data-mining-part-a-kmeans.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "import pandas\n",
    "import sklearn.svm\n",
    "import sklearn.cluster\n",
    "import sklearn.metrics\n",
    "import matplotlib.pyplot\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": [
    "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": [
    "kmeans_train_source, kmeans_predict_source, kmeans_train_target, kmeans_remove_target = sklearn.model_selection.train_test_split(x_train, y_train, test_size=0.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "distortions = []\n",
    "k = 10\n",
    "clustering = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(1, k):\n",
    "    clustering = sklearn.cluster.KMeans(n_clusters=i, init='random', n_init=10, max_iter=300, tol=1e-04, random_state=0)\n",
    "    clustering.fit(kmeans_train_source)\n",
    "    distortions.append(clustering.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "matplotlib.pyplot.plot(range(1, k), distortions, marker='o')\n",
    "matplotlib.pyplot.xlabel('Number of clusters')\n",
    "matplotlib.pyplot.ylabel('Distortion')\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "means_array = numpy.zeros(9, dtype=numpy.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "means_dict = dict(enumerate(means_array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(clustering.predict(kmeans_train_source))):\n",
    "    x = kmeans_train_target.iloc[i]\n",
    "    means_dict[clustering.predict(kmeans_train_source)[i]] = means_dict[clustering.predict(kmeans_train_source)[i]] + x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "means_df = pandas.DataFrame.from_dict(list(means_dict.items())).drop([0], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique, counts = numpy.unique(clustering.predict(kmeans_train_source), return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_denominators = dict(zip(unique, counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getList(dict):\n",
    "    return [*dict]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in getList(mean_denominators):\n",
    "    means_df.iloc[i][1] = round(means_df.iloc[i]/mean_denominators.get(i), 2).item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "means_df = means_df.rename(columns={1: \"cluster_means\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = pandas.DataFrame(index=kmeans_remove_target.index, columns=['pH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(clustering.predict(kmeans_predict_source))):\n",
    "    y_pred.iloc[i] = means_df.loc[clustering.predict(kmeans_predict_source)[i]].item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "variance = round(abs(sum(kmeans_remove_target.to_numpy() - y_pred.to_numpy().flatten()))/len(kmeans_remove_target), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = pandas.concat([kmeans_train_source, kmeans_train_target], axis=1, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dataset = pandas.concat([kmeans_predict_source, y_pred], axis=1, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_split = pandas.concat([train_dataset, test_dataset], axis=0, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = pandas.concat([first_split.iloc[:, :10], first_split.iloc[:, 11:]], axis=1, join='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = first_split.iloc[:, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 26,
     "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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = x_test.iloc[:, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = pandas.concat([x_test.iloc[:, :10], tmp], axis=1, join='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = svm_clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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       0.00      0.00      0.00        15\n",
      "           5       0.66      0.66      0.66       171\n",
      "           6       0.56      0.61      0.59       160\n",
      "           7       0.54      0.44      0.48        48\n",
      "           8       0.00      0.00      0.00         6\n",
      "\n",
      "    accuracy                           0.58       400\n",
      "   macro avg       0.29      0.29      0.29       400\n",
      "weighted avg       0.57      0.58      0.58       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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