lazyfca_heart_desease.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from importlib import reload\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import model_selection\n",
    "from sklearn import metrics\n",
    "import fca_lazy_clf as fca\n",
    "from datetime import datetime\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>2.3</td>\n",
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       "      <td>1.4</td>\n",
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       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#https://archive.ics.uci.edu/ml/datasets/Heart+Disease\n",
    "data = pd.read_csv('heart-disease-uci.zip')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attribute Information:\n",
    "1. age\n",
    "2. sex\n",
    "3. chest pain type (4 values)\n",
    "4. resting blood pressure\n",
    "5. serum cholestoral in mg/dl\n",
    "6. fasting blood sugar > 120 mg/dl\n",
    "7. resting electrocardiographic results (values 0,1,2)\n",
    "8. maximum heart rate achieved\n",
    "9. exercise induced angina\n",
    "10. oldpeak = ST depression induced by exercise relative to rest\n",
    "11. the slope of the peak exercise ST segment\n",
    "12. number of major vessels (0-3) colored by flourosopy\n",
    "13. thal: 3 = normal; 6 = fixed defect; 7 = reversable defect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('heart-disease-uci.zip')\n",
    "data = data.dropna()\n",
    "\n",
    "intervals = 5\n",
    "for attr in ['age', 'trestbps', 'chol', 'thalach', 'oldpeak']:\n",
    "    min_val = data[attr].min()\n",
    "    max_val = data[attr].max()\n",
    "    gap = max_val - min_val\n",
    "    k = 0\n",
    "    for i in np.linspace(min_val + gap / intervals, max_val - gap / intervals, intervals):\n",
    "        data[attr + '_' + str(k)] = (data[attr] >= i).astype(int)\n",
    "        k += 1\n",
    "    data = data.drop(attr, axis=1)\n",
    "    \n",
    "for attr in ['cp', 'restecg', 'slope', 'ca', 'thal']:\n",
    "    for i in data[attr].unique():\n",
    "        data[attr + '_' + str(i)] = (data[attr] == i).astype(int)\n",
    "    data = data.drop(attr, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>fbs</th>\n",
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       "      <th>trestbps_0</th>\n",
       "      <th>...</th>\n",
       "      <th>slope_1</th>\n",
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       "      <td>4</td>\n",
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       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   sex  fbs  exang  target  age_0  age_1  age_2  age_3  age_4  trestbps_0  \\\n",
       "0    1    1      0       1      1      1      1      1      0           1   \n",
       "1    1    0      0       1      0      0      0      0      0           1   \n",
       "2    0    0      0       1      1      0      0      0      0           1   \n",
       "3    1    0      0       1      1      1      1      0      0           1   \n",
       "4    0    0      1       1      1      1      1      0      0           1   \n",
       "\n",
       "   ...  slope_1  ca_0  ca_2  ca_1  ca_3  ca_4  thal_1  thal_2  thal_3  thal_0  \n",
       "0  ...        0     1     0     0     0     0       1       0       0       0  \n",
       "1  ...        0     1     0     0     0     0       0       1       0       0  \n",
       "2  ...        0     1     0     0     0     0       0       1       0       0  \n",
       "3  ...        0     1     0     0     0     0       0       1       0       0  \n",
       "4  ...        0     1     0     0     0     0       0       1       0       0  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lazy classifying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Parameters: LazyClassifier(bias='random', random=True, sample_share=0.3, threshold=0.1)\n",
      "\n",
      "   accuracy  precision    recall  seconds\n",
      "0      0.85   0.911111  0.788462        6\n",
      "1      0.73   0.829268  0.629630        6\n",
      "2      0.92   0.901639  0.964912        4\n",
      "3      0.87   0.866667  0.912281        5\n",
      "4      0.83   0.901961  0.793103        6\n",
      "5      0.81   0.948718  0.685185        6\n",
      "6      0.83   0.816327  0.833333        5\n",
      "7      0.78   0.788462  0.788462        5\n",
      "8      0.78   0.895833  0.716667        5\n",
      "9      0.74   0.860000  0.693548        4\n",
      "\n",
      "Accuracy: 0.8140000000000001\n",
      "\n",
      "Parameters: LazyClassifier(bias='random', random=True, sample_share=0.3,\n",
      "               threshold=0.30000000000000004)\n",
      "\n",
      "   accuracy  precision    recall  seconds\n",
      "0      0.82   0.925000  0.711538        5\n",
      "1      0.71   0.931034  0.500000        5\n",
      "2      0.86   0.864407  0.894737        4\n",
      "3      0.81   0.851852  0.807018        4\n",
      "4      0.79   0.911111  0.706897        5\n",
      "5      0.76   0.941176  0.592593        5\n",
      "6      0.85   0.851064  0.833333        5\n",
      "7      0.86   0.851852  0.884615        5\n",
      "8      0.82   0.937500  0.750000        5\n",
      "9      0.71   0.851064  0.645161        6\n",
      "\n",
      "Accuracy: 0.799\n",
      "\n",
      "Parameters: LazyClassifier(bias='random', random=True, sample_share=0.3, threshold=0.5)\n",
      "\n",
      "   accuracy  precision    recall  seconds\n",
      "0      0.81   0.923077  0.692308        5\n",
      "1      0.73   0.846154  0.611111        6\n",
      "2      0.84   0.918367  0.789474        4\n",
      "3      0.82   0.933333  0.736842        4\n",
      "4      0.83   0.859649  0.844828        4\n",
      "5      0.76   0.916667  0.611111        4\n",
      "6      0.84   0.863636  0.791667        5\n",
      "7      0.83   0.796610  0.903846        4\n",
      "8      0.77   0.911111  0.683333        5\n",
      "9      0.77   0.842105  0.774194        6\n",
      "\n",
      "Accuracy: 0.7999999999999998\n",
      "\n",
      "Parameters: LazyClassifier(bias='random', random=True, sample_share=0.3,\n",
      "               threshold=0.7000000000000001)\n",
      "\n",
      "   accuracy  precision    recall  seconds\n",
      "0      0.84   0.860000  0.826923        5\n",
      "1      0.73   0.846154  0.611111        5\n",
      "2      0.85   0.920000  0.807018        5\n",
      "3      0.86   0.890909  0.859649        5\n",
      "4      0.71   0.939394  0.534483        4\n",
      "5      0.82   0.973684  0.685185        4\n",
      "6      0.83   0.829787  0.812500        5\n",
      "7      0.85   0.836364  0.884615        5\n",
      "8      0.80   0.934783  0.716667        5\n",
      "9      0.70   0.833333  0.645161        4\n",
      "\n",
      "Accuracy: 0.7989999999999999\n",
      "\n",
      "Parameters: LazyClassifier(bias='random', random=True, sample_share=0.3, threshold=0.9)\n",
      "\n",
      "   accuracy  precision    recall  seconds\n",
      "0      0.80   0.833333  0.769231        5\n",
      "1      0.76   0.840909  0.685185        4\n",
      "2      0.87   0.907407  0.859649        4\n",
      "3      0.82   0.882353  0.789474        4\n",
      "4      0.81   0.882353  0.775862        5\n",
      "5      0.77   0.918919  0.629630        5\n",
      "6      0.81   0.853659  0.729167        5\n",
      "7      0.82   0.840000  0.807692        7\n",
      "8      0.79   0.882353  0.750000        5\n",
      "9      0.75   0.877551  0.693548        4\n",
      "\n",
      "Accuracy: 0.8\n",
      "\n",
      "Parameters: LazyClassifier(bias='true', random=True, sample_share=0.3, threshold=0.1)\n",
      "\n",
      "   accuracy  precision    recall  seconds\n",
      "0      0.79   0.844444  0.730769        5\n",
      "1      0.81   0.830189  0.814815        4\n",
      "2      0.88   0.924528  0.859649        4\n",
      "3      0.78   0.872340  0.719298        4\n",
      "4      0.83   0.918367  0.775862        4\n",
      "5      0.84   0.895833  0.796296        5\n",
      "6      0.83   0.844444  0.791667        5\n",
      "7      0.78   0.812500  0.750000        4\n",
      "8      0.83   0.890909  0.816667        4\n",
      "9      0.76   0.913043  0.677419        4\n",
      "\n",
      "Accuracy: 0.8130000000000001\n",
      "\n",
      "Parameters: LazyClassifier(bias='true', random=True, sample_share=0.3,\n",
      "               threshold=0.30000000000000004)\n",
      "\n",
      "   accuracy  precision    recall  seconds\n",
      "0      0.81   0.923077  0.692308        5\n",
      "1      0.78   0.833333  0.740741        5\n",
      "2      0.84   0.955556  0.754386        5\n",
      "3      0.82   0.882353  0.789474        5\n",
      "4      0.80   0.865385  0.775862        5\n",
      "5      0.79   0.971429  0.629630        4\n",
      "6      0.81   0.871795  0.708333        7\n",
      "7      0.84   0.833333  0.865385        5\n",
      "8      0.76   0.928571  0.650000        4\n",
      "9      0.70   0.820000  0.661290        4\n",
      "\n",
      "Accuracy: 0.795\n",
      "\n",
      "Parameters: LazyClassifier(bias='true', random=True, sample_share=0.3, threshold=0.5)\n",
      "\n",
      "   accuracy  precision    recall  seconds\n",
      "0      0.84   0.891304  0.788462        4\n",
      "1      0.72   0.825000  0.611111        5\n",
      "2      0.87   0.907407  0.859649        5\n",
      "3      0.82   0.897959  0.771930        4\n",
      "4      0.75   0.866667  0.672414        4\n",
      "5      0.78   0.900000  0.666667        5\n",
      "6      0.84   0.863636  0.791667        6\n",
      "7      0.84   0.860000  0.826923        5\n",
      "8      0.76   0.928571  0.650000        4\n",
      "9      0.79   0.847458  0.806452        4\n",
      "\n",
      "Accuracy: 0.8009999999999999\n",
      "\n",
      "Parameters: LazyClassifier(bias='true', random=True, sample_share=0.3,\n",
      "               threshold=0.7000000000000001)\n",
      "\n",
      "   accuracy  precision    recall  seconds\n",
      "0      0.82   0.886364  0.750000        5\n",
      "1      0.80   0.814815  0.814815        4\n",
      "2      0.87   0.940000  0.824561        4\n",
      "3      0.81   0.880000  0.771930        7\n",
      "4      0.77   0.906977  0.672414        5\n",
      "5      0.82   0.950000  0.703704        5\n",
      "6      0.82   0.840909  0.770833        6\n",
      "7      0.84   0.875000  0.807692        6\n",
      "8      0.78   0.931818  0.683333        6\n",
      "9      0.72   0.826923  0.693548        5\n",
      "\n",
      "Accuracy: 0.805\n",
      "\n",
      "Parameters: LazyClassifier(bias='true', random=True, sample_share=0.3, threshold=0.9)\n",
      "\n",
      "   accuracy  precision    recall  seconds\n",
      "0      0.82   0.854167  0.788462        6\n",
      "1      0.74   0.833333  0.648148        5\n",
      "2      0.81   0.931818  0.719298        4\n",
      "3      0.83   0.884615  0.807018        4\n",
      "4      0.83   0.901961  0.793103        4\n",
      "5      0.81   0.926829  0.703704        4\n",
      "6      0.82   0.840909  0.770833        5\n",
      "7      0.84   0.790323  0.942308        5\n",
      "8      0.86   0.910714  0.850000        4\n",
      "9      0.78   0.822581  0.822581        4\n",
      "\n",
      "Accuracy: 0.8140000000000001\n",
      "\n",
      "Parameters: LazyClassifier(bias='false', random=True, sample_share=0.3, threshold=0.1)\n",
      "\n",
      "   accuracy  precision    recall  seconds\n",
      "0      0.82   0.793103  0.884615        4\n",
      "1      0.79   0.851064  0.740741        4\n",
      "2      0.85   0.920000  0.807018        4\n",
      "3      0.84   0.847458  0.877193        4\n",
      "4      0.89   0.861538  0.965517        4\n",
      "5      0.82   0.928571  0.722222        4\n",
      "6      0.84   0.880952  0.770833        5\n",
      "7      0.82   0.814815  0.846154        4\n",
      "8      0.79   0.914894  0.716667        4\n",
      "9      0.73   0.888889  0.645161        4\n",
      "\n",
      "Accuracy: 0.819\n",
      "\n",
      "Parameters: LazyClassifier(bias='false', random=True, sample_share=0.3,\n",
      "               threshold=0.30000000000000004)\n",
      "\n",
      "   accuracy  precision    recall  seconds\n",
      "0      0.81   0.971429  0.653846        5\n",
      "1      0.73   0.864865  0.592593        5\n",
      "2      0.91   0.928571  0.912281        4\n",
      "3      0.76   0.883721  0.666667        6\n",
      "4      0.82   0.833333  0.862069        4\n",
      "5      0.80   0.947368  0.666667        4\n",
      "6      0.81   0.763636  0.875000        5\n",
      "7      0.83   0.888889  0.769231        5\n",
      "8      0.74   0.972222  0.583333        4\n",
      "9      0.73   0.888889  0.645161        4\n",
      "\n",
      "Accuracy: 0.7940000000000002\n",
      "\n",
      "Parameters: LazyClassifier(bias='false', random=True, sample_share=0.3, threshold=0.5)\n",
      "\n",
      "   accuracy  precision    recall  seconds\n",
      "0      0.83   0.948718  0.711538        5\n",
      "1      0.75   0.871795  0.629630        5\n",
      "2      0.86   0.921569  0.824561        4\n",
      "3      0.80   0.930233  0.701754        4\n",
      "4      0.77   0.857143  0.724138        5\n",
      "5      0.76   0.968750  0.574074        5\n",
      "6      0.87   0.857143  0.875000        5\n",
      "7      0.83   0.786885  0.923077        5\n",
      "8      0.71   0.942857  0.550000        5\n",
      "9      0.72   0.869565  0.645161        4\n",
      "\n",
      "Accuracy: 0.7899999999999999\n",
      "\n",
      "Parameters: LazyClassifier(bias='false', random=True, sample_share=0.3,\n",
      "               threshold=0.7000000000000001)\n",
      "\n",
      "   accuracy  precision    recall  seconds\n",
      "0      0.78   0.916667  0.634615        4\n",
      "1      0.77   0.860465  0.685185        4\n",
      "2      0.86   0.957447  0.789474        4\n",
      "3      0.82   0.867925  0.807018        4\n",
      "4      0.80   0.931818  0.706897        4\n",
      "5      0.88   0.903846  0.870370        4\n",
      "6      0.81   0.795918  0.812500        4\n",
      "7      0.84   0.821429  0.884615        4\n",
      "8      0.77   0.930233  0.666667        4\n",
      "9      0.71   0.902439  0.596774        4\n",
      "\n",
      "Accuracy: 0.8039999999999999\n",
      "\n",
      "Parameters: LazyClassifier(bias='false', random=True, sample_share=0.3, threshold=0.9)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   accuracy  precision    recall  seconds\n",
      "0      0.81   0.851064  0.769231        4\n",
      "1      0.71   0.820513  0.592593        4\n",
      "2      0.85   0.920000  0.807018        4\n",
      "3      0.84   0.847458  0.877193        5\n",
      "4      0.80   0.895833  0.741379        5\n",
      "5      0.81   0.948718  0.685185        4\n",
      "6      0.83   0.844444  0.791667        4\n",
      "7      0.84   0.833333  0.865385        5\n",
      "8      0.74   0.925000  0.616667        5\n",
      "9      0.76   0.839286  0.758065        4\n",
      "\n",
      "Accuracy: 0.799\n"
     ]
    }
   ],
   "source": [
    "results = {'accuracy': [], 'precision': [], 'recall': [], 'seconds': [], 'params': []}\n",
    "reload(fca)\n",
    "\n",
    "columns = list(data.columns)\n",
    "columns.remove('target')\n",
    "\n",
    "X = data.loc[:, columns]\n",
    "y = data.target\n",
    "for i in ['random', 'positive', 'negative']:\n",
    "    for j in np.linspace(0.1, 0.9, 5):\n",
    "        results = {'accuracy': [], 'precision': [], 'recall': [], 'seconds': []}\n",
    "        clf = fca.LazyClassifier(threshold=j, bias=i, random=True, sample_share=0.3)\n",
    "        \n",
    "        print()\n",
    "        print('Parameters:', clf)\n",
    "        print()\n",
    "        \n",
    "        for k in range(10):\n",
    "\n",
    "            X_train, X_test, y_train, y_test = model_selection\\\n",
    "                .train_test_split(X, y, test_size=0.33, random_state=k)\n",
    "\n",
    "            clf.fit(X_train, y_train)\n",
    "\n",
    "            s = datetime.now()\n",
    "            y_pred = clf.predict(X_test)\n",
    "            f = datetime.now()\n",
    "\n",
    "            results['accuracy'].append(metrics.accuracy_score(y_test, y_pred))\n",
    "            results['precision'].append(metrics.precision_score(y_test, y_pred))\n",
    "            results['recall'].append(metrics.recall_score(y_test, y_pred))\n",
    "            results['seconds'].append((f - s).seconds)\n",
    "           \n",
    "\n",
    "        print(pd.DataFrame(results))\n",
    "        print()\n",
    "        print('Accuracy:', pd.DataFrame(results)['accuracy'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   accuracy  precision    recall  seconds\n",
      "0      0.83   0.906977  0.750000       16\n",
      "1      0.75   0.853659  0.648148       16\n",
      "2      0.85   0.920000  0.807018       17\n",
      "3      0.83   0.884615  0.807018       20\n",
      "4      0.80   0.895833  0.741379       16\n",
      "5      0.83   0.930233  0.740741       16\n",
      "6      0.84   0.863636  0.791667       18\n",
      "7      0.85   0.849057  0.865385       17\n",
      "8      0.79   0.933333  0.700000       17\n",
      "9      0.72   0.826923  0.693548       17\n",
      "\n",
      "Accuracy: 0.8089999999999999\n"
     ]
    }
   ],
   "source": [
    "reload(fca)\n",
    "\n",
    "columns = list(data.columns)\n",
    "columns.remove('target')\n",
    "\n",
    "X = data.loc[:, columns]\n",
    "y = data.target\n",
    "\n",
    "results = {'accuracy': [], 'precision': [], 'recall': [], 'seconds': []}\n",
    "clf = fca.LazyClassifier(threshold=0.9, bias='negative')\n",
    "for k in range(10):\n",
    "\n",
    "    X_train, X_test, y_train, y_test = model_selection\\\n",
    "        .train_test_split(X, y, test_size=0.33, random_state=k)\n",
    "\n",
    "    clf.fit(X_train, y_train)\n",
    "\n",
    "    s = datetime.now()\n",
    "    y_pred = clf.predict(X_test)\n",
    "    f = datetime.now()\n",
    "\n",
    "    results['accuracy'].append(metrics.accuracy_score(y_test, y_pred))\n",
    "    results['precision'].append(metrics.precision_score(y_test, y_pred))\n",
    "    results['recall'].append(metrics.recall_score(y_test, y_pred))\n",
    "    results['seconds'].append((f - s).seconds)\n",
    "\n",
    "print(pd.DataFrame(results))\n",
    "print()\n",
    "print('Accuracy:', pd.DataFrame(results)['accuracy'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classical logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   accuracy  precision    recall  seconds\n",
      "0      0.83   0.843137  0.826923        0\n",
      "1      0.79   0.823529  0.777778        0\n",
      "2      0.87   0.854839  0.929825        0\n",
      "3      0.88   0.868852  0.929825        0\n",
      "4      0.81   0.830508  0.844828        0\n",
      "5      0.85   0.842105  0.888889        0\n",
      "6      0.83   0.792453  0.875000        0\n",
      "7      0.81   0.789474  0.865385        0\n",
      "8      0.81   0.872727  0.800000        0\n",
      "9      0.82   0.866667  0.838710        0\n",
      "\n",
      "Accuracy: 0.8300000000000001\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('heart-disease-uci.zip')\n",
    "data = data.dropna()\n",
    "\n",
    "columns = list(data.columns)\n",
    "columns.remove('target')\n",
    "\n",
    "results = {'accuracy': [], 'precision': [], 'recall': [], 'seconds': []}\n",
    "reload(fca)\n",
    "clf = LogisticRegression(solver='lbfgs', random_state=0)\n",
    "for k in range(10):\n",
    "\n",
    "    X_train, X_test, y_train, y_test = model_selection\\\n",
    "        .train_test_split(X, y, test_size=0.33, random_state=k)\n",
    "\n",
    "    clf.fit(X_train, y_train)\n",
    "\n",
    "    s = datetime.now()\n",
    "    y_pred = clf.predict(X_test)\n",
    "    f = datetime.now()\n",
    "\n",
    "    results['accuracy'].append(metrics.accuracy_score(y_test, y_pred))\n",
    "    results['precision'].append(metrics.precision_score(y_test, y_pred))\n",
    "    results['recall'].append(metrics.recall_score(y_test, y_pred))\n",
    "    results['seconds'].append((f - s).seconds)\n",
    "\n",
    "print(pd.DataFrame(results))\n",
    "print()\n",
    "print('Accuracy:', pd.DataFrame(results)['accuracy'].mean())"
   ]
  }
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