Merge pull request #62 from navigating-stories/fix-tests

Fix tests and CI

.github/workflows/publish-to-pypi.yml

@@ -45,24 +45,3 @@ jobs:
           path: dist/
       - name: Publish to PyPI
         uses: pypa/gh-action-pypi-publish@release/v1
-
-  publish-to-test_pypi:
-    name: Publish distribution to Test PyPI
-    needs: [build]
-    runs-on: ubuntu-latest
-    if: github.event_name == 'workflow_dispatch' || github.event_name == 'push'
-    environment:
-      name: testpypi
-      url: https://test.pypi.org/p/storynavigator
-    permissions:
-      id-token: write  # IMPORTANT: mandatory for trusted publishing
-    steps:
-      - name: Download the distribution packages
-        uses: actions/download-artifact@v3
-        with:
-          name: artifact
-          path: dist/
-      - name: Publish to TestPyPI
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          repository-url: https://test.pypi.org/legacy/

.github/workflows/test.yml

@@ -10,35 +10,46 @@ on:
   pull_request:
     branches:
       - master
+    types:
+      - opened
+      - reopened
+      - synchronize
+      - ready_for_review
+  workflow_dispatch:
 
 jobs:
-  build:
+  test:
+    if: github.event.pull_request.draft == false
+    name: Run Tests
     runs-on: ubuntu-latest
     strategy:
-      fail-fast: false
       matrix:
         os: ["ubuntu-latest", "windows-latest", "macos-latest"]
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.9"]
 
     steps:
-    - uses: actions/checkout@v3
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v3
-      with:
-        python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        python -m pip install flake8 tox
-        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
-    # - name: Lint with flake8
-    #   run: |
-    #     # stop the build if there are Python syntax errors or undefined names
-    #     flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-    #     # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-    #     flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
-    - name: Test with Tox
-      run: |
-        tox -vv
-      env:
-        QT_QPA_PLATFORM: offscreen
+      - uses: actions/checkout@v3
+        with:
+          submodules: 'true'
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - uses: actions/cache@v3
+        id: cache-virtualenv
+        with:
+          path: ${{ env.pythonLocation }}
+          key: ${{ env.pythonLocation }}-${{ hashFiles('setup.cfg') }}
+
+      - name: Install dependencies
+        if: steps.cache-virtualenv.outputs.cache-hit != 'true'
+        run: |
+          python -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+          python -m pip install -e .[dev]
+    
+      - name: Test with pytest
+        run: |
+          pytest
+        env:
+          QT_QPA_PLATFORM: offscreen

.gitignore

@@ -21,7 +21,6 @@ flenv/
 .venv/
 
 data/
-workflows/
 
 navigator.code-workspace
 

README.md

@@ -89,5 +89,18 @@ Steps to build and test from source:
 
 The Orange3 application should shortly start up with a splash screen
 
+## Testing
+
+Run tests with `pytest`.
+
+For coverage:
+
+```python
+coverage run -m pytest
+coverage report
+# or
+coverage html
+```
+
 ## Collaborators
 The Story Navigator is maintained by the [Netherlands eScience Center](https://www.esciencecenter.nl/) in collaboration with the [Faculty of Behavioural, Management and Social Sciences (BMS)](https://www.utwente.nl/en/bms/) at the [University of Twente](https://www.utwente.nl/en/).

orangecontrib/storynavigation/modules/actoranalysis.py

@@ -1,6 +1,9 @@
 """Modules required for Actor Analysis widget in Story Navigator.
 """
 
+from __future__ import annotations
+
+
 import os
 import pandas as pd
 import numpy as np
@@ -191,7 +194,7 @@ def __postag_sents(
             return self.html_result
 
     def postag_text(
-        self, text, nouns, subjs, custom, selected_prominence_metric, prominence_score_min, story_elements_df
+        self, text, nouns: bool, subjs: bool, custom: bool, selected_prominence_metric: float, prominence_score_min: int, story_elements_df: pd.DataFrame | None
     ):
         """POS-tags story text and returns HTML string which encodes the the tagged text, ready for rendering in the UI
 
@@ -201,19 +204,18 @@ def postag_text(
             subjs (boolean): whether subject tokens should be tagged
             custom (boolean): whether custom tags should be highlighted or not
             selected_prominence_metric (float): the selected metric by which to calculate the word prominence score
-            prominence_score_min (float): the minimum prominence score for an entity which qualifies it to be tagged
+            prominence_score_min (int): the minimum prominence score for an entity which qualifies it to be tagged
             story_elements_df (pandas.DataFrame): a dataframe with all required nlp tagging information
 
         Returns:
             string: HTML string representation of POS tagged text
         """
+        if story_elements_df is None or (len(story_elements_df) == 0):
+            sentences = util.preprocess_text(text)
+            return self.__print_html_no_highlighted_tokens(sentences)
 
         sorted_df = story_elements_df.sort_values(by=['storyid', 'sentence_id'], ascending=True)
         sentences = sorted_df['sentence'].unique().tolist()
-        # sentences = util.preprocess_text(text)
-
-        if story_elements_df is None or len(story_elements_df) == 0:
-            return self.__print_html_no_highlighted_tokens(sentences)
 
         selected_storyid = story_elements_df['storyid'].unique().tolist()[0]
         specific_tag_choice_html = (str(int(nouns)) + str(int(subjs)) + str(int(custom)))

orangecontrib/storynavigation/widgets/OWSNActorAnalysis.py

@@ -364,9 +364,9 @@ class Error(OWWidget.Error):
     # currently selected agent prominence metric
     agent_prominence_metric = constants.SELECTED_PROMINENCE_METRIC
     # minimum possible score for agent prominence
-    agent_prominence_score_min = 0.0
+    agent_prominence_score_min = 0
     # maximum possible score for agent prominence
-    agent_prominence_score_max = 15.0
+    agent_prominence_score_max = 15
 
     word_prominence_scores = {}
 
@@ -834,13 +834,13 @@ def show_docs(self, slider_engaged=False):
                 if feature.name.lower() == "content" or feature.name.lower() == "text":
                     if len(self.story_elements_dict) > 0:
                         value = self.actortagger.postag_text(
-                            value,
-                            self.nouns,
-                            self.subjs,
-                            self.custom,
-                            self.agent_prominence_metric,
-                            self.agent_prominence_score_min,
-                            self.story_elements_dict[str(c_index)]
+                            text=value,
+                            nouns=self.nouns,
+                            subjs=self.subjs,
+                            custom=self.custom,
+                            selected_prominence_metric=self.agent_prominence_metric,
+                            prominence_score_min=self.agent_prominence_score_min,
+                            story_elements_df=self.story_elements_dict[str(c_index)]
                         )                   
 
                 if feature in self.search_features and (len(self.regexp_filter) > 0):

orangecontrib/storynavigation/tests/test_OWSNActorAnalysis.py → tests/test_OWSNActorAnalysis.py

@@ -1,5 +1,4 @@
 import logging
-import unittest
 import os
 
 from storynavigation.widgets.OWSNActorAnalysis import OWSNActorAnalysis
@@ -37,7 +36,7 @@ def setUp(self):
     def load(self):
         self.logger.info("loading data...")
         self.widget.corpus = Corpus.from_file(os.path.join(self.current_dir, constants.TEST_DATA_FILE_NAME))
-        self.send_signal(self.widget.Inputs.corpus, self.widget.corpus)
+        self.send_signal(self.widget.Inputs.stories, self.widget.corpus)
         self.logger.info("loading completed.")
 
     def test_tagging(self):
@@ -61,13 +60,13 @@ def test_tagging(self):
 
             if len(value) > 0:
                 value = self.actortagger.postag_text(
-                    value,
-                    True,
-                    True,
-                    False,
-                    {},
-                    constants.SELECTED_PROMINENCE_METRIC,
-                    0.0
+                    text=value,
+                    nouns=True,
+                    subjs=True,
+                    custom=False,
+                    selected_prominence_metric=0,
+                    prominence_score_min=constants.SELECTED_PROMINENCE_METRIC,
+                    story_elements_df=None,
                 )
                 self.tagging_completed = True
 
@@ -89,6 +88,3 @@ def test_actor_metrics(self):
             self.logger.info("metrics calculated.")
         else:
             self.logger.info("metrics could not be calculated.")         
-
-if __name__ == '__main__':
-    unittest.main()