2. Decision Tree.ipynb

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   "source": [
    "<br><font face=\"Times New Roman\" size=5><div dir=ltr align=center>\n",
    "<font color=blue size=8>\n",
    "    Introduction to Machine Learning <br>\n",
    "<font color=red size=5>\n",
    "    Sharif University of Technology - Computer Engineering Department <br>\n",
    "    Fall 2022<br> <br>\n",
    "<font color=black size=6>\n",
    "    Homework 2: Practical - Decision Tree   \n",
    "<font color=black size=4>\n",
    "    Hamidreza Yaghoubi \n",
    "    \n",
    "<br><br>\n",
    "<font size=4>\n",
    "In this homework, we are going to implement the Classification Decision Tree. Keep in mind to complete all of the following questions and write your own codes in the TODO cells."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd3871b7",
   "metadata": {},
   "source": [
    "<font face=\"Times New Roman\" size=4><div dir=ltr>\n",
    "# Problem 2: Classification Decision Tree (100 points)\n",
    "We will implement a Classification Decision Tree from scratch in the following problem. Then we will use our model to predict malignant and benign breast cancer. For this purpose, we will use the breast_cancer.csv dataset which you can find more details about it <a href=\"https://www.kaggle.com/datasets/merishnasuwal/breast-cancer-prediction-dataset\"><font face=\"Roboto\">here</font></a>."
   ]
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  {
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   "execution_count": 1,
   "id": "ccdebb27",
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   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from math import log\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2307c50",
   "metadata": {},
   "source": [
    "<font face=\"Times New Roman\" size=4><div dir=ltr>\n",
    "## Classification Decision Tree Class (60 points)\n",
    "In this section, you only need to fill TODO parts. You can find the logic and formula in both course slides and the web, but fill it out on your own. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "618baebb",
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   "outputs": [],
   "source": [
    "class Node:\n",
    "    def __init__(self, feature=None, threshold=None, left=None, right=None, value=None):\n",
    "        self.feature = feature\n",
    "        self.threshold = threshold\n",
    "        self.left = left\n",
    "        self.right = right\n",
    "        self.value = value\n",
    "\n",
    "    def is_leaf(self):\n",
    "        if self.value is not None:\n",
    "            return True\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "71b3b483",
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2022-10-22T21:30:25.404649Z"
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   "outputs": [],
   "source": [
    "class DecisionTree:\n",
    "    def __init__(self, max_depth=None, min_samples_split=2):\n",
    "        self.max_depth = max_depth\n",
    "        self.min_samples_split = min_samples_split\n",
    "        self.root = None\n",
    "\n",
    "    def is_splitting_finished(self, depth, num_class_labels, num_samples):\n",
    "        if depth > self.max_depth :\n",
    "            return True\n",
    "        if num_samples < self.min_samples_split :\n",
    "            return True\n",
    "        #TODO\n",
    "        return None\n",
    "\n",
    "    def split(self, X, y, feature, threshold):\n",
    "        left_indexes = X[feature] <= threshold\n",
    "        right_indexes = -left_indexes\n",
    "        X_left = X[left_indexes]\n",
    "        y_left = y[left_indexes]\n",
    "        X_right = X[right_indexes]\n",
    "        y_right = y[right_indexes]\n",
    "\n",
    "        return X_left, X_right, y_left, y_right\n",
    "\n",
    "    def entropy(self, y):\n",
    "        true_value = len(y[y == 1])\n",
    "        false_value = len(y[y == 0])\n",
    "        true_prob = true_value / (true_value + false_value)\n",
    "        false_prob = false_value / (true_value + false_value)\n",
    "        probs = [false_prob , true_prob]\n",
    "        ent =0                \n",
    "        for prob in probs:\n",
    "            if(prob == 0):\n",
    "                continue\n",
    "            ent += - prob * log(prob,2)\n",
    "        \n",
    "        #TODO\n",
    "        return ent\n",
    "\n",
    "\n",
    "    def information_gain(self, X, y, feature, threshold):\n",
    "        X_left, X_right, y_left, y_right = self.split(X, y, feature, threshold)\n",
    "        parent_E = self.entropy(y)\n",
    "        left_E = self.entropy(y_left)\n",
    "        right_E = self.entropy(y_right)\n",
    "        left_w = len(y_left) / (len(y))\n",
    "        right_w = len(y_right) / (len(y))\n",
    "        ig = parent_E - (left_E*left_w) - (right_E*right_w)\n",
    "\n",
    "        #TODO\n",
    "        \n",
    "        return ig\n",
    "\n",
    "    def best_split(self, X, y):\n",
    "        best_feature = None\n",
    "        best_threshold = None\n",
    "        max_ig = -float(\"inf\")\n",
    "        for fe in X.columns :\n",
    "            Xi = X[fe]\n",
    "            thresholds =  set(Xi)\n",
    "            for th in thresholds:\n",
    "               X_left, X_right, y_left, y_right = self.split(X, y, fe, th) \n",
    "               if(len(X_left) != 0 and len(X_right)!=0):\n",
    "                n_ig = self.information_gain(X,y,fe,th)\n",
    "                if n_ig > max_ig :\n",
    "                    max_ig = n_ig \n",
    "                    best_feature = fe \n",
    "                    best_threshold = th\n",
    "        \n",
    "        return best_feature, best_threshold\n",
    "\n",
    "    def build_tree(self, X, y, depth=0):\n",
    "        if self.is_splitting_finished(depth, len(X.columns), len(X)):\n",
    "            return None\n",
    "\n",
    "        best_feature, best_threshold = self.best_split(X, y)\n",
    "        X_left, X_right, y_left, y_right = self.split(X, y, best_feature, best_threshold)\n",
    "\n",
    "        left_node = self.build_tree(X_left, y_left, depth=depth + 1)\n",
    "        right_node = self.build_tree(X_right, y_right, depth=depth + 1)\n",
    "\n",
    "        value = None\n",
    "        if left_node is None or right_node is None:\n",
    "            true_value = len(y[y == 1])\n",
    "            false_value = len(y[y == 0])\n",
    "            if true_value >= false_value:\n",
    "                value = 1\n",
    "            else:\n",
    "                value = 0\n",
    "\n",
    "        return Node(feature=best_feature, threshold=best_threshold, left=left_node, right=right_node, value=value)\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        self.root = self.build_tree(X, y)\n",
    "\n",
    "    def predict(self, X):\n",
    "        tree = self.root\n",
    "        predicted_value = []\n",
    "        for index in list(X.index):\n",
    "            data = X.loc[index]\n",
    "            current_tree = tree\n",
    "            for depth in range(self.max_depth):\n",
    "                if Node.is_leaf(current_tree):\n",
    "                    predicted_value.append(current_tree.value)\n",
    "                    break\n",
    "                feature = current_tree.feature\n",
    "                threshold = current_tree.threshold\n",
    "                if data[feature] <= threshold:\n",
    "                    current_tree = current_tree.left\n",
    "                if data[feature] > threshold:\n",
    "                    current_tree = current_tree.right\n",
    "\n",
    "        return predicted_value\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e2197f0",
   "metadata": {},
   "source": [
    "<font face=\"Times New Roman\" size=4><div dir=ltr>\n",
    "## Data Prepration (20 points)\n",
    "In this section, you must perform a good EDA for data. Then split it into train and validation data. We will then use the validation data to find the best model hyperparameters.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "da5a4d94",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-22T21:30:26.199958Z",
     "start_time": "2022-10-22T21:30:26.192910Z"
    }
   },
   "outputs": [],
   "source": [
    "breast_cancer_pdf = pd.read_csv(\"breast_cancer.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc9056fd",
   "metadata": {},
   "source": [
    "u can see the data in the following table:"
   ]
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   "id": "27207399",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_radius</th>\n",
       "      <th>mean_texture</th>\n",
       "      <th>mean_perimeter</th>\n",
       "      <th>mean_area</th>\n",
       "      <th>mean_smoothness</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 6 columns</p>\n",
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      "text/plain": [
       "     mean_radius  mean_texture  mean_perimeter  mean_area  mean_smoothness  \\\n",
       "0          17.99         10.38          122.80     1001.0          0.11840   \n",
       "1          20.57         17.77          132.90     1326.0          0.08474   \n",
       "2          19.69         21.25          130.00     1203.0          0.10960   \n",
       "3          11.42         20.38           77.58      386.1          0.14250   \n",
       "4          20.29         14.34          135.10     1297.0          0.10030   \n",
       "..           ...           ...             ...        ...              ...   \n",
       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
       "566        16.60         28.08          108.30      858.1          0.08455   \n",
       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
       "568         7.76         24.54           47.92      181.0          0.05263   \n",
       "\n",
       "     diagnosis  \n",
       "0            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  \n",
       "..         ...  \n",
       "564          0  \n",
       "565          0  \n",
       "566          0  \n",
       "567          0  \n",
       "568          1  \n",
       "\n",
       "[569 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "breast_cancer_pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d3758b",
   "metadata": {},
   "source": [
    "u can see the data information in the following table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1feeea2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 6 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   mean_radius      569 non-null    float64\n",
      " 1   mean_texture     569 non-null    float64\n",
      " 2   mean_perimeter   569 non-null    float64\n",
      " 3   mean_area        569 non-null    float64\n",
      " 4   mean_smoothness  569 non-null    float64\n",
      " 5   diagnosis        569 non-null    int64  \n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 26.8 KB\n"
     ]
    }
   ],
   "source": [
    "breast_cancer_pdf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8148495c",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_radius</th>\n",
       "      <th>mean_texture</th>\n",
       "      <th>mean_perimeter</th>\n",
       "      <th>mean_area</th>\n",
       "      <th>mean_smoothness</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.627417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.483918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean_radius  mean_texture  mean_perimeter    mean_area  \\\n",
       "count   569.000000    569.000000      569.000000   569.000000   \n",
       "mean     14.127292     19.289649       91.969033   654.889104   \n",
       "std       3.524049      4.301036       24.298981   351.914129   \n",
       "min       6.981000      9.710000       43.790000   143.500000   \n",
       "25%      11.700000     16.170000       75.170000   420.300000   \n",
       "50%      13.370000     18.840000       86.240000   551.100000   \n",
       "75%      15.780000     21.800000      104.100000   782.700000   \n",
       "max      28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       mean_smoothness   diagnosis  \n",
       "count       569.000000  569.000000  \n",
       "mean          0.096360    0.627417  \n",
       "std           0.014064    0.483918  \n",
       "min           0.052630    0.000000  \n",
       "25%           0.086370    0.000000  \n",
       "50%           0.095870    1.000000  \n",
       "75%           0.105300    1.000000  \n",
       "max           0.163400    1.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breast_cancer_pdf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c3cd85b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean_radius        0\n",
       "mean_texture       0\n",
       "mean_perimeter     0\n",
       "mean_area          0\n",
       "mean_smoothness    0\n",
       "diagnosis          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breast_cancer_pdf.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "aab114dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean_radius        0\n",
       "mean_texture       0\n",
       "mean_perimeter     0\n",
       "mean_area          0\n",
       "mean_smoothness    0\n",
       "diagnosis          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breast_cancer_pdf.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b15fdf7",
   "metadata": {},
   "source": [
    "u can see features in the following table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1b795759",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mean_radius', 'mean_texture', 'mean_perimeter', 'mean_area',\n",
       "       'mean_smoothness', 'diagnosis'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breast_cancer_pdf.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1efe904",
   "metadata": {},
   "source": [
    "in the below plots, you can see the distribution of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ed7377e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='mean_radius', ylabel='Density'>"
      ]
     },
     "execution_count": 24,
     "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": [
    "# plot the distribution of mean radius\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')\n",
    "sns.distplot(breast_cancer_pdf['mean_radius'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "51bbad11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='mean_texture', ylabel='Density'>"
      ]
     },
     "execution_count": 14,
     "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": [
    "sns.distplot(breast_cancer_pdf['mean_texture'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d4587f57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='mean_perimeter', ylabel='Density'>"
      ]
     },
     "execution_count": 15,
     "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": [
    "sns.distplot(breast_cancer_pdf['mean_perimeter'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7f9790eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='mean_area', ylabel='Density'>"
      ]
     },
     "execution_count": 16,
     "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": [
    "sns.distplot(breast_cancer_pdf['mean_area'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "501ab6a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='mean_smoothness', ylabel='Density'>"
      ]
     },
     "execution_count": 17,
     "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": [
    "sns.distplot(breast_cancer_pdf['mean_smoothness'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56813fae",
   "metadata": {},
   "source": [
    "in the next plot you can see count of positive and negative diagnosis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "412cf1e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f5094beac20>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(breast_cancer_pdf['diagnosis'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16e988b4",
   "metadata": {},
   "source": [
    "in the next plot you can see the correlation between features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "aaa70e6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10,8))\n",
    "corr = breast_cancer_pdf.corr()\n",
    "sns.heatmap(corr, annot=True, mask=np.zeros_like(corr, dtype=np.bool),\n",
    "           cmap = sns.diverging_palette(240, 10, as_cmap = True), \n",
    "           square = True, ax = ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7d67f2d",
   "metadata": {},
   "source": [
    "in the next table you can see the correlation between features and diagnosis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "981cfa1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean_perimeter    -0.742636\n",
       "mean_radius       -0.730029\n",
       "mean_area         -0.708984\n",
       "mean_texture      -0.415185\n",
       "mean_smoothness   -0.358560\n",
       "diagnosis          1.000000\n",
       "Name: diagnosis, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breast_cancer_pdf.corr()['diagnosis'].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00ba0e8c",
   "metadata": {},
   "source": [
    "split data into train and test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "39ba4c34",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-22T21:30:38.003703Z",
     "start_time": "2022-10-22T21:30:37.996292Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "X = breast_cancer_pdf.drop(\"diagnosis\",axis=1,inplace=False)\n",
    "\n",
    "y = breast_cancer_pdf[\"diagnosis\"]\n",
    "\n",
    "\n",
    "x_train, x_val, y_train, y_val = train_test_split(X, y, test_size=0.70, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cefd7a92",
   "metadata": {},
   "source": [
    "<font face=\"Times New Roman\" size=4><div dir=ltr>\n",
    "## Training And Tuning Hyperparameters (20 points)\n",
    "In this section, you only need to find the best hyperparameters for your model. You can test different values and permutations of hyperparameters by adding them to the lists below. Your model must have at least accuracy=0.85 on validation data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "assigned-estate",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-22T21:36:15.733600Z",
     "start_time": "2022-10-22T21:36:15.730426Z"
    }
   },
   "outputs": [],
   "source": [
    "max_depths = [10,7]\n",
    "min_samples_splits = [4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "58003410",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of training set for [min_samples_splits=4-max_depths=10] =0.9294117647058824\n",
      "accuracy of validation set for [min_samples_splits=4-max_depths=10] =0.9047619047619048\n",
      "------------------------------------------------\n",
      "accuracy of training set for [min_samples_splits=4-max_depths=7] =0.9294117647058824\n",
      "accuracy of validation set for [min_samples_splits=4-max_depths=7] =0.9047619047619048\n",
      "------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "best_max_depth = None\n",
    "best_min_samples_split = None\n",
    "best_accuracy = 0\n",
    "best_model = None\n",
    "for max_depth in max_depths:\n",
    "    for min_samples_split in min_samples_splits:\n",
    "        clf = DecisionTree(max_depth, min_samples_split)\n",
    "        clf.fit(x_train, y_train)\n",
    "        y_val_pred = clf.predict(x_val)\n",
    "        y_train_pred = clf.predict(x_train)\n",
    "        accuracy = accuracy_score(y_val_pred, y_val)\n",
    "        train_accuracy = accuracy_score(y_train_pred, y_train)\n",
    "        print(f\"accuracy of training set for [min_samples_splits={min_samples_split}-max_depths={max_depth}] ={train_accuracy}\")\n",
    "        print(f\"accuracy of validation set for [min_samples_splits={min_samples_split}-max_depths={max_depth}] ={accuracy}\")\n",
    "        print(\"------------------------------------------------\")\n",
    "        if accuracy >= best_accuracy:\n",
    "            best_accuracy = accuracy\n",
    "            best_max_depth = max_depth\n",
    "            best_min_samples_split = min_samples_split\n",
    "            best_model = clf\n"
   ]
  }
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