data-mining-part-a-svm-mean-completion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "import pandas\n",
    "import sklearn.svm\n",
    "import sklearn.metrics\n",
    "import sklearn.model_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_df = pandas.read_csv('winequality-red.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = init_df.iloc[:, :11]\n",
    "y = init_df['quality']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x, y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_n = int(x_train.shape[0] * 33 / 100) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_indices = numpy.random.choice(x_train.index, replace_n, replace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_pH = 0\n",
    "for i in x_train.index:\n",
    "    if i not in replace_indices:\n",
    "        mean_pH += x_train.pH[i]\n",
    "mean_pH = round(mean_pH / (len(x_train) - replace_n), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in replace_indices:\n",
    "    x_train.at[i, 'pH'] = mean_pH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1000, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf = sklearn.svm.SVC(gamma=0.01, C=1000, kernel='rbf')\n",
    "svm_clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = svm_clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           3       0.00      0.00      0.00         0\n",
      "           4       0.05      0.10      0.07        10\n",
      "           5       0.70      0.69      0.69       183\n",
      "           6       0.59      0.57      0.58       155\n",
      "           7       0.51      0.48      0.49        48\n",
      "           8       0.00      0.00      0.00         4\n",
      "\n",
      "    accuracy                           0.59       400\n",
      "   macro avg       0.31      0.31      0.31       400\n",
      "weighted avg       0.61      0.59      0.60       400\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/andreas/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(sklearn.metrics.classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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