ML_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=ltr align=center>\n",
    "<font color=05fffb size=7>\n",
    "    Introduction to Machine Learning <br>\n",
    "<font color=05ff3b size=5>\n",
    "    Electrical Engineering Department <br>\n",
    "    Spring 2024<br>\n",
    "<font color=fffb03 size=6>\n",
    "    Project <br>\n",
    "<font color=f703ff size=5>\n",
    "    AmirHossein Zahedi 99101705"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=ltr align=left>\n",
    "<font color=05fffb size=6>\n",
    "    Libraries <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler, MinMaxScaler\n",
    "from scipy.stats import ttest_ind\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, average_precision_score, classification_report\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import time\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.decomposition import PCA\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=ltr align=left>\n",
    "<font color=05fffb size=6>\n",
    "    Part 2 -> Data Review <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=ltr align=left>\n",
    "<font color=fffb03 size=4>\n",
    "    Part 2.1 -> Reading <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=\"rtl\">\n",
    "در این بخش داده را می خوانیم و مشاهده می کنیم که 2000 سطر و 10 ستون ویژگی دارد. عددی یا کیفی بودن هر ویژگی نیز مشخص شده است.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 10 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   Id       2000 non-null   int64 \n",
      " 1   Age      2000 non-null   int64 \n",
      " 2   MF       2000 non-null   object\n",
      " 3   LoE      1990 non-null   object\n",
      " 4   YoW      2000 non-null   int64 \n",
      " 5   YoCW     2000 non-null   int64 \n",
      " 6   Income   2000 non-null   int64 \n",
      " 7   Housing  1990 non-null   object\n",
      " 8   Car      1990 non-null   object\n",
      " 9   Res      2000 non-null   object\n",
      "dtypes: int64(5), object(5)\n",
      "memory usage: 156.4+ KB\n"
     ]
    }
   ],
   "source": [
    "## Reading data\n",
    "data_raw = pd.read_csv('train_data.csv')\n",
    "data_raw.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=ltr align=left>\n",
    "<font color=fffb03 size=4>\n",
    "    Part 2.2 -> Preprocessing <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=\"rtl\">\n",
    "در این بخش داده های پرت را با استفاده از فرض توزیع گوسی برای آن ها حذف می کنیم به این صورت که داده هایی که بیش از دو برابر انحراف معیار از میانگین فاصله داشته باشند، که 5 درصد از توزیع را شامل می شوند، به عنوان داده های پرت در نظر گرفته شده و حذف می شوند.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=\"rtl\">\n",
    "مشاهده می کنیم که 5 داده از 2000 داده حذف می شوند.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1995 entries, 0 to 1999\n",
      "Data columns (total 10 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   Id       1995 non-null   int64 \n",
      " 1   Age      1995 non-null   int64 \n",
      " 2   MF       1995 non-null   object\n",
      " 3   LoE      1985 non-null   object\n",
      " 4   YoW      1995 non-null   int64 \n",
      " 5   YoCW     1995 non-null   int64 \n",
      " 6   Income   1995 non-null   int64 \n",
      " 7   Housing  1985 non-null   object\n",
      " 8   Car      1985 non-null   object\n",
      " 9   Res      1995 non-null   object\n",
      "dtypes: int64(5), object(5)\n",
      "memory usage: 171.4+ KB\n"
     ]
    }
   ],
   "source": [
    "## Removing outliers\n",
    "number_cols = data_raw.select_dtypes(include=['int64']).columns\n",
    "data_prep = data_raw\n",
    "for i in number_cols:\n",
    "    mean_col = np.mean(data_raw[i])\n",
    "    std_col = np.std(data_raw[i])\n",
    "    l_bound = mean_col - 2*std_col\n",
    "    h_bound = mean_col + 2*std_col\n",
    "    data_prep = data_raw[(data_raw[i] >= l_bound) & (data_raw[i] <= h_bound)]\n",
    "data_prep.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=\"rtl\">\n",
    "در این بخش داده ها (سطر ها) را که مقادیر نا مشخص را در ویژگی های خود دارند حذف می کنیم. 30 سطر حذف می شوند. اگر دیتای اولیه را ببینیم متوجه می شویم که سه ویژگی در 10 سطر مقادیر nan داشتند که در این بخش آن 30 سطر در کل حذف شدند. در نهایت 1965 سطر پس از مراحل پیش پردازش باقی می ماند.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1965 entries, 0 to 1999\n",
      "Data columns (total 10 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   Id       1965 non-null   int64 \n",
      " 1   Age      1965 non-null   int64 \n",
      " 2   MF       1965 non-null   object\n",
      " 3   LoE      1965 non-null   object\n",
      " 4   YoW      1965 non-null   int64 \n",
      " 5   YoCW     1965 non-null   int64 \n",
      " 6   Income   1965 non-null   int64 \n",
      " 7   Housing  1965 non-null   object\n",
      " 8   Car      1965 non-null   object\n",
      " 9   Res      1965 non-null   object\n",
      "dtypes: int64(5), object(5)\n",
      "memory usage: 168.9+ KB\n"
     ]
    }
   ],
   "source": [
    "## Removing NANs\n",
    "data_prep = data_prep.dropna()\n",
    "data_prep.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=\"rtl\">\n",
    "در این قسمت ویژگی های کیفی را به عددی تغییر می دهیم. برای مثال اگر ویژگی حاوی سه حالت کیفی باشد، به آن مقادیر، 0، 1 و 2 تعلق می گیرند تا ویژگی کمی شود. داده های تغییر یافته را نیز می توانیم مشاهده کنیم. برای آنکه ترتیب را بدانیم تا در قسمت t-test استفاده کنیم، روش اول را کنار گذاشته و مقادیر کیفی را دستی عددی می کنیم.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['F' 'M']\n",
      "['Bach' 'Ad. Dip' 'Dip' 'Mst' 'P. Doct' 'Doct']\n",
      "['R' 'O' 'N']\n",
      "[False True]\n",
      "['Accept' 'Reject']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of         Id  Age  MF  LoE  YoW  YoCW   Income  Housing  Car  Res\n",
       "0        0   60   0    2    4     4  4759451        1    0    1\n",
       "1        1   60   0    2    4     4  4759451        1    0    1\n",
       "2        2   76   1    2    8     8  8237019        1    1    1\n",
       "3        3   28   1    1    4     4  9114804        1    0    1\n",
       "4        4   44   1    1   16     5   998868        1    0    0\n",
       "...    ...  ...  ..  ...  ...   ...      ...      ...  ...  ...\n",
       "1995  1995   61   1    0    9     4  5316246        1    1    1\n",
       "1996  1996   50   1    1    3     3  2062048        1    1    0\n",
       "1997  1997   52   1    3    8     6  5811902        1    0    1\n",
       "1998  1998   41   1    4   15    14  5866909        1    0    1\n",
       "1999  1999   66   0    2    3     3  8199311        1    1    1\n",
       "\n",
       "[1965 rows x 10 columns]>"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Changing object columns to numerical\n",
    "# object_cols = data_prep.select_dtypes(include=['object']).columns\n",
    "# for i in object_cols:\n",
    "#     encoder = LabelEncoder()\n",
    "#     data_prep[i] = encoder.fit_transform(data_prep[i])\n",
    "# data_prep.info\n",
    "\n",
    "print(data_prep['MF'].unique())\n",
    "print(data_prep['LoE'].unique())\n",
    "print(data_prep['Housing'].unique())\n",
    "print(data_prep['Car'].unique())\n",
    "print(data_prep['Res'].unique())\n",
    "\n",
    "data_prep['MF'] = data_prep['MF'].map({'M':1 ,'F':0})\n",
    "data_prep['LoE'] = data_prep['LoE'].map({'Dip':0 ,'Ad. Dip':1 ,'Bach':2 ,'Mst':3 ,'Doct':4 ,'P. Doct':5})\n",
    "data_prep['Housing'] = data_prep['Housing'].map({'O':2 ,'R':1 ,'N':0})\n",
    "data_prep['Car'] = data_prep['Car'].map({True:1 ,False:0})\n",
    "data_prep['Res'] = data_prep['Res'].map({'Accept':1 ,'Reject':0})\n",
    "data_prep.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=\"rtl\">\n",
    "در اینجا ویژگی آخر را که پذیرفته شدن یا نشدن بود را به عنوان برچسب برای هر سطر انتخاب می کنیم. 0 به معنای پذیرفته نشدن و 1 به معنای پذیرش است. همچنین پس از جدا سازی داده از لیبل ها، آن ها را نرمالیزه می کنیم با نرمالیزیشن MinMax، که اینگونه همه ویژگی ها مقادیر بین 0 و 1 به خود می گیرند.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1965, 9)\n",
      "(1965,)\n"
     ]
    }
   ],
   "source": [
    "## Seperating data from label\n",
    "data = data_prep.drop(columns='Res')\n",
    "labels = data_prep['Res']\n",
    "\n",
    "## Normalization (z-score)\n",
    "#scaler = StandardScaler()\n",
    "#data = scaler.fit_transform(data)\n",
    "scaler = MinMaxScaler()\n",
    "data = scaler.fit_transform(data)\n",
    "\n",
    "print(data.shape)\n",
    "print(labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=ltr align=left>\n",
    "<font color=fffb03 size=4>\n",
    "    Part 2.3 -> Plotting <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=\"rtl\">\n",
    "در این بخش هر ویژگی را در نظر گرفته و برای دو کلاس پذیرش و عدم پذیرش، هیستوگرام هر کدام را می کشیم. <br>\n",
    "در این بخش به دنبال ویژگی یا ویژگی هایی هستیم که با استفاده از هیستوگرام آن ها جدایی پذیری خوبی برای دو کلاس داشته باشند. <br>\n",
    "هیچ کدام از ویژگی ها به اندازه کافی نمی توانند تفکیک پذیری دو کلاس را تامین کنند و نیازمند ترکیب خطی یا غیر خطی ویژگی ها هستیم تا تفکیک پذیری حاصل شود اما با این حال، به نظر ویژگی درامد به دلیل توزیع مکمل، و به خصوص ویژگی سطح آموزش (LoE) ویژگی های بهتری باشند.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s2aM33njDYWr42a699lodPHhQy5Yt06+//qo33njD/m0v6cyU9yeeeEJr1qxRWlqavv/+e23evFktWrSQJEVERGjVqlVKTU3Vtm3b9M0339jXnev06dMaNWyUftj+gxL/lajCwkJl/JGhjD8yHL6Bdq4ut3RRTk6O9u7Za1+27J1l6hneU61CW6l169b2n7vvvlsNGjTQkiVL1KRJEw0fPlyPPvqoPvnkE6WmpmrNmjX697//LenMPYmzsrJ0//33a8uWLfrll1/07rvvav8v+yVJUZOiNHvWbP3rn//Sr7/8qj279+iDJR/ordlvOYxv0fxF+urzr7R/335NjpqsE8dP6P5h90uSrgq6SiaTSatWrdKff/6pkydP2rf77rvvFB4eXupxAwBQ1VSFXOSee+7Rli1b9N5776mwsFDp6elKT08/by7So0cPnTp1Srt377YvW7Bggfr166e2bdsanovs3Xsm54mOjlZcXJxef/117du3Tzt37tTChQs1a9Ysh/HNmTNHy5cv188//6yxY8cqMzNTjz76qCSpcePG5CKodDhXVN5zxRdffMG5AsAVhSILAOCiBAcHa9u2berRo4eioqLUunVrhYWF6euvv9bcuXNL3OaOO+7Q+PHj9cQTT+j6669XSkqKpk6dal/v6uqqv//+Ww899JCaNWumwYMHq0+fPoqJiZF05ptOY8eOVYsWLXT77bfruuuuK/VexYcPH1bSiiQd/f2owruGq11IO/vPlv9uKfW4fP181WdAHy3/95k/ov7M+FNfr/pafe/oW6ytyWTSoEGD7FPv586dq3vuuUdjxoxR8+bNNXLkSJ06dUqS5Ofnp2+++UYnT55Ut27d1L59e82fP19uNc58O2zI8CF6JfEV/Xvpv9Wrcy/d0+ce/fu9f+vqxlc77HNy9GTNeW2OwrqEaVPKJr39/tv2GToBgQGKmhylmJgYBQQE2L8plpeXp+XLl2vkyJGlHjcAAFVNVchFPvvsMx0+fFjXX3+9AgIC7D8pKSmlHpefn58GDRqk9957T5L0xx9/6Msvv7Q/QPpshuQi//9N9ccee0z/+te/tGjRIoWGhqpbt25atGiRgoODHfY5Y8YMzZw5U23bttV3332nTz/91P6N80aNGik6OppcBJUK54rKea6IiYnRs88+K39/f84VAK4YJlvR08CqsaysLPn4+OjEiRMXvPdlWVksFq1YsUI/eP9g2C2uortHG9KPQ59rjO/TaC42F7XNamtoLJ2hsr8/VSWOVUFRLPv27VviQwHLIi8vT6mpqQoODrY/tLDodmGVWaBXoKH9Wa1WZWVlydvbWy4uxtX9yxvLPbv36P6B9+v7Hd+rjlcdh3VGH3tZx3go7ZA6hXbSqvWr1LpN69Ib2iQPq4dDLOfMmaNPP/1USUlJpW5W0u9iEWd8PqJk1TkXqQqKYnkp530QRyMZFcvzfQZUF87KRcpr586d6tWrl/bv3y8vr/M/T+5yOXDggIKDg7V9+3Zdf/31pbYrKZbkIlXH+WJd3nNFZfv/qyo7N5ZV+VxRkks9V1wM8hFjEEfjVOdYGn1d2ojrdecqay7CM1kAg1SFghWA82vRqoWmvDBFhw4eUotWJU/rr0rc3Nwu+KBNAABQeYSGhio+Pl4HDhxQaGhoRQ/nkpGLAM7BuQIAKheKLAAAnGXw0MEVPQTDjBo1qqKHAAAALtLw4cMregiGIRcBnIdzBQBUHhRZAACoxIIaB+n3rN8rehgAAKCaatKkibjLOIAL4VwBoDrjppgAAAAAAAAAAADlQJEFAABUW6dPn9Zzzz2n4OBg1a5dW02bNtULL7wgq/V/D4q32WyKjo5WYGCgateure7du2v37t0O/eTn5+vJJ59U/fr15enpqYEDB+rw4cOX+3AAAAAAAMBlRpEFAABUWzNnztSbb76p2bNna8+ePYqPj9fLL7/s8ODN+Ph4zZo1S7Nnz9bmzZtlNpsVFham7Oxse5uIiAgtX75cy5Yt0/r163Xy5En1799fhYWFFXFYAAAAAADgMuGZLAAAoNrasGGD7rjjDvXr10/SmXtJv//++9qyZYukM7NYEhISNGXKFA0aNEiStHjxYvn7+2vp0qUaPXq0Tpw4oQULFujdd99Vr169JElLlixRUFCQVq9erd69e1fMwQEAAAAAAKejyAIAAKqtm2++WW+++ab27dunZs2a6YcfftD69euVkJAgSUpNTVV6errCw8Pt27i7u6tbt25KSUnR6NGjtXXrVlksFoc2gYGBat26tVJSUkossuTn5ys/P9/+OisrS5JksVhksVgMObaiflxsxk1cNmpsVU3RcVfX4zcKcTSOUbG0WCyy2WyyWq0Ot0msTooe0lwUB5RfeWNptVpls9lksVjk6urqsI7zBQAAqAoosgAAgGrrmWee0YkTJ9S8eXO5urqqsLBQL730kh544AFJUnp6uiTJ39/fYTt/f3+lpaXZ29SsWVP16tUr1qZo+3PFxcUpJiam2PKkpCR5eHhc8nGdLTQ71LC+VqxYYVhfVVFycnJFD+GKQByNc6mxrFGjhsxms06ePKmCggKDRlU1nX0LSFyai41lQUGBcnNztW7dOp0+fdphXU5OjpFDAwAAcAqKLAAApzCZTFq+fLnuvPPOih5KmRUUFKjHTT2U8FaCbup0k2H9Llq0SBERETp+/LhhfZbktttu0+TJk3XPPfc4dT9Xkg8++EBLlizR0qVL1apVK+3YsUMREREKDAzU8OHD7e1MJpPDdjabrdiyc52vzaRJkxQZGWl/nZWVpaCgIIWHh8vb2/sSjuh/LBaLkpOTtdNrp6wmY76dPenmSYb0U9UUxTIsLExubm4VPZwqizgax6hY5uXl6dChQ6pTp45q1apl4AgrB1dXV3300UfnzUVsNpuys7Pl5eV1wfP65VBQUKDWrVtr4cKF6tq1q2H9Llq0SJGRkTp27JhhfZ7LZrPppptu0uTJk+232CyLvLw81a5dW7feemux38OimZ6AM1XVv1tatmypxYsXG36uuBx/t9x0002aNGnSRZ0rAKAyo8gCAJWMV+yrl3V/2ZOjLnqb9PR0vfTSS/ryyy/1+++/q2HDhrr++usVERGhnj17OmGUF2fFZyu05O0l+nHHj8o8lqlV61epdZvWF9zuvYXvqdHVjRwKLI28G9n/7enpqWuuuUbjx4/Xww8/XObx3Hffferbt+9FHcP5fPDeB4p+Nlp7Du1xWP7000/bL2y4uBh3i6gr2YQJE/Tss8/q/vvvlySFhoYqLS1NcXFxGj58uMxms6Qzv/MBAQH27TIyMuyzW8xmswoKCpSZmekwmyUjI0NdunQpcb/u7u5yd3cvttzNzc3wi89Wk9WwIkt1vzDujPenOiKOxrnUWBYWFspkMsnFxcXxcyM6+tIHdzHKsb+y5iLFju0cRbe1KoqDUT7++GO99dZb2rp1q/7++29t375d119//QW3+9e//qXGjRvrlltusS87u/hT3lzkgQceUP/+/Q07xpIuxFqt1nLlIi4uLjKZTCX+PnOuqALO8/+vyWZTrfx8mdzdJSOKmE48V1SU8p4r5s2bp8aNGzsUWIw4Vxj9d0tpRZupU6fq6aef1p133snfLQCuCJzJAAAX5cCBA2rfvr2++eYbxcfHa+fOnVq5cqV69OihsWPHVvTwJEk5p3J0U6ebNDlm8kVt9/Zbb2vIQ0OKLZ81d5aOHj2qH374Qffdd58eeeQRrVq1qsz91q5dWw0bNryosZRH7969deLEiYsaW3WXk5NT7A87V1dX+0W34OBgmc1mh1vyFBQUaO3atfYCSvv27eXm5ubQ5ujRo9q1a1epRRYAQPlVhVzk1KlT6tq1q2bMmHFR2yUmJuqxxx4rtnzhwoXkIsBF4lxRec8V/fr141wB4IpCkQUAcFHGjBkjk8mkTZs26Z577lGzZs3UqlUrRUZGauPGjaVu98wzz6hZs2by8PBQ06ZNNXXqVIeHmf7www/q0aOHvLy85O3trfbt22vLli2SpLS0NA0YMED16tWTp6enWrVqdd5nQ9zzwD0a/+x43dL9llLbnGvnjp068NsB9exd/BttPj4+MpvNuuaaazR58mT5+voqKSnJvv7EiRMaNWqUGjZsKG9vb91222364Ycf7OsXLVqkunXrOvSZ9FWSbr/1djVt0FSd23TWrLhZDvchP3H8hCaOm6i217RV0wZNdVvH25T8VbJSvktR5D8ilXUiS428G6mRdyO9+v+zn1xdXdWnTx+9//77ZT7u6m7AgAH2bzceOHBAy5cv16xZs3TXXXdJOvONwIiICMXGxmr58uXatWuXHn74YXl4eGjIkDMFOR8fH40YMUJRUVH6+uuvtX37dj344IMKDQ1Vr169KvLwAOCKVBVykWHDhun555+/qM+Bbdu2af/+/erXr1+xdXXr1jU8F/n888/Vvn171apVS02bNlVMTIxDLnL8+HGNGjVK/v7+qlWrllq3bq0vvvhCa9as0SOPPKITJ07IZDLJZDIp+v9nGJCLoDLhXFG5zxV9+/blXAHgisHtwgAAZXbs2DGtXLlSL730kjw9PYutPzchP5uXl5cWLVqkwMBA7dy5UyNHjlSdOnU0evRopZ9M130P3KdWbVrpyzVfysXFRbt37lZmQaaOZB/RiNEjZCmw6D9f/UceHh7a9/M+5bnk6Uj2EcOObeP3G9X02qby8vYqtU1hYaE++ugjHTt2zH77CpvNpn79+snX11crVqyQj4+P3nrrLfXs2VP79u2Tr69vsX5WrVqlcSPH6YWZL6hjl45KS03TxKcmSpIiJ0XKarXqwbsf1KmTp5Q4P1GNgxtr38/75Orqqhs73qiYGTF6JfYVrdu6TpIc3oubbrpJr7zyimFxudIlJiZq6tSpGjNmjDIyMhQYGKjRo0fr+eeft7eZOHGicnNzNWbMGGVmZqpjx45KSkqSl9f/fldee+011ahRQ4MHD1Zubq569uypRYsWydXVtSIOCwCuWM7KRSRp6NChateunebOnStXV1ft2LHD/nk/duxYFRQUaN26dfL09NRPP/2kOnXqGHps69atU7Nmzc77bC4jc5EHH3xQb7zxhm655Rb9+uuvGjVqlCRp2rRpslqt6tOnj7Kzs7VkyRJdc801+umnn+Tq6qouXbooISFBzz//vPbu3StJDrEgF0FlYPS5wsvLSxMnnsnXOVcYc67o0KGD4uPjjQwNAFQYiiwAgDLbv3+/bDabmjdvftHbPvfcc/Z/N2nSRFFRUfrggw/sFzZ+P/y7Hh/3uK5tdq0kqem1Te3tjxw+or4D+6pFqxaSpMbBjS/lMEp06OAh+Qf4l7hu7KNjNW7kOOXl5amwsFC+vr726fnffvutdu7cqYyMDPszNl555RV98skn+s9//mP/I+RsL730ksaOH6vBQwfbj2fClAl66fmXFDkpUt99+512bN2hNZvX6JqQa4ods5fPmYfzNvQ/ayq/7cx/GjVqpIMHD8pqtXJ/4zLw8vJSQkKCEhISSm1T9K276PPcB7xWrVpKTExUYmKi8YMEANg5Mxc5ePCgJkyYYO87JCTE3v7gwYO6++67FRoaKklq2rSpjHbgwAEFBgaWuO6BBx6Qq6urobnIs88+q+HDh9uP58UXX9TEiRM1bdo0rV69Wps2bdKePXvUrFmzYsfs4+Mjk8lkf3aZ9L/n25CLoDJwxrmiqMjCueLSzhVFOFcAuJJwFgMAlJnNduZKvqkcD678z3/+o5tvvllms1l16tTR1KlTdfDgQfv6UWNHacKTE3TfwPs0e9ZsHfjtgH3do48/qtdffl13hN2hV156RT/t+umSj+VceXl5JT6IXJKmxU3Tjh07lJycrOuvv16vvfaarr32TDFo69atOnnypPz8/FSnTh37T2pqqn799dcS+9u6dasSZiYoJCDE/jNx3ET9kf6HcnNytXvnbgU0CrAXWC5G7dq1ZbValZ+ff9HbAgBQ2TkzF4mMjNRjjz2mXr16acaMGQ6f4+PGjdP06dPVtWtXTZs2TT/++OOlH8w5cnNzVatWrRLXvfbaa4bnIi+88IJD+5EjR+ro0aPKycnRjh07dNVVV9kvml4McpH/WbdunQYMGKDAwECZTCZ98skn9nUWi0XPPPOMQkND5enpqcDAQD300EM6csRxpnZ+fr6efPJJ1a9fX56enho4cKAOHz7s0CYzM1PDhg2Tj4+PfHx8NGzYsGIPGq9uOFdwrgCAy4kiCwCgzEJCQmQymbRnz56L2m7jxo26//771adPH33xxRfavn27pkyZooKCAnubqMlR+ua/36hneE99v/Z79ejQQ199/pUkacjwIUr5MUV333+3fv7pZ/Xt1ldvv/m2ocfm6+erE8dPlLiuoX9DXXvtterRo4c+/PBDjR07Vj/9dKbQY7VaFRAQoB07djj87N27VxMmTCixP6vVqqjJUUpan2T/Wb1htdZvXy/3Wu6l/tFUFseOHZOHh4dq165d7j4AAKisnJmLREdHa/fu3erXr5+++eYbtWzZUsuXL5ckPfbYY/rtt980bNgw7dy5UzfeeKPhsxfr16+vzMzMEteZzWbDc5GYmBiH9jt37tQvv/yiWrVqXVIeQS7yP6dOnVLbtm01e/bsYutycnK0bds2TZ06Vdu2bdPHH3+sffv2aeDAgQ7tIiIitHz5ci1btkzr16/XyZMn1b9/fxUWFtrbDBkyRDt27NDKlSu1cuVK7dixQ8OGDXP68VVmnCs4VwDA5cTtwqqQ6DXRFT0EANWcr6+vevfurTlz5mjcuHHF7m98/PjxEu9v/P3336tx48aaMmWKfVlaWlqxdteEXKNrQq7RqCdGacwjY/TBkg/UZ0AfSVKjqxrpoREP6aERDykuOk5LFy/Vo48/atixtW7TWu/+613ZbLbzfuPt2muv1d13361Jkybp008/1Q033KD09HTVqFFDTZo0KdO+brjhBv36y68Kvia4xPUtWrfQ0d+P6tdffi1xNktNt5oOf1ifbffu3brhhhvKNA4AAKoaZ+cizZo1U7NmzTR+/Hg98MADWrhwoe666y5JUlBQkB5//HE9/vjjmjRpkubPn68nn3zSsGMresbD5cpF9u7da/+G+7natGmjw4cPa9++fSV+Q71mTXKRsujTp4/69OlT4jofHx8lJyc7LEtMTFSHDh108OBBXX311Tpx4oQWLFigd9991/5g9CVLligoKEirV69W7969tWfPHq1cuVIbN25Ux44dJUnz589X586dtXfvXl133XXOPchKinNF5T9X7Nq1i3MFgCsGM1kAABfln//8pwoLC9WhQwd99NFH+uWXX7Rnzx698cYb6ty5c4nbXHvttTp48KCWLVumX3/9VW+88Yb9217SmSnvU6KmKOW7FB0+eFibN27WD9t+UMh1Z+5v/Pwzz2vN6jU6eOCgdu7Yqe/Xfq9rrys50ZekzGOZ2vXjLu37eZ8k6ddfftWuH3cp44+MUrfpcksX5eTkaO+evReMQVRUlD7//HNt2bJFvXr1UufOnXXnnXdq1apVOnDggFJSUvTcc89py5YtJW7//PPP6z/v/0evxr6qvXv26pe9v+jTjz7VzBdmSpI639xZHbt21Khho7Tum3U6eOCgvkn6Rt8mfytJuqrxVTp18pS+W/Odjv19TLk5ufa+169fr/Dw8AseAwAAVZWzcpEnnnhCa9asUVpamr7//ntt3rxZLVqceR5cRESEVq1apdTUVG3btk3ffPONfV1Jjh07ph07dti/Qb53717t2LFD6enppW7To0cPnTp1Srt3775gDIzIRd555x37N/L37NmjDz74wP4sim7duunWW2/V3XffreTkZKWmpuqrr77SypUrJZ15TsXJkyf19ddf66+//lJOTo69b3KR8jtx4oRMJpP94v/WrVtlsVgc4hkYGKjWrVsrJSVFkrRhwwb5+PjYCyyS1KlTJ/n4+NjbVFecKyr3ueK7777jXAHgisFMFgDARQkODta2bdv00ksvKSoqSkePHlWDBg3Uvn17zZ07t8Rt7rjjDo0fP15PPPGE8vPz1a9fP02dOtX+IHFXV1dlHsvUU6Of0l8Zf8nXz1d9BvRR1OQoSZK10KopUVN09MhR1fGqo+69uis6LrrUMSZ9laTIf0TaX495ZIwkKfLZSHuf5yra5/J/L9ek6EnnjUFoaKh69eql559/XitWrNCKFSs0ZcoUPfroo/rzzz9lNpt16623yt/fv8Tte/furcX/XqzXZr6mf77+T7m5uenakGv1wPAH7G3mvztfLz73osY8Oka5Oblq0rSJfVw3dbxJw0YM0z8e/ocyj2WeOa5JUTpy5IhSUlK0ZMmS844fAICqzFm5yN9//62HHnpIf/zxh+rXr69BgwYpJiZGklRYWKixY8fq8OHD8vb21u23367XXnut1DF+9tlneuSRR+yv77//fknStGnT7Ps8l5+fnwYNGqT33ntPcXFx542BEbnIF198oRdeeEHx8fFyc3NT8+bN7Q/IlqSPPvpITz/9tB544AGdOnVK1157rWbMmCFJ6tKlix5//HHdd999+vvvvzVt2jQ9//zz5CKXIC8vT88++6yGDBkib29vSVJ6erpq1qypevXqObT19/e3X4RPT09Xw4YNi/XXsGHD816oz8/Pd3gWRlZWlqQzz4qxWCwObS0Wi2w2m6xWq6xWq8M60/8/+6QkRc9FsZ2nzcWwnbPvC2ncuLG2bNmi2NhYh3PFDTfcoDlz5jgcS9GxDRgwQBEREfZzRd++ffXcc88pJiZGVqtVJpNJf/31l8O54q677tK0adNktVp1+vRph3NF7969NWvWrGJxK/LJJ59oxIgR9tdF54rnn39e06ZN+9+xnxXLevXq6a677tKSJUsUGxvr0N+571GrVq3Us2dPTZ06VV9++aW++OILPffccw7niltuuUUNGjRw2Lbov2FhYfrss880ffp0h3PFo48+am/z4YcfasKECQ7nitjYWFmtVnXq1EmjR4+2nyuKjuv3339XSkqK3nnnnVJjcy6r1SqbzSaLxSJXV9cybVOSot/vc3/PcXGIo3GqcyxdbMbO/yjqz8hYlrUvk82oT7sqLCsrSz4+Pjpx4oQ9mblUFotFK1as0A/eP8hqurhEAI5cbC5qm9WWWF4i4micolj27dtXbm5u5eojLy9PqampCg4Otj9/40j2kQtsVfECvQIN7c9qtSorK0s5LjnSxT+T0nB7du/R/QPv1/c7vlcdrzoO6y7l2N966y29+OKLDg8pNfz9tklxU+KUl5en+fPnl3mzkn4Xizjj8xElqyq5SHT3aEP6qWqKYnkp530QRyMZFcvzfQZUF0W5iLe3t1xcKv5GDzt37lSvXr20f/9+eXl5GdZvSbmI0axWq5566ilykVKYTCYtX75cd955Z7F1FotF9957rw4ePKg1a9bYj3Xp0qV65JFHij0YPCwsTNdcc43efPNNxcbGavHixdq713E2dkhIiEaMGKFnn322xPFER0fbi4hnW7p0qTw8PByW1ahRQ2azWUFBQapZs+bFHDacZPfu3brrrru0detWQ88VCxcu1CuvvFKmWTKXYurUqcrOzlZCQkKZtykoKNChQ4eUnp6u06dPO29wAHCWnJwcDRky5IK5CDNZAKCc4tbHlfvCZR2XOurq1VUZpzJUw1J1TsXOKAx4yOPC7S6TFq1aaMoLU3To4CG1aFX6tP6LcejQIa1YsUKtWrUypL/zadCggUaOHOn0/QAAAOcIDQ1VfHy8Dhw4oNDQUEP6JBep3CwWiwYPHqzU1FR98803DhdwzGazCgoKlJmZ6TCbJSMjQ126dLG3+eOPP4r1++eff5Y6k0mSJk2apMjI/838zsrKUlBQkMLDw4tdRMrLy9OhQ4dUp06diyrI2mw2ZWdny8vL67zPDsGFnRvLzp07a+bMmTp27JgaNWpkyD4OHTqkb7/9Vq1atXJ6UTMoKEjDhg27qP3k5eWpdu3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      "text/plain": [
       "<Figure size 2000x1600 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plotting histograms of features\n",
    "number_features = np.size(data,1)\n",
    "feature_names = data_raw.columns\n",
    "class1_indexes = np.where(labels == 0)\n",
    "class2_indexes = np.where(labels == 1)\n",
    "\n",
    "plt.figure(figsize=(20, 16))\n",
    "for i in range(number_features):\n",
    "    plt.subplot(3,3,i+1)\n",
    "    plt.hist(data[class2_indexes][:,i], bins=20, align='mid', alpha=0.5, color='green', label='Class 2 (Accept)')\n",
    "    plt.hist(data[class1_indexes][:,i], bins=20, align='mid', alpha=0.5, color='red', label='Class 1 (Reject)')\n",
    "    plt.title('Histogram of ' + feature_names[i])\n",
    "    plt.ylabel('Repeats')\n",
    "    plt.legend(title='label')\n",
    "    plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=\"rtl\">\n",
    "در این قسمت هیستوگام لیبل ها را رسم می کنیم، همانطور که مشاهده می شود، داده ها نامتعادل هستند و تعداد پذیرش ها در حدود 4 برابر تعداد داده های عدم پذیرش هستند. <br>\n",
    "از آنجا که در داده های نامتعادل، هدف بیشتر طبقه بندی کلاس نادر تر است، معیار هایی که کلاس بزرگتر را دخیل می کنند چندان برای معیار ارزیابی مناسب نیستند. برای مثال معیار صحت (Accuracy)  چندان مناسب نیست زیرا که Accuracy به دلیل بسیار بیشتر بودن یکی از کلاس ها صحت خیلی خوبی نشان می دهد که درست نیست. <br>\n",
    "معیار هایی مانند Precision و Recall عملکرد مناسب تری دارند زیرا که داده های درست طبقه بندی شده کلاس بزرگتر را دخیل نمیکنند. به نظر من معیار F1-Score بهترین نتیجه را می دهد زیرا که داده های غلط طبقه بندی شده و درست طبقه بندی شده برای کلاس نادر تر را در نظر میگیرد اما داده های درست طبقه بندی شده کلاس بزرگ که عمده داده ها هستند را در نظر نمی گیرد. به همین دلیل معیار F1-Score معیار مناسب تر و نزدیک به واقعیت تری است. <br>\n",
    "با استفاده از Precision و Recall می توان نمودار PRC را کشید و مساحت زیر آن را بدست آورد که با نام AUPRC شناخته می شود و معیار مناسبی است.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plotting histograms of labels\n",
    "plt.figure(figsize=(6, 3))\n",
    "plt.hist(labels, bins=10,color='Purple',align='mid')\n",
    "plt.title('Histogram of Labels')\n",
    "plt.ylabel('Repeats')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=\"rtl\">\n",
    "همانند بخش قبلی در این بخش نیز با 5 جفت یافت شده نمی توان حرف دقیقی درباره تفکیک زد و صرفا م یتوان پراکندگی آن ها را مشاهده کرد. <br>\n",
    "به دلیل آنکه بعضی از ویژگی ها باینری هستند یا به طور کل گسسته هستند ممکن است پراکندگی خوبی در آن ویژگی ها به نمایش در نیاید. <br>\n",
    "نمودار های رسم شده به صورت رندوم بین درآمد و جنسیت، سطح آموزش و سن، خودرو و درآمد، درآمد و سابقه کار شغل فعلی، خانه داشتن و سابقه کار کلی است. البته اگر ران مجدد بگیرید تغییر خواهد کرد. <br>\n",
    "با توجه به مقادیر همبستگی، کلا ویژگی ها همبستگی کمی دارند و در بین جفت ویژگی های گفته شده، خودرو و درامد کمترین ضریب همبستگی را دارند که اصلا منطقی نیست و توقع برعکس می رفت. بقیه جفت ها نیز ضریب همبستگی در حدود 0.02 دارند که کم است. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between Income and MF = 0.023926623482023637\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between LoE and Age = 0.023433219436316047\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between Car and Income = -0.0028611681224039345\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between Income and YoCW = -0.032044356430030105\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between Housing and YoW = 0.0254928927990373\n"
     ]
    }
   ],
   "source": [
    "## Plotting 2D scatterplots of features\n",
    "for i in range(5):\n",
    "    f1,f2 = np.random.choice(number_features, 2, replace=False)\n",
    "    class1_indexes = np.where(labels == 0)\n",
    "    class2_indexes = np.where(labels == 1)\n",
    "\n",
    "    plt.figure(figsize=(6, 3))\n",
    "    plt.scatter(data[class1_indexes][:,f1], data[class1_indexes][:,f2], color='Green', label='Accepted', alpha=0.5)\n",
    "    plt.scatter(data[class2_indexes][:,f1], data[class2_indexes][:,f2], color='Red', label='Rejected', alpha=0.5)\n",
    "    plt.title('Scatter plot of ' + feature_names[f1] + ' vs ' + feature_names[f2])\n",
    "    plt.xlabel(feature_names[f1])\n",
    "    plt.ylabel(feature_names[f2])\n",
    "    plt.legend(title='label')\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "    \n",
    "    # Calculating correlations\n",
    "    correlation = np.corrcoef(data[:,f1], data[:,f2])[0,1]\n",
    "    print('Correlation between', feature_names[f1],'and',feature_names[f2],'=',correlation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=ltr align=left>\n",
    "<font color=fffb03 size=4>\n",
    "    Part 2.4 -> T-Test <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=\"rtl\">\n",
    "ابتدا با توجه به شروط، دیتا یا لیبل را تقسیم کرده و سپس معنا دار بودن اختلاف میانگین دسته ها را با ttest_ind مشخص می کنیم. p-value نشان دهنده معنا دار بودن است اگر خیلی کوچک باشد. و t-test نشان دهنده اختلاف میانگین هاست که هر چه بزرگتر باشد، گزاره معنا دار تر است و بزرگ یا کوچکتر بودن آن شرط دوم گزاره را مشخص می کند. <br>\n",
    "در گزاره اول برا اساس سطح آموزشی تقسیم بندی می کنیم و تست را بر روی برچسب ها که نشان دهنده اعتبار هستند اعمال می کنیم. میبینیم با توجه به p-value خیلی کوچک و t-test بزرگتر از 0 می توانیم تایید کنیم که آموزش سطح بالاتر با پذیرفته شدن رابطه ای معنادار دارد. <br> \n",
    "در گزاره دوم و سوم مشاهده می کنیم که اختلاف معناداری وجود ندارد پس گزاره ها صحیح نیست. <br>\n",
    "گزاره چهارم معنا دار است چون p-value خیلی کوچکی دارد اما شرط دوم را قانع نمی کند یعنی که افراد مرد بالای 50 سال اتفاقا نرخ پذیرش بیشتری از مردان زیر 30 سال دارند. این مسئله با t-test مشخص شده است. پس این گزاره نیز صحیح نیست. <br>\n",
    "گزاره پنجم نیز p-value در حد 0.3 دارد پس نمی تواند معناداری کافی را داشته باشد. این گزاره نیز درست نیست.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".افرادی که سطح آموزشی بالاتری دارند از اعتبار بهتری برخوردار هستند   ->  t-stat = 12.353710553688835    p-value = 8.10830875208501e-34\n",
      "The Phrase is correct\n",
      "\n",
      ".عموما افراد با درآمد بالاتر يا صاحب خانه هستند يا ماشين دارند   ->  t-stat = 0.41642299269833954    p-value = 0.6771460321674014\n",
      "The Phrase is not correct\n",
      "\n",
      ".افرادي كه در 5 سال اخير شغل خود را تغيير نداده اند اعتبار بالاتري دارند   ->  t-stat = -0.343499414540515    p-value = 0.7312595663353922\n",
      "The Phrase is not correct\n",
      "\n",
      ".مردان با سن بالاي 50 سال نسبت به مردان زير 30 سال نرخ پذيرش كمتري در اعتبارسنجي دارند   ->  t-stat = 4.431853249284261    p-value = 1.0196722966607323e-05\n",
      "The Phrase is not correct\n",
      "\n",
      ".مردان درآمد بيشتري از زنان دارند   ->  t-stat = 1.060390709366515    p-value = 0.28909733705971086\n",
      "The Phrase is not correct\n",
      "\n"
     ]
    }
   ],
   "source": [
    "##### Phrase 1\n",
    "#  Finding data with higher education\n",
    "h_LoE = labels[ data_prep['LoE'] > np.mean(data_prep['LoE'])]\n",
    "l_LoE = labels[ data_prep['LoE'] <= np.mean(data_prep['LoE'])]\n",
    "\n",
    "# Calculating p-value\n",
    "t_stat,p_value = ttest_ind(h_LoE, l_LoE)\n",
    "\n",
    "print('.افرادی که سطح آموزشی بالاتری دارند از اعتبار بهتری برخوردار هستند   ->  t-stat =',t_stat,'   p-value =',p_value)\n",
    "if (p_value < 0.05  and  t_stat > 0):\n",
    "    print('The Phrase is correct\\n')\n",
    "else:\n",
    "    print('The Phrase is not correct\\n')\n",
    "    \n",
    "    \n",
    "##### Phrase 2\n",
    "#  Finding data with house or car\n",
    "h_HouseCar = data_prep[ (data_prep['Housing'] == 2) | (data_prep['Car'] == 1)]\n",
    "l_HouseCar = data_prep[ (data_prep['Housing'] != 2) & (data_prep['Car'] == 0)]\n",
    "\n",
    "# Calculating p-value\n",
    "t_stat,p_value = ttest_ind(h_HouseCar['Income'], l_HouseCar['Income'])\n",
    "\n",
    "print('.عموما افراد با درآمد بالاتر يا صاحب خانه هستند يا ماشين دارند   ->  t-stat =',t_stat,'   p-value =',p_value)\n",
    "if (p_value < 0.05 and t_stat > 0):\n",
    "    print('The Phrase is correct\\n')\n",
    "else:\n",
    "    print('The Phrase is not correct\\n')\n",
    "\n",
    "\n",
    "##### Phrase 3\n",
    "#  Finding data with more stability in current job\n",
    "h_YoCW = labels[ data_prep['YoCW'] >= 5]\n",
    "l_YoCW = labels[ data_prep['YoCW'] < 5]\n",
    "\n",
    "# Calculating p-value\n",
    "t_stat,p_value = ttest_ind(h_YoCW, l_YoCW)\n",
    "\n",
    "print('.افرادي كه در 5 سال اخير شغل خود را تغيير نداده اند اعتبار بالاتري دارند   ->  t-stat =',t_stat,'   p-value =',p_value)\n",
    "if (p_value < 0.05 and t_stat > 0):\n",
    "    print('The Phrase is correct\\n')\n",
    "else:\n",
    "    print('The Phrase is not correct\\n')\n",
    "\n",
    "\n",
    "##### Phrase 4\n",
    "#  Finding data with male age seperation\n",
    "h_Age = labels[ (data_prep['MF'] == 1) & (data_prep['Age'] > 50)]\n",
    "l_Age = labels[ (data_prep['MF'] == 1) & (data_prep['Age'] < 30)]\n",
    "\n",
    "# Calculating p-value\n",
    "t_stat,p_value = ttest_ind(h_Age, l_Age)\n",
    "\n",
    "print('.مردان با سن بالاي 50 سال نسبت به مردان زير 30 سال نرخ پذيرش كمتري در اعتبارسنجي دارند   ->  t-stat =',t_stat,'   p-value =',p_value)\n",
    "if (p_value < 0.05 and t_stat < 0):\n",
    "    print('The Phrase is correct\\n')\n",
    "else:\n",
    "    print('The Phrase is not correct\\n')\n",
    "\n",
    "\n",
    "##### Phrase 5\n",
    "#  Finding data with men or women\n",
    "male_MF = data_prep[ data_prep['MF'] == 1]\n",
    "female_MF = data_prep[ data_prep['MF'] == 0]\n",
    "\n",
    "# Calculating p-value\n",
    "t_stat,p_value = ttest_ind(male_MF['Income'], female_MF['Income'])\n",
    "\n",
    "print('.مردان درآمد بيشتري از زنان دارند   ->  t-stat =',t_stat,'   p-value =',p_value)\n",
    "if (p_value < 0.05 and t_stat > 0):\n",
    "    print('The Phrase is correct\\n')\n",
    "else:\n",
    "    print('The Phrase is not correct\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=ltr align=left>\n",
    "<font color=05fffb size=6>\n",
    "    Part 3 -> Training & Testing <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=ltr align=left>\n",
    "<font color=fffb03 size=4>\n",
    "    Part 3.1 -> Searching for best model and its parameters <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=\"rtl\">\n",
    "همانطور که خواسته شده از سه روش طبقه بندی کننده Decision Tree، MLP  و C-SVM از نوع چند جمله ای استفاده می کنیم همچنین پارامتر های طبقه بندی کننده Random Forest را نیز مورد بررسی قرار می دهیم با اینکه برگرفته از درخت تصمیم است. طبقه بندی کننده ها را با پارامتر های مختلف می سازیم و ارزیابی می کنیم. در این مرحله بهترین پارامتر های هر کدام از طبقه بندی ها یافت می شود که در خروجی نیز معلوم است.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 72 candidates, totalling 288 fits\n",
      "Best parameters found for MLP:  {'activation': 'relu', 'alpha': 0.05, 'hidden_layer_sizes': (100, 50), 'learning_rate': 'constant', 'solver': 'adam'}\n",
      "Best cross-validation f1-score of MLP: 92.69\n",
      "Fitting 5 folds for each of 576 candidates, totalling 2880 fits\n",
      "Best parameters found for DT:  {'criterion': 'entropy', 'max_depth': 10, 'max_features': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'splitter': 'random'}\n",
      "Best cross-validation f1-score of DT: 93.50\n",
      "Fitting 5 folds for each of 864 candidates, totalling 4320 fits\n",
      "Best parameters found for RF:  {'criterion': 'gini', 'max_depth': 10, 'max_features': None, 'min_samples_leaf': 5, 'min_samples_split': 2, 'n_estimators': 10}\n",
      "Best cross-validation f1-score of RF: 94.47\n",
      "Best parameters found for C-SVM: C= 1 Degree= 4\n"
     ]
    }
   ],
   "source": [
    "# Splitting data for training and testing\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(data,labels,test_size=0.2,random_state=2024)\n",
    "\n",
    "##### MLP\n",
    "# Defining model \n",
    "MLP_Model = MLPClassifier(max_iter=1000,random_state=2024)\n",
    "\n",
    "# Searching for best parameters of MLP classifier model\n",
    "param_grid = {\n",
    "    'hidden_layer_sizes': [(50,),(100,),(100, 50)],\n",
    "    'activation': ['tanh','relu','logistic'],\n",
    "    'solver': ['sgd','adam'],\n",
    "    'alpha': [0.0001,0.05],\n",
    "    'learning_rate': ['constant','adaptive']\n",
    "}\n",
    "grid_search = GridSearchCV(estimator=MLP_Model,param_grid=param_grid,cv=4,n_jobs=-1,scoring='f1',verbose=2)\n",
    "grid_search.fit(X_train,Y_train)\n",
    "print(\"Best parameters found for MLP: \", grid_search.best_params_)\n",
    "print(\"Best cross-validation f1-score of MLP: {:.2f}\".format(grid_search.best_score_*100))\n",
    "\n",
    "# Performing best model on the data and testing\n",
    "MLP_Model_best = grid_search.best_estimator_\n",
    "MLP_Model_best.fit(X_train,Y_train)\n",
    "\n",
    "MLP_Y_pred = MLP_Model_best.predict(X_test)\n",
    "\n",
    "\n",
    "##### Decision Tree\n",
    "# Defining model \n",
    "DT_Model = DecisionTreeClassifier(random_state=2024)\n",
    "\n",
    "# Searching for best parameters of DT classifier model\n",
    "param_grid = {\n",
    "    'criterion': ['gini','entropy'],\n",
    "    'splitter': ['best','random'],\n",
    "    'max_depth': [None,10,20,30],\n",
    "    'min_samples_split': [2,10,20],\n",
    "    'min_samples_leaf': [1,5,10],\n",
    "    'max_features': [None,'auto','sqrt','log2']\n",
    "}\n",
    "grid_search = GridSearchCV(estimator=DT_Model,param_grid=param_grid,cv=5,n_jobs=-1,scoring='f1',verbose=2)\n",
    "grid_search.fit(X_train,Y_train)\n",
    "\n",
    "print(\"Best parameters found for DT: \",grid_search.best_params_)\n",
    "print(\"Best cross-validation f1-score of DT: {:.2f}\".format(grid_search.best_score_*100))\n",
    "\n",
    "# Performing best model on the data and testing\n",
    "DT_Model_best = grid_search.best_estimator_\n",
    "DT_Model_best.fit(X_train,Y_train)\n",
    "\n",
    "DT_Y_pred = DT_Model_best.predict(X_test)\n",
    "\n",
    "\n",
    "##### Random Forest\n",
    "# Defining model \n",
    "RF_Model = RandomForestClassifier(random_state=2024)\n",
    "\n",
    "# Searching for best parameters of RF classifier model\n",
    "param_grid = {\n",
    "    'n_estimators': [10,30,50],\n",
    "    'criterion': ['gini','entropy'],\n",
    "    'max_depth': [None, 10,20,30],\n",
    "    'min_samples_split': [2,10,20],\n",
    "    'min_samples_leaf': [1,5,10],\n",
    "    'max_features': [None,'auto','sqrt','log2']\n",
    "}\n",
    "grid_search = GridSearchCV(estimator=RF_Model,param_grid=param_grid,cv=5,n_jobs=-1,scoring='f1',verbose=2)\n",
    "grid_search.fit(X_train,Y_train)\n",
    "\n",
    "print(\"Best parameters found for RF: \",grid_search.best_params_)\n",
    "print(\"Best cross-validation f1-score of RF: {:.2f}\".format(grid_search.best_score_*100))\n",
    "\n",
    "# Performing best model on the data and testing\n",
    "RF_Model_best = grid_search.best_estimator_\n",
    "RF_Model_best.fit(X_train,Y_train)\n",
    "\n",
    "RF_Y_pred = RF_Model_best.predict(X_test)\n",
    "\n",
    "\n",
    "##### C-SVM (POLY)  \n",
    "# Defining degrees for range of 1 to 4   &    Using 11 different amounts for C\n",
    "degree = [1, 2, 3, 4]\n",
    "C = [0.05, 0.1, 0.5, 1, 5, 10, 20]\n",
    "f1_max = 0\n",
    "C_max = 0\n",
    "gamma_max = 0\n",
    "for j in degree:\n",
    "    for i in C:\n",
    "        # Model fitting\n",
    "        SVM_Model = SVC(kernel='poly',C=i,degree=j)\n",
    "        SVM_Model.fit(X_train,Y_train)\n",
    "\n",
    "        # Validation\n",
    "        SVM_Y_pred = SVM_Model.predict(X_test)\n",
    "        SVM_f1 = f1_score(Y_test,SVM_Y_pred)\n",
    "        \n",
    "        if SVM_f1 > f1_max:\n",
    "            f1_max = SVM_f1\n",
    "            C_max = i\n",
    "            degree_max = j\n",
    "print(\"Best parameters found for C-SVM: C=\",C_max,'Degree=',degree_max)\n",
    "\n",
    "# Performing best model on the data and testing\n",
    "SVM_Model_best = SVC(kernel='poly', C=C_max, degree=degree_max)\n",
    "SVM_Model_best.fit(X_train,Y_train)\n",
    "\n",
    "SVM_Y_pred = SVM_Model_best.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=\"rtl\">\n",
    "در بخش قبل 80 درصد داده را برای آموزش و 20 درصد را برای ارزیابی استفاده می کردیم و بهترین طبقه بند های هر سه گروه بدست آمد. در این جا معیار های ارزیابی را با توجه به داده تست داده شده، برای هر کدام به علاوه Logistic Regression بدست می آوریم تا بالاخره مدل طبقه بند مطلوب را انتخاب کنیم. <br>\n",
    "مشاهده می کنیم که فرق چندان جدی ای وجود ندارد اما به هر حال به خصوص با معیار هایی که برای داده های نا متعادل مناسب هستند، طبقه بندی کننده های Random Forest که برگرفته از درخت تصمیم است و شبکه عصبی مصنوعی بهتر هستند. با توجه به آنکه محدودیت زمان بندی 200 میلی ثانیه ای گذاشته شده است، RF را با پارامتر های بهینه به عنوان طبقه بند مناسب بر میگزینیم.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP :\n",
      "Accuracy -> 86.25954198473282\n",
      "Precision -> 86.24641833810888\n",
      "Recall -> 98.0456026058632\n",
      "F1 Score -> 91.76829268292683\n",
      "AUPRC -> 86.08753814282447\n",
      "\n",
      "DT :\n",
      "Accuracy -> 85.75063613231552\n",
      "Precision -> 87.91540785498489\n",
      "Recall -> 94.78827361563518\n",
      "F1 Score -> 91.22257053291534\n",
      "AUPRC -> 87.40474416722313\n",
      "\n",
      "RF :\n",
      "Accuracy -> 86.00508905852418\n",
      "Precision -> 86.62790697674419\n",
      "Recall -> 97.06840390879479\n",
      "F1 Score -> 91.55145929339477\n",
      "AUPRC -> 86.37840297779893\n",
      "\n",
      "C-SVM :\n",
      "Accuracy -> 84.47837150127226\n",
      "Precision -> 83.97790055248619\n",
      "Recall -> 99.0228013029316\n",
      "F1 Score -> 90.88191330343797\n",
      "AUPRC -> 83.92062838108785\n",
      "\n",
      "Logistic Regression :\n",
      "Accuracy -> 82.95165394402035\n",
      "Precision -> 83.14917127071824\n",
      "Recall -> 98.0456026058632\n",
      "F1 Score -> 89.98505231689087\n",
      "AUPRC -> 83.05082359140887\n"
     ]
    }
   ],
   "source": [
    "### Finding best model\n",
    "## MLP\n",
    "accuracie = accuracy_score(Y_test,MLP_Y_pred)\n",
    "precision = precision_score(Y_test,MLP_Y_pred)\n",
    "recall = recall_score(Y_test,MLP_Y_pred)\n",
    "f1score = f1_score(Y_test,MLP_Y_pred)\n",
    "auprc = average_precision_score(Y_test,MLP_Y_pred)\n",
    "print('MLP :')\n",
    "print('Accuracy ->',accuracie*100)\n",
    "print('Precision ->',precision*100)\n",
    "print('Recall ->',recall*100)\n",
    "print('F1 Score ->',f1score*100)\n",
    "print('AUPRC ->',auprc*100)\n",
    "\n",
    "## DT\n",
    "accuracie = accuracy_score(Y_test,DT_Y_pred)\n",
    "precision = precision_score(Y_test,DT_Y_pred)\n",
    "recall = recall_score(Y_test,DT_Y_pred)\n",
    "f1score = f1_score(Y_test,DT_Y_pred)\n",
    "auprc = average_precision_score(Y_test,DT_Y_pred)\n",
    "print('\\nDT :')\n",
    "print('Accuracy ->',accuracie*100)\n",
    "print('Precision ->',precision*100)\n",
    "print('Recall ->',recall*100)\n",
    "print('F1 Score ->',f1score*100)\n",
    "print('AUPRC ->',auprc*100)\n",
    "\n",
    "## RF\n",
    "accuracie = accuracy_score(Y_test,RF_Y_pred)\n",
    "precision = precision_score(Y_test,RF_Y_pred)\n",
    "recall = recall_score(Y_test,RF_Y_pred)\n",
    "f1score = f1_score(Y_test,RF_Y_pred)\n",
    "auprc = average_precision_score(Y_test,RF_Y_pred)\n",
    "print('\\nRF :')\n",
    "print('Accuracy ->',accuracie*100)\n",
    "print('Precision ->',precision*100)\n",
    "print('Recall ->',recall*100)\n",
    "print('F1 Score ->',f1score*100)\n",
    "print('AUPRC ->',auprc*100)\n",
    "\n",
    "## C-SVM\n",
    "accuracie = accuracy_score(Y_test,SVM_Y_pred)\n",
    "precision = precision_score(Y_test,SVM_Y_pred)\n",
    "recall = recall_score(Y_test,SVM_Y_pred)\n",
    "f1score = f1_score(Y_test,SVM_Y_pred)\n",
    "auprc = average_precision_score(Y_test,SVM_Y_pred)\n",
    "print('\\nC-SVM :')\n",
    "print('Accuracy ->',accuracie*100)\n",
    "print('Precision ->',precision*100)\n",
    "print('Recall ->',recall*100)\n",
    "print('F1 Score ->',f1score*100)\n",
    "print('AUPRC ->',auprc*100)\n",
    "\n",
    "## LR\n",
    "# Model fitting\n",
    "LR_Model = LogisticRegression()\n",
    "LR_Model.fit(X_train,Y_train)\n",
    "# Predicting\n",
    "LR_Y_pred = LR_Model.predict(X_test)\n",
    "\n",
    "accuracie = accuracy_score(Y_test,LR_Y_pred)\n",
    "precision = precision_score(Y_test,LR_Y_pred)\n",
    "recall = recall_score(Y_test,LR_Y_pred)\n",
    "f1score = f1_score(Y_test,LR_Y_pred)\n",
    "auprc = average_precision_score(Y_test,LR_Y_pred)\n",
    "print('\\nLogistic Regression :')\n",
    "print('Accuracy ->',accuracie*100)\n",
    "print('Precision ->',precision*100)\n",
    "print('Recall ->',recall*100)\n",
    "print('F1 Score ->',f1score*100)\n",
    "print('AUPRC ->',auprc*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=ltr align=left>\n",
    "<font color=fffb03 size=4>\n",
    "    Part 3.2 -> Training & Testing <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=\"rtl\">\n",
    "در این مرحله 5 بار داده ها را به آموزش و تست تقسیم کرده، مدل را آموزش داده و از آن تست می گیریم. معیار های ارزیابی را هر 5 بار حساب کرده و در نهایت میانگین ها را گزارش می دهیم. همچنین ماتریس کانفیوژن آخرین طبقه بندی نیز رسم می شود. از بهترین مدل که Random Forest با پارامتر های تعیین شده در بخش قبل استفاده می کنیم. <br>\n",
    "مشاهده می شود که F-score در حدود 94 و AUPRC در حدود 90 درصد است که نشان دهنده انجام مناسب طبقه بندی است.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Time 1 = 171.70405387878418 ms\n",
      "Train Time 2 = 103.10888290405273 ms\n",
      "Train Time 3 = 137.62307167053223 ms\n",
      "Train Time 4 = 120.39327621459961 ms\n",
      "Train Time 5 = 126.47747993469238 ms\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy -> mean = 90.02544529262086 variance = 8.65010456526101e-05\n",
      "Precision -> mean = 89.90147431317789 variance = 0.0001556490803427168\n",
      "Recall -> mean = 98.80835145709128 variance = 3.761569456146073e-05\n",
      "F1 Score -> mean = 94.13714268611753 variance = 3.431896405421917e-05\n",
      "AUPRC -> mean = 89.79302466137258 variance= 0.00014938768422021804\n"
     ]
    }
   ],
   "source": [
    "## Training & Validating\n",
    "accuracies = np.zeros(5)\n",
    "precisions = np.zeros(5)\n",
    "recalls = np.zeros(5)\n",
    "f1scores = np.zeros(5)\n",
    "auprcs = np.zeros(5)\n",
    "\n",
    "for i in range(5):\n",
    "    # Splitting data for training and testing\n",
    "    X_train, X_test, Y_train, Y_test = train_test_split(data,labels,test_size=0.2)\n",
    "    \n",
    "    # Training model and calculating time\n",
    "    RF_Model_best = grid_search.best_estimator_\n",
    "    model = RF_Model_best\n",
    "    start = time.time()\n",
    "    model.fit(X_train,Y_train)\n",
    "    train_time = time.time() - start\n",
    "    print('Train Time',i+1,'=',train_time*1000,'ms')\n",
    "    \n",
    "    # Testing model\n",
    "    Y_pred = model.predict(X_test)\n",
    "    \n",
    "    # Meterics\n",
    "    accuracies[i] = accuracy_score(Y_test,Y_pred)\n",
    "    precisions[i] = precision_score(Y_test,Y_pred)\n",
    "    recalls[i] = recall_score(Y_test,Y_pred)\n",
    "    f1scores[i] = f1_score(Y_test,Y_pred)\n",
    "    auprcs[i] = average_precision_score(Y_test,Y_pred)\n",
    "\n",
    "## Confusion matrix\n",
    "conf_matrix = confusion_matrix(Y_test, Y_pred)\n",
    "plt.figure(figsize=(4, 3))\n",
    "plt.matshow(conf_matrix, cmap='Blues', fignum=1)\n",
    "plt.title('Confusion Matrix', pad=20)\n",
    "plt.xlabel('Predicted (Reject=0 & Accept=1)')\n",
    "plt.ylabel('True (Reject=0 & Accept=1)')\n",
    "for (i, j), val in np.ndenumerate(conf_matrix):\n",
    "    plt.text(j,i,f'{val}',ha='center',va='center',color='red')\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "\n",
    "## Printing meterics   \n",
    "print('\\nAccuracy -> mean =',np.mean(accuracies)*100,'variance =',np.var(accuracies))\n",
    "print('Precision -> mean =',np.mean(precisions)*100,'variance =',np.var(precisions))\n",
    "print('Recall -> mean =',np.mean(recalls)*100,'variance =',np.var(recalls))\n",
    "print('F1 Score -> mean =',np.mean(f1scores)*100,'variance =',np.var(f1scores))\n",
    "print('AUPRC -> mean =',np.mean(auprcs)*100,'variance=',np.var(auprcs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=ltr align=left>\n",
    "<font color=fffb03 size=4>\n",
    "    Part 3.3 -> Testing test data <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=\"rtl\">\n",
    "حال داده تست داده شده را به مدل ترین شده در مرحله قبل می دهیم و خروجی آن را برچسب هایی را که میزند در یک فایل تکست ذخیره می کنیم.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "## Reading test data\n",
    "data_test_raw = pd.read_csv('test_data_1.csv')\n",
    "\n",
    "## Preprocessing\n",
    "# Removing outliers\n",
    "number_cols = data_test_raw.select_dtypes(include=['int64']).columns\n",
    "data_prep_test = data_test_raw\n",
    "for i in number_cols:\n",
    "    mean_col = np.mean(data_test_raw[i])\n",
    "    std_col = np.std(data_test_raw[i])\n",
    "    l_bound = mean_col - 2*std_col\n",
    "    h_bound = mean_col + 2*std_col\n",
    "    data_prep_test = data_test_raw[(data_test_raw[i] >= l_bound) & (data_test_raw[i] <= h_bound)]\n",
    "    \n",
    "# Removing NANs\n",
    "data_prep_test = data_prep_test.dropna()\n",
    "\n",
    "# Changing object columns to numerical\n",
    "data_prep_test['MF'] = data_prep_test['MF'].map({'M':1 ,'F':0})\n",
    "data_prep_test['LoE'] = data_prep_test['LoE'].map({'Dip':0 ,'Ad. Dip':1 ,'Bach':2 ,'Mst':3 ,'Doct':4 ,'P. Doct':5})\n",
    "data_prep_test['Housing'] = data_prep_test['Housing'].map({'O':2 ,'R':1 ,'N':0})\n",
    "data_prep_test['Car'] = data_prep_test['Car'].map({True:1 ,False:0})\n",
    "\n",
    "#Normalization (z-score)\n",
    "scaler = MinMaxScaler()\n",
    "data_test = scaler.fit_transform(data_prep_test)\n",
    "\n",
    "\n",
    "## Predicting\n",
    "Y_pred_test = model.predict(data_test)\n",
    "with open('predicted_labels.txt', 'w') as f:\n",
    "    for i in range(np.size(Y_pred_test)):\n",
    "        print(Y_pred_test[i])\n",
    "        f.write(str(Y_pred_test[i]))\n",
    "        f.write('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=ltr align=left>\n",
    "<font color=05fffb size=6>\n",
    "    Part 4 -> Model simplification <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=\"rtl\">\n",
    "برای این بخس ابتدا همبستگی تک تک ویژگی ها را با برچسب ها می سنجیم و به ترتیب ویژگی های با کمترین همبستگی را حذف می کنیم و هر بار AUPRC را مورد محاسبه قرار می دهیم تا ببینیم پایینتر از 80 درصد آمده است یا خیر. مشاهده می کنیم که حتی اگر تمام فیچر ها حذف شوند و صرفا سطح آموزشی (LoE) باقی بماند، باز هم AUPRC بالای 80 درصد است. <br>\n",
    "با تغییر آستانه به حدود 88 درصد، تعداد ویژگی های حذف شده کم تر شده و به 1 یا 3 یا بیشتر می رسد.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation of feature Id with labels is  [-0.01976041]\n",
      "Correlation of feature Age with labels is  [0.05081657]\n",
      "Correlation of feature MF with labels is  [0.0197225]\n",
      "Correlation of feature LoE with labels is  [0.35473572]\n",
      "Correlation of feature YoW with labels is  [-0.02246378]\n",
      "Correlation of feature YoCW with labels is  [-0.01286578]\n",
      "Correlation of feature Income with labels is  [0.21124357]\n",
      "Correlation of feature Housing with labels is  [-0.02565218]\n",
      "Correlation of feature Car with labels is  [0.03033649]\n",
      "\n",
      "Deleted features are : ['YoCW', 'MF', 'Id', 'YoW', 'Housing', 'Car', 'Age', 'Income']\n",
      "\n",
      "Accuracy = 79.89821882951654\n",
      "Precision = 81.81818181818183\n",
      "Recall = 94.25675675675676\n",
      "F1 Score = 87.59811616954474\n",
      "AUPRC = 81.4448643647117\n"
     ]
    }
   ],
   "source": [
    "# Calculating correlations between features and labels\n",
    "L = np.size(data,1)\n",
    "correlations = np.zeros((L,1))\n",
    "for i in range(L):\n",
    "    correlations[i] = (np.corrcoef(data[:,i],labels)[0,1])\n",
    "    print('Correlation of feature ' + feature_names[i] + ' with labels is ',correlations[i])\n",
    "\n",
    "# Deleting least correlated features\n",
    "auprc_reduction = 100\n",
    "counter = 1\n",
    "data_reduced = data\n",
    "deleted_features = []\n",
    "names = feature_names\n",
    "while auprc_reduction > 80 and counter < L :\n",
    "    counter += 1\n",
    "    \n",
    "    # Reducing feature\n",
    "    feature_min_corr = np.argmin(np.abs(correlations))\n",
    "    deleted_features.append(names[feature_min_corr])\n",
    "    correlations = np.delete(correlations,feature_min_corr,axis=0)\n",
    "    names = np.delete(names,feature_min_corr,axis=0)\n",
    "    data_reduced = np.delete(data_reduced,feature_min_corr,axis=1)\n",
    "    \n",
    "    # Splitting data for training and testing\n",
    "    X_train, X_test, Y_train, Y_test = train_test_split(data_reduced,labels,test_size=0.2)\n",
    "    \n",
    "    # Training model and calculating time\n",
    "    RF_Model_best = grid_search.best_estimator_\n",
    "    model = RF_Model_best\n",
    "    model.fit(X_train,Y_train)\n",
    "    \n",
    "    # Testing model\n",
    "    Y_pred = model.predict(X_test)\n",
    "    \n",
    "    # Updating AUPRC\n",
    "    auprc_reduction = average_precision_score(Y_test,Y_pred)*100\n",
    "   \n",
    "print('\\nDeleted features are :', deleted_features)\n",
    "\n",
    "# Meterics\n",
    "accuracie = accuracy_score(Y_test,Y_pred)\n",
    "precision = precision_score(Y_test,Y_pred)\n",
    "recall = recall_score(Y_test,Y_pred)\n",
    "f1score = f1_score(Y_test,Y_pred)\n",
    "auprc = average_precision_score(Y_test,Y_pred)\n",
    "\n",
    "# Printing meterics   \n",
    "print('\\nAccuracy =',accuracie*100)\n",
    "print('Precision =',precision*100)\n",
    "print('Recall =',recall*100)\n",
    "print('F1 Score =',f1score*100)\n",
    "print('AUPRC =',auprc*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=\"rtl\">\n",
    "در این بخش ابتدا میانگین داده را 0 می کنیم تا بتوانیم PCA را بر روی آن اعمال کنیم. پس از اعمال، تا زمانی که AUPRC به کوچکتر از 80 درصد نرسیده یا تعداد کامپوننت نگه داشته شده توسط PCA به یک نرسیده ادامه می دهد و کاهش بعد می کند. <br>\n",
    "مشاهده می کنیم که همانند بخش قبلی این بار هم از 9 بعد ابتدایی، 8 بعد با واریانس کمتر حذف می شوند و داده با یک بعد با بیشترین واریانس نیز AUPCR بالای 80 درصد را حفظ می کند. پس 1 ویژگی کافی است و اینطور است که مدل ساده شده و حجم محاسبات نیز کاهش می یابد. <br>\n",
    "با تغییر آستانه به حدود 88 درصد، تعداد کامپوننت های حذف شده کم تر شده و به 1 الی 5 یا بیشتر می رسد.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of reduced dimensions : 8\n",
      "\n",
      "Accuracy = 77.09923664122137\n",
      "Precision = 81.04956268221575\n",
      "Recall = 91.74917491749174\n",
      "F1 Score = 86.06811145510835\n",
      "AUPRC = 80.72362819038452\n"
     ]
    }
   ],
   "source": [
    "# Performing PCA on data\n",
    "L = np.size(data,1)\n",
    "data_mean = np.mean(data,0)\n",
    "data_meanless = data - data_mean \n",
    "pca = PCA()\n",
    "pca.fit(data_meanless)\n",
    "\n",
    "# Reducing least varianced dimensions with PCA\n",
    "auprc_reduction = 100\n",
    "number_components = np.size(data,1) + 1\n",
    "while auprc_reduction > 80 and number_components > 1 :\n",
    "    number_components -= 1\n",
    "    \n",
    "    # Reducing dimension\n",
    "    data_PCA = np.dot(pca.transform(data_meanless)[:,0:number_components], pca.components_[0:number_components,:])\n",
    "    data_PCA = data_PCA + data_mean\n",
    "    \n",
    "    # Splitting data for training and testing\n",
    "    X_train, X_test, Y_train, Y_test = train_test_split(data_PCA,labels,test_size=0.2)\n",
    "    \n",
    "    # Training model and calculating time\n",
    "    RF_Model_best = grid_search.best_estimator_\n",
    "    model = RF_Model_best\n",
    "    model.fit(X_train,Y_train)\n",
    "    \n",
    "    # Testing model\n",
    "    Y_pred = model.predict(X_test)\n",
    "    \n",
    "    # Updating AUPRC\n",
    "    auprc_reduction = average_precision_score(Y_test,Y_pred)*100\n",
    "   \n",
    "print('Number of reduced dimensions :', L-number_components)\n",
    "\n",
    "# Meterics\n",
    "accuracie = accuracy_score(Y_test,Y_pred)\n",
    "precision = precision_score(Y_test,Y_pred)\n",
    "recall = recall_score(Y_test,Y_pred)\n",
    "f1score = f1_score(Y_test,Y_pred)\n",
    "auprc = average_precision_score(Y_test,Y_pred)\n",
    "\n",
    "# Printing meterics   \n",
    "print('\\nAccuracy =',accuracie*100)\n",
    "print('Precision =',precision*100)\n",
    "print('Recall =',recall*100)\n",
    "print('F1 Score =',f1score*100)\n",
    "print('AUPRC =',auprc*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=ltr align=left>\n",
    "<font color=05fffb size=6>\n",
    "    Part 6 -> Learning using ensemble <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=ltr align=left>\n",
    "<font color=fffb03 size=4>\n",
    "    Part 6.1 -> Data partition method <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=\"rtl\">\n",
    "در این قسمت مدل درخت تصمیم را انتخاب می کنیم و با تعداد های 5، 10 و 20 به هر دو صورت رای گیری نرم و سخت، مدل ترکیبی را آموزش داده و تست می گیریم تا بهترین تعداد و بهترین شیوه بدست آید. در این بخش تقسیم بندی بر روی خود داده انجام شده است و نه ویژگی ها. <br>\n",
    "5 بار آموزش و ارزیابی را تکرار می کنیم و معیار های میانگین را بدست می آوریم.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Ensemble of 5 weak learners with hard voting (data splitting) :\n",
      "Accuracy -> mean = 85.3944020356234 variance = 0.0002605390776243283\n",
      "Precision -> mean = 91.55799958701033 variance = 7.127061535344667e-05\n",
      "Recall -> mean = 90.09000700336485 variance = 0.0005446972585858604\n",
      "F1 Score -> mean = 90.79742155037114 variance = 0.0001232614488134557\n",
      "AUPRC -> mean = 90.41701292554443 variance= 5.216152642870208e-05\n",
      "\n",
      "Ensemble of 5 weak learners with soft voting (data splitting) :\n",
      "Accuracy -> mean = 85.19083969465649 variance = 0.000164196595639985\n",
      "Precision -> mean = 91.5884463478183 variance = 6.468292900513068e-05\n",
      "Recall -> mean = 89.78044284561051 variance = 0.0003762892164146761\n",
      "F1 Score -> mean = 90.65874584768896 variance = 7.468405606412722e-05\n",
      "AUPRC -> mean = 90.41404213294196 variance= 4.371656655550438e-05\n",
      "\n",
      "Ensemble of 10 weak learners with hard voting (data splitting) :\n",
      "Accuracy -> mean = 85.80152671755725 variance = 3.988371566018562e-05\n",
      "Precision -> mean = 91.50329444816097 variance = 6.878449325087544e-05\n",
      "Recall -> mean = 90.6174085824258 variance = 0.00012793275303018983\n",
      "F1 Score -> mean = 91.04992975539605 variance = 2.389837307134321e-05\n",
      "AUPRC -> mean = 90.39366379243468 variance= 5.428519192733445e-05\n",
      "\n",
      "Ensemble of 10 weak learners with soft voting (data splitting) :\n",
      "Accuracy -> mean = 85.85241730279898 variance = 9.996827431708848e-05\n",
      "Precision -> mean = 91.39857467068501 variance = 8.11585626593374e-05\n",
      "Recall -> mean = 90.8062709186946 variance = 0.00015287013456487562\n",
      "F1 Score -> mean = 91.09415846188092 variance = 5.175971681258948e-05\n",
      "AUPRC -> mean = 90.32236295413657 variance= 7.468861954824898e-05\n",
      "\n",
      "Ensemble of 20 weak learners with hard voting (data splitting) :\n",
      "Accuracy -> mean = 84.7328244274809 variance = 0.0001294925833122907\n",
      "Precision -> mean = 91.76776755550658 variance = 0.00017568979337409546\n",
      "Recall -> mean = 88.86958364973839 variance = 0.00014161436552186067\n",
      "F1 Score -> mean = 90.28464369847958 variance = 6.138472638217264e-05\n",
      "AUPRC -> mean = 90.45587470996203 variance= 0.0001848770604207354\n",
      "\n",
      "Ensemble of 20 weak learners with soft voting (data splitting) :\n",
      "Accuracy -> mean = 85.19083969465647 variance = 0.00013570822731128065\n",
      "Precision -> mean = 91.87288837516408 variance = 0.0002128595600809151\n",
      "Recall -> mean = 89.37121026797936 variance = 0.00010279941743482403\n",
      "F1 Score -> mean = 90.59501012462515 variance = 6.777102399804776e-05\n",
      "AUPRC -> mean = 90.60460157495488 variance= 0.00022078378264826526\n"
     ]
    }
   ],
   "source": [
    "# Training and testing for different numbers of weak learners\n",
    "leaneres_number = [5,10,20]\n",
    "for i in range(len(leaneres_number)):\n",
    "    auprc_max = 0\n",
    "    accuracies_hard = np.zeros(5)\n",
    "    precisions_hard = np.zeros(5)\n",
    "    recalls_hard = np.zeros(5)\n",
    "    f1scores_hard = np.zeros(5)\n",
    "    auprcs_hard = np.zeros(5)\n",
    "    \n",
    "    accuracies_soft = np.zeros(5)\n",
    "    precisions_soft = np.zeros(5)\n",
    "    recalls_soft = np.zeros(5)\n",
    "    f1scores_soft = np.zeros(5)\n",
    "    auprcs_soft = np.zeros(5)\n",
    "    \n",
    "    # 5 iterations\n",
    "    for t in range(5):\n",
    "        # Splitting data for training and testing\n",
    "        X_train, X_test, Y_train, Y_test = train_test_split(data,labels,test_size=0.2)\n",
    "\n",
    "        # Splitting data to subsets\n",
    "        number_learner = leaneres_number[i]\n",
    "        X_splitted = np.array_split(X_train,number_learner)\n",
    "        Y_splitted = np.array_split(Y_train,number_learner)\n",
    "        \n",
    "        # Defining learners\n",
    "        models = []\n",
    "        for j in range(number_learner):\n",
    "            model = DecisionTreeClassifier()\n",
    "            model.fit(X_splitted[j],Y_splitted[j])\n",
    "            models.append(('DT model ' + str(j), model))\n",
    "        \n",
    "        # Defining ensemble of learners and fitting it\n",
    "        model_ensemble_hard = VotingClassifier(models,voting='hard')\n",
    "        model_ensemble_soft = VotingClassifier(models,voting='soft')\n",
    "        model_ensemble_hard.fit(X_train,Y_train)\n",
    "        model_ensemble_soft.fit(X_train,Y_train)\n",
    "        \n",
    "        # Predicting\n",
    "        Ensemble_Y_pred_hard = model_ensemble_hard.predict(X_test)\n",
    "        Ensemble_Y_pred_soft = model_ensemble_soft.predict(X_test)\n",
    "        \n",
    "        # Meterics\n",
    "        accuracies_hard[t] = accuracy_score(Y_test,Ensemble_Y_pred_hard)\n",
    "        precisions_hard[t] = precision_score(Y_test,Ensemble_Y_pred_hard)\n",
    "        recalls_hard[t] = recall_score(Y_test,Ensemble_Y_pred_hard)\n",
    "        f1scores_hard[t] = f1_score(Y_test,Ensemble_Y_pred_hard)\n",
    "        auprcs_hard[t] = average_precision_score(Y_test,Ensemble_Y_pred_hard)\n",
    "        \n",
    "        accuracies_soft[t] = accuracy_score(Y_test,Ensemble_Y_pred_soft)\n",
    "        precisions_soft[t] = precision_score(Y_test,Ensemble_Y_pred_soft)\n",
    "        recalls_soft[t] = recall_score(Y_test,Ensemble_Y_pred_soft)\n",
    "        f1scores_soft[t] = f1_score(Y_test,Ensemble_Y_pred_soft)\n",
    "        auprcs_soft[t] = average_precision_score(Y_test,Ensemble_Y_pred_soft)\n",
    "        \n",
    "    print('\\nEnsemble of',number_learner,'weak learners with hard voting (data splitting) :')\n",
    "    print('Accuracy -> mean =',np.mean(accuracies_hard)*100,'variance =',np.var(accuracies_hard))\n",
    "    print('Precision -> mean =',np.mean(precisions_hard)*100,'variance =',np.var(precisions_hard))\n",
    "    print('Recall -> mean =',np.mean(recalls_hard)*100,'variance =',np.var(recalls_hard))\n",
    "    print('F1 Score -> mean =',np.mean(f1scores_hard)*100,'variance =',np.var(f1scores_hard))\n",
    "    print('AUPRC -> mean =',np.mean(auprcs_hard)*100,'variance=',np.var(auprcs_hard))\n",
    "    if np.mean(auprcs_hard)*100 >= auprc_max:\n",
    "        auprc_max = np.mean(auprcs_hard)*100\n",
    "        hard_or_soft = 1\n",
    "        best_number = number_learner\n",
    "    \n",
    "    print('\\nEnsemble of',number_learner,'weak learners with soft voting (data splitting) :')\n",
    "    print('Accuracy -> mean =',np.mean(accuracies_soft)*100,'variance =',np.var(accuracies_soft))\n",
    "    print('Precision -> mean =',np.mean(precisions_soft)*100,'variance =',np.var(precisions_soft))\n",
    "    print('Recall -> mean =',np.mean(recalls_soft)*100,'variance =',np.var(recalls_soft))\n",
    "    print('F1 Score -> mean =',np.mean(f1scores_soft)*100,'variance =',np.var(f1scores_soft))\n",
    "    print('AUPRC -> mean =',np.mean(auprcs_soft)*100,'variance=',np.var(auprcs_soft))\n",
    "    if np.mean(auprcs_hard)*100 >= auprc_max:\n",
    "        auprc_max = np.mean(auprcs_soft)*100\n",
    "        hard_or_soft = 2\n",
    "        best_number = number_learner\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=\"rtl\">\n",
    "بهترین مدل با استفاده از بخش قبل را بر روی داده آموزش داده و تست می کنیم. می بینیم که معیار ها بسیار پیشرفت کرده اند.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best number of weak learners is 20 with soft voting\n",
      "\n",
      "Best ensemble :\n",
      "Accuracy -> 96.69211195928753\n",
      "Precision -> 97.80564263322884\n",
      "Recall -> 98.11320754716981\n",
      "F1 Score -> 97.95918367346938\n",
      "AUPRC -> 97.48697070683492\n"
     ]
    }
   ],
   "source": [
    "# Best ensemble model\n",
    "if hard_or_soft == 1:\n",
    "    print('Best number of weak learners is',best_number,'with hard voting')\n",
    "elif hard_or_soft == 2:\n",
    "    print('Best number of weak learners is',best_number,'with soft voting')\n",
    "    \n",
    "# Splitting data to subsets\n",
    "number_learner = leaneres_number[i]\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(data,labels,test_size=0.2)\n",
    "X_splitted = np.array_split(X_train,number_learner)\n",
    "Y_splitted = np.array_split(Y_train,number_learner)\n",
    "\n",
    "# Defining learners\n",
    "models = []\n",
    "for j in range(best_number):\n",
    "    model = DecisionTreeClassifier()\n",
    "    model.fit(X_splitted[j],Y_splitted[j])\n",
    "    models.append(('DT model ' + str(j), model))\n",
    "\n",
    "# Defining ensemble of learners and fitting it\n",
    "if hard_or_soft == 1:\n",
    "    model_ensemble = VotingClassifier(models,voting='hard')\n",
    "elif hard_or_soft == 2:\n",
    "    model_ensemble = VotingClassifier(models,voting='soft')\n",
    "model_ensemble.fit(X_train,Y_train)\n",
    "\n",
    "# Predicting\n",
    "Ensemble_Y_pred = model_ensemble_hard.predict(X_test)\n",
    "\n",
    "# Meterics\n",
    "accuracie = accuracy_score(Y_test,Ensemble_Y_pred)\n",
    "precision = precision_score(Y_test,Ensemble_Y_pred)\n",
    "recall = recall_score(Y_test,Ensemble_Y_pred)\n",
    "f1score = f1_score(Y_test,Ensemble_Y_pred)\n",
    "auprc = average_precision_score(Y_test,Ensemble_Y_pred)\n",
    "print('\\nBest ensemble :')\n",
    "print('Accuracy ->',accuracie*100)\n",
    "print('Precision ->',precision*100)\n",
    "print('Recall ->',recall*100)\n",
    "print('F1 Score ->',f1score*100)\n",
    "print('AUPRC ->',auprc*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=ltr align=left>\n",
    "<font color=fffb03 size=4>\n",
    "    Part 6.2 -> Features partition method <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=\"rtl\">\n",
    "این بخش همانند قبل است منتها تقسیم بندی بر روی ویژگی ها انجام می شود و نه سطر ها.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Ensemble of 2 weak learners with hard voting (data splitting) :\n",
      "Accuracy -> mean = 85.13994910941476 variance = 0.00012327693931330096\n",
      "Precision -> mean = 91.0594307661425 variance = 0.00020108523126046774\n",
      "Recall -> mean = 90.0200667644157 variance = 0.00033500854330974016\n",
      "F1 Score -> mean = 90.51468226361443 variance = 7.03238723625175e-05\n",
      "AUPRC -> mean = 89.84676337146193 variance= 0.00018598952547302461\n",
      "\n",
      "Ensemble of 2 weak learners with soft voting (data splitting) :\n",
      "Accuracy -> mean = 85.08905852417303 variance = 8.701901598585964e-05\n",
      "Precision -> mean = 91.16804379511278 variance = 0.00014205026093531295\n",
      "Recall -> mean = 89.84237096204701 variance = 0.00042124736745951483\n",
      "F1 Score -> mean = 90.47370839900827 variance = 4.2100595845599945e-05\n",
      "AUPRC -> mean = 89.92828495403764 variance= 0.00011307964809609508\n",
      "\n",
      "Ensemble of 3 weak learners with hard voting (data splitting) :\n",
      "Accuracy -> mean = 83.96946564885496 variance = 2.848836832870414e-05\n",
      "Precision -> mean = 90.75169519958811 variance = 0.00011569472402582132\n",
      "Recall -> mean = 88.82093434391264 variance = 0.00019817225218739346\n",
      "F1 Score -> mean = 89.76125945844466 variance = 2.6205168026690662e-05\n",
      "AUPRC -> mean = 89.45087315877956 variance= 9.929236931275937e-05\n",
      "\n",
      "Ensemble of 3 weak learners with soft voting (data splitting) :\n",
      "Accuracy -> mean = 83.61323155216284 variance = 4.8171240992171846e-05\n",
      "Precision -> mean = 90.64571358753442 variance = 9.149580391796005e-05\n",
      "Recall -> mean = 88.43026465791574 variance = 0.00012192222842713444\n",
      "F1 Score -> mean = 89.51623879690057 variance = 3.553176777766958e-05\n",
      "AUPRC -> mean = 89.31486843066017 variance= 9.253663129926641e-05\n",
      "\n",
      "Ensemble of 4 weak learners with hard voting (data splitting) :\n",
      "Accuracy -> mean = 84.98727735368956 variance = 0.0005775369215728169\n",
      "Precision -> mean = 91.50174678901338 variance = 0.00028926330516902134\n",
      "Recall -> mean = 89.19858672334759 variance = 0.0004813416411125957\n",
      "F1 Score -> mean = 90.33192769981154 variance = 0.0003557615587549997\n",
      "AUPRC -> mean = 90.14926242166639 variance= 0.00040351949567733964\n",
      "\n",
      "Ensemble of 4 weak learners with soft voting (data splitting) :\n",
      "Accuracy -> mean = 84.93638676844783 variance = 0.0005397250872456277\n",
      "Precision -> mean = 91.38928259723176 variance = 0.00027470863283993977\n",
      "Recall -> mean = 89.2656147211563 variance = 0.00042517060665242464\n",
      "F1 Score -> mean = 90.31251918077155 variance = 0.00033014425158968224\n",
      "AUPRC -> mean = 90.05776859731131 variance= 0.0003837454855114987\n"
     ]
    }
   ],
   "source": [
    "# Training and testing for different numbers of weak learners\n",
    "leaneres_number = [2,3,4]\n",
    "for i in range(len(leaneres_number)):\n",
    "    auprc_max = 0\n",
    "    accuracies_hard = np.zeros(5)\n",
    "    precisions_hard = np.zeros(5)\n",
    "    recalls_hard = np.zeros(5)\n",
    "    f1scores_hard = np.zeros(5)\n",
    "    auprcs_hard = np.zeros(5)\n",
    "    \n",
    "    accuracies_soft = np.zeros(5)\n",
    "    precisions_soft = np.zeros(5)\n",
    "    recalls_soft = np.zeros(5)\n",
    "    f1scores_soft = np.zeros(5)\n",
    "    auprcs_soft = np.zeros(5)\n",
    "    \n",
    "    # 5 iterations\n",
    "    for t in range(5):\n",
    "        # Splitting data for training and testing\n",
    "        X_train, X_test, Y_train, Y_test = train_test_split(data,labels,test_size=0.2)\n",
    "\n",
    "        # Splitting data to subsets\n",
    "        number_learner = leaneres_number[i]\n",
    "        X_features_splitted = np.array_split(X_train,number_learner,axis=1)\n",
    "        \n",
    "        # Defining learners\n",
    "        models = []\n",
    "        for j in range(number_learner):\n",
    "            model = DecisionTreeClassifier()\n",
    "            model.fit(X_features_splitted[j],Y_train)\n",
    "            models.append(('DT model ' + str(j), model))\n",
    "        \n",
    "        # Defining ensemble of learners and fitting it\n",
    "        model_ensemble_hard = VotingClassifier(models,voting='hard')\n",
    "        model_ensemble_soft = VotingClassifier(models,voting='soft')\n",
    "        model_ensemble_hard.fit(X_train,Y_train)\n",
    "        model_ensemble_soft.fit(X_train,Y_train)\n",
    "        \n",
    "        # Predicting\n",
    "        Ensemble_Y_pred_hard = model_ensemble_hard.predict(X_test)\n",
    "        Ensemble_Y_pred_soft = model_ensemble_soft.predict(X_test)\n",
    "        \n",
    "        # Meterics\n",
    "        accuracies_hard[t] = accuracy_score(Y_test,Ensemble_Y_pred_hard)\n",
    "        precisions_hard[t] = precision_score(Y_test,Ensemble_Y_pred_hard)\n",
    "        recalls_hard[t] = recall_score(Y_test,Ensemble_Y_pred_hard)\n",
    "        f1scores_hard[t] = f1_score(Y_test,Ensemble_Y_pred_hard)\n",
    "        auprcs_hard[t] = average_precision_score(Y_test,Ensemble_Y_pred_hard)\n",
    "        \n",
    "        accuracies_soft[t] = accuracy_score(Y_test,Ensemble_Y_pred_soft)\n",
    "        precisions_soft[t] = precision_score(Y_test,Ensemble_Y_pred_soft)\n",
    "        recalls_soft[t] = recall_score(Y_test,Ensemble_Y_pred_soft)\n",
    "        f1scores_soft[t] = f1_score(Y_test,Ensemble_Y_pred_soft)\n",
    "        auprcs_soft[t] = average_precision_score(Y_test,Ensemble_Y_pred_soft)\n",
    "        \n",
    "    print('\\nEnsemble of',number_learner,'weak learners with hard voting (data splitting) :')\n",
    "    print('Accuracy -> mean =',np.mean(accuracies_hard)*100,'variance =',np.var(accuracies_hard))\n",
    "    print('Precision -> mean =',np.mean(precisions_hard)*100,'variance =',np.var(precisions_hard))\n",
    "    print('Recall -> mean =',np.mean(recalls_hard)*100,'variance =',np.var(recalls_hard))\n",
    "    print('F1 Score -> mean =',np.mean(f1scores_hard)*100,'variance =',np.var(f1scores_hard))\n",
    "    print('AUPRC -> mean =',np.mean(auprcs_hard)*100,'variance=',np.var(auprcs_hard))\n",
    "    if np.mean(auprcs_hard)*100 >= auprc_max:\n",
    "        auprc_max = np.mean(auprcs_hard)*100\n",
    "        hard_or_soft = 1\n",
    "        best_number = number_learner\n",
    "    \n",
    "    print('\\nEnsemble of',number_learner,'weak learners with soft voting (data splitting) :')\n",
    "    print('Accuracy -> mean =',np.mean(accuracies_soft)*100,'variance =',np.var(accuracies_soft))\n",
    "    print('Precision -> mean =',np.mean(precisions_soft)*100,'variance =',np.var(precisions_soft))\n",
    "    print('Recall -> mean =',np.mean(recalls_soft)*100,'variance =',np.var(recalls_soft))\n",
    "    print('F1 Score -> mean =',np.mean(f1scores_soft)*100,'variance =',np.var(f1scores_soft))\n",
    "    print('AUPRC -> mean =',np.mean(auprcs_soft)*100,'variance=',np.var(auprcs_soft))\n",
    "    if np.mean(auprcs_hard)*100 >= auprc_max:\n",
    "        auprc_max = np.mean(auprcs_soft)*100\n",
    "        hard_or_soft = 2\n",
    "        best_number = number_learner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div dir=\"rtl\">\n",
    "بهترین مدل با استفاده از بخش قبل را بر روی داده آموزش داده و تست می کنیم. می بینیم که معیار ها بسیار پیشرفت کرده اند. <br>\n",
    "هر دو تقسیم داده یا ویژگی بسیار بسیار خوب و در حدود 98 درصد عمل کرده اند. اما گویا تقسیم بندی ویژگی شاید در حد یک درصد بهتر است.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best number of weak learners is 4 with soft voting\n",
      "\n",
      "Best ensemble :\n",
      "Accuracy -> 97.70992366412213\n",
      "Precision -> 98.39228295819936\n",
      "Recall -> 98.70967741935483\n",
      "F1 Score -> 98.55072463768116\n",
      "AUPRC -> 98.14051681841204\n"
     ]
    }
   ],
   "source": [
    "# Best ensemble model\n",
    "if hard_or_soft == 1:\n",
    "    print('Best number of weak learners is',best_number,'with hard voting')\n",
    "elif hard_or_soft == 2:\n",
    "    print('Best number of weak learners is',best_number,'with soft voting')\n",
    "    \n",
    "# Splitting data to subsets\n",
    "number_learner = leaneres_number[i]\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(data,labels,test_size=0.2)\n",
    "X_features_splitted = np.array_split(X_train,number_learner,axis=1)\n",
    "\n",
    "# Defining learners\n",
    "models = []\n",
    "for j in range(best_number):\n",
    "    model = DecisionTreeClassifier()\n",
    "    model.fit(X_features_splitted[j],Y_train)\n",
    "    models.append(('DT model ' + str(j), model))\n",
    "\n",
    "# Defining ensemble of learners and fitting it\n",
    "if hard_or_soft == 1:\n",
    "    model_ensemble = VotingClassifier(models,voting='hard')\n",
    "elif hard_or_soft == 2:\n",
    "    model_ensemble = VotingClassifier(models,voting='soft')\n",
    "model_ensemble.fit(X_train,Y_train)\n",
    "\n",
    "# Predicting\n",
    "Ensemble_Y_pred = model_ensemble_hard.predict(X_test)\n",
    "\n",
    "# Meterics\n",
    "accuracie = accuracy_score(Y_test,Ensemble_Y_pred)\n",
    "precision = precision_score(Y_test,Ensemble_Y_pred)\n",
    "recall = recall_score(Y_test,Ensemble_Y_pred)\n",
    "f1score = f1_score(Y_test,Ensemble_Y_pred)\n",
    "auprc = average_precision_score(Y_test,Ensemble_Y_pred)\n",
    "print('\\nBest ensemble :')\n",
    "print('Accuracy ->',accuracie*100)\n",
    "print('Precision ->',precision*100)\n",
    "print('Recall ->',recall*100)\n",
    "print('F1 Score ->',f1score*100)\n",
    "print('AUPRC ->',auprc*100)"
   ]
  }
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