missing_val_indices.append(first[label_matches[0]])
+
+            if len(missing_val_indices) > 0:
+                X_missing_val = (
+                    iloc_pandas_on_spark(X_train_all, missing_val_indices)
+                    if is_spark_dataframe
+                    else X_train_all.iloc[missing_val_indices]
+                    if data_is_df
+                    else X_train_all[missing_val_indices]
+                )
+                y_missing_val = (
+                    iloc_pandas_on_spark(y_train_all, missing_val_indices)
+                    if is_spark_dataframe
+                    else y_train_all.iloc[missing_val_indices]
+                    if isinstance(y_train_all, (pd.Series, psSeries))
+                    else y_train_all[missing_val_indices]
+                )
+                X_val = concat(X_missing_val, X_val)
+                y_val = concat(y_missing_val, y_val) if data_is_df else np.concatenate([y_missing_val, y_val])
+
+                # Handle sample_weight if present
+                if (
+                    "sample_weight" in state.fit_kwargs
+                    and hasattr(state, "weight_val")
+                    and state.weight_val is not None
+                ):
+                    sample_weight_source = (
+                        state.sample_weight_all
+                        if hasattr(state, "sample_weight_all")
+                        else state.fit_kwargs.get("sample_weight")
+                    )
+                    if sample_weight_source is not None and max(missing_val_indices) < len(sample_weight_source):
+                        missing_weights = (
+                            sample_weight_source[missing_val_indices]
+                            if isinstance(sample_weight_source, np.ndarray)
+                            else sample_weight_source.iloc[missing_val_indices]
+                        )
+                        state.weight_val = concat(missing_weights, state.weight_val)
+
+        return X_train, X_val, y_train, y_val
+
+    def _handle_missing_labels_no_overlap(
+        self,
+        state,
+        X_train,
+        X_val,
+        y_train,
+        y_val,
+        X_train_all,
+        y_train_all,
+        is_spark_dataframe,
+        data_is_df,
+        split_ratio,
+    ):
+        """Handle missing labels intelligently to avoid overlap when possible.
+
+        This is the slower but more precise version that:
+        - For single-instance classes: Adds to both sets (unavoidable overlap)
+        - For multi-instance classes: Re-splits them properly to avoid overlap
+
+        Args:
+            state: The state object containing fit parameters
+            X_train, X_val: Training and validation features
+            y_train, y_val: Training and validation labels
+            X_train_all, y_train_all: Complete dataset
+            is_spark_dataframe: Whether data is pandas_on_spark
+            data_is_df: Whether data is DataFrame/Series
+            split_ratio: The ratio for splitting
+
+        Returns:
+            Tuple of (X_train, X_val, y_train, y_val) with missing labels handled
+        """
+        # Check which labels are present in train and val sets
+        if is_spark_dataframe:
+            label_set_train, _ = unique_pandas_on_spark(y_train)
+            label_set_val, _ = unique_pandas_on_spark(y_val)
+            label_set_all, first = unique_value_first_index(y_train_all)
+        else:
+            label_set_all, first = unique_value_first_index(y_train_all)
+            label_set_train = np.unique(y_train)
+            label_set_val = np.unique(y_val)
+
+        # Find missing labels
+        missing_in_train = np.setdiff1d(label_set_all, label_set_train)
+        missing_in_val = np.setdiff1d(label_set_all, label_set_val)
+
+        # Handle missing labels intelligently
+        # For classes with only 1 instance: add to both sets (unavoidable overlap)
+        # For classes with multiple instances: move/split them properly to avoid overlap
+
+        if len(missing_in_train) > 0:
+            # Process missing labels in training set
+            for label in missing_in_train:
+                # Find all indices for this label in the original data
+                if is_spark_dataframe:
+                    label_indices = np.where(y_train_all.to_numpy() == label)[0].tolist()
+                else:
+                    label_indices = np.where(np.asarray(y_train_all) == label)[0].tolist()
+
+                num_instances = len(label_indices)
+
+                if num_instances == 1:
+                    # Single instance: must add to both train and val (unavoidable overlap)
+                    X_single = (
+                        iloc_pandas_on_spark(X_train_all, label_indices)
+                        if is_spark_dataframe
+                        else X_train_all.iloc[label_indices]
+                        if data_is_df
+                        else X_train_all[label_indices]
+                    )
+                    y_single = (
+                        iloc_pandas_on_spark(y_train_all, label_indices)
+                        if is_spark_dataframe
+                        else y_train_all.iloc[label_indices]
+                        if isinstance(y_train_all, (pd.Series, psSeries))
+                        else y_train_all[label_indices]
+                    )
+                    X_train = concat(X_single, X_train)
+                    y_train = concat(y_single, y_train) if data_is_df else np.concatenate([y_single, y_train])
+
+                    # Handle sample_weight
+                    if "sample_weight" in state.fit_kwargs:
+                        sample_weight_source = (
+                            state.sample_weight_all
+                            if hasattr(state, "sample_weight_all")
+                            else state.fit_kwargs.get("sample_weight")
+                        )
+                        if sample_weight_source is not None and label_indices[0] < len(sample_weight_source):
+                            single_weight = (
+                                sample_weight_source[label_indices]
+                                if isinstance(sample_weight_source, np.ndarray)
+                                else sample_weight_source.iloc[label_indices]
+                            )
+                            state.fit_kwargs["sample_weight"] = concat(single_weight, state.fit_kwargs["sample_weight"])
+                else:
+                    # Multiple instances: move some from val to train (no overlap needed)
+                    # Calculate how many to move to train (leave at least 1 in val)
+                    num_to_train = max(1, min(num_instances - 1, int(num_instances * (1 - split_ratio))))
+                    indices_to_move = label_indices[:num_to_train]
+
+                    X_to_move = (
+                        iloc_pandas_on_spark(X_train_all, indices_to_move)
+                        if is_spark_dataframe
+                        else X_train_all.iloc[indices_to_move]
+                        if data_is_df
+                        else X_train_all[indices_to_move]
+                    )
+                    y_to_move = (
+                        iloc_pandas_on_spark(y_train_all, indices_to_move)
+                        if is_spark_dataframe
+                        else y_train_all.iloc[indices_to_move]
+                        if isinstance(y_train_all, (pd.Series, psSeries))
+                        else y_train_all[indices_to_move]
+                    )
+
+                    # Add to train
+                    X_train = concat(X_to_move, X_train)
+                    y_train = concat(y_to_move, y_train) if data_is_df else np.concatenate([y_to_move, y_train])
+
+                    # Remove from val (they are currently all in val)
+                    if is_spark_dataframe:
+                        val_mask = ~y_val.isin([label])
+                        X_val = X_val[val_mask]
+                        y_val = y_val[val_mask]
+                    else:
+                        val_mask = np.asarray(y_val) != label
+                        if data_is_df:
+                            X_val = X_val[val_mask]
+                            y_val = y_val[val_mask]
+                        else:
+                            X_val = X_val[val_mask]
+                            y_val = y_val[val_mask]
+
+                    # Add remaining instances back to val
+                    remaining_indices = label_indices[num_to_train:]
+                    if len(remaining_indices) > 0:
+                        X_remaining = (
+                            iloc_pandas_on_spark(X_train_all, remaining_indices)
+                            if is_spark_dataframe
+                            else X_train_all.iloc[remaining_indices]
+                            if data_is_df
+                            else X_train_all[remaining_indices]
+                        )
+                        y_remaining = (
+                            iloc_pandas_on_spark(y_train_all, remaining_indices)
+                            if is_spark_dataframe
+                            else y_train_all.iloc[remaining_indices]
+                            if isinstance(y_train_all, (pd.Series, psSeries))
+                            else y_train_all[remaining_indices]
+                        )
+                        X_val = concat(X_remaining, X_val)
+                        y_val = concat(y_remaining, y_val) if data_is_df else np.concatenate([y_remaining, y_val])
+
+                    # Handle sample_weight
+                    if "sample_weight" in state.fit_kwargs:
+                        sample_weight_source = (
+                            state.sample_weight_all
+                            if hasattr(state, "sample_weight_all")
+                            else state.fit_kwargs.get("sample_weight")
+                        )
+                        if sample_weight_source is not None and max(indices_to_move) < len(sample_weight_source):
+                            weights_to_move = (
+                                sample_weight_source[indices_to_move]
+                                if isinstance(sample_weight_source, np.ndarray)
+                                else sample_weight_source.iloc[indices_to_move]
+                            )
+                            state.fit_kwargs["sample_weight"] = concat(
+                                weights_to_move, state.fit_kwargs["sample_weight"]
+                            )
+
+                            if (
+                                len(remaining_indices) > 0
+                                and hasattr(state, "weight_val")
+                                and state.weight_val is not None
+                            ):
+                                # Remove and re-add weights for val
+                                if isinstance(state.weight_val, np.ndarray):
+                                    state.weight_val = state.weight_val[val_mask]
+                                else:
+                                    state.weight_val = state.weight_val[val_mask]
+
+                                if max(remaining_indices) < len(sample_weight_source):
+                                    remaining_weights = (
+                                        sample_weight_source[remaining_indices]
+                                        if isinstance(sample_weight_source, np.ndarray)
+                                        else sample_weight_source.iloc[remaining_indices]
+                                    )
+                                    state.weight_val = concat(remaining_weights, state.weight_val)
+
+        if len(missing_in_val) > 0:
+            # Process missing labels in validation set
+            for label in missing_in_val:
+                # Find all indices for this label in the original data
+                if is_spark_dataframe:
+                    label_indices = np.where(y_train_all.to_numpy() == label)[0].tolist()
+                else:
+                    label_indices = np.where(np.asarray(y_train_all) == label)[0].tolist()
+
+                num_instances = len(label_indices)
+
+                if num_instances == 1:
+                    # Single instance: must add to both train and val (unavoidable overlap)
+                    X_single = (
+                        iloc_pandas_on_spark(X_train_all, label_indices)
+                        if is_spark_dataframe
+                        else X_train_all.iloc[label_indices]
+                        if data_is_df
+                        else X_train_all[label_indices]
+                    )
+                    y_single = (
+                        iloc_pandas_on_spark(y_train_all, label_indices)
+                        if is_spark_dataframe
+                        else y_train_all.iloc[label_indices]
+                        if isinstance(y_train_all, (pd.Series, psSeries))
+                        else y_train_all[label_indices]
+                    )
+                    X_val = concat(X_single, X_val)
+                    y_val = concat(y_single, y_val) if data_is_df else np.concatenate([y_single, y_val])
+
+                    # Handle sample_weight
+                    if "sample_weight" in state.fit_kwargs and hasattr(state, "weight_val"):
+                        sample_weight_source = (
+                            state.sample_weight_all
+                            if hasattr(state, "sample_weight_all")
+                            else state.fit_kwargs.get("sample_weight")
+                        )
+                        if sample_weight_source is not None and label_indices[0] < len(sample_weight_source):
+                            single_weight = (
+                                sample_weight_source[label_indices]
+                                if isinstance(sample_weight_source, np.ndarray)
+                                else sample_weight_source.iloc[label_indices]
+                            )
+                            if state.weight_val is not None:
+                                state.weight_val = concat(single_weight, state.weight_val)
+                else:
+                    # Multiple instances: move some from train to val (no overlap needed)
+                    # Calculate how many to move to val (leave at least 1 in train)
+                    num_to_val = max(1, min(num_instances - 1, int(num_instances * split_ratio)))
+                    indices_to_move = label_indices[:num_to_val]
+
+                    X_to_move = (
+                        iloc_pandas_on_spark(X_train_all, indices_to_move)
+                        if is_spark_dataframe
+                        else X_train_all.iloc[indices_to_move]
+                        if data_is_df
+                        else X_train_all[indices_to_move]
+                    )
+                    y_to_move = (
+                        iloc_pandas_on_spark(y_train_all, indices_to_move)
+                        if is_spark_dataframe
+                        else y_train_all.iloc[indices_to_move]
+                        if isinstance(y_train_all, (pd.Series, psSeries))
+                        else y_train_all[indices_to_move]
+                    )
+
+                    # Add to val
+                    X_val = concat(X_to_move, X_val)
+                    y_val = concat(y_to_move, y_val) if data_is_df else np.concatenate([y_to_move, y_val])
+
+                    # Remove from train (they are currently all in train)
+                    if is_spark_dataframe:
+                        train_mask = ~y_train.isin([label])
+                        X_train = X_train[train_mask]
+                        y_train = y_train[train_mask]
+                    else:
+                        train_mask = np.asarray(y_train) != label
+                        if data_is_df:
+                            X_train = X_train[train_mask]
+                            y_train = y_train[train_mask]
+                        else:
+                            X_train = X_train[train_mask]
+                            y_train = y_train[train_mask]
+
+                    # Add remaining instances back to train
+                    remaining_indices = label_indices[num_to_val:]
+                    if len(remaining_indices) > 0:
+                        X_remaining = (
+                            iloc_pandas_on_spark(X_train_all, remaining_indices)
+                            if is_spark_dataframe
+                            else X_train_all.iloc[remaining_indices]
+                            if data_is_df
+                            else X_train_all[remaining_indices]
+                        )
+                        y_remaining = (
+                            iloc_pandas_on_spark(y_train_all, remaining_indices)
+                            if is_spark_dataframe
+                            else y_train_all.iloc[remaining_indices]
+                            if isinstance(y_train_all, (pd.Series, psSeries))
+                            else y_train_all[remaining_indices]
+                        )
+                        X_train = concat(X_remaining, X_train)
+                        y_train = concat(y_remaining, y_train) if data_is_df else np.concatenate([y_remaining, y_train])
+
+                    # Handle sample_weight
+                    if "sample_weight" in state.fit_kwargs:
+                        sample_weight_source = (
+                            state.sample_weight_all
+                            if hasattr(state, "sample_weight_all")
+                            else state.fit_kwargs.get("sample_weight")
+                        )
+                        if sample_weight_source is not None and max(indices_to_move) < len(sample_weight_source):
+                            weights_to_move = (
+                                sample_weight_source[indices_to_move]
+                                if isinstance(sample_weight_source, np.ndarray)
+                                else sample_weight_source.iloc[indices_to_move]
+                            )
+                            if hasattr(state, "weight_val") and state.weight_val is not None:
+                                state.weight_val = concat(weights_to_move, state.weight_val)
+
+                            if len(remaining_indices) > 0:
+                                # Remove and re-add weights for train
+                                if isinstance(state.fit_kwargs["sample_weight"], np.ndarray):
+                                    state.fit_kwargs["sample_weight"] = state.fit_kwargs["sample_weight"][train_mask]
+                                else:
+                                    state.fit_kwargs["sample_weight"] = state.fit_kwargs["sample_weight"][train_mask]
+
+                                if max(remaining_indices) < len(sample_weight_source):
+                                    remaining_weights = (
+                                        sample_weight_source[remaining_indices]
+                                        if isinstance(sample_weight_source, np.ndarray)
+                                        else sample_weight_source.iloc[remaining_indices]
+                                    )
+                                    state.fit_kwargs["sample_weight"] = concat(
+                                        remaining_weights, state.fit_kwargs["sample_weight"]
+                                    )
+
+        return X_train, X_val, y_train, y_val
+
     def prepare_data(
         self,
         state,
@@ -377,6 +836,7 @@ class GenericTask(Task):
         n_splits,
         data_is_df,
         sample_weight_full,
+        allow_label_overlap=True,
     ) -> int:
         X_val, y_val = state.X_val, state.y_val
         if issparse(X_val):
@@ -505,59 +965,46 @@ class GenericTask(Task):
             elif self.is_classification():
                 # for classification, make sure the labels are complete in both
                 # training and validation data
-                label_set, first = unique_value_first_index(y_train_all)
-                rest = []
-                last = 0
-                first.sort()
-                for i in range(len(first)):
-                    rest.extend(range(last, first[i]))
-                    last = first[i] + 1
-                rest.extend(range(last, len(y_train_all)))
-                X_first = X_train_all.iloc[first] if data_is_df else X_train_all[first]
-                if len(first) < len(y_train_all) / 2:
-                    # Get X_rest and y_rest with drop, sparse matrix can't apply np.delete
-                    X_rest = (
-                        np.delete(X_train_all, first, axis=0)
-                        if isinstance(X_train_all, np.ndarray)
-                        else X_train_all.drop(first.tolist())
-                        if data_is_df
-                        else X_train_all[rest]
-                    )
-                    y_rest = (
-                        np.delete(y_train_all, first, axis=0)
-                        if isinstance(y_train_all, np.ndarray)
-                        else y_train_all.drop(first.tolist())
-                        if data_is_df
-                        else y_train_all[rest]
+                stratify = y_train_all if split_type == "stratified" else None
+                X_train, X_val, y_train, y_val = self._train_test_split(
+                    state, X_train_all, y_train_all, split_ratio=split_ratio, stratify=stratify
+                )
+
+                # Handle missing labels using the appropriate strategy
+                if allow_label_overlap:
+                    # Fast version: adds first instance to set with missing label (may create overlap)
+                    X_train, X_val, y_train, y_val = self._handle_missing_labels_fast(
+                        state,
+                        X_train,
+                        X_val,
+                        y_train,
+                        y_val,
+                        X_train_all,
+                        y_train_all,
+                        is_spark_dataframe,
+                        data_is_df,
                     )
                 else:
-                    X_rest = (
-                        iloc_pandas_on_spark(X_train_all, rest)
-                        if is_spark_dataframe
-                        else X_train_all.iloc[rest]
-                        if data_is_df
-                        else X_train_all[rest]
+                    # Precise version: avoids overlap when possible (slower)
+                    X_train, X_val, y_train, y_val = self._handle_missing_labels_no_overlap(
+                        state,
+                        X_train,
+                        X_val,
+                        y_train,
+                        y_val,
+                        X_train_all,
+                        y_train_all,
+                        is_spark_dataframe,
+                        data_is_df,
+                        split_ratio,
                     )
-                    y_rest = (
-                        iloc_pandas_on_spark(y_train_all, rest)
-                        if is_spark_dataframe
-                        else y_train_all.iloc[rest]
-                        if data_is_df
-                        else y_train_all[rest]
-                    )
-                stratify = y_rest if split_type == "stratified" else None
-                X_train, X_val, y_train, y_val = self._train_test_split(
-                    state, X_rest, y_rest, first, rest, split_ratio, stratify
-                )
-                X_train = concat(X_first, X_train)
-                y_train = concat(label_set, y_train) if data_is_df else np.concatenate([label_set, y_train])
-                X_val = concat(X_first, X_val)
-                y_val = concat(label_set, y_val) if data_is_df else np.concatenate([label_set, y_val])
 
                 if isinstance(y_train, (psDataFrame, pd.DataFrame)) and y_train.shape[1] == 1:
                     y_train = y_train[y_train.columns[0]]
                     y_val = y_val[y_val.columns[0]]
-                    y_train.name = y_val.name = y_rest.name
+                    # Only set name if y_train_all is a Series (not a DataFrame)
+                    if isinstance(y_train_all, (pd.Series, psSeries)):
+                        y_train.name = y_val.name = y_train_all.name
 
             elif self.is_regression():
                 X_train, X_val, y_train, y_val = self._train_test_split(
@@ -746,7 +1193,10 @@ class GenericTask(Task):
             elif isinstance(kf, TimeSeriesSplit):
                 kf = kf.split(X_train_split, y_train_split)
             else:
-                kf = kf.split(X_train_split)
+                try:
+                    kf = kf.split(X_train_split)
+                except TypeError:
+                    kf = kf.split(X_train_split, y_train_split)
 
         for train_index, val_index in kf:
             if shuffle:

flaml/automl/task/time_series_task.py

@@ -151,7 +151,7 @@ class TimeSeriesTask(Task):
                 raise ValueError("Must supply either X_train_all and y_train_all, or dataframe and label")
 
             try:
-                dataframe[self.time_col] = pd.to_datetime(dataframe[self.time_col])
+                dataframe.loc[:, self.time_col] = pd.to_datetime(dataframe[self.time_col])
             except Exception:
                 raise ValueError(
                     f"For '{TS_FORECAST}' task, time column {self.time_col} must contain timestamp values."
@@ -386,9 +386,8 @@ class TimeSeriesTask(Task):
         return X
 
     def preprocess(self, X, transformer=None):
-        if isinstance(X, pd.DataFrame) or isinstance(X, np.ndarray) or isinstance(X, pd.Series):
-            X = X.copy()
-            X = normalize_ts_data(X, self.target_names, self.time_col)
+        if isinstance(X, (pd.DataFrame, np.ndarray, pd.Series)):
+            X = normalize_ts_data(X.copy(), self.target_names, self.time_col)
             return self._preprocess(X, transformer)
         elif isinstance(X, int):
             return X
@@ -529,7 +528,7 @@ def remove_ts_duplicates(
     duplicates = X.duplicated()
 
     if any(duplicates):
-        logger.warning("Duplicate timestamp values found in timestamp column. " f"\n{X.loc[duplicates, X][time_col]}")
+        logger.warning("Duplicate timestamp values found in timestamp column. " f"\n{X.loc[duplicates, time_col]}")
         X = X.drop_duplicates()
         logger.warning("Removed duplicate rows based on all columns")
         assert (

flaml/automl/time_series/sklearn.py

@@ -17,24 +17,30 @@ from sklearn.preprocessing import StandardScaler
 
 
 def make_lag_features(X: pd.DataFrame, y: pd.Series, lags: int):
-    """Transform input data X, y into autoregressive form - shift
-    them appropriately based on horizon and create `lags` columns.
+    """Transform input data X, y into autoregressive form by creating `lags` columns.
+
+    This function is called automatically by FLAML during the training process
+    to convert time series data into a format suitable for sklearn-based regression
+    models (e.g., lgbm, rf, xgboost). Users do NOT need to manually call this function
+    or create lagged features themselves.
 
     Parameters
     ----------
     X : pandas.DataFrame
-        Input features.
+        Input feature DataFrame, which may contain temporal features and/or exogenous variables.
 
     y : array_like, (1d)
-        Target vector.
+        Target vector (time series values to forecast).
 
-    horizon : int
-        length of X for `predict` method
+    lags : int
+        Number of lagged time steps to use as features.
 
     Returns
     -------
     pandas.DataFrame
-        shifted dataframe with `lags` columns
+        Shifted dataframe with `lags` columns for each original feature.
+        The target variable y is also lagged to prevent data leakage
+        (i.e., we use y(t-1), y(t-2), ..., y(t-lags) to predict y(t)).
     """
     lag_features = []
 
@@ -55,6 +61,17 @@ def make_lag_features(X: pd.DataFrame, y: pd.Series, lags: int):
 
 
 class SklearnWrapper:
+    """Wrapper class for using sklearn-based models for time series forecasting.
+
+    This wrapper automatically handles the transformation of time series data into
+    a supervised learning format by creating lagged features. It trains separate
+    models for each step in the forecast horizon.
+
+    Users typically don't interact with this class directly - it's used internally
+    by FLAML when sklearn-based estimators (lgbm, rf, xgboost, etc.) are selected
+    for time series forecasting tasks.
+    """
+
     def __init__(
         self,
         model_class: type,
@@ -76,6 +93,8 @@ class SklearnWrapper:
             self.pca = None
 
     def fit(self, X: pd.DataFrame, y: pd.Series, **kwargs):
+        if "is_retrain" in kwargs:
+            kwargs.pop("is_retrain")
         self._X = X
         self._y = y
 
@@ -92,7 +111,14 @@ class SklearnWrapper:
 
         for i, model in enumerate(self.models):
             offset = i + self.lags
-            model.fit(X_trans[: len(X) - offset], y[offset:], **fit_params)
+            if len(X) - offset > 2:
+                # series with length 2 will meet All features are either constant or ignored.
+                # TODO: see why the non-constant features are ignored. Selector?
+                model.fit(X_trans[: len(X) - offset], y[offset:], **fit_params)
+            elif len(X) > offset and "catboost" not in str(model).lower():
+                model.fit(X_trans[: len(X) - offset], y[offset:], **fit_params)
+            else:
+                print("[INFO]: Length of data should longer than period + lags.")
         return self
 
     def predict(self, X, X_train=None, y_train=None):

flaml/automl/time_series/tcn.py

@@ -264,7 +264,8 @@ class TCNEstimator(TimeSeriesEstimator):
     def predict(self, X):
         X = self.enrich(X)
         if isinstance(X, TimeSeriesDataset):
-            df = X.X_val
+            # Use X_train if X_val is empty (e.g., when computing training metrics)
+            df = X.X_val if len(X.test_data) > 0 else X.X_train
         else:
             df = X
         dataset = DataframeDataset(

flaml/automl/time_series/tft.py

@@ -1,3 +1,4 @@
+import inspect
 import time
 
 try:
@@ -106,12 +107,17 @@ class TemporalFusionTransformerEstimator(TimeSeriesEstimator):
     def fit(self, X_train, y_train, budget=None, **kwargs):
         import warnings
 
-        import pytorch_lightning as pl
+        try:
+            import lightning.pytorch as pl
+            from lightning.pytorch.callbacks import EarlyStopping, LearningRateMonitor
+            from lightning.pytorch.loggers import TensorBoardLogger
+        except ImportError:
+            import pytorch_lightning as pl
+            from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
+            from pytorch_lightning.loggers import TensorBoardLogger
         import torch
         from pytorch_forecasting import TemporalFusionTransformer
         from pytorch_forecasting.metrics import QuantileLoss
-        from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
-        from pytorch_lightning.loggers import TensorBoardLogger
 
         # a bit of monkey patching to fix the MacOS test
         # all the log_prediction method appears to do is plot stuff, which ?breaks github tests
@@ -132,12 +138,26 @@ class TemporalFusionTransformerEstimator(TimeSeriesEstimator):
         lr_logger = LearningRateMonitor()  # log the learning rate
         logger = TensorBoardLogger(kwargs.get("log_dir", "lightning_logs"))  # logging results to a tensorboard
         default_trainer_kwargs = dict(
-            gpus=self._kwargs.get("gpu_per_trial", [0]) if torch.cuda.is_available() else None,
             max_epochs=max_epochs,
             gradient_clip_val=gradient_clip_val,
             callbacks=[lr_logger, early_stop_callback],
             logger=logger,
         )
+
+        # PyTorch Lightning >=2.0 replaced `gpus` with `accelerator`/`devices`.
+        # Also, passing `gpus=None` is not accepted on newer versions.
+        trainer_sig_params = inspect.signature(pl.Trainer.__init__).parameters
+        if torch.cuda.is_available() and "gpus" in trainer_sig_params:
+            gpus = self._kwargs.get("gpu_per_trial", None)
+            if gpus is not None:
+                default_trainer_kwargs["gpus"] = gpus
+        elif torch.cuda.is_available() and "devices" in trainer_sig_params:
+            devices = self._kwargs.get("gpu_per_trial", None)
+            if devices == -1:
+                devices = "auto"
+            if devices is not None:
+                default_trainer_kwargs["accelerator"] = "gpu"
+                default_trainer_kwargs["devices"] = devices
         trainer = pl.Trainer(
             **default_trainer_kwargs,
         )
@@ -157,7 +177,14 @@ class TemporalFusionTransformerEstimator(TimeSeriesEstimator):
             val_dataloaders=val_dataloader,
         )
         best_model_path = trainer.checkpoint_callback.best_model_path
-        best_tft = TemporalFusionTransformer.load_from_checkpoint(best_model_path)
+        # PyTorch 2.6 changed `torch.load` default `weights_only` from False -> True.
+        # Some Lightning checkpoints (including those produced here) can require full unpickling.
+        # This path is generated locally during training, so it's trusted.
+        load_sig_params = inspect.signature(TemporalFusionTransformer.load_from_checkpoint).parameters
+        if "weights_only" in load_sig_params:
+            best_tft = TemporalFusionTransformer.load_from_checkpoint(best_model_path, weights_only=False)
+        else:
+            best_tft = TemporalFusionTransformer.load_from_checkpoint(best_model_path)
         train_time = time.time() - current_time
         self._model = best_tft
         return train_time
@@ -170,7 +197,11 @@ class TemporalFusionTransformerEstimator(TimeSeriesEstimator):
         last_data_cols = self.group_ids.copy()
         last_data_cols.append(self.target_names[0])
         last_data = self.data[lambda x: x.time_idx == x.time_idx.max()][last_data_cols]
-        decoder_data = X.X_val if isinstance(X, TimeSeriesDataset) else X
+        # Use X_train if test_data is empty (e.g., when computing training metrics)
+        if isinstance(X, TimeSeriesDataset):
+            decoder_data = X.X_val if len(X.test_data) > 0 else X.X_train
+        else:
+            decoder_data = X
         if "time_idx" not in decoder_data:
             decoder_data = add_time_idx_col(decoder_data)
         decoder_data["time_idx"] += encoder_data["time_idx"].max() + 1 - decoder_data["time_idx"].min()

flaml/automl/time_series/ts_data.py

@@ -9,6 +9,7 @@ import numpy as np
 try:
     import pandas as pd
     from pandas import DataFrame, Series, to_datetime
+    from pandas.api.types import is_datetime64_any_dtype
     from scipy.sparse import issparse
     from sklearn.compose import ColumnTransformer
     from sklearn.impute import SimpleImputer
@@ -120,7 +121,12 @@ class TimeSeriesDataset:
 
     @property
     def X_all(self) -> pd.DataFrame:
-        return pd.concat([self.X_train, self.X_val], axis=0)
+        # Remove empty or all-NA columns before concatenation
+        X_train_filtered = self.X_train.dropna(axis=1, how="all")
+        X_val_filtered = self.X_val.dropna(axis=1, how="all")
+
+        # Concatenate the filtered DataFrames
+        return pd.concat([X_train_filtered, X_val_filtered], axis=0)
 
     @property
     def y_train(self) -> pd.DataFrame:
@@ -392,6 +398,15 @@ class DataTransformerTS:
         assert len(self.num_columns) == 0, "Trying to call fit() twice, something is wrong"
 
         for column in X.columns:
+            # Never treat the time column as a feature for sklearn preprocessing
+            if column == self.time_col:
+                continue
+
+            # Robust datetime detection (covers datetime64[ms/us/ns], tz-aware, etc.)
+            if is_datetime64_any_dtype(X[column]):
+                self.datetime_columns.append(column)
+                continue
+
             # sklearn/utils/validation.py needs int/float values
             if X[column].dtype.name in ("object", "category", "string"):
                 if (
@@ -462,7 +477,7 @@ class DataTransformerTS:
                 if "__NAN__" not in X[col].cat.categories:
                     X[col] = X[col].cat.add_categories("__NAN__").fillna("__NAN__")
             else:
-                X[col] = X[col].fillna("__NAN__")
+                X[col] = X[col].fillna("__NAN__").infer_objects(copy=False)
                 X[col] = X[col].astype("category")
 
         for column in self.num_columns:
@@ -531,14 +546,12 @@ def normalize_ts_data(X_train_all, target_names, time_col, y_train_all=None):
 
 
 def validate_data_basic(X_train_all, y_train_all):
-    assert isinstance(X_train_all, np.ndarray) or issparse(X_train_all) or isinstance(X_train_all, pd.DataFrame), (
-        "X_train_all must be a numpy array, a pandas dataframe, " "or Scipy sparse matrix."
-    )
+    assert isinstance(X_train_all, (np.ndarray, DataFrame)) or issparse(
+        X_train_all
+    ), "X_train_all must be a numpy array, a pandas dataframe, or Scipy sparse matrix."
 
-    assert (
-        isinstance(y_train_all, np.ndarray)
-        or isinstance(y_train_all, pd.Series)
-        or isinstance(y_train_all, pd.DataFrame)
+    assert isinstance(
+        y_train_all, (np.ndarray, pd.Series, pd.DataFrame)
     ), "y_train_all must be a numpy array or a pandas series or DataFrame."
 
     assert X_train_all.size != 0 and y_train_all.size != 0, "Input data must not be empty, use None if no data"

flaml/automl/time_series/ts_model.py

@@ -194,7 +194,13 @@ class Orbit(TimeSeriesEstimator):
 
         elif isinstance(X, TimeSeriesDataset):
             data = X
-            X = data.test_data[[self.time_col] + X.regressors]
+            # By default we predict on the dataset's test partition.
+            # Some internal call paths (e.g., training-metric logging) may pass a
+            # dataset whose test partition is empty; fall back to train partition.
+            if data.test_data is not None and len(data.test_data):
+                X = data.test_data[data.regressors + [data.time_col]]
+            else:
+                X = data.train_data[data.regressors + [data.time_col]]
 
         if self._model is not None:
             forecast = self._model.predict(X, **kwargs)
@@ -301,7 +307,13 @@ class Prophet(TimeSeriesEstimator):
 
         if isinstance(X, TimeSeriesDataset):
             data = X
-            X = data.test_data[data.regressors + [data.time_col]]
+            # By default we predict on the dataset's test partition.
+            # Some internal call paths (e.g., training-metric logging) may pass a
+            # dataset whose test partition is empty; fall back to train partition.
+            if data.test_data is not None and len(data.test_data):
+                X = data.test_data[data.regressors + [data.time_col]]
+            else:
+                X = data.train_data[data.regressors + [data.time_col]]
 
         X = X.rename(columns={self.time_col: "ds"})
         if self._model is not None:
@@ -327,11 +339,19 @@ class StatsModelsEstimator(TimeSeriesEstimator):
 
         if isinstance(X, TimeSeriesDataset):
             data = X
-            X = data.test_data[data.regressors + [data.time_col]]
+            # By default we predict on the dataset's test partition.
+            # Some internal call paths (e.g., training-metric logging) may pass a
+            # dataset whose test partition is empty; fall back to train partition.
+            if data.test_data is not None and len(data.test_data):
+                X = data.test_data[data.regressors + [data.time_col]]
+            else:
+                X = data.train_data[data.regressors + [data.time_col]]
         else:
             X = X[self.regressors + [self.time_col]]
 
         if isinstance(X, DataFrame):
+            if X.shape[0] == 0:
+                return pd.Series([], name=self.target_names[0], dtype=float)
             start = X[self.time_col].iloc[0]
             end = X[self.time_col].iloc[-1]
             if len(self.regressors):
@@ -829,6 +849,13 @@ class TS_SKLearn(TimeSeriesEstimator):
         if isinstance(X, TimeSeriesDataset):
             data = X
             X = data.test_data
+            # By default we predict on the dataset's test partition.
+            # Some internal call paths (e.g., training-metric logging) may pass a
+            # dataset whose test partition is empty; fall back to train partition.
+            if data.test_data is not None and len(data.test_data):
+                X = data.test_data
+            else:
+                X = data.train_data
 
         if self._model is not None:
             X = X[self.regressors]

flaml/default/estimator.py

@@ -95,6 +95,27 @@ def flamlize_estimator(super_class, name: str, task: str, alternatives=None):
         def fit(self, X, y, *args, **params):
             hyperparams, estimator_name, X, y_transformed = self.suggest_hyperparams(X, y)
             self.set_params(**hyperparams)
+
+            # Transform eval_set if present
+            if "eval_set" in params and params["eval_set"] is not None:
+                transformed_eval_set = []
+                for eval_X, eval_y in params["eval_set"]:
+                    # Transform features
+                    eval_X_transformed = self._feature_transformer.transform(eval_X)
+                    # Transform labels if applicable
+                    if self._label_transformer and estimator_name in [
+                        "rf",
+                        "extra_tree",
+                        "xgboost",
+                        "xgb_limitdepth",
+                        "choose_xgb",
+                    ]:
+                        eval_y_transformed = self._label_transformer.transform(eval_y)
+                        transformed_eval_set.append((eval_X_transformed, eval_y_transformed))
+                    else:
+                        transformed_eval_set.append((eval_X_transformed, eval_y))
+                params["eval_set"] = transformed_eval_set
+
             if self._label_transformer and estimator_name in [
                 "rf",
                 "extra_tree",

flaml/default/greedy.py

@@ -32,6 +32,7 @@ def construct_portfolio(regret_matrix, meta_features, regret_bound):
     if meta_features is not None:
         scaler = RobustScaler()
         meta_features = meta_features.loc[tasks]
+        meta_features = meta_features.astype(float)
         meta_features.loc[:, :] = scaler.fit_transform(meta_features)
         nearest_task = {}
         for t in tasks:

flaml/default/portfolio.py

@@ -26,6 +26,7 @@ def config_predictor_tuple(tasks, configs, meta_features, regret_matrix):
     # pre-processing
     scaler = RobustScaler()
     meta_features_norm = meta_features.loc[tasks]  # this makes a copy
+    meta_features_norm = meta_features_norm.astype(float)
     meta_features_norm.loc[:, :] = scaler.fit_transform(meta_features_norm)
 
     proc = {

flaml/fabric/mlflow.py

@@ -1,10 +1,14 @@
+import atexit
+import functools
 import json
+import logging
 import os
 import pickle
 import random
-import sys
 import tempfile
 import time
+import warnings
+from concurrent.futures import ThreadPoolExecutor, wait
 from typing import MutableMapping
 
 import mlflow
@@ -12,14 +16,15 @@ import pandas as pd
 from mlflow.entities import Metric, Param, RunTag
 from mlflow.exceptions import MlflowException
 from mlflow.utils.autologging_utils import AUTOLOGGING_INTEGRATIONS, autologging_is_disabled
+from packaging.requirements import Requirement
 from scipy.sparse import issparse
 from sklearn import tree
 
 try:
-    from pyspark.ml import Pipeline as SparkPipeline
+    from pyspark.ml import PipelineModel as SparkPipelineModel
 except ImportError:
 
-    class SparkPipeline:
+    class SparkPipelineModel:
         pass
 
 
@@ -32,6 +37,84 @@ from flaml.version import __version__
 
 SEARCH_MAX_RESULTS = 5000  # Each train should not have more than 5000 trials
 IS_RENAME_CHILD_RUN = os.environ.get("FLAML_IS_RENAME_CHILD_RUN", "false").lower() == "true"
+REMOVE_REQUIREMENT_LIST = [
+    "synapseml-cognitive",
+    "synapseml-core",
+    "synapseml-deep-learning",
+    "synapseml-internal",
+    "synapseml-mlflow",
+    "synapseml-opencv",
+    "synapseml-vw",
+    "synapseml-lightgbm",
+    "synapseml-utils",
+    "nni",
+    "optuna",
+]
+OPTIONAL_REMOVE_REQUIREMENT_LIST = ["pytorch-lightning", "transformers"]
+
+os.environ["MLFLOW_ENABLE_ARTIFACTS_PROGRESS_BAR"] = os.environ.get("MLFLOW_ENABLE_ARTIFACTS_PROGRESS_BAR", "false")
+
+MLFLOW_NUM_WORKERS = int(os.environ.get("FLAML_MLFLOW_NUM_WORKERS", os.cpu_count() * 4 if os.cpu_count() else 2))
+executor = ThreadPoolExecutor(max_workers=MLFLOW_NUM_WORKERS)
+atexit.register(lambda: executor.shutdown(wait=True))
+
+IS_CLEAN_LOGS = os.environ.get("FLAML_IS_CLEAN_LOGS", "1")
+if IS_CLEAN_LOGS == "1":
+    logging.getLogger("synapse.ml").setLevel(logging.CRITICAL)
+    logging.getLogger("mlflow.utils").setLevel(logging.CRITICAL)
+    logging.getLogger("mlflow.utils.environment").setLevel(logging.CRITICAL)
+    logging.getLogger("mlflow.models.model").setLevel(logging.CRITICAL)
+    warnings.simplefilter("ignore", category=FutureWarning)
+    warnings.simplefilter("ignore", category=UserWarning)
+
+
+def convert_requirement(requirement_list: list[str]):
+    ret = (
+        [Requirement(s.strip().lower()) for s in requirement_list]
+        if mlflow.__version__ <= "2.17.0"
+        else requirement_list
+    )
+    return ret
+
+
+def time_it(func_or_code=None):
+    """
+    Decorator or function that measures execution time.
+
+    Can be used in three ways:
+    1. As a decorator with no arguments: @time_it
+    2. As a decorator with arguments: @time_it()
+    3. As a function call with a string of code to execute and time: time_it("some_code()")
+
+    Args:
+        func_or_code (callable or str, optional): Either a function to decorate or
+            a string of code to execute and time.
+
+    Returns:
+        callable or None: Returns a decorated function if used as a decorator,
+            or None if used to execute a string of code.
+    """
+
+    def decorator(func):
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):
+            start_time = time.time()
+            result = func(*args, **kwargs)
+            end_time = time.time()
+            logger.debug(f"Execution of {func.__name__} took {end_time - start_time:.4f} seconds")
+            return result
+
+        return wrapper
+
+    if callable(func_or_code):
+        return decorator(func_or_code)
+    elif func_or_code is None:
+        return decorator
+    else:
+        start_time = time.time()
+        exec(func_or_code)
+        end_time = time.time()
+        logger.debug(f"Execution\n```\n{func_or_code}\n```\ntook {end_time - start_time:.4f} seconds")
 
 
 def flatten_dict(d: MutableMapping, sep: str = ".") -> MutableMapping:
@@ -49,23 +132,28 @@ def is_autolog_enabled():
     return not all(autologging_is_disabled(k) for k in AUTOLOGGING_INTEGRATIONS.keys())
 
 
-def get_mlflow_log_latency(model_history=False):
+def get_mlflow_log_latency(model_history=False, delete_run=True):
+    try:
+        FLAML_MLFLOW_LOG_LATENCY = float(os.getenv("FLAML_MLFLOW_LOG_LATENCY", 0))
+    except ValueError:
+        FLAML_MLFLOW_LOG_LATENCY = 0
+    if FLAML_MLFLOW_LOG_LATENCY >= 0.1:
+        return FLAML_MLFLOW_LOG_LATENCY
     st = time.time()
     with mlflow.start_run(nested=True, run_name="get_mlflow_log_latency") as run:
         if model_history:
             sk_model = tree.DecisionTreeClassifier()
-            mlflow.sklearn.log_model(sk_model, "sk_models")
-            mlflow.sklearn.log_model(Pipeline([("estimator", sk_model)]), "sk_pipeline")
+            mlflow.sklearn.log_model(sk_model, "model")
             with tempfile.TemporaryDirectory() as tmpdir:
-                pickle_fpath = os.path.join(tmpdir, f"tmp_{int(time.time()*1000)}")
+                pickle_fpath = os.path.join(tmpdir, f"tmp_{int(time.time() * 1000)}")
                 with open(pickle_fpath, "wb") as f:
                     pickle.dump(sk_model, f)
-                mlflow.log_artifact(pickle_fpath, "sk_model1")
-                mlflow.log_artifact(pickle_fpath, "sk_model2")
+                mlflow.log_artifact(pickle_fpath, "sk_model")
         mlflow.set_tag("synapseml.ui.visible", "false")  # not shown inline in fabric
-    mlflow.delete_run(run.info.run_id)
+    if delete_run:
+        mlflow.delete_run(run.info.run_id)
     et = time.time()
-    return et - st
+    return 3 * (et - st)
 
 
 def infer_signature(X_train=None, y_train=None, dataframe=None, label=None):
@@ -98,12 +186,76 @@ def infer_signature(X_train=None, y_train=None, dataframe=None, label=None):
                 )
 
 
+def update_and_install_requirements(
+    run_id=None,
+    model_name=None,
+    model_version=None,
+    remove_list=None,
+    artifact_path="model",
+    dst_path=None,
+    install_with_ipython=False,
+):
+    if not (run_id or (model_name and model_version)):
+        raise ValueError(
+            "Please provide `run_id` or both `model_name` and `model_version`. If all three are provided, `run_id` will be used."
+        )
+
+    if install_with_ipython:
+        from IPython import get_ipython
+
+    if not remove_list:
+        remove_list = [
+            "synapseml-cognitive",
+            "synapseml-core",
+            "synapseml-deep-learning",
+            "synapseml-internal",
+            "synapseml-mlflow",
+            "synapseml-opencv",
+            "synapseml-vw",
+            "synapseml-lightgbm",
+            "synapseml-utils",
+            "flaml",  # flaml is needed for AutoML models, should be pre-installed in the runtime
+            "pyspark",  # fabric internal pyspark should be pre-installed in the runtime
+        ]
+
+    # Download model artifacts
+    client = mlflow.MlflowClient()
+    if not run_id:
+        run_id = client.get_model_version(model_name, model_version).run_id
+    if not dst_path:
+        dst_path = os.path.join(tempfile.gettempdir(), "model_artifacts")
+    os.makedirs(dst_path, exist_ok=True)
+    client.download_artifacts(run_id, artifact_path, dst_path)
+    requirements_path = os.path.join(dst_path, artifact_path, "requirements.txt")
+    with open(requirements_path) as f:
+        reqs = f.read().splitlines()
+        old_reqs = [Requirement(req) for req in reqs if req]
+        old_reqs_dict = {req.name: str(req) for req in old_reqs}
+        for req in remove_list:
+            req = Requirement(req)
+            if req.name in old_reqs_dict:
+                old_reqs_dict.pop(req.name, None)
+        new_reqs_list = list(old_reqs_dict.values())
+
+    with open(requirements_path, "w") as f:
+        f.write("\n".join(new_reqs_list))
+
+    if install_with_ipython:
+        get_ipython().run_line_magic("pip", f"install -r {requirements_path} -q")
+    else:
+        logger.info(f"You can run `pip install -r {requirements_path}` to install dependencies.")
+    return requirements_path
+
+
 def _mlflow_wrapper(evaluation_func, mlflow_exp_id, mlflow_config=None, extra_tags=None, autolog=False):
     def wrapped(*args, **kwargs):
         if mlflow_config is not None:
-            from synapse.ml.mlflow import set_mlflow_env_config
+            try:
+                from synapse.ml.mlflow import set_mlflow_env_config
 
-            set_mlflow_env_config(mlflow_config)
+                set_mlflow_env_config(mlflow_config)
+            except Exception:
+                pass
         import mlflow
 
         if mlflow_exp_id is not None:
@@ -124,7 +276,20 @@ def _mlflow_wrapper(evaluation_func, mlflow_exp_id, mlflow_config=None, extra_ta
 
 
 def _get_notebook_name():
-    return None
+    try:
+        import re
+
+        from synapse.ml.mlflow import get_mlflow_env_config
+        from synapse.ml.mlflow.shared_platform_utils import get_artifact
+
+        notebook_id = get_mlflow_env_config(False).artifact_id
+        current_notebook = get_artifact(notebook_id)
+        notebook_name = re.sub("\\W+", "-", current_notebook.displayName).strip()
+
+        return notebook_name
+    except Exception as e:
+        logger.debug(f"Failed to get notebook name: {e}")
+        return None
 
 
 def safe_json_dumps(obj):
@@ -163,6 +328,8 @@ class MLflowIntegration:
         self.has_model = False
         self.only_history = False
         self._do_log_model = True
+        self.futures = {}
+        self.futures_log_model = {}
 
         self.extra_tag = (
             extra_tag
@@ -170,6 +337,9 @@ class MLflowIntegration:
             else {"extra_tag.sid": f"flaml_{__version__}_{int(time.time())}_{random.randint(1001, 9999)}"}
         )
         self.start_time = time.time()
+        self.experiment_type = experiment_type
+        self.update_autolog_state()
+
         self.mlflow_client = mlflow.tracking.MlflowClient()
         parent_run_info = mlflow.active_run().info if mlflow.active_run() is not None else None
         if parent_run_info:
@@ -188,8 +358,6 @@ class MLflowIntegration:
                 mlflow.set_experiment(experiment_name=mlflow_exp_name)
             self.experiment_id = mlflow.tracking.fluent._active_experiment_id
         self.experiment_name = mlflow.get_experiment(self.experiment_id).name
-        self.experiment_type = experiment_type
-        self.update_autolog_state()
 
         if self.autolog:
             # only end user created parent run in autolog scenario
@@ -197,9 +365,12 @@ class MLflowIntegration:
 
     def set_mlflow_config(self):
         if self.driver_mlflow_env_config is not None:
-            from synapse.ml.mlflow import set_mlflow_env_config
+            try:
+                from synapse.ml.mlflow import set_mlflow_env_config
 
-            set_mlflow_env_config(self.driver_mlflow_env_config)
+                set_mlflow_env_config(self.driver_mlflow_env_config)
+            except Exception:
+                pass
 
     def wrap_evaluation_function(self, evaluation_function):
         wrapped_evaluation_function = _mlflow_wrapper(
@@ -267,6 +438,7 @@ class MLflowIntegration:
         else:
             _tags = []
         self.mlflow_client.log_batch(run_id=target_id, metrics=_metrics, params=[], tags=_tags)
+        return f"Successfully copy_mlflow_run run_id {src_id} to run_id {target_id}"
 
     def record_trial(self, result, trial, metric):
         if isinstance(result, dict):
@@ -334,12 +506,31 @@ class MLflowIntegration:
                 self.copy_mlflow_run(best_mlflow_run_id, self.parent_run_id)
                 self.has_summary = True
 
-    def log_model(self, model, estimator, signature=None):
+    def log_model(self, model, estimator, signature=None, run_id=None):
         if not self._do_log_model:
             return
         logger.debug(f"logging model {estimator}")
+        ret_message = f"Successfully log_model {estimator} to run_id {run_id}"
+        optional_remove_list = (
+            [] if estimator in ["transformer", "transformer_ms", "tcn", "tft"] else OPTIONAL_REMOVE_REQUIREMENT_LIST
+        )
+        run = mlflow.active_run()
+        if run and run.info.run_id == self.parent_run_id:
+            logger.debug(
+                f"Current active run_id {run.info.run_id} == parent_run_id {self.parent_run_id}, Starting run_id {run_id}"
+            )
+            mlflow.start_run(run_id=run_id, nested=True)
+        elif run and run.info.run_id != run_id:
+            ret_message = (
+                f"Error: Should log_model {estimator} to run_id {run_id}, but logged to run_id {run.info.run_id}"
+            )
+            logger.error(ret_message)
+        else:
+            logger.debug(f"No active run, start run_id {run_id}")
+            mlflow.start_run(run_id=run_id)
+        logger.debug(f"logged model {estimator} to run_id {mlflow.active_run().info.run_id}")
         if estimator.endswith("_spark"):
-            mlflow.spark.log_model(model, estimator, signature=signature)
+            # mlflow.spark.log_model(model, estimator, signature=signature)
             mlflow.spark.log_model(model, "model", signature=signature)
         elif estimator in ["lgbm"]:
             mlflow.lightgbm.log_model(model, estimator, signature=signature)
@@ -352,42 +543,93 @@ class MLflowIntegration:
         elif estimator in ["prophet"]:
             mlflow.prophet.log_model(model, estimator, signature=signature)
         elif estimator in ["orbit"]:
-            pass
+            logger.warning(f"Unsupported model: {estimator}. No model logged.")
         else:
             mlflow.sklearn.log_model(model, estimator, signature=signature)
+        future = executor.submit(
+            lambda: mlflow.models.model.update_model_requirements(
+                model_uri=f"runs:/{run_id}/{'model' if estimator.endswith('_spark') else estimator}",
+                operation="remove",
+                requirement_list=convert_requirement(REMOVE_REQUIREMENT_LIST + optional_remove_list),
+            )
+        )
+        self.futures[future] = f"run_{run_id}_requirements_updated"
+        if not run or run.info.run_id == self.parent_run_id:
+            logger.debug(f"Ending current run_id {mlflow.active_run().info.run_id}")
+            mlflow.end_run()
+        return ret_message
 
-    def _pickle_and_log_artifact(self, obj, artifact_name, pickle_fname="temp_.pkl"):
+    def _pickle_and_log_artifact(self, obj, artifact_name, pickle_fname="temp_.pkl", run_id=None):
         if not self._do_log_model:
-            return
+            return True
         with tempfile.TemporaryDirectory() as tmpdir:
             pickle_fpath = os.path.join(tmpdir, pickle_fname)
             try:
                 with open(pickle_fpath, "wb") as f:
                     pickle.dump(obj, f)
-                mlflow.log_artifact(pickle_fpath, artifact_name)
+                self.mlflow_client.log_artifact(run_id, pickle_fpath, artifact_name)
+                return True
             except Exception as e:
-                logger.debug(f"Failed to pickle and log artifact {artifact_name}, error: {e}")
+                logger.debug(f"Failed to pickle and log {artifact_name}, error: {e}")
+                return False
 
-    def pickle_and_log_automl_artifacts(self, automl, model, estimator, signature=None):
+    def _log_pipeline(self, pipeline, flavor_name, pipeline_name, signature, run_id, estimator=None):
+        logger.debug(f"logging pipeline {flavor_name}:{pipeline_name}:{estimator}")
+        ret_message = f"Successfully _log_pipeline {flavor_name}:{pipeline_name}:{estimator} to run_id {run_id}"
+        optional_remove_list = (
+            [] if estimator in ["transformer", "transformer_ms", "tcn", "tft"] else OPTIONAL_REMOVE_REQUIREMENT_LIST
+        )
+        run = mlflow.active_run()
+        if run and run.info.run_id == self.parent_run_id:
+            logger.debug(
+                f"Current active run_id {run.info.run_id} == parent_run_id {self.parent_run_id}, Starting run_id {run_id}"
+            )
+            mlflow.start_run(run_id=run_id, nested=True)
+        elif run and run.info.run_id != run_id:
+            ret_message = f"Error: Should _log_pipeline {flavor_name}:{pipeline_name}:{estimator} model to run_id {run_id}, but logged to run_id {run.info.run_id}"
+            logger.error(ret_message)
+        else:
+            logger.debug(f"No active run, start run_id {run_id}")
+            mlflow.start_run(run_id=run_id)
+        logger.debug(
+            f"logging pipeline {flavor_name}:{pipeline_name}:{estimator} to run_id {mlflow.active_run().info.run_id}"
+        )
+        if flavor_name == "sklearn":
+            mlflow.sklearn.log_model(pipeline, pipeline_name, signature=signature)
+        elif flavor_name == "spark":
+            mlflow.spark.log_model(pipeline, pipeline_name, signature=signature)
+        else:
+            logger.warning(f"Unsupported pipeline flavor: {flavor_name}. No model logged.")
+        future = executor.submit(
+            lambda: mlflow.models.model.update_model_requirements(
+                model_uri=f"runs:/{run_id}/{pipeline_name}",
+                operation="remove",
+                requirement_list=convert_requirement(REMOVE_REQUIREMENT_LIST + optional_remove_list),
+            )
+        )
+        self.futures[future] = f"run_{run_id}_requirements_updated"
+        if not run or run.info.run_id == self.parent_run_id:
+            logger.debug(f"Ending current run_id {mlflow.active_run().info.run_id}")
+            mlflow.end_run()
+        return ret_message
+
+    def pickle_and_log_automl_artifacts(self, automl, model, estimator, signature=None, run_id=None):
         """log automl artifacts to mlflow
         load back with `automl = mlflow.pyfunc.load_model(model_run_id_or_uri)`, then do prediction with `automl.predict(X)`
         """
-        logger.debug(f"logging automl artifacts {estimator}")
-        self._pickle_and_log_artifact(automl.feature_transformer, "feature_transformer", "feature_transformer.pkl")
-        self._pickle_and_log_artifact(automl.label_transformer, "label_transformer", "label_transformer.pkl")
-        # Test test_mlflow 1 and 4 will get error: TypeError: cannot pickle '_io.TextIOWrapper' object
-        # try:
-        #     self._pickle_and_log_artifact(automl, "automl", "automl.pkl")
-        # except TypeError:
-        #     pass
+        logger.debug(f"logging automl estimator {estimator}")
+        # self._pickle_and_log_artifact(
+        #     automl.feature_transformer, "feature_transformer", "feature_transformer.pkl", run_id
+        # )
+        # self._pickle_and_log_artifact(automl.label_transformer, "label_transformer", "label_transformer.pkl", run_id)
         if estimator.endswith("_spark"):
             # spark pipeline is not supported yet
             return
         feature_transformer = automl.feature_transformer
-        if isinstance(feature_transformer, Pipeline):
+        if isinstance(feature_transformer, Pipeline) and not estimator.endswith("_spark"):
             pipeline = feature_transformer
             pipeline.steps.append(("estimator", model))
-        elif isinstance(feature_transformer, SparkPipeline):
+        elif isinstance(feature_transformer, SparkPipelineModel) and estimator.endswith("_spark"):
             pipeline = feature_transformer
             pipeline.stages.append(model)
         elif not estimator.endswith("_spark"):
@@ -395,24 +637,26 @@ class MLflowIntegration:
             steps.append(("estimator", model))
             pipeline = Pipeline(steps)
         else:
-            stages = [feature_transformer]
+            stages = []
+            if feature_transformer is not None:
+                stages.append(feature_transformer)
             stages.append(model)
-            pipeline = SparkPipeline(stages=stages)
-        if isinstance(pipeline, SparkPipeline):
+            pipeline = SparkPipelineModel(stages=stages)
+        if isinstance(pipeline, SparkPipelineModel):
             logger.debug(f"logging spark pipeline {estimator}")
-            mlflow.spark.log_model(pipeline, "automl_pipeline", signature=signature)
+            self._log_pipeline(pipeline, "spark", "model", signature, run_id, estimator)
         else:
             # Add a log named "model" to fit default settings
             logger.debug(f"logging sklearn pipeline {estimator}")
-            mlflow.sklearn.log_model(pipeline, "automl_pipeline", signature=signature)
-            mlflow.sklearn.log_model(pipeline, "model", signature=signature)
+            self._log_pipeline(pipeline, "sklearn", "model", signature, run_id, estimator)
+        return f"Successfully pickle_and_log_automl_artifacts {estimator} to run_id {run_id}"
 
-    def record_state(self, automl, search_state, estimator):
+    @time_it
+    def record_state(self, automl, search_state, estimator, is_log_model=True):
         _st = time.time()
         automl_metric_name = (
             automl._state.metric if isinstance(automl._state.metric, str) else automl._state.error_metric
         )
-
         if automl._state.error_metric.startswith("1-"):
             automl_metric_value = 1 - search_state.val_loss
         elif automl._state.error_metric.startswith("-"):
@@ -425,6 +669,8 @@ class MLflowIntegration:
         else:
             config = search_state.config
 
+        self.automl_user_configurations = safe_json_dumps(automl._automl_user_configurations)
+
         info = {
             "metrics": {
                 "iter_counter": automl._track_iter,
@@ -445,7 +691,7 @@ class MLflowIntegration:
                 "flaml.meric": automl_metric_name,
                 "flaml.run_source": "flaml-automl",
                 "flaml.log_type": self.log_type,
-                "flaml.automl_user_configurations": safe_json_dumps(automl._automl_user_configurations),
+                "flaml.automl_user_configurations": self.automl_user_configurations,
             },
             "params": {
                 "sample_size": search_state.sample_size,
@@ -472,33 +718,70 @@ class MLflowIntegration:
                 run_name = f"{self.parent_run_name}_child_{self.child_counter}"
             else:
                 run_name = None
+            _t1 = time.time()
+            wait(self.futures_log_model)
+            _t2 = time.time() - _t1
+            logger.debug(f"wait futures_log_model in record_state took {_t2} seconds")
             with mlflow.start_run(nested=True, run_name=run_name) as child_run:
-                self._log_info_to_run(info, child_run.info.run_id, log_params=True)
-                if automl._state.model_history:
-                    self.log_model(
-                        search_state.trained_estimator._model, estimator, signature=automl.estimator_signature
-                    )
-                    self.pickle_and_log_automl_artifacts(
-                        automl, search_state.trained_estimator, estimator, signature=automl.pipeline_signature
-                    )
+                future = executor.submit(lambda: self._log_info_to_run(info, child_run.info.run_id, log_params=True))
+                self.futures[future] = f"iter_{automl._track_iter}_log_info_to_run"
+                future = executor.submit(lambda: self._log_automl_configurations(child_run.info.run_id))
+                self.futures[future] = f"iter_{automl._track_iter}_log_automl_configurations"
+                if automl._state.model_history and is_log_model:
+                    if estimator.endswith("_spark"):
+                        future = executor.submit(
+                            lambda: self.log_model(
+                                search_state.trained_estimator._model,
+                                estimator,
+                                automl.estimator_signature,
+                                child_run.info.run_id,
+                            )
+                        )
+                        self.futures_log_model[future] = f"record_state-log_model_{estimator}"
+                    else:
+                        future = executor.submit(
+                            lambda: self.pickle_and_log_automl_artifacts(
+                                automl,
+                                search_state.trained_estimator,
+                                estimator,
+                                automl.pipeline_signature,
+                                child_run.info.run_id,
+                            )
+                        )
+                        self.futures_log_model[future] = f"record_state-pickle_and_log_automl_artifacts_{estimator}"
                 self.manual_run_ids.append(child_run.info.run_id)
             self.child_counter += 1
+        return f"Successfully record_state iteration {automl._track_iter}"
 
+    @time_it
     def log_automl(self, automl):
         self.set_best_iter(automl)
         if self.autolog:
             if self.parent_run_id is not None:
                 mlflow.start_run(run_id=self.parent_run_id, experiment_id=self.experiment_id)
-                mlflow.log_metric("best_validation_loss", automl._state.best_loss)
-                mlflow.log_metric("best_iteration", automl._best_iteration)
-                mlflow.log_metric("num_child_runs", len(self.infos))
-                if automl._trained_estimator is not None and not self.has_model:
-                    self.log_model(
-                        automl._trained_estimator._model, automl.best_estimator, signature=automl.estimator_signature
-                    )
-                    self.pickle_and_log_automl_artifacts(
-                        automl, automl.model, automl.best_estimator, signature=automl.pipeline_signature
-                    )
+                mlflow.log_metrics(
+                    {
+                        "best_validation_loss": automl._state.best_loss,
+                        "best_iteration": automl._best_iteration,
+                        "num_child_runs": len(self.infos),
+                    }
+                )
+                if (
+                    automl._trained_estimator is not None
+                    and not self.has_model
+                    and automl._trained_estimator._model is not None
+                ):
+                    if automl.best_estimator.endswith("_spark"):
+                        self.log_model(
+                            automl._trained_estimator._model,
+                            automl.best_estimator,
+                            automl.estimator_signature,
+                            self.parent_run_id,
+                        )
+                    else:
+                        self.pickle_and_log_automl_artifacts(
+                            automl, automl.model, automl.best_estimator, automl.pipeline_signature, self.parent_run_id
+                        )
                     self.has_model = True
 
             self.adopt_children(automl)
@@ -514,31 +797,68 @@ class MLflowIntegration:
                 conf = automl._config_history[automl._best_iteration][1].copy()
                 if "ml" in conf.keys():
                     conf = conf["ml"]
-
-                mlflow.log_params(conf)
-                mlflow.log_param("best_learner", automl._best_estimator)
+                params_arr = [
+                    Param(key, str(value)) for key, value in {**conf, "best_learner": automl._best_estimator}.items()
+                ]
+                self.mlflow_client.log_batch(run_id=self.parent_run_id, metrics=[], params=params_arr, tags=[])
                 if not self.has_summary:
                     logger.info(f"logging best model {automl.best_estimator}")
-                    self.copy_mlflow_run(best_mlflow_run_id, self.parent_run_id)
+                    future = executor.submit(lambda: self.copy_mlflow_run(best_mlflow_run_id, self.parent_run_id))
+                    self.futures[future] = "log_automl_copy_mlflow_run"
+                    future = executor.submit(lambda: self._log_automl_configurations(self.parent_run_id))
+                    self.futures[future] = "log_automl_log_automl_configurations"
                     self.has_summary = True
-                    if automl._trained_estimator is not None and not self.has_model:
-                        self.log_model(
-                            automl._trained_estimator._model,
-                            automl.best_estimator,
-                            signature=automl.estimator_signature,
-                        )
-                        self.pickle_and_log_automl_artifacts(
-                            automl, automl.model, automl.best_estimator, signature=automl.pipeline_signature
-                        )
+                    _t1 = time.time()
+                    wait(self.futures_log_model)
+                    _t2 = time.time() - _t1
+                    logger.debug(f"wait futures_log_model in log_automl took {_t2} seconds")
+                    if (
+                        automl._trained_estimator is not None
+                        and not self.has_model
+                        and automl._trained_estimator._model is not None
+                    ):
+                        if automl.best_estimator.endswith("_spark"):
+                            future = executor.submit(
+                                lambda: self.log_model(
+                                    automl._trained_estimator._model,
+                                    automl.best_estimator,
+                                    signature=automl.estimator_signature,
+                                    run_id=self.parent_run_id,
+                                )
+                            )
+                            self.futures_log_model[future] = f"log_automl-log_model_{automl.best_estimator}"
+                        else:
+                            future = executor.submit(
+                                lambda: self.pickle_and_log_automl_artifacts(
+                                    automl,
+                                    automl.model,
+                                    automl.best_estimator,
+                                    signature=automl.pipeline_signature,
+                                    run_id=self.parent_run_id,
+                                )
+                            )
+                            self.futures_log_model[
+                                future
+                            ] = f"log_automl-pickle_and_log_automl_artifacts_{automl.best_estimator}"
                         self.has_model = True
 
     def resume_mlflow(self):
         if len(self.resume_params) > 0:
             mlflow.autolog(**self.resume_params)
 
+    def _log_automl_configurations(self, run_id):
+        self.mlflow_client.log_text(
+            run_id=run_id,
+            text=self.automl_user_configurations,
+            artifact_file="automl_configurations/automl_user_configurations.json",
+        )
+        return f"Successfully _log_automl_configurations to run_id {run_id}"
+
     def _log_info_to_run(self, info, run_id, log_params=False):
         _metrics = [Metric(key, value, int(time.time() * 1000), 0) for key, value in info["metrics"].items()]
-        _tags = [RunTag(key, str(value)) for key, value in info["tags"].items()]
+        _tags = [
+            RunTag(key, str(value)[:5000]) for key, value in info["tags"].items()
+        ]  # AML will raise error if value length > 5000
         _params = [
             Param(key, str(value))
             for key, value in info["params"].items()
@@ -554,6 +874,7 @@ class MLflowIntegration:
                     _tags = [RunTag("mlflow.parentRunId", run_id)]
                     self.mlflow_client.log_batch(run_id=run.info.run_id, metrics=_metrics, params=[], tags=_tags)
             del info["submetrics"]["values"]
+        return f"Successfully _log_info_to_run to run_id {run_id}"
 
     def adopt_children(self, result=None):
         """
@@ -575,6 +896,7 @@ class MLflowIntegration:
                 ),
             )
             self.child_counter = 0
+            num_infos = len(self.infos)
 
             # From latest to earliest, remove duplicate cross-validation runs
             _exist_child_run_params = []  # for deduplication of cross-validation child runs
@@ -639,22 +961,37 @@ class MLflowIntegration:
                             )
                         self.mlflow_client.set_tag(child_run_id, "flaml.child_counter", self.child_counter)
 
-                    # merge autolog child run and corresponding manual run
-                    flaml_info = self.infos[self.child_counter]
-                    child_run = self.mlflow_client.get_run(child_run_id)
-                    self._log_info_to_run(flaml_info, child_run_id, log_params=False)
+                    # Merge autolog child run and corresponding FLAML trial info (if available).
+                    # In nested scenarios (e.g., Tune -> AutoML -> MLflow autolog), MLflow can create
+                    # more child runs than the number of FLAML trials recorded in self.infos.
+                    # TODO: need more tests in nested scenarios.
+                    flaml_info = None
+                    child_run = None
+                    if self.child_counter < num_infos:
+                        flaml_info = self.infos[self.child_counter]
+                        child_run = self.mlflow_client.get_run(child_run_id)
+                        self._log_info_to_run(flaml_info, child_run_id, log_params=False)
 
-                    if self.experiment_type == "automl":
-                        if "learner" not in child_run.data.params:
-                            self.mlflow_client.log_param(child_run_id, "learner", flaml_info["params"]["learner"])
-                        if "sample_size" not in child_run.data.params:
-                            self.mlflow_client.log_param(
-                                child_run_id, "sample_size", flaml_info["params"]["sample_size"]
-                            )
+                        if self.experiment_type == "automl":
+                            if "learner" not in child_run.data.params:
+                                self.mlflow_client.log_param(child_run_id, "learner", flaml_info["params"]["learner"])
+                            if "sample_size" not in child_run.data.params:
+                                self.mlflow_client.log_param(
+                                    child_run_id, "sample_size", flaml_info["params"]["sample_size"]
+                                )
+                    else:
+                        logger.debug(
+                            "No corresponding FLAML info for MLflow child run %s (child_counter=%s, infos=%s); skipping merge.",
+                            child_run_id,
+                            self.child_counter,
+                            num_infos,
+                        )
 
-                    if self.child_counter == best_iteration:
+                    if flaml_info is not None and self.child_counter == best_iteration:
                         self.mlflow_client.set_tag(child_run_id, "flaml.best_run", True)
                         if result is not None:
+                            if child_run is None:
+                                child_run = self.mlflow_client.get_run(child_run_id)
                             result.best_run_id = child_run_id
                             result.best_run_name = child_run.info.run_name
                             self.best_run_id = child_run_id
@@ -678,7 +1015,7 @@ class MLflowIntegration:
         self.resume_mlflow()
 
 
-def register_automl_pipeline(automl, model_name=None, signature=None):
+def register_automl_pipeline(automl, model_name=None, signature=None, artifact_path="model"):
     pipeline = automl.automl_pipeline
     if pipeline is None:
         logger.warning("pipeline not found, cannot register it")
@@ -688,7 +1025,7 @@ def register_automl_pipeline(automl, model_name=None, signature=None):
     if automl.best_run_id is None:
         mlflow.sklearn.log_model(
             pipeline,
-            "automl_pipeline",
+            artifact_path,
             registered_model_name=model_name,
             signature=automl.pipeline_signature if signature is None else signature,
         )
@@ -698,5 +1035,5 @@ def register_automl_pipeline(automl, model_name=None, signature=None):
         return mvs[0]
     else:
         best_run = mlflow.get_run(automl.best_run_id)
-        model_uri = f"runs:/{best_run.info.run_id}/automl_pipeline"
+        model_uri = f"runs:/{best_run.info.run_id}/{artifact_path}"
         return mlflow.register_model(model_uri, model_name)

flaml/onlineml/README.md

@@ -1,6 +1,6 @@
 # ChaCha for Online AutoML
 
-FLAML includes *ChaCha* which is an automatic hyperparameter tuning solution for online machine learning. Online machine learning has the following properties: (1) data comes in sequential order; and (2) the performance of the machine learning model is evaluated online, i.e., at every iteration. *ChaCha* performs online AutoML respecting the aforementioned properties of online learning, and at the same time respecting the following constraints: (1) only a small constant number of 'live' models are allowed to perform online learning at the same time;  and (2) no model persistence or offline training is allowed, which means that once we decide to replace a 'live' model with a new one, the replaced model can no longer be retrieved.
+FLAML includes *ChaCha* which is an automatic hyperparameter tuning solution for online machine learning. Online machine learning has the following properties: (1) data comes in sequential order; and (2) the performance of the machine learning model is evaluated online, i.e., at every iteration. *ChaCha* performs online AutoML respecting the aforementioned properties of online learning, and at the same time respecting the following constraints: (1) only a small constant number of 'live' models are allowed to perform online learning at the same time; and (2) no model persistence or offline training is allowed, which means that once we decide to replace a 'live' model with a new one, the replaced model can no longer be retrieved.
 
 For more technical details about *ChaCha*, please check our paper.
 

flaml/tune/logger.py

@@ -0,0 +1,37 @@
+import logging
+import os
+
+
+class ColoredFormatter(logging.Formatter):
+    # ANSI escape codes for colors
+    COLORS = {
+        # logging.DEBUG: "\033[36m",  # Cyan
+        # logging.INFO: "\033[32m",   # Green
+        logging.WARNING: "\033[33m",  # Yellow
+        logging.ERROR: "\033[31m",  # Red
+        logging.CRITICAL: "\033[1;31m",  # Bright Red
+    }
+    RESET = "\033[0m"  # Reset to default
+
+    def __init__(self, fmt, datefmt, use_color=True):
+        super().__init__(fmt, datefmt)
+        self.use_color = use_color
+
+    def format(self, record):
+        formatted = super().format(record)
+        if self.use_color:
+            color = self.COLORS.get(record.levelno, "")
+            if color:
+                return f"{color}{formatted}{self.RESET}"
+        return formatted
+
+
+logger = logging.getLogger(__name__)
+use_color = True
+if os.getenv("FLAML_LOG_NO_COLOR"):
+    use_color = False
+
+logger_formatter = ColoredFormatter(
+    "[%(name)s: %(asctime)s] {%(lineno)d} %(levelname)s - %(message)s", "%m-%d %H:%M:%S", use_color
+)
+logger.propagate = False

flaml/tune/searcher/blendsearch.py

@@ -217,7 +217,24 @@ class BlendSearch(Searcher):
         if global_search_alg is not None:
             self._gs = global_search_alg
         elif getattr(self, "__name__", None) != "CFO":
-            if space and self._ls.hierarchical:
+            # Use define-by-run for OptunaSearch when needed:
+            # - Hierarchical/conditional spaces are best supported via define-by-run.
+            # - Ray Tune domain/grid specs can trigger an "unresolved search space" warning
+            #   unless we switch to define-by-run.
+            use_define_by_run = bool(getattr(self._ls, "hierarchical", False))
+            if (not use_define_by_run) and isinstance(space, dict) and space:
+                try:
+                    from .variant_generator import parse_spec_vars
+
+                    _, domain_vars, grid_vars = parse_spec_vars(space)
+                    use_define_by_run = bool(domain_vars or grid_vars)
+                except Exception:
+                    # Be conservative: if we can't determine whether the space is
+                    # unresolved, fall back to the original behavior.
+                    use_define_by_run = False
+
+            self._use_define_by_run = use_define_by_run
+            if use_define_by_run:
                 from functools import partial
 
                 gs_space = partial(define_by_run_func, space=space)
@@ -244,13 +261,32 @@ class BlendSearch(Searcher):
                     evaluated_rewards=evaluated_rewards,
                 )
             except (AssertionError, ValueError):
-                self._gs = GlobalSearch(
-                    space=gs_space,
-                    metric=metric,
-                    mode=mode,
-                    seed=gs_seed,
-                    sampler=sampler,
-                )
+                try:
+                    self._gs = GlobalSearch(
+                        space=gs_space,
+                        metric=metric,
+                        mode=mode,
+                        seed=gs_seed,
+                        sampler=sampler,
+                    )
+                except ValueError:
+                    # Ray Tune's OptunaSearch converts Tune domains into Optuna
+                    # distributions. Optuna disallows integer log distributions
+                    # with step != 1 (e.g., qlograndint with q>1), which can
+                    # raise here. Fall back to FLAML's OptunaSearch wrapper,
+                    # which handles these spaces more permissively.
+                    if getattr(GlobalSearch, "__module__", "").startswith("ray.tune"):
+                        from .suggestion import OptunaSearch as _FallbackOptunaSearch
+
+                        self._gs = _FallbackOptunaSearch(
+                            space=gs_space,
+                            metric=metric,
+                            mode=mode,
+                            seed=gs_seed,
+                            sampler=sampler,
+                        )
+                    else:
+                        raise
             self._gs.space = space
         else:
             self._gs = None
@@ -468,7 +504,7 @@ class BlendSearch(Searcher):
                             self._ls_bound_max,
                             self._subspace.get(trial_id, self._ls.space),
                         )
-                    if self._gs is not None and self._experimental and (not self._ls.hierarchical):
+                    if self._gs is not None and self._experimental and (not getattr(self, "_use_define_by_run", False)):
                         self._gs.add_evaluated_point(flatten_dict(config), objective)
                         # TODO: recover when supported
                         # converted = convert_key(config, self._gs.space)

flaml/tune/searcher/flow2.py

@@ -641,8 +641,10 @@ class FLOW2(Searcher):
             else:
                 # key must be in space
                 domain = space[key]
-                if self.hierarchical and not (
-                    domain is None or type(domain) in (str, int, float) or isinstance(domain, sample.Domain)
+                if (
+                    self.hierarchical
+                    and domain is not None
+                    and not isinstance(domain, (str, int, float, sample.Domain))
                 ):
                     # not domain or hashable
                     # get rid of list type for hierarchical search space.

flaml/tune/searcher/online_searcher.py

@@ -207,7 +207,7 @@ class ChampionFrontierSearcher(BaseSearcher):
                     hyperparameter_config_groups.append(partial_new_configs)
                     # does not have searcher_trial_ids
                     searcher_trial_ids_groups.append([])
-            elif isinstance(config_domain, Float) or isinstance(config_domain, Categorical):
+            elif isinstance(config_domain, (Float, Categorical)):
                 # otherwise we need to deal with them in group
                 nonpoly_config[k] = v
                 if k not in self._space_of_nonpoly_hp:

flaml/tune/searcher/search_thread.py

@@ -25,6 +25,31 @@ from .flow2 import FLOW2
 logger = logging.getLogger(__name__)
 
 
+def _recursive_dict_update(target: Dict, source: Dict) -> None:
+    """Recursively update target dictionary with source dictionary.
+
+    Unlike dict.update(), this function merges nested dictionaries instead of
+    replacing them entirely. This is crucial for configurations with nested
+    structures (e.g., XGBoost params).
+
+    Args:
+        target: The dictionary to be updated (modified in place).
+        source: The dictionary containing values to merge into target.
+
+    Example:
+        >>> target = {'params': {'eta': 0.1, 'max_depth': 3}}
+        >>> source = {'params': {'verbosity': 0}}
+        >>> _recursive_dict_update(target, source)
+        >>> target
+        {'params': {'eta': 0.1, 'max_depth': 3, 'verbosity': 0}}
+    """
+    for key, value in source.items():
+        if isinstance(value, dict) and key in target and isinstance(target[key], dict):
+            _recursive_dict_update(target[key], value)
+        else:
+            target[key] = value
+
+
 class SearchThread:
     """Class of global or local search thread."""
 
@@ -65,7 +90,7 @@ class SearchThread:
             try:
                 config = self._search_alg.suggest(trial_id)
                 if isinstance(self._search_alg._space, dict):
-                    config.update(self._const)
+                    _recursive_dict_update(config, self._const)
                 else:
                     # define by run
                     config, self.space = unflatten_hierarchical(config, self._space)

flaml/tune/searcher/suggestion.py

@@ -35,6 +35,73 @@ from ..sample import (
     Quantized,
     Uniform,
 )
+
+# If Ray is installed, flaml.tune may re-export Ray Tune sampling functions.
+# In that case, the search space contains Ray Tune Domain/Sampler objects,
+# which should be accepted by our Optuna search-space conversion.
+try:
+    from ray import __version__ as _ray_version  # type: ignore
+
+    if str(_ray_version).startswith("1."):
+        from ray.tune.sample import (  # type: ignore
+            Categorical as _RayCategorical,
+        )
+        from ray.tune.sample import (
+            Domain as _RayDomain,
+        )
+        from ray.tune.sample import (
+            Float as _RayFloat,
+        )
+        from ray.tune.sample import (
+            Integer as _RayInteger,
+        )
+        from ray.tune.sample import (
+            LogUniform as _RayLogUniform,
+        )
+        from ray.tune.sample import (
+            Quantized as _RayQuantized,
+        )
+        from ray.tune.sample import (
+            Uniform as _RayUniform,
+        )
+    else:
+        from ray.tune.search.sample import (  # type: ignore
+            Categorical as _RayCategorical,
+        )
+        from ray.tune.search.sample import (
+            Domain as _RayDomain,
+        )
+        from ray.tune.search.sample import (
+            Float as _RayFloat,
+        )
+        from ray.tune.search.sample import (
+            Integer as _RayInteger,
+        )
+        from ray.tune.search.sample import (
+            LogUniform as _RayLogUniform,
+        )
+        from ray.tune.search.sample import (
+            Quantized as _RayQuantized,
+        )
+        from ray.tune.search.sample import (
+            Uniform as _RayUniform,
+        )
+
+    _FLOAT_TYPES = (Float, _RayFloat)
+    _INTEGER_TYPES = (Integer, _RayInteger)
+    _CATEGORICAL_TYPES = (Categorical, _RayCategorical)
+    _DOMAIN_TYPES = (Domain, _RayDomain)
+    _QUANTIZED_TYPES = (Quantized, _RayQuantized)
+    _UNIFORM_TYPES = (Uniform, _RayUniform)
+    _LOGUNIFORM_TYPES = (LogUniform, _RayLogUniform)
+except Exception:  # pragma: no cover
+    _FLOAT_TYPES = (Float,)
+    _INTEGER_TYPES = (Integer,)
+    _CATEGORICAL_TYPES = (Categorical,)
+    _DOMAIN_TYPES = (Domain,)
+    _QUANTIZED_TYPES = (Quantized,)
+    _UNIFORM_TYPES = (Uniform,)
+    _LOGUNIFORM_TYPES = (LogUniform,)
 from ..trial import flatten_dict, unflatten_dict
 from .variant_generator import parse_spec_vars
 
@@ -850,19 +917,22 @@ class OptunaSearch(Searcher):
         def resolve_value(domain: Domain) -> ot.distributions.BaseDistribution:
             quantize = None
 
-            sampler = domain.get_sampler()
-            if isinstance(sampler, Quantized):
+            # Ray Tune Domains and FLAML Domains both provide get_sampler(), but
+            # fall back to the .sampler attribute for robustness.
+            sampler = domain.get_sampler() if hasattr(domain, "get_sampler") else getattr(domain, "sampler", None)
+
+            if isinstance(sampler, _QUANTIZED_TYPES) or type(sampler).__name__ == "Quantized":
                 quantize = sampler.q
-                sampler = sampler.sampler
-                if isinstance(sampler, LogUniform):
+                sampler = getattr(sampler, "sampler", None) or sampler.get_sampler()
+                if isinstance(sampler, _LOGUNIFORM_TYPES) or type(sampler).__name__ == "LogUniform":
                     logger.warning(
                         "Optuna does not handle quantization in loguniform "
                         "sampling. The parameter will be passed but it will "
                         "probably be ignored."
                     )
 
-            if isinstance(domain, Float):
-                if isinstance(sampler, LogUniform):
+            if isinstance(domain, _FLOAT_TYPES) or type(domain).__name__ == "Float":
+                if isinstance(sampler, _LOGUNIFORM_TYPES) or type(sampler).__name__ == "LogUniform":
                     if quantize:
                         logger.warning(
                             "Optuna does not support both quantization and "
@@ -870,17 +940,17 @@ class OptunaSearch(Searcher):
                         )
                     return ot.distributions.LogUniformDistribution(domain.lower, domain.upper)
 
-                elif isinstance(sampler, Uniform):
+                elif isinstance(sampler, _UNIFORM_TYPES) or type(sampler).__name__ == "Uniform":
                     if quantize:
                         return ot.distributions.DiscreteUniformDistribution(domain.lower, domain.upper, quantize)
                     return ot.distributions.UniformDistribution(domain.lower, domain.upper)
 
-            elif isinstance(domain, Integer):
-                if isinstance(sampler, LogUniform):
+            elif isinstance(domain, _INTEGER_TYPES) or type(domain).__name__ == "Integer":
+                if isinstance(sampler, _LOGUNIFORM_TYPES) or type(sampler).__name__ == "LogUniform":
                     # ``step`` argument Deprecated in v2.0.0. ``step`` argument should be 1 in Log Distribution
                     # The removal of this feature is currently scheduled for v4.0.0,
                     return ot.distributions.IntLogUniformDistribution(domain.lower, domain.upper - 1, step=1)
-                elif isinstance(sampler, Uniform):
+                elif isinstance(sampler, _UNIFORM_TYPES) or type(sampler).__name__ == "Uniform":
                     # Upper bound should be inclusive for quantization and
                     # exclusive otherwise
                     return ot.distributions.IntUniformDistribution(
@@ -888,16 +958,16 @@ class OptunaSearch(Searcher):
                         domain.upper - int(bool(not quantize)),
                         step=quantize or 1,
                     )
-            elif isinstance(domain, Categorical):
-                if isinstance(sampler, Uniform):
+            elif isinstance(domain, _CATEGORICAL_TYPES) or type(domain).__name__ == "Categorical":
+                if isinstance(sampler, _UNIFORM_TYPES) or type(sampler).__name__ == "Uniform":
                     return ot.distributions.CategoricalDistribution(domain.categories)
 
             raise ValueError(
                 "Optuna search does not support parameters of type "
-                "`{}` with samplers of type `{}`".format(type(domain).__name__, type(domain.sampler).__name__)
+                "`{}` with samplers of type `{}`".format(type(domain).__name__, type(sampler).__name__)
             )
 
         # Parameter name is e.g. "a/b/c" for nested dicts
         values = {"/".join(path): resolve_value(domain) for path, domain in domain_vars}
 
-        return values
+        return values

flaml/tune/space.py

@@ -261,7 +261,7 @@ def add_cost_to_space(space: Dict, low_cost_point: Dict, choice_cost: Dict):
                         low_cost[i] = point
                 if len(low_cost) > len(domain.categories):
                     if domain.ordered:
-                        low_cost[-1] = int(np.where(ind == low_cost[-1])[0])
+                        low_cost[-1] = int(np.where(ind == low_cost[-1])[0].item())
                     domain.low_cost_point = low_cost[-1]
                 return
         if low_cost:

flaml/tune/spark/utils.py

@@ -162,6 +162,10 @@ def broadcast_code(custom_code="", file_name="mylearner"):
     assert isinstance(MyLargeLGBM(), LGBMEstimator)
     ```
     """
+    # Check if Spark is available
+    spark_available, _ = check_spark()
+
+    # Write to local driver file system
     flaml_path = os.path.dirname(os.path.abspath(__file__))
     custom_code = textwrap.dedent(custom_code)
     custom_path = os.path.join(flaml_path, file_name + ".py")
@@ -169,6 +173,24 @@ def broadcast_code(custom_code="", file_name="mylearner"):
     with open(custom_path, "w") as f:
         f.write(custom_code)
 
+    # If using Spark, broadcast the code content to executors
+    if spark_available:
+        spark = SparkSession.builder.getOrCreate()
+        bc_code = spark.sparkContext.broadcast(custom_code)
+
+        # Execute a job to ensure the code is distributed to all executors
+        def _write_code(bc):
+            code = bc.value
+            import os
+
+            module_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), file_name + ".py")
+            os.makedirs(os.path.dirname(module_path), exist_ok=True)
+            with open(module_path, "w") as f:
+                f.write(code)
+            return True
+
+        spark.sparkContext.parallelize(range(1)).map(lambda _: _write_code(bc_code)).collect()
+
     return custom_path
 
 

flaml/tune/tune.py

@@ -21,11 +21,11 @@ except (ImportError, AssertionError):
     from .analysis import ExperimentAnalysis as EA
 else:
     ray_available = True
-
 import logging
 
 from flaml.tune.spark.utils import PySparkOvertimeMonitor, check_spark
 
+from .logger import logger, logger_formatter
 from .result import DEFAULT_METRIC
 from .trial import Trial
 
@@ -41,8 +41,6 @@ except ImportError:
     internal_mlflow = False
 
 
-logger = logging.getLogger(__name__)
-logger.propagate = False
 _use_ray = True
 _runner = None
 _verbose = 0
@@ -521,10 +519,6 @@ def run(
             elif not logger.hasHandlers():
                 # Add the console handler.
                 _ch = logging.StreamHandler(stream=sys.stdout)
-                logger_formatter = logging.Formatter(
-                    "[%(name)s: %(asctime)s] {%(lineno)d} %(levelname)s - %(message)s",
-                    "%m-%d %H:%M:%S",
-                )
                 _ch.setFormatter(logger_formatter)
                 logger.addHandler(_ch)
             if verbose <= 2:
@@ -752,10 +746,16 @@ def run(
             max_concurrent = max(1, search_alg.max_concurrent)
         else:
             max_concurrent = max(1, max_spark_parallelism)
+        passed_in_n_concurrent_trials = max(n_concurrent_trials, max_concurrent)
         n_concurrent_trials = min(
             n_concurrent_trials if n_concurrent_trials > 0 else num_executors,
             max_concurrent,
         )
+        if n_concurrent_trials < passed_in_n_concurrent_trials:
+            logger.warning(
+                f"The actual concurrent trials is {n_concurrent_trials}. You can set the environment "
+                f"variable `FLAML_MAX_CONCURRENT` to '{passed_in_n_concurrent_trials}' to override the detected num of executors."
+            )
         with parallel_backend("spark"):
             with Parallel(n_jobs=n_concurrent_trials, verbose=max(0, (verbose - 1) * 50)) as parallel:
                 try:
@@ -776,8 +776,8 @@ def run(
                         and (num_samples < 0 or num_trials < num_samples)
                         and num_failures < upperbound_num_failures
                     ):
-                        if automl_info and automl_info[0] > 0 and time_budget_s < np.inf:
-                            time_budget_s -= automl_info[0]
+                        if automl_info and automl_info[1] == "all" and automl_info[0] > 0 and time_budget_s < np.inf:
+                            time_budget_s -= automl_info[0] * n_concurrent_trials
                             logger.debug(f"Remaining time budget with mlflow log latency: {time_budget_s} seconds.")
                         while len(_runner.running_trials) < n_concurrent_trials:
                             # suggest trials for spark
@@ -802,9 +802,17 @@ def run(
                         )
                         results = None
                         with PySparkOvertimeMonitor(time_start, time_budget_s, force_cancel, parallel=parallel):
-                            results = parallel(
-                                delayed(evaluation_function)(trial_to_run.config) for trial_to_run in trials_to_run
-                            )
+                            try:
+                                results = parallel(
+                                    delayed(evaluation_function)(trial_to_run.config) for trial_to_run in trials_to_run
+                                )
+                            except RuntimeError as e:
+                                logger.warning(f"RuntimeError: {e}")
+                                results = None
+                                logger.info(
+                                    "Encountered RuntimeError. Waiting 10 seconds for Spark cluster to recover before retrying."
+                                )
+                                time.sleep(10)
                         # results = [evaluation_function(trial_to_run.config) for trial_to_run in trials_to_run]
                         while results:
                             result = results.pop(0)

flaml/version.py

@@ -1 +1 @@
-__version__ = "2.3.4"
+__version__ = "2.5.0"

pyproject.toml

@@ -2,7 +2,6 @@
 license_file = "LICENSE"
 description-file = "README.md"
 
-
 [tool.pytest.ini_options]
 addopts = '-m "not conda"'
 markers = [

pytest.ini

@@ -0,0 +1,3 @@
+[pytest]
+markers =
+    spark: mark a test as requiring Spark

setup.py

@@ -51,60 +51,59 @@ setuptools.setup(
             "joblib<=1.3.2",
         ],
         "test": [
+            "numpy>=1.17,<2.0.0; python_version<'3.13'",
+            "numpy>=1.17; python_version>='3.13'",
             "jupyter",
             "lightgbm>=2.3.1",
-            "xgboost>=0.90,<2.0.0",
+            "xgboost>=0.90,<2.0.0; python_version<'3.11'",
+            "xgboost>=2.0.0; python_version>='3.11'",
             "scipy>=1.4.1",
             "pandas>=1.1.4,<2.0.0; python_version<'3.10'",
             "pandas>=1.1.4; python_version>='3.10'",
-            "scikit-learn>=1.0.0",
+            "scikit-learn>=1.2.0",
             "thop",
             "pytest>=6.1.1",
+            "pytest-rerunfailures>=13.0",
             "coverage>=5.3",
             "pre-commit",
             "torch",
             "torchvision",
-            "catboost>=0.26,<1.2; python_version<'3.11'",
-            "catboost>=0.26; python_version>='3.11'",
+            "catboost>=0.26",
             "rgf-python",
             "optuna>=2.8.0,<=3.6.1",
             "openml",
             "statsmodels>=0.12.2",
-            "psutil==5.8.0",
+            "psutil",
             "dataclasses",
-            "transformers[torch]==4.26",
+            "transformers[torch]",
             "datasets",
-            "nltk<=3.8.1",  # 3.8.2 doesn't work with mlflow
+            "evaluate",
+            "nltk!=3.8.2",  # 3.8.2 doesn't work with mlflow
             "rouge_score",
-            "hcrystalball==0.1.10",
+            "hcrystalball",
             "seqeval",
-            "pytorch-forecasting>=0.9.0,<=0.10.1; python_version<'3.11'",
-            # "pytorch-forecasting==0.10.1; python_version=='3.11'",
-            "mlflow==2.15.1",
+            "pytorch-forecasting",
+            "mlflow-skinny<=2.22.1",  # Refer to https://mvnrepository.com/artifact/org.mlflow/mlflow-spark
             "joblibspark>=0.5.0",
             "joblib<=1.3.2",
             "nbconvert",
             "nbformat",
             "ipykernel",
-            "pytorch-lightning<1.9.1",  # test_forecast_panel
-            "tensorboardX==2.6",  # test_forecast_panel
-            "requests<2.29.0",  # https://github.com/docker/docker-py/issues/3113
+            "pytorch-lightning",  # test_forecast_panel
+            "tensorboardX",  # test_forecast_panel
+            "requests",  # https://github.com/docker/docker-py/issues/3113
             "packaging",
-            "pydantic==1.10.9",
-            "sympy",
-            "wolframalpha",
             "dill",  # a drop in replacement of pickle
         ],
         "catboost": [
-            "catboost>=0.26,<1.2; python_version<'3.11'",
-            "catboost>=0.26,<=1.2.5; python_version>='3.11'",
+            "catboost>=0.26",
         ],
         "blendsearch": [
             "optuna>=2.8.0,<=3.6.1",
             "packaging",
         ],
         "ray": [
-            "ray[tune]~=1.13",
+            "ray[tune]>=1.13,<2.5.0",
         ],
         "azureml": [
             "azureml-mlflow",
@@ -117,47 +116,35 @@ setuptools.setup(
             "scikit-learn",
         ],
         "hf": [
-            "transformers[torch]==4.26",
+            "transformers[torch]>=4.26",
             "datasets",
             "nltk<=3.8.1",
             "rouge_score",
             "seqeval",
         ],
         "nlp": [  # for backward compatibility; hf is the new option name
-            "transformers[torch]==4.26",
+            "transformers[torch]>=4.26",
             "datasets",
             "nltk<=3.8.1",
             "rouge_score",
             "seqeval",
         ],
         "ts_forecast": [
-            "holidays<0.14",  # to prevent installation error for prophet
-            "prophet>=1.0.1",
+            "holidays",
+            "prophet>=1.1.5",
             "statsmodels>=0.12.2",
-            "hcrystalball==0.1.10",
+            "hcrystalball>=0.1.10",
         ],
         "forecast": [
-            "holidays<0.14",  # to prevent installation error for prophet
-            "prophet>=1.0.1",
+            "holidays",
+            "prophet>=1.1.5",
             "statsmodels>=0.12.2",
-            "hcrystalball==0.1.10",
-            "pytorch-forecasting>=0.9.0; python_version<'3.11'",
-            # "pytorch-forecasting==0.10.1; python_version=='3.11'",
-            "pytorch-lightning==1.9.0",
-            "tensorboardX==2.6",
+            "hcrystalball>=0.1.10",
+            "pytorch-forecasting>=0.10.4",
+            "pytorch-lightning>=1.9.0",
+            "tensorboardX>=2.6",
         ],
         "benchmark": ["catboost>=0.26", "psutil==5.8.0", "xgboost==1.3.3", "pandas==1.1.4"],
-        "openai": ["openai==0.27.8", "diskcache"],
-        "autogen": ["openai==0.27.8", "diskcache", "termcolor"],
-        "mathchat": ["openai==0.27.8", "diskcache", "termcolor", "sympy", "pydantic==1.10.9", "wolframalpha"],
-        "retrievechat": [
-            "openai==0.27.8",
-            "diskcache",
-            "termcolor",
-            "chromadb",
-            "tiktoken",
-            "sentence_transformers",
-        ],
         "synapse": [
             "joblibspark>=0.5.0",
             "optuna>=2.8.0,<=3.6.1",
@@ -170,10 +157,9 @@ setuptools.setup(
         "Operating System :: OS Independent",
         # Specify the Python versions you support here.
         "Programming Language :: Python :: 3",
-        "Programming Language :: Python :: 3.8",
-        "Programming Language :: Python :: 3.9",
         "Programming Language :: Python :: 3.10",
         "Programming Language :: Python :: 3.11",
+        "Programming Language :: Python :: 3.12",
     ],
-    python_requires=">=3.8",
+    python_requires=">=3.10",
 )

test/automl/test_custom_hp.py

@@ -4,8 +4,17 @@ import pytest
 
 from flaml import AutoML, tune
 
+try:
+    import transformers
 
-@pytest.mark.skipif(sys.platform == "darwin", reason="do not run on mac os")
+    _transformers_installed = True
+except ImportError:
+    _transformers_installed = False
+
+
+@pytest.mark.skipif(
+    sys.platform == "darwin" or not _transformers_installed, reason="do not run on mac os or transformers not installed"
+)
 def test_custom_hp_nlp():
     from test.nlp.utils import get_automl_settings, get_toy_data_seqclassification
 
@@ -63,5 +72,39 @@ def test_custom_hp():
     print(automl.best_config_per_estimator)
 
 
+def test_lgbm_objective():
+    """Test that objective parameter can be set via custom_hp for LGBMEstimator"""
+    import numpy as np
+
+    # Create a simple regression dataset
+    np.random.seed(42)
+    X_train = np.random.rand(100, 5)
+    y_train = np.random.rand(100) * 100  # Scale to avoid division issues with MAPE
+
+    automl = AutoML()
+    settings = {
+        "time_budget": 3,
+        "metric": "mape",
+        "task": "regression",
+        "estimator_list": ["lgbm"],
+        "verbose": 0,
+        "custom_hp": {"lgbm": {"objective": {"domain": "mape"}}},  # Fixed value, not tuned
+    }
+
+    automl.fit(X_train, y_train, **settings)
+
+    # Verify that objective was set correctly
+    assert "objective" in automl.best_config, "objective should be in best_config"
+    assert automl.best_config["objective"] == "mape", "objective should be 'mape'"
+
+    # Verify the model has the correct objective
+    if hasattr(automl.model, "estimator") and hasattr(automl.model.estimator, "get_params"):
+        model_params = automl.model.estimator.get_params()
+        assert model_params.get("objective") == "mape", "Model should use 'mape' objective"
+
+    print("Test passed: objective parameter works correctly with LGBMEstimator")
+
+
 if __name__ == "__main__":
     test_custom_hp()
+    test_lgbm_objective()

test/automl/test_extra_models.py

@@ -1,3 +1,4 @@
+import atexit
 import os
 import sys
 import unittest
@@ -15,8 +16,18 @@ from sklearn.model_selection import train_test_split
 
 from flaml import AutoML
 from flaml.automl.ml import sklearn_metric_loss_score
+from flaml.automl.spark import disable_spark_ansi_mode, restore_spark_ansi_mode
 from flaml.tune.spark.utils import check_spark
 
+try:
+    import pytorch_lightning
+
+    _pl_installed = True
+except ImportError:
+    _pl_installed = False
+
+pytestmark = pytest.mark.spark
+
 leaderboard = defaultdict(dict)
 
 warnings.simplefilter(action="ignore")
@@ -37,7 +48,7 @@ else:
             .config(
                 "spark.jars.packages",
                 (
-                    "com.microsoft.azure:synapseml_2.12:1.0.2,"
+                    "com.microsoft.azure:synapseml_2.12:1.1.0,"
                     "org.apache.hadoop:hadoop-azure:3.3.5,"
                     "com.microsoft.azure:azure-storage:8.6.6,"
                     f"org.mlflow:mlflow-spark_2.12:{mlflow.__version__}"
@@ -61,6 +72,9 @@ else:
     except ImportError:
         skip_spark = True
 
+spark, ansi_conf, adjusted = disable_spark_ansi_mode()
+atexit.register(restore_spark_ansi_mode, spark, ansi_conf, adjusted)
+
 
 def _test_regular_models(estimator_list, task):
     if isinstance(estimator_list, str):
@@ -174,7 +188,11 @@ def _test_sparse_matrix_classification(estimator):
         "n_jobs": 1,
         "model_history": True,
     }
-    X_train = scipy.sparse.random(1554, 21, dtype=int)
+    # NOTE: Avoid `dtype=int` here. On some NumPy/SciPy combinations (notably
+    # Windows + Python 3.13), `scipy.sparse.random(..., dtype=int)` may trigger
+    # integer sampling paths which raise "low is out of bounds for int32".
+    # A float sparse matrix is sufficient to validate sparse-input support.
+    X_train = scipy.sparse.random(1554, 21, dtype=np.float32)
     y_train = np.random.randint(3, size=1554)
     automl_experiment.fit(X_train=X_train, y_train=y_train, **automl_settings)
 
@@ -269,7 +287,11 @@ class TestExtraModel(unittest.TestCase):
 
     @unittest.skipIf(skip_spark, reason="Spark is not installed. Skip all spark tests.")
     def test_default_spark(self):
-        _test_spark_models(None, "classification")
+        # TODO: remove the estimator assignment once SynapseML supports spark 4+.
+        from flaml.automl.spark.utils import _spark_major_minor_version
+
+        estimator_list = ["rf_spark"] if _spark_major_minor_version[0] >= 4 else None
+        _test_spark_models(estimator_list, "classification")
 
     def test_svc(self):
         _test_regular_models("svc", "classification")
@@ -300,7 +322,7 @@ class TestExtraModel(unittest.TestCase):
     def test_avg(self):
         _test_forecast("avg")
 
-    @unittest.skipIf(skip_spark, reason="Skip on Mac or Windows")
+    @unittest.skipIf(skip_spark or not _pl_installed, reason="Skip on Mac or Windows or no pytorch_lightning.")
     def test_tcn(self):
         _test_forecast("tcn")
 

test/automl/test_forecast.py

@@ -10,7 +10,7 @@ from flaml import AutoML
 from flaml.automl.task.time_series_task import TimeSeriesTask
 
 
-def test_forecast_automl(budget=10, estimators_when_no_prophet=["arima", "sarimax", "holt-winters"]):
+def test_forecast_automl(budget=20, estimators_when_no_prophet=["arima", "sarimax", "holt-winters"]):
     # using dataframe
     import statsmodels.api as sm
 
@@ -477,7 +477,10 @@ def test_forecast_classification(budget=5):
 def get_stalliion_data():
     from pytorch_forecasting.data.examples import get_stallion_data
 
-    data = get_stallion_data()
+    # data = get_stallion_data()
+    data = pd.read_parquet(
+        "https://raw.githubusercontent.com/sktime/pytorch-forecasting/refs/heads/main/examples/data/stallion.parquet"
+    )
     # add time index - For datasets with no missing values, FLAML will automate this process
     data["time_idx"] = data["date"].dt.year * 12 + data["date"].dt.month
     data["time_idx"] -= data["time_idx"].min()
@@ -507,8 +510,12 @@ def get_stalliion_data():
     "3.11" in sys.version,
     reason="do not run on py 3.11",
 )
-def test_forecast_panel(budget=5):
-    data, special_days = get_stalliion_data()
+def test_forecast_panel(budget=30):
+    try:
+        data, special_days = get_stalliion_data()
+    except ImportError:
+        print("pytorch_forecasting not installed")
+        return
     time_horizon = 6  # predict six months
     training_cutoff = data["time_idx"].max() - time_horizon
     data["time_idx"] = data["time_idx"].astype("int")
@@ -674,11 +681,55 @@ def test_cv_step():
     print("yahoo!")
 
 
+def test_log_training_metric_ts_models():
+    """Test that log_training_metric=True works with time series models (arima, sarimax, holt-winters)."""
+    import statsmodels.api as sm
+
+    from flaml.automl.task.time_series_task import TimeSeriesTask
+
+    estimators_all = TimeSeriesTask("forecast").estimators.keys()
+    estimators_to_test = ["xgboost", "arima", "lassolars", "tcn", "snaive", "prophet", "orbit"]
+    estimators = [
+        est for est in estimators_to_test if est in estimators_all
+    ]  # not all estimators available in current python env
+    print(f"Testing estimators: {estimators}")
+
+    # Prepare data
+    data = sm.datasets.co2.load_pandas().data["co2"]
+    data = data.resample("MS").mean()
+    data = data.bfill().ffill()
+    data = data.to_frame().reset_index()
+    data = data.rename(columns={"index": "ds", "co2": "y"})
+    num_samples = data.shape[0]
+    time_horizon = 12
+    split_idx = num_samples - time_horizon
+    df = data[:split_idx]
+
+    # Test each time series model with log_training_metric=True
+    for estimator in estimators:
+        print(f"\nTesting {estimator} with log_training_metric=True")
+        automl = AutoML()
+        settings = {
+            "time_budget": 3,
+            "metric": "mape",
+            "task": "forecast",
+            "eval_method": "holdout",
+            "label": "y",
+            "log_training_metric": True,  # This should not cause errors
+            "estimator_list": [estimator],
+        }
+        automl.fit(dataframe=df, **settings, period=time_horizon, force_cancel=True)
+        print(f"  ✅ {estimator} SUCCESS with log_training_metric=True")
+        if automl.best_estimator:
+            assert automl.best_estimator == estimator
+
+
 if __name__ == "__main__":
     # test_forecast_automl(60)
     # test_multivariate_forecast_num(5)
     # test_multivariate_forecast_cat(5)
-    test_numpy()
+    # test_numpy()
     # test_forecast_classification(5)
     # test_forecast_panel(5)
     # test_cv_step()
+    test_log_training_metric_ts_models()

test/automl/test_max_iter_1.py

@@ -0,0 +1,51 @@
+import mlflow
+import numpy as np
+import pandas as pd
+
+from flaml import AutoML
+
+
+def test_max_iter_1():
+    date_rng = pd.date_range(start="2024-01-01", periods=100, freq="H")
+    X = pd.DataFrame({"ds": date_rng})
+    y_train_24h = np.random.rand(len(X)) * 100
+
+    # AutoML
+    settings = {
+        "max_iter": 1,
+        "estimator_list": ["xgboost", "lgbm"],
+        "starting_points": {"xgboost": {}, "lgbm": {}},
+        "task": "ts_forecast",
+        "log_file_name": "test_max_iter_1.log",
+        "seed": 41,
+        "mlflow_exp_name": "TestExp-max_iter-1",
+        "use_spark": False,
+        "n_concurrent_trials": 1,
+        "verbose": 1,
+        "featurization": "off",
+        "metric": "rmse",
+        "mlflow_logging": True,
+    }
+
+    automl = AutoML(**settings)
+
+    with mlflow.start_run(run_name="AutoMLModel-XGBoost-and-LGBM-max_iter_1"):
+        automl.fit(
+            X_train=X,
+            y_train=y_train_24h,
+            period=24,
+            X_val=X,
+            y_val=y_train_24h,
+            split_ratio=0,
+            force_cancel=False,
+        )
+
+    assert automl.model is not None, "AutoML failed to return a model"
+    assert automl.best_run_id is not None, "Best run ID should not be None with mlflow logging"
+
+    print("Best model:", automl.model)
+    print("Best run ID:", automl.best_run_id)
+
+
+if __name__ == "__main__":
+    test_max_iter_1()

test/automl/test_mlflow.py

@@ -10,6 +10,18 @@ from flaml import AutoML
 
 
 class TestMLFlowLoggingParam:
+    def test_update_and_install_requirements(self):
+        import mlflow
+        from sklearn import tree
+
+        from flaml.fabric.mlflow import update_and_install_requirements
+
+        with mlflow.start_run(run_name="test") as run:
+            sk_model = tree.DecisionTreeClassifier()
+            mlflow.sklearn.log_model(sk_model, "model", registered_model_name="test")
+
+        update_and_install_requirements(run_id=run.info.run_id)
+
     def test_should_start_new_run_by_default(self, automl_settings):
         with mlflow.start_run() as parent_run:
             automl = AutoML()

test/automl/test_multiclass.py

@@ -181,6 +181,49 @@ class TestMultiClass(unittest.TestCase):
         }
         automl.fit(X_train=X_train, y_train=y_train, **settings)
 
+    def test_ensemble_final_estimator_params_not_tuned(self):
+        """Test that final_estimator parameters in ensemble are not automatically tuned.
+
+        This test verifies that when a custom final_estimator is provided with specific
+        parameters, those parameters are used as-is without any hyperparameter tuning.
+        """
+        from sklearn.linear_model import LogisticRegression
+
+        automl = AutoML()
+        X_train, y_train = load_wine(return_X_y=True)
+
+        # Create a LogisticRegression with specific non-default parameters
+        custom_params = {
+            "C": 0.5,  # Non-default value
+            "max_iter": 50,  # Non-default value
+            "random_state": 42,
+        }
+        final_est = LogisticRegression(**custom_params)
+
+        settings = {
+            "time_budget": 5,
+            "estimator_list": ["rf", "lgbm"],
+            "task": "classification",
+            "ensemble": {
+                "final_estimator": final_est,
+                "passthrough": False,
+            },
+            "n_jobs": 1,
+        }
+        automl.fit(X_train=X_train, y_train=y_train, **settings)
+
+        # Verify that the final estimator in the stacker uses the exact parameters we specified
+        if hasattr(automl.model, "final_estimator_"):
+            # The model is a StackingClassifier
+            fitted_final_estimator = automl.model.final_estimator_
+            assert (
+                abs(fitted_final_estimator.C - custom_params["C"]) < 1e-9
+            ), f"Expected C={custom_params['C']}, but got {fitted_final_estimator.C}"
+            assert (
+                fitted_final_estimator.max_iter == custom_params["max_iter"]
+            ), f"Expected max_iter={custom_params['max_iter']}, but got {fitted_final_estimator.max_iter}"
+            print("✓ Final estimator parameters were preserved (not tuned)")
+
     def test_dataframe(self):
         self.test_classification(True)
 
@@ -235,6 +278,34 @@ class TestMultiClass(unittest.TestCase):
         except ImportError:
             pass
 
+    def test_invalid_custom_metric(self):
+        """Test that proper error is raised when custom_metric is called instead of passed."""
+        from sklearn.datasets import load_iris
+
+        X_train, y_train = load_iris(return_X_y=True)
+
+        # Test with non-callable metric in __init__
+        with self.assertRaises(ValueError) as context:
+            automl = AutoML(metric=123)  # passing an int instead of function
+        self.assertIn("must be either a string or a callable function", str(context.exception))
+        self.assertIn("but got int", str(context.exception))
+
+        # Test with non-callable metric in fit
+        automl = AutoML()
+        with self.assertRaises(ValueError) as context:
+            automl.fit(X_train=X_train, y_train=y_train, metric=[], task="classification", time_budget=1)
+        self.assertIn("must be either a string or a callable function", str(context.exception))
+        self.assertIn("but got list", str(context.exception))
+
+        # Test with tuple (simulating result of calling a function that returns tuple)
+        with self.assertRaises(ValueError) as context:
+            automl = AutoML()
+            automl.fit(
+                X_train=X_train, y_train=y_train, metric=(0.5, {"loss": 0.5}), task="classification", time_budget=1
+            )
+        self.assertIn("must be either a string or a callable function", str(context.exception))
+        self.assertIn("but got tuple", str(context.exception))
+
     def test_classification(self, as_frame=False):
         automl_experiment = AutoML()
         automl_settings = {
@@ -368,7 +439,11 @@ class TestMultiClass(unittest.TestCase):
             "n_jobs": 1,
             "model_history": True,
         }
-        X_train = scipy.sparse.random(1554, 21, dtype=int)
+        # NOTE: Avoid `dtype=int` here. On some NumPy/SciPy combinations (notably
+        # Windows + Python 3.13), `scipy.sparse.random(..., dtype=int)` may trigger
+        # integer sampling paths which raise "low is out of bounds for int32".
+        # A float sparse matrix is sufficient to validate sparse-input support.
+        X_train = scipy.sparse.random(1554, 21, dtype=np.float32)
         y_train = np.random.randint(3, size=1554)
         automl_experiment.fit(X_train=X_train, y_train=y_train, **automl_settings)
         print(automl_experiment.classes_)
@@ -531,6 +606,32 @@ class TestMultiClass(unittest.TestCase):
         print(f"Best accuracy on validation data: {new_automl_val_accuracy:.4g}")
         # print('Training duration of best run: {0:.4g} s'.format(new_automl_experiment.best_config_train_time))
 
+    def test_starting_points_should_improve_performance(self):
+        N = 10000  # a large N is needed to see the improvement
+        X_train, y_train = load_iris(return_X_y=True)
+        X_train = np.concatenate([X_train + 0.1 * i for i in range(N)], axis=0)
+        y_train = np.concatenate([y_train] * N, axis=0)
+
+        am1 = AutoML()
+        am1.fit(X_train, y_train, estimator_list=["lgbm"], time_budget=3, seed=11)
+
+        am2 = AutoML()
+        am2.fit(
+            X_train,
+            y_train,
+            estimator_list=["lgbm"],
+            time_budget=2,
+            seed=11,
+            starting_points=am1.best_config_per_estimator,
+        )
+
+        print(f"am1.best_loss: {am1.best_loss:.4f}")
+        print(f"am2.best_loss: {am2.best_loss:.4f}")
+
+        assert np.round(am2.best_loss, 4) <= np.round(
+            am1.best_loss, 4
+        ), "Starting points should help improve the performance!"
+
 
 if __name__ == "__main__":
     unittest.main()

test/automl/test_no_overlap.py

@@ -0,0 +1,272 @@
+"""Test to ensure correct label overlap handling for classification tasks"""
+import numpy as np
+import pandas as pd
+from sklearn.datasets import load_iris, make_classification
+
+from flaml import AutoML
+
+
+def test_allow_label_overlap_true():
+    """Test with allow_label_overlap=True (fast mode, default)"""
+    # Load iris dataset
+    dic_data = load_iris(as_frame=True)
+    iris_data = dic_data["frame"]
+
+    # Prepare data
+    x_train = iris_data[["sepal length (cm)", "sepal width (cm)", "petal length (cm)", "petal width (cm)"]].to_numpy()
+    y_train = iris_data["target"]
+
+    # Train with fast mode (default)
+    automl = AutoML()
+    automl_settings = {
+        "max_iter": 5,
+        "metric": "accuracy",
+        "task": "classification",
+        "estimator_list": ["lgbm"],
+        "eval_method": "holdout",
+        "split_type": "stratified",
+        "keep_search_state": True,
+        "retrain_full": False,
+        "auto_augment": False,
+        "verbose": 0,
+        "allow_label_overlap": True,  # Fast mode
+    }
+    automl.fit(x_train, y_train, **automl_settings)
+
+    # Check results
+    input_size = len(x_train)
+    train_size = len(automl._state.X_train)
+    val_size = len(automl._state.X_val)
+
+    # With stratified split on balanced data, fast mode may have no overlap
+    assert (
+        train_size + val_size >= input_size
+    ), f"Inconsistent sizes. Input: {input_size}, Train: {train_size}, Val: {val_size}"
+
+    # Verify all classes are represented in both sets
+    train_labels = set(np.unique(automl._state.y_train))
+    val_labels = set(np.unique(automl._state.y_val))
+    all_labels = set(np.unique(y_train))
+
+    assert train_labels == all_labels, f"Not all labels in train. All: {all_labels}, Train: {train_labels}"
+    assert val_labels == all_labels, f"Not all labels in val. All: {all_labels}, Val: {val_labels}"
+
+    print(
+        f"✓ Test passed (fast mode): Input: {input_size}, Train: {train_size}, Val: {val_size}, "
+        f"Overlap: {train_size + val_size - input_size}"
+    )
+
+
+def test_allow_label_overlap_false():
+    """Test with allow_label_overlap=False (precise mode)"""
+    # Load iris dataset
+    dic_data = load_iris(as_frame=True)
+    iris_data = dic_data["frame"]
+
+    # Prepare data
+    x_train = iris_data[["sepal length (cm)", "sepal width (cm)", "petal length (cm)", "petal width (cm)"]].to_numpy()
+    y_train = iris_data["target"]
+
+    # Train with precise mode
+    automl = AutoML()
+    automl_settings = {
+        "max_iter": 5,
+        "metric": "accuracy",
+        "task": "classification",
+        "estimator_list": ["lgbm"],
+        "eval_method": "holdout",
+        "split_type": "stratified",
+        "keep_search_state": True,
+        "retrain_full": False,
+        "auto_augment": False,
+        "verbose": 0,
+        "allow_label_overlap": False,  # Precise mode
+    }
+    automl.fit(x_train, y_train, **automl_settings)
+
+    # Check that there's no overlap (or minimal overlap for single-instance classes)
+    input_size = len(x_train)
+    train_size = len(automl._state.X_train)
+    val_size = len(automl._state.X_val)
+
+    # Verify all classes are represented
+    all_labels = set(np.unique(y_train))
+
+    # Should have no overlap or minimal overlap
+    overlap = train_size + val_size - input_size
+    assert overlap <= len(all_labels), f"Excessive overlap: {overlap}"
+
+    # Verify all classes are represented
+    train_labels = set(np.unique(automl._state.y_train))
+    val_labels = set(np.unique(automl._state.y_val))
+
+    combined_labels = train_labels.union(val_labels)
+    assert combined_labels == all_labels, f"Not all labels present. All: {all_labels}, Combined: {combined_labels}"
+
+    print(
+        f"✓ Test passed (precise mode): Input: {input_size}, Train: {train_size}, Val: {val_size}, "
+        f"Overlap: {overlap}"
+    )
+
+
+def test_uniform_split_with_overlap_control():
+    """Test with uniform split and both overlap modes"""
+    # Load iris dataset
+    dic_data = load_iris(as_frame=True)
+    iris_data = dic_data["frame"]
+
+    # Prepare data
+    x_train = iris_data[["sepal length (cm)", "sepal width (cm)", "petal length (cm)", "petal width (cm)"]].to_numpy()
+    y_train = iris_data["target"]
+
+    # Test precise mode with uniform split
+    automl = AutoML()
+    automl_settings = {
+        "max_iter": 5,
+        "metric": "accuracy",
+        "task": "classification",
+        "estimator_list": ["lgbm"],
+        "eval_method": "holdout",
+        "split_type": "uniform",
+        "keep_search_state": True,
+        "retrain_full": False,
+        "auto_augment": False,
+        "verbose": 0,
+        "allow_label_overlap": False,  # Precise mode
+    }
+    automl.fit(x_train, y_train, **automl_settings)
+
+    input_size = len(x_train)
+    train_size = len(automl._state.X_train)
+    val_size = len(automl._state.X_val)
+
+    # Verify all classes are represented
+    train_labels = set(np.unique(automl._state.y_train))
+    val_labels = set(np.unique(automl._state.y_val))
+    all_labels = set(np.unique(y_train))
+
+    combined_labels = train_labels.union(val_labels)
+    assert combined_labels == all_labels, "Not all labels present with uniform split"
+
+    print(f"✓ Test passed (uniform split): Input: {input_size}, Train: {train_size}, Val: {val_size}")
+
+
+def test_with_sample_weights():
+    """Test label overlap handling with sample weights"""
+    # Create a simple dataset
+    X, y = make_classification(
+        n_samples=200,
+        n_features=10,
+        n_informative=5,
+        n_redundant=2,
+        n_classes=3,
+        n_clusters_per_class=1,
+        random_state=42,
+    )
+
+    # Create sample weights (giving more weight to some samples)
+    sample_weight = np.random.uniform(0.5, 2.0, size=len(y))
+
+    # Test fast mode with sample weights
+    automl_fast = AutoML()
+    automl_fast.fit(
+        X,
+        y,
+        task="classification",
+        metric="accuracy",
+        estimator_list=["lgbm"],
+        eval_method="holdout",
+        split_type="stratified",
+        max_iter=3,
+        keep_search_state=True,
+        retrain_full=False,
+        auto_augment=False,
+        verbose=0,
+        allow_label_overlap=True,  # Fast mode
+        sample_weight=sample_weight,
+    )
+
+    # Verify all labels present
+    train_labels_fast = set(np.unique(automl_fast._state.y_train))
+    val_labels_fast = set(np.unique(automl_fast._state.y_val))
+    all_labels = set(np.unique(y))
+
+    assert train_labels_fast == all_labels, "Not all labels in train (fast mode with weights)"
+    assert val_labels_fast == all_labels, "Not all labels in val (fast mode with weights)"
+
+    # Test precise mode with sample weights
+    automl_precise = AutoML()
+    automl_precise.fit(
+        X,
+        y,
+        task="classification",
+        metric="accuracy",
+        estimator_list=["lgbm"],
+        eval_method="holdout",
+        split_type="stratified",
+        max_iter=3,
+        keep_search_state=True,
+        retrain_full=False,
+        auto_augment=False,
+        verbose=0,
+        allow_label_overlap=False,  # Precise mode
+        sample_weight=sample_weight,
+    )
+
+    # Verify all labels present
+    train_labels_precise = set(np.unique(automl_precise._state.y_train))
+    val_labels_precise = set(np.unique(automl_precise._state.y_val))
+
+    combined_labels = train_labels_precise.union(val_labels_precise)
+    assert combined_labels == all_labels, "Not all labels present (precise mode with weights)"
+
+    print("✓ Test passed with sample weights (fast and precise modes)")
+
+
+def test_single_instance_class():
+    """Test handling of single-instance classes"""
+    # Create imbalanced dataset where one class has only 1 instance
+    X = np.random.randn(50, 4)
+    y = np.array([0] * 40 + [1] * 9 + [2] * 1)  # Class 2 has only 1 instance
+
+    # Test precise mode - should add single instance to both sets
+    automl = AutoML()
+    automl.fit(
+        X,
+        y,
+        task="classification",
+        metric="accuracy",
+        estimator_list=["lgbm"],
+        eval_method="holdout",
+        split_type="uniform",
+        max_iter=3,
+        keep_search_state=True,
+        retrain_full=False,
+        auto_augment=False,
+        verbose=0,
+        allow_label_overlap=False,  # Precise mode
+    )
+
+    # Verify all labels present
+    train_labels = set(np.unique(automl._state.y_train))
+    val_labels = set(np.unique(automl._state.y_val))
+    all_labels = set(np.unique(y))
+
+    # Single-instance class should be in both sets
+    combined_labels = train_labels.union(val_labels)
+    assert combined_labels == all_labels, "Not all labels present with single-instance class"
+
+    # Check that single-instance class (label 2) is in both sets
+    assert 2 in train_labels, "Single-instance class not in train"
+    assert 2 in val_labels, "Single-instance class not in val"
+
+    print("✓ Test passed with single-instance class")
+
+
+if __name__ == "__main__":
+    test_allow_label_overlap_true()
+    test_allow_label_overlap_false()
+    test_uniform_split_with_overlap_control()
+    test_with_sample_weights()
+    test_single_instance_class()
+    print("\n✓ All tests passed!")

test/automl/test_notebook_example.py

@@ -1,8 +1,23 @@
 import sys
 
 import pytest
-from minio.error import ServerError
-from openml.exceptions import OpenMLServerException
+
+try:
+    from minio.error import ServerError
+except ImportError:
+
+    class ServerError(Exception):
+        pass
+
+
+try:
+    from openml.exceptions import OpenMLServerException
+except ImportError:
+
+    class OpenMLServerException(Exception):
+        pass
+
+
 from requests.exceptions import ChunkedEncodingError, SSLError
 
 
@@ -64,6 +79,9 @@ def test_automl(budget=5, dataset_format="dataframe", hpo_method=None):
     automl.fit(X_train=X_train, y_train=y_train, **settings)
     """ retrieve best config and best learner """
     print("Best ML leaner:", automl.best_estimator)
+    if not automl.best_estimator:
+        print("Training budget is not sufficient")
+        return
     print("Best hyperparmeter config:", automl.best_config)
     print(f"Best accuracy on validation data: {1 - automl.best_loss:.4g}")
     print(f"Training duration of best run: {automl.best_config_train_time:.4g} s")

test/automl/test_preprocess_api.py

@@ -0,0 +1,236 @@
+"""Tests for the public preprocessor APIs."""
+import unittest
+
+import numpy as np
+import pandas as pd
+from sklearn.datasets import load_breast_cancer, load_diabetes
+
+from flaml import AutoML
+
+
+class TestPreprocessAPI(unittest.TestCase):
+    """Test cases for the public preprocess() API methods."""
+
+    def test_automl_preprocess_before_fit(self):
+        """Test that calling preprocess before fit raises an error."""
+        automl = AutoML()
+        X_test = np.array([[1, 2, 3], [4, 5, 6]])
+
+        with self.assertRaises(AttributeError) as context:
+            automl.preprocess(X_test)
+        # Check that an error is raised about not being fitted
+        self.assertIn("fit()", str(context.exception))
+
+    def test_automl_preprocess_classification(self):
+        """Test task-level preprocessing for classification."""
+        # Load dataset
+        X, y = load_breast_cancer(return_X_y=True)
+        X_train, y_train = X[:400], y[:400]
+        X_test = X[400:450]
+
+        # Train AutoML
+        automl = AutoML()
+        automl_settings = {
+            "max_iter": 5,
+            "task": "classification",
+            "metric": "accuracy",
+            "estimator_list": ["lgbm"],
+            "verbose": 0,
+        }
+        automl.fit(X_train, y_train, **automl_settings)
+
+        # Test task-level preprocessing
+        X_preprocessed = automl.preprocess(X_test)
+
+        # Verify the output is not None and has the right shape
+        self.assertIsNotNone(X_preprocessed)
+        self.assertEqual(X_preprocessed.shape[0], X_test.shape[0])
+
+    def test_automl_preprocess_regression(self):
+        """Test task-level preprocessing for regression."""
+        # Load dataset
+        X, y = load_diabetes(return_X_y=True)
+        X_train, y_train = X[:300], y[:300]
+        X_test = X[300:350]
+
+        # Train AutoML
+        automl = AutoML()
+        automl_settings = {
+            "max_iter": 5,
+            "task": "regression",
+            "metric": "r2",
+            "estimator_list": ["lgbm"],
+            "verbose": 0,
+        }
+        automl.fit(X_train, y_train, **automl_settings)
+
+        # Test task-level preprocessing
+        X_preprocessed = automl.preprocess(X_test)
+
+        # Verify the output
+        self.assertIsNotNone(X_preprocessed)
+        self.assertEqual(X_preprocessed.shape[0], X_test.shape[0])
+
+    def test_automl_preprocess_with_dataframe(self):
+        """Test task-level preprocessing with pandas DataFrame."""
+        # Create a simple dataset
+        X_train = pd.DataFrame(
+            {
+                "feature1": [1, 2, 3, 4, 5] * 20,
+                "feature2": [5, 4, 3, 2, 1] * 20,
+                "category": ["a", "b", "a", "b", "a"] * 20,
+            }
+        )
+        y_train = pd.Series([0, 1, 0, 1, 0] * 20)
+
+        X_test = pd.DataFrame(
+            {
+                "feature1": [6, 7, 8],
+                "feature2": [1, 2, 3],
+                "category": ["a", "b", "a"],
+            }
+        )
+
+        # Train AutoML
+        automl = AutoML()
+        automl_settings = {
+            "max_iter": 5,
+            "task": "classification",
+            "metric": "accuracy",
+            "estimator_list": ["lgbm"],
+            "verbose": 0,
+        }
+        automl.fit(X_train, y_train, **automl_settings)
+
+        # Test preprocessing
+        X_preprocessed = automl.preprocess(X_test)
+
+        # Verify the output - check the number of rows matches
+        self.assertIsNotNone(X_preprocessed)
+        preprocessed_len = len(X_preprocessed) if hasattr(X_preprocessed, "__len__") else X_preprocessed.shape[0]
+        self.assertEqual(preprocessed_len, len(X_test))
+
+    def test_estimator_preprocess(self):
+        """Test estimator-level preprocessing."""
+        # Load dataset
+        X, y = load_breast_cancer(return_X_y=True)
+        X_train, y_train = X[:400], y[:400]
+        X_test = X[400:450]
+
+        # Train AutoML
+        automl = AutoML()
+        automl_settings = {
+            "max_iter": 5,
+            "task": "classification",
+            "metric": "accuracy",
+            "estimator_list": ["lgbm"],
+            "verbose": 0,
+        }
+        automl.fit(X_train, y_train, **automl_settings)
+
+        # Get the trained estimator
+        estimator = automl.model
+        self.assertIsNotNone(estimator)
+
+        # First apply task-level preprocessing
+        X_task_preprocessed = automl.preprocess(X_test)
+
+        # Then apply estimator-level preprocessing
+        X_estimator_preprocessed = estimator.preprocess(X_task_preprocessed)
+
+        # Verify the output
+        self.assertIsNotNone(X_estimator_preprocessed)
+        self.assertEqual(X_estimator_preprocessed.shape[0], X_test.shape[0])
+
+    def test_preprocess_pipeline(self):
+        """Test the complete preprocessing pipeline (task-level then estimator-level)."""
+        # Load dataset
+        X, y = load_breast_cancer(return_X_y=True)
+        X_train, y_train = X[:400], y[:400]
+        X_test = X[400:450]
+
+        # Train AutoML
+        automl = AutoML()
+        automl_settings = {
+            "max_iter": 5,
+            "task": "classification",
+            "metric": "accuracy",
+            "estimator_list": ["lgbm"],
+            "verbose": 0,
+        }
+        automl.fit(X_train, y_train, **automl_settings)
+
+        # Apply the complete preprocessing pipeline
+        X_task_preprocessed = automl.preprocess(X_test)
+        X_final = automl.model.preprocess(X_task_preprocessed)
+
+        # Verify predictions work with preprocessed data
+        # The internal predict already does this preprocessing,
+        # but we verify our manual preprocessing gives consistent results
+        y_pred_manual = automl.model._model.predict(X_final)
+        y_pred_auto = automl.predict(X_test)
+
+        # Both should give the same predictions
+        np.testing.assert_array_equal(y_pred_manual, y_pred_auto)
+
+    def test_preprocess_with_mixed_types(self):
+        """Test preprocessing with mixed data types."""
+        # Create dataset with mixed types
+        X_train = pd.DataFrame(
+            {
+                "numeric1": np.random.rand(100),
+                "numeric2": np.random.randint(0, 100, 100),
+                "categorical": np.random.choice(["cat", "dog", "bird"], 100),
+                "boolean": np.random.choice([True, False], 100),
+            }
+        )
+        y_train = pd.Series(np.random.randint(0, 2, 100))
+
+        X_test = pd.DataFrame(
+            {
+                "numeric1": np.random.rand(10),
+                "numeric2": np.random.randint(0, 100, 10),
+                "categorical": np.random.choice(["cat", "dog", "bird"], 10),
+                "boolean": np.random.choice([True, False], 10),
+            }
+        )
+
+        # Train AutoML
+        automl = AutoML()
+        automl_settings = {
+            "max_iter": 5,
+            "task": "classification",
+            "metric": "accuracy",
+            "estimator_list": ["lgbm"],
+            "verbose": 0,
+        }
+        automl.fit(X_train, y_train, **automl_settings)
+
+        # Test preprocessing
+        X_preprocessed = automl.preprocess(X_test)
+
+        # Verify the output
+        self.assertIsNotNone(X_preprocessed)
+
+    def test_estimator_preprocess_without_automl(self):
+        """Test that estimator.preprocess() can be used independently."""
+        from flaml.automl.model import LGBMEstimator
+
+        # Create a simple estimator
+        X_train = np.random.rand(100, 5)
+        y_train = np.random.randint(0, 2, 100)
+
+        estimator = LGBMEstimator(task="classification")
+        estimator.fit(X_train, y_train)
+
+        # Test preprocessing
+        X_test = np.random.rand(10, 5)
+        X_preprocessed = estimator.preprocess(X_test)
+
+        # Verify the output
+        self.assertIsNotNone(X_preprocessed)
+        self.assertEqual(X_preprocessed.shape, X_test.shape)
+
+
+if __name__ == "__main__":
+    unittest.main()

test/automl/test_python_log.py

@@ -38,7 +38,7 @@ class TestLogging(unittest.TestCase):
                 "keep_search_state": True,
                 "learner_selector": "roundrobin",
             }
-            X_train, y_train = fetch_california_housing(return_X_y=True)
+            X_train, y_train = fetch_california_housing(return_X_y=True, data_home="test")
             n = len(y_train) >> 1
             print(automl.model, automl.classes_, automl.predict(X_train))
             automl.fit(

test/automl/test_regression.py

@@ -47,7 +47,7 @@ class TestRegression(unittest.TestCase):
             "n_jobs": 1,
             "model_history": True,
         }
-        X_train, y_train = fetch_california_housing(return_X_y=True)
+        X_train, y_train = fetch_california_housing(return_X_y=True, data_home="test")
         n = int(len(y_train) * 9 // 10)
         automl.fit(X_train=X_train[:n], y_train=y_train[:n], X_val=X_train[n:], y_val=y_train[n:], **automl_settings)
         assert automl._state.eval_method == "holdout"
@@ -130,7 +130,7 @@ class TestRegression(unittest.TestCase):
         )
         automl.fit(X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **settings)
 
-    def test_parallel(self, hpo_method=None):
+    def test_parallel_and_pickle(self, hpo_method=None):
         automl_experiment = AutoML()
         automl_settings = {
             "time_budget": 10,
@@ -141,7 +141,7 @@ class TestRegression(unittest.TestCase):
             "n_concurrent_trials": 10,
             "hpo_method": hpo_method,
         }
-        X_train, y_train = fetch_california_housing(return_X_y=True)
+        X_train, y_train = fetch_california_housing(return_X_y=True, data_home="test")
         try:
             automl_experiment.fit(X_train=X_train, y_train=y_train, **automl_settings)
             print(automl_experiment.predict(X_train))
@@ -153,6 +153,18 @@ class TestRegression(unittest.TestCase):
         except ImportError:
             return
 
+        # test pickle and load_pickle, should work for prediction
+        automl_experiment.pickle("automl_xgboost_spark.pkl")
+        automl_loaded = AutoML().load_pickle("automl_xgboost_spark.pkl")
+        assert automl_loaded.best_estimator == automl_experiment.best_estimator
+        assert automl_loaded.best_loss == automl_experiment.best_loss
+        automl_loaded.predict(X_train)
+
+        import shutil
+
+        shutil.rmtree("automl_xgboost_spark.pkl", ignore_errors=True)
+        shutil.rmtree("automl_xgboost_spark.pkl.flaml_artifacts", ignore_errors=True)
+
     def test_sparse_matrix_regression_holdout(self):
         X_train = scipy.sparse.random(8, 100)
         y_train = np.random.uniform(size=8)
@@ -268,7 +280,7 @@ def test_reproducibility_of_regression_models(estimator: str):
         "skip_transform": True,
         "retrain_full": True,
     }
-    X, y = fetch_california_housing(return_X_y=True, as_frame=True)
+    X, y = fetch_california_housing(return_X_y=True, as_frame=True, data_home="test")
     automl.fit(X_train=X, y_train=y, **automl_settings)
     best_model = automl.model
     assert best_model is not None
@@ -314,7 +326,7 @@ def test_reproducibility_of_catboost_regression_model():
         "skip_transform": True,
         "retrain_full": True,
     }
-    X, y = fetch_california_housing(return_X_y=True, as_frame=True)
+    X, y = fetch_california_housing(return_X_y=True, as_frame=True, data_home="test")
     automl.fit(X_train=X, y_train=y, **automl_settings)
     best_model = automl.model
     assert best_model is not None
@@ -360,7 +372,7 @@ def test_reproducibility_of_lgbm_regression_model():
         "skip_transform": True,
         "retrain_full": True,
     }
-    X, y = fetch_california_housing(return_X_y=True, as_frame=True)
+    X, y = fetch_california_housing(return_X_y=True, as_frame=True, data_home="test")
     automl.fit(X_train=X, y_train=y, **automl_settings)
     best_model = automl.model
     assert best_model is not None
@@ -424,7 +436,7 @@ def test_reproducibility_of_underlying_regression_models(estimator: str):
         "skip_transform": True,
         "retrain_full": False,
     }
-    X, y = fetch_california_housing(return_X_y=True, as_frame=True)
+    X, y = fetch_california_housing(return_X_y=True, as_frame=True, data_home="test")
     automl.fit(X_train=X, y_train=y, **automl_settings)
     best_model = automl.model
     assert best_model is not None

test/automl/test_score.py

@@ -142,7 +142,7 @@ class TestScore:
     def test_regression(self):
         automl_experiment = AutoML()
 
-        X_train, y_train = fetch_california_housing(return_X_y=True)
+        X_train, y_train = fetch_california_housing(return_X_y=True, data_home="test")
         n = int(len(y_train) * 9 // 10)
 
         for each_estimator in [

test/automl/test_sklearn_17_compat.py

@@ -0,0 +1,89 @@
+"""Test sklearn 1.7+ compatibility for estimator type detection.
+
+This test ensures that FLAML estimators are properly recognized as
+regressors or classifiers by sklearn's is_regressor() and is_classifier()
+functions, which is required for sklearn 1.7+ ensemble methods.
+"""
+
+import pytest
+from sklearn.base import is_classifier, is_regressor
+
+from flaml.automl.model import (
+    ExtraTreesEstimator,
+    LGBMEstimator,
+    RandomForestEstimator,
+    XGBoostSklearnEstimator,
+)
+
+
+def test_extra_trees_regressor_type():
+    """Test that ExtraTreesEstimator with regression task is recognized as regressor."""
+    est = ExtraTreesEstimator(task="regression")
+    assert is_regressor(est), "ExtraTreesEstimator(task='regression') should be recognized as a regressor"
+    assert not is_classifier(est), "ExtraTreesEstimator(task='regression') should not be recognized as a classifier"
+
+
+def test_extra_trees_classifier_type():
+    """Test that ExtraTreesEstimator with classification task is recognized as classifier."""
+    est = ExtraTreesEstimator(task="binary")
+    assert is_classifier(est), "ExtraTreesEstimator(task='binary') should be recognized as a classifier"
+    assert not is_regressor(est), "ExtraTreesEstimator(task='binary') should not be recognized as a regressor"
+
+    est = ExtraTreesEstimator(task="multiclass")
+    assert is_classifier(est), "ExtraTreesEstimator(task='multiclass') should be recognized as a classifier"
+    assert not is_regressor(est), "ExtraTreesEstimator(task='multiclass') should not be recognized as a regressor"
+
+
+def test_random_forest_regressor_type():
+    """Test that RandomForestEstimator with regression task is recognized as regressor."""
+    est = RandomForestEstimator(task="regression")
+    assert is_regressor(est), "RandomForestEstimator(task='regression') should be recognized as a regressor"
+    assert not is_classifier(est), "RandomForestEstimator(task='regression') should not be recognized as a classifier"
+
+
+def test_random_forest_classifier_type():
+    """Test that RandomForestEstimator with classification task is recognized as classifier."""
+    est = RandomForestEstimator(task="binary")
+    assert is_classifier(est), "RandomForestEstimator(task='binary') should be recognized as a classifier"
+    assert not is_regressor(est), "RandomForestEstimator(task='binary') should not be recognized as a regressor"
+
+
+def test_lgbm_regressor_type():
+    """Test that LGBMEstimator with regression task is recognized as regressor."""
+    est = LGBMEstimator(task="regression")
+    assert is_regressor(est), "LGBMEstimator(task='regression') should be recognized as a regressor"
+    assert not is_classifier(est), "LGBMEstimator(task='regression') should not be recognized as a classifier"
+
+
+def test_lgbm_classifier_type():
+    """Test that LGBMEstimator with classification task is recognized as classifier."""
+    est = LGBMEstimator(task="binary")
+    assert is_classifier(est), "LGBMEstimator(task='binary') should be recognized as a classifier"
+    assert not is_regressor(est), "LGBMEstimator(task='binary') should not be recognized as a regressor"
+
+
+def test_xgboost_regressor_type():
+    """Test that XGBoostSklearnEstimator with regression task is recognized as regressor."""
+    est = XGBoostSklearnEstimator(task="regression")
+    assert is_regressor(est), "XGBoostSklearnEstimator(task='regression') should be recognized as a regressor"
+    assert not is_classifier(est), "XGBoostSklearnEstimator(task='regression') should not be recognized as a classifier"
+
+
+def test_xgboost_classifier_type():
+    """Test that XGBoostSklearnEstimator with classification task is recognized as classifier."""
+    est = XGBoostSklearnEstimator(task="binary")
+    assert is_classifier(est), "XGBoostSklearnEstimator(task='binary') should be recognized as a classifier"
+    assert not is_regressor(est), "XGBoostSklearnEstimator(task='binary') should not be recognized as a regressor"
+
+
+if __name__ == "__main__":
+    # Run all tests
+    test_extra_trees_regressor_type()
+    test_extra_trees_classifier_type()
+    test_random_forest_regressor_type()
+    test_random_forest_classifier_type()
+    test_lgbm_regressor_type()
+    test_lgbm_classifier_type()
+    test_xgboost_regressor_type()
+    test_xgboost_classifier_type()
+    print("All sklearn 1.7+ compatibility tests passed!")

test/automl/test_training_log.py

@@ -30,7 +30,7 @@ class TestTrainingLog(unittest.TestCase):
                 "keep_search_state": True,
                 "estimator_list": estimator_list,
             }
-            X_train, y_train = fetch_california_housing(return_X_y=True)
+            X_train, y_train = fetch_california_housing(return_X_y=True, data_home="test")
             automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
             # Check if the training log file is populated.
             self.assertTrue(os.path.exists(filename))

test/automl/test_warmstart.py

@@ -108,7 +108,14 @@ class TestWarmStart(unittest.TestCase):
 
     def test_FLAML_sample_size_in_starting_points(self):
         from minio.error import ServerError
-        from openml.exceptions import OpenMLServerException
+
+        try:
+            from openml.exceptions import OpenMLServerException
+        except ImportError:
+
+            class OpenMLServerException(Exception):
+                pass
+
         from requests.exceptions import ChunkedEncodingError, SSLError
 
         from flaml import AutoML

test/check_dependency.py

@@ -0,0 +1,60 @@
+import subprocess
+from importlib.metadata import distributions
+
+installed_libs = sorted(f"{dist.metadata['Name']}=={dist.version}" for dist in distributions())
+
+first_tier_dependencies = [
+    "numpy",
+    "jupyter",
+    "lightgbm",
+    "xgboost",
+    "scipy",
+    "pandas",
+    "scikit-learn",
+    "thop",
+    "pytest",
+    "pytest-rerunfailures",
+    "coverage",
+    "pre-commit",
+    "torch",
+    "torchvision",
+    "catboost",
+    "rgf-python",
+    "optuna",
+    "openml",
+    "statsmodels",
+    "psutil",
+    "dataclasses",
+    "transformers[torch]",
+    "transformers",
+    "datasets",
+    "evaluate",
+    "nltk",
+    "rouge_score",
+    "hcrystalball",
+    "seqeval",
+    "pytorch-forecasting",
+    "mlflow-skinny",
+    "joblibspark",
+    "joblib",
+    "nbconvert",
+    "nbformat",
+    "ipykernel",
+    "pytorch-lightning",
+    "tensorboardX",
+    "requests",
+    "packaging",
+    "dill",
+    "ray",
+    "prophet",
+]
+
+
+for lib in installed_libs:
+    lib_name = lib.split("==")[0]
+    if lib_name in first_tier_dependencies:
+        print(lib)
+
+# print current commit hash
+commit_hash = subprocess.check_output(["git", "rev-parse", "HEAD"]).decode("utf-8").strip()
+print(f"Current commit hash: {commit_hash}")

test/conftest.py

@@ -2,11 +2,24 @@ from typing import Any, Dict, List, Union
 
 import numpy as np
 import pandas as pd
-from catboost import CatBoostClassifier, CatBoostRegressor, Pool
+import pytest
 from sklearn.metrics import f1_score, r2_score
 
+try:
+    from catboost import CatBoostClassifier, CatBoostRegressor, Pool
+except ImportError:  # pragma: no cover
+    CatBoostClassifier = None
+    CatBoostRegressor = None
+    Pool = None
 
-def evaluate_cv_folds_with_underlying_model(X_train_all, y_train_all, kf, model: Any, task: str) -> pd.DataFrame:
+
+def _is_catboost_model_type(model_type: type) -> bool:
+    if CatBoostClassifier is not None and CatBoostRegressor is not None:
+        return model_type is CatBoostClassifier or model_type is CatBoostRegressor
+    return getattr(model_type, "__module__", "").startswith("catboost")
+
+
+def evaluate_cv_folds_with_underlying_model(X_train_all, y_train_all, kf, model: Any, task: str) -> List[float]:
     """Mimic the FLAML CV process to calculate the metrics across each fold.
 
     :param X_train_all: X training data
@@ -17,7 +30,7 @@ def evaluate_cv_folds_with_underlying_model(X_train_all, y_train_all, kf, model:
     :return: An array containing the metrics
     """
     rng = np.random.RandomState(2020)
-    all_fold_metrics: List[Dict[str, Union[int, float]]] = []
+    all_fold_metrics: List[float] = []
     for train_index, val_index in kf.split(X_train_all, y_train_all):
         X_train_split, y_train_split = X_train_all, y_train_all
         train_index = rng.permutation(train_index)
@@ -25,9 +38,11 @@ def evaluate_cv_folds_with_underlying_model(X_train_all, y_train_all, kf, model:
         X_val = X_train_split.iloc[val_index]
         y_train, y_val = y_train_split[train_index], y_train_split[val_index]
         model_type = type(model)
-        if model_type is not CatBoostClassifier and model_type is not CatBoostRegressor:
+        if not _is_catboost_model_type(model_type):
             model.fit(X_train, y_train)
         else:
+            if Pool is None:
+                pytest.skip("catboost is not installed")
             use_best_model = True
             n = max(int(len(y_train) * 0.9), len(y_train) - 1000) if use_best_model else len(y_train)
             X_tr, y_tr = (X_train)[:n], y_train[:n]
@@ -38,5 +53,5 @@ def evaluate_cv_folds_with_underlying_model(X_train_all, y_train_all, kf, model:
             reproduced_metric = 1 - f1_score(y_val, y_pred_classes)
         else:
             reproduced_metric = 1 - r2_score(y_val, y_pred_classes)
-        all_fold_metrics.append(reproduced_metric)
+        all_fold_metrics.append(float(reproduced_metric))
     return all_fold_metrics

test/default/test_defaults.py

@@ -60,7 +60,7 @@ def test_housing(as_frame=True):
         "starting_points": "data",
         "max_iter": 0,
     }
-    X_train, y_train = fetch_california_housing(return_X_y=True, as_frame=as_frame)
+    X_train, y_train = fetch_california_housing(return_X_y=True, as_frame=as_frame, data_home="test")
     automl.fit(X_train, y_train, **automl_settings)
 
 
@@ -115,7 +115,7 @@ def test_suggest_classification():
 
 def test_suggest_regression():
     location = "test/default"
-    X_train, y_train = fetch_california_housing(return_X_y=True, as_frame=True)
+    X_train, y_train = fetch_california_housing(return_X_y=True, as_frame=True, data_home="test")
     suggested = suggest_hyperparams("regression", X_train, y_train, "lgbm", location=location)
     print(suggested)
     suggested = preprocess_and_suggest_hyperparams("regression", X_train, y_train, "xgboost", location=location)
@@ -137,7 +137,7 @@ def test_rf():
     print(rf)
 
     location = "test/default"
-    X_train, y_train = fetch_california_housing(return_X_y=True, as_frame=True)
+    X_train, y_train = fetch_california_housing(return_X_y=True, as_frame=True, data_home="test")
     rf = RandomForestRegressor(default_location=location)
     rf.fit(X_train[:100], y_train[:100])
     rf.predict(X_train)
@@ -155,7 +155,7 @@ def test_extratrees():
     print(classifier)
 
     location = "test/default"
-    X_train, y_train = fetch_california_housing(return_X_y=True, as_frame=True)
+    X_train, y_train = fetch_california_housing(return_X_y=True, as_frame=True, data_home="test")
     regressor = ExtraTreesRegressor(default_location=location)
     regressor.fit(X_train[:100], y_train[:100])
     regressor.predict(X_train)
@@ -175,7 +175,7 @@ def test_lgbm():
     print(classifier.classes_)
 
     location = "test/default"
-    X_train, y_train = fetch_california_housing(return_X_y=True, as_frame=True)
+    X_train, y_train = fetch_california_housing(return_X_y=True, as_frame=True, data_home="test")
     regressor = LGBMRegressor(default_location=location)
     regressor.fit(X_train, y_train)
     regressor.predict(X_train)
@@ -183,6 +183,8 @@ def test_lgbm():
 
 
 def test_xgboost():
+    import numpy as np
+
     from flaml.default import XGBClassifier, XGBRegressor
 
     X_train, y_train = load_breast_cancer(return_X_y=True, as_frame=True)
@@ -194,12 +196,71 @@ def test_xgboost():
     print(classifier.classes_)
 
     location = "test/default"
-    X_train, y_train = fetch_california_housing(return_X_y=True, as_frame=True)
+    X_train, y_train = fetch_california_housing(return_X_y=True, as_frame=True, data_home="test")
     regressor = XGBRegressor(default_location=location)
     regressor.fit(X_train[:100], y_train[:100])
     regressor.predict(X_train)
     print(regressor)
 
+    # Test eval_set with categorical features (Issue: eval_set not preprocessed)
+    np.random.seed(42)
+    n = 500
+    df = pd.DataFrame(
+        {
+            "num1": np.random.randn(n),
+            "num2": np.random.rand(n) * 10,
+            "cat1": np.random.choice(["A", "B", "C"], size=n),
+            "cat2": np.random.choice(["X", "Y"], size=n),
+            "target": np.random.choice([0, 1], size=n),
+        }
+    )
+
+    X = df.drop(columns="target")
+    y = df["target"]
+
+    X_train_cat, X_valid_cat, y_train_cat, y_valid_cat = train_test_split(X, y, test_size=0.2, random_state=0)
+
+    # Convert categorical columns to pandas 'category' dtype
+    for col in X_train_cat.select_dtypes(include="object").columns:
+        X_train_cat[col] = X_train_cat[col].astype("category")
+        X_valid_cat[col] = X_valid_cat[col].astype("category")
+
+    # Test XGBClassifier with eval_set
+    classifier_eval = XGBClassifier(
+        tree_method="hist",
+        enable_categorical=True,
+        eval_metric="logloss",
+        use_label_encoder=False,
+        early_stopping_rounds=10,
+        random_state=0,
+        n_estimators=10,
+    )
+    classifier_eval.fit(X_train_cat, y_train_cat, eval_set=[(X_valid_cat, y_valid_cat)], verbose=False)
+    y_pred = classifier_eval.predict(X_valid_cat)
+    assert len(y_pred) == len(y_valid_cat)
+
+    # Test XGBRegressor with eval_set
+    y_reg = df["num1"]  # Use num1 as target for regression
+    X_reg = df.drop(columns=["num1", "target"])
+
+    X_train_reg, X_valid_reg, y_train_reg, y_valid_reg = train_test_split(X_reg, y_reg, test_size=0.2, random_state=0)
+
+    for col in X_train_reg.select_dtypes(include="object").columns:
+        X_train_reg[col] = X_train_reg[col].astype("category")
+        X_valid_reg[col] = X_valid_reg[col].astype("category")
+
+    regressor_eval = XGBRegressor(
+        tree_method="hist",
+        enable_categorical=True,
+        eval_metric="rmse",
+        early_stopping_rounds=10,
+        random_state=0,
+        n_estimators=10,
+    )
+    regressor_eval.fit(X_train_reg, y_train_reg, eval_set=[(X_valid_reg, y_valid_reg)], verbose=False)
+    y_pred = regressor_eval.predict(X_valid_reg)
+    assert len(y_pred) == len(y_valid_reg)
+
 
 def test_nobudget():
     X_train, y_train = load_breast_cancer(return_X_y=True, as_frame=True)

test/nlp/test_autohf_classificationhead.py

@@ -3,6 +3,12 @@ import shutil
 import sys
 
 import pytest
+
+try:
+    import transformers
+except ImportError:
+    pytest.skip("transformers not installed", allow_module_level=True)
+
 from utils import (
     get_automl_settings,
     get_toy_data_binclassification,
@@ -24,6 +30,8 @@ model_path_list = [
 if sys.platform.startswith("darwin") and sys.version_info[0] == 3 and sys.version_info[1] == 11:
     pytest.skip("skipping Python 3.11 on MacOS", allow_module_level=True)
 
+pytestmark = pytest.mark.spark  # set to spark as parallel testing raised RuntimeError
+
 
 def test_switch_1_1():
     data_idx, model_path_idx = 0, 0

test/nlp/test_autohf_cv.py

@@ -5,8 +5,20 @@ import sys
 import pytest
 from utils import get_automl_settings, get_toy_data_seqclassification
 
+try:
+    import transformers
 
-@pytest.mark.skipif(sys.platform in ["darwin", "win32"], reason="do not run on mac os or windows")
+    _transformers_installed = True
+except ImportError:
+    _transformers_installed = False
+
+pytestmark = pytest.mark.spark  # set to spark as parallel testing raised MlflowException of changing parameter
+
+
+@pytest.mark.skipif(
+    sys.platform in ["darwin", "win32"] or not _transformers_installed,
+    reason="do not run on mac os or windows or transformers not installed",
+)
 def test_cv():
     import requests
 

test/nlp/test_autohf_multichoice_classification.py

@@ -5,8 +5,18 @@ import sys
 import pytest
 from utils import get_automl_settings, get_toy_data_multiplechoiceclassification
 
+try:
+    import transformers
 
-@pytest.mark.skipif(sys.platform in ["darwin", "win32"], reason="do not run on mac os or windows")
+    _transformers_installed = True
+except ImportError:
+    _transformers_installed = False
+
+
+@pytest.mark.skipif(
+    sys.platform in ["darwin", "win32"] or not _transformers_installed,
+    reason="do not run on mac os or windows or transformers not installed",
+)
 def test_mcc():
     import requests
 

test/nlp/test_default.py

@@ -7,8 +7,24 @@ from utils import get_automl_settings, get_toy_data_seqclassification
 
 from flaml.default import portfolio
 
-if sys.platform.startswith("darwin") and sys.version_info[0] == 3 and sys.version_info[1] == 11:
-    pytest.skip("skipping Python 3.11 on MacOS", allow_module_level=True)
+try:
+    import transformers
+
+    _transformers_installed = True
+except ImportError:
+    _transformers_installed = False
+
+if (
+    sys.platform.startswith("darwin")
+    and sys.version_info >= (3, 11)
+    or not _transformers_installed
+    or sys.platform == "win32"
+):
+    pytest.skip("skipping Python 3.11 on MacOS or without transformers or on Windows", allow_module_level=True)
+
+pytestmark = (
+    pytest.mark.spark
+)  # set to spark as parallel testing raised ValueError: Feature NonExisting not implemented.
 
 
 def pop_args(fit_kwargs):
@@ -24,23 +40,34 @@ def test_build_portfolio(path="./test/nlp/default", strategy="greedy"):
     portfolio.main()
 
 
-@pytest.mark.skipif(sys.platform == "win32", reason="do not run on windows")
 def test_starting_point_not_in_search_space():
-    from flaml import AutoML
+    """Regression test for invalid starting points and custom_hp.
+
+    This test must not require network access to Hugging Face.
+    """
 
     """
         test starting_points located outside of the search space, and custom_hp is not set
     """
+    from flaml.automl.state import SearchState
+    from flaml.automl.task.factory import task_factory
+
     this_estimator_name = "transformer"
-    X_train, y_train, X_val, y_val, _ = get_toy_data_seqclassification()
+    X_train, y_train, _, _, _ = get_toy_data_seqclassification()
+    task = task_factory("seq-classification", X_train, y_train)
+    estimator_class = task.estimator_class_from_str(this_estimator_name)
+    estimator_class.init()
 
-    automl = AutoML()
-    automl_settings = get_automl_settings(estimator_name=this_estimator_name)
-
-    automl_settings["starting_points"] = {this_estimator_name: [{"learning_rate": 2e-3}]}
-
-    automl.fit(X_train, y_train, **automl_settings)
-    assert automl._search_states[this_estimator_name].init_config[0]["learning_rate"] != 2e-3
+    # SearchState is where invalid starting points are filtered out when max_iter > 1.
+    search_state = SearchState(
+        learner_class=estimator_class,
+        data=X_train,
+        task=task,
+        starting_point={"learning_rate": 2e-3},
+        max_iter=3,
+        budget=10,
+    )
+    assert search_state.init_config and search_state.init_config[0].get("learning_rate") != 2e-3
 
     """
         test starting_points located outside of the search space, and custom_hp is set
@@ -48,39 +75,60 @@ def test_starting_point_not_in_search_space():
 
     from flaml import tune
 
-    X_train, y_train, X_val, y_val, _ = get_toy_data_seqclassification()
+    X_train, y_train, _, _, _ = get_toy_data_seqclassification()
 
     this_estimator_name = "transformer_ms"
-    automl = AutoML()
-    automl_settings = get_automl_settings(estimator_name=this_estimator_name)
+    task = task_factory("seq-classification", X_train, y_train)
+    estimator_class = task.estimator_class_from_str(this_estimator_name)
+    estimator_class.init()
 
-    automl_settings["custom_hp"] = {
-        this_estimator_name: {
-            "model_path": {
-                "domain": "albert-base-v2",
-            },
-            "learning_rate": {
-                "domain": tune.choice([1e-4, 1e-5]),
-            },
-            "per_device_train_batch_size": {
-                "domain": 2,
-            },
-        }
+    custom_hp = {
+        "model_path": {
+            "domain": "albert-base-v2",
+        },
+        "learning_rate": {
+            "domain": tune.choice([1e-4, 1e-5]),
+        },
+        "per_device_train_batch_size": {
+            "domain": 2,
+        },
     }
-    automl_settings["starting_points"] = "data:test/nlp/default/"
 
-    automl.fit(X_train, y_train, **automl_settings)
-    assert len(automl._search_states[this_estimator_name].init_config[0]) == len(
-        automl._search_states[this_estimator_name]._search_space_domain
-    ) - len(automl_settings["custom_hp"][this_estimator_name]), (
+    # Simulate a suggested starting point (e.g. from portfolio) which becomes invalid
+    # after custom_hp constrains the space.
+    invalid_starting_points = [
+        {
+            "learning_rate": 1e-5,
+            "num_train_epochs": 1.0,
+            "per_device_train_batch_size": 8,
+            "seed": 43,
+            "global_max_steps": 100,
+            "model_path": "google/electra-base-discriminator",
+        }
+    ]
+
+    search_state = SearchState(
+        learner_class=estimator_class,
+        data=X_train,
+        task=task,
+        starting_point=invalid_starting_points,
+        custom_hp=custom_hp,
+        max_iter=3,
+        budget=10,
+    )
+
+    assert search_state.init_config, "Expected a non-empty init_config list"
+    init_config0 = search_state.init_config[0]
+    assert init_config0 is not None
+    assert len(init_config0) == len(search_state._search_space_domain) - len(custom_hp), (
         "The search space is updated with the custom_hp on {} hyperparameters of "
         "the specified estimator without an initial value. Thus a valid init config "
         "should only contain the cardinality of the search space minus {}".format(
-            len(automl_settings["custom_hp"][this_estimator_name]),
-            len(automl_settings["custom_hp"][this_estimator_name]),
+            len(custom_hp),
+            len(custom_hp),
         )
     )
-    assert automl._search_states[this_estimator_name].search_space["model_path"] == "albert-base-v2"
+    assert search_state.search_space["model_path"] == "albert-base-v2"
 
     if os.path.exists("test/data/output/"):
         try:
@@ -89,7 +137,6 @@ def test_starting_point_not_in_search_space():
             print("PermissionError when deleting test/data/output/")
 
 
-@pytest.mark.skipif(sys.platform == "win32", reason="do not run on windows")
 def test_points_to_evaluate():
     from flaml import AutoML
 
@@ -102,7 +149,13 @@ def test_points_to_evaluate():
 
     automl_settings["custom_hp"] = {"transformer_ms": {"model_path": {"domain": "google/electra-small-discriminator"}}}
 
-    automl.fit(X_train, y_train, **automl_settings)
+    try:
+        automl.fit(X_train, y_train, **automl_settings)
+    except OSError as e:
+        message = str(e)
+        if "Too Many Requests" in message or "rate limit" in message.lower():
+            pytest.skip(f"Skipping HF model load/training: {message}")
+        raise
 
     if os.path.exists("test/data/output/"):
         try:
@@ -112,7 +165,6 @@ def test_points_to_evaluate():
 
 
 # TODO: implement _test_zero_shot_model
-@pytest.mark.skipif(sys.platform == "win32", reason="do not run on windows")
 def test_zero_shot_nomodel():
     from flaml.default import preprocess_and_suggest_hyperparams
 
@@ -137,7 +189,14 @@ def test_zero_shot_nomodel():
     fit_kwargs = automl_settings.pop("fit_kwargs_by_estimator", {}).get(estimator_name)
     fit_kwargs.update(automl_settings)
     pop_args(fit_kwargs)
-    model.fit(X_train, y_train, **fit_kwargs)
+
+    try:
+        model.fit(X_train, y_train, **fit_kwargs)
+    except OSError as e:
+        message = str(e)
+        if "Too Many Requests" in message or "rate limit" in message.lower():
+            pytest.skip(f"Skipping HF model load/training: {message}")
+        raise
 
     if os.path.exists("test/data/output/"):
         try:

test/object_store.py

@@ -7,7 +7,7 @@ from sklearn.model_selection import train_test_split
 from flaml import tune
 from flaml.automl.model import LGBMEstimator
 
-data = fetch_california_housing(return_X_y=False, as_frame=True)
+data = fetch_california_housing(return_X_y=False, as_frame=True, data_home="test")
 X, y = data.data, data.target
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
 X_train_ref = ray.put(X_train)

test/reg.py

@@ -11,7 +11,7 @@ automl_settings = {
     "task": "regression",
     "log_file_name": "test/california.log",
 }
-X_train, y_train = fetch_california_housing(return_X_y=True)
+X_train, y_train = fetch_california_housing(return_X_y=True, data_home="test")
 # Train with labeled input data
 automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
 print(automl.model)

test/spark/test_0sparkml.py

@@ -1,13 +1,17 @@
+import atexit
 import os
 import sys
 import warnings
 
 import mlflow
+import numpy as np
 import pytest
 import sklearn.datasets as skds
 from packaging.version import Version
 
 from flaml import AutoML
+from flaml.automl.data import auto_convert_dtypes_pandas, auto_convert_dtypes_spark, get_random_dataframe
+from flaml.automl.spark import disable_spark_ansi_mode, restore_spark_ansi_mode
 from flaml.tune.spark.utils import check_spark
 
 warnings.simplefilter(action="ignore")
@@ -27,7 +31,7 @@ else:
             .config(
                 "spark.jars.packages",
                 (
-                    "com.microsoft.azure:synapseml_2.12:1.0.4,"
+                    "com.microsoft.azure:synapseml_2.12:1.1.0,"
                     "org.apache.hadoop:hadoop-azure:3.3.5,"
                     "com.microsoft.azure:azure-storage:8.6.6,"
                     f"org.mlflow:mlflow-spark_2.12:{mlflow.__version__}"
@@ -53,15 +57,25 @@ else:
     except ImportError:
         skip_spark = True
 
+spark, ansi_conf, adjusted = disable_spark_ansi_mode()
+atexit.register(restore_spark_ansi_mode, spark, ansi_conf, adjusted)
+
 if sys.version_info >= (3, 11):
     skip_py311 = True
 else:
     skip_py311 = False
 
-pytestmark = pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests.")
+pytestmark = [pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests."), pytest.mark.spark]
 
 
 def _test_spark_synapseml_lightgbm(spark=None, task="classification"):
+    # TODO: remove the estimator assignment once SynapseML supports spark 4+.
+    from flaml.automl.spark.utils import _spark_major_minor_version
+
+    if _spark_major_minor_version[0] >= 4:
+        # skip synapseml lightgbm test for spark 4+
+        return
+
     if task == "classification":
         metric = "accuracy"
         X_train, y_train = skds.load_iris(return_X_y=True, as_frame=True)
@@ -151,27 +165,32 @@ def test_spark_synapseml_rank():
     _test_spark_synapseml_lightgbm(spark, "rank")
 
 
-def test_spark_input_df():
-    df = (
-        spark.read.format("csv")
-        .option("header", True)
-        .option("inferSchema", True)
-        .load("wasbs://publicwasb@mmlspark.blob.core.windows.net/company_bankruptcy_prediction_data.csv")
-    )
+def test_spark_input_df_and_pickle():
+    import pandas as pd
+
+    file_url = "https://mmlspark.blob.core.windows.net/publicwasb/company_bankruptcy_prediction_data.csv"
+    df = pd.read_csv(file_url)
+    df = spark.createDataFrame(df)
     train, test = df.randomSplit([0.8, 0.2], seed=1)
     feature_cols = df.columns[1:]
     featurizer = VectorAssembler(inputCols=feature_cols, outputCol="features")
     train_data = featurizer.transform(train)["Bankrupt?", "features"]
     test_data = featurizer.transform(test)["Bankrupt?", "features"]
     automl = AutoML()
+
+    # TODO: remove the estimator assignment once SynapseML supports spark 4+.
+    from flaml.automl.spark.utils import _spark_major_minor_version
+
+    estimator_list = ["rf_spark"] if _spark_major_minor_version[0] >= 4 else None
+
     settings = {
         "time_budget": 30,  # total running time in seconds
         "metric": "roc_auc",
-        # "estimator_list": ["lgbm_spark"],  # list of ML learners; we tune lightgbm in this example
         "task": "classification",  # task type
         "log_file_name": "flaml_experiment.log",  # flaml log file
         "seed": 7654321,  # random seed
         "eval_method": "holdout",
+        "estimator_list": estimator_list,  # TODO: remove once SynapseML supports spark 4+
     }
     df = to_pandas_on_spark(to_pandas_on_spark(train_data).to_spark(index_col="index"))
 
@@ -182,6 +201,22 @@ def test_spark_input_df():
         **settings,
     )
 
+    # test pickle and load_pickle, should work for prediction
+    automl.pickle("automl_spark.pkl")
+    automl_loaded = AutoML().load_pickle("automl_spark.pkl")
+    assert automl_loaded.best_estimator == automl.best_estimator
+    assert automl_loaded.best_loss == automl.best_loss
+    automl_loaded.predict(df)
+    automl_loaded.model.estimator.transform(test_data)
+
+    import shutil
+
+    shutil.rmtree("automl_spark.pkl", ignore_errors=True)
+    shutil.rmtree("automl_spark.pkl.flaml_artifacts", ignore_errors=True)
+
+    if estimator_list == ["rf_spark"]:
+        return
+
     try:
         model = automl.model.estimator
         predictions = model.transform(test_data)
@@ -296,11 +331,88 @@ def _test_spark_large_df():
     print("time cost in minutes: ", (end_time - start_time) / 60)
 
 
+def test_get_random_dataframe():
+    # Test with default parameters
+    df = get_random_dataframe(n_rows=50, ratio_none=0.2, seed=123)
+    assert df.shape == (50, 14)  # Default is 200 rows and 14 columns
+
+    # Test column types
+    assert "timestamp" in df.columns and np.issubdtype(df["timestamp"].dtype, np.datetime64)
+    assert "id" in df.columns and np.issubdtype(df["id"].dtype, np.integer)
+    assert "score" in df.columns and np.issubdtype(df["score"].dtype, np.floating)
+    assert "category" in df.columns and df["category"].dtype.name == "category"
+
+
+def test_auto_convert_dtypes_pandas():
+    # Create a test DataFrame with various types
+    import pandas as pd
+
+    test_df = pd.DataFrame(
+        {
+            "int_col": ["1", "2", "3", "4", "5", "6", "6"],
+            "float_col": ["1.1", "2.2", "3.3", "NULL", "5.5", "6.6", "6.6"],
+            "date_col": ["2021-01-01", "2021-02-01", "NA", "2021-04-01", "2021-05-01", "2021-06-01", "2021-06-01"],
+            "cat_col": ["A", "B", "A", "A", "B", "A", "B"],
+            "string_col": ["text1", "text2", "text3", "text4", "text5", "text6", "text7"],
+        }
+    )
+
+    # Convert dtypes
+    converted_df, schema = auto_convert_dtypes_pandas(test_df)
+
+    # Check conversions
+    assert schema["int_col"] == "int"
+    assert schema["float_col"] == "double"
+    assert schema["date_col"] == "timestamp"
+    assert schema["cat_col"] == "category"
+    assert schema["string_col"] == "string"
+
+
+def test_auto_convert_dtypes_spark():
+    """Test auto_convert_dtypes_spark function with various data types."""
+    import pandas as pd
+
+    # Create a test DataFrame with various types
+    test_pdf = pd.DataFrame(
+        {
+            "int_col": ["1", "2", "3", "4", "NA"],
+            "float_col": ["1.1", "2.2", "3.3", "NULL", "5.5"],
+            "date_col": ["2021-01-01", "2021-02-01", "NA", "2021-04-01", "2021-05-01"],
+            "cat_col": ["A", "B", "A", "C", "B"],
+            "string_col": ["text1", "text2", "text3", "text4", "text5"],
+        }
+    )
+
+    # Convert pandas DataFrame to Spark DataFrame
+    test_df = spark.createDataFrame(test_pdf)
+
+    # Convert dtypes
+    converted_df, schema = auto_convert_dtypes_spark(test_df)
+
+    # Check conversions
+    assert schema["int_col"] == "int"
+    assert schema["float_col"] == "double"
+    assert schema["date_col"] == "timestamp"
+    assert schema["cat_col"] == "string"  # Conceptual category in schema
+    assert schema["string_col"] == "string"
+
+    # Verify the actual data types from the Spark DataFrame
+    spark_dtypes = dict(converted_df.dtypes)
+    assert spark_dtypes["int_col"] == "int"
+    assert spark_dtypes["float_col"] == "double"
+    assert spark_dtypes["date_col"] == "timestamp"
+    assert spark_dtypes["cat_col"] == "string"  # In Spark, categories are still strings
+    assert spark_dtypes["string_col"] == "string"
+
+
 if __name__ == "__main__":
-    test_spark_synapseml_classification()
-    test_spark_synapseml_regression()
-    test_spark_synapseml_rank()
-    test_spark_input_df()
+    # test_spark_synapseml_classification()
+    # test_spark_synapseml_regression()
+    # test_spark_synapseml_rank()
+    test_spark_input_df_and_pickle()
+    # test_get_random_dataframe()
+    # test_auto_convert_dtypes_pandas()
+    # test_auto_convert_dtypes_spark()
 
     # import cProfile
     # import pstats

test/spark/test_automl.py

@@ -25,13 +25,13 @@ os.environ["FLAML_MAX_CONCURRENT"] = "2"
 spark_available, _ = check_spark()
 skip_spark = not spark_available
 
-pytestmark = pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests.")
+pytestmark = [pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests."), pytest.mark.spark]
 
 
-def test_parallel_xgboost(hpo_method=None, data_size=1000):
+def test_parallel_xgboost_and_pickle(hpo_method=None, data_size=1000):
     automl_experiment = AutoML()
     automl_settings = {
-        "time_budget": 10,
+        "time_budget": 30,
         "metric": "ap",
         "task": "classification",
         "log_file_name": "test/sparse_classification.log",
@@ -53,15 +53,27 @@ def test_parallel_xgboost(hpo_method=None, data_size=1000):
     print(automl_experiment.best_iteration)
     print(automl_experiment.best_estimator)
 
+    # test pickle and load_pickle, should work for prediction
+    automl_experiment.pickle("automl_xgboost_spark.pkl")
+    automl_loaded = AutoML().load_pickle("automl_xgboost_spark.pkl")
+    assert automl_loaded.best_estimator == automl_experiment.best_estimator
+    assert automl_loaded.best_loss == automl_experiment.best_loss
+    automl_loaded.predict(X_train)
+
+    import shutil
+
+    shutil.rmtree("automl_xgboost_spark.pkl", ignore_errors=True)
+    shutil.rmtree("automl_xgboost_spark.pkl.flaml_artifacts", ignore_errors=True)
+
 
 def test_parallel_xgboost_others():
     # use random search as the hpo_method
-    test_parallel_xgboost(hpo_method="random")
+    test_parallel_xgboost_and_pickle(hpo_method="random")
 
 
 @pytest.mark.skip(reason="currently not supporting too large data, will support spark dataframe in the future")
 def test_large_dataset():
-    test_parallel_xgboost(data_size=90000000)
+    test_parallel_xgboost_and_pickle(data_size=90000000)
 
 
 @pytest.mark.skipif(
@@ -95,10 +107,10 @@ def test_custom_learner(data_size=1000):
 
 
 if __name__ == "__main__":
-    test_parallel_xgboost()
-    test_parallel_xgboost_others()
-    # test_large_dataset()
-    if skip_my_learner:
-        print("please run pytest in the root directory of FLAML, i.e., the directory that contains the setup.py file")
-    else:
-        test_custom_learner()
+    test_parallel_xgboost_and_pickle()
+    # test_parallel_xgboost_others()
+    # # test_large_dataset()
+    # if skip_my_learner:
+    #     print("please run pytest in the root directory of FLAML, i.e., the directory that contains the setup.py file")
+    # else:
+    #     test_custom_learner()

test/spark/test_ensemble.py

@@ -1,6 +1,7 @@
 import os
 import unittest
 
+import pytest
 from sklearn.datasets import load_wine
 
 from flaml import AutoML
@@ -24,6 +25,8 @@ if os.path.exists(os.path.join(os.getcwd(), "test", "spark", "custom_mylearner.p
 else:
     skip_my_learner = True
 
+pytestmark = pytest.mark.spark
+
 
 class TestEnsemble(unittest.TestCase):
     def setUp(self) -> None:

test/spark/test_exceptions.py

@@ -9,7 +9,7 @@ from flaml.tune.spark.utils import check_spark
 spark_available, _ = check_spark()
 skip_spark = not spark_available
 
-pytestmark = pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests.")
+pytestmark = [pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests."), pytest.mark.spark]
 
 os.environ["FLAML_MAX_CONCURRENT"] = "2"
 
@@ -22,7 +22,7 @@ def base_automl(n_concurrent_trials=1, use_ray=False, use_spark=False, verbose=0
     except (ServerError, Exception):
         from sklearn.datasets import fetch_california_housing
 
-        X_train, y_train = fetch_california_housing(return_X_y=True)
+        X_train, y_train = fetch_california_housing(return_X_y=True, data_home="test")
     automl = AutoML()
     settings = {
         "time_budget": 3,  # total running time in seconds

test/spark/test_mlflow.py

@@ -1,3 +1,4 @@
+import atexit
 import importlib
 import os
 import sys
@@ -13,6 +14,7 @@ from sklearn.metrics import r2_score
 from sklearn.model_selection import train_test_split
 
 import flaml
+from flaml.automl.spark import disable_spark_ansi_mode, restore_spark_ansi_mode
 from flaml.automl.spark.utils import to_pandas_on_spark
 
 try:
@@ -21,6 +23,7 @@ try:
     from pyspark.ml.feature import VectorAssembler
 except ImportError:
     pass
+pytestmark = pytest.mark.spark
 warnings.filterwarnings("ignore")
 
 skip_spark = importlib.util.find_spec("pyspark") is None
@@ -119,6 +122,29 @@ def _check_mlflow_logging(possible_num_runs, metric, is_parent_run, experiment_i
     # mlflow.delete_experiment(experiment_id)
 
 
+@pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests.")
+def test_automl_nonsparkdata_noautolog_noparentrun():
+    experiment_id = _test_automl_nonsparkdata(is_autolog=False, is_parent_run=False)
+    _check_mlflow_logging(0, "r2", False, experiment_id, is_automl=True)  # no logging
+
+
+@pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests.")
+def test_automl_sparkdata_noautolog_noparentrun():
+    experiment_id = _test_automl_sparkdata(is_autolog=False, is_parent_run=False)
+    _check_mlflow_logging(0, "mse", False, experiment_id, is_automl=True)  # no logging
+
+
+@pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests.")
+def test_tune_noautolog_noparentrun_parallel():
+    experiment_id = _test_tune(is_autolog=False, is_parent_run=False, is_parallel=True)
+    _check_mlflow_logging(0, "r2", False, experiment_id)
+
+
+def test_tune_noautolog_noparentrun_nonparallel():
+    experiment_id = _test_tune(is_autolog=False, is_parent_run=False, is_parallel=False)
+    _check_mlflow_logging(3, "r2", False, experiment_id, skip_tags=True)
+
+
 @pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests.")
 def test_tune_autolog_parentrun_parallel():
     experiment_id = _test_tune(is_autolog=True, is_parent_run=True, is_parallel=True)
@@ -130,6 +156,16 @@ def test_tune_autolog_parentrun_nonparallel():
     _check_mlflow_logging(3, "r2", True, experiment_id)
 
 
+def test_tune_autolog_noparentrun_nonparallel():
+    experiment_id = _test_tune(is_autolog=True, is_parent_run=False, is_parallel=False)
+    _check_mlflow_logging(3, "r2", False, experiment_id)
+
+
+def test_tune_noautolog_parentrun_nonparallel():
+    experiment_id = _test_tune(is_autolog=False, is_parent_run=True, is_parallel=False)
+    _check_mlflow_logging(3, "r2", True, experiment_id)
+
+
 @pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests.")
 def test_tune_autolog_noparentrun_parallel():
     experiment_id = _test_tune(is_autolog=True, is_parent_run=False, is_parallel=True)
@@ -142,28 +178,12 @@ def test_tune_noautolog_parentrun_parallel():
     _check_mlflow_logging([4, 3], "r2", True, experiment_id)
 
 
-def test_tune_autolog_noparentrun_nonparallel():
-    experiment_id = _test_tune(is_autolog=True, is_parent_run=False, is_parallel=False)
-    _check_mlflow_logging(3, "r2", False, experiment_id)
-
-
-def test_tune_noautolog_parentrun_nonparallel():
-    experiment_id = _test_tune(is_autolog=False, is_parent_run=True, is_parallel=False)
-    _check_mlflow_logging(3, "r2", True, experiment_id)
-
-
-@pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests.")
-def test_tune_noautolog_noparentrun_parallel():
-    experiment_id = _test_tune(is_autolog=False, is_parent_run=False, is_parallel=True)
-    _check_mlflow_logging(0, "r2", False, experiment_id)
-
-
-def test_tune_noautolog_noparentrun_nonparallel():
-    experiment_id = _test_tune(is_autolog=False, is_parent_run=False, is_parallel=False)
-    _check_mlflow_logging(3, "r2", False, experiment_id, skip_tags=True)
-
-
 def _test_automl_sparkdata(is_autolog, is_parent_run):
+    # TODO: remove the estimator assignment once SynapseML supports spark 4+.
+    from flaml.automl.spark.utils import _spark_major_minor_version
+
+    estimator_list = ["rf_spark"] if _spark_major_minor_version[0] >= 4 else None
+
     mlflow.end_run()
     mlflow_exp_name = f"test_mlflow_integration_{int(time.time())}"
     mlflow_experiment = mlflow.set_experiment(mlflow_exp_name)
@@ -174,6 +194,9 @@ def _test_automl_sparkdata(is_autolog, is_parent_run):
     if is_parent_run:
         mlflow.start_run(run_name=f"automl_sparkdata_autolog_{is_autolog}")
     spark = pyspark.sql.SparkSession.builder.getOrCreate()
+    spark, ansi_conf, adjusted = disable_spark_ansi_mode()
+    atexit.register(restore_spark_ansi_mode, spark, ansi_conf, adjusted)
+
     pd_df = load_diabetes(as_frame=True).frame
     df = spark.createDataFrame(pd_df)
     df = df.repartition(4).cache()
@@ -192,6 +215,7 @@ def _test_automl_sparkdata(is_autolog, is_parent_run):
         "log_type": "all",
         "n_splits": 2,
         "model_history": True,
+        "estimator_list": estimator_list,
     }
     df = to_pandas_on_spark(to_pandas_on_spark(train_data).to_spark(index_col="index"))
     automl.fit(
@@ -251,12 +275,6 @@ def test_automl_sparkdata_noautolog_parentrun():
     _check_mlflow_logging(3, "mse", True, experiment_id, is_automl=True)
 
 
-@pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests.")
-def test_automl_sparkdata_noautolog_noparentrun():
-    experiment_id = _test_automl_sparkdata(is_autolog=False, is_parent_run=False)
-    _check_mlflow_logging(0, "mse", False, experiment_id, is_automl=True)  # no logging
-
-
 @pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests.")
 def test_automl_nonsparkdata_autolog_parentrun():
     experiment_id = _test_automl_nonsparkdata(is_autolog=True, is_parent_run=True)
@@ -275,12 +293,6 @@ def test_automl_nonsparkdata_noautolog_parentrun():
     _check_mlflow_logging([4, 3], "r2", True, experiment_id, is_automl=True)
 
 
-@pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests.")
-def test_automl_nonsparkdata_noautolog_noparentrun():
-    experiment_id = _test_automl_nonsparkdata(is_autolog=False, is_parent_run=False)
-    _check_mlflow_logging(0, "r2", False, experiment_id, is_automl=True)  # no logging
-
-
 @pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests.")
 def test_exit_pyspark_autolog():
     import pyspark
@@ -318,6 +330,9 @@ def _init_spark_for_main():
         "https://mmlspark.blob.core.windows.net/publicwasb/log_model_allowlist.txt",
     )
 
+    spark, ansi_conf, adjusted = disable_spark_ansi_mode()
+    atexit.register(restore_spark_ansi_mode, spark, ansi_conf, adjusted)
+
 
 if __name__ == "__main__":
     _init_spark_for_main()

test/spark/test_multiclass.py

@@ -2,6 +2,7 @@ import os
 import unittest
 
 import numpy as np
+import pytest
 import scipy.sparse
 from sklearn.datasets import load_iris, load_wine
 
@@ -12,6 +13,7 @@ from flaml.tune.spark.utils import check_spark
 
 spark_available, _ = check_spark()
 skip_spark = not spark_available
+pytestmark = pytest.mark.spark
 
 os.environ["FLAML_MAX_CONCURRENT"] = "2"
 
@@ -260,7 +262,11 @@ class TestMultiClass(unittest.TestCase):
             "n_concurrent_trials": 2,
             "use_spark": True,
         }
-        X_train = scipy.sparse.random(1554, 21, dtype=int)
+        # NOTE: Avoid `dtype=int` here. On some NumPy/SciPy combinations (notably
+        # Windows + Python 3.13), `scipy.sparse.random(..., dtype=int)` may trigger
+        # integer sampling paths which raise "low is out of bounds for int32".
+        # A float sparse matrix is sufficient to validate sparse-input support.
+        X_train = scipy.sparse.random(1554, 21, dtype=np.float32)
         y_train = np.random.randint(3, size=1554)
         automl_experiment.fit(X_train=X_train, y_train=y_train, **automl_settings)
         print(automl_experiment.classes_)

test/spark/test_notebook.py

@@ -9,7 +9,7 @@ from flaml.tune.spark.utils import check_spark
 spark_available, _ = check_spark()
 skip_spark = not spark_available
 
-pytestmark = pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests.")
+pytestmark = [pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests."), pytest.mark.spark]
 
 here = os.path.abspath(os.path.dirname(__file__))
 os.environ["FLAML_MAX_CONCURRENT"] = "2"

test/spark/test_overtime.py

@@ -25,7 +25,7 @@ try:
 except ImportError:
     skip_spark = True
 
-pytestmark = pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests.")
+pytestmark = [pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests."), pytest.mark.spark]
 
 
 def test_overtime():

test/spark/test_performance.py

@@ -2,8 +2,23 @@ import os
 import sys
 
 import pytest
-from minio.error import ServerError
-from openml.exceptions import OpenMLServerException
+
+try:
+    from minio.error import ServerError
+except ImportError:
+
+    class ServerError(Exception):
+        pass
+
+
+try:
+    from openml.exceptions import OpenMLServerException
+except ImportError:
+
+    class OpenMLServerException(Exception):
+        pass
+
+
 from requests.exceptions import ChunkedEncodingError, SSLError
 
 from flaml.tune.spark.utils import check_spark
@@ -11,19 +26,19 @@ from flaml.tune.spark.utils import check_spark
 spark_available, _ = check_spark()
 skip_spark = not spark_available
 
-pytestmark = pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests.")
+pytestmark = [pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests."), pytest.mark.spark]
 
 os.environ["FLAML_MAX_CONCURRENT"] = "2"
 
 
-def run_automl(budget=3, dataset_format="dataframe", hpo_method=None):
+def run_automl(budget=30, dataset_format="dataframe", hpo_method=None):
     import urllib3
 
     from flaml.automl.data import load_openml_dataset
 
     performance_check_budget = 3600
     if sys.platform == "darwin" or "nt" in os.name or "3.10" not in sys.version:
-        budget = 3  # revise the buget if the platform is not linux + python 3.10
+        budget = 30  # revise the buget if the platform is not linux + python 3.10
     if budget >= performance_check_budget:
         max_iter = 60
         performance_check_budget = None
@@ -76,6 +91,11 @@ def run_automl(budget=3, dataset_format="dataframe", hpo_method=None):
     print("Best ML leaner:", automl.best_estimator)
     print("Best hyperparmeter config:", automl.best_config)
     print(f"Best accuracy on validation data: {1 - automl.best_loss:.4g}")
+    if performance_check_budget is not None and automl.best_estimator is None:
+        # skip the performance check if no model is trained
+        # this happens sometimes in github actions ubuntu python 3.12 environment
+        print("Warning: no model is trained, skip performance check")
+        return
     print(f"Training duration of best run: {automl.best_config_train_time:.4g} s")
     print(automl.model.estimator)
     print(automl.best_config_per_estimator)

test/spark/test_tune.py

@@ -14,7 +14,7 @@ from flaml.tune.spark.utils import check_spark
 spark_available, _ = check_spark()
 skip_spark = not spark_available
 
-pytestmark = pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests.")
+pytestmark = [pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests."), pytest.mark.spark]
 
 os.environ["FLAML_MAX_CONCURRENT"] = "2"
 X, y = load_breast_cancer(return_X_y=True)

test/spark/test_utils.py

@@ -1,3 +1,4 @@
+import atexit
 import os
 from functools import partial
 from timeit import timeit
@@ -14,6 +15,7 @@ try:
     from pyspark.sql import SparkSession
 
     from flaml.automl.ml import sklearn_metric_loss_score
+    from flaml.automl.spark import disable_spark_ansi_mode, restore_spark_ansi_mode
     from flaml.automl.spark.metrics import spark_metric_loss_score
     from flaml.automl.spark.utils import (
         iloc_pandas_on_spark,
@@ -24,6 +26,7 @@ try:
         unique_value_first_index,
     )
     from flaml.tune.spark.utils import (
+        _spark_major_minor_version,
         check_spark,
         get_broadcast_data,
         get_n_cpus,
@@ -35,8 +38,39 @@ try:
 except ImportError:
     print("Spark is not installed. Skip all spark tests.")
     skip_spark = True
+    _spark_major_minor_version = (0, 0)
 
-pytestmark = pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests.")
+
+pytestmark = [pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests."), pytest.mark.spark]
+
+
+@pytest.mark.skipif(_spark_major_minor_version[0] < 4, reason="Requires Spark 4.0+")
+def test_to_pandas_on_spark_temp_override():
+    import pyspark.pandas as ps
+    from pyspark.sql import Row
+
+    from flaml.automl.spark.utils import to_pandas_on_spark
+
+    spark_session = SparkSession.builder.getOrCreate()
+    spark, ansi_conf, adjusted = disable_spark_ansi_mode()
+    atexit.register(restore_spark_ansi_mode, spark, ansi_conf, adjusted)
+
+    # Ensure we can toggle options
+    orig = ps.get_option("compute.fail_on_ansi_mode")
+
+    try:
+        spark_session.conf.set("spark.sql.ansi.enabled", "true")
+        ps.set_option("compute.fail_on_ansi_mode", True)
+
+        # create tiny spark df
+        sdf = spark_session.createDataFrame([Row(a=1, b=2)])
+        # Should not raise as our function temporarily disables fail_on_ansi_mode
+        pds = to_pandas_on_spark(sdf)
+        assert "a" in pds.columns
+    finally:
+        # restore test environment
+        ps.set_option("compute.fail_on_ansi_mode", orig)
+        spark_session.conf.set("spark.sql.ansi.enabled", "false")
 
 
 def test_with_parameters_spark():

test/test_autovw.py

@@ -5,17 +5,38 @@ import sys
 import unittest
 
 import numpy as np
-import openml
+
+try:
+    import openml
+except ImportError:
+    openml = None
 import pandas as pd
 import pytest
 import scipy.sparse
-from minio.error import ServerError
+
+try:
+    from minio.error import ServerError
+except ImportError:
+
+    class ServerError(Exception):
+        pass
+
+
 from requests.exceptions import SSLError
 from sklearn.metrics import mean_absolute_error, mean_squared_error
 
 from flaml import AutoVW
 from flaml.tune import loguniform, polynomial_expansion_set
 
+try:
+    from vowpalwabbit import pyvw
+except ImportError:
+    skip_vw_test = True
+else:
+    skip_vw_test = False
+
+pytest.skip("skipping if no openml", allow_module_level=True) if openml is None else None
+
 VW_DS_DIR = "test/data/"
 NS_LIST = list(string.ascii_lowercase) + list(string.ascii_uppercase)
 logger = logging.getLogger(__name__)
@@ -351,14 +372,9 @@ def get_vw_tuning_problem(tuning_hp="NamesapceInteraction"):
     return vw_oml_problem_args, vw_online_aml_problem
 
 
-@pytest.mark.skipif(
-    "3.10" in sys.version or "3.11" in sys.version,
-    reason="do not run on py >= 3.10",
-)
+@pytest.mark.skipif(skip_vw_test, reason="vowpalwabbit not installed")
 class TestAutoVW(unittest.TestCase):
     def test_vw_oml_problem_and_vanilla_vw(self):
-        from vowpalwabbit import pyvw
-
         try:
             vw_oml_problem_args, vw_online_aml_problem = get_vw_tuning_problem()
         except (SSLError, ServerError, Exception) as e:

test/tune/test_lexiflow.py

@@ -4,10 +4,17 @@ from collections import defaultdict
 
 import numpy as np
 import pytest
-import thop
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
+
+try:
+    import thop
+    import torch
+    import torch.nn as nn
+    import torch.nn.functional as F
+except ImportError:
+    thop = None
+    torch = None
+    nn = None
+    F = None
 
 try:
     import torchvision
@@ -16,6 +23,11 @@ except ImportError:
 
 from flaml import tune
 
+if thop is None or torch is None or nn is None or F is None or torchvision is None:
+    pytest.skip(
+        "skipping test_lexiflow.py because torch, torchvision or thop is not installed.", allow_module_level=True
+    )
+
 DEVICE = torch.device("cpu")
 BATCHSIZE = 128
 N_TRAIN_EXAMPLES = BATCHSIZE * 30

test/tune/test_search_thread.py

@@ -0,0 +1,99 @@
+"""Tests for SearchThread nested dictionary update fix."""
+
+import pytest
+
+from flaml.tune.searcher.search_thread import _recursive_dict_update
+
+
+def test_recursive_dict_update_simple():
+    """Test simple non-nested dictionary update."""
+    target = {"a": 1, "b": 2}
+    source = {"c": 3}
+    _recursive_dict_update(target, source)
+    assert target == {"a": 1, "b": 2, "c": 3}
+
+
+def test_recursive_dict_update_override():
+    """Test that source values override target values for non-dict values."""
+    target = {"a": 1, "b": 2}
+    source = {"b": 3}
+    _recursive_dict_update(target, source)
+    assert target == {"a": 1, "b": 3}
+
+
+def test_recursive_dict_update_nested():
+    """Test nested dictionary merge (the main use case for XGBoost params)."""
+    target = {
+        "num_boost_round": 10,
+        "params": {
+            "max_depth": 12,
+            "eta": 0.020168455186106736,
+            "min_child_weight": 1.4504723523894132,
+            "scale_pos_weight": 3.794258636185337,
+            "gamma": 0.4985070123025904,
+        },
+    }
+    source = {
+        "params": {
+            "verbosity": 3,
+            "booster": "gbtree",
+            "eval_metric": "auc",
+            "tree_method": "hist",
+            "objective": "binary:logistic",
+        }
+    }
+    _recursive_dict_update(target, source)
+
+    # Check that sampled params are preserved
+    assert target["params"]["max_depth"] == 12
+    assert target["params"]["eta"] == 0.020168455186106736
+    assert target["params"]["min_child_weight"] == 1.4504723523894132
+    assert target["params"]["scale_pos_weight"] == 3.794258636185337
+    assert target["params"]["gamma"] == 0.4985070123025904
+
+    # Check that const params are added
+    assert target["params"]["verbosity"] == 3
+    assert target["params"]["booster"] == "gbtree"
+    assert target["params"]["eval_metric"] == "auc"
+    assert target["params"]["tree_method"] == "hist"
+    assert target["params"]["objective"] == "binary:logistic"
+
+    # Check top-level param is preserved
+    assert target["num_boost_round"] == 10
+
+
+def test_recursive_dict_update_deeply_nested():
+    """Test deeply nested dictionary merge."""
+    target = {"a": {"b": {"c": 1, "d": 2}}}
+    source = {"a": {"b": {"e": 3}}}
+    _recursive_dict_update(target, source)
+    assert target == {"a": {"b": {"c": 1, "d": 2, "e": 3}}}
+
+
+def test_recursive_dict_update_mixed_types():
+    """Test that non-dict values in source replace dict values in target."""
+    target = {"a": {"b": 1}}
+    source = {"a": 2}
+    _recursive_dict_update(target, source)
+    assert target == {"a": 2}
+
+
+def test_recursive_dict_update_empty_dicts():
+    """Test with empty dictionaries."""
+    target = {}
+    source = {"a": 1}
+    _recursive_dict_update(target, source)
+    assert target == {"a": 1}
+
+    target = {"a": 1}
+    source = {}
+    _recursive_dict_update(target, source)
+    assert target == {"a": 1}
+
+
+def test_recursive_dict_update_none_values():
+    """Test that None values are properly handled."""
+    target = {"a": 1, "b": None}
+    source = {"b": 2, "c": None}
+    _recursive_dict_update(target, source)
+    assert target == {"a": 1, "b": 2, "c": None}

test/tune/test_searcher.py

@@ -324,3 +324,26 @@ def test_no_optuna():
     import flaml.tune.searcher.suggestion
 
     subprocess.check_call([sys.executable, "-m", "pip", "install", "optuna==2.8.0"])
+
+
+def test_unresolved_search_space(caplog):
+    import logging
+
+    from flaml import tune
+    from flaml.tune.searcher.blendsearch import BlendSearch
+
+    if caplog is not None:
+        caplog.set_level(logging.INFO)
+
+    BlendSearch(metric="loss", mode="min", space={"lr": tune.uniform(0.001, 0.1), "depth": tune.randint(1, 10)})
+    try:
+        text = caplog.text
+    except AttributeError:
+        text = ""
+    assert (
+        "unresolved search space" not in text and text
+    ), "BlendSearch should not produce warning about unresolved search space"
+
+
+if __name__ == "__main__":
+    test_unresolved_search_space(None)

test/tune/test_tune.py

@@ -53,6 +53,11 @@ def _easy_objective(config):
 
 
 def test_nested_run():
+    """
+    nested tuning example: Tune -> AutoML -> MLflow autolog
+    mlflow logging is complicated in nested tuning. It's better to turn off mlflow autologging to avoid
+    potential issues in FLAML's mlflow_integration.adopt_children() function.
+    """
     from flaml import AutoML, tune
 
     data, labels = sklearn.datasets.load_breast_cancer(return_X_y=True)

test/tune_example.py

@@ -6,12 +6,12 @@ from sklearn.model_selection import train_test_split
 from flaml import tune
 from flaml.automl.model import LGBMEstimator
 
-data = fetch_california_housing(return_X_y=False, as_frame=True)
+data = fetch_california_housing(return_X_y=False, as_frame=True, data_home="test")
 df, X, y = data.frame, data.data, data.target
 df_train, _, X_train, X_test, _, y_test = train_test_split(df, X, y, test_size=0.33, random_state=42)
 csv_file_name = "test/housing.csv"
 df_train.to_csv(csv_file_name, index=False)
-# X, y = fetch_california_housing(return_X_y=True, as_frame=True)
+# X, y = fetch_california_housing(return_X_y=True, as_frame=True, data_home="test")
 # X_train, X_test, y_train, y_test = train_test_split(
 #     X, y, test_size=0.33, random_state=42
 # )

tutorials/flaml-tutorial-automl-24.md

@@ -4,7 +4,7 @@
 
 **Date and Time**: 09.09.2024, 15:30-17:00
 
-Location:  Sorbonne University, 4 place Jussieu, 75005 Paris
+Location: Sorbonne University, 4 place Jussieu, 75005 Paris
 
 Duration: 1.5 hours
 

tutorials/flaml-tutorial-pydata-23.md

@@ -4,7 +4,7 @@
 
 **Date and Time**: 04-26, 09:00–10:30 PT.
 
-Location:  Microsoft Conference Center, Seattle, WA.
+Location: Microsoft Conference Center, Seattle, WA.
 
 Duration: 1.5 hours
 

website/docs/Best-Practices.md

@@ -0,0 +1,159 @@
+# Best Practices
+
+This page collects practical guidance for using FLAML effectively across common tasks.
+
+## General tips
+
+- Start simple: set `task`, `time_budget`, and keep `metric="auto"` unless you have a strong reason to override.
+- Prefer correct splits: ensure your evaluation strategy matches your data (time series vs i.i.d., grouped data, etc.).
+- Keep estimator lists explicit when debugging: start with a small `estimator_list` and expand.
+- Use built-in discovery helpers to avoid stale hardcoded lists:
+
+```python
+from flaml import AutoML
+from flaml.automl.task.factory import task_factory
+
+automl = AutoML()
+print("Built-in sklearn metrics:", sorted(automl.supported_metrics[0]))
+print(
+    "classification estimators:",
+    sorted(task_factory("classification").estimators.keys()),
+)
+```
+
+## Classification
+
+- **Metric**: for binary classification, `metric="roc_auc"` is common; for multiclass, `metric="log_loss"` is often robust.
+- **Imbalanced data**:
+  - pass `sample_weight` to `AutoML.fit()`;
+  - consider setting class weights via `custom_hp` / `fit_kwargs_by_estimator` for specific estimators (see [FAQ](FAQ)).
+- **Probability vs label metrics**: use `roc_auc` / `log_loss` when you care about calibrated probabilities.
+- **Label overlap control** (holdout evaluation only):
+  - By default, FLAML uses a fast strategy (`allow_label_overlap=True`) that ensures all labels are present in both training and validation sets by adding missing labels' first instances to both sets. This is efficient but may create minor overlap.
+  - For strict no-overlap validation, use `allow_label_overlap=False`. This slower but more precise strategy intelligently re-splits multi-instance classes to avoid overlap while maintaining label completeness.
+
+```python
+from flaml import AutoML
+
+# Fast version (default): allows overlap for efficiency
+automl_fast = AutoML()
+automl_fast.fit(
+    X_train,
+    y_train,
+    task="classification",
+    eval_method="holdout",
+    allow_label_overlap=True,
+)  # default
+
+# Precise version: avoids overlap when possible
+automl_precise = AutoML()
+automl_precise.fit(
+    X_train,
+    y_train,
+    task="classification",
+    eval_method="holdout",
+    allow_label_overlap=False,
+)  # slower but more precise
+```
+
+Note: This only affects holdout evaluation. CV and custom validation sets are unaffected.
+
+## Regression
+
+- **Default metric**: `metric="r2"` (minimizes `1 - r2`).
+- If your target scale matters (e.g., dollar error), consider `mae`/`rmse`.
+
+## Learning to rank
+
+- Use `task="rank"` with group information (`groups` / `groups_val`) so metrics like `ndcg` and `ndcg@k` are meaningful.
+- If you pass `metric="ndcg@10"`, also pass `groups` so FLAML can compute group-aware NDCG.
+
+## Time series forecasting
+
+- Use time-aware splitting. For holdout validation, set `eval_method="holdout"` and use a time-ordered dataset.
+- Prefer supplying a DataFrame with a clear time column when possible.
+- Optional time-series estimators depend on optional dependencies. To list what is available in your environment:
+
+```python
+from flaml.automl.task.factory import task_factory
+
+print("forecast:", sorted(task_factory("forecast").estimators.keys()))
+```
+
+## NLP (Transformers)
+
+- Install the optional dependency: `pip install "flaml[hf]"`.
+- When you provide a custom metric, ensure it returns `(metric_to_minimize, metrics_to_log)` with stable keys.
+
+## Speed, stability, and tricky settings
+
+- **Time budget vs convergence**: if you see warnings about not all estimators converging, increase `time_budget` or reduce `estimator_list`.
+- **Memory pressure / OOM**:
+  - set `free_mem_ratio` (e.g., `0.2`) to keep free memory above a threshold;
+  - set `model_history=False` to reduce stored artifacts;
+- **Reproducibility**: set `seed` and keep `n_jobs` fixed; expect some runtime variance.
+
+## Persisting models
+
+FLAML supports **both** MLflow logging and pickle-based persistence. For production deployment, MLflow logging is typically the most important option because it plugs into the MLflow ecosystem (tracking, model registry, serving, governance). For quick local reuse, persisting the whole `AutoML` object via pickle is often the most convenient.
+
+### Option 1: MLflow logging (recommended for production)
+
+When you run `AutoML.fit()` inside an MLflow run, FLAML can log metrics/params automatically (disable via `mlflow_logging=False` if needed). To persist the trained `AutoML` object as a model artifact and reuse MLflow tooling end-to-end:
+
+```python
+import mlflow
+import numpy as np
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from flaml import AutoML
+
+X, y = load_iris(return_X_y=True, as_frame=True)
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42
+)
+
+automl = AutoML()
+mlflow.set_experiment("flaml")
+with mlflow.start_run(run_name="flaml_run") as run:
+    automl.fit(X_train, y_train, task="classification", time_budget=3)
+
+run_id = run.info.run_id
+
+# Later (or in a different process)
+automl2 = mlflow.sklearn.load_model(f"runs:/{run_id}/model")
+assert np.array_equal(automl2.predict(X_test), automl.predict(X_test))
+```
+
+### Option 2: Pickle the full `AutoML` instance (convenient)
+
+Pickling stores the *entire* `AutoML` instance (not just the best estimator). This is useful when you prefer not to rely on MLflow or when you want to reuse additional attributes of the AutoML object without retraining.
+
+In Microsoft Fabric scenarios, additional attributes is particularly important for re-plotting visualization figures without requiring model retraining.
+
+```python
+import mlflow
+import numpy as np
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from flaml import AutoML
+
+X, y = load_iris(return_X_y=True, as_frame=True)
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42
+)
+
+automl = AutoML()
+mlflow.set_experiment("flaml")
+with mlflow.start_run(run_name="flaml_run") as run:
+    automl.fit(X_train, y_train, task="classification", time_budget=3)
+
+automl.pickle("automl.pkl")
+automl2 = AutoML.load_pickle("automl.pkl")
+assert np.array_equal(automl2.predict(X_test), automl.predict(X_test))
+assert automl.best_config == automl2.best_config
+assert automl.best_loss == automl2.best_loss
+assert automl.mlflow_integration.infos == automl2.mlflow_integration.infos
+```
+
+See also: [Task-Oriented AutoML](Use-Cases/Task-Oriented-AutoML) and [FAQ](FAQ).

website/docs/Contribute.md

@@ -49,7 +49,7 @@ print(flaml.__version__)
 ```
 
 - Please ensure all **code snippets and error messages are formatted in
-  appropriate code blocks**.  See [Creating and highlighting code blocks](https://help.github.com/articles/creating-and-highlighting-code-blocks)
+  appropriate code blocks**. See [Creating and highlighting code blocks](https://help.github.com/articles/creating-and-highlighting-code-blocks)
   for more details.
 
 ## Becoming a Reviewer
@@ -62,10 +62,10 @@ There is currently no formal reviewer solicitation process. Current reviewers id
 
 ```bash
 git clone https://github.com/microsoft/FLAML.git
-pip install -e FLAML[notebook,autogen]
+pip install -e ".[notebook]"
 ```
 
-In case the `pip install` command fails, try escaping the brackets such as `pip install -e FLAML\[notebook,autogen\]`.
+In case the `pip install` command fails, try escaping the brackets such as `pip install -e .\[notebook\]`.
 
 ### Docker
 
@@ -88,7 +88,7 @@ Run `pre-commit install` to install pre-commit into your git hooks. Before you c
 
 ### Coverage
 
-Any code you commit should not decrease coverage. To run all unit tests, install the \[test\] option under FLAML/:
+Any code you commit should not decrease coverage. To run all unit tests, install the [test] option under FLAML/:
 
 ```bash
 pip install -e."[test]"

website/docs/Examples/AutoML-Classification.md

@@ -2,7 +2,7 @@
 
 ### Prerequisites
 
-Install the \[automl\] option.
+Install the [automl] option.
 
 ```bash
 pip install "flaml[automl]"