val1features.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "from sklearn import model_selection\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import accuracy_score, f1_score, classification_report, precision_score, recall_score, confusion_matrix\n",
    "from sklearn import tree\n",
    "from sklearn import svm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler,SMOTE, ADASYN\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import matplotlib\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import r2_score\n",
    "import statsmodels.api as sm\n",
    "from sklearn.linear_model import RidgeCV, LassoCV, Ridge, Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>SCRMSD</th>\n",
       "      <th>CARMSD</th>\n",
       "      <th>CHI1</th>\n",
       "      <th>SELFDIHED</th>\n",
       "      <th>SELFVDW</th>\n",
       "      <th>SELFELEC</th>\n",
       "      <th>SelfALL</th>\n",
       "      <th>MBDIHED</th>\n",
       "      <th>MBVDW</th>\n",
       "      <th>MBELEC</th>\n",
       "      <th>MBALL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LOW</td>\n",
       "      <td>1.153631</td>\n",
       "      <td>1.325141</td>\n",
       "      <td>-51.881355</td>\n",
       "      <td>0.651907</td>\n",
       "      <td>2.523383</td>\n",
       "      <td>20.251547</td>\n",
       "      <td>33.503959</td>\n",
       "      <td>0.377640</td>\n",
       "      <td>-11.113391</td>\n",
       "      <td>-5.697512</td>\n",
       "      <td>-14.855245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LOW</td>\n",
       "      <td>1.577676</td>\n",
       "      <td>1.108865</td>\n",
       "      <td>-55.229710</td>\n",
       "      <td>1.275343</td>\n",
       "      <td>2.216418</td>\n",
       "      <td>21.343843</td>\n",
       "      <td>34.027217</td>\n",
       "      <td>0.581738</td>\n",
       "      <td>-8.664580</td>\n",
       "      <td>-5.200302</td>\n",
       "      <td>-15.724896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LOW</td>\n",
       "      <td>1.274470</td>\n",
       "      <td>1.020357</td>\n",
       "      <td>-61.589970</td>\n",
       "      <td>0.645298</td>\n",
       "      <td>1.744053</td>\n",
       "      <td>21.457868</td>\n",
       "      <td>30.968461</td>\n",
       "      <td>0.155810</td>\n",
       "      <td>-11.826216</td>\n",
       "      <td>-6.000686</td>\n",
       "      <td>-16.761202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LOW</td>\n",
       "      <td>1.193524</td>\n",
       "      <td>0.924958</td>\n",
       "      <td>-58.549500</td>\n",
       "      <td>1.267476</td>\n",
       "      <td>1.457724</td>\n",
       "      <td>21.573995</td>\n",
       "      <td>33.366320</td>\n",
       "      <td>0.042535</td>\n",
       "      <td>-11.986884</td>\n",
       "      <td>-5.565793</td>\n",
       "      <td>-14.073723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LOW</td>\n",
       "      <td>0.920896</td>\n",
       "      <td>0.746715</td>\n",
       "      <td>-67.387482</td>\n",
       "      <td>0.939653</td>\n",
       "      <td>2.051272</td>\n",
       "      <td>19.523036</td>\n",
       "      <td>32.674128</td>\n",
       "      <td>0.073776</td>\n",
       "      <td>-11.359334</td>\n",
       "      <td>-5.591370</td>\n",
       "      <td>-16.054609</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  location    SCRMSD    CARMSD       CHI1  SELFDIHED   SELFVDW   SELFELEC  \\\n",
       "0      LOW  1.153631  1.325141 -51.881355   0.651907  2.523383  20.251547   \n",
       "1      LOW  1.577676  1.108865 -55.229710   1.275343  2.216418  21.343843   \n",
       "2      LOW  1.274470  1.020357 -61.589970   0.645298  1.744053  21.457868   \n",
       "3      LOW  1.193524  0.924958 -58.549500   1.267476  1.457724  21.573995   \n",
       "4      LOW  0.920896  0.746715 -67.387482   0.939653  2.051272  19.523036   \n",
       "\n",
       "     SelfALL   MBDIHED      MBVDW    MBELEC      MBALL  \n",
       "0  33.503959  0.377640 -11.113391 -5.697512 -14.855245  \n",
       "1  34.027217  0.581738  -8.664580 -5.200302 -15.724896  \n",
       "2  30.968461  0.155810 -11.826216 -6.000686 -16.761202  \n",
       "3  33.366320  0.042535 -11.986884 -5.565793 -14.073723  \n",
       "4  32.674128  0.073776 -11.359334 -5.591370 -16.054609  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('0100val1ft.csv',delimiter=\",\")\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['location'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location     0\n",
       "SCRMSD       0\n",
       "CARMSD       0\n",
       "CHI1         0\n",
       "SELFDIHED    0\n",
       "SELFVDW      0\n",
       "SELFELEC     0\n",
       "SelfALL      0\n",
       "MBDIHED      0\n",
       "MBVDW        0\n",
       "MBELEC       0\n",
       "MBALL        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "enc=preprocessing.LabelEncoder()\n",
    "\n",
    "df['location'] = enc.fit_transform(df['location'])\n",
    "y = df[['location']]\n",
    "x = df[df.columns[1:]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=77)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3288230142402797"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm = LinearRegression()\n",
    "lm.fit(X_train, y_train)\n",
    "lm.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3454280302171473"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The linear model is: Y = 5.3105 + 0.17316*cav1 + -0.19608*cav2 + -0.00083269*cav3+ -0.026388*cav4\n"
     ]
    }
   ],
   "source": [
    "        print(\"The linear model is: Y = {:.5} + {:.5}*cav1 + {:.5}*cav2 + {:.5}*cav3+ {:.5}*cav4\".format(lm.intercept_[0], lm.coef_[0][0], lm.coef_[0][1], lm.coef_[0][2], lm.coef_[0][3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'CHI2'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2656\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2657\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2658\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'CHI2'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-0ebab30dc142>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumn_stack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'SCRMSD'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'CARMSD'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'CHI1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'CHI2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'SELFDIHED'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'SELFVDW'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'SELFELEC'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'SelfALL'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'MBDIHED'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'MBVDW'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'MBELEC'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'MBALL'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'SCHBOND#'\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[0m\u001b[1;32m      2\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'location'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mX2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_constant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOLS\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mest2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2925\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2926\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2927\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2928\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2929\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2657\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2658\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2659\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   2660\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2661\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'CHI2'"
     ]
    }
   ],
   "source": [
    "X = np.column_stack((df['SCRMSD'], df['CARMSD'], df['CHI1'],df['CHI2'],df['SELFDIHED'],df['SELFVDW'],df['SELFELEC'],df['SelfALL'],df['MBDIHED'],df['MBVDW'],df['MBELEC'],df['MBALL'],df['SCHBOND#']))\n",
    "y = df['location']\n",
    "X2 = sm.add_constant(X)\n",
    "est = sm.OLS(y, X2)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = LassoCV()\n",
    "reg.fit(x, y)\n",
    "print(\"Best alpha using built-in LassoCV: %f\" % reg.alpha_)\n",
    "print(\"Best score using built-in LassoCV: %f\" %reg.score(x,y))\n",
    "coef = pd.Series(reg.coef_, index = x.columns)\n",
    "\n",
    "print(\"Lasso picked \" + str(sum(coef != 0)) + \" variables and eliminated the other \" +  str(sum(coef == 0)) + \" variables\")\n",
    "\n",
    "imp_coef = coef.sort_values()\n",
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize'] = (8.0, 10.0)\n",
    "imp_coef.plot(kind = \"barh\")\n",
    "plt.title(\"Feature importance using Lasso Model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5834313725490196"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train and fit Logistic Regression model.\n",
    "LoRmod=LogisticRegression(max_iter=10000)\n",
    "LoRmod.fit(X_train,y_train)\n",
    "#Score LR training\n",
    "LoRmod.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 58.90196078431372\n",
      "F1 score: 56.21661834240884\n",
      "Recall score: 57.336928463480795\n",
      "Precision score: 56.29953272881719\n",
      "Confusion matrix: [[440 156 116]\n",
      " [273 261 278]\n",
      " [ 75 150 801]]\n",
      "Classification report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.56      0.62      0.59       712\n",
      "           1       0.46      0.32      0.38       812\n",
      "           2       0.67      0.78      0.72      1026\n",
      "\n",
      "    accuracy                           0.59      2550\n",
      "   macro avg       0.56      0.57      0.56      2550\n",
      "weighted avg       0.57      0.59      0.57      2550\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/metrics/_classification.py:1321: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'macro'). You may use labels=[pos_label] to specify a single positive class.\n",
      "  % (pos_label, average), UserWarning)\n"
     ]
    }
   ],
   "source": [
    "#Predict from manufactured test and score\n",
    "LoRpred= LoRmod.predict(X_test)\n",
    "print('Accuracy score:', accuracy_score(y_test,LoRpred)*100)\n",
    "print('F1 score:', f1_score(y_test, LoRpred,pos_label='positive',average='macro')*100)\n",
    "print('Recall score:', recall_score(y_test, LoRpred,pos_label='positive',average='macro')*100)\n",
    "print('Precision score:', precision_score(y_test, LoRpred,pos_label='positive',average='macro')*100)\n",
    "print('Confusion matrix:', confusion_matrix(y_test, LoRpred))\n",
    "print('Classification report:', classification_report(y_test, LoRpred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n",
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "k_list=[]\n",
    "for i in range(1,100):\n",
    "  classifier=KNeighborsClassifier(n_neighbors=i)\n",
    "  classifier.fit(X_train,y_train)\n",
    "  k_list.append(classifier.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,100),k_list)\n",
    "plt.ylabel('Validation acc')\n",
    "plt.title('nice')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5958823529411764"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNNmod=KNeighborsClassifier(n_neighbors=30)\n",
    "KNNmod.fit(X_train,y_train)\n",
    "#Score RF fit.\n",
    "KNNmod.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 55.490196078431374\n",
      "F1 score: 52.14844430138035\n",
      "Recall score: 53.330801514814915\n",
      "Precision score: 52.402448861752035\n",
      "Confusion matrix: [[375 193 144]\n",
      " [261 232 319]\n",
      " [ 95 123 808]]\n",
      "Classification report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.51      0.53      0.52       712\n",
      "           1       0.42      0.29      0.34       812\n",
      "           2       0.64      0.79      0.70      1026\n",
      "\n",
      "    accuracy                           0.55      2550\n",
      "   macro avg       0.52      0.53      0.52      2550\n",
      "weighted avg       0.53      0.55      0.54      2550\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/metrics/_classification.py:1321: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'macro'). You may use labels=[pos_label] to specify a single positive class.\n",
      "  % (pos_label, average), UserWarning)\n"
     ]
    }
   ],
   "source": [
    "#Predict from manufactured test and score\n",
    "KNNpred = KNNmod.predict(X_test)\n",
    "print('Accuracy score:', accuracy_score(y_test,KNNpred)*100)\n",
    "print('F1 score:', f1_score(y_test, KNNpred,pos_label='positive',average='macro')*100)\n",
    "print('Recall score:', recall_score(y_test, KNNpred,pos_label='positive',average='macro')*100)\n",
    "print('Precision score:', precision_score(y_test, KNNpred,pos_label='positive',average='macro')*100)\n",
    "print('Confusion matrix:', confusion_matrix(y_test, KNNpred))\n",
    "print('Classification report:', classification_report(y_test, KNNpred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.702843137254902"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVCmod=SVC(kernel='rbf',gamma=0.1)\n",
    "SVCmod.fit(X_train,y_train)\n",
    "#Score DT fit.\n",
    "SVCmod.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.702843137254902, 0.7993137254901961, 0.8728431372549019, 0.9227450980392157, 0.9546078431372549, 0.9750980392156863, 0.9878431372549019, 0.9949019607843137, 0.9979411764705882, 0.9988235294117647]\n"
     ]
    }
   ],
   "source": [
    "gammalist=[]\n",
    "for i in range(1,11):\n",
    "    SVCmod=SVC(kernel='rbf',gamma=float(i/10))\n",
    "    SVCmod.fit(X_train,y_train)\n",
    "    gammalist.append(SVCmod.score(X_train, y_train))\n",
    "    \n",
    "print(gammalist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,11),gammalist)\n",
    "plt.ylabel('Validation acc')\n",
    "plt.title('nice')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7993137254901961"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVCmod=SVC(kernel='rbf',gamma=0.2)\n",
    "SVCmod.fit(X_train,y_train)\n",
    "#Score DT fit.\n",
    "SVCmod.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 55.607843137254896\n",
      "F1 score: 52.98058277282326\n",
      "Recall score: 53.69929129581311\n",
      "Precision score: 52.98295205288716\n",
      "Confusion matrix: [[373 208 131]\n",
      " [257 267 288]\n",
      " [100 148 778]]\n",
      "Classification report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.51      0.52      0.52       712\n",
      "           1       0.43      0.33      0.37       812\n",
      "           2       0.65      0.76      0.70      1026\n",
      "\n",
      "    accuracy                           0.56      2550\n",
      "   macro avg       0.53      0.54      0.53      2550\n",
      "weighted avg       0.54      0.56      0.54      2550\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/metrics/_classification.py:1321: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'macro'). You may use labels=[pos_label] to specify a single positive class.\n",
      "  % (pos_label, average), UserWarning)\n"
     ]
    }
   ],
   "source": [
    "#Predict from manufactured test and score\n",
    "SVCpred = SVCmod.predict(X_test)\n",
    "print('Accuracy score:', accuracy_score(y_test,SVCpred)*100)\n",
    "print('F1 score:', f1_score(y_test, SVCpred,pos_label='positive',average='macro')*100)\n",
    "print('Recall score:', recall_score(y_test, SVCpred,pos_label='positive',average='macro')*100)\n",
    "print('Precision score:', precision_score(y_test, SVCpred,pos_label='positive',average='macro')*100)\n",
    "print('Confusion matrix:', confusion_matrix(y_test, SVCpred))\n",
    "print('Classification report:', classification_report(y_test, SVCpred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train and fit Random Forest model.\n",
    "RFmod=RandomForestClassifier(n_estimators=100,random_state=77)\n",
    "RFmod.fit(X_train,y_train)\n",
    "#Score RF fit.\n",
    "RFmod.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 58.58823529411765\n",
      "F1 score: 56.09561108427231\n",
      "Recall score: 56.81528834283104\n",
      "Precision score: 56.26819008245375\n",
      "Confusion matrix: [[405 187 120]\n",
      " [242 289 281]\n",
      " [ 95 131 800]]\n",
      "Classification report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.55      0.57      0.56       712\n",
      "           1       0.48      0.36      0.41       812\n",
      "           2       0.67      0.78      0.72      1026\n",
      "\n",
      "    accuracy                           0.59      2550\n",
      "   macro avg       0.56      0.57      0.56      2550\n",
      "weighted avg       0.57      0.59      0.57      2550\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/metrics/_classification.py:1321: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'macro'). You may use labels=[pos_label] to specify a single positive class.\n",
      "  % (pos_label, average), UserWarning)\n"
     ]
    }
   ],
   "source": [
    "#Predict from manufactured test and score\n",
    "RFpred = RFmod.predict(X_test)\n",
    "print('Accuracy score:', accuracy_score(y_test,RFpred)*100)\n",
    "print('F1 score:', f1_score(y_test, RFpred,pos_label='positive',average='macro')*100)\n",
    "print('Recall score:', recall_score(y_test, RFpred,pos_label='positive',average='macro')*100)\n",
    "print('Precision score:', precision_score(y_test, RFpred,pos_label='positive',average='macro')*100)\n",
    "print('Confusion matrix:', confusion_matrix(y_test, RFpred))\n",
    "print('Classification report:', classification_report(y_test, RFpred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3288230142402797"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm = LinearRegression()\n",
    "lm.fit(X_train, y_train)\n",
    "lm.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3454280302171473"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(\"The linear model is: Y = {:.5} + {:.5}*x1 + {:.5}*x2 + {:.5}*x3+ {:.5}*cav4\".format(lm.intercept_[0], lm.coef_[0][0], lm.coef_[0][1], lm.coef_[0][2], lm.coef_[0][3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.column_stack((df['SCRMSD'], df['CARMSD'], df['CHI1'],df['CHI2'],df['SELFDIHED'],df['SELFVDW'],df['SELFELEC'],df['SelfALL'],df['MBDIHED'],df['MBVDW'],df['MBELEC'],df['MBALL'],df['SCHBOND#']))\n",
    "y = df['location']\n",
    "X2 = sm.add_constant(X)\n",
    "est = sm.OLS(y, X2)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_importances(coef, names, top=-1):\n",
    "    imp = coef\n",
    "    imp, names = zip(*sorted(list(zip(imp, names))))\n",
    "\n",
    "    # Show all features\n",
    "    if top == -1:\n",
    "        top = len(names)\n",
    "\n",
    "    plt.barh(range(top), imp[::-1][0:top], align='center')\n",
    "    plt.yticks(range(top), names[::-1][0:top])\n",
    "    plt.show()\n",
    "\n",
    "# whatever your features are called\n",
    "features_names = ['cav1', 'cav2','cav3','cav4']\n",
    "svm = svm.SVC(kernel='linear')\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "# Specify your top n features you want to visualize.\n",
    "# You can also discard the abs() function \n",
    "# if you are interested in negative contribution of features\n",
    "f_importances(abs(svm.coef_[0]), features_names, top=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7106862745098039"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DTmod=DecisionTreeClassifier(criterion='gini', random_state = 77,max_depth=10)\n",
    "DTmod.fit(X_train,y_train)\n",
    "#Score DT fit.\n",
    "DTmod.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 54.509803921568626\n",
      "F1 score: 52.636571653390476\n",
      "Recall score: 52.99196320475302\n",
      "Precision score: 52.52073558059025\n",
      "Confusion matrix: [[368 223 121]\n",
      " [247 299 266]\n",
      " [128 175 723]]\n",
      "Classification report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.52      0.51       712\n",
      "           1       0.43      0.37      0.40       812\n",
      "           2       0.65      0.70      0.68      1026\n",
      "\n",
      "    accuracy                           0.55      2550\n",
      "   macro avg       0.53      0.53      0.53      2550\n",
      "weighted avg       0.54      0.55      0.54      2550\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/metrics/_classification.py:1321: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'macro'). You may use labels=[pos_label] to specify a single positive class.\n",
      "  % (pos_label, average), UserWarning)\n"
     ]
    }
   ],
   "source": [
    "#Predict from manufactured test and score\n",
    "DTpred = DTmod.predict(X_test)\n",
    "print('Accuracy score:', accuracy_score(y_test,DTpred)*100)\n",
    "print('F1 score:', f1_score(y_test, DTpred,pos_label='positive',average='macro')*100)\n",
    "print('Recall score:', recall_score(y_test, DTpred,pos_label='positive',average='macro')*100)\n",
    "print('Precision score:', precision_score(y_test, DTpred,pos_label='positive',average='macro')*100)\n",
    "print('Confusion matrix:', confusion_matrix(y_test, DTpred))\n",
    "print('Classification report:', classification_report(y_test, DTpred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['SCRMSD', 'CARMSD', 'CHI1', 'SELFDIHED', 'SELFVDW', 'SELFELEC', 'SelfALL', 'MBDIHED', 'MBVDW', 'MBELEC', 'MBALL']\n"
     ]
    }
   ],
   "source": [
    "featurelist=list(df.columns[1:])\n",
    "plt.bar(range(len(DTmod.feature_importances_)), DTmod.feature_importances_)\n",
    "plt.xlabel(\"feature\")\n",
    "plt.ylabel(\"importance\")\n",
    "plt.title(\"feature importance\")\n",
    "plt.xticks(range(len(DTmod.feature_importances_)), featurelist,rotation='vertical')\n",
    "plt.show()\n",
    "print(featurelist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alia/.local/lib/python3.5/site-packages/ipykernel_launcher.py:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train and fit Random Forest model.\n",
    "RFmod=RandomForestClassifier(n_estimators=100,random_state=77)\n",
    "RFmod.fit(X_train,y_train)\n",
    "#Score RF fit.\n",
    "RFmod.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 58.58823529411765\n",
      "F1 score: 56.09561108427231\n",
      "Recall score: 56.81528834283104\n",
      "Precision score: 56.26819008245375\n",
      "Confusion matrix: [[405 187 120]\n",
      " [242 289 281]\n",
      " [ 95 131 800]]\n",
      "Classification report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.55      0.57      0.56       712\n",
      "           1       0.48      0.36      0.41       812\n",
      "           2       0.67      0.78      0.72      1026\n",
      "\n",
      "    accuracy                           0.59      2550\n",
      "   macro avg       0.56      0.57      0.56      2550\n",
      "weighted avg       0.57      0.59      0.57      2550\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/metrics/_classification.py:1321: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'macro'). You may use labels=[pos_label] to specify a single positive class.\n",
      "  % (pos_label, average), UserWarning)\n"
     ]
    }
   ],
   "source": [
    "#Predict from manufactured test and score\n",
    "RFpred = RFmod.predict(X_test)\n",
    "print('Accuracy score:', accuracy_score(y_test,RFpred)*100)\n",
    "print('F1 score:', f1_score(y_test, RFpred,pos_label='positive',average='macro')*100)\n",
    "print('Recall score:', recall_score(y_test, RFpred,pos_label='positive',average='macro')*100)\n",
    "print('Precision score:', precision_score(y_test, RFpred,pos_label='positive',average='macro')*100)\n",
    "print('Confusion matrix:', confusion_matrix(y_test, RFpred))\n",
    "print('Classification report:', classification_report(y_test, RFpred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['SCRMSD', 'CARMSD', 'CHI1', 'SELFDIHED', 'SELFVDW', 'SELFELEC', 'SelfALL', 'MBDIHED', 'MBVDW', 'MBELEC', 'MBALL']\n"
     ]
    }
   ],
   "source": [
    "featurelist=list(df.columns[1:])\n",
    "plt.bar(range(len(RFmod.feature_importances_)), RFmod.feature_importances_)\n",
    "plt.xlabel(\"feature\")\n",
    "plt.ylabel(\"importance\")\n",
    "plt.title(\"feature importance\")\n",
    "plt.xticks(range(len(RFmod.feature_importances_)), featurelist,rotation='vertical')\n",
    "plt.show()\n",
    "print(featurelist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kfold = model_selection.KFold(n_splits=10, random_state=77)\n",
    "cv_LoR = model_selection.cross_val_score(LogisticRegression(max_iter=10000), X_train, y_train, cv=kfold, scoring='accuracy')\n",
    "cv_DT = model_selection.cross_val_score(DecisionTreeClassifier(criterion='gini', random_state = 77,max_depth=10), X_train, y_train, cv=kfold, scoring='accuracy')\n",
    "cv_RF = model_selection.cross_val_score(RandomForestClassifier(n_estimators=100), X_train, y_train, cv=kfold, scoring='accuracy')\n",
    "cv_SVC = model_selection.cross_val_score(SVC(kernel='rbf',gamma=0.2), X_train, y_train, cv=kfold, scoring='accuracy')\n",
    "cv_KNN = model_selection.cross_val_score(KNeighborsClassifier(n_neighbors=60), X_train, y_train, cv=kfold, scoring='accuracy')\n",
    "results=[cv_LoR,cv_DT, cv_RF,cv_SVC,cv_KNN]\n",
    "names=[\"LoR\",\"DT\",\"RF\",\"SVC,\"\"KNN\"]\n",
    "fig = plt.figure()\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "plt.show()"
   ]
  }
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