hpo-optuna.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0cef04ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Experiment: artifact_location='./mlruns/1', creation_time=1667238424038, experiment_id='1', last_update_time=1667238424038, lifecycle_stage='active', name='experiment-001', tags={}>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import optuna\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from sklearn.metrics import accuracy_score\n",
    "import mlflow\n",
    "\n",
    "optuna.logging.set_verbosity(optuna.logging.WARNING)\n",
    "\n",
    "mlflow.set_tracking_uri(\"http://127.0.0.1:5000\")\n",
    "mlflow.set_experiment(\"experiment-001\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "964133c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((250, 10), (250,))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = make_classification(\n",
    "    n_samples=250, \n",
    "    n_features=10,\n",
    "    n_informative=5, \n",
    "    n_redundant=3,\n",
    "    random_state=42, \n",
    "    shuffle=True\n",
    ")\n",
    "\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cbb62c60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((200, 10), (50, 10), (200,), (50,))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, \n",
    "    y, \n",
    "    test_size=0.20, \n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "52186298",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_accuracy: 0.84\n",
      "test_precision: 1.0\n",
      "test_recall: 0.7037037037037037\n",
      "test_f1_score: 0.8260869565217391\n",
      "CPU times: total: 1.31 s\n",
      "Wall time: 5.48 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "with mlflow.start_run():\n",
    "    mlflow.sklearn.autolog(exclusive=False)\n",
    "    \n",
    "    n_estimators = 50\n",
    "    max_depth = 5\n",
    "    \n",
    "    mlflow.log_param(\"max_depth\", max_depth)\n",
    "    mlflow.log_param(\"n_estimators\", n_estimators)\n",
    "    \n",
    "    model = RandomForestClassifier(\n",
    "        random_state=42, \n",
    "        max_depth=max_depth,\n",
    "        n_estimators=n_estimators\n",
    "    )\n",
    "    \n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    y_pred = model.predict(X_test)\n",
    "    # y_proba = model.predict_proba(X_test)\n",
    "    \n",
    "    mlflow.log_dict(\n",
    "        {\n",
    "            \"y_test\": [int(x) for x in y_test],\n",
    "            \"y_pred\": [int(x) for x in y_pred]\n",
    "        }, \n",
    "        \"ytest-ypred.json\"\n",
    "    )\n",
    "    \n",
    "    test_acc = accuracy_score(y_test, y_pred)\n",
    "    \n",
    "    test_precision, test_recall, test_f1, _ = precision_recall_fscore_support(\n",
    "        y_test, \n",
    "        y_pred, \n",
    "        average='binary'\n",
    "    )\n",
    "    \n",
    "    \n",
    "    mlflow.log_metric(\"test_accuracy\", test_acc)\n",
    "    mlflow.log_metric(\"test_precision\", test_precision)\n",
    "    mlflow.log_metric(\"test_recall\", test_recall)\n",
    "    mlflow.log_metric(\"test_f1_score\", test_f1)\n",
    "    \n",
    "    print(\"test_accuracy:\", test_acc)\n",
    "    print(\"test_precision:\", test_precision)\n",
    "    print(\"test_recall:\", test_recall)\n",
    "    print(\"test_f1_score:\", test_f1)\n",
    "    \n",
    "    mlflow.sklearn.autolog(disable=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c43e795a",
   "metadata": {},
   "source": [
    "## RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "878529cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
      "CPU times: total: 734 ms\n",
      "Wall time: 1min 4s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=50,\n",
       "                   n_jobs=4,\n",
       "                   param_distributions={&#x27;bootstrap&#x27;: [True, False],\n",
       "                                        &#x27;max_depth&#x27;: [10, 35, 60, 85, 110,\n",
       "                                                      None],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                                        &#x27;n_estimators&#x27;: [100, 200, 300, 400,\n",
       "                                                         500]},\n",
       "                   random_state=42, scoring=&#x27;average_precision&#x27;, verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=50,\n",
       "                   n_jobs=4,\n",
       "                   param_distributions={&#x27;bootstrap&#x27;: [True, False],\n",
       "                                        &#x27;max_depth&#x27;: [10, 35, 60, 85, 110,\n",
       "                                                      None],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                                        &#x27;n_estimators&#x27;: [100, 200, 300, 400,\n",
       "                                                         500]},\n",
       "                   random_state=42, scoring=&#x27;average_precision&#x27;, verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=50,\n",
       "                   n_jobs=4,\n",
       "                   param_distributions={'bootstrap': [True, False],\n",
       "                                        'max_depth': [10, 35, 60, 85, 110,\n",
       "                                                      None],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 4],\n",
       "                                        'min_samples_split': [2, 5, 10],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500]},\n",
       "                   random_state=42, scoring='average_precision', verbose=2)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "import numpy as np\n",
    "SEED=42\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in range(100,505,100)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(10, 110, num = 5)]\n",
    "max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]\n",
    "# Create the random grid\n",
    "random_grid = {\n",
    "    'n_estimators': n_estimators,\n",
    "    'max_features': max_features,\n",
    "    'max_depth': max_depth,\n",
    "    'min_samples_split': min_samples_split,\n",
    "    'min_samples_leaf': min_samples_leaf,\n",
    "    'bootstrap': bootstrap\n",
    "}\n",
    "\n",
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations, and use all available cores\n",
    "rf_random = RandomizedSearchCV(\n",
    "    estimator=RandomForestClassifier(), \n",
    "    param_distributions=random_grid,\n",
    "    scoring=\"average_precision\",\n",
    "    random_state=SEED,\n",
    "    n_iter=50,\n",
    "    verbose=2,\n",
    "    n_jobs=4,\n",
    "    cv=5,\n",
    ")\n",
    "\n",
    "# Fit the random search model\n",
    "rf_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "cad507dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_accuracy: 0.82\n",
      "test_precision: 0.875\n",
      "test_recall: 0.7777777777777778\n",
      "test_f1_score: 0.823529411764706\n"
     ]
    }
   ],
   "source": [
    "y_pred = rf_random.predict(X_test)\n",
    "\n",
    "test_acc = accuracy_score(y_test, y_pred)\n",
    "\n",
    "test_precision, test_recall, test_f1, _ = precision_recall_fscore_support(\n",
    "    y_test, \n",
    "    y_pred, \n",
    "    average='binary'\n",
    ")\n",
    "\n",
    "print(\"test_accuracy:\", test_acc)\n",
    "print(\"test_precision:\", test_precision)\n",
    "print(\"test_recall:\", test_recall)\n",
    "print(\"test_f1_score:\", test_f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6e1419d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9169154567638319"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.cv_results_[\"mean_test_score\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b64c3c1",
   "metadata": {},
   "source": [
    "## Optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da334892",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import model_selection\n",
    "\n",
    "\n",
    "#Step 1. Define an objective function to be maximized.\n",
    "def objective(trial):   \n",
    "    # Step 2. Setup values for the hyperparameters:\n",
    "    rf_n_estimators = trial.suggest_int(\"rf_n_estimators\", 10, 1000)\n",
    "    rf_max_depth = trial.suggest_int(\"rf_max_depth\", 2, 32, log=True)\n",
    "    classifier_obj = RandomForestClassifier(\n",
    "        max_depth=rf_max_depth, \n",
    "        n_estimators=rf_n_estimators\n",
    "    )\n",
    "\n",
    "    # Step 3: Scoring method:\n",
    "    score = model_selection.cross_val_score(classifier_obj, X, y, n_jobs=-1, cv=3)\n",
    "    accuracy = score.mean()\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a177de1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "64e1afb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 15.6 ms\n",
      "Wall time: 9.18 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "def objective(trial):\n",
    "    # Number of trees in random forest\n",
    "    n_estimators = trial.suggest_int(name=\"n_estimators\", low=100, high=500, step=100)\n",
    "\n",
    "    # Number of features to consider at every split\n",
    "    max_features = trial.suggest_categorical(name=\"max_features\", choices=['auto', 'sqrt']) \n",
    "\n",
    "    # Maximum number of levels in tree\n",
    "    max_depth = trial.suggest_int(name=\"max_depth\", low=10, high=110, step=20)\n",
    "\n",
    "    # Minimum number of samples required to split a node\n",
    "    min_samples_split = trial.suggest_int(name=\"min_samples_split\", low=2, high=10, step=2)\n",
    "\n",
    "    # Minimum number of samples required at each leaf node\n",
    "    min_samples_leaf = trial.suggest_int(name=\"min_samples_leaf\", low=1, high=4, step=1)\n",
    "    \n",
    "    params = {\n",
    "        \"n_estimators\": n_estimators,\n",
    "        \"max_features\": max_features,\n",
    "        \"max_depth\": max_depth,\n",
    "        \"min_samples_split\": min_samples_split,\n",
    "        \"min_samples_leaf\": min_samples_leaf\n",
    "    }\n",
    "    \n",
    "    model = RandomForestClassifier(random_state=SEED, **params)\n",
    "    \n",
    "    cv_score = cross_val_score(model, X_train, y_train, n_jobs=4, cv=5)\n",
    "    mean_cv_accuracy = cv_score.mean()\n",
    "\n",
    "    return mean_cv_accuracy\n",
    "\n",
    "optuna.logging.set_verbosity(optuna.logging.WARNING)\n",
    "\n",
    "study = optuna.create_study()\n",
    "study.optimize(objective, n_trials=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1a963007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 500,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 30,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 3}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "study.best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "12ec905e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Documents\\GitProjects\\alfa-uav-fault-detection\\venv\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_accuracy: 0.82\n",
      "test_precision: 0.9090909090909091\n",
      "test_recall: 0.7407407407407407\n",
      "test_f1_score: 0.8163265306122449\n"
     ]
    }
   ],
   "source": [
    "best_model = RandomForestClassifier(random_state=SEED, **study.best_params)\n",
    "best_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = best_model.predict(X_test)\n",
    "\n",
    "test_acc = accuracy_score(y_test, y_pred)\n",
    "\n",
    "test_precision, test_recall, test_f1, _ = precision_recall_fscore_support(\n",
    "    y_test, \n",
    "    y_pred, \n",
    "    average='binary'\n",
    ")\n",
    "\n",
    "print(\"test_accuracy:\", test_acc)\n",
    "print(\"test_precision:\", test_precision)\n",
    "print(\"test_recall:\", test_recall)\n",
    "print(\"test_f1_score:\", test_f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb1eb1ed",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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