data-mining-part-a-logistic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "import sklearn.svm\n",
    "import sklearn.metrics\n",
    "import sklearn.preprocessing\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 = pandas.concat([init_df.iloc[:, :8], init_df.iloc[:, 9:]], axis=1, join='inner')\n",
    "y = init_df['pH']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mul_dict = {}\n",
    "mul_dict['fixed acidity'] = 10\n",
    "mul_dict['volatile acidity'] = 1000\n",
    "mul_dict['citric acid'] = 100\n",
    "mul_dict['residual sugar'] = 10\n",
    "mul_dict['chlorides'] = 1000\n",
    "mul_dict['free sulfur dioxide'] = 1\n",
    "mul_dict['total sulfur dioxide'] = 1\n",
    "mul_dict['density'] = 100000\n",
    "mul_dict['sulphates'] = 100\n",
    "mul_dict['pH'] = 100\n",
    "mul_dict['alcohol'] = 10\n",
    "mul_dict['quality'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in x.columns:\n",
    "    x[i] = x[i] * mul_dict.get(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x.astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = (y * 100).astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "logistic_train_source, logistic_predict_source, logistic_train_target, logistic_remove_target = sklearn.model_selection.train_test_split(x_train, y_train, test_size=0.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "logisticRegr = sklearn.linear_model.LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/andreas/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logisticRegr.fit(logistic_train_source, logistic_train_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = logisticRegr.predict(logistic_predict_source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "variance = round(abs(sum(logistic_remove_target - y_pred) / len(logistic_remove_target)) / 100, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_partinion_a = pandas.concat([logistic_train_source.iloc[:, :8], logistic_train_target, logistic_train_source.iloc[:, 8:-1]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_partinion_b = pandas.concat([logistic_predict_source.iloc[:, :8], logistic_remove_target, logistic_predict_source.iloc[:, 8:-1]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = pandas.concat([x_train_partinion_a, x_train_partinion_b], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = pandas.concat([logistic_train_source['quality'], logistic_predict_source['quality']], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in x_train.columns:\n",
    "    x_train[i] = x_train[i] / mul_dict.get(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = x_test['quality']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = pandas.concat([x_test.iloc[:, :8], y_test, x_test.iloc[:, 8:-1]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in x_test.columns:\n",
    "    x_test[i] = x_test[i] / mul_dict.get(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 22,
     "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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = svm_clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           3       0.00      0.00      0.00         3\n",
      "           4       0.02      0.08      0.03        13\n",
      "           5       0.53      0.48      0.50       161\n",
      "           6       0.49      0.42      0.45       166\n",
      "           7       0.24      0.11      0.15        53\n",
      "           8       0.00      0.00      0.00         4\n",
      "\n",
      "    accuracy                           0.38       400\n",
      "   macro avg       0.21      0.18      0.19       400\n",
      "weighted avg       0.45      0.38      0.41       400\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/andreas/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted 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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