Boston.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Boston Housing Price dataset\n",
    "\n",
    "\n",
    "We will be attempting to predict the median price of homes in a given Boston suburb in the mid-1970s, given a few data points about the \n",
    "suburb at the time, such as the crime rate, the local property tax rate, etc.\n",
    "\n",
    "The dataset has very few data points, only 506 in \n",
    "total, split between 404 training samples and 102 test samples, and each \"feature\" in the input data (e.g. the crime rate is a feature) has \n",
    "a different scale. For instance some values are proportions, which take a values between 0 and 1, others take values between 1 and 12, \n",
    "others between 0 and 100...\n",
    "\n",
    "Let's take a look at the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-17 12:35:56.805769: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-06-17 12:35:57.210456: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "keras.__version__\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import boston_housing\n",
    "\n",
    "(train_data, train_targets), (test_data, test_targets) =  boston_housing.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We take the default splitting of keras between training and testing dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 13)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "As you can see, we have 404 training samples and 102 test samples. The data comprises 13 features. The 13 features in the input data are as \n",
    "follow:\n",
    "\n",
    "1. Per capita crime rate.\n",
    "2. Proportion of residential land zoned for lots over 25,000 square feet.\n",
    "3. Proportion of non-retail business acres per town.\n",
    "4. Charles River dummy variable (= 1 if tract bounds river; 0 otherwise).\n",
    "5. Nitric oxides concentration (parts per 10 million).\n",
    "6. Average number of rooms per dwelling.\n",
    "7. Proportion of owner-occupied units built prior to 1940.\n",
    "8. Weighted distances to five Boston employment centres.\n",
    "9. Index of accessibility to radial highways.\n",
    "10. Full-value property-tax rate per $10,000.\n",
    "11. Pupil-teacher ratio by town.\n",
    "12. 1000 * (Bk - 0.63) ** 2 where Bk is the proportion of Black people by town.\n",
    "13. % lower status of the population.\n",
    "\n",
    "The targets are the median values of owner-occupied homes, in thousands of dollars:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15.2, 42.3, 50. , 21.1, 17.7, 18.5, 11.3, 15.6, 15.6, 14.4, 12.1,\n",
       "       17.9, 23.1, 19.9, 15.7,  8.8, 50. , 22.5, 24.1, 27.5, 10.9, 30.8,\n",
       "       32.9, 24. , 18.5, 13.3, 22.9, 34.7, 16.6, 17.5, 22.3, 16.1, 14.9,\n",
       "       23.1, 34.9, 25. , 13.9, 13.1, 20.4, 20. , 15.2, 24.7, 22.2, 16.7,\n",
       "       12.7, 15.6, 18.4, 21. , 30.1, 15.1, 18.7,  9.6, 31.5, 24.8, 19.1,\n",
       "       22. , 14.5, 11. , 32. , 29.4, 20.3, 24.4, 14.6, 19.5, 14.1, 14.3,\n",
       "       15.6, 10.5,  6.3, 19.3, 19.3, 13.4, 36.4, 17.8, 13.5, 16.5,  8.3,\n",
       "       14.3, 16. , 13.4, 28.6, 43.5, 20.2, 22. , 23. , 20.7, 12.5, 48.5,\n",
       "       14.6, 13.4, 23.7, 50. , 21.7, 39.8, 38.7, 22.2, 34.9, 22.5, 31.1,\n",
       "       28.7, 46. , 41.7, 21. , 26.6, 15. , 24.4, 13.3, 21.2, 11.7, 21.7,\n",
       "       19.4, 50. , 22.8, 19.7, 24.7, 36.2, 14.2, 18.9, 18.3, 20.6, 24.6,\n",
       "       18.2,  8.7, 44. , 10.4, 13.2, 21.2, 37. , 30.7, 22.9, 20. , 19.3,\n",
       "       31.7, 32. , 23.1, 18.8, 10.9, 50. , 19.6,  5. , 14.4, 19.8, 13.8,\n",
       "       19.6, 23.9, 24.5, 25. , 19.9, 17.2, 24.6, 13.5, 26.6, 21.4, 11.9,\n",
       "       22.6, 19.6,  8.5, 23.7, 23.1, 22.4, 20.5, 23.6, 18.4, 35.2, 23.1,\n",
       "       27.9, 20.6, 23.7, 28. , 13.6, 27.1, 23.6, 20.6, 18.2, 21.7, 17.1,\n",
       "        8.4, 25.3, 13.8, 22.2, 18.4, 20.7, 31.6, 30.5, 20.3,  8.8, 19.2,\n",
       "       19.4, 23.1, 23. , 14.8, 48.8, 22.6, 33.4, 21.1, 13.6, 32.2, 13.1,\n",
       "       23.4, 18.9, 23.9, 11.8, 23.3, 22.8, 19.6, 16.7, 13.4, 22.2, 20.4,\n",
       "       21.8, 26.4, 14.9, 24.1, 23.8, 12.3, 29.1, 21. , 19.5, 23.3, 23.8,\n",
       "       17.8, 11.5, 21.7, 19.9, 25. , 33.4, 28.5, 21.4, 24.3, 27.5, 33.1,\n",
       "       16.2, 23.3, 48.3, 22.9, 22.8, 13.1, 12.7, 22.6, 15. , 15.3, 10.5,\n",
       "       24. , 18.5, 21.7, 19.5, 33.2, 23.2,  5. , 19.1, 12.7, 22.3, 10.2,\n",
       "       13.9, 16.3, 17. , 20.1, 29.9, 17.2, 37.3, 45.4, 17.8, 23.2, 29. ,\n",
       "       22. , 18. , 17.4, 34.6, 20.1, 25. , 15.6, 24.8, 28.2, 21.2, 21.4,\n",
       "       23.8, 31. , 26.2, 17.4, 37.9, 17.5, 20. ,  8.3, 23.9,  8.4, 13.8,\n",
       "        7.2, 11.7, 17.1, 21.6, 50. , 16.1, 20.4, 20.6, 21.4, 20.6, 36.5,\n",
       "        8.5, 24.8, 10.8, 21.9, 17.3, 18.9, 36.2, 14.9, 18.2, 33.3, 21.8,\n",
       "       19.7, 31.6, 24.8, 19.4, 22.8,  7.5, 44.8, 16.8, 18.7, 50. , 50. ,\n",
       "       19.5, 20.1, 50. , 17.2, 20.8, 19.3, 41.3, 20.4, 20.5, 13.8, 16.5,\n",
       "       23.9, 20.6, 31.5, 23.3, 16.8, 14. , 33.8, 36.1, 12.8, 18.3, 18.7,\n",
       "       19.1, 29. , 30.1, 50. , 50. , 22. , 11.9, 37.6, 50. , 22.7, 20.8,\n",
       "       23.5, 27.9, 50. , 19.3, 23.9, 22.6, 15.2, 21.7, 19.2, 43.8, 20.3,\n",
       "       33.2, 19.9, 22.5, 32.7, 22. , 17.1, 19. , 15. , 16.1, 25.1, 23.7,\n",
       "       28.7, 37.2, 22.6, 16.4, 25. , 29.8, 22.1, 17.4, 18.1, 30.3, 17.5,\n",
       "       24.7, 12.6, 26.5, 28.7, 13.3, 10.4, 24.4, 23. , 20. , 17.8,  7. ,\n",
       "       11.8, 24.4, 13.8, 19.4, 25.2, 19.4, 19.4, 29.1])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_targets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The prices are typically between 10000 and 50,000. If that sounds cheap, remember this was the mid-1970s, and these prices are not \n",
    "inflation-adjusted.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_boston_trainX = pd.DataFrame(train_data)\n",
    "df_boston_testX = pd.DataFrame(test_data)\n",
    "df_boston_trainY = pd.DataFrame(train_targets)\n",
    "df_boston_testY = pd.DataFrame(test_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': '0'}>, <Axes: title={'center': '1'}>,\n",
       "        <Axes: title={'center': '2'}>, <Axes: title={'center': '3'}>],\n",
       "       [<Axes: title={'center': '4'}>, <Axes: title={'center': '5'}>,\n",
       "        <Axes: title={'center': '6'}>, <Axes: title={'center': '7'}>],\n",
       "       [<Axes: title={'center': '8'}>, <Axes: title={'center': '9'}>,\n",
       "        <Axes: title={'center': '10'}>, <Axes: title={'center': '11'}>],\n",
       "       [<Axes: title={'center': '12'}>, <Axes: >, <Axes: >, <Axes: >]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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AkCnCEQAAAAAAIFOEIwAAAAAAQKYIRwAAAAAAgEwRjgAAAAAAAJkiHAEAAAAAADLl/wOeiDICyHO7bQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2000x1500 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_boston_trainX.hist(bins=50, figsize=(20,15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the data\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do a standard normalization of the inputs (mean 0 and standard deviation 1) and a min-max normalization of the ouput (minimum to 0 and maximum to 1). This is a very simple and standard preprocessing in ML. In addition, this enables to have inputs and ouput on the same scale. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformer = StandardScaler().fit(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_prepared = transformer.transform(train_data)\n",
    "test_data_prepared = transformer.transform(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.27224633, -0.48361547, -0.43576161, ...,  1.14850044,\n",
       "         0.44807713,  0.8252202 ],\n",
       "       [-0.40342651,  2.99178419, -1.33391162, ..., -1.71818909,\n",
       "         0.43190599, -1.32920239],\n",
       "       [ 0.1249402 , -0.48361547,  1.0283258 , ...,  0.78447637,\n",
       "         0.22061726, -1.30850006],\n",
       "       ...,\n",
       "       [-0.40202987,  0.99079651, -0.7415148 , ..., -0.71712291,\n",
       "         0.07943894, -0.67776904],\n",
       "       [-0.17292018, -0.48361547,  1.24588095, ..., -1.71818909,\n",
       "        -0.98764362,  0.42083466],\n",
       "       [-0.40422614,  2.04394792, -1.20161456, ..., -1.30866202,\n",
       "         0.23317118, -1.15392266]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': '0'}>, <Axes: title={'center': '1'}>,\n",
       "        <Axes: title={'center': '2'}>, <Axes: title={'center': '3'}>],\n",
       "       [<Axes: title={'center': '4'}>, <Axes: title={'center': '5'}>,\n",
       "        <Axes: title={'center': '6'}>, <Axes: title={'center': '7'}>],\n",
       "       [<Axes: title={'center': '8'}>, <Axes: title={'center': '9'}>,\n",
       "        <Axes: title={'center': '10'}>, <Axes: title={'center': '11'}>],\n",
       "       [<Axes: title={'center': '12'}>, <Axes: >, <Axes: >, <Axes: >]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(train_data_prepared).hist(bins=50, figsize=(20,15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that a standard normalization is not sufficient to get distribution close to a normal one. But this is not important in our case because we are not looking for the best performances for the Boston dataset: we just want to compare the optimizers on a given architecture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "minY = np.min(train_targets)\n",
    "maxY = np.max(train_targets)\n",
    "\n",
    "train_targets = (train_targets-minY)/(maxY-minY)\n",
    "test_targets = (test_targets-minY)/(maxY-minY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "1.0\n",
      "0.35\n",
      "0.37666666666666665\n"
     ]
    }
   ],
   "source": [
    "print(np.min(train_targets))\n",
    "print(np.max(train_targets))\n",
    "\n",
    "print(np.median(train_targets))\n",
    "print(np.median(test_targets))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We record the preprocessing data in csv files that we will use in the C/C++ code to train the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('boston_inputs_train.csv', train_data_prepared, delimiter=\",\")\n",
    "np.savetxt('boston_outputs_train.csv', train_targets, delimiter=\",\")\n",
    "np.savetxt('boston_inputs_test.csv', test_data_prepared, delimiter=\",\")\n",
    "np.savetxt('boston_outputs_test.csv', test_targets, delimiter=\",\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}