test

Author: Samoed

.github/workflows/build-docs.yaml

@@ -0,0 +1,62 @@
+# Simple workflow for deploying static content to GitHub Pages
+name: Deploy static content to Pages
+
+on:
+  # Runs on pushes targeting the default branch
+  push:
+    branches:
+      - "dev"
+      - "feat/docs_ci"
+
+  # Allows you to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued.
+# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete.
+concurrency:
+  group: "pages"
+  cancel-in-progress: false
+
+jobs:
+  # Single deploy job since we're just deploying
+  deploy:
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Setup Python 3.10
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+          cache: "pip"
+
+      - name: Install dependencies
+        run: |
+          pip install .[docs]
+
+      - name: Setup Pages
+        uses: actions/configure-pages@v5
+
+      - name: Build docs
+        run: |
+          make docs
+
+      - name: Upload artifact
+        uses: actions/upload-pages-artifact@v3
+        with:
+          # Upload entire repository
+          path: 'docs/_build/html'
+
+      - name: Deploy to GitHub Pages
+        id: deployment
+        uses: actions/deploy-pages@v4

autointent/context/embedder.py

@@ -16,9 +16,7 @@
 
 
 class EmbedderDumpMetadata(TypedDict):
-    """
-    Metadata for saving and loading an Embedder instance.
-    """
+    """Metadata for saving and loading an Embedder instance."""
 
     batch_size: int
     """Batch size used for embedding calculations."""

autointent/modules/base.py

@@ -40,10 +40,7 @@ def score(self, context: Context, metric_fn: METRIC_FN) -> float:
 
     @abstractmethod
     def get_assets(self) -> Artifact:
-        """
-        Return useful assets that represent intermediate data into context.
-
-        """
+        """Return useful assets that represent intermediate data into context."""
 
     @abstractmethod
     def clear_cache(self) -> None:

autointent/modules/prediction/base.py

@@ -53,10 +53,7 @@ def score(self, context: Context, metric_fn: PredictionMetricFn) -> float:
         return metric_fn(labels, self._predictions)
 
     def get_assets(self) -> PredictorArtifact:
-        """
-        Return useful assets that represent intermediate data into context.
-
-        """
+        """Return useful assets that represent intermediate data into context."""
         return PredictorArtifact(labels=self._predictions)
 
     def clear_cache(self) -> None:

autointent/modules/scoring/dnnc/dnnc.py

@@ -49,7 +49,8 @@ class DNNCScorer(ScoringModule):
           archivePrefix={arXiv},
           primaryClass={cs.CL},
           url={https://arxiv.org/abs/2010.13009},
-    }
+        }
+
     """
 
     name = "dnnc"

docs/source/learn/dialogue_systems.rst

@@ -4,7 +4,7 @@ Dialogue Systems
 В этом разделе вы познакомитесь с основами построения диалоговых систем.
 
 Intents
------
+-------
 
 Диалоговая система в широком смысле --- это текстовый интерфейс взаимодействия с некоторым сервисом (будь то сервис по заказу еды или по получению информации о банковском счете). Обычно сервис поддерживает конечное количество API методов, которые вызываются во время диалога с пользователем. Чтобы определить, какой метод нужен в данный момент диалога используются классификаторы интентов. Если рассуждать в терминах машинного обучения, то это задача классификации текстов. 
 

pyproject.toml

@@ -76,7 +76,6 @@ target-version = "py310"
 [tool.ruff.lint]
 select = ["ALL"]
 ignore = [
-    "D",       # pydocstyle
     "TD",      # todos
     "FIX",     # fixmes
     "S311",    # random usage
@@ -92,7 +91,7 @@ ignore = [
 "autointent/modules/*" = ["ARG002", "ARG003"]  # unused argument
 "docs/*" = ["INP001", "A001", "D"]
 "*/utils.py" = ["D104", "D100"]
-"tutorials/*" = ["INP001", "T"]
+"tutorials/*" = ["INP001", "T", "D"]
 
 [tool.ruff.lint.pylint]
 max-args = 10

tutorials/modules/prediction/argmax.py

@@ -18,5 +18,6 @@
 # %%
 predictor = ArgmaxPredictor()
 scores = np.array([[0.1, 0.9], [0.8, 0.2], [0.3, 0.7]])
+predictor.fit(scores, [0, 1, 0])
 predictions = predictor.predict(scores)
 np.testing.assert_array_equal(predictions, np.array([1, 0, 1]))

tutorials/pipeline_optimization/demo.py

@@ -55,4 +55,4 @@ def optimize(task_type: TaskType) -> None:
 
 
 # %%
-optimize("multiclass")
+# optimize("multiclass")