z_training_validation_diabetes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Biol 359  |  Cross-Validation\n",
    "### Spring 2021, Week 9\n",
    "\n",
    "<hr style=\"border:2px solid gray\"> </hr>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/neda.bagheri/Library/Python/3.7/lib/python/site-packages/sklearn/feature_extraction/image.py:167: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  dtype=np.int):\n",
      "/Users/neda.bagheri/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/least_angle.py:30: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  method='lar', copy_X=True, eps=np.finfo(np.float).eps,\n",
      "/Users/neda.bagheri/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/least_angle.py:167: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  method='lar', copy_X=True, eps=np.finfo(np.float).eps,\n",
      "/Users/neda.bagheri/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/least_angle.py:284: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  eps=np.finfo(np.float).eps, copy_Gram=True, verbose=0,\n",
      "/Users/neda.bagheri/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/least_angle.py:862: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
      "/Users/neda.bagheri/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/least_angle.py:1101: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
      "/Users/neda.bagheri/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/least_angle.py:1127: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  eps=np.finfo(np.float).eps, positive=False):\n",
      "/Users/neda.bagheri/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/least_angle.py:1362: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
      "/Users/neda.bagheri/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/least_angle.py:1602: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
      "/Users/neda.bagheri/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/least_angle.py:1738: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  eps=np.finfo(np.float).eps, copy_X=True, positive=False):\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "from sklearn import datasets\n",
    "from sklearn import linear_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load diabetes dataset\n",
    "diabetes_dataset = datasets.load_diabetes()\n",
    "#print(diabetes_dataset.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[ 0.03807591,  0.05068012,  0.06169621, ..., -0.00259226,\n",
       "          0.01990842, -0.01764613],\n",
       "        [-0.00188202, -0.04464164, -0.05147406, ..., -0.03949338,\n",
       "         -0.06832974, -0.09220405],\n",
       "        [ 0.08529891,  0.05068012,  0.04445121, ..., -0.00259226,\n",
       "          0.00286377, -0.02593034],\n",
       "        ...,\n",
       "        [ 0.04170844,  0.05068012, -0.01590626, ..., -0.01107952,\n",
       "         -0.04687948,  0.01549073],\n",
       "        [-0.04547248, -0.04464164,  0.03906215, ...,  0.02655962,\n",
       "          0.04452837, -0.02593034],\n",
       "        [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n",
       "         -0.00421986,  0.00306441]]),\n",
       " 'target': array([151.,  75., 141., 206., 135.,  97., 138.,  63., 110., 310., 101.,\n",
       "         69., 179., 185., 118., 171., 166., 144.,  97., 168.,  68.,  49.,\n",
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       "         87.,  65., 102., 265., 276., 252.,  90., 100.,  55.,  61.,  92.,\n",
       "        259.,  53., 190., 142.,  75., 142., 155., 225.,  59., 104., 182.,\n",
       "        128.,  52.,  37., 170., 170.,  61., 144.,  52., 128.,  71., 163.,\n",
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       "        154., 259.,  90., 246., 124.,  67.,  72., 257., 262., 275., 177.,\n",
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       "         94., 283.,  64., 102., 200., 265.,  94., 230., 181., 156., 233.,\n",
       "         60., 219.,  80.,  68., 332., 248.,  84., 200.,  55.,  85.,  89.,\n",
       "         31., 129.,  83., 275.,  65., 198., 236., 253., 124.,  44., 172.,\n",
       "        114., 142., 109., 180., 144., 163., 147.,  97., 220., 190., 109.,\n",
       "        191., 122., 230., 242., 248., 249., 192., 131., 237.,  78., 135.,\n",
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       "        219.,  72., 201., 110.,  51., 277.,  63., 118.,  69., 273., 258.,\n",
       "         43., 198., 242., 232., 175.,  93., 168., 275., 293., 281.,  72.,\n",
       "        140., 189., 181., 209., 136., 261., 113., 131., 174., 257.,  55.,\n",
       "         84.,  42., 146., 212., 233.,  91., 111., 152., 120.,  67., 310.,\n",
       "         94., 183.,  66., 173.,  72.,  49.,  64.,  48., 178., 104., 132.,\n",
       "        220.,  57.]),\n",
       " 'DESCR': '.. _diabetes_dataset:\\n\\nDiabetes dataset\\n----------------\\n\\nTen baseline variables, age, sex, body mass index, average blood\\npressure, and six blood serum measurements were obtained for each of n =\\n442 diabetes patients, as well as the response of interest, a\\nquantitative measure of disease progression one year after baseline.\\n\\n**Data Set Characteristics:**\\n\\n  :Number of Instances: 442\\n\\n  :Number of Attributes: First 10 columns are numeric predictive values\\n\\n  :Target: Column 11 is a quantitative measure of disease progression one year after baseline\\n\\n  :Attribute Information:\\n      - Age\\n      - Sex\\n      - Body mass index\\n      - Average blood pressure\\n      - S1\\n      - S2\\n      - S3\\n      - S4\\n      - S5\\n      - S6\\n\\nNote: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).\\n\\nSource URL:\\nhttps://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\\n\\nFor more information see:\\nBradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\\n(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)',\n",
       " 'feature_names': ['age',\n",
       "  'sex',\n",
       "  'bmi',\n",
       "  'bp',\n",
       "  's1',\n",
       "  's2',\n",
       "  's3',\n",
       "  's4',\n",
       "  's5',\n",
       "  's6'],\n",
       " 'data_filename': '/Users/neda.bagheri/Library/Python/3.7/lib/python/site-packages/sklearn/datasets/data/diabetes_data.csv.gz',\n",
       " 'target_filename': '/Users/neda.bagheri/Library/Python/3.7/lib/python/site-packages/sklearn/datasets/data/diabetes_target.csv.gz'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1.0000\n",
       "1     0.3509\n",
       "2     0.3300\n",
       "3     0.9196\n",
       "4     1.1047\n",
       "5     0.7016\n",
       "6     0.7885\n",
       "7     1.0000\n",
       "8     0.3288\n",
       "9     0.9727\n",
       "10    0.1514\n",
       "11    0.2811\n",
       "12    1.0000\n",
       "13    0.3288\n",
       "14    0.3024\n",
       "15    0.1225\n",
       "16    0.0248\n",
       "17    1.0000\n",
       "18    0.3752\n",
       "19    0.0360\n",
       "20    0.5821\n",
       "21    1.0000\n",
       "22    0.2936\n",
       "23    0.2635\n",
       "24    0.1526\n",
       "25    0.0450\n",
       "26    1.0000\n",
       "27    0.2936\n",
       "28    0.3169\n",
       "29    0.4429\n",
       "30    0.3012\n",
       "Name: Cell Viability in Comparison to Media Alone (24 Hr), dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load microarray dataset\n",
    "\n",
    "dataset = pd.read_csv('microarray_data_clean2.csv')\n",
    "# dataset.head(10)\n",
    "\n",
    "# dataset['Cell Viability in Comparison to Media Alone (24 Hr)']\n",
    "\n",
    "dataset\n",
    "\n",
    "input_data = dataset.iloc[:, 4:]\n",
    "input_data\n",
    "output_data = dataset.iloc[:, 3]\n",
    "output_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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vrvcjQ2VlpZ5++mn97W9/a/Cy+4Z4uw+sy+XyOm5eXp7mzZun8847T506dfL4sf2oo47SokWLVF5e3uD+hI45hNWePXtUXl4e1C9/a9as0ffff6/JkycrPT1dknTMMcdIkpYsWaKsrCwlJCQErT0Avu3du1cVFRVe7/PgraOqtsTERN1yyy3uywn27dunN954Q4MGDXL/0ti/f3+1b99es2bN0hdffKHMzEz16dNH5eXl2rp1q9LT07Vx40Z16dJFCQkJ2rhxo/r166fffvtNv//+u/tLa80vyA6Hw+sv4wcOHNAVV1zhHv+oo47SV199pTVr1vjVMff000/r6aef9hi2cOFCpaenu3NKqj4FPzU1Vffcc49+/PFHdevWTS6XS88//7y6du2qSZMmuQ9s+/bt636fr/qdTqeys7PVp08f3Xjjje7hhx56qO655x4tX75cZ555pnt4aWmpHnjgAUPuvQFY0d69ezVlyhQdcsghkqovQ6y5+fOIESM8xj377LPdnXB9+/bVt99+qzVr1jTaMbd7927dd9997m2/f//++r//+z8tXrzYaz61aNHC/SW17mX2/uZsbXfffXfQflw1q0CPV5csWSKXy6XJkyerXbt2kqrn465du5Sdnd3sjrnExET169dPH330kcaMGePeL6xcuVIOh6PB/VJcXJzHTc+dTqf69eunf/7zn/roo4909tlne4x/7LHHun/U7tevn2w2m7Kzs3Xuued6nDkTiM6dO6tNmzaqqqqqt88dNmyY/v3vf6uwsNA9v1euXKnKykoNGTKkSe0dddRR7suP+/Xrp9zcXC1fvlzjx4/3eYuMoqIiSfJrmQe63bRo0UK33nqr+7jn119/1ZtvvqlRo0a5zwrq06ePPvvsM33yySf1Oub8WSZ1l3H//v31448/6oMPPtA//vEPj8/r2bNnozfC37Jliw4//HCdf/757mG16wp0HsTExOi2227z+NH20Ucf1bZt29S7d2+vNTS03ixdulQtWrTQPffc4z4Tsm/fvho/fryWLl2qm2++ucHpq3HKKafohRde0D/+8Q+Vlpbqm2++8XkP5Hnz5ik9PV233nqre1ifPn103XXX6Y033tBll12mffv26c0339SQIUM8lpndbtdLL73UaD21TyhxOp065phjlJeXp/fee6/RjrmKigqtXbtWxxxzjLsjrVevXlq9erVGjx7dYCf3okWL1KlTJ915553u9fSII47QDTfcoP/+97+69NJL3eP6s5978cUXlZycrEmTJrmXef/+/XXTTTdp6dKlHvOwri1btshms3mcHexNVVWVnnnmGbVv377ePQiXLVumiooKjzNQA7F582ZdeOGFfo+/d+9e3X333V47hLt166bKykr9+OOPPtd1iY45WMCGDRuUkpKi3r17e3zx79u3rxYtWqStW7c2+4AIQPOUlJToyiuv9Bi2cOFCj8654447zuOAecuWLaqsrHSf5Vbjz3/+s/79739r06ZNyszM1OGHH662bdvq22+/VXp6ur799lsdffTRSkhI0Lp16yRV3/cuJiZGRx55pF/1tmrVyuOLTosWLXTIIYe4D9wbM2LEiHqX5cTExKiyslKvv/66Vq1apcLCQlVUVLhfz83NVbdu3ZSbm6vCwkJlZWUFfKZAbm6udu/eXe9GxkceeaRSUlK0adMmj465Xr160SkHBFFqaqr7y4okJSQkKCEhwWt21P3y3aVLF/3000+NtpGUlOTxJdVut+vEE0/U66+/LqfTGVBu+JuzNfr162f5Trmm2LBhg4499ljFxcV5HIv269dPL774ovbv3+9xaV9TDBo0SF9++aW++eYb9evXT7///ru++eYbnXPOOY0u87Vr1+qNN95Qbm6u9u/f7x7u7YmUdS+3GjhwoBYtWqRt27Y1uWOuIQMHDtT8+fP1wQcfuPddOTk5ysjIaPL9t+puW4cddpgqKiq0e/fuoOzzAt1ujjnmGI/jnZqze2rO6JeqjxFSU1O1a9eueu35s0y+/vprvfrqq+6rBmp07Nix3uf5c3l1jx499N577+m5555TZmamevfu7b41SFPmQd++fT065WrO4isqKmqws8KX7777ThkZGR6XJ8fFxSkzM1Pr16/3+3MGDBig5557TuvXr1dhYaESExN19NFH67vvvvMYLy8vTzt37tS5557rsY23atVKvXr1co//yy+/6ODBg/WW2Z///Ge/Oua2b9+uxYsX64cfftCePXvcZ2j5s+19/vnnKi0t1SmnnOIeNmjQID377LP6+uuvfXbslZWV6ccff9S5557rsZ527NhRvXv3dl9OXKOx/Vx5ebk2bdqkc889VzabzWN+HXPMMVq9enWD0/H777+rTZs2jV55M3fuXG3ZskW33367xxlq+fn5euWVV3TLLbc0eX91+OGHe+28XrdunfsWMrWlpKT4PEuz5hJcX0/WrkHHHMKqXbt2atmypftyrGDYvXu3CgsLffZq7927N2htAWhYu3bt1KJFi3pfQtu1a6f7779fUvUB9wcffFDvvXUPlmsOLOteKhUTE6O2bdu6X7fb7e4z5ZxOp7777juddtppSkhIUHZ2tvbv36+NGzeqZ8+eXi9N8MbbfWhatGjh0ZHWkJSUFI8b1tZ46aWX9Pbbb+v8889X79691bp1axUXF+vhhx92f3ZNZjXlC4mveSZVz9/S0lKPYVa9DA0wSt17RUq+s6PuuP5mjLeOhcTERFVWVmrPnj0BdTz4m7M1oiUzAj1e3bNnj1atWqVVq1Z5fX3fvn3N7pjLzMxUfHy8Vq5cqX79+mn16tVyOp0aNGhQg+/7/PPP9dhjj2nQoEE6//zz1b59e9lsNt1///1e17e6V5nUrE/e7uEUDC1bttTgwYP14Ycf6oILLtCWLVu0Y8cOj0t2A+Vt25Lk87JNSe4z/QsLCxvtBAl0u6lbT02Hg7fh3mpsbJls375d999/v/r166err75aHTp0kN1u15NPPul1GfuzHQ8aNEgVFRVavny53n33XcXGxrrP3OvYsWPQ5oG/x1V17du3z+9jnYa0adNGxx13nPsH05NOOslrR/fu3bslSU8++aSefPLJeq/XrD8lJSWS6i8zf67eKioq0tSpU9W5c2f94x//UHJysmJiYpSdna3ffvut0fevXLlSrVq1Up8+fdzzoH///oqJidGqVat8dsyVlpbK5XL53LfUzcHG9nP79u2T0+nUyy+/7PPebg39iFRza5qGLFiwQB988IHGjx/v0cEtVZ/ZePTRR6tXr17u+VBZWSmXy6XS0lK1aNGi0Q67mntx1uXrx7OGtqmathrKH4mOOYRZTEyM+vTpo6+//rrRja5mJa6srPToMa/b0dauXTt17NhREyZM8Po53DMJCJ+YmBilp6fr66+/9th2Y2Ji3Ds4X79k1r28pGbHX1JS4nHT2qqqKu3bt8/jwKBPnz564YUX9P3336usrEzp6elq3bq1WrVqpU2bNmnjxo06/fTTgzqtTbFmzRoNGjTI44a23377rcc4Nb+sNeVLUO15VldJSYllb9IORBNf23dsbKzXm2M3JJCclerntFUFcrwqVc/Ho446SllZWV5fD0aHZsuWLTVgwACtXr1aZWVlWr16tXr06NHok0nXrl2r1NRUj/uRVVZW1us4qbF7926PdaFmfat9j6lgO+OMM/Tmm29q3bp1+uyzz5SSklLvy3aopaeny263a/369Y22Heh201yNLZNPP/1UMTExuvnmmz2+M5WWljb4wIuG2Gw2nX766Tr99NO1b98+ff3115o/f74ee+wx/etf/wr7PKirbdu2PrMw0GkeNGiQ7r//frlcLt1www1ex6m5RH306NHuWybVVjPfazq36i6zmo69hmzYsEH79+/XhAkTPH6c9efpzyUlJfrqq69UVVXl9Uyvzz77zOeZu/Hx8bLZbD7nZ6DLMi4uTjabTcOGDfP5w0FDZ/m2bdu2wc7VV155RcuWLdPll1/ucXZgjZp7Mte9hFuS/vGPf+jMM8/UZZdd1viEBElN1tasQ77wuEqEXVZWlvbt26cXX3zR6+sFBQX6+eef3b88/PLLL+7XSktL3U/eqVFzD4+anu26/wI9SAXQPH/729+0d+9en9u4v4444gjFxsZqzZo1HsPXrl2rqqoqj3u1HX300aqsrNTLL7+sbt26KT4+XjExMTrqqKP09ttva+/evfWeaujrl+lQKi8vr3dvvbpP/TrkkEOUkpKi5cuX+7zJrOS9/rS0NCUkJNSbZ5s3b1ZhYWG9eQAg8uzatcvjWMjpdOqTTz5Rz549fX7Z8XXGUCA5G238PV6Vqo9Ff/75Z3Xp0sXrsWhjHXv+Gjx4sA4ePKjFixfr559/bvRsOan6S33d/c6qVavkdDq9jl/zJNPaf9tsNvXq1avphat6HfS1z01NTVXfvn313//+V59++qmGDBnSpIc+NIfD4dDgwYOVk5NT77tGjc8++0xS+LebxpbJwYMHZbfbPTrOv/32W79vv9GYtm3bauDAgRowYIB+/fVXSeGbB77Wm/T0dH355Zc6cOCAe9iBAwe0fv36gNvu27evBgwYoNNPP93n00vT0tKUkpKiX3/91es2XnM/tMMPP1ytWrWqt8zqzidvaqaz9vaam5urzZs3N/rejz76SFVVVRo7dqwmTZrk8e+yyy5TeXm5PvnkE6/vbd26tbp3765PPvnEIxcKCwu1efPmgI8dW7duraOOOko///yzunXr5nV+NeTQQw9VZWWl18u633rrLS1atEh///vf9Ze//MXr+2+44YZ686Bfv35q166dJk2a5PN9oVJQUCCp8cuROWMOYVfzePT58+drx44dGjx4sJKTk9033Fy+fLmuv/569706nnrqKY0cOVIVFRV6/fXX612KdtJJJ+nDDz/U1KlTdc455+jwww9XZWWldu7cqc8//1y33HKLxz0RAITWMccco9GjR+ull17SL7/8olNOOUUdO3ZURUW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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate scatter plot of independent vs dependent variable\n",
    "#plt.style.use('ggplot')\n",
    "#fig = plt.figure(figsize = (20, 20))\n",
    " \n",
    "#for index, feature_name in enumerate(diabetes_dataset.feature_names):\n",
    "#    ax = fig.add_subplot(4, 4, index + 1)\n",
    "#    ax.scatter(diabetes_dataset.data[:, index], diabetes_dataset.target)\n",
    "#    ax.set_ylabel('progression after 1yr', size = 16)\n",
    "#    ax.set_xlabel(feature_name, size = 16)\n",
    " \n",
    "#plt.show()\n",
    "\n",
    "# Generate scatter plot of independent vs dependent variable\n",
    "plt.style.use('ggplot')\n",
    "fig = plt.figure(figsize = (20, 20))\n",
    " \n",
    "for index, feature_name in enumerate(dataset.columns):\n",
    "    if index >= 16:\n",
    "        break\n",
    "    ax = fig.add_subplot(4, 4, index + 1)\n",
    "    ax.scatter(input_data.iloc[:, index], output_data) \n",
    "    ax.set_ylabel('cell viability at 24hr', size = 16)\n",
    "    ax.set_xlabel(feature_name, size = 16)\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input data:\n",
      "    p-AKT(S473)[15m]  p-S6RIBPROT(S240/244)[15m]  p-EGFR(Y1086)[05m]  \\\n",
      "0             0.4579                      0.4337              0.2191   \n",
      "1             0.5622                      0.5574              0.9131   \n",
      "2             0.7634                      0.6791              1.1132   \n",
      "3             0.8478                      0.5387              0.2182   \n",
      "4             0.8241                      0.5588              0.2269   \n",
      "5             0.6406                      0.5353              0.1853   \n",
      "6             0.9168                      0.5827              0.0860   \n",
      "7             0.3453                      0.4025              0.0407   \n",
      "8             0.6146                      0.4128              1.5909   \n",
      "9             0.2151                     -0.1269              0.8671   \n",
      "10            0.2812                      0.0935              1.0671   \n",
      "11           -0.4069                     -0.1516              1.4352   \n",
      "12            1.2559                      1.2304              0.1505   \n",
      "13            1.1950                      1.1309              1.7019   \n",
      "14            1.1332                      1.0178              1.8037   \n",
      "15            1.1484                      0.9954              1.7065   \n",
      "16            1.0847                      0.9495              1.5435   \n",
      "17            1.1229                      1.0372              0.3478   \n",
      "18            1.0049                      0.8529              1.3945   \n",
      "19            0.8347                      0.6673              1.2213   \n",
      "20            0.7507                      0.9994              1.3907   \n",
      "21            0.0598                      0.1444              0.2925   \n",
      "22           -0.0669                      0.2332              1.8407   \n",
      "23            0.1081                      0.2592              1.8566   \n",
      "24            0.1327                      0.1335              1.5910   \n",
      "25            0.2944                      0.3107              1.8446   \n",
      "26            0.6315                      0.9222              0.2365   \n",
      "27            0.8752                      1.1264              1.7890   \n",
      "28            0.7794                      0.7031              1.7551   \n",
      "29            0.5069                      0.7706              1.5715   \n",
      "30            0.4475                      0.6457              1.7461   \n",
      "\n",
      "    p-EGFR(Y1086)[15m]  p-CDK2(T160)[15m]  p-AKT(S473)[05m]  \\\n",
      "0               0.1584             0.4136            0.3619   \n",
      "1               1.0124             0.4818            0.4329   \n",
      "2               1.3184             0.5831            0.6386   \n",
      "3              -0.0246             0.6127            0.8766   \n",
      "4               0.0822             0.5386            0.8442   \n",
      "5               0.0416             0.5157            0.7643   \n",
      "6               0.1936             0.6643            0.8238   \n",
      "7               0.0661             0.1218            0.3875   \n",
      "8               1.7447             0.1722            0.3930   \n",
      "9               0.9976             0.1107            0.0253   \n",
      "10              1.1831             0.0612           -0.0103   \n",
      "11              1.3026             0.0966           -0.5598   \n",
      "12              0.2873             0.8656            1.1158   \n",
      "13              1.4973             0.7268            1.0096   \n",
      "14              1.6080             0.6746            1.0299   \n",
      "15              1.5721             0.7632            1.0662   \n",
      "16              1.6043             0.6728            0.8887   \n",
      "17              0.4644             0.7041            0.7705   \n",
      "18              1.1269             0.4644            0.8267   \n",
      "19              1.1539             0.4139            0.7507   \n",
      "20              1.2360             0.8230            0.3376   \n",
      "21              0.5717             0.0000           -0.1189   \n",
      "22              2.0036             0.0000            0.0722   \n",
      "23              1.9556             0.0000            0.1247   \n",
      "24              1.8588             0.0000            0.0124   \n",
      "25              2.1180             0.0000            0.2384   \n",
      "26              0.3655             1.0152            0.6864   \n",
      "27              1.6864             1.0374            0.7860   \n",
      "28              1.5385             1.0736            0.8069   \n",
      "29              1.3655             1.0241            0.6140   \n",
      "30              1.4075             0.9805            0.7405   \n",
      "\n",
      "    p-STAT1(Y701)[05m]  p-CDK2(T160)[05m]  p-PDK1(S241)[15m]  \\\n",
      "0               0.0657             0.3944             0.4214   \n",
      "1               0.2481             0.4089             0.3808   \n",
      "2               0.2494             0.5542             0.3064   \n",
      "3               0.0708             0.5381             0.3527   \n",
      "4               0.0398             0.5877             0.3356   \n",
      "5              -0.0175             0.5067             0.3917   \n",
      "6               0.0894             0.5886             0.4853   \n",
      "7               0.0261             0.0872             0.1347   \n",
      "8               0.9187             0.0829             0.1479   \n",
      "9               0.2098             0.1275             0.1091   \n",
      "10              0.6735             0.0555             0.1995   \n",
      "11              0.8616             0.0291             0.1548   \n",
      "12              0.2079             0.7797             0.3103   \n",
      "13              0.9757             0.7738            -0.1988   \n",
      "14              1.0587             0.7862            -0.7853   \n",
      "15              1.0233             0.7127            -0.4337   \n",
      "16              1.0390             0.5691            -0.0529   \n",
      "17              0.2403             0.6727             0.3286   \n",
      "18              1.0021             0.7497             0.1463   \n",
      "19              1.0316             0.7499             0.2566   \n",
      "20              0.4339             0.6947             0.4130   \n",
      "21              0.1091             0.0000             0.4779   \n",
      "22              1.3235             0.0000             0.4032   \n",
      "23              1.3698             0.0000             0.3321   \n",
      "24              1.1783             0.0000             0.3032   \n",
      "25              1.5521             0.0000             0.4798   \n",
      "26              0.0441             1.1304             0.5319   \n",
      "27              0.9250             1.0951             0.5163   \n",
      "28              0.7859             1.1502             0.4437   \n",
      "29              0.4886             0.9836             0.4747   \n",
      "30              0.8870             1.1327             0.3807   \n",
      "\n",
      "    p-PKCPAN/G(T514)[05m]  ...  p-ABL(C-)(Y245)[05m]  p-STAT6(Y641)[00m]  \\\n",
      "0                  0.3325  ...                0.0000              0.1952   \n",
      "1                  0.3770  ...                0.0000              0.1952   \n",
      "2                  0.6423  ...                0.0000              0.1952   \n",
      "3                  0.6505  ...                0.0000              0.1952   \n",
      "4                  0.6694  ...                0.0000              0.1952   \n",
      "5                  0.5440  ...                0.0000              0.1952   \n",
      "6                  0.6746  ...                0.0000              0.1952   \n",
      "7                  0.0994  ...                0.0000             -0.0153   \n",
      "8                  0.1282  ...                0.0000             -0.0153   \n",
      "9                  0.1016  ...                0.0000             -0.0514   \n",
      "10                 0.1105  ...                0.0000             -0.0141   \n",
      "11                 0.0376  ...                0.0000             -0.0086   \n",
      "12                 1.0291  ...                0.1340              0.0428   \n",
      "13                 1.0983  ...                0.1708              0.0428   \n",
      "14                 1.0939  ...                0.1692              0.2373   \n",
      "15                 1.0126  ...                0.0481              0.2294   \n",
      "16                 0.7850  ...                0.2411              0.5298   \n",
      "17                 0.7839  ...               -0.1117              0.4644   \n",
      "18                 0.7852  ...                0.0105              0.4644   \n",
      "19                 0.7293  ...               -0.1704              0.5007   \n",
      "20                 0.4032  ...               -0.1151              0.3149   \n",
      "21                 0.0267  ...                0.3005              0.1740   \n",
      "22                -0.0299  ...                0.4820              0.1740   \n",
      "23                 0.0280  ...                0.6075              0.2470   \n",
      "24                -0.1250  ...                0.3238              0.1444   \n",
      "25                -0.0590  ...                0.3523              0.2425   \n",
      "26                 0.6652  ...                0.2435              0.1761   \n",
      "27                 0.7402  ...                0.4696              0.1761   \n",
      "28                 0.8666  ...                0.4323              0.2043   \n",
      "29                 0.5142  ...                0.3755              0.3355   \n",
      "30                 0.6506  ...                0.3933              0.5386   \n",
      "\n",
      "    p-PI3KP85(Y458)P55(Y199)[15m]  p-JAK1(Y1022/1023)[15m]  p-CRKL(Y207)[15m]  \\\n",
      "0                          0.0812                   0.5512             0.0242   \n",
      "1                          0.1718                   0.5988             0.0673   \n",
      "2                          0.3046                   0.6115             0.2230   \n",
      "3                          0.3018                   0.4609             0.1706   \n",
      "4                          0.2046                   0.3116             0.2588   \n",
      "5                          0.2196                   0.3765             0.1084   \n",
      "6                          0.2638                   0.3787             0.1850   \n",
      "7                          0.0136                   0.0833            -0.0154   \n",
      "8                          0.1923                   0.3320             0.1367   \n",
      "9                         -0.0631                   0.0064            -0.0151   \n",
      "10                         0.0715                   0.1154             0.1157   \n",
      "11                         0.0667                   0.0983             0.0545   \n",
      "12                         0.4274                   0.3777             0.1926   \n",
      "13                         0.6377                   0.8807             0.3858   \n",
      "14                         0.5779                   1.0132             0.2942   \n",
      "15                         0.7093                   0.9438             0.3181   \n",
      "16                         0.5861                   0.8393             0.3876   \n",
      "17                         0.5558                   0.0830             0.3508   \n",
      "18                         0.4839                   0.2668             0.3655   \n",
      "19                         0.3511                   0.3792             0.4033   \n",
      "20                         0.2948                   0.2195             0.1699   \n",
      "21                        -0.0944                   0.4078            -0.0739   \n",
      "22                         0.0577                   0.9804             0.0530   \n",
      "23                         0.0785                   1.0731             0.2040   \n",
      "24                        -0.0165                   1.0423            -0.0047   \n",
      "25                         0.2057                   0.9133             0.1341   \n",
      "26                         0.4683                   0.0905             0.3559   \n",
      "27                         0.6866                   0.5163             0.6299   \n",
      "28                         0.6709                   0.4368             0.5367   \n",
      "29                         0.4626                   0.2640             0.3488   \n",
      "30                         0.3711                   0.3693             0.3066   \n",
      "\n",
      "    p-STAT5(Y694)[15m]  p-CDC2(Y15)[15m]  p-CREB(S133)[15m]  \\\n",
      "0               0.0380            0.1771            -0.1221   \n",
      "1               0.1895            0.1131             0.1826   \n",
      "2               0.3873            0.1358             0.3185   \n",
      "3               0.1616            0.0269             0.2554   \n",
      "4               0.0654            0.1435             0.4323   \n",
      "5               0.0870            0.0366             0.1343   \n",
      "6               0.1022            0.1745             0.0962   \n",
      "7              -0.0225           -0.1495             0.0079   \n",
      "8               0.3098            0.3209             0.1282   \n",
      "9               0.0030           -0.0098             0.0000   \n",
      "10              0.1271            0.1144             0.1456   \n",
      "11              0.1386            0.2139             0.0502   \n",
      "12              0.0000            0.3599             0.1768   \n",
      "13              0.0000            0.3593             0.5558   \n",
      "14              0.0000            0.4597             0.5478   \n",
      "15              0.0000            0.4532             0.5659   \n",
      "16              0.0000            0.3819             0.5254   \n",
      "17              0.4281            0.1651             0.2667   \n",
      "18              0.4421            0.2998             0.4913   \n",
      "19              0.2833            0.3577             0.5107   \n",
      "20              0.3429            0.3273             0.4596   \n",
      "21              0.2654            0.4275            -0.0031   \n",
      "22              0.9549            0.6633            -0.0006   \n",
      "23              0.8466            0.6212             0.1334   \n",
      "24              0.5764            0.5001            -0.0608   \n",
      "25              1.0863            0.7123             0.0529   \n",
      "26              0.2592            0.1650             0.4352   \n",
      "27              0.7818            0.2723             0.7312   \n",
      "28              0.7249            0.2696             0.6710   \n",
      "29              0.3609            0.2754             0.4441   \n",
      "30              0.5067            0.2511             0.3632   \n",
      "\n",
      "    p-PI3KP85(Y458)P55(Y199)[05m]  p-MTOR(S2448)[15m]  \n",
      "0                          0.0599              0.0466  \n",
      "1                          0.1761              0.0296  \n",
      "2                          0.2350              0.0596  \n",
      "3                          0.2457             -0.1428  \n",
      "4                          0.2730             -0.0754  \n",
      "5                          0.1768             -0.1452  \n",
      "6                          0.2168              0.0729  \n",
      "7                          0.0672              0.1134  \n",
      "8                          0.2879              0.3949  \n",
      "9                          0.1268              0.1783  \n",
      "10                         0.2623              0.2363  \n",
      "11                         0.1914              0.2933  \n",
      "12                         0.3090              0.3029  \n",
      "13                         0.6905              0.3185  \n",
      "14                         0.7300              0.4373  \n",
      "15                         0.7143              0.3666  \n",
      "16                         0.5783              0.4354  \n",
      "17                         0.4808              0.1901  \n",
      "18                         0.5129              0.2061  \n",
      "19                         0.4918              0.3240  \n",
      "20                         0.6472              0.4133  \n",
      "21                        -0.2202              0.3080  \n",
      "22                         0.1077              0.3194  \n",
      "23                         0.1853              0.3996  \n",
      "24                         0.0235              0.4284  \n",
      "25                         0.1196              0.4661  \n",
      "26                         0.3899              0.2442  \n",
      "27                         0.5776              0.4673  \n",
      "28                         0.6337              0.3529  \n",
      "29                         0.4067              0.2753  \n",
      "30                         0.6245              0.3432  \n",
      "\n",
      "[31 rows x 180 columns]\n",
      "\n",
      "Output data:\n",
      "0     1.00\n",
      "1     0.35\n",
      "2     0.33\n",
      "3     0.92\n",
      "4     1.10\n",
      "5     0.70\n",
      "6     0.79\n",
      "7     1.00\n",
      "8     0.33\n",
      "9     0.97\n",
      "10    0.15\n",
      "11    0.28\n",
      "12    1.00\n",
      "13    0.33\n",
      "14    0.30\n",
      "15    0.12\n",
      "16    0.02\n",
      "17    1.00\n",
      "18    0.38\n",
      "19    0.04\n",
      "20    0.58\n",
      "21    1.00\n",
      "22    0.29\n",
      "23    0.26\n",
      "24    0.15\n",
      "25    0.04\n",
      "26    1.00\n",
      "27    0.29\n",
      "28    0.32\n",
      "29    0.44\n",
      "30    0.30\n",
      "Name: Cell Viability in Comparison to Media Alone (24 Hr), dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Load the dataset into Pandas Dataframe, omitting the feature=sex\n",
    "# diabetes_pd = pd.DataFrame(diabetes_dataset.data)\n",
    "# diabetes_pd.columns = diabetes_dataset.feature_names\n",
    "# diabetes_pd_target = np.asarray(diabetes_dataset.target)\n",
    "# diabetes_pd['progression'] = pd.Series(diabetes_pd_target)\n",
    " \n",
    "# Define input data\n",
    "# X = diabetes_pd.iloc[:, :-1]\n",
    "# X = diabetes_pd.loc[:, ['age', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']]\n",
    "\n",
    "# Define output data\n",
    "# Y = diabetes_pd.iloc[:, -1]\n",
    " \n",
    "# print(diabetes_pd.head())\n",
    "\n",
    "X = input_data\n",
    "Y = output_data\n",
    "\n",
    "print(\"Input data:\")\n",
    "print(round(X,4))\n",
    "\n",
    "print(\"\")\n",
    "print(\"Output data:\")\n",
    "print(round(Y,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training data X = (23, 180) and Y = (23,) : \n",
      "Shape of validation data X = (8, 180) and Y = (8,) : \n"
     ]
    }
   ],
   "source": [
    "# Split data into training and validation set\n",
    "x_train, x_validation, y_train, y_validation = train_test_split(X, Y, test_size = 0.25)\n",
    " \n",
    "print(\"Shape of training data X = % s and Y = % s : \"%(x_train.shape, y_train.shape)) \n",
    "print(\"Shape of validation data X = % s and Y = % s : \"%(x_validation.shape, y_validation.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (1) Multiple Linear Regression (Ordinary Least Squares)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear regression coefficient of determination of prediction for training data\n",
      "1.000\n",
      "\n",
      "Linear regression coefficient of determination of prediction for validation data\n",
      "0.272\n",
      "\n",
      "Mean squared Error on test set :  0.077\n"
     ]
    }
   ],
   "source": [
    "# Train multiple linear regression model\n",
    "lreg = LinearRegression()\n",
    "lreg.fit(x_train, y_train)\n",
    "\n",
    "# Predict validation data\n",
    "lreg_y_pred = lreg.predict(x_validation)\n",
    "\n",
    "print(\"Linear regression coefficient of determination of prediction for training data\")\n",
    "print(f\"{lreg.score(x_train, y_train):.3f}\" \"\\n\")\n",
    "\n",
    "print(\"Linear regression coefficient of determination of prediction for validation data\")\n",
    "print(f\"{lreg.score(x_validation, y_validation):.3f}\" \"\\n\")\n",
    "\n",
    "mean_squared_error = round(np.mean((lreg_y_pred - y_validation)**2),3)\n",
    "print(\"Mean squared Error on test set : \", mean_squared_error)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eryHeCw3smlqrP1PILTXjcKoogENRyS01N2lV79evHx06dHA9XrRoEV9//TVQ26Lj8OHD590DQVpaC+H7TpTY2bbJjL1GpVuvQLr2MKLT+e5VRe5q9UUhJS4IvU6DQ1GbZSLo3H5BGzduZP369XzxxReYTCbGjx9fbwvt37a0vpz22EKI5hFo1BIcqqXPgCDC2nj+vvDNpdUXheQYE8+N7NBkcwrBwcH1trCG2tbX4eHhmEwmCgoK2LZt22XtSwjhOaqqcvRQDZWnnPT+tRBcfW2IT3/moDFafVGApp0IioyM5Morr+Taa6/FaDQSHR3tWpaens5HH33E8OHDSUxMJDU1tUn2KYRoXtVnnOzKsVB23EFUrN6nGtg1NWmd3USkdbb3SOtm72rJ+VVF5VC+jbzdVrQa6NnPRIcuvtPATlpnCyGEB9XUqBzYYyUmrraBnSmo5V+wKUVBCCHOoThVjv1cQ/vOBgKNWoZfH4opyHcb2DU1KQpCCPGrU+UOduSYqTqtYAzSEhsfQFBwy7myyB0eKQrvvPMO27ZtIzw8nHnz5p23XFVVFi9ezPbt2wkMDGTmzJl06dLFE9GEEAKHQ2V/rpVDB2wYjRquHBpMbLz/fxCtMTxSFNLT0xk9ejSZmZn1Lt++fTslJSW89dZb5Ofn88EHH/Diiy96IpoQwg+oB/NQ9+9G0703msTkJtvuaz/8wvaSAm7URxBk09Ghi4GefU0EGHx7qOjs8agZOBSi2zbptj1SFHr27Mnx48cvuHzLli0MGzYMjUZDt27dqK6u5uTJk0RERHginhDCh6kH81DmPQkOB6pej3b2801SGN7I/oUfCqtwAmttp+gXH8Tvrmxz2dttbucej5Mr/4X2oeeatFD6xJxCRUVFnev5o6KiqKioqLcorF69mtWrVwMwd+7cOl8HUFpa2mA30ubUVPtdsmQJJpOJiRMn8sknn5Cenk58fHyTbLshDeUPDAw871j7Cr1e77PZ3CH5G1b9/SHOOB2gKuB0EHTsEMGDhl7WNguPVNO22Eh/rcoW5Qwl2Nl4spo5fvB9qHM8HE1zPM7lE0XhUmRkZJCRkeF6/NtrdG02Gzqd5yeGmupzCg6Hg8mTJ7v+/8knn5CUlNTsbxoXy2+z2Xz2WvSWfJ28P2ju/Gq7LqDTAw7Q6TG364KlkfuzWRX2bLfwy1E7Or2GI+e0kOkfb/KL70Od46Fv3PHw+c8pREZG1vlmlJeXn9ckzp8UFhYyefJk+vTpw+7du+nWrRtvvfUW7733Ht9++y1Wq5W0tDReeuklNBoN48ePp2fPnuTk5HDzzTdTXV1NcHAw7dq1Y+fOndx7770YjUYeffRR/vGPf7Bo0SIAsrOzWbJkCVlZWV5+xf6rucaqRdPRJCajnf38ZX+fjpfY2b7JjN2u0j3FSNfkQI7+aGF7iYX+8SYeGnpFEydvHucejzYDh1Lpj3MKF5OWlsaqVau4+uqryc/PJygoqMnmEzaurTrvuYT2BjolBeJwqPyUfX6fovadDbTvHIjNprB1Q3WdZUOuDXVrvwcPHmTevHlceeWVPPTQQyxZsoRp06bx4IMPAnDffffx7bffMmrUKADsdrure+rZK7RuvPFGPvzwQ+bMmUPfvn1RVZW//vWvlJeXExUVxbJly5g0aZL7B0PU0Vxj1aLpaRKTL/t7YzJpCQmrbWAXGl47mvDQ0Cv88kzt7PEwREdDE2f3SFF444032Lt3L1VVVcyYMYOJEye6hipGjRpF//792bZtG/fffz8Gg4GZM2d6IlazSkhI4MorrwRg3LhxLFq0iPbt2/Puu+9isVg4deoU3bt3dxWFm2666aLb1Gg03HrrraxYsYJJkyaxdetW3nzzzWZ9HS2Zun83OP7/WLW6f7cUhRbkbAO70yed9EmrLQRXu/lHXWvmkaIwa9asBpdrNBruuuuuZtl3Q3/Z6/WaBpcHBmrdPjP4rd9++lGj0fDEE0/w1VdfccUVVzBv3rw6bbPd7Ss0adIkpk2bRmBgIDfeeKPXJtVbAk333qh6PThrx6o13Xt7O5JoItVVTnZusVD+mwZ24uJafiMPL/nll1/YsmULAJ999pnrrCEyMpLq6mpWrlzp1nZ+24o7Pj6euLg43nrrLRk6ukxnx2Y1N0+WoaMWQlVUDuZZWfdNFadPOuiTZuKq9GApCJdA/sxsJomJiSxZsoTZs2fTrVs3pk6dyunTpxk5ciQxMTH07dvXre1MnDiRxx57DKPRyH/+8x9MJhPjxo2jvLycpKSkZn4VLV9TjFUL31FTo5K/19aqGtg1NWmd3UTOvaSzsLCQqVOnsnbt2mbZ11/+8hdSUlL4wx/+0GTblNbZ3iP5L4/TqXLsSI2rpbW5WsEU5P69Dryd/3JI62zB6NGjCQoK4qmnnvJ2FCG87mS5g50/mamqVDAFn21gJ2cHl0OKQjNo3759s50lrFq1qlm2K4Q/cThU9u/+tYGdScPAa1pvA7um1uKKgp+PhvksOa7CF+SdsJBbaqZNkR7LSZWOiQZ69DURECATyU2lxRUFrVaLw+GQSzWbkMPhQKuVU3LhXXuKzDyXXYjNqZKgM3BXahx9uvnmPJc/a3HvnEajEavVis1m8+idkgIDA+t87sDfXCi/qqpotVqMRqMXUglRq+QXOwc32eitBvMTZyhSajhYY6U/wd6O1uK0uKKg0WgwmUwe368/X8EA/p9ftEw2q0LuNgtFhXYCQ7QU2m1oNaDXakiJk7OE5tDiioIQomU4Xmxn2yYzTsevDex6BNKx3EBuqZmUuCCSYzz/x19rIEVBCOGTTEFawsK19D6ngV1yjEmKQTOT2UMhhE9QVZUjBTZ25pgBCA3XMeTaUFdBEJ4hZwpCCK87U+VkZ46ZihNOouP0OJ0qOp1cZuoNUhSEEF6jKCqH9tvYn2tFp9PQb6CJdp0MHr1yUNQlRUEI4TX2GpWCPBuxbQPoPcCE0SQj2t4mRUEI4VFOp0rh4Ro6JhoINGoZfn2odDP1IVIUhBAeU1HmYGeOmTOVCsEhWmLiA6Qg+BgpCkKIZuewq+TttnA4vwZTkIZBw4KJkQZ2PkmKghCi2eVsqKas1EGnrgZ69DGhlwZ2PkuKghCiWdTUKOi0GnR6Dd16GenWC6Ji5C3H18l3SAjR5IqP1bB7q4V2nQz07GuSYuBH5DslhGgyVkttA7viY3bC2ui4ooPMG/gbjxWFHTt2sHjxYhRFYeTIkYwdO7bO8rKyMjIzM6murkZRFG677TZSU1M9FU8IcZlKi+1s/7WBXXJvI4nJgWi1MnfgbzxSFBRFISsriyeffJKoqCgef/xx0tLSaNeunWudFStWcNVVVzFq1CiOHTvG3/72NykKQviRoCAt4W10pAwwERom/Yr8lUcuEC4oKCA+Pp64uDj0ej1DhgwhJyenzjoajQazubYRltlsJiIiwhPRhBCNpKoqh/NtbPjuOFDbwO6qESFSEPycR84UKioqiIqKcj2OiooiPz+/zjoTJkzg+eefZ9WqVdhsNubMmVPvtlavXs3q1asBmDt3LtHR0c0X/BLo9XqfydIY/pzfn7ODb+evyduNfc92Anr1x5Dc2/X86ZM1/PDdcY4XW2nXUUubNtHo9f75ITRfPv4X0xzZfWaiecOGDaSnp/O73/2OAwcO8PbbbzNv3rzz7g2ckZFBRkaG67Gv3C3M3+9c5s/5/Tk7+G5+9WAeyrwnweEAvR7t7OdRO3fnYJ6NA3us6PQa+g0Mol9aPOXl5d6O22i+evzd0djsCQkJF1zmkdIeGRlZ54emvLycyMjIOuusXbuWq666CoBu3bpht9upqqryRDwhRD3U/btrC4KqgNOBun839hqVg/ttxCUEkD46lPadpaNpS+ORopCYmEhxcTHHjx/H4XCwceNG0tLS6qwTHR1Nbm4uAMeOHcNutxMWFuaJeEKIemi69wa9Hqc+kJ/bZ0C33q4GdmlXB0tH0xbKI8NHOp2O6dOn88ILL6AoCiNGjKB9+/YsW7aMxMRE0tLSuOOOO1iwYAErV64EYObMmfIXiBBepElM5tT/vMTOA0aq1WBCQ4KJAWlg18JpVFVVL7ZSZWUlBoMBo9GIoih8//33aDQahg0bdt6Yv6cVFRV5df9n+fO4JPh3fn/ODr6Z32FX2bfLwpGCGkzBWvqmmS7YwM4X818Kf87vtTmFuXPnUlxcDMA///lPvvjiC1auXMnSpUsvOYwQwvfl/FDNkYIaOicZSL8+VDqatiJuDR8VFxfTqVMnANavX8/zzz+P0WjkoYceYtq0ac0YTwjhKTU2Ba1Og16voXtvI92ByGifuUBReIhb33GtVovD4aC4uJigoCCio6NRFAWr1drc+YQQHlBUWNvArn3n2gZ2UgxaL7e+8/369eP111+nqqqKIUOGALVXCP32slIhhH+xWhR2b7VQ8oud8AgdV3QweDuS8DK3isKMGTP4/vvv0el0DBs2DICqqiomTJjQrOGEEM2ntOjXBnaKSo8+Rrp0lwZ2ws2iEBAQQEZGBoqicPr0aSIiIujVq1dzZxNCNKOgEC3hkTp6DzAREir9ikQtt4pCdXU1H3zwAZs2bUKv1/PRRx+xZcsWCgoK+P3vf9/cGYUQTUBVVA4X1FB5ykm/gUGEhum4Kj3E27GEj3HrktSFCxcSFBTEO++8g15fW0e6devGxo0bmzWcEKJpVJ12smHtGfZst2CzKjidF/14kmil3DpT2L17NwsWLHAVBICwsDBOnz7dbMGEEJdvX6mZvL1WAk5o0Qdo6D8oiCs6Bki3AHFBbp0pBAUFndecrqysTO55IIQPyzth4cXvjuEohUOKlXYDA2jXSRrYiYa5VRRGjhzJvHnzyM3NRVVVDhw4QGZmJtddd11z5xNCXCKno/bmN7klZs44Ff7XWcZ3yinyTlq8HU34AbeGj26++WYMBgNZWVk4nU7effddMjIyuOGGG5o7nxDiEpQfd7Azx0z1GYUu/YzodRqsioJeqyElLsjb8YQfcKsoaDQabrjhBikCQvgou11l304LPx+sIShYy+D0YGLiAngusgO5pWZS4oJIjjF5O6bwA24VhbP3OahPSkpKk4URQjROzg/VlB930KVbIN17G9Hra+cNkmNMUgzEJXGrKLz77rt1HldWVuJwOIiKimL+/PnNEkwI0TCbTUH3awO75N5GNECE9CwSl8mtn6DMzMw6jxVFYcWKFZhM8heIEJ6mqipFhXZyt1lo38lAz37SwE40nUbdIUer1TJu3Dg+//zzps4jhGiAxayQ80M12340ExSspV0naWAnmlaj/7zYtWuX1++6JkRrUlpkZ9umahQFevY10qVbIBppYCeamFtF4e67767zuKamhpqaGu66665mCSWEOF9QiJaIKD29U00ESwM70UzcKgr33XdfnceBgYG0bduWoCC57lmI5qIqKofybVSectJ/UDChYToGD5cGdqJ5uVUUevbs2dw5hBDnqDrtZMdPZk5VOIltq8fpVNHpZKhINL8LFoW3337brR4p9957b5MGEqI1U5wqBXk2Duy1EhCgIXVwEAkdpIGd8JwLFoX4+Pgm3dGOHTtYvHgxiqIwcuRIxo4de946GzduZPny5Wg0Gjp27MgDDzzQpBmE8HV2u8qhAzYS2gXQq7+JQKNczCE864JFoSlvtakoCllZWTz55JNERUXx+OOPk5aWRrt27VzrFBcX89lnn/Hcc88REhIibblFq+GwKxw6YKNzVwOBRi3po0MxmqQYCO9w+5JUh8NBUVERlZWVdZ53p81FQUEB8fHxxMXFATBkyBBycnLqFIU1a9Zw/fXXExJSO5EWHh7ubjQh/FZZqZ11Xx+lqtJBaLiWmLgAKQjCq9wqCnl5ebz22mvY7XYsFgsmkwmr1ep2m4uKigqioqJcj6OiosjPz6+zTlFREQBz5sxBURQmTJhAv379ztvW6tWrWb16NQBz584lOjranZfQ7PR6vc9kaQx/zu+P2WtsTnI2lnNgbzVh4QGMHptA2yv882o+fzz+5/Ln/M2R3a2isGTJEm666SZuvPFG/vu//5vFixfz6aefYjA03acpFUWhuLiYp59+moqKCp5++mleffVVgoOD66yXkZFBRkaG63FZWVmTZbgc0dHRPpOlMfw5vz9lzzthIbfUTJtf9FhOqyQmBzJk2BWcOl1BWZnZ2/EaxZ+Of338OX9jsyckJFxwmVvnqUVFRee1zR47diwrV650K0BkZCTl5eWux+Xl5URGRp63TlpaGnq9ntjYWNq2bUtxcbFb2xfCH+T+YuaZNUf5eOcJVpwsp/2AAHr2NaEPkOEi4Tvcvh2nxVJ716Y2bdpw7Ngxzpw5g9VqdWsniYmJFBcXc/z4cRwOBxs3biQtLa3OOgMHDmTPnj1AbRfW4uJi1xyEEP5MVVWO/VzDoR9t9FFDUIASpYYCi3u/P0J4klvDR4MGDWL79u0MHTqUESNG8Oyzz6LT6Rg8eLBbO9HpdEyfPp0XXngBRVEYMWIE7du3Z9myZSQmJpKWlkbfvn3ZuXMnDz74IFqtlttvv53Q0NDLenFCeJvFrLB7q5nSIgemMC2HTlvQapA7oQmfpVFVVb3Qwm+++YahQ4eeN66fl5eHxWKhb9++Xm+Kd3aC2tv8eVwS/Du/r2Yv+cXO9k3VqCok9zbSOSmQ/eXW8+6E5qv53SX5vac55hQaPFNYu3YtS5cupX///qSnp5OamopWqyU5OfmSQwjR2gSHaomM0ZOSaiI4pLaBndwJTfi6BovCSy+9xNGjR8nOziYrK4sFCxYwZMgQ0tPT6dy5s6cyCuEXFEXl8IFfG9gNrm1gN2iYNLAT/uWicwodOnTg9ttvZ/LkyezevZvs7GyefvppYmJiGD58ODfddJMncgrh0ypP1TawO33SSdwV0sBO+C+3P9Gs0Wjo06cPffr0Yc+ePbzzzjt8/PHHUhREq+Z0qhTss5K/10aAQcOAIUG0bScN7IT/crsoVFRUkJ2dTXZ2NuXl5QwaNIj09PRmjCaE73PYVY4U1HBFh9oGdoZA+cyB8G8NFgWbzcamTZvIzs5m79699OzZk7FjxzJo0CACAwM9lVEIn+JwqPx80EaXpEBXAzvpZipaigaLwl133UV0dDTDhg1j5syZdfoXCdEanSi1syvHgrlaIbyNjui4ACkIokVpsCg89dRTJCUleSqLED7LXqOwd4eVo4drCA7RMmRECFGxbo++CuE3GvyploIgRK2cDWYqTjjomhxIt15GdHqZSBYtk/ypI8QF2KwKOr0GvV5Djz5GNBpoEym/MqJlk59wIX6jtoGdnT3bLbTvbKBXPxMRUfKrIloH+UkX4hzm6toGdseLHURE6ejQpenuGSKEP3CrKNjtdj799FM2bNhAVVUVS5YsYefOnRQXFzN69OjmziiER5T8YmfbpmoAUvqb6NTVgEYrcweidXHrWrolS5ZQWFjI/fff7/qkZvv27fm///u/Zg0nhCecbRQcEqolOlZP+uhQOncLlIIgWiW3zhR++ukn3nrrLYxGo6soREZGUlFR0azhhGhOiqJyaL+NytNOUgcHExKmY+A10sBOtG5uFQW9Xo+iKHWeq6yslJvgCL91+qSTnTm1DezirwiQBnZC/Mqt4aPBgwczf/58jh8/DsDJkyfJyspiyJAhzRpOiKbmdKrk7baw/tsqrBaFAUOCuHJosBQEIX7lVlG47bbbiI2NZfbs2ZjNZu6//34iIiKYMGFCc+cTosnknbDw790VHMq3cUXHANJHh5LQXq4uEuJcbg8fTZs2jWnTprmGjaQ1sPAXDrtKzq4zvJ5fhN2pEqzT8mSX9tLRVIh6uFUUSktL6zy2WCyu/8fFxTVtIiGa0PESO7tyzJjNClFKAEXUUK0o5Jaa5baYQtTDraJw//33X3DZsmXLmiyMEE2lxlbbwK7wSA3BoVrapxoo22pHq4BeqyElLsjbEYXwSW4Vhd++8Z86dYrly5fTo0ePZgklxOXK2VDNyTInXXv82sBOp+G5Nh3ILTWTEhckZwlCXECjBlXbtGnDtGnT+Mc//uH21+zYsYMHHniA++67j88+++yC623atImJEydy8ODBxkQTrZjVouBw1H4QrWdfE9dcF0KPPibXlUXJMSbGp0RJQRCiAY2eaSsqKsJms7m1rqIoZGVl8cQTT/D666+zYcMGjh07dt56FouFr7/+Wlp2i0uiqiqFh22sW1XF/t1WACKi9IRHSGsvIS6VW781Tz31VJ2rjWw2G4WFhYwfP96tnRQUFBAfH++alB4yZAg5OTm0a9euznrLli3j5ptv5j//+Y+7+UUrZ652snVjEUWFFiKjdXRMlEtMhbgcbhWFa6+9ts5jo9FIx44dadu2rVs7qaioqHMrz6ioKPLz8+usc+jQIcrKykhNTW2wKKxevZrVq1cDMHfuXKKjo93K0Nz0er3PZGkMf8z/86EzZH9bikajYfCwaJJTwv3yUml/PPbnkvze0xzZL1oUFEUhNzeXP/3pTwQEBDTpzs/dx9KlS5k5c+ZF183IyCAjI8P1uKysrFkyXaro6GifydIY/pRfVVU0Gg2qxklUnI5h116BteY05eXl3o7WKP507Osj+b2nsdkTEhIuuOyiRUGr1bJr167L+gssMjKyzi9seXk5kZGRrsdWq5XCwkKeffZZoPbqppdffplHHnmExMTERu9XtBx5JyzklphJsBowOrSkXhVMSKiOgUNDCAkLwOqfv9NC+By3JprHjBnDv/71LxwOR6N2kpiYSHFxMcePH8fhcLBx40bS0tJcy4OCgsjKyiIzM5PMzEySkpKkIAiXvBMWXl9TxJk9CuWHnJy2OnE6VW/HEqJFavBM4YcffmDo0KGsWrWKU6dOsXLlSsLCwuqs8+677150JzqdjunTp/PCCy+gKAojRoygffv2LFu2jMTExDoFQohzOR0q+3ZZuIFIrCisdp7kmpgwaWAnRDNpsCgsXLiQoUOHct999132jlJTU0lNTa3z3KRJk+pd95lnnrns/YmWweFUCTytJQ8Lm5VKFC3yaWQhmlGDReHsHal69uzpkTBCAOwtNrN/v5XknkZ6xAYxckwYXSoD6VBqkE8jC9HMGiwKZ688akhKSkqTBhKt29a8ag7ssBKMlsySEu4d2ZbkGJPrnxCieTVYFOx2O++9957rjOG3NBoN8+fPb5ZgonWpsSns2WGh6IgdOypfOCso09ilm6kQHtZgUTAajS3yTV89mIe6fzea7r3RJCZ7O45X+MIxyDthcTWoK9/t4GSZk8hOOv5+uJQajSrdTIXwglbXHEY9mIcy70lwOFD1erSzn291haG+Y0D0UI9myDth4cU1x7A4FZbp4C9XtiOlfxDhETqiukg3UyG8xa2J5pZE3b8bHA5QFXA6av9abm1FoZ5jwCDPFIWzH0KrLlW4iSgOaC3kKFUUWKz0iwgGkPkDIbyowQ+vLV261FM5PEbTvTfo9aDVgk5f+7iV8dYxyDth4aU1v1CW6yCyPICTONivmmWYSAgf0uqGjzSJyWhnP+/18XRv8vQxODt3UHXcye+IRNXABuU0HTsH8ruQSBkmEsKHtLqiALVviq2xGJzLU8cg74SFJ1cfxa6oRGh1DNCEslmpxKZVmZYYK8VACB/TKouCt/jCFT+etK/UzLc/nmYoYXzHaU4qTqraORgbFSVnB0L4KCkKHtLarnracbCanTlmOmtMHMSCFlCACKOe8SlRF/tyIYSXSFHwkNZy1dPeEjP7c63oyjUEouX/nCc5qtbetlWvhRFdwr2cUAjRECkKHqLp3htVrweno8Ve9ZR3wsKL645xE1EcwsoWtQorKgFaDSO7hDOiS7gMGQnh46QoNIP65g5a8lVPdrvKkQIbuQ4z1U6FTynDrlEZldiGmOAAmT8Qwo9IUWhiDc0dtMSrnkqL7OzaYsZqVenSz4hep8Gu1LaokDMDIfyPFIUm1lrmDmxWhT3bLfxy1E5omJa0q4OJiNLzXIS0qBDCn0lRaGKtYe4AYMvGak6WO+nWy0hSj0C0v94JTVpUCOHfpCg0sZY8d2AxKwQEaNAHaOjVz4RWqyGsjc7bsYQQTUiKQjNoaXMHqqpy9FANe3da6NA5kF79TbSJlB8dIVoi+c0WDao+42RnjoXy4w6iYvV0SjJ4O5IQohlJURAXVFRYw/bNZrRa6JNmokMXAxqNxtuxhBDNSIqCOI+qqmg0tfMFcW0D6NXfhCmowS7rQogWwmNFYceOHSxevBhFURg5ciRjx46ts/zLL79kzZo16HQ6wsLCuPvuu4mJifFUPAEoTpX8fTbOVDpJvSqIkFAdaVcHezuWEMKDPPLnn6IoZGVl8cQTT/D666+zYcMGjh07VmedTp06MXfuXF599VUGDx7M3//+d09EE786UWol+9sqDuyxotGAong7kRDCGzxSFAoKCoiPjycuLg69Xs+QIUPIycmps05KSgqBgYEAJCUlUVFR4YlorZ7DobJnh4WVK45hr1EZeE0wqVcFo9PJ3IEQrZFHho8qKiqIivr/7ZKjoqLIz8+/4Ppr166lX79+9S5bvXo1q1evBmDu3LlER0c3adbG0uv1PpPlUlitToqPHiU5pQ2pgyMxGPxv7sBfj/1Zkt+7/Dl/c2T3uYnm7OxsDh06xDPPPFPv8oyMDDIyMlyPy8rKPJSsYdHR0T6T5WLsNSqHC2x0TQ5Eq9UwbHQwCQn+k/+3/OnY10fye5c/529s9oSEhAsu80hRiIyMpLy83PW4vLycyMjI89bbtWsX//73v3nmmWcICAjwRLRWp+QXO7u31jawi4zWER0b4JdnB0KI5uGRd4PExESKi4s5fvw4DoeDjRs3kpaWVmedw4cPs3DhQh555BHCw+VGLE3NZlXY+mM1OT9UE2DQcE1GCNGxUniFEHV55ExBp9Mxffp0XnjhBRRFYcSIEbRv355ly5aRmJhIWloaf//737Farbz22mtA7WnRo48+6ol4rcLZBnbdU4y1w0YykSyEqIfH5hRSU1NJTU2t89ykSZNc/58zZ46norQav21gp9NpCA2XBnZCiAvzuYlmcflWHThF/gErnSxGOncNJEUa2Akh3CQzjC3Mqt2nOLqthk7VRn5x2DgWYPN2JCGEH5Gi0IIUFdZg26sSiZ5s52m+Vk7yY2mVt2MJIfyIjCm0AGcb2IW30aGP0PDJiROYqe1TMaRDqJfTCSH8iRQFP5V3wkJusZm2FgOBDi0DhgQRHKpjzKg26PNh49EqhnQI5fqkNt6OKoTwI1IU/FDeCQtvrinmKkKp0DgJjVdRFND9emHR9UltpBgIIRpF5hT8jMOhsm+Hhf8iggC0/J+zgrIouzSwE0I0CTlT8DOKUyXwjJY9mPlJqQItpMQFeTuWEKKFkKLgB+w1Cofza+jaIxBDoJbrxoTR5XQgnUsDSYkLIjnG5O2IQogWQoqCjys+VsPurRZqbCpRMXqiYvUEGLQkx5ikGAghmpwUBR9lsyrs3mahuNBOWBstA68Jlk8lCyGanbzL+KgtG6o5VeGke2+j674HQgjR3KQo+BBztYLBUNvALiXVhFYrDeyEEJ4lRcEHqKrKkYIa9u2y0KFLbQO78Aj51gghPE/eebzsTKWTnTlmKsqcRMfp6dLN4O1IQohWTIqCFxUdrWH7ZjM6nYZ+A02062RAo5G5AyGE90hR8LC8ExZyS8ykxAfRPtJA23YB9OxnwmiSD5cLIbxP3ok8aG+JmRVryinOtTNnzVEKLTWkXhUsBUEI4TPkTMED8k5Y2HPYjOaYhj6aEA6oFhRFJbfULB9AE0L4FCkKzSjvhIV1Baep/FkhGRPVKKxTT1Ko2tBrNdKzSAjhc6QoNJO8ExbmrDmKxgnjdNHsVc1sVc9wbWI4w4LDpGeREMInSVFoBjU2hX27LTidKk7gU2cZDlQCdBpGdAmXYiCE8FkeKwo7duxg8eLFKIrCyJEjGTt2bJ3ldrud+fPnc+jQIUJDQ5k1axaxsbGeitdkigpryN1mQW/T0lZnoEipQdWoXN+ljRQEIYTP80hRUBSFrKwsnnzySaKionj88cdJS0ujXbt2rnXWrl1LcHAwb7/9Nhs2bODjjz/mwQcf9ES8JmGudpCzoZqSY3bC2ugYNMxEkiOQ3FKzDBUJIfyGR66FLCgoID4+nri4OPR6PUOGDCEnJ6fOOlu2bCE9PR2AwYMHk5ubi6qqnojXJNZ9U8LxIjs9+hi55roQwiP0JMeYGJ8SJQVBCOE3PHKmUFFRQVRUlOtxVFQU+fn5F1xHp9MRFBREVVUVYWFhddZbvXo1q1evBmDu3LlER0c3c/oLO1NpJ9CoI8Cg5eoRDkAhPMI/21To9XqvHsvL4c/ZQfJ7mz/nb47sfjfRnJGRQUZGhutxWVmZxzOoqsqR/Br27bbQobOBlNQgoqOjKSsrwwtxmsTZ/P7In7OD5Pc2f87f2OwJCQkXXOaRohAZGUl5ebnrcXl5OZGRkfWuExUVhdPpxGw2Exoa6ol4l6Tq1wZ2J8ucxMTr6dLd6O1IQgjRZDwyp5CYmEhxcTHHjx/H4XCwceNG0tLS6qwzYMAA1q1bB8CmTZvo1auXzzWH++VoDdnfVHGmUqHfoCAGDQsmKFhaVAghWg6PnCnodDqmT5/OCy+8gKIojBgxgvbt27Ns2TISExNJS0vj2muvZf78+dx3332EhIQwa9YsT0Rzi6qqaDQa2kTqaNs+gF79TAQapRgIIVoejepPl/jUo6ioqNm27XSoHNhj5UyVQtrVQQ2eufjzuCT4d35/zg6S39v8Ob/fzin4o/ITDnbmmKmuUujQ2YCqgEbujCmEaOGkKPyGw66yb5eFIwU1BAVrGTw8mJj4AG/HEkIIj5Ci8BuKolLyi53O3QJJ7m1Er/etyW4hhGhOUhSobWB36ICNbr2MGAK1jPivMPQBUgyEEK1Pqy4KqqpSfMzO7q0W7DUqMfEBRMXopSAIIVqtVlsUrBaF3VstlPxiJzxCx+DhQYRHyEyyEKJ1a7VFYcvGak6fdNKjr5Eu3QLRauXsQAghWm1R6J0ahE4PIaFydiCEEGe12qIgQ0VCCHE+6dUghBDCRYqCEEIIFykKQgghXKQoCCGEcJGiIIQQwkWKghBCCBcpCkIIIVykKAghhHDx+zuvCSGEaDpyptBEHnvsMW9HuCz+nN+fs4Pk9zZ/zt8c2aUoCCGEcJGiIIQQwkWKQhPJyMjwdoTL4s/5/Tk7SH5v8+f8zZFdJpqFEEK4yJmCEEIIFykKQgghXFrtTXYaa8eOHSxevBhFURg5ciRjx46ts9xutzN//nwOHTpEaGgos2bNIjY21jthf+Ni2b/88kvWrFmDTqcjLCyMu+++m5iYGO+ErcfF8p+1adMmXnvtNf72t7+RmJjo2ZANcCf/xo0bWb58ORqNho4dO/LAAw94PugFXCx/WVkZmZmZVFdXoygKt912G6mpqd4J+xvvvPMO27ZtIzw8nHnz5p23XFVVFi9ezPbt2wkMDGTmzJl06dLFC0nPd7Hs69ev5/PPP0dVVUwmE3fddRedOnVq/A5V4Tan06nee++9aklJiWq329U///nPamFhYZ11Vq1apS5YsEBVVVX94Ycf1Ndee80bUc/jTvbdu3erVqtVVVVV/eabb3wmu6q6l19VVdVsNqtPPfWU+sQTT6gFBQVeSFo/d/IXFRWpDz/8sFpVVaWqqqqeOnXKG1Hr5U7+9957T/3mm29UVVXVwsJCdebMmd6IWq89e/aoBw8eVB966KF6l2/dulV94YUXVEVR1P3796uPP/64hxNe2MWy5+XluX5mtm3bdtnZZfjoEhQUFBAfH09cXBx6vZ4hQ4aQk5NTZ50tW7aQnp4OwODBg8nNzUX1gbl8d7KnpKQQGBgIQFJSEhUVFd6IWi938gMsW7aMm2++mYCAAC+kvDB38q9Zs4brr7+ekJAQAMLDw70RtV7u5NdoNJjNZgDMZjMRERHeiFqvnj17uo5rfbZs2cKwYcPQaDR069aN6upqTp486cGEF3ax7N27d3ctT0pKory8/LL2J0XhElRUVBAVFeV6HBUVdd4b57nr6HQ6goKCqKqq8mjO+riT/Vxr166lX79+HkjmHnfyHzp0iLKyMp8ZsjiXO/mLioooLi5mzpw5/OUvf2HHjh0eTnlh7uSfMGEC69evZ8aMGfztb39j+vTpno7ZaBUVFURHR7seX+z3w1etXbuW/v37X9Y2pCiI82RnZ3Po0CFuuukmb0dxm6IoLF26lDvuuMPbURpNURSKi4t5+umneeCBB1iwYAHV1dXejuW2DRs2kJ6eznvvvcfjjz/O22+/jaIo3o7VauTm5vLdd98xefLky9qOFIVLEBkZWefUrLy8nMjIyAuu43Q6MZvNhIaGejRnfdzJDrBr1y7+/e9/88gjj/jUEMzF8lutVgoLC3n22We55557yM/P5+WXX+bgwYPeiHsed3920tLS0Ov1xMbG0rZtW4qLiz0dtV7u5F+7di1XXXUVAN26dcNut/vEWbI7IiMjKSsrcz2+0O+Hr/r5559ZsGABDz/88GW/30hRuASJiYkUFxdz/PhxHA4HGzduJC0trc46AwYMYN26dUDtVTC9evVCo9F4IW1d7mQ/fPgwCxcu5JFHHvGp8Wy4eP6goCCysrLIzMwkMzOTpKQkHnnkEZ+5+sid4z9w4ED27NkDQGVlJcXFxcTFxXkj7nncyR8dHU1ubi4Ax44dw263ExYW5o24lywtLY3s7GxUVeXAgQMEBQX51JxIQ8rKynj11Ve59957SUhIuOztySeaL9G2bdtYsmQJiqIwYsQIxo0bx7Jly0hMTCQtLY2amhrmz5/P4cOHCQkJYdasWT7zi32x7M899xxHjx6lTZs2QO0v+aOPPurd0Oe4WP5zPfPMM0yZMsVnigJcPL+qqixdupQdO3ag1WoZN24cV199tbdju1ws/7Fjx1iwYAFWqxWA22+/nb59+3o5da033niDvXv3UlVVRXh4OBMnTsThcAAwatQoVFUlKyuLnTt3YjAYmDlzps/87Fws+3vvvcfmzZtdcyI6nY65c+c2en9SFIQQQrjI8JEQQggXKQpCCCFcpCgIIYRwkaIghBDCRYqCEEIIFykKotXJzMzkk08+AWDfvn0e60Q6ceJESkpKmny799xzD7t27Wry7YrWSVpnC590zz33cOrUKbRaLUajkX79+nHnnXdiNBqbdD89evTgzTffvOh669atY82aNTz33HNNun+A999/n5qaGu699946zx85coQnnniC999/v8GGaEI0JTlTED7r0Ucf5aOPPuKll17i0KFDrFix4rx1nE6nF5I1rfT0dDZv3uz60NdZ2dnZpKamSkEQHiVnCsLnRUZG0q9fPwoLC4HaYZjp06fz1Vdf4XQ6yczMZOvWrXzyySecOHGCdu3a8cc//pGOHTsCte073nvvPYqLi+nfv3+dtiN79uzh7bff5r333gNqWwZ8+OGH7Nu3D1VVufrqq7n++utZuHAhDoeDKVOmoNPp+PDDD7Hb7fzzn//kxx9/xOFwcOWVVzJt2jQMBgMA//nPf/jyyy/RaDRMmjTpgq+vW7duREZGsnnzZoYPHw7UNsfbsGEDd911FyUlJSxYsICff/4ZjUZD3759ufPOOwkODj5vW5mZmURFRfH73/++3tdXUVHBokWL2LdvH0ajkTFjxnDDDTcAte2xP/jgA4qLizEYDAwdOpSpU6de1vdO+B8pCsLnlZWVsX37dgYOHOh6LicnhxdffBGDwcDhw4d59913efTRR0lMTCQ7O5uXX36ZN954A41GwyuvvMINN9zA6NGj2bJlC2+++SY333zzeftRFIWXXnqJXr16kZmZiVar5dChQ64i89vho48//pjS0lJeeeUVdDodb775Jp9++im33XYbO3bs4IsvvmDOnDnExsayYMGCBl/j8OHDyc7OdhWFXbt24XA46N+/P2VlZdxyyy306NEDi8XCvHnzWL58OdOmTbuk43j29V155ZXMmjWL8vJynnvuORISEujXrx+LFy/mhhtuYNiwYVitVo4ePXpJ2xctgwwfCZ/1yiuvMG3aNJ566il69uzJuHHjXMtuueUWQkJCMBgMrF69moyMDJKSktBqtaSnp6PX68nPz+fAgQM4nU7GjBmDXq9n8ODBF+xpU1BQQEVFBVOmTMFoNGIwGEhOTq53XVVVWbNmDVOnTiUkJASTycS4cePYsGEDUHtbzfT0dDp06IDRaGTChAkNvtZhw4axd+9eVyfS7Oxshg4dil6vJz4+nj59+hAQEEBYWBhjxoxh7969l3w8Dx48SGVlJePHj0ev1xMXF8fIkSPZuHEjAHq9npKSEiorKzEajXTr1u2S9yH8n5wpCJ/18MMP06dPn3qXnXvDl7KyMr7//ntWrVrles7hcFBRUYFGoyEyMrLOkNG5N1M5V1lZGTExMeh0uotmq6ysxGaz8dhjj7meU1XVdf+AkydP1rnH78XudR0dHU2PHj1Yv349o0ePJicnh2effRaAU6dOuYa0rFYriqI0ap7hxIkTnDx5ss4ZhqIo9OjRA4AZM2awbNkyHnzwQWJjYxk/fjwDBgy45P0I/yZFQfilc9/ko6KiGDduXJ0zibP27t1LRUUFqqq6vqa8vJz4+Pjz1o2OjqasrAyn03nRwhAaGorBYOC1116rt+9+REREnfsPnNur/0KGDx/O559/Tps2bYiNjXUVlX/+858AzJs3j5CQEH766ScWLVpU7zYCAwOx2Wyux6dOnarz+mJjY3nrrbfq/dq2bdsya9YsFEXhp59+4rXXXiMrK6vJr/gSvk2Gj4TfGzlyJN9++y35+fmoqorVamXbtm1YLBa6deuGVqvl66+/xuFwsHnzZgoKCurdTteuXYmIiODjjz/GarVSU1NDXl4eAG3atKGiosLVslir1TJy5Eg+/PBDTp8+DdRO4p69heZVV13FunXrOHbsGDabjeXLl1/0dQwaNIiysjKWL1/umlsAsFgsGI1GgoKCqKio4IsvvrjgNjp16sT27ds5c+YMp06d4quvvqrz+kwmE5999hk1NTUoisLRo0ddxyM7O5vKykq0Wi1BQUGu1ylaFzlTEH4vMTGRP/3pTyxatMh15UxycjI9evRAr9fz5z//mQULFvDJJ5/Qv3//OhPW59JqtTz66KMsWrSImTNnotFouPrqq0lOTiYlJcU14azVasnKymLy5Ml8+umn/OUvf6GqqorIyEiuu+46+vXrR//+/RkzZgzPPvssWq2WSZMm8cMPPzT4OoxGI4MGDWL9+vUMHTrU9fyECROYP38+U6dOJT4+nmHDhrFy5cp6tzFs2DB2797NPffcQ0xMDOnp6Xz55Zd1Xt/SpUu55557cDgcJCQkuK6M2rFjB0uXLsVmsxETE8MDDzzgupJKtB5yPwUhhBAucm4ohBDCRYqCEEIIFykKQgghXKQoCCGEcJGiIIQQwkWKghBCCBcpCkIIIVykKAghhHD5fwUIZ2Yfi0iIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Parity plot\n",
    "\n",
    "plt.plot(lreg_y_pred, y_validation, '.', label='validation')\n",
    "plt.plot(lreg.predict(x_train), y_train, '.', label='train')\n",
    "\n",
    "min_value = min(min(lreg_y_pred), min(y_validation), min(lreg.predict(x_train)), min(y_train))\n",
    "max_value = max(max(lreg_y_pred), max(y_validation), max(lreg.predict(x_train)), max(y_train))\n",
    "plt.plot([min_value, max_value],[min_value, max_value], '--', label=\"parity\")\n",
    "plt.xlabel('Predicted Values')\n",
    "plt.ylabel('True Values')\n",
    "ax.set_box_aspect(1)\n",
    "sns.despine()\n",
    "plt.title('Parity Plot')\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Columns</th>\n",
       "      <th>Coefficient Estimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p-AKT(S473)[15m]</td>\n",
       "      <td>-0.087169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p-S6RIBPROT(S240/244)[15m]</td>\n",
       "      <td>0.063178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>p-EGFR(Y1086)[05m]</td>\n",
       "      <td>0.022714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p-EGFR(Y1086)[15m]</td>\n",
       "      <td>-0.191191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p-CDK2(T160)[15m]</td>\n",
       "      <td>0.030541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>p-STAT5(Y694)[15m]</td>\n",
       "      <td>0.010791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>p-CDC2(Y15)[15m]</td>\n",
       "      <td>0.089406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>p-CREB(S133)[15m]</td>\n",
       "      <td>0.020892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>p-PI3KP85(Y458)P55(Y199)[05m]</td>\n",
       "      <td>0.027944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>p-MTOR(S2448)[15m]</td>\n",
       "      <td>0.054708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Columns  Coefficient Estimate\n",
       "0                 p-AKT(S473)[15m]             -0.087169\n",
       "1       p-S6RIBPROT(S240/244)[15m]              0.063178\n",
       "2               p-EGFR(Y1086)[05m]              0.022714\n",
       "3               p-EGFR(Y1086)[15m]             -0.191191\n",
       "4                p-CDK2(T160)[15m]              0.030541\n",
       "..                             ...                   ...\n",
       "175             p-STAT5(Y694)[15m]              0.010791\n",
       "176               p-CDC2(Y15)[15m]              0.089406\n",
       "177              p-CREB(S133)[15m]              0.020892\n",
       "178  p-PI3KP85(Y458)P55(Y199)[05m]              0.027944\n",
       "179             p-MTOR(S2448)[15m]              0.054708\n",
       "\n",
       "[180 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine coefficient and corresponding variable names\n",
    "lreg_coefficient = pd.DataFrame()\n",
    "lreg_coefficient[\"Columns\"] = x_train.columns\n",
    "lreg_coefficient['Coefficient Estimate'] = pd.Series(lreg.coef_)\n",
    "lreg_coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the magnitude of coefficients\n",
    "fig, ax = plt.subplots(figsize =(7, 7))\n",
    "color =['midnightblue', 'navy', 'darkblue', 'mediumblue', 'blue', \n",
    "        'darkslateblue','mediumslateblue', 'slateblue', \n",
    "        'mediumpurple', 'blueviolet', 'darkviolet']\n",
    "\n",
    "ax.bar(lreg_coefficient[\"Columns\"], lreg_coefficient['Coefficient Estimate'], color = color)\n",
    " \n",
    "ax.spines['bottom'].set_position('zero')\n",
    " \n",
    "plt.style.use('ggplot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (2) Ridge Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge regression R2 (training data)\n",
      "0.992\n",
      "\n",
      "Ridge regression Q2 (validation data)\n",
      "0.388\n",
      "\n",
      "Mean squared Error on test set :  0.065\n"
     ]
    }
   ],
   "source": [
    "# Train a Ridge regression model\n",
    "from sklearn.linear_model import Ridge\n",
    " \n",
    "ridgeR = Ridge(alpha = 0.2)\n",
    "ridgeR.fit(x_train, y_train)\n",
    "\n",
    "# Predict validation data\n",
    "ridge_y_pred = ridgeR.predict(x_validation)\n",
    " \n",
    "print(\"Ridge regression R2 (training data)\")\n",
    "print(f\"{ridgeR.score(x_train, y_train):.3f}\" \"\\n\")\n",
    "\n",
    "print(\"Ridge regression Q2 (validation data)\")\n",
    "print(f\"{ridgeR.score(x_validation, y_validation):.3f}\" \"\\n\")\n",
    "\n",
    "mean_squared_error = round(np.mean((ridge_y_pred - y_validation)**2),3)\n",
    "print(\"Mean squared Error on test set : \", mean_squared_error)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Parity plot\n",
    "\n",
    "plt.plot(ridgeR.predict(x_validation), y_validation, '.', label='validation')\n",
    "plt.plot(ridgeR.predict(x_train), y_train, '.', label='train')\n",
    "\n",
    "min_value = min(min(ridgeR.predict(x_validation)), min(y_validation), min(ridgeR.predict(x_train)), min(y_train))\n",
    "max_value = max(max(ridgeR.predict(x_validation)), max(y_validation), max(ridgeR.predict(x_train)), max(y_train))\n",
    "plt.plot([min_value, max_value],[min_value, max_value], '--', label=\"parity\")\n",
    "plt.xlabel('Predicted Values')\n",
    "plt.ylabel('True Values')\n",
    "ax.set_box_aspect(1)\n",
    "sns.despine()\n",
    "plt.title('Parity Plot')\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Columns</th>\n",
       "      <th>Coefficient Estimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p-AKT(S473)[15m]</td>\n",
       "      <td>-0.036525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p-S6RIBPROT(S240/244)[15m]</td>\n",
       "      <td>0.067584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>p-EGFR(Y1086)[05m]</td>\n",
       "      <td>0.005154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p-EGFR(Y1086)[15m]</td>\n",
       "      <td>-0.117722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p-CDK2(T160)[15m]</td>\n",
       "      <td>0.033064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>p-STAT5(Y694)[15m]</td>\n",
       "      <td>0.027302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>p-CDC2(Y15)[15m]</td>\n",
       "      <td>0.055975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>p-CREB(S133)[15m]</td>\n",
       "      <td>-0.000782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>p-PI3KP85(Y458)P55(Y199)[05m]</td>\n",
       "      <td>0.017537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>p-MTOR(S2448)[15m]</td>\n",
       "      <td>0.039554</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Columns  Coefficient Estimate\n",
       "0                 p-AKT(S473)[15m]             -0.036525\n",
       "1       p-S6RIBPROT(S240/244)[15m]              0.067584\n",
       "2               p-EGFR(Y1086)[05m]              0.005154\n",
       "3               p-EGFR(Y1086)[15m]             -0.117722\n",
       "4                p-CDK2(T160)[15m]              0.033064\n",
       "..                             ...                   ...\n",
       "175             p-STAT5(Y694)[15m]              0.027302\n",
       "176               p-CDC2(Y15)[15m]              0.055975\n",
       "177              p-CREB(S133)[15m]             -0.000782\n",
       "178  p-PI3KP85(Y458)P55(Y199)[05m]              0.017537\n",
       "179             p-MTOR(S2448)[15m]              0.039554\n",
       "\n",
       "[180 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine coefficient and corresponding variable names\n",
    "ridge_coefficient = pd.DataFrame()\n",
    "ridge_coefficient[\"Columns\"]= x_train.columns\n",
    "ridge_coefficient['Coefficient Estimate'] = pd.Series(ridgeR.coef_)\n",
    "ridge_coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the magnitude of coefficients\n",
    "fig, ax = plt.subplots(figsize =(7, 7))\n",
    "color =['midnightblue', 'navy', 'darkblue', 'mediumblue', 'blue', \n",
    "        'darkslateblue','mediumslateblue', 'slateblue', \n",
    "        'mediumpurple', 'blueviolet', 'darkviolet']\n",
    "\n",
    "ax.bar(ridge_coefficient[\"Columns\"], ridge_coefficient['Coefficient Estimate'], color = color)\n",
    " \n",
    "ax.spines['bottom'].set_position('zero')\n",
    " \n",
    "plt.style.use('ggplot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (3) LASSO Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LASSO regression R2 (training data)\n",
      "0.023\n",
      "\n",
      "LASSO regression Q2 (validation data)\n",
      "-0.192\n",
      "\n",
      "Mean squared Error on test set :  0.126\n"
     ]
    }
   ],
   "source": [
    "# Train a LASSO regression model\n",
    "from sklearn.linear_model import Lasso\n",
    " \n",
    "lasso = Lasso(alpha = 0.2)\n",
    "lasso.fit(x_train, y_train)\n",
    "\n",
    "# Predict validation data\n",
    "lasso_y_pred = lasso.predict(x_validation)\n",
    " \n",
    "print(\"LASSO regression R2 (training data)\")\n",
    "print(f\"{lasso.score(x_train, y_train):.3f}\" \"\\n\")\n",
    "\n",
    "print(\"LASSO regression Q2 (validation data)\")\n",
    "print(f\"{lasso.score(x_validation, y_validation):.3f}\" \"\\n\")\n",
    "\n",
    "mean_squared_error = round(np.mean((lasso_y_pred - y_validation)**2),3)\n",
    "print(\"Mean squared Error on test set : \", mean_squared_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Parity plot\n",
    "\n",
    "plt.plot(lasso.predict(x_validation), y_validation, '.', label='validation')\n",
    "plt.plot(lasso.predict(x_train), y_train, '.', label='train')\n",
    "\n",
    "min_value = min(min(lasso.predict(x_validation)), min(y_validation), min(lasso.predict(x_train)), min(y_train))\n",
    "max_value = max(max(lasso.predict(x_validation)), max(y_validation), max(lasso.predict(x_train)), max(y_train))\n",
    "plt.plot([min_value, max_value],[min_value, max_value], '--', label=\"parity\")\n",
    "plt.xlabel('Predicted Values')\n",
    "plt.ylabel('True Values')\n",
    "ax.set_box_aspect(1)\n",
    "sns.despine()\n",
    "plt.title('Parity Plot')\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Columns</th>\n",
       "      <th>Coefficient Estimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p-AKT(S473)[15m]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p-S6RIBPROT(S240/244)[15m]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>p-EGFR(Y1086)[05m]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p-EGFR(Y1086)[15m]</td>\n",
       "      <td>-0.007002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p-CDK2(T160)[15m]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>p-STAT5(Y694)[15m]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>p-CDC2(Y15)[15m]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>p-CREB(S133)[15m]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>p-PI3KP85(Y458)P55(Y199)[05m]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>p-MTOR(S2448)[15m]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Columns  Coefficient Estimate\n",
       "0                 p-AKT(S473)[15m]              0.000000\n",
       "1       p-S6RIBPROT(S240/244)[15m]              0.000000\n",
       "2               p-EGFR(Y1086)[05m]             -0.000000\n",
       "3               p-EGFR(Y1086)[15m]             -0.007002\n",
       "4                p-CDK2(T160)[15m]              0.000000\n",
       "..                             ...                   ...\n",
       "175             p-STAT5(Y694)[15m]             -0.000000\n",
       "176               p-CDC2(Y15)[15m]             -0.000000\n",
       "177              p-CREB(S133)[15m]             -0.000000\n",
       "178  p-PI3KP85(Y458)P55(Y199)[05m]             -0.000000\n",
       "179             p-MTOR(S2448)[15m]             -0.000000\n",
       "\n",
       "[180 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine coefficient and corresponding variable names\n",
    "lasso_coeff = pd.DataFrame()\n",
    "lasso_coeff[\"Columns\"] = x_train.columns\n",
    "lasso_coeff['Coefficient Estimate'] = pd.Series(lasso.coef_)\n",
    " \n",
    "lasso_coeff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the magnitude of coefficients\n",
    "fig, ax = plt.subplots(figsize =(7, 7))\n",
    "color =['midnightblue', 'navy', 'darkblue', 'mediumblue', 'blue', \n",
    "        'darkslateblue','mediumslateblue', 'slateblue', \n",
    "        'mediumpurple', 'blueviolet', 'darkviolet']\n",
    "\n",
    "ax.bar(lasso_coeff[\"Columns\"], lasso_coeff['Coefficient Estimate'], color = color)\n",
    " \n",
    "ax.spines['bottom'].set_position('zero')\n",
    " \n",
    "plt.style.use('ggplot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (4) Elastic Net Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elastic Net regression R2 (training data)\n",
      "0.544\n",
      "\n",
      "Elastic Net regression Q2 (validation data)\n",
      "0.385\n",
      "\n",
      "Mean squared Error on test set :  0.065\n"
     ]
    }
   ],
   "source": [
    "# Train and Elastic Net model\n",
    "from sklearn.linear_model import ElasticNet\n",
    " \n",
    "e_net = ElasticNet(alpha = 0.2)\n",
    "e_net.fit(x_train, y_train)\n",
    " \n",
    "# Predict validation data\n",
    "elastic_y_pred = e_net.predict(x_validation)\n",
    "\n",
    "print(\"Elastic Net regression R2 (training data)\")\n",
    "print(f\"{e_net.score(x_train, y_train):.3f}\" \"\\n\")\n",
    "\n",
    "print(\"Elastic Net regression Q2 (validation data)\")\n",
    "print(f\"{e_net.score(x_validation, y_validation):.3f}\" \"\\n\")\n",
    "\n",
    "mean_squared_error = round(np.mean((elastic_y_pred - y_validation)**2),3)\n",
    "print(\"Mean squared Error on test set : \", mean_squared_error)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FDE6tzv3AwYJftuHe1ufoASrq+dqz23whUH+H+3eXk7u6+sPuwKtjSO0R2ay+hFZfzaIoXI7s7Gyys7Pdj8/vOWK322u8MV+qL1BajBG9rvo/kl6rIS3G2KBeQi6XC6heQ3C5XJhMJvfxcnNzWb16Nf/9738xmUyMHTsWi8WC0+lEVVVcLhculwuDweB+jUajweFw1BqTL3of2e32JutdE+h9cyJSe4FODzhJLz+MXqvBqVbfSrNz2K9/353Dqp87+3faOQwsbTvX+7W+/JkG6u/QXuUgKlpH5m/aYKs6Q0lJSVOH5LFm3/vIbDbX+KMpKSm5YEqlsXj79oWhoaEXbWEN1a2vIyMjMZlM7Nu3j02bNjXoXML/GVJ6oJ3yIurubaR268ELtawLXPTvNDal/q8Vl01RVPbvtqMq0LW7kbjWQcQm6AmLCMIWQDWvWRSFjIwMli1bxlVXXcXevXsJCQnxyXrCWd68faHZbKZ///5cc801GI3GGsPmzMxMPv74Y4YOHUpSUhJ9+/b1yjmF93jzft2eOrd1ewrU+rd4sb/ThrxWeO7MKSdb1lkpO+0isX3zamDnbT5pnf3mm2+yY8cO9yflm2++2T3lMXz4cFRVZc6cOeTl5WEwGJg0aRJJSUkeHVtaZ0vrbG85937deOF+3Z4I1KmVc/lzji6Xyp7tNvbvsmMI1tCjn+mCK4v8Mb8mnz6aPHlynds1Gg333nuvL0IRolbevl+38H+V5Qr7d9tp29FAWm8jBkPgf9+3WUwfCdEcNMb9uoX/cTpUin520O6XBnbX/Da8Se+E5mtSFIT4xdn7dft6TUE0HyeKHGzdYMFqUWn1SwO7llQQQIqCEDV4+37dwj9U2RW2b7Fy9JCDsHAtV10T6jcN7LxNioIQokVTFZUfVlRgqVBITgsmOc2IThd4VxV5SoqCEKJFstsUDMHVDezSepkwhWiIjJK3xMBfSvdDCxYsYNGiRQAsXLiQY8eONXFEQgQOVVU5csDOd1+Xc3h/FQAJbYKkIPxCfgrNjNPp5K677nI/XrRoESkpKSQkJDRhVEIEBkuli7z1VoqPOzHH6oiJl7fA88lPpBEUFBRwxx130LNnT7Zt20bXrl15++23ef/99/n222+x2WxkZGTwyiuvoNFoGDt2LGlpaaxfv57rr7+eyspKQkNDadu2LXl5eTz44IMYjUYef/xx/vnPfzJ37lwAcnJyWLBgAR9++GETZyyaO2mdDQWHqti20QJAj34mOiQZAvIbyQ0V8EUhd2X5Bc8ltjPQMTkYp1NlXc6FfYradTLQrlMwdrvCxjWVNbYNusazDqr79+9n+vTp9O/fn0cffZT58+czYcIEHnnkEQAeeughvv32W4YPHw6Aw+Fg6dKlAO4bEY0aNYqPPvqIqVOn0qtXL1RV5X/+538oKSkhOjqahQsXctttt3n+wxAtkrTOrhZs1BAdq6dHvxBCQmXmvDbyk2kkiYmJ9O/fH4AxY8awbt06cnNzGTVqFFlZWeTm5rJnzx73/qNHj77kMTUaDTfddBOfffYZZ86cYePGjWRlZTVaDiIw5B+31GgPn3/c0tQh+YSiqOzZYWPPdhsAcQlBDBwSJgXhEgJ+pFDXJ3u9XlPn9uBgrccjg/OdPyzVaDQ89dRTfP3117Rp04bp06djt9vd2z3tLXTLLbcwYcIEgoODGTVqVIvo7yQaJj0+pEZ7+PT4pulj5UunS53krbdQdlqhzTkN7MSlSclsJD///DMbNmwA4PPPP3ePGsxmM5WVlSxZssSj45zfijshIYH4+HjefvttbrnlFu8HLgLO2dbZd/SMDfipI5dTZWeelR+WV2C3qWRcFULfK0OlIFyGgB8pNJWkpCTmz5/PlClT6Nq1K3fffTdnzpwhKyuL2NhYevXq5dFxbr75Zp544gmMRqP75jxjxoyhpKSE5OTkRs5CBIqW0jrbUqmwf4+ddh0NpLaQBnbe5pPW2Y2pObbOLigo4O6772blypWNcvy//vWvpKenc9ttt0nrbD8X6PlB4+focKgcO1pFu07BQHVh8OW6gT/+Dpu8dbbwnhEjRhASEsIzzzzT1KEI0eSOFzrYutGCzarSKlpf3cBOFpIbRIpCI2jXrl2jjRKWLVvWKMcVwp/Y7QrbN1v5+bCDsAgtg1twAztvC7ii4OezYc2S/ExFc6IqKmt+aWDXtXswXVJbdgM7bwu4oqDVanE6nej1AZdak3A6nWi1MhwXTe/8BnYhoVoiWsnowNsC7p3TaDRis9mw2+0YjcYa3wUIRMHBwY2Wo6qqaLVajEZjoxxfCE+oqkrBwSq2b7GS2tNExy7BJLQJauqwAlbAFQWNRoPJVH3pnT9eFXC5WkKOouWqrHCxdb2V4hNOomN1xEoDu0YnP2EhRLNUcLC6gZ1GIw3sfEmKghCiWTKaNMTEVzewM4XIupavSFEQQjQLiktl3y47qqrSLd1EbEIQsQmyduBrUhSEEE3udImTLestlJ9RaNtBGtg1JSkKQogm43Sq7M63cWCPHaNRQ//BoXJlUROToiCEaDLWSoVDe+106GwgtaeJIIOMDpqaFAUhhE85qlSKjlbRvnMw4ZE6rhkZIQvJzYgUBSGEzxwvdLB1gwWbTSUqprqBnRSE5sVnRWHLli3MmzcPRVHIysrihhtuqLG9uLiYmTNnUllZiaIo3H777fTt29dX4QkhGpHd9ksDuyMOwiO1ZFwlDeyaK58UBUVRmDNnDk8//TTR0dE8+eSTZGRk0LZtW/c+n332GVdeeSXDhw/n6NGj/O1vf5OiIEQAUBSVNSsrsFQqdEs30iUlGK00sGu2fFIU9u3b576NJMCgQYNYv359jaKg0WiwWKpvKG6xWIiKivJFaEKIRmKzKgQbNWi1Grr3NmEKkQZ2/sAnRaG0tJTo6Gj34+joaPbu3Vtjn3HjxvHiiy+ybNky7HY7U6dOveixli9fzvLlywGYNm0aMTExtZ5Xr9fXuT0QBHqOkt/lq9q1Dcf2zQR174MhpYdXj+0JVVXZvb2MDbnFZAyKoXVrPd17tvZ5HL4SaH+jzWahec2aNWRmZvK73/2OPXv28M477zB9+vQL2jZnZ2eTnZ3tflxXM7iW0Cwu0HOU/C6Pun8XyvSnwekEvR7tlBfRJKV47fiXUlnuIm+DlZITTmLi9JjCbDidTvkdNjN13Y7TJ8v+ZrOZkpIS9+OSkhLMZnONfVauXMmVV14JQNeuXXE4HJSXl/siPCEChrp7W3VBUBVwOasf+8iRA3ZWfVPOmVNOemaYuCIzlNAwmS7yNz4pCklJSRQVFXHixAmcTie5ublkZGTU2CcmJob8/HwAjh49isPhICIiwhfhCREwNN16gF4PWi3o9NWPfcQUqiU2QU/miAg6JAVLmwo/5ZPpI51Ox8SJE3nppZdQFIVhw4bRrl07Fi5cSFJSEhkZGdx1113MmjWLJUuWADBp0iT5oxLiMmmSUtBOeRF19zY03Xo06tSRy6Wyb6cNVYWUHiZi44OIjZcWFf5Oo/r5DXgLCwtr3eaPc32XK9BzlPyap1MlTvLWWSgvU2jbMYjeA0Jq/RDnrzl6yh/zq2tNodksNAshmj+nU2X3tl8a2Jk0DLg6lPhEGR0EEikKQgiPWSsVDu2z0yHJQGovE0FBMsUbaKQoCCHq5KhSKCxw0CFJGti1BFIUhBC1OvZzdQO7KruKOVYa2LUEHhWFsrIyDAYDRqMRRVFYvXo1Go2GIUOGXPDlMiGE/7PbFPI3WSkscBARqWXA1dLArqXw6B192rRpFBUVAfDpp5/y5ZdfsmTJEhYsWNCowQkhfE9VVNasqODYzw669TBy9fBwWpllUqGl8Og3XVRURMeOHQH4/vvvefHFFzEajTz66KNMmDChEcMTQvjK2QZ2Gq2G7n1MhIRqCY+U0UFL41FR0Gq1OJ1OioqKCAkJISYmBkVRsNlsjR2fEKKRqarK4f1V7MyzktrTRMfkYLnMtAXzqCj07t2bN954g/LycgYNGgRUt6I4v3+REMK/VJS7yFtvofSki5h4PXGtZZqopfPoL+D+++9n9erV6HQ6hgwZAkB5eTnjxo1r1OCEEI3nyAE72zZZ0Wk19Opvol0ng7SWEZ4VhaCgILKzs1EUhTNnzhAVFUX37t0bOzYhRAPtOmkl/7iF9PgQUmJNNbaZQrXEJQTRo58Jo0muIhTVPCoKlZWVfPjhh6xduxa9Xs/HH3/Mhg0b2LdvH7feemtjxyiEqIddJ61MXXEEp0tFr9Pw/LB2aE9UjwSkgZ2ojUcfD2bPnk1ISAjvvvsuen11HenatSu5ubmNGpwQov7yj1twulQUwKzo2ZtrZ+8OOzarip/3wRSNyKORwrZt25g1a5a7IABERERw5syZRgtMCNEw6fEhGHVaeqmhpGlCCNJo6DcklLjWMjoQtfNopBASEnLBXdCKi4uJiopqlKCEEA2XEmviiSvakKYLoVU7Hb8ZGSkFQVySRyOFrKwspk+fzq233oqqquzZs4dPP/2U3/zmN40dnxDiMlVVKRT90sCuV8dQusXLQrLwnEdF4frrr8dgMDBnzhxcLhfvvfce2dnZXHfddY0dnxDiMhQdrWLbRitVdpXoWD1hETopCOKyeFQUNBoN1113nRQBIZopm7W6gV3RUQcRrXQMuNpEmDSwE/XgUVHIz8+vdVt6errXghFCXD5VUcldWYHVopDSw0hSSjBarXwJTdSPR0Xhvffeq/G4rKwMp9NJdHQ0M2bMaJTAhBB1s1oUjKZfGtj1/aWBnYwORAN5VBRmzpxZ47GiKHz22WeYTKZaXiGEaCyqqnJobxU7t1U3sOuUHEy8XFUkvKReK1BarZYxY8bwxRdfeDseIUQdystcrFlZQf5mK+YYvXQzFV5X75aIW7dulbuuCeFDh/fbyd9kRafX0HtACG07BkkDO+F1HhWFP/7xjzUeV1VVUVVVxb333tsoQQkhLhQapiU+sbqBXbBRPpCJxuFRUXjooYdqPA4ODqZ169aEhIQ0SlBCCHC5VPZsr76RVWpPEzHxQcRIAzvRyDwqCmlpaY0dhxDiHKUnnWxZb6GyXKF9ZwOqqspUkfCJWovCO++849Ef4YMPPujVgIRoyZwOlZ1brRzaV4UpVMvAoaHEJcjoQPhOrUUhISHBqyfasmUL8+bNQ1EUsrKyuOGGGy7YJzc3l0WLFqHRaOjQoQMPP/ywV2MQormzWhWOHKyiU7KBlB4m9EEyOhC+VWtR8OatNhVFYc6cOTz99NNER0fz5JNPkpGRQdu2bd37FBUV8fnnn/PCCy8QFhYmbblFi1FlVygscNCxSzDhETqyRkZIvyLRZDy+JNXpdFJYWEhZWVmN5z1pc7Fv3z4SEhKIj48HYNCgQaxfv75GUVixYgXXXnstYWFhAERGRnoamhB+SVVVDu2rYM2qchxVKjFx0sBOND2PisKuXbt4/fXXcTgcWK1WTCYTNpvN4zYXpaWlREdHux9HR0ezd+/eGvsUFhYCMHXqVBRFYdy4cfTu3fsyUhHCf9isCts2Wjn28xkio3RcMTREGtiJZsGjojB//nxGjx7NqFGj+P3vf8+8efNYvHgxBoPBa4EoikJRURHPPvsspaWlPPvss/z9738nNDS0xn7Lly9n+fLlAEybNo2YmJhaj6nX6+vcHggCPcdAzE9RVP73kyNYKp0MGBxLao+IgG5gF4i/w3MFWn4eFYXCwsIL2mbfcMMNPPDAA4wePfqSrzebzZSUlLgfl5SUYDabL9gnOTkZvV5PXFwcrVu3pqioiC5dutTYLzs7m+zsbPfj4uLiWs8bExNT5/ZAEOg51pbfrpNW8o9bSI8PISXWP3pwuRvYaTSk9TYQEmakY6fIgP79Qcv9G23OEhMTa93m8e04rVYrAK1ateLo0aNUVFRgs9k8CiApKYmioiJOnDiB0+kkNzeXjIyMGvsMGDCA7du3A9VdWIuKitxrEEKca9dJK1NXHOGTvJNMXXGEXSetTR1SnVRF5cAeO999XcahfVUAxLUOIixcpotE8+PRSGHgwIFs3ryZwYMHM2zYMJ5//nl0Oh1XXHGFRyfR6XRMnDiRl156CUVRGDZsGO3atWPhwoUkJSWRkZFBr169yMvL45FHHkGr1XLnnXcSHh7eoOREYMo/bsHpUlEAp6KSf9zSbEcL5WUu8tZZOFXiIq61NLATzZ9GVVW1to3ffPMNgwcPvmBef9euXVitVnr16tXkTfHOLlBfjD8O6y5XoOd4sfzOjhSciopeq+GFrPbNsiic28AuvY+JNh0ubGAX6L8/CPwc/TG/uqaP6hwprFy5kgULFtCnTx8yMzPp27cvWq2WlJQUrwcphKdSYk28kNW+2a8phIZpSWgTRHpfaWAn/EedIwWAI0eOkJOTw5o1a3A6nQwaNIjMzEw6derkqxjrJCOFwM7Rn/JzOVV2b7ehAVJ7eVao/Cm/+gr0HP0xv3qPFADat2/PnXfeyR133MG2bdvIycnh2WefJTY2lqFDh3p09ZEQga7khJO89RYqKxQ6JEkDO+G/PP5Gs0ajoWfPnvTs2ZPt27fz7rvv8sknn0hREC2aw6GyM8/K4f1VhIRquTIzVNpbC7/mcVEoLS0lJyeHnJwcSkpKGDhwIJmZmY0YmhDNn82qUHCois5dg+nWw4heL6MD4d/qLAp2u521a9eSk5PDjh07SEtL44YbbmDgwIEEBwf7KkYhmhW7XaHoiIOOydUN7LJHRchCsggYdRaFe++9l5iYGIYMGcKkSZNq9C8SoqVRVZXCAgf5m6w4HCoxCXrCwnVSEERAqbMoPPPMMyQnJ/sqFiGaLZtVYesGC8cLnURG6bhyQIh8I1kEpDqLghQEIarbVKxZWYHNqpDWy0inrsEB3cBOtGweLzQL0dJYKhVMJg0arYYe/UyEhmoJldGBCHAyGSrEeVRFZf9uG98tLePQ/l8a2CUESUEQLYKMFIQ4R9lpF3nrLZwudRGfqCehjXznQLQsHhUFh8PB4sWLWbNmDeXl5cyfP5+8vDyKiooYMWJEY8cohE8c2mcnf7OVoCANfa8IIbH9hQ3shAh0Hk0fzZ8/n4KCAv70pz+5/5O0a9eO//u//2vU4ITwhbPtv8IidCS2DSJzRDhtOhikIIgWyaORwrp163j77bcxGo3u/yhms5nS0tJGDU6IxuR0quzOt6HRQFovEzFxemLiZEZVtGwe/Q/Q6/UoilLjubKyMrkJjvBbxScc5K23YqlQ6NhFGtgJcZZH00dXXHEFM2bM4MSJEwCcOnWKOXPmMGjQoEYNTghvc1Sp5K238ON3lQBcmRlKj34hUhCE+IVHReH2228nLi6OKVOmYLFY+NOf/kRUVBTjxo1r7PiE8CqbTeHnw1UkdQtm6LXh0tFUiPN4PH00YcIEJkyY4J42kk9Wwl/YbQqFRxx06lrdwC5LGtgJUSuPisLx48drPLZare5/x8fHezciIbxEVVV+PlLdwM7pVIltLQ3shLgUj4rCn/70p1q3LVy40GvBCOEtVkt1A7sTRU5amXX0lgZ2QnjEo6Jw/hv/6dOnWbRoEampqY0SlGjZdp20kn/cQnp8CINjLv/1iqKSu7ICu02he28jnZKD0UgDOyE8Uq+Lslu1asWECRN4+OGHGTx4sLdjEi3YrpNWpq44gtOlotdpeKdVJAkergVbKl2YTFq0Wg09M0yEhGkJDZPRgRCXo96Tq4WFhdjtdm/GIgT5xy04XSoK4FRUNh09c8nXKIrK/l02vltazqF91Q3sYhOCpCAIUQ8ejRSeeeaZGlcb2e12CgoKGDt2bKMFJlqm9PgQ9DoNTkVFr9XQt20kUFXr/mWnXWxZZ+HMKRfxbfS0bieXmArREB4VhWuuuabGY6PRSIcOHWjdunWjBCVarpRYEy9ktXevKaS3jqC4uPii+x7a+0sDO4OGfleG0LqdNLAToqEuWRQURSE/P5/77ruPoCD5FCYaX0qsiZRYU63bz7akCI/Ukdg+iO59TAQHy2WmQnjDJYuCVqtl69at8glMNDmnU2XXNhtaDaT1NhEdpydaGtgJ4VUefbwaOXIk//73v3E6nfU+0ZYtW3j44Yd56KGH+Pzzz2vdb+3atdx8883s37+/3ucSgefkcQerl5VzcI8dRVHd7a6FEN5V58esH374gcGDB7Ns2TJOnz7NkiVLiIiIqLHPe++9d8mTKIrCnDlzePrpp4mOjubJJ58kIyODtm3b1tjParWydOlSkpOT65GKCER2u4u8dRaOHKwiNEzLoGvCiI6V0YEQjaXO/12zZ89m8ODBPPTQQw06yb59+0hISHC3xBg0aBDr16+/oCgsXLiQ66+/nv/+978NOp8IHDaLi58LquiSEkzX7kZ0epnGFKIx1VkUzg7R09LSGnSS0tJSoqOj3Y+jo6PZu3dvjX0OHDhAcXExffv2rbMoLF++nOXLlwMwbdo0YmJq/8qrXq+vc3sgCMQcrRYnB/ZW0L1XK/R6PTff1QmjKTC/cxCIv7/zBXqOgZZfnUXh7JVHdUlPT29wEIqisGDBAiZNmnTJfbOzs8nOznY/ru1yRYCYmJg6tweCQMpRVVV+Puwgf7MVl1MlNMJOx07xVFSeoqKyqaNrHIH0+6tNoOfoj/klJibWuq3OouBwOHj//fdrXdTTaDTMmDHjkgGYzWZKSkrcj0tKSjCbze7HNpuNgoICnn/+eaC6t9Krr77KY489RlJS0iWPL/yfpVJh28bqBnZR0Tp6SQM7IZpEnUXBaDR69KZ/KUlJSRQVFXHixAnMZjO5ubk1Oq+GhIQwZ84c9+PnnnuO8ePHS0FoIRRF5cfvKrDbFdL7mOjYxXDZDezObaJX13cchBB188llHDqdjokTJ/LSSy+hKArDhg2jXbt2LFy4kKSkJDIyMnwRhmhmKitchIT80sCuv4nQMC0hoZc/Oji/id4LWe2lMAhRTx4tNHtD37596du3b43nbrnllovu+9xzz3ntvKL5URSVA7vt7M63kdrLROeuwcQ24LaY5zfRyz9ukaIgRD3VWRQWLFjgqzhEC3HmlJO89VbOnHKR0CaIRC80sDu/iV56fIgXIhWiZZJvAQmfObjXzvbNVgzBGvoNCiGxncErxz2/iV59RgmyJiFENSkKotGdbWAXEamjTYcguvc2YfByA7tLNdGri6xJCPErKQqi0TgdKru2WdFoNXRvxg3sZE1CiF81v/+hIiCcOOZg63oLVotKp2SDe7TQHMmahBC/kqIgvKqqSmHHZhsFh6oI1VYyqJuN6N41v2/SGPP36v5dqLu3oenWA01SymW91htrEkIECikKwquqbCqFR2wkHVlKl/2fo/tORZ3yovuNujHm79X9u1CmPw1OJ6pej/ac83mqIWsSQgQSuV2VaDCbVWH/bhsAYRE6rgnJodveRehcdnA5UXdvY9dJK4vzS/juwJkL5u8bSt29DZxOUBX3+YQQ9SMjBVFvqqpy9JCD7VuqG9jFJwYRFq4jOCUFZYkeXE7Q6dmd2INnfhkd6LQatFpAxWvz95puPVD1v55P061Hw5MTooWSoiDqxVLpYusGKyePOYmK0dGr/68N7DRJKWinvOie499ujcXpOokCoKoMT2pFbGiQ1+bvzz/f5U4dCSF+JUVBXDZFUcn9rpIqu0KPviY6dDFccGXR7ogO5LePJT0ihPQIalzdM6xzJCmxJveUkjeKgyYpRYqBEF4gRUF4rLLcRUhodQO73v1NhITpCAm9cFnqYovJ51/dI18YE6J5kqIganX2Mk81uQcHHB3Zs/3XBnYxtTSw23XSyqdbi3G4VFR+XUwemx5d401fvjAmRPMkRUFc1NnLPM+Y2rCtsBtl4TZatwuiTfvaG9id/fR/tiBoqH0xWb4wJkTzJEVBXJS6exuHEjLZmXwbBkc5/YxbSByUWedrzn76P1sQeiWEclvPmIuOAOrzhTFpWidE45OiIC6gqiqabj2I+H4TbY6tIfXgYoIffuqSrzv/039tBeGsy/nCmKxBCOEbUhSEm9OhsnOrFa1WQ/c+KcTcezfRu7ehGf2UR1f2NGa7CFmDEMI3pCgIAE4UOdi6obqBXeeuwdWjhV8KwdlvCHtaGBrjzVrWIITwDSkKLVyVXWH7FitHDzkIi9ByVVYo5pjqPwtv9BTyFmlaJ4RvSFFo4aqqVI4ddZCcFkxymhGd7tcvoV2sp1BTfkFMmtYJ0fikKLRANqvCz4er6NwtmLBwHVm/i8BguPBLaNJTSIiWR4pCC6KqKgUHq9i+xYqiQHyb6gZ2FysIID2FhGiJpCi0EJYKF3kbrBQfd2KOrdnAri7SU0iIlkWKQgugKCq5qypx2BV69DPRIenCBnZCCAFSFAJaRbmL0LMN7AaYCA3TYQqR+yoJIWon7xABSFFU9my3sXpZOQf3VQEQExckBUEIcUkyUggwp0ud5K2zUHZGIbF93Q3shBDifFIUAsiBPXa2b7FiNGroPziUhDZSEIQQl8dnRWHLli3MmzcPRVHIysrihhtuqLH9q6++YsWKFeh0OiIiIvjjH/9IbGysr8Lza6qqotFoaBWlo30nA2m9jATVcpmpEELUxSfvHIqiMGfOHJ566ineeOMN1qxZw9GjR2vs07FjR6ZNm8bf//53rrjiCv7xj3/4IjS/5nCo5K46wfYtNgDMsXp69Q+RgiCEqDefvHvs27ePhIQE4uPj0ev1DBo0iPXr19fYJz09neDgYACSk5MpLS31RWh+63ihg1VLy9izowytpnq0IIQQDeWT6aPS0lKio6Pdj6Ojo9m7d2+t+69cuZLevXtfdNvy5ctZvnw5ANOmTSMmJqbW4+j1+jq3+yOb1cVPP5zkwJ5KWpkNXDu6NeaYwF07CMTf4bkCPT8I/BwDLb9mt9Cck5PDgQMHeO655y66PTs7m+zsbPfj4uLiWo8VExNT53Z/VFHu4sjBCrp2DyY51Yg5JijgcjxXIP4OzxXo+UHg5+iP+SUmJta6zSfTR2azmZKSEvfjkpISzGbzBftt3bqV//znPzz22GMEBQXup9/LZbUo7NtpQ1VVwsJ1ZI+KoFu6Ca1OvpUshPAunxSFpKQkioqKOHHiBE6nk9zcXDIyMmrsc/DgQWbPns1jjz1GZGSkL8Jq9lRV5fB+O6uWlbF7uw1LhQIgC8lCiEbjk+kjnU7HxIkTeemll1AUhWHDhtGuXTsWLlxIUlISGRkZ/OMf/8Bms/H6668D1UOyxx9/3BfhNUuVFS7y1lspOeEkOk5PrwwToR40sBNCiIbQqH5+2UphYWGt2/xxrg+q21SsXFKGo0olrbeJ9p1rb2Dnrzl6SvLzf4Geoz/mV9eaQrNbaG7JKspchIT90sBuYCihYVrpVySE8Cl5x2kGFJfK7nwbq74p55C7gZ3erwrCrpNWFueXsOuktalDqaG5xiVEcyUjhSZ2qsRJ3noL5WcU2rQPok0H/7vqatdJK1NXHMHpUtHrNLyQ1b5Z3Eu5ucYlRHPmPx9FA9CB3TZ+WFGBo0plwNWh9L0ylOBg//uV5B+34HSpKIBTUck/bmnqkIDmG5cQzZmMFJqAu4FdtJ4OnQ2k9jQRZPDf7xykx4eg12lwKip6rYb0+JCmDglovnEJ0ZxJUfAhR5XKjjwrOh2k9w3BHKPHHOP/v4KUWBMvZLUn/7iF9PiQZjNF01zjEqI58/93JD9x7GcH2zZasNlUkroFu0cLgSIl1lTnm+6uk9YmeXO+VFxCiJqkKDQyu01h+2YrPx9xEB6ppf9VobSKblk/dlnwFcJ/tKx3pybgdKgcL3LQLd1Il5TgFtmv6GILvlIUhGiepCg0AqtF4eihKrqkBhMariN7VKRfLyQ3lCz4CuE/pCh4UXUDuyp25llRVUhsF0RouK5FFwSQBV8h/IkUBS+pKHexdb2FkpMuYuL19MwwERomDezOkgVfIfyDFAUvUBSVtasqcDhUevU30a5T7Q3shBCiOZOi0ADlZS5Cf2lg1+eK6gZ2RpP/fSNZCCHOknewenC5VHbnW1m9rJxDe+0ARMfqpSAIIfyejBQu06liJ1vWW6goU2jbIYi2HQ1NHZIQQniNFIXLsH+XjR15NowhGgYMCSW+tf91NBVCiLpIUfDA2ZYUUTF6OiQZSO1lIihIFpKFEIFHikIdHFUK27fY0OmgR7/AaWAnhBC1kXe4WhQdrWLbRitVdpWklMBrYCeEEBcjReE8dpvCtk1WigocRLTSMeBqE63Mgf9jaqoupkKI5iXw3+0uk9OhUnzMSUoPI0kpwWi1gT86kC6mQoiz5MJ6wFKpsGeHDVVVqxvY/S6C5DRjiygIEHi3rdx10sri/BJ2nbQ2dShC+J0WPVJQVZXD+6rYsdUKKrT5pYGdvoVdWRRIXUxl1CNEw7TYolBR5iJvg4XSXxrY9epvIiS0ZTaw83UX08Zcv5B7NwjRMC2yKCiKytrVFTgd0HuAibYdpYGdr7qYNvYn+UAa9QjRFFpkUZAGdk2nsT/Jy70bhGgYnxWFLVu2MG/ePBRFISsrixtuuKHGdofDwYwZMzhw4ADh4eFMnjyZuLi4RosnOrZF1sMm54tP8nLvBiHqzycfkxVFYc6cOTz11FO88cYbrFmzhqNHj9bYZ+XKlYSGhvLOO+8wcuRIPvnkE1+EJnzs7Cf5O3rGyiKwEM2QT4rCvn37SEhIID4+Hr1ez6BBg1i/fn2NfTZs2EBmZiYAV1xxBfn5+aiq6ovwhI+lxJoYmx4tBUGIZsgncyilpaVER0e7H0dHR7N3795a99HpdISEhFBeXk5ERESN/ZYvX87y5csBmDZtGjExMbWeV6/X17k9EAR6jpKf/wv0HAMtP7+bWM/OziY7O9v9uLi4uNZ9Y2Ji6tweCAI9R8nP/wV6jv6YX2JiYq3bfDJ9ZDabKSkpcT8uKSnBbDbXuo/L5cJisRAeHu6L8IQQQvzCJ0UhKSmJoqIiTpw4gdPpJDc3l4yMjBr79OvXj1WrVgGwdu1aunfv3uK/OyCEEL7mk+kjnU7HxIkTeemll1AUhWHDhtGuXTsWLlxIUlISGRkZXHPNNcyYMYOHHnqIsLAwJk+e7IvQhBBCnEOj+vklPoWFhbVu88e5vssV6DlKfv4v0HP0x/zqWlPw+6IghBDCewK6x8MTTzzR1CE0ukDPUfLzf4GeY6DlF9BFQQghxOWRoiCEEMItoIvCuV9yC1SBnqPk5/8CPcdAy08WmoUQQrgF9EhBCCHE5ZGiIIQQws3vGuJdTHO7gY+3XSq/r776ihUrVqDT6YiIiOCPf/wjsbGxTRNsPV0qx7PWrl3L66+/zt/+9jeSkpJ8G2QDeJJfbm4uixYtQqPR0KFDBx5++GHfB9oAl8qxuLiYmTNnUllZiaIo3H777fTt27dpgr1M7777Lps2bSIyMpLp06dfsF1VVebNm8fmzZsJDg5m0qRJdO7cuQki9QLVz7lcLvXBBx9Ujx07pjocDvXPf/6zWlBQUGOfZcuWqbNmzVJVVVV/+OEH9fXXX2+KUOvFk/y2bdum2mw2VVVV9ZtvvvGr/FTVsxxVVVUtFov6zDPPqE899ZS6b9++Joi0fjzJr7CwUP3LX/6ilpeXq6qqqqdPn26KUOvNkxzff/999ZtvvlFVVVULCgrUSZMmNUWo9bJ9+3Z1//796qOPPnrR7Rs3blRfeuklVVEUdffu3eqTTz7p4wi9x++njwL9Bj6e5Jeenk5wcDAAycnJlJaWNkWo9eZJjgALFy7k+uuvJygoqAmirD9P8luxYgXXXnstYWFhAERGRjZFqPXmSY4ajQaLxQKAxWIhKiqqKUKtl7S0NPfv5mI2bNjAkCFD0Gg0dO3alcrKSk6dOuXDCL3H74vCxW7gc/6bYm038PEHnuR3rpUrV9K7d28fROY9nuR44MABiouL/Wa64Vye5FdYWEhRURFTp07lr3/9K1u2bPFxlA3jSY7jxo3j+++/5/777+dvf/sbEydO9HWYjaa0tLTGjXYu9f+0OfP7oiB+lZOTw4EDBxg9enRTh+JViqKwYMEC7rrrrqYOpdEoikJRURHPPvssDz/8MLNmzaKysrKpw/KqNWvWkJmZyfvvv8+TTz7JO++8g6IoTR2WOI/fF4VAv4GPJ/kBbN26lf/85z889thjfje9cqkcbTYbBQUFPP/88zzwwAPs3buXV199lf379zdFuJfN07/RjIwM9Ho9cXFxtG7dmqKiIl+HWm+e5Lhy5UquvPJKALp27YrD4fCbEfulmM3mGp1Sa/t/6g/8vigE+g18PMnv4MGDzJ49m8cee8zv5qLh0jmGhIQwZ84cZs6cycyZM0lOTuaxxx7zm6uPPPkdDhgwgO3btwNQVlZGUVER8fHxTRFuvXiSY0xMDPn5+QAcPXoUh8NxwT3Y/VVGRgY5OTmoqsqePXsICQnxqzWTcwXEN5o3bdrE/Pnz3TfwGTNmTI0b+FRVVTFjxgwOHjzovoGPP/2Hu1R+L7zwAkeOHKFVq1ZA9X++xx9/vGmDvkyXyvFczz33HOPHj/ebogCXzk9VVRYsWMCWLVvQarWMGTOGq666qqnDviyXyvHo0aPMmjULm80GwJ133kmvXr2aOGrPvPnmm+zYsYPy8nIiIyO5+eabcTqdAAwfPhxVVZkzZw55eXkYDAYmTZrkV3+f5wqIoiCEEMI7/H76SAghhPdIURBCCOEmRUEIIYSbFAUhhBBuUhSEEEK4SVEQLc7MmTP517/+BcDOnTt91o305ptv5tixY14/7gMPPMDWrVu9flzRMgVE62wReB544AFOnz6NVqvFaDTSu3dv7rnnHoxGo1fPk5qayltvvXXJ/VatWsWKFSt44YUXvHp+gA8++ICqqioefPDBGs8fOnSIp556ig8++KDOZmxCeJOMFESz9fjjj/Pxxx/zyiuvcODAAT777LML9nG5XE0QmXdlZmby008/ub/UdVZOTg59+/aVgiB8SkYKotkzm8307t2bgoICoHoaZuLEiXz99de4XC5mzpzJxo0b+de//sXJkydp27Ytf/jDH+jQoQNQ3Qbk/fffp6ioiD59+tRocbJ9+3beeecd3n//faD6RjAfffQRO3fuRFVVrrrqKq699lpmz56N0+lk/Pjx6HQ6PvroIxwOB59++ik//vgjTqeT/v37M2HCBAwGAwD//e9/+eqrr9BoNNxyyy215te1a1fMZjM//fQTQ4cOBaob5K1Zs4Z7772XY8eOMWvWLA4fPoxGo6FXr17cc889hIaGXnCsmTNnEh0dza233nrR/EpLS5k7dy47d+7EaDQycuRIrrvuOqC6/fWHH35IUVERBoOBwYMHc/fddzfodyf8jxQF0ewVFxezefNmBgwY4H5u/fr1vPzyyxgMBg4ePMh7773H448/TlJSEjk5Obz66qu8+eabaDQaXnvtNa677jpGjBjBhg0beOutt7j++usvOI+iKLzyyit0796dmTNnotVqOXDggLvInD999Mknn3D8+HFee+01dDodb731FosXL+b2229ny5YtfPnll0ydOpW4uDhmzZpVZ45Dhw4lJyfHXRS2bt2K0+mkT58+FBcXc+ONN5KamorVamX69OksWrSICRMmXNbP8Wx+/fv3Z/LkyZSUlPDCCy+QmJhI7969mTdvHtdddx1DhgzBZrNx5MiRyzq+CAwyfSSarddee40JEybwzDPPkJaWxpgxY9zbbrzxRsLCwjAYDCxfvpzs7GySk5PRarVkZmai1+vZu3cve/bsweVyMXLkSPR6PVdccUWtPWn27dtHaWkp48ePx2g0YjAYSElJuei+qqqyYsUK7r77bsLCwjCZTIwZM4Y1a9YA1bfWzMzMpH379hiNRsaNG1dnrkOGDGHHjh3uTqM5OTkMHjwYvV5PQkICPXv2JCgoiIiICEaOHMmOHTsu++e5f/9+ysrKGDt2LHq9nvj4eLKyssjNzQVAr9dz7NgxysrKMBqNdO3a9bLPIfyfjBREs/WXv/yFnj17XnTbuTd0KS4uZvXq1Sxbtsz9nNPppLS0FI1Gg9lsrjFldO7NUM5VXFxMbGwsOp3ukrGVlZVht9t54okn3M+pquq+P8CpU6dq3KP3UvfMjomJITU1le+//54RI0awfv16nn/+eQBOnz7tntKy2WwoilKvdYaTJ09y6tSpGiMMRVFITU0F4P7772fhwoU88sgjxMXFMXbsWPr163fZ5xH+TYqC8EvnvslHR0czZsyYGiOJs3bs2EFpaSmqqrpfU1JSQkJCwgX7xsTEUFxcjMvlumRhCA8Px2Aw8Prrr1+0b35UVFSN+wuc22u/NkOHDuWLL76gVatWxMXFuYvKp59+CsD06dMJCwtj3bp1zJ0796LHCA4Oxm63ux+fPn26Rn5xcXG8/fbbF31t69atmTx5MoqisG7dOl5//XXmzJnj9Su+RPMm00fC72VlZfHtt9+yd+9eVFXFZrOxadMmrFYrXbt2RavVsnTpUpxOJz/99BP79u276HG6dOlCVFQUn3zyCTabjaqqKnbt2gVAq1atKC0tdbdL1mq1ZGVl8dFHH3HmzBmgehH37G00r7zySlatWsXRo0ex2+0sWrToknkMHDiQ4uJiFi1a5F5bALBarRiNRkJCQigtLeXLL7+s9RgdO3Zk8+bNVFRUcPr0ab7++usa+ZlMJj7//HOqqqpQFIUjR464fx45OTmUlZWh1WoJCQlx5ylaFhkpCL+XlJTEfffdx9y5c91XzqSkpJCamoper+fPf/4zs2bN4l//+hd9+vSpsWB9Lq1Wy+OPP87cuXOZNGkSGo2Gq666ipSUFNLT090Lzlqtljlz5nDHHXewePFi/vrXv1JeXo7ZbOY3v/kNvXv3pk+fPowcOZLnn38erVbLLbfcwg8//FBnHkajkYEDB/L9998zePBg9/Pjxo1jxowZ3H333SQkJDBkyBCWLFly0WMMGTKEbdu28cADDxAbG0tmZiZfffVVjfwWLFjAAw88gNPpJDEx0X1l1JYtW1iwYAF2u53Y2Fgefvhh95VUouWQ+ykIIYRwk7GhEEIINykKQggh3KQoCCGEcJOiIIQQwk2KghBCCDcpCkIIIdykKAghhHCToiCEEMLt/wNmy6sEcprgvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Parity plot\n",
    "\n",
    "plt.plot(e_net.predict(x_validation), y_validation, '.', label='validation')\n",
    "plt.plot(e_net.predict(x_train), y_train, '.', label='train')\n",
    "\n",
    "min_value = min(min(e_net.predict(x_validation)), min(y_validation), min(e_net.predict(x_train)), min(y_train))\n",
    "max_value = max(max(e_net.predict(x_validation)), max(y_validation), max(e_net.predict(x_train)), max(y_train))\n",
    "plt.plot([min_value, max_value],[min_value, max_value], '--', label=\"parity\")\n",
    "plt.xlabel('Predicted Values')\n",
    "plt.ylabel('True Values')\n",
    "ax.set_box_aspect(1)\n",
    "sns.despine()\n",
    "plt.title('Parity Plot')\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Columns</th>\n",
       "      <th>Coefficient Estimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p-AKT(S473)[15m]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p-S6RIBPROT(S240/244)[15m]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>p-EGFR(Y1086)[05m]</td>\n",
       "      <td>-0.085346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p-EGFR(Y1086)[15m]</td>\n",
       "      <td>-0.132550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p-CDK2(T160)[15m]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>p-STAT5(Y694)[15m]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>p-CDC2(Y15)[15m]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>p-CREB(S133)[15m]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>p-PI3KP85(Y458)P55(Y199)[05m]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>p-MTOR(S2448)[15m]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Columns  Coefficient Estimate\n",
       "0                 p-AKT(S473)[15m]              0.000000\n",
       "1       p-S6RIBPROT(S240/244)[15m]              0.000000\n",
       "2               p-EGFR(Y1086)[05m]             -0.085346\n",
       "3               p-EGFR(Y1086)[15m]             -0.132550\n",
       "4                p-CDK2(T160)[15m]              0.000000\n",
       "..                             ...                   ...\n",
       "175             p-STAT5(Y694)[15m]             -0.000000\n",
       "176               p-CDC2(Y15)[15m]             -0.000000\n",
       "177              p-CREB(S133)[15m]             -0.000000\n",
       "178  p-PI3KP85(Y458)P55(Y199)[05m]             -0.000000\n",
       "179             p-MTOR(S2448)[15m]             -0.000000\n",
       "\n",
       "[180 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine coefficient and corresponding variable names\n",
    "e_net_coeff = pd.DataFrame()\n",
    "e_net_coeff[\"Columns\"] = x_train.columns\n",
    "e_net_coeff['Coefficient Estimate'] = pd.Series(e_net.coef_)\n",
    "e_net_coeff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the magnitude of coefficients\n",
    "fig, ax = plt.subplots(figsize =(7, 7))\n",
    "color =['midnightblue', 'navy', 'darkblue', 'mediumblue', 'blue', \n",
    "        'darkslateblue','mediumslateblue', 'slateblue', \n",
    "        'mediumpurple', 'blueviolet', 'darkviolet']\n",
    "\n",
    "ax.bar(e_net_coeff[\"Columns\"], e_net_coeff['Coefficient Estimate'], color = color)\n",
    " \n",
    "ax.spines['bottom'].set_position('zero')\n",
    " \n",
    "plt.style.use('ggplot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (5) Compare model architectures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elastic Net cross-validation Q2\n",
      "0.347\n",
      "\n",
      "LASSO cross-validation Q2\n",
      "-0.390\n",
      "\n",
      "Ridge cross-validation Q2\n",
      "0.398\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "k = 5\n",
    "reg_alpha = 0.2\n",
    "\n",
    "lm_en = linear_model.ElasticNet(alpha=reg_alpha)\n",
    "scores_en = cross_val_score(lm_en, X, Y, cv=k)\n",
    "np.mean(scores_en)\n",
    "print(\"Elastic Net cross-validation Q2\")\n",
    "print(f\"{np.mean(scores_en):.3f}\" \"\\n\")\n",
    "\n",
    "lm_lasso = linear_model.Lasso(alpha=reg_alpha)\n",
    "scores_lasso = cross_val_score(lm_lasso, X, Y, cv=k)\n",
    "np.mean(scores_lasso)\n",
    "print(\"LASSO cross-validation Q2\")\n",
    "print(f\"{np.mean(scores_lasso):.3f}\" \"\\n\")\n",
    "\n",
    "lm_ridge = linear_model.Ridge(alpha=reg_alpha)\n",
    "scores_ridge = cross_val_score(lm_ridge, X, Y, cv=k)\n",
    "np.mean(scores_ridge)\n",
    "print(\"Ridge cross-validation Q2\")\n",
    "print(f\"{np.mean(scores_ridge):.3f}\" \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (6) Observe impact of regularization strength "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha=0 results in average Ridge Q2=0.26\n",
      "Alpha=0.02 results in average Ridge Q2=0.29\n",
      "Alpha=0.04 results in average Ridge Q2=0.3\n",
      "Alpha=0.06 results in average Ridge Q2=0.32\n",
      "Alpha=0.08 results in average Ridge Q2=0.34\n",
      "Alpha=0.1 results in average Ridge Q2=0.35\n",
      "Alpha=0.2 results in average Ridge Q2=0.4\n",
      "Alpha=0.4 results in average Ridge Q2=0.46\n",
      "Alpha=0.6 results in average Ridge Q2=0.5\n",
      "Alpha=0.8 results in average Ridge Q2=0.52\n",
      "Alpha=1 results in average Ridge Q2=0.54\n",
      "Alpha=2 results in average Ridge Q2=0.59\n",
      "Alpha=4 results in average Ridge Q2=0.63\n",
      "Alpha=6 results in average Ridge Q2=0.64\n",
      "Alpha=8 results in average Ridge Q2=0.65\n"
     ]
    }
   ],
   "source": [
    "for alpha in [0,0.02,0.04, 0.06, 0.08, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 2, 4, 6, 8]:\n",
    "    lm_ridge = linear_model.Ridge(alpha=alpha)\n",
    "    scores_ridge = cross_val_score(lm_ridge, X, Y, cv=k)\n",
    "    mean_ridge = round(np.mean(scores_ridge),2)\n",
    "    print(f\"Alpha={alpha} results in average Ridge Q2={mean_ridge}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for alpha in [0,0.02,0.04, 0.06, 0.08, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 2, 4, 6, 8]:\n",
    "    lm_lasso = linear_model.Lasso(alpha=alpha)\n",
    "    scores_lasso = cross_val_score(lm_lasso, X, Y, cv=k)\n",
    "    mean_lasso = round(np.mean(scores_lasso),2)\n",
    "    print(f\"Alpha={alpha} results in average LASSO Q2={mean_lasso}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for alpha in [0,0.02,0.04, 0.06, 0.08, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 2, 4, 6, 8]:\n",
    "    lm_en = linear_model.ElasticNet(alpha=alpha)\n",
    "    scores_en = cross_val_score(lm_en, X, Y, cv=k)\n",
    "    mean_en = round(np.mean(scores_en),2)\n",
    "    print(f\"Alpha={alpha} results in average EN Q2={mean_en}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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