DBSCAN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
<<<<<<< Updated upstream
   "execution_count": 95,
=======
   "execution_count": 1,
>>>>>>> Stashed changes
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn import metrics\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(260601, 39)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/train_values.csv')\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>building_id</th>\n",
       "      <th>geo_level_1_id</th>\n",
       "      <th>geo_level_2_id</th>\n",
       "      <th>geo_level_3_id</th>\n",
       "      <th>count_floors_pre_eq</th>\n",
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       "      <th>height_percentage</th>\n",
       "      <th>land_surface_condition</th>\n",
       "      <th>foundation_type</th>\n",
       "      <th>...</th>\n",
       "      <th>has_secondary_use_agriculture</th>\n",
       "      <th>has_secondary_use_hotel</th>\n",
       "      <th>has_secondary_use_rental</th>\n",
       "      <th>has_secondary_use_institution</th>\n",
       "      <th>has_secondary_use_school</th>\n",
       "      <th>has_secondary_use_industry</th>\n",
       "      <th>has_secondary_use_health_post</th>\n",
       "      <th>has_secondary_use_gov_office</th>\n",
       "      <th>has_secondary_use_use_police</th>\n",
       "      <th>has_secondary_use_other</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   building_id  geo_level_1_id  geo_level_2_id  geo_level_3_id  \\\n",
       "0       802906               6             487           12198   \n",
       "1        28830               8             900            2812   \n",
       "2        94947              21             363            8973   \n",
       "3       590882              22             418           10694   \n",
       "4       201944              11             131            1488   \n",
       "\n",
       "   count_floors_pre_eq  age  area_percentage  height_percentage  \\\n",
       "0                    2   30                6                  5   \n",
       "1                    2   10                8                  7   \n",
       "2                    2   10                5                  5   \n",
       "3                    2   10                6                  5   \n",
       "4                    3   30                8                  9   \n",
       "\n",
       "  land_surface_condition foundation_type  ... has_secondary_use_agriculture  \\\n",
       "0                      t               r  ...                             0   \n",
       "1                      o               r  ...                             0   \n",
       "2                      t               r  ...                             0   \n",
       "3                      t               r  ...                             0   \n",
       "4                      t               r  ...                             0   \n",
       "\n",
       "  has_secondary_use_hotel has_secondary_use_rental  \\\n",
       "0                       0                        0   \n",
       "1                       0                        0   \n",
       "2                       0                        0   \n",
       "3                       0                        0   \n",
       "4                       0                        0   \n",
       "\n",
       "  has_secondary_use_institution has_secondary_use_school  \\\n",
       "0                             0                        0   \n",
       "1                             0                        0   \n",
       "2                             0                        0   \n",
       "3                             0                        0   \n",
       "4                             0                        0   \n",
       "\n",
       "   has_secondary_use_industry  has_secondary_use_health_post  \\\n",
       "0                           0                              0   \n",
       "1                           0                              0   \n",
       "2                           0                              0   \n",
       "3                           0                              0   \n",
       "4                           0                              0   \n",
       "\n",
       "   has_secondary_use_gov_office  has_secondary_use_use_police  \\\n",
       "0                             0                             0   \n",
       "1                             0                             0   \n",
       "2                             0                             0   \n",
       "3                             0                             0   \n",
       "4                             0                             0   \n",
       "\n",
       "   has_secondary_use_other  \n",
       "0                        0  \n",
       "1                        0  \n",
       "2                        0  \n",
       "3                        0  \n",
       "4                        0  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot_encode(column_name, data):\n",
    "    data = data.join(pd.get_dummies(data[column_name], prefix=column_name))\n",
    "    data = data.drop(column_name, axis=1)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = one_hot_encode('land_surface_condition', data)\n",
    "data = one_hot_encode('foundation_type', data)\n",
    "data = one_hot_encode('roof_type', data)\n",
    "data = one_hot_encode('ground_floor_type', data)\n",
    "data = one_hot_encode('other_floor_type', data)\n",
    "data = one_hot_encode('position', data)\n",
    "data = one_hot_encode('plan_configuration', data)\n",
    "data = one_hot_encode('legal_ownership_status', data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>plan_configuration_u</th>\n",
       "      <th>legal_ownership_status_a</th>\n",
       "      <th>legal_ownership_status_r</th>\n",
       "      <th>legal_ownership_status_v</th>\n",
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      ],
      "text/plain": [
       "   building_id  geo_level_1_id  geo_level_2_id  geo_level_3_id  \\\n",
       "0       802906               6             487           12198   \n",
       "1        28830               8             900            2812   \n",
       "2        94947              21             363            8973   \n",
       "3       590882              22             418           10694   \n",
       "4       201944              11             131            1488   \n",
       "\n",
       "   count_floors_pre_eq  age  area_percentage  height_percentage  \\\n",
       "0                    2   30                6                  5   \n",
       "1                    2   10                8                  7   \n",
       "2                    2   10                5                  5   \n",
       "3                    2   10                6                  5   \n",
       "4                    3   30                8                  9   \n",
       "\n",
       "   has_superstructure_adobe_mud  has_superstructure_mud_mortar_stone  ...  \\\n",
       "0                             1                                    1  ...   \n",
       "1                             0                                    1  ...   \n",
       "2                             0                                    1  ...   \n",
       "3                             0                                    1  ...   \n",
       "4                             1                                    0  ...   \n",
       "\n",
       "   plan_configuration_m  plan_configuration_n  plan_configuration_o  \\\n",
       "0                     0                     0                     0   \n",
       "1                     0                     0                     0   \n",
       "2                     0                     0                     0   \n",
       "3                     0                     0                     0   \n",
       "4                     0                     0                     0   \n",
       "\n",
       "   plan_configuration_q  plan_configuration_s  plan_configuration_u  \\\n",
       "0                     0                     0                     0   \n",
       "1                     0                     0                     0   \n",
       "2                     0                     0                     0   \n",
       "3                     0                     0                     0   \n",
       "4                     0                     0                     0   \n",
       "\n",
       "   legal_ownership_status_a  legal_ownership_status_r  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   legal_ownership_status_v  legal_ownership_status_w  \n",
       "0                         1                         0  \n",
       "1                         1                         0  \n",
       "2                         1                         0  \n",
       "3                         1                         0  \n",
       "4                         1                         0  \n",
       "\n",
       "[5 rows x 69 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
<<<<<<< Updated upstream
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1  0  1  2]\n",
      "Estimated number of clusters: 3\n",
      "Estimated number of noise points: 260592\n"
     ]
    }
   ],
   "source": [
    "db = DBSCAN(eps=42, min_samples=3).fit(data)  #metric='cosine'\n",
    "core_samples_mask = np.zeros_like(db.labels_, dtype=bool)\n",
    "core_samples_mask[db.core_sample_indices_] = True\n",
    "labels = db.labels_\n",
    "print(np.unique(labels))\n",
    "\n",
    "# Number of clusters in labels, ignoring noise if present.\n",
    "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
    "n_noise_ = list(labels).count(-1)\n",
    "\n",
    "print('Estimated number of clusters: %d' % n_clusters_)\n",
    "print('Estimated number of noise points: %d' % n_noise_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_minmax = pd.DataFrame(preprocessing.MinMaxScaler().fit_transform(data), columns=data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=3)\n",
    "finished_pca = pca.fit_transform(data_minmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
=======
>>>>>>> Stashed changes
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.15436169 0.09901098 0.08511   ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
<<<<<<< Updated upstream
       "pandas.core.indexing._iLocIndexer"
      ]
     },
     "execution_count": 114,
=======
       "DBSCAN(algorithm='auto', eps=0.3, leaf_size=30, metric='euclidean',\n",
       "    metric_params=None, min_samples=7, n_jobs=None, p=None)"
      ]
     },
     "execution_count": 7,
>>>>>>> Stashed changes
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(pca.explained_variance_ratio_)\n",
    "pcadf = pd.DataFrame(pca.components_,columns=data_minmax.columns,index = ['PC-1','PC-2','PC-3'])\n",
    "type(pcadf.iloc(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_vals = pd.read_csv('data/train_labels.csv')\n",
    "y_vals = y_vals['damage_grade']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = pd.Series(labels)\n",
    "label = [x+1 for x in label]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 2, 2, 2, 2]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label = [i + 1 if i!=3 else i**0 for i in label]\n",
    "label[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     148262\n",
       "False    112339\n",
       "Name: damage_grade, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_vals == label).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5689233732794579\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(y_vals, label, average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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