regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import math\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from keras.losses import mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "sns.set_style('darkgrid')\n",
    "plt.rcParams['figure.dpi'] = 108\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = pd.read_csv(\"model_data/data.csv\",sep =\"\\t\")\n",
    "dt.reset_index()\n",
    "y = dt.Real\n",
    "test_dt =pd.read_csv(\"model_data/test_data.csv\",sep =\"\\t\")\n",
    "dt = dt.drop('Real',axis =1)\n",
    "len_dt = len(dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "frames = [dt, test_dt]\n",
    " \n",
    "train_test = pd.concat(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standard\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(train_test)\n",
    "train_test_standard= scaler.transform(train_test)\n",
    "#dt = scaler.transform(dt)\n",
    "#test_dt = scaler.transform(test_dt)\n",
    "ds = train_test_standard[:len_dt]\n",
    "test_dt =train_test_standard[len_dt:]\n",
    "#dt['Real'] =dt.apply(lambda x: 0 if x.Real ==-1 else 1, axis =1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7955\n",
      "[[-0.11048675 -0.43852401  0.28149735  0.12572772 -0.13898814]\n",
      " [ 0.03533805  0.30677984 -0.27427674 -0.08039888 -0.13898814]\n",
      " [-0.11135786 -0.43852401 -0.31333114 -0.08039888 -0.12762523]\n",
      " ...\n",
      " [-0.0981169  -0.20183968 -0.16011774  0.16320528 -0.13898814]\n",
      " [-0.10883163 -0.32269976 -0.29831022 -0.00544375 -0.13898814]\n",
      " [-0.11249031  0.20102727 -0.31032696 -0.08039888 -0.12762523]]\n",
      "7955\n",
      "7950    1\n",
      "7951   -1\n",
      "7952    1\n",
      "7953    1\n",
      "7954   -1\n",
      "Name: Real, dtype: int64\n",
      "[[-0.11074808 -0.44355984 -0.22921398 -0.08039888 -0.12762523]\n",
      " [-0.1063925  -0.39320148 -0.28929767 -0.08039888 -0.13141287]\n",
      " [-0.09471955 -0.05580041 -0.26526419 -0.08039888 -0.1352005 ]\n",
      " ...\n",
      " [-0.11022541 -0.42845233 -0.31032696 -0.08039888 -0.13141287]\n",
      " [-0.10769918 -0.4234165  -0.30732277 -0.08039888 -0.13141287]\n",
      " [-0.0913222  -0.40830899 -0.31032696 -0.08039888 -0.13141287]]\n"
     ]
    }
   ],
   "source": [
    "#ds =dt.drop('Real',axis=1)\n",
    "print(len_dt)\n",
    "#print(train_test_standard)\n",
    "print(ds)\n",
    "print(len(ds))\n",
    "print(y.tail())\n",
    "\n",
    "print(test_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly =PolynomialFeatures(2)\n",
    "ds=poly.fit_transform(ds)\n",
    "test_dt = poly.fit_transform(test_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 6364 instances. Test set has 1591 instances.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(ds, y, test_size=0.2, random_state=1)\n",
    "print(\"Training set has {} instances. Test set has {} instances.\".format(X_train.shape[0], X_test.shape[0]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0042"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "ridgecv = RidgeCV(alphas=[0.00387,0.00385,0.00386,0.005,0.0042,0.01,0.0045])\n",
    "ridgecv.fit(X_train, y_train)\n",
    "a= ridgecv.alpha_ \n",
    "ridgecv.alpha_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "      normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "ridge = Ridge(alpha=0.01)\n",
    "ridge.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00000000e+00 -1.02665108e+00 -7.13784886e-02  1.50713125e-01\n",
      "  2.04757503e+00 -1.78768309e+02  3.10745764e-03 -6.51664603e-03\n",
      "  2.79961282e-01 -4.45687257e-02 -7.87177481e+00  5.58769941e-03\n",
      "  1.17046910e-02 -8.10867632e-03  5.97695815e-03 -1.04100822e-02\n",
      " -2.65575178e-02 -1.59736982e-04 -2.95891820e-03  1.37558507e+01\n",
      "  2.45093630e+01]\n",
      "-24.522174921977985\n"
     ]
    }
   ],
   "source": [
    "print (ridge.coef_)\n",
    "print (ridge.intercept_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.02571389, -1.3328108 ,  0.7650074 , ...,  0.73692011,\n",
       "       -0.62917052,  0.78246999])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3223    1\n",
      "6133   -1\n",
      "5856    1\n",
      "3922    1\n",
      "7409   -1\n",
      "       ..\n",
      "5706    1\n",
      "1317    1\n",
      "6983    1\n",
      "7915   -1\n",
      "4733    1\n",
      "Name: Real, Length: 1591, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.860852973046809"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " -0.63591465]\n",
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0.4886433032836711, 0.35508592757684604, 0.26913698746341563, 0.24504521986691452, 0.33257406765464986, 0.5173018202703249, 0.34775509599190885, 0.5189390235847443, 0.5082732029532295, 0.4109060415727647, 0.5173338963389056, 0.347187306866243, 0.6023430731816997, 0.33701889511886035, 0.3463508109138262, 0.20725698104906254, 0.35083121965382635, 0.5151855607914363, 0.5868693215047809, 0.31700930810727895, 0.5111541801007767, 0.3524074168406812, 0.5167907157470962, 0.5145053732240639, 0.612563093655935, 0.34680342799526, 0.3532801746104294, 0.517407906149481, 0.349284552845201, 0.5079584706171482, 0.3488335906436004, 0.6027376248278717, 0.5392221692968658, 0.3459081490007697, 0.3459314423192902, 0.3468458671341852, 0.3467980381673433, 0.3461706208401655]\n",
      "   Id  Predicted\n",
      "0   1   0.318178\n",
      "1   2   0.285502\n",
      "2   3   0.285780\n",
      "3   4   0.111092\n",
      "4   5   0.938920\n"
     ]
    }
   ],
   "source": [
    "y_real_test = ridge.predict(test_dt)\n",
    "print(y_real_test)\n",
    "\n",
    "y_test_result = list(map(lambda x: 1 / (1 + math.exp(-x)),y_real_test))\n",
    "print(y_test_result)\n",
    "result = pd.read_csv('data/sample.csv',sep = ',')\n",
    "print(result.head())\n",
    "result['Predicted'] = y_test_result\n",
    "result.to_csv('predictions/result3.csv',sep = ',',index =False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4944622396635717\n"
     ]
    }
   ],
   "source": [
    "\n",
    "median = np.median(y_test_result)\n",
    "print(median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "X_train = X_train/ 100.0\n",
    "X_test = X_test / 100.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "\u001B[0;32m<ipython-input-11-c3a55fa4d482>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      4\u001B[0m \u001B[0mtest_mses\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mlist\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      5\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 6\u001B[0;31m X_grid = np.linspace(min(X_train.min(), X_test.min()),\n\u001B[0m\u001B[1;32m      7\u001B[0m                      max(X_train.max(), X_test.max()), num=1001)\n\u001B[1;32m      8\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/generic.py\u001B[0m in \u001B[0;36m__nonzero__\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m   1477\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0m__nonzero__\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1478\u001B[0m         raise ValueError(\n\u001B[0;32m-> 1479\u001B[0;31m             \u001B[0;34mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   1480\u001B[0m             \u001B[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1481\u001B[0m         )\n",
      "\u001B[0;31mValueError\u001B[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    }
   ],
   "source": [
    "degrees = list(range(12))\n",
    "models = list()\n",
    "train_mses = list()\n",
    "test_mses = list()\n",
    "\n",
    "X_grid = np.linspace(min(X_train.min(), X_test.min()),\n",
    "                     max(X_train.max(), X_test.max()), num=1001)\n",
    "\n",
    "plt.figure(figsize=(20,16))\n",
    "for i, degree in enumerate(degrees):\n",
    "    plt.subplot(len(degrees)//2, 2, i+1)\n",
    "\n",
    "    # Transform features\n",
    "    poly = PolynomialFeatures(degree=degree)\n",
    "    Phi_train, Phi_test = poly.fit_transform(X_train), poly.fit_transform(X_test)\n",
    "    Phi_grid = poly.fit_transform(X_grid[:,np.newaxis])\n",
    "\n",
    "    # Fit model\n",
    "    lr_poly = Ridge(alpha = 0.002).fit(Phi_train, y_train)\n",
    "    models.append(lr_poly)\n",
    "    \n",
    "    # Evaluate\n",
    "    train_mse = mean_squared_error(lr_poly.predict(Phi_train), y_train)\n",
    "    train_mses.append(train_mse)\n",
    "    test_mse = mean_squared_error(lr_poly.predict(Phi_test), y_test)\n",
    "    test_mses.append(test_mse)\n",
    "\n",
    "    plt.plot(X_grid, lr_poly.predict(Phi_grid), 'k', label='Prediction')\n",
    "    plt.scatter(X_train, y_train, color='b', marker='.', label='Train')\n",
    "    #plt.scatter(X_test_s, y_test, color='r', marker='.', label='Test')\n",
    "    plt.title('Degree {} | Train MSE {:.3f}'.format(degree, train_mse))\n",
    "    plt.legend()\n",
    "\n",
    "plt.suptitle('Polynomial ridge regression for different polynomial degrees', y=1.05, fontsize=32)\n",
    "plt.tight_layout()"
   ]
  }
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