leu89features.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 20,
   "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>CHI2</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",
       "      <th>SCHBOND#</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LOW</td>\n",
       "      <td>0.355345</td>\n",
       "      <td>0.256193</td>\n",
       "      <td>-147.515411</td>\n",
       "      <td>53.508400</td>\n",
       "      <td>3.887751</td>\n",
       "      <td>0.403015</td>\n",
       "      <td>8.067721</td>\n",
       "      <td>20.031511</td>\n",
       "      <td>1.017953</td>\n",
       "      <td>-9.429284</td>\n",
       "      <td>-30.596049</td>\n",
       "      <td>-38.942355</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LOW</td>\n",
       "      <td>0.605549</td>\n",
       "      <td>0.127604</td>\n",
       "      <td>-158.578873</td>\n",
       "      <td>64.957588</td>\n",
       "      <td>3.310061</td>\n",
       "      <td>0.351081</td>\n",
       "      <td>8.016348</td>\n",
       "      <td>20.840695</td>\n",
       "      <td>0.732616</td>\n",
       "      <td>-9.951947</td>\n",
       "      <td>-25.882230</td>\n",
       "      <td>-34.407366</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LOW</td>\n",
       "      <td>0.647490</td>\n",
       "      <td>0.255585</td>\n",
       "      <td>-165.717285</td>\n",
       "      <td>56.551693</td>\n",
       "      <td>3.377258</td>\n",
       "      <td>0.330532</td>\n",
       "      <td>8.587244</td>\n",
       "      <td>24.702449</td>\n",
       "      <td>1.153298</td>\n",
       "      <td>-10.808672</td>\n",
       "      <td>-30.765902</td>\n",
       "      <td>-39.712960</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LOW</td>\n",
       "      <td>0.822144</td>\n",
       "      <td>0.414241</td>\n",
       "      <td>-166.462830</td>\n",
       "      <td>50.175953</td>\n",
       "      <td>2.890941</td>\n",
       "      <td>0.557575</td>\n",
       "      <td>8.490858</td>\n",
       "      <td>20.957459</td>\n",
       "      <td>0.242466</td>\n",
       "      <td>-10.311595</td>\n",
       "      <td>-28.185566</td>\n",
       "      <td>-37.839885</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LOW</td>\n",
       "      <td>0.608519</td>\n",
       "      <td>0.241132</td>\n",
       "      <td>-149.661606</td>\n",
       "      <td>70.794235</td>\n",
       "      <td>4.456279</td>\n",
       "      <td>0.441016</td>\n",
       "      <td>8.031332</td>\n",
       "      <td>21.847960</td>\n",
       "      <td>0.662844</td>\n",
       "      <td>-11.297873</td>\n",
       "      <td>-28.420036</td>\n",
       "      <td>-38.408539</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  location    SCRMSD    CARMSD        CHI1       CHI2  SELFDIHED   SELFVDW  \\\n",
       "0      LOW  0.355345  0.256193 -147.515411  53.508400   3.887751  0.403015   \n",
       "1      LOW  0.605549  0.127604 -158.578873  64.957588   3.310061  0.351081   \n",
       "2      LOW  0.647490  0.255585 -165.717285  56.551693   3.377258  0.330532   \n",
       "3      LOW  0.822144  0.414241 -166.462830  50.175953   2.890941  0.557575   \n",
       "4      LOW  0.608519  0.241132 -149.661606  70.794235   4.456279  0.441016   \n",
       "\n",
       "   SELFELEC    SelfALL   MBDIHED      MBVDW     MBELEC      MBALL  SCHBOND#  \n",
       "0  8.067721  20.031511  1.017953  -9.429284 -30.596049 -38.942355         3  \n",
       "1  8.016348  20.840695  0.732616  -9.951947 -25.882230 -34.407366         1  \n",
       "2  8.587244  24.702449  1.153298 -10.808672 -30.765902 -39.712960         1  \n",
       "3  8.490858  20.957459  0.242466 -10.311595 -28.185566 -37.839885         1  \n",
       "4  8.031332  21.847960  0.662844 -11.297873 -28.420036 -38.408539         1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('0100leu89ft.csv',delimiter=\",\")\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['location'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location     0\n",
       "SCRMSD       0\n",
       "CARMSD       0\n",
       "CHI1         0\n",
       "CHI2         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",
       "SCHBOND#     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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": 66,
   "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": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04071726247299379"
      ]
     },
     "execution_count": 67,
     "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": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04390480977444511"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The linear model is: Y = 2.3241 + -0.14473*cav1 + 0.12332*cav2 + 0.0031391*cav3+ 0.0036297*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": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               location   R-squared:                       0.042\n",
      "Model:                            OLS   Adj. R-squared:                  0.041\n",
      "Method:                 Least Squares   F-statistic:                     42.99\n",
      "Date:                Fri, 01 May 2020   Prob (F-statistic):          1.33e-108\n",
      "Time:                        17:10:34   Log-Likelihood:                -15328.\n",
      "No. Observations:               12750   AIC:                         3.068e+04\n",
      "Df Residuals:                   12736   BIC:                         3.079e+04\n",
      "Df Model:                          13                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          2.1634      0.214     10.115      0.000       1.744       2.583\n",
      "x1            -0.0960      0.037     -2.611      0.009      -0.168      -0.024\n",
      "x2             0.0810      0.047      1.739      0.082      -0.010       0.172\n",
      "x3             0.0028      0.000      7.236      0.000       0.002       0.004\n",
      "x4             0.0039      0.000     12.199      0.000       0.003       0.005\n",
      "x5             0.0028      0.014      0.206      0.837      -0.024       0.029\n",
      "x6             0.0101      0.009      1.098      0.272      -0.008       0.028\n",
      "x7             0.0192      0.011      1.688      0.092      -0.003       0.042\n",
      "x8             0.0106      0.004      2.863      0.004       0.003       0.018\n",
      "x9             0.0544      0.020      2.735      0.006       0.015       0.093\n",
      "x10            0.0421      0.016      2.678      0.007       0.011       0.073\n",
      "x11            0.0259      0.016      1.604      0.109      -0.006       0.058\n",
      "x12            0.0030      0.015      0.196      0.845      -0.027       0.033\n",
      "x13           -0.0364      0.016     -2.297      0.022      -0.068      -0.005\n",
      "==============================================================================\n",
      "Omnibus:                   124088.368   Durbin-Watson:                   0.035\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1114.322\n",
      "Skew:                          -0.192   Prob(JB):                    1.07e-242\n",
      "Kurtosis:                       1.604   Cond. No.                     4.91e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 4.91e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "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": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha using built-in LassoCV: 3.095375\n",
      "Best score using built-in LassoCV: 0.000000\n",
      "Lasso picked 1 variables and eliminated the other 12 variables\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Feature importance using Lasso Model')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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": 75,
   "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>CHI2</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",
       "      <th>SCHBOND#</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12750.000000</td>\n",
       "      <td>12750.000000</td>\n",
       "      <td>12750.000000</td>\n",
       "      <td>12750.000000</td>\n",
       "      <td>12750.000000</td>\n",
       "      <td>12750.000000</td>\n",
       "      <td>12750.000000</td>\n",
       "      <td>12750.000000</td>\n",
       "      <td>12750.000000</td>\n",
       "      <td>12750.000000</td>\n",
       "      <td>12750.000000</td>\n",
       "      <td>12750.000000</td>\n",
       "      <td>12750.000000</td>\n",
       "      <td>12750.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.098039</td>\n",
       "      <td>0.894963</td>\n",
       "      <td>0.582334</td>\n",
       "      <td>-140.592907</td>\n",
       "      <td>57.864762</td>\n",
       "      <td>3.615084</td>\n",
       "      <td>0.891645</td>\n",
       "      <td>7.605594</td>\n",
       "      <td>23.783152</td>\n",
       "      <td>0.974003</td>\n",
       "      <td>-11.516121</td>\n",
       "      <td>-26.637363</td>\n",
       "      <td>-36.703792</td>\n",
       "      <td>1.216784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.822627</td>\n",
       "      <td>0.406708</td>\n",
       "      <td>0.219794</td>\n",
       "      <td>30.701559</td>\n",
       "      <td>25.369650</td>\n",
       "      <td>0.586932</td>\n",
       "      <td>0.800441</td>\n",
       "      <td>0.719131</td>\n",
       "      <td>2.071232</td>\n",
       "      <td>0.569191</td>\n",
       "      <td>1.653128</td>\n",
       "      <td>1.741880</td>\n",
       "      <td>2.218219</td>\n",
       "      <td>0.466529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.148588</td>\n",
       "      <td>0.023591</td>\n",
       "      <td>-179.930328</td>\n",
       "      <td>-179.957214</td>\n",
       "      <td>2.248211</td>\n",
       "      <td>-1.095073</td>\n",
       "      <td>5.346896</td>\n",
       "      <td>17.133294</td>\n",
       "      <td>0.027781</td>\n",
       "      <td>-16.781096</td>\n",
       "      <td>-34.598112</td>\n",
       "      <td>-44.192224</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.599010</td>\n",
       "      <td>0.423036</td>\n",
       "      <td>-157.849636</td>\n",
       "      <td>54.899139</td>\n",
       "      <td>3.190220</td>\n",
       "      <td>0.318531</td>\n",
       "      <td>7.107777</td>\n",
       "      <td>22.328397</td>\n",
       "      <td>0.566145</td>\n",
       "      <td>-12.658761</td>\n",
       "      <td>-27.783855</td>\n",
       "      <td>-38.224054</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.793289</td>\n",
       "      <td>0.573771</td>\n",
       "      <td>-152.530533</td>\n",
       "      <td>60.267228</td>\n",
       "      <td>3.537322</td>\n",
       "      <td>0.797801</td>\n",
       "      <td>7.563736</td>\n",
       "      <td>23.639213</td>\n",
       "      <td>0.855990</td>\n",
       "      <td>-11.606491</td>\n",
       "      <td>-26.578857</td>\n",
       "      <td>-36.765803</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.093560</td>\n",
       "      <td>0.729284</td>\n",
       "      <td>-144.635643</td>\n",
       "      <td>65.639850</td>\n",
       "      <td>3.952597</td>\n",
       "      <td>1.357529</td>\n",
       "      <td>8.069070</td>\n",
       "      <td>25.097037</td>\n",
       "      <td>1.251116</td>\n",
       "      <td>-10.487245</td>\n",
       "      <td>-25.441361</td>\n",
       "      <td>-35.246472</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.371461</td>\n",
       "      <td>1.647659</td>\n",
       "      <td>179.093369</td>\n",
       "      <td>179.877319</td>\n",
       "      <td>7.327453</td>\n",
       "      <td>5.263958</td>\n",
       "      <td>11.326908</td>\n",
       "      <td>32.662894</td>\n",
       "      <td>4.889210</td>\n",
       "      <td>-3.652790</td>\n",
       "      <td>-19.451467</td>\n",
       "      <td>-27.711963</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           location        SCRMSD        CARMSD          CHI1          CHI2  \\\n",
       "count  12750.000000  12750.000000  12750.000000  12750.000000  12750.000000   \n",
       "mean       1.098039      0.894963      0.582334   -140.592907     57.864762   \n",
       "std        0.822627      0.406708      0.219794     30.701559     25.369650   \n",
       "min        0.000000      0.148588      0.023591   -179.930328   -179.957214   \n",
       "25%        0.000000      0.599010      0.423036   -157.849636     54.899139   \n",
       "50%        1.000000      0.793289      0.573771   -152.530533     60.267228   \n",
       "75%        2.000000      1.093560      0.729284   -144.635643     65.639850   \n",
       "max        2.000000      2.371461      1.647659    179.093369    179.877319   \n",
       "\n",
       "          SELFDIHED       SELFVDW      SELFELEC       SelfALL       MBDIHED  \\\n",
       "count  12750.000000  12750.000000  12750.000000  12750.000000  12750.000000   \n",
       "mean       3.615084      0.891645      7.605594     23.783152      0.974003   \n",
       "std        0.586932      0.800441      0.719131      2.071232      0.569191   \n",
       "min        2.248211     -1.095073      5.346896     17.133294      0.027781   \n",
       "25%        3.190220      0.318531      7.107777     22.328397      0.566145   \n",
       "50%        3.537322      0.797801      7.563736     23.639213      0.855990   \n",
       "75%        3.952597      1.357529      8.069070     25.097037      1.251116   \n",
       "max        7.327453      5.263958     11.326908     32.662894      4.889210   \n",
       "\n",
       "              MBVDW        MBELEC         MBALL      SCHBOND#  \n",
       "count  12750.000000  12750.000000  12750.000000  12750.000000  \n",
       "mean     -11.516121    -26.637363    -36.703792      1.216784  \n",
       "std        1.653128      1.741880      2.218219      0.466529  \n",
       "min      -16.781096    -34.598112    -44.192224      0.000000  \n",
       "25%      -12.658761    -27.783855    -38.224054      1.000000  \n",
       "50%      -11.606491    -26.578857    -36.765803      1.000000  \n",
       "75%      -10.487245    -25.441361    -35.246472      1.000000  \n",
       "max       -3.652790    -19.451467    -27.711963      4.000000  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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": [
       "0.435"
      ]
     },
     "execution_count": 76,
     "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": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 45.490196078431374\n",
      "F1 score: 41.941965413552616\n",
      "Recall score: 42.60263481036743\n",
      "Precision score: 45.39441998018306\n",
      "Confusion matrix: [[168 133 411]\n",
      " [100 293 419]\n",
      " [101 226 699]]\n",
      "Classification report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.46      0.24      0.31       712\n",
      "           1       0.45      0.36      0.40       812\n",
      "           2       0.46      0.68      0.55      1026\n",
      "\n",
      "    accuracy                           0.45      2550\n",
      "   macro avg       0.45      0.43      0.42      2550\n",
      "weighted avg       0.45      0.45      0.43      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": 87,
   "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": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 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": 89,
   "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": [
       "0.4615686274509804"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNNmod=KNeighborsClassifier(n_neighbors=60)\n",
    "KNNmod.fit(X_train,y_train)\n",
    "#Score RF fit.\n",
    "KNNmod.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 43.333333333333336\n",
      "F1 score: 41.01134561397049\n",
      "Recall score: 41.197190962683734\n",
      "Precision score: 42.70254384727512\n",
      "Confusion matrix: [[200 142 370]\n",
      " [129 284 399]\n",
      " [151 254 621]]\n",
      "Classification report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.42      0.28      0.34       712\n",
      "           1       0.42      0.35      0.38       812\n",
      "           2       0.45      0.61      0.51      1026\n",
      "\n",
      "    accuracy                           0.43      2550\n",
      "   macro avg       0.43      0.41      0.41      2550\n",
      "weighted avg       0.43      0.43      0.42      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": 91,
   "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": [
       "0.7528431372549019"
      ]
     },
     "execution_count": 91,
     "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": 97,
   "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": [
      "[0.7528431372549019, 0.9163725490196079, 0.9790196078431372, 0.9957843137254901, 0.9990196078431373, 0.9999019607843137, 1.0, 1.0, 1.0, 1.0]\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": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 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": 110,
   "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": [
       "0.9957843137254901"
      ]
     },
     "execution_count": 110,
     "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": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 40.431372549019606\n",
      "F1 score: 36.473173698556685\n",
      "Recall score: 37.55223319898035\n",
      "Precision score: 38.78764331469888\n",
      "Confusion matrix: [[163 147 402]\n",
      " [124 201 487]\n",
      " [145 214 667]]\n",
      "Classification report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.38      0.23      0.28       712\n",
      "           1       0.36      0.25      0.29       812\n",
      "           2       0.43      0.65      0.52      1026\n",
      "\n",
      "    accuracy                           0.40      2550\n",
      "   macro avg       0.39      0.38      0.36      2550\n",
      "weighted avg       0.39      0.40      0.38      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": 100,
   "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": 100,
     "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": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 47.411764705882355\n",
      "F1 score: 45.714587156141164\n",
      "Recall score: 45.686265508074\n",
      "Precision score: 46.59623373979149\n",
      "Confusion matrix: [[250 146 316]\n",
      " [157 330 325]\n",
      " [171 226 629]]\n",
      "Classification report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.43      0.35      0.39       712\n",
      "           1       0.47      0.41      0.44       812\n",
      "           2       0.50      0.61      0.55      1026\n",
      "\n",
      "    accuracy                           0.47      2550\n",
      "   macro avg       0.47      0.46      0.46      2550\n",
      "weighted avg       0.47      0.47      0.47      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": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04071726247299379"
      ]
     },
     "execution_count": 102,
     "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": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04390480977444511"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The linear model is: Y = 1.106 + 0.0028371*cav1 + 0.0053549*cav2 + 0.022616*cav3+ 0.0022858*cav4\n"
     ]
    }
   ],
   "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": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               location   R-squared:                       0.042\n",
      "Model:                            OLS   Adj. R-squared:                  0.041\n",
      "Method:                 Least Squares   F-statistic:                     42.99\n",
      "Date:                Fri, 01 May 2020   Prob (F-statistic):          1.33e-108\n",
      "Time:                        17:21:19   Log-Likelihood:                -15328.\n",
      "No. Observations:               12750   AIC:                         3.068e+04\n",
      "Df Residuals:                   12736   BIC:                         3.079e+04\n",
      "Df Model:                          13                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          2.1634      0.214     10.115      0.000       1.744       2.583\n",
      "x1            -0.0960      0.037     -2.611      0.009      -0.168      -0.024\n",
      "x2             0.0810      0.047      1.739      0.082      -0.010       0.172\n",
      "x3             0.0028      0.000      7.236      0.000       0.002       0.004\n",
      "x4             0.0039      0.000     12.199      0.000       0.003       0.005\n",
      "x5             0.0028      0.014      0.206      0.837      -0.024       0.029\n",
      "x6             0.0101      0.009      1.098      0.272      -0.008       0.028\n",
      "x7             0.0192      0.011      1.688      0.092      -0.003       0.042\n",
      "x8             0.0106      0.004      2.863      0.004       0.003       0.018\n",
      "x9             0.0544      0.020      2.735      0.006       0.015       0.093\n",
      "x10            0.0421      0.016      2.678      0.007       0.011       0.073\n",
      "x11            0.0259      0.016      1.604      0.109      -0.006       0.058\n",
      "x12            0.0030      0.015      0.196      0.845      -0.027       0.033\n",
      "x13           -0.0364      0.016     -2.297      0.022      -0.068      -0.005\n",
      "==============================================================================\n",
      "Omnibus:                   124088.368   Durbin-Watson:                   0.035\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1114.322\n",
      "Skew:                          -0.192   Prob(JB):                    1.07e-242\n",
      "Kurtosis:                       1.604   Cond. No.                     4.91e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 4.91e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "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": 62,
   "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"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "shape mismatch: objects cannot be broadcast to a single shape",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-62-7369ff63ee48>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;31m# You can also discard the abs() function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;31m# if you are interested in negative contribution of features\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mf_importances\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeatures_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-62-7369ff63ee48>\u001b[0m in \u001b[0;36mf_importances\u001b[0;34m(coef, names, top)\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mtop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbarh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mtop\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mtop\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\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/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mbarh\u001b[0;34m(y, width, height, left, align, **kwargs)\u001b[0m\n\u001b[1;32m   2469\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mbarh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft\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[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2470\u001b[0m     return gca().barh(\n\u001b[0;32m-> 2471\u001b[0;31m         y, width, height=height, left=left, align=align, **kwargs)\n\u001b[0m\u001b[1;32m   2472\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2473\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mbarh\u001b[0;34m(self, y, width, height, left, align, **kwargs)\u001b[0m\n\u001b[1;32m   2461\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'orientation'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'horizontal'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2462\u001b[0m         patches = self.bar(x=left, height=height, width=width, bottom=y,\n\u001b[0;32m-> 2463\u001b[0;31m                            align=align, **kwargs)\n\u001b[0m\u001b[1;32m   2464\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mpatches\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2465\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1808\u001b[0m                         \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1809\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1810\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\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;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1811\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1812\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[1;32m   2249\u001b[0m         x, height, width, y, linewidth = np.broadcast_arrays(\n\u001b[1;32m   2250\u001b[0m             \u001b[0;31m# Make args iterable too.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2251\u001b[0;31m             np.atleast_1d(x), height, width, y, linewidth)\n\u001b[0m\u001b[1;32m   2252\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2253\u001b[0m         \u001b[0;31m# Now that units have been converted, set the tick locations.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mbroadcast_arrays\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/numpy/lib/stride_tricks.py\u001b[0m in \u001b[0;36mbroadcast_arrays\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    262\u001b[0m     \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_m\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubok\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_m\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 264\u001b[0;31m     \u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_broadcast_shape\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[0m\n\u001b[0m\u001b[1;32m    265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marray\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\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/numpy/lib/stride_tricks.py\u001b[0m in \u001b[0;36m_broadcast_shape\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m    189\u001b[0m     \u001b[0;31m# use the old-iterator because np.nditer does not handle size 0 arrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m     \u001b[0;31m# consistently\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 191\u001b[0;31m     \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbroadcast\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[0;36m32\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    192\u001b[0m     \u001b[0;31m# unfortunately, it cannot handle 32 or more arguments directly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mpos\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m31\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: shape mismatch: objects cannot be broadcast to a single shape"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5463725490196079"
      ]
     },
     "execution_count": 111,
     "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": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 44.43137254901961\n",
      "F1 score: 43.002389832842766\n",
      "Recall score: 42.910995812173105\n",
      "Precision score: 43.877740806360116\n",
      "Confusion matrix: [[237 120 355]\n",
      " [161 316 335]\n",
      " [198 248 580]]\n",
      "Classification report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.40      0.33      0.36       712\n",
      "           1       0.46      0.39      0.42       812\n",
      "           2       0.46      0.57      0.51      1026\n",
      "\n",
      "    accuracy                           0.44      2550\n",
      "   macro avg       0.44      0.43      0.43      2550\n",
      "weighted avg       0.44      0.44      0.44      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": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['SCRMSD', 'CARMSD', 'CHI1', 'CHI2', 'SELFDIHED', 'SELFVDW', 'SELFELEC', 'SelfALL', 'MBDIHED', 'MBVDW', 'MBELEC', 'MBALL', 'SCHBOND#']\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": 114,
   "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": 114,
     "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": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 47.411764705882355\n",
      "F1 score: 45.714587156141164\n",
      "Recall score: 45.686265508074\n",
      "Precision score: 46.59623373979149\n",
      "Confusion matrix: [[250 146 316]\n",
      " [157 330 325]\n",
      " [171 226 629]]\n",
      "Classification report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.43      0.35      0.39       712\n",
      "           1       0.47      0.41      0.44       812\n",
      "           2       0.50      0.61      0.55      1026\n",
      "\n",
      "    accuracy                           0.47      2550\n",
      "   macro avg       0.47      0.46      0.46      2550\n",
      "weighted avg       0.47      0.47      0.47      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": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['SCRMSD', 'CARMSD', 'CHI1', 'CHI2', 'SELFDIHED', 'SELFVDW', 'SELFELEC', 'SelfALL', 'MBDIHED', 'MBVDW', 'MBELEC', 'MBALL', 'SCHBOND#']\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": 119,
   "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 576x720 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()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}