Exemple d’un batch de test

SalimM21 · SalimM21 · commit ee0487a54cbe · 2025-10-31T21:51:28.000+01:00
diff --git a/data/X_test.pkl b/data/X_test.pkl
diff --git a/data/X_train.pkl b/data/X_train.pkl
diff --git a/data/processed_test.csv b/data/processed_test.csv
diff --git a/data/processed_train.csv b/data/processed_train.csv
diff --git a/data/y_test.pkl b/data/y_test.pkl
diff --git a/data/y_train.pkl b/data/y_train.pkl
diff --git a/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/meta.yaml b/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/meta.yaml
@@ -0,0 +1,14 @@
+artifact_uri: file:///c:/Users/PC/Desktop/sprint5/Churn-Prediction-avec-MLflow/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/artifacts
+end_time: 1761903195677
+entry_point_name: ''
+experiment_id: '667014208243130233'
+lifecycle_stage: active
+run_id: 9ef1f5bd1b494066bbf7d0c26b56384f
+run_name: flawless-asp-958
+source_name: ''
+source_type: 4
+source_version: ''
+start_time: 1761903188164
+status: 3
+tags: []
+user_id: PC
diff --git a/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/metrics/accuracy b/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/metrics/accuracy
@@ -0,0 +1,2 @@
+1761903188256 0.5 0
+1761903188256 0.5 0
diff --git a/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/metrics/f1 b/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/metrics/f1
@@ -0,0 +1,2 @@
+1761903188251 0.5 0
+1761903188251 0.5 0
diff --git a/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/outputs/m-4cd66135667c4e2cb0101afb74becdba/meta.yaml b/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/outputs/m-4cd66135667c4e2cb0101afb74becdba/meta.yaml
@@ -0,0 +1,6 @@
+destination_id: m-4cd66135667c4e2cb0101afb74becdba
+destination_type: MODEL_OUTPUT
+source_id: m-4cd66135667c4e2cb0101afb74becdba
+source_type: RUN_OUTPUT
+step: 0
+tags: {}
diff --git a/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/params/note b/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/params/note
@@ -0,0 +1 @@
+demo run created by notebook
diff --git a/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/tags/imbalance_strategy b/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/tags/imbalance_strategy
@@ -0,0 +1 @@
+none
diff --git a/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/tags/mlflow.runName b/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/tags/mlflow.runName
@@ -0,0 +1 @@
+flawless-asp-958
diff --git a/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/tags/mlflow.source.git.commit b/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/tags/mlflow.source.git.commit
@@ -0,0 +1 @@
+7d7b02a1d9989bc64551a48a0fdd7dd157da51d2
diff --git a/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/tags/mlflow.source.name b/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/tags/mlflow.source.name
@@ -0,0 +1 @@
+c:\Users\PC\Desktop\sprint5\Churn-Prediction-avec-MLflow\venv\Lib\site-packages\ipykernel_launcher.py
diff --git a/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/tags/mlflow.source.type b/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/tags/mlflow.source.type
@@ -0,0 +1 @@
+LOCAL
diff --git a/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/tags/mlflow.user b/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/tags/mlflow.user
@@ -0,0 +1 @@
+PC
diff --git a/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/tags/model_name b/mlruns/667014208243130233/9ef1f5bd1b494066bbf7d0c26b56384f/tags/model_name
@@ -0,0 +1 @@
+DummyModel
diff --git a/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/artifacts/MLmodel b/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/artifacts/MLmodel
@@ -0,0 +1,22 @@
+artifact_path: file:///c:/Users/PC/Desktop/sprint5/Churn-Prediction-avec-MLflow/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/artifacts
+flavors:
+  python_function:
+    env:
+      conda: conda.yaml
+      virtualenv: python_env.yaml
+    loader_module: mlflow.sklearn
+    model_path: model.pkl
+    predict_fn: predict
+    python_version: 3.12.2
+  sklearn:
+    code: null
+    pickled_model: model.pkl
+    serialization_format: cloudpickle
+    sklearn_version: 1.7.2
+mlflow_version: 3.5.1
+model_id: m-4cd66135667c4e2cb0101afb74becdba
+model_size_bytes: 477
+model_uuid: m-4cd66135667c4e2cb0101afb74becdba
+prompts: null
+run_id: 9ef1f5bd1b494066bbf7d0c26b56384f
+utc_time_created: '2025-10-31 09:33:08.356111'
diff --git a/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/artifacts/conda.yaml b/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/artifacts/conda.yaml
@@ -0,0 +1,15 @@
+channels:
+- conda-forge
+dependencies:
+- python=3.12.2
+- pip<=24.0
+- pip:
+  - mlflow==3.5.1
+  - cloudpickle==3.1.1
+  - numpy==2.3.4
+  - pandas==2.3.3
+  - psutil==7.1.1
+  - pyarrow==21.0.0
+  - scikit-learn==1.7.2
+  - scipy==1.16.2
+name: mlflow-env
diff --git a/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/artifacts/model.pkl b/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/artifacts/model.pkl
diff --git a/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/artifacts/python_env.yaml b/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/artifacts/python_env.yaml
@@ -0,0 +1,7 @@
+python: 3.12.2
+build_dependencies:
+- pip==24.0
+- setuptools
+- wheel
+dependencies:
+- -r requirements.txt
diff --git a/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/artifacts/requirements.txt b/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/artifacts/requirements.txt
@@ -0,0 +1,8 @@
+mlflow==3.5.1
+cloudpickle==3.1.1
+numpy==2.3.4
+pandas==2.3.3
+psutil==7.1.1
+pyarrow==21.0.0
+scikit-learn==1.7.2
+scipy==1.16.2
diff --git a/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/meta.yaml b/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/meta.yaml
@@ -0,0 +1,10 @@
+artifact_location: file:///c:/Users/PC/Desktop/sprint5/Churn-Prediction-avec-MLflow/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/artifacts
+creation_timestamp: 1761903188309
+experiment_id: '667014208243130233'
+last_updated_timestamp: 1761903195660
+model_id: m-4cd66135667c4e2cb0101afb74becdba
+model_type: null
+name: model
+source_run_id: 9ef1f5bd1b494066bbf7d0c26b56384f
+status: 2
+status_message: null
diff --git a/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/metrics/accuracy b/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/metrics/accuracy
@@ -0,0 +1 @@
+1761903188256 0.5 0 9ef1f5bd1b494066bbf7d0c26b56384f
diff --git a/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/metrics/f1 b/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/metrics/f1
@@ -0,0 +1 @@
+1761903188251 0.5 0 9ef1f5bd1b494066bbf7d0c26b56384f
diff --git a/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/params/note b/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/params/note
@@ -0,0 +1 @@
+demo run created by notebook
diff --git a/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/tags/mlflow.source.git.commit b/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/tags/mlflow.source.git.commit
@@ -0,0 +1 @@
+7d7b02a1d9989bc64551a48a0fdd7dd157da51d2
diff --git a/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/tags/mlflow.source.name b/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/tags/mlflow.source.name
@@ -0,0 +1 @@
+c:\Users\PC\Desktop\sprint5\Churn-Prediction-avec-MLflow\venv\Lib\site-packages\ipykernel_launcher.py
diff --git a/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/tags/mlflow.source.type b/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/tags/mlflow.source.type
@@ -0,0 +1 @@
+LOCAL
diff --git a/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/tags/mlflow.user b/mlruns/667014208243130233/models/m-4cd66135667c4e2cb0101afb74becdba/tags/mlflow.user
@@ -0,0 +1 @@
+PC
diff --git a/notebooks/3_training_mlflow.ipynb b/notebooks/3_training_mlflow.ipynb
@@ -347,7 +347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 171,
    "id": "661c451b",
    "metadata": {},
    "outputs": [
@@ -359,13 +359,29 @@
          "name": "index",
          "rawType": "int64",
          "type": "integer"
+        },
+        {
+         "name": "metrics.accuracy",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "metrics.f1",
+         "rawType": "float64",
+         "type": "float"
         }
        ],
-       "ref": "ed29b0ae-02af-44b5-8e8b-3a1d02787b41",
-       "rows": [],
+       "ref": "723820ad-eff1-43d0-8403-e4115ac8b38a",
+       "rows": [
+        [
+         "0",
+         "0.5",
+         "0.5"
+        ]
+       ],
        "shape": {
-        "columns": 0,
-        "rows": 0
+        "columns": 2,
+        "rows": 1
        }
       },
       "text/html": [
@@ -387,20 +403,26 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>metrics.accuracy</th>\n",
+       "      <th>metrics.f1</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: []\n",
-       "Index: []"
+       "   metrics.accuracy  metrics.f1\n",
+       "0               0.5         0.5"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 171,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -411,22 +433,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 172,
    "id": "eaa49d1c",
    "metadata": {},
    "outputs": [
     {
-     "ename": "KeyError",
-     "evalue": "'metrics.f1'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
-      "\u001b[32m~\\AppData\\Local\\Temp\\ipykernel_15856\\758707258.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m best_run = runs.sort_values(\u001b[33m\"metrics.f1\"\u001b[39m, ascending=\u001b[38;5;28;01mFalse\u001b[39;00m).iloc[\u001b[32m0\u001b[39m]\n\u001b[32m      2\u001b[39m md(f\"**Meilleur modèle :** {best_run[\u001b[33m'tags.model_name'\u001b[39m]} (F1 = {best_run[\u001b[33m'metrics.f1'\u001b[39m]:.3f})\")\n",
-      "\u001b[32mc:\\Users\\PC\\Desktop\\sprint5\\Churn-Prediction-avec-MLflow\\venv\\Lib\\site-packages\\pandas\\core\\frame.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(self, by, axis, ascending, inplace, kind, na_position, ignore_index, key)\u001b[39m\n\u001b[32m   7207\u001b[39m             )\n\u001b[32m   7208\u001b[39m         \u001b[38;5;28;01melif\u001b[39;00m len(by):\n\u001b[32m   7209\u001b[39m             \u001b[38;5;66;03m# len(by) == 1\u001b[39;00m\n\u001b[32m   7210\u001b[39m \n\u001b[32m-> \u001b[39m\u001b[32m7211\u001b[39m             k = self._get_label_or_level_values(by[\u001b[32m0\u001b[39m], axis=axis)\n\u001b[32m   7212\u001b[39m \n\u001b[32m   7213\u001b[39m             \u001b[38;5;66;03m# need to rewrap column in Series to apply key function\u001b[39;00m\n\u001b[32m   7214\u001b[39m             \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;28;01mis\u001b[39;00m \u001b[38;5;28;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "\u001b[32mc:\\Users\\PC\\Desktop\\sprint5\\Churn-Prediction-avec-MLflow\\venv\\Lib\\site-packages\\pandas\\core\\generic.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(self, key, axis)\u001b[39m\n\u001b[32m   1910\u001b[39m             values = self.xs(key, axis=other_axes[\u001b[32m0\u001b[39m])._values\n\u001b[32m   1911\u001b[39m         \u001b[38;5;28;01melif\u001b[39;00m self._is_level_reference(key, axis=axis):\n\u001b[32m   1912\u001b[39m             values = self.axes[axis].get_level_values(key)._values\n\u001b[32m   1913\u001b[39m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1914\u001b[39m             \u001b[38;5;28;01mraise\u001b[39;00m KeyError(key)\n\u001b[32m   1915\u001b[39m \n\u001b[32m   1916\u001b[39m         \u001b[38;5;66;03m# Check for duplicates\u001b[39;00m\n\u001b[32m   1917\u001b[39m         \u001b[38;5;28;01mif\u001b[39;00m values.ndim > \u001b[32m1\u001b[39m:\n",
-      "\u001b[31mKeyError\u001b[39m: 'metrics.f1'"
-     ]
+     "data": {
+      "text/markdown": [
+       "**Meilleur modèle :** DummyModel (F1 = 0.500)"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -444,22 +466,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 173,
    "id": "3f546c09",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'best_run' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[66]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# Chargement et test du modèle\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m model_uri = \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mruns:/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[43mbest_run\u001b[49m[\u001b[33m'\u001b[39m\u001b[33mrun_id\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m/model\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m      3\u001b[39m loaded_model = mlflow.sklearn.load_model(model_uri)\n",
-      "\u001b[31mNameError\u001b[39m: name 'best_run' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Chargement et test du modèle\n",
     "model_uri = f\"runs:/{best_run['run_id']}/model\"\n",
@@ -468,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 174,
    "id": "638439a2",
    "metadata": {},
    "outputs": [],
@@ -491,13 +501,30 @@
    "execution_count": null,
    "id": "a5fa9d0a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Prédiction exemple :\n",
+      "[0 0 0 0 0]\n"
+     ]
+    }
+   ],
    "source": [
     "# Exemple d’un batch de test\n",
     "X_test = joblib.load(\"data/X_test.pkl\")\n",
     "print(\"✅ Prédiction exemple :\")\n",
     "print(loaded_model.predict(X_test[:5]))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0983ddc2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/notebooks/mlruns/0/meta.yaml b/notebooks/mlruns/0/meta.yaml
@@ -0,0 +1,6 @@
+artifact_location: file:///c:/Users/PC/Desktop/sprint5/Churn-Prediction-avec-MLflow/notebooks/mlruns/0
+creation_time: 1761902129601
+experiment_id: '0'
+last_update_time: 1761902129601
+lifecycle_stage: active
+name: Default
diff --git a/notebooks/mlruns/444479824665946921/meta.yaml b/notebooks/mlruns/444479824665946921/meta.yaml
@@ -0,0 +1,6 @@
+artifact_location: file:///c:/Users/PC/Desktop/sprint5/Churn-Prediction-avec-MLflow/notebooks/mlruns/444479824665946921
+creation_time: 1761902200854
+experiment_id: '444479824665946921'
+last_update_time: 1761902200854
+lifecycle_stage: active
+name: Churn_Prediction_Models
diff --git a/src/models/__pycache__/forest_script_new.cpython-312.pyc b/src/models/__pycache__/forest_script_new.cpython-312.pyc
diff --git a/src/models/__pycache__/logistic_script_new.cpython-312.pyc b/src/models/__pycache__/logistic_script_new.cpython-312.pyc
diff --git a/src/models/__pycache__/xgboost_script_new.cpython-312.pyc b/src/models/__pycache__/xgboost_script_new.cpython-312.pyc

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	`1`	`+1761903188256 0.5 0`
	`2`	`+1761903188256 0.5 0`
Original file line number	Diff line number	Diff line change
`@@ -0,0 +1,2 @@`
	`1`	`+1761903188251 0.5 0`
	`2`	`+1761903188251 0.5 0`