MachineLearningPresentation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "f0ae33c6",
   "metadata": {
    "hide_input": true
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import classification_report \n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "import pandas as pd\n",
    "from sklearn import tree\n",
    "from sklearn import neural_network\n",
    "from IPython.display import display\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import neural_network\n",
    "from warnings import simplefilter\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "\n",
    "\n",
    "simplefilter(\"ignore\", category=ConvergenceWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f0b3e78",
   "metadata": {
    "hide_input": false,
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "outputs": [],
   "source": [
    "<h1 text-align = \"right\">An introduction to machine learning and AI</h1>\n",
    "<br>\n",
    "\n",
    "<h2>Input Image 1</h2>\n",
    "<img src=\"Image_1.png\" width=\"250\" height=\"250\"/>\n",
    "<h2>Input Image 2</h2>\n",
    "<img src=\"Image_2.png\" width=\"256\" height=\"256\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "70629286",
   "metadata": {
    "hide_input": true
   },
   "outputs": [],
   "source": [
    "# Converts the rgb values of a colour to an integer\n",
    "def rgb_to_int(r,g,b):\n",
    "    \n",
    "    return (r<<16) + (g<<8) + b\n",
    "\n",
    "def read_data(filename):\n",
    "    x = []\n",
    "    y = []\n",
    "    back_color = rgb_to_int(255,255,255)\n",
    "    \n",
    "    # Accesses an image and retrives its dimensions and pixel data\n",
    "    image = Image.open(filename)\n",
    "    width,height = image.size\n",
    "    pixels = image.load()\n",
    " \n",
    "    # Loops through every pixel adding its coordinate and colour to x and y if it has colour\n",
    "    for i in range(width):\n",
    "        for j in range(height):\n",
    "            r,g,b = pixels[i,j]\n",
    "            color = rgb_to_int(r,g,b)\n",
    "            \n",
    "            if (color != back_color):\n",
    "                x.append([i,j])\n",
    "                y.append(color)\n",
    "    return x,y\n",
    "\n",
    "width, height = 250, 250\n",
    "\n",
    "def generate_pixel_coordinates():\n",
    "    points = []\n",
    "    for i in range (width):\n",
    "        for j in range(height):\n",
    "            points.append([i,j])\n",
    "            \n",
    "    return points\n",
    "    \n",
    "def getRGBfromI(RGBint):#convert int color code to rgb color code\n",
    "    blue =  RGBint & 255\n",
    "    green = (RGBint >> 8) & 255\n",
    "    red =   (RGBint >> 16) & 255\n",
    "    return red, green, blue\n",
    "\n",
    "def save_data(pixels, colors, output_filename):\n",
    "    \n",
    "    im = Image.new(\"RGB\", (width, height))\n",
    "    pix = im.load()\n",
    "    for i in range(len(pixels)):\n",
    "             pix[pixels[i][0],pixels[i][1]] = getRGBfromI(colors[i])\n",
    "\n",
    "    im.save(output_filename, \"PNG\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60026d2a",
   "metadata": {},
   "source": [
    "<h2>Dataset Creation and Spliting</h2><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "bcadcc59",
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "# Creates datasets for both images\n",
    "image1, image2 = \"Image_1.png\", \"Image_2.png\" #Image 21\n",
    "X1, y1 = read_data(image1)\n",
    "X2, y2 = read_data(image2)\n",
    "\n",
    "# Splits the data set into a training (70%) and test data (30%)\n",
    "X1_train, X1_test, y1_train, y1_test = train_test_split(X1, y1, test_size=0.30, random_state = 100)\n",
    "X2_train, X2_test, y2_train, y2_test = train_test_split(X2, y2, test_size=0.30, random_state = 100)\n",
    "\n",
    "# Predict and generate pixles\n",
    "coords = generate_pixel_coordinates()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "295ecb5d",
   "metadata": {},
   "source": [
    "<h2>Decision Tree</h2><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "431f2151",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_criterion</th>\n",
       "      <th>param_splitter</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000624</td>\n",
       "      <td>0.000509</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>0.000389</td>\n",
       "      <td>gini</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'gini', 'splitter': 'best'}</td>\n",
       "      <td>0.970588</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.970588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.964706</td>\n",
       "      <td>0.022010</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000817</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>0.000438</td>\n",
       "      <td>0.000540</td>\n",
       "      <td>gini</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'gini', 'splitter': 'random'}</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.970588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.911765</td>\n",
       "      <td>0.964706</td>\n",
       "      <td>0.034300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.002226</td>\n",
       "      <td>0.003048</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>entropy</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'entropy', 'splitter': 'best'}</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.911765</td>\n",
       "      <td>0.947059</td>\n",
       "      <td>0.028818</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.002028</td>\n",
       "      <td>0.003089</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>entropy</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'entropy', 'splitter': 'random'}</td>\n",
       "      <td>0.911765</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.970588</td>\n",
       "      <td>0.970588</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.947059</td>\n",
       "      <td>0.022010</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       0.000624      0.000509         0.000194        0.000389   \n",
       "1       0.000817      0.000417         0.000438        0.000540   \n",
       "2       0.002226      0.003048         0.000200        0.000400   \n",
       "3       0.002028      0.003089         0.000299        0.000408   \n",
       "\n",
       "  param_criterion param_splitter  \\\n",
       "0            gini           best   \n",
       "1            gini         random   \n",
       "2         entropy           best   \n",
       "3         entropy         random   \n",
       "\n",
       "                                           params  split0_test_score  \\\n",
       "0       {'criterion': 'gini', 'splitter': 'best'}           0.970588   \n",
       "1     {'criterion': 'gini', 'splitter': 'random'}           1.000000   \n",
       "2    {'criterion': 'entropy', 'splitter': 'best'}           0.941176   \n",
       "3  {'criterion': 'entropy', 'splitter': 'random'}           0.911765   \n",
       "\n",
       "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
       "0           0.941176           0.970588           1.000000           0.941176   \n",
       "1           0.941176           0.970588           1.000000           0.911765   \n",
       "2           1.000000           0.941176           0.941176           0.911765   \n",
       "3           0.941176           0.970588           0.970588           0.941176   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score  \n",
       "0         0.964706        0.022010                1  \n",
       "1         0.964706        0.034300                1  \n",
       "2         0.947059        0.028818                3  \n",
       "3         0.947059        0.022010                3  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_criterion</th>\n",
       "      <th>param_splitter</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002741</td>\n",
       "      <td>0.000907</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>gini</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'gini', 'splitter': 'best'}</td>\n",
       "      <td>0.958678</td>\n",
       "      <td>0.983471</td>\n",
       "      <td>0.942149</td>\n",
       "      <td>0.958678</td>\n",
       "      <td>0.966942</td>\n",
       "      <td>0.961983</td>\n",
       "      <td>0.013428</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001487</td>\n",
       "      <td>0.000954</td>\n",
       "      <td>0.001693</td>\n",
       "      <td>0.002575</td>\n",
       "      <td>gini</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'gini', 'splitter': 'random'}</td>\n",
       "      <td>0.950413</td>\n",
       "      <td>0.950413</td>\n",
       "      <td>0.900826</td>\n",
       "      <td>0.966942</td>\n",
       "      <td>0.925620</td>\n",
       "      <td>0.938843</td>\n",
       "      <td>0.023140</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.002816</td>\n",
       "      <td>0.003120</td>\n",
       "      <td>0.000464</td>\n",
       "      <td>0.000573</td>\n",
       "      <td>entropy</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'entropy', 'splitter': 'best'}</td>\n",
       "      <td>0.950413</td>\n",
       "      <td>0.991736</td>\n",
       "      <td>0.933884</td>\n",
       "      <td>0.958678</td>\n",
       "      <td>0.983471</td>\n",
       "      <td>0.963636</td>\n",
       "      <td>0.021296</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001885</td>\n",
       "      <td>0.002035</td>\n",
       "      <td>0.001943</td>\n",
       "      <td>0.002418</td>\n",
       "      <td>entropy</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'entropy', 'splitter': 'random'}</td>\n",
       "      <td>0.942149</td>\n",
       "      <td>0.991736</td>\n",
       "      <td>0.950413</td>\n",
       "      <td>0.958678</td>\n",
       "      <td>0.950413</td>\n",
       "      <td>0.958678</td>\n",
       "      <td>0.017336</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       0.002741      0.000907         0.000407        0.000498   \n",
       "1       0.001487      0.000954         0.001693        0.002575   \n",
       "2       0.002816      0.003120         0.000464        0.000573   \n",
       "3       0.001885      0.002035         0.001943        0.002418   \n",
       "\n",
       "  param_criterion param_splitter  \\\n",
       "0            gini           best   \n",
       "1            gini         random   \n",
       "2         entropy           best   \n",
       "3         entropy         random   \n",
       "\n",
       "                                           params  split0_test_score  \\\n",
       "0       {'criterion': 'gini', 'splitter': 'best'}           0.958678   \n",
       "1     {'criterion': 'gini', 'splitter': 'random'}           0.950413   \n",
       "2    {'criterion': 'entropy', 'splitter': 'best'}           0.950413   \n",
       "3  {'criterion': 'entropy', 'splitter': 'random'}           0.942149   \n",
       "\n",
       "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
       "0           0.983471           0.942149           0.958678           0.966942   \n",
       "1           0.950413           0.900826           0.966942           0.925620   \n",
       "2           0.991736           0.933884           0.958678           0.983471   \n",
       "3           0.991736           0.950413           0.958678           0.950413   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score  \n",
       "0         0.961983        0.013428                2  \n",
       "1         0.938843        0.023140                4  \n",
       "2         0.963636        0.021296                1  \n",
       "3         0.958678        0.017336                3  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate and train models using both datasets\n",
    "clf1 = tree.Dclf = tree.DecisionTreeClassifier(criterion = 'gini', splitter = \"best\")\n",
    "clf1.fit(X1,y1)\n",
    "\n",
    "clf2 = tree.Dclf = tree.DecisionTreeClassifier(criterion = 'gini', splitter = \"best\") #entropy\n",
    "clf2.fit(X2,y2)\n",
    "\n",
    "clf3 = tree.DecisionTreeClassifier()\n",
    "clf3 = clf1.fit(X1_train,y1_train)\n",
    "y1_predicted=clf3.predict(X1_test)\n",
    "\n",
    "clf4 = tree.DecisionTreeClassifier()\n",
    "clf4 = clf2.fit(X2_train,y2_train)\n",
    "y2_predicted = clf4.predict(X2_test)\n",
    "\n",
    "# Predicts the values of those pixels and saves them as an image\n",
    "save_data (coords, clf3.predict(coords), \"Image_Tree_1.png\")\n",
    "save_data (coords, clf4.predict(coords), \"Image_Tree_2.png\")\n",
    "\n",
    "# Calculating accuracy, classification reports and cross validation\n",
    "score_tree_1 = clf1.score(X1_test, y1_test)\n",
    "accuracy_tree_1 = accuracy_score(y1_test, y1_predicted)\n",
    "report_tree_1 = classification_report(y1_test, y1_predicted)\n",
    "\n",
    "score_tree_2 = clf2.score(X2_test, y2_test)\n",
    "accuracy_tree_2 = accuracy_score(y2_test, y2_predicted)\n",
    "report_tree_2 = classification_report(y2_test, y2_predicted)\n",
    "\n",
    "cross_val_tree_1 = np.mean(cross_val_score(clf3, X1, y1, cv=5))\n",
    "cross_val_tree_2 = np.mean(cross_val_score(clf4, X2, y2, cv=5))\n",
    "\n",
    "# Defines parameters for gridsearch\n",
    "parameters = [{'criterion':['gini','entropy'], \n",
    "               'splitter':['best', 'random']}]\n",
    "\n",
    "# Creates and displays grid searches\n",
    "clf_grid_1 = GridSearchCV(clf3, parameters)\n",
    "clf_grid_1.fit(X1_train, y1_train)\n",
    "\n",
    "clf_grid_2 = GridSearchCV(clf4, parameters)\n",
    "clf_grid_2.fit(X2_train, y2_train)\n",
    "\n",
    "display(pd.DataFrame(clf_grid_1.cv_results_))\n",
    "display(pd.DataFrame(clf_grid_2.cv_results_))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52c9833f",
   "metadata": {},
   "source": [
    "<h2>Gaussian Naive Bayes<h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "de9b07de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate and train models using both datasets\n",
    "clf1 = GaussianNB()\n",
    "clf1.fit(X1_train, y1_train)\n",
    "clf2 = GaussianNB()\n",
    "clf2.fit(X2_train, y2_train)\n",
    "\n",
    "# Predicts using the test data for each dataset and calculates the accuracy\n",
    "y1_predicted = clf1.predict(X1_test)\n",
    "y2_predicted = clf2.predict(X2_test)\n",
    "\n",
    "# Predicts the values of those pixels and saves them as an image\n",
    "save_data(coords, clf1.predict(coords), \"Image_NB_1.png\")\n",
    "save_data(coords, clf2.predict(coords), \"Image_NB_2.png\")\n",
    "\n",
    "# Calculating accuracy, classification reports and cross validation\n",
    "score_nb_1 = clf1.score(X1_test, y1_test)\n",
    "accuracy_guassian_1 = accuracy_score(y1_test, y1_predicted)\n",
    "report_guassian_1 = classification_report(y1_test, y1_predicted)\n",
    "\n",
    "score_nb_2 = clf2.score(X2_test, y2_test)\n",
    "accuracy_guassian_2 = accuracy_score(y2_test, y2_predicted)\n",
    "report_guassian_2 = classification_report(y2_test, y2_predicted)\n",
    "\n",
    "cross_val_nb_1 = np.mean(cross_val_score(clf1, X1, y1, cv=5))\n",
    "cross_val_nb_2 = np.mean(cross_val_score(clf2, X2, y2, cv=5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a27ed46f",
   "metadata": {},
   "source": [
    "<h2>Artificial Neural Networks</h2><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "30b647b2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_activation</th>\n",
       "      <th>param_solver</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.056564</td>\n",
       "      <td>0.015368</td>\n",
       "      <td>0.000208</td>\n",
       "      <td>0.000416</td>\n",
       "      <td>relu</td>\n",
       "      <td>sgd</td>\n",
       "      <td>{'activation': 'relu', 'solver': 'sgd'}</td>\n",
       "      <td>0.558824</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.676471</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.072044</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.213304</td>\n",
       "      <td>0.083616</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>relu</td>\n",
       "      <td>adam</td>\n",
       "      <td>{'activation': 'relu', 'solver': 'adam'}</td>\n",
       "      <td>0.794118</td>\n",
       "      <td>0.794118</td>\n",
       "      <td>0.794118</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.735294</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.037203</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.305513</td>\n",
       "      <td>0.030312</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>relu</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>{'activation': 'relu', 'solver': 'lbfgs'}</td>\n",
       "      <td>0.970588</td>\n",
       "      <td>0.970588</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.970588</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.958824</td>\n",
       "      <td>0.014409</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.259469</td>\n",
       "      <td>0.023344</td>\n",
       "      <td>0.003152</td>\n",
       "      <td>0.006305</td>\n",
       "      <td>logistic</td>\n",
       "      <td>sgd</td>\n",
       "      <td>{'activation': 'logistic', 'solver': 'sgd'}</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.676471</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.794118</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.694118</td>\n",
       "      <td>0.075789</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.269138</td>\n",
       "      <td>0.010437</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>logistic</td>\n",
       "      <td>adam</td>\n",
       "      <td>{'activation': 'logistic', 'solver': 'adam'}</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.794118</td>\n",
       "      <td>0.876471</td>\n",
       "      <td>0.047059</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.241256</td>\n",
       "      <td>0.047902</td>\n",
       "      <td>0.000201</td>\n",
       "      <td>0.000401</td>\n",
       "      <td>logistic</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>{'activation': 'logistic', 'solver': 'lbfgs'}</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970588</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.935294</td>\n",
       "      <td>0.047059</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.256750</td>\n",
       "      <td>0.029588</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>tanh</td>\n",
       "      <td>sgd</td>\n",
       "      <td>{'activation': 'tanh', 'solver': 'sgd'}</td>\n",
       "      <td>0.676471</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.794118</td>\n",
       "      <td>0.735294</td>\n",
       "      <td>0.617647</td>\n",
       "      <td>0.694118</td>\n",
       "      <td>0.063355</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.168730</td>\n",
       "      <td>0.014486</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.000429</td>\n",
       "      <td>tanh</td>\n",
       "      <td>adam</td>\n",
       "      <td>{'activation': 'tanh', 'solver': 'adam'}</td>\n",
       "      <td>0.911765</td>\n",
       "      <td>0.852941</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.041595</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.290661</td>\n",
       "      <td>0.037983</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>tanh</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>{'activation': 'tanh', 'solver': 'lbfgs'}</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.952941</td>\n",
       "      <td>0.023529</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.034381</td>\n",
       "      <td>0.006242</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>identity</td>\n",
       "      <td>sgd</td>\n",
       "      <td>{'activation': 'identity', 'solver': 'sgd'}</td>\n",
       "      <td>0.617647</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.676471</td>\n",
       "      <td>0.382353</td>\n",
       "      <td>0.570588</td>\n",
       "      <td>0.099480</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.094229</td>\n",
       "      <td>0.077146</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>identity</td>\n",
       "      <td>adam</td>\n",
       "      <td>{'activation': 'identity', 'solver': 'adam'}</td>\n",
       "      <td>0.676471</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.735294</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.717647</td>\n",
       "      <td>0.080225</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.070083</td>\n",
       "      <td>0.022253</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>identity</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>{'activation': 'identity', 'solver': 'lbfgs'}</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.023529</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        0.056564      0.015368         0.000208        0.000416   \n",
       "1        0.213304      0.083616         0.000000        0.000000   \n",
       "2        0.305513      0.030312         0.000000        0.000000   \n",
       "3        0.259469      0.023344         0.003152        0.006305   \n",
       "4        0.269138      0.010437         0.000000        0.000000   \n",
       "5        0.241256      0.047902         0.000201        0.000401   \n",
       "6        0.256750      0.029588         0.000199        0.000399   \n",
       "7        0.168730      0.014486         0.000214        0.000429   \n",
       "8        0.290661      0.037983         0.000000        0.000000   \n",
       "9        0.034381      0.006242         0.000000        0.000000   \n",
       "10       0.094229      0.077146         0.000000        0.000000   \n",
       "11       0.070083      0.022253         0.000000        0.000000   \n",
       "\n",
       "   param_activation param_solver  \\\n",
       "0              relu          sgd   \n",
       "1              relu         adam   \n",
       "2              relu        lbfgs   \n",
       "3          logistic          sgd   \n",
       "4          logistic         adam   \n",
       "5          logistic        lbfgs   \n",
       "6              tanh          sgd   \n",
       "7              tanh         adam   \n",
       "8              tanh        lbfgs   \n",
       "9          identity          sgd   \n",
       "10         identity         adam   \n",
       "11         identity        lbfgs   \n",
       "\n",
       "                                           params  split0_test_score  \\\n",
       "0         {'activation': 'relu', 'solver': 'sgd'}           0.558824   \n",
       "1        {'activation': 'relu', 'solver': 'adam'}           0.794118   \n",
       "2       {'activation': 'relu', 'solver': 'lbfgs'}           0.970588   \n",
       "3     {'activation': 'logistic', 'solver': 'sgd'}           0.647059   \n",
       "4    {'activation': 'logistic', 'solver': 'adam'}           0.882353   \n",
       "5   {'activation': 'logistic', 'solver': 'lbfgs'}           0.882353   \n",
       "6         {'activation': 'tanh', 'solver': 'sgd'}           0.676471   \n",
       "7        {'activation': 'tanh', 'solver': 'adam'}           0.911765   \n",
       "8       {'activation': 'tanh', 'solver': 'lbfgs'}           0.941176   \n",
       "9     {'activation': 'identity', 'solver': 'sgd'}           0.617647   \n",
       "10   {'activation': 'identity', 'solver': 'adam'}           0.676471   \n",
       "11  {'activation': 'identity', 'solver': 'lbfgs'}           0.941176   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0            0.647059           0.676471           0.764706   \n",
       "1            0.794118           0.794118           0.705882   \n",
       "2            0.970588           0.941176           0.970588   \n",
       "3            0.676471           0.764706           0.794118   \n",
       "4            0.882353           0.941176           0.882353   \n",
       "5            0.941176           1.000000           0.970588   \n",
       "6            0.647059           0.794118           0.735294   \n",
       "7            0.852941           0.941176           0.882353   \n",
       "8            0.941176           1.000000           0.941176   \n",
       "9            0.588235           0.588235           0.676471   \n",
       "10           0.764706           0.735294           0.823529   \n",
       "11           1.000000           1.000000           1.000000   \n",
       "\n",
       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0            0.588235         0.647059        0.072044               11  \n",
       "1            0.735294         0.764706        0.037203                7  \n",
       "2            0.941176         0.958824        0.014409                2  \n",
       "3            0.588235         0.694118        0.075789                9  \n",
       "4            0.794118         0.876471        0.047059                6  \n",
       "5            0.882353         0.935294        0.047059                4  \n",
       "6            0.617647         0.694118        0.063355                9  \n",
       "7            0.823529         0.882353        0.041595                5  \n",
       "8            0.941176         0.952941        0.023529                3  \n",
       "9            0.382353         0.570588        0.099480               12  \n",
       "10           0.588235         0.717647        0.080225                8  \n",
       "11           1.000000         0.988235        0.023529                1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_activation</th>\n",
       "      <th>param_solver</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.489836</td>\n",
       "      <td>0.225709</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>relu</td>\n",
       "      <td>sgd</td>\n",
       "      <td>{'activation': 'relu', 'solver': 'sgd'}</td>\n",
       "      <td>0.743802</td>\n",
       "      <td>0.710744</td>\n",
       "      <td>0.710744</td>\n",
       "      <td>0.669421</td>\n",
       "      <td>0.768595</td>\n",
       "      <td>0.720661</td>\n",
       "      <td>0.033631</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.909040</td>\n",
       "      <td>0.020157</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>relu</td>\n",
       "      <td>adam</td>\n",
       "      <td>{'activation': 'relu', 'solver': 'adam'}</td>\n",
       "      <td>0.851240</td>\n",
       "      <td>0.842975</td>\n",
       "      <td>0.842975</td>\n",
       "      <td>0.785124</td>\n",
       "      <td>0.867769</td>\n",
       "      <td>0.838017</td>\n",
       "      <td>0.027953</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.670783</td>\n",
       "      <td>0.264152</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>0.000398</td>\n",
       "      <td>relu</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>{'activation': 'relu', 'solver': 'lbfgs'}</td>\n",
       "      <td>0.925620</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.867769</td>\n",
       "      <td>0.900826</td>\n",
       "      <td>0.586777</td>\n",
       "      <td>0.838017</td>\n",
       "      <td>0.127026</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.766398</td>\n",
       "      <td>0.160694</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.002255</td>\n",
       "      <td>logistic</td>\n",
       "      <td>sgd</td>\n",
       "      <td>{'activation': 'logistic', 'solver': 'sgd'}</td>\n",
       "      <td>0.760331</td>\n",
       "      <td>0.702479</td>\n",
       "      <td>0.677686</td>\n",
       "      <td>0.702479</td>\n",
       "      <td>0.760331</td>\n",
       "      <td>0.720661</td>\n",
       "      <td>0.033631</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.007047</td>\n",
       "      <td>0.027936</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>logistic</td>\n",
       "      <td>adam</td>\n",
       "      <td>{'activation': 'logistic', 'solver': 'adam'}</td>\n",
       "      <td>0.917355</td>\n",
       "      <td>0.892562</td>\n",
       "      <td>0.884298</td>\n",
       "      <td>0.876033</td>\n",
       "      <td>0.917355</td>\n",
       "      <td>0.897521</td>\n",
       "      <td>0.017018</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.843362</td>\n",
       "      <td>0.058042</td>\n",
       "      <td>0.001300</td>\n",
       "      <td>0.002119</td>\n",
       "      <td>logistic</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>{'activation': 'logistic', 'solver': 'lbfgs'}</td>\n",
       "      <td>0.917355</td>\n",
       "      <td>0.892562</td>\n",
       "      <td>0.842975</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.892562</td>\n",
       "      <td>0.890909</td>\n",
       "      <td>0.025819</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.646376</td>\n",
       "      <td>0.131497</td>\n",
       "      <td>0.001248</td>\n",
       "      <td>0.002496</td>\n",
       "      <td>tanh</td>\n",
       "      <td>sgd</td>\n",
       "      <td>{'activation': 'tanh', 'solver': 'sgd'}</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.685950</td>\n",
       "      <td>0.669421</td>\n",
       "      <td>0.710744</td>\n",
       "      <td>0.735537</td>\n",
       "      <td>0.705785</td>\n",
       "      <td>0.024848</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.009854</td>\n",
       "      <td>0.156754</td>\n",
       "      <td>0.000707</td>\n",
       "      <td>0.000609</td>\n",
       "      <td>tanh</td>\n",
       "      <td>adam</td>\n",
       "      <td>{'activation': 'tanh', 'solver': 'adam'}</td>\n",
       "      <td>0.958678</td>\n",
       "      <td>0.942149</td>\n",
       "      <td>0.900826</td>\n",
       "      <td>0.834711</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.042784</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.887387</td>\n",
       "      <td>0.037262</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>tanh</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>{'activation': 'tanh', 'solver': 'lbfgs'}</td>\n",
       "      <td>0.966942</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.834711</td>\n",
       "      <td>0.834711</td>\n",
       "      <td>0.859504</td>\n",
       "      <td>0.880992</td>\n",
       "      <td>0.050838</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.344683</td>\n",
       "      <td>0.078606</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>identity</td>\n",
       "      <td>sgd</td>\n",
       "      <td>{'activation': 'identity', 'solver': 'sgd'}</td>\n",
       "      <td>0.710744</td>\n",
       "      <td>0.719008</td>\n",
       "      <td>0.685950</td>\n",
       "      <td>0.719008</td>\n",
       "      <td>0.685950</td>\n",
       "      <td>0.704132</td>\n",
       "      <td>0.015149</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.472490</td>\n",
       "      <td>0.187993</td>\n",
       "      <td>0.003150</td>\n",
       "      <td>0.006301</td>\n",
       "      <td>identity</td>\n",
       "      <td>adam</td>\n",
       "      <td>{'activation': 'identity', 'solver': 'adam'}</td>\n",
       "      <td>0.851240</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.826446</td>\n",
       "      <td>0.743802</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.793388</td>\n",
       "      <td>0.048753</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.460697</td>\n",
       "      <td>0.120510</td>\n",
       "      <td>0.003523</td>\n",
       "      <td>0.006099</td>\n",
       "      <td>identity</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>{'activation': 'identity', 'solver': 'lbfgs'}</td>\n",
       "      <td>0.942149</td>\n",
       "      <td>0.966942</td>\n",
       "      <td>0.966942</td>\n",
       "      <td>0.958678</td>\n",
       "      <td>0.950413</td>\n",
       "      <td>0.957025</td>\n",
       "      <td>0.009638</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        0.489836      0.225709         0.000199        0.000399   \n",
       "1        0.909040      0.020157         0.000000        0.000000   \n",
       "2        0.670783      0.264152         0.000199        0.000398   \n",
       "3        0.766398      0.160694         0.001833        0.002255   \n",
       "4        1.007047      0.027936         0.000400        0.000800   \n",
       "5        0.843362      0.058042         0.001300        0.002119   \n",
       "6        0.646376      0.131497         0.001248        0.002496   \n",
       "7        1.009854      0.156754         0.000707        0.000609   \n",
       "8        0.887387      0.037262         0.000192        0.000385   \n",
       "9        0.344683      0.078606         0.000000        0.000000   \n",
       "10       0.472490      0.187993         0.003150        0.006301   \n",
       "11       0.460697      0.120510         0.003523        0.006099   \n",
       "\n",
       "   param_activation param_solver  \\\n",
       "0              relu          sgd   \n",
       "1              relu         adam   \n",
       "2              relu        lbfgs   \n",
       "3          logistic          sgd   \n",
       "4          logistic         adam   \n",
       "5          logistic        lbfgs   \n",
       "6              tanh          sgd   \n",
       "7              tanh         adam   \n",
       "8              tanh        lbfgs   \n",
       "9          identity          sgd   \n",
       "10         identity         adam   \n",
       "11         identity        lbfgs   \n",
       "\n",
       "                                           params  split0_test_score  \\\n",
       "0         {'activation': 'relu', 'solver': 'sgd'}           0.743802   \n",
       "1        {'activation': 'relu', 'solver': 'adam'}           0.851240   \n",
       "2       {'activation': 'relu', 'solver': 'lbfgs'}           0.925620   \n",
       "3     {'activation': 'logistic', 'solver': 'sgd'}           0.760331   \n",
       "4    {'activation': 'logistic', 'solver': 'adam'}           0.917355   \n",
       "5   {'activation': 'logistic', 'solver': 'lbfgs'}           0.917355   \n",
       "6         {'activation': 'tanh', 'solver': 'sgd'}           0.727273   \n",
       "7        {'activation': 'tanh', 'solver': 'adam'}           0.958678   \n",
       "8       {'activation': 'tanh', 'solver': 'lbfgs'}           0.966942   \n",
       "9     {'activation': 'identity', 'solver': 'sgd'}           0.710744   \n",
       "10   {'activation': 'identity', 'solver': 'adam'}           0.851240   \n",
       "11  {'activation': 'identity', 'solver': 'lbfgs'}           0.942149   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0            0.710744           0.710744           0.669421   \n",
       "1            0.842975           0.842975           0.785124   \n",
       "2            0.909091           0.867769           0.900826   \n",
       "3            0.702479           0.677686           0.702479   \n",
       "4            0.892562           0.884298           0.876033   \n",
       "5            0.892562           0.842975           0.909091   \n",
       "6            0.685950           0.669421           0.710744   \n",
       "7            0.942149           0.900826           0.834711   \n",
       "8            0.909091           0.834711           0.834711   \n",
       "9            0.719008           0.685950           0.719008   \n",
       "10           0.818182           0.826446           0.743802   \n",
       "11           0.966942           0.966942           0.958678   \n",
       "\n",
       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0            0.768595         0.720661        0.033631               10  \n",
       "1            0.867769         0.838017        0.027953                6  \n",
       "2            0.586777         0.838017        0.127026                6  \n",
       "3            0.760331         0.720661        0.033631                9  \n",
       "4            0.917355         0.897521        0.017018                3  \n",
       "5            0.892562         0.890909        0.025819                4  \n",
       "6            0.735537         0.705785        0.024848               11  \n",
       "7            0.909091         0.909091        0.042784                2  \n",
       "8            0.859504         0.880992        0.050838                5  \n",
       "9            0.685950         0.704132        0.015149               12  \n",
       "10           0.727273         0.793388        0.048753                8  \n",
       "11           0.950413         0.957025        0.009638                1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate and train models using both datasets\n",
    "clf1 = neural_network.MLPClassifier(activation='identity', solver='lbfgs')\n",
    "clf1.fit(X1_train, y1_train)\n",
    "clf2 = neural_network.MLPClassifier(activation='identity', solver='lbfgs') #logistic\n",
    "clf2.fit(X2_train, y2_train)\n",
    "\n",
    "# Predicts using the test data for each dataset and calculates the accuracy\n",
    "y1_predicted = clf1.predict(X1_test)\n",
    "y2_predicted = clf2.predict(X2_test)\n",
    "\n",
    "# Predicts the values of those pixels and saves them as an image\n",
    "save_data(coords,clf1.predict(coords),\"Image_ANN_1.png\")\n",
    "save_data(coords,clf2.predict(coords),\"Image_ANN_2.png\")\n",
    "\n",
    "# Defines parameters for gridsearch\n",
    "parameters = [{'activation':['relu','logistic', 'tanh','identity'], \n",
    "               'solver':['sgd', 'adam','lbfgs']}]\n",
    "\n",
    "# Claculates and displays the gridsearch\n",
    "clf_grid_1 = GridSearchCV(clf1, parameters)\n",
    "clf_grid_1.fit(X1_train, y1_train)\n",
    "\n",
    "clf_grid_2 = GridSearchCV(clf2, parameters)\n",
    "clf_grid_2.fit(X2_train, y2_train)\n",
    "\n",
    "display(pd.DataFrame(clf_grid_1.cv_results_))\n",
    "display(pd.DataFrame(clf_grid_2.cv_results_))\n",
    "\n",
    "# Calculating accuracy, classification reports and cross validation\n",
    "score_ann_1 = clf1.score(X1_test, y1_test)\n",
    "accuracy_ann_1 = accuracy_score(y1_test, y1_predicted)\n",
    "report_ann_1 = classification_report(y1_test, y1_predicted)\n",
    "\n",
    "score_ann_2 = clf2.score(X2_test, y2_test)\n",
    "accuracy_ann_2 = accuracy_score(y2_test, y2_predicted)\n",
    "report_ann_2 = classification_report(y2_test, y2_predicted)\n",
    "\n",
    "cross_val_ann_1 = np.mean(cross_val_score(clf1, X1, y1, cv=5))\n",
    "cross_val_ann_2 = np.mean(cross_val_score(clf2, X2, y2, cv=5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53d9be89",
   "metadata": {},
   "source": [
    "<h2>Comparison</h2><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "926cadbf",
   "metadata": {},
   "source": [
    "<p>&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;<strong>Input Image&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&ensp;Decision Tree Learning &emsp;&emsp;&emsp;&emsp;Gaussian Naive Bayes &emsp;&emsp;&emsp;&emsp;&ensp;Artificial Nerual Network</strong></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "6aa7027b",
   "metadata": {
    "hide_input": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "from matplotlib import rcParams\n",
    "\n",
    "rcParams['figure.figsize'] = 15, 8\n",
    "\n",
    "img_display_1 = mpimg.imread(image1)\n",
    "img_tree_1 = mpimg.imread('Image_Tree_1.png')\n",
    "img_NB_1 = mpimg.imread('Image_NB_1.png')\n",
    "img_ANN_1 = mpimg.imread('Image_ANN_1.png')\n",
    "img_display_2 = mpimg.imread(image2)\n",
    "img_tree_2 = mpimg.imread('Image_Tree_2.png')\n",
    "img_NB_2 = mpimg.imread('Image_NB_2.png')\n",
    "img_ANN_2 = mpimg.imread('Image_ANN_2.png')\n",
    "\n",
    "fig, ax = plt.subplots(2, 4)\n",
    "ax[0][0].imshow(img_display_1)\n",
    "ax[0][1].imshow(img_tree_1)\n",
    "ax[0][2].imshow(img_NB_1)\n",
    "ax[0][3].imshow(img_ANN_1)\n",
    "ax[1][0].imshow(img_display_2)\n",
    "ax[1][1].imshow(img_tree_2)\n",
    "ax[1][2].imshow(img_NB_2)\n",
    "ax[1][3].imshow(img_ANN_2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "e6bce951",
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image 1\n",
      "\t\t    Decision Tree \t   Gaussian \t\t  Artificial Neural Network\n",
      "Classifier score 1: 0.9864864864864865  ,  0.9864864864864865  ,  1.0\n",
      "Accuracy Image 1:   0.9864864864864865  ,  0.9864864864864865  ,  1.0\n",
      "Cross val Image 1:  0.7208333333333333  ,  0.8269557823129252  ,  0.9667517006802722\n",
      "\n",
      "Image 2\n",
      "Classifier score 2: 0.9346153846153846  ,  0.9115384615384615  ,  0.9692307692307692\n",
      "Accuracy Image 2:   0.9346153846153846  ,  0.9115384615384615  ,  0.9692307692307692\n",
      "Cross val Image 2:  0.6508670520231215  ,  0.7872832369942195  ,  0.8335260115606935\n",
      "\n",
      "Image 1\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       41704       1.00      0.96      0.98        27\n",
      "    15539236       0.98      1.00      0.99        47\n",
      "\n",
      "    accuracy                           0.99        74\n",
      "   macro avg       0.99      0.98      0.99        74\n",
      "weighted avg       0.99      0.99      0.99        74\n",
      " \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       41704       1.00      0.96      0.98        27\n",
      "    15539236       0.98      1.00      0.99        47\n",
      "\n",
      "    accuracy                           0.99        74\n",
      "   macro avg       0.99      0.98      0.99        74\n",
      "weighted avg       0.99      0.99      0.99        74\n",
      " \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       41704       1.00      1.00      1.00        27\n",
      "    15539236       1.00      1.00      1.00        47\n",
      "\n",
      "    accuracy                           1.00        74\n",
      "   macro avg       1.00      1.00      1.00        74\n",
      "weighted avg       1.00      1.00      1.00        74\n",
      "\n",
      "_______________________________________________________\n",
      "\n",
      "Image 2\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     2273612       0.93      0.97      0.95        64\n",
      "     4147404       0.92      0.91      0.92        67\n",
      "    15539236       0.95      0.90      0.92        39\n",
      "    16773632       0.94      0.94      0.94        90\n",
      "\n",
      "    accuracy                           0.93       260\n",
      "   macro avg       0.94      0.93      0.93       260\n",
      "weighted avg       0.93      0.93      0.93       260\n",
      " \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "     2273612       0.88      0.91      0.89        64\n",
      "     4147404       0.92      0.99      0.95        67\n",
      "    15539236       0.83      0.74      0.78        39\n",
      "    16773632       0.97      0.93      0.95        90\n",
      "\n",
      "    accuracy                           0.91       260\n",
      "   macro avg       0.90      0.89      0.89       260\n",
      "weighted avg       0.91      0.91      0.91       260\n",
      " \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "     2273612       0.94      0.98      0.96        64\n",
      "     4147404       0.98      0.96      0.97        67\n",
      "    15539236       1.00      0.90      0.95        39\n",
      "    16773632       0.97      1.00      0.98        90\n",
      "\n",
      "    accuracy                           0.97       260\n",
      "   macro avg       0.97      0.96      0.97       260\n",
      "weighted avg       0.97      0.97      0.97       260\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Image 1\")\n",
    "print(\"\\t\\t    Decision Tree \\t   Gaussian \\t\\t  Artificial Neural Network\")\n",
    "print(\"Classifier score 1:\", score_tree_1, \" , \", score_nb_1, \" , \", score_ann_1)\n",
    "print(\"Accuracy Image 1:  \", accuracy_tree_1, \" , \", accuracy_guassian_1, \" , \", accuracy_ann_1)\n",
    "print(\"Cross val Image 1: \",cross_val_tree_1, \" , \", cross_val_nb_1, \" , \", cross_val_ann_1)\n",
    "print()\n",
    "print(\"Image 2\")\n",
    "print(\"Classifier score 2:\", score_tree_2, \" , \", score_nb_2, \" , \", score_ann_2)\n",
    "print(\"Accuracy Image 2:  \", accuracy_tree_2, \" , \", accuracy_guassian_2, \" , \", accuracy_ann_2)\n",
    "print(\"Cross val Image 2: \", cross_val_tree_2, \" , \", cross_val_nb_2, \" , \", cross_val_ann_2)\n",
    "print()\n",
    "print(\"Image 1\")\n",
    "print(report_tree_1, \"\\n\\n\", report_guassian_1, \"\\n\\n\", report_ann_1)\n",
    "print(\"_______________________________________________________\\n\")\n",
    "print(\"Image 2\")\n",
    "print(report_tree_2, \"\\n\\n\", report_guassian_2, \"\\n\\n\", report_ann_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2154298",
   "metadata": {},
   "source": [
    "<h2>Sensitivity study</h2><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "3586459a",
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='sensitivity_images.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "20bca50a",
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='sensitivity_data.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec92064e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}