✅ test automodel (#40)

* test automodel

* updates

* add CI autolabel

* min deps

* fix

.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,5 +1,5 @@
 ---
-name: Bug report
+name: 🐛 Bug report
 about: Create a report to help us improve
 title: ''
 labels: ''

.github/ISSUE_TEMPLATE/feature_request.md

@@ -1,5 +1,5 @@
 ---
-name: Feature request
+name: 🚀 Feature request
 about: Suggest an idea for this project
 title: ''
 labels: ''

.github/labeler.yml

@@ -7,3 +7,8 @@ example:
 
 test:
 - tests/**/*
+
+CI:
+- .github/**/*
+- "*.yaml"
+- "*.yml"

.github/pull_request_template.md

@@ -8,11 +8,12 @@ Fixes # (issue)
 
 #### Type of change
 <!-- Please delete options that are not relevant. -->
-- [ ] Documentation Update
-- [ ] Bug fix (non-breaking change which fixes an issue)
-- [ ] New feature (non-breaking change which adds functionality)
-- [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
-- [ ] This change requires a documentation update
+- [ ] 📚  Documentation Update
+- [ ] 🧪 Tests Cases
+- [ ] 🐞 Bug fix (non-breaking change which fixes an issue)
+- [ ] 🔬 New feature (non-breaking change which adds functionality)
+- [ ] 🚨 Breaking change (fix or feature that would cause existing functionality to not work as expected)
+- [ ] 📝 This change requires a documentation update
 
 
 #### Checklist
@@ -22,3 +23,4 @@ Fixes # (issue)
 - [ ] I have commented my code, particularly in hard-to-understand areas
 - [ ] I have made corresponding changes to the documentation
 - [ ] My changes generate no new warnings
+- [ ] Did you update CHANGELOG in case of a major change?

gradsflow/core/autoclassifier.py

@@ -71,7 +71,7 @@ def forward(self, x):
         return self.model(x)
 
     # noinspection PyTypeChecker
-    def _create_hparam_config(self) -> Dict[str, str]:
+    def _create_search_space(self) -> Dict[str, str]:
         """Create hyperparameter config from `ray.tune`
 
         Returns:

gradsflow/core/automodel.py

@@ -88,7 +88,7 @@ def __init__(
         )
 
     @abstractmethod
-    def _create_hparam_config(self) -> Dict[str, str]:
+    def _create_search_space(self) -> Dict[str, str]:
         raise NotImplementedError
 
     @abstractmethod
@@ -144,7 +144,7 @@ def hp_tune(
         trainer_config = trainer_config or {}
         ray_config = ray_config or {}
 
-        search_space = self._create_hparam_config()
+        search_space = self._create_search_space()
         trainable = self.objective
 
         analysis = tune.run(

pyproject.toml

@@ -28,7 +28,7 @@ classifiers = [
 requires-python = ">=3.7"
 requires = [
     "smart_open==5.1",
-    "lightning-flash[all]==0.4.0",
+    "lightning-flash[image,text]==0.4.0",
     "pytorch-lightning==1.4.0",
     "ray[tune]==1.6",
     "loguru~=0.5"

tests/core/test_automodel.py

@@ -13,8 +13,10 @@
 #  limitations under the License.
 
 from pathlib import Path
+from unittest.mock import MagicMock, patch
 
 import pytest
+import torch
 from flash.image import ImageClassificationData
 
 from gradsflow.core.automodel import AutoModel
@@ -34,3 +36,21 @@ def test_build_model():
     model = AutoModel(datamodule)
     with pytest.raises(NotImplementedError):
         model.build_model({"lr": 1})
+
+
+def test_create_search_space():
+    model = AutoModel(datamodule)
+    with pytest.raises(NotImplementedError):
+        model._create_search_space()
+
+
+@patch("gradsflow.core.automodel.pl")
+def test_objective(mock_pl):
+    optimization_metric = "val_accuracy"
+    model = AutoModel(datamodule, optimization_metric=optimization_metric)
+
+    model.build_model = MagicMock()
+    trainer = mock_pl.Trainer = MagicMock()
+    trainer.callback_metrics = {optimization_metric: torch.as_tensor([1])}
+
+    model.objective({}, {})