LinearRegression.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear Regression"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First import the data set, and tools that will be used throughout."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.datasets import load_diabetes\n",
    "from sklearn.model_selection import train_test_split\n",
    "data_before_split = load_breast_cancer()\n",
    "diabetes_data = load_diabetes()\n",
    "two_var_data = data_before_split.data[:, :2]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_test_split2(data, train_size):\n",
    "    slice = int(data.shape[0] * train_size)\n",
    "    train = data[:, 0]\n",
    "    target = data[:, 1]\n",
    "    return np.array(train[:slice]), np.array(train[slice:]), np.array(target[:slice]), np.array(target[slice:]) "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a linear regression model, the class must be made to have it's parameters, attributes and methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearRegression:\n",
    "    \"\"\"The class fits a line to the data that can be used to predict known or unknown quantities based on the input.\"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        self.a : np.ndarray\n",
    "\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        self.a = np.linalg.inv(X.T@X)@X.T@y\n",
    "        for s in step:\n",
    "            self.a = self.a - loss_gradient(self.a, X, y) \n",
    "        \n",
    "    def predict(self, X):\n",
    "        return X * self.a\n",
    "    "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Addition of Sample Data to test the Linear Regression Model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "LinAlgError",
     "evalue": "0-dimensional array given. Array must be at least two-dimensional",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mLinAlgError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[6], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m model \u001b[39m=\u001b[39m LinearRegression()\n\u001b[1;32m      3\u001b[0m model\u001b[39m.\u001b[39mfit(X_train, y_train)\n\u001b[0;32m----> 4\u001b[0m predicted_vals \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39;49mpredict(X_test)\n",
      "Cell \u001b[0;32mIn[5], line 22\u001b[0m, in \u001b[0;36mLinearRegression.predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mpredict\u001b[39m(\u001b[39mself\u001b[39m, X):\n\u001b[1;32m     12\u001b[0m     \u001b[39m# y_transpose_x = (self.target * self.data).sum()\u001b[39;00m\n\u001b[1;32m     13\u001b[0m     \u001b[39m# lst_matrix = []\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[39m# inverted_matrix = np.linalg.inv(x_transposition)\u001b[39;00m\n\u001b[1;32m     21\u001b[0m     \u001b[39m# a = y_transpose_x * inverted_matrix\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m     a \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mlinalg\u001b[39m.\u001b[39;49minv(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdata\u001b[39m.\u001b[39;49mT\u001b[39m@self\u001b[39;49m\u001b[39m.\u001b[39;49mdata)\u001b[39m@self\u001b[39m\u001b[39m.\u001b[39mdata\u001b[39m.\u001b[39mT\u001b[39m@self\u001b[39m\u001b[39m.\u001b[39mtarget\n\u001b[1;32m     23\u001b[0m     \u001b[39mprint\u001b[39m(a)\n\u001b[1;32m     24\u001b[0m     \u001b[39mprint\u001b[39m(X \u001b[39m*\u001b[39m a)\n",
      "File \u001b[0;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36minv\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m~/anaconda3/envs/owkin1/lib/python3.9/site-packages/numpy/linalg/linalg.py:532\u001b[0m, in \u001b[0;36minv\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m    471\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    472\u001b[0m \u001b[39mCompute the (multiplicative) inverse of a matrix.\u001b[39;00m\n\u001b[1;32m    473\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    529\u001b[0m \n\u001b[1;32m    530\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    531\u001b[0m a, wrap \u001b[39m=\u001b[39m _makearray(a)\n\u001b[0;32m--> 532\u001b[0m _assert_stacked_2d(a)\n\u001b[1;32m    533\u001b[0m _assert_stacked_square(a)\n\u001b[1;32m    534\u001b[0m t, result_t \u001b[39m=\u001b[39m _commonType(a)\n",
      "File \u001b[0;32m~/anaconda3/envs/owkin1/lib/python3.9/site-packages/numpy/linalg/linalg.py:183\u001b[0m, in \u001b[0;36m_assert_stacked_2d\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[39mfor\u001b[39;00m a \u001b[39min\u001b[39;00m arrays:\n\u001b[1;32m    182\u001b[0m     \u001b[39mif\u001b[39;00m a\u001b[39m.\u001b[39mndim \u001b[39m<\u001b[39m \u001b[39m2\u001b[39m:\n\u001b[0;32m--> 183\u001b[0m         \u001b[39mraise\u001b[39;00m LinAlgError(\u001b[39m'\u001b[39m\u001b[39m%d\u001b[39;00m\u001b[39m-dimensional array given. Array must be \u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m    184\u001b[0m                 \u001b[39m'\u001b[39m\u001b[39mat least two-dimensional\u001b[39m\u001b[39m'\u001b[39m \u001b[39m%\u001b[39m a\u001b[39m.\u001b[39mndim)\n",
      "\u001b[0;31mLinAlgError\u001b[0m: 0-dimensional array given. Array must be at least two-dimensional"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(two_var_data[:, 0], two_var_data[:, 1], test_size=0.2)\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "predicted_vals = model.predict(X_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to view the linear model in terms of the data, the same range of inputs are graphed, both with the data and the predicted values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'predicted_vals' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m f \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39mfigure()\n\u001b[1;32m      2\u001b[0m plt\u001b[39m.\u001b[39mscatter(two_var_data[:, \u001b[39m0\u001b[39m], two_var_data[:, \u001b[39m1\u001b[39m], color\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mblue\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m plt\u001b[39m.\u001b[39mplot(X_test, predicted_vals, color\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mred\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m      4\u001b[0m plt\u001b[39m.\u001b[39mxlabel(\u001b[39m'\u001b[39m\u001b[39mMean Radius\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m      5\u001b[0m plt\u001b[39m.\u001b[39mylabel(\u001b[39m'\u001b[39m\u001b[39mMean Texture\u001b[39m\u001b[39m'\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'predicted_vals' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure()\n",
    "plt.scatter(two_var_data[:, 0], two_var_data[:, 1], color='blue')\n",
    "plt.plot(X_test, predicted_vals, color='red')\n",
    "plt.xlabel('Mean Radius')\n",
    "plt.ylabel('Mean Texture')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 38,  44,  50,  56],\n",
       "       [ 83,  98, 113, 128]])"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "B = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])\n",
    "np.dot(A, B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 2 is different from 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[267], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m B\u001b[39m@A\u001b[39;49m\n",
      "\u001b[0;31mValueError\u001b[0m: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 2 is different from 4)"
     ]
    }
   ],
   "source": [
    "B@A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.91730313,  0.88251177],\n",
       "       [ 0.38986869,  0.75652795],\n",
       "       [-0.85447155,  2.39054959],\n",
       "       [ 1.03185426, -0.54426348],\n",
       "       [-1.65188224,  0.17613845],\n",
       "       [ 1.01467872,  1.23464917],\n",
       "       [ 0.37783511,  0.97558001],\n",
       "       [-0.50824628, -0.68860627],\n",
       "       [ 0.37237847,  0.08881998],\n",
       "       [-0.59271073, -1.45696844]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.random.randn(10, 2)\n",
    "y = np.random.randn(10,)*0.02 + X@np.array([2,3])\n",
    "w = np.array([1,1])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pred(X, w):\n",
    "    return X@w\n",
    "\n",
    "def loss(w, X, y):\n",
    "    return np.mean(np.square(pred(X, w) - y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0007340440724757174"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss(np.array([2,3]), X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.97060029, 3.02343755])"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_ = np.linalg.inv(X.T@X)@X.T@y\n",
    "w_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.34527926500567613"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss(w_, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_candidate_1 = np.linspace(-1,1,100)\n",
    "w_candidate_2 = np.linspace(-1,1,100)\n",
    "w_values = []\n",
    "loss_values =[]\n",
    "for w_1 in w_candidate_1:\n",
    "    for w_2 in w_candidate_2:\n",
    "         w = np.array([w_1, w_2])\n",
    "         w_values.append(w)\n",
    "         loss_val = loss(w, X, y)\n",
    "         loss_values.append(loss_val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Loss')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "f = plt.figure()\n",
    "plt.plot(w_values, loss_values, color='blue')\n",
    "plt.xlabel('W values')\n",
    "plt.ylabel('Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -176.9546503   -299.16778989   594.53059596   368.72525305\n",
      " -1738.75600274  1129.63256557   828.17945958   772.15459391\n",
      "   916.42575935   -99.24908976]\n",
      "[[ 6.76097729e+00 -1.51618591e+01  2.71829294e+00  1.31427653e+01\n",
      "   1.94751623e+01  6.65188338e+00 -3.89927457e+01  2.64917430e+01\n",
      "   1.49439928e+01  1.06960496e-01]\n",
      " [ 9.33215557e+00 -1.51618591e+01 -6.89359088e+00  2.07595619e+01\n",
      "  -9.77547059e+01  8.23528212e+01 -3.28946321e+01  5.49851130e+01\n",
      "   2.80092938e+01  5.18060837e-01]\n",
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      "  -4.99815313e+01  1.06960496e-01]\n",
      " [-1.18800653e+01 -1.51618591e+01 -1.84278515e+01  1.71757040e+00\n",
      "  -4.27284412e+01  4.02574399e+01 -2.37474617e+01  2.64917430e+01\n",
      "   2.14181669e+01 -8.11504632e+00]\n",
      " [-1.25228598e+01  1.33553397e+01  4.11658282e+01  1.39933076e+01\n",
      "  -3.79435487e+01  1.69948557e+00 -2.98455753e+01  3.01958811e+01\n",
      "   7.11481778e+01 -1.05816484e+01]\n",
      " [-1.59535214e+00  1.33553397e+01  2.70683986e+01  1.06038331e+01\n",
      "  -2.11964246e+01 -1.56339067e+01  2.19883904e+01 -3.04949970e+01\n",
      "   4.22775382e+01 -3.59294257e+00]\n",
      " [-9.52557566e-01 -1.51618591e+01  1.80973070e+01  3.09152906e+01\n",
      "   6.49316419e+01 -5.34843757e+01  1.28412200e+01 -3.04949970e+01\n",
      "   7.91846965e+00 -1.53744087e+00]\n",
      " [ 4.18979900e+00  1.33553397e+01 -3.89332036e+01 -3.00190820e+01\n",
      "   6.73240882e+01 -6.05592297e+01  4.94299017e+01 -5.89883670e+01\n",
      "  -3.40258248e+01  4.21796391e+00]\n",
      " [-1.31656544e+01 -1.51618591e+01 -1.20199289e+01  1.69511636e+01\n",
      "  -1.28856508e+02  3.70737556e+01 -2.98455753e+01  5.49851130e+01\n",
      "   9.74625629e+01 -3.59294257e+00]\n",
      " [-1.18800653e+01  1.33553397e+01  3.34763212e+01  2.71068923e+01\n",
      "   2.42600549e+01 -4.42870655e+01 -2.67965185e+01 -2.00162701e+00\n",
      "   6.94105930e+01 -3.59294257e+00]\n",
      " [-4.16653042e+00  1.33553397e+01  3.66802824e+01  1.94900958e+01\n",
      "   6.01467493e+01 -5.52530892e+01 -2.37474617e+01 -2.00162701e+00\n",
      "   5.01467924e+01  5.18060837e-01]\n",
      " [ 4.83259358e+00 -1.51618591e+01 -1.39423057e+01 -5.89922619e+00\n",
      "  -2.35888709e+01  1.44342228e+01  2.19883904e+01 -2.00162701e+00\n",
      "  -9.99201976e+00  2.16246220e+00]\n",
      " [-2.23814671e+00  1.33553397e+01  9.12621548e+00 -1.22465567e+01\n",
      "  -7.14377967e+01  3.63662702e+01 -2.40406402e+00 -2.00162701e+00\n",
      "   4.13001467e+01  6.68456595e+00]\n",
      " [ 9.33215557e+00  1.33553397e+01 -3.31660733e+01 -1.35160228e+01\n",
      "  -1.55173417e+02 -3.60665493e+00  6.74310640e+00  2.64917430e+01\n",
      "   1.21312585e+02 -3.04139845e-01]\n",
      " [-9.52557566e-01  1.33553397e+01  3.53986979e+01 -2.07138955e+01\n",
      "  -4.27284412e+01  5.97132884e+01 -3.59436889e+01  3.93137595e+01\n",
      "  -3.86870412e+00  2.98466288e+00]\n",
      " [-1.44512436e+01 -1.51618591e+01  7.95916171e-01  1.31427653e+01\n",
      "  -2.19769467e+02  1.02869898e+02  1.58902768e+01  2.64917430e+01\n",
      "   7.74302151e+01  2.98466288e+00]\n",
      " [-2.88094128e+00 -1.51618591e+01 -1.26607212e+01 -3.36029399e+00\n",
      "  -5.94755653e+01  5.40534052e+01  6.44992786e-01 -2.00162701e+00\n",
      "  -1.18298498e+01 -2.35964155e+00]\n",
      " [-1.12372707e+01 -1.51618591e+01 -1.52238902e+01  4.25650259e+00\n",
      "  -1.12109384e+02  5.47608906e+01  2.50374472e+01 -2.00162701e+00\n",
      "   3.51851849e+01 -1.94854121e+00]\n",
      " [ 1.89740741e+01  1.33553397e+01 -4.59819184e+01 -9.70762448e+00\n",
      "   1.55844601e+02 -1.08668237e+02  2.19883904e+01 -5.89883670e+01\n",
      "  -3.90130273e+01  5.18060837e-01]\n",
      " [-1.05944761e+01 -1.51618591e+01  2.45052296e+01  4.25650259e+00\n",
      "  -7.14377967e+01  7.98766223e+01 -2.98455753e+01  2.64917430e+01\n",
      "  -9.99201976e+00  2.98466288e+00]\n",
      " [ 2.90420986e+00  1.33553397e+01 -6.25279863e+00  4.48104300e-01\n",
      "   6.49316419e+01 -4.03958958e+01  9.79216321e+00 -3.04949970e+01\n",
      "  -1.96072125e+01  3.39576322e+00]]\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "x and y must be the same size",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[7], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m Predictions \u001b[39m=\u001b[39m Regressed_data\u001b[39m.\u001b[39mpredict(X_test2)\n\u001b[1;32m      5\u001b[0m f \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39mfigure()\n\u001b[0;32m----> 6\u001b[0m plt\u001b[39m.\u001b[39;49mscatter(diabetes_data\u001b[39m.\u001b[39;49mdata, diabetes_data\u001b[39m.\u001b[39;49mtarget, color\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mblue\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m      7\u001b[0m plt\u001b[39m.\u001b[39mplot(X_test2, Predictions, color\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mred\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m      8\u001b[0m plt\u001b[39m.\u001b[39mxlabel(\u001b[39m'\u001b[39m\u001b[39mX values\u001b[39m\u001b[39m'\u001b[39m)\n",
      "File \u001b[0;32m~/anaconda3/envs/owkin1/lib/python3.9/site-packages/matplotlib/pyplot.py:2862\u001b[0m, in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[1;32m   2857\u001b[0m \u001b[39m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[39m.\u001b[39mscatter)\n\u001b[1;32m   2858\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mscatter\u001b[39m(\n\u001b[1;32m   2859\u001b[0m         x, y, s\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, c\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, marker\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, cmap\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, norm\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,\n\u001b[1;32m   2860\u001b[0m         vmin\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, vmax\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, alpha\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, linewidths\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m,\n\u001b[1;32m   2861\u001b[0m         edgecolors\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, plotnonfinite\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m-> 2862\u001b[0m     __ret \u001b[39m=\u001b[39m gca()\u001b[39m.\u001b[39;49mscatter(\n\u001b[1;32m   2863\u001b[0m         x, y, s\u001b[39m=\u001b[39;49ms, c\u001b[39m=\u001b[39;49mc, marker\u001b[39m=\u001b[39;49mmarker, cmap\u001b[39m=\u001b[39;49mcmap, norm\u001b[39m=\u001b[39;49mnorm,\n\u001b[1;32m   2864\u001b[0m         vmin\u001b[39m=\u001b[39;49mvmin, vmax\u001b[39m=\u001b[39;49mvmax, alpha\u001b[39m=\u001b[39;49malpha, linewidths\u001b[39m=\u001b[39;49mlinewidths,\n\u001b[1;32m   2865\u001b[0m         edgecolors\u001b[39m=\u001b[39;49medgecolors, plotnonfinite\u001b[39m=\u001b[39;49mplotnonfinite,\n\u001b[1;32m   2866\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m({\u001b[39m\"\u001b[39;49m\u001b[39mdata\u001b[39;49m\u001b[39m\"\u001b[39;49m: data} \u001b[39mif\u001b[39;49;00m data \u001b[39mis\u001b[39;49;00m \u001b[39mnot\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m \u001b[39melse\u001b[39;49;00m {}), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   2867\u001b[0m     sci(__ret)\n\u001b[1;32m   2868\u001b[0m     \u001b[39mreturn\u001b[39;00m __ret\n",
      "File \u001b[0;32m~/anaconda3/envs/owkin1/lib/python3.9/site-packages/matplotlib/__init__.py:1442\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1439\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m   1440\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39minner\u001b[39m(ax, \u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m   1441\u001b[0m     \u001b[39mif\u001b[39;00m data \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 1442\u001b[0m         \u001b[39mreturn\u001b[39;00m func(ax, \u001b[39m*\u001b[39;49m\u001b[39mmap\u001b[39;49m(sanitize_sequence, args), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1444\u001b[0m     bound \u001b[39m=\u001b[39m new_sig\u001b[39m.\u001b[39mbind(ax, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m   1445\u001b[0m     auto_label \u001b[39m=\u001b[39m (bound\u001b[39m.\u001b[39marguments\u001b[39m.\u001b[39mget(label_namer)\n\u001b[1;32m   1446\u001b[0m                   \u001b[39mor\u001b[39;00m bound\u001b[39m.\u001b[39mkwargs\u001b[39m.\u001b[39mget(label_namer))\n",
      "File \u001b[0;32m~/anaconda3/envs/owkin1/lib/python3.9/site-packages/matplotlib/axes/_axes.py:4584\u001b[0m, in \u001b[0;36mAxes.scatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m   4582\u001b[0m y \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mma\u001b[39m.\u001b[39mravel(y)\n\u001b[1;32m   4583\u001b[0m \u001b[39mif\u001b[39;00m x\u001b[39m.\u001b[39msize \u001b[39m!=\u001b[39m y\u001b[39m.\u001b[39msize:\n\u001b[0;32m-> 4584\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mx and y must be the same size\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m   4586\u001b[0m \u001b[39mif\u001b[39;00m s \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   4587\u001b[0m     s \u001b[39m=\u001b[39m (\u001b[39m20\u001b[39m \u001b[39mif\u001b[39;00m mpl\u001b[39m.\u001b[39mrcParams[\u001b[39m'\u001b[39m\u001b[39m_internal.classic_mode\u001b[39m\u001b[39m'\u001b[39m] \u001b[39melse\u001b[39;00m\n\u001b[1;32m   4588\u001b[0m          mpl\u001b[39m.\u001b[39mrcParams[\u001b[39m'\u001b[39m\u001b[39mlines.markersize\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m*\u001b[39m\u001b[39m*\u001b[39m \u001b[39m2.0\u001b[39m)\n",
      "\u001b[0;31mValueError\u001b[0m: x and y must be the same size"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train2, X_test2, y_train2, y_test2 = train_test_split(diabetes_data.data, diabetes_data.target, test_size=0.2)\n",
    "Regressed_data = LinearRegression()\n",
    "Regressed_data.fit(X_train2, y_train2)\n",
    "Predictions = Regressed_data.predict(X_test2)\n",
    "f = plt.figure()\n",
    "plt.scatter(diabetes_data.data, diabetes_data.target, color='blue')\n",
    "plt.plot(X_test2, Predictions, color='red')\n",
    "plt.xlabel('X values')\n",
    "plt.ylabel('Y values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m f \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39mfigure()\n\u001b[1;32m      2\u001b[0m plt\u001b[39m.\u001b[39mscatter(diabetes_data\u001b[39m.\u001b[39mdata, diabetes_data\u001b[39m.\u001b[39mtarget, color\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mblue\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m      3\u001b[0m plt\u001b[39m.\u001b[39mplot(X_test2[:, \u001b[39m0\u001b[39m], Predictions[:, \u001b[39m0\u001b[39m], color\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mred\u001b[39m\u001b[39m'\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "f = plt.figure()\n",
    "plt.scatter(diabetes_data.data, diabetes_data.target, color='blue')\n",
    "plt.plot(X_test2[:, 0], Predictions[:, 0], color='red')\n",
    "plt.plot(X_test2[:, 1], Predictions[:, 1], color='green')\n",
    "plt.plot(X_test2[:, 3], Predictions[:, 3], color='black')\n",
    "plt.plot(X_test2[:, 4], Predictions[:, 4], color='yellow')\n",
    "plt.xlabel('X values')\n",
    "plt.ylabel('Y values')\n",
    "print(diabetes_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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