CI move to github action

.github/workflows/test.yml

@@ -0,0 +1,75 @@
+name: Test
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+
+# Cancel in-progress workflows when pushing
+# a new commit on the same branch
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  test_kit:
+    name: Test
+    runs-on: ubuntu-latest
+    env:
+      CONDA_ENV: 'testcondaenv'
+
+    defaults:
+      run:
+        # Need to use this shell to get cond working properly.
+        # See https://github.com/marketplace/actions/setup-miniconda#important
+        shell: 'bash -l {0}'
+
+    steps:
+      - uses: actions/checkout@v3
+      - name: Setup Conda
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          activate-environment: ${{ env.CONDA_ENV }}
+          python-version: "3.10"
+          # Use miniforge to only get conda-forge as default channel.
+          miniforge-version: latest
+
+      - name: Install benchopt and its dependencies
+        run: |
+            pip install -r requirements.txt
+
+      - name: 'Run the tests'
+        run: ramp-test
+
+  flake8:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python
+      uses: actions/setup-python@v3
+      with:
+        python-version: "3.10"
+
+    - name: Install dependencies
+      run: pip install flake8
+    - name: Flake8 linter
+      run: flake8 .
+
+  nbconvert:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python
+      uses: actions/setup-python@v3
+      with:
+        python-version: "3.10"
+
+    - name: Install dependencies
+      run: pip install seaborn nbconvert[test]
+
+    - name: Check the starting-kiyt notebook
+      run: jupyter nbconvert --execute variable_stars_starting_kit.ipynb --to html --ExecutePreprocessor.kernel_name=$IPYTHON_KERNEL

template_starting_kit.ipynb

@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -70,7 +70,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Submission\n",
+    "# Submission format\n",
     "\n",
     "Here, you should describe the submission format. This is the format the participants should follow to submit their predictions on the RAMP plateform.\n",
     "\n",
@@ -83,72 +83,69 @@
    "source": [
     "## The pipeline workflow\n",
     "\n",
-    "The input data are stored in a dataframe. To go from a dataframe to a numpy array we will a scikit-learn column transformer. The first example we will write will jus t consist in\n",
-    "selecting a subset of columns we want to work with."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Testing using a scikit-learn pipeline"
+    "The input data are stored in a dataframe. To go from a dataframe to a numpy array we will a scikit-learn column transformer. The first example we will write will just consist in selecting a subset of columns we want to work with."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import problem\n",
-    "from sklearn.model_selection import cross_val_score\n",
+    "# %load submissions/starting_kit/estimator.py\n",
     "\n",
-    "X_df, y = problem.get_train_data()\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.linear_model import LogisticRegression\n",
     "\n",
-    "scores = cross_val_score(get_estimator(), X_df, y, cv=2, scoring='accuracy')\n",
-    "print(scores)"
+    "\n",
+    "def get_estimator():\n",
+    "    pipe = make_pipeline(\n",
+    "        StandardScaler(),\n",
+    "        LogisticRegression()\n",
+    "    )\n",
+    "\n",
+    "    return pipe\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Submission\n",
-    "\n",
-    "To submit your code, you can refer to the [online documentation](https://paris-saclay-cds.github.io/ramp-docs/ramp-workflow/stable/using_kits.html)."
+    "## Testing using a scikit-learn pipeline"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[0;31mInit signature:\u001b[0m\n",
-      "\u001b[0mrw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore_types\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBalancedAccuracy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
-      "\u001b[0;34m\u001b[0m    \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'balanced_accuracy'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-      "\u001b[0;34m\u001b[0m    \u001b[0mprecision\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-      "\u001b[0;34m\u001b[0m    \u001b[0madjusted\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-      "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mDocstring:\u001b[0m      <no docstring>\n",
-      "\u001b[0;31mFile:\u001b[0m           ~/Work/ramp/ramp-workflow/rampwf/score_types/balanced_accuracy.py\n",
-      "\u001b[0;31mType:\u001b[0m           type\n",
-      "\u001b[0;31mSubclasses:\u001b[0m     "
+      "[0.97222222 0.96527778 0.97212544 0.95121951 0.96167247]\n"
      ]
     }
    ],
    "source": [
-    "import rampwf as rw\n",
-    "rw.score_types.BalancedAccuracy?"
+    "import problem\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "X_df, y = problem.get_train_data()\n",
+    "\n",
+    "scores = cross_val_score(get_estimator(), X_df, y, cv=5, scoring='accuracy')\n",
+    "print(scores)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "## Submission\n",
+    "\n",
+    "To submit your code, you can refer to the [online documentation](https://paris-saclay-cds.github.io/ramp-docs/ramp-workflow/stable/using_kits.html)."
+   ]
   }
  ],
  "metadata": {