notebooks/SupportVectorMachine.ipynb

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   "source": [
    "import os\n",
    "import numpy as np; np.random.seed(42)\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import plotly.graph_objects as go\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from plotly import offline\n",
    "from sklearn.externals import joblib as jb\n",
    "\n",
    "offline.init_notebook_mode(connected=True)"
   ]
  },
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   "execution_count": 3,
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     "end_time": "2019-09-06T13:34:36.392953Z",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>sysclass</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>dir</th>\n",
       "      <th>vel</th>\n",
       "      <th>size</th>\n",
       "      <th>ttyyyxx1</th>\n",
       "      <th>ttyyyxx2</th>\n",
       "      <th>ttyyyxx3</th>\n",
       "      <th>...</th>\n",
       "      <th>maxz</th>\n",
       "      <th>meanvil</th>\n",
       "      <th>ttvil</th>\n",
       "      <th>meanprec</th>\n",
       "      <th>maxprec</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12871</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.75</td>\n",
       "      <td>-58.57</td>\n",
       "      <td>-999.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>22.28</td>\n",
       "      <td>0.18</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.18</td>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
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       "      <th>12872</th>\n",
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       "      <td>-2.73</td>\n",
       "      <td>-58.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28.17</td>\n",
       "      <td>0.30</td>\n",
       "      <td>9.10</td>\n",
       "      <td>1.80</td>\n",
       "      <td>4.16</td>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>23.01</td>\n",
       "      <td>0.18</td>\n",
       "      <td>3.76</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.32</td>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <th>12874</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>-2.75</td>\n",
       "      <td>-58.57</td>\n",
       "      <td>180.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.28</td>\n",
       "      <td>0.18</td>\n",
       "      <td>2.28</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.09</td>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>21.46</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.96</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.97</td>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
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       "       time  sysclass   lat    lon    dir   vel  size  ttyyyxx1  ttyyyxx2  \\\n",
       "12871   0.0       0.0 -2.75 -58.57 -999.0   0.0  13.0       0.0       0.0   \n",
       "12872   0.2       1.0 -2.73 -58.57    0.0  11.1  30.0       0.0       0.0   \n",
       "12873   0.0       0.0 -2.73 -58.57 -999.0   0.0  21.0       0.0       0.0   \n",
       "12874   0.2       1.0 -2.75 -58.57  180.0  11.1  13.0       0.0       0.0   \n",
       "12875   0.0       0.0 -1.71 -59.63 -999.0   0.0  12.0       0.0       0.0   \n",
       "\n",
       "       ttyyyxx3  ...   maxz  meanvil  ttvil  meanprec  maxprec  year  month  \\\n",
       "12871       0.0  ...  22.28     0.18   2.31      0.97     1.18  2014      8   \n",
       "12872       0.0  ...  28.17     0.30   9.10      1.80     4.16  2014      8   \n",
       "12873       0.0  ...  23.01     0.18   3.76      1.02     1.32  2014      8   \n",
       "12874       0.0  ...  22.28     0.18   2.28      0.97     1.09  2014      8   \n",
       "12875       0.0  ...  21.46     0.16   1.96      0.92     0.97  2014      8   \n",
       "\n",
       "       day  hour  minute  \n",
       "12871   27     2      24  \n",
       "12872   27     2      24  \n",
       "12873   27     3       0  \n",
       "12874   27     3       0  \n",
       "12875   27     3      48  \n",
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    "files = ['august.csv', 'september.csv', 'october.csv'] \n",
    "PATH ='../data/private/csv/fam/'\n",
    "\n",
    "df = [pd.read_csv(os.path.join(PATH, file)) \n",
    "      for file in files]\n",
    "df = pd.concat(df, sort=False)\n",
    "\n",
    "query = '(month == 8 and day >= 27) or (month == 9) or (month == 10 and day <= 7)'\n",
    "df = df.query(query)\n",
    "df.head()"
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   "source": [
    "query1 = 'ttyyyxx3 == 0'\n",
    "query2 = '(ttyyyxx3 >= 1) and (ttyyyxx3 <= 10)'\n",
    "query3 = '(ttyyyxx3 > 10)'\n",
    "\n",
    "class1 = df.query(query1)\n",
    "class2 = df.query(query2)\n",
    "class3 = df.query(query3)\n",
    "\n",
    "total1 = class1['ttyyyxx3'].count()\n",
    "total2 = class2['ttyyyxx3'].count()\n",
    "total3 = class3['ttyyyxx3'].count()\n",
    "\n",
    "fig = go.Figure()\n",
    "labels = ['Classe 1 - sem detecção de raios (0 raios)', \n",
    "          'Classe 2 - atividade moderada (de 1 a 10 raios detectados)', \n",
    "          'Classe 3 - atividade extrema (mais de 10 raios detectados)']\n",
    "values = [total1, total2, total3]\n",
    "\n",
    "colors = ['#140304', '#3C090C', '#DC1F2A']\n",
    "fig.add_trace(\n",
    "    go.Pie(labels=labels, values=values, hole=.5,\n",
    "           marker=dict(colors=colors, \n",
    "                    line=dict(color='#A0A0A0', width=1)))\n",
    ")\n",
    "\n",
    "fig.update_layout(\n",
    "    template='plotly_dark',\n",
    "    title='Definição e distribuição das classes'\n",
    ")\n",
    "\n",
    "# offline.plot(fig, image_filename='definition-classes-fam', image='png', image_width=1000, image_height=600)\n",
    "offline.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-06T13:34:56.264627Z",
     "start_time": "2019-09-06T13:34:55.868843Z"
    }
   },
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   ],
   "source": [
    "columns = [\n",
    "    'ttyyyxx3', 'label', 'riverfrac', 'convfrac', 'strafrac', \n",
    "    'maxz', 'ttvil', 'maxprec',\n",
    "]\n",
    "\n",
    "samples = 1\n",
    "samples *=  100\n",
    "samples = int((min(values)*samples)/100)\n",
    "\n",
    "df1 = class1.sample(samples)\n",
    "df2 = class2.sample(samples)\n",
    "df3 = class3.sample(samples)\n",
    "\n",
    "df1['label'] = 0\n",
    "df2['label'] = 1\n",
    "df3['label'] = 2\n",
    "\n",
    "df_ = pd.concat([df1, df2, df3], sort=False).sample(samples*3)\n",
    "\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "a = df_[df_['label'] == 0][['ttvil']]\n",
    "b = df_[df_['label'] == 1][['ttvil']]\n",
    "c = df_[df_['label'] == 2][['ttvil']]\n",
    "\n",
    "aa = df_[df_['label'] == 0][['maxz']]\n",
    "bb = df_[df_['label'] == 1][['maxz']]\n",
    "cc = df_[df_['label'] == 2][['maxz']]\n",
    "\n",
    "fig.add_trace(\n",
    "    go.Scatter(x=aa.values.ravel(), y=a.values.ravel(),\n",
    "              mode='markers', name='Subconjunto sem detecção de raios', marker=dict(opacity=.2))\n",
    ")\n",
    "fig.add_trace(\n",
    "    go.Scatter(x=bb.values.ravel(), y=b.values.ravel(),\n",
    "              mode='markers', name='Subconjunto com atividade moderada de raios', marker=dict(opacity=.2))\n",
    ")\n",
    "fig.add_trace(\n",
    "    go.Scatter(x=cc.values.ravel(), y=c.values.ravel(),\n",
    "              mode='markers', name='Subconjunto com atividade extrema de raios', \n",
    "               marker=dict(opacity=.2, line=dict(color='yellow', width=1)))\n",
    ")\n",
    "fig.update_xaxes(title_text='Máxima precipitação (dBZ)')\n",
    "fig.update_yaxes(title_text='VIL (kg m<sup>-2</sup>)')\n",
    "fig.update_layout(\n",
    "    template='plotly_dark',\n",
    "    title='Disperção dos índices de ttvil e maxz das 3 classes'\n",
    ")\n",
    "\n",
    "# offline.plot(fig, image_filename='scatter-of-indexes-ttvilXmaxz-fam', image='png', image_width=1000, image_height=600)\n",
    "\n",
    "offline.iplot(fig)"
   ]
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('0c3db878-a9d9-4781-8633-14b652d3b6d5');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df_[columns[2:]], \\\n",
    "                                                    df_[['label']], \\\n",
    "                                                    test_size=.2)\n",
    "svm = SVC(kernel='rbf', degree=2, gamma='scale').fit(X_train, y_train['label'].values)\n",
    "y_pred = svm.predict(X_test)\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "tc1 = cm[0][0]\n",
    "fc1 = cm[0][1]+cm[0][2]\n",
    "\n",
    "tc2 = cm[1][1]\n",
    "fc2 = cm[1][0]+cm[1][2]\n",
    "\n",
    "tc3 = cm[2][2]\n",
    "fc3 = cm[2][0]+cm[2][1]\n",
    "\n",
    "accuracy = (tc1+tc2+tc3)/(tc1+fc1+tc2+fc2+tc3+fc3)\n",
    "precisionc1 = (tc1)/(tc1+fc1)\n",
    "precisionc2 = (tc2)/(tc2+fc2)\n",
    "precisionc3 = (tc3)/(tc3+fc3)\n",
    "\n",
    "fig = go.Figure()\n",
    "labels = ['Acurácia', 'Precisão da classe 1', \n",
    "          'Precisão da classe 2', 'Precisão da classe 3']\n",
    "values = ['%.2f'%(accuracy*100), '%.2f'%(precisionc1*100),\n",
    "         '%.2f'%(precisionc2*100), '%.2f'%(precisionc3*100)]\n",
    "colors = ['#C61D1D', '#30FF41', '#0090FF', '#F18F01']\n",
    "fig.add_trace(\n",
    "    go.Bar(y=labels, x=values, orientation='h', \n",
    "           text=['%.2f%%'%(float(x)) for x in values], \n",
    "           marker_color=colors,\n",
    "           textposition='inside')\n",
    ")\n",
    "\n",
    "\n",
    "fig.update_layout(\n",
    "    template='plotly_dark',\n",
    "    title='Métricas da classificação SVM',\n",
    ")\n",
    "# offline.plot(fig, image_filename='metrics-svm-rbf-scale-degree2-fam', image='png')\n",
    "offline.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-06T15:13:29.415845Z",
     "start_time": "2019-09-06T15:13:25.580488Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize = (15, 8)\n",
    "fig, ax = plt.subplots(1, 1, figsize=figsize)\n",
    "cmap = plt.cm.Blues\n",
    "divider = make_axes_locatable(ax)\n",
    "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.4)\n",
    "\n",
    "total_samples = cm.sum(axis=1)[:, np.newaxis]\n",
    "normed_conf_mat = cm.astype('float') / total_samples\n",
    "labels = ['Sem raios', 'Atividade moderada', 'Atividade extrema']\n",
    "label_font = dict(fontweight='normal', size=14)\n",
    "title_font = dict(fontweight='normal', size=18)\n",
    "\n",
    "\n",
    "matshow = ax.matshow(cm, cmap=cmap)\n",
    "cb = plt.colorbar(matshow, cax=cax, orientation='vertical')\n",
    "\n",
    "for i in range(cm.shape[0]):\n",
    "    for j in range(cm.shape[1]):\n",
    "        cell_text = str(cm[i, j])\n",
    "        cell_text += '\\n(%.2f%%)' % (normed_conf_mat[i, j] * 100)\n",
    "        ax.text(x=j,\n",
    "                y=i,\n",
    "                s=cell_text,\n",
    "                va='center',\n",
    "                ha='center',\n",
    "                color=\"white\" if normed_conf_mat[i, j] > 0.5 else \"black\")\n",
    "\n",
    "ax.set_xticklabels([''] + labels)\n",
    "ax.set_yticklabels([''] + labels, rotation=90, va='center')\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "\n",
    "#     ax.set_title('Matriz de confusão para %d testes'%((n*2)*0.2), fontdict=title_font)\n",
    "ax.set_xlabel('Classe prevista', fontdict=label_font)\n",
    "ax.set_ylabel('Classe real', fontdict=label_font)\n",
    "cb.ax.set_ylabel('Ocorrências', va='center', rotation=-270, labelpad=-60)\n",
    "ax.set_title('Matriz de consusão resultada da\\\n",
    "classificação\\n por SVM', fontdict=title_font)\n",
    "\n",
    "plt.savefig('confusion_matrix.png', dpi=600, \\\n",
    "           bbox_inches='tight', transparent=\"False\", pad_inches=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-06T15:00:25.613835Z",
     "start_time": "2019-09-06T15:00:25.609154Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['svm-rbf-scale-degree2.pkl']"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jb.dump(svm, 'svm-rbf-scale-degree2.pkl')"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}