first_experiment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5f05cdda",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "56014f75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), array([900, 100], dtype=int64))"
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 1: Create an imbalanced binary classification dataset\n",
    "X, y = make_classification(n_samples=1000, n_features=10, n_informative=2, n_redundant=8, \n",
    "                           weights=[0.9, 0.1], flip_y=0, random_state=42)\n",
    "\n",
    "np.unique(y, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e94ae830",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f7d21a3c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.97      0.96       270\n",
      "           1       0.62      0.50      0.56        30\n",
      "\n",
      "    accuracy                           0.92       300\n",
      "   macro avg       0.79      0.73      0.76       300\n",
      "weighted avg       0.91      0.92      0.92       300\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Define the model hyperparameters\n",
    "params = {\n",
    "    \"solver\": \"lbfgs\",\n",
    "    \"max_iter\": 1000,\n",
    "    \"multi_class\": \"auto\",\n",
    "    \"random_state\": 8888,\n",
    "}\n",
    "\n",
    "# Train the model\n",
    "lr = LogisticRegression(**params)\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred = lr.predict(X_test)\n",
    "\n",
    "report = classification_report(y_test, y_pred)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c37eb3c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0': {'precision': 0.9456521739130435,\n",
       "  'recall': 0.9666666666666667,\n",
       "  'f1-score': 0.956043956043956,\n",
       "  'support': 270.0},\n",
       " '1': {'precision': 0.625,\n",
       "  'recall': 0.5,\n",
       "  'f1-score': 0.5555555555555556,\n",
       "  'support': 30.0},\n",
       " 'accuracy': 0.92,\n",
       " 'macro avg': {'precision': 0.7853260869565217,\n",
       "  'recall': 0.7333333333333334,\n",
       "  'f1-score': 0.7557997557997558,\n",
       "  'support': 300.0},\n",
       " 'weighted avg': {'precision': 0.9135869565217392,\n",
       "  'recall': 0.92,\n",
       "  'f1-score': 0.915995115995116,\n",
       "  'support': 300.0}}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report_dict = classification_report(y_test, y_pred, output_dict=True)\n",
    "report_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "66f89a13",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mlflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f380ca75",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024/07/29 13:57:02 INFO mlflow.tracking.fluent: Experiment with name 'First Experiment' does not exist. Creating a new experiment.\n",
      "Registered model 'tracking-quickstart' already exists. Creating a new version of this model...\n",
      "2024/07/29 13:57:04 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: tracking-quickstart, version 2\n",
      "Created version '2' of model 'tracking-quickstart'.\n"
     ]
    }
   ],
   "source": [
    "mlflow.set_experiment(\"First Experiment\")\n",
    "mlflow.set_tracking_uri(uri=\"http://127.0.0.1:5000/\")\n",
    "\n",
    "with mlflow.start_run():\n",
    "    mlflow.log_params(params)\n",
    "    mlflow.log_metrics({\n",
    "        'accuracy': report_dict['accuracy'],\n",
    "        'recall_class_0': report_dict['0']['recall'],\n",
    "        'recall_class_1': report_dict['1']['recall'],\n",
    "        'f1_score_macro': report_dict['macro avg']['f1-score']\n",
    "    })\n",
    "    mlflow.sklearn.log_model(lr, \"Logistic Regression\")  "
   ]
  }
 ],
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