Ex1_0_Tracking.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Eksempel av MLFlow Tracking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mlflow\n",
    "import pandas as pd\n",
    "from mlflow.models import infer_signature\n",
    "from sklearn import datasets\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelltrening\n",
    "Man trener en modell som vanlig, her har vi hentet inn iris-datasettet fra sklearn.datasets og lager en enkel LogisticRegression-modell.\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Load the Iris dataset\n",
    "X, y = datasets.load_iris(return_X_y=True)\n",
    "\n",
    "# Split the data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")\n",
    "\n",
    "# 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",
    "# Calculate metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vi kan logge resultatene våre i MLflow\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Opprett et nytt MLflow-eksperiment\n",
    "mlflow.set_experiment(\"Iris\")\n",
    "\n",
    "# Start en MLflow-run\n",
    "with mlflow.start_run():\n",
    "    # Logg hyperparametrene\n",
    "    mlflow.log_params(params)\n",
    "\n",
    "    # Logg tapsmetrikken\n",
    "    mlflow.log_metric(\"accuracy\", accuracy)\n",
    "\n",
    "    # Sett en tag som vi kan bruke for å minne oss selv på hva denne kjøringen var for\n",
    "    mlflow.set_tag(\"Opplæringsinfo\", \"Grunnleggende LR-modell for irisdata\")\n",
    "\n",
    "    # Utled modellens signatur\n",
    "    signature = infer_signature(X_train, lr.predict(X_train))\n",
    "\n",
    "    # Logg modellen\n",
    "    model_info = mlflow.sklearn.log_model(\n",
    "        sk_model=lr,\n",
    "        artifact_path=\"iris_model\",\n",
    "        signature=signature,\n",
    "        input_example=X_train,\n",
    "        registered_model_name=\"iris\",\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vi har lagret modellen, hva nå?\n",
    "Etter at modellen er lagret så kan vi studere resultatene i MLflow UI.\n",
    "For å gjøre dette kan vi kjøre følgende kommando i terminalen:\n",
    "```bash\n",
    "    mlflow ui\n",
    "```\n",
    "\n",
    "Dette vil starte en lokal server som vi kan åpne i nettleseren vår.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gjenbruk\n",
    "Vi har studert modellene og våre og funnet den vi vil ha, hvordan gjenbruker vi den? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_model = mlflow.pyfunc.load_model(model_info.model_uri)\n",
    "\n",
    "predictions = loaded_model.predict(X_test)\n",
    "\n",
    "iris_feature_names = datasets.load_iris().feature_names\n",
    "\n",
    "result = pd.DataFrame(X_test, columns=iris_feature_names)\n",
    "result[\"actual_class\"] = y_test\n",
    "result[\"predicted_class\"] = predictions\n",
    "\n",
    "result[:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Enklere logging gjennom autolog\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "\n",
    "# Enable autologging\n",
    "mlflow.sklearn.autolog()\n",
    "\n",
    "# Load the Iris dataset\n",
    "iris = load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create a Logistic Regression model\n",
    "lr = LogisticRegression(max_iter=200)\n",
    "\n",
    "# Start an MLflow experiment\n",
    "mlflow.set_experiment(\"Iris Autologging\")\n",
    "with mlflow.start_run():\n",
    "    # Train the model\n",
    "    lr.fit(X_train, y_train)\n",
    "    \n",
    "    # Make predictions and calculate accuracy\n",
    "    y_pred = lr.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    \n",
    "    # No need to explicitly log anything, autologging will handle it\n"
   ]
  }
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