Feat/pipeline simpler fitting (#36)

Co-authored-by: Roman Solomatin <[email protected]>
Co-authored-by: Darinka <[email protected]>

Author: 3 people

.github/workflows/test-inference.yaml

@@ -0,0 +1,40 @@
+name: test inference
+
+on:
+  push:
+    branches:
+      - dev
+  pull_request:
+    branches:
+      - dev
+
+jobs:
+  test:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ ubuntu-latest ]
+        python-version: [ "3.10", "3.11", "3.12" ]
+        include:
+          - os: windows-latest
+            python-version: "3.10"
+
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+
+    - name: Setup Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+        cache: "pip"
+
+    - name: Install dependencies
+      run: |
+          pip install .
+          pip install pytest pytest-asyncio
+
+    - name: Run tests
+      run: |
+        pytest tests/pipeline/test_inference.py

.github/workflows/tests.yaml .github/workflows/test-nodes.yaml.github/workflows/tests.yaml renamed to .github/workflows/test-nodes.yaml

@@ -1,4 +1,4 @@
-name: Run Tests
+name: test nodes
 
 on:
   push:
@@ -37,4 +37,4 @@ jobs:
 
     - name: Run tests
       run: |
-        pytest
+        pytest tests/nodes

.github/workflows/test-optimization.yaml

@@ -0,0 +1,40 @@
+name: test optimization
+
+on:
+  push:
+    branches:
+      - dev
+  pull_request:
+    branches:
+      - dev
+
+jobs:
+  test:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ ubuntu-latest ]
+        python-version: [ "3.10", "3.11", "3.12" ]
+        include:
+          - os: windows-latest
+            python-version: "3.10"
+
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+
+    - name: Setup Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+        cache: "pip"
+
+    - name: Install dependencies
+      run: |
+          pip install .
+          pip install pytest pytest-asyncio
+
+    - name: Run tests
+      run: |
+        pytest tests/pipeline/test_optimization.py

.github/workflows/unit-tests.yaml

@@ -0,0 +1,40 @@
+name: unit tests
+
+on:
+  push:
+    branches:
+      - dev
+  pull_request:
+    branches:
+      - dev
+
+jobs:
+  test:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ ubuntu-latest ]
+        python-version: [ "3.10", "3.11", "3.12" ]
+        include:
+          - os: windows-latest
+            python-version: "3.10"
+
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+
+    - name: Setup Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+        cache: "pip"
+
+    - name: Install dependencies
+      run: |
+          pip install .
+          pip install pytest pytest-asyncio
+
+    - name: Run tests
+      run: |
+        pytest --ignore=tests/nodes --ignore=tests/pipeline

…tent/pipeline/optimization/utils/name.py autointent/configs/name.pyautointent/pipeline/optimization/utils/name.py renamed to autointent/configs/name.py autointent/pipeline/optimization/utils/name.py renamed to autointent/configs/name.py

@@ -1,4 +1,5 @@
 import random
+from datetime import datetime
 
 adjectives = [
     "adorable",
@@ -342,3 +343,9 @@ def generate_name() -> str:
     adjective = random.choice(adjectives)
     noun = random.choice(nouns)
     return f"{adjective}_{noun}"
+
+
+def get_run_name(run_name: str | None = None) -> str:
+    if run_name is None:
+        run_name = generate_name()
+    return f"{run_name}_{datetime.now().strftime('%m-%d-%Y_%H-%M-%S')}"  # noqa: DTZ005

autointent/configs/node.py

@@ -9,7 +9,7 @@ class InferenceNodeConfig:
     node_type: str = MISSING
     module_type: str = MISSING
     module_config: dict[str, Any] = MISSING
-    load_path: str = MISSING
+    load_path: str | None = None
     _target_: str = "autointent.nodes.InferenceNode"
 
 

autointent/configs/optimization_cli.py

@@ -6,7 +6,7 @@
 from hydra.core.config_store import ConfigStore
 from omegaconf import MISSING
 
-from autointent.pipeline.optimization.utils import generate_name
+from .name import generate_name
 
 
 @dataclass
@@ -28,6 +28,8 @@ class LoggingConfig:
     run_name: str | None = None
     dirpath: Path | None = None
     dump_dir: Path | None = None
+    dump_modules: bool = False
+    clear_ram: bool = True
 
     def __post_init__(self) -> None:
         self.define_run_name()
@@ -44,7 +46,6 @@ def define_dirpath(self) -> None:
         if self.run_name is None:
             raise ValueError
         self.dirpath = dirpath / self.run_name
-        self.dirpath.mkdir(parents=True)
 
     def define_dump_dir(self) -> None:
         if self.dump_dir is None:
@@ -57,6 +58,7 @@ def define_dump_dir(self) -> None:
 class VectorIndexConfig:
     db_dir: Path | None = None
     device: str = "cpu"
+    save_db: bool = False
 
 
 @dataclass

autointent/context/context.py

@@ -1,42 +1,79 @@
+import json
+import logging
 from dataclasses import asdict
 from pathlib import Path
 from typing import Any
 
+import yaml
+
+from autointent.configs.optimization_cli import (
+    AugmentationConfig,
+    DataConfig,
+    EmbedderConfig,
+    LoggingConfig,
+    VectorIndexConfig,
+)
+
 from .data_handler import DataAugmenter, DataHandler, Dataset
 from .optimization_info import OptimizationInfo
+from .utils import NumpyEncoder, load_data
 from .vector_index_client import VectorIndex, VectorIndexClient
 
 
 class Context:
-    def __init__(  # noqa: PLR0913
+    data_handler: DataHandler
+    vector_index_client: VectorIndexClient
+    optimization_info: OptimizationInfo
+
+    def __init__(
         self,
-        dataset: Dataset,
-        test_dataset: Dataset | None = None,
-        device: str = "cpu",
-        multilabel_generation_config: str | None = None,
-        regex_sampling: int = 0,
         seed: int = 42,
-        db_dir: str | Path | None = None,
-        dump_dir: str | Path | None = None,
-        force_multilabel: bool = False,
-        embedder_batch_size: int = 32,
-        embedder_max_length: int | None = None,
     ) -> None:
-        augmenter = DataAugmenter(multilabel_generation_config, regex_sampling, seed)
+        self.seed = seed
+        self._logger = logging.getLogger(__name__)
+
+    def configure_logging(self, config: LoggingConfig) -> None:
+        self.logging_config = config
+        self.optimization_info = OptimizationInfo()
+
+    def configure_vector_index(self, config: VectorIndexConfig, embedder_config: EmbedderConfig | None = None) -> None:
+        self.vector_index_config = config
+        if embedder_config is None:
+            embedder_config = EmbedderConfig()
+        self.embedder_config = embedder_config
+
+        self.vector_index_client = VectorIndexClient(
+            self.vector_index_config.device,
+            self.vector_index_config.db_dir,
+            self.embedder_config.batch_size,
+            self.embedder_config.max_length,
+        )
+
+    def configure_data(self, config: DataConfig, augmentation_config: AugmentationConfig | None = None) -> None:
+        if augmentation_config is not None:
+            self.augmentation_config = AugmentationConfig()
+            augmenter = DataAugmenter(
+                self.augmentation_config.multilabel_generation_config,
+                self.augmentation_config.regex_sampling,
+                self.seed,
+            )
+        else:
+            augmenter = None
+
         self.data_handler = DataHandler(
-            dataset, test_dataset, random_seed=seed, force_multilabel=force_multilabel, augmenter=augmenter
+            dataset=load_data(config.train_path),
+            test_dataset=None if config.test_path is None else load_data(config.test_path),
+            random_seed=self.seed,
+            force_multilabel=config.force_multilabel,
+            augmenter=augmenter,
+        )
+
+    def set_datasets(
+        self, train_data: Dataset, val_data: Dataset | None = None, force_multilabel: bool = False
+    ) -> None:
+        self.data_handler = DataHandler(
+            dataset=train_data, test_dataset=val_data, random_seed=self.seed, force_multilabel=force_multilabel
         )
-        self.optimization_info = OptimizationInfo()
-        self.vector_index_client = VectorIndexClient(device, db_dir, embedder_batch_size, embedder_max_length)
-
-        self.db_dir = self.vector_index_client.db_dir
-        self.embedder_max_length = embedder_max_length
-        self.embedder_batch_size = embedder_batch_size
-        self.device = device
-        self.multilabel = self.data_handler.multilabel
-        self.n_classes = self.data_handler.n_classes
-        self.seed = seed
-        self.dump_dir = Path.cwd() / "modules_dumps" if dump_dir is None else Path(dump_dir)
 
     def get_best_index(self) -> VectorIndex:
         model_name = self.optimization_info.get_best_embedder()
@@ -48,10 +85,79 @@ def get_inference_config(self) -> dict[str, Any]:
             cfg.pop("_target_")
         return {
             "metadata": {
-                "device": self.device,
-                "multilabel": self.multilabel,
-                "n_classes": self.n_classes,
+                "device": self.get_device(),
+                "multilabel": self.is_multilabel(),
+                "n_classes": self.get_n_classes(),
                 "seed": self.seed,
             },
             "nodes_configs": nodes_configs,
         }
+
+    def dump(self) -> None:
+        self._logger.debug("dumping logs...")
+        optimization_results = self.optimization_info.dump_evaluation_results()
+
+        logs_dir = self.logging_config.dirpath
+        if logs_dir is None:
+            msg = "something's wrong with LoggingConfig"
+            raise ValueError(msg)
+
+        # create appropriate directory
+        logs_dir.mkdir(parents=True, exist_ok=True)
+
+        # dump search space and evaluation results
+        logs_path = logs_dir / "logs.json"
+        with logs_path.open("w") as file:
+            json.dump(optimization_results, file, indent=4, ensure_ascii=False, cls=NumpyEncoder)
+        # config_path = logs_dir / "config.yaml"
+        # with config_path.open("w") as file:
+        #     yaml.dump(self.config, file)
+
+        # self._logger.info(make_report(optimization_results, nodes=nodes))
+
+        # dump train and test data splits
+        train_data, test_data = self.data_handler.dump()
+        train_path = logs_dir / "train_data.json"
+        test_path = logs_dir / "test_data.json"
+        with train_path.open("w") as file:
+            json.dump(train_data, file, indent=4, ensure_ascii=False)
+        with test_path.open("w") as file:
+            json.dump(test_data, file, indent=4, ensure_ascii=False)
+
+        self._logger.info("logs and other assets are saved to %s", logs_dir)
+
+        # dump optimization results (config for inference)
+        inference_config = self.get_inference_config()
+        inference_config_path = logs_dir / "inference_config.yaml"
+        with inference_config_path.open("w") as file:
+            yaml.dump(inference_config, file)
+
+    def get_db_dir(self) -> Path:
+        return self.vector_index_client.db_dir
+
+    def get_device(self) -> str:
+        return self.vector_index_client.device
+
+    def get_batch_size(self) -> int:
+        return self.vector_index_client.embedder_batch_size
+
+    def get_max_length(self) -> int | None:
+        return self.vector_index_client.embedder_max_length
+
+    def get_dump_dir(self) -> Path | None:
+        if self.logging_config.dump_modules:
+            return self.logging_config.dump_dir
+        return None
+
+    def is_multilabel(self) -> bool:
+        return self.data_handler.multilabel
+
+    def get_n_classes(self) -> int:
+        return self.data_handler.n_classes
+
+    def is_ram_to_clear(self) -> bool:
+        return self.logging_config.clear_ram
+
+    def has_saved_modules(self) -> bool:
+        node_types = ["regexp", "retrieval", "scoring", "prediction"]
+        return any(len(self.optimization_info.modules.get(nt)) > 0 for nt in node_types)