classifier_farsi_name.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "49232219-0157-4543-b16c-20cc21a98681",
   "metadata": {},
   "source": [
    "# Gender Recognition of Farsi names using methods of Statistical Learning and Deep Learning\n",
    "> Writen by ***Alireza Khalilian*** - *Aug 2024*\n",
    "\n",
    "In this notebook, we explore the intriguing problem of gender recognition (or gender classification) for Farsi (Persian) names. We employ various classification methods, including KNN, SVM, Random Forests, ANN, CNN, and RNN, to determine the most effective approach for this task. Each model's performance will be compared against a baseline dummy classifier, and hypothesis testing will be conducted on the accuracy of each model using an alpha value of 0.05.\n",
    "\n",
    "# Table of Contentes\n",
    "- [__Dataset__](#Dataset)\n",
    "- [__Preprocessing__](#Preprocessing)\n",
    "- [__Visualization__](#Visualization)\n",
    "- [__Classification__](#Classification)\n",
    "    - [Basic Models](#Basic-Models)\n",
    "      - [KNN](#K-Nearest-Neighbours)\n",
    "      - [SVM](#Kerneled-SVM)\n",
    "      - [Random Forest](#Random-Forests)\n",
    "      - [Sensitivity Analysis](#Sensetivity-Analysis)\n",
    "    - [Artificial Neural Networks]()\n",
    "        - [Dense Neural Networks]()\n",
    "        - [Recursive Neural Networks]()\n",
    "- [__Comparing Results__](#Comparing-the-Results)\n",
    "- [__Conclusion__](#Conclusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "095da2b1-21ef-4de9-974c-66ee54c8d062",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-31 13:55:30.926781: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-08-31 13:55:31.763293: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import char_map\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import itertools\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from tensorflow import keras\n",
    "import seaborn as sns\n",
    "import math\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import scipy\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_recall_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52ec3b0b-47b9-4a7f-b45f-3a64fc425b53",
   "metadata": {},
   "source": [
    "# Dataset\n",
    "The dataset was initially provided by the PayPing company, though it is relatively small, containing only about 6,000 samples. Another notable dataset is available in the Persian-gender-detection package within [this repository](https://github.com/zeoses/persian-gender-detection/tree/main), which includes approximately 19,000 entries, making it more suitable for real-world applications. Additionally, a list of other available datasets is as follows:\n",
    "- [Persian-gender-detection](https://github.com/zeoses/persian-gender-detection/tree/main)\n",
    "- [Persian(Farsi) Names Dataset](https://www.kaggle.com/datasets/hamedetezadi/persianfarsi-names-dataset)\n",
    "- [nabidam - Persian Names](https://github.com/nabidam/persian-names)\n",
    "- [Dataset for names of people in Persian language](https://www.kaggle.com/datasets/rezaali/dataset-for-names-of-people-in-persian-language).\n",
    "\n",
    "Additionally, since Iran shares a common history with several other countries, such as Tajikistan, Azerbaijan, Afghanistan, and Iraq, names from these regions can be combined and used together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1ffda930-fe9a-436b-91cb-5681904387a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('FirstNames.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99935384-a041-4cb5-af7c-4eb8c5d280b6",
   "metadata": {},
   "source": [
    "# Preprocessing\n",
    "In order to make the data suitable for any machine learning task, the first step that needs to be done is the preprocessing of the data. This step is crucial because it directly impacts the performance, accuracy, and reliability of the models. Various steps can be taken to prepare the data for the model including:\n",
    "\n",
    "1. Handling Missing Data\n",
    "2. Normalization/Standardization\n",
    "3. Feature Engineering\n",
    "4. Handling Categorical Data\n",
    "5. Removing Duplicates\n",
    "6. Handling Text Data\n",
    "7. Splitting the Data\n",
    "\n",
    "## Cleaning Up the labels\n",
    "The provided dataset contains four columns: the first column lists names in Farsi, the second and third columns specify gender, and the fourth column indicates the commonness of the name in the Farsi language. However, the labeling of names is not suitable for our classification problem. Each name is tagged either in the \"Pesar\" column, which indicates a male name, or in the \"Dokhtar\" column, which indicates a female name.\n",
    "\n",
    "We can approach this problem in two different ways:\n",
    "\n",
    "- __One Hot Encoding of the lables__:  In this approach, each label is mapped to a vector where each element is either 0 or 1. We will create a vector $[1,0]$ for males and another vector $[0,1]$ for females. This approach is useful when the machine learning model supports __multiclass classification__ (e.g., Softmax Classification).\n",
    " \n",
    "- __Binary labels__: Binary labels are simpler and can be used with standard models such as traditional SVM classifiers or SGD classifiers. Note that modified versions of these models support multiclass classification.\n",
    "\n",
    "Here we will regenerate both type of labels so that we can use either of them when neccessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b469d328-5629-4b61-9a44-ec1f353e4e43",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Function for generating the binary labels\n",
    "def binary_labels(inp_row):\n",
    "\n",
    "    if inp_row['Pesar'] == 1.0  and inp_row['Dokhtar'] == 1.0:\n",
    "        return 2\n",
    "        \n",
    "    elif inp_row['Pesar'] == 1.0:\n",
    "        return 0\n",
    "\n",
    "    elif inp_row['Dokhtar'] == 1.0:\n",
    "        return 1\n",
    "\n",
    "    else: return -1\n",
    "\n",
    "## Function for generating the one-hot-encoded labels\n",
    "def one_hot_encoded_labels(inp_row):\n",
    "    \n",
    "    if inp_row['Dokhtar'] == 1.0 and inp_row['Pesar'] == 1.0:\n",
    "        return np.array([1,1])\n",
    "\n",
    "    elif inp_row['Pesar'] == 1.0:\n",
    "        return np.array([1,0])\n",
    "\n",
    "    elif inp_row['Dokhtar'] == 1.0:\n",
    "        return np.array([0,1])\n",
    "\n",
    "\n",
    "    else: return np.array([0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9974e1ef-80b0-4dcd-839f-2c80b4879ec7",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Create two additional columns, One for one-hot-encoeded labels and one for binary labels.\n",
    "df['gender_bin'] = df.apply( binary_labels , axis = 1)\n",
    "df['gender_one_hot'] = df.apply( one_hot_encoded_labels , axis = 1)\n",
    "\n",
    "## Remove the unneccessary columns by droping them.\n",
    "df = df.drop(['Pesar' , 'Dokhtar', 'Rate'] , axis = 1)\n",
    "df.columns = ['name', 'gender_bin', 'gender_one_hot']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51cdbfdc-88ae-4ca9-ba31-59ad100d6a22",
   "metadata": {},
   "source": [
    "## Checking for the unlabeled or mislabeled data\n",
    "There are over 6000 data points in this dataset. All of these data needs to be examined to find any mislabeled (names with both _Pesar_ label and _Dokhtar_ labels) or unlabeled names (names with neither _Pesar_ label or _Dokhtar_ label). Fortunatly, when the new labels were created, these cases were considered. A binary label of $2$ means a mislabeled entry and a binary label of $-1$ means an unlabeled entry. These entries, if found, need to be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f612c6a5-658e-4161-913a-86f0bbe6e9b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mislabels: 291 \t Unlabeled: 0\n"
     ]
    }
   ],
   "source": [
    "miss_count = df['gender_bin'].where(df['gender_bin'] == 2).count()\n",
    "unlab_count = df['gender_bin'].where(df['gender_bin'] == -1).count()\n",
    "print(f'Mislabels: {miss_count} \\t Unlabeled: {unlab_count}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "79cc193c-0ed6-4d6d-852c-8b04258dc3ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(index = df[df['gender_bin'] == 2].index, axis = 0)\n",
    "df = df.drop(index = df[df['gender_bin'] == -1].index, axis = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb7f35e6-a437-44ec-a483-94d8c220d764",
   "metadata": {},
   "source": [
    "## Cleaning Up NaNs\n",
    "In any dataset, unassigned entries can be found. These are anomalies which can create problems for the models later on so they should be removed too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b27b7089-843d-4105-b5d6-48c86fb7a5df",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(index = df[df['name'] == np.nan].index, axis = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e6e7fb4-c53e-4789-a832-d6888a602f1a",
   "metadata": {},
   "source": [
    "## Cleaning up the _Name_ column\n",
    "The Farsi language uses the Arabic script, but it only utilizes a portion of the symbols found in the Arabic writing system. For example, symbols like __Tashdid__, while occasionally used, are not common in Farsi. Additionally, many other symbols, such as diacritics, are unnecessary for our purposes. Therefore, we should either remove or convert these unused symbols to the standard 32 Farsi letters; Including a space character for names like \"علی رضا,\" this gives us 33 unique tokens to work with.\n",
    "\n",
    "Fortunatly, PayPing provided a substitute table (can be found in `char_map.py`) which we'll use to clean up the names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3a518dfc-7cec-4c8c-8614-a57fffeb6b7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For our purposes, we will convert all the characters in name to \"FARSI\" characters.\n",
    "df['name'] = df['name'].apply( lambda x: ''.join([char_map.char_mappings[let] if let not in char_map.valid_chars else let for let in x]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88f79423-b7c8-4ce3-b4fc-3c199141755c",
   "metadata": {},
   "source": [
    "## Vectorizing the Names\n",
    "Machine learning models require numerical data to function. Therefore, names must be converted into numerical vectors before being input into a model. In Natural Language Processing, several techniques are available for text vectorization. Since our classification task operates at the character level, we need to assign vectors to each character and then combine them into a single vector.\n",
    "\n",
    "For this task, three methods can be used.\n",
    "\n",
    "### Ordinal Vectorization\n",
    "For ordinal vectorization, a number will be assigned to each letter of the alphabet. Afterwards, a vector of fixed size will be used to represent the names where each element of it will corespond to a letter in the alphabet.\n",
    "$$ a = 1,\\;b = 2,\\;c = 3 $$\n",
    "$$(abaac)_5 = [1 , 2, 1, 1, 3]$$\n",
    "$$(abaac)_7 = [1 , 2, 1, 1, 3, 0, 0]$$  \n",
    "\n",
    "### Binary Vectorization\n",
    "In this case, each letter will be assigned a unique binary vector. These binary vectors will later be combined to create a fixed size vector representing the name.\n",
    "$$ a = [1,0],\\;b = [0,1],\\;c = [1,1] $$\n",
    "$$(abaac)_{10} = [1, 0, 0, 1, 1, 0, 1, 0, 1, 1]$$\n",
    "$$(abaac)_{16} = [1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0]$$\n",
    "\n",
    "### One Hot Encoding\n",
    "Just like in the one hot encoded labels, a vector with the size of our alphabet will be used to represent each letter. For example, if we have $ Alphabet = \\{ a , b, c\\}$ as our alphabet, a vector of size $3$ is used where all the elements but one are zeros. The nonzero element in this case represent different characters of the alphabet. Finally, like in the binary vectorization, all character vectors are combined (with apropriate padding) to create a vector representation of names.\n",
    "$$ a = [1,0,0] $$\n",
    "$$ b = [0,1,0] $$\n",
    "$$ c = [0,0,1] $$\n",
    "$$(abaac)_{15} = [1,0,0,0,1,0,1,0,0,1,0,0,0,0,1]$$\n",
    "$$(abaac)_{20} = [1,0,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0]$$\n",
    "\n",
    "For our case, we will incoporate one hot encodeing as it was successfully used in previous studies [[1]](./2106.10156v2.pdf)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b5b40bd8-e256-46e9-b306-356a19af3584",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5883.000000\n",
       "mean        5.491926\n",
       "std         1.565979\n",
       "min         2.000000\n",
       "25%         4.000000\n",
       "50%         5.000000\n",
       "75%         6.000000\n",
       "max        12.000000\n",
       "Name: length, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## calculate the length of each name. We will use the maximum length name to determine our fixed size vector.\n",
    "df['length'] = df['name'].apply( len )\n",
    "df['length'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "450f4858-7fe2-471f-93f5-8beb1d301bca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One Hot Encoded Vector Size: 396\n"
     ]
    }
   ],
   "source": [
    "set_of_chars = char_map.valid_chars\n",
    "unique_char_count = len(set_of_chars)\n",
    "word_vector_size = unique_char_count * df['length'].max()\n",
    "\n",
    "print('One Hot Encoded Vector Size:', word_vector_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a96c42cd-83f5-482d-8a67-b0cdab89d8f5",
   "metadata": {},
   "source": [
    "Hence, each name will be represented as a $ (1 \\times 396) $ vector. However, we can limit the number of characters to like $8$ characters so the vector will be of size $( 1 \\times 264 )$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2fd60415-08e2-405c-8fec-707e5f02c2f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'م': 0, 'ق': 1, 'ص': 2, ' ': 3, 'د': 4, 'گ': 5, 'خ': 6, 'ت': 7, 'ج': 8, 'ر': 9, 'پ': 10, 'ف': 11, 'ل': 12, 'ژ': 13, 'ط': 14, 'غ': 15, 'ه': 16, 'ک': 17, 'و': 18, 'ض': 19, 'س': 20, 'ش': 21, 'ع': 22, 'ب': 23, 'ی': 24, 'ا': 25, 'ظ': 26, 'چ': 27, 'ذ': 28, 'ن': 29, 'ح': 30, 'ث': 31, 'ز': 32}\n"
     ]
    }
   ],
   "source": [
    "## Create a table lookup for the characters | assign each character a unique number\n",
    "char_table_lookup = {key : val for val , key in enumerate(set_of_chars)}\n",
    "print(char_table_lookup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "72c8571a-fa85-4cbc-b28a-db63ed9c20d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def letter_encoder(type_ , num_of_letters = 12):\n",
    "\n",
    "    total_number_of_features = num_of_letters * unique_char_count\n",
    "    ohe_features = unique_char_count\n",
    "\n",
    "    def numerical_encoder(name):\n",
    "\n",
    "        if len(name) > num_of_letters:\n",
    "            name = name[:num_of_letters]\n",
    "\n",
    "        enc = [-1] * num_of_letters\n",
    "\n",
    "        for i , ch in enumerate(name):\n",
    "            enc[i] = char_table_lookup[ch]\n",
    "                \n",
    "        return np.array(enc)\n",
    "\n",
    "    def one_hot_encoder(name):\n",
    "        enc = []\n",
    "        if len(name) > num_of_letters:\n",
    "            name = name[:num_of_letters]\n",
    "\n",
    "        for ch in name:\n",
    "            letter_encoding = [0] * ohe_features\n",
    "            letter_encoding[char_table_lookup[ch]] = 1\n",
    "            enc.extend(letter_encoding)\n",
    "\n",
    "        if len(name) < num_of_letters:\n",
    "            enc.extend( [0] * (num_of_letters - len(name)) * ohe_features)\n",
    "\n",
    "        return np.array(enc)\n",
    "\n",
    "    if type_ == 'ohe':\n",
    "        return one_hot_encoder\n",
    "\n",
    "    elif type_ == 'numerical':\n",
    "        return numerical_encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0b1da57f-8ac9-48f3-a83b-f77f4f42b402",
   "metadata": {},
   "outputs": [
    {
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       "      <th>name</th>\n",
       "      <th>gender_bin</th>\n",
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       "      <th>0</th>\n",
       "      <td>ابده</td>\n",
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       "      <td>[1, 0]</td>\n",
       "      <td>4</td>\n",
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       "     name  gender_bin gender_one_hot  length  \\\n",
       "0    ابده           0         [1, 0]       4   \n",
       "1    ابیش           0         [1, 0]       4   \n",
       "2  اپروند           0         [1, 0]       6   \n",
       "3  اپرویژ           0         [1, 0]       6   \n",
       "4   اپیوه           0         [1, 0]       5   \n",
       "\n",
       "                                              encode  \n",
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       "2  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "3  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "4  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder = letter_encoder('ohe' , 10)\n",
    "df['encode'] = df['name'].apply(encoder)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66dca3aa-3bc7-4c52-8fad-4aff3898163e",
   "metadata": {},
   "source": [
    "# Visualization\n",
    "Visualizing data before solving a problem is essential because it helps us understand the data's structure, identify patterns, detect outliers, and generate hypotheses. It guides model selection, informs data preprocessing, and aids in communicating findings. By revealing critical insights early on, visualization ensures a more informed and effective approach to problem-solving.\n",
    "\n",
    "## Male VS Female\n",
    "Let's start by examining the distribution of each class in our dataset, the number of male and female names. A significant imbalance in class distribution can introduce bias into the model, affecting its performance. Therefore, it's important to assess whether we need to adjust the __class weights__ in the models to address any disparities.  \n",
    "However as we can see in the _Gender Count_ figure bellow, the imbalance is negligible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d6c3e94e-268e-4a83-94f3-1bda727559b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Boys: 2841 \t Number of Girls: 3042  \t Total Entry: 5883\n"
     ]
    }
   ],
   "source": [
    "number_of_girls = df['gender_bin'][df['gender_bin'] == 1].count()\n",
    "number_of_boys = df['gender_bin'][df['gender_bin'] == 0].count()\n",
    "\n",
    "print(f'Number of Boys: {number_of_boys} \\t Number of Girls: {number_of_girls}  \\t Total Entry: {df.shape[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "916f7751-7601-4ce3-8d41-a8a2a13aabb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 4))\n",
    "ax = sns.barplot( {'Boys': [number_of_boys], 'Girls': [number_of_girls]}, orient='h', width=0.4 , palette = \"bright\")\n",
    "ax.grid(True)\n",
    "_ = ax.set_title('Gender Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1032b90-139d-4755-98dc-0aac091b280a",
   "metadata": {},
   "source": [
    "## Name Lengths\n",
    "Another interesting aspect to explore is plotting the length of each name. This is particularly useful if we plan to limit our vectorization to less than 12 characters.\n",
    "\n",
    "### Result\n",
    "As it is vivid in the graphs down bellow, the average name length is between $5$ and $6$ with the names between $4$ and $8$ being most common in both genders. So, if we plan to reduce the size of the name vectors, we can set it to around $8$ and not miss out on much."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "860665d2-059e-49ea-8ce2-69789be385b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x759ff78c6da0>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,3)\n",
    "fig.set_figheight(3)\n",
    "fig.set_figwidth(10)\n",
    "fig.tight_layout()\n",
    "\n",
    "df['length'].where(df['gender_bin'] == 1).hist(bins =9, ax = axs[0], color = 'orange')\n",
    "df['length'].where(df['gender_bin'] == 0).hist(bins = 9, ax = axs[1], color = 'blue')\n",
    "df['length'].hist(bins = 9, ax = axs[2], color = 'red')\n",
    "\n",
    "mean_girls = df['length'].where(df['gender_bin'] == 1).mean()\n",
    "mean_boys =  df['length'].where(df['gender_bin'] == 0).mean()\n",
    "mean_ = df['length'].mean()\n",
    "\n",
    "axs[0].set_title('Girls name length')\n",
    "axs[1].set_title('Boys name length')\n",
    "axs[2].set_title('Overall name length')\n",
    "\n",
    "axs[0].plot( [mean_girls , mean_girls] , [0,1050] , c = 'black')\n",
    "axs[1].plot( [mean_boys , mean_boys] , [0,1050] , c = 'black')\n",
    "axs[2].plot( [mean_ , mean_] , [0,2000] , c = 'black')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "303ce709-35c6-43ad-ba4e-fde2720785ac",
   "metadata": {},
   "source": [
    "## Character Frequencies\n",
    "Just like the distribution of lengths, character frequencies can also be charted to determine the most usefull characters in the whole dataset. This can also be used to reduce the number of features in One Hot Encoding by removing the less usefull characters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e5d8ae37-afbd-4236-82b6-430d148b7052",
   "metadata": {},
   "outputs": [],
   "source": [
    "char_freq_male = {key : 0 for key in set_of_chars}\n",
    "char_freq_female = {key : 0 for key in set_of_chars}\n",
    "char_freq = {key : 0 for key in set_of_chars}\n",
    "\n",
    "for idx, row in df.iterrows():\n",
    "    for ch in row['name']:\n",
    "        if row['gender_bin'] == 1:\n",
    "            char_freq_female[ch] += 1\n",
    "        if row['gender_bin'] == 0:\n",
    "            char_freq_male[ch] += 1\n",
    "\n",
    "        char_freq[ch] += 1\n",
    "\n",
    "ordered_frq_female = list(set_of_chars)\n",
    "ordered_frq_female.sort( key = lambda x : char_freq_female[x])\n",
    "\n",
    "ordered_frq_male = list(set_of_chars)\n",
    "ordered_frq_male.sort( key = lambda x : char_freq_male[x])\n",
    "\n",
    "ordered_frq = list(set_of_chars)\n",
    "ordered_frq.sort( key = lambda x : char_freq[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d757817f-5f34-429f-a7d1-beeb36ac250a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Char Frequency Male')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2100x700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,3)\n",
    "fig.set_figwidth(21)\n",
    "fig.set_figheight(7)\n",
    "fig.tight_layout()\n",
    "\n",
    "axs[0].grid(True)\n",
    "axs[1].grid(True)\n",
    "axs[2].grid(True)\n",
    "\n",
    "sns.barplot(char_freq, order = ordered_frq, ax = axs[0] , color='teal')\n",
    "\n",
    "sns.barplot(char_freq_female, order = ordered_frq_female, ax = axs[1], color='gold')\n",
    "sns.barplot(char_freq_male, order = ordered_frq_female, ax = axs[1], color='black' , alpha = 0.15)\n",
    "\n",
    "sns.barplot(char_freq_male, order = ordered_frq_male, ax = axs[2], color='coral')\n",
    "sns.barplot(char_freq_female, order = ordered_frq_male, ax = axs[2], color='black' , alpha = 0.15)\n",
    "\n",
    "axs[0].set_title('Char Frequency Totla')\n",
    "axs[1].set_title('Char Frequency Female')\n",
    "axs[2].set_title('Char Frequency Male')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccacbe93-c0f5-41f4-86c8-68d11cb3b8a7",
   "metadata": {},
   "source": [
    "We can See that the data (char frequencies) has an almost __exponantial__ trend which is expected. Also, in the plots regarding male and female frequencies, we also ploted the opposite sides bar in gray so we can compare them easily. For example it is vivid that the letter 'د' is used most commonly in male name and the letter 'ه' is more common in female names."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "637b1e77-36d6-4e8a-af85-a3be02bb8bfd",
   "metadata": {},
   "source": [
    "## Possibility for Feature Engineering\n",
    "As we mentioned before, we can limit both the _number of characters_ to consider for each name and also we can limit the encoding of the characters to only those with consider repetition in our dataset. However these are methods of __Dimensionality Reduction__ to make our models lighter, less prone to overfitting and avoid some nasty side effects like the _Curse of Dimentionality_. \n",
    "\n",
    "One other method that we can use to go beyond the limits of our 33 characters is to create more features using __Polynomial Combinations__. In this method, new features will be added to our dataset which are polynomial combinations (specificaly $degree = n$ combinations) of our previous features. Using this, we can also consider the effect of character combinations (eg, 'مه' in names such as 'مهسا' and 'مهتاب' and etc.). However, this will increase the size of features with an alarming rate.\n",
    "Consider our current $33$ characters and $10$ name length; In total, we have $ 330 $ features. The number of features after polynomial combinations of degree $2$ can be caluculated as:\n",
    "$$ \\dbinom{k + n}{k} - 1 = \\dbinom{2 + 330}{2} - 1 = \\frac{332!}{330! \\times 2!} - 1 = 54,945$$\n",
    "And with degree $3$:\n",
    "$$ \\dbinom{k + n}{k} - 1 = \\dbinom{3 + 330}{3} - 1 = \\frac{333!}{330! \\times 3!} - 1 = 6,099,005$$\n",
    "\n",
    "There are other methods of Dimension Reduction such as **_PCA_** to reduce the size of these massive vectors but, after previously implementing it in the _test phase_, we didn't see considerable improvement in accuracy, hence we will be using our $330$ featured vector."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35bace3d-5d56-4516-8e7c-475234cae074",
   "metadata": {},
   "source": [
    "# Classification\n",
    "In this section, we will tackle different Machine Learning models to find the best one for our task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "fbb526a7-58f0-49de-ac48-ddae979e63fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5883, 330)\n"
     ]
    }
   ],
   "source": [
    "X_ = np.stack(df['encode'].to_numpy() , axis = 0)\n",
    "\n",
    "y_sl = df['gender_bin'].to_numpy()\n",
    "y_dl = np.stack(df['gender_one_hot'].to_numpy() , axis = 0)\n",
    "print(X_.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "257b9ee5-e0c4-46f8-97c9-ccccd7c16c8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train_sl, y_test_sl = train_test_split(X_ , y_sl , test_size = 0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c28b65c3-e8c0-4735-9c11-bd30a8de9c57",
   "metadata": {},
   "source": [
    "## Basic Models\n",
    "In this section, we will perform classification on our data using basic models such as KNN, Kerneled-SVM and RandomForests. In some cases, where the complexity (for example non-linearity) of the data is low, these basic models will perform just as good as a complex model (ie. CNNs) without the processing overhead.\n",
    "\n",
    "### The Dummy Classifier\n",
    "Before we begin to train model, we use a dummy classifier in order to compare our results to it afterwards. This classifier randomly guesses one of the labels each time it is called.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d51e278f-d96c-4555-88c1-d974d176b7c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DummyClassifier(strategy=&#x27;uniform&#x27;)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DummyClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.dummy.DummyClassifier.html\">?<span>Documentation for DummyClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DummyClassifier(strategy=&#x27;uniform&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DummyClassifier(strategy='uniform')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy = DummyClassifier(strategy='uniform')\n",
    "dummy.fit(X_train, y_train_sl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7fb17045-18b0-49a6-a82a-5cadc25db77a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.48      0.48      0.48       723\n",
      "           1       0.50      0.51      0.51       748\n",
      "\n",
      "    accuracy                           0.49      1471\n",
      "   macro avg       0.49      0.49      0.49      1471\n",
      "weighted avg       0.49      0.49      0.49      1471\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report( y_test_sl , dummy.predict(X_test) ))\n",
    "dummy_acc = accuracy_score(y_test_sl , dummy.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c2136c4-8e98-4487-8981-a3d27b6fc8c0",
   "metadata": {},
   "source": [
    "#### Some random tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4d840271-ca73-4457-87bd-42f217469566",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "میترا => پسر است\n",
      "رها => پسر است\n",
      "حمید => دختر است\n",
      "زیبا => پسر است\n",
      "فریبا => دختر است\n"
     ]
    }
   ],
   "source": [
    "names_ = ['میترا' , 'رها' , 'حمید' , 'زیبا' , 'فریبا']\n",
    "for name in names_:\n",
    "    pred_ = dummy.predict([encoder(name)])\n",
    "    if pred_:\n",
    "        print(name, \"=>\" , 'دختر است')\n",
    "\n",
    "    else:\n",
    "        print(name, \"=>\" , 'پسر است')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee3c92ed-7364-4879-9830-1a00fc3161d9",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbours\n",
    "The first model that we encoporate is KNN. This model is lightweight and there is a meaningful interpetation behind it. KNN works great with medium sized datasets however, if we have large input features, for example if we decide to add polynomial relations between features, the models performance will be poor due to the curse of dimentionality.\n",
    "#### Advantages\n",
    "- No training phase (traditional).\n",
    "- Non-parametric (KNN doesn’t assume any underlying distribution of the data).\n",
    "- Handles non-linearity.\n",
    "#### Disadvantages\n",
    "- Memory Intensive.\n",
    "- Must have small amount of features.\n",
    "- Sensitive to noise.\n",
    "- Poorly handles imbalanced classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ee0eec2f-19ad-4e2b-9576-505293fa10a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_cls = KNeighborsClassifier(n_neighbors=16, n_jobs=5, weights = 'distance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "05d93af7-a714-48b9-9086-bf3dca2a070d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_jobs=5, n_neighbors=16, weights=&#x27;distance&#x27;)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_jobs=5, n_neighbors=16, weights=&#x27;distance&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_jobs=5, n_neighbors=16, weights='distance')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_cls.fit(X_train, y_train_sl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bf337bb4-60c9-417c-b141-567f6910c82f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.64      0.67       723\n",
      "           1       0.68      0.75      0.71       748\n",
      "\n",
      "    accuracy                           0.70      1471\n",
      "   macro avg       0.70      0.69      0.69      1471\n",
      "weighted avg       0.70      0.70      0.69      1471\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report( y_test_sl , knn_cls.predict(X_test) ))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a25abf18-a754-486f-9ca3-b1c57eaf28ba",
   "metadata": {},
   "source": [
    "#### Parameter Optimization\n",
    "Afterwards, we perform grid-search on __number of neighbours__ to determine which parameter for the $K$ works best on our _Training Data_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "55f3e7b3-9209-4e84-baf2-9859dcd664bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = [{'n_neighbors' : np.arange(5,20), 'weights' : ['distance' , 'uniform']}]\n",
    "gs = GridSearchCV( knn_cls , param_grid , cv = 5 , scoring='accuracy' , return_train_score=True)\n",
    "\n",
    "# gs.fit(X_train , y_train_sl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8939a943-06af-4b75-a46f-ad53a8c55d3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print('Best Parameter' , gs.best_params_ , 'Best Score', gs.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d82916e-2e31-4fd0-b89e-c994b3311fc8",
   "metadata": {},
   "source": [
    "Hence, we will create the classifier agan (already did) using $16$ for the number of neighbours."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b9f6c6d-c507-47a5-b275-bb42f11afcc8",
   "metadata": {},
   "source": [
    "#### Hypothesis Testing\n",
    "Now that we have a model, we form our null hypothesis:\n",
    "> There is no difference between K-NN model and the Dummy classifier.\n",
    "\n",
    "To test this, we will calculate the P-Value by assuming that the dummy classifier use a __Bionomial Distribution__ to calculate labels with $ p = \\frac{1}{2}$.  \n",
    "Afterwards, using the bionomial distribution, we'll calculate the probablity of achiving the accuracy of K-NN (or more significant)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1dad7879-4084-46a9-a8b1-9f36665ba360",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P-Value: 0.0\n"
     ]
    }
   ],
   "source": [
    "## Calculate the p-value\n",
    "knn_acc = accuracy_score(y_test_sl , knn_cls.predict(X_test))\n",
    "sample_size = y_test_sl.shape[0]\n",
    "\n",
    "p_bionom_cdf = 1 - scipy.stats.binom.cdf( knn_acc * sample_size , sample_size , dummy_acc ) ## 0.5 because dummy's guesses are uniform\n",
    "print('P-Value:' , p_bionom_cdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b181503-ca05-4e0f-bc52-bed2a5d8bd05",
   "metadata": {},
   "source": [
    "Just for the good measure, we will also perform a randomized test to determine an aproximate for the P-Value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c078a980-5ac1-4252-bcf1-9335bab4f53b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P-Value (random-test): 0.0\n"
     ]
    }
   ],
   "source": [
    "itterations = 10000\n",
    "resulted_acc = np.zeros(itterations)\n",
    "\n",
    "for i in range(itterations):\n",
    "    cur_acc = accuracy_score(y_test_sl , dummy.predict(X_test))\n",
    "    resulted_acc[i] = cur_acc\n",
    "\n",
    "p_random = np.sum(resulted_acc > knn_acc) / itterations\n",
    "print('P-Value (random-test):' , p_random)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1353f6f-8c4b-4ad6-bfeb-5c0edfb39ab0",
   "metadata": {},
   "source": [
    "The calculated P-Value from both tests are 0. Hence it beats our significance level $(0.05)$ and we conclude that the null hypothesis is wrong."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e1dc5e6-232e-44f3-81a9-d02f8e652f77",
   "metadata": {},
   "source": [
    "#### Some random tests\n",
    "Here, we will give some random names to the model so we can visualize the models response in action. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "eeab4007-4f21-4aa4-b7dd-ba73d0e56a77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "میترا => دختر است\n",
      "رها => دختر است\n",
      "حمید => پسر است\n",
      "زیبا => دختر است\n",
      "فریبا => دختر است\n"
     ]
    }
   ],
   "source": [
    "names_ = ['میترا' , 'رها' , 'حمید' , 'زیبا' , 'فریبا']\n",
    "for name in names_:\n",
    "    pred_ = knn_cls.predict([encoder(name)])\n",
    "    if pred_:\n",
    "        print(name, \"=>\" , 'دختر است')\n",
    "\n",
    "    else:\n",
    "        print(name, \"=>\" , 'پسر است')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38808e1e-1baf-4161-9157-9ac7b39a9961",
   "metadata": {},
   "source": [
    "### Kerneled SVM\n",
    "Next, we will use a kernelized SVM. Since our dataset is medium sized and our feature vector is not that large, SVM is a good choice to classify the data. However, as our dataset gets larger, the SVM can become computationaly expensive. But, one major advantage of Kernelized SVM is that we can use kernels to simulate the relationship between features.\n",
    "\n",
    "#### Advantages\n",
    "- Usually yields the best hyperplane seperator. \n",
    "- Can use Kernel to handle non-linearity.\n",
    "- Effective for medium sized data sets.\n",
    "#### Disadvantages\n",
    "- Susceptible to noise.\n",
    "- Bad for large datasets.\n",
    "\n",
    "The other procedures are as same as KNN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ef736faa-c34c-4c6b-b929-25daa1904f92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=1, coef0=0.1, kernel=&#x27;poly&#x27;)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=1, coef0=0.1, kernel=&#x27;poly&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=1, coef0=0.1, kernel='poly')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = SVC(\n",
    "    C=1,\n",
    "    kernel='poly',\n",
    "    degree=3,\n",
    "    gamma='scale',\n",
    "    coef0=0.1,\n",
    "    tol=0.001,\n",
    ")\n",
    "svc.fit(X_train , y_train_sl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e1125564-5760-482d-87ec-1114985aa803",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.73      0.74       723\n",
      "           1       0.75      0.76      0.76       748\n",
      "\n",
      "    accuracy                           0.75      1471\n",
      "   macro avg       0.75      0.75      0.75      1471\n",
      "weighted avg       0.75      0.75      0.75      1471\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report( y_test_sl , svc.predict(X_test) ))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f64ac5e8-53b6-4e68-aba7-b8811158018d",
   "metadata": {},
   "source": [
    "#### Parameter Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "93ca1914-f5f3-48ee-8b06-02f51fcc6869",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = [{'kernel' : ['rbf'] , 'C' : [0.4, 0.6, 0.8, 1], 'gamma' : ['scale' , 'auto']},\n",
    "              {'kernel' : ['poly'] , 'degree' : [2,3,4,5,6,7,8], 'C' : [0.4, 0.6, 0.8, 1], 'gamma' : ['scale' , 'auto']}]\n",
    "\n",
    "gs = GridSearchCV( svc , param_grid , cv = 5 , scoring='accuracy' , return_train_score=True)\n",
    "\n",
    "# gs.fit(X_train , y_train_sl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2cff36f9-3951-4e48-890b-59839fdcc407",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print('Best Parameter' , gs.best_params_ , 'Best Score', gs.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ac82765-514f-42dd-b5c9-6e68aa970b1d",
   "metadata": {},
   "source": [
    "#### Hypothesis Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "57361486-7aff-4670-ab06-8c4a273f664a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P-Value: 0.0\n",
      "P-Value (random-test): 0.0\n"
     ]
    }
   ],
   "source": [
    "svc_acc = accuracy_score(y_test_sl , svc.predict(X_test))\n",
    "sample_size = y_test_sl.shape[0]\n",
    "\n",
    "p_bionom_cdf = 1 - scipy.stats.binom.cdf( svc_acc * sample_size , sample_size , dummy_acc ) ## 0.5 because dummy's guesses are uniform\n",
    "print('P-Value:' , p_bionom_cdf)\n",
    "\n",
    "itterations = 10000\n",
    "resulted_acc = np.zeros(itterations)\n",
    "\n",
    "for i in range(itterations):\n",
    "    cur_acc = accuracy_score(y_test_sl , dummy.predict(X_test))\n",
    "    resulted_acc[i] = cur_acc\n",
    "\n",
    "p_random = np.sum(resulted_acc > svc_acc) / itterations\n",
    "print('P-Value (random-test):' , p_random)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d459833-c4ab-4d3d-9a8e-e9a33a866814",
   "metadata": {},
   "source": [
    "#### Some random tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "578d9dee-d12b-4f7a-8e78-d488face92b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "میترا => دختر است\n",
      "ارمین => پسر است\n",
      "دلارام => دختر است\n",
      "زیبا => دختر است\n",
      "اشکان => پسر است\n"
     ]
    }
   ],
   "source": [
    "names_ = ['میترا' , 'ارمین' , 'دلارام' , 'زیبا' , 'اشکان']\n",
    "for name in names_:\n",
    "    pred_ = svc.predict([encoder(name)])\n",
    "    if pred_:\n",
    "        print(name, \"=>\" , 'دختر است')\n",
    "\n",
    "    else:\n",
    "        print(name, \"=>\" , 'پسر است')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e144bbc-7c8c-4582-8522-4fed15d0a334",
   "metadata": {},
   "source": [
    "### Random Forests\n",
    "Finally, we will tackle Random Forests. They work by creating multiple decision trees during training and merging their outputs to improve accuracy and control overfitting. Each tree in the forest is trained on a random subset of the data and uses a random subset of features to make decisions, which leads to a more generalized model. They usually have high accuracies however, interpreting them (knowing why they work) is very difficult.\n",
    "\n",
    "#### Advantages\n",
    "- High Accuracy.\n",
    "- Less prone to Overfiting.\n",
    "- Effective for medium sized data sets.\n",
    "#### Disadvantages\n",
    "- Complex and sometimes computationaly expensive.\n",
    "- Slow predictions.\n",
    "\n",
    "The other procedures are as same as KNN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6e489487-47cd-4a18-91e4-e4367f79b316",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_features=90, min_impurity_decrease=0,\n",
       "                       min_samples_leaf=5, n_estimators=500, n_jobs=10)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_features=90, min_impurity_decrease=0,\n",
       "                       min_samples_leaf=5, n_estimators=500, n_jobs=10)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_features=90, min_impurity_decrease=0,\n",
       "                       min_samples_leaf=5, n_estimators=500, n_jobs=10)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(\n",
    "    \n",
    "    n_estimators = 500,\n",
    "    criterion = 'gini',\n",
    "    max_depth = None,\n",
    "    min_samples_leaf = 5,\n",
    "    max_features = 90,\n",
    "    max_leaf_nodes = None,\n",
    "    min_impurity_decrease =0,\n",
    "    bootstrap = True,\n",
    "    oob_score = False,\n",
    "    n_jobs = 10\n",
    ")\n",
    "\n",
    "rfc.fit(X_train , y_train_sl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "345fee36-7748-4b74-8dc3-2b8a45b90093",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.71      0.72       723\n",
      "           1       0.73      0.75      0.74       748\n",
      "\n",
      "    accuracy                           0.73      1471\n",
      "   macro avg       0.73      0.73      0.73      1471\n",
      "weighted avg       0.73      0.73      0.73      1471\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report( y_test_sl , rfc.predict(X_test) ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c0399c61-0661-48fc-8a64-77a9702df99c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seaching if the Shallow trees in greater numbers work better than dense trees in lower numbers.\n",
    "param_grid = [\n",
    "    {'n_estimators' : [500],  'max_features' : [10,20,30,50,70,90], 'min_samples_leaf' : [5,10,15,20], 'max_depth' : [10,20,30,40,None]}\n",
    "             ]\n",
    "gs = GridSearchCV( rfc , param_grid , cv = 5 , scoring='accuracy' , return_train_score=True)\n",
    "# gs.fit(X_train, y_train_sl)\n",
    "# print(gs.best_params_)\n",
    "# gs.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3437b0bf-5986-4212-81f2-a1012ff1d103",
   "metadata": {},
   "source": [
    "#### Some Random Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "92d3e8ac-120c-4c56-bc65-8abe961b4795",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "میترا => دختر است\n",
      "ارمین => پسر است\n",
      "دلارام => دختر است\n",
      "زیبا => دختر است\n",
      "اشکان => پسر است\n"
     ]
    }
   ],
   "source": [
    "names_ = ['میترا' , 'ارمین' , 'دلارام' , 'زیبا' , 'اشکان']\n",
    "for name in names_:\n",
    "    pred_ = rfc.predict([encoder(name)])\n",
    "    if pred_:\n",
    "        print(name, \"=>\" , 'دختر است')\n",
    "\n",
    "    else:\n",
    "        print(name, \"=>\" , 'پسر است')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dda7b5a-737a-47db-b6a3-8337d547398e",
   "metadata": {},
   "source": [
    "### Comparing Results\n",
    "Since the p-values from all three models were significantly low, we will not be able to compare them, but on the other hand, other classification parameters such as __Accuracy, F1, Percision and Recall__ are comparable so, lets graph them. \n",
    "\n",
    "Note, that all tests were perform on the __Test Set__. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e55efb28-4684-448d-bc9b-589ade48fc13",
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy_predic = dummy.predict(X_test)\n",
    "knn_predict = knn_cls.predict(X_test)\n",
    "svc_predict = svc.predict(X_test)\n",
    "rfc_predict = rfc.predict(X_test)\n",
    "\n",
    "acc_dummy = accuracy_score(y_test_sl , dummy_predic )\n",
    "perc_dummy = precision_score(y_test_sl , dummy_predic )\n",
    "rec_dummy = recall_score(y_test_sl , dummy_predic )\n",
    "f1_dummy = f1_score(y_test_sl , dummy_predic )\n",
    "#------------------------------------------------------------------------------------------------\n",
    "acc_knn = accuracy_score(y_test_sl , knn_predict )\n",
    "perc_knn = precision_score(y_test_sl , knn_predict )\n",
    "rec_knn = recall_score(y_test_sl , knn_predict )\n",
    "f1_knn = f1_score(y_test_sl , knn_predict )\n",
    "#------------------------------------------------------------------------------------------------\n",
    "acc_svc = accuracy_score(y_test_sl , svc_predict )\n",
    "perc_svc = precision_score(y_test_sl , svc_predict )\n",
    "rec_svc = recall_score(y_test_sl , svc_predict )\n",
    "f1_svc = f1_score(y_test_sl , svc_predict )\n",
    "#------------------------------------------------------------------------------------------------\n",
    "acc_rfc = accuracy_score(y_test_sl , rfc_predict )\n",
    "perc_rfc = precision_score(y_test_sl , rfc_predict )\n",
    "rec_rfc = recall_score(y_test_sl , rfc_predict )\n",
    "f1_rfc = f1_score(y_test_sl , rfc_predict )\n",
    "\n",
    "comp_results = [\n",
    "         { 'classifier': \"Dummy\", 'Accuracy' : acc_dummy, 'Percision' : perc_dummy , 'Recall' : rec_dummy, 'F1': f1_dummy },\n",
    "         { 'classifier': \"KNN\", 'Accuracy' : acc_knn, 'Percision' : perc_knn , 'Recall' : rec_knn, 'F1': f1_knn },\n",
    "         { 'classifier': \"SVM\",  'Accuracy' : acc_svc, 'Percision' : perc_svc , 'Recall' : rec_svc, 'F1': f1_svc },\n",
    "         { 'classifier': \"Random-Forest\",  'Accuracy' : acc_rfc, 'Percision' : perc_rfc , 'Recall' : rec_rfc, 'F1': f1_rfc },\n",
    "]\n",
    "\n",
    "res_df = pd.DataFrame(comp_results)\n",
    "res_df.head()\n",
    "\n",
    "res_df.set_index('classifier', inplace=True)\n",
    "res_df = res_df.stack().to_frame('value').reset_index()\n",
    "res_df.rename(columns={'level_1':'Metric'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d69a3c64-59f2-45ad-8b61-0532f12eddeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'F1'}, xlabel='classifier', ylabel='value'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2)\n",
    "fig.set_figwidth(10)\n",
    "fig.set_figheight(5)\n",
    "fig.tight_layout()\n",
    "\n",
    "axs[0].grid(True)\n",
    "axs[1].grid(True)\n",
    "\n",
    "axs[0].set_title('Accuracy')\n",
    "axs[1].set_title('F1')\n",
    "\n",
    "sns.barplot( data = res_df[ res_df['Metric'] == 'Accuracy' ] , x = 'classifier' , y = 'value' , hue = 'classifier' , ax = axs[0] , width=0.5 )\n",
    "sns.barplot( data = res_df[ res_df['Metric'] == 'F1' ] , x = 'classifier' , y = 'value' , hue = 'classifier' , ax = axs[1] , width=0.5 )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0460c614-29f8-47cd-8b56-3038839766f6",
   "metadata": {},
   "source": [
    "Here in these charts, we did not include the _Percision_ and _Recall_ but instead we added __F1__ metric which is the _Harmonic Mean_ of percision and recall. \n",
    "\n",
    "As it is vivid in the graphs, the __SVM__ classifier beats others both in Accuracy and F1 score but it is nearly followed with an _Optimized_ Random Forst Classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1eaaee5-09f8-47ba-93f6-614b49e6d196",
   "metadata": {},
   "source": [
    "### Sensetivity Analysis\n",
    "For the sensitivity testing of the models, a few options are available. First, we can chose to peroform either __Local Sensitivity Analysis__ or __Global Sensitivity Analysis__. To keep things simple, a local sensitivity analysis method called OAT (one at a time) will be performed on the KNN model (since it can give us meaningfull output for the OAT analysis) however it is worth mentioning that other options like __Variance-Based methods__, __Moris Method__ and __SHAP__ are also feasible. But, their practicality in our cases can be further disccused (since our models are classification models and Sensitivity analysis is mostly performed on regression models).\n",
    "\n",
    "#### OAT\n",
    "One-at-a-Time (OAT) Sensitivity Analysis is a method used to assess the influence of individual input variables on the output of a model. In this method, we vary one input variable at a time while keeping the others constant, and observe the effect on the model's output. This approach is straightforward and easy to implement, making it a popular choice for initial sensitivity analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6dee9c3a-6da2-46a4-9773-8342c3551a1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_svm = np.zeros((330,))\n",
    "\n",
    "for case, label in zip(X_test, y_test_sl):\n",
    "    pred_ = svc.predict([case])\n",
    "    \n",
    "    case_X_epand = np.tile(case, 330).reshape(330 , case.shape[0])\n",
    "    oat_input_vector = np.identity(330)\n",
    "    \n",
    "    oat_input_vector = np.logical_or(oat_input_vector , case_X_epand).astype(np.uint8)\n",
    "    pred_oat = svc.predict(oat_input_vector)\n",
    "\n",
    "    miss_pred = (pred_oat != pred_).astype(np.uint8)\n",
    "    results_svm += miss_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d15c236b-280c-467f-9b46-ba5fac741ec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_rfc = np.zeros((330,))\n",
    "\n",
    "for case, label in zip(X_test, y_test_sl):\n",
    "    pred_ = rfc.predict([case])\n",
    "    \n",
    "    case_X_epand = np.tile(case, 330).reshape(330 , case.shape[0])\n",
    "    oat_input_vector = np.identity(330)\n",
    "    \n",
    "    oat_input_vector = np.logical_or(oat_input_vector , case_X_epand).astype(np.uint8)\n",
    "    pred_oat = rfc.predict(oat_input_vector)\n",
    "\n",
    "    miss_pred = (pred_oat != pred_).astype(np.uint8)\n",
    "    results_rfc += miss_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e9494c43-196a-4fdc-a82f-4acd41dd5696",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_knn = np.zeros((330,))\n",
    "\n",
    "for case, label in zip(X_test, y_test_sl):\n",
    "    pred_ = knn_cls.predict([case])\n",
    "    \n",
    "    case_X_epand = np.tile(case, 330).reshape(330 , case.shape[0])\n",
    "    oat_input_vector = np.identity(330)\n",
    "    \n",
    "    oat_input_vector = np.logical_or(oat_input_vector , case_X_epand).astype(np.uint8)\n",
    "    pred_oat = knn_cls.predict(oat_input_vector)\n",
    "\n",
    "    miss_pred = (pred_oat != pred_).astype(np.uint8)\n",
    "    results_knn += miss_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b13855b5-c3f6-4597-821a-f2eec9b993f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1100 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_svm /= results_svm.max()\n",
    "results_knn /= results_knn.max()\n",
    "results_rfc /= results_rfc.max()\n",
    "\n",
    "fig, ax = plt.subplots(3,1)\n",
    "fig.set_figwidth(14)\n",
    "fig.set_figheight(11)\n",
    "fig.tight_layout(pad = 5)\n",
    "\n",
    "\n",
    "sns.heatmap(results_svm.reshape(10,33), ax = ax[0])\n",
    "ax[0].set_title('SVM OAT Sensitivity')\n",
    "\n",
    "sns.heatmap(results_knn.reshape(10,33), ax = ax[1])\n",
    "ax[1].set_title('KNN OAT Sensitivity')\n",
    "\n",
    "sns.heatmap(results_rfc.reshape(10,33), ax = ax[2])\n",
    "ax[2].set_title('Random OAT Forest Sensitivity')\n",
    "\n",
    "for ax_ in ax:\n",
    "    ax_.set_xlabel('Alphabet Letter')\n",
    "    ax_.set_ylabel('Character Position')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abc2959b-194d-4167-80a9-cef13617398f",
   "metadata": {},
   "source": [
    "As we can clearly see in our sensitivity heat maps, KNN seemes more prone to noisy data meaning that a typo in the names will most likely reduce the accuracy of KNN significantly. On the other hand, Random forests are generaly less affected by noise. However, it is clear that some character in certain positions, for example letter 25 in the 4th and 5th positions ( letter 'ه' probably beacuse of names such as 'سمانه' and 'عرفانه' ) is very tricky because it will always cause the model to missclassify. (Note that the character orders __must__ be viewd using the `char_table_lookup` variable and not in ordinal, alphabetical order).\n",
    "\n",
    "The most interesting results however is the SVM sensitivity heat map. It appears that the SVM is also sensitive to letter 25 in the 4th and 5th positions but, it also apears that changes to any letters in last 3 positions (positions 10, 9, 8) is very unlikly to change the outcome of the model. In another word, all of the models (and especially SVM) will care less about the last letters of a _long_ name. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3bd60df-485e-469d-8d12-f32d3f9c6251",
   "metadata": {},
   "source": [
    "## Artificial Neural Networks\n",
    "Artificial Neural Networks (ANNs) are computational models inspired by the human brain's structure and function. They consist of interconnected layers of nodes (neurons) that work together to process information. ANNs are widely used in machine learning for tasks like image recognition, natural language processing, and more.\n",
    "\n",
    "In comparison to traditional machine learning (statistical learning), they are more powerfull tools in learning complex patterns between different features (non-linear patterns) however, they require more data and are prone to overfiting.\n",
    "\n",
    "Some Advantages of Neural Networks:\n",
    "- Handle Non-linear Relationships\n",
    "- Feature Learnin (Little feature engineering required)\n",
    "\n",
    "Some Disadvantages of neural networks:\n",
    "- Requires Large Amounts of Data\n",
    "- Overfitting\n",
    "- Hyperparameter Tuning\n",
    "\n",
    "Here, we encoporate two different ANN types; Dense Networks which are among the most simplest of neural networks and Recursive Networks which are the cutting edge technology in the field of NLP. \n",
    "> Unfortunatly, because of limited time, we wont be able to tune the hyper parameters of the networks so much. But, just like the case with the other methods, aproaches such as Gridseatch Cross validationa and Random Search Crossvalidation can be use to tune ANN hyper parameters as well.  \n",
    "> It is worth mentioning that the parameters were tuned to some extent; For example the learning rate was chosen such that overfriting dosen't take over the network so much. \n",
    "\n",
    "### Dense Network\n",
    "Fully connected networks, composed entirely of Dense layers, are a fundamental type of neural network where each neuron in a layer is connected to every neuron in the preceding layer. These networks are simple yet powerful, capable of learning complex patterns and functioning as universal approximators, meaning they can model any continuous function given enough neurons. However, they tend to have a large number of parameters, making them prone to overfitting (as we'll see shortly), especially on small datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "aef0a1d0-5227-4d65-a31c-696815f4ba68",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train_dl, y_test_dl = train_test_split(X_ , y_dl , test_size = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "1f9420a1-1cc6-4fe7-9726-25717cefe6ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dense = keras.models.Sequential()\n",
    "model_dense.add(keras.layers.Input(shape = (330,)))\n",
    "model_dense.add(keras.layers.Dense(1000, activation = 'relu'))\n",
    "model_dense.add(keras.layers.Dense(1000, activation = 'relu' , kernel_regularizer = keras.regularizers.l2(1e-2)))\n",
    "model_dense.add(keras.layers.Dropout(0.5))\n",
    "model_dense.add(keras.layers.Dense(500, activation = 'elu' , kernel_regularizer = keras.regularizers.l2(1e-2)))\n",
    "model_dense.add(keras.layers.Dense(2, activation = 'softmax', kernel_regularizer = keras.regularizers.l2(1e-4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "318aa519-c36e-4c54-82be-41c45e2e3ae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_6\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_6\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1000</span>)           │       <span style=\"color: #00af00; text-decoration-color: #00af00\">331,000</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1000</span>)           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,001,000</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1000</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">500</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">500,500</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)              │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,002</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_20 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1000\u001b[0m)           │       \u001b[38;5;34m331,000\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_21 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1000\u001b[0m)           │     \u001b[38;5;34m1,001,000\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_8 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1000\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_22 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m500\u001b[0m)            │       \u001b[38;5;34m500,500\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_23 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)              │         \u001b[38;5;34m1,002\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,833,502</span> (6.99 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,833,502\u001b[0m (6.99 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,833,502</span> (6.99 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,833,502\u001b[0m (6.99 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_dense.compile(\n",
    "    loss = keras.losses.CategoricalCrossentropy(),\n",
    "    optimizer = keras.optimizers.Adam(learning_rate=8e-5),\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "model_dense.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "eb4573fc-7016-48f7-9d24-0dcd3fc061d4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 54ms/step - accuracy: 0.5206 - loss: 16.5778 - val_accuracy: 0.6364 - val_loss: 14.2942\n",
      "Epoch 2/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 41ms/step - accuracy: 0.6115 - loss: 13.6191 - val_accuracy: 0.6500 - val_loss: 11.7236\n",
      "Epoch 3/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 41ms/step - accuracy: 0.6526 - loss: 11.1621 - val_accuracy: 0.6591 - val_loss: 9.5993\n",
      "Epoch 4/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 41ms/step - accuracy: 0.6887 - loss: 9.1350 - val_accuracy: 0.6682 - val_loss: 7.8478\n",
      "Epoch 5/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 44ms/step - accuracy: 0.7015 - loss: 7.4595 - val_accuracy: 0.6791 - val_loss: 6.4114\n",
      "Epoch 6/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 44ms/step - accuracy: 0.7146 - loss: 6.0845 - val_accuracy: 0.6899 - val_loss: 5.2472\n",
      "Epoch 7/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 41ms/step - accuracy: 0.7414 - loss: 4.9700 - val_accuracy: 0.6872 - val_loss: 4.3035\n",
      "Epoch 8/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 40ms/step - accuracy: 0.7572 - loss: 4.0568 - val_accuracy: 0.7044 - val_loss: 3.5456\n",
      "Epoch 9/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 49ms/step - accuracy: 0.7742 - loss: 3.3282 - val_accuracy: 0.7008 - val_loss: 2.9407\n",
      "Epoch 10/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - accuracy: 0.7812 - loss: 2.7439 - val_accuracy: 0.7081 - val_loss: 2.4513\n",
      "Epoch 11/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 41ms/step - accuracy: 0.8057 - loss: 2.2523 - val_accuracy: 0.7108 - val_loss: 2.0640\n",
      "Epoch 12/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 39ms/step - accuracy: 0.8145 - loss: 1.8744 - val_accuracy: 0.7208 - val_loss: 1.7549\n",
      "Epoch 13/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 42ms/step - accuracy: 0.8183 - loss: 1.5732 - val_accuracy: 0.7162 - val_loss: 1.5128\n",
      "Epoch 14/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - accuracy: 0.8293 - loss: 1.3228 - val_accuracy: 0.7126 - val_loss: 1.3279\n",
      "Epoch 15/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 44ms/step - accuracy: 0.8390 - loss: 1.1305 - val_accuracy: 0.7189 - val_loss: 1.1826\n",
      "Epoch 16/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 41ms/step - accuracy: 0.8670 - loss: 0.9586 - val_accuracy: 0.7144 - val_loss: 1.0697\n",
      "Epoch 17/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 41ms/step - accuracy: 0.8857 - loss: 0.8271 - val_accuracy: 0.7189 - val_loss: 0.9831\n",
      "Epoch 18/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 39ms/step - accuracy: 0.9005 - loss: 0.7266 - val_accuracy: 0.7226 - val_loss: 0.9260\n",
      "Epoch 19/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - accuracy: 0.9083 - loss: 0.6367 - val_accuracy: 0.7253 - val_loss: 0.8778\n",
      "Epoch 20/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 37ms/step - accuracy: 0.9178 - loss: 0.5699 - val_accuracy: 0.7126 - val_loss: 0.8581\n",
      "Epoch 21/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - accuracy: 0.9238 - loss: 0.5137 - val_accuracy: 0.7099 - val_loss: 0.8236\n",
      "Epoch 22/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 38ms/step - accuracy: 0.9429 - loss: 0.4600 - val_accuracy: 0.7189 - val_loss: 0.8062\n",
      "Epoch 23/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 38ms/step - accuracy: 0.9480 - loss: 0.4272 - val_accuracy: 0.7144 - val_loss: 0.8008\n",
      "Epoch 24/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 38ms/step - accuracy: 0.9541 - loss: 0.3918 - val_accuracy: 0.7180 - val_loss: 0.7833\n",
      "Epoch 25/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 37ms/step - accuracy: 0.9580 - loss: 0.3725 - val_accuracy: 0.7208 - val_loss: 0.7799\n",
      "Epoch 26/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - accuracy: 0.9632 - loss: 0.3428 - val_accuracy: 0.7226 - val_loss: 0.7784\n",
      "Epoch 27/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - accuracy: 0.9704 - loss: 0.3223 - val_accuracy: 0.7153 - val_loss: 0.7847\n",
      "Epoch 28/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 39ms/step - accuracy: 0.9734 - loss: 0.3051 - val_accuracy: 0.7108 - val_loss: 0.7763\n",
      "Epoch 29/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 37ms/step - accuracy: 0.9773 - loss: 0.2898 - val_accuracy: 0.7162 - val_loss: 0.7946\n",
      "Epoch 30/30\n",
      "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 0.9806 - loss: 0.2774 - val_accuracy: 0.7108 - val_loss: 0.7795\n"
     ]
    }
   ],
   "source": [
    "history_dense = model_dense.fit( x= X_train, y= y_train_dl, batch_size=64 , epochs=30, validation_split=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "a7292f08-2faa-4ec0-97a4-3537d8a4901d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.75      0.74       707\n",
      "           1       0.76      0.74      0.75       764\n",
      "\n",
      "    accuracy                           0.75      1471\n",
      "   macro avg       0.75      0.75      0.75      1471\n",
      "weighted avg       0.75      0.75      0.75      1471\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred = model_dense.predict(X_test)\n",
    "pred = np.argmax(pred , axis = 1)\n",
    "print(classification_report(np.argmax(y_test_dl , axis = 1) , pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "856271d2-19b7-4f98-ae9b-f8bc9d9d0627",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x759fa810aef0>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2)\n",
    "fig.set_figwidth(8)\n",
    "fig.set_figheight(3.5)\n",
    "fig.tight_layout()\n",
    "\n",
    "ax[0].plot(history_dense.history['loss'], label='Training Loss')\n",
    "ax[0].plot(history_dense.history['val_loss'], label='Validation Loss')\n",
    "ax[0].set_xlabel('Epochs')\n",
    "ax[0].set_ylabel('Loss')\n",
    "ax[0].set_title('Training and Validation Loss')\n",
    "ax[0].grid(True)\n",
    "ax[0].legend()\n",
    "\n",
    "ax[1].plot(history_dense.history['accuracy'], label='Training Accuracy')\n",
    "ax[1].plot(history_dense.history['val_accuracy'], label='Validation Accuracy')\n",
    "ax[1].set_xlabel('Epochs')\n",
    "ax[1].set_ylabel('Accuracy')\n",
    "ax[1].set_title('Training and Validation Accuracy')\n",
    "ax[1].grid(True)\n",
    "ax[1].legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4d6e995-2c09-450b-a1f7-a4e4fdcd8ce4",
   "metadata": {},
   "source": [
    "As it is clear, our model definatly overfited (probably because of the large number of parameters) but it didnt cause the model to decrease the Validation accuracy which is good. However, the performance is very similar to the traditional techniques (close to KNN) as it is vivid in the classification summary.\n",
    "\n",
    "However, Dense ANN which uses softmax as output is a probablistic model which means that we can plot the __Percision Recall__ Curve to chose a good tradeoff between the two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "29fb744d-38cb-4e9a-8d26-fec7ca23962d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "Best Threshold:  0.52187335\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision, recall, thresholds = precision_recall_curve( y_test_dl[:,0] , model_dense.predict(X_test)[:,0] )\n",
    "plt.figure(figsize=(7, 3))\n",
    "plt.plot( thresholds , precision[:-1] , label = 'Precision' )\n",
    "plt.plot( thresholds , recall[:-1] , label = 'Recall' )\n",
    "\n",
    "best_tradoff = np.argmin(np.abs(precision - recall))\n",
    "plt.plot( [thresholds[best_tradoff] , thresholds[best_tradoff]] , [0, precision[best_tradoff]] , c='red' , linestyle='dashed' , alpha = 0.5)\n",
    "plt.plot( [0 , thresholds[best_tradoff]] , [precision[best_tradoff], precision[best_tradoff]] , c='red', linestyle='dashed'  , alpha = 0.5)\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.xlabel('Thresholds')\n",
    "plt.ylabel('Score')\n",
    "plt.ylim([0,1])\n",
    "plt.xlim([0,1])\n",
    "plt.title('Precision Recall Curve')\n",
    "\n",
    "print('Best Threshold: ' , thresholds[best_tradoff])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "841cef12-29ff-4895-9b80-59bf5763f680",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.76      0.76       764\n",
      "           1       0.74      0.74      0.74       707\n",
      "\n",
      "    accuracy                           0.75      1471\n",
      "   macro avg       0.75      0.75      0.75      1471\n",
      "weighted avg       0.75      0.75      0.75      1471\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred = model_dense.predict(X_test)\n",
    "pred = (pred[:,0] > thresholds[best_tradoff]).astype(np.uint8)\n",
    "print(classification_report(y_test_dl[:,0] , pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84b79f05-574f-4a40-b5b3-0c12464bebf6",
   "metadata": {},
   "source": [
    "So, if we chose our thersholds as it was given above, we will create a good ballance between percision and recall.\n",
    "#### Some Random Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "b3a15ae7-fbd4-4f92-9c33-644b256442ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step\n",
      "میترا => دختر است \tScore: 0.034042817\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
      "اسد => پسر است \tScore: 0.95439184\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
      "دلارام => دختر است \tScore: 0.21576478\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
      "زیبا => دختر است \tScore: 0.14846905\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "اشکان => پسر است \tScore: 0.91729313\n"
     ]
    }
   ],
   "source": [
    "names_ = ['میترا' , 'اسد' , 'دلارام' , 'زیبا' , 'اشکان']\n",
    "for name in names_:\n",
    "    word_vec = encoder(name).reshape(-1 , 330, 1)\n",
    "    pred_ = model_dense.predict([word_vec])\n",
    "\n",
    "    if pred_[0][0] > thresholds[best_tradoff]:\n",
    "        print(name, \"=>\" , 'پسر است', '\\tScore:' , pred_[0][0])\n",
    "\n",
    "    else:\n",
    "        print(name, \"=>\" , 'دختر است' , '\\tScore:' , pred_[0][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70e41d93-78a6-4434-915b-9b7c68d745b2",
   "metadata": {},
   "source": [
    "### Recursive Neural Nets\n",
    "> Note that the model structure was borrowed from [Maël Fabien](https://maelfabien.github.io/machinelearning/NLP_7/#building-the-model) article on Gender Classification of French names.\n",
    "\n",
    "Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units are crucial in Natural Language Processing (NLP). LSTMs improve RNNs by managing long-term dependencies, enabling the model to retain important information over longer sequences. This makes them highly effective for tasks like language modeling, text generation, and sentiment analysis, where context over extended sequences is vital.\n",
    "\n",
    "\n",
    "Despite their strengths, RNNs with LSTM units have some downsides. They can be computationally expensive and slow to train, especially on large datasets, due to their complex architecture. LSTMs also struggle with capturing very long-term dependencies in extremely lengthy sequences. Additionally, they require a large amount of memory and can be difficult to parallelize, which limits their efficiency in certain applications. Newer models like Transformers often outperform LSTMs in many NLP tasks by handling long-range dependencies more effectively and scaling better with data but their implementation is beyond the scope of this project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "5131f1f7-043d-4271-ade0-43c77bb267e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_ = X_.reshape((X_.shape[0], 10 , 33))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "1626fb80-e7fe-4d08-b5c2-a4a958be4e9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train_dl, y_test_dl = train_test_split(X_ , y_dl , test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "ccd5a13c-3210-4279-b166-0b02bec0fd2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_7\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_7\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ bidirectional_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1000</span>)       │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,136,000</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1000</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ bidirectional_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1000</span>)           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">6,004,000</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1000</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">100,100</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_25 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">202</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ bidirectional_4 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m1000\u001b[0m)       │     \u001b[38;5;34m2,136,000\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_9 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m1000\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ bidirectional_5 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1000\u001b[0m)           │     \u001b[38;5;34m6,004,000\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_10 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1000\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_24 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │       \u001b[38;5;34m100,100\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_25 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)              │           \u001b[38;5;34m202\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_2 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)              │             \u001b[38;5;34m0\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">8,240,302</span> (31.43 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,240,302\u001b[0m (31.43 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">8,240,302</span> (31.43 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,240,302\u001b[0m (31.43 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_rnn = keras.models.Sequential()\n",
    "model_rnn.add(keras.layers.Input(shape = (X_train.shape[1],X_train.shape[2],)))\n",
    "model_rnn.add(keras.layers.Bidirectional(keras.layers.LSTM(500, return_sequences=True), backward_layer=keras.layers.LSTM(500, return_sequences=True, go_backwards=True)))\n",
    "model_rnn.add(keras.layers.Dropout(0.2))\n",
    "model_rnn.add(keras.layers.Bidirectional(keras.layers.LSTM(500)))\n",
    "model_rnn.add(keras.layers.Dropout(0.2))\n",
    "model_rnn.add(keras.layers.Dense(100, activity_regularizer=keras.regularizers.l2(0.001)))\n",
    "model_rnn.add(keras.layers.Dense(2,))\n",
    "model_rnn.add(keras.layers.Activation('softmax'))\n",
    "model_rnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "0611badf-f9c1-45e3-9acf-e0f884dec726",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_schedule = keras.optimizers.schedules.PolynomialDecay(\n",
    "    initial_learning_rate = 0.005,\n",
    "    decay_steps = 100,\n",
    "    end_learning_rate=0.0001,\n",
    "    power=6,\n",
    "    cycle=False,\n",
    "    name=\"PolynomialDecay\",\n",
    ")\n",
    "\n",
    "model_rnn.compile(\n",
    "    loss = keras.losses.CategoricalFocalCrossentropy(),\n",
    "    optimizer = keras.optimizers.Adam(learning_rate=lr_schedule),\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "74dc6728-6571-40b4-b83b-bb66ac723dc4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 1s/step - accuracy: 0.5120 - loss: 0.0611 - val_accuracy: 0.5692 - val_loss: 0.0451\n",
      "Epoch 2/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 0.6294 - loss: 0.0444 - val_accuracy: 0.6559 - val_loss: 0.0425\n",
      "Epoch 3/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 2s/step - accuracy: 0.6814 - loss: 0.0424 - val_accuracy: 0.6729 - val_loss: 0.0419\n",
      "Epoch 4/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 0.6882 - loss: 0.0419 - val_accuracy: 0.6780 - val_loss: 0.0418\n",
      "Epoch 5/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 1s/step - accuracy: 0.6863 - loss: 0.0418 - val_accuracy: 0.6763 - val_loss: 0.0417\n",
      "Epoch 6/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 2s/step - accuracy: 0.6904 - loss: 0.0418 - val_accuracy: 0.6890 - val_loss: 0.0416\n",
      "Epoch 7/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 2s/step - accuracy: 0.6936 - loss: 0.0418 - val_accuracy: 0.6797 - val_loss: 0.0416\n",
      "Epoch 8/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 2s/step - accuracy: 0.7018 - loss: 0.0415 - val_accuracy: 0.6907 - val_loss: 0.0415\n",
      "Epoch 9/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 2s/step - accuracy: 0.6957 - loss: 0.0417 - val_accuracy: 0.6805 - val_loss: 0.0415\n",
      "Epoch 10/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 1s/step - accuracy: 0.6903 - loss: 0.0415 - val_accuracy: 0.6848 - val_loss: 0.0414\n",
      "Epoch 11/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 1s/step - accuracy: 0.6939 - loss: 0.0414 - val_accuracy: 0.6933 - val_loss: 0.0413\n",
      "Epoch 12/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 1s/step - accuracy: 0.7000 - loss: 0.0413 - val_accuracy: 0.6848 - val_loss: 0.0413\n",
      "Epoch 13/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 1s/step - accuracy: 0.6876 - loss: 0.0414 - val_accuracy: 0.7052 - val_loss: 0.0412\n",
      "Epoch 14/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 1s/step - accuracy: 0.7091 - loss: 0.0412 - val_accuracy: 0.6890 - val_loss: 0.0412\n",
      "Epoch 15/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 1s/step - accuracy: 0.7075 - loss: 0.0410 - val_accuracy: 0.6984 - val_loss: 0.0411\n",
      "Epoch 16/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 1s/step - accuracy: 0.7143 - loss: 0.0409 - val_accuracy: 0.7052 - val_loss: 0.0410\n",
      "Epoch 17/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 1s/step - accuracy: 0.7051 - loss: 0.0410 - val_accuracy: 0.6992 - val_loss: 0.0409\n",
      "Epoch 18/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 1s/step - accuracy: 0.7150 - loss: 0.0408 - val_accuracy: 0.7018 - val_loss: 0.0409\n",
      "Epoch 19/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 1s/step - accuracy: 0.7079 - loss: 0.0409 - val_accuracy: 0.7060 - val_loss: 0.0408\n",
      "Epoch 20/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 1s/step - accuracy: 0.6993 - loss: 0.0409 - val_accuracy: 0.6967 - val_loss: 0.0408\n",
      "Epoch 21/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 1s/step - accuracy: 0.7076 - loss: 0.0406 - val_accuracy: 0.7103 - val_loss: 0.0407\n",
      "Epoch 22/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 1s/step - accuracy: 0.7108 - loss: 0.0409 - val_accuracy: 0.7043 - val_loss: 0.0407\n",
      "Epoch 23/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 0.7140 - loss: 0.0405 - val_accuracy: 0.6924 - val_loss: 0.0407\n",
      "Epoch 24/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.7053 - loss: 0.0405 - val_accuracy: 0.7077 - val_loss: 0.0406\n",
      "Epoch 25/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 1s/step - accuracy: 0.7010 - loss: 0.0405 - val_accuracy: 0.7196 - val_loss: 0.0406\n",
      "Epoch 26/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.7015 - loss: 0.0406 - val_accuracy: 0.7060 - val_loss: 0.0405\n",
      "Epoch 27/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 2s/step - accuracy: 0.7064 - loss: 0.0403 - val_accuracy: 0.7094 - val_loss: 0.0405\n",
      "Epoch 28/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 1s/step - accuracy: 0.7272 - loss: 0.0400 - val_accuracy: 0.7120 - val_loss: 0.0404\n",
      "Epoch 29/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 1s/step - accuracy: 0.7168 - loss: 0.0402 - val_accuracy: 0.7094 - val_loss: 0.0403\n",
      "Epoch 30/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 1s/step - accuracy: 0.7224 - loss: 0.0401 - val_accuracy: 0.7094 - val_loss: 0.0403\n",
      "Epoch 31/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 0.7105 - loss: 0.0401 - val_accuracy: 0.7111 - val_loss: 0.0403\n",
      "Epoch 32/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 2s/step - accuracy: 0.7171 - loss: 0.0402 - val_accuracy: 0.7154 - val_loss: 0.0402\n",
      "Epoch 33/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 2s/step - accuracy: 0.7185 - loss: 0.0400 - val_accuracy: 0.7154 - val_loss: 0.0402\n",
      "Epoch 34/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 1s/step - accuracy: 0.7150 - loss: 0.0399 - val_accuracy: 0.7239 - val_loss: 0.0401\n",
      "Epoch 35/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 1s/step - accuracy: 0.7252 - loss: 0.0398 - val_accuracy: 0.7103 - val_loss: 0.0401\n",
      "Epoch 36/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 1s/step - accuracy: 0.7143 - loss: 0.0401 - val_accuracy: 0.7103 - val_loss: 0.0400\n",
      "Epoch 37/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 1s/step - accuracy: 0.7209 - loss: 0.0398 - val_accuracy: 0.7094 - val_loss: 0.0400\n",
      "Epoch 38/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 1s/step - accuracy: 0.7095 - loss: 0.0399 - val_accuracy: 0.7213 - val_loss: 0.0400\n",
      "Epoch 39/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 0.7088 - loss: 0.0399 - val_accuracy: 0.7094 - val_loss: 0.0399\n",
      "Epoch 40/40\n",
      "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 0.7288 - loss: 0.0396 - val_accuracy: 0.7120 - val_loss: 0.0399\n",
      "Epoch 1/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 879ms/step - accuracy: 0.7268 - loss: 0.0383 - val_accuracy: 0.7196 - val_loss: 0.0386\n",
      "Epoch 2/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 869ms/step - accuracy: 0.7137 - loss: 0.0378 - val_accuracy: 0.7103 - val_loss: 0.0386\n",
      "Epoch 3/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 853ms/step - accuracy: 0.7230 - loss: 0.0378 - val_accuracy: 0.7145 - val_loss: 0.0386\n",
      "Epoch 4/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 820ms/step - accuracy: 0.7188 - loss: 0.0382 - val_accuracy: 0.7188 - val_loss: 0.0384\n",
      "Epoch 5/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 1s/step - accuracy: 0.7214 - loss: 0.0378 - val_accuracy: 0.6899 - val_loss: 0.0387\n",
      "Epoch 6/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 896ms/step - accuracy: 0.7145 - loss: 0.0379 - val_accuracy: 0.7188 - val_loss: 0.0383\n",
      "Epoch 7/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 868ms/step - accuracy: 0.7194 - loss: 0.0375 - val_accuracy: 0.7094 - val_loss: 0.0384\n",
      "Epoch 8/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 887ms/step - accuracy: 0.7093 - loss: 0.0374 - val_accuracy: 0.7196 - val_loss: 0.0383\n",
      "Epoch 9/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 892ms/step - accuracy: 0.7135 - loss: 0.0375 - val_accuracy: 0.7094 - val_loss: 0.0382\n",
      "Epoch 10/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 902ms/step - accuracy: 0.6935 - loss: 0.0378 - val_accuracy: 0.7128 - val_loss: 0.0382\n",
      "Epoch 11/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 905ms/step - accuracy: 0.7173 - loss: 0.0372 - val_accuracy: 0.7171 - val_loss: 0.0382\n",
      "Epoch 12/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 883ms/step - accuracy: 0.7259 - loss: 0.0373 - val_accuracy: 0.7171 - val_loss: 0.0380\n",
      "Epoch 13/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 930ms/step - accuracy: 0.7101 - loss: 0.0372 - val_accuracy: 0.7179 - val_loss: 0.0380\n",
      "Epoch 14/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 896ms/step - accuracy: 0.7245 - loss: 0.0370 - val_accuracy: 0.7188 - val_loss: 0.0380\n",
      "Epoch 15/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 838ms/step - accuracy: 0.7077 - loss: 0.0375 - val_accuracy: 0.7196 - val_loss: 0.0379\n",
      "Epoch 16/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 900ms/step - accuracy: 0.7329 - loss: 0.0364 - val_accuracy: 0.7145 - val_loss: 0.0379\n",
      "Epoch 17/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 827ms/step - accuracy: 0.7204 - loss: 0.0368 - val_accuracy: 0.7213 - val_loss: 0.0377\n",
      "Epoch 18/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 883ms/step - accuracy: 0.7173 - loss: 0.0369 - val_accuracy: 0.7264 - val_loss: 0.0377\n",
      "Epoch 19/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 826ms/step - accuracy: 0.7175 - loss: 0.0369 - val_accuracy: 0.7179 - val_loss: 0.0378\n",
      "Epoch 20/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 838ms/step - accuracy: 0.7263 - loss: 0.0366 - val_accuracy: 0.7171 - val_loss: 0.0377\n",
      "Epoch 21/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 827ms/step - accuracy: 0.7264 - loss: 0.0366 - val_accuracy: 0.7179 - val_loss: 0.0377\n",
      "Epoch 22/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 876ms/step - accuracy: 0.7270 - loss: 0.0365 - val_accuracy: 0.7188 - val_loss: 0.0374\n",
      "Epoch 23/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 804ms/step - accuracy: 0.7338 - loss: 0.0363 - val_accuracy: 0.7035 - val_loss: 0.0376\n",
      "Epoch 24/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 813ms/step - accuracy: 0.7438 - loss: 0.0360 - val_accuracy: 0.7230 - val_loss: 0.0375\n",
      "Epoch 25/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 863ms/step - accuracy: 0.7397 - loss: 0.0360 - val_accuracy: 0.7103 - val_loss: 0.0376\n",
      "Epoch 26/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 901ms/step - accuracy: 0.7305 - loss: 0.0362 - val_accuracy: 0.7239 - val_loss: 0.0374\n",
      "Epoch 27/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 906ms/step - accuracy: 0.7293 - loss: 0.0359 - val_accuracy: 0.7179 - val_loss: 0.0373\n",
      "Epoch 28/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 798ms/step - accuracy: 0.7368 - loss: 0.0359 - val_accuracy: 0.6958 - val_loss: 0.0380\n",
      "Epoch 29/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 809ms/step - accuracy: 0.7336 - loss: 0.0359 - val_accuracy: 0.7188 - val_loss: 0.0372\n",
      "Epoch 30/30\n",
      "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 828ms/step - accuracy: 0.7441 - loss: 0.0355 - val_accuracy: 0.7111 - val_loss: 0.0373\n",
      "Epoch 1/20\n",
      "\u001b[1m56/56\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 584ms/step - accuracy: 0.7290 - loss: 0.0355 - val_accuracy: 0.7171 - val_loss: 0.0367\n",
      "Epoch 2/20\n",
      "\u001b[1m56/56\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 603ms/step - accuracy: 0.7066 - loss: 0.0359 - val_accuracy: 0.7196 - val_loss: 0.0365\n",
      "Epoch 3/20\n",
      "\u001b[1m56/56\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 573ms/step - accuracy: 0.7324 - loss: 0.0349 - val_accuracy: 0.7179 - val_loss: 0.0365\n",
      "Epoch 4/20\n",
      "\u001b[1m56/56\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 574ms/step - accuracy: 0.7185 - loss: 0.0357 - val_accuracy: 0.7205 - val_loss: 0.0365\n",
      "Epoch 5/20\n",
      "\u001b[1m56/56\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 567ms/step - accuracy: 0.7361 - loss: 0.0345 - val_accuracy: 0.7222 - val_loss: 0.0363\n",
      "Epoch 6/20\n",
      "\u001b[1m56/56\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 582ms/step - accuracy: 0.7246 - loss: 0.0350 - val_accuracy: 0.7179 - val_loss: 0.0363\n",
      "Epoch 7/20\n",
      "\u001b[1m56/56\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 560ms/step - accuracy: 0.7344 - loss: 0.0346 - val_accuracy: 0.7035 - val_loss: 0.0370\n",
      "Epoch 8/20\n",
      "\u001b[1m56/56\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 573ms/step - accuracy: 0.7364 - loss: 0.0343 - val_accuracy: 0.7145 - val_loss: 0.0365\n",
      "Epoch 9/20\n",
      "\u001b[1m56/56\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 561ms/step - accuracy: 0.7377 - loss: 0.0342 - val_accuracy: 0.7222 - val_loss: 0.0363\n",
      "Epoch 10/20\n",
      "\u001b[1m56/56\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 570ms/step - accuracy: 0.7312 - loss: 0.0345 - val_accuracy: 0.7205 - val_loss: 0.0361\n",
      "Epoch 11/20\n",
      "\u001b[1m56/56\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 557ms/step - accuracy: 0.7341 - loss: 0.0344 - val_accuracy: 0.7298 - val_loss: 0.0362\n",
      "Epoch 12/20\n",
      "\u001b[1m56/56\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 572ms/step - accuracy: 0.7359 - loss: 0.0342 - val_accuracy: 0.7120 - val_loss: 0.0365\n",
      "Epoch 13/20\n",
      "\u001b[1m56/56\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 568ms/step - accuracy: 0.7412 - loss: 0.0337 - val_accuracy: 0.7154 - val_loss: 0.0361\n",
      "Epoch 14/20\n",
      "\u001b[1m56/56\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 604ms/step - accuracy: 0.7376 - loss: 0.0338 - val_accuracy: 0.7290 - val_loss: 0.0360\n",
      "Epoch 15/20\n",
      "\u001b[1m56/56\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 571ms/step - accuracy: 0.7506 - loss: 0.0332 - val_accuracy: 0.7230 - val_loss: 0.0359\n",
      "Epoch 16/20\n",
      "\u001b[1m56/56\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 568ms/step - accuracy: 0.7457 - loss: 0.0334 - val_accuracy: 0.7213 - val_loss: 0.0360\n",
      "Epoch 17/20\n",
      "\u001b[1m56/56\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 555ms/step - accuracy: 0.7301 - loss: 0.0339 - val_accuracy: 0.7307 - val_loss: 0.0357\n",
      "Epoch 18/20\n",
      "\u001b[1m56/56\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 570ms/step - accuracy: 0.7548 - loss: 0.0331 - val_accuracy: 0.7281 - val_loss: 0.0357\n",
      "Epoch 19/20\n",
      "\u001b[1m56/56\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 661ms/step - accuracy: 0.7543 - loss: 0.0330 - val_accuracy: 0.7247 - val_loss: 0.0360\n",
      "Epoch 20/20\n",
      "\u001b[1m56/56\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 673ms/step - accuracy: 0.7570 - loss: 0.0325 - val_accuracy: 0.7179 - val_loss: 0.0360\n",
      "Epoch 1/10\n",
      "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 478ms/step - accuracy: 0.7353 - loss: 0.0331 - val_accuracy: 0.7230 - val_loss: 0.0353\n",
      "Epoch 2/10\n",
      "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 484ms/step - accuracy: 0.7434 - loss: 0.0326 - val_accuracy: 0.7315 - val_loss: 0.0354\n",
      "Epoch 3/10\n",
      "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 476ms/step - accuracy: 0.7630 - loss: 0.0317 - val_accuracy: 0.7162 - val_loss: 0.0352\n",
      "Epoch 4/10\n",
      "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 464ms/step - accuracy: 0.7522 - loss: 0.0322 - val_accuracy: 0.7290 - val_loss: 0.0356\n",
      "Epoch 5/10\n",
      "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m50s\u001b[0m 451ms/step - accuracy: 0.7592 - loss: 0.0315 - val_accuracy: 0.7315 - val_loss: 0.0353\n",
      "Epoch 6/10\n",
      "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 467ms/step - accuracy: 0.7854 - loss: 0.0299 - val_accuracy: 0.7358 - val_loss: 0.0351\n",
      "Epoch 7/10\n",
      "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 458ms/step - accuracy: 0.7542 - loss: 0.0315 - val_accuracy: 0.7230 - val_loss: 0.0351\n",
      "Epoch 8/10\n",
      "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 462ms/step - accuracy: 0.7701 - loss: 0.0304 - val_accuracy: 0.7426 - val_loss: 0.0348\n",
      "Epoch 9/10\n",
      "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 464ms/step - accuracy: 0.7674 - loss: 0.0308 - val_accuracy: 0.7213 - val_loss: 0.0354\n",
      "Epoch 10/10\n",
      "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 463ms/step - accuracy: 0.7917 - loss: 0.0297 - val_accuracy: 0.7230 - val_loss: 0.0356\n"
     ]
    }
   ],
   "source": [
    "history_rnn_1 = model_rnn.fit( x= X_train, y= y_train_dl, batch_size=256 , epochs=40, validation_split=0.25)\n",
    "history_rnn_2 = model_rnn.fit( x= X_train, y= y_train_dl, batch_size=128 , epochs=30, validation_split=0.25)\n",
    "history_rnn_3 = model_rnn.fit( x= X_train, y= y_train_dl, batch_size=64 , epochs=20, validation_split=0.25)\n",
    "history_rnn_4 = model_rnn.fit( x= X_train, y= y_train_dl, batch_size=32 , epochs=10, validation_split=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e40b7f-fcb7-4868-9acc-847c185794a4",
   "metadata": {},
   "source": [
    "This training approach while uncoventional, progressively reduces the batch size while simultaneously decreasing the number of epochs. The idea is to start with a __larger batch__ size to allow for faster and more stable convergence in the earlier stages, where the model can make broad adjustments. As training progresses, reducing the batch size allows for finer adjustments to the model's weights, potentially leading to better generalization. This strategy can help avoid getting stuck in local minima and improve the model's performance by making more precise updates in the later stages of training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "fe7f6b3d-e715-4ffe-a866-6add6758c69f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 126ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      0.70      0.69       558\n",
      "           1       0.72      0.71      0.72       619\n",
      "\n",
      "    accuracy                           0.71      1177\n",
      "   macro avg       0.71      0.71      0.71      1177\n",
      "weighted avg       0.71      0.71      0.71      1177\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred = model_rnn.predict(X_test)\n",
    "pred = np.argmax(pred , axis = 1)\n",
    "print(classification_report(np.argmax(y_test_dl , axis = 1) , pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "7bf95edb-9e16-4fb0-b022-0642d1a907f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x759fb7ce3100>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2)\n",
    "fig.set_figwidth(8)\n",
    "fig.set_figheight(3.5)\n",
    "fig.tight_layout()\n",
    "\n",
    "history_train_loss = history1.history['loss'] + history2.history['loss'] + history3.history['loss'] + history4.history['loss']\n",
    "history_val_loss = history1.history['val_loss'] + history2.history['val_loss'] + history3.history['val_loss'] + history4.history['val_loss']\n",
    "history_train_acc = history1.history['accuracy'] + history2.history['accuracy'] + history3.history['accuracy'] + history4.history['accuracy']\n",
    "history_val_acc = history1.history['val_accuracy'] + history2.history['val_accuracy'] + history3.history['val_accuracy'] + history4.history['val_accuracy']\n",
    "\n",
    "ax[0].plot(history_train_loss, label='Training Loss')\n",
    "ax[0].plot(history_val_loss, label='Validation Loss')\n",
    "ax[0].set_xlabel('Epochs')\n",
    "ax[0].set_ylabel('Loss')\n",
    "ax[0].set_title('Training and Validation Loss')\n",
    "ax[0].grid(True)\n",
    "ax[0].legend()\n",
    "\n",
    "ax[1].plot(history_train_acc, label='Training Accuracy')\n",
    "ax[1].plot(history_val_acc, label='Validation Accuracy')\n",
    "ax[1].set_xlabel('Epochs')\n",
    "ax[1].set_ylabel('Accuracy')\n",
    "ax[1].set_title('Training and Validation Accuracy')\n",
    "ax[1].grid(True)\n",
    "ax[1].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "a1e7c17c-1653-44a0-af06-b9cca1ed6f22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 120ms/step\n",
      "Best Threshold:  0.5044263\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision, recall, thresholds = precision_recall_curve( y_test_dl[:,0] , model_rnn.predict(X_test)[:,0] )\n",
    "plt.figure(figsize=(7, 3))\n",
    "plt.plot( thresholds , precision[:-1] , label = 'Precision' )\n",
    "plt.plot( thresholds , recall[:-1] , label = 'Recall' )\n",
    "\n",
    "best_tradoff = np.argmin(np.abs(precision - recall))\n",
    "plt.plot( [thresholds[best_tradoff] , thresholds[best_tradoff]] , [0, precision[best_tradoff]] , c='red' , linestyle='dashed' , alpha = 0.5)\n",
    "plt.plot( [0 , thresholds[best_tradoff]] , [precision[best_tradoff], precision[best_tradoff]] , c='red', linestyle='dashed'  , alpha = 0.5)\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.xlabel('Thresholds')\n",
    "plt.ylabel('Score')\n",
    "plt.ylim([0,1])\n",
    "plt.xlim([0,1])\n",
    "plt.title('Precision Recall Curve')\n",
    "\n",
    "print('Best Threshold: ' , thresholds[best_tradoff])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "f62aec9c-9b52-466a-9883-80d9bb0e12b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 125ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.72      0.72       619\n",
      "           1       0.69      0.68      0.69       558\n",
      "\n",
      "    accuracy                           0.70      1177\n",
      "   macro avg       0.70      0.70      0.70      1177\n",
      "weighted avg       0.70      0.70      0.70      1177\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred = model_rnn.predict(X_test)\n",
    "pred = (pred[:,0] > thresholds[best_tradoff]).astype(np.uint8)\n",
    "print(classification_report(y_test_dl[:,0] , pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "145f37df-97ba-49b9-b870-edfb2e9be1ea",
   "metadata": {},
   "source": [
    "As we can see clearly, using a gradually reduce learning rate (using schedules) had helped in reducing the overfitting in our network. However, it looks like that the Dense model actually did a better job at learning the patterns in our data. This can be due to many factors; For example, it could indicate that our data is __Non-Sequential__. Also, the lower performance could mean that we have a case of __Vanishing Gradient__. One other reaseon, might be the size of our dataset, since a dataset of only about $3500$ entries might not be big enought to train an RNN.\n",
    "\n",
    "# Comparing the Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "3bee8432-b170-443b-810d-768fd0b2a970",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 161ms/step\n",
      "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step\n"
     ]
    }
   ],
   "source": [
    "## Create random tests from dataset\n",
    "random_indicies = np.arange(0, 1000)\n",
    "random_indicies = np.random.shuffle(random_indicies)\n",
    "\n",
    "X_test = np.stack(df['encode'].to_numpy() , axis = 0)[random_indicies]\n",
    "y_test = df['gender_bin'].to_numpy()[random_indicies].reshape(-1)\n",
    "\n",
    "X_test = X_test.reshape( X_test.shape[1] , 10, 33)\n",
    "rnn_predict = model_rnn.predict(X_test)\n",
    "X_test = X_test.reshape( X_test.shape[0] , -1)\n",
    "dummy_predict = dummy.predict(X_test)\n",
    "dense_predict = model_dense.predict(X_test)\n",
    "svm_pred = svc.predict(X_test)\n",
    "\n",
    "acc_dummy = accuracy_score(y_test , dummy_predict )\n",
    "perc_dummy = precision_score(y_test , dummy_predict )\n",
    "rec_dummy = recall_score(y_test , dummy_predict )\n",
    "f1_dummy = f1_score(y_test , dummy_predict )\n",
    "#------------------------------------------------------------------------------------------------\n",
    "acc_rnn = accuracy_score(y_test , rnn_predict[:,0] < 0.5 )\n",
    "perc_rnn = precision_score(y_test , rnn_predict[:,0] < 0.5 )\n",
    "rec_rnn = recall_score(y_test , rnn_predict[:,0] < 0.5 )\n",
    "f1_rnn = f1_score(y_test , rnn_predict[:,0] < 0.5 )\n",
    "#------------------------------------------------------------------------------------------------\n",
    "acc_dense = accuracy_score(y_test , dense_predict[:,0] < 0.5 )\n",
    "perc_dense = precision_score(y_test , dense_predict[:,0] < 0.5 )\n",
    "rec_dense = recall_score(y_test , dense_predict[:,0] < 0.5 )\n",
    "f1_dense = f1_score(y_test , dense_predict[:,0] < 0.5 )\n",
    "#------------------------------------------------------------------------------------------------\n",
    "acc_svc = accuracy_score(y_test , svm_pred )\n",
    "perc_svc = precision_score(y_test , svm_pred )\n",
    "rec_svc = recall_score(y_test , svm_pred )\n",
    "f1_svc = f1_score(y_test , svm_pred )\n",
    "\n",
    "\n",
    "comp_results = [\n",
    "         { 'classifier': \"Dummy\", 'Accuracy' : acc_dummy, 'Percision' : perc_dummy , 'Recall' : rec_dummy, 'F1': f1_dummy },\n",
    "         { 'classifier': \"Dense\", 'Accuracy' : acc_dense, 'Percision' : perc_dense , 'Recall' : rec_dense, 'F1': f1_dense },\n",
    "         { 'classifier': \"RNN\",  'Accuracy' : acc_rnn, 'Percision' : perc_rnn , 'Recall' : rec_rnn, 'F1': f1_rnn },\n",
    "         { 'classifier': \"SVM\",  'Accuracy' : acc_svc, 'Percision' : perc_svc , 'Recall' : rec_svc, 'F1': f1_svc },\n",
    "]\n",
    "\n",
    "res_df = pd.DataFrame(comp_results)\n",
    "res_df.head()\n",
    "\n",
    "res_df.set_index('classifier', inplace=True)\n",
    "res_df = res_df.stack().to_frame('value').reset_index()\n",
    "res_df.rename(columns={'level_1':'Metric'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "2c5ccfaa-6def-4e18-b726-320fda701118",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'F1'}, xlabel='classifier', ylabel='value'>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2)\n",
    "fig.set_figwidth(10)\n",
    "fig.set_figheight(5)\n",
    "fig.tight_layout()\n",
    "\n",
    "axs[0].grid(True)\n",
    "axs[1].grid(True)\n",
    "\n",
    "axs[0].set_title('Accuracy')\n",
    "axs[1].set_title('F1')\n",
    "\n",
    "sns.barplot( data = res_df[ res_df['Metric'] == 'Accuracy' ] , x = 'classifier' , y = 'value' , hue = 'classifier' , ax = axs[0] , width=0.5 )\n",
    "sns.barplot( data = res_df[ res_df['Metric'] == 'F1' ] , x = 'classifier' , y = 'value' , hue = 'classifier' , ax = axs[1] , width=0.5 )"
   ]
  },
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    "As evident from the graphs, the __RNN__ was outperformed by both the __SVM__ and __Dense__ networks, which performed almost equally well. This unusual occurrence calls for further investigation; however, this is beyond the scope of this notebook as it would require a more in-depth analysis of the network structure, learning methods, and other factors.\n",
    "\n",
    "Another point worth addressing is that nearly all models achieved around $75\\%$ test accuracy, suggesting that the dataset may not be classifiable beyond this point—potentially an inherent characteristic of the Farsi language. Gathering more data could help clarify this claim.\n",
    "\n",
    "Despite the similarity in performance between the SVM and Dense networks, with SVM performing slightly better, this marks a clear victory for the SVM. It outperformed all the models, is less sensitive to noise, and is also much simpler compared to the other models."
   ]
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    "# Conclusion\n",
    "\n",
    "In conclusion, the dataset of Farsi names appears to be classifiable using machine learning techniques, achieving an accuracy of around $75\\%$. The most effective model for this task is the Support Vector Machine with a polynomial kernel of degree $3$.\n",
    "\n",
    "Further research could explore additional approaches, such as using **Transforms** for classification, more advanced feature engineering, and increasing the dataset size beyond the current $6,000$ data points. Also, model structures from [[2]](./2102.03692v1.pdf) , [[3]](./2106.10156v2.pdf) , [[4]](./Ritwick_Ghosh__Paper.pdf) could also be implemented to directly compare the results with these papers but it was curently skiped as the number of network structures used in these papers are large (This could also be used to investigate the claim that Farsi names aren't classifiable beyond the $75\\%$ accuracy mark).\n",
    "\n",
    "However, for real-world applications, it could be argued that creating a Look-Up Table for gender classification of names might be more practical and viable. Given that the set of Farsi names is relatively static and unlikely to grow significantly in the coming century, a look-up table could be a more efficient solution."
   ]
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