final_project_portion_random_forests.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COMP 562 Final Project\n",
    "### Multinomial Image Classification using SVMs, Neural Networks, and Random Forests\n",
    "Dataset of 365 classes, each with 50 samples. Each sample is an image. We use 10 of these classes for this assignment because of time and computational constraints. Thus, we have a dataset of 500 samples.\n",
    "\n",
    "We converted each sample into a histogram of oriented gradients (HOGs)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Andrew: Random Forests "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the data using Nate's code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(48188, 3780)\n",
      "48188\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "database_path = Path().cwd() / 'hog_matrix_2'\n",
    "with open(database_path / 'labels.pkl', \"rb\") as fp:\n",
    "    label_list = pickle.load(fp)\n",
    "\n",
    "hog_matrix = np.load(database_path / 'hog_matrix.npy')\n",
    "\n",
    "print(hog_matrix.shape)\n",
    "print(len(label_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from scipy.stats import randint\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_and_train_accuracy(classifier: RandomForestClassifier, features_training: np.ndarray, features_testing: np.ndarray, labels_training: np.ndarray, labels_testing: np.ndarray) -> tuple:\n",
    "    \"\"\"Given a RandomForestClassifier, a training set, and a testing set, returns the accuracy of the classifer on both the test and training set. Useful to determine\n",
    "     if your model is overfitting.\"\"\"\n",
    "    classifier_test_preds: np.ndarray = classifier.predict(features_testing)\n",
    "    classifier_test_acc: float = accuracy_score(labels_testing, classifier_test_preds)\n",
    "    print(\"Test Set Accuracy:\", classifier_test_acc)\n",
    "\n",
    "    classifier_train_preds: np.ndarray = classifier.predict(features_training)\n",
    "    classifier_train_acc: float = accuracy_score(labels_training, classifier_train_preds)\n",
    "    print(\"Train Set Accuracy:\", classifier_train_acc)\n",
    "\n",
    "    return classifier_test_acc, classifier_train_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Explanation of the data\n",
    "# 10 classes, each class has about 5,000 images (samples)\n",
    "# Thus, there are 48,188 images in total. Nate converted each one to a HOG as a vector of length 8,100. \n",
    "# Thus, we have 48,1888 HOGs, each of length 8100, and each with a label denoting which class it is from  \n",
    "# Each element of the vector is a feature, so each sample has 8100 features and 1 class label that we predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((33731, 3780), (14457, 3780), 33731, 14457)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Randomly split the data into 70% training and 30% Testing\n",
    "features_train, features_test, class_train, class_test = train_test_split(hog_matrix, label_list, test_size=0.3)\n",
    "\n",
    "# Verify that training and testing sets were correctly split\n",
    "print(features_train.shape[0] + features_test.shape[0] == hog_matrix.shape[0])\n",
    "features_train.shape, features_test.shape, len(class_train), len(class_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do a single decision tree at first just for comparison, but don't spend any time trying to optimize the single tree. Instead just jump straight into random forests \n",
    "# dt: DecisionTreeClassifier = DecisionTreeClassifier()\n",
    "# dt.fit(features_train, class_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Training Set: 0.26582278481012656\n",
      "Accuracy on Training Set: 0.9996442441670866\n"
     ]
    }
   ],
   "source": [
    "# Evaluate Accuracy of Single Decision Tree\n",
    "dt_preds_test: np.ndarray = dt.predict(features_test)\n",
    "dt_acc_test: float = sum([dt_preds_test[i] == class_test[i] for i in range(len(dt_preds_test))]) / len(dt_preds_test)\n",
    "print(f\"Accuracy on Training Set: {dt_acc_test}\")\n",
    "\n",
    "dt_preds_train: np.ndarray = dt.predict(features_train)\n",
    "dt_acc_train: float = sum([dt_preds_train[i] == class_train[i] for i in range(len(dt_preds_train))]) / len(dt_preds_train)\n",
    "print(f\"Accuracy on Training Set: {dt_acc_train}\")\n",
    "#9m01s. Clearly over fit!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pilot RandomForestClassifier\n",
    "Why Random Forest?\n",
    "* Should reduce overfitting by using mutliple, minimally correlated decision trees to make predictions\n",
    "* Should improve classification accuracy \n",
    "\n",
    "To save computational effort upfront, we started by training a classifier with 30 estimators (decision trees). \n",
    "\n",
    "We try 4 max_depths: 5, 7, 9, 11, to demonstrate that as the maximum depth of estimators increases, the model begins to overfit and model accuracy improves only minimally. \n",
    " - max_depth must be at least 4 because we attempt to classify 10 classes, and a decision tree can make at most as many classifications as it has leaf nodes. \n",
    " - A decision tree of depth 3 can classify into at most 8 classes, and a decision tree of depth 4 can classify into at most 16 classes. \n",
    "\n",
    "How each tree is formed\n",
    "* Each tree is made from a random subset of features and a bootstrapped random sample of samples. Max # of features sampled is set as the sqrt of total # of features.\n",
    "* max # of samples is set to features_train.shape[0] = 33731.   \n",
    "* the rest of the parameters are set to scikitlearn's defaults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy: 0.3583731064536211\n",
      "Train Set Accuracy: 0.384927811212238\n",
      "Done with depth = 5\n",
      "Test Set Accuracy: 0.3960019367780314\n",
      "Train Set Accuracy: 0.48975719664403666\n",
      "Done with depth = 7\n",
      "Test Set Accuracy: 0.41488552258421524\n",
      "Train Set Accuracy: 0.6582668761673238\n",
      "Done with depth = 9\n",
      "Test Set Accuracy: 0.41979663830670266\n",
      "Train Set Accuracy: 0.8517980492721828\n",
      "Done with depth = 11\n"
     ]
    }
   ],
   "source": [
    "depth_list: list = [5, 7, 9, 11]\n",
    "n_estimators: int = 30\n",
    "test_accs: list = []\n",
    "train_accs: list = []\n",
    "\n",
    "for depth in depth_list:\n",
    "    rf_30_depth = RandomForestClassifier(n_estimators=n_estimators, max_depth=depth)\n",
    "    rf_30_depth.fit(features_train, class_train)\n",
    "    results: tuple = test_and_train_accuracy(rf_30_depth, features_train, features_test, class_train, class_test)\n",
    "    test_accs.append(results[0])\n",
    "    train_accs.append((results[1]))\n",
    "    print(\"Done with depth =\", depth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18033dd83d0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"Max Depth\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.plot(depth_list, test_accs, label = \"Test Set\")\n",
    "plt.plot(depth_list, train_accs, label = \"Training Set\");\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy: 0.43203984229093173\n",
      "Train Set Accuracy: 0.6864605259257063\n",
      "Done with num_estimators = 50\n",
      "Test Set Accuracy: 0.44538977657881995\n",
      "Train Set Accuracy: 0.7168776496397972\n",
      "Done with num_estimators = 100\n",
      "Test Set Accuracy: 0.4503008923013073\n",
      "Train Set Accuracy: 0.7211467196347573\n",
      "Done with num_estimators = 150\n",
      "Test Set Accuracy: 0.4522376703327108\n",
      "Train Set Accuracy: 0.727579970946607\n",
      "Done with num_estimators = 200\n"
     ]
    }
   ],
   "source": [
    "# Now, we have roughly determined that using a max_depth of 9 is a decent tradeoff between computational effort and model performance. \n",
    "# In this cell, we will run a model with a max_depth of 9 on an increasing number of estimators to determine how increasing the number\n",
    "estimators_list: list = list(range(50, 201, 50))\n",
    "max_depth: int = 9\n",
    "test_accs_estimator_experiment: list = []\n",
    "train_accs_estimator_experiment: list = []\n",
    "\n",
    "for estimators in estimators_list:\n",
    "    rf_estimators_9 = RandomForestClassifier(n_estimators=estimators, max_depth=max_depth)\n",
    "    rf_estimators_9.fit(features_train, class_train)\n",
    "    results: tuple = test_and_train_accuracy(rf_estimators_9, features_train, features_test, class_train, class_test)\n",
    "    test_accs_estimator_experiment.append(results[0])\n",
    "    train_accs_estimator_experiment.append((results[1]))\n",
    "    print(\"Done with num_estimators =\", estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x180349bead0>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"Num Estimators\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.plot(estimators_list, test_accs_estimator_experiment, label = \"Test Set\")\n",
    "plt.plot(estimators_list, train_accs_estimator_experiment, label = \"Training Set\");\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 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-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 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-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 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-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 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-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=9)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=9)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=9)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It appears as though a classifier with 100 estimators and max depth of 9 has similar performance to classifiers with more estimators and deeper trees. \n",
    "# So, if we're gonna want to improve classification accuracy, we should try modifying some other params. First, lets fit our baseline classifier\n",
    "rf_100_9: RandomForestClassifier = RandomForestClassifier(n_estimators=100, max_depth=9)\n",
    "rf_100_9.fit(features_train, class_train) \n",
    "# Time: 5m39s for hog_matrix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy: 0.44314173064951234\n",
      "Train Set Accuracy: 0.686018158236057\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.44314173064951234, 0.686018158236057)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_and_train_accuracy(rf_100_9, features_train, features_test, class_train, class_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.44314173064951234\n",
      "Precision: [0.59036145 0.58722359 0.3042362  0.41766382 0.30822597 0.7038835\n",
      " 0.39059675 0.43531828 0.40677966 0.43219512]\n",
      "Recall: [0.82094241 0.45136922 0.23867069 0.73007968 0.31995885 0.18637532\n",
      " 0.20970874 0.72272727 0.28458498 0.46387435]\n"
     ]
    }
   ],
   "source": [
    "rf_100_9_preds: np.ndarray = rf_100_9.predict(features_test)\n",
    "accuracy: float = accuracy_score(class_test, rf_100_9_preds)\n",
    "precision: float = precision_score(class_test, rf_100_9_preds, average=None)\n",
    "recall: float = recall_score(class_test, rf_100_9_preds, average=None)\n",
    "\n",
    "# IDK HOW TO INTERPRET\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=5,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001810450CB90&gt;,\n",
       "                                        &#x27;min_samples_leaf&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001810452DAD0&gt;,\n",
       "                                        &#x27;min_samples_split&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001802FFA4ED0&gt;,\n",
       "                                        &#x27;n_estimators&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x0000018033E42A10&gt;})</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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=5,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001810450CB90&gt;,\n",
       "                                        &#x27;min_samples_leaf&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001810452DAD0&gt;,\n",
       "                                        &#x27;min_samples_split&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001802FFA4ED0&gt;,\n",
       "                                        &#x27;n_estimators&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x0000018033E42A10&gt;})</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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" 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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" 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=5,\n",
       "                   param_distributions={'max_depth': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001810450CB90>,\n",
       "                                        'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001810452DAD0>,\n",
       "                                        'min_samples_split': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001802FFA4ED0>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x0000018033E42A10>})"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Randomized Hyperparameter Tuning using 5-fold cross validation. max_layers limited to 10 so as to minimize overfitting but still have high prediction accuracy\n",
    "param_dist: dict = {\n",
    "    'n_estimators': randint(100, 120),\n",
    "    'max_depth': randint(8,10),\n",
    "    'min_samples_leaf': randint(1, int(features_train.shape[0] * 0.001)),\n",
    "    \"min_samples_split\": randint(2, int(features_train.shape[0] * 0.05)),\n",
    "}\n",
    "\n",
    "# Create a random forest classifier\n",
    "rf_hyperparam_tuning: RandomForestClassifier = RandomForestClassifier()\n",
    "\n",
    "# Use random search to find the best hyperparameters\n",
    "rand_search = RandomizedSearchCV(rf_hyperparam_tuning, \n",
    "                                 param_distributions = param_dist, \n",
    "                                 n_iter=5, \n",
    "                                 cv=5,\n",
    "                                 refit=True)\n",
    "\n",
    "# Fit the random search object to the data\n",
    "rand_search.fit(features_train, class_train) # took 91m20s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 8,\n",
       " 'min_samples_leaf': 7,\n",
       " 'min_samples_split': 94,\n",
       " 'n_estimators': 100}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params = rand_search.best_params_\n",
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy: 0.4292038458878052\n",
      "Train Set Accuracy: 0.5550087456642258\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.4292038458878052, 0.5550087456642258)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_best: RandomForestClassifier = RandomForestClassifier(n_estimators=100, max_depth=8, min_samples_leaf=7, min_samples_split=94)\n",
    "rf_best.fit(features_train, class_train)\n",
    "test_and_train_accuracy(rf_best, features_train, features_test, class_train, class_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy: 0.4593622466625164\n",
      "Train Set Accuracy: 0.7501111736977855\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.4593622466625164, 0.7501111736977855)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_100_9_94_100: RandomForestClassifier = RandomForestClassifier(max_depth = 15, min_samples_leaf=7, min_samples_split=94)\n",
    "# Changing the min_samples_leaf and min_samples_split parameters enabled us to reduce overfitting while increasing accuracy! \n",
    "# Let's try again but with more estimators\n",
    "rf_100_9_94_100.fit(features_train, class_train)\n",
    "test_and_train_accuracy(rf_100_9_94_100, features_train, features_test, class_train, class_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy: 0.4729888635263194\n",
      "Train Set Accuracy: 0.7621772256974296\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.4729888635263194, 0.7621772256974296)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_250_9_94_100: RandomForestClassifier = RandomForestClassifier(n_estimators = 250, max_depth = 15, min_samples_leaf=7, min_samples_split=94)\n",
    "rf_250_9_94_100.fit(features_train, class_train)\n",
    "test_and_train_accuracy(rf_250_9_94_100, features_train, features_test, class_train, class_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((38550, 3780), (9638, 3780), 38550, 9638)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split into 80/20 and rerun rf_250_9_94_100\n",
    "features_train, features_test, class_train, class_test = train_test_split(hog_matrix, label_list, test_size=0.2)\n",
    "\n",
    "# Verify that training and testing sets were correctly split\n",
    "print(features_train.shape[0] + features_test.shape[0] == hog_matrix.shape[0])\n",
    "features_train.shape, features_test.shape, len(class_train), len(class_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy: 0.4782112471467109\n",
      "Train Set Accuracy: 0.7584954604409857\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.4782112471467109, 0.7584954604409857)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_250_9_94_100: RandomForestClassifier = RandomForestClassifier(n_estimators = 250, max_depth = 15, min_samples_leaf=7, min_samples_split=94)\n",
    "rf_250_9_94_100.fit(features_train, class_train)\n",
    "test_and_train_accuracy(rf_250_9_94_100, features_train, features_test, class_train, class_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_250_9_94_100: np.ndarray = rf_250_9_94_100.predict(features_test)\n",
    "labels: list = list(set(label_list))\n",
    "cm_250_9_94_100: np.ndarray = confusion_matrix(class_test, rf_250_9_94_100, labels=labels)\n",
    "ConfusionMatrixDisplay(confusion_matrix=cm_250_9_94_100, display_labels=labels).plot(xticks_rotation = \"vertical\");\n",
    "\n",
    "# Some of the most misclassified: deli and bakery, garage outdoor and airfield, garage indoor and airfield, galley and fastfood restaurant, garage indoor and garage outdoor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Discuss error reduction methods\n",
    "# Reducing correlation between trees, increasing the strength of each tree, tinkering with depth, bias-variance tradeoff, etc. \n",
    "# Hyperparameters?"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  },
  "orig_nbformat": 4
 },
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 "nbformat_minor": 2
}