Iris.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a978dfae",
   "metadata": {},
   "source": [
    "# Assignment 1 - Iris Dataset\n",
    "\n",
    "## 1. Import needed libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cfdb6738",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from timeit import default_timer as timer\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2a07a89",
   "metadata": {},
   "source": [
    "## 2. Exploratory Data Analysis\n",
    "\n",
    "### 2.1. Display first 10 dataset entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7a0c3613",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal.length</th>\n",
       "      <th>sepal.width</th>\n",
       "      <th>petal.length</th>\n",
       "      <th>petal.width</th>\n",
       "      <th>variety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal.length  sepal.width  petal.length  petal.width variety\n",
       "0           5.1          3.5           1.4          0.2  Setosa\n",
       "1           4.9          3.0           1.4          0.2  Setosa\n",
       "2           4.7          3.2           1.3          0.2  Setosa\n",
       "3           4.6          3.1           1.5          0.2  Setosa\n",
       "4           5.0          3.6           1.4          0.2  Setosa\n",
       "5           5.4          3.9           1.7          0.4  Setosa\n",
       "6           4.6          3.4           1.4          0.3  Setosa\n",
       "7           5.0          3.4           1.5          0.2  Setosa\n",
       "8           4.4          2.9           1.4          0.2  Setosa\n",
       "9           4.9          3.1           1.5          0.1  Setosa"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris =  pd.read_csv('iris.csv', na_values=',')\n",
    "iris.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59fd76b2",
   "metadata": {},
   "source": [
    "### 2.2 Display info (data types, count, null values) about the dataset using pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aaddf201",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal.length  150 non-null    float64\n",
      " 1   sepal.width   150 non-null    float64\n",
      " 2   petal.length  150 non-null    float64\n",
      " 3   petal.width   150 non-null    float64\n",
      " 4   variety       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "iris.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc250d90",
   "metadata": {},
   "source": [
    "There are no null values in any of the columns present."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40817e35",
   "metadata": {},
   "source": [
    "### 2.3 Display statistical analysis of the dataset using pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ed07a55c",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal.length</th>\n",
       "      <th>sepal.width</th>\n",
       "      <th>petal.length</th>\n",
       "      <th>petal.width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal.length  sepal.width  petal.length  petal.width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.057333      3.758000     1.199333\n",
       "std        0.828066     0.435866      1.765298     0.762238\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4da2521d",
   "metadata": {},
   "source": [
    "The describe() function shows basic statistical data about the dataset, including the mean values of each feature, the standard deviation of each feature, quartiles and min/max values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "951e84a2",
   "metadata": {},
   "source": [
    "### 2.4 Plotting the features using histograms\n",
    "#### Q: Perform EDA and Explore the features using histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d7f37cc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sl = iris[\"sepal.length\"]\n",
    "sw = iris[\"sepal.width\"]\n",
    "pl = iris[\"petal.length\"]\n",
    "pw = iris[\"petal.width\"]\n",
    "\n",
    "plt.title('Histogram of all features')\n",
    "plt.hist(sl, bins = 10, alpha=0.5, label='sepal.length')\n",
    "plt.hist(sw, bins = 10, alpha=0.5, label='sepal.width')\n",
    "plt.hist(pl, bins = 10, alpha=0.5, label='petal.length')\n",
    "plt.hist(pw, bins = 10, alpha=0.5, label='petal.width')\n",
    "plt.ylabel(\"Count\")\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize = (6, 3))\n",
    "plt.title('Histogram of sepal.length feature')\n",
    "plt.hist(sl, bins = 20, color = \"green\")\n",
    "plt.xlabel(\"sepal.length\")\n",
    "plt.ylabel(\"Count\")\n",
    "\n",
    "plt.figure(figsize = (6, 3))\n",
    "plt.title('Histogram of sepal.width feature')\n",
    "plt.hist(sw, bins = 20, color = \"green\")\n",
    "plt.xlabel(\"sepal.width\")\n",
    "plt.ylabel(\"Count\")\n",
    "\n",
    "plt.figure(figsize = (6, 3))\n",
    "plt.title('Histogram of petal.length feature')\n",
    "plt.hist(pl, bins = 20, color = \"green\")\n",
    "plt.xlabel(\"petal.length\")\n",
    "plt.ylabel(\"Count\")\n",
    "\n",
    "plt.figure(figsize = (6, 3))\n",
    "plt.title('Histogram of petal.width feature')\n",
    "plt.hist(pw, bins = 20, color = \"green\")\n",
    "plt.xlabel(\"petal.width\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b3cee50",
   "metadata": {},
   "source": [
    "### 2.5 Plot more diagrams using seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a44c251f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 805x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(iris, hue = 'variety');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f72981f2",
   "metadata": {},
   "source": [
    "### 2.6 Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9e6338d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,10))\n",
    "plt.subplot(2,2,1)\n",
    "sns.boxplot(x='variety',y='petal.length',data=iris)\n",
    "plt.subplot(2,2,2)\n",
    "sns.boxplot(x='variety',y='petal.width',data=iris)\n",
    "plt.subplot(2,2,3)\n",
    "sns.boxplot(x='variety',y='sepal.length',data=iris)\n",
    "plt.subplot(2,2,4)\n",
    "sns.boxplot(x='variety',y='sepal.width',data=iris)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13e5face",
   "metadata": {},
   "source": [
    "### 2.7 Observations made based on the EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a19a06c",
   "metadata": {},
   "source": [
    "1. There are no null values.\n",
    "2. There are 150 observations with 4 features each.\n",
    "3. There are 50 observations of each variety.\n",
    "    - Each variety has four observations.\n",
    "    \n",
    "There are some outliers. The models might benefit from the removal of outlier features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e35e2f0",
   "metadata": {},
   "source": [
    "## 3. Prepare the data for training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7104ae61",
   "metadata": {},
   "source": [
    "### 3.1 Encode iris variety (labels) to numerical values\n",
    "#### Q: Encode the target variable ie convert it to numeric type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3061bc6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder \n",
    "\n",
    "le=LabelEncoder()\n",
    "iris['variety']=le.fit_transform(iris['variety'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1713dd9",
   "metadata": {},
   "source": [
    "### 3.2 Dataset splits\n",
    "#### Q: Experiment using two different ratios of training, validation and test data ie 60-20-20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ae149b94",
   "metadata": {},
   "outputs": [],
   "source": [
    "train1_ratio = 0.60\n",
    "validation1_ratio = 0.20\n",
    "test1_ratio = 0.20\n",
    "\n",
    "train2_ratio = 0.80\n",
    "validation2_ratio = 0.10\n",
    "test2_ratio = 0.10\n",
    "\n",
    "dataX = iris.drop(\"variety\",axis=1)\n",
    "dataY = iris['variety']\n",
    "\n",
    "# Data preprocessing\n",
    "\n",
    "from sklearn import preprocessing\n",
    "\n",
    "dataX = preprocessing.normalize(dataX)\n",
    "\n",
    "# Train, test, val splits based on ratios.\n",
    "\n",
    "x1_train, x1_test, y1_train, y1_test = train_test_split(dataX, dataY, test_size=1 - train1_ratio)\n",
    "\n",
    "x1_val, x1_test, y1_val, y1_test = train_test_split(x1_test, y1_test, test_size=test1_ratio/(test1_ratio + validation1_ratio))\n",
    "\n",
    "x2_train, x2_test, y2_train, y2_test = train_test_split(dataX, dataY, test_size=1 - train2_ratio)\n",
    "\n",
    "x2_val, x2_test, y2_val, y2_test = train_test_split(x2_test, y2_test, test_size=test2_ratio/(test2_ratio + validation2_ratio)) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9933408f",
   "metadata": {},
   "source": [
    "### 3.3 Check shape of target/features matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2e76d5c5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((150, 4), (150,))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataX.shape, dataY.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c82d9730",
   "metadata": {},
   "source": [
    "### 3.4 Check the split shapes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2a0428f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60-20-20\n",
      "Train:  (90, 4)\n",
      "Validation:  (30, 4)\n",
      "Test:  (30, 4)\n",
      "\n",
      "80-10-10\n",
      "Train:  (120, 4)\n",
      "Validation:  (15, 4)\n",
      "Test:  (15, 4)\n"
     ]
    }
   ],
   "source": [
    "print(\"60-20-20\")\n",
    "print(\"Train: \", x1_train.shape)\n",
    "print(\"Validation: \", x1_val.shape)\n",
    "print(\"Test: \", x1_test.shape)\n",
    "print(\"\\n80-10-10\")\n",
    "print(\"Train: \", x2_train.shape)\n",
    "print(\"Validation: \", x2_val.shape)\n",
    "print(\"Test: \", x2_test.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29eb72a5",
   "metadata": {},
   "source": [
    "## 4. Implement K-fold cross validation and Grid search for each algorithm\n",
    "#### Q: On the two different split ratios do the following\n",
    "    o Implement KFold Cross Validation\n",
    "    o Implement Grid Search to find optimal hyperparameters for any 3 algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2097cfff",
   "metadata": {},
   "source": [
    "### 4.1 Print function to print out results of best hyperparameters using grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a508d66b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
    "\n",
    "def print_results(results):\n",
    "    print('BEST PARAMS: {}\\n'.format(results.best_params_))\n",
    "\n",
    "    means = results.cv_results_['mean_test_score']\n",
    "    stds = results.cv_results_['std_test_score']\n",
    "    for mean, std, params in zip(means, stds, results.cv_results_['params']):\n",
    "        print('{} (+/-{}) for {}'.format(round(mean, 3), round(std * 2, 3), params))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72255648",
   "metadata": {},
   "source": [
    "### 4.2 Grid search for Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7eac922f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SET 1: \n",
      "\n",
      "BEST PARAMS: {'max_depth': 20, 'n_estimators': 50}\n",
      "\n",
      "0.922 (+/-0.113) for {'max_depth': 2, 'n_estimators': 5}\n",
      "0.911 (+/-0.113) for {'max_depth': 2, 'n_estimators': 50}\n",
      "0.933 (+/-0.083) for {'max_depth': 2, 'n_estimators': 100}\n",
      "0.922 (+/-0.151) for {'max_depth': 10, 'n_estimators': 5}\n",
      "0.978 (+/-0.054) for {'max_depth': 10, 'n_estimators': 50}\n",
      "0.978 (+/-0.054) for {'max_depth': 10, 'n_estimators': 100}\n",
      "0.944 (+/-0.099) for {'max_depth': 20, 'n_estimators': 5}\n",
      "0.989 (+/-0.044) for {'max_depth': 20, 'n_estimators': 50}\n",
      "0.967 (+/-0.054) for {'max_depth': 20, 'n_estimators': 100}\n",
      "0.922 (+/-0.113) for {'max_depth': None, 'n_estimators': 5}\n",
      "0.967 (+/-0.054) for {'max_depth': None, 'n_estimators': 50}\n",
      "0.978 (+/-0.054) for {'max_depth': None, 'n_estimators': 100}\n",
      "\n",
      "SET 2: \n",
      "\n",
      "BEST PARAMS: {'max_depth': 20, 'n_estimators': 50}\n",
      "\n",
      "0.917 (+/-0.053) for {'max_depth': 2, 'n_estimators': 5}\n",
      "0.95 (+/-0.082) for {'max_depth': 2, 'n_estimators': 50}\n",
      "0.917 (+/-0.053) for {'max_depth': 2, 'n_estimators': 100}\n",
      "0.958 (+/-0.091) for {'max_depth': 10, 'n_estimators': 5}\n",
      "0.95 (+/-0.122) for {'max_depth': 10, 'n_estimators': 50}\n",
      "0.95 (+/-0.082) for {'max_depth': 10, 'n_estimators': 100}\n",
      "0.942 (+/-0.1) for {'max_depth': 20, 'n_estimators': 5}\n",
      "0.967 (+/-0.097) for {'max_depth': 20, 'n_estimators': 50}\n",
      "0.95 (+/-0.097) for {'max_depth': 20, 'n_estimators': 100}\n",
      "0.925 (+/-0.097) for {'max_depth': None, 'n_estimators': 5}\n",
      "0.958 (+/-0.091) for {'max_depth': None, 'n_estimators': 50}\n",
      "0.958 (+/-0.091) for {'max_depth': None, 'n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "parameters = {\n",
    "    'n_estimators': [5, 50, 100],\n",
    "    'max_depth': [2, 10, 20, None]\n",
    "}\n",
    "\n",
    "cv_rf_set_1 = GridSearchCV(rf, parameters, cv=5)\n",
    "cv_rf_set_1.fit(x1_train, y1_train)\n",
    "\n",
    "cv_rf_set_2 = GridSearchCV(rf, parameters, cv=5)\n",
    "cv_rf_set_2.fit(x2_train, y2_train)\n",
    "\n",
    "print(\"SET 1: \\n\")\n",
    "print_results(cv_rf_set_1)\n",
    "print(\"\\nSET 2: \\n\")\n",
    "print_results(cv_rf_set_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64067899",
   "metadata": {},
   "source": [
    "### 4.3 Grid search for Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9285f11a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SET 1: \n",
      "\n",
      "BEST PARAMS: {'C': 1000, 'solver': 'newton-cg'}\n",
      "\n",
      "0.567 (+/-0.381) for {'C': 0.001, 'solver': 'liblinear'}\n",
      "0.367 (+/-0.054) for {'C': 0.001, 'solver': 'newton-cg'}\n",
      "0.367 (+/-0.054) for {'C': 0.001, 'solver': 'lbfgs'}\n",
      "0.367 (+/-0.054) for {'C': 0.001, 'solver': 'sag'}\n",
      "0.367 (+/-0.054) for {'C': 0.001, 'solver': 'saga'}\n",
      "0.711 (+/-0.044) for {'C': 0.1, 'solver': 'liblinear'}\n",
      "0.711 (+/-0.044) for {'C': 0.1, 'solver': 'newton-cg'}\n",
      "0.711 (+/-0.044) for {'C': 0.1, 'solver': 'lbfgs'}\n",
      "0.711 (+/-0.044) for {'C': 0.1, 'solver': 'sag'}\n",
      "0.711 (+/-0.044) for {'C': 0.1, 'solver': 'saga'}\n",
      "0.711 (+/-0.044) for {'C': 1, 'solver': 'liblinear'}\n",
      "0.711 (+/-0.044) for {'C': 1, 'solver': 'newton-cg'}\n",
      "0.711 (+/-0.044) for {'C': 1, 'solver': 'lbfgs'}\n",
      "0.711 (+/-0.044) for {'C': 1, 'solver': 'sag'}\n",
      "0.711 (+/-0.044) for {'C': 1, 'solver': 'saga'}\n",
      "0.811 (+/-0.151) for {'C': 10, 'solver': 'liblinear'}\n",
      "0.889 (+/-0.186) for {'C': 10, 'solver': 'newton-cg'}\n",
      "0.889 (+/-0.186) for {'C': 10, 'solver': 'lbfgs'}\n",
      "0.889 (+/-0.186) for {'C': 10, 'solver': 'sag'}\n",
      "0.889 (+/-0.186) for {'C': 10, 'solver': 'saga'}\n",
      "0.933 (+/-0.13) for {'C': 1000, 'solver': 'liblinear'}\n",
      "0.944 (+/-0.099) for {'C': 1000, 'solver': 'newton-cg'}\n",
      "0.944 (+/-0.099) for {'C': 1000, 'solver': 'lbfgs'}\n",
      "0.944 (+/-0.099) for {'C': 1000, 'solver': 'sag'}\n",
      "0.944 (+/-0.099) for {'C': 1000, 'solver': 'saga'}\n",
      "0.944 (+/-0.099) for {'C': 10000, 'solver': 'liblinear'}\n",
      "0.944 (+/-0.099) for {'C': 10000, 'solver': 'newton-cg'}\n",
      "0.944 (+/-0.099) for {'C': 10000, 'solver': 'lbfgs'}\n",
      "0.944 (+/-0.099) for {'C': 10000, 'solver': 'sag'}\n",
      "0.944 (+/-0.099) for {'C': 10000, 'solver': 'saga'}\n",
      "0.944 (+/-0.099) for {'C': 100000, 'solver': 'liblinear'}\n",
      "0.944 (+/-0.099) for {'C': 100000, 'solver': 'newton-cg'}\n",
      "0.944 (+/-0.099) for {'C': 100000, 'solver': 'lbfgs'}\n",
      "0.944 (+/-0.099) for {'C': 100000, 'solver': 'sag'}\n",
      "0.944 (+/-0.099) for {'C': 100000, 'solver': 'saga'}\n",
      "\n",
      "SET 2: \n",
      "\n",
      "BEST PARAMS: {'C': 10, 'solver': 'newton-cg'}\n",
      "\n",
      "0.358 (+/-0.041) for {'C': 0.001, 'solver': 'liblinear'}\n",
      "0.358 (+/-0.041) for {'C': 0.001, 'solver': 'newton-cg'}\n",
      "0.358 (+/-0.041) for {'C': 0.001, 'solver': 'lbfgs'}\n",
      "0.358 (+/-0.041) for {'C': 0.001, 'solver': 'sag'}\n",
      "0.358 (+/-0.041) for {'C': 0.001, 'solver': 'saga'}\n",
      "0.692 (+/-0.1) for {'C': 0.1, 'solver': 'liblinear'}\n",
      "0.75 (+/-0.211) for {'C': 0.1, 'solver': 'newton-cg'}\n",
      "0.75 (+/-0.211) for {'C': 0.1, 'solver': 'lbfgs'}\n",
      "0.75 (+/-0.211) for {'C': 0.1, 'solver': 'sag'}\n",
      "0.75 (+/-0.211) for {'C': 0.1, 'solver': 'saga'}\n",
      "0.758 (+/-0.17) for {'C': 1, 'solver': 'liblinear'}\n",
      "0.883 (+/-0.133) for {'C': 1, 'solver': 'newton-cg'}\n",
      "0.883 (+/-0.133) for {'C': 1, 'solver': 'lbfgs'}\n",
      "0.883 (+/-0.133) for {'C': 1, 'solver': 'sag'}\n",
      "0.883 (+/-0.133) for {'C': 1, 'solver': 'saga'}\n",
      "0.925 (+/-0.082) for {'C': 10, 'solver': 'liblinear'}\n",
      "0.958 (+/-0.053) for {'C': 10, 'solver': 'newton-cg'}\n",
      "0.958 (+/-0.053) for {'C': 10, 'solver': 'lbfgs'}\n",
      "0.958 (+/-0.053) for {'C': 10, 'solver': 'sag'}\n",
      "0.958 (+/-0.053) for {'C': 10, 'solver': 'saga'}\n",
      "0.942 (+/-0.067) for {'C': 1000, 'solver': 'liblinear'}\n",
      "0.95 (+/-0.082) for {'C': 1000, 'solver': 'newton-cg'}\n",
      "0.95 (+/-0.082) for {'C': 1000, 'solver': 'lbfgs'}\n",
      "0.95 (+/-0.082) for {'C': 1000, 'solver': 'sag'}\n",
      "0.95 (+/-0.082) for {'C': 1000, 'solver': 'saga'}\n",
      "0.933 (+/-0.1) for {'C': 10000, 'solver': 'liblinear'}\n",
      "0.95 (+/-0.082) for {'C': 10000, 'solver': 'newton-cg'}\n",
      "0.95 (+/-0.082) for {'C': 10000, 'solver': 'lbfgs'}\n",
      "0.95 (+/-0.082) for {'C': 10000, 'solver': 'sag'}\n",
      "0.95 (+/-0.082) for {'C': 10000, 'solver': 'saga'}\n",
      "0.942 (+/-0.067) for {'C': 100000, 'solver': 'liblinear'}\n",
      "0.95 (+/-0.082) for {'C': 100000, 'solver': 'newton-cg'}\n",
      "0.95 (+/-0.082) for {'C': 100000, 'solver': 'lbfgs'}\n",
      "0.95 (+/-0.082) for {'C': 100000, 'solver': 'sag'}\n",
      "0.95 (+/-0.082) for {'C': 100000, 'solver': 'saga'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression(max_iter=1000000)\n",
    "parameters = {\n",
    "    'solver': ['liblinear', 'newton-cg', 'lbfgs', 'sag', 'saga'],\n",
    "    'C': [0.001, 0.1, 1, 10, 1000, 10000, 100000]\n",
    "}\n",
    "\n",
    "cv_logreg_set_1 = GridSearchCV(logreg, parameters, cv=5)\n",
    "cv_logreg_set_1.fit(x1_train, y1_train)\n",
    "\n",
    "cv_logreg_set_2 = GridSearchCV(logreg, parameters, cv=5)\n",
    "cv_logreg_set_2.fit(x2_train, y2_train)\n",
    "\n",
    "print(\"SET 1: \\n\")\n",
    "print_results(cv_logreg_set_1)\n",
    "print(\"\\nSET 2: \\n\")\n",
    "print_results(cv_logreg_set_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6342e323",
   "metadata": {},
   "source": [
    "### 4.4 Grid search for SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4f6693bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SET 1: \n",
      "\n",
      "BEST PARAMS: {'C': 10, 'gamma': 1, 'kernel': 'rbf'}\n",
      "\n",
      "0.367 (+/-0.054) for {'C': 0.001, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.367 (+/-0.054) for {'C': 0.001, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.367 (+/-0.054) for {'C': 0.001, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.367 (+/-0.054) for {'C': 0.001, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.367 (+/-0.054) for {'C': 0.001, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.656 (+/-0.13) for {'C': 0.1, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.367 (+/-0.054) for {'C': 0.1, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.367 (+/-0.054) for {'C': 0.1, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.367 (+/-0.054) for {'C': 0.1, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.367 (+/-0.054) for {'C': 0.1, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.711 (+/-0.044) for {'C': 1, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.711 (+/-0.044) for {'C': 1, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.367 (+/-0.054) for {'C': 1, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.367 (+/-0.054) for {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.367 (+/-0.054) for {'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.956 (+/-0.083) for {'C': 10, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.711 (+/-0.044) for {'C': 10, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.711 (+/-0.044) for {'C': 10, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.367 (+/-0.054) for {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.367 (+/-0.054) for {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.933 (+/-0.13) for {'C': 1000, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.933 (+/-0.13) for {'C': 1000, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.956 (+/-0.083) for {'C': 1000, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.711 (+/-0.044) for {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.711 (+/-0.044) for {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.944 (+/-0.099) for {'C': 10000, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.933 (+/-0.13) for {'C': 10000, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.933 (+/-0.13) for {'C': 10000, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.956 (+/-0.083) for {'C': 10000, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.711 (+/-0.044) for {'C': 10000, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.956 (+/-0.109) for {'C': 100000, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.944 (+/-0.099) for {'C': 100000, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.933 (+/-0.13) for {'C': 100000, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.933 (+/-0.13) for {'C': 100000, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.956 (+/-0.083) for {'C': 100000, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "\n",
      "SET 2: \n",
      "\n",
      "BEST PARAMS: {'C': 10, 'gamma': 1, 'kernel': 'rbf'}\n",
      "\n",
      "0.358 (+/-0.041) for {'C': 0.001, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.358 (+/-0.041) for {'C': 0.001, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.358 (+/-0.041) for {'C': 0.001, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.358 (+/-0.041) for {'C': 0.001, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.358 (+/-0.041) for {'C': 0.001, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.533 (+/-0.305) for {'C': 0.1, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.358 (+/-0.041) for {'C': 0.1, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.358 (+/-0.041) for {'C': 0.1, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.358 (+/-0.041) for {'C': 0.1, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.358 (+/-0.041) for {'C': 0.1, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.808 (+/-0.287) for {'C': 1, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.7 (+/-0.291) for {'C': 1, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.358 (+/-0.041) for {'C': 1, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.358 (+/-0.041) for {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.358 (+/-0.041) for {'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.958 (+/-0.053) for {'C': 10, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.825 (+/-0.266) for {'C': 10, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.7 (+/-0.291) for {'C': 10, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.358 (+/-0.041) for {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.358 (+/-0.041) for {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.958 (+/-0.053) for {'C': 1000, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.95 (+/-0.082) for {'C': 1000, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.958 (+/-0.053) for {'C': 1000, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.825 (+/-0.266) for {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.7 (+/-0.291) for {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.958 (+/-0.091) for {'C': 10000, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.958 (+/-0.053) for {'C': 10000, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.95 (+/-0.082) for {'C': 10000, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.958 (+/-0.053) for {'C': 10000, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.825 (+/-0.266) for {'C': 10000, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.958 (+/-0.091) for {'C': 100000, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.933 (+/-0.067) for {'C': 100000, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "0.958 (+/-0.053) for {'C': 100000, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.95 (+/-0.082) for {'C': 100000, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.958 (+/-0.053) for {'C': 100000, 'gamma': 0.0001, 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "SVM = SVC()\n",
    "\n",
    "parameters = {\n",
    "              'C': [0.001, 0.1, 1, 10, 1000, 10000, 100000],\n",
    "              'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
    "              'kernel': ['rbf']\n",
    "}\n",
    "\n",
    "cv_SVM_set_1 = GridSearchCV(SVM, parameters, cv=5)\n",
    "cv_SVM_set_1.fit(x1_train, y1_train)\n",
    "\n",
    "cv_SVM_set_2 = GridSearchCV(SVM, parameters, cv=5)\n",
    "cv_SVM_set_2.fit(x2_train, y2_train)\n",
    "\n",
    "print(\"SET 1: \\n\")\n",
    "print_results(cv_SVM_set_1)\n",
    "print(\"\\nSET 2: \\n\")\n",
    "print_results(cv_SVM_set_2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6162534",
   "metadata": {},
   "source": [
    "## 5. Training the models\n",
    "#### Q:  Analyze the results on Validation set and test set and mention which model performed the best and why?\n",
    "    Compare the performance of models (using precision, recall, accuracy, latency).\n",
    "    What was the best proportion or split ratio of data from the set of experiments\n",
    "    you conducted?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d559a6e",
   "metadata": {},
   "source": [
    "### 5.1 Fit the models with the best params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a8b27781",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(max_depth=20, n_estimators=50)\n",
      "RandomForestClassifier(max_depth=20, n_estimators=50)\n",
      "LogisticRegression(C=1000, solver='newton-cg')\n",
      "LogisticRegression(C=10, solver='newton-cg')\n",
      "SVC(C=10, gamma=1)\n",
      "SVC(C=10, gamma=1)\n"
     ]
    }
   ],
   "source": [
    "# Save best models for each dataset split.\n",
    "\n",
    "rf_best_1 = RandomForestClassifier(**cv_rf_set_1.best_params_)\n",
    "print(rf_best_1.fit(x1_train, y1_train))\n",
    "\n",
    "rf_best_2 = RandomForestClassifier(**cv_rf_set_2.best_params_)\n",
    "print(rf_best_2.fit(x2_train, y2_train))\n",
    "\n",
    "lr_best_1 = LogisticRegression(**cv_logreg_set_1.best_params_)\n",
    "print(lr_best_1.fit(x1_train, y1_train))\n",
    "\n",
    "lr_best_2 = LogisticRegression(**cv_logreg_set_2.best_params_)\n",
    "print(lr_best_2.fit(x2_train, y2_train))\n",
    "\n",
    "SVM_best_1 = SVC(**cv_SVM_set_1.best_params_)\n",
    "print(SVM_best_1.fit(x1_train, y1_train))\n",
    "\n",
    "SVM_best_2 = SVC(**cv_SVM_set_2.best_params_)\n",
    "print(SVM_best_2.fit(x2_train, y2_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d98baa7",
   "metadata": {},
   "source": [
    "### 5.2 Evaluate models on validation sets and print out results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0e826dc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate models on Validation sets\n",
      "\n",
      "\n",
      "Random Forest (best model) - 60/20/20 - SET 1\n",
      "Max depth: 10 / # Of est: 50 \n",
      "-- Accuracy: 0.9667 / Precision: 0.9667 / Recall: 0.9667 / Latency: 0.005326300000000117\n",
      "\n",
      "Random Forest (best model) - 80/10/10 - SET 2\n",
      "Max depth: 10 / # Of est: 100 \n",
      "-- Accuracy: 1.0 / Precision: 1.0 / Recall: 1.0 / Latency: 0.008474700000000723\n",
      "\n",
      "Logistic Regression (best model) - 60/20/20 - SET 1\n",
      "LR Algorithm: newton-cg / Inverse of regularization strength: 10000 \n",
      "-- Accuracy: 0.9333 / Precision: 0.9333 / Recall: 0.9333 / Latency: 0.00010919999999714491\n",
      "\n",
      "Logistic Regression (best model) - 80/10/10 - SET 2\n",
      "LR Algorithm: newton-cg / Inverse of regularization strength: 1 \n",
      "-- Accuracy: 1.0 / Precision: 1.0 / Recall: 1.0 / Latency: 0.00011130000000036944\n",
      "\n",
      "Support Vector Machine (best model) - 60/20/20 - SET 1\n",
      "SVM Kernel: rbf / Inverse of regularization strength: 1000 / Gamma: 1 \n",
      "-- Accuracy: 0.9333 / Precision: 0.9333 / Recall: 0.9333 / Latency: 0.0003831999999981406\n",
      "\n",
      "Support Vector Machine (best model) - 80/10/10 - SET 2\n",
      "SVM Kernel: rbf / Inverse of regularization strength: 1000 / Gamma: 0.01 \n",
      "-- Accuracy: 1.0 / Precision: 1.0 / Recall: 1.0 / Latency: 0.00023209999999806996\n"
     ]
    }
   ],
   "source": [
    "print(\"Evaluate models on Validation sets\\n\")\n",
    "print(\"\\nRandom Forest (best model) - 60/20/20 - SET 1\")\n",
    "for mdl in [rf_best_1]:\n",
    "    start = timer()\n",
    "    y_pred = mdl.predict(x1_val)\n",
    "    latency = (timer() - start)\n",
    "    accuracy = round(accuracy_score(y1_val, y_pred), 4)\n",
    "    precision = round(precision_score(y1_val, y_pred, average='micro'), 4)\n",
    "    recall = round(recall_score(y1_val, y_pred, average='micro'), 4)\n",
    "    print('Max depth: {} / # Of est: {} \\n-- Accuracy: {} / Precision: {} / Recall: {} / Latency: {}'.format(mdl.max_depth,\n",
    "                                                                         mdl.n_estimators,\n",
    "                                                                         accuracy,\n",
    "                                                                         precision,\n",
    "                                                                         recall,\n",
    "                                                                         latency))\n",
    "    \n",
    "print(\"\\nRandom Forest (best model) - 80/10/10 - SET 2\")\n",
    "for mdl in [rf_best_2]:\n",
    "    start = timer()\n",
    "    y_pred = mdl.predict(x2_val)\n",
    "    latency = (timer() - start)\n",
    "    accuracy = round(accuracy_score(y2_val, y_pred), 4)\n",
    "    precision = round(precision_score(y2_val, y_pred, average='micro'), 4)\n",
    "    recall = round(recall_score(y2_val, y_pred, average='micro'), 4)\n",
    "    print('Max depth: {} / # Of est: {} \\n-- Accuracy: {} / Precision: {} / Recall: {} / Latency: {}'.format(mdl.max_depth,\n",
    "                                                                                                           mdl.n_estimators,\n",
    "                                                                                                           accuracy,\n",
    "                                                                                                           precision,\n",
    "                                                                                                           recall,\n",
    "                                                                                                           latency))\n",
    "print(\"\\nLogistic Regression (best model) - 60/20/20 - SET 1\")\n",
    "for mdl in [lr_best_1]:\n",
    "    start = timer()\n",
    "    y_pred = mdl.predict(x1_val)\n",
    "    latency = (timer() - start)\n",
    "    accuracy = round(accuracy_score(y1_val, y_pred), 4)\n",
    "    precision = round(precision_score(y1_val, y_pred, average='micro'), 4)\n",
    "    recall = round(recall_score(y1_val, y_pred, average='micro'), 4)\n",
    "    print('LR Algorithm: {} / Inverse of regularization strength: {} \\n-- Accuracy: {} / Precision: {} / Recall: {} / Latency: {}'.format(mdl.solver,\n",
    "                                                                                                                                         mdl.C,\n",
    "                                                                                                                                         accuracy,\n",
    "                                                                                                                                         precision,\n",
    "                                                                                                                                         recall,\n",
    "                                                                                                                                         latency))\n",
    "    \n",
    "print(\"\\nLogistic Regression (best model) - 80/10/10 - SET 2\")\n",
    "for mdl in [lr_best_2]:\n",
    "    start = timer()\n",
    "    y_pred = mdl.predict(x2_val)\n",
    "    latency = (timer() - start)\n",
    "    accuracy = round(accuracy_score(y2_val, y_pred), 4)\n",
    "    precision = round(precision_score(y2_val, y_pred, average='micro'), 4)\n",
    "    recall = round(recall_score(y2_val, y_pred, average='micro'), 4)\n",
    "    print('LR Algorithm: {} / Inverse of regularization strength: {} \\n-- Accuracy: {} / Precision: {} / Recall: {} / Latency: {}'.format(mdl.solver,\n",
    "                                                                                                                         mdl.C,\n",
    "                                                                                                                         accuracy,\n",
    "                                                                                                                         precision,\n",
    "                                                                                                                         recall,\n",
    "                                                                                                                         latency))\n",
    "    \n",
    "print(\"\\nSupport Vector Machine (best model) - 60/20/20 - SET 1\")\n",
    "for mdl in [SVM_best_1]:\n",
    "    start = timer()\n",
    "    y_pred = mdl.predict(x1_val)\n",
    "    latency = (timer() - start)\n",
    "    accuracy = round(accuracy_score(y1_val, y_pred), 4)\n",
    "    precision = round(precision_score(y1_val, y_pred, average='micro'), 4)\n",
    "    recall = round(recall_score(y1_val, y_pred, average='micro'), 4)\n",
    "    print('SVM Kernel: {} / Inverse of regularization strength: {} / Gamma: {} \\n-- Accuracy: {} / Precision: {} / Recall: {} / Latency: {}'.format(mdl.kernel,\n",
    "                                                                                                                                   mdl.C,\n",
    "                                                                                                                                   mdl.gamma,\n",
    "                                                                                                                                   accuracy,\n",
    "                                                                                                                                   precision,\n",
    "                                                                                                                                   recall,\n",
    "                                                                                                                                   latency))\n",
    "print(\"\\nSupport Vector Machine (best model) - 80/10/10 - SET 2\")\n",
    "for mdl in [SVM_best_2]:\n",
    "    start = timer()\n",
    "    y_pred = mdl.predict(x2_val)\n",
    "    latency = (timer() - start)\n",
    "    accuracy = round(accuracy_score(y2_val, y_pred), 4)\n",
    "    precision = round(precision_score(y2_val, y_pred, average='micro'), 4)\n",
    "    recall = round(recall_score(y2_val, y_pred, average='micro'), 4)\n",
    "    print('SVM Kernel: {} / Inverse of regularization strength: {} / Gamma: {} \\n-- Accuracy: {} / Precision: {} / Recall: {} / Latency: {}'.format(mdl.kernel,\n",
    "                                                                                                                                    mdl.C,\n",
    "                                                                                                                                    mdl.gamma,\n",
    "                                                                                                                                    accuracy,\n",
    "                                                                                                                                    precision,\n",
    "                                                                                                                                    recall,\n",
    "                                                                                                                                    latency))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f57d029f",
   "metadata": {},
   "source": [
    "### 5.3 Evaluate models on test sets and print out results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ec161994",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate models on Test sets\n",
      "\n",
      "\n",
      "Random Forest (best model) - 60/20/20 - SET 1\n",
      "\n",
      "A: 0.967 / P: 0.967 / R: 0.967 / L: 0.005837399999997217\n",
      "\n",
      "Random Forest (best model) - 80/10/10 - SET 2\n",
      "\n",
      "A: 1.0 / P: 1.0 / R: 1.0 / L: 0.008471799999998808\n",
      "\n",
      "Logistic Regression (best model) - 60/20/20 - SET 1\n",
      "\n",
      "A: 0.9 / P: 0.9 / R: 0.9 / L: 0.00010699999999985721\n",
      "\n",
      "Logistic Regression (best model) - 80/10/10 - SET 2\n",
      "\n",
      "A: 1.0 / P: 1.0 / R: 1.0 / L: 0.00010080000000201039\n",
      "\n",
      "Support Vector Machine (best model) - 60/20/20 - SET 1\n",
      "\n",
      "A: 0.9 / P: 0.9 / R: 0.9 / L: 0.00022370000000293544\n",
      "\n",
      "Support Vector Machine (best model) - 80/10/10 - SET 2\n",
      "\n",
      "A: 1.0 / P: 1.0 / R: 1.0 / L: 0.0003331000000024176\n"
     ]
    }
   ],
   "source": [
    "print(\"Evaluate models on Test sets\\n\")\n",
    "print(\"\\nRandom Forest (best model) - 60/20/20 - SET 1\")\n",
    "\n",
    "for mdl in [rf_best_1]:\n",
    "    start = timer()\n",
    "    y_pred = mdl.predict(x1_test)\n",
    "    latency = (timer() - start)\n",
    "    accuracy = round(accuracy_score(y1_test, y_pred), 3)\n",
    "    precision = round(precision_score(y1_test, y_pred, average='micro'), 3)\n",
    "    recall = round(recall_score(y1_test, y_pred, average='micro'), 3)\n",
    "    print('\\nA: {} / P: {} / R: {} / L: {}'.format(accuracy,precision,recall,latency))\n",
    "\n",
    "print(\"\\nRandom Forest (best model) - 80/10/10 - SET 2\")\n",
    "\n",
    "for mdl in [rf_best_2]:\n",
    "    start = timer()\n",
    "    y_pred = mdl.predict(x2_test)\n",
    "    latency = (timer() - start)\n",
    "    accuracy = round(accuracy_score(y2_test, y_pred), 3)\n",
    "    precision = round(precision_score(y2_test, y_pred, average='micro'), 3)\n",
    "    recall = round(recall_score(y2_test, y_pred, average='micro'), 3)\n",
    "    print('\\nA: {} / P: {} / R: {} / L: {}'.format(accuracy,precision,recall,latency))\n",
    "    \n",
    "print(\"\\nLogistic Regression (best model) - 60/20/20 - SET 1\")\n",
    "for mdl in [lr_best_1]:\n",
    "    start = timer()\n",
    "    y_pred = mdl.predict(x1_test)\n",
    "    latency = (timer() - start)\n",
    "    accuracy = round(accuracy_score(y1_test, y_pred), 3)\n",
    "    precision = round(precision_score(y1_test, y_pred, average='micro'), 3)\n",
    "    recall = round(recall_score(y1_test, y_pred, average='micro'), 3)\n",
    "    print('\\nA: {} / P: {} / R: {} / L: {}'.format(accuracy,precision,recall,latency))\n",
    "    \n",
    "print(\"\\nLogistic Regression (best model) - 80/10/10 - SET 2\")\n",
    "for mdl in [lr_best_2]:\n",
    "    start = timer()\n",
    "    y_pred = mdl.predict(x2_test)\n",
    "    latency = (timer() - start)\n",
    "    accuracy = round(accuracy_score(y2_test, y_pred), 3)\n",
    "    precision = round(precision_score(y2_test, y_pred, average='micro'), 3)\n",
    "    recall = round(recall_score(y2_test, y_pred, average='micro'), 3)\n",
    "    print('\\nA: {} / P: {} / R: {} / L: {}'.format(accuracy,precision,recall,latency))\n",
    "    \n",
    "print(\"\\nSupport Vector Machine (best model) - 60/20/20 - SET 1\")\n",
    "for mdl in [SVM_best_1]:\n",
    "    start = timer()\n",
    "    y_pred = mdl.predict(x1_test)\n",
    "    latency = (timer() - start)\n",
    "    accuracy = round(accuracy_score(y1_test, y_pred), 3)\n",
    "    precision = round(precision_score(y1_test, y_pred, average='micro'), 3)\n",
    "    recall = round(recall_score(y1_test, y_pred, average='micro'), 3)\n",
    "    print('\\nA: {} / P: {} / R: {} / L: {}'.format(accuracy,precision,recall,latency))\n",
    "\n",
    "print(\"\\nSupport Vector Machine (best model) - 80/10/10 - SET 2\")\n",
    "for mdl in [SVM_best_2]:\n",
    "    start = timer()\n",
    "    y_pred = mdl.predict(x2_test)\n",
    "    latency = (timer() - start)\n",
    "    accuracy = round(accuracy_score(y2_test, y_pred), 3)\n",
    "    precision = round(precision_score(y2_test, y_pred, average='micro'), 3)\n",
    "    recall = round(recall_score(y2_test, y_pred, average='micro'), 3)\n",
    "    print('\\nA: {} / P: {} / R: {} / L: {}'.format(accuracy,precision,recall,latency))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b66be690",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression SET 1 -- Accuracy: 0.967 / Precision: 0.967 / Recall: 0.967 / Latency: 0.2ms\n",
      "Logistic Regression SET 2 -- Accuracy: 1.0 / Precision: 1.0 / Recall: 1.0 / Latency: 0.1ms\n",
      "Random Forest SET 1 -- Accuracy: 0.967 / Precision: 0.967 / Recall: 0.967 / Latency: 3.6ms\n",
      "Random Forest SET 2 -- Accuracy: 1.0 / Precision: 1.0 / Recall: 1.0 / Latency: 4.3ms\n",
      "SVM SET 1 -- Accuracy: 0.967 / Precision: 0.967 / Recall: 0.967 / Latency: 0.3ms\n",
      "SVM SET 1 -- Accuracy: 1.0 / Precision: 1.0 / Recall: 1.0 / Latency: 0.3ms\n"
     ]
    }
   ],
   "source": [
    "def evaluate_model(name, model, features, labels):\n",
    "    start = timer()\n",
    "    pred = model.predict(features)\n",
    "    end = timer()\n",
    "    accuracy = round(accuracy_score(labels, pred), 3)\n",
    "    precision = round(precision_score(labels, pred, average='micro'), 3)\n",
    "    recall = round(recall_score(labels, pred, average='micro'), 3)\n",
    "    print('{} -- Accuracy: {} / Precision: {} / Recall: {} / Latency: {}ms'.format(name,\n",
    "                                                                                   accuracy,\n",
    "                                                                                   precision,\n",
    "                                                                                   recall,\n",
    "                                                                                   round((end - start)*1000, 1)))\n",
    "\n",
    "evaluate_model('Logistic Regression SET 1', lr_best_1, x1_test, y1_test)\n",
    "evaluate_model('Logistic Regression SET 2', lr_best_2, x2_test, y2_test)\n",
    "evaluate_model('Random Forest SET 1', rf_best_1, x1_test, y1_test)\n",
    "evaluate_model('Random Forest SET 2', rf_best_2, x2_test, y2_test)\n",
    "evaluate_model('SVM SET 1', SVM_best_1, x1_test, y1_test)\n",
    "evaluate_model('SVM SET 1', SVM_best_2, x2_test, y2_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "458fd244",
   "metadata": {},
   "source": [
    "**1. Validation performance:**\n",
    "\n",
    "- SET 1: \n",
    "    \n",
    "    - From the results, it seems as though Random Forest performed the best on accuracy, precision and recall metrics with scores of 1 for all metrics. The latency metric shows a time of 0.005 to fit the data, which is the worst out of the other algorithms. Logistic Regression performed the fastest with a time of 0.0001s, however, the other metrics show that it is not the most accurate or precise in this case.\n",
    "    \n",
    "    \n",
    "- SET 2: \n",
    "    \n",
    "    - With the validation set 2, all three algorithms performed the best on accuracy, precision and recall metrics. The fastest algorithm is, once again, the logistic regression with a time of 0.0001s.\n",
    "\n",
    "**2. Test performance:**\n",
    "    \n",
    "- SET 1: \n",
    "    \n",
    "    - For the test set 1 performance, Random Forest performed the best on accuracy, precision and recall metrics. However, it is once again not the best performer when it comes to latency. Logistic regression once again performed the fastest with a time of 0.0001s\n",
    "    \n",
    "    \n",
    "- SET 2: \n",
    "    \n",
    "    - All three algorithms with the test set 2 performed the best on accuracy, precision and recall metrics with scores of 1. The fastest algorithm is logistic regression.\n",
    "    \n",
    "**Summary:**\n",
    "\n",
    "   - From the results, it is obvious that choosing Logistic regression in this case proved to be the fastest, yet not the most precise when using the 60-20-20 split. All three algorithms however produced perfect scores of 1 on accuracy, precision and recall metrics on the 80-10-10 split (both val and test sets), with logistic regression performing the best on the latency metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "989e8240",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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