RANDOM_DATA_GENERATOR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.datasets import make_blobs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining constants and creating data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.55083801, 0.27433042, 0.15834485, 0.86471684, 0.14003355,\n",
       "       0.12594939, 0.20671995, 0.35875021, 0.87714981, 0.31900044])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NUM_TANKS = 50\n",
    "# l= CENTER_BOX_FOR_HOUSES[0]\n",
    "# r= CENTER_BOX_FOR_HOUSES[1]\n",
    "# tanks_x_y = [abs((l-r)*np.random.random(10) + l) for i in range(NUM_TANKS)]\n",
    "# tanks_x_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "RANDOM_SEED = 32\n",
    "\n",
    "np.random.seed(RANDOM_SEED)\n",
    "NUM_HOUSES = 200\n",
    "NUM_CLUSTERS_OF_HOUSES = 6\n",
    "N_FEATURES = 2\n",
    "CLUSTER_STD = 100.0\n",
    "CENTER_BOX_FOR_HOUSES = (0,1000)\n",
    "MEAN_DEMAND_WATER_PER_HOUSE = 100 # LITERS\n",
    "VAR_DEMAND_WATER_PER_HOUSE = 50 # LITERS\n",
    "MEAN_AREA_PER_HOUSE = 100 # METERS SQUARE\n",
    "VAR_AREA_PER_HOUSE = 50 # METERS SQUARE PER HOUSE\n",
    "RAINFALL_ESTIMATE = 100 # mm\n",
    "FC_TANK_PLACEMENT = 1000 # Rs.\n",
    "FIXED_COST_PIPE_PER_METER = 120 # Rs.\n",
    "COST_PER_LITERS_TANK = 500 # Rs\n",
    "\n",
    "house_data_x_y_labels = make_blobs(n_samples = NUM_HOUSES, centers = NUM_CLUSTERS_OF_HOUSES, n_features=N_FEATURES, cluster_std =CLUSTER_STD, center_box = CENTER_BOX_FOR_HOUSES, shuffle = True, random_state = RANDOM_SEED)\n",
    "\n",
    "house_data_x_y = np.array(house_data_x_y_labels[0])\n",
    "house_data_labels= np.array(house_data_x_y_labels[1])\n",
    "\n",
    "house_data_x = house_data_x_y[:,0]\n",
    "house_data_y = house_data_x_y[:,1]\n",
    "\n",
    "# demand\n",
    "demand_per_house = np.array([np.random.normal(loc = MEAN_DEMAND_WATER_PER_HOUSE, scale = VAR_DEMAND_WATER_PER_HOUSE) for i in range(NUM_HOUSES)])\n",
    "\n",
    "#area\n",
    "area_per_house = np.array([np.random.normal(loc = MEAN_AREA_PER_HOUSE, scale = VAR_AREA_PER_HOUSE) for i in range(NUM_HOUSES)])\n",
    "\n",
    "# rainfall estimation\n",
    "rainfall_estimate_for_city =  np.array([RAINFALL_ESTIMATE for i in range(NUM_HOUSES)]) # FLOAT\n",
    "\n",
    "# capacity of the tanks per house (DERIVED)\n",
    "capacity_for_tank_per_house = np.array([area_per_house[i]* RAINFALL_ESTIMATE/1000  for i in range(NUM_HOUSES)])\n",
    "\n",
    "# average_length_of_pipe_per_house\n",
    "average_length_of_pipe_per_house = np.random.normal(loc = 30, scale = 40, size = NUM_HOUSES)\n",
    "\n",
    "# fixed_cost_of_tank_placement_per_house\n",
    "fixed_cost_of_tank_placement_per_house = np.array([FC_TANK_PLACEMENT for i in range(NUM_HOUSES)])\n",
    "\n",
    "# cost_per_liters_tank\n",
    "cost_per_liters_tank_per_house = np.array([COST_PER_LITERS_TANK for i in range(NUM_HOUSES)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "House data x, y (200, 2)\n",
      "House_data_labels (200,)\n",
      "House_data_x (200,)\n",
      "House_data_y (200,)\n",
      "Demand_per_house (200,)\n",
      "Capacity_for_tank_per_house (200,)\n",
      "Rainfall_estimate_for_city (200,)\n",
      "Average_length_of_pipe_per_house (200,)\n",
      "fixed_cost_of_tank_placement_per_house (200,)\n",
      "cost_per_liters_tank_per_house (200,)\n"
     ]
    }
   ],
   "source": [
    "print('House data x, y',(house_data_x_y).shape)\n",
    "print('House_data_labels',(house_data_labels).shape )\n",
    "print('House_data_x', (house_data_x.shape) )\n",
    "print('House_data_y', (house_data_y.shape))\n",
    "print('Demand_per_house', (demand_per_house.shape))\n",
    "print('Capacity_for_tank_per_house', (capacity_for_tank_per_house.shape))\n",
    "print('Rainfall_estimate_for_city', (rainfall_estimate_for_city.shape))\n",
    "print('Average_length_of_pipe_per_house', (average_length_of_pipe_per_house.shape))\n",
    "print('fixed_cost_of_tank_placement_per_house', (fixed_cost_of_tank_placement_per_house.shape))\n",
    "print('cost_per_liters_tank_per_house', (cost_per_liters_tank_per_house.shape))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For tanks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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": [
    "# sns.scatterplot(house_data_x, house_data_y, hue = house_data_labels)\n",
    "plt.scatter(x= house_data_x, y= house_data_y, c = house_data_labels, )\n",
    "plt.xlabel(\"house x\")\n",
    "plt.ylabel(\"house y\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data to csv for further coding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>house_data_x</th>\n",
       "      <th>house_data_y</th>\n",
       "      <th>demand_per_house_liters</th>\n",
       "      <th>area_per_house_metersq</th>\n",
       "      <th>capacity_for_tank_per_house_lt</th>\n",
       "      <th>rainfall_estimate_for_city_mm</th>\n",
       "      <th>average_length_of_pipe_per_house</th>\n",
       "      <th>fixed_cost_of_tank_placement_per_house</th>\n",
       "      <th>cost_per_liters_tank_per_house</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>503.705</td>\n",
       "      <td>717.782</td>\n",
       "      <td>82.555</td>\n",
       "      <td>134.387</td>\n",
       "      <td>13.439</td>\n",
       "      <td>100</td>\n",
       "      <td>10.892</td>\n",
       "      <td>1000</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-16.408</td>\n",
       "      <td>1218.144</td>\n",
       "      <td>149.185</td>\n",
       "      <td>136.867</td>\n",
       "      <td>13.687</td>\n",
       "      <td>100</td>\n",
       "      <td>-8.318</td>\n",
       "      <td>1000</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>234.719</td>\n",
       "      <td>878.906</td>\n",
       "      <td>129.046</td>\n",
       "      <td>43.958</td>\n",
       "      <td>4.396</td>\n",
       "      <td>100</td>\n",
       "      <td>-16.879</td>\n",
       "      <td>1000</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>871.943</td>\n",
       "      <td>354.177</td>\n",
       "      <td>103.514</td>\n",
       "      <td>102.750</td>\n",
       "      <td>10.275</td>\n",
       "      <td>100</td>\n",
       "      <td>53.069</td>\n",
       "      <td>1000</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>87.194</td>\n",
       "      <td>332.712</td>\n",
       "      <td>138.877</td>\n",
       "      <td>228.421</td>\n",
       "      <td>22.842</td>\n",
       "      <td>100</td>\n",
       "      <td>-6.246</td>\n",
       "      <td>1000</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   house_data_x  house_data_y  demand_per_house_liters  \\\n",
       "0       503.705       717.782                   82.555   \n",
       "1       -16.408      1218.144                  149.185   \n",
       "2       234.719       878.906                  129.046   \n",
       "3       871.943       354.177                  103.514   \n",
       "4        87.194       332.712                  138.877   \n",
       "\n",
       "   area_per_house_metersq  capacity_for_tank_per_house_lt  \\\n",
       "0                 134.387                          13.439   \n",
       "1                 136.867                          13.687   \n",
       "2                  43.958                           4.396   \n",
       "3                 102.750                          10.275   \n",
       "4                 228.421                          22.842   \n",
       "\n",
       "   rainfall_estimate_for_city_mm  average_length_of_pipe_per_house  \\\n",
       "0                            100                            10.892   \n",
       "1                            100                            -8.318   \n",
       "2                            100                           -16.879   \n",
       "3                            100                            53.069   \n",
       "4                            100                            -6.246   \n",
       "\n",
       "   fixed_cost_of_tank_placement_per_house  cost_per_liters_tank_per_house  \n",
       "0                                    1000                             500  \n",
       "1                                    1000                             500  \n",
       "2                                    1000                             500  \n",
       "3                                    1000                             500  \n",
       "4                                    1000                             500  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data = None, columns = None)\n",
    "df['house_data_x'] = house_data_x\n",
    "df['house_data_y'] = house_data_y\n",
    "df['demand_per_house_liters'] = demand_per_house\n",
    "df['area_per_house_metersq'] = area_per_house\n",
    "df['capacity_for_tank_per_house_lt'] = capacity_for_tank_per_house\n",
    "df['rainfall_estimate_for_city_mm'] = rainfall_estimate_for_city\n",
    "df['average_length_of_pipe_per_house'] = average_length_of_pipe_per_house\n",
    "df['fixed_cost_of_tank_placement_per_house'] = fixed_cost_of_tank_placement_per_house\n",
    "df['cost_per_liters_tank_per_house'] = cost_per_liters_tank_per_house\n",
    "\n",
    "\n",
    "df = df.round(3)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"data.csv\", index= False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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