Merge pull request #2 from mijanr/main

push chnages in main to test

.github/workflows/update_readme.yaml

@@ -0,0 +1,46 @@
+name: Update README
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  update-readme:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v2
+
+      - name: Set up Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.9
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install mlflow tabulate
+
+      - name: Run script
+        run: python results/gen_result.py
+
+      - name: Commit results if it changed
+        run: |
+          git diff --exit-code --quiet results/best_runs.md || (git add results/best_runs.md && git commit -m "Update best runs")
+
+      - name: Replace results in README
+        run: |
+          results=$(cat results/best_runs.md) # Read the results
+
+          # Replace the results in the README
+          awk -v r="$results" '/<!--START-->/ {print; print r; f=1} /<!--END-->/ {f=0} !f' README.md > temp && mv temp README.md
+
+      - name: Commit and push if it changed
+        run: |
+          git diff
+          git config --global user.email "[email protected]"
+          git config --global user.name "Md Mijanur Rahman"
+          git commit -am "Update README with test results" || exit 0
+          git push

results/gen_result.py

@@ -0,0 +1,27 @@
+"""
+We will generate a table here with the results of the experiments from mlruns
+"""
+
+import mlflow
+import git
+
+basePath = git.Repo('.', search_parent_directories=True).working_tree_dir
+mlflow.set_tracking_uri(f"file:{basePath}/mlruns")
+
+def get_best_runs():  
+
+    all_runs = mlflow.search_runs(search_all_experiments=True)
+    columns = ['tags.mlflow.runName', 'params.model_name', 'metrics.accuracy']
+    all_runs = all_runs[columns]
+    best_runs = all_runs.groupby(['tags.mlflow.runName', 'params.model_name']).agg({'metrics.accuracy': 'max'}).reset_index()
+    best_runs = best_runs.pivot(index='tags.mlflow.runName', columns='params.model_name', values='metrics.accuracy').reset_index()
+    best_runs.columns.name = None
+    best_runs = best_runs.rename(columns={'tags.mlflow.runName': 'Dataset'})
+    # save as markdown file
+    with open('best_runs.md', 'w') as f:
+        f.write(best_runs.to_markdown(index=False))
+
+
+if __name__ == "__main__":
+    get_best_runs()
+