Example of use of MLFlow with XGBoost and LogisticRegression

titanic/titanic_models.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import mlflow\n",
+    "from mlflow.models import infer_signature\n",
+    "\n",
+    "\n",
+    "from sklearn import datasets\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix,roc_auc_score\n",
+    "\n",
+    "from xgboost import XGBClassifier\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import pandas as pd\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gen_dirname = os.path.dirname(os.path.abspath(''))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type_of_dataset = \"gentle\"\n",
+    "\n",
+    "labeled_data = pd.read_csv(os.path.join(gen_dirname,f\"data\\{type_of_dataset}\\labelled.csv\"))\n",
+    "\n",
+    "labels = labeled_data[\"Survived\"]\n",
+    "inputs = labeled_data.drop(\"Survived\",axis=\"columns\")\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(inputs,labels,test_size=0.3,random_state=42) # We are fixing the split so every run is comparable "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list_models = []"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### XGBoost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the model hyperparameters\n",
+    "params_xgb = {\n",
+    "    \"n_estimators\":20,\n",
+    "    \"max_depth\":100,\n",
+    "    \"learning_rate\": 0.3,\n",
+    "    \"objective\": \"binary:logistic\",\n",
+    "}\n",
+    "\n",
+    "# Create model instance\n",
+    "bst = XGBClassifier(**params_xgb)\n",
+    "\n",
+    "# Fit the model\n",
+    "bst.fit(X_train, y_train)\n",
+    "\n",
+    "# # Infer the model signature\n",
+    "# signature = infer_signature(X_train, bst.predict(X_train))\n",
+    "\n",
+    "# # Log the model\n",
+    "# model_info = mlflow.xgboost.autolog()\n",
+    "\n",
+    "# Register in list \n",
+    "list_models.append([\"XGBoost\",params_xgb,bst,mlflow.xgboost.log_model])#,model_info])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\RT277831\\Documents\\Projets\\Dauphine\\ML_OPS\\venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define the model hyperparameters\n",
+    "params_lr = {\n",
+    "    \"solver\": \"lbfgs\",\n",
+    "    \"max_iter\": 1000,\n",
+    "    \"multi_class\": \"auto\",\n",
+    "    \"random_state\": 8888,\n",
+    "}\n",
+    "\n",
+    "# Create model instance\n",
+    "lr = LogisticRegression(**params_lr)\n",
+    "\n",
+    "# Fit the model\n",
+    "lr.fit(X_train, y_train)\n",
+    "\n",
+    "# # Log the model\n",
+    "# model_info = mlflow.sklearn.autolog()\n",
+    "\n",
+    "# Register in list \n",
+    "list_models.append([\"LogisticRegression\",params_lr,lr,mlflow.sklearn.log_model])#,model_info])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluation metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are gonna set our metrics that will help compare our different models, because it is a classification task we are gonna focus on AUC, accuracy, recall, confusion matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def eval_metrics(actual, preds):\n",
+    "    # Calculate metrics\n",
+    "\n",
+    "    # Accuracy\n",
+    "    accuracy = accuracy_score(actual, preds)\n",
+    "\n",
+    "    # recall\n",
+    "    recall = recall_score(actual, preds)\n",
+    "\n",
+    "    # AUC\n",
+    "    auc = roc_auc_score(actual, preds)\n",
+    "\n",
+    "    # Confusion matrix\n",
+    "    cnf_matr = confusion_matrix(actual,preds)\n",
+    "\n",
+    "    return accuracy, recall, auc, cnf_matr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## MLFlow part"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set our tracking server uri for logging\n",
+    "mlflow.set_tracking_uri(uri=\"http://127.0.0.1:5000\")\n",
+    "\n",
+    "# Create a new MLflow Experiment\n",
+    "mlflow.set_experiment(\"Titanic MLFlow demo\")\n",
+    "\n",
+    "\n",
+    "for name,params,model,log_model in list_models:\n",
+    "\n",
+    "    # Start an MLflow run\n",
+    "    with mlflow.start_run():\n",
+    "        # Log the hyperparameters\n",
+    "        mlflow.log_params(params)\n",
+    "        \n",
+    "        preds = model.predict(X_test)\n",
+    "\n",
+    "        # Log the metric\n",
+    "        accuracy, recall, auc, cnf_matr = eval_metrics(y_test,preds)\n",
+    "        mlflow.log_metric(\"accuracy\", accuracy)\n",
+    "        mlflow.log_metric(\"recall\", recall)\n",
+    "        mlflow.log_metric(\"auc\", auc)\n",
+    "\n",
+    "        fig, ax = plt.subplots()\n",
+    "\n",
+    "        sns.heatmap(cnf_matr, annot=True)\n",
+    "        ax.set_title(\"Feature confusion Matrix\", fontsize=14)\n",
+    "        plt.tight_layout()\n",
+    "        plt.close(fig)\n",
+    "\n",
+    "        mlflow.log_figure(fig, \"confusion_matrix.png\")\n",
+    "\n",
+    "        # Set a tag that we can use to remind ourselves what this run was for\n",
+    "        mlflow.set_tag(\"Training Info\", f\"{name} model training for {type_of_dataset} titanic dataset\")\n",
+    "\n",
+    "        mlflow.set_tag(\"mlflow.runName\", f\"{name}\")\n",
+    "\n",
+    "\n",
+    "        # model_info = log_model()\n",
+    "        # # Infer the model signature\n",
+    "        # signature = infer_signature(X_train, model.predict(X_train))\n",
+    "\n",
+    "        # model_info =log_model(\n",
+    "        #     artifact_path=f\"{type_of_dataset}_{name}\",\n",
+    "        #     signature=signature,\n",
+    "        #     input_example=X_train,\n",
+    "        #     registered_model_name=f\" {name}\",\n",
+    "        # )"
+   ]
+  }
+ ],
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