LinearReg2.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error,accuracy_score\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import itertools\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.linear_model import Ridge,Lasso,ElasticNet\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('network_backup_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Week #</th>\n",
       "      <th>Day of Week</th>\n",
       "      <th>Backup Start Time - Hour of Day</th>\n",
       "      <th>Work-Flow-ID</th>\n",
       "      <th>File Name</th>\n",
       "      <th>Size of Backup (GB)</th>\n",
       "      <th>Backup Time (hour)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "      <td>work_flow_0</td>\n",
       "      <td>File_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "      <td>work_flow_2</td>\n",
       "      <td>File_12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "      <td>work_flow_2</td>\n",
       "      <td>File_13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "      <td>work_flow_2</td>\n",
       "      <td>File_14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "      <td>work_flow_2</td>\n",
       "      <td>File_16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Week # Day of Week  Backup Start Time - Hour of Day Work-Flow-ID File Name  \\\n",
       "0       1      Monday                                1  work_flow_0    File_0   \n",
       "1       1      Monday                                1  work_flow_2   File_12   \n",
       "2       1      Monday                                1  work_flow_2   File_13   \n",
       "3       1      Monday                                1  work_flow_2   File_14   \n",
       "4       1      Monday                                1  work_flow_2   File_16   \n",
       "\n",
       "   Size of Backup (GB)  Backup Time (hour)  \n",
       "0                  0.0                   0  \n",
       "1                  0.0                   0  \n",
       "2                  0.0                   0  \n",
       "3                  0.0                   0  \n",
       "4                  0.0                   0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = dataset.replace({'Day of Week': {'Monday': 1, 'Tuesday': 2, 'Wednesday': 3, 'Thursday': 4, 'Friday': 5,\n",
    "                                  'Saturday': 6, 'Sunday': 7},\n",
    "                  'Work-Flow-ID': {'work_flow_0': 0, 'work_flow_1': 1, 'work_flow_2': 2, 'work_flow_3': 3,\n",
    "                                   'work_flow_4': 4},\n",
    "                  'File Name': {'File_0': 0, 'File_1': 1, 'File_2': 2, 'File_3': 3, 'File_4': 4, 'File_5': 5,\n",
    "                                'File_6': 6, 'File_7': 7, 'File_8': 8, 'File_9': 9, 'File_10': 10, 'File_11': 11,\n",
    "                                'File_12': 12, 'File_13': 13, 'File_14': 14, 'File_15': 15, 'File_16': 16,\n",
    "                                'File_17': 17, 'File_18': 18, 'File_19': 19, 'File_20': 20, 'File_21': 21,\n",
    "                                'File_22': 22,\n",
    "                                'File_23': 23, 'File_24': 24, 'File_25': 25, 'File_26': 26, 'File_27': 27,\n",
    "                                'File_28': 28, 'File_29': 29, }})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "    .dataframe thead th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Week #</th>\n",
       "      <th>Day of Week</th>\n",
       "      <th>Backup Start Time - Hour of Day</th>\n",
       "      <th>Work-Flow-ID</th>\n",
       "      <th>File Name</th>\n",
       "      <th>Size of Backup (GB)</th>\n",
       "      <th>Backup Time (hour)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Week #  Day of Week  Backup Start Time - Hour of Day  Work-Flow-ID  \\\n",
       "0       1            1                                1             0   \n",
       "1       1            1                                1             2   \n",
       "2       1            1                                1             2   \n",
       "3       1            1                                1             2   \n",
       "4       1            1                                1             2   \n",
       "\n",
       "   File Name  Size of Backup (GB)  Backup Time (hour)  \n",
       "0          0                  0.0                   0  \n",
       "1         12                  0.0                   0  \n",
       "2         13                  0.0                   0  \n",
       "3         14                  0.0                   0  \n",
       "4         16                  0.0                   0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = df[\n",
    "    ['Week #', 'Day of Week', 'Backup Start Time - Hour of Day', 'Work-Flow-ID', 'File Name']].values\n",
    "y = df['Size of Backup (GB)'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: [ -4.46137250e-05  -2.40527826e-03   1.38306408e-03  -4.14520552e-04\n",
      "   4.85929375e-04]\n",
      "Intercept: 0.049413880522\n",
      "RMSE without cross validation: 0.106503866998\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n",
    "linreg = LinearRegression()\n",
    "linreg.fit(X_train, y_train)\n",
    "print (\"Coefficients:\", linreg.coef_)\n",
    "print (\"Intercept:\", linreg.intercept_)\n",
    "y_predict = linreg.predict(X_test)\n",
    "print(\"RMSE without cross validation:\", np.sqrt(mean_squared_error(y_test, y_predict)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def perform_10fold(X,y,regressor):\n",
    "    kf = KFold(n_splits=10,shuffle = True,random_state = 0)\n",
    "    i = 1\n",
    "    bestModel = None\n",
    "    tr_e = 0\n",
    "    ts_e = 0\n",
    "    min_ts_e = 10\n",
    "    for train_index, test_index in kf.split(X):\n",
    "        #print(\"Fold : \",i)\n",
    "        X_train, X_test = X[train_index], X[test_index]\n",
    "        y_train, y_test = y[train_index], y[test_index]\n",
    "        regressor.fit(X_train,y_train)\n",
    "        train_preds = regressor.predict(X_train)\n",
    "        test_preds = regressor.predict(X_test)\n",
    "        test_error = mean_squared_error(y_test,test_preds)\n",
    "        tr_e += mean_squared_error(y_train,train_preds)\n",
    "        ts_e += test_error\n",
    "        #print(\"Training RMSE : \",np.sqrt(mean_squared_error(y_train,train_preds)))\n",
    "        #print(\"Test RMSE : \",np.sqrt(test_error))\n",
    "        if(test_error<min_ts_e):\n",
    "            min_ts_e = test_error\n",
    "            bestModel = regressor\n",
    "        i = i+1\n",
    "    return np.sqrt(tr_e/10),np.sqrt(ts_e/10), bestModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_graph(x,y,xlabel,ylabel,title):\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.scatter(x, y, color='blue', lw=1, label=y,s=3)\n",
    "    #plt.plot([y.min(), y.max()], [0,0], 'k--', lw=4)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_graph_time(y1,y2,y1label,y2label,ylabel,title):\n",
    "    plt.title(title)\n",
    "    plt.scatter(np.arange(y1.shape[0]), y1, color='blue', label=y1label,s=3)\n",
    "    plt.scatter(np.arange(y1.shape[0]), y2, color='red', label=y2label,s=3)\n",
    "    plt.xlabel('Time')\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.legend(loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "Average Training RMSE :  0.103585393643\n",
      "Average Test RMSE :  0.103675847676\n"
     ]
    }
   ],
   "source": [
    "regressor = LinearRegression()\n",
    "avg_tr,avg_ts,bestModel = perform_10fold(X,y,regressor)\n",
    "print(\"------------------------------\")\n",
    "print(\"Average Training RMSE : \",avg_tr)\n",
    "print(\"Average Test RMSE : \",avg_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regressor = LinearRegression()\n",
    "regressor.fit(X,y)\n",
    "predicted = regressor.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5HWa2DNgO/DihRbHR3R+K3jtKWGLEgU+6e9Nxi9maOtvXF1ajTYoHu4eH4Tvf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\ndTa6+X8duAR4FLgPeJ27P5g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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115a25f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zJwDP4DqPD2+kUoZhGEZzqGoUVPXPqrpRVZ9S1f+tqp9Q1VVjoVwrMx623Vu8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\nPtnG7ftnHMZF42Ds/rS2bz9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ed285c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ed28550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cmke/FpHncAvXGYZhGKOMLKOP3hedniEiv8OtbvqrUrUyDMMwGkKm0UcxqnpT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\nVzQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1160be828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_graph(y,predicted,\"Actual\",\"Fitted\",\"Fitted vs Actual\")\n",
    "plot_graph_time(y,predicted,\"Actual\",\"Fitted\",\"Actual and Fitted values\",\"Actual and Fitted values over time\")\n",
    "plot_graph(predicted,y-predicted,\"Fitted\",\"Residual\",\"Residual vs Fitted\")\n",
    "plot_graph_time(y-predicted,predicted,\"Residual\",\"Fitted\",\"Residual and Fitted values\",\"Residual and Fitted values over time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part ii"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = df[\n",
    "    ['Week #', 'Day of Week', 'Backup Start Time - Hour of Day', 'Work-Flow-ID', 'File Name']].values\n",
    "y = df['Size of Backup (GB)'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: [-0.00019274 -0.00479403  0.00944936 -0.00058665  0.00420832]\n",
      "Intercept: 0.0607646798087\n",
      "RMSE without cross validation: 0.106503866998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/satya/Anaconda/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1,random_state = 0)\n",
    "scaler = StandardScaler()\n",
    "X_train_std = scaler.fit_transform(X_train)\n",
    "X_test_std = scaler.transform(X_test)\n",
    "linreg = LinearRegression()\n",
    "linreg.fit(X_train_std, y_train)\n",
    "print (\"Coefficients:\", linreg.coef_)\n",
    "print (\"Intercept:\", linreg.intercept_)\n",
    "y_predict = linreg.predict(X_test_std)\n",
    "print(\"RMSE without cross validation:\", np.sqrt(mean_squared_error(y_test, y_predict)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def perform_10fold_std(X,y,regressor):\n",
    "    kf = KFold(n_splits=10,random_state = 0,shuffle = True)\n",
    "    i = 1\n",
    "    bestModel = None\n",
    "    tr_e = 0\n",
    "    ts_e = 0\n",
    "    min_ts_e = 10\n",
    "    for train_index, test_index in kf.split(X):\n",
    "        print(\"Fold : \",i)\n",
    "        scaler = StandardScaler()\n",
    "        X_train, X_test = X[train_index], X[test_index]\n",
    "        y_train, y_test = y[train_index], y[test_index]\n",
    "        scaler.fit_transform(X_train)\n",
    "        scaler.transform(X_test)\n",
    "        regressor.fit(X_train,y_train)\n",
    "        train_preds = regressor.predict(X_train)\n",
    "        test_preds = regressor.predict(X_test)\n",
    "        test_error = mean_squared_error(y_test,test_preds)\n",
    "        tr_e += mean_squared_error(y_train,train_preds)\n",
    "        ts_e += test_error\n",
    "        print(\"Training RMSE : \",np.sqrt(mean_squared_error(y_train,train_preds)))\n",
    "        print(\"Test RMSE : \",np.sqrt(test_error))\n",
    "        if(test_error<min_ts_e):\n",
    "            min_ts_e = test_error\n",
    "            bestModel = regressor\n",
    "        i = i+1\n",
    "    return np.sqrt(tr_e/10),np.sqrt(ts_e/10), bestModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold :  1\n",
      "Training RMSE :  0.103265021252\n",
      "Test RMSE :  0.106503866998\n",
      "Fold :  2\n",
      "Training RMSE :  0.103428926322\n",
      "Test RMSE :  0.105029409306\n",
      "Fold :  3\n",
      "Training RMSE :  0.101891490799\n",
      "Test RMSE :  0.117793845398\n",
      "Fold :  4\n",
      "Training RMSE :  0.104325747394\n",
      "Test RMSE :  0.0967244993663\n",
      "Fold :  5\n",
      "Training RMSE :  0.104118712604\n",
      "Test RMSE :  0.0987136373113\n",
      "Fold :  6\n",
      "Training RMSE :  0.104606731957\n",
      "Test RMSE :  0.0939770759809\n",
      "Fold :  7\n",
      "Training RMSE :  0.103710941139\n",
      "Test RMSE :  0.102543127434\n",
      "Fold :  8\n",
      "Training RMSE :  0.102680720217\n",
      "Test RMSE :  0.111465416023\n",
      "Fold :  9\n",
      "Training RMSE :  0.103622312889\n",
      "Test RMSE :  0.103306632317\n",
      "Fold :  10\n",
      "Training RMSE :  0.104198598987\n",
      "Test RMSE :  0.0979498611003\n",
      "------------------------------\n",
      "Average Training RMSE :  0.103587856885"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/satya/Anaconda/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Average Test RMSE :  0.103627896737\n"
     ]
    }
   ],
   "source": [
    "regressor = LinearRegression()\n",
    "avg_tr,avg_ts,bestModel = perform_10fold_std(X,y,regressor)\n",
    "print(\"------------------------------\")\n",
    "print(\"Average Training RMSE : \",avg_tr)\n",
    "print(\"Average Test RMSE : \",avg_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/satya/Anaconda/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "X2 = scaler.fit_transform(X)\n",
    "regressor.fit(X2,y)\n",
    "predicted = regressor.predict(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5HWa2DNgO/DihRbHR3R+K3jtKWGLEgU+6e9Nxi9maOtvXF1ajTYoHu4eH4Tvf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\ndTa6+X8duAR4FLgPeJ27P5g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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115db1e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zJwDP4DqPD2+kUoZhGEZzqGoUVPXPqrpRVZ9S1f+tqp9Q1VVjoVwrMx623Vu8\nGNrb3TkEre7mEO5thBsnWzg0klB+6fvhZPbPVIpsutBKRzM32entdZuk9PaqTp3qrqdODSOzrW1k\nV656NujwN3IJsdOXLy+Em/v7nYz+/jAbkvhhEsK97e0jMrq7R2TXg69XiI12Yh3b20f7Z1n8TWJ8\n/yyLr1MI/fx4EiKMh4ZGy4mPenZf8/0tdJiEkKcacJOdVqOZm+xI2nQ8XJQKKbOtDbZsCScPyuto\n8uqT1wiZoeV1dcFzz0FnpzvXK6/V3bvbbjA8vP39oSE49thyMkPrOGsWPPEE9PS4c73yoPgmO0X6\nFIyI3t6R80C0YMfAQPbzRUiLTEceWV6er1dnp7uOz2Xo6Bg5h3BzW9voc72EDpN2b+hFd/fob5TF\n1yuEjr6MOP5kZUpF2Lhx5Oz7Z1lCh7FPCP87LWX8Y1dXeYMAo/2tv99dx+cy+OEaQl5NZFUhgJ/i\nJpulHkWqIY04WmWP5lC0+p7Fjdz3uRX3s231fZ6ThNA3dDiEDuNGxJNYx8HB1gzvRsRDCjYf5RmF\n1+YdRYQ34mhVo9DqmUmr6zcZaUSYhOiz8GlkoSAErViwaFXqNgqteoQyCqEjU2en883OzjDyQifu\nnh4nr6cnjDzVyZdhhJY3a5YLk1mzwshT1SAdsT5dXU5WV1cYeX19Wnenrk9o97Z6nK6Hokahakdz\ntD/zF4G9gOles9NLgrVh1UCojuYpU1xUEoFt2+rXy2/TreKlTZHndyY++2z98gCmTnUd4u3tsHlz\n/fJC69jqYRJaHrR+vF68GC680PWbnX9+/fJC6xc6Tk+bBps2uT6555u8s33IjubvAt8EtgCvB74P\nBAjOiUWIDj+fEB1+PvFoprKjmsZCZjzyxR8BY9TG9Omjz63GJZfA1q3u3IqEjtObNo0+jweKGIVO\nVf0VIOomsp0OvKGxajWeuFQRqoQWmsceG32ul7jUE6L0M1kJPQqkEaNKfGMQYkJWqFF2MaEzycFB\nN8ppcDCMvNCELiyOBUWMwsZoQ5z7ROQEEXknsHOD9Wo4fmQvM0syOQvyqKNc5DzqqDCzLsuW+LJm\nZ/rDUxcscJF0wYLy8iB8giwzhHYs9fNLkbX4YRb33+8KJfffX9t7eW7+0pegr88Z/+FhOPHE8voB\n3HST03Hu3DAzif0hzmVmEiffOf98Fx7nnx8m3flxJsRM556e0eeiNHXGdbVOB+AAYAZuk5zvAv8J\nLCjSYdGIoxGjj4p2VvmdRnkdt2U6v5IdUv7veGZtd3d1OX6Hty/Dv65FP78zNKujtRb98oYrlnFz\n0Y7LWnRM0zX5u2wHZ1bHYy0EyWVGAAAgAElEQVSzVosMGqhFP39QQ5Z+oTp0y/ihH0/ywruMjnmD\nB2qRlxWvy84W91cPCDVIBBt9VJyige9HyLyRI2UiZ6jILuKeE8mWWYs8P0LGS3G0tZWX5+uUJa8W\nmUVHdzQzTHwdQ4RJkVFLtcjznw2hX1GKyvTjSV54l9ExRBxULRavy4ZJqFFqwYwCcC1wTfIoIrwR\nRyOMgr+WTB5ZJe+y8rJkJ6mllJtVSsl6pha9Gl1TKCuzCCFqCj61DEH2M4wsmXmZUy16xdQypLlI\nTSHU+js+RUvOReNrR4eT19FRXIc82bWU7IvE61rkFQmTWglpFPb3jgOBrwFfLiK8EUcjjMJEHpts\nNIZawrjIsxZn6qfV/bDZ+jW0+Qi4vsx7IY5WndFcK82MIM2OnMb2WJhMfEJP3KuVokahyOS1Hb2f\nU6IawzdU9WX1dnKXoZmrpIYkXqmxrw8eeGDyfNtIx8Jk4rNsGSxZ4hbkq2fxvbKEnLx2M7AyOt8I\nfBL4YH3qGaed5jKAtBUbJ/K3jXQsTCY+xx7rDH4zDEItFKkpTFfVjYl701S1KZO2J0pNwTAMYywJ\nWVO4IeXejbWrZBiGYbQ6mUZBRF4kIvsDnSKyr4jsFx2vA7rGTEPDKIjttWsY9ZNXU3gzcAZuJvNX\nvePjwKmNV80wamPJEtdZu2RJszXJJtTG862MGefxTZE+hcNV9dIx0qcq1qdgZNHs0R1FaG93q4TW\nsw93q2MjqVqTkH0K+4tIZTknEZklIv9el3aGEYBkiXQ8jO448khnEOrZh7vVsZFU45siNYU/qOq+\niXu3qOp+DdUsA6spGDFWIjWM4oSsKbSJyDRPcCcwLed5owlMxnbciV4inYxhajSfIkbhB8CvROSD\nIvIB4Be43deMFmI8dLKGppWbi0J0KJ98sgvTk092vyeDkZgMbvRpRfdWbT4CEJFDgDcCAlytqlc1\nWrEsrPkonfHQyTqZCNGhnNyzOnRzWSvGmcnWJDiW7g3ZfISq/lxVP6WqnwQ2iMjZBZU4RETuFZFV\nIvLZlP8/ISJ3icjtIvIrEdmjiFxje1q51DweCF1iC9GhPG3a6HPo5rJWrF1O9CbBJC3p3iKr5gHz\ngC8Ba3H7K3y0wDttwGrgJUAHcBuwV+KZ1wNd0fWHgYuryW2lVVIny8qWodzZyv7V6BUsy7i90f4V\ny493NGvFcEnzg1r2A8mT29MzsqNgq7o/JNS7dDawJ3AacDfwW+CjwJ+LCI3eXwhc5f0+BTgl5/l9\ngd9Vk9ssoxBvehHvbOYfIvXJTB5FNuEoKq+93elXJgFl6Vd2s5UsWWU308nTr8hmNT5DQyObI8Ub\n3tTrXlXn71k6FtkAJ0ns5lA6JrfFrDce5oVJmTCOdx3LOsqQFyZlyHNzaD+sxxCGMArbgOuBv/Hu\nrSkiNHr2XcC3vd+LgbNynj8L+JeM/47DrdS6cvfddy/vKzWQLKHkRcyykSmkvLzEXdZwhXbzZJM3\nHnQMnemG1m/KlNZOd9XkhfbDsgVQJ7eYUcjrUzgc+CtwrYgsE5GDcR3NRUl7VlMfFBkE5gNfSftf\nVYdUdb6qzp89e3YNKpQn2d7a3h7+GyFlxiNUspg+vXaZefr199cuL4/u7urPJNv9BwbC6tCIMA79\nvTw3lwkTTU2RjimFehxHI7XkEAXYti2svEYwlvGwTDqulcxgV9XLVPWfgL8FrsOtefRCEfmmiLyp\ngOxhYDfvdx/wYPIhEXkj8DngMG3Sctxp7Lrr6PM557gOIT8C9Pe7e0ND5b5xzjluZElMnKDKRLI4\ncYts/35XF5x5Zm3yli2DGTNGJ/Lubtd5OjgI999fu45DQ9v7ITh5GzZUfz9pqG+6ybk7ltfR4a5j\nHWshdu+sWdDb6+61tY1clzWCaWExOOj84ZxzatfxL38Z0Sn5nTJhsmiRc6fvvoEBp9/SpbXJWrx4\nJB52dkJPj/PPWHaZeB2/47u5t7dcGMf4702d6nSdNav2dBwXUj74wdHxcGBgJK5Xk5ks6MTx0M8X\nYsqk41IUqU7EB7Aj8CHgmgLPtgNrgDmMdDS/IvHMvrjO6LlFdRirPgW/yhaiw8/vHAvRUZbcmD1U\n51vszq4uJ7+rq7w8Xye/Y6+sPyblhQyTEB3Nvk4h9EvK6OlxOvb0hNMx7pso625fnt/f1iqdtqHj\nTOg0kox3nZ3ud2dneZlZ0Mg9mosewFuAP0UZ/+eie0twtQKAXwIPA7dGxxXVZDbDKITIMOLE19Y2\nknjqax8c3WYZtw3PmlVepu/OrDbRWhKW72ZfZll/9PXLC5OiOvrhkPdOUXlF9StK0giEDuM4U0tm\n4rWEsS8v7qhvb9/+uaIyixrWMmGc539lwjhEOk4W5urt38ijJYxCI44QRqFIBChawkiW2IvIyysN\n9Pa6/6qNWkh+N6sUWVSe6mh3Zunoj3qploCK1BT6+0fkVavlFM0w8jInn6IlvalTi7m5qH5Fa3XJ\nTCxLZuyHbW21ZbpZmVotNRJfXl6m29Hh/uvoyJdX1LAWLVH7z+W5Kw7jqVPz5fnuzQvHomGSdKMZ\nhSYYBT8x9PTU3+ySbGZKfiuOQHEk6e/PNyS+vK6uYqW1wUHnprTnfXmxYSji5qwMKKlfEfKajpKj\nK5J+Eus6MOASdpHhtb685LNlmnh8eXmZUFHZfjPL4ODo+OD7VRxn4vtZ8tL80J+DEMsbGBgJd3/Y\nY9KP/BFJRYfNVmse9PXr70/3q7jAJOKeyYrTSXnJglvWddF0l/SPrKbfvHSUDJOkG+J3+/tHywjR\nDJyFGYUM/FJf8vBLTmn/p5W2s2R1d2f/l1cayHqutzd9bHo1eXFNoVa9suT5JXtft6S/Zflhcv5A\nmrxYxywZyWer+WEsL2/8d9kwqRZn0uTFpdeyR5K0b8e1pSJHMl6nDW9OizdZ8bFImNRy1JLuiqSR\nImEc+2nZdJwXH+JaUy3yQlDUKJQYdDa+2bw5+z/V0eck69cXXw7hmWeK6bNsWTGZ69e7tXSqkZTz\n6KPl9Mri1JQ999avd2ff37L8MOmGtBEzsY5ZMpLPVvPDWN7y5dXlQfGlLtavrx5nfJmxfocfXkx+\nFjvuOFrHb31r+2dqWW8pDr+YtKVS0uJNVnysN44lidPdrFnbu72oTkmqhXEcnmXdkhYmMZs2VX8/\nbVTSmC2cV8RytNJRb00hq0RVtGTlT6bJKw0kSxhFSzAhj2oTf4ocfhNElh8VKdH7R96MUpHaagow\nuvbX1VW/X/vfLVKqq3YUraEMDGxfsyuia9Kfag0P3x+zwrjWGqZfA0xzk9/XVUSe3ymel1bLhH1e\nGNfq7qwwyAqTWsOqnlFJWPNROn6GlGyr9dsc/Y6itCaOtCPZbphsKy7S1JCWeJL6xYkubnOtJeL6\n8vz2S9+NHR3Fmzj89tykfkldqyWEtA7AWGZHx+h+mSz94pFEfsYRZzzJNnu/jT05pLKam/PiTK0J\nPtnG7ftnHMZF42Ds/rS2bz9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115db11d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JpK938hrnfRbJDLCsuiraPsocq1zrutc13WOtak+3vGPWbQepksaycSbR5zY6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\nQUREYgoKIiIS+/+IBCRrc3dnzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ed4a0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cmke/FpHncAvXGYZhGKOMLKOP3hedniEiv8OtbvqrUrUyDMMwGkKm0UcxqnpT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\nVzQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1160c9b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_graph(y,predicted,\"Actual\",\"Fitted\",\"Fitted vs Actual\")\n",
    "plot_graph_time(y,predicted,\"Actual\",\"Fitted\",\"Actual and Fitted values\",\"Actual and Fitted values over time\")\n",
    "plot_graph(predicted,y-predicted,\"Fitted\",\"Residual\",\"Residual vs Fitted\")\n",
    "plot_graph_time(y-predicted,predicted,\"Residual\",\"Fitted\",\"Residual and Fitted values\",\"Residual and Fitted values over time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part iii"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import f_regression,mutual_info_regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = df[\n",
    "    ['Week #', 'Day of Week', 'Backup Start Time - Hour of Day', 'Work-Flow-ID', 'File Name']].values\n",
    "y = df['Size of Backup (GB)'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  8.45006257e-03,   3.88163798e+01,   1.50740934e+02,\n",
       "         2.61386654e+01,   2.53200943e+01])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selectbest = SelectKBest(f_regression,k=3)\n",
    "X_f = selectbest.fit_transform(X, y)\n",
    "selectbest.scores_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best features from f_regression = Day of the week, Backup time - hour of day, workflow id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/satya/Anaconda/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by the scale function.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.00301695,  0.22228916,  0.23209516,  0.27939148,  0.42824185])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selectbest2 = SelectKBest(mutual_info_regression,k=3)\n",
    "X_mi = selectbest2.fit_transform(X, y)\n",
    "selectbest2.scores_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best features from mutual_info_regression = Backup time - hour of day, workflow id, file name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "Average Training RMSE :  0.103585682142\n",
      "Average Test RMSE :  0.103670661831\n"
     ]
    }
   ],
   "source": [
    "regressor = LinearRegression()\n",
    "avg_tr,avg_ts,bestModel_f = perform_10fold(X_f,y,regressor)\n",
    "print(\"------------------------------\")\n",
    "print(\"Average Training RMSE : \",avg_tr)\n",
    "print(\"Average Test RMSE : \",avg_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7rH4b1IJErM+hrB9CRGS+Uc2C5ledzdY2stKaR3Zf0ZyM9HNEROYD1Sy6LB1j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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115dcc470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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slXDzIWBJdH0EcEktuSH3aG7UIIyXI17mO7kvdbsdSf3K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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115dcc2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Q7Kp6UEFhEK9mdf+tbparP9nW3teP+dTqGNZOcPlxm1qdKLsNCrW8dqL+ar6X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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115ef0828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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b8+hXIvIcbuE6wzAMY4SRZfTRu6PTc0Tkt7jVTX9ZqlaGYRhGQ8g0+ihGVW8u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\nCoZhGEY/ZhQMwzCMfswoGIZhGP38/9RdfTe46qOjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1160be9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "regressor.fit(X_f,y)\n",
    "predicted = regressor.predict(X_f)\n",
    "plot_graph(y,predicted,\"Actual\",\"Fitted\",\"Fitted vs Actual\")\n",
    "plot_graph_time(y,predicted,\"Actual\",\"Fitted\",\"Actual and Fitted values\",\"Actual and Fitted values over time\")\n",
    "plot_graph(predicted,y-predicted,\"Fitted\",\"Residual\",\"Residual vs Fitted\")\n",
    "plot_graph_time(y-predicted,predicted,\"Residual\",\"Fitted\",\"Residual and Fitted values\",\"Residual and Fitted values over time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "Average Training RMSE :  0.103694528194\n",
      "Average Test RMSE :  0.103772293071\n"
     ]
    }
   ],
   "source": [
    "regressor = LinearRegression()\n",
    "avg_tr,avg_ts,bestModel_mi = perform_10fold(X_mi,y,regressor)\n",
    "print(\"------------------------------\")\n",
    "print(\"Average Training RMSE : \",avg_tr)\n",
    "print(\"Average Test RMSE : \",avg_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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F90XSFmAD8PpSIypXy/2V1AFcAVw4XQGVrMjvt4vkUtQbSM4avy7pFRHxeMmx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\nzHI5WZiZWS4nCzMzy+VkYWZmuf4/a2MhrjOLwIIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115db1588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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q3BkzZgRRzjAMY6yRpydfWRpZfTQIzPTO+4CHM54ZFJF2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1159a8d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0sl1qLqC79hKgte3yKuCrEfFoRDwAfBeozPhI3RwKtwErJC2XNJ/kjbk5t8xm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b0877f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JTdUvmmM0+rLZMAzDmPjUNQoAIjIL2B0YWfRXVa8vSinDMAyjOdQ1CiLyIVwX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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11af87cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "regressor.fit(X_mi,y)\n",
    "predicted = regressor.predict(X_mi)\n",
    "plot_graph(y,predicted,\"Actual\",\"Fitted\",\"Fitted vs Actual\")\n",
    "plot_graph_time(y,predicted,\"Actual\",\"Fitted\",\"Actual and Fitted values\",\"Actual and Fitted values over time\")\n",
    "plot_graph(predicted,y-predicted,\"Fitted\",\"Residual\",\"Residual vs Fitted\")\n",
    "plot_graph_time(y-predicted,predicted,\"Residual\",\"Fitted\",\"Residual and Fitted values\",\"Residual and Fitted values over time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Part iv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mask = []\n",
    "for i in range(0,6):\n",
    "    for j in itertools.combinations([0,1,2,3,4],i):\n",
    "        mask_temp = [False,False,False,False,False]\n",
    "        for index in j:\n",
    "            mask_temp[index] = True\n",
    "        mask.append(mask_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoding :  [False, False, False, False, False]\n",
      "Average Training RMSE :  0.103587856885\n",
      "Average Test RMSE :  0.103627896737\n"
     ]
    }
   ],
   "source": [
    "#Without one hot encoding\n",
    "tr_l = []\n",
    "ts_l = []\n",
    "print(\"Encoding : \",mask[0])\n",
    "regressor = LinearRegression()\n",
    "avg_tr,avg_ts,bestModel = perform_10fold(X,y,regressor)\n",
    "tr_l.append(avg_tr)\n",
    "ts_l.append(avg_ts)\n",
    "print(\"Average Training RMSE : \",avg_tr)\n",
    "print(\"Average Test RMSE : \",avg_ts)\n",
    "#predicted = bestModel.predict(X)\n",
    "#plot_graph_time(y,predicted,\"Actual\",\"Fitted\",\"Actual and Fitted values\",\"Actual and Fitted values over time\")\n",
    "#plot_graph_time(y-predicted,predicted,\"Residual\",\"Fitted\",\"Residual and Fitted values\",\"Residual and Fitted values over time\")\n",
    "min_ts_e = avg_ts\n",
    "bestModel_all = bestModel\n",
    "best_mask = mask[0]\n",
    "comb_num = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##############################\n",
      "Encoding :  [True, False, False, False, False]\n",
      "Average Training RMSE :  0.103582694803\n",
      "Average Test RMSE :  0.103720392945\n",
      "##############################\n",
      "Encoding :  [False, True, False, False, False]\n",
      "Average Training RMSE :  0.102151365372\n",
      "Average Test RMSE :  0.102219981311\n",
      "##############################\n",
      "Encoding :  [False, False, True, False, False]\n",
      "Average Training RMSE :  0.102362582\n",
      "Average Test RMSE :  0.102454594314\n",
      "##############################\n",
      "Encoding :  [False, False, False, True, False]\n",
      "Average Training RMSE :  0.0913448742395\n",
      "Average Test RMSE :  0.0913876247056\n",
      "##############################\n",
      "Encoding :  [False, False, False, False, True]\n",
      "Average Training RMSE :  0.0913359452862\n",
      "Average Test RMSE :  0.0915388299517\n",
      "##############################\n",
      "Encoding :  [True, True, False, False, False]\n",
      "Average Training RMSE :  0.102146464374\n",
      "Average Test RMSE :  0.102309283172\n",
      "##############################\n",
      "Encoding :  [True, False, True, False, False]\n",
      "Average Training RMSE :  0.102356556194\n",
      "Average Test RMSE :  0.102540081691\n",
      "##############################\n",
      "Encoding :  [True, False, False, True, False]\n",
      "Average Training RMSE :  0.091339891868\n",
      "Average Test RMSE :  0.0914653225294\n",
      "##############################\n",
      "Encoding :  [True, False, False, False, True]\n",
      "Average Training RMSE :  0.0913305641747\n",
      "Average Test RMSE :  0.0916130371878\n",
      "##############################\n",
      "Encoding :  [False, True, True, False, False]\n",
      "Average Training RMSE :  0.100906053113\n",
      "Average Test RMSE :  0.101020190683\n",
      "##############################\n",
      "Encoding :  [False, True, False, True, False]\n",
      "Average Training RMSE :  0.0897611150021\n",
      "Average Test RMSE :  0.0898246557368\n",
      "##############################\n",
      "Encoding :  [False, True, False, False, True]\n",
      "Average Training RMSE :  0.0897523017686\n",
      "Average Test RMSE :  0.0899654588358\n",
      "##############################\n",
      "Encoding :  [False, False, True, True, False]\n",
      "Average Training RMSE :  0.0899555883518\n",
      "Average Test RMSE :  0.0900366434602\n",
      "##############################\n",
      "Encoding :  [False, False, True, False, True]\n",
      "Average Training RMSE :  0.0899472516438\n",
      "Average Test RMSE :  0.0902001711434\n",
      "##############################\n",
      "Encoding :  [False, False, False, True, True]\n",
      "Average Training RMSE :  0.091340419237\n",
      "Average Test RMSE :  0.091540668254\n",
      "##############################\n",
      "Encoding :  [True, True, True, False, False]\n",
      "Average Training RMSE :  0.100900943028\n",
      "Average Test RMSE :  0.10110090435\n",
      "##############################\n",
      "Encoding :  [True, True, False, True, False]\n",
      "Average Training RMSE :  0.0897586928766\n",
      "Average Test RMSE :  0.0899187921991\n",
      "##############################\n",
      "Encoding :  [True, True, False, False, True]\n",
      "Average Training RMSE :  0.0897476188212\n",
      "Average Test RMSE :  0.0900510303835\n",
      "##############################\n",
      "Encoding :  [True, False, True, True, False]\n",
      "Average Training RMSE :  0.0899513794303\n",
      "Average Test RMSE :  0.0901054080674\n",
      "##############################\n",
      "Encoding :  [True, False, True, False, True]\n",
      "Average Training RMSE :  0.0899679431143\n",
      "Average Test RMSE :  0.0902812089422\n",
      "##############################\n",
      "Encoding :  [True, False, False, True, True]\n",
      "Average Training RMSE :  0.0913351778858\n",
      "Average Test RMSE :  0.0916081653326\n",
      "##############################\n",
      "Encoding :  [False, True, True, True, False]\n",
      "Average Training RMSE :  0.0883442861787\n",
      "Average Test RMSE :  0.0884437842078\n",
      "##############################\n",
      "Encoding :  [False, True, True, False, True]\n",
      "Average Training RMSE :  0.0885056630476\n",
      "Average Test RMSE :  0.0887858949512\n",
      "##############################\n",
      "Encoding :  [False, True, False, True, True]\n",
      "Average Training RMSE :  0.0897870110187\n",
      "Average Test RMSE :  0.0900269806253\n",
      "##############################\n",
      "Encoding :  [False, False, True, True, True]\n",
      "Average Training RMSE :  0.0899730220284\n",
      "Average Test RMSE :  0.0901786948626\n",
      "##############################\n",
      "Encoding :  [True, True, True, True, False]\n",
      "Average Training RMSE :  0.088442211613\n",
      "Average Test RMSE :  0.0885847836358\n",
      "##############################\n",
      "Encoding :  [True, True, True, False, True]\n",
      "Average Training RMSE :  0.0883349941039\n",
      "Average Test RMSE :  0.0886634369554\n",
      "##############################\n",
      "Encoding :  [True, True, False, True, True]\n",
      "Average Training RMSE :  0.0897499726105\n",
      "Average Test RMSE :  0.0900613723211\n",
      "##############################\n",
      "Encoding :  [True, False, True, True, True]\n",
      "Average Training RMSE :  0.0899590938804\n",
      "Average Test RMSE :  0.0902700966677\n",
      "##############################\n",
      "Encoding :  [False, True, True, True, True]\n",
      "Average Training RMSE :  0.0883379759816\n",
      "Average Test RMSE :  0.0885926646416\n",
      "##############################\n",
      "Encoding :  [True, True, True, True, True]\n",
      "Average Training RMSE :  0.0883521278874\n",
      "Average Test RMSE :  0.0886614787309\n",
      "--------------------------------\n",
      "Best Combination :  [False, True, True, True, False]\n",
      "Combination Number :  22\n",
      "Training RMSE :  0.0883442861787\n",
      "Test RMSE :  0.0884437842078\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,32):\n",
    "    print(\"##############################\")\n",
    "    print(\"Encoding : \",mask[i])\n",
    "    encoder = OneHotEncoder(categorical_features = mask[i])\n",
    "    X_encoded = encoder.fit_transform(X).toarray()\n",
    "    regressor = LinearRegression()\n",
    "    avg_tr,avg_ts,bestModel = perform_10fold(X_encoded,y,regressor)\n",
    "    tr_l.append(avg_tr)\n",
    "    ts_l.append(avg_ts)\n",
    "    print(\"Average Training RMSE : \",avg_tr)\n",
    "    print(\"Average Test RMSE : \",avg_ts)\n",
    "    #predicted = bestModel.predict(X_encoded)\n",
    "    #plot_graph(y,predicted,\"Actual\",\"Fitted\",\"Fitted vs Actual\")\n",
    "    #plot_graph_time(y,predicted,\"Actual\",\"Fitted\",\"Actual and Fitted values\",\"Actual and Fitted values over time\")\n",
    "    #plot_graph(predicted,y-predicted,\"Fitted\",\"Residual\",\"Residual vs Fitted\")\n",
    "    #plot_graph_time(y-predicted,predicted,\"Residual\",\"Fitted\",\"Residual and Fitted values\",\"Residual and Fitted values over time\")\n",
    "    if(avg_ts<min_ts_e):\n",
    "            min_ts_e = avg_ts\n",
    "            min_tr_e = avg_tr\n",
    "            bestModel_all = bestModel\n",
    "            best_mask = mask[i]\n",
    "            comb_num = i\n",
    "print(\"--------------------------------\")\n",
    "print(\"Best Combination : \",best_mask)\n",
    "print(\"Combination Number : \",comb_num)\n",
    "print(\"Training RMSE : \",min_tr_e)\n",
    "print(\"Test RMSE : \",min_ts_e)\n",
    "#print(\"Coefficients : \",bestModel_all.coef_)\n",
    "#print(\"Intercept : \",bestModel_all.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rHTvau4aISJn6GVCmgdWJ9yPAw3nHmNkg8Exgv7s/6e6PArj7PwLfBV7Y9RIX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\nkgoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115cb2748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mmj1i5qw9GPNP80nT0XwJ3iB4jwAzgY/6+6YcCxZ4UwEuWJDftIArVkBn57i2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\nT9Nb5g8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115cb2978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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m/T3vmXu+e/f87s2LFoXBjjHKuu1CuEnR9HTopmsWutoODdXXHTXW9HTcyO16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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f7e9630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dePqeRqNolZaGYRhGxJ7wAGfelkae2VNT/JhkGIaxb7JwYesWFrVStdAAEJFp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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f7fc978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "encoder = OneHotEncoder(categorical_features = [False, True, True, False, True])\n",
    "X_encoded = encoder.fit_transform(X).toarray()\n",
    "regressor.fit(X_encoded,y)\n",
    "predicted = regressor.predict(X_encoded)\n",
    "plot_graph(y,predicted,\"Actual\",\"Fitted\",\"Fitted vs Actual\")\n",
    "plot_graph_time(y,predicted,\"Actual\",\"Fitted\",\"Actual and Fitted values\",\"Actual and Fitted values over time\")\n",
    "plot_graph(predicted,y-predicted,\"Fitted\",\"Residual\",\"Residual vs Fitted\")\n",
    "plot_graph_time(y-predicted,predicted,\"Residual\",\"Fitted\",\"Residual and Fitted values\",\"Residual and Fitted values over time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(0,32) :\n",
    "    if ts_l[i] > 1:\n",
    "        ts_l[i] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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U9ZT+7FhVO0XkXOAvQDVwk6q+LCKXAQtU9R7g68DPReR8TGjpdI1jZvdNm8xw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\nvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f623630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Training and Test RMSE for each combination\")\n",
    "plt.plot(range(1,33), tr_l, label=\"Training RMSE\")\n",
    "plt.plot(range(1,33), ts_l, color='red', label='Test RMSE')\n",
    "plt.legend(loc = 'upper left')\n",
    "plt.xlabel(\"Combinations\")\n",
    "plt.ylabel(\"Training and Test RMSE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_ridge = Ridge()\n",
    "new_lasso = Lasso()\n",
    "new_elasticnet = ElasticNet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def partv(regressor,parameters):\n",
    "    best_score = 10\n",
    "    for i in range(1,32):\n",
    "        print(\"##############################\")\n",
    "        print(\"Encoding : \",mask[i])\n",
    "        encoder = OneHotEncoder(categorical_features = mask[i])\n",
    "        X_encoded = encoder.fit_transform(X).toarray()\n",
    "        clf = GridSearchCV(regressor, parameters, cv=10,scoring = 'neg_mean_squared_error')\n",
    "        clf.fit(X_encoded,y)\n",
    "        score = np.sqrt(-1 * clf.best_score_)\n",
    "        print(\"Best Parameter : \",clf.best_params_)\n",
    "        print(\"RMSE : \", score)\n",
    "        if score < best_score:\n",
    "            best_score = score\n",
    "            best_param = clf.best_params_\n",
    "            best_estimator = clf.best_estimator_\n",
    "            best_mask = mask[i]\n",
    "    print(\"---------------------------\")\n",
    "    print(\"Best Combination : \",best_mask)\n",
    "    print(\"Parameter : \",best_param)\n",
    "    print(\"Test RMSE : \",best_score)\n",
    "    print(\"Coefficients : \",best_estimator.coef_)\n",
    "    print(\"Intercept : \",best_estimator.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##############################\n",
      "Encoding :  [True, False, False, False, False]\n",
      "Best Parameter :  {'alpha': 10000}\n",
      "RMSE :  0.103673664218\n",
      "##############################\n",
      "Encoding :  [False, True, False, False, False]\n",
      "Best Parameter :  {'alpha': 10}\n",
      "RMSE :  0.102231852837\n",
      "##############################\n",
      "Encoding :  [False, False, True, False, False]\n",
      "Best Parameter :  {'alpha': 10}\n",
      "RMSE :  0.102465499369\n",
      "##############################\n",
      "Encoding :  [False, False, False, True, False]\n",
      "Best Parameter :  {'alpha': 10}\n",
      "RMSE :  0.0914970533256\n",
      "##############################\n",
      "Encoding :  [False, False, False, False, True]\n",
      "Best Parameter :  {'alpha': 1}\n",
      "RMSE :  0.0914984086895\n",
      "##############################\n",
      "Encoding :  [True, True, False, False, False]\n",
      "Best Parameter :  {'alpha': 100}\n",
      "RMSE :  0.102242640467\n",
      "##############################\n",
      "Encoding :  [True, False, True, False, False]\n",
      "Best Parameter :  {'alpha': 100}\n",
      "RMSE :  0.102551175986\n",
      "##############################\n",
      "Encoding :  [True, False, False, True, False]\n",
      "Best Parameter :  {'alpha': 100}\n",
      "RMSE :  0.091587458889\n",
      "##############################\n",
      "Encoding :  [True, False, False, False, True]\n",
      "Best Parameter :  {'alpha': 10}\n",
      "RMSE :  0.0916257948086\n",
      "##############################\n",
      "Encoding :  [False, True, True, False, False]\n",
      "Best Parameter :  {'alpha': 10}\n",
      "RMSE :  0.101000281118\n",
      "##############################\n",
      "Encoding :  [False, True, False, True, False]\n",
      "Best Parameter :  {'alpha': 10}\n",
      "RMSE :  0.0899065396966\n",
      "##############################\n",
      "Encoding :  [False, True, False, False, True]\n",
      "Best Parameter :  {'alpha': 1}\n",
      "RMSE :  0.0899078982454\n",
      "##############################\n",
      "Encoding :  [False, False, True, True, False]\n",
      "Best Parameter :  {'alpha': 10}\n",
      "RMSE :  0.0901236495225\n",
      "##############################\n",
      "Encoding :  [False, False, True, False, True]\n",
      "Best Parameter :  {'alpha': 1}\n",
      "RMSE :  0.0901250391688\n",
      "##############################\n",
      "Encoding :  [False, False, False, True, True]\n",
      "Best Parameter :  {'alpha': 10}\n",
      "RMSE :  0.0914981449718\n",
      "##############################\n",
      "Encoding :  [True, True, True, False, False]\n",
      "Best Parameter :  {'alpha': 100}\n",
      "RMSE :  0.101012882124\n",
      "##############################\n",
      "Encoding :  [True, True, False, True, False]\n",
      "Best Parameter :  {'alpha': 100}\n",
      "RMSE :  0.0899235386051\n",
      "##############################\n",
      "Encoding :  [True, True, False, False, True]\n",
      "Best Parameter :  {'alpha': 10}\n",
      "RMSE :  0.0899386414402\n",
      "##############################\n",
      "Encoding :  [True, False, True, True, False]\n",
      "Best Parameter :  {'alpha': 100}\n",
      "RMSE :  0.0902158424055\n",
      "##############################\n",
      "Encoding :  [True, False, True, False, True]\n",
      "Best Parameter :  {'alpha': 10}\n",
      "RMSE :  0.0902613218117\n",
      "##############################\n",
      "Encoding :  [True, False, False, True, True]\n",
      "Best Parameter :  {'alpha': 100}\n",
      "RMSE :  0.0915844182216\n",
      "##############################\n",
      "Encoding :  [False, True, True, True, False]\n",
      "Best Parameter :  {'alpha': 10}\n",
      "RMSE :  0.0885040092843\n",
      "##############################\n",
      "Encoding :  [False, True, True, False, True]\n",
      "Best Parameter :  {'alpha': 1}\n",
      "RMSE :  0.0885053878658\n",
      "##############################\n",
      "Encoding :  [False, True, False, True, True]\n",
      "Best Parameter :  {'alpha': 10}\n",
      "RMSE :  0.0899076481742\n",
      "##############################\n",
      "Encoding :  [False, False, True, True, True]\n",
      "Best Parameter :  {'alpha': 10}\n",
      "RMSE :  0.0901247541556\n",
      "##############################\n",
      "Encoding :  [True, True, True, True, False]\n",
      "Best Parameter :  {'alpha': 100}\n",
      "RMSE :  0.0885231446107\n",
      "##############################\n",
      "Encoding :  [True, True, True, False, True]\n",
      "Best Parameter :  {'alpha': 10}\n",
      "RMSE :  0.0885456845641\n",
      "##############################\n",
      "Encoding :  [True, True, False, True, True]\n",
      "Best Parameter :  {'alpha': 100}\n",
      "RMSE :  0.0899204800161\n",
      "##############################\n",
      "Encoding :  [True, False, True, True, True]\n",
      "Best Parameter :  {'alpha': 100}\n",
      "RMSE :  0.0902127341858\n",
      "##############################\n",
      "Encoding :  [False, True, True, True, True]\n",
      "Best Parameter :  {'alpha': 10}\n",
      "RMSE :  0.0885051231976\n",
      "##############################\n",
      "Encoding :  [True, True, True, True, True]\n",
      "Best Parameter :  {'alpha': 100}\n",
      "RMSE :  0.0885200104551\n",
      "---------------------------\n",
      "Best Combination :  [False, True, True, True, False]\n",
      "Parameter :  {'alpha': 10}\n",
      "Test RMSE :  0.0885040092843\n",
      "Coefficients :  [  3.91627621e-02  -1.28132184e-02  -2.01924610e-02  -5.22906083e-03\n",
      "  -5.68395769e-03   3.26492675e-03   1.49100903e-03  -2.01413694e-02\n",
      "  -2.09906028e-02   7.76661954e-03   3.33486570e-02  -1.98564497e-03\n",
      "   2.00234054e-03   3.89679066e-02  -1.36046079e-02  -4.00945986e-02\n",
      "  -5.71782631e-02   7.19095630e-02   1.12276439e-05   7.06996262e-05]\n",
      "Intercept :  0.0597754701781\n"
     ]
    }
   ],
   "source": [
    "parameters = {'alpha':[0.0001,0.001,0.1,1,10,100,1000,10000]}\n",
    "partv(new_ridge,parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##############################\n",
      "Encoding :  [True, False, False, False, False]\n",
      "Best Parameter :  {'alpha': 0.001}\n",
      "RMSE :  0.103671129332\n",
      "##############################\n",
      "Encoding :  [False, True, False, False, False]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.102233153569\n",
      "##############################\n",
      "Encoding :  [False, False, True, False, False]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.102467016538\n",
      "##############################\n",
      "Encoding :  [False, False, False, True, False]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0914981026218\n",
      "##############################\n",
      "Encoding :  [False, False, False, False, True]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0915321257068\n",
      "##############################\n",
      "Encoding :  [True, True, False, False, False]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.102232343758\n",
      "##############################\n",
      "Encoding :  [True, False, True, False, False]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.102494247758\n",
      "##############################\n",
      "Encoding :  [True, False, False, True, False]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0915211593896\n",
      "##############################\n",
      "Encoding :  [True, False, False, False, True]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0915557705191\n",
      "##############################\n",
      "Encoding :  [False, True, True, False, False]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.101003330763\n",
      "##############################\n",
      "Encoding :  [False, True, False, True, False]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0899096268223\n",
      "##############################\n",
      "Encoding :  [False, True, False, False, True]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0899442791106\n",
      "##############################\n",
      "Encoding :  [False, False, True, True, False]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0901267187371\n",
      "##############################\n",
      "Encoding :  [False, False, True, False, True]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0901609601543\n",
      "##############################\n",
      "Encoding :  [False, False, False, True, True]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0914976673852\n",
      "##############################\n",
      "Encoding :  [True, True, True, False, False]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.101002545316\n",
      "##############################\n",
      "Encoding :  [True, True, False, True, False]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0899088675582\n",
      "##############################\n",
      "Encoding :  [True, True, False, False, True]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0899435020743\n",
      "##############################\n",
      "Encoding :  [True, False, True, True, False]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0901485250867\n",
      "##############################\n",
      "Encoding :  [True, False, True, False, True]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0901833625418\n",
      "##############################\n",
      "Encoding :  [True, False, False, True, True]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0915207258373\n",
      "##############################\n",
      "Encoding :  [False, True, True, True, False]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0885094021684\n",
      "##############################\n",
      "Encoding :  [False, True, True, False, True]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.088544404102\n",
      "##############################\n",
      "Encoding :  [False, True, False, True, True]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0899091683803\n",
      "##############################\n",
      "Encoding :  [False, False, True, True, True]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0901262755346\n",
      "##############################\n",
      "Encoding :  [True, True, True, True, False]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0885087077711\n",
      "##############################\n",
      "Encoding :  [True, True, True, False, True]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0885436922198\n",
      "##############################\n",
      "Encoding :  [True, True, False, True, True]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0899084089169\n",
      "##############################\n",
      "Encoding :  [True, False, True, True, True]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0901480839546\n",
      "##############################\n",
      "Encoding :  [False, True, True, True, True]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0885089569755\n",
      "##############################\n",
      "Encoding :  [True, True, True, True, True]\n",
      "Best Parameter :  {'alpha': 0.0001}\n",
      "RMSE :  0.0885082624122\n",
      "---------------------------\n",
      "Best Combination :  [True, True, True, True, True]\n",
      "Parameter :  {'alpha': 0.0001}\n",
      "Test RMSE :  0.0885082624122\n",
      "Coefficients :  [ -0.00000000e+00  -0.00000000e+00   0.00000000e+00  -0.00000000e+00\n",
      "   0.00000000e+00  -0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00  -0.00000000e+00   0.00000000e+00\n",
      "  -0.00000000e+00  -0.00000000e+00  -0.00000000e+00   4.37513766e-02\n",
      "  -6.96907942e-03  -1.44485670e-02  -0.00000000e+00  -0.00000000e+00\n",
      "   7.71880392e-03   5.93830959e-03  -2.10345963e-02  -2.18506978e-02\n",
      "   5.78177598e-03   3.14450311e-02  -2.79293029e-03   2.96261921e-06\n",
      "   5.17787530e-02  -0.00000000e+00  -2.56788220e-02  -4.23859230e-02\n",
      "   8.64849397e-02  -0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "  -0.00000000e+00   0.00000000e+00  -0.00000000e+00  -0.00000000e+00\n",
      "   0.00000000e+00  -0.00000000e+00  -0.00000000e+00  -0.00000000e+00\n",
      "  -0.00000000e+00   0.00000000e+00  -0.00000000e+00  -0.00000000e+00\n",
      "  -0.00000000e+00  -0.00000000e+00  -0.00000000e+00  -0.00000000e+00\n",
      "  -0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "  -0.00000000e+00   0.00000000e+00   0.00000000e+00]\n",
      "Intercept :  0.0431284722921\n"
     ]
    }
   ],
   "source": [
    "parameters = {'alpha':[0.0001,0.001,0.1,1,10,100,1000,10000]}\n",
    "partv(new_lasso,parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##############################\n",
      "Encoding :  [True, False, False, False, False]\n",
      "Best Parameter :  {'alpha': 0.001, 'l1_ratio': 0.5}\n",
      "RMSE :  0.103670353304\n",
      "##############################\n",
      "Encoding :  [False, True, False, False, False]\n",
      "Best Parameter :  {'alpha': 0.0001, 'l1_ratio': 0.1}\n",
      "RMSE :  0.10223179185\n",
      "##############################\n",
      "Encoding :  [False, False, True, False, False]\n",
      "Best Parameter :  {'alpha': 0.0001, 'l1_ratio': 0.1}\n",
      "RMSE :  0.102465502253\n",
      "##############################\n",
      "Encoding :  [False, False, False, True, False]\n",
      "Best Parameter :  {'alpha': 0.0001, 'l1_ratio': 0.1}\n",
      "RMSE :  0.0914970673675\n",
      "##############################\n",
      "Encoding :  [False, False, False, False, True]\n",
      "Best Parameter :  {'alpha': 0.0001, 'l1_ratio': 0.1}\n",
      "RMSE :  0.091498284852\n",
      "##############################\n",
      "Encoding :  [True, True, False, False, False]\n",
      "Best Parameter :  {'alpha': 0.0001, 'l1_ratio': 0.7}\n",
      "RMSE :  0.102232032984\n",
      "##############################\n",
      "Encoding :  [True, False, True, False, False]\n",
      "Best Parameter :  {'alpha': 0.001, 'l1_ratio': 0.1}\n",
      "RMSE :  0.102493882397\n",
      "##############################\n",
      "Encoding :  [True, False, False, True, False]\n",
      "Best Parameter :  {'alpha': 0.001, 'l1_ratio': 0.1}\n",
      "RMSE :  0.0915195064478\n",
      "##############################\n",
      "Encoding :  [True, False, False, False, True]\n",
      "Best Parameter :  {'alpha': 0.0001, 'l1_ratio': 0.99}\n",
      "RMSE :  0.0915557666355\n",
      "##############################\n",
      "Encoding :  [False, True, True, False, False]\n",
      "Best Parameter :  {'alpha': 0.0001, 'l1_ratio': 0.1}\n",
      "RMSE :  0.101000229377\n",
      "##############################\n",
      "Encoding :  [False, True, False, True, False]\n",
      "Best Parameter :  {'alpha': 0.0001, 'l1_ratio': 0.1}\n",
      "RMSE :  0.0899065485882\n",
      "##############################\n",
      "Encoding :  [False, True, False, False, True]\n",
      "Best Parameter :  {'alpha': 0.0001, 'l1_ratio': 0.1}\n",
      "RMSE :  0.0899078066042\n",
      "##############################\n",
      "Encoding :  [False, False, True, True, False]\n",
      "Best Parameter :  {'alpha': 0.0001, 'l1_ratio': 0.1}\n",
      "RMSE :  0.090123706818\n",
      "##############################\n",
      "Encoding :  [False, False, True, False, True]\n",
      "Best Parameter :  {'alpha': 0.0001, 'l1_ratio': 0.1}\n",
      "RMSE :  0.0901248924712\n",
      "##############################\n",
      "Encoding :  [False, False, False, True, True]\n",
      "Best Parameter :  {'alpha': 0.001, 'l1_ratio': 0.1}\n",
      "RMSE :  0.091497010609\n",
      "##############################\n",
      "Encoding :  [True, True, True, False, False]\n",
      "Best Parameter :  {'alpha': 0.0001, 'l1_ratio': 0.5}\n",
      "RMSE :  0.101001170756\n",
      "##############################\n",
      "Encoding :  [True, True, False, True, False]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-38-29647316a7cd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mnew_elasticnet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mElasticNet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mparameters\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'alpha'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.0001\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.001\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'l1_ratio'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m.95\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m.99\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mpartv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_elasticnet\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-35-14144547f41a>\u001b[0m in \u001b[0;36mpartv\u001b[0;34m(regressor, parameters)\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mX_encoded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mencoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mregressor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mscoring\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'neg_mean_squared_error'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m         \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_encoded\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m         \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_score_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Best Parameter : \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Anaconda/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    637\u001b[0m                                   error_score=self.error_score)\n\u001b[1;32m    638\u001b[0m           for parameters, (train, test) in product(candidate_params,\n\u001b[0;32m--> 639\u001b[0;31m                                                    cv.split(X, y, groups)))\n\u001b[0m\u001b[1;32m    640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    641\u001b[0m         \u001b[0;31m# if one choose to see train score, \"out\" will contain train score info\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Anaconda/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    777\u001b[0m             \u001b[0;31m# was dispatched. In particular this covers the edge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    778\u001b[0m             \u001b[0;31m# case of Parallel used with an exhausted iterator.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 779\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    780\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    781\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Anaconda/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    623\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    626\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Anaconda/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    586\u001b[0m         \u001b[0mdispatch_timestamp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    587\u001b[0m         \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBatchCompletionCallBack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdispatch_timestamp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 588\u001b[0;31m         \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    589\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    590\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Anaconda/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    109\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Anaconda/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    330\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 332\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    333\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\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[0m\n",
      "\u001b[0;32m~/Anaconda/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\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[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\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[0m\n",
      "\u001b[0;32m~/Anaconda/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\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[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\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[0m\n",
      "\u001b[0;32m~/Anaconda/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)\u001b[0m\n\u001b[1;32m    456\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    457\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 458\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    459\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    460\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Anaconda/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, check_input)\u001b[0m\n\u001b[1;32m    750\u001b[0m                           \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    751\u001b[0m                           \u001b[0mselection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 752\u001b[0;31m                           check_input=False)\n\u001b[0m\u001b[1;32m    753\u001b[0m             \u001b[0mcoef_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mthis_coef\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    754\u001b[0m             \u001b[0mdual_gaps_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mthis_dual_gap\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Anaconda/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py\u001b[0m in \u001b[0;36menet_path\u001b[0;34m(X, y, l1_ratio, eps, n_alphas, alphas, precompute, Xy, copy_X, coef_init, verbose, return_n_iter, positive, check_input, **params)\u001b[0m\n\u001b[1;32m    475\u001b[0m             model = cd_fast.enet_coordinate_descent(\n\u001b[1;32m    476\u001b[0m                 \u001b[0mcoef_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml1_reg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml2_reg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_iter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrng\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m                 positive)\n\u001b[0m\u001b[1;32m    478\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    479\u001b[0m             raise ValueError(\"Precompute should be one of True, False, \"\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "new_elasticnet = ElasticNet(max_iter = 10000)\n",
    "parameters = {'alpha':[0.0001,0.001,0.1,1,10,100,1000,10000],'l1_ratio':[.1, .5, .7, .9, .95, .99, 1]}\n",
    "partv(new_elasticnet,parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}